From evgeny.burovskiy at gmail.com Fri Jul 8 19:36:41 2016 From: evgeny.burovskiy at gmail.com (Evgeni Burovski) Date: Sat, 9 Jul 2016 00:36:41 +0100 Subject: [SciPy-Dev] scipy 0.18.0 release candidate 2 Message-ID: On behalf of the scipy development team, I am pleased to announce the second release candidate for scipy 0.18.0. Please test it --- both the release itself on your machines and your code against this release --- and report results on the issue tracker or scipy-dev mailing list. Source tarballs and release notes are available from Github releases, https://github.com/scipy/scipy/releases/tag/v0.18.0rc2 OS X and Linux wheels are being built now, and I will upload them to PyPI when they are ready. I would like to flag two areas where additional scrutiny and testing would be most appreciated: * Precision loss in some corner cases was reported to occur on Fedora 23 and 24 with OpenBLAS, see https://github.com/scipy/scipy/issues/6286. * On Windows, a CRT incompatibility was reported when building with MSVC and MKL, see https://github.com/scipy/scipy/issues/6336. There are additional issues on s390x (https://github.com/scipy/scipy/issues/6338), so if you are using this platform, we appreciate your help resolving them. The (revised) release schedule is: 25 July: final release Thanks to everyone who contributed! Cheers, Evgeni From evgeny.burovskiy at gmail.com Sun Jul 10 18:39:59 2016 From: evgeny.burovskiy at gmail.com (Evgeni Burovski) Date: Sun, 10 Jul 2016 23:39:59 +0100 Subject: [SciPy-Dev] scipy 0.18.0 release candidate 2 In-Reply-To: References: Message-ID: OS X and linux wheels are on PyPI now: $ pip install scipy --pre Collecting scipy Downloading scipy-0.18.0rc2-cp35-cp35m-manylinux1_x86_64.whl (42.0MB) 100% |????????????????????????????????| 42.0MB 33kB/s For those who would want to verify the wheels, MD5 and SHA256 checksums are listed in the README file in https://github.com/scipy/scipy/releases/tag/v0.18.0rc2 On Sat, Jul 9, 2016 at 12:36 AM, Evgeni Burovski wrote: > On behalf of the scipy development team, I am pleased to announce the > second release candidate for scipy 0.18.0. > > Please test it --- both the release itself on your machines and your > code against this release --- and report results on the issue tracker > or scipy-dev mailing list. > > Source tarballs and release notes are available from Github releases, > https://github.com/scipy/scipy/releases/tag/v0.18.0rc2 > > OS X and Linux wheels are being built now, and I will upload them to > PyPI when they are ready. > > I would like to flag two areas where additional scrutiny and testing > would be most appreciated: > > * Precision loss in some corner cases was reported to occur on Fedora > 23 and 24 with OpenBLAS, see > https://github.com/scipy/scipy/issues/6286. > > * On Windows, a CRT incompatibility was reported when building with > MSVC and MKL, see https://github.com/scipy/scipy/issues/6336. > > There are additional issues on s390x > (https://github.com/scipy/scipy/issues/6338), so if you are using this > platform, we appreciate your help resolving them. > > The (revised) release schedule is: > > 25 July: final release > > Thanks to everyone who contributed! > > Cheers, > > Evgeni From matthew.brett at gmail.com Mon Jul 11 01:33:34 2016 From: matthew.brett at gmail.com (Matthew Brett) Date: Sun, 10 Jul 2016 22:33:34 -0700 Subject: [SciPy-Dev] scipy 0.18.0 release candidate 2 In-Reply-To: References: Message-ID: On Sun, Jul 10, 2016 at 3:39 PM, Evgeni Burovski wrote: > OS X and linux wheels are on PyPI now: > > $ pip install scipy --pre > Collecting scipy > Downloading scipy-0.18.0rc2-cp35-cp35m-manylinux1_x86_64.whl (42.0MB) > 100% |????????????????????????????????| 42.0MB 33kB/s > > For those who would want to verify the wheels, MD5 and SHA256 > checksums are listed in the README file in > https://github.com/scipy/scipy/releases/tag/v0.18.0rc2 Thanks very much for doing that, Matthew From Nicolas.Rougier at inria.fr Fri Jul 15 09:48:34 2016 From: Nicolas.Rougier at inria.fr (Nicolas P. Rougier) Date: Fri, 15 Jul 2016 08:48:34 -0500 Subject: [SciPy-Dev] 100 Numpy exercises complete ! Message-ID: <4E3FBC3F-C790-4B75-BF49-15DB12EDA24E@inria.fr> Hi all, It's my great pleasure to announce that "100 Numpy exercises" is now complete. I've also made a notebook out of them such that you can now test them on binder. https://github.com/rougier/numpy-100 If you spot errors or have better solutions to propose, PR welcome. (I'm still fighting to fix exercise #54 that does not work as expected). Nicolas From joseph.p.hickey at nasa.gov Fri Jul 15 10:36:02 2016 From: joseph.p.hickey at nasa.gov (Hickey, Joseph P. (GRC-LSS0)[ZIN TECHNOLOGIES INC]) Date: Fri, 15 Jul 2016 10:36:02 -0400 Subject: [SciPy-Dev] Cross Compiling SciPy? Message-ID: <5788F4D2.6040601@nasa.gov> Hello all -- apologies if similar question(s) have been asked before, but I didn't see anything in the archives. I'm trying to compile NumPy + SciPy for an embedded target -- PowerPC target CPU, x86-64 host machine. The build system I'm using is based on Yocto/OpenEmbedded, basically a fairly standard cross compile setup. I'm using tagged versions from the git repositories, specifically v1.10.4 of NumPy and v0.17.1 for SciPy, and Python 2.7 as provided by the upstream "Poky" distribution. So far, NumPy has been relatively painless, the "setup.py" script seems to obey the BUILD_SYS/HOST_SYS variables and successfully builds for the target architecture. SciPy, on the other hand, is more problematic. The provided "setup.py" script was not even getting started because it attempts this: from numpy.distutils.core import setup But the "setup.py" script is actually running on the host machine in this case, so it is importing the setup from the host installation, NOT the target's numpy installation. If I install numpy on the host machine as well, this import now works, but it creates a mixture of binaries -- the build fails later on when it attempts to link binaries built using the native gcc with other binaries built using the cross compiler. I've also tried the previous release (v0.16.1 tag) with similar results. Has anyone successfully cross compiled SciPy that could provide some guidance here? Is there anything special I need to do with the NumPy cross installation such that the SciPy build can find it? Thanks in advance -Joe From pav at iki.fi Fri Jul 15 15:01:33 2016 From: pav at iki.fi (Pauli Virtanen) Date: Fri, 15 Jul 2016 19:01:33 +0000 (UTC) Subject: [SciPy-Dev] Cross Compiling SciPy? References: <5788F4D2.6040601@nasa.gov> Message-ID: Fri, 15 Jul 2016 10:36:02 -0400, Hickey, Joseph P. (GRC-LSS0)[ZIN TECHNOLOGIES INC] kirjoitti: > Has anyone successfully cross compiled SciPy that could provide some > guidance here? Is there anything special I need to do with the NumPy > cross installation such that the SciPy build can find it? As far as I know, the build system (numpy.distutils) has no support for cross compilation. It may be someone has patched it to work, but I do not remember seeing such patches. -- Pauli Virtanen From fabian.rost at tu-dresden.de Mon Jul 18 05:07:10 2016 From: fabian.rost at tu-dresden.de (Fabian Rost) Date: Mon, 18 Jul 2016 09:07:10 +0000 Subject: [SciPy-Dev] Adding a multistart algorithm to global optimizers in scipy.optimize Message-ID: <1468832830091.20616@tu-dresden.de> Hello everyone, in GitHub issue #6381 I suggested to add a global optimizer algorithm to scipy.optimize. As @andyfaff pointed out I should rather started the discussion here. As I already have some feedback by @andyfaff, I'll copy-paste my idea and his reply here. I'll also add my comments on his reply below. My issue: I regularly come across the following task: To find a the global minimum of a function I run local optimizers from multiple random initial guesses. For some situations, e.g. fitting parameters in systems biology, it was found that this type of algorithm performs better than others (Raue et al., 2013). I wonder if there is any interest to include such an algorithm into scipy. I wrote some code, documentation and 2 examples here: http://nbviewer.jupyter.org/github/fabianrost84/multistart-minimize/blob/master/multistart-minimize.ipynb @andyfaff's reply: This is possibly something that would've been better to raise on the mailing list first. There are several approaches to the type of problem you described. Firstly (and probably best), you can use one of the global optimizers, optimize.basinhopping or optimize.differential_evolution, they are designed for this very task. They will be a lot more effective than brute search followed by local minimisation as the dimensionality of the problem increases. Remember that you can do brute search and polish using the optimize.brute, although the grid search is done over a regular grid. Finally, and this is not well known, you can do a brute search and polish (of the best solution) with differential_evolution. Here the brute search would examine the problem space by latin hypercube sampling. You would probably want to increase the popsize by a bit. By setting maxiter to 0 no evolution is done on the population, and the best solution from the hypercube sampling would be polished with L-BFGS-B. I do appreciate that isn't the same as performing a local minimisation of all the brute search locations, but I don't think that is going to outperform differential_evolution or basinhopping in the first place. basinhopping does do a local minimisation step as part of it's operation anyway. my reply to that: Thanks very much for your comments! You point out that basinhopping or differential evolution will be a lot more effective than and that they probably will outperform a multistart algorithm. Concerning that I'd like to point to the paper Raue et al., 2013 once more. One of there problems is 11-dimensional. They compare the efficiency of multiple optimizers among them differential evolution and a multistart optimizer. I think they do not include basinhooping or simulated annealing. They find that their multistart optimizer outperform all other tested optimizers for the problem at hand. I should also mention they perform there study using MatLab, not Python. I found another study performed in Mathematica. This study finds optimization problems for which a multistart algorithm gives better results than simulated annealing or differential evolution. So doesn't that mean your point concerning effectiveness does not hold in general? I was aware of brute, I was not aware of the brute search with differential_evolution. Thanks. Let me mention that the latin hypercube sampling points could of course also be used as initials for multistart. That's what's done by Raue et al., 2013 and it's probably better than my simple random uniform implementation. Let me also mention that a multistart algorithms are available in Excel's solver add-on, in MatLab, in R and in Mathematica, but not for scipy. Best, Fabian -- -------------------------------- Fabian Rost (Dipl.-Phys.) Wissenschaftlicher Mitarbeiter Technische Universit?t Dresden Centre for Information Services and High Performance Computing (ZIH) Dept. for Innovative Methods of Computing (IMC) 01062 Dresden Tel.: +49 (351) 463-38780 Fax: +49 (351) 463-38245 E-Mail: fabian.rost at tu-dresden.de Web: http://imc.zih.tu-dresden.de/ -------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From m.schultz at fz-juelich.de Mon Jul 18 06:11:31 2016 From: m.schultz at fz-juelich.de (Schultz, Martin) Date: Mon, 18 Jul 2016 10:11:31 +0000 Subject: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test Message-ID: <4A980E5ADF859E4E9007AA5514CF9C8E6262BFAF@MBX2010-E01.ad.fz-juelich.de> Dear list, the routine below evaluates the Mann-Kendall test for non-parametrically checking the significance of any trend estimate. It would provide a nice complement to the existing theilslopes routine and others in scipy.stats. I found this code when searching for "Mann Kendall python" and saw that the original link was no longer existing. In my opinion it would be good to preserve this piece of work and make it available to others. I tested this routine on hundreds of datasets and it seemed to work well. This implementation was also compared to a few calculations with the Matlab implementation of this test and provided identical results. Best regards, Martin 
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 29 09:16:06 2015
@author: Michael Schramm
"""

from __future__ import division
import numpy as np
from scipy.stats import norm, mstats


def mk_test(x, alpha = 0.05):
    """
    This function is derived from code originally posted by Sat Kumar Tomer (satkumartomer at gmail.com)
    See also: http://vsp.pnnl.gov/help/Vsample/Design_Trend_Mann_Kendall.htm

    The purpose of the Mann-Kendall (MK) test (Mann 1945, Kendall 1975, Gilbert 1987) is to statistically assess if there is a monotonic upward or downward trend of the variable of interest over time. A monotonic upward (downward) trend means that the variable consistently increases (decreases) through time, but the trend may or may not be linear. The MK test can be used in place of a parametric linear regression analysis, which can be used to test if the slope of the estimated linear regression line is different from zero. The regression analysis requires that the residuals from the fitted regression line be normally distributed; an assumption not required by the MK test, that is, the MK test is a non-parametric (distribution-free) test.
    Hirsch, Slack and Smith (1982, page 107) indicate that the MK test is best viewed as an exploratory analysis and is most appropriately used to identify stations where changes are significant or of large magnitude and to quantify these findings.

    Input:
        x:   a vector of data
        alpha: significance level (0.05 default)

    Output:
        trend: tells the trend (increasing, decreasing or no trend)
        h: True (if trend is present) or False (if trend is absence)
        p: p value of the significance test
        z: normalized test statistics

    Examples
    --------
      >>> x = np.random.rand(100)
      >>> trend,h,p,z = mk_test(x,0.05)
    """
    n = len(x)

    # calculate S
    s = 0
    for k in range(n-1):
        for j in range(k+1,n):
            s += np.sign(x[j] - x[k])

    # calculate the unique data
    unique_x = np.unique(x)
    g = len(unique_x)

    # calculate the var(s)
    if n == g: # there is no tie
        var_s = (n*(n-1)*(2*n+5))/18
    else: # there are some ties in data
        tp = np.zeros(unique_x.shape)
        for i in range(len(unique_x)):
            tp[i] = sum(unique_x[i] == x)
        var_s = (n*(n-1)*(2*n+5) + np.sum(tp*(tp-1)*(2*tp+5)))/18

    if s>0:
        z = (s - 1)/np.sqrt(var_s)
    elif s == 0:
            z = 0
    elif s<0:
        z = (s + 1)/np.sqrt(var_s)

    # calculate the p_value
    p = 2*(1-norm.cdf(abs(z))) # two tail test
    h = abs(z) > norm.ppf(1-alpha/2)

    if (z<0) and h:
        trend = 'decreasing'
    elif (z>0) and h:
        trend = 'increasing'
    else:
        trend = 'no trend'

    return trend, h, p, z
------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, Prof. Dr. Sebastian M. Schmidt ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ -------------- next part -------------- An HTML attachment was scrubbed... URL: From peridot.faceted at gmail.com Mon Jul 18 07:31:56 2016 From: peridot.faceted at gmail.com (Anne Archibald) Date: Mon, 18 Jul 2016 11:31:56 +0000 Subject: [SciPy-Dev] Adding a multistart algorithm to global optimizers in scipy.optimize In-Reply-To: <1468832830091.20616@tu-dresden.de> References: <1468832830091.20616@tu-dresden.de> Message-ID: So, their lack of comparison with basinhopping is unfortunate, because what you're describing is basically what basinhopping is for: it starts a local optimizer at a point, runs it downhill, then jumps to another starting point to see if it does better. So your algorithm is roughly equivalent to basinhopping with the "step" function being "choose a new point completely at random". But in fact for basinhopping or most other global optimizers, it's a good habit to run many starting points anyway, which can be done in parallel. So in this setting, basinhopping shifts some of the work from completely random starting points to those near other minima. You can always set the maximum number of iterations to 1. It wouldn't hurt to set up a convenient wrapper that starts many global optimizations in parallel at random points; they need not even all use the same algorithm, and accepting an Executor object (from concurrent.futures) allows you to achieve parallelism using whatever underlying implementation the user prefers: thread-based, process-based, or in fact ipython provides an executor that distributes work across the ipython cluster. The only headache is that concurrent.futures is not standard in python2, although there is a backport. Somewhere I have a trivial shim of a SerialExecutor that lets you use the interface without needing concurrent.futures as long ass you don't actually want parallelism. Anne On Mon, Jul 18, 2016 at 11:07 AM Fabian Rost wrote: > Hello everyone, > > > in GitHub issue #6381 I > suggested to add a global optimizer algorithm to scipy.optimize. As > @andyfaff pointed out I should rather started the discussion here. As I > already have some feedback by @andyfaff, I'll copy-paste my idea and his > reply here. I'll also add my comments on his reply below. > > > My issue: > > I regularly come across the following task: To find a the global minimum > of a function I run local optimizers from multiple random initial guesses. > For some situations, e.g. fitting parameters in systems biology, it was > found that this type of algorithm performs better than others (Raue et > al., 2013 ). I wonder if > there is any interest to include such an algorithm into scipy. I wrote > some code, documentation and 2 examples here: > > http://nbviewer.jupyter.org/github/fabianrost84/multistart-minimize/blob/master/multistart-minimize.ipynb > > > @andyfaff's reply: > > This is possibly something that would've been better to raise on the > mailing list first. There are several approaches to the type of problem you > described. Firstly (and probably best), you can use one of the global > optimizers, optimize.basinhopping or optimize.differential_evolution, > they are designed for this very task. They will be a lot more effective > than brute search followed by local minimisation as the dimensionality of > the problem increases. > Remember that you can do brute search and polish using the optimize.brute, > although the grid search is done over a regular grid. > Finally, and this is not well known, you can do a brute search and polish > (of the best solution) with differential_evolution. Here the brute search > would examine the problem space by latin hypercube > sampling. You > would probably want to increase the popsize by a bit. By setting maxiter > to 0 no evolution is done on the population, and the best solution from the > hypercube sampling would be polished with L-BFGS-B. > > I do appreciate that isn't the same as performing a local minimisation of > all the brute search locations, but I don't think that is going to > outperform differential_evolution or basinhopping in the first place. > basinhopping does do a local minimisation step as part of it's operation > anyway. > > > my reply to that: > > Thanks very much for your comments! > > You point out that basinhopping or differential evolution will be a lot > more effective than and that they probably will outperform a multistart > algorithm. > > Concerning that I'd like to point to the paper Raue et al., 2013 > once more. One of > there problems is 11-dimensional. They compare the efficiency > of multiple optimizers among them differential evolution and a multistart > optimizer. I think they do not include basinhooping or simulated annealing. > They find that their multistart optimizer outperform all other tested > optimizers for the problem at hand. I should also mention they perform > there study using MatLab, not Python. I found another study performed in > Mathematica > . This > study finds optimization problems for which a multistart algorithm > gives better results than simulated annealing or differential evolution. So > doesn't that mean your point concerning effectiveness does not hold in > general? > > I was aware of brute, I was not aware of the brute search with > differential_evolution. Thanks. Let me mention that the latin hypercube > sampling points could of course also be used as initials for multistart. > That's what's done by Raue et al., 2013 > and it's probably better > than my simple random uniform implementation. > > Let me also mention that a multistart algorithms are available in Excel's > solver add-on > , > in MatLab > , > in R and in > Mathematica , > but not for scipy. > > > Best, > > > Fabian > > > > > -- > -------------------------------- > Fabian Rost (Dipl.