[Scipy-svn] r7050 - in trunk: doc/source scipy/odr scipy/optimize scipy/spatial scipy/special scipy/stats scipy/weave
scipy-svn at scipy.org
scipy-svn at scipy.org
Sat Jan 15 09:45:36 EST 2011
Author: rgommers
Date: 2011-01-15 08:45:35 -0600 (Sat, 15 Jan 2011)
New Revision: 7050
Modified:
trunk/doc/source/io.rst
trunk/doc/source/maxentropy.rst
trunk/doc/source/release.rst
trunk/doc/source/sparse.rst
trunk/doc/source/stats.mstats.rst
trunk/doc/source/stats.rst
trunk/scipy/odr/odrpack.py
trunk/scipy/optimize/anneal.py
trunk/scipy/spatial/distance.py
trunk/scipy/spatial/kdtree.py
trunk/scipy/special/basic.py
trunk/scipy/stats/info.py
trunk/scipy/weave/catalog.py
Log:
BUG: fix doc build. Some irregular indentation caused LaTeX errors.
Also fix a lot of warnings in the doc build.
The errors came from the Output class in odrpack.py and were of the following
nature:
"Perhaps a missing \item
\cr
\end{tabular}
"
Modified: trunk/doc/source/io.rst
===================================================================
--- trunk/doc/source/io.rst 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/doc/source/io.rst 2011-01-15 14:45:35 UTC (rev 7050)
@@ -32,7 +32,6 @@
:toctree: generated/
save_as_module
- npfile
Wav sound files (:mod:`scipy.io.wavfile`)
=========================================
Modified: trunk/doc/source/maxentropy.rst
===================================================================
--- trunk/doc/source/maxentropy.rst 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/doc/source/maxentropy.rst 2011-01-15 14:45:35 UTC (rev 7050)
@@ -66,6 +66,8 @@
Utilities
=========
+.. automodule:: scipy.maxentropy.maxentutils
+
.. autosummary::
:toctree: generated/
Modified: trunk/doc/source/release.rst
===================================================================
--- trunk/doc/source/release.rst 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/doc/source/release.rst 2011-01-15 14:45:35 UTC (rev 7050)
@@ -10,5 +10,3 @@
release.0.7.2
release.0.7.1
release.0.7.0
-
-.. include:: ../release/0.9.0-notes.rst
Modified: trunk/doc/source/sparse.rst
===================================================================
--- trunk/doc/source/sparse.rst 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/doc/source/sparse.rst 2011-01-15 14:45:35 UTC (rev 7050)
@@ -32,8 +32,6 @@
identity
kron
kronsum
- lil_eye
- lil_diags
spdiags
tril
triu
Modified: trunk/doc/source/stats.mstats.rst
===================================================================
--- trunk/doc/source/stats.mstats.rst 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/doc/source/stats.mstats.rst 2011-01-15 14:45:35 UTC (rev 7050)
@@ -46,16 +46,12 @@
plotting_positions
pointbiserialr
rankdata
- samplestd
- samplevar
scoreatpercentile
sem
signaltonoise
skew
skewtest
spearmanr
- std
- stderr
theilslopes
threshold
tmax
@@ -73,9 +69,7 @@
ttest_onesamp
ttest_rel
tvar
- var
variation
winsorize
- z
zmap
- zs
+ zscore
Modified: trunk/doc/source/stats.rst
===================================================================
--- trunk/doc/source/stats.rst 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/doc/source/stats.rst 2011-01-15 14:45:35 UTC (rev 7050)
@@ -1,9 +1,9 @@
-.. module:: scipy.stats
-
==========================================
Statistical functions (:mod:`scipy.stats`)
==========================================
+.. module:: scipy.stats
+
This module contains a large number of probability distributions as
well as a growing library of statistical functions.
