From scipy-svn at scipy.org Wed Oct 1 05:49:53 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 1 Oct 2008 04:49:53 -0500 (CDT) Subject: [Scipy-svn] r4762 - branches Message-ID: <20081001094953.E16DD39C0F1@scipy.org> Author: peridot Date: 2008-10-01 04:49:49 -0500 (Wed, 01 Oct 2008) New Revision: 4762 Added: branches/spatial/ Log: creating branch for spatial Copied: branches/spatial (from rev 4761, trunk) From scipy-svn at scipy.org Wed Oct 1 05:52:47 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 1 Oct 2008 04:52:47 -0500 (CDT) Subject: [Scipy-svn] r4763 - in trunk/scipy: . integrate Message-ID: <20081001095247.E93DA39C0F1@scipy.org> Author: peridot Date: 2008-10-01 04:52:45 -0500 (Wed, 01 Oct 2008) New Revision: 4763 Removed: trunk/scipy/spatial/ Modified: trunk/scipy/__init__.py trunk/scipy/integrate/setup.py trunk/scipy/setup.py Log: Removed spatial; it's in a branch now so that it won't interfere with 0.7. Modified: trunk/scipy/__init__.py =================================================================== --- trunk/scipy/__init__.py 2008-10-01 09:49:49 UTC (rev 4762) +++ trunk/scipy/__init__.py 2008-10-01 09:52:45 UTC (rev 4763) @@ -66,7 +66,7 @@ # Remove subpackage names from __all__ such that they are not imported via # "from scipy import *". This works around a numpy bug present in < 1.2. subpackages = """cluster constants fftpack integrate interpolate io lib linalg -linsolve maxentropy misc ndimage odr optimize signal sparse special spatial +linsolve maxentropy misc ndimage odr optimize signal sparse special splinalg stats stsci weave""".split() for name in subpackages: try: Modified: trunk/scipy/integrate/setup.py =================================================================== --- trunk/scipy/integrate/setup.py 2008-10-01 09:49:49 UTC (rev 4762) +++ trunk/scipy/integrate/setup.py 2008-10-01 09:52:45 UTC (rev 4763) @@ -51,6 +51,11 @@ sources=['vode.pyf'], libraries=libs, **newblas) + # lsodar + config.add_extension('lsodar', + sources=['lsodar.pyf'], + libraries=libs, + **newblas) config.add_data_dir('tests') return config Modified: trunk/scipy/setup.py =================================================================== --- trunk/scipy/setup.py 2008-10-01 09:49:49 UTC (rev 4762) +++ trunk/scipy/setup.py 2008-10-01 09:52:45 UTC (rev 4763) @@ -18,7 +18,6 @@ config.add_subpackage('signal') config.add_subpackage('sparse') config.add_subpackage('special') - config.add_subpackage('spatial') config.add_subpackage('stats') config.add_subpackage('ndimage') config.add_subpackage('stsci') From scipy-svn at scipy.org Wed Oct 1 15:38:41 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 1 Oct 2008 14:38:41 -0500 (CDT) Subject: [Scipy-svn] r4764 - trunk/scipy/integrate Message-ID: <20081001193841.16F3A39C1AE@scipy.org> Author: rkern Date: 2008-10-01 14:38:40 -0500 (Wed, 01 Oct 2008) New Revision: 4764 Modified: trunk/scipy/integrate/setup.py Log: BUG: Remove mistaken commit. Modified: trunk/scipy/integrate/setup.py =================================================================== --- trunk/scipy/integrate/setup.py 2008-10-01 09:52:45 UTC (rev 4763) +++ trunk/scipy/integrate/setup.py 2008-10-01 19:38:40 UTC (rev 4764) @@ -51,11 +51,6 @@ sources=['vode.pyf'], libraries=libs, **newblas) - # lsodar - config.add_extension('lsodar', - sources=['lsodar.pyf'], - libraries=libs, - **newblas) config.add_data_dir('tests') return config From scipy-svn at scipy.org Fri Oct 3 10:31:42 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 3 Oct 2008 09:31:42 -0500 (CDT) Subject: [Scipy-svn] r4765 - trunk/scipy/special/cephes Message-ID: <20081003143142.96D0D39C088@scipy.org> Author: oliphant Date: 2008-10-03 09:31:41 -0500 (Fri, 03 Oct 2008) New Revision: 4765 Modified: trunk/scipy/special/cephes/jv.c Log: Fix jv(3,4) case by special casing. The problem is in the recur function which seems to have a special-case problem with n=3 and x=4 because pk=qk in that case. Modified: trunk/scipy/special/cephes/jv.c =================================================================== --- trunk/scipy/special/cephes/jv.c 2008-10-01 19:38:40 UTC (rev 4764) +++ trunk/scipy/special/cephes/jv.c 2008-10-03 14:31:41 UTC (rev 4765) @@ -50,6 +50,10 @@ #include "mconf.h" #define CEPHES_DEBUG 0 +#if CEPHES_DEBUG +#include +#endif + #ifdef DEC #define MAXGAM 34.84425627277176174 #else @@ -94,6 +98,15 @@ double k, q, t, y, an; int i, sign, nint; + /* The recursion used when n = 3 and x = 4 in recur gives + the wrong answer. + + Simple fix for now: + */ + if ((n==3) && (x == 4)) { + return 0.43017147387562193; + } + nint = 0; /* Flag for integer n */ sign = 1; /* Flag for sign inversion */ an = fabs(n); From scipy-svn at scipy.org Fri Oct 3 14:57:21 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 3 Oct 2008 13:57:21 -0500 (CDT) Subject: [Scipy-svn] r4766 - trunk/scipy/stats Message-ID: <20081003185721.2347F39C107@scipy.org> Author: oliphant Date: 2008-10-03 13:57:20 -0500 (Fri, 03 Oct 2008) New Revision: 4766 Modified: trunk/scipy/stats/continuous.lyx trunk/scipy/stats/distributions.py Log: Improve docstring of lognorm a bit. Modified: trunk/scipy/stats/continuous.lyx =================================================================== --- trunk/scipy/stats/continuous.lyx 2008-10-03 14:31:41 UTC (rev 4765) +++ trunk/scipy/stats/continuous.lyx 2008-10-03 18:57:20 UTC (rev 4766) @@ -1,18 +1,19 @@ -#LyX 1.3 created this file. For more info see http://www.lyx.org/ -\lyxformat 221 +#LyX 1.4.3 created this file. For more info see http://www.lyx.org/ +\lyxformat 245 +\begin_document +\begin_header \textclass article \language english \inputencoding auto \fontscheme default \graphics default \paperfontsize default -\spacing single -\papersize Default -\paperpackage a4 -\use_geometry 1 -\use_amsmath 1 -\use_natbib 0 -\use_numerical_citations 0 +\spacing single +\papersize default +\use_geometry true +\use_amsmath 2 +\cite_engine basic +\use_bibtopic false \paperorientation portrait \leftmargin 1in \topmargin 1in @@ -23,43 +24,49 @@ \paragraph_separation indent \defskip medskip \quotes_language english -\quotes_times 2 \papercolumns 1 \papersides 1 \paperpagestyle default +\tracking_changes false +\output_changes true +\end_header -\layout Title +\begin_body +\begin_layout Title Continuous Statistical Distributions -\layout Section +\end_layout +\begin_layout Section Overview -\layout Standard +\end_layout +\begin_layout Standard All distributions will have location (L) and Scale (S) parameters along with any shape parameters needed, the names for the shape parameters will vary. Standard form for the distributions will be given where \begin_inset Formula $L=0.0$ -\end_inset +\end_inset and \begin_inset Formula $S=1.0.$ -\end_inset +\end_inset The nonstandard forms can be obtained for the various functions using (note \begin_inset Formula $U$ -\end_inset +\end_inset is a standard uniform random variate). -\layout Standard -\align center +\end_layout -\size small +\begin_layout Standard +\align center -\begin_inset Tabular +\size small +\begin_inset Tabular @@ -69,591 +76,612 @@ \begin_inset Text -\layout Standard +\begin_layout Standard +Function Name +\end_layout -Function Name -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Standard Function +\end_layout -Standard Function -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Transformation +\end_layout -Transformation -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Cumulative Distribution Function (CDF) +\end_layout -Cumulative Distribution Function (CDF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $F\left(x\right)$ +\end_inset -\begin_inset Formula $F\left(x\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $F\left(x;L,S\right)=F\left(\frac{\left(x-L\right)}{S}\right)$ +\end_inset -\begin_inset Formula $F\left(x;L,S\right)=F\left(\frac{\left(x-L\right)}{S}\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Probability Density Function (PDF) +\end_layout -Probability Density Function (PDF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $f\left(x\right)=F^{\prime}\left(x\right)$ +\end_inset -\begin_inset Formula $f\left(x\right)=F^{\prime}\left(x\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $f\left(x;L,S\right)=\frac{1}{S}f\left(\frac{\left(x-L\right)}{S}\right)$ +\end_inset -\begin_inset Formula $f\left(x;L,S\right)=\frac{1}{S}f\left(\frac{\left(x-L\right)}{S}\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Percent Point Function (PPF) +\end_layout -Percent Point Function (PPF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $G\left(q\right)=F^{-1}\left(q\right)$ +\end_inset -\begin_inset Formula $G\left(q\right)=F^{-1}\left(q\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $G\left(q;L,S\right)=L+SG\left(q\right)$ +\end_inset -\begin_inset Formula $G\left(q;L,S\right)=L+SG\left(q\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Probability Sparsity Function (PSF) +\end_layout -Probability Sparsity Function (PSF) -\end_inset +\end_inset \begin_inset Text -\layout Standard - - +\begin_layout Standard \begin_inset Formula $g\left(q\right)=G^{\prime}\left(q\right)$ -\end_inset +\end_inset -\end_inset +\end_layout + +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $g\left(q;L,S\right)=Sg\left(q\right)$ +\end_inset -\begin_inset Formula $g\left(q;L,S\right)=Sg\left(q\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Hazard Function (HF) +\end_layout -Hazard Function (HF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $h_{a}\left(x\right)=\frac{f\left(x\right)}{1-F\left(x\right)}$ +\end_inset -\begin_inset Formula $h_{a}\left(x\right)=\frac{f\left(x\right)}{1-F\left(x\right)}$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $h_{a}\left(x;L,S\right)=\frac{1}{S}h_{a}\left(\frac{\left(x-L\right)}{S}\right)$ +\end_inset -\begin_inset Formula $h_{a}\left(x;L,S\right)=\frac{1}{S}h_{a}\left(\frac{\left(x-L\right)}{S}\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Cumulative Hazard Functon (CHF) +\end_layout -Cumulative Hazard Functon (CHF) -\end_inset +\end_inset \begin_inset Text -\layout Standard - - +\begin_layout Standard \begin_inset Formula $H_{a}\left(x\right)=$ -\end_inset +\end_inset \begin_inset Formula $\log\frac{1}{1-F\left(x\right)}$ -\end_inset +\end_inset -\end_inset +\end_layout + +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $H_{a}\left(x;L,S\right)=H_{a}\left(\frac{\left(x-L\right)}{S}\right)$ +\end_inset -\begin_inset Formula $H_{a}\left(x;L,S\right)=H_{a}\left(\frac{\left(x-L\right)}{S}\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Survival Function (SF) +\end_layout -Survival Function (SF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $S\left(x\right)=1-F\left(x\right)$ +\end_inset -\begin_inset Formula $S\left(x\right)=1-F\left(x\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $S\left(x;L,S\right)=S\left(\frac{\left(x-L\right)}{S}\right)$ +\end_inset -\begin_inset Formula $S\left(x;L,S\right)=S\left(\frac{\left(x-L\right)}{S}\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Inverse Survival Function (ISF) +\end_layout -Inverse Survival Function (ISF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $Z\left(\alpha\right)=S^{-1}\left(\alpha\right)=G\left(1-\alpha\right)$ +\end_inset -\begin_inset Formula $Z\left(\alpha\right)=S^{-1}\left(\alpha\right)=G\left(1-\alpha\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $Z\left(\alpha;L,S\right)=L+SZ\left(\alpha\right)$ +\end_inset -\begin_inset Formula $Z\left(\alpha;L,S\right)=L+SZ\left(\alpha\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Moment Generating Function (MGF) +\end_layout -Moment Generating Function (MGF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $M_{Y}\left(t\right)=E\left[e^{Yt}\right]$ +\end_inset -\begin_inset Formula $M_{Y}\left(t\right)=E\left[e^{Yt}\right]$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $M_{X}\left(t\right)=e^{Lt}M_{Y}\left(St\right)$ +\end_inset -\begin_inset Formula $M_{X}\left(t\right)=e^{Lt}M_{Y}\left(St\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Random Variates +\end_layout -Random Variates -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $Y=G\left(U\right)$ +\end_inset -\begin_inset Formula $Y=G\left(U\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $X=L+SY$ +\end_inset -\begin_inset Formula $X=L+SY$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +(Differential) Entropy +\end_layout -(Differential) Entropy -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $h\left[Y\right]=-\int f\left(y\right)\log f\left(y\right)dy$ +\end_inset -\begin_inset Formula $h\left[Y\right]=-\int f\left(y\right)\log f\left(y\right)dy$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $h\left[X\right]=h\left[Y\right]+\log S$ +\end_inset -\begin_inset Formula $h\left[X\right]=h\left[Y\right]+\log S$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +(Non-central) Moments +\end_layout -(Non-central) Moments -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $\mu_{n}^{\prime}=E\left[Y^{n}\right]$ +\end_inset -\begin_inset Formula $\mu_{n}^{\prime}=E\left[Y^{n}\right]$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard - - +\begin_layout Standard \begin_inset Formula $E\left[X^{n}\right]=L^{n}\sum_{k=0}^{N}\left(\begin{array}{c} n\\ k\end{array}\right)\left(\frac{S}{L}\right)^{k}\mu_{k}^{\prime}$ -\end_inset +\end_inset -\end_inset +\end_layout + +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Central Moments +\end_layout -Central Moments -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $\mu_{n}=E\left[\left(Y-\mu\right)^{n}\right]$ +\end_inset -\begin_inset Formula $\mu_{n}=E\left[\left(Y-\mu\right)^{n}\right]$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $E\left[\left(X-\mu_{X}\right)^{n}\right]=S^{n}\mu_{n}$ +\end_inset -\begin_inset Formula $E\left[\left(X-\mu_{X}\right)^{n}\right]=S^{n}\mu_{n}$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +mean (mode, median), var +\end_layout -mean (mode, median), var -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $\mu,\,\mu_{2}$ +\end_inset -\begin_inset Formula $\mu,\,\mu_{2}$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard - - +\begin_layout Standard \begin_inset Formula $L+S\mu,\, S^{2}\mu_{2}$ -\end_inset +\end_inset -\end_inset +\end_layout + +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +skewness, kurtosis +\end_layout -skewness, kurtosis -\end_inset +\end_inset \begin_inset Text -\layout Standard - - +\begin_layout Standard \begin_inset Formula $\gamma_{1}=\frac{\mu_{3}}{\left(\mu_{2}\right)^{3/2}},\,$ -\end_inset +\end_inset \begin_inset Formula $\gamma_{2}=\frac{\mu_{4}}{\left(\mu_{2}\right)^{2}}-3$ -\end_inset +\end_inset -\end_inset +\end_layout + +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $\gamma_{1},\,\gamma_{2}$ +\end_inset -\begin_inset Formula $\gamma_{1},\,\gamma_{2}$ -\end_inset +\end_layout - -\end_inset +\end_inset -\end_inset +\end_inset -\layout Standard +\end_layout -\SpecialChar ~ +\begin_layout Standard +\InsetSpace ~ -\layout Subsection +\end_layout +\begin_layout Subsection Moments -\layout Standard +\end_layout +\begin_layout Standard Non-central moments are defined using the PDF \begin_inset Formula \[ \mu_{n}^{\prime}=\int_{-\infty}^{\infty}x^{n}f\left(x\right)dx.\] -\end_inset +\end_inset Note, that these can always be computed using the PPF. Substitute \begin_inset Formula $x=G\left(q\right)$ -\end_inset +\end_inset in the above equation and get \begin_inset Formula \[ \mu_{n}^{\prime}=\int_{0}^{1}G^{n}\left(q\right)dq\] -\end_inset +\end_inset which may be easier to compute numerically. Note that \begin_inset Formula $q=F\left(x\right)$ -\end_inset +\end_inset so that \begin_inset Formula $dq=f\left(x\right)dx.$ -\end_inset +\end_inset Central moments are computed similarly \begin_inset Formula $\mu=\mu_{1}^{\prime}$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -663,7 +691,7 @@ n\\ k\end{array}\right)\left(-\mu\right)^{k}\mu_{n-k}^{\prime}\end{eqnarray*} -\end_inset +\end_inset In particular \begin_inset Formula \begin{eqnarray*} @@ -672,157 +700,162 @@ \mu_{4} & = & \mu_{4}^{\prime}-4\mu\mu_{3}^{\prime}+6\mu^{2}\mu_{2}^{\prime}-3\mu^{4}\\ & = & \mu_{4}^{\prime}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}\end{eqnarray*} -\end_inset +\end_inset Skewness is defined as \begin_inset Formula \[ \gamma_{1}=\sqrt{\beta_{1}}=\frac{\mu_{3}}{\mu_{2}^{3/2}}\] -\end_inset +\end_inset while (Fisher) kurtosis is \begin_inset Formula \[ \gamma_{2}=\frac{\mu_{4}}{\mu_{2}^{2}}-3,\] -\end_inset +\end_inset so that a normal distribution has a kurtosis of zero. -\layout Subsection +\end_layout +\begin_layout Subsection Median and mode -\layout Standard +\end_layout +\begin_layout Standard The median, \begin_inset Formula $m_{n}$ -\end_inset +\end_inset is defined as the point at which half of the density is on one side and half on the other. In other words, \begin_inset Formula $F\left(m_{n}\right)=\frac{1}{2}$ -\end_inset +\end_inset so that \begin_inset Formula \[ m_{n}=G\left(\frac{1}{2}\right).\] -\end_inset +\end_inset In addition, the mode, \begin_inset Formula $m_{d}$ -\end_inset +\end_inset , is defined as the value for which the probability density function reaches it's peak \begin_inset Formula \[ m_{d}=\arg\max_{x}f\left(x\right).\] -\end_inset +\end_inset -\layout Subsection +\end_layout +\begin_layout Subsection Fitting data -\layout Standard +\end_layout +\begin_layout Standard To fit data to a distribution, maximizing the likelihood function is common. Alternatively, some distributions have well-known minimum variance unbiased estimators. These will be chosen by default, but the likelihood function will always be available for minimizing. -\layout Standard +\end_layout +\begin_layout Standard If \begin_inset Formula $f\left(x;\boldsymbol{\theta}\right)$ -\end_inset +\end_inset is the PDF of a random-variable where \begin_inset Formula $\boldsymbol{\theta}$ -\end_inset +\end_inset is a vector of parameters ( -\emph on +\emph on e.g. \begin_inset Formula $L$ -\end_inset +\end_inset -\emph default +\emph default and \begin_inset Formula $S$ -\end_inset +\end_inset ), then for a collection of \begin_inset Formula $N$ -\end_inset +\end_inset independent samples from this distribution, the joint distribution the random vector \begin_inset Formula $\mathbf{x}$ -\end_inset +\end_inset is \begin_inset Formula \[ f\left(\mathbf{x};\boldsymbol{\theta}\right)=\prod_{i=1}^{N}f\left(x_{i};\boldsymbol{\theta}\right).\] -\end_inset +\end_inset The maximum likelihood estimate of the parameters \begin_inset Formula $\boldsymbol{\theta}$ -\end_inset +\end_inset are the parameters which maximize this function with \begin_inset Formula $\mathbf{x}$ -\end_inset +\end_inset fixed and given by the data: \begin_inset Formula \begin{eqnarray*} \boldsymbol{\theta}_{es} & = & \arg\max_{\boldsymbol{\theta}}f\left(\mathbf{x};\boldsymbol{\theta}\right)\\ & = & \arg\min_{\boldsymbol{\theta}}l_{\mathbf{x}}\left(\boldsymbol{\theta}\right).\end{eqnarray*} -\end_inset +\end_inset Where \begin_inset Formula \begin{eqnarray*} l_{\mathbf{x}}\left(\boldsymbol{\theta}\right) & = & -\sum_{i=1}^{N}\log f\left(x_{i};\boldsymbol{\theta}\right)\\ & = & -N\overline{\log f\left(x_{i};\boldsymbol{\theta}\right)}\end{eqnarray*} -\end_inset +\end_inset Note that if \begin_inset Formula $\boldsymbol{\theta}$ -\end_inset +\end_inset includes only shape parameters, the location and scale-parameters can be fit by replacing \begin_inset Formula $x_{i}$ -\end_inset +\end_inset with \begin_inset Formula $\left(x_{i}-L\right)/S$ -\end_inset +\end_inset in the log-likelihood function adding \begin_inset Formula $N\log S$ -\end_inset +\end_inset and minimizing, thus \begin_inset Formula \begin{eqnarray*} l_{\mathbf{x}}\left(L,S;\boldsymbol{\theta}\right) & = & N\log S-\sum_{i=1}^{N}\log f\left(\frac{x_{i}-L}{S};\boldsymbol{\theta}\right)\\ & = & N\log S+l_{\frac{\mathbf{x}-S}{L}}\left(\boldsymbol{\theta}\right)\end{eqnarray*} -\end_inset +\end_inset If desired, sample estimates for \begin_inset Formula $L$ -\end_inset +\end_inset and \begin_inset Formula $S$ -\end_inset +\end_inset (not necessarily maximum likelihood estimates) can be obtained from samples estimates of the mean and variance using @@ -830,185 +863,194 @@ \hat{S} & = & \sqrt{\frac{\hat{\mu}_{2}}{\mu_{2}}}\\ \hat{L} & = & \hat{\mu}-\hat{S}\mu\end{eqnarray*} -\end_inset +\end_inset where \begin_inset Formula $\mu$ -\end_inset +\end_inset and \begin_inset Formula $\mu_{2}$ -\end_inset +\end_inset are assumed known as the mean and variance of the -\series bold +\series bold untransformed -\series default +\series default distribution (when \begin_inset Formula $L=0$ -\end_inset +\end_inset and \begin_inset Formula $S=1$ -\end_inset +\end_inset ) and \begin_inset Formula \begin{eqnarray*} \hat{\mu} & = & \frac{1}{N}\sum_{i=1}^{N}x_{i}=\bar{\mathbf{x}}\\ \hat{\mu}_{2} & = & \frac{1}{N-1}\sum_{i=1}^{N}\left(x_{i}-\hat{\mu}\right)^{2}=\frac{N}{N-1}\overline{\left(\mathbf{x}-\bar{\mathbf{x}}\right)^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Subsection +\end_layout +\begin_layout Subsection Standard notation for mean -\layout Standard +\end_layout +\begin_layout Standard We will use \begin_inset Formula \[ \overline{y\left(\mathbf{x}\right)}=\frac{1}{N}\sum_{i=1}^{N}y\left(x_{i}\right)\] -\end_inset +\end_inset where \begin_inset Formula $N$ -\end_inset +\end_inset should be clear from context as the number of samples \begin_inset Formula $x_{i}$ -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Alpha -\layout Standard +\end_layout +\begin_layout Standard One shape parameters \begin_inset Formula $\alpha>0$ -\end_inset +\end_inset (paramter \begin_inset Formula $\beta$ -\end_inset +\end_inset in DATAPLOT is a scale-parameter). Standard form is \begin_inset Formula $x>0:$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\alpha\right) & = & \frac{1}{x^{2}\Phi\left(\alpha\right)\sqrt{2\pi}}\exp\left(-\frac{1}{2}\left(\alpha-\frac{1}{x}\right)^{2}\right)\\ F\left(x;\alpha\right) & = & \frac{\Phi\left(\alpha-\frac{1}{x}\right)}{\Phi\left(\alpha\right)}\\ G\left(q;\alpha\right) & = & \left[\alpha-\Phi^{-1}\left(q\Phi\left(\alpha\right)\right)\right]^{-1}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=\frac{1}{\Phi\left(a\right)\sqrt{2\pi}}\int_{0}^{\infty}\frac{e^{xt}}{x^{2}}\exp\left(-\frac{1}{2}\left(\alpha-\frac{1}{x}\right)^{2}\right)dx\] -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard No moments? \begin_inset Formula \[ l_{\mathbf{x}}\left(\alpha\right)=N\log\left[\Phi\left(\alpha\right)\sqrt{2\pi}\right]+2N\overline{\log\mathbf{x}}+\frac{N}{2}\alpha^{2}-\alpha\overline{\mathbf{x}^{-1}}+\frac{1}{2}\overline{\mathbf{x}^{-2}}\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Anglit -\layout Standard +\end_layout +\begin_layout Standard Defined over \begin_inset Formula $x\in\left[-\frac{\pi}{4},\frac{\pi}{4}\right]$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \sin\left(2x+\frac{\pi}{2}\right)=\cos\left(2x\right)\\ F\left(x\right) & = & \sin^{2}\left(x+\frac{\pi}{4}\right)\\ G\left(q\right) & = & \arcsin\left(\sqrt{q}\right)-\frac{\pi}{4}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & 0\\ \mu_{2} & = & \frac{\pi^{2}}{16}-\frac{1}{2}\\ \gamma_{1} & = & 0\\ \gamma_{2} & = & -2\frac{\pi^{4}-96}{\left(\pi^{2}-8\right)^{2}}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & 1-\log2\\ & \approx & 0.30685281944005469058\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} M\left(t\right) & = & \int_{-\frac{\pi}{4}}^{\frac{\pi}{4}}\cos\left(2x\right)e^{xt}dx\\ & = & \frac{4\cosh\left(\frac{\pi t}{4}\right)}{t^{2}+4}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ l_{\mathbf{x}}\left(\cdot\right)=-N\overline{\log\left[\cos\left(2\mathbf{x}\right)\right]}\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Arcsine -\layout Standard +\end_layout +\begin_layout Standard Defined over \begin_inset Formula $x\in\left(0,1\right)$ -\end_inset +\end_inset . To get the JKB definition put \begin_inset Formula $x=\frac{u+1}{2}.$ -\end_inset +\end_inset i.e. \begin_inset Formula $L=-1$ -\end_inset +\end_inset and \begin_inset Formula $S=2.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1016,20 +1058,20 @@ F\left(x\right) & = & \frac{2}{\pi}\arcsin\left(\sqrt{x}\right)\\ G\left(q\right) & = & \sin^{2}\left(\frac{\pi}{2}q\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=E^{t/2}I_{0}\left(\frac{t}{2}\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} \mu_{n}^{\prime} & = & \frac{1}{\pi}\int_{0}^{1}dx\, x^{n-1/2}\left(1-x\right)^{-1/2}\\ & = & \frac{1}{\pi}B\left(\frac{1}{2},n+\frac{1}{2}\right)=\frac{\left(2n-1\right)!!}{2^{n}n!}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1038,45 +1080,47 @@ \gamma_{1} & = & 0\\ \gamma_{2} & = & -\frac{3}{2}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]\approx-0.24156447527049044468\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ l_{\mathbf{x}}\left(\cdot\right)=N\log\pi+\frac{N}{2}\overline{\log\mathbf{x}}+\frac{N}{2}\overline{\log\left(1-\mathbf{x}\right)}\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Beta -\layout Standard +\end_layout +\begin_layout Standard Two shape parameters -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ a,b>0\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;a,b\right) & = & \frac{\Gamma\left(a+b\right)}{\Gamma\left(a\right)\Gamma\left(b\right)}x^{a-1}\left(1-x\right)^{b-1}I_{\left(0,1\right)}\left(x\right)\\ F\left(x;a,b\right) & = & \int_{0}^{x}f\left(y;a,b\right)dy=I\left(x,a,b\right)\\ @@ -1088,66 +1132,68 @@ \gamma_{2} & = & \frac{6\left(a^{3}+a^{2}\left(1-2b\right)+b^{2}\left(b+1\right)-2ab\left(b+2\right)\right)}{ab\left(a+b+2\right)\left(a+b+3\right)}\\ m_{d} & = & \frac{\left(a-1\right)}{\left(a+b-2\right)}\, a+b\neq2\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula $f\left(x;a,1\right)$ -\end_inset +\end_inset is also called the Power-function distribution. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ l_{\mathbf{x}}\left(a,b\right)=-N\log\Gamma\left(a+b\right)+N\log\Gamma\left(a\right)+N\log\Gamma\left(b\right)-N\left(a-1\right)\overline{\log\mathbf{x}}-N\left(b-1\right)\overline{\log\left(1-\mathbf{x}\right)}\] -\end_inset +\end_inset All of the \begin_inset Formula $x_{i}\in\left[0,1\right]$ -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Beta Prime -\layout Standard +\end_layout +\begin_layout Standard Defined over \begin_inset Formula $00.$ -\end_inset +\end_inset (Note the CDF evaluation uses Eq. 3.194.1 on pg. 313 of Gradshteyn & Ryzhik (sixth edition). -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\alpha,\beta\right) & = & \frac{\Gamma\left(\alpha+\beta\right)}{\Gamma\left(\alpha\right)\Gamma\left(\beta\right)}x^{\alpha-1}\left(1+x\right)^{-\alpha-\beta}\\ F\left(x;\alpha,\beta\right) & = & \frac{\Gamma\left(\alpha+\beta\right)}{\alpha\Gamma\left(\alpha\right)\Gamma\left(\beta\right)}x^{\alpha}\,_{2}F_{1}\left(\alpha+\beta,\alpha;1+\alpha;-x\right)\\ G\left(q;\alpha,\beta\right) & = & F^{-1}\left(x;\alpha,\beta\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=\left\{ \begin{array}{ccc} \frac{\Gamma\left(n+\alpha\right)\Gamma\left(\beta-n\right)}{\Gamma\left(\alpha\right)\Gamma\left(\beta\right)}=\frac{\left(\alpha\right)_{n}}{\left(\beta-n\right)_{n}} & & \beta>n\\ \infty & & \textrm{otherwise}\end{array}\right.\] -\end_inset +\end_inset Therefore, \begin_inset Formula \begin{eqnarray*} @@ -1157,20 +1203,21 @@ \gamma_{2} & = & \frac{\mu_{4}}{\mu_{2}^{2}}-3\\ \mu_{4} & = & \frac{\alpha\left(\alpha+1\right)\left(\alpha+2\right)\left(\alpha+3\right)}{\left(\beta-4\right)\left(\beta-3\right)\left(\beta-2\right)\left(\beta-1\right)}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}\quad\beta>4\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Bradford -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} c & > & 0\\ k & = & \log\left(1+c\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1185,37 +1232,38 @@ m_{d} & = & 0\\ m_{n} & = & \sqrt{1+c}-1\end{eqnarray*} -\end_inset +\end_inset where \begin_inset Formula $\textrm{Ei}\left(\textrm{z}\right)$ -\end_inset +\end_inset is the exponential integral function. Also \begin_inset Formula \[ h\left[X\right]=\frac{1}{2}\log\left(1+c\right)-\log\left(\frac{c}{\log\left(1+c\right)}\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Burr -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} c & > & 0\\ d & > & 0\\ k & = & \Gamma\left(d\right)\Gamma\left(1-\frac{2}{c}\right)\Gamma\left(\frac{2}{c}+d\right)-\Gamma^{2}\left(1-\frac{1}{c}\right)\Gamma^{2}\left(\frac{1}{c}+d\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c,d\right) & = & \frac{cd}{x^{c+1}\left(1+x^{-c}\right)^{d+1}}I_{\left(0,\infty\right)}\left(x\right)\\ F\left(x;c,d\right) & = & \left(1+x^{-c}\right)^{-d}\\ @@ -1230,15 +1278,16 @@ m_{d} & = & \left(\frac{cd-1}{c+1}\right)^{1/c}\,\textrm{if }cd>1\,\textrm{otherwise }0\\ m_{n} & = & \left(2^{1/d}-1\right)^{-1/c}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Cauchy -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{1}{\pi\left(1+x^{2}\right)}\\ F\left(x\right) & = & \frac{1}{2}+\frac{1}{\pi}\tan^{-1}x\\ @@ -1246,7 +1295,7 @@ m_{d} & = & 0\\ m_{n} & = & 0\end{eqnarray*} -\end_inset +\end_inset No finite moments. This is the t distribution with one degree of freedom. @@ -1255,35 +1304,37 @@ h\left[X\right] & = & \log\left(4\pi\right)\\ & \approx & 2.5310242469692907930.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Chi -\layout Standard +\end_layout +\begin_layout Standard Generated by taking the (positive) square-root of chi-squared variates. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\nu\right) & = & \frac{x^{\nu-1}e^{-x^{2}/2}}{2^{\nu/2-1}\Gamma\left(\frac{\nu}{2}\right)}I_{\left(0,\infty\right)}\left(x\right)\\ F\left(x;\nu\right) & = & \Gamma\left(\frac{\nu}{2},\frac{x^{2}}{2}\right)\\ G\left(\alpha;\nu\right) & = & \sqrt{2\Gamma^{-1}\left(\frac{\nu}{2},\alpha\right)}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\Gamma\left(\frac{v}{2}\right)\,_{1}F_{1}\left(\frac{v}{2};\frac{1}{2};\frac{t^{2}}{2}\right)+\frac{t}{\sqrt{2}}\Gamma\left(\frac{1+\nu}{2}\right)\,_{1}F_{1}\left(\frac{1+\nu}{2};\frac{3}{2};\frac{t^{2}}{2}\right)\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \frac{\sqrt{2}\Gamma\left(\frac{\nu+1}{2}\right)}{\Gamma\left(\frac{\nu}{2}\right)}\\ \mu_{2} & = & \nu-\mu^{2}\\ @@ -1292,50 +1343,53 @@ m_{d} & = & \sqrt{\nu-1}\quad\nu\geq1\\ m_{n} & = & \sqrt{2\Gamma^{-1}\left(\frac{\nu}{2},\frac{1}{2}\right)}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Chi-squared -\layout Standard +\end_layout +\begin_layout Standard This is the gamma distribution with \begin_inset Formula $L=0.0$ -\end_inset +\end_inset and \begin_inset Formula $S=2.0$ -\end_inset +\end_inset and \begin_inset Formula $\alpha=\nu/2$ -\end_inset +\end_inset where \begin_inset Formula $\nu$ -\end_inset +\end_inset is called the degrees of freedom. If \begin_inset Formula $Z_{1}\ldots Z_{\nu}$ -\end_inset +\end_inset are all standard normal distributions, then \begin_inset Formula $W=\sum_{k}Z_{k}^{2}$ -\end_inset +\end_inset has (standard) chi-square distribution with \begin_inset Formula $\nu$ -\end_inset +\end_inset degrees of freedom. -\layout Standard +\end_layout +\begin_layout Standard The standard form (most often used in standard form only) is \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1343,13 +1397,13 @@ F\left(x;\alpha\right) & = & \Gamma\left(\frac{\nu}{2},\frac{x}{2}\right)\\ G\left(q;\alpha\right) & = & 2\Gamma^{-1}\left(\frac{\nu}{2},q\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\frac{\Gamma\left(\frac{\nu}{2}\right)}{\left(\frac{1}{2}-t\right)^{\nu/2}}\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1359,19 +1413,21 @@ \gamma_{2} & = & \frac{12}{\nu}\\ m_{d} & = & \frac{\nu}{2}-1\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Cosine -\layout Standard +\end_layout +\begin_layout Standard Approximation to the normal distribution. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{1}{2\pi}\left[1+\cos x\right]I_{\left[-\pi,\pi\right]}\left(x\right)\\ F\left(x\right) & = & \frac{1}{2\pi}\left[\pi+x+\sin x\right]I_{\left[-\pi,\pi\right]}\left(x\right)+I_{\left(\pi,\infty\right)}\left(x\right)\\ @@ -1382,33 +1438,35 @@ \gamma_{1} & = & 0\\ \gamma_{2} & = & \frac{-6\left(\pi^{4}-90\right)}{5\left(\pi^{2}-6\right)^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(4\pi\right)-1\\ & \approx & 1.5310242469692907930.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Double Gamma -\layout Standard +\end_layout +\begin_layout Standard The double gamma is the signed version of the Gamma distribution. For \begin_inset Formula $\alpha>0:$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\alpha\right) & = & \frac{1}{2\Gamma\left(\alpha\right)}\left|x\right|^{\alpha-1}e^{-\left|x\right|}\\ F\left(x;\alpha\right) & = & \left\{ \begin{array}{ccc} @@ -1418,21 +1476,21 @@ -\Gamma^{-1}\left(\alpha,\left|2q-1\right|\right) & & q\leq\frac{1}{2}\\ \Gamma^{-1}\left(\alpha,\left|2q-1\right|\right) & & q>\frac{1}{2}\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=\frac{1}{2\left(1-t\right)^{a}}+\frac{1}{2\left(1+t\right)^{a}}\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu=m_{n} & = & 0\\ \mu_{2} & = & \alpha\left(\alpha+1\right)\\ @@ -1440,24 +1498,28 @@ \gamma_{2} & = & \frac{\left(\alpha+2\right)\left(\alpha+3\right)}{\alpha\left(\alpha+1\right)}-3\\ m_{d} & = & \textrm{NA}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Doubly Non-central F* -\layout Section +\end_layout +\begin_layout Section Doubly Non-central t* -\layout Section +\end_layout +\begin_layout Section Double Weibull -\layout Standard +\end_layout +\begin_layout Standard This is a signed form of the Weibull distribution. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c\right) & = & \frac{c}{2}\left|x\right|^{c-1}\exp\left(-\left|x\right|^{c}\right)\\ F\left(x;c\right) & = & \left\{ \begin{array}{ccc} @@ -1467,7 +1529,7 @@ -\log^{1/c}\left(\frac{1}{2q}\right) & & q\leq\frac{1}{2}\\ \log^{1/c}\left(\frac{1}{2q-1}\right) & & q>\frac{1}{2}\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ @@ -1475,7 +1537,7 @@ \Gamma\left(1+\frac{n}{c}\right) & n\textrm{ even}\\ 0 & n\textrm{ odd}\end{cases}\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1485,34 +1547,38 @@ \gamma_{2} & = & \frac{\Gamma\left(1+\frac{4}{c}\right)}{\Gamma^{2}\left(1+\frac{2}{c}\right)}\\ m_{d} & = & \textrm{NA bimodal}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Erlang -\layout Standard +\end_layout +\begin_layout Standard This is just the Gamma distribution with shape parameter \begin_inset Formula $\alpha=n$ -\end_inset +\end_inset an integer. -\layout Section +\end_layout +\begin_layout Section Exponential -\layout Standard +\end_layout +\begin_layout Standard This is a special case of the Gamma (and Erlang) distributions with shape parameter \begin_inset Formula $\left(\alpha=1\right)$ -\end_inset +\end_inset and the same location and scale parameters. The standard form is therefore ( \begin_inset Formula $x\geq0$ -\end_inset +\end_inset ) \begin_inset Formula \begin{eqnarray*} @@ -1520,25 +1586,25 @@ F\left(x\right) & = & \Gamma\left(1,x\right)=1-e^{-x}\\ G\left(q\right) & = & -\log\left(1-q\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=n!\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=\frac{1}{1-t}\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1548,31 +1614,33 @@ \gamma_{2} & = & 6\\ m_{d} & = & 0\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=1.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Exponentiated Weibull -\layout Standard +\end_layout +\begin_layout Standard Two positive shape parameters \begin_inset Formula $a$ -\end_inset +\end_inset and \begin_inset Formula $c$ -\end_inset +\end_inset and \begin_inset Formula $x\in\left(0,\infty\right)$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1580,22 +1648,24 @@ F\left(x;a,c\right) & = & \left[1-\exp\left(-x^{c}\right)\right]^{a}\\ G\left(q;a,c\right) & = & \left[-\log\left(1-q^{1/a}\right)\right]^{1/c}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Exponential Power -\layout Standard +\end_layout +\begin_layout Standard One positive shape parameter \begin_inset Formula $b$ -\end_inset +\end_inset . Defined for \begin_inset Formula $x\geq0.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1603,84 +1673,88 @@ F\left(x;b\right) & = & 1-\exp\left[1-e^{x^{b}}\right]\\ G\left(q;b\right) & = & \log^{1/b}\left[1-\log\left(1-q\right)\right]\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Fatigue Life (Birnbaum-Sanders) -\layout Standard +\end_layout +\begin_layout Standard This distribution's pdf is the average of the inverse-Gaussian \begin_inset Formula $\left(\mu=1\right)$ -\end_inset +\end_inset and reciprocal inverse-Gaussian pdf \begin_inset Formula $\left(\mu=1\right)$ -\end_inset +\end_inset . We follow the notation of JKB here with \begin_inset Formula $\beta=S.$ -\end_inset +\end_inset for \begin_inset Formula $x>0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c\right) & = & \frac{x+1}{2c\sqrt{2\pi x^{3}}}\exp\left(-\frac{\left(x-1\right)^{2}}{2xc^{2}}\right)\\ F\left(x;c\right) & = & \Phi\left(\frac{1}{c}\left(\sqrt{x}-\frac{1}{\sqrt{x}}\right)\right)\\ G\left(q;c\right) & = & \frac{1}{4}\left[c\Phi^{-1}\left(q\right)+\sqrt{c^{2}\left(\Phi^{-1}\left(q\right)\right)^{2}+4}\right]^{2}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=c\sqrt{2\pi}\exp\left[\frac{1}{c^{2}}\left(1-\sqrt{1-2c^{2}t}\right)\right]\left(1+\frac{1}{\sqrt{1-2c^{2}t}}\right)\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \frac{c^{2}}{2}+1\\ \mu_{2} & = & c^{2}\left(\frac{5}{4}c^{2}+1\right)\\ \gamma_{1} & = & \frac{4c\sqrt{11c^{2}+6}}{\left(5c^{2}+4\right)^{3/2}}\\ \gamma_{2} & = & \frac{6c^{2}\left(93c^{2}+41\right)}{\left(5c^{2}+4\right)^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Fisk (Log Logistic) -\layout Standard +\end_layout +\begin_layout Standard Special case of the Burr distribution with \begin_inset Formula $d=1$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} c & > & 0\\ k & = & \Gamma\left(1-\frac{2}{c}\right)\Gamma\left(\frac{2}{c}+1\right)-\Gamma^{2}\left(1-\frac{1}{c}\right)\Gamma^{2}\left(\frac{1}{c}+1\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c,d\right) & = & \frac{cx^{c-1}}{\left(1+x^{c}\right)^{2}}I_{\left(0,\infty\right)}\left(x\right)\\ F\left(x;c,d\right) & = & \left(1+x^{-c}\right)^{-1}\\ @@ -1695,45 +1769,47 @@ m_{d} & = & \left(\frac{c-1}{c+1}\right)^{1/c}\,\textrm{if }c>1\,\textrm{otherwise }0\\ m_{n} & = & 1\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=2-\log c.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Folded Cauchy -\layout Standard +\end_layout +\begin_layout Standard This formula can be expressed in terms of the standard formulas for the Cauchy distribution (call the cdf \begin_inset Formula $C\left(x\right)$ -\end_inset +\end_inset and the pdf \begin_inset Formula $d\left(x\right)$ -\end_inset +\end_inset ). if \begin_inset Formula $Y$ -\end_inset +\end_inset is cauchy then \begin_inset Formula $\left|Y\right|$ -\end_inset +\end_inset is folded cauchy. Note that \begin_inset Formula $x\geq0.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1741,54 +1817,57 @@ F\left(x;c\right) & = & \frac{1}{\pi}\tan^{-1}\left(x-c\right)+\frac{1}{\pi}\tan^{-1}\left(x+c\right)\\ G\left(q;c\right) & = & F^{-1}\left(x;c\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard No moments -\layout Section +\end_layout +\begin_layout Section Folded Normal -\layout Standard +\end_layout +\begin_layout Standard If \begin_inset Formula $Z$ -\end_inset +\end_inset is Normal with mean \begin_inset Formula $L$ -\end_inset +\end_inset and \begin_inset Formula $\sigma=S$ -\end_inset +\end_inset , then \begin_inset Formula $\left|Z\right|$ -\end_inset +\end_inset is a folded normal with shape parameter \begin_inset Formula $c=\left|L\right|/S$ -\end_inset +\end_inset , location parameter \begin_inset Formula $0$ -\end_inset +\end_inset and scale parameter \begin_inset Formula $S$ -\end_inset +\end_inset . This is a special case of the non-central chi distribution with one-degree of freedom and non-centrality parameter \begin_inset Formula $c^{2}.$ -\end_inset +\end_inset Note that \begin_inset Formula $c\geq0$ -\end_inset +\end_inset . The standard form of the folded normal is @@ -1797,13 +1876,13 @@ F\left(x;c\right) & = & \Phi\left(x-c\right)-\Phi\left(-x-c\right)=\Phi\left(x-c\right)+\Phi\left(x+c\right)-1\\ G\left(\alpha;c\right) & = & F^{-1}\left(x;c\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\exp\left[\frac{t}{2}\left(t-2c\right)\right]\left(1+e^{2ct}\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1814,38 +1893,40 @@ \gamma_{1} & = & \frac{\sqrt{\frac{2}{\pi}}p^{3}\left(4-\frac{\pi}{p^{2}}\left(2c^{2}+1\right)\right)+2ck\left(6p^{2}+3cpk\sqrt{2\pi}+\pi c\left(k^{2}-1\right)\right)}{\pi\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{c^{4}+6c^{2}+3+6\left(c^{2}+1\right)\mu^{2}-3\mu^{4}-4p\mu\left(\sqrt{\frac{2}{\pi}}\left(c^{2}+2\right)+\frac{ck}{p}\left(c^{2}+3\right)\right)}{\mu_{2}^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Fratio (or F) -\layout Standard +\end_layout +\begin_layout Standard Defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset . The distribution of \begin_inset Formula $\left(X_{1}/X_{2}\right)\left(\nu_{2}/\nu_{1}\right)$ -\end_inset +\end_inset if \begin_inset Formula $X_{1}$ -\end_inset +\end_inset is chi-squared with \begin_inset Formula $v_{1}$ -\end_inset +\end_inset degrees of freedom and \begin_inset Formula $X_{2}$ -\end_inset +\end_inset is chi-squared with \begin_inset Formula $v_{2}$ -\end_inset +\end_inset degrees of freedom. @@ -1854,7 +1935,7 @@ F\left(x;v_{1},v_{2}\right) & = & I\left(\frac{\nu_{1}}{2},\frac{\nu_{2}}{2},\frac{\nu_{2}x}{\nu_{2}+\nu_{1}x}\right)\\ G\left(q;\nu_{1},\nu_{2}\right) & = & \left[\frac{\nu_{2}}{I^{-1}\left(\nu_{1}/2,\nu_{2}/2,q\right)}-\frac{\nu_{1}}{\nu_{2}}\right]^{-1}.\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1863,22 +1944,24 @@ \gamma_{1} & = & \frac{2\left(2\nu_{1}+\nu_{2}-2\right)}{\nu_{2}-6}\sqrt{\frac{2\left(\nu_{2}-4\right)}{\nu_{1}\left(\nu_{1}+\nu_{2}-2\right)}}\quad\nu_{2}>6\\ \gamma_{2} & = & \frac{3\left[8+\left(\nu_{2}-6\right)\gamma_{1}^{2}\right]}{2\nu-16}\quad\nu_{2}>8\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Fr?chet (ExtremeLB, Extreme Value II, Weibull minimum) -\layout Standard +\end_layout +\begin_layout Standard A type of extreme-value distribution with a lower bound. Defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset and \begin_inset Formula $c>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1886,13 +1969,13 @@ F\left(x;c\right) & = & 1-\exp\left(-x^{c}\right)\\ G\left(q;c\right) & = & \left[-\log\left(1-q\right)\right]^{1/c}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\Gamma\left(1+\frac{n}{c}\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1903,37 +1986,39 @@ m_{d} & = & \left(\frac{c}{1+c}\right)^{1/c}\\ m_{n} & = & G\left(\frac{1}{2};c\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=-\frac{\gamma}{c}-\log\left(c\right)+\gamma+1\] -\end_inset +\end_inset where \begin_inset Formula $\gamma$ -\end_inset +\end_inset is Euler's constant and equal to \begin_inset Formula \[ \gamma\approx0.57721566490153286061.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Fr?chet (left-skewed, Extreme Value Type III, Weibull maximum) -\layout Standard +\end_layout +\begin_layout Standard Defined for \begin_inset Formula $x<0$ -\end_inset +\end_inset and \begin_inset Formula $c>0$ -\end_inset +\end_inset . @@ -1942,53 +2027,56 @@ F\left(x;c\right) & = & \exp\left(-\left(-x\right)^{c}\right)\\ G\left(q;c\right) & = & -\left(-\log q\right)^{1/c}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard The mean is the negative of the right-skewed Frechet distribution given above, and the other statistical parameters can be computed from -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=\left(-1\right)^{n}\Gamma\left(1+\frac{n}{c}\right).\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=-\frac{\gamma}{c}-\log\left(c\right)+\gamma+1\] -\end_inset +\end_inset where \begin_inset Formula $\gamma$ -\end_inset +\end_inset is Euler's constant and equal to \begin_inset Formula \[ \gamma\approx0.57721566490153286061.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gamma -\layout Standard +\end_layout +\begin_layout Standard The standard form for the gamma distribution is \begin_inset Formula $\left(\alpha>0\right)$ -\end_inset +\end_inset valid for \begin_inset Formula $x\geq0$ -\end_inset +\end_inset . \begin_inset Formula \begin{eqnarray*} @@ -1996,13 +2084,13 @@ F\left(x;\alpha\right) & = & \Gamma\left(\alpha,x\right)\\ G\left(q;\alpha\right) & = & \Gamma^{-1}\left(\alpha,q\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\frac{1}{\left(1-t\right)^{\alpha}}\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2012,62 +2100,64 @@ \gamma_{2} & = & \frac{6}{\alpha}\\ m_{d} & = & \alpha-1\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=\Psi\left(a\right)\left[1-a\right]+a+\log\Gamma\left(a\right)\] -\end_inset +\end_inset where \begin_inset Formula \[ \Psi\left(a\right)=\frac{\Gamma^{\prime}\left(a\right)}{\Gamma\left(a\right)}.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Logistic -\layout Standard +\end_layout +\begin_layout Standard Has been used in the analysis of extreme values. Has one shape parameter \begin_inset Formula $c>0.$ -\end_inset +\end_inset And \begin_inset Formula $x>0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c\right) & = & \frac{c\exp\left(-x\right)}{\left[1+\exp\left(-x\right)\right]^{c+1}}\\ F\left(x;c\right) & = & \frac{1}{\left[1+\exp\left(-x\right)\right]^{c}}\\ G\left(q;c\right) & = & -\log\left(q^{-1/c}-1\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=\frac{c}{1-t}\,_{2}F_{1}\left(1+c,\,1-t\,;\,2-t\,;-1\right)\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \gamma+\psi_{0}\left(c\right)\\ \mu_{2} & = & \frac{\pi^{2}}{6}+\psi_{1}\left(c\right)\\ @@ -2076,7 +2166,7 @@ m_{d} & = & \log c\\ m_{n} & = & -\log\left(2^{1/c}-1\right)\end{eqnarray*} -\end_inset +\end_inset Note that the polygamma function is \begin_inset Formula \begin{eqnarray*} @@ -2084,42 +2174,44 @@ & = & \left(-1\right)^{n+1}n!\sum_{k=0}^{\infty}\frac{1}{\left(z+k\right)^{n+1}}\\ & = & \left(-1\right)^{n+1}n!\zeta\left(n+1,z\right)\end{eqnarray*} -\end_inset +\end_inset where \begin_inset Formula $\zeta\left(k,x\right)$ -\end_inset +\end_inset is a generalization of the Riemann zeta function called the Hurwitz zeta function Note that \begin_inset Formula $\zeta\left(n\right)\equiv\zeta\left(n,1\right)$ -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Pareto -\layout Standard +\end_layout +\begin_layout Standard Shape parameter \begin_inset Formula $c\neq0$ -\end_inset +\end_inset and defined for \begin_inset Formula $x\geq0$ -\end_inset +\end_inset for all \begin_inset Formula $c$ -\end_inset +\end_inset and \begin_inset Formula $x<\frac{1}{\left|c\right|}$ -\end_inset +\end_inset if \begin_inset Formula $c$ -\end_inset +\end_inset is negative. @@ -2128,29 +2220,29 @@ F\left(x;c\right) & = & 1-\frac{1}{\left(1+cx\right)^{1/c}}\\ G\left(q;c\right) & = & \frac{1}{c}\left[\left(\frac{1}{1-q}\right)^{c}-1\right]\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=\left\{ \begin{array}{cc} \left(-\frac{t}{c}\right)^{\frac{1}{c}}e^{-\frac{t}{c}}\left[\Gamma\left(1-\frac{1}{c}\right)+\Gamma\left(-\frac{1}{c},-\frac{t}{c}\right)-\pi\csc\left(\frac{\pi}{c}\right)/\Gamma\left(\frac{1}{c}\right)\right] & c>0\\ \left(\frac{\left|c\right|}{t}\right)^{1/\left|c\right|}\Gamma\left[\frac{1}{\left|c\right|},\frac{t}{\left|c\right|}\right] & c<0\end{array}\right.\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=\frac{\left(-1\right)^{n}}{c^{n}}\sum_{k=0}^{n}\left(\begin{array}{c} n\\ k\end{array}\right)\frac{\left(-1\right)^{k}}{1-ck}\quad cn<1\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2159,7 +2251,7 @@ \mu_{3}^{\prime} & = & \frac{6}{\left(1-c\right)\left(1-2c\right)\left(1-3c\right)}\quad c<\frac{1}{3}\\ \mu_{4}^{\prime} & = & \frac{24}{\left(1-c\right)\left(1-2c\right)\left(1-3c\right)\left(1-4c\right)}\quad c<\frac{1}{4}\end{eqnarray*} -\end_inset +\end_inset Thus, \begin_inset Formula \begin{eqnarray*} @@ -2168,38 +2260,40 @@ \gamma_{1} & = & \frac{\mu_{3}^{\prime}-3\mu\mu_{2}-\mu^{3}}{\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{\mu_{4}^{\prime}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=1+c\quad c>0\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Exponential -\layout Standard +\end_layout +\begin_layout Standard Three positive shape parameters for \begin_inset Formula $x\geq0.$ -\end_inset +\end_inset Note that \begin_inset Formula $a,b,$ -\end_inset +\end_inset and \begin_inset Formula $c$ -\end_inset +\end_inset are all \begin_inset Formula $>0.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2207,29 +2301,32 @@ F\left(x;a,b,c\right) & = & 1-\exp\left[ax-bx+\frac{b}{c}\left(1-e^{-cx}\right)\right]\\ G\left(q;a,b,c\right) & = & F^{-1}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Extreme Value -\layout Standard +\end_layout +\begin_layout Standard Extreme value distributions with shape parameter \begin_inset Formula $c$ -\end_inset +\end_inset . -\layout Standard +\end_layout +\begin_layout Standard For \begin_inset Formula $c>0$ -\end_inset +\end_inset defined on \begin_inset Formula $-\infty-1\] -\end_inset +\end_inset So, \begin_inset Formula \begin{eqnarray*} @@ -2254,19 +2351,19 @@ \mu_{3}^{\prime} & = & \frac{1}{c^{3}}\left(1-3\Gamma\left(1+c\right)+3\Gamma\left(1+2c\right)-\Gamma\left(1+3c\right)\right)\quad c>-\frac{1}{3}\\ \mu_{4}^{\prime} & = & \frac{1}{c^{4}}\left(1-4\Gamma\left(1+c\right)+6\Gamma\left(1+2c\right)-4\Gamma\left(1+3c\right)+\Gamma\left(1+4c\right)\right)\quad c>-\frac{1}{4}\end{eqnarray*} -\end_inset +\end_inset For \begin_inset Formula $c<0$ -\end_inset +\end_inset defined on \begin_inset Formula $\frac{1}{c}\leq x<\infty.$ -\end_inset +\end_inset For \begin_inset Formula $c=0$ -\end_inset +\end_inset defined over all space \begin_inset Formula \begin{eqnarray*} @@ -2274,7 +2371,7 @@ F\left(x;0\right) & = & \exp\left[-e^{-x}\right]\\ G\left(q;0\right) & = & -\log\left(-\log q\right)\end{eqnarray*} -\end_inset +\end_inset This is just the (left-skewed) Gumbel distribution for c=0. @@ -2284,30 +2381,32 @@ \gamma_{1} & = & \frac{12\sqrt{6}}{\pi^{3}}\zeta\left(3\right)\\ \gamma_{2} & = & \frac{12}{5}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Gamma -\layout Standard +\end_layout +\begin_layout Standard A general probability form that reduces to many common distributions: \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula $a>0$ -\end_inset +\end_inset and \begin_inset Formula $c\neq0.$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;a,c\right) & = & \frac{\left|c\right|x^{ca-1}}{\Gamma\left(a\right)}\exp\left(-x^{c}\right)\\ F\left(x;a,c\right) & = & \begin{array}{cc} @@ -2316,13 +2415,13 @@ G\left(q;a,c\right) & = & \left\{ \Gamma^{-1}\left[a,\Gamma\left(a\right)q\right]\right\} ^{1/c}\quad c>0\\ & & \left\{ \Gamma^{-1}\left[a,\Gamma\left(a\right)\left(1-q\right)\right]\right\} ^{1/c}\quad c<0\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\frac{\Gamma\left(a+\frac{n}{c}\right)}{\Gamma\left(a\right)}\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2332,47 +2431,49 @@ \gamma_{2} & = & \frac{\Gamma\left(a+\frac{4}{c}\right)/\Gamma\left(a\right)-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\\ m_{d} & = & \left(\frac{ac-1}{c}\right)^{1/c}.\end{eqnarray*} -\end_inset +\end_inset Special cases are Weibull \begin_inset Formula $\left(a=1\right)$ -\end_inset +\end_inset , half-normal \begin_inset Formula $\left(a=1/2,c=2\right)$ -\end_inset +\end_inset and ordinary gamma distributions \begin_inset Formula $c=1.$ -\end_inset +\end_inset If \begin_inset Formula $c=-1$ -\end_inset +\end_inset then it is the inverted gamma distribution. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=a-a\Psi\left(a\right)+\frac{1}{c}\Psi\left(a\right)+\log\Gamma\left(a\right)-\log\left|c\right|.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Half-Logistic -\layout Standard +\end_layout +\begin_layout Standard For \begin_inset Formula $x\in\left[0,1/c\right]$ -\end_inset +\end_inset and \begin_inset Formula $c>0$ -\end_inset +\end_inset we have \begin_inset Formula \begin{eqnarray*} @@ -2380,34 +2481,36 @@ F\left(x;c\right) & = & \frac{1-\left(1-cx\right)^{1/c}}{1+\left(1-cx\right)^{1/c}}\\ G\left(q;c\right) & = & \frac{1}{c}\left[1-\left(\frac{1-q}{1+q}\right)^{c}\right]\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & 2-\left(2c+1\right)\log2.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gilbrat -\layout Standard +\end_layout +\begin_layout Standard Special case of the log-normal with \begin_inset Formula $\sigma=1$ -\end_inset +\end_inset and \begin_inset Formula $S=1.0$ -\end_inset +\end_inset (typically also \begin_inset Formula $L=0.0$ -\end_inset +\end_inset ) \begin_inset Formula \begin{eqnarray*} @@ -2415,7 +2518,7 @@ F\left(x;\sigma\right) & = & \Phi\left(\log x\right)=\frac{1}{2}\left[1+\textrm{erf}\left(\frac{\log x}{\sqrt{2}}\right)\right]\\ G\left(q;\sigma\right) & = & \exp\left\{ \Phi^{-1}\left(q\right)\right\} \end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2424,58 +2527,60 @@ \gamma_{1} & = & \sqrt{e-1}\left(2+e\right)\\ \gamma_{2} & = & e^{4}+2e^{3}+3e^{2}-6\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(\sqrt{2\pi e}\right)\\ & \approx & 1.4189385332046727418\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gompertz (Truncated Gumbel) -\layout Standard +\end_layout +\begin_layout Standard For \begin_inset Formula $x\geq0$ -\end_inset +\end_inset and \begin_inset Formula $c>0$ -\end_inset +\end_inset . In JKB the two shape parameters \begin_inset Formula $b,a$ -\end_inset +\end_inset are reduced to the single shape-parameter \begin_inset Formula $c=b/a$ -\end_inset +\end_inset . As \begin_inset Formula $a$ -\end_inset +\end_inset is just a scale parameter when \begin_inset Formula $a\neq0$ -\end_inset +\end_inset . If \begin_inset Formula $a=0,$ -\end_inset +\end_inset the distribution reduces to the exponential distribution scaled by \begin_inset Formula $1/b.$ -\end_inset +\end_inset Thus, the standard form is given as \begin_inset Formula \begin{eqnarray*} @@ -2483,45 +2588,47 @@ F\left(x;c\right) & = & 1-\exp\left[-c\left(e^{x}-1\right)\right]\\ G\left(q;c\right) & = & \log\left[1-\frac{1}{c}\log\left(1-q\right)\right]\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=1-\log\left(c\right)-e^{c}\textrm{Ei}\left(1,c\right),\] -\end_inset +\end_inset where \begin_inset Formula \[ \textrm{Ei}\left(n,x\right)=\int_{1}^{\infty}t^{-n}\exp\left(-xt\right)dt\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gumbel (LogWeibull, Fisher-Tippetts, Type I Extreme Value) -\layout Standard +\end_layout +\begin_layout Standard One of a clase of extreme value distributions (right-skewed). -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \exp\left(-\left(x+e^{-x}\right)\right)\\ F\left(x\right) & = & \exp\left(-e^{-x}\right)\\ G\left(q\right) & = & -\log\left(-\log\left(q\right)\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\Gamma\left(1-t\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2532,80 +2639,83 @@ m_{d} & = & 0\\ m_{n} & = & -\log\left(\log2\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]\approx1.0608407169541684911\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gumbel Left-skewed (for minimum order statistic) -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \exp\left(x-e^{x}\right)\\ F\left(x\right) & = & 1-\exp\left(-e^{x}\right)\\ G\left(q\right) & = & \log\left(-\log\left(1-q\right)\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\Gamma\left(1+t\right)\] -\end_inset +\end_inset Note, that \begin_inset Formula $\mu$ -\end_inset +\end_inset is negative the mean for the right-skewed distribution. Similar for median and mode. All other moments are the same. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]\approx1.0608407169541684911.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section HalfCauchy -\layout Standard +\end_layout +\begin_layout Standard If \begin_inset Formula $Z$ -\end_inset +\end_inset is Hyperbolic Secant distributed then \begin_inset Formula $e^{Z}$ -\end_inset +\end_inset is Half-Cauchy distributed. Also, if \begin_inset Formula $W$ -\end_inset +\end_inset is (standard) Cauchy distributed, then \begin_inset Formula $\left|W\right|$ -\end_inset +\end_inset is Half-Cauchy distributed. Special case of the Folded Cauchy distribution with \begin_inset Formula $c=0.$ -\end_inset +\end_inset The standard form is \begin_inset Formula \begin{eqnarray*} @@ -2613,68 +2723,70 @@ F\left(x\right) & = & \frac{2}{\pi}\arctan\left(x\right)I_{\left[0,\infty\right]}\left(x\right)\\ G\left(q\right) & = & \tan\left(\frac{\pi}{2}q\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\cos t+\frac{2}{\pi}\left[\textrm{Si}\left(t\right)\cos t-\textrm{Ci}\left(\textrm{-}t\right)\sin t\right]\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} m_{d} & = & 0\\ m_{n} & = & \tan\left(\frac{\pi}{4}\right)\end{eqnarray*} -\end_inset +\end_inset No moments, as the integrals diverge. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(2\pi\right)\\ & \approx & 1.8378770664093454836.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section HalfNormal -\layout Standard +\end_layout +\begin_layout Standard This is a special case of the chi distribution with \begin_inset Formula $L=a$ -\end_inset +\end_inset and \begin_inset Formula $S=b$ -\end_inset +\end_inset and \begin_inset Formula $\nu=1.$ -\end_inset +\end_inset This is also a special case of the folded normal with shape parameter \begin_inset Formula $c=0$ -\end_inset +\end_inset and \begin_inset Formula $S=S.$ -\end_inset +\end_inset If \begin_inset Formula $Z$ -\end_inset +\end_inset is (standard) normally distributed then, \begin_inset Formula $\left|Z\right|$ -\end_inset +\end_inset is half-normal. The standard form is @@ -2683,18 +2795,18 @@ F\left(x\right) & = & 2\Phi\left(x\right)-1\\ G\left(q\right) & = & \Phi^{-1}\left(\frac{1+q}{2}\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\sqrt{2\pi}e^{t^{2}/2}\Phi\left(t\right)\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \sqrt{\frac{2}{\pi}}\\ \mu_{2} & = & 1-\frac{2}{\pi}\\ @@ -2703,40 +2815,42 @@ m_{d} & = & 0\\ m_{n} & = & \Phi^{-1}\left(\frac{3}{4}\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(\sqrt{\frac{\pi e}{2}}\right)\\ & \approx & 0.72579135264472743239.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Half-Logistic -\layout Standard +\end_layout +\begin_layout Standard In the limit as \begin_inset Formula $c\rightarrow\infty$ -\end_inset +\end_inset for the generalized half-logistic we have the half-logistic defined over \begin_inset Formula $x\geq0.$ -\end_inset +\end_inset Also, the distribution of \begin_inset Formula $\left|X\right|$ -\end_inset +\end_inset where \begin_inset Formula $X$ -\end_inset +\end_inset has logistic distribtution. @@ -2745,22 +2859,22 @@ F\left(x\right) & = & \frac{1-e^{-x}}{1+e^{-x}}=\tanh\left(\frac{x}{2}\right)\\ G\left(q\right) & = & \log\left(\frac{1+q}{1-q}\right)=2\textrm{arctanh}\left(q\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=1-t\psi_{0}\left(\frac{1}{2}-\frac{t}{2}\right)+t\psi_{0}\left(1-\frac{t}{2}\right)\] -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=2\left(1-2^{1-n}\right)n!\zeta\left(n\right)\quad n\neq1\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2769,28 +2883,30 @@ \mu_{3}^{\prime} & = & 9\zeta\left(3\right)\\ \mu_{4}^{\prime} & = & 42\zeta\left(4\right)=\frac{7\pi^{4}}{15}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & 2-\log\left(2\right)\\ & \approx & 1.3068528194400546906.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Hyperbolic Secant -\layout Standard +\end_layout +\begin_layout Standard Related to the logistic distribution and used in lifetime analysis. Standard form is (defined over all \begin_inset Formula $x$ -\end_inset +\end_inset ) \begin_inset Formula \begin{eqnarray*} @@ -2798,13 +2914,13 @@ F\left(x\right) & = & \frac{2}{\pi}\arctan\left(e^{x}\right)\\ G\left(q\right) & = & \log\left(\tan\left(\frac{\pi}{2}q\right)\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\sec\left(\frac{\pi}{2}t\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2813,30 +2929,30 @@ 0 & n\textrm{ odd}\\ C_{n/2}\frac{\pi^{n}}{2^{n}} & n\textrm{ even}\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset where \begin_inset Formula $C_{m}$ -\end_inset +\end_inset is an integer given by \begin_inset Formula \begin{eqnarray*} C_{m} & = & \frac{\left(2m\right)!\left[\zeta\left(2m+1,\frac{1}{4}\right)-\zeta\left(2m+1,\frac{3}{4}\right)\right]}{\pi^{2m+1}2^{2m}}\\ & = & 4\left(-1\right)^{m-1}\frac{16^{m}}{2m+1}B_{2m+1}\left(\frac{1}{4}\right)\end{eqnarray*} -\end_inset +\end_inset where \begin_inset Formula $B_{2m+1}\left(\frac{1}{4}\right)$ -\end_inset +\end_inset is the Bernoulli polynomial of order \begin_inset Formula $2m+1$ -\end_inset +\end_inset evaluated at \begin_inset Formula $1/4.$ -\end_inset +\end_inset Thus \begin_inset Formula \[ @@ -2844,90 +2960,93 @@ 0 & n\textrm{ odd}\\ 4\left(-1\right)^{n/2-1}\frac{\left(2\pi\right)^{n}}{n+1}B_{n+1}\left(\frac{1}{4}\right) & n\textrm{ even}\end{array}\right.\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} m_{d}=m_{n}=\mu & = & 0\\ \mu_{2} & = & \frac{\pi^{2}}{4}\\ \gamma_{1} & = & 0\\ \gamma_{2} & = & 2\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=\log\left(2\pi\right).\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gauss Hypergeometric -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula $x\in\left[0,1\right]$ -\end_inset +\end_inset , \begin_inset Formula $\alpha>0,\,\beta>0$ -\end_inset +\end_inset \begin_inset Formula \[ C^{-1}=B\left(\alpha,\beta\right)\,_{2}F_{1}\left(\gamma,\alpha;\alpha+\beta;-z\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} f\left(x;\alpha,\beta,\gamma,z\right) & = & Cx^{\alpha-1}\frac{\left(1-x\right)^{\beta-1}}{\left(1+zx\right)^{\gamma}}\\ \mu_{n}^{\prime} & = & \frac{B\left(n+\alpha,\beta\right)}{B\left(\alpha,\beta\right)}\frac{\,_{2}F_{1}\left(\gamma,\alpha+n;\alpha+\beta+n;-z\right)}{\,_{2}F_{1}\left(\gamma,\alpha;\alpha+\beta;-z\right)}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Inverted Gamma -\layout Standard +\end_layout +\begin_layout Standard Special case of the generalized Gamma distribution with \begin_inset Formula $c=-1$ -\end_inset +\end_inset and \begin_inset Formula $a>0$ -\end_inset +\end_inset , \begin_inset Formula $x>0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;a\right) & = & \frac{x^{-a-1}}{\Gamma\left(a\right)}\exp\left(-\frac{1}{x}\right)\\ F\left(x;a\right) & = & \frac{\Gamma\left(a,\frac{1}{x}\right)}{\Gamma\left(a\right)}\\ G\left(q;a\right) & = & \left\{ \Gamma^{-1}\left[a,\Gamma\left(a\right)q\right]\right\} ^{-1}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\frac{\Gamma\left(a-n\right)}{\Gamma\left(a\right)}\quad a>n\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2936,54 +3055,56 @@ \gamma_{1} & = & \frac{\frac{1}{\left(a-3\right)\left(a-2\right)\left(a-1\right)}-3\mu\mu_{2}-\mu^{3}}{\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{\frac{1}{\left(a-4\right)\left(a-3\right)\left(a-2\right)\left(a-1\right)}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ m_{d}=\frac{1}{a+1}\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=a-\left(a+1\right)\Psi\left(a\right)+\log\Gamma\left(a\right).\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Inverse Normal (Inverse Gaussian) -\layout Standard +\end_layout +\begin_layout Standard The standard form involves the shape parameter \begin_inset Formula $\mu$ -\end_inset +\end_inset (in most definitions, \begin_inset Formula $L=0.0$ -\end_inset +\end_inset is used). (In terms of the regress documentation \begin_inset Formula $\mu=A/B$ -\end_inset +\end_inset ) and \begin_inset Formula $B=S$ -\end_inset +\end_inset and \begin_inset Formula $L$ -\end_inset +\end_inset is not a parameter in that distribution. A standard form is \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2991,12 +3112,12 @@ F\left(x;\mu\right) & = & \Phi\left(\frac{1}{\sqrt{x}}\frac{x-\mu}{\mu}\right)+\exp\left(\frac{2}{\mu}\right)\Phi\left(-\frac{1}{\sqrt{x}}\frac{x+\mu}{\mu}\right)\\ G\left(q;\mu\right) & = & F^{-1}\left(q;\mu\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \mu\\ \mu_{2} & = & \mu^{3}\\ @@ -3004,80 +3125,83 @@ \gamma_{2} & = & 15\mu\\ m_{d} & = & \frac{\mu}{2}\left(\sqrt{9\mu^{2}+4}-3\mu\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard This is related to the canonical form or JKB \begin_inset Quotes eld -\end_inset +\end_inset two-parameter \begin_inset Quotes erd -\end_inset +\end_inset inverse Gaussian when written in it's full form with scale parameter \begin_inset Formula $S$ -\end_inset +\end_inset and location parameter \begin_inset Formula $L$ -\end_inset +\end_inset by taking \begin_inset Formula $L=0$ -\end_inset +\end_inset and \begin_inset Formula $S\equiv\lambda,$ -\end_inset +\end_inset then \begin_inset Formula $\mu S$ -\end_inset +\end_inset is equal to \begin_inset Formula $\mu_{2}$ -\end_inset +\end_inset where -\bar under +\bar under -\bar default +\bar default \begin_inset Formula $\mu_{2}$ -\end_inset +\end_inset is the parameter used by JKB. We prefer this form because of it's consistent use of the scale parameter. Notice that in JKB the skew \begin_inset Formula $\left(\sqrt{\beta_{1}}\right)$ -\end_inset +\end_inset and the kurtosis ( \begin_inset Formula $\beta_{2}-3$ -\end_inset +\end_inset ) are both functions only of \begin_inset Formula $\mu_{2}/\lambda=\mu S/S=\mu$ -\end_inset +\end_inset as shown here, while the variance and mean of the standard form here are transformed appropriately. -\layout Section +\end_layout +\begin_layout Section Inverted Weibull -\layout Standard +\end_layout +\begin_layout Standard Shape parameter \begin_inset Formula $c>0$ -\end_inset +\end_inset and \begin_inset Formula $x>0$ -\end_inset +\end_inset . Then @@ -3086,35 +3210,37 @@ F\left(x;c\right) & = & \exp\left(-x^{-c}\right)\\ G\left(q;c\right) & = & \left(-\log q\right)^{-1/c}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=1+\gamma+\frac{\gamma}{c}-\log\left(c\right)\] -\end_inset +\end_inset where \begin_inset Formula $\gamma$ -\end_inset +\end_inset is Euler's constant. -\layout Section +\end_layout +\begin_layout Section Johnson SB -\layout Standard +\end_layout +\begin_layout Standard Defined for \begin_inset Formula $x\in\left(0,1\right)$ -\end_inset +\end_inset with two shape parameters \begin_inset Formula $a$ -\end_inset +\end_inset and \begin_inset Formula $b>0.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3122,25 +3248,27 @@ F\left(x;a,b\right) & = & \Phi\left(a+b\log\frac{x}{1-x}\right)\\ G\left(q;a,b\right) & = & \frac{1}{1+\exp\left[-\frac{1}{b}\left(\Phi^{-1}\left(q\right)-a\right)\right]}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Johnson SU -\layout Standard +\end_layout +\begin_layout Standard Defined for all \begin_inset Formula $x$ -\end_inset +\end_inset with two shape parameters \begin_inset Formula $a$ -\end_inset +\end_inset and \begin_inset Formula $b>0$ -\end_inset +\end_inset . @@ -3149,21 +3277,24 @@ F\left(x;a,b\right) & = & \Phi\left(a+b\log\left(x+\sqrt{x^{2}+1}\right)\right)\\ G\left(q;a,b\right) & = & \sinh\left[\frac{\Phi^{-1}\left(q\right)-a}{b}\right]\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section KSone -\layout Section +\end_layout +\begin_layout Section KStwo -\layout Section +\end_layout +\begin_layout Section Laplace (Double Exponential, Bilateral Expoooonential) -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{1}{2}e^{-\left|x\right|}\\ F\left(x\right) & = & \left\{ \begin{array}{ccc} @@ -3173,7 +3304,7 @@ \log\left(2q\right) & & q\leq\frac{1}{2}\\ -\log\left(2-2q\right) & & q>\frac{1}{2}\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3182,95 +3313,98 @@ \gamma_{1} & = & 0\\ \gamma_{2} & = & 3\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard The ML estimator of the location parameter is \begin_inset Formula \[ \hat{L}=\textrm{median}\left(X_{i}\right)\] -\end_inset +\end_inset where \begin_inset Formula $X_{i}$ -\end_inset +\end_inset is a sequence of \begin_inset Formula $N$ -\end_inset +\end_inset mutually independent Laplace RV's and the median is some number between the \begin_inset Formula $\frac{1}{2}N\textrm{th}$ -\end_inset +\end_inset and the \begin_inset Formula $(N/2+1)\textrm{th}$ -\end_inset +\end_inset order statistic ( -\emph on +\emph on e.g. -\emph default +\emph default take the average of these two) when \begin_inset Formula $N$ -\end_inset +\end_inset is even. Also, \begin_inset Formula \[ \hat{S}=\frac{1}{N}\sum_{j=1}^{N}\left|X_{j}-\hat{L}\right|.\] -\end_inset +\end_inset Replace \begin_inset Formula $\hat{L}$ -\end_inset +\end_inset with \begin_inset Formula $L$ -\end_inset +\end_inset if it is known. If \begin_inset Formula $L$ -\end_inset +\end_inset is known then this estimator is distributed as \begin_inset Formula $\left(2N\right)^{-1}S\cdot\chi_{2N}^{2}$ -\end_inset +\end_inset . -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(2e\right)\\ & \approx & 1.6931471805599453094.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Left-skewed L?vy -\layout Standard +\end_layout +\begin_layout Standard Special case of L?vy-stable distribution with \begin_inset Formula $\alpha=\frac{1}{2}$ -\end_inset +\end_inset and \begin_inset Formula $\beta=-1$ -\end_inset +\end_inset the support is \begin_inset Formula $x<0$ -\end_inset +\end_inset . In standard form @@ -3279,26 +3413,28 @@ F\left(x\right) & = & 2\Phi\left(\frac{1}{\sqrt{\left|x\right|}}\right)-1\\ G\left(q\right) & = & -\left[\Phi^{-1}\left(\frac{q+1}{2}\right)\right]^{-2}.\end{eqnarray*} -\end_inset +\end_inset No moments. -\layout Section +\end_layout +\begin_layout Section L?vy -\layout Standard +\end_layout +\begin_layout Standard A special case of L?vy-stable distributions with \begin_inset Formula $\alpha=\frac{1}{2}$ -\end_inset +\end_inset and \begin_inset Formula $\beta=1$ -\end_inset +\end_inset . In standard form it is defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset as \begin_inset Formula \begin{eqnarray*} @@ -3306,37 +3442,39 @@ F\left(x\right) & = & 2\left[1-\Phi\left(\frac{1}{\sqrt{x}}\right)\right]\\ G\left(q\right) & = & \left[\Phi^{-1}\left(1-\frac{q}{2}\right)\right]^{-2}.\end{eqnarray*} -\end_inset +\end_inset It has no finite moments. -\layout Section +\end_layout +\begin_layout Section Logistic (Sech-squared) -\layout Standard +\end_layout +\begin_layout Standard A special case of the Generalized Logistic distribution with \begin_inset Formula $c=1.$ -\end_inset +\end_inset Defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{\exp\left(-x\right)}{\left[1+\exp\left(-x\right)\right]^{2}}\\ F\left(x\right) & = & \frac{1}{1+\exp\left(-x\right)}\\ G\left(q\right) & = & -\log\left(1/q-1\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \gamma+\psi_{0}\left(1\right)=0\\ \mu_{2} & = & \frac{\pi^{2}}{6}+\psi_{1}\left(1\right)=\frac{\pi^{2}}{3}\\ @@ -3345,30 +3483,32 @@ m_{d} & = & \log1=0\\ m_{n} & = & -\log\left(2-1\right)=0\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=1.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Log Double Exponential (Log-Laplace) -\layout Standard +\end_layout +\begin_layout Standard Defined over \begin_inset Formula $x>0$ -\end_inset +\end_inset with \begin_inset Formula $c>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3382,30 +3522,32 @@ \left(2q\right)^{1/c} & & 0\leq q<\frac{1}{2}\\ \left(2-2q\right)^{-1/c} & & \frac{1}{2}\leq q\leq1\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=\log\left(\frac{2e}{c}\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Log Gamma -\layout Standard +\end_layout +\begin_layout Standard A single shape parameter \begin_inset Formula $c>0$ -\end_inset +\end_inset (Defined for all \begin_inset Formula $x$ -\end_inset +\end_inset ) \begin_inset Formula \begin{eqnarray*} @@ -3413,13 +3555,13 @@ F\left(x;c\right) & = & \frac{\Gamma\left(c,e^{x}\right)}{\Gamma\left(c\right)}\\ G\left(q;c\right) & = & \log\left[\Gamma^{-1}\left[c,q\Gamma\left(c\right)\right]\right]\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\int_{0}^{\infty}\left[\log y\right]^{n}y^{c-1}\exp\left(-y\right)dy.\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3428,43 +3570,45 @@ \gamma_{1} & = & \frac{\mu_{3}^{\prime}-3\mu\mu_{2}-\mu^{3}}{\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{\mu_{4}^{\prime}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Log Normal (Cobb-Douglass) -\layout Standard +\end_layout +\begin_layout Standard Has one shape parameter \begin_inset Formula $\sigma$ -\end_inset +\end_inset >0. (Notice that the \begin_inset Quotes eld -\end_inset +\end_inset Regress \begin_inset Quotes erd -\end_inset +\end_inset \begin_inset Formula $A=\log S$ -\end_inset +\end_inset where \begin_inset Formula $S$ -\end_inset +\end_inset is the scale parameter and \begin_inset Formula $A$ -\end_inset +\end_inset is the mean of the underlying normal distribution). The standard form is \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3472,7 +3616,7 @@ F\left(x;\sigma\right) & = & \Phi\left(\frac{\log x}{\sigma}\right)\\ G\left(q;\sigma\right) & = & \exp\left\{ \sigma\Phi^{-1}\left(q\right)\right\} \end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3481,45 +3625,80 @@ \gamma_{1} & = & \sqrt{p-1}\left(2+p\right)\\ \gamma_{2} & = & p^{4}+2p^{3}+3p^{2}-6\quad\quad p=e^{\sigma^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard Notice that using JKB notation we have \begin_inset Formula $\theta=L,$ -\end_inset +\end_inset \begin_inset Formula $\zeta=\log S$ -\end_inset +\end_inset and we have given the so-called antilognormal form of the distribution. This is more consistent with the location, scale parameter description of general probability distributions. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=\frac{1}{2}\left[1+\log\left(2\pi\right)+2\log\left(\sigma\right)\right].\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Standard +Also, note that if +\begin_inset Formula $X$ +\end_inset + + is a log-normally distributed random-variable with +\begin_inset Formula $L=0$ +\end_inset + + and +\begin_inset Formula $S$ +\end_inset + + and shape parameter +\begin_inset Formula $\sigma.$ +\end_inset + + Then, +\begin_inset Formula $\log X$ +\end_inset + + is normally distributed with variance +\begin_inset Formula $\sigma^{2}$ +\end_inset + + and mean +\begin_inset Formula $\log S.$ +\end_inset + + +\end_layout + +\begin_layout Section Nakagami -\layout Standard +\end_layout +\begin_layout Standard Generalization of the chi distribution. Shape parameter is \begin_inset Formula $\nu>0.$ -\end_inset +\end_inset Defined for \begin_inset Formula $x>0.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3527,7 +3706,7 @@ F\left(x;\nu\right) & = & \Gamma\left(\nu,\nu x^{2}\right)\\ G\left(q;\nu\right) & = & \sqrt{\frac{1}{\nu}\Gamma^{-1}\left(v,q\right)}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3536,70 +3715,75 @@ \gamma_{1} & = & \frac{\mu\left(1-4v\mu_{2}\right)}{2\nu\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{-6\mu^{4}\nu+\left(8\nu-2\right)\mu^{2}-2\nu+1}{\nu\mu_{2}^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Noncentral beta* -\layout Standard +\end_layout +\begin_layout Standard Defined over \begin_inset Formula $x\in\left[0,1\right]$ -\end_inset +\end_inset with \begin_inset Formula $a>0$ -\end_inset +\end_inset and \begin_inset Formula $b>0$ -\end_inset +\end_inset and \begin_inset Formula $c\geq0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ F\left(x;a,b,c\right)=\sum_{j=0}^{\infty}\frac{e^{-c/2}\left(\frac{c}{2}\right)^{j}}{j!}I_{B}\left(a+j,b;0\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Noncentral chi* -\layout Section +\end_layout +\begin_layout Section Noncentral chi-squared -\layout Standard +\end_layout +\begin_layout Standard The distribution of \begin_inset Formula $\sum_{i=1}^{\nu}\left(Z_{i}+\delta_{i}\right)^{2}$ -\end_inset +\end_inset where \begin_inset Formula $Z_{i}$ -\end_inset +\end_inset are independent standard normal variables and \begin_inset Formula $\delta_{i}$ -\end_inset +\end_inset are constants. \begin_inset Formula $\lambda=\sum_{i=1}^{\nu}\delta_{i}^{2}>0.$ -\end_inset +\end_inset (In communications it is called the Marcum-Q function). Can be thought of as a Generalized Rayleigh-Rice distribution. For \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3607,7 +3791,7 @@ F\left(x;\nu,\lambda\right) & = & \sum_{j=0}^{\infty}\left\{ \frac{\left(\lambda/2\right)^{j}}{j!}e^{-\lambda/2}\right\} \textrm{Pr}\left[\chi_{\nu+2j}^{2}\leq x\right]\\ G\left(q;\nu,\lambda\right) & = & F^{-1}\left(x;\nu,\lambda\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3616,68 +3800,72 @@ \gamma_{1} & = & \frac{\sqrt{8}\left(\nu+3\lambda\right)}{\left(\nu+2\lambda\right)^{3/2}}\\ \gamma_{2} & = & \frac{12\left(\nu+4\lambda\right)}{\left(\nu+2\lambda\right)^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Noncentral F -\layout Standard +\end_layout +\begin_layout Standard Let \begin_inset Formula $\lambda>0$ -\end_inset +\end_inset and \begin_inset Formula $\nu_{1}>0$ -\end_inset +\end_inset and \begin_inset Formula $\nu_{2}>0.$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\lambda,\nu_{1},\nu_{2}\right) & = & \exp\left[\frac{\lambda}{2}+\frac{\left(\lambda\nu_{1}x\right)}{2\left(\nu_{1}x+\nu_{2}\right)}\right]\nu_{1}^{\nu_{1}/2}\nu_{2}^{\nu_{2}/2}x^{\nu_{1}/2-1}\\ & & \times\left(\nu_{2}+\nu_{1}x\right)^{-\left(\nu_{1}+\nu_{2}\right)/2}\frac{\Gamma\left(\frac{\nu_{1}}{2}\right)\Gamma\left(1+\frac{\nu_{2}}{2}\right)L_{\nu_{2}/2}^{\nu_{1}/2-1}\left(-\frac{\lambda\nu_{1}x}{2\left(\nu_{1}x+\nu_{2}\right)}\right)}{B\left(\frac{\nu_{1}}{2},\frac{\nu_{2}}{2}\right)\Gamma\left(\frac{\nu_{1}+\nu_{2}}{2}\right)}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Noncentral t -\layout Standard +\end_layout +\begin_layout Standard The distribution of the ratio \begin_inset Formula \[ \frac{U+\lambda}{\chi_{\nu}/\sqrt{\nu}}\] -\end_inset +\end_inset where \begin_inset Formula $U$ -\end_inset +\end_inset and \begin_inset Formula $\chi_{\nu}$ -\end_inset +\end_inset are independent and distributed as a standard normal and chi with \begin_inset Formula $\nu$ -\end_inset +\end_inset degrees of freedom. Note \begin_inset Formula $\lambda>0$ -\end_inset +\end_inset and \begin_inset Formula $\nu>0$ -\end_inset +\end_inset . @@ -3689,77 +3877,81 @@ & & \times\left(\frac{\nu}{\nu+x^{2}}\right)^{\left(\nu-1\right)/2}Hh_{\nu}\left(-\frac{\lambda x}{\sqrt{\nu+x^{2}}}\right)\\ F\left(x;\lambda,\nu\right) & =\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Normal -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{e^{-x^{2}/2}}{\sqrt{2\pi}}\\ F\left(x\right) & = & \Phi\left(x\right)=\frac{1}{2}+\frac{1}{2}\textrm{erf}\left(\frac{\textrm{x}}{\sqrt{2}}\right)\\ G\left(q\right) & = & \Phi^{-1}\left(q\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard -\align center +\end_layout +\begin_layout Standard +\align center \begin_inset Formula \begin{eqnarray*} m_{d}=m_{n}=\mu & = & 0\\ \mu_{2} & = & 1\\ \gamma_{1} & = & 0\\ \gamma_{2} & = & 0\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(\sqrt{2\pi e}\right)\\ & \approx & 1.4189385332046727418\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Maxwell -\layout Standard +\end_layout +\begin_layout Standard This is a special case of the Chi distribution with \begin_inset Formula $L=0$ -\end_inset +\end_inset and \begin_inset Formula $S=S=\frac{1}{\sqrt{a}}$ -\end_inset +\end_inset and \begin_inset Formula $\nu=3.$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \sqrt{\frac{2}{\pi}}x^{2}e^{-x^{2}/2}I_{\left(0,\infty\right)}\left(x\right)\\ F\left(x\right) & = & \Gamma\left(\frac{3}{2},\frac{x^{2}}{2}\right)\\ G\left(\alpha\right) & = & \sqrt{2\Gamma^{-1}\left(\frac{3}{2},\alpha\right)}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & 2\sqrt{\frac{2}{\pi}}\\ \mu_{2} & = & 3-\frac{8}{\pi}\\ @@ -3768,37 +3960,39 @@ m_{d} & = & \sqrt{2}\\ m_{n} & = & \sqrt{2\Gamma^{-1}\left(\frac{3}{2},\frac{1}{2}\right)}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=\log\left(\sqrt{\frac{2\pi}{e}}\right)+\gamma.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Mielke's Beta-Kappa -\layout Standard +\end_layout +\begin_layout Standard A generalized F distribution. Two shape parameters \begin_inset Formula $\kappa$ -\end_inset +\end_inset and \begin_inset Formula $\theta$ -\end_inset +\end_inset , and \begin_inset Formula $x>0$ -\end_inset +\end_inset . The \begin_inset Formula $\beta$ -\end_inset +\end_inset in the DATAPLOT reference is a scale parameter. \begin_inset Formula \begin{eqnarray*} @@ -3806,107 +4000,112 @@ F\left(x;\kappa,\theta\right) & = & \frac{x^{\kappa}}{\left(1+x^{\theta}\right)^{\kappa/\theta}}\\ G\left(q;\kappa,\theta\right) & = & \left(\frac{q^{\theta/\kappa}}{1-q^{\theta/\kappa}}\right)^{1/\theta}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Pareto -\layout Standard +\end_layout +\begin_layout Standard For \begin_inset Formula $x\geq1$ -\end_inset +\end_inset and \begin_inset Formula $b>0$ -\end_inset +\end_inset . Standard form is -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;b\right) & = & \frac{b}{x^{b+1}}\\ F\left(x;b\right) & = & 1-\frac{1}{x^{b}}\\ G\left(q;b\right) & = & \left(1-q\right)^{-1/b}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \frac{b}{b-1}\quad b>1\\ \mu_{2} & = & \frac{b}{\left(b-2\right)\left(b-1\right)^{2}}\quad b>2\\ \gamma_{1} & = & \frac{2\left(b+1\right)\sqrt{b-2}}{\left(b-3\right)\sqrt{b}}\quad b>3\\ \gamma_{2} & = & \frac{6\left(b^{3}+b^{2}-6b-2\right)}{b\left(b^{2}-7b+12\right)}\quad b>4\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left(X\right)=\frac{1}{c}+1-\log\left(c\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Pareto Second Kind (Lomax) -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula $c>0.$ -\end_inset +\end_inset This is Pareto of the first kind with \begin_inset Formula $L=-1.0$ -\end_inset +\end_inset so \begin_inset Formula $x\geq0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c\right) & = & \frac{c}{\left(1+x\right)^{c+1}}\\ F\left(x;c\right) & = & 1-\frac{1}{\left(1+x\right)^{c}}\\ G\left(q;c\right) & = & \left(1-q\right)^{-1/c}-1\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=\frac{1}{c}+1-\log\left(c\right).\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Power Log Normal -\layout Standard +\end_layout +\begin_layout Standard A generalization of the log-normal distribution \begin_inset Formula $\sigma>0$ -\end_inset +\end_inset and \begin_inset Formula $c>0$ -\end_inset +\end_inset and \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3914,13 +4113,13 @@ F\left(x;\sigma,c\right) & = & 1-\left(\Phi\left(-\frac{\log x}{\sigma}\right)\right)^{c}\\ G\left(q;\sigma,c\right) & = & \exp\left[-\sigma\Phi^{-1}\left[\left(1-q\right)^{1/c}\right]\right]\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\int_{0}^{1}\exp\left[-n\sigma\Phi^{-1}\left(y^{1/c}\right)\right]dy\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3929,21 +4128,23 @@ \gamma_{1} & = & \frac{\mu_{3}^{\prime}-3\mu\mu_{2}-\mu^{3}}{\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{\mu_{4}^{\prime}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset This distribution reduces to the log-normal distribution when \begin_inset Formula $c=1.$ -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Power Normal -\layout Standard +\end_layout +\begin_layout Standard A generalization of the normal distribution, \begin_inset Formula $c>0$ -\end_inset +\end_inset for \begin_inset Formula \begin{eqnarray*} @@ -3951,13 +4152,13 @@ F\left(x;c\right) & = & 1-\left(\Phi\left(-x\right)\right)^{c}\\ G\left(q;c\right) & = & -\Phi^{-1}\left[\left(1-q\right)^{1/c}\right]\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\left(-1\right)^{n}\int_{0}^{1}\left[\Phi^{-1}\left(y^{1/c}\right)\right]^{n}dy\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3966,40 +4167,42 @@ \gamma_{1} & = & \frac{\mu_{3}^{\prime}-3\mu\mu_{2}-\mu^{3}}{\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{\mu_{4}^{\prime}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset For \begin_inset Formula $c=1$ -\end_inset +\end_inset this reduces to the normal distribution. -\layout Section +\end_layout +\begin_layout Section Power-function -\layout Standard +\end_layout +\begin_layout Standard A special case of the beta distribution with \begin_inset Formula $b=1$ -\end_inset +\end_inset : defined for \begin_inset Formula $x\in\left[0,1\right]$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ a>0\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;a\right) & = & ax^{a-1}\\ F\left(x;a\right) & = & x^{a}\\ @@ -4010,63 +4213,67 @@ \gamma_{2} & = & \frac{6\left(a^{3}-a^{2}-6a+2\right)}{a\left(a+3\right)\left(a+4\right)}\\ m_{d} & = & 1\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=1-\frac{1}{a}-\log\left(a\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section R-distribution -\layout Standard +\end_layout +\begin_layout Standard A general-purpose distribution with a variety of shapes controlled by \begin_inset Formula $c>0.$ -\end_inset +\end_inset Range of standard distribution is \begin_inset Formula $x\in\left[-1,1\right]$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} f\left(x;c\right) & = & \frac{\left(1-x^{2}\right)^{c/2-1}}{B\left(\frac{1}{2},\frac{c}{2}\right)}\\ F\left(x;c\right) & = & \frac{1}{2}+\frac{x}{B\left(\frac{1}{2},\frac{c}{2}\right)}\,_{2}F_{1}\left(\frac{1}{2},1-\frac{c}{2};\frac{3}{2};x^{2}\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\frac{\left(1+\left(-1\right)^{n}\right)}{2}B\left(\frac{n+1}{2},\frac{c}{2}\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Rayleigh -\layout Standard +\end_layout +\begin_layout Standard This is Chi distribution with \begin_inset Formula $L=0.0$ -\end_inset +\end_inset and \begin_inset Formula $\nu=2$ -\end_inset +\end_inset and \begin_inset Formula $S=S$ -\end_inset +\end_inset (no location parameter is generally used), the mode of the distribution is \begin_inset Formula $S.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4074,7 +4281,7 @@ F\left(r\right) & = & 1-e^{-r^{2}/2}I_{[0,\infty)}\left(x\right)\\ G\left(q\right) & = & \sqrt{-2\log\left(1-q\right)}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4085,69 +4292,73 @@ m_{d} & = & 1\\ m_{n} & = & \sqrt{2\log\left(2\right)}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=\frac{\gamma}{2}+\log\left(\frac{e}{\sqrt{2}}\right).\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=\sqrt{2^{n}}\Gamma\left(\frac{n}{2}+1\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Rice* -\layout Standard +\end_layout +\begin_layout Standard Defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset and \begin_inset Formula $b>0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;b\right) & = & x\exp\left(-\frac{x^{2}+b^{2}}{2}\right)I_{0}\left(xb\right)\\ F\left(x;b\right) & = & \int_{0}^{x}\alpha\exp\left(-\frac{\alpha^{2}+b^{2}}{2}\right)I_{0}\left(\alpha b\right)d\alpha\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=\sqrt{2^{n}}\Gamma\left(1+\frac{n}{2}\right)\,_{1}F_{1}\left(-\frac{n}{2};1;-\frac{b^{2}}{2}\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Reciprocal -\layout Standard +\end_layout +\begin_layout Standard Shape parameters \begin_inset Formula $a,b>0$ -\end_inset +\end_inset \begin_inset Formula $x\in\left[a,b\right]$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4155,7 +4366,7 @@ F\left(x;a,b\right) & = & \frac{\log\left(x/a\right)}{\log\left(b/a\right)}\\ G\left(q;a,b\right) & = & a\exp\left(q\log\left(b/a\right)\right)=a\left(\frac{b}{a}\right)^{q}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4167,58 +4378,62 @@ m_{d} & = & a\\ m_{n} & = & \sqrt{ab}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=\frac{1}{2}\log\left(ab\right)+\log\left[\log\left(\frac{b}{a}\right)\right].\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Reciprocal Inverse Gaussian -\layout Standard +\end_layout +\begin_layout Standard The pdf is found from the inverse gaussian (IG), \begin_inset Formula $f_{RIG}\left(x;\mu\right)=\frac{1}{x^{2}}f_{IG}\left(\frac{1}{x};\mu\right)$ -\end_inset +\end_inset defined for \begin_inset Formula $x\geq0$ -\end_inset +\end_inset as -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f_{IG}\left(x;\mu\right) & = & \frac{1}{\sqrt{2\pi x^{3}}}\exp\left(-\frac{\left(x-\mu\right)^{2}}{2x\mu^{2}}\right).\\ F_{IG}\left(x;\mu\right) & = & \Phi\left(\frac{1}{\sqrt{x}}\frac{x-\mu}{\mu}\right)+\exp\left(\frac{2}{\mu}\right)\Phi\left(-\frac{1}{\sqrt{x}}\frac{x+\mu}{\mu}\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f_{RIG}\left(x;\mu\right) & = & \frac{1}{\sqrt{2\pi x}}\exp\left(-\frac{\left(1-\mu x\right)^{2}}{2x\mu^{2}}\right)\\ F_{RIG}\left(x;\mu\right) & = & 1-F_{IG}\left(\frac{1}{x},\mu\right)\\ & = & 1-\Phi\left(\frac{1}{\sqrt{x}}\frac{1-\mu x}{\mu}\right)-\exp\left(\frac{2}{\mu}\right)\Phi\left(-\frac{1}{\sqrt{x}}\frac{1+\mu x}{\mu}\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Semicircular -\layout Standard +\end_layout +\begin_layout Standard Defined on \begin_inset Formula $x\in\left[-1,1\right]$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4226,7 +4441,7 @@ F\left(x\right) & = & \frac{1}{2}+\frac{1}{\pi}\left[x\sqrt{1-x^{2}}+\arcsin x\right]\\ G\left(q\right) & = & F^{-1}\left(q\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4235,39 +4450,42 @@ \gamma_{1} & = & 0\\ \gamma_{2} & = & -1\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=0.64472988584940017414.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Studentized Range* -\layout Section +\end_layout +\begin_layout Section Student t -\layout Standard +\end_layout +\begin_layout Standard Shape parameter \begin_inset Formula $\nu>0.$ -\end_inset +\end_inset \begin_inset Formula $I\left(a,b,x\right)$ -\end_inset +\end_inset is the incomplete beta integral and \begin_inset Formula $I^{-1}\left(a,b,I\left(a,b,x\right)\right)=x$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\nu\right) & = & \frac{\Gamma\left(\frac{\nu+1}{2}\right)}{\sqrt{\pi\nu}\Gamma\left(\frac{\nu}{2}\right)\left[1+\frac{x^{2}}{\nu}\right]^{\frac{\nu+1}{2}}}\\ F\left(x;\nu\right) & = & \left\{ \begin{array}{ccc} @@ -4277,50 +4495,52 @@ -\sqrt{\frac{\nu}{I^{-1}\left(\frac{\nu}{2},\frac{1}{2},2q\right)}-\nu} & & q\leq\frac{1}{2}\\ \sqrt{\frac{\nu}{I^{-1}\left(\frac{\nu}{2},\frac{1}{2},2-2q\right)}-\nu} & & q\geq\frac{1}{2}\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} m_{n}=m_{d}=\mu & = & 0\\ \mu_{2} & = & \frac{\nu}{\nu-2}\quad\nu>2\\ \gamma_{1} & = & 0\quad\nu>3\\ \gamma_{2} & = & \frac{6}{\nu-4}\quad\nu>4\end{eqnarray*} -\end_inset +\end_inset As \begin_inset Formula $\nu\rightarrow\infty,$ -\end_inset +\end_inset this distribution approaches the standard normal distribution. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=\frac{1}{4}\log\left(\frac{\pi c\Gamma^{2}\left(\frac{c}{2}\right)}{\Gamma^{2}\left(\frac{c+1}{2}\right)}\right)-\frac{\left(c+1\right)}{4}\left[\Psi\left(\frac{c}{2}\right)-cZ\left(c\right)+\pi\tan\left(\frac{\pi c}{2}\right)+\gamma+2\log2\right]\] -\end_inset +\end_inset where \begin_inset Formula \[ Z\left(c\right)=\,_{3}F_{2}\left(1,1,1+\frac{c}{2};\frac{3}{2},2;1\right)=\sum_{k=0}^{\infty}\frac{k!}{k+1}\frac{\Gamma\left(\frac{c}{2}+1+k\right)}{\Gamma\left(\frac{c}{2}+1\right)}\frac{\Gamma\left(\frac{3}{2}\right)}{\Gamma\left(\frac{3}{2}+k\right)}\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Student Z -\layout Standard +\end_layout +\begin_layout Standard The student Z distriubtion is defined over all space with one shape parameter \begin_inset Formula $\nu>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4330,7 +4550,7 @@ 1-Q\left(x;\nu\right) & & x\geq0\end{array}\right.\\ Q\left(x;\nu\right) & = & \frac{\left|x\right|^{1-n}\Gamma\left(\frac{n}{2}\right)\,_{2}F_{1}\left(\frac{n-1}{2},\frac{n}{2};\frac{n+1}{2};-\frac{1}{x^{2}}\right)}{2\sqrt{\pi}\Gamma\left(\frac{n+1}{2}\right)}\end{eqnarray*} -\end_inset +\end_inset Interesting moments are \begin_inset Formula \begin{eqnarray*} @@ -4339,26 +4559,29 @@ \gamma_{1} & = & 0\\ \gamma_{2} & = & \frac{6}{\nu-5}.\end{eqnarray*} -\end_inset +\end_inset The moment generating function is \begin_inset Formula \[ \theta\left(t\right)=2\sqrt{\left|\frac{t}{2}\right|^{\nu-1}}\frac{K_{\left(n-1\right)/2}\left(\left|t\right|\right)}{\Gamma\left(\frac{\nu-1}{2}\right)}.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Symmetric Power* -\layout Section +\end_layout +\begin_layout Section Triangular -\layout Standard +\end_layout +\begin_layout Standard One shape parameter \begin_inset Formula $c\in[0,1]$ -\end_inset +\end_inset giving the distance to the peak as a percentage of the total extent of the non-zero portion. @@ -4366,7 +4589,7 @@ meter is the width of the non-zero portion. In standard form we have \begin_inset Formula $x\in\left[0,1\right].$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4380,7 +4603,7 @@ \sqrt{cq} & & q0$ -\end_inset +\end_inset . Note, the PDF and CDF functions are periodic and are always defined over \begin_inset Formula $x\in\left[-\pi,\pi\right]$ -\end_inset +\end_inset regardless of the location parameter. Thus, if an input beyond this range is given, it is converted to the equivalent angle in this range. For values of \begin_inset Formula $b<100$ -\end_inset +\end_inset the PDF and CDF formulas below are used. Otherwise, a normal approximation with variance \begin_inset Formula $1/b$ -\end_inset +\end_inset is used. @@ -4610,51 +4842,53 @@ F\left(x;b\right) & = & \frac{1}{2}+\frac{x}{2\pi}+\sum_{k=1}^{\infty}\frac{I_{k}\left(b\right)\sin\left(kx\right)}{I_{0}\left(b\right)\pi k}\\ G\left(q;b\right) & = & F^{-1}\left(x;b\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & 0\\ \mu_{2} & = & \int_{-\pi}^{\pi}x^{2}f\left(x;b\right)dx\\ \gamma_{1} & = & 0\\ \gamma_{2} & = & \frac{\int_{-\pi}^{\pi}x^{4}f\left(x;b\right)dx}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset This can be used for defining circular variance. -\layout Section +\end_layout +\begin_layout Section Wald -\layout Standard +\end_layout +\begin_layout Standard Special case of the Inverse Normal with shape parameter set to \begin_inset Formula $1.0$ -\end_inset +\end_inset . Defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset . -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{1}{\sqrt{2\pi x^{3}}}\exp\left(-\frac{\left(x-1\right)^{2}}{2x}\right).\\ F\left(x\right) & = & \Phi\left(\frac{x-1}{\sqrt{x}}\right)+\exp\left(2\right)\Phi\left(-\frac{x+1}{\sqrt{x}}\right)\\ G\left(q;\mu\right) & = & F^{-1}\left(q;\mu\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & 1\\ \mu_{2} & = & 1\\ @@ -4662,24 +4896,27 @@ \gamma_{2} & = & 15\\ m_{d} & = & \frac{1}{2}\left(\sqrt{13}-3\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Wishart* -\layout Section +\end_layout +\begin_layout Section Wrapped Cauchy -\layout Standard +\end_layout +\begin_layout Standard For \begin_inset Formula $x\in\left[0,2\pi\right]$ -\end_inset +\end_inset \begin_inset Formula $c\in\left(0,1\right)$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4693,19 +4930,22 @@ r_{c}\left(q\right) & & 0\leq q<\frac{1}{2}\\ 2\pi-r_{c}\left(1-q\right) & & \frac{1}{2}\leq q\leq1\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \] -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=\log\left(2\pi\left(1-c^{2}\right)\right).\] -\end_inset +\end_inset -\the_end +\end_layout + +\end_body +\end_document Modified: trunk/scipy/stats/distributions.py =================================================================== --- trunk/scipy/stats/distributions.py 2008-10-03 14:31:41 UTC (rev 4765) +++ trunk/scipy/stats/distributions.py 2008-10-03 18:57:20 UTC (rev 4766) @@ -2280,7 +2280,6 @@ ## Log-Laplace (Log Double Exponential) ## - class loglaplace_gen(rv_continuous): def _pdf(self, x, c): cd2 = c/2.0 @@ -2336,6 +2335,10 @@ lognorm.pdf(x,s) = 1/(s*x*sqrt(2*pi)) * exp(-1/2*(log(x)/s)**2) for x > 0, s > 0. + +If log x is normally distributed with mean mu and variance sigma**2, +then x is log-normally distributed with shape paramter sigma and scale +parameter exp(mu). """ ) From scipy-svn at scipy.org Sat Oct 4 18:22:56 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 4 Oct 2008 17:22:56 -0500 (CDT) Subject: [Scipy-svn] r4767 - trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC Message-ID: <20081004222256.DFF2C39C088@scipy.org> Author: wnbell Date: 2008-10-04 17:22:39 -0500 (Sat, 04 Oct 2008) New Revision: 4767 Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c Log: rename c_abs() and c_abs1() to slu_c_abs() and slu_c_abs() to avoid conflict with Python headers hopefully resolves ticket #735 Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c 2008-10-03 18:57:20 UTC (rev 4766) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c 2008-10-04 22:22:39 UTC (rev 4767) @@ -118,7 +118,7 @@ for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; ++i) { irow = Astore->rowind[i]; - r[irow] = SUPERLU_MAX( r[irow], c_abs1(&Aval[i]) ); + r[irow] = SUPERLU_MAX( r[irow], slu_c_cabs1(&Aval[i]) ); } /* Find the maximum and minimum scale factors. */ @@ -153,7 +153,7 @@ for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; ++i) { irow = Astore->rowind[i]; - c[j] = SUPERLU_MAX( c[j], c_abs1(&Aval[i]) * r[irow] ); + c[j] = SUPERLU_MAX( c[j], slu_c_cabs1(&Aval[i]) * r[irow] ); } /* Find the maximum and minimum scale factors. */ Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c 2008-10-03 18:57:20 UTC (rev 4766) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c 2008-10-04 22:22:39 UTC (rev 4767) @@ -276,21 +276,21 @@ than SAFE2, then SAFE1 is added to the i-th component of the numerator and denominator before dividing. */ - for (i = 0; i < A->nrow; ++i) rwork[i] = c_abs1( &Bptr[i] ); + for (i = 0; i < A->nrow; ++i) rwork[i] = slu_c_cabs1( &Bptr[i] ); /* Compute abs(op(A))*abs(X) + abs(B). */ if (notran) { for (k = 0; k < A->ncol; ++k) { - xk = c_abs1( &Xptr[k] ); + xk = slu_c_cabs1( &Xptr[k] ); for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) - rwork[Astore->rowind[i]] += c_abs1(&Aval[i]) * xk; + rwork[Astore->rowind[i]] += slu_c_cabs1(&Aval[i]) * xk; } } else { for (k = 0; k < A->ncol; ++k) { s = 0.; for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) { irow = Astore->rowind[i]; - s += c_abs1(&Aval[i]) * c_abs1(&Xptr[irow]); + s += slu_c_cabs1(&Aval[i]) * slu_c_cabs1(&Xptr[irow]); } rwork[k] += s; } @@ -298,9 +298,9 @@ s = 0.; for (i = 0; i < A->nrow; ++i) { if (rwork[i] > safe2) - s = SUPERLU_MAX( s, c_abs1(&work[i]) / rwork[i] ); + s = SUPERLU_MAX( s, slu_c_cabs1(&work[i]) / rwork[i] ); else - s = SUPERLU_MAX( s, (c_abs1(&work[i]) + safe1) / + s = SUPERLU_MAX( s, (slu_c_cabs1(&work[i]) + safe1) / (rwork[i] + safe1) ); } berr[j] = s; @@ -351,22 +351,22 @@ inv(op(A)) * diag(W), where W = abs(R) + NZ*EPS*( abs(op(A))*abs(X)+abs(B) ))) */ - for (i = 0; i < A->nrow; ++i) rwork[i] = c_abs1( &Bptr[i] ); + for (i = 0; i < A->nrow; ++i) rwork[i] = slu_c_cabs1( &Bptr[i] ); /* Compute abs(op(A))*abs(X) + abs(B). */ if ( notran ) { for (k = 0; k < A->ncol; ++k) { - xk = c_abs1( &Xptr[k] ); + xk = slu_c_cabs1( &Xptr[k] ); for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) - rwork[Astore->rowind[i]] += c_abs1(&Aval[i]) * xk; + rwork[Astore->rowind[i]] += slu_c_cabs1(&Aval[i]) * xk; } } else { for (k = 0; k < A->ncol; ++k) { s = 0.; for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) { irow = Astore->rowind[i]; - xk = c_abs1( &Xptr[irow] ); - s += c_abs1(&Aval[i]) * xk; + xk = slu_c_cabs1( &Xptr[irow] ); + s += slu_c_cabs1(&Aval[i]) * xk; } rwork[k] += s; } @@ -374,9 +374,9 @@ for (i = 0; i < A->nrow; ++i) if (rwork[i] > safe2) - rwork[i] = c_abs(&work[i]) + (iwork[i]+1)*eps*rwork[i]; + rwork[i] = slu_c_cabs(&work[i]) + (iwork[i]+1)*eps*rwork[i]; else - rwork[i] = c_abs(&work[i])+(iwork[i]+1)*eps*rwork[i]+safe1; + rwork[i] = slu_c_cabs(&work[i])+(iwork[i]+1)*eps*rwork[i]+safe1; kase = 0; do { @@ -424,13 +424,13 @@ lstres = 0.; if ( notran && colequ ) { for (i = 0; i < A->nrow; ++i) - lstres = SUPERLU_MAX( lstres, C[i] * c_abs1( &Xptr[i]) ); + lstres = SUPERLU_MAX( lstres, C[i] * slu_c_cabs1( &Xptr[i]) ); } else if ( !notran && rowequ ) { for (i = 0; i < A->nrow; ++i) - lstres = SUPERLU_MAX( lstres, R[i] * c_abs1( &Xptr[i]) ); + lstres = SUPERLU_MAX( lstres, R[i] * slu_c_cabs1( &Xptr[i]) ); } else { for (i = 0; i < A->nrow; ++i) - lstres = SUPERLU_MAX( lstres, c_abs1( &Xptr[i]) ); + lstres = SUPERLU_MAX( lstres, slu_c_cabs1( &Xptr[i]) ); } if ( lstres != 0. ) ferr[j] /= lstres; Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c 2008-10-03 18:57:20 UTC (rev 4766) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c 2008-10-04 22:22:39 UTC (rev 4767) @@ -106,14 +106,14 @@ L20: if (*n == 1) { v[0] = x[0]; - *est = c_abs(&v[0]); + *est = slu_c_cabs(&v[0]); /* ... QUIT */ goto L150; } *est = scsum1_(n, x, &c__1); for (i = 0; i < *n; ++i) { - d__1 = c_abs(&x[i]); + d__1 = slu_c_cabs(&x[i]); if (d__1 > safmin) { d__1 = 1 / d__1; x[i].r *= d__1; @@ -158,7 +158,7 @@ if (*est <= estold) goto L120; for (i = 0; i < *n; ++i) { - d__1 = c_abs(&x[i]); + d__1 = slu_c_cabs(&x[i]); if (d__1 > safmin) { d__1 = 1 / d__1; x[i].r *= d__1; Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c 2008-10-03 18:57:20 UTC (rev 4766) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c 2008-10-04 22:22:39 UTC (rev 4767) @@ -72,7 +72,7 @@ value = 0.; for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; i++) - value = SUPERLU_MAX( value, c_abs( &Aval[i]) ); + value = SUPERLU_MAX( value, slu_c_cabs( &Aval[i]) ); } else if (lsame_(norm, "O") || *(unsigned char *)norm == '1') { /* Find norm1(A). */ @@ -80,7 +80,7 @@ for (j = 0; j < A->ncol; ++j) { sum = 0.; for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; i++) - sum += c_abs( &Aval[i] ); + sum += slu_c_cabs( &Aval[i] ); value = SUPERLU_MAX(value,sum); } @@ -92,7 +92,7 @@ for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; i++) { irow = Astore->rowind[i]; - rwork[irow] += c_abs( &Aval[i] ); + rwork[irow] += slu_c_cabs( &Aval[i] ); } value = 0.; for (i = 0; i < A->nrow; ++i) Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c 2008-10-03 18:57:20 UTC (rev 4766) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c 2008-10-04 22:22:39 UTC (rev 4767) @@ -106,7 +106,7 @@ diag = EMPTY; old_pivptr = nsupc; for (isub = nsupc; isub < nsupr; ++isub) { - rtemp = c_abs1 (&lu_col_ptr[isub]); + rtemp = slu_c_cabs1 (&lu_col_ptr[isub]); if ( rtemp > pivmax ) { pivmax = rtemp; pivptr = isub; @@ -127,7 +127,7 @@ /* Choose appropriate pivotal element by our policy. */ if ( *usepr ) { - rtemp = c_abs1 (&lu_col_ptr[old_pivptr]); + rtemp = slu_c_cabs1 (&lu_col_ptr[old_pivptr]); if ( rtemp != 0.0 && rtemp >= thresh ) pivptr = old_pivptr; else @@ -136,7 +136,7 @@ if ( *usepr == 0 ) { /* Use diagonal pivot? */ if ( diag >= 0 ) { /* diagonal exists */ - rtemp = c_abs1 (&lu_col_ptr[diag]); + rtemp = slu_c_cabs1 (&lu_col_ptr[diag]); if ( rtemp != 0.0 && rtemp >= thresh ) pivptr = diag; } *pivrow = lsub_ptr[pivptr]; Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c 2008-10-03 18:57:20 UTC (rev 4766) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c 2008-10-04 22:22:39 UTC (rev 4767) @@ -83,15 +83,15 @@ maxaj = 0.; oldcol = inv_perm_c[j]; for (i = Astore->colptr[oldcol]; i < Astore->colptr[oldcol+1]; ++i) - maxaj = SUPERLU_MAX( maxaj, c_abs1( &Aval[i]) ); + maxaj = SUPERLU_MAX( maxaj, slu_c_cabs1( &Aval[i]) ); maxuj = 0.; for (i = Ustore->colptr[j]; i < Ustore->colptr[j+1]; i++) - maxuj = SUPERLU_MAX( maxuj, c_abs1( &Uval[i]) ); + maxuj = SUPERLU_MAX( maxuj, slu_c_cabs1( &Uval[i]) ); /* Supernode */ for (i = 0; i < nz_in_U; ++i) - maxuj = SUPERLU_MAX( maxuj, c_abs1( &luval[i]) ); + maxuj = SUPERLU_MAX( maxuj, slu_c_cabs1( &luval[i]) ); ++nz_in_U; luval += nsupr; Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c 2008-10-03 18:57:20 UTC (rev 4766) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c 2008-10-04 22:22:39 UTC (rev 4767) @@ -414,8 +414,8 @@ err = xnorm = 0.0; for (i = 0; i < X->nrow; i++) { c_sub(&temp, &soln_work[i], &xtrue[i]); - err = SUPERLU_MAX(err, c_abs(&temp)); - xnorm = SUPERLU_MAX(xnorm, c_abs(&soln_work[i])); + err = SUPERLU_MAX(err, slu_c_cabs(&temp)); + xnorm = SUPERLU_MAX(xnorm, slu_c_cabs(&soln_work[i])); } err = err / xnorm; printf("||X - Xtrue||/||X|| = %e\n", err); Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c 2008-10-03 18:57:20 UTC (rev 4766) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c 2008-10-04 22:22:39 UTC (rev 4767) @@ -45,7 +45,7 @@ /* Returns sqrt(z.r^2 + z.i^2) */ -double c_abs(complex *z) +double slu_c_cabs(complex *z) { float temp; float real = z->r; @@ -68,7 +68,7 @@ /* Approximates the abs */ /* Returns abs(z.r) + abs(z.i) */ -double c_abs1(complex *z) +double slu_c_cabs1(complex *z) { float real = z->r; float imag = z->i; Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h 2008-10-03 18:57:20 UTC (rev 4766) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h 2008-10-04 22:22:39 UTC (rev 4767) @@ -57,8 +57,8 @@ /* Prototypes for functions in scomplex.c */ void c_div(complex *, complex *, complex *); -double c_abs(complex *); /* exact */ -double c_abs1(complex *); /* approximate */ +double slu_c_cabs(complex *); /* exact */ +double slu_c_cabs1(complex *); /* approximate */ void c_exp(complex *, complex *); void r_cnjg(complex *, complex *); double r_imag(complex *); Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c 2008-10-03 18:57:20 UTC (rev 4766) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c 2008-10-04 22:22:39 UTC (rev 4767) @@ -42,7 +42,7 @@ int i__1, i__2; float ret_val; /* Builtin functions */ - double c_abs(complex *); + double slu_c_cabs(complex *); /* Local variables */ static int i, nincx; static float stemp; @@ -69,7 +69,7 @@ /* NEXT LINE MODIFIED. */ - stemp += c_abs(&CX(i)); + stemp += slu_c_cabs(&CX(i)); /* L10: */ } ret_val = stemp; @@ -83,7 +83,7 @@ /* NEXT LINE MODIFIED. */ - stemp += c_abs(&CX(i)); + stemp += slu_c_cabs(&CX(i)); /* L30: */ } ret_val = stemp; From scipy-svn at scipy.org Sat Oct 4 18:33:59 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 4 Oct 2008 17:33:59 -0500 (CDT) Subject: [Scipy-svn] r4768 - trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC Message-ID: <20081004223359.C562F39C088@scipy.org> Author: wnbell Date: 2008-10-04 17:33:54 -0500 (Sat, 04 Oct 2008) New Revision: 4768 Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c Log: fix spelling in c_abs renaming Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c 2008-10-04 22:22:39 UTC (rev 4767) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c 2008-10-04 22:33:54 UTC (rev 4768) @@ -118,7 +118,7 @@ for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; ++i) { irow = Astore->rowind[i]; - r[irow] = SUPERLU_MAX( r[irow], slu_c_cabs1(&Aval[i]) ); + r[irow] = SUPERLU_MAX( r[irow], slu_c_abs1(&Aval[i]) ); } /* Find the maximum and minimum scale factors. */ @@ -153,7 +153,7 @@ for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; ++i) { irow = Astore->rowind[i]; - c[j] = SUPERLU_MAX( c[j], slu_c_cabs1(&Aval[i]) * r[irow] ); + c[j] = SUPERLU_MAX( c[j], slu_c_abs1(&Aval[i]) * r[irow] ); } /* Find the maximum and minimum scale factors. */ Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c 2008-10-04 22:22:39 UTC (rev 4767) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c 2008-10-04 22:33:54 UTC (rev 4768) @@ -276,21 +276,21 @@ than SAFE2, then SAFE1 is added to the i-th component of the numerator and denominator before dividing. */ - for (i = 0; i < A->nrow; ++i) rwork[i] = slu_c_cabs1( &Bptr[i] ); + for (i = 0; i < A->nrow; ++i) rwork[i] = slu_c_abs1( &Bptr[i] ); /* Compute abs(op(A))*abs(X) + abs(B). */ if (notran) { for (k = 0; k < A->ncol; ++k) { - xk = slu_c_cabs1( &Xptr[k] ); + xk = slu_c_abs1( &Xptr[k] ); for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) - rwork[Astore->rowind[i]] += slu_c_cabs1(&Aval[i]) * xk; + rwork[Astore->rowind[i]] += slu_c_abs1(&Aval[i]) * xk; } } else { for (k = 0; k < A->ncol; ++k) { s = 0.; for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) { irow = Astore->rowind[i]; - s += slu_c_cabs1(&Aval[i]) * slu_c_cabs1(&Xptr[irow]); + s += slu_c_abs1(&Aval[i]) * slu_c_abs1(&Xptr[irow]); } rwork[k] += s; } @@ -298,9 +298,9 @@ s = 0.; for (i = 0; i < A->nrow; ++i) { if (rwork[i] > safe2) - s = SUPERLU_MAX( s, slu_c_cabs1(&work[i]) / rwork[i] ); + s = SUPERLU_MAX( s, slu_c_abs1(&work[i]) / rwork[i] ); else - s = SUPERLU_MAX( s, (slu_c_cabs1(&work[i]) + safe1) / + s = SUPERLU_MAX( s, (slu_c_abs1(&work[i]) + safe1) / (rwork[i] + safe1) ); } berr[j] = s; @@ -351,22 +351,22 @@ inv(op(A)) * diag(W), where W = abs(R) + NZ*EPS*( abs(op(A))*abs(X)+abs(B) ))) */ - for (i = 0; i < A->nrow; ++i) rwork[i] = slu_c_cabs1( &Bptr[i] ); + for (i = 0; i < A->nrow; ++i) rwork[i] = slu_c_abs1( &Bptr[i] ); /* Compute abs(op(A))*abs(X) + abs(B). */ if ( notran ) { for (k = 0; k < A->ncol; ++k) { - xk = slu_c_cabs1( &Xptr[k] ); + xk = slu_c_abs1( &Xptr[k] ); for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) - rwork[Astore->rowind[i]] += slu_c_cabs1(&Aval[i]) * xk; + rwork[Astore->rowind[i]] += slu_c_abs1(&Aval[i]) * xk; } } else { for (k = 0; k < A->ncol; ++k) { s = 0.; for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) { irow = Astore->rowind[i]; - xk = slu_c_cabs1( &Xptr[irow] ); - s += slu_c_cabs1(&Aval[i]) * xk; + xk = slu_c_abs1( &Xptr[irow] ); + s += slu_c_abs1(&Aval[i]) * xk; } rwork[k] += s; } @@ -374,9 +374,9 @@ for (i = 0; i < A->nrow; ++i) if (rwork[i] > safe2) - rwork[i] = slu_c_cabs(&work[i]) + (iwork[i]+1)*eps*rwork[i]; + rwork[i] = slu_c_abs(&work[i]) + (iwork[i]+1)*eps*rwork[i]; else - rwork[i] = slu_c_cabs(&work[i])+(iwork[i]+1)*eps*rwork[i]+safe1; + rwork[i] = slu_c_abs(&work[i])+(iwork[i]+1)*eps*rwork[i]+safe1; kase = 0; do { @@ -424,13 +424,13 @@ lstres = 0.; if ( notran && colequ ) { for (i = 0; i < A->nrow; ++i) - lstres = SUPERLU_MAX( lstres, C[i] * slu_c_cabs1( &Xptr[i]) ); + lstres = SUPERLU_MAX( lstres, C[i] * slu_c_abs1( &Xptr[i]) ); } else if ( !notran && rowequ ) { for (i = 0; i < A->nrow; ++i) - lstres = SUPERLU_MAX( lstres, R[i] * slu_c_cabs1( &Xptr[i]) ); + lstres = SUPERLU_MAX( lstres, R[i] * slu_c_abs1( &Xptr[i]) ); } else { for (i = 0; i < A->nrow; ++i) - lstres = SUPERLU_MAX( lstres, slu_c_cabs1( &Xptr[i]) ); + lstres = SUPERLU_MAX( lstres, slu_c_abs1( &Xptr[i]) ); } if ( lstres != 0. ) ferr[j] /= lstres; Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c 2008-10-04 22:22:39 UTC (rev 4767) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c 2008-10-04 22:33:54 UTC (rev 4768) @@ -106,14 +106,14 @@ L20: if (*n == 1) { v[0] = x[0]; - *est = slu_c_cabs(&v[0]); + *est = slu_c_abs(&v[0]); /* ... QUIT */ goto L150; } *est = scsum1_(n, x, &c__1); for (i = 0; i < *n; ++i) { - d__1 = slu_c_cabs(&x[i]); + d__1 = slu_c_abs(&x[i]); if (d__1 > safmin) { d__1 = 1 / d__1; x[i].r *= d__1; @@ -158,7 +158,7 @@ if (*est <= estold) goto L120; for (i = 0; i < *n; ++i) { - d__1 = slu_c_cabs(&x[i]); + d__1 = slu_c_abs(&x[i]); if (d__1 > safmin) { d__1 = 1 / d__1; x[i].r *= d__1; Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c 2008-10-04 22:22:39 UTC (rev 4767) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c 2008-10-04 22:33:54 UTC (rev 4768) @@ -72,7 +72,7 @@ value = 0.; for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; i++) - value = SUPERLU_MAX( value, slu_c_cabs( &Aval[i]) ); + value = SUPERLU_MAX( value, slu_c_abs( &Aval[i]) ); } else if (lsame_(norm, "O") || *(unsigned char *)norm == '1') { /* Find norm1(A). */ @@ -80,7 +80,7 @@ for (j = 0; j < A->ncol; ++j) { sum = 0.; for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; i++) - sum += slu_c_cabs( &Aval[i] ); + sum += slu_c_abs( &Aval[i] ); value = SUPERLU_MAX(value,sum); } @@ -92,7 +92,7 @@ for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; i++) { irow = Astore->rowind[i]; - rwork[irow] += slu_c_cabs( &Aval[i] ); + rwork[irow] += slu_c_abs( &Aval[i] ); } value = 0.; for (i = 0; i < A->nrow; ++i) Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c 2008-10-04 22:22:39 UTC (rev 4767) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c 2008-10-04 22:33:54 UTC (rev 4768) @@ -106,7 +106,7 @@ diag = EMPTY; old_pivptr = nsupc; for (isub = nsupc; isub < nsupr; ++isub) { - rtemp = slu_c_cabs1 (&lu_col_ptr[isub]); + rtemp = slu_c_abs1 (&lu_col_ptr[isub]); if ( rtemp > pivmax ) { pivmax = rtemp; pivptr = isub; @@ -127,7 +127,7 @@ /* Choose appropriate pivotal element by our policy. */ if ( *usepr ) { - rtemp = slu_c_cabs1 (&lu_col_ptr[old_pivptr]); + rtemp = slu_c_abs1 (&lu_col_ptr[old_pivptr]); if ( rtemp != 0.0 && rtemp >= thresh ) pivptr = old_pivptr; else @@ -136,7 +136,7 @@ if ( *usepr == 0 ) { /* Use diagonal pivot? */ if ( diag >= 0 ) { /* diagonal exists */ - rtemp = slu_c_cabs1 (&lu_col_ptr[diag]); + rtemp = slu_c_abs1 (&lu_col_ptr[diag]); if ( rtemp != 0.0 && rtemp >= thresh ) pivptr = diag; } *pivrow = lsub_ptr[pivptr]; Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c 2008-10-04 22:22:39 UTC (rev 4767) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c 2008-10-04 22:33:54 UTC (rev 4768) @@ -83,15 +83,15 @@ maxaj = 0.; oldcol = inv_perm_c[j]; for (i = Astore->colptr[oldcol]; i < Astore->colptr[oldcol+1]; ++i) - maxaj = SUPERLU_MAX( maxaj, slu_c_cabs1( &Aval[i]) ); + maxaj = SUPERLU_MAX( maxaj, slu_c_abs1( &Aval[i]) ); maxuj = 0.; for (i = Ustore->colptr[j]; i < Ustore->colptr[j+1]; i++) - maxuj = SUPERLU_MAX( maxuj, slu_c_cabs1( &Uval[i]) ); + maxuj = SUPERLU_MAX( maxuj, slu_c_abs1( &Uval[i]) ); /* Supernode */ for (i = 0; i < nz_in_U; ++i) - maxuj = SUPERLU_MAX( maxuj, slu_c_cabs1( &luval[i]) ); + maxuj = SUPERLU_MAX( maxuj, slu_c_abs1( &luval[i]) ); ++nz_in_U; luval += nsupr; Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c 2008-10-04 22:22:39 UTC (rev 4767) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c 2008-10-04 22:33:54 UTC (rev 4768) @@ -414,8 +414,8 @@ err = xnorm = 0.0; for (i = 0; i < X->nrow; i++) { c_sub(&temp, &soln_work[i], &xtrue[i]); - err = SUPERLU_MAX(err, slu_c_cabs(&temp)); - xnorm = SUPERLU_MAX(xnorm, slu_c_cabs(&soln_work[i])); + err = SUPERLU_MAX(err, slu_c_abs(&temp)); + xnorm = SUPERLU_MAX(xnorm, slu_c_abs(&soln_work[i])); } err = err / xnorm; printf("||X - Xtrue||/||X|| = %e\n", err); Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c 2008-10-04 22:22:39 UTC (rev 4767) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c 2008-10-04 22:33:54 UTC (rev 4768) @@ -45,7 +45,7 @@ /* Returns sqrt(z.r^2 + z.i^2) */ -double slu_c_cabs(complex *z) +double slu_c_abs(complex *z) { float temp; float real = z->r; @@ -68,7 +68,7 @@ /* Approximates the abs */ /* Returns abs(z.r) + abs(z.i) */ -double slu_c_cabs1(complex *z) +double slu_c_abs1(complex *z) { float real = z->r; float imag = z->i; Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h 2008-10-04 22:22:39 UTC (rev 4767) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h 2008-10-04 22:33:54 UTC (rev 4768) @@ -57,8 +57,8 @@ /* Prototypes for functions in scomplex.c */ void c_div(complex *, complex *, complex *); -double slu_c_cabs(complex *); /* exact */ -double slu_c_cabs1(complex *); /* approximate */ +double slu_c_abs(complex *); /* exact */ +double slu_c_abs1(complex *); /* approximate */ void c_exp(complex *, complex *); void r_cnjg(complex *, complex *); double r_imag(complex *); Modified: trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c =================================================================== --- trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c 2008-10-04 22:22:39 UTC (rev 4767) +++ trunk/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c 2008-10-04 22:33:54 UTC (rev 4768) @@ -42,7 +42,7 @@ int i__1, i__2; float ret_val; /* Builtin functions */ - double slu_c_cabs(complex *); + double slu_c_abs(complex *); /* Local variables */ static int i, nincx; static float stemp; @@ -69,7 +69,7 @@ /* NEXT LINE MODIFIED. */ - stemp += slu_c_cabs(&CX(i)); + stemp += slu_c_abs(&CX(i)); /* L10: */ } ret_val = stemp; @@ -83,7 +83,7 @@ /* NEXT LINE MODIFIED. */ - stemp += slu_c_cabs(&CX(i)); + stemp += slu_c_abs(&CX(i)); /* L30: */ } ret_val = stemp; From scipy-svn at scipy.org Sat Oct 4 20:43:10 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 4 Oct 2008 19:43:10 -0500 (CDT) Subject: [Scipy-svn] r4769 - branches/spatial/scipy/spatial/tests Message-ID: <20081005004310.34C0C39C249@scipy.org> Author: peridot Date: 2008-10-04 19:43:08 -0500 (Sat, 04 Oct 2008) New Revision: 4769 Modified: branches/spatial/scipy/spatial/tests/test_kdtree.py Log: Nosification of tests; some tests are applied more generally now. Modified: branches/spatial/scipy/spatial/tests/test_kdtree.py =================================================================== --- branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-04 22:33:54 UTC (rev 4768) +++ branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-05 00:43:08 UTC (rev 4769) @@ -5,64 +5,17 @@ import numpy as np from scipy.spatial import KDTree, distance -class CheckSmall(NumpyTestCase): - def setUp(self): - self.data = np.array([[0,0,0], - [0,0,1], - [0,1,0], - [0,1,1], - [1,0,0], - [1,0,1], - [1,1,0], - [1,1,1]]) - self.kdtree = KDTree(self.data) - +class ConsistencyTests: def test_nearest(self): - assert_array_equal( - self.kdtree.query((0,0,0.1), 1), - (0.1,0)) - def test_nearest_two(self): - assert_array_equal( - self.kdtree.query((0,0,0.1), 2), - ([0.1,0.9],[0,1])) -class CheckSmallNonLeaf(NumpyTestCase): - def setUp(self): - self.data = np.array([[0,0,0], - [0,0,1], - [0,1,0], - [0,1,1], - [1,0,0], - [1,0,1], - [1,1,0], - [1,1,1]]) - self.kdtree = KDTree(self.data,leafsize=1) - - def test_nearest(self): - assert_array_equal( - self.kdtree.query((0,0,0.1), 1), - (0.1,0)) - def test_nearest_two(self): - assert_array_equal( - self.kdtree.query((0,0,0.1), 2), - ([0.1,0.9],[0,1])) - -class CheckRandom(NumpyTestCase): - def setUp(self): - self.n = 1000 - self.k = 4 - self.data = np.random.randn(self.n, self.k) - self.kdtree = KDTree(self.data) - - def test_nearest(self): - x = np.random.randn(self.k) + x = self.x d, i = self.kdtree.query(x, 1) assert_almost_equal(d**2,np.sum((x-self.data[i])**2)) eps = 1e-8 assert np.all(np.sum((self.data-x[np.newaxis,:])**2,axis=1)>d**2-eps) def test_m_nearest(self): - x = np.random.randn(self.k) - m = 10 + x = self.x + m = self.m dd, ii = self.kdtree.query(x, m) d = np.amax(dd) i = ii[np.argmax(dd)] @@ -71,8 +24,8 @@ assert_equal(np.sum(np.sum((self.data-x[np.newaxis,:])**2,axis=1) Author: matthew.brett at gmail.com Date: 2008-10-04 19:58:08 -0500 (Sat, 04 Oct 2008) New Revision: 4770 Modified: trunk/scipy/io/matlab/mio.py trunk/scipy/io/matlab/mio4.py trunk/scipy/io/matlab/mio5.py trunk/scipy/io/matlab/miobase.py trunk/scipy/io/matlab/tests/test_mio.py Log: Centralize recognition of matfile type, apply patch for ticket #743 by Ray Jones, with thanks Modified: trunk/scipy/io/matlab/mio.py =================================================================== --- trunk/scipy/io/matlab/mio.py 2008-10-05 00:43:08 UTC (rev 4769) +++ trunk/scipy/io/matlab/mio.py 2008-10-05 00:58:08 UTC (rev 4770) @@ -6,7 +6,9 @@ import os import sys +import warnings +from miobase import get_matfile_version from mio4 import MatFile4Reader, MatFile4Writer from mio5 import MatFile5Reader, MatFile5Writer @@ -57,11 +59,16 @@ raise IOError, 'Reader needs file name or open file-like object' byte_stream = file_name - MR = MatFile4Reader(byte_stream, **kwargs) - if MR.format_looks_right(): - return MR - return MatFile5Reader(byte_stream, **kwargs) - + mv = get_matfile_version(byte_stream) + if mv == '4': + return MatFile4Reader(byte_stream, **kwargs) + elif mv == '5': + return MatFile5Reader(byte_stream, **kwargs) + elif mv == '7': + raise NotImplementedError('Please use PyTables for matlab HDF files') + else: + raise TypeError('Did not recognize version %s' % mv) + def loadmat(file_name, mdict=None, appendmat=True, basename='raw', **kwargs): ''' Load Matlab(tm) file @@ -88,10 +95,17 @@ matlab_compatible - returns matrices as would be loaded by matlab (implies squeeze_me=False, chars_as_strings=False, mat_dtype=True) + struct_as_record - whether to load matlab structs as numpy record arrays, or + as old-style numpy arrays with dtype=object. + (warns if not set, and defaults to False. non-recarrays + cannot be exported via savemat.) v4 (Level 1.0), v6 and v7.1 matfiles are supported. ''' + if not kwargs.get('struct_as_record', False): + warnings.warn("loading matlab structures as arrays of dtype=object is deprecated", + DeprecationWarning, stacklevel=2) MR = mat_reader_factory(file_name, appendmat, **kwargs) matfile_dict = MR.get_variables() if mdict is not None: @@ -125,7 +139,7 @@ if format == '4': MW = MatFile4Writer(file_stream) elif format == '5': - MW = MatFile5Writer(file_stream) + MW = MatFile5Writer(file_stream, unicode_strings=True) else: raise ValueError, 'Format should be 4 or 5' MW.put_variables(mdict) Modified: trunk/scipy/io/matlab/mio4.py =================================================================== --- trunk/scipy/io/matlab/mio4.py 2008-10-05 00:43:08 UTC (rev 4769) +++ trunk/scipy/io/matlab/mio4.py 2008-10-05 00:58:08 UTC (rev 4770) @@ -198,15 +198,6 @@ def matrix_getter_factory(self): return self._array_reader.matrix_getter_factory() - def format_looks_right(self): - # Mat4 files have a zero somewhere in first 4 bytes - self.mat_stream.seek(0) - mopt_bytes = np.ndarray(shape=(4,), - dtype=np.uint8, - buffer = self.mat_stream.read(4)) - self.mat_stream.seek(0) - return 0 in mopt_bytes - def guess_byte_order(self): self.mat_stream.seek(0) mopt = self.read_dtype(np.dtype('i4')) Modified: trunk/scipy/io/matlab/mio5.py =================================================================== --- trunk/scipy/io/matlab/mio5.py 2008-10-05 00:43:08 UTC (rev 4769) +++ trunk/scipy/io/matlab/mio5.py 2008-10-05 00:58:08 UTC (rev 4770) @@ -56,6 +56,10 @@ mxCELL_CLASS = 1 mxSTRUCT_CLASS = 2 +# The March 2008 edition of "Matlab 7 MAT-File Format" says that +# mxOBJECT_CLASS = 3, whereas matrix.h says that mxLOGICAL = 3. +# Matlab 2008a appears to save logicals as type 9, so we assume that +# the document is correct. See type 18, below. mxOBJECT_CLASS = 3 mxCHAR_CLASS = 4 mxSPARSE_CLASS = 5 @@ -67,6 +71,14 @@ mxUINT16_CLASS = 11 mxINT32_CLASS = 12 mxUINT32_CLASS = 13 +# The following are not in the March 2008 edition of "Matlab 7 +# MAT-File Format," but were guessed from matrix.h. +mxINT64_CLASS = 14 +mxUINT64_CLASS = 15 +mxFUNCTION_CLASS = 16 +# Not doing anything with these at the moment. +mxOPAQUE_CLASS = 17 +mxOBJECT_CLASS_FROM_MATRIX_H = 18 mdtypes_template = { miINT8: 'i1', @@ -102,6 +114,8 @@ mxUINT16_CLASS: 'u2', mxINT32_CLASS: 'i4', mxUINT32_CLASS: 'u4', + mxINT64_CLASS: 'i8', + mxUINT64_CLASS: 'u8', mxSINGLE_CLASS: 'f4', mxDOUBLE_CLASS: 'f8', } @@ -117,9 +131,11 @@ 'i1': miINT8, 'i2': miINT16, 'i4': miINT32, + 'i8': miINT64, 'u1': miUINT8, + 'u2': miUINT16, 'u4': miUINT32, - 'u2': miUINT16, + 'u8': miUINT64, 'S1': miUINT8, 'U1': miUTF16, } @@ -132,8 +148,10 @@ 'c16': mxDOUBLE_CLASS, 'f4': mxSINGLE_CLASS, 'c8': mxSINGLE_CLASS, + 'i8': mxINT64_CLASS, 'i4': mxINT32_CLASS, 'i2': mxINT16_CLASS, + 'u8': mxUINT64_CLASS, 'u2': mxUINT16_CLASS, 'u1': mxUINT8_CLASS, 'S1': mxUINT8_CLASS, @@ -163,12 +181,10 @@ mxUINT16_CLASS, mxINT32_CLASS, mxUINT32_CLASS, + mxINT64_CLASS, + mxUINT64_CLASS, ) -class mat_struct(object): - ''' Placeholder for holding read data from structs ''' - pass - class mat_obj(object): ''' Placeholder for holding read data from objects ''' pass @@ -180,13 +196,13 @@ factory function ''' - def __init__(self, mat_stream, dtypes, processor_func, codecs, class_dtypes): + def __init__(self, mat_stream, dtypes, processor_func, codecs, class_dtypes, struct_as_record): super(Mat5ArrayReader, self).__init__(mat_stream, dtypes, - processor_func, - ) + processor_func) self.codecs = codecs self.class_dtypes = class_dtypes + self.struct_as_record = struct_as_record def read_element(self, copy=True): raw_tag = self.mat_stream.read(8) @@ -228,7 +244,7 @@ buffer=raw_str) if copy: el = el.copy() - + return el def matrix_getter_factory(self): @@ -274,9 +290,11 @@ if mc == mxCELL_CLASS: return Mat5CellMatrixGetter(self, header) if mc == mxSTRUCT_CLASS: - return Mat5StructMatrixGetter(self, header) + return Mat5StructMatrixGetter(self, header, self.struct_as_record) if mc == mxOBJECT_CLASS: return Mat5ObjectMatrixGetter(self, header) + if mc == mxFUNCTION_CLASS: + return Mat5FunctionMatrixGetter(self, header) raise TypeError, 'No reader for class code %s' % mc @@ -294,7 +312,8 @@ array_reader.dtypes, array_reader.processor_func, array_reader.codecs, - array_reader.class_dtypes) + array_reader.class_dtypes, + array_reader.struct_as_record) class Mat5MatrixGetter(MatMatrixGetter): @@ -322,6 +341,7 @@ self.mat_stream = array_reader.mat_stream self.data_position = self.mat_stream.tell() self.header = {} + self.name = '' self.is_global = False self.mat_dtype = 'f8' @@ -420,44 +440,92 @@ def get_item(self): return self.read_element() +class mat_struct(object): + ''' Placeholder for holding read data from structs ''' + pass -class Mat5StructMatrixGetter(Mat5CellMatrixGetter): - def __init__(self, *args, **kwargs): - super(Mat5StructMatrixGetter, self).__init__(*args, **kwargs) - self.obj_template = mat_struct() +class Mat5StructMatrixGetter(Mat5MatrixGetter): + def __init__(self, array_reader, header, struct_as_record): + super(Mat5StructMatrixGetter, self).__init__(array_reader, header) + self.struct_as_record = struct_as_record def get_raw_array(self): namelength = self.read_element()[0] - # get field names names = self.read_element() - splitnames = [names[i:i+namelength] for i in \ - xrange(0,len(names),namelength)] - self.obj_template._fieldnames = [x.tostring().strip('\x00') - for x in splitnames] - return super(Mat5StructMatrixGetter, self).get_raw_array() + field_names = [names[i:i+namelength].tostring().strip('\x00') + for i in xrange(0,len(names),namelength)] + tupdims = tuple(self.header['dims'][::-1]) + length = np.product(tupdims) + result = np.empty(length, dtype=[(field_name, object) + for field_name in field_names]) + for i in range(length): + for field_name in field_names: + result[i][field_name] = self.read_element() + + if not self.struct_as_record: + # Backward compatibility with previous format + self.obj_template = mat_struct() + self.obj_template._fieldnames = field_names + newresult = np.empty(length, dtype=object) + for i in range(length): + item = pycopy(self.obj_template) + for name in field_names: + item.__dict__[name] = result[i][name] + newresult[i] = item + result = newresult + + return result.reshape(tupdims).T - def get_item(self): - item = pycopy(self.obj_template) - for element in item._fieldnames: - item.__dict__[element] = self.read_element() - return item +class MatlabObject: + def __init__(self, classname, field_names): + self.__dict__['classname'] = classname + self.__dict__['mobj_recarray'] = np.empty((1,1), dtype=[(field_name, object) + for field_name in field_names]) + def __getattr__(self, name): + mobj_recarray = self.__dict__['mobj_recarray'] + if name in mobj_recarray.dtype.fields: + return mobj_recarray[0,0][name] + else: + raise AttributeError, "no field named %s in MatlabObject"%(name) -class Mat5ObjectMatrixGetter(Mat5StructMatrixGetter): - def __init__(self, *args, **kwargs): - super(Mat5StructMatrixGetter, self).__init__(*args, **kwargs) - self.obj_template = mat_obj() + def __setattr__(self, name, value): + if name in self.__dict__['mobj_recarray'].dtype.fields: + self.__dict__['mobj_recarray'][0,0][name] = value + else: + self.__dict__[name] = value + - def get_raw_array(self): - self.obj_template._classname = self.read_element().tostring() - return super(Mat5ObjectMatrixGetter, self).get_raw_array() +class Mat5ObjectMatrixGetter(Mat5MatrixGetter): + def get_array(self): + '''Matlab ojects are essentially structs, with an extra field, the classname.''' + classname = self.read_element().tostring() + namelength = self.read_element()[0] + names = self.read_element() + field_names = [names[i:i+namelength].tostring().strip('\x00') + for i in xrange(0,len(names),namelength)] + result = MatlabObject(classname, field_names) + for field_name in field_names: + result.__setattr__(field_name, self.read_element()) + return result + + +class MatlabFunctionMatrix: + ''' Opaque object representing an array of function handles. ''' + def __init__(self, arr): + self.arr = arr + +class Mat5FunctionMatrixGetter(Mat5CellMatrixGetter): + def get_array(self): + return MatlabFunctionMatrix(self.get_raw_array()) + + class MatFile5Reader(MatFileReader): ''' Reader for Mat 5 mat files - Adds the following attribute to base class - + uint16_codec - char codec to use for uint16 char arrays (defaults to system default codec) ''' @@ -469,6 +537,7 @@ squeeze_me=False, chars_as_strings=True, matlab_compatible=False, + struct_as_record=False, uint16_codec=None ): self.codecs = {} @@ -478,6 +547,7 @@ None, None, None, + struct_as_record ) super(MatFile5Reader, self).__init__( mat_stream, @@ -534,6 +604,8 @@ return self._array_reader.matrix_getter_factory() def guess_byte_order(self): + ''' Guess byte order. + Sets stream pointer to 0 ''' self.mat_stream.seek(126) mi = self.mat_stream.read(2) self.mat_stream.seek(0) @@ -549,16 +621,7 @@ hdict['__version__'] = '%d.%d' % (v_major, v_minor) return hdict - def format_looks_right(self): - # Mat4 files have a zero somewhere in first 4 bytes - self.mat_stream.seek(0) - mopt_bytes = np.ndarray(shape=(4,), - dtype=np.uint8, - buffer = self.mat_stream.read(4)) - self.mat_stream.seek(0) - return 0 not in mopt_bytes - class Mat5MatrixWriter(MatStreamWriter): mat_tag = np.zeros((), mdtypes_template['tag_full']) @@ -644,7 +707,6 @@ class Mat5NumericWriter(Mat5MatrixWriter): - def write(self): imagf = self.arr.dtype.kind == 'c' try: @@ -684,10 +746,8 @@ class Mat5SparseWriter(Mat5MatrixWriter): - def write(self): ''' Sparse matrices are 2D - ''' A = self.arr.tocsc() # convert to sparse CSC format A.sort_indices() # MATLAB expects sorted row indices @@ -704,6 +764,80 @@ self.update_matrix_tag() +class Mat5CompositeWriter(Mat5MatrixWriter): + def __init__(self, file_stream, arr, name, is_global=False, unicode_strings=False): + super(Mat5CompositeWriter, self).__init__(file_stream, arr, name, is_global) + self.unicode_strings = unicode_strings + + +class Mat5CellWriter(Mat5CompositeWriter): + def write(self): + self.write_header(mclass=mxCELL_CLASS) + # loop over data, column major + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + MW = MWG.matrix_writer_factory(el, '') + MW.write() + self.update_matrix_tag() + +class Mat5FunctionWriter(Mat5CompositeWriter): + def __init__(self, file_stream, arr, name, is_global=False, unicode_strings=False): + super(Mat5FunctionWriter, self).__init__(file_stream, arr.arr, name, is_global) + + def write(self): + self.write_header(mclass=mxFUNCTION_CLASS) + # loop over data, column major + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + MW = MWG.matrix_writer_factory(el, '') + MW.write() + self.update_matrix_tag() + + +class Mat5StructWriter(Mat5CompositeWriter): + def write(self): + self.write_header(mclass=mxSTRUCT_CLASS) + + # write fieldnames + fieldnames = [f[0] for f in self.arr.dtype.descr] + self.write_element(np.array([32], dtype='i4')) + self.write_element(np.array(fieldnames, dtype='S32'), mdtype=miINT8) + + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + for f in fieldnames: + MW = MWG.matrix_writer_factory(el[f], '') + MW.write() + self.update_matrix_tag() + +class Mat5ObjectWriter(Mat5CompositeWriter): + def __init__(self, file_stream, arr, name, is_global=False, unicode_strings=False): + super(Mat5ObjectWriter, self).__init__(file_stream, arr.__dict__['mobj_recarray'], name, is_global) + self.classname = arr.classname + + def write(self): + self.write_header(mclass=mxOBJECT_CLASS) + + # write classnames + self.write_element(np.array(self.classname, dtype='S'), mdtype=miINT8) + + # write fieldnames + fieldnames = [f[0] for f in self.arr.dtype.descr] + self.write_element(np.array([32], dtype='i4')) + self.write_element(np.array(fieldnames, dtype='S32'), mdtype=miINT8) + + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + for f in fieldnames: + MW = MWG.matrix_writer_factory(el[f], '') + MW.write() + self.update_matrix_tag() + + class Mat5WriterGetter(object): ''' Wraps stream and options, provides methods for getting Writer objects ''' def __init__(self, stream, unicode_strings): @@ -722,15 +856,18 @@ if have_sparse: if scipy.sparse.issparse(arr): return Mat5SparseWriter(self.stream, arr, name, is_global) + + if isinstance(arr, MatlabFunctionMatrix): + return Mat5FunctionWriter(self.stream, arr, name, is_global, self.unicode_strings) + if isinstance(arr, MatlabObject): + return Mat5ObjectWriter(self.stream, arr, name, is_global, self.unicode_strings) + arr = np.array(arr) if arr.dtype.hasobject: - types, arr_type = self.classify_mobjects(arr) - if arr_type == 'c': - return Mat5CellWriter(self.stream, arr, name, is_global, types) - elif arr_type == 's': - return Mat5StructWriter(self.stream, arr, name, is_global) - elif arr_type == 'o': - return Mat5ObjectWriter(self.stream, arr, name, is_global) + if arr.dtype.fields == None: + return Mat5CellWriter(self.stream, arr, name, is_global, self.unicode_strings) + else: + return Mat5StructWriter(self.stream, arr, name, is_global, self.unicode_strings) if arr.dtype.kind in ('U', 'S'): if self.unicode_strings: return Mat5UniCharWriter(self.stream, arr, name, is_global) @@ -739,47 +876,6 @@ else: return Mat5NumericWriter(self.stream, arr, name, is_global) - def classify_mobjects(self, objarr): - ''' Function to classify objects passed for writing - returns - types - S1 array of same shape as objarr with codes for each object - i - invalid object - a - ndarray - s - matlab struct - o - matlab object - arr_type - one of - c - cell array - s - struct array - o - object array - ''' - n = objarr.size - types = np.empty((n,), dtype='S1') - types[:] = 'i' - type_set = set() - flato = objarr.flat - for i in range(n): - obj = flato[i] - if isinstance(obj, np.ndarray): - types[i] = 'a' - continue - try: - fns = tuple(obj._fieldnames) - except AttributeError: - continue - try: - cn = obj._classname - except AttributeError: - types[i] = 's' - type_set.add(fns) - continue - types[i] = 'o' - type_set.add((cn, fns)) - arr_type = 'c' - if len(set(types))==1 and len(type_set) == 1: - arr_type = types[0] - return types.reshape(objarr.shape), arr_type - - class MatFile5Writer(MatFileWriter): ''' Class for writing mat5 files ''' def __init__(self, file_stream, @@ -815,6 +911,8 @@ def put_variables(self, mdict): for name, var in mdict.items(): + if name[0] == '_': + continue is_global = name in self.global_vars self.writer_getter.rewind() self.writer_getter.matrix_writer_factory( Modified: trunk/scipy/io/matlab/miobase.py =================================================================== --- trunk/scipy/io/matlab/miobase.py 2008-10-05 00:43:08 UTC (rev 4769) +++ trunk/scipy/io/matlab/miobase.py 2008-10-05 00:58:08 UTC (rev 4770) @@ -22,6 +22,38 @@ res *= e return res +def get_matfile_version(fileobj): + ''' Return '4', '5', or '7' depending on apparent mat file type + Inputs + fileobj - file object implementing seek() and read() + Outputs + version_str - one of (strings) 4, 5, or 7 + + Has the side effect of setting the file read pointer to 0 + ''' + # Mat4 files have a zero somewhere in first 4 bytes + fileobj.seek(0) + mopt_bytes = np.ndarray(shape=(4,), + dtype=np.uint8, + buffer = fileobj.read(4)) + if 0 in mopt_bytes: + fileobj.seek(0) + return '4' + # For 5 or 7 we need to read an integer in the header + # bytes 124 through 128 contain a version integer + # and an endian test string + fileobj.seek(124) + tst_str = fileobj.read(4) + fileobj.seek(0) + maj_ind = int(tst_str[2] == 'I') + verb = ord(tst_str[maj_ind]) + if verb == 1: + return '5' + elif verb == 2: + return '7' + raise ValueError('Unknown mat file type, version %d' % verb) + + class ByteOrder(object): ''' Namespace for byte ordering ''' little_endian = sys.byteorder == 'little' @@ -50,7 +82,7 @@ Attaches to initialized stream Base class for "getters" - which do store state of what they are - reading on itialization, and therefore need to be initialized + reading on initialization, and therefore need to be initialized before each read, and "readers" which do not store state, and only need to be initialized once on object creation @@ -96,14 +128,13 @@ matlab_compatible - returns matrices as would be loaded by matlab (implies squeeze_me=False, chars_as_strings=False mat_dtype=True) + struct_as_record - return strutures as numpy records (only from v5 files) To make this class functional, you will need to override the following methods: set_dtypes - sets data types defs from byte order matrix_getter_factory - gives object to fetch next matrix from stream - format_looks_right - returns True if format looks correct for - this file type (Mat4, Mat5) guess_byte_order - guesses file byte order from file """ @@ -113,6 +144,7 @@ squeeze_me=False, chars_as_strings=True, matlab_compatible=False, + struct_as_record=False ): # Initialize stream self.mat_stream = mat_stream @@ -177,7 +209,8 @@ 'get/set order code') def set_dtypes(self): - assert False, 'Not implemented' + ''' Set dtype endianness. In this case we have no dtypes ''' + pass def convert_dtypes(self, dtype_template): dtypes = dtype_template.copy() @@ -187,16 +220,13 @@ def matrix_getter_factory(self): assert False, 'Not implemented' - - def format_looks_right(self): - "Return True if the format looks right for this object" - assert False, 'Not implemented' - + def file_header(self): return {} def guess_byte_order(self): - assert 0, 'Not implemented' + ''' As we do not know what file type we have, assume native ''' + return ByteOrder.native_code def get_processor_func(self): ''' Processing to apply to read matrices @@ -227,7 +257,7 @@ str_arr = arr.reshape( (small_product(n_dims), dims[-1])) - arr = np.empty(n_dims, dtype=object) + arr = np.empty(n_dims, dtype='U%d' % dims[-1]) for i in range(0, n_dims[-1]): arr[...,i] = self.chars_to_str(str_arr[i]) else: # return string Modified: trunk/scipy/io/matlab/tests/test_mio.py =================================================================== --- trunk/scipy/io/matlab/tests/test_mio.py 2008-10-05 00:43:08 UTC (rev 4769) +++ trunk/scipy/io/matlab/tests/test_mio.py 2008-10-05 00:58:08 UTC (rev 4770) @@ -6,11 +6,11 @@ from tempfile import mkdtemp from numpy.testing import * from numpy import arange, array, pi, cos, exp, sin, sqrt, ndarray, \ - zeros, reshape, transpose + zeros, reshape, transpose, dtype, empty import scipy.sparse as SP from scipy.io.matlab.mio import loadmat, savemat -from scipy.io.matlab.mio5 import mat_obj, mat_struct +from scipy.io.matlab.mio5 import MatlabObject import shutil import gzip @@ -30,7 +30,7 @@ self._check_level(level_label, ev, actual[i]) return # object, as container for matlab structs and objects - elif isinstance(expected, mat_struct) or isinstance(expected, mat_obj): + elif isinstance(expected, MatlabObject): assert isinstance(actual, typex), \ "Different types %s and %s at %s" % (typex, typac, label) ex_fields = dir(expected) @@ -62,7 +62,7 @@ def _check_case(self, name, files, case): for file_name in files: - matdict = loadmat(file_name) + matdict = loadmat(file_name, struct_as_record=True) label = "test %s; file %s" % (name, file_name) for k, expected in case.items(): k_label = "%s, variable %s" % (label, k) @@ -164,10 +164,8 @@ 'expected': {'testsparsefloat': SP.csc_matrix(array([[-1+2j,0,2],[0,-3j,0]]))}, }, ] -st = mat_struct() -st.stringfield = u'Rats live on no evil star.' -st.doublefield = array([sqrt(2),exp(1),pi]) -st.complexfield = (1+1j)*array([sqrt(2),exp(1),pi]) +st = array([(u'Rats live on no evil star.', array([sqrt(2),exp(1),pi]), (1+1j)*array([sqrt(2),exp(1),pi]))], + dtype=[(n, object) for n in ['stringfield', 'doublefield', 'complexfield']]) case_table5.append( {'name': 'struct', 'expected': {'teststruct': st} @@ -182,25 +180,24 @@ {'name': 'cellnest', 'expected': {'testcellnest': a}, }) -st = mat_struct() -st.one = array(1) -st.two = mat_struct() -st.two.three = u'number 3' +st = empty((1,1), dtype=[(n, object) for n in ['one', 'two']]) +st[0,0]['one'] = array(1) +st[0,0]['two'] = empty((1,1), dtype=[('three', object)]) +st[0,0]['two'][0,0]['three'] = u'number 3' case_table5.append( {'name': 'structnest', 'expected': {'teststructnest': st} }) -a = array([mat_struct(), mat_struct()]) -a[0].one = array(1) -a[0].two = array(2) -a[1].one = u'number 1' -a[1].two = u'number 2' +a = empty((2,1), dtype=[(n, object) for n in ['one', 'two']]) +a[0,0]['one'] = array(1) +a[0,0]['two'] = array(2) +a[1,0]['one'] = u'number 1' +a[1,0]['two'] = u'number 2' case_table5.append( {'name': 'structarr', 'expected': {'teststructarr': a} }) -a = mat_obj() -a._classname = 'inline' +a = MatlabObject('inline', ['expr', 'args', 'isEmpty', 'numArgs', 'version']) a.expr = u'x' a.inputExpr = u' x = INLINE_INPUTS_{1};' a.args = u'x' @@ -255,7 +252,7 @@ mat_stream.close() mat_stream = gzip.open( fname,mode='rb') - actual = loadmat(mat_stream) + actual = loadmat(mat_stream, struct_as_record=True) mat_stream.close() finally: shutil.rmtree(tmpdir) From scipy-svn at scipy.org Sat Oct 4 21:55:02 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 4 Oct 2008 20:55:02 -0500 (CDT) Subject: [Scipy-svn] r4771 - trunk/scipy/io/matlab/tests Message-ID: <20081005015502.E8F9839C088@scipy.org> Author: matthew.brett at gmail.com Date: 2008-10-04 20:54:59 -0500 (Sat, 04 Oct 2008) New Revision: 4771 Added: trunk/scipy/io/matlab/tests/gen_unittests.m trunk/scipy/io/matlab/tests/gen_unittests4.m trunk/scipy/io/matlab/tests/save_test.m Log: Restore matlab m files for creating test mat files Added: trunk/scipy/io/matlab/tests/gen_unittests.m =================================================================== --- trunk/scipy/io/matlab/tests/gen_unittests.m 2008-10-05 00:58:08 UTC (rev 4770) +++ trunk/scipy/io/matlab/tests/gen_unittests.m 2008-10-05 01:54:59 UTC (rev 4771) @@ -0,0 +1,92 @@ +% Generates mat files for loadmat unit tests +% This is the version for matlab 5 and higher +% Uses save_test.m function + +% work out matlab version and file suffix for test files +global FILEPREFIX FILESUFFIX +FILEPREFIX = [fullfile(pwd, 'data') filesep]; +temp = ver('MATLAB'); +mlv = temp.Version; +FILESUFFIX = ['_' mlv '_' computer '.mat']; + +% basic double array +save_test('testdouble', 0:pi/4:2*pi); + +% string +save_test('teststring', '"Do nine men interpret?" "Nine men," I nod.') + +% complex +theta = 0:pi/4:2*pi; +save_test('testcomplex', cos(theta) + 1j*sin(theta)); + +% asymmetric array to check indexing +a = zeros(3, 5); +a(:,1) = [1:3]'; +a(1,:) = 1:5; + +% 2D matrix +save_test('testmatrix', a); + +% minus number - tests signed int +save_test('testminus', -1); + +% single character +save_test('testonechar', 'r'); + +% string array +save_test('teststringarray', ['one '; 'two '; 'three']); + +% sparse array +save_test('testsparse', sparse(a)); + +% sparse complex array +b = sparse(a); +b(1,1) = b(1,1) + j; +save_test('testsparsecomplex', b); + +% Two variables in same file +save([FILEPREFIX 'testmulti' FILESUFFIX], 'a', 'theta') + + +% struct +save_test('teststruct', ... + struct('stringfield','Rats live on no evil star.',... + 'doublefield',[sqrt(2) exp(1) pi],... + 'complexfield',(1+1j)*[sqrt(2) exp(1) pi])); + +% cell +save_test('testcell', ... + {['This cell contains this string and 3 arrays of increasing' ... + ' length'], 1., 1.:2., 1.:3.}); + +% Empty cells in two cell matrices +save_test('testemptycell', {1, 2, [], [], 3}); + +% 3D matrix +save_test('test3dmatrix', reshape(1:24,[2 3 4])) + +% nested cell array +save_test('testcellnest', {1, {2, 3, {4, 5}}}); + +% nested struct +save_test('teststructnest', struct('one', 1, 'two', ... + struct('three', 'number 3'))); + +% array of struct +save_test('teststructarr', [struct('one', 1, 'two', 2) ... + struct('one', 'number 1', 'two', 'number 2')]); + +% matlab object +save_test('testobject', inline('x')) + +% array of matlab objects +%save_test('testobjarr', [inline('x') inline('y')]) + +% unicode test +if str2num(mlv) > 7 % function added 7.0.1 + fid = fopen([FILEPREFIX 'japanese_utf8.txt']); + from_japan = fread(fid, 'uint8')'; + fclose(fid); + save_test('testunicode', native2unicode(from_japan, 'utf-8')); +end + \ No newline at end of file Property changes on: trunk/scipy/io/matlab/tests/gen_unittests.m ___________________________________________________________________ Name: svn:executable + * Added: trunk/scipy/io/matlab/tests/gen_unittests4.m =================================================================== --- trunk/scipy/io/matlab/tests/gen_unittests4.m 2008-10-05 00:58:08 UTC (rev 4770) +++ trunk/scipy/io/matlab/tests/gen_unittests4.m 2008-10-05 01:54:59 UTC (rev 4771) @@ -0,0 +1,50 @@ +% Generates mat files for loadmat unit tests +% Uses save_test.m function +% This is the version for matlab 4 + +% work out matlab version and file suffix for test files +global FILEPREFIX FILESUFFIX +sepchar = '/'; +if strcmp(computer, 'PCWIN'), sepchar = '\'; end +FILEPREFIX = [pwd sepchar 'data' sepchar]; +mlv = version; +FILESUFFIX = ['_' mlv '_' computer '.mat']; + +% basic double array +save_test('testdouble', 0:pi/4:2*pi); + +% string +save_test('teststring', '"Do nine men interpret?" "Nine men," I nod.') + +% complex +theta = 0:pi/4:2*pi; +save_test('testcomplex', cos(theta) + 1j*sin(theta)); + +% asymmetric array to check indexing +a = zeros(3, 5); +a(:,1) = [1:3]'; +a(1,:) = 1:5; + +% 2D matrix +save_test('testmatrix', a); + +% minus number - tests signed int +save_test('testminus', -1); + +% single character +save_test('testonechar', 'r'); + +% string array +save_test('teststringarray', ['one '; 'two '; 'three']); + +% sparse array +save_test('testsparse', sparse(a)); + +% sparse complex array +b = sparse(a); +b(1,1) = b(1,1) + j; +save_test('testsparsecomplex', b); + +% Two variables in same file +save([FILEPREFIX 'testmulti' FILESUFFIX], 'a', 'theta') + Property changes on: trunk/scipy/io/matlab/tests/gen_unittests4.m ___________________________________________________________________ Name: svn:executable + * Added: trunk/scipy/io/matlab/tests/save_test.m =================================================================== --- trunk/scipy/io/matlab/tests/save_test.m 2008-10-05 00:58:08 UTC (rev 4770) +++ trunk/scipy/io/matlab/tests/save_test.m 2008-10-05 01:54:59 UTC (rev 4771) @@ -0,0 +1,6 @@ +function save_test(test_name, v) +% saves variable passed in m with filename from prefix + +global FILEPREFIX FILESUFFIX +eval([test_name ' = v;']); +save([FILEPREFIX test_name FILESUFFIX], test_name) \ No newline at end of file Property changes on: trunk/scipy/io/matlab/tests/save_test.m ___________________________________________________________________ Name: svn:executable + * From scipy-svn at scipy.org Sat Oct 4 22:23:45 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 4 Oct 2008 21:23:45 -0500 (CDT) Subject: [Scipy-svn] r4772 - in trunk/scipy/sparse: . sparsetools Message-ID: <20081005022345.1751239C088@scipy.org> Author: wnbell Date: 2008-10-04 21:23:37 -0500 (Sat, 04 Oct 2008) New Revision: 4772 Modified: trunk/scipy/sparse/dia.py trunk/scipy/sparse/sparsetools/coo.h trunk/scipy/sparse/sparsetools/coo.i trunk/scipy/sparse/sparsetools/coo.py trunk/scipy/sparse/sparsetools/coo_wrap.cxx trunk/scipy/sparse/sparsetools/csc.i trunk/scipy/sparse/sparsetools/csc.py trunk/scipy/sparse/sparsetools/csc_wrap.cxx trunk/scipy/sparse/sparsetools/csr.h trunk/scipy/sparse/sparsetools/csr.i trunk/scipy/sparse/sparsetools/csr.py trunk/scipy/sparse/sparsetools/csr_wrap.cxx trunk/scipy/sparse/sparsetools/dia.py trunk/scipy/sparse/sparsetools/dia_wrap.cxx trunk/scipy/sparse/sparsetools/sparsetools.i Log: minor additions to sparsetools Modified: trunk/scipy/sparse/dia.py =================================================================== --- trunk/scipy/sparse/dia.py 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/dia.py 2008-10-05 02:23:37 UTC (rev 4772) @@ -80,10 +80,10 @@ # Try interpreting it as (data, diags) data, diags = arg1 except: - raise ValueError, "unrecognized form for dia_matrix constructor" + raise ValueError('unrecognized form for dia_matrix constructor') else: if shape is None: - raise ValueError,'expected a shape argument' + raise ValueError('expected a shape argument') self.data = atleast_2d(array(arg1[0],dtype=dtype,copy=copy)) self.diags = atleast_1d(array(arg1[1],dtype='i',copy=copy)) self.shape = shape @@ -103,18 +103,18 @@ #check format if self.diags.ndim != 1: - raise ValueError,'diags array must have rank 1' + raise ValueError('diags array must have rank 1') if self.data.ndim != 2: - raise ValueError,'data array must have rank 2' + raise ValueError('data array must have rank 2') if self.data.shape[0] != len(self.diags): - raise ValueError,'number of diagonals (%d) ' \ + raise ValueError('number of diagonals (%d) ' \ 'does not match the number of diags (%d)' \ - % (self.data.shape[0], len(self.diags)) + % (self.data.shape[0], len(self.diags))) if len(unique(self.diags)) != len(self.diags): - raise ValueError,'offset array contains duplicate values' + raise ValueError('offset array contains duplicate values') def __repr__(self): nnz = self.getnnz() Modified: trunk/scipy/sparse/sparsetools/coo.h =================================================================== --- trunk/scipy/sparse/sparsetools/coo.h 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/coo.h 2008-10-05 02:23:37 UTC (rev 4772) @@ -2,6 +2,7 @@ #define __COO_H__ #include +#include /* * Compute B = A for COO matrix A, CSR matrix B @@ -149,4 +150,26 @@ } } +/* + * Count the number of occupied diagonals in COO matrix A + * + * Input Arguments: + * I nnz - number of nonzeros in A + * I Ai[nnz(A)] - row indices + * I Aj[nnz(A)] - column indices + * + */ +template +I coo_count_diagonals(const I nnz, + const I Ai[], + const I Aj[]) +{ + std::set diagonals; + for(I n = 0; n < nnz; n++){ + diagonals.insert(Aj[n] - Ai[n]); + } + return diagonals.size(); +} + + #endif Modified: trunk/scipy/sparse/sparsetools/coo.i =================================================================== --- trunk/scipy/sparse/sparsetools/coo.i 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/coo.i 2008-10-05 02:23:37 UTC (rev 4772) @@ -14,4 +14,6 @@ INSTANTIATE_ALL(coo_matvec) +INSTANTIATE_INDEX(coo_count_diagonals) + Modified: trunk/scipy/sparse/sparsetools/coo.py =================================================================== --- trunk/scipy/sparse/sparsetools/coo.py 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/coo.py 2008-10-05 02:23:37 UTC (rev 4772) @@ -1,5 +1,5 @@ # This file was automatically generated by SWIG (http://www.swig.org). -# Version 1.3.34 +# Version 1.3.36 # # Don't modify this file, modify the SWIG interface instead. # This file is compatible with both classic and new-style classes. @@ -49,7 +49,11 @@ +def coo_count_diagonals(*args): + """coo_count_diagonals(int nnz, int Ai, int Aj) -> int""" + return _coo.coo_count_diagonals(*args) + def coo_tocsr(*args): """ coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, Modified: trunk/scipy/sparse/sparsetools/coo_wrap.cxx =================================================================== --- trunk/scipy/sparse/sparsetools/coo_wrap.cxx 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/coo_wrap.cxx 2008-10-05 02:23:37 UTC (rev 4772) @@ -1,6 +1,6 @@ /* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). - * Version 1.3.34 + * Version 1.3.36 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make @@ -73,6 +73,12 @@ # endif #endif +#ifndef SWIG_MSC_UNSUPPRESS_4505 +# if defined(_MSC_VER) +# pragma warning(disable : 4505) /* unreferenced local function has been removed */ +# endif +#endif + #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) @@ -2516,7 +2522,7 @@ #define SWIG_name "_coo" -#define SWIGVERSION 0x010334 +#define SWIGVERSION 0x010336 #define SWIG_VERSION SWIGVERSION @@ -2544,7 +2550,9 @@ PyObject_ptr(PyObject *obj, bool initial_ref = true) :_obj(obj) { - if (initial_ref) Py_XINCREF(_obj); + if (initial_ref) { + Py_XINCREF(_obj); + } } PyObject_ptr & operator=(const PyObject_ptr& item) @@ -3089,6 +3097,16 @@ return res; } + + #define SWIG_From_long PyInt_FromLong + + +SWIGINTERNINLINE PyObject * +SWIG_From_int (int value) +{ + return SWIG_From_long (value); +} + #ifdef __cplusplus extern "C" { #endif @@ -12950,6 +12968,76 @@ } +SWIGINTERN PyObject *_wrap_coo_count_diagonals(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + int arg1 ; + int *arg2 ; + int *arg3 ; + int val1 ; + int ecode1 = 0 ; + PyArrayObject *array2 = NULL ; + int is_new_object2 ; + PyArrayObject *array3 = NULL ; + int is_new_object3 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + int result; + + if (!PyArg_ParseTuple(args,(char *)"OOO:coo_count_diagonals",&obj0,&obj1,&obj2)) SWIG_fail; + ecode1 = SWIG_AsVal_int(obj0, &val1); + if (!SWIG_IsOK(ecode1)) { + SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "coo_count_diagonals" "', argument " "1"" of type '" "int""'"); + } + arg1 = static_cast< int >(val1); + { + npy_intp size[1] = { + -1 + }; + array2 = obj_to_array_contiguous_allow_conversion(obj1, PyArray_INT, &is_new_object2); + if (!array2 || !require_dimensions(array2,1) || !require_size(array2,size,1) + || !require_contiguous(array2) || !require_native(array2)) SWIG_fail; + + arg2 = (int*) array2->data; + } + { + npy_intp size[1] = { + -1 + }; + array3 = obj_to_array_contiguous_allow_conversion(obj2, PyArray_INT, &is_new_object3); + if (!array3 || !require_dimensions(array3,1) || !require_size(array3,size,1) + || !require_contiguous(array3) || !require_native(array3)) SWIG_fail; + + arg3 = (int*) array3->data; + } + result = (int)coo_count_diagonals< int >(arg1,(int const (*))arg2,(int const (*))arg3); + resultobj = SWIG_From_int(static_cast< int >(result)); + { + if (is_new_object2 && array2) { + Py_DECREF(array2); + } + } + { + if (is_new_object3 && array3) { + Py_DECREF(array3); + } + } + return resultobj; +fail: + { + if (is_new_object2 && array2) { + Py_DECREF(array2); + } + } + { + if (is_new_object3 && array3) { + Py_DECREF(array3); + } + } + return NULL; +} + + static PyMethodDef SwigMethods[] = { { (char *)"coo_tocsr", _wrap_coo_tocsr, METH_VARARGS, (char *)"\n" "coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, \n" @@ -13067,6 +13155,7 @@ "coo_matvec(int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, \n" " npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx)\n" ""}, + { (char *)"coo_count_diagonals", _wrap_coo_count_diagonals, METH_VARARGS, (char *)"coo_count_diagonals(int nnz, int Ai, int Aj) -> int"}, { NULL, NULL, 0, NULL } }; Modified: trunk/scipy/sparse/sparsetools/csc.i =================================================================== --- trunk/scipy/sparse/sparsetools/csc.i 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/csc.i 2008-10-05 02:23:37 UTC (rev 4772) @@ -8,7 +8,7 @@ %include "csc.h" -%template(csc_matmat_pass1) csc_matmat_pass1; +INSTANTIATE_INDEX(csc_matmat_pass1); INSTANTIATE_ALL(csc_diagonal) INSTANTIATE_ALL(csc_tocsr) Modified: trunk/scipy/sparse/sparsetools/csc.py =================================================================== --- trunk/scipy/sparse/sparsetools/csc.py 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/csc.py 2008-10-05 02:23:37 UTC (rev 4772) @@ -1,5 +1,5 @@ # This file was automatically generated by SWIG (http://www.swig.org). -# Version 1.3.34 +# Version 1.3.36 # # Don't modify this file, modify the SWIG interface instead. # This file is compatible with both classic and new-style classes. Modified: trunk/scipy/sparse/sparsetools/csc_wrap.cxx =================================================================== --- trunk/scipy/sparse/sparsetools/csc_wrap.cxx 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/csc_wrap.cxx 2008-10-05 02:23:37 UTC (rev 4772) @@ -1,6 +1,6 @@ /* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). - * Version 1.3.34 + * Version 1.3.36 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make @@ -73,6 +73,12 @@ # endif #endif +#ifndef SWIG_MSC_UNSUPPRESS_4505 +# if defined(_MSC_VER) +# pragma warning(disable : 4505) /* unreferenced local function has been removed */ +# endif +#endif + #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) @@ -2516,7 +2522,7 @@ #define SWIG_name "_csc" -#define SWIGVERSION 0x010334 +#define SWIGVERSION 0x010336 #define SWIG_VERSION SWIGVERSION @@ -2544,7 +2550,9 @@ PyObject_ptr(PyObject *obj, bool initial_ref = true) :_obj(obj) { - if (initial_ref) Py_XINCREF(_obj); + if (initial_ref) { + Py_XINCREF(_obj); + } } PyObject_ptr & operator=(const PyObject_ptr& item) Modified: trunk/scipy/sparse/sparsetools/csr.h =================================================================== --- trunk/scipy/sparse/sparsetools/csr.h 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/csr.h 2008-10-05 02:23:37 UTC (rev 4772) @@ -1,6 +1,7 @@ #ifndef __CSR_H__ #define __CSR_H__ +#include #include #include #include @@ -39,8 +40,8 @@ const I N = std::min(n_row, n_col); for(I i = 0; i < N; i++){ - I row_start = Ap[i]; - I row_end = Ap[i+1]; + const I row_start = Ap[i]; + const I row_end = Ap[i+1]; T diag = 0; for(I jj = row_start; jj < row_end; jj++){ @@ -742,6 +743,7 @@ * * Note: * The column indicies within each row must be in sorted order. + * Explicit zeros are retained. * Ap, Aj, and Ax will be modified *inplace* * */ @@ -951,7 +953,29 @@ } +/* + * Count the number of occupied diagonals in CSR matrix A + * + * Input Arguments: + * I nnz - number of nonzeros in A + * I Ai[nnz(A)] - row indices + * I Aj[nnz(A)] - column indices + * + */ +template +I csr_count_diagonals(const I n_row, + const I Ap[], + const I Aj[]) +{ + std::set diagonals; + + for(I i = 0; i < n_row; i++){ + for(I jj = Ap[i]; jj < Ap[i+1]; jj++){ + diagonals.insert(Aj[jj] - i); + } + } + return diagonals.size(); +} - #endif Modified: trunk/scipy/sparse/sparsetools/csr.i =================================================================== --- trunk/scipy/sparse/sparsetools/csr.i 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/csr.i 2008-10-05 02:23:37 UTC (rev 4772) @@ -9,12 +9,11 @@ %include "csr.h" -%template(expandptr) expandptr; -%template(csr_count_blocks) csr_count_blocks; -%template(csr_matmat_pass1) csr_matmat_pass1; -%template(csr_has_sorted_indices) csr_has_sorted_indices; +INSTANTIATE_INDEX(expandptr) +INSTANTIATE_INDEX(csr_matmat_pass1) +INSTANTIATE_INDEX(csr_count_blocks) +INSTANTIATE_INDEX(csr_has_sorted_indices) - INSTANTIATE_ALL(csr_diagonal) INSTANTIATE_ALL(csr_scale_rows) INSTANTIATE_ALL(csr_scale_columns) Modified: trunk/scipy/sparse/sparsetools/csr.py =================================================================== --- trunk/scipy/sparse/sparsetools/csr.py 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/csr.py 2008-10-05 02:23:37 UTC (rev 4772) @@ -1,5 +1,5 @@ # This file was automatically generated by SWIG (http://www.swig.org). -# Version 1.3.34 +# Version 1.3.36 # # Don't modify this file, modify the SWIG interface instead. # This file is compatible with both classic and new-style classes. @@ -53,10 +53,6 @@ """expandptr(int n_row, int Ap, int Bi)""" return _csr.expandptr(*args) -def csr_count_blocks(*args): - """csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int""" - return _csr.csr_count_blocks(*args) - def csr_matmat_pass1(*args): """ csr_matmat_pass1(int n_row, int n_col, int Ap, int Aj, int Bp, int Bj, @@ -64,6 +60,10 @@ """ return _csr.csr_matmat_pass1(*args) +def csr_count_blocks(*args): + """csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int""" + return _csr.csr_count_blocks(*args) + def csr_has_sorted_indices(*args): """csr_has_sorted_indices(int n_row, int Ap, int Aj) -> bool""" return _csr.csr_has_sorted_indices(*args) Modified: trunk/scipy/sparse/sparsetools/csr_wrap.cxx =================================================================== --- trunk/scipy/sparse/sparsetools/csr_wrap.cxx 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/csr_wrap.cxx 2008-10-05 02:23:37 UTC (rev 4772) @@ -1,6 +1,6 @@ /* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). - * Version 1.3.34 + * Version 1.3.36 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make @@ -73,6 +73,12 @@ # endif #endif +#ifndef SWIG_MSC_UNSUPPRESS_4505 +# if defined(_MSC_VER) +# pragma warning(disable : 4505) /* unreferenced local function has been removed */ +# endif +#endif + #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) @@ -2530,7 +2536,7 @@ #define SWIG_name "_csr" -#define SWIGVERSION 0x010334 +#define SWIGVERSION 0x010336 #define SWIG_VERSION SWIGVERSION @@ -2558,7 +2564,9 @@ PyObject_ptr(PyObject *obj, bool initial_ref = true) :_obj(obj) { - if (initial_ref) Py_XINCREF(_obj); + if (initial_ref) { + Py_XINCREF(_obj); + } } PyObject_ptr & operator=(const PyObject_ptr& item) @@ -3176,59 +3184,71 @@ } -SWIGINTERN PyObject *_wrap_csr_count_blocks(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { +SWIGINTERN PyObject *_wrap_csr_matmat_pass1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; int arg2 ; - int arg3 ; - int arg4 ; + int *arg3 ; + int *arg4 ; int *arg5 ; int *arg6 ; - int result; + int *arg7 ; int val1 ; int ecode1 = 0 ; int val2 ; int ecode2 = 0 ; - int val3 ; - int ecode3 = 0 ; - int val4 ; - int ecode4 = 0 ; + PyArrayObject *array3 = NULL ; + int is_new_object3 ; + PyArrayObject *array4 = NULL ; + int is_new_object4 ; PyArrayObject *array5 = NULL ; int is_new_object5 ; PyArrayObject *array6 = NULL ; int is_new_object6 ; + PyArrayObject *temp7 = NULL ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; PyObject * obj4 = 0 ; PyObject * obj5 = 0 ; + PyObject * obj6 = 0 ; - if (!PyArg_ParseTuple(args,(char *)"OOOOOO:csr_count_blocks",&obj0,&obj1,&obj2,&obj3,&obj4,&obj5)) SWIG_fail; + if (!PyArg_ParseTuple(args,(char *)"OOOOOOO:csr_matmat_pass1",&obj0,&obj1,&obj2,&obj3,&obj4,&obj5,&obj6)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { - SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "csr_count_blocks" "', argument " "1"" of type '" "int""'"); + SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "csr_matmat_pass1" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { - SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "csr_count_blocks" "', argument " "2"" of type '" "int""'"); + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "csr_matmat_pass1" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); - ecode3 = SWIG_AsVal_int(obj2, &val3); - if (!SWIG_IsOK(ecode3)) { - SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "csr_count_blocks" "', argument " "3"" of type '" "int""'"); - } - arg3 = static_cast< int >(val3); - ecode4 = SWIG_AsVal_int(obj3, &val4); - if (!SWIG_IsOK(ecode4)) { - SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "csr_count_blocks" "', argument " "4"" of type '" "int""'"); - } - arg4 = static_cast< int >(val4); { npy_intp size[1] = { -1 }; + array3 = obj_to_array_contiguous_allow_conversion(obj2, PyArray_INT, &is_new_object3); + if (!array3 || !require_dimensions(array3,1) || !require_size(array3,size,1) + || !require_contiguous(array3) || !require_native(array3)) SWIG_fail; + + arg3 = (int*) array3->data; + } + { + npy_intp size[1] = { + -1 + }; + array4 = obj_to_array_contiguous_allow_conversion(obj3, PyArray_INT, &is_new_object4); + if (!array4 || !require_dimensions(array4,1) || !require_size(array4,size,1) + || !require_contiguous(array4) || !require_native(array4)) SWIG_fail; + + arg4 = (int*) array4->data; + } + { + npy_intp size[1] = { + -1 + }; array5 = obj_to_array_contiguous_allow_conversion(obj4, PyArray_INT, &is_new_object5); if (!array5 || !require_dimensions(array5,1) || !require_size(array5,size,1) || !require_contiguous(array5) || !require_native(array5)) SWIG_fail; @@ -3245,9 +3265,24 @@ arg6 = (int*) array6->data; } - result = (int)csr_count_blocks< int >(arg1,arg2,arg3,arg4,(int const (*))arg5,(int const (*))arg6); - resultobj = SWIG_From_int(static_cast< int >(result)); { + temp7 = obj_to_array_no_conversion(obj6,PyArray_INT); + if (!temp7 || !require_contiguous(temp7) || !require_native(temp7)) SWIG_fail; + arg7 = (int*) array_data(temp7); + } + csr_matmat_pass1< int >(arg1,arg2,(int const (*))arg3,(int const (*))arg4,(int const (*))arg5,(int const (*))arg6,arg7); + resultobj = SWIG_Py_Void(); + { + if (is_new_object3 && array3) { + Py_DECREF(array3); + } + } + { + if (is_new_object4 && array4) { + Py_DECREF(array4); + } + } + { if (is_new_object5 && array5) { Py_DECREF(array5); } @@ -3260,6 +3295,16 @@ return resultobj; fail: { + if (is_new_object3 && array3) { + Py_DECREF(array3); + } + } + { + if (is_new_object4 && array4) { + Py_DECREF(array4); + } + } + { if (is_new_object5 && array5) { Py_DECREF(array5); } @@ -3273,71 +3318,59 @@ } -SWIGINTERN PyObject *_wrap_csr_matmat_pass1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { +SWIGINTERN PyObject *_wrap_csr_count_blocks(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; int arg2 ; - int *arg3 ; - int *arg4 ; + int arg3 ; + int arg4 ; int *arg5 ; int *arg6 ; - int *arg7 ; int val1 ; int ecode1 = 0 ; int val2 ; int ecode2 = 0 ; - PyArrayObject *array3 = NULL ; - int is_new_object3 ; - PyArrayObject *array4 = NULL ; - int is_new_object4 ; + int val3 ; + int ecode3 = 0 ; + int val4 ; + int ecode4 = 0 ; PyArrayObject *array5 = NULL ; int is_new_object5 ; PyArrayObject *array6 = NULL ; int is_new_object6 ; - PyArrayObject *temp7 = NULL ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; PyObject * obj4 = 0 ; PyObject * obj5 = 0 ; - PyObject * obj6 = 0 ; + int result; - if (!PyArg_ParseTuple(args,(char *)"OOOOOOO:csr_matmat_pass1",&obj0,&obj1,&obj2,&obj3,&obj4,&obj5,&obj6)) SWIG_fail; + if (!PyArg_ParseTuple(args,(char *)"OOOOOO:csr_count_blocks",&obj0,&obj1,&obj2,&obj3,&obj4,&obj5)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { - SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "csr_matmat_pass1" "', argument " "1"" of type '" "int""'"); + SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "csr_count_blocks" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { - SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "csr_matmat_pass1" "', argument " "2"" of type '" "int""'"); + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "csr_count_blocks" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); + ecode3 = SWIG_AsVal_int(obj2, &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "csr_count_blocks" "', argument " "3"" of type '" "int""'"); + } + arg3 = static_cast< int >(val3); + ecode4 = SWIG_AsVal_int(obj3, &val4); + if (!SWIG_IsOK(ecode4)) { + SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "csr_count_blocks" "', argument " "4"" of type '" "int""'"); + } + arg4 = static_cast< int >(val4); { npy_intp size[1] = { -1 }; - array3 = obj_to_array_contiguous_allow_conversion(obj2, PyArray_INT, &is_new_object3); - if (!array3 || !require_dimensions(array3,1) || !require_size(array3,size,1) - || !require_contiguous(array3) || !require_native(array3)) SWIG_fail; - - arg3 = (int*) array3->data; - } - { - npy_intp size[1] = { - -1 - }; - array4 = obj_to_array_contiguous_allow_conversion(obj3, PyArray_INT, &is_new_object4); - if (!array4 || !require_dimensions(array4,1) || !require_size(array4,size,1) - || !require_contiguous(array4) || !require_native(array4)) SWIG_fail; - - arg4 = (int*) array4->data; - } - { - npy_intp size[1] = { - -1 - }; array5 = obj_to_array_contiguous_allow_conversion(obj4, PyArray_INT, &is_new_object5); if (!array5 || !require_dimensions(array5,1) || !require_size(array5,size,1) || !require_contiguous(array5) || !require_native(array5)) SWIG_fail; @@ -3354,24 +3387,9 @@ arg6 = (int*) array6->data; } + result = (int)csr_count_blocks< int >(arg1,arg2,arg3,arg4,(int const (*))arg5,(int const (*))arg6); + resultobj = SWIG_From_int(static_cast< int >(result)); { - temp7 = obj_to_array_no_conversion(obj6,PyArray_INT); - if (!temp7 || !require_contiguous(temp7) || !require_native(temp7)) SWIG_fail; - arg7 = (int*) array_data(temp7); - } - csr_matmat_pass1< int >(arg1,arg2,(int const (*))arg3,(int const (*))arg4,(int const (*))arg5,(int const (*))arg6,arg7); - resultobj = SWIG_Py_Void(); - { - if (is_new_object3 && array3) { - Py_DECREF(array3); - } - } - { - if (is_new_object4 && array4) { - Py_DECREF(array4); - } - } - { if (is_new_object5 && array5) { Py_DECREF(array5); } @@ -3384,16 +3402,6 @@ return resultobj; fail: { - if (is_new_object3 && array3) { - Py_DECREF(array3); - } - } - { - if (is_new_object4 && array4) { - Py_DECREF(array4); - } - } - { if (is_new_object5 && array5) { Py_DECREF(array5); } @@ -3412,7 +3420,6 @@ int arg1 ; int *arg2 ; int *arg3 ; - bool result; int val1 ; int ecode1 = 0 ; PyArrayObject *array2 = NULL ; @@ -3422,6 +3429,7 @@ PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; + bool result; if (!PyArg_ParseTuple(args,(char *)"OOO:csr_has_sorted_indices",&obj0,&obj1,&obj2)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); @@ -45431,11 +45439,11 @@ static PyMethodDef SwigMethods[] = { { (char *)"expandptr", _wrap_expandptr, METH_VARARGS, (char *)"expandptr(int n_row, int Ap, int Bi)"}, - { (char *)"csr_count_blocks", _wrap_csr_count_blocks, METH_VARARGS, (char *)"csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int"}, { (char *)"csr_matmat_pass1", _wrap_csr_matmat_pass1, METH_VARARGS, (char *)"\n" "csr_matmat_pass1(int n_row, int n_col, int Ap, int Aj, int Bp, int Bj, \n" " int Cp)\n" ""}, + { (char *)"csr_count_blocks", _wrap_csr_count_blocks, METH_VARARGS, (char *)"csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int"}, { (char *)"csr_has_sorted_indices", _wrap_csr_has_sorted_indices, METH_VARARGS, (char *)"csr_has_sorted_indices(int n_row, int Ap, int Aj) -> bool"}, { (char *)"csr_diagonal", _wrap_csr_diagonal, METH_VARARGS, (char *)"\n" "csr_diagonal(int n_row, int n_col, int Ap, int Aj, signed char Ax, \n" Modified: trunk/scipy/sparse/sparsetools/dia.py =================================================================== --- trunk/scipy/sparse/sparsetools/dia.py 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/dia.py 2008-10-05 02:23:37 UTC (rev 4772) @@ -1,5 +1,5 @@ # This file was automatically generated by SWIG (http://www.swig.org). -# Version 1.3.34 +# Version 1.3.36 # # Don't modify this file, modify the SWIG interface instead. # This file is compatible with both classic and new-style classes. Modified: trunk/scipy/sparse/sparsetools/dia_wrap.cxx =================================================================== --- trunk/scipy/sparse/sparsetools/dia_wrap.cxx 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/dia_wrap.cxx 2008-10-05 02:23:37 UTC (rev 4772) @@ -1,6 +1,6 @@ /* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). - * Version 1.3.34 + * Version 1.3.36 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make @@ -73,6 +73,12 @@ # endif #endif +#ifndef SWIG_MSC_UNSUPPRESS_4505 +# if defined(_MSC_VER) +# pragma warning(disable : 4505) /* unreferenced local function has been removed */ +# endif +#endif + #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) @@ -2516,7 +2522,7 @@ #define SWIG_name "_dia" -#define SWIGVERSION 0x010334 +#define SWIGVERSION 0x010336 #define SWIG_VERSION SWIGVERSION @@ -2544,7 +2550,9 @@ PyObject_ptr(PyObject *obj, bool initial_ref = true) :_obj(obj) { - if (initial_ref) Py_XINCREF(_obj); + if (initial_ref) { + Py_XINCREF(_obj); + } } PyObject_ptr & operator=(const PyObject_ptr& item) Modified: trunk/scipy/sparse/sparsetools/sparsetools.i =================================================================== --- trunk/scipy/sparse/sparsetools/sparsetools.i 2008-10-05 01:54:59 UTC (rev 4771) +++ trunk/scipy/sparse/sparsetools/sparsetools.i 2008-10-05 02:23:37 UTC (rev 4772) @@ -189,3 +189,9 @@ %enddef +%define INSTANTIATE_INDEX( f_name ) +/* 32-bit indices */ +%template(f_name) f_name; +/* 64-bit indices would go here */ +%enddef + From scipy-svn at scipy.org Sat Oct 4 22:28:16 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 4 Oct 2008 21:28:16 -0500 (CDT) Subject: [Scipy-svn] r4773 - trunk/scipy/sparse Message-ID: <20081005022816.C593939C088@scipy.org> Author: wnbell Date: 2008-10-04 21:28:14 -0500 (Sat, 04 Oct 2008) New Revision: 4773 Modified: trunk/scipy/sparse/dia.py Log: renamed diags to offsets to avoid possible confusion Modified: trunk/scipy/sparse/dia.py =================================================================== --- trunk/scipy/sparse/dia.py 2008-10-05 02:23:37 UTC (rev 4772) +++ trunk/scipy/sparse/dia.py 2008-10-05 02:28:14 UTC (rev 4773) @@ -26,9 +26,9 @@ to construct an empty matrix with shape (M, N), dtype is optional, defaulting to dtype='d'. - dia_matrix((data, diags), shape=(M, N)) + dia_matrix((data, offsets), shape=(M, N)) where the ``data[k,:]`` stores the diagonal entries for - diagonal ``diag[k]`` (See example below) + diagonal ``offsets[k]`` (See example below) Examples @@ -42,8 +42,8 @@ [0, 0, 0, 0]], dtype=int8) >>> data = array([[1,2,3,4]]).repeat(3,axis=0) - >>> diags = array([0,-1,2]) - >>> dia_matrix( (data,diags), shape=(4,4)).todense() + >>> offsets = array([0,-1,2]) + >>> dia_matrix( (data,offsets), shape=(4,4)).todense() matrix([[1, 0, 3, 0], [1, 2, 0, 4], [0, 2, 3, 0], @@ -57,36 +57,36 @@ if isspmatrix_dia(arg1): if copy: arg1 = arg1.copy() - self.data = arg1.data - self.diags = arg1.diags - self.shape = arg1.shape + self.data = arg1.data + self.offsets = arg1.offsets + self.shape = arg1.shape elif isspmatrix(arg1): if isspmatrix_dia(arg1) and copy: A = arg1.copy() else: A = arg1.todia() - self.data = A.data - self.diags = A.diags - self.shape = A.shape + self.data = A.data + self.offsets = A.offsets + self.shape = A.shape elif isinstance(arg1, tuple): if isshape(arg1): # It's a tuple of matrix dimensions (M, N) # create empty matrix self.shape = arg1 #spmatrix checks for errors here self.data = zeros( (0,0), getdtype(dtype, default=float)) - self.diags = zeros( (0), dtype=intc) + self.offsets = zeros( (0), dtype=intc) else: try: - # Try interpreting it as (data, diags) - data, diags = arg1 + # Try interpreting it as (data, offsets) + data, offsets = arg1 except: raise ValueError('unrecognized form for dia_matrix constructor') else: if shape is None: raise ValueError('expected a shape argument') - self.data = atleast_2d(array(arg1[0],dtype=dtype,copy=copy)) - self.diags = atleast_1d(array(arg1[1],dtype='i',copy=copy)) - self.shape = shape + self.data = atleast_2d(array(arg1[0],dtype=dtype,copy=copy)) + self.offsets = atleast_1d(array(arg1[1],dtype='i',copy=copy)) + self.shape = shape else: #must be dense, convert to COO first, then to DIA try: @@ -96,24 +96,24 @@ " %s_matrix constructor" % self.format from coo import coo_matrix A = coo_matrix(arg1).todia() - self.data = A.data - self.diags = A.diags - self.shape = A.shape + self.data = A.data + self.offsets = A.offsets + self.shape = A.shape #check format - if self.diags.ndim != 1: - raise ValueError('diags array must have rank 1') + if self.offsets.ndim != 1: + raise ValueError('offsets array must have rank 1') if self.data.ndim != 2: raise ValueError('data array must have rank 2') - if self.data.shape[0] != len(self.diags): + if self.data.shape[0] != len(self.offsets): raise ValueError('number of diagonals (%d) ' \ - 'does not match the number of diags (%d)' \ - % (self.data.shape[0], len(self.diags))) + 'does not match the number of offsets (%d)' \ + % (self.data.shape[0], len(self.offsets))) - if len(unique(self.diags)) != len(self.diags): + if len(unique(self.offsets)) != len(self.offsets): raise ValueError('offset array contains duplicate values') def __repr__(self): @@ -131,7 +131,7 @@ """ M,N = self.shape nnz = 0 - for k in self.diags: + for k in self.offsets: if k > 0: nnz += min(M,N-k) else: @@ -149,7 +149,7 @@ M,N = self.shape - dia_matvec(M,N, len(self.diags), L, self.diags, self.data, x.ravel(), y.ravel()) + dia_matvec(M,N, len(self.offsets), L, self.offsets, self.data, x.ravel(), y.ravel()) return y @@ -177,7 +177,7 @@ row = arange(len_data).reshape(1,-1).repeat(num_data,axis=0) col = row.copy() - for i,k in enumerate(self.diags): + for i,k in enumerate(self.offsets): row[i,:] -= k row,col,data = row.ravel(),col.ravel(),self.data.ravel() @@ -198,9 +198,9 @@ but with different data. By default the structure arrays are copied. """ if copy: - return dia_matrix( (data,self.diags.copy()), shape=self.shape) + return dia_matrix( (data,self.offsets.copy()), shape=self.shape) else: - return dia_matrix( (data,self.diags), shape=self.shape) + return dia_matrix( (data,self.offsets), shape=self.shape) from sputils import _isinstance From scipy-svn at scipy.org Sat Oct 4 23:37:15 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 4 Oct 2008 22:37:15 -0500 (CDT) Subject: [Scipy-svn] r4774 - trunk/scipy/cluster/tests Message-ID: <20081005033715.6406639C088@scipy.org> Author: alan.mcintyre Date: 2008-10-04 22:36:54 -0500 (Sat, 04 Oct 2008) New Revision: 4774 Modified: trunk/scipy/cluster/tests/test_distance.py Log: Don't print debug information for low verbosity. Modified: trunk/scipy/cluster/tests/test_distance.py =================================================================== --- trunk/scipy/cluster/tests/test_distance.py 2008-10-05 02:28:14 UTC (rev 4773) +++ trunk/scipy/cluster/tests/test_distance.py 2008-10-05 03:36:54 UTC (rev 4774) @@ -243,7 +243,8 @@ w = 1.0 / X1.std(axis=0) Y1 = cdist(X1, X2, 'wminkowski', p=3.8, w=w) Y2 = cdist(X1, X2, 'test_wminkowski', p=3.8, w=w) - print (Y1-Y2).max() + if verbose > 2: + print (Y1-Y2).max() self.failUnless(within_tol(Y1, Y2, eps)) def test_cdist_wminkowski_random_p4d6(self): @@ -255,7 +256,8 @@ w = 1.0 / X1.std(axis=0) Y1 = cdist(X1, X2, 'wminkowski', p=4.6, w=w) Y2 = cdist(X1, X2, 'test_wminkowski', p=4.6, w=w) - print (Y1-Y2).max() + if verbose > 2: + print (Y1-Y2).max() self.failUnless(within_tol(Y1, Y2, eps)) def test_cdist_wminkowski_random_p1d23(self): @@ -267,7 +269,8 @@ w = 1.0 / X1.std(axis=0) Y1 = cdist(X1, X2, 'wminkowski', p=1.23, w=w) Y2 = cdist(X1, X2, 'test_wminkowski', p=1.23, w=w) - print (Y1-Y2).max() + if verbose > 2: + print (Y1-Y2).max() self.failUnless(within_tol(Y1, Y2, eps)) From scipy-svn at scipy.org Sun Oct 5 00:49:49 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 4 Oct 2008 23:49:49 -0500 (CDT) Subject: [Scipy-svn] r4775 - branches/mb_mio_refactor branches/mb_mio_refactor/matlab trunk/scipy/io/matlab/tests trunk/scipy/io/matlab/tests/data Message-ID: <20081005044949.E436B39C088@scipy.org> Author: matthew.brett at gmail.com Date: 2008-10-04 23:49:23 -0500 (Sat, 04 Oct 2008) New Revision: 4775 Added: branches/mb_mio_refactor/matlab/byteordercodes.py trunk/scipy/io/matlab/tests/data/testhdf5_7.4_GLNX86.mat trunk/scipy/io/matlab/tests/test_byteordercodes.py Modified: branches/mb_mio_refactor/__init__.py branches/mb_mio_refactor/matlab/__init__.py branches/mb_mio_refactor/matlab/mio.py branches/mb_mio_refactor/matlab/mio4.py branches/mb_mio_refactor/matlab/mio5.py branches/mb_mio_refactor/matlab/miobase.py trunk/scipy/io/matlab/tests/save_test.m trunk/scipy/io/matlab/tests/test_mio.py Log: Fixing long-broken mio tests Modified: branches/mb_mio_refactor/__init__.py =================================================================== --- branches/mb_mio_refactor/__init__.py 2008-10-05 03:36:54 UTC (rev 4774) +++ branches/mb_mio_refactor/__init__.py 2008-10-05 04:49:23 UTC (rev 4775) @@ -80,9 +80,10 @@ from netcdf import netcdf_file, netcdf_variable from recaster import sctype_attributes, Recaster +import matlab.byteordercodes as byteordercodes from data_store import save_as_module from mmio import mminfo, mmread, mmwrite __all__ = filter(lambda s:not s.startswith('_'),dir()) -from scipy.testing.pkgtester import Tester +from numpy.testing import Tester test = Tester().test Modified: branches/mb_mio_refactor/matlab/__init__.py =================================================================== --- branches/mb_mio_refactor/matlab/__init__.py 2008-10-05 03:36:54 UTC (rev 4774) +++ branches/mb_mio_refactor/matlab/__init__.py 2008-10-05 04:49:23 UTC (rev 4775) @@ -0,0 +1,5 @@ +# Matlab file read and write utilities +from mio import loadmat, savemat + +from numpy.testing import Tester +test = Tester().test Added: branches/mb_mio_refactor/matlab/byteordercodes.py =================================================================== --- branches/mb_mio_refactor/matlab/byteordercodes.py 2008-10-05 03:36:54 UTC (rev 4774) +++ branches/mb_mio_refactor/matlab/byteordercodes.py 2008-10-05 04:49:23 UTC (rev 4775) @@ -0,0 +1,68 @@ +''' Byteorder utilities for system - numpy byteorder encoding + +Converts a variety of string codes for little endian, big endian, +native byte order and swapped byte order to explicit numpy endian +codes - one of '<' (little endian) or '>' (big endian) + +''' + +import sys + +sys_is_le = sys.byteorder == 'little' +native_code = sys_is_le and '<' or '>' +swapped_code = sys_is_le and '>' or '<' + +aliases = {'little': ('little', '<', 'l', 'le'), + 'big': ('big', '>', 'b', 'be'), + 'native': ('native', '='), + 'swapped': ('swapped', 'S')} + +def to_numpy_code(code): + ''' Convert various order codings to numpy format + Parameters + ---------- + code : {'little','big','l','b','le','be','<','>', + 'native','=', + 'swapped', 's'} string + code is converted to lower case before parsing + + Returns + ------- + out_code : {'<','>'} string + where '<' is the numpy dtype code for little + endian, and '>' is the code for big endian + + + Examples + -------- + >>> import sys + >>> from imagers.byteorder import to_numpy_code, sys_is_le + >>> sys_is_le == (sys.byteorder == 'little') + True + >>> to_numpy_code('big') + '>' + >>> to_numpy_code('little') + '<' + >>> nc = to_numpy_code('native') + >>> nc == '<' if sys_is_le else nc == '>' + True + >>> sc = to_numpy_code('swapped') + >>> sc == '>' if sys_is_le else sc == '<' + True + ''' + code = code.lower() + if code is None: + return native_code + if code in aliases['little']: + return '<' + elif code in aliases['big']: + return '>' + elif code in aliases['native']: + return native_code + elif code in aliases['swapped']: + return swapped_code + else: + raise ValueError( + 'We cannot handle byte order %s' % code) + + Modified: branches/mb_mio_refactor/matlab/mio.py =================================================================== --- branches/mb_mio_refactor/matlab/mio.py 2008-10-05 03:36:54 UTC (rev 4774) +++ branches/mb_mio_refactor/matlab/mio.py 2008-10-05 04:49:23 UTC (rev 4775) @@ -6,6 +6,7 @@ import os import sys +import warnings from miobase import get_matfile_version from mio4 import MatFile4Reader, MatFile4Writer @@ -58,13 +59,13 @@ raise IOError, 'Reader needs file name or open file-like object' byte_stream = file_name - mv = get_matfile_version(byte_stream) - if mv == '4': + mjv, mnv = get_matfile_version(byte_stream) + if mjv == 0: return MatFile4Reader(byte_stream, **kwargs) - elif mv == '5': + elif mjv == 1: return MatFile5Reader(byte_stream, **kwargs) - elif mv == '7': - raise NotImplementedError('Please use PyTables for matlab HDF files') + elif mjv == 2: + raise NotImplementedError('Please use PyTables for matlab v7.3 (HDF) files') else: raise TypeError('Did not recognize version %s' % mv) @@ -94,10 +95,17 @@ matlab_compatible - returns matrices as would be loaded by matlab (implies squeeze_me=False, chars_as_strings=False, mat_dtype=True) + struct_as_record - whether to load matlab structs as numpy record arrays, or + as old-style numpy arrays with dtype=object. + (warns if not set, and defaults to False. non-recarrays + cannot be exported via savemat.) v4 (Level 1.0), v6 and v7.1 matfiles are supported. ''' + if not kwargs.get('struct_as_record', False): + warnings.warn("loading matlab structures as arrays of dtype=object is deprecated", + DeprecationWarning, stacklevel=2) MR = mat_reader_factory(file_name, appendmat, **kwargs) matfile_dict = MR.get_variables() if mdict is not None: @@ -131,7 +139,7 @@ if format == '4': MW = MatFile4Writer(file_stream) elif format == '5': - MW = MatFile5Writer(file_stream) + MW = MatFile5Writer(file_stream, unicode_strings=True) else: raise ValueError, 'Format should be 4 or 5' MW.put_variables(mdict) Modified: branches/mb_mio_refactor/matlab/mio4.py =================================================================== --- branches/mb_mio_refactor/matlab/mio4.py 2008-10-05 03:36:54 UTC (rev 4774) +++ branches/mb_mio_refactor/matlab/mio4.py 2008-10-05 04:49:23 UTC (rev 4775) @@ -1,10 +1,14 @@ ''' Classes for read / write of matlab (TM) 4 files ''' +import sys -import numpy as N +import numpy as np -from miobase import * +from miobase import MatFileReader, MatArrayReader, MatMatrixGetter, \ + MatFileWriter, MatStreamWriter, spsparse +SYS_LITTLE_ENDIAN = sys.byteorder == 'little' + miDOUBLE = 0 miSINGLE = 1 miINT32 = 2 @@ -76,7 +80,7 @@ header['mclass'] = T header['dims'] = (data['mrows'], data['ncols']) header['is_complex'] = data['imagf'] == 1 - remaining_bytes = header['dtype'].itemsize * N.product(header['dims']) + remaining_bytes = header['dtype'].itemsize * np.product(header['dims']) if header['is_complex'] and not header['mclass'] == mxSPARSE_CLASS: remaining_bytes *= 2 next_pos = self.mat_stream.tell() + remaining_bytes @@ -109,10 +113,10 @@ num_bytes = dt.itemsize for d in dims: num_bytes *= d - arr = N.ndarray(shape=dims, - dtype=dt, - buffer=self.mat_stream.read(num_bytes), - order='F') + arr = np.ndarray(shape=dims, + dtype=dt, + buffer=self.mat_stream.read(num_bytes), + order='F') if copy: arr = arr.copy() return arr @@ -122,9 +126,9 @@ def __init__(self, array_reader, header): super(Mat4FullGetter, self).__init__(array_reader, header) if header['is_complex']: - self.mat_dtype = N.dtype(N.complex128) + self.mat_dtype = np.dtype(np.complex128) else: - self.mat_dtype = N.dtype(N.float64) + self.mat_dtype = np.dtype(np.float64) def get_raw_array(self): if self.header['is_complex']: @@ -137,12 +141,12 @@ class Mat4CharGetter(Mat4MatrixGetter): def get_raw_array(self): - arr = self.read_array().astype(N.uint8) + arr = self.read_array().astype(np.uint8) # ascii to unicode S = arr.tostring().decode('ascii') - return N.ndarray(shape=self.header['dims'], - dtype=N.dtype('U1'), - buffer = N.array(S)).copy() + return np.ndarray(shape=self.header['dims'], + dtype=np.dtype('U1'), + buffer = np.array(S)).copy() class Mat4SparseGetter(Mat4MatrixGetter): @@ -166,17 +170,17 @@ res = self.read_array() tmp = res[:-1,:] dims = res[-1,0:2] - I = N.ascontiguousarray(tmp[:,0],dtype='intc') #fixes byte order also - J = N.ascontiguousarray(tmp[:,1],dtype='intc') + I = np.ascontiguousarray(tmp[:,0],dtype='intc') #fixes byte order also + J = np.ascontiguousarray(tmp[:,1],dtype='intc') I -= 1 # for 1-based indexing J -= 1 if res.shape[1] == 3: - V = N.ascontiguousarray(tmp[:,2],dtype='float') + V = np.ascontiguousarray(tmp[:,2],dtype='float') else: - V = N.ascontiguousarray(tmp[:,2],dtype='complex') + V = np.ascontiguousarray(tmp[:,2],dtype='complex') V.imag = tmp[:,3] - if have_sparse: - return scipy.sparse.coo_matrix((V,(I,J)), dims) + if spsparse: + return spsparse.coo_matrix((V,(I,J)), dims) return (dims, I, J, V) @@ -200,11 +204,11 @@ def guess_byte_order(self): self.mat_stream.seek(0) - mopt = self.read_dtype(N.dtype('i4')) + mopt = self.read_dtype(np.dtype('i4')) self.mat_stream.seek(0) if mopt < 0 or mopt > 5000: - return ByteOrder.swapped_code - return ByteOrder.native_code + return SYS_LITTLE_ENDIAN and '>' or '<' + return SYS_LITTLE_ENDIAN and '<' or '>' class Mat4MatrixWriter(MatStreamWriter): @@ -218,8 +222,8 @@ ''' if dims is None: dims = self.arr.shape - header = N.empty((), mdtypes_template['header']) - M = not ByteOrder.little_endian + header = np.empty((), mdtypes_template['header']) + M = not SYS_LITTLE_ENDIAN O = 0 header['mopt'] = (M * 1000 + O * 100 + @@ -233,7 +237,7 @@ self.write_string(self.name + '\0') def arr_to_2d(self): - self.arr = N.atleast_2d(self.arr) + self.arr = np.atleast_2d(self.arr) dims = self.arr.shape if len(dims) > 2: self.arr = self.arr.reshape(-1,dims[-1]) @@ -275,12 +279,12 @@ T=mxCHAR_CLASS) if self.arr.dtype.kind == 'U': # Recode unicode to ascii - n_chars = N.product(dims) - st_arr = N.ndarray(shape=(), - dtype=self.arr_dtype_number(n_chars), - buffer=self.arr) + n_chars = np.product(dims) + st_arr = np.ndarray(shape=(), + dtype=self.arr_dtype_number(n_chars), + buffer=self.arr) st = st_arr.item().encode('ascii') - self.arr = N.ndarray(shape=dims, dtype='S1', buffer=st) + self.arr = np.ndarray(shape=dims, dtype='S1', buffer=st) self.write_bytes(self.arr) @@ -292,7 +296,7 @@ ''' A = self.arr.tocoo() #convert to sparse COO format (ijv) imagf = A.dtype.kind == 'c' - ijv = N.zeros((A.nnz + 1, 3+imagf), dtype='f8') + ijv = np.zeros((A.nnz + 1, 3+imagf), dtype='f8') ijv[:-1,0] = A.row ijv[:-1,1] = A.col ijv[:-1,0:2] += 1 # 1 based indexing @@ -314,16 +318,16 @@ arr - array to write name - name in matlab (TM) workspace ''' - if have_sparse: - if scipy.sparse.issparse(arr): + if spsparse: + if spsparse.issparse(arr): return Mat4SparseWriter(stream, arr, name) - arr = N.array(arr) + arr = np.array(arr) dtt = arr.dtype.type - if dtt is N.object_: + if dtt is np.object_: raise TypeError, 'Cannot save object arrays in Mat4' - elif dtt is N.void: + elif dtt is np.void: raise TypeError, 'Cannot save void type arrays' - elif dtt in (N.unicode_, N.string_): + elif dtt in (np.unicode_, np.string_): return Mat4CharWriter(stream, arr, name) else: return Mat4NumericWriter(stream, arr, name) Modified: branches/mb_mio_refactor/matlab/mio5.py =================================================================== --- branches/mb_mio_refactor/matlab/mio5.py 2008-10-05 03:36:54 UTC (rev 4774) +++ branches/mb_mio_refactor/matlab/mio5.py 2008-10-05 04:49:23 UTC (rev 4775) @@ -26,17 +26,15 @@ # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. +import sys import zlib +from cStringIO import StringIO from copy import copy as pycopy -from cStringIO import StringIO -import numpy as N -from miobase import * +import numpy as np -try: # Python 2.3 support - from sets import Set as set -except: - pass +from miobase import MatFileReader, MatArrayReader, MatMatrixGetter, \ + MatFileWriter, MatStreamWriter, spsparse miINT8 = 1 miUINT8 = 2 @@ -56,6 +54,10 @@ mxCELL_CLASS = 1 mxSTRUCT_CLASS = 2 +# The March 2008 edition of "Matlab 7 MAT-File Format" says that +# mxOBJECT_CLASS = 3, whereas matrix.h says that mxLOGICAL = 3. +# Matlab 2008a appears to save logicals as type 9, so we assume that +# the document is correct. See type 18, below. mxOBJECT_CLASS = 3 mxCHAR_CLASS = 4 mxSPARSE_CLASS = 5 @@ -67,6 +69,14 @@ mxUINT16_CLASS = 11 mxINT32_CLASS = 12 mxUINT32_CLASS = 13 +# The following are not in the March 2008 edition of "Matlab 7 +# MAT-File Format," but were guessed from matrix.h. +mxINT64_CLASS = 14 +mxUINT64_CLASS = 15 +mxFUNCTION_CLASS = 16 +# Not doing anything with these at the moment. +mxOPAQUE_CLASS = 17 +mxOBJECT_CLASS_FROM_MATRIX_H = 18 mdtypes_template = { miINT8: 'i1', @@ -87,6 +97,7 @@ ('version', 'u2'), ('endian_test', 'S2')], 'tag_full': [('mdtype', 'u4'), ('byte_count', 'u4')], + 'tag_smalldata':[('byte_count_mdtype', 'u4'), ('data', 'S4')], 'array_flags': [('data_type', 'u4'), ('byte_count', 'u4'), ('flags_class','u4'), @@ -101,6 +112,8 @@ mxUINT16_CLASS: 'u2', mxINT32_CLASS: 'i4', mxUINT32_CLASS: 'u4', + mxINT64_CLASS: 'i8', + mxUINT64_CLASS: 'u8', mxSINGLE_CLASS: 'f4', mxDOUBLE_CLASS: 'f8', } @@ -116,9 +129,11 @@ 'i1': miINT8, 'i2': miINT16, 'i4': miINT32, + 'i8': miINT64, 'u1': miUINT8, + 'u2': miUINT16, 'u4': miUINT32, - 'u2': miUINT16, + 'u8': miUINT64, 'S1': miUINT8, 'U1': miUTF16, } @@ -131,8 +146,10 @@ 'c16': mxDOUBLE_CLASS, 'f4': mxSINGLE_CLASS, 'c8': mxSINGLE_CLASS, + 'i8': mxINT64_CLASS, 'i4': mxINT32_CLASS, 'i2': mxINT16_CLASS, + 'u8': mxUINT64_CLASS, 'u2': mxUINT16_CLASS, 'u1': mxUINT8_CLASS, 'S1': mxUINT8_CLASS, @@ -162,16 +179,10 @@ mxUINT16_CLASS, mxINT32_CLASS, mxUINT32_CLASS, + mxINT64_CLASS, + mxUINT64_CLASS, ) -class mat_struct(object): - ''' Placeholder for holding read data from structs ''' - pass - -class mat_obj(object): - ''' Placeholder for holding read data from objects ''' - pass - class Mat5ArrayReader(MatArrayReader): ''' Class to get Mat5 arrays @@ -179,36 +190,55 @@ factory function ''' - def __init__(self, mat_stream, dtypes, processor_func, codecs, class_dtypes): + def __init__(self, mat_stream, dtypes, processor_func, codecs, class_dtypes, struct_as_record): super(Mat5ArrayReader, self).__init__(mat_stream, dtypes, - processor_func, - ) + processor_func) self.codecs = codecs self.class_dtypes = class_dtypes + self.struct_as_record = struct_as_record def read_element(self, copy=True): - mdtype, byte_count, buf = tagparse(self.mat_stream, swapf) - if mdtype == miMATRIX: - # Can this use buf or not? - return self.current_getter(byte_count).get_array() - elif mdtype in self.codecs: # encoded char data + raw_tag = self.mat_stream.read(8) + tag = np.ndarray(shape=(), + dtype=self.dtypes['tag_full'], + buffer=raw_tag) + mdtype = tag['mdtype'].item() + # Byte count if this is small data element + byte_count = mdtype >> 16 + if byte_count: # small data element format + if byte_count > 4: + raise ValueError, 'Too many bytes for sde format' + mdtype = mdtype & 0xFFFF + if mdtype == miMATRIX: + raise TypeError('Cannot have matrix in SDE format') + raw_str = raw_tag[4:byte_count+4] + else: # regular element + byte_count = tag['byte_count'].item() + # Deal with miMATRIX type (cannot pass byte string) + if mdtype == miMATRIX: + return self.current_getter(byte_count).get_array() + # All other types can be read from string + raw_str = self.mat_stream.read(byte_count) + # Seek to next 64-bit boundary + mod8 = byte_count % 8 + if mod8: + self.mat_stream.seek(8 - mod8, 1) + + if mdtype in self.codecs: # encoded char data codec = self.codecs[mdtype] if not codec: raise TypeError, 'Do not support encoding %d' % mdtype - el = buf.decode(codec) + el = raw_str.decode(codec) else: # numeric data dt = self.dtypes[mdtype] el_count = byte_count // dt.itemsize - el = N.ndarray(shape=(el_count,), - dtype=dt, - buffer=buf) + el = np.ndarray(shape=(el_count,), + dtype=dt, + buffer=raw_str) if copy: el = el.copy() - # Seek to next 64-bit boundary - mod8 = byte_count % 8 - if mod8: - self.mat_stream.seek(8 - mod8, 1) + return el def matrix_getter_factory(self): @@ -254,9 +284,11 @@ if mc == mxCELL_CLASS: return Mat5CellMatrixGetter(self, header) if mc == mxSTRUCT_CLASS: - return Mat5StructMatrixGetter(self, header) + return Mat5StructMatrixGetter(self, header, self.struct_as_record) if mc == mxOBJECT_CLASS: return Mat5ObjectMatrixGetter(self, header) + if mc == mxFUNCTION_CLASS: + return Mat5FunctionMatrixGetter(self, header) raise TypeError, 'No reader for class code %s' % mc @@ -274,7 +306,8 @@ array_reader.dtypes, array_reader.processor_func, array_reader.codecs, - array_reader.class_dtypes) + array_reader.class_dtypes, + array_reader.struct_as_record) class Mat5MatrixGetter(MatMatrixGetter): @@ -302,11 +335,12 @@ self.mat_stream = array_reader.mat_stream self.data_position = self.mat_stream.tell() self.header = {} + self.name = '' self.is_global = False self.mat_dtype = 'f8' def get_raw_array(self): - return N.array([[]]) + return np.array([[]]) class Mat5NumericMatrixGetter(Mat5MatrixGetter): @@ -314,7 +348,7 @@ def __init__(self, array_reader, header): super(Mat5NumericMatrixGetter, self).__init__(array_reader, header) if header['is_logical']: - self.mat_dtype = N.dtype('bool') + self.mat_dtype = np.dtype('bool') else: self.mat_dtype = self.class_dtypes[header['mclass']] @@ -326,10 +360,10 @@ res = res + (res_j * 1j) else: res = self.read_element() - return N.ndarray(shape=self.header['dims'], - dtype=res.dtype, - buffer=res, - order='F') + return np.ndarray(shape=self.header['dims'], + dtype=res.dtype, + buffer=res, + order='F') class Mat5SparseMatrixGetter(Mat5MatrixGetter): @@ -360,39 +394,38 @@ nnz = indptr[-1] rowind = rowind[:nnz] data = data[:nnz] - if have_sparse: - from scipy.sparse import csc_matrix - return csc_matrix((data,rowind,indptr), shape=(M,N)) + if spsparse: + return spsparse.csc_matrix((data,rowind,indptr), shape=(M,N)) else: - return (dims, data, rowind, indptr) + return ((M,N), data, rowind, indptr) class Mat5CharMatrixGetter(Mat5MatrixGetter): def get_raw_array(self): res = self.read_element() # Convert non-string types to unicode - if isinstance(res, N.ndarray): - if res.dtype.type == N.uint16: + if isinstance(res, np.ndarray): + if res.dtype.type == np.uint16: codec = miUINT16_codec if self.codecs['uint16_len'] == 1: - res = res.astype(N.uint8) - elif res.dtype.type in (N.uint8, N.int8): + res = res.astype(np.uint8) + elif res.dtype.type in (np.uint8, np.int8): codec = 'ascii' else: raise TypeError, 'Did not expect type %s' % res.dtype res = res.tostring().decode(codec) - return N.ndarray(shape=self.header['dims'], - dtype=N.dtype('U1'), - buffer=N.array(res), - order='F').copy() + return np.ndarray(shape=self.header['dims'], + dtype=np.dtype('U1'), + buffer=np.array(res), + order='F').copy() class Mat5CellMatrixGetter(Mat5MatrixGetter): def get_raw_array(self): # Account for fortran indexing of cells tupdims = tuple(self.header['dims'][::-1]) - length = N.product(tupdims) - result = N.empty(length, dtype=object) + length = np.product(tupdims) + result = np.empty(length, dtype=object) for i in range(length): result[i] = self.get_item() return result.reshape(tupdims).T @@ -400,47 +433,97 @@ def get_item(self): return self.read_element() +class mat_struct(object): + ''' Placeholder for holding read data from structs ''' + pass -class Mat5StructMatrixGetter(Mat5CellMatrixGetter): - def __init__(self, *args, **kwargs): - super(Mat5StructMatrixGetter, self).__init__(*args, **kwargs) - self.obj_template = mat_struct() +class Mat5StructMatrixGetter(Mat5MatrixGetter): + def __init__(self, array_reader, header, struct_as_record): + super(Mat5StructMatrixGetter, self).__init__(array_reader, header) + self.struct_as_record = struct_as_record def get_raw_array(self): namelength = self.read_element()[0] - # get field names names = self.read_element() - splitnames = [names[i:i+namelength] for i in \ - xrange(0,len(names),namelength)] - self.obj_template._fieldnames = [x.tostring().strip('\x00') - for x in splitnames] - return super(Mat5StructMatrixGetter, self).get_raw_array() + field_names = [names[i:i+namelength].tostring().strip('\x00') + for i in xrange(0,len(names),namelength)] + tupdims = tuple(self.header['dims'][::-1]) + length = np.product(tupdims) + result = np.empty(length, dtype=[(field_name, object) + for field_name in field_names]) + for i in range(length): + for field_name in field_names: + result[i][field_name] = self.read_element() + + if not self.struct_as_record: + # Backward compatibility with previous format + self.obj_template = mat_struct() + self.obj_template._fieldnames = field_names + newresult = np.empty(length, dtype=object) + for i in range(length): + item = pycopy(self.obj_template) + for name in field_names: + item.__dict__[name] = result[i][name] + newresult[i] = item + result = newresult + + return result.reshape(tupdims).T - def get_item(self): - item = pycopy(self.obj_template) - for element in item._fieldnames: - item.__dict__[element] = self.read_element() - return item +class MatlabObject(object): + ''' Class to contain read data from matlab objects ''' + def __init__(self, classname, field_names): + self.__dict__['classname'] = classname + self.__dict__['mobj_recarray'] = np.empty((1,1), dtype=[(field_name, object) + for field_name in field_names]) + def __getattr__(self, name): + mobj_recarray = self.__dict__['mobj_recarray'] + if name in mobj_recarray.dtype.fields: + return mobj_recarray[0,0][name] + else: + raise AttributeError, "no field named %s in MatlabObject"%(name) -class Mat5ObjectMatrixGetter(Mat5StructMatrixGetter): - def __init__(self, *args, **kwargs): - super(Mat5StructMatrixGetter, self).__init__(*args, **kwargs) - self.obj_template = mat_obj() + def __setattr__(self, name, value): + if name in self.__dict__['mobj_recarray'].dtype.fields: + self.__dict__['mobj_recarray'][0,0][name] = value + else: + self.__dict__[name] = value + - def get_raw_array(self): - self.obj_template._classname = self.read_element().tostring() - return super(Mat5ObjectMatrixGetter, self).get_raw_array() +class Mat5ObjectMatrixGetter(Mat5MatrixGetter): + def get_array(self): + '''Matlab ojects are essentially structs, with an extra field, the classname.''' + classname = self.read_element().tostring() + namelength = self.read_element()[0] + names = self.read_element() + field_names = [names[i:i+namelength].tostring().strip('\x00') + for i in xrange(0,len(names),namelength)] + result = MatlabObject(classname, field_names) + for field_name in field_names: + result.__setattr__(field_name, self.read_element()) + return result + + +class MatlabFunctionMatrix: + ''' Opaque object representing an array of function handles. ''' + def __init__(self, arr): + self.arr = arr + +class Mat5FunctionMatrixGetter(Mat5CellMatrixGetter): + def get_array(self): + return MatlabFunctionMatrix(self.get_raw_array()) + + class MatFile5Reader(MatFileReader): ''' Reader for Mat 5 mat files - Adds the following attribute to base class - + uint16_codec - char codec to use for uint16 char arrays (defaults to system default codec) ''' + def __init__(self, mat_stream, byte_order=None, @@ -448,6 +531,7 @@ squeeze_me=False, chars_as_strings=True, matlab_compatible=False, + struct_as_record=False, uint16_codec=None ): self.codecs = {} @@ -457,6 +541,7 @@ None, None, None, + struct_as_record ) super(MatFile5Reader, self).__init__( mat_stream, @@ -533,7 +618,7 @@ class Mat5MatrixWriter(MatStreamWriter): - mat_tag = N.zeros((), mdtypes_template['tag_full']) + mat_tag = np.zeros((), mdtypes_template['tag_full']) mat_tag['mdtype'] = miMATRIX def __init__(self, file_stream, arr, name, is_global=False): @@ -545,20 +630,32 @@ def write_element(self, arr, mdtype=None): # write tag, data - tag = N.zeros((), mdtypes_template['tag_full']) if mdtype is None: - tag['mdtype'] = np_to_mtypes[arr.dtype.str[1:]] + mdtype = np_to_mtypes[arr.dtype.str[1:]] + byte_count = arr.size*arr.itemsize + if byte_count <= 4: + self.write_smalldata_element(arr, mdtype, byte_count) else: - tag['mdtype'] = mdtype + self.write_regular_element(arr, mdtype, byte_count) - tag['byte_count'] = arr.size*arr.itemsize + def write_smalldata_element(self, arr, mdtype, byte_count): + # write tag with embedded data + tag = np.zeros((), mdtypes_template['tag_smalldata']) + tag['byte_count_mdtype'] = (byte_count << 16) + mdtype + # if arr.tostring is < 4, the element will be zero-padded as needed. + tag['data'] = arr.tostring(order='F') + self.write_dtype(tag) + + def write_regular_element(self, arr, mdtype, byte_count): + # write tag, data + tag = np.zeros((), mdtypes_template['tag_full']) + tag['mdtype'] = mdtype + tag['byte_count'] = byte_count padding = (8 - tag['byte_count']) % 8 - self.write_dtype(tag) self.write_bytes(arr) - # pad to next 64-bit boundary - self.write_bytes(N.zeros((padding,),'u1')) + self.write_bytes(np.zeros((padding,),'u1')) def write_header(self, mclass, is_global=False, @@ -568,14 +665,14 @@ ''' Write header for given data options mclass - mat5 matrix class is_global - True if matrix is global - is_complex - True is matrix is complex + is_complex - True if matrix is complex is_logical - True if matrix is logical nzmax - max non zero elements for sparse arrays ''' self._mat_tag_pos = self.file_stream.tell() self.write_dtype(self.mat_tag) # write array flags (complex, global, logical, class, nzmax) - af = N.zeros((), mdtypes_template['array_flags']) + af = np.zeros((), mdtypes_template['array_flags']) af['data_type'] = miUINT32 af['byte_count'] = 8 flags = is_complex << 3 | is_global << 2 | is_logical << 1 @@ -584,13 +681,13 @@ self.write_dtype(af) # write array shape if self.arr.ndim < 2: - new_arr = N.atleast_2d(self.arr) + new_arr = np.atleast_2d(self.arr) if type(new_arr) != type(self.arr): raise ValueError("Array should be 2-dimensional.") self.arr = new_arr - self.write_element(N.array(self.arr.shape, dtype='i4')) + self.write_element(np.array(self.arr.shape, dtype='i4')) # write name - self.write_element(N.array([ord(c) for c in self.name], 'i1')) + self.write_element(np.array([ord(c) for c in self.name], 'i1')) def update_matrix_tag(self): curr_pos = self.file_stream.tell() @@ -604,7 +701,6 @@ class Mat5NumericWriter(Mat5MatrixWriter): - def write(self): imagf = self.arr.dtype.kind == 'c' try: @@ -630,12 +726,12 @@ self.write_header(mclass=mxCHAR_CLASS) if self.arr.dtype.kind == 'U': # Recode unicode using self.codec - n_chars = N.product(self.arr.shape) - st_arr = N.ndarray(shape=(), - dtype=self.arr_dtype_number(n_chars), - buffer=self.arr) + n_chars = np.product(self.arr.shape) + st_arr = np.ndarray(shape=(), + dtype=self.arr_dtype_number(n_chars), + buffer=self.arr) st = st_arr.item().encode(self.codec) - self.arr = N.ndarray(shape=(len(st)), dtype='u1', buffer=st) + self.arr = np.ndarray(shape=(len(st)), dtype='u1', buffer=st) self.write_element(self.arr,mdtype=miUTF8) self.update_matrix_tag() @@ -644,10 +740,8 @@ class Mat5SparseWriter(Mat5MatrixWriter): - def write(self): ''' Sparse matrices are 2D - ''' A = self.arr.tocsc() # convert to sparse CSC format A.sort_indices() # MATLAB expects sorted row indices @@ -664,6 +758,80 @@ self.update_matrix_tag() +class Mat5CompositeWriter(Mat5MatrixWriter): + def __init__(self, file_stream, arr, name, is_global=False, unicode_strings=False): + super(Mat5CompositeWriter, self).__init__(file_stream, arr, name, is_global) + self.unicode_strings = unicode_strings + + +class Mat5CellWriter(Mat5CompositeWriter): + def write(self): + self.write_header(mclass=mxCELL_CLASS) + # loop over data, column major + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + MW = MWG.matrix_writer_factory(el, '') + MW.write() + self.update_matrix_tag() + +class Mat5FunctionWriter(Mat5CompositeWriter): + def __init__(self, file_stream, arr, name, is_global=False, unicode_strings=False): + super(Mat5FunctionWriter, self).__init__(file_stream, arr.arr, name, is_global) + + def write(self): + self.write_header(mclass=mxFUNCTION_CLASS) + # loop over data, column major + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + MW = MWG.matrix_writer_factory(el, '') + MW.write() + self.update_matrix_tag() + + +class Mat5StructWriter(Mat5CompositeWriter): + def write(self): + self.write_header(mclass=mxSTRUCT_CLASS) + + # write fieldnames + fieldnames = [f[0] for f in self.arr.dtype.descr] + self.write_element(np.array([32], dtype='i4')) + self.write_element(np.array(fieldnames, dtype='S32'), mdtype=miINT8) + + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + for f in fieldnames: + MW = MWG.matrix_writer_factory(el[f], '') + MW.write() + self.update_matrix_tag() + +class Mat5ObjectWriter(Mat5CompositeWriter): + def __init__(self, file_stream, arr, name, is_global=False, unicode_strings=False): + super(Mat5ObjectWriter, self).__init__(file_stream, arr.__dict__['mobj_recarray'], name, is_global) + self.classname = arr.classname + + def write(self): + self.write_header(mclass=mxOBJECT_CLASS) + + # write classnames + self.write_element(np.array(self.classname, dtype='S'), mdtype=miINT8) + + # write fieldnames + fieldnames = [f[0] for f in self.arr.dtype.descr] + self.write_element(np.array([32], dtype='i4')) + self.write_element(np.array(fieldnames, dtype='S32'), mdtype=miINT8) + + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + for f in fieldnames: + MW = MWG.matrix_writer_factory(el[f], '') + MW.write() + self.update_matrix_tag() + + class Mat5WriterGetter(object): ''' Wraps stream and options, provides methods for getting Writer objects ''' def __init__(self, stream, unicode_strings): @@ -679,18 +847,21 @@ arr - array to write name - name in matlab (TM) workspace ''' - if have_sparse: - if scipy.sparse.issparse(arr): + if spsparse: + if spsparse.issparse(arr): return Mat5SparseWriter(self.stream, arr, name, is_global) - arr = N.array(arr) + + if isinstance(arr, MatlabFunctionMatrix): + return Mat5FunctionWriter(self.stream, arr, name, is_global, self.unicode_strings) + if isinstance(arr, MatlabObject): + return Mat5ObjectWriter(self.stream, arr, name, is_global, self.unicode_strings) + + arr = np.array(arr) if arr.dtype.hasobject: - types, arr_type = self.classify_mobjects(arr) - if arr_type == 'c': - return Mat5CellWriter(self.stream, arr, name, is_global, types) - elif arr_type == 's': - return Mat5StructWriter(self.stream, arr, name, is_global) - elif arr_type == 'o': - return Mat5ObjectWriter(self.stream, arr, name, is_global) + if arr.dtype.fields == None: + return Mat5CellWriter(self.stream, arr, name, is_global, self.unicode_strings) + else: + return Mat5StructWriter(self.stream, arr, name, is_global, self.unicode_strings) if arr.dtype.kind in ('U', 'S'): if self.unicode_strings: return Mat5UniCharWriter(self.stream, arr, name, is_global) @@ -699,47 +870,6 @@ else: return Mat5NumericWriter(self.stream, arr, name, is_global) - def classify_mobjects(self, objarr): - ''' Function to classify objects passed for writing - returns - types - S1 array of same shape as objarr with codes for each object - i - invalid object - a - ndarray - s - matlab struct - o - matlab object - arr_type - one of - c - cell array - s - struct array - o - object array - ''' - n = objarr.size - types = N.empty((n,), dtype='S1') - types[:] = 'i' - type_set = set() - flato = objarr.flat - for i in range(n): - obj = flato[i] - if isinstance(obj, N.ndarray): - types[i] = 'a' - continue - try: - fns = tuple(obj._fieldnames) - except AttributeError: - continue - try: - cn = obj._classname - except AttributeError: - types[i] = 's' - type_set.add(fns) - continue - types[i] = 'o' - type_set.add((cn, fns)) - arr_type = 'c' - if len(set(types))==1 and len(type_set) == 1: - arr_type = types[0] - return types.reshape(objarr.shape), arr_type - - class MatFile5Writer(MatFileWriter): ''' Class for writing mat5 files ''' def __init__(self, file_stream, @@ -757,11 +887,11 @@ unicode_strings) # write header import os, time - hdr = N.zeros((), mdtypes_template['file_header']) + hdr = np.zeros((), mdtypes_template['file_header']) hdr['description']='MATLAB 5.0 MAT-file Platform: %s, Created on: %s' % ( os.name,time.asctime()) hdr['version']= 0x0100 - hdr['endian_test']=N.ndarray(shape=(),dtype='S2',buffer=N.uint16(0x4d49)) + hdr['endian_test']=np.ndarray(shape=(),dtype='S2',buffer=np.uint16(0x4d49)) file_stream.write(hdr.tostring()) def get_unicode_strings(self): @@ -775,6 +905,8 @@ def put_variables(self, mdict): for name, var in mdict.items(): + if name[0] == '_': + continue is_global = name in self.global_vars self.writer_getter.rewind() self.writer_getter.matrix_writer_factory( @@ -785,7 +917,7 @@ stream = self.writer_getter.stream if self.do_compression: str = zlib.compress(stream.getvalue(stream.tell())) - tag = N.empty((), mdtypes_template['tag_full']) + tag = np.empty((), mdtypes_template['tag_full']) tag['mdtype'] = miCOMPRESSED tag['byte_count'] = len(str) self.file_stream.write(tag.tostring() + str) Modified: branches/mb_mio_refactor/matlab/miobase.py =================================================================== --- branches/mb_mio_refactor/matlab/miobase.py 2008-10-05 03:36:54 UTC (rev 4774) +++ branches/mb_mio_refactor/matlab/miobase.py 2008-10-05 04:49:23 UTC (rev 4775) @@ -6,15 +6,16 @@ import sys -import numpy as N +import numpy as np +import byteordercodes as sibc + +# sparse module if available try: - import scipy.sparse - have_sparse = 1 + import scipy.sparse as spsparse except ImportError: - have_sparse = 0 + spsparse = None - def small_product(arr): ''' Faster than product for small arrays ''' res = 1 @@ -23,57 +24,67 @@ return res def get_matfile_version(fileobj): - ''' Return '4', '5', or '7' depending on apparent mat file type - Inputs - fileobj - file object implementing seek() and read() - Outputs - version_str - one of (strings) 4, 5, or 7 - + ''' Return major, minor tuple depending on apparent mat file type + + Where: + + #. 0,x -> version 4 format mat files + #. 1,x -> version 5 format mat files + #. 2,x -> version 7.3 format mat files (HDF format) + + Parameters + ---------- + fileobj : {file-like} + object implementing seek() and read() + + Returns + ------- + major_version : {0, 1, 2} + major matlab file format version + minor_version : int + major matlab file format version + + Notes + ----- Has the side effect of setting the file read pointer to 0 ''' # Mat4 files have a zero somewhere in first 4 bytes fileobj.seek(0) - mopt_bytes = N.ndarray(shape=(4,), - dtype=N.uint8, + mopt_bytes = np.ndarray(shape=(4,), + dtype=np.uint8, buffer = fileobj.read(4)) if 0 in mopt_bytes: fileobj.seek(0) - return '4' - # For 5 or 7 we need to read an integer in the header - # bytes 124 through 128 contain a version integer - # and an endian test string + return (0,0) + + # For 5 format or 7.3 format we need to read an integer in the + # header. Bytes 124 through 128 contain a version integer and an + # endian test string fileobj.seek(124) tst_str = fileobj.read(4) fileobj.seek(0) maj_ind = int(tst_str[2] == 'I') - verb = ord(tst_str[maj_ind]) - if verb == 1: - return '5' - elif verb == 2: - return '7' - raise ValueError('Unknown mat file type, version %d' % verb) + maj_val = ord(tst_str[maj_ind]) + min_val = ord(tst_str[1-maj_ind]) + ret = (maj_val, min_val) + if maj_val in (1, 2): + return ret + else: + raise ValueError('Unknown mat file type, version %s' % ret) class ByteOrder(object): ''' Namespace for byte ordering ''' - little_endian = sys.byteorder == 'little' - native_code = little_endian and '<' or '>' - swapped_code = little_endian and '>' or '<' + little_endian = sibc.sys_is_le + native_code = sibc.native_code + swapped_code = sibc.swapped_code + to_numpy_code = sibc.to_numpy_code - def to_numpy_code(code): - if code is None: - return ByteOrder.native_code - if code in ('little', '<', 'l', 'L'): - return '<' - elif code in ('BIG', '>', 'B', 'b'): - return '>' - elif code in ('native', '='): - return ByteOrder.native_code - elif code in ('swapped'): - return ByteOrder.swapped_code - else: - raise ValueError, 'We cannot handle byte order %s' % byte_order - to_numpy_code = staticmethod(to_numpy_code) +ByteOrder = np.deprecate_with_doc(""" +We no longer use the ByteOrder class, and deprecate it; we will remove +it in future versions of scipy. Please use the +scipy.io.byteordercodes module instead. +""")(ByteOrder) class MatStreamAgent(object): @@ -103,10 +114,10 @@ a_dtype is assumed to be correct endianness ''' num_bytes = a_dtype.itemsize - arr = N.ndarray(shape=(), - dtype=a_dtype, - buffer=self.mat_stream.read(num_bytes), - order='F') + arr = np.ndarray(shape=(), + dtype=a_dtype, + buffer=self.mat_stream.read(num_bytes), + order='F') return arr def read_ztstring(self, num_bytes): @@ -128,6 +139,7 @@ matlab_compatible - returns matrices as would be loaded by matlab (implies squeeze_me=False, chars_as_strings=False mat_dtype=True) + struct_as_record - return strutures as numpy records (only from v5 files) To make this class functional, you will need to override the following methods: @@ -136,12 +148,14 @@ matrix_getter_factory - gives object to fetch next matrix from stream guess_byte_order - guesses file byte order from file """ + def __init__(self, mat_stream, byte_order=None, mat_dtype=False, squeeze_me=False, chars_as_strings=True, matlab_compatible=False, + struct_as_record=False ): # Initialize stream self.mat_stream = mat_stream @@ -197,7 +211,7 @@ def get_order_code(self): return self._order_code def set_order_code(self, order_code): - order_code = ByteOrder.to_numpy_code(order_code) + order_code = sibc.to_numpy_code(order_code) self._order_code = order_code self.set_dtypes() order_code = property(get_order_code, @@ -212,8 +226,7 @@ def convert_dtypes(self, dtype_template): dtypes = dtype_template.copy() for k in dtypes: - dtypes[k] = N.dtype(dtypes[k]).newbyteorder( - self.order_code) + dtypes[k] = np.dtype(dtypes[k]).newbyteorder(self.order_code) return dtypes def matrix_getter_factory(self): @@ -255,7 +268,7 @@ str_arr = arr.reshape( (small_product(n_dims), dims[-1])) - arr = N.empty(n_dims, dtype=object) + arr = np.empty(n_dims, dtype='U%d' % dims[-1]) for i in range(0, n_dims[-1]): arr[...,i] = self.chars_to_str(str_arr[i]) else: # return string @@ -266,9 +279,9 @@ if getter.mat_dtype is not None: arr = arr.astype(getter.mat_dtype) if self.squeeze_me: - arr = N.squeeze(arr) + arr = np.squeeze(arr) if not arr.size: - arr = N.array([]) + arr = np.array([]) elif not arr.shape: # 0d coverted to scalar arr = arr.item() return arr @@ -276,10 +289,10 @@ def chars_to_str(self, str_arr): ''' Convert string array to string ''' - dt = N.dtype('U' + str(small_product(str_arr.shape))) - return N.ndarray(shape=(), - dtype = dt, - buffer = str_arr.copy()).item() + dt = np.dtype('U' + str(small_product(str_arr.shape))) + return np.ndarray(shape=(), + dtype = dt, + buffer = str_arr.copy()).item() def get_variables(self, variable_names=None): ''' get variables from stream as dictionary @@ -380,7 +393,7 @@ def arr_dtype_number(self, num): ''' Return dtype for given number of items per element''' - return N.dtype(self.arr.dtype.str[:2] + str(num)) + return np.dtype(self.arr.dtype.str[:2] + str(num)) def arr_to_chars(self): ''' Convert string array to char array ''' @@ -388,9 +401,9 @@ if not dims: dims = [1] dims.append(int(self.arr.dtype.str[2:])) - self.arr = N.ndarray(shape=dims, - dtype=self.arr_dtype_number(1), - buffer=self.arr) + self.arr = np.ndarray(shape=dims, + dtype=self.arr_dtype_number(1), + buffer=self.arr) def write_bytes(self, arr): self.file_stream.write(arr.tostring(order='F')) Added: trunk/scipy/io/matlab/tests/data/testhdf5_7.4_GLNX86.mat =================================================================== (Binary files differ) Property changes on: trunk/scipy/io/matlab/tests/data/testhdf5_7.4_GLNX86.mat ___________________________________________________________________ Name: svn:mime-type + application/octet-stream Modified: trunk/scipy/io/matlab/tests/save_test.m =================================================================== --- trunk/scipy/io/matlab/tests/save_test.m 2008-10-05 03:36:54 UTC (rev 4774) +++ trunk/scipy/io/matlab/tests/save_test.m 2008-10-05 04:49:23 UTC (rev 4775) @@ -3,4 +3,4 @@ global FILEPREFIX FILESUFFIX eval([test_name ' = v;']); -save([FILEPREFIX test_name FILESUFFIX], test_name) \ No newline at end of file +save([FILEPREFIX test_name FILESUFFIX], test_name, '-V7.3') \ No newline at end of file Added: trunk/scipy/io/matlab/tests/test_byteordercodes.py =================================================================== --- trunk/scipy/io/matlab/tests/test_byteordercodes.py 2008-10-05 03:36:54 UTC (rev 4774) +++ trunk/scipy/io/matlab/tests/test_byteordercodes.py 2008-10-05 04:49:23 UTC (rev 4775) @@ -0,0 +1,30 @@ +''' Tests for byteorder module ''' + +import sys + +import numpy as np + +from numpy.testing import assert_raises + +import scipy.io.byteordercodes as sibc + +def test_native(): + native_is_le = sys.byteorder == 'little' + assert sibc.sys_is_le == native_is_le + +def test_to_numpy(): + if sys.byteorder == 'little': + assert sibc.to_numpy_code('native') == '<' + assert sibc.to_numpy_code('swapped') == '>' + else: + assert sibc.to_numpy_code('native') == '>' + assert sibc.to_numpy_code('swapped') == '<' + assert sibc.to_numpy_code('native') == sibc.to_numpy_code('=') + assert sibc.to_numpy_code('big') == '>' + for code in ('little', '<', 'l', 'L', 'le'): + assert sibc.to_numpy_code(code) == '<' + for code in ('big', '>', 'b', 'B', 'be'): + assert sibc.to_numpy_code(code) == '>' + assert_raises(ValueError, sibc.to_numpy_code, 'silly string') + + Modified: trunk/scipy/io/matlab/tests/test_mio.py =================================================================== --- trunk/scipy/io/matlab/tests/test_mio.py 2008-10-05 03:36:54 UTC (rev 4774) +++ trunk/scipy/io/matlab/tests/test_mio.py 2008-10-05 04:49:23 UTC (rev 4775) @@ -17,7 +17,7 @@ test_data_path = os.path.join(os.path.dirname(__file__), 'data') -def _check_level(self, label, expected, actual): +def _check_level(label, expected, actual): """ Check one level of a potentially nested object / list """ # object array is returned from cell array in mat file typex = type(expected) @@ -27,7 +27,7 @@ assert len(expected) == len(actual), "Different list lengths at %s" % label for i, ev in enumerate(expected): level_label = "%s, [%d], " % (label, i) - self._check_level(level_label, ev, actual[i]) + _check_level(level_label, ev, actual[i]) return # object, as container for matlab structs and objects elif isinstance(expected, MatlabObject): @@ -42,7 +42,7 @@ ev = expected.__dict__[k] v = actual.__dict__[k] level_label = "%s, property %s, " % (label, k) - self._check_level(level_label, ev, v) + _check_level(level_label, ev, v) return # hoping this is a single value, which might be an array if SP.issparse(expected): @@ -60,34 +60,24 @@ "Types %s and %s do not match at %s" % (typex, typac, label) assert_equal(actual, expected, err_msg=label) -def _check_case(self, name, files, case): +def _check_case(name, files, case, *args, **kwargs): for file_name in files: - matdict = loadmat(file_name, struct_as_record=True) + matdict = loadmat(file_name, *args, **kwargs) label = "test %s; file %s" % (name, file_name) for k, expected in case.items(): k_label = "%s, variable %s" % (label, k) assert k in matdict, "Missing key at %s" % k_label - self._check_level(k_label, expected, matdict[k]) + _check_level(k_label, expected, matdict[k]) -# Add the load tests dynamically, with given parameters -def _make_check_case(name, files, expected): - def cc(self): - self._check_case(name, files, expected) - cc.__doc__ = "check loadmat case %s" % name - return cc +# Round trip tests +def _rt_check_case(name, expected, format): + mat_stream = StringIO() + savemat(mat_stream, expected, format=format) + mat_stream.seek(0) + _check_case(name, [mat_stream], expected, struct_as_record=True) -# Add the round trip tests dynamically, with given parameters -def _make_rt_check_case(name, expected, format): - def cc(self): - mat_stream = StringIO() - savemat(mat_stream, expected, format=format) - mat_stream.seek(0) - self._check_case(name, [mat_stream], expected) - cc.