-Phys.) > Wissenschaftlicher Mitarbeiter > Technische Universit?t Dresden > Centre for Information Services and High Performance Computing (ZIH) > Dept. for Innovative Methods of Computing (IMC) > 01062 Dresden > Tel.: +49 (351) 463-38780 > Fax: +49 (351) 463-38245 > E-Mail: fabian.rost at tu-dresden.de > Web: http://imc.zih.tu-dresden.de/ > -------------------------------- > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > -------------- next part -------------- An HTML attachment was scrubbed... URL: From fabian.rost at tu-dresden.de Mon Jul 18 08:03:20 2016 From: fabian.rost at tu-dresden.de (Fabian Rost) Date: Mon, 18 Jul 2016 12:03:20 +0000 Subject: [SciPy-Dev] Adding a multistart algorithm to global optimizers in scipy.optimize In-Reply-To: References: <1468832830091.20616@tu-dresden.de>, Message-ID: <1468843400808.14239@tu-dresden.de> Actually, a wrapper that starts many optimizations at random points would not only work for global optimizers but also for local optimizers. However, this wrapper would be limited to optimizers that use an initial guess (optimize.brute and differential_evolution would be excluded). Furthermore, this wrapper would turn any local optimizer into a global optimizer. In fact, the code I suggested here is just a wrapper around optimize.minimize. I realized that the documentation of optimize.basinhopping suggests that "the algorithm can be run from a number of different random starting points". So a convenient wrapper to do this definitely would not hurt. I wonder what this wrapper should return. It could return just the global optimum. But for analysis it might also be good to get all the OptimizeResults. This could help to judge how many local optima there are and if more or less initial guesses are needed to find the global optimum. Parallelization is certainly a good idea. Fabian ________________________________ Von: SciPy-Dev im Auftrag von Anne Archibald Gesendet: Montag, 18. Juli 2016 13:31 An: SciPy Developers List Betreff: Re: [SciPy-Dev] Adding a multistart algorithm to global optimizers in scipy.optimize So, their lack of comparison with basinhopping is unfortunate, because what you're describing is basically what basinhopping is for: it starts a local optimizer at a point, runs it downhill, then jumps to another starting point to see if it does better. So your algorithm is roughly equivalent to basinhopping with the "step" function being "choose a new point completely at random". But in fact for basinhopping or most other global optimizers, it's a good habit to run many starting points anyway, which can be done in parallel. So in this setting, basinhopping shifts some of the work from completely random starting points to those near other minima. You can always set the maximum number of iterations to 1. It wouldn't hurt to set up a convenient wrapper that starts many global optimizations in parallel at random points; they need not even all use the same algorithm, and accepting an Executor object (from concurrent.futures) allows you to achieve parallelism using whatever underlying implementation the user prefers: thread-based, process-based, or in fact ipython provides an executor that distributes work across the ipython cluster. The only headache is that concurrent.futures is not standard in python2, although there is a backport. Somewhere I have a trivial shim of a SerialExecutor that lets you use the interface without needing concurrent.futures as long ass you don't actually want parallelism. Anne On Mon, Jul 18, 2016 at 11:07 AM Fabian Rost > wrote: Hello everyone, in GitHub issue #6381 I suggested to add a global optimizer algorithm to scipy.optimize. As @andyfaff pointed out I should rather started the discussion here. As I already have some feedback by @andyfaff, I'll copy-paste my idea and his reply here. I'll also add my comments on his reply below. My issue: I regularly come across the following task: To find a the global minimum of a function I run local optimizers from multiple random initial guesses. For some situations, e.g. fitting parameters in systems biology, it was found that this type of algorithm performs better than others (Raue et al., 2013). I wonder if there is any interest to include such an algorithm into scipy. I wrote some code, documentation and 2 examples here: http://nbviewer.jupyter.org/github/fabianrost84/multistart-minimize/blob/master/multistart-minimize.ipynb @andyfaff's reply: This is possibly something that would've been better to raise on the mailing list first. There are several approaches to the type of problem you described. Firstly (and probably best), you can use one of the global optimizers, optimize.basinhopping or optimize.differential_evolution, they are designed for this very task. They will be a lot more effective than brute search followed by local minimisation as the dimensionality of the problem increases. Remember that you can do brute search and polish using the optimize.brute, although the grid search is done over a regular grid. Finally, and this is not well known, you can do a brute search and polish (of the best solution) with differential_evolution. Here the brute search would examine the problem space by latin hypercube sampling. You would probably want to increase the popsize by a bit. By setting maxiter to 0 no evolution is done on the population, and the best solution from the hypercube sampling would be polished with L-BFGS-B. I do appreciate that isn't the same as performing a local minimisation of all the brute search locations, but I don't think that is going to outperform differential_evolution or basinhopping in the first place. basinhopping does do a local minimisation step as part of it's operation anyway. my reply to that: Thanks very much for your comments! You point out that basinhopping or differential evolution will be a lot more effective than and that they probably will outperform a multistart algorithm. Concerning that I'd like to point to the paper Raue et al., 2013 once more. One of there problems is 11-dimensional. They compare the efficiency of multiple optimizers among them differential evolution and a multistart optimizer. I think they do not include basinhooping or simulated annealing. They find that their multistart optimizer outperform all other tested optimizers for the problem at hand. I should also mention they perform there study using MatLab, not Python. I found another study performed in Mathematica. This study finds optimization problems for which a multistart algorithm gives better results than simulated annealing or differential evolution. So doesn't that mean your point concerning effectiveness does not hold in general? I was aware of brute, I was not aware of the brute search with differential_evolution. Thanks. Let me mention that the latin hypercube sampling points could of course also be used as initials for multistart. That's what's done by Raue et al., 2013 and it's probably better than my simple random uniform implementation. Let me also mention that a multistart algorithms are available in Excel's solver add-on, in MatLab, in R and in Mathematica, but not for scipy. Best, Fabian -- -------------------------------- Fabian Rost (Dipl.-Phys.) Wissenschaftlicher Mitarbeiter Technische Universit?t Dresden Centre for Information Services and High Performance Computing (ZIH) Dept. for Innovative Methods of Computing (IMC) 01062 Dresden Tel.: +49 (351) 463-38780 Fax: +49 (351) 463-38245 E-Mail: fabian.rost at tu-dresden.de Web: http://imc.zih.tu-dresden.de/ -------------------------------- _______________________________________________ SciPy-Dev mailing list SciPy-Dev at scipy.org https://mail.scipy.org/mailman/listinfo/scipy-dev -------------- next part -------------- An HTML attachment was scrubbed... URL: From peridot.faceted at gmail.com Mon Jul 18 08:35:44 2016 From: peridot.faceted at gmail.com (Anne Archibald) Date: Mon, 18 Jul 2016 12:35:44 +0000 Subject: [SciPy-Dev] Adding a multistart algorithm to global optimizers in scipy.optimize In-Reply-To: <1468843400808.14239@tu-dresden.de> References: <1468832830091.20616@tu-dresden.de> <1468843400808.14239@tu-dresden.de> Message-ID: On Mon, Jul 18, 2016 at 2:03 PM Fabian Rost wrote: > Actually, a wrapper that starts many optimizations at random points would > not only work for global optimizers but also for local optimizers. However, > this wrapper would be limited to optimizers that use an initial guess > (optimize.brute and differential_evolution would be excluded). Furthermore, > this wrapper would turn any local optimizer into a global optimizer. In > fact, the code I suggested here > is > just a wrapper around optimize.minimize. > > I realized that the documentation of optimize.basinhopping suggests that > "the algorithm can be run from a number of different random starting > points". So a convenient wrapper to do this definitely would not hurt. > > I wonder what this wrapper should return. It could return just the global > optimum. But for analysis it might also be good to get all the > OptimizeResults. This could help to judge how many local optima there are > and if more or less initial guesses are needed to find the global optimum. > > Parallelization is certainly a good idea. > What to return is a question the other global optimizers have already had to face. The basinhopping code just returns the global optimum, I believe, but also provides a callback that allows users to see each local minimum. Presumably they can also stop the global optimization if they're happy, by raising an exception if nothing else will work. This may require some thought in the context of parallelization, since it's not so easy to send results back from subprocesses. It might be necessary to "open the hood" of basinhopping and run just the local optimizations in subprocesses. Obviously not everyone is going to want parallelization, but global optimization tends to be a "big" problem, and making it easy for people to use parallelization, to the degree and in the way that they prefer, would make the code in scipy much more useful. For really complex and difficult optimizations, of course, people are going to want to switch to more elaborate specialized codes. But it we make that less necessary, so much the better. Anne -------------- next part -------------- An HTML attachment was scrubbed... URL: From pav at iki.fi Mon Jul 18 19:30:10 2016 From: pav at iki.fi (Pauli Virtanen) Date: Mon, 18 Jul 2016 23:30:10 +0000 (UTC) Subject: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test References: <4A980E5ADF859E4E9007AA5514CF9C8E6262BFAF@MBX2010-E01.ad.fz-juelich.de> Message-ID: Hi, Mon, 18 Jul 2016 10:11:31 +0000, Schultz, Martin kirjoitti: > the routine below evaluates the Mann-Kendall test for > non-parametrically checking the significance of any trend estimate. > It would provide a nice complement to the existing theilslopes > routine and others in scipy.stats. I found this code when searching > for "Mann Kendall python" and saw that the original link was no > longer existing. In my opinion it would be good to preserve this > piece of work and make it available to others. I tested this > routine on hundreds of datasets and it seemed to work well. This > implementation was also compared to a few calculations with the > Matlab implementation of this test and provided identical results. Thanks for the information and code. Note that the following technical points would need to be addressed for including such a routine in Scipy: - No license information, so the code or works derived from it cannot be used. - Tests. While checking the results manually is valuable, it would also be necessary to write automated tests --- to make it possible for other people to verify the correctness, and to ensure the code stays working in the future. If you do not have time for this yourself, getting it done would need someone else to volunteer. -- Pauli Virtanen From m.schultz at fz-juelich.de Tue Jul 19 02:50:55 2016 From: m.schultz at fz-juelich.de (Schultz, Martin) Date: Tue, 19 Jul 2016 06:50:55 +0000 Subject: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test In-Reply-To: References: <4A980E5ADF859E4E9007AA5514CF9C8E6262BFAF@MBX2010-E01.ad.fz-juelich.de> Message-ID: <4A980E5ADF859E4E9007AA5514CF9C8E6262D119@MBX2010-E01.ad.fz-juelich.de> Hi Pauli, thank you for the feedback. I would be willing to inquire with the original author(s) about licensing, and I can provide a few small test datasets with results for inclusion into an automated test, but I don't think I would be able to write the automated test myself. So, if someone volunteers on that end, I will communicate the data with her or him. May I get back to you in case of questions concerning the license terms etc. if needed? Martin Schultz -----Original Message----- From: SciPy-Dev [mailto:scipy-dev-bounces at scipy.org] On Behalf Of Pauli Virtanen Sent: Tuesday, July 19, 2016 1:30 AM To: scipy-dev at scipy.org Subject: Re: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test Hi, Mon, 18 Jul 2016 10:11:31 +0000, Schultz, Martin kirjoitti: > the routine below evaluates the Mann-Kendall test for > non-parametrically checking the significance of any trend estimate. > It would provide a nice complement to the existing theilslopes > routine and others in scipy.stats. I found this code when searching > for "Mann Kendall python" and saw that the original link was no > longer existing. In my opinion it would be good to preserve this > piece of work and make it available to others. I tested this > routine on hundreds of datasets and it seemed to work well. This > implementation was also compared to a few calculations with the > Matlab implementation of this test and provided identical results. Thanks for the information and code. Note that the following technical points would need to be addressed for including such a routine in Scipy: - No license information, so the code or works derived from it cannot be used. - Tests. While checking the results manually is valuable, it would also be necessary to write automated tests --- to make it possible for other people to verify the correctness, and to ensure the code stays working in the future. If you do not have time for this yourself, getting it done would need someone else to volunteer. -- Pauli Virtanen _______________________________________________ SciPy-Dev mailing list SciPy-Dev at scipy.org https://mail.scipy.org/mailman/listinfo/scipy-dev ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, Prof. Dr. Sebastian M. Schmidt ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ From josef.pktd at gmail.com Tue Jul 19 04:04:41 2016 From: josef.pktd at gmail.com (josef.pktd at gmail.com) Date: Tue, 19 Jul 2016 04:04:41 -0400 Subject: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test In-Reply-To: <4A980E5ADF859E4E9007AA5514CF9C8E6262D119@MBX2010-E01.ad.fz-juelich.de> References: <4A980E5ADF859E4E9007AA5514CF9C8E6262BFAF@MBX2010-E01.ad.fz-juelich.de> <4A980E5ADF859E4E9007AA5514CF9C8E6262D119@MBX2010-E01.ad.fz-juelich.de> Message-ID: On Tue, Jul 19, 2016 at 2:50 AM, Schultz, Martin wrote: > Hi Pauli, > > thank you for the feedback. I would be willing to inquire with the original author(s) about licensing, and I can provide a few small test datasets with results for inclusion into an automated test, but I don't think I would be able to write the automated test myself. So, if someone volunteers on that end, I will communicate the data with her or him. May I get back to you in case of questions concerning the license terms etc. if needed? > > Martin Schultz > > > -----Original Message----- > From: SciPy-Dev [mailto:scipy-dev-bounces at scipy.org] On Behalf Of Pauli Virtanen > Sent: Tuesday, July 19, 2016 1:30 AM > To: scipy-dev at scipy.org > Subject: Re: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test > > Hi, > > Mon, 18 Jul 2016 10:11:31 +0000, Schultz, Martin kirjoitti: >> the routine below evaluates the Mann-Kendall test for >> non-parametrically checking the significance of any trend estimate. >> It would provide a nice complement to the existing theilslopes >> routine and others in scipy.stats. I found this code when searching >> for "Mann Kendall python" and saw that the original link was no >> longer existing. In my opinion it would be good to preserve this >> piece of work and make it available to others. I tested this >> routine on hundreds of datasets and it seemed to work well. This >> implementation was also compared to a few calculations with the >> Matlab implementation of this test and provided identical results. > > Thanks for the information and code. > > Note that the following technical points would need to be > addressed for including such a routine in Scipy: > > - No license information, so the code or works derived from it > cannot be used. > > - Tests. While checking the results manually is valuable, > it would also be necessary to write automated tests --- > to make it possible for other people to verify the correctness, > and to ensure the code stays working in the future. > > If you do not have time for this yourself, getting it done would need > someone else to volunteer. The function looks interesting, I've never heard of this hypothesis test. The implementation in the function is very straightforward and I expect that it will be pretty slow for longer arrays, especially when there are just a few ties. Maybe it is possible to streamline the function along some of the existing functions. I don't know if it can be based on ranked data, which IIRC are already in cython. (shapiro which is another all pair comparison algorithm is in Fortran, IIRC) (aside: the vsp package in the docstring looks interesting, and might have a few more things that are missing in scipy.stats/statsmodels.) Josef > > -- > Pauli Virtanen > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > > > ------------------------------------------------------------------------------------------------ > ------------------------------------------------------------------------------------------------ > Forschungszentrum Juelich GmbH > 52425 Juelich > Sitz der Gesellschaft: Juelich > Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 > Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher > Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), > Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, > Prof. Dr. Sebastian M. Schmidt > ------------------------------------------------------------------------------------------------ > ------------------------------------------------------------------------------------------------ > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev From pgmdevlist at gmail.com Tue Jul 19 04:21:48 2016 From: pgmdevlist at gmail.com (Pierre GM) Date: Tue, 19 Jul 2016 04:21:48 -0400 Subject: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test In-Reply-To: References: <4A980E5ADF859E4E9007AA5514CF9C8E6262BFAF@MBX2010-E01.ad.fz-juelich.de> <4A980E5ADF859E4E9007AA5514CF9C8E6262D119@MBX2010-E01.ad.fz-juelich.de> Message-ID: Just so you know, there's a Kendall's tau routine in scipy.mstats. On 19 July 2016 at 10:04:48, josef.pktd at gmail.com (josef.pktd at gmail.com) wrote: On Tue, Jul 19, 2016 at 2:50 AM, Schultz, Martin wrote: > Hi Pauli, > > thank you for the feedback. I would be willing to inquire with the original author(s) about licensing, and I can provide a few small test datasets with results for inclusion into an automated test, but I don't think I would be able to write the automated test myself. So, if someone volunteers on that end, I will communicate the data with her or him. May I get back to you in case of questions concerning the license terms etc. if needed? > > Martin Schultz > > > -----Original Message----- > From: SciPy-Dev [mailto:scipy-dev-bounces at scipy.org] On Behalf Of Pauli Virtanen > Sent: Tuesday, July 19, 2016 1:30 AM > To: scipy-dev at scipy.org > Subject: Re: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test > > Hi, > > Mon, 18 Jul 2016 10:11:31 +0000, Schultz, Martin kirjoitti: >> the routine below evaluates the Mann-Kendall test for >> non-parametrically checking the significance of any trend estimate. >> It would provide a nice complement to the existing theilslopes >> routine and others in scipy.stats. I found this code when searching >> for "Mann Kendall python" and saw that the original link was no >> longer existing. In my opinion it would be good to preserve this >> piece of work and make it available to others. I tested this >> routine on hundreds of datasets and it seemed to work well. This >> implementation was also compared to a few calculations with the >> Matlab implementation of this test and provided identical results. > > Thanks for the information and code. > > Note that the following technical points would need to be > addressed for including such a routine in Scipy: > > - No license information, so the code or works derived from it > cannot be used. > > - Tests. While checking the results manually is valuable, > it would also be necessary to write automated tests --- > to make it possible for other people to verify the correctness, > and to ensure the code stays working in the future. > > If you do not have time for this yourself, getting it done would need > someone else to volunteer. The function looks interesting, I've never heard of this hypothesis test. The implementation in the function is very straightforward and I expect that it will be pretty slow for longer arrays, especially when there are just a few ties. Maybe it is possible to streamline the function along some of the existing functions. I don't know if it can be based on ranked data, which IIRC are already in cython. (shapiro which is another all pair comparison algorithm is in Fortran, IIRC) (aside: the vsp package in the docstring looks interesting, and might have a few more things that are missing in scipy.stats/statsmodels.) Josef > > -- > Pauli Virtanen > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > > > ------------------------------------------------------------------------------------------------ > ------------------------------------------------------------------------------------------------ > Forschungszentrum Juelich GmbH > 52425 Juelich > Sitz der Gesellschaft: Juelich > Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 > Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher > Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), > Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, > Prof. Dr. Sebastian M. Schmidt > ------------------------------------------------------------------------------------------------ > ------------------------------------------------------------------------------------------------ > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev _______________________________________________ SciPy-Dev mailing list SciPy-Dev at scipy.org https://mail.scipy.org/mailman/listinfo/scipy-dev -------------- next part -------------- An HTML attachment was scrubbed... URL: From josef.pktd at gmail.com Tue Jul 19 04:32:34 2016 From: josef.pktd at gmail.com (josef.pktd at gmail.com) Date: Tue, 19 Jul 2016 04:32:34 -0400 Subject: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test In-Reply-To: References: <4A980E5ADF859E4E9007AA5514CF9C8E6262BFAF@MBX2010-E01.ad.fz-juelich.de> <4A980E5ADF859E4E9007AA5514CF9C8E6262D119@MBX2010-E01.ad.fz-juelich.de> Message-ID: On Tue, Jul 19, 2016 at 4:21 AM, Pierre GM wrote: > Just so you know, there's a Kendall's tau routine in scipy.mstats. the kendall_tau in scipy.stats has been rewritten with a mix of numpy and cython, and, based on how far I followed the PR, has very good performance now. I don't know if anything can be reused because the hypothesis is pretty different, sign instead of correlation and univariate instead of bivariate. Still only mstats has the seasonal version of kendall_tau. vsp package also has a seasonal version of the mann-kendall trend test Josef > > > On 19 July 2016 at 10:04:48, josef.pktd at gmail.com (josef.pktd at gmail.com) > wrote: > > On Tue, Jul 19, 2016 at 2:50 AM, Schultz, Martin > wrote: >> Hi Pauli, >> >> thank you for the feedback. I would be willing to inquire with the >> original author(s) about licensing, and I can provide a few small test >> datasets with results for inclusion into an automated test, but I don't >> think I would be able to write the automated test myself. So, if someone >> volunteers on that end, I will communicate the data with her or him. May I >> get back to you in case of questions concerning the license terms etc. if >> needed? >> >> Martin Schultz >> >> >> -----Original Message----- >> From: SciPy-Dev [mailto:scipy-dev-bounces at scipy.org] On Behalf Of Pauli >> Virtanen >> Sent: Tuesday, July 19, 2016 1:30 AM >> To: scipy-dev at scipy.org >> Subject: Re: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall >> test >> >> Hi, >> >> Mon, 18 Jul 2016 10:11:31 +0000, Schultz, Martin kirjoitti: >>> the routine below evaluates the Mann-Kendall test for >>> non-parametrically checking the significance of any trend estimate. >>> It would provide a nice complement to the existing theilslopes >>> routine and others in scipy.stats. I found this code when searching >>> for "Mann Kendall python" and saw that the original link was no >>> longer existing. In my opinion it would be good to preserve this >>> piece of work and make it available to others. I tested this >>> routine on hundreds of datasets and it seemed to work well. This >>> implementation was also compared to a few calculations with the >>> Matlab implementation of this test and provided identical results. >> >> Thanks for the information and code. >> >> Note that the following technical points would need to be >> addressed for including such a routine in Scipy: >> >> - No license information, so the code or works derived from it >> cannot be used. >> >> - Tests. While checking the results manually is valuable, >> it would also be necessary to write automated tests --- >> to make it possible for other people to verify the correctness, >> and to ensure the code stays working in the future. >> >> If you do not have time for this yourself, getting it done would need >> someone else to volunteer. > > The function looks interesting, I've never heard of this hypothesis test. > > The implementation in the function is very straightforward and I > expect that it will be pretty slow for longer arrays, especially when > there are just a few ties. > Maybe it is possible to streamline the function along some of the > existing functions. I don't know if it can be based on ranked data, > which IIRC are already in cython. > (shapiro which is another all pair comparison algorithm is in Fortran, IIRC) > > > (aside: the vsp package in the docstring looks interesting, and might > have a few more things that are missing in scipy.stats/statsmodels.) > > Josef > >> >> -- >> Pauli Virtanen >> >> _______________________________________________ >> SciPy-Dev mailing list >> SciPy-Dev at scipy.org >> https://mail.scipy.org/mailman/listinfo/scipy-dev >> >> >> >> ------------------------------------------------------------------------------------------------ >> >> ------------------------------------------------------------------------------------------------ >> Forschungszentrum Juelich GmbH >> 52425 Juelich >> Sitz der Gesellschaft: Juelich >> Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 >> Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher >> Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), >> Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, >> Prof. Dr. Sebastian M. Schmidt >> >> ------------------------------------------------------------------------------------------------ >> >> ------------------------------------------------------------------------------------------------ >> >> _______________________________________________ >> SciPy-Dev mailing list >> SciPy-Dev at scipy.org >> https://mail.scipy.org/mailman/listinfo/scipy-dev > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev From pmhobson at gmail.com Tue Jul 19 21:24:16 2016 From: pmhobson at gmail.com (Paul Hobson) Date: Tue, 19 Jul 2016 18:24:16 -0700 Subject: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test In-Reply-To: References: <4A980E5ADF859E4E9007AA5514CF9C8E6262BFAF@MBX2010-E01.ad.fz-juelich.de> <4A980E5ADF859E4E9007AA5514CF9C8E6262D119@MBX2010-E01.ad.fz-juelich.de> Message-ID: On Tue, Jul 19, 2016 at 1:04 AM, wrote: > The function looks interesting, I've never heard of this hypothesis test. > > EPA guidance specifies that the Mann-Kendall test be used to determine if there's a trend (seasonal, or long-term) in pollutant concentrations. Even though regulators sometimes *require* that you use their ProUCL[0] software, this would be still be a very welcome addition to the stats module for environmental engineers like me. Martin: if you get the pull request started, I'll help your write the tests. Just @-mention me in the PR's discussion (@phobson). -Paul [0] https://www.epa.gov/land-research/proucl-software -------------- next part -------------- An HTML attachment was scrubbed... URL: From hamed.dadgour at gmail.com Fri Jul 22 19:17:53 2016 From: hamed.dadgour at gmail.com (Hamed Dadgour) Date: Fri, 22 Jul 2016 16:17:53 -0700 Subject: [SciPy-Dev] Fwd: Scipy installation issue In-Reply-To: References: Message-ID: Hi there, I am trying to install Scipy after I successfully installed Numpy on Python3.6. I did try to install Scipy from PyCharm by going to settings -> project interpretor -> add Scipy. However, I keep getting this error message (see the screenshot). It seems that Python3.6 can't find my Fortran compiler. However, I have already installed the Fortran compiler on my machine. *How do I tell PyCharm where it should look for Fortran compiler?