@@ -62,8 +62,6 @@
foldcauchy
f
foldnorm
- fretchet_r
- fretcher_l
genlogistic
genpareto
genexpon
@@ -113,7 +111,7 @@
truncnorm
tukeylambda
uniform
- von_mises
+ vonmises
wald
weibull_min
weibull_max
@@ -151,9 +149,7 @@
gmean
hmean
- mean
cmedian
- median
mode
tmean
tvar
@@ -186,16 +182,9 @@
:toctree: generated/
obrientransform
- samplevar
- samplestd
signaltonoise
bayes_mvs
- var
- std
- stderr
sem
- z
- zs
zmap
zscore
@@ -205,8 +194,6 @@
threshold
trimboth
trim1
- cov
- corrcoef
.. autosummary::
:toctree: generated/
@@ -252,7 +239,6 @@
:toctree: generated/
glm
- anova
Plot-tests
==========
Modified: trunk/scipy/odr/odrpack.py
===================================================================
--- trunk/scipy/odr/odrpack.py 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/scipy/odr/odrpack.py 2011-01-15 14:45:35 UTC (rev 7050)
@@ -536,54 +536,50 @@
The Output class stores the output of an ODR run.
Takes one argument for initialization, the return value from the
- function odr.
+ function `odr`.
Attributes
----------
- beta :
- estimated parameter values [beta.shape == (q,)]
- sd_beta :
- standard errors of the estimated parameters
- [sd_beta.shape == (p,)]
- cov_beta :
- covariance matrix of the estimated parameters
- [cov_beta.shape == (p, p)]
+ beta : ndarray
+ Estimated parameter values, of shape (q,).
+ sd_beta : ndarray
+ Standard errors of the estimated parameters, of shape (p,).
+ cov_beta : ndarray
+ Covariance matrix of the estimated parameters, of shape (p,p).
+ delta : ndarray, optional
+ Array of estimated errors in input variables, of same shape as `x`.
+ eps : ndarray, optional
+ Array of estimated errors in response variables, of same shape as `y`.
+ xplus : ndarray, optional
+ Array of ``x + delta``.
+ y : ndarray, optional
+ Array ``y = fcn(x + delta)``.
+ res_var : float, optional
+ Residual variance.
+ sum_sqare : float, optional
+ Sum of squares error.
+ sum_square_delta : float, optional
+ Sum of squares of delta error.
+ sum_square_eps : float, optional
+ Sum of squares of eps error.
+ inv_condnum : float, optional
+ Inverse condition number (cf. ODRPACK UG p. 77).
+ rel_error : float, optional
+ Relative error in function values computed within fcn.
+ work : ndarray, optional
+ Final work array.
+ work_ind : dict, optional
+ Indices into work for drawing out values (cf. ODRPACK UG p. 83).
+ info : int, optional
+ Reason for returning, as output by ODRPACK (cf. ODRPACK UG p. 38).
+ stopreason : list of str, optional
+ `info` interpreted into English.
- Following are present if odr() was run with "full_output=1".
+ Notes
+ -----
+ The attributes listed as "optional" above are only present if `odr` was run
+ with ``full_output=1``.
- delta :
- array of estimated errors in input variables
- [delta.shape == data.x.shape]
- eps :
- array of estimated errors in response variables
- [eps.shape == data.y.shape]
- xplus :
- array of x + delta [xplus.shape == data.x.shape]
- y :
- array of y = fcn(x + delta) [y.shape == data.y.shape]
- res_var : float
- residual variance
- sum_sqare : float
- sum of squares error
- sum_square_delta : float
- sum of squares of delta error
- sum_square_eps : float
- sum of squares of eps error
- inv_condnum : float
- inverse condition number (cf. ODRPACK UG p. 77)
- rel_error : float
- relative error in function values computed within fcn
- work : ndarray
- final work array
- work_ind : dictionary
- indices into work for drawing out values
- (cf. ODRPACK UG p. 83)
- info : int
- reason for returning (as output by ODRPACK)
- (cf. ODRPACK UG p. 38)
- stopreason : list of strings
- "info" interpreted into English
-
"""
def __init__(self, output):
Modified: trunk/scipy/optimize/anneal.py
===================================================================
--- trunk/scipy/optimize/anneal.py 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/scipy/optimize/anneal.py 2011-01-15 14:45:35 UTC (rev 7050)
@@ -198,7 +198,7 @@
dwell : int
The number of times to search the space at each temperature.
- Outputs
+ Returns
-------
xmin : ndarray
Point giving smallest value found.