__doc__ = "check loadmat case %s" % name - return cc - # Define cases to test -theta = pi/4*arange(9,dtype=float) +theta = pi/4*arange(9,dtype=float).reshape(9,1) case_table4 = [ {'name': 'double', 'expected': {'testdouble': theta} @@ -224,7 +214,7 @@ filt = os.path.join(test_data_path, 'test%s_*.mat' % name) files = glob(filt) assert files, "No files for test %s using filter %s" % (name, filt) - yield _make_check_case, name, files, expected + yield _check_case, name, files, expected # round trip tests def test_round_trip(): @@ -232,7 +222,7 @@ name = case['name'] + '_round_trip' expected = case['expected'] format = case in case_table4 and '4' or '5' - yield _make_rt_check_case, name, expected, format + yield _rt_check_case, name, expected, format def test_gzip_simple(): xdense = zeros((20,20)) @@ -261,3 +251,9 @@ expected['x'].todense()) +def test_mat73(): + # Check any hdf5 files raise an error + filenames = glob( + os.path.join(test_data_path, 'testhdf5*.mat')) + for filename in filenames: + assert_raises(NotImplementedError, loadmat, filename, struct_as_record=True) From scipy-svn at scipy.org Sun Oct 5 01:02:36 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 5 Oct 2008 00:02:36 -0500 (CDT) Subject: [Scipy-svn] r4776 - in branches/mb_mio_refactor: . arff/tests Message-ID: <20081005050236.EA47E39C088@scipy.org> Author: matthew.brett at gmail.com Date: 2008-10-05 00:02:32 -0500 (Sun, 05 Oct 2008) New Revision: 4776 Modified: branches/mb_mio_refactor/SConscript branches/mb_mio_refactor/arff/tests/test_data.py branches/mb_mio_refactor/arff/tests/test_header.py branches/mb_mio_refactor/array_import.py branches/mb_mio_refactor/fopen.py branches/mb_mio_refactor/mmio.py branches/mb_mio_refactor/netcdf.py branches/mb_mio_refactor/npfile.py branches/mb_mio_refactor/wavfile.py Log: Completed copy of scipy working tree into branch Modified: branches/mb_mio_refactor/SConscript =================================================================== --- branches/mb_mio_refactor/SConscript 2008-10-05 04:49:23 UTC (rev 4775) +++ branches/mb_mio_refactor/SConscript 2008-10-05 05:02:32 UTC (rev 4776) @@ -1,11 +1,7 @@ # Last Change: Wed Mar 05 03:00 PM 2008 J # vim:syntax=python -from os.path import join - -from numpy.distutils.misc_util import get_numpy_include_dirs from numscons import GetNumpyEnvironment env = GetNumpyEnvironment(ARGUMENTS) -env.AppendUnique(CPPPATH = get_numpy_include_dirs()) -env.DistutilsPythonExtension('numpyio', source = 'numpyiomodule.c') +env.NumpyPythonExtension('numpyio', source = 'numpyiomodule.c') Modified: branches/mb_mio_refactor/arff/tests/test_data.py =================================================================== --- branches/mb_mio_refactor/arff/tests/test_data.py 2008-10-05 04:49:23 UTC (rev 4775) +++ branches/mb_mio_refactor/arff/tests/test_data.py 2008-10-05 05:02:32 UTC (rev 4776) @@ -2,9 +2,10 @@ """Tests for parsing full arff files.""" import os -import numpy as N -from scipy.testing import * +import numpy as np +from numpy.testing import * + from scipy.io.arff.arffread import loadarff data_path = os.path.join(os.path.dirname(__file__), 'data') @@ -16,8 +17,8 @@ (1, 2, 3, 4, 'class3')] missing = os.path.join(data_path, 'missing.arff') -expect_missing_raw = N.array([[1, 5], [2, 4], [N.nan, N.nan]]) -expect_missing = N.empty(3, [('yop', N.float), ('yap', N.float)]) +expect_missing_raw = np.array([[1, 5], [2, 4], [np.nan, np.nan]]) +expect_missing = np.empty(3, [('yop', np.float), ('yap', np.float)]) expect_missing['yop'] = expect_missing_raw[:, 0] expect_missing['yap'] = expect_missing_raw[:, 1] Modified: branches/mb_mio_refactor/arff/tests/test_header.py =================================================================== --- branches/mb_mio_refactor/arff/tests/test_header.py 2008-10-05 04:49:23 UTC (rev 4775) +++ branches/mb_mio_refactor/arff/tests/test_header.py 2008-10-05 05:02:32 UTC (rev 4776) @@ -2,10 +2,9 @@ """Test for parsing arff headers only.""" import os -from scipy.testing import * +from numpy.testing import * -from scipy.io.arff.arffread import read_header, MetaData, parse_type, \ - ParseArffError +from scipy.io.arff.arffread import read_header, parse_type, ParseArffError data_path = os.path.join(os.path.dirname(__file__), 'data') Modified: branches/mb_mio_refactor/array_import.py =================================================================== --- branches/mb_mio_refactor/array_import.py 2008-10-05 04:49:23 UTC (rev 4775) +++ branches/mb_mio_refactor/array_import.py 2008-10-05 05:02:32 UTC (rev 4776) @@ -169,7 +169,7 @@ return lines[:-1] def __del__(self): - if hasattr(self.file,'close') and self.should_close_file: + if hasattr(getattr(self, 'file', None),'close') and self.should_close_file: self.file.close() def __getitem__(self, item): Modified: branches/mb_mio_refactor/fopen.py =================================================================== --- branches/mb_mio_refactor/fopen.py 2008-10-05 04:49:23 UTC (rev 4775) +++ branches/mb_mio_refactor/fopen.py 2008-10-05 05:02:32 UTC (rev 4776) @@ -2,7 +2,8 @@ # Author: Travis Oliphant -import struct, os, sys +import struct +import sys import types from numpy import * Modified: branches/mb_mio_refactor/mmio.py =================================================================== --- branches/mb_mio_refactor/mmio.py 2008-10-05 04:49:23 UTC (rev 4775) +++ branches/mb_mio_refactor/mmio.py 2008-10-05 05:02:32 UTC (rev 4776) @@ -10,9 +10,8 @@ # import os -from numpy import asarray, real, imag, conj, zeros, ndarray, \ - empty, concatenate, ones, ascontiguousarray, \ - vstack, savetxt, fromfile, fromstring +from numpy import asarray, real, imag, conj, zeros, ndarray, concatenate, \ + ones, ascontiguousarray, vstack, savetxt, fromfile, fromstring __all__ = ['mminfo','mmread','mmwrite', 'MMFile'] Modified: branches/mb_mio_refactor/netcdf.py =================================================================== --- branches/mb_mio_refactor/netcdf.py 2008-10-05 04:49:23 UTC (rev 4775) +++ branches/mb_mio_refactor/netcdf.py 2008-10-05 05:02:32 UTC (rev 4776) @@ -15,7 +15,6 @@ __all__ = ['netcdf_file', 'netcdf_variable'] import struct -import itertools import mmap from numpy import ndarray, zeros, array Modified: branches/mb_mio_refactor/npfile.py =================================================================== --- branches/mb_mio_refactor/npfile.py 2008-10-05 04:49:23 UTC (rev 4775) +++ branches/mb_mio_refactor/npfile.py 2008-10-05 05:02:32 UTC (rev 4776) @@ -6,7 +6,7 @@ import sys -import numpy as N +import numpy as np __all__ = ['sys_endian_code', 'npfile'] @@ -40,9 +40,9 @@ Example use: >>> from StringIO import StringIO - >>> import numpy as N + >>> import numpy as np >>> from scipy.io import npfile - >>> arr = N.arange(10).reshape(5,2) + >>> arr = np.arange(10).reshape(5,2) >>> # Make file-like object (could also be file name) >>> my_file = StringIO() >>> npf = npfile(my_file) @@ -167,7 +167,7 @@ (if None from self.order) ''' endian, order = self._endian_order(endian, order) - data = N.asarray(data) + data = np.asarray(data) dt_endian = self._endian_from_dtype(data.dtype) if not endian == 'dtype': if dt_endian != endian: @@ -194,7 +194,7 @@ arr - array from file with given dtype (dt) ''' endian, order = self._endian_order(endian, order) - dt = N.dtype(dt) + dt = np.dtype(dt) try: shape = list(shape) except TypeError: @@ -203,7 +203,7 @@ if minus_ones == 0: pass elif minus_ones == 1: - known_dimensions_size = -N.product(shape,axis=0) * dt.itemsize + known_dimensions_size = -np.product(shape,axis=0) * dt.itemsize unknown_dimension_size, illegal = divmod(self.remaining_bytes(), known_dimensions_size) if illegal: @@ -212,10 +212,10 @@ else: raise ValueError( "illegal -1 count; can only specify one unknown dimension") - sz = dt.itemsize * N.product(shape) + sz = dt.itemsize * np.product(shape) dt_endian = self._endian_from_dtype(dt) buf = self.file.read(sz) - arr = N.ndarray(shape=shape, + arr = np.ndarray(shape=shape, dtype=dt, buffer=buf, order=order) @@ -223,7 +223,7 @@ return arr.byteswap() return arr.copy() -npfile = N.deprecate_with_doc(""" +npfile = np.deprecate_with_doc(""" You can achieve the same effect as using npfile, using ndarray.tofile and numpy.fromfile. Modified: branches/mb_mio_refactor/wavfile.py =================================================================== --- branches/mb_mio_refactor/wavfile.py 2008-10-05 04:49:23 UTC (rev 4775) +++ branches/mb_mio_refactor/wavfile.py 2008-10-05 05:02:32 UTC (rev 4776) @@ -4,7 +4,7 @@ # assumes file pointer is immediately # after the 'fmt ' id def _read_fmt_chunk(fid): - res = struct.unpack('lhHLLHH',fid.read(20)) + res = struct.unpack('ihHIIHH',fid.read(20)) size, comp, noc, rate, sbytes, ba, bits = res if (comp != 1 or size > 16): print "Warning: unfamiliar format bytes..." @@ -96,7 +96,7 @@ bits = data.dtype.itemsize * 8 sbytes = rate*(bits / 8)*noc ba = noc * (bits / 8) - fid.write(struct.pack('lhHLLHH', 16, 1, noc, rate, sbytes, ba, bits)) + fid.write(struct.pack('ihHIIHH', 16, 1, noc, rate, sbytes, ba, bits)) # data chunk fid.write('data') fid.write(struct.pack('i', data.nbytes)) From scipy-svn at scipy.org Sun Oct 5 01:52:04 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 5 Oct 2008 00:52:04 -0500 (CDT) Subject: [Scipy-svn] r4777 - in branches/spatial/scipy/spatial: . tests Message-ID: <20081005055204.010D339C088@scipy.org> Author: peridot Date: 2008-10-05 00:51:47 -0500 (Sun, 05 Oct 2008) New Revision: 4777 Modified: branches/spatial/scipy/spatial/kdtree.py branches/spatial/scipy/spatial/tests/test_kdtree.py Log: Added all-neighbors code, both array-of-points and between two kd-trees. Not optimized. Modified: branches/spatial/scipy/spatial/kdtree.py =================================================================== --- branches/spatial/scipy/spatial/kdtree.py 2008-10-05 05:02:32 UTC (rev 4776) +++ branches/spatial/scipy/spatial/kdtree.py 2008-10-05 05:51:47 UTC (rev 4777) @@ -4,6 +4,14 @@ from heapq import heappush, heappop def distance_p(x,y,p=2): + """Compute the pth power of the L**p distance between x and y + + For efficiency, this function computes the L**p distance but does + not extract the pth root. If p is 1 or infinity, this is equal to + the actual L**p distance. + """ + x = np.asarray(x) + y = np.asarray(y) if p==np.inf: return np.amax(np.abs(y-x),axis=-1) elif p==1: @@ -11,12 +19,59 @@ else: return np.sum(np.abs(y-x)**p,axis=-1) def distance(x,y,p=2): + """Compute the L**p distance between x and y""" + x = np.asarray(x) + y = np.asarray(y) if p==np.inf or p==1: return distance_p(x,y,p) else: return distance_p(x,y,p)**(1./p) +class Rectangle(object): + """Hyperrectangle class. + Represents a Cartesian product of intervals. + """ + def __init__(self, maxes, mins): + """Construct a hyperrectangle.""" + self.maxes = np.maximum(maxes,mins).astype(np.float) + self.mins = np.minimum(maxes,mins).astype(np.float) + self.k, = self.maxes.shape + + def __repr__(self): + return "" % zip(self.mins, self.maxes) + + def volume(self): + """Total volume.""" + return np.prod(self.maxes-self.mins) + + def split(self, d, split): + """Produce two hyperrectangles by splitting along axis d.""" + mid = np.copy(self.maxes) + mid[d] = split + less = Rectangle(self.mins, mid) + mid = np.copy(self.mins) + mid[d] = split + greater = Rectangle(mid, self.maxes) + return less, greater + + def min_distance_point(self, x, p=2.): + """Compute the minimum distance between x and a point in the hyperrectangle.""" + return distance(0, np.maximum(0,np.maximum(self.mins-x,x-self.maxes)),p) + + def max_distance_point(self, x, p=2.): + """Compute the maximum distance between x and a point in the hyperrectangle.""" + return distance(0, np.maximum(self.maxes-x,x-self.mins),p) + + def min_distance_rectangle(self, other, p=2.): + """Compute the minimum distance between points in the two hyperrectangles.""" + return distance(0, np.maximum(0,np.maximum(self.mins-other.maxes,other.mins-self.maxes)),p) + + def max_distance_rectangle(self, other, p=2.): + """Compute the maximum distance between points in the two hyperrectangles.""" + return distance(0, np.maximum(self.maxes-other.mins,other.maxes-self.mins),p) + + class KDTree(object): """kd-tree for quick nearest-neighbor lookup @@ -42,6 +97,11 @@ For large dimensions (20 is already large) do not expect this to run significantly faster than brute force. High-dimensional nearest-neighbor queries are a substantial open problem in computer science. + + The tree also supports all-neighbors queries, both with arrays of points + and with other kd-trees. These do use a reasonably efficient algorithm, + but the kd-tree is not necessarily the best data structure for this + sort of calculation. """ def __init__(self, data, leafsize=10): @@ -302,4 +362,135 @@ raise ValueError("Requested %s nearest neighbors; acceptable numbers are integers greater than or equal to one, or None") + def __query_ball_point(self, x, r, p=2., eps=0): + R = Rectangle(self.maxes, self.mins) + def traverse_checking(node, rect): + if rect.min_distance_point(x,p)>=r/(1.+eps): + return [] + elif rect.max_distance_point(x,p)r/(1.+eps): + return + elif rect1.max_distance_rectangle(rect2, p)=self.d/(1.+self.eps)) + +class test_random_ball(ball_consistency): + + def setUp(self): + n = 1000 + k = 4 + self.data = np.random.randn(n,k) + self.T = KDTree(self.data) + self.x = np.random.randn(k) + self.p = 2. + self.eps = 0 + self.d = 0.2 + +class test_random_ball_approx(test_random_ball): + + def setUp(self): + test_random_ball.setUp(self) + self.eps = 0.1 + +class test_random_ball_far(test_random_ball): + + def setUp(self): + test_random_ball.setUp(self) + self.d = 2. + +class test_random_ball_l1(test_random_ball): + + def setUp(self): + test_random_ball.setUp(self) + self.p = 1 + +class test_random_ball_linf(test_random_ball): + + def setUp(self): + test_random_ball.setUp(self) + self.p = np.inf + +def test_random_ball_vectorized(): + + n = 20 + k = 5 + T = KDTree(np.random.randn(n,k)) + + r = T.query_ball_point(np.random.randn(2,3,k),1) + assert_equal(r.shape,(2,3)) + assert isinstance(r[0,0],list) + +class two_trees_consistency: + + def test_all_in_ball(self): + r = self.T1.query_ball_tree(self.T2, self.d, p=self.p, eps=self.eps) + for i, l in enumerate(r): + for j in l: + assert distance(self.data1[i],self.data2[j],self.p)<=self.d*(1.+self.eps) + def test_found_all(self): + r = self.T1.query_ball_tree(self.T2, self.d, p=self.p, eps=self.eps) + for i, l in enumerate(r): + c = np.ones(self.T2.n,dtype=np.bool) + c[l] = False + assert np.all(distance(self.data2[c],self.data1[i],self.p)>=self.d/(1.+self.eps)) + +class test_two_random_trees(two_trees_consistency): + + def setUp(self): + n = 100 + k = 4 + self.data1 = np.random.randn(n,k) + self.T1 = KDTree(self.data1,leafsize=2) + self.data2 = np.random.randn(n,k) + self.T2 = KDTree(self.data2,leafsize=2) + self.p = 2. + self.eps = 0 + self.d = 0.2 + +class test_two_random_trees_far(test_two_random_trees): + + def setUp(self): + test_two_random_trees.setUp(self) + self.d = 2 + +class test_two_random_trees_linf(test_two_random_trees): + + def setUp(self): + test_two_random_trees.setUp(self) + self.p = np.inf + + +class test_rectangle: + + def setUp(self): + self.rect = Rectangle([0,0],[1,1]) + + def test_min_inside(self): + assert_almost_equal(self.rect.min_distance_point([0.5,0.5]),0) + def test_min_one_side(self): + assert_almost_equal(self.rect.min_distance_point([0.5,1.5]),0.5) + def test_min_two_sides(self): + assert_almost_equal(self.rect.min_distance_point([2,2]),np.sqrt(2)) + def test_max_inside(self): + assert_almost_equal(self.rect.max_distance_point([0.5,0.5]),1/np.sqrt(2)) + def test_max_one_side(self): + assert_almost_equal(self.rect.max_distance_point([0.5,1.5]),np.hypot(0.5,1.5)) + def test_max_two_sides(self): + assert_almost_equal(self.rect.max_distance_point([2,2]),2*np.sqrt(2)) + + def test_split(self): + less, greater = self.rect.split(0,0.1) + assert_array_equal(less.maxes,[0.1,1]) + assert_array_equal(less.mins,[0,0]) + assert_array_equal(greater.maxes,[1,1]) + assert_array_equal(greater.mins,[0.1,0]) + + +def test_distance_l2(): + assert_almost_equal(distance([0,0],[1,1],2),np.sqrt(2)) +def test_distance_l1(): + assert_almost_equal(distance([0,0],[1,1],1),2) +def test_distance_linf(): + assert_almost_equal(distance([0,0],[1,1],np.inf),1) +def test_distance_vectorization(): + x = np.random.randn(10,1,3) + y = np.random.randn(1,7,3) + assert_equal(distance(x,y).shape,(10,7)) From scipy-svn at scipy.org Sun Oct 5 04:00:06 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 5 Oct 2008 03:00:06 -0500 (CDT) Subject: [Scipy-svn] r4778 - in branches/spatial/scipy/spatial: . tests Message-ID: <20081005080006.6D27239C088@scipy.org> Author: peridot Date: 2008-10-05 02:59:51 -0500 (Sun, 05 Oct 2008) New Revision: 4778 Modified: branches/spatial/scipy/spatial/kdtree.py branches/spatial/scipy/spatial/tests/test_kdtree.py Log: Added neighbor-counting code. Modified: branches/spatial/scipy/spatial/kdtree.py =================================================================== --- branches/spatial/scipy/spatial/kdtree.py 2008-10-05 05:51:47 UTC (rev 4777) +++ branches/spatial/scipy/spatial/kdtree.py 2008-10-05 07:59:51 UTC (rev 4778) @@ -132,12 +132,14 @@ class leafnode(node): def __init__(self, idx): self.idx = idx + self.children = len(idx) class innernode(node): def __init__(self, split_dim, split, less, greater): self.split_dim = split_dim self.split = split self.less = less self.greater = greater + self.children = less.children+greater.children def __build(self, idx, maxes, mins): if len(idx)<=self.leafsize: @@ -494,3 +496,79 @@ return results + def count_neighbors(self, other, r, p=2.): + """Count how many nearby pairs can be formed. + + Count the number of pairs (x1,x2) can be formed, with x1 drawn + from self and x2 drawn from other, and where distance(x1,x2,p)<=r. + This is the "two-point correlation" described in Gray and Moore 2000, + "N-body problems in statistical learning", and the code here is based + on their algorithm. + + Parameters + ========== + + other : KDTree + + r : float or one-dimensional array of floats + The radius to produce a count for. Multiple radii are searched with a single + tree traversal. + p : float, 1<=p<=infinity + Which Minkowski p-norm to use + + Returns + ======= + + result : integer or one-dimensional array of integers + The number of pairs. Note that this is internally stored in a numpy int, + and so may overflow if very large (two billion). + """ + + def traverse(node1, rect1, node2, rect2, idx): + min_r = rect1.min_distance_rectangle(rect2,p) + max_r = rect1.max_distance_rectangle(rect2,p) + c_greater = r[idx]>max_r + result[idx[c_greater]] += node1.children*node2.children + idx = idx[(min_r<=r[idx]) & (r[idx]<=max_r)] + if len(idx)==0: + return + + if isinstance(node1,KDTree.leafnode): + if isinstance(node2,KDTree.leafnode): + ds = distance(self.data[node1.idx][:,np.newaxis,:], + other.data[node2.idx][np.newaxis,:,:], + p).ravel() + ds.sort() + result[idx] += np.searchsorted(ds,r[idx],side='right') + else: + less, greater = rect2.split(node2.split_dim, node2.split) + traverse(node1, rect1, node2.less, less, idx) + traverse(node1, rect1, node2.greater, greater, idx) + else: + if isinstance(node2,KDTree.leafnode): + less, greater = rect1.split(node1.split_dim, node1.split) + traverse(node1.less, less, node2, rect2, idx) + traverse(node1.greater, greater, node2, rect2, idx) + else: + less1, greater1 = rect1.split(node1.split_dim, node1.split) + less2, greater2 = rect2.split(node2.split_dim, node2.split) + traverse(node1.less,less1,node2.less,less2,idx) + traverse(node1.less,less1,node2.greater,greater2,idx) + traverse(node1.greater,greater1,node2.less,less2,idx) + traverse(node1.greater,greater1,node2.greater,greater2,idx) + R1 = Rectangle(self.maxes, self.mins) + R2 = Rectangle(other.maxes, other.mins) + if np.shape(r) == (): + r = np.array([r]) + result = np.zeros(1,dtype=int) + traverse(self.tree, R1, other.tree, R2, np.arange(1)) + return result[0] + elif len(np.shape(r))==1: + r = np.asarray(r) + n, = r.shape + result = np.zeros(n,dtype=int) + traverse(self.tree, R1, other.tree, R2, np.arange(n)) + return result + else: + raise ValueError("r must be either a single value or a one-dimensional array of values") + Modified: branches/spatial/scipy/spatial/tests/test_kdtree.py =================================================================== --- branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-05 05:51:47 UTC (rev 4777) +++ branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-05 07:59:51 UTC (rev 4778) @@ -311,3 +311,28 @@ x = np.random.randn(10,1,3) y = np.random.randn(1,7,3) assert_equal(distance(x,y).shape,(10,7)) + +class test_count_neighbors: + + def setUp(self): + n = 100 + k = 4 + self.T1 = KDTree(np.random.randn(n,k),leafsize=2) + self.T2 = KDTree(np.random.randn(n,k),leafsize=2) + + def test_one_radius(self): + r = 0.2 + assert_equal(self.T1.count_neighbors(self.T2, r), + np.sum([len(l) for l in self.T1.query_ball_tree(self.T2,r)])) + + def test_large_radius(self): + r = 1000 + assert_equal(self.T1.count_neighbors(self.T2, r), + np.sum([len(l) for l in self.T1.query_ball_tree(self.T2,r)])) + + def test_multiple_radius(self): + rs = np.exp(np.linspace(np.log(0.01),np.log(10),10)) + results = self.T1.count_neighbors(self.T2, rs) + assert np.all(np.diff(results)>=0) + for r,result in zip(rs, results): + assert_equal(self.T1.count_neighbors(self.T2, r), result) From scipy-svn at scipy.org Sun Oct 5 04:01:28 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 5 Oct 2008 03:01:28 -0500 (CDT) Subject: [Scipy-svn] r4779 - branches/spatial/scipy/spatial/tests Message-ID: <20081005080128.2A30D39C088@scipy.org> Author: peridot Date: 2008-10-05 03:01:17 -0500 (Sun, 05 Oct 2008) New Revision: 4779 Modified: branches/spatial/scipy/spatial/tests/test_kdtree.py Log: Added boilerplate to allow running all tests in this module. Modified: branches/spatial/scipy/spatial/tests/test_kdtree.py =================================================================== --- branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-05 07:59:51 UTC (rev 4778) +++ branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-05 08:01:17 UTC (rev 4779) @@ -336,3 +336,7 @@ assert np.all(np.diff(results)>=0) for r,result in zip(rs, results): assert_equal(self.T1.count_neighbors(self.T2, r), result) + + +if __name__=="__main__": + run_module_suite() From scipy-svn at scipy.org Sun Oct 5 04:55:43 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 5 Oct 2008 03:55:43 -0500 (CDT) Subject: [Scipy-svn] r4780 - in branches/spatial/scipy/spatial: . tests Message-ID: <20081005085543.05C0F39C088@scipy.org> Author: peridot Date: 2008-10-05 03:55:42 -0500 (Sun, 05 Oct 2008) New Revision: 4780 Modified: branches/spatial/scipy/spatial/kdtree.py branches/spatial/scipy/spatial/tests/test_kdtree.py Log: Added distance matrix implementation, sparse distance matrix, and a few comments. Modified: branches/spatial/scipy/spatial/kdtree.py =================================================================== --- branches/spatial/scipy/spatial/kdtree.py 2008-10-05 08:01:17 UTC (rev 4779) +++ branches/spatial/scipy/spatial/kdtree.py 2008-10-05 08:55:42 UTC (rev 4780) @@ -2,6 +2,7 @@ # Released under the scipy license import numpy as np from heapq import heappush, heappop +import scipy.sparse def distance_p(x,y,p=2): """Compute the pth power of the L**p distance between x and y @@ -46,7 +47,12 @@ return np.prod(self.maxes-self.mins) def split(self, d, split): - """Produce two hyperrectangles by splitting along axis d.""" + """Produce two hyperrectangles by splitting along axis d. + + In general, if you need to compute maximum and minimum + distances to the children, it can be done more efficiently + by updating the maximum and minimum distances to the parent. + """ # FIXME: do this mid = np.copy(self.maxes) mid[d] = split less = Rectangle(self.mins, mid) @@ -572,3 +578,98 @@ else: raise ValueError("r must be either a single value or a one-dimensional array of values") + def sparse_distance_matrix(self, other, max_distance, p=2.): + """Compute a sparse distance matrix + + Computes a distance matrix between two KDTrees, leaving as zero + any distance greater than max_distance. + + Parameters + ========== + + other : KDTree + + max_distance : positive float + + Returns + ======= + + result : dok_matrix + Sparse matrix representing the results in "dictionary of keys" format. + """ + result = scipy.sparse.dok_matrix((self.n,other.n)) + + def traverse(node1, rect1, node2, rect2): + if rect1.min_distance_rectangle(rect2, p)>max_distance: + return + elif isinstance(node1, KDTree.leafnode): + if isinstance(node2, KDTree.leafnode): + for i in node1.idx: + for j in node2.idx: + d = distance(self.data[i],other.data[j],p) + if d<=max_distance: + result[i,j] = d + else: + less, greater = rect2.split(node2.split_dim, node2.split) + traverse(node1,rect1,node2.less,less) + traverse(node1,rect1,node2.greater,greater) + elif isinstance(node2, KDTree.leafnode): + less, greater = rect1.split(node1.split_dim, node1.split) + traverse(node1.less,less,node2,rect2) + traverse(node1.greater,greater,node2,rect2) + else: + less1, greater1 = rect1.split(node1.split_dim, node1.split) + less2, greater2 = rect2.split(node2.split_dim, node2.split) + traverse(node1.less,less1,node2.less,less2) + traverse(node1.less,less1,node2.greater,greater2) + traverse(node1.greater,greater1,node2.less,less2) + traverse(node1.greater,greater1,node2.greater,greater2) + traverse(self.tree, Rectangle(self.maxes, self.mins), + other.tree, Rectangle(other.maxes, other.mins)) + + return result + + +def distance_matrix(x,y,p=2,threshold=1000000): + """Compute the distance matrix. + + Computes the matrix of all pairwise distances. + + Parameters + ========== + + x : array-like, m by k + y : array-like, n by k + p : float 1<=p<=infinity + Which Minkowski p-norm to use. + threshold : positive integer + If m*n*k>threshold use a python loop instead of creating + a very large temporary. + + Returns + ======= + + result : array-like, m by n + + + """ + + x = np.asarray(x) + m, k = x.shape + y = np.asarray(y) + n, kk = y.shape + + if k != kk: + raise ValueError("x contains %d-dimensional vectors but y contains %d-dimensional vectors" % (k, kk)) + + if m*n*k <= threshold: + return distance(x[:,np.newaxis,:],y[np.newaxis,:,:],p) + else: + result = np.empty((m,n),dtype=np.float) #FIXME: figure out the best dtype + if m Author: matthew.brett at gmail.com Date: 2008-10-05 04:36:46 -0500 (Sun, 05 Oct 2008) New Revision: 4781 Removed: trunk/scipy/io/tests/gen_unittests.m trunk/scipy/io/tests/gen_unittests4.m trunk/scipy/io/tests/save_test.m Log: Removed m files; now moved to matlab test directory Deleted: trunk/scipy/io/tests/gen_unittests.m =================================================================== --- trunk/scipy/io/tests/gen_unittests.m 2008-10-05 08:55:42 UTC (rev 4780) +++ trunk/scipy/io/tests/gen_unittests.m 2008-10-05 09:36:46 UTC (rev 4781) @@ -1,92 +0,0 @@ -% Generates mat files for loadmat unit tests -% This is the version for matlab 5 and higher -% Uses save_test.m function - -% work out matlab version and file suffix for test files -global FILEPREFIX FILESUFFIX -FILEPREFIX = [fullfile(pwd, 'data') filesep]; -temp = ver('MATLAB'); -mlv = temp.Version; -FILESUFFIX = ['_' mlv '_' computer '.mat']; - -% basic double array -save_test('testdouble', 0:pi/4:2*pi); - -% string -save_test('teststring', '"Do nine men interpret?" "Nine men," I nod.') - -% complex -theta = 0:pi/4:2*pi; -save_test('testcomplex', cos(theta) + 1j*sin(theta)); - -% asymmetric array to check indexing -a = zeros(3, 5); -a(:,1) = [1:3]'; -a(1,:) = 1:5; - -% 2D matrix -save_test('testmatrix', a); - -% minus number - tests signed int -save_test('testminus', -1); - -% single character -save_test('testonechar', 'r'); - -% string array -save_test('teststringarray', ['one '; 'two '; 'three']); - -% sparse array -save_test('testsparse', sparse(a)); - -% sparse complex array -b = sparse(a); -b(1,1) = b(1,1) + j; -save_test('testsparsecomplex', b); - -% Two variables in same file -save([FILEPREFIX 'testmulti' FILESUFFIX], 'a', 'theta') - - -% struct -save_test('teststruct', ... - struct('stringfield','Rats live on no evil star.',... - 'doublefield',[sqrt(2) exp(1) pi],... - 'complexfield',(1+1j)*[sqrt(2) exp(1) pi])); - -% cell -save_test('testcell', ... - {['This cell contains this string and 3 arrays of increasing' ... - ' length'], 1., 1.:2., 1.:3.}); - -% Empty cells in two cell matrices -save_test('testemptycell', {1, 2, [], [], 3}); - -% 3D matrix -save_test('test3dmatrix', reshape(1:24,[2 3 4])) - -% nested cell array -save_test('testcellnest', {1, {2, 3, {4, 5}}}); - -% nested struct -save_test('teststructnest', struct('one', 1, 'two', ... - struct('three', 'number 3'))); - -% array of struct -save_test('teststructarr', [struct('one', 1, 'two', 2) ... - struct('one', 'number 1', 'two', 'number 2')]); - -% matlab object -save_test('testobject', inline('x')) - -% array of matlab objects -%save_test('testobjarr', [inline('x') inline('y')]) - -% unicode test -if str2num(mlv) > 7 % function added 7.0.1 - fid = fopen([FILEPREFIX 'japanese_utf8.txt']); - from_japan = fread(fid, 'uint8')'; - fclose(fid); - save_test('testunicode', native2unicode(from_japan, 'utf-8')); -end - \ No newline at end of file Deleted: trunk/scipy/io/tests/gen_unittests4.m =================================================================== --- trunk/scipy/io/tests/gen_unittests4.m 2008-10-05 08:55:42 UTC (rev 4780) +++ trunk/scipy/io/tests/gen_unittests4.m 2008-10-05 09:36:46 UTC (rev 4781) @@ -1,50 +0,0 @@ -% Generates mat files for loadmat unit tests -% Uses save_test.m function -% This is the version for matlab 4 - -% work out matlab version and file suffix for test files -global FILEPREFIX FILESUFFIX -sepchar = '/'; -if strcmp(computer, 'PCWIN'), sepchar = '\'; end -FILEPREFIX = [pwd sepchar 'data' sepchar]; -mlv = version; -FILESUFFIX = ['_' mlv '_' computer '.mat']; - -% basic double array -save_test('testdouble', 0:pi/4:2*pi); - -% string -save_test('teststring', '"Do nine men interpret?" "Nine men," I nod.') - -% complex -theta = 0:pi/4:2*pi; -save_test('testcomplex', cos(theta) + 1j*sin(theta)); - -% asymmetric array to check indexing -a = zeros(3, 5); -a(:,1) = [1:3]'; -a(1,:) = 1:5; - -% 2D matrix -save_test('testmatrix', a); - -% minus number - tests signed int -save_test('testminus', -1); - -% single character -save_test('testonechar', 'r'); - -% string array -save_test('teststringarray', ['one '; 'two '; 'three']); - -% sparse array -save_test('testsparse', sparse(a)); - -% sparse complex array -b = sparse(a); -b(1,1) = b(1,1) + j; -save_test('testsparsecomplex', b); - -% Two variables in same file -save([FILEPREFIX 'testmulti' FILESUFFIX], 'a', 'theta') - Deleted: trunk/scipy/io/tests/save_test.m =================================================================== --- trunk/scipy/io/tests/save_test.m 2008-10-05 08:55:42 UTC (rev 4780) +++ trunk/scipy/io/tests/save_test.m 2008-10-05 09:36:46 UTC (rev 4781) @@ -1,6 +0,0 @@ -function save_test(test_name, v) -% saves variable passed in m with filename from prefix - -global FILEPREFIX FILESUFFIX -eval([test_name ' = v;']); -save([FILEPREFIX test_name FILESUFFIX], test_name) \ No newline at end of file From scipy-svn at scipy.org Sun Oct 5 10:11:41 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 5 Oct 2008 09:11:41 -0500 (CDT) Subject: [Scipy-svn] r4782 - branches/spatial/scipy/spatial/tests Message-ID: <20081005141141.9835039C089@scipy.org> Author: peridot Date: 2008-10-05 09:11:35 -0500 (Sun, 05 Oct 2008) New Revision: 4782 Modified: branches/spatial/scipy/spatial/tests/test_kdtree.py Log: Cut down size of test cases (run time 30s -> 3s) Modified: branches/spatial/scipy/spatial/tests/test_kdtree.py =================================================================== --- branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-05 09:36:46 UTC (rev 4781) +++ branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-05 14:11:35 UTC (rev 4782) @@ -75,10 +75,10 @@ class test_random(ConsistencyTests): def setUp(self): - self.n = 1000 + self.n = 100 self.k = 4 self.data = np.random.randn(self.n, self.k) - self.kdtree = KDTree(self.data) + self.kdtree = KDTree(self.data,leafsize=2) self.x = np.random.randn(self.k) self.d = 0.2 self.m = 10 @@ -192,10 +192,10 @@ class test_random_ball(ball_consistency): def setUp(self): - n = 1000 + n = 100 k = 4 self.data = np.random.randn(n,k) - self.T = KDTree(self.data) + self.T = KDTree(self.data,leafsize=2) self.x = np.random.randn(k) self.p = 2. self.eps = 0 @@ -252,7 +252,7 @@ class test_two_random_trees(two_trees_consistency): def setUp(self): - n = 100 + n = 50 k = 4 self.data1 = np.random.randn(n,k) self.T1 = KDTree(self.data1,leafsize=2) @@ -315,8 +315,8 @@ class test_count_neighbors: def setUp(self): - n = 100 - k = 4 + n = 50 + k = 2 self.T1 = KDTree(np.random.randn(n,k),leafsize=2) self.T2 = KDTree(np.random.randn(n,k),leafsize=2) @@ -331,7 +331,7 @@ np.sum([len(l) for l in self.T1.query_ball_tree(self.T2,r)])) def test_multiple_radius(self): - rs = np.exp(np.linspace(np.log(0.01),np.log(10),10)) + rs = np.exp(np.linspace(np.log(0.01),np.log(10),3)) results = self.T1.count_neighbors(self.T2, rs) assert np.all(np.diff(results)>=0) for r,result in zip(rs, results): @@ -339,7 +339,7 @@ class test_sparse_distance_matrix: def setUp(self): - n = 100 + n = 50 k = 4 self.T1 = KDTree(np.random.randn(n,k),leafsize=2) self.T2 = KDTree(np.random.randn(n,k),leafsize=2) From scipy-svn at scipy.org Sun Oct 5 12:55:27 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 5 Oct 2008 11:55:27 -0500 (CDT) Subject: [Scipy-svn] r4783 - in trunk/scipy/io: . arff/tests matlab matlab/tests Message-ID: <20081005165527.B431639C088@scipy.org> Author: matthew.brett at gmail.com Date: 2008-10-05 11:55:22 -0500 (Sun, 05 Oct 2008) New Revision: 4783 Added: trunk/scipy/io/matlab/byteordercodes.py Modified: trunk/scipy/io/__init__.py trunk/scipy/io/arff/tests/test_data.py trunk/scipy/io/matlab/mio.py trunk/scipy/io/matlab/mio4.py trunk/scipy/io/matlab/mio5.py trunk/scipy/io/matlab/miobase.py trunk/scipy/io/matlab/tests/test_byteordercodes.py trunk/scipy/io/matlab/tests/test_mio.py Log: Take matlab io tests out for now, known to fail (and have been failing silently for a long time) Modified: trunk/scipy/io/__init__.py =================================================================== --- trunk/scipy/io/__init__.py 2008-10-05 14:11:35 UTC (rev 4782) +++ trunk/scipy/io/__init__.py 2008-10-05 16:55:22 UTC (rev 4783) @@ -80,6 +80,7 @@ from netcdf import netcdf_file, netcdf_variable from recaster import sctype_attributes, Recaster +import matlab.byteordercodes as byteordercodes from data_store import save_as_module from mmio import mminfo, mmread, mmwrite Modified: trunk/scipy/io/arff/tests/test_data.py =================================================================== --- trunk/scipy/io/arff/tests/test_data.py 2008-10-05 14:11:35 UTC (rev 4782) +++ trunk/scipy/io/arff/tests/test_data.py 2008-10-05 16:55:22 UTC (rev 4783) @@ -2,6 +2,8 @@ """Tests for parsing full arff files.""" import os +import numpy as np + from numpy.testing import * from scipy.io.arff.arffread import loadarff Added: trunk/scipy/io/matlab/byteordercodes.py =================================================================== --- trunk/scipy/io/matlab/byteordercodes.py 2008-10-05 14:11:35 UTC (rev 4782) +++ trunk/scipy/io/matlab/byteordercodes.py 2008-10-05 16:55:22 UTC (rev 4783) @@ -0,0 +1,68 @@ +''' Byteorder utilities for system - numpy byteorder encoding + +Converts a variety of string codes for little endian, big endian, +native byte order and swapped byte order to explicit numpy endian +codes - one of '<' (little endian) or '>' (big endian) + +''' + +import sys + +sys_is_le = sys.byteorder == 'little' +native_code = sys_is_le and '<' or '>' +swapped_code = sys_is_le and '>' or '<' + +aliases = {'little': ('little', '<', 'l', 'le'), + 'big': ('big', '>', 'b', 'be'), + 'native': ('native', '='), + 'swapped': ('swapped', 'S')} + +def to_numpy_code(code): + ''' Convert various order codings to numpy format + Parameters + ---------- + code : {'little','big','l','b','le','be','<','>', + 'native','=', + 'swapped', 's'} string + code is converted to lower case before parsing + + Returns + ------- + out_code : {'<','>'} string + where '<' is the numpy dtype code for little + endian, and '>' is the code for big endian + + + Examples + -------- + >>> import sys + >>> from imagers.byteorder import to_numpy_code, sys_is_le + >>> sys_is_le == (sys.byteorder == 'little') + True + >>> to_numpy_code('big') + '>' + >>> to_numpy_code('little') + '<' + >>> nc = to_numpy_code('native') + >>> nc == '<' if sys_is_le else nc == '>' + True + >>> sc = to_numpy_code('swapped') + >>> sc == '>' if sys_is_le else sc == '<' + True + ''' + code = code.lower() + if code is None: + return native_code + if code in aliases['little']: + return '<' + elif code in aliases['big']: + return '>' + elif code in aliases['native']: + return native_code + elif code in aliases['swapped']: + return swapped_code + else: + raise ValueError( + 'We cannot handle byte order %s' % code) + + Modified: trunk/scipy/io/matlab/mio.py =================================================================== --- trunk/scipy/io/matlab/mio.py 2008-10-05 14:11:35 UTC (rev 4782) +++ trunk/scipy/io/matlab/mio.py 2008-10-05 16:55:22 UTC (rev 4783) @@ -59,13 +59,13 @@ raise IOError, 'Reader needs file name or open file-like object' byte_stream = file_name - mv = get_matfile_version(byte_stream) - if mv == '4': + mjv, mnv = get_matfile_version(byte_stream) + if mjv == 0: return MatFile4Reader(byte_stream, **kwargs) - elif mv == '5': + elif mjv == 1: return MatFile5Reader(byte_stream, **kwargs) - elif mv == '7': - raise NotImplementedError('Please use PyTables for matlab HDF files') + elif mjv == 2: + raise NotImplementedError('Please use PyTables for matlab v7.3 (HDF) files') else: raise TypeError('Did not recognize version %s' % mv) Modified: trunk/scipy/io/matlab/mio4.py =================================================================== --- trunk/scipy/io/matlab/mio4.py 2008-10-05 14:11:35 UTC (rev 4782) +++ trunk/scipy/io/matlab/mio4.py 2008-10-05 16:55:22 UTC (rev 4783) @@ -1,10 +1,14 @@ ''' Classes for read / write of matlab (TM) 4 files ''' +import sys import numpy as np -from miobase import * +from miobase import MatFileReader, MatArrayReader, MatMatrixGetter, \ + MatFileWriter, MatStreamWriter, spsparse +SYS_LITTLE_ENDIAN = sys.byteorder == 'little' + miDOUBLE = 0 miSINGLE = 1 miINT32 = 2 @@ -175,8 +179,8 @@ else: V = np.ascontiguousarray(tmp[:,2],dtype='complex') V.imag = tmp[:,3] - if have_sparse: - return scipy.sparse.coo_matrix((V,(I,J)), dims) + if spsparse: + return spsparse.coo_matrix((V,(I,J)), dims) return (dims, I, J, V) @@ -203,8 +207,8 @@ mopt = self.read_dtype(np.dtype('i4')) self.mat_stream.seek(0) if mopt < 0 or mopt > 5000: - return ByteOrder.swapped_code - return ByteOrder.native_code + return SYS_LITTLE_ENDIAN and '>' or '<' + return SYS_LITTLE_ENDIAN and '<' or '>' class Mat4MatrixWriter(MatStreamWriter): @@ -219,7 +223,7 @@ if dims is None: dims = self.arr.shape header = np.empty((), mdtypes_template['header']) - M = not ByteOrder.little_endian + M = not SYS_LITTLE_ENDIAN O = 0 header['mopt'] = (M * 1000 + O * 100 + @@ -314,8 +318,8 @@ arr - array to write name - name in matlab (TM) workspace ''' - if have_sparse: - if scipy.sparse.issparse(arr): + if spsparse: + if spsparse.issparse(arr): return Mat4SparseWriter(stream, arr, name) arr = np.array(arr) dtt = arr.dtype.type Modified: trunk/scipy/io/matlab/mio5.py =================================================================== --- trunk/scipy/io/matlab/mio5.py 2008-10-05 14:11:35 UTC (rev 4782) +++ trunk/scipy/io/matlab/mio5.py 2008-10-05 16:55:22 UTC (rev 4783) @@ -4,40 +4,16 @@ # Small fragments of current code adapted from matfile.py by Heiko # Henkelmann -## Notice in matfile.py file - -# Copyright (c) 2003 Heiko Henkelmann - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to -# deal in the Software without restriction, including without limitation the -# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or -# sell copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING -# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER -# DEALINGS IN THE SOFTWARE. - +import sys import zlib -from copy import copy as pycopy from cStringIO import StringIO +from copy import copy as pycopy + import numpy as np -from miobase import * +from miobase import MatFileReader, MatArrayReader, MatMatrixGetter, \ + MatFileWriter, MatStreamWriter, spsparse -try: # Python 2.3 support - from sets import Set as set -except: - pass - miINT8 = 1 miUINT8 = 2 miINT16 = 3 @@ -185,10 +161,6 @@ mxUINT64_CLASS, ) -class mat_obj(object): - ''' Placeholder for holding read data from objects ''' - pass - class Mat5ArrayReader(MatArrayReader): ''' Class to get Mat5 arrays @@ -290,7 +262,7 @@ if mc == mxCELL_CLASS: return Mat5CellMatrixGetter(self, header) if mc == mxSTRUCT_CLASS: - return Mat5StructMatrixGetter(self, header, self.struct_as_record) + return Mat5StructMatrixGetter(self, header) if mc == mxOBJECT_CLASS: return Mat5ObjectMatrixGetter(self, header) if mc == mxFUNCTION_CLASS: @@ -400,11 +372,10 @@ nnz = indptr[-1] rowind = rowind[:nnz] data = data[:nnz] - if have_sparse: - from scipy.sparse import csc_matrix - return csc_matrix((data,rowind,indptr), shape=(M,N)) + if spsparse: + return spsparse.csc_matrix((data,rowind,indptr), shape=(M,N)) else: - return (dims, data, rowind, indptr) + return ((M,N), data, rowind, indptr) class Mat5CharMatrixGetter(Mat5MatrixGetter): @@ -445,9 +416,9 @@ pass class Mat5StructMatrixGetter(Mat5MatrixGetter): - def __init__(self, array_reader, header, struct_as_record): + def __init__(self, array_reader, header): super(Mat5StructMatrixGetter, self).__init__(array_reader, header) - self.struct_as_record = struct_as_record + self.struct_as_record = array_reader.struct_as_record def get_raw_array(self): namelength = self.read_element()[0] @@ -456,27 +427,26 @@ for i in xrange(0,len(names),namelength)] tupdims = tuple(self.header['dims'][::-1]) length = np.product(tupdims) - result = np.empty(length, dtype=[(field_name, object) - for field_name in field_names]) - for i in range(length): - for field_name in field_names: - result[i][field_name] = self.read_element() - - if not self.struct_as_record: - # Backward compatibility with previous format + if self.struct_as_record: + result = np.empty(length, dtype=[(field_name, object) + for field_name in field_names]) + for i in range(length): + for field_name in field_names: + result[i][field_name] = self.read_element() + else: # Backward compatibility with previous format self.obj_template = mat_struct() self.obj_template._fieldnames = field_names - newresult = np.empty(length, dtype=object) + result = np.empty(length, dtype=object) for i in range(length): item = pycopy(self.obj_template) for name in field_names: - item.__dict__[name] = result[i][name] - newresult[i] = item - result = newresult + item.__dict__[name] = self.read_element() + result[i] = item return result.reshape(tupdims).T -class MatlabObject: +class MatlabObject(object): + ''' Class to contain read data from matlab objects ''' def __init__(self, classname, field_names): self.__dict__['classname'] = classname self.__dict__['mobj_recarray'] = np.empty((1,1), dtype=[(field_name, object) @@ -540,7 +510,35 @@ struct_as_record=False, uint16_codec=None ): + ''' + mat_stream : file-like + object with file API, open for reading + byte_order : {None, string} + specification of byte order, one of: + ('native', '=', 'little', '<', 'BIG', '>') + mat_dtype : {True, False} boolean + If True, return arrays in same dtype as loaded into matlab + otherwise return with dtype with which they were saved + squeeze_me : {False, True} boolean + If True, squeezes dimensions of size 1 from arrays + chars_as_strings : {True, False} boolean + If True, convert char arrays to string arrays + matlab_compatible : {False, True} boolean + If True, returns matrices as would be loaded by matlab + (implies squeeze_me=False, chars_as_strings=False + mat_dtype=True, struct_as_record=True) + struct_as_record : {False, True} boolean + If True, return strutures as numpy records, + otherwise, return as custom object (for + compatibility with scipy 0.6) + uint16_codec : {None, string} + Set codec to use for uint16 char arrays + (e.g. 'utf-8'). Use system default codec if None + ''' self.codecs = {} + # Missing inputs to array reader set later (processor func + # below, dtypes, codecs via our own set_dtype function, called + # from parent __init__) self._array_reader = Mat5ArrayReader( mat_stream, None, @@ -853,8 +851,8 @@ arr - array to write name - name in matlab (TM) workspace ''' - if have_sparse: - if scipy.sparse.issparse(arr): + if spsparse: + if spsparse.issparse(arr): return Mat5SparseWriter(self.stream, arr, name, is_global) if isinstance(arr, MatlabFunctionMatrix): Modified: trunk/scipy/io/matlab/miobase.py =================================================================== --- trunk/scipy/io/matlab/miobase.py 2008-10-05 14:11:35 UTC (rev 4782) +++ trunk/scipy/io/matlab/miobase.py 2008-10-05 16:55:22 UTC (rev 4783) @@ -4,17 +4,16 @@ Base classes for matlab (TM) file stream reading """ -import sys - import numpy as np +import byteordercodes as boc + +# sparse module if available try: - import scipy.sparse - have_sparse = 1 + import scipy.sparse as spsparse except ImportError: - have_sparse = 0 + spsparse = None - def small_product(arr): ''' Faster than product for small arrays ''' res = 1 @@ -23,12 +22,28 @@ return res def get_matfile_version(fileobj): - ''' Return '4', '5', or '7' depending on apparent mat file type - Inputs - fileobj - file object implementing seek() and read() - Outputs - version_str - one of (strings) 4, 5, or 7 - + ''' Return major, minor tuple depending on apparent mat file type + + Where: + + #. 0,x -> version 4 format mat files + #. 1,x -> version 5 format mat files + #. 2,x -> version 7.3 format mat files (HDF format) + + Parameters + ---------- + fileobj : {file-like} + object implementing seek() and read() + + Returns + ------- + major_version : {0, 1, 2} + major matlab file format version + minor_version : int + major matlab file format version + + Notes + ----- Has the side effect of setting the file read pointer to 0 ''' # Mat4 files have a zero somewhere in first 4 bytes @@ -38,42 +53,36 @@ buffer = fileobj.read(4)) if 0 in mopt_bytes: fileobj.seek(0) - return '4' - # For 5 or 7 we need to read an integer in the header - # bytes 124 through 128 contain a version integer - # and an endian test string + return (0,0) + + # For 5 format or 7.3 format we need to read an integer in the + # header. Bytes 124 through 128 contain a version integer and an + # endian test string fileobj.seek(124) tst_str = fileobj.read(4) fileobj.seek(0) maj_ind = int(tst_str[2] == 'I') - verb = ord(tst_str[maj_ind]) - if verb == 1: - return '5' - elif verb == 2: - return '7' - raise ValueError('Unknown mat file type, version %d' % verb) + maj_val = ord(tst_str[maj_ind]) + min_val = ord(tst_str[1-maj_ind]) + ret = (maj_val, min_val) + if maj_val in (1, 2): + return ret + else: + raise ValueError('Unknown mat file type, version %s' % ret) class ByteOrder(object): ''' Namespace for byte ordering ''' - little_endian = sys.byteorder == 'little' - native_code = little_endian and '<' or '>' - swapped_code = little_endian and '>' or '<' + little_endian = boc.sys_is_le + native_code = boc.native_code + swapped_code = boc.swapped_code + to_numpy_code = boc.to_numpy_code - def to_numpy_code(code): - if code is None: - return ByteOrder.native_code - if code in ('little', '<', 'l', 'L'): - return '<' - elif code in ('BIG', '>', 'B', 'b'): - return '>' - elif code in ('native', '='): - return ByteOrder.native_code - elif code in ('swapped'): - return ByteOrder.swapped_code - else: - raise ValueError, 'We cannot handle byte order %s' % byte_order - to_numpy_code = staticmethod(to_numpy_code) +ByteOrder = np.deprecate_with_doc(""" +We no longer use the ByteOrder class, and deprecate it; we will remove +it in future versions of scipy. Please use the +scipy.io.matlab.byteordercodes module instead. +""")(ByteOrder) class MatStreamAgent(object): @@ -116,20 +125,6 @@ class MatFileReader(MatStreamAgent): """ Base object for reading mat files - mat_stream - initialized byte stream object - file io interface object - byte_order - byte order ('native', 'little', 'BIG') - in ('native', '=') - or in ('little', '<') - or in ('BIG', '>') - mat_dtype - return arrays in same dtype as loaded into matlab - (instead of the dtype with which they were saved) - squeeze_me - whether to squeeze unit dimensions or not - chars_as_strings - whether to convert char arrays to string arrays - matlab_compatible - returns matrices as would be loaded by matlab - (implies squeeze_me=False, chars_as_strings=False - mat_dtype=True) - struct_as_record - return strutures as numpy records (only from v5 files) - To make this class functional, you will need to override the following methods: @@ -146,6 +141,25 @@ matlab_compatible=False, struct_as_record=False ): + ''' + mat_stream : file-like + object with file API, open for reading + byte_order : {None, string} + specification of byte order, one of: + ('native', '=', 'little', '<', 'BIG', '>') + mat_dtype : {True, False} boolean + If True, return arrays in same dtype as loaded into matlab + otherwise return with dtype with which they were saved + squeeze_me : {False, True} boolean + If True, squeezes dimensions of size 1 from arrays + chars_as_strings : {True, False} boolean + If True, convert char arrays to string arrays + matlab_compatible : {False, True} boolean + If True, returns matrices as would be loaded by matlab + (implies squeeze_me=False, chars_as_strings=False + mat_dtype=True) + + ''' # Initialize stream self.mat_stream = mat_stream self.dtypes = {} @@ -195,12 +209,12 @@ chars_as_strings = property(get_chars_as_strings, set_chars_as_strings, None, - 'get/set squeeze me property') + 'get/set chars_as_strings property') def get_order_code(self): return self._order_code def set_order_code(self, order_code): - order_code = ByteOrder.to_numpy_code(order_code) + order_code = boc.to_numpy_code(order_code) self._order_code = order_code self.set_dtypes() order_code = property(get_order_code, Modified: trunk/scipy/io/matlab/tests/test_byteordercodes.py =================================================================== --- trunk/scipy/io/matlab/tests/test_byteordercodes.py 2008-10-05 14:11:35 UTC (rev 4782) +++ trunk/scipy/io/matlab/tests/test_byteordercodes.py 2008-10-05 16:55:22 UTC (rev 4783) @@ -6,7 +6,7 @@ from numpy.testing import assert_raises -import scipy.io.byteordercodes as sibc +import scipy.io.matlab.byteordercodes as sibc def test_native(): native_is_le = sys.byteorder == 'little' Modified: trunk/scipy/io/matlab/tests/test_mio.py =================================================================== --- trunk/scipy/io/matlab/tests/test_mio.py 2008-10-05 14:11:35 UTC (rev 4782) +++ trunk/scipy/io/matlab/tests/test_mio.py 2008-10-05 16:55:22 UTC (rev 4783) @@ -57,12 +57,13 @@ assert_array_almost_equal(actual, expected, err_msg=label, decimal=5) else: assert isinstance(expected, typac), \ - "Types %s and %s do not match at %s" % (typex, typac, label) + "Expected %s and actual %s do not match at %s" % \ + (typex, typac, label) assert_equal(actual, expected, err_msg=label) -def _check_case(name, files, case, *args, **kwargs): +def _check_case(name, files, case): for file_name in files: - matdict = loadmat(file_name, *args, **kwargs) + matdict = loadmat(file_name, struct_as_record=True) label = "test %s; file %s" % (name, file_name) for k, expected in case.items(): k_label = "%s, variable %s" % (label, k) @@ -74,7 +75,7 @@ mat_stream = StringIO() savemat(mat_stream, expected, format=format) mat_stream.seek(0) - _check_case(name, [mat_stream], expected, struct_as_record=True) + _check_case(name, [mat_stream], expected) # Define cases to test theta = pi/4*arange(9,dtype=float).reshape(9,1) @@ -207,6 +208,7 @@ }) # generator for load tests + at dec.knownfailureif(True) def test_load(): for case in case_table4 + case_table5: name = case['name'] @@ -216,13 +218,14 @@ assert files, "No files for test %s using filter %s" % (name, filt) yield _check_case, name, files, expected - # round trip tests +# generator for round trip tests + at dec.knownfailureif(True) def test_round_trip(): for case in case_table4 + case_table5_rt: name = case['name'] + '_round_trip' expected = case['expected'] format = case in case_table4 and '4' or '5' - yield _rt_check_case, name, expected, format + #yield _rt_check_case, name, expected, format def test_gzip_simple(): xdense = zeros((20,20)) From scipy-svn at scipy.org Mon Oct 6 00:29:48 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 5 Oct 2008 23:29:48 -0500 (CDT) Subject: [Scipy-svn] r4784 - trunk/scipy/sparse/sparsetools Message-ID: <20081006042948.B42CE39C088@scipy.org> Author: wnbell Date: 2008-10-05 23:29:46 -0500 (Sun, 05 Oct 2008) New Revision: 4784 Modified: trunk/scipy/sparse/sparsetools/csr.h Log: added csr_toell() Modified: trunk/scipy/sparse/sparsetools/csr.h =================================================================== --- trunk/scipy/sparse/sparsetools/csr.h 2008-10-05 16:55:22 UTC (rev 4783) +++ trunk/scipy/sparse/sparsetools/csr.h 2008-10-06 04:29:46 UTC (rev 4784) @@ -381,7 +381,55 @@ +/* + * Compute B = A for CSR matrix A, ELL matrix B + * + * Input Arguments: + * I n_row - number of rows in A + * I n_col - number of columns in A + * I Ap[n_row+1] - row pointer + * I Aj[nnz(A)] - column indices + * T Ax[nnz(A)] - nonzeros + * I row_length - maximum nnz in a row of A + * + * Output Arguments: + * I Bj[n_row * row_length] - column indices + * T Bx[n_row * row_length] - nonzeros + * + * Note: + * Output arrays Bj, Bx must be preallocated + * Duplicate entries in A are not merged. + * Explicit zeros in A are carried over to B. + * Rows with fewer than row_length columns are padded with zeros. + * + */ +template +void csr_toell(const I n_row, + const I n_col, + const I Ap[], + const I Aj[], + const T Ax[], + const I row_length, + I Bj[], + T Bx[]) +{ + const I ell_nnz = row_length * n_row; + std::fill(Bj, Bj + ell_nnz, 0); + std::fill(Bx, Bx + ell_nnz, 0); + for(I i = 0; i < n_row; i++){ + I * Bj_row = Bj + row_length * i; + T * Bx_row = Bx + row_length * i; + for(I jj = Ap[i]; jj < Ap[i+1]; jj++){ + *Bj_row = Aj[jj]; + *Bx_row = Ax[jj]; + Bj_row++; + Bx_row++; + } + } +} + + /* * Compute C = A*B for CSR matrices A,B * From scipy-svn at scipy.org Mon Oct 6 03:36:57 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 6 Oct 2008 02:36:57 -0500 (CDT) Subject: [Scipy-svn] r4785 - trunk/scipy/io Message-ID: <20081006073657.14F0439C088@scipy.org> Author: wnbell Date: 2008-10-06 02:36:43 -0500 (Mon, 06 Oct 2008) New Revision: 4785 Modified: trunk/scipy/io/mmio.py Log: writing of MatrixMarket now works for all integer kinds Modified: trunk/scipy/io/mmio.py =================================================================== --- trunk/scipy/io/mmio.py 2008-10-06 04:29:46 UTC (rev 4784) +++ trunk/scipy/io/mmio.py 2008-10-06 07:36:43 UTC (rev 4785) @@ -479,14 +479,15 @@ precision = 16 if field is None: - if typecode in 'li': + kind = a.dtype.kind + if kind == 'i': field = 'integer' - elif typecode in 'df': + elif kind == 'f': field = 'real' - elif typecode in 'DF': + elif kind == 'c': field = 'complex' else: - raise TypeError,'unexpected typecode '+typecode + raise TypeError('unexpected dtype kind ' + kind) if rep == self.FORMAT_ARRAY: symm = self._get_symmetry(a) @@ -570,23 +571,6 @@ savetxt(stream, IJV, fmt=fmt) - - ### Old method - ## line template - #template = '%i %i ' + template - #I,J,V = coo.row + 1, coo.col + 1, coo.data # change base 0 -> base 1 - #if field in (self.FIELD_REAL, self.FIELD_INTEGER): - # for ijv_tuple in izip(I,J,V): - # stream.writelines(template % ijv_tuple) - #elif field == self.FIELD_COMPLEX: - # for ijv_tuple in izip(I,J,V.real,V.imag): - # stream.writelines(template % ijv_tuple) - #elif field == self.FIELD_PATTERN: - # raise NotImplementedError,`field` - #else: - # raise TypeError,'Unknown field type %s'% `field` - - #------------------------------------------------------------------------------- if __name__ == '__main__': import sys From scipy-svn at scipy.org Mon Oct 6 07:50:02 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 6 Oct 2008 06:50:02 -0500 (CDT) Subject: [Scipy-svn] r4786 - trunk/scipy/optimize/tests Message-ID: <20081006115002.D1C1C39C088@scipy.org> Author: uwe.schmitt Date: 2008-10-06 06:49:39 -0500 (Mon, 06 Oct 2008) New Revision: 4786 Modified: trunk/scipy/optimize/tests/test_nnls.py Log: fixed comments in test_nnls.py Modified: trunk/scipy/optimize/tests/test_nnls.py =================================================================== --- trunk/scipy/optimize/tests/test_nnls.py 2008-10-06 07:36:43 UTC (rev 4785) +++ trunk/scipy/optimize/tests/test_nnls.py 2008-10-06 11:49:39 UTC (rev 4786) @@ -1,6 +1,6 @@ -""" Unit tests for nonlinear solvers -Author: Ondrej Certik -May 2007 +""" Unit tests for nonnegative least squares +Author: Uwe Schmitt +Sep 2008 """ from numpy.testing import * @@ -11,10 +11,6 @@ class TestNNLS(TestCase): - """ Test case for a simple constrained entropy maximization problem - (the machine translation example of Berger et al in - Computational Linguistics, vol 22, num 1, pp 39--72, 1996.) - """ def test_nnls(self): a=arange(25.0).reshape(-1,5) From scipy-svn at scipy.org Thu Oct 9 01:27:01 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Thu, 9 Oct 2008 00:27:01 -0500 (CDT) Subject: [Scipy-svn] r4787 - in trunk/scipy/stats: . tests Message-ID: <20081009052701.D9BF239C260@scipy.org> Author: peridot Date: 2008-10-09 00:26:59 -0500 (Thu, 09 Oct 2008) New Revision: 4787 Modified: trunk/scipy/stats/distributions.py trunk/scipy/stats/tests/test_distributions.py Log: Fixed and installed tests for scipy.stats.vonmises. Closes tickets #747, #748, and #749. Modified: trunk/scipy/stats/distributions.py =================================================================== --- trunk/scipy/stats/distributions.py 2008-10-06 11:49:39 UTC (rev 4786) +++ trunk/scipy/stats/distributions.py 2008-10-09 05:26:59 UTC (rev 4787) @@ -3083,10 +3083,7 @@ def _rvs(self, b): return mtrand.vonmises(0.0, b, size=self._size) def _pdf(self, x, b): - x = arr(angle(exp(1j*x))) - Px = where(b < 100, exp(b*cos(x)) / (2*pi*special.i0(b)), - norm.pdf(x, 0.0, sqrt(1.0/b))) - return Px + return exp(b*cos(x)) / (2*pi*special.i0(b)) def _cdf(self, x, b): x = arr(angle(exp(1j*x))) eps2 = sqrt(eps) @@ -3102,7 +3099,7 @@ vals = ones(len(c_xsimple),float) putmask(vals, c_bad, nan) putmask(vals, c_xsimple, x / 2.0/pi) - st = sqrt(b-0.5) + st = 1./sqrt(b-0.5) st = where(isnan(st),0.0,st) putmask(vals, c_xnormal, norm.cdf(x, scale=st)) @@ -3120,7 +3117,8 @@ if (j == 500): print "Warning: did not converge..." put(vals, indxiter, val) - return vals + 0.5 + vals[c_xiter]+=0.5 + return numpy.clip(vals,0,1) def _stats(self, b): return 0, None, 0, None vonmises = vonmises_gen(name='vonmises', longname="A Von Mises", Modified: trunk/scipy/stats/tests/test_distributions.py =================================================================== --- trunk/scipy/stats/tests/test_distributions.py 2008-10-06 11:49:39 UTC (rev 4786) +++ trunk/scipy/stats/tests/test_distributions.py 2008-10-09 05:26:59 UTC (rev 4787) @@ -33,7 +33,8 @@ 'halflogistic','fatiguelife','foldnorm','ncx2','t','nct', 'weibull_min','weibull_max','dweibull','maxwell','rayleigh', 'genlogistic', 'logistic','gumbel_l','gumbel_r','gompertz', - 'hypsecant', 'laplace', 'reciprocal','triang','tukeylambda'] + 'hypsecant', 'laplace', 'reciprocal','triang','tukeylambda', + 'vonmises'] # check function for test generator def check_distribution(dist, args, alpha): @@ -63,6 +64,9 @@ vals = rand(nargs) vals[1] = vals[0] + 1.0 args = tuple(vals) + elif dist == 'vonmises': + yield check_distribution, dist, (100,), alpha + args = tuple(1.0+rand(nargs)) else: args = tuple(1.0+rand(nargs)) yield check_distribution, dist, args, alpha From scipy-svn at scipy.org Thu Oct 9 03:29:57 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Thu, 9 Oct 2008 02:29:57 -0500 (CDT) Subject: [Scipy-svn] r4788 - trunk/scipy/stats Message-ID: <20081009072957.D6BBC39C260@scipy.org> Author: peridot Date: 2008-10-09 02:29:54 -0500 (Thu, 09 Oct 2008) New Revision: 4788 Added: trunk/scipy/stats/vonmises.py trunk/scipy/stats/vonmises_cython.c trunk/scipy/stats/vonmises_cython.pyx Modified: trunk/scipy/stats/distributions.py trunk/scipy/stats/setup.py Log: Replaced implementation of von Mises CDF with cython implementation that should have better numerical performance. Note that for large k it uses array operations rather than cython code since we need scipy.stats.norm.cdf and scipy.special.i0. Note also that the C code is *not* automatically rebuilt when the cython code changes. Modified: trunk/scipy/stats/distributions.py =================================================================== --- trunk/scipy/stats/distributions.py 2008-10-09 05:26:59 UTC (rev 4787) +++ trunk/scipy/stats/distributions.py 2008-10-09 07:29:54 UTC (rev 4788) @@ -21,6 +21,7 @@ from numpy import flatnonzero as nonzero from scipy.special import gammaln as gamln from copy import copy +import vonmises_cython __all__ = [ 'rv_continuous', @@ -3079,46 +3080,14 @@ eps = numpy.finfo(float).eps + class vonmises_gen(rv_continuous): def _rvs(self, b): return mtrand.vonmises(0.0, b, size=self._size) def _pdf(self, x, b): return exp(b*cos(x)) / (2*pi*special.i0(b)) def _cdf(self, x, b): - x = arr(angle(exp(1j*x))) - eps2 = sqrt(eps) - - c_xsimple = atleast_1d((b==0)&(x==x)) - c_xiter = atleast_1d((b<100)&(b > 0)&(x==x)) - c_xnormal = atleast_1d((b>=100)&(x==x)) - c_bad = atleast_1d((b<=0) | (x != x)) - - indxiter = nonzero(c_xiter) - xiter = take(x, indxiter, 0) - - vals = ones(len(c_xsimple),float) - putmask(vals, c_bad, nan) - putmask(vals, c_xsimple, x / 2.0/pi) - st = 1./sqrt(b-0.5) - st = where(isnan(st),0.0,st) - putmask(vals, c_xnormal, norm.cdf(x, scale=st)) - - biter = take(atleast_1d(b)*(x==x), indxiter, 0) - if len(xiter) > 0: - fac = special.i0(biter) - x2 = xiter - val = x2 / 2.0/ pi - for j in range(1,501): - trm1 = special.iv(j,biter)/j/fac - trm2 = sin(j*x2)/pi - val += trm1*trm2 - if all(trm1 < eps2): - break - if (j == 500): - print "Warning: did not converge..." - put(vals, indxiter, val) - vals[c_xiter]+=0.5 - return numpy.clip(vals,0,1) + return vonmises_cython.von_mises_cdf(b,x) def _stats(self, b): return 0, None, 0, None vonmises = vonmises_gen(name='vonmises', longname="A Von Mises", Modified: trunk/scipy/stats/setup.py =================================================================== --- trunk/scipy/stats/setup.py 2008-10-09 05:26:59 UTC (rev 4787) +++ trunk/scipy/stats/setup.py 2008-10-09 07:29:54 UTC (rev 4788) @@ -2,6 +2,7 @@ from os.path import join + def configuration(parent_package='',top_path=None): from numpy.distutils.misc_util import Configuration config = Configuration('stats', parent_package, top_path) @@ -18,6 +19,11 @@ libraries=['statlib'], ) + # add vonmises_cython module + config.add_extension('vonmises_cython', + sources=['vonmises_cython.c'], # FIXME: use cython source + ) + # add futil module config.add_extension('futil', sources=['futil.f'], Added: trunk/scipy/stats/vonmises.py =================================================================== --- trunk/scipy/stats/vonmises.py 2008-10-09 05:26:59 UTC (rev 4787) +++ trunk/scipy/stats/vonmises.py 2008-10-09 07:29:54 UTC (rev 4788) @@ -0,0 +1,45 @@ +import numpy as np +import scipy.stats +from scipy.special import i0 +import numpy.testing + +def von_mises_cdf_series(k,x,p): + x = float(x) + s = np.sin(x) + c = np.cos(x) + sn = np.sin(p*x) + cn = np.cos(p*x) + R = 0 + V = 0 + for n in range(p-1,0,-1): + sn, cn = sn*c - cn*s, cn*c + sn*s + R = 1./(2*n/k + R) + V = R*(sn/n+V) + + return 0.5+x/(2*np.pi) + V/np.pi + +def von_mises_cdf_normalapprox(k,x,C1): + b = np.sqrt(2/np.pi)*np.exp(k)/i0(k) + z = b*np.sin(x/2.) + C = 24*k + chi = z - z**3/((C-2*z**2-16)/3.-(z**4+7/4.*z**2+167./2)/(C+C1-z**2+3))**2 + return scipy.stats.norm.cdf(z) + +def von_mises_cdf(k,x): + ix = 2*np.pi*np.round(x/(2*np.pi)) + x = x-ix + k = float(k) + + # These values should give 12 decimal digits + CK=50 + a = [28., 0.5, 100., 5.0] + C1 = 50.1 + + if kob_refcnt) + #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) + #define Py_SIZE(ob) (((PyVarObject*)(ob))->ob_size) + #define PyVarObject_HEAD_INIT(type, size) \ + PyObject_HEAD_INIT(type) size, + #define PyType_Modified(t) + + typedef struct { + void *buf; + Py_ssize_t len; + int readonly; + const char *format; + int ndim; + Py_ssize_t *shape; + Py_ssize_t *strides; + Py_ssize_t *suboffsets; + Py_ssize_t itemsize; + void *internal; + } Py_buffer; + + #define PyBUF_SIMPLE 0 + #define PyBUF_WRITABLE 0x0001 + #define PyBUF_LOCK 0x0002 + #define PyBUF_FORMAT 0x0004 + #define PyBUF_ND 0x0008 + #define PyBUF_STRIDES (0x0010 | PyBUF_ND) + #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES) + #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES) + #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES) + #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES) + +#endif +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" +#endif +#if PY_MAJOR_VERSION >= 3 + #define Py_TPFLAGS_CHECKTYPES 0 + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyString_Type PyBytes_Type + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define PyBytes_Type PyString_Type +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyMethod_New(func, self, klass) PyInstanceMethod_New(func) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif +#else + #define _USE_MATH_DEFINES +#endif +#ifdef __cplusplus +#define __PYX_EXTERN_C extern "C" +#else +#define __PYX_EXTERN_C extern +#endif +#include +#define __PYX_HAVE_API__scipy__stats__vonmises_cython +#include "math.h" +#include "numpy/arrayobject.h" + + +#ifdef __GNUC__ +#define INLINE __inline__ +#elif _WIN32 +#define INLINE __inline +#else +#define INLINE +#endif + +typedef struct {PyObject **p; char *s; long n; char is_unicode; char intern; char is_identifier;} __Pyx_StringTabEntry; /*proto*/ + + + +static int __pyx_skip_dispatch = 0; + + +/* Type Conversion Predeclarations */ + +#if PY_MAJOR_VERSION < 3 +#define __Pyx_PyBytes_FromString PyString_FromString +#define __Pyx_PyBytes_AsString PyString_AsString +#else +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_AsString PyBytes_AsString +#endif + +#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) +static INLINE int __Pyx_PyObject_IsTrue(PyObject* x); +static INLINE PY_LONG_LONG __pyx_PyInt_AsLongLong(PyObject* x); +static INLINE unsigned PY_LONG_LONG __pyx_PyInt_AsUnsignedLongLong(PyObject* x); +static INLINE Py_ssize_t __pyx_PyIndex_AsSsize_t(PyObject* b); + +#define __pyx_PyInt_AsLong(x) (PyInt_CheckExact(x) ? PyInt_AS_LONG(x) : PyInt_AsLong(x)) +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) + +static INLINE unsigned char __pyx_PyInt_unsigned_char(PyObject* x); +static INLINE unsigned short __pyx_PyInt_unsigned_short(PyObject* x); +static INLINE char __pyx_PyInt_char(PyObject* x); +static INLINE short __pyx_PyInt_short(PyObject* x); +static INLINE int __pyx_PyInt_int(PyObject* x); +static INLINE long __pyx_PyInt_long(PyObject* x); +static INLINE signed char __pyx_PyInt_signed_char(PyObject* x); +static INLINE signed short __pyx_PyInt_signed_short(PyObject* x); +static INLINE signed int __pyx_PyInt_signed_int(PyObject* x); +static INLINE signed long __pyx_PyInt_signed_long(PyObject* x); +static INLINE long double __pyx_PyInt_long_double(PyObject* x); +#ifdef __GNUC__ +/* Test for GCC > 2.95 */ +#if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) +#define likely(x) __builtin_expect(!!(x), 1) +#define unlikely(x) __builtin_expect(!!(x), 0) +#else /* __GNUC__ > 2 ... */ +#define likely(x) (x) +#define unlikely(x) (x) +#endif /* __GNUC__ > 2 ... */ +#else /* __GNUC__ */ +#define likely(x) (x) +#define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; +static const char **__pyx_f; +static INLINE void __Pyx_SafeReleaseBuffer(PyObject* obj, Py_buffer* info); +static INLINE void __Pyx_ZeroBuffer(Py_buffer* buf); /*proto*/ +static INLINE const char* __Pyx_ConsumeWhitespace(const char* ts); /*proto*/ +static INLINE const char* __Pyx_BufferTypestringCheckEndian(const char* ts); /*proto*/ +static void __Pyx_BufferNdimError(Py_buffer* buffer, int expected_ndim); /*proto*/ +static const char* __Pyx_BufferTypestringCheck_item_double(const char* ts); /*proto*/ + +static int __Pyx_GetBuffer_double(PyObject* obj, Py_buffer* buf, int flags, int nd); /*proto*/ + +static void __Pyx_RaiseBufferFallbackError(void); /*proto*/ +#define __Pyx_BufPtrStrided1d(buf, i0, s0) ((char*)buf + i0 * s0) + +static INLINE void __Pyx_RaiseArgtupleTooLong(Py_ssize_t num_expected, Py_ssize_t num_found); /*proto*/ +#if (PY_MAJOR_VERSION < 3) && !(Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_NEWBUFFER) +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); +static void __Pyx_ReleaseBuffer(PyObject *obj, Py_buffer *view); +#else +#define __Pyx_GetBuffer PyObject_GetBuffer +#define __Pyx_ReleaseBuffer PyObject_ReleaseBuffer +#endif + +Py_ssize_t __Pyx_zeros[] = {0}; +Py_ssize_t __Pyx_minusones[] = {-1}; + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/ + +static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/ + +static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/ +static int __Pyx_EndUnpack(PyObject *); /*proto*/ + +static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/ + +static INLINE PyObject *__Pyx_GetItemInt(PyObject *o, Py_ssize_t i, int is_unsigned) { + PyObject *r; + if (PyList_CheckExact(o) && 0 <= i && i < PyList_GET_SIZE(o)) { + r = PyList_GET_ITEM(o, i); + Py_INCREF(r); + } + else if (PyTuple_CheckExact(o) && 0 <= i && i < PyTuple_GET_SIZE(o)) { + r = PyTuple_GET_ITEM(o, i); + Py_INCREF(r); + } + else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_item && (likely(i >= 0) || !is_unsigned)) + r = PySequence_GetItem(o, i); + else { + PyObject *j = (likely(i >= 0) || !is_unsigned) ? PyInt_FromLong(i) : PyLong_FromUnsignedLongLong((sizeof(unsigned long long) > sizeof(Py_ssize_t) ? (1ULL << (sizeof(Py_ssize_t)*8)) : 0) + i); + if (!j) + return 0; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + } + return r; +} + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ + +static void __Pyx_WriteUnraisable(const char *name); /*proto*/ + +static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, long size); /*proto*/ + +static PyObject *__Pyx_ImportModule(char *name); /*proto*/ + +static void __Pyx_AddTraceback(const char *funcname); /*proto*/ + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ + +/* Type declarations */ + +typedef npy_int8 __pyx_t_5numpy_int8_t; + +typedef npy_int16 __pyx_t_5numpy_int16_t; + +typedef npy_int32 __pyx_t_5numpy_int32_t; + +typedef npy_int64 __pyx_t_5numpy_int64_t; + +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +typedef npy_float32 __pyx_t_5numpy_float32_t; + +typedef npy_float64 __pyx_t_5numpy_float64_t; + +typedef npy_long __pyx_t_5numpy_int_t; + +typedef npy_longlong __pyx_t_5numpy_long_t; + +typedef npy_ulong __pyx_t_5numpy_uint_t; + +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +typedef npy_double __pyx_t_5numpy_float_t; + +typedef npy_double __pyx_t_5numpy_double_t; + +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +/* Module declarations from numpy */ + +/* Module declarations from numpy */ + +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +/* Module declarations from cython */ + +/* Module declarations from scipy.stats.vonmises_cython */ + +static double __pyx_f_5scipy_5stats_15vonmises_cython_von_mises_cdf_series(double, double, unsigned int); /*proto*/ + + +/* Implementation of scipy.stats.vonmises_cython */ +static PyObject *__pyx_int_2; +static PyObject *__pyx_int_24; +static PyObject *__pyx_int_3; +static PyObject *__pyx_int_16; +static PyObject *__pyx_int_4; +static PyObject *__pyx_int_0; +static char __pyx_k_numpy[] = "numpy"; +static PyObject *__pyx_kp_numpy; +static char __pyx_k_np[] = "np"; +static PyObject *__pyx_kp_np; +static char __pyx_k_17[] = "scipy.stats"; +static PyObject *__pyx_kp_17; +static char __pyx_k_scipy[] = "scipy"; +static PyObject *__pyx_kp_scipy; +static char __pyx_k_18[] = "scipy.special"; +static PyObject *__pyx_kp_18; +static char __pyx_k_i0[] = "i0"; +static PyObject *__pyx_kp_i0; +static char __pyx_k_19[] = "numpy.testing"; +static PyObject *__pyx_kp_19; +static char __pyx_k_pi[] = "pi"; +static PyObject *__pyx_kp_pi; +static char __pyx_k_sqrt[] = "sqrt"; +static PyObject *__pyx_kp_sqrt; +static char __pyx_k_exp[] = "exp"; +static PyObject *__pyx_kp_exp; +static char __pyx_k_sin[] = "sin"; +static PyObject *__pyx_kp_sin; +static char __pyx_k_stats[] = "stats"; +static PyObject *__pyx_kp_stats; +static char __pyx_k_norm[] = "norm"; +static PyObject *__pyx_kp_norm; +static char __pyx_k_cdf[] = "cdf"; +static PyObject *__pyx_kp_cdf; +static char __pyx_k_asarray[] = "asarray"; +static PyObject *__pyx_kp_asarray; +static char __pyx_k_ndim[] = "ndim"; +static PyObject *__pyx_kp_ndim; +static char __pyx_k_20[] = "atleast_1d"; +static PyObject *__pyx_kp_20; +static char __pyx_k_round[] = "round"; +static PyObject *__pyx_kp_round; +static char __pyx_k_broadcast_arrays[] = "broadcast_arrays"; +static PyObject *__pyx_kp_broadcast_arrays; +static char __pyx_k_empty[] = "empty"; +static PyObject *__pyx_kp_empty; +static char __pyx_k_shape[] = "shape"; +static PyObject *__pyx_kp_shape; +static char __pyx_k_dtype[] = "dtype"; +static PyObject *__pyx_kp_dtype; +static char __pyx_k_float[] = "float"; +static PyObject *__pyx_kp_float; +static char __pyx_k_astype[] = "astype"; +static PyObject *__pyx_kp_astype; +static char __pyx_k_21[] = "von_mises_cdf_normalapprox"; +static PyObject *__pyx_kp_21; +static char __pyx_k___getbuffer__[] = "__getbuffer__"; +static PyObject *__pyx_kp___getbuffer__; +static char __pyx_k_RuntimeError[] = "RuntimeError"; +static PyObject *__pyx_kp_RuntimeError; +static char __pyx_k_ValueError[] = "ValueError"; +static PyObject *__pyx_kp_ValueError; +static PyObject *__pyx_kp_1; +static PyObject *__pyx_kp_16; +static PyObject *__pyx_builtin_RuntimeError; +static PyObject *__pyx_builtin_ValueError; +static char __pyx_k_1[] = "Py_intptr_t and Py_ssize_t differs in size, numpy.pxd does not support this"; +static char __pyx_k_2[] = "b"; +static char __pyx_k_3[] = "B"; +static char __pyx_k_4[] = "h"; +static char __pyx_k_5[] = "H"; +static char __pyx_k_6[] = "i"; +static char __pyx_k_7[] = "I"; +static char __pyx_k_8[] = "l"; +static char __pyx_k_9[] = "L"; +static char __pyx_k_10[] = "q"; +static char __pyx_k_11[] = "Q"; +static char __pyx_k_12[] = "f"; +static char __pyx_k_13[] = "d"; +static char __pyx_k_14[] = "g"; +static char __pyx_k_15[] = "O"; +static char __pyx_k_16[] = "only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)"; + +/* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":12 + * + * + * cdef double von_mises_cdf_series(double k,double x,unsigned int p): # <<<<<<<<<<<<<< + * cdef double s, c, sn, cn, R, V + * cdef unsigned int n + */ + +static double __pyx_f_5scipy_5stats_15vonmises_cython_von_mises_cdf_series(double __pyx_v_k, double __pyx_v_x, unsigned int __pyx_v_p) { + double __pyx_v_s; + double __pyx_v_c; + double __pyx_v_sn; + double __pyx_v_cn; + double __pyx_v_R; + double __pyx_v_V; + unsigned int __pyx_v_n; + double __pyx_r; + double __pyx_1; + double __pyx_2; + PyObject *__pyx_3 = 0; + PyObject *__pyx_4 = 0; + PyObject *__pyx_5 = 0; + PyObject *__pyx_6 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":15 + * cdef double s, c, sn, cn, R, V + * cdef unsigned int n + * s = sin(x) # <<<<<<<<<<<<<< + * c = cos(x) + * sn = sin(p*x) + */ + __pyx_v_s = sin(__pyx_v_x); + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":16 + * cdef unsigned int n + * s = sin(x) + * c = cos(x) # <<<<<<<<<<<<<< + * sn = sin(p*x) + * cn = cos(p*x) + */ + __pyx_v_c = cos(__pyx_v_x); + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":17 + * s = sin(x) + * c = cos(x) + * sn = sin(p*x) # <<<<<<<<<<<<<< + * cn = cos(p*x) + * R = 0 + */ + __pyx_v_sn = sin((__pyx_v_p * __pyx_v_x)); + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":18 + * c = cos(x) + * sn = sin(p*x) + * cn = cos(p*x) # <<<<<<<<<<<<<< + * R = 0 + * V = 0 + */ + __pyx_v_cn = cos((__pyx_v_p * __pyx_v_x)); + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":19 + * sn = sin(p*x) + * cn = cos(p*x) + * R = 0 # <<<<<<<<<<<<<< + * V = 0 + * for n in range(p-1,0,-1): + */ + __pyx_v_R = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":20 + * cn = cos(p*x) + * R = 0 + * V = 0 # <<<<<<<<<<<<<< + * for n in range(p-1,0,-1): + * sn, cn = sn*c - cn*s, cn*c + sn*s + */ + __pyx_v_V = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":21 + * R = 0 + * V = 0 + * for n in range(p-1,0,-1): # <<<<<<<<<<<<<< + * sn, cn = sn*c - cn*s, cn*c + sn*s + * R = 1./(2*n/k + R) + */ + for (__pyx_v_n = (__pyx_v_p - 1); __pyx_v_n > 0; __pyx_v_n-=1) { + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":22 + * V = 0 + * for n in range(p-1,0,-1): + * sn, cn = sn*c - cn*s, cn*c + sn*s # <<<<<<<<<<<<<< + * R = 1./(2*n/k + R) + * V = R*(sn/n+V) + */ + __pyx_1 = ((__pyx_v_sn * __pyx_v_c) - (__pyx_v_cn * __pyx_v_s)); + __pyx_2 = ((__pyx_v_cn * __pyx_v_c) + (__pyx_v_sn * __pyx_v_s)); + __pyx_v_sn = __pyx_1; + __pyx_v_cn = __pyx_2; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":23 + * for n in range(p-1,0,-1): + * sn, cn = sn*c - cn*s, cn*c + sn*s + * R = 1./(2*n/k + R) # <<<<<<<<<<<<<< + * V = R*(sn/n+V) + * + */ + __pyx_v_R = (1. / (((2 * __pyx_v_n) / __pyx_v_k) + __pyx_v_R)); + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":24 + * sn, cn = sn*c - cn*s, cn*c + sn*s + * R = 1./(2*n/k + R) + * V = R*(sn/n+V) # <<<<<<<<<<<<<< + * + * return 0.5+x/(2*np.pi) + V/np.pi + */ + __pyx_v_V = (__pyx_v_R * ((__pyx_v_sn / __pyx_v_n) + __pyx_v_V)); + } + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":26 + * V = R*(sn/n+V) + * + * return 0.5+x/(2*np.pi) + V/np.pi # <<<<<<<<<<<<<< + * + * def von_mises_cdf_normalapprox(k,x,C1): + */ + __pyx_3 = PyFloat_FromDouble(0.5); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_4 = PyFloat_FromDouble(__pyx_v_x); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_6 = PyObject_GetAttr(__pyx_5, __pyx_kp_pi); if (unlikely(!__pyx_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_5 = PyNumber_Multiply(__pyx_int_2, __pyx_6); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_6); __pyx_6 = 0; + __pyx_6 = __Pyx_PyNumber_Divide(__pyx_4, __pyx_5); if (unlikely(!__pyx_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_4 = PyNumber_Add(__pyx_3, __pyx_6); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + Py_DECREF(__pyx_6); __pyx_6 = 0; + __pyx_5 = PyFloat_FromDouble(__pyx_v_V); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_6 = PyObject_GetAttr(__pyx_3, __pyx_kp_pi); if (unlikely(!__pyx_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_3 = __Pyx_PyNumber_Divide(__pyx_5, __pyx_6); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + Py_DECREF(__pyx_6); __pyx_6 = 0; + __pyx_5 = PyNumber_Add(__pyx_4, __pyx_3); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_1 = __pyx_PyFloat_AsDouble(__pyx_5); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 26; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_r = __pyx_1; + goto __pyx_L0; + + __pyx_r = 0; + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_3); + Py_XDECREF(__pyx_4); + Py_XDECREF(__pyx_5); + Py_XDECREF(__pyx_6); + __Pyx_WriteUnraisable("scipy.stats.vonmises_cython.von_mises_cdf_series"); + __pyx_r = 0; + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":28 + * return 0.5+x/(2*np.pi) + V/np.pi + * + * def von_mises_cdf_normalapprox(k,x,C1): # <<<<<<<<<<<<<< + * b = np.sqrt(2/np.pi)*np.exp(k)/i0(k) + * z = b*np.sin(x/2.) + */ + +static PyObject *__pyx_pf_5scipy_5stats_15vonmises_cython_von_mises_cdf_normalapprox(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static PyObject *__pyx_pf_5scipy_5stats_15vonmises_cython_von_mises_cdf_normalapprox(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_k = 0; + PyObject *__pyx_v_x = 0; + PyObject *__pyx_v_C1 = 0; + PyObject *__pyx_v_b; + PyObject *__pyx_v_z; + PyObject *__pyx_v_C; + PyObject *__pyx_v_chi; + PyObject *__pyx_r; + PyObject *__pyx_1 = 0; + PyObject *__pyx_2 = 0; + PyObject *__pyx_3 = 0; + PyObject *__pyx_4 = 0; + PyObject *__pyx_5 = 0; + PyObject *__pyx_6 = 0; + static char *__pyx_argnames[] = {"k","x","C1",0}; + __pyx_self = __pyx_self; + if (likely(!__pyx_kwds) && likely(PyTuple_GET_SIZE(__pyx_args) == 3)) { + __pyx_v_k = PyTuple_GET_ITEM(__pyx_args, 0); + __pyx_v_x = PyTuple_GET_ITEM(__pyx_args, 1); + __pyx_v_C1 = PyTuple_GET_ITEM(__pyx_args, 2); + } + else { + if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "OOO", __pyx_argnames, &__pyx_v_k, &__pyx_v_x, &__pyx_v_C1))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 28; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } + goto __pyx_L4; + __pyx_L3_error:; + __Pyx_AddTraceback("scipy.stats.vonmises_cython.von_mises_cdf_normalapprox"); + return NULL; + __pyx_L4:; + __pyx_v_b = Py_None; Py_INCREF(Py_None); + __pyx_v_z = Py_None; Py_INCREF(Py_None); + __pyx_v_C = Py_None; Py_INCREF(Py_None); + __pyx_v_chi = Py_None; Py_INCREF(Py_None); + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":29 + * + * def von_mises_cdf_normalapprox(k,x,C1): + * b = np.sqrt(2/np.pi)*np.exp(k)/i0(k) # <<<<<<<<<<<<<< + * z = b*np.sin(x/2.) + * C = 24*k + */ + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_sqrt); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_1, __pyx_kp_pi); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_1 = __Pyx_PyNumber_Divide(__pyx_int_2, __pyx_3); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_3, 0, __pyx_1); + __pyx_1 = 0; + __pyx_1 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_exp); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_k); + PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_k); + __pyx_4 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + __pyx_3 = PyNumber_Multiply(__pyx_1, __pyx_4); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(__pyx_4); __pyx_4 = 0; + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_i0); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_k); + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_v_k); + __pyx_4 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + __pyx_2 = __Pyx_PyNumber_Divide(__pyx_3, __pyx_4); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 29; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + Py_DECREF(__pyx_4); __pyx_4 = 0; + Py_DECREF(__pyx_v_b); + __pyx_v_b = __pyx_2; + __pyx_2 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":30 + * def von_mises_cdf_normalapprox(k,x,C1): + * b = np.sqrt(2/np.pi)*np.exp(k)/i0(k) + * z = b*np.sin(x/2.) # <<<<<<<<<<<<<< + * C = 24*k + * chi = z - z**3/((C-2*z**2-16)/3.-(z**4+7/4.*z**2+167./2)/(C+C1-z**2+3))**2 + */ + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 30; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_1, __pyx_kp_sin); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 30; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_4 = PyFloat_FromDouble(2.); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 30; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = __Pyx_PyNumber_Divide(__pyx_v_x, __pyx_4); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 30; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 30; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_2); + __pyx_2 = 0; + __pyx_4 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 30; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + __pyx_2 = PyNumber_Multiply(__pyx_v_b, __pyx_4); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 30; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + Py_DECREF(__pyx_v_z); + __pyx_v_z = __pyx_2; + __pyx_2 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":31 + * b = np.sqrt(2/np.pi)*np.exp(k)/i0(k) + * z = b*np.sin(x/2.) + * C = 24*k # <<<<<<<<<<<<<< + * chi = z - z**3/((C-2*z**2-16)/3.-(z**4+7/4.*z**2+167./2)/(C+C1-z**2+3))**2 + * return scipy.stats.norm.cdf(z) + */ + __pyx_3 = PyNumber_Multiply(__pyx_int_24, __pyx_v_k); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 31; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_v_C); + __pyx_v_C = __pyx_3; + __pyx_3 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":32 + * z = b*np.sin(x/2.) + * C = 24*k + * chi = z - z**3/((C-2*z**2-16)/3.-(z**4+7/4.*z**2+167./2)/(C+C1-z**2+3))**2 # <<<<<<<<<<<<<< + * return scipy.stats.norm.cdf(z) + * + */ + __pyx_1 = PyNumber_Power(__pyx_v_z, __pyx_int_3, Py_None); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_4 = PyNumber_Power(__pyx_v_z, __pyx_int_2, Py_None); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyNumber_Multiply(__pyx_int_2, __pyx_4); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + __pyx_3 = PyNumber_Subtract(__pyx_v_C, __pyx_2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_4 = PyNumber_Subtract(__pyx_3, __pyx_int_16); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_2 = PyFloat_FromDouble(3.); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = __Pyx_PyNumber_Divide(__pyx_4, __pyx_2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_4 = PyNumber_Power(__pyx_v_z, __pyx_int_4, Py_None); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyFloat_FromDouble((7 / 4.)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyNumber_Power(__pyx_v_z, __pyx_int_2, Py_None); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_6 = PyNumber_Multiply(__pyx_2, __pyx_5); if (unlikely(!__pyx_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_2 = PyNumber_Add(__pyx_4, __pyx_6); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + Py_DECREF(__pyx_6); __pyx_6 = 0; + __pyx_5 = PyFloat_FromDouble((167. / 2)); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_4 = PyNumber_Add(__pyx_2, __pyx_5); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_6 = PyNumber_Add(__pyx_v_C, __pyx_v_C1); if (unlikely(!__pyx_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyNumber_Power(__pyx_v_z, __pyx_int_2, Py_None); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyNumber_Subtract(__pyx_6, __pyx_2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_6); __pyx_6 = 0; + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_6 = PyNumber_Add(__pyx_5, __pyx_int_3); if (unlikely(!__pyx_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_2 = __Pyx_PyNumber_Divide(__pyx_4, __pyx_6); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + Py_DECREF(__pyx_6); __pyx_6 = 0; + __pyx_5 = PyNumber_Subtract(__pyx_3, __pyx_2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_4 = PyNumber_Power(__pyx_5, __pyx_int_2, Py_None); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_6 = __Pyx_PyNumber_Divide(__pyx_1, __pyx_4); if (unlikely(!__pyx_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(__pyx_4); __pyx_4 = 0; + __pyx_3 = PyNumber_Subtract(__pyx_v_z, __pyx_6); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 32; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_6); __pyx_6 = 0; + Py_DECREF(__pyx_v_chi); + __pyx_v_chi = __pyx_3; + __pyx_3 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":33 + * C = 24*k + * chi = z - z**3/((C-2*z**2-16)/3.-(z**4+7/4.*z**2+167./2)/(C+C1-z**2+3))**2 + * return scipy.stats.norm.cdf(z) # <<<<<<<<<<<<<< + * + * cimport cython + */ + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_scipy); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 33; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyObject_GetAttr(__pyx_2, __pyx_kp_stats); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 33; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_1 = PyObject_GetAttr(__pyx_5, __pyx_kp_norm); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 33; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_4 = PyObject_GetAttr(__pyx_1, __pyx_kp_cdf); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 33; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_6 = PyTuple_New(1); if (unlikely(!__pyx_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 33; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_z); + PyTuple_SET_ITEM(__pyx_6, 0, __pyx_v_z); + __pyx_3 = PyObject_Call(__pyx_4, ((PyObject *)__pyx_6), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 33; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + Py_DECREF(((PyObject *)__pyx_6)); __pyx_6 = 0; + __pyx_r = __pyx_3; + __pyx_3 = 0; + goto __pyx_L0; + + __pyx_r = Py_None; Py_INCREF(Py_None); + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_1); + Py_XDECREF(__pyx_2); + Py_XDECREF(__pyx_3); + Py_XDECREF(__pyx_4); + Py_XDECREF(__pyx_5); + Py_XDECREF(__pyx_6); + __Pyx_AddTraceback("scipy.stats.vonmises_cython.von_mises_cdf_normalapprox"); + __pyx_r = NULL; + __pyx_L0:; + Py_DECREF(__pyx_v_b); + Py_DECREF(__pyx_v_z); + Py_DECREF(__pyx_v_C); + Py_DECREF(__pyx_v_chi); + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":37 + * cimport cython + * @cython.boundscheck(False) + * def von_mises_cdf(k,x): # <<<<<<<<<<<<<< + * cdef np.ndarray[double, ndim=1] temp, temp_xs, temp_ks + * cdef unsigned int i, p + */ + +static PyObject *__pyx_pf_5scipy_5stats_15vonmises_cython_von_mises_cdf(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static PyObject *__pyx_pf_5scipy_5stats_15vonmises_cython_von_mises_cdf(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_k = 0; + PyObject *__pyx_v_x = 0; + PyArrayObject *__pyx_v_temp; + PyArrayObject *__pyx_v_temp_xs; + PyArrayObject *__pyx_v_temp_ks; + unsigned int __pyx_v_i; + unsigned int __pyx_v_p; + double __pyx_v_a1; + double __pyx_v_a2; + double __pyx_v_a3; + double __pyx_v_a4; + double __pyx_v_C1; + double __pyx_v_CK; + PyObject *__pyx_v_zerodim; + PyObject *__pyx_v_ix; + PyObject *__pyx_v_bx; + PyObject *__pyx_v_bk; + PyObject *__pyx_v_result; + PyObject *__pyx_v_c_small_k; + Py_buffer __pyx_bstruct_temp; + Py_ssize_t __pyx_bstride_0_temp = 0; + Py_ssize_t __pyx_bshape_0_temp = 0; + Py_buffer __pyx_bstruct_temp_xs; + Py_ssize_t __pyx_bstride_0_temp_xs = 0; + Py_ssize_t __pyx_bshape_0_temp_xs = 0; + Py_buffer __pyx_bstruct_temp_ks; + Py_ssize_t __pyx_bstride_0_temp_ks = 0; + Py_ssize_t __pyx_bshape_0_temp_ks = 0; + PyObject *__pyx_r; + PyObject *__pyx_1 = 0; + PyObject *__pyx_2 = 0; + PyObject *__pyx_3 = 0; + int __pyx_4; + double __pyx_5; + double __pyx_6; + double __pyx_7; + double __pyx_8; + PyObject *__pyx_9 = 0; + PyObject *__pyx_10 = 0; + Py_ssize_t __pyx_11 = 0; + int __pyx_12; + PyArrayObject *__pyx_t_1 = NULL; + int __pyx_t_2; + PyObject *__pyx_t_3 = NULL; + PyObject *__pyx_t_4 = NULL; + PyObject *__pyx_t_5 = NULL; + unsigned int __pyx_t_6; + unsigned int __pyx_t_7; + unsigned int __pyx_t_8; + unsigned int __pyx_t_9; + unsigned int __pyx_t_10; + unsigned int __pyx_t_11; + unsigned int __pyx_t_12; + unsigned int __pyx_t_13; + unsigned int __pyx_t_14; + static char *__pyx_argnames[] = {"k","x",0}; + __pyx_self = __pyx_self; + if (likely(!__pyx_kwds) && likely(PyTuple_GET_SIZE(__pyx_args) == 2)) { + __pyx_v_k = PyTuple_GET_ITEM(__pyx_args, 0); + __pyx_v_x = PyTuple_GET_ITEM(__pyx_args, 1); + } + else { + if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "OO", __pyx_argnames, &__pyx_v_k, &__pyx_v_x))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 37; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } + goto __pyx_L4; + __pyx_L3_error:; + __Pyx_AddTraceback("scipy.stats.vonmises_cython.von_mises_cdf"); + return NULL; + __pyx_L4:; + Py_INCREF(__pyx_v_k); + Py_INCREF(__pyx_v_x); + __pyx_v_temp = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); + __pyx_bstruct_temp.buf = NULL; + __pyx_v_temp_xs = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); + __pyx_bstruct_temp_xs.buf = NULL; + __pyx_v_temp_ks = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); + __pyx_bstruct_temp_ks.buf = NULL; + __pyx_v_zerodim = Py_None; Py_INCREF(Py_None); + __pyx_v_ix = Py_None; Py_INCREF(Py_None); + __pyx_v_bx = Py_None; Py_INCREF(Py_None); + __pyx_v_bk = Py_None; Py_INCREF(Py_None); + __pyx_v_result = Py_None; Py_INCREF(Py_None); + __pyx_v_c_small_k = Py_None; Py_INCREF(Py_None); + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":42 + * cdef double a1, a2, a3, a4, C1, CK + * #k,x = np.broadcast_arrays(np.asarray(k),np.asarray(x)) + * k = np.asarray(k) # <<<<<<<<<<<<<< + * x = np.asarray(x) + * zerodim = k.ndim==0 and x.ndim==0 + */ + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 42; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_asarray); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 42; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 42; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_k); + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_v_k); + __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 42; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + Py_DECREF(__pyx_v_k); + __pyx_v_k = __pyx_3; + __pyx_3 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":43 + * #k,x = np.broadcast_arrays(np.asarray(k),np.asarray(x)) + * k = np.asarray(k) + * x = np.asarray(x) # <<<<<<<<<<<<<< + * zerodim = k.ndim==0 and x.ndim==0 + * + */ + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_asarray); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_x); + PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_x); + __pyx_2 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + Py_DECREF(__pyx_v_x); + __pyx_v_x = __pyx_2; + __pyx_2 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":44 + * k = np.asarray(k) + * x = np.asarray(x) + * zerodim = k.ndim==0 and x.ndim==0 # <<<<<<<<<<<<<< + * + * k = np.atleast_1d(k) + */ + __pyx_3 = PyObject_GetAttr(__pyx_v_k, __pyx_kp_ndim); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 44; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_RichCompare(__pyx_3, __pyx_int_0, Py_EQ); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 44; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_4 = __Pyx_PyObject_IsTrue(__pyx_1); if (unlikely(__pyx_4 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 44; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (__pyx_4) { + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_2 = PyObject_GetAttr(__pyx_v_x, __pyx_kp_ndim); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 44; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_RichCompare(__pyx_2, __pyx_int_0, Py_EQ); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 44; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + } + Py_DECREF(__pyx_v_zerodim); + __pyx_v_zerodim = __pyx_1; + __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":46 + * zerodim = k.ndim==0 and x.ndim==0 + * + * k = np.atleast_1d(k) # <<<<<<<<<<<<<< + * x = np.atleast_1d(x) + * ix = np.round(x/(2*np.pi)) + */ + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_20); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_k); + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_v_k); + __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + Py_DECREF(__pyx_v_k); + __pyx_v_k = __pyx_3; + __pyx_3 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":47 + * + * k = np.atleast_1d(k) + * x = np.atleast_1d(x) # <<<<<<<<<<<<<< + * ix = np.round(x/(2*np.pi)) + * x = x-ix + */ + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 47; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_20); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 47; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 47; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_x); + PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_x); + __pyx_2 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 47; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + Py_DECREF(__pyx_v_x); + __pyx_v_x = __pyx_2; + __pyx_2 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":48 + * k = np.atleast_1d(k) + * x = np.atleast_1d(x) + * ix = np.round(x/(2*np.pi)) # <<<<<<<<<<<<<< + * x = x-ix + * + */ + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_1, __pyx_kp_round); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_pi); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_2 = PyNumber_Multiply(__pyx_int_2, __pyx_1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_1 = __Pyx_PyNumber_Divide(__pyx_v_x, __pyx_2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_2, 0, __pyx_1); + __pyx_1 = 0; + __pyx_1 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + Py_DECREF(__pyx_v_ix); + __pyx_v_ix = __pyx_1; + __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":49 + * x = np.atleast_1d(x) + * ix = np.round(x/(2*np.pi)) + * x = x-ix # <<<<<<<<<<<<<< + * + * # These values should give 12 decimal digits + */ + __pyx_3 = PyNumber_Subtract(__pyx_v_x, __pyx_v_ix); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_v_x); + __pyx_v_x = __pyx_3; + __pyx_3 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":52 + * + * # These values should give 12 decimal digits + * CK=50 # <<<<<<<<<<<<<< + * a1, a2, a3, a4 = [28., 0.5, 100., 5.0] + * C1 = 50.1 + */ + __pyx_v_CK = 50; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":53 + * # These values should give 12 decimal digits + * CK=50 + * a1, a2, a3, a4 = [28., 0.5, 100., 5.0] # <<<<<<<<<<<<<< + * C1 = 50.1 + * + */ + __pyx_5 = 28.; + __pyx_6 = 0.5; + __pyx_7 = 100.; + __pyx_8 = 5.0; + __pyx_v_a1 = __pyx_5; + __pyx_v_a2 = __pyx_6; + __pyx_v_a3 = __pyx_7; + __pyx_v_a4 = __pyx_8; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":54 + * CK=50 + * a1, a2, a3, a4 = [28., 0.5, 100., 5.0] + * C1 = 50.1 # <<<<<<<<<<<<<< + * + * bx, bk = np.broadcast_arrays(x,k) + */ + __pyx_v_C1 = 50.1; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":56 + * C1 = 50.1 + * + * bx, bk = np.broadcast_arrays(x,k) # <<<<<<<<<<<<<< + * result = np.empty(bx.shape,dtype=np.float) + * + */ + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 56; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_broadcast_arrays); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 56; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_3 = PyTuple_New(2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 56; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_x); + PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_x); + Py_INCREF(__pyx_v_k); + PyTuple_SET_ITEM(__pyx_3, 1, __pyx_v_k); + __pyx_2 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 56; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + if (PyTuple_CheckExact(__pyx_2) && PyTuple_GET_SIZE(__pyx_2) == 2) { + PyObject* tuple = __pyx_2; + __pyx_3 = PyTuple_GET_ITEM(tuple, 0); + Py_INCREF(__pyx_3); + Py_DECREF(__pyx_v_bx); + __pyx_v_bx = __pyx_3; + __pyx_3 = 0; + __pyx_3 = PyTuple_GET_ITEM(tuple, 1); + Py_INCREF(__pyx_3); + Py_DECREF(__pyx_v_bk); + __pyx_v_bk = __pyx_3; + __pyx_3 = 0; + Py_DECREF(__pyx_2); __pyx_2 = 0; + } + else { + __pyx_1 = PyObject_GetIter(__pyx_2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 56; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_3 = __Pyx_UnpackItem(__pyx_1, 0); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 56; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_v_bx); + __pyx_v_bx = __pyx_3; + __pyx_3 = 0; + __pyx_3 = __Pyx_UnpackItem(__pyx_1, 1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 56; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_v_bk); + __pyx_v_bk = __pyx_3; + __pyx_3 = 0; + if (__Pyx_EndUnpack(__pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 56; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + } + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":57 + * + * bx, bk = np.broadcast_arrays(x,k) + * result = np.empty(bx.shape,dtype=np.float) # <<<<<<<<<<<<<< + * + * c_small_k = bk(1+a1+a2*temp_ks[i]-a3/(temp_ks[i]+a4)) + */ + __pyx_10 = PyObject_GetItem(__pyx_v_bk, __pyx_v_c_small_k); if (!__pyx_10) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_10, __pyx_kp_astype); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_10); __pyx_10 = 0; + __pyx_9 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_9, __pyx_kp_float); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_9); __pyx_9 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_3); + __pyx_3 = 0; + __pyx_10 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + if (!(__Pyx_TypeTest(__pyx_10, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = ((PyArrayObject *)__pyx_10); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_temp_ks, &__pyx_bstruct_temp_ks); + __pyx_t_2 = __Pyx_GetBuffer_double((PyObject*)__pyx_t_1, &__pyx_bstruct_temp_ks, PyBUF_FORMAT| PyBUF_STRIDES, 1); + if (unlikely(__pyx_t_2 < 0)) + { + PyErr_Fetch(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5); + if (unlikely(__Pyx_GetBuffer_double((PyObject*)__pyx_v_temp_ks, &__pyx_bstruct_temp_ks, PyBUF_FORMAT| PyBUF_STRIDES, 1) == -1)) { + Py_XDECREF(__pyx_t_3); Py_XDECREF(__pyx_t_4); Py_XDECREF(__pyx_t_5); + __Pyx_RaiseBufferFallbackError(); + } else { + PyErr_Restore(__pyx_t_3, __pyx_t_4, __pyx_t_5); + } + } + __pyx_bstride_0_temp_ks = __pyx_bstruct_temp_ks.strides[0]; + __pyx_bshape_0_temp_ks = __pyx_bstruct_temp_ks.shape[0]; + if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = 0; + Py_DECREF(((PyObject *)__pyx_v_temp_ks)); + __pyx_v_temp_ks = ((PyArrayObject *)__pyx_10); + __pyx_10 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":63 + * temp_xs = bx[c_small_k].astype(np.float) + * temp_ks = bk[c_small_k].astype(np.float) + * for i in range(len(temp)): # <<<<<<<<<<<<<< + * p = (1+a1+a2*temp_ks[i]-a3/(temp_ks[i]+a4)) + * temp[i] = von_mises_cdf_series(temp_ks[i],temp_xs[i],p) + */ + __pyx_11 = PyObject_Length(((PyObject *)__pyx_v_temp)); if (unlikely(__pyx_11 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 63; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + for (__pyx_v_i = 0; __pyx_v_i < __pyx_11; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":64 + * temp_ks = bk[c_small_k].astype(np.float) + * for i in range(len(temp)): + * p = (1+a1+a2*temp_ks[i]-a3/(temp_ks[i]+a4)) # <<<<<<<<<<<<<< + * temp[i] = von_mises_cdf_series(temp_ks[i],temp_xs[i],p) + * if temp[i]<0: + */ + __pyx_t_6 = __pyx_v_i; + __pyx_5 = *((double *)((double *)__Pyx_BufPtrStrided1d(__pyx_bstruct_temp_ks.buf, __pyx_t_6, __pyx_bstride_0_temp_ks))); + __pyx_t_7 = __pyx_v_i; + __pyx_6 = *((double *)((double *)__Pyx_BufPtrStrided1d(__pyx_bstruct_temp_ks.buf, __pyx_t_7, __pyx_bstride_0_temp_ks))); + __pyx_v_p = ((int)(((1 + __pyx_v_a1) + (__pyx_v_a2 * __pyx_5)) - (__pyx_v_a3 / (__pyx_6 + __pyx_v_a4)))); + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":65 + * for i in range(len(temp)): + * p = (1+a1+a2*temp_ks[i]-a3/(temp_ks[i]+a4)) + * temp[i] = von_mises_cdf_series(temp_ks[i],temp_xs[i],p) # <<<<<<<<<<<<<< + * if temp[i]<0: + * temp[i]=0 + */ + __pyx_t_8 = __pyx_v_i; + __pyx_7 = *((double *)((double *)__Pyx_BufPtrStrided1d(__pyx_bstruct_temp_ks.buf, __pyx_t_8, __pyx_bstride_0_temp_ks))); + __pyx_t_9 = __pyx_v_i; + __pyx_8 = *((double *)((double *)__Pyx_BufPtrStrided1d(__pyx_bstruct_temp_xs.buf, __pyx_t_9, __pyx_bstride_0_temp_xs))); + __pyx_t_10 = __pyx_v_i; + *((double *)__Pyx_BufPtrStrided1d(__pyx_bstruct_temp.buf, __pyx_t_10, __pyx_bstride_0_temp)) = __pyx_f_5scipy_5stats_15vonmises_cython_von_mises_cdf_series(__pyx_7, __pyx_8, __pyx_v_p); + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":66 + * p = (1+a1+a2*temp_ks[i]-a3/(temp_ks[i]+a4)) + * temp[i] = von_mises_cdf_series(temp_ks[i],temp_xs[i],p) + * if temp[i]<0: # <<<<<<<<<<<<<< + * temp[i]=0 + * elif temp[i]>1: + */ + __pyx_t_11 = __pyx_v_i; + __pyx_5 = *((double *)((double *)__Pyx_BufPtrStrided1d(__pyx_bstruct_temp.buf, __pyx_t_11, __pyx_bstride_0_temp))); + __pyx_4 = (__pyx_5 < 0); + if (__pyx_4) { + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":67 + * temp[i] = von_mises_cdf_series(temp_ks[i],temp_xs[i],p) + * if temp[i]<0: + * temp[i]=0 # <<<<<<<<<<<<<< + * elif temp[i]>1: + * temp[i]=1 + */ + __pyx_t_12 = __pyx_v_i; + *((double *)__Pyx_BufPtrStrided1d(__pyx_bstruct_temp.buf, __pyx_t_12, __pyx_bstride_0_temp)) = 0; + goto __pyx_L7; + } + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":68 + * if temp[i]<0: + * temp[i]=0 + * elif temp[i]>1: # <<<<<<<<<<<<<< + * temp[i]=1 + * result[c_small_k] = temp + */ + __pyx_t_13 = __pyx_v_i; + __pyx_6 = *((double *)((double *)__Pyx_BufPtrStrided1d(__pyx_bstruct_temp.buf, __pyx_t_13, __pyx_bstride_0_temp))); + __pyx_4 = (__pyx_6 > 1); + if (__pyx_4) { + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":69 + * temp[i]=0 + * elif temp[i]>1: + * temp[i]=1 # <<<<<<<<<<<<<< + * result[c_small_k] = temp + * result[~c_small_k] = von_mises_cdf_normalapprox(bk[~c_small_k],bx[~c_small_k],C1) + */ + __pyx_t_14 = __pyx_v_i; + *((double *)__Pyx_BufPtrStrided1d(__pyx_bstruct_temp.buf, __pyx_t_14, __pyx_bstride_0_temp)) = 1; + goto __pyx_L7; + } + __pyx_L7:; + } + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":70 + * elif temp[i]>1: + * temp[i]=1 + * result[c_small_k] = temp # <<<<<<<<<<<<<< + * result[~c_small_k] = von_mises_cdf_normalapprox(bk[~c_small_k],bx[~c_small_k],C1) + * + */ + if (PyObject_SetItem(__pyx_v_result, __pyx_v_c_small_k, ((PyObject *)__pyx_v_temp)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 70; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":71 + * temp[i]=1 + * result[c_small_k] = temp + * result[~c_small_k] = von_mises_cdf_normalapprox(bk[~c_small_k],bx[~c_small_k],C1) # <<<<<<<<<<<<<< + * + * if not zerodim: + */ + __pyx_9 = __Pyx_GetName(__pyx_m, __pyx_kp_21); if (unlikely(!__pyx_9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyNumber_Invert(__pyx_v_c_small_k); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetItem(__pyx_v_bk, __pyx_3); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_1 = PyNumber_Invert(__pyx_v_c_small_k); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_10 = PyObject_GetItem(__pyx_v_bx, __pyx_1); if (!__pyx_10) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_3 = PyFloat_FromDouble(__pyx_v_C1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(3); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_2); + PyTuple_SET_ITEM(__pyx_1, 1, __pyx_10); + PyTuple_SET_ITEM(__pyx_1, 2, __pyx_3); + __pyx_2 = 0; + __pyx_10 = 0; + __pyx_3 = 0; + __pyx_2 = PyObject_Call(__pyx_9, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_9); __pyx_9 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + __pyx_10 = PyNumber_Invert(__pyx_v_c_small_k); if (unlikely(!__pyx_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetItem(__pyx_v_result, __pyx_10, __pyx_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_10); __pyx_10 = 0; + Py_DECREF(__pyx_2); __pyx_2 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":73 + * result[~c_small_k] = von_mises_cdf_normalapprox(bk[~c_small_k],bx[~c_small_k],C1) + * + * if not zerodim: # <<<<<<<<<<<<<< + * return result+(2*np.pi)*ix + * else: + */ + __pyx_4 = __Pyx_PyObject_IsTrue(__pyx_v_zerodim); if (unlikely(__pyx_4 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_12 = (!__pyx_4); + if (__pyx_12) { + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":74 + * + * if not zerodim: + * return result+(2*np.pi)*ix # <<<<<<<<<<<<<< + * else: + * return (result+(2*np.pi)*ix)[0] + */ + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_9 = PyObject_GetAttr(__pyx_3, __pyx_kp_pi); if (unlikely(!__pyx_9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_1 = PyNumber_Multiply(__pyx_int_2, __pyx_9); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_9); __pyx_9 = 0; + __pyx_2 = PyNumber_Multiply(__pyx_1, __pyx_v_ix); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_10 = PyNumber_Add(__pyx_v_result, __pyx_2); if (unlikely(!__pyx_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_r = __pyx_10; + __pyx_10 = 0; + goto __pyx_L0; + goto __pyx_L8; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":76 + * return result+(2*np.pi)*ix + * else: + * return (result+(2*np.pi)*ix)[0] # <<<<<<<<<<<<<< + */ + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 76; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_9 = PyObject_GetAttr(__pyx_3, __pyx_kp_pi); if (unlikely(!__pyx_9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 76; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_1 = PyNumber_Multiply(__pyx_int_2, __pyx_9); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 76; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_9); __pyx_9 = 0; + __pyx_2 = PyNumber_Multiply(__pyx_1, __pyx_v_ix); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 76; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_10 = PyNumber_Add(__pyx_v_result, __pyx_2); if (unlikely(!__pyx_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 76; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_3 = __Pyx_GetItemInt(__pyx_10, 0, 0); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 76; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_10); __pyx_10 = 0; + __pyx_r = __pyx_3; + __pyx_3 = 0; + goto __pyx_L0; + } + __pyx_L8:; + + __pyx_r = Py_None; Py_INCREF(Py_None); + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_1); + Py_XDECREF(__pyx_2); + Py_XDECREF(__pyx_3); + Py_XDECREF(__pyx_9); + Py_XDECREF(__pyx_10); + { PyObject *__pyx_type, *__pyx_value, *__pyx_tb; + PyErr_Fetch(&__pyx_type, &__pyx_value, &__pyx_tb); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_temp, &__pyx_bstruct_temp); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_temp_xs, &__pyx_bstruct_temp_xs); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_temp_ks, &__pyx_bstruct_temp_ks); + PyErr_Restore(__pyx_type, __pyx_value, __pyx_tb);} + __Pyx_AddTraceback("scipy.stats.vonmises_cython.von_mises_cdf"); + __pyx_r = NULL; + goto __pyx_L2; + __pyx_L0:; + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_temp, &__pyx_bstruct_temp); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_temp_xs, &__pyx_bstruct_temp_xs); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_temp_ks, &__pyx_bstruct_temp_ks); + __pyx_L2:; + Py_DECREF(__pyx_v_temp); + Py_DECREF(__pyx_v_temp_xs); + Py_DECREF(__pyx_v_temp_ks); + Py_DECREF(__pyx_v_zerodim); + Py_DECREF(__pyx_v_ix); + Py_DECREF(__pyx_v_bx); + Py_DECREF(__pyx_v_bk); + Py_DECREF(__pyx_v_result); + Py_DECREF(__pyx_v_c_small_k); + Py_DECREF(__pyx_v_k); + Py_DECREF(__pyx_v_x); + return __pyx_r; +} + +/* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":36 + * # experimental exception made for __getbuffer__ and __releasebuffer__ + * # -- the details of this may change. + * def __getbuffer__(ndarray self, Py_buffer* info, int flags): # <<<<<<<<<<<<<< + * # This implementation of getbuffer is geared towards Cython + * # requirements, and does not yet fullfill the PEP (specifically, + */ + +static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { + int __pyx_v_t; + char *__pyx_v_f; + int __pyx_r; + int __pyx_1; + PyObject *__pyx_2 = 0; + PyObject *__pyx_3 = 0; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":42 + * # so the flags are not even checked). + * + * if sizeof(npy_intp) != sizeof(Py_ssize_t): # <<<<<<<<<<<<<< + * raise RuntimeError("Py_intptr_t and Py_ssize_t differs in size, numpy.pxd does not support this") + * + */ + __pyx_1 = ((sizeof(npy_intp)) != (sizeof(Py_ssize_t))); + if (__pyx_1) { + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":43 + * + * if sizeof(npy_intp) != sizeof(Py_ssize_t): + * raise RuntimeError("Py_intptr_t and Py_ssize_t differs in size, numpy.pxd does not support this") # <<<<<<<<<<<<<< + * + * info.buf = PyArray_DATA(self) + */ + __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_kp_1); + PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_1); + __pyx_3 = PyObject_Call(__pyx_builtin_RuntimeError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + __Pyx_Raise(__pyx_3, 0, 0); + Py_DECREF(__pyx_3); __pyx_3 = 0; + {__pyx_filename = __pyx_f[1]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + goto __pyx_L5; + } + __pyx_L5:; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":45 + * raise RuntimeError("Py_intptr_t and Py_ssize_t differs in size, numpy.pxd does not support this") + * + * info.buf = PyArray_DATA(self) # <<<<<<<<<<<<<< + * info.ndim = PyArray_NDIM(self) + * info.strides = PyArray_STRIDES(self) + */ + __pyx_v_info->buf = PyArray_DATA(((PyArrayObject *)__pyx_v_self)); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":46 + * + * info.buf = PyArray_DATA(self) + * info.ndim = PyArray_NDIM(self) # <<<<<<<<<<<<<< + * info.strides = PyArray_STRIDES(self) + * info.shape = PyArray_DIMS(self) + */ + __pyx_v_info->ndim = PyArray_NDIM(((PyArrayObject *)__pyx_v_self)); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":47 + * info.buf = PyArray_DATA(self) + * info.ndim = PyArray_NDIM(self) + * info.strides = PyArray_STRIDES(self) # <<<<<<<<<<<<<< + * info.shape = PyArray_DIMS(self) + * info.suboffsets = NULL + */ + __pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(((PyArrayObject *)__pyx_v_self))); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":48 + * info.ndim = PyArray_NDIM(self) + * info.strides = PyArray_STRIDES(self) + * info.shape = PyArray_DIMS(self) # <<<<<<<<<<<<<< + * info.suboffsets = NULL + * info.itemsize = PyArray_ITEMSIZE(self) + */ + __pyx_v_info->shape = ((Py_ssize_t *)PyArray_DIMS(((PyArrayObject *)__pyx_v_self))); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":49 + * info.strides = PyArray_STRIDES(self) + * info.shape = PyArray_DIMS(self) + * info.suboffsets = NULL # <<<<<<<<<<<<<< + * info.itemsize = PyArray_ITEMSIZE(self) + * info.readonly = not PyArray_ISWRITEABLE(self) + */ + __pyx_v_info->suboffsets = NULL; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":50 + * info.shape = PyArray_DIMS(self) + * info.suboffsets = NULL + * info.itemsize = PyArray_ITEMSIZE(self) # <<<<<<<<<<<<<< + * info.readonly = not PyArray_ISWRITEABLE(self) + * + */ + __pyx_v_info->itemsize = PyArray_ITEMSIZE(((PyArrayObject *)__pyx_v_self)); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":51 + * info.suboffsets = NULL + * info.itemsize = PyArray_ITEMSIZE(self) + * info.readonly = not PyArray_ISWRITEABLE(self) # <<<<<<<<<<<<<< + * + * # Formats that are not tested and working in Cython are not + */ + __pyx_v_info->readonly = (!PyArray_ISWRITEABLE(((PyArrayObject *)__pyx_v_self))); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":55 + * # Formats that are not tested and working in Cython are not + * # made available from this pxd file yet. + * cdef int t = PyArray_TYPE(self) # <<<<<<<<<<<<<< + * cdef char* f = NULL + * if t == NPY_BYTE: f = "b" + */ + __pyx_v_t = PyArray_TYPE(((PyArrayObject *)__pyx_v_self)); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":56 + * # made available from this pxd file yet. + * cdef int t = PyArray_TYPE(self) + * cdef char* f = NULL # <<<<<<<<<<<<<< + * if t == NPY_BYTE: f = "b" + * elif t == NPY_UBYTE: f = "B" + */ + __pyx_v_f = NULL; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":57 + * cdef int t = PyArray_TYPE(self) + * cdef char* f = NULL + * if t == NPY_BYTE: f = "b" # <<<<<<<<<<<<<< + * elif t == NPY_UBYTE: f = "B" + * elif t == NPY_SHORT: f = "h" + */ + switch (__pyx_v_t) { + case NPY_BYTE: + __pyx_v_f = __pyx_k_2; + break; + case NPY_UBYTE: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":58 + * cdef char* f = NULL + * if t == NPY_BYTE: f = "b" + * elif t == NPY_UBYTE: f = "B" # <<<<<<<<<<<<<< + * elif t == NPY_SHORT: f = "h" + * elif t == NPY_USHORT: f = "H" + */ + __pyx_v_f = __pyx_k_3; + break; + case NPY_SHORT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":59 + * if t == NPY_BYTE: f = "b" + * elif t == NPY_UBYTE: f = "B" + * elif t == NPY_SHORT: f = "h" # <<<<<<<<<<<<<< + * elif t == NPY_USHORT: f = "H" + * elif t == NPY_INT: f = "i" + */ + __pyx_v_f = __pyx_k_4; + break; + case NPY_USHORT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":60 + * elif t == NPY_UBYTE: f = "B" + * elif t == NPY_SHORT: f = "h" + * elif t == NPY_USHORT: f = "H" # <<<<<<<<<<<<<< + * elif t == NPY_INT: f = "i" + * elif t == NPY_UINT: f = "I" + */ + __pyx_v_f = __pyx_k_5; + break; + case NPY_INT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":61 + * elif t == NPY_SHORT: f = "h" + * elif t == NPY_USHORT: f = "H" + * elif t == NPY_INT: f = "i" # <<<<<<<<<<<<<< + * elif t == NPY_UINT: f = "I" + * elif t == NPY_LONG: f = "l" + */ + __pyx_v_f = __pyx_k_6; + break; + case NPY_UINT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":62 + * elif t == NPY_USHORT: f = "H" + * elif t == NPY_INT: f = "i" + * elif t == NPY_UINT: f = "I" # <<<<<<<<<<<<<< + * elif t == NPY_LONG: f = "l" + * elif t == NPY_ULONG: f = "L" + */ + __pyx_v_f = __pyx_k_7; + break; + case NPY_LONG: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":63 + * elif t == NPY_INT: f = "i" + * elif t == NPY_UINT: f = "I" + * elif t == NPY_LONG: f = "l" # <<<<<<<<<<<<<< + * elif t == NPY_ULONG: f = "L" + * elif t == NPY_LONGLONG: f = "q" + */ + __pyx_v_f = __pyx_k_8; + break; + case NPY_ULONG: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":64 + * elif t == NPY_UINT: f = "I" + * elif t == NPY_LONG: f = "l" + * elif t == NPY_ULONG: f = "L" # <<<<<<<<<<<<<< + * elif t == NPY_LONGLONG: f = "q" + * elif t == NPY_ULONGLONG: f = "Q" + */ + __pyx_v_f = __pyx_k_9; + break; + case NPY_LONGLONG: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":65 + * elif t == NPY_LONG: f = "l" + * elif t == NPY_ULONG: f = "L" + * elif t == NPY_LONGLONG: f = "q" # <<<<<<<<<<<<<< + * elif t == NPY_ULONGLONG: f = "Q" + * elif t == NPY_FLOAT: f = "f" + */ + __pyx_v_f = __pyx_k_10; + break; + case NPY_ULONGLONG: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":66 + * elif t == NPY_ULONG: f = "L" + * elif t == NPY_LONGLONG: f = "q" + * elif t == NPY_ULONGLONG: f = "Q" # <<<<<<<<<<<<<< + * elif t == NPY_FLOAT: f = "f" + * elif t == NPY_DOUBLE: f = "d" + */ + __pyx_v_f = __pyx_k_11; + break; + case NPY_FLOAT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":67 + * elif t == NPY_LONGLONG: f = "q" + * elif t == NPY_ULONGLONG: f = "Q" + * elif t == NPY_FLOAT: f = "f" # <<<<<<<<<<<<<< + * elif t == NPY_DOUBLE: f = "d" + * elif t == NPY_LONGDOUBLE: f = "g" + */ + __pyx_v_f = __pyx_k_12; + break; + case NPY_DOUBLE: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":68 + * elif t == NPY_ULONGLONG: f = "Q" + * elif t == NPY_FLOAT: f = "f" + * elif t == NPY_DOUBLE: f = "d" # <<<<<<<<<<<<<< + * elif t == NPY_LONGDOUBLE: f = "g" + * elif t == NPY_OBJECT: f = "O" + */ + __pyx_v_f = __pyx_k_13; + break; + case NPY_LONGDOUBLE: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":69 + * elif t == NPY_FLOAT: f = "f" + * elif t == NPY_DOUBLE: f = "d" + * elif t == NPY_LONGDOUBLE: f = "g" # <<<<<<<<<<<<<< + * elif t == NPY_OBJECT: f = "O" + * + */ + __pyx_v_f = __pyx_k_14; + break; + case NPY_OBJECT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":70 + * elif t == NPY_DOUBLE: f = "d" + * elif t == NPY_LONGDOUBLE: f = "g" + * elif t == NPY_OBJECT: f = "O" # <<<<<<<<<<<<<< + * + * if f == NULL: + */ + __pyx_v_f = __pyx_k_15; + break; + } + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":72 + * elif t == NPY_OBJECT: f = "O" + * + * if f == NULL: # <<<<<<<<<<<<<< + * raise ValueError("only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)" % t) + * info.format = f + */ + __pyx_1 = (__pyx_v_f == NULL); + if (__pyx_1) { + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":73 + * + * if f == NULL: + * raise ValueError("only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)" % t) # <<<<<<<<<<<<<< + * info.format = f + * + */ + __pyx_2 = PyInt_FromLong(__pyx_v_t); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyNumber_Remainder(__pyx_kp_16, __pyx_2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_2, 0, __pyx_3); + __pyx_3 = 0; + __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + __Pyx_Raise(__pyx_3, 0, 0); + Py_DECREF(__pyx_3); __pyx_3 = 0; + {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + goto __pyx_L6; + } + __pyx_L6:; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":74 + * if f == NULL: + * raise ValueError("only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)" % t) + * info.format = f # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_info->format = __pyx_v_f; + + __pyx_r = 0; + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_2); + Py_XDECREF(__pyx_3); + __Pyx_AddTraceback("numpy.ndarray.__getbuffer__"); + __pyx_r = -1; + __pyx_L0:; + return __pyx_r; +} + +static struct PyMethodDef __pyx_methods[] = { + {"von_mises_cdf_normalapprox", (PyCFunction)__pyx_pf_5scipy_5stats_15vonmises_cython_von_mises_cdf_normalapprox, METH_VARARGS|METH_KEYWORDS, 0}, + {"von_mises_cdf", (PyCFunction)__pyx_pf_5scipy_5stats_15vonmises_cython_von_mises_cdf, METH_VARARGS|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; + +static void __pyx_init_filenames(void); /*proto*/ + +#if PY_MAJOR_VERSION >= 3 +static struct PyModuleDef __pyx_moduledef = { + PyModuleDef_HEAD_INIT, + "vonmises_cython", + 0, /* m_doc */ + -1, /* m_size */ + __pyx_methods /* m_methods */, + NULL, /* m_reload */ + NULL, /* m_traverse */ + NULL, /* m_clear */ + NULL /* m_free */ +}; +#endif + +static __Pyx_StringTabEntry __pyx_string_tab[] = { + {&__pyx_kp_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 1, 1, 1}, + {&__pyx_kp_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 1, 1}, + {&__pyx_kp_17, __pyx_k_17, sizeof(__pyx_k_17), 1, 1, 1}, + {&__pyx_kp_scipy, __pyx_k_scipy, sizeof(__pyx_k_scipy), 0, 1, 1}, + {&__pyx_kp_18, __pyx_k_18, sizeof(__pyx_k_18), 1, 1, 1}, + {&__pyx_kp_i0, __pyx_k_i0, sizeof(__pyx_k_i0), 1, 1, 1}, + {&__pyx_kp_19, __pyx_k_19, sizeof(__pyx_k_19), 1, 1, 1}, + {&__pyx_kp_pi, __pyx_k_pi, sizeof(__pyx_k_pi), 1, 1, 1}, + {&__pyx_kp_sqrt, __pyx_k_sqrt, sizeof(__pyx_k_sqrt), 1, 1, 1}, + {&__pyx_kp_exp, __pyx_k_exp, sizeof(__pyx_k_exp), 1, 1, 1}, + {&__pyx_kp_sin, __pyx_k_sin, sizeof(__pyx_k_sin), 1, 1, 1}, + {&__pyx_kp_stats, __pyx_k_stats, sizeof(__pyx_k_stats), 1, 1, 1}, + {&__pyx_kp_norm, __pyx_k_norm, sizeof(__pyx_k_norm), 1, 1, 1}, + {&__pyx_kp_cdf, __pyx_k_cdf, sizeof(__pyx_k_cdf), 1, 1, 1}, + {&__pyx_kp_asarray, __pyx_k_asarray, sizeof(__pyx_k_asarray), 1, 1, 1}, + {&__pyx_kp_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 1, 1, 1}, + {&__pyx_kp_20, __pyx_k_20, sizeof(__pyx_k_20), 1, 1, 1}, + {&__pyx_kp_round, __pyx_k_round, sizeof(__pyx_k_round), 1, 1, 1}, + {&__pyx_kp_broadcast_arrays, __pyx_k_broadcast_arrays, sizeof(__pyx_k_broadcast_arrays), 1, 1, 1}, + {&__pyx_kp_empty, __pyx_k_empty, sizeof(__pyx_k_empty), 1, 1, 1}, + {&__pyx_kp_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 1, 1, 1}, + {&__pyx_kp_dtype, __pyx_k_dtype, sizeof(__pyx_k_dtype), 1, 1, 1}, + {&__pyx_kp_float, __pyx_k_float, sizeof(__pyx_k_float), 1, 1, 1}, + {&__pyx_kp_astype, __pyx_k_astype, sizeof(__pyx_k_astype), 1, 1, 1}, + {&__pyx_kp_21, __pyx_k_21, sizeof(__pyx_k_21), 0, 1, 1}, + {&__pyx_kp___getbuffer__, __pyx_k___getbuffer__, sizeof(__pyx_k___getbuffer__), 0, 1, 1}, + {&__pyx_kp_RuntimeError, __pyx_k_RuntimeError, sizeof(__pyx_k_RuntimeError), 1, 1, 1}, + {&__pyx_kp_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 1, 1, 1}, + {&__pyx_kp_1, __pyx_k_1, sizeof(__pyx_k_1), 0, 0, 0}, + {&__pyx_kp_16, __pyx_k_16, sizeof(__pyx_k_16), 0, 0, 0}, + {0, 0, 0, 0, 0, 0} +}; +static int __Pyx_InitCachedBuiltins(void) { + __pyx_builtin_RuntimeError = __Pyx_GetName(__pyx_b, __pyx_kp_RuntimeError); if (!__pyx_builtin_RuntimeError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_builtin_ValueError = __Pyx_GetName(__pyx_b, __pyx_kp_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + return 0; + __pyx_L1_error:; + return -1; +} + +static int __Pyx_InitGlobals(void) { + __pyx_int_2 = PyInt_FromLong(2); if (unlikely(!__pyx_int_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + __pyx_int_24 = PyInt_FromLong(24); if (unlikely(!__pyx_int_24)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + __pyx_int_3 = PyInt_FromLong(3); if (unlikely(!__pyx_int_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + __pyx_int_16 = PyInt_FromLong(16); if (unlikely(!__pyx_int_16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + __pyx_int_4 = PyInt_FromLong(4); if (unlikely(!__pyx_int_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + return 0; + __pyx_L1_error:; + return -1; +} + +#if PY_MAJOR_VERSION < 3 +PyMODINIT_FUNC initvonmises_cython(void); /*proto*/ +PyMODINIT_FUNC initvonmises_cython(void) +#else +PyMODINIT_FUNC PyInit_vonmises_cython(void); /*proto*/ +PyMODINIT_FUNC PyInit_vonmises_cython(void) +#endif +{ + PyObject *__pyx_1 = 0; + PyObject *__pyx_2 = 0; + __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + /*--- Libary function declarations ---*/ + __pyx_init_filenames(); + /*--- Initialize various global constants etc. ---*/ + if (unlikely(__Pyx_InitGlobals() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + /*--- Module creation code ---*/ + #if PY_MAJOR_VERSION < 3 + __pyx_m = Py_InitModule4("vonmises_cython", __pyx_methods, 0, 0, PYTHON_API_VERSION); + #else + __pyx_m = PyModule_Create(&__pyx_moduledef); + #endif + if (!__pyx_m) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + #if PY_MAJOR_VERSION < 3 + Py_INCREF(__pyx_m); + #endif + __pyx_b = PyImport_AddModule(__Pyx_BUILTIN_MODULE_NAME); + if (!__pyx_b) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + /*--- Builtin init code ---*/ + if (unlikely(__Pyx_InitCachedBuiltins() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_skip_dispatch = 0; + /*--- Global init code ---*/ + /*--- Function export code ---*/ + /*--- Type init code ---*/ + /*--- Type import code ---*/ + __pyx_ptype_5numpy_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject)); if (unlikely(!__pyx_ptype_5numpy_ndarray)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 24; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + /*--- Function import code ---*/ + /*--- Execution code ---*/ + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":1 + * import numpy as np # <<<<<<<<<<<<<< + * import scipy.stats + * from scipy.special import i0 + */ + __pyx_1 = __Pyx_Import(__pyx_kp_numpy, 0); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_m, __pyx_kp_np, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":2 + * import numpy as np + * import scipy.stats # <<<<<<<<<<<<<< + * from scipy.special import i0 + * import numpy.testing + */ + __pyx_1 = __Pyx_Import(__pyx_kp_17, 0); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_m, __pyx_kp_scipy, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":3 + * import numpy as np + * import scipy.stats + * from scipy.special import i0 # <<<<<<<<<<<<<< + * import numpy.testing + * cimport numpy as np + */ + __pyx_1 = PyList_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_kp_i0); + PyList_SET_ITEM(__pyx_1, 0, __pyx_kp_i0); + __pyx_2 = __Pyx_Import(__pyx_kp_18, ((PyObject *)__pyx_1)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_i0); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_m, __pyx_kp_i0, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(__pyx_2); __pyx_2 = 0; + + /* "/home/peridot/software/scipy/svn/trunk/scipy/stats/vonmises_cython.pyx":4 + * import scipy.stats + * from scipy.special import i0 + * import numpy.testing # <<<<<<<<<<<<<< + * cimport numpy as np + * + */ + __pyx_2 = __Pyx_Import(__pyx_kp_19, 0); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_m, __pyx_kp_numpy, __pyx_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":36 + * # experimental exception made for __getbuffer__ and __releasebuffer__ + * # -- the details of this may change. + * def __getbuffer__(ndarray self, Py_buffer* info, int flags): # <<<<<<<<<<<<<< + * # This implementation of getbuffer is geared towards Cython + * # requirements, and does not yet fullfill the PEP (specifically, + */ + #if PY_MAJOR_VERSION < 3 + return; + #else + return __pyx_m; + #endif + __pyx_L1_error:; + Py_XDECREF(__pyx_1); + Py_XDECREF(__pyx_2); + __Pyx_AddTraceback("scipy.stats.vonmises_cython"); + #if PY_MAJOR_VERSION >= 3 + return NULL; + #endif +} + +static const char *__pyx_filenames[] = { + "vonmises_cython.pyx", + "numpy.pxd", +}; + +/* Runtime support code */ + +static void __pyx_init_filenames(void) { + __pyx_f = __pyx_filenames; +} + +static INLINE void __Pyx_SafeReleaseBuffer(PyObject* obj, Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(obj, info); +} + +static INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} + +static INLINE const char* __Pyx_ConsumeWhitespace(const char* ts) { + while (1) { + switch (*ts) { + case 10: + case 13: + case ' ': + ++ts; + default: + return ts; + } + } +} + +static INLINE const char* __Pyx_BufferTypestringCheckEndian(const char* ts) { + int num = 1; + int little_endian = ((char*)&num)[0]; + int ok = 1; + switch (*ts) { + case '@': + case '=': + ++ts; break; + case '<': + if (little_endian) ++ts; + else ok = 0; + break; + case '>': + case '!': + if (!little_endian) ++ts; + else ok = 0; + break; + } + if (!ok) { + PyErr_Format(PyExc_ValueError, "Buffer has wrong endianness (rejecting on '%s')", ts); + return NULL; + } + return ts; +} + +static void __Pyx_BufferNdimError(Py_buffer* buffer, int expected_ndim) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + expected_ndim, buffer->ndim); +} + + +static const char* __Pyx_BufferTypestringCheck_item_double(const char* ts) { + if (*ts == '1') ++ts; + if (*ts != 'd') { + PyErr_Format(PyExc_ValueError, "Buffer datatype mismatch (expected 'd', got '%s')", ts); + return NULL; + } else return ts + 1; + +} + +static int __Pyx_GetBuffer_double(PyObject* obj, Py_buffer* buf, int flags, int nd) { + const char* ts; + if (obj == Py_None) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + __Pyx_BufferNdimError(buf, nd); + goto fail; + } + ts = buf->format; + ts = __Pyx_ConsumeWhitespace(ts); + ts = __Pyx_BufferTypestringCheckEndian(ts); + if (!ts) goto fail; + ts = __Pyx_ConsumeWhitespace(ts); + ts = __Pyx_BufferTypestringCheck_item_double(ts); + if (!ts) goto fail; + ts = __Pyx_ConsumeWhitespace(ts); + if (*ts != 0) { + PyErr_Format(PyExc_ValueError, + "Expected non-struct buffer data type (expected end, got '%s')", ts); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static void __Pyx_RaiseBufferFallbackError(void) { + PyErr_Format(PyExc_ValueError, + "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); +} + + +static INLINE void __Pyx_RaiseArgtupleTooLong( + Py_ssize_t num_expected, + Py_ssize_t num_found) +{ + const char* error_message = + #if PY_VERSION_HEX < 0x02050000 + "function takes at most %d positional arguments (%d given)"; + #else + "function takes at most %zd positional arguments (%zd given)"; + #endif + PyErr_Format(PyExc_TypeError, error_message, num_expected, num_found); +} + +#if (PY_MAJOR_VERSION < 3) && !(Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_NEWBUFFER) +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pf_5numpy_7ndarray___getbuffer__(obj, view, flags); + else { + PyErr_Format(PyExc_TypeError, "'%100s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; + } +} + +static void __Pyx_ReleaseBuffer(PyObject *obj, Py_buffer *view) { + +} + +#endif + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) { + PyObject *__import__ = 0; + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + __import__ = PyObject_GetAttrString(__pyx_b, "__import__"); + if (!__import__) + goto bad; + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + module = PyObject_CallFunction(__import__, "OOOO", + name, global_dict, empty_dict, list); +bad: + Py_XDECREF(empty_list); + Py_XDECREF(__import__); + Py_XDECREF(empty_dict); + return module; +} + +static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) { + PyObject *result; + result = PyObject_GetAttr(dict, name); + if (!result) + PyErr_SetObject(PyExc_NameError, name); + return result; +} + +static PyObject *__Pyx_UnpackItem(PyObject *iter, Py_ssize_t index) { + PyObject *item; + if (!(item = PyIter_Next(iter))) { + if (!PyErr_Occurred()) { + PyErr_Format(PyExc_ValueError, + #if PY_VERSION_HEX < 0x02050000 + "need more than %d values to unpack", (int)index); + #else + "need more than %zd values to unpack", index); + #endif + } + } + return item; +} + +static int __Pyx_EndUnpack(PyObject *iter) { + PyObject *item; + if ((item = PyIter_Next(iter))) { + Py_DECREF(item); + PyErr_SetString(PyExc_ValueError, "too many values to unpack"); + return -1; + } + else if (!PyErr_Occurred()) + return 0; + else + return -1; +} + +static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (!type) { + PyErr_Format(PyExc_SystemError, "Missing type object"); + return 0; + } + if (obj == Py_None || PyObject_TypeCheck(obj, type)) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %s to %s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { + Py_XINCREF(type); + Py_XINCREF(value); + Py_XINCREF(tb); + /* First, check the traceback argument, replacing None with NULL. */ + if (tb == Py_None) { + Py_DECREF(tb); + tb = 0; + } + else if (tb != NULL && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + /* Next, replace a missing value with None */ + if (value == NULL) { + value = Py_None; + Py_INCREF(value); + } + #if PY_VERSION_HEX < 0x02050000 + if (!PyClass_Check(type)) + #else + if (!PyType_Check(type)) + #endif + { + /* Raising an instance. The value should be a dummy. */ + if (value != Py_None) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + /* Normalize to raise , */ + Py_DECREF(value); + value = type; + #if PY_VERSION_HEX < 0x02050000 + if (PyInstance_Check(type)) { + type = (PyObject*) ((PyInstanceObject*)type)->in_class; + Py_INCREF(type); + } + else { + type = 0; + PyErr_SetString(PyExc_TypeError, + "raise: exception must be an old-style class or instance"); + goto raise_error; + } + #else + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + #endif + } + PyErr_Restore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} + +static void __Pyx_WriteUnraisable(const char *name) { + PyObject *old_exc, *old_val, *old_tb; + PyObject *ctx; + PyErr_Fetch(&old_exc, &old_val, &old_tb); + #if PY_MAJOR_VERSION < 3 + ctx = PyString_FromString(name); + #else + ctx = PyUnicode_FromString(name); + #endif + PyErr_Restore(old_exc, old_val, old_tb); + if (!ctx) + ctx = Py_None; + PyErr_WriteUnraisable(ctx); +} + +#ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, + long size) +{ + PyObject *py_module = 0; + PyObject *result = 0; + PyObject *py_name = 0; + + #if PY_MAJOR_VERSION < 3 + py_name = PyString_FromString(module_name); + #else + py_name = PyUnicode_FromString(module_name); + #endif + if (!py_name) + goto bad; + + py_module = __Pyx_ImportModule(module_name); + if (!py_module) + goto bad; + result = PyObject_GetAttrString(py_module, class_name); + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%s.%s is not a type object", + module_name, class_name); + goto bad; + } + if (((PyTypeObject *)result)->tp_basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%s.%s does not appear to be the correct type object", + module_name, class_name); + goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(py_name); + Py_XDECREF(result); + return 0; +} +#endif + +#ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + + #if PY_MAJOR_VERSION < 3 + py_name = PyString_FromString(name); + #else + py_name = PyUnicode_FromString(name); + #endif + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; +} +#endif + +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" + +static void __Pyx_AddTraceback(const char *funcname) { + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + PyObject *py_globals = 0; + PyObject *empty_string = 0; + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(__pyx_filename); + #else + py_srcfile = PyUnicode_FromString(__pyx_filename); + #endif + if (!py_srcfile) goto bad; + if (__pyx_clineno) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_globals = PyModule_GetDict(__pyx_m); + if (!py_globals) goto bad; + #if PY_MAJOR_VERSION < 3 + empty_string = PyString_FromStringAndSize("", 0); + #else + empty_string = PyBytes_FromStringAndSize("", 0); + #endif + if (!empty_string) goto bad; + py_code = PyCode_New( + 0, /*int argcount,*/ + #if PY_MAJOR_VERSION >= 3 + 0, /*int kwonlyargcount,*/ + #endif + 0, /*int nlocals,*/ + 0, /*int stacksize,*/ + 0, /*int flags,*/ + empty_string, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + __pyx_lineno, /*int firstlineno,*/ + empty_string /*PyObject *lnotab*/ + ); + if (!py_code) goto bad; + py_frame = PyFrame_New( + PyThreadState_Get(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + py_globals, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + py_frame->f_lineno = __pyx_lineno; + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + Py_XDECREF(empty_string); + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode && (!t->is_identifier)) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else /* Python 3+ has unicode identifiers */ + if (t->is_identifier || (t->is_unicode && t->intern)) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->is_unicode) { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +/* Type Conversion Functions */ + +static INLINE Py_ssize_t __pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject* x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} + +static INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + if (x == Py_True) return 1; + else if (x == Py_False) return 0; + else return PyObject_IsTrue(x); +} + +static INLINE PY_LONG_LONG __pyx_PyInt_AsLongLong(PyObject* x) { + if (PyInt_CheckExact(x)) { + return PyInt_AS_LONG(x); + } + else if (PyLong_CheckExact(x)) { + return PyLong_AsLongLong(x); + } + else { + PY_LONG_LONG val; + PyObject* tmp = PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; + val = __pyx_PyInt_AsLongLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static INLINE unsigned PY_LONG_LONG __pyx_PyInt_AsUnsignedLongLong(PyObject* x) { + if (PyInt_CheckExact(x)) { + long val = PyInt_AS_LONG(x); + if (unlikely(val < 0)) { + PyErr_SetString(PyExc_TypeError, "Negative assignment to unsigned type."); + return (unsigned PY_LONG_LONG)-1; + } + return val; + } + else if (PyLong_CheckExact(x)) { + return PyLong_AsUnsignedLongLong(x); + } + else { + PY_LONG_LONG val; + PyObject* tmp = PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; + val = __pyx_PyInt_AsUnsignedLongLong(tmp); + Py_DECREF(tmp); + return val; + } +} + + +static INLINE unsigned char __pyx_PyInt_unsigned_char(PyObject* x) { + if (sizeof(unsigned char) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + unsigned char val = (unsigned char)long_val; + if (unlikely((val != long_val) || (long_val < 0))) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to unsigned char"); + return (unsigned char)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE unsigned short __pyx_PyInt_unsigned_short(PyObject* x) { + if (sizeof(unsigned short) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + unsigned short val = (unsigned short)long_val; + if (unlikely((val != long_val) || (long_val < 0))) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to unsigned short"); + return (unsigned short)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE char __pyx_PyInt_char(PyObject* x) { + if (sizeof(char) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + char val = (char)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to char"); + return (char)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE short __pyx_PyInt_short(PyObject* x) { + if (sizeof(short) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + short val = (short)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to short"); + return (short)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE int __pyx_PyInt_int(PyObject* x) { + if (sizeof(int) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + int val = (int)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); + return (int)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE long __pyx_PyInt_long(PyObject* x) { + if (sizeof(long) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + long val = (long)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); + return (long)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE signed char __pyx_PyInt_signed_char(PyObject* x) { + if (sizeof(signed char) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + signed char val = (signed char)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed char"); + return (signed char)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE signed short __pyx_PyInt_signed_short(PyObject* x) { + if (sizeof(signed short) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + signed short val = (signed short)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed short"); + return (signed short)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE signed int __pyx_PyInt_signed_int(PyObject* x) { + if (sizeof(signed int) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + signed int val = (signed int)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed int"); + return (signed int)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE signed long __pyx_PyInt_signed_long(PyObject* x) { + if (sizeof(signed long) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + signed long val = (signed long)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed long"); + return (signed long)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE long double __pyx_PyInt_long_double(PyObject* x) { + if (sizeof(long double) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + long double val = (long double)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to long double"); + return (long double)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + Added: trunk/scipy/stats/vonmises_cython.pyx =================================================================== --- trunk/scipy/stats/vonmises_cython.pyx 2008-10-09 05:26:59 UTC (rev 4787) +++ trunk/scipy/stats/vonmises_cython.pyx 2008-10-09 07:29:54 UTC (rev 4788) @@ -0,0 +1,76 @@ +import numpy as np +import scipy.stats +from scipy.special import i0 +import numpy.testing +cimport numpy as np + +cdef extern from "math.h": + double cos(double theta) + double sin(double theta) + + +cdef double von_mises_cdf_series(double k,double x,unsigned int p): + cdef double s, c, sn, cn, R, V + cdef unsigned int n + s = sin(x) + c = cos(x) + sn = sin(p*x) + cn = cos(p*x) + R = 0 + V = 0 + for n in range(p-1,0,-1): + sn, cn = sn*c - cn*s, cn*c + sn*s + R = 1./(2*n/k + R) + V = R*(sn/n+V) + + return 0.5+x/(2*np.pi) + V/np.pi + +def von_mises_cdf_normalapprox(k,x,C1): + b = np.sqrt(2/np.pi)*np.exp(k)/i0(k) + z = b*np.sin(x/2.) + C = 24*k + chi = z - z**3/((C-2*z**2-16)/3.-(z**4+7/4.*z**2+167./2)/(C+C1-z**2+3))**2 + return scipy.stats.norm.cdf(z) + +cimport cython + at cython.boundscheck(False) +def von_mises_cdf(k,x): + cdef np.ndarray[double, ndim=1] temp, temp_xs, temp_ks + cdef unsigned int i, p + cdef double a1, a2, a3, a4, C1, CK + #k,x = np.broadcast_arrays(np.asarray(k),np.asarray(x)) + k = np.asarray(k) + x = np.asarray(x) + zerodim = k.ndim==0 and x.ndim==0 + + k = np.atleast_1d(k) + x = np.atleast_1d(x) + ix = np.round(x/(2*np.pi)) + x = x-ix + + # These values should give 12 decimal digits + CK=50 + a1, a2, a3, a4 = [28., 0.5, 100., 5.0] + C1 = 50.1 + + bx, bk = np.broadcast_arrays(x,k) + result = np.empty(bx.shape,dtype=np.float) + + c_small_k = bk(1+a1+a2*temp_ks[i]-a3/(temp_ks[i]+a4)) + temp[i] = von_mises_cdf_series(temp_ks[i],temp_xs[i],p) + if temp[i]<0: + temp[i]=0 + elif temp[i]>1: + temp[i]=1 + result[c_small_k] = temp + result[~c_small_k] = von_mises_cdf_normalapprox(bk[~c_small_k],bx[~c_small_k],C1) + + if not zerodim: + return result+(2*np.pi)*ix + else: + return (result+(2*np.pi)*ix)[0] From scipy-svn at scipy.org Thu Oct 9 23:17:29 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Thu, 9 Oct 2008 22:17:29 -0500 (CDT) Subject: [Scipy-svn] r4789 - trunk/scipy/io/matlab Message-ID: <20081010031729.B06D639C1AE@scipy.org> Author: wnbell Date: 2008-10-09 22:17:27 -0500 (Thu, 09 Oct 2008) New Revision: 4789 Modified: trunk/scipy/io/matlab/mio4.py Log: (hopefully) fix ticket #736 Modified: trunk/scipy/io/matlab/mio4.py =================================================================== --- trunk/scipy/io/matlab/mio4.py 2008-10-09 07:29:54 UTC (rev 4788) +++ trunk/scipy/io/matlab/mio4.py 2008-10-10 03:17:27 UTC (rev 4789) @@ -67,7 +67,7 @@ ''' Read header, return matrix getter ''' data = self.read_dtype(self.dtypes['header']) header = {} - header['name'] = self.read_ztstring(data['namlen']) + header['name'] = self.read_ztstring(int(data['namlen'])) if data['mopt'] < 0 or data['mopt'] > 5000: ValueError, 'Mat 4 mopt wrong format, byteswapping problem?' M,rest = divmod(data['mopt'], 1000) From scipy-svn at scipy.org Fri Oct 10 03:01:00 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 10 Oct 2008 02:01:00 -0500 (CDT) Subject: [Scipy-svn] r4790 - trunk/scipy/interpolate Message-ID: <20081010070100.2DCAD39C107@scipy.org> Author: cdavid Date: 2008-10-10 02:00:56 -0500 (Fri, 10 Oct 2008) New Revision: 4790 Modified: trunk/scipy/interpolate/SConscript Log: Update numscons build to cope with recent changes in scipy.interpolate. Modified: trunk/scipy/interpolate/SConscript =================================================================== --- trunk/scipy/interpolate/SConscript 2008-10-10 03:17:27 UTC (rev 4789) +++ trunk/scipy/interpolate/SConscript 2008-10-10 07:00:56 UTC (rev 4790) @@ -1,4 +1,4 @@ -# Last Change: Thu Jun 12 07:00 PM 2008 J +# Last Change: Fri Oct 10 03:00 PM 2008 J # vim:syntax=python from os.path import join as pjoin @@ -34,7 +34,11 @@ env.PrependUnique(LIBPATH = ['.']) # Build _fitpack -env.NumpyPythonExtension('_fitpack', source = '_fitpackmodule.c') +env.NumpyPythonExtension('_fitpack', source = 'src/_fitpackmodule.c') # Build dfitpack -env.NumpyPythonExtension('dfitpack', source = 'fitpack.pyf') +env.NumpyPythonExtension('dfitpack', source = 'src/fitpack.pyf') + +# Build _interpolate +env.NumpyPythonExtension('_interpolate', source = 'src/_interpolate.cpp', + CXXFILESUFFIX = ".cpp") From scipy-svn at scipy.org Fri Oct 10 03:02:24 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 10 Oct 2008 02:02:24 -0500 (CDT) Subject: [Scipy-svn] r4791 - trunk/scipy/ndimage Message-ID: <20081010070224.E92EA39C107@scipy.org> Author: cdavid Date: 2008-10-10 02:02:20 -0500 (Fri, 10 Oct 2008) New Revision: 4791 Modified: trunk/scipy/ndimage/SConscript Log: Update numscons build of scipy.ndimage. Modified: trunk/scipy/ndimage/SConscript =================================================================== --- trunk/scipy/ndimage/SConscript 2008-10-10 07:00:56 UTC (rev 4790) +++ trunk/scipy/ndimage/SConscript 2008-10-10 07:02:20 UTC (rev 4791) @@ -1,4 +1,4 @@ -# Last Change: Wed Mar 05 09:00 PM 2008 J +# Last Change: Fri Oct 10 03:00 PM 2008 J from os.path import join from numscons import GetNumpyEnvironment @@ -10,11 +10,3 @@ ndimage_src = ["nd_image.c", "ni_filters.c", "ni_fourier.c", "ni_interpolation.c", "ni_measure.c", "ni_morphology.c", "ni_support.c"] env.NumpyPythonExtension('_nd_image', source = [join('src', i) for i in ndimage_src]) - -segment_src = ['Segmenter_EXT.c', 'Segmenter_IMPL.c'] -env.NumpyPythonExtension('_segment', source = [join('src', 'segment', i) - for i in segment_src]) - -register_src = ['Register_EXT.c', 'Register_IMPL.c'] -env.NumpyPythonExtension('_register', source = [join('src', 'register', i) - for i in register_src]) From scipy-svn at scipy.org Fri Oct 10 13:13:14 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 10 Oct 2008 12:13:14 -0500 (CDT) Subject: [Scipy-svn] r4792 - in trunk/scipy/sparse: . tests Message-ID: <20081010171314.4038339C1AE@scipy.org> Author: wnbell Date: 2008-10-10 12:13:10 -0500 (Fri, 10 Oct 2008) New Revision: 4792 Modified: trunk/scipy/sparse/csr.py trunk/scipy/sparse/sputils.py trunk/scipy/sparse/tests/test_base.py trunk/scipy/sparse/tests/test_sputils.py Log: fix CSR[:,array([1,2])] slicing problem Modified: trunk/scipy/sparse/csr.py =================================================================== --- trunk/scipy/sparse/csr.py 2008-10-10 07:02:20 UTC (rev 4791) +++ trunk/scipy/sparse/csr.py 2008-10-10 17:13:10 UTC (rev 4792) @@ -338,7 +338,7 @@ return i0, i1 - elif isscalar( sl ): + elif isintlike( sl ): if sl < 0: sl += num Modified: trunk/scipy/sparse/sputils.py =================================================================== --- trunk/scipy/sparse/sputils.py 2008-10-10 07:02:20 UTC (rev 4791) +++ trunk/scipy/sparse/sputils.py 2008-10-10 17:13:10 UTC (rev 4792) @@ -82,13 +82,16 @@ """Is x appropriate as an index into a sparse matrix? Returns True if it can be cast safely to a machine int. """ - try: - if int(x) == x: - return True - else: + if issequence(x): + return False + else: + try: + if int(x) == x: + return True + else: + return False + except TypeError: return False - except TypeError: - return False def isshape(x): """Is x a valid 2-tuple of dimensions? Modified: trunk/scipy/sparse/tests/test_base.py =================================================================== --- trunk/scipy/sparse/tests/test_base.py 2008-10-10 07:02:20 UTC (rev 4791) +++ trunk/scipy/sparse/tests/test_base.py 2008-10-10 17:13:10 UTC (rev 4792) @@ -700,20 +700,28 @@ assert_equal(A[2,3], B[2,3]) assert_equal(A[-1,8], B[-1,8]) assert_equal(A[-1,-2],B[-1,-2]) + assert_equal(A[array(-1),-2],B[-1,-2]) + assert_equal(A[-1,array(-2)],B[-1,-2]) + assert_equal(A[array(-1),array(-2)],B[-1,-2]) # [i,1:2] assert_equal(A[2,:].todense(), B[2,:]) assert_equal(A[2,5:-2].todense(),B[2,5:-2]) + assert_equal(A[array(2),5:-2].todense(),B[2,5:-2]) # [i,[1,2]] assert_equal(A[3,[1,3]].todense(), B[3,[1,3]]) assert_equal(A[-1,[2,-5]].todense(),B[-1,[2,-5]]) + assert_equal(A[array(-1),[2,-5]].todense(),B[-1,[2,-5]]) + assert_equal(A[-1,array([2,-5])].todense(),B[-1,[2,-5]]) + assert_equal(A[array(-1),array([2,-5])].todense(),B[-1,[2,-5]]) # [1:2,j] assert_equal(A[:,2].todense(), B[:,2]) assert_equal(A[3:4,9].todense(), B[3:4,9]) assert_equal(A[1:4,-5].todense(),B[1:4,-5]) assert_equal(A[2:-1,3].todense(),B[2:-1,3]) + assert_equal(A[2:-1,array(3)].todense(),B[2:-1,3]) # [1:2,1:2] assert_equal(A[1:2,1:2].todense(),B[1:2,1:2]) @@ -725,26 +733,38 @@ assert_equal(A[:,[2,8,3,-1]].todense(),B[:,[2,8,3,-1]]) assert_equal(A[3:4,[9]].todense(), B[3:4,[9]]) assert_equal(A[1:4,[-1,-5]].todense(), B[1:4,[-1,-5]]) + assert_equal(A[1:4,array([-1,-5])].todense(), B[1:4,[-1,-5]]) # [[1,2],j] assert_equal(A[[1,3],3].todense(), B[[1,3],3]) assert_equal(A[[2,-5],-4].todense(), B[[2,-5],-4]) + assert_equal(A[array([2,-5]),-4].todense(), B[[2,-5],-4]) + assert_equal(A[[2,-5],array(-4)].todense(), B[[2,-5],-4]) + assert_equal(A[array([2,-5]),array(-4)].todense(), B[[2,-5],-4]) # [[1,2],1:2] assert_equal(A[[1,3],:].todense(), B[[1,3],:]) assert_equal(A[[2,-5],8:-1].todense(),B[[2,-5],8:-1]) + assert_equal(A[array([2,-5]),8:-1].todense(),B[[2,-5],8:-1]) # [[1,2],[1,2]] assert_equal(A[[1,3],[2,4]], B[[1,3],[2,4]]) assert_equal(A[[-1,-3],[2,-4]],B[[-1,-3],[2,-4]]) + assert_equal(A[array([-1,-3]),[2,-4]],B[[-1,-3],[2,-4]]) + assert_equal(A[[-1,-3],array([2,-4])],B[[-1,-3],[2,-4]]) + assert_equal(A[array([-1,-3]),array([2,-4])],B[[-1,-3],[2,-4]]) # [[[1],[2]],[1,2]] assert_equal(A[[[1],[3]],[2,4]].todense(), B[[[1],[3]],[2,4]]) assert_equal(A[[[-1],[-3],[-2]],[2,-4]].todense(),B[[[-1],[-3],[-2]],[2,-4]]) + assert_equal(A[array([[-1],[-3],[-2]]),[2,-4]].todense(),B[[[-1],[-3],[-2]],[2,-4]]) + assert_equal(A[[[-1],[-3],[-2]],array([2,-4])].todense(),B[[[-1],[-3],[-2]],[2,-4]]) + assert_equal(A[array([[-1],[-3],[-2]]),array([2,-4])].todense(),B[[[-1],[-3],[-2]],[2,-4]]) # [i] assert_equal(A[1,:].todense(), B[1,:]) assert_equal(A[-2,:].todense(),B[-2,:]) + assert_equal(A[array(-2),:].todense(),B[-2,:]) # [1:2] assert_equal(A[1:4].todense(), B[1:4]) @@ -753,14 +773,17 @@ # [[1,2]] assert_equal(A[[1,3]].todense(), B[[1,3]]) assert_equal(A[[-1,-3]].todense(),B[[-1,-3]]) + assert_equal(A[array([-1,-3])].todense(),B[[-1,-3]]) # [[1,2],:][:,[1,2]] assert_equal(A[[1,3],:][:,[2,4]].todense(), B[[1,3],:][:,[2,4]] ) assert_equal(A[[-1,-3],:][:,[2,-4]].todense(), B[[-1,-3],:][:,[2,-4]] ) + assert_equal(A[array([-1,-3]),:][:,array([2,-4])].todense(), B[[-1,-3],:][:,[2,-4]] ) # [:,[1,2]][[1,2],:] assert_equal(A[:,[1,3]][[2,4],:].todense(), B[:,[1,3]][[2,4],:] ) assert_equal(A[:,[-1,-3]][[2,-4],:].todense(), B[:,[-1,-3]][[2,-4],:] ) + assert_equal(A[:,array([-1,-3])][array([2,-4]),:].todense(), B[:,[-1,-3]][[2,-4],:] ) # Check bug reported by Robert Cimrman: Modified: trunk/scipy/sparse/tests/test_sputils.py =================================================================== --- trunk/scipy/sparse/tests/test_sputils.py 2008-10-10 07:02:20 UTC (rev 4791) +++ trunk/scipy/sparse/tests/test_sputils.py 2008-10-10 17:13:10 UTC (rev 4792) @@ -36,7 +36,7 @@ assert_equal(isintlike(3.0),True) assert_equal(isintlike(-4),True) assert_equal(isintlike(np.array(3)),True) - assert_equal(isintlike( np.array([3])), True) + assert_equal(isintlike(np.array([3])), False) assert_equal(isintlike(2.5),False) assert_equal(isintlike(1 + 3j),False) From scipy-svn at scipy.org Sat Oct 11 06:36:27 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 11 Oct 2008 05:36:27 -0500 (CDT) Subject: [Scipy-svn] r4793 - trunk/scipy/sparse/benchmarks Message-ID: <20081011103627.76E3239C0EA@scipy.org> Author: wnbell Date: 2008-10-11 05:36:17 -0500 (Sat, 11 Oct 2008) New Revision: 4793 Modified: trunk/scipy/sparse/benchmarks/bench_sparse.py Log: added missing import Modified: trunk/scipy/sparse/benchmarks/bench_sparse.py =================================================================== --- trunk/scipy/sparse/benchmarks/bench_sparse.py 2008-10-10 17:13:10 UTC (rev 4792) +++ trunk/scipy/sparse/benchmarks/bench_sparse.py 2008-10-11 10:36:17 UTC (rev 4793) @@ -8,7 +8,8 @@ from numpy.testing import * from scipy import sparse -from scipy.sparse import csr_matrix, coo_matrix, dia_matrix +from scipy.sparse import csr_matrix, coo_matrix, dia_matrix, lil_matrix, \ + dok_matrix def random_sparse(m,n,nnz_per_row): From scipy-svn at scipy.org Sun Oct 12 09:07:12 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 12 Oct 2008 08:07:12 -0500 (CDT) Subject: [Scipy-svn] r4794 - in branches/spatial/scipy/spatial: . tests Message-ID: <20081012130712.1394C39C05F@scipy.org> Author: peridot Date: 2008-10-12 08:06:54 -0500 (Sun, 12 Oct 2008) New Revision: 4794 Modified: branches/spatial/scipy/spatial/kdtree.py branches/spatial/scipy/spatial/tests/test_kdtree.py Log: Found and fixed a bug when the initial point is outside the tree's bounding box; renamed the dimension of the tree to m to reduce confusion. Modified: branches/spatial/scipy/spatial/kdtree.py =================================================================== --- branches/spatial/scipy/spatial/kdtree.py 2008-10-11 10:36:17 UTC (rev 4793) +++ branches/spatial/scipy/spatial/kdtree.py 2008-10-12 13:06:54 UTC (rev 4794) @@ -37,7 +37,7 @@ """Construct a hyperrectangle.""" self.maxes = np.maximum(maxes,mins).astype(np.float) self.mins = np.minimum(maxes,mins).astype(np.float) - self.k, = self.maxes.shape + self.m, = self.maxes.shape def __repr__(self): return "" % zip(self.mins, self.maxes) @@ -124,7 +124,7 @@ brute-force. """ self.data = np.asarray(data) - self.n, self.k = np.shape(self.data) + self.n, self.m = np.shape(self.data) self.leafsize = int(leafsize) if self.leafsize<1: raise ValueError("leafsize must be at least 1") @@ -192,13 +192,19 @@ def __query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf): - side_distances = [max(0,x[i]-self.maxes[i],self.mins[i]-x[i]) for i in range(self.k)] + side_distances = np.maximum(0,np.maximum(x-self.maxes,self.mins-x)) + if p!=np.inf: + side_distances**=p + min_distance = np.sum(side_distances) + else: + min_distance = np.amax(side_distances) + # priority queue for chasing nodes # entries are: # minimum distance between the cell and the target # distances between the nearest side of the cell and the target # the head node of the cell - q = [(distance_p(np.array(side_distances),0), + q = [(min_distance, tuple(side_distances), self.tree)] # priority queue for the nearest neighbors @@ -221,13 +227,7 @@ if isinstance(node, KDTree.leafnode): # brute-force data = self.data[node.idx] - a = np.abs(data-x[np.newaxis,:]) - if p==np.inf: - ds = np.amax(a,axis=1) - elif p==1: - ds = np.sum(a,axis=1) - else: - ds = np.sum(a**p,axis=1) + ds = distance_p(data,x[np.newaxis,:],p) for i in range(len(ds)): if ds[i] Author: peridot Date: 2008-10-12 08:23:48 -0500 (Sun, 12 Oct 2008) New Revision: 4795 Added: branches/spatial/scipy/spatial/ckdtree.c branches/spatial/scipy/spatial/ckdtree.pyx Modified: branches/spatial/scipy/spatial/__init__.py branches/spatial/scipy/spatial/setup.py branches/spatial/scipy/spatial/tests/test_kdtree.py Log: kd-tree implementation in compiled (cython) code. Note that the c is not automatically built. Modified: branches/spatial/scipy/spatial/__init__.py =================================================================== --- branches/spatial/scipy/spatial/__init__.py 2008-10-12 13:06:54 UTC (rev 4794) +++ branches/spatial/scipy/spatial/__init__.py 2008-10-12 13:23:48 UTC (rev 4795) @@ -5,6 +5,7 @@ from info import __doc__ from kdtree import * +from ckdtree import * __all__ = filter(lambda s:not s.startswith('_'),dir()) Added: branches/spatial/scipy/spatial/ckdtree.c =================================================================== --- branches/spatial/scipy/spatial/ckdtree.c 2008-10-12 13:06:54 UTC (rev 4794) +++ branches/spatial/scipy/spatial/ckdtree.c 2008-10-12 13:23:48 UTC (rev 4795) @@ -0,0 +1,5995 @@ +/* Generated by Cython 0.9.8.1.1 on Sun Oct 12 09:20:26 2008 */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#include "structmember.h" +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#if PY_VERSION_HEX < 0x02040000 + #define METH_COEXIST 0 +#endif +#if PY_VERSION_HEX < 0x02050000 + typedef int Py_ssize_t; + #define PY_SSIZE_T_MAX INT_MAX + #define PY_SSIZE_T_MIN INT_MIN + #define PyInt_FromSsize_t(z) PyInt_FromLong(z) + #define PyInt_AsSsize_t(o) PyInt_AsLong(o) + #define PyNumber_Index(o) PyNumber_Int(o) + #define PyIndex_Check(o) PyNumber_Check(o) +#endif +#if PY_VERSION_HEX < 0x02060000 + #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt) + #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) + #define Py_SIZE(ob) (((PyVarObject*)(ob))->ob_size) + #define PyVarObject_HEAD_INIT(type, size) \ + PyObject_HEAD_INIT(type) size, + #define PyType_Modified(t) + + typedef struct { + void *buf; + Py_ssize_t len; + int readonly; + const char *format; + int ndim; + Py_ssize_t *shape; + Py_ssize_t *strides; + Py_ssize_t *suboffsets; + Py_ssize_t itemsize; + void *internal; + } Py_buffer; + + #define PyBUF_SIMPLE 0 + #define PyBUF_WRITABLE 0x0001 + #define PyBUF_LOCK 0x0002 + #define PyBUF_FORMAT 0x0004 + #define PyBUF_ND 0x0008 + #define PyBUF_STRIDES (0x0010 | PyBUF_ND) + #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES) + #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES) + #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES) + #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES) + +#endif +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" +#endif +#if PY_MAJOR_VERSION >= 3 + #define Py_TPFLAGS_CHECKTYPES 0 + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyString_Type PyBytes_Type + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define PyBytes_Type PyString_Type +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyMethod_New(func, self, klass) PyInstanceMethod_New(func) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif +#else + #define _USE_MATH_DEFINES +#endif +#ifdef __cplusplus +#define __PYX_EXTERN_C extern "C" +#else +#define __PYX_EXTERN_C extern +#endif +#include +#define __PYX_HAVE_API__scipy__spatial__ckdtree +#include "numpy/arrayobject.h" +#include "stdlib.h" + + +#ifdef __GNUC__ +#define INLINE __inline__ +#elif _WIN32 +#define INLINE __inline +#else +#define INLINE +#endif + +typedef struct {PyObject **p; char *s; long n; char is_unicode; char intern; char is_identifier;} __Pyx_StringTabEntry; /*proto*/ + + + +static int __pyx_skip_dispatch = 0; + + +/* Type Conversion Predeclarations */ + +#if PY_MAJOR_VERSION < 3 +#define __Pyx_PyBytes_FromString PyString_FromString +#define __Pyx_PyBytes_AsString PyString_AsString +#else +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_AsString PyBytes_AsString +#endif + +#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) +static INLINE int __Pyx_PyObject_IsTrue(PyObject* x); +static INLINE PY_LONG_LONG __pyx_PyInt_AsLongLong(PyObject* x); +static INLINE unsigned PY_LONG_LONG __pyx_PyInt_AsUnsignedLongLong(PyObject* x); +static INLINE Py_ssize_t __pyx_PyIndex_AsSsize_t(PyObject* b); + +#define __pyx_PyInt_AsLong(x) (PyInt_CheckExact(x) ? PyInt_AS_LONG(x) : PyInt_AsLong(x)) +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) + +static INLINE unsigned char __pyx_PyInt_unsigned_char(PyObject* x); +static INLINE unsigned short __pyx_PyInt_unsigned_short(PyObject* x); +static INLINE char __pyx_PyInt_char(PyObject* x); +static INLINE short __pyx_PyInt_short(PyObject* x); +static INLINE int __pyx_PyInt_int(PyObject* x); +static INLINE long __pyx_PyInt_long(PyObject* x); +static INLINE signed char __pyx_PyInt_signed_char(PyObject* x); +static INLINE signed short __pyx_PyInt_signed_short(PyObject* x); +static INLINE signed int __pyx_PyInt_signed_int(PyObject* x); +static INLINE signed long __pyx_PyInt_signed_long(PyObject* x); +static INLINE long double __pyx_PyInt_long_double(PyObject* x); +#ifdef __GNUC__ +/* Test for GCC > 2.95 */ +#if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) +#define likely(x) __builtin_expect(!!(x), 1) +#define unlikely(x) __builtin_expect(!!(x), 0) +#else /* __GNUC__ > 2 ... */ +#define likely(x) (x) +#define unlikely(x) (x) +#endif /* __GNUC__ > 2 ... */ +#else /* __GNUC__ */ +#define likely(x) (x) +#define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; +static const char **__pyx_f; +static INLINE void __Pyx_SafeReleaseBuffer(PyObject* obj, Py_buffer* info); +static INLINE void __Pyx_ZeroBuffer(Py_buffer* buf); /*proto*/ +static INLINE const char* __Pyx_ConsumeWhitespace(const char* ts); /*proto*/ +static INLINE const char* __Pyx_BufferTypestringCheckEndian(const char* ts); /*proto*/ +static void __Pyx_BufferNdimError(Py_buffer* buffer, int expected_ndim); /*proto*/ +static const char* __Pyx_BufferTypestringCheck_item_double(const char* ts); /*proto*/ + +static int __Pyx_GetBuffer_double(PyObject* obj, Py_buffer* buf, int flags, int nd); /*proto*/ + +static void __Pyx_RaiseBufferFallbackError(void); /*proto*/ +static const char* __Pyx_BufferTypestringCheck_item_nn___pyx_t_5numpy_int32_t(const char* ts); /*proto*/ + +static int __Pyx_GetBuffer_nn___pyx_t_5numpy_int32_t(PyObject* obj, Py_buffer* buf, int flags, int nd); /*proto*/ +static const char* __Pyx_BufferTypestringCheck_item_int(const char* ts); /*proto*/ + +static int __Pyx_GetBuffer_int(PyObject* obj, Py_buffer* buf, int flags, int nd); /*proto*/ +static void __Pyx_RaiseBufferIndexError(int axis); /*proto*/ +#define __Pyx_BufPtrStrided2d(buf, i0, s0, i1, s1) ((char*)buf + i0 * s0 + i1 * s1) + +static INLINE void __Pyx_RaiseArgtupleTooLong(Py_ssize_t num_expected, Py_ssize_t num_found); /*proto*/ +#if (PY_MAJOR_VERSION < 3) && !(Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_NEWBUFFER) +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); +static void __Pyx_ReleaseBuffer(PyObject *obj, Py_buffer *view); +#else +#define __Pyx_GetBuffer PyObject_GetBuffer +#define __Pyx_ReleaseBuffer PyObject_ReleaseBuffer +#endif + +Py_ssize_t __Pyx_zeros[] = {0, 0}; +Py_ssize_t __Pyx_minusones[] = {-1, -1}; + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/ + +static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/ + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ + +static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/ +static int __Pyx_EndUnpack(PyObject *); /*proto*/ + +static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/ + +static int __Pyx_Print(PyObject *, int); /*proto*/ +#if PY_MAJOR_VERSION >= 3 +static PyObject* __pyx_print = 0; +static PyObject* __pyx_print_kwargs = 0; +#endif + +static INLINE PyObject *__Pyx_GetItemInt(PyObject *o, Py_ssize_t i, int is_unsigned) { + PyObject *r; + if (PyList_CheckExact(o) && 0 <= i && i < PyList_GET_SIZE(o)) { + r = PyList_GET_ITEM(o, i); + Py_INCREF(r); + } + else if (PyTuple_CheckExact(o) && 0 <= i && i < PyTuple_GET_SIZE(o)) { + r = PyTuple_GET_ITEM(o, i); + Py_INCREF(r); + } + else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_item && (likely(i >= 0) || !is_unsigned)) + r = PySequence_GetItem(o, i); + else { + PyObject *j = (likely(i >= 0) || !is_unsigned) ? PyInt_FromLong(i) : PyLong_FromUnsignedLongLong((sizeof(unsigned long long) > sizeof(Py_ssize_t) ? (1ULL << (sizeof(Py_ssize_t)*8)) : 0) + i); + if (!j) + return 0; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + } + return r; +} + +static void __Pyx_WriteUnraisable(const char *name); /*proto*/ + +static int __Pyx_SetVtable(PyObject *dict, void *vtable); /*proto*/ + +static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, long size); /*proto*/ + +static PyObject *__Pyx_ImportModule(char *name); /*proto*/ + +static void __Pyx_AddTraceback(const char *funcname); /*proto*/ + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ + +/* Type declarations */ + +typedef npy_int8 __pyx_t_5numpy_int8_t; + +typedef npy_int16 __pyx_t_5numpy_int16_t; + +typedef npy_int32 __pyx_t_5numpy_int32_t; + +typedef npy_int64 __pyx_t_5numpy_int64_t; + +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +typedef npy_float32 __pyx_t_5numpy_float32_t; + +typedef npy_float64 __pyx_t_5numpy_float64_t; + +typedef npy_long __pyx_t_5numpy_int_t; + +typedef npy_longlong __pyx_t_5numpy_long_t; + +typedef npy_ulong __pyx_t_5numpy_uint_t; + +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +typedef npy_double __pyx_t_5numpy_float_t; + +typedef npy_double __pyx_t_5numpy_double_t; + +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":15 + * + * # priority queue + * cdef union heapcontents: # <<<<<<<<<<<<<< + * int intdata + * char* ptrdata + */ + +union __pyx_t_5scipy_7spatial_7ckdtree_heapcontents { + int intdata; + char *ptrdata; +}; + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":19 + * char* ptrdata + * + * cdef struct heapitem: # <<<<<<<<<<<<<< + * double priority + * heapcontents contents + */ + +struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem { + double priority; + union __pyx_t_5scipy_7spatial_7ckdtree_heapcontents contents; +}; + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":23 + * heapcontents contents + * + * cdef struct heap: # <<<<<<<<<<<<<< + * int n + * heapitem* heap + */ + +struct __pyx_t_5scipy_7spatial_7ckdtree_heap { + int n; + struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem *heap; + int space; +}; + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":139 + * + * # Tree structure + * cdef struct innernode: # <<<<<<<<<<<<<< + * int split_dim + * int n_points + */ + +struct __pyx_t_5scipy_7spatial_7ckdtree_innernode { + int split_dim; + int n_points; + double split; + struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *less; + struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *greater; +}; + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":145 + * innernode* less + * innernode* greater + * cdef struct leafnode: # <<<<<<<<<<<<<< + * int split_dim + * int n_points + */ + +struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode { + int split_dim; + int n_points; + int start_idx; + int end_idx; +}; + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":153 + * # this is the standard trick for variable-size arrays: + * # malloc sizeof(nodeinfo)+self.m*sizeof(double) bytes. + * cdef struct nodeinfo: # <<<<<<<<<<<<<< + * innernode* node + * double side_distances[0] + */ + +struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo { + struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *node; + double side_distances[0]; +}; + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":157 + * double side_distances[0] + * + * cdef class cKDTree: # <<<<<<<<<<<<<< + * """kd-tree for quick nearest-neighbor lookup + * + */ + +struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree { + PyObject_HEAD + struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *__pyx_vtab; + struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *tree; + PyObject *data; + double *raw_data; + int n; + int m; + int leafsize; + PyObject *maxes; + double *raw_maxes; + PyObject *mins; + double *raw_mins; + PyObject *indices; + __pyx_t_5numpy_int32_t *raw_indices; +}; + + +struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree { + struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *(*__build)(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *, int, int, double *, double *); + PyObject *(*__free_tree)(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *, struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *); + void (*__query)(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *, double *, int *, double *, int, double, double, double); +}; +static struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *__pyx_vtabptr_5scipy_7spatial_7ckdtree_cKDTree; +/* Module declarations from numpy */ + +/* Module declarations from numpy */ + +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +/* Module declarations from stdlib */ + +/* Module declarations from scipy.spatial.ckdtree */ + +static PyTypeObject *__pyx_ptype_5scipy_7spatial_7ckdtree_cKDTree = 0; +static double __pyx_v_5scipy_7spatial_7ckdtree_infinity; +static double __pyx_k_17; +static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapcreate(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *, int); /*proto*/ +static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapdestroy(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *); /*proto*/ +static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapresize(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *, int); /*proto*/ +static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heappush(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *, struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem); /*proto*/ +static struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_f_5scipy_7spatial_7ckdtree_heappeek(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *); /*proto*/ +static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapremove(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *); /*proto*/ +static struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_f_5scipy_7spatial_7ckdtree_heappop(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *); /*proto*/ +static double __pyx_f_5scipy_7spatial_7ckdtree_dmax(double, double); /*proto*/ +static double __pyx_f_5scipy_7spatial_7ckdtree_dabs(double); /*proto*/ +static double __pyx_f_5scipy_7spatial_7ckdtree__distance_p(double *, double *, double, int, double); /*proto*/ + + +/* Implementation of scipy.spatial.ckdtree */ +static PyObject *__pyx_int_0; +static char __pyx_k___init__[] = "__init__"; +static PyObject *__pyx_kp___init__; +static char __pyx_k___del__[] = "__del__"; +static PyObject *__pyx_kp___del__; +static char __pyx_k_query[] = "query"; +static PyObject *__pyx_kp_query; +static char __pyx_k_numpy[] = "numpy"; +static PyObject *__pyx_kp_numpy; +static char __pyx_k_np[] = "np"; +static PyObject *__pyx_kp_np; +static char __pyx_k_kdtree[] = "kdtree"; +static PyObject *__pyx_kp_kdtree; +static char __pyx_k_inf[] = "inf"; +static PyObject *__pyx_kp_inf; +static char __pyx_k_ValueError[] = "ValueError"; +static PyObject *__pyx_kp_ValueError; +static char __pyx_k_ascontiguousarray[] = "ascontiguousarray"; +static PyObject *__pyx_kp_ascontiguousarray; +static char __pyx_k_dtype[] = "dtype"; +static PyObject *__pyx_kp_dtype; +static char __pyx_k_float[] = "float"; +static PyObject *__pyx_kp_float; +static char __pyx_k_shape[] = "shape"; +static PyObject *__pyx_kp_shape; +static char __pyx_k_amax[] = "amax"; +static PyObject *__pyx_kp_amax; +static char __pyx_k_axis[] = "axis"; +static PyObject *__pyx_kp_axis; +static char __pyx_k_amin[] = "amin"; +static PyObject *__pyx_kp_amin; +static char __pyx_k_arange[] = "arange"; +static PyObject *__pyx_kp_arange; +static char __pyx_k_int[] = "int"; +static PyObject *__pyx_kp_int; +static char __pyx_k_asarray[] = "asarray"; +static PyObject *__pyx_kp_asarray; +static char __pyx_k_astype[] = "astype"; +static PyObject *__pyx_kp_astype; +static char __pyx_k_newaxis[] = "newaxis"; +static PyObject *__pyx_kp_newaxis; +static char __pyx_k_prod[] = "prod"; +static PyObject *__pyx_kp_prod; +static char __pyx_k_reshape[] = "reshape"; +static PyObject *__pyx_kp_reshape; +static char __pyx_k_empty[] = "empty"; +static PyObject *__pyx_kp_empty; +static char __pyx_k_fill[] = "fill"; +static PyObject *__pyx_kp_fill; +static char __pyx_k_23[] = "i"; +static PyObject *__pyx_kp_23; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_kp_18; +static char __pyx_k_18[] = "Heap containing %d items cannot be resized to %d"; +static PyObject *__pyx_kp_19; +static char __pyx_k_19[] = "leafsize must be at least 1"; +static PyObject *__pyx_kp_20; +static char __pyx_k_20[] = "freeing cKDTree"; +static PyObject *__pyx_kp_21; +static PyObject *__pyx_kp_22; +static char __pyx_k_21[] = "x must consist of vectors of length %d but has shape %s"; +static char __pyx_k_22[] = "Only p-norms with 1<=p<=infinity permitted"; +static char __pyx_k___getbuffer__[] = "__getbuffer__"; +static PyObject *__pyx_kp___getbuffer__; +static char __pyx_k_RuntimeError[] = "RuntimeError"; +static PyObject *__pyx_kp_RuntimeError; +static PyObject *__pyx_kp_1; +static PyObject *__pyx_kp_16; +static PyObject *__pyx_builtin_RuntimeError; +static char __pyx_k_1[] = "Py_intptr_t and Py_ssize_t differs in size, numpy.pxd does not support this"; +static char __pyx_k_2[] = "b"; +static char __pyx_k_3[] = "B"; +static char __pyx_k_4[] = "h"; +static char __pyx_k_5[] = "H"; +static char __pyx_k_6[] = "i"; +static char __pyx_k_7[] = "I"; +static char __pyx_k_8[] = "l"; +static char __pyx_k_9[] = "L"; +static char __pyx_k_10[] = "q"; +static char __pyx_k_11[] = "Q"; +static char __pyx_k_12[] = "f"; +static char __pyx_k_13[] = "d"; +static char __pyx_k_14[] = "g"; +static char __pyx_k_15[] = "O"; +static char __pyx_k_16[] = "only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)"; + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":28 + * int space + * + * cdef inline heapcreate(heap* self,int initial_size): # <<<<<<<<<<<<<< + * self.space = initial_size + * self.heap = stdlib.malloc(sizeof(heapitem)*self.space) + */ + +static INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapcreate(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self, int __pyx_v_initial_size) { + PyObject *__pyx_r; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":29 + * + * cdef inline heapcreate(heap* self,int initial_size): + * self.space = initial_size # <<<<<<<<<<<<<< + * self.heap = stdlib.malloc(sizeof(heapitem)*self.space) + * self.n=0 + */ + __pyx_v_self->space = __pyx_v_initial_size; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":30 + * cdef inline heapcreate(heap* self,int initial_size): + * self.space = initial_size + * self.heap = stdlib.malloc(sizeof(heapitem)*self.space) # <<<<<<<<<<<<<< + * self.n=0 + * + */ + __pyx_v_self->heap = ((struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem *)malloc(((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem)) * __pyx_v_self->space))); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":31 + * self.space = initial_size + * self.heap = stdlib.malloc(sizeof(heapitem)*self.space) + * self.n=0 # <<<<<<<<<<<<<< + * + * cdef inline heapdestroy(heap* self): + */ + __pyx_v_self->n = 0; + + __pyx_r = Py_None; Py_INCREF(Py_None); + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":33 + * self.n=0 + * + * cdef inline heapdestroy(heap* self): # <<<<<<<<<<<<<< + * stdlib.free(self.heap) + * + */ + +static INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapdestroy(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self) { + PyObject *__pyx_r; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":34 + * + * cdef inline heapdestroy(heap* self): + * stdlib.free(self.heap) # <<<<<<<<<<<<<< + * + * cdef inline heapresize(heap* self, int new_space): + */ + free(__pyx_v_self->heap); + + __pyx_r = Py_None; Py_INCREF(Py_None); + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":36 + * stdlib.free(self.heap) + * + * cdef inline heapresize(heap* self, int new_space): # <<<<<<<<<<<<<< + * if new_spacen); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":38 + * cdef inline heapresize(heap* self, int new_space): + * if new_spacestdlib.realloc(self.heap,new_space*sizeof(heapitem)) + */ + __pyx_2 = PyInt_FromLong(__pyx_v_self->n); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyInt_FromLong(__pyx_v_new_space); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_4 = PyTuple_New(2); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_4, 0, __pyx_2); + PyTuple_SET_ITEM(__pyx_4, 1, __pyx_3); + __pyx_2 = 0; + __pyx_3 = 0; + __pyx_2 = PyNumber_Remainder(__pyx_kp_18, ((PyObject *)__pyx_4)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0; + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_3, 0, __pyx_2); + __pyx_2 = 0; + __pyx_4 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + __Pyx_Raise(__pyx_4, 0, 0); + Py_DECREF(__pyx_4); __pyx_4 = 0; + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + goto __pyx_L3; + } + __pyx_L3:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":39 + * if new_spacestdlib.realloc(self.heap,new_space*sizeof(heapitem)) + * + */ + __pyx_v_self->space = __pyx_v_new_space; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":40 + * raise ValueError("Heap containing %d items cannot be resized to %d" % (self.n, new_space)) + * self.space = new_space + * self.heap = stdlib.realloc(self.heap,new_space*sizeof(heapitem)) # <<<<<<<<<<<<<< + * + * cdef inline heappush(heap* self, heapitem item): + */ + __pyx_v_self->heap = ((struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem *)realloc(((void *)__pyx_v_self->heap), (__pyx_v_new_space * (sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem))))); + + __pyx_r = Py_None; Py_INCREF(Py_None); + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_2); + Py_XDECREF(__pyx_3); + Py_XDECREF(__pyx_4); + __Pyx_AddTraceback("scipy.spatial.ckdtree.heapresize"); + __pyx_r = 0; + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":42 + * self.heap = stdlib.realloc(self.heap,new_space*sizeof(heapitem)) + * + * cdef inline heappush(heap* self, heapitem item): # <<<<<<<<<<<<<< + * cdef int i + * cdef heapitem t + */ + +static INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heappush(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self, struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_v_item) { + int __pyx_v_i; + struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_v_t; + PyObject *__pyx_r; + int __pyx_1; + PyObject *__pyx_2 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":46 + * cdef heapitem t + * + * self.n += 1 # <<<<<<<<<<<<<< + * if self.n>self.space: + * heapresize(self,2*self.space+1) + */ + __pyx_v_self->n += 1; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":47 + * + * self.n += 1 + * if self.n>self.space: # <<<<<<<<<<<<<< + * heapresize(self,2*self.space+1) + * + */ + __pyx_1 = (__pyx_v_self->n > __pyx_v_self->space); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":48 + * self.n += 1 + * if self.n>self.space: + * heapresize(self,2*self.space+1) # <<<<<<<<<<<<<< + * + * i = self.n-1 + */ + __pyx_2 = __pyx_f_5scipy_7spatial_7ckdtree_heapresize(__pyx_v_self, ((2 * __pyx_v_self->space) + 1)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + goto __pyx_L3; + } + __pyx_L3:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":50 + * heapresize(self,2*self.space+1) + * + * i = self.n-1 # <<<<<<<<<<<<<< + * self.heap[i] = item + * while i>0 and self.heap[i].priorityn - 1); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":51 + * + * i = self.n-1 + * self.heap[i] = item # <<<<<<<<<<<<<< + * while i>0 and self.heap[i].priorityheap[__pyx_v_i]) = __pyx_v_item; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":52 + * i = self.n-1 + * self.heap[i] = item + * while i>0 and self.heap[i].priority 0); + if (__pyx_1) { + __pyx_1 = ((__pyx_v_self->heap[__pyx_v_i]).priority < (__pyx_v_self->heap[((__pyx_v_i - 1) / 2)]).priority); + } + if (!__pyx_1) break; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":53 + * self.heap[i] = item + * while i>0 and self.heap[i].priorityheap[((__pyx_v_i - 1) / 2)]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":54 + * while i>0 and self.heap[i].priorityheap[((__pyx_v_i - 1) / 2)]) = (__pyx_v_self->heap[__pyx_v_i]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":55 + * t = self.heap[(i-1)//2] + * self.heap[(i-1)//2] = self.heap[i] + * self.heap[i] = t # <<<<<<<<<<<<<< + * i = (i-1)//2 + * + */ + (__pyx_v_self->heap[__pyx_v_i]) = __pyx_v_t; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":56 + * self.heap[(i-1)//2] = self.heap[i] + * self.heap[i] = t + * i = (i-1)//2 # <<<<<<<<<<<<<< + * + * cdef heapitem heappeek(heap* self): + */ + __pyx_v_i = ((__pyx_v_i - 1) / 2); + } + + __pyx_r = Py_None; Py_INCREF(Py_None); + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_2); + __Pyx_AddTraceback("scipy.spatial.ckdtree.heappush"); + __pyx_r = 0; + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":58 + * i = (i-1)//2 + * + * cdef heapitem heappeek(heap* self): # <<<<<<<<<<<<<< + * return self.heap[0] + * + */ + +static struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_f_5scipy_7spatial_7ckdtree_heappeek(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self) { + struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_r; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":59 + * + * cdef heapitem heappeek(heap* self): + * return self.heap[0] # <<<<<<<<<<<<<< + * + * cdef heapremove(heap* self): + */ + __pyx_r = (__pyx_v_self->heap[0]); + goto __pyx_L0; + + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":61 + * return self.heap[0] + * + * cdef heapremove(heap* self): # <<<<<<<<<<<<<< + * cdef heapitem t + * cdef int i, j, k, l + */ + +static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapremove(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self) { + struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_v_t; + int __pyx_v_i; + int __pyx_v_j; + int __pyx_v_k; + int __pyx_v_l; + PyObject *__pyx_r; + int __pyx_1; + PyObject *__pyx_2 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":65 + * cdef int i, j, k, l + * + * self.heap[0] = self.heap[self.n-1] # <<<<<<<<<<<<<< + * self.n -= 1 + * if self.n < self.space//4 and self.space>40: #FIXME: magic number + */ + (__pyx_v_self->heap[0]) = (__pyx_v_self->heap[(__pyx_v_self->n - 1)]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":66 + * + * self.heap[0] = self.heap[self.n-1] + * self.n -= 1 # <<<<<<<<<<<<<< + * if self.n < self.space//4 and self.space>40: #FIXME: magic number + * heapresize(self,self.space//2+1) + */ + __pyx_v_self->n -= 1; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":67 + * self.heap[0] = self.heap[self.n-1] + * self.n -= 1 + * if self.n < self.space//4 and self.space>40: #FIXME: magic number # <<<<<<<<<<<<<< + * heapresize(self,self.space//2+1) + * + */ + __pyx_1 = (__pyx_v_self->n < (__pyx_v_self->space / 4)); + if (__pyx_1) { + __pyx_1 = (__pyx_v_self->space > 40); + } + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":68 + * self.n -= 1 + * if self.n < self.space//4 and self.space>40: #FIXME: magic number + * heapresize(self,self.space//2+1) # <<<<<<<<<<<<<< + * + * i=0 + */ + __pyx_2 = __pyx_f_5scipy_7spatial_7ckdtree_heapresize(__pyx_v_self, ((__pyx_v_self->space / 2) + 1)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + goto __pyx_L3; + } + __pyx_L3:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":70 + * heapresize(self,self.space//2+1) + * + * i=0 # <<<<<<<<<<<<<< + * j=1 + * k=2 + */ + __pyx_v_i = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":71 + * + * i=0 + * j=1 # <<<<<<<<<<<<<< + * k=2 + * while ((j self.heap[j].priority or + */ + __pyx_v_k = 2; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":73 + * j=1 + * k=2 + * while ((j self.heap[j].priority or + * kn); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":74 + * k=2 + * while ((j self.heap[j].priority or # <<<<<<<<<<<<<< + * k self.heap[k].priority)): + */ + __pyx_1 = ((__pyx_v_self->heap[__pyx_v_i]).priority > (__pyx_v_self->heap[__pyx_v_j]).priority); + } + if (!__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":75 + * while ((j self.heap[j].priority or + * k self.heap[k].priority)): + * if kself.heap[k].priority: + */ + __pyx_1 = (__pyx_v_k < __pyx_v_self->n); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":76 + * self.heap[i].priority > self.heap[j].priority or + * k self.heap[k].priority)): # <<<<<<<<<<<<<< + * if kself.heap[k].priority: + * l = k + */ + __pyx_1 = ((__pyx_v_self->heap[__pyx_v_i]).priority > (__pyx_v_self->heap[__pyx_v_k]).priority); + } + } + if (!__pyx_1) break; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":77 + * k self.heap[k].priority)): + * if kself.heap[k].priority: # <<<<<<<<<<<<<< + * l = k + * else: + */ + __pyx_1 = (__pyx_v_k < __pyx_v_self->n); + if (__pyx_1) { + __pyx_1 = ((__pyx_v_self->heap[__pyx_v_j]).priority > (__pyx_v_self->heap[__pyx_v_k]).priority); + } + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":78 + * self.heap[i].priority > self.heap[k].priority)): + * if kself.heap[k].priority: + * l = k # <<<<<<<<<<<<<< + * else: + * l = j + */ + __pyx_v_l = __pyx_v_k; + goto __pyx_L6; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":80 + * l = k + * else: + * l = j # <<<<<<<<<<<<<< + * t = self.heap[l] + * self.heap[l] = self.heap[i] + */ + __pyx_v_l = __pyx_v_j; + } + __pyx_L6:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":81 + * else: + * l = j + * t = self.heap[l] # <<<<<<<<<<<<<< + * self.heap[l] = self.heap[i] + * self.heap[i] = t + */ + __pyx_v_t = (__pyx_v_self->heap[__pyx_v_l]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":82 + * l = j + * t = self.heap[l] + * self.heap[l] = self.heap[i] # <<<<<<<<<<<<<< + * self.heap[i] = t + * i = l + */ + (__pyx_v_self->heap[__pyx_v_l]) = (__pyx_v_self->heap[__pyx_v_i]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":83 + * t = self.heap[l] + * self.heap[l] = self.heap[i] + * self.heap[i] = t # <<<<<<<<<<<<<< + * i = l + * j = 2*i+1 + */ + (__pyx_v_self->heap[__pyx_v_i]) = __pyx_v_t; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":84 + * self.heap[l] = self.heap[i] + * self.heap[i] = t + * i = l # <<<<<<<<<<<<<< + * j = 2*i+1 + * k = 2*i+2 + */ + __pyx_v_i = __pyx_v_l; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":85 + * self.heap[i] = t + * i = l + * j = 2*i+1 # <<<<<<<<<<<<<< + * k = 2*i+2 + * + */ + __pyx_v_j = ((2 * __pyx_v_i) + 1); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":86 + * i = l + * j = 2*i+1 + * k = 2*i+2 # <<<<<<<<<<<<<< + * + * cdef heapitem heappop(heap* self): + */ + __pyx_v_k = ((2 * __pyx_v_i) + 2); + } + + __pyx_r = Py_None; Py_INCREF(Py_None); + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_2); + __Pyx_AddTraceback("scipy.spatial.ckdtree.heapremove"); + __pyx_r = 0; + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":88 + * k = 2*i+2 + * + * cdef heapitem heappop(heap* self): # <<<<<<<<<<<<<< + * cdef heapitem it + * it = heappeek(self) + */ + +static struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_f_5scipy_7spatial_7ckdtree_heappop(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self) { + struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_v_it; + struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_r; + PyObject *__pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":90 + * cdef heapitem heappop(heap* self): + * cdef heapitem it + * it = heappeek(self) # <<<<<<<<<<<<<< + * heapremove(self) + * return it + */ + __pyx_v_it = __pyx_f_5scipy_7spatial_7ckdtree_heappeek(__pyx_v_self); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":91 + * cdef heapitem it + * it = heappeek(self) + * heapremove(self) # <<<<<<<<<<<<<< + * return it + * + */ + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapremove(__pyx_v_self); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":92 + * it = heappeek(self) + * heapremove(self) + * return it # <<<<<<<<<<<<<< + * + * + */ + __pyx_r = __pyx_v_it; + goto __pyx_L0; + + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_1); + __Pyx_WriteUnraisable("scipy.spatial.ckdtree.heappop"); + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":99 + * + * # utility functions + * cdef inline double dmax(double x, double y): # <<<<<<<<<<<<<< + * if x>y: + * return x + */ + +static INLINE double __pyx_f_5scipy_7spatial_7ckdtree_dmax(double __pyx_v_x, double __pyx_v_y) { + double __pyx_r; + int __pyx_1; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":100 + * # utility functions + * cdef inline double dmax(double x, double y): + * if x>y: # <<<<<<<<<<<<<< + * return x + * else: + */ + __pyx_1 = (__pyx_v_x > __pyx_v_y); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":101 + * cdef inline double dmax(double x, double y): + * if x>y: + * return x # <<<<<<<<<<<<<< + * else: + * return y + */ + __pyx_r = __pyx_v_x; + goto __pyx_L0; + goto __pyx_L3; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":103 + * return x + * else: + * return y # <<<<<<<<<<<<<< + * cdef inline double dabs(double x): + * if x>0: + */ + __pyx_r = __pyx_v_y; + goto __pyx_L0; + } + __pyx_L3:; + + __pyx_r = 0; + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":104 + * else: + * return y + * cdef inline double dabs(double x): # <<<<<<<<<<<<<< + * if x>0: + * return x + */ + +static INLINE double __pyx_f_5scipy_7spatial_7ckdtree_dabs(double __pyx_v_x) { + double __pyx_r; + int __pyx_1; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":105 + * return y + * cdef inline double dabs(double x): + * if x>0: # <<<<<<<<<<<<<< + * return x + * else: + */ + __pyx_1 = (__pyx_v_x > 0); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":106 + * cdef inline double dabs(double x): + * if x>0: + * return x # <<<<<<<<<<<<<< + * else: + * return -x + */ + __pyx_r = __pyx_v_x; + goto __pyx_L0; + goto __pyx_L3; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":108 + * return x + * else: + * return -x # <<<<<<<<<<<<<< + * cdef inline double _distance_p(double*x,double*y,double p,int k,double upperbound): + * """Compute the distance between x and y + */ + __pyx_r = (-__pyx_v_x); + goto __pyx_L0; + } + __pyx_L3:; + + __pyx_r = 0; + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":109 + * else: + * return -x + * cdef inline double _distance_p(double*x,double*y,double p,int k,double upperbound): # <<<<<<<<<<<<<< + * """Compute the distance between x and y + * + */ + +static INLINE double __pyx_f_5scipy_7spatial_7ckdtree__distance_p(double *__pyx_v_x, double *__pyx_v_y, double __pyx_v_p, int __pyx_v_k, double __pyx_v_upperbound) { + int __pyx_v_i; + double __pyx_v_r; + double __pyx_r; + int __pyx_1; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":118 + * cdef int i + * cdef double r + * r = 0 # <<<<<<<<<<<<<< + * if p==infinity: + * for i in range(k): + */ + __pyx_v_r = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":119 + * cdef double r + * r = 0 + * if p==infinity: # <<<<<<<<<<<<<< + * for i in range(k): + * r = dmax(r,dabs(x[i]-y[i])) + */ + __pyx_1 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":120 + * r = 0 + * if p==infinity: + * for i in range(k): # <<<<<<<<<<<<<< + * r = dmax(r,dabs(x[i]-y[i])) + * if r>upperbound: + */ + for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_k; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":121 + * if p==infinity: + * for i in range(k): + * r = dmax(r,dabs(x[i]-y[i])) # <<<<<<<<<<<<<< + * if r>upperbound: + * return r + */ + __pyx_v_r = __pyx_f_5scipy_7spatial_7ckdtree_dmax(__pyx_v_r, __pyx_f_5scipy_7spatial_7ckdtree_dabs(((__pyx_v_x[__pyx_v_i]) - (__pyx_v_y[__pyx_v_i])))); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":122 + * for i in range(k): + * r = dmax(r,dabs(x[i]-y[i])) + * if r>upperbound: # <<<<<<<<<<<<<< + * return r + * elif p==1: + */ + __pyx_1 = (__pyx_v_r > __pyx_v_upperbound); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":123 + * r = dmax(r,dabs(x[i]-y[i])) + * if r>upperbound: + * return r # <<<<<<<<<<<<<< + * elif p==1: + * for i in range(k): + */ + __pyx_r = __pyx_v_r; + goto __pyx_L0; + goto __pyx_L6; + } + __pyx_L6:; + } + goto __pyx_L3; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":124 + * if r>upperbound: + * return r + * elif p==1: # <<<<<<<<<<<<<< + * for i in range(k): + * r += dabs(x[i]-y[i]) + */ + __pyx_1 = (__pyx_v_p == 1); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":125 + * return r + * elif p==1: + * for i in range(k): # <<<<<<<<<<<<<< + * r += dabs(x[i]-y[i]) + * if r>upperbound: + */ + for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_k; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":126 + * elif p==1: + * for i in range(k): + * r += dabs(x[i]-y[i]) # <<<<<<<<<<<<<< + * if r>upperbound: + * return r + */ + __pyx_v_r += __pyx_f_5scipy_7spatial_7ckdtree_dabs(((__pyx_v_x[__pyx_v_i]) - (__pyx_v_y[__pyx_v_i]))); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":127 + * for i in range(k): + * r += dabs(x[i]-y[i]) + * if r>upperbound: # <<<<<<<<<<<<<< + * return r + * else: + */ + __pyx_1 = (__pyx_v_r > __pyx_v_upperbound); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":128 + * r += dabs(x[i]-y[i]) + * if r>upperbound: + * return r # <<<<<<<<<<<<<< + * else: + * for i in range(k): + */ + __pyx_r = __pyx_v_r; + goto __pyx_L0; + goto __pyx_L9; + } + __pyx_L9:; + } + goto __pyx_L3; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":130 + * return r + * else: + * for i in range(k): # <<<<<<<<<<<<<< + * r += dabs(x[i]-y[i])**p + * if r>upperbound: + */ + for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_k; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":131 + * else: + * for i in range(k): + * r += dabs(x[i]-y[i])**p # <<<<<<<<<<<<<< + * if r>upperbound: + * return r + */ + __pyx_v_r += pow(__pyx_f_5scipy_7spatial_7ckdtree_dabs(((__pyx_v_x[__pyx_v_i]) - (__pyx_v_y[__pyx_v_i]))), __pyx_v_p); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":132 + * for i in range(k): + * r += dabs(x[i]-y[i])**p + * if r>upperbound: # <<<<<<<<<<<<<< + * return r + * return r + */ + __pyx_1 = (__pyx_v_r > __pyx_v_upperbound); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":133 + * r += dabs(x[i]-y[i])**p + * if r>upperbound: + * return r # <<<<<<<<<<<<<< + * return r + * + */ + __pyx_r = __pyx_v_r; + goto __pyx_L0; + goto __pyx_L12; + } + __pyx_L12:; + } + } + __pyx_L3:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":134 + * if r>upperbound: + * return r + * return r # <<<<<<<<<<<<<< + * + * + */ + __pyx_r = __pyx_v_r; + goto __pyx_L0; + + __pyx_r = 0; + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":195 + * cdef object indices + * cdef np.int32_t* raw_indices + * def __init__(cKDTree self, data, int leafsize=10): # <<<<<<<<<<<<<< + * """Construct a kd-tree. + * + */ + +static int __pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static char __pyx_doc_5scipy_7spatial_7ckdtree_7cKDTree___init__[] = "Construct a kd-tree.\n\n Parameters:\n ===========\n\n data : array-like, shape (n,m)\n The n data points of dimension mto be indexed. This array is \n not copied unless this is necessary to produce a contiguous \n array of doubles, and so modifying this data will result in \n bogus results.\n leafsize : positive integer\n The number of points at which the algorithm switches over to\n brute-force.\n "; +static int __pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_data = 0; + int __pyx_v_leafsize; + PyArrayObject *__pyx_v_inner_data; + PyArrayObject *__pyx_v_inner_maxes; + PyArrayObject *__pyx_v_inner_mins; + PyArrayObject *__pyx_v_inner_indices; + Py_buffer __pyx_bstruct_inner_indices; + Py_ssize_t __pyx_bstride_0_inner_indices = 0; + Py_ssize_t __pyx_bshape_0_inner_indices = 0; + Py_buffer __pyx_bstruct_inner_maxes; + Py_ssize_t __pyx_bstride_0_inner_maxes = 0; + Py_ssize_t __pyx_bshape_0_inner_maxes = 0; + Py_buffer __pyx_bstruct_inner_data; + Py_ssize_t __pyx_bstride_0_inner_data = 0; + Py_ssize_t __pyx_bstride_1_inner_data = 0; + Py_ssize_t __pyx_bshape_0_inner_data = 0; + Py_ssize_t __pyx_bshape_1_inner_data = 0; + Py_buffer __pyx_bstruct_inner_mins; + Py_ssize_t __pyx_bstride_0_inner_mins = 0; + Py_ssize_t __pyx_bshape_0_inner_mins = 0; + int __pyx_r; + PyObject *__pyx_1 = 0; + PyObject *__pyx_2 = 0; + PyObject *__pyx_3 = 0; + PyObject *__pyx_4 = 0; + PyObject *__pyx_5 = 0; + int __pyx_6; + int __pyx_7; + PyObject *__pyx_8 = 0; + PyArrayObject *__pyx_t_1 = NULL; + int __pyx_t_2; + PyObject *__pyx_t_3 = NULL; + PyObject *__pyx_t_4 = NULL; + PyObject *__pyx_t_5 = NULL; + PyArrayObject *__pyx_t_6 = NULL; + PyArrayObject *__pyx_t_7 = NULL; + PyArrayObject *__pyx_t_8 = NULL; + static char *__pyx_argnames[] = {"data","leafsize",0}; + __pyx_v_leafsize = 10; + if (likely(!__pyx_kwds) && likely(1 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 2)) { + __pyx_v_data = PyTuple_GET_ITEM(__pyx_args, 0); + if (PyTuple_GET_SIZE(__pyx_args) > 1) { + __pyx_v_leafsize = __pyx_PyInt_int(PyTuple_GET_ITEM(__pyx_args, 1)); if (unlikely((__pyx_v_leafsize == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 195; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } + } + else { + if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "O|i", __pyx_argnames, &__pyx_v_data, &__pyx_v_leafsize))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 195; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } + goto __pyx_L4; + __pyx_L3_error:; + __Pyx_AddTraceback("scipy.spatial.ckdtree.cKDTree.__init__"); + return -1; + __pyx_L4:; + __pyx_v_inner_data = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); + __pyx_bstruct_inner_data.buf = NULL; + __pyx_v_inner_maxes = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); + __pyx_bstruct_inner_maxes.buf = NULL; + __pyx_v_inner_mins = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); + __pyx_bstruct_inner_mins.buf = NULL; + __pyx_v_inner_indices = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); + __pyx_bstruct_inner_indices.buf = NULL; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":214 + * cdef np.ndarray[double, ndim=1] inner_mins + * cdef np.ndarray[np.int32_t, ndim=1] inner_indices + * self.data = np.ascontiguousarray(data,dtype=np.float) # <<<<<<<<<<<<<< + * self.n, self.m = np.shape(self.data) + * self.leafsize = leafsize + */ + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_ascontiguousarray); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_data); + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_v_data); + __pyx_3 = PyDict_New(); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_4 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyObject_GetAttr(__pyx_4, __pyx_kp_float); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + if (PyDict_SetItem(__pyx_3, __pyx_kp_dtype, __pyx_5) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_4 = PyEval_CallObjectWithKeywords(__pyx_2, ((PyObject *)__pyx_1), ((PyObject *)__pyx_3)); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + Py_DECREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data = __pyx_4; + __pyx_4 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":215 + * cdef np.ndarray[np.int32_t, ndim=1] inner_indices + * self.data = np.ascontiguousarray(data,dtype=np.float) + * self.n, self.m = np.shape(self.data) # <<<<<<<<<<<<<< + * self.leafsize = leafsize + * if self.leafsize<1: + */ + __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_5, __pyx_kp_shape); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); + PyTuple_SET_ITEM(__pyx_1, 0, ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); + __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + if (PyTuple_CheckExact(__pyx_3) && PyTuple_GET_SIZE(__pyx_3) == 2) { + PyObject* tuple = __pyx_3; + __pyx_5 = PyTuple_GET_ITEM(tuple, 0); + Py_INCREF(__pyx_5); + __pyx_6 = __pyx_PyInt_int(__pyx_5); if (unlikely((__pyx_6 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->n = __pyx_6; + __pyx_2 = PyTuple_GET_ITEM(tuple, 1); + Py_INCREF(__pyx_2); + __pyx_6 = __pyx_PyInt_int(__pyx_2); if (unlikely((__pyx_6 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m = __pyx_6; + Py_DECREF(__pyx_3); __pyx_3 = 0; + } + else { + __pyx_4 = PyObject_GetIter(__pyx_3); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_5 = __Pyx_UnpackItem(__pyx_4, 0); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_6 = __pyx_PyInt_int(__pyx_5); if (unlikely((__pyx_6 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->n = __pyx_6; + __pyx_2 = __Pyx_UnpackItem(__pyx_4, 1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_6 = __pyx_PyInt_int(__pyx_2); if (unlikely((__pyx_6 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m = __pyx_6; + if (__Pyx_EndUnpack(__pyx_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":216 + * self.data = np.ascontiguousarray(data,dtype=np.float) + * self.n, self.m = np.shape(self.data) + * self.leafsize = leafsize # <<<<<<<<<<<<<< + * if self.leafsize<1: + * raise ValueError("leafsize must be at least 1") + */ + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->leafsize = __pyx_v_leafsize; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":217 + * self.n, self.m = np.shape(self.data) + * self.leafsize = leafsize + * if self.leafsize<1: # <<<<<<<<<<<<<< + * raise ValueError("leafsize must be at least 1") + * self.maxes = np.ascontiguousarray(np.amax(self.data,axis=0)) + */ + __pyx_7 = (((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->leafsize < 1); + if (__pyx_7) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":218 + * self.leafsize = leafsize + * if self.leafsize<1: + * raise ValueError("leafsize must be at least 1") # <<<<<<<<<<<<<< + * self.maxes = np.ascontiguousarray(np.amax(self.data,axis=0)) + * self.mins = np.ascontiguousarray(np.amin(self.data,axis=0)) + */ + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 218; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_kp_19); + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_kp_19); + __pyx_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 218; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + __Pyx_Raise(__pyx_5, 0, 0); + Py_DECREF(__pyx_5); __pyx_5 = 0; + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 218; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + goto __pyx_L5; + } + __pyx_L5:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":219 + * if self.leafsize<1: + * raise ValueError("leafsize must be at least 1") + * self.maxes = np.ascontiguousarray(np.amax(self.data,axis=0)) # <<<<<<<<<<<<<< + * self.mins = np.ascontiguousarray(np.amin(self.data,axis=0)) + * self.indices = np.ascontiguousarray(np.arange(self.n,dtype=np.int)) + */ + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_ascontiguousarray); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_4 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_4, __pyx_kp_amax); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + __pyx_5 = PyTuple_New(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); + PyTuple_SET_ITEM(__pyx_5, 0, ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); + __pyx_2 = PyDict_New(); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyDict_SetItem(__pyx_2, __pyx_kp_axis, __pyx_int_0) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_4 = PyEval_CallObjectWithKeywords(__pyx_1, ((PyObject *)__pyx_5), ((PyObject *)__pyx_2)); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_4); + __pyx_4 = 0; + __pyx_5 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + Py_DECREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes); + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes = __pyx_5; + __pyx_5 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":220 + * raise ValueError("leafsize must be at least 1") + * self.maxes = np.ascontiguousarray(np.amax(self.data,axis=0)) + * self.mins = np.ascontiguousarray(np.amin(self.data,axis=0)) # <<<<<<<<<<<<<< + * self.indices = np.ascontiguousarray(np.arange(self.n,dtype=np.int)) + * + */ + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_kp_ascontiguousarray); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_3, __pyx_kp_amin); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_5 = PyTuple_New(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); + PyTuple_SET_ITEM(__pyx_5, 0, ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); + __pyx_2 = PyDict_New(); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyDict_SetItem(__pyx_2, __pyx_kp_axis, __pyx_int_0) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyEval_CallObjectWithKeywords(__pyx_1, ((PyObject *)__pyx_5), ((PyObject *)__pyx_2)); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_3); + __pyx_3 = 0; + __pyx_5 = PyObject_Call(__pyx_4, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + Py_DECREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins); + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins = __pyx_5; + __pyx_5 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":221 + * self.maxes = np.ascontiguousarray(np.amax(self.data,axis=0)) + * self.mins = np.ascontiguousarray(np.amin(self.data,axis=0)) + * self.indices = np.ascontiguousarray(np.arange(self.n,dtype=np.int)) # <<<<<<<<<<<<<< + * + * inner_data = self.data + */ + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_ascontiguousarray); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_4 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_4, __pyx_kp_arange); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_4); __pyx_4 = 0; + __pyx_5 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->n); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_2, 0, __pyx_5); + __pyx_5 = 0; + __pyx_4 = PyDict_New(); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_8 = PyObject_GetAttr(__pyx_5, __pyx_kp_int); if (unlikely(!__pyx_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + if (PyDict_SetItem(__pyx_4, __pyx_kp_dtype, __pyx_8) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_8); __pyx_8 = 0; + __pyx_5 = PyEval_CallObjectWithKeywords(__pyx_1, ((PyObject *)__pyx_2), ((PyObject *)__pyx_4)); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0; + __pyx_8 = PyTuple_New(1); if (unlikely(!__pyx_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_8, 0, __pyx_5); + __pyx_5 = 0; + __pyx_1 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_8), NULL); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + Py_DECREF(((PyObject *)__pyx_8)); __pyx_8 = 0; + Py_DECREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices); + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices = __pyx_1; + __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":223 + * self.indices = np.ascontiguousarray(np.arange(self.n,dtype=np.int)) + * + * inner_data = self.data # <<<<<<<<<<<<<< + * self.raw_data = inner_data.data + * inner_maxes = self.maxes + */ + if (!(__Pyx_TypeTest(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 223; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_data, &__pyx_bstruct_inner_data); + __pyx_t_2 = __Pyx_GetBuffer_double((PyObject*)__pyx_t_1, &__pyx_bstruct_inner_data, PyBUF_FORMAT| PyBUF_STRIDES, 2); + if (unlikely(__pyx_t_2 < 0)) + { + PyErr_Fetch(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5); + if (unlikely(__Pyx_GetBuffer_double((PyObject*)__pyx_v_inner_data, &__pyx_bstruct_inner_data, PyBUF_FORMAT| PyBUF_STRIDES, 2) == -1)) { + Py_XDECREF(__pyx_t_3); Py_XDECREF(__pyx_t_4); Py_XDECREF(__pyx_t_5); + __Pyx_RaiseBufferFallbackError(); + } else { + PyErr_Restore(__pyx_t_3, __pyx_t_4, __pyx_t_5); + } + } + __pyx_bstride_0_inner_data = __pyx_bstruct_inner_data.strides[0]; __pyx_bstride_1_inner_data = __pyx_bstruct_inner_data.strides[1]; + __pyx_bshape_0_inner_data = __pyx_bstruct_inner_data.shape[0]; __pyx_bshape_1_inner_data = __pyx_bstruct_inner_data.shape[1]; + if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 223; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = 0; + Py_INCREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); + Py_DECREF(((PyObject *)__pyx_v_inner_data)); + __pyx_v_inner_data = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":224 + * + * inner_data = self.data + * self.raw_data = inner_data.data # <<<<<<<<<<<<<< + * inner_maxes = self.maxes + * self.raw_maxes = inner_maxes.data + */ + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_data = ((double *)__pyx_v_inner_data->data); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":225 + * inner_data = self.data + * self.raw_data = inner_data.data + * inner_maxes = self.maxes # <<<<<<<<<<<<<< + * self.raw_maxes = inner_maxes.data + * inner_mins = self.mins + */ + if (!(__Pyx_TypeTest(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 225; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_maxes, &__pyx_bstruct_inner_maxes); + __pyx_t_2 = __Pyx_GetBuffer_double((PyObject*)__pyx_t_6, &__pyx_bstruct_inner_maxes, PyBUF_FORMAT| PyBUF_STRIDES, 1); + if (unlikely(__pyx_t_2 < 0)) + { + PyErr_Fetch(&__pyx_t_5, &__pyx_t_4, &__pyx_t_3); + if (unlikely(__Pyx_GetBuffer_double((PyObject*)__pyx_v_inner_maxes, &__pyx_bstruct_inner_maxes, PyBUF_FORMAT| PyBUF_STRIDES, 1) == -1)) { + Py_XDECREF(__pyx_t_5); Py_XDECREF(__pyx_t_4); Py_XDECREF(__pyx_t_3); + __Pyx_RaiseBufferFallbackError(); + } else { + PyErr_Restore(__pyx_t_5, __pyx_t_4, __pyx_t_3); + } + } + __pyx_bstride_0_inner_maxes = __pyx_bstruct_inner_maxes.strides[0]; + __pyx_bshape_0_inner_maxes = __pyx_bstruct_inner_maxes.shape[0]; + if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 225; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = 0; + Py_INCREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes); + Py_DECREF(((PyObject *)__pyx_v_inner_maxes)); + __pyx_v_inner_maxes = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":226 + * self.raw_data = inner_data.data + * inner_maxes = self.maxes + * self.raw_maxes = inner_maxes.data # <<<<<<<<<<<<<< + * inner_mins = self.mins + * self.raw_mins = inner_mins.data + */ + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_maxes = ((double *)__pyx_v_inner_maxes->data); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":227 + * inner_maxes = self.maxes + * self.raw_maxes = inner_maxes.data + * inner_mins = self.mins # <<<<<<<<<<<<<< + * self.raw_mins = inner_mins.data + * inner_indices = self.indices + */ + if (!(__Pyx_TypeTest(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 227; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_7 = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_mins, &__pyx_bstruct_inner_mins); + __pyx_t_2 = __Pyx_GetBuffer_double((PyObject*)__pyx_t_7, &__pyx_bstruct_inner_mins, PyBUF_FORMAT| PyBUF_STRIDES, 1); + if (unlikely(__pyx_t_2 < 0)) + { + PyErr_Fetch(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5); + if (unlikely(__Pyx_GetBuffer_double((PyObject*)__pyx_v_inner_mins, &__pyx_bstruct_inner_mins, PyBUF_FORMAT| PyBUF_STRIDES, 1) == -1)) { + Py_XDECREF(__pyx_t_3); Py_XDECREF(__pyx_t_4); Py_XDECREF(__pyx_t_5); + __Pyx_RaiseBufferFallbackError(); + } else { + PyErr_Restore(__pyx_t_3, __pyx_t_4, __pyx_t_5); + } + } + __pyx_bstride_0_inner_mins = __pyx_bstruct_inner_mins.strides[0]; + __pyx_bshape_0_inner_mins = __pyx_bstruct_inner_mins.shape[0]; + if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 227; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_7 = 0; + Py_INCREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins); + Py_DECREF(((PyObject *)__pyx_v_inner_mins)); + __pyx_v_inner_mins = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":228 + * self.raw_maxes = inner_maxes.data + * inner_mins = self.mins + * self.raw_mins = inner_mins.data # <<<<<<<<<<<<<< + * inner_indices = self.indices + * self.raw_indices = inner_indices.data + */ + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_mins = ((double *)__pyx_v_inner_mins->data); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":229 + * inner_mins = self.mins + * self.raw_mins = inner_mins.data + * inner_indices = self.indices # <<<<<<<<<<<<<< + * self.raw_indices = inner_indices.data + * + */ + if (!(__Pyx_TypeTest(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 229; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_8 = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_indices, &__pyx_bstruct_inner_indices); + __pyx_t_2 = __Pyx_GetBuffer_nn___pyx_t_5numpy_int32_t((PyObject*)__pyx_t_8, &__pyx_bstruct_inner_indices, PyBUF_FORMAT| PyBUF_STRIDES, 1); + if (unlikely(__pyx_t_2 < 0)) + { + PyErr_Fetch(&__pyx_t_5, &__pyx_t_4, &__pyx_t_3); + if (unlikely(__Pyx_GetBuffer_nn___pyx_t_5numpy_int32_t((PyObject*)__pyx_v_inner_indices, &__pyx_bstruct_inner_indices, PyBUF_FORMAT| PyBUF_STRIDES, 1) == -1)) { + Py_XDECREF(__pyx_t_5); Py_XDECREF(__pyx_t_4); Py_XDECREF(__pyx_t_3); + __Pyx_RaiseBufferFallbackError(); + } else { + PyErr_Restore(__pyx_t_5, __pyx_t_4, __pyx_t_3); + } + } + __pyx_bstride_0_inner_indices = __pyx_bstruct_inner_indices.strides[0]; + __pyx_bshape_0_inner_indices = __pyx_bstruct_inner_indices.shape[0]; + if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 229; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_8 = 0; + Py_INCREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices); + Py_DECREF(((PyObject *)__pyx_v_inner_indices)); + __pyx_v_inner_indices = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":230 + * self.raw_mins = inner_mins.data + * inner_indices = self.indices + * self.raw_indices = inner_indices.data # <<<<<<<<<<<<<< + * + * self.tree = self.__build(0, self.n, self.raw_maxes, self.raw_mins) + */ + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_indices = ((__pyx_t_5numpy_int32_t *)__pyx_v_inner_indices->data); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":232 + * self.raw_indices = inner_indices.data + * + * self.tree = self.__build(0, self.n, self.raw_maxes, self.raw_mins) # <<<<<<<<<<<<<< + * + * cdef innernode* __build(cKDTree self, int start_idx, int end_idx, double* maxes, double* mins): + */ + ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->tree = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->__pyx_vtab)->__build(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self), 0, ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->n, ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_maxes, ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_mins); + + __pyx_r = 0; + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_1); + Py_XDECREF(__pyx_2); + Py_XDECREF(__pyx_3); + Py_XDECREF(__pyx_4); + Py_XDECREF(__pyx_5); + Py_XDECREF(__pyx_8); + { PyObject *__pyx_type, *__pyx_value, *__pyx_tb; + PyErr_Fetch(&__pyx_type, &__pyx_value, &__pyx_tb); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_indices, &__pyx_bstruct_inner_indices); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_maxes, &__pyx_bstruct_inner_maxes); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_data, &__pyx_bstruct_inner_data); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_mins, &__pyx_bstruct_inner_mins); + PyErr_Restore(__pyx_type, __pyx_value, __pyx_tb);} + __Pyx_AddTraceback("scipy.spatial.ckdtree.cKDTree.__init__"); + __pyx_r = -1; + goto __pyx_L2; + __pyx_L0:; + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_indices, &__pyx_bstruct_inner_indices); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_maxes, &__pyx_bstruct_inner_maxes); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_data, &__pyx_bstruct_inner_data); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_mins, &__pyx_bstruct_inner_mins); + __pyx_L2:; + Py_DECREF(__pyx_v_inner_data); + Py_DECREF(__pyx_v_inner_maxes); + Py_DECREF(__pyx_v_inner_mins); + Py_DECREF(__pyx_v_inner_indices); + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":234 + * self.tree = self.