* Here are the gcc and gfortran versions that I have on my machine. *Hameds-MacBook-Air:~ hameddadgour$ gcc --version* *Configured with: --prefix=/Library/Developer/CommandLineTools/usr --with-gxx-include-dir=/usr/include/c++/4.2.1* *Apple LLVM version 7.0.2 (clang-700.1.81)* *Target: x86_64-apple-darwin14.5.0* *Thread model: posix* *Hameds-MacBook-Air:~ hameddadgour$ gfortran --version* *GNU Fortran (Homebrew gcc 6.1.0) 6.1.0* *Copyright (C) 2016 Free Software Foundation, Inc.* *This is free software; see the source for copying conditions. There is NO* *warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.* [image: Inline image 1] -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image.png Type: image/png Size: 90075 bytes Desc: not available URL: From charlesnwoods at gmail.com Sun Jul 24 18:38:11 2016 From: charlesnwoods at gmail.com (Nathan Woods) Date: Sun, 24 Jul 2016 22:38:11 +0000 (UTC) Subject: [SciPy-Dev] (no subject) In-Reply-To: References: Message-ID: Bump.? On Wed, May 11, 2016 at 11:20 AM +0900, "Nathan Woods" wrote: There have been a number of problems (e.g. GitHub issues #5002, #4831) with the C interface of scipy.integrate.quad since work was done a couple of years ago to allow performance optimizations (https://github.com/scipy/scipy/commit/f5183a34a959d625c6b534e99df5db7ca49bf1ec). Specifically, the QuadPack Fortran library does not really accommodate multivariate integrand functions, which leads to all sorts of monkey business in the API to hide that multivariate-ness. This is further complicated by the fact that the QuadPack interface is one of the oldest parts of SciPy and uses the Python C API directly, which many (most?) of us are unfamiliar with.? I'd really like to figure out some kind of "good" solution to this. I think that the requirements of the problem are fairly simple: connect the existing scipy.integrate.quad routine to the underlying Fortran code, while allowing users to use compiled function pointers as integrand functions, if they want to (and are willing to put in some effort).? I guess that the question here is, what is the best way to go about this? Should we simply find a way to fix the existing interface code? Should we roll back the changes and disallow the use of function pointers in quad? Should we replace the existing API with something else, using Cython or f2py? Should we alter the QuadPack code itself (perhaps by adding a void* argument to the function) to make it more friendly to multivariate functions in general? The current state of affairs bothers me, especially since I was involved in the PR that ?caused the new problems, but I haven't been able to come up with a good way to fix things yet. I'd love to get some ideas and feedback on how this could be resolved. Nathan Woods -------------- next part -------------- An HTML attachment was scrubbed... URL: From evgeny.burovskiy at gmail.com Mon Jul 25 13:54:00 2016 From: evgeny.burovskiy at gmail.com (Evgeni Burovski) Date: Mon, 25 Jul 2016 18:54:00 +0100 Subject: [SciPy-Dev] ANN: scipy 0.18.0 release Message-ID: On behalf of the Scipy development team I am pleased to announce the availability of Scipy 0.18.0. This release contains several great new features and a large number of bug fixes and various improvements, as detailed in the release notes below. 99 people contributed to this release over the course of six months. Thanks to everyone who contributed! This release requires Python 2.7 or 3.4-3.5 and NumPy 1.7.2 or greater. Source tarballs and release notes can be found at https://github.com/scipy/scipy/releases/tag/v0.18.0. OS X and Linux wheels are available from PyPI. For security-conscious, the wheels themselves are signed with my GPG key. Additionally, you can checksum the wheels and verify the checksums with those listed in the README file at https://github.com/scipy/scipy/releases/tag/v0.18.0. Cheers, Evgeni -----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 ========================== SciPy 0.18.0 Release Notes ========================== .. contents:: SciPy 0.18.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.19.x branch, and on adding new features on the master branch. This release requires Python 2.7 or 3.4-3.5 and NumPy 1.7.1 or greater. Highlights of this release include: - - A new ODE solver for two-point boundary value problems, `scipy.optimize.solve_bvp`. - - A new class, `CubicSpline`, for cubic spline interpolation of data. - - N-dimensional tensor product polynomials, `scipy.interpolate.NdPPoly`. - - Spherical Voronoi diagrams, `scipy.spatial.SphericalVoronoi`. - - Support for discrete-time linear systems, `scipy.signal.dlti`. New features ============ `scipy.integrate` improvements - ------------------------------ A solver of two-point boundary value problems for ODE systems has been implemented in `scipy.integrate.solve_bvp`. The solver allows for non-separated boundary conditions, unknown parameters and certain singular terms. It finds a C1 continious solution using a fourth-order collocation algorithm. `scipy.interpolate` improvements - -------------------------------- Cubic spline interpolation is now available via `scipy.interpolate.CubicSpline`. This class represents a piecewise cubic polynomial passing through given points and C2 continuous. It is represented in the standard polynomial basis on each segment. A representation of n-dimensional tensor product piecewise polynomials is available as the `scipy.interpolate.NdPPoly` class. Univariate piecewise polynomial classes, `PPoly` and `Bpoly`, can now be evaluated on periodic domains. Use ``extrapolate="periodic"`` keyword argument for this. `scipy.fftpack` improvements - ---------------------------- `scipy.fftpack.next_fast_len` function computes the next "regular" number for FFTPACK. Padding the input to this length can give significant performance increase for `scipy.fftpack.fft`. `scipy.signal` improvements - --------------------------- Resampling using polyphase filtering has been implemented in the function `scipy.signal.resample_poly`. This method upsamples a signal, applies a zero-phase low-pass FIR filter, and downsamples using `scipy.signal.upfirdn` (which is also new in 0.18.0). This method can be faster than FFT-based filtering provided by `scipy.signal.resample` for some signals. `scipy.signal.firls`, which constructs FIR filters using least-squares error minimization, was added. `scipy.signal.sosfiltfilt`, which does forward-backward filtering like `scipy.signal.filtfilt` but for second-order sections, was added. Discrete-time linear systems ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ `scipy.signal.dlti` provides an implementation of discrete-time linear systems. Accordingly, the `StateSpace`, `TransferFunction` and `ZerosPolesGain` classes have learned a the new keyword, `dt`, which can be used to create discrete-time instances of the corresponding system representation. `scipy.sparse` improvements - --------------------------- The functions `sum`, `max`, `mean`, `min`, `transpose`, and `reshape` in `scipy.sparse` have had their signatures augmented with additional arguments and functionality so as to improve compatibility with analogously defined functions in `numpy`. Sparse matrices now have a `count_nonzero` method, which counts the number of nonzero elements in the matrix. Unlike `getnnz()` and ``nnz`` propety, which return the number of stored entries (the length of the data attribute), this method counts the actual number of non-zero entries in data. `scipy.optimize` improvements - ----------------------------- The implementation of Nelder-Mead minimization, `scipy.minimize(..., method="Nelder-Mead")`, obtained a new keyword, `initial_simplex`, which can be used to specify the initial simplex for the optimization process. Initial step size selection in CG and BFGS minimizers has been improved. We expect that this change will improve numeric stability of optimization in some cases. See pull request gh-5536 for details. Handling of infinite bounds in SLSQP optimization has been improved. We expect that this change will improve numeric stability of optimization in the some cases. See pull request gh-6024 for details. A large suite of global optimization benchmarks has been added to ``scipy/benchmarks/go_benchmark_functions``. See pull request gh-4191 for details. Nelder-Mead and Powell minimization will now only set defaults for maximum iterations or function evaluations if neither limit is set by the caller. In some cases with a slow converging function and only 1 limit set, the minimization may continue for longer than with previous versions and so is more likely to reach convergence. See issue gh-5966. `scipy.stats` improvements - -------------------------- Trapezoidal distribution has been implemented as `scipy.stats.trapz`. Skew normal distribution has been implemented as `scipy.stats.skewnorm`. Burr type XII distribution has been implemented as `scipy.stats.burr12`. Three- and four-parameter kappa distributions have been implemented as `scipy.stats.kappa3` and `scipy.stats.kappa4`, respectively. New `scipy.stats.iqr` function computes the interquartile region of a distribution. Random matrices ~~~~~~~~~~~~~~~ `scipy.stats.special_ortho_group` and `scipy.stats.ortho_group` provide generators of random matrices in the SO(N) and O(N) groups, respectively. They generate matrices in the Haar distribution, the only uniform distribution on these group manifolds. `scipy.stats.random_correlation` provides a generator for random correlation matrices, given specified eigenvalues. `scipy.linalg` improvements - --------------------------- `scipy.linalg.svd` gained a new keyword argument, ``lapack_driver``. Available drivers are ``gesdd`` (default) and ``gesvd``. `scipy.linalg.lapack.ilaver` returns the version of the LAPACK library SciPy links to. `scipy.spatial` improvements - ---------------------------- Boolean distances, `scipy.spatial.pdist`, have been sped up. Improvements vary by the function and the input size. In many cases, one can expect a speed-up of x2--x10. New class `scipy.spatial.SphericalVoronoi` constructs Voronoi diagrams on the surface of a sphere. See pull request gh-5232 for details. `scipy.cluster` improvements - ---------------------------- A new clustering algorithm, the nearest neighbor chain algorithm, has been implemented for `scipy.cluster.hierarchy.linkage`. As a result, one can expect a significant algorithmic improvement (:math:`O(N^2)` instead of :math:`O(N^3)`) for several linkage methods. `scipy.special` improvements - ---------------------------- The new function `scipy.special.loggamma` computes the principal branch of the logarithm of the Gamma function. For real input, ``loggamma`` is compatible with `scipy.special.gammaln`. For complex input, it has more consistent behavior in the complex plane and should be preferred over ``gammaln``. Vectorized forms of spherical Bessel functions have been implemented as `scipy.special.spherical_jn`, `scipy.special.spherical_kn`, `scipy.special.spherical_in` and `scipy.special.spherical_yn`. They are recommended for use over ``sph_*`` functions, which are now deprecated. Several special functions have been extended to the complex domain and/or have seen domain/stability improvements. This includes `spence`, `digamma`, `log1p` and several others. Deprecated features =================== The cross-class properties of `lti` systems have been deprecated. The following properties/setters will raise a `DeprecationWarning`: Name - (accessing/setting raises warning) - (setting raises warning) * StateSpace - (`num`, `den`, `gain`) - (`zeros`, `poles`) * TransferFunction (`A`, `B`, `C`, `D`, `gain`) - (`zeros`, `poles`) * ZerosPolesGain (`A`, `B`, `C`, `D`, `num`, `den`) - () Spherical Bessel functions, ``sph_in``, ``sph_jn``, ``sph_kn``, ``sph_yn``, ``sph_jnyn`` and ``sph_inkn`` have been deprecated in favor of `scipy.special.spherical_jn` and ``spherical_kn``, ``spherical_yn``, ``spherical_in``. The following functions in `scipy.constants` are deprecated: ``C2K``, ``K2C``, ``C2F``, ``F2C``, ``F2K`` and ``K2F``. They are superceded by a new function `scipy.constants.convert_temperature` that can perform all those conversions plus to/from the Rankine temperature scale. Backwards incompatible changes ============================== `scipy.optimize` - ---------------- The convergence criterion for ``optimize.bisect``, ``optimize.brentq``, ``optimize.brenth``, and ``optimize.ridder`` now works the same as ``numpy.allclose``. `scipy.ndimage` - --------------- The offset in ``ndimage.iterpolation.affine_transform`` is now consistently added after the matrix is applied, independent of if the matrix is specified using a one-dimensional or a two-dimensional array. `scipy.stats` - ------------- ``stats.ks_2samp`` used to return nonsensical values if the input was not real or contained nans. It now raises an exception for such inputs. Several deprecated methods of `scipy.stats` distributions have been removed: ``est_loc_scale``, ``vecfunc``, ``veccdf`` and ``vec_generic_moment``. Deprecated functions ``nanmean``, ``nanstd`` and ``nanmedian`` have been removed from `scipy.stats`. These functions were deprecated in scipy 0.15.0 in favor of their `numpy` equivalents. A bug in the ``rvs()`` method of the distributions in `scipy.stats` has been fixed. When arguments to ``rvs()`` were given that were shaped for broadcasting, in many cases the returned random samples were not random. A simple example of the problem is ``stats.norm.rvs(loc=np.zeros(10))``. Because of the bug, that call would return 10 identical values. The bug only affected code that relied on the broadcasting of the shape, location and scale parameters. The ``rvs()`` method also accepted some arguments that it should not have. There is a potential for backwards incompatibility in cases where ``rvs()`` accepted arguments that are not, in fact, compatible with broadcasting. An example is stats.gamma.rvs([2, 5, 10, 15], size=(2,2)) The shape of the first argument is not compatible with the requested size, but the function still returned an array with shape (2, 2). In scipy 0.18, that call generates a ``ValueError``. `scipy.io` - ---------- `scipy.io.netcdf` masking now gives precedence to the ``_FillValue`` attribute over the ``missing_value`` attribute, if both are given. Also, data are only treated as missing if they match one of these attributes exactly: values that differ by roundoff from ``_FillValue`` or ``missing_value`` are no longer treated as missing values. `scipy.interpolate` - ------------------- `scipy.interpolate.PiecewisePolynomial` class has been removed. It has been deprecated in scipy 0.14.0, and `scipy.interpolate.BPoly.from_derivatives` serves as a drop-in replacement. Other changes ============= Scipy now uses ``setuptools`` for its builds instead of plain distutils. This fixes usage of ``install_requires='scipy'`` in the ``setup.py`` files of projects that depend on Scipy (see Numpy issue gh-6551 for details). It potentially affects the way that build/install methods for Scipy itself behave though. Please report any unexpected behavior on the Scipy issue tracker. PR `#6240 `__ changes the interpretation of the `maxfun` option in `L-BFGS-B` based routines in the `scipy.optimize` module. An `L-BFGS-B` search consists of multiple iterations, with each iteration consisting of one or more function evaluations. Whereas the old search strategy terminated immediately upon reaching `maxfun` function evaluations, the new strategy allows the current iteration to finish despite reaching `maxfun`. The bundled copy of Qhull in the `scipy.spatial` subpackage has been upgraded to version 2015.2. The bundled copy of ARPACK in the `scipy.sparse.linalg` subpackage has been upgraded to arpack-ng 3.3.0. The bundled copy of SuperLU in the `scipy.sparse` subpackage has been upgraded to version 5.1.1. Authors ======= * @endolith * @yanxun827 + * @kleskjr + * @MYheavyGo + * @solarjoe + * Gregory Allen + * Gilles Aouizerate + * Tom Augspurger + * Henrik Bengtsson + * Felix Berkenkamp * Per Brodtkorb * Lars Buitinck * Daniel Bunting + * Evgeni Burovski * CJ Carey * Tim Cera * Grey Christoforo + * Robert Cimrman * Philip DeBoer + * Yves Delley + * D?vid Bodn?r + * Ion Elberdin + * Gabriele Farina + * Yu Feng * Andrew Fowlie + * Joseph Fox-Rabinovitz * Simon Gibbons + * Neil Girdhar + * Kolja Glogowski + * Christoph Gohlke * Ralf Gommers * Todd Goodall + * Johnnie Gray + * Alex Griffing * Olivier Grisel * Thomas Haslwanter + * Michael Hirsch + * Derek Homeier * Golnaz Irannejad + * Marek Jacob + * InSuk Joung + * Tetsuo Koyama + * Eugene Krokhalev + * Eric Larson * Denis Laxalde * Antony Lee * Jerry Li + * Henry Lin + * Nelson Liu + * Lo?c Est?ve * Lei Ma + * Osvaldo Martin + * Stefano Martina + * Nikolay Mayorov * Matthieu Melot + * Sturla Molden * Eric Moore * Alistair Muldal + * Maniteja Nandana * Tavi Nathanson + * Andrew Nelson * Joel Nothman * Behzad Nouri * Nikolai Nowaczyk + * Juan Nunez-Iglesias + * Ted Pudlik * Eric Quintero * Yoav Ram * Jonas Rauber + * Tyler Reddy + * Juha Remes * Garrett Reynolds + * Ariel Rokem + * Fabian Rost + * Bill Sacks + * Jona Sassenhagen + * Kari Schoonbee + * Marcello Seri + * Sourav Singh + * Martin Spacek + * S?ren Fuglede J?rgensen + * Bhavika Tekwani + * Martin Thoma + * Sam Tygier + * Meet Udeshi + * Utkarsh Upadhyay * Bram Vandekerckhove + * Sebasti?n Vanrell + * Ze Vinicius + * Pauli Virtanen * Stefan van der Walt * Warren Weckesser * Jakub Wilk + * Josh Wilson * Phillip J. Wolfram + * Nathan Woods * Haochen Wu * G Young + A total of 99 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. Issues closed for 0.18.0 - ------------------------ - - `#1484 `__: SVD using *GESVD lapack drivers (Trac #957) - - `#1547 `__: Inconsistent use of offset in ndimage.interpolation.affine_transform()... - - `#1609 `__: special.hyp0f1 returns nan (Trac #1082) - - `#1656 `__: fmin_slsqp enhancement (Trac #1129) - - `#2069 `__: stats broadcasting in rvs (Trac #1544) - - `#2165 `__: sph_jn returns false results for some orders/values (Trac #1640) - - `#2255 `__: Incorrect order of translation and rotation in affine_transform... - - `#2332 `__: hyp0f1 args and return values are unnumpyic (Trac #1813) - - `#2534 `__: The sparse .sum() method with uint8 dtype does not act like the... - - `#3113 `__: Implement ufuncs for CSPHJY, SPHJ, SPHY, CSPHIK, SPHI, SPHIK... - - `#3568 `__: SciPy 0.13.3 - CentOS5 - Errors in test_arpack - - `#3581 `__: optimize: stepsize in fmin_bfgs is "bad" - - `#4476 `__: scipy.sparse non-native endian bug - - `#4484 `__: ftol in optimize.fmin fails to work - - `#4510 `__: sparsetools.cxx call_thunk can segfault due to out of bounds... - - `#5051 `__: ftol and xtol for _minimize_neldermead are absolute instead of... - - `#5097 `__: proposal: spherical Voronoi diagrams - - `#5123 `__: Call to `scipy.sparse.coo_matrix` fails when passed Cython typed... - - `#5220 `__: scipy.cluster.hierarchy.