Modified: trunk/scipy/spatial/distance.py
===================================================================
--- trunk/scipy/spatial/distance.py 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/scipy/spatial/distance.py 2011-01-15 14:45:35 UTC (rev 7050)
@@ -160,17 +160,19 @@
{||u-v||}_p = (\sum{|u_i - v_i|^p})^{1/p}.
- :Parameters:
- u : ndarray
- An n-dimensional vector.
- v : ndarray
- An n-dimensional vector.
- p : ndarray
- The norm of the difference :math:`{||u-v||}_p`.
+ Parameters
+ ----------
+ u : ndarray
+ An n-dimensional vector.
+ v : ndarray
+ An n-dimensional vector.
+ p : ndarray
+ The norm of the difference :math:`{||u-v||}_p`.
- :Returns:
- d : double
- The Minkowski distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Minkowski distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -187,19 +189,21 @@
\left(\sum{(w_i |u_i - v_i|^p)}\right)^{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.
+ 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``.
+ Returns
+ -------
+ d : double
+ The Minkowski distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -217,15 +221,17 @@
{||u-v||}_2
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Euclidean distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Euclidean distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -242,15 +248,17 @@
{||u-v||}_2^2.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The squared Euclidean distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The squared Euclidean distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -266,15 +274,17 @@
\frac{1-uv^T}
{||u||_2 ||v||_2}.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Cosine distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Cosine distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -294,15 +304,17 @@
where :math:`\bar{u}` is the mean of a vectors elements and ``n``
is the common dimensionality of ``u`` and ``v``.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The correlation distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The correlation distance between vectors ``u`` and ``v``.
"""
umu = u.mean()
vmu = v.mean()
@@ -327,15 +339,17 @@
:math:`\mathtt{u[k]} = i` and :math:`\mathtt{v[k]} = j` for
:math:`k < n`.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Hamming distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Hamming distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -355,15 +369,17 @@
:math:`\mathtt{u[k]} = i` and :math:`\mathtt{v[k]} = j` for
:math:`k < n`.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Jaccard distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Jaccard distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -385,15 +401,17 @@
:math:`\mathtt{u[k]} = i` and :math:`\mathtt{v[k]} = j` for
:math:`k < n`.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Kulsinski distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Kulsinski distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -409,15 +427,17 @@
variances. It is usually computed among a larger collection
vectors.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The standardized Euclidean distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The standardized Euclidean distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -435,15 +455,17 @@
\sum_i {(u_i-v_i)}.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The City Block distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The City Block distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -460,15 +482,17 @@
where ``VI`` is the inverse covariance matrix :math:`V^{-1}`.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Mahalanobis distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Mahalanobis distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -484,15 +508,17 @@
\max_i {|u_i-v_i|}.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Chebyshev distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Chebyshev distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -507,15 +533,17 @@
\sum{|u_i-v_i|} / \sum{|u_i+v_i|}.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Bray-Curtis distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Bray-Curtis distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -532,15 +560,17 @@
{\sum_i {|u_i|+|v_i|}}.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Canberra distance between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Canberra distance between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -596,15 +626,17 @@
:math:`\mathtt{u[k]} = i` and :math:`\mathtt{v[k]} = j` for
:math:`k < n` and :math:`R = 2.0 * (c_{TF} + c_{FT})`.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Yule dissimilarity between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Yule dissimilarity between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -624,15 +656,17 @@
:math:`\mathtt{u[k]} = i` and :math:`\mathtt{v[k]} = j` for
:math:`k < n`.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Matching dissimilarity between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Matching dissimilarity between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -653,15 +687,17 @@
:math:`\mathtt{u[k]} = i` and :math:`\mathtt{v[k]} = j` for
:math:`k < n`.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Dice dissimilarity between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Dice dissimilarity between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -685,16 +721,18 @@
:math:`\mathtt{u[k]} = i` and :math:`\mathtt{v[k]} = j` for
:math:`k < n` and :math:`R = 2(c_{TF} + c_{FT})`.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Rogers-Tanimoto dissimilarity between vectors
- ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Rogers-Tanimoto dissimilarity between vectors
+ `u` and `v`.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -715,15 +753,17 @@
:math:`\mathtt{u[k]} = i` and :math:`\mathtt{v[k]} = j` for
:math:`k < n`.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Russell-Rao dissimilarity between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Russell-Rao dissimilarity between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -748,15 +788,17 @@
:math:`k < n`, :math:`R = 2 * (c_{TF} + c_{FT})` and
:math:`S = c_{FF} + c_{TT}`.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Sokal-Michener dissimilarity between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Sokal-Michener dissimilarity between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -783,15 +825,17 @@
:math:`\mathtt{u[k]} = i` and :math:`\mathtt{v[k]} = j` for
:math:`k < n` and :math:`R = 2(c_{TF} + c_{FT})`.