__build(0, self.n, self.raw_maxes, self.raw_mins) + * + * cdef innernode* __build(cKDTree self, int start_idx, int end_idx, double* maxes, double* mins): # <<<<<<<<<<<<<< + * cdef leafnode* n + * cdef innernode* ni + */ + +static struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_f_5scipy_7spatial_7ckdtree_7cKDTree___build(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *__pyx_v_self, int __pyx_v_start_idx, int __pyx_v_end_idx, double *__pyx_v_maxes, double *__pyx_v_mins) { + struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode *__pyx_v_n; + struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_v_ni; + int __pyx_v_i; + int __pyx_v_j; + int __pyx_v_t; + int __pyx_v_p; + int __pyx_v_q; + int __pyx_v_d; + double __pyx_v_size; + double __pyx_v_split; + double __pyx_v_minval; + double __pyx_v_maxval; + double *__pyx_v_mids; + struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_r; + int __pyx_1; + int __pyx_2; + long __pyx_3; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":240 + * cdef double size, split, minval, maxval + * cdef double*mids + * if end_idx-start_idx<=self.leafsize: # <<<<<<<<<<<<<< + * n = stdlib.malloc(sizeof(leafnode)) + * n.split_dim = -1 + */ + __pyx_1 = ((__pyx_v_end_idx - __pyx_v_start_idx) <= __pyx_v_self->leafsize); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":241 + * cdef double*mids + * if end_idx-start_idx<=self.leafsize: + * n = stdlib.malloc(sizeof(leafnode)) # <<<<<<<<<<<<<< + * n.split_dim = -1 + * n.start_idx = start_idx + */ + __pyx_v_n = ((struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode *)malloc((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode)))); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":242 + * if end_idx-start_idx<=self.leafsize: + * n = stdlib.malloc(sizeof(leafnode)) + * n.split_dim = -1 # <<<<<<<<<<<<<< + * n.start_idx = start_idx + * n.end_idx = end_idx + */ + __pyx_v_n->split_dim = -1; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":243 + * n = stdlib.malloc(sizeof(leafnode)) + * n.split_dim = -1 + * n.start_idx = start_idx # <<<<<<<<<<<<<< + * n.end_idx = end_idx + * return n + */ + __pyx_v_n->start_idx = __pyx_v_start_idx; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":244 + * n.split_dim = -1 + * n.start_idx = start_idx + * n.end_idx = end_idx # <<<<<<<<<<<<<< + * return n + * else: + */ + __pyx_v_n->end_idx = __pyx_v_end_idx; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":245 + * n.start_idx = start_idx + * n.end_idx = end_idx + * return n # <<<<<<<<<<<<<< + * else: + * d = 0 + */ + __pyx_r = ((struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *)__pyx_v_n); + goto __pyx_L0; + goto __pyx_L3; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":247 + * return n + * else: + * d = 0 # <<<<<<<<<<<<<< + * size = 0 + * for i in range(self.m): + */ + __pyx_v_d = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":248 + * else: + * d = 0 + * size = 0 # <<<<<<<<<<<<<< + * for i in range(self.m): + * if maxes[i]-mins[i] > size: + */ + __pyx_v_size = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":249 + * d = 0 + * size = 0 + * for i in range(self.m): # <<<<<<<<<<<<<< + * if maxes[i]-mins[i] > size: + * d = i + */ + __pyx_2 = __pyx_v_self->m; + for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":250 + * size = 0 + * for i in range(self.m): + * if maxes[i]-mins[i] > size: # <<<<<<<<<<<<<< + * d = i + * size = maxes[i]-mins[i] + */ + __pyx_1 = (((__pyx_v_maxes[__pyx_v_i]) - (__pyx_v_mins[__pyx_v_i])) > __pyx_v_size); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":251 + * for i in range(self.m): + * if maxes[i]-mins[i] > size: + * d = i # <<<<<<<<<<<<<< + * size = maxes[i]-mins[i] + * maxval = maxes[d] + */ + __pyx_v_d = __pyx_v_i; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":252 + * if maxes[i]-mins[i] > size: + * d = i + * size = maxes[i]-mins[i] # <<<<<<<<<<<<<< + * maxval = maxes[d] + * minval = mins[d] + */ + __pyx_v_size = ((__pyx_v_maxes[__pyx_v_i]) - (__pyx_v_mins[__pyx_v_i])); + goto __pyx_L6; + } + __pyx_L6:; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":253 + * d = i + * size = maxes[i]-mins[i] + * maxval = maxes[d] # <<<<<<<<<<<<<< + * minval = mins[d] + * if maxval==minval: + */ + __pyx_v_maxval = (__pyx_v_maxes[__pyx_v_d]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":254 + * size = maxes[i]-mins[i] + * maxval = maxes[d] + * minval = mins[d] # <<<<<<<<<<<<<< + * if maxval==minval: + * # all points are identical; warn user? + */ + __pyx_v_minval = (__pyx_v_mins[__pyx_v_d]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":255 + * maxval = maxes[d] + * minval = mins[d] + * if maxval==minval: # <<<<<<<<<<<<<< + * # all points are identical; warn user? + * n = stdlib.malloc(sizeof(leafnode)) + */ + __pyx_1 = (__pyx_v_maxval == __pyx_v_minval); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":257 + * if maxval==minval: + * # all points are identical; warn user? + * n = stdlib.malloc(sizeof(leafnode)) # <<<<<<<<<<<<<< + * n.split_dim = -1 + * n.start_idx = start_idx + */ + __pyx_v_n = ((struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode *)malloc((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode)))); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":258 + * # all points are identical; warn user? + * n = stdlib.malloc(sizeof(leafnode)) + * n.split_dim = -1 # <<<<<<<<<<<<<< + * n.start_idx = start_idx + * n.end_idx = end_idx + */ + __pyx_v_n->split_dim = -1; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":259 + * n = stdlib.malloc(sizeof(leafnode)) + * n.split_dim = -1 + * n.start_idx = start_idx # <<<<<<<<<<<<<< + * n.end_idx = end_idx + * return n + */ + __pyx_v_n->start_idx = __pyx_v_start_idx; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":260 + * n.split_dim = -1 + * n.start_idx = start_idx + * n.end_idx = end_idx # <<<<<<<<<<<<<< + * return n + * + */ + __pyx_v_n->end_idx = __pyx_v_end_idx; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":261 + * n.start_idx = start_idx + * n.end_idx = end_idx + * return n # <<<<<<<<<<<<<< + * + * split = (maxval+minval)/2 + */ + __pyx_r = ((struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *)__pyx_v_n); + goto __pyx_L0; + goto __pyx_L7; + } + __pyx_L7:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":263 + * return n + * + * split = (maxval+minval)/2 # <<<<<<<<<<<<<< + * + * p = start_idx + */ + __pyx_v_split = ((__pyx_v_maxval + __pyx_v_minval) / 2); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":265 + * split = (maxval+minval)/2 + * + * p = start_idx # <<<<<<<<<<<<<< + * q = end_idx-1 + * while p<=q: + */ + __pyx_v_p = __pyx_v_start_idx; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":266 + * + * p = start_idx + * q = end_idx-1 # <<<<<<<<<<<<<< + * while p<=q: + * if self.raw_data[self.raw_indices[p]*self.m+d]=split: + */ + __pyx_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_p]) * __pyx_v_self->m) + __pyx_v_d)]) < __pyx_v_split); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":269 + * while p<=q: + * if self.raw_data[self.raw_indices[p]*self.m+d]=split: + * q-=1 + */ + __pyx_v_p += 1; + goto __pyx_L10; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":270 + * if self.raw_data[self.raw_indices[p]*self.m+d]=split: # <<<<<<<<<<<<<< + * q-=1 + * else: + */ + __pyx_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_q]) * __pyx_v_self->m) + __pyx_v_d)]) >= __pyx_v_split); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":271 + * p+=1 + * elif self.raw_data[self.raw_indices[q]*self.m+d]>=split: + * q-=1 # <<<<<<<<<<<<<< + * else: + * t = self.raw_indices[p] + */ + __pyx_v_q -= 1; + goto __pyx_L10; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":273 + * q-=1 + * else: + * t = self.raw_indices[p] # <<<<<<<<<<<<<< + * self.raw_indices[p] = self.raw_indices[q] + * self.raw_indices[q] = t + */ + __pyx_v_t = (__pyx_v_self->raw_indices[__pyx_v_p]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":274 + * else: + * t = self.raw_indices[p] + * self.raw_indices[p] = self.raw_indices[q] # <<<<<<<<<<<<<< + * self.raw_indices[q] = t + * p+=1 + */ + (__pyx_v_self->raw_indices[__pyx_v_p]) = (__pyx_v_self->raw_indices[__pyx_v_q]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":275 + * t = self.raw_indices[p] + * self.raw_indices[p] = self.raw_indices[q] + * self.raw_indices[q] = t # <<<<<<<<<<<<<< + * p+=1 + * q-=1 + */ + (__pyx_v_self->raw_indices[__pyx_v_q]) = __pyx_v_t; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":276 + * self.raw_indices[p] = self.raw_indices[q] + * self.raw_indices[q] = t + * p+=1 # <<<<<<<<<<<<<< + * q-=1 + * + */ + __pyx_v_p += 1; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":277 + * self.raw_indices[q] = t + * p+=1 + * q-=1 # <<<<<<<<<<<<<< + * + * # slide midpoint if necessary + */ + __pyx_v_q -= 1; + } + __pyx_L10:; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":280 + * + * # slide midpoint if necessary + * if p==start_idx: # <<<<<<<<<<<<<< + * # no points less than split + * j = start_idx + */ + __pyx_1 = (__pyx_v_p == __pyx_v_start_idx); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":282 + * if p==start_idx: + * # no points less than split + * j = start_idx # <<<<<<<<<<<<<< + * split = self.raw_data[self.raw_indices[j]*self.m+d] + * for i in range(start_idx+1, end_idx): + */ + __pyx_v_j = __pyx_v_start_idx; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":283 + * # no points less than split + * j = start_idx + * split = self.raw_data[self.raw_indices[j]*self.m+d] # <<<<<<<<<<<<<< + * for i in range(start_idx+1, end_idx): + * if self.raw_data[self.raw_indices[i]*self.m+d]raw_data[(((__pyx_v_self->raw_indices[__pyx_v_j]) * __pyx_v_self->m) + __pyx_v_d)]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":284 + * j = start_idx + * split = self.raw_data[self.raw_indices[j]*self.m+d] + * for i in range(start_idx+1, end_idx): # <<<<<<<<<<<<<< + * if self.raw_data[self.raw_indices[i]*self.m+d]raw_data[(((__pyx_v_self->raw_indices[__pyx_v_i]) * __pyx_v_self->m) + __pyx_v_d)]) < __pyx_v_split); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":286 + * for i in range(start_idx+1, end_idx): + * if self.raw_data[self.raw_indices[i]*self.m+d]raw_data[(((__pyx_v_self->raw_indices[__pyx_v_j]) * __pyx_v_self->m) + __pyx_v_d)]); + goto __pyx_L14; + } + __pyx_L14:; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":288 + * j = i + * split = self.raw_data[self.raw_indices[j]*self.m+d] + * t = self.raw_indices[start_idx] # <<<<<<<<<<<<<< + * self.raw_indices[start_idx] = self.raw_indices[j] + * self.raw_indices[j] = t + */ + __pyx_v_t = (__pyx_v_self->raw_indices[__pyx_v_start_idx]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":289 + * split = self.raw_data[self.raw_indices[j]*self.m+d] + * t = self.raw_indices[start_idx] + * self.raw_indices[start_idx] = self.raw_indices[j] # <<<<<<<<<<<<<< + * self.raw_indices[j] = t + * p = start_idx+1 + */ + (__pyx_v_self->raw_indices[__pyx_v_start_idx]) = (__pyx_v_self->raw_indices[__pyx_v_j]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":290 + * t = self.raw_indices[start_idx] + * self.raw_indices[start_idx] = self.raw_indices[j] + * self.raw_indices[j] = t # <<<<<<<<<<<<<< + * p = start_idx+1 + * q = start_idx + */ + (__pyx_v_self->raw_indices[__pyx_v_j]) = __pyx_v_t; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":291 + * self.raw_indices[start_idx] = self.raw_indices[j] + * self.raw_indices[j] = t + * p = start_idx+1 # <<<<<<<<<<<<<< + * q = start_idx + * elif p==end_idx: + */ + __pyx_v_p = (__pyx_v_start_idx + 1); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":292 + * self.raw_indices[j] = t + * p = start_idx+1 + * q = start_idx # <<<<<<<<<<<<<< + * elif p==end_idx: + * # no points greater than split + */ + __pyx_v_q = __pyx_v_start_idx; + goto __pyx_L11; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":293 + * p = start_idx+1 + * q = start_idx + * elif p==end_idx: # <<<<<<<<<<<<<< + * # no points greater than split + * j = end_idx-1 + */ + __pyx_1 = (__pyx_v_p == __pyx_v_end_idx); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":295 + * elif p==end_idx: + * # no points greater than split + * j = end_idx-1 # <<<<<<<<<<<<<< + * split = self.raw_data[self.raw_indices[j]*self.m+d] + * for i in range(start_idx, end_idx-1): + */ + __pyx_v_j = (__pyx_v_end_idx - 1); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":296 + * # no points greater than split + * j = end_idx-1 + * split = self.raw_data[self.raw_indices[j]*self.m+d] # <<<<<<<<<<<<<< + * for i in range(start_idx, end_idx-1): + * if self.raw_data[self.raw_indices[i]*self.m+d]>split: + */ + __pyx_v_split = (__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_j]) * __pyx_v_self->m) + __pyx_v_d)]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":297 + * j = end_idx-1 + * split = self.raw_data[self.raw_indices[j]*self.m+d] + * for i in range(start_idx, end_idx-1): # <<<<<<<<<<<<<< + * if self.raw_data[self.raw_indices[i]*self.m+d]>split: + * j = i + */ + __pyx_3 = (__pyx_v_end_idx - 1); + for (__pyx_v_i = __pyx_v_start_idx; __pyx_v_i < __pyx_3; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":298 + * split = self.raw_data[self.raw_indices[j]*self.m+d] + * for i in range(start_idx, end_idx-1): + * if self.raw_data[self.raw_indices[i]*self.m+d]>split: # <<<<<<<<<<<<<< + * j = i + * split = self.raw_data[self.raw_indices[j]*self.m+d] + */ + __pyx_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_i]) * __pyx_v_self->m) + __pyx_v_d)]) > __pyx_v_split); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":299 + * for i in range(start_idx, end_idx-1): + * if self.raw_data[self.raw_indices[i]*self.m+d]>split: + * j = i # <<<<<<<<<<<<<< + * split = self.raw_data[self.raw_indices[j]*self.m+d] + * t = self.raw_indices[end_idx-1] + */ + __pyx_v_j = __pyx_v_i; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":300 + * if self.raw_data[self.raw_indices[i]*self.m+d]>split: + * j = i + * split = self.raw_data[self.raw_indices[j]*self.m+d] # <<<<<<<<<<<<<< + * t = self.raw_indices[end_idx-1] + * self.raw_indices[end_idx-1] = self.raw_indices[j] + */ + __pyx_v_split = (__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_j]) * __pyx_v_self->m) + __pyx_v_d)]); + goto __pyx_L17; + } + __pyx_L17:; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":301 + * j = i + * split = self.raw_data[self.raw_indices[j]*self.m+d] + * t = self.raw_indices[end_idx-1] # <<<<<<<<<<<<<< + * self.raw_indices[end_idx-1] = self.raw_indices[j] + * self.raw_indices[j] = t + */ + __pyx_v_t = (__pyx_v_self->raw_indices[(__pyx_v_end_idx - 1)]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":302 + * split = self.raw_data[self.raw_indices[j]*self.m+d] + * t = self.raw_indices[end_idx-1] + * self.raw_indices[end_idx-1] = self.raw_indices[j] # <<<<<<<<<<<<<< + * self.raw_indices[j] = t + * p = end_idx-1 + */ + (__pyx_v_self->raw_indices[(__pyx_v_end_idx - 1)]) = (__pyx_v_self->raw_indices[__pyx_v_j]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":303 + * t = self.raw_indices[end_idx-1] + * self.raw_indices[end_idx-1] = self.raw_indices[j] + * self.raw_indices[j] = t # <<<<<<<<<<<<<< + * p = end_idx-1 + * q = end_idx-2 + */ + (__pyx_v_self->raw_indices[__pyx_v_j]) = __pyx_v_t; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":304 + * self.raw_indices[end_idx-1] = self.raw_indices[j] + * self.raw_indices[j] = t + * p = end_idx-1 # <<<<<<<<<<<<<< + * q = end_idx-2 + * + */ + __pyx_v_p = (__pyx_v_end_idx - 1); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":305 + * self.raw_indices[j] = t + * p = end_idx-1 + * q = end_idx-2 # <<<<<<<<<<<<<< + * + * # construct new node representation + */ + __pyx_v_q = (__pyx_v_end_idx - 2); + goto __pyx_L11; + } + __pyx_L11:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":308 + * + * # construct new node representation + * ni = stdlib.malloc(sizeof(innernode)) # <<<<<<<<<<<<<< + * + * mids = stdlib.malloc(sizeof(double)*self.m) + */ + __pyx_v_ni = ((struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *)malloc((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_innernode)))); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":310 + * ni = stdlib.malloc(sizeof(innernode)) + * + * mids = stdlib.malloc(sizeof(double)*self.m) # <<<<<<<<<<<<<< + * for i in range(self.m): + * mids[i] = maxes[i] + */ + __pyx_v_mids = ((double *)malloc(((sizeof(double)) * __pyx_v_self->m))); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":311 + * + * mids = stdlib.malloc(sizeof(double)*self.m) + * for i in range(self.m): # <<<<<<<<<<<<<< + * mids[i] = maxes[i] + * mids[d] = split + */ + __pyx_2 = __pyx_v_self->m; + for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":312 + * mids = stdlib.malloc(sizeof(double)*self.m) + * for i in range(self.m): + * mids[i] = maxes[i] # <<<<<<<<<<<<<< + * mids[d] = split + * ni.less = self.__build(start_idx,p,mids,mins) + */ + (__pyx_v_mids[__pyx_v_i]) = (__pyx_v_maxes[__pyx_v_i]); + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":313 + * for i in range(self.m): + * mids[i] = maxes[i] + * mids[d] = split # <<<<<<<<<<<<<< + * ni.less = self.__build(start_idx,p,mids,mins) + * + */ + (__pyx_v_mids[__pyx_v_d]) = __pyx_v_split; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":314 + * mids[i] = maxes[i] + * mids[d] = split + * ni.less = self.__build(start_idx,p,mids,mins) # <<<<<<<<<<<<<< + * + * for i in range(self.m): + */ + __pyx_v_ni->less = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self->__pyx_vtab)->__build(__pyx_v_self, __pyx_v_start_idx, __pyx_v_p, __pyx_v_mids, __pyx_v_mins); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":316 + * ni.less = self.__build(start_idx,p,mids,mins) + * + * for i in range(self.m): # <<<<<<<<<<<<<< + * mids[i] = mins[i] + * mids[d] = split + */ + __pyx_2 = __pyx_v_self->m; + for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":317 + * + * for i in range(self.m): + * mids[i] = mins[i] # <<<<<<<<<<<<<< + * mids[d] = split + * ni.greater = self.__build(p,end_idx,maxes,mids) + */ + (__pyx_v_mids[__pyx_v_i]) = (__pyx_v_mins[__pyx_v_i]); + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":318 + * for i in range(self.m): + * mids[i] = mins[i] + * mids[d] = split # <<<<<<<<<<<<<< + * ni.greater = self.__build(p,end_idx,maxes,mids) + * + */ + (__pyx_v_mids[__pyx_v_d]) = __pyx_v_split; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":319 + * mids[i] = mins[i] + * mids[d] = split + * ni.greater = self.__build(p,end_idx,maxes,mids) # <<<<<<<<<<<<<< + * + * stdlib.free(mids) + */ + __pyx_v_ni->greater = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self->__pyx_vtab)->__build(__pyx_v_self, __pyx_v_p, __pyx_v_end_idx, __pyx_v_maxes, __pyx_v_mids); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":321 + * ni.greater = self.__build(p,end_idx,maxes,mids) + * + * stdlib.free(mids) # <<<<<<<<<<<<<< + * + * ni.split_dim = d + */ + free(__pyx_v_mids); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":323 + * stdlib.free(mids) + * + * ni.split_dim = d # <<<<<<<<<<<<<< + * ni.split = split + * + */ + __pyx_v_ni->split_dim = __pyx_v_d; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":324 + * + * ni.split_dim = d + * ni.split = split # <<<<<<<<<<<<<< + * + * return ni + */ + __pyx_v_ni->split = __pyx_v_split; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":326 + * ni.split = split + * + * return ni # <<<<<<<<<<<<<< + * + * cdef __free_tree(cKDTree self, innernode* node): + */ + __pyx_r = __pyx_v_ni; + goto __pyx_L0; + } + __pyx_L3:; + + __pyx_r = 0; + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":328 + * return ni + * + * cdef __free_tree(cKDTree self, innernode* node): # <<<<<<<<<<<<<< + * if node.split_dim!=-1: + * self.__free_tree(node.less) + */ + +static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_7cKDTree___free_tree(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *__pyx_v_self, struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_v_node) { + PyObject *__pyx_r; + int __pyx_1; + PyObject *__pyx_2 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":329 + * + * cdef __free_tree(cKDTree self, innernode* node): + * if node.split_dim!=-1: # <<<<<<<<<<<<<< + * self.__free_tree(node.less) + * self.__free_tree(node.greater) + */ + __pyx_1 = (__pyx_v_node->split_dim != -1); + if (__pyx_1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":330 + * cdef __free_tree(cKDTree self, innernode* node): + * if node.split_dim!=-1: + * self.__free_tree(node.less) # <<<<<<<<<<<<<< + * self.__free_tree(node.greater) + * stdlib.free(node) + */ + __pyx_2 = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self->__pyx_vtab)->__free_tree(__pyx_v_self, __pyx_v_node->less); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 330; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":331 + * if node.split_dim!=-1: + * self.__free_tree(node.less) + * self.__free_tree(node.greater) # <<<<<<<<<<<<<< + * stdlib.free(node) + * + */ + __pyx_2 = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self->__pyx_vtab)->__free_tree(__pyx_v_self, __pyx_v_node->greater); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 331; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + goto __pyx_L3; + } + __pyx_L3:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":332 + * self.__free_tree(node.less) + * self.__free_tree(node.greater) + * stdlib.free(node) # <<<<<<<<<<<<<< + * + * def __del__(cKDTree self): + */ + free(__pyx_v_node); + + __pyx_r = Py_None; Py_INCREF(Py_None); + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_2); + __Pyx_AddTraceback("scipy.spatial.ckdtree.cKDTree.__free_tree"); + __pyx_r = 0; + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":334 + * stdlib.free(node) + * + * def __del__(cKDTree self): # <<<<<<<<<<<<<< + * print "freeing cKDTree" + * self.__free_tree(self.tree) + */ + +static PyObject *__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___del__(PyObject *__pyx_v_self, PyObject *unused); /*proto*/ +static PyObject *__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___del__(PyObject *__pyx_v_self, PyObject *unused) { + PyObject *__pyx_r; + PyObject *__pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":335 + * + * def __del__(cKDTree self): + * print "freeing cKDTree" # <<<<<<<<<<<<<< + * self.__free_tree(self.tree) + * + */ + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 335; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_kp_20); + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_kp_20); + if (__Pyx_Print(((PyObject *)__pyx_1), 1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 335; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":336 + * def __del__(cKDTree self): + * print "freeing cKDTree" + * self.__free_tree(self.tree) # <<<<<<<<<<<<<< + * + * cdef void __query(cKDTree self, + */ + __pyx_1 = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->__pyx_vtab)->__free_tree(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self), ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->tree); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 336; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + + __pyx_r = Py_None; Py_INCREF(Py_None); + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_1); + __Pyx_AddTraceback("scipy.spatial.ckdtree.cKDTree.__del__"); + __pyx_r = NULL; + __pyx_L0:; + return __pyx_r; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":338 + * self.__free_tree(self.tree) + * + * cdef void __query(cKDTree self, # <<<<<<<<<<<<<< + * double*result_distances, + * int*result_indices, + */ + +static void __pyx_f_5scipy_7spatial_7ckdtree_7cKDTree___query(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *__pyx_v_self, double *__pyx_v_result_distances, int *__pyx_v_result_indices, double *__pyx_v_x, int __pyx_v_k, double __pyx_v_eps, double __pyx_v_p, double __pyx_v_distance_upper_bound) { + struct __pyx_t_5scipy_7spatial_7ckdtree_heap __pyx_v_q; + struct __pyx_t_5scipy_7spatial_7ckdtree_heap __pyx_v_neighbors; + int __pyx_v_i; + double __pyx_v_t; + struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *__pyx_v_inf; + struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *__pyx_v_inf2; + double __pyx_v_d; + double __pyx_v_epsfac; + double __pyx_v_min_distance; + double __pyx_v_far_min_distance; + struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_v_it; + struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_v_it2; + struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_v_neighbor; + struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode *__pyx_v_node; + struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_v_inode; + struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_v_near; + struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_v_far; + PyObject *__pyx_1 = 0; + int __pyx_2; + int __pyx_3; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":369 + * # distances between the nearest side of the cell and the target + * # the head node of the cell + * heapcreate(&q,12) # <<<<<<<<<<<<<< + * + * # priority queue for the nearest neighbors + */ + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_q), 12); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 369; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":374 + * # furthest known neighbor first + * # entries are (-distance**p, i) + * heapcreate(&neighbors,k) # <<<<<<<<<<<<<< + * + * # set up first nodeinfo + */ + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_neighbors), __pyx_v_k); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 374; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":377 + * + * # set up first nodeinfo + * inf = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) # <<<<<<<<<<<<<< + * inf.node = self.tree + * for i in range(self.m): + */ + __pyx_v_inf = ((struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *)malloc(((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo)) + (__pyx_v_self->m * (sizeof(double)))))); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":378 + * # set up first nodeinfo + * inf = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) + * inf.node = self.tree # <<<<<<<<<<<<<< + * for i in range(self.m): + * inf.side_distances[i] = 0 + */ + __pyx_v_inf->node = __pyx_v_self->tree; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":379 + * inf = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) + * inf.node = self.tree + * for i in range(self.m): # <<<<<<<<<<<<<< + * inf.side_distances[i] = 0 + * t = x[i]-self.raw_maxes[i] + */ + __pyx_2 = __pyx_v_self->m; + for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":380 + * inf.node = self.tree + * for i in range(self.m): + * inf.side_distances[i] = 0 # <<<<<<<<<<<<<< + * t = x[i]-self.raw_maxes[i] + * if t>inf.side_distances[i]: + */ + (__pyx_v_inf->side_distances[__pyx_v_i]) = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":381 + * for i in range(self.m): + * inf.side_distances[i] = 0 + * t = x[i]-self.raw_maxes[i] # <<<<<<<<<<<<<< + * if t>inf.side_distances[i]: + * inf.side_distances[i] = t + */ + __pyx_v_t = ((__pyx_v_x[__pyx_v_i]) - (__pyx_v_self->raw_maxes[__pyx_v_i])); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":382 + * inf.side_distances[i] = 0 + * t = x[i]-self.raw_maxes[i] + * if t>inf.side_distances[i]: # <<<<<<<<<<<<<< + * inf.side_distances[i] = t + * else: + */ + __pyx_3 = (__pyx_v_t > (__pyx_v_inf->side_distances[__pyx_v_i])); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":383 + * t = x[i]-self.raw_maxes[i] + * if t>inf.side_distances[i]: + * inf.side_distances[i] = t # <<<<<<<<<<<<<< + * else: + * t = self.raw_mins[i]-x[i] + */ + (__pyx_v_inf->side_distances[__pyx_v_i]) = __pyx_v_t; + goto __pyx_L5; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":385 + * inf.side_distances[i] = t + * else: + * t = self.raw_mins[i]-x[i] # <<<<<<<<<<<<<< + * if t>inf.side_distances[i]: + * inf.side_distances[i] = t + */ + __pyx_v_t = ((__pyx_v_self->raw_mins[__pyx_v_i]) - (__pyx_v_x[__pyx_v_i])); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":386 + * else: + * t = self.raw_mins[i]-x[i] + * if t>inf.side_distances[i]: # <<<<<<<<<<<<<< + * inf.side_distances[i] = t + * if p!=1 and p!=infinity: + */ + __pyx_3 = (__pyx_v_t > (__pyx_v_inf->side_distances[__pyx_v_i])); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":387 + * t = self.raw_mins[i]-x[i] + * if t>inf.side_distances[i]: + * inf.side_distances[i] = t # <<<<<<<<<<<<<< + * if p!=1 and p!=infinity: + * inf.side_distances[i]=inf.side_distances[i]**p + */ + (__pyx_v_inf->side_distances[__pyx_v_i]) = __pyx_v_t; + goto __pyx_L6; + } + __pyx_L6:; + } + __pyx_L5:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":388 + * if t>inf.side_distances[i]: + * inf.side_distances[i] = t + * if p!=1 and p!=infinity: # <<<<<<<<<<<<<< + * inf.side_distances[i]=inf.side_distances[i]**p + * + */ + __pyx_3 = (__pyx_v_p != 1); + if (__pyx_3) { + __pyx_3 = (__pyx_v_p != __pyx_v_5scipy_7spatial_7ckdtree_infinity); + } + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":389 + * inf.side_distances[i] = t + * if p!=1 and p!=infinity: + * inf.side_distances[i]=inf.side_distances[i]**p # <<<<<<<<<<<<<< + * + * # compute first distance + */ + (__pyx_v_inf->side_distances[__pyx_v_i]) = pow((__pyx_v_inf->side_distances[__pyx_v_i]), __pyx_v_p); + goto __pyx_L7; + } + __pyx_L7:; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":392 + * + * # compute first distance + * min_distance = 0. # <<<<<<<<<<<<<< + * for i in range(self.m): + * if p==infinity: + */ + __pyx_v_min_distance = 0.; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":393 + * # compute first distance + * min_distance = 0. + * for i in range(self.m): # <<<<<<<<<<<<<< + * if p==infinity: + * min_distance = dmax(min_distance,inf.side_distances[i]) + */ + __pyx_2 = __pyx_v_self->m; + for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":394 + * min_distance = 0. + * for i in range(self.m): + * if p==infinity: # <<<<<<<<<<<<<< + * min_distance = dmax(min_distance,inf.side_distances[i]) + * else: + */ + __pyx_3 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":395 + * for i in range(self.m): + * if p==infinity: + * min_distance = dmax(min_distance,inf.side_distances[i]) # <<<<<<<<<<<<<< + * else: + * min_distance += inf.side_distances[i] + */ + __pyx_v_min_distance = __pyx_f_5scipy_7spatial_7ckdtree_dmax(__pyx_v_min_distance, (__pyx_v_inf->side_distances[__pyx_v_i])); + goto __pyx_L10; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":397 + * min_distance = dmax(min_distance,inf.side_distances[i]) + * else: + * min_distance += inf.side_distances[i] # <<<<<<<<<<<<<< + * + * # fiddle approximation factor + */ + __pyx_v_min_distance += (__pyx_v_inf->side_distances[__pyx_v_i]); + } + __pyx_L10:; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":400 + * + * # fiddle approximation factor + * if eps==0: # <<<<<<<<<<<<<< + * epsfac=1 + * elif p==infinity: + */ + __pyx_3 = (__pyx_v_eps == 0); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":401 + * # fiddle approximation factor + * if eps==0: + * epsfac=1 # <<<<<<<<<<<<<< + * elif p==infinity: + * epsfac = 1/(1+eps) + */ + __pyx_v_epsfac = 1; + goto __pyx_L11; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":402 + * if eps==0: + * epsfac=1 + * elif p==infinity: # <<<<<<<<<<<<<< + * epsfac = 1/(1+eps) + * else: + */ + __pyx_3 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":403 + * epsfac=1 + * elif p==infinity: + * epsfac = 1/(1+eps) # <<<<<<<<<<<<<< + * else: + * epsfac = 1/(1+eps)**p + */ + __pyx_v_epsfac = (1 / (1 + __pyx_v_eps)); + goto __pyx_L11; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":405 + * epsfac = 1/(1+eps) + * else: + * epsfac = 1/(1+eps)**p # <<<<<<<<<<<<<< + * + * # internally we represent all distances as distance**p + */ + __pyx_v_epsfac = (1 / pow((1 + __pyx_v_eps), __pyx_v_p)); + } + __pyx_L11:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":408 + * + * # internally we represent all distances as distance**p + * if p!=infinity and distance_upper_bound!=infinity: # <<<<<<<<<<<<<< + * distance_upper_bound = distance_upper_bound**p + * + */ + __pyx_3 = (__pyx_v_p != __pyx_v_5scipy_7spatial_7ckdtree_infinity); + if (__pyx_3) { + __pyx_3 = (__pyx_v_distance_upper_bound != __pyx_v_5scipy_7spatial_7ckdtree_infinity); + } + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":409 + * # internally we represent all distances as distance**p + * if p!=infinity and distance_upper_bound!=infinity: + * distance_upper_bound = distance_upper_bound**p # <<<<<<<<<<<<<< + * + * while True: + */ + __pyx_v_distance_upper_bound = pow(__pyx_v_distance_upper_bound, __pyx_v_p); + goto __pyx_L12; + } + __pyx_L12:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":411 + * distance_upper_bound = distance_upper_bound**p + * + * while True: # <<<<<<<<<<<<<< + * if inf.node.split_dim==-1: + * node = inf.node + */ + while (1) { + __pyx_3 = 1; + if (!__pyx_3) break; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":412 + * + * while True: + * if inf.node.split_dim==-1: # <<<<<<<<<<<<<< + * node = inf.node + * + */ + __pyx_3 = (__pyx_v_inf->node->split_dim == -1); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":413 + * while True: + * if inf.node.split_dim==-1: + * node = inf.node # <<<<<<<<<<<<<< + * + * # brute-force + */ + __pyx_v_node = ((struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode *)__pyx_v_inf->node); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":416 + * + * # brute-force + * for i in range(node.start_idx,node.end_idx): # <<<<<<<<<<<<<< + * d = _distance_p( + * self.raw_data+self.raw_indices[i]*self.m, + */ + __pyx_2 = __pyx_v_node->end_idx; + for (__pyx_v_i = __pyx_v_node->start_idx; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":419 + * d = _distance_p( + * self.raw_data+self.raw_indices[i]*self.m, + * x,p,self.m,distance_upper_bound) # <<<<<<<<<<<<<< + * + * if draw_data + ((__pyx_v_self->raw_indices[__pyx_v_i]) * __pyx_v_self->m)), __pyx_v_x, __pyx_v_p, __pyx_v_self->m, __pyx_v_distance_upper_bound); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":421 + * x,p,self.m,distance_upper_bound) + * + * if draw_indices[__pyx_v_i]); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":427 + * neighbor.priority = -d + * neighbor.contents.intdata = self.raw_indices[i] + * heappush(&neighbors,neighbor) # <<<<<<<<<<<<<< + * + * # adjust upper bound for efficiency + */ + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_neighbors), __pyx_v_neighbor); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 427; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":430 + * + * # adjust upper bound for efficiency + * if neighbors.n==k: # <<<<<<<<<<<<<< + * distance_upper_bound = -heappeek(&neighbors).priority + * # done with this node, get another + */ + __pyx_3 = (__pyx_v_neighbors.n == __pyx_v_k); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":431 + * # adjust upper bound for efficiency + * if neighbors.n==k: + * distance_upper_bound = -heappeek(&neighbors).priority # <<<<<<<<<<<<<< + * # done with this node, get another + * stdlib.free(inf) + */ + __pyx_v_distance_upper_bound = (-__pyx_f_5scipy_7spatial_7ckdtree_heappeek((&__pyx_v_neighbors)).priority); + goto __pyx_L20; + } + __pyx_L20:; + goto __pyx_L18; + } + __pyx_L18:; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":433 + * distance_upper_bound = -heappeek(&neighbors).priority + * # done with this node, get another + * stdlib.free(inf) # <<<<<<<<<<<<<< + * if q.n==0: + * # no more nodes to visit + */ + free(__pyx_v_inf); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":434 + * # done with this node, get another + * stdlib.free(inf) + * if q.n==0: # <<<<<<<<<<<<<< + * # no more nodes to visit + * break + */ + __pyx_3 = (__pyx_v_q.n == 0); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":436 + * if q.n==0: + * # no more nodes to visit + * break # <<<<<<<<<<<<<< + * else: + * it = heappop(&q) + */ + goto __pyx_L14; + goto __pyx_L21; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":438 + * break + * else: + * it = heappop(&q) # <<<<<<<<<<<<<< + * inf = it.contents.ptrdata + * min_distance = it.priority + */ + __pyx_v_it = __pyx_f_5scipy_7spatial_7ckdtree_heappop((&__pyx_v_q)); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":439 + * else: + * it = heappop(&q) + * inf = it.contents.ptrdata # <<<<<<<<<<<<<< + * min_distance = it.priority + * else: + */ + __pyx_v_inf = ((struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *)__pyx_v_it.contents.ptrdata); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":440 + * it = heappop(&q) + * inf = it.contents.ptrdata + * min_distance = it.priority # <<<<<<<<<<<<<< + * else: + * inode = inf.node + */ + __pyx_v_min_distance = __pyx_v_it.priority; + } + __pyx_L21:; + goto __pyx_L15; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":442 + * min_distance = it.priority + * else: + * inode = inf.node # <<<<<<<<<<<<<< + * + * # we don't push cells that are too far onto the queue at all, + */ + __pyx_v_inode = ((struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *)__pyx_v_inf->node); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":447 + * # but since the distance_upper_bound decreases, we might get + * # here even if the cell's too far + * if min_distance>distance_upper_bound*epsfac: # <<<<<<<<<<<<<< + * # since this is the nearest cell, we're done, bail out + * stdlib.free(inf) + */ + __pyx_3 = (__pyx_v_min_distance > (__pyx_v_distance_upper_bound * __pyx_v_epsfac)); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":449 + * if min_distance>distance_upper_bound*epsfac: + * # since this is the nearest cell, we're done, bail out + * stdlib.free(inf) # <<<<<<<<<<<<<< + * # free all the nodes still on the heap + * for i in range(q.n): + */ + free(__pyx_v_inf); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":451 + * stdlib.free(inf) + * # free all the nodes still on the heap + * for i in range(q.n): # <<<<<<<<<<<<<< + * stdlib.free(q.heap[i].contents.ptrdata) + * break + */ + __pyx_2 = __pyx_v_q.n; + for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":452 + * # free all the nodes still on the heap + * for i in range(q.n): + * stdlib.free(q.heap[i].contents.ptrdata) # <<<<<<<<<<<<<< + * break + * + */ + free((__pyx_v_q.heap[__pyx_v_i]).contents.ptrdata); + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":453 + * for i in range(q.n): + * stdlib.free(q.heap[i].contents.ptrdata) + * break # <<<<<<<<<<<<<< + * + * # set up children for searching + */ + goto __pyx_L14; + goto __pyx_L22; + } + __pyx_L22:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":456 + * + * # set up children for searching + * if x[inode.split_dim]split_dim]) < __pyx_v_inode->split); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":457 + * # set up children for searching + * if x[inode.split_dim]less; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":458 + * if x[inode.split_dim]greater; + goto __pyx_L25; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":460 + * far = inode.greater + * else: + * near = inode.greater # <<<<<<<<<<<<<< + * far = inode.less + * + */ + __pyx_v_near = __pyx_v_inode->greater; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":461 + * else: + * near = inode.greater + * far = inode.less # <<<<<<<<<<<<<< + * + * # near child is at the same distance as the current node + */ + __pyx_v_far = __pyx_v_inode->less; + } + __pyx_L25:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":466 + * # we're going here next, so no point pushing it on the queue + * # no need to recompute the distance or the side_distances + * inf.node = near # <<<<<<<<<<<<<< + * + * # far child is further by an amount depending only + */ + __pyx_v_inf->node = __pyx_v_near; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":471 + * # on the split value; compute its distance and side_distances + * # and push it on the queue if it's near enough + * inf2 = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) # <<<<<<<<<<<<<< + * it2.contents.ptrdata = inf2 + * inf2.node = far + */ + __pyx_v_inf2 = ((struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *)malloc(((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo)) + (__pyx_v_self->m * (sizeof(double)))))); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":472 + * # and push it on the queue if it's near enough + * inf2 = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) + * it2.contents.ptrdata = inf2 # <<<<<<<<<<<<<< + * inf2.node = far + * # most side distances unchanged + */ + __pyx_v_it2.contents.ptrdata = ((char *)__pyx_v_inf2); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":473 + * inf2 = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) + * it2.contents.ptrdata = inf2 + * inf2.node = far # <<<<<<<<<<<<<< + * # most side distances unchanged + * for i in range(self.m): + */ + __pyx_v_inf2->node = __pyx_v_far; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":475 + * inf2.node = far + * # most side distances unchanged + * for i in range(self.m): # <<<<<<<<<<<<<< + * inf2.side_distances[i] = inf.side_distances[i] + * + */ + __pyx_2 = __pyx_v_self->m; + for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":476 + * # most side distances unchanged + * for i in range(self.m): + * inf2.side_distances[i] = inf.side_distances[i] # <<<<<<<<<<<<<< + * + * # one side distance changes + */ + (__pyx_v_inf2->side_distances[__pyx_v_i]) = (__pyx_v_inf->side_distances[__pyx_v_i]); + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":480 + * # one side distance changes + * # we can adjust the minimum distance without recomputing + * if p == infinity: # <<<<<<<<<<<<<< + * # we never use side_distances in the l_infinity case + * # inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) + */ + __pyx_3 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":483 + * # we never use side_distances in the l_infinity case + * # inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) + * far_min_distance = dmax(min_distance, dabs(inode.split-x[inode.split_dim])) # <<<<<<<<<<<<<< + * elif p == 1: + * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) + */ + __pyx_v_far_min_distance = __pyx_f_5scipy_7spatial_7ckdtree_dmax(__pyx_v_min_distance, __pyx_f_5scipy_7spatial_7ckdtree_dabs((__pyx_v_inode->split - (__pyx_v_x[__pyx_v_inode->split_dim])))); + goto __pyx_L28; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":484 + * # inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) + * far_min_distance = dmax(min_distance, dabs(inode.split-x[inode.split_dim])) + * elif p == 1: # <<<<<<<<<<<<<< + * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) + * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] + */ + __pyx_3 = (__pyx_v_p == 1); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":485 + * far_min_distance = dmax(min_distance, dabs(inode.split-x[inode.split_dim])) + * elif p == 1: + * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) # <<<<<<<<<<<<<< + * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] + * else: + */ + (__pyx_v_inf2->side_distances[__pyx_v_inode->split_dim]) = __pyx_f_5scipy_7spatial_7ckdtree_dabs((__pyx_v_inode->split - (__pyx_v_x[__pyx_v_inode->split_dim]))); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":486 + * elif p == 1: + * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) + * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] # <<<<<<<<<<<<<< + * else: + * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim])**p + */ + __pyx_v_far_min_distance = ((__pyx_v_min_distance - (__pyx_v_inf->side_distances[__pyx_v_inode->split_dim])) + (__pyx_v_inf2->side_distances[__pyx_v_inode->split_dim])); + goto __pyx_L28; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":488 + * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] + * else: + * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim])**p # <<<<<<<<<<<<<< + * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] + * + */ + (__pyx_v_inf2->side_distances[__pyx_v_inode->split_dim]) = pow(__pyx_f_5scipy_7spatial_7ckdtree_dabs((__pyx_v_inode->split - (__pyx_v_x[__pyx_v_inode->split_dim]))), __pyx_v_p); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":489 + * else: + * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim])**p + * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] # <<<<<<<<<<<<<< + * + * it2.priority = far_min_distance + */ + __pyx_v_far_min_distance = ((__pyx_v_min_distance - (__pyx_v_inf->side_distances[__pyx_v_inode->split_dim])) + (__pyx_v_inf2->side_distances[__pyx_v_inode->split_dim])); + } + __pyx_L28:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":491 + * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] + * + * it2.priority = far_min_distance # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_it2.priority = __pyx_v_far_min_distance; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":495 + * + * # far child might be too far, if so, don't bother pushing it + * if far_min_distance<=distance_upper_bound*epsfac: # <<<<<<<<<<<<<< + * heappush(&q,it2) + * else: + */ + __pyx_3 = (__pyx_v_far_min_distance <= (__pyx_v_distance_upper_bound * __pyx_v_epsfac)); + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":496 + * # far child might be too far, if so, don't bother pushing it + * if far_min_distance<=distance_upper_bound*epsfac: + * heappush(&q,it2) # <<<<<<<<<<<<<< + * else: + * stdlib.free(inf2) + */ + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_q), __pyx_v_it2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + goto __pyx_L29; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":498 + * heappush(&q,it2) + * else: + * stdlib.free(inf2) # <<<<<<<<<<<<<< + * # just in case + * it2.contents.ptrdata = 0 + */ + free(__pyx_v_inf2); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":500 + * stdlib.free(inf2) + * # just in case + * it2.contents.ptrdata = 0 # <<<<<<<<<<<<<< + * + * # fill output arrays with sorted neighbors + */ + __pyx_v_it2.contents.ptrdata = ((char *)0); + } + __pyx_L29:; + } + __pyx_L15:; + } + __pyx_L14:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":503 + * + * # fill output arrays with sorted neighbors + * for i in range(neighbors.n-1,-1,-1): # <<<<<<<<<<<<<< + * neighbor = heappop(&neighbors) # FIXME: neighbors may be realloced + * result_indices[i] = neighbor.contents.intdata + */ + for (__pyx_v_i = (__pyx_v_neighbors.n - 1); __pyx_v_i > -1; __pyx_v_i-=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":504 + * # fill output arrays with sorted neighbors + * for i in range(neighbors.n-1,-1,-1): + * neighbor = heappop(&neighbors) # FIXME: neighbors may be realloced # <<<<<<<<<<<<<< + * result_indices[i] = neighbor.contents.intdata + * if p==1 or p==infinity: + */ + __pyx_v_neighbor = __pyx_f_5scipy_7spatial_7ckdtree_heappop((&__pyx_v_neighbors)); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":505 + * for i in range(neighbors.n-1,-1,-1): + * neighbor = heappop(&neighbors) # FIXME: neighbors may be realloced + * result_indices[i] = neighbor.contents.intdata # <<<<<<<<<<<<<< + * if p==1 or p==infinity: + * result_distances[i] = -neighbor.priority + */ + (__pyx_v_result_indices[__pyx_v_i]) = __pyx_v_neighbor.contents.intdata; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":506 + * neighbor = heappop(&neighbors) # FIXME: neighbors may be realloced + * result_indices[i] = neighbor.contents.intdata + * if p==1 or p==infinity: # <<<<<<<<<<<<<< + * result_distances[i] = -neighbor.priority + * else: + */ + __pyx_3 = (__pyx_v_p == 1); + if (!__pyx_3) { + __pyx_3 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); + } + if (__pyx_3) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":507 + * result_indices[i] = neighbor.contents.intdata + * if p==1 or p==infinity: + * result_distances[i] = -neighbor.priority # <<<<<<<<<<<<<< + * else: + * result_distances[i] = (-neighbor.priority)**(1./p) + */ + (__pyx_v_result_distances[__pyx_v_i]) = (-__pyx_v_neighbor.priority); + goto __pyx_L32; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":509 + * result_distances[i] = -neighbor.priority + * else: + * result_distances[i] = (-neighbor.priority)**(1./p) # <<<<<<<<<<<<<< + * + * heapdestroy(&q) + */ + (__pyx_v_result_distances[__pyx_v_i]) = pow((-__pyx_v_neighbor.priority), (1. / __pyx_v_p)); + } + __pyx_L32:; + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":511 + * result_distances[i] = (-neighbor.priority)**(1./p) + * + * heapdestroy(&q) # <<<<<<<<<<<<<< + * heapdestroy(&neighbors) + * + */ + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapdestroy((&__pyx_v_q)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 511; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":512 + * + * heapdestroy(&q) + * heapdestroy(&neighbors) # <<<<<<<<<<<<<< + * + * def query(cKDTree self, object x, int k=1, double eps=0, double p=2, + */ + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapdestroy((&__pyx_v_neighbors)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 512; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_1); + __Pyx_WriteUnraisable("scipy.spatial.ckdtree.cKDTree.__query"); + __pyx_L0:; +} + +/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":514 + * heapdestroy(&neighbors) + * + * def query(cKDTree self, object x, int k=1, double eps=0, double p=2, # <<<<<<<<<<<<<< + * double distance_upper_bound=infinity): + * """query the kd-tree for nearest neighbors + */ + +static PyObject *__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree_query(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static char __pyx_doc_5scipy_7spatial_7ckdtree_7cKDTree_query[] = "query the kd-tree for nearest neighbors\n\n Parameters:\n ===========\n\n x : array-like, last dimension self.m\n An array of points to query.\n k : integer\n The number of nearest neighbors to return.\n eps : nonnegative float\n Return approximate nearest neighbors; the kth returned value \n is guaranteed to be no further than (1+eps) times the \n distance to the real kth nearest neighbor.\n p : float, 1<=p<=infinity\n Which Minkowski p-norm to use. \n 1 is the sum-of-absolute-values \"Manhattan\" distance\n 2 is the usual Euclidean distance\n infinity is the maximum-coordinate-difference distance\n distance_upper_bound : nonnegative float\n Return only neighbors within this distance. This is used to prune\n tree searches, so if you are doing a series of nearest-neighbor\n queries, it may help to supply the distance to the nearest neighbor\n of the most recent point.\n\n Returns:\n ========\n \n d : array of floats\n The distances to the nearest neighbors. \n If x has shape tuple+(self.m,), then d has shape tuple+(k,).\n Missing neighbors are indicated with infinite distances.\n i : array of integers\n The locations of the neighbors in self.data.\n If x has shape tuple+(self.m,), then i has shape tuple+(k,).\n Missing neighbors are indicated with self.n+1.\n "; +static PyObject *__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree_query(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_x = 0; + int __pyx_v_k; + double __pyx_v_eps; + double __pyx_v_p; + double __pyx_v_distance_upper_bound; + PyArrayObject *__pyx_v_ii; + PyArrayObject *__pyx_v_dd; + PyArrayObject *__pyx_v_xx; + int __pyx_v_c; + PyObject *__pyx_v_single; + PyObject *__pyx_v_retshape; + PyObject *__pyx_v_n; + Py_buffer __pyx_bstruct_ii; + Py_ssize_t __pyx_bstride_0_ii = 0; + Py_ssize_t __pyx_bstride_1_ii = 0; + Py_ssize_t __pyx_bshape_0_ii = 0; + Py_ssize_t __pyx_bshape_1_ii = 0; + Py_buffer __pyx_bstruct_xx; + Py_ssize_t __pyx_bstride_0_xx = 0; + Py_ssize_t __pyx_bstride_1_xx = 0; + Py_ssize_t __pyx_bshape_0_xx = 0; + Py_ssize_t __pyx_bshape_1_xx = 0; + Py_buffer __pyx_bstruct_dd; + Py_ssize_t __pyx_bstride_0_dd = 0; + Py_ssize_t __pyx_bstride_1_dd = 0; + Py_ssize_t __pyx_bshape_0_dd = 0; + Py_ssize_t __pyx_bshape_1_dd = 0; + PyObject *__pyx_r; + PyObject *__pyx_1 = 0; + PyObject *__pyx_2 = 0; + PyObject *__pyx_3 = 0; + int __pyx_4; + PyObject *__pyx_5 = 0; + Py_ssize_t __pyx_6 = 0; + PyObject *__pyx_7 = 0; + int __pyx_8; + double __pyx_9; + PyArrayObject *__pyx_t_1 = NULL; + int __pyx_t_2; + PyObject *__pyx_t_3 = NULL; + PyObject *__pyx_t_4 = NULL; + PyObject *__pyx_t_5 = NULL; + PyArrayObject *__pyx_t_6 = NULL; + PyArrayObject *__pyx_t_7 = NULL; + long __pyx_t_8; + long __pyx_t_9; + long __pyx_t_10; + long __pyx_t_11; + static char *__pyx_argnames[] = {"x","k","eps","p","distance_upper_bound",0}; + __pyx_v_k = 1; + __pyx_v_eps = ((double)0); + __pyx_v_p = ((double)2); + __pyx_v_distance_upper_bound = __pyx_k_17; + if (likely(!__pyx_kwds) && likely(1 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 5)) { + __pyx_v_x = PyTuple_GET_ITEM(__pyx_args, 0); + if (PyTuple_GET_SIZE(__pyx_args) > 1) { + __pyx_v_k = __pyx_PyInt_int(PyTuple_GET_ITEM(__pyx_args, 1)); if (unlikely((__pyx_v_k == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (PyTuple_GET_SIZE(__pyx_args) > 2) { + __pyx_v_eps = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 2)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (PyTuple_GET_SIZE(__pyx_args) > 3) { + __pyx_v_p = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 3)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (PyTuple_GET_SIZE(__pyx_args) > 4) { + __pyx_v_distance_upper_bound = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 4)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 515; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } + } + } + } + } + else { + if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "O|iddd", __pyx_argnames, &__pyx_v_x, &__pyx_v_k, &__pyx_v_eps, &__pyx_v_p, &__pyx_v_distance_upper_bound))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } + goto __pyx_L4; + __pyx_L3_error:; + __Pyx_AddTraceback("scipy.spatial.ckdtree.cKDTree.query"); + return NULL; + __pyx_L4:; + Py_INCREF(__pyx_v_x); + __pyx_v_ii = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); + __pyx_bstruct_ii.buf = NULL; + __pyx_v_dd = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); + __pyx_bstruct_dd.buf = NULL; + __pyx_v_xx = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); + __pyx_bstruct_xx.buf = NULL; + __pyx_v_single = Py_None; Py_INCREF(Py_None); + __pyx_v_retshape = Py_None; Py_INCREF(Py_None); + __pyx_v_n = Py_None; Py_INCREF(Py_None); + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":556 + * cdef np.ndarray[double, ndim=2] xx + * cdef int c + * x = np.asarray(x).astype(np.float) # <<<<<<<<<<<<<< + * if np.shape(x)[-1] != self.m: + * raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) + */ + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_asarray); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_x); + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_v_x); + __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_astype); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_1, __pyx_kp_float); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_3); + __pyx_3 = 0; + __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + Py_DECREF(__pyx_v_x); + __pyx_v_x = __pyx_3; + __pyx_3 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":557 + * cdef int c + * x = np.asarray(x).astype(np.float) + * if np.shape(x)[-1] != self.m: # <<<<<<<<<<<<<< + * raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) + * if p<1: + */ + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_shape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_x); + PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_x); + __pyx_2 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + __pyx_1 = __Pyx_GetItemInt(__pyx_2, -1, 0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_3 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_RichCompare(__pyx_1, __pyx_3, Py_NE); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_4 = __Pyx_PyObject_IsTrue(__pyx_2); if (unlikely(__pyx_4 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + if (__pyx_4) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":558 + * x = np.asarray(x).astype(np.float) + * if np.shape(x)[-1] != self.m: + * raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) # <<<<<<<<<<<<<< + * if p<1: + * raise ValueError("Only p-norms with 1<=p<=infinity permitted") + */ + __pyx_1 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_shape); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_x); + PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_x); + __pyx_5 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_2, 0, __pyx_1); + PyTuple_SET_ITEM(__pyx_2, 1, __pyx_5); + __pyx_1 = 0; + __pyx_5 = 0; + __pyx_3 = PyNumber_Remainder(__pyx_kp_21, ((PyObject *)__pyx_2)); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_3); + __pyx_3 = 0; + __pyx_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + __Pyx_Raise(__pyx_5, 0, 0); + Py_DECREF(__pyx_5); __pyx_5 = 0; + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + goto __pyx_L5; + } + __pyx_L5:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":559 + * if np.shape(x)[-1] != self.m: + * raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) + * if p<1: # <<<<<<<<<<<<<< + * raise ValueError("Only p-norms with 1<=p<=infinity permitted") + * if len(x.shape)==1: + */ + __pyx_4 = (__pyx_v_p < 1); + if (__pyx_4) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":560 + * raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) + * if p<1: + * raise ValueError("Only p-norms with 1<=p<=infinity permitted") # <<<<<<<<<<<<<< + * if len(x.shape)==1: + * single = True + */ + __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_kp_22); + PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_22); + __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + __Pyx_Raise(__pyx_3, 0, 0); + Py_DECREF(__pyx_3); __pyx_3 = 0; + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + goto __pyx_L6; + } + __pyx_L6:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":561 + * if p<1: + * raise ValueError("Only p-norms with 1<=p<=infinity permitted") + * if len(x.shape)==1: # <<<<<<<<<<<<<< + * single = True + * x = x[np.newaxis,:] + */ + __pyx_1 = PyObject_GetAttr(__pyx_v_x, __pyx_kp_shape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 561; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_6 = PyObject_Length(__pyx_1); if (unlikely(__pyx_6 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 561; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_4 = (__pyx_6 == 1); + if (__pyx_4) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":562 + * raise ValueError("Only p-norms with 1<=p<=infinity permitted") + * if len(x.shape)==1: + * single = True # <<<<<<<<<<<<<< + * x = x[np.newaxis,:] + * else: + */ + __pyx_5 = __Pyx_PyBool_FromLong(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_v_single); + __pyx_v_single = __pyx_5; + __pyx_5 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":563 + * if len(x.shape)==1: + * single = True + * x = x[np.newaxis,:] # <<<<<<<<<<<<<< + * else: + * single = False + */ + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_newaxis); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_1 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_5, 0, __pyx_3); + PyTuple_SET_ITEM(__pyx_5, 1, __pyx_1); + __pyx_3 = 0; + __pyx_1 = 0; + __pyx_2 = PyObject_GetItem(__pyx_v_x, ((PyObject *)__pyx_5)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; + Py_DECREF(__pyx_v_x); + __pyx_v_x = __pyx_2; + __pyx_2 = 0; + goto __pyx_L7; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":565 + * x = x[np.newaxis,:] + * else: + * single = False # <<<<<<<<<<<<<< + * retshape = np.shape(x)[:-1] + * n = np.prod(retshape) + */ + __pyx_3 = __Pyx_PyBool_FromLong(0); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 565; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_v_single); + __pyx_v_single = __pyx_3; + __pyx_3 = 0; + } + __pyx_L7:; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":566 + * else: + * single = False + * retshape = np.shape(x)[:-1] # <<<<<<<<<<<<<< + * n = np.prod(retshape) + * xx = np.reshape(x,(n,self.m)) + */ + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyObject_GetAttr(__pyx_1, __pyx_kp_shape); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_x); + PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_x); + __pyx_3 = PyObject_Call(__pyx_5, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + __pyx_1 = PySequence_GetSlice(__pyx_3, 0, -1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + Py_DECREF(__pyx_v_retshape); + __pyx_v_retshape = __pyx_1; + __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":567 + * single = False + * retshape = np.shape(x)[:-1] + * n = np.prod(retshape) # <<<<<<<<<<<<<< + * xx = np.reshape(x,(n,self.m)) + * dd = np.empty((n,k),dtype=np.float) + */ + __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 567; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_5, __pyx_kp_prod); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 567; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 567; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_retshape); + PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_retshape); + __pyx_1 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 567; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + Py_DECREF(__pyx_v_n); + __pyx_v_n = __pyx_1; + __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":568 + * retshape = np.shape(x)[:-1] + * n = np.prod(retshape) + * xx = np.reshape(x,(n,self.m)) # <<<<<<<<<<<<<< + * dd = np.empty((n,k),dtype=np.float) + * dd.fill(infinity) + */ + __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_5, __pyx_kp_reshape); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_3 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_n); + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_v_n); + PyTuple_SET_ITEM(__pyx_1, 1, __pyx_3); + __pyx_3 = 0; + __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_x); + PyTuple_SET_ITEM(__pyx_5, 0, __pyx_v_x); + PyTuple_SET_ITEM(__pyx_5, 1, ((PyObject *)__pyx_1)); + __pyx_1 = 0; + __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; + if (!(__Pyx_TypeTest(__pyx_3, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = ((PyArrayObject *)__pyx_3); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_xx, &__pyx_bstruct_xx); + __pyx_t_2 = __Pyx_GetBuffer_double((PyObject*)__pyx_t_1, &__pyx_bstruct_xx, PyBUF_FORMAT| PyBUF_STRIDES, 2); + if (unlikely(__pyx_t_2 < 0)) + { + PyErr_Fetch(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5); + if (unlikely(__Pyx_GetBuffer_double((PyObject*)__pyx_v_xx, &__pyx_bstruct_xx, PyBUF_FORMAT| PyBUF_STRIDES, 2) == -1)) { + Py_XDECREF(__pyx_t_3); Py_XDECREF(__pyx_t_4); Py_XDECREF(__pyx_t_5); + __Pyx_RaiseBufferFallbackError(); + } else { + PyErr_Restore(__pyx_t_3, __pyx_t_4, __pyx_t_5); + } + } + __pyx_bstride_0_xx = __pyx_bstruct_xx.strides[0]; __pyx_bstride_1_xx = __pyx_bstruct_xx.strides[1]; + __pyx_bshape_0_xx = __pyx_bstruct_xx.shape[0]; __pyx_bshape_1_xx = __pyx_bstruct_xx.shape[1]; + if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = 0; + Py_DECREF(((PyObject *)__pyx_v_xx)); + __pyx_v_xx = ((PyArrayObject *)__pyx_3); + __pyx_3 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":569 + * n = np.prod(retshape) + * xx = np.reshape(x,(n,self.m)) + * dd = np.empty((n,k),dtype=np.float) # <<<<<<<<<<<<<< + * dd.fill(infinity) + * ii = np.empty((n,k),dtype='i') + */ + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_empty); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_5 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyTuple_New(2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_n); + PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_n); + PyTuple_SET_ITEM(__pyx_3, 1, __pyx_5); + __pyx_5 = 0; + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, ((PyObject *)__pyx_3)); + __pyx_3 = 0; + __pyx_5 = PyDict_New(); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyObject_GetAttr(__pyx_3, __pyx_kp_float); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + if (PyDict_SetItem(__pyx_5, __pyx_kp_dtype, __pyx_7) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_7); __pyx_7 = 0; + __pyx_3 = PyEval_CallObjectWithKeywords(__pyx_2, ((PyObject *)__pyx_1), ((PyObject *)__pyx_5)); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; + if (!(__Pyx_TypeTest(__pyx_3, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = ((PyArrayObject *)__pyx_3); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_dd, &__pyx_bstruct_dd); + __pyx_t_2 = __Pyx_GetBuffer_double((PyObject*)__pyx_t_6, &__pyx_bstruct_dd, PyBUF_FORMAT| PyBUF_STRIDES, 2); + if (unlikely(__pyx_t_2 < 0)) + { + PyErr_Fetch(&__pyx_t_5, &__pyx_t_4, &__pyx_t_3); + if (unlikely(__Pyx_GetBuffer_double((PyObject*)__pyx_v_dd, &__pyx_bstruct_dd, PyBUF_FORMAT| PyBUF_STRIDES, 2) == -1)) { + Py_XDECREF(__pyx_t_5); Py_XDECREF(__pyx_t_4); Py_XDECREF(__pyx_t_3); + __Pyx_RaiseBufferFallbackError(); + } else { + PyErr_Restore(__pyx_t_5, __pyx_t_4, __pyx_t_3); + } + } + __pyx_bstride_0_dd = __pyx_bstruct_dd.strides[0]; __pyx_bstride_1_dd = __pyx_bstruct_dd.strides[1]; + __pyx_bshape_0_dd = __pyx_bstruct_dd.shape[0]; __pyx_bshape_1_dd = __pyx_bstruct_dd.shape[1]; + if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = 0; + Py_DECREF(((PyObject *)__pyx_v_dd)); + __pyx_v_dd = ((PyArrayObject *)__pyx_3); + __pyx_3 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":570 + * xx = np.reshape(x,(n,self.m)) + * dd = np.empty((n,k),dtype=np.float) + * dd.fill(infinity) # <<<<<<<<<<<<<< + * ii = np.empty((n,k),dtype='i') + * ii.fill(self.n) + */ + __pyx_7 = PyObject_GetAttr(((PyObject *)__pyx_v_dd), __pyx_kp_fill); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyFloat_FromDouble(__pyx_v_5scipy_7spatial_7ckdtree_infinity); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_2); + __pyx_2 = 0; + __pyx_5 = PyObject_Call(__pyx_7, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_7); __pyx_7 = 0; + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + Py_DECREF(__pyx_5); __pyx_5 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":571 + * dd = np.empty((n,k),dtype=np.float) + * dd.fill(infinity) + * ii = np.empty((n,k),dtype='i') # <<<<<<<<<<<<<< + * ii.fill(self.n) + * for c in range(n): + */ + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_empty); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_7 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_v_n); + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_v_n); + PyTuple_SET_ITEM(__pyx_1, 1, __pyx_7); + __pyx_7 = 0; + __pyx_5 = PyTuple_New(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_5, 0, ((PyObject *)__pyx_1)); + __pyx_1 = 0; + __pyx_3 = PyDict_New(); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyDict_SetItem(__pyx_3, __pyx_kp_dtype, __pyx_kp_23) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyEval_CallObjectWithKeywords(__pyx_2, ((PyObject *)__pyx_5), ((PyObject *)__pyx_3)); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + if (!(__Pyx_TypeTest(__pyx_7, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_7 = ((PyArrayObject *)__pyx_7); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_ii, &__pyx_bstruct_ii); + __pyx_t_2 = __Pyx_GetBuffer_int((PyObject*)__pyx_t_7, &__pyx_bstruct_ii, PyBUF_FORMAT| PyBUF_STRIDES, 2); + if (unlikely(__pyx_t_2 < 0)) + { + PyErr_Fetch(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5); + if (unlikely(__Pyx_GetBuffer_int((PyObject*)__pyx_v_ii, &__pyx_bstruct_ii, PyBUF_FORMAT| PyBUF_STRIDES, 2) == -1)) { + Py_XDECREF(__pyx_t_3); Py_XDECREF(__pyx_t_4); Py_XDECREF(__pyx_t_5); + __Pyx_RaiseBufferFallbackError(); + } else { + PyErr_Restore(__pyx_t_3, __pyx_t_4, __pyx_t_5); + } + } + __pyx_bstride_0_ii = __pyx_bstruct_ii.strides[0]; __pyx_bstride_1_ii = __pyx_bstruct_ii.strides[1]; + __pyx_bshape_0_ii = __pyx_bstruct_ii.shape[0]; __pyx_bshape_1_ii = __pyx_bstruct_ii.shape[1]; + if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_7 = 0; + Py_DECREF(((PyObject *)__pyx_v_ii)); + __pyx_v_ii = ((PyArrayObject *)__pyx_7); + __pyx_7 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":572 + * dd.fill(infinity) + * ii = np.empty((n,k),dtype='i') + * ii.fill(self.n) # <<<<<<<<<<<<<< + * for c in range(n): + * self.__query( + */ + __pyx_1 = PyObject_GetAttr(((PyObject *)__pyx_v_ii), __pyx_kp_fill); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->n); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyTuple_New(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_5, 0, __pyx_2); + __pyx_2 = 0; + __pyx_3 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; + Py_DECREF(__pyx_3); __pyx_3 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":573 + * ii = np.empty((n,k),dtype='i') + * ii.fill(self.n) + * for c in range(n): # <<<<<<<<<<<<<< + * self.__query( + * (dd.data)+c*k, + */ + __pyx_8 = __pyx_PyInt_int(__pyx_v_n); if (unlikely((__pyx_8 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + for (__pyx_v_c = 0; __pyx_v_c < __pyx_8; __pyx_v_c+=1) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":581 + * eps, + * p, + * distance_upper_bound) # <<<<<<<<<<<<<< + * if single: + * if k==1: + */ + ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->__pyx_vtab)->__query(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self), (((double *)__pyx_v_dd->data) + (__pyx_v_c * __pyx_v_k)), (((int *)__pyx_v_ii->data) + (__pyx_v_c * __pyx_v_k)), (((double *)__pyx_v_xx->data) + (__pyx_v_c * ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m)), __pyx_v_k, __pyx_v_eps, __pyx_v_p, __pyx_v_distance_upper_bound); + } + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":582 + * p, + * distance_upper_bound) + * if single: # <<<<<<<<<<<<<< + * if k==1: + * return dd[0,0], ii[0,0] + */ + __pyx_4 = __Pyx_PyObject_IsTrue(__pyx_v_single); if (unlikely(__pyx_4 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 582; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (__pyx_4) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":583 + * distance_upper_bound) + * if single: + * if k==1: # <<<<<<<<<<<<<< + * return dd[0,0], ii[0,0] + * else: + */ + __pyx_4 = (__pyx_v_k == 1); + if (__pyx_4) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":584 + * if single: + * if k==1: + * return dd[0,0], ii[0,0] # <<<<<<<<<<<<<< + * else: + * return dd[0], ii[0] + */ + __pyx_t_8 = 0; + __pyx_t_9 = 0; + __pyx_t_2 = -1; + if (__pyx_t_8 < 0) { + __pyx_t_8 += __pyx_bshape_0_dd; + if (unlikely(__pyx_t_8 < 0)) __pyx_t_2 = 0; + } else if (unlikely(__pyx_t_8 >= __pyx_bshape_0_dd)) __pyx_t_2 = 0; + if (__pyx_t_9 < 0) { + __pyx_t_9 += __pyx_bshape_1_dd; + if (unlikely(__pyx_t_9 < 0)) __pyx_t_2 = 1; + } else if (unlikely(__pyx_t_9 >= __pyx_bshape_1_dd)) __pyx_t_2 = 1; + if (unlikely(__pyx_t_2 != -1)) { + __Pyx_RaiseBufferIndexError(__pyx_t_2); + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + } + __pyx_9 = *((double *)((double *)__Pyx_BufPtrStrided2d(__pyx_bstruct_dd.buf, __pyx_t_8, __pyx_bstride_0_dd, __pyx_t_9, __pyx_bstride_1_dd))); + __pyx_7 = PyFloat_FromDouble(__pyx_9); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_10 = 0; + __pyx_t_11 = 0; + __pyx_t_2 = -1; + if (__pyx_t_10 < 0) { + __pyx_t_10 += __pyx_bshape_0_ii; + if (unlikely(__pyx_t_10 < 0)) __pyx_t_2 = 0; + } else if (unlikely(__pyx_t_10 >= __pyx_bshape_0_ii)) __pyx_t_2 = 0; + if (__pyx_t_11 < 0) { + __pyx_t_11 += __pyx_bshape_1_ii; + if (unlikely(__pyx_t_11 < 0)) __pyx_t_2 = 1; + } else if (unlikely(__pyx_t_11 >= __pyx_bshape_1_ii)) __pyx_t_2 = 1; + if (unlikely(__pyx_t_2 != -1)) { + __Pyx_RaiseBufferIndexError(__pyx_t_2); + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + } + __pyx_8 = *((int *)((int *)__Pyx_BufPtrStrided2d(__pyx_bstruct_ii.buf, __pyx_t_10, __pyx_bstride_0_ii, __pyx_t_11, __pyx_bstride_1_ii))); + __pyx_2 = PyInt_FromLong(__pyx_8); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_7); + PyTuple_SET_ITEM(__pyx_1, 1, __pyx_2); + __pyx_7 = 0; + __pyx_2 = 0; + __pyx_r = ((PyObject *)__pyx_1); + __pyx_1 = 0; + goto __pyx_L0; + goto __pyx_L11; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":586 + * return dd[0,0], ii[0,0] + * else: + * return dd[0], ii[0] # <<<<<<<<<<<<<< + * else: + * if k==1: + */ + __pyx_5 = __Pyx_GetItemInt(((PyObject *)__pyx_v_dd), 0, 0); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = __Pyx_GetItemInt(((PyObject *)__pyx_v_ii), 0, 0); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyTuple_New(2); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_7, 0, __pyx_5); + PyTuple_SET_ITEM(__pyx_7, 1, __pyx_3); + __pyx_5 = 0; + __pyx_3 = 0; + __pyx_r = ((PyObject *)__pyx_7); + __pyx_7 = 0; + goto __pyx_L0; + } + __pyx_L11:; + goto __pyx_L10; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":588 + * return dd[0], ii[0] + * else: + * if k==1: # <<<<<<<<<<<<<< + * return np.reshape(dd[...,0],retshape), np.reshape(ii[...,0],retshape) + * else: + */ + __pyx_4 = (__pyx_v_k == 1); + if (__pyx_4) { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":589 + * else: + * if k==1: + * return np.reshape(dd[...,0],retshape), np.reshape(ii[...,0],retshape) # <<<<<<<<<<<<<< + * else: + * return np.reshape(dd,retshape+(k,)), np.reshape(ii,retshape+(k,)) + */ + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_reshape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(Py_Ellipsis); + PyTuple_SET_ITEM(__pyx_5, 0, Py_Ellipsis); + Py_INCREF(__pyx_int_0); + PyTuple_SET_ITEM(__pyx_5, 1, __pyx_int_0); + __pyx_3 = PyObject_GetItem(((PyObject *)__pyx_v_dd), ((PyObject *)__pyx_5)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; + __pyx_7 = PyTuple_New(2); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_7, 0, __pyx_3); + Py_INCREF(__pyx_v_retshape); + PyTuple_SET_ITEM(__pyx_7, 1, __pyx_v_retshape); + __pyx_3 = 0; + __pyx_2 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_7), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + Py_DECREF(((PyObject *)__pyx_7)); __pyx_7 = 0; + __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_5, __pyx_kp_reshape); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(Py_Ellipsis); + PyTuple_SET_ITEM(__pyx_1, 0, Py_Ellipsis); + Py_INCREF(__pyx_int_0); + PyTuple_SET_ITEM(__pyx_1, 1, __pyx_int_0); + __pyx_7 = PyObject_GetItem(((PyObject *)__pyx_v_ii), ((PyObject *)__pyx_1)); if (!__pyx_7) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_5, 0, __pyx_7); + Py_INCREF(__pyx_v_retshape); + PyTuple_SET_ITEM(__pyx_5, 1, __pyx_v_retshape); + __pyx_7 = 0; + __pyx_1 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; + __pyx_7 = PyTuple_New(2); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_7, 0, __pyx_2); + PyTuple_SET_ITEM(__pyx_7, 1, __pyx_1); + __pyx_2 = 0; + __pyx_1 = 0; + __pyx_r = ((PyObject *)__pyx_7); + __pyx_7 = 0; + goto __pyx_L0; + goto __pyx_L12; + } + /*else*/ { + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":591 + * return np.reshape(dd[...,0],retshape), np.reshape(ii[...,0],retshape) + * else: + * return np.reshape(dd,retshape+(k,)), np.reshape(ii,retshape+(k,)) # <<<<<<<<<<<<<< + * + */ + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyObject_GetAttr(__pyx_3, __pyx_kp_reshape); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_3); __pyx_3 = 0; + __pyx_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_2); + __pyx_2 = 0; + __pyx_7 = PyNumber_Add(__pyx_v_retshape, ((PyObject *)__pyx_1)); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; + __pyx_3 = PyTuple_New(2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(((PyObject *)__pyx_v_dd)); + PyTuple_SET_ITEM(__pyx_3, 0, ((PyObject *)__pyx_v_dd)); + PyTuple_SET_ITEM(__pyx_3, 1, __pyx_7); + __pyx_7 = 0; + __pyx_2 = PyObject_Call(__pyx_5, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_5); __pyx_5 = 0; + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyObject_GetAttr(__pyx_1, __pyx_kp_reshape); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_5 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_3, 0, __pyx_5); + __pyx_5 = 0; + __pyx_1 = PyNumber_Add(__pyx_v_retshape, ((PyObject *)__pyx_3)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; + __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(((PyObject *)__pyx_v_ii)); + PyTuple_SET_ITEM(__pyx_5, 0, ((PyObject *)__pyx_v_ii)); + PyTuple_SET_ITEM(__pyx_5, 1, __pyx_1); + __pyx_1 = 0; + __pyx_3 = PyObject_Call(__pyx_7, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_7); __pyx_7 = 0; + Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; + __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_1, 0, __pyx_2); + PyTuple_SET_ITEM(__pyx_1, 1, __pyx_3); + __pyx_2 = 0; + __pyx_3 = 0; + __pyx_r = ((PyObject *)__pyx_1); + __pyx_1 = 0; + goto __pyx_L0; + } + __pyx_L12:; + } + __pyx_L10:; + + __pyx_r = Py_None; Py_INCREF(Py_None); + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_1); + Py_XDECREF(__pyx_2); + Py_XDECREF(__pyx_3); + Py_XDECREF(__pyx_5); + Py_XDECREF(__pyx_7); + { PyObject *__pyx_type, *__pyx_value, *__pyx_tb; + PyErr_Fetch(&__pyx_type, &__pyx_value, &__pyx_tb); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_ii, &__pyx_bstruct_ii); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_xx, &__pyx_bstruct_xx); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_dd, &__pyx_bstruct_dd); + PyErr_Restore(__pyx_type, __pyx_value, __pyx_tb);} + __Pyx_AddTraceback("scipy.spatial.ckdtree.cKDTree.query"); + __pyx_r = NULL; + goto __pyx_L2; + __pyx_L0:; + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_ii, &__pyx_bstruct_ii); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_xx, &__pyx_bstruct_xx); + __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_dd, &__pyx_bstruct_dd); + __pyx_L2:; + Py_DECREF(__pyx_v_ii); + Py_DECREF(__pyx_v_dd); + Py_DECREF(__pyx_v_xx); + Py_DECREF(__pyx_v_single); + Py_DECREF(__pyx_v_retshape); + Py_DECREF(__pyx_v_n); + Py_DECREF(__pyx_v_x); + return __pyx_r; +} + +/* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":36 + * # experimental exception made for __getbuffer__ and __releasebuffer__ + * # -- the details of this may change. + * def __getbuffer__(ndarray self, Py_buffer* info, int flags): # <<<<<<<<<<<<<< + * # This implementation of getbuffer is geared towards Cython + * # requirements, and does not yet fullfill the PEP (specifically, + */ + +static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { + int __pyx_v_t; + char *__pyx_v_f; + int __pyx_r; + int __pyx_1; + PyObject *__pyx_2 = 0; + PyObject *__pyx_3 = 0; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":42 + * # so the flags are not even checked). + * + * if sizeof(npy_intp) != sizeof(Py_ssize_t): # <<<<<<<<<<<<<< + * raise RuntimeError("Py_intptr_t and Py_ssize_t differs in size, numpy.pxd does not support this") + * + */ + __pyx_1 = ((sizeof(npy_intp)) != (sizeof(Py_ssize_t))); + if (__pyx_1) { + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":43 + * + * if sizeof(npy_intp) != sizeof(Py_ssize_t): + * raise RuntimeError("Py_intptr_t and Py_ssize_t differs in size, numpy.pxd does not support this") # <<<<<<<<<<<<<< + * + * info.buf = PyArray_DATA(self) + */ + __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_kp_1); + PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_1); + __pyx_3 = PyObject_Call(__pyx_builtin_RuntimeError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + __Pyx_Raise(__pyx_3, 0, 0); + Py_DECREF(__pyx_3); __pyx_3 = 0; + {__pyx_filename = __pyx_f[1]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + goto __pyx_L5; + } + __pyx_L5:; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":45 + * raise RuntimeError("Py_intptr_t and Py_ssize_t differs in size, numpy.pxd does not support this") + * + * info.buf = PyArray_DATA(self) # <<<<<<<<<<<<<< + * info.ndim = PyArray_NDIM(self) + * info.strides = PyArray_STRIDES(self) + */ + __pyx_v_info->buf = PyArray_DATA(((PyArrayObject *)__pyx_v_self)); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":46 + * + * info.buf = PyArray_DATA(self) + * info.ndim = PyArray_NDIM(self) # <<<<<<<<<<<<<< + * info.strides = PyArray_STRIDES(self) + * info.shape = PyArray_DIMS(self) + */ + __pyx_v_info->ndim = PyArray_NDIM(((PyArrayObject *)__pyx_v_self)); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":47 + * info.buf = PyArray_DATA(self) + * info.ndim = PyArray_NDIM(self) + * info.strides = PyArray_STRIDES(self) # <<<<<<<<<<<<<< + * info.shape = PyArray_DIMS(self) + * info.suboffsets = NULL + */ + __pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(((PyArrayObject *)__pyx_v_self))); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":48 + * info.ndim = PyArray_NDIM(self) + * info.strides = PyArray_STRIDES(self) + * info.shape = PyArray_DIMS(self) # <<<<<<<<<<<<<< + * info.suboffsets = NULL + * info.itemsize = PyArray_ITEMSIZE(self) + */ + __pyx_v_info->shape = ((Py_ssize_t *)PyArray_DIMS(((PyArrayObject *)__pyx_v_self))); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":49 + * info.strides = PyArray_STRIDES(self) + * info.shape = PyArray_DIMS(self) + * info.suboffsets = NULL # <<<<<<<<<<<<<< + * info.itemsize = PyArray_ITEMSIZE(self) + * info.readonly = not PyArray_ISWRITEABLE(self) + */ + __pyx_v_info->suboffsets = NULL; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":50 + * info.shape = PyArray_DIMS(self) + * info.suboffsets = NULL + * info.itemsize = PyArray_ITEMSIZE(self) # <<<<<<<<<<<<<< + * info.readonly = not PyArray_ISWRITEABLE(self) + * + */ + __pyx_v_info->itemsize = PyArray_ITEMSIZE(((PyArrayObject *)__pyx_v_self)); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":51 + * info.suboffsets = NULL + * info.itemsize = PyArray_ITEMSIZE(self) + * info.readonly = not PyArray_ISWRITEABLE(self) # <<<<<<<<<<<<<< + * + * # Formats that are not tested and working in Cython are not + */ + __pyx_v_info->readonly = (!PyArray_ISWRITEABLE(((PyArrayObject *)__pyx_v_self))); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":55 + * # Formats that are not tested and working in Cython are not + * # made available from this pxd file yet. + * cdef int t = PyArray_TYPE(self) # <<<<<<<<<<<<<< + * cdef char* f = NULL + * if t == NPY_BYTE: f = "b" + */ + __pyx_v_t = PyArray_TYPE(((PyArrayObject *)__pyx_v_self)); + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":56 + * # made available from this pxd file yet. + * cdef int t = PyArray_TYPE(self) + * cdef char* f = NULL # <<<<<<<<<<<<<< + * if t == NPY_BYTE: f = "b" + * elif t == NPY_UBYTE: f = "B" + */ + __pyx_v_f = NULL; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":57 + * cdef int t = PyArray_TYPE(self) + * cdef char* f = NULL + * if t == NPY_BYTE: f = "b" # <<<<<<<<<<<<<< + * elif t == NPY_UBYTE: f = "B" + * elif t == NPY_SHORT: f = "h" + */ + switch (__pyx_v_t) { + case NPY_BYTE: + __pyx_v_f = __pyx_k_2; + break; + case NPY_UBYTE: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":58 + * cdef char* f = NULL + * if t == NPY_BYTE: f = "b" + * elif t == NPY_UBYTE: f = "B" # <<<<<<<<<<<<<< + * elif t == NPY_SHORT: f = "h" + * elif t == NPY_USHORT: f = "H" + */ + __pyx_v_f = __pyx_k_3; + break; + case NPY_SHORT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":59 + * if t == NPY_BYTE: f = "b" + * elif t == NPY_UBYTE: f = "B" + * elif t == NPY_SHORT: f = "h" # <<<<<<<<<<<<<< + * elif t == NPY_USHORT: f = "H" + * elif t == NPY_INT: f = "i" + */ + __pyx_v_f = __pyx_k_4; + break; + case NPY_USHORT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":60 + * elif t == NPY_UBYTE: f = "B" + * elif t == NPY_SHORT: f = "h" + * elif t == NPY_USHORT: f = "H" # <<<<<<<<<<<<<< + * elif t == NPY_INT: f = "i" + * elif t == NPY_UINT: f = "I" + */ + __pyx_v_f = __pyx_k_5; + break; + case NPY_INT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":61 + * elif t == NPY_SHORT: f = "h" + * elif t == NPY_USHORT: f = "H" + * elif t == NPY_INT: f = "i" # <<<<<<<<<<<<<< + * elif t == NPY_UINT: f = "I" + * elif t == NPY_LONG: f = "l" + */ + __pyx_v_f = __pyx_k_6; + break; + case NPY_UINT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":62 + * elif t == NPY_USHORT: f = "H" + * elif t == NPY_INT: f = "i" + * elif t == NPY_UINT: f = "I" # <<<<<<<<<<<<<< + * elif t == NPY_LONG: f = "l" + * elif t == NPY_ULONG: f = "L" + */ + __pyx_v_f = __pyx_k_7; + break; + case NPY_LONG: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":63 + * elif t == NPY_INT: f = "i" + * elif t == NPY_UINT: f = "I" + * elif t == NPY_LONG: f = "l" # <<<<<<<<<<<<<< + * elif t == NPY_ULONG: f = "L" + * elif t == NPY_LONGLONG: f = "q" + */ + __pyx_v_f = __pyx_k_8; + break; + case NPY_ULONG: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":64 + * elif t == NPY_UINT: f = "I" + * elif t == NPY_LONG: f = "l" + * elif t == NPY_ULONG: f = "L" # <<<<<<<<<<<<<< + * elif t == NPY_LONGLONG: f = "q" + * elif t == NPY_ULONGLONG: f = "Q" + */ + __pyx_v_f = __pyx_k_9; + break; + case NPY_LONGLONG: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":65 + * elif t == NPY_LONG: f = "l" + * elif t == NPY_ULONG: f = "L" + * elif t == NPY_LONGLONG: f = "q" # <<<<<<<<<<<<<< + * elif t == NPY_ULONGLONG: f = "Q" + * elif t == NPY_FLOAT: f = "f" + */ + __pyx_v_f = __pyx_k_10; + break; + case NPY_ULONGLONG: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":66 + * elif t == NPY_ULONG: f = "L" + * elif t == NPY_LONGLONG: f = "q" + * elif t == NPY_ULONGLONG: f = "Q" # <<<<<<<<<<<<<< + * elif t == NPY_FLOAT: f = "f" + * elif t == NPY_DOUBLE: f = "d" + */ + __pyx_v_f = __pyx_k_11; + break; + case NPY_FLOAT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":67 + * elif t == NPY_LONGLONG: f = "q" + * elif t == NPY_ULONGLONG: f = "Q" + * elif t == NPY_FLOAT: f = "f" # <<<<<<<<<<<<<< + * elif t == NPY_DOUBLE: f = "d" + * elif t == NPY_LONGDOUBLE: f = "g" + */ + __pyx_v_f = __pyx_k_12; + break; + case NPY_DOUBLE: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":68 + * elif t == NPY_ULONGLONG: f = "Q" + * elif t == NPY_FLOAT: f = "f" + * elif t == NPY_DOUBLE: f = "d" # <<<<<<<<<<<<<< + * elif t == NPY_LONGDOUBLE: f = "g" + * elif t == NPY_OBJECT: f = "O" + */ + __pyx_v_f = __pyx_k_13; + break; + case NPY_LONGDOUBLE: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":69 + * elif t == NPY_FLOAT: f = "f" + * elif t == NPY_DOUBLE: f = "d" + * elif t == NPY_LONGDOUBLE: f = "g" # <<<<<<<<<<<<<< + * elif t == NPY_OBJECT: f = "O" + * + */ + __pyx_v_f = __pyx_k_14; + break; + case NPY_OBJECT: + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":70 + * elif t == NPY_DOUBLE: f = "d" + * elif t == NPY_LONGDOUBLE: f = "g" + * elif t == NPY_OBJECT: f = "O" # <<<<<<<<<<<<<< + * + * if f == NULL: + */ + __pyx_v_f = __pyx_k_15; + break; + } + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":72 + * elif t == NPY_OBJECT: f = "O" + * + * if f == NULL: # <<<<<<<<<<<<<< + * raise ValueError("only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)" % t) + * info.format = f + */ + __pyx_1 = (__pyx_v_f == NULL); + if (__pyx_1) { + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":73 + * + * if f == NULL: + * raise ValueError("only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)" % t) # <<<<<<<<<<<<<< + * info.format = f + * + */ + __pyx_2 = PyInt_FromLong(__pyx_v_t); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyNumber_Remainder(__pyx_kp_16, __pyx_2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyTuple_SET_ITEM(__pyx_2, 0, __pyx_3); + __pyx_3 = 0; + __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; + __Pyx_Raise(__pyx_3, 0, 0); + Py_DECREF(__pyx_3); __pyx_3 = 0; + {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + goto __pyx_L6; + } + __pyx_L6:; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":74 + * if f == NULL: + * raise ValueError("only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)" % t) + * info.format = f # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_info->format = __pyx_v_f; + + __pyx_r = 0; + goto __pyx_L0; + __pyx_L1_error:; + Py_XDECREF(__pyx_2); + Py_XDECREF(__pyx_3); + __Pyx_AddTraceback("numpy.ndarray.__getbuffer__"); + __pyx_r = -1; + __pyx_L0:; + return __pyx_r; +} +static struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree __pyx_vtable_5scipy_7spatial_7ckdtree_cKDTree; + +static PyObject *__pyx_tp_new_5scipy_7spatial_7ckdtree_cKDTree(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *p; + PyObject *o = (*t->tp_alloc)(t, 0); + if (!o) return 0; + p = ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)o); + p->__pyx_vtab = __pyx_vtabptr_5scipy_7spatial_7ckdtree_cKDTree; + p->data = Py_None; Py_INCREF(Py_None); + p->maxes = Py_None; Py_INCREF(Py_None); + p->mins = Py_None; Py_INCREF(Py_None); + p->indices = Py_None; Py_INCREF(Py_None); + return o; +} + +static void __pyx_tp_dealloc_5scipy_7spatial_7ckdtree_cKDTree(PyObject *o) { + struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *p = (struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)o; + Py_XDECREF(p->data); + Py_XDECREF(p->maxes); + Py_XDECREF(p->mins); + Py_XDECREF(p->indices); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_5scipy_7spatial_7ckdtree_cKDTree(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *p = (struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)o; + if (p->data) { + e = (*v)(p->data, a); if (e) return e; + } + if (p->maxes) { + e = (*v)(p->maxes, a); if (e) return e; + } + if (p->mins) { + e = (*v)(p->mins, a); if (e) return e; + } + if (p->indices) { + e = (*v)(p->indices, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_5scipy_7spatial_7ckdtree_cKDTree(PyObject *o) { + struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *p = (struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)o; + PyObject* tmp; + tmp = ((PyObject*)p->data); + p->data = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->maxes); + p->maxes = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->mins); + p->mins = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->indices); + p->indices = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + return 0; +} + +static struct PyMethodDef __pyx_methods_5scipy_7spatial_7ckdtree_cKDTree[] = { + {"__del__", (PyCFunction)__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___del__, METH_NOARGS, 0}, + {"query", (PyCFunction)__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree_query, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5scipy_7spatial_7ckdtree_7cKDTree_query}, + {0, 0, 0, 0} +}; + +static struct PyMemberDef __pyx_members_5scipy_7spatial_7ckdtree_cKDTree[] = { + {"data", T_OBJECT, offsetof(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree, data), READONLY, 0}, + {"n", T_INT, offsetof(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree, n), READONLY, 0}, + {"m", T_INT, offsetof(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree, m), READONLY, 0}, + {"leafsize", T_INT, offsetof(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree, leafsize), READONLY, 0}, + {"maxes", T_OBJECT, offsetof(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree, maxes), READONLY, 0}, + {"mins", T_OBJECT, offsetof(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree, mins), READONLY, 0}, + {0, 0, 0, 0, 0} +}; + +static PyNumberMethods __pyx_tp_as_number_cKDTree = { + 0, /*nb_add*/ + 0, /*nb_subtract*/ + 0, /*nb_multiply*/ + #if PY_MAJOR_VERSION < 3 + 0, /*nb_divide*/ + #endif + 0, /*nb_remainder*/ + 0, /*nb_divmod*/ + 0, /*nb_power*/ + 0, /*nb_negative*/ + 0, /*nb_positive*/ + 0, /*nb_absolute*/ + 0, /*nb_nonzero*/ + 0, /*nb_invert*/ + 0, /*nb_lshift*/ + 0, /*nb_rshift*/ + 0, /*nb_and*/ + 0, /*nb_xor*/ + 0, /*nb_or*/ + #if PY_MAJOR_VERSION < 3 + 0, /*nb_coerce*/ + #endif + 0, /*nb_int*/ + 0, /*nb_long*/ + 0, /*nb_float*/ + #if PY_MAJOR_VERSION < 3 + 0, /*nb_oct*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*nb_hex*/ + #endif + 0, /*nb_inplace_add*/ + 0, /*nb_inplace_subtract*/ + 0, /*nb_inplace_multiply*/ + #if PY_MAJOR_VERSION < 3 + 0, /*nb_inplace_divide*/ + #endif + 0, /*nb_inplace_remainder*/ + 0, /*nb_inplace_power*/ + 0, /*nb_inplace_lshift*/ + 0, /*nb_inplace_rshift*/ + 0, /*nb_inplace_and*/ + 0, /*nb_inplace_xor*/ + 0, /*nb_inplace_or*/ + 0, /*nb_floor_divide*/ + 0, /*nb_true_divide*/ + 0, /*nb_inplace_floor_divide*/ + 0, /*nb_inplace_true_divide*/ + #if (PY_MAJOR_VERSION >= 3) || (Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_INDEX) + 0, /*nb_index*/ + #endif +}; + +static PySequenceMethods __pyx_tp_as_sequence_cKDTree = { + 0, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + 0, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_cKDTree = { + 0, /*mp_length*/ + 0, /*mp_subscript*/ + 0, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_cKDTree = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + #if (PY_MAJOR_VERSION >= 3) || (Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_NEWBUFFER) + 0, /*bf_getbuffer*/ + #endif + #if (PY_MAJOR_VERSION >= 3) || (Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_NEWBUFFER) + 0, /*bf_releasebuffer*/ + #endif +}; + +PyTypeObject __pyx_type_5scipy_7spatial_7ckdtree_cKDTree = { + PyVarObject_HEAD_INIT(0, 0) + "scipy.spatial.ckdtree.cKDTree", /*tp_name*/ + sizeof(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_5scipy_7spatial_7ckdtree_cKDTree, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + 0, /*tp_compare*/ + 0, /*tp_repr*/ + &__pyx_tp_as_number_cKDTree, /*tp_as_number*/ + &__pyx_tp_as_sequence_cKDTree, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_cKDTree, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_cKDTree, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + "kd-tree for quick nearest-neighbor lookup\n\n This class provides an index into a set of k-dimensional points\n which can be used to rapidly look up the nearest neighbors of any\n point. \n\n The algorithm used is described in Maneewongvatana and Mount 1999. \n The general idea is that the kd-tree is a binary trie, each of whose\n nodes represents an axis-aligned hyperrectangle. Each node specifies\n an axis and splits the set of points based on whether their coordinate\n along that axis is greater than or less than a particular value. \n\n During construction, the axis and splitting point are chosen by the \n \"sliding midpoint\" rule, which ensures that the cells do not all\n become long and thin. \n\n The tree can be queried for the r closest neighbors of any given point \n (optionally returning only those within some maximum distance of the \n point). It can also be queried, with a substantial gain in efficiency, \n for the r approximate closest neighbors.\n\n For large dimensions (20 is already large) do not expect this to run \n significantly faster than brute force. High-dimensional nearest-neighbor\n queries are a substantial open problem in computer science.\n ", /*tp_doc*/ + __pyx_tp_traverse_5scipy_7spatial_7ckdtree_cKDTree, /*tp_traverse*/ + __pyx_tp_clear_5scipy_7spatial_7ckdtree_cKDTree, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_5scipy_7spatial_7ckdtree_cKDTree, /*tp_methods*/ + __pyx_members_5scipy_7spatial_7ckdtree_cKDTree, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + __pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___init__, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_5scipy_7spatial_7ckdtree_cKDTree, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ +}; + +static struct PyMethodDef __pyx_methods[] = { + {0, 0, 0, 0} +}; + +static void __pyx_init_filenames(void); /*proto*/ + +#if PY_MAJOR_VERSION >= 3 +static struct PyModuleDef __pyx_moduledef = { + PyModuleDef_HEAD_INIT, + "ckdtree", + 0, /* m_doc */ + -1, /* m_size */ + __pyx_methods /* m_methods */, + NULL, /* m_reload */ + NULL, /* m_traverse */ + NULL, /* m_clear */ + NULL /* m_free */ +}; +#endif + +static __Pyx_StringTabEntry __pyx_string_tab[] = { + {&__pyx_kp___init__, __pyx_k___init__, sizeof(__pyx_k___init__), 0, 1, 1}, + {&__pyx_kp___del__, __pyx_k___del__, sizeof(__pyx_k___del__), 0, 1, 1}, + {&__pyx_kp_query, __pyx_k_query, sizeof(__pyx_k_query), 0, 1, 1}, + {&__pyx_kp_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 1, 1, 1}, + {&__pyx_kp_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 1, 1}, + {&__pyx_kp_kdtree, __pyx_k_kdtree, sizeof(__pyx_k_kdtree), 1, 1, 1}, + {&__pyx_kp_inf, __pyx_k_inf, sizeof(__pyx_k_inf), 1, 1, 1}, + {&__pyx_kp_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 1, 1, 1}, + {&__pyx_kp_ascontiguousarray, __pyx_k_ascontiguousarray, sizeof(__pyx_k_ascontiguousarray), 1, 1, 1}, + {&__pyx_kp_dtype, __pyx_k_dtype, sizeof(__pyx_k_dtype), 1, 1, 1}, + {&__pyx_kp_float, __pyx_k_float, sizeof(__pyx_k_float), 1, 1, 1}, + {&__pyx_kp_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 1, 1, 1}, + {&__pyx_kp_amax, __pyx_k_amax, sizeof(__pyx_k_amax), 1, 1, 1}, + {&__pyx_kp_axis, __pyx_k_axis, sizeof(__pyx_k_axis), 1, 1, 1}, + {&__pyx_kp_amin, __pyx_k_amin, sizeof(__pyx_k_amin), 1, 1, 1}, + {&__pyx_kp_arange, __pyx_k_arange, sizeof(__pyx_k_arange), 1, 1, 1}, + {&__pyx_kp_int, __pyx_k_int, sizeof(__pyx_k_int), 1, 1, 1}, + {&__pyx_kp_asarray, __pyx_k_asarray, sizeof(__pyx_k_asarray), 1, 1, 1}, + {&__pyx_kp_astype, __pyx_k_astype, sizeof(__pyx_k_astype), 1, 1, 1}, + {&__pyx_kp_newaxis, __pyx_k_newaxis, sizeof(__pyx_k_newaxis), 1, 1, 1}, + {&__pyx_kp_prod, __pyx_k_prod, sizeof(__pyx_k_prod), 1, 1, 1}, + {&__pyx_kp_reshape, __pyx_k_reshape, sizeof(__pyx_k_reshape), 1, 1, 1}, + {&__pyx_kp_empty, __pyx_k_empty, sizeof(__pyx_k_empty), 1, 1, 1}, + {&__pyx_kp_fill, __pyx_k_fill, sizeof(__pyx_k_fill), 1, 1, 1}, + {&__pyx_kp_23, __pyx_k_23, sizeof(__pyx_k_23), 0, 1, 0}, + {&__pyx_kp_18, __pyx_k_18, sizeof(__pyx_k_18), 0, 0, 0}, + {&__pyx_kp_19, __pyx_k_19, sizeof(__pyx_k_19), 0, 0, 0}, + {&__pyx_kp_20, __pyx_k_20, sizeof(__pyx_k_20), 0, 0, 0}, + {&__pyx_kp_21, __pyx_k_21, sizeof(__pyx_k_21), 0, 0, 0}, + {&__pyx_kp_22, __pyx_k_22, sizeof(__pyx_k_22), 0, 0, 0}, + {&__pyx_kp___getbuffer__, __pyx_k___getbuffer__, sizeof(__pyx_k___getbuffer__), 0, 1, 1}, + {&__pyx_kp_RuntimeError, __pyx_k_RuntimeError, sizeof(__pyx_k_RuntimeError), 1, 1, 1}, + {&__pyx_kp_1, __pyx_k_1, sizeof(__pyx_k_1), 0, 0, 0}, + {&__pyx_kp_16, __pyx_k_16, sizeof(__pyx_k_16), 0, 0, 0}, + {0, 0, 0, 0, 0, 0} +}; +static int __Pyx_InitCachedBuiltins(void) { + __pyx_builtin_ValueError = __Pyx_GetName(__pyx_b, __pyx_kp_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_builtin_RuntimeError = __Pyx_GetName(__pyx_b, __pyx_kp_RuntimeError); if (!__pyx_builtin_RuntimeError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + return 0; + __pyx_L1_error:; + return -1; +} + +static int __Pyx_InitGlobals(void) { + __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + return 0; + __pyx_L1_error:; + return -1; +} + +#if PY_MAJOR_VERSION < 3 +PyMODINIT_FUNC initckdtree(void); /*proto*/ +PyMODINIT_FUNC initckdtree(void) +#else +PyMODINIT_FUNC PyInit_ckdtree(void); /*proto*/ +PyMODINIT_FUNC PyInit_ckdtree(void) +#endif +{ + PyObject *__pyx_1 = 0; + PyObject *__pyx_2 = 0; + double __pyx_3; + __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + /*--- Libary function declarations ---*/ + __pyx_init_filenames(); + /*--- Initialize various global constants etc. ---*/ + if (unlikely(__Pyx_InitGlobals() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + /*--- Module creation code ---*/ + #if PY_MAJOR_VERSION < 3 + __pyx_m = Py_InitModule4("ckdtree", __pyx_methods, 0, 0, PYTHON_API_VERSION); + #else + __pyx_m = PyModule_Create(&__pyx_moduledef); + #endif + if (!__pyx_m) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + #if PY_MAJOR_VERSION < 3 + Py_INCREF(__pyx_m); + #endif + __pyx_b = PyImport_AddModule(__Pyx_BUILTIN_MODULE_NAME); + if (!__pyx_b) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + /*--- Builtin init code ---*/ + if (unlikely(__Pyx_InitCachedBuiltins() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_skip_dispatch = 0; + /*--- Global init code ---*/ + /*--- Function export code ---*/ + /*--- Type init code ---*/ + __pyx_vtabptr_5scipy_7spatial_7ckdtree_cKDTree = &__pyx_vtable_5scipy_7spatial_7ckdtree_cKDTree; + *(void(**)(void))&__pyx_vtable_5scipy_7spatial_7ckdtree_cKDTree.__build = (void(*)(void))__pyx_f_5scipy_7spatial_7ckdtree_7cKDTree___build; + *(void(**)(void))&__pyx_vtable_5scipy_7spatial_7ckdtree_cKDTree.__free_tree = (void(*)(void))__pyx_f_5scipy_7spatial_7ckdtree_7cKDTree___free_tree; + *(void(**)(void))&__pyx_vtable_5scipy_7spatial_7ckdtree_cKDTree.__query = (void(*)(void))__pyx_f_5scipy_7spatial_7ckdtree_7cKDTree___query; + if (PyType_Ready(&__pyx_type_5scipy_7spatial_7ckdtree_cKDTree) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 157; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (__Pyx_SetVtable(__pyx_type_5scipy_7spatial_7ckdtree_cKDTree.tp_dict, __pyx_vtabptr_5scipy_7spatial_7ckdtree_cKDTree) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 157; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttrString(__pyx_m, "cKDTree", (PyObject *)&__pyx_type_5scipy_7spatial_7ckdtree_cKDTree) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 157; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_ptype_5scipy_7spatial_7ckdtree_cKDTree = &__pyx_type_5scipy_7spatial_7ckdtree_cKDTree; + /*--- Type import code ---*/ + __pyx_ptype_5numpy_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject)); if (unlikely(!__pyx_ptype_5numpy_ndarray)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 24; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + /*--- Function import code ---*/ + /*--- Execution code ---*/ + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":3 + * # Copyright Anne M. Archibald 2008 + * # Released under the scipy license + * import numpy as np # <<<<<<<<<<<<<< + * cimport numpy as np + * cimport stdlib + */ + __pyx_1 = __Pyx_Import(__pyx_kp_numpy, 0); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_m, __pyx_kp_np, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":7 + * cimport stdlib + * + * import kdtree # <<<<<<<<<<<<<< + * + * cdef double infinity = np.inf + */ + __pyx_1 = __Pyx_Import(__pyx_kp_kdtree, 0); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_m, __pyx_kp_kdtree, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":9 + * import kdtree + * + * cdef double infinity = np.inf # <<<<<<<<<<<<<< + * + * + */ + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_inf); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_3 = __pyx_PyFloat_AsDouble(__pyx_2); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_v_5scipy_7spatial_7ckdtree_infinity = __pyx_3; + + /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":515 + * + * def query(cKDTree self, object x, int k=1, double eps=0, double p=2, + * double distance_upper_bound=infinity): # <<<<<<<<<<<<<< + * """query the kd-tree for nearest neighbors + * + */ + __pyx_k_17 = __pyx_v_5scipy_7spatial_7ckdtree_infinity; + + /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/stdlib.pxd":2 + * + * cdef extern from "stdlib.h": # <<<<<<<<<<<<<< + * ctypedef unsigned long size_t + * void free(void *ptr) + */ + #if PY_MAJOR_VERSION < 3 + return; + #else + return __pyx_m; + #endif + __pyx_L1_error:; + Py_XDECREF(__pyx_1); + Py_XDECREF(__pyx_2); + __Pyx_AddTraceback("scipy.spatial.ckdtree"); + #if PY_MAJOR_VERSION >= 3 + return NULL; + #endif +} + +static const char *__pyx_filenames[] = { + "ckdtree.pyx", + "numpy.pxd", +}; + +/* Runtime support code */ + +static void __pyx_init_filenames(void) { + __pyx_f = __pyx_filenames; +} + +static INLINE void __Pyx_SafeReleaseBuffer(PyObject* obj, Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(obj, info); +} + +static INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} + +static INLINE const char* __Pyx_ConsumeWhitespace(const char* ts) { + while (1) { + switch (*ts) { + case 10: + case 13: + case ' ': + ++ts; + default: + return ts; + } + } +} + +static INLINE const char* __Pyx_BufferTypestringCheckEndian(const char* ts) { + int num = 1; + int little_endian = ((char*)&num)[0]; + int ok = 1; + switch (*ts) { + case '@': + case '=': + ++ts; break; + case '<': + if (little_endian) ++ts; + else ok = 0; + break; + case '>': + case '!': + if (!little_endian) ++ts; + else ok = 0; + break; + } + if (!ok) { + PyErr_Format(PyExc_ValueError, "Buffer has wrong endianness (rejecting on '%s')", ts); + return NULL; + } + return ts; +} + +static void __Pyx_BufferNdimError(Py_buffer* buffer, int expected_ndim) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + expected_ndim, buffer->ndim); +} + + +static const char* __Pyx_BufferTypestringCheck_item_double(const char* ts) { + if (*ts == '1') ++ts; + if (*ts != 'd') { + PyErr_Format(PyExc_ValueError, "Buffer datatype mismatch (expected 'd', got '%s')", ts); + return NULL; + } else return ts + 1; + +} + +static int __Pyx_GetBuffer_double(PyObject* obj, Py_buffer* buf, int flags, int nd) { + const char* ts; + if (obj == Py_None) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + __Pyx_BufferNdimError(buf, nd); + goto fail; + } + ts = buf->format; + ts = __Pyx_ConsumeWhitespace(ts); + ts = __Pyx_BufferTypestringCheckEndian(ts); + if (!ts) goto fail; + ts = __Pyx_ConsumeWhitespace(ts); + ts = __Pyx_BufferTypestringCheck_item_double(ts); + if (!ts) goto fail; + ts = __Pyx_ConsumeWhitespace(ts); + if (*ts != 0) { + PyErr_Format(PyExc_ValueError, + "Expected non-struct buffer data type (expected end, got '%s')", ts); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static void __Pyx_RaiseBufferFallbackError(void) { + PyErr_Format(PyExc_ValueError, + "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); +} + + +static const char* __Pyx_BufferTypestringCheck_item_nn___pyx_t_5numpy_int32_t(const char* ts) { + int ok; + if (*ts == '1') ++ts; + switch (*ts) { + case 'b': ok = (sizeof(__pyx_t_5numpy_int32_t) == sizeof(char) && (__pyx_t_5numpy_int32_t)-1 < 0); break; + case 'h': ok = (sizeof(__pyx_t_5numpy_int32_t) == sizeof(short) && (__pyx_t_5numpy_int32_t)-1 < 0); break; + case 'i': ok = (sizeof(__pyx_t_5numpy_int32_t) == sizeof(int) && (__pyx_t_5numpy_int32_t)-1 < 0); break; + case 'l': ok = (sizeof(__pyx_t_5numpy_int32_t) == sizeof(long) && (__pyx_t_5numpy_int32_t)-1 < 0); break; + case 'q': ok = (sizeof(__pyx_t_5numpy_int32_t) == sizeof(long long) && (__pyx_t_5numpy_int32_t)-1 < 0); break; default: ok = 0; + } + if (!ok) { + PyErr_Format(PyExc_ValueError, "Buffer datatype mismatch (rejecting on '%s')", ts); + return NULL; + } else return ts + 1; + +} + +static int __Pyx_GetBuffer_nn___pyx_t_5numpy_int32_t(PyObject* obj, Py_buffer* buf, int flags, int nd) { + const char* ts; + if (obj == Py_None) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + __Pyx_BufferNdimError(buf, nd); + goto fail; + } + ts = buf->format; + ts = __Pyx_ConsumeWhitespace(ts); + ts = __Pyx_BufferTypestringCheckEndian(ts); + if (!ts) goto fail; + ts = __Pyx_ConsumeWhitespace(ts); + ts = __Pyx_BufferTypestringCheck_item_nn___pyx_t_5numpy_int32_t(ts); + if (!ts) goto fail; + ts = __Pyx_ConsumeWhitespace(ts); + if (*ts != 0) { + PyErr_Format(PyExc_ValueError, + "Expected non-struct buffer data type (expected end, got '%s')", ts); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static const char* __Pyx_BufferTypestringCheck_item_int(const char* ts) { + if (*ts == '1') ++ts; + if (*ts != 'i') { + PyErr_Format(PyExc_ValueError, "Buffer datatype mismatch (expected 'i', got '%s')", ts); + return NULL; + } else return ts + 1; + +} + +static int __Pyx_GetBuffer_int(PyObject* obj, Py_buffer* buf, int flags, int nd) { + const char* ts; + if (obj == Py_None) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + __Pyx_BufferNdimError(buf, nd); + goto fail; + } + ts = buf->format; + ts = __Pyx_ConsumeWhitespace(ts); + ts = __Pyx_BufferTypestringCheckEndian(ts); + if (!ts) goto fail; + ts = __Pyx_ConsumeWhitespace(ts); + ts = __Pyx_BufferTypestringCheck_item_int(ts); + if (!ts) goto fail; + ts = __Pyx_ConsumeWhitespace(ts); + if (*ts != 0) { + PyErr_Format(PyExc_ValueError, + "Expected non-struct buffer data type (expected end, got '%s')", ts); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +}static void __Pyx_RaiseBufferIndexError(int axis) { + PyErr_Format(PyExc_IndexError, + "Out of bounds on buffer access (axis %d)", axis); +} + + +static INLINE void __Pyx_RaiseArgtupleTooLong( + Py_ssize_t num_expected, + Py_ssize_t num_found) +{ + const char* error_message = + #if PY_VERSION_HEX < 0x02050000 + "function takes at most %d positional arguments (%d given)"; + #else + "function takes at most %zd positional arguments (%zd given)"; + #endif + PyErr_Format(PyExc_TypeError, error_message, num_expected, num_found); +} + +#if (PY_MAJOR_VERSION < 3) && !(Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_NEWBUFFER) +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pf_5numpy_7ndarray___getbuffer__(obj, view, flags); + else { + PyErr_Format(PyExc_TypeError, "'%100s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; + } +} + +static void __Pyx_ReleaseBuffer(PyObject *obj, Py_buffer *view) { + +} + +#endif + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) { + PyObject *__import__ = 0; + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + __import__ = PyObject_GetAttrString(__pyx_b, "__import__"); + if (!__import__) + goto bad; + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + module = PyObject_CallFunction(__import__, "OOOO", + name, global_dict, empty_dict, list); +bad: + Py_XDECREF(empty_list); + Py_XDECREF(__import__); + Py_XDECREF(empty_dict); + return module; +} + +static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) { + PyObject *result; + result = PyObject_GetAttr(dict, name); + if (!result) + PyErr_SetObject(PyExc_NameError, name); + return result; +} + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { + Py_XINCREF(type); + Py_XINCREF(value); + Py_XINCREF(tb); + /* First, check the traceback argument, replacing None with NULL. */ + if (tb == Py_None) { + Py_DECREF(tb); + tb = 0; + } + else if (tb != NULL && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + /* Next, replace a missing value with None */ + if (value == NULL) { + value = Py_None; + Py_INCREF(value); + } + #if PY_VERSION_HEX < 0x02050000 + if (!PyClass_Check(type)) + #else + if (!PyType_Check(type)) + #endif + { + /* Raising an instance. The value should be a dummy. */ + if (value != Py_None) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + /* Normalize to raise , */ + Py_DECREF(value); + value = type; + #if PY_VERSION_HEX < 0x02050000 + if (PyInstance_Check(type)) { + type = (PyObject*) ((PyInstanceObject*)type)->in_class; + Py_INCREF(type); + } + else { + type = 0; + PyErr_SetString(PyExc_TypeError, + "raise: exception must be an old-style class or instance"); + goto raise_error; + } + #else + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + #endif + } + PyErr_Restore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} + +static PyObject *__Pyx_UnpackItem(PyObject *iter, Py_ssize_t index) { + PyObject *item; + if (!(item = PyIter_Next(iter))) { + if (!PyErr_Occurred()) { + PyErr_Format(PyExc_ValueError, + #if PY_VERSION_HEX < 0x02050000 + "need more than %d values to unpack", (int)index); + #else + "need more than %zd values to unpack", index); + #endif + } + } + return item; +} + +static int __Pyx_EndUnpack(PyObject *iter) { + PyObject *item; + if ((item = PyIter_Next(iter))) { + Py_DECREF(item); + PyErr_SetString(PyExc_ValueError, "too many values to unpack"); + return -1; + } + else if (!PyErr_Occurred()) + return 0; + else + return -1; +} + +static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (!type) { + PyErr_Format(PyExc_SystemError, "Missing type object"); + return 0; + } + if (obj == Py_None || PyObject_TypeCheck(obj, type)) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %s to %s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +#if PY_MAJOR_VERSION < 3 +static PyObject *__Pyx_GetStdout(void) { + PyObject *f = PySys_GetObject("stdout"); + if (!f) { + PyErr_SetString(PyExc_RuntimeError, "lost sys.stdout"); + } + return f; +} + +static int __Pyx_Print(PyObject *arg_tuple, int newline) { + PyObject *f; + PyObject* v; + int i; + + if (!(f = __Pyx_GetStdout())) + return -1; + for (i=0; i < PyTuple_GET_SIZE(arg_tuple); i++) { + if (PyFile_SoftSpace(f, 1)) { + if (PyFile_WriteString(" ", f) < 0) + return -1; + } + v = PyTuple_GET_ITEM(arg_tuple, i); + if (PyFile_WriteObject(v, f, Py_PRINT_RAW) < 0) + return -1; + if (PyString_Check(v)) { + char *s = PyString_AsString(v); + Py_ssize_t len = PyString_Size(v); + if (len > 0 && + isspace(Py_CHARMASK(s[len-1])) && + s[len-1] != ' ') + PyFile_SoftSpace(f, 0); + } + } + if (newline) { + if (PyFile_WriteString("\n", f) < 0) + return -1; + PyFile_SoftSpace(f, 0); + } + return 0; +} + +#else /* Python 3 has a print function */ +static int __Pyx_Print(PyObject *arg_tuple, int newline) { + PyObject* kwargs = 0; + PyObject* result = 0; + PyObject* end_string; + if (!__pyx_print) { + __pyx_print = PyObject_GetAttrString(__pyx_b, "print"); + if (!__pyx_print) + return -1; + } + if (!newline) { + if (!__pyx_print_kwargs) { + __pyx_print_kwargs = PyDict_New(); + if (!__pyx_print_kwargs) + return -1; + end_string = PyUnicode_FromStringAndSize(" ", 1); + if (!end_string) + return -1; + if (PyDict_SetItemString(__pyx_print_kwargs, "end", end_string) < 0) { + Py_DECREF(end_string); + return -1; + } + Py_DECREF(end_string); + } + kwargs = __pyx_print_kwargs; + } + result = PyObject_Call(__pyx_print, arg_tuple, kwargs); + if (!result) + return -1; + Py_DECREF(result); + return 0; +} +#endif + + +static void __Pyx_WriteUnraisable(const char *name) { + PyObject *old_exc, *old_val, *old_tb; + PyObject *ctx; + PyErr_Fetch(&old_exc, &old_val, &old_tb); + #if PY_MAJOR_VERSION < 3 + ctx = PyString_FromString(name); + #else + ctx = PyUnicode_FromString(name); + #endif + PyErr_Restore(old_exc, old_val, old_tb); + if (!ctx) + ctx = Py_None; + PyErr_WriteUnraisable(ctx); +} + +static int __Pyx_SetVtable(PyObject *dict, void *vtable) { + PyObject *pycobj = 0; + int result; + + pycobj = PyCObject_FromVoidPtr(vtable, 0); + if (!pycobj) + goto bad; + if (PyDict_SetItemString(dict, "__pyx_vtable__", pycobj) < 0) + goto bad; + result = 0; + goto done; + +bad: + result = -1; +done: + Py_XDECREF(pycobj); + return result; +} + +#ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, + long size) +{ + PyObject *py_module = 0; + PyObject *result = 0; + PyObject *py_name = 0; + + #if PY_MAJOR_VERSION < 3 + py_name = PyString_FromString(module_name); + #else + py_name = PyUnicode_FromString(module_name); + #endif + if (!py_name) + goto bad; + + py_module = __Pyx_ImportModule(module_name); + if (!py_module) + goto bad; + result = PyObject_GetAttrString(py_module, class_name); + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%s.%s is not a type object", + module_name, class_name); + goto bad; + } + if (((PyTypeObject *)result)->tp_basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%s.%s does not appear to be the correct type object", + module_name, class_name); + goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(py_name); + Py_XDECREF(result); + return 0; +} +#endif + +#ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + + #if PY_MAJOR_VERSION < 3 + py_name = PyString_FromString(name); + #else + py_name = PyUnicode_FromString(name); + #endif + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; +} +#endif + +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" + +static void __Pyx_AddTraceback(const char *funcname) { + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + PyObject *py_globals = 0; + PyObject *empty_string = 0; + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(__pyx_filename); + #else + py_srcfile = PyUnicode_FromString(__pyx_filename); + #endif + if (!py_srcfile) goto bad; + if (__pyx_clineno) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_globals = PyModule_GetDict(__pyx_m); + if (!py_globals) goto bad; + #if PY_MAJOR_VERSION < 3 + empty_string = PyString_FromStringAndSize("", 0); + #else + empty_string = PyBytes_FromStringAndSize("", 0); + #endif + if (!empty_string) goto bad; + py_code = PyCode_New( + 0, /*int argcount,*/ + #if PY_MAJOR_VERSION >= 3 + 0, /*int kwonlyargcount,*/ + #endif + 0, /*int nlocals,*/ + 0, /*int stacksize,*/ + 0, /*int flags,*/ + empty_string, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + __pyx_lineno, /*int firstlineno,*/ + empty_string /*PyObject *lnotab*/ + ); + if (!py_code) goto bad; + py_frame = PyFrame_New( + PyThreadState_Get(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + py_globals, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + py_frame->f_lineno = __pyx_lineno; + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + Py_XDECREF(empty_string); + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode && (!t->is_identifier)) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else /* Python 3+ has unicode identifiers */ + if (t->is_identifier || (t->is_unicode && t->intern)) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->is_unicode) { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +/* Type Conversion Functions */ + +static INLINE Py_ssize_t __pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject* x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} + +static INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + if (x == Py_True) return 1; + else if (x == Py_False) return 0; + else return PyObject_IsTrue(x); +} + +static INLINE PY_LONG_LONG __pyx_PyInt_AsLongLong(PyObject* x) { + if (PyInt_CheckExact(x)) { + return PyInt_AS_LONG(x); + } + else if (PyLong_CheckExact(x)) { + return PyLong_AsLongLong(x); + } + else { + PY_LONG_LONG val; + PyObject* tmp = PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; + val = __pyx_PyInt_AsLongLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static INLINE unsigned PY_LONG_LONG __pyx_PyInt_AsUnsignedLongLong(PyObject* x) { + if (PyInt_CheckExact(x)) { + long val = PyInt_AS_LONG(x); + if (unlikely(val < 0)) { + PyErr_SetString(PyExc_TypeError, "Negative assignment to unsigned type."); + return (unsigned PY_LONG_LONG)-1; + } + return val; + } + else if (PyLong_CheckExact(x)) { + return PyLong_AsUnsignedLongLong(x); + } + else { + PY_LONG_LONG val; + PyObject* tmp = PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; + val = __pyx_PyInt_AsUnsignedLongLong(tmp); + Py_DECREF(tmp); + return val; + } +} + + +static INLINE unsigned char __pyx_PyInt_unsigned_char(PyObject* x) { + if (sizeof(unsigned char) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + unsigned char val = (unsigned char)long_val; + if (unlikely((val != long_val) || (long_val < 0))) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to unsigned char"); + return (unsigned char)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE unsigned short __pyx_PyInt_unsigned_short(PyObject* x) { + if (sizeof(unsigned short) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + unsigned short val = (unsigned short)long_val; + if (unlikely((val != long_val) || (long_val < 0))) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to unsigned short"); + return (unsigned short)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE char __pyx_PyInt_char(PyObject* x) { + if (sizeof(char) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + char val = (char)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to char"); + return (char)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE short __pyx_PyInt_short(PyObject* x) { + if (sizeof(short) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + short val = (short)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to short"); + return (short)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE int __pyx_PyInt_int(PyObject* x) { + if (sizeof(int) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + int val = (int)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); + return (int)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE long __pyx_PyInt_long(PyObject* x) { + if (sizeof(long) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + long val = (long)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); + return (long)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE signed char __pyx_PyInt_signed_char(PyObject* x) { + if (sizeof(signed char) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + signed char val = (signed char)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed char"); + return (signed char)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE signed short __pyx_PyInt_signed_short(PyObject* x) { + if (sizeof(signed short) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + signed short val = (signed short)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed short"); + return (signed short)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE signed int __pyx_PyInt_signed_int(PyObject* x) { + if (sizeof(signed int) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + signed int val = (signed int)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed int"); + return (signed int)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE signed long __pyx_PyInt_signed_long(PyObject* x) { + if (sizeof(signed long) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + signed long val = (signed long)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed long"); + return (signed long)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + +static INLINE long double __pyx_PyInt_long_double(PyObject* x) { + if (sizeof(long double) < sizeof(long)) { + long long_val = __pyx_PyInt_AsLong(x); + long double val = (long double)long_val; + if (unlikely((val != long_val) )) { + PyErr_SetString(PyExc_OverflowError, "value too large to convert to long double"); + return (long double)-1; + } + return val; + } + else { + return __pyx_PyInt_AsLong(x); + } +} + Copied: branches/spatial/scipy/spatial/ckdtree.pyx (from rev 4794, branches/spatial/scipy/spatial/kdtree.py) =================================================================== --- branches/spatial/scipy/spatial/kdtree.py 2008-10-12 13:06:54 UTC (rev 4794) +++ branches/spatial/scipy/spatial/ckdtree.pyx 2008-10-12 13:23:48 UTC (rev 4795) @@ -0,0 +1,592 @@ +# Copyright Anne M. Archibald 2008 +# Released under the scipy license +import numpy as np +cimport numpy as np +cimport stdlib + +import kdtree + +cdef double infinity = np.inf + + + + +# priority queue +cdef union heapcontents: + int intdata + char* ptrdata + +cdef struct heapitem: + double priority + heapcontents contents + +cdef struct heap: + int n + heapitem* heap + int space + +cdef inline heapcreate(heap* self,int initial_size): + self.space = initial_size + self.heap = stdlib.malloc(sizeof(heapitem)*self.space) + self.n=0 + +cdef inline heapdestroy(heap* self): + stdlib.free(self.heap) + +cdef inline heapresize(heap* self, int new_space): + if new_spacestdlib.realloc(self.heap,new_space*sizeof(heapitem)) + +cdef inline heappush(heap* self, heapitem item): + cdef int i + cdef heapitem t + + self.n += 1 + if self.n>self.space: + heapresize(self,2*self.space+1) + + i = self.n-1 + self.heap[i] = item + while i>0 and self.heap[i].priority40: #FIXME: magic number + heapresize(self,self.space//2+1) + + i=0 + j=1 + k=2 + while ((j self.heap[j].priority or + k self.heap[k].priority)): + if kself.heap[k].priority: + l = k + else: + l = j + t = self.heap[l] + self.heap[l] = self.heap[i] + self.heap[i] = t + i = l + j = 2*i+1 + k = 2*i+2 + +cdef heapitem heappop(heap* self): + cdef heapitem it + it = heappeek(self) + heapremove(self) + return it + + + + + +# utility functions +cdef inline double dmax(double x, double y): + if x>y: + return x + else: + return y +cdef inline double dabs(double x): + if x>0: + return x + else: + return -x +cdef inline double _distance_p(double*x,double*y,double p,int k,double upperbound): + """Compute the distance between x and y + + Computes the Minkowski p-distance to the power p between two points. + If the distance**p is larger than upperbound, then any number larger + than upperbound may be returned (the calculation is truncated). + """ + cdef int i + cdef double r + r = 0 + if p==infinity: + for i in range(k): + r = dmax(r,dabs(x[i]-y[i])) + if r>upperbound: + return r + elif p==1: + for i in range(k): + r += dabs(x[i]-y[i]) + if r>upperbound: + return r + else: + for i in range(k): + r += dabs(x[i]-y[i])**p + if r>upperbound: + return r + return r + + + +# Tree structure +cdef struct innernode: + int split_dim + int n_points + double split + innernode* less + innernode* greater +cdef struct leafnode: + int split_dim + int n_points + int start_idx + int end_idx + +# this is the standard trick for variable-size arrays: +# malloc sizeof(nodeinfo)+self.m*sizeof(double) bytes. +cdef struct nodeinfo: + innernode* node + double side_distances[0] + +cdef class cKDTree: + """kd-tree for quick nearest-neighbor lookup + + This class provides an index into a set of k-dimensional points + which can be used to rapidly look up the nearest neighbors of any + point. + + The algorithm used is described in Maneewongvatana and Mount 1999. + The general idea is that the kd-tree is a binary trie, each of whose + nodes represents an axis-aligned hyperrectangle. Each node specifies + an axis and splits the set of points based on whether their coordinate + along that axis is greater than or less than a particular value. + + During construction, the axis and splitting point are chosen by the + "sliding midpoint" rule, which ensures that the cells do not all + become long and thin. + + The tree can be queried for the r closest neighbors of any given point + (optionally returning only those within some maximum distance of the + point). It can also be queried, with a substantial gain in efficiency, + for the r approximate closest neighbors. + + For large dimensions (20 is already large) do not expect this to run + significantly faster than brute force. High-dimensional nearest-neighbor + queries are a substantial open problem in computer science. + """ + + cdef innernode* tree + cdef readonly object data + cdef double* raw_data + cdef readonly int n, m + cdef readonly int leafsize + cdef readonly object maxes + cdef double* raw_maxes + cdef readonly object mins + cdef double* raw_mins + cdef object indices + cdef np.int32_t* raw_indices + def __init__(cKDTree self, data, int leafsize=10): + """Construct a kd-tree. + + Parameters: + =========== + + data : array-like, shape (n,m) + The n data points of dimension mto be indexed. This array is + not copied unless this is necessary to produce a contiguous + array of doubles, and so modifying this data will result in + bogus results. + leafsize : positive integer + The number of points at which the algorithm switches over to + brute-force. + """ + cdef np.ndarray[double, ndim=2] inner_data + cdef np.ndarray[double, ndim=1] inner_maxes + cdef np.ndarray[double, ndim=1] inner_mins + cdef np.ndarray[np.int32_t, ndim=1] inner_indices + self.data = np.ascontiguousarray(data,dtype=np.float) + self.n, self.m = np.shape(self.data) + self.leafsize = leafsize + if self.leafsize<1: + raise ValueError("leafsize must be at least 1") + self.maxes = np.ascontiguousarray(np.amax(self.data,axis=0)) + self.mins = np.ascontiguousarray(np.amin(self.data,axis=0)) + self.indices = np.ascontiguousarray(np.arange(self.n,dtype=np.int)) + + inner_data = self.data + self.raw_data = inner_data.data + inner_maxes = self.maxes + self.raw_maxes = inner_maxes.data + inner_mins = self.mins + self.raw_mins = inner_mins.data + inner_indices = self.indices + self.raw_indices = inner_indices.data + + self.tree = self.__build(0, self.n, self.raw_maxes, self.raw_mins) + + cdef innernode* __build(cKDTree self, int start_idx, int end_idx, double* maxes, double* mins): + cdef leafnode* n + cdef innernode* ni + cdef int i, j, t, p, q, d + cdef double size, split, minval, maxval + cdef double*mids + if end_idx-start_idx<=self.leafsize: + n = stdlib.malloc(sizeof(leafnode)) + n.split_dim = -1 + n.start_idx = start_idx + n.end_idx = end_idx + return n + else: + d = 0 + size = 0 + for i in range(self.m): + if maxes[i]-mins[i] > size: + d = i + size = maxes[i]-mins[i] + maxval = maxes[d] + minval = mins[d] + if maxval==minval: + # all points are identical; warn user? + n = stdlib.malloc(sizeof(leafnode)) + n.split_dim = -1 + n.start_idx = start_idx + n.end_idx = end_idx + return n + + split = (maxval+minval)/2 + + p = start_idx + q = end_idx-1 + while p<=q: + if self.raw_data[self.raw_indices[p]*self.m+d]=split: + q-=1 + else: + t = self.raw_indices[p] + self.raw_indices[p] = self.raw_indices[q] + self.raw_indices[q] = t + p+=1 + q-=1 + + # slide midpoint if necessary + if p==start_idx: + # no points less than split + j = start_idx + split = self.raw_data[self.raw_indices[j]*self.m+d] + for i in range(start_idx+1, end_idx): + if self.raw_data[self.raw_indices[i]*self.m+d]split: + j = i + split = self.raw_data[self.raw_indices[j]*self.m+d] + t = self.raw_indices[end_idx-1] + self.raw_indices[end_idx-1] = self.raw_indices[j] + self.raw_indices[j] = t + p = end_idx-1 + q = end_idx-2 + + # construct new node representation + ni = stdlib.malloc(sizeof(innernode)) + + mids = stdlib.malloc(sizeof(double)*self.m) + for i in range(self.m): + mids[i] = maxes[i] + mids[d] = split + ni.less = self.__build(start_idx,p,mids,mins) + + for i in range(self.m): + mids[i] = mins[i] + mids[d] = split + ni.greater = self.__build(p,end_idx,maxes,mids) + + stdlib.free(mids) + + ni.split_dim = d + ni.split = split + + return ni + + cdef __free_tree(cKDTree self, innernode* node): + if node.split_dim!=-1: + self.__free_tree(node.less) + self.__free_tree(node.greater) + stdlib.free(node) + + def __del__(cKDTree self): + print "freeing cKDTree" + self.__free_tree(self.tree) + + cdef void __query(cKDTree self, + double*result_distances, + int*result_indices, + double*x, + int k, + double eps, + double p, + double distance_upper_bound): + cdef heap q + cdef heap neighbors + + cdef int i, j + cdef double t + cdef nodeinfo* inf + cdef nodeinfo* inf2 + cdef double d + cdef double epsfac + cdef double min_distance + cdef double far_min_distance + cdef heapitem it, it2, neighbor + cdef leafnode* node + cdef innernode* inode + cdef innernode* near + cdef innernode* far + cdef double* side_distances + + # priority queue for chasing nodes + # entries are: + # minimum distance between the cell and the target + # distances between the nearest side of the cell and the target + # the head node of the cell + heapcreate(&q,12) + + # priority queue for the nearest neighbors + # furthest known neighbor first + # entries are (-distance**p, i) + heapcreate(&neighbors,k) + + # set up first nodeinfo + inf = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) + inf.node = self.tree + for i in range(self.m): + inf.side_distances[i] = 0 + t = x[i]-self.raw_maxes[i] + if t>inf.side_distances[i]: + inf.side_distances[i] = t + else: + t = self.raw_mins[i]-x[i] + if t>inf.side_distances[i]: + inf.side_distances[i] = t + if p!=1 and p!=infinity: + inf.side_distances[i]=inf.side_distances[i]**p + + # compute first distance + min_distance = 0. + for i in range(self.m): + if p==infinity: + min_distance = dmax(min_distance,inf.side_distances[i]) + else: + min_distance += inf.side_distances[i] + + # fiddle approximation factor + if eps==0: + epsfac=1 + elif p==infinity: + epsfac = 1/(1+eps) + else: + epsfac = 1/(1+eps)**p + + # internally we represent all distances as distance**p + if p!=infinity and distance_upper_bound!=infinity: + distance_upper_bound = distance_upper_bound**p + + while True: + if inf.node.split_dim==-1: + node = inf.node + + # brute-force + for i in range(node.start_idx,node.end_idx): + d = _distance_p( + self.raw_data+self.raw_indices[i]*self.m, + x,p,self.m,distance_upper_bound) + + if dit.contents.ptrdata + min_distance = it.priority + else: + inode = inf.node + + # we don't push cells that are too far onto the queue at all, + # but since the distance_upper_bound decreases, we might get + # here even if the cell's too far + if min_distance>distance_upper_bound*epsfac: + # since this is the nearest cell, we're done, bail out + stdlib.free(inf) + # free all the nodes still on the heap + for i in range(q.n): + stdlib.free(q.heap[i].contents.ptrdata) + break + + # set up children for searching + if x[inode.split_dim]stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) + it2.contents.ptrdata = inf2 + inf2.node = far + # most side distances unchanged + for i in range(self.m): + inf2.side_distances[i] = inf.side_distances[i] + + # one side distance changes + # we can adjust the minimum distance without recomputing + if p == infinity: + # we never use side_distances in the l_infinity case + # inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) + far_min_distance = dmax(min_distance, dabs(inode.split-x[inode.split_dim])) + elif p == 1: + inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) + far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] + else: + inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim])**p + far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] + + it2.priority = far_min_distance + + + # far child might be too far, if so, don't bother pushing it + if far_min_distance<=distance_upper_bound*epsfac: + heappush(&q,it2) + else: + stdlib.free(inf2) + # just in case + it2.contents.ptrdata = 0 + + # fill output arrays with sorted neighbors + for i in range(neighbors.n-1,-1,-1): + neighbor = heappop(&neighbors) # FIXME: neighbors may be realloced + result_indices[i] = neighbor.contents.intdata + if p==1 or p==infinity: + result_distances[i] = -neighbor.priority + else: + result_distances[i] = (-neighbor.priority)**(1./p) + + heapdestroy(&q) + heapdestroy(&neighbors) + + def query(cKDTree self, object x, int k=1, double eps=0, double p=2, + double distance_upper_bound=infinity): + """query the kd-tree for nearest neighbors + + Parameters: + =========== + + x : array-like, last dimension self.m + An array of points to query. + k : integer + The number of nearest neighbors to return. + eps : nonnegative float + Return approximate nearest neighbors; the kth returned value + is guaranteed to be no further than (1+eps) times the + distance to the real kth nearest neighbor. + p : float, 1<=p<=infinity + Which Minkowski p-norm to use. + 1 is the sum-of-absolute-values "Manhattan" distance + 2 is the usual Euclidean distance + infinity is the maximum-coordinate-difference distance + distance_upper_bound : nonnegative float + Return only neighbors within this distance. This is used to prune + tree searches, so if you are doing a series of nearest-neighbor + queries, it may help to supply the distance to the nearest neighbor + of the most recent point. + + Returns: + ======== + + d : array of floats + The distances to the nearest neighbors. + If x has shape tuple+(self.m,), then d has shape tuple+(k,). + Missing neighbors are indicated with infinite distances. + i : array of integers + The locations of the neighbors in self.data. + If x has shape tuple+(self.m,), then i has shape tuple+(k,). + Missing neighbors are indicated with self.n+1. + """ + cdef np.ndarray[int, ndim=2] ii + cdef np.ndarray[double, ndim=2] dd + cdef np.ndarray[double, ndim=2] xx + cdef int c + x = np.asarray(x).astype(np.float) + if np.shape(x)[-1] != self.m: + raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) + if p<1: + raise ValueError("Only p-norms with 1<=p<=infinity permitted") + if len(x.shape)==1: + single = True + x = x[np.newaxis,:] + else: + single = False + retshape = np.shape(x)[:-1] + n = np.prod(retshape) + xx = np.reshape(x,(n,self.m)) + dd = np.empty((n,k),dtype=np.float) + dd.fill(infinity) + ii = np.empty((n,k),dtype='i') + ii.fill(self.n) + for c in range(n): + self.__query( + (dd.data)+c*k, + (ii.data)+c*k, + (xx.data)+c*self.m, + k, + eps, + p, + distance_upper_bound) + if single: + if k==1: + return dd[0,0], ii[0,0] + else: + return dd[0], ii[0] + else: + if k==1: + return np.reshape(dd[...,0],retshape), np.reshape(ii[...,0],retshape) + else: + return np.reshape(dd,retshape+(k,)), np.reshape(ii,retshape+(k,)) + Property changes on: branches/spatial/scipy/spatial/ckdtree.pyx ___________________________________________________________________ Name: svn:mergeinfo + Modified: branches/spatial/scipy/spatial/setup.py =================================================================== --- branches/spatial/scipy/spatial/setup.py 2008-10-12 13:06:54 UTC (rev 4794) +++ branches/spatial/scipy/spatial/setup.py 2008-10-12 13:23:48 UTC (rev 4795) @@ -8,6 +8,7 @@ config = Configuration('spatial', parent_package, top_path) config.add_data_dir('tests') + config.add_extension('ckdtree', sources=['ckdtree.c']) # FIXME: cython return config Modified: branches/spatial/scipy/spatial/tests/test_kdtree.py =================================================================== --- branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-12 13:06:54 UTC (rev 4794) +++ branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-12 13:23:48 UTC (rev 4795) @@ -3,7 +3,7 @@ from numpy.testing import * import numpy as np -from scipy.spatial import KDTree, distance, Rectangle, distance_matrix +from scipy.spatial import KDTree, distance, Rectangle, distance_matrix, cKDTree class ConsistencyTests: def test_nearest(self): @@ -115,21 +115,25 @@ ([0.1,0.9],[0,1])) class test_small_nonleaf(test_small): def setUp(self): - self.data = np.array([[0,0,0], - [0,0,1], - [0,1,0], - [0,1,1], - [1,0,0], - [1,0,1], - [1,1,0], - [1,1,1]]) + test_small.setUp(self) self.kdtree = KDTree(self.data,leafsize=1) - self.n = self.kdtree.n - self.m = self.kdtree.m - self.x = np.random.randn(self.m) - self.d = 0.5 - self.k = 4 +class test_small_compiled(test_small): + def setUp(self): + test_small.setUp(self) + self.kdtree = cKDTree(self.data) +class test_small_nonleaf_compiled(test_small): + def setUp(self): + test_small.setUp(self) + self.kdtree = cKDTree(self.data,leafsize=1) +class test_random_compiled(test_random): + def setUp(self): + test_random.setUp(self) + self.kdtree = cKDTree(self.data) +class test_random_far_compiled(test_random_far): + def setUp(self): + test_random_far.setUp(self) + self.kdtree = cKDTree(self.data) class test_vectorization: def setUp(self): @@ -181,6 +185,45 @@ assert isinstance(d[0,0],list) assert isinstance(i[0,0],list) +class test_vectorization_compiled: + def setUp(self): + self.data = np.array([[0,0,0], + [0,0,1], + [0,1,0], + [0,1,1], + [1,0,0], + [1,0,1], + [1,1,0], + [1,1,1]]) + self.kdtree = KDTree(self.data) + + def test_single_query(self): + d, i = self.kdtree.query([0,0,0]) + assert isinstance(d,float) + assert isinstance(i,int) + + def test_vectorized_query(self): + d, i = self.kdtree.query(np.zeros((2,4,3))) + assert_equal(np.shape(d),(2,4)) + assert_equal(np.shape(i),(2,4)) + + def test_single_query_multiple_neighbors(self): + s = 23 + kk = self.kdtree.n+s + d, i = self.kdtree.query([0,0,0],k=kk) + assert_equal(np.shape(d),(kk,)) + assert_equal(np.shape(i),(kk,)) + assert np.all(~np.isfinite(d[-s:])) + assert np.all(i[-s:]==self.kdtree.n) + def test_vectorized_query_multiple_neighbors(self): + s = 23 + kk = self.kdtree.n+s + d, i = self.kdtree.query(np.zeros((2,4,3)),k=kk) + assert_equal(np.shape(d),(2,4,kk)) + assert_equal(np.shape(i),(2,4,kk)) + assert np.all(~np.isfinite(d[:,:,-s:])) + assert np.all(i[:,:,-s:]==self.kdtree.n) + class ball_consistency: def test_in_ball(self): From scipy-svn at scipy.org Mon Oct 13 11:46:21 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 13 Oct 2008 10:46:21 -0500 (CDT) Subject: [Scipy-svn] r4796 - branches/spatial/scipy/spatial Message-ID: <20081013154621.4EF5D39C088@scipy.org> Author: peridot Date: 2008-10-13 10:46:17 -0500 (Mon, 13 Oct 2008) New Revision: 4796 Modified: branches/spatial/scipy/spatial/ckdtree.c branches/spatial/scipy/spatial/ckdtree.pyx Log: renamed __del__ to __dealloc__ so that cython would actually run it (ty google) Modified: branches/spatial/scipy/spatial/ckdtree.c =================================================================== --- branches/spatial/scipy/spatial/ckdtree.c 2008-10-12 13:23:48 UTC (rev 4795) +++ branches/spatial/scipy/spatial/ckdtree.c 2008-10-13 15:46:17 UTC (rev 4796) @@ -1,4 +1,4 @@ -/* Generated by Cython 0.9.8.1.1 on Sun Oct 12 09:20:26 2008 */ +/* Generated by Cython 0.9.8.1.1 on Mon Oct 13 11:41:48 2008 */ #define PY_SSIZE_T_CLEAN #include "Python.h" @@ -293,54 +293,22 @@ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":15 - * - * # priority queue - * cdef union heapcontents: # <<<<<<<<<<<<<< - * int intdata - * char* ptrdata - */ - union __pyx_t_5scipy_7spatial_7ckdtree_heapcontents { int intdata; char *ptrdata; }; -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":19 - * char* ptrdata - * - * cdef struct heapitem: # <<<<<<<<<<<<<< - * double priority - * heapcontents contents - */ - struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem { double priority; union __pyx_t_5scipy_7spatial_7ckdtree_heapcontents contents; }; -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":23 - * heapcontents contents - * - * cdef struct heap: # <<<<<<<<<<<<<< - * int n - * heapitem* heap - */ - struct __pyx_t_5scipy_7spatial_7ckdtree_heap { int n; struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem *heap; int space; }; -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":139 - * - * # Tree structure - * cdef struct innernode: # <<<<<<<<<<<<<< - * int split_dim - * int n_points - */ - struct __pyx_t_5scipy_7spatial_7ckdtree_innernode { int split_dim; int n_points; @@ -349,14 +317,6 @@ struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *greater; }; -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":145 - * innernode* less - * innernode* greater - * cdef struct leafnode: # <<<<<<<<<<<<<< - * int split_dim - * int n_points - */ - struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode { int split_dim; int n_points; @@ -364,27 +324,11 @@ int end_idx; }; -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":153 - * # this is the standard trick for variable-size arrays: - * # malloc sizeof(nodeinfo)+self.m*sizeof(double) bytes. - * cdef struct nodeinfo: # <<<<<<<<<<<<<< - * innernode* node - * double side_distances[0] - */ - struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo { struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *node; double side_distances[0]; }; -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":157 - * double side_distances[0] - * - * cdef class cKDTree: # <<<<<<<<<<<<<< - * """kd-tree for quick nearest-neighbor lookup - * - */ - struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree { PyObject_HEAD struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *__pyx_vtab; @@ -437,8 +381,8 @@ static PyObject *__pyx_int_0; static char __pyx_k___init__[] = "__init__"; static PyObject *__pyx_kp___init__; -static char __pyx_k___del__[] = "__del__"; -static PyObject *__pyx_kp___del__; +static char __pyx_k___dealloc__[] = "__dealloc__"; +static PyObject *__pyx_kp___dealloc__; static char __pyx_k_query[] = "query"; static PyObject *__pyx_kp_query; static char __pyx_k_numpy[] = "numpy"; @@ -520,104 +464,32 @@ static char __pyx_k_15[] = "O"; static char __pyx_k_16[] = "only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)"; -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":28 - * int space - * - * cdef inline heapcreate(heap* self,int initial_size): # <<<<<<<<<<<<<< - * self.space = initial_size - * self.heap = stdlib.malloc(sizeof(heapitem)*self.space) - */ - static INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapcreate(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self, int __pyx_v_initial_size) { PyObject *__pyx_r; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":29 - * - * cdef inline heapcreate(heap* self,int initial_size): - * self.space = initial_size # <<<<<<<<<<<<<< - * self.heap = stdlib.malloc(sizeof(heapitem)*self.space) - * self.n=0 - */ __pyx_v_self->space = __pyx_v_initial_size; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":30 - * cdef inline heapcreate(heap* self,int initial_size): - * self.space = initial_size - * self.heap = stdlib.malloc(sizeof(heapitem)*self.space) # <<<<<<<<<<<<<< - * self.n=0 - * - */ __pyx_v_self->heap = ((struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem *)malloc(((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem)) * __pyx_v_self->space))); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":31 - * self.space = initial_size - * self.heap = stdlib.malloc(sizeof(heapitem)*self.space) - * self.n=0 # <<<<<<<<<<<<<< - * - * cdef inline heapdestroy(heap* self): - */ __pyx_v_self->n = 0; __pyx_r = Py_None; Py_INCREF(Py_None); return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":33 - * self.n=0 - * - * cdef inline heapdestroy(heap* self): # <<<<<<<<<<<<<< - * stdlib.free(self.heap) - * - */ - static INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapdestroy(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self) { PyObject *__pyx_r; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":34 - * - * cdef inline heapdestroy(heap* self): - * stdlib.free(self.heap) # <<<<<<<<<<<<<< - * - * cdef inline heapresize(heap* self, int new_space): - */ free(__pyx_v_self->heap); __pyx_r = Py_None; Py_INCREF(Py_None); return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":36 - * stdlib.free(self.heap) - * - * cdef inline heapresize(heap* self, int new_space): # <<<<<<<<<<<<<< - * if new_spacen); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":38 - * cdef inline heapresize(heap* self, int new_space): - * if new_spacestdlib.realloc(self.heap,new_space*sizeof(heapitem)) - */ __pyx_2 = PyInt_FromLong(__pyx_v_self->n); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_3 = PyInt_FromLong(__pyx_v_new_space); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_4 = PyTuple_New(2); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} @@ -638,23 +510,7 @@ goto __pyx_L3; } __pyx_L3:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":39 - * if new_spacestdlib.realloc(self.heap,new_space*sizeof(heapitem)) - * - */ __pyx_v_self->space = __pyx_v_new_space; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":40 - * raise ValueError("Heap containing %d items cannot be resized to %d" % (self.n, new_space)) - * self.space = new_space - * self.heap = stdlib.realloc(self.heap,new_space*sizeof(heapitem)) # <<<<<<<<<<<<<< - * - * cdef inline heappush(heap* self, heapitem item): - */ __pyx_v_self->heap = ((struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem *)realloc(((void *)__pyx_v_self->heap), (__pyx_v_new_space * (sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem))))); __pyx_r = Py_None; Py_INCREF(Py_None); @@ -669,119 +525,31 @@ return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":42 - * self.heap = stdlib.realloc(self.heap,new_space*sizeof(heapitem)) - * - * cdef inline heappush(heap* self, heapitem item): # <<<<<<<<<<<<<< - * cdef int i - * cdef heapitem t - */ - static INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heappush(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self, struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_v_item) { int __pyx_v_i; struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_v_t; PyObject *__pyx_r; int __pyx_1; PyObject *__pyx_2 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":46 - * cdef heapitem t - * - * self.n += 1 # <<<<<<<<<<<<<< - * if self.n>self.space: - * heapresize(self,2*self.space+1) - */ __pyx_v_self->n += 1; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":47 - * - * self.n += 1 - * if self.n>self.space: # <<<<<<<<<<<<<< - * heapresize(self,2*self.space+1) - * - */ __pyx_1 = (__pyx_v_self->n > __pyx_v_self->space); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":48 - * self.n += 1 - * if self.n>self.space: - * heapresize(self,2*self.space+1) # <<<<<<<<<<<<<< - * - * i = self.n-1 - */ __pyx_2 = __pyx_f_5scipy_7spatial_7ckdtree_heapresize(__pyx_v_self, ((2 * __pyx_v_self->space) + 1)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; goto __pyx_L3; } __pyx_L3:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":50 - * heapresize(self,2*self.space+1) - * - * i = self.n-1 # <<<<<<<<<<<<<< - * self.heap[i] = item - * while i>0 and self.heap[i].priorityn - 1); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":51 - * - * i = self.n-1 - * self.heap[i] = item # <<<<<<<<<<<<<< - * while i>0 and self.heap[i].priorityheap[__pyx_v_i]) = __pyx_v_item; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":52 - * i = self.n-1 - * self.heap[i] = item - * while i>0 and self.heap[i].priority 0); if (__pyx_1) { __pyx_1 = ((__pyx_v_self->heap[__pyx_v_i]).priority < (__pyx_v_self->heap[((__pyx_v_i - 1) / 2)]).priority); } if (!__pyx_1) break; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":53 - * self.heap[i] = item - * while i>0 and self.heap[i].priorityheap[((__pyx_v_i - 1) / 2)]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":54 - * while i>0 and self.heap[i].priorityheap[((__pyx_v_i - 1) / 2)]) = (__pyx_v_self->heap[__pyx_v_i]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":55 - * t = self.heap[(i-1)//2] - * self.heap[(i-1)//2] = self.heap[i] - * self.heap[i] = t # <<<<<<<<<<<<<< - * i = (i-1)//2 - * - */ (__pyx_v_self->heap[__pyx_v_i]) = __pyx_v_t; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":56 - * self.heap[(i-1)//2] = self.heap[i] - * self.heap[i] = t - * i = (i-1)//2 # <<<<<<<<<<<<<< - * - * cdef heapitem heappeek(heap* self): - */ __pyx_v_i = ((__pyx_v_i - 1) / 2); } @@ -795,24 +563,8 @@ return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":58 - * i = (i-1)//2 - * - * cdef heapitem heappeek(heap* self): # <<<<<<<<<<<<<< - * return self.heap[0] - * - */ - static struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_f_5scipy_7spatial_7ckdtree_heappeek(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self) { struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_r; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":59 - * - * cdef heapitem heappeek(heap* self): - * return self.heap[0] # <<<<<<<<<<<<<< - * - * cdef heapremove(heap* self): - */ __pyx_r = (__pyx_v_self->heap[0]); goto __pyx_L0; @@ -820,14 +572,6 @@ return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":61 - * return self.heap[0] - * - * cdef heapremove(heap* self): # <<<<<<<<<<<<<< - * cdef heapitem t - * cdef int i, j, k, l - */ - static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapremove(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self) { struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_v_t; int __pyx_v_i; @@ -837,210 +581,50 @@ PyObject *__pyx_r; int __pyx_1; PyObject *__pyx_2 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":65 - * cdef int i, j, k, l - * - * self.heap[0] = self.heap[self.n-1] # <<<<<<<<<<<<<< - * self.n -= 1 - * if self.n < self.space//4 and self.space>40: #FIXME: magic number - */ (__pyx_v_self->heap[0]) = (__pyx_v_self->heap[(__pyx_v_self->n - 1)]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":66 - * - * self.heap[0] = self.heap[self.n-1] - * self.n -= 1 # <<<<<<<<<<<<<< - * if self.n < self.space//4 and self.space>40: #FIXME: magic number - * heapresize(self,self.space//2+1) - */ __pyx_v_self->n -= 1; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":67 - * self.heap[0] = self.heap[self.n-1] - * self.n -= 1 - * if self.n < self.space//4 and self.space>40: #FIXME: magic number # <<<<<<<<<<<<<< - * heapresize(self,self.space//2+1) - * - */ __pyx_1 = (__pyx_v_self->n < (__pyx_v_self->space / 4)); if (__pyx_1) { __pyx_1 = (__pyx_v_self->space > 40); } if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":68 - * self.n -= 1 - * if self.n < self.space//4 and self.space>40: #FIXME: magic number - * heapresize(self,self.space//2+1) # <<<<<<<<<<<<<< - * - * i=0 - */ __pyx_2 = __pyx_f_5scipy_7spatial_7ckdtree_heapresize(__pyx_v_self, ((__pyx_v_self->space / 2) + 1)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; goto __pyx_L3; } __pyx_L3:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":70 - * heapresize(self,self.space//2+1) - * - * i=0 # <<<<<<<<<<<<<< - * j=1 - * k=2 - */ __pyx_v_i = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":71 - * - * i=0 - * j=1 # <<<<<<<<<<<<<< - * k=2 - * while ((j self.heap[j].priority or - */ __pyx_v_k = 2; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":73 - * j=1 - * k=2 - * while ((j self.heap[j].priority or - * kn); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":74 - * k=2 - * while ((j self.heap[j].priority or # <<<<<<<<<<<<<< - * k self.heap[k].priority)): - */ __pyx_1 = ((__pyx_v_self->heap[__pyx_v_i]).priority > (__pyx_v_self->heap[__pyx_v_j]).priority); } if (!__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":75 - * while ((j self.heap[j].priority or - * k self.heap[k].priority)): - * if kself.heap[k].priority: - */ __pyx_1 = (__pyx_v_k < __pyx_v_self->n); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":76 - * self.heap[i].priority > self.heap[j].priority or - * k self.heap[k].priority)): # <<<<<<<<<<<<<< - * if kself.heap[k].priority: - * l = k - */ __pyx_1 = ((__pyx_v_self->heap[__pyx_v_i]).priority > (__pyx_v_self->heap[__pyx_v_k]).priority); } } if (!__pyx_1) break; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":77 - * k self.heap[k].priority)): - * if kself.heap[k].priority: # <<<<<<<<<<<<<< - * l = k - * else: - */ __pyx_1 = (__pyx_v_k < __pyx_v_self->n); if (__pyx_1) { __pyx_1 = ((__pyx_v_self->heap[__pyx_v_j]).priority > (__pyx_v_self->heap[__pyx_v_k]).priority); } if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":78 - * self.heap[i].priority > self.heap[k].priority)): - * if kself.heap[k].priority: - * l = k # <<<<<<<<<<<<<< - * else: - * l = j - */ __pyx_v_l = __pyx_v_k; goto __pyx_L6; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":80 - * l = k - * else: - * l = j # <<<<<<<<<<<<<< - * t = self.heap[l] - * self.heap[l] = self.heap[i] - */ __pyx_v_l = __pyx_v_j; } __pyx_L6:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":81 - * else: - * l = j - * t = self.heap[l] # <<<<<<<<<<<<<< - * self.heap[l] = self.heap[i] - * self.heap[i] = t - */ __pyx_v_t = (__pyx_v_self->heap[__pyx_v_l]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":82 - * l = j - * t = self.heap[l] - * self.heap[l] = self.heap[i] # <<<<<<<<<<<<<< - * self.heap[i] = t - * i = l - */ (__pyx_v_self->heap[__pyx_v_l]) = (__pyx_v_self->heap[__pyx_v_i]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":83 - * t = self.heap[l] - * self.heap[l] = self.heap[i] - * self.heap[i] = t # <<<<<<<<<<<<<< - * i = l - * j = 2*i+1 - */ (__pyx_v_self->heap[__pyx_v_i]) = __pyx_v_t; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":84 - * self.heap[l] = self.heap[i] - * self.heap[i] = t - * i = l # <<<<<<<<<<<<<< - * j = 2*i+1 - * k = 2*i+2 - */ __pyx_v_i = __pyx_v_l; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":85 - * self.heap[i] = t - * i = l - * j = 2*i+1 # <<<<<<<<<<<<<< - * k = 2*i+2 - * - */ __pyx_v_j = ((2 * __pyx_v_i) + 1); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":86 - * i = l - * j = 2*i+1 - * k = 2*i+2 # <<<<<<<<<<<<<< - * - * cdef heapitem heappop(heap* self): - */ __pyx_v_k = ((2 * __pyx_v_i) + 2); } @@ -1054,45 +638,13 @@ return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":88 - * k = 2*i+2 - * - * cdef heapitem heappop(heap* self): # <<<<<<<<<<<<<< - * cdef heapitem it - * it = heappeek(self) - */ - static struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_f_5scipy_7spatial_7ckdtree_heappop(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self) { struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_v_it; struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_r; PyObject *__pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":90 - * cdef heapitem heappop(heap* self): - * cdef heapitem it - * it = heappeek(self) # <<<<<<<<<<<<<< - * heapremove(self) - * return it - */ __pyx_v_it = __pyx_f_5scipy_7spatial_7ckdtree_heappeek(__pyx_v_self); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":91 - * cdef heapitem it - * it = heappeek(self) - * heapremove(self) # <<<<<<<<<<<<<< - * return it - * - */ __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapremove(__pyx_v_self); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":92 - * it = heappeek(self) - * heapremove(self) - * return it # <<<<<<<<<<<<<< - * - * - */ __pyx_r = __pyx_v_it; goto __pyx_L0; @@ -1104,48 +656,16 @@ return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":99 - * - * # utility functions - * cdef inline double dmax(double x, double y): # <<<<<<<<<<<<<< - * if x>y: - * return x - */ - static INLINE double __pyx_f_5scipy_7spatial_7ckdtree_dmax(double __pyx_v_x, double __pyx_v_y) { double __pyx_r; int __pyx_1; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":100 - * # utility functions - * cdef inline double dmax(double x, double y): - * if x>y: # <<<<<<<<<<<<<< - * return x - * else: - */ __pyx_1 = (__pyx_v_x > __pyx_v_y); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":101 - * cdef inline double dmax(double x, double y): - * if x>y: - * return x # <<<<<<<<<<<<<< - * else: - * return y - */ __pyx_r = __pyx_v_x; goto __pyx_L0; goto __pyx_L3; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":103 - * return x - * else: - * return y # <<<<<<<<<<<<<< - * cdef inline double dabs(double x): - * if x>0: - */ __pyx_r = __pyx_v_y; goto __pyx_L0; } @@ -1156,48 +676,16 @@ return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":104 - * else: - * return y - * cdef inline double dabs(double x): # <<<<<<<<<<<<<< - * if x>0: - * return x - */ - static INLINE double __pyx_f_5scipy_7spatial_7ckdtree_dabs(double __pyx_v_x) { double __pyx_r; int __pyx_1; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":105 - * return y - * cdef inline double dabs(double x): - * if x>0: # <<<<<<<<<<<<<< - * return x - * else: - */ __pyx_1 = (__pyx_v_x > 0); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":106 - * cdef inline double dabs(double x): - * if x>0: - * return x # <<<<<<<<<<<<<< - * else: - * return -x - */ __pyx_r = __pyx_v_x; goto __pyx_L0; goto __pyx_L3; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":108 - * return x - * else: - * return -x # <<<<<<<<<<<<<< - * cdef inline double _distance_p(double*x,double*y,double p,int k,double upperbound): - * """Compute the distance between x and y - */ __pyx_r = (-__pyx_v_x); goto __pyx_L0; } @@ -1208,74 +696,18 @@ return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":109 - * else: - * return -x - * cdef inline double _distance_p(double*x,double*y,double p,int k,double upperbound): # <<<<<<<<<<<<<< - * """Compute the distance between x and y - * - */ - static INLINE double __pyx_f_5scipy_7spatial_7ckdtree__distance_p(double *__pyx_v_x, double *__pyx_v_y, double __pyx_v_p, int __pyx_v_k, double __pyx_v_upperbound) { int __pyx_v_i; double __pyx_v_r; double __pyx_r; int __pyx_1; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":118 - * cdef int i - * cdef double r - * r = 0 # <<<<<<<<<<<<<< - * if p==infinity: - * for i in range(k): - */ __pyx_v_r = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":119 - * cdef double r - * r = 0 - * if p==infinity: # <<<<<<<<<<<<<< - * for i in range(k): - * r = dmax(r,dabs(x[i]-y[i])) - */ __pyx_1 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":120 - * r = 0 - * if p==infinity: - * for i in range(k): # <<<<<<<<<<<<<< - * r = dmax(r,dabs(x[i]-y[i])) - * if r>upperbound: - */ for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_k; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":121 - * if p==infinity: - * for i in range(k): - * r = dmax(r,dabs(x[i]-y[i])) # <<<<<<<<<<<<<< - * if r>upperbound: - * return r - */ __pyx_v_r = __pyx_f_5scipy_7spatial_7ckdtree_dmax(__pyx_v_r, __pyx_f_5scipy_7spatial_7ckdtree_dabs(((__pyx_v_x[__pyx_v_i]) - (__pyx_v_y[__pyx_v_i])))); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":122 - * for i in range(k): - * r = dmax(r,dabs(x[i]-y[i])) - * if r>upperbound: # <<<<<<<<<<<<<< - * return r - * elif p==1: - */ __pyx_1 = (__pyx_v_r > __pyx_v_upperbound); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":123 - * r = dmax(r,dabs(x[i]-y[i])) - * if r>upperbound: - * return r # <<<<<<<<<<<<<< - * elif p==1: - * for i in range(k): - */ __pyx_r = __pyx_v_r; goto __pyx_L0; goto __pyx_L6; @@ -1284,52 +716,12 @@ } goto __pyx_L3; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":124 - * if r>upperbound: - * return r - * elif p==1: # <<<<<<<<<<<<<< - * for i in range(k): - * r += dabs(x[i]-y[i]) - */ __pyx_1 = (__pyx_v_p == 1); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":125 - * return r - * elif p==1: - * for i in range(k): # <<<<<<<<<<<<<< - * r += dabs(x[i]-y[i]) - * if r>upperbound: - */ for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_k; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":126 - * elif p==1: - * for i in range(k): - * r += dabs(x[i]-y[i]) # <<<<<<<<<<<<<< - * if r>upperbound: - * return r - */ __pyx_v_r += __pyx_f_5scipy_7spatial_7ckdtree_dabs(((__pyx_v_x[__pyx_v_i]) - (__pyx_v_y[__pyx_v_i]))); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":127 - * for i in range(k): - * r += dabs(x[i]-y[i]) - * if r>upperbound: # <<<<<<<<<<<<<< - * return r - * else: - */ __pyx_1 = (__pyx_v_r > __pyx_v_upperbound); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":128 - * r += dabs(x[i]-y[i]) - * if r>upperbound: - * return r # <<<<<<<<<<<<<< - * else: - * for i in range(k): - */ __pyx_r = __pyx_v_r; goto __pyx_L0; goto __pyx_L9; @@ -1339,42 +731,10 @@ goto __pyx_L3; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":130 - * return r - * else: - * for i in range(k): # <<<<<<<<<<<<<< - * r += dabs(x[i]-y[i])**p - * if r>upperbound: - */ for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_k; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":131 - * else: - * for i in range(k): - * r += dabs(x[i]-y[i])**p # <<<<<<<<<<<<<< - * if r>upperbound: - * return r - */ __pyx_v_r += pow(__pyx_f_5scipy_7spatial_7ckdtree_dabs(((__pyx_v_x[__pyx_v_i]) - (__pyx_v_y[__pyx_v_i]))), __pyx_v_p); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":132 - * for i in range(k): - * r += dabs(x[i]-y[i])**p - * if r>upperbound: # <<<<<<<<<<<<<< - * return r - * return r - */ __pyx_1 = (__pyx_v_r > __pyx_v_upperbound); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":133 - * r += dabs(x[i]-y[i])**p - * if r>upperbound: - * return r # <<<<<<<<<<<<<< - * return r - * - */ __pyx_r = __pyx_v_r; goto __pyx_L0; goto __pyx_L12; @@ -1383,14 +743,6 @@ } } __pyx_L3:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":134 - * if r>upperbound: - * return r - * return r # <<<<<<<<<<<<<< - * - * - */ __pyx_r = __pyx_v_r; goto __pyx_L0; @@ -1399,14 +751,6 @@ return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":195 - * cdef object indices - * cdef np.int32_t* raw_indices - * def __init__(cKDTree self, data, int leafsize=10): # <<<<<<<<<<<<<< - * """Construct a kd-tree. - * - */ - static int __pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static char __pyx_doc_5scipy_7spatial_7ckdtree_7cKDTree___init__[] = "Construct a kd-tree.\n\n Parameters:\n ===========\n\n data : array-like, shape (n,m)\n The n data points of dimension mto be indexed. This array is \n not copied unless this is necessary to produce a contiguous \n array of doubles, and so modifying this data will result in \n bogus results.\n leafsize : positive integer\n The number of points at which the algorithm switches over to\n brute-force.\n "; static int __pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { @@ -1471,14 +815,6 @@ __pyx_bstruct_inner_mins.buf = NULL; __pyx_v_inner_indices = ((PyArrayObject *)Py_None); Py_INCREF(Py_None); __pyx_bstruct_inner_indices.buf = NULL; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":214 - * cdef np.ndarray[double, ndim=1] inner_mins - * cdef np.ndarray[np.int32_t, ndim=1] inner_indices - * self.data = np.ascontiguousarray(data,dtype=np.float) # <<<<<<<<<<<<<< - * self.n, self.m = np.shape(self.data) - * self.leafsize = leafsize - */ __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_ascontiguousarray); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 214; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; @@ -1498,14 +834,6 @@ Py_DECREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data = __pyx_4; __pyx_4 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":215 - * cdef np.ndarray[np.int32_t, ndim=1] inner_indices - * self.data = np.ascontiguousarray(data,dtype=np.float) - * self.n, self.m = np.shape(self.data) # <<<<<<<<<<<<<< - * self.leafsize = leafsize - * if self.leafsize<1: - */ __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_2 = PyObject_GetAttr(__pyx_5, __pyx_kp_shape); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_5); __pyx_5 = 0; @@ -1543,33 +871,9 @@ if (__Pyx_EndUnpack(__pyx_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_4); __pyx_4 = 0; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":216 - * self.data = np.ascontiguousarray(data,dtype=np.float) - * self.n, self.m = np.shape(self.data) - * self.leafsize = leafsize # <<<<<<<<<<<<<< - * if self.leafsize<1: - * raise ValueError("leafsize must be at least 1") - */ ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->leafsize = __pyx_v_leafsize; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":217 - * self.n, self.m = np.shape(self.data) - * self.leafsize = leafsize - * if self.leafsize<1: # <<<<<<<<<<<<<< - * raise ValueError("leafsize must be at least 1") - * self.maxes = np.ascontiguousarray(np.amax(self.data,axis=0)) - */ __pyx_7 = (((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->leafsize < 1); if (__pyx_7) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":218 - * self.leafsize = leafsize - * if self.leafsize<1: - * raise ValueError("leafsize must be at least 1") # <<<<<<<<<<<<<< - * self.maxes = np.ascontiguousarray(np.amax(self.data,axis=0)) - * self.mins = np.ascontiguousarray(np.amin(self.data,axis=0)) - */ __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 218; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_kp_19); PyTuple_SET_ITEM(__pyx_1, 0, __pyx_kp_19); @@ -1581,14 +885,6 @@ goto __pyx_L5; } __pyx_L5:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":219 - * if self.leafsize<1: - * raise ValueError("leafsize must be at least 1") - * self.maxes = np.ascontiguousarray(np.amax(self.data,axis=0)) # <<<<<<<<<<<<<< - * self.mins = np.ascontiguousarray(np.amin(self.data,axis=0)) - * self.indices = np.ascontiguousarray(np.arange(self.n,dtype=np.int)) - */ __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_ascontiguousarray); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; @@ -1613,14 +909,6 @@ Py_DECREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes); ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes = __pyx_5; __pyx_5 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":220 - * raise ValueError("leafsize must be at least 1") - * self.maxes = np.ascontiguousarray(np.amax(self.data,axis=0)) - * self.mins = np.ascontiguousarray(np.amin(self.data,axis=0)) # <<<<<<<<<<<<<< - * self.indices = np.ascontiguousarray(np.arange(self.n,dtype=np.int)) - * - */ __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_kp_ascontiguousarray); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; @@ -1645,14 +933,6 @@ Py_DECREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins); ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins = __pyx_5; __pyx_5 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":221 - * self.maxes = np.ascontiguousarray(np.amax(self.data,axis=0)) - * self.mins = np.ascontiguousarray(np.amin(self.data,axis=0)) - * self.indices = np.ascontiguousarray(np.arange(self.n,dtype=np.int)) # <<<<<<<<<<<<<< - * - * inner_data = self.data - */ __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_ascontiguousarray); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; @@ -1682,14 +962,6 @@ Py_DECREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices); ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices = __pyx_1; __pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":223 - * self.indices = np.ascontiguousarray(np.arange(self.n,dtype=np.int)) - * - * inner_data = self.data # <<<<<<<<<<<<<< - * self.raw_data = inner_data.data - * inner_maxes = self.maxes - */ if (!(__Pyx_TypeTest(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 223; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_1 = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_data, &__pyx_bstruct_inner_data); @@ -1711,23 +983,7 @@ Py_INCREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); Py_DECREF(((PyObject *)__pyx_v_inner_data)); __pyx_v_inner_data = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->data); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":224 - * - * inner_data = self.data - * self.raw_data = inner_data.data # <<<<<<<<<<<<<< - * inner_maxes = self.maxes - * self.raw_maxes = inner_maxes.data - */ ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_data = ((double *)__pyx_v_inner_data->data); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":225 - * inner_data = self.data - * self.raw_data = inner_data.data - * inner_maxes = self.maxes # <<<<<<<<<<<<<< - * self.raw_maxes = inner_maxes.data - * inner_mins = self.mins - */ if (!(__Pyx_TypeTest(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 225; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_6 = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes); __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_maxes, &__pyx_bstruct_inner_maxes); @@ -1749,23 +1005,7 @@ Py_INCREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes); Py_DECREF(((PyObject *)__pyx_v_inner_maxes)); __pyx_v_inner_maxes = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->maxes); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":226 - * self.raw_data = inner_data.data - * inner_maxes = self.maxes - * self.raw_maxes = inner_maxes.data # <<<<<<<<<<<<<< - * inner_mins = self.mins - * self.raw_mins = inner_mins.data - */ ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_maxes = ((double *)__pyx_v_inner_maxes->data); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":227 - * inner_maxes = self.maxes - * self.raw_maxes = inner_maxes.data - * inner_mins = self.mins # <<<<<<<<<<<<<< - * self.raw_mins = inner_mins.data - * inner_indices = self.indices - */ if (!(__Pyx_TypeTest(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 227; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_7 = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins); __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_mins, &__pyx_bstruct_inner_mins); @@ -1787,23 +1027,7 @@ Py_INCREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins); Py_DECREF(((PyObject *)__pyx_v_inner_mins)); __pyx_v_inner_mins = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->mins); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":228 - * self.raw_maxes = inner_maxes.data - * inner_mins = self.mins - * self.raw_mins = inner_mins.data # <<<<<<<<<<<<<< - * inner_indices = self.indices - * self.raw_indices = inner_indices.data - */ ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_mins = ((double *)__pyx_v_inner_mins->data); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":229 - * inner_mins = self.mins - * self.raw_mins = inner_mins.data - * inner_indices = self.indices # <<<<<<<<<<<<<< - * self.raw_indices = inner_indices.data - * - */ if (!(__Pyx_TypeTest(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 229; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_8 = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices); __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_inner_indices, &__pyx_bstruct_inner_indices); @@ -1825,23 +1049,7 @@ Py_INCREF(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices); Py_DECREF(((PyObject *)__pyx_v_inner_indices)); __pyx_v_inner_indices = ((PyArrayObject *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->indices); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":230 - * self.raw_mins = inner_mins.data - * inner_indices = self.indices - * self.raw_indices = inner_indices.data # <<<<<<<<<<<<<< - * - * self.tree = self.__build(0, self.n, self.raw_maxes, self.raw_mins) - */ ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_indices = ((__pyx_t_5numpy_int32_t *)__pyx_v_inner_indices->data); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":232 - * self.raw_indices = inner_indices.data - * - * self.tree = self.__build(0, self.n, self.raw_maxes, self.raw_mins) # <<<<<<<<<<<<<< - * - * cdef innernode* __build(cKDTree self, int start_idx, int end_idx, double* maxes, double* mins): - */ ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->tree = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->__pyx_vtab)->__build(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self), 0, ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->n, ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_maxes, ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->raw_mins); __pyx_r = 0; @@ -1876,14 +1084,6 @@ return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":234 - * self.tree = self.__build(0, self.n, self.raw_maxes, self.raw_mins) - * - * cdef innernode* __build(cKDTree self, int start_idx, int end_idx, double* maxes, double* mins): # <<<<<<<<<<<<<< - * cdef leafnode* n - * cdef innernode* ni - */ - static struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_f_5scipy_7spatial_7ckdtree_7cKDTree___build(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *__pyx_v_self, int __pyx_v_start_idx, int __pyx_v_end_idx, double *__pyx_v_maxes, double *__pyx_v_mins) { struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode *__pyx_v_n; struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_v_ni; @@ -1902,694 +1102,126 @@ int __pyx_1; int __pyx_2; long __pyx_3; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":240 - * cdef double size, split, minval, maxval - * cdef double*mids - * if end_idx-start_idx<=self.leafsize: # <<<<<<<<<<<<<< - * n = stdlib.malloc(sizeof(leafnode)) - * n.split_dim = -1 - */ __pyx_1 = ((__pyx_v_end_idx - __pyx_v_start_idx) <= __pyx_v_self->leafsize); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":241 - * cdef double*mids - * if end_idx-start_idx<=self.leafsize: - * n = stdlib.malloc(sizeof(leafnode)) # <<<<<<<<<<<<<< - * n.split_dim = -1 - * n.start_idx = start_idx - */ __pyx_v_n = ((struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode *)malloc((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode)))); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":242 - * if end_idx-start_idx<=self.leafsize: - * n = stdlib.malloc(sizeof(leafnode)) - * n.split_dim = -1 # <<<<<<<<<<<<<< - * n.start_idx = start_idx - * n.end_idx = end_idx - */ __pyx_v_n->split_dim = -1; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":243 - * n = stdlib.malloc(sizeof(leafnode)) - * n.split_dim = -1 - * n.start_idx = start_idx # <<<<<<<<<<<<<< - * n.end_idx = end_idx - * return n - */ __pyx_v_n->start_idx = __pyx_v_start_idx; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":244 - * n.split_dim = -1 - * n.start_idx = start_idx - * n.end_idx = end_idx # <<<<<<<<<<<<<< - * return n - * else: - */ __pyx_v_n->end_idx = __pyx_v_end_idx; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":245 - * n.start_idx = start_idx - * n.end_idx = end_idx - * return n # <<<<<<<<<<<<<< - * else: - * d = 0 - */ __pyx_r = ((struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *)__pyx_v_n); goto __pyx_L0; goto __pyx_L3; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":247 - * return n - * else: - * d = 0 # <<<<<<<<<<<<<< - * size = 0 - * for i in range(self.m): - */ __pyx_v_d = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":248 - * else: - * d = 0 - * size = 0 # <<<<<<<<<<<<<< - * for i in range(self.m): - * if maxes[i]-mins[i] > size: - */ __pyx_v_size = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":249 - * d = 0 - * size = 0 - * for i in range(self.m): # <<<<<<<<<<<<<< - * if maxes[i]-mins[i] > size: - * d = i - */ __pyx_2 = __pyx_v_self->m; for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":250 - * size = 0 - * for i in range(self.m): - * if maxes[i]-mins[i] > size: # <<<<<<<<<<<<<< - * d = i - * size = maxes[i]-mins[i] - */ __pyx_1 = (((__pyx_v_maxes[__pyx_v_i]) - (__pyx_v_mins[__pyx_v_i])) > __pyx_v_size); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":251 - * for i in range(self.m): - * if maxes[i]-mins[i] > size: - * d = i # <<<<<<<<<<<<<< - * size = maxes[i]-mins[i] - * maxval = maxes[d] - */ __pyx_v_d = __pyx_v_i; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":252 - * if maxes[i]-mins[i] > size: - * d = i - * size = maxes[i]-mins[i] # <<<<<<<<<<<<<< - * maxval = maxes[d] - * minval = mins[d] - */ __pyx_v_size = ((__pyx_v_maxes[__pyx_v_i]) - (__pyx_v_mins[__pyx_v_i])); goto __pyx_L6; } __pyx_L6:; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":253 - * d = i - * size = maxes[i]-mins[i] - * maxval = maxes[d] # <<<<<<<<<<<<<< - * minval = mins[d] - * if maxval==minval: - */ __pyx_v_maxval = (__pyx_v_maxes[__pyx_v_d]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":254 - * size = maxes[i]-mins[i] - * maxval = maxes[d] - * minval = mins[d] # <<<<<<<<<<<<<< - * if maxval==minval: - * # all points are identical; warn user? - */ __pyx_v_minval = (__pyx_v_mins[__pyx_v_d]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":255 - * maxval = maxes[d] - * minval = mins[d] - * if maxval==minval: # <<<<<<<<<<<<<< - * # all points are identical; warn user? - * n = stdlib.malloc(sizeof(leafnode)) - */ __pyx_1 = (__pyx_v_maxval == __pyx_v_minval); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":257 - * if maxval==minval: - * # all points are identical; warn user? - * n = stdlib.malloc(sizeof(leafnode)) # <<<<<<<<<<<<<< - * n.split_dim = -1 - * n.start_idx = start_idx - */ __pyx_v_n = ((struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode *)malloc((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode)))); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":258 - * # all points are identical; warn user? - * n = stdlib.malloc(sizeof(leafnode)) - * n.split_dim = -1 # <<<<<<<<<<<<<< - * n.start_idx = start_idx - * n.end_idx = end_idx - */ __pyx_v_n->split_dim = -1; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":259 - * n = stdlib.malloc(sizeof(leafnode)) - * n.split_dim = -1 - * n.start_idx = start_idx # <<<<<<<<<<<<<< - * n.end_idx = end_idx - * return n - */ __pyx_v_n->start_idx = __pyx_v_start_idx; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":260 - * n.split_dim = -1 - * n.start_idx = start_idx - * n.end_idx = end_idx # <<<<<<<<<<<<<< - * return n - * - */ __pyx_v_n->end_idx = __pyx_v_end_idx; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":261 - * n.start_idx = start_idx - * n.end_idx = end_idx - * return n # <<<<<<<<<<<<<< - * - * split = (maxval+minval)/2 - */ __pyx_r = ((struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *)__pyx_v_n); goto __pyx_L0; goto __pyx_L7; } __pyx_L7:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":263 - * return n - * - * split = (maxval+minval)/2 # <<<<<<<<<<<<<< - * - * p = start_idx - */ __pyx_v_split = ((__pyx_v_maxval + __pyx_v_minval) / 2); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":265 - * split = (maxval+minval)/2 - * - * p = start_idx # <<<<<<<<<<<<<< - * q = end_idx-1 - * while p<=q: - */ __pyx_v_p = __pyx_v_start_idx; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":266 - * - * p = start_idx - * q = end_idx-1 # <<<<<<<<<<<<<< - * while p<=q: - * if self.raw_data[self.raw_indices[p]*self.m+d]=split: - */ __pyx_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_p]) * __pyx_v_self->m) + __pyx_v_d)]) < __pyx_v_split); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":269 - * while p<=q: - * if self.raw_data[self.raw_indices[p]*self.m+d]=split: - * q-=1 - */ __pyx_v_p += 1; goto __pyx_L10; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":270 - * if self.raw_data[self.raw_indices[p]*self.m+d]=split: # <<<<<<<<<<<<<< - * q-=1 - * else: - */ __pyx_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_q]) * __pyx_v_self->m) + __pyx_v_d)]) >= __pyx_v_split); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":271 - * p+=1 - * elif self.raw_data[self.raw_indices[q]*self.m+d]>=split: - * q-=1 # <<<<<<<<<<<<<< - * else: - * t = self.raw_indices[p] - */ __pyx_v_q -= 1; goto __pyx_L10; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":273 - * q-=1 - * else: - * t = self.raw_indices[p] # <<<<<<<<<<<<<< - * self.raw_indices[p] = self.raw_indices[q] - * self.raw_indices[q] = t - */ __pyx_v_t = (__pyx_v_self->raw_indices[__pyx_v_p]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":274 - * else: - * t = self.raw_indices[p] - * self.raw_indices[p] = self.raw_indices[q] # <<<<<<<<<<<<<< - * self.raw_indices[q] = t - * p+=1 - */ (__pyx_v_self->raw_indices[__pyx_v_p]) = (__pyx_v_self->raw_indices[__pyx_v_q]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":275 - * t = self.raw_indices[p] - * self.raw_indices[p] = self.raw_indices[q] - * self.raw_indices[q] = t # <<<<<<<<<<<<<< - * p+=1 - * q-=1 - */ (__pyx_v_self->raw_indices[__pyx_v_q]) = __pyx_v_t; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":276 - * self.raw_indices[p] = self.raw_indices[q] - * self.raw_indices[q] = t - * p+=1 # <<<<<<<<<<<<<< - * q-=1 - * - */ __pyx_v_p += 1; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":277 - * self.raw_indices[q] = t - * p+=1 - * q-=1 # <<<<<<<<<<<<<< - * - * # slide midpoint if necessary - */ __pyx_v_q -= 1; } __pyx_L10:; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":280 - * - * # slide midpoint if necessary - * if p==start_idx: # <<<<<<<<<<<<<< - * # no points less than split - * j = start_idx - */ __pyx_1 = (__pyx_v_p == __pyx_v_start_idx); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":282 - * if p==start_idx: - * # no points less than split - * j = start_idx # <<<<<<<<<<<<<< - * split = self.raw_data[self.raw_indices[j]*self.m+d] - * for i in range(start_idx+1, end_idx): - */ __pyx_v_j = __pyx_v_start_idx; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":283 - * # no points less than split - * j = start_idx - * split = self.raw_data[self.raw_indices[j]*self.m+d] # <<<<<<<<<<<<<< - * for i in range(start_idx+1, end_idx): - * if self.raw_data[self.raw_indices[i]*self.m+d]raw_data[(((__pyx_v_self->raw_indices[__pyx_v_j]) * __pyx_v_self->m) + __pyx_v_d)]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":284 - * j = start_idx - * split = self.raw_data[self.raw_indices[j]*self.m+d] - * for i in range(start_idx+1, end_idx): # <<<<<<<<<<<<<< - * if self.raw_data[self.raw_indices[i]*self.m+d]raw_data[(((__pyx_v_self->raw_indices[__pyx_v_i]) * __pyx_v_self->m) + __pyx_v_d)]) < __pyx_v_split); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":286 - * for i in range(start_idx+1, end_idx): - * if self.raw_data[self.raw_indices[i]*self.m+d]raw_data[(((__pyx_v_self->raw_indices[__pyx_v_j]) * __pyx_v_self->m) + __pyx_v_d)]); goto __pyx_L14; } __pyx_L14:; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":288 - * j = i - * split = self.raw_data[self.raw_indices[j]*self.m+d] - * t = self.raw_indices[start_idx] # <<<<<<<<<<<<<< - * self.raw_indices[start_idx] = self.raw_indices[j] - * self.raw_indices[j] = t - */ __pyx_v_t = (__pyx_v_self->raw_indices[__pyx_v_start_idx]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":289 - * split = self.raw_data[self.raw_indices[j]*self.m+d] - * t = self.raw_indices[start_idx] - * self.raw_indices[start_idx] = self.raw_indices[j] # <<<<<<<<<<<<<< - * self.raw_indices[j] = t - * p = start_idx+1 - */ (__pyx_v_self->raw_indices[__pyx_v_start_idx]) = (__pyx_v_self->raw_indices[__pyx_v_j]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":290 - * t = self.raw_indices[start_idx] - * self.raw_indices[start_idx] = self.raw_indices[j] - * self.raw_indices[j] = t # <<<<<<<<<<<<<< - * p = start_idx+1 - * q = start_idx - */ (__pyx_v_self->raw_indices[__pyx_v_j]) = __pyx_v_t; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":291 - * self.raw_indices[start_idx] = self.raw_indices[j] - * self.raw_indices[j] = t - * p = start_idx+1 # <<<<<<<<<<<<<< - * q = start_idx - * elif p==end_idx: - */ __pyx_v_p = (__pyx_v_start_idx + 1); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":292 - * self.raw_indices[j] = t - * p = start_idx+1 - * q = start_idx # <<<<<<<<<<<<<< - * elif p==end_idx: - * # no points greater than split - */ __pyx_v_q = __pyx_v_start_idx; goto __pyx_L11; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":293 - * p = start_idx+1 - * q = start_idx - * elif p==end_idx: # <<<<<<<<<<<<<< - * # no points greater than split - * j = end_idx-1 - */ __pyx_1 = (__pyx_v_p == __pyx_v_end_idx); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":295 - * elif p==end_idx: - * # no points greater than split - * j = end_idx-1 # <<<<<<<<<<<<<< - * split = self.raw_data[self.raw_indices[j]*self.m+d] - * for i in range(start_idx, end_idx-1): - */ __pyx_v_j = (__pyx_v_end_idx - 1); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":296 - * # no points greater than split - * j = end_idx-1 - * split = self.raw_data[self.raw_indices[j]*self.m+d] # <<<<<<<<<<<<<< - * for i in range(start_idx, end_idx-1): - * if self.raw_data[self.raw_indices[i]*self.m+d]>split: - */ __pyx_v_split = (__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_j]) * __pyx_v_self->m) + __pyx_v_d)]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":297 - * j = end_idx-1 - * split = self.raw_data[self.raw_indices[j]*self.m+d] - * for i in range(start_idx, end_idx-1): # <<<<<<<<<<<<<< - * if self.raw_data[self.raw_indices[i]*self.m+d]>split: - * j = i - */ __pyx_3 = (__pyx_v_end_idx - 1); for (__pyx_v_i = __pyx_v_start_idx; __pyx_v_i < __pyx_3; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":298 - * split = self.raw_data[self.raw_indices[j]*self.m+d] - * for i in range(start_idx, end_idx-1): - * if self.raw_data[self.raw_indices[i]*self.m+d]>split: # <<<<<<<<<<<<<< - * j = i - * split = self.raw_data[self.raw_indices[j]*self.m+d] - */ __pyx_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_i]) * __pyx_v_self->m) + __pyx_v_d)]) > __pyx_v_split); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":299 - * for i in range(start_idx, end_idx-1): - * if self.raw_data[self.raw_indices[i]*self.m+d]>split: - * j = i # <<<<<<<<<<<<<< - * split = self.raw_data[self.raw_indices[j]*self.m+d] - * t = self.raw_indices[end_idx-1] - */ __pyx_v_j = __pyx_v_i; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":300 - * if self.raw_data[self.raw_indices[i]*self.m+d]>split: - * j = i - * split = self.raw_data[self.raw_indices[j]*self.m+d] # <<<<<<<<<<<<<< - * t = self.raw_indices[end_idx-1] - * self.raw_indices[end_idx-1] = self.raw_indices[j] - */ __pyx_v_split = (__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_j]) * __pyx_v_self->m) + __pyx_v_d)]); goto __pyx_L17; } __pyx_L17:; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":301 - * j = i - * split = self.raw_data[self.raw_indices[j]*self.m+d] - * t = self.raw_indices[end_idx-1] # <<<<<<<<<<<<<< - * self.raw_indices[end_idx-1] = self.raw_indices[j] - * self.raw_indices[j] = t - */ __pyx_v_t = (__pyx_v_self->raw_indices[(__pyx_v_end_idx - 1)]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":302 - * split = self.raw_data[self.raw_indices[j]*self.m+d] - * t = self.raw_indices[end_idx-1] - * self.raw_indices[end_idx-1] = self.raw_indices[j] # <<<<<<<<<<<<<< - * self.raw_indices[j] = t - * p = end_idx-1 - */ (__pyx_v_self->raw_indices[(__pyx_v_end_idx - 1)]) = (__pyx_v_self->raw_indices[__pyx_v_j]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":303 - * t = self.raw_indices[end_idx-1] - * self.raw_indices[end_idx-1] = self.raw_indices[j] - * self.raw_indices[j] = t # <<<<<<<<<<<<<< - * p = end_idx-1 - * q = end_idx-2 - */ (__pyx_v_self->raw_indices[__pyx_v_j]) = __pyx_v_t; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":304 - * self.raw_indices[end_idx-1] = self.raw_indices[j] - * self.raw_indices[j] = t - * p = end_idx-1 # <<<<<<<<<<<<<< - * q = end_idx-2 - * - */ __pyx_v_p = (__pyx_v_end_idx - 1); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":305 - * self.raw_indices[j] = t - * p = end_idx-1 - * q = end_idx-2 # <<<<<<<<<<<<<< - * - * # construct new node representation - */ __pyx_v_q = (__pyx_v_end_idx - 2); goto __pyx_L11; } __pyx_L11:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":308 - * - * # construct new node representation - * ni = stdlib.malloc(sizeof(innernode)) # <<<<<<<<<<<<<< - * - * mids = stdlib.malloc(sizeof(double)*self.m) - */ __pyx_v_ni = ((struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *)malloc((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_innernode)))); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":310 - * ni = stdlib.malloc(sizeof(innernode)) - * - * mids = stdlib.malloc(sizeof(double)*self.m) # <<<<<<<<<<<<<< - * for i in range(self.m): - * mids[i] = maxes[i] - */ __pyx_v_mids = ((double *)malloc(((sizeof(double)) * __pyx_v_self->m))); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":311 - * - * mids = stdlib.malloc(sizeof(double)*self.m) - * for i in range(self.m): # <<<<<<<<<<<<<< - * mids[i] = maxes[i] - * mids[d] = split - */ __pyx_2 = __pyx_v_self->m; for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":312 - * mids = stdlib.malloc(sizeof(double)*self.m) - * for i in range(self.m): - * mids[i] = maxes[i] # <<<<<<<<<<<<<< - * mids[d] = split - * ni.less = self.__build(start_idx,p,mids,mins) - */ (__pyx_v_mids[__pyx_v_i]) = (__pyx_v_maxes[__pyx_v_i]); } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":313 - * for i in range(self.m): - * mids[i] = maxes[i] - * mids[d] = split # <<<<<<<<<<<<<< - * ni.less = self.__build(start_idx,p,mids,mins) - * - */ (__pyx_v_mids[__pyx_v_d]) = __pyx_v_split; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":314 - * mids[i] = maxes[i] - * mids[d] = split - * ni.less = self.__build(start_idx,p,mids,mins) # <<<<<<<<<<<<<< - * - * for i in range(self.m): - */ __pyx_v_ni->less = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self->__pyx_vtab)->__build(__pyx_v_self, __pyx_v_start_idx, __pyx_v_p, __pyx_v_mids, __pyx_v_mins); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":316 - * ni.less = self.__build(start_idx,p,mids,mins) - * - * for i in range(self.m): # <<<<<<<<<<<<<< - * mids[i] = mins[i] - * mids[d] = split - */ __pyx_2 = __pyx_v_self->m; for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":317 - * - * for i in range(self.m): - * mids[i] = mins[i] # <<<<<<<<<<<<<< - * mids[d] = split - * ni.greater = self.__build(p,end_idx,maxes,mids) - */ (__pyx_v_mids[__pyx_v_i]) = (__pyx_v_mins[__pyx_v_i]); } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":318 - * for i in range(self.m): - * mids[i] = mins[i] - * mids[d] = split # <<<<<<<<<<<<<< - * ni.greater = self.__build(p,end_idx,maxes,mids) - * - */ (__pyx_v_mids[__pyx_v_d]) = __pyx_v_split; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":319 - * mids[i] = mins[i] - * mids[d] = split - * ni.greater = self.__build(p,end_idx,maxes,mids) # <<<<<<<<<<<<<< - * - * stdlib.free(mids) - */ __pyx_v_ni->greater = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self->__pyx_vtab)->__build(__pyx_v_self, __pyx_v_p, __pyx_v_end_idx, __pyx_v_maxes, __pyx_v_mids); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":321 - * ni.greater = self.__build(p,end_idx,maxes,mids) - * - * stdlib.free(mids) # <<<<<<<<<<<<<< - * - * ni.split_dim = d - */ free(__pyx_v_mids); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":323 - * stdlib.free(mids) - * - * ni.split_dim = d # <<<<<<<<<<<<<< - * ni.split = split - * - */ __pyx_v_ni->split_dim = __pyx_v_d; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":324 - * - * ni.split_dim = d - * ni.split = split # <<<<<<<<<<<<<< - * - * return ni - */ __pyx_v_ni->split = __pyx_v_split; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":326 - * ni.split = split - * - * return ni # <<<<<<<<<<<<<< - * - * cdef __free_tree(cKDTree self, innernode* node): - */ __pyx_r = __pyx_v_ni; goto __pyx_L0; } @@ -2600,59 +1232,19 @@ return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":328 - * return ni - * - * cdef __free_tree(cKDTree self, innernode* node): # <<<<<<<<<<<<<< - * if node.split_dim!=-1: - * self.__free_tree(node.less) - */ - static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_7cKDTree___free_tree(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *__pyx_v_self, struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_v_node) { PyObject *__pyx_r; int __pyx_1; PyObject *__pyx_2 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":329 - * - * cdef __free_tree(cKDTree self, innernode* node): - * if node.split_dim!=-1: # <<<<<<<<<<<<<< - * self.__free_tree(node.less) - * self.__free_tree(node.greater) - */ __pyx_1 = (__pyx_v_node->split_dim != -1); if (__pyx_1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":330 - * cdef __free_tree(cKDTree self, innernode* node): - * if node.split_dim!=-1: - * self.__free_tree(node.less) # <<<<<<<<<<<<<< - * self.__free_tree(node.greater) - * stdlib.free(node) - */ __pyx_2 = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self->__pyx_vtab)->__free_tree(__pyx_v_self, __pyx_v_node->less); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 330; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":331 - * if node.split_dim!=-1: - * self.__free_tree(node.less) - * self.__free_tree(node.greater) # <<<<<<<<<<<<<< - * stdlib.free(node) - * - */ __pyx_2 = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self->__pyx_vtab)->__free_tree(__pyx_v_self, __pyx_v_node->greater); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 331; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; goto __pyx_L3; } __pyx_L3:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":332 - * self.__free_tree(node.less) - * self.__free_tree(node.greater) - * stdlib.free(node) # <<<<<<<<<<<<<< - * - * def __del__(cKDTree self): - */ free(__pyx_v_node); __pyx_r = Py_None; Py_INCREF(Py_None); @@ -2665,60 +1257,24 @@ return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":334 - * stdlib.free(node) - * - * def __del__(cKDTree self): # <<<<<<<<<<<<<< - * print "freeing cKDTree" - * self.__free_tree(self.tree) - */ - -static PyObject *__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___del__(PyObject *__pyx_v_self, PyObject *unused); /*proto*/ -static PyObject *__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___del__(PyObject *__pyx_v_self, PyObject *unused) { - PyObject *__pyx_r; +static void __pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___dealloc__(PyObject *__pyx_v_self); /*proto*/ +static void __pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___dealloc__(PyObject *__pyx_v_self) { PyObject *__pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":335 - * - * def __del__(cKDTree self): - * print "freeing cKDTree" # <<<<<<<<<<<<<< - * self.__free_tree(self.tree) - * - */ __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 335; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_kp_20); PyTuple_SET_ITEM(__pyx_1, 0, __pyx_kp_20); if (__Pyx_Print(((PyObject *)__pyx_1), 1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 335; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":336 - * def __del__(cKDTree self): - * print "freeing cKDTree" - * self.__free_tree(self.tree) # <<<<<<<<<<<<<< - * - * cdef void __query(cKDTree self, - */ __pyx_1 = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->__pyx_vtab)->__free_tree(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self), ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->tree); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 336; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - __pyx_r = Py_None; Py_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; Py_XDECREF(__pyx_1); - __Pyx_AddTraceback("scipy.spatial.ckdtree.cKDTree.__del__"); - __pyx_r = NULL; + __Pyx_AddTraceback("scipy.spatial.ckdtree.cKDTree.__dealloc__"); __pyx_L0:; - return __pyx_r; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":338 - * self.__free_tree(self.tree) - * - * cdef void __query(cKDTree self, # <<<<<<<<<<<<<< - * double*result_distances, - * int*result_indices, - */ - static void __pyx_f_5scipy_7spatial_7ckdtree_7cKDTree___query(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *__pyx_v_self, double *__pyx_v_result_distances, int *__pyx_v_result_indices, double *__pyx_v_x, int __pyx_v_k, double __pyx_v_eps, double __pyx_v_p, double __pyx_v_distance_upper_bound) { struct __pyx_t_5scipy_7spatial_7ckdtree_heap __pyx_v_q; struct __pyx_t_5scipy_7spatial_7ckdtree_heap __pyx_v_neighbors; @@ -2740,413 +1296,101 @@ PyObject *__pyx_1 = 0; int __pyx_2; int __pyx_3; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":369 - * # distances between the nearest side of the cell and the target - * # the head node of the cell - * heapcreate(&q,12) # <<<<<<<<<<<<<< - * - * # priority queue for the nearest neighbors - */ __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_q), 12); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 369; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":374 - * # furthest known neighbor first - * # entries are (-distance**p, i) - * heapcreate(&neighbors,k) # <<<<<<<<<<<<<< - * - * # set up first nodeinfo - */ __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_neighbors), __pyx_v_k); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 374; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":377 - * - * # set up first nodeinfo - * inf = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) # <<<<<<<<<<<<<< - * inf.node = self.tree - * for i in range(self.m): - */ __pyx_v_inf = ((struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *)malloc(((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo)) + (__pyx_v_self->m * (sizeof(double)))))); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":378 - * # set up first nodeinfo - * inf = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) - * inf.node = self.tree # <<<<<<<<<<<<<< - * for i in range(self.m): - * inf.side_distances[i] = 0 - */ __pyx_v_inf->node = __pyx_v_self->tree; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":379 - * inf = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) - * inf.node = self.tree - * for i in range(self.m): # <<<<<<<<<<<<<< - * inf.side_distances[i] = 0 - * t = x[i]-self.raw_maxes[i] - */ __pyx_2 = __pyx_v_self->m; for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":380 - * inf.node = self.tree - * for i in range(self.m): - * inf.side_distances[i] = 0 # <<<<<<<<<<<<<< - * t = x[i]-self.raw_maxes[i] - * if t>inf.side_distances[i]: - */ (__pyx_v_inf->side_distances[__pyx_v_i]) = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":381 - * for i in range(self.m): - * inf.side_distances[i] = 0 - * t = x[i]-self.raw_maxes[i] # <<<<<<<<<<<<<< - * if t>inf.side_distances[i]: - * inf.side_distances[i] = t - */ __pyx_v_t = ((__pyx_v_x[__pyx_v_i]) - (__pyx_v_self->raw_maxes[__pyx_v_i])); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":382 - * inf.side_distances[i] = 0 - * t = x[i]-self.raw_maxes[i] - * if t>inf.side_distances[i]: # <<<<<<<<<<<<<< - * inf.side_distances[i] = t - * else: - */ __pyx_3 = (__pyx_v_t > (__pyx_v_inf->side_distances[__pyx_v_i])); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":383 - * t = x[i]-self.raw_maxes[i] - * if t>inf.side_distances[i]: - * inf.side_distances[i] = t # <<<<<<<<<<<<<< - * else: - * t = self.raw_mins[i]-x[i] - */ (__pyx_v_inf->side_distances[__pyx_v_i]) = __pyx_v_t; goto __pyx_L5; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":385 - * inf.side_distances[i] = t - * else: - * t = self.raw_mins[i]-x[i] # <<<<<<<<<<<<<< - * if t>inf.side_distances[i]: - * inf.side_distances[i] = t - */ __pyx_v_t = ((__pyx_v_self->raw_mins[__pyx_v_i]) - (__pyx_v_x[__pyx_v_i])); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":386 - * else: - * t = self.raw_mins[i]-x[i] - * if t>inf.side_distances[i]: # <<<<<<<<<<<<<< - * inf.side_distances[i] = t - * if p!=1 and p!=infinity: - */ __pyx_3 = (__pyx_v_t > (__pyx_v_inf->side_distances[__pyx_v_i])); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":387 - * t = self.raw_mins[i]-x[i] - * if t>inf.side_distances[i]: - * inf.side_distances[i] = t # <<<<<<<<<<<<<< - * if p!=1 and p!=infinity: - * inf.side_distances[i]=inf.side_distances[i]**p - */ (__pyx_v_inf->side_distances[__pyx_v_i]) = __pyx_v_t; goto __pyx_L6; } __pyx_L6:; } __pyx_L5:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":388 - * if t>inf.side_distances[i]: - * inf.side_distances[i] = t - * if p!=1 and p!=infinity: # <<<<<<<<<<<<<< - * inf.side_distances[i]=inf.side_distances[i]**p - * - */ __pyx_3 = (__pyx_v_p != 1); if (__pyx_3) { __pyx_3 = (__pyx_v_p != __pyx_v_5scipy_7spatial_7ckdtree_infinity); } if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":389 - * inf.side_distances[i] = t - * if p!=1 and p!=infinity: - * inf.side_distances[i]=inf.side_distances[i]**p # <<<<<<<<<<<<<< - * - * # compute first distance - */ (__pyx_v_inf->side_distances[__pyx_v_i]) = pow((__pyx_v_inf->side_distances[__pyx_v_i]), __pyx_v_p); goto __pyx_L7; } __pyx_L7:; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":392 - * - * # compute first distance - * min_distance = 0. # <<<<<<<<<<<<<< - * for i in range(self.m): - * if p==infinity: - */ __pyx_v_min_distance = 0.; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":393 - * # compute first distance - * min_distance = 0. - * for i in range(self.m): # <<<<<<<<<<<<<< - * if p==infinity: - * min_distance = dmax(min_distance,inf.side_distances[i]) - */ __pyx_2 = __pyx_v_self->m; for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":394 - * min_distance = 0. - * for i in range(self.m): - * if p==infinity: # <<<<<<<<<<<<<< - * min_distance = dmax(min_distance,inf.side_distances[i]) - * else: - */ __pyx_3 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":395 - * for i in range(self.m): - * if p==infinity: - * min_distance = dmax(min_distance,inf.side_distances[i]) # <<<<<<<<<<<<<< - * else: - * min_distance += inf.side_distances[i] - */ __pyx_v_min_distance = __pyx_f_5scipy_7spatial_7ckdtree_dmax(__pyx_v_min_distance, (__pyx_v_inf->side_distances[__pyx_v_i])); goto __pyx_L10; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":397 - * min_distance = dmax(min_distance,inf.side_distances[i]) - * else: - * min_distance += inf.side_distances[i] # <<<<<<<<<<<<<< - * - * # fiddle approximation factor - */ __pyx_v_min_distance += (__pyx_v_inf->side_distances[__pyx_v_i]); } __pyx_L10:; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":400 - * - * # fiddle approximation factor - * if eps==0: # <<<<<<<<<<<<<< - * epsfac=1 - * elif p==infinity: - */ __pyx_3 = (__pyx_v_eps == 0); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":401 - * # fiddle approximation factor - * if eps==0: - * epsfac=1 # <<<<<<<<<<<<<< - * elif p==infinity: - * epsfac = 1/(1+eps) - */ __pyx_v_epsfac = 1; goto __pyx_L11; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":402 - * if eps==0: - * epsfac=1 - * elif p==infinity: # <<<<<<<<<<<<<< - * epsfac = 1/(1+eps) - * else: - */ __pyx_3 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":403 - * epsfac=1 - * elif p==infinity: - * epsfac = 1/(1+eps) # <<<<<<<<<<<<<< - * else: - * epsfac = 1/(1+eps)**p - */ __pyx_v_epsfac = (1 / (1 + __pyx_v_eps)); goto __pyx_L11; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":405 - * epsfac = 1/(1+eps) - * else: - * epsfac = 1/(1+eps)**p # <<<<<<<<<<<<<< - * - * # internally we represent all distances as distance**p - */ __pyx_v_epsfac = (1 / pow((1 + __pyx_v_eps), __pyx_v_p)); } __pyx_L11:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":408 - * - * # internally we represent all distances as distance**p - * if p!=infinity and distance_upper_bound!=infinity: # <<<<<<<<<<<<<< - * distance_upper_bound = distance_upper_bound**p - * - */ __pyx_3 = (__pyx_v_p != __pyx_v_5scipy_7spatial_7ckdtree_infinity); if (__pyx_3) { __pyx_3 = (__pyx_v_distance_upper_bound != __pyx_v_5scipy_7spatial_7ckdtree_infinity); } if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":409 - * # internally we represent all distances as distance**p - * if p!=infinity and distance_upper_bound!=infinity: - * distance_upper_bound = distance_upper_bound**p # <<<<<<<<<<<<<< - * - * while True: - */ __pyx_v_distance_upper_bound = pow(__pyx_v_distance_upper_bound, __pyx_v_p); goto __pyx_L12; } __pyx_L12:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":411 - * distance_upper_bound = distance_upper_bound**p - * - * while True: # <<<<<<<<<<<<<< - * if inf.node.split_dim==-1: - * node = inf.node - */ while (1) { __pyx_3 = 1; if (!__pyx_3) break; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":412 - * - * while True: - * if inf.node.split_dim==-1: # <<<<<<<<<<<<<< - * node = inf.node - * - */ __pyx_3 = (__pyx_v_inf->node->split_dim == -1); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":413 - * while True: - * if inf.node.split_dim==-1: - * node = inf.node # <<<<<<<<<<<<<< - * - * # brute-force - */ __pyx_v_node = ((struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode *)__pyx_v_inf->node); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":416 - * - * # brute-force - * for i in range(node.start_idx,node.end_idx): # <<<<<<<<<<<<<< - * d = _distance_p( - * self.raw_data+self.raw_indices[i]*self.m, - */ __pyx_2 = __pyx_v_node->end_idx; for (__pyx_v_i = __pyx_v_node->start_idx; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":419 - * d = _distance_p( - * self.raw_data+self.raw_indices[i]*self.m, - * x,p,self.m,distance_upper_bound) # <<<<<<<<<<<<<< - * - * if draw_data + ((__pyx_v_self->raw_indices[__pyx_v_i]) * __pyx_v_self->m)), __pyx_v_x, __pyx_v_p, __pyx_v_self->m, __pyx_v_distance_upper_bound); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":421 - * x,p,self.m,distance_upper_bound) - * - * if draw_indices[__pyx_v_i]); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":427 - * neighbor.priority = -d - * neighbor.contents.intdata = self.raw_indices[i] - * heappush(&neighbors,neighbor) # <<<<<<<<<<<<<< - * - * # adjust upper bound for efficiency - */ __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_neighbors), __pyx_v_neighbor); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 427; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":430 - * - * # adjust upper bound for efficiency - * if neighbors.n==k: # <<<<<<<<<<<<<< - * distance_upper_bound = -heappeek(&neighbors).priority - * # done with this node, get another - */ __pyx_3 = (__pyx_v_neighbors.n == __pyx_v_k); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":431 - * # adjust upper bound for efficiency - * if neighbors.n==k: - * distance_upper_bound = -heappeek(&neighbors).priority # <<<<<<<<<<<<<< - * # done with this node, get another - * stdlib.free(inf) - */ __pyx_v_distance_upper_bound = (-__pyx_f_5scipy_7spatial_7ckdtree_heappeek((&__pyx_v_neighbors)).priority); goto __pyx_L20; } @@ -3155,357 +1399,77 @@ } __pyx_L18:; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":433 - * distance_upper_bound = -heappeek(&neighbors).priority - * # done with this node, get another - * stdlib.free(inf) # <<<<<<<<<<<<<< - * if q.n==0: - * # no more nodes to visit - */ free(__pyx_v_inf); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":434 - * # done with this node, get another - * stdlib.free(inf) - * if q.n==0: # <<<<<<<<<<<<<< - * # no more nodes to visit - * break - */ __pyx_3 = (__pyx_v_q.n == 0); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":436 - * if q.n==0: - * # no more nodes to visit - * break # <<<<<<<<<<<<<< - * else: - * it = heappop(&q) - */ goto __pyx_L14; goto __pyx_L21; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":438 - * break - * else: - * it = heappop(&q) # <<<<<<<<<<<<<< - * inf = it.contents.ptrdata - * min_distance = it.priority - */ __pyx_v_it = __pyx_f_5scipy_7spatial_7ckdtree_heappop((&__pyx_v_q)); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":439 - * else: - * it = heappop(&q) - * inf = it.contents.ptrdata # <<<<<<<<<<<<<< - * min_distance = it.priority - * else: - */ __pyx_v_inf = ((struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *)__pyx_v_it.contents.ptrdata); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":440 - * it = heappop(&q) - * inf = it.contents.ptrdata - * min_distance = it.priority # <<<<<<<<<<<<<< - * else: - * inode = inf.node - */ __pyx_v_min_distance = __pyx_v_it.priority; } __pyx_L21:; goto __pyx_L15; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":442 - * min_distance = it.priority - * else: - * inode = inf.node # <<<<<<<<<<<<<< - * - * # we don't push cells that are too far onto the queue at all, - */ __pyx_v_inode = ((struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *)__pyx_v_inf->node); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":447 - * # but since the distance_upper_bound decreases, we might get - * # here even if the cell's too far - * if min_distance>distance_upper_bound*epsfac: # <<<<<<<<<<<<<< - * # since this is the nearest cell, we're done, bail out - * stdlib.free(inf) - */ __pyx_3 = (__pyx_v_min_distance > (__pyx_v_distance_upper_bound * __pyx_v_epsfac)); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":449 - * if min_distance>distance_upper_bound*epsfac: - * # since this is the nearest cell, we're done, bail out - * stdlib.free(inf) # <<<<<<<<<<<<<< - * # free all the nodes still on the heap - * for i in range(q.n): - */ free(__pyx_v_inf); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":451 - * stdlib.free(inf) - * # free all the nodes still on the heap - * for i in range(q.n): # <<<<<<<<<<<<<< - * stdlib.free(q.heap[i].contents.ptrdata) - * break - */ __pyx_2 = __pyx_v_q.n; for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":452 - * # free all the nodes still on the heap - * for i in range(q.n): - * stdlib.free(q.heap[i].contents.ptrdata) # <<<<<<<<<<<<<< - * break - * - */ free((__pyx_v_q.heap[__pyx_v_i]).contents.ptrdata); } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":453 - * for i in range(q.n): - * stdlib.free(q.heap[i].contents.ptrdata) - * break # <<<<<<<<<<<<<< - * - * # set up children for searching - */ goto __pyx_L14; goto __pyx_L22; } __pyx_L22:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":456 - * - * # set up children for searching - * if x[inode.split_dim]split_dim]) < __pyx_v_inode->split); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":457 - * # set up children for searching - * if x[inode.split_dim]less; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":458 - * if x[inode.split_dim]greater; goto __pyx_L25; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":460 - * far = inode.greater - * else: - * near = inode.greater # <<<<<<<<<<<<<< - * far = inode.less - * - */ __pyx_v_near = __pyx_v_inode->greater; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":461 - * else: - * near = inode.greater - * far = inode.less # <<<<<<<<<<<<<< - * - * # near child is at the same distance as the current node - */ __pyx_v_far = __pyx_v_inode->less; } __pyx_L25:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":466 - * # we're going here next, so no point pushing it on the queue - * # no need to recompute the distance or the side_distances - * inf.node = near # <<<<<<<<<<<<<< - * - * # far child is further by an amount depending only - */ __pyx_v_inf->node = __pyx_v_near; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":471 - * # on the split value; compute its distance and side_distances - * # and push it on the queue if it's near enough - * inf2 = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) # <<<<<<<<<<<<<< - * it2.contents.ptrdata = inf2 - * inf2.node = far - */ __pyx_v_inf2 = ((struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *)malloc(((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo)) + (__pyx_v_self->m * (sizeof(double)))))); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":472 - * # and push it on the queue if it's near enough - * inf2 = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) - * it2.contents.ptrdata = inf2 # <<<<<<<<<<<<<< - * inf2.node = far - * # most side distances unchanged - */ __pyx_v_it2.contents.ptrdata = ((char *)__pyx_v_inf2); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":473 - * inf2 = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) - * it2.contents.ptrdata = inf2 - * inf2.node = far # <<<<<<<<<<<<<< - * # most side distances unchanged - * for i in range(self.m): - */ __pyx_v_inf2->node = __pyx_v_far; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":475 - * inf2.node = far - * # most side distances unchanged - * for i in range(self.m): # <<<<<<<<<<<<<< - * inf2.side_distances[i] = inf.side_distances[i] - * - */ __pyx_2 = __pyx_v_self->m; for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":476 - * # most side distances unchanged - * for i in range(self.m): - * inf2.side_distances[i] = inf.side_distances[i] # <<<<<<<<<<<<<< - * - * # one side distance changes - */ (__pyx_v_inf2->side_distances[__pyx_v_i]) = (__pyx_v_inf->side_distances[__pyx_v_i]); } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":480 - * # one side distance changes - * # we can adjust the minimum distance without recomputing - * if p == infinity: # <<<<<<<<<<<<<< - * # we never use side_distances in the l_infinity case - * # inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) - */ __pyx_3 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":483 - * # we never use side_distances in the l_infinity case - * # inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) - * far_min_distance = dmax(min_distance, dabs(inode.split-x[inode.split_dim])) # <<<<<<<<<<<<<< - * elif p == 1: - * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) - */ __pyx_v_far_min_distance = __pyx_f_5scipy_7spatial_7ckdtree_dmax(__pyx_v_min_distance, __pyx_f_5scipy_7spatial_7ckdtree_dabs((__pyx_v_inode->split - (__pyx_v_x[__pyx_v_inode->split_dim])))); goto __pyx_L28; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":484 - * # inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) - * far_min_distance = dmax(min_distance, dabs(inode.split-x[inode.split_dim])) - * elif p == 1: # <<<<<<<<<<<<<< - * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) - * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] - */ __pyx_3 = (__pyx_v_p == 1); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":485 - * far_min_distance = dmax(min_distance, dabs(inode.split-x[inode.split_dim])) - * elif p == 1: - * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) # <<<<<<<<<<<<<< - * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] - * else: - */ (__pyx_v_inf2->side_distances[__pyx_v_inode->split_dim]) = __pyx_f_5scipy_7spatial_7ckdtree_dabs((__pyx_v_inode->split - (__pyx_v_x[__pyx_v_inode->split_dim]))); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":486 - * elif p == 1: - * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) - * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] # <<<<<<<<<<<<<< - * else: - * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim])**p - */ __pyx_v_far_min_distance = ((__pyx_v_min_distance - (__pyx_v_inf->side_distances[__pyx_v_inode->split_dim])) + (__pyx_v_inf2->side_distances[__pyx_v_inode->split_dim])); goto __pyx_L28; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":488 - * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] - * else: - * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim])**p # <<<<<<<<<<<<<< - * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] - * - */ (__pyx_v_inf2->side_distances[__pyx_v_inode->split_dim]) = pow(__pyx_f_5scipy_7spatial_7ckdtree_dabs((__pyx_v_inode->split - (__pyx_v_x[__pyx_v_inode->split_dim]))), __pyx_v_p); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":489 - * else: - * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim])**p - * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] # <<<<<<<<<<<<<< - * - * it2.priority = far_min_distance - */ __pyx_v_far_min_distance = ((__pyx_v_min_distance - (__pyx_v_inf->side_distances[__pyx_v_inode->split_dim])) + (__pyx_v_inf2->side_distances[__pyx_v_inode->split_dim])); } __pyx_L28:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":491 - * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] - * - * it2.priority = far_min_distance # <<<<<<<<<<<<<< - * - * - */ __pyx_v_it2.priority = __pyx_v_far_min_distance; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":495 - * - * # far child might be too far, if so, don't bother pushing it - * if far_min_distance<=distance_upper_bound*epsfac: # <<<<<<<<<<<<<< - * heappush(&q,it2) - * else: - */ __pyx_3 = (__pyx_v_far_min_distance <= (__pyx_v_distance_upper_bound * __pyx_v_epsfac)); if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":496 - * # far child might be too far, if so, don't bother pushing it - * if far_min_distance<=distance_upper_bound*epsfac: - * heappush(&q,it2) # <<<<<<<<<<<<<< - * else: - * stdlib.free(inf2) - */ __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_q), __pyx_v_it2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; goto __pyx_L29; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":498 - * heappush(&q,it2) - * else: - * stdlib.free(inf2) # <<<<<<<<<<<<<< - * # just in case - * it2.contents.ptrdata = 0 - */ free(__pyx_v_inf2); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":500 - * stdlib.free(inf2) - * # just in case - * it2.contents.ptrdata = 0 # <<<<<<<<<<<<<< - * - * # fill output arrays with sorted neighbors - */ __pyx_v_it2.contents.ptrdata = ((char *)0); } __pyx_L29:; @@ -3513,88 +1477,24 @@ __pyx_L15:; } __pyx_L14:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":503 - * - * # fill output arrays with sorted neighbors - * for i in range(neighbors.n-1,-1,-1): # <<<<<<<<<<<<<< - * neighbor = heappop(&neighbors) # FIXME: neighbors may be realloced - * result_indices[i] = neighbor.contents.intdata - */ for (__pyx_v_i = (__pyx_v_neighbors.n - 1); __pyx_v_i > -1; __pyx_v_i-=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":504 - * # fill output arrays with sorted neighbors - * for i in range(neighbors.n-1,-1,-1): - * neighbor = heappop(&neighbors) # FIXME: neighbors may be realloced # <<<<<<<<<<<<<< - * result_indices[i] = neighbor.contents.intdata - * if p==1 or p==infinity: - */ __pyx_v_neighbor = __pyx_f_5scipy_7spatial_7ckdtree_heappop((&__pyx_v_neighbors)); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":505 - * for i in range(neighbors.n-1,-1,-1): - * neighbor = heappop(&neighbors) # FIXME: neighbors may be realloced - * result_indices[i] = neighbor.contents.intdata # <<<<<<<<<<<<<< - * if p==1 or p==infinity: - * result_distances[i] = -neighbor.priority - */ (__pyx_v_result_indices[__pyx_v_i]) = __pyx_v_neighbor.contents.intdata; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":506 - * neighbor = heappop(&neighbors) # FIXME: neighbors may be realloced - * result_indices[i] = neighbor.contents.intdata - * if p==1 or p==infinity: # <<<<<<<<<<<<<< - * result_distances[i] = -neighbor.priority - * else: - */ __pyx_3 = (__pyx_v_p == 1); if (!__pyx_3) { __pyx_3 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); } if (__pyx_3) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":507 - * result_indices[i] = neighbor.contents.intdata - * if p==1 or p==infinity: - * result_distances[i] = -neighbor.priority # <<<<<<<<<<<<<< - * else: - * result_distances[i] = (-neighbor.priority)**(1./p) - */ (__pyx_v_result_distances[__pyx_v_i]) = (-__pyx_v_neighbor.priority); goto __pyx_L32; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":509 - * result_distances[i] = -neighbor.priority - * else: - * result_distances[i] = (-neighbor.priority)**(1./p) # <<<<<<<<<<<<<< - * - * heapdestroy(&q) - */ (__pyx_v_result_distances[__pyx_v_i]) = pow((-__pyx_v_neighbor.priority), (1. / __pyx_v_p)); } __pyx_L32:; } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":511 - * result_distances[i] = (-neighbor.priority)**(1./p) - * - * heapdestroy(&q) # <<<<<<<<<<<<<< - * heapdestroy(&neighbors) - * - */ __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapdestroy((&__pyx_v_q)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 511; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":512 - * - * heapdestroy(&q) - * heapdestroy(&neighbors) # <<<<<<<<<<<<<< - * - * def query(cKDTree self, object x, int k=1, double eps=0, double p=2, - */ __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapdestroy((&__pyx_v_neighbors)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 512; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; @@ -3605,14 +1505,6 @@ __pyx_L0:; } -/* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":514 - * heapdestroy(&neighbors) - * - * def query(cKDTree self, object x, int k=1, double eps=0, double p=2, # <<<<<<<<<<<<<< - * double distance_upper_bound=infinity): - * """query the kd-tree for nearest neighbors - */ - static PyObject *__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree_query(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static char __pyx_doc_5scipy_7spatial_7ckdtree_7cKDTree_query[] = "query the kd-tree for nearest neighbors\n\n Parameters:\n ===========\n\n x : array-like, last dimension self.m\n An array of points to query.\n k : integer\n The number of nearest neighbors to return.\n eps : nonnegative float\n Return approximate nearest neighbors; the kth returned value \n is guaranteed to be no further than (1+eps) times the \n distance to the real kth nearest neighbor.\n p : float, 1<=p<=infinity\n Which Minkowski p-norm to use. \n 1 is the sum-of-absolute-values \"Manhattan\" distance\n 2 is the usual Euclidean distance\n infinity is the maximum-coordinate-difference distance\n distance_upper_bound : nonnegative float\n Return only neighbors within this distance. This is used to prune\n tree searches, so if you are doing a series of nearest-neighbor\n queries, it may help to supply the distance to the nearest neighbor\n of the most recent point.\n\n Returns:\n ========\n \n d : array of floats\n The distances to the nearest neighbors. \n If x has shape tuple+(self.m,), then d has shape tuple+(k,).\n Missing neighbors are indicated with infinite distances.\n i : array of integers\n The locations of the neighbors in self.data.\n If x has shape tuple+(self.m,), then i has shape tuple+(k,).\n Missing neighbors are indicated with self.n+1.\n "; static PyObject *__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree_query(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { @@ -3702,14 +1594,6 @@ __pyx_v_single = Py_None; Py_INCREF(Py_None); __pyx_v_retshape = Py_None; Py_INCREF(Py_None); __pyx_v_n = Py_None; Py_INCREF(Py_None); - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":556 - * cdef np.ndarray[double, ndim=2] xx - * cdef int c - * x = np.asarray(x).astype(np.float) # <<<<<<<<<<<<<< - * if np.shape(x)[-1] != self.m: - * raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) - */ __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_asarray); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; @@ -3733,14 +1617,6 @@ Py_DECREF(__pyx_v_x); __pyx_v_x = __pyx_3; __pyx_3 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":557 - * cdef int c - * x = np.asarray(x).astype(np.float) - * if np.shape(x)[-1] != self.m: # <<<<<<<<<<<<<< - * raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) - * if p<1: - */ __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_shape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; @@ -3759,14 +1635,6 @@ __pyx_4 = __Pyx_PyObject_IsTrue(__pyx_2); if (unlikely(__pyx_4 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; if (__pyx_4) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":558 - * x = np.asarray(x).astype(np.float) - * if np.shape(x)[-1] != self.m: - * raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) # <<<<<<<<<<<<<< - * if p<1: - * raise ValueError("Only p-norms with 1<=p<=infinity permitted") - */ __pyx_1 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_shape); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} @@ -3795,24 +1663,8 @@ goto __pyx_L5; } __pyx_L5:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":559 - * if np.shape(x)[-1] != self.m: - * raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) - * if p<1: # <<<<<<<<<<<<<< - * raise ValueError("Only p-norms with 1<=p<=infinity permitted") - * if len(x.shape)==1: - */ __pyx_4 = (__pyx_v_p < 1); if (__pyx_4) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":560 - * raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x))) - * if p<1: - * raise ValueError("Only p-norms with 1<=p<=infinity permitted") # <<<<<<<<<<<<<< - * if len(x.shape)==1: - * single = True - */ __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_kp_22); PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_22); @@ -3824,39 +1676,15 @@ goto __pyx_L6; } __pyx_L6:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":561 - * if p<1: - * raise ValueError("Only p-norms with 1<=p<=infinity permitted") - * if len(x.shape)==1: # <<<<<<<<<<<<<< - * single = True - * x = x[np.newaxis,:] - */ __pyx_1 = PyObject_GetAttr(__pyx_v_x, __pyx_kp_shape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 561; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_6 = PyObject_Length(__pyx_1); if (unlikely(__pyx_6 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 561; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; __pyx_4 = (__pyx_6 == 1); if (__pyx_4) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":562 - * raise ValueError("Only p-norms with 1<=p<=infinity permitted") - * if len(x.shape)==1: - * single = True # <<<<<<<<<<<<<< - * x = x[np.newaxis,:] - * else: - */ __pyx_5 = __Pyx_PyBool_FromLong(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_v_single); __pyx_v_single = __pyx_5; __pyx_5 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":563 - * if len(x.shape)==1: - * single = True - * x = x[np.newaxis,:] # <<<<<<<<<<<<<< - * else: - * single = False - */ __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_newaxis); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; @@ -3874,28 +1702,12 @@ goto __pyx_L7; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":565 - * x = x[np.newaxis,:] - * else: - * single = False # <<<<<<<<<<<<<< - * retshape = np.shape(x)[:-1] - * n = np.prod(retshape) - */ __pyx_3 = __Pyx_PyBool_FromLong(0); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 565; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_v_single); __pyx_v_single = __pyx_3; __pyx_3 = 0; } __pyx_L7:; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":566 - * else: - * single = False - * retshape = np.shape(x)[:-1] # <<<<<<<<<<<<<< - * n = np.prod(retshape) - * xx = np.reshape(x,(n,self.m)) - */ __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_5 = PyObject_GetAttr(__pyx_1, __pyx_kp_shape); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; @@ -3910,14 +1722,6 @@ Py_DECREF(__pyx_v_retshape); __pyx_v_retshape = __pyx_1; __pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":567 - * single = False - * retshape = np.shape(x)[:-1] - * n = np.prod(retshape) # <<<<<<<<<<<<<< - * xx = np.reshape(x,(n,self.m)) - * dd = np.empty((n,k),dtype=np.float) - */ __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 567; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_2 = PyObject_GetAttr(__pyx_5, __pyx_kp_prod); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 567; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_5); __pyx_5 = 0; @@ -3930,14 +1734,6 @@ Py_DECREF(__pyx_v_n); __pyx_v_n = __pyx_1; __pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":568 - * retshape = np.shape(x)[:-1] - * n = np.prod(retshape) - * xx = np.reshape(x,(n,self.m)) # <<<<<<<<<<<<<< - * dd = np.empty((n,k),dtype=np.float) - * dd.fill(infinity) - */ __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_2 = PyObject_GetAttr(__pyx_5, __pyx_kp_reshape); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_5); __pyx_5 = 0; @@ -3976,14 +1772,6 @@ Py_DECREF(((PyObject *)__pyx_v_xx)); __pyx_v_xx = ((PyArrayObject *)__pyx_3); __pyx_3 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":569 - * n = np.prod(retshape) - * xx = np.reshape(x,(n,self.m)) - * dd = np.empty((n,k),dtype=np.float) # <<<<<<<<<<<<<< - * dd.fill(infinity) - * ii = np.empty((n,k),dtype='i') - */ __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_empty); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; @@ -4027,14 +1815,6 @@ Py_DECREF(((PyObject *)__pyx_v_dd)); __pyx_v_dd = ((PyArrayObject *)__pyx_3); __pyx_3 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":570 - * xx = np.reshape(x,(n,self.m)) - * dd = np.empty((n,k),dtype=np.float) - * dd.fill(infinity) # <<<<<<<<<<<<<< - * ii = np.empty((n,k),dtype='i') - * ii.fill(self.n) - */ __pyx_7 = PyObject_GetAttr(((PyObject *)__pyx_v_dd), __pyx_kp_fill); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_2 = PyFloat_FromDouble(__pyx_v_5scipy_7spatial_7ckdtree_infinity); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} @@ -4044,14 +1824,6 @@ Py_DECREF(__pyx_7); __pyx_7 = 0; Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; Py_DECREF(__pyx_5); __pyx_5 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":571 - * dd = np.empty((n,k),dtype=np.float) - * dd.fill(infinity) - * ii = np.empty((n,k),dtype='i') # <<<<<<<<<<<<<< - * ii.fill(self.n) - * for c in range(n): - */ __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_empty); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_3); __pyx_3 = 0; @@ -4091,14 +1863,6 @@ Py_DECREF(((PyObject *)__pyx_v_ii)); __pyx_v_ii = ((PyArrayObject *)__pyx_7); __pyx_7 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":572 - * dd.fill(infinity) - * ii = np.empty((n,k),dtype='i') - * ii.fill(self.n) # <<<<<<<<<<<<<< - * for c in range(n): - * self.__query( - */ __pyx_1 = PyObject_GetAttr(((PyObject *)__pyx_v_ii), __pyx_kp_fill); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_2 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->n); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_5 = PyTuple_New(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} @@ -4108,54 +1872,14 @@ Py_DECREF(__pyx_1); __pyx_1 = 0; Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; Py_DECREF(__pyx_3); __pyx_3 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":573 - * ii = np.empty((n,k),dtype='i') - * ii.fill(self.n) - * for c in range(n): # <<<<<<<<<<<<<< - * self.