{ward,median,centroid} does not work... - - `#5379 `__: Add a build step at the end of .travis.yml that uploads working... - - `#5440 `__: scipy.optimize.basinhopping: accept_test returning numpy.bool_... - - `#5452 `__: Error in scipy.integrate.nquad when using variable integration... - - `#5520 `__: Cannot inherit csr_matrix properly - - `#5533 `__: Kendall tau implementation uses Python mergesort - - `#5553 `__: stats.tiecorrect overflows - - `#5589 `__: Add the Type XII Burr distribution to stats. - - `#5612 `__: sparse.linalg factorizations slow for small k due to default... - - `#5626 `__: io.netcdf masking should use masked_equal rather than masked_value - - `#5637 `__: Simple cubic spline interpolation? - - `#5683 `__: BUG: Akima1DInterpolator may return nans given multidimensional... - - `#5686 `__: scipy.stats.ttest_ind_from_stats does not accept arrays - - `#5702 `__: scipy.ndimage.interpolation.affine_transform lacks documentation... - - `#5718 `__: Wrong computation of weighted minkowski distance in cdist - - `#5745 `__: move to setuptools for next release - - `#5752 `__: DOC: solve_discrete_lyapunov equation puts transpose in wrong... - - `#5760 `__: signal.ss2tf doesn't handle zero-order state-space models - - `#5764 `__: Hypergeometric function hyp0f1 behaves incorrectly for complex... - - `#5814 `__: stats NaN Policy Error message inconsistent with code - - `#5833 `__: docstring of stats.binom_test() needs an update - - `#5853 `__: Error in scipy.linalg.expm for complex matrix with shape (1,1) - - `#5856 `__: Specify Nelder-Mead initial simplex - - `#5865 `__: scipy.linalg.expm fails for certain numpy matrices - - `#5915 `__: optimize.basinhopping - variable referenced before assignment. - - `#5916 `__: LSQUnivariateSpline fitting failed with knots generated from... - - `#5927 `__: unicode vs. string comparison in scipy.stats.binned_statistic_dd - - `#5936 `__: faster implementation of ks_2samp - - `#5948 `__: csc matrix .mean returns single element matrix rather than scalar - - `#5959 `__: BUG: optimize test error for root when using lgmres - - `#5972 `__: Test failures for sparse sum tests on 32-bit Python - - `#5976 `__: Unexpected exception in scipy.sparse.bmat while using 0 x 0 matrix - - `#6008 `__: scipy.special.kl_div not available in 0.14.1 - - `#6011 `__: The von-Mises entropy is broken - - `#6016 `__: python crashes for linalg.interpolative.svd with certain large... - - `#6017 `__: Wilcoxon signed-rank test with zero_method="pratt" or "zsplit"... - - `#6028 `__: stats.distributions does not have trapezoidal distribution - - `#6035 `__: Wrong link in f_oneway - - `#6056 `__: BUG: signal.decimate should only accept discrete LTI objects - - `#6093 `__: Precision error on Linux 32 bit with openblas - - `#6101 `__: Barycentric transforms test error on Python3, 32-bit Linux - - `#6105 `__: scipy.misc.face docstring is incorrect - - `#6113 `__: scipy.linalg.logm fails for a trivial matrix - - `#6128 `__: Error in dot method of sparse COO array, when used with numpy... - - `#6132 `__: Failures with latest MKL - - `#6136 `__: Failures on `master` with MKL - - `#6162 `__: fmin_l_bfgs_b returns inconsistent results (fmin ? f(xmin)) and... - - `#6165 `__: optimize.minimize infinite loop with Newton-CG - - `#6167 `__: incorrect distribution fitting for data containing boundary values. - - `#6194 `__: lstsq() and others detect numpy.complex256 as real - - `#6216 `__: ENH: improve accuracy of ppf cdf roundtrip for bradford - - `#6217 `__: BUG: weibull_min.logpdf return nan for c=1 and x=0 - - `#6218 `__: Is there a method to cap shortest path search distances? - - `#6222 `__: PchipInterpolator no longer handles a 2-element array - - `#6226 `__: ENH: improve accuracy for logistic.ppf and logistic.isf - - `#6227 `__: ENH: improve accuracy for rayleigh.logpdf and rayleigh.logsf... - - `#6228 `__: ENH: improve accuracy of ppf cdf roundtrip for gumbel_l - - `#6235 `__: BUG: alpha.pdf and alpha.logpdf returns nan for x=0 - - `#6245 `__: ENH: improve accuracy for ppf-cdf and sf-isf roundtrips for invgamma - - `#6263 `__: BUG: stats: Inconsistency in the multivariate_normal docstring - - `#6292 `__: Python 3 unorderable type errors in test_sparsetools.TestInt32Overflow - - `#6316 `__: TestCloughTocher2DInterpolator.test_dense crashes python3.5.2rc1_64bit... - - `#6318 `__: Scipy interp1d 'nearest' not working for high values on x-axis Pull requests for 0.18.0 - ------------------------ - - `#3226 `__: DOC: Change `nb` and `na` to conventional m and n - - `#3867 `__: allow cKDTree.query taking a list input in k. - - `#4191 `__: ENH: Benchmarking global optimizers - - `#4356 `__: ENH: add PPoly.solve(y) for solving ``p(x) == y`` - - `#4370 `__: DOC separate boolean distance functions for clarity - - `#4678 `__: BUG: sparse: ensure index dtype is large enough to pass all parameters... - - `#4881 `__: scipy.signal: Add the class dlti for linear discrete-time systems.... - - `#4901 `__: MAINT: add benchmark and improve docstring for signal.lfilter - - `#5043 `__: ENH: sparse: add count_nonzero method - - `#5136 `__: Attribute kurtosistest() to Anscombe & Glynn (1983) - - `#5186 `__: ENH: Port upfirdn - - `#5232 `__: ENH: adding spherical Voronoi diagram algorithm to scipy.spatial - - `#5279 `__: ENH: Bessel filters with different normalizations, high order - - `#5384 `__: BUG: Closes #5027 distance function always casts bool to double - - `#5392 `__: ENH: Add zero_phase kwarg to signal.decimate - - `#5394 `__: MAINT: sparse: non-canonical test cleanup and fixes - - `#5424 `__: DOC: add Scipy developers guide - - `#5442 `__: STY: PEP8 amendments - - `#5472 `__: Online QR in LGMRES - - `#5526 `__: BUG: stats: Fix broadcasting in the rvs() method of the distributions. - - `#5530 `__: MAINT: sparse: set `format` attr explicitly - - `#5536 `__: optimize: fix up cg/bfgs initial step sizes - - `#5548 `__: PERF: improves performance in stats.kendalltau - - `#5549 `__: ENH: Nearest-neighbor chain algorithm for hierarchical clustering - - `#5554 `__: MAINT/BUG: closes overflow bug in stats.tiecorrect - - `#5557 `__: BUG: modify optimize.bisect to achieve desired tolerance - - `#5581 `__: DOC: Tutorial for least_squares - - `#5606 `__: ENH: differential_evolution - moving core loop of solve method... - - `#5609 `__: [MRG] test against numpy dev - - `#5611 `__: use setuptools for bdist_egg distributions - - `#5615 `__: MAINT: linalg: tighten _decomp_update + special: remove unused... - - `#5622 `__: Add SO(N) rotation matrix generator - - `#5623 `__: ENH: special: Add vectorized spherical Bessel functions. - - `#5627 `__: Response to issue #5160, implements the skew normal distribution... - - `#5628 `__: DOC: Align the description and operation - - `#5632 `__: DOC: special: Expanded docs for Airy, elliptic, Bessel functions. - - `#5633 `__: MAINT: linalg: unchecked malloc in _decomp_update - - `#5634 `__: MAINT: optimize: tighten _group_columns - - `#5640 `__: Fixes for io.netcdf masking - - `#5645 `__: MAINT: size 0 vector handling in cKDTree range queries - - `#5649 `__: MAINT: update license text - - `#5650 `__: DOC: Clarify Exponent Order in ltisys.py - - `#5651 `__: DOC: Clarify Documentation for scipy.special.gammaln - - `#5652 `__: DOC: Fixed scipy.special.betaln Doc - - `#5653 `__: [MRG] ENH: CubicSpline interpolator - - `#5654 `__: ENH: Burr12 distribution to stats module - - `#5659 `__: DOC: Define BEFORE/AFTER in runtests.py -h for bench-compare - - `#5660 `__: MAINT: remove functions deprecated before 0.16.0 - - `#5662 `__: ENH: Circular statistic optimization - - `#5663 `__: MAINT: remove uses of np.testing.rand - - `#5665 `__: MAINT: spatial: remove matching distance implementation - - `#5667 `__: Change some HTTP links to HTTPS - - `#5669 `__: DOC: zpk2sos can't do analog, array_like, etc. - - `#5670 `__: Update conf.py - - `#5672 `__: MAINT: move a sample distribution to a subclass of rv_discrete - - `#5678 `__: MAINT: stats: remove est_loc_scale method - - `#5679 `__: MAINT: DRY up generic computations for discrete distributions - - `#5680 `__: MAINT: stop shadowing builtins in stats.distributions - - `#5681 `__: forward port ENH: Re-enable broadcasting of fill_value - - `#5684 `__: BUG: Fix Akima1DInterpolator returning nans - - `#5690 `__: BUG: fix stats.ttest_ind_from_stats to handle arrays. - - `#5691 `__: BUG: fix generator in io._loadarff to comply with PEP 0479 - - `#5693 `__: ENH: use math.factorial for exact factorials - - `#5695 `__: DOC: dx might be a float, not only an integer - - `#5699 `__: MAINT: io: micro-optimize Matlab reading code for size - - `#5701 `__: Implement OptimizeResult.__dir__ - - `#5703 `__: ENH: stats: make R? printing optional in probplot - - `#5704 `__: MAINT: typo ouf->out - - `#5705 `__: BUG: fix typo in query_pairs - - `#5707 `__: DOC:Add some explanation for ftol xtol in scipy.optimize.fmin - - `#5708 `__: DOC: optimize: PEP8 minimize docstring - - `#5709 `__: MAINT: optimize Cython code for speed and size - - `#5713 `__: [DOC] Fix broken link to reference - - `#5717 `__: DOC: curve_fit raises RuntimeError on failure. - - `#5724 `__: forward port gh-5720 - - `#5728 `__: STY: remove a blank line - - `#5729 `__: ENH: spatial: speed up boolean distances - - `#5732 `__: MAINT: differential_evolution changes to default keywords break... - - `#5733 `__: TST: differential_evolution - population initiation tests - - `#5736 `__: Complex number support in log1p, expm1, and xlog1py - - `#5741 `__: MAINT: sparse: clean up extraction functions - - `#5742 `__: DOC: signal: Explain fftbins in get_window - - `#5748 `__: ENH: Add O(N) random matrix generator - - `#5749 `__: ENH: Add polyphase resampling - - `#5756 `__: RFC: Bump the minimum numpy version, drop older python versions - - `#5761 `__: DOC: Some improvements to least squares docstrings - - `#5762 `__: MAINT: spatial: distance refactoring - - `#5768 `__: DOC: Fix io.loadmat docstring for mdict param - - `#5770 `__: BUG: Accept anything np.dtype can handle for a dtype in sparse.random - - `#5772 `__: Update sparse.csgraph.laplacian docstring - - `#5777 `__: BUG: fix special.hyp0f1 to work correctly for complex inputs. - - `#5780 `__: DOC: Update PIL error install URL - - `#5781 `__: DOC: Fix documentation on solve_discrete_lyapunov - - `#5782 `__: DOC: cKDTree and KDTree now reference each other - - `#5783 `__: DOC: Clarify finish behaviour in scipy.optimize.brute - - `#5784 `__: MAINT: Change default tolerances of least_squares to 1e-8 - - `#5787 `__: BUG: Allow Processing of Zero Order State Space Models in signal.ss2tf - - `#5788 `__: DOC, BUG: Clarify and Enforce Input Types to 'Data' Objects - - `#5789 `__: ENH: sparse: speedup LIL matrix slicing (was #3338) - - `#5791 `__: DOC: README: remove coveralls.io - - `#5792 `__: MAINT: remove uses of deprecated np.random.random_integers - - `#5794 `__: fix affine_transform (fixes #1547 and #5702) - - `#5795 `__: DOC: Removed uniform method from kmeans2 doc - - `#5797 `__: DOC: Clarify the computation of weighted minkowski - - `#5798 `__: BUG: Ensure scipy's _asfarray returns ndarray - - `#5799 `__: TST: Mpmath testing patch - - `#5801 `__: allow reading of certain IDL 8.0 .sav files - - `#5803 `__: DOC: fix module name in error message - - `#5804 `__: DOC: special: Expanded docs for special functions. - - `#5805 `__: DOC: Fix order of returns in _spectral_helper - - `#5806 `__: ENH: sparse: vectorized coo_matrix.diagonal - - `#5808 `__: ENH: Added iqr function to compute IQR metric in scipy/stats/stats.py - - `#5810 `__: MAINT/BENCH: sparse: Benchmark cleanup and additions - - `#5811 `__: DOC: sparse.linalg: shape, not size - - `#5813 `__: Update sparse ARPACK functions min `ncv` value - - `#5815 `__: BUG: Error message contained wrong values - - `#5816 `__: remove dead code from stats tests - - `#5820 `__: "in"->"a" in order_filter docstring - - `#5821 `__: DOC: README: INSTALL.txt was renamed in 2014 - - `#5825 `__: DOC: typo in the docstring of least_squares - - `#5826 `__: MAINT: sparse: increase test coverage - - `#5827 `__: NdPPoly rebase - - `#5828 `__: Improve numerical stability of hyp0f1 for large orders - - `#5829 `__: ENH: sparse: Add copy parameter to all .toXXX() methods in sparse... - - `#5830 `__: DOC: rework INSTALL.rst.txt - - `#5831 `__: Adds plotting options to voronoi_plot_2d - - `#5834 `__: Update stats.binom_test() docstring - - `#5836 `__: ENH, TST: Allow SIMO tf's for tf2ss - - `#5837 `__: DOC: Image examples - - `#5838 `__: ENH: sparse: add eliminate_zeros() to coo_matrix - - `#5839 `__: BUG: Fixed name of NumpyVersion.__repr__ - - `#5845 `__: MAINT: Fixed typos in documentation - - `#5847 `__: Fix bugs in sparsetools - - `#5848 `__: BUG: sparse.linalg: add locks to ensure ARPACK threadsafety - - `#5849 `__: ENH: sparse.linalg: upgrade to superlu 5.1.1 - - `#5851 `__: ENH: expose lapack's ilaver to python to allow lapack verion... - - `#5852 `__: MAINT: runtests.py: ensure Ctrl-C interrupts the build - - `#5854 `__: DOC: Minor update to documentation - - `#5855 `__: Pr 5640 - - `#5859 `__: ENH: Add random correlation matrix generator - - `#5862 `__: BUG: Allow expm for complex matrix with shape (1, 1) - - `#5863 `__: FIX: Fix test - - `#5864 `__: DOC: add a little note about the Normal survival function (Q-function) - - `#5867 `__: Fix for #5865 - - `#5869 `__: extend normal distribution cdf to complex domain - - `#5872 `__: DOC: Note that morlet and cwt don't work together - - `#5875 `__: DOC: interp2d class description - - `#5876 `__: MAINT: spatial: remove a stray print statement - - `#5878 `__: MAINT: Fixed noisy UserWarnings in ndimage tests. Fixes #5877 - - `#5879 `__: MAINT: sparse.linalg/superlu: add explicit casts to resolve compiler... - - `#5880 `__: MAINT: signal: import gcd from math and not fractions when on... - - `#5887 `__: Neldermead initial simplex - - `#5894 `__: BUG: _CustomLinearOperator unpickalable in python3.5 - - `#5895 `__: DOC: special: slightly improve the multigammaln docstring - - `#5900 `__: Remove duplicate assignment. - - `#5901 `__: Update bundled ARPACK - - `#5904 `__: ENH: Make convolve and correlate order-agnostic - - `#5905 `__: ENH: sparse.linalg: further LGMRES cleanups - - `#5906 `__: Enhancements and cleanup in scipy.integrate (attempt #2) - - `#5907 `__: ENH: Change sparse `.sum` and `.mean` dtype casting to match... - - `#5909 `__: changes for convolution symmetry - - `#5913 `__: MAINT: basinhopping remove instance test closes #5440 - - `#5919 `__: MAINT: uninitialised var if basinhopping niter=0. closes #5915 - - `#5920 `__: BLD: Fix missing lsame.c error for MKL - - `#5921 `__: DOC: interpolate: add example showing how to work around issue... - - `#5926 `__: MAINT: spatial: upgrade to Qhull 2015.2 - - `#5928 `__: MAINT: sparse: optimize DIA sum/diagonal, csgraph.laplacian - - `#5929 `__: Update info/URL for octave-maintainers discussion - - `#5930 `__: TST: special: silence DeprecationWarnings from sph_yn - - `#5931 `__: ENH: implement the principle branch of the logarithm of Gamma. - - `#5934 `__: Typo: "mush" => "must" - - `#5935 `__: BUG:string comparison stats._binned_statistic closes #5927 - - `#5938 `__: Cythonize stats.ks_2samp for a ~33% gain in speed. - - `#5939 `__: DOC: fix optimize.fmin convergence docstring - - `#5941 `__: Fix minor typo in squareform docstring - - `#5942 `__: Update linregress stderr description. - - `#5943 `__: ENH: Improve numerical accuracy of lognorm - - `#5944 `__: Merge vonmises into stats pyx - - `#5945 `__: MAINT: interpolate: Tweak declaration to avoid cython warning... - - `#5946 `__: MAINT: sparse: clean up format conversion methods - - `#5949 `__: BUG: fix sparse .mean to return a scalar instead of a matrix - - `#5955 `__: MAINT: Replace calls to `hanning` with `hann` - - `#5956 `__: DOC: Missing periods interfering with parsing - - `#5958 `__: MAINT: add a test for lognorm.sf underflow - - `#5961 `__: MAINT _centered(): rename size to shape - - `#5962 `__: ENH: constants: Add multi-scale temperature conversion function - - `#5965 `__: ENH: special: faster way for calculating comb() for exact=True - - `#5975 `__: ENH: Improve FIR path of signal.decimate - - `#5977 `__: MAINT/BUG: sparse: remove overzealous bmat checks - - `#5978 `__: minimize_neldermead() stop at user requested maxiter or maxfev - - `#5983 `__: ENH: make sparse `sum` cast dtypes like NumPy `sum` for 32-bit... - - `#5985 `__: BUG, API: Add `jac` parameter to curve_fit - - `#5989 `__: ENH: Add firls least-squares fitting - - `#5990 `__: BUG: read tries to handle 20-bit WAV files but shouldn't - - `#5991 `__: DOC: Cleanup wav read/write docs and add tables for common types - - `#5994 `__: ENH: Add gesvd method for svd - - `#5996 `__: MAINT: Wave cleanup - - `#5997 `__: TST: Break up upfirdn tests & compare to lfilter - - `#6001 `__: Filter design docs - - `#6002 `__: COMPAT: Expand compatibility fromnumeric.py - - `#6007 `__: ENH: Skip conversion of TF to TF in freqresp - - `#6009 `__: DOC: fix incorrect versionadded for entr, rel_entr, kl_div - - `#6013 `__: Fixed the entropy calculation of the von Mises distribution. - - `#6014 `__: MAINT: make gamma, rgamma use loggamma for complex arguments - - `#6020 `__: WIP: ENH: add exact=True factorial for vectors - - `#6022 `__: Added 'lanczos' to the image interpolation function list. - - `#6024 `__: BUG: optimize: do not use dummy constraints in SLSQP when no... - - `#6025 `__: ENH: Boundary value problem solver for ODE systems - - `#6029 `__: MAINT: Future imports for optimize._lsq - - `#6030 `__: ENH: stats.trap - adding trapezoidal distribution closes #6028 - - `#6031 `__: MAINT: Some improvements to optimize._numdiff - - `#6032 `__: MAINT: Add special/_comb.c to .gitignore - - `#6033 `__: BUG: check the requested approximation rank in interpolative.svd - - `#6034 `__: DOC: Doc for mannwhitneyu in stats.py corrected - - `#6040 `__: FIX: Edit the wrong link in f_oneway - - `#6044 `__: BUG: (ordqz) always increase parameter lwork by 1. - - `#6047 `__: ENH: extend special.spence to complex arguments. - - `#6049 `__: DOC: Add documentation of PR #5640 to the 0.18.0 release notes - - `#6050 `__: MAINT: small cleanups related to loggamma - - `#6070 `__: Add asarray to explicitly cast list to numpy array in wilcoxon... - - `#6071 `__: DOC: antialiasing filter and link decimate resample, etc. - - `#6075 `__: MAINT: reimplement special.digamma for complex arguments - - `#6080 `__: avoid multiple computation in kstest - - `#6081 `__: Clarified pearson correlation return value - - `#6085 `__: ENH: allow long indices of sparse matrix with umfpack in spsolve() - - `#6086 `__: fix description for associated Laguerre polynomials - - `#6087 `__: Corrected docstring of splrep. - - `#6094 `__: ENH: special: change zeta signature to zeta(x, q=1) - - `#6095 `__: BUG: fix integer overflow in special.spence - - `#6106 `__: Fixed Issue #6105 - - `#6116 `__: BUG: matrix logarithm edge case - - `#6119 `__: TST: DeprecationWarnings in stats on python 3.5 closes #5885 - - `#6120 `__: MAINT: sparse: clean up sputils.isintlike - - `#6122 `__: DOC: optimize: linprog docs should say minimize instead of maximize - - `#6123 `__: DOC: optimize: document the `fun` field in `scipy.optimize.OptimizeResult` - - `#6124 `__: Move FFT zero-padding calculation from signaltools to fftpack - - `#6125 `__: MAINT: improve special.gammainc in the ``a ~ x`` regime. - - `#6130 `__: BUG: sparse: Fix COO dot with zero columns - - `#6138 `__: ENH: stats: Improve behavior of genextreme.sf and genextreme.isf - - `#6146 `__: MAINT: simplify the expit implementation - - `#6151 `__: MAINT: special: make generate_ufuncs.py output deterministic - - `#6152 `__: TST: special: better test for gammainc at large arguments - - `#6153 `__: ENH: Make next_fast_len public and faster - - `#6154 `__: fix typo "mush"-->"must" - - `#6155 `__: DOC: Fix some incorrect RST definition lists - - `#6160 `__: make logsumexp error out on a masked array - - `#6161 `__: added missing bracket to rosen documentation - - `#6163 `__: ENH: Added "kappa4" and "kappa3" distributions. - - `#6164 `__: DOC: Minor clean-up in integrate._bvp - - `#6169 `__: Fix mpf_assert_allclose to handle iterable results, such as maps - - `#6170 `__: Fix pchip_interpolate convenience function - - `#6172 `__: Corrected misplaced bracket in doc string - - `#6175 `__: ENH: sparse.csgraph: Pass indices to shortest_path - - `#6178 `__: TST: increase test coverage of sf and isf of a generalized extreme... - - `#6179 `__: TST: avoid a deprecation warning from numpy - - `#6181 `__: ENH: Boundary conditions for CubicSpline - - `#6182 `__: DOC: Add examples/graphs to max_len_seq - - `#6183 `__: BLD: update Bento build config files for recent changes. - - `#6184 `__: BUG: fix issue in io/wavfile for float96 input. - - `#6186 `__: ENH: Periodic extrapolation for PPoly and BPoly - - `#6192 `__: MRG: Add circle-CI - - `#6193 `__: ENH: sparse: avoid setitem densification - - `#6196 `__: Fixed missing sqrt in docstring of Mahalanobis distance in cdist,... - - `#6206 `__: MAINT: Minor changes in solve_bvp - - `#6207 `__: BUG: linalg: for BLAS, downcast complex256 to complex128, not... - - `#6209 `__: BUG: io.matlab: avoid buffer overflows in read_element_into - - `#6210 `__: BLD: use setuptools when building. - - `#6214 `__: BUG: sparse.linalg: fix bug in LGMRES breakdown handling - - `#6215 `__: MAINT: special: make loggamma use zdiv - - `#6220 `__: DOC: Add parameter - - `#6221 `__: ENH: Improve Newton solver for solve_bvp - - `#6223 `__: pchip should work for length-2 arrays - - `#6224 `__: signal.lti: deprecate cross-class properties/setters - - `#6229 `__: BUG: optimize: avoid an infinite loop in Newton-CG - - `#6230 `__: Add example for application of gaussian filter - - `#6236 `__: MAINT: gumbel_l accuracy - - `#6237 `__: MAINT: rayleigh accuracy - - `#6238 `__: MAINT: logistic accuracy - - `#6239 `__: MAINT: bradford distribution accuracy - - `#6240 `__: MAINT: avoid bad fmin in l-bfgs-b due to maxfun interruption - - `#6241 `__: MAINT: weibull_min accuracy - - `#6246 `__: ENH: Add _support_mask to distributions - - `#6247 `__: fixed a print error for an example of ode - - `#6249 `__: MAINT: change x-axis label for stats.probplot to "theoretical... - - `#6250 `__: DOC: fix typos - - `#6251 `__: MAINT: constants: filter out test noise from deprecated conversions - - `#6252 `__: MAINT: io/arff: remove unused variable - - `#6253 `__: Add examples to scipy.ndimage.filters - - `#6254 `__: MAINT: special: fix some build warnings - - `#6258 `__: MAINT: inverse gamma distribution accuracy - - `#6260 `__: MAINT: signal.decimate - Use discrete-time objects - - `#6262 `__: BUG: odr: fix string formatting - - `#6267 `__: TST: fix some test issues in interpolate and stats. - - `#6269 `__: TST: fix some warnings in the test suite - - `#6274 `__: ENH: Add sosfiltfilt - - `#6276 `__: DOC: update release notes for 0.18.0 - - `#6277 `__: MAINT: update the author name mapping - - `#6282 `__: DOC: Correcting references for scipy.stats.normaltest - - `#6283 `__: DOC: some more additions to 0.18.0 release notes. - - `#6284 `__: Add `.. versionadded::` directive to `loggamma`. - - `#6285 `__: BUG: stats: Inconsistency in the multivariate_normal docstring... - - `#6290 `__: Add author list, gh-lists to 0.18.0 release notes - - `#6293 `__: TST: special: relax a test's precision - - `#6295 `__: BUG: sparse: stop comparing None and int in bsr_matrix constructor - - `#6313 `__: MAINT: Fix for python 3.5 travis-ci build problem. - - `#6327 `__: TST: signal: use assert_allclose for testing near-equality in... - - `#6330 `__: BUG: spatial/qhull: allocate qhT via malloc to ensure CRT likes... - - `#6332 `__: TST: fix stats.iqr test to not emit warnings, and fix line lengths. - - `#6334 `__: MAINT: special: fix a test for hyp0f1 - - `#6347 `__: TST: spatial.qhull: skip a test on 32-bit platforms - - `#6350 `__: BUG: optimize/slsqp: don't overwrite an array out of bounds - - `#6351 `__: BUG: #6318 Interp1d 'nearest' integer x-axis overflow issue fixed - 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Thanks for managing this release. Great job! Warren > This release contains several great new > features and a large number of bug fixes and various improvements, as > detailed in the release notes below. > 99 people contributed to this release over the course of six months. > > Thanks to everyone who contributed! > > This release requires Python 2.7 or 3.4-3.5 and NumPy 1.7.2 or > greater. Source tarballs and release notes can be found at > https://github.com/scipy/scipy/releases/tag/v0.18.0. > > OS X and Linux wheels are available from PyPI. For security-conscious, > the wheels themselves are signed with my GPG key. Additionally, you > can checksum the wheels and verify the checksums with those listed in > the README file at > https://github.com/scipy/scipy/releases/tag/v0.18.0. > > > Cheers, > > Evgeni > > > > -----BEGIN PGP SIGNED MESSAGE----- > Hash: SHA1 > > ========================== > SciPy 0.18.0 Release Notes > ========================== > > .. contents:: > > SciPy 0.18.0 is the culmination of 6 months of hard work. It contains > many new features, numerous bug-fixes, improved test coverage and > better documentation. There have been a number of deprecations and > API changes in this release, which are documented below. All users > are encouraged to upgrade to this release, as there are a large number > of bug-fixes and optimizations. Moreover, our development attention > will now shift to bug-fix releases on the 0.19.x branch, and on adding > new features on the master branch. > > This release requires Python 2.7 or 3.4-3.5 and NumPy 1.7.1 or greater. > > Highlights of this release include: > > - - A new ODE solver for two-point boundary value problems, > `scipy.optimize.solve_bvp`. > - - A new class, `CubicSpline`, for cubic spline interpolation of data. > - - N-dimensional tensor product polynomials, `scipy.interpolate.NdPPoly`. > - - Spherical Voronoi diagrams, `scipy.spatial.SphericalVoronoi`. > - - Support for discrete-time linear systems, `scipy.signal.dlti`. > > > New features > ============ > > `scipy.integrate` improvements > - ------------------------------ > > A solver of two-point boundary value problems for ODE systems has been > implemented in `scipy.integrate.solve_bvp`. The solver allows for > non-separated > boundary conditions, unknown parameters and certain singular terms. It > finds > a C1 continious solution using a fourth-order collocation algorithm. > > > `scipy.interpolate` improvements > - -------------------------------- > > Cubic spline interpolation is now available via > `scipy.interpolate.CubicSpline`. > This class represents a piecewise cubic polynomial passing through given > points > and C2 continuous. It is represented in the standard polynomial basis on > each > segment. > > A representation of n-dimensional tensor product piecewise polynomials is > available as the `scipy.interpolate.NdPPoly` class. > > Univariate piecewise