- :Parameters:
- u : ndarray
- An :math:`n`-dimensional vector.
- v : ndarray
- An :math:`n`-dimensional vector.
+ Parameters
+ ----------
+ u : ndarray
+ An :math:`n`-dimensional vector.
+ v : ndarray
+ An :math:`n`-dimensional vector.
- :Returns:
- d : double
- The Sokal-Sneath dissimilarity between vectors ``u`` and ``v``.
+ Returns
+ -------
+ d : double
+ The Sokal-Sneath dissimilarity between vectors ``u`` and ``v``.
"""
u = np.asarray(u, order='c')
v = np.asarray(v, order='c')
@@ -999,34 +1043,36 @@
dm = pdist(X, 'sokalsneath')
- :Parameters:
- X : ndarray
- An m by n array of m original observations in an
- n-dimensional space.
- metric : string or function
- The distance metric to use. The distance function can
- be 'braycurtis', 'canberra', 'chebyshev', 'cityblock',
- 'correlation', 'cosine', 'dice', 'euclidean', 'hamming',
- 'jaccard', 'kulsinski', 'mahalanobis', 'matching',
- 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
- 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'.
- w : ndarray
- The weight vector (for weighted Minkowski).
- p : double
- The p-norm to apply (for Minkowski, weighted and unweighted)
- V : ndarray
- The variance vector (for standardized Euclidean).
- VI : ndarray
- The inverse of the covariance matrix (for Mahalanobis).
+ Parameters
+ ----------
+ X : ndarray
+ An m by n array of m original observations in an
+ n-dimensional space.
+ metric : string or function
+ The distance metric to use. The distance function can
+ be 'braycurtis', 'canberra', 'chebyshev', 'cityblock',
+ 'correlation', 'cosine', 'dice', 'euclidean', 'hamming',
+ 'jaccard', 'kulsinski', 'mahalanobis', 'matching',
+ 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
+ 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'.
+ w : ndarray
+ The weight vector (for weighted Minkowski).
+ p : double
+ The p-norm to apply (for Minkowski, weighted and unweighted)
+ V : ndarray
+ The variance vector (for standardized Euclidean).
+ VI : ndarray
+ The inverse of the covariance matrix (for Mahalanobis).
- :Returns:
- Y : ndarray
- A condensed distance matrix.
+ Returns
+ -------
+ Y : ndarray
+ A condensed distance matrix.
- :SeeAlso:
-
- squareform : converts between condensed distance matrices and
- square distance matrices.
+ See Also
+ --------
+ squareform : converts between condensed distance matrices and
+ square distance matrices.
"""
@@ -1239,11 +1285,13 @@
Converts a vector-form distance vector to a square-form distance
matrix, and vice-versa.
- :Parameters:
+ Parameters
+ ----------
X : ndarray
Either a condensed or redundant distance matrix.
- :Returns:
+ Returns
+ -------
Y : ndarray
If a condensed distance matrix is passed, a redundant
one is returned, or if a redundant one is passed, a
@@ -1358,31 +1406,33 @@
Distance matrices must be 2-dimensional numpy arrays containing
doubles. They must have a zero-diagonal, and they must be symmetric.
- :Parameters:
- D : ndarray
- The candidate object to test for validity.
- tol : double
- The distance matrix should be symmetric. tol is the maximum
- difference between the :math:`ij`th entry and the
- :math:`ji`th entry for the distance metric to be
- considered symmetric.