__query( - * (dd.data)+c*k, - */ __pyx_8 = __pyx_PyInt_int(__pyx_v_n); if (unlikely((__pyx_8 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} for (__pyx_v_c = 0; __pyx_v_c < __pyx_8; __pyx_v_c+=1) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":581 - * eps, - * p, - * distance_upper_bound) # <<<<<<<<<<<<<< - * if single: - * if k==1: - */ ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->__pyx_vtab)->__query(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self), (((double *)__pyx_v_dd->data) + (__pyx_v_c * __pyx_v_k)), (((int *)__pyx_v_ii->data) + (__pyx_v_c * __pyx_v_k)), (((double *)__pyx_v_xx->data) + (__pyx_v_c * ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m)), __pyx_v_k, __pyx_v_eps, __pyx_v_p, __pyx_v_distance_upper_bound); } - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":582 - * p, - * distance_upper_bound) - * if single: # <<<<<<<<<<<<<< - * if k==1: - * return dd[0,0], ii[0,0] - */ __pyx_4 = __Pyx_PyObject_IsTrue(__pyx_v_single); if (unlikely(__pyx_4 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 582; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (__pyx_4) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":583 - * distance_upper_bound) - * if single: - * if k==1: # <<<<<<<<<<<<<< - * return dd[0,0], ii[0,0] - * else: - */ __pyx_4 = (__pyx_v_k == 1); if (__pyx_4) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":584 - * if single: - * if k==1: - * return dd[0,0], ii[0,0] # <<<<<<<<<<<<<< - * else: - * return dd[0], ii[0] - */ __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_2 = -1; @@ -4201,14 +1925,6 @@ goto __pyx_L11; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":586 - * return dd[0,0], ii[0,0] - * else: - * return dd[0], ii[0] # <<<<<<<<<<<<<< - * else: - * if k==1: - */ __pyx_5 = __Pyx_GetItemInt(((PyObject *)__pyx_v_dd), 0, 0); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_3 = __Pyx_GetItemInt(((PyObject *)__pyx_v_ii), 0, 0); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_7 = PyTuple_New(2); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} @@ -4224,24 +1940,8 @@ goto __pyx_L10; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":588 - * return dd[0], ii[0] - * else: - * if k==1: # <<<<<<<<<<<<<< - * return np.reshape(dd[...,0],retshape), np.reshape(ii[...,0],retshape) - * else: - */ __pyx_4 = (__pyx_v_k == 1); if (__pyx_4) { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":589 - * else: - * if k==1: - * return np.reshape(dd[...,0],retshape), np.reshape(ii[...,0],retshape) # <<<<<<<<<<<<<< - * else: - * return np.reshape(dd,retshape+(k,)), np.reshape(ii,retshape+(k,)) - */ __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_reshape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; @@ -4289,13 +1989,6 @@ goto __pyx_L12; } /*else*/ { - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":591 - * return np.reshape(dd[...,0],retshape), np.reshape(ii[...,0],retshape) - * else: - * return np.reshape(dd,retshape+(k,)), np.reshape(ii,retshape+(k,)) # <<<<<<<<<<<<<< - * - */ __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_5 = PyObject_GetAttr(__pyx_3, __pyx_kp_reshape); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_3); __pyx_3 = 0; @@ -4375,14 +2068,6 @@ return __pyx_r; } -/* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":36 - * # experimental exception made for __getbuffer__ and __releasebuffer__ - * # -- the details of this may change. - * def __getbuffer__(ndarray self, Py_buffer* info, int flags): # <<<<<<<<<<<<<< - * # This implementation of getbuffer is geared towards Cython - * # requirements, and does not yet fullfill the PEP (specifically, - */ - static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) { int __pyx_v_t; @@ -4391,24 +2076,8 @@ int __pyx_1; PyObject *__pyx_2 = 0; PyObject *__pyx_3 = 0; - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":42 - * # so the flags are not even checked). - * - * if sizeof(npy_intp) != sizeof(Py_ssize_t): # <<<<<<<<<<<<<< - * raise RuntimeError("Py_intptr_t and Py_ssize_t differs in size, numpy.pxd does not support this") - * - */ __pyx_1 = ((sizeof(npy_intp)) != (sizeof(Py_ssize_t))); if (__pyx_1) { - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":43 - * - * if sizeof(npy_intp) != sizeof(Py_ssize_t): - * raise RuntimeError("Py_intptr_t and Py_ssize_t differs in size, numpy.pxd does not support this") # <<<<<<<<<<<<<< - * - * info.buf = PyArray_DATA(self) - */ __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_kp_1); PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_1); @@ -4420,261 +2089,61 @@ goto __pyx_L5; } __pyx_L5:; - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":45 - * raise RuntimeError("Py_intptr_t and Py_ssize_t differs in size, numpy.pxd does not support this") - * - * info.buf = PyArray_DATA(self) # <<<<<<<<<<<<<< - * info.ndim = PyArray_NDIM(self) - * info.strides = PyArray_STRIDES(self) - */ __pyx_v_info->buf = PyArray_DATA(((PyArrayObject *)__pyx_v_self)); - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":46 - * - * info.buf = PyArray_DATA(self) - * info.ndim = PyArray_NDIM(self) # <<<<<<<<<<<<<< - * info.strides = PyArray_STRIDES(self) - * info.shape = PyArray_DIMS(self) - */ __pyx_v_info->ndim = PyArray_NDIM(((PyArrayObject *)__pyx_v_self)); - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":47 - * info.buf = PyArray_DATA(self) - * info.ndim = PyArray_NDIM(self) - * info.strides = PyArray_STRIDES(self) # <<<<<<<<<<<<<< - * info.shape = PyArray_DIMS(self) - * info.suboffsets = NULL - */ __pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(((PyArrayObject *)__pyx_v_self))); - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":48 - * info.ndim = PyArray_NDIM(self) - * info.strides = PyArray_STRIDES(self) - * info.shape = PyArray_DIMS(self) # <<<<<<<<<<<<<< - * info.suboffsets = NULL - * info.itemsize = PyArray_ITEMSIZE(self) - */ __pyx_v_info->shape = ((Py_ssize_t *)PyArray_DIMS(((PyArrayObject *)__pyx_v_self))); - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":49 - * info.strides = PyArray_STRIDES(self) - * info.shape = PyArray_DIMS(self) - * info.suboffsets = NULL # <<<<<<<<<<<<<< - * info.itemsize = PyArray_ITEMSIZE(self) - * info.readonly = not PyArray_ISWRITEABLE(self) - */ __pyx_v_info->suboffsets = NULL; - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":50 - * info.shape = PyArray_DIMS(self) - * info.suboffsets = NULL - * info.itemsize = PyArray_ITEMSIZE(self) # <<<<<<<<<<<<<< - * info.readonly = not PyArray_ISWRITEABLE(self) - * - */ __pyx_v_info->itemsize = PyArray_ITEMSIZE(((PyArrayObject *)__pyx_v_self)); - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":51 - * info.suboffsets = NULL - * info.itemsize = PyArray_ITEMSIZE(self) - * info.readonly = not PyArray_ISWRITEABLE(self) # <<<<<<<<<<<<<< - * - * # Formats that are not tested and working in Cython are not - */ __pyx_v_info->readonly = (!PyArray_ISWRITEABLE(((PyArrayObject *)__pyx_v_self))); - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":55 - * # Formats that are not tested and working in Cython are not - * # made available from this pxd file yet. - * cdef int t = PyArray_TYPE(self) # <<<<<<<<<<<<<< - * cdef char* f = NULL - * if t == NPY_BYTE: f = "b" - */ __pyx_v_t = PyArray_TYPE(((PyArrayObject *)__pyx_v_self)); - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":56 - * # made available from this pxd file yet. - * cdef int t = PyArray_TYPE(self) - * cdef char* f = NULL # <<<<<<<<<<<<<< - * if t == NPY_BYTE: f = "b" - * elif t == NPY_UBYTE: f = "B" - */ __pyx_v_f = NULL; - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":57 - * cdef int t = PyArray_TYPE(self) - * cdef char* f = NULL - * if t == NPY_BYTE: f = "b" # <<<<<<<<<<<<<< - * elif t == NPY_UBYTE: f = "B" - * elif t == NPY_SHORT: f = "h" - */ switch (__pyx_v_t) { case NPY_BYTE: __pyx_v_f = __pyx_k_2; break; case NPY_UBYTE: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":58 - * cdef char* f = NULL - * if t == NPY_BYTE: f = "b" - * elif t == NPY_UBYTE: f = "B" # <<<<<<<<<<<<<< - * elif t == NPY_SHORT: f = "h" - * elif t == NPY_USHORT: f = "H" - */ __pyx_v_f = __pyx_k_3; break; case NPY_SHORT: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":59 - * if t == NPY_BYTE: f = "b" - * elif t == NPY_UBYTE: f = "B" - * elif t == NPY_SHORT: f = "h" # <<<<<<<<<<<<<< - * elif t == NPY_USHORT: f = "H" - * elif t == NPY_INT: f = "i" - */ __pyx_v_f = __pyx_k_4; break; case NPY_USHORT: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":60 - * elif t == NPY_UBYTE: f = "B" - * elif t == NPY_SHORT: f = "h" - * elif t == NPY_USHORT: f = "H" # <<<<<<<<<<<<<< - * elif t == NPY_INT: f = "i" - * elif t == NPY_UINT: f = "I" - */ __pyx_v_f = __pyx_k_5; break; case NPY_INT: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":61 - * elif t == NPY_SHORT: f = "h" - * elif t == NPY_USHORT: f = "H" - * elif t == NPY_INT: f = "i" # <<<<<<<<<<<<<< - * elif t == NPY_UINT: f = "I" - * elif t == NPY_LONG: f = "l" - */ __pyx_v_f = __pyx_k_6; break; case NPY_UINT: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":62 - * elif t == NPY_USHORT: f = "H" - * elif t == NPY_INT: f = "i" - * elif t == NPY_UINT: f = "I" # <<<<<<<<<<<<<< - * elif t == NPY_LONG: f = "l" - * elif t == NPY_ULONG: f = "L" - */ __pyx_v_f = __pyx_k_7; break; case NPY_LONG: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":63 - * elif t == NPY_INT: f = "i" - * elif t == NPY_UINT: f = "I" - * elif t == NPY_LONG: f = "l" # <<<<<<<<<<<<<< - * elif t == NPY_ULONG: f = "L" - * elif t == NPY_LONGLONG: f = "q" - */ __pyx_v_f = __pyx_k_8; break; case NPY_ULONG: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":64 - * elif t == NPY_UINT: f = "I" - * elif t == NPY_LONG: f = "l" - * elif t == NPY_ULONG: f = "L" # <<<<<<<<<<<<<< - * elif t == NPY_LONGLONG: f = "q" - * elif t == NPY_ULONGLONG: f = "Q" - */ __pyx_v_f = __pyx_k_9; break; case NPY_LONGLONG: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":65 - * elif t == NPY_LONG: f = "l" - * elif t == NPY_ULONG: f = "L" - * elif t == NPY_LONGLONG: f = "q" # <<<<<<<<<<<<<< - * elif t == NPY_ULONGLONG: f = "Q" - * elif t == NPY_FLOAT: f = "f" - */ __pyx_v_f = __pyx_k_10; break; case NPY_ULONGLONG: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":66 - * elif t == NPY_ULONG: f = "L" - * elif t == NPY_LONGLONG: f = "q" - * elif t == NPY_ULONGLONG: f = "Q" # <<<<<<<<<<<<<< - * elif t == NPY_FLOAT: f = "f" - * elif t == NPY_DOUBLE: f = "d" - */ __pyx_v_f = __pyx_k_11; break; case NPY_FLOAT: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":67 - * elif t == NPY_LONGLONG: f = "q" - * elif t == NPY_ULONGLONG: f = "Q" - * elif t == NPY_FLOAT: f = "f" # <<<<<<<<<<<<<< - * elif t == NPY_DOUBLE: f = "d" - * elif t == NPY_LONGDOUBLE: f = "g" - */ __pyx_v_f = __pyx_k_12; break; case NPY_DOUBLE: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":68 - * elif t == NPY_ULONGLONG: f = "Q" - * elif t == NPY_FLOAT: f = "f" - * elif t == NPY_DOUBLE: f = "d" # <<<<<<<<<<<<<< - * elif t == NPY_LONGDOUBLE: f = "g" - * elif t == NPY_OBJECT: f = "O" - */ __pyx_v_f = __pyx_k_13; break; case NPY_LONGDOUBLE: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":69 - * elif t == NPY_FLOAT: f = "f" - * elif t == NPY_DOUBLE: f = "d" - * elif t == NPY_LONGDOUBLE: f = "g" # <<<<<<<<<<<<<< - * elif t == NPY_OBJECT: f = "O" - * - */ __pyx_v_f = __pyx_k_14; break; case NPY_OBJECT: - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":70 - * elif t == NPY_DOUBLE: f = "d" - * elif t == NPY_LONGDOUBLE: f = "g" - * elif t == NPY_OBJECT: f = "O" # <<<<<<<<<<<<<< - * - * if f == NULL: - */ __pyx_v_f = __pyx_k_15; break; } - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":72 - * elif t == NPY_OBJECT: f = "O" - * - * if f == NULL: # <<<<<<<<<<<<<< - * raise ValueError("only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)" % t) - * info.format = f - */ __pyx_1 = (__pyx_v_f == NULL); if (__pyx_1) { - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":73 - * - * if f == NULL: - * raise ValueError("only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)" % t) # <<<<<<<<<<<<<< - * info.format = f - * - */ __pyx_2 = PyInt_FromLong(__pyx_v_t); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_3 = PyNumber_Remainder(__pyx_kp_16, __pyx_2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 73; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; @@ -4689,14 +2158,6 @@ goto __pyx_L6; } __pyx_L6:; - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/numpy.pxd":74 - * if f == NULL: - * raise ValueError("only objects, int and float dtypes supported for ndarray buffer access so far (dtype is %d)" % t) - * info.format = f # <<<<<<<<<<<<<< - * - * - */ __pyx_v_info->format = __pyx_v_f; __pyx_r = 0; @@ -4726,6 +2187,15 @@ static void __pyx_tp_dealloc_5scipy_7spatial_7ckdtree_cKDTree(PyObject *o) { struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *p = (struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)o; + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + ++Py_REFCNT(o); + __pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___dealloc__(o); + if (PyErr_Occurred()) PyErr_WriteUnraisable(o); + --Py_REFCNT(o); + PyErr_Restore(etype, eval, etb); + } Py_XDECREF(p->data); Py_XDECREF(p->maxes); Py_XDECREF(p->mins); @@ -4770,7 +2240,6 @@ } static struct PyMethodDef __pyx_methods_5scipy_7spatial_7ckdtree_cKDTree[] = { - {"__del__", (PyCFunction)__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___del__, METH_NOARGS, 0}, {"query", (PyCFunction)__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree_query, METH_VARARGS|METH_KEYWORDS, __pyx_doc_5scipy_7spatial_7ckdtree_7cKDTree_query}, {0, 0, 0, 0} }; @@ -4949,7 +2418,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_kp___init__, __pyx_k___init__, sizeof(__pyx_k___init__), 0, 1, 1}, - {&__pyx_kp___del__, __pyx_k___del__, sizeof(__pyx_k___del__), 0, 1, 1}, + {&__pyx_kp___dealloc__, __pyx_k___dealloc__, sizeof(__pyx_k___dealloc__), 0, 1, 1}, {&__pyx_kp_query, __pyx_k_query, sizeof(__pyx_k_query), 0, 1, 1}, {&__pyx_kp_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 1, 1, 1}, {&__pyx_kp_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 1, 1}, @@ -5047,58 +2516,19 @@ __pyx_ptype_5numpy_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject)); if (unlikely(!__pyx_ptype_5numpy_ndarray)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 24; __pyx_clineno = __LINE__; goto __pyx_L1_error;} /*--- Function import code ---*/ /*--- Execution code ---*/ - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":3 - * # Copyright Anne M. Archibald 2008 - * # Released under the scipy license - * import numpy as np # <<<<<<<<<<<<<< - * cimport numpy as np - * cimport stdlib - */ __pyx_1 = __Pyx_Import(__pyx_kp_numpy, 0); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttr(__pyx_m, __pyx_kp_np, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":7 - * cimport stdlib - * - * import kdtree # <<<<<<<<<<<<<< - * - * cdef double infinity = np.inf - */ __pyx_1 = __Pyx_Import(__pyx_kp_kdtree, 0); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (PyObject_SetAttr(__pyx_m, __pyx_kp_kdtree, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":9 - * import kdtree - * - * cdef double infinity = np.inf # <<<<<<<<<<<<<< - * - * - */ __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_inf); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; __pyx_3 = __pyx_PyFloat_AsDouble(__pyx_2); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; __pyx_v_5scipy_7spatial_7ckdtree_infinity = __pyx_3; - - /* "/home/peridot/software/scipy/svn/spatial/scipy/spatial/ckdtree.pyx":515 - * - * def query(cKDTree self, object x, int k=1, double eps=0, double p=2, - * double distance_upper_bound=infinity): # <<<<<<<<<<<<<< - * """query the kd-tree for nearest neighbors - * - */ __pyx_k_17 = __pyx_v_5scipy_7spatial_7ckdtree_infinity; - - /* "/home/peridot/devlib/lib/python2.5/site-packages/Cython-0.9.8.1.1-py2.5-linux-i686.egg/Cython/Includes/stdlib.pxd":2 - * - * cdef extern from "stdlib.h": # <<<<<<<<<<<<<< - * ctypedef unsigned long size_t - * void free(void *ptr) - */ #if PY_MAJOR_VERSION < 3 return; #else Modified: branches/spatial/scipy/spatial/ckdtree.pyx =================================================================== --- branches/spatial/scipy/spatial/ckdtree.pyx 2008-10-12 13:23:48 UTC (rev 4795) +++ branches/spatial/scipy/spatial/ckdtree.pyx 2008-10-13 15:46:17 UTC (rev 4796) @@ -331,8 +331,7 @@ self.__free_tree(node.greater) stdlib.free(node) - def __del__(cKDTree self): - print "freeing cKDTree" + def __dealloc__(cKDTree self): self.__free_tree(self.tree) cdef void __query(cKDTree self, From scipy-svn at scipy.org Mon Oct 13 11:58:55 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 13 Oct 2008 10:58:55 -0500 (CDT) Subject: [Scipy-svn] r4797 - branches/spatial/scipy/spatial Message-ID: <20081013155855.EDCE639C088@scipy.org> Author: peridot Date: 2008-10-13 10:58:54 -0500 (Mon, 13 Oct 2008) New Revision: 4797 Modified: branches/spatial/scipy/spatial/ckdtree.c branches/spatial/scipy/spatial/ckdtree.pyx Log: Docstring fix and code to avoid freeing 0 if __init__ is never called. Modified: branches/spatial/scipy/spatial/ckdtree.c =================================================================== --- branches/spatial/scipy/spatial/ckdtree.c 2008-10-13 15:46:17 UTC (rev 4796) +++ branches/spatial/scipy/spatial/ckdtree.c 2008-10-13 15:58:54 UTC (rev 4797) @@ -1,4 +1,4 @@ -/* Generated by Cython 0.9.8.1.1 on Mon Oct 13 11:41:48 2008 */ +/* Generated by Cython 0.9.8.1.1 on Mon Oct 13 11:57:06 2008 */ #define PY_SSIZE_T_CLEAN #include "Python.h" @@ -217,12 +217,6 @@ static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/ -static int __Pyx_Print(PyObject *, int); /*proto*/ -#if PY_MAJOR_VERSION >= 3 -static PyObject* __pyx_print = 0; -static PyObject* __pyx_print_kwargs = 0; -#endif - static INLINE PyObject *__Pyx_GetItemInt(PyObject *o, Py_ssize_t i, int is_unsigned) { PyObject *r; if (PyList_CheckExact(o) && 0 <= i && i < PyList_GET_SIZE(o)) { @@ -427,19 +421,17 @@ static PyObject *__pyx_kp_empty; static char __pyx_k_fill[] = "fill"; static PyObject *__pyx_kp_fill; -static char __pyx_k_23[] = "i"; -static PyObject *__pyx_kp_23; +static char __pyx_k_22[] = "i"; +static PyObject *__pyx_kp_22; static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_kp_18; static char __pyx_k_18[] = "Heap containing %d items cannot be resized to %d"; static PyObject *__pyx_kp_19; static char __pyx_k_19[] = "leafsize must be at least 1"; static PyObject *__pyx_kp_20; -static char __pyx_k_20[] = "freeing cKDTree"; static PyObject *__pyx_kp_21; -static PyObject *__pyx_kp_22; -static char __pyx_k_21[] = "x must consist of vectors of length %d but has shape %s"; -static char __pyx_k_22[] = "Only p-norms with 1<=p<=infinity permitted"; +static char __pyx_k_20[] = "x must consist of vectors of length %d but has shape %s"; +static char __pyx_k_21[] = "Only p-norms with 1<=p<=infinity permitted"; static char __pyx_k___getbuffer__[] = "__getbuffer__"; static PyObject *__pyx_kp___getbuffer__; static char __pyx_k_RuntimeError[] = "RuntimeError"; @@ -1259,18 +1251,20 @@ static void __pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___dealloc__(PyObject *__pyx_v_self); /*proto*/ static void __pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree___dealloc__(PyObject *__pyx_v_self) { - PyObject *__pyx_1 = 0; - __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 335; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - Py_INCREF(__pyx_kp_20); - PyTuple_SET_ITEM(__pyx_1, 0, __pyx_kp_20); - if (__Pyx_Print(((PyObject *)__pyx_1), 1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 335; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; - __pyx_1 = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->__pyx_vtab)->__free_tree(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self), ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->tree); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 336; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - Py_DECREF(__pyx_1); __pyx_1 = 0; + int __pyx_1; + PyObject *__pyx_2 = 0; + __pyx_1 = (((int)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->tree) == 0); + if (__pyx_1) { + goto __pyx_L0; + goto __pyx_L5; + } + __pyx_L5:; + __pyx_2 = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->__pyx_vtab)->__free_tree(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self), ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->tree); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 338; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_DECREF(__pyx_2); __pyx_2 = 0; goto __pyx_L0; __pyx_L1_error:; - Py_XDECREF(__pyx_1); + Py_XDECREF(__pyx_2); __Pyx_AddTraceback("scipy.spatial.ckdtree.cKDTree.__dealloc__"); __pyx_L0:; } @@ -1296,9 +1290,9 @@ PyObject *__pyx_1 = 0; int __pyx_2; int __pyx_3; - __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_q), 12); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 369; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_q), 12); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 371; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_neighbors), __pyx_v_k); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 374; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_neighbors), __pyx_v_k); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 376; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; __pyx_v_inf = ((struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *)malloc(((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo)) + (__pyx_v_self->m * (sizeof(double)))))); __pyx_v_inf->node = __pyx_v_self->tree; @@ -1380,14 +1374,14 @@ if (__pyx_3) { __pyx_3 = (__pyx_v_neighbors.n == __pyx_v_k); if (__pyx_3) { - __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapremove((&__pyx_v_neighbors)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 424; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapremove((&__pyx_v_neighbors)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 426; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; goto __pyx_L19; } __pyx_L19:; __pyx_v_neighbor.priority = (-__pyx_v_d); __pyx_v_neighbor.contents.intdata = (__pyx_v_self->raw_indices[__pyx_v_i]); - __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_neighbors), __pyx_v_neighbor); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 427; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_neighbors), __pyx_v_neighbor); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 429; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; __pyx_3 = (__pyx_v_neighbors.n == __pyx_v_k); if (__pyx_3) { @@ -1464,7 +1458,7 @@ __pyx_v_it2.priority = __pyx_v_far_min_distance; __pyx_3 = (__pyx_v_far_min_distance <= (__pyx_v_distance_upper_bound * __pyx_v_epsfac)); if (__pyx_3) { - __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_q), __pyx_v_it2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_q), __pyx_v_it2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 498; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; goto __pyx_L29; } @@ -1493,9 +1487,9 @@ } __pyx_L32:; } - __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapdestroy((&__pyx_v_q)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 511; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapdestroy((&__pyx_v_q)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 513; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapdestroy((&__pyx_v_neighbors)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 512; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapdestroy((&__pyx_v_neighbors)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; goto __pyx_L0; @@ -1506,7 +1500,7 @@ } static PyObject *__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree_query(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_5scipy_7spatial_7ckdtree_7cKDTree_query[] = "query the kd-tree for nearest neighbors\n\n Parameters:\n ===========\n\n x : array-like, last dimension self.m\n An array of points to query.\n k : integer\n The number of nearest neighbors to return.\n eps : nonnegative float\n Return approximate nearest neighbors; the kth returned value \n is guaranteed to be no further than (1+eps) times the \n distance to the real kth nearest neighbor.\n p : float, 1<=p<=infinity\n Which Minkowski p-norm to use. \n 1 is the sum-of-absolute-values \"Manhattan\" distance\n 2 is the usual Euclidean distance\n infinity is the maximum-coordinate-difference distance\n distance_upper_bound : nonnegative float\n Return only neighbors within this distance. This is used to prune\n tree searches, so if you are doing a series of nearest-neighbor\n queries, it may help to supply the distance to the nearest neighbor\n of the most recent point.\n\n Returns:\n ========\n \n d : array of floats\n The distances to the nearest neighbors. \n If x has shape tuple+(self.m,), then d has shape tuple+(k,).\n Missing neighbors are indicated with infinite distances.\n i : array of integers\n The locations of the neighbors in self.data.\n If x has shape tuple+(self.m,), then i has shape tuple+(k,).\n Missing neighbors are indicated with self.n+1.\n "; +static char __pyx_doc_5scipy_7spatial_7ckdtree_7cKDTree_query[] = "query the kd-tree for nearest neighbors\n\n Parameters:\n ===========\n\n x : array-like, last dimension self.m\n An array of points to query.\n k : integer\n The number of nearest neighbors to return.\n eps : nonnegative float\n Return approximate nearest neighbors; the kth returned value \n is guaranteed to be no further than (1+eps) times the \n distance to the real kth nearest neighbor.\n p : float, 1<=p<=infinity\n Which Minkowski p-norm to use. \n 1 is the sum-of-absolute-values \"Manhattan\" distance\n 2 is the usual Euclidean distance\n infinity is the maximum-coordinate-difference distance\n distance_upper_bound : nonnegative float\n Return only neighbors within this distance. This is used to prune\n tree searches, so if you are doing a series of nearest-neighbor\n queries, it may help to supply the distance to the nearest neighbor\n of the most recent point.\n\n Returns:\n ========\n \n d : array of floats\n The distances to the nearest neighbors. \n If x has shape tuple+(self.m,), then d has shape tuple+(k,).\n Missing neighbors are indicated with infinite distances.\n i : array of integers\n The locations of the neighbors in self.data.\n If x has shape tuple+(self.m,), then i has shape tuple+(k,).\n Missing neighbors are indicated with self.n.\n "; static PyObject *__pyx_pf_5scipy_7spatial_7ckdtree_7cKDTree_query(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_x = 0; int __pyx_v_k; @@ -1564,20 +1558,20 @@ if (likely(!__pyx_kwds) && likely(1 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 5)) { __pyx_v_x = PyTuple_GET_ITEM(__pyx_args, 0); if (PyTuple_GET_SIZE(__pyx_args) > 1) { - __pyx_v_k = __pyx_PyInt_int(PyTuple_GET_ITEM(__pyx_args, 1)); if (unlikely((__pyx_v_k == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __pyx_v_k = __pyx_PyInt_int(PyTuple_GET_ITEM(__pyx_args, 1)); if (unlikely((__pyx_v_k == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 516; __pyx_clineno = __LINE__; goto __pyx_L3_error;} if (PyTuple_GET_SIZE(__pyx_args) > 2) { - __pyx_v_eps = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 2)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __pyx_v_eps = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 2)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 516; __pyx_clineno = __LINE__; goto __pyx_L3_error;} if (PyTuple_GET_SIZE(__pyx_args) > 3) { - __pyx_v_p = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 3)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __pyx_v_p = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 3)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 516; __pyx_clineno = __LINE__; goto __pyx_L3_error;} if (PyTuple_GET_SIZE(__pyx_args) > 4) { - __pyx_v_distance_upper_bound = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 4)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 515; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __pyx_v_distance_upper_bound = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 4)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 517; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } } } } } else { - if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "O|iddd", __pyx_argnames, &__pyx_v_x, &__pyx_v_k, &__pyx_v_eps, &__pyx_v_p, &__pyx_v_distance_upper_bound))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "O|iddd", __pyx_argnames, &__pyx_v_x, &__pyx_v_k, &__pyx_v_eps, &__pyx_v_p, &__pyx_v_distance_upper_bound))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 516; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } goto __pyx_L4; __pyx_L3_error:; @@ -1594,107 +1588,107 @@ __pyx_v_single = Py_None; Py_INCREF(Py_None); __pyx_v_retshape = Py_None; Py_INCREF(Py_None); __pyx_v_n = Py_None; Py_INCREF(Py_None); - __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_asarray); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_asarray); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_v_x); PyTuple_SET_ITEM(__pyx_1, 0, __pyx_v_x); - __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; - __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_astype); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_astype); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_3); __pyx_3 = 0; - __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_3 = PyObject_GetAttr(__pyx_1, __pyx_kp_float); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_1, __pyx_kp_float); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_1, 0, __pyx_3); __pyx_3 = 0; - __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; Py_DECREF(__pyx_v_x); __pyx_v_x = __pyx_3; __pyx_3 = 0; - __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_shape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_shape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; - __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_v_x); PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_x); - __pyx_2 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; - __pyx_1 = __Pyx_GetItemInt(__pyx_2, -1, 0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __Pyx_GetItemInt(__pyx_2, -1, 0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; - __pyx_3 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_2 = PyObject_RichCompare(__pyx_1, __pyx_3, Py_NE); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_RichCompare(__pyx_1, __pyx_3, Py_NE); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; Py_DECREF(__pyx_3); __pyx_3 = 0; - __pyx_4 = __Pyx_PyObject_IsTrue(__pyx_2); if (unlikely(__pyx_4 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 557; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_4 = __Pyx_PyObject_IsTrue(__pyx_2); if (unlikely(__pyx_4 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; if (__pyx_4) { - __pyx_1 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_shape); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_shape); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_3); __pyx_3 = 0; - __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_v_x); PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_x); - __pyx_5 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; - __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_2, 0, __pyx_1); PyTuple_SET_ITEM(__pyx_2, 1, __pyx_5); __pyx_1 = 0; __pyx_5 = 0; - __pyx_3 = PyNumber_Remainder(__pyx_kp_21, ((PyObject *)__pyx_2)); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyNumber_Remainder(__pyx_kp_20, ((PyObject *)__pyx_2)); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; - __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_1, 0, __pyx_3); __pyx_3 = 0; - __pyx_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; __Pyx_Raise(__pyx_5, 0, 0); Py_DECREF(__pyx_5); __pyx_5 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L5; } __pyx_L5:; __pyx_4 = (__pyx_v_p < 1); if (__pyx_4) { - __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - Py_INCREF(__pyx_kp_22); - PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_22); - __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + Py_INCREF(__pyx_kp_21); + PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_21); + __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; __Pyx_Raise(__pyx_3, 0, 0); Py_DECREF(__pyx_3); __pyx_3 = 0; - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L6; } __pyx_L6:; - __pyx_1 = PyObject_GetAttr(__pyx_v_x, __pyx_kp_shape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 561; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_6 = PyObject_Length(__pyx_1); if (unlikely(__pyx_6 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 561; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_v_x, __pyx_kp_shape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_6 = PyObject_Length(__pyx_1); if (unlikely(__pyx_6 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; __pyx_4 = (__pyx_6 == 1); if (__pyx_4) { - __pyx_5 = __Pyx_PyBool_FromLong(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = __Pyx_PyBool_FromLong(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 564; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_v_single); __pyx_v_single = __pyx_5; __pyx_5 = 0; - __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_newaxis); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 565; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_newaxis); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 565; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; - __pyx_1 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 565; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 565; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_5, 0, __pyx_3); PyTuple_SET_ITEM(__pyx_5, 1, __pyx_1); __pyx_3 = 0; __pyx_1 = 0; - __pyx_2 = PyObject_GetItem(__pyx_v_x, ((PyObject *)__pyx_5)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetItem(__pyx_v_x, ((PyObject *)__pyx_5)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 565; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; Py_DECREF(__pyx_v_x); __pyx_v_x = __pyx_2; @@ -1702,56 +1696,56 @@ goto __pyx_L7; } /*else*/ { - __pyx_3 = __Pyx_PyBool_FromLong(0); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 565; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = __Pyx_PyBool_FromLong(0); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 567; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_v_single); __pyx_v_single = __pyx_3; __pyx_3 = 0; } __pyx_L7:; - __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_5 = PyObject_GetAttr(__pyx_1, __pyx_kp_shape); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyObject_GetAttr(__pyx_1, __pyx_kp_shape); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_v_x); PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_x); - __pyx_3 = PyObject_Call(__pyx_5, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_Call(__pyx_5, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_5); __pyx_5 = 0; Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0; - __pyx_1 = PySequence_GetSlice(__pyx_3, 0, -1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 566; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PySequence_GetSlice(__pyx_3, 0, -1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_3); __pyx_3 = 0; Py_DECREF(__pyx_v_retshape); __pyx_v_retshape = __pyx_1; __pyx_1 = 0; - __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 567; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_2 = PyObject_GetAttr(__pyx_5, __pyx_kp_prod); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 567; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_5, __pyx_kp_prod); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_5); __pyx_5 = 0; - __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 567; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_v_retshape); PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_retshape); - __pyx_1 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 567; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; Py_DECREF(__pyx_v_n); __pyx_v_n = __pyx_1; __pyx_1 = 0; - __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_2 = PyObject_GetAttr(__pyx_5, __pyx_kp_reshape); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_5, __pyx_kp_reshape); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_5); __pyx_5 = 0; - __pyx_3 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_v_n); PyTuple_SET_ITEM(__pyx_1, 0, __pyx_v_n); PyTuple_SET_ITEM(__pyx_1, 1, __pyx_3); __pyx_3 = 0; - __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_v_x); PyTuple_SET_ITEM(__pyx_5, 0, __pyx_v_x); PyTuple_SET_ITEM(__pyx_5, 1, ((PyObject *)__pyx_1)); __pyx_1 = 0; - __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; - if (!(__Pyx_TypeTest(__pyx_3, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (!(__Pyx_TypeTest(__pyx_3, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_1 = ((PyArrayObject *)__pyx_3); __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_xx, &__pyx_bstruct_xx); __pyx_t_2 = __Pyx_GetBuffer_double((PyObject*)__pyx_t_1, &__pyx_bstruct_xx, PyBUF_FORMAT| PyBUF_STRIDES, 2); @@ -1767,34 +1761,34 @@ } __pyx_bstride_0_xx = __pyx_bstruct_xx.strides[0]; __pyx_bstride_1_xx = __pyx_bstruct_xx.strides[1]; __pyx_bshape_0_xx = __pyx_bstruct_xx.shape[0]; __pyx_bshape_1_xx = __pyx_bstruct_xx.shape[1]; - if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_1 = 0; Py_DECREF(((PyObject *)__pyx_v_xx)); __pyx_v_xx = ((PyArrayObject *)__pyx_3); __pyx_3 = 0; - __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_empty); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_kp_empty); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - __pyx_5 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_3 = PyTuple_New(2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyTuple_New(2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_v_n); PyTuple_SET_ITEM(__pyx_3, 0, __pyx_v_n); PyTuple_SET_ITEM(__pyx_3, 1, __pyx_5); __pyx_5 = 0; - __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_1, 0, ((PyObject *)__pyx_3)); __pyx_3 = 0; - __pyx_5 = PyDict_New(); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_7 = PyObject_GetAttr(__pyx_3, __pyx_kp_float); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyDict_New(); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyObject_GetAttr(__pyx_3, __pyx_kp_float); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_3); __pyx_3 = 0; - if (PyDict_SetItem(__pyx_5, __pyx_kp_dtype, __pyx_7) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyDict_SetItem(__pyx_5, __pyx_kp_dtype, __pyx_7) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_7); __pyx_7 = 0; - __pyx_3 = PyEval_CallObjectWithKeywords(__pyx_2, ((PyObject *)__pyx_1), ((PyObject *)__pyx_5)); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyEval_CallObjectWithKeywords(__pyx_2, ((PyObject *)__pyx_1), ((PyObject *)__pyx_5)); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; - if (!(__Pyx_TypeTest(__pyx_3, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (!(__Pyx_TypeTest(__pyx_3, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_6 = ((PyArrayObject *)__pyx_3); __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_dd, &__pyx_bstruct_dd); __pyx_t_2 = __Pyx_GetBuffer_double((PyObject*)__pyx_t_6, &__pyx_bstruct_dd, PyBUF_FORMAT| PyBUF_STRIDES, 2); @@ -1810,39 +1804,39 @@ } __pyx_bstride_0_dd = __pyx_bstruct_dd.strides[0]; __pyx_bstride_1_dd = __pyx_bstruct_dd.strides[1]; __pyx_bshape_0_dd = __pyx_bstruct_dd.shape[0]; __pyx_bshape_1_dd = __pyx_bstruct_dd.shape[1]; - if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_6 = 0; Py_DECREF(((PyObject *)__pyx_v_dd)); __pyx_v_dd = ((PyArrayObject *)__pyx_3); __pyx_3 = 0; - __pyx_7 = PyObject_GetAttr(((PyObject *)__pyx_v_dd), __pyx_kp_fill); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_2 = PyFloat_FromDouble(__pyx_v_5scipy_7spatial_7ckdtree_infinity); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyObject_GetAttr(((PyObject *)__pyx_v_dd), __pyx_kp_fill); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyFloat_FromDouble(__pyx_v_5scipy_7spatial_7ckdtree_infinity); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_1, 0, __pyx_2); __pyx_2 = 0; - __pyx_5 = PyObject_Call(__pyx_7, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 570; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyObject_Call(__pyx_7, ((PyObject *)__pyx_1), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_7); __pyx_7 = 0; Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; Py_DECREF(__pyx_5); __pyx_5 = 0; - __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_empty); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_empty); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_3); __pyx_3 = 0; - __pyx_7 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(__pyx_v_n); PyTuple_SET_ITEM(__pyx_1, 0, __pyx_v_n); PyTuple_SET_ITEM(__pyx_1, 1, __pyx_7); __pyx_7 = 0; - __pyx_5 = PyTuple_New(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyTuple_New(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_5, 0, ((PyObject *)__pyx_1)); __pyx_1 = 0; - __pyx_3 = PyDict_New(); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - if (PyDict_SetItem(__pyx_3, __pyx_kp_dtype, __pyx_kp_23) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_7 = PyEval_CallObjectWithKeywords(__pyx_2, ((PyObject *)__pyx_5), ((PyObject *)__pyx_3)); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyDict_New(); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyDict_SetItem(__pyx_3, __pyx_kp_dtype, __pyx_kp_22) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyEval_CallObjectWithKeywords(__pyx_2, ((PyObject *)__pyx_5), ((PyObject *)__pyx_3)); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; - if (!(__Pyx_TypeTest(__pyx_7, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (!(__Pyx_TypeTest(__pyx_7, __pyx_ptype_5numpy_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_7 = ((PyArrayObject *)__pyx_7); __Pyx_SafeReleaseBuffer((PyObject*)__pyx_v_ii, &__pyx_bstruct_ii); __pyx_t_2 = __Pyx_GetBuffer_int((PyObject*)__pyx_t_7, &__pyx_bstruct_ii, PyBUF_FORMAT| PyBUF_STRIDES, 2); @@ -1858,25 +1852,25 @@ } __pyx_bstride_0_ii = __pyx_bstruct_ii.strides[0]; __pyx_bstride_1_ii = __pyx_bstruct_ii.strides[1]; __pyx_bshape_0_ii = __pyx_bstruct_ii.shape[0]; __pyx_bshape_1_ii = __pyx_bstruct_ii.shape[1]; - if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_7 = 0; Py_DECREF(((PyObject *)__pyx_v_ii)); __pyx_v_ii = ((PyArrayObject *)__pyx_7); __pyx_7 = 0; - __pyx_1 = PyObject_GetAttr(((PyObject *)__pyx_v_ii), __pyx_kp_fill); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_2 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->n); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_5 = PyTuple_New(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(((PyObject *)__pyx_v_ii), __pyx_kp_fill); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyInt_FromLong(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->n); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyTuple_New(1); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_5, 0, __pyx_2); __pyx_2 = 0; - __pyx_3 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; Py_DECREF(__pyx_3); __pyx_3 = 0; - __pyx_8 = __pyx_PyInt_int(__pyx_v_n); if (unlikely((__pyx_8 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_8 = __pyx_PyInt_int(__pyx_v_n); if (unlikely((__pyx_8 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 575; __pyx_clineno = __LINE__; goto __pyx_L1_error;} for (__pyx_v_c = 0; __pyx_v_c < __pyx_8; __pyx_v_c+=1) { ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->__pyx_vtab)->__query(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self), (((double *)__pyx_v_dd->data) + (__pyx_v_c * __pyx_v_k)), (((int *)__pyx_v_ii->data) + (__pyx_v_c * __pyx_v_k)), (((double *)__pyx_v_xx->data) + (__pyx_v_c * ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->m)), __pyx_v_k, __pyx_v_eps, __pyx_v_p, __pyx_v_distance_upper_bound); } - __pyx_4 = __Pyx_PyObject_IsTrue(__pyx_v_single); if (unlikely(__pyx_4 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 582; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_4 = __Pyx_PyObject_IsTrue(__pyx_v_single); if (unlikely(__pyx_4 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (__pyx_4) { __pyx_4 = (__pyx_v_k == 1); if (__pyx_4) { @@ -1893,10 +1887,10 @@ } else if (unlikely(__pyx_t_9 >= __pyx_bshape_1_dd)) __pyx_t_2 = 1; if (unlikely(__pyx_t_2 != -1)) { __Pyx_RaiseBufferIndexError(__pyx_t_2); - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } __pyx_9 = *((double *)((double *)__Pyx_BufPtrStrided2d(__pyx_bstruct_dd.buf, __pyx_t_8, __pyx_bstride_0_dd, __pyx_t_9, __pyx_bstride_1_dd))); - __pyx_7 = PyFloat_FromDouble(__pyx_9); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyFloat_FromDouble(__pyx_9); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_10 = 0; __pyx_t_11 = 0; __pyx_t_2 = -1; @@ -1910,11 +1904,11 @@ } else if (unlikely(__pyx_t_11 >= __pyx_bshape_1_ii)) __pyx_t_2 = 1; if (unlikely(__pyx_t_2 != -1)) { __Pyx_RaiseBufferIndexError(__pyx_t_2); - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } __pyx_8 = *((int *)((int *)__Pyx_BufPtrStrided2d(__pyx_bstruct_ii.buf, __pyx_t_10, __pyx_bstride_0_ii, __pyx_t_11, __pyx_bstride_1_ii))); - __pyx_2 = PyInt_FromLong(__pyx_8); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 584; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyInt_FromLong(__pyx_8); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_1, 0, __pyx_7); PyTuple_SET_ITEM(__pyx_1, 1, __pyx_2); __pyx_7 = 0; @@ -1925,9 +1919,9 @@ goto __pyx_L11; } /*else*/ { - __pyx_5 = __Pyx_GetItemInt(((PyObject *)__pyx_v_dd), 0, 0); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_3 = __Pyx_GetItemInt(((PyObject *)__pyx_v_ii), 0, 0); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_7 = PyTuple_New(2); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = __Pyx_GetItemInt(((PyObject *)__pyx_v_dd), 0, 0); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 588; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = __Pyx_GetItemInt(((PyObject *)__pyx_v_ii), 0, 0); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 588; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyTuple_New(2); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 588; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_7, 0, __pyx_5); PyTuple_SET_ITEM(__pyx_7, 1, __pyx_3); __pyx_5 = 0; @@ -1942,43 +1936,43 @@ /*else*/ { __pyx_4 = (__pyx_v_k == 1); if (__pyx_4) { - __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_reshape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_kp_reshape); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_2); __pyx_2 = 0; - __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(Py_Ellipsis); PyTuple_SET_ITEM(__pyx_5, 0, Py_Ellipsis); Py_INCREF(__pyx_int_0); PyTuple_SET_ITEM(__pyx_5, 1, __pyx_int_0); - __pyx_3 = PyObject_GetItem(((PyObject *)__pyx_v_dd), ((PyObject *)__pyx_5)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetItem(((PyObject *)__pyx_v_dd), ((PyObject *)__pyx_5)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; - __pyx_7 = PyTuple_New(2); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyTuple_New(2); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_7, 0, __pyx_3); Py_INCREF(__pyx_v_retshape); PyTuple_SET_ITEM(__pyx_7, 1, __pyx_v_retshape); __pyx_3 = 0; - __pyx_2 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_7), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_7), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; Py_DECREF(((PyObject *)__pyx_7)); __pyx_7 = 0; - __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_3 = PyObject_GetAttr(__pyx_5, __pyx_kp_reshape); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_GetAttr(__pyx_5, __pyx_kp_reshape); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_5); __pyx_5 = 0; - __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(Py_Ellipsis); PyTuple_SET_ITEM(__pyx_1, 0, Py_Ellipsis); Py_INCREF(__pyx_int_0); PyTuple_SET_ITEM(__pyx_1, 1, __pyx_int_0); - __pyx_7 = PyObject_GetItem(((PyObject *)__pyx_v_ii), ((PyObject *)__pyx_1)); if (!__pyx_7) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyObject_GetItem(((PyObject *)__pyx_v_ii), ((PyObject *)__pyx_1)); if (!__pyx_7) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; - __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_5, 0, __pyx_7); Py_INCREF(__pyx_v_retshape); PyTuple_SET_ITEM(__pyx_5, 1, __pyx_v_retshape); __pyx_7 = 0; - __pyx_1 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_3); __pyx_3 = 0; Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; - __pyx_7 = PyTuple_New(2); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 589; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyTuple_New(2); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_7, 0, __pyx_2); PyTuple_SET_ITEM(__pyx_7, 1, __pyx_1); __pyx_2 = 0; @@ -1989,41 +1983,41 @@ goto __pyx_L12; } /*else*/ { - __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_5 = PyObject_GetAttr(__pyx_3, __pyx_kp_reshape); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyObject_GetAttr(__pyx_3, __pyx_kp_reshape); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_3); __pyx_3 = 0; - __pyx_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(1); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_1, 0, __pyx_2); __pyx_2 = 0; - __pyx_7 = PyNumber_Add(__pyx_v_retshape, ((PyObject *)__pyx_1)); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyNumber_Add(__pyx_v_retshape, ((PyObject *)__pyx_1)); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(((PyObject *)__pyx_1)); __pyx_1 = 0; - __pyx_3 = PyTuple_New(2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyTuple_New(2); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(((PyObject *)__pyx_v_dd)); PyTuple_SET_ITEM(__pyx_3, 0, ((PyObject *)__pyx_v_dd)); PyTuple_SET_ITEM(__pyx_3, 1, __pyx_7); __pyx_7 = 0; - __pyx_2 = PyObject_Call(__pyx_5, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_2 = PyObject_Call(__pyx_5, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_5); __pyx_5 = 0; Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; - __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_7 = PyObject_GetAttr(__pyx_1, __pyx_kp_reshape); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_7 = PyObject_GetAttr(__pyx_1, __pyx_kp_reshape); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_1); __pyx_1 = 0; - __pyx_5 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_3, 0, __pyx_5); __pyx_5 = 0; - __pyx_1 = PyNumber_Add(__pyx_v_retshape, ((PyObject *)__pyx_3)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyNumber_Add(__pyx_v_retshape, ((PyObject *)__pyx_3)); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0; - __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_INCREF(((PyObject *)__pyx_v_ii)); PyTuple_SET_ITEM(__pyx_5, 0, ((PyObject *)__pyx_v_ii)); PyTuple_SET_ITEM(__pyx_5, 1, __pyx_1); __pyx_1 = 0; - __pyx_3 = PyObject_Call(__pyx_7, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_3 = PyObject_Call(__pyx_7, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} Py_DECREF(__pyx_7); __pyx_7 = 0; Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0; - __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_1 = PyTuple_New(2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 593; __pyx_clineno = __LINE__; goto __pyx_L1_error;} PyTuple_SET_ITEM(__pyx_1, 0, __pyx_2); PyTuple_SET_ITEM(__pyx_1, 1, __pyx_3); __pyx_2 = 0; @@ -2441,12 +2435,11 @@ {&__pyx_kp_reshape, __pyx_k_reshape, sizeof(__pyx_k_reshape), 1, 1, 1}, {&__pyx_kp_empty, __pyx_k_empty, sizeof(__pyx_k_empty), 1, 1, 1}, {&__pyx_kp_fill, __pyx_k_fill, sizeof(__pyx_k_fill), 1, 1, 1}, - {&__pyx_kp_23, __pyx_k_23, sizeof(__pyx_k_23), 0, 1, 0}, + {&__pyx_kp_22, __pyx_k_22, sizeof(__pyx_k_22), 0, 1, 0}, {&__pyx_kp_18, __pyx_k_18, sizeof(__pyx_k_18), 0, 0, 0}, {&__pyx_kp_19, __pyx_k_19, sizeof(__pyx_k_19), 0, 0, 0}, {&__pyx_kp_20, __pyx_k_20, sizeof(__pyx_k_20), 0, 0, 0}, {&__pyx_kp_21, __pyx_k_21, sizeof(__pyx_k_21), 0, 0, 0}, - {&__pyx_kp_22, __pyx_k_22, sizeof(__pyx_k_22), 0, 0, 0}, {&__pyx_kp___getbuffer__, __pyx_k___getbuffer__, sizeof(__pyx_k___getbuffer__), 0, 1, 1}, {&__pyx_kp_RuntimeError, __pyx_k_RuntimeError, sizeof(__pyx_k_RuntimeError), 1, 1, 1}, {&__pyx_kp_1, __pyx_k_1, sizeof(__pyx_k_1), 0, 0, 0}, @@ -2924,82 +2917,7 @@ return 0; } -#if PY_MAJOR_VERSION < 3 -static PyObject *__Pyx_GetStdout(void) { - PyObject *f = PySys_GetObject("stdout"); - if (!f) { - PyErr_SetString(PyExc_RuntimeError, "lost sys.stdout"); - } - return f; -} -static int __Pyx_Print(PyObject *arg_tuple, int newline) { - PyObject *f; - PyObject* v; - int i; - - if (!(f = __Pyx_GetStdout())) - return -1; - for (i=0; i < PyTuple_GET_SIZE(arg_tuple); i++) { - if (PyFile_SoftSpace(f, 1)) { - if (PyFile_WriteString(" ", f) < 0) - return -1; - } - v = PyTuple_GET_ITEM(arg_tuple, i); - if (PyFile_WriteObject(v, f, Py_PRINT_RAW) < 0) - return -1; - if (PyString_Check(v)) { - char *s = PyString_AsString(v); - Py_ssize_t len = PyString_Size(v); - if (len > 0 && - isspace(Py_CHARMASK(s[len-1])) && - s[len-1] != ' ') - PyFile_SoftSpace(f, 0); - } - } - if (newline) { - if (PyFile_WriteString("\n", f) < 0) - return -1; - PyFile_SoftSpace(f, 0); - } - return 0; -} - -#else /* Python 3 has a print function */ -static int __Pyx_Print(PyObject *arg_tuple, int newline) { - PyObject* kwargs = 0; - PyObject* result = 0; - PyObject* end_string; - if (!__pyx_print) { - __pyx_print = PyObject_GetAttrString(__pyx_b, "print"); - if (!__pyx_print) - return -1; - } - if (!newline) { - if (!__pyx_print_kwargs) { - __pyx_print_kwargs = PyDict_New(); - if (!__pyx_print_kwargs) - return -1; - end_string = PyUnicode_FromStringAndSize(" ", 1); - if (!end_string) - return -1; - if (PyDict_SetItemString(__pyx_print_kwargs, "end", end_string) < 0) { - Py_DECREF(end_string); - return -1; - } - Py_DECREF(end_string); - } - kwargs = __pyx_print_kwargs; - } - result = PyObject_Call(__pyx_print, arg_tuple, kwargs); - if (!result) - return -1; - Py_DECREF(result); - return 0; -} -#endif - - static void __Pyx_WriteUnraisable(const char *name) { PyObject *old_exc, *old_val, *old_tb; PyObject *ctx; Modified: branches/spatial/scipy/spatial/ckdtree.pyx =================================================================== --- branches/spatial/scipy/spatial/ckdtree.pyx 2008-10-13 15:46:17 UTC (rev 4796) +++ branches/spatial/scipy/spatial/ckdtree.pyx 2008-10-13 15:58:54 UTC (rev 4797) @@ -332,6 +332,9 @@ stdlib.free(node) def __dealloc__(cKDTree self): + if (self.tree) == 0: + # should happen only if __init__ was never called + return self.__free_tree(self.tree) cdef void __query(cKDTree self, @@ -546,7 +549,7 @@ i : array of integers The locations of the neighbors in self.data. If x has shape tuple+(self.m,), then i has shape tuple+(k,). - Missing neighbors are indicated with self.n+1. + Missing neighbors are indicated with self.n. """ cdef np.ndarray[int, ndim=2] ii cdef np.ndarray[double, ndim=2] dd From scipy-svn at scipy.org Fri Oct 17 06:34:08 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 17 Oct 2008 05:34:08 -0500 (CDT) Subject: [Scipy-svn] r4798 - trunk/tools/win32/build_scripts Message-ID: <20081017103408.ACEBD39C107@scipy.org> Author: cdavid Date: 2008-10-17 05:34:01 -0500 (Fri, 17 Oct 2008) New Revision: 4798 Added: trunk/tools/win32/build_scripts/pavement.py Log: Add a dummy pavement file. Added: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-13 15:58:54 UTC (rev 4797) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-17 10:34:01 UTC (rev 4798) @@ -0,0 +1,9 @@ +options( + setup=Bunch( + name = "scipy-superpack", + ) +) + + at task +def setup(): + print "Setting up package %s" % options.name From scipy-svn at scipy.org Fri Oct 17 06:34:26 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 17 Oct 2008 05:34:26 -0500 (CDT) Subject: [Scipy-svn] r4799 - trunk/tools/win32/build_scripts Message-ID: <20081017103426.57CD539C107@scipy.org> Author: cdavid Date: 2008-10-17 05:34:18 -0500 (Fri, 17 Oct 2008) New Revision: 4799 Modified: trunk/tools/win32/build_scripts/pavement.py Log: The pavement file can now bootstrap the sources. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-17 10:34:01 UTC (rev 4798) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-17 10:34:18 UTC (rev 4799) @@ -1,9 +1,97 @@ +import os +from os.path import join as pjoin, normpath, exists as pexists +import subprocess +from shutil import rmtree +import re + +SRC_ROOT = normpath(pjoin(os.getcwd(), os.pardir, os.pardir, os.pardir)) +BUILD_ROOT = os.getcwd() + +PYVER = 2.5 + options( - setup=Bunch( - name = "scipy-superpack", + clean=Bunch( + src_dir = SRC_ROOT, + pyver = PYVER + ), + build_sdist=Bunch( + src_dir = SRC_ROOT ) ) @task -def setup(): - print "Setting up package %s" % options.name +def clean(): + # Clean sdist + sdir = pjoin(options.src_dir, "dist") + if pexists(sdir): + rmtree(sdir) + mani = pjoin(options.src_dir, "MANIFEST") + if pexists(mani): + os.remove(mani) + + # Clean bootstrap directory + bdir = bootstrap_dir(options.pyver) + if pexists(bdir): + rmtree(bdir) + + at task +def build_sdist(): + cmd = ["python", "setup.py", "sdist", "--format=zip"] + st = subprocess.call(cmd, cwd=options.src_dir) + + at task + at needs('build_sdist') +#@needs('clean') +def bootstrap(): + print get_scipy_version(options.src_dir) + +# Helpers +def bootstrap_dir(pyver): + return pjoin(BUILD_ROOT, "bootstrap-%s" % pyver) + +def get_scipy_version(src_root): + version_file = pjoin(src_root, "scipy", "version.py") + if not pexists(version_file): + raise IOError("file %s not found" % version_file) + + fid = open(version_file, "r") + vregex = re.compile("version\s*=\s*'(\d+)\.(\d+)\.(\d+)'") + isrelregex = re.compile("release\s*=\s*True") + isdevregex = re.compile("release\s*=\s*False") + isdev = None + version = None + for line in fid.readlines(): + m = vregex.match(line) + if m: + version = [int(i) for i in m.groups()] + if isrelregex.match(line): + if isdev is None: + isdev = False + else: + raise RuntimeError("isdev already set ?") + if isdevregex.match(line): + if isdev is None: + isdev = True + else: + raise RuntimeError("isdev already set ?") + + verstr = ".".join([str(i) for i in version]) + if isdev: + verstr += ".dev" + verstr += get_svn_version(src_root) + return verstr + +def get_svn_version(chdir): + out = subprocess.Popen(['svn', 'info'], + stdout = subprocess.PIPE, cwd = chdir).communicate()[0] + r = re.compile('Revision: ([0-9]+)') + svnver = None + for line in out.split('\n'): + m = r.match(line) + if m: + svnver = m.group(1) + + if not svnver: + raise ValueError("Error while parsing svn version ?") + + return svnver From scipy-svn at scipy.org Fri Oct 17 06:38:30 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 17 Oct 2008 05:38:30 -0500 (CDT) Subject: [Scipy-svn] r4800 - trunk/tools/win32/build_scripts Message-ID: <20081017103830.CFA7239C107@scipy.org> Author: cdavid Date: 2008-10-17 05:38:23 -0500 (Fri, 17 Oct 2008) New Revision: 4800 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Trailing spaces. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-17 10:34:18 UTC (rev 4799) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-17 10:38:23 UTC (rev 4800) @@ -74,7 +74,7 @@ isdev = True else: raise RuntimeError("isdev already set ?") - + verstr = ".".join([str(i) for i in version]) if isdev: verstr += ".dev" @@ -82,7 +82,7 @@ return verstr def get_svn_version(chdir): - out = subprocess.Popen(['svn', 'info'], + out = subprocess.Popen(['svn', 'info'], stdout = subprocess.PIPE, cwd = chdir).communicate()[0] r = re.compile('Revision: ([0-9]+)') svnver = None From scipy-svn at scipy.org Fri Oct 17 06:44:33 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 17 Oct 2008 05:44:33 -0500 (CDT) Subject: [Scipy-svn] r4801 - trunk/tools/win32/build_scripts Message-ID: <20081017104433.2984A39C107@scipy.org> Author: cdavid Date: 2008-10-17 05:44:28 -0500 (Fri, 17 Oct 2008) New Revision: 4801 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Bootstrap is now working with paver. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-17 10:38:23 UTC (rev 4800) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-17 10:44:28 UTC (rev 4801) @@ -1,8 +1,9 @@ import os -from os.path import join as pjoin, normpath, exists as pexists +from os.path import join as pjoin, normpath, exists as pexists, dirname import subprocess from shutil import rmtree import re +from zipfile import ZipFile SRC_ROOT = normpath(pjoin(os.getcwd(), os.pardir, os.pardir, os.pardir)) BUILD_ROOT = os.getcwd() @@ -41,11 +42,35 @@ @task @needs('build_sdist') -#@needs('clean') def bootstrap(): - print get_scipy_version(options.src_dir) + prepare_scipy_sources(options.src_dir, bootstrap_dir(options.pyver)) # Helpers +def get_sdist_tarball(src_root): + """Return the name of the installer built by sdist command.""" + # Yeah, the name logic is harcoded in distutils. We have to reproduce it + # here + name = "scipy-%s.zip" % get_scipy_version(src_root) + return name + +def prepare_scipy_sources(src_root, bootstrap): + zid = ZipFile(pjoin(src_root, 'dist', get_sdist_tarball(src_root))) + root = 'scipy-%s' % get_scipy_version(src_root) + + # From the sdist-built tarball, extract all files into bootstrap directory, + # but removing the scipy-VERSION head path + for name in zid.namelist(): + cnt = zid.read(name) + if name.startswith(root): + # XXX: even on windows, the path sep in zip is '/' ? + name = name.split('/', 1)[1] + newname = pjoin(bootstrap, name) + + if not pexists(dirname(newname)): + os.makedirs(dirname(newname)) + fid = open(newname, 'wb') + fid.write(cnt) + def bootstrap_dir(pyver): return pjoin(BUILD_ROOT, "bootstrap-%s" % pyver) From scipy-svn at scipy.org Sun Oct 19 17:18:37 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 19 Oct 2008 16:18:37 -0500 (CDT) Subject: [Scipy-svn] r4802 - in trunk/scipy/sparse: . tests Message-ID: <20081019211837.9333939C088@scipy.org> Author: wnbell Date: 2008-10-19 16:18:35 -0500 (Sun, 19 Oct 2008) New Revision: 4802 Modified: trunk/scipy/sparse/dok.py trunk/scipy/sparse/tests/test_base.py Log: added DOK matrix patch by James Philbin i. Improved the docstring. ii. Fixed the constructor (this fixes #755). iii. Reduced the codepath for the common case of setting an individual element (~2x speedup). Modified: trunk/scipy/sparse/dok.py =================================================================== --- trunk/scipy/sparse/dok.py 2008-10-17 10:44:28 UTC (rev 4801) +++ trunk/scipy/sparse/dok.py 2008-10-19 21:18:35 UTC (rev 4802) @@ -14,46 +14,66 @@ class dok_matrix(spmatrix, dict): """Dictionary Of Keys based matrix. This is an efficient - structure for constructing sparse matrices + structure for constructing sparse matrices incrementally. + + This can be instatiated in several ways: + dok_matrix(D) + with a dense matrix, D + + dok_matrix(S) + with a sparse matrix, S + + dok_matrix((M,N), [dtype]) + create the matrix with initial shape (M,N) + dtype is optional, defaulting to dtype='d' + + Notes + ----- + Allows for efficient O(1) access of individual elements. + Duplicates are not allowed. + Can be efficiently converted to a coo_matrix once constructed. + + Examples + -------- + >>> from scipy.sparse import * + >>> from scipy import * + >>> S = dok_matrix((5,5), dtype=float32) + >>> for i in range(5): + >>> for j in range(5): + >>> S[i,j] = i+j # Update element + """ - def __init__(self, A=None, shape=None, dtype=None, copy=False): - """ Create a new dictionary-of-keys sparse matrix. An optional - argument A is accepted, which initializes the dok_matrix with it. - This can be a tuple of dimensions (M, N) or a (dense) array - to copy. - """ - #TODO deprecate argument A in favor of arg1 style + def __init__(self, arg1, shape=None, dtype=None, copy=False): dict.__init__(self) spmatrix.__init__(self) - self.dtype = getdtype(dtype, A, default=float) - if A is not None: - if isinstance(A, tuple): - # Interpret as dimensions - if not isshape(A): - raise TypeError, "dimensions must be a 2-tuple of positive"\ - " integers" - self.shape = A - elif isspmatrix(A): - if isspmatrix_dok(A) and copy: - A = A.copy() - else: - A = A.todok() - self.update( A ) - self.shape = A.shape - self.dtype = A.dtype + + self.dtype = getdtype(dtype, default=float) + if isinstance(arg1, tuple) and isshape(arg1): # (M,N) + M, N = arg1 + self.shape = (M, N) + elif isspmatrix(arg1): # Sparse ctor + if isspmatrix_dok(arg1) and copy: + arg1 = arg1.copy() else: - #must be dense, convert to COO first, then to DOK - try: - A = asarray(A) - except: - raise ValueError, "unrecognized form for" \ - " %s_matrix constructor" % self.format - from coo import coo_matrix - self.update( coo_matrix(A).todok() ) - self.shape = A.shape - self.dtype = A.dtype + arg1 = arg1.todok() + self.update(arg1) + self.shape = arg1.shape + self.dtype = arg1.dtype + else: # Dense ctor + try: + arg1 = asarray(arg1) + except: + raise TypeError('invalid input format') + if len(arg1.shape)!=2: + raise TypeError('expected rank <=2 dense array or matrix') + + from coo import coo_matrix + self.update( coo_matrix(arg1).todok() ) + self.shape = arg1.shape + self.dtype = arg1.dtype + def getnnz(self): return dict.__len__(self) nnz = property(fget=getnnz) @@ -177,13 +197,10 @@ def __setitem__(self, key, value): try: - assert len(key) == 2 - except (AssertionError, TypeError): - raise TypeError, "index must be a pair of integers, slices, or" \ - " sequences" - i, j = key + i, j = key + except (ValueError, TypeError): + raise TypeError, "index must be a pair of integers or slices" - # First deal with the case where both i and j are integers if isintlike(i) and isintlike(j): if i < 0: @@ -193,20 +210,14 @@ if i < 0 or i >= self.shape[0] or j < 0 or j >= self.shape[1]: raise IndexError, "index out of bounds" - if isintlike(value) and value == 0: - if key in self.keys(): # get rid of it something already there - del self[key] + + if isscalar(value): + if value==0: + del self[(i,j)] + else: + dict.__setitem__(self, (i,j), self.dtype.type(value)) else: - # Ensure value is a single element, not a sequence - if isinstance(value, float) or isintlike(value) or \ - isinstance(value, complex): - dict.__setitem__(self, (i,j), self.dtype.type(value)) - newrows = max(self.shape[0], int(key[0])+1) - newcols = max(self.shape[1], int(key[1])+1) - self.shape = (newrows, newcols) - else: - raise TypeError, "cannot set matrix element to non-scalar" - return # done + raise TypeError, "cannot set matrix element to a non-scalar" else: # Either i or j is a slice, sequence, or invalid. If i is a slice # or sequence, unfold it first and call __setitem__ recursively. Modified: trunk/scipy/sparse/tests/test_base.py =================================================================== --- trunk/scipy/sparse/tests/test_base.py 2008-10-17 10:44:28 UTC (rev 4801) +++ trunk/scipy/sparse/tests/test_base.py 2008-10-19 21:18:35 UTC (rev 4802) @@ -1163,7 +1163,26 @@ caught += 1 assert_equal(caught,5) + def test_ctor(self): + caught = 0 + # Empty ctor + try: + A = dok_matrix() + except TypeError, e: + caught+=1 + assert_equal(caught, 1) + # Dense ctor + b = matrix([[1,0,0,0],[0,0,1,0],[0,2,0,3]],'d') + A = dok_matrix(b) + assert_equal(A.todense(), b) + + # Sparse ctor + c = csr_matrix(b) + assert_equal(A.todense(), c.todense()) + + + class TestLIL( _TestCommon, _TestHorizSlicing, _TestVertSlicing, _TestBothSlicing, _TestGetSet, _TestSolve, _TestArithmetic, _TestInplaceArithmetic, From scipy-svn at scipy.org Tue Oct 21 00:04:40 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 20 Oct 2008 23:04:40 -0500 (CDT) Subject: [Scipy-svn] r4803 - trunk/tools/win32/build_scripts Message-ID: <20081021040440.1DF6839C0EA@scipy.org> Author: cdavid Date: 2008-10-20 23:04:36 -0500 (Mon, 20 Oct 2008) New Revision: 4803 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Add clean_bootstrap task. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-19 21:18:35 UTC (rev 4802) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 04:04:36 UTC (rev 4803) @@ -15,11 +15,16 @@ src_dir = SRC_ROOT, pyver = PYVER ), + clean_bootstrap=Bunch( + src_dir = SRC_ROOT, + pyver = PYVER + ), build_sdist=Bunch( src_dir = SRC_ROOT ) ) +# Clean everything, including bootstrap source tree @task def clean(): # Clean sdist @@ -35,7 +40,18 @@ if pexists(bdir): rmtree(bdir) +# Clean the bootstrap source tree for a clean build from scratch @task +def clean_bootstrap(): + bdir = bootstrap_dir(options.pyver) + for d in ["build", "dist"]: + if pexists(pjoin(bdir, d)): + shutil.rmtree(pjoin(bdir, d)) + + if pexists(pjoin(bdir, "site.cfg")): + os.remove(pjoin(bdir, "site.cfg")) + + at task def build_sdist(): cmd = ["python", "setup.py", "sdist", "--format=zip"] st = subprocess.call(cmd, cwd=options.src_dir) From scipy-svn at scipy.org Tue Oct 21 01:09:30 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 00:09:30 -0500 (CDT) Subject: [Scipy-svn] r4804 - trunk/tools/win32/build_scripts Message-ID: <20081021050930.7577839C0EA@scipy.org> Author: cdavid Date: 2008-10-21 00:09:27 -0500 (Tue, 21 Oct 2008) New Revision: 4804 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Cosmetic change to pavement file. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 04:04:36 UTC (rev 4803) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 05:09:27 UTC (rev 4804) @@ -124,7 +124,8 @@ def get_svn_version(chdir): out = subprocess.Popen(['svn', 'info'], - stdout = subprocess.PIPE, cwd = chdir).communicate()[0] + stdout = subprocess.PIPE, + cwd = chdir).communicate()[0] r = re.compile('Revision: ([0-9]+)') svnver = None for line in out.split('\n'): From scipy-svn at scipy.org Tue Oct 21 01:11:06 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 00:11:06 -0500 (CDT) Subject: [Scipy-svn] r4805 - trunk/tools/win32/build_scripts Message-ID: <20081021051106.71A8339C0EA@scipy.org> Author: cdavid Date: 2008-10-21 00:11:03 -0500 (Tue, 21 Oct 2008) New Revision: 4805 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Start the build task. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 05:09:27 UTC (rev 4804) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 05:11:03 UTC (rev 4805) @@ -8,8 +8,13 @@ SRC_ROOT = normpath(pjoin(os.getcwd(), os.pardir, os.pardir, os.pardir)) BUILD_ROOT = os.getcwd() -PYVER = 2.5 +PYVER = '2.5' +ARCH = 'nosse' +PYEXECS = {"2.5" : "C:\python25\python.exe", + "2.4" : "C:\python24\python24.exe", + "2.3" : "C:\python23\python23.exe"} + options( clean=Bunch( src_dir = SRC_ROOT, @@ -21,6 +26,10 @@ ), build_sdist=Bunch( src_dir = SRC_ROOT + ), + build=Bunch( + pyver = PYVER, + arch = ARCH ) ) @@ -61,6 +70,12 @@ def bootstrap(): prepare_scipy_sources(options.src_dir, bootstrap_dir(options.pyver)) + at task +def build(): + pyver = options.pyver + arch = options.arch + print "Building scipy binary for python %s, arch is %s" % (get_python_exec(pyver), arch) + # Helpers def get_sdist_tarball(src_root): """Return the name of the installer built by sdist command.""" @@ -137,3 +152,18 @@ raise ValueError("Error while parsing svn version ?") return svnver + +def get_python_exec(ver): + """Return the executable of python for the given version.""" + # XXX Check that the file actually exists + try: + return PYEXECS[ver] + except KeyError: + raise ValueError("Version %s not supported/recognized" % ver) + +def write_site_cfg(arch): + if pexists("site.cfg"): + os.remove("site.cfg") + f = open("site.cfg", 'w') + f.writelines(SITECFG[arch]) + f.close() From scipy-svn at scipy.org Tue Oct 21 01:31:59 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 00:31:59 -0500 (CDT) Subject: [Scipy-svn] r4806 - trunk/tools/win32/build_scripts Message-ID: <20081021053159.8A67339C0EA@scipy.org> Author: cdavid Date: 2008-10-21 00:31:55 -0500 (Tue, 21 Oct 2008) New Revision: 4806 Modified: trunk/tools/win32/build_scripts/pavement.py Log: More on build. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 05:11:03 UTC (rev 4805) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 05:31:55 UTC (rev 4806) @@ -5,6 +5,7 @@ import re from zipfile import ZipFile +BUILD_MSI = False SRC_ROOT = normpath(pjoin(os.getcwd(), os.pardir, os.pardir, os.pardir)) BUILD_ROOT = os.getcwd() @@ -15,6 +16,15 @@ "2.4" : "C:\python24\python24.exe", "2.3" : "C:\python23\python23.exe"} +_SSE3_CFG = r"""[atlas] +library_dirs = C:\local\lib\yop\sse3""" +_SSE2_CFG = r"""[atlas] +library_dirs = C:\local\lib\yop\sse2""" +_NOSSE_CFG = r"""[DEFAULT] +library_dirs = C:\local\lib\yop\nosse""" + +SITECFG = {"sse2" : _SSE2_CFG, "sse3" : _SSE3_CFG, "nosse" : _NOSSE_CFG} + options( clean=Bunch( src_dir = SRC_ROOT, @@ -74,8 +84,39 @@ def build(): pyver = options.pyver arch = options.arch + bdir = bootstrap_dir(pyver) print "Building scipy binary for python %s, arch is %s" % (get_python_exec(pyver), arch) + write_site_cfg(arch, cwd=bdir) + if BUILD_MSI: + cmd = [get_python_exec(pyver), "setup.py", "build", "-c", "mingw32", + "bdist_msi"] + else: + cmd = [get_python_exec(pyver), "setup.py", "build", "-c", "mingw32", + "bdist_wininst"] + # build_log = "build-%s-%s.log" % (arch, pyver) + # f = open(build_log, 'w') + + try: + try: + subprocess.call(cmd, #shell = True, + #stderr = subprocess.STDOUT, stdout = f, + cwd=bdir) + finally: + f.close() + except subprocess.CalledProcessError, e: + msg = """ +There was an error while executing the following command: + + %s + +Error was : %s + +Look at the build log (%s).""" % (cmd, str(e), build_log) + raise Exception(msg) + + move_binary(arch, pyver) + # Helpers def get_sdist_tarball(src_root): """Return the name of the installer built by sdist command.""" @@ -161,9 +202,13 @@ except KeyError: raise ValueError("Version %s not supported/recognized" % ver) -def write_site_cfg(arch): - if pexists("site.cfg"): - os.remove("site.cfg") - f = open("site.cfg", 'w') +def write_site_cfg(arch, cwd=None): + if not cwd: + cwd = os.getcwd() + + scfg = pjoin(cwd, "site.cfg") + if pexists(scfg): + os.remove(scfg) + f = open(scfg, 'w') f.writelines(SITECFG[arch]) f.close() From scipy-svn at scipy.org Tue Oct 21 01:44:18 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 00:44:18 -0500 (CDT) Subject: [Scipy-svn] r4807 - trunk/tools/win32/build_scripts Message-ID: <20081021054418.4F9B939C0EA@scipy.org> Author: cdavid Date: 2008-10-21 00:44:14 -0500 (Tue, 21 Oct 2008) New Revision: 4807 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Rename pavement build task to build_binary. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 05:31:55 UTC (rev 4806) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 05:44:14 UTC (rev 4807) @@ -20,7 +20,9 @@ library_dirs = C:\local\lib\yop\sse3""" _SSE2_CFG = r"""[atlas] library_dirs = C:\local\lib\yop\sse2""" -_NOSSE_CFG = r"""[DEFAULT] +_NOSSE_CFG = r"""[ATLAS] +library_dirs = fakedirectorywhichhopefullydoesnotexist +[DEFAULT] library_dirs = C:\local\lib\yop\nosse""" SITECFG = {"sse2" : _SSE2_CFG, "sse3" : _SSE3_CFG, "nosse" : _NOSSE_CFG} @@ -37,7 +39,7 @@ build_sdist=Bunch( src_dir = SRC_ROOT ), - build=Bunch( + build_binary=Bunch( pyver = PYVER, arch = ARCH ) @@ -81,7 +83,7 @@ prepare_scipy_sources(options.src_dir, bootstrap_dir(options.pyver)) @task -def build(): +def build_binary(): pyver = options.pyver arch = options.arch bdir = bootstrap_dir(pyver) From scipy-svn at scipy.org Tue Oct 21 01:58:24 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 00:58:24 -0500 (CDT) Subject: [Scipy-svn] r4808 - trunk/tools/win32/build_scripts Message-ID: <20081021055824.A95CA39C0EA@scipy.org> Author: cdavid Date: 2008-10-21 00:58:20 -0500 (Tue, 21 Oct 2008) New Revision: 4808 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Add bootstrap_arch task. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 05:44:14 UTC (rev 4807) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 05:58:20 UTC (rev 4808) @@ -20,7 +20,7 @@ library_dirs = C:\local\lib\yop\sse3""" _SSE2_CFG = r"""[atlas] library_dirs = C:\local\lib\yop\sse2""" -_NOSSE_CFG = r"""[ATLAS] +_NOSSE_CFG = r"""[atlas] library_dirs = fakedirectorywhichhopefullydoesnotexist [DEFAULT] library_dirs = C:\local\lib\yop\nosse""" @@ -42,6 +42,10 @@ build_binary=Bunch( pyver = PYVER, arch = ARCH + ), + bootstrap_arch=Bunch( + pyver = PYVER, + arch = ARCH ) ) @@ -83,6 +87,12 @@ prepare_scipy_sources(options.src_dir, bootstrap_dir(options.pyver)) @task +def bootstrap_arch(): + pyver = options.pyver + arch = options.arch + set_bootstrap_sources(arch, pyver) + + at task def build_binary(): pyver = options.pyver arch = options.arch @@ -120,6 +130,10 @@ move_binary(arch, pyver) # Helpers +def set_bootstrap_sources(arch, pyver): + bdir = bootstrap_dir(pyver) + write_site_cfg(arch, cwd=bdir) + def get_sdist_tarball(src_root): """Return the name of the installer built by sdist command.""" # Yeah, the name logic is harcoded in distutils. We have to reproduce it From scipy-svn at scipy.org Tue Oct 21 02:13:54 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 01:13:54 -0500 (CDT) Subject: [Scipy-svn] r4809 - trunk/tools/win32/build_scripts Message-ID: <20081021061354.0CF8D39C0EA@scipy.org> Author: cdavid Date: 2008-10-21 01:13:48 -0500 (Tue, 21 Oct 2008) New Revision: 4809 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Re-enable the build log. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 05:58:20 UTC (rev 4808) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 06:13:48 UTC (rev 4809) @@ -93,12 +93,12 @@ set_bootstrap_sources(arch, pyver) @task + at needs('bootstrap_arch') def build_binary(): pyver = options.pyver arch = options.arch bdir = bootstrap_dir(pyver) print "Building scipy binary for python %s, arch is %s" % (get_python_exec(pyver), arch) - write_site_cfg(arch, cwd=bdir) if BUILD_MSI: cmd = [get_python_exec(pyver), "setup.py", "build", "-c", "mingw32", @@ -106,13 +106,13 @@ else: cmd = [get_python_exec(pyver), "setup.py", "build", "-c", "mingw32", "bdist_wininst"] - # build_log = "build-%s-%s.log" % (arch, pyver) - # f = open(build_log, 'w') + build_log = "build-%s-%s.log" % (arch, pyver) + f = open(build_log, 'w') try: try: subprocess.call(cmd, #shell = True, - #stderr = subprocess.STDOUT, stdout = f, + stderr = subprocess.STDOUT, stdout = f, cwd=bdir) finally: f.close() From scipy-svn at scipy.org Tue Oct 21 02:23:32 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 01:23:32 -0500 (CDT) Subject: [Scipy-svn] r4810 - trunk/tools/win32/build_scripts Message-ID: <20081021062332.A044039C0EA@scipy.org> Author: cdavid Date: 2008-10-21 01:23:28 -0500 (Tue, 21 Oct 2008) New Revision: 4810 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Bootstrap build now OK. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 06:13:48 UTC (rev 4809) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 06:23:28 UTC (rev 4810) @@ -1,7 +1,7 @@ import os from os.path import join as pjoin, normpath, exists as pexists, dirname import subprocess -from shutil import rmtree +from shutil import rmtree, move as shmove import re from zipfile import ZipFile @@ -41,7 +41,8 @@ ), build_binary=Bunch( pyver = PYVER, - arch = ARCH + arch = ARCH, + src_root = SRC_ROOT ), bootstrap_arch=Bunch( pyver = PYVER, @@ -97,8 +98,10 @@ def build_binary(): pyver = options.pyver arch = options.arch + scipy_verstr = get_scipy_version(options.src_root) bdir = bootstrap_dir(pyver) - print "Building scipy binary for python %s, arch is %s" % (get_python_exec(pyver), arch) + print "Building scipy (version %s) binary for python %s, arch is %s" % \ + (scipy_verstr, get_python_exec(pyver), arch) if BUILD_MSI: cmd = [get_python_exec(pyver), "setup.py", "build", "-c", "mingw32", @@ -127,7 +130,7 @@ Look at the build log (%s).""" % (cmd, str(e), build_log) raise Exception(msg) - move_binary(arch, pyver) + move_binary(arch, pyver, bdir, scipy_verstr) # Helpers def set_bootstrap_sources(arch, pyver): @@ -228,3 +231,28 @@ f = open(scfg, 'w') f.writelines(SITECFG[arch]) f.close() + +def move_binary(arch, pyver, cwd, scipy_verstr): + if not pexists(pjoin(cwd, "binaries")): + os.makedirs(pjoin(cwd, "binaries")) + + shmove(pjoin(cwd, 'dist', get_windist_exec(pyver, scipy_verstr)), + pjoin(cwd, 'binaries', get_binary_name(arch, scipy_verstr))) + +def get_binary_name(arch, scipy_verstr): + if BUILD_MSI: + ext = '.msi' + else: + ext = '.exe' + return "scipy-%s-%s%s" % (scipy_verstr, arch, ext) + +def get_windist_exec(pyver, scipy_verstr): + """Return the name of the installer built by wininst command.""" + # Yeah, the name logic is harcoded in distutils. We have to reproduce it + # here + if BUILD_MSI: + ext = '.msi' + else: + ext = '.exe' + name = "scipy-%s.win32-py%s%s" % (scipy_verstr, pyver, ext) + return name From scipy-svn at scipy.org Tue Oct 21 02:26:57 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 01:26:57 -0500 (CDT) Subject: [Scipy-svn] r4811 - trunk/tools/win32/build_scripts Message-ID: <20081021062657.CDB5D39C0F1@scipy.org> Author: cdavid Date: 2008-10-21 01:26:54 -0500 (Tue, 21 Oct 2008) New Revision: 4811 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Fix clean_bootstrap task. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 06:23:28 UTC (rev 4810) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 06:26:54 UTC (rev 4811) @@ -72,7 +72,7 @@ bdir = bootstrap_dir(options.pyver) for d in ["build", "dist"]: if pexists(pjoin(bdir, d)): - shutil.rmtree(pjoin(bdir, d)) + rmtree(pjoin(bdir, d)) if pexists(pjoin(bdir, "site.cfg")): os.remove(pjoin(bdir, "site.cfg")) From scipy-svn at scipy.org Tue Oct 21 02:43:20 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 01:43:20 -0500 (CDT) Subject: [Scipy-svn] r4812 - trunk/tools/win32/build_scripts Message-ID: <20081021064320.AD2DC39C0F1@scipy.org> Author: cdavid Date: 2008-10-21 01:43:17 -0500 (Tue, 21 Oct 2008) New Revision: 4812 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Generate nsis script in bootstrap task. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 06:26:54 UTC (rev 4811) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 06:43:17 UTC (rev 4812) @@ -44,6 +44,10 @@ arch = ARCH, src_root = SRC_ROOT ), + bootstrap=Bunch( + pyver = PYVER, + src_root = SRC_ROOT + ), bootstrap_arch=Bunch( pyver = PYVER, arch = ARCH @@ -85,7 +89,9 @@ @task @needs('build_sdist') def bootstrap(): - prepare_scipy_sources(options.src_dir, bootstrap_dir(options.pyver)) + bdir = bootstrap_dir(options.pyver) + prepare_scipy_sources(options.src_dir, bdir) + prepare_nsis_script(bootstrap, pyver, get_scipy_version(options.src_root)) @task def bootstrap_arch(): @@ -162,6 +168,20 @@ fid = open(newname, 'wb') fid.write(cnt) +def prepare_nsis_script(bootstrap, pyver, numver): + tpl = pjoin('nsis_scripts', 'scipy-superinstaller.nsi.in') + source = open(tpl, 'r') + target = open(pjoin(bootstrap, 'scipy-superinstaller.nsi'), 'w') + + installer_name = 'scipy-%s-win32-superpack-python%s.exe' % (numver, pyver) + cnt = "".join(source.readlines()) + cnt = cnt.replace('@SCIPY_INSTALLER_NAME@', installer_name) + for arch in ['nosse', 'sse2', 'sse3']: + cnt = cnt.replace('@%s_BINARY@' % arch.upper(), + get_binary_name(arch)) + + target.write(cnt) + def bootstrap_dir(pyver): return pjoin(BUILD_ROOT, "bootstrap-%s" % pyver) From scipy-svn at scipy.org Tue Oct 21 03:22:40 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 02:22:40 -0500 (CDT) Subject: [Scipy-svn] r4813 - trunk/tools/win32/build_scripts Message-ID: <20081021072240.8157839C0EA@scipy.org> Author: cdavid Date: 2008-10-21 02:22:36 -0500 (Tue, 21 Oct 2008) New Revision: 4813 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Fix nsis script generation. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 06:43:17 UTC (rev 4812) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 07:22:36 UTC (rev 4813) @@ -51,6 +51,10 @@ bootstrap_arch=Bunch( pyver = PYVER, arch = ARCH + ), + bootstrap_nsis=Bunch( + pyver = PYVER, + src_root = SRC_ROOT ) ) @@ -91,7 +95,6 @@ def bootstrap(): bdir = bootstrap_dir(options.pyver) prepare_scipy_sources(options.src_dir, bdir) - prepare_nsis_script(bootstrap, pyver, get_scipy_version(options.src_root)) @task def bootstrap_arch(): @@ -100,7 +103,15 @@ set_bootstrap_sources(arch, pyver) @task +def bootstrap_nsis(): + pyver = options.pyver + bdir = bootstrap_dir(options.pyver) + prepare_nsis_script(bdir, pyver, get_scipy_version(options.src_root)) + + at task @needs('bootstrap_arch') + at needs('bootstrap_nsis') + at needs('clean_bootstrap') def build_binary(): pyver = options.pyver arch = options.arch @@ -168,17 +179,17 @@ fid = open(newname, 'wb') fid.write(cnt) -def prepare_nsis_script(bootstrap, pyver, numver): +def prepare_nsis_script(bdir, pyver, numver): tpl = pjoin('nsis_scripts', 'scipy-superinstaller.nsi.in') source = open(tpl, 'r') - target = open(pjoin(bootstrap, 'scipy-superinstaller.nsi'), 'w') + target = open(pjoin(bdir, 'scipy-superinstaller.nsi'), 'w') installer_name = 'scipy-%s-win32-superpack-python%s.exe' % (numver, pyver) cnt = "".join(source.readlines()) cnt = cnt.replace('@SCIPY_INSTALLER_NAME@', installer_name) for arch in ['nosse', 'sse2', 'sse3']: cnt = cnt.replace('@%s_BINARY@' % arch.upper(), - get_binary_name(arch)) + get_binary_name(arch, numver)) target.write(cnt) From scipy-svn at scipy.org Tue Oct 21 03:26:07 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 02:26:07 -0500 (CDT) Subject: [Scipy-svn] r4814 - trunk/tools/win32/build_scripts Message-ID: <20081021072607.F382F39C0EA@scipy.org> Author: cdavid Date: 2008-10-21 02:26:04 -0500 (Tue, 21 Oct 2008) New Revision: 4814 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Make build_binary a raw task. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 07:22:36 UTC (rev 4813) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 07:26:04 UTC (rev 4814) @@ -109,9 +109,6 @@ prepare_nsis_script(bdir, pyver, get_scipy_version(options.src_root)) @task - at needs('bootstrap_arch') - at needs('bootstrap_nsis') - at needs('clean_bootstrap') def build_binary(): pyver = options.pyver arch = options.arch From scipy-svn at scipy.org Tue Oct 21 03:36:40 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 02:36:40 -0500 (CDT) Subject: [Scipy-svn] r4815 - trunk/tools/win32/build_scripts Message-ID: <20081021073640.5659A39C0EA@scipy.org> Author: cdavid Date: 2008-10-21 02:36:36 -0500 (Tue, 21 Oct 2008) New Revision: 4815 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Better error checking when running scipy setup.py from paver. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 07:26:04 UTC (rev 4814) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 07:36:36 UTC (rev 4815) @@ -128,12 +128,15 @@ try: try: - subprocess.call(cmd, #shell = True, + st = subprocess.call(cmd, #shell = True, stderr = subprocess.STDOUT, stdout = f, cwd=bdir) + if st: + raise RuntimeError("The cmd failed with status %d" % st) finally: f.close() - except subprocess.CalledProcessError, e: + except (subprocess.CalledProcessError, RuntimeError), e: + print e msg = """ There was an error while executing the following command: @@ -146,6 +149,14 @@ move_binary(arch, pyver, bdir, scipy_verstr) + at task + at needs('build_binary') + at needs('bootstrap_arch') + at needs('bootstrap_nsis') + at needs('bootstrap') + at needs('clean') +def build_all(): + pass # Helpers def set_bootstrap_sources(arch, pyver): bdir = bootstrap_dir(pyver) From scipy-svn at scipy.org Tue Oct 21 05:28:38 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 04:28:38 -0500 (CDT) Subject: [Scipy-svn] r4816 - trunk/tools/win32/build_scripts Message-ID: <20081021092838.C158639C0F1@scipy.org> Author: cdavid Date: 2008-10-21 04:28:34 -0500 (Tue, 21 Oct 2008) New Revision: 4816 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Put clean action into separate function. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 07:36:36 UTC (rev 4815) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 09:28:34 UTC (rev 4816) @@ -61,19 +61,8 @@ # Clean everything, including bootstrap source tree @task def clean(): - # Clean sdist - sdir = pjoin(options.src_dir, "dist") - if pexists(sdir): - rmtree(sdir) - mani = pjoin(options.src_dir, "MANIFEST") - if pexists(mani): - os.remove(mani) + raw_clean(options.src_dir, options.pyver) - # Clean bootstrap directory - bdir = bootstrap_dir(options.pyver) - if pexists(bdir): - rmtree(bdir) - # Clean the bootstrap source tree for a clean build from scratch @task def clean_bootstrap(): @@ -295,3 +284,17 @@ ext = '.exe' name = "scipy-%s.win32-py%s%s" % (scipy_verstr, pyver, ext) return name + +def raw_clean(src_dir, pyver): + # Clean sdist + sdir = pjoin(src_dir, "dist") + if pexists(sdir): + rmtree(sdir) + mani = pjoin(src_dir, "MANIFEST") + if pexists(mani): + os.remove(mani) + + # Clean bootstrap directory + bdir = bootstrap_dir(pyver) + if pexists(bdir): + rmtree(bdir) From scipy-svn at scipy.org Tue Oct 21 05:30:09 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 04:30:09 -0500 (CDT) Subject: [Scipy-svn] r4817 - trunk/tools/win32/build_scripts Message-ID: <20081021093009.4079039C0F1@scipy.org> Author: cdavid Date: 2008-10-21 04:30:05 -0500 (Tue, 21 Oct 2008) New Revision: 4817 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Put clean_bootstrap action into separate function. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 09:28:34 UTC (rev 4816) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 09:30:05 UTC (rev 4817) @@ -66,14 +66,8 @@ # Clean the bootstrap source tree for a clean build from scratch @task def clean_bootstrap(): - bdir = bootstrap_dir(options.pyver) - for d in ["build", "dist"]: - if pexists(pjoin(bdir, d)): - rmtree(pjoin(bdir, d)) + raw_clean_bootstrap(options.pyver) - if pexists(pjoin(bdir, "site.cfg")): - os.remove(pjoin(bdir, "site.cfg")) - @task def build_sdist(): cmd = ["python", "setup.py", "sdist", "--format=zip"] @@ -298,3 +292,13 @@ bdir = bootstrap_dir(pyver) if pexists(bdir): rmtree(bdir) + +def raw_clean_bootstrap(pyver): + bdir = bootstrap_dir(pyver) + for d in ["build", "dist"]: + if pexists(pjoin(bdir, d)): + rmtree(pjoin(bdir, d)) + + if pexists(pjoin(bdir, "site.cfg")): + os.remove(pjoin(bdir, "site.cfg")) + From scipy-svn at scipy.org Tue Oct 21 05:39:51 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 04:39:51 -0500 (CDT) Subject: [Scipy-svn] r4818 - trunk/tools/win32/build_scripts Message-ID: <20081021093951.E972339C2EA@scipy.org> Author: cdavid Date: 2008-10-21 04:39:47 -0500 (Tue, 21 Oct 2008) New Revision: 4818 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Build nsis installer should work now. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 09:30:05 UTC (rev 4817) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 09:39:47 UTC (rev 4818) @@ -70,14 +70,12 @@ @task def build_sdist(): - cmd = ["python", "setup.py", "sdist", "--format=zip"] - st = subprocess.call(cmd, cwd=options.src_dir) + raw_build_sdist(options.src_dir) @task @needs('build_sdist') def bootstrap(): - bdir = bootstrap_dir(options.pyver) - prepare_scipy_sources(options.src_dir, bdir) + raw_bootstrap(options.pyver, options.src_dir) @task def bootstrap_arch(): @@ -95,51 +93,21 @@ def build_binary(): pyver = options.pyver arch = options.arch + raw_build_arch(pyver, arch, options.src_root) + + at task + at needs('clean') + at needs('bootstrap') +def build_nsis(): scipy_verstr = get_scipy_version(options.src_root) - bdir = bootstrap_dir(pyver) - print "Building scipy (version %s) binary for python %s, arch is %s" % \ - (scipy_verstr, get_python_exec(pyver), arch) + bdir = bootstrap_dir(options.pyver) - if BUILD_MSI: - cmd = [get_python_exec(pyver), "setup.py", "build", "-c", "mingw32", - "bdist_msi"] - else: - cmd = [get_python_exec(pyver), "setup.py", "build", "-c", "mingw32", - "bdist_wininst"] - build_log = "build-%s-%s.log" % (arch, pyver) - f = open(build_log, 'w') + for arch in ['nosse', 'sse2', 'sse3']: + raw_clean_bootstrap(options.pyver) + set_bootstrap_sources(arch, options.pyver) + prepare_nsis_script(bdir, pyver, scipy_verstr) + raw_build_arch(options.pyver, arch, options.src_root) - try: - try: - st = subprocess.call(cmd, #shell = True, - stderr = subprocess.STDOUT, stdout = f, - cwd=bdir) - if st: - raise RuntimeError("The cmd failed with status %d" % st) - finally: - f.close() - except (subprocess.CalledProcessError, RuntimeError), e: - print e - msg = """ -There was an error while executing the following command: - - %s - -Error was : %s - -Look at the build log (%s).""" % (cmd, str(e), build_log) - raise Exception(msg) - - move_binary(arch, pyver, bdir, scipy_verstr) - - at task - at needs('build_binary') - at needs('bootstrap_arch') - at needs('bootstrap_nsis') - at needs('bootstrap') - at needs('clean') -def build_all(): - pass # Helpers def set_bootstrap_sources(arch, pyver): bdir = bootstrap_dir(pyver) @@ -302,3 +270,50 @@ if pexists(pjoin(bdir, "site.cfg")): os.remove(pjoin(bdir, "site.cfg")) +def raw_build_sdist(cwd): + cmd = ["python", "setup.py", "sdist", "--format=zip"] + st = subprocess.call(cmd, cwd=cwd) + +def raw_bootstrap(pyver, src_dir): + bdir = bootstrap_dir(pyver) + prepare_scipy_sources(src_dir, bdir) + +def raw_build_arch(pyver, arch, src_root): + scipy_verstr = get_scipy_version(src_root) + bdir = bootstrap_dir(pyver) + + print "Building scipy (version %s) binary for python %s, arch is %s" % \ + (scipy_verstr, get_python_exec(pyver), arch) + + if BUILD_MSI: + cmd = [get_python_exec(pyver), "setup.py", "build", "-c", "mingw32", + "bdist_msi"] + else: + cmd = [get_python_exec(pyver), "setup.py", "build", "-c", "mingw32", + "bdist_wininst"] + build_log = "build-%s-%s.log" % (arch, pyver) + f = open(build_log, 'w') + + try: + try: + st = subprocess.call(cmd, #shell = True, + stderr = subprocess.STDOUT, stdout = f, + cwd=bdir) + if st: + raise RuntimeError("The cmd failed with status %d" % st) + finally: + f.close() + except (subprocess.CalledProcessError, RuntimeError), e: + print e + msg = """ +There was an error while executing the following command: + + %s + +Error was : %s + +Look at the build log (%s).""" % (cmd, str(e), build_log) + raise Exception(msg) + + move_binary(arch, pyver, bdir, scipy_verstr) + From scipy-svn at scipy.org Tue Oct 21 05:55:58 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 04:55:58 -0500 (CDT) Subject: [Scipy-svn] r4819 - trunk/tools/win32/build_scripts Message-ID: <20081021095558.E937339C0EA@scipy.org> Author: cdavid Date: 2008-10-21 04:55:54 -0500 (Tue, 21 Oct 2008) New Revision: 4819 Modified: trunk/tools/win32/build_scripts/pavement.py Log: Fix wrong order of dependencies for build_nsis task. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 09:39:47 UTC (rev 4818) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 09:55:54 UTC (rev 4819) @@ -96,8 +96,8 @@ raw_build_arch(pyver, arch, options.src_root) @task + at needs('bootstrap') @needs('clean') - at needs('bootstrap') def build_nsis(): scipy_verstr = get_scipy_version(options.src_root) bdir = bootstrap_dir(options.pyver) From scipy-svn at scipy.org Tue Oct 21 05:58:42 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 04:58:42 -0500 (CDT) Subject: [Scipy-svn] r4820 - trunk/tools/win32/build_scripts Message-ID: <20081021095842.2643539C0EA@scipy.org> Author: cdavid Date: 2008-10-21 04:58:35 -0500 (Tue, 21 Oct 2008) New Revision: 4820 Modified: trunk/tools/win32/build_scripts/pavement.py Log: nsis_script preparation is common to all arches. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 09:55:54 UTC (rev 4819) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 09:58:35 UTC (rev 4820) @@ -102,10 +102,10 @@ scipy_verstr = get_scipy_version(options.src_root) bdir = bootstrap_dir(options.pyver) + prepare_nsis_script(bdir, options.pyver, scipy_verstr) for arch in ['nosse', 'sse2', 'sse3']: raw_clean_bootstrap(options.pyver) set_bootstrap_sources(arch, options.pyver) - prepare_nsis_script(bdir, pyver, scipy_verstr) raw_build_arch(options.pyver, arch, options.src_root) # Helpers From scipy-svn at scipy.org Tue Oct 21 06:40:22 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 05:40:22 -0500 (CDT) Subject: [Scipy-svn] r4821 - trunk/tools/win32/build_scripts Message-ID: <20081021104022.84E0E39C0EA@scipy.org> Author: cdavid Date: 2008-10-21 05:40:18 -0500 (Tue, 21 Oct 2008) New Revision: 4821 Modified: trunk/tools/win32/build_scripts/pavement.py Log: nsis task to build the nsis installer from scratch should now work. Modified: trunk/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-21 09:58:35 UTC (rev 4820) +++ trunk/tools/win32/build_scripts/pavement.py 2008-10-21 10:40:18 UTC (rev 4821) @@ -108,6 +108,8 @@ set_bootstrap_sources(arch, options.pyver) raw_build_arch(options.pyver, arch, options.src_root) + raw_build_nsis(options.pyver) + # Helpers def set_bootstrap_sources(arch, pyver): bdir = bootstrap_dir(pyver) @@ -317,3 +319,9 @@ move_binary(arch, pyver, bdir, scipy_verstr) +def raw_build_nsis(pyver): + bdir = bootstrap_dir(options.pyver) + st = subprocess.call(['makensis', 'scipy-superinstaller.nsi'], + cwd=bdir) + if st: + raise RuntimeError("Error while executing makensis command") From scipy-svn at scipy.org Tue Oct 21 06:47:01 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 05:47:01 -0500 (CDT) Subject: [Scipy-svn] r4822 - trunk/tools/win32/build_scripts/nsis_scripts Message-ID: <20081021104701.1B25E39C0EA@scipy.org> Author: cdavid Date: 2008-10-21 05:46:57 -0500 (Tue, 21 Oct 2008) New Revision: 4822 Modified: trunk/tools/win32/build_scripts/nsis_scripts/scipy-superinstaller.nsi.in Log: Disable LZMA compressor for scipy nsis installer: do not give much, and is slow. Modified: trunk/tools/win32/build_scripts/nsis_scripts/scipy-superinstaller.nsi.in =================================================================== --- trunk/tools/win32/build_scripts/nsis_scripts/scipy-superinstaller.nsi.in 2008-10-21 10:40:18 UTC (rev 4821) +++ trunk/tools/win32/build_scripts/nsis_scripts/scipy-superinstaller.nsi.in 2008-10-21 10:46:57 UTC (rev 4822) @@ -3,8 +3,8 @@ !include "MUI2.nsh" -;SetCompress off ; Useful to disable compression under development -SetCompressor /Solid LZMA ; Useful to disable compression under development +SetCompress off ; Useful to disable compression under development +;SetCompressor /Solid LZMA ; Useful to disable compression under development ;-------------------------------- ;General From scipy-svn at scipy.org Tue Oct 21 06:51:25 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 21 Oct 2008 05:51:25 -0500 (CDT) Subject: [Scipy-svn] r4823 - trunk/tools/win32/build_scripts Message-ID: <20081021105125.8D1AD39C0EA@scipy.org> Author: cdavid Date: 2008-10-21 05:51:18 -0500 (Tue, 21 Oct 2008) New Revision: 4823 Removed: trunk/tools/win32/build_scripts/build.py trunk/tools/win32/build_scripts/prepare_bootstrap.py Log: Remove unused code. Deleted: trunk/tools/win32/build_scripts/build.py =================================================================== --- trunk/tools/win32/build_scripts/build.py 2008-10-21 10:46:57 UTC (rev 4822) +++ trunk/tools/win32/build_scripts/build.py 2008-10-21 10:51:18 UTC (rev 4823) @@ -1,176 +0,0 @@ -"""Python script to build windows binaries to be fed to the "superpack". - -The script is pretty dumb: it assumes python executables are installed the -standard way, and the location for blas/lapack/atlas is harcoded.""" - -import sys -import subprocess -import os -import shutil -from os.path import join as pjoin, split as psplit, dirname, exists as pexists -import re - -PYEXECS = {"2.5" : "C:\python25\python.exe", - "2.4" : "C:\python24\python24.exe", - "2.3" : "C:\python23\python23.exe"} - -_SSE3_CFG = r"""[atlas] -library_dirs = C:\local\lib\yop\sse3""" -_SSE2_CFG = r"""[atlas] -library_dirs = C:\local\lib\yop\sse2""" -_NOSSE_CFG = r"""[DEFAULT] -library_dirs = C:\local\lib\yop\nosse""" - -SITECFG = {"sse2" : _SSE2_CFG, "sse3" : _SSE3_CFG, "nosse" : _NOSSE_CFG} - -def get_svn_version(chdir): - out = subprocess.Popen(['svn', 'info'], - stdout = subprocess.PIPE, cwd = chdir).communicate()[0] - r = re.compile('Revision: ([0-9]+)') - svnver = None - for line in out.split('\n'): - m = r.match(line) - if m: - svnver = m.group(1) - - if not svnver: - raise ValueError("Error while parsing svn version ?") - - return svnver - -def get_scipy_version(src_root): - version_file = pjoin(src_root, "scipy", "version.py") - if not pexists(version_file): - raise IOError("file %s not found" % version_file) - - fid = open(version_file, "r") - vregex = re.compile("version\s*=\s*'(\d+)\.(\d+)\.(\d+)'") - isrelregex = re.compile("release\s*=\s*True") - isdevregex = re.compile("release\s*=\s*False") - isdev = None - version = None - for line in fid.readlines(): - m = vregex.match(line) - if m: - version = [int(i) for i in m.groups()] - if isrelregex.match(line): - if isdev is None: - isdev = False - else: - raise RuntimeError("isdev already set ?") - if isdevregex.match(line): - if isdev is None: - isdev = True - else: - raise RuntimeError("isdev already set ?") - - verstr = ".".join([str(i) for i in version]) - if isdev: - verstr += ".dev" - verstr += get_svn_version(src_root) - return verstr - -def get_python_exec(ver): - """Return the executable of python for the given version.""" - # XXX Check that the file actually exists - try: - return PYEXECS[ver] - except KeyError: - raise ValueError("Version %s not supported/recognized" % ver) - -def get_clean(): - if os.path.exists("build"): - shutil.rmtree("build") - if os.path.exists("dist"): - shutil.rmtree("dist") - -def write_site_cfg(arch): - if os.path.exists("site.cfg"): - os.remove("site.cfg") - f = open("site.cfg", 'w') - f.writelines(SITECFG[arch]) - f.close() - -def build(arch, pyver): - print "Building scipy binary for python %s, arch is %s" % (get_python_exec(pyver), arch) - get_clean() - write_site_cfg(arch) - - if BUILD_MSI: - cmd = "%s setup.py build -c mingw32 bdist_msi" % get_python_exec(pyver) - else: - cmd = "%s setup.py build -c mingw32 bdist_wininst" % get_python_exec(pyver) - build_log = "build-%s-%s.log" % (arch, pyver) - f = open(build_log, 'w') - - try: - try: - subprocess.check_call(cmd, shell = True, stderr = subprocess.STDOUT, stdout = f) - finally: - f.close() - except subprocess.CalledProcessError, e: - msg = """ -There was an error while executing the following command: - - %s - -Error was : %s - -Look at the build log (%s).""" % (cmd, str(e), build_log) - raise Exception(msg) - - move_binary(arch, pyver) - -def move_binary(arch, pyver): - if not os.path.exists("binaries"): - os.makedirs("binaries") - - shutil.move(os.path.join('dist', get_windist_exec(pyver)), - os.path.join("binaries", get_binary_name(arch))) - -def get_binary_name(arch): - if BUILD_MSI: - ext = '.msi' - else: - ext = '.exe' - return "scipy-%s-%s%s" % (get_scipy_version(), arch, ext) - -def get_windist_exec(pyver): - """Return the name of the installer built by wininst command.""" - # Yeah, the name logic is harcoded in distutils. We have to reproduce it - # here - if BUILD_MSI: - ext = '.msi' - else: - ext = '.exe' - name = "scipy-%s.win32-py%s%s" % (get_scipy_version(ROOT), pyver, ext) - return name - -if __name__ == '__main__': - ROOT = pjoin("..", "..", "..", "..") - from optparse import OptionParser - parser = OptionParser() - parser.add_option("-a", "--arch", dest="arch", - help = "Architecture to build (sse2, sse3, nosse, etc...)") - parser.add_option("-p", "--pyver", dest="pyver", - help = "Python version (2.4, 2.5, etc...)") - parser.add_option("-m", "--build-msi", dest="msi", - help = "0 or 1. If 1, build a msi instead of an exe.") - - opts, args = parser.parse_args() - arch = opts.arch - pyver = opts.pyver - msi = opts.msi - - if not pyver: - pyver = "2.5" - if not msi: - BUILD_MSI = False - else: - BUILD_MSI = True - - if not arch: - for arch in SITECFG.keys(): - build(arch, pyver) - else: - build(arch, pyver) Deleted: trunk/tools/win32/build_scripts/prepare_bootstrap.py =================================================================== --- trunk/tools/win32/build_scripts/prepare_bootstrap.py 2008-10-21 10:46:57 UTC (rev 4822) +++ trunk/tools/win32/build_scripts/prepare_bootstrap.py 2008-10-21 10:51:18 UTC (rev 4823) @@ -1,122 +0,0 @@ -import os -import shutil -import subprocess -from os.path import join as pjoin, split as psplit, dirname, exists as pexists -import re -from zipfile import ZipFile - -def get_sdist_tarball(src_root): - """Return the name of the installer built by sdist command.""" - # Yeah, the name logic is harcoded in distutils. We have to reproduce it - # here - name = "scipy-%s.zip" % get_scipy_version(src_root) - return name - -def build_sdist(chdir): - cwd = os.getcwd() - try: - os.chdir(chdir) - cmd = ["python", "setup.py", "sdist", "--format=zip"] - subprocess.call(cmd) - except Exception, e: - raise RuntimeError("Error while executing cmd (%s)" % e) - finally: - os.chdir(cwd) - -def prepare_scipy_sources(src_root, bootstrap = 'bootstrap'): - zid = ZipFile(pjoin(src_root, 'dist', get_sdist_tarball(src_root))) - root = 'scipy-%s' % get_scipy_version(src_root) - - # From the sdist-built tarball, extract all files into bootstrap directory, - # but removing the numpy-VERSION head path - for name in zid.namelist(): - cnt = zid.read(name) - if name.startswith(root): - # XXX: even on windows, the path sep in zip is '/' ? - name = name.split('/', 1)[1] - newname = pjoin(bootstrap, name) - - if not os.path.exists(dirname(newname)): - os.makedirs(dirname(newname)) - fid = open(newname, 'wb') - fid.write(cnt) - -def get_svn_version(chdir): - out = subprocess.Popen(['svn', 'info'], - stdout = subprocess.PIPE, cwd = chdir).communicate()[0] - r = re.compile('Revision: ([0-9]+)') - svnver = None - for line in out.split('\n'): - m = r.match(line) - if m: - svnver = m.group(1) - - if not svnver: - raise ValueError("Error while parsing svn version ?") - - return svnver - -def get_scipy_version(src_root): - version_file = pjoin(src_root, "scipy", "version.py") - if not pexists(version_file): - raise IOError("file %s not found" % version_file) - - fid = open(version_file, "r") - vregex = re.compile("version\s*=\s*'(\d+)\.(\d+)\.(\d+)'") - isrelregex = re.compile("release\s*=\s*True") - isdevregex = re.compile("release\s*=\s*False") - isdev = None - version = None - for line in fid.readlines(): - m = vregex.match(line) - if m: - version = [int(i) for i in m.groups()] - if isrelregex.match(line): - if isdev is None: - isdev = False - else: - raise RuntimeError("isdev already set ?") - if isdevregex.match(line): - if isdev is None: - isdev = True - else: - raise RuntimeError("isdev already set ?") - - verstr = ".".join([str(i) for i in version]) - if isdev: - verstr += ".dev" - verstr += get_svn_version(src_root) - return verstr - -def prepare_nsis_script(bootstrap, pyver, numver): - tpl = os.path.join('nsis_scripts', 'scipy-superinstaller.nsi.in') - source = open(tpl, 'r') - target = open(pjoin(bootstrap, 'scipy-superinstaller.nsi'), 'w') - - installer_name = 'scipy-%s-win32-superpack-python%s.exe' % (numver, pyver) - cnt = "".join(source.readlines()) - cnt = cnt.replace('@SCIPY_INSTALLER_NAME@', installer_name) - for arch in ['nosse', 'sse2', 'sse3']: - cnt = cnt.replace('@%s_BINARY@' % arch.upper(), - get_binary_name(arch)) - - target.write(cnt) - -def get_binary_name(arch): - return "scipy-%s-%s.exe" % (get_scipy_version(ROOT), arch) - -def prepare_bootstrap(src_root, pyver): - bootstrap = "bootstrap-%s" % pyver - if os.path.exists(bootstrap): - shutil.rmtree(bootstrap) - os.makedirs(bootstrap) - - build_sdist(src_root) - prepare_scipy_sources(src_root, bootstrap) - - shutil.copy('build.py', bootstrap) - prepare_nsis_script(bootstrap, pyver, get_scipy_version(src_root)) - -if __name__ == '__main__': - ROOT = os.path.join("..", "..", "..") - prepare_bootstrap(ROOT, "2.5") From scipy-svn at scipy.org Wed Oct 22 04:16:07 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 22 Oct 2008 03:16:07 -0500 (CDT) Subject: [Scipy-svn] r4824 - trunk/scipy/stats Message-ID: <20081022081607.A9B6439C0F1@scipy.org> Author: cdavid Date: 2008-10-22 03:15:58 -0500 (Wed, 22 Oct 2008) New Revision: 4824 Modified: trunk/scipy/stats/morestats.py Log: Use np instead of numpy in morestats. Modified: trunk/scipy/stats/morestats.py =================================================================== --- trunk/scipy/stats/morestats.py 2008-10-21 10:51:18 UTC (rev 4823) +++ trunk/scipy/stats/morestats.py 2008-10-22 08:15:58 UTC (rev 4824) @@ -10,7 +10,7 @@ atleast_1d, sqrt, ceil, floor, array, poly1d, compress, not_equal, \ pi, exp, ravel, angle import scipy -import numpy +import numpy as np import types import scipy.optimize as optimize import scipy.special as special @@ -784,7 +784,7 @@ Returns pval -- Probability that null test is rejected for this set of x and n even though it is true. """ - x = atleast_1d(x).astype(numpy.integer) + x = atleast_1d(x).astype(np.integer) if len(x) == 2: n = x[1]+x[0] x = x[0] @@ -792,7 +792,7 @@ x = x[0] if n is None or n < x: raise ValueError, "n must be >= x" - n = numpy.int_(n) + n = np.int_(n) else: raise ValueError, "Incorrect length for x." From scipy-svn at scipy.org Wed Oct 22 04:29:59 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 22 Oct 2008 03:29:59 -0500 (CDT) Subject: [Scipy-svn] r4825 - trunk/scipy/stats/tests Message-ID: <20081022082959.5881339C0F1@scipy.org> Author: cdavid Date: 2008-10-22 03:29:55 -0500 (Wed, 22 Oct 2008) New Revision: 4825 Modified: trunk/scipy/stats/tests/test_stats.py Log: Replace numpy imports with np. Modified: trunk/scipy/stats/tests/test_stats.py =================================================================== --- trunk/scipy/stats/tests/test_stats.py 2008-10-22 08:15:58 UTC (rev 4824) +++ trunk/scipy/stats/tests/test_stats.py 2008-10-22 08:29:55 UTC (rev 4825) @@ -8,7 +8,7 @@ from numpy.testing import * from numpy import array, arange, zeros, ravel, float32, float64, power -import numpy +import numpy as np import scipy.stats as stats @@ -87,12 +87,12 @@ def test_rounding2(self): """ W.II.A.2. Y = 2-INT(EXP(LOG(SQR(2)*SQR(2)))) (Y should be 0)""" - y=2-int(numpy.exp(numpy.log(numpy.sqrt(2.)*numpy.sqrt(2.)))) + y=2-int(np.exp(np.log(np.sqrt(2.)*np.sqrt(2.)))) assert_equal(y,0) def test_rounding3(self): """ W.II.A.3. Y = INT(3-EXP(LOG(SQR(2)*SQR(2)))) (Y should be 1)""" - y=(int(round((3-numpy.exp(numpy.log(numpy.sqrt(2.0)*numpy.sqrt(2.0))))))) + y=(int(round((3-np.exp(np.log(np.sqrt(2.0)*np.sqrt(2.0))))))) assert_equal(y,1) class TestBasicStats(TestCase): @@ -142,7 +142,8 @@ ## assert_almost_equal(y, 0.0) def test_meanBIG(self): - y = stats.mean(BIG) + y = np.mean(BIG) + assert_almost_equal(y, 99999995.00) def test_stdBIG(self): @@ -187,11 +188,11 @@ self.X = X.copy() self.Xall = X.copy() - self.Xall[:] = numpy.nan + self.Xall[:] = np.nan self.Xsome = X.copy() self.Xsomet = X.copy() - self.Xsome[0] = numpy.nan + self.Xsome[0] = np.nan self.Xsomet = self.Xsomet[1:] def test_nanmean_none(self): @@ -207,7 +208,7 @@ def test_nanmean_all(self): """Check nanmean when all values are nan.""" m = stats.nanmean(self.Xall) - assert numpy.isnan(m) + assert np.isnan(m) def test_nanstd_none(self): """Check nanstd when no values are nan.""" @@ -222,7 +223,7 @@ def test_nanstd_all(self): """Check nanstd when all values are nan.""" s = stats.nanstd(self.Xall) - assert numpy.isnan(s) + assert np.isnan(s) def test_nanmedian_none(self): """Check nanmedian when no values are nan.""" @@ -237,7 +238,7 @@ def test_nanmedian_all(self): """Check nanmedian when all values are nan.""" m = stats.nanmedian(self.Xall) - assert numpy.isnan(m) + assert np.isnan(m) class TestCorr(TestCase): """ W.II.D. Compute a correlation matrix on all the variables. @@ -483,9 +484,9 @@ def test_regress_simple(self): """Regress a line with sinusoidal noise.""" - x = numpy.linspace(0, 100, 100) - y = 0.2 * numpy.linspace(0, 100, 100) + 10 - y += numpy.sin(numpy.linspace(0, 20, 100)) + x = np.linspace(0, 100, 100) + y = 0.2 * np.linspace(0, 100, 100) + 10 + y += np.sin(np.linspace(0, 20, 100)) res = stats.linregress(x, y) assert_almost_equal(res[4], 4.3609875083149268e-3) From scipy-svn at scipy.org Wed Oct 22 04:31:37 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 22 Oct 2008 03:31:37 -0500 (CDT) Subject: [Scipy-svn] r4826 - trunk/scipy/stats/tests Message-ID: <20081022083137.7627F39C0F1@scipy.org> Author: cdavid Date: 2008-10-22 03:31:32 -0500 (Wed, 22 Oct 2008) New Revision: 4826 Modified: trunk/scipy/stats/tests/test_stats.py Log: Revert bogus change. Modified: trunk/scipy/stats/tests/test_stats.py =================================================================== --- trunk/scipy/stats/tests/test_stats.py 2008-10-22 08:29:55 UTC (rev 4825) +++ trunk/scipy/stats/tests/test_stats.py 2008-10-22 08:31:32 UTC (rev 4826) @@ -142,7 +142,7 @@ ## assert_almost_equal(y, 0.0) def test_meanBIG(self): - y = np.mean(BIG) + y = stats.mean(BIG) assert_almost_equal(y, 99999995.00) From scipy-svn at scipy.org Wed Oct 22 09:53:07 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 22 Oct 2008 08:53:07 -0500 (CDT) Subject: [Scipy-svn] r4827 - trunk/scipy Message-ID: <20081022135307.414D039C0EA@scipy.org> Author: cdavid Date: 2008-10-22 08:52:41 -0500 (Wed, 22 Oct 2008) New Revision: 4827 Modified: trunk/scipy/__init__.py Log: Emit a warning if numpy is < 1.2.0. Modified: trunk/scipy/__init__.py =================================================================== --- trunk/scipy/__init__.py 2008-10-22 08:31:32 UTC (rev 4826) +++ trunk/scipy/__init__.py 2008-10-22 13:52:41 UTC (rev 4827) @@ -29,6 +29,14 @@ from numpy.fft import fft, ifft from numpy.lib.scimath import * +# Emit a warning if numpy is too old +majver, minver = [float(i) for i in _num.version.version.split('.')[:2]] +if majver < 1 or (majver == 1 and minver < 2): + import warnings + warnings.warn("Numpy 1.2.0 or above is recommended for this version of " \ + "scipy (detected version %s)" % _num.version.version, + UserWarning) + __all__ += ['oldnumeric']+_num.__all__ __all__ += ['randn', 'rand', 'fft', 'ifft'] From scipy-svn at scipy.org Thu Oct 23 08:33:46 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Thu, 23 Oct 2008 07:33:46 -0500 (CDT) Subject: [Scipy-svn] r4828 - in branches/refactor_fft: . scipy scipy/cluster scipy/cluster/src scipy/cluster/tests scipy/fftpack/benchmarks scipy/fftpack/tests scipy/integrate scipy/integrate/tests scipy/interpolate scipy/interpolate/tests scipy/io scipy/io/arff scipy/io/arff/tests scipy/io/matlab scipy/io/matlab/tests scipy/io/matlab/tests/data scipy/io/tests scipy/lib/blas/tests scipy/lib/lapack/tests scipy/linalg scipy/linalg/benchmarks scipy/linalg/tests scipy/maxentropy scipy/maxentropy/examples scipy/maxentropy/tests scipy/misc scipy/misc/tests scipy/ndimage scipy/odr/tests scipy/optimize scipy/optimize/nnls scipy/optimize/tests scipy/signal scipy/sparse scipy/sparse/benchmarks scipy/sparse/linalg/dsolve/SuperLU/SRC scipy/sparse/linalg/dsolve/tests scipy/sparse/linalg/dsolve/umfpack scipy/sparse/linalg/dsolve/umfpack/tests scipy/sparse/linalg/eigen/arpack scipy/sparse/linalg/eigen/arpack/tests scipy/sparse/linalg/eigen/lobpcg scipy/sparse/linalg/eigen/lobpcg/tests scipy/sparse/linalg/isolve scipy/sparse/linalg/isolve/tests scipy/sparse/linalg/tests scipy/sparse/sparsetools scipy/sparse/tests scipy/special scipy/special/cephes scipy/special/tests scipy/stats scipy/stats/tests scipy/stsci/convolve/lib scipy/stsci/image/lib scipy/weave scipy/weave/examples scipy/weave/tests tools tools/win32 tools/win32/build_scripts tools/win32/build_scripts/nsis_scripts Message-ID: <20081023123346.8F14439C05F@scipy.org> Author: cdavid Date: 2008-10-23 07:30:20 -0500 (Thu, 23 Oct 2008) New Revision: 4828 Added: branches/refactor_fft/scipy/cluster/tests/cdist-X1.txt branches/refactor_fft/scipy/cluster/tests/cdist-X2.txt branches/refactor_fft/scipy/io/matlab/byteordercodes.py branches/refactor_fft/scipy/io/matlab/tests/data/testhdf5_7.4_GLNX86.mat branches/refactor_fft/scipy/io/matlab/tests/gen_unittests.m branches/refactor_fft/scipy/io/matlab/tests/gen_unittests4.m branches/refactor_fft/scipy/io/matlab/tests/save_test.m branches/refactor_fft/scipy/io/matlab/tests/test_byteordercodes.py branches/refactor_fft/scipy/optimize/nnls.py branches/refactor_fft/scipy/optimize/nnls/ branches/refactor_fft/scipy/optimize/nnls/nnls.f branches/refactor_fft/scipy/optimize/nnls/nnls.pyf branches/refactor_fft/scipy/optimize/tests/test_nnls.py branches/refactor_fft/scipy/stats/vonmises.py branches/refactor_fft/scipy/stats/vonmises_cython.c branches/refactor_fft/scipy/stats/vonmises_cython.pyx branches/refactor_fft/tools/ branches/refactor_fft/tools/win32/ branches/refactor_fft/tools/win32/build_scripts/ branches/refactor_fft/tools/win32/build_scripts/nsis_scripts/ branches/refactor_fft/tools/win32/build_scripts/nsis_scripts/scipy-superinstaller.nsi.in branches/refactor_fft/tools/win32/build_scripts/pavement.py Removed: branches/refactor_fft/scipy/io/tests/gen_unittests.m branches/refactor_fft/scipy/io/tests/gen_unittests4.m branches/refactor_fft/scipy/io/tests/save_test.m branches/refactor_fft/scipy/optimize/nnls/nnls.f branches/refactor_fft/scipy/optimize/nnls/nnls.pyf branches/refactor_fft/scipy/weave/tests/test_scxx.py branches/refactor_fft/tools/win32/ branches/refactor_fft/tools/win32/build_scripts/ branches/refactor_fft/tools/win32/build_scripts/nsis_scripts/ branches/refactor_fft/tools/win32/build_scripts/nsis_scripts/scipy-superinstaller.nsi.in branches/refactor_fft/tools/win32/build_scripts/pavement.py Modified: branches/refactor_fft/ branches/refactor_fft/THANKS.txt branches/refactor_fft/scipy/__init__.py branches/refactor_fft/scipy/cluster/distance.py branches/refactor_fft/scipy/cluster/hierarchy.py branches/refactor_fft/scipy/cluster/src/distance.c branches/refactor_fft/scipy/cluster/src/distance.h branches/refactor_fft/scipy/cluster/src/distance_wrap.c branches/refactor_fft/scipy/cluster/tests/test_distance.py branches/refactor_fft/scipy/cluster/tests/test_hierarchy.py branches/refactor_fft/scipy/cluster/tests/test_vq.py branches/refactor_fft/scipy/fftpack/benchmarks/bench_basic.py branches/refactor_fft/scipy/fftpack/benchmarks/bench_pseudo_diffs.py branches/refactor_fft/scipy/fftpack/tests/test_basic.py branches/refactor_fft/scipy/fftpack/tests/test_helper.py branches/refactor_fft/scipy/fftpack/tests/test_pseudo_diffs.py branches/refactor_fft/scipy/integrate/ode.py branches/refactor_fft/scipy/integrate/quadpack.py branches/refactor_fft/scipy/integrate/quadrature.py branches/refactor_fft/scipy/integrate/tests/test_integrate.py branches/refactor_fft/scipy/integrate/tests/test_quadpack.py branches/refactor_fft/scipy/interpolate/SConscript branches/refactor_fft/scipy/interpolate/interpolate.py branches/refactor_fft/scipy/interpolate/polyint.py branches/refactor_fft/scipy/interpolate/tests/test_fitpack.py branches/refactor_fft/scipy/io/__init__.py branches/refactor_fft/scipy/io/arff/arffread.py branches/refactor_fft/scipy/io/arff/tests/test_data.py branches/refactor_fft/scipy/io/arff/tests/test_header.py branches/refactor_fft/scipy/io/fopen.py branches/refactor_fft/scipy/io/matlab/mio.py branches/refactor_fft/scipy/io/matlab/mio4.py branches/refactor_fft/scipy/io/matlab/mio5.py branches/refactor_fft/scipy/io/matlab/miobase.py branches/refactor_fft/scipy/io/matlab/tests/test_mio.py branches/refactor_fft/scipy/io/mmio.py branches/refactor_fft/scipy/io/netcdf.py branches/refactor_fft/scipy/io/tests/test_mmio.py branches/refactor_fft/scipy/lib/blas/tests/test_blas.py branches/refactor_fft/scipy/lib/blas/tests/test_fblas.py branches/refactor_fft/scipy/lib/lapack/tests/test_lapack.py branches/refactor_fft/scipy/linalg/benchmarks/bench_basic.py branches/refactor_fft/scipy/linalg/benchmarks/bench_decom.py branches/refactor_fft/scipy/linalg/matfuncs.py branches/refactor_fft/scipy/linalg/tests/test_atlas_version.py branches/refactor_fft/scipy/linalg/tests/test_basic.py branches/refactor_fft/scipy/linalg/tests/test_blas.py branches/refactor_fft/scipy/linalg/tests/test_decomp.py branches/refactor_fft/scipy/linalg/tests/test_fblas.py branches/refactor_fft/scipy/linalg/tests/test_lapack.py branches/refactor_fft/scipy/linalg/tests/test_matfuncs.py branches/refactor_fft/scipy/maxentropy/examples/bergerexample.py branches/refactor_fft/scipy/maxentropy/examples/conditionalexample2.py branches/refactor_fft/scipy/maxentropy/maxentropy.py branches/refactor_fft/scipy/maxentropy/maxentutils.py branches/refactor_fft/scipy/maxentropy/tests/test_maxentropy.py branches/refactor_fft/scipy/misc/common.py branches/refactor_fft/scipy/misc/pilutil.py branches/refactor_fft/scipy/misc/tests/test_pilutil.py branches/refactor_fft/scipy/ndimage/SConscript branches/refactor_fft/scipy/ndimage/fourier.py branches/refactor_fft/scipy/ndimage/interpolation.py branches/refactor_fft/scipy/ndimage/morphology.py branches/refactor_fft/scipy/odr/tests/test_odr.py branches/refactor_fft/scipy/optimize/SConscript branches/refactor_fft/scipy/optimize/linesearch.py branches/refactor_fft/scipy/optimize/minpack.py branches/refactor_fft/scipy/optimize/optimize.py branches/refactor_fft/scipy/optimize/setup.py branches/refactor_fft/scipy/optimize/slsqp.py branches/refactor_fft/scipy/optimize/tests/test_optimize.py branches/refactor_fft/scipy/optimize/tests/test_slsqp.py branches/refactor_fft/scipy/optimize/tests/test_zeros.py branches/refactor_fft/scipy/optimize/zeros.py branches/refactor_fft/scipy/signal/bsplines.py branches/refactor_fft/scipy/signal/signaltools.py branches/refactor_fft/scipy/signal/wavelets.py branches/refactor_fft/scipy/sparse/benchmarks/bench_sparse.py branches/refactor_fft/scipy/sparse/bsr.py branches/refactor_fft/scipy/sparse/compressed.py branches/refactor_fft/scipy/sparse/construct.py branches/refactor_fft/scipy/sparse/coo.py branches/refactor_fft/scipy/sparse/csc.py branches/refactor_fft/scipy/sparse/csr.py branches/refactor_fft/scipy/sparse/dia.py branches/refactor_fft/scipy/sparse/dok.py branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c branches/refactor_fft/scipy/sparse/linalg/dsolve/tests/test_linsolve.py branches/refactor_fft/scipy/sparse/linalg/dsolve/umfpack/tests/test_umfpack.py branches/refactor_fft/scipy/sparse/linalg/dsolve/umfpack/umfpack.py branches/refactor_fft/scipy/sparse/linalg/eigen/arpack/speigs.py branches/refactor_fft/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py branches/refactor_fft/scipy/sparse/linalg/eigen/lobpcg/lobpcg.py branches/refactor_fft/scipy/sparse/linalg/eigen/lobpcg/tests/test_lobpcg.py branches/refactor_fft/scipy/sparse/linalg/isolve/iterative.py branches/refactor_fft/scipy/sparse/linalg/isolve/minres.py branches/refactor_fft/scipy/sparse/linalg/isolve/tests/test_iterative.py branches/refactor_fft/scipy/sparse/linalg/tests/test_interface.py branches/refactor_fft/scipy/sparse/sparsetools/coo.h branches/refactor_fft/scipy/sparse/sparsetools/coo.i branches/refactor_fft/scipy/sparse/sparsetools/coo.py branches/refactor_fft/scipy/sparse/sparsetools/coo_wrap.cxx branches/refactor_fft/scipy/sparse/sparsetools/csc.i branches/refactor_fft/scipy/sparse/sparsetools/csc.py branches/refactor_fft/scipy/sparse/sparsetools/csc_wrap.cxx branches/refactor_fft/scipy/sparse/sparsetools/csr.h branches/refactor_fft/scipy/sparse/sparsetools/csr.i branches/refactor_fft/scipy/sparse/sparsetools/csr.py branches/refactor_fft/scipy/sparse/sparsetools/csr_wrap.cxx branches/refactor_fft/scipy/sparse/sparsetools/dia.py branches/refactor_fft/scipy/sparse/sparsetools/dia_wrap.cxx branches/refactor_fft/scipy/sparse/sparsetools/sparsetools.i branches/refactor_fft/scipy/sparse/spfuncs.py branches/refactor_fft/scipy/sparse/sputils.py branches/refactor_fft/scipy/sparse/tests/test_base.py branches/refactor_fft/scipy/sparse/tests/test_extract.py branches/refactor_fft/scipy/sparse/tests/test_sputils.py branches/refactor_fft/scipy/special/cephes/jv.c branches/refactor_fft/scipy/special/cephes_doc.h branches/refactor_fft/scipy/special/info.py branches/refactor_fft/scipy/special/tests/test_basic.py branches/refactor_fft/scipy/stats/_support.py branches/refactor_fft/scipy/stats/continuous.lyx branches/refactor_fft/scipy/stats/distributions.py branches/refactor_fft/scipy/stats/mmorestats.py branches/refactor_fft/scipy/stats/morestats.py branches/refactor_fft/scipy/stats/mstats.py branches/refactor_fft/scipy/stats/setup.py branches/refactor_fft/scipy/stats/stats.py branches/refactor_fft/scipy/stats/tests/test_distributions.py branches/refactor_fft/scipy/stats/tests/test_mmorestats.py branches/refactor_fft/scipy/stats/tests/test_mstats.py branches/refactor_fft/scipy/stats/tests/test_stats.py branches/refactor_fft/scipy/stsci/convolve/lib/Convolve.py branches/refactor_fft/scipy/stsci/convolve/lib/iraf_frame.py branches/refactor_fft/scipy/stsci/convolve/lib/lineshape.py branches/refactor_fft/scipy/stsci/image/lib/_image.py branches/refactor_fft/scipy/stsci/image/lib/combine.py branches/refactor_fft/scipy/weave/accelerate_tools.py branches/refactor_fft/scipy/weave/examples/vq.py branches/refactor_fft/scipy/weave/tests/test_blitz_tools.py branches/refactor_fft/scipy/weave/tests/test_c_spec.py branches/refactor_fft/scipy/weave/tests/test_ext_tools.py branches/refactor_fft/scipy/weave/tests/test_inline_tools.py branches/refactor_fft/scipy/weave/tests/test_numpy_scalar_spec.py branches/refactor_fft/scipy/weave/tests/test_scxx_dict.py branches/refactor_fft/scipy/weave/tests/test_scxx_object.py branches/refactor_fft/scipy/weave/tests/test_scxx_sequence.py branches/refactor_fft/scipy/weave/tests/test_size_check.py branches/refactor_fft/scipy/weave/tests/weave_test_utils.py Log: Merged revisions 4694-4827 via svnmerge from http://svn.scipy.org/svn/scipy/trunk ........ r4694 | damian.eads | 2008-09-08 04:28:06 +0900 (Mon, 08 Sep 2008) | 1 line Added cdist function for computing distances between two collections of vectors. Added tests for the cdist function. ........ r4696 | damian.eads | 2008-09-08 14:01:06 +0900 (Mon, 08 Sep 2008) | 1 line RSTified more hierarchy docs. ........ r4697 | pierregm | 2008-09-08 14:01:53 +0900 (Mon, 08 Sep 2008) | 1 line mstats.mode: make sure that mode returns a tuple. ........ r4700 | pierregm | 2008-09-08 17:05:22 +0900 (Mon, 08 Sep 2008) | 1 line * force compatibility between mstats.mode and stats.mode ........ r4703 | damian.eads | 2008-09-08 23:47:40 +0900 (Mon, 08 Sep 2008) | 1 line RSTified more hierarchy docs. ........ r4704 | ptvirtan | 2008-09-09 03:57:39 +0900 (Tue, 09 Sep 2008) | 1 line Fix Bessel K_nu function name in docstrings: 'modified Bessel function of the second kind' is the more common name, rather than the 'third kind' ........ r4705 | alan.mcintyre | 2008-09-09 22:46:55 +0900 (Tue, 09 Sep 2008) | 4 lines Standardize NumPy import as "import numpy as np". Removed unused numpy import. Clean up alignment of multiline statements. ........ r4706 | alan.mcintyre | 2008-09-09 22:55:11 +0900 (Tue, 09 Sep 2008) | 2 lines Standardize NumPy import as "import numpy as np". ........ r4707 | alan.mcintyre | 2008-09-09 23:16:57 +0900 (Tue, 09 Sep 2008) | 3 lines Standardize NumPy import as "import numpy as np". Removed unused numpy import. ........ r4708 | cdavid | 2008-09-10 02:11:12 +0900 (Wed, 10 Sep 2008) | 2 lines Start the prepare bootstrap for scipy. ........ r4709 | cdavid | 2008-09-10 02:12:17 +0900 (Wed, 10 Sep 2008) | 2 lines Add func to build scipy tarball. ........ r4710 | cdavid | 2008-09-10 02:12:59 +0900 (Wed, 10 Sep 2008) | 2 lines Fix mising dot between release version and dev. ........ r4711 | cdavid | 2008-09-10 02:13:49 +0900 (Wed, 10 Sep 2008) | 2 lines Full version string correctly generated. ........ r4712 | cdavid | 2008-09-10 02:14:37 +0900 (Wed, 10 Sep 2008) | 2 lines Bootsrap script can now prepare scipy sources. ........ r4713 | cdavid | 2008-09-10 02:15:25 +0900 (Wed, 10 Sep 2008) | 2 lines Remove global var reference. ........ r4714 | cdavid | 2008-09-10 02:16:19 +0900 (Wed, 10 Sep 2008) | 2 lines Add build script for scipy. ........ r4715 | cdavid | 2008-09-10 02:17:02 +0900 (Wed, 10 Sep 2008) | 2 lines Copy build script in bootstrapped sources. ........ r4716 | cdavid | 2008-09-10 02:17:57 +0900 (Wed, 10 Sep 2008) | 2 lines Remove numpy references in scipy build script. ........ r4717 | cdavid | 2008-09-10 02:18:55 +0900 (Wed, 10 Sep 2008) | 2 lines Add nsis script to bootstap. ........ r4718 | cdavid | 2008-09-10 02:19:48 +0900 (Wed, 10 Sep 2008) | 2 lines Add nsis script to build super installer. ........ r4719 | cdavid | 2008-09-10 02:20:26 +0900 (Wed, 10 Sep 2008) | 2 lines Fix numpy script leftover. ........ r4720 | cdavid | 2008-09-10 02:21:11 +0900 (Wed, 10 Sep 2008) | 2 lines Forgot to pass ROOT dir to scipy_version func. ........ r4721 | cdavid | 2008-09-10 02:22:00 +0900 (Wed, 10 Sep 2008) | 2 lines Rename nsis script. ........ r4722 | cdavid | 2008-09-10 03:11:27 +0900 (Wed, 10 Sep 2008) | 1 line Fix missing imports. ........ r4723 | alan.mcintyre | 2008-09-19 04:12:41 +0900 (Fri, 19 Sep 2008) | 3 lines Removed unused imports. PEP8 conformance (one import per line). ........ r4724 | alan.mcintyre | 2008-09-19 04:15:47 +0900 (Fri, 19 Sep 2008) | 3 lines Removed unused imports. Standardized NumPy import as "import numpy as np". ........ r4725 | alan.mcintyre | 2008-09-19 04:23:58 +0900 (Fri, 19 Sep 2008) | 4 lines Removed unused imports. Standardized NumPy import as "import numpy as np". PEP8 conformance (one import per line). ........ r4726 | alan.mcintyre | 2008-09-19 04:27:00 +0900 (Fri, 19 Sep 2008) | 2 lines Removed unused imports. ........ r4727 | alan.mcintyre | 2008-09-19 04:28:38 +0900 (Fri, 19 Sep 2008) | 2 lines Removed unused imports. ........ r4728 | alan.mcintyre | 2008-09-19 04:30:13 +0900 (Fri, 19 Sep 2008) | 2 lines Remove unused imports. ........ r4729 | alan.mcintyre | 2008-09-19 04:31:49 +0900 (Fri, 19 Sep 2008) | 2 lines Remove unused/redundant imports. ........ r4730 | alan.mcintyre | 2008-09-19 04:34:57 +0900 (Fri, 19 Sep 2008) | 2 lines Removed unused/redundant imports. ........ r4731 | alan.mcintyre | 2008-09-19 04:37:37 +0900 (Fri, 19 Sep 2008) | 2 lines Removed unused imports. ........ r4732 | alan.mcintyre | 2008-09-19 04:45:20 +0900 (Fri, 19 Sep 2008) | 3 lines Removed unused imports. Removed test_scxx.py (the nose framework doesn't need to import the test_scxx* modules to find the tests). ........ r4733 | alan.mcintyre | 2008-09-19 04:50:45 +0900 (Fri, 19 Sep 2008) | 2 lines Removed unused imports. ........ r4734 | alan.mcintyre | 2008-09-19 04:55:02 +0900 (Fri, 19 Sep 2008) | 2 lines Removed unused imports. ........ r4735 | alan.mcintyre | 2008-09-19 04:57:42 +0900 (Fri, 19 Sep 2008) | 2 lines Removed unused imports. ........ r4736 | alan.mcintyre | 2008-09-19 05:00:22 +0900 (Fri, 19 Sep 2008) | 3 lines Removed unused imports. Moved imports to the top of modules. ........ r4737 | alan.mcintyre | 2008-09-19 06:20:05 +0900 (Fri, 19 Sep 2008) | 3 lines Removed unused imports. Standardize NumPy import as "import numpy as np". ........ r4738 | alan.mcintyre | 2008-09-21 08:00:11 +0900 (Sun, 21 Sep 2008) | 2 lines Remove unused imports. ........ r4739 | alan.mcintyre | 2008-09-21 08:01:08 +0900 (Sun, 21 Sep 2008) | 2 lines Silence debugging print statements when verbosity < 3. ........ r4740 | damian.eads | 2008-09-21 12:15:27 +0900 (Sun, 21 Sep 2008) | 1 line Corrected spelling of hierarchical. ........ r4741 | oliphant | 2008-09-25 02:17:51 +0900 (Thu, 25 Sep 2008) | 1 line Remove typecode from _support ........ r4742 | cdavid | 2008-09-26 13:00:25 +0900 (Fri, 26 Sep 2008) | 2 lines Deprecate scipy.stats.var for numpy.var. ........ r4743 | cdavid | 2008-09-26 13:01:16 +0900 (Fri, 26 Sep 2008) | 2 lines scipy.stats.warn raise a DeprecationWarning. ........ r4744 | cdavid | 2008-09-26 13:02:08 +0900 (Fri, 26 Sep 2008) | 2 lines Deprecate scipy.stats.std. ........ r4745 | cdavid | 2008-09-26 13:02:51 +0900 (Fri, 26 Sep 2008) | 2 lines Fix deprecation warning for stats.var. ........ r4746 | cdavid | 2008-09-26 13:03:37 +0900 (Fri, 26 Sep 2008) | 2 lines Deprecate stats.mean. ........ r4747 | cdavid | 2008-09-26 13:04:19 +0900 (Fri, 26 Sep 2008) | 2 lines Deprecate stats.median. ........ r4748 | cdavid | 2008-09-26 13:05:03 +0900 (Fri, 26 Sep 2008) | 2 lines Deprecate stats.cov. ........ r4749 | cdavid | 2008-09-26 13:05:42 +0900 (Fri, 26 Sep 2008) | 2 lines Deprecate stats.corrcoef. ........ r4750 | cdavid | 2008-09-26 13:06:26 +0900 (Fri, 26 Sep 2008) | 2 lines Fix cov/corrcoef deprecation. ........ r4751 | cdavid | 2008-09-26 13:25:25 +0900 (Fri, 26 Sep 2008) | 1 line Do not modify error handling when importing scipy. ........ r4752 | oliphant | 2008-09-26 21:53:59 +0900 (Fri, 26 Sep 2008) | 1 line Fix distributions to return numpy scalars instead of 0-d arrays. ........ r4753 | oliphant | 2008-09-28 06:46:03 +0900 (Sun, 28 Sep 2008) | 1 line Fix error in est_loc_scale. ........ r4754 | uwe.schmitt | 2008-09-30 05:13:24 +0900 (Tue, 30 Sep 2008) | 1 line added nnls, incl. test script ........ r4755 | uwe.schmitt | 2008-09-30 15:45:52 +0900 (Tue, 30 Sep 2008) | 1 line removed obsolete default parameters from nnls() ........ r4756 | uwe.schmitt | 2008-09-30 15:48:15 +0900 (Tue, 30 Sep 2008) | 1 line removed NNLS type from setup.py and Sconscript ........ r4757 | uwe.schmitt | 2008-09-30 16:00:07 +0900 (Tue, 30 Sep 2008) | 1 line renamed NNLS.F to nnls.f ........ r4758 | damian.eads | 2008-10-01 04:14:59 +0900 (Wed, 01 Oct 2008) | 1 line Fixed minor bug in cophenet. ........ r4759 | peridot | 2008-10-01 08:55:21 +0900 (Wed, 01 Oct 2008) | 2 lines New module for spatial data structure. Currently contains one pure-python implementation of a kd-tree. ........ r4760 | damian.eads | 2008-10-01 09:10:16 +0900 (Wed, 01 Oct 2008) | 1 line Added order keyword in asarray statements to ensure contiguity of data prior to passing to C functions. ........ r4761 | damian.eads | 2008-10-01 09:14:20 +0900 (Wed, 01 Oct 2008) | 1 line Added order keyword in asarray statements to ensure contiguity of data prior to passing to C functions. ........ r4763 | peridot | 2008-10-01 18:52:45 +0900 (Wed, 01 Oct 2008) | 2 lines Removed spatial; it's in a branch now so that it won't interfere with 0.7. ........ r4764 | rkern | 2008-10-02 04:38:40 +0900 (Thu, 02 Oct 2008) | 1 line BUG: Remove mistaken commit. ........ r4765 | oliphant | 2008-10-03 23:31:41 +0900 (Fri, 03 Oct 2008) | 1 line Fix jv(3,4) case by special casing. The problem is in the recur function which seems to have a special-case problem with n=3 and x=4 because pk=qk in that case. ........ r4766 | oliphant | 2008-10-04 03:57:20 +0900 (Sat, 04 Oct 2008) | 1 line Improve docstring of lognorm a bit. ........ r4767 | wnbell | 2008-10-05 07:22:39 +0900 (Sun, 05 Oct 2008) | 4 lines rename c_abs() and c_abs1() to slu_c_abs() and slu_c_abs() to avoid conflict with Python headers hopefully resolves ticket #735 ........ r4768 | wnbell | 2008-10-05 07:33:54 +0900 (Sun, 05 Oct 2008) | 2 lines fix spelling in c_abs renaming ........ r4770 | matthew.brett at gmail.com | 2008-10-05 09:58:08 +0900 (Sun, 05 Oct 2008) | 1 line Centralize recognition of matfile type, apply patch for ticket #743 by Ray Jones, with thanks ........ r4771 | matthew.brett at gmail.com | 2008-10-05 10:54:59 +0900 (Sun, 05 Oct 2008) | 1 line Restore matlab m files for creating test mat files ........ r4772 | wnbell | 2008-10-05 11:23:37 +0900 (Sun, 05 Oct 2008) | 2 lines minor additions to sparsetools ........ r4773 | wnbell | 2008-10-05 11:28:14 +0900 (Sun, 05 Oct 2008) | 2 lines renamed diags to offsets to avoid possible confusion ........ r4774 | alan.mcintyre | 2008-10-05 12:36:54 +0900 (Sun, 05 Oct 2008) | 2 lines Don't print debug information for low verbosity. ........ r4775 | matthew.brett at gmail.com | 2008-10-05 13:49:23 +0900 (Sun, 05 Oct 2008) | 1 line Fixing long-broken mio tests ........ r4781 | matthew.brett at gmail.com | 2008-10-05 18:36:46 +0900 (Sun, 05 Oct 2008) | 1 line Removed m files; now moved to matlab test directory ........ r4783 | matthew.brett at gmail.com | 2008-10-06 01:55:22 +0900 (Mon, 06 Oct 2008) | 1 line Take matlab io tests out for now, known to fail (and have been failing silently for a long time) ........ r4784 | wnbell | 2008-10-06 13:29:46 +0900 (Mon, 06 Oct 2008) | 2 lines added csr_toell() ........ r4785 | wnbell | 2008-10-06 16:36:43 +0900 (Mon, 06 Oct 2008) | 2 lines writing of MatrixMarket now works for all integer kinds ........ r4786 | uwe.schmitt | 2008-10-06 20:49:39 +0900 (Mon, 06 Oct 2008) | 1 line fixed comments in test_nnls.py ........ r4787 | peridot | 2008-10-09 14:26:59 +0900 (Thu, 09 Oct 2008) | 2 lines Fixed and installed tests for scipy.stats.vonmises. Closes tickets #747, #748, and #749. ........ r4788 | peridot | 2008-10-09 16:29:54 +0900 (Thu, 09 Oct 2008) | 4 lines Replaced implementation of von Mises CDF with cython implementation that should have better numerical performance. Note that for large k it uses array operations rather than cython code since we need scipy.stats.norm.cdf and scipy.special.i0. Note also that the C code is *not* automatically rebuilt when the cython code changes. ........ r4789 | wnbell | 2008-10-10 12:17:27 +0900 (Fri, 10 Oct 2008) | 2 lines (hopefully) fix ticket #736 ........ r4790 | cdavid | 2008-10-10 16:00:56 +0900 (Fri, 10 Oct 2008) | 1 line Update numscons build to cope with recent changes in scipy.interpolate. ........ r4791 | cdavid | 2008-10-10 16:02:20 +0900 (Fri, 10 Oct 2008) | 1 line Update numscons build of scipy.ndimage. ........ r4792 | wnbell | 2008-10-11 02:13:10 +0900 (Sat, 11 Oct 2008) | 2 lines fix CSR[:,array([1,2])] slicing problem ........ r4793 | wnbell | 2008-10-11 19:36:17 +0900 (Sat, 11 Oct 2008) | 2 lines added missing import ........ r4798 | cdavid | 2008-10-17 19:34:01 +0900 (Fri, 17 Oct 2008) | 1 line Add a dummy pavement file. ........ r4799 | cdavid | 2008-10-17 19:34:18 +0900 (Fri, 17 Oct 2008) | 1 line The pavement file can now bootstrap the sources. ........ r4800 | cdavid | 2008-10-17 19:38:23 +0900 (Fri, 17 Oct 2008) | 1 line Trailing spaces. ........ r4801 | cdavid | 2008-10-17 19:44:28 +0900 (Fri, 17 Oct 2008) | 1 line Bootstrap is now working with paver. ........ r4802 | wnbell | 2008-10-20 06:18:35 +0900 (Mon, 20 Oct 2008) | 7 lines added DOK matrix patch by James Philbin i. Improved the docstring. ii. Fixed the constructor (this fixes #755). iii. Reduced the codepath for the common case of setting an individual element (~2x speedup). ........ r4803 | cdavid | 2008-10-21 13:04:36 +0900 (Tue, 21 Oct 2008) | 1 line Add clean_bootstrap task. ........ r4804 | cdavid | 2008-10-21 14:09:27 +0900 (Tue, 21 Oct 2008) | 1 line Cosmetic change to pavement file. ........ r4805 | cdavid | 2008-10-21 14:11:03 +0900 (Tue, 21 Oct 2008) | 1 line Start the build task. ........ r4806 | cdavid | 2008-10-21 14:31:55 +0900 (Tue, 21 Oct 2008) | 1 line More on build. ........ r4807 | cdavid | 2008-10-21 14:44:14 +0900 (Tue, 21 Oct 2008) | 1 line Rename pavement build task to build_binary. ........ r4808 | cdavid | 2008-10-21 14:58:20 +0900 (Tue, 21 Oct 2008) | 1 line Add bootstrap_arch task. ........ r4809 | cdavid | 2008-10-21 15:13:48 +0900 (Tue, 21 Oct 2008) | 1 line Re-enable the build log. ........ r4810 | cdavid | 2008-10-21 15:23:28 +0900 (Tue, 21 Oct 2008) | 1 line Bootstrap build now OK. ........ r4811 | cdavid | 2008-10-21 15:26:54 +0900 (Tue, 21 Oct 2008) | 1 line Fix clean_bootstrap task. ........ r4812 | cdavid | 2008-10-21 15:43:17 +0900 (Tue, 21 Oct 2008) | 1 line Generate nsis script in bootstrap task. ........ r4813 | cdavid | 2008-10-21 16:22:36 +0900 (Tue, 21 Oct 2008) | 1 line Fix nsis script generation. ........ r4814 | cdavid | 2008-10-21 16:26:04 +0900 (Tue, 21 Oct 2008) | 1 line Make build_binary a raw task. ........ r4815 | cdavid | 2008-10-21 16:36:36 +0900 (Tue, 21 Oct 2008) | 1 line Better error checking when running scipy setup.py from paver. ........ r4816 | cdavid | 2008-10-21 18:28:34 +0900 (Tue, 21 Oct 2008) | 1 line Put clean action into separate function. ........ r4817 | cdavid | 2008-10-21 18:30:05 +0900 (Tue, 21 Oct 2008) | 1 line Put clean_bootstrap action into separate function. ........ r4818 | cdavid | 2008-10-21 18:39:47 +0900 (Tue, 21 Oct 2008) | 1 line Build nsis installer should work now. ........ r4819 | cdavid | 2008-10-21 18:55:54 +0900 (Tue, 21 Oct 2008) | 1 line Fix wrong order of dependencies for build_nsis task. ........ r4820 | cdavid | 2008-10-21 18:58:35 +0900 (Tue, 21 Oct 2008) | 1 line nsis_script preparation is common to all arches. ........ r4821 | cdavid | 2008-10-21 19:40:18 +0900 (Tue, 21 Oct 2008) | 1 line nsis task to build the nsis installer from scratch should now work. ........ r4822 | cdavid | 2008-10-21 19:46:57 +0900 (Tue, 21 Oct 2008) | 1 line Disable LZMA compressor for scipy nsis installer: do not give much, and is slow. ........ r4823 | cdavid | 2008-10-21 19:51:18 +0900 (Tue, 21 Oct 2008) | 1 line Remove unused code. ........ r4824 | cdavid | 2008-10-22 17:15:58 +0900 (Wed, 22 Oct 2008) | 1 line Use np instead of numpy in morestats. ........ r4825 | cdavid | 2008-10-22 17:29:55 +0900 (Wed, 22 Oct 2008) | 1 line Replace numpy imports with np. ........ r4826 | cdavid | 2008-10-22 17:31:32 +0900 (Wed, 22 Oct 2008) | 1 line Revert bogus change. ........ r4827 | cdavid | 2008-10-22 22:52:41 +0900 (Wed, 22 Oct 2008) | 1 line Emit a warning if numpy is < 1.2.0. ........ Property changes on: branches/refactor_fft ___________________________________________________________________ Name: svnmerge-integrated - /branches/build_with_scons:1-3868 /branches/scipy.scons:1-3533 /branches/sparse_build_reduce_mem:1-4005 /branches/testing_cleanup:1-3662 /trunk:1-4693 + /branches/build_with_scons:1-3868 /branches/scipy.scons:1-3533 /branches/sparse_build_reduce_mem:1-4005 /branches/testing_cleanup:1-3662 /trunk:1-4827 Modified: branches/refactor_fft/THANKS.txt =================================================================== --- branches/refactor_fft/THANKS.txt 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/THANKS.txt 2008-10-23 12:30:20 UTC (rev 4828) @@ -30,7 +30,8 @@ Travis Vaught -- initial work on stats module clean up Jeff Whitaker -- Mac OS X support David Cournapeau -- bug-fixes, refactor of fftpack and cluster, numscons build. -Damian Eads -- hiearchical clustering +Damian Eads -- hierarchical clustering, dendrogram plotting, + distance functions, vq documentation Testing: Modified: branches/refactor_fft/scipy/__init__.py =================================================================== --- branches/refactor_fft/scipy/__init__.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/__init__.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -28,8 +28,15 @@ from numpy.random import rand, randn from numpy.fft import fft, ifft from numpy.lib.scimath import * -_num.seterr(all='ignore') +# Emit a warning if numpy is too old +majver, minver = [float(i) for i in _num.version.version.split('.')[:2]] +if majver < 1 or (majver == 1 and minver < 2): + import warnings + warnings.warn("Numpy 1.2.0 or above is recommended for this version of " \ + "scipy (detected version %s)" % _num.version.version, + UserWarning) + __all__ += ['oldnumeric']+_num.__all__ __all__ += ['randn', 'rand', 'fft', 'ifft'] @@ -67,7 +74,7 @@ # Remove subpackage names from __all__ such that they are not imported via # "from scipy import *". This works around a numpy bug present in < 1.2. subpackages = """cluster constants fftpack integrate interpolate io lib linalg -linsolve maxentropy misc ndimage odr optimize signal sparse special +linsolve maxentropy misc ndimage odr optimize signal sparse special splinalg stats stsci weave""".split() for name in subpackages: try: Modified: branches/refactor_fft/scipy/cluster/distance.py =================================================================== --- branches/refactor_fft/scipy/cluster/distance.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/cluster/distance.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -193,12 +193,41 @@ d : double The Minkowski distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') if p < 1: raise ValueError("p must be at least 1") return (abs(u-v)**p).sum() ** (1.0 / p) +def wminkowski(u, v, p, w): + """ + Computes the weighted Minkowski distance between two vectors ``u`` + and ``v``, defined as + + .. math:: + + \sum {(w_i*|u_i - v_i|)^p})^(1/p). + + :Parameters: + u : ndarray + An :math:`n`-dimensional vector. + v : ndarray + An :math:`n`-dimensional vector. + p : ndarray + The norm of the difference :math:`${||u-v||}_p$`. + w : ndarray + The weight vector. + + :Returns: + d : double + The Minkowski distance between vectors ``u`` and ``v``. + """ + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') + if p < 1: + raise ValueError("p must be at least 1") + return ((w * abs(u-v))**p).sum() ** (1.0 / p) + def euclidean(u, v): """ Computes the Euclidean distance between two n-vectors ``u`` and ``v``, @@ -218,8 +247,8 @@ d : double The Euclidean distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') q=np.matrix(u-v) return np.sqrt((q*q.T).sum()) @@ -243,8 +272,8 @@ d : double The squared Euclidean distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') return ((u-v)*(u-v).T).sum() def cosine(u, v): @@ -266,8 +295,8 @@ d : double The Cosine distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') return (1.0 - (np.dot(u, v.T) / \ (np.sqrt(np.dot(u, u.T)) * np.sqrt(np.dot(v, v.T))))) @@ -327,8 +356,8 @@ d : double The Hamming distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') return (u != v).mean() def jaccard(u, v): @@ -355,8 +384,8 @@ d : double The Jaccard distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') return (np.double(np.bitwise_and((u != v), np.bitwise_or(u != 0, v != 0)).sum()) / np.double(np.bitwise_or(u != 0, v != 0).sum())) @@ -385,8 +414,8 @@ d : double The Kulsinski distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') n = len(u) (nff, nft, ntf, ntt) = _nbool_correspond_all(u, v) @@ -409,9 +438,9 @@ d : double The standardized Euclidean distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) - V = np.asarray(V) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') + V = np.asarray(V, order='c') if len(V.shape) != 1 or V.shape[0] != u.shape[0] or u.shape[0] != v.shape[0]: raise TypeError('V must be a 1-D array of the same dimension as u and v.') return np.sqrt(((u-v)**2 / V).sum()) @@ -435,8 +464,8 @@ d : double The City Block distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') return abs(u-v).sum() def mahalanobis(u, v, VI): @@ -459,9 +488,9 @@ d : double The Mahalanobis distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) - VI = np.asarray(VI) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') + VI = np.asarray(VI, order='c') return np.sqrt(np.dot(np.dot((u-v),VI),(u-v).T).sum()) def chebyshev(u, v): @@ -482,8 +511,8 @@ d : double The Chebyshev distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') return max(abs(u-v)) def braycurtis(u, v): @@ -505,8 +534,8 @@ d : double The Bray-Curtis distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') return abs(u-v).sum() / abs(u+v).sum() def canberra(u, v): @@ -530,8 +559,8 @@ d : double The Canberra distance between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') return abs(u-v).sum() / (abs(u).sum() + abs(v).sum()) def _nbool_correspond_all(u, v): @@ -597,8 +626,8 @@ d : double The Yule dissimilarity between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') (nff, nft, ntf, ntt) = _nbool_correspond_all(u, v) return float(2.0 * ntf * nft) / float(ntt * nff + ntf * nft) @@ -625,8 +654,8 @@ d : double The Matching dissimilarity between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') (nft, ntf) = _nbool_correspond_ft_tf(u, v) return float(nft + ntf) / float(len(u)) @@ -654,8 +683,8 @@ d : double The Dice dissimilarity between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') if u.dtype == np.bool: ntt = (u & v).sum() else: @@ -687,8 +716,8 @@ The Rogers-Tanimoto dissimilarity between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') (nff, nft, ntf, ntt) = _nbool_correspond_all(u, v) return float(2.0 * (ntf + nft)) / float(ntt + nff + (2.0 * (ntf + nft))) @@ -716,8 +745,8 @@ d : double The Russell-Rao dissimilarity between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') if u.dtype == np.bool: ntt = (u & v).sum() else: @@ -749,8 +778,8 @@ d : double The Sokal-Michener dissimilarity between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') if u.dtype == np.bool: ntt = (u & v).sum() nff = (~u & ~v).sum() @@ -784,8 +813,8 @@ d : double The Sokal-Sneath dissimilarity between vectors ``u`` and ``v``. """ - u = np.asarray(u) - v = np.asarray(v) + u = np.asarray(u, order='c') + v = np.asarray(v, order='c') if u.dtype == np.bool: ntt = (u & v).sum() else: @@ -796,7 +825,7 @@ def pdist(X, metric='euclidean', p=2, V=None, VI=None): """ - Computes the distance between m original observations in + Computes the pairwise distances between m original observations in n-dimensional space. Returns a condensed distance matrix Y. For each :math:`$i$` and :math:`$j$` (where :math:`$i #include +extern PyObject *cdist_euclidean_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const double *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_euclidean(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue("d", 0.0); +} + +extern PyObject *cdist_canberra_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const double *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_canberra(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue("d", 0.0); +} + +extern PyObject *cdist_bray_curtis_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const double *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_bray_curtis(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue("d", 0.0); +} + + +extern PyObject *cdist_mahalanobis_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *covinv_, *dm_; + int mA, mB, n; + double *dm; + const double *XA, *XB; + const double *covinv; + if (!PyArg_ParseTuple(args, "O!O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &covinv_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + covinv = (const double*)covinv_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_mahalanobis(XA, XB, covinv, dm, mA, mB, n); + } + return Py_BuildValue("d", 0.0); +} + + +extern PyObject *cdist_chebyshev_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const double *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_chebyshev(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue("d", 0.0); +} + + +extern PyObject *cdist_cosine_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_, *normsA_, *normsB_; + int mA, mB, n; + double *dm; + const double *XA, *XB, *normsA, *normsB; + if (!PyArg_ParseTuple(args, "O!O!O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_, + &PyArray_Type, &normsA_, + &PyArray_Type, &normsB_)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + dm = (double*)dm_->data; + normsA = (const double*)normsA_->data; + normsB = (const double*)normsB_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_cosine(XA, XB, dm, mA, mB, n, normsA, normsB); + } + return Py_BuildValue("d", 0.0); +} + +extern PyObject *cdist_seuclidean_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_, *var_; + int mA, mB, n; + double *dm; + const double *XA, *XB, *var; + if (!PyArg_ParseTuple(args, "O!O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &var_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + dm = (double*)dm_->data; + var = (double*)var_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_seuclidean(XA, XB, var, dm, mA, mB, n); + } + return Py_BuildValue("d", 0.0); +} + +extern PyObject *cdist_city_block_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const double *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_city_block(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue("d", 0.0); +} + +extern PyObject *cdist_hamming_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const double *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_hamming(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue("d", 0.0); +} + +extern PyObject *cdist_hamming_bool_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const char *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const char*)XA_->data; + XB = (const char*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_hamming_bool(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue("d", 0.0); +} + +extern PyObject *cdist_jaccard_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const double *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_jaccard(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue("d", 0.0); +} + +extern PyObject *cdist_jaccard_bool_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const char *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const char*)XA_->data; + XB = (const char*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_jaccard_bool(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue("d", 0.0); +} + +extern PyObject *cdist_minkowski_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const double *XA, *XB; + double p; + if (!PyArg_ParseTuple(args, "O!O!O!d", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_, + &p)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + cdist_minkowski(XA, XB, dm, mA, mB, n, p); + } + return Py_BuildValue("d", 0.0); +} + +extern PyObject *cdist_weighted_minkowski_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_, *w_; + int mA, mB, n; + double *dm; + const double *XA, *XB, *w; + double p; + if (!PyArg_ParseTuple(args, "O!O!O!dO!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_, + &p, + &PyArray_Type, &w_)) { + return 0; + } + else { + XA = (const double*)XA_->data; + XB = (const double*)XB_->data; + w = (const double*)w_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + cdist_weighted_minkowski(XA, XB, dm, mA, mB, n, p, w); + } + return Py_BuildValue("d", 0.0); +} + +extern PyObject *cdist_yule_bool_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const char *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const char*)XA_->data; + XB = (const char*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_yule_bool(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue(""); +} + +extern PyObject *cdist_matching_bool_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const char *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const char*)XA_->data; + XB = (const char*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_matching_bool(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue(""); +} + +extern PyObject *cdist_dice_bool_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const char *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const char*)XA_->data; + XB = (const char*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_dice_bool(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue(""); +} + +extern PyObject *cdist_rogerstanimoto_bool_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const char *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const char*)XA_->data; + XB = (const char*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_rogerstanimoto_bool(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue(""); +} + +extern PyObject *cdist_russellrao_bool_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const char *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const char*)XA_->data; + XB = (const char*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_russellrao_bool(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue(""); +} + +extern PyObject *cdist_kulsinski_bool_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const char *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const char*)XA_->data; + XB = (const char*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_kulsinski_bool(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue(""); +} + +extern PyObject *cdist_sokalmichener_bool_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const char *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const char*)XA_->data; + XB = (const char*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_sokalmichener_bool(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue(""); +} + +extern PyObject *cdist_sokalsneath_bool_wrap(PyObject *self, PyObject *args) { + PyArrayObject *XA_, *XB_, *dm_; + int mA, mB, n; + double *dm; + const char *XA, *XB; + if (!PyArg_ParseTuple(args, "O!O!O!", + &PyArray_Type, &XA_, &PyArray_Type, &XB_, + &PyArray_Type, &dm_)) { + return 0; + } + else { + XA = (const char*)XA_->data; + XB = (const char*)XB_->data; + dm = (double*)dm_->data; + mA = XA_->dimensions[0]; + mB = XB_->dimensions[0]; + n = XA_->dimensions[1]; + + cdist_sokalsneath_bool(XA, XB, dm, mA, mB, n); + } + return Py_BuildValue(""); +} + +/***************************** pdist ***/ + extern PyObject *pdist_euclidean_wrap(PyObject *self, PyObject *args) { PyArrayObject *X_, *dm_; int m, n; @@ -324,7 +848,31 @@ return Py_BuildValue("d", 0.0); } +extern PyObject *pdist_weighted_minkowski_wrap(PyObject *self, PyObject *args) { + PyArrayObject *X_, *dm_, *w_; + int m, n; + double *dm, *X, *w; + double p; + if (!PyArg_ParseTuple(args, "O!O!dO!", + &PyArray_Type, &X_, + &PyArray_Type, &dm_, + &p, + &PyArray_Type, &w_)) { + return 0; + } + else { + X = (double*)X_->data; + dm = (double*)dm_->data; + w = (const double*)w_->data; + m = X_->dimensions[0]; + n = X_->dimensions[1]; + pdist_weighted_minkowski(X, dm, m, n, p, w); + } + return Py_BuildValue("d", 0.0); +} + + extern PyObject *pdist_yule_bool_wrap(PyObject *self, PyObject *args) { PyArrayObject *X_, *dm_; int m, n; @@ -533,6 +1081,28 @@ static PyMethodDef _distanceWrapMethods[] = { + {"cdist_bray_curtis_wrap", cdist_bray_curtis_wrap, METH_VARARGS}, + {"cdist_canberra_wrap", cdist_canberra_wrap, METH_VARARGS}, + {"cdist_chebyshev_wrap", cdist_chebyshev_wrap, METH_VARARGS}, + {"cdist_city_block_wrap", cdist_city_block_wrap, METH_VARARGS}, + {"cdist_cosine_wrap", cdist_cosine_wrap, METH_VARARGS}, + {"cdist_dice_bool_wrap", cdist_dice_bool_wrap, METH_VARARGS}, + {"cdist_euclidean_wrap", cdist_euclidean_wrap, METH_VARARGS}, + {"cdist_hamming_wrap", cdist_hamming_wrap, METH_VARARGS}, + {"cdist_hamming_bool_wrap", cdist_hamming_bool_wrap, METH_VARARGS}, + {"cdist_jaccard_wrap", cdist_jaccard_wrap, METH_VARARGS}, + {"cdist_jaccard_bool_wrap", cdist_jaccard_bool_wrap, METH_VARARGS}, + {"cdist_kulsinski_bool_wrap", cdist_kulsinski_bool_wrap, METH_VARARGS}, + {"cdist_mahalanobis_wrap", cdist_mahalanobis_wrap, METH_VARARGS}, + {"cdist_matching_bool_wrap", cdist_matching_bool_wrap, METH_VARARGS}, + {"cdist_minkowski_wrap", cdist_minkowski_wrap, METH_VARARGS}, + {"cdist_weighted_minkowski_wrap", cdist_weighted_minkowski_wrap, METH_VARARGS}, + {"cdist_rogerstanimoto_bool_wrap", cdist_rogerstanimoto_bool_wrap, METH_VARARGS}, + {"cdist_russellrao_bool_wrap", cdist_russellrao_bool_wrap, METH_VARARGS}, + {"cdist_seuclidean_wrap", cdist_seuclidean_wrap, METH_VARARGS}, + {"cdist_sokalmichener_bool_wrap", cdist_sokalmichener_bool_wrap, METH_VARARGS}, + {"cdist_sokalsneath_bool_wrap", cdist_sokalsneath_bool_wrap, METH_VARARGS}, + {"cdist_yule_bool_wrap", cdist_yule_bool_wrap, METH_VARARGS}, {"pdist_bray_curtis_wrap", pdist_bray_curtis_wrap, METH_VARARGS}, {"pdist_canberra_wrap", pdist_canberra_wrap, METH_VARARGS}, {"pdist_chebyshev_wrap", pdist_chebyshev_wrap, METH_VARARGS}, @@ -548,6 +1118,7 @@ {"pdist_mahalanobis_wrap", pdist_mahalanobis_wrap, METH_VARARGS}, {"pdist_matching_bool_wrap", pdist_matching_bool_wrap, METH_VARARGS}, {"pdist_minkowski_wrap", pdist_minkowski_wrap, METH_VARARGS}, + {"pdist_weighted_minkowski_wrap", pdist_weighted_minkowski_wrap, METH_VARARGS}, {"pdist_rogerstanimoto_bool_wrap", pdist_rogerstanimoto_bool_wrap, METH_VARARGS}, {"pdist_russellrao_bool_wrap", pdist_russellrao_bool_wrap, METH_VARARGS}, {"pdist_seuclidean_wrap", pdist_seuclidean_wrap, METH_VARARGS}, Copied: branches/refactor_fft/scipy/cluster/tests/cdist-X1.txt (from rev 4827, trunk/scipy/cluster/tests/cdist-X1.txt) Copied: branches/refactor_fft/scipy/cluster/tests/cdist-X2.txt (from rev 4827, trunk/scipy/cluster/tests/cdist-X2.txt) Modified: branches/refactor_fft/scipy/cluster/tests/test_distance.py =================================================================== --- branches/refactor_fft/scipy/cluster/tests/test_distance.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/cluster/tests/test_distance.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -34,17 +34,16 @@ # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -import sys import os.path import numpy as np from numpy.testing import * -from scipy.cluster.hierarchy import linkage, from_mlab_linkage, numobs_linkage -from scipy.cluster.distance import squareform, pdist, matching, jaccard, dice, sokalsneath, rogerstanimoto, russellrao, yule, numobs_dm, numobs_y +from scipy.cluster.distance import squareform, pdist, cdist, matching, \ + jaccard, dice, sokalsneath, rogerstanimoto, russellrao, yule -#from scipy.cluster.hierarchy import pdist, euclidean - _filenames = ["iris.txt", + "cdist-X1.txt", + "cdist-X2.txt", "pdist-hamming-ml.txt", "pdist-boolean-inp.txt", "pdist-jaccard-ml.txt", @@ -97,6 +96,365 @@ #print np.abs(Y_test2 - Y_right).max() #print np.abs(Y_test1 - Y_right).max() +class TestCdist(TestCase): + """ + Test suite for the pdist function. + """ + + def test_cdist_euclidean_random(self): + "Tests cdist(X, 'euclidean') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'euclidean') + Y2 = cdist(X1, X2, 'test_euclidean') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_sqeuclidean_random(self): + "Tests cdist(X, 'sqeuclidean') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'sqeuclidean') + Y2 = cdist(X1, X2, 'test_sqeuclidean') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_cityblock_random(self): + "Tests cdist(X, 'sqeuclidean') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'cityblock') + Y2 = cdist(X1, X2, 'test_cityblock') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_hamming_double_random(self): + "Tests cdist(X, 'hamming') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'hamming') + Y2 = cdist(X1, X2, 'test_hamming') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_hamming_bool_random(self): + "Tests cdist(X, 'hamming') on random boolean data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'hamming') + Y2 = cdist(X1, X2, 'test_hamming') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_jaccard_double_random(self): + "Tests cdist(X, 'jaccard') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'jaccard') + Y2 = cdist(X1, X2, 'test_jaccard') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_jaccard_bool_random(self): + "Tests cdist(X, 'jaccard') on random boolean data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'jaccard') + Y2 = cdist(X1, X2, 'test_jaccard') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_chebychev_random(self): + "Tests cdist(X, 'chebychev') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'chebychev') + Y2 = cdist(X1, X2, 'test_chebychev') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_minkowski_random_p3d8(self): + "Tests cdist(X, 'minkowski') on random data. (p=3.8)" + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'minkowski', p=3.8) + Y2 = cdist(X1, X2, 'test_minkowski', p=3.8) + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_minkowski_random_p4d6(self): + "Tests cdist(X, 'minkowski') on random data. (p=4.6)" + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'minkowski', p=4.6) + Y2 = cdist(X1, X2, 'test_minkowski', p=4.6) + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_minkowski_random_p1d23(self): + "Tests cdist(X, 'minkowski') on random data. (p=1.23)" + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'minkowski', p=1.23) + Y2 = cdist(X1, X2, 'test_minkowski', p=1.23) + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + + def test_cdist_wminkowski_random_p3d8(self): + "Tests cdist(X, 'wminkowski') on random data. (p=3.8)" + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + w = 1.0 / X1.std(axis=0) + Y1 = cdist(X1, X2, 'wminkowski', p=3.8, w=w) + Y2 = cdist(X1, X2, 'test_wminkowski', p=3.8, w=w) + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_wminkowski_random_p4d6(self): + "Tests cdist(X, 'wminkowski') on random data. (p=4.6)" + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + w = 1.0 / X1.std(axis=0) + Y1 = cdist(X1, X2, 'wminkowski', p=4.6, w=w) + Y2 = cdist(X1, X2, 'test_wminkowski', p=4.6, w=w) + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_wminkowski_random_p1d23(self): + "Tests cdist(X, 'wminkowski') on random data. (p=1.23)" + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + w = 1.0 / X1.std(axis=0) + Y1 = cdist(X1, X2, 'wminkowski', p=1.23, w=w) + Y2 = cdist(X1, X2, 'test_wminkowski', p=1.23, w=w) + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + + def test_cdist_seuclidean_random(self): + "Tests cdist(X, 'seuclidean') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'seuclidean') + Y2 = cdist(X1, X2, 'test_seuclidean') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_sqeuclidean_random(self): + "Tests cdist(X, 'sqeuclidean') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'sqeuclidean') + Y2 = cdist(X1, X2, 'test_sqeuclidean') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_cosine_random(self): + "Tests cdist(X, 'cosine') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'cosine') + Y2 = cdist(X1, X2, 'test_cosine') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_correlation_random(self): + "Tests cdist(X, 'correlation') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'correlation') + Y2 = cdist(X1, X2, 'test_correlation') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_mahalanobis_random(self): + "Tests cdist(X, 'mahalanobis') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] + X2 = eo['cdist-X2'] + Y1 = cdist(X1, X2, 'mahalanobis') + Y2 = cdist(X1, X2, 'test_mahalanobis') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_canberra_random(self): + "Tests cdist(X, 'canberra') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'canberra') + Y2 = cdist(X1, X2, 'test_canberra') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_braycurtis_random(self): + "Tests cdist(X, 'braycurtis') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'braycurtis') + Y2 = cdist(X1, X2, 'test_braycurtis') + if verbose > 2: + print Y1, Y2 + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_yule_random(self): + "Tests cdist(X, 'yule') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'yule') + Y2 = cdist(X1, X2, 'test_yule') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_matching_random(self): + "Tests cdist(X, 'matching') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'matching') + Y2 = cdist(X1, X2, 'test_matching') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_kulsinski_random(self): + "Tests cdist(X, 'kulsinski') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'kulsinski') + Y2 = cdist(X1, X2, 'test_kulsinski') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_dice_random(self): + "Tests cdist(X, 'dice') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'dice') + Y2 = cdist(X1, X2, 'test_dice') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_rogerstanimoto_random(self): + "Tests cdist(X, 'rogerstanimoto') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'rogerstanimoto') + Y2 = cdist(X1, X2, 'test_rogerstanimoto') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_russellrao_random(self): + "Tests cdist(X, 'russellrao') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'russellrao') + Y2 = cdist(X1, X2, 'test_russellrao') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_sokalmichener_random(self): + "Tests cdist(X, 'sokalmichener') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'sokalmichener') + Y2 = cdist(X1, X2, 'test_sokalmichener') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + + def test_cdist_sokalsneath_random(self): + "Tests cdist(X, 'sokalsneath') on random data." + eps = 1e-07 + # Get the data: the input matrix and the right output. + X1 = eo['cdist-X1'] < 0.5 + X2 = eo['cdist-X2'] < 0.5 + Y1 = cdist(X1, X2, 'sokalsneath') + Y2 = cdist(X1, X2, 'test_sokalsneath') + if verbose > 2: + print (Y1-Y2).max() + self.failUnless(within_tol(Y1, Y2, eps)) + class TestPdist(TestCase): """ Test suite for the pdist function. Modified: branches/refactor_fft/scipy/cluster/tests/test_hierarchy.py =================================================================== --- branches/refactor_fft/scipy/cluster/tests/test_hierarchy.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/cluster/tests/test_hierarchy.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -33,14 +33,13 @@ # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -import sys import os.path import numpy as np from numpy.testing import * from scipy.cluster.hierarchy import linkage, from_mlab_linkage, numobs_linkage, inconsistent -from scipy.cluster.distance import squareform, pdist, matching, jaccard, dice, sokalsneath, rogerstanimoto, russellrao, yule, numobs_dm, numobs_y +from scipy.cluster.distance import squareform, pdist, numobs_dm, numobs_y _tdist = np.array([[0, 662, 877, 255, 412, 996], [662, 0, 295, 468, 268, 400], Modified: branches/refactor_fft/scipy/cluster/tests/test_vq.py =================================================================== --- branches/refactor_fft/scipy/cluster/tests/test_vq.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/cluster/tests/test_vq.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -3,13 +3,12 @@ # David Cournapeau # Last Change: Tue Jun 24 04:00 PM 2008 J -import sys import os.path import numpy as np from numpy.testing import * -from scipy.cluster.vq import kmeans, kmeans2, py_vq, py_vq2, _py_vq_1d, vq, ClusterError +from scipy.cluster.vq import kmeans, kmeans2, py_vq, py_vq2, vq, ClusterError try: from scipy.cluster import _vq TESTC=True Modified: branches/refactor_fft/scipy/fftpack/benchmarks/bench_basic.py =================================================================== --- branches/refactor_fft/scipy/fftpack/benchmarks/bench_basic.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/fftpack/benchmarks/bench_basic.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -2,11 +2,9 @@ """ import sys from numpy.testing import * -from scipy.fftpack import ifft,fft,fftn,ifftn,rfft,irfft -from scipy.fftpack import _fftpack as fftpack +from scipy.fftpack import ifft, fft, fftn, irfft -from numpy import arange, add, array, asarray, zeros, dot, exp, pi,\ - swapaxes, double, cdouble +from numpy import arange, asarray, zeros, dot, exp, pi, double, cdouble import numpy.fft from numpy.random import rand Modified: branches/refactor_fft/scipy/fftpack/benchmarks/bench_pseudo_diffs.py =================================================================== --- branches/refactor_fft/scipy/fftpack/benchmarks/bench_pseudo_diffs.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/fftpack/benchmarks/bench_pseudo_diffs.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -2,12 +2,10 @@ """ import sys -from numpy import arange, add, array, sin, cos, pi,exp,tanh,sum,sign +from numpy import arange, sin, cos, pi, exp, tanh, sign from numpy.testing import * -from scipy.fftpack import diff,fft,ifft,tilbert,itilbert,hilbert,ihilbert,rfft -from scipy.fftpack import shift -from scipy.fftpack import fftfreq +from scipy.fftpack import diff, fft, ifft, tilbert, hilbert, shift, fftfreq def random(size): return rand(*size) Modified: branches/refactor_fft/scipy/fftpack/tests/test_basic.py =================================================================== --- branches/refactor_fft/scipy/fftpack/tests/test_basic.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/fftpack/tests/test_basic.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -10,7 +10,7 @@ Run tests if fftpack is not installed: python tests/test_basic.py """ -import sys + from numpy.testing import * from scipy.fftpack import ifft,fft,fftn,ifftn,rfft,irfft Modified: branches/refactor_fft/scipy/fftpack/tests/test_helper.py =================================================================== --- branches/refactor_fft/scipy/fftpack/tests/test_helper.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/fftpack/tests/test_helper.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -11,7 +11,6 @@ python tests/test_helper.py [] """ -import sys from numpy.testing import * from scipy.fftpack import fftshift,ifftshift,fftfreq,rfftfreq Modified: branches/refactor_fft/scipy/fftpack/tests/test_pseudo_diffs.py =================================================================== --- branches/refactor_fft/scipy/fftpack/tests/test_pseudo_diffs.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/fftpack/tests/test_pseudo_diffs.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -10,13 +10,12 @@ Run tests if fftpack is not installed: python tests/test_pseudo_diffs.py [] """ -import sys + from numpy.testing import * -from scipy.fftpack import diff,fft,ifft,tilbert,itilbert,hilbert,ihilbert,rfft -from scipy.fftpack import shift -from scipy.fftpack import fftfreq +from scipy.fftpack import diff, fft, ifft, tilbert, itilbert, hilbert, \ + ihilbert, shift, fftfreq -from numpy import arange, add, array, sin, cos, pi,exp,tanh,sum,sign +from numpy import arange, sin, cos, pi, exp, tanh, sum, sign def random(size): return rand(*size) Modified: branches/refactor_fft/scipy/integrate/ode.py =================================================================== --- branches/refactor_fft/scipy/integrate/ode.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/integrate/ode.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -147,7 +147,7 @@ __version__ = "$Id$" __docformat__ = "restructuredtext en" -from numpy import asarray, array, zeros, sin, int32, isscalar +from numpy import asarray, array, zeros, int32, isscalar import re, sys #------------------------------------------------------------------------------ Modified: branches/refactor_fft/scipy/integrate/quadpack.py =================================================================== --- branches/refactor_fft/scipy/integrate/quadpack.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/integrate/quadpack.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -6,10 +6,10 @@ import _quadpack import sys import numpy +from numpy import inf, Inf error = _quadpack.error - def quad_explain(output=sys.stdout): output.write(""" Extra information for quad() inputs and outputs: @@ -117,8 +117,6 @@ return -from numpy import inf, Inf - def quad(func, a, b, args=(), full_output=0, epsabs=1.49e-8, epsrel=1.49e-8, limit=50, points=None, weight=None, wvar=None, wopts=None, maxp1=50, limlst=50): Modified: branches/refactor_fft/scipy/integrate/quadrature.py =================================================================== --- branches/refactor_fft/scipy/integrate/quadrature.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/integrate/quadrature.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -6,7 +6,6 @@ from scipy.special import gammaln from numpy import sum, ones, add, diff, isinf, isscalar, \ asarray, real, trapz, arange, empty -import scipy as sp import numpy as np def fixed_quad(func,a,b,args=(),n=5): Modified: branches/refactor_fft/scipy/integrate/tests/test_integrate.py =================================================================== --- branches/refactor_fft/scipy/integrate/tests/test_integrate.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/integrate/tests/test_integrate.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -4,9 +4,8 @@ """ import numpy -from numpy import (arange, zeros, array, dot, sqrt, cos, sin, absolute, - eye, pi, exp, allclose) -from scipy.linalg import norm +from numpy import arange, zeros, array, dot, sqrt, cos, sin, eye, pi, exp, \ + allclose from numpy.testing import * from scipy.integrate import odeint, ode Modified: branches/refactor_fft/scipy/integrate/tests/test_quadpack.py =================================================================== --- branches/refactor_fft/scipy/integrate/tests/test_quadpack.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/integrate/tests/test_quadpack.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,4 +1,3 @@ -import numpy from numpy import sqrt, cos, sin, arctan, exp, log, pi, Inf from numpy.testing import * from scipy.integrate import quad, dblquad, tplquad Modified: branches/refactor_fft/scipy/interpolate/SConscript =================================================================== --- branches/refactor_fft/scipy/interpolate/SConscript 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/interpolate/SConscript 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,4 +1,4 @@ -# Last Change: Thu Jun 12 07:00 PM 2008 J +# Last Change: Fri Oct 10 03:00 PM 2008 J # vim:syntax=python from os.path import join as pjoin @@ -34,7 +34,11 @@ env.PrependUnique(LIBPATH = ['.']) # Build _fitpack -env.NumpyPythonExtension('_fitpack', source = '_fitpackmodule.c') +env.NumpyPythonExtension('_fitpack', source = 'src/_fitpackmodule.c') # Build dfitpack -env.NumpyPythonExtension('dfitpack', source = 'fitpack.pyf') +env.NumpyPythonExtension('dfitpack', source = 'src/fitpack.pyf') + +# Build _interpolate +env.NumpyPythonExtension('_interpolate', source = 'src/_interpolate.cpp', + CXXFILESUFFIX = ".cpp") Modified: branches/refactor_fft/scipy/interpolate/interpolate.py =================================================================== --- branches/refactor_fft/scipy/interpolate/interpolate.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/interpolate/interpolate.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -6,11 +6,10 @@ __all__ = ['interp1d', 'interp2d', 'spline', 'spleval', 'splmake', 'spltopp', 'ppform', 'lagrange'] -from numpy import shape, sometrue, rank, array, transpose, \ - swapaxes, searchsorted, clip, take, ones, putmask, less, greater, \ - logical_or, atleast_1d, atleast_2d, meshgrid, ravel, dot, poly1d +from numpy import shape, sometrue, rank, array, transpose, searchsorted, \ + ones, logical_or, atleast_1d, atleast_2d, meshgrid, ravel, \ + dot, poly1d import numpy as np -import scipy.linalg as slin import scipy.special as spec import math Modified: branches/refactor_fft/scipy/interpolate/polyint.py =================================================================== --- branches/refactor_fft/scipy/interpolate/polyint.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/interpolate/polyint.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,6 +1,5 @@ import numpy as np from scipy import factorial -from numpy import poly1d __all__ = ["KroghInterpolator", "krogh_interpolate", "BarycentricInterpolator", "barycentric_interpolate", "PiecewisePolynomial", "piecewise_polynomial_interpolate","approximate_taylor_polynomial"] Modified: branches/refactor_fft/scipy/interpolate/tests/test_fitpack.py =================================================================== --- branches/refactor_fft/scipy/interpolate/tests/test_fitpack.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/interpolate/tests/test_fitpack.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -12,13 +12,10 @@ """ #import libwadpy -import sys from numpy.testing import * from numpy import array, diff -from scipy.interpolate.fitpack2 import UnivariateSpline,LSQUnivariateSpline,\ - InterpolatedUnivariateSpline -from scipy.interpolate.fitpack2 import LSQBivariateSpline, \ - SmoothBivariateSpline, RectBivariateSpline +from scipy.interpolate.fitpack2 import UnivariateSpline, LSQBivariateSpline, \ + SmoothBivariateSpline, RectBivariateSpline class TestUnivariateSpline(TestCase): def test_linear_constant(self): Modified: branches/refactor_fft/scipy/io/__init__.py =================================================================== --- branches/refactor_fft/scipy/io/__init__.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/__init__.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -80,6 +80,7 @@ from netcdf import netcdf_file, netcdf_variable from recaster import sctype_attributes, Recaster +import matlab.byteordercodes as byteordercodes from data_store import save_as_module from mmio import mminfo, mmread, mmwrite Modified: branches/refactor_fft/scipy/io/arff/arffread.py =================================================================== --- branches/refactor_fft/scipy/io/arff/arffread.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/arff/arffread.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -2,7 +2,6 @@ # Last Change: Mon Aug 20 08:00 PM 2007 J import re import itertools -import sys import numpy as np Modified: branches/refactor_fft/scipy/io/arff/tests/test_data.py =================================================================== --- branches/refactor_fft/scipy/io/arff/tests/test_data.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/arff/tests/test_data.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -2,7 +2,8 @@ """Tests for parsing full arff files.""" import os -import numpy as N +import numpy as np + from numpy.testing import * from scipy.io.arff.arffread import loadarff Modified: branches/refactor_fft/scipy/io/arff/tests/test_header.py =================================================================== --- branches/refactor_fft/scipy/io/arff/tests/test_header.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/arff/tests/test_header.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -4,8 +4,7 @@ from numpy.testing import * -from scipy.io.arff.arffread import read_header, MetaData, parse_type, \ - ParseArffError +from scipy.io.arff.arffread import read_header, parse_type, ParseArffError data_path = os.path.join(os.path.dirname(__file__), 'data') Modified: branches/refactor_fft/scipy/io/fopen.py =================================================================== --- branches/refactor_fft/scipy/io/fopen.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/fopen.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -2,7 +2,8 @@ # Author: Travis Oliphant -import struct, os, sys +import struct +import sys import types from numpy import * Copied: branches/refactor_fft/scipy/io/matlab/byteordercodes.py (from rev 4827, trunk/scipy/io/matlab/byteordercodes.py) Modified: branches/refactor_fft/scipy/io/matlab/mio.py =================================================================== --- branches/refactor_fft/scipy/io/matlab/mio.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/matlab/mio.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -6,7 +6,9 @@ import os import sys +import warnings +from miobase import get_matfile_version from mio4 import MatFile4Reader, MatFile4Writer from mio5 import MatFile5Reader, MatFile5Writer @@ -57,11 +59,16 @@ raise IOError, 'Reader needs file name or open file-like object' byte_stream = file_name - MR = MatFile4Reader(byte_stream, **kwargs) - if MR.format_looks_right(): - return MR - return MatFile5Reader(byte_stream, **kwargs) - + mjv, mnv = get_matfile_version(byte_stream) + if mjv == 0: + return MatFile4Reader(byte_stream, **kwargs) + elif mjv == 1: + return MatFile5Reader(byte_stream, **kwargs) + elif mjv == 2: + raise NotImplementedError('Please use PyTables for matlab v7.3 (HDF) files') + else: + raise TypeError('Did not recognize version %s' % mv) + def loadmat(file_name, mdict=None, appendmat=True, basename='raw', **kwargs): ''' Load Matlab(tm) file @@ -88,10 +95,17 @@ matlab_compatible - returns matrices as would be loaded by matlab (implies squeeze_me=False, chars_as_strings=False, mat_dtype=True) + struct_as_record - whether to load matlab structs as numpy record arrays, or + as old-style numpy arrays with dtype=object. + (warns if not set, and defaults to False. non-recarrays + cannot be exported via savemat.) v4 (Level 1.0), v6 and v7.1 matfiles are supported. ''' + if not kwargs.get('struct_as_record', False): + warnings.warn("loading matlab structures as arrays of dtype=object is deprecated", + DeprecationWarning, stacklevel=2) MR = mat_reader_factory(file_name, appendmat, **kwargs) matfile_dict = MR.get_variables() if mdict is not None: @@ -125,7 +139,7 @@ if format == '4': MW = MatFile4Writer(file_stream) elif format == '5': - MW = MatFile5Writer(file_stream) + MW = MatFile5Writer(file_stream, unicode_strings=True) else: raise ValueError, 'Format should be 4 or 5' MW.put_variables(mdict) Modified: branches/refactor_fft/scipy/io/matlab/mio4.py =================================================================== --- branches/refactor_fft/scipy/io/matlab/mio4.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/matlab/mio4.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,10 +1,14 @@ ''' Classes for read / write of matlab (TM) 4 files ''' +import sys import numpy as np -from miobase import * +from miobase import MatFileReader, MatArrayReader, MatMatrixGetter, \ + MatFileWriter, MatStreamWriter, spsparse +SYS_LITTLE_ENDIAN = sys.byteorder == 'little' + miDOUBLE = 0 miSINGLE = 1 miINT32 = 2 @@ -63,7 +67,7 @@ ''' Read header, return matrix getter ''' data = self.read_dtype(self.dtypes['header']) header = {} - header['name'] = self.read_ztstring(data['namlen']) + header['name'] = self.read_ztstring(int(data['namlen'])) if data['mopt'] < 0 or data['mopt'] > 5000: ValueError, 'Mat 4 mopt wrong format, byteswapping problem?' M,rest = divmod(data['mopt'], 1000) @@ -175,8 +179,8 @@ else: V = np.ascontiguousarray(tmp[:,2],dtype='complex') V.imag = tmp[:,3] - if have_sparse: - return scipy.sparse.coo_matrix((V,(I,J)), dims) + if spsparse: + return spsparse.coo_matrix((V,(I,J)), dims) return (dims, I, J, V) @@ -198,22 +202,13 @@ def matrix_getter_factory(self): return self._array_reader.matrix_getter_factory() - def format_looks_right(self): - # Mat4 files have a zero somewhere in first 4 bytes - self.mat_stream.seek(0) - mopt_bytes = np.ndarray(shape=(4,), - dtype=np.uint8, - buffer = self.mat_stream.read(4)) - self.mat_stream.seek(0) - return 0 in mopt_bytes - def guess_byte_order(self): self.mat_stream.seek(0) mopt = self.read_dtype(np.dtype('i4')) self.mat_stream.seek(0) if mopt < 0 or mopt > 5000: - return ByteOrder.swapped_code - return ByteOrder.native_code + return SYS_LITTLE_ENDIAN and '>' or '<' + return SYS_LITTLE_ENDIAN and '<' or '>' class Mat4MatrixWriter(MatStreamWriter): @@ -228,7 +223,7 @@ if dims is None: dims = self.arr.shape header = np.empty((), mdtypes_template['header']) - M = not ByteOrder.little_endian + M = not SYS_LITTLE_ENDIAN O = 0 header['mopt'] = (M * 1000 + O * 100 + @@ -323,8 +318,8 @@ arr - array to write name - name in matlab (TM) workspace ''' - if have_sparse: - if scipy.sparse.issparse(arr): + if spsparse: + if spsparse.issparse(arr): return Mat4SparseWriter(stream, arr, name) arr = np.array(arr) dtt = arr.dtype.type Modified: branches/refactor_fft/scipy/io/matlab/mio5.py =================================================================== --- branches/refactor_fft/scipy/io/matlab/mio5.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/matlab/mio5.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -4,40 +4,16 @@ # Small fragments of current code adapted from matfile.py by Heiko # Henkelmann -## Notice in matfile.py file - -# Copyright (c) 2003 Heiko Henkelmann - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to -# deal in the Software without restriction, including without limitation the -# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or -# sell copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING -# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER -# DEALINGS IN THE SOFTWARE. - +import sys import zlib +from cStringIO import StringIO from copy import copy as pycopy -from cStringIO import StringIO + import numpy as np -from miobase import * +from miobase import MatFileReader, MatArrayReader, MatMatrixGetter, \ + MatFileWriter, MatStreamWriter, spsparse -try: # Python 2.3 support - from sets import Set as set -except: - pass - miINT8 = 1 miUINT8 = 2 miINT16 = 3 @@ -56,6 +32,10 @@ mxCELL_CLASS = 1 mxSTRUCT_CLASS = 2 +# The March 2008 edition of "Matlab 7 MAT-File Format" says that +# mxOBJECT_CLASS = 3, whereas matrix.h says that mxLOGICAL = 3. +# Matlab 2008a appears to save logicals as type 9, so we assume that +# the document is correct. See type 18, below. mxOBJECT_CLASS = 3 mxCHAR_CLASS = 4 mxSPARSE_CLASS = 5 @@ -67,6 +47,14 @@ mxUINT16_CLASS = 11 mxINT32_CLASS = 12 mxUINT32_CLASS = 13 +# The following are not in the March 2008 edition of "Matlab 7 +# MAT-File Format," but were guessed from matrix.h. +mxINT64_CLASS = 14 +mxUINT64_CLASS = 15 +mxFUNCTION_CLASS = 16 +# Not doing anything with these at the moment. +mxOPAQUE_CLASS = 17 +mxOBJECT_CLASS_FROM_MATRIX_H = 18 mdtypes_template = { miINT8: 'i1', @@ -102,6 +90,8 @@ mxUINT16_CLASS: 'u2', mxINT32_CLASS: 'i4', mxUINT32_CLASS: 'u4', + mxINT64_CLASS: 'i8', + mxUINT64_CLASS: 'u8', mxSINGLE_CLASS: 'f4', mxDOUBLE_CLASS: 'f8', } @@ -117,9 +107,11 @@ 'i1': miINT8, 'i2': miINT16, 'i4': miINT32, + 'i8': miINT64, 'u1': miUINT8, + 'u2': miUINT16, 'u4': miUINT32, - 'u2': miUINT16, + 'u8': miUINT64, 'S1': miUINT8, 'U1': miUTF16, } @@ -132,8 +124,10 @@ 'c16': mxDOUBLE_CLASS, 'f4': mxSINGLE_CLASS, 'c8': mxSINGLE_CLASS, + 'i8': mxINT64_CLASS, 'i4': mxINT32_CLASS, 'i2': mxINT16_CLASS, + 'u8': mxUINT64_CLASS, 'u2': mxUINT16_CLASS, 'u1': mxUINT8_CLASS, 'S1': mxUINT8_CLASS, @@ -163,16 +157,10 @@ mxUINT16_CLASS, mxINT32_CLASS, mxUINT32_CLASS, + mxINT64_CLASS, + mxUINT64_CLASS, ) -class mat_struct(object): - ''' Placeholder for holding read data from structs ''' - pass - -class mat_obj(object): - ''' Placeholder for holding read data from objects ''' - pass - class Mat5ArrayReader(MatArrayReader): ''' Class to get Mat5 arrays @@ -180,13 +168,13 @@ factory function ''' - def __init__(self, mat_stream, dtypes, processor_func, codecs, class_dtypes): + def __init__(self, mat_stream, dtypes, processor_func, codecs, class_dtypes, struct_as_record): super(Mat5ArrayReader, self).__init__(mat_stream, dtypes, - processor_func, - ) + processor_func) self.codecs = codecs self.class_dtypes = class_dtypes + self.struct_as_record = struct_as_record def read_element(self, copy=True): raw_tag = self.mat_stream.read(8) @@ -228,7 +216,7 @@ buffer=raw_str) if copy: el = el.copy() - + return el def matrix_getter_factory(self): @@ -277,6 +265,8 @@ return Mat5StructMatrixGetter(self, header) if mc == mxOBJECT_CLASS: return Mat5ObjectMatrixGetter(self, header) + if mc == mxFUNCTION_CLASS: + return Mat5FunctionMatrixGetter(self, header) raise TypeError, 'No reader for class code %s' % mc @@ -294,7 +284,8 @@ array_reader.dtypes, array_reader.processor_func, array_reader.codecs, - array_reader.class_dtypes) + array_reader.class_dtypes, + array_reader.struct_as_record) class Mat5MatrixGetter(MatMatrixGetter): @@ -322,6 +313,7 @@ self.mat_stream = array_reader.mat_stream self.data_position = self.mat_stream.tell() self.header = {} + self.name = '' self.is_global = False self.mat_dtype = 'f8' @@ -380,11 +372,10 @@ nnz = indptr[-1] rowind = rowind[:nnz] data = data[:nnz] - if have_sparse: - from scipy.sparse import csc_matrix - return csc_matrix((data,rowind,indptr), shape=(M,N)) + if spsparse: + return spsparse.csc_matrix((data,rowind,indptr), shape=(M,N)) else: - return (dims, data, rowind, indptr) + return ((M,N), data, rowind, indptr) class Mat5CharMatrixGetter(Mat5MatrixGetter): @@ -420,44 +411,91 @@ def get_item(self): return self.read_element() +class mat_struct(object): + ''' Placeholder for holding read data from structs ''' + pass -class Mat5StructMatrixGetter(Mat5CellMatrixGetter): - def __init__(self, *args, **kwargs): - super(Mat5StructMatrixGetter, self).__init__(*args, **kwargs) - self.obj_template = mat_struct() +class Mat5StructMatrixGetter(Mat5MatrixGetter): + def __init__(self, array_reader, header): + super(Mat5StructMatrixGetter, self).__init__(array_reader, header) + self.struct_as_record = array_reader.struct_as_record def get_raw_array(self): namelength = self.read_element()[0] - # get field names names = self.read_element() - splitnames = [names[i:i+namelength] for i in \ - xrange(0,len(names),namelength)] - self.obj_template._fieldnames = [x.tostring().strip('\x00') - for x in splitnames] - return super(Mat5StructMatrixGetter, self).get_raw_array() + field_names = [names[i:i+namelength].tostring().strip('\x00') + for i in xrange(0,len(names),namelength)] + tupdims = tuple(self.header['dims'][::-1]) + length = np.product(tupdims) + if self.struct_as_record: + result = np.empty(length, dtype=[(field_name, object) + for field_name in field_names]) + for i in range(length): + for field_name in field_names: + result[i][field_name] = self.read_element() + else: # Backward compatibility with previous format + self.obj_template = mat_struct() + self.obj_template._fieldnames = field_names + result = np.empty(length, dtype=object) + for i in range(length): + item = pycopy(self.obj_template) + for name in field_names: + item.__dict__[name] = self.read_element() + result[i] = item + + return result.reshape(tupdims).T - def get_item(self): - item = pycopy(self.obj_template) - for element in item._fieldnames: - item.__dict__[element] = self.read_element() - return item +class MatlabObject(object): + ''' Class to contain read data from matlab objects ''' + def __init__(self, classname, field_names): + self.__dict__['classname'] = classname + self.__dict__['mobj_recarray'] = np.empty((1,1), dtype=[(field_name, object) + for field_name in field_names]) + def __getattr__(self, name): + mobj_recarray = self.__dict__['mobj_recarray'] + if name in mobj_recarray.dtype.fields: + return mobj_recarray[0,0][name] + else: + raise AttributeError, "no field named %s in MatlabObject"%(name) -class Mat5ObjectMatrixGetter(Mat5StructMatrixGetter): - def __init__(self, *args, **kwargs): - super(Mat5StructMatrixGetter, self).__init__(*args, **kwargs) - self.obj_template = mat_obj() + def __setattr__(self, name, value): + if name in self.__dict__['mobj_recarray'].dtype.fields: + self.__dict__['mobj_recarray'][0,0][name] = value + else: + self.__dict__[name] = value + - def get_raw_array(self): - self.obj_template._classname = self.read_element().tostring() - return super(Mat5ObjectMatrixGetter, self).get_raw_array() +class Mat5ObjectMatrixGetter(Mat5MatrixGetter): + def get_array(self): + '''Matlab ojects are essentially structs, with an extra field, the classname.''' + classname = self.read_element().tostring() + namelength = self.read_element()[0] + names = self.read_element() + field_names = [names[i:i+namelength].tostring().strip('\x00') + for i in xrange(0,len(names),namelength)] + result = MatlabObject(classname, field_names) + for field_name in field_names: + result.__setattr__(field_name, self.read_element()) + return result + + +class MatlabFunctionMatrix: + ''' Opaque object representing an array of function handles. ''' + def __init__(self, arr): + self.arr = arr + +class Mat5FunctionMatrixGetter(Mat5CellMatrixGetter): + def get_array(self): + return MatlabFunctionMatrix(self.get_raw_array()) + + class MatFile5Reader(MatFileReader): ''' Reader for Mat 5 mat files - Adds the following attribute to base class - + uint16_codec - char codec to use for uint16 char arrays (defaults to system default codec) ''' @@ -469,15 +507,45 @@ squeeze_me=False, chars_as_strings=True, matlab_compatible=False, + struct_as_record=False, uint16_codec=None ): + ''' + mat_stream : file-like + object with file API, open for reading + byte_order : {None, string} + specification of byte order, one of: + ('native', '=', 'little', '<', 'BIG', '>') + mat_dtype : {True, False} boolean + If True, return arrays in same dtype as loaded into matlab + otherwise return with dtype with which they were saved + squeeze_me : {False, True} boolean + If True, squeezes dimensions of size 1 from arrays + chars_as_strings : {True, False} boolean + If True, convert char arrays to string arrays + matlab_compatible : {False, True} boolean + If True, returns matrices as would be loaded by matlab + (implies squeeze_me=False, chars_as_strings=False + mat_dtype=True, struct_as_record=True) + struct_as_record : {False, True} boolean + If True, return strutures as numpy records, + otherwise, return as custom object (for + compatibility with scipy 0.6) + uint16_codec : {None, string} + Set codec to use for uint16 char arrays + (e.g. 'utf-8'). Use system default codec if None + ''' self.codecs = {} + # Missing inputs to array reader set later (processor func + # below, dtypes, codecs via our own set_dtype function, called + # from parent __init__) self._array_reader = Mat5ArrayReader( mat_stream, None, None, None, None, + struct_as_record ) super(MatFile5Reader, self).__init__( mat_stream, @@ -534,6 +602,8 @@ return self._array_reader.matrix_getter_factory() def guess_byte_order(self): + ''' Guess byte order. + Sets stream pointer to 0 ''' self.mat_stream.seek(126) mi = self.mat_stream.read(2) self.mat_stream.seek(0) @@ -549,16 +619,7 @@ hdict['__version__'] = '%d.%d' % (v_major, v_minor) return hdict - def format_looks_right(self): - # Mat4 files have a zero somewhere in first 4 bytes - self.mat_stream.seek(0) - mopt_bytes = np.ndarray(shape=(4,), - dtype=np.uint8, - buffer = self.mat_stream.read(4)) - self.mat_stream.seek(0) - return 0 not in mopt_bytes - class Mat5MatrixWriter(MatStreamWriter): mat_tag = np.zeros((), mdtypes_template['tag_full']) @@ -644,7 +705,6 @@ class Mat5NumericWriter(Mat5MatrixWriter): - def write(self): imagf = self.arr.dtype.kind == 'c' try: @@ -684,10 +744,8 @@ class Mat5SparseWriter(Mat5MatrixWriter): - def write(self): ''' Sparse matrices are 2D - ''' A = self.arr.tocsc() # convert to sparse CSC format A.sort_indices() # MATLAB expects sorted row indices @@ -704,6 +762,80 @@ self.update_matrix_tag() +class Mat5CompositeWriter(Mat5MatrixWriter): + def __init__(self, file_stream, arr, name, is_global=False, unicode_strings=False): + super(Mat5CompositeWriter, self).__init__(file_stream, arr, name, is_global) + self.unicode_strings = unicode_strings + + +class Mat5CellWriter(Mat5CompositeWriter): + def write(self): + self.write_header(mclass=mxCELL_CLASS) + # loop over data, column major + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + MW = MWG.matrix_writer_factory(el, '') + MW.write() + self.update_matrix_tag() + +class Mat5FunctionWriter(Mat5CompositeWriter): + def __init__(self, file_stream, arr, name, is_global=False, unicode_strings=False): + super(Mat5FunctionWriter, self).__init__(file_stream, arr.arr, name, is_global) + + def write(self): + self.write_header(mclass=mxFUNCTION_CLASS) + # loop over data, column major + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + MW = MWG.matrix_writer_factory(el, '') + MW.write() + self.update_matrix_tag() + + +class Mat5StructWriter(Mat5CompositeWriter): + def write(self): + self.write_header(mclass=mxSTRUCT_CLASS) + + # write fieldnames + fieldnames = [f[0] for f in self.arr.dtype.descr] + self.write_element(np.array([32], dtype='i4')) + self.write_element(np.array(fieldnames, dtype='S32'), mdtype=miINT8) + + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + for f in fieldnames: + MW = MWG.matrix_writer_factory(el[f], '') + MW.write() + self.update_matrix_tag() + +class Mat5ObjectWriter(Mat5CompositeWriter): + def __init__(self, file_stream, arr, name, is_global=False, unicode_strings=False): + super(Mat5ObjectWriter, self).__init__(file_stream, arr.__dict__['mobj_recarray'], name, is_global) + self.classname = arr.classname + + def write(self): + self.write_header(mclass=mxOBJECT_CLASS) + + # write classnames + self.write_element(np.array(self.classname, dtype='S'), mdtype=miINT8) + + # write fieldnames + fieldnames = [f[0] for f in self.arr.dtype.descr] + self.write_element(np.array([32], dtype='i4')) + self.write_element(np.array(fieldnames, dtype='S32'), mdtype=miINT8) + + A = np.atleast_2d(self.arr).flatten('F') + MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) + for el in A: + for f in fieldnames: + MW = MWG.matrix_writer_factory(el[f], '') + MW.write() + self.update_matrix_tag() + + class Mat5WriterGetter(object): ''' Wraps stream and options, provides methods for getting Writer objects ''' def __init__(self, stream, unicode_strings): @@ -719,18 +851,21 @@ arr - array to write name - name in matlab (TM) workspace ''' - if have_sparse: - if scipy.sparse.issparse(arr): + if spsparse: + if spsparse.issparse(arr): return Mat5SparseWriter(self.stream, arr, name, is_global) + + if isinstance(arr, MatlabFunctionMatrix): + return Mat5FunctionWriter(self.stream, arr, name, is_global, self.unicode_strings) + if isinstance(arr, MatlabObject): + return Mat5ObjectWriter(self.stream, arr, name, is_global, self.unicode_strings) + arr = np.array(arr) if arr.dtype.hasobject: - types, arr_type = self.classify_mobjects(arr) - if arr_type == 'c': - return Mat5CellWriter(self.stream, arr, name, is_global, types) - elif arr_type == 's': - return Mat5StructWriter(self.stream, arr, name, is_global) - elif arr_type == 'o': - return Mat5ObjectWriter(self.stream, arr, name, is_global) + if arr.dtype.fields == None: + return Mat5CellWriter(self.stream, arr, name, is_global, self.unicode_strings) + else: + return Mat5StructWriter(self.stream, arr, name, is_global, self.unicode_strings) if arr.dtype.kind in ('U', 'S'): if self.unicode_strings: return Mat5UniCharWriter(self.stream, arr, name, is_global) @@ -739,47 +874,6 @@ else: return Mat5NumericWriter(self.stream, arr, name, is_global) - def classify_mobjects(self, objarr): - ''' Function to classify objects passed for writing - returns - types - S1 array of same shape as objarr with codes for each object - i - invalid object - a - ndarray - s - matlab struct - o - matlab object - arr_type - one of - c - cell array - s - struct array - o - object array - ''' - n = objarr.size - types = np.empty((n,), dtype='S1') - types[:] = 'i' - type_set = set() - flato = objarr.flat - for i in range(n): - obj = flato[i] - if isinstance(obj, np.ndarray): - types[i] = 'a' - continue - try: - fns = tuple(obj._fieldnames) - except AttributeError: - continue - try: - cn = obj._classname - except AttributeError: - types[i] = 's' - type_set.add(fns) - continue - types[i] = 'o' - type_set.add((cn, fns)) - arr_type = 'c' - if len(set(types))==1 and len(type_set) == 1: - arr_type = types[0] - return types.reshape(objarr.shape), arr_type - - class MatFile5Writer(MatFileWriter): ''' Class for writing mat5 files ''' def __init__(self, file_stream, @@ -815,6 +909,8 @@ def put_variables(self, mdict): for name, var in mdict.items(): + if name[0] == '_': + continue is_global = name in self.global_vars self.writer_getter.rewind() self.writer_getter.matrix_writer_factory( Modified: branches/refactor_fft/scipy/io/matlab/miobase.py =================================================================== --- branches/refactor_fft/scipy/io/matlab/miobase.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/matlab/miobase.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -4,17 +4,16 @@ Base classes for matlab (TM) file stream reading """ -import sys - import numpy as np +import byteordercodes as boc + +# sparse module if available try: - import scipy.sparse - have_sparse = 1 + import scipy.sparse as spsparse except ImportError: - have_sparse = 0 + spsparse = None - def small_product(arr): ''' Faster than product for small arrays ''' res = 1 @@ -22,26 +21,68 @@ res *= e return res +def get_matfile_version(fileobj): + ''' Return major, minor tuple depending on apparent mat file type + + Where: + + #. 0,x -> version 4 format mat files + #. 1,x -> version 5 format mat files + #. 2,x -> version 7.3 format mat files (HDF format) + + Parameters + ---------- + fileobj : {file-like} + object implementing seek() and read() + + Returns + ------- + major_version : {0, 1, 2} + major matlab file format version + minor_version : int + major matlab file format version + + Notes + ----- + Has the side effect of setting the file read pointer to 0 + ''' + # Mat4 files have a zero somewhere in first 4 bytes + fileobj.seek(0) + mopt_bytes = np.ndarray(shape=(4,), + dtype=np.uint8, + buffer = fileobj.read(4)) + if 0 in mopt_bytes: + fileobj.seek(0) + return (0,0) + + # For 5 format or 7.3 format we need to read an integer in the + # header. Bytes 124 through 128 contain a version integer and an + # endian test string + fileobj.seek(124) + tst_str = fileobj.read(4) + fileobj.seek(0) + maj_ind = int(tst_str[2] == 'I') + maj_val = ord(tst_str[maj_ind]) + min_val = ord(tst_str[1-maj_ind]) + ret = (maj_val, min_val) + if maj_val in (1, 2): + return ret + else: + raise ValueError('Unknown mat file type, version %s' % ret) + + class ByteOrder(object): ''' Namespace for byte ordering ''' - little_endian = sys.byteorder == 'little' - native_code = little_endian and '<' or '>' - swapped_code = little_endian and '>' or '<' + little_endian = boc.sys_is_le + native_code = boc.native_code + swapped_code = boc.swapped_code + to_numpy_code = boc.to_numpy_code - def to_numpy_code(code): - if code is None: - return ByteOrder.native_code - if code in ('little', '<', 'l', 'L'): - return '<' - elif code in ('BIG', '>', 'B', 'b'): - return '>' - elif code in ('native', '='): - return ByteOrder.native_code - elif code in ('swapped'): - return ByteOrder.swapped_code - else: - raise ValueError, 'We cannot handle byte order %s' % byte_order - to_numpy_code = staticmethod(to_numpy_code) +ByteOrder = np.deprecate_with_doc(""" +We no longer use the ByteOrder class, and deprecate it; we will remove +it in future versions of scipy. Please use the +scipy.io.matlab.byteordercodes module instead. +""")(ByteOrder) class MatStreamAgent(object): @@ -50,7 +91,7 @@ Attaches to initialized stream Base class for "getters" - which do store state of what they are - reading on itialization, and therefore need to be initialized + reading on initialization, and therefore need to be initialized before each read, and "readers" which do not store state, and only need to be initialized once on object creation @@ -84,26 +125,11 @@ class MatFileReader(MatStreamAgent): """ Base object for reading mat files - mat_stream - initialized byte stream object - file io interface object - byte_order - byte order ('native', 'little', 'BIG') - in ('native', '=') - or in ('little', '<') - or in ('BIG', '>') - mat_dtype - return arrays in same dtype as loaded into matlab - (instead of the dtype with which they were saved) - squeeze_me - whether to squeeze unit dimensions or not - chars_as_strings - whether to convert char arrays to string arrays - matlab_compatible - returns matrices as would be loaded by matlab - (implies squeeze_me=False, chars_as_strings=False - mat_dtype=True) - To make this class functional, you will need to override the following methods: set_dtypes - sets data types defs from byte order matrix_getter_factory - gives object to fetch next matrix from stream - format_looks_right - returns True if format looks correct for - this file type (Mat4, Mat5) guess_byte_order - guesses file byte order from file """ @@ -113,7 +139,27 @@ squeeze_me=False, chars_as_strings=True, matlab_compatible=False, + struct_as_record=False ): + ''' + mat_stream : file-like + object with file API, open for reading + byte_order : {None, string} + specification of byte order, one of: + ('native', '=', 'little', '<', 'BIG', '>') + mat_dtype : {True, False} boolean + If True, return arrays in same dtype as loaded into matlab + otherwise return with dtype with which they were saved + squeeze_me : {False, True} boolean + If True, squeezes dimensions of size 1 from arrays + chars_as_strings : {True, False} boolean + If True, convert char arrays to string arrays + matlab_compatible : {False, True} boolean + If True, returns matrices as would be loaded by matlab + (implies squeeze_me=False, chars_as_strings=False + mat_dtype=True) + + ''' # Initialize stream self.mat_stream = mat_stream self.dtypes = {} @@ -163,12 +209,12 @@ chars_as_strings = property(get_chars_as_strings, set_chars_as_strings, None, - 'get/set squeeze me property') + 'get/set chars_as_strings property') def get_order_code(self): return self._order_code def set_order_code(self, order_code): - order_code = ByteOrder.to_numpy_code(order_code) + order_code = boc.to_numpy_code(order_code) self._order_code = order_code self.set_dtypes() order_code = property(get_order_code, @@ -177,7 +223,8 @@ 'get/set order code') def set_dtypes(self): - assert False, 'Not implemented' + ''' Set dtype endianness. In this case we have no dtypes ''' + pass def convert_dtypes(self, dtype_template): dtypes = dtype_template.copy() @@ -187,16 +234,13 @@ def matrix_getter_factory(self): assert False, 'Not implemented' - - def format_looks_right(self): - "Return True if the format looks right for this object" - assert False, 'Not implemented' - + def file_header(self): return {} def guess_byte_order(self): - assert 0, 'Not implemented' + ''' As we do not know what file type we have, assume native ''' + return ByteOrder.native_code def get_processor_func(self): ''' Processing to apply to read matrices @@ -227,7 +271,7 @@ str_arr = arr.reshape( (small_product(n_dims), dims[-1])) - arr = np.empty(n_dims, dtype=object) + arr = np.empty(n_dims, dtype='U%d' % dims[-1]) for i in range(0, n_dims[-1]): arr[...,i] = self.chars_to_str(str_arr[i]) else: # return string Copied: branches/refactor_fft/scipy/io/matlab/tests/data/testhdf5_7.4_GLNX86.mat (from rev 4827, trunk/scipy/io/matlab/tests/data/testhdf5_7.4_GLNX86.mat) Copied: branches/refactor_fft/scipy/io/matlab/tests/gen_unittests.m (from rev 4827, trunk/scipy/io/matlab/tests/gen_unittests.m) Copied: branches/refactor_fft/scipy/io/matlab/tests/gen_unittests4.m (from rev 4827, trunk/scipy/io/matlab/tests/gen_unittests4.m) Copied: branches/refactor_fft/scipy/io/matlab/tests/save_test.m (from rev 4827, trunk/scipy/io/matlab/tests/save_test.m) Copied: branches/refactor_fft/scipy/io/matlab/tests/test_byteordercodes.py (from rev 4827, trunk/scipy/io/matlab/tests/test_byteordercodes.py) Modified: branches/refactor_fft/scipy/io/matlab/tests/test_mio.py =================================================================== --- branches/refactor_fft/scipy/io/matlab/tests/test_mio.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/matlab/tests/test_mio.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -3,26 +3,21 @@ import os from glob import glob from cStringIO import StringIO -from tempfile import mkstemp, mkdtemp +from tempfile import mkdtemp from numpy.testing import * -from numpy import arange, array, eye, pi, cos, exp, sin, sqrt, ndarray, \ - zeros, reshape, transpose, empty +from numpy import arange, array, pi, cos, exp, sin, sqrt, ndarray, \ + zeros, reshape, transpose, dtype, empty import scipy.sparse as SP from scipy.io.matlab.mio import loadmat, savemat -from scipy.io.matlab.mio5 import mat_obj, mat_struct +from scipy.io.matlab.mio5 import MatlabObject import shutil import gzip -try: # Python 2.3 support - from sets import Set as set -except: - pass - test_data_path = os.path.join(os.path.dirname(__file__), 'data') -def _check_level(self, label, expected, actual): +def _check_level(label, expected, actual): """ Check one level of a potentially nested object / list """ # object array is returned from cell array in mat file typex = type(expected) @@ -32,10 +27,10 @@ assert len(expected) == len(actual), "Different list lengths at %s" % label for i, ev in enumerate(expected): level_label = "%s, [%d], " % (label, i) - self._check_level(level_label, ev, actual[i]) + _check_level(level_label, ev, actual[i]) return # object, as container for matlab structs and objects - elif isinstance(expected, mat_struct) or isinstance(expected, mat_obj): + elif isinstance(expected, MatlabObject): assert isinstance(actual, typex), \ "Different types %s and %s at %s" % (typex, typac, label) ex_fields = dir(expected) @@ -47,7 +42,7 @@ ev = expected.__dict__[k] v = actual.__dict__[k] level_label = "%s, property %s, " % (label, k) - self._check_level(level_label, ev, v) + _check_level(level_label, ev, v) return # hoping this is a single value, which might be an array if SP.issparse(expected): @@ -62,37 +57,28 @@ assert_array_almost_equal(actual, expected, err_msg=label, decimal=5) else: assert isinstance(expected, typac), \ - "Types %s and %s do not match at %s" % (typex, typac, label) + "Expected %s and actual %s do not match at %s" % \ + (typex, typac, label) assert_equal(actual, expected, err_msg=label) -def _check_case(self, name, files, case): +def _check_case(name, files, case): for file_name in files: - matdict = loadmat(file_name) + matdict = loadmat(file_name, struct_as_record=True) label = "test %s; file %s" % (name, file_name) for k, expected in case.items(): k_label = "%s, variable %s" % (label, k) assert k in matdict, "Missing key at %s" % k_label - self._check_level(k_label, expected, matdict[k]) + _check_level(k_label, expected, matdict[k]) -# Add the load tests dynamically, with given parameters -def _make_check_case(name, files, expected): - def cc(self): - self._check_case(name, files, expected) - cc.__doc__ = "check loadmat case %s" % name - return cc +# Round trip tests +def _rt_check_case(name, expected, format): + mat_stream = StringIO() + savemat(mat_stream, expected, format=format) + mat_stream.seek(0) + _check_case(name, [mat_stream], expected) -# Add the round trip tests dynamically, with given parameters -def _make_rt_check_case(name, expected, format): - def cc(self): - mat_stream = StringIO() - savemat(mat_stream, expected, format=format) - mat_stream.seek(0) - self._check_case(name, [mat_stream], expected) - cc.__doc__ = "check loadmat case %s" % name - return cc - # Define cases to test -theta = pi/4*arange(9,dtype=float) +theta = pi/4*arange(9,dtype=float).reshape(9,1) case_table4 = [ {'name': 'double', 'expected': {'testdouble': theta} @@ -169,10 +155,8 @@ 'expected': {'testsparsefloat': SP.csc_matrix(array([[-1+2j,0,2],[0,-3j,0]]))}, }, ] -st = mat_struct() -st.stringfield = u'Rats live on no evil star.' -st.doublefield = array([sqrt(2),exp(1),pi]) -st.complexfield = (1+1j)*array([sqrt(2),exp(1),pi]) +st = array([(u'Rats live on no evil star.', array([sqrt(2),exp(1),pi]), (1+1j)*array([sqrt(2),exp(1),pi]))], + dtype=[(n, object) for n in ['stringfield', 'doublefield', 'complexfield']]) case_table5.append( {'name': 'struct', 'expected': {'teststruct': st} @@ -187,25 +171,24 @@ {'name': 'cellnest', 'expected': {'testcellnest': a}, }) -st = mat_struct() -st.one = array(1) -st.two = mat_struct() -st.two.three = u'number 3' +st = empty((1,1), dtype=[(n, object) for n in ['one', 'two']]) +st[0,0]['one'] = array(1) +st[0,0]['two'] = empty((1,1), dtype=[('three', object)]) +st[0,0]['two'][0,0]['three'] = u'number 3' case_table5.append( {'name': 'structnest', 'expected': {'teststructnest': st} }) -a = array([mat_struct(), mat_struct()]) -a[0].one = array(1) -a[0].two = array(2) -a[1].one = u'number 1' -a[1].two = u'number 2' +a = empty((2,1), dtype=[(n, object) for n in ['one', 'two']]) +a[0,0]['one'] = array(1) +a[0,0]['two'] = array(2) +a[1,0]['one'] = u'number 1' +a[1,0]['two'] = u'number 2' case_table5.append( {'name': 'structarr', 'expected': {'teststructarr': a} }) -a = mat_obj() -a._classname = 'inline' +a = MatlabObject('inline', ['expr', 'args', 'isEmpty', 'numArgs', 'version']) a.expr = u'x' a.inputExpr = u' x = INLINE_INPUTS_{1};' a.args = u'x' @@ -225,6 +208,7 @@ }) # generator for load tests + at dec.knownfailureif(True) def test_load(): for case in case_table4 + case_table5: name = case['name'] @@ -232,15 +216,16 @@ filt = os.path.join(test_data_path, 'test%s_*.mat' % name) files = glob(filt) assert files, "No files for test %s using filter %s" % (name, filt) - yield _make_check_case, name, files, expected + yield _check_case, name, files, expected - # round trip tests +# generator for round trip tests + at dec.knownfailureif(True) def test_round_trip(): for case in case_table4 + case_table5_rt: name = case['name'] + '_round_trip' expected = case['expected'] format = case in case_table4 and '4' or '5' - yield _make_rt_check_case, name, expected, format + #yield _rt_check_case, name, expected, format def test_gzip_simple(): xdense = zeros((20,20)) @@ -260,7 +245,7 @@ mat_stream.close() mat_stream = gzip.open( fname,mode='rb') - actual = loadmat(mat_stream) + actual = loadmat(mat_stream, struct_as_record=True) mat_stream.close() finally: shutil.rmtree(tmpdir) @@ -269,3 +254,9 @@ expected['x'].todense()) +def test_mat73(): + # Check any hdf5 files raise an error + filenames = glob( + os.path.join(test_data_path, 'testhdf5*.mat')) + for filename in filenames: + assert_raises(NotImplementedError, loadmat, filename, struct_as_record=True) Modified: branches/refactor_fft/scipy/io/mmio.py =================================================================== --- branches/refactor_fft/scipy/io/mmio.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/mmio.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -10,9 +10,8 @@ # import os -from numpy import asarray, real, imag, conj, zeros, ndarray, \ - empty, concatenate, ones, ascontiguousarray, \ - vstack, savetxt, fromfile, fromstring +from numpy import asarray, real, imag, conj, zeros, ndarray, concatenate, \ + ones, ascontiguousarray, vstack, savetxt, fromfile, fromstring __all__ = ['mminfo','mmread','mmwrite', 'MMFile'] @@ -480,14 +479,15 @@ precision = 16 if field is None: - if typecode in 'li': + kind = a.dtype.kind + if kind == 'i': field = 'integer' - elif typecode in 'df': + elif kind == 'f': field = 'real' - elif typecode in 'DF': + elif kind == 'c': field = 'complex' else: - raise TypeError,'unexpected typecode '+typecode + raise TypeError('unexpected dtype kind ' + kind) if rep == self.FORMAT_ARRAY: symm = self._get_symmetry(a) @@ -571,23 +571,6 @@ savetxt(stream, IJV, fmt=fmt) - - ### Old method - ## line template - #template = '%i %i ' + template - #I,J,V = coo.row + 1, coo.col + 1, coo.data # change base 0 -> base 1 - #if field in (self.FIELD_REAL, self.FIELD_INTEGER): - # for ijv_tuple in izip(I,J,V): - # stream.writelines(template % ijv_tuple) - #elif field == self.FIELD_COMPLEX: - # for ijv_tuple in izip(I,J,V.real,V.imag): - # stream.writelines(template % ijv_tuple) - #elif field == self.FIELD_PATTERN: - # raise NotImplementedError,`field` - #else: - # raise TypeError,'Unknown field type %s'% `field` - - #------------------------------------------------------------------------------- if __name__ == '__main__': import sys Modified: branches/refactor_fft/scipy/io/netcdf.py =================================================================== --- branches/refactor_fft/scipy/io/netcdf.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/netcdf.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -15,7 +15,6 @@ __all__ = ['netcdf_file', 'netcdf_variable'] import struct -import itertools import mmap from numpy import ndarray, zeros, array Deleted: branches/refactor_fft/scipy/io/tests/gen_unittests.m =================================================================== --- branches/refactor_fft/scipy/io/tests/gen_unittests.m 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/tests/gen_unittests.m 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,92 +0,0 @@ -% Generates mat files for loadmat unit tests -% This is the version for matlab 5 and higher -% Uses save_test.m function - -% work out matlab version and file suffix for test files -global FILEPREFIX FILESUFFIX -FILEPREFIX = [fullfile(pwd, 'data') filesep]; -temp = ver('MATLAB'); -mlv = temp.Version; -FILESUFFIX = ['_' mlv '_' computer '.mat']; - -% basic double array -save_test('testdouble', 0:pi/4:2*pi); - -% string -save_test('teststring', '"Do nine men interpret?" "Nine men," I nod.') - -% complex -theta = 0:pi/4:2*pi; -save_test('testcomplex', cos(theta) + 1j*sin(theta)); - -% asymmetric array to check indexing -a = zeros(3, 5); -a(:,1) = [1:3]'; -a(1,:) = 1:5; - -% 2D matrix -save_test('testmatrix', a); - -% minus number - tests signed int -save_test('testminus', -1); - -% single character -save_test('testonechar', 'r'); - -% string array -save_test('teststringarray', ['one '; 'two '; 'three']); - -% sparse array -save_test('testsparse', sparse(a)); - -% sparse complex array -b = sparse(a); -b(1,1) = b(1,1) + j; -save_test('testsparsecomplex', b); - -% Two variables in same file -save([FILEPREFIX 'testmulti' FILESUFFIX], 'a', 'theta') - - -% struct -save_test('teststruct', ... - struct('stringfield','Rats live on no evil star.',... - 'doublefield',[sqrt(2) exp(1) pi],... - 'complexfield',(1+1j)*[sqrt(2) exp(1) pi])); - -% cell -save_test('testcell', ... - {['This cell contains this string and 3 arrays of increasing' ... - ' length'], 1., 1.:2., 1.:3.}); - -% Empty cells in two cell matrices -save_test('testemptycell', {1, 2, [], [], 3}); - -% 3D matrix -save_test('test3dmatrix', reshape(1:24,[2 3 4])) - -% nested cell array -save_test('testcellnest', {1, {2, 3, {4, 5}}}); - -% nested struct -save_test('teststructnest', struct('one', 1, 'two', ... - struct('three', 'number 3'))); - -% array of struct -save_test('teststructarr', [struct('one', 1, 'two', 2) ... - struct('one', 'number 1', 'two', 'number 2')]); - -% matlab object -save_test('testobject', inline('x')) - -% array of matlab objects -%save_test('testobjarr', [inline('x') inline('y')]) - -% unicode test -if str2num(mlv) > 7 % function added 7.0.1 - fid = fopen([FILEPREFIX 'japanese_utf8.txt']); - from_japan = fread(fid, 'uint8')'; - fclose(fid); - save_test('testunicode', native2unicode(from_japan, 'utf-8')); -end - \ No newline at end of file Deleted: branches/refactor_fft/scipy/io/tests/gen_unittests4.m =================================================================== --- branches/refactor_fft/scipy/io/tests/gen_unittests4.m 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/tests/gen_unittests4.m 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,50 +0,0 @@ -% Generates mat files for loadmat unit tests -% Uses save_test.m function -% This is the version for matlab 4 - -% work out matlab version and file suffix for test files -global FILEPREFIX FILESUFFIX -sepchar = '/'; -if strcmp(computer, 'PCWIN'), sepchar = '\'; end -FILEPREFIX = [pwd sepchar 'data' sepchar]; -mlv = version; -FILESUFFIX = ['_' mlv '_' computer '.mat']; - -% basic double array -save_test('testdouble', 0:pi/4:2*pi); - -% string -save_test('teststring', '"Do nine men interpret?" "Nine men," I nod.') - -% complex -theta = 0:pi/4:2*pi; -save_test('testcomplex', cos(theta) + 1j*sin(theta)); - -% asymmetric array to check indexing -a = zeros(3, 5); -a(:,1) = [1:3]'; -a(1,:) = 1:5; - -% 2D matrix -save_test('testmatrix', a); - -% minus number - tests signed int -save_test('testminus', -1); - -% single character -save_test('testonechar', 'r'); - -% string array -save_test('teststringarray', ['one '; 'two '; 'three']); - -% sparse array -save_test('testsparse', sparse(a)); - -% sparse complex array -b = sparse(a); -b(1,1) = b(1,1) + j; -save_test('testsparsecomplex', b); - -% Two variables in same file -save([FILEPREFIX 'testmulti' FILESUFFIX], 'a', 'theta') - Deleted: branches/refactor_fft/scipy/io/tests/save_test.m =================================================================== --- branches/refactor_fft/scipy/io/tests/save_test.m 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/tests/save_test.m 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,6 +0,0 @@ -function save_test(test_name, v) -% saves variable passed in m with filename from prefix - -global FILEPREFIX FILESUFFIX -eval([test_name ' = v;']); -save([FILEPREFIX test_name FILESUFFIX], test_name) \ No newline at end of file Modified: branches/refactor_fft/scipy/io/tests/test_mmio.py =================================================================== --- branches/refactor_fft/scipy/io/tests/test_mmio.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/io/tests/test_mmio.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -4,7 +4,6 @@ from numpy import array,transpose from numpy.testing import * -import scipy import scipy.sparse from scipy.io.mmio import mminfo,mmread,mmwrite Modified: branches/refactor_fft/scipy/lib/blas/tests/test_blas.py =================================================================== --- branches/refactor_fft/scipy/lib/blas/tests/test_blas.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/lib/blas/tests/test_blas.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -12,10 +12,9 @@ python tests/test_blas.py [] """ -import sys import math -from numpy import arange, add, array +from numpy import array from numpy.testing import * from scipy.lib.blas import fblas from scipy.lib.blas import cblas Modified: branches/refactor_fft/scipy/lib/blas/tests/test_fblas.py =================================================================== --- branches/refactor_fft/scipy/lib/blas/tests/test_fblas.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/lib/blas/tests/test_fblas.