polynomial classes, `PPoly` and `Bpoly`, can now be > evaluated on periodic domains. Use ``extrapolate="periodic"`` keyword > argument for this. > > > `scipy.fftpack` improvements > - ---------------------------- > > `scipy.fftpack.next_fast_len` function computes the next "regular" number > for > FFTPACK. Padding the input to this length can give significant performance > increase for `scipy.fftpack.fft`. > > > `scipy.signal` improvements > - --------------------------- > > Resampling using polyphase filtering has been implemented in the function > `scipy.signal.resample_poly`. This method upsamples a signal, applies a > zero-phase low-pass FIR filter, and downsamples using > `scipy.signal.upfirdn` > (which is also new in 0.18.0). This method can be faster than FFT-based > filtering provided by `scipy.signal.resample` for some signals. > > `scipy.signal.firls`, which constructs FIR filters using least-squares > error > minimization, was added. > > `scipy.signal.sosfiltfilt`, which does forward-backward filtering like > `scipy.signal.filtfilt` but for second-order sections, was added. > > > Discrete-time linear systems > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ > > `scipy.signal.dlti` provides an implementation of discrete-time linear > systems. > Accordingly, the `StateSpace`, `TransferFunction` and `ZerosPolesGain` > classes > have learned a the new keyword, `dt`, which can be used to create > discrete-time > instances of the corresponding system representation. > > > `scipy.sparse` improvements > - --------------------------- > > The functions `sum`, `max`, `mean`, `min`, `transpose`, and `reshape` in > `scipy.sparse` have had their signatures augmented with additional > arguments > and functionality so as to improve compatibility with analogously defined > functions in `numpy`. > > Sparse matrices now have a `count_nonzero` method, which counts the number > of > nonzero elements in the matrix. Unlike `getnnz()` and ``nnz`` propety, > which return the number of stored entries (the length of the data > attribute), > this method counts the actual number of non-zero entries in data. > > > `scipy.optimize` improvements > - ----------------------------- > > The implementation of Nelder-Mead minimization, > `scipy.minimize(..., method="Nelder-Mead")`, obtained a new keyword, > `initial_simplex`, which can be used to specify the initial simplex for the > optimization process. > > Initial step size selection in CG and BFGS minimizers has been improved. We > expect that this change will improve numeric stability of optimization in > some > cases. See pull request gh-5536 for details. > > Handling of infinite bounds in SLSQP optimization has been improved. We > expect > that this change will improve numeric stability of optimization in the some > cases. See pull request gh-6024 for details. > > A large suite of global optimization benchmarks has been added to > ``scipy/benchmarks/go_benchmark_functions``. See pull request gh-4191 > for details. > > Nelder-Mead and Powell minimization will now only set defaults for > maximum iterations or function evaluations if neither limit is set by > the caller. In some cases with a slow converging function and only 1 > limit set, the minimization may continue for longer than with previous > versions and so is more likely to reach convergence. See issue gh-5966. > > `scipy.stats` improvements > - -------------------------- > > Trapezoidal distribution has been implemented as `scipy.stats.trapz`. > Skew normal distribution has been implemented as `scipy.stats.skewnorm`. > Burr type XII distribution has been implemented as `scipy.stats.burr12`. > Three- and four-parameter kappa distributions have been implemented as > `scipy.stats.kappa3` and `scipy.stats.kappa4`, respectively. > > New `scipy.stats.iqr` function computes the interquartile region of a > distribution. > > Random matrices > ~~~~~~~~~~~~~~~ > > `scipy.stats.special_ortho_group` and `scipy.stats.ortho_group` provide > generators of random matrices in the SO(N) and O(N) groups, respectively. > They > generate matrices in the Haar distribution, the only uniform distribution > on > these group manifolds. > > `scipy.stats.random_correlation` provides a generator for random > correlation matrices, given specified eigenvalues. > > > `scipy.linalg` improvements > - --------------------------- > > `scipy.linalg.svd` gained a new keyword argument, ``lapack_driver``. > Available > drivers are ``gesdd`` (default) and ``gesvd``. > > `scipy.linalg.lapack.ilaver` returns the version of the LAPACK library > SciPy > links to. > > > `scipy.spatial` improvements > - ---------------------------- > > Boolean distances, `scipy.spatial.pdist`, have been sped up. Improvements > vary > by the function and the input size. In many cases, one can expect a > speed-up > of x2--x10. > > New class `scipy.spatial.SphericalVoronoi` constructs Voronoi diagrams on > the > surface of a sphere. See pull request gh-5232 for details. > > `scipy.cluster` improvements > - ---------------------------- > > A new clustering algorithm, the nearest neighbor chain algorithm, has been > implemented for `scipy.cluster.hierarchy.linkage`. As a result, one can > expect > a significant algorithmic improvement (:math:`O(N^2)` instead of > :math:`O(N^3)`) > for several linkage methods. > > > `scipy.special` improvements > - ---------------------------- > > The new function `scipy.special.loggamma` computes the principal branch of > the > logarithm of the Gamma function. For real input, ``loggamma`` is compatible > with `scipy.special.gammaln`. For complex input, it has more consistent > behavior in the complex plane and should be preferred over ``gammaln``. > > Vectorized forms of spherical Bessel functions have been implemented as > `scipy.special.spherical_jn`, `scipy.special.spherical_kn`, > `scipy.special.spherical_in` and `scipy.special.spherical_yn`. > They are recommended for use over ``sph_*`` functions, which are now > deprecated. > > Several special functions have been extended to the complex domain and/or > have seen domain/stability improvements. This includes `spence`, `digamma`, > `log1p` and several others. > > > Deprecated features > =================== > > The cross-class properties of `lti` systems have been deprecated. The > following properties/setters will raise a `DeprecationWarning`: > > Name - (accessing/setting raises warning) - (setting raises warning) > * StateSpace - (`num`, `den`, `gain`) - (`zeros`, `poles`) > * TransferFunction (`A`, `B`, `C`, `D`, `gain`) - (`zeros`, `poles`) > * ZerosPolesGain (`A`, `B`, `C`, `D`, `num`, `den`) - () > > Spherical Bessel functions, ``sph_in``, ``sph_jn``, ``sph_kn``, ``sph_yn``, > ``sph_jnyn`` and ``sph_inkn`` have been deprecated in favor of > `scipy.special.spherical_jn` and ``spherical_kn``, ``spherical_yn``, > ``spherical_in``. > > The following functions in `scipy.constants` are deprecated: ``C2K``, > ``K2C``, > ``C2F``, ``F2C``, ``F2K`` and ``K2F``. They are superceded by a new > function > `scipy.constants.convert_temperature` that can perform all those > conversions > plus to/from the Rankine temperature scale. > > > Backwards incompatible changes > ============================== > > `scipy.optimize` > - ---------------- > > The convergence criterion for ``optimize.bisect``, > ``optimize.brentq``, ``optimize.brenth``, and ``optimize.ridder`` now > works the same as ``numpy.allclose``. > > `scipy.ndimage` > - --------------- > > The offset in ``ndimage.iterpolation.affine_transform`` > is now consistently added after the matrix is applied, > independent of if the matrix is specified using a one-dimensional > or a two-dimensional array. > > `scipy.stats` > - ------------- > > ``stats.ks_2samp`` used to return nonsensical values if the input was > not real or contained nans. It now raises an exception for such inputs. > > Several deprecated methods of `scipy.stats` distributions have been > removed: > ``est_loc_scale``, ``vecfunc``, ``veccdf`` and ``vec_generic_moment``. > > Deprecated functions ``nanmean``, ``nanstd`` and ``nanmedian`` have been > removed > from `scipy.stats`. These functions were deprecated in scipy 0.15.0 in > favor > of their `numpy` equivalents. > > A bug in the ``rvs()`` method of the distributions in `scipy.stats` has > been fixed. When arguments to ``rvs()`` were given that were shaped for > broadcasting, in many cases the returned random samples were not random. > A simple example of the problem is ``stats.norm.rvs(loc=np.zeros(10))``. > Because of the bug, that call would return 10 identical values. The bug > only affected code that relied on the broadcasting of the shape, location > and scale parameters. > > The ``rvs()`` method also accepted some arguments that it should not have. > There is a potential for backwards incompatibility in cases where ``rvs()`` > accepted arguments that are not, in fact, compatible with broadcasting. > An example is > > stats.gamma.rvs([2, 5, 10, 15], size=(2,2)) > > The shape of the first argument is not compatible with the requested size, > but the function still returned an array with shape (2, 2). In scipy 0.18, > that call generates a ``ValueError``. > > `scipy.io` > - ---------- > > `scipy.io.netcdf` masking now gives precedence to the ``_FillValue`` > attribute > over the ``missing_value`` attribute, if both are given. Also, data are > only > treated as missing if they match one of these attributes exactly: values > that > differ by roundoff from ``_FillValue`` or ``missing_value`` are no longer > treated as missing values. > > `scipy.interpolate` > - ------------------- > > `scipy.interpolate.PiecewisePolynomial` class has been removed. It has been > deprecated in scipy 0.14.0, and > `scipy.interpolate.BPoly.from_derivatives` serves > as a drop-in replacement. > > > Other changes > ============= > > Scipy now uses ``setuptools`` for its builds instead of plain distutils. > This > fixes usage of ``install_requires='scipy'`` in the ``setup.py`` files of > projects that depend on Scipy (see Numpy issue gh-6551 for details). It > potentially affects the way that build/install methods for Scipy itself > behave > though. Please report any unexpected behavior on the Scipy issue tracker. > > PR `#6240 `__ > changes the interpretation of the `maxfun` option in `L-BFGS-B` based > routines > in the `scipy.optimize` module. > An `L-BFGS-B` search consists of multiple iterations, > with each iteration consisting of one or more function evaluations. > Whereas the old search strategy terminated immediately upon reaching > `maxfun` > function evaluations, the new strategy allows the current iteration > to finish despite reaching `maxfun`. > > The bundled copy of Qhull in the `scipy.spatial` subpackage has been > upgraded to > version 2015.2. > > The bundled copy of ARPACK in the `scipy.sparse.linalg` subpackage has been > upgraded to arpack-ng 3.3.0. > > The bundled copy of SuperLU in the `scipy.sparse` subpackage has been > upgraded > to version 5.1.1. > > > Authors > ======= > > * @endolith > * @yanxun827 + > * @kleskjr + > * @MYheavyGo + > * @solarjoe + > * Gregory Allen + > * Gilles Aouizerate + > * Tom Augspurger + > * Henrik Bengtsson + > * Felix Berkenkamp > * Per Brodtkorb > * Lars Buitinck > * Daniel Bunting + > * Evgeni Burovski > * CJ Carey > * Tim Cera > * Grey Christoforo + > * Robert Cimrman > * Philip DeBoer + > * Yves Delley + > * D?vid Bodn?r + > * Ion Elberdin + > * Gabriele Farina + > * Yu Feng > * Andrew Fowlie + > * Joseph Fox-Rabinovitz > * Simon Gibbons + > * Neil Girdhar + > * Kolja Glogowski + > * Christoph Gohlke > * Ralf Gommers > * Todd Goodall + > * Johnnie Gray + > * Alex Griffing > * Olivier Grisel > * Thomas Haslwanter + > * Michael Hirsch + > * Derek Homeier > * Golnaz Irannejad + > * Marek Jacob + > * InSuk Joung + > * Tetsuo Koyama + > * Eugene Krokhalev + > * Eric Larson > * Denis Laxalde > * Antony Lee > * Jerry Li + > * Henry Lin + > * Nelson Liu + > * Lo?c Est?ve > * Lei Ma + > * Osvaldo Martin + > * Stefano Martina + > * Nikolay Mayorov > * Matthieu Melot + > * Sturla Molden > * Eric Moore > * Alistair Muldal + > * Maniteja Nandana > * Tavi Nathanson + > * Andrew Nelson > * Joel Nothman > * Behzad Nouri > * Nikolai Nowaczyk + > * Juan Nunez-Iglesias + > * Ted Pudlik > * Eric Quintero > * Yoav Ram > * Jonas Rauber + > * Tyler Reddy + > * Juha Remes > * Garrett Reynolds + > * Ariel Rokem + > * Fabian Rost + > * Bill Sacks + > * Jona Sassenhagen + > * Kari Schoonbee + > * Marcello Seri + > * Sourav Singh + > * Martin Spacek + > * S?ren Fuglede J?rgensen + > * Bhavika Tekwani + > * Martin Thoma + > * Sam Tygier + > * Meet Udeshi + > * Utkarsh Upadhyay > * Bram Vandekerckhove + > * Sebasti?n Vanrell + > * Ze Vinicius + > * Pauli Virtanen > * Stefan van der Walt > * Warren Weckesser > * Jakub Wilk + > * Josh Wilson > * Phillip J. Wolfram + > * Nathan Woods > * Haochen Wu > * G Young + > > A total of 99 people contributed to this release. > People with a "+" by their names contributed a patch for the first time. > This list of names is automatically generated, and may not be fully > complete. > > > Issues closed for 0.18.0 > - ------------------------ > > - - `#1484 `__: SVD using > *GESVD lapack drivers (Trac #957) > - - `#1547 `__: > Inconsistent use of offset in > ndimage.interpolation.affine_transform()... > - - `#1609 `__: > special.hyp0f1 returns nan (Trac #1082) > - - `#1656 `__: fmin_slsqp > enhancement (Trac #1129) > - - `#2069 `__: stats > broadcasting in rvs (Trac #1544) > - - `#2165 `__: sph_jn > returns false results for some orders/values (Trac #1640) > - - `#2255 `__: Incorrect > order of translation and rotation in affine_transform... > - - `#2332 `__: hyp0f1 > args and return values are unnumpyic (Trac #1813) > - - `#2534 `__: The sparse > .sum() method with uint8 dtype does not act like the... > - - `#3113 `__: Implement > ufuncs for CSPHJY, SPHJ, SPHY, CSPHIK, SPHI, SPHIK... > - - `#3568 `__: SciPy > 0.13.3 - CentOS5 - Errors in test_arpack > - - `#3581 `__: optimize: > stepsize in fmin_bfgs is "bad" > - - `#4476 `__: > scipy.sparse non-native endian bug > - - `#4484 `__: ftol in > optimize.fmin fails to work > - - `#4510 `__: > sparsetools.cxx call_thunk can segfault due to out of bounds... > - - `#5051 `__: ftol and > xtol for _minimize_neldermead are absolute instead of... > - - `#5097 `__: proposal: > spherical Voronoi diagrams > - - `#5123 `__: Call to > `scipy.sparse.coo_matrix` fails when passed Cython typed... > - - `#5220 `__: > scipy.cluster.hierarchy.{ward,median,centroid} does not work... > - - `#5379 `__: Add a > build step at the end of .travis.yml that uploads working... > - - `#5440 `__: > scipy.optimize.basinhopping: accept_test returning numpy.bool_... > - - `#5452 `__: Error in > scipy.integrate.nquad when using variable integration... > - - `#5520 `__: Cannot > inherit csr_matrix properly > - - `#5533 `__: Kendall > tau implementation uses Python mergesort > - - `#5553 `__: > stats.tiecorrect overflows > - - `#5589 `__: Add the > Type XII Burr distribution to stats. > - - `#5612 `__: > sparse.linalg factorizations slow for small k due to default... > - - `#5626 `__: io.netcdf > masking should use masked_equal rather than masked_value > - - `#5637 `__: Simple > cubic spline interpolation? > - - `#5683 `__: BUG: > Akima1DInterpolator may return nans given multidimensional... > - - `#5686 `__: > scipy.stats.ttest_ind_from_stats does not accept arrays > - - `#5702 `__: > scipy.ndimage.interpolation.affine_transform lacks documentation... > - - `#5718 `__: Wrong > computation of weighted minkowski distance in cdist > - - `#5745 `__: move to > setuptools for next release > - - `#5752 `__: DOC: > solve_discrete_lyapunov equation puts transpose in wrong... > - - `#5760 `__: > signal.ss2tf doesn't handle zero-order state-space models > - - `#5764 `__: > Hypergeometric function hyp0f1 behaves incorrectly for complex... > - - `#5814 `__: stats NaN > Policy Error message inconsistent with code > - - `#5833 `__: docstring > of stats.binom_test() needs an update > - - `#5853 `__: Error in > scipy.linalg.expm for complex matrix with shape (1,1) > - - `#5856 `__: Specify > Nelder-Mead initial simplex > - - `#5865 `__: > scipy.linalg.expm fails for certain numpy matrices > - - `#5915 `__: > optimize.basinhopping - variable referenced before assignment. > - - `#5916 `__: > LSQUnivariateSpline fitting failed with knots generated from... > - - `#5927 `__: unicode > vs. string comparison in scipy.stats.binned_statistic_dd > - - `#5936 `__: faster > implementation of ks_2samp > - - `#5948 `__: csc matrix > .mean returns single element matrix rather than scalar > - - `#5959 `__: BUG: > optimize test error for root when using lgmres > - - `#5972 `__: Test > failures for sparse sum tests on 32-bit Python > - - `#5976 `__: Unexpected > exception in scipy.sparse.bmat while using 0 x 0 matrix > - - `#6008 `__: > scipy.special.kl_div not available in 0.14.1 > - - `#6011 `__: The > von-Mises entropy is broken > - - `#6016 `__: python > crashes for linalg.interpolative.svd with certain large... > - - `#6017 `__: Wilcoxon > signed-rank test with zero_method="pratt" or "zsplit"... > - - `#6028 `__: > stats.distributions does not have trapezoidal distribution > - - `#6035 `__: Wrong link > in f_oneway > - - `#6056 `__: BUG: > signal.decimate should only accept discrete LTI objects > - - `#6093 `__: Precision > error on Linux 32 bit with openblas > - - `#6101 `__: > Barycentric transforms test error on Python3, 32-bit Linux > - - `#6105 `__: > scipy.misc.face docstring is incorrect > - - `#6113 `__: > scipy.linalg.logm fails for a trivial matrix > - - `#6128 `__: Error in > dot method of sparse COO array, when used with numpy... > - - `#6132 `__: Failures > with latest MKL > - - `#6136 `__: Failures > on `master` with MKL > - - `#6162 `__: > fmin_l_bfgs_b returns inconsistent results (fmin ? f(xmin)) and... > - - `#6165 `__: > optimize.minimize infinite loop with Newton-CG > - - `#6167 `__: incorrect > distribution fitting for data containing boundary values. > - - `#6194 `__: lstsq() > and others detect numpy.complex256 as real > - - `#6216 `__: ENH: > improve accuracy of ppf cdf roundtrip for bradford > - - `#6217 `__: BUG: > weibull_min.logpdf return nan for c=1 and x=0 > - - `#6218 `__: Is there a > method to cap shortest path search distances? > - - `#6222 `__: > PchipInterpolator no longer handles a 2-element array > - - `#6226 `__: ENH: > improve accuracy for logistic.ppf and logistic.isf > - - `#6227 `__: ENH: > improve accuracy for rayleigh.logpdf and rayleigh.logsf... > - - `#6228 `__: ENH: > improve accuracy of ppf cdf roundtrip for gumbel_l > - - `#6235 `__: BUG: > alpha.pdf and alpha.logpdf returns nan for x=0 > - - `#6245 `__: ENH: > improve accuracy for ppf-cdf and sf-isf roundtrips for invgamma > - - `#6263 `__: BUG: > stats: Inconsistency in the multivariate_normal docstring > - - `#6292 `__: Python 3 > unorderable type errors in test_sparsetools.TestInt32Overflow > - - `#6316 `__: > TestCloughTocher2DInterpolator.test_dense crashes > python3.5.2rc1_64bit... > - - `#6318 `__: Scipy > interp1d 'nearest' not working for high values on x-axis > > > Pull requests for 0.18.0 > - ------------------------ > > - - `#3226 `__: DOC: Change > `nb` and `na` to conventional m and n > - - `#3867 `__: allow > cKDTree.query taking a list input in k. > - - `#4191 `__: ENH: > Benchmarking global optimizers > - - `#4356 `__: ENH: add > PPoly.solve(y) for solving ``p(x) == y`` > - - `#4370 `__: DOC separate > boolean distance functions for clarity > - - `#4678 `__: BUG: sparse: > ensure index dtype is large enough to pass all parameters... > - - `#4881 `__: > scipy.signal: Add the class dlti for linear discrete-time systems.... > - - `#4901 `__: MAINT: add > benchmark and improve docstring for signal.lfilter > - - `#5043 `__: ENH: sparse: > add count_nonzero method > - - `#5136 `__: Attribute > kurtosistest() to Anscombe & Glynn (1983) > - - `#5186 `__: ENH: Port > upfirdn > - - `#5232 `__: ENH: adding > spherical Voronoi diagram algorithm to scipy.spatial > - - `#5279 `__: ENH: Bessel > filters with different normalizations, high order > - - `#5384 `__: BUG: Closes > #5027 distance function always casts bool to double > - - `#5392 `__: ENH: Add > zero_phase kwarg to signal.decimate > - - `#5394 `__: MAINT: > sparse: non-canonical test cleanup and fixes > - - `#5424 `__: DOC: add > Scipy developers guide > - - `#5442 `__: STY: PEP8 > amendments > - - `#5472 `__: Online QR in > LGMRES > - - `#5526 `__: BUG: stats: > Fix broadcasting in the rvs() method of the distributions. > - - `#5530 `__: MAINT: > sparse: set `format` attr explicitly > - - `#5536 `__: optimize: > fix up cg/bfgs initial step sizes > - - `#5548 `__: PERF: > improves performance in stats.kendalltau > - - `#5549 `__: ENH: > Nearest-neighbor chain algorithm for hierarchical clustering > - - `#5554 `__: MAINT/BUG: > closes overflow bug in stats.tiecorrect > - - `#5557 `__: BUG: modify > optimize.bisect to achieve desired tolerance > - - `#5581 `__: DOC: > Tutorial for least_squares > - - `#5606 `__: ENH: > differential_evolution - moving core loop of solve method... > - - `#5609 `__: [MRG] test > against numpy dev > - - `#5611 `__: use > setuptools for bdist_egg distributions > - - `#5615 `__: MAINT: > linalg: tighten _decomp_update + special: remove unused... > - - `#5622 `__: Add SO(N) > rotation matrix generator > - - `#5623 `__: ENH: > special: Add vectorized spherical Bessel functions. > - - `#5627 `__: Response to > issue #5160, implements the skew normal distribution... > - - `#5628 `__: DOC: Align > the description and operation > - - `#5632 `__: DOC: > special: Expanded docs for Airy, elliptic, Bessel functions. > - - `#5633 `__: MAINT: > linalg: unchecked malloc in _decomp_update > - - `#5634 `__: MAINT: > optimize: tighten _group_columns > - - `#5640 `__: Fixes for > io.netcdf masking > - - `#5645 `__: MAINT: size > 0 vector handling in cKDTree range queries > - - `#5649 `__: MAINT: > update license text > - - `#5650 `__: DOC: Clarify > Exponent Order in ltisys.py > - - `#5651 `__: DOC: Clarify > Documentation for scipy.special.gammaln > - - `#5652 `__: DOC: Fixed > scipy.special.betaln Doc > - - `#5653 `__: [MRG] ENH: > CubicSpline interpolator > - - `#5654 `__: ENH: Burr12 > distribution to stats module > - - `#5659 `__: DOC: Define > BEFORE/AFTER in runtests.py -h for bench-compare > - - `#5660 `__: MAINT: > remove functions deprecated before 0.16.0 > - - `#5662 `__: ENH: > Circular statistic optimization > - - `#5663 `__: MAINT: > remove uses of np.testing.rand > - - `#5665 `__: MAINT: > spatial: remove matching distance implementation > - - `#5667 `__: Change some > HTTP links to HTTPS > - - `#5669 `__: DOC: zpk2sos > can't do analog, array_like, etc. > - - `#5670 `__: Update conf.py > - - `#5672 `__: MAINT: move > a sample distribution to a subclass of rv_discrete > - - `#5678 `__: MAINT: > stats: remove est_loc_scale method > - - `#5679 `__: MAINT: DRY > up generic computations for discrete distributions > - - `#5680 `__: MAINT: stop > shadowing builtins in stats.distributions > - - `#5681 `__: forward port > ENH: Re-enable broadcasting of fill_value > - - `#5684 `__: BUG: Fix > Akima1DInterpolator returning nans > - - `#5690 `__: BUG: fix > stats.ttest_ind_from_stats to handle arrays. > - - `#5691 `__: BUG: fix > generator in io._loadarff to comply with PEP 0479 > - - `#5693 `__: ENH: use > math.factorial for exact factorials > - - `#5695 `__: DOC: dx > might be a float, not only an integer > - - `#5699 `__: MAINT: io: > micro-optimize Matlab reading code for size > - - `#5701 `__: Implement > OptimizeResult.