- throw : bool
- An exception is thrown if the distance matrix passed is not
- valid.
- name : string
- the name of the variable to checked. This is useful ifa
- throw is set to ``True`` so the offending variable can be
- identified in the exception message when an exception is
- thrown.
- warning : boolx
- Instead of throwing an exception, a warning message is
- raised.
+ Parameters
+ ----------
+ D : ndarray
+ The candidate object to test for validity.
+ tol : double
+ The distance matrix should be symmetric. tol is the maximum
+ difference between the :math:`ij`th entry and the
+ :math:`ji`th entry for the distance metric to be
+ considered symmetric.
+ throw : bool
+ An exception is thrown if the distance matrix passed is not
+ valid.
+ name : string
+ the name of the variable to checked. This is useful ifa
+ throw is set to ``True`` so the offending variable can be
+ identified in the exception message when an exception is
+ thrown.
+ warning : boolx
+ Instead of throwing an exception, a warning message is
+ raised.
- :Returns:
- Returns ``True`` if the variable ``D`` passed is a valid
- distance matrix. Small numerical differences in ``D`` and
- ``D.T`` and non-zeroness of the diagonal are ignored if they are
- within the tolerance specified by ``tol``.
+ Returns
+ -------
+ Returns ``True`` if the variable ``D`` passed is a valid
+ distance matrix. Small numerical differences in ``D`` and
+ ``D.T`` and non-zeroness of the diagonal are ignored if they are
+ within the tolerance specified by ``tol``.
"""
D = np.asarray(D, order='c')
valid = True
@@ -1436,24 +1486,22 @@
coefficient :math:`{n \choose 2}` for some positive integer n.
- :Parameters:
- y : ndarray
- The condensed distance matrix.
+ Parameters
+ ----------
+ y : ndarray
+ The condensed distance matrix.
+ warning : bool, optional
+ Invokes a warning if the variable passed is not a valid
+ condensed distance matrix. The warning message explains why
+ the distance matrix is not valid. 'name' is used when
+ referencing the offending variable.
+ throws : throw, optional
+ Throws an exception if the variable passed is not a valid
+ condensed distance matrix.
+ name : bool, optional
+ Used when referencing the offending variable in the
+ warning or exception message.
- warning : bool
- Invokes a warning if the variable passed is not a valid
- condensed distance matrix. The warning message explains why
- the distance matrix is not valid. 'name' is used when
- referencing the offending variable.
-
- throws : throw
- Throws an exception if the variable passed is not a valid
- condensed distance matrix.
-
- name : bool
- Used when referencing the offending variable in the
- warning or exception message.
-
"""
y = np.asarray(y, order='c')
valid = True
@@ -1493,12 +1541,15 @@
Returns the number of original observations that correspond to a
square, redudant distance matrix ``D``.
- :Parameters:
- d : ndarray
- The target distance matrix.
+ Parameters
+ ----------
+ d : ndarray
+ The target distance matrix.
- :Returns:
- The number of observations in the redundant distance matrix.
+ Returns
+ -------
+ numobs : int
+ The number of observations in the redundant distance matrix.
"""
d = np.asarray(d, order='c')
is_valid_dm(d, tol=np.inf, throw=True, name='d')
@@ -1509,15 +1560,17 @@
Returns the number of original observations that correspond to a
condensed distance matrix ``Y``.
- :Parameters:
- Y : ndarray
- The number of original observations in the condensed
- observation ``Y``.
+ Parameters
+ ----------
+ Y : ndarray
+ The number of original observations in the condensed
+ observation ``Y``.
- :Returns:
- n : int
- The number of observations in the condensed distance matrix
- passed.
+ Returns
+ -------
+ n : int
+ The number of observations in the condensed distance matrix
+ passed.
"""
Y = np.asarray(Y, order='c')
is_valid_y(Y, throw=True, name='Y')
@@ -1727,34 +1780,36 @@
dm = cdist(XA, XB, 'sokalsneath')
- :Parameters:
- XA : ndarray
- An :math:`m_A` by :math:`n` array of :math:`m_A`
- original observations in an :math:`n`-dimensional space.