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -6,10 +6,8 @@ # !! Complex calculations really aren't checked that carefully. # !! Only real valued complex numbers are used in tests. -import sys -from numpy import zeros, transpose, newaxis, shape, float32, \ - float64, complex64, complex128, arange, array, common_type, \ - conjugate +from numpy import zeros, transpose, newaxis, shape, float32, float64, \ + complex64, complex128, arange, array, common_type, conjugate from numpy.testing import * from scipy.lib.blas import fblas Modified: branches/refactor_fft/scipy/lib/lapack/tests/test_lapack.py =================================================================== --- branches/refactor_fft/scipy/lib/lapack/tests/test_lapack.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/lib/lapack/tests/test_lapack.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -14,10 +14,8 @@ can be run (see the isrunnable method). ''' -import os -import sys from numpy.testing import * -from numpy import dot, ones, zeros +from numpy import ones from scipy.lib.lapack import flapack, clapack Modified: branches/refactor_fft/scipy/linalg/benchmarks/bench_basic.py =================================================================== --- branches/refactor_fft/scipy/linalg/benchmarks/bench_basic.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/linalg/benchmarks/bench_basic.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,19 +1,13 @@ import sys -import numpy -from numpy import arange, add, array, dot, zeros, identity, conjugate, transpose - from numpy.testing import * +import numpy.linalg as linalg -from scipy.linalg import solve,inv,det,lstsq, toeplitz, hankel, tri, triu, \ - tril, pinv, pinv2, solve_banded - def random(size): return rand(*size) class TestSolve(TestCase): def bench_random(self): - import numpy.linalg as linalg basic_solve = linalg.solve print print ' Solving system of linear equations' @@ -53,7 +47,6 @@ class TestInv(TestCase): def bench_random(self): - import numpy.linalg as linalg basic_inv = linalg.inv print print ' Finding matrix inverse' @@ -92,7 +85,6 @@ class TestDet(TestCase): def bench_random(self): - import numpy.linalg as linalg basic_det = linalg.det print print ' Finding matrix determinant' Modified: branches/refactor_fft/scipy/linalg/benchmarks/bench_decom.py =================================================================== --- branches/refactor_fft/scipy/linalg/benchmarks/bench_decom.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/linalg/benchmarks/bench_decom.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -3,10 +3,7 @@ """ import sys -import numpy from numpy import linalg -from scipy.linalg import eigvals - from numpy.testing import * def random(size): Modified: branches/refactor_fft/scipy/linalg/matfuncs.py =================================================================== --- branches/refactor_fft/scipy/linalg/matfuncs.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/linalg/matfuncs.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -9,15 +9,15 @@ from numpy import asarray, Inf, dot, floor, eye, diag, exp, \ product, logical_not, ravel, transpose, conjugate, \ - cast, log, ogrid, isfinite, imag, real, absolute, amax, sign, \ + cast, log, ogrid, imag, real, absolute, amax, sign, \ isfinite, sqrt, identity, single from numpy import matrix as mat -import numpy as sb +import numpy as np from basic import solve, inv, norm, triu, all_mat from decomp import eig, schur, rsf2csf, orth, svd -eps = sb.finfo(float).eps -feps = sb.finfo(single).eps +eps = np.finfo(float).eps +feps = np.finfo(single).eps def expm(A,q=7): """Compute the matrix exponential using Pade approximation. @@ -142,7 +142,7 @@ if tol is None: tol = {0:feps*1e3, 1:eps*1e6}[_array_precision[arr.dtype.char]] if (arr.dtype.char in ['F', 'D','G']) and \ - sb.allclose(arr.imag, 0.0, atol=tol): + np.allclose(arr.imag, 0.0, atol=tol): arr = arr.real return arr @@ -455,11 +455,11 @@ # Shifting to avoid zero eigenvalues. How to ensure that shifting does # not change the spectrum too much? vals = svd(a,compute_uv=0) - max_sv = sb.amax(vals) + max_sv = np.amax(vals) #min_nonzero_sv = vals[(vals>max_sv*errtol).tolist().count(1)-1] #c = 0.5/min_nonzero_sv c = 0.5/max_sv - S0 = a + c*sb.identity(a.shape[0]) + S0 = a + c*np.identity(a.shape[0]) prev_errest = errest for i in range(100): iS0 = inv(S0) @@ -508,7 +508,7 @@ T, Z = rsf2csf(T,Z) n,n = T.shape - R = sb.zeros((n,n),T.dtype.char) + R = np.zeros((n,n),T.dtype.char) for j in range(n): R[j,j] = sqrt(T[j,j]) for i in range(j-1,-1,-1): @@ -521,7 +521,7 @@ X = (Z * R * Z.H) if disp: - nzeig = sb.any(sb.diag(T)==0) + nzeig = np.any(diag(T)==0) if nzeig: print "Matrix is singular and may not have a square root." return X.A Modified: branches/refactor_fft/scipy/linalg/tests/test_atlas_version.py =================================================================== --- branches/refactor_fft/scipy/linalg/tests/test_atlas_version.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/linalg/tests/test_atlas_version.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -3,7 +3,6 @@ # Created by: Pearu Peterson, October 2003 # -import sys from numpy.testing import * import scipy.linalg.atlas_version Modified: branches/refactor_fft/scipy/linalg/tests/test_basic.py =================================================================== --- branches/refactor_fft/scipy/linalg/tests/test_basic.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/linalg/tests/test_basic.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -19,10 +19,9 @@ python tests/test_basic.py """ -import numpy from numpy import arange, add, array, dot, zeros, identity, conjugate, transpose +import numpy.linalg as linalg -import sys from numpy.testing import * from scipy.linalg import solve,inv,det,lstsq, toeplitz, hankel, tri, triu, \ @@ -50,33 +49,33 @@ for b in ([[1,0,0,0],[0,0,0,1],[0,1,0,0],[0,1,0,0]], [[2,1],[-30,4],[2,3],[1,3]]): x = solve_banded((l,u),ab,b) - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) class TestSolve(TestCase): def test_20Feb04_bug(self): a = [[1,1],[1.0,0]] # ok x0 = solve(a,[1,0j]) - assert_array_almost_equal(numpy.dot(a,x0),[1,0]) + assert_array_almost_equal(dot(a,x0),[1,0]) a = [[1,1],[1.2,0]] # gives failure with clapack.zgesv(..,rowmajor=0) b = [1,0j] x0 = solve(a,b) - assert_array_almost_equal(numpy.dot(a,x0),[1,0]) + assert_array_almost_equal(dot(a,x0),[1,0]) def test_simple(self): a = [[1,20],[-30,4]] for b in ([[1,0],[0,1]],[1,0], [[2,1],[-30,4]]): x = solve(a,b) - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) def test_simple_sym(self): a = [[2,3],[3,5]] for lower in [0,1]: for b in ([[1,0],[0,1]],[1,0]): x = solve(a,b,sym_pos=1,lower=lower) - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) def test_simple_sym_complex(self): a = [[5,2],[2,4]] @@ -85,7 +84,7 @@ [0,2]], ]: x = solve(a,b,sym_pos=1) - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) def test_simple_complex(self): a = array([[5,2],[2j,4]],'D') @@ -96,7 +95,7 @@ array([1,0],'D'), ]: x = solve(a,b) - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) def test_nils_20Feb04(self): n = 2 @@ -117,7 +116,7 @@ for i in range(4): b = random([n,3]) x = solve(a,b) - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) def test_random_complex(self): n = 20 @@ -126,7 +125,7 @@ for i in range(2): b = random([n,3]) x = solve(a,b) - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) def test_random_sym(self): n = 20 @@ -138,7 +137,7 @@ for i in range(4): b = random([n]) x = solve(a,b,sym_pos=1) - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) def test_random_sym_complex(self): n = 20 @@ -147,11 +146,11 @@ for i in range(n): a[i,i] = abs(20*(.1+a[i,i])) for j in range(i): - a[i,j] = numpy.conjugate(a[j,i]) + a[i,j] = conjugate(a[j,i]) b = random([n])+2j*random([n]) for i in range(2): x = solve(a,b,sym_pos=1) - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) class TestInv(TestCase): @@ -159,11 +158,11 @@ def test_simple(self): a = [[1,2],[3,4]] a_inv = inv(a) - assert_array_almost_equal(numpy.dot(a,a_inv), + assert_array_almost_equal(dot(a,a_inv), [[1,0],[0,1]]) a = [[1,2,3],[4,5,6],[7,8,10]] a_inv = inv(a) - assert_array_almost_equal(numpy.dot(a,a_inv), + assert_array_almost_equal(dot(a,a_inv), [[1,0,0],[0,1,0],[0,0,1]]) def test_random(self): @@ -172,12 +171,12 @@ a = random([n,n]) for i in range(n): a[i,i] = 20*(.1+a[i,i]) a_inv = inv(a) - assert_array_almost_equal(numpy.dot(a,a_inv), - numpy.identity(n)) + assert_array_almost_equal(dot(a,a_inv), + identity(n)) def test_simple_complex(self): a = [[1,2],[3,4j]] a_inv = inv(a) - assert_array_almost_equal(numpy.dot(a,a_inv), + assert_array_almost_equal(dot(a,a_inv), [[1,0],[0,1]]) def test_random_complex(self): @@ -186,8 +185,8 @@ a = random([n,n])+2j*random([n,n]) for i in range(n): a[i,i] = 20*(.1+a[i,i]) a_inv = inv(a) - assert_array_almost_equal(numpy.dot(a,a_inv), - numpy.identity(n)) + assert_array_almost_equal(dot(a,a_inv), + identity(n)) class TestDet(TestCase): @@ -203,7 +202,6 @@ assert_almost_equal(a_det,-6+4j) def test_random(self): - import numpy.linalg as linalg basic_det = linalg.det n = 20 for i in range(4): @@ -213,7 +211,6 @@ assert_almost_equal(d1,d2) def test_random_complex(self): - import numpy.linalg as linalg basic_det = linalg.det n = 20 for i in range(4): @@ -246,7 +243,7 @@ for b in ([[1,0],[0,1]],[1,0], [[2,1],[-30,4]]): x = lstsq(a,b)[0] - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) def test_simple_overdet(self): a = [[1,2],[4,5],[3,4]] @@ -271,7 +268,7 @@ for i in range(4): b = random([n,3]) x = lstsq(a,b)[0] - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) def test_random_complex_exact(self): n = 20 @@ -280,7 +277,7 @@ for i in range(2): b = random([n,3]) x = lstsq(a,b)[0] - assert_array_almost_equal(numpy.dot(a,x),b) + assert_array_almost_equal(dot(a,x),b) def test_random_overdet(self): n = 20 Modified: branches/refactor_fft/scipy/linalg/tests/test_blas.py =================================================================== --- branches/refactor_fft/scipy/linalg/tests/test_blas.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/linalg/tests/test_blas.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -12,13 +12,9 @@ python tests/test_blas.py [] """ -import sys import math -from numpy import arange, add, array - from numpy.testing import * - from scipy.linalg import fblas, cblas Modified: branches/refactor_fft/scipy/linalg/tests/test_decomp.py =================================================================== --- branches/refactor_fft/scipy/linalg/tests/test_decomp.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/linalg/tests/test_decomp.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -14,7 +14,6 @@ python tests/test_decomp.py """ -import sys from numpy.testing import * from scipy.linalg import eig,eigvals,lu,svd,svdvals,cholesky,qr, \ @@ -25,10 +24,9 @@ from numpy import array, transpose, sometrue, diag, ones, linalg, \ argsort, zeros, arange, float32, complex64, dot, conj, identity, \ - ravel, sqrt, iscomplex, shape, sort, sign, conjugate, sign, bmat, \ + ravel, sqrt, iscomplex, shape, sort, conjugate, bmat, sign, \ asarray, matrix, isfinite, all - from numpy.random import rand def random(size): Modified: branches/refactor_fft/scipy/linalg/tests/test_fblas.py =================================================================== --- branches/refactor_fft/scipy/linalg/tests/test_fblas.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/linalg/tests/test_fblas.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -6,11 +6,8 @@ # !! Complex calculations really aren't checked that carefully. # !! Only real valued complex numbers are used in tests. -import sys - -from numpy import dot, float32, float64, complex64, complex128, \ - arange, array, zeros, shape, transpose, newaxis, \ - common_type, conjugate +from numpy import float32, float64, complex64, complex128, arange, array, \ + zeros, shape, transpose, newaxis, common_type, conjugate from scipy.linalg import fblas from numpy.testing import * Modified: branches/refactor_fft/scipy/linalg/tests/test_lapack.py =================================================================== --- branches/refactor_fft/scipy/linalg/tests/test_lapack.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/linalg/tests/test_lapack.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -3,8 +3,6 @@ # Created by: Pearu Peterson, September 2002 # - -import sys from numpy.testing import * from numpy import ones Modified: branches/refactor_fft/scipy/linalg/tests/test_matfuncs.py =================================================================== --- branches/refactor_fft/scipy/linalg/tests/test_matfuncs.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/linalg/tests/test_matfuncs.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -6,15 +6,11 @@ """ -import sys - -import numpy from numpy import array, identity, dot, sqrt - from numpy.testing import * import scipy.linalg -from scipy.linalg import signm,logm,funm, sqrtm, expm, expm2, expm3 +from scipy.linalg import signm, logm, sqrtm, expm, expm2, expm3 class TestSignM(TestCase): Modified: branches/refactor_fft/scipy/maxentropy/examples/bergerexample.py =================================================================== --- branches/refactor_fft/scipy/maxentropy/examples/bergerexample.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/maxentropy/examples/bergerexample.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -20,8 +20,6 @@ __author__ = 'Ed Schofield' __version__= '2.1' - -import math from scipy import maxentropy a_grave = u'\u00e0' Modified: branches/refactor_fft/scipy/maxentropy/examples/conditionalexample2.py =================================================================== --- branches/refactor_fft/scipy/maxentropy/examples/conditionalexample2.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/maxentropy/examples/conditionalexample2.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -18,9 +18,7 @@ __author__ = 'Ed Schofield' -import math from scipy import maxentropy, sparse -import numpy samplespace = ['dans', 'en', '??', 'au cours de', 'pendant'] # Occurrences of French words, and their 'next English word' contexts, in Modified: branches/refactor_fft/scipy/maxentropy/maxentropy.py =================================================================== --- branches/refactor_fft/scipy/maxentropy/maxentropy.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/maxentropy/maxentropy.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -70,8 +70,8 @@ import math, types, cPickle -import numpy -from scipy import optimize, sparse +import numpy as np +from scipy import optimize from scipy.linalg import norm from scipy.maxentropy.maxentutils import * @@ -194,7 +194,7 @@ " using setfeaturesandsamplespace()" # First convert K to a numpy array if necessary - K = numpy.asarray(K, float) + K = np.asarray(K, float) # Store the desired feature expectations as a member variable self.K = K @@ -212,7 +212,7 @@ # self.gradevals = 0 # Make a copy of the parameters - oldparams = numpy.array(self.params) + oldparams = np.array(self.params) callback = self.log @@ -272,7 +272,7 @@ + "' is unsupported. Options are 'CG', 'LBFGSB', " \ "'Nelder-Mead', 'Powell', and 'BFGS'" - if numpy.any(self.params != newparams): + if np.any(self.params != newparams): self.setparams(newparams) self.func_calls = func_calls @@ -322,7 +322,7 @@ self.setparams(params) # Subsumes both small and large cases: - L = self.lognormconst() - numpy.dot(self.params, self.K) + L = self.lognormconst() - np.dot(self.params, self.K) if self.verbose and self.external is None: print " dual is ", L @@ -332,7 +332,7 @@ # Define 0 / 0 = 0 here; this allows a variance term of # sigma_i^2==0 to indicate that feature i should be ignored. if self.sigma2 is not None and ignorepenalty==False: - ratios = numpy.nan_to_num(self.params**2 / self.sigma2) + ratios = np.nan_to_num(self.params**2 / self.sigma2) # Why does the above convert inf to 1.79769e+308? L += 0.5 * ratios.sum() @@ -396,7 +396,7 @@ self.test() if not self.callingback and self.external is None: - if self.mindual > -numpy.inf and self.dual() < self.mindual: + if self.mindual > -np.inf and self.dual() < self.mindual: raise DivergenceError, "dual is below the threshold 'mindual'" \ " and may be diverging to -inf. Fix the constraints" \ " or lower the threshold!" @@ -428,7 +428,7 @@ if self.sigma2 is not None and ignorepenalty==False: penalty = self.params / self.sigma2 G += penalty - features_to_kill = numpy.where(numpy.isnan(penalty))[0] + features_to_kill = np.where(np.isnan(penalty))[0] G[features_to_kill] = 0.0 if self.verbose and self.external is None: normG = norm(G) @@ -449,7 +449,7 @@ return G - def crossentropy(self, fx, log_prior_x=None, base=numpy.e): + def crossentropy(self, fx, log_prior_x=None, base=np.e): """Returns the cross entropy H(q, p) of the empirical distribution q of the data (with the given feature matrix fx) with respect to the model p. For discrete distributions this is @@ -466,9 +466,9 @@ For continuous distributions this makes no sense! """ H = -self.logpdf(fx, log_prior_x).mean() - if base != numpy.e: + if base != np.e: # H' = H * log_{base} (e) - return H / numpy.log(base) + return H / np.log(base) else: return H @@ -483,7 +483,7 @@ Z = E_aux_dist [{exp (params.f(X))} / aux_dist(X)] using a sample from aux_dist. """ - return numpy.exp(self.lognormconst()) + return np.exp(self.lognormconst()) def setsmooth(sigma): @@ -507,7 +507,7 @@ length as the model's feature vector f. """ - self.params = numpy.array(params, float) # make a copy + self.params = np.array(params, float) # make a copy # Log the new params to disk self.logparams() @@ -546,7 +546,7 @@ raise ValueError, "specify the number of features / parameters" # Set parameters, clearing cache variables - self.setparams(numpy.zeros(m, float)) + self.setparams(np.zeros(m, float)) # These bounds on the param values are only effective for the # L-BFGS-B optimizer: @@ -595,7 +595,7 @@ return # Check whether the params are NaN - if not numpy.all(self.params == self.params): + if not np.all(self.params == self.params): raise FloatingPointError, "some of the parameters are NaN" if self.verbose: @@ -775,15 +775,15 @@ raise AttributeError, "prior probability mass function not set" def p(x): - f_x = numpy.array([f[i](x) for i in range(len(f))], float) + f_x = np.array([f[i](x) for i in range(len(f))], float) # Do we have a prior distribution p_0? if priorlogpmf is not None: priorlogprob_x = priorlogpmf(x) - return math.exp(numpy.dot(self.params, f_x) + priorlogprob_x \ + return math.exp(np.dot(self.params, f_x) + priorlogprob_x \ - logZ) else: - return math.exp(numpy.dot(self.params, f_x) - logZ) + return math.exp(np.dot(self.params, f_x) - logZ) return p @@ -893,7 +893,7 @@ # As an optimization, p_tilde need not be copied or stored at all, since # it is only used by this function. - self.p_tilde_context = numpy.empty(numcontexts, float) + self.p_tilde_context = np.empty(numcontexts, float) for w in xrange(numcontexts): self.p_tilde_context[w] = self.p_tilde[0, w*S : (w+1)*S].sum() @@ -932,7 +932,7 @@ if self.priorlogprobs is not None: log_p_dot += self.priorlogprobs - self.logZ = numpy.zeros(numcontexts, float) + self.logZ = np.zeros(numcontexts, float) for w in xrange(numcontexts): self.logZ[w] = logsumexp(log_p_dot[w*S: (w+1)*S]) return self.logZ @@ -972,8 +972,7 @@ logZs = self.lognormconst() - L = numpy.dot(self.p_tilde_context, logZs) - numpy.dot(self.params, \ - self.K) + L = np.dot(self.p_tilde_context, logZs) - np.dot(self.params, self.K) if self.verbose and self.external is None: print " dual is ", L @@ -1069,7 +1068,7 @@ log_p_dot += self.priorlogprobs if not hasattr(self, 'logZ'): # Compute the norm constant (quickly!) - self.logZ = numpy.zeros(numcontexts, float) + self.logZ = np.zeros(numcontexts, float) for w in xrange(numcontexts): self.logZ[w] = logsumexp(log_p_dot[w*S : (w+1)*S]) # Renormalize @@ -1366,8 +1365,8 @@ # -log(n-1) + logsumexp(2*log|Z_k - meanZ|) self.logZapprox = logsumexp(logZs) - math.log(ttrials) - stdevlogZ = numpy.array(logZs).std() - mus = numpy.array(mus) + stdevlogZ = np.array(logZs).std() + mus = np.array(mus) self.varE = columnvariances(mus) self.mu = columnmeans(mus) return @@ -1459,7 +1458,7 @@ log_Z_est = self.lognormconst() def p(fx): - return numpy.exp(innerprodtranspose(fx, self.params) - log_Z_est) + return np.exp(innerprodtranspose(fx, self.params) - log_Z_est) return p @@ -1486,7 +1485,7 @@ """ log_Z_est = self.lognormconst() if len(fx.shape) == 1: - logpdf = numpy.dot(self.params, fx) - log_Z_est + logpdf = np.dot(self.params, fx) - log_Z_est else: logpdf = innerprodtranspose(fx, self.params) - log_Z_est if log_prior_x is not None: @@ -1536,8 +1535,8 @@ # Use Kersten-Deylon accelerated SA, based on the rate of # changes of sign of the gradient. (If frequent swaps, the # stepsize is too large.) - #n += (numpy.dot(y_k, y_kminus1) < 0) # an indicator fn - if numpy.dot(y_k, y_kminus1) < 0: + #n += (np.dot(y_k, y_kminus1) < 0) # an indicator fn + if np.dot(y_k, y_kminus1) < 0: n += 1 else: # Store iterations of sign switches (for plotting @@ -1590,7 +1589,7 @@ if self.verbose: print "SA: after iteration " + str(k) print " approx dual fn is: " + str(self.logZapprox \ - - numpy.dot(self.params, K)) + - np.dot(self.params, K)) print " norm(mu_est - k) = " + str(norm_y_k) # Update params (after the convergence tests too ... don't waste the @@ -1682,7 +1681,7 @@ self.external = None self.clearcache() - meandual = numpy.average(dualapprox,axis=0) + meandual = np.average(dualapprox,axis=0) self.external_duals[self.iters] = dualapprox self.external_gradnorms[self.iters] = gradnorms @@ -1692,7 +1691,7 @@ (len(self.externalFs), meandual) print "** Mean mean square error of the (unregularized) feature" \ " expectation estimates from the external samples =" \ - " mean(|| \hat{\mu_e} - k ||,axis=0) =", numpy.average(gradnorms,axis=0) + " mean(|| \hat{\mu_e} - k ||,axis=0) =", np.average(gradnorms,axis=0) # Track the parameter vector params with the lowest mean dual estimate # so far: if meandual < self.bestdual: Modified: branches/refactor_fft/scipy/maxentropy/maxentutils.py =================================================================== --- branches/refactor_fft/scipy/maxentropy/maxentutils.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/maxentropy/maxentutils.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -17,7 +17,9 @@ __author__ = "Ed Schofield" __version__ = '2.0' -import random, math, bisect, cmath +import random +import math +import cmath import numpy from numpy import log, exp, asarray, ndarray from scipy import sparse Modified: branches/refactor_fft/scipy/maxentropy/tests/test_maxentropy.py =================================================================== --- branches/refactor_fft/scipy/maxentropy/tests/test_maxentropy.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/maxentropy/tests/test_maxentropy.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -6,9 +6,8 @@ Copyright: Ed Schofield, 2003-2005 """ -import sys from numpy.testing import * -from numpy import arange, add, array, dot, zeros, identity, log, exp, ones +from numpy import arange, log, exp, ones from scipy.maxentropy.maxentropy import * class TestMaxentropy(TestCase): Modified: branches/refactor_fft/scipy/misc/common.py =================================================================== --- branches/refactor_fft/scipy/misc/common.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/misc/common.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -3,16 +3,9 @@ (special, linalg) """ -import sys +from numpy import exp, asarray, arange, newaxis, hstack, product, array, \ + where, zeros, extract, place, pi, sqrt, eye, poly1d, dot, r_ -import numpy - -from numpy import exp, asarray, arange, \ - newaxis, hstack, product, array, where, \ - zeros, extract, place, pi, sqrt, eye, poly1d, dot, r_ - -from numpy import who - __all__ = ['factorial','factorial2','factorialk','comb', 'central_diff_weights', 'derivative', 'pade', 'lena'] Modified: branches/refactor_fft/scipy/misc/pilutil.py =================================================================== --- branches/refactor_fft/scipy/misc/pilutil.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/misc/pilutil.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,6 +1,5 @@ # Functions which need the PIL -import types import numpy import tempfile Modified: branches/refactor_fft/scipy/misc/tests/test_pilutil.py =================================================================== --- branches/refactor_fft/scipy/misc/tests/test_pilutil.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/misc/tests/test_pilutil.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,5 +1,4 @@ import os.path -import glob import numpy as np from numpy.testing import * Modified: branches/refactor_fft/scipy/ndimage/SConscript =================================================================== --- branches/refactor_fft/scipy/ndimage/SConscript 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/ndimage/SConscript 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,4 +1,4 @@ -# Last Change: Wed Mar 05 09:00 PM 2008 J +# Last Change: Fri Oct 10 03:00 PM 2008 J from os.path import join from numscons import GetNumpyEnvironment @@ -10,11 +10,3 @@ ndimage_src = ["nd_image.c", "ni_filters.c", "ni_fourier.c", "ni_interpolation.c", "ni_measure.c", "ni_morphology.c", "ni_support.c"] env.NumpyPythonExtension('_nd_image', source = [join('src', i) for i in ndimage_src]) - -segment_src = ['Segmenter_EXT.c', 'Segmenter_IMPL.c'] -env.NumpyPythonExtension('_segment', source = [join('src', 'segment', i) - for i in segment_src]) - -register_src = ['Register_EXT.c', 'Register_IMPL.c'] -env.NumpyPythonExtension('_register', source = [join('src', 'register', i) - for i in register_src]) Modified: branches/refactor_fft/scipy/ndimage/fourier.py =================================================================== --- branches/refactor_fft/scipy/ndimage/fourier.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/ndimage/fourier.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -29,7 +29,6 @@ # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import types -import math import numpy import _ni_support import _nd_image Modified: branches/refactor_fft/scipy/ndimage/interpolation.py =================================================================== --- branches/refactor_fft/scipy/ndimage/interpolation.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/ndimage/interpolation.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -28,9 +28,7 @@ # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -import types import math -import warnings import numpy import _ni_support import _nd_image Modified: branches/refactor_fft/scipy/ndimage/morphology.py =================================================================== --- branches/refactor_fft/scipy/ndimage/morphology.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/ndimage/morphology.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -32,7 +32,6 @@ import _ni_support import _nd_image import filters -import types def _center_is_true(structure, origin): Modified: branches/refactor_fft/scipy/odr/tests/test_odr.py =================================================================== --- branches/refactor_fft/scipy/odr/tests/test_odr.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/odr/tests/test_odr.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,6 +1,3 @@ -# Standard library imports. -import cPickle - # Scipy imports. import numpy as np from numpy import pi Modified: branches/refactor_fft/scipy/optimize/SConscript =================================================================== --- branches/refactor_fft/scipy/optimize/SConscript 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/SConscript 2008-10-23 12:30:20 UTC (rev 4828) @@ -73,6 +73,10 @@ src = [pjoin('minpack2', i) for i in ['dcsrch.f', 'dcstep.f', 'minpack2.pyf']] env.NumpyPythonExtension('minpack2', source = src) +# _nnls pyextension +src = [pjoin('nnls', i) for i in ['nnls.f', 'nnls.pyf']] +env.NumpyPythonExtension('_nnls', source = src) + # moduleTNC pyextension env.NumpyPythonExtension('moduleTNC', source = [pjoin('tnc', i) for i in \ Modified: branches/refactor_fft/scipy/optimize/linesearch.py =================================================================== --- branches/refactor_fft/scipy/optimize/linesearch.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/linesearch.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -2,7 +2,6 @@ from scipy.optimize import minpack2 import numpy -import sys import __builtin__ pymin = __builtin__.min Modified: branches/refactor_fft/scipy/optimize/minpack.py =================================================================== --- branches/refactor_fft/scipy/optimize/minpack.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/minpack.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -2,7 +2,7 @@ from numpy import atleast_1d, dot, take, triu, shape, eye, \ transpose, zeros, product, greater, array, \ - any, all, where, isscalar, asarray, ndarray + all, where, isscalar, asarray error = _minpack.error Copied: branches/refactor_fft/scipy/optimize/nnls (from rev 4827, trunk/scipy/optimize/nnls) Deleted: branches/refactor_fft/scipy/optimize/nnls/nnls.f =================================================================== --- trunk/scipy/optimize/nnls/nnls.f 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/nnls/nnls.f 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,477 +0,0 @@ -C SUBROUTINE NNLS (A,MDA,M,N,B,X,RNORM,W,ZZ,INDEX,MODE) -C -C Algorithm NNLS: NONNEGATIVE LEAST SQUARES -C -c The original version of this code was developed by -c Charles L. Lawson and Richard J. Hanson at Jet Propulsion Laboratory -c 1973 JUN 15, and published in the book -c "SOLVING LEAST SQUARES PROBLEMS", Prentice-HalL, 1974. -c Revised FEB 1995 to accompany reprinting of the book by SIAM. -c -C GIVEN AN M BY N MATRIX, A, AND AN M-VECTOR, B, COMPUTE AN -C N-VECTOR, X, THAT SOLVES THE LEAST SQUARES PROBLEM -C -C A * X = B SUBJECT TO X .GE. 0 -C ------------------------------------------------------------------ -c Subroutine Arguments -c -C A(),MDA,M,N MDA IS THE FIRST DIMENSIONING PARAMETER FOR THE -C ARRAY, A(). ON ENTRY A() CONTAINS THE M BY N -C MATRIX, A. ON EXIT A() CONTAINS -C THE PRODUCT MATRIX, Q*A , WHERE Q IS AN -C M BY M ORTHOGONAL MATRIX GENERATED IMPLICITLY BY -C THIS SUBROUTINE. -C B() ON ENTRY B() CONTAINS THE M-VECTOR, B. ON EXIT B() CON- -C TAINS Q*B. -C X() ON ENTRY X() NEED NOT BE INITIALIZED. ON EXIT X() WILL -C CONTAIN THE SOLUTION VECTOR. -C RNORM ON EXIT RNORM CONTAINS THE EUCLIDEAN NORM OF THE -C RESIDUAL VECTOR. -C W() AN N-ARRAY OF WORKING SPACE. ON EXIT W() WILL CONTAIN -C THE DUAL SOLUTION VECTOR. W WILL SATISFY W(I) = 0. -C FOR ALL I IN SET P AND W(I) .LE. 0. FOR ALL I IN SET Z -C ZZ() AN M-ARRAY OF WORKING SPACE. -C INDEX() AN INTEGER WORKING ARRAY OF LENGTH AT LEAST N. -C ON EXIT THE CONTENTS OF THIS ARRAY DEFINE THE SETS -C P AND Z AS FOLLOWS.. -C -C INDEX(1) THRU INDEX(NSETP) = SET P. -C INDEX(IZ1) THRU INDEX(IZ2) = SET Z. -C IZ1 = NSETP + 1 = NPP1 -C IZ2 = N -C MODE THIS IS A SUCCESS-FAILURE FLAG WITH THE FOLLOWING -C MEANINGS. -C 1 THE SOLUTION HAS BEEN COMPUTED SUCCESSFULLY. -C 2 THE DIMENSIONS OF THE PROBLEM ARE BAD. -C EITHER M .LE. 0 OR N .LE. 0. -C 3 ITERATION COUNT EXCEEDED. MORE THAN 3*N ITERATIONS. -C -C ------------------------------------------------------------------ - SUBROUTINE NNLS (A,MDA,M,N,B,X,RNORM,W,ZZ,INDEX,MODE) -C ------------------------------------------------------------------ - integer I, II, IP, ITER, ITMAX, IZ, IZ1, IZ2, IZMAX, J, JJ, JZ, L - integer M, MDA, MODE,N, NPP1, NSETP, RTNKEY -c integer INDEX(N) -c double precision A(MDA,N), B(M), W(N), X(N), ZZ(M) - integer INDEX(*) - double precision A(MDA,*), B(*), W(*), X(*), ZZ(*) - double precision ALPHA, ASAVE, CC, DIFF, DUMMY, FACTOR, RNORM - double precision SM, SS, T, TEMP, TWO, UNORM, UP, WMAX - double precision ZERO, ZTEST - parameter(FACTOR = 0.01d0) - parameter(TWO = 2.0d0, ZERO = 0.0d0) -C ------------------------------------------------------------------ - MODE=1 - IF (M .le. 0 .or. N .le. 0) then - MODE=2 - RETURN - endif - ITER=0 - ITMAX=3*N -C -C INITIALIZE THE ARRAYS INDEX() AND X(). -C - DO 20 I=1,N - X(I)=ZERO - 20 INDEX(I)=I -C - IZ2=N - IZ1=1 - NSETP=0 - NPP1=1 -C ****** MAIN LOOP BEGINS HERE ****** - 30 CONTINUE -C QUIT IF ALL COEFFICIENTS ARE ALREADY IN THE SOLUTION. -C OR IF M COLS OF A HAVE BEEN TRIANGULARIZED. -C - IF (IZ1 .GT.IZ2.OR.NSETP.GE.M) GO TO 350 -C -C COMPUTE COMPONENTS OF THE DUAL (NEGATIVE GRADIENT) VECTOR W(). -C - DO 50 IZ=IZ1,IZ2 - J=INDEX(IZ) - SM=ZERO - DO 40 L=NPP1,M - 40 SM=SM+A(L,J)*B(L) - W(J)=SM - 50 continue -C FIND LARGEST POSITIVE W(J). - 60 continue - WMAX=ZERO - DO 70 IZ=IZ1,IZ2 - J=INDEX(IZ) - IF (W(J) .gt. WMAX) then - WMAX=W(J) - IZMAX=IZ - endif - 70 CONTINUE -C -C IF WMAX .LE. 0. GO TO TERMINATION. -C THIS INDICATES SATISFACTION OF THE KUHN-TUCKER CONDITIONS. -C - IF (WMAX .le. ZERO) go to 350 - IZ=IZMAX - J=INDEX(IZ) -C -C THE SIGN OF W(J) IS OK FOR J TO BE MOVED TO SET P. -C BEGIN THE TRANSFORMATION AND CHECK NEW DIAGONAL ELEMENT TO AVOID -C NEAR LINEAR DEPENDENCE. -C - ASAVE=A(NPP1,J) - CALL H12 (1,NPP1,NPP1+1,M,A(1,J),1,UP,DUMMY,1,1,0) - UNORM=ZERO - IF (NSETP .ne. 0) then - DO 90 L=1,NSETP - 90 UNORM=UNORM+A(L,J)**2 - endif - UNORM=sqrt(UNORM) - IF (DIFF(UNORM+ABS(A(NPP1,J))*FACTOR,UNORM) .gt. ZERO) then -C -C COL J IS SUFFICIENTLY INDEPENDENT. COPY B INTO ZZ, UPDATE ZZ -C AND SOLVE FOR ZTEST ( = PROPOSED NEW VALUE FOR X(J) ). -C - DO 120 L=1,M - 120 ZZ(L)=B(L) - CALL H12 (2,NPP1,NPP1+1,M,A(1,J),1,UP,ZZ,1,1,1) - ZTEST=ZZ(NPP1)/A(NPP1,J) -C -C SEE IF ZTEST IS POSITIVE -C - IF (ZTEST .gt. ZERO) go to 140 - endif -C -C REJECT J AS A CANDIDATE TO BE MOVED FROM SET Z TO SET P. -C RESTORE A(NPP1,J), SET W(J)=0., AND LOOP BACK TO TEST DUAL -C COEFFS AGAIN. -C - A(NPP1,J)=ASAVE - W(J)=ZERO - GO TO 60 -C -C THE INDEX J=INDEX(IZ) HAS BEEN SELECTED TO BE MOVED FROM -C SET Z TO SET P. UPDATE B, UPDATE INDICES, APPLY HOUSEHOLDER -C TRANSFORMATIONS TO COLS IN NEW SET Z, ZERO SUBDIAGONAL ELTS IN -C COL J, SET W(J)=0. -C - 140 continue - DO 150 L=1,M - 150 B(L)=ZZ(L) -C - INDEX(IZ)=INDEX(IZ1) - INDEX(IZ1)=J - IZ1=IZ1+1 - NSETP=NPP1 - NPP1=NPP1+1 -C - IF (IZ1 .le. IZ2) then - DO 160 JZ=IZ1,IZ2 - JJ=INDEX(JZ) - CALL H12 (2,NSETP,NPP1,M,A(1,J),1,UP,A(1,JJ),1,MDA,1) - 160 continue - endif -C - IF (NSETP .ne. M) then - DO 180 L=NPP1,M - 180 A(L,J)=ZERO - endif -C - W(J)=ZERO -C SOLVE THE TRIANGULAR SYSTEM. -C STORE THE SOLUTION TEMPORARILY IN ZZ(). - RTNKEY = 1 - GO TO 400 - 200 CONTINUE -C -C ****** SECONDARY LOOP BEGINS HERE ****** -C -C ITERATION COUNTER. -C - 210 continue - ITER=ITER+1 - IF (ITER .gt. ITMAX) then - MODE=3 - write (*,'(/a)') ' NNLS quitting on iteration count.' - GO TO 350 - endif -C -C SEE IF ALL NEW CONSTRAINED COEFFS ARE FEASIBLE. -C IF NOT COMPUTE ALPHA. -C - ALPHA=TWO - DO 240 IP=1,NSETP - L=INDEX(IP) - IF (ZZ(IP) .le. ZERO) then - T=-X(L)/(ZZ(IP)-X(L)) - IF (ALPHA .gt. T) then - ALPHA=T - JJ=IP - endif - endif - 240 CONTINUE -C -C IF ALL NEW CONSTRAINED COEFFS ARE FEASIBLE THEN ALPHA WILL -C STILL = 2. IF SO EXIT FROM SECONDARY LOOP TO MAIN LOOP. -C - IF (ALPHA.EQ.TWO) GO TO 330 -C -C OTHERWISE USE ALPHA WHICH WILL BE BETWEEN 0. AND 1. TO -C INTERPOLATE BETWEEN THE OLD X AND THE NEW ZZ. -C - DO 250 IP=1,NSETP - L=INDEX(IP) - X(L)=X(L)+ALPHA*(ZZ(IP)-X(L)) - 250 continue -C -C MODIFY A AND B AND THE INDEX ARRAYS TO MOVE COEFFICIENT I -C FROM SET P TO SET Z. -C - I=INDEX(JJ) - 260 continue - X(I)=ZERO -C - IF (JJ .ne. NSETP) then - JJ=JJ+1 - DO 280 J=JJ,NSETP - II=INDEX(J) - INDEX(J-1)=II - CALL G1 (A(J-1,II),A(J,II),CC,SS,A(J-1,II)) - A(J,II)=ZERO - DO 270 L=1,N - IF (L.NE.II) then -c -c Apply procedure G2 (CC,SS,A(J-1,L),A(J,L)) -c - TEMP = A(J-1,L) - A(J-1,L) = CC*TEMP + SS*A(J,L) - A(J,L) =-SS*TEMP + CC*A(J,L) - endif - 270 CONTINUE -c -c Apply procedure G2 (CC,SS,B(J-1),B(J)) -c - TEMP = B(J-1) - B(J-1) = CC*TEMP + SS*B(J) - B(J) =-SS*TEMP + CC*B(J) - 280 continue - endif -c - NPP1=NSETP - NSETP=NSETP-1 - IZ1=IZ1-1 - INDEX(IZ1)=I -C -C SEE IF THE REMAINING COEFFS IN SET P ARE FEASIBLE. THEY SHOULD -C BE BECAUSE OF THE WAY ALPHA WAS DETERMINED. -C IF ANY ARE INFEASIBLE IT IS DUE TO ROUND-OFF ERROR. ANY -C THAT ARE NONPOSITIVE WILL BE SET TO ZERO -C AND MOVED FROM SET P TO SET Z. -C - DO 300 JJ=1,NSETP - I=INDEX(JJ) - IF (X(I) .le. ZERO) go to 260 - 300 CONTINUE -C -C COPY B( ) INTO ZZ( ). THEN SOLVE AGAIN AND LOOP BACK. -C - DO 310 I=1,M - 310 ZZ(I)=B(I) - RTNKEY = 2 - GO TO 400 - 320 CONTINUE - GO TO 210 -C ****** END OF SECONDARY LOOP ****** -C - 330 continue - DO 340 IP=1,NSETP - I=INDEX(IP) - 340 X(I)=ZZ(IP) -C ALL NEW COEFFS ARE POSITIVE. LOOP BACK TO BEGINNING. - GO TO 30 -C -C ****** END OF MAIN LOOP ****** -C -C COME TO HERE FOR TERMINATION. -C COMPUTE THE NORM OF THE FINAL RESIDUAL VECTOR. -C - 350 continue - SM=ZERO - IF (NPP1 .le. M) then - DO 360 I=NPP1,M - 360 SM=SM+B(I)**2 - else - DO 380 J=1,N - 380 W(J)=ZERO - endif - RNORM=sqrt(SM) - RETURN -C -C THE FOLLOWING BLOCK OF CODE IS USED AS AN INTERNAL SUBROUTINE -C TO SOLVE THE TRIANGULAR SYSTEM, PUTTING THE SOLUTION IN ZZ(). -C - 400 continue - DO 430 L=1,NSETP - IP=NSETP+1-L - IF (L .ne. 1) then - DO 410 II=1,IP - ZZ(II)=ZZ(II)-A(II,JJ)*ZZ(IP+1) - 410 continue - endif - JJ=INDEX(IP) - ZZ(IP)=ZZ(IP)/A(IP,JJ) - 430 continue - go to (200, 320), RTNKEY - END - - - double precision FUNCTION DIFF(X,Y) -c -c Function used in tests that depend on machine precision. -c -c The original version of this code was developed by -c Charles L. Lawson and Richard J. Hanson at Jet Propulsion Laboratory -c 1973 JUN 7, and published in the book -c "SOLVING LEAST SQUARES PROBLEMS", Prentice-HalL, 1974. -c Revised FEB 1995 to accompany reprinting of the book by SIAM. -C - double precision X, Y - DIFF=X-Y - RETURN - END - - -C SUBROUTINE H12 (MODE,LPIVOT,L1,M,U,IUE,UP,C,ICE,ICV,NCV) -C -C CONSTRUCTION AND/OR APPLICATION OF A SINGLE -C HOUSEHOLDER TRANSFORMATION.. Q = I + U*(U**T)/B -C -c The original version of this code was developed by -c Charles L. Lawson and Richard J. Hanson at Jet Propulsion Laboratory -c 1973 JUN 12, and published in the book -c "SOLVING LEAST SQUARES PROBLEMS", Prentice-HalL, 1974. -c Revised FEB 1995 to accompany reprinting of the book by SIAM. -C ------------------------------------------------------------------ -c Subroutine Arguments -c -C MODE = 1 OR 2 Selects Algorithm H1 to construct and apply a -c Householder transformation, or Algorithm H2 to apply a -c previously constructed transformation. -C LPIVOT IS THE INDEX OF THE PIVOT ELEMENT. -C L1,M IF L1 .LE. M THE TRANSFORMATION WILL BE CONSTRUCTED TO -C ZERO ELEMENTS INDEXED FROM L1 THROUGH M. IF L1 GT. M -C THE SUBROUTINE DOES AN IDENTITY TRANSFORMATION. -C U(),IUE,UP On entry with MODE = 1, U() contains the pivot -c vector. IUE is the storage increment between elements. -c On exit when MODE = 1, U() and UP contain quantities -c defining the vector U of the Householder transformation. -c on entry with MODE = 2, U() and UP should contain -c quantities previously computed with MODE = 1. These will -c not be modified during the entry with MODE = 2. -C C() ON ENTRY with MODE = 1 or 2, C() CONTAINS A MATRIX WHICH -c WILL BE REGARDED AS A SET OF VECTORS TO WHICH THE -c HOUSEHOLDER TRANSFORMATION IS TO BE APPLIED. -c ON EXIT C() CONTAINS THE SET OF TRANSFORMED VECTORS. -C ICE STORAGE INCREMENT BETWEEN ELEMENTS OF VECTORS IN C(). -C ICV STORAGE INCREMENT BETWEEN VECTORS IN C(). -C NCV NUMBER OF VECTORS IN C() TO BE TRANSFORMED. IF NCV .LE. 0 -C NO OPERATIONS WILL BE DONE ON C(). -C ------------------------------------------------------------------ - SUBROUTINE H12 (MODE,LPIVOT,L1,M,U,IUE,UP,C,ICE,ICV,NCV) -C ------------------------------------------------------------------ - integer I, I2, I3, I4, ICE, ICV, INCR, IUE, J - integer L1, LPIVOT, M, MODE, NCV - double precision B, C(*), CL, CLINV, ONE, SM -c double precision U(IUE,M) - double precision U(IUE,*) - double precision UP - parameter(ONE = 1.0d0) -C ------------------------------------------------------------------ - IF (0.GE.LPIVOT.OR.LPIVOT.GE.L1.OR.L1.GT.M) RETURN - CL=abs(U(1,LPIVOT)) - IF (MODE.EQ.2) GO TO 60 -C ****** CONSTRUCT THE TRANSFORMATION. ****** - DO 10 J=L1,M - 10 CL=MAX(abs(U(1,J)),CL) - IF (CL) 130,130,20 - 20 CLINV=ONE/CL - SM=(U(1,LPIVOT)*CLINV)**2 - DO 30 J=L1,M - 30 SM=SM+(U(1,J)*CLINV)**2 - CL=CL*SQRT(SM) - IF (U(1,LPIVOT)) 50,50,40 - 40 CL=-CL - 50 UP=U(1,LPIVOT)-CL - U(1,LPIVOT)=CL - GO TO 70 -C ****** APPLY THE TRANSFORMATION I+U*(U**T)/B TO C. ****** -C - 60 IF (CL) 130,130,70 - 70 IF (NCV.LE.0) RETURN - B= UP*U(1,LPIVOT) -C B MUST BE NONPOSITIVE HERE. IF B = 0., RETURN. -C - IF (B) 80,130,130 - 80 B=ONE/B - I2=1-ICV+ICE*(LPIVOT-1) - INCR=ICE*(L1-LPIVOT) - DO 120 J=1,NCV - I2=I2+ICV - I3=I2+INCR - I4=I3 - SM=C(I2)*UP - DO 90 I=L1,M - SM=SM+C(I3)*U(1,I) - 90 I3=I3+ICE - IF (SM) 100,120,100 - 100 SM=SM*B - C(I2)=C(I2)+SM*UP - DO 110 I=L1,M - C(I4)=C(I4)+SM*U(1,I) - 110 I4=I4+ICE - 120 CONTINUE - 130 RETURN - END - - - - SUBROUTINE G1 (A,B,CTERM,STERM,SIG) -c -C COMPUTE ORTHOGONAL ROTATION MATRIX.. -c -c The original version of this code was developed by -c Charles L. Lawson and Richard J. Hanson at Jet Propulsion Laboratory -c 1973 JUN 12, and published in the book -c "SOLVING LEAST SQUARES PROBLEMS", Prentice-HalL, 1974. -c Revised FEB 1995 to accompany reprinting of the book by SIAM. -C -C COMPUTE.. MATRIX (C, S) SO THAT (C, S)(A) = (SQRT(A**2+B**2)) -C (-S,C) (-S,C)(B) ( 0 ) -C COMPUTE SIG = SQRT(A**2+B**2) -C SIG IS COMPUTED LAST TO ALLOW FOR THE POSSIBILITY THAT -C SIG MAY BE IN THE SAME LOCATION AS A OR B . -C ------------------------------------------------------------------ - double precision A, B, CTERM, ONE, SIG, STERM, XR, YR, ZERO - parameter(ONE = 1.0d0, ZERO = 0.0d0) -C ------------------------------------------------------------------ - if (abs(A) .gt. abs(B)) then - XR=B/A - YR=sqrt(ONE+XR**2) - CTERM=sign(ONE/YR,A) - STERM=CTERM*XR - SIG=abs(A)*YR - RETURN - endif - - if (B .ne. ZERO) then - XR=A/B - YR=sqrt(ONE+XR**2) - STERM=sign(ONE/YR,B) - CTERM=STERM*XR - SIG=abs(B)*YR - RETURN - endif - - SIG=ZERO - CTERM=ZERO - STERM=ONE - RETURN - END Copied: branches/refactor_fft/scipy/optimize/nnls/nnls.f (from rev 4827, trunk/scipy/optimize/nnls/nnls.f) Deleted: branches/refactor_fft/scipy/optimize/nnls/nnls.pyf =================================================================== --- trunk/scipy/optimize/nnls/nnls.pyf 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/nnls/nnls.pyf 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,22 +0,0 @@ -! -*- f90 -*- -! Note: the context of this file is case sensitive. - -python module _nnls ! in - interface ! in :_nnls - subroutine nnls(a,mda,m,n,b,x,rnorm,w,zz,index_bn,mode) ! in :nnls:NNLS.F - double precision dimension(mda,*), intent(copy) :: a - integer optional,check(shape(a,0)==mda),depend(a) :: mda=shape(a,0) - integer :: m - integer :: n - double precision dimension(*), intent(copy) :: b - double precision dimension(n), intent(out) :: x - double precision, intent(out) :: rnorm - double precision dimension(*) :: w - double precision dimension(*) :: zz - integer dimension(*) :: index_bn - integer , intent(out):: mode - end subroutine nnls -end python module _nnls - -! This file was auto-generated with f2py (version:2_5878). -! See http://cens.ioc.ee/projects/f2py2e/ Copied: branches/refactor_fft/scipy/optimize/nnls/nnls.pyf (from rev 4827, trunk/scipy/optimize/nnls/nnls.pyf) Copied: branches/refactor_fft/scipy/optimize/nnls.py (from rev 4827, trunk/scipy/optimize/nnls.py) Modified: branches/refactor_fft/scipy/optimize/optimize.py =================================================================== --- branches/refactor_fft/scipy/optimize/optimize.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/optimize.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -24,7 +24,7 @@ import numpy from numpy import atleast_1d, eye, mgrid, argmin, zeros, shape, empty, \ - squeeze, isscalar, vectorize, asarray, absolute, sqrt, Inf, asfarray, isinf + squeeze, vectorize, asarray, absolute, sqrt, Inf, asfarray, isinf import linesearch # These have been copied from Numeric's MLab.py Modified: branches/refactor_fft/scipy/optimize/setup.py =================================================================== --- branches/refactor_fft/scipy/optimize/setup.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/setup.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -41,6 +41,9 @@ sources = ['slsqp.pyf', 'slsqp_optmz.f'] config.add_extension('_slsqp', sources=[join('slsqp', x) for x in sources]) + config.add_extension('_nnls', sources=[join('nnls', x) \ + for x in ["nnls.f","nnls.pyf"]]) + config.add_data_dir('tests') config.add_data_dir('benchmarks') return config Modified: branches/refactor_fft/scipy/optimize/slsqp.py =================================================================== --- branches/refactor_fft/scipy/optimize/slsqp.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/slsqp.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -8,8 +8,8 @@ __all__ = ['approx_jacobian','fmin_slsqp'] from _slsqp import slsqp -from numpy import zeros, array, identity, linalg, rank, squeeze, append, \ - asfarray,product, concatenate, finfo, sqrt, vstack, transpose +from numpy import zeros, array, linalg, append, asfarray, concatenate, finfo, \ + sqrt, vstack from optimize import approx_fprime, wrap_function __docformat__ = "restructuredtext en" Copied: branches/refactor_fft/scipy/optimize/tests/test_nnls.py (from rev 4827, trunk/scipy/optimize/tests/test_nnls.py) Modified: branches/refactor_fft/scipy/optimize/tests/test_optimize.py =================================================================== --- branches/refactor_fft/scipy/optimize/tests/test_optimize.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/tests/test_optimize.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -13,8 +13,7 @@ from scipy import optimize from scipy.optimize import leastsq -from numpy import array, zeros, float64, dot, log, exp, inf, \ - pi, sin, cos +from numpy import array, zeros, float64, dot, log, exp, inf, sin, cos import numpy as np from scipy.optimize.tnc import RCSTRINGS, MSG_NONE import numpy.random @@ -264,14 +263,14 @@ return err def test_basic(self): - p0 = numpy.array([0,0,0]) + p0 = array([0,0,0]) params_fit, ier = leastsq(self.residuals, p0, args=(self.y_meas, self.x)) assert ier in (1,2,3,4), 'solution not found (ier=%d)'%ier assert_array_almost_equal( params_fit, self.abc, decimal=2) # low precision due to random def test_full_output(self): - p0 = numpy.array([0,0,0]) + p0 = array([0,0,0]) full_output = leastsq(self.residuals, p0, args=(self.y_meas, self.x), full_output=True) @@ -279,8 +278,8 @@ assert ier in (1,2,3,4), 'solution not found: %s'%mesg def test_input_untouched(self): - p0 = numpy.array([0,0,0],dtype=numpy.float64) - p0_copy = numpy.array(p0, copy=True) + p0 = array([0,0,0],dtype=float64) + p0_copy = array(p0, copy=True) full_output = leastsq(self.residuals, p0, args=(self.y_meas, self.x), full_output=True) Modified: branches/refactor_fft/scipy/optimize/tests/test_slsqp.py =================================================================== --- branches/refactor_fft/scipy/optimize/tests/test_slsqp.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/tests/test_slsqp.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -2,7 +2,6 @@ import numpy as np from scipy.optimize import fmin_slsqp -from numpy import matrix, diag class TestSLSQP(TestCase): Modified: branches/refactor_fft/scipy/optimize/tests/test_zeros.py =================================================================== --- branches/refactor_fft/scipy/optimize/tests/test_zeros.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/tests/test_zeros.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -7,8 +7,7 @@ from scipy.optimize import zeros as cc # Import testing parameters -from scipy.optimize._tstutils import methods, mstrings, functions, \ - fstrings, description +from scipy.optimize._tstutils import functions, fstrings class TestBasic(TestCase) : def run_check(self, method, name): Modified: branches/refactor_fft/scipy/optimize/zeros.py =================================================================== --- branches/refactor_fft/scipy/optimize/zeros.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/optimize/zeros.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,7 +1,7 @@ ## Automatically adapted for scipy Oct 07, 2005 by convertcode.py import _zeros -from numpy import sqrt, sign, finfo +from numpy import finfo _iter = 100 _xtol = 1e-12 Modified: branches/refactor_fft/scipy/signal/bsplines.py =================================================================== --- branches/refactor_fft/scipy/signal/bsplines.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/signal/bsplines.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -3,7 +3,7 @@ import scipy.special from numpy import logical_and, asarray, pi, zeros_like, \ piecewise, array, arctan2, tan, zeros, arange, floor -from numpy.core.umath import sqrt, exp, greater, less, equal, cos, add, sin, \ +from numpy.core.umath import sqrt, exp, greater, less, cos, add, sin, \ less_equal, greater_equal from spline import * # C-modules from scipy.misc import comb Modified: branches/refactor_fft/scipy/signal/signaltools.py =================================================================== --- branches/refactor_fft/scipy/signal/signaltools.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/signal/signaltools.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -4,17 +4,16 @@ import types import sigtools from scipy import special, linalg -from scipy.fftpack import fft, ifft, ifftshift, fft2, ifft2 +from scipy.fftpack import fft, ifft, ifftshift, fft2, ifft2, fftn, ifftn from numpy import polyadd, polymul, polydiv, polysub, \ roots, poly, polyval, polyder, cast, asarray, isscalar, atleast_1d, \ ones, sin, linspace, real, extract, real_if_close, zeros, array, arange, \ where, sqrt, rank, newaxis, argmax, product, cos, pi, exp, \ ravel, size, less_equal, sum, r_, iscomplexobj, take, \ - argsort, allclose, expand_dims, unique, prod, sort, reshape, c_, \ - transpose, dot, any, minimum, maximum, mean, cosh, arccosh, \ + argsort, allclose, expand_dims, unique, prod, sort, reshape, \ + transpose, dot, any, mean, cosh, arccosh, \ arccos, concatenate -import numpy -from scipy.fftpack import fftn, ifftn, fft +import numpy as np from scipy.misc import factorial _modedict = {'valid':0, 'same':1, 'full':2} @@ -94,8 +93,8 @@ """ s1 = array(in1.shape) s2 = array(in2.shape) - complex_result = (numpy.issubdtype(in1.dtype, numpy.complex) or - numpy.issubdtype(in2.dtype, numpy.complex)) + complex_result = (np.issubdtype(in1.dtype, np.complex) or + np.issubdtype(in2.dtype, np.complex)) size = s1+s2-1 IN1 = fftn(in1,size) IN1 *= fftn(in2,size) @@ -864,7 +863,7 @@ p = zeros(x.shape) p[x > 1] = cosh(order * arccosh(x[x > 1])) p[x < -1] = (1 - 2*(order%2)) * cosh(order * arccosh(-x[x < -1])) - p[numpy.abs(x) <=1 ] = cos(order * arccos(x[numpy.abs(x) <= 1])) + p[np.abs(x) <=1 ] = cos(order * arccos(x[np.abs(x) <= 1])) # Appropriate IDFT and filling up # depending on even/odd M @@ -1004,11 +1003,11 @@ mult -- The multiplicity of each root """ if rtype in ['max','maximum']: - comproot = numpy.maximum + comproot = np.maximum elif rtype in ['min','minimum']: - comproot = numpy.minimum + comproot = np.minimum elif rtype in ['avg','mean']: - comproot = numpy.mean + comproot = np.mean p = asarray(p)*1.0 tol = abs(tol) p, indx = cmplx_sort(p) @@ -1381,7 +1380,7 @@ sl = [slice(None)]*len(x.shape) newshape = list(x.shape) newshape[axis] = num - N = int(numpy.minimum(num,Nx)) + N = int(np.minimum(num,Nx)) Y = zeros(newshape,'D') sl[axis] = slice(0,(N+1)/2) Y[sl] = X[sl] Modified: branches/refactor_fft/scipy/signal/wavelets.py =================================================================== --- branches/refactor_fft/scipy/signal/wavelets.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/signal/wavelets.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,45 +1,45 @@ __all__ = ['daub','qmf','cascade','morlet'] -import numpy as sb +import numpy as np from numpy.dual import eig from scipy.misc import comb -from scipy import linspace, pi, exp, zeros +from scipy import linspace, pi, exp def daub(p): """The coefficients for the FIR low-pass filter producing Daubechies wavelets. p>=1 gives the order of the zero at f=1/2. There are 2p filter coefficients. """ - sqrt = sb.sqrt + sqrt = np.sqrt assert(p>=1) if p==1: c = 1/sqrt(2) - return sb.array([c,c]) + return np.array([c,c]) elif p==2: f = sqrt(2)/8 c = sqrt(3) - return f*sb.array([1+c,3+c,3-c,1-c]) + return f*np.array([1+c,3+c,3-c,1-c]) elif p==3: tmp = 12*sqrt(10) z1 = 1.5 + sqrt(15+tmp)/6 - 1j*(sqrt(15)+sqrt(tmp-15))/6 - z1c = sb.conj(z1) + z1c = np.conj(z1) f = sqrt(2)/8 - d0 = sb.real((1-z1)*(1-z1c)) - a0 = sb.real(z1*z1c) - a1 = 2*sb.real(z1) - return f/d0*sb.array([a0, 3*a0-a1, 3*a0-3*a1+1, a0-3*a1+3, 3-a1, 1]) + d0 = np.real((1-z1)*(1-z1c)) + a0 = np.real(z1*z1c) + a1 = 2*np.real(z1) + return f/d0*np.array([a0, 3*a0-a1, 3*a0-3*a1+1, a0-3*a1+3, 3-a1, 1]) elif p<35: # construct polynomial and factor it if p<35: P = [comb(p-1+k,k,exact=1) for k in range(p)][::-1] - yj = sb.roots(P) + yj = np.roots(P) else: # try different polynomial --- needs work P = [comb(p-1+k,k,exact=1)/4.0**k for k in range(p)][::-1] - yj = sb.roots(P) / 4 + yj = np.roots(P) / 4 # for each root, compute two z roots, select the one with |z|>1 # Build up final polynomial - c = sb.poly1d([1,1])**p - q = sb.poly1d([1]) + c = np.poly1d([1,1])**p + q = np.poly1d([1]) for k in range(p-1): yval = yj[k] part = 2*sqrt(yval*(yval-1)) @@ -49,9 +49,9 @@ z1 = const - part q = q * [1,-z1] - q = c * sb.real(q) + q = c * np.real(q) # Normalize result - q = q / sb.sum(q) * sqrt(2) + q = q / np.sum(q) * sqrt(2) return q.c[::-1] else: raise ValueError, "Polynomial factorization does not work "\ @@ -62,7 +62,7 @@ """ N = len(hk)-1 asgn = [{0:1,1:-1}[k%2] for k in range(N+1)] - return hk[::-1]*sb.array(asgn) + return hk[::-1]*np.array(asgn) def wavedec(amn,hk): gk = qmf(hk) @@ -96,55 +96,55 @@ N = len(hk)-1 - if (J > 30 - sb.log2(N+1)): + if (J > 30 - np.log2(N+1)): raise ValueError, "Too many levels." if (J < 1): raise ValueError, "Too few levels." # construct matrices needed - nn,kk = sb.ogrid[:N,:N] - s2 = sb.sqrt(2) + nn,kk = np.ogrid[:N,:N] + s2 = np.sqrt(2) # append a zero so that take works - thk = sb.r_[hk,0] + thk = np.r_[hk,0] gk = qmf(hk) - tgk = sb.r_[gk,0] + tgk = np.r_[gk,0] - indx1 = sb.clip(2*nn-kk,-1,N+1) - indx2 = sb.clip(2*nn-kk+1,-1,N+1) - m = sb.zeros((2,2,N,N),'d') - m[0,0] = sb.take(thk,indx1,0) - m[0,1] = sb.take(thk,indx2,0) - m[1,0] = sb.take(tgk,indx1,0) - m[1,1] = sb.take(tgk,indx2,0) + indx1 = np.clip(2*nn-kk,-1,N+1) + indx2 = np.clip(2*nn-kk+1,-1,N+1) + m = np.zeros((2,2,N,N),'d') + m[0,0] = np.take(thk,indx1,0) + m[0,1] = np.take(thk,indx2,0) + m[1,0] = np.take(tgk,indx1,0) + m[1,1] = np.take(tgk,indx2,0) m *= s2 # construct the grid of points - x = sb.arange(0,N*(1<= 0" % \ minor_name - if numpy.diff(self.indptr).min() < 0: + if diff(self.indptr).min() < 0: raise ValueError,'index pointer values must form a " \ "non-decreasing sequence' @@ -260,7 +258,7 @@ raise ValueError('inconsistent shapes') if isdense(other): - return numpy.multiply(self.todense(),other) + return multiply(self.todense(),other) else: other = self.__class__(other) return self._binopt(other,'_elmul_') @@ -541,7 +539,7 @@ index = self.indices[indices] - start data = self.data[indices] - indptr = numpy.array([0, len(indices)]) + indptr = array([0, len(indices)]) return self.__class__((data, index, indptr), shape=shape, \ dtype=self.dtype) Modified: branches/refactor_fft/scipy/sparse/construct.py =================================================================== --- branches/refactor_fft/scipy/sparse/construct.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/construct.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -15,16 +15,13 @@ from sputils import upcast -from csr import csr_matrix, isspmatrix_csr -from csc import csc_matrix, isspmatrix_csc +from csr import csr_matrix +from csc import csc_matrix from bsr import bsr_matrix from coo import coo_matrix -from dok import dok_matrix from lil import lil_matrix from dia import dia_matrix -from base import isspmatrix - def spdiags(data, diags, m, n, format=None): """Return a sparse matrix from diagonals. Modified: branches/refactor_fft/scipy/sparse/coo.py =================================================================== --- branches/refactor_fft/scipy/sparse/coo.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/coo.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -7,14 +7,13 @@ from itertools import izip from warnings import warn -from numpy import array, asarray, empty, intc, zeros, \ - unique, searchsorted, atleast_2d, rank, deprecate, hstack +from numpy import array, asarray, empty, intc, zeros, unique, searchsorted,\ + atleast_2d, rank, deprecate, hstack -from sparsetools import coo_tocsr, coo_tocsc, coo_todense, coo_matvec +from sparsetools import coo_tocsr, coo_todense, coo_matvec from base import isspmatrix from data import _data_matrix from sputils import upcast, to_native, isshape, getdtype -from spfuncs import estimate_blocksize class coo_matrix(_data_matrix): """A sparse matrix in COOrdinate format. Modified: branches/refactor_fft/scipy/sparse/csc.py =================================================================== --- branches/refactor_fft/scipy/sparse/csc.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/csc.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -6,15 +6,9 @@ from warnings import warn -import numpy -from numpy import array, matrix, asarray, asmatrix, zeros, rank, intc, \ - empty, hstack, isscalar, ndarray, shape, searchsorted, where, \ - concatenate, deprecate, transpose, ravel - -from base import spmatrix, isspmatrix +from numpy import asarray, intc, empty, searchsorted, deprecate from sparsetools import csc_tocsr -from sputils import upcast, to_native, isdense, isshape, getdtype, \ - isscalarlike, isintlike +from sputils import upcast, isintlike from compressed import _cs_matrix Modified: branches/refactor_fft/scipy/sparse/csr.py =================================================================== --- branches/refactor_fft/scipy/sparse/csr.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/csr.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -7,16 +7,12 @@ from warnings import warn -import numpy -from numpy import array, matrix, asarray, asmatrix, zeros, rank, intc, \ - empty, hstack, isscalar, ndarray, shape, searchsorted, where, \ - concatenate, deprecate, arange, ones, ravel +from numpy import asarray, asmatrix, zeros, intc, empty, isscalar, array, \ + searchsorted, where, deprecate, arange, ones, ravel -from base import spmatrix, isspmatrix from sparsetools import csr_tocsc, csr_tobsr, csr_count_blocks, \ get_csr_submatrix -from sputils import upcast, to_native, isdense, isshape, getdtype, \ - isscalarlike, isintlike +from sputils import upcast, isintlike from compressed import _cs_matrix @@ -319,7 +315,7 @@ index = self.indices[indices] - start data = self.data[indices] - indptr = numpy.array([0, len(indices)]) + indptr = array([0, len(indices)]) return csr_matrix( (data, index, indptr), shape=(1, stop-start) ) def _get_submatrix( self, row_slice, col_slice ): @@ -342,7 +338,7 @@ return i0, i1 - elif isscalar( sl ): + elif isintlike( sl ): if sl < 0: sl += num Modified: branches/refactor_fft/scipy/sparse/dia.py =================================================================== --- branches/refactor_fft/scipy/sparse/dia.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/dia.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -4,14 +4,12 @@ __all__ = ['dia_matrix','isspmatrix_dia'] -from numpy import asarray, asmatrix, matrix, zeros, arange, array, \ - empty_like, intc, atleast_1d, atleast_2d, add, multiply, \ - unique, hstack +from numpy import asarray, zeros, arange, array, intc, atleast_1d, \ + atleast_2d, unique, hstack from base import isspmatrix, _formats from data import _data_matrix -from sputils import isscalarlike, isshape, upcast, getdtype, isdense - +from sputils import isshape, upcast, getdtype from sparsetools import dia_matvec class dia_matrix(_data_matrix): @@ -28,9 +26,9 @@ to construct an empty matrix with shape (M, N), dtype is optional, defaulting to dtype='d'. - dia_matrix((data, diags), shape=(M, N)) + dia_matrix((data, offsets), shape=(M, N)) where the ``data[k,:]`` stores the diagonal entries for - diagonal ``diag[k]`` (See example below) + diagonal ``offsets[k]`` (See example below) Examples @@ -44,8 +42,8 @@ [0, 0, 0, 0]], dtype=int8) >>> data = array([[1,2,3,4]]).repeat(3,axis=0) - >>> diags = array([0,-1,2]) - >>> dia_matrix( (data,diags), shape=(4,4)).todense() + >>> offsets = array([0,-1,2]) + >>> dia_matrix( (data,offsets), shape=(4,4)).todense() matrix([[1, 0, 3, 0], [1, 2, 0, 4], [0, 2, 3, 0], @@ -59,36 +57,36 @@ if isspmatrix_dia(arg1): if copy: arg1 = arg1.copy() - self.data = arg1.data - self.diags = arg1.diags - self.shape = arg1.shape + self.data = arg1.data + self.offsets = arg1.offsets + self.shape = arg1.shape elif isspmatrix(arg1): if isspmatrix_dia(arg1) and copy: A = arg1.copy() else: A = arg1.todia() - self.data = A.data - self.diags = A.diags - self.shape = A.shape + self.data = A.data + self.offsets = A.offsets + self.shape = A.shape elif isinstance(arg1, tuple): if isshape(arg1): # It's a tuple of matrix dimensions (M, N) # create empty matrix self.shape = arg1 #spmatrix checks for errors here self.data = zeros( (0,0), getdtype(dtype, default=float)) - self.diags = zeros( (0), dtype=intc) + self.offsets = zeros( (0), dtype=intc) else: try: - # Try interpreting it as (data, diags) - data, diags = arg1 + # Try interpreting it as (data, offsets) + data, offsets = arg1 except: - raise ValueError, "unrecognized form for dia_matrix constructor" + raise ValueError('unrecognized form for dia_matrix constructor') else: if shape is None: - raise ValueError,'expected a shape argument' - self.data = atleast_2d(array(arg1[0],dtype=dtype,copy=copy)) - self.diags = atleast_1d(array(arg1[1],dtype='i',copy=copy)) - self.shape = shape + raise ValueError('expected a shape argument') + self.data = atleast_2d(array(arg1[0],dtype=dtype,copy=copy)) + self.offsets = atleast_1d(array(arg1[1],dtype='i',copy=copy)) + self.shape = shape else: #must be dense, convert to COO first, then to DIA try: @@ -98,25 +96,25 @@ " %s_matrix constructor" % self.format from coo import coo_matrix A = coo_matrix(arg1).todia() - self.data = A.data - self.diags = A.diags - self.shape = A.shape + self.data = A.data + self.offsets = A.offsets + self.shape = A.shape #check format - if self.diags.ndim != 1: - raise ValueError,'diags array must have rank 1' + if self.offsets.ndim != 1: + raise ValueError('offsets array must have rank 1') if self.data.ndim != 2: - raise ValueError,'data array must have rank 2' + raise ValueError('data array must have rank 2') - if self.data.shape[0] != len(self.diags): - raise ValueError,'number of diagonals (%d) ' \ - 'does not match the number of diags (%d)' \ - % (self.data.shape[0], len(self.diags)) + if self.data.shape[0] != len(self.offsets): + raise ValueError('number of diagonals (%d) ' \ + 'does not match the number of offsets (%d)' \ + % (self.data.shape[0], len(self.offsets))) - if len(unique(self.diags)) != len(self.diags): - raise ValueError,'offset array contains duplicate values' + if len(unique(self.offsets)) != len(self.offsets): + raise ValueError('offset array contains duplicate values') def __repr__(self): nnz = self.getnnz() @@ -133,7 +131,7 @@ """ M,N = self.shape nnz = 0 - for k in self.diags: + for k in self.offsets: if k > 0: nnz += min(M,N-k) else: @@ -151,7 +149,7 @@ M,N = self.shape - dia_matvec(M,N, len(self.diags), L, self.diags, self.data, x.ravel(), y.ravel()) + dia_matvec(M,N, len(self.offsets), L, self.offsets, self.data, x.ravel(), y.ravel()) return y @@ -179,7 +177,7 @@ row = arange(len_data).reshape(1,-1).repeat(num_data,axis=0) col = row.copy() - for i,k in enumerate(self.diags): + for i,k in enumerate(self.offsets): row[i,:] -= k row,col,data = row.ravel(),col.ravel(),self.data.ravel() @@ -200,9 +198,9 @@ but with different data. By default the structure arrays are copied. """ if copy: - return dia_matrix( (data,self.diags.copy()), shape=self.shape) + return dia_matrix( (data,self.offsets.copy()), shape=self.shape) else: - return dia_matrix( (data,self.diags), shape=self.shape) + return dia_matrix( (data,self.offsets), shape=self.shape) from sputils import _isinstance Modified: branches/refactor_fft/scipy/sparse/dok.py =================================================================== --- branches/refactor_fft/scipy/sparse/dok.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/dok.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -7,53 +7,73 @@ import operator from itertools import izip -from numpy import asarray, asmatrix, intc, isscalar, array, matrix +from numpy import asarray, intc, isscalar from base import spmatrix,isspmatrix from sputils import isdense, getdtype, isshape, isintlike, isscalarlike class dok_matrix(spmatrix, dict): """Dictionary Of Keys based matrix. This is an efficient - structure for constructing sparse matrices + structure for constructing sparse matrices incrementally. + + This can be instatiated in several ways: + dok_matrix(D) + with a dense matrix, D + + dok_matrix(S) + with a sparse matrix, S + + dok_matrix((M,N), [dtype]) + create the matrix with initial shape (M,N) + dtype is optional, defaulting to dtype='d' + + Notes + ----- + Allows for efficient O(1) access of individual elements. + Duplicates are not allowed. + Can be efficiently converted to a coo_matrix once constructed. + + Examples + -------- + >>> from scipy.sparse import * + >>> from scipy import * + >>> S = dok_matrix((5,5), dtype=float32) + >>> for i in range(5): + >>> for j in range(5): + >>> S[i,j] = i+j # Update element + """ - def __init__(self, A=None, shape=None, dtype=None, copy=False): - """ Create a new dictionary-of-keys sparse matrix. An optional - argument A is accepted, which initializes the dok_matrix with it. - This can be a tuple of dimensions (M, N) or a (dense) array - to copy. - """ - #TODO deprecate argument A in favor of arg1 style + def __init__(self, arg1, shape=None, dtype=None, copy=False): dict.__init__(self) spmatrix.__init__(self) - self.dtype = getdtype(dtype, A, default=float) - if A is not None: - if isinstance(A, tuple): - # Interpret as dimensions - if not isshape(A): - raise TypeError, "dimensions must be a 2-tuple of positive"\ - " integers" - self.shape = A - elif isspmatrix(A): - if isspmatrix_dok(A) and copy: - A = A.copy() - else: - A = A.todok() - self.update( A ) - self.shape = A.shape - self.dtype = A.dtype + + self.dtype = getdtype(dtype, default=float) + if isinstance(arg1, tuple) and isshape(arg1): # (M,N) + M, N = arg1 + self.shape = (M, N) + elif isspmatrix(arg1): # Sparse ctor + if isspmatrix_dok(arg1) and copy: + arg1 = arg1.copy() else: - #must be dense, convert to COO first, then to DOK - try: - A = asarray(A) - except: - raise ValueError, "unrecognized form for" \ - " %s_matrix constructor" % self.format - from coo import coo_matrix - self.update( coo_matrix(A).todok() ) - self.shape = A.shape - self.dtype = A.dtype + arg1 = arg1.todok() + self.update(arg1) + self.shape = arg1.shape + self.dtype = arg1.dtype + else: # Dense ctor + try: + arg1 = asarray(arg1) + except: + raise TypeError('invalid input format') + if len(arg1.shape)!=2: + raise TypeError('expected rank <=2 dense array or matrix') + + from coo import coo_matrix + self.update( coo_matrix(arg1).todok() ) + self.shape = arg1.shape + self.dtype = arg1.dtype + def getnnz(self): return dict.__len__(self) nnz = property(fget=getnnz) @@ -177,13 +197,10 @@ def __setitem__(self, key, value): try: - assert len(key) == 2 - except (AssertionError, TypeError): - raise TypeError, "index must be a pair of integers, slices, or" \ - " sequences" - i, j = key + i, j = key + except (ValueError, TypeError): + raise TypeError, "index must be a pair of integers or slices" - # First deal with the case where both i and j are integers if isintlike(i) and isintlike(j): if i < 0: @@ -193,20 +210,14 @@ if i < 0 or i >= self.shape[0] or j < 0 or j >= self.shape[1]: raise IndexError, "index out of bounds" - if isintlike(value) and value == 0: - if key in self.keys(): # get rid of it something already there - del self[key] + + if isscalar(value): + if value==0: + del self[(i,j)] + else: + dict.__setitem__(self, (i,j), self.dtype.type(value)) else: - # Ensure value is a single element, not a sequence - if isinstance(value, float) or isintlike(value) or \ - isinstance(value, complex): - dict.__setitem__(self, (i,j), self.dtype.type(value)) - newrows = max(self.shape[0], int(key[0])+1) - newcols = max(self.shape[1], int(key[1])+1) - self.shape = (newrows, newcols) - else: - raise TypeError, "cannot set matrix element to non-scalar" - return # done + raise TypeError, "cannot set matrix element to a non-scalar" else: # Either i or j is a slice, sequence, or invalid. If i is a slice # or sequence, unfold it first and call __setitem__ recursively. Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c 2008-10-23 12:30:20 UTC (rev 4828) @@ -118,7 +118,7 @@ for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; ++i) { irow = Astore->rowind[i]; - r[irow] = SUPERLU_MAX( r[irow], c_abs1(&Aval[i]) ); + r[irow] = SUPERLU_MAX( r[irow], slu_c_abs1(&Aval[i]) ); } /* Find the maximum and minimum scale factors. */ @@ -153,7 +153,7 @@ for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; ++i) { irow = Astore->rowind[i]; - c[j] = SUPERLU_MAX( c[j], c_abs1(&Aval[i]) * r[irow] ); + c[j] = SUPERLU_MAX( c[j], slu_c_abs1(&Aval[i]) * r[irow] ); } /* Find the maximum and minimum scale factors. */ Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c 2008-10-23 12:30:20 UTC (rev 4828) @@ -276,21 +276,21 @@ than SAFE2, then SAFE1 is added to the i-th component of the numerator and denominator before dividing. */ - for (i = 0; i < A->nrow; ++i) rwork[i] = c_abs1( &Bptr[i] ); + for (i = 0; i < A->nrow; ++i) rwork[i] = slu_c_abs1( &Bptr[i] ); /* Compute abs(op(A))*abs(X) + abs(B). */ if (notran) { for (k = 0; k < A->ncol; ++k) { - xk = c_abs1( &Xptr[k] ); + xk = slu_c_abs1( &Xptr[k] ); for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) - rwork[Astore->rowind[i]] += c_abs1(&Aval[i]) * xk; + rwork[Astore->rowind[i]] += slu_c_abs1(&Aval[i]) * xk; } } else { for (k = 0; k < A->ncol; ++k) { s = 0.; for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) { irow = Astore->rowind[i]; - s += c_abs1(&Aval[i]) * c_abs1(&Xptr[irow]); + s += slu_c_abs1(&Aval[i]) * slu_c_abs1(&Xptr[irow]); } rwork[k] += s; } @@ -298,9 +298,9 @@ s = 0.; for (i = 0; i < A->nrow; ++i) { if (rwork[i] > safe2) - s = SUPERLU_MAX( s, c_abs1(&work[i]) / rwork[i] ); + s = SUPERLU_MAX( s, slu_c_abs1(&work[i]) / rwork[i] ); else - s = SUPERLU_MAX( s, (c_abs1(&work[i]) + safe1) / + s = SUPERLU_MAX( s, (slu_c_abs1(&work[i]) + safe1) / (rwork[i] + safe1) ); } berr[j] = s; @@ -351,22 +351,22 @@ inv(op(A)) * diag(W), where W = abs(R) + NZ*EPS*( abs(op(A))*abs(X)+abs(B) ))) */ - for (i = 0; i < A->nrow; ++i) rwork[i] = c_abs1( &Bptr[i] ); + for (i = 0; i < A->nrow; ++i) rwork[i] = slu_c_abs1( &Bptr[i] ); /* Compute abs(op(A))*abs(X) + abs(B). */ if ( notran ) { for (k = 0; k < A->ncol; ++k) { - xk = c_abs1( &Xptr[k] ); + xk = slu_c_abs1( &Xptr[k] ); for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) - rwork[Astore->rowind[i]] += c_abs1(&Aval[i]) * xk; + rwork[Astore->rowind[i]] += slu_c_abs1(&Aval[i]) * xk; } } else { for (k = 0; k < A->ncol; ++k) { s = 0.; for (i = Astore->colptr[k]; i < Astore->colptr[k+1]; ++i) { irow = Astore->rowind[i]; - xk = c_abs1( &Xptr[irow] ); - s += c_abs1(&Aval[i]) * xk; + xk = slu_c_abs1( &Xptr[irow] ); + s += slu_c_abs1(&Aval[i]) * xk; } rwork[k] += s; } @@ -374,9 +374,9 @@ for (i = 0; i < A->nrow; ++i) if (rwork[i] > safe2) - rwork[i] = c_abs(&work[i]) + (iwork[i]+1)*eps*rwork[i]; + rwork[i] = slu_c_abs(&work[i]) + (iwork[i]+1)*eps*rwork[i]; else - rwork[i] = c_abs(&work[i])+(iwork[i]+1)*eps*rwork[i]+safe1; + rwork[i] = slu_c_abs(&work[i])+(iwork[i]+1)*eps*rwork[i]+safe1; kase = 0; do { @@ -424,13 +424,13 @@ lstres = 0.; if ( notran && colequ ) { for (i = 0; i < A->nrow; ++i) - lstres = SUPERLU_MAX( lstres, C[i] * c_abs1( &Xptr[i]) ); + lstres = SUPERLU_MAX( lstres, C[i] * slu_c_abs1( &Xptr[i]) ); } else if ( !notran && rowequ ) { for (i = 0; i < A->nrow; ++i) - lstres = SUPERLU_MAX( lstres, R[i] * c_abs1( &Xptr[i]) ); + lstres = SUPERLU_MAX( lstres, R[i] * slu_c_abs1( &Xptr[i]) ); } else { for (i = 0; i < A->nrow; ++i) - lstres = SUPERLU_MAX( lstres, c_abs1( &Xptr[i]) ); + lstres = SUPERLU_MAX( lstres, slu_c_abs1( &Xptr[i]) ); } if ( lstres != 0. ) ferr[j] /= lstres; Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c 2008-10-23 12:30:20 UTC (rev 4828) @@ -106,14 +106,14 @@ L20: if (*n == 1) { v[0] = x[0]; - *est = c_abs(&v[0]); + *est = slu_c_abs(&v[0]); /* ... QUIT */ goto L150; } *est = scsum1_(n, x, &c__1); for (i = 0; i < *n; ++i) { - d__1 = c_abs(&x[i]); + d__1 = slu_c_abs(&x[i]); if (d__1 > safmin) { d__1 = 1 / d__1; x[i].r *= d__1; @@ -158,7 +158,7 @@ if (*est <= estold) goto L120; for (i = 0; i < *n; ++i) { - d__1 = c_abs(&x[i]); + d__1 = slu_c_abs(&x[i]); if (d__1 > safmin) { d__1 = 1 / d__1; x[i].r *= d__1; Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c 2008-10-23 12:30:20 UTC (rev 4828) @@ -72,7 +72,7 @@ value = 0.; for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; i++) - value = SUPERLU_MAX( value, c_abs( &Aval[i]) ); + value = SUPERLU_MAX( value, slu_c_abs( &Aval[i]) ); } else if (lsame_(norm, "O") || *(unsigned char *)norm == '1') { /* Find norm1(A). */ @@ -80,7 +80,7 @@ for (j = 0; j < A->ncol; ++j) { sum = 0.; for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; i++) - sum += c_abs( &Aval[i] ); + sum += slu_c_abs( &Aval[i] ); value = SUPERLU_MAX(value,sum); } @@ -92,7 +92,7 @@ for (j = 0; j < A->ncol; ++j) for (i = Astore->colptr[j]; i < Astore->colptr[j+1]; i++) { irow = Astore->rowind[i]; - rwork[irow] += c_abs( &Aval[i] ); + rwork[irow] += slu_c_abs( &Aval[i] ); } value = 0.; for (i = 0; i < A->nrow; ++i) Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c 2008-10-23 12:30:20 UTC (rev 4828) @@ -106,7 +106,7 @@ diag = EMPTY; old_pivptr = nsupc; for (isub = nsupc; isub < nsupr; ++isub) { - rtemp = c_abs1 (&lu_col_ptr[isub]); + rtemp = slu_c_abs1 (&lu_col_ptr[isub]); if ( rtemp > pivmax ) { pivmax = rtemp; pivptr = isub; @@ -127,7 +127,7 @@ /* Choose appropriate pivotal element by our policy. */ if ( *usepr ) { - rtemp = c_abs1 (&lu_col_ptr[old_pivptr]); + rtemp = slu_c_abs1 (&lu_col_ptr[old_pivptr]); if ( rtemp != 0.0 && rtemp >= thresh ) pivptr = old_pivptr; else @@ -136,7 +136,7 @@ if ( *usepr == 0 ) { /* Use diagonal pivot? */ if ( diag >= 0 ) { /* diagonal exists */ - rtemp = c_abs1 (&lu_col_ptr[diag]); + rtemp = slu_c_abs1 (&lu_col_ptr[diag]); if ( rtemp != 0.0 && rtemp >= thresh ) pivptr = diag; } *pivrow = lsub_ptr[pivptr]; Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c 2008-10-23 12:30:20 UTC (rev 4828) @@ -83,15 +83,15 @@ maxaj = 0.; oldcol = inv_perm_c[j]; for (i = Astore->colptr[oldcol]; i < Astore->colptr[oldcol+1]; ++i) - maxaj = SUPERLU_MAX( maxaj, c_abs1( &Aval[i]) ); + maxaj = SUPERLU_MAX( maxaj, slu_c_abs1( &Aval[i]) ); maxuj = 0.; for (i = Ustore->colptr[j]; i < Ustore->colptr[j+1]; i++) - maxuj = SUPERLU_MAX( maxuj, c_abs1( &Uval[i]) ); + maxuj = SUPERLU_MAX( maxuj, slu_c_abs1( &Uval[i]) ); /* Supernode */ for (i = 0; i < nz_in_U; ++i) - maxuj = SUPERLU_MAX( maxuj, c_abs1( &luval[i]) ); + maxuj = SUPERLU_MAX( maxuj, slu_c_abs1( &luval[i]) ); ++nz_in_U; luval += nsupr; Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c 2008-10-23 12:30:20 UTC (rev 4828) @@ -414,8 +414,8 @@ err = xnorm = 0.0; for (i = 0; i < X->nrow; i++) { c_sub(&temp, &soln_work[i], &xtrue[i]); - err = SUPERLU_MAX(err, c_abs(&temp)); - xnorm = SUPERLU_MAX(xnorm, c_abs(&soln_work[i])); + err = SUPERLU_MAX(err, slu_c_abs(&temp)); + xnorm = SUPERLU_MAX(xnorm, slu_c_abs(&soln_work[i])); } err = err / xnorm; printf("||X - Xtrue||/||X|| = %e\n", err); Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c 2008-10-23 12:30:20 UTC (rev 4828) @@ -45,7 +45,7 @@ /* Returns sqrt(z.r^2 + z.i^2) */ -double c_abs(complex *z) +double slu_c_abs(complex *z) { float temp; float real = z->r; @@ -68,7 +68,7 @@ /* Approximates the abs */ /* Returns abs(z.r) + abs(z.i) */ -double c_abs1(complex *z) +double slu_c_abs1(complex *z) { float real = z->r; float imag = z->i; Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.h 2008-10-23 12:30:20 UTC (rev 4828) @@ -57,8 +57,8 @@ /* Prototypes for functions in scomplex.c */ void c_div(complex *, complex *, complex *); -double c_abs(complex *); /* exact */ -double c_abs1(complex *); /* approximate */ +double slu_c_abs(complex *); /* exact */ +double slu_c_abs1(complex *); /* approximate */ void c_exp(complex *, complex *); void r_cnjg(complex *, complex *); double r_imag(complex *); Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c 2008-10-23 12:30:20 UTC (rev 4828) @@ -42,7 +42,7 @@ int i__1, i__2; float ret_val; /* Builtin functions */ - double c_abs(complex *); + double slu_c_abs(complex *); /* Local variables */ static int i, nincx; static float stemp; @@ -69,7 +69,7 @@ /* NEXT LINE MODIFIED. */ - stemp += c_abs(&CX(i)); + stemp += slu_c_abs(&CX(i)); /* L10: */ } ret_val = stemp; @@ -83,7 +83,7 @@ /* NEXT LINE MODIFIED. */ - stemp += c_abs(&CX(i)); + stemp += slu_c_abs(&CX(i)); /* L30: */ } ret_val = stemp; Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/tests/test_linsolve.py =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/tests/test_linsolve.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/tests/test_linsolve.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -4,7 +4,7 @@ from numpy.testing import * from scipy.linalg import norm, inv -from scipy.sparse import spdiags, csc_matrix, SparseEfficiencyWarning +from scipy.sparse import spdiags, SparseEfficiencyWarning from scipy.sparse.linalg.dsolve import spsolve, use_solver warnings.simplefilter('ignore',SparseEfficiencyWarning) Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/umfpack/tests/test_umfpack.py =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/umfpack/tests/test_umfpack.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/umfpack/tests/test_umfpack.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -6,19 +6,16 @@ """ import warnings - -from numpy import transpose, array, arange - import random from numpy.testing import * from scipy import rand, matrix, diag, eye -from scipy.sparse import csc_matrix, dok_matrix, spdiags, SparseEfficiencyWarning +from scipy.sparse import csc_matrix, spdiags, SparseEfficiencyWarning from scipy.sparse.linalg import linsolve warnings.simplefilter('ignore',SparseEfficiencyWarning) -import numpy as nm +import numpy as np try: import scipy.sparse.linalg.dsolve.umfpack as um except (ImportError, AttributeError): @@ -112,8 +109,8 @@ self.a = spdiags([[1, 2, 3, 4, 5], [6, 5, 8, 9, 10]], [0, 1], 5, 5) #print "The sparse matrix (constructed from diagonals):" #print self.a - self.b = array([1, 2, 3, 4, 5]) - self.b2 = array([5, 4, 3, 2, 1]) + self.b = np.array([1, 2, 3, 4, 5]) + self.b2 = np.array([5, 4, 3, 2, 1]) @@ -174,7 +171,7 @@ self.real_matrices = [csc_matrix(x).astype('d') for x \ in self.real_matrices] - self.complex_matrices = [x.astype(nm.complex128) + self.complex_matrices = [x.astype(np.complex128) for x in self.real_matrices] # Skip methods if umfpack not present Modified: branches/refactor_fft/scipy/sparse/linalg/dsolve/umfpack/umfpack.py =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/dsolve/umfpack/umfpack.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/linalg/dsolve/umfpack/umfpack.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -7,9 +7,9 @@ #from base import Struct, pause -import numpy as nm +import numpy as np import scipy.sparse as sp -import re, imp +import re try: # Silence import error. import _umfpack as _um except: @@ -275,8 +275,8 @@ raise TypeError, 'wrong family: %s' % family self.family = family - self.control = nm.zeros( (UMFPACK_CONTROL, ), dtype = nm.double ) - self.info = nm.zeros( (UMFPACK_INFO, ), dtype = nm.double ) + self.control = np.zeros( (UMFPACK_CONTROL, ), dtype = np.double ) + self.info = np.zeros( (UMFPACK_INFO, ), dtype = np.double ) self._symbolic = None self._numeric = None self.mtx = None @@ -328,19 +328,19 @@ ## # Should check types of indices to correspond to familyTypes. if self.family[1] == 'i': - if (indx.dtype != nm.dtype('i')) \ - or mtx.indptr.dtype != nm.dtype('i'): + if (indx.dtype != np.dtype('i')) \ + or mtx.indptr.dtype != np.dtype('i'): raise ValueError, 'matrix must have int indices' else: - if (indx.dtype != nm.dtype('l')) \ - or mtx.indptr.dtype != nm.dtype('l'): + if (indx.dtype != np.dtype('l')) \ + or mtx.indptr.dtype != np.dtype('l'): raise ValueError, 'matrix must have long indices' if self.isReal: - if mtx.data.dtype != nm.dtype(' +#include /* * Compute B = A for COO matrix A, CSR matrix B @@ -149,4 +150,26 @@ } } +/* + * Count the number of occupied diagonals in COO matrix A + * + * Input Arguments: + * I nnz - number of nonzeros in A + * I Ai[nnz(A)] - row indices + * I Aj[nnz(A)] - column indices + * + */ +template +I coo_count_diagonals(const I nnz, + const I Ai[], + const I Aj[]) +{ + std::set diagonals; + for(I n = 0; n < nnz; n++){ + diagonals.insert(Aj[n] - Ai[n]); + } + return diagonals.size(); +} + + #endif Modified: branches/refactor_fft/scipy/sparse/sparsetools/coo.i =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/coo.i 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/coo.i 2008-10-23 12:30:20 UTC (rev 4828) @@ -14,4 +14,6 @@ INSTANTIATE_ALL(coo_matvec) +INSTANTIATE_INDEX(coo_count_diagonals) + Modified: branches/refactor_fft/scipy/sparse/sparsetools/coo.py =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/coo.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/coo.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,5 +1,5 @@ # This file was automatically generated by SWIG (http://www.swig.org). -# Version 1.3.34 +# Version 1.3.36 # # Don't modify this file, modify the SWIG interface instead. # This file is compatible with both classic and new-style classes. @@ -49,7 +49,11 @@ +def coo_count_diagonals(*args): + """coo_count_diagonals(int nnz, int Ai, int Aj) -> int""" + return _coo.coo_count_diagonals(*args) + def coo_tocsr(*args): """ coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, Modified: branches/refactor_fft/scipy/sparse/sparsetools/coo_wrap.cxx =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/coo_wrap.cxx 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/coo_wrap.cxx 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,6 +1,6 @@ /* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). - * Version 1.3.34 + * Version 1.3.36 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make @@ -73,6 +73,12 @@ # endif #endif +#ifndef SWIG_MSC_UNSUPPRESS_4505 +# if defined(_MSC_VER) +# pragma warning(disable : 4505) /* unreferenced local function has been removed */ +# endif +#endif + #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) @@ -2516,7 +2522,7 @@ #define SWIG_name "_coo" -#define SWIGVERSION 0x010334 +#define SWIGVERSION 0x010336 #define SWIG_VERSION SWIGVERSION @@ -2544,7 +2550,9 @@ PyObject_ptr(PyObject *obj, bool initial_ref = true) :_obj(obj) { - if (initial_ref) Py_XINCREF(_obj); + if (initial_ref) { + Py_XINCREF(_obj); + } } PyObject_ptr & operator=(const PyObject_ptr& item) @@ -3089,6 +3097,16 @@ return res; } + + #define SWIG_From_long PyInt_FromLong + + +SWIGINTERNINLINE PyObject * +SWIG_From_int (int value) +{ + return SWIG_From_long (value); +} + #ifdef __cplusplus extern "C" { #endif @@ -12950,6 +12968,76 @@ } +SWIGINTERN PyObject *_wrap_coo_count_diagonals(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + int arg1 ; + int *arg2 ; + int *arg3 ; + int val1 ; + int ecode1 = 0 ; + PyArrayObject *array2 = NULL ; + int is_new_object2 ; + PyArrayObject *array3 = NULL ; + int is_new_object3 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + int result; + + if (!PyArg_ParseTuple(args,(char *)"OOO:coo_count_diagonals",&obj0,&obj1,&obj2)) SWIG_fail; + ecode1 = SWIG_AsVal_int(obj0, &val1); + if (!SWIG_IsOK(ecode1)) { + SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "coo_count_diagonals" "', argument " "1"" of type '" "int""'"); + } + arg1 = static_cast< int >(val1); + { + npy_intp size[1] = { + -1 + }; + array2 = obj_to_array_contiguous_allow_conversion(obj1, PyArray_INT, &is_new_object2); + if (!array2 || !require_dimensions(array2,1) || !require_size(array2,size,1) + || !require_contiguous(array2) || !require_native(array2)) SWIG_fail; + + arg2 = (int*) array2->data; + } + { + npy_intp size[1] = { + -1 + }; + array3 = obj_to_array_contiguous_allow_conversion(obj2, PyArray_INT, &is_new_object3); + if (!array3 || !require_dimensions(array3,1) || !require_size(array3,size,1) + || !require_contiguous(array3) || !require_native(array3)) SWIG_fail; + + arg3 = (int*) array3->data; + } + result = (int)coo_count_diagonals< int >(arg1,(int const (*))arg2,(int const (*))arg3); + resultobj = SWIG_From_int(static_cast< int >(result)); + { + if (is_new_object2 && array2) { + Py_DECREF(array2); + } + } + { + if (is_new_object3 && array3) { + Py_DECREF(array3); + } + } + return resultobj; +fail: + { + if (is_new_object2 && array2) { + Py_DECREF(array2); + } + } + { + if (is_new_object3 && array3) { + Py_DECREF(array3); + } + } + return NULL; +} + + static PyMethodDef SwigMethods[] = { { (char *)"coo_tocsr", _wrap_coo_tocsr, METH_VARARGS, (char *)"\n" "coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, \n" @@ -13067,6 +13155,7 @@ "coo_matvec(int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, \n" " npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx)\n" ""}, + { (char *)"coo_count_diagonals", _wrap_coo_count_diagonals, METH_VARARGS, (char *)"coo_count_diagonals(int nnz, int Ai, int Aj) -> int"}, { NULL, NULL, 0, NULL } }; Modified: branches/refactor_fft/scipy/sparse/sparsetools/csc.i =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/csc.i 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/csc.i 2008-10-23 12:30:20 UTC (rev 4828) @@ -8,7 +8,7 @@ %include "csc.h" -%template(csc_matmat_pass1) csc_matmat_pass1; +INSTANTIATE_INDEX(csc_matmat_pass1); INSTANTIATE_ALL(csc_diagonal) INSTANTIATE_ALL(csc_tocsr) Modified: branches/refactor_fft/scipy/sparse/sparsetools/csc.py =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/csc.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/csc.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,5 +1,5 @@ # This file was automatically generated by SWIG (http://www.swig.org). -# Version 1.3.34 +# Version 1.3.36 # # Don't modify this file, modify the SWIG interface instead. # This file is compatible with both classic and new-style classes. Modified: branches/refactor_fft/scipy/sparse/sparsetools/csc_wrap.cxx =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/csc_wrap.cxx 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/csc_wrap.cxx 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,6 +1,6 @@ /* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). - * Version 1.3.34 + * Version 1.3.36 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make @@ -73,6 +73,12 @@ # endif #endif +#ifndef SWIG_MSC_UNSUPPRESS_4505 +# if defined(_MSC_VER) +# pragma warning(disable : 4505) /* unreferenced local function has been removed */ +# endif +#endif + #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) @@ -2516,7 +2522,7 @@ #define SWIG_name "_csc" -#define SWIGVERSION 0x010334 +#define SWIGVERSION 0x010336 #define SWIG_VERSION SWIGVERSION @@ -2544,7 +2550,9 @@ PyObject_ptr(PyObject *obj, bool initial_ref = true) :_obj(obj) { - if (initial_ref) Py_XINCREF(_obj); + if (initial_ref) { + Py_XINCREF(_obj); + } } PyObject_ptr & operator=(const PyObject_ptr& item) Modified: branches/refactor_fft/scipy/sparse/sparsetools/csr.h =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/csr.h 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/csr.h 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,6 +1,7 @@ #ifndef __CSR_H__ #define __CSR_H__ +#include #include #include #include @@ -39,8 +40,8 @@ const I N = std::min(n_row, n_col); for(I i = 0; i < N; i++){ - I row_start = Ap[i]; - I row_end = Ap[i+1]; + const I row_start = Ap[i]; + const I row_end = Ap[i+1]; T diag = 0; for(I jj = row_start; jj < row_end; jj++){ @@ -380,7 +381,55 @@ +/* + * Compute B = A for CSR matrix A, ELL matrix B + * + * Input Arguments: + * I n_row - number of rows in A + * I n_col - number of columns in A + * I Ap[n_row+1] - row pointer + * I Aj[nnz(A)] - column indices + * T Ax[nnz(A)] - nonzeros + * I row_length - maximum nnz in a row of A + * + * Output Arguments: + * I Bj[n_row * row_length] - column indices + * T Bx[n_row * row_length] - nonzeros + * + * Note: + * Output arrays Bj, Bx must be preallocated + * Duplicate entries in A are not merged. + * Explicit zeros in A are carried over to B. + * Rows with fewer than row_length columns are padded with zeros. + * + */ +template +void csr_toell(const I n_row, + const I n_col, + const I Ap[], + const I Aj[], + const T Ax[], + const I row_length, + I Bj[], + T Bx[]) +{ + const I ell_nnz = row_length * n_row; + std::fill(Bj, Bj + ell_nnz, 0); + std::fill(Bx, Bx + ell_nnz, 0); + for(I i = 0; i < n_row; i++){ + I * Bj_row = Bj + row_length * i; + T * Bx_row = Bx + row_length * i; + for(I jj = Ap[i]; jj < Ap[i+1]; jj++){ + *Bj_row = Aj[jj]; + *Bx_row = Ax[jj]; + Bj_row++; + Bx_row++; + } + } +} + + /* * Compute C = A*B for CSR matrices A,B * @@ -742,6 +791,7 @@ * * Note: * The column indicies within each row must be in sorted order. + * Explicit zeros are retained. * Ap, Aj, and Ax will be modified *inplace* * */ @@ -951,7 +1001,29 @@ } +/* + * Count the number of occupied diagonals in CSR matrix A + * + * Input Arguments: + * I nnz - number of nonzeros in A + * I Ai[nnz(A)] - row indices + * I Aj[nnz(A)] - column indices + * + */ +template +I csr_count_diagonals(const I n_row, + const I Ap[], + const I Aj[]) +{ + std::set diagonals; + + for(I i = 0; i < n_row; i++){ + for(I jj = Ap[i]; jj < Ap[i+1]; jj++){ + diagonals.insert(Aj[jj] - i); + } + } + return diagonals.size(); +} - #endif Modified: branches/refactor_fft/scipy/sparse/sparsetools/csr.i =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/csr.i 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/csr.i 2008-10-23 12:30:20 UTC (rev 4828) @@ -9,12 +9,11 @@ %include "csr.h" -%template(expandptr) expandptr; -%template(csr_count_blocks) csr_count_blocks; -%template(csr_matmat_pass1) csr_matmat_pass1; -%template(csr_has_sorted_indices) csr_has_sorted_indices; +INSTANTIATE_INDEX(expandptr) +INSTANTIATE_INDEX(csr_matmat_pass1) +INSTANTIATE_INDEX(csr_count_blocks) +INSTANTIATE_INDEX(csr_has_sorted_indices) - INSTANTIATE_ALL(csr_diagonal) INSTANTIATE_ALL(csr_scale_rows) INSTANTIATE_ALL(csr_scale_columns) Modified: branches/refactor_fft/scipy/sparse/sparsetools/csr.py =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/csr.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/csr.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,5 +1,5 @@ # This file was automatically generated by SWIG (http://www.swig.org). -# Version 1.3.34 +# Version 1.3.36 # # Don't modify this file, modify the SWIG interface instead. # This file is compatible with both classic and new-style classes. @@ -53,10 +53,6 @@ """expandptr(int n_row, int Ap, int Bi)""" return _csr.expandptr(*args) -def csr_count_blocks(*args): - """csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int""" - return _csr.csr_count_blocks(*args) - def csr_matmat_pass1(*args): """ csr_matmat_pass1(int n_row, int n_col, int Ap, int Aj, int Bp, int Bj, @@ -64,6 +60,10 @@ """ return _csr.csr_matmat_pass1(*args) +def csr_count_blocks(*args): + """csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int""" + return _csr.csr_count_blocks(*args) + def csr_has_sorted_indices(*args): """csr_has_sorted_indices(int n_row, int Ap, int Aj) -> bool""" return _csr.csr_has_sorted_indices(*args) Modified: branches/refactor_fft/scipy/sparse/sparsetools/csr_wrap.cxx =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/csr_wrap.cxx 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/csr_wrap.cxx 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,6 +1,6 @@ /* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). - * Version 1.3.34 + * Version 1.3.36 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make @@ -73,6 +73,12 @@ # endif #endif +#ifndef SWIG_MSC_UNSUPPRESS_4505 +# if defined(_MSC_VER) +# pragma warning(disable : 4505) /* unreferenced local function has been removed */ +# endif +#endif + #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) @@ -2530,7 +2536,7 @@ #define SWIG_name "_csr" -#define SWIGVERSION 0x010334 +#define SWIGVERSION 0x010336 #define SWIG_VERSION SWIGVERSION @@ -2558,7 +2564,9 @@ PyObject_ptr(PyObject *obj, bool initial_ref = true) :_obj(obj) { - if (initial_ref) Py_XINCREF(_obj); + if (initial_ref) { + Py_XINCREF(_obj); + } } PyObject_ptr & operator=(const PyObject_ptr& item) @@ -3176,59 +3184,71 @@ } -SWIGINTERN PyObject *_wrap_csr_count_blocks(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { +SWIGINTERN PyObject *_wrap_csr_matmat_pass1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; int arg2 ; - int arg3 ; - int arg4 ; + int *arg3 ; + int *arg4 ; int *arg5 ; int *arg6 ; - int result; + int *arg7 ; int val1 ; int ecode1 = 0 ; int val2 ; int ecode2 = 0 ; - int val3 ; - int ecode3 = 0 ; - int val4 ; - int ecode4 = 0 ; + PyArrayObject *array3 = NULL ; + int is_new_object3 ; + PyArrayObject *array4 = NULL ; + int is_new_object4 ; PyArrayObject *array5 = NULL ; int is_new_object5 ; PyArrayObject *array6 = NULL ; int is_new_object6 ; + PyArrayObject *temp7 = NULL ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; PyObject * obj4 = 0 ; PyObject * obj5 = 0 ; + PyObject * obj6 = 0 ; - if (!PyArg_ParseTuple(args,(char *)"OOOOOO:csr_count_blocks",&obj0,&obj1,&obj2,&obj3,&obj4,&obj5)) SWIG_fail; + if (!PyArg_ParseTuple(args,(char *)"OOOOOOO:csr_matmat_pass1",&obj0,&obj1,&obj2,&obj3,&obj4,&obj5,&obj6)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { - SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "csr_count_blocks" "', argument " "1"" of type '" "int""'"); + SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "csr_matmat_pass1" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { - SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "csr_count_blocks" "', argument " "2"" of type '" "int""'"); + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "csr_matmat_pass1" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); - ecode3 = SWIG_AsVal_int(obj2, &val3); - if (!SWIG_IsOK(ecode3)) { - SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "csr_count_blocks" "', argument " "3"" of type '" "int""'"); - } - arg3 = static_cast< int >(val3); - ecode4 = SWIG_AsVal_int(obj3, &val4); - if (!SWIG_IsOK(ecode4)) { - SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "csr_count_blocks" "', argument " "4"" of type '" "int""'"); - } - arg4 = static_cast< int >(val4); { npy_intp size[1] = { -1 }; + array3 = obj_to_array_contiguous_allow_conversion(obj2, PyArray_INT, &is_new_object3); + if (!array3 || !require_dimensions(array3,1) || !require_size(array3,size,1) + || !require_contiguous(array3) || !require_native(array3)) SWIG_fail; + + arg3 = (int*) array3->data; + } + { + npy_intp size[1] = { + -1 + }; + array4 = obj_to_array_contiguous_allow_conversion(obj3, PyArray_INT, &is_new_object4); + if (!array4 || !require_dimensions(array4,1) || !require_size(array4,size,1) + || !require_contiguous(array4) || !require_native(array4)) SWIG_fail; + + arg4 = (int*) array4->data; + } + { + npy_intp size[1] = { + -1 + }; array5 = obj_to_array_contiguous_allow_conversion(obj4, PyArray_INT, &is_new_object5); if (!array5 || !require_dimensions(array5,1) || !require_size(array5,size,1) || !require_contiguous(array5) || !require_native(array5)) SWIG_fail; @@ -3245,9 +3265,24 @@ arg6 = (int*) array6->data; } - result = (int)csr_count_blocks< int >(arg1,arg2,arg3,arg4,(int const (*))arg5,(int const (*))arg6); - resultobj = SWIG_From_int(static_cast< int >(result)); { + temp7 = obj_to_array_no_conversion(obj6,PyArray_INT); + if (!temp7 || !require_contiguous(temp7) || !require_native(temp7)) SWIG_fail; + arg7 = (int*) array_data(temp7); + } + csr_matmat_pass1< int >(arg1,arg2,(int const (*))arg3,(int const (*))arg4,(int const (*))arg5,(int const (*))arg6,arg7); + resultobj = SWIG_Py_Void(); + { + if (is_new_object3 && array3) { + Py_DECREF(array3); + } + } + { + if (is_new_object4 && array4) { + Py_DECREF(array4); + } + } + { if (is_new_object5 && array5) { Py_DECREF(array5); } @@ -3260,6 +3295,16 @@ return resultobj; fail: { + if (is_new_object3 && array3) { + Py_DECREF(array3); + } + } + { + if (is_new_object4 && array4) { + Py_DECREF(array4); + } + } + { if (is_new_object5 && array5) { Py_DECREF(array5); } @@ -3273,71 +3318,59 @@ } -SWIGINTERN PyObject *_wrap_csr_matmat_pass1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { +SWIGINTERN PyObject *_wrap_csr_count_blocks(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { PyObject *resultobj = 0; int arg1 ; int arg2 ; - int *arg3 ; - int *arg4 ; + int arg3 ; + int arg4 ; int *arg5 ; int *arg6 ; - int *arg7 ; int val1 ; int ecode1 = 0 ; int val2 ; int ecode2 = 0 ; - PyArrayObject *array3 = NULL ; - int is_new_object3 ; - PyArrayObject *array4 = NULL ; - int is_new_object4 ; + int val3 ; + int ecode3 = 0 ; + int val4 ; + int ecode4 = 0 ; PyArrayObject *array5 = NULL ; int is_new_object5 ; PyArrayObject *array6 = NULL ; int is_new_object6 ; - PyArrayObject *temp7 = NULL ; PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; PyObject * obj3 = 0 ; PyObject * obj4 = 0 ; PyObject * obj5 = 0 ; - PyObject * obj6 = 0 ; + int result; - if (!PyArg_ParseTuple(args,(char *)"OOOOOOO:csr_matmat_pass1",&obj0,&obj1,&obj2,&obj3,&obj4,&obj5,&obj6)) SWIG_fail; + if (!PyArg_ParseTuple(args,(char *)"OOOOOO:csr_count_blocks",&obj0,&obj1,&obj2,&obj3,&obj4,&obj5)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); if (!SWIG_IsOK(ecode1)) { - SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "csr_matmat_pass1" "', argument " "1"" of type '" "int""'"); + SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "csr_count_blocks" "', argument " "1"" of type '" "int""'"); } arg1 = static_cast< int >(val1); ecode2 = SWIG_AsVal_int(obj1, &val2); if (!SWIG_IsOK(ecode2)) { - SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "csr_matmat_pass1" "', argument " "2"" of type '" "int""'"); + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "csr_count_blocks" "', argument " "2"" of type '" "int""'"); } arg2 = static_cast< int >(val2); + ecode3 = SWIG_AsVal_int(obj2, &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "csr_count_blocks" "', argument " "3"" of type '" "int""'"); + } + arg3 = static_cast< int >(val3); + ecode4 = SWIG_AsVal_int(obj3, &val4); + if (!SWIG_IsOK(ecode4)) { + SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "csr_count_blocks" "', argument " "4"" of type '" "int""'"); + } + arg4 = static_cast< int >(val4); { npy_intp size[1] = { -1 }; - array3 = obj_to_array_contiguous_allow_conversion(obj2, PyArray_INT, &is_new_object3); - if (!array3 || !require_dimensions(array3,1) || !require_size(array3,size,1) - || !require_contiguous(array3) || !require_native(array3)) SWIG_fail; - - arg3 = (int*) array3->data; - } - { - npy_intp size[1] = { - -1 - }; - array4 = obj_to_array_contiguous_allow_conversion(obj3, PyArray_INT, &is_new_object4); - if (!array4 || !require_dimensions(array4,1) || !require_size(array4,size,1) - || !require_contiguous(array4) || !require_native(array4)) SWIG_fail; - - arg4 = (int*) array4->data; - } - { - npy_intp size[1] = { - -1 - }; array5 = obj_to_array_contiguous_allow_conversion(obj4, PyArray_INT, &is_new_object5); if (!array5 || !require_dimensions(array5,1) || !require_size(array5,size,1) || !require_contiguous(array5) || !require_native(array5)) SWIG_fail; @@ -3354,24 +3387,9 @@ arg6 = (int*) array6->data; } + result = (int)csr_count_blocks< int >(arg1,arg2,arg3,arg4,(int const (*))arg5,(int const (*))arg6); + resultobj = SWIG_From_int(static_cast< int >(result)); { - temp7 = obj_to_array_no_conversion(obj6,PyArray_INT); - if (!temp7 || !require_contiguous(temp7) || !require_native(temp7)) SWIG_fail; - arg7 = (int*) array_data(temp7); - } - csr_matmat_pass1< int >(arg1,arg2,(int const (*))arg3,(int const (*))arg4,(int const (*))arg5,(int const (*))arg6,arg7); - resultobj = SWIG_Py_Void(); - { - if (is_new_object3 && array3) { - Py_DECREF(array3); - } - } - { - if (is_new_object4 && array4) { - Py_DECREF(array4); - } - } - { if (is_new_object5 && array5) { Py_DECREF(array5); } @@ -3384,16 +3402,6 @@ return resultobj; fail: { - if (is_new_object3 && array3) { - Py_DECREF(array3); - } - } - { - if (is_new_object4 && array4) { - Py_DECREF(array4); - } - } - { if (is_new_object5 && array5) { Py_DECREF(array5); } @@ -3412,7 +3420,6 @@ int arg1 ; int *arg2 ; int *arg3 ; - bool result; int val1 ; int ecode1 = 0 ; PyArrayObject *array2 = NULL ; @@ -3422,6 +3429,7 @@ PyObject * obj0 = 0 ; PyObject * obj1 = 0 ; PyObject * obj2 = 0 ; + bool result; if (!PyArg_ParseTuple(args,(char *)"OOO:csr_has_sorted_indices",&obj0,&obj1,&obj2)) SWIG_fail; ecode1 = SWIG_AsVal_int(obj0, &val1); @@ -45431,11 +45439,11 @@ static PyMethodDef SwigMethods[] = { { (char *)"expandptr", _wrap_expandptr, METH_VARARGS, (char *)"expandptr(int n_row, int Ap, int Bi)"}, - { (char *)"csr_count_blocks", _wrap_csr_count_blocks, METH_VARARGS, (char *)"csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int"}, { (char *)"csr_matmat_pass1", _wrap_csr_matmat_pass1, METH_VARARGS, (char *)"\n" "csr_matmat_pass1(int n_row, int n_col, int Ap, int Aj, int Bp, int Bj, \n" " int Cp)\n" ""}, + { (char *)"csr_count_blocks", _wrap_csr_count_blocks, METH_VARARGS, (char *)"csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int"}, { (char *)"csr_has_sorted_indices", _wrap_csr_has_sorted_indices, METH_VARARGS, (char *)"csr_has_sorted_indices(int n_row, int Ap, int Aj) -> bool"}, { (char *)"csr_diagonal", _wrap_csr_diagonal, METH_VARARGS, (char *)"\n" "csr_diagonal(int n_row, int n_col, int Ap, int Aj, signed char Ax, \n" Modified: branches/refactor_fft/scipy/sparse/sparsetools/dia.py =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/dia.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/dia.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,5 +1,5 @@ # This file was automatically generated by SWIG (http://www.swig.org). -# Version 1.3.34 +# Version 1.3.36 # # Don't modify this file, modify the SWIG interface instead. # This file is compatible with both classic and new-style classes. Modified: branches/refactor_fft/scipy/sparse/sparsetools/dia_wrap.cxx =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/dia_wrap.cxx 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/dia_wrap.cxx 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,6 +1,6 @@ /* ---------------------------------------------------------------------------- * This file was automatically generated by SWIG (http://www.swig.org). - * Version 1.3.34 + * Version 1.3.36 * * This file is not intended to be easily readable and contains a number of * coding conventions designed to improve portability and efficiency. Do not make @@ -73,6 +73,12 @@ # endif #endif +#ifndef SWIG_MSC_UNSUPPRESS_4505 +# if defined(_MSC_VER) +# pragma warning(disable : 4505) /* unreferenced local function has been removed */ +# endif +#endif + #ifndef SWIGUNUSEDPARM # ifdef __cplusplus # define SWIGUNUSEDPARM(p) @@ -2516,7 +2522,7 @@ #define SWIG_name "_dia" -#define SWIGVERSION 0x010334 +#define SWIGVERSION 0x010336 #define SWIG_VERSION SWIGVERSION @@ -2544,7 +2550,9 @@ PyObject_ptr(PyObject *obj, bool initial_ref = true) :_obj(obj) { - if (initial_ref) Py_XINCREF(_obj); + if (initial_ref) { + Py_XINCREF(_obj); + } } PyObject_ptr & operator=(const PyObject_ptr& item) Modified: branches/refactor_fft/scipy/sparse/sparsetools/sparsetools.i =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/sparsetools.i 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sparsetools/sparsetools.i 2008-10-23 12:30:20 UTC (rev 4828) @@ -189,3 +189,9 @@ %enddef +%define INSTANTIATE_INDEX( f_name ) +/* 32-bit indices */ +%template(f_name) f_name; +/* 64-bit indices would go here */ +%enddef + Modified: branches/refactor_fft/scipy/sparse/spfuncs.py =================================================================== --- branches/refactor_fft/scipy/sparse/spfuncs.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/spfuncs.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -3,15 +3,8 @@ __all__ = ['count_blocks','estimate_blocksize'] -from numpy import empty, ravel - -from base import isspmatrix from csr import isspmatrix_csr, csr_matrix from csc import isspmatrix_csc -from bsr import isspmatrix_bsr -from sputils import upcast - -import sparsetools from sparsetools import csr_count_blocks def extract_diagonal(A): Modified: branches/refactor_fft/scipy/sparse/sputils.py =================================================================== --- branches/refactor_fft/scipy/sparse/sputils.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/sputils.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -82,13 +82,16 @@ """Is x appropriate as an index into a sparse matrix? Returns True if it can be cast safely to a machine int. """ - try: - if int(x) == x: - return True - else: + if issequence(x): + return False + else: + try: + if int(x) == x: + return True + else: + return False + except TypeError: return False - except TypeError: - return False def isshape(x): """Is x a valid 2-tuple of dimensions? Modified: branches/refactor_fft/scipy/sparse/tests/test_base.py =================================================================== --- branches/refactor_fft/scipy/sparse/tests/test_base.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/tests/test_base.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -15,9 +15,10 @@ import warnings -import numpy -from numpy import arange, zeros, array, dot, ones, matrix, asmatrix, \ - asarray, vstack, ndarray, transpose, diag +import numpy as np +from numpy import arange, zeros, array, dot, matrix, asmatrix, asarray, \ + vstack, ndarray, transpose, diag, kron, inf, conjugate, \ + int8 import random from numpy.testing import * @@ -94,12 +95,12 @@ mats.append( [[0,1],[0,2],[0,3]] ) mats.append( [[0,0,1],[0,0,2],[0,3,0]] ) - mats.append( numpy.kron(mats[0],[[1,2]]) ) - mats.append( numpy.kron(mats[0],[[1],[2]]) ) - mats.append( numpy.kron(mats[1],[[1,2],[3,4]]) ) - mats.append( numpy.kron(mats[2],[[1,2],[3,4]]) ) - mats.append( numpy.kron(mats[3],[[1,2],[3,4]]) ) - mats.append( numpy.kron(mats[3],[[1,2,3,4]]) ) + mats.append( kron(mats[0],[[1,2]]) ) + mats.append( kron(mats[0],[[1],[2]]) ) + mats.append( kron(mats[1],[[1,2],[3,4]]) ) + mats.append( kron(mats[2],[[1,2],[3,4]]) ) + mats.append( kron(mats[3],[[1,2],[3,4]]) ) + mats.append( kron(mats[3],[[1,2,3,4]]) ) for m in mats: assert_equal(self.spmatrix(m).diagonal(),diag(m)) @@ -257,7 +258,7 @@ assert_array_equal((self.datsp / self.datsp).todense(),expected) denom = self.spmatrix(matrix([[1,0,0,4],[-1,0,0,0],[0,8,0,-5]],'d')) - res = matrix([[1,0,0,0.5],[-3,0,numpy.inf,0],[0,0.25,0,0]],'d') + res = matrix([[1,0,0,0.5],[-3,0,inf,0],[0,0.25,0,0]],'d') assert_array_equal((self.datsp / denom).todense(),res) # complex @@ -421,7 +422,7 @@ def test_tobsr(self): x = array([[1,0,2,0],[0,0,0,0],[0,0,4,5]]) y = array([[0,1,2],[3,0,5]]) - A = numpy.kron(x,y) + A = kron(x,y) Asp = self.spmatrix(A) for format in ['bsr']: fn = getattr(Asp, 'to' + format ) @@ -584,16 +585,16 @@ Wagner for a 64-bit machine, 02 March 2005 (EJS) """ n = 20 - numpy.random.seed(0) #make tests repeatable + np.random.seed(0) #make tests repeatable A = zeros((n,n), dtype=complex) - x = numpy.random.rand(n) - y = numpy.random.rand(n-1)+1j*numpy.random.rand(n-1) - r = numpy.random.rand(n) + x = np.random.rand(n) + y = np.random.rand(n-1)+1j*np.random.rand(n-1) + r = np.random.rand(n) for i in range(len(x)): A[i,i] = x[i] for i in range(len(y)): A[i,i+1] = y[i] - A[i+1,i] = numpy.conjugate(y[i]) + A[i+1,i] = conjugate(y[i]) A = self.spmatrix(A) x = splu(A).solve(r) assert_almost_equal(A*x,r) @@ -699,20 +700,28 @@ assert_equal(A[2,3], B[2,3]) assert_equal(A[-1,8], B[-1,8]) assert_equal(A[-1,-2],B[-1,-2]) + assert_equal(A[array(-1),-2],B[-1,-2]) + assert_equal(A[-1,array(-2)],B[-1,-2]) + assert_equal(A[array(-1),array(-2)],B[-1,-2]) # [i,1:2] assert_equal(A[2,:].todense(), B[2,:]) assert_equal(A[2,5:-2].todense(),B[2,5:-2]) + assert_equal(A[array(2),5:-2].todense(),B[2,5:-2]) # [i,[1,2]] assert_equal(A[3,[1,3]].todense(), B[3,[1,3]]) assert_equal(A[-1,[2,-5]].todense(),B[-1,[2,-5]]) + assert_equal(A[array(-1),[2,-5]].todense(),B[-1,[2,-5]]) + assert_equal(A[-1,array([2,-5])].todense(),B[-1,[2,-5]]) + assert_equal(A[array(-1),array([2,-5])].todense(),B[-1,[2,-5]]) # [1:2,j] assert_equal(A[:,2].todense(), B[:,2]) assert_equal(A[3:4,9].todense(), B[3:4,9]) assert_equal(A[1:4,-5].todense(),B[1:4,-5]) assert_equal(A[2:-1,3].todense(),B[2:-1,3]) + assert_equal(A[2:-1,array(3)].todense(),B[2:-1,3]) # [1:2,1:2] assert_equal(A[1:2,1:2].todense(),B[1:2,1:2]) @@ -724,26 +733,38 @@ assert_equal(A[:,[2,8,3,-1]].todense(),B[:,[2,8,3,-1]]) assert_equal(A[3:4,[9]].todense(), B[3:4,[9]]) assert_equal(A[1:4,[-1,-5]].todense(), B[1:4,[-1,-5]]) + assert_equal(A[1:4,array([-1,-5])].todense(), B[1:4,[-1,-5]]) # [[1,2],j] assert_equal(A[[1,3],3].todense(), B[[1,3],3]) assert_equal(A[[2,-5],-4].todense(), B[[2,-5],-4]) + assert_equal(A[array([2,-5]),-4].todense(), B[[2,-5],-4]) + assert_equal(A[[2,-5],array(-4)].todense(), B[[2,-5],-4]) + assert_equal(A[array([2,-5]),array(-4)].todense(), B[[2,-5],-4]) # [[1,2],1:2] assert_equal(A[[1,3],:].todense(), B[[1,3],:]) assert_equal(A[[2,-5],8:-1].todense(),B[[2,-5],8:-1]) + assert_equal(A[array([2,-5]),8:-1].todense(),B[[2,-5],8:-1]) # [[1,2],[1,2]] assert_equal(A[[1,3],[2,4]], B[[1,3],[2,4]]) assert_equal(A[[-1,-3],[2,-4]],B[[-1,-3],[2,-4]]) + assert_equal(A[array([-1,-3]),[2,-4]],B[[-1,-3],[2,-4]]) + assert_equal(A[[-1,-3],array([2,-4])],B[[-1,-3],[2,-4]]) + assert_equal(A[array([-1,-3]),array([2,-4])],B[[-1,-3],[2,-4]]) # [[[1],[2]],[1,2]] assert_equal(A[[[1],[3]],[2,4]].todense(), B[[[1],[3]],[2,4]]) assert_equal(A[[[-1],[-3],[-2]],[2,-4]].todense(),B[[[-1],[-3],[-2]],[2,-4]]) + assert_equal(A[array([[-1],[-3],[-2]]),[2,-4]].todense(),B[[[-1],[-3],[-2]],[2,-4]]) + assert_equal(A[[[-1],[-3],[-2]],array([2,-4])].todense(),B[[[-1],[-3],[-2]],[2,-4]]) + assert_equal(A[array([[-1],[-3],[-2]]),array([2,-4])].todense(),B[[[-1],[-3],[-2]],[2,-4]]) # [i] assert_equal(A[1,:].todense(), B[1,:]) assert_equal(A[-2,:].todense(),B[-2,:]) + assert_equal(A[array(-2),:].todense(),B[-2,:]) # [1:2] assert_equal(A[1:4].todense(), B[1:4]) @@ -752,19 +773,22 @@ # [[1,2]] assert_equal(A[[1,3]].todense(), B[[1,3]]) assert_equal(A[[-1,-3]].todense(),B[[-1,-3]]) + assert_equal(A[array([-1,-3])].todense(),B[[-1,-3]]) # [[1,2],:][:,[1,2]] assert_equal(A[[1,3],:][:,[2,4]].todense(), B[[1,3],:][:,[2,4]] ) assert_equal(A[[-1,-3],:][:,[2,-4]].todense(), B[[-1,-3],:][:,[2,-4]] ) + assert_equal(A[array([-1,-3]),:][:,array([2,-4])].todense(), B[[-1,-3],:][:,[2,-4]] ) # [:,[1,2]][[1,2],:] assert_equal(A[:,[1,3]][[2,4],:].todense(), B[:,[1,3]][[2,4],:] ) assert_equal(A[:,[-1,-3]][[2,-4],:].todense(), B[:,[-1,-3]][[2,-4],:] ) + assert_equal(A[:,array([-1,-3])][array([2,-4]),:].todense(), B[:,[-1,-3]][[2,-4],:] ) # Check bug reported by Robert Cimrman: # http://thread.gmane.org/gmane.comp.python.scientific.devel/7986 - s = slice(numpy.int8(2),numpy.int8(4),None) + s = slice(int8(2),int8(4),None) assert_equal(A[s,:].todense(), B[2:4,:]) assert_equal(A[:,s].todense(), B[:,2:4]) @@ -920,9 +944,9 @@ def test_constructor4(self): """using (data, ij) format""" - row = numpy.array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2]) - col = numpy.array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1]) - data = numpy.array([ 6., 10., 3., 9., 1., 4., + row = array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2]) + col = array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1]) + data = array([ 6., 10., 3., 9., 1., 4., 11., 2., 8., 5., 7.]) ij = vstack((row,col)) @@ -994,9 +1018,9 @@ def test_constructor4(self): """using (data, ij) format""" - row = numpy.array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2]) - col = numpy.array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1]) - data = numpy.array([ 6., 10., 3., 9., 1., 4., + row = array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2]) + col = array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1]) + data = array([ 6., 10., 3., 9., 1., 4., 11., 2., 8., 5., 7.]) ij = vstack((row,col)) @@ -1139,7 +1163,26 @@ caught += 1 assert_equal(caught,5) + def test_ctor(self): + caught = 0 + # Empty ctor + try: + A = dok_matrix() + except TypeError, e: + caught+=1 + assert_equal(caught, 1) + # Dense ctor + b = matrix([[1,0,0,0],[0,0,1,0],[0,2,0,3]],'d') + A = dok_matrix(b) + assert_equal(A.todense(), b) + + # Sparse ctor + c = csr_matrix(b) + assert_equal(A.todense(), c.todense()) + + + class TestLIL( _TestCommon, _TestHorizSlicing, _TestVertSlicing, _TestBothSlicing, _TestGetSet, _TestSolve, _TestArithmetic, _TestInplaceArithmetic, @@ -1295,9 +1338,9 @@ spmatrix = coo_matrix def test_constructor1(self): """unsorted triplet format""" - row = numpy.array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2]) - col = numpy.array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1]) - data = numpy.array([ 6., 10., 3., 9., 1., 4., + row = array([2, 3, 1, 3, 0, 1, 3, 0, 2, 1, 2]) + col = array([0, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1]) + data = array([ 6., 10., 3., 9., 1., 4., 11., 2., 8., 5., 7.]) coo = coo_matrix((data,(row,col)),(4,3)) @@ -1306,9 +1349,9 @@ def test_constructor2(self): """unsorted triplet format with duplicates (which are summed)""" - row = numpy.array([0,1,2,2,2,2,0,0,2,2]) - col = numpy.array([0,2,0,2,1,1,1,0,0,2]) - data = numpy.array([2,9,-4,5,7,0,-1,2,1,-5]) + row = array([0,1,2,2,2,2,0,0,2,2]) + col = array([0,2,0,2,1,1,1,0,0,2]) + data = array([2,9,-4,5,7,0,-1,2,1,-5]) coo = coo_matrix((data,(row,col)),(3,3)) mat = matrix([[4,-1,0],[0,0,9],[-3,7,0]]) @@ -1327,14 +1370,14 @@ def test_constructor4(self): """from dense matrix""" - mat = numpy.array([[0,1,0,0], + mat = array([[0,1,0,0], [7,0,3,0], [0,4,0,0]]) coo = coo_matrix(mat) assert_array_equal(coo.todense(),mat) #upgrade rank 1 arrays to row matrix - mat = numpy.array([0,1,0,0]) + mat = array([0,1,0,0]) coo = coo_matrix(mat) assert_array_equal(coo.todense(),mat.reshape(1,-1)) @@ -1366,7 +1409,7 @@ data[3] = array([[ 0, 5, 10], [15, 0, 25]]) - A = numpy.kron( [[1,0,2,0],[0,0,0,0],[0,0,4,5]], [[0,1,2],[3,0,5]] ) + A = kron( [[1,0,2,0],[0,0,0,0],[0,0,4,5]], [[0,1,2],[3,0,5]] ) Asp = bsr_matrix((data,indices,indptr),shape=(6,12)) assert_equal(Asp.todense(),A) @@ -1385,7 +1428,7 @@ assert_equal(bsr_matrix(A,blocksize=(2,2)).todense(),A) assert_equal(bsr_matrix(A,blocksize=(2,3)).todense(),A) - A = numpy.kron( [[1,0,2,0],[0,0,0,0],[0,0,4,5]], [[0,1,2],[3,0,5]] ) + A = kron( [[1,0,2,0],[0,0,0,0],[0,0,4,5]], [[0,1,2],[3,0,5]] ) assert_equal(bsr_matrix(A).todense(),A) assert_equal(bsr_matrix(A,shape=(6,12)).todense(),A) assert_equal(bsr_matrix(A,blocksize=(1,1)).todense(),A) @@ -1395,11 +1438,11 @@ assert_equal(bsr_matrix(A,blocksize=(3,12)).todense(),A) assert_equal(bsr_matrix(A,blocksize=(6,12)).todense(),A) - A = numpy.kron( [[1,0,2,0],[0,1,0,0],[0,0,0,0]], [[0,1,2],[3,0,5]] ) + A = kron( [[1,0,2,0],[0,1,0,0],[0,0,0,0]], [[0,1,2],[3,0,5]] ) assert_equal(bsr_matrix(A,blocksize=(2,3)).todense(),A) def test_eliminate_zeros(self): - data = numpy.kron([1, 0, 0, 0, 2, 0, 3, 0], [[1,1],[1,1]]).T + data = kron([1, 0, 0, 0, 2, 0, 3, 0], [[1,1],[1,1]]).T data = data.reshape(-1,2,2) indices = array( [1, 2, 3, 4, 5, 6, 7, 8] ) indptr = array( [0, 3, 8] ) Modified: branches/refactor_fft/scipy/sparse/tests/test_extract.py =================================================================== --- branches/refactor_fft/scipy/sparse/tests/test_extract.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/tests/test_extract.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,9 +1,6 @@ """test sparse matrix construction functions""" -import numpy -from numpy import array, matrix from numpy.testing import * - from scipy.sparse import csr_matrix import numpy as np Modified: branches/refactor_fft/scipy/sparse/tests/test_sputils.py =================================================================== --- branches/refactor_fft/scipy/sparse/tests/test_sputils.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/sparse/tests/test_sputils.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -36,7 +36,7 @@ assert_equal(isintlike(3.0),True) assert_equal(isintlike(-4),True) assert_equal(isintlike(np.array(3)),True) - assert_equal(isintlike( np.array([3])), True) + assert_equal(isintlike(np.array([3])), False) assert_equal(isintlike(2.5),False) assert_equal(isintlike(1 + 3j),False) Modified: branches/refactor_fft/scipy/special/cephes/jv.c =================================================================== --- branches/refactor_fft/scipy/special/cephes/jv.c 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/special/cephes/jv.c 2008-10-23 12:30:20 UTC (rev 4828) @@ -50,6 +50,10 @@ #include "mconf.h" #define CEPHES_DEBUG 0 +#if CEPHES_DEBUG +#include +#endif + #ifdef DEC #define MAXGAM 34.84425627277176174 #else @@ -94,6 +98,15 @@ double k, q, t, y, an; int i, sign, nint; + /* The recursion used when n = 3 and x = 4 in recur gives + the wrong answer. + + Simple fix for now: + */ + if ((n==3) && (x == 4)) { + return 0.43017147387562193; + } + nint = 0; /* Flag for integer n */ sign = 1; /* Flag for sign inversion */ an = fabs(n); Modified: branches/refactor_fft/scipy/special/cephes_doc.h =================================================================== --- branches/refactor_fft/scipy/special/cephes_doc.h 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/special/cephes_doc.h 2008-10-23 12:30:20 UTC (rev 4828) @@ -80,20 +80,20 @@ #define jn_doc "y=jn(n,x) returns the Bessel function of integer order n at x." #define jv_doc "y=jv(v,z) returns the Bessel function of real order v at complex z." #define jve_doc "y=jve(v,z) returns the exponentially scaled Bessel function of real order\nv at complex z: jve(v,z) = jv(v,z) * exp(-abs(z.imag))" -#define k0_doc "y=i0(x) returns the modified Bessel function of the third kind of\norder 0 at x." -#define k0e_doc "y=k0e(x) returns the exponentially scaled modified Bessel function\nof the third kind of order 0 at x. k0e(x) = exp(x) * k0(x)." -#define k1_doc "y=i1(x) returns the modified Bessel function of the third kind of\norder 1 at x." -#define k1e_doc "y=k1e(x) returns the exponentially scaled modified Bessel function\nof the third kind of order 1 at x. k1e(x) = exp(x) * k1(x)" +#define k0_doc "y=k0(x) returns the modified Bessel function of the second kind (sometimes called the third kind) of\norder 0 at x." +#define k0e_doc "y=k0e(x) returns the exponentially scaled modified Bessel function\nof the second kind (sometimes called the third kind) of order 0 at x. k0e(x) = exp(x) * k0(x)." +#define k1_doc "y=i1(x) returns the modified Bessel function of the second kind (sometimes called the third kind) of\norder 1 at x." +#define k1e_doc "y=k1e(x) returns the exponentially scaled modified Bessel function\nof the second kind (sometimes called the third kind) of order 1 at x. k1e(x) = exp(x) * k1(x)" #define kei_doc "y=kei(x) returns the Kelvin function ker x" #define keip_doc "y=keip(x) returns the derivative of the Kelvin function kei x" #define kelvin_doc "(Be, Ke, Bep, Kep)=kelvin(x) returns the tuple (Be, Ke, Bep, Kep) which containes \ncomplex numbers representing the real and imaginary Kelvin functions \nand their derivatives evaluated at x. For example, \nkelvin(x)[0].real = ber x and kelvin(x)[0].imag = bei x with similar \nrelationships for ker and kei." #define ker_doc "y=ker(x) returns the Kelvin function ker x" #define kerp_doc "y=kerp(x) returns the derivative of the Kelvin function ker x" -#define kn_doc "y=kn(n,x) returns the modified Bessel function of the third kind for\ninteger order n at x." +#define kn_doc "y=kn(n,x) returns the modified Bessel function of the second kind (sometimes called the third kind) for\ninteger order n at x." #define kolmogi_doc "y=kolmogi(p) returns y such that kolmogorov(y) = p" #define kolmogorov_doc "p=kolmogorov(y) returns the complementary cumulative distribution \nfunction of Kolmogorov's limiting distribution (Kn* for large n) \nof a two-sided test for equality between an empirical and a theoretical \ndistribution. It is equal to the (limit as n->infinity of the) probability \nthat sqrt(n) * max absolute deviation > y." -#define kv_doc "y=kv(v,z) returns the modified Bessel function of the third kind for\nreal order v at complex z." -#define kve_doc "y=kve(v,z) returns the exponentially scaled, modified Bessel function\nof the third kind for real order v at complex z: kve(v,z) = kv(v,z) * exp(z)" +#define kv_doc "y=kv(v,z) returns the modified Bessel function of the second kind (sometimes called the third kind) for\nreal order v at complex z." +#define kve_doc "y=kve(v,z) returns the exponentially scaled, modified Bessel function\nof the second kind (sometimes called the third kind) for real order v at complex z: kve(v,z) = kv(v,z) * exp(z)" #define log1p_doc "y=log1p(x) calculates log(1+x) for use when x is near zero." #define lpmv_doc "y=lpmv(m,v,x) returns the associated legendre function of integer order\nm and nonnegative degree v: |x|<=1." #define mathieu_a_doc "lmbda=mathieu_a(m,q) returns the characteristic value for the even solution, \nce_m(z,q), of Mathieu's equation" Modified: branches/refactor_fft/scipy/special/info.py =================================================================== --- branches/refactor_fft/scipy/special/info.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/special/info.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -25,9 +25,9 @@ * yn -- Bessel function of second kind (integer order). * yv -- Bessel function of the second kind (real-valued order). * yve -- Exponentially scaled Bessel function of the second kind. -* kn -- Modified Bessel function of the third kind (integer order). -* kv -- Modified Bessel function of the third kind (real order). -* kve -- Exponentially scaled modified Bessel function of the third kind. +* kn -- Modified Bessel function of the second kind (integer order). +* kv -- Modified Bessel function of the second kind (real order). +* kve -- Exponentially scaled modified Bessel function of the second kind. * iv -- Modified Bessel function. * ive -- Exponentially scaled modified Bessel function. * hankel1 -- Hankel function of the first kind. @@ -60,10 +60,10 @@ * i0e -- Exponentially scaled modified Bessel function of order 0. * i1 -- Modified Bessel function of order 1. * i1e -- Exponentially scaled modified Bessel function of order 1. -* k0 -- Modified Bessel function of the third kind of order 0. -* k0e -- Exponentially scaled modified Bessel function of the third kind of order 0. -* k1 -- Modified Bessel function of the third kind of order 1. -* k1e -- Exponentially scaled modified Bessel function of the third kind of order 1. +* k0 -- Modified Bessel function of the second kind of order 0. +* k0e -- Exponentially scaled modified Bessel function of the second kind of order 0. +* k1 -- Modified Bessel function of the second kind of order 1. +* k1e -- Exponentially scaled modified Bessel function of the second kind of order 1. Integrals of Bessel Functions ............................. Modified: branches/refactor_fft/scipy/special/tests/test_basic.py =================================================================== --- branches/refactor_fft/scipy/special/tests/test_basic.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/special/tests/test_basic.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -32,7 +32,7 @@ #8 test_sh_jacobi #8 test_sh_legendre -from numpy import dot, array +from numpy import array from numpy.testing import * Modified: branches/refactor_fft/scipy/stats/_support.py =================================================================== --- branches/refactor_fft/scipy/stats/_support.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/_support.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -48,7 +48,7 @@ except TypeError: uniques = np.concatenate([uniques,np.array([item])]) else: # IT MUST BE A 2+D ARRAY - if inarray.typecode() != 'O': # not an Object array + if inarray.dtype.char != 'O': # not an Object array for item in inarray[1:]: if not np.sum(np.alltrue(np.equal(uniques,item),1),axis=0): try: Modified: branches/refactor_fft/scipy/stats/continuous.lyx =================================================================== --- branches/refactor_fft/scipy/stats/continuous.lyx 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/continuous.lyx 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,18 +1,19 @@ -#LyX 1.3 created this file. For more info see http://www.lyx.org/ -\lyxformat 221 +#LyX 1.4.3 created this file. For more info see http://www.lyx.org/ +\lyxformat 245 +\begin_document +\begin_header \textclass article \language english \inputencoding auto \fontscheme default \graphics default \paperfontsize default -\spacing single -\papersize Default -\paperpackage a4 -\use_geometry 1 -\use_amsmath 1 -\use_natbib 0 -\use_numerical_citations 0 +\spacing single +\papersize default +\use_geometry true +\use_amsmath 2 +\cite_engine basic +\use_bibtopic false \paperorientation portrait \leftmargin 1in \topmargin 1in @@ -23,43 +24,49 @@ \paragraph_separation indent \defskip medskip \quotes_language english -\quotes_times 2 \papercolumns 1 \papersides 1 \paperpagestyle default +\tracking_changes false +\output_changes true +\end_header -\layout Title +\begin_body +\begin_layout Title Continuous Statistical Distributions -\layout Section +\end_layout +\begin_layout Section Overview -\layout Standard +\end_layout +\begin_layout Standard All distributions will have location (L) and Scale (S) parameters along with any shape parameters needed, the names for the shape parameters will vary. Standard form for the distributions will be given where \begin_inset Formula $L=0.0$ -\end_inset +\end_inset and \begin_inset Formula $S=1.0.$ -\end_inset +\end_inset The nonstandard forms can be obtained for the various functions using (note \begin_inset Formula $U$ -\end_inset +\end_inset is a standard uniform random variate). -\layout Standard -\align center +\end_layout -\size small +\begin_layout Standard +\align center -\begin_inset Tabular +\size small +\begin_inset Tabular @@ -69,591 +76,612 @@ \begin_inset Text -\layout Standard +\begin_layout Standard +Function Name +\end_layout -Function Name -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Standard Function +\end_layout -Standard Function -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Transformation +\end_layout -Transformation -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Cumulative Distribution Function (CDF) +\end_layout -Cumulative Distribution Function (CDF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $F\left(x\right)$ +\end_inset -\begin_inset Formula $F\left(x\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $F\left(x;L,S\right)=F\left(\frac{\left(x-L\right)}{S}\right)$ +\end_inset -\begin_inset Formula $F\left(x;L,S\right)=F\left(\frac{\left(x-L\right)}{S}\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Probability Density Function (PDF) +\end_layout -Probability Density Function (PDF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $f\left(x\right)=F^{\prime}\left(x\right)$ +\end_inset -\begin_inset Formula $f\left(x\right)=F^{\prime}\left(x\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $f\left(x;L,S\right)=\frac{1}{S}f\left(\frac{\left(x-L\right)}{S}\right)$ +\end_inset -\begin_inset Formula $f\left(x;L,S\right)=\frac{1}{S}f\left(\frac{\left(x-L\right)}{S}\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Percent Point Function (PPF) +\end_layout -Percent Point Function (PPF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $G\left(q\right)=F^{-1}\left(q\right)$ +\end_inset -\begin_inset Formula $G\left(q\right)=F^{-1}\left(q\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $G\left(q;L,S\right)=L+SG\left(q\right)$ +\end_inset -\begin_inset Formula $G\left(q;L,S\right)=L+SG\left(q\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Probability Sparsity Function (PSF) +\end_layout -Probability Sparsity Function (PSF) -\end_inset +\end_inset \begin_inset Text -\layout Standard - - +\begin_layout Standard \begin_inset Formula $g\left(q\right)=G^{\prime}\left(q\right)$ -\end_inset +\end_inset -\end_inset +\end_layout + +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $g\left(q;L,S\right)=Sg\left(q\right)$ +\end_inset -\begin_inset Formula $g\left(q;L,S\right)=Sg\left(q\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Hazard Function (HF) +\end_layout -Hazard Function (HF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $h_{a}\left(x\right)=\frac{f\left(x\right)}{1-F\left(x\right)}$ +\end_inset -\begin_inset Formula $h_{a}\left(x\right)=\frac{f\left(x\right)}{1-F\left(x\right)}$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $h_{a}\left(x;L,S\right)=\frac{1}{S}h_{a}\left(\frac{\left(x-L\right)}{S}\right)$ +\end_inset -\begin_inset Formula $h_{a}\left(x;L,S\right)=\frac{1}{S}h_{a}\left(\frac{\left(x-L\right)}{S}\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Cumulative Hazard Functon (CHF) +\end_layout -Cumulative Hazard Functon (CHF) -\end_inset +\end_inset \begin_inset Text -\layout Standard - - +\begin_layout Standard \begin_inset Formula $H_{a}\left(x\right)=$ -\end_inset +\end_inset \begin_inset Formula $\log\frac{1}{1-F\left(x\right)}$ -\end_inset +\end_inset -\end_inset +\end_layout + +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $H_{a}\left(x;L,S\right)=H_{a}\left(\frac{\left(x-L\right)}{S}\right)$ +\end_inset -\begin_inset Formula $H_{a}\left(x;L,S\right)=H_{a}\left(\frac{\left(x-L\right)}{S}\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Survival Function (SF) +\end_layout -Survival Function (SF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $S\left(x\right)=1-F\left(x\right)$ +\end_inset -\begin_inset Formula $S\left(x\right)=1-F\left(x\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $S\left(x;L,S\right)=S\left(\frac{\left(x-L\right)}{S}\right)$ +\end_inset -\begin_inset Formula $S\left(x;L,S\right)=S\left(\frac{\left(x-L\right)}{S}\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Inverse Survival Function (ISF) +\end_layout -Inverse Survival Function (ISF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $Z\left(\alpha\right)=S^{-1}\left(\alpha\right)=G\left(1-\alpha\right)$ +\end_inset -\begin_inset Formula $Z\left(\alpha\right)=S^{-1}\left(\alpha\right)=G\left(1-\alpha\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $Z\left(\alpha;L,S\right)=L+SZ\left(\alpha\right)$ +\end_inset -\begin_inset Formula $Z\left(\alpha;L,S\right)=L+SZ\left(\alpha\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Moment Generating Function (MGF) +\end_layout -Moment Generating Function (MGF) -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $M_{Y}\left(t\right)=E\left[e^{Yt}\right]$ +\end_inset -\begin_inset Formula $M_{Y}\left(t\right)=E\left[e^{Yt}\right]$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $M_{X}\left(t\right)=e^{Lt}M_{Y}\left(St\right)$ +\end_inset -\begin_inset Formula $M_{X}\left(t\right)=e^{Lt}M_{Y}\left(St\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Random Variates +\end_layout -Random Variates -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $Y=G\left(U\right)$ +\end_inset -\begin_inset Formula $Y=G\left(U\right)$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $X=L+SY$ +\end_inset -\begin_inset Formula $X=L+SY$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +(Differential) Entropy +\end_layout -(Differential) Entropy -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $h\left[Y\right]=-\int f\left(y\right)\log f\left(y\right)dy$ +\end_inset -\begin_inset Formula $h\left[Y\right]=-\int f\left(y\right)\log f\left(y\right)dy$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $h\left[X\right]=h\left[Y\right]+\log S$ +\end_inset -\begin_inset Formula $h\left[X\right]=h\left[Y\right]+\log S$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +(Non-central) Moments +\end_layout -(Non-central) Moments -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $\mu_{n}^{\prime}=E\left[Y^{n}\right]$ +\end_inset -\begin_inset Formula $\mu_{n}^{\prime}=E\left[Y^{n}\right]$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard - - +\begin_layout Standard \begin_inset Formula $E\left[X^{n}\right]=L^{n}\sum_{k=0}^{N}\left(\begin{array}{c} n\\ k\end{array}\right)\left(\frac{S}{L}\right)^{k}\mu_{k}^{\prime}$ -\end_inset +\end_inset -\end_inset +\end_layout + +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +Central Moments +\end_layout -Central Moments -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $\mu_{n}=E\left[\left(Y-\mu\right)^{n}\right]$ +\end_inset -\begin_inset Formula $\mu_{n}=E\left[\left(Y-\mu\right)^{n}\right]$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $E\left[\left(X-\mu_{X}\right)^{n}\right]=S^{n}\mu_{n}$ +\end_inset -\begin_inset Formula $E\left[\left(X-\mu_{X}\right)^{n}\right]=S^{n}\mu_{n}$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +mean (mode, median), var +\end_layout -mean (mode, median), var -\end_inset +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $\mu,\,\mu_{2}$ +\end_inset -\begin_inset Formula $\mu,\,\mu_{2}$ -\end_inset +\end_layout - -\end_inset +\end_inset \begin_inset Text -\layout Standard - - +\begin_layout Standard \begin_inset Formula $L+S\mu,\, S^{2}\mu_{2}$ -\end_inset +\end_inset -\end_inset +\end_layout + +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +skewness, kurtosis +\end_layout -skewness, kurtosis -\end_inset +\end_inset \begin_inset Text -\layout Standard - - +\begin_layout Standard \begin_inset Formula $\gamma_{1}=\frac{\mu_{3}}{\left(\mu_{2}\right)^{3/2}},\,$ -\end_inset +\end_inset \begin_inset Formula $\gamma_{2}=\frac{\mu_{4}}{\left(\mu_{2}\right)^{2}}-3$ -\end_inset +\end_inset -\end_inset +\end_layout + +\end_inset \begin_inset Text -\layout Standard +\begin_layout Standard +\begin_inset Formula $\gamma_{1},\,\gamma_{2}$ +\end_inset -\begin_inset Formula $\gamma_{1},\,\gamma_{2}$ -\end_inset +\end_layout - -\end_inset +\end_inset -\end_inset +\end_inset -\layout Standard +\end_layout -\SpecialChar ~ +\begin_layout Standard +\InsetSpace ~ -\layout Subsection +\end_layout +\begin_layout Subsection Moments -\layout Standard +\end_layout +\begin_layout Standard Non-central moments are defined using the PDF \begin_inset Formula \[ \mu_{n}^{\prime}=\int_{-\infty}^{\infty}x^{n}f\left(x\right)dx.\] -\end_inset +\end_inset Note, that these can always be computed using the PPF. Substitute \begin_inset Formula $x=G\left(q\right)$ -\end_inset +\end_inset in the above equation and get \begin_inset Formula \[ \mu_{n}^{\prime}=\int_{0}^{1}G^{n}\left(q\right)dq\] -\end_inset +\end_inset which may be easier to compute numerically. Note that \begin_inset Formula $q=F\left(x\right)$ -\end_inset +\end_inset so that \begin_inset Formula $dq=f\left(x\right)dx.$ -\end_inset +\end_inset Central moments are computed similarly \begin_inset Formula $\mu=\mu_{1}^{\prime}$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -663,7 +691,7 @@ n\\ k\end{array}\right)\left(-\mu\right)^{k}\mu_{n-k}^{\prime}\end{eqnarray*} -\end_inset +\end_inset In particular \begin_inset Formula \begin{eqnarray*} @@ -672,157 +700,162 @@ \mu_{4} & = & \mu_{4}^{\prime}-4\mu\mu_{3}^{\prime}+6\mu^{2}\mu_{2}^{\prime}-3\mu^{4}\\ & = & \mu_{4}^{\prime}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}\end{eqnarray*} -\end_inset +\end_inset Skewness is defined as \begin_inset Formula \[ \gamma_{1}=\sqrt{\beta_{1}}=\frac{\mu_{3}}{\mu_{2}^{3/2}}\] -\end_inset +\end_inset while (Fisher) kurtosis is \begin_inset Formula \[ \gamma_{2}=\frac{\mu_{4}}{\mu_{2}^{2}}-3,\] -\end_inset +\end_inset so that a normal distribution has a kurtosis of zero. -\layout Subsection +\end_layout +\begin_layout Subsection Median and mode -\layout Standard +\end_layout +\begin_layout Standard The median, \begin_inset Formula $m_{n}$ -\end_inset +\end_inset is defined as the point at which half of the density is on one side and half on the other. In other words, \begin_inset Formula $F\left(m_{n}\right)=\frac{1}{2}$ -\end_inset +\end_inset so that \begin_inset Formula \[ m_{n}=G\left(\frac{1}{2}\right).\] -\end_inset +\end_inset In addition, the mode, \begin_inset Formula $m_{d}$ -\end_inset +\end_inset , is defined as the value for which the probability density function reaches it's peak \begin_inset Formula \[ m_{d}=\arg\max_{x}f\left(x\right).\] -\end_inset +\end_inset -\layout Subsection +\end_layout +\begin_layout Subsection Fitting data -\layout Standard +\end_layout +\begin_layout Standard To fit data to a distribution, maximizing the likelihood function is common. Alternatively, some distributions have well-known minimum variance unbiased estimators. These will be chosen by default, but the likelihood function will always be available for minimizing. -\layout Standard +\end_layout +\begin_layout Standard If \begin_inset Formula $f\left(x;\boldsymbol{\theta}\right)$ -\end_inset +\end_inset is the PDF of a random-variable where \begin_inset Formula $\boldsymbol{\theta}$ -\end_inset +\end_inset is a vector of parameters ( -\emph on +\emph on e.g. \begin_inset Formula $L$ -\end_inset +\end_inset -\emph default +\emph default and \begin_inset Formula $S$ -\end_inset +\end_inset ), then for a collection of \begin_inset Formula $N$ -\end_inset +\end_inset independent samples from this distribution, the joint distribution the random vector \begin_inset Formula $\mathbf{x}$ -\end_inset +\end_inset is \begin_inset Formula \[ f\left(\mathbf{x};\boldsymbol{\theta}\right)=\prod_{i=1}^{N}f\left(x_{i};\boldsymbol{\theta}\right).\] -\end_inset +\end_inset The maximum likelihood estimate of the parameters \begin_inset Formula $\boldsymbol{\theta}$ -\end_inset +\end_inset are the parameters which maximize this function with \begin_inset Formula $\mathbf{x}$ -\end_inset +\end_inset fixed and given by the data: \begin_inset Formula \begin{eqnarray*} \boldsymbol{\theta}_{es} & = & \arg\max_{\boldsymbol{\theta}}f\left(\mathbf{x};\boldsymbol{\theta}\right)\\ & = & \arg\min_{\boldsymbol{\theta}}l_{\mathbf{x}}\left(\boldsymbol{\theta}\right).\end{eqnarray*} -\end_inset +\end_inset Where \begin_inset Formula \begin{eqnarray*} l_{\mathbf{x}}\left(\boldsymbol{\theta}\right) & = & -\sum_{i=1}^{N}\log f\left(x_{i};\boldsymbol{\theta}\right)\\ & = & -N\overline{\log f\left(x_{i};\boldsymbol{\theta}\right)}\end{eqnarray*} -\end_inset +\end_inset Note that if \begin_inset Formula $\boldsymbol{\theta}$ -\end_inset +\end_inset includes only shape parameters, the location and scale-parameters can be fit by replacing \begin_inset Formula $x_{i}$ -\end_inset +\end_inset with \begin_inset Formula $\left(x_{i}-L\right)/S$ -\end_inset +\end_inset in the log-likelihood function adding \begin_inset Formula $N\log S$ -\end_inset +\end_inset and minimizing, thus \begin_inset Formula \begin{eqnarray*} l_{\mathbf{x}}\left(L,S;\boldsymbol{\theta}\right) & = & N\log S-\sum_{i=1}^{N}\log f\left(\frac{x_{i}-L}{S};\boldsymbol{\theta}\right)\\ & = & N\log S+l_{\frac{\mathbf{x}-S}{L}}\left(\boldsymbol{\theta}\right)\end{eqnarray*} -\end_inset +\end_inset If desired, sample estimates for \begin_inset Formula $L$ -\end_inset +\end_inset and \begin_inset Formula $S$ -\end_inset +\end_inset (not necessarily maximum likelihood estimates) can be obtained from samples estimates of the mean and variance using @@ -830,185 +863,194 @@ \hat{S} & = & \sqrt{\frac{\hat{\mu}_{2}}{\mu_{2}}}\\ \hat{L} & = & \hat{\mu}-\hat{S}\mu\end{eqnarray*} -\end_inset +\end_inset where \begin_inset Formula $\mu$ -\end_inset +\end_inset and \begin_inset Formula $\mu_{2}$ -\end_inset +\end_inset are assumed known as the mean and variance of the -\series bold +\series bold untransformed -\series default +\series default distribution (when \begin_inset Formula $L=0$ -\end_inset +\end_inset and \begin_inset Formula $S=1$ -\end_inset +\end_inset ) and \begin_inset Formula \begin{eqnarray*} \hat{\mu} & = & \frac{1}{N}\sum_{i=1}^{N}x_{i}=\bar{\mathbf{x}}\\ \hat{\mu}_{2} & = & \frac{1}{N-1}\sum_{i=1}^{N}\left(x_{i}-\hat{\mu}\right)^{2}=\frac{N}{N-1}\overline{\left(\mathbf{x}-\bar{\mathbf{x}}\right)^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Subsection +\end_layout +\begin_layout Subsection Standard notation for mean -\layout Standard +\end_layout +\begin_layout Standard We will use \begin_inset Formula \[ \overline{y\left(\mathbf{x}\right)}=\frac{1}{N}\sum_{i=1}^{N}y\left(x_{i}\right)\] -\end_inset +\end_inset where \begin_inset Formula $N$ -\end_inset +\end_inset should be clear from context as the number of samples \begin_inset Formula $x_{i}$ -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Alpha -\layout Standard +\end_layout +\begin_layout Standard One shape parameters \begin_inset Formula $\alpha>0$ -\end_inset +\end_inset (paramter \begin_inset Formula $\beta$ -\end_inset +\end_inset in DATAPLOT is a scale-parameter). Standard form is \begin_inset Formula $x>0:$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\alpha\right) & = & \frac{1}{x^{2}\Phi\left(\alpha\right)\sqrt{2\pi}}\exp\left(-\frac{1}{2}\left(\alpha-\frac{1}{x}\right)^{2}\right)\\ F\left(x;\alpha\right) & = & \frac{\Phi\left(\alpha-\frac{1}{x}\right)}{\Phi\left(\alpha\right)}\\ G\left(q;\alpha\right) & = & \left[\alpha-\Phi^{-1}\left(q\Phi\left(\alpha\right)\right)\right]^{-1}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=\frac{1}{\Phi\left(a\right)\sqrt{2\pi}}\int_{0}^{\infty}\frac{e^{xt}}{x^{2}}\exp\left(-\frac{1}{2}\left(\alpha-\frac{1}{x}\right)^{2}\right)dx\] -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard No moments? \begin_inset Formula \[ l_{\mathbf{x}}\left(\alpha\right)=N\log\left[\Phi\left(\alpha\right)\sqrt{2\pi}\right]+2N\overline{\log\mathbf{x}}+\frac{N}{2}\alpha^{2}-\alpha\overline{\mathbf{x}^{-1}}+\frac{1}{2}\overline{\mathbf{x}^{-2}}\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Anglit -\layout Standard +\end_layout +\begin_layout Standard Defined over \begin_inset Formula $x\in\left[-\frac{\pi}{4},\frac{\pi}{4}\right]$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \sin\left(2x+\frac{\pi}{2}\right)=\cos\left(2x\right)\\ F\left(x\right) & = & \sin^{2}\left(x+\frac{\pi}{4}\right)\\ G\left(q\right) & = & \arcsin\left(\sqrt{q}\right)-\frac{\pi}{4}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & 0\\ \mu_{2} & = & \frac{\pi^{2}}{16}-\frac{1}{2}\\ \gamma_{1} & = & 0\\ \gamma_{2} & = & -2\frac{\pi^{4}-96}{\left(\pi^{2}-8\right)^{2}}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & 1-\log2\\ & \approx & 0.30685281944005469058\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} M\left(t\right) & = & \int_{-\frac{\pi}{4}}^{\frac{\pi}{4}}\cos\left(2x\right)e^{xt}dx\\ & = & \frac{4\cosh\left(\frac{\pi t}{4}\right)}{t^{2}+4}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ l_{\mathbf{x}}\left(\cdot\right)=-N\overline{\log\left[\cos\left(2\mathbf{x}\right)\right]}\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Arcsine -\layout Standard +\end_layout +\begin_layout Standard Defined over \begin_inset Formula $x\in\left(0,1\right)$ -\end_inset +\end_inset . To get the JKB definition put \begin_inset Formula $x=\frac{u+1}{2}.$ -\end_inset +\end_inset i.e. \begin_inset Formula $L=-1$ -\end_inset +\end_inset and \begin_inset Formula $S=2.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1016,20 +1058,20 @@ F\left(x\right) & = & \frac{2}{\pi}\arcsin\left(\sqrt{x}\right)\\ G\left(q\right) & = & \sin^{2}\left(\frac{\pi}{2}q\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=E^{t/2}I_{0}\left(\frac{t}{2}\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} \mu_{n}^{\prime} & = & \frac{1}{\pi}\int_{0}^{1}dx\, x^{n-1/2}\left(1-x\right)^{-1/2}\\ & = & \frac{1}{\pi}B\left(\frac{1}{2},n+\frac{1}{2}\right)=\frac{\left(2n-1\right)!!}{2^{n}n!}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1038,45 +1080,47 @@ \gamma_{1} & = & 0\\ \gamma_{2} & = & -\frac{3}{2}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]\approx-0.24156447527049044468\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ l_{\mathbf{x}}\left(\cdot\right)=N\log\pi+\frac{N}{2}\overline{\log\mathbf{x}}+\frac{N}{2}\overline{\log\left(1-\mathbf{x}\right)}\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Beta -\layout Standard +\end_layout +\begin_layout Standard Two shape parameters -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ a,b>0\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;a,b\right) & = & \frac{\Gamma\left(a+b\right)}{\Gamma\left(a\right)\Gamma\left(b\right)}x^{a-1}\left(1-x\right)^{b-1}I_{\left(0,1\right)}\left(x\right)\\ F\left(x;a,b\right) & = & \int_{0}^{x}f\left(y;a,b\right)dy=I\left(x,a,b\right)\\ @@ -1088,66 +1132,68 @@ \gamma_{2} & = & \frac{6\left(a^{3}+a^{2}\left(1-2b\right)+b^{2}\left(b+1\right)-2ab\left(b+2\right)\right)}{ab\left(a+b+2\right)\left(a+b+3\right)}\\ m_{d} & = & \frac{\left(a-1\right)}{\left(a+b-2\right)}\, a+b\neq2\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula $f\left(x;a,1\right)$ -\end_inset +\end_inset is also called the Power-function distribution. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ l_{\mathbf{x}}\left(a,b\right)=-N\log\Gamma\left(a+b\right)+N\log\Gamma\left(a\right)+N\log\Gamma\left(b\right)-N\left(a-1\right)\overline{\log\mathbf{x}}-N\left(b-1\right)\overline{\log\left(1-\mathbf{x}\right)}\] -\end_inset +\end_inset All of the \begin_inset Formula $x_{i}\in\left[0,1\right]$ -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Beta Prime -\layout Standard +\end_layout +\begin_layout Standard Defined over \begin_inset Formula $00.$ -\end_inset +\end_inset (Note the CDF evaluation uses Eq. 3.194.1 on pg. 313 of Gradshteyn & Ryzhik (sixth edition). -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\alpha,\beta\right) & = & \frac{\Gamma\left(\alpha+\beta\right)}{\Gamma\left(\alpha\right)\Gamma\left(\beta\right)}x^{\alpha-1}\left(1+x\right)^{-\alpha-\beta}\\ F\left(x;\alpha,\beta\right) & = & \frac{\Gamma\left(\alpha+\beta\right)}{\alpha\Gamma\left(\alpha\right)\Gamma\left(\beta\right)}x^{\alpha}\,_{2}F_{1}\left(\alpha+\beta,\alpha;1+\alpha;-x\right)\\ G\left(q;\alpha,\beta\right) & = & F^{-1}\left(x;\alpha,\beta\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=\left\{ \begin{array}{ccc} \frac{\Gamma\left(n+\alpha\right)\Gamma\left(\beta-n\right)}{\Gamma\left(\alpha\right)\Gamma\left(\beta\right)}=\frac{\left(\alpha\right)_{n}}{\left(\beta-n\right)_{n}} & & \beta>n\\ \infty & & \textrm{otherwise}\end{array}\right.\] -\end_inset +\end_inset Therefore, \begin_inset Formula \begin{eqnarray*} @@ -1157,20 +1203,21 @@ \gamma_{2} & = & \frac{\mu_{4}}{\mu_{2}^{2}}-3\\ \mu_{4} & = & \frac{\alpha\left(\alpha+1\right)\left(\alpha+2\right)\left(\alpha+3\right)}{\left(\beta-4\right)\left(\beta-3\right)\left(\beta-2\right)\left(\beta-1\right)}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}\quad\beta>4\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Bradford -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} c & > & 0\\ k & = & \log\left(1+c\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1185,37 +1232,38 @@ m_{d} & = & 0\\ m_{n} & = & \sqrt{1+c}-1\end{eqnarray*} -\end_inset +\end_inset where \begin_inset Formula $\textrm{Ei}\left(\textrm{z}\right)$ -\end_inset +\end_inset is the exponential integral function. Also \begin_inset Formula \[ h\left[X\right]=\frac{1}{2}\log\left(1+c\right)-\log\left(\frac{c}{\log\left(1+c\right)}\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Burr -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} c & > & 0\\ d & > & 0\\ k & = & \Gamma\left(d\right)\Gamma\left(1-\frac{2}{c}\right)\Gamma\left(\frac{2}{c}+d\right)-\Gamma^{2}\left(1-\frac{1}{c}\right)\Gamma^{2}\left(\frac{1}{c}+d\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c,d\right) & = & \frac{cd}{x^{c+1}\left(1+x^{-c}\right)^{d+1}}I_{\left(0,\infty\right)}\left(x\right)\\ F\left(x;c,d\right) & = & \left(1+x^{-c}\right)^{-d}\\ @@ -1230,15 +1278,16 @@ m_{d} & = & \left(\frac{cd-1}{c+1}\right)^{1/c}\,\textrm{if }cd>1\,\textrm{otherwise }0\\ m_{n} & = & \left(2^{1/d}-1\right)^{-1/c}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Cauchy -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{1}{\pi\left(1+x^{2}\right)}\\ F\left(x\right) & = & \frac{1}{2}+\frac{1}{\pi}\tan^{-1}x\\ @@ -1246,7 +1295,7 @@ m_{d} & = & 0\\ m_{n} & = & 0\end{eqnarray*} -\end_inset +\end_inset No finite moments. This is the t distribution with one degree of freedom. @@ -1255,35 +1304,37 @@ h\left[X\right] & = & \log\left(4\pi\right)\\ & \approx & 2.5310242469692907930.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Chi -\layout Standard +\end_layout +\begin_layout Standard Generated by taking the (positive) square-root of chi-squared variates. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\nu\right) & = & \frac{x^{\nu-1}e^{-x^{2}/2}}{2^{\nu/2-1}\Gamma\left(\frac{\nu}{2}\right)}I_{\left(0,\infty\right)}\left(x\right)\\ F\left(x;\nu\right) & = & \Gamma\left(\frac{\nu}{2},\frac{x^{2}}{2}\right)\\ G\left(\alpha;\nu\right) & = & \sqrt{2\Gamma^{-1}\left(\frac{\nu}{2},\alpha\right)}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\Gamma\left(\frac{v}{2}\right)\,_{1}F_{1}\left(\frac{v}{2};\frac{1}{2};\frac{t^{2}}{2}\right)+\frac{t}{\sqrt{2}}\Gamma\left(\frac{1+\nu}{2}\right)\,_{1}F_{1}\left(\frac{1+\nu}{2};\frac{3}{2};\frac{t^{2}}{2}\right)\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \frac{\sqrt{2}\Gamma\left(\frac{\nu+1}{2}\right)}{\Gamma\left(\frac{\nu}{2}\right)}\\ \mu_{2} & = & \nu-\mu^{2}\\ @@ -1292,50 +1343,53 @@ m_{d} & = & \sqrt{\nu-1}\quad\nu\geq1\\ m_{n} & = & \sqrt{2\Gamma^{-1}\left(\frac{\nu}{2},\frac{1}{2}\right)}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Chi-squared -\layout Standard +\end_layout +\begin_layout Standard This is the gamma distribution with \begin_inset Formula $L=0.0$ -\end_inset +\end_inset and \begin_inset Formula $S=2.0$ -\end_inset +\end_inset and \begin_inset Formula $\alpha=\nu/2$ -\end_inset +\end_inset where \begin_inset Formula $\nu$ -\end_inset +\end_inset is called the degrees of freedom. If \begin_inset Formula $Z_{1}\ldots Z_{\nu}$ -\end_inset +\end_inset are all standard normal distributions, then \begin_inset Formula $W=\sum_{k}Z_{k}^{2}$ -\end_inset +\end_inset has (standard) chi-square distribution with \begin_inset Formula $\nu$ -\end_inset +\end_inset degrees of freedom. -\layout Standard +\end_layout +\begin_layout Standard The standard form (most often used in standard form only) is \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1343,13 +1397,13 @@ F\left(x;\alpha\right) & = & \Gamma\left(\frac{\nu}{2},\frac{x}{2}\right)\\ G\left(q;\alpha\right) & = & 2\Gamma^{-1}\left(\frac{\nu}{2},q\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\frac{\Gamma\left(\frac{\nu}{2}\right)}{\left(\frac{1}{2}-t\right)^{\nu/2}}\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1359,19 +1413,21 @@ \gamma_{2} & = & \frac{12}{\nu}\\ m_{d} & = & \frac{\nu}{2}-1\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Cosine -\layout Standard +\end_layout +\begin_layout Standard Approximation to the normal distribution. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{1}{2\pi}\left[1+\cos x\right]I_{\left[-\pi,\pi\right]}\left(x\right)\\ F\left(x\right) & = & \frac{1}{2\pi}\left[\pi+x+\sin x\right]I_{\left[-\pi,\pi\right]}\left(x\right)+I_{\left(\pi,\infty\right)}\left(x\right)\\ @@ -1382,33 +1438,35 @@ \gamma_{1} & = & 0\\ \gamma_{2} & = & \frac{-6\left(\pi^{4}-90\right)}{5\left(\pi^{2}-6\right)^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(4\pi\right)-1\\ & \approx & 1.5310242469692907930.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Double Gamma -\layout Standard +\end_layout +\begin_layout Standard The double gamma is the signed version of the Gamma distribution. For \begin_inset Formula $\alpha>0:$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\alpha\right) & = & \frac{1}{2\Gamma\left(\alpha\right)}\left|x\right|^{\alpha-1}e^{-\left|x\right|}\\ F\left(x;\alpha\right) & = & \left\{ \begin{array}{ccc} @@ -1418,21 +1476,21 @@ -\Gamma^{-1}\left(\alpha,\left|2q-1\right|\right) & & q\leq\frac{1}{2}\\ \Gamma^{-1}\left(\alpha,\left|2q-1\right|\right) & & q>\frac{1}{2}\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=\frac{1}{2\left(1-t\right)^{a}}+\frac{1}{2\left(1+t\right)^{a}}\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu=m_{n} & = & 0\\ \mu_{2} & = & \alpha\left(\alpha+1\right)\\ @@ -1440,24 +1498,28 @@ \gamma_{2} & = & \frac{\left(\alpha+2\right)\left(\alpha+3\right)}{\alpha\left(\alpha+1\right)}-3\\ m_{d} & = & \textrm{NA}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Doubly Non-central F* -\layout Section +\end_layout +\begin_layout Section Doubly Non-central t* -\layout Section +\end_layout +\begin_layout Section Double Weibull -\layout Standard +\end_layout +\begin_layout Standard This is a signed form of the Weibull distribution. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c\right) & = & \frac{c}{2}\left|x\right|^{c-1}\exp\left(-\left|x\right|^{c}\right)\\ F\left(x;c\right) & = & \left\{ \begin{array}{ccc} @@ -1467,7 +1529,7 @@ -\log^{1/c}\left(\frac{1}{2q}\right) & & q\leq\frac{1}{2}\\ \log^{1/c}\left(\frac{1}{2q-1}\right) & & q>\frac{1}{2}\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ @@ -1475,7 +1537,7 @@ \Gamma\left(1+\frac{n}{c}\right) & n\textrm{ even}\\ 0 & n\textrm{ odd}\end{cases}\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1485,34 +1547,38 @@ \gamma_{2} & = & \frac{\Gamma\left(1+\frac{4}{c}\right)}{\Gamma^{2}\left(1+\frac{2}{c}\right)}\\ m_{d} & = & \textrm{NA bimodal}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Erlang -\layout Standard +\end_layout +\begin_layout Standard This is just the Gamma distribution with shape parameter \begin_inset Formula $\alpha=n$ -\end_inset +\end_inset an integer. -\layout Section +\end_layout +\begin_layout Section Exponential -\layout Standard +\end_layout +\begin_layout Standard This is a special case of the Gamma (and Erlang) distributions with shape parameter \begin_inset Formula $\left(\alpha=1\right)$ -\end_inset +\end_inset and the same location and scale parameters. The standard form is therefore ( \begin_inset Formula $x\geq0$ -\end_inset +\end_inset ) \begin_inset Formula \begin{eqnarray*} @@ -1520,25 +1586,25 @@ F\left(x\right) & = & \Gamma\left(1,x\right)=1-e^{-x}\\ G\left(q\right) & = & -\log\left(1-q\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=n!\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=\frac{1}{1-t}\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1548,31 +1614,33 @@ \gamma_{2} & = & 6\\ m_{d} & = & 0\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=1.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Exponentiated Weibull -\layout Standard +\end_layout +\begin_layout Standard Two positive shape parameters \begin_inset Formula $a$ -\end_inset +\end_inset and \begin_inset Formula $c$ -\end_inset +\end_inset and \begin_inset Formula $x\in\left(0,\infty\right)$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1580,22 +1648,24 @@ F\left(x;a,c\right) & = & \left[1-\exp\left(-x^{c}\right)\right]^{a}\\ G\left(q;a,c\right) & = & \left[-\log\left(1-q^{1/a}\right)\right]^{1/c}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Exponential Power -\layout Standard +\end_layout +\begin_layout Standard One positive shape parameter \begin_inset Formula $b$ -\end_inset +\end_inset . Defined for \begin_inset Formula $x\geq0.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1603,84 +1673,88 @@ F\left(x;b\right) & = & 1-\exp\left[1-e^{x^{b}}\right]\\ G\left(q;b\right) & = & \log^{1/b}\left[1-\log\left(1-q\right)\right]\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Fatigue Life (Birnbaum-Sanders) -\layout Standard +\end_layout +\begin_layout Standard This distribution's pdf is the average of the inverse-Gaussian \begin_inset Formula $\left(\mu=1\right)$ -\end_inset +\end_inset and reciprocal inverse-Gaussian pdf \begin_inset Formula $\left(\mu=1\right)$ -\end_inset +\end_inset . We follow the notation of JKB here with \begin_inset Formula $\beta=S.$ -\end_inset +\end_inset for \begin_inset Formula $x>0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c\right) & = & \frac{x+1}{2c\sqrt{2\pi x^{3}}}\exp\left(-\frac{\left(x-1\right)^{2}}{2xc^{2}}\right)\\ F\left(x;c\right) & = & \Phi\left(\frac{1}{c}\left(\sqrt{x}-\frac{1}{\sqrt{x}}\right)\right)\\ G\left(q;c\right) & = & \frac{1}{4}\left[c\Phi^{-1}\left(q\right)+\sqrt{c^{2}\left(\Phi^{-1}\left(q\right)\right)^{2}+4}\right]^{2}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=c\sqrt{2\pi}\exp\left[\frac{1}{c^{2}}\left(1-\sqrt{1-2c^{2}t}\right)\right]\left(1+\frac{1}{\sqrt{1-2c^{2}t}}\right)\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \frac{c^{2}}{2}+1\\ \mu_{2} & = & c^{2}\left(\frac{5}{4}c^{2}+1\right)\\ \gamma_{1} & = & \frac{4c\sqrt{11c^{2}+6}}{\left(5c^{2}+4\right)^{3/2}}\\ \gamma_{2} & = & \frac{6c^{2}\left(93c^{2}+41\right)}{\left(5c^{2}+4\right)^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Fisk (Log Logistic) -\layout Standard +\end_layout +\begin_layout Standard Special case of the Burr distribution with \begin_inset Formula $d=1$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} c & > & 0\\ k & = & \Gamma\left(1-\frac{2}{c}\right)\Gamma\left(\frac{2}{c}+1\right)-\Gamma^{2}\left(1-\frac{1}{c}\right)\Gamma^{2}\left(\frac{1}{c}+1\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c,d\right) & = & \frac{cx^{c-1}}{\left(1+x^{c}\right)^{2}}I_{\left(0,\infty\right)}\left(x\right)\\ F\left(x;c,d\right) & = & \left(1+x^{-c}\right)^{-1}\\ @@ -1695,45 +1769,47 @@ m_{d} & = & \left(\frac{c-1}{c+1}\right)^{1/c}\,\textrm{if }c>1\,\textrm{otherwise }0\\ m_{n} & = & 1\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=2-\log c.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Folded Cauchy -\layout Standard +\end_layout +\begin_layout Standard This formula can be expressed in terms of the standard formulas for the Cauchy distribution (call the cdf \begin_inset Formula $C\left(x\right)$ -\end_inset +\end_inset and the pdf \begin_inset Formula $d\left(x\right)$ -\end_inset +\end_inset ). if \begin_inset Formula $Y$ -\end_inset +\end_inset is cauchy then \begin_inset Formula $\left|Y\right|$ -\end_inset +\end_inset is folded cauchy. Note that \begin_inset Formula $x\geq0.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1741,54 +1817,57 @@ F\left(x;c\right) & = & \frac{1}{\pi}\tan^{-1}\left(x-c\right)+\frac{1}{\pi}\tan^{-1}\left(x+c\right)\\ G\left(q;c\right) & = & F^{-1}\left(x;c\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard No moments -\layout Section +\end_layout +\begin_layout Section Folded Normal -\layout Standard +\end_layout +\begin_layout Standard If \begin_inset Formula $Z$ -\end_inset +\end_inset is Normal with mean \begin_inset Formula $L$ -\end_inset +\end_inset and \begin_inset Formula $\sigma=S$ -\end_inset +\end_inset , then \begin_inset Formula $\left|Z\right|$ -\end_inset +\end_inset is a folded normal with shape parameter \begin_inset Formula $c=\left|L\right|/S$ -\end_inset +\end_inset , location parameter \begin_inset Formula $0$ -\end_inset +\end_inset and scale parameter \begin_inset Formula $S$ -\end_inset +\end_inset . This is a special case of the non-central chi distribution with one-degree of freedom and non-centrality parameter \begin_inset Formula $c^{2}.$ -\end_inset +\end_inset Note that \begin_inset Formula $c\geq0$ -\end_inset +\end_inset . The standard form of the folded normal is @@ -1797,13 +1876,13 @@ F\left(x;c\right) & = & \Phi\left(x-c\right)-\Phi\left(-x-c\right)=\Phi\left(x-c\right)+\Phi\left(x+c\right)-1\\ G\left(\alpha;c\right) & = & F^{-1}\left(x;c\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\exp\left[\frac{t}{2}\left(t-2c\right)\right]\left(1+e^{2ct}\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1814,38 +1893,40 @@ \gamma_{1} & = & \frac{\sqrt{\frac{2}{\pi}}p^{3}\left(4-\frac{\pi}{p^{2}}\left(2c^{2}+1\right)\right)+2ck\left(6p^{2}+3cpk\sqrt{2\pi}+\pi c\left(k^{2}-1\right)\right)}{\pi\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{c^{4}+6c^{2}+3+6\left(c^{2}+1\right)\mu^{2}-3\mu^{4}-4p\mu\left(\sqrt{\frac{2}{\pi}}\left(c^{2}+2\right)+\frac{ck}{p}\left(c^{2}+3\right)\right)}{\mu_{2}^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Fratio (or F) -\layout Standard +\end_layout +\begin_layout Standard Defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset . The distribution of \begin_inset Formula $\left(X_{1}/X_{2}\right)\left(\nu_{2}/\nu_{1}\right)$ -\end_inset +\end_inset if \begin_inset Formula $X_{1}$ -\end_inset +\end_inset is chi-squared with \begin_inset Formula $v_{1}$ -\end_inset +\end_inset degrees of freedom and \begin_inset Formula $X_{2}$ -\end_inset +\end_inset is chi-squared with \begin_inset Formula $v_{2}$ -\end_inset +\end_inset degrees of freedom. @@ -1854,7 +1935,7 @@ F\left(x;v_{1},v_{2}\right) & = & I\left(\frac{\nu_{1}}{2},\frac{\nu_{2}}{2},\frac{\nu_{2}x}{\nu_{2}+\nu_{1}x}\right)\\ G\left(q;\nu_{1},\nu_{2}\right) & = & \left[\frac{\nu_{2}}{I^{-1}\left(\nu_{1}/2,\nu_{2}/2,q\right)}-\frac{\nu_{1}}{\nu_{2}}\right]^{-1}.\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1863,22 +1944,24 @@ \gamma_{1} & = & \frac{2\left(2\nu_{1}+\nu_{2}-2\right)}{\nu_{2}-6}\sqrt{\frac{2\left(\nu_{2}-4\right)}{\nu_{1}\left(\nu_{1}+\nu_{2}-2\right)}}\quad\nu_{2}>6\\ \gamma_{2} & = & \frac{3\left[8+\left(\nu_{2}-6\right)\gamma_{1}^{2}\right]}{2\nu-16}\quad\nu_{2}>8\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Fr?chet (ExtremeLB, Extreme Value II, Weibull minimum) -\layout Standard +\end_layout +\begin_layout Standard A type of extreme-value distribution with a lower bound. Defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset and \begin_inset Formula $c>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1886,13 +1969,13 @@ F\left(x;c\right) & = & 1-\exp\left(-x^{c}\right)\\ G\left(q;c\right) & = & \left[-\log\left(1-q\right)\right]^{1/c}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\Gamma\left(1+\frac{n}{c}\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -1903,37 +1986,39 @@ m_{d} & = & \left(\frac{c}{1+c}\right)^{1/c}\\ m_{n} & = & G\left(\frac{1}{2};c\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=-\frac{\gamma}{c}-\log\left(c\right)+\gamma+1\] -\end_inset +\end_inset where \begin_inset Formula $\gamma$ -\end_inset +\end_inset is Euler's constant and equal to \begin_inset Formula \[ \gamma\approx0.57721566490153286061.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Fr?chet (left-skewed, Extreme Value Type III, Weibull maximum) -\layout Standard +\end_layout +\begin_layout Standard Defined for \begin_inset Formula $x<0$ -\end_inset +\end_inset and \begin_inset Formula $c>0$ -\end_inset +\end_inset . @@ -1942,53 +2027,56 @@ F\left(x;c\right) & = & \exp\left(-\left(-x\right)^{c}\right)\\ G\left(q;c\right) & = & -\left(-\log q\right)^{1/c}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard The mean is the negative of the right-skewed Frechet distribution given above, and the other statistical parameters can be computed from -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=\left(-1\right)^{n}\Gamma\left(1+\frac{n}{c}\right).\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=-\frac{\gamma}{c}-\log\left(c\right)+\gamma+1\] -\end_inset +\end_inset where \begin_inset Formula $\gamma$ -\end_inset +\end_inset is Euler's constant and equal to \begin_inset Formula \[ \gamma\approx0.57721566490153286061.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gamma -\layout Standard +\end_layout +\begin_layout Standard The standard form for the gamma distribution is \begin_inset Formula $\left(\alpha>0\right)$ -\end_inset +\end_inset valid for \begin_inset Formula $x\geq0$ -\end_inset +\end_inset . \begin_inset Formula \begin{eqnarray*} @@ -1996,13 +2084,13 @@ F\left(x;\alpha\right) & = & \Gamma\left(\alpha,x\right)\\ G\left(q;\alpha\right) & = & \Gamma^{-1}\left(\alpha,q\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\frac{1}{\left(1-t\right)^{\alpha}}\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2012,62 +2100,64 @@ \gamma_{2} & = & \frac{6}{\alpha}\\ m_{d} & = & \alpha-1\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=\Psi\left(a\right)\left[1-a\right]+a+\log\Gamma\left(a\right)\] -\end_inset +\end_inset where \begin_inset Formula \[ \Psi\left(a\right)=\frac{\Gamma^{\prime}\left(a\right)}{\Gamma\left(a\right)}.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Logistic -\layout Standard +\end_layout +\begin_layout Standard Has been used in the analysis of extreme values. Has one shape parameter \begin_inset Formula $c>0.$ -\end_inset +\end_inset And \begin_inset Formula $x>0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c\right) & = & \frac{c\exp\left(-x\right)}{\left[1+\exp\left(-x\right)\right]^{c+1}}\\ F\left(x;c\right) & = & \frac{1}{\left[1+\exp\left(-x\right)\right]^{c}}\\ G\left(q;c\right) & = & -\log\left(q^{-1/c}-1\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=\frac{c}{1-t}\,_{2}F_{1}\left(1+c,\,1-t\,;\,2-t\,;-1\right)\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \gamma+\psi_{0}\left(c\right)\\ \mu_{2} & = & \frac{\pi^{2}}{6}+\psi_{1}\left(c\right)\\ @@ -2076,7 +2166,7 @@ m_{d} & = & \log c\\ m_{n} & = & -\log\left(2^{1/c}-1\right)\end{eqnarray*} -\end_inset +\end_inset Note that the polygamma function is \begin_inset Formula \begin{eqnarray*} @@ -2084,42 +2174,44 @@ & = & \left(-1\right)^{n+1}n!\sum_{k=0}^{\infty}\frac{1}{\left(z+k\right)^{n+1}}\\ & = & \left(-1\right)^{n+1}n!\zeta\left(n+1,z\right)\end{eqnarray*} -\end_inset +\end_inset where \begin_inset Formula $\zeta\left(k,x\right)$ -\end_inset +\end_inset is a generalization of the Riemann zeta function called the Hurwitz zeta function Note that \begin_inset Formula $\zeta\left(n\right)\equiv\zeta\left(n,1\right)$ -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Pareto -\layout Standard +\end_layout +\begin_layout Standard Shape parameter \begin_inset Formula $c\neq0$ -\end_inset +\end_inset and defined for \begin_inset Formula $x\geq0$ -\end_inset +\end_inset for all \begin_inset Formula $c$ -\end_inset +\end_inset and \begin_inset Formula $x<\frac{1}{\left|c\right|}$ -\end_inset +\end_inset if \begin_inset Formula $c$ -\end_inset +\end_inset is negative. @@ -2128,29 +2220,29 @@ F\left(x;c\right) & = & 1-\frac{1}{\left(1+cx\right)^{1/c}}\\ G\left(q;c\right) & = & \frac{1}{c}\left[\left(\frac{1}{1-q}\right)^{c}-1\right]\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=\left\{ \begin{array}{cc} \left(-\frac{t}{c}\right)^{\frac{1}{c}}e^{-\frac{t}{c}}\left[\Gamma\left(1-\frac{1}{c}\right)+\Gamma\left(-\frac{1}{c},-\frac{t}{c}\right)-\pi\csc\left(\frac{\pi}{c}\right)/\Gamma\left(\frac{1}{c}\right)\right] & c>0\\ \left(\frac{\left|c\right|}{t}\right)^{1/\left|c\right|}\Gamma\left[\frac{1}{\left|c\right|},\frac{t}{\left|c\right|}\right] & c<0\end{array}\right.\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=\frac{\left(-1\right)^{n}}{c^{n}}\sum_{k=0}^{n}\left(\begin{array}{c} n\\ k\end{array}\right)\frac{\left(-1\right)^{k}}{1-ck}\quad cn<1\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2159,7 +2251,7 @@ \mu_{3}^{\prime} & = & \frac{6}{\left(1-c\right)\left(1-2c\right)\left(1-3c\right)}\quad c<\frac{1}{3}\\ \mu_{4}^{\prime} & = & \frac{24}{\left(1-c\right)\left(1-2c\right)\left(1-3c\right)\left(1-4c\right)}\quad c<\frac{1}{4}\end{eqnarray*} -\end_inset +\end_inset Thus, \begin_inset Formula \begin{eqnarray*} @@ -2168,38 +2260,40 @@ \gamma_{1} & = & \frac{\mu_{3}^{\prime}-3\mu\mu_{2}-\mu^{3}}{\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{\mu_{4}^{\prime}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=1+c\quad c>0\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Exponential -\layout Standard +\end_layout +\begin_layout Standard Three positive shape parameters for \begin_inset Formula $x\geq0.$ -\end_inset +\end_inset Note that \begin_inset Formula $a,b,$ -\end_inset +\end_inset and \begin_inset Formula $c$ -\end_inset +\end_inset are all \begin_inset Formula $>0.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2207,29 +2301,32 @@ F\left(x;a,b,c\right) & = & 1-\exp\left[ax-bx+\frac{b}{c}\left(1-e^{-cx}\right)\right]\\ G\left(q;a,b,c\right) & = & F^{-1}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Extreme Value -\layout Standard +\end_layout +\begin_layout Standard Extreme value distributions with shape parameter \begin_inset Formula $c$ -\end_inset +\end_inset . -\layout Standard +\end_layout +\begin_layout Standard For \begin_inset Formula $c>0$ -\end_inset +\end_inset defined on \begin_inset Formula $-\infty-1\] -\end_inset +\end_inset So, \begin_inset Formula \begin{eqnarray*} @@ -2254,19 +2351,19 @@ \mu_{3}^{\prime} & = & \frac{1}{c^{3}}\left(1-3\Gamma\left(1+c\right)+3\Gamma\left(1+2c\right)-\Gamma\left(1+3c\right)\right)\quad c>-\frac{1}{3}\\ \mu_{4}^{\prime} & = & \frac{1}{c^{4}}\left(1-4\Gamma\left(1+c\right)+6\Gamma\left(1+2c\right)-4\Gamma\left(1+3c\right)+\Gamma\left(1+4c\right)\right)\quad c>-\frac{1}{4}\end{eqnarray*} -\end_inset +\end_inset For \begin_inset Formula $c<0$ -\end_inset +\end_inset defined on \begin_inset Formula $\frac{1}{c}\leq x<\infty.$ -\end_inset +\end_inset For \begin_inset Formula $c=0$ -\end_inset +\end_inset defined over all space \begin_inset Formula \begin{eqnarray*} @@ -2274,7 +2371,7 @@ F\left(x;0\right) & = & \exp\left[-e^{-x}\right]\\ G\left(q;0\right) & = & -\log\left(-\log q\right)\end{eqnarray*} -\end_inset +\end_inset This is just the (left-skewed) Gumbel distribution for c=0. @@ -2284,30 +2381,32 @@ \gamma_{1} & = & \frac{12\sqrt{6}}{\pi^{3}}\zeta\left(3\right)\\ \gamma_{2} & = & \frac{12}{5}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Gamma -\layout Standard +\end_layout +\begin_layout Standard A general probability form that reduces to many common distributions: \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula $a>0$ -\end_inset +\end_inset and \begin_inset Formula $c\neq0.$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;a,c\right) & = & \frac{\left|c\right|x^{ca-1}}{\Gamma\left(a\right)}\exp\left(-x^{c}\right)\\ F\left(x;a,c\right) & = & \begin{array}{cc} @@ -2316,13 +2415,13 @@ G\left(q;a,c\right) & = & \left\{ \Gamma^{-1}\left[a,\Gamma\left(a\right)q\right]\right\} ^{1/c}\quad c>0\\ & & \left\{ \Gamma^{-1}\left[a,\Gamma\left(a\right)\left(1-q\right)\right]\right\} ^{1/c}\quad c<0\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\frac{\Gamma\left(a+\frac{n}{c}\right)}{\Gamma\left(a\right)}\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2332,47 +2431,49 @@ \gamma_{2} & = & \frac{\Gamma\left(a+\frac{4}{c}\right)/\Gamma\left(a\right)-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\\ m_{d} & = & \left(\frac{ac-1}{c}\right)^{1/c}.\end{eqnarray*} -\end_inset +\end_inset Special cases are Weibull \begin_inset Formula $\left(a=1\right)$ -\end_inset +\end_inset , half-normal \begin_inset Formula $\left(a=1/2,c=2\right)$ -\end_inset +\end_inset and ordinary gamma distributions \begin_inset Formula $c=1.$ -\end_inset +\end_inset If \begin_inset Formula $c=-1$ -\end_inset +\end_inset then it is the inverted gamma distribution. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=a-a\Psi\left(a\right)+\frac{1}{c}\Psi\left(a\right)+\log\Gamma\left(a\right)-\log\left|c\right|.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Generalized Half-Logistic -\layout Standard +\end_layout +\begin_layout Standard For \begin_inset Formula $x\in\left[0,1/c\right]$ -\end_inset +\end_inset and \begin_inset Formula $c>0$ -\end_inset +\end_inset we have \begin_inset Formula \begin{eqnarray*} @@ -2380,34 +2481,36 @@ F\left(x;c\right) & = & \frac{1-\left(1-cx\right)^{1/c}}{1+\left(1-cx\right)^{1/c}}\\ G\left(q;c\right) & = & \frac{1}{c}\left[1-\left(\frac{1-q}{1+q}\right)^{c}\right]\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & 2-\left(2c+1\right)\log2.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gilbrat -\layout Standard +\end_layout +\begin_layout Standard Special case of the log-normal with \begin_inset Formula $\sigma=1$ -\end_inset +\end_inset and \begin_inset Formula $S=1.0$ -\end_inset +\end_inset (typically also \begin_inset Formula $L=0.0$ -\end_inset +\end_inset ) \begin_inset Formula \begin{eqnarray*} @@ -2415,7 +2518,7 @@ F\left(x;\sigma\right) & = & \Phi\left(\log x\right)=\frac{1}{2}\left[1+\textrm{erf}\left(\frac{\log x}{\sqrt{2}}\right)\right]\\ G\left(q;\sigma\right) & = & \exp\left\{ \Phi^{-1}\left(q\right)\right\} \end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2424,58 +2527,60 @@ \gamma_{1} & = & \sqrt{e-1}\left(2+e\right)\\ \gamma_{2} & = & e^{4}+2e^{3}+3e^{2}-6\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(\sqrt{2\pi e}\right)\\ & \approx & 1.4189385332046727418\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gompertz (Truncated Gumbel) -\layout Standard +\end_layout +\begin_layout Standard For \begin_inset Formula $x\geq0$ -\end_inset +\end_inset and \begin_inset Formula $c>0$ -\end_inset +\end_inset . In JKB the two shape parameters \begin_inset Formula $b,a$ -\end_inset +\end_inset are reduced to the single shape-parameter \begin_inset Formula $c=b/a$ -\end_inset +\end_inset . As \begin_inset Formula $a$ -\end_inset +\end_inset is just a scale parameter when \begin_inset Formula $a\neq0$ -\end_inset +\end_inset . If \begin_inset Formula $a=0,$ -\end_inset +\end_inset the distribution reduces to the exponential distribution scaled by \begin_inset Formula $1/b.$ -\end_inset +\end_inset Thus, the standard form is given as \begin_inset Formula \begin{eqnarray*} @@ -2483,45 +2588,47 @@ F\left(x;c\right) & = & 1-\exp\left[-c\left(e^{x}-1\right)\right]\\ G\left(q;c\right) & = & \log\left[1-\frac{1}{c}\log\left(1-q\right)\right]\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=1-\log\left(c\right)-e^{c}\textrm{Ei}\left(1,c\right),\] -\end_inset +\end_inset where \begin_inset Formula \[ \textrm{Ei}\left(n,x\right)=\int_{1}^{\infty}t^{-n}\exp\left(-xt\right)dt\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gumbel (LogWeibull, Fisher-Tippetts, Type I Extreme Value) -\layout Standard +\end_layout +\begin_layout Standard One of a clase of extreme value distributions (right-skewed). -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \exp\left(-\left(x+e^{-x}\right)\right)\\ F\left(x\right) & = & \exp\left(-e^{-x}\right)\\ G\left(q\right) & = & -\log\left(-\log\left(q\right)\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\Gamma\left(1-t\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2532,80 +2639,83 @@ m_{d} & = & 0\\ m_{n} & = & -\log\left(\log2\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]\approx1.0608407169541684911\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gumbel Left-skewed (for minimum order statistic) -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \exp\left(x-e^{x}\right)\\ F\left(x\right) & = & 1-\exp\left(-e^{x}\right)\\ G\left(q\right) & = & \log\left(-\log\left(1-q\right)\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\Gamma\left(1+t\right)\] -\end_inset +\end_inset Note, that \begin_inset Formula $\mu$ -\end_inset +\end_inset is negative the mean for the right-skewed distribution. Similar for median and mode. All other moments are the same. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]\approx1.0608407169541684911.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section HalfCauchy -\layout Standard +\end_layout +\begin_layout Standard If \begin_inset Formula $Z$ -\end_inset +\end_inset is Hyperbolic Secant distributed then \begin_inset Formula $e^{Z}$ -\end_inset +\end_inset is Half-Cauchy distributed. Also, if \begin_inset Formula $W$ -\end_inset +\end_inset is (standard) Cauchy distributed, then \begin_inset Formula $\left|W\right|$ -\end_inset +\end_inset is Half-Cauchy distributed. Special case of the Folded Cauchy distribution with \begin_inset Formula $c=0.$ -\end_inset +\end_inset The standard form is \begin_inset Formula \begin{eqnarray*} @@ -2613,68 +2723,70 @@ F\left(x\right) & = & \frac{2}{\pi}\arctan\left(x\right)I_{\left[0,\infty\right]}\left(x\right)\\ G\left(q\right) & = & \tan\left(\frac{\pi}{2}q\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\cos t+\frac{2}{\pi}\left[\textrm{Si}\left(t\right)\cos t-\textrm{Ci}\left(\textrm{-}t\right)\sin t\right]\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} m_{d} & = & 0\\ m_{n} & = & \tan\left(\frac{\pi}{4}\right)\end{eqnarray*} -\end_inset +\end_inset No moments, as the integrals diverge. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(2\pi\right)\\ & \approx & 1.8378770664093454836.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section HalfNormal -\layout Standard +\end_layout +\begin_layout Standard This is a special case of the chi distribution with \begin_inset Formula $L=a$ -\end_inset +\end_inset and \begin_inset Formula $S=b$ -\end_inset +\end_inset and \begin_inset Formula $\nu=1.$ -\end_inset +\end_inset This is also a special case of the folded normal with shape parameter \begin_inset Formula $c=0$ -\end_inset +\end_inset and \begin_inset Formula $S=S.$ -\end_inset +\end_inset If \begin_inset Formula $Z$ -\end_inset +\end_inset is (standard) normally distributed then, \begin_inset Formula $\left|Z\right|$ -\end_inset +\end_inset is half-normal. The standard form is @@ -2683,18 +2795,18 @@ F\left(x\right) & = & 2\Phi\left(x\right)-1\\ G\left(q\right) & = & \Phi^{-1}\left(\frac{1+q}{2}\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\sqrt{2\pi}e^{t^{2}/2}\Phi\left(t\right)\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \sqrt{\frac{2}{\pi}}\\ \mu_{2} & = & 1-\frac{2}{\pi}\\ @@ -2703,40 +2815,42 @@ m_{d} & = & 0\\ m_{n} & = & \Phi^{-1}\left(\frac{3}{4}\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(\sqrt{\frac{\pi e}{2}}\right)\\ & \approx & 0.72579135264472743239.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Half-Logistic -\layout Standard +\end_layout +\begin_layout Standard In the limit as \begin_inset Formula $c\rightarrow\infty$ -\end_inset +\end_inset for the generalized half-logistic we have the half-logistic defined over \begin_inset Formula $x\geq0.$ -\end_inset +\end_inset Also, the distribution of \begin_inset Formula $\left|X\right|$ -\end_inset +\end_inset where \begin_inset Formula $X$ -\end_inset +\end_inset has logistic distribtution. @@ -2745,22 +2859,22 @@ F\left(x\right) & = & \frac{1-e^{-x}}{1+e^{-x}}=\tanh\left(\frac{x}{2}\right)\\ G\left(q\right) & = & \log\left(\frac{1+q}{1-q}\right)=2\textrm{arctanh}\left(q\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ M\left(t\right)=1-t\psi_{0}\left(\frac{1}{2}-\frac{t}{2}\right)+t\psi_{0}\left(1-\frac{t}{2}\right)\] -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=2\left(1-2^{1-n}\right)n!\zeta\left(n\right)\quad n\neq1\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2769,28 +2883,30 @@ \mu_{3}^{\prime} & = & 9\zeta\left(3\right)\\ \mu_{4}^{\prime} & = & 42\zeta\left(4\right)=\frac{7\pi^{4}}{15}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & 2-\log\left(2\right)\\ & \approx & 1.3068528194400546906.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Hyperbolic Secant -\layout Standard +\end_layout +\begin_layout Standard Related to the logistic distribution and used in lifetime analysis. Standard form is (defined over all \begin_inset Formula $x$ -\end_inset +\end_inset ) \begin_inset Formula \begin{eqnarray*} @@ -2798,13 +2914,13 @@ F\left(x\right) & = & \frac{2}{\pi}\arctan\left(e^{x}\right)\\ G\left(q\right) & = & \log\left(\tan\left(\frac{\pi}{2}q\right)\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ M\left(t\right)=\sec\left(\frac{\pi}{2}t\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2813,30 +2929,30 @@ 0 & n\textrm{ odd}\\ C_{n/2}\frac{\pi^{n}}{2^{n}} & n\textrm{ even}\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset where \begin_inset Formula $C_{m}$ -\end_inset +\end_inset is an integer given by \begin_inset Formula \begin{eqnarray*} C_{m} & = & \frac{\left(2m\right)!\left[\zeta\left(2m+1,\frac{1}{4}\right)-\zeta\left(2m+1,\frac{3}{4}\right)\right]}{\pi^{2m+1}2^{2m}}\\ & = & 4\left(-1\right)^{m-1}\frac{16^{m}}{2m+1}B_{2m+1}\left(\frac{1}{4}\right)\end{eqnarray*} -\end_inset +\end_inset where \begin_inset Formula $B_{2m+1}\left(\frac{1}{4}\right)$ -\end_inset +\end_inset is the Bernoulli polynomial of order \begin_inset Formula $2m+1$ -\end_inset +\end_inset evaluated at \begin_inset Formula $1/4.$ -\end_inset +\end_inset Thus \begin_inset Formula \[ @@ -2844,90 +2960,93 @@ 0 & n\textrm{ odd}\\ 4\left(-1\right)^{n/2-1}\frac{\left(2\pi\right)^{n}}{n+1}B_{n+1}\left(\frac{1}{4}\right) & n\textrm{ even}\end{array}\right.\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} m_{d}=m_{n}=\mu & = & 0\\ \mu_{2} & = & \frac{\pi^{2}}{4}\\ \gamma_{1} & = & 0\\ \gamma_{2} & = & 2\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=\log\left(2\pi\right).\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Gauss Hypergeometric -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula $x\in\left[0,1\right]$ -\end_inset +\end_inset , \begin_inset Formula $\alpha>0,\,\beta>0$ -\end_inset +\end_inset \begin_inset Formula \[ C^{-1}=B\left(\alpha,\beta\right)\,_{2}F_{1}\left(\gamma,\alpha;\alpha+\beta;-z\right)\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} f\left(x;\alpha,\beta,\gamma,z\right) & = & Cx^{\alpha-1}\frac{\left(1-x\right)^{\beta-1}}{\left(1+zx\right)^{\gamma}}\\ \mu_{n}^{\prime} & = & \frac{B\left(n+\alpha,\beta\right)}{B\left(\alpha,\beta\right)}\frac{\,_{2}F_{1}\left(\gamma,\alpha+n;\alpha+\beta+n;-z\right)}{\,_{2}F_{1}\left(\gamma,\alpha;\alpha+\beta;-z\right)}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Inverted Gamma -\layout Standard +\end_layout +\begin_layout Standard Special case of the generalized Gamma distribution with \begin_inset Formula $c=-1$ -\end_inset +\end_inset and \begin_inset Formula $a>0$ -\end_inset +\end_inset , \begin_inset Formula $x>0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;a\right) & = & \frac{x^{-a-1}}{\Gamma\left(a\right)}\exp\left(-\frac{1}{x}\right)\\ F\left(x;a\right) & = & \frac{\Gamma\left(a,\frac{1}{x}\right)}{\Gamma\left(a\right)}\\ G\left(q;a\right) & = & \left\{ \Gamma^{-1}\left[a,\Gamma\left(a\right)q\right]\right\} ^{-1}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\frac{\Gamma\left(a-n\right)}{\Gamma\left(a\right)}\quad a>n\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2936,54 +3055,56 @@ \gamma_{1} & = & \frac{\frac{1}{\left(a-3\right)\left(a-2\right)\left(a-1\right)}-3\mu\mu_{2}-\mu^{3}}{\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{\frac{1}{\left(a-4\right)\left(a-3\right)\left(a-2\right)\left(a-1\right)}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ m_{d}=\frac{1}{a+1}\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=a-\left(a+1\right)\Psi\left(a\right)+\log\Gamma\left(a\right).\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Inverse Normal (Inverse Gaussian) -\layout Standard +\end_layout +\begin_layout Standard The standard form involves the shape parameter \begin_inset Formula $\mu$ -\end_inset +\end_inset (in most definitions, \begin_inset Formula $L=0.0$ -\end_inset +\end_inset is used). (In terms of the regress documentation \begin_inset Formula $\mu=A/B$ -\end_inset +\end_inset ) and \begin_inset Formula $B=S$ -\end_inset +\end_inset and \begin_inset Formula $L$ -\end_inset +\end_inset is not a parameter in that distribution. A standard form is \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -2991,12 +3112,12 @@ F\left(x;\mu\right) & = & \Phi\left(\frac{1}{\sqrt{x}}\frac{x-\mu}{\mu}\right)+\exp\left(\frac{2}{\mu}\right)\Phi\left(-\frac{1}{\sqrt{x}}\frac{x+\mu}{\mu}\right)\\ G\left(q;\mu\right) & = & F^{-1}\left(q;\mu\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \mu\\ \mu_{2} & = & \mu^{3}\\ @@ -3004,80 +3125,83 @@ \gamma_{2} & = & 15\mu\\ m_{d} & = & \frac{\mu}{2}\left(\sqrt{9\mu^{2}+4}-3\mu\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard This is related to the canonical form or JKB \begin_inset Quotes eld -\end_inset +\end_inset two-parameter \begin_inset Quotes erd -\end_inset +\end_inset inverse Gaussian when written in it's full form with scale parameter \begin_inset Formula $S$ -\end_inset +\end_inset and location parameter \begin_inset Formula $L$ -\end_inset +\end_inset by taking \begin_inset Formula $L=0$ -\end_inset +\end_inset and \begin_inset Formula $S\equiv\lambda,$ -\end_inset +\end_inset then \begin_inset Formula $\mu S$ -\end_inset +\end_inset is equal to \begin_inset Formula $\mu_{2}$ -\end_inset +\end_inset where -\bar under +\bar under -\bar default +\bar default \begin_inset Formula $\mu_{2}$ -\end_inset +\end_inset is the parameter used by JKB. We prefer this form because of it's consistent use of the scale parameter. Notice that in JKB the skew \begin_inset Formula $\left(\sqrt{\beta_{1}}\right)$ -\end_inset +\end_inset and the kurtosis ( \begin_inset Formula $\beta_{2}-3$ -\end_inset +\end_inset ) are both functions only of \begin_inset Formula $\mu_{2}/\lambda=\mu S/S=\mu$ -\end_inset +\end_inset as shown here, while the variance and mean of the standard form here are transformed appropriately. -\layout Section +\end_layout +\begin_layout Section Inverted Weibull -\layout Standard +\end_layout +\begin_layout Standard Shape parameter \begin_inset Formula $c>0$ -\end_inset +\end_inset and \begin_inset Formula $x>0$ -\end_inset +\end_inset . Then @@ -3086,35 +3210,37 @@ F\left(x;c\right) & = & \exp\left(-x^{-c}\right)\\ G\left(q;c\right) & = & \left(-\log q\right)^{-1/c}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=1+\gamma+\frac{\gamma}{c}-\log\left(c\right)\] -\end_inset +\end_inset where \begin_inset Formula $\gamma$ -\end_inset +\end_inset is Euler's constant. -\layout Section +\end_layout +\begin_layout Section Johnson SB -\layout Standard +\end_layout +\begin_layout Standard Defined for \begin_inset Formula $x\in\left(0,1\right)$ -\end_inset +\end_inset with two shape parameters \begin_inset Formula $a$ -\end_inset +\end_inset and \begin_inset Formula $b>0.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3122,25 +3248,27 @@ F\left(x;a,b\right) & = & \Phi\left(a+b\log\frac{x}{1-x}\right)\\ G\left(q;a,b\right) & = & \frac{1}{1+\exp\left[-\frac{1}{b}\left(\Phi^{-1}\left(q\right)-a\right)\right]}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Johnson SU -\layout Standard +\end_layout +\begin_layout Standard Defined for all \begin_inset Formula $x$ -\end_inset +\end_inset with two shape parameters \begin_inset Formula $a$ -\end_inset +\end_inset and \begin_inset Formula $b>0$ -\end_inset +\end_inset . @@ -3149,21 +3277,24 @@ F\left(x;a,b\right) & = & \Phi\left(a+b\log\left(x+\sqrt{x^{2}+1}\right)\right)\\ G\left(q;a,b\right) & = & \sinh\left[\frac{\Phi^{-1}\left(q\right)-a}{b}\right]\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section KSone -\layout Section +\end_layout +\begin_layout Section KStwo -\layout Section +\end_layout +\begin_layout Section Laplace (Double Exponential, Bilateral Expoooonential) -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{1}{2}e^{-\left|x\right|}\\ F\left(x\right) & = & \left\{ \begin{array}{ccc} @@ -3173,7 +3304,7 @@ \log\left(2q\right) & & q\leq\frac{1}{2}\\ -\log\left(2-2q\right) & & q>\frac{1}{2}\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3182,95 +3313,98 @@ \gamma_{1} & = & 0\\ \gamma_{2} & = & 3\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard The ML estimator of the location parameter is \begin_inset Formula \[ \hat{L}=\textrm{median}\left(X_{i}\right)\] -\end_inset +\end_inset where \begin_inset Formula $X_{i}$ -\end_inset +\end_inset is a sequence of \begin_inset Formula $N$ -\end_inset +\end_inset mutually independent Laplace RV's and the median is some number between the \begin_inset Formula $\frac{1}{2}N\textrm{th}$ -\end_inset +\end_inset and the \begin_inset Formula $(N/2+1)\textrm{th}$ -\end_inset +\end_inset order statistic ( -\emph on +\emph on e.g. -\emph default +\emph default take the average of these two) when \begin_inset Formula $N$ -\end_inset +\end_inset is even. Also, \begin_inset Formula \[ \hat{S}=\frac{1}{N}\sum_{j=1}^{N}\left|X_{j}-\hat{L}\right|.\] -\end_inset +\end_inset Replace \begin_inset Formula $\hat{L}$ -\end_inset +\end_inset with \begin_inset Formula $L$ -\end_inset +\end_inset if it is known. If \begin_inset Formula $L$ -\end_inset +\end_inset is known then this estimator is distributed as \begin_inset Formula $\left(2N\right)^{-1}S\cdot\chi_{2N}^{2}$ -\end_inset +\end_inset . -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(2e\right)\\ & \approx & 1.6931471805599453094.\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Left-skewed L?vy -\layout Standard +\end_layout +\begin_layout Standard Special case of L?vy-stable distribution with \begin_inset Formula $\alpha=\frac{1}{2}$ -\end_inset +\end_inset and \begin_inset Formula $\beta=-1$ -\end_inset +\end_inset the support is \begin_inset Formula $x<0$ -\end_inset +\end_inset . In standard form @@ -3279,26 +3413,28 @@ F\left(x\right) & = & 2\Phi\left(\frac{1}{\sqrt{\left|x\right|}}\right)-1\\ G\left(q\right) & = & -\left[\Phi^{-1}\left(\frac{q+1}{2}\right)\right]^{-2}.\end{eqnarray*} -\end_inset +\end_inset No moments. -\layout Section +\end_layout +\begin_layout Section L?vy -\layout Standard +\end_layout +\begin_layout Standard A special case of L?vy-stable distributions with \begin_inset Formula $\alpha=\frac{1}{2}$ -\end_inset +\end_inset and \begin_inset Formula $\beta=1$ -\end_inset +\end_inset . In standard form it is defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset as \begin_inset Formula \begin{eqnarray*} @@ -3306,37 +3442,39 @@ F\left(x\right) & = & 2\left[1-\Phi\left(\frac{1}{\sqrt{x}}\right)\right]\\ G\left(q\right) & = & \left[\Phi^{-1}\left(1-\frac{q}{2}\right)\right]^{-2}.\end{eqnarray*} -\end_inset +\end_inset It has no finite moments. -\layout Section +\end_layout +\begin_layout Section Logistic (Sech-squared) -\layout Standard +\end_layout +\begin_layout Standard A special case of the Generalized Logistic distribution with \begin_inset Formula $c=1.$ -\end_inset +\end_inset Defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{\exp\left(-x\right)}{\left[1+\exp\left(-x\right)\right]^{2}}\\ F\left(x\right) & = & \frac{1}{1+\exp\left(-x\right)}\\ G\left(q\right) & = & -\log\left(1/q-1\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \gamma+\psi_{0}\left(1\right)=0\\ \mu_{2} & = & \frac{\pi^{2}}{6}+\psi_{1}\left(1\right)=\frac{\pi^{2}}{3}\\ @@ -3345,30 +3483,32 @@ m_{d} & = & \log1=0\\ m_{n} & = & -\log\left(2-1\right)=0\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=1.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Log Double Exponential (Log-Laplace) -\layout Standard +\end_layout +\begin_layout Standard Defined over \begin_inset Formula $x>0$ -\end_inset +\end_inset with \begin_inset Formula $c>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3382,30 +3522,32 @@ \left(2q\right)^{1/c} & & 0\leq q<\frac{1}{2}\\ \left(2-2q\right)^{-1/c} & & \frac{1}{2}\leq q\leq1\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=\log\left(\frac{2e}{c}\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Log Gamma -\layout Standard +\end_layout +\begin_layout Standard A single shape parameter \begin_inset Formula $c>0$ -\end_inset +\end_inset (Defined for all \begin_inset Formula $x$ -\end_inset +\end_inset ) \begin_inset Formula \begin{eqnarray*} @@ -3413,13 +3555,13 @@ F\left(x;c\right) & = & \frac{\Gamma\left(c,e^{x}\right)}{\Gamma\left(c\right)}\\ G\left(q;c\right) & = & \log\left[\Gamma^{-1}\left[c,q\Gamma\left(c\right)\right]\right]\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\int_{0}^{\infty}\left[\log y\right]^{n}y^{c-1}\exp\left(-y\right)dy.\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3428,43 +3570,45 @@ \gamma_{1} & = & \frac{\mu_{3}^{\prime}-3\mu\mu_{2}-\mu^{3}}{\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{\mu_{4}^{\prime}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Log Normal (Cobb-Douglass) -\layout Standard +\end_layout +\begin_layout Standard Has one shape parameter \begin_inset Formula $\sigma$ -\end_inset +\end_inset >0. (Notice that the \begin_inset Quotes eld -\end_inset +\end_inset Regress \begin_inset Quotes erd -\end_inset +\end_inset \begin_inset Formula $A=\log S$ -\end_inset +\end_inset where \begin_inset Formula $S$ -\end_inset +\end_inset is the scale parameter and \begin_inset Formula $A$ -\end_inset +\end_inset is the mean of the underlying normal distribution). The standard form is \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3472,7 +3616,7 @@ F\left(x;\sigma\right) & = & \Phi\left(\frac{\log x}{\sigma}\right)\\ G\left(q;\sigma\right) & = & \exp\left\{ \sigma\Phi^{-1}\left(q\right)\right\} \end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3481,45 +3625,80 @@ \gamma_{1} & = & \sqrt{p-1}\left(2+p\right)\\ \gamma_{2} & = & p^{4}+2p^{3}+3p^{2}-6\quad\quad p=e^{\sigma^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout +\begin_layout Standard Notice that using JKB notation we have \begin_inset Formula $\theta=L,$ -\end_inset +\end_inset \begin_inset Formula $\zeta=\log S$ -\end_inset +\end_inset and we have given the so-called antilognormal form of the distribution. This is more consistent with the location, scale parameter description of general probability distributions. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=\frac{1}{2}\left[1+\log\left(2\pi\right)+2\log\left(\sigma\right)\right].\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Standard +Also, note that if +\begin_inset Formula $X$ +\end_inset + + is a log-normally distributed random-variable with +\begin_inset Formula $L=0$ +\end_inset + + and +\begin_inset Formula $S$ +\end_inset + + and shape parameter +\begin_inset Formula $\sigma.$ +\end_inset + + Then, +\begin_inset Formula $\log X$ +\end_inset + + is normally distributed with variance +\begin_inset Formula $\sigma^{2}$ +\end_inset + + and mean +\begin_inset Formula $\log S.$ +\end_inset + + +\end_layout + +\begin_layout Section Nakagami -\layout Standard +\end_layout +\begin_layout Standard Generalization of the chi distribution. Shape parameter is \begin_inset Formula $\nu>0.$ -\end_inset +\end_inset Defined for \begin_inset Formula $x>0.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3527,7 +3706,7 @@ F\left(x;\nu\right) & = & \Gamma\left(\nu,\nu x^{2}\right)\\ G\left(q;\nu\right) & = & \sqrt{\frac{1}{\nu}\Gamma^{-1}\left(v,q\right)}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3536,70 +3715,75 @@ \gamma_{1} & = & \frac{\mu\left(1-4v\mu_{2}\right)}{2\nu\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{-6\mu^{4}\nu+\left(8\nu-2\right)\mu^{2}-2\nu+1}{\nu\mu_{2}^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Noncentral beta* -\layout Standard +\end_layout +\begin_layout Standard Defined over \begin_inset Formula $x\in\left[0,1\right]$ -\end_inset +\end_inset with \begin_inset Formula $a>0$ -\end_inset +\end_inset and \begin_inset Formula $b>0$ -\end_inset +\end_inset and \begin_inset Formula $c\geq0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ F\left(x;a,b,c\right)=\sum_{j=0}^{\infty}\frac{e^{-c/2}\left(\frac{c}{2}\right)^{j}}{j!}I_{B}\left(a+j,b;0\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Noncentral chi* -\layout Section +\end_layout +\begin_layout Section Noncentral chi-squared -\layout Standard +\end_layout +\begin_layout Standard The distribution of \begin_inset Formula $\sum_{i=1}^{\nu}\left(Z_{i}+\delta_{i}\right)^{2}$ -\end_inset +\end_inset where \begin_inset Formula $Z_{i}$ -\end_inset +\end_inset are independent standard normal variables and \begin_inset Formula $\delta_{i}$ -\end_inset +\end_inset are constants. \begin_inset Formula $\lambda=\sum_{i=1}^{\nu}\delta_{i}^{2}>0.$ -\end_inset +\end_inset (In communications it is called the Marcum-Q function). Can be thought of as a Generalized Rayleigh-Rice distribution. For \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3607,7 +3791,7 @@ F\left(x;\nu,\lambda\right) & = & \sum_{j=0}^{\infty}\left\{ \frac{\left(\lambda/2\right)^{j}}{j!}e^{-\lambda/2}\right\} \textrm{Pr}\left[\chi_{\nu+2j}^{2}\leq x\right]\\ G\left(q;\nu,\lambda\right) & = & F^{-1}\left(x;\nu,\lambda\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3616,68 +3800,72 @@ \gamma_{1} & = & \frac{\sqrt{8}\left(\nu+3\lambda\right)}{\left(\nu+2\lambda\right)^{3/2}}\\ \gamma_{2} & = & \frac{12\left(\nu+4\lambda\right)}{\left(\nu+2\lambda\right)^{2}}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Noncentral F -\layout Standard +\end_layout +\begin_layout Standard Let \begin_inset Formula $\lambda>0$ -\end_inset +\end_inset and \begin_inset Formula $\nu_{1}>0$ -\end_inset +\end_inset and \begin_inset Formula $\nu_{2}>0.$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\lambda,\nu_{1},\nu_{2}\right) & = & \exp\left[\frac{\lambda}{2}+\frac{\left(\lambda\nu_{1}x\right)}{2\left(\nu_{1}x+\nu_{2}\right)}\right]\nu_{1}^{\nu_{1}/2}\nu_{2}^{\nu_{2}/2}x^{\nu_{1}/2-1}\\ & & \times\left(\nu_{2}+\nu_{1}x\right)^{-\left(\nu_{1}+\nu_{2}\right)/2}\frac{\Gamma\left(\frac{\nu_{1}}{2}\right)\Gamma\left(1+\frac{\nu_{2}}{2}\right)L_{\nu_{2}/2}^{\nu_{1}/2-1}\left(-\frac{\lambda\nu_{1}x}{2\left(\nu_{1}x+\nu_{2}\right)}\right)}{B\left(\frac{\nu_{1}}{2},\frac{\nu_{2}}{2}\right)\Gamma\left(\frac{\nu_{1}+\nu_{2}}{2}\right)}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Noncentral t -\layout Standard +\end_layout +\begin_layout Standard The distribution of the ratio \begin_inset Formula \[ \frac{U+\lambda}{\chi_{\nu}/\sqrt{\nu}}\] -\end_inset +\end_inset where \begin_inset Formula $U$ -\end_inset +\end_inset and \begin_inset Formula $\chi_{\nu}$ -\end_inset +\end_inset are independent and distributed as a standard normal and chi with \begin_inset Formula $\nu$ -\end_inset +\end_inset degrees of freedom. Note \begin_inset Formula $\lambda>0$ -\end_inset +\end_inset and \begin_inset Formula $\nu>0$ -\end_inset +\end_inset . @@ -3689,77 +3877,81 @@ & & \times\left(\frac{\nu}{\nu+x^{2}}\right)^{\left(\nu-1\right)/2}Hh_{\nu}\left(-\frac{\lambda x}{\sqrt{\nu+x^{2}}}\right)\\ F\left(x;\lambda,\nu\right) & =\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Normal -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{e^{-x^{2}/2}}{\sqrt{2\pi}}\\ F\left(x\right) & = & \Phi\left(x\right)=\frac{1}{2}+\frac{1}{2}\textrm{erf}\left(\frac{\textrm{x}}{\sqrt{2}}\right)\\ G\left(q\right) & = & \Phi^{-1}\left(q\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard -\align center +\end_layout +\begin_layout Standard +\align center \begin_inset Formula \begin{eqnarray*} m_{d}=m_{n}=\mu & = & 0\\ \mu_{2} & = & 1\\ \gamma_{1} & = & 0\\ \gamma_{2} & = & 0\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} h\left[X\right] & = & \log\left(\sqrt{2\pi e}\right)\\ & \approx & 1.4189385332046727418\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Maxwell -\layout Standard +\end_layout +\begin_layout Standard This is a special case of the Chi distribution with \begin_inset Formula $L=0$ -\end_inset +\end_inset and \begin_inset Formula $S=S=\frac{1}{\sqrt{a}}$ -\end_inset +\end_inset and \begin_inset Formula $\nu=3.$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \sqrt{\frac{2}{\pi}}x^{2}e^{-x^{2}/2}I_{\left(0,\infty\right)}\left(x\right)\\ F\left(x\right) & = & \Gamma\left(\frac{3}{2},\frac{x^{2}}{2}\right)\\ G\left(\alpha\right) & = & \sqrt{2\Gamma^{-1}\left(\frac{3}{2},\alpha\right)}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & 2\sqrt{\frac{2}{\pi}}\\ \mu_{2} & = & 3-\frac{8}{\pi}\\ @@ -3768,37 +3960,39 @@ m_{d} & = & \sqrt{2}\\ m_{n} & = & \sqrt{2\Gamma^{-1}\left(\frac{3}{2},\frac{1}{2}\right)}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=\log\left(\sqrt{\frac{2\pi}{e}}\right)+\gamma.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Mielke's Beta-Kappa -\layout Standard +\end_layout +\begin_layout Standard A generalized F distribution. Two shape parameters \begin_inset Formula $\kappa$ -\end_inset +\end_inset and \begin_inset Formula $\theta$ -\end_inset +\end_inset , and \begin_inset Formula $x>0$ -\end_inset +\end_inset . The \begin_inset Formula $\beta$ -\end_inset +\end_inset in the DATAPLOT reference is a scale parameter. \begin_inset Formula \begin{eqnarray*} @@ -3806,107 +4000,112 @@ F\left(x;\kappa,\theta\right) & = & \frac{x^{\kappa}}{\left(1+x^{\theta}\right)^{\kappa/\theta}}\\ G\left(q;\kappa,\theta\right) & = & \left(\frac{q^{\theta/\kappa}}{1-q^{\theta/\kappa}}\right)^{1/\theta}\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Pareto -\layout Standard +\end_layout +\begin_layout Standard For \begin_inset Formula $x\geq1$ -\end_inset +\end_inset and \begin_inset Formula $b>0$ -\end_inset +\end_inset . Standard form is -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;b\right) & = & \frac{b}{x^{b+1}}\\ F\left(x;b\right) & = & 1-\frac{1}{x^{b}}\\ G\left(q;b\right) & = & \left(1-q\right)^{-1/b}\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & \frac{b}{b-1}\quad b>1\\ \mu_{2} & = & \frac{b}{\left(b-2\right)\left(b-1\right)^{2}}\quad b>2\\ \gamma_{1} & = & \frac{2\left(b+1\right)\sqrt{b-2}}{\left(b-3\right)\sqrt{b}}\quad b>3\\ \gamma_{2} & = & \frac{6\left(b^{3}+b^{2}-6b-2\right)}{b\left(b^{2}-7b+12\right)}\quad b>4\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left(X\right)=\frac{1}{c}+1-\log\left(c\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Pareto Second Kind (Lomax) -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula $c>0.$ -\end_inset +\end_inset This is Pareto of the first kind with \begin_inset Formula $L=-1.0$ -\end_inset +\end_inset so \begin_inset Formula $x\geq0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;c\right) & = & \frac{c}{\left(1+x\right)^{c+1}}\\ F\left(x;c\right) & = & 1-\frac{1}{\left(1+x\right)^{c}}\\ G\left(q;c\right) & = & \left(1-q\right)^{-1/c}-1\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=\frac{1}{c}+1-\log\left(c\right).\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Power Log Normal -\layout Standard +\end_layout +\begin_layout Standard A generalization of the log-normal distribution \begin_inset Formula $\sigma>0$ -\end_inset +\end_inset and \begin_inset Formula $c>0$ -\end_inset +\end_inset and \begin_inset Formula $x>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3914,13 +4113,13 @@ F\left(x;\sigma,c\right) & = & 1-\left(\Phi\left(-\frac{\log x}{\sigma}\right)\right)^{c}\\ G\left(q;\sigma,c\right) & = & \exp\left[-\sigma\Phi^{-1}\left[\left(1-q\right)^{1/c}\right]\right]\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\int_{0}^{1}\exp\left[-n\sigma\Phi^{-1}\left(y^{1/c}\right)\right]dy\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3929,21 +4128,23 @@ \gamma_{1} & = & \frac{\mu_{3}^{\prime}-3\mu\mu_{2}-\mu^{3}}{\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{\mu_{4}^{\prime}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset This distribution reduces to the log-normal distribution when \begin_inset Formula $c=1.$ -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Power Normal -\layout Standard +\end_layout +\begin_layout Standard A generalization of the normal distribution, \begin_inset Formula $c>0$ -\end_inset +\end_inset for \begin_inset Formula \begin{eqnarray*} @@ -3951,13 +4152,13 @@ F\left(x;c\right) & = & 1-\left(\Phi\left(-x\right)\right)^{c}\\ G\left(q;c\right) & = & -\Phi^{-1}\left[\left(1-q\right)^{1/c}\right]\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\left(-1\right)^{n}\int_{0}^{1}\left[\Phi^{-1}\left(y^{1/c}\right)\right]^{n}dy\] -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -3966,40 +4167,42 @@ \gamma_{1} & = & \frac{\mu_{3}^{\prime}-3\mu\mu_{2}-\mu^{3}}{\mu_{2}^{3/2}}\\ \gamma_{2} & = & \frac{\mu_{4}^{\prime}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset For \begin_inset Formula $c=1$ -\end_inset +\end_inset this reduces to the normal distribution. -\layout Section +\end_layout +\begin_layout Section Power-function -\layout Standard +\end_layout +\begin_layout Standard A special case of the beta distribution with \begin_inset Formula $b=1$ -\end_inset +\end_inset : defined for \begin_inset Formula $x\in\left[0,1\right]$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ a>0\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;a\right) & = & ax^{a-1}\\ F\left(x;a\right) & = & x^{a}\\ @@ -4010,63 +4213,67 @@ \gamma_{2} & = & \frac{6\left(a^{3}-a^{2}-6a+2\right)}{a\left(a+3\right)\left(a+4\right)}\\ m_{d} & = & 1\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=1-\frac{1}{a}-\log\left(a\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section R-distribution -\layout Standard +\end_layout +\begin_layout Standard A general-purpose distribution with a variety of shapes controlled by \begin_inset Formula $c>0.$ -\end_inset +\end_inset Range of standard distribution is \begin_inset Formula $x\in\left[-1,1\right]$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} f\left(x;c\right) & = & \frac{\left(1-x^{2}\right)^{c/2-1}}{B\left(\frac{1}{2},\frac{c}{2}\right)}\\ F\left(x;c\right) & = & \frac{1}{2}+\frac{x}{B\left(\frac{1}{2},\frac{c}{2}\right)}\,_{2}F_{1}\left(\frac{1}{2},1-\frac{c}{2};\frac{3}{2};x^{2}\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \mu_{n}^{\prime}=\frac{\left(1+\left(-1\right)^{n}\right)}{2}B\left(\frac{n+1}{2},\frac{c}{2}\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Rayleigh -\layout Standard +\end_layout +\begin_layout Standard This is Chi distribution with \begin_inset Formula $L=0.0$ -\end_inset +\end_inset and \begin_inset Formula $\nu=2$ -\end_inset +\end_inset and \begin_inset Formula $S=S$ -\end_inset +\end_inset (no location parameter is generally used), the mode of the distribution is \begin_inset Formula $S.$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4074,7 +4281,7 @@ F\left(r\right) & = & 1-e^{-r^{2}/2}I_{[0,\infty)}\left(x\right)\\ G\left(q\right) & = & \sqrt{-2\log\left(1-q\right)}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4085,69 +4292,73 @@ m_{d} & = & 1\\ m_{n} & = & \sqrt{2\log\left(2\right)}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=\frac{\gamma}{2}+\log\left(\frac{e}{\sqrt{2}}\right).\] -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=\sqrt{2^{n}}\Gamma\left(\frac{n}{2}+1\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Rice* -\layout Standard +\end_layout +\begin_layout Standard Defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset and \begin_inset Formula $b>0$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;b\right) & = & x\exp\left(-\frac{x^{2}+b^{2}}{2}\right)I_{0}\left(xb\right)\\ F\left(x;b\right) & = & \int_{0}^{x}\alpha\exp\left(-\frac{\alpha^{2}+b^{2}}{2}\right)I_{0}\left(\alpha b\right)d\alpha\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ \mu_{n}^{\prime}=\sqrt{2^{n}}\Gamma\left(1+\frac{n}{2}\right)\,_{1}F_{1}\left(-\frac{n}{2};1;-\frac{b^{2}}{2}\right)\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Reciprocal -\layout Standard +\end_layout +\begin_layout Standard Shape parameters \begin_inset Formula $a,b>0$ -\end_inset +\end_inset \begin_inset Formula $x\in\left[a,b\right]$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4155,7 +4366,7 @@ F\left(x;a,b\right) & = & \frac{\log\left(x/a\right)}{\log\left(b/a\right)}\\ G\left(q;a,b\right) & = & a\exp\left(q\log\left(b/a\right)\right)=a\left(\frac{b}{a}\right)^{q}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4167,58 +4378,62 @@ m_{d} & = & a\\ m_{n} & = & \sqrt{ab}\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=\frac{1}{2}\log\left(ab\right)+\log\left[\log\left(\frac{b}{a}\right)\right].\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Reciprocal Inverse Gaussian -\layout Standard +\end_layout +\begin_layout Standard The pdf is found from the inverse gaussian (IG), \begin_inset Formula $f_{RIG}\left(x;\mu\right)=\frac{1}{x^{2}}f_{IG}\left(\frac{1}{x};\mu\right)$ -\end_inset +\end_inset defined for \begin_inset Formula $x\geq0$ -\end_inset +\end_inset as -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f_{IG}\left(x;\mu\right) & = & \frac{1}{\sqrt{2\pi x^{3}}}\exp\left(-\frac{\left(x-\mu\right)^{2}}{2x\mu^{2}}\right).\\ F_{IG}\left(x;\mu\right) & = & \Phi\left(\frac{1}{\sqrt{x}}\frac{x-\mu}{\mu}\right)+\exp\left(\frac{2}{\mu}\right)\Phi\left(-\frac{1}{\sqrt{x}}\frac{x+\mu}{\mu}\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f_{RIG}\left(x;\mu\right) & = & \frac{1}{\sqrt{2\pi x}}\exp\left(-\frac{\left(1-\mu x\right)^{2}}{2x\mu^{2}}\right)\\ F_{RIG}\left(x;\mu\right) & = & 1-F_{IG}\left(\frac{1}{x},\mu\right)\\ & = & 1-\Phi\left(\frac{1}{\sqrt{x}}\frac{1-\mu x}{\mu}\right)-\exp\left(\frac{2}{\mu}\right)\Phi\left(-\frac{1}{\sqrt{x}}\frac{1+\mu x}{\mu}\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Semicircular -\layout Standard +\end_layout +\begin_layout Standard Defined on \begin_inset Formula $x\in\left[-1,1\right]$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4226,7 +4441,7 @@ F\left(x\right) & = & \frac{1}{2}+\frac{1}{\pi}\left[x\sqrt{1-x^{2}}+\arcsin x\right]\\ G\left(q\right) & = & F^{-1}\left(q\right)\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4235,39 +4450,42 @@ \gamma_{1} & = & 0\\ \gamma_{2} & = & -1\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=0.64472988584940017414.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Studentized Range* -\layout Section +\end_layout +\begin_layout Section Student t -\layout Standard +\end_layout +\begin_layout Standard Shape parameter \begin_inset Formula $\nu>0.$ -\end_inset +\end_inset \begin_inset Formula $I\left(a,b,x\right)$ -\end_inset +\end_inset is the incomplete beta integral and \begin_inset Formula $I^{-1}\left(a,b,I\left(a,b,x\right)\right)=x$ -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x;\nu\right) & = & \frac{\Gamma\left(\frac{\nu+1}{2}\right)}{\sqrt{\pi\nu}\Gamma\left(\frac{\nu}{2}\right)\left[1+\frac{x^{2}}{\nu}\right]^{\frac{\nu+1}{2}}}\\ F\left(x;\nu\right) & = & \left\{ \begin{array}{ccc} @@ -4277,50 +4495,52 @@ -\sqrt{\frac{\nu}{I^{-1}\left(\frac{\nu}{2},\frac{1}{2},2q\right)}-\nu} & & q\leq\frac{1}{2}\\ \sqrt{\frac{\nu}{I^{-1}\left(\frac{\nu}{2},\frac{1}{2},2-2q\right)}-\nu} & & q\geq\frac{1}{2}\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} m_{n}=m_{d}=\mu & = & 0\\ \mu_{2} & = & \frac{\nu}{\nu-2}\quad\nu>2\\ \gamma_{1} & = & 0\quad\nu>3\\ \gamma_{2} & = & \frac{6}{\nu-4}\quad\nu>4\end{eqnarray*} -\end_inset +\end_inset As \begin_inset Formula $\nu\rightarrow\infty,$ -\end_inset +\end_inset this distribution approaches the standard normal distribution. -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \[ h\left[X\right]=\frac{1}{4}\log\left(\frac{\pi c\Gamma^{2}\left(\frac{c}{2}\right)}{\Gamma^{2}\left(\frac{c+1}{2}\right)}\right)-\frac{\left(c+1\right)}{4}\left[\Psi\left(\frac{c}{2}\right)-cZ\left(c\right)+\pi\tan\left(\frac{\pi c}{2}\right)+\gamma+2\log2\right]\] -\end_inset +\end_inset where \begin_inset Formula \[ Z\left(c\right)=\,_{3}F_{2}\left(1,1,1+\frac{c}{2};\frac{3}{2},2;1\right)=\sum_{k=0}^{\infty}\frac{k!}{k+1}\frac{\Gamma\left(\frac{c}{2}+1+k\right)}{\Gamma\left(\frac{c}{2}+1\right)}\frac{\Gamma\left(\frac{3}{2}\right)}{\Gamma\left(\frac{3}{2}+k\right)}\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Student Z -\layout Standard +\end_layout +\begin_layout Standard The student Z distriubtion is defined over all space with one shape parameter \begin_inset Formula $\nu>0$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4330,7 +4550,7 @@ 1-Q\left(x;\nu\right) & & x\geq0\end{array}\right.\\ Q\left(x;\nu\right) & = & \frac{\left|x\right|^{1-n}\Gamma\left(\frac{n}{2}\right)\,_{2}F_{1}\left(\frac{n-1}{2},\frac{n}{2};\frac{n+1}{2};-\frac{1}{x^{2}}\right)}{2\sqrt{\pi}\Gamma\left(\frac{n+1}{2}\right)}\end{eqnarray*} -\end_inset +\end_inset Interesting moments are \begin_inset Formula \begin{eqnarray*} @@ -4339,26 +4559,29 @@ \gamma_{1} & = & 0\\ \gamma_{2} & = & \frac{6}{\nu-5}.\end{eqnarray*} -\end_inset +\end_inset The moment generating function is \begin_inset Formula \[ \theta\left(t\right)=2\sqrt{\left|\frac{t}{2}\right|^{\nu-1}}\frac{K_{\left(n-1\right)/2}\left(\left|t\right|\right)}{\Gamma\left(\frac{\nu-1}{2}\right)}.\] -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Symmetric Power* -\layout Section +\end_layout +\begin_layout Section Triangular -\layout Standard +\end_layout +\begin_layout Standard One shape parameter \begin_inset Formula $c\in[0,1]$ -\end_inset +\end_inset giving the distance to the peak as a percentage of the total extent of the non-zero portion. @@ -4366,7 +4589,7 @@ meter is the width of the non-zero portion. In standard form we have \begin_inset Formula $x\in\left[0,1\right].$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4380,7 +4603,7 @@ \sqrt{cq} & & q0$ -\end_inset +\end_inset . Note, the PDF and CDF functions are periodic and are always defined over \begin_inset Formula $x\in\left[-\pi,\pi\right]$ -\end_inset +\end_inset regardless of the location parameter. Thus, if an input beyond this range is given, it is converted to the equivalent angle in this range. For values of \begin_inset Formula $b<100$ -\end_inset +\end_inset the PDF and CDF formulas below are used. Otherwise, a normal approximation with variance \begin_inset Formula $1/b$ -\end_inset +\end_inset is used. @@ -4610,51 +4842,53 @@ F\left(x;b\right) & = & \frac{1}{2}+\frac{x}{2\pi}+\sum_{k=1}^{\infty}\frac{I_{k}\left(b\right)\sin\left(kx\right)}{I_{0}\left(b\right)\pi k}\\ G\left(q;b\right) & = & F^{-1}\left(x;b\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & 0\\ \mu_{2} & = & \int_{-\pi}^{\pi}x^{2}f\left(x;b\right)dx\\ \gamma_{1} & = & 0\\ \gamma_{2} & = & \frac{\int_{-\pi}^{\pi}x^{4}f\left(x;b\right)dx}{\mu_{2}^{2}}-3\end{eqnarray*} -\end_inset +\end_inset This can be used for defining circular variance. -\layout Section +\end_layout +\begin_layout Section Wald -\layout Standard +\end_layout +\begin_layout Standard Special case of the Inverse Normal with shape parameter set to \begin_inset Formula $1.0$ -\end_inset +\end_inset . Defined for \begin_inset Formula $x>0$ -\end_inset +\end_inset . -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} f\left(x\right) & = & \frac{1}{\sqrt{2\pi x^{3}}}\exp\left(-\frac{\left(x-1\right)^{2}}{2x}\right).\\ F\left(x\right) & = & \Phi\left(\frac{x-1}{\sqrt{x}}\right)+\exp\left(2\right)\Phi\left(-\frac{x+1}{\sqrt{x}}\right)\\ G\left(q;\mu\right) & = & F^{-1}\left(q;\mu\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Standard +\end_layout - +\begin_layout Standard \begin_inset Formula \begin{eqnarray*} \mu & = & 1\\ \mu_{2} & = & 1\\ @@ -4662,24 +4896,27 @@ \gamma_{2} & = & 15\\ m_{d} & = & \frac{1}{2}\left(\sqrt{13}-3\right)\end{eqnarray*} -\end_inset +\end_inset -\layout Section +\end_layout +\begin_layout Section Wishart* -\layout Section +\end_layout +\begin_layout Section Wrapped Cauchy -\layout Standard +\end_layout +\begin_layout Standard For \begin_inset Formula $x\in\left[0,2\pi\right]$ -\end_inset +\end_inset \begin_inset Formula $c\in\left(0,1\right)$ -\end_inset +\end_inset \begin_inset Formula \begin{eqnarray*} @@ -4693,19 +4930,22 @@ r_{c}\left(q\right) & & 0\leq q<\frac{1}{2}\\ 2\pi-r_{c}\left(1-q\right) & & \frac{1}{2}\leq q\leq1\end{array}\right.\end{eqnarray*} -\end_inset +\end_inset \begin_inset Formula \[ \] -\end_inset +\end_inset \begin_inset Formula \[ h\left[X\right]=\log\left(2\pi\left(1-c^{2}\right)\right).\] -\end_inset +\end_inset -\the_end +\end_layout + +\end_body +\end_document Modified: branches/refactor_fft/scipy/stats/distributions.py =================================================================== --- branches/refactor_fft/scipy/stats/distributions.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/distributions.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -21,6 +21,7 @@ from numpy import flatnonzero as nonzero from scipy.special import gammaln as gamln from copy import copy +import vonmises_cython __all__ = [ 'rv_continuous', @@ -478,6 +479,8 @@ goodargs = argsreduce(cond, *((x,)+args+(scale,))) scale, goodargs = goodargs[-1], goodargs[:-1] place(output,cond,self._pdf(*goodargs) / scale) + if output.ndim == 0: + return output[()] return output def cdf(self,x,*args,**kwds): @@ -507,6 +510,8 @@ place(output,cond2,1.0) goodargs = argsreduce(cond, *((x,)+args)) place(output,cond,self._cdf(*goodargs)) + if output.ndim == 0: + return output[()] return output def sf(self,x,*args,**kwds): @@ -536,6 +541,8 @@ place(output,cond2,1.0) goodargs = argsreduce(cond, *((x,)+args)) place(output,cond,self._sf(*goodargs)) + if output.ndim == 0: + return output[()] return output def ppf(self,q,*args,**kwds): @@ -565,6 +572,8 @@ goodargs = argsreduce(cond, *((q,)+args+(scale,loc))) scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2] place(output,cond,self._ppf(*goodargs)*scale + loc) + if output.ndim == 0: + return output[()] return output def isf(self,q,*args,**kwds): @@ -594,6 +603,8 @@ goodargs = argsreduce(cond, *((1.0-q,)+args+(scale,loc))) scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2] place(output,cond,self._ppf(*goodargs)*scale + loc) + if output.ndim == 0: + return output[()] return output def stats(self,*args,**kwds): @@ -768,7 +779,7 @@ return optimize.fmin(self.nnlf,x0,args=(ravel(data),),disp=0) def est_loc_scale(self, data, *args): - mu, mu2, g1, g2 = self.stats(*args,**{'moments':'mv'}) + mu, mu2 = self.stats(*args,**{'moments':'mv'}) muhat = st.nanmean(data) mu2hat = st.nanstd(data) Shat = sqrt(mu2hat / mu2) @@ -2270,7 +2281,6 @@ ## Log-Laplace (Log Double Exponential) ## - class loglaplace_gen(rv_continuous): def _pdf(self, x, c): cd2 = c/2.0 @@ -2326,6 +2336,10 @@ lognorm.pdf(x,s) = 1/(s*x*sqrt(2*pi)) * exp(-1/2*(log(x)/s)**2) for x > 0, s > 0. + +If log x is normally distributed with mean mu and variance sigma**2, +then x is log-normally distributed with shape paramter sigma and scale +parameter exp(mu). """ ) @@ -3066,48 +3080,14 @@ eps = numpy.finfo(float).eps + class vonmises_gen(rv_continuous): def _rvs(self, b): return mtrand.vonmises(0.0, b, size=self._size) def _pdf(self, x, b): - x = arr(angle(exp(1j*x))) - Px = where(b < 100, exp(b*cos(x)) / (2*pi*special.i0(b)), - norm.pdf(x, 0.0, sqrt(1.0/b))) - return Px + return exp(b*cos(x)) / (2*pi*special.i0(b)) def _cdf(self, x, b): - x = arr(angle(exp(1j*x))) - eps2 = sqrt(eps) - - c_xsimple = atleast_1d((b==0)&(x==x)) - c_xiter = atleast_1d((b<100)&(b > 0)&(x==x)) - c_xnormal = atleast_1d((b>=100)&(x==x)) - c_bad = atleast_1d((b<=0) | (x != x)) - - indxiter = nonzero(c_xiter) - xiter = take(x, indxiter, 0) - - vals = ones(len(c_xsimple),float) - putmask(vals, c_bad, nan) - putmask(vals, c_xsimple, x / 2.0/pi) - st = sqrt(b-0.5) - st = where(isnan(st),0.0,st) - putmask(vals, c_xnormal, norm.cdf(x, scale=st)) - - biter = take(atleast_1d(b)*(x==x), indxiter, 0) - if len(xiter) > 0: - fac = special.i0(biter) - x2 = xiter - val = x2 / 2.0/ pi - for j in range(1,501): - trm1 = special.iv(j,biter)/j/fac - trm2 = sin(j*x2)/pi - val += trm1*trm2 - if all(trm1 < eps2): - break - if (j == 500): - print "Warning: did not converge..." - put(vals, indxiter, val) - return vals + 0.5 + return vonmises_cython.von_mises_cdf(b,x) def _stats(self, b): return 0, None, 0, None vonmises = vonmises_gen(name='vonmises', longname="A Von Mises", @@ -3536,6 +3516,8 @@ place(output,(1-cond0)*(cond1==cond1),self.badvalue) goodargs = argsreduce(cond, *((k,)+args)) place(output,cond,self._pmf(*goodargs)) + if output.ndim == 0: + return output[()] return output def cdf(self, k, *args, **kwds): @@ -3564,6 +3546,8 @@ place(output,cond2*(cond0==cond0), 1.0) goodargs = argsreduce(cond, *((k,)+args)) place(output,cond,self._cdf(*goodargs)) + if output.ndim == 0: + return output[()] return output def sf(self,k,*args,**kwds): @@ -3592,6 +3576,8 @@ place(output,cond2,1.0) goodargs = argsreduce(cond, *((k,)+args)) place(output,cond,self._sf(*goodargs)) + if output.ndim == 0: + return output[()] return output def ppf(self,q,*args,**kwds): @@ -3620,6 +3606,8 @@ goodargs = argsreduce(cond, *((q,)+args+(loc,))) loc, goodargs = goodargs[-1], goodargs[:-1] place(output,cond,self._ppf(*goodargs) + loc) + if output.ndim == 0: + return output[()] return output def isf(self,q,*args,**kwds): @@ -3649,6 +3637,8 @@ goodargs = argsreduce(cond, *((q,)+args+(loc,))) loc, goodargs = goodargs[-1], goodargs[:-1] place(output,cond,self._ppf(*goodargs) + loc) + if output.ndim == 0: + return output[()] return output def stats(self, *args, **kwds): Modified: branches/refactor_fft/scipy/stats/mmorestats.py =================================================================== --- branches/refactor_fft/scipy/stats/mmorestats.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/mmorestats.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -18,20 +18,16 @@ 'trimmed_mean_ci',] import numpy as np -from numpy import bool_, float_, int_, ndarray, array as narray +from numpy import float_, int_, ndarray import numpy.ma as ma -from numpy.ma import masked, nomask, MaskedArray -#from numpy.ma.extras import apply_along_axis, dot, median +from numpy.ma import MaskedArray import scipy.stats.mstats as mstats -#from numpy.ma.mstats import trim_both, trimmed_stde, mquantiles, stde_median from scipy.stats.distributions import norm, beta, t, binom -from scipy.stats.morestats import find_repeats - #####-------------------------------------------------------------------------- #---- --- Quantiles --- #####-------------------------------------------------------------------------- Modified: branches/refactor_fft/scipy/stats/morestats.py =================================================================== --- branches/refactor_fft/scipy/stats/morestats.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/morestats.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -5,13 +5,12 @@ import statlib import stats import distributions -import inspect from numpy import isscalar, r_, log, sum, around, unique, asarray from numpy import zeros, arange, sort, amin, amax, any, where, \ - array, atleast_1d, sqrt, ceil, floor, array, poly1d, compress, not_equal, \ + atleast_1d, sqrt, ceil, floor, array, poly1d, compress, not_equal, \ pi, exp, ravel, angle import scipy -import numpy +import numpy as np import types import scipy.optimize as optimize import scipy.special as special @@ -785,7 +784,7 @@ Returns pval -- Probability that null test is rejected for this set of x and n even though it is true. """ - x = atleast_1d(x).astype(numpy.integer) + x = atleast_1d(x).astype(np.integer) if len(x) == 2: n = x[1]+x[0] x = x[0] @@ -793,7 +792,7 @@ x = x[0] if n is None or n < x: raise ValueError, "n must be >= x" - n = numpy.int_(n) + n = np.int_(n) else: raise ValueError, "Incorrect length for x." Modified: branches/refactor_fft/scipy/stats/mstats.py =================================================================== --- branches/refactor_fft/scipy/stats/mstats.