__dir__ > - - `#5703 `__: ENH: stats: > make R? printing optional in probplot > - - `#5704 `__: MAINT: typo > ouf->out > - - `#5705 `__: BUG: fix > typo in query_pairs > - - `#5707 `__: DOC:Add some > explanation for ftol xtol in scipy.optimize.fmin > - - `#5708 `__: DOC: > optimize: PEP8 minimize docstring > - - `#5709 `__: MAINT: > optimize Cython code for speed and size > - - `#5713 `__: [DOC] Fix > broken link to reference > - - `#5717 `__: DOC: > curve_fit raises RuntimeError on failure. > - - `#5724 `__: forward port > gh-5720 > - - `#5728 `__: STY: remove > a blank line > - - `#5729 `__: ENH: > spatial: speed up boolean distances > - - `#5732 `__: MAINT: > differential_evolution changes to default keywords break... > - - `#5733 `__: TST: > differential_evolution - population initiation tests > - - `#5736 `__: Complex > number support in log1p, expm1, and xlog1py > - - `#5741 `__: MAINT: > sparse: clean up extraction functions > - - `#5742 `__: DOC: signal: > Explain fftbins in get_window > - - `#5748 `__: ENH: Add > O(N) random matrix generator > - - `#5749 `__: ENH: Add > polyphase resampling > - - `#5756 `__: RFC: Bump > the minimum numpy version, drop older python versions > - - `#5761 `__: DOC: Some > improvements to least squares docstrings > - - `#5762 `__: MAINT: > spatial: distance refactoring > - - `#5768 `__: DOC: Fix > io.loadmat docstring for mdict param > - - `#5770 `__: BUG: Accept > anything np.dtype can handle for a dtype in sparse.random > - - `#5772 `__: Update > sparse.csgraph.laplacian docstring > - - `#5777 `__: BUG: fix > special.hyp0f1 to work correctly for complex inputs. > - - `#5780 `__: DOC: Update > PIL error install URL > - - `#5781 `__: DOC: Fix > documentation on solve_discrete_lyapunov > - - `#5782 `__: DOC: cKDTree > and KDTree now reference each other > - - `#5783 `__: DOC: Clarify > finish behaviour in scipy.optimize.brute > - - `#5784 `__: MAINT: > Change default tolerances of least_squares to 1e-8 > - - `#5787 `__: BUG: Allow > Processing of Zero Order State Space Models in signal.ss2tf > - - `#5788 `__: DOC, BUG: > Clarify and Enforce Input Types to 'Data' Objects > - - `#5789 `__: ENH: sparse: > speedup LIL matrix slicing (was #3338) > - - `#5791 `__: DOC: README: > remove coveralls.io > - - `#5792 `__: MAINT: > remove uses of deprecated np.random.random_integers > - - `#5794 `__: fix > affine_transform (fixes #1547 and #5702) > - - `#5795 `__: DOC: Removed > uniform method from kmeans2 doc > - - `#5797 `__: DOC: Clarify > the computation of weighted minkowski > - - `#5798 `__: BUG: Ensure > scipy's _asfarray returns ndarray > - - `#5799 `__: TST: Mpmath > testing patch > - - `#5801 `__: allow > reading of certain IDL 8.0 .sav files > - - `#5803 `__: DOC: fix > module name in error message > - - `#5804 `__: DOC: > special: Expanded docs for special functions. > - - `#5805 `__: DOC: Fix > order of returns in _spectral_helper > - - `#5806 `__: ENH: sparse: > vectorized coo_matrix.diagonal > - - `#5808 `__: ENH: Added > iqr function to compute IQR metric in scipy/stats/stats.py > - - `#5810 `__: MAINT/BENCH: > sparse: Benchmark cleanup and additions > - - `#5811 `__: DOC: > sparse.linalg: shape, not size > - - `#5813 `__: Update > sparse ARPACK functions min `ncv` value > - - `#5815 `__: BUG: Error > message contained wrong values > - - `#5816 `__: remove dead > code from stats tests > - - `#5820 `__: "in"->"a" in > order_filter docstring > - - `#5821 `__: DOC: README: > INSTALL.txt was renamed in 2014 > - - `#5825 `__: DOC: typo in > the docstring of least_squares > - - `#5826 `__: MAINT: > sparse: increase test coverage > - - `#5827 `__: NdPPoly rebase > - - `#5828 `__: Improve > numerical stability of hyp0f1 for large orders > - - `#5829 `__: ENH: sparse: > Add copy parameter to all .toXXX() methods in sparse... > - - `#5830 `__: DOC: rework > INSTALL.rst.txt > - - `#5831 `__: Adds > plotting options to voronoi_plot_2d > - - `#5834 `__: Update > stats.binom_test() docstring > - - `#5836 `__: ENH, TST: > Allow SIMO tf's for tf2ss > - - `#5837 `__: DOC: Image > examples > - - `#5838 `__: ENH: sparse: > add eliminate_zeros() to coo_matrix > - - `#5839 `__: BUG: Fixed > name of NumpyVersion.__repr__ > - - `#5845 `__: MAINT: Fixed > typos in documentation > - - `#5847 `__: Fix bugs in > sparsetools > - - `#5848 `__: BUG: > sparse.linalg: add locks to ensure ARPACK threadsafety > - - `#5849 `__: ENH: > sparse.linalg: upgrade to superlu 5.1.1 > - - `#5851 `__: ENH: expose > lapack's ilaver to python to allow lapack verion... > - - `#5852 `__: MAINT: > runtests.py: ensure Ctrl-C interrupts the build > - - `#5854 `__: DOC: Minor > update to documentation > - - `#5855 `__: Pr 5640 > - - `#5859 `__: ENH: Add > random correlation matrix generator > - - `#5862 `__: BUG: Allow > expm for complex matrix with shape (1, 1) > - - `#5863 `__: FIX: Fix test > - - `#5864 `__: DOC: add a > little note about the Normal survival function (Q-function) > - - `#5867 `__: Fix for #5865 > - - `#5869 `__: extend > normal distribution cdf to complex domain > - - `#5872 `__: DOC: Note > that morlet and cwt don't work together > - - `#5875 `__: DOC: > interp2d class description > - - `#5876 `__: MAINT: > spatial: remove a stray print statement > - - `#5878 `__: MAINT: Fixed > noisy UserWarnings in ndimage tests. Fixes #5877 > - - `#5879 `__: MAINT: > sparse.linalg/superlu: add explicit casts to resolve compiler... > - - `#5880 `__: MAINT: > signal: import gcd from math and not fractions when on... > - - `#5887 `__: Neldermead > initial simplex > - - `#5894 `__: BUG: > _CustomLinearOperator unpickalable in python3.5 > - - `#5895 `__: DOC: > special: slightly improve the multigammaln docstring > - - `#5900 `__: Remove > duplicate assignment. > - - `#5901 `__: Update bundled > ARPACK > - - `#5904 `__: ENH: Make > convolve and correlate order-agnostic > - - `#5905 `__: ENH: > sparse.linalg: further LGMRES cleanups > - - `#5906 `__: Enhancements > and cleanup in scipy.integrate (attempt #2) > - - `#5907 `__: ENH: Change > sparse `.sum` and `.mean` dtype casting to match... > - - `#5909 `__: changes for > convolution symmetry > - - `#5913 `__: MAINT: > basinhopping remove instance test closes #5440 > - - `#5919 `__: MAINT: > uninitialised var if basinhopping niter=0. closes #5915 > - - `#5920 `__: BLD: Fix > missing lsame.c error for MKL > - - `#5921 `__: DOC: > interpolate: add example showing how to work around issue... > - - `#5926 `__: MAINT: > spatial: upgrade to Qhull 2015.2 > - - `#5928 `__: MAINT: > sparse: optimize DIA sum/diagonal, csgraph.laplacian > - - `#5929 `__: Update > info/URL for octave-maintainers discussion > - - `#5930 `__: TST: > special: silence DeprecationWarnings from sph_yn > - - `#5931 `__: ENH: > implement the principle branch of the logarithm of Gamma. > - - `#5934 `__: Typo: "mush" => > "must" > - - `#5935 `__: BUG:string > comparison stats._binned_statistic closes #5927 > - - `#5938 `__: Cythonize > stats.ks_2samp for a ~33% gain in speed. > - - `#5939 `__: DOC: fix > optimize.fmin convergence docstring > - - `#5941 `__: Fix minor > typo in squareform docstring > - - `#5942 `__: Update > linregress stderr description. > - - `#5943 `__: ENH: Improve > numerical accuracy of lognorm > - - `#5944 `__: Merge > vonmises into stats pyx > - - `#5945 `__: MAINT: > interpolate: Tweak declaration to avoid cython warning... > - - `#5946 `__: MAINT: > sparse: clean up format conversion methods > - - `#5949 `__: BUG: fix > sparse .mean to return a scalar instead of a matrix > - - `#5955 `__: MAINT: > Replace calls to `hanning` with `hann` > - - `#5956 `__: DOC: Missing > periods interfering with parsing > - - `#5958 `__: MAINT: add a > test for lognorm.sf underflow > - - `#5961 `__: MAINT > _centered(): rename size to shape > - - `#5962 `__: ENH: > constants: Add multi-scale temperature conversion function > - - `#5965 `__: ENH: > special: faster way for calculating comb() for exact=True > - - `#5975 `__: ENH: Improve > FIR path of signal.decimate > - - `#5977 `__: MAINT/BUG: > sparse: remove overzealous bmat checks > - - `#5978 `__: > minimize_neldermead() stop at user requested maxiter or maxfev > - - `#5983 `__: ENH: make > sparse `sum` cast dtypes like NumPy `sum` for 32-bit... > - - `#5985 `__: BUG, API: > Add `jac` parameter to curve_fit > - - `#5989 `__: ENH: Add > firls least-squares fitting > - - `#5990 `__: BUG: read > tries to handle 20-bit WAV files but shouldn't > - - `#5991 `__: DOC: Cleanup > wav read/write docs and add tables for common types > - - `#5994 `__: ENH: Add > gesvd method for svd > - - `#5996 `__: MAINT: Wave > cleanup > - - `#5997 `__: TST: Break > up upfirdn tests & compare to lfilter > - - `#6001 `__: Filter design > docs > - - `#6002 `__: COMPAT: > Expand compatibility fromnumeric.py > - - `#6007 `__: ENH: Skip > conversion of TF to TF in freqresp > - - `#6009 `__: DOC: fix > incorrect versionadded for entr, rel_entr, kl_div > - - `#6013 `__: Fixed the > entropy calculation of the von Mises distribution. > - - `#6014 `__: MAINT: make > gamma, rgamma use loggamma for complex arguments > - - `#6020 `__: WIP: ENH: > add exact=True factorial for vectors > - - `#6022 `__: Added > 'lanczos' to the image interpolation function list. > - - `#6024 `__: BUG: > optimize: do not use dummy constraints in SLSQP when no... > - - `#6025 `__: ENH: > Boundary value problem solver for ODE systems > - - `#6029 `__: MAINT: > Future imports for optimize._lsq > - - `#6030 `__: ENH: > stats.trap - adding trapezoidal distribution closes #6028 > - - `#6031 `__: MAINT: Some > improvements to optimize._numdiff > - - `#6032 `__: MAINT: Add > special/_comb.c to .gitignore > - - `#6033 `__: BUG: check > the requested approximation rank in interpolative.svd > - - `#6034 `__: DOC: Doc for > mannwhitneyu in stats.py corrected > - - `#6040 `__: FIX: Edit > the wrong link in f_oneway > - - `#6044 `__: BUG: (ordqz) > always increase parameter lwork by 1. > - - `#6047 `__: ENH: extend > special.spence to complex arguments. > - - `#6049 `__: DOC: Add > documentation of PR #5640 to the 0.18.0 release notes > - - `#6050 `__: MAINT: small > cleanups related to loggamma > - - `#6070 `__: Add asarray > to explicitly cast list to numpy array in wilcoxon... > - - `#6071 `__: DOC: > antialiasing filter and link decimate resample, etc. > - - `#6075 `__: MAINT: > reimplement special.digamma for complex arguments > - - `#6080 `__: avoid > multiple computation in kstest > - - `#6081 `__: Clarified > pearson correlation return value > - - `#6085 `__: ENH: allow > long indices of sparse matrix with umfpack in spsolve() > - - `#6086 `__: fix > description for associated Laguerre polynomials > - - `#6087 `__: Corrected > docstring of splrep. > - - `#6094 `__: ENH: > special: change zeta signature to zeta(x, q=1) > - - `#6095 `__: BUG: fix > integer overflow in special.spence > - - `#6106 `__: Fixed Issue > #6105 > - - `#6116 `__: BUG: matrix > logarithm edge case > - - `#6119 `__: TST: > DeprecationWarnings in stats on python 3.5 closes #5885 > - - `#6120 `__: MAINT: > sparse: clean up sputils.isintlike > - - `#6122 `__: DOC: > optimize: linprog docs should say minimize instead of maximize > - - `#6123 `__: DOC: > optimize: document the `fun` field in `scipy.optimize.OptimizeResult` > - - `#6124 `__: Move FFT > zero-padding calculation from signaltools to fftpack > - - `#6125 `__: MAINT: > improve special.gammainc in the ``a ~ x`` regime. > - - `#6130 `__: BUG: sparse: > Fix COO dot with zero columns > - - `#6138 `__: ENH: stats: > Improve behavior of genextreme.sf and genextreme.isf > - - `#6146 `__: MAINT: > simplify the expit implementation > - - `#6151 `__: MAINT: > special: make generate_ufuncs.py output deterministic > - - `#6152 `__: TST: > special: better test for gammainc at large arguments > - - `#6153 `__: ENH: Make > next_fast_len public and faster > - - `#6154 `__: fix typo > "mush"-->"must" > - - `#6155 `__: DOC: Fix > some incorrect RST definition lists > - - `#6160 `__: make > logsumexp error out on a masked array > - - `#6161 `__: added > missing bracket to rosen documentation > - - `#6163 `__: ENH: Added > "kappa4" and "kappa3" distributions. > - - `#6164 `__: DOC: Minor > clean-up in integrate._bvp > - - `#6169 `__: Fix > mpf_assert_allclose to handle iterable results, such as maps > - - `#6170 `__: Fix > pchip_interpolate convenience function > - - `#6172 `__: Corrected > misplaced bracket in doc string > - - `#6175 `__: ENH: > sparse.csgraph: Pass indices to shortest_path > - - `#6178 `__: TST: > increase test coverage of sf and isf of a generalized extreme... > - - `#6179 `__: TST: avoid a > deprecation warning from numpy > - - `#6181 `__: ENH: > Boundary conditions for CubicSpline > - - `#6182 `__: DOC: Add > examples/graphs to max_len_seq > - - `#6183 `__: BLD: update > Bento build config files for recent changes. > - - `#6184 `__: BUG: fix > issue in io/wavfile for float96 input. > - - `#6186 `__: ENH: > Periodic extrapolation for PPoly and BPoly > - - `#6192 `__: MRG: Add > circle-CI > - - `#6193 `__: ENH: sparse: > avoid setitem densification > - - `#6196 `__: Fixed > missing sqrt in docstring of Mahalanobis distance in cdist,... > - - `#6206 `__: MAINT: Minor > changes in solve_bvp > - - `#6207 `__: BUG: linalg: > for BLAS, downcast complex256 to complex128, not... > - - `#6209 `__: BUG: > io.matlab: avoid buffer overflows in read_element_into > - - `#6210 `__: BLD: use > setuptools when building. > - - `#6214 `__: BUG: > sparse.linalg: fix bug in LGMRES breakdown handling > - - `#6215 `__: MAINT: > special: make loggamma use zdiv > - - `#6220 `__: DOC: Add > parameter > - - `#6221 `__: ENH: Improve > Newton solver for solve_bvp > - - `#6223 `__: pchip should > work for length-2 arrays > - - `#6224 `__: signal.lti: > deprecate cross-class properties/setters > - - `#6229 `__: BUG: > optimize: avoid an infinite loop in Newton-CG > - - `#6230 `__: Add example > for application of gaussian filter > - - `#6236 `__: MAINT: > gumbel_l accuracy > - - `#6237 `__: MAINT: > rayleigh accuracy > - - `#6238 `__: MAINT: > logistic accuracy > - - `#6239 `__: MAINT: > bradford distribution accuracy > - - `#6240 `__: MAINT: avoid > bad fmin in l-bfgs-b due to maxfun interruption > - - `#6241 `__: MAINT: > weibull_min accuracy > - - `#6246 `__: ENH: Add > _support_mask to distributions > - - `#6247 `__: fixed a > print error for an example of ode > - - `#6249 `__: MAINT: > change x-axis label for stats.probplot to "theoretical... > - - `#6250 `__: DOC: fix typos > - - `#6251 `__: MAINT: > constants: filter out test noise from deprecated conversions > - - `#6252 `__: MAINT: > io/arff: remove unused variable > - - `#6253 `__: Add examples > to scipy.ndimage.filters > - - `#6254 `__: MAINT: > special: fix some build warnings > - - `#6258 `__: MAINT: > inverse gamma distribution accuracy > - - `#6260 `__: MAINT: > signal.decimate - Use discrete-time objects > - - `#6262 `__: BUG: odr: > fix string formatting > - - `#6267 `__: TST: fix > some test issues in interpolate and stats. > - - `#6269 `__: TST: fix > some warnings in the test suite > - - `#6274 `__: ENH: Add > sosfiltfilt > - - `#6276 `__: DOC: update > release notes for 0.18.0 > - - `#6277 `__: MAINT: > update the author name mapping > - - `#6282 `__: DOC: > Correcting references for scipy.stats.normaltest > - - `#6283 `__: DOC: some > more additions to 0.18.0 release notes. > - - `#6284 `__: Add `.. > versionadded::` directive to `loggamma`. > - - `#6285 `__: BUG: stats: > Inconsistency in the multivariate_normal docstring... > - - `#6290 `__: Add author > list, gh-lists to 0.18.0 release notes > - - `#6293 `__: TST: > special: relax a test's precision > - - `#6295 `__: BUG: sparse: > stop comparing None and int in bsr_matrix constructor > - - `#6313 `__: MAINT: Fix > for python 3.5 travis-ci build problem. > - - `#6327 `__: TST: signal: > use assert_allclose for testing near-equality in... > - - `#6330 `__: BUG: > spatial/qhull: allocate qhT via malloc to ensure CRT likes... > - - `#6332 `__: TST: fix > stats.iqr test to not emit warnings, and fix line lengths. > - - `#6334 `__: MAINT: > special: fix a test for hyp0f1 > - - `#6347 `__: TST: > spatial.qhull: skip a test on 32-bit platforms > - - `#6350 `__: BUG: > optimize/slsqp: don't overwrite an array out of bounds > - - `#6351 `__: BUG: #6318 > Interp1d 'nearest' integer x-axis overflow issue fixed > - - `#6355 `__: Backports for > 0.18.0 > > > Checksums > ========= > > MD5 > ~~~ > > e9ddb97336421e110a3f20e1b1f6c057 > > scipy-0.18.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl > f203cdea575c4e5d9c11f88480e609c4 > scipy-0.18.0-cp27-cp27m-manylinux1_i686.whl > ccca6aeb87717ca383a15e1d3cd3b3e9 > scipy-0.18.0-cp27-cp27m-manylinux1_x86_64.whl > 3426dd5731fcdb9f56ee30623def9ac0 > scipy-0.18.0-cp27-cp27mu-manylinux1_i686.whl > 96f1e6ac2ed3a845802050840929681a > scipy-0.18.0-cp27-cp27mu-manylinux1_x86_64.whl > aec151bd64fee0651dc804f8b57a1948 > > scipy-0.18.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl > 6e7bcd119bfcd3504f5d432424779fd2 > scipy-0.18.0-cp34-cp34m-manylinux1_i686.whl > d8b88298b048884225708494bc51f249 > scipy-0.18.0-cp34-cp34m-manylinux1_x86_64.whl > 3886c82aa705b44662964d5d1de179b8 > > scipy-0.18.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl > 1bdc3664de8456bbc45620afbfc3d2cf > scipy-0.18.0-cp35-cp35m-manylinux1_i686.whl > 8f28a30aba8dd7fa4c1704088347684e > scipy-0.18.0-cp35-cp35m-manylinux1_x86_64.whl > d70e7f533622ab705bc016dac328d93e scipy-0.18.0.tar.gz > 59bceff108f58b0e72dfac6fb719476e scipy-0.18.0.tar.xz > 9ec1363dde2f2c16e833d3cd09f0dd13 scipy-0.18.0.zip > > SHA256 > ~~~~~~ > > c1357a8d7da900f3eb72c817a554fa82f71a123d232c1d28c4ef5624694ec937 > > scipy-0.18.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl > f1ce34715420670253aef746ca9ac0de6af0db975bc2f7145697b227ed43a411 > scipy-0.18.0-cp27-cp27m-manylinux1_i686.whl > b10ccbb451d781f5a31bce940150c626260369c31dc7c6cc609c012aae5b8b77 > scipy-0.18.0-cp27-cp27m-manylinux1_x86_64.whl > 919c035bfd63a8ecd9076e46488108db0847a13ff7bb3e2eb52561af68ffb798 > scipy-0.18.0-cp27-cp27mu-manylinux1_i686.whl > 7b77d7721a2017fe6fe2369edf6631c1cb4d2665f7a0e0562ed3796e8d8007d4 > scipy-0.18.0-cp27-cp27mu-manylinux1_x86_64.whl > 61441deb6c9ff093c60b48362a2104fa22212bb8baa09b62643f71df3f1fd361 > > scipy-0.18.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl > 41252b1083e657be1beb391c8508fc70298cc3c47214d7b874e5b50e2676229e > scipy-0.18.0-cp34-cp34m-manylinux1_i686.whl > 1709f0696508047742a80c5f3c7699c7bd28f006cbbe4d2b001ed6ee9fc862a8 > scipy-0.18.0-cp34-cp34m-manylinux1_x86_64.whl > 5ed943f23f7553c34aec6444a3f929b9eccb0f330b7b4416a3132dd0c4c1068f > > scipy-0.18.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl > d71c4ac8019b5820acd881d1178461e4d900e8dcfe920f3b63faf9381db24a1c > scipy-0.18.0-cp35-cp35m-manylinux1_i686.whl > ab9db1de7140eaf602d4ba190bbbe07ca608d5d298203afbfeceb170a3f03ef4 > scipy-0.18.0-cp35-cp35m-manylinux1_x86_64.whl > f01784fb1c2bc246d4211f2482ecf4369db5abaecb9d5afb9d94f6c59663286a > scipy-0.18.0.tar.gz > fc2d01887276839b1c709e661ba318d8db69a5c398217c79b97d6460a93c2d41 > scipy-0.18.0.tar.xz > a4f9fb8cddbe681e2d5465a8dcf715ef454b16fe8c9eddf92bffb10bb50ac75e > scipy-0.18.0.zip > -----BEGIN PGP SIGNATURE----- > Version: GnuPG v1.4.11 (GNU/Linux) > > iQEcBAEBAgAGBQJXljjOAAoJEIp0pQ0zQcu+748IAL8pSPGDmi0PQOASCM00DUgK > 2tq/3gnI5RIqpMDshQmcuI0/+6iqn0dGYVMJVIWYYBCVSm5D13xtz/3jTC9XGB6E > hsnVdSBI/RiZHoEpB7VguzyvOjZIFngkFk5XUpGbr8uaqYcfdQhzKfLnzq7ZHcuB > hUoYcku7gdQY5LZDlFTKZYjEBJM9odHa4uYIwezic09Rvr5X1PVjwIhahSXZekpo > zH37Zwi/bMNJxjUWtLonIdOB1xFi3rSFgEkycGNYWaiwtxy8dJGrkstiLf9T6HCC > ZV0lIVJcgUDG8VuRg8D7dQAoNSpDkYuR3HA17OvPfaKXETb6BDPcFFtqSxwvOF8= > =GM4X > -----END PGP SIGNATURE----- > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > -------------- next part -------------- An HTML attachment was scrubbed... URL: From matthew.brett at gmail.com Mon Jul 25 15:17:17 2016 From: matthew.brett at gmail.com (Matthew Brett) Date: Mon, 25 Jul 2016 20:17:17 +0100 Subject: [SciPy-Dev] ANN: scipy 0.18.0 release In-Reply-To: References: Message-ID: On Mon, Jul 25, 2016 at 8:08 PM, Warren Weckesser wrote: > > > On Mon, Jul 25, 2016 at 1:54 PM, Evgeni Burovski > wrote: >> >> On behalf of the Scipy development team I am pleased to announce the >> availability of Scipy 0.18.0. > > > > Thanks for managing this release. Great job! Nice - much thanks from me too, Matthew From nikolay.mayorov at zoho.com Mon Jul 25 15:18:21 2016 From: nikolay.mayorov at zoho.com (Nikolay Mayorov) Date: Tue, 26 Jul 2016 00:18:21 +0500 Subject: [SciPy-Dev] ANN: scipy 0.18.0 release In-Reply-To: References: Message-ID: <156237e65a8.1197a477b34741.2476452405675206093@zoho.com> Thank you Evgeni for doing this super useful and hard release work. Well done! Nikolay. ---- On Tue, 26 Jul 2016 00:08:29 +0500 Warren Weckesser <warren.weckesser at gmail.com>wrote ---- On Mon, Jul 25, 2016 at 1:54 PM, Evgeni Burovski <evgeny.burovskiy at gmail.com> wrote: On behalf of the Scipy development team I am pleased to announce the availability of Scipy 0.18.0. Thanks for managing this release. Great job! Warren This release contains several great new features and a large number of bug fixes and various improvements, as detailed in the release notes below. 99 people contributed to this release over the course of six months. Thanks to everyone who contributed! This release requires Python 2.7 or 3.4-3.5 and NumPy 1.7.2 or greater. Source tarballs and release notes can be found at https://github.com/scipy/scipy/releases/tag/v0.18.0. OS X and Linux wheels are available from PyPI. For security-conscious, the wheels themselves are signed with my GPG key. Additionally, you can checksum the wheels and verify the checksums with those listed in the README file at https://github.com/scipy/scipy/releases/tag/v0.18.0. Cheers, Evgeni -----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 ========================== SciPy 0.18.0 Release Notes ========================== .. contents:: SciPy 0.18.