- XB : ndarray
- An :math:`m_B` by :math:`n` array of :math:`m_B`
- original observations in an :math:`n`-dimensional space.
- metric : string or function
- The distance metric to use. The distance function can
- be 'braycurtis', 'canberra', 'chebyshev', 'cityblock',
- 'correlation', 'cosine', 'dice', 'euclidean', 'hamming',
- 'jaccard', 'kulsinski', 'mahalanobis', 'matching',
- 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
- 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski',
- 'yule'.
- w : ndarray
- The weight vector (for weighted Minkowski).
- p : double
- The p-norm to apply (for Minkowski, weighted and unweighted)
- V : ndarray
- The variance vector (for standardized Euclidean).
- VI : ndarray
- The inverse of the covariance matrix (for Mahalanobis).
+ Parameters
+ ----------
+ XA : ndarray
+ An :math:`m_A` by :math:`n` array of :math:`m_A`
+ original observations in an :math:`n`-dimensional space.
+ XB : ndarray
+ An :math:`m_B` by :math:`n` array of :math:`m_B`
+ original observations in an :math:`n`-dimensional space.
+ metric : string or function
+ The distance metric to use. The distance function can
+ be 'braycurtis', 'canberra', 'chebyshev', 'cityblock',
+ 'correlation', 'cosine', 'dice', 'euclidean', 'hamming',
+ 'jaccard', 'kulsinski', 'mahalanobis', 'matching',
+ 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
+ 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski',
+ 'yule'.
+ w : ndarray
+ The weight vector (for weighted Minkowski).
+ p : double
+ The p-norm to apply (for Minkowski, weighted and unweighted)
+ V : ndarray
+ The variance vector (for standardized Euclidean).
+ VI : ndarray
+ The inverse of the covariance matrix (for Mahalanobis).
- :Returns:
- Y : ndarray
- A :math:`m_A` by :math:`m_B` distance matrix.
+ Returns
+ -------
+ Y : ndarray
+ A :math:`m_A` by :math:`m_B` distance matrix.
"""
Modified: trunk/scipy/spatial/kdtree.py
===================================================================
--- trunk/scipy/spatial/kdtree.py 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/scipy/spatial/kdtree.py 2011-01-15 14:45:35 UTC (rev 7050)
@@ -116,13 +116,12 @@
def __init__(self, data, leafsize=10):
"""Construct a kd-tree.
- Parameters:
- ===========
-
- data : array-like, shape (n,k)
+ Parameters
+ ----------
+ data : array_like, shape (n,k)
The data points to be indexed. This array is not copied, and
so modifying this data will result in bogus results.
- leafsize : positive integer
+ leafsize : positive int
The number of points at which the algorithm switches over to
brute-force.
"""
Modified: trunk/scipy/special/basic.py
===================================================================
--- trunk/scipy/special/basic.py 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/scipy/special/basic.py 2011-01-15 14:45:35 UTC (rev 7050)
@@ -59,16 +59,22 @@
"""Compute nt (<=1200) zeros of the bessel functions Jn and Jn'
and arange them in order of their magnitudes.
- Outputs (all are arrays of length nt):
+ Returns
+ -------
+ zo[l-1] : ndarray
+ Value of the lth zero of of Jn(x) and Jn'(x). Of length `nt`.
+ n[l-1] : ndarray
+ Order of the Jn(x) or Jn'(x) associated with lth zero. Of length `nt`.
+ m[l-1] : ndarray
+ Serial number of the zeros of Jn(x) or Jn'(x) associated
+ with lth zero. Of length `nt`.
+ t[l-1] : ndarray
+ 0 if lth zero in zo is zero of Jn(x), 1 if it is a zero of Jn'(x). Of
+ length `nt`.
- zo[l-1] -- Value of the lth zero of of Jn(x) and Jn'(x)
- n[l-1] -- Order of the Jn(x) or Jn'(x) associated with lth zero
- m[l-1] -- Serial number of the zeros of Jn(x) or Jn'(x) associated
- with lth zero.
- t[l-1] -- 0 if lth zero in zo is zero of Jn(x), 1 if it is a zero
- of Jn'(x)
-
- See jn_zeros, jnp_zeros to get separated arrays of zeros.