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/mstats.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -256,15 +256,24 @@ def _mode1D(a): (rep,cnt) = find_repeats(a) if not cnt.ndim: - return (0,0) + return (0, 0) elif cnt.size: return (rep[cnt.argmax()], cnt.max()) return (a[0], 1) # if axis is None: output = _mode1D(ma.ravel(a)) + output = (ma.array(output[0]), ma.array(output[1])) else: output = ma.apply_along_axis(_mode1D, axis, a) + newshape = list(a.shape) + newshape[axis] = 1 + slices = [slice(None)] * output.ndim + slices[axis] = 0 + modes = output[tuple(slices)].reshape(newshape) + slices[axis] = 1 + counts = output[tuple(slices)].reshape(newshape) + output = (modes, counts) return output mode.__doc__ = stats.mode.__doc__ Modified: branches/refactor_fft/scipy/stats/setup.py =================================================================== --- branches/refactor_fft/scipy/stats/setup.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/setup.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -2,6 +2,7 @@ from os.path import join + def configuration(parent_package='',top_path=None): from numpy.distutils.misc_util import Configuration config = Configuration('stats', parent_package, top_path) @@ -18,6 +19,11 @@ libraries=['statlib'], ) + # add vonmises_cython module + config.add_extension('vonmises_cython', + sources=['vonmises_cython.c'], # FIXME: use cython source + ) + # add futil module config.add_extension('futil', sources=['futil.f'], Modified: branches/refactor_fft/scipy/stats/stats.py =================================================================== --- branches/refactor_fft/scipy/stats/stats.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/stats.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -404,6 +404,13 @@ all values in the array if axis=None. The return value will have a floating point dtype even if the input data are integers. """ + warnings.warn("""\ +scipy.stats.mean is deprecated; please update your code to use numpy.mean. +Please note that: + - numpy.mean axis argument defaults to None, not 0 + - numpy.mean has a ddof argument to replace bias in a more general manner. + scipy.stats.mean(a, bias=True) can be replaced by numpy.mean(x, +axis=0, ddof=1).""", DeprecationWarning) a, axis = _chk_asarray(a, axis) return a.mean(axis) @@ -475,6 +482,13 @@ The median of each remaining axis, or of all of the values in the array if axis is None. """ + warnings.warn("""\ +scipy.stats.median is deprecated; please update your code to use numpy.median. +Please note that: + - numpy.median axis argument defaults to None, not 0 + - numpy.median has a ddof argument to replace bias in a more general manner. + scipy.stats.median(a, bias=True) can be replaced by numpy.median(x, +axis=0, ddof=1).""", DeprecationWarning) a, axis = _chk_asarray(a, axis) if axis != 0: a = np.rollaxis(a, axis, 0) @@ -1185,13 +1199,19 @@ sd = samplestd(instack,axis) return np.where(sd == 0, 0, m/sd) - def var(a, axis=0, bias=False): """ Returns the estimated population variance of the values in the passed array (i.e., N-1). Axis can equal None (ravel array first), or an integer (the axis over which to operate). """ + warnings.warn("""\ +scipy.stats.var is deprecated; please update your code to use numpy.var. +Please note that: + - numpy.var axis argument defaults to None, not 0 + - numpy.var has a ddof argument to replace bias in a more general manner. + scipy.stats.var(a, bias=True) can be replaced by numpy.var(x, +axis=0, ddof=1).""", DeprecationWarning) a, axis = _chk_asarray(a, axis) mn = np.expand_dims(mean(a,axis),axis) deviations = a - mn @@ -1208,6 +1228,13 @@ the passed array (i.e., N-1). Axis can equal None (ravel array first), or an integer (the axis over which to operate). """ + warnings.warn("""\ +scipy.stats.std is deprecated; please update your code to use numpy.std. +Please note that: + - numpy.std axis argument defaults to None, not 0 + - numpy.std has a ddof argument to replace bias in a more general manner. + scipy.stats.std(a, bias=True) can be replaced by numpy.std(x, +axis=0, ddof=1).""", DeprecationWarning) return np.sqrt(var(a,axis,bias)) @@ -1360,6 +1387,12 @@ If rowvar is False, then each row is a variable with observations in the columns. """ + warnings.warn("""\ +scipy.stats.cov is deprecated; please update your code to use numpy.cov. +Please note that: + - numpy.cov rowvar argument defaults to true, not false + - numpy.cov bias argument defaults to false, not true +""", DeprecationWarning) m = asarray(m) if y is None: y = m @@ -1390,6 +1423,12 @@ If rowvar is True, then each row is a variables with observations in the columns. """ + warnings.warn("""\ +scipy.stats.corrcoef is deprecated; please update your code to use numpy.corrcoef. +Please note that: + - numpy.corrcoef rowvar argument defaults to true, not false + - numpy.corrcoef bias argument defaults to false, not true +""", DeprecationWarning) if y is not None: x = np.transpose([x,y]) y = None Modified: branches/refactor_fft/scipy/stats/tests/test_distributions.py =================================================================== --- branches/refactor_fft/scipy/stats/tests/test_distributions.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/tests/test_distributions.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -33,7 +33,8 @@ 'halflogistic','fatiguelife','foldnorm','ncx2','t','nct', 'weibull_min','weibull_max','dweibull','maxwell','rayleigh', 'genlogistic', 'logistic','gumbel_l','gumbel_r','gompertz', - 'hypsecant', 'laplace', 'reciprocal','triang','tukeylambda'] + 'hypsecant', 'laplace', 'reciprocal','triang','tukeylambda', + 'vonmises'] # check function for test generator def check_distribution(dist, args, alpha): @@ -63,6 +64,9 @@ vals = rand(nargs) vals[1] = vals[0] + 1.0 args = tuple(vals) + elif dist == 'vonmises': + yield check_distribution, dist, (100,), alpha + args = tuple(1.0+rand(nargs)) else: args = tuple(1.0+rand(nargs)) yield check_distribution, dist, args, alpha Modified: branches/refactor_fft/scipy/stats/tests/test_mmorestats.py =================================================================== --- branches/refactor_fft/scipy/stats/tests/test_mmorestats.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/tests/test_mmorestats.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -9,7 +9,6 @@ import numpy as np import numpy.ma as ma -from numpy.ma import masked import scipy.stats.mstats as ms import scipy.stats.mmorestats as mms Modified: branches/refactor_fft/scipy/stats/tests/test_mstats.py =================================================================== --- branches/refactor_fft/scipy/stats/tests/test_mstats.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/tests/test_mstats.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -348,10 +348,10 @@ assert_equal(mstats.mode(ma1, axis=None), (0,3)) assert_equal(mstats.mode(a2, axis=None), (3,4)) assert_equal(mstats.mode(ma2, axis=None), (0,3)) - assert_equal(mstats.mode(a2, axis=0), [[0,0,0,1,1],[1,1,1,1,1]]) - assert_equal(mstats.mode(ma2, axis=0), [[0,0,0,1,1],[1,1,1,1,1]]) - assert_equal(mstats.mode(a2, axis=-1), [[0,3],[3,3],[3,1]]) - assert_equal(mstats.mode(ma2, axis=-1), [[0,3],[1,1],[0,0]]) + assert_equal(mstats.mode(a2, axis=0), ([[0,0,0,1,1]],[[1,1,1,1,1]])) + assert_equal(mstats.mode(ma2, axis=0), ([[0,0,0,1,1]],[[1,1,1,1,1]])) + assert_equal(mstats.mode(a2, axis=-1), ([[0],[3],[3]], [[3],[3],[1]])) + assert_equal(mstats.mode(ma2, axis=-1), ([[0],[1],[0]], [[3],[1],[0]])) class TestPercentile(TestCase): Modified: branches/refactor_fft/scipy/stats/tests/test_stats.py =================================================================== --- branches/refactor_fft/scipy/stats/tests/test_stats.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stats/tests/test_stats.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -6,10 +6,9 @@ """ -import sys from numpy.testing import * from numpy import array, arange, zeros, ravel, float32, float64, power -import numpy +import numpy as np import scipy.stats as stats @@ -88,12 +87,12 @@ def test_rounding2(self): """ W.II.A.2. Y = 2-INT(EXP(LOG(SQR(2)*SQR(2)))) (Y should be 0)""" - y=2-int(numpy.exp(numpy.log(numpy.sqrt(2.)*numpy.sqrt(2.)))) + y=2-int(np.exp(np.log(np.sqrt(2.)*np.sqrt(2.)))) assert_equal(y,0) def test_rounding3(self): """ W.II.A.3. Y = INT(3-EXP(LOG(SQR(2)*SQR(2)))) (Y should be 1)""" - y=(int(round((3-numpy.exp(numpy.log(numpy.sqrt(2.0)*numpy.sqrt(2.0))))))) + y=(int(round((3-np.exp(np.log(np.sqrt(2.0)*np.sqrt(2.0))))))) assert_equal(y,1) class TestBasicStats(TestCase): @@ -144,6 +143,7 @@ def test_meanBIG(self): y = stats.mean(BIG) + assert_almost_equal(y, 99999995.00) def test_stdBIG(self): @@ -188,11 +188,11 @@ self.X = X.copy() self.Xall = X.copy() - self.Xall[:] = numpy.nan + self.Xall[:] = np.nan self.Xsome = X.copy() self.Xsomet = X.copy() - self.Xsome[0] = numpy.nan + self.Xsome[0] = np.nan self.Xsomet = self.Xsomet[1:] def test_nanmean_none(self): @@ -208,7 +208,7 @@ def test_nanmean_all(self): """Check nanmean when all values are nan.""" m = stats.nanmean(self.Xall) - assert numpy.isnan(m) + assert np.isnan(m) def test_nanstd_none(self): """Check nanstd when no values are nan.""" @@ -223,7 +223,7 @@ def test_nanstd_all(self): """Check nanstd when all values are nan.""" s = stats.nanstd(self.Xall) - assert numpy.isnan(s) + assert np.isnan(s) def test_nanmedian_none(self): """Check nanmedian when no values are nan.""" @@ -238,7 +238,7 @@ def test_nanmedian_all(self): """Check nanmedian when all values are nan.""" m = stats.nanmedian(self.Xall) - assert numpy.isnan(m) + assert np.isnan(m) class TestCorr(TestCase): """ W.II.D. Compute a correlation matrix on all the variables. @@ -484,9 +484,9 @@ def test_regress_simple(self): """Regress a line with sinusoidal noise.""" - x = numpy.linspace(0, 100, 100) - y = 0.2 * numpy.linspace(0, 100, 100) + 10 - y += numpy.sin(numpy.linspace(0, 20, 100)) + x = np.linspace(0, 100, 100) + y = 0.2 * np.linspace(0, 100, 100) + 10 + y += np.sin(np.linspace(0, 20, 100)) res = stats.linregress(x, y) assert_almost_equal(res[4], 4.3609875083149268e-3) Copied: branches/refactor_fft/scipy/stats/vonmises.py (from rev 4827, trunk/scipy/stats/vonmises.py) Copied: branches/refactor_fft/scipy/stats/vonmises_cython.c (from rev 4827, trunk/scipy/stats/vonmises_cython.c) Copied: branches/refactor_fft/scipy/stats/vonmises_cython.pyx (from rev 4827, trunk/scipy/stats/vonmises_cython.pyx) Modified: branches/refactor_fft/scipy/stsci/convolve/lib/Convolve.py =================================================================== --- branches/refactor_fft/scipy/stsci/convolve/lib/Convolve.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stsci/convolve/lib/Convolve.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,4 +1,4 @@ -import numpy as num +import numpy as np import _correlate import numpy.fft as dft import iraf_frame @@ -16,12 +16,12 @@ } def _condition_inputs(data, kernel): - data, kernel = num.asarray(data), num.asarray(kernel) - if num.rank(data) == 0: + data, kernel = np.asarray(data), np.asarray(kernel) + if np.rank(data) == 0: data.shape = (1,) - if num.rank(kernel) == 0: + if np.rank(kernel) == 0: kernel.shape = (1,) - if num.rank(data) > 1 or num.rank(kernel) > 1: + if np.rank(data) > 1 or np.rank(kernel) > 1: raise ValueError("arrays must be 1D") if len(data) < len(kernel): data, kernel = kernel, data @@ -30,25 +30,25 @@ def correlate(data, kernel, mode=FULL): """correlate(data, kernel, mode=FULL) - >>> correlate(num.arange(8), [1, 2], mode=VALID) + >>> correlate(np.arange(8), [1, 2], mode=VALID) array([ 2, 5, 8, 11, 14, 17, 20]) - >>> correlate(num.arange(8), [1, 2], mode=SAME) + >>> correlate(np.arange(8), [1, 2], mode=SAME) array([ 0, 2, 5, 8, 11, 14, 17, 20]) - >>> correlate(num.arange(8), [1, 2], mode=FULL) + >>> correlate(np.arange(8), [1, 2], mode=FULL) array([ 0, 2, 5, 8, 11, 14, 17, 20, 7]) - >>> correlate(num.arange(8), [1, 2, 3], mode=VALID) + >>> correlate(np.arange(8), [1, 2, 3], mode=VALID) array([ 8, 14, 20, 26, 32, 38]) - >>> correlate(num.arange(8), [1, 2, 3], mode=SAME) + >>> correlate(np.arange(8), [1, 2, 3], mode=SAME) array([ 3, 8, 14, 20, 26, 32, 38, 20]) - >>> correlate(num.arange(8), [1, 2, 3], mode=FULL) + >>> correlate(np.arange(8), [1, 2, 3], mode=FULL) array([ 0, 3, 8, 14, 20, 26, 32, 38, 20, 7]) - >>> correlate(num.arange(8), [1, 2, 3, 4, 5, 6], mode=VALID) + >>> correlate(np.arange(8), [1, 2, 3, 4, 5, 6], mode=VALID) array([ 70, 91, 112]) - >>> correlate(num.arange(8), [1, 2, 3, 4, 5, 6], mode=SAME) + >>> correlate(np.arange(8), [1, 2, 3, 4, 5, 6], mode=SAME) array([ 17, 32, 50, 70, 91, 112, 85, 60]) - >>> correlate(num.arange(8), [1, 2, 3, 4, 5, 6], mode=FULL) + >>> correlate(np.arange(8), [1, 2, 3, 4, 5, 6], mode=FULL) array([ 0, 6, 17, 32, 50, 70, 91, 112, 85, 60, 38, 20, 7]) - >>> correlate(num.arange(8), 1+1j) + >>> correlate(np.arange(8), 1+1j) Traceback (most recent call last): ... TypeError: array cannot be safely cast to required type @@ -66,17 +66,17 @@ result_type = max(kernel.dtype.name, data.dtype.name) if mode == VALID: - wdata = num.concatenate((kdata, data, kdata)) + wdata = np.concatenate((kdata, data, kdata)) result = wdata.astype(result_type) _correlate.Correlate1d(kernel, wdata, result) return result[lenk+halfk:-lenk-halfk+even] elif mode == SAME: - wdata = num.concatenate((kdata, data, kdata)) + wdata = np.concatenate((kdata, data, kdata)) result = wdata.astype(result_type) _correlate.Correlate1d(kernel, wdata, result) return result[lenk:-lenk] elif mode == FULL: - wdata = num.concatenate((kdata, data, kdata)) + wdata = np.concatenate((kdata, data, kdata)) result = wdata.astype(result_type) _correlate.Correlate1d(kernel, wdata, result) return result[halfk+1:-halfk-1+even] @@ -102,25 +102,25 @@ sequences a and v; mode can be 0 (VALID), 1 (SAME), or 2 (FULL) to specify size of the resulting sequence. - >>> convolve(num.arange(8), [1, 2], mode=VALID) + >>> convolve(np.arange(8), [1, 2], mode=VALID) array([ 1, 4, 7, 10, 13, 16, 19]) - >>> convolve(num.arange(8), [1, 2], mode=SAME) + >>> convolve(np.arange(8), [1, 2], mode=SAME) array([ 0, 1, 4, 7, 10, 13, 16, 19]) - >>> convolve(num.arange(8), [1, 2], mode=FULL) + >>> convolve(np.arange(8), [1, 2], mode=FULL) array([ 0, 1, 4, 7, 10, 13, 16, 19, 14]) - >>> convolve(num.arange(8), [1, 2, 3], mode=VALID) + >>> convolve(np.arange(8), [1, 2, 3], mode=VALID) array([ 4, 10, 16, 22, 28, 34]) - >>> convolve(num.arange(8), [1, 2, 3], mode=SAME) + >>> convolve(np.arange(8), [1, 2, 3], mode=SAME) array([ 1, 4, 10, 16, 22, 28, 34, 32]) - >>> convolve(num.arange(8), [1, 2, 3], mode=FULL) + >>> convolve(np.arange(8), [1, 2, 3], mode=FULL) array([ 0, 1, 4, 10, 16, 22, 28, 34, 32, 21]) - >>> convolve(num.arange(8), [1, 2, 3, 4, 5, 6], mode=VALID) + >>> convolve(np.arange(8), [1, 2, 3, 4, 5, 6], mode=VALID) array([35, 56, 77]) - >>> convolve(num.arange(8), [1, 2, 3, 4, 5, 6], mode=SAME) + >>> convolve(np.arange(8), [1, 2, 3, 4, 5, 6], mode=SAME) array([ 4, 10, 20, 35, 56, 77, 90, 94]) - >>> convolve(num.arange(8), [1, 2, 3, 4, 5, 6], mode=FULL) + >>> convolve(np.arange(8), [1, 2, 3, 4, 5, 6], mode=FULL) array([ 0, 1, 4, 10, 20, 35, 56, 77, 90, 94, 88, 71, 42]) - >>> convolve([1.,2.], num.arange(10.)) + >>> convolve([1.,2.], np.arange(10.)) array([ 0., 1., 4., 7., 10., 13., 16., 19., 22., 25., 18.]) """ data, kernel = _condition_inputs(data, kernel) @@ -131,15 +131,15 @@ def _gaussian(sigma, mew, npoints, sigmas): - ox = num.arange(mew-sigmas*sigma, - mew+sigmas*sigma, - 2*sigmas*sigma/npoints, type=num.float64) + ox = np.arange(mew-sigmas*sigma, + mew+sigmas*sigma, + 2*sigmas*sigma/npoints, type=np.float64) x = ox-mew x /= sigma x = x * x x *= -1/2 - x = num.exp(x) - return ox, 1/(sigma * num.sqrt(2*num.pi)) * x + x = np.exp(x) + return ox, 1/(sigma * np.sqrt(2*np.pi)) * x def _correlate2d_fft(data0, kernel0, output=None, mode="nearest", cval=0.0): """_correlate2d_fft does 2d correlation of 'data' with 'kernel', storing @@ -153,17 +153,17 @@ """ shape = data0.shape kshape = kernel0.shape - oversized = (num.array(shape) + num.array(kshape)) + oversized = (np.array(shape) + np.array(kshape)) dy = kshape[0] // 2 dx = kshape[1] // 2 - kernel = num.zeros(oversized, dtype=num.float64) + kernel = np.zeros(oversized, dtype=np.float64) kernel[:kshape[0], :kshape[1]] = kernel0[::-1,::-1] # convolution <-> correlation data = iraf_frame.frame(data0, oversized, mode=mode, cval=cval) - complex_result = (isinstance(data, num.complexfloating) or - isinstance(kernel, num.complexfloating)) + complex_result = (isinstance(data, np.complexfloating) or + isinstance(kernel, np.complexfloating)) Fdata = dft.fft2(data) del data @@ -171,7 +171,7 @@ Fkernel = dft.fft2(kernel) del kernel - num.multiply(Fdata, Fkernel, Fdata) + np.multiply(Fdata, Fkernel, Fdata) del Fkernel if complex_result: @@ -196,14 +196,14 @@ commutative, _fix_data_kernel reverses kernel and data if necessary and panics if there's no good order. """ - data, kernel = map(num.asarray, [data, kernel]) - if num.rank(data) == 0: + data, kernel = map(np.asarray, [data, kernel]) + if np.rank(data) == 0: data.shape = (1,1) - elif num.rank(data) == 1: + elif np.rank(data) == 1: data.shape = (1,) + data.shape - if num.rank(kernel) == 0: + if np.rank(kernel) == 0: kernel.shape = (1,1) - elif num.rank(kernel) == 1: + elif np.rank(kernel) == 1: kernel.shape = (1,) + kernel.shape if (kernel.shape[0] > data.shape[0] and kernel.shape[1] > data.shape[1]): @@ -226,12 +226,12 @@ If fft is True, the correlation is performed using the FFT, else the correlation is performed using the naive approach. - >>> a = num.arange(20*20) + >>> a = np.arange(20*20) >>> a = a.reshape((20,20)) - >>> b = num.ones((5,5), dtype=num.float64) + >>> b = np.ones((5,5), dtype=np.float64) >>> rn = correlate2d(a, b, fft=0) >>> rf = correlate2d(a, b, fft=1) - >>> num.alltrue(num.ravel(rn-rf<1e-10)) + >>> np.alltrue(np.ravel(rn-rf<1e-10)) True """ data, kernel = _fix_data_kernel(data, kernel) @@ -252,12 +252,12 @@ 'reflect' elements beyond boundary come from reflection on same array edge. 'constant' elements beyond boundary are set to 'cval' - >>> a = num.arange(20*20) + >>> a = np.arange(20*20) >>> a = a.reshape((20,20)) - >>> b = num.ones((5,5), dtype=num.float64) + >>> b = np.ones((5,5), dtype=np.float64) >>> rn = convolve2d(a, b, fft=0) >>> rf = convolve2d(a, b, fft=1) - >>> num.alltrue(num.ravel(rn-rf<1e-10)) + >>> np.alltrue(np.ravel(rn-rf<1e-10)) True """ data, kernel = _fix_data_kernel(data, kernel) @@ -269,8 +269,8 @@ def _boxcar(data, output, boxshape, mode, cval): if len(boxshape) == 1: - _correlate.Boxcar2d(data[num.newaxis,...], 1, boxshape[0], - output[num.newaxis,...], mode, cval) + _correlate.Boxcar2d(data[np.newaxis,...], 1, boxshape[0], + output[np.newaxis,...], mode, cval) elif len(boxshape) == 2: _correlate.Boxcar2d(data, boxshape[0], boxshape[1], output, mode, cval) else: @@ -290,19 +290,19 @@ 'reflect' elements beyond boundary come from reflection on same array edge. 'constant' elements beyond boundary are set to 'cval' - >>> boxcar(num.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="nearest").astype(num.longlong) + >>> boxcar(np.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="nearest").astype(np.longlong) array([ 6, 3, 0, 0, 0, 333, 666], dtype=int64) - >>> boxcar(num.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="wrap").astype(num.longlong) + >>> boxcar(np.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="wrap").astype(np.longlong) array([336, 3, 0, 0, 0, 333, 336], dtype=int64) - >>> boxcar(num.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="reflect").astype(num.longlong) + >>> boxcar(np.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="reflect").astype(np.longlong) array([ 6, 3, 0, 0, 0, 333, 666], dtype=int64) - >>> boxcar(num.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="constant").astype(num.longlong) + >>> boxcar(np.array([10, 0, 0, 0, 0, 0, 1000]), (3,), mode="constant").astype(np.longlong) array([ 3, 3, 0, 0, 0, 333, 333], dtype=int64) - >>> a = num.zeros((10,10)) + >>> a = np.zeros((10,10)) >>> a[0,0] = 100 >>> a[5,5] = 1000 >>> a[9,9] = 10000 - >>> boxcar(a, (3,3)).astype(num.longlong) + >>> boxcar(a, (3,3)).astype(np.longlong) array([[ 44, 22, 0, 0, 0, 0, 0, 0, 0, 0], [ 22, 11, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], @@ -313,7 +313,7 @@ [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 1111, 2222], [ 0, 0, 0, 0, 0, 0, 0, 0, 2222, 4444]], dtype=int64) - >>> boxcar(a, (3,3), mode="wrap").astype(num.longlong) + >>> boxcar(a, (3,3), mode="wrap").astype(np.longlong) array([[1122, 11, 0, 0, 0, 0, 0, 0, 1111, 1122], [ 11, 11, 0, 0, 0, 0, 0, 0, 0, 11], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], @@ -324,7 +324,7 @@ [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1111, 0, 0, 0, 0, 0, 0, 0, 1111, 1111], [1122, 11, 0, 0, 0, 0, 0, 0, 1111, 1122]], dtype=int64) - >>> boxcar(a, (3,3), mode="reflect").astype(num.longlong) + >>> boxcar(a, (3,3), mode="reflect").astype(np.longlong) array([[ 44, 22, 0, 0, 0, 0, 0, 0, 0, 0], [ 22, 11, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], @@ -335,7 +335,7 @@ [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 1111, 2222], [ 0, 0, 0, 0, 0, 0, 0, 0, 2222, 4444]], dtype=int64) - >>> boxcar(a, (3,3), mode="constant").astype(num.longlong) + >>> boxcar(a, (3,3), mode="constant").astype(np.longlong) array([[ 11, 11, 0, 0, 0, 0, 0, 0, 0, 0], [ 11, 11, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], @@ -347,9 +347,9 @@ [ 0, 0, 0, 0, 0, 0, 0, 0, 1111, 1111], [ 0, 0, 0, 0, 0, 0, 0, 0, 1111, 1111]], dtype=int64) - >>> a = num.zeros((10,10)) + >>> a = np.zeros((10,10)) >>> a[3:6,3:6] = 111 - >>> boxcar(a, (3,3)).astype(num.longlong) + >>> boxcar(a, (3,3)).astype(np.longlong) array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 12, 24, 37, 24, 12, 0, 0, 0], @@ -363,11 +363,11 @@ """ mode = pix_modes[ mode ] if output is None: - woutput = data.astype(num.float64) + woutput = data.astype(np.float64) else: woutput = output _fbroadcast(_boxcar, len(boxshape), data.shape, - (data, woutput), (boxshape, mode, cval)) + (data, woutput), (boxshape, mode, cval)) if output is None: return woutput Modified: branches/refactor_fft/scipy/stsci/convolve/lib/iraf_frame.py =================================================================== --- branches/refactor_fft/scipy/stsci/convolve/lib/iraf_frame.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stsci/convolve/lib/iraf_frame.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,4 +1,4 @@ -import numpy as num +import numpy as np """This module defines the function frame() which creates a framed copy of an input array with the boundary pixels @@ -12,7 +12,7 @@ and the contents of 'a' in the center. The boundary pixels are copied from the nearest edge pixel in 'a'. - >>> a = num.arange(16) + >>> a = np.arange(16) >>> a.shape=(4,4) >>> frame_nearest(a, (8,8)) array([[ 0, 0, 0, 1, 2, 3, 3, 3], @@ -26,8 +26,8 @@ """ - b = num.zeros(shape, dtype=a.dtype) - delta = (num.array(b.shape) - num.array(a.shape)) + b = np.zeros(shape, dtype=a.dtype) + delta = (np.array(b.shape) - np.array(a.shape)) dy = delta[0] // 2 dx = delta[1] // 2 my = a.shape[0] + dy @@ -51,7 +51,7 @@ and the contents of 'a' in the center. The boundary pixels are reflected from the nearest edge pixels in 'a'. - >>> a = num.arange(16) + >>> a = np.arange(16) >>> a.shape = (4,4) >>> frame_reflect(a, (8,8)) array([[ 5, 4, 4, 5, 6, 7, 7, 6], @@ -64,8 +64,8 @@ [ 9, 8, 8, 9, 10, 11, 11, 10]]) """ - b = num.zeros(shape, dtype=a.dtype) - delta = (num.array(b.shape) - num.array(a.shape)) + b = np.zeros(shape, dtype=a.dtype) + delta = (np.array(b.shape) - np.array(a.shape)) dy = delta[0] // 2 dx = delta[1] // 2 my = a.shape[0] + dy @@ -89,7 +89,7 @@ and the contents of 'a' in the center. The boundary pixels are wrapped around to the opposite edge pixels in 'a'. - >>> a = num.arange(16) + >>> a = np.arange(16) >>> a.shape=(4,4) >>> frame_wrap(a, (8,8)) array([[10, 11, 8, 9, 10, 11, 8, 9], @@ -103,8 +103,8 @@ """ - b = num.zeros(shape, dtype=a.dtype) - delta = (num.array(b.shape) - num.array(a.shape)) + b = np.zeros(shape, dtype=a.dtype) + delta = (np.array(b.shape) - np.array(a.shape)) dy = delta[0] // 2 dx = delta[1] // 2 my = a.shape[0] + dy @@ -128,7 +128,7 @@ and the contents of 'a' in the center. The boundary pixels are copied from the nearest edge pixel in 'a'. - >>> a = num.arange(16) + >>> a = np.arange(16) >>> a.shape=(4,4) >>> frame_constant(a, (8,8), cval=42) array([[42, 42, 42, 42, 42, 42, 42, 42], @@ -142,8 +142,8 @@ """ - b = num.zeros(shape, dtype=a.dtype) - delta = (num.array(b.shape) - num.array(a.shape)) + b = np.zeros(shape, dtype=a.dtype) + delta = (np.array(b.shape) - np.array(a.shape)) dy = delta[0] // 2 dx = delta[1] // 2 my = a.shape[0] + dy @@ -183,7 +183,7 @@ """unframe extracts the center slice of framed array 'a' which had 'shape' prior to framing.""" - delta = num.array(a.shape) - num.array(shape) + delta = np.array(a.shape) - np.array(shape) dy = delta[0]//2 dx = delta[1]//2 my = shape[0] + dy Modified: branches/refactor_fft/scipy/stsci/convolve/lib/lineshape.py =================================================================== --- branches/refactor_fft/scipy/stsci/convolve/lib/lineshape.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stsci/convolve/lib/lineshape.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -40,11 +40,8 @@ __date__ = "$Date: 2007/03/14 16:35:57 $"[7:-11] __version__ = "$Revision: 1.1 $"[11:-2] - -import numpy as num from convolve._lineshape import * - class Profile(object): """An base object to provide a convolution kernel.""" Modified: branches/refactor_fft/scipy/stsci/image/lib/_image.py =================================================================== --- branches/refactor_fft/scipy/stsci/image/lib/_image.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stsci/image/lib/_image.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,7 +1,6 @@ -import numpy as num +import numpy as np import scipy.stsci.convolve import scipy.stsci.convolve._correlate as _correlate -MLab=num def _translate(a, dx, dy, output=None, mode="nearest", cval=0.0): """_translate does positive sub-pixel shifts using bilinear interpolation.""" @@ -14,10 +13,8 @@ y = (1-dx) * dy z = dx * dy - kernel = num.array([ - [ z, y ], - [ x, w ], - ]) + kernel = np.array([[ z, y ], + [ x, w ]]) return convolve.correlate2d(a, kernel, output, mode, cval) @@ -33,7 +30,7 @@ 'reflect' elements beyond boundary come from reflection on same array edge. 'constant' elements beyond boundary are set to 'cval' """ - a = num.asarray(a) + a = np.asarray(a) sdx, sdy = -sdx, -sdy # Flip sign to match IRAF sign convention @@ -51,11 +48,11 @@ rotation = 0 dx, dy = abs(sdx), abs(sdy) - b = MLab.rot90(a, rotation) + b = np.rot90(a, rotation) c = _correlate.Shift2d(b, int(dx), int(dy), mode=convolve.pix_modes[mode]) d = _translate(c, dx % 1, dy % 1, output, mode, cval) if output is not None: - output._copyFrom(MLab.rot90(output, -rotation%4)) + output._copyFrom(np.rot90(output, -rotation%4)) else: - return MLab.rot90(d, -rotation % 4).astype(a.type()) + return np.rot90(d, -rotation % 4).astype(a.type()) Modified: branches/refactor_fft/scipy/stsci/image/lib/combine.py =================================================================== --- branches/refactor_fft/scipy/stsci/image/lib/combine.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/stsci/image/lib/combine.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,10 +1,8 @@ -import numpy as num +import numpy as np from _combine import combine as _comb -import operator as _operator - def _combine_f(funcstr, arrays, output=None, outtype=None, nlow=0, nhigh=0, badmasks=None): - arrays = [ num.asarray(a) for a in arrays ] + arrays = [ np.asarray(a) for a in arrays ] shape = arrays[0].shape if output is None: if outtype is not None: @@ -44,7 +42,7 @@ indicates that a particular pixel is not to be included in the median calculation. - >>> a = num.arange(4) + >>> a = np.arange(4) >>> a = a.reshape((2,2)) >>> arrays = [a*16, a*4, a*2, a*8] >>> median(arrays) @@ -56,10 +54,10 @@ >>> median(arrays, nlow=1) array([[ 0, 8], [16, 24]]) - >>> median(arrays, outtype=num.float32) + >>> median(arrays, outtype=np.float32) array([[ 0., 6.], [ 12., 18.]], dtype=float32) - >>> bm = num.zeros((4,2,2), dtype=num.bool8) + >>> bm = np.zeros((4,2,2), dtype=np.bool8) >>> bm[2,...] = 1 >>> median(arrays, badmasks=bm) array([[ 0, 8], @@ -94,7 +92,7 @@ indicates that a particular pixel is not to be included in the average calculation. - >>> a = num.arange(4) + >>> a = np.arange(4) >>> a = a.reshape((2,2)) >>> arrays = [a*16, a*4, a*2, a*8] >>> average(arrays) @@ -106,10 +104,10 @@ >>> average(arrays, nlow=1) array([[ 0, 9], [18, 28]]) - >>> average(arrays, outtype=num.float32) + >>> average(arrays, outtype=np.float32) array([[ 0. , 7.5], [ 15. , 22.5]], dtype=float32) - >>> bm = num.zeros((4,2,2), dtype=num.bool8) + >>> bm = np.zeros((4,2,2), dtype=np.bool8) >>> bm[2,...] = 1 >>> average(arrays, badmasks=bm) array([[ 0, 9], @@ -145,7 +143,7 @@ indicates that a particular pixel is not to be included in the minimum calculation. - >>> a = num.arange(4) + >>> a = np.arange(4) >>> a = a.reshape((2,2)) >>> arrays = [a*16, a*4, a*2, a*8] >>> minimum(arrays) @@ -157,10 +155,10 @@ >>> minimum(arrays, nlow=1) array([[ 0, 4], [ 8, 12]]) - >>> minimum(arrays, outtype=num.float32) + >>> minimum(arrays, outtype=np.float32) array([[ 0., 2.], [ 4., 6.]], dtype=float32) - >>> bm = num.zeros((4,2,2), dtype=num.bool8) + >>> bm = np.zeros((4,2,2), dtype=np.bool8) >>> bm[2,...] = 1 >>> minimum(arrays, badmasks=bm) array([[ 0, 4], @@ -178,9 +176,9 @@ boolean value is true where each of the arrays values is < the low or >= the high threshholds. - >>> a=num.arange(100) + >>> a=np.arange(100) >>> a=a.reshape((10,10)) - >>> (threshhold(a, 1, 50)).astype(num.int8) + >>> (threshhold(a, 1, 50)).astype(np.int8) array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], @@ -191,7 +189,7 @@ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int8) - >>> (threshhold([ range(10)]*10, 3, 7)).astype(num.int8) + >>> (threshhold([ range(10)]*10, 3, 7)).astype(np.int8) array([[1, 1, 1, 0, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 0, 1, 1, 1], @@ -202,7 +200,7 @@ [1, 1, 1, 0, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 0, 1, 1, 1]], dtype=int8) - >>> (threshhold(a, high=50)).astype(num.int8) + >>> (threshhold(a, high=50)).astype(np.int8) array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], @@ -213,7 +211,7 @@ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int8) - >>> (threshhold(a, low=50)).astype(num.int8) + >>> (threshhold(a, low=50)).astype(np.int8) array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], @@ -227,12 +225,12 @@ """ - if not isinstance(arrays[0], num.ndarray): - return threshhold( num.asarray(arrays), low, high, outputs) + if not isinstance(arrays[0], np.ndarray): + return threshhold( np.asarray(arrays), low, high, outputs) if outputs is None: - outs = num.zeros(shape=(len(arrays),)+arrays[0].shape, - dtype=num.bool8) + outs = np.zeros(shape=(len(arrays),)+arrays[0].shape, + dtype=np.bool8) else: outs = outputs @@ -241,12 +239,12 @@ out[:] = 0 if high is not None: - num.greater_equal(a, high, out) + np.greater_equal(a, high, out) if low is not None: - num.logical_or(out, a < low, out) + np.logical_or(out, a < low, out) else: if low is not None: - num.less(a, low, out) + np.less(a, low, out) if outputs is None: return outs @@ -254,16 +252,16 @@ def _bench(): """time a 10**6 element median""" import time - a = num.arange(10**6) + a = np.arange(10**6) a = a.reshape((1000, 1000)) arrays = [a*2, a*64, a*16, a*8] t0 = time.clock() median(arrays) print "maskless:", time.clock()-t0 - a = num.arange(10**6) + a = np.arange(10**6) a = a.reshape((1000, 1000)) arrays = [a*2, a*64, a*16, a*8] t0 = time.clock() - median(arrays, badmasks=num.zeros((1000,1000), dtype=num.bool8)) + median(arrays, badmasks=np.zeros((1000,1000), dtype=np.bool8)) print "masked:", time.clock()-t0 Modified: branches/refactor_fft/scipy/weave/accelerate_tools.py =================================================================== --- branches/refactor_fft/scipy/weave/accelerate_tools.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/accelerate_tools.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -109,7 +109,7 @@ Double = Double() String = String() -import numpy as nx +import numpy as np class Vector(Type_Descriptor): cxxtype = 'PyArrayObject*' @@ -214,12 +214,12 @@ int : Integer, float : Double, str: String, - (nx.ndarray,1,int): IntegerVector, - (nx.ndarray,2,int): Integermatrix, - (nx.ndarray,1,nx.long): LongVector, - (nx.ndarray,2,nx.long): Longmatrix, - (nx.ndarray,1,float): DoubleVector, - (nx.ndarray,2,float): Doublematrix, + (np.ndarray,1,int): IntegerVector, + (np.ndarray,2,int): Integermatrix, + (np.ndarray,1,np.long): LongVector, + (np.ndarray,2,np.long): Longmatrix, + (np.ndarray,1,float): DoubleVector, + (np.ndarray,2,float): Doublematrix, XRangeType : XRange, } @@ -260,7 +260,7 @@ try: return typedefs[T] except: - if isinstance(T,nx.ndarray): + if isinstance(T,np.ndarray): return typedefs[(T,len(x.shape),x.dtype.char)] elif issubclass(T, InstanceType): return Instance(x) Modified: branches/refactor_fft/scipy/weave/examples/vq.py =================================================================== --- branches/refactor_fft/scipy/weave/examples/vq.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/examples/vq.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -14,7 +14,6 @@ # [25 29] [ 2.49147272 3.83021021] # speed up: 32.56 -import numpy from numpy import * import sys sys.path.insert(0,'..') Modified: branches/refactor_fft/scipy/weave/tests/test_blitz_tools.py =================================================================== --- branches/refactor_fft/scipy/weave/tests/test_blitz_tools.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/tests/test_blitz_tools.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,7 +1,7 @@ import os import time -from numpy import dot, float32, float64, complex64, complex128, \ +from numpy import float32, float64, complex64, complex128, \ zeros, random, array, sum, abs, allclose from numpy.testing import * Modified: branches/refactor_fft/scipy/weave/tests/test_c_spec.py =================================================================== --- branches/refactor_fft/scipy/weave/tests/test_c_spec.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/tests/test_c_spec.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,4 +1,3 @@ -import time import os import sys Modified: branches/refactor_fft/scipy/weave/tests/test_ext_tools.py =================================================================== --- branches/refactor_fft/scipy/weave/tests/test_ext_tools.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/tests/test_ext_tools.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,5 +1,3 @@ -import time - from numpy.testing import * from scipy.weave import ext_tools, c_spec Modified: branches/refactor_fft/scipy/weave/tests/test_inline_tools.py =================================================================== --- branches/refactor_fft/scipy/weave/tests/test_inline_tools.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/tests/test_inline_tools.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -2,7 +2,6 @@ from numpy.testing import * from scipy.weave import inline_tools -from test_scxx import * class TestInline(TestCase): """ These are long running tests... Modified: branches/refactor_fft/scipy/weave/tests/test_numpy_scalar_spec.py =================================================================== --- branches/refactor_fft/scipy/weave/tests/test_numpy_scalar_spec.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/tests/test_numpy_scalar_spec.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,4 +1,3 @@ -import time import os import sys Deleted: branches/refactor_fft/scipy/weave/tests/test_scxx.py =================================================================== --- branches/refactor_fft/scipy/weave/tests/test_scxx.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/tests/test_scxx.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,14 +0,0 @@ -""" Test refcounting and behavior of SCXX. -""" -import unittest -import time -import os,sys - -from numpy.testing import * -from test_scxx_object import * -from test_scxx_sequence import * -from test_scxx_dict import * - - -if __name__ == "__main__": - nose.run(argv=['', __file__]) Modified: branches/refactor_fft/scipy/weave/tests/test_scxx_dict.py =================================================================== --- branches/refactor_fft/scipy/weave/tests/test_scxx_dict.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/tests/test_scxx_dict.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,7 +1,6 @@ """ Test refcounting and behavior of SCXX. """ -import time -import os + import sys from numpy.testing import * Modified: branches/refactor_fft/scipy/weave/tests/test_scxx_object.py =================================================================== --- branches/refactor_fft/scipy/weave/tests/test_scxx_object.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/tests/test_scxx_object.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,7 +1,6 @@ """ Test refcounting and behavior of SCXX. """ -import time -import os + import sys from numpy.testing import * Modified: branches/refactor_fft/scipy/weave/tests/test_scxx_sequence.py =================================================================== --- branches/refactor_fft/scipy/weave/tests/test_scxx_sequence.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/tests/test_scxx_sequence.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -2,7 +2,7 @@ """ import time -import os,sys +import sys from numpy.testing import * @@ -18,8 +18,6 @@ # operator[] (get) # operator[] (set) DONE -from UserList import UserList - class _TestSequenceBase(TestCase): seq_type = None Modified: branches/refactor_fft/scipy/weave/tests/test_size_check.py =================================================================== --- branches/refactor_fft/scipy/weave/tests/test_size_check.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/tests/test_size_check.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,4 +1,3 @@ -import os import numpy as np from numpy.testing import * Modified: branches/refactor_fft/scipy/weave/tests/weave_test_utils.py =================================================================== --- branches/refactor_fft/scipy/weave/tests/weave_test_utils.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/scipy/weave/tests/weave_test_utils.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,7 +1,4 @@ import os -import sys -import string -import pprint def remove_whitespace(in_str): out = in_str.replace(" ","") Copied: branches/refactor_fft/tools (from rev 4827, trunk/tools) Copied: branches/refactor_fft/tools/win32 (from rev 4827, trunk/tools/win32) Copied: branches/refactor_fft/tools/win32/build_scripts (from rev 4827, trunk/tools/win32/build_scripts) Copied: branches/refactor_fft/tools/win32/build_scripts/nsis_scripts (from rev 4827, trunk/tools/win32/build_scripts/nsis_scripts) Deleted: branches/refactor_fft/tools/win32/build_scripts/nsis_scripts/scipy-superinstaller.nsi.in =================================================================== --- trunk/tools/win32/build_scripts/nsis_scripts/scipy-superinstaller.nsi.in 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/tools/win32/build_scripts/nsis_scripts/scipy-superinstaller.nsi.in 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,121 +0,0 @@ -;-------------------------------- -;Include Modern UI - -!include "MUI2.nsh" - -SetCompress off ; Useful to disable compression under development -;SetCompressor /Solid LZMA ; Useful to disable compression under development - -;-------------------------------- -;General - -;Name and file -Name "Scipy super installer" -OutFile "@SCIPY_INSTALLER_NAME@" - -;Default installation folder -InstallDir "$TEMP" - -;-------------------------------- -;Interface Settings - -!define MUI_ABORTWARNING - -;-------------------------------- -;Pages - -;!insertmacro MUI_PAGE_LICENSE "${NSISDIR}\Docs\Modern UI\License.txt" -;!insertmacro MUI_PAGE_COMPONENTS -;!insertmacro MUI_PAGE_DIRECTORY -;!insertmacro MUI_PAGE_INSTFILES - -;!insertmacro MUI_UNPAGE_CONFIRM -;!insertmacro MUI_UNPAGE_INSTFILES - -;-------------------------------- -;Languages - -!insertmacro MUI_LANGUAGE "English" - -;-------------------------------- -;Component Sections - -!include 'Sections.nsh' -!include LogicLib.nsh - -Var HasSSE2 -Var HasSSE3 -Var CPUSSE - -Section "Core" SecCore - - ;SectionIn RO - SetOutPath "$INSTDIR" - - ;Create uninstaller - ;WriteUninstaller "$INSTDIR\Uninstall.exe" - - DetailPrint "Install dir for actual installers is $INSTDIR" - - StrCpy $CPUSSE "0" - CpuCaps::hasSSE2 - Pop $0 - StrCpy $HasSSE2 $0 - - CpuCaps::hasSSE3 - Pop $0 - StrCpy $HasSSE3 $0 - - ; Debug - StrCmp $HasSSE2 "Y" include_sse2 no_include_sse2 - include_sse2: - DetailPrint '"Target CPU handles SSE2"' - StrCpy $CPUSSE "2" - goto done_sse2 - no_include_sse2: - DetailPrint '"Target CPU does NOT handle SSE2"' - goto done_sse2 - done_sse2: - - StrCmp $HasSSE3 "Y" include_sse3 no_include_sse3 - include_sse3: - DetailPrint '"Target CPU handles SSE3"' - StrCpy $CPUSSE "3" - goto done_sse3 - no_include_sse3: - DetailPrint '"Target CPU does NOT handle SSE3"' - goto done_sse3 - done_sse3: - - ClearErrors - - ; Install files conditionaly on detected cpu - ${Switch} $CPUSSE - ${Case} "3" - DetailPrint '"Install SSE 3"' - File "binaries\@SSE3_BINARY@" - ExecWait '"$INSTDIR\@SSE3_BINARY@"' - ${Break} - ${Case} "2" - DetailPrint '"Install SSE 2"' - File "binaries\@SSE2_BINARY@" - ExecWait '"$INSTDIR\@SSE2_BINARY@"' - ${Break} - ${Default} - DetailPrint '"Install NO SSE"' - File "binaries\@NOSSE_BINARY@" - ExecWait '"$INSTDIR\@NOSSE_BINARY@"' - ${Break} - ${EndSwitch} - - ; Handle errors when executing installers - IfErrors error no_error - - error: - messageBox MB_OK "Executing scipy installer failed" - goto done - no_error: - goto done - done: - -SectionEnd Copied: branches/refactor_fft/tools/win32/build_scripts/nsis_scripts/scipy-superinstaller.nsi.in (from rev 4827, trunk/tools/win32/build_scripts/nsis_scripts/scipy-superinstaller.nsi.in) Deleted: branches/refactor_fft/tools/win32/build_scripts/pavement.py =================================================================== --- trunk/tools/win32/build_scripts/pavement.py 2008-10-22 13:52:41 UTC (rev 4827) +++ branches/refactor_fft/tools/win32/build_scripts/pavement.py 2008-10-23 12:30:20 UTC (rev 4828) @@ -1,327 +0,0 @@ -import os -from os.path import join as pjoin, normpath, exists as pexists, dirname -import subprocess -from shutil import rmtree, move as shmove -import re -from zipfile import ZipFile - -BUILD_MSI = False -SRC_ROOT = normpath(pjoin(os.getcwd(), os.pardir, os.pardir, os.pardir)) -BUILD_ROOT = os.getcwd() - -PYVER = '2.5' -ARCH = 'nosse' - -PYEXECS = {"2.5" : "C:\python25\python.exe", - "2.4" : "C:\python24\python24.exe", - "2.3" : "C:\python23\python23.exe"} - -_SSE3_CFG = r"""[atlas] -library_dirs = C:\local\lib\yop\sse3""" -_SSE2_CFG = r"""[atlas] -library_dirs = C:\local\lib\yop\sse2""" -_NOSSE_CFG = r"""[atlas] -library_dirs = fakedirectorywhichhopefullydoesnotexist -[DEFAULT] -library_dirs = C:\local\lib\yop\nosse""" - -SITECFG = {"sse2" : _SSE2_CFG, "sse3" : _SSE3_CFG, "nosse" : _NOSSE_CFG} - -options( - clean=Bunch( - src_dir = SRC_ROOT, - pyver = PYVER - ), - clean_bootstrap=Bunch( - src_dir = SRC_ROOT, - pyver = PYVER - ), - build_sdist=Bunch( - src_dir = SRC_ROOT - ), - build_binary=Bunch( - pyver = PYVER, - arch = ARCH, - src_root = SRC_ROOT - ), - bootstrap=Bunch( - pyver = PYVER, - src_root = SRC_ROOT - ), - bootstrap_arch=Bunch( - pyver = PYVER, - arch = ARCH - ), - bootstrap_nsis=Bunch( - pyver = PYVER, - src_root = SRC_ROOT - ) -) - -# Clean everything, including bootstrap source tree - at task -def clean(): - raw_clean(options.src_dir, options.pyver) - -# Clean the bootstrap source tree for a clean build from scratch - at task -def clean_bootstrap(): - raw_clean_bootstrap(options.pyver) - - at task -def build_sdist(): - raw_build_sdist(options.src_dir) - - at task - at needs('build_sdist') -def bootstrap(): - raw_bootstrap(options.pyver, options.src_dir) - - at task -def bootstrap_arch(): - pyver = options.pyver - arch = options.arch - set_bootstrap_sources(arch, pyver) - - at task -def bootstrap_nsis(): - pyver = options.pyver - bdir = bootstrap_dir(options.pyver) - prepare_nsis_script(bdir, pyver, get_scipy_version(options.src_root)) - - at task -def build_binary(): - pyver = options.pyver - arch = options.arch - raw_build_arch(pyver, arch, options.src_root) - - at task - at needs('bootstrap') - at needs('clean') -def build_nsis(): - scipy_verstr = get_scipy_version(options.src_root) - bdir = bootstrap_dir(options.pyver) - - prepare_nsis_script(bdir, options.pyver, scipy_verstr) - for arch in ['nosse', 'sse2', 'sse3']: - raw_clean_bootstrap(options.pyver) - set_bootstrap_sources(arch, options.pyver) - raw_build_arch(options.pyver, arch, options.src_root) - - raw_build_nsis(options.pyver) - -# Helpers -def set_bootstrap_sources(arch, pyver): - bdir = bootstrap_dir(pyver) - write_site_cfg(arch, cwd=bdir) - -def get_sdist_tarball(src_root): - """Return the name of the installer built by sdist command.""" - # Yeah, the name logic is harcoded in distutils. We have to reproduce it - # here - name = "scipy-%s.zip" % get_scipy_version(src_root) - return name - -def prepare_scipy_sources(src_root, bootstrap): - zid = ZipFile(pjoin(src_root, 'dist', get_sdist_tarball(src_root))) - root = 'scipy-%s' % get_scipy_version(src_root) - - # From the sdist-built tarball, extract all files into bootstrap directory, - # but removing the scipy-VERSION head path - for name in zid.namelist(): - cnt = zid.read(name) - if name.startswith(root): - # XXX: even on windows, the path sep in zip is '/' ? - name = name.split('/', 1)[1] - newname = pjoin(bootstrap, name) - - if not pexists(dirname(newname)): - os.makedirs(dirname(newname)) - fid = open(newname, 'wb') - fid.write(cnt) - -def prepare_nsis_script(bdir, pyver, numver): - tpl = pjoin('nsis_scripts', 'scipy-superinstaller.nsi.in') - source = open(tpl, 'r') - target = open(pjoin(bdir, 'scipy-superinstaller.nsi'), 'w') - - installer_name = 'scipy-%s-win32-superpack-python%s.exe' % (numver, pyver) - cnt = "".join(source.readlines()) - cnt = cnt.replace('@SCIPY_INSTALLER_NAME@', installer_name) - for arch in ['nosse', 'sse2', 'sse3']: - cnt = cnt.replace('@%s_BINARY@' % arch.upper(), - get_binary_name(arch, numver)) - - target.write(cnt) - -def bootstrap_dir(pyver): - return pjoin(BUILD_ROOT, "bootstrap-%s" % pyver) - -def get_scipy_version(src_root): - version_file = pjoin(src_root, "scipy", "version.py") - if not pexists(version_file): - raise IOError("file %s not found" % version_file) - - fid = open(version_file, "r") - vregex = re.compile("version\s*=\s*'(\d+)\.(\d+)\.(\d+)'") - isrelregex = re.compile("release\s*=\s*True") - isdevregex = re.compile("release\s*=\s*False") - isdev = None - version = None - for line in fid.readlines(): - m = vregex.match(line) - if m: - version = [int(i) for i in m.groups()] - if isrelregex.match(line): - if isdev is None: - isdev = False - else: - raise RuntimeError("isdev already set ?") - if isdevregex.match(line): - if isdev is None: - isdev = True - else: - raise RuntimeError("isdev already set ?") - - verstr = ".".join([str(i) for i in version]) - if isdev: - verstr += ".dev" - verstr += get_svn_version(src_root) - return verstr - -def get_svn_version(chdir): - out = subprocess.Popen(['svn', 'info'], - stdout = subprocess.PIPE, - cwd = chdir).communicate()[0] - r = re.compile('Revision: ([0-9]+)') - svnver = None - for line in out.split('\n'): - m = r.match(line) - if m: - svnver = m.group(1) - - if not svnver: - raise ValueError("Error while parsing svn version ?") - - return svnver - -def get_python_exec(ver): - """Return the executable of python for the given version.""" - # XXX Check that the file actually exists - try: - return PYEXECS[ver] - except KeyError: - raise ValueError("Version %s not supported/recognized" % ver) - -def write_site_cfg(arch, cwd=None): - if not cwd: - cwd = os.getcwd() - - scfg = pjoin(cwd, "site.cfg") - if pexists(scfg): - os.remove(scfg) - f = open(scfg, 'w') - f.writelines(SITECFG[arch]) - f.close() - -def move_binary(arch, pyver, cwd, scipy_verstr): - if not pexists(pjoin(cwd, "binaries")): - os.makedirs(pjoin(cwd, "binaries")) - - shmove(pjoin(cwd, 'dist', get_windist_exec(pyver, scipy_verstr)), - pjoin(cwd, 'binaries', get_binary_name(arch, scipy_verstr))) - -def get_binary_name(arch, scipy_verstr): - if BUILD_MSI: - ext = '.msi' - else: - ext = '.exe' - return "scipy-%s-%s%s" % (scipy_verstr, arch, ext) - -def get_windist_exec(pyver, scipy_verstr): - """Return the name of the installer built by wininst command.""" - # Yeah, the name logic is harcoded in distutils. We have to reproduce it - # here - if BUILD_MSI: - ext = '.msi' - else: - ext = '.exe' - name = "scipy-%s.win32-py%s%s" % (scipy_verstr, pyver, ext) - return name - -def raw_clean(src_dir, pyver): - # Clean sdist - sdir = pjoin(src_dir, "dist") - if pexists(sdir): - rmtree(sdir) - mani = pjoin(src_dir, "MANIFEST") - if pexists(mani): - os.remove(mani) - - # Clean bootstrap directory - bdir = bootstrap_dir(pyver) - if pexists(bdir): - rmtree(bdir) - -def raw_clean_bootstrap(pyver): - bdir = bootstrap_dir(pyver) - for d in ["build", "dist"]: - if pexists(pjoin(bdir, d)): - rmtree(pjoin(bdir, d)) - - if pexists(pjoin(bdir, "site.cfg")): - os.remove(pjoin(bdir, "site.cfg")) - -def raw_build_sdist(cwd): - cmd = ["python", "setup.py", "sdist", "--format=zip"] - st = subprocess.call(cmd, cwd=cwd) - -def raw_bootstrap(pyver, src_dir): - bdir = bootstrap_dir(pyver) - prepare_scipy_sources(src_dir, bdir) - -def raw_build_arch(pyver, arch, src_root): - scipy_verstr = get_scipy_version(src_root) - bdir = bootstrap_dir(pyver) - - print "Building scipy (version %s) binary for python %s, arch is %s" % \ - (scipy_verstr, get_python_exec(pyver), arch) - - if BUILD_MSI: - cmd = [get_python_exec(pyver), "setup.py", "build", "-c", "mingw32", - "bdist_msi"] - else: - cmd = [get_python_exec(pyver), "setup.py", "build", "-c", "mingw32", - "bdist_wininst"] - build_log = "build-%s-%s.log" % (arch, pyver) - f = open(build_log, 'w') - - try: - try: - st = subprocess.call(cmd, #shell = True, - stderr = subprocess.STDOUT, stdout = f, - cwd=bdir) - if st: - raise RuntimeError("The cmd failed with status %d" % st) - finally: - f.close() - except (subprocess.CalledProcessError, RuntimeError), e: - print e - msg = """ -There was an error while executing the following command: - - %s - -Error was : %s - -Look at the build log (%s).""" % (cmd, str(e), build_log) - raise Exception(msg) - - move_binary(arch, pyver, bdir, scipy_verstr) - -def raw_build_nsis(pyver): - bdir = bootstrap_dir(options.pyver) - st = subprocess.call(['makensis', 'scipy-superinstaller.nsi'], - cwd=bdir) - if st: - raise RuntimeError("Error while executing makensis command") Copied: branches/refactor_fft/tools/win32/build_scripts/pavement.py (from rev 4827, trunk/tools/win32/build_scripts/pavement.py) From scipy-svn at scipy.org Sat Oct 25 03:36:46 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 02:36:46 -0500 (CDT) Subject: [Scipy-svn] r4829 - trunk/scipy/stats/tests Message-ID: <20081025073646.5033E39C107@scipy.org> Author: cdavid Date: 2008-10-25 02:36:42 -0500 (Sat, 25 Oct 2008) New Revision: 4829 Modified: trunk/scipy/stats/tests/test_stats.py Log: Remove double TestMedian, which shadows some median tests. Modified: trunk/scipy/stats/tests/test_stats.py =================================================================== --- trunk/scipy/stats/tests/test_stats.py 2008-10-23 12:30:20 UTC (rev 4828) +++ trunk/scipy/stats/tests/test_stats.py 2008-10-25 07:36:42 UTC (rev 4829) @@ -625,15 +625,6 @@ A += val assert_almost_equal(stats.mean(a,axis=None),A/(5*3.0*5)) -class TestMedian(TestCase): - def test_basic(self): - a1 = [3,4,5,10,-3,-5,6] - a2 = [3,-6,-2,8,7,4,2,1] - a3 = [3.,4,5,10,-3,-5,-6,7.0] - assert_equal(stats.median(a1),4) - assert_equal(stats.median(a2),2.5) - assert_equal(stats.median(a3),3.5) - class TestPercentile(TestCase): def setUp(self): self.a1 = [3,4,5,10,-3,-5,6] From scipy-svn at scipy.org Sat Oct 25 05:13:09 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 04:13:09 -0500 (CDT) Subject: [Scipy-svn] r4830 - trunk/scipy/stats/tests Message-ID: <20081025091309.76F1539C107@scipy.org> Author: cdavid Date: 2008-10-25 04:13:05 -0500 (Sat, 25 Oct 2008) New Revision: 4830 Modified: trunk/scipy/stats/tests/test_stats.py Log: Add old median test to new median test. Modified: trunk/scipy/stats/tests/test_stats.py =================================================================== --- trunk/scipy/stats/tests/test_stats.py 2008-10-25 07:36:42 UTC (rev 4829) +++ trunk/scipy/stats/tests/test_stats.py 2008-10-25 09:13:05 UTC (rev 4830) @@ -685,6 +685,14 @@ assert_almost_equal(stats.median(data1),2.5) assert_almost_equal(stats.median(data2),5) + def test_basic2(self): + a1 = [3,4,5,10,-3,-5,6] + a2 = [3,-6,-2,8,7,4,2,1] + a3 = [3.,4,5,10,-3,-5,-6,7.0] + assert_equal(stats.median(a1),4) + assert_equal(stats.median(a2),2.5) + assert_equal(stats.median(a3),3.5) + class TestMode(TestCase): def test_basic(self): data1 = [3,5,1,10,23,3,2,6,8,6,10,6] From scipy-svn at scipy.org Sat Oct 25 05:13:38 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 04:13:38 -0500 (CDT) Subject: [Scipy-svn] r4831 - trunk/scipy/stats/tests Message-ID: <20081025091338.437BE39C107@scipy.org> Author: cdavid Date: 2008-10-25 04:13:34 -0500 (Sat, 25 Oct 2008) New Revision: 4831 Modified: trunk/scipy/stats/tests/test_stats.py Log: Add regression test for #760. Modified: trunk/scipy/stats/tests/test_stats.py =================================================================== --- trunk/scipy/stats/tests/test_stats.py 2008-10-25 09:13:05 UTC (rev 4830) +++ trunk/scipy/stats/tests/test_stats.py 2008-10-25 09:13:34 UTC (rev 4831) @@ -693,6 +693,11 @@ assert_equal(stats.median(a2),2.5) assert_equal(stats.median(a3),3.5) + def test_axis(self): + a1 = np.array([[3,4,5], [10,-3,-5]]) + assert_equal(stats.median(a1), np.array([6.5, 0.5, 0.])) + assert_equal(stats.median(a1, axis=-1), np.array([4., -3])) + class TestMode(TestCase): def test_basic(self): data1 = [3,5,1,10,23,3,2,6,8,6,10,6] From scipy-svn at scipy.org Sat Oct 25 05:26:36 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 04:26:36 -0500 (CDT) Subject: [Scipy-svn] r4832 - trunk/scipy/stats/tests Message-ID: <20081025092636.10B5A39C107@scipy.org> Author: cdavid Date: 2008-10-25 04:26:32 -0500 (Sat, 25 Oct 2008) New Revision: 4832 Modified: trunk/scipy/stats/tests/test_stats.py Log: BUG: Fix bug 760. median was using old numpy.median behavior wrt axis argument. Modified: trunk/scipy/stats/tests/test_stats.py =================================================================== --- trunk/scipy/stats/tests/test_stats.py 2008-10-25 09:13:34 UTC (rev 4831) +++ trunk/scipy/stats/tests/test_stats.py 2008-10-25 09:26:32 UTC (rev 4832) @@ -694,6 +694,7 @@ assert_equal(stats.median(a3),3.5) def test_axis(self): + """Regression test for #760.""" a1 = np.array([[3,4,5], [10,-3,-5]]) assert_equal(stats.median(a1), np.array([6.5, 0.5, 0.])) assert_equal(stats.median(a1, axis=-1), np.array([4., -3])) From scipy-svn at scipy.org Sat Oct 25 06:29:32 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 05:29:32 -0500 (CDT) Subject: [Scipy-svn] r4833 - trunk/scipy/stats Message-ID: <20081025102932.8DBD739C107@scipy.org> Author: cdavid Date: 2008-10-25 05:29:28 -0500 (Sat, 25 Oct 2008) New Revision: 4833 Modified: trunk/scipy/stats/stats.py Log: Forgot to add one file for #760 fix. Modified: trunk/scipy/stats/stats.py =================================================================== --- trunk/scipy/stats/stats.py 2008-10-25 09:26:32 UTC (rev 4832) +++ trunk/scipy/stats/stats.py 2008-10-25 10:29:28 UTC (rev 4833) @@ -489,10 +489,7 @@ - numpy.median has a ddof argument to replace bias in a more general manner. scipy.stats.median(a, bias=True) can be replaced by numpy.median(x, axis=0, ddof=1).""", DeprecationWarning) - a, axis = _chk_asarray(a, axis) - if axis != 0: - a = np.rollaxis(a, axis, 0) - return np.median(a) + return np.median(a, axis) def mode(a, axis=0): """Returns an array of the modal (most common) value in the passed array. From scipy-svn at scipy.org Sat Oct 25 17:26:47 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 16:26:47 -0500 (CDT) Subject: [Scipy-svn] r4834 - trunk/scipy/sparse Message-ID: <20081025212647.9EBB039C05F@scipy.org> Author: wnbell Date: 2008-10-25 16:26:45 -0500 (Sat, 25 Oct 2008) New Revision: 4834 Modified: trunk/scipy/sparse/coo.py trunk/scipy/sparse/dia.py Log: removed sum_duplicates option from coo_matrix.tocsr() and coo_matrix.tocsc() Modified: trunk/scipy/sparse/coo.py =================================================================== --- trunk/scipy/sparse/coo.py 2008-10-25 10:29:28 UTC (rev 4833) +++ trunk/scipy/sparse/coo.py 2008-10-25 21:26:45 UTC (rev 4834) @@ -233,12 +233,25 @@ coo_todense(M, N, self.nnz, self.row, self.col, self.data, B.ravel() ) return B - def tocsc(self,sum_duplicates=True): + def tocsc(self): """Return a copy of this matrix in Compressed Sparse Column format - By default sum_duplicates=True and any duplicate - matrix entries are added together. + Duplicate entries will be summed together. + Example + ------- + >>> from numpy import array + >>> from scipy.sparse import coo_matrix + >>> row = array([0,0,1,3,1,0,0]) + >>> col = array([0,2,1,3,1,0,0]) + >>> data = array([1,1,1,1,1,1,1]) + >>> A = coo_matrix( (data,(row,col)), shape=(4,4)).tocsc() + >>> A.todense() + matrix([[3, 0, 1, 0], + [0, 2, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 1]]) + """ from csc import csc_matrix if self.nnz == 0: @@ -253,16 +266,28 @@ indptr, indices, data) A = csc_matrix((data, indices, indptr), self.shape) - if sum_duplicates: - A.sum_duplicates() + A.sum_duplicates() return A - def tocsr(self,sum_duplicates=True): + def tocsr(self): """Return a copy of this matrix in Compressed Sparse Row format - By default sum_duplicates=True and any duplicate - matrix entries are added together. + Duplicate entries will be summed together. + Example + ------- + >>> from numpy import array + >>> from scipy.sparse import coo_matrix + >>> row = array([0,0,1,3,1,0,0]) + >>> col = array([0,2,1,3,1,0,0]) + >>> data = array([1,1,1,1,1,1,1]) + >>> A = coo_matrix( (data,(row,col)), shape=(4,4)).tocsr() + >>> A.todense() + matrix([[3, 0, 1, 0], + [0, 2, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 1]]) + """ from csr import csr_matrix if self.nnz == 0: @@ -277,8 +302,7 @@ indptr, indices, data) A = csr_matrix((data, indices, indptr), self.shape) - if sum_duplicates: - A.sum_duplicates() + A.sum_duplicates() return A Modified: trunk/scipy/sparse/dia.py =================================================================== --- trunk/scipy/sparse/dia.py 2008-10-25 10:29:28 UTC (rev 4833) +++ trunk/scipy/sparse/dia.py 2008-10-25 21:26:45 UTC (rev 4834) @@ -164,11 +164,11 @@ def tocsr(self): #this could be faster - return self.tocoo().tocsr(sum_duplicates=False) + return self.tocoo().tocsr() def tocsc(self): #this could be faster - return self.tocoo().tocsc(sum_duplicates=False) + return self.tocoo().tocsc() def tocoo(self): num_data = len(self.data) From scipy-svn at scipy.org Sat Oct 25 18:19:52 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 17:19:52 -0500 (CDT) Subject: [Scipy-svn] r4835 - in trunk/scipy/sparse: . tests Message-ID: <20081025221952.2343239C1AE@scipy.org> Author: wnbell Date: 2008-10-25 17:19:48 -0500 (Sat, 25 Oct 2008) New Revision: 4835 Modified: trunk/scipy/sparse/base.py trunk/scipy/sparse/dok.py trunk/scipy/sparse/lil.py trunk/scipy/sparse/sputils.py trunk/scipy/sparse/tests/test_base.py trunk/scipy/sparse/tests/test_sputils.py Log: sparse matrices now conform to spmatrix( (M,N) ) -> empty M-by-N matrix added tests for invalid shapes in case above Modified: trunk/scipy/sparse/base.py =================================================================== --- trunk/scipy/sparse/base.py 2008-10-25 21:26:45 UTC (rev 4834) +++ trunk/scipy/sparse/base.py 2008-10-25 22:19:48 UTC (rev 4835) @@ -70,7 +70,7 @@ raise TypeError('invalid shape') if not (shape[0] >= 1 and shape[1] >= 1): - raise TypeError('invalid shape') + raise ValueError('invalid shape') if (self._shape != shape) and (self._shape is not None): try: Modified: trunk/scipy/sparse/dok.py =================================================================== --- trunk/scipy/sparse/dok.py 2008-10-25 21:26:45 UTC (rev 4834) +++ trunk/scipy/sparse/dok.py 2008-10-25 22:19:48 UTC (rev 4835) @@ -13,8 +13,10 @@ from sputils import isdense, getdtype, isshape, isintlike, isscalarlike class dok_matrix(spmatrix, dict): - """Dictionary Of Keys based matrix. This is an efficient - structure for constructing sparse matrices incrementally. + """Dictionary Of Keys based sparse matrix. + + This is an efficient structure for constructing sparse + matrices incrementally. This can be instatiated in several ways: dok_matrix(D) Modified: trunk/scipy/sparse/lil.py =================================================================== --- trunk/scipy/sparse/lil.py 2008-10-25 21:26:45 UTC (rev 4834) +++ trunk/scipy/sparse/lil.py 2008-10-25 22:19:48 UTC (rev 4835) @@ -16,23 +16,22 @@ from sputils import getdtype,isshape,issequence,isscalarlike class lil_matrix(spmatrix): - """Row-based linked list matrix + """Row-based linked list sparse matrix + This is an efficient structure for constructing sparse + matrices incrementally. This can be instantiated in several ways: - csc_matrix(D) + lil_matrix(D) with a dense matrix or rank-2 ndarray D - csc_matrix(S) + lil_matrix(S) with another sparse matrix S (equivalent to S.tocsc()) - csc_matrix((M, N), [dtype]) + lil_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'. - csc_matrix((data, ij), [shape=(M, N)]) - where ``data`` and ``ij`` satisfy ``a[ij[0, k], ij[1, k]] = data[k]`` - Notes ----- @@ -60,40 +59,25 @@ """ - def __init__(self, A=None, shape=None, dtype=None, copy=False): - """ Create a new list-of-lists sparse matrix. An optional - argument A is accepted, which initializes the lil_matrix with it. - This can be a tuple of dimensions (M, N) or a dense array / - matrix to copy, or a sparse matrix. - """ + def __init__(self, arg1, shape=None, dtype=None, copy=False): spmatrix.__init__(self) - self.dtype = getdtype(dtype, A, default=float) + self.dtype = getdtype(dtype, arg1, default=float) # First get the shape - if A is None: - if not isshape(shape): - raise TypeError("need a valid shape") - M, N = shape - self.shape = (M,N) - self.rows = numpy.empty((M,), dtype=object) - self.data = numpy.empty((M,), dtype=object) - for i in range(M): - self.rows[i] = [] - self.data[i] = [] - elif isspmatrix(A): - if isspmatrix_lil(A) and copy: - A = A.copy() + if isspmatrix(arg1): + if isspmatrix_lil(arg1) and copy: + A = arg1.copy() else: - A = A.tolil() + A = arg1.tolil() self.shape = A.shape self.dtype = A.dtype self.rows = A.rows self.data = A.data - elif isinstance(A,tuple): - if isshape(A): + elif isinstance(arg1,tuple): + if isshape(arg1): if shape is not None: raise ValueError('invalid use of shape parameter') - M, N = A + M, N = arg1 self.shape = (M,N) self.rows = numpy.empty((M,), dtype=object) self.data = numpy.empty((M,), dtype=object) @@ -101,11 +85,11 @@ self.rows[i] = [] self.data[i] = [] else: - raise TypeError,'unrecognized lil_matrix constructor usage' + raise TypeError('unrecognized lil_matrix constructor usage') else: #assume A is dense try: - A = asmatrix(A) + A = asmatrix(arg1) except TypeError: raise TypeError, "unsupported matrix type" else: @@ -334,7 +318,7 @@ def _mul_scalar(self, other): if other == 0: # Multiply by zero: return the zero matrix - new = lil_matrix(shape=self.shape, dtype=self.dtype) + new = lil_matrix(self.shape, dtype=self.dtype) else: new = self.copy() # Multiply this scalar by every element. Modified: trunk/scipy/sparse/sputils.py =================================================================== --- trunk/scipy/sparse/sputils.py 2008-10-25 21:26:45 UTC (rev 4834) +++ trunk/scipy/sparse/sputils.py 2008-10-25 22:19:48 UTC (rev 4835) @@ -100,7 +100,7 @@ # Assume it's a tuple of matrix dimensions (M, N) (M, N) = x assert isintlike(M) and isintlike(N) # raises TypeError unless integers - assert M > 0 and N > 0 + #assert M > 0 and N > 0 except (ValueError, TypeError, AssertionError): return False else: Modified: trunk/scipy/sparse/tests/test_base.py =================================================================== --- trunk/scipy/sparse/tests/test_base.py 2008-10-25 21:26:45 UTC (rev 4834) +++ trunk/scipy/sparse/tests/test_base.py 2008-10-25 22:19:48 UTC (rev 4835) @@ -34,7 +34,6 @@ warnings.simplefilter('ignore',SparseEfficiencyWarning) -#TODO check that invalid shape in constructor raises exception #TODO check that spmatrix( ... , copy=X ) is respected #TODO test prune #TODO test has_sorted_indices @@ -44,14 +43,25 @@ def setUp(self): self.dat = matrix([[1,0,0,2],[3,0,1,0],[0,2,0,0]],'d') self.datsp = self.spmatrix(self.dat) + + def test_empty(self): + """create empty matrices""" + + assert_equal(self.spmatrix((3,3)).todense(), np.zeros((3,3))) + assert_equal(self.spmatrix((3,3)).nnz, 0) + def test_invalid_shapes(self): + assert_raises(ValueError, self.spmatrix, (-1,3) ) + assert_raises(ValueError, self.spmatrix, (3,-1) ) + assert_raises(ValueError, self.spmatrix, (-1,-1) ) + def test_repr(self): repr(self.datsp) def test_str(self): str(self.datsp) - def test_empty(self): + def test_empty_arithmetic(self): """Test manipulating empty matrices. Fails in SciPy SVN <= r1768 """ shape = (5, 5) @@ -304,17 +314,11 @@ assert(isinstance( M * array([1,2,3]), ndarray)) assert(isinstance( M * matrix([1,2,3]).T, matrix)) - #ensure exception is raised for improper dimensions bad_vecs = [array([1,2]), array([1,2,3,4]), array([[1],[2]]), matrix([1,2,3]), matrix([[1],[2]])] - caught = 0 for x in bad_vecs: - try: - y = M * x - except ValueError: - caught += 1 - assert_equal(caught,len(bad_vecs)) + assert_raises(ValueError, M.__mul__, x) # Should this be supported or not?! #flat = array([1,2,3]) @@ -1371,8 +1375,8 @@ def test_constructor4(self): """from dense matrix""" mat = array([[0,1,0,0], - [7,0,3,0], - [0,4,0,0]]) + [7,0,3,0], + [0,4,0,0]]) coo = coo_matrix(mat) assert_array_equal(coo.todense(),mat) @@ -1386,10 +1390,16 @@ spmatrix = dia_matrix def test_constructor1(self): - pass - #TODO add test + D = matrix([[1, 0, 3, 0], + [1, 2, 0, 4], + [0, 2, 3, 0], + [0, 0, 3, 4]]) + data = np.array([[1,2,3,4]]).repeat(3,axis=0) + offsets = np.array([0,-1,2]) + assert_equal(dia_matrix( (data,offsets), shape=(4,4)).todense(), D) + class TestBSR(_TestCommon, _TestArithmetic, _TestInplaceArithmetic, _TestMatvecOutput, TestCase): spmatrix = bsr_matrix Modified: trunk/scipy/sparse/tests/test_sputils.py =================================================================== --- trunk/scipy/sparse/tests/test_sputils.py 2008-10-25 21:26:45 UTC (rev 4834) +++ trunk/scipy/sparse/tests/test_sputils.py 2008-10-25 22:19:48 UTC (rev 4835) @@ -47,10 +47,9 @@ assert_equal(isshape( (1,2) ),True) assert_equal(isshape( (5,2) ),True) - assert_equal(isshape( (-1,4) ),False) assert_equal(isshape( (1.5,2) ),False) - assert_equal(isshape( (0,4) ),False) assert_equal(isshape( (2,2,2) ),False) + assert_equal(isshape( ([2],2) ),False) def test_issequence(self): assert_equal(issequence( (1,) ),True) From scipy-svn at scipy.org Sat Oct 25 18:34:38 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 17:34:38 -0500 (CDT) Subject: [Scipy-svn] r4836 - trunk/scipy/sparse Message-ID: <20081025223438.528C039C226@scipy.org> Author: wnbell Date: 2008-10-25 17:34:36 -0500 (Sat, 25 Oct 2008) New Revision: 4836 Modified: trunk/scipy/sparse/lil.py Log: cleaned up lil_matrix imports Modified: trunk/scipy/sparse/lil.py =================================================================== --- trunk/scipy/sparse/lil.py 2008-10-25 22:19:48 UTC (rev 4835) +++ trunk/scipy/sparse/lil.py 2008-10-25 22:34:36 UTC (rev 4836) @@ -5,15 +5,12 @@ __all__ = ['lil_matrix','isspmatrix_lil'] -import copy from bisect import bisect_left -import numpy -from numpy import isscalar, asmatrix, asarray, intc, concatenate, array, \ - cumsum, zeros, unravel_index +import numpy as np from base import spmatrix, isspmatrix -from sputils import getdtype,isshape,issequence,isscalarlike +from sputils import getdtype, isshape, issequence, isscalarlike class lil_matrix(spmatrix): """Row-based linked list sparse matrix @@ -79,8 +76,8 @@ raise ValueError('invalid use of shape parameter') M, N = arg1 self.shape = (M,N) - self.rows = numpy.empty((M,), dtype=object) - self.data = numpy.empty((M,), dtype=object) + self.rows = np.empty((M,), dtype=object) + self.data = np.empty((M,), dtype=object) for i in range(M): self.rows[i] = [] self.data[i] = [] @@ -89,9 +86,9 @@ else: #assume A is dense try: - A = asmatrix(arg1) + A = np.asmatrix(arg1) except TypeError: - raise TypeError, "unsupported matrix type" + raise TypeError('unsupported matrix type') else: from csr import csr_matrix A = csr_matrix(A).tolil() @@ -161,7 +158,7 @@ j += self.shape[1] if j < 0 or j > self.shape[1]: - raise IndexError,'column index out of bounds' + raise IndexError('column index out of bounds') pos = bisect_left(row, j) if pos != len(data) and row[pos] == j: @@ -194,10 +191,10 @@ try: i, j = index except (AssertionError, TypeError): - raise IndexError, "invalid index" + raise IndexError('invalid index') - if isscalar(i): - if isscalar(j): + if np.isscalar(i): + if np.isscalar(j): return self._get1(i, j) if isinstance(j, slice): j = self._slicetoseq(j, self.shape[1]) @@ -208,7 +205,7 @@ elif issequence(i) or isinstance(i, slice): if isinstance(i, slice): i = self._slicetoseq(i, self.shape[0]) - if isscalar(j): + if np.isscalar(j): return self.__class__([[self._get1(ii, j)] for ii in i]) if isinstance(j, slice): j = self._slicetoseq(j, self.shape[1]) @@ -233,7 +230,7 @@ j += self.shape[1] if j < 0 or j >= self.shape[1]: - raise IndexError,'column index out of bounds' + raise IndexError('column index out of bounds') pos = bisect_left(row, j) if x != 0: @@ -258,22 +255,22 @@ if issequence(j): if isinstance(x, spmatrix): x = x.todense() - x = numpy.asarray(x).squeeze() - if isscalar(x) or x.size == 1: + x = np.asarray(x).squeeze() + if np.isscalar(x) or x.size == 1: for jj in j: self._insertat2(row, data, jj, x) else: # x must be one D. maybe check these things out for jj, xx in zip(j, x): self._insertat2(row, data, jj, xx) - elif isscalar(j): + elif np.isscalar(j): self._insertat2(row, data, j, x) else: - raise ValueError, "invalid column value: %s" % str(j) + raise ValueError('invalid column value: %s' % str(j)) def __setitem__(self, index, x): - if isscalar(x): + if np.isscalar(x): x = self.dtype.type(x) elif not isinstance(x, spmatrix): x = lil_matrix(x) @@ -281,7 +278,7 @@ try: i, j = index except (ValueError, TypeError): - raise IndexError, "invalid index" + raise IndexError('invalid index') if isspmatrix(x): if (isinstance(i, slice) and (i == slice(None))) and \ @@ -292,12 +289,12 @@ self.data = x.data return - if isscalar(i): + if np.isscalar(i): row = self.rows[i] data = self.data[i] self._insertat3(row, data, j, x) elif issequence(i) and issequence(j): - if isscalar(x): + if np.isscalar(x): for ii, jj in zip(i, j): self._insertat(ii, jj, x) else: @@ -306,14 +303,14 @@ elif isinstance(i, slice) or issequence(i): rows = self.rows[i] datas = self.data[i] - if isscalar(x): + if np.isscalar(x): for row, data in zip(rows, datas): self._insertat3(row, data, j, x) else: for row, data, xx in zip(rows, datas, x): self._insertat3(row, data, j, xx) else: - raise ValueError, "invalid index value: %s" % str((i, j)) + raise ValueError('invalid index value: %s' % str((i, j))) def _mul_scalar(self, other): if other == 0: @@ -322,16 +319,16 @@ else: new = self.copy() # Multiply this scalar by every element. - new.data = numpy.array([[val*other for val in rowvals] for - rowvals in new.data], dtype=object) + new.data = np.array([[val*other for val in rowvals] for + rowvals in new.data], dtype=object) return new def __truediv__(self, other): # self / other if isscalarlike(other): new = self.copy() # Divide every element by this scalar - new.data = numpy.array([[val/other for val in rowvals] for - rowvals in new.data], dtype=object) + new.data = np.array([[val/other for val in rowvals] for + rowvals in new.data], dtype=object) return new else: return self.tocsr() / other @@ -341,7 +338,7 @@ # """Point-wise multiplication by another lil_matrix. # # """ -# if isscalar(other): +# if np.isscalar(other): # return self.__mul__(other) # # if isspmatrix_lil(other): @@ -370,22 +367,23 @@ # "with another lil_matrix.") def copy(self): + from copy import deepcopy new = lil_matrix(self.shape, dtype=self.dtype) - new.data = copy.deepcopy(self.data) - new.rows = copy.deepcopy(self.rows) + new.data = deepcopy(self.data) + new.rows = deepcopy(self.rows) return new def reshape(self,shape): - new = lil_matrix(shape,dtype=self.dtype) + new = lil_matrix(shape, dtype=self.dtype) j_max = self.shape[1] for i,row in enumerate(self.rows): for col,j in enumerate(row): - new_r,new_c = unravel_index(i*j_max + j,shape) + new_r,new_c = np.unravel_index(i*j_max + j,shape) new[new_r,new_c] = self[i,j] return new def __add__(self, other): - if isscalar(other) and other != 0: + if np.isscalar(other) and other != 0: raise ValueError("Refusing to destroy sparsity. " "Use x.todense() + c instead.") else: @@ -400,7 +398,7 @@ def toarray(self): - d = zeros(self.shape, dtype=self.dtype) + d = np.zeros(self.shape, dtype=self.dtype) for i, row in enumerate(self.rows): for pos, j in enumerate(row): d[i, j] = self.data[i][pos] @@ -419,20 +417,20 @@ """ Return Compressed Sparse Row format arrays for this matrix. """ - indptr = asarray([len(x) for x in self.rows], dtype=intc) - indptr = concatenate( ( array([0],dtype=intc), cumsum(indptr) ) ) + indptr = np.asarray([len(x) for x in self.rows], dtype=np.intc) + indptr = np.concatenate( (np.array([0], dtype=np.intc), np.cumsum(indptr)) ) nnz = indptr[-1] indices = [] for x in self.rows: indices.extend(x) - indices = asarray(indices,dtype=intc) + indices = np.asarray(indices, dtype=np.intc) data = [] for x in self.data: data.extend(x) - data = asarray(data,dtype=self.dtype) + data = np.asarray(data, dtype=self.dtype) from csr import csr_matrix return csr_matrix((data, indices, indptr), shape=self.shape) From scipy-svn at scipy.org Sat Oct 25 18:43:46 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 17:43:46 -0500 (CDT) Subject: [Scipy-svn] r4837 - trunk/scipy/sparse Message-ID: <20081025224346.5327A39C088@scipy.org> Author: wnbell Date: 2008-10-25 17:43:44 -0500 (Sat, 25 Oct 2008) New Revision: 4837 Modified: trunk/scipy/sparse/dia.py Log: cleaned up dia_matrix imports Modified: trunk/scipy/sparse/dia.py =================================================================== --- trunk/scipy/sparse/dia.py 2008-10-25 22:34:36 UTC (rev 4836) +++ trunk/scipy/sparse/dia.py 2008-10-25 22:43:44 UTC (rev 4837) @@ -4,8 +4,7 @@ __all__ = ['dia_matrix','isspmatrix_dia'] -from numpy import asarray, zeros, arange, array, intc, atleast_1d, \ - atleast_2d, unique, hstack +import numpy as np from base import isspmatrix, _formats from data import _data_matrix @@ -72,9 +71,9 @@ if isshape(arg1): # It's a tuple of matrix dimensions (M, N) # create empty matrix - self.shape = arg1 #spmatrix checks for errors here - self.data = zeros( (0,0), getdtype(dtype, default=float)) - self.offsets = zeros( (0), dtype=intc) + self.shape = arg1 #spmatrix checks for errors here + self.data = np.zeros( (0,0), getdtype(dtype, default=float)) + self.offsets = np.zeros( (0), dtype=np.intc) else: try: # Try interpreting it as (data, offsets) @@ -84,16 +83,16 @@ else: if shape is None: raise ValueError('expected a shape argument') - self.data = atleast_2d(array(arg1[0],dtype=dtype,copy=copy)) - self.offsets = atleast_1d(array(arg1[1],dtype='i',copy=copy)) + self.data = np.atleast_2d(np.array(arg1[0], dtype=dtype, copy=copy)) + self.offsets = np.atleast_1d(np.array(arg1[1], dtype=np.intc, copy=copy)) self.shape = shape else: #must be dense, convert to COO first, then to DIA try: - arg1 = asarray(arg1) + arg1 = np.asarray(arg1) except: - raise ValueError, "unrecognized form for" \ - " %s_matrix constructor" % self.format + raise ValueError("unrecognized form for" \ + " %s_matrix constructor" % self.format) from coo import coo_matrix A = coo_matrix(arg1).todia() self.data = A.data @@ -113,7 +112,7 @@ 'does not match the number of offsets (%d)' \ % (self.data.shape[0], len(self.offsets))) - if len(unique(self.offsets)) != len(self.offsets): + if len(np.unique(self.offsets)) != len(self.offsets): raise ValueError('offset array contains duplicate values') def __repr__(self): @@ -143,7 +142,7 @@ def _mul_vector(self, other): x = other - y = zeros( self.shape[0], dtype=upcast(self.dtype,x.dtype)) + y = np.zeros( self.shape[0], dtype=upcast(self.dtype,x.dtype)) L = self.data.shape[1] @@ -154,7 +153,7 @@ return y def _mul_dense_matrix(self, other): - return hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) + return np.hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) def todia(self,copy=False): if copy: @@ -174,7 +173,7 @@ num_data = len(self.data) len_data = self.data.shape[1] - row = arange(len_data).reshape(1,-1).repeat(num_data,axis=0) + row = np.arange(len_data).reshape(1,-1).repeat(num_data,axis=0) col = row.copy() for i,k in enumerate(self.offsets): @@ -187,10 +186,9 @@ mask &= (col < self.shape[1]) mask &= data != 0 row,col,data = row[mask],col[mask],data[mask] - #row,col,data = row.reshape(-1),col.reshape(-1),data.reshape(-1) from coo import coo_matrix - return coo_matrix((data,(row,col)),shape=self.shape) + return coo_matrix((data,(row,col)), shape=self.shape) # needed by _data_matrix def _with_data(self, data, copy=True): @@ -198,7 +196,7 @@ but with different data. By default the structure arrays are copied. """ if copy: - return dia_matrix( (data,self.offsets.copy()), shape=self.shape) + return dia_matrix( (data, self.offsets.copy()), shape=self.shape) else: return dia_matrix( (data,self.offsets), shape=self.shape) From scipy-svn at scipy.org Sat Oct 25 19:13:11 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 18:13:11 -0500 (CDT) Subject: [Scipy-svn] r4838 - trunk/scipy/sparse Message-ID: <20081025231311.6732239C088@scipy.org> Author: wnbell Date: 2008-10-25 18:13:09 -0500 (Sat, 25 Oct 2008) New Revision: 4838 Modified: trunk/scipy/sparse/bsr.py Log: cleaned up bsr_matrix imports Modified: trunk/scipy/sparse/bsr.py =================================================================== --- trunk/scipy/sparse/bsr.py 2008-10-25 22:43:44 UTC (rev 4837) +++ trunk/scipy/sparse/bsr.py 2008-10-25 23:13:09 UTC (rev 4838) @@ -6,9 +6,11 @@ from warnings import warn -from numpy import zeros, intc, array, asarray, arange, diff, tile, rank, \ - ravel, empty, empty_like +import numpy as np +#from numpy import zeros, intc, array, asarray, arange, diff, tile, rank, \ +# ravel, empty, empty_like + from data import _data_matrix from compressed import _cs_matrix from base import isspmatrix, _formats @@ -97,10 +99,10 @@ if isspmatrix(arg1): - if arg1.format == self.format and copy: + if isspmatrix_bsr(arg1) and copy: arg1 = arg1.copy() else: - arg1 = getattr(arg1,'to' + self.format)(blocksize=blocksize) + arg1 = arg1.tobsr(blocksize=blocksize) self._set_self( arg1 ) elif isinstance(arg1,tuple): @@ -113,16 +115,16 @@ blocksize = (1,1) else: if not isshape(blocksize): - raise ValueError,'invalid blocksize=%s',blocksize + raise ValueError('invalid blocksize=%s' % blocksize) blocksize = tuple(blocksize) - self.data = zeros( (0,) + blocksize, getdtype(dtype, default=float) ) - self.indices = zeros( 0, dtype=intc ) + self.data = np.zeros( (0,) + blocksize, getdtype(dtype, default=float) ) + self.indices = np.zeros( 0, dtype=np.intc ) R,C = blocksize if (M % R) != 0 or (N % C) != 0: raise ValueError, 'shape must be multiple of blocksize' - self.indptr = zeros(M/R + 1, dtype=intc ) + self.indptr = np.zeros(M/R + 1, dtype=np.intc ) elif len(arg1) == 2: # (data,(row,col)) format @@ -132,21 +134,20 @@ elif len(arg1) == 3: # (data,indices,indptr) format (data, indices, indptr) = arg1 - self.indices = array(indices, copy=copy) - self.indptr = array(indptr, copy=copy) - self.data = array(data, copy=copy, \ - dtype=getdtype(dtype, data)) + self.indices = np.array(indices, copy=copy) + self.indptr = np.array(indptr, copy=copy) + self.data = np.array(data, copy=copy, dtype=getdtype(dtype, data)) else: - raise ValueError,'unrecognized bsr_matrix constructor usage' + raise ValueError('unrecognized bsr_matrix constructor usage') else: #must be dense try: - arg1 = asarray(arg1) + arg1 = np.asarray(arg1) except: - raise ValueError, "unrecognized form for" \ - " %s_matrix constructor" % self.format + raise ValueError("unrecognized form for" \ + " %s_matrix constructor" % self.format) from coo import coo_matrix - arg1 = self.__class__( coo_matrix(arg1), blocksize=blocksize ) + arg1 = coo_matrix(arg1).tobsr(blocksize=blocksize) self._set_self( arg1 ) if shape is not None: @@ -193,14 +194,14 @@ % self.indices.dtype.name ) # only support 32-bit ints for now - self.indptr = asarray(self.indptr,intc) - self.indices = asarray(self.indices,intc) + self.indptr = np.asarray(self.indptr, np.intc) + self.indices = np.asarray(self.indices, np.intc) self.data = to_native(self.data) # check array shapes - if (rank(self.indices) != 1) or (rank(self.indptr) != 1): + if np.rank(self.indices) != 1 or np.rank(self.indptr) != 1: raise ValueError,"indices, and indptr should be rank 1" - if rank(self.data) != 3: + if np.rank(self.data) != 3: raise ValueError,"data should be rank 3" # check index pointer @@ -260,9 +261,9 @@ """ M,N = self.shape R,C = self.blocksize - y = empty( min(M,N), dtype=upcast(self.dtype) ) + y = np.empty(min(M,N), dtype=upcast(self.dtype)) sparsetools.bsr_diagonal(M/R, N/C, R, C, \ - self.indptr, self.indices, ravel(self.data), y) + self.indptr, self.indices, np.ravel(self.data), y) return y ########################## @@ -292,7 +293,7 @@ M,N = self.shape R,C = self.blocksize - result = zeros( self.shape[0], dtype=upcast(self.dtype, other.dtype) ) + result = np.zeros(self.shape[0], dtype=upcast(self.dtype, other.dtype)) bsr_matvec(M/R, N/C, R, C, \ self.indptr, self.indices, self.data.ravel(), @@ -305,7 +306,7 @@ M,N = self.shape n_vecs = other.shape[1] #number of column vectors - result = zeros( (M,n_vecs), dtype=upcast(self.dtype,other.dtype) ) + result = np.zeros((M,n_vecs), dtype=upcast(self.dtype,other.dtype)) bsr_matvecs(M/R, N/C, n_vecs, R, C, \ self.indptr, self.indices, self.data.ravel(), \ @@ -313,17 +314,11 @@ return result - #def _mul_dense_matrix(self, other): - # # TODO make sparse * dense matrix multiplication more efficient - # # matvec each column of other - # result = hstack( [ self * col.reshape(-1,1) for col in asarray(other).T ] ) - # return result - def _mul_sparse_matrix(self, other): M, K1 = self.shape K2, N = other.shape - indptr = empty_like( self.indptr ) + indptr = np.empty_like( self.indptr ) R,n = self.blocksize @@ -336,7 +331,7 @@ from csr import isspmatrix_csr if isspmatrix_csr(other) and n == 1: - other = other.tobsr(blocksize=(n,C),copy=False) #convert to this format + other = other.tobsr(blocksize=(n,C), copy=False) #lightweight conversion else: other = other.tobsr(blocksize=(n,C)) @@ -346,15 +341,16 @@ indptr) bnnz = indptr[-1] - indices = empty( bnnz, dtype=intc) - data = empty( R*C*bnnz, dtype=upcast(self.dtype,other.dtype)) + indices = np.empty(bnnz, dtype=np.intc) + data = np.empty(R*C*bnnz, dtype=upcast(self.dtype,other.dtype)) bsr_matmat_pass2( M/R, N/C, R, C, n, \ - self.indptr, self.indices, ravel(self.data), \ - other.indptr, other.indices, ravel(other.data), \ + self.indptr, self.indices, np.ravel(self.data), \ + other.indptr, other.indices, np.ravel(other.data), \ indptr, indices, data) data = data.reshape(-1,R,C) + #TODO eliminate zeros return bsr_matrix((data,indices,indptr),shape=(M,N),blocksize=(R,C)) @@ -391,13 +387,13 @@ M,N = self.shape R,C = self.blocksize - row = (R * arange(M/R)).repeat(diff(self.indptr)) + row = (R * np.arange(M/R)).repeat(np.diff(self.indptr)) row = row.repeat(R*C).reshape(-1,R,C) - row += tile( arange(R).reshape(-1,1), (1,C) ) + row += np.tile(np.arange(R).reshape(-1,1), (1,C)) row = row.reshape(-1) col = (C * self.indices).repeat(R*C).reshape(-1,R,C) - col += tile( arange(C), (R,1) ) + col += np.tile(np.arange(C), (R,1)) col = col.reshape(-1) data = self.data.reshape(-1) @@ -406,7 +402,7 @@ data = data.copy() from coo import coo_matrix - return coo_matrix( (data,(row,col)), shape=self.shape ) + return coo_matrix((data,(row,col)), shape=self.shape) def transpose(self): @@ -416,17 +412,17 @@ NBLK = self.nnz/(R*C) if self.nnz == 0: - return bsr_matrix((N,M),blocksize=(C,R)) + return bsr_matrix((N,M), blocksize=(C,R)) - indptr = empty( N/C + 1, dtype=self.indptr.dtype) - indices = empty( NBLK, dtype=self.indices.dtype) - data = empty( (NBLK,C,R), dtype=self.data.dtype) + indptr = np.empty( N/C + 1, dtype=self.indptr.dtype) + indices = np.empty( NBLK, dtype=self.indices.dtype) + data = np.empty( (NBLK,C,R), dtype=self.data.dtype) bsr_transpose(M/R, N/C, R, C, \ self.indptr, self.indices, self.data.ravel(), \ indptr, indices, data.ravel()) - return bsr_matrix( (data,indices,indptr), shape=(N,M) ) + return bsr_matrix((data,indices,indptr), shape=(N,M)) ############################################################## @@ -510,13 +506,13 @@ R,C = self.blocksize max_bnnz = len(self.data) + len(other.data) - indptr = empty_like(self.indptr) - indices = empty( max_bnnz, dtype=intc ) - data = empty( R*C*max_bnnz, dtype=upcast(self.dtype,other.dtype) ) + indptr = np.empty_like(self.indptr) + indices = np.empty(max_bnnz, dtype=np.intc) + data = np.empty(R*C*max_bnnz, dtype=upcast(self.dtype,other.dtype)) fn(in_shape[0]/R, in_shape[1]/C, R, C, \ - self.indptr, self.indices, ravel(self.data), - other.indptr, other.indices, ravel(other.data), + self.indptr, self.indices, np.ravel(self.data), + other.indptr, other.indices, np.ravel(other.data), indptr, indices, data) actual_bnnz = indptr[-1] From scipy-svn at scipy.org Sat Oct 25 19:30:40 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 18:30:40 -0500 (CDT) Subject: [Scipy-svn] r4839 - trunk/scipy/sparse Message-ID: <20081025233040.21D4939C088@scipy.org> Author: wnbell Date: 2008-10-25 18:30:38 -0500 (Sat, 25 Oct 2008) New Revision: 4839 Modified: trunk/scipy/sparse/bsr.py trunk/scipy/sparse/coo.py Log: cleaned up coo_matrix imports Modified: trunk/scipy/sparse/bsr.py =================================================================== --- trunk/scipy/sparse/bsr.py 2008-10-25 23:13:09 UTC (rev 4838) +++ trunk/scipy/sparse/bsr.py 2008-10-25 23:30:38 UTC (rev 4839) @@ -8,9 +8,6 @@ import numpy as np -#from numpy import zeros, intc, array, asarray, arange, diff, tile, rank, \ -# ravel, empty, empty_like - from data import _data_matrix from compressed import _cs_matrix from base import isspmatrix, _formats Modified: trunk/scipy/sparse/coo.py =================================================================== --- trunk/scipy/sparse/coo.py 2008-10-25 23:13:09 UTC (rev 4838) +++ trunk/scipy/sparse/coo.py 2008-10-25 23:30:38 UTC (rev 4839) @@ -4,12 +4,13 @@ __all__ = ['coo_matrix', 'isspmatrix_coo'] -from itertools import izip from warnings import warn -from numpy import array, asarray, empty, intc, zeros, unique, searchsorted,\ - atleast_2d, rank, deprecate, hstack +import numpy as np +#from numpy import array, asarray, empty, intc, zeros, unique, searchsorted,\ +# atleast_2d, rank, deprecate, hstack + from sparsetools import coo_tocsr, coo_todense, coo_matvec from base import isspmatrix from data import _data_matrix @@ -108,9 +109,9 @@ if isshape(arg1): M, N = arg1 self.shape = (M,N) - self.row = array([], dtype=intc) - self.col = array([], dtype=intc) - self.data = array([], getdtype(dtype, default=float)) + self.row = np.array([], dtype=np.intc) + self.col = np.array([], dtype=np.intc) + self.data = np.array([], getdtype(dtype, default=float)) else: try: obj, ij = arg1 @@ -123,9 +124,9 @@ except TypeError: raise TypeError('invalid input format') - self.row = array(ij[0], copy=copy, dtype=intc) - self.col = array(ij[1], copy=copy, dtype=intc) - self.data = array( obj, copy=copy) + self.row = np.array(ij[0], copy=copy, dtype=np.intc) + self.col = np.array(ij[1], copy=copy, dtype=np.intc) + self.data = np.array( obj, copy=copy) if shape is None: if len(self.row) == 0 or len(self.col) == 0: @@ -145,9 +146,9 @@ warn('coo_matrix(None, shape=(M,N)) is deprecated, ' \ 'use coo_matrix( (M,N) ) instead', DeprecationWarning) self.shape = shape - self.data = array([], getdtype(dtype, default=float)) - self.row = array([], dtype=intc) - self.col = array([], dtype=intc) + self.data = np.array([], getdtype(dtype, default=float)) + self.row = np.array([], dtype=np.intc) + self.col = np.array([], dtype=np.intc) else: if isspmatrix(arg1): if isspmatrix_coo(arg1) and copy: @@ -164,11 +165,11 @@ else: #dense argument try: - M = atleast_2d(asarray(arg1)) + M = np.atleast_2d(np.asarray(arg1)) except: raise TypeError('invalid input format') - if len(M.shape) != 2: + if np.rank(M) != 2: raise TypeError('expected rank <= 2 array or matrix') self.shape = M.shape self.row,self.col = (M != 0).nonzero() @@ -178,10 +179,10 @@ def getnnz(self): nnz = len(self.data) - if (nnz != len(self.row)) or (nnz != len(self.col)): + if nnz != len(self.row) or nnz != len(self.col): raise ValueError('row, column, and data array must all be the same length') - if rank(self.data) != 1 or rank(self.row) != 1 or rank(self.col) != 1: + if np.rank(self.data) != 1 or np.rank(self.row) != 1 or np.rank(self.col) != 1: raise ValueError('row, column, and data arrays must have rank 1') return nnz @@ -200,8 +201,8 @@ % self.col.dtype.name ) # only support 32-bit ints for now - self.row = asarray(self.row, dtype=intc) - self.col = asarray(self.col, dtype=intc) + self.row = np.asarray(self.row, dtype=np.intc) + self.col = np.asarray(self.col, dtype=np.intc) self.data = to_native(self.data) if nnz > 0: @@ -215,22 +216,22 @@ raise ValueError('negative column index found') - @deprecate + @np.deprecate def rowcol(self, num): return (self.row[num], self.col[num]) - @deprecate + @np.deprecate def getdata(self, num): return self.data[num] - def transpose(self,copy=False): + def transpose(self, copy=False): M,N = self.shape - return coo_matrix((self.data,(self.col,self.row)),(N,M),copy=copy) + return coo_matrix((self.data, (self.col, self.row)), shape=(N,M), copy=copy) def toarray(self): - B = zeros(self.shape, dtype=self.dtype) + B = np.zeros(self.shape, dtype=self.dtype) M,N = self.shape - coo_todense(M, N, self.nnz, self.row, self.col, self.data, B.ravel() ) + coo_todense(M, N, self.nnz, self.row, self.col, self.data, B.ravel()) return B def tocsc(self): @@ -257,16 +258,18 @@ if self.nnz == 0: return csc_matrix(self.shape, dtype=self.dtype) else: - indptr = empty(self.shape[1] + 1,dtype=intc) - indices = empty(self.nnz, dtype=intc) - data = empty(self.nnz, dtype=upcast(self.dtype)) + M,N = self.shape + indptr = np.empty(N + 1, dtype=np.intc) + indices = np.empty(self.nnz, dtype=np.intc) + data = np.empty(self.nnz, dtype=upcast(self.dtype)) - coo_tocsr(self.shape[1], self.shape[0], self.nnz, \ + coo_tocsr(N, M, self.nnz, \ self.col, self.row, self.data, \ indptr, indices, data) - A = csc_matrix((data, indices, indptr), self.shape) + A = csc_matrix((data, indices, indptr), shape=self.shape) A.sum_duplicates() + return A def tocsr(self): @@ -293,19 +296,20 @@ if self.nnz == 0: return csr_matrix(self.shape, dtype=self.dtype) else: - indptr = empty(self.shape[0] + 1,dtype=intc) - indices = empty(self.nnz, dtype=intc) - data = empty(self.nnz, dtype=upcast(self.dtype)) + M,N = self.shape + indptr = np.empty(M + 1, dtype=np.intc) + indices = np.empty(self.nnz, dtype=np.intc) + data = np.empty(self.nnz, dtype=upcast(self.dtype)) - coo_tocsr(self.shape[0], self.shape[1], self.nnz, \ + coo_tocsr(M, N, self.nnz, \ self.row, self.col, self.data, \ indptr, indices, data) - A = csr_matrix((data, indices, indptr), self.shape) + A = csr_matrix((data, indices, indptr), shape=self.shape) A.sum_duplicates() + return A - def tocoo(self, copy=False): if copy: return self.copy() @@ -316,7 +320,7 @@ from dia import dia_matrix ks = self.col - self.row #the diagonal for each nonzero - diags = unique(ks) + diags = np.unique(ks) if len(diags) > 100: #probably undesired, should we do something? @@ -324,15 +328,16 @@ pass #initialize and fill in data array - data = zeros( (len(diags), self.col.max()+1), dtype=self.dtype) - data[ searchsorted(diags,ks), self.col ] = self.data + data = np.zeros( (len(diags), self.col.max()+1), dtype=self.dtype) + data[ np.searchsorted(diags,ks), self.col ] = self.data - return dia_matrix((data,diags),shape=self.shape) + return dia_matrix((data,diags), shape=self.shape) def todok(self): + from itertools import izip from dok import dok_matrix - dok = dok_matrix((self.shape),dtype=self.dtype) + dok = dok_matrix((self.shape), dtype=self.dtype) dok.update( izip(izip(self.row,self.col),self.data) ) @@ -358,12 +363,12 @@ def _mul_vector(self, other): #output array - result = zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) + result = np.zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) coo_matvec(self.nnz, self.row, self.col, self.data, other, result) return result def _mul_dense_matrix(self, other): - return hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) + return np.hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) from sputils import _isinstance From scipy-svn at scipy.org Sat Oct 25 21:16:06 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 20:16:06 -0500 (CDT) Subject: [Scipy-svn] r4840 - in trunk/scipy/sparse: . linalg tests Message-ID: <20081026011606.A681B39C088@scipy.org> Author: wnbell Date: 2008-10-25 20:16:01 -0500 (Sat, 25 Oct 2008) New Revision: 4840 Modified: trunk/scipy/sparse/base.py trunk/scipy/sparse/compressed.py trunk/scipy/sparse/coo.py trunk/scipy/sparse/dia.py trunk/scipy/sparse/dok.py trunk/scipy/sparse/lil.py trunk/scipy/sparse/linalg/interface.py trunk/scipy/sparse/tests/test_base.py Log: cleaned up sp_matrix imports cleaned up multiplication handlers Modified: trunk/scipy/sparse/base.py =================================================================== --- trunk/scipy/sparse/base.py 2008-10-25 23:30:38 UTC (rev 4839) +++ trunk/scipy/sparse/base.py 2008-10-26 01:16:01 UTC (rev 4840) @@ -5,9 +5,7 @@ from warnings import warn -import numpy -from numpy import asarray, asmatrix, asanyarray, ones, deprecate, ravel, \ - matrix +import numpy as np from sputils import isdense, isscalarlike, isintlike @@ -100,7 +98,7 @@ return self else: for fp_type in fp_types: - if self.dtype <= numpy.dtype(fp_type): + if self.dtype <= np.dtype(fp_type): return self.astype(fp_type) raise TypeError,'cannot upcast [%s] to a floating \ @@ -137,15 +135,15 @@ format = 'und' return format - @deprecate + @np.deprecate def rowcol(self, num): return (None, None) - @deprecate + @np.deprecate def getdata(self, num): return None - @deprecate + @np.deprecate def listprint(self, start, stop): """Provides a way to print over a single index. """ @@ -212,10 +210,12 @@ else: return getattr(self,'to' + format)() - # default operations use the CSR format as a base - # and operations return in csr format - # thus, a new sparse matrix format just needs to define - # a tocsr method + ################################################################### + # NOTE: All arithmetic operations use csr_matrix by default. + # Therefore a new sparse matrix format just needs to define a + # .tocsr() method to provide arithmetic support. Any of these + # methods can be overridden for efficiency. + #################################################################### def multiply(self, other): """Point-wise multiplication by another matrix @@ -239,13 +239,34 @@ return self.tocsr().__rsub__(other) # old __mul__ interfaces - def matvec(self, other): + @np.deprecate + def matvec(self,other): return self * other - def matmat(self, other): + + @np.deprecate + def matmat(self,other): return self * other + + @np.deprecate def dot(self, other): return self * other + @np.deprecate + def rmatvec(self, other, conjugate=True): + """Multiplies the vector 'other' by the sparse matrix, returning a + dense vector as a result. + + If 'conjugate' is True: + - returns A.transpose().conj() * other + Otherwise: + - returns A.transpose() * other. + + """ + if conjugate: + return self.conj().transpose() * other + else: + return self.transpose() * other + def __mul__(self, other): """interpret other and call one of the following @@ -268,18 +289,18 @@ other.shape except AttributeError: # If it's a list or whatever, treat it like a matrix - other = asanyarray(other) + other = np.asanyarray(other) - if isdense(other) and asarray(other).squeeze().ndim <= 1: + if isdense(other) and np.asarray(other).squeeze().ndim <= 1: ## # dense row or column vector if other.shape != (N,) and other.shape != (N,1): raise ValueError('dimension mismatch') - result = self._mul_vector(ravel(other)) + result = self._mul_vector(np.ravel(other)) - if isinstance(other, matrix): - result = asmatrix(result) + if isinstance(other, np.matrix): + result = np.asmatrix(result) if other.ndim == 2 and other.shape[1] == 1: # If 'other' was an (nx1) column vector, reshape the result @@ -294,10 +315,10 @@ if other.shape[0] != self.shape[1]: raise ValueError('dimension mismatch') - result = self._mul_dense_matrix(asarray(other)) + result = self._mul_dense_matrix(np.asarray(other)) - if isinstance(other, matrix): - result = asmatrix(result) + if isinstance(other, np.matrix): + result = np.asmatrix(result) return result else: @@ -316,9 +337,21 @@ def _mul_sparse_matrix(self, other): return self.tocsr()._mul_sparse_matrix(other) - def __rmul__(self, other): - return self.tocsr().__rmul__(other) + def __rmul__(self, other): # other * self + if isscalarlike(other): + return self.__mul__(other) + else: + # Don't use asarray unless we have to + try: + tr = other.transpose() + except AttributeError: + tr = np.asarray(other).transpose() + return (self.transpose() * tr).transpose() + #################### + # Other Arithmetic # + #################### + def __truediv__(self, other): if isscalarlike(other): return self * (1./other) @@ -349,12 +382,12 @@ def __pow__(self, other): if self.shape[0] != self.shape[1]: - raise TypeError,'matrix is not square' + raise TypeError('matrix is not square') if isintlike(other): other = int(other) if other < 0: - raise ValueError,'exponent must be >= 0' + raise ValueError('exponent must be >= 0') if other == 0: from construct import identity @@ -367,7 +400,7 @@ result = result*self return result elif isscalarlike(other): - raise ValueError,'exponent must be an integer' + raise ValueError('exponent must be an integer') elif isspmatrix(other): warn('Using ** for elementwise multiplication is deprecated.'\ 'Use .multiply() instead', DeprecationWarning) @@ -460,36 +493,11 @@ a[0, i] = 1 return a * self - - def rmatvec(self, other, conjugate=True): - """Multiplies the vector 'other' by the sparse matrix, returning a - dense vector as a result. - - If 'conjugate' is True: - - returns A.transpose().conj() * other - Otherwise: - - returns A.transpose() * other. - - """ - return self.tocsr().rmatvec(other, conjugate=conjugate) - - #def rmatmat(self, other, conjugate=True): - # """ If 'conjugate' is True: - # returns other * A.transpose().conj(), - # where 'other' is a matrix. Otherwise: - # returns other * A.transpose(). - # """ - # other = csc_matrix(other) - # if conjugate: - # return other.matmat(self.transpose()).conj() - # else: - # return other.matmat(self.transpose()) - #def __array__(self): # return self.toarray() def todense(self): - return asmatrix(self.toarray()) + return np.asmatrix(self.toarray()) def toarray(self): return self.tocoo().toarray() @@ -522,13 +530,13 @@ m, n = self.shape if axis == 0: # sum over columns - return asmatrix(ones((1, m), dtype=self.dtype)) * self + return np.asmatrix(np.ones((1, m), dtype=self.dtype)) * self elif axis == 1: # sum over rows - return self * asmatrix(ones((n, 1), dtype=self.dtype)) + return self * np.asmatrix(np.ones((n, 1), dtype=self.dtype)) elif axis is None: # sum over rows and columns - return ( self * asmatrix(ones((n, 1), dtype=self.dtype)) ).sum() + return ( self * np.asmatrix(np.ones((n, 1), dtype=self.dtype)) ).sum() else: raise ValueError, "axis out of bounds" Modified: trunk/scipy/sparse/compressed.py =================================================================== --- trunk/scipy/sparse/compressed.py 2008-10-25 23:30:38 UTC (rev 4839) +++ trunk/scipy/sparse/compressed.py 2008-10-26 01:16:01 UTC (rev 4840) @@ -5,8 +5,7 @@ from warnings import warn -from numpy import array, asarray, zeros, rank, intc, empty, isscalar, \ - empty_like, where, concatenate, deprecate, diff, multiply +import numpy as np from base import spmatrix, isspmatrix, SparseEfficiencyWarning from data import _data_matrix @@ -15,7 +14,6 @@ isscalarlike, isintlike - class _cs_matrix(_data_matrix): """base matrix class for compressed row and column oriented matrices""" @@ -43,9 +41,9 @@ # create empty matrix self.shape = arg1 #spmatrix checks for errors here M, N = self.shape - self.data = zeros(0, getdtype(dtype, default=float)) - self.indices = zeros(0, intc) - self.indptr = zeros(self._swap((M,N))[0] + 1, dtype=intc) + self.data = np.zeros(0, getdtype(dtype, default=float)) + self.indices = np.zeros(0, np.intc) + self.indptr = np.zeros(self._swap((M,N))[0] + 1, dtype=np.intc) else: if len(arg1) == 2: # (data, ij) format @@ -55,9 +53,9 @@ elif len(arg1) == 3: # (data, indices, indptr) format (data, indices, indptr) = arg1 - self.indices = array(indices, copy=copy) - self.indptr = array(indptr, copy=copy) - self.data = array(data, copy=copy, dtype=getdtype(dtype, data)) + self.indices = np.array(indices, copy=copy) + self.indptr = np.array(indptr, copy=copy) + self.data = np.array(data, copy=copy, dtype=getdtype(dtype, data)) else: raise ValueError, "unrecognized %s_matrix constructor usage" %\ self.format @@ -65,7 +63,7 @@ else: #must be dense try: - arg1 = asarray(arg1) + arg1 = np.asarray(arg1) except: raise ValueError, "unrecognized %s_matrix constructor usage" % \ self.format @@ -128,14 +126,13 @@ % self.indices.dtype.name ) # only support 32-bit ints for now - self.indptr = asarray(self.indptr,dtype=intc) - self.indices = asarray(self.indices,dtype=intc) + self.indptr = np.asarray(self.indptr, dtype=np.intc) + self.indices = np.asarray(self.indices, dtype=np.intc) self.data = to_native(self.data) # check array shapes - if (rank(self.data) != 1) or (rank(self.indices) != 1) or \ - (rank(self.indptr) != 1): - raise ValueError,"data, indices, and indptr should be rank 1" + if np.rank(self.data) != 1 or np.rank(self.indices) != 1 or np.rank(self.indptr) != 1: + raise ValueError('data, indices, and indptr should be rank 1') # check index pointer if (len(self.indptr) != major_dim + 1 ): @@ -164,7 +161,7 @@ if self.indices.min() < 0: raise ValueError, "%s index values must be >= 0" % \ minor_name - if diff(self.indptr).min() < 0: + if np.diff(self.indptr).min() < 0: raise ValueError,'index pointer values must form a " \ "non-decreasing sequence' @@ -225,18 +222,6 @@ raise NotImplementedError - def __rmul__(self, other): # other * self - if isscalarlike(other): - return self.__mul__(other) - else: - # Don't use asarray unless we have to - try: - tr = other.transpose() - except AttributeError: - tr = asarray(other).transpose() - return (self.transpose() * tr).transpose() - - def __truediv__(self,other): if isscalarlike(other): return self * (1./other) @@ -258,7 +243,7 @@ raise ValueError('inconsistent shapes') if isdense(other): - return multiply(self.todense(),other) + return np.multiply(self.todense(),other) else: other = self.__class__(other) return self._binopt(other,'_elmul_') @@ -272,7 +257,7 @@ M,N = self.shape #output array - result = zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) + result = np.zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) # csr_matvec or csc_matvec fn = getattr(sparsetools,self.format + '_matvec') @@ -285,7 +270,7 @@ M,N = self.shape n_vecs = other.shape[1] #number of column vectors - result = zeros( (M,n_vecs), dtype=upcast(self.dtype,other.dtype) ) + result = np.zeros( (M,n_vecs), dtype=upcast(self.dtype,other.dtype) ) # csr_matvecs or csc_matvecs fn = getattr(sparsetools,self.format + '_matvecs') @@ -299,7 +284,7 @@ K2, N = other.shape major_axis = self._swap((M,N))[0] - indptr = empty( major_axis + 1, dtype=intc ) + indptr = np.empty(major_axis + 1, dtype=np.intc) other = self.__class__(other) #convert to this format fn = getattr(sparsetools, self.format + '_matmat_pass1') @@ -308,8 +293,8 @@ indptr) nnz = indptr[-1] - indices = empty( nnz, dtype=intc) - data = empty( nnz, dtype=upcast(self.dtype,other.dtype)) + indices = np.empty(nnz, dtype=np.intc) + data = np.empty(nnz, dtype=upcast(self.dtype,other.dtype)) fn = getattr(sparsetools, self.format + '_matmat_pass2') fn( M, N, self.indptr, self.indices, self.data, \ @@ -318,125 +303,8 @@ return self.__class__((data,indices,indptr),shape=(M,N)) - def matvec(self,other): - return self * other - def matmat(self,other): - return self * other - - #def matmat(self, other): - # if isspmatrix(other): - # M, K1 = self.shape - # K2, N = other.shape - # if (K1 != K2): - # raise ValueError, "shape mismatch error" - - # #return self._binopt(other,'mu',in_shape=(M,N),out_shape=(M,N)) - - # major_axis = self._swap((M,N))[0] - # indptr = empty( major_axis + 1, dtype=intc ) - - # other = self.__class__(other) #convert to this format - # fn = getattr(sparsetools, self.format + '_matmat_pass1') - # fn( M, N, self.indptr, self.indices, \ - # other.indptr, other.indices, \ - # indptr) - - # nnz = indptr[-1] - # indices = empty( nnz, dtype=intc) - # data = empty( nnz, dtype=upcast(self.dtype,other.dtype)) - - # fn = getattr(sparsetools, self.format + '_matmat_pass2') - # fn( M, N, self.indptr, self.indices, self.data, \ - # other.indptr, other.indices, other.data, \ - # indptr, indices, data) - - # return self.__class__((data,indices,indptr),shape=(M,N)) - - - # elif isdense(other): - # # TODO make sparse * dense matrix multiplication more efficient - # - # # matvec each column of other - # result = hstack( [ self * col.reshape(-1,1) for col in asarray(other).T ] ) - # if isinstance(other, matrix): - # result = asmatrix(result) - # return result - - # else: - # raise TypeError, "need a dense or sparse matrix" - - - #def matvec(self, other): - # """Sparse matrix vector product (self * other) - - # 'other' may be a rank 1 array of length N or a rank 2 array - # or matrix with shape (N,1). - - # """ - # #If the optional 'output' parameter is defined, it will - # #be used to store the result. Otherwise, a new vector - # #will be allocated. - - # if isdense(other): - # M,N = self.shape - - # if other.shape != (N,) and other.shape != (N,1): - # raise ValueError, "dimension mismatch" - - # # csrmux, cscmux - # fn = getattr(sparsetools,self.format + '_matvec') - - # #output array - # y = zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) - - # #if output is None: - # # y = empty( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) - # #else: - # # if output.shape != (M,) and output.shape != (M,1): - # # raise ValueError, "output array has improper dimensions" - # # if not output.flags.c_contiguous: - # # raise ValueError, "output array must be contiguous" - # # if output.dtype != upcast(self.dtype,other.dtype): - # # raise ValueError, "output array has dtype=%s "\ - # # "dtype=%s is required" % \ - # # (output.dtype,upcast(self.dtype,other.dtype)) - # # y = output - - # fn(self.shape[0], self.shape[1], \ - # self.indptr, self.indices, self.data, numpy.ravel(other), y) - - # if isinstance(other, matrix): - # y = asmatrix(y) - - # if other.ndim == 2 and other.shape[1] == 1: - # # If 'other' was an (nx1) column vector, reshape the result - # y = y.reshape(-1,1) - - # return y - - # elif isspmatrix(other): - # raise TypeError, "use matmat() for sparse * sparse" - - # else: - # raise TypeError, "need a dense vector" - - def rmatvec(self, other, conjugate=True): - """Multiplies the vector 'other' by the sparse matrix, returning a - dense vector as a result. - - If 'conjugate' is True: - - returns A.transpose().conj() * other - Otherwise: - - returns A.transpose() * other. - - """ - if conjugate: - return self.transpose().conj().matvec( other ) - else: - return self.transpose().matvec( other ) - - @deprecate + @np.deprecate def getdata(self, ind): return self.data[ind] @@ -445,7 +313,7 @@ """ #TODO support k-th diagonal fn = getattr(sparsetools, self.format + "_diagonal") - y = empty( min(self.shape), dtype=upcast(self.dtype) ) + y = np.empty( min(self.shape), dtype=upcast(self.dtype) ) fn(self.shape[0], self.shape[1], self.indptr, self.indices, self.data, y) return y @@ -506,7 +374,7 @@ start = self.indptr[major_index] end = self.indptr[major_index+1] - indxs = where(minor_index == self.indices[start:end])[0] + indxs = np.where(minor_index == self.indices[start:end])[0] num_matches = len(indxs) @@ -539,7 +407,7 @@ index = self.indices[indices] - start data = self.data[indices] - indptr = array([0, len(indices)]) + indptr = np.array([0, len(indices)]) return self.__class__((data, index, indptr), shape=shape, \ dtype=self.dtype) @@ -563,7 +431,7 @@ return i0, i1 - elif isscalar( sl ): + elif np.isscalar( sl ): if sl < 0: sl += num @@ -612,7 +480,7 @@ start = self.indptr[major_index] end = self.indptr[major_index+1] - indxs = where(minor_index == self.indices[start:end])[0] + indxs = np.where(minor_index == self.indices[start:end])[0] num_matches = len(indxs) @@ -627,10 +495,10 @@ newindx = self.indices[start:end].searchsorted(minor_index) newindx += start - val = array([val],dtype=self.data.dtype) - minor_index = array([minor_index],dtype=self.indices.dtype) - self.data = concatenate((self.data[:newindx],val,self.data[newindx:])) - self.indices = concatenate((self.indices[:newindx],minor_index,self.indices[newindx:])) + val = np.array([val],dtype=self.data.dtype) + minor_index = np.array([minor_index],dtype=self.indices.dtype) + self.data = np.concatenate((self.data[:newindx],val,self.data[newindx:])) + self.indices = np.concatenate((self.indices[:newindx],minor_index,self.indices[newindx:])) self.indptr[major_index+1:] += 1 @@ -670,7 +538,7 @@ data = data.copy() minor_indices = minor_indices.copy() - major_indices = empty(len(minor_indices),dtype=intc) + major_indices = np.empty(len(minor_indices), dtype=np.intc) sparsetools.expandptr(major_dim,self.indptr,major_indices) @@ -814,9 +682,9 @@ fn = getattr(sparsetools, self.format + op + self.format) maxnnz = self.nnz + other.nnz - indptr = empty_like(self.indptr) - indices = empty( maxnnz, dtype=intc ) - data = empty( maxnnz, dtype=upcast(self.dtype,other.dtype) ) + indptr = np.empty_like(self.indptr) + indices = np.empty(maxnnz, dtype=np.intc) + data = np.empty(maxnnz, dtype=upcast(self.dtype,other.dtype)) fn(in_shape[0], in_shape[1], \ self.indptr, self.indices, self.data, Modified: trunk/scipy/sparse/coo.py =================================================================== --- trunk/scipy/sparse/coo.py 2008-10-25 23:30:38 UTC (rev 4839) +++ trunk/scipy/sparse/coo.py 2008-10-26 01:16:01 UTC (rev 4840) @@ -8,9 +8,6 @@ import numpy as np -#from numpy import array, asarray, empty, intc, zeros, unique, searchsorted,\ -# atleast_2d, rank, deprecate, hstack - from sparsetools import coo_tocsr, coo_todense, coo_matvec from base import isspmatrix from data import _data_matrix Modified: trunk/scipy/sparse/dia.py =================================================================== --- trunk/scipy/sparse/dia.py 2008-10-25 23:30:38 UTC (rev 4839) +++ trunk/scipy/sparse/dia.py 2008-10-26 01:16:01 UTC (rev 4840) @@ -2,7 +2,7 @@ __docformat__ = "restructuredtext en" -__all__ = ['dia_matrix','isspmatrix_dia'] +__all__ = ['dia_matrix', 'isspmatrix_dia'] import numpy as np Modified: trunk/scipy/sparse/dok.py =================================================================== --- trunk/scipy/sparse/dok.py 2008-10-25 23:30:38 UTC (rev 4839) +++ trunk/scipy/sparse/dok.py 2008-10-26 01:16:01 UTC (rev 4840) @@ -476,7 +476,7 @@ base[newkey] = self[key] return base, ext - +# TODO update these w/ new multiplication handlers # def matvec(self, other): # if isdense(other): # if other.shape[0] != self.shape[1]: Modified: trunk/scipy/sparse/lil.py =================================================================== --- trunk/scipy/sparse/lil.py 2008-10-25 23:30:38 UTC (rev 4839) +++ trunk/scipy/sparse/lil.py 2008-10-26 01:16:01 UTC (rev 4840) @@ -382,21 +382,6 @@ new[new_r,new_c] = self[i,j] return new - def __add__(self, other): - if np.isscalar(other) and other != 0: - raise ValueError("Refusing to destroy sparsity. " - "Use x.todense() + c instead.") - else: - return spmatrix.__add__(self, other) - - def __rmul__(self, other): # other * self - if isscalarlike(other): - # Multiplication by a scalar is symmetric - return self.__mul__(other) - else: - return spmatrix.__rmul__(self, other) - - def toarray(self): d = np.zeros(self.shape, dtype=self.dtype) for i, row in enumerate(self.rows): Modified: trunk/scipy/sparse/linalg/interface.py =================================================================== --- trunk/scipy/sparse/linalg/interface.py 2008-10-25 23:30:38 UTC (rev 4839) +++ trunk/scipy/sparse/linalg/interface.py 2008-10-26 01:16:01 UTC (rev 4840) @@ -127,8 +127,14 @@ matmat=matmat, dtype=A.dtype) elif isspmatrix(A): - return LinearOperator(A.shape, A.matvec, rmatvec=A.rmatvec, - matmat=A.dot, dtype=A.dtype) + def matvec(v): + return A * v + def rmatvec(v): + return A.conj().transpose() * v + def matmat(V): + return A * V + return LinearOperator(A.shape, matvec, rmatvec=rmatvec, + matmat=matmat, dtype=A.dtype) else: if hasattr(A,'shape') and hasattr(A,'matvec'): Modified: trunk/scipy/sparse/tests/test_base.py =================================================================== --- trunk/scipy/sparse/tests/test_base.py 2008-10-25 23:30:38 UTC (rev 4839) +++ trunk/scipy/sparse/tests/test_base.py 2008-10-26 01:16:01 UTC (rev 4840) @@ -355,21 +355,18 @@ csp = bsp.tocsc() c = b assert_array_almost_equal((asp*csp).todense(), a*c) - assert_array_almost_equal((asp.matmat(csp)).todense(), a*c) assert_array_almost_equal( asp*c, a*c) assert_array_almost_equal( a*csp, a*c) assert_array_almost_equal( a2*csp, a*c) csp = bsp.tocsr() assert_array_almost_equal((asp*csp).todense(), a*c) - assert_array_almost_equal((asp.matmat(csp)).todense(), a*c) assert_array_almost_equal( asp*c, a*c) assert_array_almost_equal( a*csp, a*c) assert_array_almost_equal( a2*csp, a*c) csp = bsp.tocoo() assert_array_almost_equal((asp*csp).todense(), a*c) - assert_array_almost_equal((asp.matmat(csp)).todense(), a*c) assert_array_almost_equal( asp*c, a*c) assert_array_almost_equal( a*csp, a*c) @@ -526,47 +523,6 @@ assert_array_equal(self.dat/17.3,a.todense()) -class _TestMatvecOutput: - """test using the matvec() output parameter""" - def test_matvec_output(self): - pass #Currently disabled - -# #flat array -# x = array([1.25, -6.5, 0.125, -3.75],dtype='d') -# y = zeros(3,dtype='d') -# -# self.datsp.matvec(x,y) -# assert_array_equal(self.datsp*x,y) -# -# #column vector -# x = array([1.25, -6.5, 0.125, -3.75],dtype='d') -# x = x.reshape(4,1) -# y = zeros((3,1),dtype='d') -# -# self.datsp.matvec(x,y) -# assert_array_equal(self.datsp*x,y) -# -# # improper output type -# x = array([1.25, -6.5, 0.125, -3.75],dtype='d') -# y = zeros(3,dtype='i') -# -# self.assertRaises( ValueError, self.datsp.matvec, x, y ) -# -# # improper output shape -# x = array([1.25, -6.5, 0.125, -3.75],dtype='d') -# y = zeros(2,dtype='d') -# -# self.assertRaises( ValueError, self.datsp.matvec, x, y ) -# -# # proper upcast output type -# x = array([1.25, -6.5, 0.125, -3.75],dtype='complex64') -# x.imag = [1,2,3,4] -# y = zeros(3,dtype='complex128') -# -# self.datsp.matvec(x,y) -# assert_array_equal(self.datsp*x,y) -# assert_equal((self.datsp*x).dtype,y.dtype) - class _TestGetSet: def test_setelement(self): a = self.spmatrix((3,4)) @@ -893,7 +849,7 @@ class TestCSR(_TestCommon, _TestGetSet, _TestSolve, - _TestInplaceArithmetic, _TestArithmetic, _TestMatvecOutput, + _TestInplaceArithmetic, _TestArithmetic, _TestHorizSlicing, _TestVertSlicing, _TestBothSlicing, _TestFancyIndexing, TestCase): spmatrix = csr_matrix @@ -990,7 +946,7 @@ class TestCSC(_TestCommon, _TestGetSet, _TestSolve, - _TestInplaceArithmetic, _TestArithmetic, _TestMatvecOutput, + _TestInplaceArithmetic, _TestArithmetic, _TestHorizSlicing, _TestVertSlicing, _TestBothSlicing, _TestFancyIndexing, TestCase): spmatrix = csc_matrix @@ -1400,8 +1356,7 @@ -class TestBSR(_TestCommon, _TestArithmetic, _TestInplaceArithmetic, - _TestMatvecOutput, TestCase): +class TestBSR(_TestCommon, _TestArithmetic, _TestInplaceArithmetic, TestCase): spmatrix = bsr_matrix def test_constructor1(self): From scipy-svn at scipy.org Sat Oct 25 21:33:42 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 20:33:42 -0500 (CDT) Subject: [Scipy-svn] r4841 - trunk/scipy/sparse Message-ID: <20081026013342.65F3839C088@scipy.org> Author: wnbell Date: 2008-10-25 20:33:39 -0500 (Sat, 25 Oct 2008) New Revision: 4841 Modified: trunk/scipy/sparse/csc.py trunk/scipy/sparse/csr.py Log: cleaned up csr_matrix and csc_matrix imports Modified: trunk/scipy/sparse/csc.py =================================================================== --- trunk/scipy/sparse/csc.py 2008-10-26 01:16:01 UTC (rev 4840) +++ trunk/scipy/sparse/csc.py 2008-10-26 01:33:39 UTC (rev 4841) @@ -6,7 +6,8 @@ from warnings import warn -from numpy import asarray, intc, empty, searchsorted, deprecate +import numpy as np + from sparsetools import csc_tocsr from sputils import upcast, isintlike @@ -96,11 +97,11 @@ for r in xrange(self.shape[0]): yield csr[r,:] - @deprecate + @np.deprecate def rowcol(self, ind): #TODO remove after 0.7 row = self.indices[ind] - col = searchsorted(self.indptr, ind+1)-1 + col = np.searchsorted(self.indptr, ind+1) - 1 return (row, col) def tocsc(self, copy=False): @@ -110,16 +111,17 @@ return self def tocsr(self): - indptr = empty(self.shape[0] + 1, dtype=intc) - indices = empty(self.nnz, dtype=intc) - data = empty(self.nnz, dtype=upcast(self.dtype)) + M,N = self.shape + indptr = np.empty(M + 1, dtype=np.intc) + indices = np.empty(self.nnz, dtype=np.intc) + data = np.empty(self.nnz, dtype=upcast(self.dtype)) - csc_tocsr(self.shape[0], self.shape[1], \ + csc_tocsr(M, N, \ self.indptr, self.indices, self.data, \ indptr, indices, data) from csr import csr_matrix - A = csr_matrix((data, indices, indptr), self.shape) + A = csr_matrix((data, indices, indptr), shape=self.shape) A.has_sorted_indices = True return A @@ -137,8 +139,8 @@ if isintlike(col) or isinstance(col,slice): return self.T[col,row].T else: - row = asarray(row, dtype='intc') - col = asarray(col, dtype='intc') + row = np.asarray(row, dtype=np.intc) + col = np.asarray(col, dtype=np.intc) if len(row.shape) == 1: return self.T[col,row] elif len(row.shape) == 2: Modified: trunk/scipy/sparse/csr.py =================================================================== --- trunk/scipy/sparse/csr.py 2008-10-26 01:16:01 UTC (rev 4840) +++ trunk/scipy/sparse/csr.py 2008-10-26 01:33:39 UTC (rev 4841) @@ -7,8 +7,7 @@ from warnings import warn -from numpy import asarray, asmatrix, zeros, intc, empty, isscalar, array, \ - searchsorted, where, deprecate, arange, ones, ravel +import numpy as np from sparsetools import csr_tocsc, csr_tobsr, csr_count_blocks, \ get_csr_submatrix @@ -91,13 +90,13 @@ def transpose(self, copy=False): from csc import csc_matrix M,N = self.shape - return csc_matrix((self.data,self.indices,self.indptr),(N,M),copy=copy) + return csc_matrix((self.data,self.indices,self.indptr), shape=(N,M), copy=copy) - @deprecate + @np.deprecate def rowcol(self, ind): #TODO remove after 0.7 col = self.indices[ind] - row = searchsorted(self.indptr, ind+1)-1 + row = np.searchsorted(self.indptr, ind+1)-1 return (row, col) @@ -105,7 +104,7 @@ from lil import lil_matrix lil = lil_matrix(self.shape,dtype=self.dtype) - self.sort_indices() #lil_matrix needs sorted rows + self.sort_indices() #lil_matrix needs sorted column indices ptr,ind,dat = self.indptr,self.indices,self.data rows, data = lil.rows, lil.data @@ -125,28 +124,30 @@ return self def tocsc(self): - indptr = empty(self.shape[1] + 1, dtype=intc) - indices = empty(self.nnz, dtype=intc) - data = empty(self.nnz, dtype=upcast(self.dtype)) + indptr = np.empty(self.shape[1] + 1, dtype=np.intc) + indices = np.empty(self.nnz, dtype=np.intc) + data = np.empty(self.nnz, dtype=upcast(self.dtype)) csr_tocsc(self.shape[0], self.shape[1], \ self.indptr, self.indices, self.data, \ indptr, indices, data) from csc import csc_matrix - A = csc_matrix((data, indices, indptr), self.shape) + A = csc_matrix((data, indices, indptr), shape=self.shape) A.has_sorted_indices = True return A - def tobsr(self,blocksize=None,copy=True): + def tobsr(self, blocksize=None, copy=True): from bsr import bsr_matrix if blocksize is None: from spfuncs import estimate_blocksize return self.tobsr(blocksize=estimate_blocksize(self)) + elif blocksize == (1,1): arg1 = (self.data.reshape(-1,1,1),self.indices,self.indptr) - return bsr_matrix( arg1, shape=self.shape, copy=copy ) + return bsr_matrix(arg1, shape=self.shape, copy=copy ) + else: R,C = blocksize M,N = self.shape @@ -156,14 +157,14 @@ blks = csr_count_blocks(M,N,R,C,self.indptr,self.indices) - indptr = empty( M/R + 1, dtype=intc ) - indices = empty( blks, dtype=intc ) - data = zeros( (blks,R,C), dtype=self.dtype) + indptr = np.empty(M/R + 1, dtype=np.intc) + indices = np.empty(blks, dtype=np.intc) + data = np.zeros((blks,R,C), dtype=self.dtype) csr_tobsr(M, N, R, C, self.indptr, self.indices, self.data, \ indptr, indices, data.ravel() ) - return bsr_matrix( (data,indices,indptr), shape=self.shape ) + return bsr_matrix((data,indices,indptr), shape=self.shape) # these functions are used by the parent class (_cs_matrix) # to remove redudancy between csc_matrix and csr_matrix @@ -176,7 +177,7 @@ def __getitem__(self, key): def asindices(x): try: - x = asarray(x,dtype='intc') + x = np.asarray(x, dtype=np.intc) except: raise IndexError('invalid index') else: @@ -201,11 +202,11 @@ indices = indices.copy() indices[indices < 0] += N - indptr = arange(len(indices) + 1, dtype='intc') - data = ones(len(indices), dtype=self.dtype) + indptr = np.arange(len(indices) + 1, dtype=np.intc) + data = np.ones(len(indices), dtype=self.dtype) shape = (len(indices),N) - return csr_matrix( (data,indices,indptr), shape=shape) + return csr_matrix((data,indices,indptr), shape=shape) if isinstance(key, tuple): @@ -245,10 +246,10 @@ val = [] for i,j in zip(row,col): val.append(self._get_single_element(i,j)) - return asmatrix(val) + return np.asmatrix(val) elif len(row.shape) == 2: - row = ravel(row) #[[[1],[2]],[1,2]] + row = np.ravel(row) #[[[1],[2]],[1,2]] P = extractor(row, self.shape[0]) return (P*self)[:,col] @@ -276,7 +277,7 @@ start = self.indptr[row] end = self.indptr[row+1] - indxs = where(col == self.indices[start:end])[0] + indxs = np.where(col == self.indices[start:end])[0] num_matches = len(indxs) @@ -288,7 +289,7 @@ else: raise ValueError('nonzero entry (%d,%d) occurs more than once' % (row,col) ) - def _get_row_slice(self, i, cslice ): + def _get_row_slice(self, i, cslice): """Returns a copy of row self[i, cslice] """ if i < 0: @@ -315,7 +316,7 @@ index = self.indices[indices] - start data = self.data[indices] - indptr = array([0, len(indices)]) + indptr = np.array([0, len(indices)]) return csr_matrix( (data, index, indptr), shape=(1, stop-start) ) def _get_submatrix( self, row_slice, col_slice ): From scipy-svn at scipy.org Sat Oct 25 21:45:22 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sat, 25 Oct 2008 20:45:22 -0500 (CDT) Subject: [Scipy-svn] r4842 - trunk/scipy/sparse Message-ID: <20081026014522.82AE339C088@scipy.org> Author: wnbell Date: 2008-10-25 20:43:51 -0500 (Sat, 25 Oct 2008) New Revision: 4842 Modified: trunk/scipy/sparse/construct.py Log: cleaned up imports in construction.py Modified: trunk/scipy/sparse/construct.py =================================================================== --- trunk/scipy/sparse/construct.py 2008-10-26 01:33:39 UTC (rev 4841) +++ trunk/scipy/sparse/construct.py 2008-10-26 01:43:51 UTC (rev 4842) @@ -9,9 +9,7 @@ from warnings import warn -import numpy -from numpy import ones, arange, intc, asarray, rank, zeros, \ - cumsum, concatenate, empty +import numpy as np from sputils import upcast @@ -84,28 +82,28 @@ """ if format in ['csr','csc']: - indptr = arange(n+1, dtype=intc) - indices = arange(n, dtype=intc) - data = ones(n, dtype=dtype) + indptr = np.arange(n+1, dtype=np.intc) + indices = np.arange(n, dtype=np.intc) + data = np.ones(n, dtype=dtype) cls = eval('%s_matrix' % format) return cls((data,indices,indptr),(n,n)) elif format == 'coo': - row = arange(n, dtype=intc) - col = arange(n, dtype=intc) - data = ones(n, dtype=dtype) + row = np.arange(n, dtype=np.intc) + col = np.arange(n, dtype=np.intc) + data = np.ones(n, dtype=dtype) return coo_matrix((data,(row,col)),(n,n)) elif format == 'dia': - data = ones(n, dtype=dtype) + data = np.ones(n, dtype=dtype) diags = [0] - return dia_matrix( (data,diags), shape=(n,n) ) + return dia_matrix((data,diags), shape=(n,n)) else: - return identity( n, dtype=dtype, format='csr').asformat(format) + return identity(n, dtype=dtype, format='csr').asformat(format) def eye(m, n, k=0, dtype='d', format=None): """eye(m, n) returns a sparse (m x n) matrix where the k-th diagonal is all ones and everything else is zeros. """ - diags = ones((1, m), dtype=dtype) + diags = np.ones((1, m), dtype=dtype) return spdiags(diags, k, m, n).asformat(format) def kron(A, B, format=None): @@ -148,7 +146,7 @@ #B is fairly dense, use BSR A = csr_matrix(A,copy=True) - output_shape = (A.shape[0]*B.shape[0],A.shape[1]*B.shape[1]) + output_shape = (A.shape[0]*B.shape[0], A.shape[1]*B.shape[1]) if A.nnz == 0 or B.nnz == 0: # kronecker product is the zero matrix @@ -158,11 +156,11 @@ data = A.data.repeat(B.size).reshape(-1,B.shape[0],B.shape[1]) data = data * B - return bsr_matrix((data,A.indices,A.indptr),shape=output_shape) + return bsr_matrix((data,A.indices,A.indptr), shape=output_shape) else: #use COO A = coo_matrix(A) - output_shape = (A.shape[0]*B.shape[0],A.shape[1]*B.shape[1]) + output_shape = (A.shape[0]*B.shape[0], A.shape[1]*B.shape[1]) if A.nnz == 0 or B.nnz == 0: # kronecker product is the zero matrix @@ -231,7 +229,7 @@ return (L+R).asformat(format) #since L + R is not always same format -def hstack( blocks, format=None, dtype=None ): +def hstack(blocks, format=None, dtype=None): """Stack sparse matrices horizontally (column wise) Parameters @@ -256,7 +254,7 @@ """ return bmat([blocks], format=format, dtype=dtype) -def vstack( blocks, format=None, dtype=None ): +def vstack(blocks, format=None, dtype=None): """Stack sparse matrices vertically (row wise) Parameters @@ -282,7 +280,7 @@ """ return bmat([ [b] for b in blocks ], format=format, dtype=dtype) -def bmat( blocks, format=None, dtype=None ): +def bmat(blocks, format=None, dtype=None): """Build a sparse matrix from sparse sub-blocks Parameters @@ -313,16 +311,16 @@ """ - blocks = asarray(blocks, dtype='object') + blocks = np.asarray(blocks, dtype='object') - if rank(blocks) != 2: + if np.rank(blocks) != 2: raise ValueError('blocks must have rank 2') M,N = blocks.shape - block_mask = zeros( blocks.shape, dtype='bool' ) - brow_lengths = zeros( blocks.shape[0], dtype=int ) - bcol_lengths = zeros( blocks.shape[1], dtype=int ) + block_mask = np.zeros(blocks.shape, dtype=np.bool) + brow_lengths = np.zeros(blocks.shape[0], dtype=np.intc) + bcol_lengths = np.zeros(blocks.shape[1], dtype=np.intc) # convert everything to COO format for i in range(M): @@ -355,12 +353,12 @@ if dtype is None: dtype = upcast( *tuple([A.dtype for A in blocks[block_mask]]) ) - row_offsets = concatenate(([0],cumsum(brow_lengths))) - col_offsets = concatenate(([0],cumsum(bcol_lengths))) + row_offsets = np.concatenate(([0], np.cumsum(brow_lengths))) + col_offsets = np.concatenate(([0], np.cumsum(bcol_lengths))) - data = empty(nnz, dtype=dtype) - row = empty(nnz, dtype=intc) - col = empty(nnz, dtype=intc) + data = np.empty(nnz, dtype=dtype) + row = np.empty(nnz, dtype=np.intc) + col = np.empty(nnz, dtype=np.intc) nnz = 0 for i in range(M): @@ -376,8 +374,8 @@ nnz += A.nnz - shape = (sum(brow_lengths),sum(bcol_lengths)) - return coo_matrix( (data, (row, col)), shape=shape ).asformat(format) + shape = (np.sum(brow_lengths), np.sum(bcol_lengths)) + return coo_matrix((data, (row, col)), shape=shape).asformat(format) @@ -387,13 +385,11 @@ __all__ += [ 'speye','spidentity', 'spkron', 'lil_eye', 'lil_diags' ] -from numpy import deprecate +spkron = np.deprecate(kron, oldname='spkron', newname='scipy.sparse.kron') +speye = np.deprecate(eye, oldname='speye', newname='scipy.sparse.eye') +spidentity = np.deprecate(identity, oldname='spidentity', newname='scipy.sparse.identity') -spkron = deprecate(kron, oldname='spkron', newname='scipy.sparse.kron') -speye = deprecate(eye, oldname='speye', newname='scipy.sparse.eye') -spidentity = deprecate(identity, oldname='spidentity', newname='scipy.sparse.identity') - def lil_eye((r,c), k=0, dtype='d'): """Generate a lil_matrix of dimensions (r,c) with the k-th diagonal set to 1. @@ -413,13 +409,11 @@ warn("lil_eye is deprecated." \ "use scipy.sparse.eye(r, c, k, format='lil') instead", \ DeprecationWarning) - return eye(r,c,k,dtype=dtype,format='lil') + return eye(r, c, k, dtype=dtype, format='lil') -from numpy import clip -from itertools import izip #TODO remove this function -def lil_diags(diags,offsets,(m,n),dtype='d'): +def lil_diags(diags, offsets, (m,n), dtype='d'): """Generate a lil_matrix with the given diagonals. Parameters @@ -449,7 +443,7 @@ raise ValueError("Number of diagonals provided should " "agree with offsets.") - sort_indices = numpy.argsort(offsets_unsorted) + sort_indices = np.argsort(offsets_unsorted) diags = [diags_unsorted[k] for k in sort_indices] offsets = [offsets_unsorted[k] for k in sort_indices] @@ -459,8 +453,10 @@ "diagonal %s." % k) out = lil_matrix((m,n),dtype=dtype) + + from itertools import izip for k,diag in izip(offsets,diags): - for ix,c in enumerate(xrange(clip(k,0,n),clip(m+k,0,n))): + for ix,c in enumerate(xrange(np.clip(k,0,n),np.clip(m+k,0,n))): out.rows[c-k].append(c) out.data[c-k].append(diag[ix]) return out From scipy-svn at scipy.org Sun Oct 26 02:48:56 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 01:48:56 -0500 (CDT) Subject: [Scipy-svn] r4843 - branches/refactor_fft/scipy/cluster Message-ID: <20081026064856.1B62A39C088@scipy.org> Author: cdavid Date: 2008-10-26 01:48:52 -0500 (Sun, 26 Oct 2008) New Revision: 4843 Removed: branches/refactor_fft/scipy/cluster/SConstruct Log: svnmerge seems to have screwed up with renames; start over manually... Deleted: branches/refactor_fft/scipy/cluster/SConstruct =================================================================== --- branches/refactor_fft/scipy/cluster/SConstruct 2008-10-26 01:43:51 UTC (rev 4842) +++ branches/refactor_fft/scipy/cluster/SConstruct 2008-10-26 06:48:52 UTC (rev 4843) @@ -1,2 +0,0 @@ -from numscons import GetInitEnvironment -GetInitEnvironment(ARGUMENTS).DistutilsSConscript('SConscript') From scipy-svn at scipy.org Sun Oct 26 02:50:26 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 01:50:26 -0500 (CDT) Subject: [Scipy-svn] r4844 - branches/refactor_fft/scipy/cluster Message-ID: <20081026065026.E6FB539C088@scipy.org> Author: cdavid Date: 2008-10-26 01:50:22 -0500 (Sun, 26 Oct 2008) New Revision: 4844 Added: branches/refactor_fft/scipy/cluster/SConstruct Log: Put updated SConstruct file. Added: branches/refactor_fft/scipy/cluster/SConstruct =================================================================== --- branches/refactor_fft/scipy/cluster/SConstruct 2008-10-26 06:48:52 UTC (rev 4843) +++ branches/refactor_fft/scipy/cluster/SConstruct 2008-10-26 06:50:22 UTC (rev 4844) @@ -0,0 +1,2 @@ +from numscons import GetInitEnvironment +GetInitEnvironment(ARGUMENTS).DistutilsSConscript('SConscript') From scipy-svn at scipy.org Sun Oct 26 05:20:59 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 04:20:59 -0500 (CDT) Subject: [Scipy-svn] r4845 - in trunk/scipy: . cluster interpolate io/matlab io/matlab/tests ndimage optimize/tests sparse sparse/linalg/isolve sparse/linalg/isolve/tests sparse/sparsetools sparse/tests stats Message-ID: <20081026092059.1E1DE39C05F@scipy.org> Author: jarrod.millman Date: 2008-10-26 04:20:24 -0500 (Sun, 26 Oct 2008) New Revision: 4845 Modified: trunk/scipy/__init__.py trunk/scipy/cluster/distance.py trunk/scipy/cluster/hierarchy.py trunk/scipy/interpolate/interpolate_wrapper.py trunk/scipy/interpolate/setup.py trunk/scipy/io/matlab/byteordercodes.py trunk/scipy/io/matlab/mio.py trunk/scipy/io/matlab/mio5.py trunk/scipy/io/matlab/miobase.py trunk/scipy/io/matlab/tests/test_byteordercodes.py trunk/scipy/io/matlab/tests/test_mio.py trunk/scipy/ndimage/filters.py trunk/scipy/optimize/tests/test_nnls.py trunk/scipy/sparse/base.py trunk/scipy/sparse/construct.py trunk/scipy/sparse/dok.py trunk/scipy/sparse/lil.py trunk/scipy/sparse/linalg/isolve/iterative.py trunk/scipy/sparse/linalg/isolve/tests/test_iterative.py trunk/scipy/sparse/sparsetools/bsr.py trunk/scipy/sparse/sparsetools/coo.py trunk/scipy/sparse/sparsetools/csc.py trunk/scipy/sparse/sparsetools/csr.py trunk/scipy/sparse/sparsetools/dia.py trunk/scipy/sparse/tests/test_base.py trunk/scipy/stats/distributions.py Log: ran reindent Modified: trunk/scipy/__init__.py =================================================================== --- trunk/scipy/__init__.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/__init__.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -74,7 +74,7 @@ # Remove subpackage names from __all__ such that they are not imported via # "from scipy import *". This works around a numpy bug present in < 1.2. subpackages = """cluster constants fftpack integrate interpolate io lib linalg -linsolve maxentropy misc ndimage odr optimize signal sparse special +linsolve maxentropy misc ndimage odr optimize signal sparse special splinalg stats stsci weave""".split() for name in subpackages: try: Modified: trunk/scipy/cluster/distance.py =================================================================== --- trunk/scipy/cluster/distance.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/cluster/distance.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -954,8 +954,8 @@ d(u,v) = \sum_u {|u_i-v_i|} {|u_i|+|v_i|} - + 12. ``Y = pdist(X, 'braycurtis')`` Computes the Bray-Curtis distance between the points. The @@ -1285,8 +1285,8 @@ it is known that ``X - X.T1`` is small and ``diag(X)`` is close to zero. These values are ignored any way so they do not disrupt the squareform transformation. - + Calling Conventions ------------------- @@ -1692,8 +1692,8 @@ d(u,v) = \sum_u {|u_i-v_i|} {|u_i|+|v_i|} - + 12. ``Y = cdist(X, 'braycurtis')`` Computes the Bray-Curtis distance between the points. The Modified: trunk/scipy/cluster/hierarchy.py =================================================================== --- trunk/scipy/cluster/hierarchy.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/cluster/hierarchy.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -345,7 +345,7 @@ Performs centroid/UPGMC linkage on the condensed distance matrix ``y``. See ``linkage`` for more information on the return - structure and algorithm. + structure and algorithm. 2. Z = centroid(X) @@ -427,7 +427,7 @@ def linkage(y, method='single', metric='euclidean'): - """ + """ Performs hierarchical/agglomerative clustering on the condensed distance matrix y. y must be a {n \choose 2} sized vector where n is the number of original observations paired @@ -665,7 +665,7 @@ The number of leaf nodes (original observations) belonging to the cluster node nd. If the target node is a leaf, 1 is returned. - + :Returns: c : int @@ -713,7 +713,7 @@ the list. For example, the statement: - + ids = root.preOrder(lambda x: x.id) returns a list of the node ids corresponding to the leaf nodes @@ -730,7 +730,7 @@ :Returns: - L : list - The pre-order traversal. + The pre-order traversal. """ # Do a preorder traversal, caching the result. To avoid having to do @@ -773,7 +773,7 @@ Converts a hierarchical clustering encoded in the matrix Z (by linkage) into an easy-to-use tree object. The reference r to the root cnode object is returned. - + Each cnode object has a left, right, dist, id, and count attribute. The left and right attributes point to cnode objects that were combined to generate the cluster. If both are None then @@ -885,12 +885,12 @@ hierarchical clustering defined by the linkage matrix ``Z`` of a set of :math:`$n$` observations in :math:`$m$` dimensions. ``Y`` is the condensed distance matrix from which - ``Z`` was generated. + ``Z`` was generated. :Returns: - c : ndarray The cophentic correlation distance (if ``y`` is passed). - + - d : ndarray The cophenetic distance matrix in condensed form. The :math:`$ij$`th entry is the cophenetic distance between @@ -964,8 +964,8 @@ The :math:`$(n-1)$` by 4 matrix encoding the linkage (hierarchical clustering). See ``linkage`` documentation for more information on its form. - + :Returns: - R : ndarray A :math:`$(n-1)$` by 5 matrix where the ``i``'th row Modified: trunk/scipy/interpolate/interpolate_wrapper.py =================================================================== --- trunk/scipy/interpolate/interpolate_wrapper.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/interpolate/interpolate_wrapper.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -119,20 +119,20 @@ return new_y def block(x, y, new_x): - """ Essentially a step function. - - For each new_x[i], finds largest j such that - x[j] < new_x[j], and returns y[j]. - """ - # find index of values in x that preceed values in x - # This code is a little strange -- we really want a routine that - # returns the index of values where x[j] < x[index] - TINY = 1e-10 - indices = np.searchsorted(x, new_x+TINY)-1 + """ Essentially a step function. + + For each new_x[i], finds largest j such that + x[j] < new_x[j], and returns y[j]. + """ + # find index of values in x that preceed values in x + # This code is a little strange -- we really want a routine that + # returns the index of values where x[j] < x[index] + TINY = 1e-10 + indices = np.searchsorted(x, new_x+TINY)-1 - # If the value is at the front of the list, it'll have -1. - # In this case, we will use the first (0), element in the array. - # take requires the index array to be an Int - indices = np.atleast_1d(np.clip(indices, 0, np.Inf).astype(np.int)) - new_y = np.take(y, indices, axis=-1) - return new_y \ No newline at end of file + # If the value is at the front of the list, it'll have -1. + # In this case, we will use the first (0), element in the array. + # take requires the index array to be an Int + indices = np.atleast_1d(np.clip(indices, 0, np.Inf).astype(np.int)) + new_y = np.take(y, indices, axis=-1) + return new_y Modified: trunk/scipy/interpolate/setup.py =================================================================== --- trunk/scipy/interpolate/setup.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/interpolate/setup.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -22,9 +22,9 @@ ) config.add_extension('_interpolate', - sources=['src/_interpolate.cpp'], - include_dirs = ['src'], - depends = ['src/interpolate.h']) + sources=['src/_interpolate.cpp'], + include_dirs = ['src'], + depends = ['src/interpolate.h']) config.add_data_dir('tests') Modified: trunk/scipy/io/matlab/byteordercodes.py =================================================================== --- trunk/scipy/io/matlab/byteordercodes.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/io/matlab/byteordercodes.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -18,21 +18,21 @@ 'swapped': ('swapped', 'S')} def to_numpy_code(code): - ''' Convert various order codings to numpy format + ''' Convert various order codings to numpy format Parameters ---------- code : {'little','big','l','b','le','be','<','>', 'native','=', 'swapped', 's'} string code is converted to lower case before parsing - + Returns ------- out_code : {'<','>'} string - where '<' is the numpy dtype code for little + where '<' is the numpy dtype code for little endian, and '>' is the code for big endian - + Examples -------- >>> import sys @@ -64,5 +64,3 @@ else: raise ValueError( 'We cannot handle byte order %s' % code) - - Modified: trunk/scipy/io/matlab/mio.py =================================================================== --- trunk/scipy/io/matlab/mio.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/io/matlab/mio.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -68,7 +68,7 @@ raise NotImplementedError('Please use PyTables for matlab v7.3 (HDF) files') else: raise TypeError('Did not recognize version %s' % mv) - + def loadmat(file_name, mdict=None, appendmat=True, basename='raw', **kwargs): ''' Load Matlab(tm) file @@ -96,8 +96,8 @@ (implies squeeze_me=False, chars_as_strings=False, mat_dtype=True) struct_as_record - whether to load matlab structs as numpy record arrays, or - as old-style numpy arrays with dtype=object. - (warns if not set, and defaults to False. non-recarrays + as old-style numpy arrays with dtype=object. + (warns if not set, and defaults to False. non-recarrays cannot be exported via savemat.) v4 (Level 1.0), v6 and v7.1 matfiles are supported. Modified: trunk/scipy/io/matlab/mio5.py =================================================================== --- trunk/scipy/io/matlab/mio5.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/io/matlab/mio5.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -202,7 +202,7 @@ mod8 = byte_count % 8 if mod8: self.mat_stream.seek(8 - mod8, 1) - + if mdtype in self.codecs: # encoded char data codec = self.codecs[mdtype] if not codec: @@ -216,7 +216,7 @@ buffer=raw_str) if copy: el = el.copy() - + return el def matrix_getter_factory(self): @@ -428,7 +428,7 @@ tupdims = tuple(self.header['dims'][::-1]) length = np.product(tupdims) if self.struct_as_record: - result = np.empty(length, dtype=[(field_name, object) + result = np.empty(length, dtype=[(field_name, object) for field_name in field_names]) for i in range(length): for field_name in field_names: @@ -442,14 +442,14 @@ for name in field_names: item.__dict__[name] = self.read_element() result[i] = item - + return result.reshape(tupdims).T class MatlabObject(object): ''' Class to contain read data from matlab objects ''' def __init__(self, classname, field_names): self.__dict__['classname'] = classname - self.__dict__['mobj_recarray'] = np.empty((1,1), dtype=[(field_name, object) + self.__dict__['mobj_recarray'] = np.empty((1,1), dtype=[(field_name, object) for field_name in field_names]) def __getattr__(self, name): @@ -464,8 +464,8 @@ self.__dict__['mobj_recarray'][0,0][name] = value else: self.__dict__[name] = value - + class Mat5ObjectMatrixGetter(Mat5MatrixGetter): def get_array(self): '''Matlab ojects are essentially structs, with an extra field, the classname.''' @@ -495,7 +495,7 @@ class MatFile5Reader(MatFileReader): ''' Reader for Mat 5 mat files Adds the following attribute to base class - + uint16_codec - char codec to use for uint16 char arrays (defaults to system default codec) ''' @@ -513,9 +513,9 @@ ''' mat_stream : file-like object with file API, open for reading - byte_order : {None, string} + byte_order : {None, string} specification of byte order, one of: - ('native', '=', 'little', '<', 'BIG', '>') + ('native', '=', 'little', '<', 'BIG', '>') mat_dtype : {True, False} boolean If True, return arrays in same dtype as loaded into matlab otherwise return with dtype with which they were saved @@ -527,7 +527,7 @@ If True, returns matrices as would be loaded by matlab (implies squeeze_me=False, chars_as_strings=False mat_dtype=True, struct_as_record=True) - struct_as_record : {False, True} boolean + struct_as_record : {False, True} boolean If True, return strutures as numpy records, otherwise, return as custom object (for compatibility with scipy 0.6) @@ -766,8 +766,8 @@ def __init__(self, file_stream, arr, name, is_global=False, unicode_strings=False): super(Mat5CompositeWriter, self).__init__(file_stream, arr, name, is_global) self.unicode_strings = unicode_strings - + class Mat5CellWriter(Mat5CompositeWriter): def write(self): self.write_header(mclass=mxCELL_CLASS) @@ -797,12 +797,12 @@ class Mat5StructWriter(Mat5CompositeWriter): def write(self): self.write_header(mclass=mxSTRUCT_CLASS) - + # write fieldnames fieldnames = [f[0] for f in self.arr.dtype.descr] self.write_element(np.array([32], dtype='i4')) self.write_element(np.array(fieldnames, dtype='S32'), mdtype=miINT8) - + A = np.atleast_2d(self.arr).flatten('F') MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) for el in A: @@ -826,7 +826,7 @@ fieldnames = [f[0] for f in self.arr.dtype.descr] self.write_element(np.array([32], dtype='i4')) self.write_element(np.array(fieldnames, dtype='S32'), mdtype=miINT8) - + A = np.atleast_2d(self.arr).flatten('F') MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) for el in A: @@ -835,7 +835,7 @@ MW.write() self.update_matrix_tag() - + class Mat5WriterGetter(object): ''' Wraps stream and options, provides methods for getting Writer objects ''' def __init__(self, stream, unicode_strings): @@ -854,12 +854,12 @@ if spsparse: if spsparse.issparse(arr): return Mat5SparseWriter(self.stream, arr, name, is_global) - + if isinstance(arr, MatlabFunctionMatrix): return Mat5FunctionWriter(self.stream, arr, name, is_global, self.unicode_strings) if isinstance(arr, MatlabObject): return Mat5ObjectWriter(self.stream, arr, name, is_global, self.unicode_strings) - + arr = np.array(arr) if arr.dtype.hasobject: if arr.dtype.fields == None: Modified: trunk/scipy/io/matlab/miobase.py =================================================================== --- trunk/scipy/io/matlab/miobase.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/io/matlab/miobase.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -29,7 +29,7 @@ #. 0,x -> version 4 format mat files #. 1,x -> version 5 format mat files #. 2,x -> version 7.3 format mat files (HDF format) - + Parameters ---------- fileobj : {file-like} @@ -54,7 +54,7 @@ if 0 in mopt_bytes: fileobj.seek(0) return (0,0) - + # For 5 format or 7.3 format we need to read an integer in the # header. Bytes 124 through 128 contain a version integer and an # endian test string @@ -144,9 +144,9 @@ ''' mat_stream : file-like object with file API, open for reading - byte_order : {None, string} + byte_order : {None, string} specification of byte order, one of: - ('native', '=', 'little', '<', 'BIG', '>') + ('native', '=', 'little', '<', 'BIG', '>') mat_dtype : {True, False} boolean If True, return arrays in same dtype as loaded into matlab otherwise return with dtype with which they were saved @@ -158,7 +158,7 @@ If True, returns matrices as would be loaded by matlab (implies squeeze_me=False, chars_as_strings=False mat_dtype=True) - + ''' # Initialize stream self.mat_stream = mat_stream @@ -234,7 +234,7 @@ def matrix_getter_factory(self): assert False, 'Not implemented' - + def file_header(self): return {} Modified: trunk/scipy/io/matlab/tests/test_byteordercodes.py =================================================================== --- trunk/scipy/io/matlab/tests/test_byteordercodes.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/io/matlab/tests/test_byteordercodes.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -11,13 +11,13 @@ def test_native(): native_is_le = sys.byteorder == 'little' assert sibc.sys_is_le == native_is_le - + def test_to_numpy(): if sys.byteorder == 'little': assert sibc.to_numpy_code('native') == '<' assert sibc.to_numpy_code('swapped') == '>' else: - assert sibc.to_numpy_code('native') == '>' + assert sibc.to_numpy_code('native') == '>' assert sibc.to_numpy_code('swapped') == '<' assert sibc.to_numpy_code('native') == sibc.to_numpy_code('=') assert sibc.to_numpy_code('big') == '>' @@ -26,5 +26,3 @@ for code in ('big', '>', 'b', 'B', 'be'): assert sibc.to_numpy_code(code) == '>' assert_raises(ValueError, sibc.to_numpy_code, 'silly string') - - Modified: trunk/scipy/io/matlab/tests/test_mio.py =================================================================== --- trunk/scipy/io/matlab/tests/test_mio.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/io/matlab/tests/test_mio.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -70,7 +70,7 @@ assert k in matdict, "Missing key at %s" % k_label _check_level(k_label, expected, matdict[k]) -# Round trip tests +# Round trip tests def _rt_check_case(name, expected, format): mat_stream = StringIO() savemat(mat_stream, expected, format=format) @@ -155,7 +155,7 @@ 'expected': {'testsparsefloat': SP.csc_matrix(array([[-1+2j,0,2],[0,-3j,0]]))}, }, ] -st = array([(u'Rats live on no evil star.', array([sqrt(2),exp(1),pi]), (1+1j)*array([sqrt(2),exp(1),pi]))], +st = array([(u'Rats live on no evil star.', array([sqrt(2),exp(1),pi]), (1+1j)*array([sqrt(2),exp(1),pi]))], dtype=[(n, object) for n in ['stringfield', 'doublefield', 'complexfield']]) case_table5.append( {'name': 'struct', @@ -253,7 +253,7 @@ assert_array_almost_equal(actual['x'].todense(), expected['x'].todense()) - + def test_mat73(): # Check any hdf5 files raise an error filenames = glob( Modified: trunk/scipy/ndimage/filters.py =================================================================== --- trunk/scipy/ndimage/filters.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/ndimage/filters.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -45,7 +45,7 @@ def moredoc(*args): def decorate(f): if f.__doc__ is not None: - for a in args: + for a in args: f.__doc__ += a return f return decorate Modified: trunk/scipy/optimize/tests/test_nnls.py =================================================================== --- trunk/scipy/optimize/tests/test_nnls.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/optimize/tests/test_nnls.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -22,6 +22,3 @@ if __name__ == "__main__": run_module_suite() - - - Modified: trunk/scipy/sparse/base.py =================================================================== --- trunk/scipy/sparse/base.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/base.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -211,8 +211,8 @@ return getattr(self,'to' + format)() ################################################################### - # NOTE: All arithmetic operations use csr_matrix by default. - # Therefore a new sparse matrix format just needs to define a + # NOTE: All arithmetic operations use csr_matrix by default. + # Therefore a new sparse matrix format just needs to define a # .tocsr() method to provide arithmetic support. Any of these # methods can be overridden for efficiency. #################################################################### Modified: trunk/scipy/sparse/construct.py =================================================================== --- trunk/scipy/sparse/construct.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/construct.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -453,7 +453,7 @@ "diagonal %s." % k) out = lil_matrix((m,n),dtype=dtype) - + from itertools import izip for k,diag in izip(offsets,diags): for ix,c in enumerate(xrange(np.clip(k,0,n),np.clip(m+k,0,n))): Modified: trunk/scipy/sparse/dok.py =================================================================== --- trunk/scipy/sparse/dok.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/dok.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -14,8 +14,8 @@ class dok_matrix(spmatrix, dict): """Dictionary Of Keys based sparse matrix. - - This is an efficient structure for constructing sparse + + This is an efficient structure for constructing sparse matrices incrementally. This can be instatiated in several ways: Modified: trunk/scipy/sparse/lil.py =================================================================== --- trunk/scipy/sparse/lil.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/lil.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -15,7 +15,7 @@ class lil_matrix(spmatrix): """Row-based linked list sparse matrix - This is an efficient structure for constructing sparse + This is an efficient structure for constructing sparse matrices incrementally. This can be instantiated in several ways: Modified: trunk/scipy/sparse/linalg/isolve/iterative.py =================================================================== --- trunk/scipy/sparse/linalg/isolve/iterative.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/linalg/isolve/iterative.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -400,7 +400,7 @@ b or use xtype='f','d','F',or 'D' callback -- an optional user-supplied function to call after each iteration. It is called as callback(rk), where rk is the - the current relative residual + the current relative residual """ A,M,x,b,postprocess = make_system(A,M,x0,b,xtype) @@ -442,7 +442,7 @@ if resid_ready and callback is not None: callback(resid) resid_ready = False - + break elif (ijob == 1): work[slice2] *= sclr2 @@ -452,7 +452,7 @@ if not first_pass and old_ijob==3: resid_ready = True - first_pass = False + first_pass = False elif (ijob == 3): work[slice2] *= sclr2 work[slice2] += sclr1*matvec(work[slice1]) @@ -466,7 +466,7 @@ info = -1 ftflag = False bnrm2, resid, info = stoptest(work[slice1], b, bnrm2, tol, info) - + old_ijob = ijob ijob = 2 Modified: trunk/scipy/sparse/linalg/isolve/tests/test_iterative.py =================================================================== --- trunk/scipy/sparse/linalg/isolve/tests/test_iterative.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/linalg/isolve/tests/test_iterative.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -61,7 +61,7 @@ #data[1,:] = -1 #A = spdiags( data, [0,-1], 10, 10, format='csr') #self.cases.append( (A,False,True) ) - + def test_maxiter(self): """test whether maxiter is respected""" @@ -76,7 +76,7 @@ residuals.append( norm(b - A*x) ) x, info = solver(A, b, x0=x0, tol=1e-8, maxiter=3, callback=callback) - + assert(len(residuals) in [2,3]) # TODO enforce this condition instead! @@ -169,8 +169,8 @@ class TestGMRES(TestCase): - def test_callback(self): - + def test_callback(self): + def store_residual(r, rvec): rvec[rvec.nonzero()[0].max()+1] = r Modified: trunk/scipy/sparse/sparsetools/bsr.py =================================================================== --- trunk/scipy/sparse/sparsetools/bsr.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/sparsetools/bsr.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -51,484 +51,483 @@ def bsr_diagonal(*args): - """ - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax, signed char Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax, unsigned char Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax, short Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax, unsigned short Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax, int Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax, unsigned int Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax, long long Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax, unsigned long long Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax, float Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax, double Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax, long double Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, npy_cfloat_wrapper Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, npy_cdouble_wrapper Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Yx) """ - return _bsr.bsr_diagonal(*args) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax, signed char Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax, unsigned char Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax, short Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax, unsigned short Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax, int Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax, unsigned int Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax, long long Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax, unsigned long long Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax, float Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax, double Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax, long double Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, npy_cfloat_wrapper Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, npy_cdouble_wrapper Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Yx) + """ + return _bsr.bsr_diagonal(*args) def bsr_scale_rows(*args): - """ - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax, signed char Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax, unsigned char Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax, short Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax, unsigned short Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax, int Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax, unsigned int Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax, long long Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax, unsigned long long Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax, float Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax, double Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax, long double Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx) """ - return _bsr.bsr_scale_rows(*args) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax, signed char Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax, unsigned char Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax, short Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax, unsigned short Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax, int Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax, unsigned int Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax, long long Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax, unsigned long long Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax, float Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax, double Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax, long double Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx) + """ + return _bsr.bsr_scale_rows(*args) def bsr_scale_columns(*args): - """ - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax, signed char Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax, unsigned char Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax, short Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax, unsigned short Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax, int Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax, unsigned int Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax, long long Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax, unsigned long long Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax, float Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax, double Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax, long double Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx) """ - return _bsr.bsr_scale_columns(*args) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax, signed char Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax, unsigned char Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax, short Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax, unsigned short Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax, int Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax, unsigned int Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax, long long Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax, unsigned long long Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax, float Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax, double Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax, long double Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx) + """ + return _bsr.bsr_scale_columns(*args) def bsr_transpose(*args): - """ - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx) """ - return _bsr.bsr_transpose(*args) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx) + """ + return _bsr.bsr_transpose(*args) def bsr_matmat_pass2(*args): - """ - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, signed char Ax, int Bp, int Bj, signed char Bx, - int Cp, int Cj, signed char Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, unsigned char Ax, int Bp, int Bj, unsigned char Bx, - int Cp, int Cj, unsigned char Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, short Ax, int Bp, int Bj, short Bx, - int Cp, int Cj, short Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, unsigned short Ax, int Bp, int Bj, - unsigned short Bx, int Cp, int Cj, unsigned short Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, int Ax, int Bp, int Bj, int Bx, int Cp, - int Cj, int Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, unsigned int Ax, int Bp, int Bj, unsigned int Bx, - int Cp, int Cj, unsigned int Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, long long Ax, int Bp, int Bj, long long Bx, - int Cp, int Cj, long long Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, unsigned long long Ax, int Bp, int Bj, - unsigned long long Bx, int Cp, int Cj, unsigned long long Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, float Ax, int Bp, int Bj, float Bx, - int Cp, int Cj, float Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, double Ax, int Bp, int Bj, double Bx, - int Cp, int Cj, double Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, long double Ax, int Bp, int Bj, long double Bx, - int Cp, int Cj, long double Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, npy_cfloat_wrapper Ax, int Bp, int Bj, - npy_cfloat_wrapper Bx, int Cp, int Cj, npy_cfloat_wrapper Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, npy_cdouble_wrapper Ax, int Bp, int Bj, - npy_cdouble_wrapper Bx, int Cp, int Cj, - npy_cdouble_wrapper Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, npy_clongdouble_wrapper Ax, int Bp, - int Bj, npy_clongdouble_wrapper Bx, int Cp, - int Cj, npy_clongdouble_wrapper Cx) """ - return _bsr.bsr_matmat_pass2(*args) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, signed char Ax, int Bp, int Bj, signed char Bx, + int Cp, int Cj, signed char Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, unsigned char Ax, int Bp, int Bj, unsigned char Bx, + int Cp, int Cj, unsigned char Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, short Ax, int Bp, int Bj, short Bx, + int Cp, int Cj, short Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, unsigned short Ax, int Bp, int Bj, + unsigned short Bx, int Cp, int Cj, unsigned short Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, int Ax, int Bp, int Bj, int Bx, int Cp, + int Cj, int Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, unsigned int Ax, int Bp, int Bj, unsigned int Bx, + int Cp, int Cj, unsigned int Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, long long Ax, int Bp, int Bj, long long Bx, + int Cp, int Cj, long long Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, unsigned long long Ax, int Bp, int Bj, + unsigned long long Bx, int Cp, int Cj, unsigned long long Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, float Ax, int Bp, int Bj, float Bx, + int Cp, int Cj, float Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, double Ax, int Bp, int Bj, double Bx, + int Cp, int Cj, double Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, long double Ax, int Bp, int Bj, long double Bx, + int Cp, int Cj, long double Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, npy_cfloat_wrapper Ax, int Bp, int Bj, + npy_cfloat_wrapper Bx, int Cp, int Cj, npy_cfloat_wrapper Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, npy_cdouble_wrapper Ax, int Bp, int Bj, + npy_cdouble_wrapper Bx, int Cp, int Cj, + npy_cdouble_wrapper Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, npy_clongdouble_wrapper Ax, int Bp, + int Bj, npy_clongdouble_wrapper Bx, int Cp, + int Cj, npy_clongdouble_wrapper Cx) + """ + return _bsr.bsr_matmat_pass2(*args) def bsr_matvec(*args): - """ - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax, signed char Xx, signed char Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax, unsigned char Xx, unsigned char Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax, short Xx, short Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax, unsigned short Xx, unsigned short Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax, int Xx, int Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax, unsigned int Xx, unsigned int Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax, long long Xx, long long Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax, unsigned long long Xx, - unsigned long long Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax, float Xx, float Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax, double Xx, double Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax, long double Xx, long double Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx, - npy_clongdouble_wrapper Yx) """ - return _bsr.bsr_matvec(*args) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax, signed char Xx, signed char Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax, unsigned char Xx, unsigned char Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax, short Xx, short Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax, unsigned short Xx, unsigned short Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax, int Xx, int Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax, unsigned int Xx, unsigned int Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax, long long Xx, long long Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax, unsigned long long Xx, + unsigned long long Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax, float Xx, float Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax, double Xx, double Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax, long double Xx, long double Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx, + npy_clongdouble_wrapper Yx) + """ + return _bsr.bsr_matvec(*args) def bsr_matvecs(*args): - """ - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, signed char Ax, signed char Xx, - signed char Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, unsigned char Ax, unsigned char Xx, - unsigned char Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, short Ax, short Xx, short Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, unsigned short Ax, unsigned short Xx, - unsigned short Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, int Ax, int Xx, int Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, unsigned int Ax, unsigned int Xx, - unsigned int Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, long long Ax, long long Xx, long long Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, unsigned long long Ax, unsigned long long Xx, - unsigned long long Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, float Ax, float Xx, float Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, double Ax, double Xx, double Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, long double Ax, long double Xx, - long double Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx, - npy_clongdouble_wrapper Yx) """ - return _bsr.bsr_matvecs(*args) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, signed char Ax, signed char Xx, + signed char Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, unsigned char Ax, unsigned char Xx, + unsigned char Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, short Ax, short Xx, short Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, unsigned short Ax, unsigned short Xx, + unsigned short Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, int Ax, int Xx, int Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, unsigned int Ax, unsigned int Xx, + unsigned int Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, long long Ax, long long Xx, long long Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, unsigned long long Ax, unsigned long long Xx, + unsigned long long Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, float Ax, float Xx, float Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, double Ax, double Xx, double Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, long double Ax, long double Xx, + long double Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx, + npy_clongdouble_wrapper Yx) + """ + return _bsr.bsr_matvecs(*args) def bsr_elmul_bsr(*args): - """ - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx, - int Cp, int Cj, signed char Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx, - int Cp, int Cj, unsigned char Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx, int Cp, - int Cj, short Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx, - int Cp, int Cj, unsigned short Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, - int Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx, - int Cp, int Cj, unsigned int Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx, - int Cp, int Cj, long long Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx, int Cp, - int Cj, float Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx, int Cp, - int Cj, double Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx, - int Cp, int Cj, long double Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _bsr.bsr_elmul_bsr(*args) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx, + int Cp, int Cj, signed char Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx, + int Cp, int Cj, unsigned char Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx, int Cp, + int Cj, short Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx, + int Cp, int Cj, unsigned short Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, + int Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx, + int Cp, int Cj, unsigned int Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx, + int Cp, int Cj, long long Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx, int Cp, + int Cj, float Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx, int Cp, + int Cj, double Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx, + int Cp, int Cj, long double Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _bsr.bsr_elmul_bsr(*args) def bsr_eldiv_bsr(*args): - """ - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx, - int Cp, int Cj, signed char Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx, - int Cp, int Cj, unsigned char Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx, int Cp, - int Cj, short Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx, - int Cp, int Cj, unsigned short Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, - int Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx, - int Cp, int Cj, unsigned int Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx, - int Cp, int Cj, long long Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx, int Cp, - int Cj, float Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx, int Cp, - int Cj, double Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx, - int Cp, int Cj, long double Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _bsr.bsr_eldiv_bsr(*args) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx, + int Cp, int Cj, signed char Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx, + int Cp, int Cj, unsigned char Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx, int Cp, + int Cj, short Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx, + int Cp, int Cj, unsigned short Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, + int Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx, + int Cp, int Cj, unsigned int Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx, + int Cp, int Cj, long long Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx, int Cp, + int Cj, float Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx, int Cp, + int Cj, double Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx, + int Cp, int Cj, long double Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _bsr.bsr_eldiv_bsr(*args) def bsr_plus_bsr(*args): - """ - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx, - int Cp, int Cj, signed char Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx, - int Cp, int Cj, unsigned char Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx, int Cp, - int Cj, short Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx, - int Cp, int Cj, unsigned short Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, - int Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx, - int Cp, int Cj, unsigned int Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx, - int Cp, int Cj, long long Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx, int Cp, - int Cj, float Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx, int Cp, - int Cj, double Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx, - int Cp, int Cj, long double Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _bsr.bsr_plus_bsr(*args) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx, + int Cp, int Cj, signed char Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx, + int Cp, int Cj, unsigned char Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx, int Cp, + int Cj, short Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx, + int Cp, int Cj, unsigned short Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, + int Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx, + int Cp, int Cj, unsigned int Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx, + int Cp, int Cj, long long Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx, int Cp, + int Cj, float Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx, int Cp, + int Cj, double Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx, + int Cp, int Cj, long double Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _bsr.bsr_plus_bsr(*args) def bsr_minus_bsr(*args): - """ - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx, - int Cp, int Cj, signed char Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx, - int Cp, int Cj, unsigned char Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx, int Cp, - int Cj, short Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx, - int Cp, int Cj, unsigned short Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, - int Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx, - int Cp, int Cj, unsigned int Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx, - int Cp, int Cj, long long Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx, int Cp, - int Cj, float Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx, int Cp, - int Cj, double Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx, - int Cp, int Cj, long double Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _bsr.bsr_minus_bsr(*args) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx, + int Cp, int Cj, signed char Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx, + int Cp, int Cj, unsigned char Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx, int Cp, + int Cj, short Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx, + int Cp, int Cj, unsigned short Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, + int Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx, + int Cp, int Cj, unsigned int Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx, + int Cp, int Cj, long long Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx, int Cp, + int Cj, float Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx, int Cp, + int Cj, double Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx, + int Cp, int Cj, long double Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _bsr.bsr_minus_bsr(*args) def bsr_sort_indices(*args): - """ - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax) """ - return _bsr.bsr_sort_indices(*args) - + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax) + """ + return _bsr.bsr_sort_indices(*args) Modified: trunk/scipy/sparse/sparsetools/coo.py =================================================================== --- trunk/scipy/sparse/sparsetools/coo.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/sparsetools/coo.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -50,135 +50,134 @@ def coo_count_diagonals(*args): - """coo_count_diagonals(int nnz, int Ai, int Aj) -> int""" - return _coo.coo_count_diagonals(*args) + """coo_count_diagonals(int nnz, int Ai, int Aj) -> int""" + return _coo.coo_count_diagonals(*args) def coo_tocsr(*args): - """ - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, - int Bp, int Bj, short Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, - int Bp, int Bj, int Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, - int Bp, int Bj, long long Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, - int Bp, int Bj, float Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, - int Bp, int Bj, double Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, - int Bp, int Bj, long double Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx) """ - return _coo.coo_tocsr(*args) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, + int Bp, int Bj, short Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, + int Bp, int Bj, int Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, + int Bp, int Bj, long long Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, + int Bp, int Bj, float Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, + int Bp, int Bj, double Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, + int Bp, int Bj, long double Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx) + """ + return _coo.coo_tocsr(*args) def coo_tocsc(*args): - """ - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, - int Bp, int Bi, signed char Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, - int Bp, int Bi, unsigned char Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, - int Bp, int Bi, short Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, - int Bp, int Bi, unsigned short Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, - int Bp, int Bi, int Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, - int Bp, int Bi, unsigned int Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, - int Bp, int Bi, long long Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, - int Bp, int Bi, float Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, - int Bp, int Bi, double Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, - int Bp, int Bi, long double Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx) """ - return _coo.coo_tocsc(*args) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, + int Bp, int Bi, signed char Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, + int Bp, int Bi, unsigned char Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, + int Bp, int Bi, short Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, + int Bp, int Bi, unsigned short Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, + int Bp, int Bi, int Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, + int Bp, int Bi, unsigned int Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, + int Bp, int Bi, long long Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, + int Bp, int Bi, float Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, + int Bp, int Bi, double Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, + int Bp, int Bi, long double Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx) + """ + return _coo.coo_tocsc(*args) def coo_todense(*args): - """ - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, - signed char Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, - unsigned char Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, - short Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, - unsigned short Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, - int Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, - unsigned int Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, - long long Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, - unsigned long long Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, - float Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, - double Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, - long double Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Bx) """ - return _coo.coo_todense(*args) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, + signed char Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, + unsigned char Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, + short Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, + unsigned short Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, + int Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, + unsigned int Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, + long long Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, + unsigned long long Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, + float Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, + double Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, + long double Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Bx) + """ + return _coo.coo_todense(*args) def coo_matvec(*args): - """ - coo_matvec(int nnz, int Ai, int Aj, signed char Ax, signed char Xx, - signed char Yx) - coo_matvec(int nnz, int Ai, int Aj, unsigned char Ax, unsigned char Xx, - unsigned char Yx) - coo_matvec(int nnz, int Ai, int Aj, short Ax, short Xx, short Yx) - coo_matvec(int nnz, int Ai, int Aj, unsigned short Ax, unsigned short Xx, - unsigned short Yx) - coo_matvec(int nnz, int Ai, int Aj, int Ax, int Xx, int Yx) - coo_matvec(int nnz, int Ai, int Aj, unsigned int Ax, unsigned int Xx, - unsigned int Yx) - coo_matvec(int nnz, int Ai, int Aj, long long Ax, long long Xx, - long long Yx) - coo_matvec(int nnz, int Ai, int Aj, unsigned long long Ax, unsigned long long Xx, - unsigned long long Yx) - coo_matvec(int nnz, int Ai, int Aj, float Ax, float Xx, float Yx) - coo_matvec(int nnz, int Ai, int Aj, double Ax, double Xx, double Yx) - coo_matvec(int nnz, int Ai, int Aj, long double Ax, long double Xx, - long double Yx) - coo_matvec(int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - coo_matvec(int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - coo_matvec(int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) """ - return _coo.coo_matvec(*args) - + coo_matvec(int nnz, int Ai, int Aj, signed char Ax, signed char Xx, + signed char Yx) + coo_matvec(int nnz, int Ai, int Aj, unsigned char Ax, unsigned char Xx, + unsigned char Yx) + coo_matvec(int nnz, int Ai, int Aj, short Ax, short Xx, short Yx) + coo_matvec(int nnz, int Ai, int Aj, unsigned short Ax, unsigned short Xx, + unsigned short Yx) + coo_matvec(int nnz, int Ai, int Aj, int Ax, int Xx, int Yx) + coo_matvec(int nnz, int Ai, int Aj, unsigned int Ax, unsigned int Xx, + unsigned int Yx) + coo_matvec(int nnz, int Ai, int Aj, long long Ax, long long Xx, + long long Yx) + coo_matvec(int nnz, int Ai, int Aj, unsigned long long Ax, unsigned long long Xx, + unsigned long long Yx) + coo_matvec(int nnz, int Ai, int Aj, float Ax, float Xx, float Yx) + coo_matvec(int nnz, int Ai, int Aj, double Ax, double Xx, double Yx) + coo_matvec(int nnz, int Ai, int Aj, long double Ax, long double Xx, + long double Yx) + coo_matvec(int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + coo_matvec(int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + coo_matvec(int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) + """ + return _coo.coo_matvec(*args) Modified: trunk/scipy/sparse/sparsetools/csc.py =================================================================== --- trunk/scipy/sparse/sparsetools/csc.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/sparsetools/csc.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -50,357 +50,356 @@ def csc_matmat_pass1(*args): - """ - csc_matmat_pass1(int n_row, int n_col, int Ap, int Ai, int Bp, int Bi, - int Cp) """ - return _csc.csc_matmat_pass1(*args) + csc_matmat_pass1(int n_row, int n_col, int Ap, int Ai, int Bp, int Bi, + int Cp) + """ + return _csc.csc_matmat_pass1(*args) def csc_diagonal(*args): - """ - csc_diagonal(int n_row, int n_col, int Ap, int Aj, signed char Ax, - signed char Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - unsigned char Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, short Ax, short Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - unsigned short Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, int Ax, int Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - unsigned int Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, long long Ax, - long long Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - unsigned long long Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, float Ax, float Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, double Ax, double Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, long double Ax, - long double Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Yx) """ - return _csc.csc_diagonal(*args) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, signed char Ax, + signed char Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + unsigned char Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, short Ax, short Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + unsigned short Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, int Ax, int Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + unsigned int Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, long long Ax, + long long Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + unsigned long long Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, float Ax, float Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, double Ax, double Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, long double Ax, + long double Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Yx) + """ + return _csc.csc_diagonal(*args) def csc_tocsr(*args): - """ - csc_tocsr(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bj, signed char Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bj, unsigned char Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bj, short Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bj, unsigned short Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bj, int Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bj, unsigned int Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bj, long long Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bj, float Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bj, double Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bj, long double Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx) """ - return _csc.csc_tocsr(*args) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bj, signed char Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bj, unsigned char Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bj, short Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bj, unsigned short Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bj, int Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bj, unsigned int Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bj, long long Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bj, float Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bj, double Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bj, long double Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx) + """ + return _csc.csc_tocsr(*args) def csc_matmat_pass2(*args): - """ - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bi, signed char Bx, int Cp, int Ci, - signed char Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bi, unsigned char Bx, int Cp, - int Ci, unsigned char Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bi, short Bx, int Cp, int Ci, short Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bi, unsigned short Bx, int Cp, - int Ci, unsigned short Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bi, int Bx, int Cp, int Ci, int Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bi, unsigned int Bx, int Cp, - int Ci, unsigned int Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bi, long long Bx, int Cp, int Ci, - long long Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx, - int Cp, int Ci, unsigned long long Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bi, float Bx, int Cp, int Ci, float Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bi, double Bx, int Cp, int Ci, double Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bi, long double Bx, int Cp, int Ci, - long double Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx, - int Cp, int Ci, npy_cfloat_wrapper Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx, - int Cp, int Ci, npy_cdouble_wrapper Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx, - int Cp, int Ci, npy_clongdouble_wrapper Cx) """ - return _csc.csc_matmat_pass2(*args) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bi, signed char Bx, int Cp, int Ci, + signed char Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bi, unsigned char Bx, int Cp, + int Ci, unsigned char Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bi, short Bx, int Cp, int Ci, short Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bi, unsigned short Bx, int Cp, + int Ci, unsigned short Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bi, int Bx, int Cp, int Ci, int Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bi, unsigned int Bx, int Cp, + int Ci, unsigned int Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bi, long long Bx, int Cp, int Ci, + long long Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx, + int Cp, int Ci, unsigned long long Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bi, float Bx, int Cp, int Ci, float Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bi, double Bx, int Cp, int Ci, double Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bi, long double Bx, int Cp, int Ci, + long double Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx, + int Cp, int Ci, npy_cfloat_wrapper Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx, + int Cp, int Ci, npy_cdouble_wrapper Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx, + int Cp, int Ci, npy_clongdouble_wrapper Cx) + """ + return _csc.csc_matmat_pass2(*args) def csc_matvec(*args): - """ - csc_matvec(int n_row, int n_col, int Ap, int Ai, signed char Ax, - signed char Xx, signed char Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - unsigned char Xx, unsigned char Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, short Ax, short Xx, - short Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - unsigned short Xx, unsigned short Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, int Ax, int Xx, - int Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - unsigned int Xx, unsigned int Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, long long Ax, - long long Xx, long long Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - unsigned long long Xx, unsigned long long Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, float Ax, float Xx, - float Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, double Ax, double Xx, - double Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, long double Ax, - long double Xx, long double Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx, npy_cfloat_wrapper Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx, npy_cdouble_wrapper Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) """ - return _csc.csc_matvec(*args) + csc_matvec(int n_row, int n_col, int Ap, int Ai, signed char Ax, + signed char Xx, signed char Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + unsigned char Xx, unsigned char Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, short Ax, short Xx, + short Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + unsigned short Xx, unsigned short Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, int Ax, int Xx, + int Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + unsigned int Xx, unsigned int Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, long long Ax, + long long Xx, long long Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + unsigned long long Xx, unsigned long long Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, float Ax, float Xx, + float Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, double Ax, double Xx, + double Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, long double Ax, + long double Xx, long double Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx, npy_cfloat_wrapper Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx, npy_cdouble_wrapper Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) + """ + return _csc.csc_matvec(*args) def csc_matvecs(*args): - """ - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, signed char Ax, - signed char Xx, signed char Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned char Ax, - unsigned char Xx, unsigned char Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, short Ax, - short Xx, short Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned short Ax, - unsigned short Xx, unsigned short Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, int Ax, - int Xx, int Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned int Ax, - unsigned int Xx, unsigned int Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, long long Ax, - long long Xx, long long Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned long long Ax, - unsigned long long Xx, - unsigned long long Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, float Ax, - float Xx, float Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, double Ax, - double Xx, double Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, long double Ax, - long double Xx, long double Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx, - npy_clongdouble_wrapper Yx) """ - return _csc.csc_matvecs(*args) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, signed char Ax, + signed char Xx, signed char Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned char Ax, + unsigned char Xx, unsigned char Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, short Ax, + short Xx, short Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned short Ax, + unsigned short Xx, unsigned short Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, int Ax, + int Xx, int Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned int Ax, + unsigned int Xx, unsigned int Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, long long Ax, + long long Xx, long long Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned long long Ax, + unsigned long long Xx, + unsigned long long Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, float Ax, + float Xx, float Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, double Ax, + double Xx, double Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, long double Ax, + long double Xx, long double Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx, + npy_clongdouble_wrapper Yx) + """ + return _csc.csc_matvecs(*args) def csc_elmul_csc(*args): - """ - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bi, signed char Bx, int Cp, int Ci, - signed char Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bi, unsigned char Bx, int Cp, - int Ci, unsigned char Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bi, short Bx, int Cp, int Ci, short Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bi, unsigned short Bx, int Cp, - int Ci, unsigned short Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bi, int Bx, int Cp, int Ci, int Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bi, unsigned int Bx, int Cp, - int Ci, unsigned int Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bi, long long Bx, int Cp, int Ci, - long long Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx, - int Cp, int Ci, unsigned long long Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bi, float Bx, int Cp, int Ci, float Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bi, double Bx, int Cp, int Ci, double Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bi, long double Bx, int Cp, int Ci, - long double Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx, - int Cp, int Ci, npy_cfloat_wrapper Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx, - int Cp, int Ci, npy_cdouble_wrapper Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx, - int Cp, int Ci, npy_clongdouble_wrapper Cx) """ - return _csc.csc_elmul_csc(*args) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bi, signed char Bx, int Cp, int Ci, + signed char Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bi, unsigned char Bx, int Cp, + int Ci, unsigned char Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bi, short Bx, int Cp, int Ci, short Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bi, unsigned short Bx, int Cp, + int Ci, unsigned short Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bi, int Bx, int Cp, int Ci, int Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bi, unsigned int Bx, int Cp, + int Ci, unsigned int Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bi, long long Bx, int Cp, int Ci, + long long Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx, + int Cp, int Ci, unsigned long long Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bi, float Bx, int Cp, int Ci, float Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bi, double Bx, int Cp, int Ci, double Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bi, long double Bx, int Cp, int Ci, + long double Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx, + int Cp, int Ci, npy_cfloat_wrapper Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx, + int Cp, int Ci, npy_cdouble_wrapper Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx, + int Cp, int Ci, npy_clongdouble_wrapper Cx) + """ + return _csc.csc_elmul_csc(*args) def csc_eldiv_csc(*args): - """ - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bi, signed char Bx, int Cp, int Ci, - signed char Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bi, unsigned char Bx, int Cp, - int Ci, unsigned char Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bi, short Bx, int Cp, int Ci, short Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bi, unsigned short Bx, int Cp, - int Ci, unsigned short Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bi, int Bx, int Cp, int Ci, int Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bi, unsigned int Bx, int Cp, - int Ci, unsigned int Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bi, long long Bx, int Cp, int Ci, - long long Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx, - int Cp, int Ci, unsigned long long Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bi, float Bx, int Cp, int Ci, float Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bi, double Bx, int Cp, int Ci, double Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bi, long double Bx, int Cp, int Ci, - long double Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx, - int Cp, int Ci, npy_cfloat_wrapper Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx, - int Cp, int Ci, npy_cdouble_wrapper Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx, - int Cp, int Ci, npy_clongdouble_wrapper Cx) """ - return _csc.csc_eldiv_csc(*args) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bi, signed char Bx, int Cp, int Ci, + signed char Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bi, unsigned char Bx, int Cp, + int Ci, unsigned char Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bi, short Bx, int Cp, int Ci, short Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bi, unsigned short Bx, int Cp, + int Ci, unsigned short Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bi, int Bx, int Cp, int Ci, int Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bi, unsigned int Bx, int Cp, + int Ci, unsigned int Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bi, long long Bx, int Cp, int Ci, + long long Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx, + int Cp, int Ci, unsigned long long Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bi, float Bx, int Cp, int Ci, float Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bi, double Bx, int Cp, int Ci, double Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bi, long double Bx, int Cp, int Ci, + long double Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx, + int Cp, int Ci, npy_cfloat_wrapper Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx, + int Cp, int Ci, npy_cdouble_wrapper Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx, + int Cp, int Ci, npy_clongdouble_wrapper Cx) + """ + return _csc.csc_eldiv_csc(*args) def csc_plus_csc(*args): - """ - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bi, signed char Bx, int Cp, int Ci, - signed char Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bi, unsigned char Bx, int Cp, - int Ci, unsigned char Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bi, short Bx, int Cp, int Ci, short Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bi, unsigned short Bx, int Cp, - int Ci, unsigned short Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bi, int Bx, int Cp, int Ci, int Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bi, unsigned int Bx, int Cp, - int Ci, unsigned int Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bi, long long Bx, int Cp, int Ci, - long long Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx, - int Cp, int Ci, unsigned long long Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bi, float Bx, int Cp, int Ci, float Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bi, double Bx, int Cp, int Ci, double Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bi, long double Bx, int Cp, int Ci, - long double Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx, - int Cp, int Ci, npy_cfloat_wrapper Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx, - int Cp, int Ci, npy_cdouble_wrapper Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx, - int Cp, int Ci, npy_clongdouble_wrapper Cx) """ - return _csc.csc_plus_csc(*args) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bi, signed char Bx, int Cp, int Ci, + signed char Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bi, unsigned char Bx, int Cp, + int Ci, unsigned char Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bi, short Bx, int Cp, int Ci, short Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bi, unsigned short Bx, int Cp, + int Ci, unsigned short Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bi, int Bx, int Cp, int Ci, int Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bi, unsigned int Bx, int Cp, + int Ci, unsigned int Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bi, long long Bx, int Cp, int Ci, + long long Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx, + int Cp, int Ci, unsigned long long Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bi, float Bx, int Cp, int Ci, float Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bi, double Bx, int Cp, int Ci, double Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bi, long double Bx, int Cp, int Ci, + long double Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx, + int Cp, int Ci, npy_cfloat_wrapper Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx, + int Cp, int Ci, npy_cdouble_wrapper Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx, + int Cp, int Ci, npy_clongdouble_wrapper Cx) + """ + return _csc.csc_plus_csc(*args) def csc_minus_csc(*args): - """ - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bi, signed char Bx, int Cp, int Ci, - signed char Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bi, unsigned char Bx, int Cp, - int Ci, unsigned char Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bi, short Bx, int Cp, int Ci, short Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bi, unsigned short Bx, int Cp, - int Ci, unsigned short Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bi, int Bx, int Cp, int Ci, int Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bi, unsigned int Bx, int Cp, - int Ci, unsigned int Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bi, long long Bx, int Cp, int Ci, - long long Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx, - int Cp, int Ci, unsigned long long Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bi, float Bx, int Cp, int Ci, float Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bi, double Bx, int Cp, int Ci, double Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bi, long double Bx, int Cp, int Ci, - long double Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx, - int Cp, int Ci, npy_cfloat_wrapper Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx, - int Cp, int Ci, npy_cdouble_wrapper Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx, - int Cp, int Ci, npy_clongdouble_wrapper Cx) """ - return _csc.csc_minus_csc(*args) - + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bi, signed char Bx, int Cp, int Ci, + signed char Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bi, unsigned char Bx, int Cp, + int Ci, unsigned char Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bi, short Bx, int Cp, int Ci, short Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bi, unsigned short Bx, int Cp, + int Ci, unsigned short Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bi, int Bx, int Cp, int Ci, int Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bi, unsigned int Bx, int Cp, + int Ci, unsigned int Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bi, long long Bx, int Cp, int Ci, + long long Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx, + int Cp, int Ci, unsigned long long Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bi, float Bx, int Cp, int Ci, float Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bi, double Bx, int Cp, int Ci, double Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bi, long double Bx, int Cp, int Ci, + long double Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx, + int Cp, int Ci, npy_cfloat_wrapper Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx, + int Cp, int Ci, npy_cdouble_wrapper Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx, + int Cp, int Ci, npy_clongdouble_wrapper Cx) + """ + return _csc.csc_minus_csc(*args) Modified: trunk/scipy/sparse/sparsetools/csr.py =================================================================== --- trunk/scipy/sparse/sparsetools/csr.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/sparsetools/csr.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -50,569 +50,568 @@ def expandptr(*args): - """expandptr(int n_row, int Ap, int Bi)""" - return _csr.expandptr(*args) + """expandptr(int n_row, int Ap, int Bi)""" + return _csr.expandptr(*args) def csr_matmat_pass1(*args): - """ - csr_matmat_pass1(int n_row, int n_col, int Ap, int Aj, int Bp, int Bj, - int Cp) """ - return _csr.csr_matmat_pass1(*args) + csr_matmat_pass1(int n_row, int n_col, int Ap, int Aj, int Bp, int Bj, + int Cp) + """ + return _csr.csr_matmat_pass1(*args) def csr_count_blocks(*args): - """csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int""" - return _csr.csr_count_blocks(*args) + """csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int""" + return _csr.csr_count_blocks(*args) def csr_has_sorted_indices(*args): - """csr_has_sorted_indices(int n_row, int Ap, int Aj) -> bool""" - return _csr.csr_has_sorted_indices(*args) + """csr_has_sorted_indices(int n_row, int Ap, int Aj) -> bool""" + return _csr.csr_has_sorted_indices(*args) def csr_diagonal(*args): - """ - csr_diagonal(int n_row, int n_col, int Ap, int Aj, signed char Ax, - signed char Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - unsigned char Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, short Ax, short Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - unsigned short Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, int Ax, int Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - unsigned int Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, long long Ax, - long long Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - unsigned long long Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, float Ax, float Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, double Ax, double Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, long double Ax, - long double Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Yx) """ - return _csr.csr_diagonal(*args) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, signed char Ax, + signed char Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + unsigned char Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, short Ax, short Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + unsigned short Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, int Ax, int Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + unsigned int Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, long long Ax, + long long Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + unsigned long long Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, float Ax, float Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, double Ax, double Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, long double Ax, + long double Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Yx) + """ + return _csr.csr_diagonal(*args) def csr_scale_rows(*args): - """ - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, signed char Ax, - signed char Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - unsigned char Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - unsigned short Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - unsigned int Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, long long Ax, - long long Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - unsigned long long Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, long double Ax, - long double Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx) """ - return _csr.csr_scale_rows(*args) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, signed char Ax, + signed char Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + unsigned char Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + unsigned short Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + unsigned int Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, long long Ax, + long long Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + unsigned long long Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, long double Ax, + long double Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx) + """ + return _csr.csr_scale_rows(*args) def csr_scale_columns(*args): - """ - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, signed char Ax, - signed char Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - unsigned char Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - unsigned short Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - unsigned int Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, long long Ax, - long long Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - unsigned long long Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, long double Ax, - long double Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx) """ - return _csr.csr_scale_columns(*args) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, signed char Ax, + signed char Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + unsigned char Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + unsigned short Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + unsigned int Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, long long Ax, + long long Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + unsigned long long Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, long double Ax, + long double Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx) + """ + return _csr.csr_scale_columns(*args) def csr_tocsc(*args): - """ - csr_tocsc(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bi, signed char Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bi, unsigned char Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bi, short Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bi, unsigned short Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bi, int Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bi, unsigned int Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bi, long long Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bi, float Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bi, double Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bi, long double Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx) """ - return _csr.csr_tocsc(*args) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bi, signed char Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bi, unsigned char Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bi, short Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bi, unsigned short Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bi, int Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bi, unsigned int Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bi, long long Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bi, float Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bi, double Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bi, long double Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx) + """ + return _csr.csr_tocsc(*args) def csr_tobsr(*args): - """ - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx) """ - return _csr.csr_tobsr(*args) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx) + """ + return _csr.csr_tobsr(*args) def csr_matmat_pass2(*args): - """ - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx, int Cp, int Cj, - signed char Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx, int Cp, - int Cj, unsigned char Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bj, short Bx, int Cp, int Cj, short Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx, int Cp, - int Cj, unsigned short Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bj, int Bx, int Cp, int Cj, int Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx, int Cp, - int Cj, unsigned int Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bj, long long Bx, int Cp, int Cj, - long long Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bj, float Bx, int Cp, int Cj, float Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bj, double Bx, int Cp, int Cj, double Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bj, long double Bx, int Cp, int Cj, - long double Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx, - int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _csr.csr_matmat_pass2(*args) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx, int Cp, int Cj, + signed char Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx, int Cp, + int Cj, unsigned char Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bj, short Bx, int Cp, int Cj, short Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx, int Cp, + int Cj, unsigned short Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bj, int Bx, int Cp, int Cj, int Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx, int Cp, + int Cj, unsigned int Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bj, long long Bx, int Cp, int Cj, + long long Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bj, float Bx, int Cp, int Cj, float Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bj, double Bx, int Cp, int Cj, double Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bj, long double Bx, int Cp, int Cj, + long double Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx, + int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _csr.csr_matmat_pass2(*args) def csr_matvec(*args): - """ - csr_matvec(int n_row, int n_col, int Ap, int Aj, signed char Ax, - signed char Xx, signed char Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - unsigned char Xx, unsigned char Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx, - short Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - unsigned short Xx, unsigned short Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx, - int Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - unsigned int Xx, unsigned int Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, long long Ax, - long long Xx, long long Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - unsigned long long Xx, unsigned long long Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx, - float Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx, - double Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, long double Ax, - long double Xx, long double Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx, npy_cfloat_wrapper Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx, npy_cdouble_wrapper Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) """ - return _csr.csr_matvec(*args) + csr_matvec(int n_row, int n_col, int Ap, int Aj, signed char Ax, + signed char Xx, signed char Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + unsigned char Xx, unsigned char Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx, + short Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + unsigned short Xx, unsigned short Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx, + int Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + unsigned int Xx, unsigned int Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, long long Ax, + long long Xx, long long Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + unsigned long long Xx, unsigned long long Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx, + float Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx, + double Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, long double Ax, + long double Xx, long double Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx, npy_cfloat_wrapper Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx, npy_cdouble_wrapper Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) + """ + return _csr.csr_matvec(*args) def csr_matvecs(*args): - """ - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, signed char Ax, - signed char Xx, signed char Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned char Ax, - unsigned char Xx, unsigned char Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, short Ax, - short Xx, short Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned short Ax, - unsigned short Xx, unsigned short Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, int Ax, - int Xx, int Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned int Ax, - unsigned int Xx, unsigned int Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, long long Ax, - long long Xx, long long Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned long long Ax, - unsigned long long Xx, - unsigned long long Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, float Ax, - float Xx, float Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, double Ax, - double Xx, double Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, long double Ax, - long double Xx, long double Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx, - npy_clongdouble_wrapper Yx) """ - return _csr.csr_matvecs(*args) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, signed char Ax, + signed char Xx, signed char Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned char Ax, + unsigned char Xx, unsigned char Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, short Ax, + short Xx, short Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned short Ax, + unsigned short Xx, unsigned short Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, int Ax, + int Xx, int Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned int Ax, + unsigned int Xx, unsigned int Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, long long Ax, + long long Xx, long long Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned long long Ax, + unsigned long long Xx, + unsigned long long Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, float Ax, + float Xx, float Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, double Ax, + double Xx, double Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, long double Ax, + long double Xx, long double Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx, + npy_clongdouble_wrapper Yx) + """ + return _csr.csr_matvecs(*args) def csr_elmul_csr(*args): - """ - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx, int Cp, int Cj, - signed char Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx, int Cp, - int Cj, unsigned char Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bj, short Bx, int Cp, int Cj, short Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx, int Cp, - int Cj, unsigned short Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bj, int Bx, int Cp, int Cj, int Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx, int Cp, - int Cj, unsigned int Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bj, long long Bx, int Cp, int Cj, - long long Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bj, float Bx, int Cp, int Cj, float Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bj, double Bx, int Cp, int Cj, double Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bj, long double Bx, int Cp, int Cj, - long double Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx, - int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _csr.csr_elmul_csr(*args) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx, int Cp, int Cj, + signed char Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx, int Cp, + int Cj, unsigned char Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bj, short Bx, int Cp, int Cj, short Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx, int Cp, + int Cj, unsigned short Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bj, int Bx, int Cp, int Cj, int Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx, int Cp, + int Cj, unsigned int Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bj, long long Bx, int Cp, int Cj, + long long Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bj, float Bx, int Cp, int Cj, float Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bj, double Bx, int Cp, int Cj, double Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bj, long double Bx, int Cp, int Cj, + long double Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx, + int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _csr.csr_elmul_csr(*args) def csr_eldiv_csr(*args): - """ - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx, int Cp, int Cj, - signed char Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx, int Cp, - int Cj, unsigned char Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bj, short Bx, int Cp, int Cj, short Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx, int Cp, - int Cj, unsigned short Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bj, int Bx, int Cp, int Cj, int Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx, int Cp, - int Cj, unsigned int Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bj, long long Bx, int Cp, int Cj, - long long Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bj, float Bx, int Cp, int Cj, float Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bj, double Bx, int Cp, int Cj, double Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bj, long double Bx, int Cp, int Cj, - long double Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx, - int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _csr.csr_eldiv_csr(*args) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx, int Cp, int Cj, + signed char Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx, int Cp, + int Cj, unsigned char Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bj, short Bx, int Cp, int Cj, short Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx, int Cp, + int Cj, unsigned short Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bj, int Bx, int Cp, int Cj, int Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx, int Cp, + int Cj, unsigned int Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bj, long long Bx, int Cp, int Cj, + long long Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bj, float Bx, int Cp, int Cj, float Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bj, double Bx, int Cp, int Cj, double Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bj, long double Bx, int Cp, int Cj, + long double Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx, + int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _csr.csr_eldiv_csr(*args) def csr_plus_csr(*args): - """ - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx, int Cp, int Cj, - signed char Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx, int Cp, - int Cj, unsigned char Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bj, short Bx, int Cp, int Cj, short Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx, int Cp, - int Cj, unsigned short Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bj, int Bx, int Cp, int Cj, int Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx, int Cp, - int Cj, unsigned int Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bj, long long Bx, int Cp, int Cj, - long long Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bj, float Bx, int Cp, int Cj, float Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bj, double Bx, int Cp, int Cj, double Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bj, long double Bx, int Cp, int Cj, - long double Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx, - int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _csr.csr_plus_csr(*args) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx, int Cp, int Cj, + signed char Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx, int Cp, + int Cj, unsigned char Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bj, short Bx, int Cp, int Cj, short Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx, int Cp, + int Cj, unsigned short Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bj, int Bx, int Cp, int Cj, int Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx, int Cp, + int Cj, unsigned int Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bj, long long Bx, int Cp, int Cj, + long long Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bj, float Bx, int Cp, int Cj, float Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bj, double Bx, int Cp, int Cj, double Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bj, long double Bx, int Cp, int Cj, + long double Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx, + int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _csr.csr_plus_csr(*args) def csr_minus_csr(*args): - """ - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx, int Cp, int Cj, - signed char Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx, int Cp, - int Cj, unsigned char Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bj, short Bx, int Cp, int Cj, short Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx, int Cp, - int Cj, unsigned short Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bj, int Bx, int Cp, int Cj, int Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx, int Cp, - int Cj, unsigned int Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bj, long long Bx, int Cp, int Cj, - long long Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bj, float Bx, int Cp, int Cj, float Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bj, double Bx, int Cp, int Cj, double Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bj, long double Bx, int Cp, int Cj, - long double Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx, - int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _csr.csr_minus_csr(*args) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx, int Cp, int Cj, + signed char Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx, int Cp, + int Cj, unsigned char Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bj, short Bx, int Cp, int Cj, short Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx, int Cp, + int Cj, unsigned short Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bj, int Bx, int Cp, int Cj, int Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx, int Cp, + int Cj, unsigned int Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bj, long long Bx, int Cp, int Cj, + long long Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bj, float Bx, int Cp, int Cj, float Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bj, double Bx, int Cp, int Cj, double Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bj, long double Bx, int Cp, int Cj, + long double Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx, + int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _csr.csr_minus_csr(*args) def csr_sort_indices(*args): - """ - csr_sort_indices(int n_row, int Ap, int Aj, signed char Ax) - csr_sort_indices(int n_row, int Ap, int Aj, unsigned char Ax) - csr_sort_indices(int n_row, int Ap, int Aj, short Ax) - csr_sort_indices(int n_row, int Ap, int Aj, unsigned short Ax) - csr_sort_indices(int n_row, int Ap, int Aj, int Ax) - csr_sort_indices(int n_row, int Ap, int Aj, unsigned int Ax) - csr_sort_indices(int n_row, int Ap, int Aj, long long Ax) - csr_sort_indices(int n_row, int Ap, int Aj, unsigned long long Ax) - csr_sort_indices(int n_row, int Ap, int Aj, float Ax) - csr_sort_indices(int n_row, int Ap, int Aj, double Ax) - csr_sort_indices(int n_row, int Ap, int Aj, long double Ax) - csr_sort_indices(int n_row, int Ap, int Aj, npy_cfloat_wrapper Ax) - csr_sort_indices(int n_row, int Ap, int Aj, npy_cdouble_wrapper Ax) - csr_sort_indices(int n_row, int Ap, int Aj, npy_clongdouble_wrapper Ax) """ - return _csr.csr_sort_indices(*args) + csr_sort_indices(int n_row, int Ap, int Aj, signed char Ax) + csr_sort_indices(int n_row, int Ap, int Aj, unsigned char Ax) + csr_sort_indices(int n_row, int Ap, int Aj, short Ax) + csr_sort_indices(int n_row, int Ap, int Aj, unsigned short Ax) + csr_sort_indices(int n_row, int Ap, int Aj, int Ax) + csr_sort_indices(int n_row, int Ap, int Aj, unsigned int Ax) + csr_sort_indices(int n_row, int Ap, int Aj, long long Ax) + csr_sort_indices(int n_row, int Ap, int Aj, unsigned long long Ax) + csr_sort_indices(int n_row, int Ap, int Aj, float Ax) + csr_sort_indices(int n_row, int Ap, int Aj, double Ax) + csr_sort_indices(int n_row, int Ap, int Aj, long double Ax) + csr_sort_indices(int n_row, int Ap, int Aj, npy_cfloat_wrapper Ax) + csr_sort_indices(int n_row, int Ap, int Aj, npy_cdouble_wrapper Ax) + csr_sort_indices(int n_row, int Ap, int Aj, npy_clongdouble_wrapper Ax) + """ + return _csr.csr_sort_indices(*args) def csr_eliminate_zeros(*args): - """ - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, signed char Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned char Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, short Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned short Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, int Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned int Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, long long Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, float Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, double Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, long double Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax) """ - return _csr.csr_eliminate_zeros(*args) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, signed char Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned char Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, short Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned short Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, int Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned int Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, long long Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, float Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, double Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, long double Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax) + """ + return _csr.csr_eliminate_zeros(*args) def csr_sum_duplicates(*args): - """ - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, signed char Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned char Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, short Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned short Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, int Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned int Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, long long Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, float Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, double Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, long double Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax) """ - return _csr.csr_sum_duplicates(*args) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, signed char Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned char Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, short Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned short Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, int Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned int Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, long long Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, float Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, double Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, long double Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax) + """ + return _csr.csr_sum_duplicates(*args) def get_csr_submatrix(*args): - """ - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(signed char)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(unsigned char)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, short Ax, int ir0, - int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(short)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(unsigned short)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, int Ax, int ir0, - int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(int)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(unsigned int)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, long long Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(long long)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int ir0, int ir1, int ic0, int ic1, - std::vector<(int)> Bp, std::vector<(int)> Bj, - std::vector<(unsigned long long)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, float Ax, int ir0, - int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(float)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, double Ax, int ir0, - int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(double)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, long double Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(long double)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int ir0, int ir1, int ic0, int ic1, - std::vector<(int)> Bp, std::vector<(int)> Bj, - std::vector<(npy_cfloat_wrapper)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int ir0, int ir1, int ic0, int ic1, - std::vector<(int)> Bp, std::vector<(int)> Bj, - std::vector<(npy_cdouble_wrapper)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int ir0, int ir1, int ic0, int ic1, - std::vector<(int)> Bp, std::vector<(int)> Bj, - std::vector<(npy_clongdouble_wrapper)> Bx) """ - return _csr.get_csr_submatrix(*args) - + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(signed char)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(unsigned char)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, short Ax, int ir0, + int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(short)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(unsigned short)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, int Ax, int ir0, + int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(int)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(unsigned int)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, long long Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(long long)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int ir0, int ir1, int ic0, int ic1, + std::vector<(int)> Bp, std::vector<(int)> Bj, + std::vector<(unsigned long long)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, float Ax, int ir0, + int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(float)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, double Ax, int ir0, + int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(double)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, long double Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(long double)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int ir0, int ir1, int ic0, int ic1, + std::vector<(int)> Bp, std::vector<(int)> Bj, + std::vector<(npy_cfloat_wrapper)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int ir0, int ir1, int ic0, int ic1, + std::vector<(int)> Bp, std::vector<(int)> Bj, + std::vector<(npy_cdouble_wrapper)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int ir0, int ir1, int ic0, int ic1, + std::vector<(int)> Bp, std::vector<(int)> Bj, + std::vector<(npy_clongdouble_wrapper)> Bx) + """ + return _csr.get_csr_submatrix(*args) Modified: trunk/scipy/sparse/sparsetools/dia.py =================================================================== --- trunk/scipy/sparse/sparsetools/dia.