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.19.x branch, and on adding new features on the master branch. This release requires Python 2.7 or 3.4-3.5 and NumPy 1.7.1 or greater. Highlights of this release include: - - A new ODE solver for two-point boundary value problems, `scipy.optimize.solve_bvp`. - - A new class, `CubicSpline`, for cubic spline interpolation of data. - - N-dimensional tensor product polynomials, `scipy.interpolate.NdPPoly`. - - Spherical Voronoi diagrams, `scipy.spatial.SphericalVoronoi`. - - Support for discrete-time linear systems, `scipy.signal.dlti`. New features ============ `scipy.integrate` improvements - ------------------------------ A solver of two-point boundary value problems for ODE systems has been implemented in `scipy.integrate.solve_bvp`. The solver allows for non-separated boundary conditions, unknown parameters and certain singular terms. It finds a C1 continious solution using a fourth-order collocation algorithm. `scipy.interpolate` improvements - -------------------------------- Cubic spline interpolation is now available via `scipy.interpolate.CubicSpline`. This class represents a piecewise cubic polynomial passing through given points and C2 continuous. It is represented in the standard polynomial basis on each segment. A representation of n-dimensional tensor product piecewise polynomials is available as the `scipy.interpolate.NdPPoly` class. Univariate piecewise polynomial classes, `PPoly` and `Bpoly`, can now be evaluated on periodic domains. Use ``extrapolate="periodic"`` keyword argument for this. `scipy.fftpack` improvements - ---------------------------- `scipy.fftpack.next_fast_len` function computes the next "regular" number for FFTPACK. Padding the input to this length can give significant performance increase for `scipy.fftpack.fft`. `scipy.signal` improvements - --------------------------- Resampling using polyphase filtering has been implemented in the function `scipy.signal.resample_poly`. This method upsamples a signal, applies a zero-phase low-pass FIR filter, and downsamples using `scipy.signal.upfirdn` (which is also new in 0.18.0). This method can be faster than FFT-based filtering provided by `scipy.signal.resample` for some signals. `scipy.signal.firls`, which constructs FIR filters using least-squares error minimization, was added. `scipy.signal.sosfiltfilt`, which does forward-backward filtering like `scipy.signal.filtfilt` but for second-order sections, was added. Discrete-time linear systems ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ `scipy.signal.dlti` provides an implementation of discrete-time linear systems. Accordingly, the `StateSpace`, `TransferFunction` and `ZerosPolesGain` classes have learned a the new keyword, `dt`, which can be used to create discrete-time instances of the corresponding system representation. `scipy.sparse` improvements - --------------------------- The functions `sum`, `max`, `mean`, `min`, `transpose`, and `reshape` in `scipy.sparse` have had their signatures augmented with additional arguments and functionality so as to improve compatibility with analogously defined functions in `numpy`. Sparse matrices now have a `count_nonzero` method, which counts the number of nonzero elements in the matrix. Unlike `getnnz()` and ``nnz`` propety, which return the number of stored entries (the length of the data attribute), this method counts the actual number of non-zero entries in data. `scipy.optimize` improvements - ----------------------------- The implementation of Nelder-Mead minimization, `scipy.minimize(..., method="Nelder-Mead")`, obtained a new keyword, `initial_simplex`, which can be used to specify the initial simplex for the optimization process. Initial step size selection in CG and BFGS minimizers has been improved. We expect that this change will improve numeric stability of optimization in some cases. See pull request gh-5536 for details. Handling of infinite bounds in SLSQP optimization has been improved. We expect that this change will improve numeric stability of optimization in the some cases. See pull request gh-6024 for details. A large suite of global optimization benchmarks has been added to ``scipy/benchmarks/go_benchmark_functions``. See pull request gh-4191 for details. Nelder-Mead and Powell minimization will now only set defaults for maximum iterations or function evaluations if neither limit is set by the caller. In some cases with a slow converging function and only 1 limit set, the minimization may continue for longer than with previous versions and so is more likely to reach convergence. See issue gh-5966. `scipy.stats` improvements - -------------------------- Trapezoidal distribution has been implemented as `scipy.stats.trapz`. Skew normal distribution has been implemented as `scipy.stats.skewnorm`. Burr type XII distribution has been implemented as `scipy.stats.burr12`. Three- and four-parameter kappa distributions have been implemented as `scipy.stats.kappa3` and `scipy.stats.kappa4`, respectively. New `scipy.stats.iqr` function computes the interquartile region of a distribution. Random matrices ~~~~~~~~~~~~~~~ `scipy.stats.special_ortho_group` and `scipy.stats.ortho_group` provide generators of random matrices in the SO(N) and O(N) groups, respectively. They generate matrices in the Haar distribution, the only uniform distribution on these group manifolds. `scipy.stats.random_correlation` provides a generator for random correlation matrices, given specified eigenvalues. `scipy.linalg` improvements - --------------------------- `scipy.linalg.svd` gained a new keyword argument, ``lapack_driver``. Available drivers are ``gesdd`` (default) and ``gesvd``. `scipy.linalg.lapack.ilaver` returns the version of the LAPACK library SciPy links to. `scipy.spatial` improvements - ---------------------------- Boolean distances, `scipy.spatial.pdist`, have been sped up. Improvements vary by the function and the input size. In many cases, one can expect a speed-up of x2--x10. New class `scipy.spatial.SphericalVoronoi` constructs Voronoi diagrams on the surface of a sphere. See pull request gh-5232 for details. `scipy.cluster` improvements - ---------------------------- A new clustering algorithm, the nearest neighbor chain algorithm, has been implemented for `scipy.cluster.hierarchy.linkage`. As a result, one can expect a significant algorithmic improvement (:math:`O(N^2)` instead of :math:`O(N^3)`) for several linkage methods. `scipy.special` improvements - ---------------------------- The new function `scipy.special.loggamma` computes the principal branch of the logarithm of the Gamma function. For real input, ``loggamma`` is compatible with `scipy.special.gammaln`. For complex input, it has more consistent behavior in the complex plane and should be preferred over ``gammaln``. Vectorized forms of spherical Bessel functions have been implemented as `scipy.special.spherical_jn`, `scipy.special.spherical_kn`, `scipy.special.spherical_in` and `scipy.special.spherical_yn`. They are recommended for use over ``sph_*`` functions, which are now deprecated. Several special functions have been extended to the complex domain and/or have seen domain/stability improvements. This includes `spence`, `digamma`, `log1p` and several others. Deprecated features =================== The cross-class properties of `lti` systems have been deprecated. The following properties/setters will raise a `DeprecationWarning`: Name - (accessing/setting raises warning) - (setting raises warning) * StateSpace - (`num`, `den`, `gain`) - (`zeros`, `poles`) * TransferFunction (`A`, `B`, `C`, `D`, `gain`) - (`zeros`, `poles`) * ZerosPolesGain (`A`, `B`, `C`, `D`, `num`, `den`) - () Spherical Bessel functions, ``sph_in``, ``sph_jn``, ``sph_kn``, ``sph_yn``, ``sph_jnyn`` and ``sph_inkn`` have been deprecated in favor of `scipy.special.spherical_jn` and ``spherical_kn``, ``spherical_yn``, ``spherical_in``. The following functions in `scipy.constants` are deprecated: ``C2K``, ``K2C``, ``C2F``, ``F2C``, ``F2K`` and ``K2F``. They are superceded by a new function `scipy.constants.convert_temperature` that can perform all those conversions plus to/from the Rankine temperature scale. Backwards incompatible changes ============================== `scipy.optimize` - ---------------- The convergence criterion for ``optimize.bisect``, ``optimize.brentq``, ``optimize.brenth``, and ``optimize.ridder`` now works the same as ``numpy.allclose``. `scipy.ndimage` - --------------- The offset in ``ndimage.iterpolation.affine_transform`` is now consistently added after the matrix is applied, independent of if the matrix is specified using a one-dimensional or a two-dimensional array. `scipy.stats` - ------------- ``stats.ks_2samp`` used to return nonsensical values if the input was not real or contained nans. It now raises an exception for such inputs. Several deprecated methods of `scipy.stats` distributions have been removed: ``est_loc_scale``, ``vecfunc``, ``veccdf`` and ``vec_generic_moment``. Deprecated functions ``nanmean``, ``nanstd`` and ``nanmedian`` have been removed from `scipy.stats`. These functions were deprecated in scipy 0.15.0 in favor of their `numpy` equivalents. A bug in the ``rvs()`` method of the distributions in `scipy.stats` has been fixed. When arguments to ``rvs()`` were given that were shaped for broadcasting, in many cases the returned random samples were not random. A simple example of the problem is ``stats.norm.rvs(loc=np.zeros(10))``. Because of the bug, that call would return 10 identical values. The bug only affected code that relied on the broadcasting of the shape, location and scale parameters. The ``rvs()`` method also accepted some arguments that it should not have. There is a potential for backwards incompatibility in cases where ``rvs()`` accepted arguments that are not, in fact, compatible with broadcasting. An example is stats.gamma.rvs([2, 5, 10, 15], size=(2,2)) The shape of the first argument is not compatible with the requested size, but the function still returned an array with shape (2, 2). In scipy 0.18, that call generates a ``ValueError``. `scipy.io` - ---------- `scipy.io.netcdf` masking now gives precedence to the ``_FillValue`` attribute over the ``missing_value`` attribute, if both are given. Also, data are only treated as missing if they match one of these attributes exactly: values that differ by roundoff from ``_FillValue`` or ``missing_value`` are no longer treated as missing values. `scipy.interpolate` - ------------------- `scipy.interpolate.PiecewisePolynomial` class has been removed. It has been deprecated in scipy 0.14.0, and `scipy.interpolate.BPoly.from_derivatives` serves as a drop-in replacement. Other changes ============= Scipy now uses ``setuptools`` for its builds instead of plain distutils. This fixes usage of ``install_requires='scipy'`` in the ``setup.py`` files of projects that depend on Scipy (see Numpy issue gh-6551 for details). It potentially affects the way that build/install methods for Scipy itself behave though. Please report any unexpected behavior on the Scipy issue tracker. PR `#6240 <https://github.com/scipy/scipy/pull/6240>`__ changes the interpretation of the `maxfun` option in `L-BFGS-B` based routines in the `scipy.optimize` module. An `L-BFGS-B` search consists of multiple iterations, with each iteration consisting of one or more function evaluations. Whereas the old search strategy terminated immediately upon reaching `maxfun` function evaluations, the new strategy allows the current iteration to finish despite reaching `maxfun`. The bundled copy of Qhull in the `scipy.spatial` subpackage has been upgraded to version 2015.2. The bundled copy of ARPACK in the `scipy.sparse.linalg` subpackage has been upgraded to arpack-ng 3.3.0. The bundled copy of SuperLU in the `scipy.sparse` subpackage has been upgraded to version 5.1.1. Authors ======= * @endolith * @yanxun827 + * @kleskjr + * @MYheavyGo + * @solarjoe + * Gregory Allen + * Gilles Aouizerate + * Tom Augspurger + * Henrik Bengtsson + * Felix Berkenkamp * Per Brodtkorb * Lars Buitinck * Daniel Bunting + * Evgeni Burovski * CJ Carey * Tim Cera * Grey Christoforo + * Robert Cimrman * Philip DeBoer + * Yves Delley + * D?vid Bodn?r + * Ion Elberdin + * Gabriele Farina + * Yu Feng * Andrew Fowlie + * Joseph Fox-Rabinovitz * Simon Gibbons + * Neil Girdhar + * Kolja Glogowski + * Christoph Gohlke * Ralf Gommers * Todd Goodall + * Johnnie Gray + * Alex Griffing * Olivier Grisel * Thomas Haslwanter + * Michael Hirsch + * Derek Homeier * Golnaz Irannejad + * Marek Jacob + * InSuk Joung + * Tetsuo Koyama + * Eugene Krokhalev + * Eric Larson * Denis Laxalde * Antony Lee * Jerry Li + * Henry Lin + * Nelson Liu + * Lo?c Est?ve * Lei Ma + * Osvaldo Martin + * Stefano Martina + * Nikolay Mayorov * Matthieu Melot + * Sturla Molden * Eric Moore * Alistair Muldal + * Maniteja Nandana * Tavi Nathanson + * Andrew Nelson * Joel Nothman * Behzad Nouri * Nikolai Nowaczyk + * Juan Nunez-Iglesias + * Ted Pudlik * Eric Quintero * Yoav Ram * Jonas Rauber + * Tyler Reddy + * Juha Remes * Garrett Reynolds + * Ariel Rokem + * Fabian Rost + * Bill Sacks + * Jona Sassenhagen + * Kari Schoonbee + * Marcello Seri + * Sourav Singh + * Martin Spacek + * S?ren Fuglede J?rgensen + * Bhavika Tekwani + * Martin Thoma + * Sam Tygier + * Meet Udeshi + * Utkarsh Upadhyay * Bram Vandekerckhove + * Sebasti?n Vanrell + * Ze Vinicius + * Pauli Virtanen * Stefan van der Walt * Warren Weckesser * Jakub Wilk + * Josh Wilson * Phillip J. Wolfram + * Nathan Woods * Haochen Wu * G Young + A total of 99 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. Issues closed for 0.18.0 - ------------------------ - - `#1484 <https://github.com/scipy/scipy/issues/1484>`__: SVD using *GESVD lapack drivers (Trac #957) - - `#1547 <https://github.com/scipy/scipy/issues/1547>`__: Inconsistent use of offset in ndimage.interpolation.affine_transform()... - - `#1609 <https://github.com/scipy/scipy/issues/1609>`__: special.hyp0f1 returns nan (Trac #1082) - - `#1656 <https://github.com/scipy/scipy/issues/1656>`__: fmin_slsqp enhancement (Trac #1129) - - `#2069 <https://github.com/scipy/scipy/issues/2069>`__: stats broadcasting in rvs (Trac #1544) - - `#2165 <https://github.com/scipy/scipy/issues/2165>`__: sph_jn returns false results for some orders/values (Trac #1640) - - `#2255 <https://github.com/scipy/scipy/issues/2255>`__: Incorrect order of translation and rotation in affine_transform... - - `#2332 <https://github.com/scipy/scipy/issues/2332>`__: hyp0f1 args and return values are unnumpyic (Trac #1813) - - `#2534 <https://github.com/scipy/scipy/issues/2534>`__: The sparse .sum() method with uint8 dtype does not act like the... - - `#3113 <https://github.com/scipy/scipy/issues/3113>`__: Implement ufuncs for CSPHJY, SPHJ, SPHY, CSPHIK, SPHI, SPHIK... - - `#3568 <https://github.com/scipy/scipy/issues/3568>`__: SciPy 0.13.3 - CentOS5 - Errors in test_arpack - - `#3581 <https://github.com/scipy/scipy/issues/3581>`__: optimize: stepsize in fmin_bfgs is "bad" - - `#4476 <https://github.com/scipy/scipy/issues/4476>`__: scipy.sparse non-native endian bug - - `#4484 <https://github.com/scipy/scipy/issues/4484>`__: ftol in optimize.fmin fails to work - - `#4510 <https://github.com/scipy/scipy/issues/4510>`__: sparsetools.cxx call_thunk can segfault due to out of bounds... - - `#5051 <https://github.com/scipy/scipy/issues/5051>`__: ftol and xtol for _minimize_neldermead are absolute instead of... - - `#5097 <https://github.com/scipy/scipy/issues/5097>`__: proposal: spherical Voronoi diagrams - - `#5123 <https://github.com/scipy/scipy/issues/5123>`__: Call to `scipy.sparse.coo_matrix` fails when passed Cython typed... - - `#5220 <https://github.com/scipy/scipy/issues/5220>`__: scipy.cluster.hierarchy.{ward,median,centroid} does not work... - - `#5379 <https://github.com/scipy/scipy/issues/5379>`__: Add a build step at the end of .travis.yml that uploads working... - - `#5440 <https://github.com/scipy/scipy/issues/5440>`__: scipy.optimize.basinhopping: accept_test returning numpy.bool_... - - `#5452 <https://github.com/scipy/scipy/issues/5452>`__: Error in scipy.integrate.nquad when using variable integration... - - `#5520 <https://github.com/scipy/scipy/issues/5520>`__: Cannot inherit csr_matrix properly - - `#5533 <https://github.com/scipy/scipy/issues/5533>`__: Kendall tau implementation uses Python mergesort - - `#5553 <https://github.com/scipy/scipy/issues/5553>`__: stats.tiecorrect overflows - - `#5589 <https://github.com/scipy/scipy/issues/5589>`__: Add the Type XII Burr distribution to stats. - - `#5612 <https://github.com/scipy/scipy/issues/5612>`__: sparse.linalg factorizations slow for small k due to default... - - `#5626 <https://github.com/scipy/scipy/issues/5626>`__: io.netcdf masking should use masked_equal rather than masked_value - - `#5637 <https://github.com/scipy/scipy/issues/5637>`__: Simple cubic spline interpolation? - - `#5683 <https://github.com/scipy/scipy/issues/5683>`__: BUG: Akima1DInterpolator may return nans given multidimensional... - - `#5686 <https://github.com/scipy/scipy/issues/5686>`__: scipy.stats.ttest_ind_from_stats does not accept arrays - - `#5702 <https://github.com/scipy/scipy/issues/5702>`__: scipy.ndimage.interpolation.affine_transform lacks documentation... - - `#5718 <https://github.com/scipy/scipy/issues/5718>`__: Wrong computation of weighted minkowski distance in cdist - - `#5745 <https://github.com/scipy/scipy/issues/5745>`__: move to setuptools for next release - - `#5752 <https://github.com/scipy/scipy/issues/5752>`__: DOC: solve_discrete_lyapunov equation puts transpose in wrong... - - `#5760 <https://github.com/scipy/scipy/issues/5760>`__: signal.ss2tf doesn't handle zero-order state-space models - - `#5764 <https://github.com/scipy/scipy/issues/5764>`__: Hypergeometric function hyp0f1 behaves incorrectly for complex... - - `#5814 <https://github.com/scipy/scipy/issues/5814>`__: stats NaN Policy Error message inconsistent with code - - `#5833 <https://github.com/scipy/scipy/issues/5833>`__: docstring of stats.binom_test() needs an update - - `#5853 <https://github.com/scipy/scipy/issues/5853>`__: Error in scipy.linalg.expm for complex matrix with shape (1,1) - - `#5856 <https://github.com/scipy/scipy/issues/5856>`__: Specify Nelder-Mead initial simplex - - `#5865 <https://github.com/scipy/scipy/issues/5865>`__: scipy.linalg.expm fails for certain numpy matrices - - `#5915 <https://github.com/scipy/scipy/issues/5915>`__: optimize.basinhopping - variable referenced before assignment. - - `#5916 <https://github.com/scipy/scipy/issues/5916>`__: LSQUnivariateSpline fitting failed with knots generated from... - - `#5927 <https://github.com/scipy/scipy/issues/5927>`__: unicode vs. string comparison in scipy.stats.binned_statistic_dd - - `#5936 <https://github.com/scipy/scipy/issues/5936>`__: faster implementation of ks_2samp - - `#5948 <https://github.com/scipy/scipy/issues/5948>`__: csc matrix .mean returns single element matrix rather than scalar - - `#5959 <https://github.com/scipy/scipy/issues/5959>`__: BUG: optimize test error for root when using lgmres - - `#5972 <https://github.com/scipy/scipy/issues/5972>`__: Test failures for sparse sum tests on 32-bit Python - - `#5976 <https://github.com/scipy/scipy/issues/5976>`__: Unexpected exception in scipy.sparse.bmat while using 0 x 0 matrix - - `#6008 <https://github.com/scipy/scipy/issues/6008>`__: scipy.special.kl_div not available in 0.14.1 - - `#6011 <https://github.com/scipy/scipy/issues/6011>`__: The von-Mises entropy is broken - - `#6016 <https://github.com/scipy/scipy/issues/6016>`__: python crashes for linalg.interpolative.svd with certain large... - - `#6017 <https://github.com/scipy/scipy/issues/6017>`__: Wilcoxon signed-rank test with zero_method="pratt" or "zsplit"... - - `#6028 <https://github.com/scipy/scipy/issues/6028>`__: stats.distributions does not have trapezoidal distribution - - `#6035 <https://github.com/scipy/scipy/issues/6035>`__: Wrong link in f_oneway - - `#6056 <https://github.com/scipy/scipy/issues/6056>`__: BUG: signal.decimate should only accept discrete LTI objects - - `#6093 <https://github.com/scipy/scipy/issues/6093>`__: Precision error on Linux 32 bit with openblas - - `#6101 <https://github.com/scipy/scipy/issues/6101>`__: Barycentric transforms test error on Python3, 32-bit Linux - - `#6105 <https://github.com/scipy/scipy/issues/6105>`__: scipy.misc.face docstring is incorrect - - `#6113 <https://github.com/scipy/scipy/issues/6113>`__: scipy.linalg.logm fails for a trivial matrix - - `#6128 <https://github.com/scipy/scipy/issues/6128>`__: Error in dot method of sparse COO array, when used with numpy... - - `#6132 <https://github.com/scipy/scipy/issues/6132>`__: Failures with latest MKL - - `#6136 <https://github.com/scipy/scipy/issues/6136>`__: Failures on `master` with MKL - - `#6162 <https://github.com/scipy/scipy/issues/6162>`__: fmin_l_bfgs_b returns inconsistent results (fmin ? f(xmin)) and... - - `#6165 <https://github.com/scipy/scipy/issues/6165>`__: optimize.minimize infinite loop with Newton-CG - - `#6167 <https://github.com/scipy/scipy/issues/6167>`__: incorrect distribution fitting for data containing boundary values. - - `#6194 <https://github.com/scipy/scipy/issues/6194>`__: lstsq() and others detect numpy.complex256 as real - - `#6216 <https://github.com/scipy/scipy/issues/6216>`__: ENH: improve accuracy of ppf cdf roundtrip for bradford - - `#6217 <https://github.com/scipy/scipy/issues/6217>`__: BUG: weibull_min.logpdf return nan for c=1 and x=0 - - `#6218 <https://github.com/scipy/scipy/issues/6218>`__: Is there a method to cap shortest path search distances? - - `#6222 <https://github.com/scipy/scipy/issues/6222>`__: PchipInterpolator no longer handles a 2-element array - - `#6226 <https://github.com/scipy/scipy/issues/6226>`__: ENH: improve accuracy for logistic.ppf and logistic.isf - - `#6227 <https://github.com/scipy/scipy/issues/6227>`__: ENH: improve accuracy for rayleigh.logpdf and rayleigh.logsf... - - `#6228 <https://github.com/scipy/scipy/issues/6228>`__: ENH: improve accuracy of ppf cdf roundtrip for gumbel_l - - `#6235 <https://github.com/scipy/scipy/issues/6235>`__: BUG: alpha.pdf and alpha.logpdf returns nan for x=0 - - `#6245 <https://github.com/scipy/scipy/issues/6245>`__: ENH: improve accuracy for ppf-cdf and sf-isf roundtrips for invgamma - - `#6263 <https://github.com/scipy/scipy/issues/6263>`__: BUG: stats: Inconsistency in the multivariate_normal docstring - - `#6292 <https://github.com/scipy/scipy/issues/6292>`__: Python 3 unorderable type errors in test_sparsetools.TestInt32Overflow - - `#6316 <https://github.com/scipy/scipy/issues/6316>`__: TestCloughTocher2DInterpolator.test_dense crashes python3.5.2rc1_64bit... - - `#6318 <https://github.com/scipy/scipy/issues/6318>`__: Scipy interp1d 'nearest' not working for high values on x-axis Pull requests for 0.18.0 - ------------------------ - - `#3226 <https://github.com/scipy/scipy/pull/3226>`__: DOC: Change `nb` and `na` to conventional m and n - - `#3867 <https://github.com/scipy/scipy/pull/3867>`__: allow cKDTree.query taking a list input in k. - - `#4191 <https://github.com/scipy/scipy/pull/4191>`__: ENH: Benchmarking global optimizers - - `#4356 <https://github.com/scipy/scipy/pull/4356>`__: ENH: add PPoly.solve(y) for solving ``p(x) == y`` - - `#4370 <https://github.com/scipy/scipy/pull/4370>`__: DOC separate boolean distance functions for clarity - - `#4678 <https://github.com/scipy/scipy/pull/4678>`__: BUG: sparse: ensure index dtype is large enough to pass all parameters... - - `#4881 <https://github.com/scipy/scipy/pull/4881>`__: scipy.signal: Add the class dlti for linear discrete-time systems.... - - `#4901 <https://github.com/scipy/scipy/pull/4901>`__: MAINT: add benchmark and improve docstring for signal.lfilter - - `#5043 <https://github.com/scipy/scipy/pull/5043>`__: ENH: sparse: add count_nonzero method - - `#5136 <https://github.com/scipy/scipy/pull/5136>`__: Attribute kurtosistest() to Anscombe & Glynn (1983) - - `#5186 <https://github.com/scipy/scipy/pull/5186>`__: ENH: Port upfirdn - - `#5232 <https://github.com/scipy/scipy/pull/5232>`__: ENH: adding spherical Voronoi diagram algorithm to scipy.spatial - - `#5279 <https://github.com/scipy/scipy/pull/5279>`__: ENH: Bessel filters with different normalizations, high order - - `#5384 <https://github.com/scipy/scipy/pull/5384>`__: BUG: Closes #5027 distance function always casts bool to double - - `#5392 <https://github.com/scipy/scipy/pull/5392>`__: ENH: Add zero_phase kwarg to signal.decimate - - `#5394 <https://github.com/scipy/scipy/pull/5394>`__: MAINT: sparse: non-canonical test cleanup and fixes - - `#5424 <https://github.com/scipy/scipy/pull/5424>`__: DOC: add Scipy developers guide - - `#5442 <https://github.com/scipy/scipy/pull/5442>`__: STY: PEP8 amendments - - `#5472 <https://github.com/scipy/scipy/pull/5472>`__: Online QR in LGMRES - - `#5526 <https://github.com/scipy/scipy/pull/5526>`__: BUG: stats: Fix broadcasting in the rvs() method of the distributions. - - `#5530 <https://github.com/scipy/scipy/pull/5530>`__: MAINT: sparse: set `format` attr explicitly - - `#5536 <https://github.com/scipy/scipy/pull/5536>`__: optimize: fix up cg/bfgs initial step sizes - - `#5548 <https://github.com/scipy/scipy/pull/5548>`__: PERF: improves performance in stats.kendalltau - - `#5549 <https://github.com/scipy/scipy/pull/5549>`__: ENH: Nearest-neighbor chain algorithm for hierarchical clustering - - `#5554 <https://github.com/scipy/scipy/pull/5554>`__: MAINT/BUG: closes overflow bug in stats.tiecorrect - - `#5557 <https://github.com/scipy/scipy/pull/5557>`__: BUG: modify optimize.bisect to achieve desired tolerance - - `#5581 <https://github.com/scipy/scipy/pull/5581>`__: DOC: Tutorial for least_squares - - `#5606 <https://github.com/scipy/scipy/pull/5606>`__: ENH: differential_evolution - moving core loop of solve method... - - `#5609 <https://github.com/scipy/scipy/pull/5609>`__: [MRG] test against numpy dev - - `#5611 <https://github.com/scipy/scipy/pull/5611>`__: use setuptools for bdist_egg distributions - - `#5615 <https://github.com/scipy/scipy/pull/5615>`__: MAINT: linalg: tighten _decomp_update + special: remove unused... - - `#5622 <https://github.com/scipy/scipy/pull/5622>`__: Add SO(N) rotation matrix generator - - `#5623 <https://github.com/scipy/scipy/pull/5623>`__: ENH: special: Add vectorized spherical Bessel functions. - - `#5627 <https://github.com/scipy/scipy/pull/5627>`__: Response to issue #5160, implements the skew normal distribution... - - `#5628 <https://github.com/scipy/scipy/pull/5628>`__: DOC: Align the description and operation - - `#5632 <https://github.com/scipy/scipy/pull/5632>`__: DOC: special: Expanded docs for Airy, elliptic, Bessel functions. - - `#5633 <https://github.com/scipy/scipy/pull/5633>`__: MAINT: linalg: unchecked malloc in _decomp_update - - `#5634 <https://github.com/scipy/scipy/pull/5634>`__: MAINT: optimize: tighten _group_columns - - `#5640 <https://github.com/scipy/scipy/pull/5640>`__: Fixes for io.netcdf masking - - `#5645 <https://github.com/scipy/scipy/pull/5645>`__: MAINT: size 0 vector handling in cKDTree range queries - - `#5649 <https://github.com/scipy/scipy/pull/5649>`__: MAINT: update license text - - `#5650 <https://github.com/scipy/scipy/pull/5650>`__: DOC: Clarify Exponent Order in ltisys.py - - `#5651 <https://github.com/scipy/scipy/pull/5651>`__: DOC: Clarify Documentation for scipy.special.gammaln - - `#5652 <https://github.com/scipy/scipy/pull/5652>`__: DOC: Fixed scipy.special.betaln Doc - - `#5653 <https://github.com/scipy/scipy/pull/5653>`__: [MRG] ENH: CubicSpline interpolator - - `#5654 <https://github.com/scipy/scipy/pull/5654>`__: ENH: Burr12 distribution to stats module - - `#5659 <https://github.com/scipy/scipy/pull/5659>`__: DOC: Define BEFORE/AFTER in runtests.py -h for bench-compare - - `#5660 <https://github.com/scipy/scipy/pull/5660>`__: MAINT: remove functions deprecated before 0.16.0 - - `#5662 <https://github.com/scipy/scipy/pull/5662>`__: ENH: Circular statistic optimization - - `#5663 <https://github.com/scipy/scipy/pull/5663>`__: MAINT: remove uses of np.testing.rand - - `#5665 <https://github.com/scipy/scipy/pull/5665>`__: MAINT: spatial: remove matching distance implementation - - `#5667 <https://github.com/scipy/scipy/pull/5667>`__: Change some HTTP links to HTTPS - - `#5669 <https://github.com/scipy/scipy/pull/5669>`__: DOC: zpk2sos can't do analog, array_like, etc. - - `#5670 <https://github.com/scipy/scipy/pull/5670>`__: Update conf.py - - `#5672 <https://github.com/scipy/scipy/pull/5672>`__: MAINT: move a sample distribution to a subclass of rv_discrete - - `#5678 <https://github.com/scipy/scipy/pull/5678>`__: MAINT: stats: remove est_loc_scale method - - `#5679 <https://github.com/scipy/scipy/pull/5679>`__: MAINT: DRY up generic computations for discrete distributions - - `#5680 <https://github.com/scipy/scipy/pull/5680>`__: MAINT: stop shadowing builtins in stats.distributions - - `#5681 <https://github.com/scipy/scipy/pull/5681>`__: forward port ENH: Re-enable broadcasting of fill_value - - `#5684 <https://github.com/scipy/scipy/pull/5684>`__: BUG: Fix Akima1DInterpolator returning nans - - `#5690 <https://github.com/scipy/scipy/pull/5690>`__: BUG: fix stats.ttest_ind_from_stats to handle arrays. - - `#5691 <https://github.com/scipy/scipy/pull/5691>`__: BUG: fix generator in io._loadarff to comply with PEP 0479 - - `#5693 <https://github.com/scipy/scipy/pull/5693>`__: ENH: use math.factorial for exact factorials - - `#5695 <https://github.com/scipy/scipy/pull/5695>`__: DOC: dx might be a float, not only an integer - - `#5699 <https://github.com/scipy/scipy/pull/5699>`__: MAINT: io: micro-optimize Matlab reading code for size - - `#5701 <https://github.com/scipy/scipy/pull/5701>`__: Implement OptimizeResult.