+ See Also
+ --------
+ jn_zeros, jnp_zeros : to get separated arrays of zeros.
"""
if not isscalar(nt) or (floor(nt)!=nt) or (nt>1200):
raise ValueError("Number must be integer <= 1200.")
@@ -521,7 +527,7 @@
Pmn_z : (m+1, n+1) array
Values for all orders 0..m and degrees 0..n
Pmn_d_z : (m+1, n+1) array
- Derivatives for all orders 0..m and degrees 0..n
+ Derivatives for all orders 0..m and degrees 0..n
"""
if not isscalar(m) or (abs(m)>n):
raise ValueError("m must be <= n.")
@@ -636,12 +642,12 @@
"""Compute the zeros of Airy Functions Ai(x) and Ai'(x), a and a'
respectively, and the associated values of Ai(a') and Ai'(a).
- Outputs:
-
- a[l-1] -- the lth zero of Ai(x)
- ap[l-1] -- the lth zero of Ai'(x)
- ai[l-1] -- Ai(ap[l-1])
- aip[l-1] -- Ai'(a[l-1])
+ Returns
+ -------
+ a[l-1] -- the lth zero of Ai(x)
+ ap[l-1] -- the lth zero of Ai'(x)
+ ai[l-1] -- Ai(ap[l-1])
+ aip[l-1] -- Ai'(a[l-1])
"""
kf = 1
if not isscalar(nt) or (floor(nt)!=nt) or (nt<=0):
@@ -652,12 +658,12 @@
"""Compute the zeros of Airy Functions Bi(x) and Bi'(x), b and b'
respectively, and the associated values of Ai(b') and Ai'(b).
- Outputs:
-
- b[l-1] -- the lth zero of Bi(x)
- bp[l-1] -- the lth zero of Bi'(x)
- bi[l-1] -- Bi(bp[l-1])
- bip[l-1] -- Bi'(b[l-1])
+ Returns
+ -------
+ b[l-1] -- the lth zero of Bi(x)
+ bp[l-1] -- the lth zero of Bi'(x)
+ bi[l-1] -- Bi(bp[l-1])
+ bip[l-1] -- Bi'(b[l-1])
"""
kf = 2
if not isscalar(nt) or (floor(nt)!=nt) or (nt<=0):
Modified: trunk/scipy/stats/info.py
===================================================================
--- trunk/scipy/stats/info.py 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/scipy/stats/info.py 2011-01-15 14:45:35 UTC (rev 7050)
@@ -116,7 +116,7 @@
truncnorm Truncated Normal
tukeylambda Tukey-Lambda
uniform Uniform
- von_mises Von-Mises (Circular)
+ vonmises Von-Mises (Circular)
wald Wald
weibull_min Minimum Weibull (see Frechet)
weibull_max Maximum Weibull (see Frechet)
@@ -178,16 +178,9 @@
================= ==============================================================
obrientransform _
-samplevar _
-samplestd _
signaltonoise _
bayes_mvs _
-var _
-std _
-stderr _
sem _
-z _
-zs _
zmap _
================= ==============================================================
@@ -195,8 +188,6 @@
threshold _
trimboth _
trim1 _
-cov _
-corrcoef _
================= ==============================================================
================= ==============================================================
@@ -238,7 +229,6 @@
================= ==============================================================
glm _
-anova _
================= ==============================================================
Modified: trunk/scipy/weave/catalog.py
===================================================================
--- trunk/scipy/weave/catalog.py 2011-01-15 14:05:17 UTC (rev 7049)
+++ trunk/scipy/weave/catalog.py 2011-01-15 14:45:35 UTC (rev 7050)
@@ -131,12 +131,15 @@
def is_writable(dir):
"""Determine whether a given directory is writable in a portable manner.
- :Parameters:
- - dir: string
- A string represeting a path to a directory on the filesystem.
+ Parameters
+ ----------
+ dir: str
+ A string represeting a path to a directory on the filesystem.
- :Returns:
- True or False.
+ Returns
+ -------
+ res : bool
+ True or False.
"""
# Do NOT use a hardcoded name here due to the danger from race conditions
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