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/sparsetools/dia.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -51,40 +51,39 @@ def dia_matvec(*args): - """ - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - signed char diags, signed char Xx, signed char Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - unsigned char diags, unsigned char Xx, unsigned char Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - short diags, short Xx, short Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - unsigned short diags, unsigned short Xx, - unsigned short Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - int diags, int Xx, int Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - unsigned int diags, unsigned int Xx, unsigned int Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - long long diags, long long Xx, long long Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - unsigned long long diags, unsigned long long Xx, - unsigned long long Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - float diags, float Xx, float Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - double diags, double Xx, double Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - long double diags, long double Xx, long double Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - npy_cfloat_wrapper diags, npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - npy_cdouble_wrapper diags, npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - npy_clongdouble_wrapper diags, npy_clongdouble_wrapper Xx, - npy_clongdouble_wrapper Yx) """ - return _dia.dia_matvec(*args) - + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + signed char diags, signed char Xx, signed char Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + unsigned char diags, unsigned char Xx, unsigned char Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + short diags, short Xx, short Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + unsigned short diags, unsigned short Xx, + unsigned short Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + int diags, int Xx, int Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + unsigned int diags, unsigned int Xx, unsigned int Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + long long diags, long long Xx, long long Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + unsigned long long diags, unsigned long long Xx, + unsigned long long Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + float diags, float Xx, float Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + double diags, double Xx, double Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + long double diags, long double Xx, long double Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + npy_cfloat_wrapper diags, npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + npy_cdouble_wrapper diags, npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + npy_clongdouble_wrapper diags, npy_clongdouble_wrapper Xx, + npy_clongdouble_wrapper Yx) + """ + return _dia.dia_matvec(*args) Modified: trunk/scipy/sparse/tests/test_base.py =================================================================== --- trunk/scipy/sparse/tests/test_base.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/sparse/tests/test_base.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -43,10 +43,10 @@ def setUp(self): self.dat = matrix([[1,0,0,2],[3,0,1,0],[0,2,0,0]],'d') self.datsp = self.spmatrix(self.dat) - + def test_empty(self): """create empty matrices""" - + assert_equal(self.spmatrix((3,3)).todense(), np.zeros((3,3))) assert_equal(self.spmatrix((3,3)).nnz, 0) @@ -946,7 +946,7 @@ class TestCSC(_TestCommon, _TestGetSet, _TestSolve, - _TestInplaceArithmetic, _TestArithmetic, + _TestInplaceArithmetic, _TestArithmetic, _TestHorizSlicing, _TestVertSlicing, _TestBothSlicing, _TestFancyIndexing, TestCase): spmatrix = csc_matrix @@ -1264,10 +1264,10 @@ assert_array_equal(C.A, D.A) def test_fancy_indexing(self): - M = arange(25).reshape(5,5) + M = arange(25).reshape(5,5) A = lil_matrix( M ) - assert_equal(A[array([1,2,3]),2:3].todense(), M[array([1,2,3]),2:3]) + assert_equal(A[array([1,2,3]),2:3].todense(), M[array([1,2,3]),2:3]) def test_point_wise_multiply(self): l = lil_matrix((4,3)) Modified: trunk/scipy/stats/distributions.py =================================================================== --- trunk/scipy/stats/distributions.py 2008-10-26 06:50:22 UTC (rev 4844) +++ trunk/scipy/stats/distributions.py 2008-10-26 09:20:24 UTC (rev 4845) @@ -542,7 +542,7 @@ goodargs = argsreduce(cond, *((x,)+args)) place(output,cond,self._sf(*goodargs)) if output.ndim == 0: - return output[()] + return output[()] return output def ppf(self,q,*args,**kwds): @@ -573,7 +573,7 @@ scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2] place(output,cond,self._ppf(*goodargs)*scale + loc) if output.ndim == 0: - return output[()] + return output[()] return output def isf(self,q,*args,**kwds): @@ -3517,7 +3517,7 @@ goodargs = argsreduce(cond, *((k,)+args)) place(output,cond,self._pmf(*goodargs)) if output.ndim == 0: - return output[()] + return output[()] return output def cdf(self, k, *args, **kwds): @@ -3547,7 +3547,7 @@ goodargs = argsreduce(cond, *((k,)+args)) place(output,cond,self._cdf(*goodargs)) if output.ndim == 0: - return output[()] + return output[()] return output def sf(self,k,*args,**kwds): @@ -3577,7 +3577,7 @@ goodargs = argsreduce(cond, *((k,)+args)) place(output,cond,self._sf(*goodargs)) if output.ndim == 0: - return output[()] + return output[()] return output def ppf(self,q,*args,**kwds): @@ -3607,7 +3607,7 @@ loc, goodargs = goodargs[-1], goodargs[:-1] place(output,cond,self._ppf(*goodargs) + loc) if output.ndim == 0: - return output[()] + return output[()] return output def isf(self,q,*args,**kwds): @@ -3638,7 +3638,7 @@ loc, goodargs = goodargs[-1], goodargs[:-1] place(output,cond,self._ppf(*goodargs) + loc) if output.ndim == 0: - return output[()] + return output[()] return output def stats(self, *args, **kwds): From scipy-svn at scipy.org Sun Oct 26 07:03:13 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 06:03:13 -0500 (CDT) Subject: [Scipy-svn] r4846 - in branches/refactor_fft: . scipy scipy/cluster scipy/interpolate scipy/io/matlab scipy/io/matlab/tests scipy/ndimage scipy/optimize/tests scipy/sparse scipy/sparse/linalg scipy/sparse/linalg/isolve scipy/sparse/linalg/isolve/tests scipy/sparse/sparsetools scipy/sparse/tests scipy/stats scipy/stats/tests Message-ID: <20081026110313.057EA39C05F@scipy.org> Author: cdavid Date: 2008-10-26 06:02:22 -0500 (Sun, 26 Oct 2008) New Revision: 4846 Modified: branches/refactor_fft/ branches/refactor_fft/scipy/__init__.py branches/refactor_fft/scipy/cluster/distance.py branches/refactor_fft/scipy/cluster/hierarchy.py branches/refactor_fft/scipy/interpolate/interpolate_wrapper.py branches/refactor_fft/scipy/interpolate/setup.py branches/refactor_fft/scipy/io/matlab/byteordercodes.py branches/refactor_fft/scipy/io/matlab/mio.py branches/refactor_fft/scipy/io/matlab/mio5.py branches/refactor_fft/scipy/io/matlab/miobase.py branches/refactor_fft/scipy/io/matlab/tests/test_byteordercodes.py branches/refactor_fft/scipy/io/matlab/tests/test_mio.py branches/refactor_fft/scipy/ndimage/filters.py branches/refactor_fft/scipy/optimize/tests/test_nnls.py branches/refactor_fft/scipy/sparse/base.py branches/refactor_fft/scipy/sparse/bsr.py branches/refactor_fft/scipy/sparse/compressed.py branches/refactor_fft/scipy/sparse/construct.py branches/refactor_fft/scipy/sparse/coo.py branches/refactor_fft/scipy/sparse/csc.py branches/refactor_fft/scipy/sparse/csr.py branches/refactor_fft/scipy/sparse/dia.py branches/refactor_fft/scipy/sparse/dok.py branches/refactor_fft/scipy/sparse/lil.py branches/refactor_fft/scipy/sparse/linalg/interface.py branches/refactor_fft/scipy/sparse/linalg/isolve/iterative.py branches/refactor_fft/scipy/sparse/linalg/isolve/tests/test_iterative.py branches/refactor_fft/scipy/sparse/sparsetools/bsr.py branches/refactor_fft/scipy/sparse/sparsetools/coo.py branches/refactor_fft/scipy/sparse/sparsetools/csc.py branches/refactor_fft/scipy/sparse/sparsetools/csr.py branches/refactor_fft/scipy/sparse/sparsetools/dia.py branches/refactor_fft/scipy/sparse/sputils.py branches/refactor_fft/scipy/sparse/tests/test_base.py branches/refactor_fft/scipy/sparse/tests/test_sputils.py branches/refactor_fft/scipy/stats/distributions.py branches/refactor_fft/scipy/stats/stats.py branches/refactor_fft/scipy/stats/tests/test_stats.py Log: Merged revisions 4828-4845 via svnmerge from http://svn.scipy.org/svn/scipy/trunk ........ r4829 | cdavid | 2008-10-25 16:36:42 +0900 (Sat, 25 Oct 2008) | 1 line Remove double TestMedian, which shadows some median tests. ........ r4830 | cdavid | 2008-10-25 18:13:05 +0900 (Sat, 25 Oct 2008) | 1 line Add old median test to new median test. ........ r4831 | cdavid | 2008-10-25 18:13:34 +0900 (Sat, 25 Oct 2008) | 1 line Add regression test for #760. ........ r4832 | cdavid | 2008-10-25 18:26:32 +0900 (Sat, 25 Oct 2008) | 1 line BUG: Fix bug 760. median was using old numpy.median behavior wrt axis argument. ........ r4833 | cdavid | 2008-10-25 19:29:28 +0900 (Sat, 25 Oct 2008) | 1 line Forgot to add one file for #760 fix. ........ r4834 | wnbell | 2008-10-26 06:26:45 +0900 (Sun, 26 Oct 2008) | 2 lines removed sum_duplicates option from coo_matrix.tocsr() and coo_matrix.tocsc() ........ r4835 | wnbell | 2008-10-26 07:19:48 +0900 (Sun, 26 Oct 2008) | 3 lines sparse matrices now conform to spmatrix( (M,N) ) -> empty M-by-N matrix added tests for invalid shapes in case above ........ r4836 | wnbell | 2008-10-26 07:34:36 +0900 (Sun, 26 Oct 2008) | 2 lines cleaned up lil_matrix imports ........ r4837 | wnbell | 2008-10-26 07:43:44 +0900 (Sun, 26 Oct 2008) | 2 lines cleaned up dia_matrix imports ........ r4838 | wnbell | 2008-10-26 08:13:09 +0900 (Sun, 26 Oct 2008) | 2 lines cleaned up bsr_matrix imports ........ r4839 | wnbell | 2008-10-26 08:30:38 +0900 (Sun, 26 Oct 2008) | 2 lines cleaned up coo_matrix imports ........ r4840 | wnbell | 2008-10-26 10:16:01 +0900 (Sun, 26 Oct 2008) | 3 lines cleaned up sp_matrix imports cleaned up multiplication handlers ........ r4841 | wnbell | 2008-10-26 10:33:39 +0900 (Sun, 26 Oct 2008) | 2 lines cleaned up csr_matrix and csc_matrix imports ........ r4842 | wnbell | 2008-10-26 10:43:51 +0900 (Sun, 26 Oct 2008) | 2 lines cleaned up imports in construction.py ........ r4845 | jarrod.millman | 2008-10-26 18:20:24 +0900 (Sun, 26 Oct 2008) | 2 lines ran reindent ........ Property changes on: branches/refactor_fft ___________________________________________________________________ Name: svnmerge-integrated - /branches/build_with_scons:1-3868 /branches/scipy.scons:1-3533 /branches/sparse_build_reduce_mem:1-4005 /branches/testing_cleanup:1-3662 /trunk:1-4827 + /branches/build_with_scons:1-3868 /branches/scipy.scons:1-3533 /branches/sparse_build_reduce_mem:1-4005 /branches/testing_cleanup:1-3662 /trunk:1-4845 Modified: branches/refactor_fft/scipy/__init__.py =================================================================== --- branches/refactor_fft/scipy/__init__.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/__init__.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -74,7 +74,7 @@ # Remove subpackage names from __all__ such that they are not imported via # "from scipy import *". This works around a numpy bug present in < 1.2. subpackages = """cluster constants fftpack integrate interpolate io lib linalg -linsolve maxentropy misc ndimage odr optimize signal sparse special +linsolve maxentropy misc ndimage odr optimize signal sparse special splinalg stats stsci weave""".split() for name in subpackages: try: Modified: branches/refactor_fft/scipy/cluster/distance.py =================================================================== --- branches/refactor_fft/scipy/cluster/distance.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/cluster/distance.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -954,8 +954,8 @@ d(u,v) = \sum_u {|u_i-v_i|} {|u_i|+|v_i|} - + 12. ``Y = pdist(X, 'braycurtis')`` Computes the Bray-Curtis distance between the points. The @@ -1285,8 +1285,8 @@ it is known that ``X - X.T1`` is small and ``diag(X)`` is close to zero. These values are ignored any way so they do not disrupt the squareform transformation. - + Calling Conventions ------------------- @@ -1692,8 +1692,8 @@ d(u,v) = \sum_u {|u_i-v_i|} {|u_i|+|v_i|} - + 12. ``Y = cdist(X, 'braycurtis')`` Computes the Bray-Curtis distance between the points. The Modified: branches/refactor_fft/scipy/cluster/hierarchy.py =================================================================== --- branches/refactor_fft/scipy/cluster/hierarchy.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/cluster/hierarchy.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -345,7 +345,7 @@ Performs centroid/UPGMC linkage on the condensed distance matrix ``y``. See ``linkage`` for more information on the return - structure and algorithm. + structure and algorithm. 2. Z = centroid(X) @@ -427,7 +427,7 @@ def linkage(y, method='single', metric='euclidean'): - """ + """ Performs hierarchical/agglomerative clustering on the condensed distance matrix y. y must be a {n \choose 2} sized vector where n is the number of original observations paired @@ -665,7 +665,7 @@ The number of leaf nodes (original observations) belonging to the cluster node nd. If the target node is a leaf, 1 is returned. - + :Returns: c : int @@ -713,7 +713,7 @@ the list. For example, the statement: - + ids = root.preOrder(lambda x: x.id) returns a list of the node ids corresponding to the leaf nodes @@ -730,7 +730,7 @@ :Returns: - L : list - The pre-order traversal. + The pre-order traversal. """ # Do a preorder traversal, caching the result. To avoid having to do @@ -773,7 +773,7 @@ Converts a hierarchical clustering encoded in the matrix Z (by linkage) into an easy-to-use tree object. The reference r to the root cnode object is returned. - + Each cnode object has a left, right, dist, id, and count attribute. The left and right attributes point to cnode objects that were combined to generate the cluster. If both are None then @@ -885,12 +885,12 @@ hierarchical clustering defined by the linkage matrix ``Z`` of a set of :math:`$n$` observations in :math:`$m$` dimensions. ``Y`` is the condensed distance matrix from which - ``Z`` was generated. + ``Z`` was generated. :Returns: - c : ndarray The cophentic correlation distance (if ``y`` is passed). - + - d : ndarray The cophenetic distance matrix in condensed form. The :math:`$ij$`th entry is the cophenetic distance between @@ -964,8 +964,8 @@ The :math:`$(n-1)$` by 4 matrix encoding the linkage (hierarchical clustering). See ``linkage`` documentation for more information on its form. - + :Returns: - R : ndarray A :math:`$(n-1)$` by 5 matrix where the ``i``'th row Modified: branches/refactor_fft/scipy/interpolate/interpolate_wrapper.py =================================================================== --- branches/refactor_fft/scipy/interpolate/interpolate_wrapper.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/interpolate/interpolate_wrapper.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -119,20 +119,20 @@ return new_y def block(x, y, new_x): - """ Essentially a step function. - - For each new_x[i], finds largest j such that - x[j] < new_x[j], and returns y[j]. - """ - # find index of values in x that preceed values in x - # This code is a little strange -- we really want a routine that - # returns the index of values where x[j] < x[index] - TINY = 1e-10 - indices = np.searchsorted(x, new_x+TINY)-1 + """ Essentially a step function. + + For each new_x[i], finds largest j such that + x[j] < new_x[j], and returns y[j]. + """ + # find index of values in x that preceed values in x + # This code is a little strange -- we really want a routine that + # returns the index of values where x[j] < x[index] + TINY = 1e-10 + indices = np.searchsorted(x, new_x+TINY)-1 - # If the value is at the front of the list, it'll have -1. - # In this case, we will use the first (0), element in the array. - # take requires the index array to be an Int - indices = np.atleast_1d(np.clip(indices, 0, np.Inf).astype(np.int)) - new_y = np.take(y, indices, axis=-1) - return new_y \ No newline at end of file + # If the value is at the front of the list, it'll have -1. + # In this case, we will use the first (0), element in the array. + # take requires the index array to be an Int + indices = np.atleast_1d(np.clip(indices, 0, np.Inf).astype(np.int)) + new_y = np.take(y, indices, axis=-1) + return new_y Modified: branches/refactor_fft/scipy/interpolate/setup.py =================================================================== --- branches/refactor_fft/scipy/interpolate/setup.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/interpolate/setup.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -22,9 +22,9 @@ ) config.add_extension('_interpolate', - sources=['src/_interpolate.cpp'], - include_dirs = ['src'], - depends = ['src/interpolate.h']) + sources=['src/_interpolate.cpp'], + include_dirs = ['src'], + depends = ['src/interpolate.h']) config.add_data_dir('tests') Modified: branches/refactor_fft/scipy/io/matlab/byteordercodes.py =================================================================== --- branches/refactor_fft/scipy/io/matlab/byteordercodes.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/io/matlab/byteordercodes.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -18,21 +18,21 @@ 'swapped': ('swapped', 'S')} def to_numpy_code(code): - ''' Convert various order codings to numpy format + ''' Convert various order codings to numpy format Parameters ---------- code : {'little','big','l','b','le','be','<','>', 'native','=', 'swapped', 's'} string code is converted to lower case before parsing - + Returns ------- out_code : {'<','>'} string - where '<' is the numpy dtype code for little + where '<' is the numpy dtype code for little endian, and '>' is the code for big endian - + Examples -------- >>> import sys @@ -64,5 +64,3 @@ else: raise ValueError( 'We cannot handle byte order %s' % code) - - Modified: branches/refactor_fft/scipy/io/matlab/mio.py =================================================================== --- branches/refactor_fft/scipy/io/matlab/mio.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/io/matlab/mio.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -68,7 +68,7 @@ raise NotImplementedError('Please use PyTables for matlab v7.3 (HDF) files') else: raise TypeError('Did not recognize version %s' % mv) - + def loadmat(file_name, mdict=None, appendmat=True, basename='raw', **kwargs): ''' Load Matlab(tm) file @@ -96,8 +96,8 @@ (implies squeeze_me=False, chars_as_strings=False, mat_dtype=True) struct_as_record - whether to load matlab structs as numpy record arrays, or - as old-style numpy arrays with dtype=object. - (warns if not set, and defaults to False. non-recarrays + as old-style numpy arrays with dtype=object. + (warns if not set, and defaults to False. non-recarrays cannot be exported via savemat.) v4 (Level 1.0), v6 and v7.1 matfiles are supported. Modified: branches/refactor_fft/scipy/io/matlab/mio5.py =================================================================== --- branches/refactor_fft/scipy/io/matlab/mio5.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/io/matlab/mio5.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -202,7 +202,7 @@ mod8 = byte_count % 8 if mod8: self.mat_stream.seek(8 - mod8, 1) - + if mdtype in self.codecs: # encoded char data codec = self.codecs[mdtype] if not codec: @@ -216,7 +216,7 @@ buffer=raw_str) if copy: el = el.copy() - + return el def matrix_getter_factory(self): @@ -428,7 +428,7 @@ tupdims = tuple(self.header['dims'][::-1]) length = np.product(tupdims) if self.struct_as_record: - result = np.empty(length, dtype=[(field_name, object) + result = np.empty(length, dtype=[(field_name, object) for field_name in field_names]) for i in range(length): for field_name in field_names: @@ -442,14 +442,14 @@ for name in field_names: item.__dict__[name] = self.read_element() result[i] = item - + return result.reshape(tupdims).T class MatlabObject(object): ''' Class to contain read data from matlab objects ''' def __init__(self, classname, field_names): self.__dict__['classname'] = classname - self.__dict__['mobj_recarray'] = np.empty((1,1), dtype=[(field_name, object) + self.__dict__['mobj_recarray'] = np.empty((1,1), dtype=[(field_name, object) for field_name in field_names]) def __getattr__(self, name): @@ -464,8 +464,8 @@ self.__dict__['mobj_recarray'][0,0][name] = value else: self.__dict__[name] = value - + class Mat5ObjectMatrixGetter(Mat5MatrixGetter): def get_array(self): '''Matlab ojects are essentially structs, with an extra field, the classname.''' @@ -495,7 +495,7 @@ class MatFile5Reader(MatFileReader): ''' Reader for Mat 5 mat files Adds the following attribute to base class - + uint16_codec - char codec to use for uint16 char arrays (defaults to system default codec) ''' @@ -513,9 +513,9 @@ ''' mat_stream : file-like object with file API, open for reading - byte_order : {None, string} + byte_order : {None, string} specification of byte order, one of: - ('native', '=', 'little', '<', 'BIG', '>') + ('native', '=', 'little', '<', 'BIG', '>') mat_dtype : {True, False} boolean If True, return arrays in same dtype as loaded into matlab otherwise return with dtype with which they were saved @@ -527,7 +527,7 @@ If True, returns matrices as would be loaded by matlab (implies squeeze_me=False, chars_as_strings=False mat_dtype=True, struct_as_record=True) - struct_as_record : {False, True} boolean + struct_as_record : {False, True} boolean If True, return strutures as numpy records, otherwise, return as custom object (for compatibility with scipy 0.6) @@ -766,8 +766,8 @@ def __init__(self, file_stream, arr, name, is_global=False, unicode_strings=False): super(Mat5CompositeWriter, self).__init__(file_stream, arr, name, is_global) self.unicode_strings = unicode_strings - + class Mat5CellWriter(Mat5CompositeWriter): def write(self): self.write_header(mclass=mxCELL_CLASS) @@ -797,12 +797,12 @@ class Mat5StructWriter(Mat5CompositeWriter): def write(self): self.write_header(mclass=mxSTRUCT_CLASS) - + # write fieldnames fieldnames = [f[0] for f in self.arr.dtype.descr] self.write_element(np.array([32], dtype='i4')) self.write_element(np.array(fieldnames, dtype='S32'), mdtype=miINT8) - + A = np.atleast_2d(self.arr).flatten('F') MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) for el in A: @@ -826,7 +826,7 @@ fieldnames = [f[0] for f in self.arr.dtype.descr] self.write_element(np.array([32], dtype='i4')) self.write_element(np.array(fieldnames, dtype='S32'), mdtype=miINT8) - + A = np.atleast_2d(self.arr).flatten('F') MWG = Mat5WriterGetter(self.file_stream, self.unicode_strings) for el in A: @@ -835,7 +835,7 @@ MW.write() self.update_matrix_tag() - + class Mat5WriterGetter(object): ''' Wraps stream and options, provides methods for getting Writer objects ''' def __init__(self, stream, unicode_strings): @@ -854,12 +854,12 @@ if spsparse: if spsparse.issparse(arr): return Mat5SparseWriter(self.stream, arr, name, is_global) - + if isinstance(arr, MatlabFunctionMatrix): return Mat5FunctionWriter(self.stream, arr, name, is_global, self.unicode_strings) if isinstance(arr, MatlabObject): return Mat5ObjectWriter(self.stream, arr, name, is_global, self.unicode_strings) - + arr = np.array(arr) if arr.dtype.hasobject: if arr.dtype.fields == None: Modified: branches/refactor_fft/scipy/io/matlab/miobase.py =================================================================== --- branches/refactor_fft/scipy/io/matlab/miobase.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/io/matlab/miobase.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -29,7 +29,7 @@ #. 0,x -> version 4 format mat files #. 1,x -> version 5 format mat files #. 2,x -> version 7.3 format mat files (HDF format) - + Parameters ---------- fileobj : {file-like} @@ -54,7 +54,7 @@ if 0 in mopt_bytes: fileobj.seek(0) return (0,0) - + # For 5 format or 7.3 format we need to read an integer in the # header. Bytes 124 through 128 contain a version integer and an # endian test string @@ -144,9 +144,9 @@ ''' mat_stream : file-like object with file API, open for reading - byte_order : {None, string} + byte_order : {None, string} specification of byte order, one of: - ('native', '=', 'little', '<', 'BIG', '>') + ('native', '=', 'little', '<', 'BIG', '>') mat_dtype : {True, False} boolean If True, return arrays in same dtype as loaded into matlab otherwise return with dtype with which they were saved @@ -158,7 +158,7 @@ If True, returns matrices as would be loaded by matlab (implies squeeze_me=False, chars_as_strings=False mat_dtype=True) - + ''' # Initialize stream self.mat_stream = mat_stream @@ -234,7 +234,7 @@ def matrix_getter_factory(self): assert False, 'Not implemented' - + def file_header(self): return {} Modified: branches/refactor_fft/scipy/io/matlab/tests/test_byteordercodes.py =================================================================== --- branches/refactor_fft/scipy/io/matlab/tests/test_byteordercodes.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/io/matlab/tests/test_byteordercodes.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -11,13 +11,13 @@ def test_native(): native_is_le = sys.byteorder == 'little' assert sibc.sys_is_le == native_is_le - + def test_to_numpy(): if sys.byteorder == 'little': assert sibc.to_numpy_code('native') == '<' assert sibc.to_numpy_code('swapped') == '>' else: - assert sibc.to_numpy_code('native') == '>' + assert sibc.to_numpy_code('native') == '>' assert sibc.to_numpy_code('swapped') == '<' assert sibc.to_numpy_code('native') == sibc.to_numpy_code('=') assert sibc.to_numpy_code('big') == '>' @@ -26,5 +26,3 @@ for code in ('big', '>', 'b', 'B', 'be'): assert sibc.to_numpy_code(code) == '>' assert_raises(ValueError, sibc.to_numpy_code, 'silly string') - - Modified: branches/refactor_fft/scipy/io/matlab/tests/test_mio.py =================================================================== --- branches/refactor_fft/scipy/io/matlab/tests/test_mio.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/io/matlab/tests/test_mio.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -70,7 +70,7 @@ assert k in matdict, "Missing key at %s" % k_label _check_level(k_label, expected, matdict[k]) -# Round trip tests +# Round trip tests def _rt_check_case(name, expected, format): mat_stream = StringIO() savemat(mat_stream, expected, format=format) @@ -155,7 +155,7 @@ 'expected': {'testsparsefloat': SP.csc_matrix(array([[-1+2j,0,2],[0,-3j,0]]))}, }, ] -st = array([(u'Rats live on no evil star.', array([sqrt(2),exp(1),pi]), (1+1j)*array([sqrt(2),exp(1),pi]))], +st = array([(u'Rats live on no evil star.', array([sqrt(2),exp(1),pi]), (1+1j)*array([sqrt(2),exp(1),pi]))], dtype=[(n, object) for n in ['stringfield', 'doublefield', 'complexfield']]) case_table5.append( {'name': 'struct', @@ -253,7 +253,7 @@ assert_array_almost_equal(actual['x'].todense(), expected['x'].todense()) - + def test_mat73(): # Check any hdf5 files raise an error filenames = glob( Modified: branches/refactor_fft/scipy/ndimage/filters.py =================================================================== --- branches/refactor_fft/scipy/ndimage/filters.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/ndimage/filters.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -45,7 +45,7 @@ def moredoc(*args): def decorate(f): if f.__doc__ is not None: - for a in args: + for a in args: f.__doc__ += a return f return decorate Modified: branches/refactor_fft/scipy/optimize/tests/test_nnls.py =================================================================== --- branches/refactor_fft/scipy/optimize/tests/test_nnls.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/optimize/tests/test_nnls.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -22,6 +22,3 @@ if __name__ == "__main__": run_module_suite() - - - Modified: branches/refactor_fft/scipy/sparse/base.py =================================================================== --- branches/refactor_fft/scipy/sparse/base.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/base.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -5,9 +5,7 @@ from warnings import warn -import numpy -from numpy import asarray, asmatrix, asanyarray, ones, deprecate, ravel, \ - matrix +import numpy as np from sputils import isdense, isscalarlike, isintlike @@ -70,7 +68,7 @@ raise TypeError('invalid shape') if not (shape[0] >= 1 and shape[1] >= 1): - raise TypeError('invalid shape') + raise ValueError('invalid shape') if (self._shape != shape) and (self._shape is not None): try: @@ -100,7 +98,7 @@ return self else: for fp_type in fp_types: - if self.dtype <= numpy.dtype(fp_type): + if self.dtype <= np.dtype(fp_type): return self.astype(fp_type) raise TypeError,'cannot upcast [%s] to a floating \ @@ -137,15 +135,15 @@ format = 'und' return format - @deprecate + @np.deprecate def rowcol(self, num): return (None, None) - @deprecate + @np.deprecate def getdata(self, num): return None - @deprecate + @np.deprecate def listprint(self, start, stop): """Provides a way to print over a single index. """ @@ -212,10 +210,12 @@ else: return getattr(self,'to' + format)() - # default operations use the CSR format as a base - # and operations return in csr format - # thus, a new sparse matrix format just needs to define - # a tocsr method + ################################################################### + # NOTE: All arithmetic operations use csr_matrix by default. + # Therefore a new sparse matrix format just needs to define a + # .tocsr() method to provide arithmetic support. Any of these + # methods can be overridden for efficiency. + #################################################################### def multiply(self, other): """Point-wise multiplication by another matrix @@ -239,13 +239,34 @@ return self.tocsr().__rsub__(other) # old __mul__ interfaces - def matvec(self, other): + @np.deprecate + def matvec(self,other): return self * other - def matmat(self, other): + + @np.deprecate + def matmat(self,other): return self * other + + @np.deprecate def dot(self, other): return self * other + @np.deprecate + def rmatvec(self, other, conjugate=True): + """Multiplies the vector 'other' by the sparse matrix, returning a + dense vector as a result. + + If 'conjugate' is True: + - returns A.transpose().conj() * other + Otherwise: + - returns A.transpose() * other. + + """ + if conjugate: + return self.conj().transpose() * other + else: + return self.transpose() * other + def __mul__(self, other): """interpret other and call one of the following @@ -268,18 +289,18 @@ other.shape except AttributeError: # If it's a list or whatever, treat it like a matrix - other = asanyarray(other) + other = np.asanyarray(other) - if isdense(other) and asarray(other).squeeze().ndim <= 1: + if isdense(other) and np.asarray(other).squeeze().ndim <= 1: ## # dense row or column vector if other.shape != (N,) and other.shape != (N,1): raise ValueError('dimension mismatch') - result = self._mul_vector(ravel(other)) + result = self._mul_vector(np.ravel(other)) - if isinstance(other, matrix): - result = asmatrix(result) + if isinstance(other, np.matrix): + result = np.asmatrix(result) if other.ndim == 2 and other.shape[1] == 1: # If 'other' was an (nx1) column vector, reshape the result @@ -294,10 +315,10 @@ if other.shape[0] != self.shape[1]: raise ValueError('dimension mismatch') - result = self._mul_dense_matrix(asarray(other)) + result = self._mul_dense_matrix(np.asarray(other)) - if isinstance(other, matrix): - result = asmatrix(result) + if isinstance(other, np.matrix): + result = np.asmatrix(result) return result else: @@ -316,9 +337,21 @@ def _mul_sparse_matrix(self, other): return self.tocsr()._mul_sparse_matrix(other) - def __rmul__(self, other): - return self.tocsr().__rmul__(other) + def __rmul__(self, other): # other * self + if isscalarlike(other): + return self.__mul__(other) + else: + # Don't use asarray unless we have to + try: + tr = other.transpose() + except AttributeError: + tr = np.asarray(other).transpose() + return (self.transpose() * tr).transpose() + #################### + # Other Arithmetic # + #################### + def __truediv__(self, other): if isscalarlike(other): return self * (1./other) @@ -349,12 +382,12 @@ def __pow__(self, other): if self.shape[0] != self.shape[1]: - raise TypeError,'matrix is not square' + raise TypeError('matrix is not square') if isintlike(other): other = int(other) if other < 0: - raise ValueError,'exponent must be >= 0' + raise ValueError('exponent must be >= 0') if other == 0: from construct import identity @@ -367,7 +400,7 @@ result = result*self return result elif isscalarlike(other): - raise ValueError,'exponent must be an integer' + raise ValueError('exponent must be an integer') elif isspmatrix(other): warn('Using ** for elementwise multiplication is deprecated.'\ 'Use .multiply() instead', DeprecationWarning) @@ -460,36 +493,11 @@ a[0, i] = 1 return a * self - - def rmatvec(self, other, conjugate=True): - """Multiplies the vector 'other' by the sparse matrix, returning a - dense vector as a result. - - If 'conjugate' is True: - - returns A.transpose().conj() * other - Otherwise: - - returns A.transpose() * other. - - """ - return self.tocsr().rmatvec(other, conjugate=conjugate) - - #def rmatmat(self, other, conjugate=True): - # """ If 'conjugate' is True: - # returns other * A.transpose().conj(), - # where 'other' is a matrix. Otherwise: - # returns other * A.transpose(). - # """ - # other = csc_matrix(other) - # if conjugate: - # return other.matmat(self.transpose()).conj() - # else: - # return other.matmat(self.transpose()) - #def __array__(self): # return self.toarray() def todense(self): - return asmatrix(self.toarray()) + return np.asmatrix(self.toarray()) def toarray(self): return self.tocoo().toarray() @@ -522,13 +530,13 @@ m, n = self.shape if axis == 0: # sum over columns - return asmatrix(ones((1, m), dtype=self.dtype)) * self + return np.asmatrix(np.ones((1, m), dtype=self.dtype)) * self elif axis == 1: # sum over rows - return self * asmatrix(ones((n, 1), dtype=self.dtype)) + return self * np.asmatrix(np.ones((n, 1), dtype=self.dtype)) elif axis is None: # sum over rows and columns - return ( self * asmatrix(ones((n, 1), dtype=self.dtype)) ).sum() + return ( self * np.asmatrix(np.ones((n, 1), dtype=self.dtype)) ).sum() else: raise ValueError, "axis out of bounds" Modified: branches/refactor_fft/scipy/sparse/bsr.py =================================================================== --- branches/refactor_fft/scipy/sparse/bsr.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/bsr.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -6,8 +6,7 @@ from warnings import warn -from numpy import zeros, intc, array, asarray, arange, diff, tile, rank, \ - ravel, empty, empty_like +import numpy as np from data import _data_matrix from compressed import _cs_matrix @@ -97,10 +96,10 @@ if isspmatrix(arg1): - if arg1.format == self.format and copy: + if isspmatrix_bsr(arg1) and copy: arg1 = arg1.copy() else: - arg1 = getattr(arg1,'to' + self.format)(blocksize=blocksize) + arg1 = arg1.tobsr(blocksize=blocksize) self._set_self( arg1 ) elif isinstance(arg1,tuple): @@ -113,16 +112,16 @@ blocksize = (1,1) else: if not isshape(blocksize): - raise ValueError,'invalid blocksize=%s',blocksize + raise ValueError('invalid blocksize=%s' % blocksize) blocksize = tuple(blocksize) - self.data = zeros( (0,) + blocksize, getdtype(dtype, default=float) ) - self.indices = zeros( 0, dtype=intc ) + self.data = np.zeros( (0,) + blocksize, getdtype(dtype, default=float) ) + self.indices = np.zeros( 0, dtype=np.intc ) R,C = blocksize if (M % R) != 0 or (N % C) != 0: raise ValueError, 'shape must be multiple of blocksize' - self.indptr = zeros(M/R + 1, dtype=intc ) + self.indptr = np.zeros(M/R + 1, dtype=np.intc ) elif len(arg1) == 2: # (data,(row,col)) format @@ -132,21 +131,20 @@ elif len(arg1) == 3: # (data,indices,indptr) format (data, indices, indptr) = arg1 - self.indices = array(indices, copy=copy) - self.indptr = array(indptr, copy=copy) - self.data = array(data, copy=copy, \ - dtype=getdtype(dtype, data)) + self.indices = np.array(indices, copy=copy) + self.indptr = np.array(indptr, copy=copy) + self.data = np.array(data, copy=copy, dtype=getdtype(dtype, data)) else: - raise ValueError,'unrecognized bsr_matrix constructor usage' + raise ValueError('unrecognized bsr_matrix constructor usage') else: #must be dense try: - arg1 = asarray(arg1) + arg1 = np.asarray(arg1) except: - raise ValueError, "unrecognized form for" \ - " %s_matrix constructor" % self.format + raise ValueError("unrecognized form for" \ + " %s_matrix constructor" % self.format) from coo import coo_matrix - arg1 = self.__class__( coo_matrix(arg1), blocksize=blocksize ) + arg1 = coo_matrix(arg1).tobsr(blocksize=blocksize) self._set_self( arg1 ) if shape is not None: @@ -193,14 +191,14 @@ % self.indices.dtype.name ) # only support 32-bit ints for now - self.indptr = asarray(self.indptr,intc) - self.indices = asarray(self.indices,intc) + self.indptr = np.asarray(self.indptr, np.intc) + self.indices = np.asarray(self.indices, np.intc) self.data = to_native(self.data) # check array shapes - if (rank(self.indices) != 1) or (rank(self.indptr) != 1): + if np.rank(self.indices) != 1 or np.rank(self.indptr) != 1: raise ValueError,"indices, and indptr should be rank 1" - if rank(self.data) != 3: + if np.rank(self.data) != 3: raise ValueError,"data should be rank 3" # check index pointer @@ -260,9 +258,9 @@ """ M,N = self.shape R,C = self.blocksize - y = empty( min(M,N), dtype=upcast(self.dtype) ) + y = np.empty(min(M,N), dtype=upcast(self.dtype)) sparsetools.bsr_diagonal(M/R, N/C, R, C, \ - self.indptr, self.indices, ravel(self.data), y) + self.indptr, self.indices, np.ravel(self.data), y) return y ########################## @@ -292,7 +290,7 @@ M,N = self.shape R,C = self.blocksize - result = zeros( self.shape[0], dtype=upcast(self.dtype, other.dtype) ) + result = np.zeros(self.shape[0], dtype=upcast(self.dtype, other.dtype)) bsr_matvec(M/R, N/C, R, C, \ self.indptr, self.indices, self.data.ravel(), @@ -305,7 +303,7 @@ M,N = self.shape n_vecs = other.shape[1] #number of column vectors - result = zeros( (M,n_vecs), dtype=upcast(self.dtype,other.dtype) ) + result = np.zeros((M,n_vecs), dtype=upcast(self.dtype,other.dtype)) bsr_matvecs(M/R, N/C, n_vecs, R, C, \ self.indptr, self.indices, self.data.ravel(), \ @@ -313,17 +311,11 @@ return result - #def _mul_dense_matrix(self, other): - # # TODO make sparse * dense matrix multiplication more efficient - # # matvec each column of other - # result = hstack( [ self * col.reshape(-1,1) for col in asarray(other).T ] ) - # return result - def _mul_sparse_matrix(self, other): M, K1 = self.shape K2, N = other.shape - indptr = empty_like( self.indptr ) + indptr = np.empty_like( self.indptr ) R,n = self.blocksize @@ -336,7 +328,7 @@ from csr import isspmatrix_csr if isspmatrix_csr(other) and n == 1: - other = other.tobsr(blocksize=(n,C),copy=False) #convert to this format + other = other.tobsr(blocksize=(n,C), copy=False) #lightweight conversion else: other = other.tobsr(blocksize=(n,C)) @@ -346,15 +338,16 @@ indptr) bnnz = indptr[-1] - indices = empty( bnnz, dtype=intc) - data = empty( R*C*bnnz, dtype=upcast(self.dtype,other.dtype)) + indices = np.empty(bnnz, dtype=np.intc) + data = np.empty(R*C*bnnz, dtype=upcast(self.dtype,other.dtype)) bsr_matmat_pass2( M/R, N/C, R, C, n, \ - self.indptr, self.indices, ravel(self.data), \ - other.indptr, other.indices, ravel(other.data), \ + self.indptr, self.indices, np.ravel(self.data), \ + other.indptr, other.indices, np.ravel(other.data), \ indptr, indices, data) data = data.reshape(-1,R,C) + #TODO eliminate zeros return bsr_matrix((data,indices,indptr),shape=(M,N),blocksize=(R,C)) @@ -391,13 +384,13 @@ M,N = self.shape R,C = self.blocksize - row = (R * arange(M/R)).repeat(diff(self.indptr)) + row = (R * np.arange(M/R)).repeat(np.diff(self.indptr)) row = row.repeat(R*C).reshape(-1,R,C) - row += tile( arange(R).reshape(-1,1), (1,C) ) + row += np.tile(np.arange(R).reshape(-1,1), (1,C)) row = row.reshape(-1) col = (C * self.indices).repeat(R*C).reshape(-1,R,C) - col += tile( arange(C), (R,1) ) + col += np.tile(np.arange(C), (R,1)) col = col.reshape(-1) data = self.data.reshape(-1) @@ -406,7 +399,7 @@ data = data.copy() from coo import coo_matrix - return coo_matrix( (data,(row,col)), shape=self.shape ) + return coo_matrix((data,(row,col)), shape=self.shape) def transpose(self): @@ -416,17 +409,17 @@ NBLK = self.nnz/(R*C) if self.nnz == 0: - return bsr_matrix((N,M),blocksize=(C,R)) + return bsr_matrix((N,M), blocksize=(C,R)) - indptr = empty( N/C + 1, dtype=self.indptr.dtype) - indices = empty( NBLK, dtype=self.indices.dtype) - data = empty( (NBLK,C,R), dtype=self.data.dtype) + indptr = np.empty( N/C + 1, dtype=self.indptr.dtype) + indices = np.empty( NBLK, dtype=self.indices.dtype) + data = np.empty( (NBLK,C,R), dtype=self.data.dtype) bsr_transpose(M/R, N/C, R, C, \ self.indptr, self.indices, self.data.ravel(), \ indptr, indices, data.ravel()) - return bsr_matrix( (data,indices,indptr), shape=(N,M) ) + return bsr_matrix((data,indices,indptr), shape=(N,M)) ############################################################## @@ -510,13 +503,13 @@ R,C = self.blocksize max_bnnz = len(self.data) + len(other.data) - indptr = empty_like(self.indptr) - indices = empty( max_bnnz, dtype=intc ) - data = empty( R*C*max_bnnz, dtype=upcast(self.dtype,other.dtype) ) + indptr = np.empty_like(self.indptr) + indices = np.empty(max_bnnz, dtype=np.intc) + data = np.empty(R*C*max_bnnz, dtype=upcast(self.dtype,other.dtype)) fn(in_shape[0]/R, in_shape[1]/C, R, C, \ - self.indptr, self.indices, ravel(self.data), - other.indptr, other.indices, ravel(other.data), + self.indptr, self.indices, np.ravel(self.data), + other.indptr, other.indices, np.ravel(other.data), indptr, indices, data) actual_bnnz = indptr[-1] Modified: branches/refactor_fft/scipy/sparse/compressed.py =================================================================== --- branches/refactor_fft/scipy/sparse/compressed.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/compressed.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -5,8 +5,7 @@ from warnings import warn -from numpy import array, asarray, zeros, rank, intc, empty, isscalar, \ - empty_like, where, concatenate, deprecate, diff, multiply +import numpy as np from base import spmatrix, isspmatrix, SparseEfficiencyWarning from data import _data_matrix @@ -15,7 +14,6 @@ isscalarlike, isintlike - class _cs_matrix(_data_matrix): """base matrix class for compressed row and column oriented matrices""" @@ -43,9 +41,9 @@ # create empty matrix self.shape = arg1 #spmatrix checks for errors here M, N = self.shape - self.data = zeros(0, getdtype(dtype, default=float)) - self.indices = zeros(0, intc) - self.indptr = zeros(self._swap((M,N))[0] + 1, dtype=intc) + self.data = np.zeros(0, getdtype(dtype, default=float)) + self.indices = np.zeros(0, np.intc) + self.indptr = np.zeros(self._swap((M,N))[0] + 1, dtype=np.intc) else: if len(arg1) == 2: # (data, ij) format @@ -55,9 +53,9 @@ elif len(arg1) == 3: # (data, indices, indptr) format (data, indices, indptr) = arg1 - self.indices = array(indices, copy=copy) - self.indptr = array(indptr, copy=copy) - self.data = array(data, copy=copy, dtype=getdtype(dtype, data)) + self.indices = np.array(indices, copy=copy) + self.indptr = np.array(indptr, copy=copy) + self.data = np.array(data, copy=copy, dtype=getdtype(dtype, data)) else: raise ValueError, "unrecognized %s_matrix constructor usage" %\ self.format @@ -65,7 +63,7 @@ else: #must be dense try: - arg1 = asarray(arg1) + arg1 = np.asarray(arg1) except: raise ValueError, "unrecognized %s_matrix constructor usage" % \ self.format @@ -128,14 +126,13 @@ % self.indices.dtype.name ) # only support 32-bit ints for now - self.indptr = asarray(self.indptr,dtype=intc) - self.indices = asarray(self.indices,dtype=intc) + self.indptr = np.asarray(self.indptr, dtype=np.intc) + self.indices = np.asarray(self.indices, dtype=np.intc) self.data = to_native(self.data) # check array shapes - if (rank(self.data) != 1) or (rank(self.indices) != 1) or \ - (rank(self.indptr) != 1): - raise ValueError,"data, indices, and indptr should be rank 1" + if np.rank(self.data) != 1 or np.rank(self.indices) != 1 or np.rank(self.indptr) != 1: + raise ValueError('data, indices, and indptr should be rank 1') # check index pointer if (len(self.indptr) != major_dim + 1 ): @@ -164,7 +161,7 @@ if self.indices.min() < 0: raise ValueError, "%s index values must be >= 0" % \ minor_name - if diff(self.indptr).min() < 0: + if np.diff(self.indptr).min() < 0: raise ValueError,'index pointer values must form a " \ "non-decreasing sequence' @@ -225,18 +222,6 @@ raise NotImplementedError - def __rmul__(self, other): # other * self - if isscalarlike(other): - return self.__mul__(other) - else: - # Don't use asarray unless we have to - try: - tr = other.transpose() - except AttributeError: - tr = asarray(other).transpose() - return (self.transpose() * tr).transpose() - - def __truediv__(self,other): if isscalarlike(other): return self * (1./other) @@ -258,7 +243,7 @@ raise ValueError('inconsistent shapes') if isdense(other): - return multiply(self.todense(),other) + return np.multiply(self.todense(),other) else: other = self.__class__(other) return self._binopt(other,'_elmul_') @@ -272,7 +257,7 @@ M,N = self.shape #output array - result = zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) + result = np.zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) # csr_matvec or csc_matvec fn = getattr(sparsetools,self.format + '_matvec') @@ -285,7 +270,7 @@ M,N = self.shape n_vecs = other.shape[1] #number of column vectors - result = zeros( (M,n_vecs), dtype=upcast(self.dtype,other.dtype) ) + result = np.zeros( (M,n_vecs), dtype=upcast(self.dtype,other.dtype) ) # csr_matvecs or csc_matvecs fn = getattr(sparsetools,self.format + '_matvecs') @@ -299,7 +284,7 @@ K2, N = other.shape major_axis = self._swap((M,N))[0] - indptr = empty( major_axis + 1, dtype=intc ) + indptr = np.empty(major_axis + 1, dtype=np.intc) other = self.__class__(other) #convert to this format fn = getattr(sparsetools, self.format + '_matmat_pass1') @@ -308,8 +293,8 @@ indptr) nnz = indptr[-1] - indices = empty( nnz, dtype=intc) - data = empty( nnz, dtype=upcast(self.dtype,other.dtype)) + indices = np.empty(nnz, dtype=np.intc) + data = np.empty(nnz, dtype=upcast(self.dtype,other.dtype)) fn = getattr(sparsetools, self.format + '_matmat_pass2') fn( M, N, self.indptr, self.indices, self.data, \ @@ -318,125 +303,8 @@ return self.__class__((data,indices,indptr),shape=(M,N)) - def matvec(self,other): - return self * other - def matmat(self,other): - return self * other - - #def matmat(self, other): - # if isspmatrix(other): - # M, K1 = self.shape - # K2, N = other.shape - # if (K1 != K2): - # raise ValueError, "shape mismatch error" - - # #return self._binopt(other,'mu',in_shape=(M,N),out_shape=(M,N)) - - # major_axis = self._swap((M,N))[0] - # indptr = empty( major_axis + 1, dtype=intc ) - - # other = self.__class__(other) #convert to this format - # fn = getattr(sparsetools, self.format + '_matmat_pass1') - # fn( M, N, self.indptr, self.indices, \ - # other.indptr, other.indices, \ - # indptr) - - # nnz = indptr[-1] - # indices = empty( nnz, dtype=intc) - # data = empty( nnz, dtype=upcast(self.dtype,other.dtype)) - - # fn = getattr(sparsetools, self.format + '_matmat_pass2') - # fn( M, N, self.indptr, self.indices, self.data, \ - # other.indptr, other.indices, other.data, \ - # indptr, indices, data) - - # return self.__class__((data,indices,indptr),shape=(M,N)) - - - # elif isdense(other): - # # TODO make sparse * dense matrix multiplication more efficient - # - # # matvec each column of other - # result = hstack( [ self * col.reshape(-1,1) for col in asarray(other).T ] ) - # if isinstance(other, matrix): - # result = asmatrix(result) - # return result - - # else: - # raise TypeError, "need a dense or sparse matrix" - - - #def matvec(self, other): - # """Sparse matrix vector product (self * other) - - # 'other' may be a rank 1 array of length N or a rank 2 array - # or matrix with shape (N,1). - - # """ - # #If the optional 'output' parameter is defined, it will - # #be used to store the result. Otherwise, a new vector - # #will be allocated. - - # if isdense(other): - # M,N = self.shape - - # if other.shape != (N,) and other.shape != (N,1): - # raise ValueError, "dimension mismatch" - - # # csrmux, cscmux - # fn = getattr(sparsetools,self.format + '_matvec') - - # #output array - # y = zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) - - # #if output is None: - # # y = empty( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) - # #else: - # # if output.shape != (M,) and output.shape != (M,1): - # # raise ValueError, "output array has improper dimensions" - # # if not output.flags.c_contiguous: - # # raise ValueError, "output array must be contiguous" - # # if output.dtype != upcast(self.dtype,other.dtype): - # # raise ValueError, "output array has dtype=%s "\ - # # "dtype=%s is required" % \ - # # (output.dtype,upcast(self.dtype,other.dtype)) - # # y = output - - # fn(self.shape[0], self.shape[1], \ - # self.indptr, self.indices, self.data, numpy.ravel(other), y) - - # if isinstance(other, matrix): - # y = asmatrix(y) - - # if other.ndim == 2 and other.shape[1] == 1: - # # If 'other' was an (nx1) column vector, reshape the result - # y = y.reshape(-1,1) - - # return y - - # elif isspmatrix(other): - # raise TypeError, "use matmat() for sparse * sparse" - - # else: - # raise TypeError, "need a dense vector" - - def rmatvec(self, other, conjugate=True): - """Multiplies the vector 'other' by the sparse matrix, returning a - dense vector as a result. - - If 'conjugate' is True: - - returns A.transpose().conj() * other - Otherwise: - - returns A.transpose() * other. - - """ - if conjugate: - return self.transpose().conj().matvec( other ) - else: - return self.transpose().matvec( other ) - - @deprecate + @np.deprecate def getdata(self, ind): return self.data[ind] @@ -445,7 +313,7 @@ """ #TODO support k-th diagonal fn = getattr(sparsetools, self.format + "_diagonal") - y = empty( min(self.shape), dtype=upcast(self.dtype) ) + y = np.empty( min(self.shape), dtype=upcast(self.dtype) ) fn(self.shape[0], self.shape[1], self.indptr, self.indices, self.data, y) return y @@ -506,7 +374,7 @@ start = self.indptr[major_index] end = self.indptr[major_index+1] - indxs = where(minor_index == self.indices[start:end])[0] + indxs = np.where(minor_index == self.indices[start:end])[0] num_matches = len(indxs) @@ -539,7 +407,7 @@ index = self.indices[indices] - start data = self.data[indices] - indptr = array([0, len(indices)]) + indptr = np.array([0, len(indices)]) return self.__class__((data, index, indptr), shape=shape, \ dtype=self.dtype) @@ -563,7 +431,7 @@ return i0, i1 - elif isscalar( sl ): + elif np.isscalar( sl ): if sl < 0: sl += num @@ -612,7 +480,7 @@ start = self.indptr[major_index] end = self.indptr[major_index+1] - indxs = where(minor_index == self.indices[start:end])[0] + indxs = np.where(minor_index == self.indices[start:end])[0] num_matches = len(indxs) @@ -627,10 +495,10 @@ newindx = self.indices[start:end].searchsorted(minor_index) newindx += start - val = array([val],dtype=self.data.dtype) - minor_index = array([minor_index],dtype=self.indices.dtype) - self.data = concatenate((self.data[:newindx],val,self.data[newindx:])) - self.indices = concatenate((self.indices[:newindx],minor_index,self.indices[newindx:])) + val = np.array([val],dtype=self.data.dtype) + minor_index = np.array([minor_index],dtype=self.indices.dtype) + self.data = np.concatenate((self.data[:newindx],val,self.data[newindx:])) + self.indices = np.concatenate((self.indices[:newindx],minor_index,self.indices[newindx:])) self.indptr[major_index+1:] += 1 @@ -670,7 +538,7 @@ data = data.copy() minor_indices = minor_indices.copy() - major_indices = empty(len(minor_indices),dtype=intc) + major_indices = np.empty(len(minor_indices), dtype=np.intc) sparsetools.expandptr(major_dim,self.indptr,major_indices) @@ -814,9 +682,9 @@ fn = getattr(sparsetools, self.format + op + self.format) maxnnz = self.nnz + other.nnz - indptr = empty_like(self.indptr) - indices = empty( maxnnz, dtype=intc ) - data = empty( maxnnz, dtype=upcast(self.dtype,other.dtype) ) + indptr = np.empty_like(self.indptr) + indices = np.empty(maxnnz, dtype=np.intc) + data = np.empty(maxnnz, dtype=upcast(self.dtype,other.dtype)) fn(in_shape[0], in_shape[1], \ self.indptr, self.indices, self.data, Modified: branches/refactor_fft/scipy/sparse/construct.py =================================================================== --- branches/refactor_fft/scipy/sparse/construct.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/construct.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -9,9 +9,7 @@ from warnings import warn -import numpy -from numpy import ones, arange, intc, asarray, rank, zeros, \ - cumsum, concatenate, empty +import numpy as np from sputils import upcast @@ -84,28 +82,28 @@ """ if format in ['csr','csc']: - indptr = arange(n+1, dtype=intc) - indices = arange(n, dtype=intc) - data = ones(n, dtype=dtype) + indptr = np.arange(n+1, dtype=np.intc) + indices = np.arange(n, dtype=np.intc) + data = np.ones(n, dtype=dtype) cls = eval('%s_matrix' % format) return cls((data,indices,indptr),(n,n)) elif format == 'coo': - row = arange(n, dtype=intc) - col = arange(n, dtype=intc) - data = ones(n, dtype=dtype) + row = np.arange(n, dtype=np.intc) + col = np.arange(n, dtype=np.intc) + data = np.ones(n, dtype=dtype) return coo_matrix((data,(row,col)),(n,n)) elif format == 'dia': - data = ones(n, dtype=dtype) + data = np.ones(n, dtype=dtype) diags = [0] - return dia_matrix( (data,diags), shape=(n,n) ) + return dia_matrix((data,diags), shape=(n,n)) else: - return identity( n, dtype=dtype, format='csr').asformat(format) + return identity(n, dtype=dtype, format='csr').asformat(format) def eye(m, n, k=0, dtype='d', format=None): """eye(m, n) returns a sparse (m x n) matrix where the k-th diagonal is all ones and everything else is zeros. """ - diags = ones((1, m), dtype=dtype) + diags = np.ones((1, m), dtype=dtype) return spdiags(diags, k, m, n).asformat(format) def kron(A, B, format=None): @@ -148,7 +146,7 @@ #B is fairly dense, use BSR A = csr_matrix(A,copy=True) - output_shape = (A.shape[0]*B.shape[0],A.shape[1]*B.shape[1]) + output_shape = (A.shape[0]*B.shape[0], A.shape[1]*B.shape[1]) if A.nnz == 0 or B.nnz == 0: # kronecker product is the zero matrix @@ -158,11 +156,11 @@ data = A.data.repeat(B.size).reshape(-1,B.shape[0],B.shape[1]) data = data * B - return bsr_matrix((data,A.indices,A.indptr),shape=output_shape) + return bsr_matrix((data,A.indices,A.indptr), shape=output_shape) else: #use COO A = coo_matrix(A) - output_shape = (A.shape[0]*B.shape[0],A.shape[1]*B.shape[1]) + output_shape = (A.shape[0]*B.shape[0], A.shape[1]*B.shape[1]) if A.nnz == 0 or B.nnz == 0: # kronecker product is the zero matrix @@ -231,7 +229,7 @@ return (L+R).asformat(format) #since L + R is not always same format -def hstack( blocks, format=None, dtype=None ): +def hstack(blocks, format=None, dtype=None): """Stack sparse matrices horizontally (column wise) Parameters @@ -256,7 +254,7 @@ """ return bmat([blocks], format=format, dtype=dtype) -def vstack( blocks, format=None, dtype=None ): +def vstack(blocks, format=None, dtype=None): """Stack sparse matrices vertically (row wise) Parameters @@ -282,7 +280,7 @@ """ return bmat([ [b] for b in blocks ], format=format, dtype=dtype) -def bmat( blocks, format=None, dtype=None ): +def bmat(blocks, format=None, dtype=None): """Build a sparse matrix from sparse sub-blocks Parameters @@ -313,16 +311,16 @@ """ - blocks = asarray(blocks, dtype='object') + blocks = np.asarray(blocks, dtype='object') - if rank(blocks) != 2: + if np.rank(blocks) != 2: raise ValueError('blocks must have rank 2') M,N = blocks.shape - block_mask = zeros( blocks.shape, dtype='bool' ) - brow_lengths = zeros( blocks.shape[0], dtype=int ) - bcol_lengths = zeros( blocks.shape[1], dtype=int ) + block_mask = np.zeros(blocks.shape, dtype=np.bool) + brow_lengths = np.zeros(blocks.shape[0], dtype=np.intc) + bcol_lengths = np.zeros(blocks.shape[1], dtype=np.intc) # convert everything to COO format for i in range(M): @@ -355,12 +353,12 @@ if dtype is None: dtype = upcast( *tuple([A.dtype for A in blocks[block_mask]]) ) - row_offsets = concatenate(([0],cumsum(brow_lengths))) - col_offsets = concatenate(([0],cumsum(bcol_lengths))) + row_offsets = np.concatenate(([0], np.cumsum(brow_lengths))) + col_offsets = np.concatenate(([0], np.cumsum(bcol_lengths))) - data = empty(nnz, dtype=dtype) - row = empty(nnz, dtype=intc) - col = empty(nnz, dtype=intc) + data = np.empty(nnz, dtype=dtype) + row = np.empty(nnz, dtype=np.intc) + col = np.empty(nnz, dtype=np.intc) nnz = 0 for i in range(M): @@ -376,8 +374,8 @@ nnz += A.nnz - shape = (sum(brow_lengths),sum(bcol_lengths)) - return coo_matrix( (data, (row, col)), shape=shape ).asformat(format) + shape = (np.sum(brow_lengths), np.sum(bcol_lengths)) + return coo_matrix((data, (row, col)), shape=shape).asformat(format) @@ -387,13 +385,11 @@ __all__ += [ 'speye','spidentity', 'spkron', 'lil_eye', 'lil_diags' ] -from numpy import deprecate +spkron = np.deprecate(kron, oldname='spkron', newname='scipy.sparse.kron') +speye = np.deprecate(eye, oldname='speye', newname='scipy.sparse.eye') +spidentity = np.deprecate(identity, oldname='spidentity', newname='scipy.sparse.identity') -spkron = deprecate(kron, oldname='spkron', newname='scipy.sparse.kron') -speye = deprecate(eye, oldname='speye', newname='scipy.sparse.eye') -spidentity = deprecate(identity, oldname='spidentity', newname='scipy.sparse.identity') - def lil_eye((r,c), k=0, dtype='d'): """Generate a lil_matrix of dimensions (r,c) with the k-th diagonal set to 1. @@ -413,13 +409,11 @@ warn("lil_eye is deprecated." \ "use scipy.sparse.eye(r, c, k, format='lil') instead", \ DeprecationWarning) - return eye(r,c,k,dtype=dtype,format='lil') + return eye(r, c, k, dtype=dtype, format='lil') -from numpy import clip -from itertools import izip #TODO remove this function -def lil_diags(diags,offsets,(m,n),dtype='d'): +def lil_diags(diags, offsets, (m,n), dtype='d'): """Generate a lil_matrix with the given diagonals. Parameters @@ -449,7 +443,7 @@ raise ValueError("Number of diagonals provided should " "agree with offsets.") - sort_indices = numpy.argsort(offsets_unsorted) + sort_indices = np.argsort(offsets_unsorted) diags = [diags_unsorted[k] for k in sort_indices] offsets = [offsets_unsorted[k] for k in sort_indices] @@ -459,8 +453,10 @@ "diagonal %s." % k) out = lil_matrix((m,n),dtype=dtype) + + from itertools import izip for k,diag in izip(offsets,diags): - for ix,c in enumerate(xrange(clip(k,0,n),clip(m+k,0,n))): + for ix,c in enumerate(xrange(np.clip(k,0,n),np.clip(m+k,0,n))): out.rows[c-k].append(c) out.data[c-k].append(diag[ix]) return out Modified: branches/refactor_fft/scipy/sparse/coo.py =================================================================== --- branches/refactor_fft/scipy/sparse/coo.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/coo.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -4,11 +4,9 @@ __all__ = ['coo_matrix', 'isspmatrix_coo'] -from itertools import izip from warnings import warn -from numpy import array, asarray, empty, intc, zeros, unique, searchsorted,\ - atleast_2d, rank, deprecate, hstack +import numpy as np from sparsetools import coo_tocsr, coo_todense, coo_matvec from base import isspmatrix @@ -108,9 +106,9 @@ if isshape(arg1): M, N = arg1 self.shape = (M,N) - self.row = array([], dtype=intc) - self.col = array([], dtype=intc) - self.data = array([], getdtype(dtype, default=float)) + self.row = np.array([], dtype=np.intc) + self.col = np.array([], dtype=np.intc) + self.data = np.array([], getdtype(dtype, default=float)) else: try: obj, ij = arg1 @@ -123,9 +121,9 @@ except TypeError: raise TypeError('invalid input format') - self.row = array(ij[0], copy=copy, dtype=intc) - self.col = array(ij[1], copy=copy, dtype=intc) - self.data = array( obj, copy=copy) + self.row = np.array(ij[0], copy=copy, dtype=np.intc) + self.col = np.array(ij[1], copy=copy, dtype=np.intc) + self.data = np.array( obj, copy=copy) if shape is None: if len(self.row) == 0 or len(self.col) == 0: @@ -145,9 +143,9 @@ warn('coo_matrix(None, shape=(M,N)) is deprecated, ' \ 'use coo_matrix( (M,N) ) instead', DeprecationWarning) self.shape = shape - self.data = array([], getdtype(dtype, default=float)) - self.row = array([], dtype=intc) - self.col = array([], dtype=intc) + self.data = np.array([], getdtype(dtype, default=float)) + self.row = np.array([], dtype=np.intc) + self.col = np.array([], dtype=np.intc) else: if isspmatrix(arg1): if isspmatrix_coo(arg1) and copy: @@ -164,11 +162,11 @@ else: #dense argument try: - M = atleast_2d(asarray(arg1)) + M = np.atleast_2d(np.asarray(arg1)) except: raise TypeError('invalid input format') - if len(M.shape) != 2: + if np.rank(M) != 2: raise TypeError('expected rank <= 2 array or matrix') self.shape = M.shape self.row,self.col = (M != 0).nonzero() @@ -178,10 +176,10 @@ def getnnz(self): nnz = len(self.data) - if (nnz != len(self.row)) or (nnz != len(self.col)): + if nnz != len(self.row) or nnz != len(self.col): raise ValueError('row, column, and data array must all be the same length') - if rank(self.data) != 1 or rank(self.row) != 1 or rank(self.col) != 1: + if np.rank(self.data) != 1 or np.rank(self.row) != 1 or np.rank(self.col) != 1: raise ValueError('row, column, and data arrays must have rank 1') return nnz @@ -200,8 +198,8 @@ % self.col.dtype.name ) # only support 32-bit ints for now - self.row = asarray(self.row, dtype=intc) - self.col = asarray(self.col, dtype=intc) + self.row = np.asarray(self.row, dtype=np.intc) + self.col = np.asarray(self.col, dtype=np.intc) self.data = to_native(self.data) if nnz > 0: @@ -215,73 +213,100 @@ raise ValueError('negative column index found') - @deprecate + @np.deprecate def rowcol(self, num): return (self.row[num], self.col[num]) - @deprecate + @np.deprecate def getdata(self, num): return self.data[num] - def transpose(self,copy=False): + def transpose(self, copy=False): M,N = self.shape - return coo_matrix((self.data,(self.col,self.row)),(N,M),copy=copy) + return coo_matrix((self.data, (self.col, self.row)), shape=(N,M), copy=copy) def toarray(self): - B = zeros(self.shape, dtype=self.dtype) + B = np.zeros(self.shape, dtype=self.dtype) M,N = self.shape - coo_todense(M, N, self.nnz, self.row, self.col, self.data, B.ravel() ) + coo_todense(M, N, self.nnz, self.row, self.col, self.data, B.ravel()) return B - def tocsc(self,sum_duplicates=True): + def tocsc(self): """Return a copy of this matrix in Compressed Sparse Column format - By default sum_duplicates=True and any duplicate - matrix entries are added together. + Duplicate entries will be summed together. + Example + ------- + >>> from numpy import array + >>> from scipy.sparse import coo_matrix + >>> row = array([0,0,1,3,1,0,0]) + >>> col = array([0,2,1,3,1,0,0]) + >>> data = array([1,1,1,1,1,1,1]) + >>> A = coo_matrix( (data,(row,col)), shape=(4,4)).tocsc() + >>> A.todense() + matrix([[3, 0, 1, 0], + [0, 2, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 1]]) + """ from csc import csc_matrix if self.nnz == 0: return csc_matrix(self.shape, dtype=self.dtype) else: - indptr = empty(self.shape[1] + 1,dtype=intc) - indices = empty(self.nnz, dtype=intc) - data = empty(self.nnz, dtype=upcast(self.dtype)) + M,N = self.shape + indptr = np.empty(N + 1, dtype=np.intc) + indices = np.empty(self.nnz, dtype=np.intc) + data = np.empty(self.nnz, dtype=upcast(self.dtype)) - coo_tocsr(self.shape[1], self.shape[0], self.nnz, \ + coo_tocsr(N, M, self.nnz, \ self.col, self.row, self.data, \ indptr, indices, data) - A = csc_matrix((data, indices, indptr), self.shape) - if sum_duplicates: - A.sum_duplicates() + A = csc_matrix((data, indices, indptr), shape=self.shape) + A.sum_duplicates() + return A - def tocsr(self,sum_duplicates=True): + def tocsr(self): """Return a copy of this matrix in Compressed Sparse Row format - By default sum_duplicates=True and any duplicate - matrix entries are added together. + Duplicate entries will be summed together. + Example + ------- + >>> from numpy import array + >>> from scipy.sparse import coo_matrix + >>> row = array([0,0,1,3,1,0,0]) + >>> col = array([0,2,1,3,1,0,0]) + >>> data = array([1,1,1,1,1,1,1]) + >>> A = coo_matrix( (data,(row,col)), shape=(4,4)).tocsr() + >>> A.todense() + matrix([[3, 0, 1, 0], + [0, 2, 0, 0], + [0, 0, 0, 0], + [0, 0, 0, 1]]) + """ from csr import csr_matrix if self.nnz == 0: return csr_matrix(self.shape, dtype=self.dtype) else: - indptr = empty(self.shape[0] + 1,dtype=intc) - indices = empty(self.nnz, dtype=intc) - data = empty(self.nnz, dtype=upcast(self.dtype)) + M,N = self.shape + indptr = np.empty(M + 1, dtype=np.intc) + indices = np.empty(self.nnz, dtype=np.intc) + data = np.empty(self.nnz, dtype=upcast(self.dtype)) - coo_tocsr(self.shape[0], self.shape[1], self.nnz, \ + coo_tocsr(M, N, self.nnz, \ self.row, self.col, self.data, \ indptr, indices, data) - A = csr_matrix((data, indices, indptr), self.shape) - if sum_duplicates: - A.sum_duplicates() + A = csr_matrix((data, indices, indptr), shape=self.shape) + A.sum_duplicates() + return A - def tocoo(self, copy=False): if copy: return self.copy() @@ -292,7 +317,7 @@ from dia import dia_matrix ks = self.col - self.row #the diagonal for each nonzero - diags = unique(ks) + diags = np.unique(ks) if len(diags) > 100: #probably undesired, should we do something? @@ -300,15 +325,16 @@ pass #initialize and fill in data array - data = zeros( (len(diags), self.col.max()+1), dtype=self.dtype) - data[ searchsorted(diags,ks), self.col ] = self.data + data = np.zeros( (len(diags), self.col.max()+1), dtype=self.dtype) + data[ np.searchsorted(diags,ks), self.col ] = self.data - return dia_matrix((data,diags),shape=self.shape) + return dia_matrix((data,diags), shape=self.shape) def todok(self): + from itertools import izip from dok import dok_matrix - dok = dok_matrix((self.shape),dtype=self.dtype) + dok = dok_matrix((self.shape), dtype=self.dtype) dok.update( izip(izip(self.row,self.col),self.data) ) @@ -334,12 +360,12 @@ def _mul_vector(self, other): #output array - result = zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) + result = np.zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) coo_matvec(self.nnz, self.row, self.col, self.data, other, result) return result def _mul_dense_matrix(self, other): - return hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) + return np.hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) from sputils import _isinstance Modified: branches/refactor_fft/scipy/sparse/csc.py =================================================================== --- branches/refactor_fft/scipy/sparse/csc.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/csc.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -6,7 +6,8 @@ from warnings import warn -from numpy import asarray, intc, empty, searchsorted, deprecate +import numpy as np + from sparsetools import csc_tocsr from sputils import upcast, isintlike @@ -96,11 +97,11 @@ for r in xrange(self.shape[0]): yield csr[r,:] - @deprecate + @np.deprecate def rowcol(self, ind): #TODO remove after 0.7 row = self.indices[ind] - col = searchsorted(self.indptr, ind+1)-1 + col = np.searchsorted(self.indptr, ind+1) - 1 return (row, col) def tocsc(self, copy=False): @@ -110,16 +111,17 @@ return self def tocsr(self): - indptr = empty(self.shape[0] + 1, dtype=intc) - indices = empty(self.nnz, dtype=intc) - data = empty(self.nnz, dtype=upcast(self.dtype)) + M,N = self.shape + indptr = np.empty(M + 1, dtype=np.intc) + indices = np.empty(self.nnz, dtype=np.intc) + data = np.empty(self.nnz, dtype=upcast(self.dtype)) - csc_tocsr(self.shape[0], self.shape[1], \ + csc_tocsr(M, N, \ self.indptr, self.indices, self.data, \ indptr, indices, data) from csr import csr_matrix - A = csr_matrix((data, indices, indptr), self.shape) + A = csr_matrix((data, indices, indptr), shape=self.shape) A.has_sorted_indices = True return A @@ -137,8 +139,8 @@ if isintlike(col) or isinstance(col,slice): return self.T[col,row].T else: - row = asarray(row, dtype='intc') - col = asarray(col, dtype='intc') + row = np.asarray(row, dtype=np.intc) + col = np.asarray(col, dtype=np.intc) if len(row.shape) == 1: return self.T[col,row] elif len(row.shape) == 2: Modified: branches/refactor_fft/scipy/sparse/csr.py =================================================================== --- branches/refactor_fft/scipy/sparse/csr.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/csr.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -7,8 +7,7 @@ from warnings import warn -from numpy import asarray, asmatrix, zeros, intc, empty, isscalar, array, \ - searchsorted, where, deprecate, arange, ones, ravel +import numpy as np from sparsetools import csr_tocsc, csr_tobsr, csr_count_blocks, \ get_csr_submatrix @@ -91,13 +90,13 @@ def transpose(self, copy=False): from csc import csc_matrix M,N = self.shape - return csc_matrix((self.data,self.indices,self.indptr),(N,M),copy=copy) + return csc_matrix((self.data,self.indices,self.indptr), shape=(N,M), copy=copy) - @deprecate + @np.deprecate def rowcol(self, ind): #TODO remove after 0.7 col = self.indices[ind] - row = searchsorted(self.indptr, ind+1)-1 + row = np.searchsorted(self.indptr, ind+1)-1 return (row, col) @@ -105,7 +104,7 @@ from lil import lil_matrix lil = lil_matrix(self.shape,dtype=self.dtype) - self.sort_indices() #lil_matrix needs sorted rows + self.sort_indices() #lil_matrix needs sorted column indices ptr,ind,dat = self.indptr,self.indices,self.data rows, data = lil.rows, lil.data @@ -125,28 +124,30 @@ return self def tocsc(self): - indptr = empty(self.shape[1] + 1, dtype=intc) - indices = empty(self.nnz, dtype=intc) - data = empty(self.nnz, dtype=upcast(self.dtype)) + indptr = np.empty(self.shape[1] + 1, dtype=np.intc) + indices = np.empty(self.nnz, dtype=np.intc) + data = np.empty(self.nnz, dtype=upcast(self.dtype)) csr_tocsc(self.shape[0], self.shape[1], \ self.indptr, self.indices, self.data, \ indptr, indices, data) from csc import csc_matrix - A = csc_matrix((data, indices, indptr), self.shape) + A = csc_matrix((data, indices, indptr), shape=self.shape) A.has_sorted_indices = True return A - def tobsr(self,blocksize=None,copy=True): + def tobsr(self, blocksize=None, copy=True): from bsr import bsr_matrix if blocksize is None: from spfuncs import estimate_blocksize return self.tobsr(blocksize=estimate_blocksize(self)) + elif blocksize == (1,1): arg1 = (self.data.reshape(-1,1,1),self.indices,self.indptr) - return bsr_matrix( arg1, shape=self.shape, copy=copy ) + return bsr_matrix(arg1, shape=self.shape, copy=copy ) + else: R,C = blocksize M,N = self.shape @@ -156,14 +157,14 @@ blks = csr_count_blocks(M,N,R,C,self.indptr,self.indices) - indptr = empty( M/R + 1, dtype=intc ) - indices = empty( blks, dtype=intc ) - data = zeros( (blks,R,C), dtype=self.dtype) + indptr = np.empty(M/R + 1, dtype=np.intc) + indices = np.empty(blks, dtype=np.intc) + data = np.zeros((blks,R,C), dtype=self.dtype) csr_tobsr(M, N, R, C, self.indptr, self.indices, self.data, \ indptr, indices, data.ravel() ) - return bsr_matrix( (data,indices,indptr), shape=self.shape ) + return bsr_matrix((data,indices,indptr), shape=self.shape) # these functions are used by the parent class (_cs_matrix) # to remove redudancy between csc_matrix and csr_matrix @@ -176,7 +177,7 @@ def __getitem__(self, key): def asindices(x): try: - x = asarray(x,dtype='intc') + x = np.asarray(x, dtype=np.intc) except: raise IndexError('invalid index') else: @@ -201,11 +202,11 @@ indices = indices.copy() indices[indices < 0] += N - indptr = arange(len(indices) + 1, dtype='intc') - data = ones(len(indices), dtype=self.dtype) + indptr = np.arange(len(indices) + 1, dtype=np.intc) + data = np.ones(len(indices), dtype=self.dtype) shape = (len(indices),N) - return csr_matrix( (data,indices,indptr), shape=shape) + return csr_matrix((data,indices,indptr), shape=shape) if isinstance(key, tuple): @@ -245,10 +246,10 @@ val = [] for i,j in zip(row,col): val.append(self._get_single_element(i,j)) - return asmatrix(val) + return np.asmatrix(val) elif len(row.shape) == 2: - row = ravel(row) #[[[1],[2]],[1,2]] + row = np.ravel(row) #[[[1],[2]],[1,2]] P = extractor(row, self.shape[0]) return (P*self)[:,col] @@ -276,7 +277,7 @@ start = self.indptr[row] end = self.indptr[row+1] - indxs = where(col == self.indices[start:end])[0] + indxs = np.where(col == self.indices[start:end])[0] num_matches = len(indxs) @@ -288,7 +289,7 @@ else: raise ValueError('nonzero entry (%d,%d) occurs more than once' % (row,col) ) - def _get_row_slice(self, i, cslice ): + def _get_row_slice(self, i, cslice): """Returns a copy of row self[i, cslice] """ if i < 0: @@ -315,7 +316,7 @@ index = self.indices[indices] - start data = self.data[indices] - indptr = array([0, len(indices)]) + indptr = np.array([0, len(indices)]) return csr_matrix( (data, index, indptr), shape=(1, stop-start) ) def _get_submatrix( self, row_slice, col_slice ): Modified: branches/refactor_fft/scipy/sparse/dia.py =================================================================== --- branches/refactor_fft/scipy/sparse/dia.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/dia.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -2,10 +2,9 @@ __docformat__ = "restructuredtext en" -__all__ = ['dia_matrix','isspmatrix_dia'] +__all__ = ['dia_matrix', 'isspmatrix_dia'] -from numpy import asarray, zeros, arange, array, intc, atleast_1d, \ - atleast_2d, unique, hstack +import numpy as np from base import isspmatrix, _formats from data import _data_matrix @@ -72,9 +71,9 @@ if isshape(arg1): # It's a tuple of matrix dimensions (M, N) # create empty matrix - self.shape = arg1 #spmatrix checks for errors here - self.data = zeros( (0,0), getdtype(dtype, default=float)) - self.offsets = zeros( (0), dtype=intc) + self.shape = arg1 #spmatrix checks for errors here + self.data = np.zeros( (0,0), getdtype(dtype, default=float)) + self.offsets = np.zeros( (0), dtype=np.intc) else: try: # Try interpreting it as (data, offsets) @@ -84,16 +83,16 @@ else: if shape is None: raise ValueError('expected a shape argument') - self.data = atleast_2d(array(arg1[0],dtype=dtype,copy=copy)) - self.offsets = atleast_1d(array(arg1[1],dtype='i',copy=copy)) + self.data = np.atleast_2d(np.array(arg1[0], dtype=dtype, copy=copy)) + self.offsets = np.atleast_1d(np.array(arg1[1], dtype=np.intc, copy=copy)) self.shape = shape else: #must be dense, convert to COO first, then to DIA try: - arg1 = asarray(arg1) + arg1 = np.asarray(arg1) except: - raise ValueError, "unrecognized form for" \ - " %s_matrix constructor" % self.format + raise ValueError("unrecognized form for" \ + " %s_matrix constructor" % self.format) from coo import coo_matrix A = coo_matrix(arg1).todia() self.data = A.data @@ -113,7 +112,7 @@ 'does not match the number of offsets (%d)' \ % (self.data.shape[0], len(self.offsets))) - if len(unique(self.offsets)) != len(self.offsets): + if len(np.unique(self.offsets)) != len(self.offsets): raise ValueError('offset array contains duplicate values') def __repr__(self): @@ -143,7 +142,7 @@ def _mul_vector(self, other): x = other - y = zeros( self.shape[0], dtype=upcast(self.dtype,x.dtype)) + y = np.zeros( self.shape[0], dtype=upcast(self.dtype,x.dtype)) L = self.data.shape[1] @@ -154,7 +153,7 @@ return y def _mul_dense_matrix(self, other): - return hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) + return np.hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) def todia(self,copy=False): if copy: @@ -164,17 +163,17 @@ def tocsr(self): #this could be faster - return self.tocoo().tocsr(sum_duplicates=False) + return self.tocoo().tocsr() def tocsc(self): #this could be faster - return self.tocoo().tocsc(sum_duplicates=False) + return self.tocoo().tocsc() def tocoo(self): num_data = len(self.data) len_data = self.data.shape[1] - row = arange(len_data).reshape(1,-1).repeat(num_data,axis=0) + row = np.arange(len_data).reshape(1,-1).repeat(num_data,axis=0) col = row.copy() for i,k in enumerate(self.offsets): @@ -187,10 +186,9 @@ mask &= (col < self.shape[1]) mask &= data != 0 row,col,data = row[mask],col[mask],data[mask] - #row,col,data = row.reshape(-1),col.reshape(-1),data.reshape(-1) from coo import coo_matrix - return coo_matrix((data,(row,col)),shape=self.shape) + return coo_matrix((data,(row,col)), shape=self.shape) # needed by _data_matrix def _with_data(self, data, copy=True): @@ -198,7 +196,7 @@ but with different data. By default the structure arrays are copied. """ if copy: - return dia_matrix( (data,self.offsets.copy()), shape=self.shape) + return dia_matrix( (data, self.offsets.copy()), shape=self.shape) else: return dia_matrix( (data,self.offsets), shape=self.shape) Modified: branches/refactor_fft/scipy/sparse/dok.py =================================================================== --- branches/refactor_fft/scipy/sparse/dok.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/dok.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -13,9 +13,11 @@ from sputils import isdense, getdtype, isshape, isintlike, isscalarlike class dok_matrix(spmatrix, dict): - """Dictionary Of Keys based matrix. This is an efficient - structure for constructing sparse matrices incrementally. + """Dictionary Of Keys based sparse matrix. + This is an efficient structure for constructing sparse + matrices incrementally. + This can be instatiated in several ways: dok_matrix(D) with a dense matrix, D @@ -474,7 +476,7 @@ base[newkey] = self[key] return base, ext - +# TODO update these w/ new multiplication handlers # def matvec(self, other): # if isdense(other): # if other.shape[0] != self.shape[1]: Modified: branches/refactor_fft/scipy/sparse/lil.py =================================================================== --- branches/refactor_fft/scipy/sparse/lil.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/lil.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -5,34 +5,30 @@ __all__ = ['lil_matrix','isspmatrix_lil'] -import copy from bisect import bisect_left -import numpy -from numpy import isscalar, asmatrix, asarray, intc, concatenate, array, \ - cumsum, zeros, unravel_index +import numpy as np from base import spmatrix, isspmatrix -from sputils import getdtype,isshape,issequence,isscalarlike +from sputils import getdtype, isshape, issequence, isscalarlike class lil_matrix(spmatrix): - """Row-based linked list matrix + """Row-based linked list sparse matrix + This is an efficient structure for constructing sparse + matrices incrementally. This can be instantiated in several ways: - csc_matrix(D) + lil_matrix(D) with a dense matrix or rank-2 ndarray D - csc_matrix(S) + lil_matrix(S) with another sparse matrix S (equivalent to S.tocsc()) - csc_matrix((M, N), [dtype]) + lil_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'. - csc_matrix((data, ij), [shape=(M, N)]) - where ``data`` and ``ij`` satisfy ``a[ij[0, k], ij[1, k]] = data[k]`` - Notes ----- @@ -60,54 +56,39 @@ """ - def __init__(self, A=None, shape=None, dtype=None, copy=False): - """ Create a new list-of-lists sparse matrix. An optional - argument A is accepted, which initializes the lil_matrix with it. - This can be a tuple of dimensions (M, N) or a dense array / - matrix to copy, or a sparse matrix. - """ + def __init__(self, arg1, shape=None, dtype=None, copy=False): spmatrix.__init__(self) - self.dtype = getdtype(dtype, A, default=float) + self.dtype = getdtype(dtype, arg1, default=float) # First get the shape - if A is None: - if not isshape(shape): - raise TypeError("need a valid shape") - M, N = shape - self.shape = (M,N) - self.rows = numpy.empty((M,), dtype=object) - self.data = numpy.empty((M,), dtype=object) - for i in range(M): - self.rows[i] = [] - self.data[i] = [] - elif isspmatrix(A): - if isspmatrix_lil(A) and copy: - A = A.copy() + if isspmatrix(arg1): + if isspmatrix_lil(arg1) and copy: + A = arg1.copy() else: - A = A.tolil() + A = arg1.tolil() self.shape = A.shape self.dtype = A.dtype self.rows = A.rows self.data = A.data - elif isinstance(A,tuple): - if isshape(A): + elif isinstance(arg1,tuple): + if isshape(arg1): if shape is not None: raise ValueError('invalid use of shape parameter') - M, N = A + M, N = arg1 self.shape = (M,N) - self.rows = numpy.empty((M,), dtype=object) - self.data = numpy.empty((M,), dtype=object) + self.rows = np.empty((M,), dtype=object) + self.data = np.empty((M,), dtype=object) for i in range(M): self.rows[i] = [] self.data[i] = [] else: - raise TypeError,'unrecognized lil_matrix constructor usage' + raise TypeError('unrecognized lil_matrix constructor usage') else: #assume A is dense try: - A = asmatrix(A) + A = np.asmatrix(arg1) except TypeError: - raise TypeError, "unsupported matrix type" + raise TypeError('unsupported matrix type') else: from csr import csr_matrix A = csr_matrix(A).tolil() @@ -177,7 +158,7 @@ j += self.shape[1] if j < 0 or j > self.shape[1]: - raise IndexError,'column index out of bounds' + raise IndexError('column index out of bounds') pos = bisect_left(row, j) if pos != len(data) and row[pos] == j: @@ -210,10 +191,10 @@ try: i, j = index except (AssertionError, TypeError): - raise IndexError, "invalid index" + raise IndexError('invalid index') - if isscalar(i): - if isscalar(j): + if np.isscalar(i): + if np.isscalar(j): return self._get1(i, j) if isinstance(j, slice): j = self._slicetoseq(j, self.shape[1]) @@ -224,7 +205,7 @@ elif issequence(i) or isinstance(i, slice): if isinstance(i, slice): i = self._slicetoseq(i, self.shape[0]) - if isscalar(j): + if np.isscalar(j): return self.__class__([[self._get1(ii, j)] for ii in i]) if isinstance(j, slice): j = self._slicetoseq(j, self.shape[1]) @@ -249,7 +230,7 @@ j += self.shape[1] if j < 0 or j >= self.shape[1]: - raise IndexError,'column index out of bounds' + raise IndexError('column index out of bounds') pos = bisect_left(row, j) if x != 0: @@ -274,22 +255,22 @@ if issequence(j): if isinstance(x, spmatrix): x = x.todense() - x = numpy.asarray(x).squeeze() - if isscalar(x) or x.size == 1: + x = np.asarray(x).squeeze() + if np.isscalar(x) or x.size == 1: for jj in j: self._insertat2(row, data, jj, x) else: # x must be one D. maybe check these things out for jj, xx in zip(j, x): self._insertat2(row, data, jj, xx) - elif isscalar(j): + elif np.isscalar(j): self._insertat2(row, data, j, x) else: - raise ValueError, "invalid column value: %s" % str(j) + raise ValueError('invalid column value: %s' % str(j)) def __setitem__(self, index, x): - if isscalar(x): + if np.isscalar(x): x = self.dtype.type(x) elif not isinstance(x, spmatrix): x = lil_matrix(x) @@ -297,7 +278,7 @@ try: i, j = index except (ValueError, TypeError): - raise IndexError, "invalid index" + raise IndexError('invalid index') if isspmatrix(x): if (isinstance(i, slice) and (i == slice(None))) and \ @@ -308,12 +289,12 @@ self.data = x.data return - if isscalar(i): + if np.isscalar(i): row = self.rows[i] data = self.data[i] self._insertat3(row, data, j, x) elif issequence(i) and issequence(j): - if isscalar(x): + if np.isscalar(x): for ii, jj in zip(i, j): self._insertat(ii, jj, x) else: @@ -322,32 +303,32 @@ elif isinstance(i, slice) or issequence(i): rows = self.rows[i] datas = self.data[i] - if isscalar(x): + if np.isscalar(x): for row, data in zip(rows, datas): self._insertat3(row, data, j, x) else: for row, data, xx in zip(rows, datas, x): self._insertat3(row, data, j, xx) else: - raise ValueError, "invalid index value: %s" % str((i, j)) + raise ValueError('invalid index value: %s' % str((i, j))) def _mul_scalar(self, other): if other == 0: # Multiply by zero: return the zero matrix - new = lil_matrix(shape=self.shape, dtype=self.dtype) + new = lil_matrix(self.shape, dtype=self.dtype) else: new = self.copy() # Multiply this scalar by every element. - new.data = numpy.array([[val*other for val in rowvals] for - rowvals in new.data], dtype=object) + new.data = np.array([[val*other for val in rowvals] for + rowvals in new.data], dtype=object) return new def __truediv__(self, other): # self / other if isscalarlike(other): new = self.copy() # Divide every element by this scalar - new.data = numpy.array([[val/other for val in rowvals] for - rowvals in new.data], dtype=object) + new.data = np.array([[val/other for val in rowvals] for + rowvals in new.data], dtype=object) return new else: return self.tocsr() / other @@ -357,7 +338,7 @@ # """Point-wise multiplication by another lil_matrix. # # """ -# if isscalar(other): +# if np.isscalar(other): # return self.__mul__(other) # # if isspmatrix_lil(other): @@ -386,37 +367,23 @@ # "with another lil_matrix.") def copy(self): + from copy import deepcopy new = lil_matrix(self.shape, dtype=self.dtype) - new.data = copy.deepcopy(self.data) - new.rows = copy.deepcopy(self.rows) + new.data = deepcopy(self.data) + new.rows = deepcopy(self.rows) return new def reshape(self,shape): - new = lil_matrix(shape,dtype=self.dtype) + new = lil_matrix(shape, dtype=self.dtype) j_max = self.shape[1] for i,row in enumerate(self.rows): for col,j in enumerate(row): - new_r,new_c = unravel_index(i*j_max + j,shape) + new_r,new_c = np.unravel_index(i*j_max + j,shape) new[new_r,new_c] = self[i,j] return new - def __add__(self, other): - if isscalar(other) and other != 0: - raise ValueError("Refusing to destroy sparsity. " - "Use x.todense() + c instead.") - else: - return spmatrix.__add__(self, other) - - def __rmul__(self, other): # other * self - if isscalarlike(other): - # Multiplication by a scalar is symmetric - return self.__mul__(other) - else: - return spmatrix.__rmul__(self, other) - - def toarray(self): - d = zeros(self.shape, dtype=self.dtype) + d = np.zeros(self.shape, dtype=self.dtype) for i, row in enumerate(self.rows): for pos, j in enumerate(row): d[i, j] = self.data[i][pos] @@ -435,20 +402,20 @@ """ Return Compressed Sparse Row format arrays for this matrix. """ - indptr = asarray([len(x) for x in self.rows], dtype=intc) - indptr = concatenate( ( array([0],dtype=intc), cumsum(indptr) ) ) + indptr = np.asarray([len(x) for x in self.rows], dtype=np.intc) + indptr = np.concatenate( (np.array([0], dtype=np.intc), np.cumsum(indptr)) ) nnz = indptr[-1] indices = [] for x in self.rows: indices.extend(x) - indices = asarray(indices,dtype=intc) + indices = np.asarray(indices, dtype=np.intc) data = [] for x in self.data: data.extend(x) - data = asarray(data,dtype=self.dtype) + data = np.asarray(data, dtype=self.dtype) from csr import csr_matrix return csr_matrix((data, indices, indptr), shape=self.shape) Modified: branches/refactor_fft/scipy/sparse/linalg/interface.py =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/interface.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/linalg/interface.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -127,8 +127,14 @@ matmat=matmat, dtype=A.dtype) elif isspmatrix(A): - return LinearOperator(A.shape, A.matvec, rmatvec=A.rmatvec, - matmat=A.dot, dtype=A.dtype) + def matvec(v): + return A * v + def rmatvec(v): + return A.conj().transpose() * v + def matmat(V): + return A * V + return LinearOperator(A.shape, matvec, rmatvec=rmatvec, + matmat=matmat, dtype=A.dtype) else: if hasattr(A,'shape') and hasattr(A,'matvec'): Modified: branches/refactor_fft/scipy/sparse/linalg/isolve/iterative.py =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/isolve/iterative.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/linalg/isolve/iterative.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -400,7 +400,7 @@ b or use xtype='f','d','F',or 'D' callback -- an optional user-supplied function to call after each iteration. It is called as callback(rk), where rk is the - the current relative residual + the current relative residual """ A,M,x,b,postprocess = make_system(A,M,x0,b,xtype) @@ -442,7 +442,7 @@ if resid_ready and callback is not None: callback(resid) resid_ready = False - + break elif (ijob == 1): work[slice2] *= sclr2 @@ -452,7 +452,7 @@ if not first_pass and old_ijob==3: resid_ready = True - first_pass = False + first_pass = False elif (ijob == 3): work[slice2] *= sclr2 work[slice2] += sclr1*matvec(work[slice1]) @@ -466,7 +466,7 @@ info = -1 ftflag = False bnrm2, resid, info = stoptest(work[slice1], b, bnrm2, tol, info) - + old_ijob = ijob ijob = 2 Modified: branches/refactor_fft/scipy/sparse/linalg/isolve/tests/test_iterative.py =================================================================== --- branches/refactor_fft/scipy/sparse/linalg/isolve/tests/test_iterative.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/linalg/isolve/tests/test_iterative.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -61,7 +61,7 @@ #data[1,:] = -1 #A = spdiags( data, [0,-1], 10, 10, format='csr') #self.cases.append( (A,False,True) ) - + def test_maxiter(self): """test whether maxiter is respected""" @@ -76,7 +76,7 @@ residuals.append( norm(b - A*x) ) x, info = solver(A, b, x0=x0, tol=1e-8, maxiter=3, callback=callback) - + assert(len(residuals) in [2,3]) # TODO enforce this condition instead! @@ -169,8 +169,8 @@ class TestGMRES(TestCase): - def test_callback(self): - + def test_callback(self): + def store_residual(r, rvec): rvec[rvec.nonzero()[0].max()+1] = r Modified: branches/refactor_fft/scipy/sparse/sparsetools/bsr.py =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/bsr.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/sparsetools/bsr.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -51,484 +51,483 @@ def bsr_diagonal(*args): - """ - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax, signed char Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax, unsigned char Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax, short Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax, unsigned short Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax, int Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax, unsigned int Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax, long long Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax, unsigned long long Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax, float Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax, double Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax, long double Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, npy_cfloat_wrapper Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, npy_cdouble_wrapper Yx) - bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Yx) """ - return _bsr.bsr_diagonal(*args) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax, signed char Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax, unsigned char Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax, short Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax, unsigned short Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax, int Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax, unsigned int Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax, long long Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax, unsigned long long Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax, float Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax, double Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax, long double Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, npy_cfloat_wrapper Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, npy_cdouble_wrapper Yx) + bsr_diagonal(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Yx) + """ + return _bsr.bsr_diagonal(*args) def bsr_scale_rows(*args): - """ - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax, signed char Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax, unsigned char Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax, short Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax, unsigned short Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax, int Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax, unsigned int Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax, long long Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax, unsigned long long Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax, float Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax, double Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax, long double Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx) - bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx) """ - return _bsr.bsr_scale_rows(*args) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax, signed char Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax, unsigned char Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax, short Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax, unsigned short Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax, int Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax, unsigned int Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax, long long Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax, unsigned long long Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax, float Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax, double Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax, long double Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx) + bsr_scale_rows(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx) + """ + return _bsr.bsr_scale_rows(*args) def bsr_scale_columns(*args): - """ - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax, signed char Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax, unsigned char Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax, short Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax, unsigned short Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax, int Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax, unsigned int Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax, long long Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax, unsigned long long Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax, float Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax, double Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax, long double Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx) - bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx) """ - return _bsr.bsr_scale_columns(*args) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax, signed char Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax, unsigned char Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax, short Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax, unsigned short Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax, int Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax, unsigned int Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax, long long Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax, unsigned long long Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax, float Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax, double Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax, long double Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx) + bsr_scale_columns(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx) + """ + return _bsr.bsr_scale_columns(*args) def bsr_transpose(*args): - """ - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx) - bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx) """ - return _bsr.bsr_transpose(*args) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx) + bsr_transpose(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx) + """ + return _bsr.bsr_transpose(*args) def bsr_matmat_pass2(*args): - """ - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, signed char Ax, int Bp, int Bj, signed char Bx, - int Cp, int Cj, signed char Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, unsigned char Ax, int Bp, int Bj, unsigned char Bx, - int Cp, int Cj, unsigned char Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, short Ax, int Bp, int Bj, short Bx, - int Cp, int Cj, short Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, unsigned short Ax, int Bp, int Bj, - unsigned short Bx, int Cp, int Cj, unsigned short Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, int Ax, int Bp, int Bj, int Bx, int Cp, - int Cj, int Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, unsigned int Ax, int Bp, int Bj, unsigned int Bx, - int Cp, int Cj, unsigned int Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, long long Ax, int Bp, int Bj, long long Bx, - int Cp, int Cj, long long Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, unsigned long long Ax, int Bp, int Bj, - unsigned long long Bx, int Cp, int Cj, unsigned long long Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, float Ax, int Bp, int Bj, float Bx, - int Cp, int Cj, float Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, double Ax, int Bp, int Bj, double Bx, - int Cp, int Cj, double Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, long double Ax, int Bp, int Bj, long double Bx, - int Cp, int Cj, long double Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, npy_cfloat_wrapper Ax, int Bp, int Bj, - npy_cfloat_wrapper Bx, int Cp, int Cj, npy_cfloat_wrapper Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, npy_cdouble_wrapper Ax, int Bp, int Bj, - npy_cdouble_wrapper Bx, int Cp, int Cj, - npy_cdouble_wrapper Cx) - bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, - int Aj, npy_clongdouble_wrapper Ax, int Bp, - int Bj, npy_clongdouble_wrapper Bx, int Cp, - int Cj, npy_clongdouble_wrapper Cx) """ - return _bsr.bsr_matmat_pass2(*args) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, signed char Ax, int Bp, int Bj, signed char Bx, + int Cp, int Cj, signed char Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, unsigned char Ax, int Bp, int Bj, unsigned char Bx, + int Cp, int Cj, unsigned char Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, short Ax, int Bp, int Bj, short Bx, + int Cp, int Cj, short Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, unsigned short Ax, int Bp, int Bj, + unsigned short Bx, int Cp, int Cj, unsigned short Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, int Ax, int Bp, int Bj, int Bx, int Cp, + int Cj, int Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, unsigned int Ax, int Bp, int Bj, unsigned int Bx, + int Cp, int Cj, unsigned int Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, long long Ax, int Bp, int Bj, long long Bx, + int Cp, int Cj, long long Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, unsigned long long Ax, int Bp, int Bj, + unsigned long long Bx, int Cp, int Cj, unsigned long long Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, float Ax, int Bp, int Bj, float Bx, + int Cp, int Cj, float Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, double Ax, int Bp, int Bj, double Bx, + int Cp, int Cj, double Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, long double Ax, int Bp, int Bj, long double Bx, + int Cp, int Cj, long double Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, npy_cfloat_wrapper Ax, int Bp, int Bj, + npy_cfloat_wrapper Bx, int Cp, int Cj, npy_cfloat_wrapper Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, npy_cdouble_wrapper Ax, int Bp, int Bj, + npy_cdouble_wrapper Bx, int Cp, int Cj, + npy_cdouble_wrapper Cx) + bsr_matmat_pass2(int n_brow, int n_bcol, int R, int C, int N, int Ap, + int Aj, npy_clongdouble_wrapper Ax, int Bp, + int Bj, npy_clongdouble_wrapper Bx, int Cp, + int Cj, npy_clongdouble_wrapper Cx) + """ + return _bsr.bsr_matmat_pass2(*args) def bsr_matvec(*args): - """ - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax, signed char Xx, signed char Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax, unsigned char Xx, unsigned char Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax, short Xx, short Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax, unsigned short Xx, unsigned short Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax, int Xx, int Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax, unsigned int Xx, unsigned int Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax, long long Xx, long long Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax, unsigned long long Xx, - unsigned long long Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax, float Xx, float Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax, double Xx, double Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax, long double Xx, long double Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx, - npy_clongdouble_wrapper Yx) """ - return _bsr.bsr_matvec(*args) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax, signed char Xx, signed char Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax, unsigned char Xx, unsigned char Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax, short Xx, short Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax, unsigned short Xx, unsigned short Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax, int Xx, int Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax, unsigned int Xx, unsigned int Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax, long long Xx, long long Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax, unsigned long long Xx, + unsigned long long Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax, float Xx, float Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax, double Xx, double Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax, long double Xx, long double Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + bsr_matvec(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx, + npy_clongdouble_wrapper Yx) + """ + return _bsr.bsr_matvec(*args) def bsr_matvecs(*args): - """ - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, signed char Ax, signed char Xx, - signed char Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, unsigned char Ax, unsigned char Xx, - unsigned char Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, short Ax, short Xx, short Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, unsigned short Ax, unsigned short Xx, - unsigned short Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, int Ax, int Xx, int Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, unsigned int Ax, unsigned int Xx, - unsigned int Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, long long Ax, long long Xx, long long Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, unsigned long long Ax, unsigned long long Xx, - unsigned long long Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, float Ax, float Xx, float Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, double Ax, double Xx, double Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, long double Ax, long double Xx, - long double Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, - int Aj, npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx, - npy_clongdouble_wrapper Yx) """ - return _bsr.bsr_matvecs(*args) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, signed char Ax, signed char Xx, + signed char Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, unsigned char Ax, unsigned char Xx, + unsigned char Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, short Ax, short Xx, short Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, unsigned short Ax, unsigned short Xx, + unsigned short Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, int Ax, int Xx, int Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, unsigned int Ax, unsigned int Xx, + unsigned int Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, long long Ax, long long Xx, long long Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, unsigned long long Ax, unsigned long long Xx, + unsigned long long Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, float Ax, float Xx, float Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, double Ax, double Xx, double Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, long double Ax, long double Xx, + long double Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + bsr_matvecs(int n_brow, int n_bcol, int n_vecs, int R, int C, int Ap, + int Aj, npy_clongdouble_wrapper Ax, npy_clongdouble_wrapper Xx, + npy_clongdouble_wrapper Yx) + """ + return _bsr.bsr_matvecs(*args) def bsr_elmul_bsr(*args): - """ - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx, - int Cp, int Cj, signed char Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx, - int Cp, int Cj, unsigned char Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx, int Cp, - int Cj, short Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx, - int Cp, int Cj, unsigned short Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, - int Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx, - int Cp, int Cj, unsigned int Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx, - int Cp, int Cj, long long Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx, int Cp, - int Cj, float Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx, int Cp, - int Cj, double Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx, - int Cp, int Cj, long double Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _bsr.bsr_elmul_bsr(*args) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx, + int Cp, int Cj, signed char Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx, + int Cp, int Cj, unsigned char Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx, int Cp, + int Cj, short Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx, + int Cp, int Cj, unsigned short Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, + int Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx, + int Cp, int Cj, unsigned int Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx, + int Cp, int Cj, long long Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx, int Cp, + int Cj, float Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx, int Cp, + int Cj, double Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx, + int Cp, int Cj, long double Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + bsr_elmul_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _bsr.bsr_elmul_bsr(*args) def bsr_eldiv_bsr(*args): - """ - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx, - int Cp, int Cj, signed char Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx, - int Cp, int Cj, unsigned char Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx, int Cp, - int Cj, short Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx, - int Cp, int Cj, unsigned short Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, - int Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx, - int Cp, int Cj, unsigned int Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx, - int Cp, int Cj, long long Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx, int Cp, - int Cj, float Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx, int Cp, - int Cj, double Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx, - int Cp, int Cj, long double Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _bsr.bsr_eldiv_bsr(*args) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx, + int Cp, int Cj, signed char Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx, + int Cp, int Cj, unsigned char Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx, int Cp, + int Cj, short Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx, + int Cp, int Cj, unsigned short Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, + int Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx, + int Cp, int Cj, unsigned int Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx, + int Cp, int Cj, long long Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx, int Cp, + int Cj, float Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx, int Cp, + int Cj, double Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx, + int Cp, int Cj, long double Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + bsr_eldiv_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _bsr.bsr_eldiv_bsr(*args) def bsr_plus_bsr(*args): - """ - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx, - int Cp, int Cj, signed char Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx, - int Cp, int Cj, unsigned char Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx, int Cp, - int Cj, short Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx, - int Cp, int Cj, unsigned short Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, - int Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx, - int Cp, int Cj, unsigned int Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx, - int Cp, int Cj, long long Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx, int Cp, - int Cj, float Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx, int Cp, - int Cj, double Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx, - int Cp, int Cj, long double Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _bsr.bsr_plus_bsr(*args) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx, + int Cp, int Cj, signed char Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx, + int Cp, int Cj, unsigned char Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx, int Cp, + int Cj, short Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx, + int Cp, int Cj, unsigned short Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, + int Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx, + int Cp, int Cj, unsigned int Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx, + int Cp, int Cj, long long Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx, int Cp, + int Cj, float Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx, int Cp, + int Cj, double Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx, + int Cp, int Cj, long double Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + bsr_plus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _bsr.bsr_plus_bsr(*args) def bsr_minus_bsr(*args): - """ - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx, - int Cp, int Cj, signed char Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx, - int Cp, int Cj, unsigned char Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx, int Cp, - int Cj, short Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx, - int Cp, int Cj, unsigned short Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, - int Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx, - int Cp, int Cj, unsigned int Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx, - int Cp, int Cj, long long Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx, int Cp, - int Cj, float Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx, int Cp, - int Cj, double Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx, - int Cp, int Cj, long double Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _bsr.bsr_minus_bsr(*args) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx, + int Cp, int Cj, signed char Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx, + int Cp, int Cj, unsigned char Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx, int Cp, + int Cj, short Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx, + int Cp, int Cj, unsigned short Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx, int Cp, int Cj, + int Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx, + int Cp, int Cj, unsigned int Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx, + int Cp, int Cj, long long Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx, int Cp, + int Cj, float Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx, int Cp, + int Cj, double Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx, + int Cp, int Cj, long double Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + bsr_minus_bsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx, int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _bsr.bsr_minus_bsr(*args) def bsr_sort_indices(*args): - """ - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - signed char Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned char Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - short Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned short Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - int Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned int Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long long Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - unsigned long long Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - float Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - double Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - long double Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax) - bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax) """ - return _bsr.bsr_sort_indices(*args) - + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + signed char Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned char Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + short Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned short Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + int Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned int Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long long Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + unsigned long long Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + float Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + double Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + long double Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax) + bsr_sort_indices(int n_brow, int n_bcol, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax) + """ + return _bsr.bsr_sort_indices(*args) Modified: branches/refactor_fft/scipy/sparse/sparsetools/coo.py =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/coo.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/sparsetools/coo.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -50,135 +50,134 @@ def coo_count_diagonals(*args): - """coo_count_diagonals(int nnz, int Ai, int Aj) -> int""" - return _coo.coo_count_diagonals(*args) + """coo_count_diagonals(int nnz, int Ai, int Aj) -> int""" + return _coo.coo_count_diagonals(*args) def coo_tocsr(*args): - """ - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, - int Bp, int Bj, short Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, - int Bp, int Bj, int Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, - int Bp, int Bj, long long Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, - int Bp, int Bj, float Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, - int Bp, int Bj, double Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, - int Bp, int Bj, long double Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx) - coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx) """ - return _coo.coo_tocsr(*args) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, + int Bp, int Bj, short Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, + int Bp, int Bj, int Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, + int Bp, int Bj, long long Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, + int Bp, int Bj, float Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, + int Bp, int Bj, double Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, + int Bp, int Bj, long double Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx) + coo_tocsr(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx) + """ + return _coo.coo_tocsr(*args) def coo_tocsc(*args): - """ - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, - int Bp, int Bi, signed char Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, - int Bp, int Bi, unsigned char Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, - int Bp, int Bi, short Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, - int Bp, int Bi, unsigned short Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, - int Bp, int Bi, int Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, - int Bp, int Bi, unsigned int Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, - int Bp, int Bi, long long Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, - int Bp, int Bi, float Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, - int Bp, int Bi, double Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, - int Bp, int Bi, long double Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx) - coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx) """ - return _coo.coo_tocsc(*args) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, + int Bp, int Bi, signed char Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, + int Bp, int Bi, unsigned char Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, + int Bp, int Bi, short Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, + int Bp, int Bi, unsigned short Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, + int Bp, int Bi, int Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, + int Bp, int Bi, unsigned int Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, + int Bp, int Bi, long long Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, + int Bp, int Bi, float Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, + int Bp, int Bi, double Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, + int Bp, int Bi, long double Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx) + coo_tocsc(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx) + """ + return _coo.coo_tocsc(*args) def coo_todense(*args): - """ - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, - signed char Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, - unsigned char Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, - short Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, - unsigned short Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, - int Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, - unsigned int Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, - long long Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, - unsigned long long Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, - float Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, - double Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, - long double Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Bx) - coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Bx) """ - return _coo.coo_todense(*args) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, signed char Ax, + signed char Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned char Ax, + unsigned char Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, short Ax, + short Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned short Ax, + unsigned short Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, int Ax, + int Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned int Ax, + unsigned int Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, long long Ax, + long long Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, unsigned long long Ax, + unsigned long long Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, float Ax, + float Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, double Ax, + double Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, long double Ax, + long double Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Bx) + coo_todense(int n_row, int n_col, int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Bx) + """ + return _coo.coo_todense(*args) def coo_matvec(*args): - """ - coo_matvec(int nnz, int Ai, int Aj, signed char Ax, signed char Xx, - signed char Yx) - coo_matvec(int nnz, int Ai, int Aj, unsigned char Ax, unsigned char Xx, - unsigned char Yx) - coo_matvec(int nnz, int Ai, int Aj, short Ax, short Xx, short Yx) - coo_matvec(int nnz, int Ai, int Aj, unsigned short Ax, unsigned short Xx, - unsigned short Yx) - coo_matvec(int nnz, int Ai, int Aj, int Ax, int Xx, int Yx) - coo_matvec(int nnz, int Ai, int Aj, unsigned int Ax, unsigned int Xx, - unsigned int Yx) - coo_matvec(int nnz, int Ai, int Aj, long long Ax, long long Xx, - long long Yx) - coo_matvec(int nnz, int Ai, int Aj, unsigned long long Ax, unsigned long long Xx, - unsigned long long Yx) - coo_matvec(int nnz, int Ai, int Aj, float Ax, float Xx, float Yx) - coo_matvec(int nnz, int Ai, int Aj, double Ax, double Xx, double Yx) - coo_matvec(int nnz, int Ai, int Aj, long double Ax, long double Xx, - long double Yx) - coo_matvec(int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - coo_matvec(int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - coo_matvec(int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) """ - return _coo.coo_matvec(*args) - + coo_matvec(int nnz, int Ai, int Aj, signed char Ax, signed char Xx, + signed char Yx) + coo_matvec(int nnz, int Ai, int Aj, unsigned char Ax, unsigned char Xx, + unsigned char Yx) + coo_matvec(int nnz, int Ai, int Aj, short Ax, short Xx, short Yx) + coo_matvec(int nnz, int Ai, int Aj, unsigned short Ax, unsigned short Xx, + unsigned short Yx) + coo_matvec(int nnz, int Ai, int Aj, int Ax, int Xx, int Yx) + coo_matvec(int nnz, int Ai, int Aj, unsigned int Ax, unsigned int Xx, + unsigned int Yx) + coo_matvec(int nnz, int Ai, int Aj, long long Ax, long long Xx, + long long Yx) + coo_matvec(int nnz, int Ai, int Aj, unsigned long long Ax, unsigned long long Xx, + unsigned long long Yx) + coo_matvec(int nnz, int Ai, int Aj, float Ax, float Xx, float Yx) + coo_matvec(int nnz, int Ai, int Aj, double Ax, double Xx, double Yx) + coo_matvec(int nnz, int Ai, int Aj, long double Ax, long double Xx, + long double Yx) + coo_matvec(int nnz, int Ai, int Aj, npy_cfloat_wrapper Ax, npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + coo_matvec(int nnz, int Ai, int Aj, npy_cdouble_wrapper Ax, npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + coo_matvec(int nnz, int Ai, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) + """ + return _coo.coo_matvec(*args) Modified: branches/refactor_fft/scipy/sparse/sparsetools/csc.py =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/csc.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/sparsetools/csc.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -50,357 +50,356 @@ def csc_matmat_pass1(*args): - """ - csc_matmat_pass1(int n_row, int n_col, int Ap, int Ai, int Bp, int Bi, - int Cp) """ - return _csc.csc_matmat_pass1(*args) + csc_matmat_pass1(int n_row, int n_col, int Ap, int Ai, int Bp, int Bi, + int Cp) + """ + return _csc.csc_matmat_pass1(*args) def csc_diagonal(*args): - """ - csc_diagonal(int n_row, int n_col, int Ap, int Aj, signed char Ax, - signed char Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - unsigned char Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, short Ax, short Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - unsigned short Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, int Ax, int Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - unsigned int Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, long long Ax, - long long Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - unsigned long long Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, float Ax, float Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, double Ax, double Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, long double Ax, - long double Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Yx) - csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Yx) """ - return _csc.csc_diagonal(*args) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, signed char Ax, + signed char Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + unsigned char Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, short Ax, short Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + unsigned short Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, int Ax, int Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + unsigned int Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, long long Ax, + long long Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + unsigned long long Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, float Ax, float Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, double Ax, double Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, long double Ax, + long double Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Yx) + csc_diagonal(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Yx) + """ + return _csc.csc_diagonal(*args) def csc_tocsr(*args): - """ - csc_tocsr(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bj, signed char Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bj, unsigned char Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bj, short Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bj, unsigned short Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bj, int Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bj, unsigned int Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bj, long long Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bj, float Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bj, double Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bj, long double Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx) - csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx) """ - return _csc.csc_tocsr(*args) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bj, signed char Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bj, unsigned char Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bj, short Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bj, unsigned short Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bj, int Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bj, unsigned int Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bj, long long Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bj, float Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bj, double Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bj, long double Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx) + csc_tocsr(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx) + """ + return _csc.csc_tocsr(*args) def csc_matmat_pass2(*args): - """ - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bi, signed char Bx, int Cp, int Ci, - signed char Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bi, unsigned char Bx, int Cp, - int Ci, unsigned char Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bi, short Bx, int Cp, int Ci, short Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bi, unsigned short Bx, int Cp, - int Ci, unsigned short Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bi, int Bx, int Cp, int Ci, int Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bi, unsigned int Bx, int Cp, - int Ci, unsigned int Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bi, long long Bx, int Cp, int Ci, - long long Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx, - int Cp, int Ci, unsigned long long Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bi, float Bx, int Cp, int Ci, float Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bi, double Bx, int Cp, int Ci, double Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bi, long double Bx, int Cp, int Ci, - long double Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx, - int Cp, int Ci, npy_cfloat_wrapper Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx, - int Cp, int Ci, npy_cdouble_wrapper Cx) - csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx, - int Cp, int Ci, npy_clongdouble_wrapper Cx) """ - return _csc.csc_matmat_pass2(*args) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bi, signed char Bx, int Cp, int Ci, + signed char Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bi, unsigned char Bx, int Cp, + int Ci, unsigned char Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bi, short Bx, int Cp, int Ci, short Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bi, unsigned short Bx, int Cp, + int Ci, unsigned short Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bi, int Bx, int Cp, int Ci, int Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bi, unsigned int Bx, int Cp, + int Ci, unsigned int Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bi, long long Bx, int Cp, int Ci, + long long Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx, + int Cp, int Ci, unsigned long long Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bi, float Bx, int Cp, int Ci, float Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bi, double Bx, int Cp, int Ci, double Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bi, long double Bx, int Cp, int Ci, + long double Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx, + int Cp, int Ci, npy_cfloat_wrapper Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx, + int Cp, int Ci, npy_cdouble_wrapper Cx) + csc_matmat_pass2(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx, + int Cp, int Ci, npy_clongdouble_wrapper Cx) + """ + return _csc.csc_matmat_pass2(*args) def csc_matvec(*args): - """ - csc_matvec(int n_row, int n_col, int Ap, int Ai, signed char Ax, - signed char Xx, signed char Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - unsigned char Xx, unsigned char Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, short Ax, short Xx, - short Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - unsigned short Xx, unsigned short Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, int Ax, int Xx, - int Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - unsigned int Xx, unsigned int Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, long long Ax, - long long Xx, long long Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - unsigned long long Xx, unsigned long long Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, float Ax, float Xx, - float Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, double Ax, double Xx, - double Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, long double Ax, - long double Xx, long double Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx, npy_cfloat_wrapper Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx, npy_cdouble_wrapper Yx) - csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) """ - return _csc.csc_matvec(*args) + csc_matvec(int n_row, int n_col, int Ap, int Ai, signed char Ax, + signed char Xx, signed char Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + unsigned char Xx, unsigned char Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, short Ax, short Xx, + short Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + unsigned short Xx, unsigned short Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, int Ax, int Xx, + int Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + unsigned int Xx, unsigned int Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, long long Ax, + long long Xx, long long Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + unsigned long long Xx, unsigned long long Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, float Ax, float Xx, + float Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, double Ax, double Xx, + double Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, long double Ax, + long double Xx, long double Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx, npy_cfloat_wrapper Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx, npy_cdouble_wrapper Yx) + csc_matvec(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) + """ + return _csc.csc_matvec(*args) def csc_matvecs(*args): - """ - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, signed char Ax, - signed char Xx, signed char Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned char Ax, - unsigned char Xx, unsigned char Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, short Ax, - short Xx, short Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned short Ax, - unsigned short Xx, unsigned short Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, int Ax, - int Xx, int Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned int Ax, - unsigned int Xx, unsigned int Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, long long Ax, - long long Xx, long long Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned long long Ax, - unsigned long long Xx, - unsigned long long Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, float Ax, - float Xx, float Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, double Ax, - double Xx, double Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, long double Ax, - long double Xx, long double Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx, - npy_clongdouble_wrapper Yx) """ - return _csc.csc_matvecs(*args) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, signed char Ax, + signed char Xx, signed char Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned char Ax, + unsigned char Xx, unsigned char Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, short Ax, + short Xx, short Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned short Ax, + unsigned short Xx, unsigned short Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, int Ax, + int Xx, int Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned int Ax, + unsigned int Xx, unsigned int Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, long long Ax, + long long Xx, long long Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, unsigned long long Ax, + unsigned long long Xx, + unsigned long long Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, float Ax, + float Xx, float Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, double Ax, + double Xx, double Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, long double Ax, + long double Xx, long double Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + csc_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Ai, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx, + npy_clongdouble_wrapper Yx) + """ + return _csc.csc_matvecs(*args) def csc_elmul_csc(*args): - """ - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bi, signed char Bx, int Cp, int Ci, - signed char Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bi, unsigned char Bx, int Cp, - int Ci, unsigned char Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bi, short Bx, int Cp, int Ci, short Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bi, unsigned short Bx, int Cp, - int Ci, unsigned short Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bi, int Bx, int Cp, int Ci, int Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bi, unsigned int Bx, int Cp, - int Ci, unsigned int Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bi, long long Bx, int Cp, int Ci, - long long Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx, - int Cp, int Ci, unsigned long long Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bi, float Bx, int Cp, int Ci, float Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bi, double Bx, int Cp, int Ci, double Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bi, long double Bx, int Cp, int Ci, - long double Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx, - int Cp, int Ci, npy_cfloat_wrapper Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx, - int Cp, int Ci, npy_cdouble_wrapper Cx) - csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx, - int Cp, int Ci, npy_clongdouble_wrapper Cx) """ - return _csc.csc_elmul_csc(*args) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bi, signed char Bx, int Cp, int Ci, + signed char Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bi, unsigned char Bx, int Cp, + int Ci, unsigned char Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bi, short Bx, int Cp, int Ci, short Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bi, unsigned short Bx, int Cp, + int Ci, unsigned short Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bi, int Bx, int Cp, int Ci, int Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bi, unsigned int Bx, int Cp, + int Ci, unsigned int Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bi, long long Bx, int Cp, int Ci, + long long Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx, + int Cp, int Ci, unsigned long long Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bi, float Bx, int Cp, int Ci, float Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bi, double Bx, int Cp, int Ci, double Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bi, long double Bx, int Cp, int Ci, + long double Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx, + int Cp, int Ci, npy_cfloat_wrapper Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx, + int Cp, int Ci, npy_cdouble_wrapper Cx) + csc_elmul_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx, + int Cp, int Ci, npy_clongdouble_wrapper Cx) + """ + return _csc.csc_elmul_csc(*args) def csc_eldiv_csc(*args): - """ - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bi, signed char Bx, int Cp, int Ci, - signed char Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bi, unsigned char Bx, int Cp, - int Ci, unsigned char Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bi, short Bx, int Cp, int Ci, short Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bi, unsigned short Bx, int Cp, - int Ci, unsigned short Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bi, int Bx, int Cp, int Ci, int Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bi, unsigned int Bx, int Cp, - int Ci, unsigned int Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bi, long long Bx, int Cp, int Ci, - long long Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx, - int Cp, int Ci, unsigned long long Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bi, float Bx, int Cp, int Ci, float Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bi, double Bx, int Cp, int Ci, double Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bi, long double Bx, int Cp, int Ci, - long double Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx, - int Cp, int Ci, npy_cfloat_wrapper Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx, - int Cp, int Ci, npy_cdouble_wrapper Cx) - csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx, - int Cp, int Ci, npy_clongdouble_wrapper Cx) """ - return _csc.csc_eldiv_csc(*args) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bi, signed char Bx, int Cp, int Ci, + signed char Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bi, unsigned char Bx, int Cp, + int Ci, unsigned char Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bi, short Bx, int Cp, int Ci, short Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bi, unsigned short Bx, int Cp, + int Ci, unsigned short Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bi, int Bx, int Cp, int Ci, int Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bi, unsigned int Bx, int Cp, + int Ci, unsigned int Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bi, long long Bx, int Cp, int Ci, + long long Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx, + int Cp, int Ci, unsigned long long Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bi, float Bx, int Cp, int Ci, float Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bi, double Bx, int Cp, int Ci, double Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bi, long double Bx, int Cp, int Ci, + long double Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx, + int Cp, int Ci, npy_cfloat_wrapper Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx, + int Cp, int Ci, npy_cdouble_wrapper Cx) + csc_eldiv_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx, + int Cp, int Ci, npy_clongdouble_wrapper Cx) + """ + return _csc.csc_eldiv_csc(*args) def csc_plus_csc(*args): - """ - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bi, signed char Bx, int Cp, int Ci, - signed char Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bi, unsigned char Bx, int Cp, - int Ci, unsigned char Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bi, short Bx, int Cp, int Ci, short Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bi, unsigned short Bx, int Cp, - int Ci, unsigned short Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bi, int Bx, int Cp, int Ci, int Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bi, unsigned int Bx, int Cp, - int Ci, unsigned int Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bi, long long Bx, int Cp, int Ci, - long long Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx, - int Cp, int Ci, unsigned long long Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bi, float Bx, int Cp, int Ci, float Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bi, double Bx, int Cp, int Ci, double Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bi, long double Bx, int Cp, int Ci, - long double Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx, - int Cp, int Ci, npy_cfloat_wrapper Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx, - int Cp, int Ci, npy_cdouble_wrapper Cx) - csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx, - int Cp, int Ci, npy_clongdouble_wrapper Cx) """ - return _csc.csc_plus_csc(*args) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bi, signed char Bx, int Cp, int Ci, + signed char Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bi, unsigned char Bx, int Cp, + int Ci, unsigned char Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bi, short Bx, int Cp, int Ci, short Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bi, unsigned short Bx, int Cp, + int Ci, unsigned short Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bi, int Bx, int Cp, int Ci, int Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bi, unsigned int Bx, int Cp, + int Ci, unsigned int Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bi, long long Bx, int Cp, int Ci, + long long Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx, + int Cp, int Ci, unsigned long long Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bi, float Bx, int Cp, int Ci, float Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bi, double Bx, int Cp, int Ci, double Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bi, long double Bx, int Cp, int Ci, + long double Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx, + int Cp, int Ci, npy_cfloat_wrapper Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx, + int Cp, int Ci, npy_cdouble_wrapper Cx) + csc_plus_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx, + int Cp, int Ci, npy_clongdouble_wrapper Cx) + """ + return _csc.csc_plus_csc(*args) def csc_minus_csc(*args): - """ - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, - int Bp, int Bi, signed char Bx, int Cp, int Ci, - signed char Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, - int Bp, int Bi, unsigned char Bx, int Cp, - int Ci, unsigned char Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, - int Bi, short Bx, int Cp, int Ci, short Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, - int Bp, int Bi, unsigned short Bx, int Cp, - int Ci, unsigned short Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, - int Bi, int Bx, int Cp, int Ci, int Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, - int Bp, int Bi, unsigned int Bx, int Cp, - int Ci, unsigned int Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, - int Bp, int Bi, long long Bx, int Cp, int Ci, - long long Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx, - int Cp, int Ci, unsigned long long Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, - int Bi, float Bx, int Cp, int Ci, float Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, - int Bi, double Bx, int Cp, int Ci, double Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, - int Bp, int Bi, long double Bx, int Cp, int Ci, - long double Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx, - int Cp, int Ci, npy_cfloat_wrapper Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx, - int Cp, int Ci, npy_cdouble_wrapper Cx) - csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx, - int Cp, int Ci, npy_clongdouble_wrapper Cx) """ - return _csc.csc_minus_csc(*args) - + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, signed char Ax, + int Bp, int Bi, signed char Bx, int Cp, int Ci, + signed char Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned char Ax, + int Bp, int Bi, unsigned char Bx, int Cp, + int Ci, unsigned char Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, short Ax, int Bp, + int Bi, short Bx, int Cp, int Ci, short Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned short Ax, + int Bp, int Bi, unsigned short Bx, int Cp, + int Ci, unsigned short Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, int Ax, int Bp, + int Bi, int Bx, int Cp, int Ci, int Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned int Ax, + int Bp, int Bi, unsigned int Bx, int Cp, + int Ci, unsigned int Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, long long Ax, + int Bp, int Bi, long long Bx, int Cp, int Ci, + long long Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx, + int Cp, int Ci, unsigned long long Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, float Ax, int Bp, + int Bi, float Bx, int Cp, int Ci, float Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, double Ax, int Bp, + int Bi, double Bx, int Cp, int Ci, double Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, long double Ax, + int Bp, int Bi, long double Bx, int Cp, int Ci, + long double Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx, + int Cp, int Ci, npy_cfloat_wrapper Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx, + int Cp, int Ci, npy_cdouble_wrapper Cx) + csc_minus_csc(int n_row, int n_col, int Ap, int Ai, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx, + int Cp, int Ci, npy_clongdouble_wrapper Cx) + """ + return _csc.csc_minus_csc(*args) Modified: branches/refactor_fft/scipy/sparse/sparsetools/csr.py =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/csr.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/sparsetools/csr.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -50,569 +50,568 @@ def expandptr(*args): - """expandptr(int n_row, int Ap, int Bi)""" - return _csr.expandptr(*args) + """expandptr(int n_row, int Ap, int Bi)""" + return _csr.expandptr(*args) def csr_matmat_pass1(*args): - """ - csr_matmat_pass1(int n_row, int n_col, int Ap, int Aj, int Bp, int Bj, - int Cp) """ - return _csr.csr_matmat_pass1(*args) + csr_matmat_pass1(int n_row, int n_col, int Ap, int Aj, int Bp, int Bj, + int Cp) + """ + return _csr.csr_matmat_pass1(*args) def csr_count_blocks(*args): - """csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int""" - return _csr.csr_count_blocks(*args) + """csr_count_blocks(int n_row, int n_col, int R, int C, int Ap, int Aj) -> int""" + return _csr.csr_count_blocks(*args) def csr_has_sorted_indices(*args): - """csr_has_sorted_indices(int n_row, int Ap, int Aj) -> bool""" - return _csr.csr_has_sorted_indices(*args) + """csr_has_sorted_indices(int n_row, int Ap, int Aj) -> bool""" + return _csr.csr_has_sorted_indices(*args) def csr_diagonal(*args): - """ - csr_diagonal(int n_row, int n_col, int Ap, int Aj, signed char Ax, - signed char Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - unsigned char Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, short Ax, short Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - unsigned short Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, int Ax, int Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - unsigned int Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, long long Ax, - long long Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - unsigned long long Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, float Ax, float Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, double Ax, double Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, long double Ax, - long double Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Yx) - csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Yx) """ - return _csr.csr_diagonal(*args) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, signed char Ax, + signed char Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + unsigned char Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, short Ax, short Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + unsigned short Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, int Ax, int Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + unsigned int Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, long long Ax, + long long Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + unsigned long long Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, float Ax, float Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, double Ax, double Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, long double Ax, + long double Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Yx) + csr_diagonal(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Yx) + """ + return _csr.csr_diagonal(*args) def csr_scale_rows(*args): - """ - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, signed char Ax, - signed char Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - unsigned char Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - unsigned short Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - unsigned int Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, long long Ax, - long long Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - unsigned long long Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, long double Ax, - long double Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx) - csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx) """ - return _csr.csr_scale_rows(*args) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, signed char Ax, + signed char Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + unsigned char Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + unsigned short Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + unsigned int Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, long long Ax, + long long Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + unsigned long long Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, long double Ax, + long double Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx) + csr_scale_rows(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx) + """ + return _csr.csr_scale_rows(*args) def csr_scale_columns(*args): - """ - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, signed char Ax, - signed char Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - unsigned char Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - unsigned short Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - unsigned int Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, long long Ax, - long long Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - unsigned long long Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, long double Ax, - long double Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx) - csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx) """ - return _csr.csr_scale_columns(*args) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, signed char Ax, + signed char Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + unsigned char Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + unsigned short Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + unsigned int Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, long long Ax, + long long Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + unsigned long long Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, long double Ax, + long double Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx) + csr_scale_columns(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx) + """ + return _csr.csr_scale_columns(*args) def csr_tocsc(*args): - """ - csr_tocsc(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bi, signed char Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bi, unsigned char Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bi, short Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bi, unsigned short Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bi, int Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bi, unsigned int Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bi, long long Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bi, unsigned long long Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bi, float Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bi, double Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bi, long double Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bi, npy_cfloat_wrapper Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bi, npy_cdouble_wrapper Bx) - csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bi, npy_clongdouble_wrapper Bx) """ - return _csr.csr_tocsc(*args) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bi, signed char Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bi, unsigned char Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bi, short Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bi, unsigned short Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bi, int Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bi, unsigned int Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bi, long long Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bi, unsigned long long Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bi, float Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bi, double Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bi, long double Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bi, npy_cfloat_wrapper Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bi, npy_cdouble_wrapper Bx) + csr_tocsc(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bi, npy_clongdouble_wrapper Bx) + """ + return _csr.csr_tocsc(*args) def csr_tobsr(*args): - """ - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - signed char Ax, int Bp, int Bj, signed char Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned char Ax, int Bp, int Bj, unsigned char Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - short Ax, int Bp, int Bj, short Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned short Ax, int Bp, int Bj, unsigned short Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - int Ax, int Bp, int Bj, int Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned int Ax, int Bp, int Bj, unsigned int Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long long Ax, int Bp, int Bj, long long Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - unsigned long long Ax, int Bp, int Bj, unsigned long long Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - float Ax, int Bp, int Bj, float Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - double Ax, int Bp, int Bj, double Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - long double Ax, int Bp, int Bj, long double Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx) - csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, - npy_clongdouble_wrapper Ax, int Bp, int Bj, - npy_clongdouble_wrapper Bx) """ - return _csr.csr_tobsr(*args) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + signed char Ax, int Bp, int Bj, signed char Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned char Ax, int Bp, int Bj, unsigned char Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + short Ax, int Bp, int Bj, short Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned short Ax, int Bp, int Bj, unsigned short Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + int Ax, int Bp, int Bj, int Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned int Ax, int Bp, int Bj, unsigned int Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long long Ax, int Bp, int Bj, long long Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + unsigned long long Ax, int Bp, int Bj, unsigned long long Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + float Ax, int Bp, int Bj, float Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + double Ax, int Bp, int Bj, double Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + long double Ax, int Bp, int Bj, long double Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cfloat_wrapper Ax, int Bp, int Bj, npy_cfloat_wrapper Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_cdouble_wrapper Ax, int Bp, int Bj, npy_cdouble_wrapper Bx) + csr_tobsr(int n_row, int n_col, int R, int C, int Ap, int Aj, + npy_clongdouble_wrapper Ax, int Bp, int Bj, + npy_clongdouble_wrapper Bx) + """ + return _csr.csr_tobsr(*args) def csr_matmat_pass2(*args): - """ - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx, int Cp, int Cj, - signed char Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx, int Cp, - int Cj, unsigned char Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bj, short Bx, int Cp, int Cj, short Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx, int Cp, - int Cj, unsigned short Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bj, int Bx, int Cp, int Cj, int Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx, int Cp, - int Cj, unsigned int Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bj, long long Bx, int Cp, int Cj, - long long Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bj, float Bx, int Cp, int Cj, float Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bj, double Bx, int Cp, int Cj, double Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bj, long double Bx, int Cp, int Cj, - long double Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx, - int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _csr.csr_matmat_pass2(*args) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx, int Cp, int Cj, + signed char Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx, int Cp, + int Cj, unsigned char Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bj, short Bx, int Cp, int Cj, short Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx, int Cp, + int Cj, unsigned short Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bj, int Bx, int Cp, int Cj, int Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx, int Cp, + int Cj, unsigned int Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bj, long long Bx, int Cp, int Cj, + long long Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bj, float Bx, int Cp, int Cj, float Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bj, double Bx, int Cp, int Cj, double Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bj, long double Bx, int Cp, int Cj, + long double Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + csr_matmat_pass2(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx, + int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _csr.csr_matmat_pass2(*args) def csr_matvec(*args): - """ - csr_matvec(int n_row, int n_col, int Ap, int Aj, signed char Ax, - signed char Xx, signed char Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - unsigned char Xx, unsigned char Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx, - short Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - unsigned short Xx, unsigned short Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx, - int Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - unsigned int Xx, unsigned int Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, long long Ax, - long long Xx, long long Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - unsigned long long Xx, unsigned long long Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx, - float Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx, - double Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, long double Ax, - long double Xx, long double Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx, npy_cfloat_wrapper Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx, npy_cdouble_wrapper Yx) - csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) """ - return _csr.csr_matvec(*args) + csr_matvec(int n_row, int n_col, int Ap, int Aj, signed char Ax, + signed char Xx, signed char Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + unsigned char Xx, unsigned char Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, short Ax, short Xx, + short Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + unsigned short Xx, unsigned short Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, int Ax, int Xx, + int Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + unsigned int Xx, unsigned int Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, long long Ax, + long long Xx, long long Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + unsigned long long Xx, unsigned long long Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, float Ax, float Xx, + float Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, double Ax, double Xx, + double Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, long double Ax, + long double Xx, long double Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx, npy_cfloat_wrapper Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx, npy_cdouble_wrapper Yx) + csr_matvec(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx, npy_clongdouble_wrapper Yx) + """ + return _csr.csr_matvec(*args) def csr_matvecs(*args): - """ - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, signed char Ax, - signed char Xx, signed char Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned char Ax, - unsigned char Xx, unsigned char Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, short Ax, - short Xx, short Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned short Ax, - unsigned short Xx, unsigned short Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, int Ax, - int Xx, int Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned int Ax, - unsigned int Xx, unsigned int Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, long long Ax, - long long Xx, long long Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned long long Ax, - unsigned long long Xx, - unsigned long long Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, float Ax, - float Xx, float Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, double Ax, - double Xx, double Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, long double Ax, - long double Xx, long double Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_cfloat_wrapper Ax, - npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_cdouble_wrapper Ax, - npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_clongdouble_wrapper Ax, - npy_clongdouble_wrapper Xx, - npy_clongdouble_wrapper Yx) """ - return _csr.csr_matvecs(*args) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, signed char Ax, + signed char Xx, signed char Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned char Ax, + unsigned char Xx, unsigned char Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, short Ax, + short Xx, short Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned short Ax, + unsigned short Xx, unsigned short Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, int Ax, + int Xx, int Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned int Ax, + unsigned int Xx, unsigned int Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, long long Ax, + long long Xx, long long Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, unsigned long long Ax, + unsigned long long Xx, + unsigned long long Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, float Ax, + float Xx, float Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, double Ax, + double Xx, double Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, long double Ax, + long double Xx, long double Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_cfloat_wrapper Ax, + npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_cdouble_wrapper Ax, + npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + csr_matvecs(int n_row, int n_col, int n_vecs, int Ap, int Aj, npy_clongdouble_wrapper Ax, + npy_clongdouble_wrapper Xx, + npy_clongdouble_wrapper Yx) + """ + return _csr.csr_matvecs(*args) def csr_elmul_csr(*args): - """ - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx, int Cp, int Cj, - signed char Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx, int Cp, - int Cj, unsigned char Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bj, short Bx, int Cp, int Cj, short Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx, int Cp, - int Cj, unsigned short Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bj, int Bx, int Cp, int Cj, int Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx, int Cp, - int Cj, unsigned int Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bj, long long Bx, int Cp, int Cj, - long long Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bj, float Bx, int Cp, int Cj, float Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bj, double Bx, int Cp, int Cj, double Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bj, long double Bx, int Cp, int Cj, - long double Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx, - int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _csr.csr_elmul_csr(*args) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx, int Cp, int Cj, + signed char Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx, int Cp, + int Cj, unsigned char Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bj, short Bx, int Cp, int Cj, short Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx, int Cp, + int Cj, unsigned short Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bj, int Bx, int Cp, int Cj, int Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx, int Cp, + int Cj, unsigned int Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bj, long long Bx, int Cp, int Cj, + long long Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bj, float Bx, int Cp, int Cj, float Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bj, double Bx, int Cp, int Cj, double Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bj, long double Bx, int Cp, int Cj, + long double Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + csr_elmul_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx, + int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _csr.csr_elmul_csr(*args) def csr_eldiv_csr(*args): - """ - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx, int Cp, int Cj, - signed char Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx, int Cp, - int Cj, unsigned char Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bj, short Bx, int Cp, int Cj, short Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx, int Cp, - int Cj, unsigned short Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bj, int Bx, int Cp, int Cj, int Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx, int Cp, - int Cj, unsigned int Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bj, long long Bx, int Cp, int Cj, - long long Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bj, float Bx, int Cp, int Cj, float Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bj, double Bx, int Cp, int Cj, double Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bj, long double Bx, int Cp, int Cj, - long double Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx, - int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _csr.csr_eldiv_csr(*args) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx, int Cp, int Cj, + signed char Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx, int Cp, + int Cj, unsigned char Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bj, short Bx, int Cp, int Cj, short Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx, int Cp, + int Cj, unsigned short Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bj, int Bx, int Cp, int Cj, int Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx, int Cp, + int Cj, unsigned int Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bj, long long Bx, int Cp, int Cj, + long long Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bj, float Bx, int Cp, int Cj, float Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bj, double Bx, int Cp, int Cj, double Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bj, long double Bx, int Cp, int Cj, + long double Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + csr_eldiv_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx, + int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _csr.csr_eldiv_csr(*args) def csr_plus_csr(*args): - """ - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx, int Cp, int Cj, - signed char Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx, int Cp, - int Cj, unsigned char Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bj, short Bx, int Cp, int Cj, short Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx, int Cp, - int Cj, unsigned short Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bj, int Bx, int Cp, int Cj, int Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx, int Cp, - int Cj, unsigned int Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bj, long long Bx, int Cp, int Cj, - long long Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bj, float Bx, int Cp, int Cj, float Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bj, double Bx, int Cp, int Cj, double Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bj, long double Bx, int Cp, int Cj, - long double Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx, - int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _csr.csr_plus_csr(*args) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx, int Cp, int Cj, + signed char Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx, int Cp, + int Cj, unsigned char Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bj, short Bx, int Cp, int Cj, short Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx, int Cp, + int Cj, unsigned short Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bj, int Bx, int Cp, int Cj, int Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx, int Cp, + int Cj, unsigned int Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bj, long long Bx, int Cp, int Cj, + long long Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bj, float Bx, int Cp, int Cj, float Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bj, double Bx, int Cp, int Cj, double Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bj, long double Bx, int Cp, int Cj, + long double Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + csr_plus_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx, + int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _csr.csr_plus_csr(*args) def csr_minus_csr(*args): - """ - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int Bp, int Bj, signed char Bx, int Cp, int Cj, - signed char Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int Bp, int Bj, unsigned char Bx, int Cp, - int Cj, unsigned char Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, - int Bj, short Bx, int Cp, int Cj, short Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int Bp, int Bj, unsigned short Bx, int Cp, - int Cj, unsigned short Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, - int Bj, int Bx, int Cp, int Cj, int Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int Bp, int Bj, unsigned int Bx, int Cp, - int Cj, unsigned int Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, - int Bp, int Bj, long long Bx, int Cp, int Cj, - long long Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int Bp, int Bj, unsigned long long Bx, - int Cp, int Cj, unsigned long long Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, - int Bj, float Bx, int Cp, int Cj, float Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, - int Bj, double Bx, int Cp, int Cj, double Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, - int Bp, int Bj, long double Bx, int Cp, int Cj, - long double Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int Bp, int Bj, npy_cfloat_wrapper Bx, - int Cp, int Cj, npy_cfloat_wrapper Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int Bp, int Bj, npy_cdouble_wrapper Bx, - int Cp, int Cj, npy_cdouble_wrapper Cx) - csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int Bp, int Bj, npy_clongdouble_wrapper Bx, - int Cp, int Cj, npy_clongdouble_wrapper Cx) """ - return _csr.csr_minus_csr(*args) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int Bp, int Bj, signed char Bx, int Cp, int Cj, + signed char Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int Bp, int Bj, unsigned char Bx, int Cp, + int Cj, unsigned char Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, short Ax, int Bp, + int Bj, short Bx, int Cp, int Cj, short Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int Bp, int Bj, unsigned short Bx, int Cp, + int Cj, unsigned short Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, int Ax, int Bp, + int Bj, int Bx, int Cp, int Cj, int Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int Bp, int Bj, unsigned int Bx, int Cp, + int Cj, unsigned int Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, long long Ax, + int Bp, int Bj, long long Bx, int Cp, int Cj, + long long Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int Bp, int Bj, unsigned long long Bx, + int Cp, int Cj, unsigned long long Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, float Ax, int Bp, + int Bj, float Bx, int Cp, int Cj, float Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, double Ax, int Bp, + int Bj, double Bx, int Cp, int Cj, double Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, long double Ax, + int Bp, int Bj, long double Bx, int Cp, int Cj, + long double Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int Bp, int Bj, npy_cfloat_wrapper Bx, + int Cp, int Cj, npy_cfloat_wrapper Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int Bp, int Bj, npy_cdouble_wrapper Bx, + int Cp, int Cj, npy_cdouble_wrapper Cx) + csr_minus_csr(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int Bp, int Bj, npy_clongdouble_wrapper Bx, + int Cp, int Cj, npy_clongdouble_wrapper Cx) + """ + return _csr.csr_minus_csr(*args) def csr_sort_indices(*args): - """ - csr_sort_indices(int n_row, int Ap, int Aj, signed char Ax) - csr_sort_indices(int n_row, int Ap, int Aj, unsigned char Ax) - csr_sort_indices(int n_row, int Ap, int Aj, short Ax) - csr_sort_indices(int n_row, int Ap, int Aj, unsigned short Ax) - csr_sort_indices(int n_row, int Ap, int Aj, int Ax) - csr_sort_indices(int n_row, int Ap, int Aj, unsigned int Ax) - csr_sort_indices(int n_row, int Ap, int Aj, long long Ax) - csr_sort_indices(int n_row, int Ap, int Aj, unsigned long long Ax) - csr_sort_indices(int n_row, int Ap, int Aj, float Ax) - csr_sort_indices(int n_row, int Ap, int Aj, double Ax) - csr_sort_indices(int n_row, int Ap, int Aj, long double Ax) - csr_sort_indices(int n_row, int Ap, int Aj, npy_cfloat_wrapper Ax) - csr_sort_indices(int n_row, int Ap, int Aj, npy_cdouble_wrapper Ax) - csr_sort_indices(int n_row, int Ap, int Aj, npy_clongdouble_wrapper Ax) """ - return _csr.csr_sort_indices(*args) + csr_sort_indices(int n_row, int Ap, int Aj, signed char Ax) + csr_sort_indices(int n_row, int Ap, int Aj, unsigned char Ax) + csr_sort_indices(int n_row, int Ap, int Aj, short Ax) + csr_sort_indices(int n_row, int Ap, int Aj, unsigned short Ax) + csr_sort_indices(int n_row, int Ap, int Aj, int Ax) + csr_sort_indices(int n_row, int Ap, int Aj, unsigned int Ax) + csr_sort_indices(int n_row, int Ap, int Aj, long long Ax) + csr_sort_indices(int n_row, int Ap, int Aj, unsigned long long Ax) + csr_sort_indices(int n_row, int Ap, int Aj, float Ax) + csr_sort_indices(int n_row, int Ap, int Aj, double Ax) + csr_sort_indices(int n_row, int Ap, int Aj, long double Ax) + csr_sort_indices(int n_row, int Ap, int Aj, npy_cfloat_wrapper Ax) + csr_sort_indices(int n_row, int Ap, int Aj, npy_cdouble_wrapper Ax) + csr_sort_indices(int n_row, int Ap, int Aj, npy_clongdouble_wrapper Ax) + """ + return _csr.csr_sort_indices(*args) def csr_eliminate_zeros(*args): - """ - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, signed char Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned char Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, short Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned short Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, int Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned int Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, long long Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, float Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, double Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, long double Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax) - csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax) """ - return _csr.csr_eliminate_zeros(*args) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, signed char Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned char Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, short Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned short Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, int Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned int Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, long long Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, float Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, double Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, long double Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax) + csr_eliminate_zeros(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax) + """ + return _csr.csr_eliminate_zeros(*args) def csr_sum_duplicates(*args): - """ - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, signed char Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned char Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, short Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned short Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, int Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned int Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, long long Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, float Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, double Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, long double Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax) - csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax) """ - return _csr.csr_sum_duplicates(*args) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, signed char Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned char Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, short Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned short Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, int Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned int Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, long long Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, float Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, double Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, long double Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax) + csr_sum_duplicates(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax) + """ + return _csr.csr_sum_duplicates(*args) def get_csr_submatrix(*args): - """ - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, signed char Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(signed char)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(unsigned char)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, short Ax, int ir0, - int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(short)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(unsigned short)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, int Ax, int ir0, - int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(int)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(unsigned int)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, long long Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(long long)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, - int ir0, int ir1, int ic0, int ic1, - std::vector<(int)> Bp, std::vector<(int)> Bj, - std::vector<(unsigned long long)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, float Ax, int ir0, - int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(float)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, double Ax, int ir0, - int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(double)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, long double Ax, - int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, - std::vector<(int)> Bj, std::vector<(long double)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, - int ir0, int ir1, int ic0, int ic1, - std::vector<(int)> Bp, std::vector<(int)> Bj, - std::vector<(npy_cfloat_wrapper)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, - int ir0, int ir1, int ic0, int ic1, - std::vector<(int)> Bp, std::vector<(int)> Bj, - std::vector<(npy_cdouble_wrapper)> Bx) - get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, - int ir0, int ir1, int ic0, int ic1, - std::vector<(int)> Bp, std::vector<(int)> Bj, - std::vector<(npy_clongdouble_wrapper)> Bx) """ - return _csr.get_csr_submatrix(*args) - + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, signed char Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(signed char)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned char Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(unsigned char)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, short Ax, int ir0, + int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(short)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned short Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(unsigned short)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, int Ax, int ir0, + int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(int)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned int Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(unsigned int)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, long long Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(long long)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, unsigned long long Ax, + int ir0, int ir1, int ic0, int ic1, + std::vector<(int)> Bp, std::vector<(int)> Bj, + std::vector<(unsigned long long)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, float Ax, int ir0, + int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(float)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, double Ax, int ir0, + int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(double)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, long double Ax, + int ir0, int ir1, int ic0, int ic1, std::vector<(int)> Bp, + std::vector<(int)> Bj, std::vector<(long double)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_cfloat_wrapper Ax, + int ir0, int ir1, int ic0, int ic1, + std::vector<(int)> Bp, std::vector<(int)> Bj, + std::vector<(npy_cfloat_wrapper)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_cdouble_wrapper Ax, + int ir0, int ir1, int ic0, int ic1, + std::vector<(int)> Bp, std::vector<(int)> Bj, + std::vector<(npy_cdouble_wrapper)> Bx) + get_csr_submatrix(int n_row, int n_col, int Ap, int Aj, npy_clongdouble_wrapper Ax, + int ir0, int ir1, int ic0, int ic1, + std::vector<(int)> Bp, std::vector<(int)> Bj, + std::vector<(npy_clongdouble_wrapper)> Bx) + """ + return _csr.get_csr_submatrix(*args) Modified: branches/refactor_fft/scipy/sparse/sparsetools/dia.py =================================================================== --- branches/refactor_fft/scipy/sparse/sparsetools/dia.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/sparsetools/dia.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -51,40 +51,39 @@ def dia_matvec(*args): - """ - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - signed char diags, signed char Xx, signed char Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - unsigned char diags, unsigned char Xx, unsigned char Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - short diags, short Xx, short Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - unsigned short diags, unsigned short Xx, - unsigned short Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - int diags, int Xx, int Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - unsigned int diags, unsigned int Xx, unsigned int Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - long long diags, long long Xx, long long Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - unsigned long long diags, unsigned long long Xx, - unsigned long long Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - float diags, float Xx, float Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - double diags, double Xx, double Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - long double diags, long double Xx, long double Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - npy_cfloat_wrapper diags, npy_cfloat_wrapper Xx, - npy_cfloat_wrapper Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - npy_cdouble_wrapper diags, npy_cdouble_wrapper Xx, - npy_cdouble_wrapper Yx) - dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, - npy_clongdouble_wrapper diags, npy_clongdouble_wrapper Xx, - npy_clongdouble_wrapper Yx) """ - return _dia.dia_matvec(*args) - + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + signed char diags, signed char Xx, signed char Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + unsigned char diags, unsigned char Xx, unsigned char Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + short diags, short Xx, short Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + unsigned short diags, unsigned short Xx, + unsigned short Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + int diags, int Xx, int Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + unsigned int diags, unsigned int Xx, unsigned int Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + long long diags, long long Xx, long long Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + unsigned long long diags, unsigned long long Xx, + unsigned long long Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + float diags, float Xx, float Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + double diags, double Xx, double Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + long double diags, long double Xx, long double Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + npy_cfloat_wrapper diags, npy_cfloat_wrapper Xx, + npy_cfloat_wrapper Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + npy_cdouble_wrapper diags, npy_cdouble_wrapper Xx, + npy_cdouble_wrapper Yx) + dia_matvec(int n_row, int n_col, int n_diags, int L, int offsets, + npy_clongdouble_wrapper diags, npy_clongdouble_wrapper Xx, + npy_clongdouble_wrapper Yx) + """ + return _dia.dia_matvec(*args) Modified: branches/refactor_fft/scipy/sparse/sputils.py =================================================================== --- branches/refactor_fft/scipy/sparse/sputils.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/sputils.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -100,7 +100,7 @@ # Assume it's a tuple of matrix dimensions (M, N) (M, N) = x assert isintlike(M) and isintlike(N) # raises TypeError unless integers - assert M > 0 and N > 0 + #assert M > 0 and N > 0 except (ValueError, TypeError, AssertionError): return False else: Modified: branches/refactor_fft/scipy/sparse/tests/test_base.py =================================================================== --- branches/refactor_fft/scipy/sparse/tests/test_base.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/tests/test_base.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -34,7 +34,6 @@ warnings.simplefilter('ignore',SparseEfficiencyWarning) -#TODO check that invalid shape in constructor raises exception #TODO check that spmatrix( ... , copy=X ) is respected #TODO test prune #TODO test has_sorted_indices @@ -45,13 +44,24 @@ self.dat = matrix([[1,0,0,2],[3,0,1,0],[0,2,0,0]],'d') self.datsp = self.spmatrix(self.dat) + def test_empty(self): + """create empty matrices""" + + assert_equal(self.spmatrix((3,3)).todense(), np.zeros((3,3))) + assert_equal(self.spmatrix((3,3)).nnz, 0) + + def test_invalid_shapes(self): + assert_raises(ValueError, self.spmatrix, (-1,3) ) + assert_raises(ValueError, self.spmatrix, (3,-1) ) + assert_raises(ValueError, self.spmatrix, (-1,-1) ) + def test_repr(self): repr(self.datsp) def test_str(self): str(self.datsp) - def test_empty(self): + def test_empty_arithmetic(self): """Test manipulating empty matrices. Fails in SciPy SVN <= r1768 """ shape = (5, 5) @@ -304,17 +314,11 @@ assert(isinstance( M * array([1,2,3]), ndarray)) assert(isinstance( M * matrix([1,2,3]).T, matrix)) - #ensure exception is raised for improper dimensions bad_vecs = [array([1,2]), array([1,2,3,4]), array([[1],[2]]), matrix([1,2,3]), matrix([[1],[2]])] - caught = 0 for x in bad_vecs: - try: - y = M * x - except ValueError: - caught += 1 - assert_equal(caught,len(bad_vecs)) + assert_raises(ValueError, M.__mul__, x) # Should this be supported or not?! #flat = array([1,2,3]) @@ -351,21 +355,18 @@ csp = bsp.tocsc() c = b assert_array_almost_equal((asp*csp).todense(), a*c) - assert_array_almost_equal((asp.matmat(csp)).todense(), a*c) assert_array_almost_equal( asp*c, a*c) assert_array_almost_equal( a*csp, a*c) assert_array_almost_equal( a2*csp, a*c) csp = bsp.tocsr() assert_array_almost_equal((asp*csp).todense(), a*c) - assert_array_almost_equal((asp.matmat(csp)).todense(), a*c) assert_array_almost_equal( asp*c, a*c) assert_array_almost_equal( a*csp, a*c) assert_array_almost_equal( a2*csp, a*c) csp = bsp.tocoo() assert_array_almost_equal((asp*csp).todense(), a*c) - assert_array_almost_equal((asp.matmat(csp)).todense(), a*c) assert_array_almost_equal( asp*c, a*c) assert_array_almost_equal( a*csp, a*c) @@ -522,47 +523,6 @@ assert_array_equal(self.dat/17.3,a.todense()) -class _TestMatvecOutput: - """test using the matvec() output parameter""" - def test_matvec_output(self): - pass #Currently disabled - -# #flat array -# x = array([1.25, -6.5, 0.125, -3.75],dtype='d') -# y = zeros(3,dtype='d') -# -# self.datsp.matvec(x,y) -# assert_array_equal(self.datsp*x,y) -# -# #column vector -# x = array([1.25, -6.5, 0.125, -3.75],dtype='d') -# x = x.reshape(4,1) -# y = zeros((3,1),dtype='d') -# -# self.datsp.matvec(x,y) -# assert_array_equal(self.datsp*x,y) -# -# # improper output type -# x = array([1.25, -6.5, 0.125, -3.75],dtype='d') -# y = zeros(3,dtype='i') -# -# self.assertRaises( ValueError, self.datsp.matvec, x, y ) -# -# # improper output shape -# x = array([1.25, -6.5, 0.125, -3.75],dtype='d') -# y = zeros(2,dtype='d') -# -# self.assertRaises( ValueError, self.datsp.matvec, x, y ) -# -# # proper upcast output type -# x = array([1.25, -6.5, 0.125, -3.75],dtype='complex64') -# x.imag = [1,2,3,4] -# y = zeros(3,dtype='complex128') -# -# self.datsp.matvec(x,y) -# assert_array_equal(self.datsp*x,y) -# assert_equal((self.datsp*x).dtype,y.dtype) - class _TestGetSet: def test_setelement(self): a = self.spmatrix((3,4)) @@ -889,7 +849,7 @@ class TestCSR(_TestCommon, _TestGetSet, _TestSolve, - _TestInplaceArithmetic, _TestArithmetic, _TestMatvecOutput, + _TestInplaceArithmetic, _TestArithmetic, _TestHorizSlicing, _TestVertSlicing, _TestBothSlicing, _TestFancyIndexing, TestCase): spmatrix = csr_matrix @@ -986,7 +946,7 @@ class TestCSC(_TestCommon, _TestGetSet, _TestSolve, - _TestInplaceArithmetic, _TestArithmetic, _TestMatvecOutput, + _TestInplaceArithmetic, _TestArithmetic, _TestHorizSlicing, _TestVertSlicing, _TestBothSlicing, _TestFancyIndexing, TestCase): spmatrix = csc_matrix @@ -1304,10 +1264,10 @@ assert_array_equal(C.A, D.A) def test_fancy_indexing(self): - M = arange(25).reshape(5,5) + M = arange(25).reshape(5,5) A = lil_matrix( M ) - assert_equal(A[array([1,2,3]),2:3].todense(), M[array([1,2,3]),2:3]) + assert_equal(A[array([1,2,3]),2:3].todense(), M[array([1,2,3]),2:3]) def test_point_wise_multiply(self): l = lil_matrix((4,3)) @@ -1371,8 +1331,8 @@ def test_constructor4(self): """from dense matrix""" mat = array([[0,1,0,0], - [7,0,3,0], - [0,4,0,0]]) + [7,0,3,0], + [0,4,0,0]]) coo = coo_matrix(mat) assert_array_equal(coo.todense(),mat) @@ -1386,12 +1346,17 @@ spmatrix = dia_matrix def test_constructor1(self): - pass - #TODO add test + D = matrix([[1, 0, 3, 0], + [1, 2, 0, 4], + [0, 2, 3, 0], + [0, 0, 3, 4]]) + data = np.array([[1,2,3,4]]).repeat(3,axis=0) + offsets = np.array([0,-1,2]) + assert_equal(dia_matrix( (data,offsets), shape=(4,4)).todense(), D) -class TestBSR(_TestCommon, _TestArithmetic, _TestInplaceArithmetic, - _TestMatvecOutput, TestCase): + +class TestBSR(_TestCommon, _TestArithmetic, _TestInplaceArithmetic, TestCase): spmatrix = bsr_matrix def test_constructor1(self): Modified: branches/refactor_fft/scipy/sparse/tests/test_sputils.py =================================================================== --- branches/refactor_fft/scipy/sparse/tests/test_sputils.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/sparse/tests/test_sputils.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -47,10 +47,9 @@ assert_equal(isshape( (1,2) ),True) assert_equal(isshape( (5,2) ),True) - assert_equal(isshape( (-1,4) ),False) assert_equal(isshape( (1.5,2) ),False) - assert_equal(isshape( (0,4) ),False) assert_equal(isshape( (2,2,2) ),False) + assert_equal(isshape( ([2],2) ),False) def test_issequence(self): assert_equal(issequence( (1,) ),True) Modified: branches/refactor_fft/scipy/stats/distributions.py =================================================================== --- branches/refactor_fft/scipy/stats/distributions.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/stats/distributions.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -542,7 +542,7 @@ goodargs = argsreduce(cond, *((x,)+args)) place(output,cond,self._sf(*goodargs)) if output.ndim == 0: - return output[()] + return output[()] return output def ppf(self,q,*args,**kwds): @@ -573,7 +573,7 @@ scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2] place(output,cond,self._ppf(*goodargs)*scale + loc) if output.ndim == 0: - return output[()] + return output[()] return output def isf(self,q,*args,**kwds): @@ -3517,7 +3517,7 @@ goodargs = argsreduce(cond, *((k,)+args)) place(output,cond,self._pmf(*goodargs)) if output.ndim == 0: - return output[()] + return output[()] return output def cdf(self, k, *args, **kwds): @@ -3547,7 +3547,7 @@ goodargs = argsreduce(cond, *((k,)+args)) place(output,cond,self._cdf(*goodargs)) if output.ndim == 0: - return output[()] + return output[()] return output def sf(self,k,*args,**kwds): @@ -3577,7 +3577,7 @@ goodargs = argsreduce(cond, *((k,)+args)) place(output,cond,self._sf(*goodargs)) if output.ndim == 0: - return output[()] + return output[()] return output def ppf(self,q,*args,**kwds): @@ -3607,7 +3607,7 @@ loc, goodargs = goodargs[-1], goodargs[:-1] place(output,cond,self._ppf(*goodargs) + loc) if output.ndim == 0: - return output[()] + return output[()] return output def isf(self,q,*args,**kwds): @@ -3638,7 +3638,7 @@ loc, goodargs = goodargs[-1], goodargs[:-1] place(output,cond,self._ppf(*goodargs) + loc) if output.ndim == 0: - return output[()] + return output[()] return output def stats(self, *args, **kwds): Modified: branches/refactor_fft/scipy/stats/stats.py =================================================================== --- branches/refactor_fft/scipy/stats/stats.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/stats/stats.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -489,10 +489,7 @@ - numpy.median has a ddof argument to replace bias in a more general manner. scipy.stats.median(a, bias=True) can be replaced by numpy.median(x, axis=0, ddof=1).""", DeprecationWarning) - a, axis = _chk_asarray(a, axis) - if axis != 0: - a = np.rollaxis(a, axis, 0) - return np.median(a) + return np.median(a, axis) def mode(a, axis=0): """Returns an array of the modal (most common) value in the passed array. Modified: branches/refactor_fft/scipy/stats/tests/test_stats.py =================================================================== --- branches/refactor_fft/scipy/stats/tests/test_stats.py 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/stats/tests/test_stats.py 2008-10-26 11:02:22 UTC (rev 4846) @@ -625,15 +625,6 @@ A += val assert_almost_equal(stats.mean(a,axis=None),A/(5*3.0*5)) -class TestMedian(TestCase): - def test_basic(self): - a1 = [3,4,5,10,-3,-5,6] - a2 = [3,-6,-2,8,7,4,2,1] - a3 = [3.,4,5,10,-3,-5,-6,7.0] - assert_equal(stats.median(a1),4) - assert_equal(stats.median(a2),2.5) - assert_equal(stats.median(a3),3.5) - class TestPercentile(TestCase): def setUp(self): self.a1 = [3,4,5,10,-3,-5,6] @@ -694,6 +685,20 @@ assert_almost_equal(stats.median(data1),2.5) assert_almost_equal(stats.median(data2),5) + def test_basic2(self): + a1 = [3,4,5,10,-3,-5,6] + a2 = [3,-6,-2,8,7,4,2,1] + a3 = [3.,4,5,10,-3,-5,-6,7.0] + assert_equal(stats.median(a1),4) + assert_equal(stats.median(a2),2.5) + assert_equal(stats.median(a3),3.5) + + def test_axis(self): + """Regression test for #760.""" + a1 = np.array([[3,4,5], [10,-3,-5]]) + assert_equal(stats.median(a1), np.array([6.5, 0.5, 0.])) + assert_equal(stats.median(a1, axis=-1), np.array([4., -3])) + class TestMode(TestCase): def test_basic(self): data1 = [3,5,1,10,23,3,2,6,8,6,10,6] From scipy-svn at scipy.org Sun Oct 26 13:51:39 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 12:51:39 -0500 (CDT) Subject: [Scipy-svn] r4847 - / Message-ID: <20081026175139.40E4A39C05F@scipy.org> Author: ptvirtan Date: 2008-10-26 12:51:28 -0500 (Sun, 26 Oct 2008) New Revision: 4847 Added: scipy-docs/ Log: Add location for Scipy documentation From scipy-svn at scipy.org Sun Oct 26 13:52:01 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 12:52:01 -0500 (CDT) Subject: [Scipy-svn] r4848 - scipy-docs Message-ID: <20081026175201.58D9A39C05F@scipy.org> Author: ptvirtan Date: 2008-10-26 12:51:47 -0500 (Sun, 26 Oct 2008) New Revision: 4848 Added: scipy-docs/branches/ scipy-docs/tags/ scipy-docs/trunk/ Log: Add location for Scipy documentation From scipy-svn at scipy.org Sun Oct 26 13:55:07 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 12:55:07 -0500 (CDT) Subject: [Scipy-svn] r4849 - in scipy-docs/trunk: . source source/_static source/_templates Message-ID: <20081026175507.7A18C39C05F@scipy.org> Author: ptvirtan Date: 2008-10-26 12:54:24 -0500 (Sun, 26 Oct 2008) New Revision: 4849 Added: scipy-docs/trunk/Makefile scipy-docs/trunk/README.txt scipy-docs/trunk/postprocess.py scipy-docs/trunk/source/ scipy-docs/trunk/source/_static/ scipy-docs/trunk/source/_static/scipy.css scipy-docs/trunk/source/_static/scipyshiny_small.png scipy-docs/trunk/source/_templates/ scipy-docs/trunk/source/_templates/indexsidebar.html scipy-docs/trunk/source/cluster.distance.rst scipy-docs/trunk/source/cluster.hierarchy.rst scipy-docs/trunk/source/cluster.rst scipy-docs/trunk/source/cluster.vq.rst scipy-docs/trunk/source/conf.py scipy-docs/trunk/source/constants.rst scipy-docs/trunk/source/fftpack.rst scipy-docs/trunk/source/generated/ scipy-docs/trunk/source/index.rst scipy-docs/trunk/source/integrate.rst scipy-docs/trunk/source/interpolate.rst scipy-docs/trunk/source/io.rst scipy-docs/trunk/source/linalg.rst scipy-docs/trunk/source/maxentropy.rst scipy-docs/trunk/source/misc.rst scipy-docs/trunk/source/ndimage.rst scipy-docs/trunk/source/odr.rst scipy-docs/trunk/source/optimize.rst scipy-docs/trunk/source/signal.rst scipy-docs/trunk/source/sparse.linalg.rst scipy-docs/trunk/source/sparse.rst scipy-docs/trunk/source/special.rst scipy-docs/trunk/source/stats.rst scipy-docs/trunk/source/weave.rst Log: Import initial version of Scipy documentation Added: scipy-docs/trunk/Makefile =================================================================== --- scipy-docs/trunk/Makefile 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/Makefile 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,100 @@ +# Makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = LANG=C sphinx-build +PAPER = + +# Internal variables. +PAPEROPT_a4 = -D latex_paper_size=a4 +PAPEROPT_letter = -D latex_paper_size=letter +ALLSPHINXOPTS = -d build/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source + +.PHONY: help clean html web pickle htmlhelp latex changes linkcheck + +help: + @echo "Please use \`make ' where is one of" + @echo " dist to make a distribution-ready tree" + @echo " html to make standalone HTML files" + @echo " pickle to make pickle files (usable by e.g. sphinx-web)" + @echo " htmlhelp to make HTML files and a HTML help project" + @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" + @echo " changes to make an overview over all changed/added/deprecated items" + @echo " linkcheck to check all external links for integrity" + +clean: + -rm -rf build/* source/generated + +dist: html + test -d build/latex || make latex + -make -C build/latex all-pdf + -rm -rf build/dist + mkdir -p build/dist + cp -r build/html build/dist/reference + touch build/dist/index.html + perl -pi -e 's#^\s*(
  • SciPy.*?Reference Guide.*?»
    • )$$#
  • Numpy and Scipy Documentation »
    • $$1#;' build/dist/*.html build/dist/*/*.html build/dist/*/*/*.html + (cd build/html && zip -9qr ../dist/scipy-html.zip .) + cp build/latex/*.pdf build/dist + cd build/dist && tar czf ../dist.tar.gz * + +generate: build/generate-stamp +build/generate-stamp: $(wildcard source/*.rst) ext + mkdir -p build + ./ext/autosummary_generate.py source/*.rst \ + -p dump.xml -o source/generated + touch build/generate-stamp + +ext: + svn co http://sphinx.googlecode.com/svn/contrib/trunk/numpyext ext + +html: generate + mkdir -p build/html build/doctrees + $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) build/html + python postprocess.py html build/html/*.html + @echo + @echo "Build finished. The HTML pages are in build/html." + +pickle: generate + mkdir -p build/pickle build/doctrees + $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) build/pickle + @echo + @echo "Build finished; now you can process the pickle files or run" + @echo " sphinx-web build/pickle" + @echo "to start the sphinx-web server." + +web: pickle + +htmlhelp: generate + mkdir -p build/htmlhelp build/doctrees + $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) build/htmlhelp + @echo + @echo "Build finished; now you can run HTML Help Workshop with the" \ + ".hhp project file in build/htmlhelp." + +latex: generate + mkdir -p build/latex build/doctrees + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) build/latex + python postprocess.py tex build/latex/*.tex + @echo + @echo "Build finished; the LaTeX files are in build/latex." + @echo "Run \`make all-pdf' or \`make all-ps' in that directory to" \ + "run these through (pdf)latex." + +coverage: build + mkdir -p build/coverage build/doctrees + $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) build/coverage + @echo "Coverage finished; see c.txt and python.txt in build/coverage" + +changes: generate + mkdir -p build/changes build/doctrees + $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) build/changes + @echo + @echo "The overview file is in build/changes." + +linkcheck: generate + mkdir -p build/linkcheck build/doctrees + $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) build/linkcheck + @echo + @echo "Link check complete; look for any errors in the above output " \ + "or in build/linkcheck/output.txt." Property changes on: scipy-docs/trunk/Makefile ___________________________________________________________________ Name: svn:eol-style + native Added: scipy-docs/trunk/README.txt =================================================================== --- scipy-docs/trunk/README.txt 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/README.txt 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,12 @@ +SciPy Reference Guide +===================== + +Instructions +------------ +1. Optionally download an XML dump of the newest docstrings from the doc wiki + at ``/pydocweb/dump`` and save it as ``dump.xml``. +2. Run ``make html`` or ``make dist`` + +You can also run ``summarize.py`` to see which parts of the Numpy +namespace are documented. + Property changes on: scipy-docs/trunk/README.txt ___________________________________________________________________ Name: svn:eol-style + native Added: scipy-docs/trunk/postprocess.py =================================================================== --- scipy-docs/trunk/postprocess.py 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/postprocess.py 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,54 @@ +#!/usr/bin/env python +""" +%prog MODE FILES... + +Post-processes HTML and Latex files output by Sphinx. +MODE is either 'html' or 'tex'. + +""" +import re, optparse + +def main(): + p = optparse.OptionParser(__doc__) + options, args = p.parse_args() + + if len(args) < 1: + p.error('no mode given') + + mode = args.pop(0) + + if mode not in ('html', 'tex'): + p.error('unknown mode %s' % mode) + + for fn in args: + f = open(fn, 'r') + try: + if mode == 'html': + lines = process_html(fn, f.readlines()) + elif mode == 'tex': + lines = process_tex(f.readlines()) + finally: + f.close() + + f = open(fn, 'w') + f.write("".join(lines)) + f.close() + +def process_html(fn, lines): + return lines + +def process_tex(lines): + """ + Remove unnecessary section titles from the LaTeX file. + + """ + new_lines = [] + for line in lines: + if re.match(r'^\\(section|subsection|subsubsection|paragraph|subparagraph){(numpy|scipy)\.', line): + pass # skip! + else: + new_lines.append(line) + return new_lines + +if __name__ == "__main__": + main() Property changes on: scipy-docs/trunk/postprocess.py ___________________________________________________________________ Name: svn:executable + Added: scipy-docs/trunk/source/_static/scipy.css =================================================================== --- scipy-docs/trunk/source/_static/scipy.css 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/_static/scipy.css 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,156 @@ + at import "default.css"; + +/** + * Spacing fixes + */ + +div.body p, div.body dd, div.body li { + line-height: 125%; +} + +ul.simple { + margin-top: 0; + margin-bottom: 0; + padding-top: 0; + padding-bottom: 0; +} + +/* spacing around blockquoted fields in parameters/attributes/returns */ +td.field-body > blockquote { + margin-top: 0.1em; + margin-bottom: 0.5em; +} + +/* spacing around example code */ +div.highlight > pre { + padding: 2px 5px 2px 5px; +} + +/* spacing in see also definition lists */ +dl.last > dd { + margin-top: 1px; + margin-bottom: 5px; + margin-left: 30px; +} + +/** + * Hide dummy toctrees + */ + +ul { + padding-top: 0; + padding-bottom: 0; + margin-top: 0; + margin-bottom: 0; +} +ul li { + padding-top: 0; + padding-bottom: 0; + margin-top: 0; + margin-bottom: 0; +} +ul li a.reference { + padding-top: 0; + padding-bottom: 0; + margin-top: 0; + margin-bottom: 0; +} + +/** + * Make high-level subsections easier to distinguish from top-level ones + */ +div.body h3 { + background-color: transparent; +} + +div.body h4 { + border: none; + background-color: transparent; +} + +/** + * Scipy colors + */ + +body { + background-color: rgb(100,135,220); +} + +div.document { + background-color: rgb(230,230,230); +} + +div.sphinxsidebar { + background-color: rgb(230,230,230); + overflow: hidden; +} + +div.related { + background-color: rgb(100,135,220); +} + +div.sphinxsidebar h3 { + color: rgb(0,102,204); +} + +div.sphinxsidebar h3 a { + color: rgb(0,102,204); +} + +div.sphinxsidebar h4 { + color: rgb(0,82,194); +} + +div.sphinxsidebar p { + color: black; +} + +div.sphinxsidebar a { + color: #355f7c; +} + +div.sphinxsidebar ul.want-points { + list-style: disc; +} + +.field-list th { + color: rgb(0,102,204); +} + +/** + * Extra admonitions + */ + +div.tip { + background-color: #ffffe4; + border: 1px solid #ee6; +} + +/* +div.admonition-example { + background-color: #e4ffe4; + border: 1px solid #ccc; +}*/ + + +/** + * Styling for field lists + */ + +table.field-list th { + border-left: 1px solid #aaa !important; + padding-left: 5px; +} + +table.field-list { + border-collapse: separate; + border-spacing: 10px; +} + +/** + * Styling for footnotes + */ + +table.footnote td, table.footnote th { + border: none; +} Added: scipy-docs/trunk/source/_static/scipyshiny_small.png =================================================================== (Binary files differ) Property changes on: scipy-docs/trunk/source/_static/scipyshiny_small.png ___________________________________________________________________ Name: svn:mime-type + image/png Added: scipy-docs/trunk/source/_templates/indexsidebar.html =================================================================== --- scipy-docs/trunk/source/_templates/indexsidebar.html 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/_templates/indexsidebar.html 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,5 @@ +

    Resources

    + Added: scipy-docs/trunk/source/cluster.distance.rst =================================================================== --- scipy-docs/trunk/source/cluster.distance.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/cluster.distance.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,6 @@ +===================================================== +Distance computations (:mod:`scipy.cluster.distance`) +===================================================== + +.. automodule:: scipy.cluster.distance + :members: Added: scipy-docs/trunk/source/cluster.hierarchy.rst =================================================================== --- scipy-docs/trunk/source/cluster.hierarchy.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/cluster.hierarchy.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,6 @@ +======================================================== +Hierarchical clustering (:mod:`scipy.cluster.hierarchy`) +======================================================== + +.. automodule:: scipy.cluster.hierarchy + :members: Added: scipy-docs/trunk/source/cluster.rst =================================================================== --- scipy-docs/trunk/source/cluster.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/cluster.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,11 @@ +========================================= +Clustering package (:mod:`scipy.cluster`) +========================================= + +.. toctree:: + + cluster.distance + cluster.hierarchy + cluster.vq + +.. automodule:: scipy.cluster Added: scipy-docs/trunk/source/cluster.vq.rst =================================================================== --- scipy-docs/trunk/source/cluster.vq.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/cluster.vq.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,6 @@ +==================================================================== +K-means clustering and vector quantization (:mod:`scipy.cluster.vq`) +==================================================================== + +.. automodule:: scipy.cluster.vq + :members: Added: scipy-docs/trunk/source/conf.py =================================================================== --- scipy-docs/trunk/source/conf.py 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/conf.py 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,233 @@ +# -*- coding: utf-8 -*- + +import sys, os + +# If your extensions are in another directory, add it here. If the directory +# is relative to the documentation root, use os.path.abspath to make it +# absolute, like shown here. +sys.path.append(os.path.abspath('../ext')) + +# Check Sphinx version +import sphinx +if sphinx.__version__ < "0.5": + raise RuntimeError("Sphinx 0.5.dev or newer required") + + +# ----------------------------------------------------------------------------- +# General configuration +# ----------------------------------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be extensions +# coming with Sphinx (named 'sphinx.ext.*') or your custom ones. +extensions = ['sphinx.ext.autodoc', 'sphinx.ext.pngmath', 'numpydoc', + 'phantom_import', 'autosummary', 'sphinx.ext.intersphinx', + 'sphinx.ext.coverage'] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix of source filenames. +source_suffix = '.rst' + +# The master toctree document. +master_doc = 'index' + +# General substitutions. +project = 'SciPy' +copyright = '2008, The Scipy community' + +# The default replacements for |version| and |release|, also used in various +# other places throughout the built documents. +# +# The short X.Y version. +version = '0.7' +# The full version, including alpha/beta/rc tags. +release = '0.7.dev' + +# There are two options for replacing |today|: either, you set today to some +# non-false value, then it is used: +#today = '' +# Else, today_fmt is used as the format for a strftime call. +today_fmt = '%B %d, %Y' + +# List of documents that shouldn't be included in the build. +#unused_docs = [] + +# The reST default role (used for this markup: `text`) to use for all documents. +default_role = "autolink" + +# List of directories, relative to source directories, that shouldn't be searched +# for source files. +exclude_dirs = [] + +# If true, '()' will be appended to :func: etc. cross-reference text. +add_function_parentheses = False + +# If true, the current module name will be prepended to all description +# unit titles (such as .. function::). +#add_module_names = True + +# If true, sectionauthor and moduleauthor directives will be shown in the +# output. They are ignored by default. +#show_authors = False + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + + +# ----------------------------------------------------------------------------- +# HTML output +# ----------------------------------------------------------------------------- + +# The style sheet to use for HTML and HTML Help pages. A file of that name +# must exist either in Sphinx' static/ path, or in one of the custom paths +# given in html_static_path. +html_style = 'scipy.css' + +# The name for this set of Sphinx documents. If None, it defaults to +# " v documentation". +html_title = "%s v%s Reference Guide (DRAFT)" % (project, version) + +# The name of an image file (within the static path) to place at the top of +# the sidebar. +html_logo = '_static/scipyshiny_small.png' + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, +# using the given strftime format. +html_last_updated_fmt = '%b %d, %Y' + +# Correct index page +#html_index = "index" + +# If true, SmartyPants will be used to convert quotes and dashes to +# typographically correct entities. +#html_use_smartypants = True + +# Custom sidebar templates, maps document names to template names. +html_sidebars = { + 'index': 'indexsidebar.html' +} + +# Additional templates that should be rendered to pages, maps page names to +# template names. +html_additional_pages = {} + +# If false, no module index is generated. +html_use_modindex = True + +# If true, the reST sources are included in the HTML build as _sources/. +#html_copy_source = True + +# If true, an OpenSearch description file will be output, and all pages will +# contain a tag referring to it. The value of this option must be the +# base URL from which the finished HTML is served. +#html_use_opensearch = '' + +# If nonempty, this is the file name suffix for HTML files (e.g. ".html"). +html_file_suffix = '.html' + +# Output file base name for HTML help builder. +htmlhelp_basename = 'NumPydoc' + +# Pngmath should try to align formulas properly +pngmath_use_preview = True + + +# ----------------------------------------------------------------------------- +# LaTeX output +# ----------------------------------------------------------------------------- + +# The paper size ('letter' or 'a4'). +#latex_paper_size = 'letter' + +# The font size ('10pt', '11pt' or '12pt'). +#latex_font_size = '10pt' + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, author, document class [howto/manual]). +_stdauthor = 'Written by the SciPy community' +latex_documents = [ + ('index', 'scipy-ref.tex', 'SciPy Reference Guide', + _stdauthor, 'manual'), +# ('user/index', 'scipy-user.tex', 'SciPy User Guide', +# _stdauthor, 'manual'), +] + +# The name of an image file (relative to this directory) to place at the top of +# the title page. +#latex_logo = None + +# For "manual" documents, if this is true, then toplevel headings are parts, +# not chapters. +#latex_use_parts = False + +# Additional stuff for the LaTeX preamble. +latex_preamble = r''' +\usepackage{amsmath} + +% In the parameters section, place a newline after the Parameters +% header +\usepackage{expdlist} +\let\latexdescription=\description +\def\description{\latexdescription{}{} \breaklabel} + +% Make Examples/etc section headers smaller and more compact +\makeatletter +\titleformat{\paragraph}{\normalsize\py at HeaderFamily}% + {\py at TitleColor}{0em}{\py at TitleColor}{\py at NormalColor} +\titlespacing*{\paragraph}{0pt}{1ex}{0pt} +\makeatother + +% Fix footer/header +\renewcommand{\chaptermark}[1]{\markboth{\MakeUppercase{\thechapter.\ #1}}{}} +\renewcommand{\sectionmark}[1]{\markright{\MakeUppercase{\thesection.\ #1}}} +''' + +# Documents to append as an appendix to all manuals. +#latex_appendices = [] + +# If false, no module index is generated. +latex_use_modindex = False + + +# ----------------------------------------------------------------------------- +# Intersphinx configuration +# ----------------------------------------------------------------------------- +intersphinx_mapping = { + 'http://docs.python.org/dev': None, + 'http://www.elisanet.fi/ptvirtan/tmp/numpy-refguide': None, +} + + +# ----------------------------------------------------------------------------- +# Numpy extensions +# ----------------------------------------------------------------------------- + +# If we want to do a phantom import from an XML file for all autodocs +phantom_import_file = 'dump.xml' + +# Edit links +#numpydoc_edit_link = '`Edit `__' + +# ----------------------------------------------------------------------------- +# Coverage checker +# ----------------------------------------------------------------------------- +coverage_ignore_modules = r""" + """.split() +coverage_ignore_functions = r""" + test($|_) (some|all)true bitwise_not cumproduct pkgload + generic\. + """.split() +coverage_ignore_classes = r""" + """.split() + +coverage_c_path = [] +coverage_c_regexes = {} +coverage_ignore_c_items = {} + + Added: scipy-docs/trunk/source/constants.rst =================================================================== --- scipy-docs/trunk/source/constants.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/constants.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,582 @@ +================================== +Constants (:mod:`scipy.constants`) +================================== + +.. module:: scipy.constants + +Physical and mathematical constants and units. + +Mathematical constants +====================== + +============ ================================================================= +``pi`` Pi +``golden`` Golden ratio +============ ================================================================= + +Physical constants +================== + +============= ================================================================= +``c`` speed of light in vacuum +``mu_0`` the magnetic constant :math:`\mu_0` +``epsilon_0`` the electric constant (vacuum permittivity), :math:`\epsilon_0` +``h`` the Planck constant :math:`h` +``hbar`` :math:`\hbar = h/(2\pi)` +``G`` Newtonian constant of gravitation +``g`` standard acceleration of gravity +``e`` elementary charge +``R`` molar gas constant +``alpha`` fine-structure constant +``N_A`` Avogadro constant +``k`` Boltzmann constant +``sigma`` Stefan-Boltzmann constant :math:`\sigma` +``Wien`` Wien displacement law constant +``Rydberg`` Rydberg constant +``m_e`` electron mass +``m_p`` proton mass +``m_n`` neutron mass +============= ================================================================= + + +Constants database +================== + +In addition to the above variables containing physical constants, +:mod:`scipy.constants` also contains a database of additional physical +constants. + +.. autosummary:: + :toctree: generated/ + + value + unit + precision + find + +.. data:: physical_constants + + Dictionary of physical constants, of the format + ``physical_constants[name] = (value, unit, uncertainty)``. + + Available constants: + + ====================================================================== ==== + ``alpha particle mass`` + ``alpha particle mass energy equivalent`` + ``alpha particle mass energy equivalent in MeV`` + ``alpha particle mass in u`` + ``alpha particle molar mass`` + ``alpha particle-electron mass ratio`` + ``alpha particle-proton mass ratio`` + ``Angstrom star`` + ``atomic mass constant`` + ``atomic mass constant energy equivalent`` + ``atomic mass constant energy equivalent in MeV`` + ``atomic mass unit-electron volt relationship`` + ``atomic mass unit-hartree relationship`` + ``atomic mass unit-hertz relationship`` + ``atomic mass unit-inverse meter relationship`` + ``atomic mass unit-joule relationship`` + ``atomic mass unit-kelvin relationship`` + ``atomic mass unit-kilogram relationship`` + ``atomic unit of 1st hyperpolarizablity`` + ``atomic unit of 2nd hyperpolarizablity`` + ``atomic unit of action`` + ``atomic unit of charge`` + ``atomic unit of charge density`` + ``atomic unit of current`` + ``atomic unit of electric dipole moment`` + ``atomic unit of electric field`` + ``atomic unit of electric field gradient`` + ``atomic unit of electric polarizablity`` + ``atomic unit of electric potential`` + ``atomic unit of electric quadrupole moment`` + ``atomic unit of energy`` + ``atomic unit of force`` + ``atomic unit of length`` + ``atomic unit of magnetic dipole moment`` + ``atomic unit of magnetic flux density`` + ``atomic unit of magnetizability`` + ``atomic unit of mass`` + ``atomic unit of momentum`` + ``atomic unit of permittivity`` + ``atomic unit of time`` + ``atomic unit of velocity`` + ``Avogadro constant`` + ``Bohr magneton`` + ``Bohr magneton in eV/T`` + ``Bohr magneton in Hz/T`` + ``Bohr magneton in inverse meters per tesla`` + ``Bohr magneton in K/T`` + ``Bohr radius`` + ``Boltzmann constant`` + ``Boltzmann constant in eV/K`` + ``Boltzmann constant in Hz/K`` + ``Boltzmann constant in inverse meters per kelvin`` + ``characteristic impedance of vacuum`` + ``classical electron radius`` + ``Compton wavelength`` + ``Compton wavelength over 2 pi`` + ``conductance quantum`` + ``conventional value of Josephson constant`` + ``conventional value of von Klitzing constant`` + ``Cu x unit`` + ``deuteron magnetic moment`` + ``deuteron magnetic moment to Bohr magneton ratio`` + ``deuteron magnetic moment to nuclear magneton ratio`` + ``deuteron mass`` + ``deuteron mass energy equivalent`` + ``deuteron mass energy equivalent in MeV`` + ``deuteron mass in u`` + ``deuteron molar mass`` + ``deuteron rms charge radius`` + ``deuteron-electron magnetic moment ratio`` + ``deuteron-electron mass ratio`` + ``deuteron-neutron magnetic moment ratio`` + ``deuteron-proton magnetic moment ratio`` + ``deuteron-proton mass ratio`` + ``electric constant`` + ``electron charge to mass quotient`` + ``electron g factor`` + ``electron gyromagnetic ratio`` + ``electron gyromagnetic ratio over 2 pi`` + ``electron magnetic moment`` + ``electron magnetic moment anomaly`` + ``electron magnetic moment to Bohr magneton ratio`` + ``electron magnetic moment to nuclear magneton ratio`` + ``electron mass`` + ``electron mass energy equivalent`` + ``electron mass energy equivalent in MeV`` + ``electron mass in u`` + ``electron molar mass`` + ``electron to alpha particle mass ratio`` + ``electron to shielded helion magnetic moment ratio`` + ``electron to shielded proton magnetic moment ratio`` + ``electron volt`` + ``electron volt-atomic mass unit relationship`` + ``electron volt-hartree relationship`` + ``electron volt-hertz relationship`` + ``electron volt-inverse meter relationship`` + ``electron volt-joule relationship`` + ``electron volt-kelvin relationship`` + ``electron volt-kilogram relationship`` + ``electron-deuteron magnetic moment ratio`` + ``electron-deuteron mass ratio`` + ``electron-muon magnetic moment ratio`` + ``electron-muon mass ratio`` + ``electron-neutron magnetic moment ratio`` + ``electron-neutron mass ratio`` + ``electron-proton magnetic moment ratio`` + ``electron-proton mass ratio`` + ``electron-tau mass ratio`` + ``elementary charge`` + ``elementary charge over h`` + ``Faraday constant`` + ``Faraday constant for conventional electric current`` + ``Fermi coupling constant`` + ``fine-structure constant`` + ``first radiation constant`` + ``first radiation constant for spectral radiance`` + ``Hartree energy`` + ``Hartree energy in eV`` + ``hartree-atomic mass unit relationship`` + ``hartree-electron volt relationship`` + ``hartree-hertz relationship`` + ``hartree-inverse meter relationship`` + ``hartree-joule relationship`` + ``hartree-kelvin relationship`` + ``hartree-kilogram relationship`` + ``helion mass`` + ``helion mass energy equivalent`` + ``helion mass energy equivalent in MeV`` + ``helion mass in u`` + ``helion molar mass`` + ``helion-electron mass ratio`` + ``helion-proton mass ratio`` + ``hertz-atomic mass unit relationship`` + ``hertz-electron volt relationship`` + ``hertz-hartree relationship`` + ``hertz-inverse meter relationship`` + ``hertz-joule relationship`` + ``hertz-kelvin relationship`` + ``hertz-kilogram relationship`` + ``inverse fine-structure constant`` + ``inverse meter-atomic mass unit relationship`` + ``inverse meter-electron volt relationship`` + ``inverse meter-hartree relationship`` + ``inverse meter-hertz relationship`` + ``inverse meter-joule relationship`` + ``inverse meter-kelvin relationship`` + ``inverse meter-kilogram relationship`` + ``inverse of conductance quantum`` + ``Josephson constant`` + ``joule-atomic mass unit relationship`` + ``joule-electron volt relationship`` + ``joule-hartree relationship`` + ``joule-hertz relationship`` + ``joule-inverse meter relationship`` + ``joule-kelvin relationship`` + ``joule-kilogram relationship`` + ``kelvin-atomic mass unit relationship`` + ``kelvin-electron volt relationship`` + ``kelvin-hartree relationship`` + ``kelvin-hertz relationship`` + ``kelvin-inverse meter relationship`` + ``kelvin-joule relationship`` + ``kelvin-kilogram relationship`` + ``kilogram-atomic mass unit relationship`` + ``kilogram-electron volt relationship`` + ``kilogram-hartree relationship`` + ``kilogram-hertz relationship`` + ``kilogram-inverse meter relationship`` + ``kilogram-joule relationship`` + ``kilogram-kelvin relationship`` + ``lattice parameter of silicon`` + ``Loschmidt constant (273.15 K, 101.325 kPa)`` + ``magnetic constant`` + ``magnetic flux quantum`` + ``Mo x unit`` + ``molar gas constant`` + ``molar mass constant`` + ``molar mass of carbon-12`` + ``molar Planck constant`` + ``molar Planck constant times c`` + ``molar volume of ideal gas (273.15 K, 100 kPa)`` + ``molar volume of ideal gas (273.15 K, 101.325 kPa)`` + ``molar volume of silicon`` + ``muon Compton wavelength`` + ``muon Compton wavelength over 2 pi`` + ``muon g factor`` + ``muon magnetic moment`` + ``muon magnetic moment anomaly`` + ``muon magnetic moment to Bohr magneton ratio`` + ``muon magnetic moment to nuclear magneton ratio`` + ``muon mass`` + ``muon mass energy equivalent`` + ``muon mass energy equivalent in MeV`` + ``muon mass in u`` + ``muon molar mass`` + ``muon-electron mass ratio`` + ``muon-neutron mass ratio`` + ``muon-proton magnetic moment ratio`` + ``muon-proton mass ratio`` + ``muon-tau mass ratio`` + ``natural unit of action`` + ``natural unit of action in eV s`` + ``natural unit of energy`` + ``natural unit of energy in MeV`` + ``natural unit of length`` + ``natural unit of mass`` + ``natural unit of momentum`` + ``natural unit of momentum in MeV/c`` + ``natural unit of time`` + ``natural unit of velocity`` + ``neutron Compton wavelength`` + ``neutron Compton wavelength over 2 pi`` + ``neutron g factor`` + ``neutron gyromagnetic ratio`` + ``neutron gyromagnetic ratio over 2 pi`` + ``neutron magnetic moment`` + ``neutron magnetic moment to Bohr magneton ratio`` + ``neutron magnetic moment to nuclear magneton ratio`` + ``neutron mass`` + ``neutron mass energy equivalent`` + ``neutron mass energy equivalent in MeV`` + ``neutron mass in u`` + ``neutron molar mass`` + ``neutron to shielded proton magnetic moment ratio`` + ``neutron-electron magnetic moment ratio`` + ``neutron-electron mass ratio`` + ``neutron-muon mass ratio`` + ``neutron-proton magnetic moment ratio`` + ``neutron-proton mass ratio`` + ``neutron-tau mass ratio`` + ``Newtonian constant of gravitation`` + ``Newtonian constant of gravitation over h-bar c`` + ``nuclear magneton`` + ``nuclear magneton in eV/T`` + ``nuclear magneton in inverse meters per tesla`` + ``nuclear magneton in K/T`` + ``nuclear magneton in MHz/T`` + ``Planck constant`` + ``Planck constant in eV s`` + ``Planck constant over 2 pi`` + ``Planck constant over 2 pi in eV s`` + ``Planck constant over 2 pi times c in MeV fm`` + ``Planck length`` + ``Planck mass`` + ``Planck temperature`` + ``Planck time`` + ``proton charge to mass quotient`` + ``proton Compton wavelength`` + ``proton Compton wavelength over 2 pi`` + ``proton g factor`` + ``proton gyromagnetic ratio`` + ``proton gyromagnetic ratio over 2 pi`` + ``proton magnetic moment`` + ``proton magnetic moment to Bohr magneton ratio`` + ``proton magnetic moment to nuclear magneton ratio`` + ``proton magnetic shielding correction`` + ``proton mass`` + ``proton mass energy equivalent`` + ``proton mass energy equivalent in MeV`` + ``proton mass in u`` + ``proton molar mass`` + ``proton rms charge radius`` + ``proton-electron mass ratio`` + ``proton-muon mass ratio`` + ``proton-neutron magnetic moment ratio`` + ``proton-neutron mass ratio`` + ``proton-tau mass ratio`` + ``quantum of circulation`` + ``quantum of circulation times 2`` + ``Rydberg constant`` + ``Rydberg constant times c in Hz`` + ``Rydberg constant times hc in eV`` + ``Rydberg constant times hc in J`` + ``Sackur-Tetrode constant (1 K, 100 kPa)`` + ``Sackur-Tetrode constant (1 K, 101.325 kPa)`` + ``second radiation constant`` + ``shielded helion gyromagnetic ratio`` + ``shielded helion gyromagnetic ratio over 2 pi`` + ``shielded helion magnetic moment`` + ``shielded helion magnetic moment to Bohr magneton ratio`` + ``shielded helion magnetic moment to nuclear magneton ratio`` + ``shielded helion to proton magnetic moment ratio`` + ``shielded helion to shielded proton magnetic moment ratio`` + ``shielded proton gyromagnetic ratio`` + ``shielded proton gyromagnetic ratio over 2 pi`` + ``shielded proton magnetic moment`` + ``shielded proton magnetic moment to Bohr magneton ratio`` + ``shielded proton magnetic moment to nuclear magneton ratio`` + ``speed of light in vacuum`` + ``standard acceleration of gravity`` + ``standard atmosphere`` + ``Stefan-Boltzmann constant`` + ``tau Compton wavelength`` + ``tau Compton wavelength over 2 pi`` + ``tau mass`` + ``tau mass energy equivalent`` + ``tau mass energy equivalent in MeV`` + ``tau mass in u`` + ``tau molar mass`` + ``tau-electron mass ratio`` + ``tau-muon mass ratio`` + ``tau-neutron mass ratio`` + ``tau-proton mass ratio`` + ``Thomson cross section`` + ``unified atomic mass unit`` + ``von Klitzing constant`` + ``weak mixing angle`` + ``Wien displacement law constant`` + ``{220} lattice spacing of silicon`` + ====================================================================== ==== + + +Unit prefixes +============= + +SI +-- + +============ ================================================================= +``yotta`` :math:`10^{24}` +``zetta`` :math:`10^{21}` +``exa`` :math:`10^{18}` +``peta`` :math:`10^{15}` +``tera`` :math:`10^{12}` +``giga`` :math:`10^{9}` +``mega`` :math:`10^{6}` +``kilo`` :math:`10^{3}` +``hecto`` :math:`10^{2}` +``deka`` :math:`10^{1}` +``deci`` :math:`10^{-1}` +``centi`` :math:`10^{-2}` +``milli`` :math:`10^{-3}` +``micro`` :math:`10^{-6}` +``nano`` :math:`10^{-9}` +``pico`` :math:`10^{-12}` +``femto`` :math:`10^{-15}` +``atto`` :math:`10^{-18}` +``zepto`` :math:`10^{-21}` +============ ================================================================= + + +Binary +------ + +============ ================================================================= +``kibi`` :math:`2^{10}` +``mebi`` :math:`2^{20}` +``gibi`` :math:`2^{30}` +``tebi`` :math:`2^{40}` +``pebi`` :math:`2^{50}` +``exbi`` :math:`2^{60}` +``zebi`` :math:`2^{70}` +``yobi`` :math:`2^{80}` +============ ================================================================= + +Units +===== + +Weight +------ + +================= ============================================================ +``gram`` :math:`10^{-3}` kg +``metric_ton`` :math:`10^{3}` kg +``grain`` one grain in kg +``lb`` one pound (avoirdupous) in kg +``oz`` one ounce in kg +``stone`` one stone in kg +``grain`` one grain in kg +``long_ton`` one long ton in kg +``short_ton`` one short ton in kg +``troy_ounce`` one Troy ounce in kg +``troy_pound`` one Troy pound in kg +``carat`` one carat in kg +``m_u`` atomic mass constant (in kg) +================= ============================================================ + +Angle +----- + +================= ============================================================ +``degree`` degree in radians +``arcmin`` arc minute in radians +``arcsec`` arc second in radians +================= ============================================================ + + +Time +---- + +================= ============================================================ +``minute`` one minute in seconds +``hour`` one hour in seconds +``day`` one day in seconds +``week`` one week in seconds +``year`` one year (365 days) in seconds +``Julian_year`` one Julian year (365.25 days) in seconds +================= ============================================================ + + +Length +------ + +================= ============================================================ +``inch`` one inch in meters +``foot`` one foot in meters +``yard`` one yard in meters +``mile`` one mile in meters +``mil`` one mil in meters +``pt`` one point in meters +``survey_foot`` one survey foot in meters +``survey_mile`` one survey mile in meters +``nautical_mile`` one nautical mile in meters +``fermi`` one Fermi in meters +``angstrom`` one ?ngstr?m in meters +``micron`` one micron in meters +``au`` one astronomical unit in meters +``light_year`` one light year in meters +``parsec`` one parsec in meters +================= ============================================================ + +Pressure +-------- + +================= ============================================================ +``atm`` standard atmosphere in pascals +``bar`` one bar in pascals +``torr`` one torr (mmHg) in pascals +``psi`` one psi in pascals +================= ============================================================ + +Area +---- + +================= ============================================================ +``hectare`` one hectare in square meters +``acre`` one acre in square meters +================= ============================================================ + + +Volume +------ + +=================== ======================================================== +``liter`` one liter in cubic meters +``gallon`` one gallon (US) in cubic meters +``gallon_imp`` one gallon (UK) in cubic meters +``fluid_ounce`` one fluid ounce (US) in cubic meters +``fluid_ounce_imp`` one fluid ounce (UK) in cubic meters +``bbl`` one barrel in cubic meters +=================== ======================================================== + +Speed +----- + +================= ========================================================== +``kmh`` kilometers per hour in meters per second +``mph`` miles per hour in meters per second +``mach`` one Mach (approx., at 15 ?C, 1 atm) in meters per second +``knot`` one knot in meters per second +================= ========================================================== + + +Temperature +----------- + +===================== ======================================================= +``zero_Celsius`` zero of Celsius scale in Kelvin +``degree_Fahrenheit`` one Fahrenheit (only differences) in Kelvins +===================== ======================================================= + +.. autosummary:: + :toctree: generated/ + + C2K + K2C + F2C + C2F + F2K + K2F + +Energy +------ + +==================== ======================================================= +``eV`` one electron volt in Joules +``calorie`` one calorie (thermochemical) in Joules +``calorie_IT`` one calorie (International Steam Table calorie, 1956) in Joules +``erg`` one erg in Joules +``Btu`` one British thermal unit (International Steam Table) in Joules +``Btu_th`` one British thermal unit (thermochemical) in Joules +``ton_TNT`` one ton of TNT in Joules +==================== ======================================================= + +Power +----- + +==================== ======================================================= +``hp`` one horsepower in watts +==================== ======================================================= + +Force +----- + +==================== ======================================================= +``dyn`` one dyne in watts +``lbf`` one pound force in watts +``kgf`` one kilogram force in watts +==================== ======================================================= + +Optics +------ + +.. autosummary:: + :toctree: generated/ + + lambda2nu + nu2lambda Added: scipy-docs/trunk/source/fftpack.rst =================================================================== --- scipy-docs/trunk/source/fftpack.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/fftpack.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,78 @@ +========================================= +Fourier transforms (:mod:`scipy.fftpack`) +========================================= + +.. module:: scipy.fftpack + +Fast Fourier transforms +======================= + +.. autosummary:: + :toctree: generated/ + + fft + ifft + fftn + ifftn + fft2 + ifft2 + rfft + irfft + +Differential and pseudo-differential operators +============================================== + +.. autosummary:: + :toctree: generated/ + + diff + tilbert + itilbert + hilbert + ihilbert + cs_diff + sc_diff + ss_diff + cc_diff + shift + +Helper functions +================ + +.. autosummary:: + :toctree: generated/ + + fftshift + ifftshift + dftfreq + rfftfreq + +Convolutions (:mod:`scipy.fftpack.convolve`) +============================================ + +.. module:: scipy.fftpack.convolve + +.. autosummary:: + :toctree: generated/ + + convolve + convolve_z + init_convolution_kernel + destroy_convolve_cache + + +:mod:`scipy.fftpack._fftpack` +============================= + +.. module:: scipy.fftpack._fftpack + +.. autosummary:: + :toctree: generated/ + + drfft + zfft + zrfft + zfftnd + destroy_drfft_cache + destroy_zfft_cache + destroy_zfftnd_cache Added: scipy-docs/trunk/source/index.rst =================================================================== --- scipy-docs/trunk/source/index.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/index.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,28 @@ +############### +SciPy reference +############### + +SciPy (pronounced "Sigh Pie") is open-source software for mathematics, +science, and engineering. + +.. toctree:: + :maxdepth: 1 + + cluster + constants + fftpack + integrate + interpolate + io + linalg + maxentropy + misc + ndimage + odr + optimize + signal + sparse + sparse.linalg + special + stats + weave Added: scipy-docs/trunk/source/integrate.rst =================================================================== --- scipy-docs/trunk/source/integrate.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/integrate.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,44 @@ +============================================= +Integration and ODEs (:mod:`scipy.integrate`) +============================================= + +.. module:: scipy.integrate + + +Integrating functions, given function object +============================================ + +.. autosummary:: + :toctree: generated/ + + quad + dblquad + tplquad + fixed_quad + quadrature + romberg + +Integrating functions, given fixed samples +========================================== + +.. autosummary:: + :toctree: generated/ + + trapz + cumtrapz + simps + romb + +.. seealso:: + + :mod:`scipy.special` for orthogonal polynomials (special) for Gaussian + quadrature roots and weights for other weighting factors and regions. + +Integrators of ODE systems +========================== + +.. autosummary:: + :toctree: generated/ + + odeint + ode Added: scipy-docs/trunk/source/interpolate.rst =================================================================== --- scipy-docs/trunk/source/interpolate.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/interpolate.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,84 @@ +======================================== +Interpolation (:mod:`scipy.interpolate`) +======================================== + +.. module:: scipy.interpolate + +Univariate interpolation +======================== + +.. autosummary:: + :toctree: generated/ + + interp1d + BarycentricInterpolator + KroghInterpolator + PiecewisePolynomial + barycentric_interpolate + krogh_interpolate + piecewise_polynomial_interpolate + + +Multivariate interpolation +========================== + +.. autosummary:: + :toctree: generated/ + + interp2d + Rbf + + +1-D Splines +=========== + +.. autosummary:: + :toctree: generated/ + + UnivariateSpline + InterpolatedUnivariateSpline + LSQUnivariateSpline + +Low-level interface to FITPACK functions: + +.. autosummary:: + :toctree: generated/ + + splrep + splprep + splev + splint + sproot + spalde + bisplrep + bisplev + + +2-D Splines +=========== + +.. seealso:: scipy.ndimage.map_coordinates + +.. autosummary:: + :toctree: generated/ + + BivariateSpline + SmoothBivariateSpline + LSQBivariateSpline + +Low-level interface to FITPACK functions: + +.. autosummary:: + :toctree: generated/ + + bisplrep + bisplev + +Additional tools +================ + +.. autosummary:: + :toctree: generated/ + + lagrange + approximate_taylor_polynomial Added: scipy-docs/trunk/source/io.rst =================================================================== --- scipy-docs/trunk/source/io.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/io.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,67 @@ +================================== +Input and output (:mod:`scipy.io`) +================================== + +.. seealso:: :ref:`numpy-reference.routines.io` (in Numpy) + +.. module:: scipy.io + +MATLAB? files +============= + +.. autosummary:: + :toctree: generated/ + + loadmat + savemat + +Matrix Market files +=================== + +.. autosummary:: + :toctree: generated/ + + mminfo + mmread + mmwrite + +Other +===== + +.. autosummary:: + :toctree: generated/ + + save_as_module + npfile + +Wav sound files (:mod:`scipy.io.wavfile`) +========================================= + +.. module:: scipy.io.wavfile + +.. autosummary:: + :toctree: generated/ + + read + write + +Arff files (:mod:`scipy.io.arff`) +================================= + +.. automodule:: scipy.io.arff + +.. autosummary:: + :toctree: generated/ + + loadarff + +Netcdf (:mod:`scipy.io.netcdf`) +=============================== + +.. module:: scipy.io.netcdf + +.. autosummary:: + :toctree: generated/ + + netcdf_file + netcdf_variable Added: scipy-docs/trunk/source/linalg.rst =================================================================== --- scipy-docs/trunk/source/linalg.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/linalg.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,81 @@ +==================================== +Linear algebra (:mod:`scipy.linalg`) +==================================== + +.. module:: scipy.linalg + +Basics +====== + +.. autosummary:: + :toctree: generated/ + + inv + solve + solve_banded + solveh_banded + det + norm + lstsq + pinv + pinv2 + +Eigenvalues and Decompositions +============================== + +.. autosummary:: + :toctree: generated/ + + eig + eigvals + eig_banded + eigvals_banded + lu + lu_factor + lu_solve + svd + svdvals + diagsvd + orth + cholesky + cholesky_banded + cho_factor + cho_solve + qr + schur + rsf2csf + hessenberg + +Matrix Functions +================ + +.. autosummary:: + :toctree: generated/ + + expm + expm2 + expm3 + logm + cosm + sinm + tanm + coshm + sinhm + tanhm + signm + sqrtm + funm + +Iterative linear systems solutions +================================== + +.. autosummary:: + :toctree: generated/ + + cg + cgs + qmr + gmres + bicg + bicgstab + Added: scipy-docs/trunk/source/maxentropy.rst =================================================================== --- scipy-docs/trunk/source/maxentropy.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/maxentropy.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,84 @@ +================================================ +Maximum entropy models (:mod:`scipy.maxentropy`) +================================================ + +.. automodule:: scipy.maxentropy + + +Models +====== + +.. autoclass:: model + +.. autosummary:: + :toctree: generated/ + + model.beginlogging + model.endlogging + model.clearcache + model.crossentropy + model.dual + model.fit + model.grad + model.log + model.logparams + model.normconst + model.reset + model.setcallback + model.setparams + model.setsmooth + model.expectations + model.lognormconst + model.logpmf + model.pmf_function + model.setfeaturesandsamplespace + +.. autoclass:: bigmodel + +.. autosummary:: + :toctree: generated/ + + bigmodel.estimate + bigmodel.logpdf + bigmodel.pdf + bigmodel.pdf_function + bigmodel.resample + bigmodel.setsampleFgen + bigmodel.settestsamples + bigmodel.stochapprox + bigmodel.test + +.. autoclass:: conditionalmodel + +.. autosummary:: + :toctree: generated/ + + conditionalmodel.dual + conditionalmodel.expectations + conditionalmodel.fit + conditionalmodel.lognormconst + conditionalmodel.logpmf + +Utilities +========= + +.. autosummary:: + :toctree: generated/ + + arrayexp + arrayexpcomplex + columnmeans + columnvariances + densefeaturematrix + densefeatures + dotprod + flatten + innerprod + innerprodtranspose + logsumexp + logsumexp_naive + robustlog + rowmeans + sample_wr + sparsefeaturematrix + sparsefeatures Added: scipy-docs/trunk/source/misc.rst =================================================================== --- scipy-docs/trunk/source/misc.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/misc.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,6 @@ +========================================== +Miscellaneous routines (:mod:`scipy.misc`) +========================================== + +.. automodule:: scipy.misc + :members: Added: scipy-docs/trunk/source/ndimage.rst =================================================================== --- scipy-docs/trunk/source/ndimage.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/ndimage.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,122 @@ +========================================================= +Multi-dimensional image processing (:mod:`scipy.ndimage`) +========================================================= + +.. module:: scipy.ndimage + +Functions for multi-dimensional image processing. + +Filters :mod:`scipy.ndimage.filters` +==================================== + +.. module:: scipy.ndimage.filters + +.. autosummary:: + :toctree: generated/ + + convolve + convolve1d + correlate + correlate1d + gaussian_filter + gaussian_filter1d + gaussian_gradient_magnitude + gaussian_laplace + generic_filter + generic_filter1d + generic_gradient_magnitude + generic_laplace + laplace + maximum_filter + maximum_filter1d + median_filter + minimum_filter + minimum_filter1d + percentile_filter + prewitt + rank_filter + sobel + uniform_filter + uniform_filter1d + +Fourier filters :mod:`scipy.ndimage.fourier` +============================================ + +.. module:: scipy.ndimage.fourier + +.. autosummary:: + :toctree: generated/ + + fourier_ellipsoid + fourier_gaussian + fourier_shift + fourier_uniform + +Interpolation :mod:`scipy.ndimage.interpolation` +================================================ + +.. module:: scipy.ndimage.interpolation + +.. autosummary:: + :toctree: generated/ + + affine_transform + geometric_transform + map_coordinates + rotate + shift + spline_filter + spline_filter1d + zoom + +Measurements :mod:`scipy.ndimage.measurements` +============================================== + +.. module:: scipy.ndimage.measurements + +.. autosummary:: + :toctree: generated/ + + center_of_mass + extrema + find_objects + histogram + label + maximum + maximum_position + mean + minimum + minimum_position + standard_deviation + sum + variance + watershed_ift + +Morphology :mod:`scipy.ndimage.morphology` +========================================== + +.. module:: scipy.ndimage.morphology + +.. autosummary:: + :toctree: generated/ + + binary_closing + binary_dilation + binary_erosion + binary_fill_holes + binary_hit_or_miss + binary_opening + binary_propagation + black_tophat + distance_transform_bf + distance_transform_cdt + distance_transform_edt + generate_binary_structure + grey_closing + grey_dilation + grey_erosion + grey_opening + iterate_structure + morphological_gradient + morphological_laplace + white_tophat Added: scipy-docs/trunk/source/odr.rst =================================================================== --- scipy-docs/trunk/source/odr.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/odr.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,34 @@ +================================================= +Orthogonal distance regression (:mod:`scipy.odr`) +================================================= + +.. automodule:: scipy.odr + +.. autoclass:: Data + + .. automethod:: set_meta + +.. autoclass:: Model + + .. automethod:: set_meta + +.. autoclass:: ODR + + .. automethod:: restart + + .. automethod:: run + + .. automethod:: set_iprint + + .. note:: XXX: Sphinx doesn't like "set_job" docstring; after it's fixed, + add "set_job" here. + +.. autoclass:: Output + + .. automethod:: pprint + +.. autoexception:: odr_error + +.. autoexception:: odr_stop + +.. autofunction:: odr Added: scipy-docs/trunk/source/optimize.rst =================================================================== --- scipy-docs/trunk/source/optimize.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/optimize.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,105 @@ +===================================================== +Optimization and root finding (:mod:`scipy.optimize`) +===================================================== + +.. module:: scipy.optimize + +Optimization +============ + +General-purpose +--------------- + +.. autosummary:: + :toctree: generated/ + + fmin + fmin_powell + fmin_cg + fmin_bfgs + fmin_ncg + leastsq + + +Constrained (multivariate) +-------------------------- + +.. autosummary:: + :toctree: generated/ + + fmin_l_bfgs_b + fmin_tnc + fmin_cobyla + +Global +------ + +.. autosummary:: + :toctree: generated/ + + anneal + brute + +Scalar function minimizers +-------------------------- + +.. autosummary:: + :toctree: generated/ + + fminbound + golden + bracket + +.. note:: + + XXX: Sphinx didn't like "brent" docstring; add it to the list after + it's fixed. + +Root finding +============ + +.. autosummary:: + :toctree: generated/ + + fsolve + +Scalar function solvers +----------------------- + +.. autosummary:: + :toctree: generated/ + + brentq + brenth + ridder + bisect + newton + +Fixed point finding: + +.. autosummary:: + :toctree: generated/ + + fixed_point + +General-purpose nonlinear (multidimensional) +-------------------------------------------- + +.. autosummary:: + :toctree: generated/ + + broyden1 + broyden2 + broyden3 + broyden_generalized + anderson + anderson2 + +Utility Functions +================= + +.. autosummary:: + :toctree: generated/ + + line_search + check_grad Added: scipy-docs/trunk/source/signal.rst =================================================================== --- scipy-docs/trunk/source/signal.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/signal.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,157 @@ +======================================= +Signal processing (:mod:`scipy.signal`) +======================================= + +.. module:: scipy.signal + +Convolution +=========== + +.. autosummary:: + :toctree: generated/ + + convolve + correlate + fftconvolve + convolve2d + correlate2d + sepfir2d + +B-splines +========= + +.. autosummary:: + :toctree: generated/ + + bspline + gauss_spline + cspline1d + qspline1d + cspline2d + qspline2d + spline_filter + +Filtering +========= + +.. autosummary:: + :toctree: generated/ + + order_filter + medfilt + medfilt2 + wiener + + symiirorder1 + symiirorder2 + lfilter + + deconvolve + + hilbert + get_window + + detrend + resample + +Filter design +============= + +.. autosummary:: + :toctree: generated/ + + remez + firwin + iirdesign + iirfilter + freqs + freqz + + unique_roots + residue + residuez + invres + +Matlab-style IIR filter design +============================== + +.. autosummary:: + :toctree: generated/ + + butter + buttord + cheby1 + cheb1ord + cheby2 + cheb2ord + ellip + ellipord + bessel + +Linear Systems +============== + +.. autosummary:: + :toctree: generated/ + + lti + lsim + impulse + step + +LTI Reresentations +================== + +.. autosummary:: + :toctree: generated/ + + tf2zpk + zpk2tf + tf2ss + ss2tf + zpk2ss + ss2zpk + +Waveforms +========= + +.. autosummary:: + :toctree: generated/ + + sawtooth + square + gausspulse + chirp + +Window functions +================ + +.. autosummary:: + :toctree: generated/ + + boxcar + triang + parzen + bohman + blackman + blackmanharris + nuttall + flattop + bartlett + hann + barthann + hamming + kaiser + gaussian + general_gaussian + slepian + +Wavelets +======== + +.. autosummary:: + :toctree: generated/ + + daub + qmf + cascade Added: scipy-docs/trunk/source/sparse.linalg.rst =================================================================== --- scipy-docs/trunk/source/sparse.linalg.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/sparse.linalg.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,6 @@ +================================================== +Sparse linear algebra (:mod:`scipy.sparse.linalg`) +================================================== + +.. automodule:: scipy.sparse.linalg + :members: Added: scipy-docs/trunk/source/sparse.rst =================================================================== --- scipy-docs/trunk/source/sparse.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/sparse.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,64 @@ +===================================== +Sparse matrices (:mod:`scipy.sparse`) +===================================== + +.. automodule:: scipy.sparse + + +Sparse matrix classes +===================== + +.. autosummary:: + :toctree: generated/ + + csc_matrix + csr_matrix + bsr_matrix + lil_matrix + dok_matrix + coo_matrix + dia_matrix + + +Functions +========= + +Building sparse matrices: + +.. autosummary:: + :toctree: generated/ + + eye + identity + kron + kronsum + lil_eye + lil_diags + spdiags + tril + triu + bmat + hstack + vstack + +Identifying sparse matrices: + +.. autosummary:: + :toctree: generated/ + + issparse + isspmatrix + isspmatrix_csc + isspmatrix_csr + isspmatrix_bsr + isspmatrix_lil + isspmatrix_dok + isspmatrix_coo + isspmatrix_dia + +Exceptions +========== + +.. autoexception:: SparseEfficiencyWarning + +.. autoexception:: SparseWarning Added: scipy-docs/trunk/source/special.rst =================================================================== --- scipy-docs/trunk/source/special.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/special.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,480 @@ +======================================== +Special functions (:mod:`scipy.special`) +======================================== + +.. module:: scipy.special + +Nearly all of the functions below are universal functions and follow +broadcasting and automatic array-looping rules. Exceptions are noted. + +Error handling +============== + +Errors are handled by returning nans, or other appropriate values. +Some of the special function routines will print an error message +when an error occurs. By default this printing +is disabled. To enable such messages use errprint(1) +To disable such messages use errprint(0). + +Example: + >>> print scipy.special.bdtr(-1,10,0.3) + >>> scipy.special.errprint(1) + >>> print scipy.special.bdtr(-1,10,0.3) + +.. autosummary:: + :toctree: generated/ + + errprint + errstate + +Available functions +=================== + +Airy functions +-------------- + +.. autosummary:: + :toctree: generated/ + + airy + airye + ai_zeros + bi_zeros + + +Elliptic Functions and Integrals +-------------------------------- + +.. autosummary:: + :toctree: generated/ + + ellipj + ellipk + ellipkinc + ellipe + ellipeinc + +Bessel Functions +---------------- + +.. autosummary:: + :toctree: generated/ + + jn + jv + jve + yn + yv + yve + kn + kv + kve + iv + ive + hankel1 + hankel1e + hankel2 + hankel2e + +The following is not an universal function: + +.. autosummary:: + :toctree: generated/ + + lmbda + +Zeros of Bessel Functions +^^^^^^^^^^^^^^^^^^^^^^^^^ + +These are not universal functions: + +.. autosummary:: + :toctree: generated/ + + jnjnp_zeros + jnyn_zeros + jn_zeros + jnp_zeros + yn_zeros + ynp_zeros + y0_zeros + y1_zeros + y1p_zeros + +Faster versions of common Bessel Functions +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autosummary:: + :toctree: generated/ + + j0 + j1 + y0 + y1 + i0 + i0e + i1 + i1e + k0 + k0e + k1 + k1e + +Integrals of Bessel Functions +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autosummary:: + :toctree: generated/ + + itj0y0 + it2j0y0 + iti0k0 + it2i0k0 + besselpoly + +Derivatives of Bessel Functions +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autosummary:: + :toctree: generated/ + + jvp + yvp + kvp + ivp + h1vp + h2vp + +Spherical Bessel Functions +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +These are not universal functions: + +.. autosummary:: + :toctree: generated/ + + sph_jn + sph_yn + sph_jnyn + sph_in + sph_kn + sph_inkn + +Ricatti-Bessel Functions +^^^^^^^^^^^^^^^^^^^^^^^^ + +These are not universal functions: + +.. autosummary:: + :toctree: generated/ + + riccati_jn + riccati_yn + +Struve Functions +---------------- + +.. autosummary:: + :toctree: generated/ + + struve + modstruve + itstruve0 + it2struve0 + itmodstruve0 + + +Raw Statistical Functions +------------------------- + +.. seealso:: :mod:`scipy.stats`: Friendly versions of these functions. + +.. autosummary:: + :toctree: generated/ + + bdtr + bdtrc + bdtri + btdtr + btdtri + fdtr + fdtrc + fdtri + gdtr + gdtrc + gdtria + gdtrib + gdtrix + nbdtr + nbdtrc + nbdtri + pdtr + pdtrc + pdtri + stdtr + stdtridf + stdtrit + chdtr + chdtrc + chdtri + ndtr + ndtri + smirnov + smirnovi + kolmogorov + kolmogi + tklmbda + +Gamma and Related Functions +--------------------------- + +.. autosummary:: + :toctree: generated/ + + gamma + gammaln + gammainc + gammaincinv + gammaincc + gammainccinv + beta + betaln + betainc + betaincinv + psi + rgamma + polygamma + + +Error Function and Fresnel Integrals +------------------------------------ + +.. autosummary:: + :toctree: generated/ + + erf + erfc + erfinv + erfcinv + erf_zeros + fresnel + fresnel_zeros + modfresnelp + modfresnelm + +These are not universal functions: + +.. autosummary:: + :toctree: generated/ + + fresnelc_zeros + fresnels_zeros + +Legendre Functions +------------------ + +.. autosummary:: + :toctree: generated/ + + lpmv + sph_harm + +These are not universal functions: + +.. autosummary:: + :toctree: generated/ + + lpn + lqn + lpmn + lqmn + +Orthogonal polynomials +---------------------- + +These functions all return a polynomial class which can then be +evaluated: ``vals = chebyt(n)(x)``. + +The class also has an attribute 'weights' which return the roots, +weights, and total weights for the appropriate form of Gaussian +quadrature. These are returned in an n x 3 array with roots in +the first column, weights in the second column, and total weights +in the final column + +.. autosummary:: + :toctree: generated/ + + legendre + chebyt + chebyu + chebyc + chebys + jacobi + laguerre + genlaguerre + hermite + hermitenorm + gegenbauer + sh_legendre + sh_chebyt + sh_chebyu + sh_jacobi + +Hypergeometric Functions +------------------------ + +.. autosummary:: + :toctree: generated/ + + hyp2f1 + hyp1f1 + hyperu + hyp0f1 + hyp2f0 + hyp1f2 + hyp3f0 + + +Parabolic Cylinder Functions +---------------------------- + +.. autosummary:: + :toctree: generated/ + + pbdv + pbvv + pbwa + +These are not universal functions: + +.. autosummary:: + :toctree: generated/ + + pbdv_seq + pbvv_seq + pbdn_seq + +Mathieu and Related Functions +----------------------------- + +.. autosummary:: + :toctree: generated/ + + mathieu_a + mathieu_b + +These are not universal functions: + +.. autosummary:: + :toctree: generated/ + + mathieu_even_coef + mathieu_odd_coef + +The following return both function and first derivative: + +.. autosummary:: + :toctree: generated/ + + mathieu_cem + mathieu_sem + mathieu_modcem1 + mathieu_modcem2 + mathieu_modsem1 + mathieu_modsem2 + +Spheroidal Wave Functions +------------------------- + +.. autosummary:: + :toctree: generated/ + + pro_ang1 + pro_rad1 + pro_rad2 + obl_ang1 + obl_rad1 + obl_rad2 + pro_cv + obl_cv + pro_cv_seq + obl_cv_seq + +The following functions require pre-computed characteristic value: + +.. autosummary:: + :toctree: generated/ + + pro_ang1_cv + pro_rad1_cv + pro_rad2_cv + obl_ang1_cv + obl_rad1_cv + obl_rad2_cv + +Kelvin Functions +---------------- + +.. autosummary:: + :toctree: generated/ + + kelvin + kelvin_zeros + ber + bei + berp + beip + ker + kei + kerp + keip + +These are not universal functions: + +.. autosummary:: + :toctree: generated/ + + ber_zeros + bei_zeros + berp_zeros + beip_zeros + ker_zeros + kei_zeros + kerp_zeros + keip_zeros + +Other Special Functions +----------------------- + +.. autosummary:: + :toctree: generated/ + + expn + exp1 + expi + wofz + dawsn + shichi + sici + spence + zeta + zetac + +Convenience Functions +--------------------- + +.. autosummary:: + :toctree: generated/ + + cbrt + exp10 + exp2 + radian + cosdg + sindg + tandg + cotdg + log1p + expm1 + cosm1 + round Added: scipy-docs/trunk/source/stats.rst =================================================================== --- scipy-docs/trunk/source/stats.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/stats.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,254 @@ +.. module:: scipy.stats + +========================================== +Statistical functions (:mod:`scipy.stats`) +========================================== + +This module contains a large number of probability distributions as +well as a growing library of statistical functions. + +Each included distribution is an instance of the class rv_continous: + +.. autosummary:: + :toctree: generated/ + + rv_continuous + rv_continuous.pdf + rv_continuous.cdf + rv_continuous.sf + rv_continuous.ppf + rv_continuous.isf + rv_continuous.stats + + +Continuous distributions +======================== + +.. autosummary:: + :toctree: generated/ + + norm + alpha + anglit + arcsine + beta + betaprime + bradford + burr + fisk + cauchy + chi + chi2 + cosine + dgamma + dweibull + erlang + expon + exponweib + exponpow + fatiguelife + foldcauchy + f + foldnorm + fretchet_r + fretcher_l + genlogistic + genpareto + genexpon + genextreme + gausshyper + gamma + gengamma + genhalflogistic + gompertz + gumbel_r + gumbel_l + halfcauchy + halflogistic + halfnorm + hypsecant + invgamma + invnorm + invweibull + johnsonsb + johnsonsu + laplace + logistic + loggamma + loglaplace + lognorm + gilbrat + lomax + maxwell + mielke + nakagami + ncx2 + ncf + t + nct + pareto + powerlaw + powerlognorm + powernorm + rdist + reciprocal + rayleigh + rice + recipinvgauss + semicircular + triang + truncexpon + truncnorm + tukeylambda + uniform + von_mises + wald + weibull_min + weibull_max + wrapcauchy + ksone + kstwobign + +Discrete distributions +====================== + +.. autosummary:: + :toctree: generated/ + + binom + bernoulli + nbinom + geom + hypergeom + logser + poisson + planck + boltzmann + randint + zipf + dlaplace + +Statistical functions +===================== + +.. autosummary:: + :toctree: generated/ + + gmean + hmean + mean + cmedian + median + mode + tmean + tvar + tmin + tmax + tstd + tsem + moment + variation + skew + kurtosis + describe + skewtest + kurtosistest + normaltest + + +.. autosummary:: + :toctree: generated/ + + itemfreq + scoreatpercentile + percentileofscore + histogram2 + histogram + cumfreq + relfreq + +.. autosummary:: + :toctree: generated/ + + obrientransform + samplevar + samplestd + signaltonoise + bayes_mvs + var + std + stderr + sem + z + zs + zmap + +.. autosummary:: + :toctree: generated/ + + threshold + trimboth + trim1 + cov + corrcoef + +.. autosummary:: + :toctree: generated/ + + f_oneway + paired + pearsonr + spearmanr + pointbiserialr + kendalltau + linregress + +.. autosummary:: + :toctree: generated/ + + ttest_1samp + ttest_ind + ttest_rel + kstest + chisquare + ks_2samp + meanwhitneyu + tiecorrect + ranksums + wilcoxon + kruskal + friedmanchisquare + +.. autosummary:: + :toctree: generated/ + + ansari + bartlett + levene + shapiro + anderson + binom_test + fligner + mood + oneway + + +.. autosummary:: + :toctree: generated/ + + glm + anova + +Plot-tests +========== + +.. autosummary:: + :toctree: generated/ + + probplot + ppcc_max + ppcc_plot + + +For many more stat related functions install the software R and the +interface package rpy. Added: scipy-docs/trunk/source/weave.rst =================================================================== --- scipy-docs/trunk/source/weave.rst 2008-10-26 17:51:47 UTC (rev 4848) +++ scipy-docs/trunk/source/weave.rst 2008-10-26 17:54:24 UTC (rev 4849) @@ -0,0 +1,6 @@ +====================================== +C/C++ integration (:mod:`scipy.weave`) +====================================== + +.. automodule:: scipy.weave + :members: From scipy-svn at scipy.org Sun Oct 26 14:10:36 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 13:10:36 -0500 (CDT) Subject: [Scipy-svn] r4850 - in scipy-docs/trunk: . frontpage frontpage/_static frontpage/_templates Message-ID: <20081026181036.BF2BE39C05F@scipy.org> Author: ptvirtan Date: 2008-10-26 13:10:15 -0500 (Sun, 26 Oct 2008) New Revision: 4850 Added: scipy-docs/trunk/frontpage/ scipy-docs/trunk/frontpage/Makefile scipy-docs/trunk/frontpage/README.txt scipy-docs/trunk/frontpage/_static/ scipy-docs/trunk/frontpage/_static/scipy.css scipy-docs/trunk/frontpage/_templates/ scipy-docs/trunk/frontpage/_templates/download.html scipy-docs/trunk/frontpage/_templates/indexcontent.html scipy-docs/trunk/frontpage/_templates/indexsidebar.html scipy-docs/trunk/frontpage/_templates/layout.html scipy-docs/trunk/frontpage/conf.py scipy-docs/trunk/frontpage/contents.rst scipy-docs/trunk/frontpage/scipyshiny_small.png Log: Add docs.scipy.org front page source Added: scipy-docs/trunk/frontpage/Makefile =================================================================== --- scipy-docs/trunk/frontpage/Makefile 2008-10-26 17:54:24 UTC (rev 4849) +++ scipy-docs/trunk/frontpage/Makefile 2008-10-26 18:10:15 UTC (rev 4850) @@ -0,0 +1,84 @@ +# Makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +PAPER = + +# Internal variables. +PAPEROPT_a4 = -D latex_paper_size=a4 +PAPEROPT_letter = -D latex_paper_size=letter +ALLSPHINXOPTS = -d _build/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . + +.PHONY: help clean html web pickle htmlhelp latex changes linkcheck + +help: + @echo "Please use \`make ' where is one of" + @echo " html to make standalone HTML files" + @echo " pickle to make pickle files" + @echo " json to make JSON files" + @echo " htmlhelp to make HTML files and a HTML help project" + @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" + @echo " changes to make an overview over all changed/added/deprecated items" + @echo " linkcheck to check all external links for integrity" + +clean: + -rm -rf _build/* + +dist: html + rm -rf _build/doc + cp -r _build/html _build/doc + rm _build/doc/objects.inv + rm _build/doc/contents.html + rm _build/doc/search.html + rm _build/doc/searchindex.js + cd _build/doc && tar czf ../frontpage.tar.gz * + +html: + mkdir -p _build/html _build/doctrees + $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) _build/html + @echo + @echo "Build finished. The HTML pages are in _build/html." + +pickle: + mkdir -p _build/pickle _build/doctrees + $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) _build/pickle + @echo + @echo "Build finished; now you can process the pickle files." + +web: pickle + +json: + mkdir -p _build/json _build/doctrees + $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) _build/json + @echo + @echo "Build finished; now you can process the JSON files." + +htmlhelp: + mkdir -p _build/htmlhelp _build/doctrees + $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) _build/htmlhelp + @echo + @echo "Build finished; now you can run HTML Help Workshop with the" \ + ".hhp project file in _build/htmlhelp." + +latex: + mkdir -p _build/latex _build/doctrees + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) _build/latex + @echo + @echo "Build finished; the LaTeX files are in _build/latex." + @echo "Run \`make all-pdf' or \`make all-ps' in that directory to" \ + "run these through (pdf)latex." + +changes: + mkdir -p _build/changes _build/doctrees + $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) _build/changes + @echo + @echo "The overview file is in _build/changes." + +linkcheck: + mkdir -p _build/linkcheck _build/doctrees + $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) _build/linkcheck + @echo + @echo "Link check complete; look for any errors in the above output " \ + "or in _build/linkcheck/output.txt." Property changes on: scipy-docs/trunk/frontpage/Makefile ___________________________________________________________________ Name: svn:eol-style + native Added: scipy-docs/trunk/frontpage/README.txt =================================================================== --- scipy-docs/trunk/frontpage/README.txt 2008-10-26 17:54:24 UTC (rev 4849) +++ scipy-docs/trunk/frontpage/README.txt 2008-10-26 18:10:15 UTC (rev 4850) @@ -0,0 +1,7 @@ +docs.scipy.org front page +========================= + +Instructions +------------ +1. Run ``make html`` or ``make dist`` + Property changes on: scipy-docs/trunk/frontpage/README.txt ___________________________________________________________________ Name: svn:eol-style + native Added: scipy-docs/trunk/frontpage/_static/scipy.css =================================================================== --- scipy-docs/trunk/frontpage/_static/scipy.css 2008-10-26 17:54:24 UTC (rev 4849) +++ scipy-docs/trunk/frontpage/_static/scipy.css 2008-10-26 18:10:15 UTC (rev 4850) @@ -0,0 +1,156 @@ + at import "default.css"; + +/** + * Spacing fixes + */ + +div.body p, div.body dd, div.body li { + line-height: 125%; +} + +ul.simple { + margin-top: 0; + margin-bottom: 0; + padding-top: 0; + padding-bottom: 0; +} + +/* spacing around blockquoted fields in parameters/attributes/returns */ +td.field-body > blockquote { + margin-top: 0.1em; + margin-bottom: 0.5em; +} + +/* spacing around example code */ +div.highlight > pre { + padding: 2px 5px 2px 5px; +} + +/* spacing in see also definition lists */ +dl.last > dd { + margin-top: 1px; + margin-bottom: 5px; + margin-left: 30px; +} + +/** + * Hide dummy toctrees + */ + +ul { + padding-top: 0; + padding-bottom: 0; + margin-top: 0; + margin-bottom: 0; +} +ul li { + padding-top: 0; + padding-bottom: 0; + margin-top: 0; + margin-bottom: 0; +} +ul li a.reference { + padding-top: 0; + padding-bottom: 0; + margin-top: 0; + margin-bottom: 0; +} + +/** + * Make high-level subsections easier to distinguish from top-level ones + */ +div.body h3 { + background-color: transparent; +} + +div.body h4 { + border: none; + background-color: transparent; +} + +/** + * Scipy colors + */ + +body { + background-color: rgb(100,135,220); +} + +div.document { + background-color: rgb(230,230,230); +} + +div.sphinxsidebar { + background-color: rgb(230,230,230); + overflow: hidden; +} + +div.related { + background-color: rgb(100,135,220); +} + +div.sphinxsidebar h3 { + color: rgb(0,102,204); +} + +div.sphinxsidebar h3 a { + color: rgb(0,102,204); +} + +div.sphinxsidebar h4 { + color: rgb(0,82,194); +} + +div.sphinxsidebar p { + color: black; +} + +div.sphinxsidebar a { + color: #355f7c; +} + +div.sphinxsidebar ul.want-points { + list-style: disc; +} + +.field-list th { + color: rgb(0,102,204); +} + +/** + * Extra admonitions + */ + +div.tip { + background-color: #ffffe4; + border: 1px solid #ee6; +} + +/* +div.admonition-example { + background-color: #e4ffe4; + border: 1px solid #ccc; +}*/ + + +/** + * Styling for field lists + */ + +table.field-list th { + border-left: 1px solid #aaa !important; + padding-left: 5px; +} + +table.field-list { + border-collapse: separate; + border-spacing: 10px; +} + +/** + * Styling for footnotes + */ + +table.footnote td, table.footnote th { + border: none; +} Added: scipy-docs/trunk/frontpage/_templates/download.html =================================================================== --- scipy-docs/trunk/frontpage/_templates/download.html 2008-10-26 17:54:24 UTC (rev 4849) +++ scipy-docs/trunk/frontpage/_templates/download.html 2008-10-26 18:10:15 UTC (rev 4850) @@ -0,0 +1,52 @@ +{% extends "layout.html" %} +{% set title = 'Download' %} +{% block body %} + +

    Download Scipy and Numpy Documentation

    + +

    This documentation on this site is available for download in + the following formats:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    HTML (zip)ca. 3 MBNumpy HTML documentation, packaged.
    PDF (Numpy reference)ca. 2 MBNumpy reference guide, as PDF.
    PDF (Numpy user guide)ca. 0.5 MBNumpy's User Guide (work-in-progress) part, as PDF.
    HTML (zip)ca. 3 MBScipy HTML documentation, packaged.
    PDF (Scipy reference)ca. 2 MBScipy reference guide (work-in-progress), as PDF.
    + +

    Additionally, you may be interested in

    + + + + + + + + + + +
    Guide to NumpyThe original Numpy reference book, by Travis Oliphant.
    Scipy.org websiteAdditional community-contributed documentation, tutorials, + examples, etc.
    + +{% endblock %} Added: scipy-docs/trunk/frontpage/_templates/indexcontent.html =================================================================== --- scipy-docs/trunk/frontpage/_templates/indexcontent.html 2008-10-26 17:54:24 UTC (rev 4849) +++ scipy-docs/trunk/frontpage/_templates/indexcontent.html 2008-10-26 18:10:15 UTC (rev 4850) @@ -0,0 +1,43 @@ +{% extends "defindex.html" %} +{% block tables %} +

    Parts of the documentation:

    + + +
    +

    Numpy Reference
    + reference documentation for Numpy

    +

    Guide to Numpy
    + book describing Numpy, by Travis Oliphant

    +     		+

    SciPy.org
    + more documentation on the scipy.org website

    +
    + +

    Work in progress:

    + + +
    +

    Numpy User Guide
    + user guide for Numpy

    +     		+

    Scipy Reference Guide
    + reference documentation for Scipy

    +
    + +

    Participate:

    + + +
    +

    Reporting bugs
    + reporting defects in Numpy and Scipy

    +

    Developer Zone
    + information about Numpy and Scipy development

    +     		+

    Numpy Documentation Marathon
    + improve this documentation

    +
    + +{% endblock %} + +{% block sidebarsearch %} +{% endblock %} Added: scipy-docs/trunk/frontpage/_templates/indexsidebar.html =================================================================== --- scipy-docs/trunk/frontpage/_templates/indexsidebar.html 2008-10-26 17:54:24 UTC (rev 4849) +++ scipy-docs/trunk/frontpage/_templates/indexsidebar.html 2008-10-26 18:10:15 UTC (rev 4850) @@ -0,0 +1,12 @@ +

    Download

    + + +

    Resources

    + Added: scipy-docs/trunk/frontpage/_templates/layout.html =================================================================== --- scipy-docs/trunk/frontpage/_templates/layout.html 2008-10-26 17:54:24 UTC (rev 4849) +++ scipy-docs/trunk/frontpage/_templates/layout.html 2008-10-26 18:10:15 UTC (rev 4850) @@ -0,0 +1,4 @@ +{% extends "!layout.html" %} +{% block rootrellink %} +
  • {{ shorttitle }}{{ reldelim1 }}
    • +{% endblock %} Added: scipy-docs/trunk/frontpage/conf.py =================================================================== --- scipy-docs/trunk/frontpage/conf.py 2008-10-26 17:54:24 UTC (rev 4849) +++ scipy-docs/trunk/frontpage/conf.py 2008-10-26 18:10:15 UTC (rev 4850) @@ -0,0 +1,189 @@ +# -*- coding: utf-8 -*- +# +# Numpy/Scipy Documentation documentation build configuration file, created by +# sphinx-quickstart on Thu Oct 23 20:48:29 2008. +# +# This file is execfile()d with the current directory set to its containing dir. +# +# The contents of this file are pickled, so don't put values in the namespace +# that aren't pickleable (module imports are okay, they're removed automatically). +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +import sys, os + +# If your extensions are in another directory, add it here. If the directory +# is relative to the documentation root, use os.path.abspath to make it +# absolute, like shown here. +#sys.path.append(os.path.abspath('.')) + +# General configuration +# --------------------- + +# Add any Sphinx extension module names here, as strings. They can be extensions +# coming with Sphinx (named 'sphinx.ext.*') or your custom ones. +extensions = [] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix of source filenames. +source_suffix = '.rst' + +# The encoding of source files. +#source_encoding = 'utf-8' + +# The master toctree document. +master_doc = 'contents' + +# General information about the project. +project = u'Numpy and Scipy' +copyright = u'2008, Scipy Community' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = '' +# The full version, including alpha/beta/rc tags. +release = '' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +#language = None + +# There are two options for replacing |today|: either, you set today to some +# non-false value, then it is used: +#today = '' +# Else, today_fmt is used as the format for a strftime call. +#today_fmt = '%B %d, %Y' + +# List of documents that shouldn't be included in the build. +#unused_docs = [] + +# List of directories, relative to source directory, that shouldn't be searched +# for source files. +exclude_trees = ['_build'] + +# The reST default role (used for this markup: `text`) to use for all documents. +#default_role = None + +# If true, '()' will be appended to :func: etc. cross-reference text. +#add_function_parentheses = True + +# If true, the current module name will be prepended to all description +# unit titles (such as .. function::). +#add_module_names = True + +# If true, sectionauthor and moduleauthor directives will be shown in the +# output. They are ignored by default. +#show_authors = False + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + + +# Options for HTML output +# ----------------------- + +# The style sheet to use for HTML and HTML Help pages. A file of that name +# must exist either in Sphinx' static/ path, or in one of the custom paths +# given in html_static_path. +html_style = 'scipy.css' + +# The name for this set of Sphinx documents. If None, it defaults to +# " v documentation". +html_title = "Numpy and Scipy Documentation" + +# A shorter title for the navigation bar. Default is the same as html_title. +#html_short_title = None + +# The name of an image file (relative to this directory) to place at the top +# of the sidebar. +html_logo = "scipyshiny_small.png" + +# The name of an image file (within the static path) to use as favicon of the +# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 +# pixels large. +#html_favicon = None + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, +# using the given strftime format. +#html_last_updated_fmt = '%b %d, %Y' + +# If true, SmartyPants will be used to convert quotes and dashes to +# typographically correct entities. +#html_use_smartypants = True + +# Custom sidebar templates, maps document names to template names. +html_sidebars = { + 'index': 'indexsidebar.html' +} + +# Additional templates that should be rendered to pages, maps page names to +# template names. +html_additional_pages = { + 'index': 'indexcontent.html', + 'download': 'download.html', +} + +# If false, no module index is generated. +html_use_modindex = False + +# If false, no index is generated. +html_use_index = False + +# If true, the index is split into individual pages for each letter. +#html_split_index = False + +# If true, the reST sources are included in the HTML build as _sources/. +html_copy_source = False + +# If true, an OpenSearch description file will be output, and all pages will +# contain a tag referring to it. The value of this option must be the +# base URL from which the finished HTML is served. +#html_use_opensearch = '' + +# If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml"). +#html_file_suffix = '' + +# Output file base name for HTML help builder. +htmlhelp_basename = 'NumpyScipyDocumentationdoc' + + +# Options for LaTeX output +# ------------------------ + +# The paper size ('letter' or 'a4'). +#latex_paper_size = 'letter' + +# The font size ('10pt', '11pt' or '12pt'). +#latex_font_size = '10pt' + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, author, document class [howto/manual]). +latex_documents = [ ] + +# The name of an image file (relative to this directory) to place at the top of +# the title page. +#latex_logo = None + +# For "manual" documents, if this is true, then toplevel headings are parts, +# not chapters. +#latex_use_parts = False + +# Additional stuff for the LaTeX preamble. +#latex_preamble = '' + +# Documents to append as an appendix to all manuals. +#latex_appendices = [] + +# If false, no module index is generated. +#latex_use_modindex = True Added: scipy-docs/trunk/frontpage/contents.rst =================================================================== --- scipy-docs/trunk/frontpage/contents.rst 2008-10-26 17:54:24 UTC (rev 4849) +++ scipy-docs/trunk/frontpage/contents.rst 2008-10-26 18:10:15 UTC (rev 4850) @@ -0,0 +1 @@ +.. All the content is under _templates/ Added: scipy-docs/trunk/frontpage/scipyshiny_small.png =================================================================== (Binary files differ) Property changes on: scipy-docs/trunk/frontpage/scipyshiny_small.png ___________________________________________________________________ Name: svn:mime-type + image/png From scipy-svn at scipy.org Sun Oct 26 14:14:08 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 13:14:08 -0500 (CDT) Subject: [Scipy-svn] r4851 - scipy-docs/trunk/source Message-ID: <20081026181408.6A8EA39C05F@scipy.org> Author: ptvirtan Date: 2008-10-26 13:13:58 -0500 (Sun, 26 Oct 2008) New Revision: 4851 Removed: scipy-docs/trunk/source/generated/ Log: Remove extraneous directory From scipy-svn at scipy.org Sun Oct 26 14:15:44 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 13:15:44 -0500 (CDT) Subject: [Scipy-svn] r4852 - in scipy-docs/trunk: . source Message-ID: <20081026181544.E28CC39C05F@scipy.org> Author: ptvirtan Date: 2008-10-26 13:15:34 -0500 (Sun, 26 Oct 2008) New Revision: 4852 Modified: scipy-docs/trunk/ scipy-docs/trunk/source/ Log: Set svn:ignore Property changes on: scipy-docs/trunk ___________________________________________________________________ Name: svn:ignore + build *.pyc *~ Property changes on: scipy-docs/trunk/source ___________________________________________________________________ Name: svn:ignore + generated From scipy-svn at scipy.org Sun Oct 26 22:39:57 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 21:39:57 -0500 (CDT) Subject: [Scipy-svn] r4853 - trunk/scipy/sparse Message-ID: <20081027023957.66C0439C05F@scipy.org> Author: wnbell Date: 2008-10-26 21:39:52 -0500 (Sun, 26 Oct 2008) New Revision: 4853 Modified: trunk/scipy/sparse/base.py trunk/scipy/sparse/extract.py Log: minor edits Modified: trunk/scipy/sparse/base.py =================================================================== --- trunk/scipy/sparse/base.py 2008-10-26 18:15:34 UTC (rev 4852) +++ trunk/scipy/sparse/base.py 2008-10-27 02:39:52 UTC (rev 4853) @@ -514,7 +514,7 @@ def todia(self): return self.tocoo().todia() - def tobsr(self,blocksize=None): + def tobsr(self, blocksize=None): return self.tocsr().tobsr(blocksize=blocksize) def copy(self): Modified: trunk/scipy/sparse/extract.py =================================================================== --- trunk/scipy/sparse/extract.py 2008-10-26 18:15:34 UTC (rev 4852) +++ trunk/scipy/sparse/extract.py 2008-10-27 02:39:52 UTC (rev 4853) @@ -37,7 +37,7 @@ Examples -------- >>> from scipy.sparse import csr_matrix - >>> A = csr_matrix( [[1,2,0,0,3],[4,5,0,6,7],[0,0,8,9,0]] ) + >>> A = csr_matrix( [[1,2,0,0,3],[4,5,0,6,7],[0,0,8,9,0]], dtype='int32' ) >>> A.todense() matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], @@ -103,7 +103,7 @@ Examples -------- >>> from scipy.sparse import csr_matrix - >>> A = csr_matrix( [[1,2,0,0,3],[4,5,0,6,7],[0,0,8,9,0]] ) + >>> A = csr_matrix( [[1,2,0,0,3],[4,5,0,6,7],[0,0,8,9,0]], dtype='int32' ) >>> A.todense() matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], From scipy-svn at scipy.org Sun Oct 26 23:08:09 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 22:08:09 -0500 (CDT) Subject: [Scipy-svn] r4854 - in trunk/scipy/sparse: . benchmarks Message-ID: <20081027030809.4CA7339C05F@scipy.org> Author: wnbell Date: 2008-10-26 22:08:05 -0500 (Sun, 26 Oct 2008) New Revision: 4854 Modified: trunk/scipy/sparse/base.py trunk/scipy/sparse/benchmarks/bench_sparse.py trunk/scipy/sparse/bsr.py trunk/scipy/sparse/compressed.py trunk/scipy/sparse/coo.py trunk/scipy/sparse/dia.py trunk/scipy/sparse/dok.py Log: cleaned up dok_matrix imports use native matrix * vector product in dok_matrix renamed _mul_densematrix to _mul_multivector Modified: trunk/scipy/sparse/base.py =================================================================== --- trunk/scipy/sparse/base.py 2008-10-27 02:39:52 UTC (rev 4853) +++ trunk/scipy/sparse/base.py 2008-10-27 03:08:05 UTC (rev 4854) @@ -272,8 +272,8 @@ self._mul_scalar() self._mul_vector() + self._mul_multivector() self._mul_sparse_matrix() - self._mul_dense_matrix() """ M,N = self.shape @@ -310,12 +310,12 @@ elif len(other.shape) == 2: ## - # dense 2D array or matrix + # dense 2D array or matrix ("multivector") if other.shape[0] != self.shape[1]: raise ValueError('dimension mismatch') - result = self._mul_dense_matrix(np.asarray(other)) + result = self._mul_multivector(np.asarray(other)) if isinstance(other, np.matrix): result = np.asmatrix(result) @@ -331,8 +331,8 @@ def _mul_vector(self, other): return self.tocsr()._mul_vector(other) - def _mul_dense_matrix(self, other): - return self.tocsr()._mul_dense_matrix(other) + def _mul_multivector(self, other): + return self.tocsr()._mul_multivector(other) def _mul_sparse_matrix(self, other): return self.tocsr()._mul_sparse_matrix(other) Modified: trunk/scipy/sparse/benchmarks/bench_sparse.py =================================================================== --- trunk/scipy/sparse/benchmarks/bench_sparse.py 2008-10-27 02:39:52 UTC (rev 4853) +++ trunk/scipy/sparse/benchmarks/bench_sparse.py 2008-10-27 03:08:05 UTC (rev 4854) @@ -130,6 +130,8 @@ matrices = [] matrices.append(('Identity', sparse.identity(10**4,format='dia'))) matrices.append(('Identity', sparse.identity(10**4,format='csr'))) + matrices.append(('Poisson5pt', poisson2d(300,format='lil'))) + matrices.append(('Poisson5pt', poisson2d(300,format='dok'))) matrices.append(('Poisson5pt', poisson2d(300,format='dia'))) matrices.append(('Poisson5pt', poisson2d(300,format='coo'))) matrices.append(('Poisson5pt', poisson2d(300,format='csr'))) Modified: trunk/scipy/sparse/bsr.py =================================================================== --- trunk/scipy/sparse/bsr.py 2008-10-27 02:39:52 UTC (rev 4853) +++ trunk/scipy/sparse/bsr.py 2008-10-27 03:08:05 UTC (rev 4854) @@ -298,7 +298,7 @@ return result - def _mul_dense_matrix(self,other): + def _mul_multivector(self,other): R,C = self.blocksize M,N = self.shape n_vecs = other.shape[1] #number of column vectors Modified: trunk/scipy/sparse/compressed.py =================================================================== --- trunk/scipy/sparse/compressed.py 2008-10-27 02:39:52 UTC (rev 4853) +++ trunk/scipy/sparse/compressed.py 2008-10-27 03:08:05 UTC (rev 4854) @@ -266,7 +266,7 @@ return result - def _mul_dense_matrix(self,other): + def _mul_multivector(self, other): M,N = self.shape n_vecs = other.shape[1] #number of column vectors Modified: trunk/scipy/sparse/coo.py =================================================================== --- trunk/scipy/sparse/coo.py 2008-10-27 02:39:52 UTC (rev 4853) +++ trunk/scipy/sparse/coo.py 2008-10-27 03:08:05 UTC (rev 4854) @@ -364,7 +364,7 @@ coo_matvec(self.nnz, self.row, self.col, self.data, other, result) return result - def _mul_dense_matrix(self, other): + def _mul_multivector(self, other): return np.hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) from sputils import _isinstance Modified: trunk/scipy/sparse/dia.py =================================================================== --- trunk/scipy/sparse/dia.py 2008-10-27 02:39:52 UTC (rev 4853) +++ trunk/scipy/sparse/dia.py 2008-10-27 03:08:05 UTC (rev 4854) @@ -152,7 +152,7 @@ return y - def _mul_dense_matrix(self, other): + def _mul_multimatrix(self, other): return np.hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) def todia(self,copy=False): Modified: trunk/scipy/sparse/dok.py =================================================================== --- trunk/scipy/sparse/dok.py 2008-10-27 02:39:52 UTC (rev 4853) +++ trunk/scipy/sparse/dok.py 2008-10-27 03:08:05 UTC (rev 4854) @@ -7,10 +7,10 @@ import operator from itertools import izip -from numpy import asarray, intc, isscalar +import numpy as np -from base import spmatrix,isspmatrix -from sputils import isdense, getdtype, isshape, isintlike, isscalarlike +from base import spmatrix, isspmatrix +from sputils import isdense, getdtype, isshape, isintlike, isscalarlike, upcast class dok_matrix(spmatrix, dict): """Dictionary Of Keys based sparse matrix. @@ -64,7 +64,7 @@ self.dtype = arg1.dtype else: # Dense ctor try: - arg1 = asarray(arg1) + arg1 = np.asarray(arg1) except: raise TypeError('invalid input format') @@ -213,7 +213,7 @@ if i < 0 or i >= self.shape[0] or j < 0 or j >= self.shape[1]: raise IndexError, "index out of bounds" - if isscalar(value): + if np.isscalar(value): if value==0: del self[(i,j)] else: @@ -243,7 +243,7 @@ else: raise NotImplementedError, "setting a 2-d slice of" \ " a dok_matrix is not yet supported" - elif isscalar(value): + elif np.isscalar(value): for element in seq: self[element, j] = value else: @@ -282,7 +282,7 @@ else: raise NotImplementedError, "setting a 2-d slice of" \ " a dok_matrix is not yet supported" - elif isscalar(value): + elif np.isscalar(value): for element in seq: self[i, element] = value else: @@ -363,13 +363,28 @@ return new def _mul_scalar(self, other): - new = dok_matrix(self.shape, dtype=self.dtype) # Multiply this scalar by every element. + new = dok_matrix(self.shape, dtype=self.dtype) for (key, val) in self.iteritems(): new[key] = val * other - #new.dtype.char = self.dtype.char return new + def _mul_vector(self, other): + #matrix * vector + result = np.zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) + for (i,j),v in self.iteritems(): + result[i] += v * other[j] + return result + + def _mul_multivector(self, other): + #matrix * multivector + M,N = self.shape + n_vecs = other.shape[1] #number of column vectors + result = np.zeros( (M,n_vecs), dtype=upcast(self.dtype,other.dtype) ) + for (i,j),v in self.iteritems(): + result[i,:] += v * other[j,:] + return result + def __imul__(self, other): if isscalarlike(other): # Multiply this scalar by every element. @@ -427,7 +442,6 @@ def copy(self): new = dok_matrix(self.shape, dtype=self.dtype) new.update(self) - new.shape = self.shape return new def take(self, cols_or_rows, columns=1): @@ -475,51 +489,16 @@ newkey = (key[0]-num, key[1]) base[newkey] = self[key] return base, ext - -# TODO update these w/ new multiplication handlers -# def matvec(self, other): -# if isdense(other): -# if other.shape[0] != self.shape[1]: -# raise ValueError, "dimensions do not match" -# new = [0] * self.shape[0] -# for key in self.keys(): -# new[int(key[0])] += self[key] * other[int(key[1])] -# new = array(new) -# if isinstance(other, matrix): -# new = asmatrix(new) -# # Do we need to return the transpose? -# if other.shape[1] == 1: -# new = new.T -# return new -# else: -# raise TypeError, "need a dense vector" -# -# def rmatvec(self, other, conjugate=True): -# if isdense(other): -# if other.shape[-1] != self.shape[0]: -# raise ValueError, "dimensions do not match" -# new = [0] * self.shape[1] -# for key in self.keys(): -# new[int(key[1])] += other[int(key[0])] * conj(self[key]) -# new = array(new) -# if isinstance(other, matrix): -# new = asmatrix(new) -# # Do we need to return the transpose? -# if other.shape[1] == 1: -# new = new.T -# return new -# else: -# raise TypeError, "need a dense vector" - + def tocoo(self): """ Return a copy of this matrix in COOrdinate format""" from coo import coo_matrix if self.nnz == 0: return coo_matrix(self.shape, dtype=self.dtype) else: - data = asarray(self.values(), dtype=self.dtype) - indices = asarray(self.keys(), dtype=intc).T - return coo_matrix((data,indices),shape=self.shape,dtype=self.dtype) + data = np.asarray(self.values(), dtype=self.dtype) + indices = np.asarray(self.keys(), dtype=np.intc).T + return coo_matrix((data,indices), shape=self.shape, dtype=self.dtype) def todok(self,copy=False): if copy: From scipy-svn at scipy.org Sun Oct 26 23:56:27 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 22:56:27 -0500 (CDT) Subject: [Scipy-svn] r4855 - in trunk/scipy/sparse: . tests Message-ID: <20081027035627.02A7B39C05F@scipy.org> Author: wnbell Date: 2008-10-26 22:56:22 -0500 (Sun, 26 Oct 2008) New Revision: 4855 Modified: trunk/scipy/sparse/bsr.py trunk/scipy/sparse/compressed.py trunk/scipy/sparse/coo.py trunk/scipy/sparse/dia.py trunk/scipy/sparse/dok.py trunk/scipy/sparse/extract.py trunk/scipy/sparse/lil.py trunk/scipy/sparse/tests/test_base.py trunk/scipy/sparse/tests/test_extract.py Log: added scipy.sparse.find() fixed handling of dtype= constructor argument in dense case Modified: trunk/scipy/sparse/bsr.py =================================================================== --- trunk/scipy/sparse/bsr.py 2008-10-27 03:08:05 UTC (rev 4854) +++ trunk/scipy/sparse/bsr.py 2008-10-27 03:56:22 UTC (rev 4855) @@ -126,7 +126,7 @@ elif len(arg1) == 2: # (data,(row,col)) format from coo import coo_matrix - self._set_self( coo_matrix(arg1).tobsr(blocksize=blocksize) ) + self._set_self( coo_matrix(arg1, dtype=dtype).tobsr(blocksize=blocksize) ) elif len(arg1) == 3: # (data,indices,indptr) format @@ -144,7 +144,7 @@ raise ValueError("unrecognized form for" \ " %s_matrix constructor" % self.format) from coo import coo_matrix - arg1 = coo_matrix(arg1).tobsr(blocksize=blocksize) + arg1 = coo_matrix(arg1, dtype=dtype).tobsr(blocksize=blocksize) self._set_self( arg1 ) if shape is not None: Modified: trunk/scipy/sparse/compressed.py =================================================================== --- trunk/scipy/sparse/compressed.py 2008-10-27 03:08:05 UTC (rev 4854) +++ trunk/scipy/sparse/compressed.py 2008-10-27 03:56:22 UTC (rev 4855) @@ -68,7 +68,7 @@ raise ValueError, "unrecognized %s_matrix constructor usage" % \ self.format from coo import coo_matrix - self._set_self( self.__class__(coo_matrix(arg1)) ) + self._set_self( self.__class__(coo_matrix(arg1, dtype=dtype)) ) # Read matrix dimensions given, if any if shape is not None: Modified: trunk/scipy/sparse/coo.py =================================================================== --- trunk/scipy/sparse/coo.py 2008-10-27 03:08:05 UTC (rev 4854) +++ trunk/scipy/sparse/coo.py 2008-10-27 03:56:22 UTC (rev 4855) @@ -172,6 +172,10 @@ self.row,self.col = (M != 0).nonzero() self.data = M[self.row,self.col] + if dtype is not None: + self.data = self.data.astype(dtype) + + self._check() def getnnz(self): Modified: trunk/scipy/sparse/dia.py =================================================================== --- trunk/scipy/sparse/dia.py 2008-10-27 03:08:05 UTC (rev 4854) +++ trunk/scipy/sparse/dia.py 2008-10-27 03:56:22 UTC (rev 4855) @@ -94,7 +94,7 @@ raise ValueError("unrecognized form for" \ " %s_matrix constructor" % self.format) from coo import coo_matrix - A = coo_matrix(arg1).todia() + A = coo_matrix(arg1, dtype=dtype).todia() self.data = A.data self.offsets = A.offsets self.shape = A.shape Modified: trunk/scipy/sparse/dok.py =================================================================== --- trunk/scipy/sparse/dok.py 2008-10-27 03:08:05 UTC (rev 4854) +++ trunk/scipy/sparse/dok.py 2008-10-27 03:56:22 UTC (rev 4855) @@ -72,7 +72,7 @@ raise TypeError('expected rank <=2 dense array or matrix') from coo import coo_matrix - self.update( coo_matrix(arg1).todok() ) + self.update( coo_matrix(arg1, dtype=dtype).todok() ) self.shape = arg1.shape self.dtype = arg1.dtype Modified: trunk/scipy/sparse/extract.py =================================================================== --- trunk/scipy/sparse/extract.py 2008-10-27 03:08:05 UTC (rev 4854) +++ trunk/scipy/sparse/extract.py 2008-10-27 03:56:22 UTC (rev 4855) @@ -3,11 +3,43 @@ __docformat__ = "restructuredtext en" -__all__ = ['tril', 'triu'] +__all__ = ['find', 'tril', 'triu'] from coo import coo_matrix +def find(A): + """Return the indices and values of the nonzero elements of a matrix + + Parameters + ---------- + A : dense or sparse matrix + Matrix whose nonzero elements are desired. + + Returns + ------- + (I,J,V) : tuple of arrays + I,J, and V contain the row indices, column indices, and values + of the nonzero matrix entries. + + + Example + ------- + >>> from scipy.sparse import csr_matrix + >>> A = csr_matrix([[7.0, 8.0, 0],[0, 0, 9.0]]) + >>> find(A) + (array([0, 0, 1], dtype=int32), array([0, 1, 2], dtype=int32), array([ 7., 8., 9.])) + + """ + + A = coo_matrix(A).tocsr() #sums duplicates + A.eliminate_zeros() #removes explicit zeros + A = A.tocoo(copy=False) #(cheaply) convert to COO + + return A.row,A.col,A.data + + + def tril(A, k=0, format=None): """Return the lower triangular portion of a matrix in sparse format Modified: trunk/scipy/sparse/lil.py =================================================================== --- trunk/scipy/sparse/lil.py 2008-10-27 03:08:05 UTC (rev 4854) +++ trunk/scipy/sparse/lil.py 2008-10-27 03:56:22 UTC (rev 4855) @@ -91,7 +91,7 @@ raise TypeError('unsupported matrix type') else: from csr import csr_matrix - A = csr_matrix(A).tolil() + A = csr_matrix(A, dtype=dtype).tolil() self.shape = A.shape self.dtype = A.dtype Modified: trunk/scipy/sparse/tests/test_base.py =================================================================== --- trunk/scipy/sparse/tests/test_base.py 2008-10-27 03:08:05 UTC (rev 4854) +++ trunk/scipy/sparse/tests/test_base.py 2008-10-27 03:56:22 UTC (rev 4855) @@ -151,14 +151,32 @@ def test_from_array(self): A = array([[1,0,0],[2,3,4],[0,5,0],[0,0,0]]) assert_array_equal(self.spmatrix(A).todense(), A) + + A = array([[1.0 + 3j, 0, 0], + [ 0, 2.0 + 5, 0], + [ 0, 0, 0]]) + assert_array_equal(self.spmatrix(A).todense(), A) + assert_array_equal(self.spmatrix(A, dtype='int16').todense(), A.astype('int16')) def test_from_matrix(self): A = matrix([[1,0,0],[2,3,4],[0,5,0],[0,0,0]]) assert_array_equal(self.spmatrix(A).todense(), A) + + A = matrix([[1.0 + 3j, 0, 0], + [ 0, 2.0 + 5, 0], + [ 0, 0, 0]]) + assert_array_equal(self.spmatrix(A).todense(), A) + assert_array_equal(self.spmatrix(A, dtype='int16').todense(), A.astype('int16')) def test_from_list(self): A = [[1,0,0],[2,3,4],[0,5,0],[0,0,0]] assert_array_equal(self.spmatrix(A).todense(), A) + + A = [[1.0 + 3j, 0, 0], + [ 0, 2.0 + 5, 0], + [ 0, 0, 0]] + assert_array_equal(self.spmatrix(A).todense(), array(A)) + assert_array_equal(self.spmatrix(A, dtype='int16').todense(), array(A).astype('int16')) #def test_array(self): # """test array(A) where A is in sparse format""" Modified: trunk/scipy/sparse/tests/test_extract.py =================================================================== --- trunk/scipy/sparse/tests/test_extract.py 2008-10-27 03:08:05 UTC (rev 4854) +++ trunk/scipy/sparse/tests/test_extract.py 2008-10-27 03:56:22 UTC (rev 4855) @@ -26,6 +26,11 @@ self.cases = cases + def find(self): + for A in self.cases: + I,J,V = find(A) + assert_equal( A.toarray(), csr_matrix(((I,J),V), shape=A.shape) ) + def test_tril(self): for A in self.cases: B = A.toarray() From scipy-svn at scipy.org Mon Oct 27 00:14:57 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 23:14:57 -0500 (CDT) Subject: [Scipy-svn] r4856 - in trunk/scipy/sparse: . tests Message-ID: <20081027041457.0BAA539C05F@scipy.org> Author: wnbell Date: 2008-10-26 23:14:53 -0500 (Sun, 26 Oct 2008) New Revision: 4856 Modified: trunk/scipy/sparse/base.py trunk/scipy/sparse/bsr.py trunk/scipy/sparse/compressed.py trunk/scipy/sparse/dia.py trunk/scipy/sparse/dok.py trunk/scipy/sparse/lil.py trunk/scipy/sparse/tests/test_base.py Log: fixed handling of dtype= constructor argument in sparse case ensure that .astype(dtype) maintains sparse format Modified: trunk/scipy/sparse/base.py =================================================================== --- trunk/scipy/sparse/base.py 2008-10-27 03:56:22 UTC (rev 4855) +++ trunk/scipy/sparse/base.py 2008-10-27 04:14:53 UTC (rev 4856) @@ -87,7 +87,7 @@ raise NotImplementedError def astype(self, t): - return self.tocsr().astype(t) + return self.tocsr().astype(t).asformat(self.format) def asfptype(self): """Upcast matrix to a floating point format (if necessary)""" Modified: trunk/scipy/sparse/bsr.py =================================================================== --- trunk/scipy/sparse/bsr.py 2008-10-27 03:56:22 UTC (rev 4855) +++ trunk/scipy/sparse/bsr.py 2008-10-27 04:14:53 UTC (rev 4856) @@ -156,17 +156,20 @@ M = len(self.indptr) - 1 N = self.indices.max() + 1 except: - raise ValueError,'unable to infer matrix dimensions' + raise ValueError('unable to infer matrix dimensions') else: R,C = self.blocksize self.shape = (M*R,N*C) if self.shape is None: if shape is None: - #infer shape here - raise ValueError,'need to infer shape' + #TODO infer shape here + raise ValueError('need to infer shape') else: self.shape = shape + + if dtype is not None: + self.data = self.data.astype(dtype) self.check_format(full_check=False) Modified: trunk/scipy/sparse/compressed.py =================================================================== --- trunk/scipy/sparse/compressed.py 2008-10-27 03:56:22 UTC (rev 4855) +++ trunk/scipy/sparse/compressed.py 2008-10-27 04:14:53 UTC (rev 4856) @@ -83,6 +83,9 @@ raise ValueError,'unable to infer matrix dimensions' else: self.shape = self._swap((major_dim,minor_dim)) + + if dtype is not None: + self.data = self.data.astype(dtype) self.check_format(full_check=False) Modified: trunk/scipy/sparse/dia.py =================================================================== --- trunk/scipy/sparse/dia.py 2008-10-27 03:56:22 UTC (rev 4855) +++ trunk/scipy/sparse/dia.py 2008-10-27 04:14:53 UTC (rev 4856) @@ -100,6 +100,9 @@ self.shape = A.shape + if dtype is not None: + self.data = self.data.astype(dtype) + #check format if self.offsets.ndim != 1: raise ValueError('offsets array must have rank 1') Modified: trunk/scipy/sparse/dok.py =================================================================== --- trunk/scipy/sparse/dok.py 2008-10-27 03:56:22 UTC (rev 4855) +++ trunk/scipy/sparse/dok.py 2008-10-27 04:14:53 UTC (rev 4856) @@ -59,6 +59,10 @@ arg1 = arg1.copy() else: arg1 = arg1.todok() + + if dtype is not None: + arg1 = arg1.astype(dtype) + self.update(arg1) self.shape = arg1.shape self.dtype = arg1.dtype Modified: trunk/scipy/sparse/lil.py =================================================================== --- trunk/scipy/sparse/lil.py 2008-10-27 03:56:22 UTC (rev 4855) +++ trunk/scipy/sparse/lil.py 2008-10-27 04:14:53 UTC (rev 4856) @@ -66,6 +66,10 @@ A = arg1.copy() else: A = arg1.tolil() + + if dtype is not None: + A = A.astype(dtype) + self.shape = A.shape self.dtype = A.dtype self.rows = A.rows Modified: trunk/scipy/sparse/tests/test_base.py =================================================================== --- trunk/scipy/sparse/tests/test_base.py 2008-10-27 03:56:22 UTC (rev 4855) +++ trunk/scipy/sparse/tests/test_base.py 2008-10-27 04:14:53 UTC (rev 4856) @@ -150,13 +150,13 @@ def test_from_array(self): A = array([[1,0,0],[2,3,4],[0,5,0],[0,0,0]]) - assert_array_equal(self.spmatrix(A).todense(), A) + assert_array_equal(self.spmatrix(A).toarray(), A) A = array([[1.0 + 3j, 0, 0], [ 0, 2.0 + 5, 0], [ 0, 0, 0]]) - assert_array_equal(self.spmatrix(A).todense(), A) - assert_array_equal(self.spmatrix(A, dtype='int16').todense(), A.astype('int16')) + assert_array_equal(self.spmatrix(A).toarray(), A) + assert_array_equal(self.spmatrix(A, dtype='int16').toarray(), A.astype('int16')) def test_from_matrix(self): A = matrix([[1,0,0],[2,3,4],[0,5,0],[0,0,0]]) @@ -165,8 +165,8 @@ A = matrix([[1.0 + 3j, 0, 0], [ 0, 2.0 + 5, 0], [ 0, 0, 0]]) - assert_array_equal(self.spmatrix(A).todense(), A) - assert_array_equal(self.spmatrix(A, dtype='int16').todense(), A.astype('int16')) + assert_array_equal(self.spmatrix(A).toarray(), A) + assert_array_equal(self.spmatrix(A, dtype='int16').toarray(), A.astype('int16')) def test_from_list(self): A = [[1,0,0],[2,3,4],[0,5,0],[0,0,0]] @@ -175,9 +175,27 @@ A = [[1.0 + 3j, 0, 0], [ 0, 2.0 + 5, 0], [ 0, 0, 0]] - assert_array_equal(self.spmatrix(A).todense(), array(A)) + assert_array_equal(self.spmatrix(A).toarray(), array(A)) assert_array_equal(self.spmatrix(A, dtype='int16').todense(), array(A).astype('int16')) + def test_from_sparse(self): + D = array([[1,0,0],[2,3,4],[0,5,0],[0,0,0]]) + S = csr_matrix(D) + assert_array_equal(self.spmatrix(S).toarray(), D) + S = self.spmatrix(D) + assert_array_equal(self.spmatrix(S).toarray(), D) + + + D = array([[1.0 + 3j, 0, 0], + [ 0, 2.0 + 5, 0], + [ 0, 0, 0]]) + S = csr_matrix(D) + assert_array_equal(self.spmatrix(S).toarray(), D) + assert_array_equal(self.spmatrix(S, dtype='int16').toarray(), D.astype('int16')) + S = self.spmatrix(D) + assert_array_equal(self.spmatrix(S).toarray(), D) + assert_array_equal(self.spmatrix(S, dtype='int16').toarray(), D.astype('int16')) + #def test_array(self): # """test array(A) where A is in sparse format""" # assert_equal( array(self.datsp), self.dat ) From scipy-svn at scipy.org Mon Oct 27 00:23:01 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 26 Oct 2008 23:23:01 -0500 (CDT) Subject: [Scipy-svn] r4857 - trunk/scipy/sparse/tests Message-ID: <20081027042301.962D039C088@scipy.org> Author: wnbell Date: 2008-10-26 23:22:59 -0500 (Sun, 26 Oct 2008) New Revision: 4857 Modified: trunk/scipy/sparse/tests/test_base.py Log: test that .astype(dtype) preserves sparse format Modified: trunk/scipy/sparse/tests/test_base.py =================================================================== --- trunk/scipy/sparse/tests/test_base.py 2008-10-27 04:14:53 UTC (rev 4856) +++ trunk/scipy/sparse/tests/test_base.py 2008-10-27 04:22:59 UTC (rev 4857) @@ -224,12 +224,24 @@ dense_dot_dense = dot(dat, b) check2 = dot(self.datsp.toarray(), b) assert_array_equal(dense_dot_dense, check2) + + def test_astype(self): + D = array([[1.0 + 3j, 0, 0], + [ 0, 2.0 + 5, 0], + [ 0, 0, 0]]) + S = self.spmatrix(D) + for x in supported_dtypes: + assert_equal(S.astype(x).dtype, D.astype(x).dtype) # correct type + assert_equal(S.astype(x).toarray(), D.astype(x)) # correct values + assert_equal(S.astype(x).format, S.format) # format preserved + def test_asfptype(self): A = self.spmatrix( arange(6,dtype='int32').reshape(2,3) ) assert_equal( A.dtype , 'int32' ) assert_equal( A.asfptype().dtype, 'float64' ) + assert_equal( A.asfptype().format, A.format ) assert_equal( A.astype('int16').asfptype().dtype , 'float32' ) assert_equal( A.astype('complex128').asfptype().dtype , 'complex128' ) @@ -815,21 +827,6 @@ self.Asp = self.spmatrix(self.A) self.Bsp = self.spmatrix(self.B) - def test_astype(self): - self.arith_init() - - #check whether dtype and value is preserved in conversion - for x in supported_dtypes: - A = self.A.astype(x) - B = self.B.astype(x) - - Asp = self.spmatrix(A) - Bsp = self.spmatrix(B) - assert_equal(Asp.dtype,A.dtype) - assert_equal(Bsp.dtype,B.dtype) - assert_array_equal(Asp.todense(),A) - assert_array_equal(Bsp.todense(),B) - def test_add_sub(self): self.arith_init() From scipy-svn at scipy.org Tue Oct 28 01:11:21 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 28 Oct 2008 00:11:21 -0500 (CDT) Subject: [Scipy-svn] r4858 - in branches/refactor_fft: . scipy/sparse scipy/sparse/benchmarks scipy/sparse/tests Message-ID: <20081028051121.AF00D39C1AE@scipy.org> Author: cdavid Date: 2008-10-28 00:11:07 -0500 (Tue, 28 Oct 2008) New Revision: 4858 Modified: branches/refactor_fft/ branches/refactor_fft/scipy/sparse/base.py branches/refactor_fft/scipy/sparse/benchmarks/bench_sparse.py branches/refactor_fft/scipy/sparse/bsr.py branches/refactor_fft/scipy/sparse/compressed.py branches/refactor_fft/scipy/sparse/coo.py branches/refactor_fft/scipy/sparse/dia.py branches/refactor_fft/scipy/sparse/dok.py branches/refactor_fft/scipy/sparse/extract.py branches/refactor_fft/scipy/sparse/lil.py branches/refactor_fft/scipy/sparse/tests/test_base.py branches/refactor_fft/scipy/sparse/tests/test_extract.py Log: Merged revisions 4846-4857 via svnmerge from http://svn.scipy.org/svn/scipy/trunk ........ r4853 | wnbell | 2008-10-27 11:39:52 +0900 (Mon, 27 Oct 2008) | 2 lines minor edits ........ r4854 | wnbell | 2008-10-27 12:08:05 +0900 (Mon, 27 Oct 2008) | 4 lines cleaned up dok_matrix imports use native matrix * vector product in dok_matrix renamed _mul_densematrix to _mul_multivector ........ r4855 | wnbell | 2008-10-27 12:56:22 +0900 (Mon, 27 Oct 2008) | 3 lines added scipy.sparse.find() fixed handling of dtype= constructor argument in dense case ........ r4856 | wnbell | 2008-10-27 13:14:53 +0900 (Mon, 27 Oct 2008) | 3 lines fixed handling of dtype= constructor argument in sparse case ensure that .astype(dtype) maintains sparse format ........ r4857 | wnbell | 2008-10-27 13:22:59 +0900 (Mon, 27 Oct 2008) | 2 lines test that .astype(dtype) preserves sparse format ........ Property changes on: branches/refactor_fft ___________________________________________________________________ Name: svnmerge-integrated - /branches/build_with_scons:1-3868 /branches/scipy.scons:1-3533 /branches/sparse_build_reduce_mem:1-4005 /branches/testing_cleanup:1-3662 /trunk:1-4845 + /branches/build_with_scons:1-3868 /branches/scipy.scons:1-3533 /branches/sparse_build_reduce_mem:1-4005 /branches/testing_cleanup:1-3662 /trunk:1-4857 Modified: branches/refactor_fft/scipy/sparse/base.py =================================================================== --- branches/refactor_fft/scipy/sparse/base.py 2008-10-27 04:22:59 UTC (rev 4857) +++ branches/refactor_fft/scipy/sparse/base.py 2008-10-28 05:11:07 UTC (rev 4858) @@ -87,7 +87,7 @@ raise NotImplementedError def astype(self, t): - return self.tocsr().astype(t) + return self.tocsr().astype(t).asformat(self.format) def asfptype(self): """Upcast matrix to a floating point format (if necessary)""" @@ -272,8 +272,8 @@ self._mul_scalar() self._mul_vector() + self._mul_multivector() self._mul_sparse_matrix() - self._mul_dense_matrix() """ M,N = self.shape @@ -310,12 +310,12 @@ elif len(other.shape) == 2: ## - # dense 2D array or matrix + # dense 2D array or matrix ("multivector") if other.shape[0] != self.shape[1]: raise ValueError('dimension mismatch') - result = self._mul_dense_matrix(np.asarray(other)) + result = self._mul_multivector(np.asarray(other)) if isinstance(other, np.matrix): result = np.asmatrix(result) @@ -331,8 +331,8 @@ def _mul_vector(self, other): return self.tocsr()._mul_vector(other) - def _mul_dense_matrix(self, other): - return self.tocsr()._mul_dense_matrix(other) + def _mul_multivector(self, other): + return self.tocsr()._mul_multivector(other) def _mul_sparse_matrix(self, other): return self.tocsr()._mul_sparse_matrix(other) @@ -514,7 +514,7 @@ def todia(self): return self.tocoo().todia() - def tobsr(self,blocksize=None): + def tobsr(self, blocksize=None): return self.tocsr().tobsr(blocksize=blocksize) def copy(self): Modified: branches/refactor_fft/scipy/sparse/benchmarks/bench_sparse.py =================================================================== --- branches/refactor_fft/scipy/sparse/benchmarks/bench_sparse.py 2008-10-27 04:22:59 UTC (rev 4857) +++ branches/refactor_fft/scipy/sparse/benchmarks/bench_sparse.py 2008-10-28 05:11:07 UTC (rev 4858) @@ -130,6 +130,8 @@ matrices = [] matrices.append(('Identity', sparse.identity(10**4,format='dia'))) matrices.append(('Identity', sparse.identity(10**4,format='csr'))) + matrices.append(('Poisson5pt', poisson2d(300,format='lil'))) + matrices.append(('Poisson5pt', poisson2d(300,format='dok'))) matrices.append(('Poisson5pt', poisson2d(300,format='dia'))) matrices.append(('Poisson5pt', poisson2d(300,format='coo'))) matrices.append(('Poisson5pt', poisson2d(300,format='csr'))) Modified: branches/refactor_fft/scipy/sparse/bsr.py =================================================================== --- branches/refactor_fft/scipy/sparse/bsr.py 2008-10-27 04:22:59 UTC (rev 4857) +++ branches/refactor_fft/scipy/sparse/bsr.py 2008-10-28 05:11:07 UTC (rev 4858) @@ -126,7 +126,7 @@ elif len(arg1) == 2: # (data,(row,col)) format from coo import coo_matrix - self._set_self( coo_matrix(arg1).tobsr(blocksize=blocksize) ) + self._set_self( coo_matrix(arg1, dtype=dtype).tobsr(blocksize=blocksize) ) elif len(arg1) == 3: # (data,indices,indptr) format @@ -144,7 +144,7 @@ raise ValueError("unrecognized form for" \ " %s_matrix constructor" % self.format) from coo import coo_matrix - arg1 = coo_matrix(arg1).tobsr(blocksize=blocksize) + arg1 = coo_matrix(arg1, dtype=dtype).tobsr(blocksize=blocksize) self._set_self( arg1 ) if shape is not None: @@ -156,17 +156,20 @@ M = len(self.indptr) - 1 N = self.indices.max() + 1 except: - raise ValueError,'unable to infer matrix dimensions' + raise ValueError('unable to infer matrix dimensions') else: R,C = self.blocksize self.shape = (M*R,N*C) if self.shape is None: if shape is None: - #infer shape here - raise ValueError,'need to infer shape' + #TODO infer shape here + raise ValueError('need to infer shape') else: self.shape = shape + + if dtype is not None: + self.data = self.data.astype(dtype) self.check_format(full_check=False) @@ -298,7 +301,7 @@ return result - def _mul_dense_matrix(self,other): + def _mul_multivector(self,other): R,C = self.blocksize M,N = self.shape n_vecs = other.shape[1] #number of column vectors Modified: branches/refactor_fft/scipy/sparse/compressed.py =================================================================== --- branches/refactor_fft/scipy/sparse/compressed.py 2008-10-27 04:22:59 UTC (rev 4857) +++ branches/refactor_fft/scipy/sparse/compressed.py 2008-10-28 05:11:07 UTC (rev 4858) @@ -68,7 +68,7 @@ raise ValueError, "unrecognized %s_matrix constructor usage" % \ self.format from coo import coo_matrix - self._set_self( self.__class__(coo_matrix(arg1)) ) + self._set_self( self.__class__(coo_matrix(arg1, dtype=dtype)) ) # Read matrix dimensions given, if any if shape is not None: @@ -83,6 +83,9 @@ raise ValueError,'unable to infer matrix dimensions' else: self.shape = self._swap((major_dim,minor_dim)) + + if dtype is not None: + self.data = self.data.astype(dtype) self.check_format(full_check=False) @@ -266,7 +269,7 @@ return result - def _mul_dense_matrix(self,other): + def _mul_multivector(self, other): M,N = self.shape n_vecs = other.shape[1] #number of column vectors Modified: branches/refactor_fft/scipy/sparse/coo.py =================================================================== --- branches/refactor_fft/scipy/sparse/coo.py 2008-10-27 04:22:59 UTC (rev 4857) +++ branches/refactor_fft/scipy/sparse/coo.py 2008-10-28 05:11:07 UTC (rev 4858) @@ -172,6 +172,10 @@ self.row,self.col = (M != 0).nonzero() self.data = M[self.row,self.col] + if dtype is not None: + self.data = self.data.astype(dtype) + + self._check() def getnnz(self): @@ -364,7 +368,7 @@ coo_matvec(self.nnz, self.row, self.col, self.data, other, result) return result - def _mul_dense_matrix(self, other): + def _mul_multivector(self, other): return np.hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) from sputils import _isinstance Modified: branches/refactor_fft/scipy/sparse/dia.py =================================================================== --- branches/refactor_fft/scipy/sparse/dia.py 2008-10-27 04:22:59 UTC (rev 4857) +++ branches/refactor_fft/scipy/sparse/dia.py 2008-10-28 05:11:07 UTC (rev 4858) @@ -94,12 +94,15 @@ raise ValueError("unrecognized form for" \ " %s_matrix constructor" % self.format) from coo import coo_matrix - A = coo_matrix(arg1).todia() + A = coo_matrix(arg1, dtype=dtype).todia() self.data = A.data self.offsets = A.offsets self.shape = A.shape + if dtype is not None: + self.data = self.data.astype(dtype) + #check format if self.offsets.ndim != 1: raise ValueError('offsets array must have rank 1') @@ -152,7 +155,7 @@ return y - def _mul_dense_matrix(self, other): + def _mul_multimatrix(self, other): return np.hstack( [ self._mul_vector(col).reshape(-1,1) for col in other.T ] ) def todia(self,copy=False): Modified: branches/refactor_fft/scipy/sparse/dok.py =================================================================== --- branches/refactor_fft/scipy/sparse/dok.py 2008-10-27 04:22:59 UTC (rev 4857) +++ branches/refactor_fft/scipy/sparse/dok.py 2008-10-28 05:11:07 UTC (rev 4858) @@ -7,10 +7,10 @@ import operator from itertools import izip -from numpy import asarray, intc, isscalar +import numpy as np -from base import spmatrix,isspmatrix -from sputils import isdense, getdtype, isshape, isintlike, isscalarlike +from base import spmatrix, isspmatrix +from sputils import isdense, getdtype, isshape, isintlike, isscalarlike, upcast class dok_matrix(spmatrix, dict): """Dictionary Of Keys based sparse matrix. @@ -59,12 +59,16 @@ arg1 = arg1.copy() else: arg1 = arg1.todok() + + if dtype is not None: + arg1 = arg1.astype(dtype) + self.update(arg1) self.shape = arg1.shape self.dtype = arg1.dtype else: # Dense ctor try: - arg1 = asarray(arg1) + arg1 = np.asarray(arg1) except: raise TypeError('invalid input format') @@ -72,7 +76,7 @@ raise TypeError('expected rank <=2 dense array or matrix') from coo import coo_matrix - self.update( coo_matrix(arg1).todok() ) + self.update( coo_matrix(arg1, dtype=dtype).todok() ) self.shape = arg1.shape self.dtype = arg1.dtype @@ -213,7 +217,7 @@ if i < 0 or i >= self.shape[0] or j < 0 or j >= self.shape[1]: raise IndexError, "index out of bounds" - if isscalar(value): + if np.isscalar(value): if value==0: del self[(i,j)] else: @@ -243,7 +247,7 @@ else: raise NotImplementedError, "setting a 2-d slice of" \ " a dok_matrix is not yet supported" - elif isscalar(value): + elif np.isscalar(value): for element in seq: self[element, j] = value else: @@ -282,7 +286,7 @@ else: raise NotImplementedError, "setting a 2-d slice of" \ " a dok_matrix is not yet supported" - elif isscalar(value): + elif np.isscalar(value): for element in seq: self[i, element] = value else: @@ -363,13 +367,28 @@ return new def _mul_scalar(self, other): - new = dok_matrix(self.shape, dtype=self.dtype) # Multiply this scalar by every element. + new = dok_matrix(self.shape, dtype=self.dtype) for (key, val) in self.iteritems(): new[key] = val * other - #new.dtype.char = self.dtype.char return new + def _mul_vector(self, other): + #matrix * vector + result = np.zeros( self.shape[0], dtype=upcast(self.dtype,other.dtype) ) + for (i,j),v in self.iteritems(): + result[i] += v * other[j] + return result + + def _mul_multivector(self, other): + #matrix * multivector + M,N = self.shape + n_vecs = other.shape[1] #number of column vectors + result = np.zeros( (M,n_vecs), dtype=upcast(self.dtype,other.dtype) ) + for (i,j),v in self.iteritems(): + result[i,:] += v * other[j,:] + return result + def __imul__(self, other): if isscalarlike(other): # Multiply this scalar by every element. @@ -427,7 +446,6 @@ def copy(self): new = dok_matrix(self.shape, dtype=self.dtype) new.update(self) - new.shape = self.shape return new def take(self, cols_or_rows, columns=1): @@ -475,51 +493,16 @@ newkey = (key[0]-num, key[1]) base[newkey] = self[key] return base, ext - -# TODO update these w/ new multiplication handlers -# def matvec(self, other): -# if isdense(other): -# if other.shape[0] != self.shape[1]: -# raise ValueError, "dimensions do not match" -# new = [0] * self.shape[0] -# for key in self.keys(): -# new[int(key[0])] += self[key] * other[int(key[1])] -# new = array(new) -# if isinstance(other, matrix): -# new = asmatrix(new) -# # Do we need to return the transpose? -# if other.shape[1] == 1: -# new = new.T -# return new -# else: -# raise TypeError, "need a dense vector" -# -# def rmatvec(self, other, conjugate=True): -# if isdense(other): -# if other.shape[-1] != self.shape[0]: -# raise ValueError, "dimensions do not match" -# new = [0] * self.shape[1] -# for key in self.keys(): -# new[int(key[1])] += other[int(key[0])] * conj(self[key]) -# new = array(new) -# if isinstance(other, matrix): -# new = asmatrix(new) -# # Do we need to return the transpose? -# if other.shape[1] == 1: -# new = new.T -# return new -# else: -# raise TypeError, "need a dense vector" - + def tocoo(self): """ Return a copy of this matrix in COOrdinate format""" from coo import coo_matrix if self.nnz == 0: return coo_matrix(self.shape, dtype=self.dtype) else: - data = asarray(self.values(), dtype=self.dtype) - indices = asarray(self.keys(), dtype=intc).T - return coo_matrix((data,indices),shape=self.shape,dtype=self.dtype) + data = np.asarray(self.values(), dtype=self.dtype) + indices = np.asarray(self.keys(), dtype=np.intc).T + return coo_matrix((data,indices), shape=self.shape, dtype=self.dtype) def todok(self,copy=False): if copy: Modified: branches/refactor_fft/scipy/sparse/extract.py =================================================================== --- branches/refactor_fft/scipy/sparse/extract.py 2008-10-27 04:22:59 UTC (rev 4857) +++ branches/refactor_fft/scipy/sparse/extract.py 2008-10-28 05:11:07 UTC (rev 4858) @@ -3,11 +3,43 @@ __docformat__ = "restructuredtext en" -__all__ = ['tril', 'triu'] +__all__ = ['find', 'tril', 'triu'] from coo import coo_matrix +def find(A): + """Return the indices and values of the nonzero elements of a matrix + + Parameters + ---------- + A : dense or sparse matrix + Matrix whose nonzero elements are desired. + + Returns + ------- + (I,J,V) : tuple of arrays + I,J, and V contain the row indices, column indices, and values + of the nonzero matrix entries. + + + Example + ------- + >>> from scipy.sparse import csr_matrix + >>> A = csr_matrix([[7.0, 8.0, 0],[0, 0, 9.0]]) + >>> find(A) + (array([0, 0, 1], dtype=int32), array([0, 1, 2], dtype=int32), array([ 7., 8., 9.])) + + """ + + A = coo_matrix(A).tocsr() #sums duplicates + A.eliminate_zeros() #removes explicit zeros + A = A.tocoo(copy=False) #(cheaply) convert to COO + + return A.row,A.col,A.data + + + def tril(A, k=0, format=None): """Return the lower triangular portion of a matrix in sparse format @@ -37,7 +69,7 @@ Examples -------- >>> from scipy.sparse import csr_matrix - >>> A = csr_matrix( [[1,2,0,0,3],[4,5,0,6,7],[0,0,8,9,0]] ) + >>> A = csr_matrix( [[1,2,0,0,3],[4,5,0,6,7],[0,0,8,9,0]], dtype='int32' ) >>> A.todense() matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], @@ -103,7 +135,7 @@ Examples -------- >>> from scipy.sparse import csr_matrix - >>> A = csr_matrix( [[1,2,0,0,3],[4,5,0,6,7],[0,0,8,9,0]] ) + >>> A = csr_matrix( [[1,2,0,0,3],[4,5,0,6,7],[0,0,8,9,0]], dtype='int32' ) >>> A.todense() matrix([[1, 2, 0, 0, 3], [4, 5, 0, 6, 7], Modified: branches/refactor_fft/scipy/sparse/lil.py =================================================================== --- branches/refactor_fft/scipy/sparse/lil.py 2008-10-27 04:22:59 UTC (rev 4857) +++ branches/refactor_fft/scipy/sparse/lil.py 2008-10-28 05:11:07 UTC (rev 4858) @@ -66,6 +66,10 @@ A = arg1.copy() else: A = arg1.tolil() + + if dtype is not None: + A = A.astype(dtype) + self.shape = A.shape self.dtype = A.dtype self.rows = A.rows @@ -91,7 +95,7 @@ raise TypeError('unsupported matrix type') else: from csr import csr_matrix - A = csr_matrix(A).tolil() + A = csr_matrix(A, dtype=dtype).tolil() self.shape = A.shape self.dtype = A.dtype Modified: branches/refactor_fft/scipy/sparse/tests/test_base.py =================================================================== --- branches/refactor_fft/scipy/sparse/tests/test_base.py 2008-10-27 04:22:59 UTC (rev 4857) +++ branches/refactor_fft/scipy/sparse/tests/test_base.py 2008-10-28 05:11:07 UTC (rev 4858) @@ -150,16 +150,52 @@ def test_from_array(self): A = array([[1,0,0],[2,3,4],[0,5,0],[0,0,0]]) - assert_array_equal(self.spmatrix(A).todense(), A) + assert_array_equal(self.spmatrix(A).toarray(), A) + + A = array([[1.0 + 3j, 0, 0], + [ 0, 2.0 + 5, 0], + [ 0, 0, 0]]) + assert_array_equal(self.spmatrix(A).toarray(), A) + assert_array_equal(self.spmatrix(A, dtype='int16').toarray(), A.astype('int16')) def test_from_matrix(self): A = matrix([[1,0,0],[2,3,4],[0,5,0],[0,0,0]]) assert_array_equal(self.spmatrix(A).todense(), A) + + A = matrix([[1.0 + 3j, 0, 0], + [ 0, 2.0 + 5, 0], + [ 0, 0, 0]]) + assert_array_equal(self.spmatrix(A).toarray(), A) + assert_array_equal(self.spmatrix(A, dtype='int16').toarray(), A.astype('int16')) def test_from_list(self): A = [[1,0,0],[2,3,4],[0,5,0],[0,0,0]] assert_array_equal(self.spmatrix(A).todense(), A) + + A = [[1.0 + 3j, 0, 0], + [ 0, 2.0 + 5, 0], + [ 0, 0, 0]] + assert_array_equal(self.spmatrix(A).toarray(), array(A)) + assert_array_equal(self.spmatrix(A, dtype='int16').todense(), array(A).astype('int16')) + def test_from_sparse(self): + D = array([[1,0,0],[2,3,4],[0,5,0],[0,0,0]]) + S = csr_matrix(D) + assert_array_equal(self.spmatrix(S).toarray(), D) + S = self.spmatrix(D) + assert_array_equal(self.spmatrix(S).toarray(), D) + + + D = array([[1.0 + 3j, 0, 0], + [ 0, 2.0 + 5, 0], + [ 0, 0, 0]]) + S = csr_matrix(D) + assert_array_equal(self.spmatrix(S).toarray(), D) + assert_array_equal(self.spmatrix(S, dtype='int16').toarray(), D.astype('int16')) + S = self.spmatrix(D) + assert_array_equal(self.spmatrix(S).toarray(), D) + assert_array_equal(self.spmatrix(S, dtype='int16').toarray(), D.astype('int16')) + #def test_array(self): # """test array(A) where A is in sparse format""" # assert_equal( array(self.datsp), self.dat ) @@ -188,12 +224,24 @@ dense_dot_dense = dot(dat, b) check2 = dot(self.datsp.toarray(), b) assert_array_equal(dense_dot_dense, check2) + + def test_astype(self): + D = array([[1.0 + 3j, 0, 0], + [ 0, 2.0 + 5, 0], + [ 0, 0, 0]]) + S = self.spmatrix(D) + for x in supported_dtypes: + assert_equal(S.astype(x).dtype, D.astype(x).dtype) # correct type + assert_equal(S.astype(x).toarray(), D.astype(x)) # correct values + assert_equal(S.astype(x).format, S.format) # format preserved + def test_asfptype(self): A = self.spmatrix( arange(6,dtype='int32').reshape(2,3) ) assert_equal( A.dtype , 'int32' ) assert_equal( A.asfptype().dtype, 'float64' ) + assert_equal( A.asfptype().format, A.format ) assert_equal( A.astype('int16').asfptype().dtype , 'float32' ) assert_equal( A.astype('complex128').asfptype().dtype , 'complex128' ) @@ -779,21 +827,6 @@ self.Asp = self.spmatrix(self.A) self.Bsp = self.spmatrix(self.B) - def test_astype(self): - self.arith_init() - - #check whether dtype and value is preserved in conversion - for x in supported_dtypes: - A = self.A.astype(x) - B = self.B.astype(x) - - Asp = self.spmatrix(A) - Bsp = self.spmatrix(B) - assert_equal(Asp.dtype,A.dtype) - assert_equal(Bsp.dtype,B.dtype) - assert_array_equal(Asp.todense(),A) - assert_array_equal(Bsp.todense(),B) - def test_add_sub(self): self.arith_init() Modified: branches/refactor_fft/scipy/sparse/tests/test_extract.py =================================================================== --- branches/refactor_fft/scipy/sparse/tests/test_extract.py 2008-10-27 04:22:59 UTC (rev 4857) +++ branches/refactor_fft/scipy/sparse/tests/test_extract.py 2008-10-28 05:11:07 UTC (rev 4858) @@ -26,6 +26,11 @@ self.cases = cases + def find(self): + for A in self.cases: + I,J,V = find(A) + assert_equal( A.toarray(), csr_matrix(((I,J),V), shape=A.shape) ) + def test_tril(self): for A in self.cases: B = A.toarray() From scipy-svn at scipy.org Tue Oct 28 01:18:05 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 28 Oct 2008 00:18:05 -0500 (CDT) Subject: [Scipy-svn] r4859 - branches/refactor_fft/scipy/fftpack/fftpack Message-ID: <20081028051805.6EC4739C1AE@scipy.org> Author: cdavid Date: 2008-10-28 00:18:00 -0500 (Tue, 28 Oct 2008) New Revision: 4859 Added: branches/refactor_fft/scipy/fftpack/fftpack/SConscript Removed: branches/refactor_fft/scipy/fftpack/fftpack/SConstruct Log: Update fftpack backend numscons build. Copied: branches/refactor_fft/scipy/fftpack/fftpack/SConscript (from rev 4845, branches/refactor_fft/scipy/fftpack/fftpack/SConstruct) =================================================================== --- branches/refactor_fft/scipy/fftpack/fftpack/SConstruct 2008-10-26 09:20:24 UTC (rev 4845) +++ branches/refactor_fft/scipy/fftpack/fftpack/SConscript 2008-10-28 05:18:00 UTC (rev 4859) @@ -0,0 +1,34 @@ +# Last Change: Tue Oct 28 02:00 PM 2008 J +# vim:syntax=python +from os.path import join as pjoin + +from numpy.distutils.misc_util import get_numpy_include_dirs +from numscons import GetNumpyEnvironment +from numscons import CheckF77Clib + +env = GetNumpyEnvironment(ARGUMENTS) +env.Tool('numpyf2py') + +env.AppendUnique(CPPPATH = get_numpy_include_dirs()) +env.AppendUnique(CPPPATH = env['F2PYINCLUDEDIR']) +env.AppendUnique(CPPPATH = [pjoin(env['build_dir'], "src")]) + +config = env.NumpyConfigure(custom_tests = {'CheckF77Clib' : CheckF77Clib}) +if not config.CheckF77Clib(): + raise Exception("Could not get f77/c++ link information") +config.Finish() + +# Build dfftpack +src = env.NumpyGlob(pjoin('DFFTPACK', '*.f')) +dfftpack = env.NumpyStaticExtLibrary('dfftpack', source = [str(s) for s in src]) +env.PrependUnique(LIBS = ['dfftpack']) +env.PrependUnique(LIBPATH = env['build_dir']) + +# Build the fftpack wrapper +src = [pjoin("src/fftpack", i) for i in ['zfft.cxx','drfft.cxx', 'zfftnd.cxx']] +src.append(env.NumpyFromFTemplate('fftpack.pyf', 'fftpack.pyf.src')) +env.NumpyPythonExtension('_fftpack', src + ['src/zrfft.c']) + +# Build convolve +src = ['src/fftpack/convolve.cxx', 'convolve.pyf'] +env.NumpyPythonExtension('convolve', src) Deleted: branches/refactor_fft/scipy/fftpack/fftpack/SConstruct =================================================================== --- branches/refactor_fft/scipy/fftpack/fftpack/SConstruct 2008-10-28 05:11:07 UTC (rev 4858) +++ branches/refactor_fft/scipy/fftpack/fftpack/SConstruct 2008-10-28 05:18:00 UTC (rev 4859) @@ -1,34 +0,0 @@ -# Last Change: Sun May 18 06:00 PM 2008 J -# vim:syntax=python -from os.path import join as pjoin - -from numpy.distutils.misc_util import get_numpy_include_dirs -from numscons import GetNumpyEnvironment -from numscons import CheckF77Clib - -env = GetNumpyEnvironment(ARGUMENTS) -env.Tool('numpyf2py') - -env.AppendUnique(CPPPATH = get_numpy_include_dirs()) -env.AppendUnique(CPPPATH = env['F2PYINCLUDEDIR']) -env.AppendUnique(CPPPATH = [pjoin(env['build_dir'], "src")]) - -config = env.NumpyConfigure(custom_tests = {'CheckF77Clib' : CheckF77Clib}) -if not config.CheckF77Clib(): - raise Exception("Could not get f77/c++ link information") -config.Finish() - -# Build dfftpack -src = env.NumpyGlob(pjoin('DFFTPACK', '*.f')) -dfftpack = env.NumpyStaticExtLibrary('dfftpack', source = [str(s) for s in src]) -env.PrependUnique(LIBS = ['dfftpack']) -env.PrependUnique(LIBPATH = env['build_dir']) - -# Build the fftpack wrapper -src = [pjoin("src/fftpack", i) for i in ['zfft.cxx','drfft.cxx', 'zfftnd.cxx']] -src.append(env.NumpyFromFTemplate('fftpack.pyf', 'fftpack.pyf.src')) -env.NumpyPythonExtension('_fftpack', src + ['src/zrfft.c']) - -# Build convolve -src = ['src/fftpack/convolve.cxx', 'convolve.pyf'] -env.NumpyPythonExtension('convolve', src) From scipy-svn at scipy.org Tue Oct 28 01:24:59 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 28 Oct 2008 00:24:59 -0500 (CDT) Subject: [Scipy-svn] r4860 - branches/refactor_fft/scipy/fftpack/fftpack Message-ID: <20081028052459.BEA1939C1AE@scipy.org> Author: cdavid Date: 2008-10-28 00:24:55 -0500 (Tue, 28 Oct 2008) New Revision: 4860 Added: branches/refactor_fft/scipy/fftpack/fftpack/SConstruct Log: Forgot to add SConstruct. Added: branches/refactor_fft/scipy/fftpack/fftpack/SConstruct =================================================================== --- branches/refactor_fft/scipy/fftpack/fftpack/SConstruct 2008-10-28 05:18:00 UTC (rev 4859) +++ branches/refactor_fft/scipy/fftpack/fftpack/SConstruct 2008-10-28 05:24:55 UTC (rev 4860) @@ -0,0 +1,2 @@ +from numscons import GetInitEnvironment +GetInitEnvironment(ARGUMENTS).DistutilsSConscript('SConscript') From scipy-svn at scipy.org Tue Oct 28 01:33:14 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 28 Oct 2008 00:33:14 -0500 (CDT) Subject: [Scipy-svn] r4861 - in branches/refactor_fft/scipy/fftpack: . fftpack Message-ID: <20081028053314.4D02B39C1AE@scipy.org> Author: cdavid Date: 2008-10-28 00:33:09 -0500 (Tue, 28 Oct 2008) New Revision: 4861 Modified: branches/refactor_fft/scipy/fftpack/fftpack/SConscript branches/refactor_fft/scipy/fftpack/setupscons.py Log: Update fftpack backend numscons build. Modified: branches/refactor_fft/scipy/fftpack/fftpack/SConscript =================================================================== --- branches/refactor_fft/scipy/fftpack/fftpack/SConscript 2008-10-28 05:24:55 UTC (rev 4860) +++ branches/refactor_fft/scipy/fftpack/fftpack/SConscript 2008-10-28 05:33:09 UTC (rev 4861) @@ -7,11 +7,11 @@ from numscons import CheckF77Clib env = GetNumpyEnvironment(ARGUMENTS) -env.Tool('numpyf2py') +env.Tool('f2py') -env.AppendUnique(CPPPATH = get_numpy_include_dirs()) -env.AppendUnique(CPPPATH = env['F2PYINCLUDEDIR']) -env.AppendUnique(CPPPATH = [pjoin(env['build_dir'], "src")]) +env.AppendUnique(CPPPATH = ['src']) +# env.AppendUnique(CPPPATH = get_numpy_include_dirs()) +# env.AppendUnique(CPPPATH = env['F2PYINCLUDEDIR']) config = env.NumpyConfigure(custom_tests = {'CheckF77Clib' : CheckF77Clib}) if not config.CheckF77Clib(): @@ -19,15 +19,14 @@ config.Finish() # Build dfftpack -src = env.NumpyGlob(pjoin('DFFTPACK', '*.f')) -dfftpack = env.NumpyStaticExtLibrary('dfftpack', source = [str(s) for s in src]) +src = env.Glob(pjoin('DFFTPACK', '*.f')) +dfftpack = env.DistutilsStaticExtLibrary('dfftpack', source = [str(s) for s in src]) env.PrependUnique(LIBS = ['dfftpack']) -env.PrependUnique(LIBPATH = env['build_dir']) +env.PrependUnique(LIBPATH = '.') # Build the fftpack wrapper src = [pjoin("src/fftpack", i) for i in ['zfft.cxx','drfft.cxx', 'zfftnd.cxx']] -src.append(env.NumpyFromFTemplate('fftpack.pyf', 'fftpack.pyf.src')) -env.NumpyPythonExtension('_fftpack', src + ['src/zrfft.c']) +env.NumpyPythonExtension('_fftpack', src + ['src/zrfft.c', 'fftpack.pyf']) # Build convolve src = ['src/fftpack/convolve.cxx', 'convolve.pyf'] Modified: branches/refactor_fft/scipy/fftpack/setupscons.py =================================================================== --- branches/refactor_fft/scipy/fftpack/setupscons.py 2008-10-28 05:24:55 UTC (rev 4860) +++ branches/refactor_fft/scipy/fftpack/setupscons.py 2008-10-28 05:33:09 UTC (rev 4861) @@ -14,7 +14,7 @@ config.add_subpackage('fftpack') # Build optional backends - config.add_subpackage('backends') + # config.add_subpackage('backends') return config From scipy-svn at scipy.org Wed Oct 29 18:42:41 2008 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 29 Oct 2008 17:42:41 -0500 (CDT) Subject: [Scipy-svn] r4862 - in trunk/scipy/stats: . tests Message-ID: <20081029224241.1901E39C300@scipy.org> Author: ptvirtan Date: 2008-10-29 17:42:23 -0500 (Wed, 29 Oct 2008) New Revision: 4862 Modified: trunk/scipy/stats/distributions.py trunk/scipy/stats/tests/test_distributions.py Log: Fix #761 Modified: trunk/scipy/stats/distributions.py =================================================================== --- trunk/scipy/stats/distributions.py 2008-10-28 05:33:09 UTC (rev 4861) +++ trunk/scipy/stats/distributions.py 2008-10-29 22:42:23 UTC (rev 4862) @@ -325,7 +325,7 @@ longname = hstr + name if self.__doc__ is None: self.__doc__ = rv_continuous.__doc__ - else: + if self.__doc__ is not None: self.__doc__ = self.__doc__.replace("A Generic",longname) if name is not None: self.__doc__ = self.__doc__.replace("generic",name) @@ -3408,7 +3408,7 @@ longname = hstr + name if self.__doc__ is None: self.__doc__ = rv_discrete.__doc__ - else: + if self.__doc__ is not None: self.__doc__ = self.__doc__.replace("A Generic",longname) if name is not None: self.__doc__ = self.__doc__.replace("generic",name) Modified: trunk/scipy/stats/tests/test_distributions.py =================================================================== --- trunk/scipy/stats/tests/test_distributions.py 2008-10-28 05:33:09 UTC (rev 4861) +++ trunk/scipy/stats/tests/test_distributions.py 2008-10-29 22:42:23 UTC (rev 4862) @@ -216,5 +216,13 @@ def test_zero(self): assert_equal(stats.expon.pdf(0),1) +class TestDocstring(TestCase): + def test_docstrings(self): + """See ticket #761""" + if stats.rayleigh.__doc__ is not None: + self.failUnless("rayleigh" in stats.rayleigh.__doc__.lower()) + if stats.bernoulli.__doc__ is not None: + self.failUnless("bernoulli" in stats.bernoulli.__doc__.lower()) + if __name__ == "__main__": run_module_suite()