__dir__ - - `#5703 <https://github.com/scipy/scipy/pull/5703>`__: ENH: stats: make R? printing optional in probplot - - `#5704 <https://github.com/scipy/scipy/pull/5704>`__: MAINT: typo ouf->out - - `#5705 <https://github.com/scipy/scipy/pull/5705>`__: BUG: fix typo in query_pairs - - `#5707 <https://github.com/scipy/scipy/pull/5707>`__: DOC:Add some explanation for ftol xtol in scipy.optimize.fmin - - `#5708 <https://github.com/scipy/scipy/pull/5708>`__: DOC: optimize: PEP8 minimize docstring - - `#5709 <https://github.com/scipy/scipy/pull/5709>`__: MAINT: optimize Cython code for speed and size - - `#5713 <https://github.com/scipy/scipy/pull/5713>`__: [DOC] Fix broken link to reference - - `#5717 <https://github.com/scipy/scipy/pull/5717>`__: DOC: curve_fit raises RuntimeError on failure. - - `#5724 <https://github.com/scipy/scipy/pull/5724>`__: forward port gh-5720 - - `#5728 <https://github.com/scipy/scipy/pull/5728>`__: STY: remove a blank line - - `#5729 <https://github.com/scipy/scipy/pull/5729>`__: ENH: spatial: speed up boolean distances - - `#5732 <https://github.com/scipy/scipy/pull/5732>`__: MAINT: differential_evolution changes to default keywords break... - - `#5733 <https://github.com/scipy/scipy/pull/5733>`__: TST: differential_evolution - population initiation tests - - `#5736 <https://github.com/scipy/scipy/pull/5736>`__: Complex number support in log1p, expm1, and xlog1py - - `#5741 <https://github.com/scipy/scipy/pull/5741>`__: MAINT: sparse: clean up extraction functions - - `#5742 <https://github.com/scipy/scipy/pull/5742>`__: DOC: signal: Explain fftbins in get_window - - `#5748 <https://github.com/scipy/scipy/pull/5748>`__: ENH: Add O(N) random matrix generator - - `#5749 <https://github.com/scipy/scipy/pull/5749>`__: ENH: Add polyphase resampling - - `#5756 <https://github.com/scipy/scipy/pull/5756>`__: RFC: Bump the minimum numpy version, drop older python versions - - `#5761 <https://github.com/scipy/scipy/pull/5761>`__: DOC: Some improvements to least squares docstrings - - `#5762 <https://github.com/scipy/scipy/pull/5762>`__: MAINT: spatial: distance refactoring - - `#5768 <https://github.com/scipy/scipy/pull/5768>`__: DOC: Fix io.loadmat docstring for mdict param - - `#5770 <https://github.com/scipy/scipy/pull/5770>`__: BUG: Accept anything np.dtype can handle for a dtype in sparse.random - - `#5772 <https://github.com/scipy/scipy/pull/5772>`__: Update sparse.csgraph.laplacian docstring - - `#5777 <https://github.com/scipy/scipy/pull/5777>`__: BUG: fix special.hyp0f1 to work correctly for complex inputs. - - `#5780 <https://github.com/scipy/scipy/pull/5780>`__: DOC: Update PIL error install URL - - `#5781 <https://github.com/scipy/scipy/pull/5781>`__: DOC: Fix documentation on solve_discrete_lyapunov - - `#5782 <https://github.com/scipy/scipy/pull/5782>`__: DOC: cKDTree and KDTree now reference each other - - `#5783 <https://github.com/scipy/scipy/pull/5783>`__: DOC: Clarify finish behaviour in scipy.optimize.brute - - `#5784 <https://github.com/scipy/scipy/pull/5784>`__: MAINT: Change default tolerances of least_squares to 1e-8 - - `#5787 <https://github.com/scipy/scipy/pull/5787>`__: BUG: Allow Processing of Zero Order State Space Models in signal.ss2tf - - `#5788 <https://github.com/scipy/scipy/pull/5788>`__: DOC, BUG: Clarify and Enforce Input Types to 'Data' Objects - - `#5789 <https://github.com/scipy/scipy/pull/5789>`__: ENH: sparse: speedup LIL matrix slicing (was #3338) - - `#5791 <https://github.com/scipy/scipy/pull/5791>`__: DOC: README: remove coveralls.io - - `#5792 <https://github.com/scipy/scipy/pull/5792>`__: MAINT: remove uses of deprecated np.random.random_integers - - `#5794 <https://github.com/scipy/scipy/pull/5794>`__: fix affine_transform (fixes #1547 and #5702) - - `#5795 <https://github.com/scipy/scipy/pull/5795>`__: DOC: Removed uniform method from kmeans2 doc - - `#5797 <https://github.com/scipy/scipy/pull/5797>`__: DOC: Clarify the computation of weighted minkowski - - `#5798 <https://github.com/scipy/scipy/pull/5798>`__: BUG: Ensure scipy's _asfarray returns ndarray - - `#5799 <https://github.com/scipy/scipy/pull/5799>`__: TST: Mpmath testing patch - - `#5801 <https://github.com/scipy/scipy/pull/5801>`__: allow reading of certain IDL 8.0 .sav files - - `#5803 <https://github.com/scipy/scipy/pull/5803>`__: DOC: fix module name in error message - - `#5804 <https://github.com/scipy/scipy/pull/5804>`__: DOC: special: Expanded docs for special functions. - - `#5805 <https://github.com/scipy/scipy/pull/5805>`__: DOC: Fix order of returns in _spectral_helper - - `#5806 <https://github.com/scipy/scipy/pull/5806>`__: ENH: sparse: vectorized coo_matrix.diagonal - - `#5808 <https://github.com/scipy/scipy/pull/5808>`__: ENH: Added iqr function to compute IQR metric in scipy/stats/stats.py - - `#5810 <https://github.com/scipy/scipy/pull/5810>`__: MAINT/BENCH: sparse: Benchmark cleanup and additions - - `#5811 <https://github.com/scipy/scipy/pull/5811>`__: DOC: sparse.linalg: shape, not size - - `#5813 <https://github.com/scipy/scipy/pull/5813>`__: Update sparse ARPACK functions min `ncv` value - - `#5815 <https://github.com/scipy/scipy/pull/5815>`__: BUG: Error message contained wrong values - - `#5816 <https://github.com/scipy/scipy/pull/5816>`__: remove dead code from stats tests - - `#5820 <https://github.com/scipy/scipy/pull/5820>`__: "in"->"a" in order_filter docstring - - `#5821 <https://github.com/scipy/scipy/pull/5821>`__: DOC: README: INSTALL.txt was renamed in 2014 - - `#5825 <https://github.com/scipy/scipy/pull/5825>`__: DOC: typo in the docstring of least_squares - - `#5826 <https://github.com/scipy/scipy/pull/5826>`__: MAINT: sparse: increase test coverage - - `#5827 <https://github.com/scipy/scipy/pull/5827>`__: NdPPoly rebase - - `#5828 <https://github.com/scipy/scipy/pull/5828>`__: Improve numerical stability of hyp0f1 for large orders - - `#5829 <https://github.com/scipy/scipy/pull/5829>`__: ENH: sparse: Add copy parameter to all .toXXX() methods in sparse... - - `#5830 <https://github.com/scipy/scipy/pull/5830>`__: DOC: rework INSTALL.rst.txt - - `#5831 <https://github.com/scipy/scipy/pull/5831>`__: Adds plotting options to voronoi_plot_2d - - `#5834 <https://github.com/scipy/scipy/pull/5834>`__: Update stats.binom_test() docstring - - `#5836 <https://github.com/scipy/scipy/pull/5836>`__: ENH, TST: Allow SIMO tf's for tf2ss - - `#5837 <https://github.com/scipy/scipy/pull/5837>`__: DOC: Image examples - - `#5838 <https://github.com/scipy/scipy/pull/5838>`__: ENH: sparse: add eliminate_zeros() to coo_matrix - - `#5839 <https://github.com/scipy/scipy/pull/5839>`__: BUG: Fixed name of NumpyVersion.__repr__ - - `#5845 <https://github.com/scipy/scipy/pull/5845>`__: MAINT: Fixed typos in documentation - - `#5847 <https://github.com/scipy/scipy/pull/5847>`__: Fix bugs in sparsetools - - `#5848 <https://github.com/scipy/scipy/pull/5848>`__: BUG: sparse.linalg: add locks to ensure ARPACK threadsafety - - `#5849 <https://github.com/scipy/scipy/pull/5849>`__: ENH: sparse.linalg: upgrade to superlu 5.1.1 - - `#5851 <https://github.com/scipy/scipy/pull/5851>`__: ENH: expose lapack's ilaver to python to allow lapack verion... - - `#5852 <https://github.com/scipy/scipy/pull/5852>`__: MAINT: runtests.py: ensure Ctrl-C interrupts the build - - `#5854 <https://github.com/scipy/scipy/pull/5854>`__: DOC: Minor update to documentation - - `#5855 <https://github.com/scipy/scipy/pull/5855>`__: Pr 5640 - - `#5859 <https://github.com/scipy/scipy/pull/5859>`__: ENH: Add random correlation matrix generator - - `#5862 <https://github.com/scipy/scipy/pull/5862>`__: BUG: Allow expm for complex matrix with shape (1, 1) - - `#5863 <https://github.com/scipy/scipy/pull/5863>`__: FIX: Fix test - - `#5864 <https://github.com/scipy/scipy/pull/5864>`__: DOC: add a little note about the Normal survival function (Q-function) - - `#5867 <https://github.com/scipy/scipy/pull/5867>`__: Fix for #5865 - - `#5869 <https://github.com/scipy/scipy/pull/5869>`__: extend normal distribution cdf to complex domain - - `#5872 <https://github.com/scipy/scipy/pull/5872>`__: DOC: Note that morlet and cwt don't work together - - `#5875 <https://github.com/scipy/scipy/pull/5875>`__: DOC: interp2d class description - - `#5876 <https://github.com/scipy/scipy/pull/5876>`__: MAINT: spatial: remove a stray print statement - - `#5878 <https://github.com/scipy/scipy/pull/5878>`__: MAINT: Fixed noisy UserWarnings in ndimage tests. Fixes #5877 - - `#5879 <https://github.com/scipy/scipy/pull/5879>`__: MAINT: sparse.linalg/superlu: add explicit casts to resolve compiler... - - `#5880 <https://github.com/scipy/scipy/pull/5880>`__: MAINT: signal: import gcd from math and not fractions when on... - - `#5887 <https://github.com/scipy/scipy/pull/5887>`__: Neldermead initial simplex - - `#5894 <https://github.com/scipy/scipy/pull/5894>`__: BUG: _CustomLinearOperator unpickalable in python3.5 - - `#5895 <https://github.com/scipy/scipy/pull/5895>`__: DOC: special: slightly improve the multigammaln docstring - - `#5900 <https://github.com/scipy/scipy/pull/5900>`__: Remove duplicate assignment. - - `#5901 <https://github.com/scipy/scipy/pull/5901>`__: Update bundled ARPACK - - `#5904 <https://github.com/scipy/scipy/pull/5904>`__: ENH: Make convolve and correlate order-agnostic - - `#5905 <https://github.com/scipy/scipy/pull/5905>`__: ENH: sparse.linalg: further LGMRES cleanups - - `#5906 <https://github.com/scipy/scipy/pull/5906>`__: Enhancements and cleanup in scipy.integrate (attempt #2) - - `#5907 <https://github.com/scipy/scipy/pull/5907>`__: ENH: Change sparse `.sum` and `.mean` dtype casting to match... - - `#5909 <https://github.com/scipy/scipy/pull/5909>`__: changes for convolution symmetry - - `#5913 <https://github.com/scipy/scipy/pull/5913>`__: MAINT: basinhopping remove instance test closes #5440 - - `#5919 <https://github.com/scipy/scipy/pull/5919>`__: MAINT: uninitialised var if basinhopping niter=0. closes #5915 - - `#5920 <https://github.com/scipy/scipy/pull/5920>`__: BLD: Fix missing lsame.c error for MKL - - `#5921 <https://github.com/scipy/scipy/pull/5921>`__: DOC: interpolate: add example showing how to work around issue... - - `#5926 <https://github.com/scipy/scipy/pull/5926>`__: MAINT: spatial: upgrade to Qhull 2015.2 - - `#5928 <https://github.com/scipy/scipy/pull/5928>`__: MAINT: sparse: optimize DIA sum/diagonal, csgraph.laplacian - - `#5929 <https://github.com/scipy/scipy/pull/5929>`__: Update info/URL for octave-maintainers discussion - - `#5930 <https://github.com/scipy/scipy/pull/5930>`__: TST: special: silence DeprecationWarnings from sph_yn - - `#5931 <https://github.com/scipy/scipy/pull/5931>`__: ENH: implement the principle branch of the logarithm of Gamma. - - `#5934 <https://github.com/scipy/scipy/pull/5934>`__: Typo: "mush" => "must" - - `#5935 <https://github.com/scipy/scipy/pull/5935>`__: BUG:string comparison stats._binned_statistic closes #5927 - - `#5938 <https://github.com/scipy/scipy/pull/5938>`__: Cythonize stats.ks_2samp for a ~33% gain in speed. - - `#5939 <https://github.com/scipy/scipy/pull/5939>`__: DOC: fix optimize.fmin convergence docstring - - `#5941 <https://github.com/scipy/scipy/pull/5941>`__: Fix minor typo in squareform docstring - - `#5942 <https://github.com/scipy/scipy/pull/5942>`__: Update linregress stderr description. - - `#5943 <https://github.com/scipy/scipy/pull/5943>`__: ENH: Improve numerical accuracy of lognorm - - `#5944 <https://github.com/scipy/scipy/pull/5944>`__: Merge vonmises into stats pyx - - `#5945 <https://github.com/scipy/scipy/pull/5945>`__: MAINT: interpolate: Tweak declaration to avoid cython warning... - - `#5946 <https://github.com/scipy/scipy/pull/5946>`__: MAINT: sparse: clean up format conversion methods - - `#5949 <https://github.com/scipy/scipy/pull/5949>`__: BUG: fix sparse .mean to return a scalar instead of a matrix - - `#5955 <https://github.com/scipy/scipy/pull/5955>`__: MAINT: Replace calls to `hanning` with `hann` - - `#5956 <https://github.com/scipy/scipy/pull/5956>`__: DOC: Missing periods interfering with parsing - - `#5958 <https://github.com/scipy/scipy/pull/5958>`__: MAINT: add a test for lognorm.sf underflow - - `#5961 <https://github.com/scipy/scipy/pull/5961>`__: MAINT _centered(): rename size to shape - - `#5962 <https://github.com/scipy/scipy/pull/5962>`__: ENH: constants: Add multi-scale temperature conversion function - - `#5965 <https://github.com/scipy/scipy/pull/5965>`__: ENH: special: faster way for calculating comb() for exact=True - - `#5975 <https://github.com/scipy/scipy/pull/5975>`__: ENH: Improve FIR path of signal.decimate - - `#5977 <https://github.com/scipy/scipy/pull/5977>`__: MAINT/BUG: sparse: remove overzealous bmat checks - - `#5978 <https://github.com/scipy/scipy/pull/5978>`__: minimize_neldermead() stop at user requested maxiter or maxfev - - `#5983 <https://github.com/scipy/scipy/pull/5983>`__: ENH: make sparse `sum` cast dtypes like NumPy `sum` for 32-bit... - - `#5985 <https://github.com/scipy/scipy/pull/5985>`__: BUG, API: Add `jac` parameter to curve_fit - - `#5989 <https://github.com/scipy/scipy/pull/5989>`__: ENH: Add firls least-squares fitting - - `#5990 <https://github.com/scipy/scipy/pull/5990>`__: BUG: read tries to handle 20-bit WAV files but shouldn't - - `#5991 <https://github.com/scipy/scipy/pull/5991>`__: DOC: Cleanup wav read/write docs and add tables for common types - - `#5994 <https://github.com/scipy/scipy/pull/5994>`__: ENH: Add gesvd method for svd - - `#5996 <https://github.com/scipy/scipy/pull/5996>`__: MAINT: Wave cleanup - - `#5997 <https://github.com/scipy/scipy/pull/5997>`__: TST: Break up upfirdn tests & compare to lfilter - - `#6001 <https://github.com/scipy/scipy/pull/6001>`__: Filter design docs - - `#6002 <https://github.com/scipy/scipy/pull/6002>`__: COMPAT: Expand compatibility fromnumeric.py - - `#6007 <https://github.com/scipy/scipy/pull/6007>`__: ENH: Skip conversion of TF to TF in freqresp - - `#6009 <https://github.com/scipy/scipy/pull/6009>`__: DOC: fix incorrect versionadded for entr, rel_entr, kl_div - - `#6013 <https://github.com/scipy/scipy/pull/6013>`__: Fixed the entropy calculation of the von Mises distribution. - - `#6014 <https://github.com/scipy/scipy/pull/6014>`__: MAINT: make gamma, rgamma use loggamma for complex arguments - - `#6020 <https://github.com/scipy/scipy/pull/6020>`__: WIP: ENH: add exact=True factorial for vectors - - `#6022 <https://github.com/scipy/scipy/pull/6022>`__: Added 'lanczos' to the image interpolation function list. - - `#6024 <https://github.com/scipy/scipy/pull/6024>`__: BUG: optimize: do not use dummy constraints in SLSQP when no... - - `#6025 <https://github.com/scipy/scipy/pull/6025>`__: ENH: Boundary value problem solver for ODE systems - - `#6029 <https://github.com/scipy/scipy/pull/6029>`__: MAINT: Future imports for optimize._lsq - - `#6030 <https://github.com/scipy/scipy/pull/6030>`__: ENH: stats.trap - adding trapezoidal distribution closes #6028 - - `#6031 <https://github.com/scipy/scipy/pull/6031>`__: MAINT: Some improvements to optimize._numdiff - - `#6032 <https://github.com/scipy/scipy/pull/6032>`__: MAINT: Add special/_comb.c to .gitignore - - `#6033 <https://github.com/scipy/scipy/pull/6033>`__: BUG: check the requested approximation rank in interpolative.svd - - `#6034 <https://github.com/scipy/scipy/pull/6034>`__: DOC: Doc for mannwhitneyu in stats.py corrected - - `#6040 <https://github.com/scipy/scipy/pull/6040>`__: FIX: Edit the wrong link in f_oneway - - `#6044 <https://github.com/scipy/scipy/pull/6044>`__: BUG: (ordqz) always increase parameter lwork by 1. - - `#6047 <https://github.com/scipy/scipy/pull/6047>`__: ENH: extend special.spence to complex arguments. - - `#6049 <https://github.com/scipy/scipy/pull/6049>`__: DOC: Add documentation of PR #5640 to the 0.18.0 release notes - - `#6050 <https://github.com/scipy/scipy/pull/6050>`__: MAINT: small cleanups related to loggamma - - `#6070 <https://github.com/scipy/scipy/pull/6070>`__: Add asarray to explicitly cast list to numpy array in wilcoxon... - - `#6071 <https://github.com/scipy/scipy/pull/6071>`__: DOC: antialiasing filter and link decimate resample, etc. - - `#6075 <https://github.com/scipy/scipy/pull/6075>`__: MAINT: reimplement special.digamma for complex arguments - - `#6080 <https://github.com/scipy/scipy/pull/6080>`__: avoid multiple computation in kstest - - `#6081 <https://github.com/scipy/scipy/pull/6081>`__: Clarified pearson correlation return value - - `#6085 <https://github.com/scipy/scipy/pull/6085>`__: ENH: allow long indices of sparse matrix with umfpack in spsolve() - - `#6086 <https://github.com/scipy/scipy/pull/6086>`__: fix description for associated Laguerre polynomials - - `#6087 <https://github.com/scipy/scipy/pull/6087>`__: Corrected docstring of splrep. - - `#6094 <https://github.com/scipy/scipy/pull/6094>`__: ENH: special: change zeta signature to zeta(x, q=1) - - `#6095 <https://github.com/scipy/scipy/pull/6095>`__: BUG: fix integer overflow in special.spence - - `#6106 <https://github.com/scipy/scipy/pull/6106>`__: Fixed Issue #6105 - - `#6116 <https://github.com/scipy/scipy/pull/6116>`__: BUG: matrix logarithm edge case - - `#6119 <https://github.com/scipy/scipy/pull/6119>`__: TST: DeprecationWarnings in stats on python 3.5 closes #5885 - - `#6120 <https://github.com/scipy/scipy/pull/6120>`__: MAINT: sparse: clean up sputils.isintlike - - `#6122 <https://github.com/scipy/scipy/pull/6122>`__: DOC: optimize: linprog docs should say minimize instead of maximize - - `#6123 <https://github.com/scipy/scipy/pull/6123>`__: DOC: optimize: document the `fun` field in `scipy.optimize.OptimizeResult` - - `#6124 <https://github.com/scipy/scipy/pull/6124>`__: Move FFT zero-padding calculation from signaltools to fftpack - - `#6125 <https://github.com/scipy/scipy/pull/6125>`__: MAINT: improve special.gammainc in the ``a ~ x`` regime. - - `#6130 <https://github.com/scipy/scipy/pull/6130>`__: BUG: sparse: Fix COO dot with zero columns - - `#6138 <https://github.com/scipy/scipy/pull/6138>`__: ENH: stats: Improve behavior of genextreme.sf and genextreme.isf - - `#6146 <https://github.com/scipy/scipy/pull/6146>`__: MAINT: simplify the expit implementation - - `#6151 <https://github.com/scipy/scipy/pull/6151>`__: MAINT: special: make generate_ufuncs.py output deterministic - - `#6152 <https://github.com/scipy/scipy/pull/6152>`__: TST: special: better test for gammainc at large arguments - - `#6153 <https://github.com/scipy/scipy/pull/6153>`__: ENH: Make next_fast_len public and faster - - `#6154 <https://github.com/scipy/scipy/pull/6154>`__: fix typo "mush"-->"must" - - `#6155 <https://github.com/scipy/scipy/pull/6155>`__: DOC: Fix some incorrect RST definition lists - - `#6160 <https://github.com/scipy/scipy/pull/6160>`__: make logsumexp error out on a masked array - - `#6161 <https://github.com/scipy/scipy/pull/6161>`__: added missing bracket to rosen documentation - - `#6163 <https://github.com/scipy/scipy/pull/6163>`__: ENH: Added "kappa4" and "kappa3" distributions. - - `#6164 <https://github.com/scipy/scipy/pull/6164>`__: DOC: Minor clean-up in integrate._bvp - - `#6169 <https://github.com/scipy/scipy/pull/6169>`__: Fix mpf_assert_allclose to handle iterable results, such as maps - - `#6170 <https://github.com/scipy/scipy/pull/6170>`__: Fix pchip_interpolate convenience function - - `#6172 <https://github.com/scipy/scipy/pull/6172>`__: Corrected misplaced bracket in doc string - - `#6175 <https://github.com/scipy/scipy/pull/6175>`__: ENH: sparse.csgraph: Pass indices to shortest_path - - `#6178 <https://github.com/scipy/scipy/pull/6178>`__: TST: increase test coverage of sf and isf of a generalized extreme... - - `#6179 <https://github.com/scipy/scipy/pull/6179>`__: TST: avoid a deprecation warning from numpy - - `#6181 <https://github.com/scipy/scipy/pull/6181>`__: ENH: Boundary conditions for CubicSpline - - `#6182 <https://github.com/scipy/scipy/pull/6182>`__: DOC: Add examples/graphs to max_len_seq - - `#6183 <https://github.com/scipy/scipy/pull/6183>`__: BLD: update Bento build config files for recent changes. - - `#6184 <https://github.com/scipy/scipy/pull/6184>`__: BUG: fix issue in io/wavfile for float96 input. - - `#6186 <https://github.com/scipy/scipy/pull/6186>`__: ENH: Periodic extrapolation for PPoly and BPoly - - `#6192 <https://github.com/scipy/scipy/pull/6192>`__: MRG: Add circle-CI - - `#6193 <https://github.com/scipy/scipy/pull/6193>`__: ENH: sparse: avoid setitem densification - - `#6196 <https://github.com/scipy/scipy/pull/6196>`__: Fixed missing sqrt in docstring of Mahalanobis distance in cdist,... - - `#6206 <https://github.com/scipy/scipy/pull/6206>`__: MAINT: Minor changes in solve_bvp - - `#6207 <https://github.com/scipy/scipy/pull/6207>`__: BUG: linalg: for BLAS, downcast complex256 to complex128, not... - - `#6209 <https://github.com/scipy/scipy/pull/6209>`__: BUG: io.matlab: avoid buffer overflows in read_element_into - - `#6210 <https://github.com/scipy/scipy/pull/6210>`__: BLD: use setuptools when building. - - `#6214 <https://github.com/scipy/scipy/pull/6214>`__: BUG: sparse.linalg: fix bug in LGMRES breakdown handling - - `#6215 <https://github.com/scipy/scipy/pull/6215>`__: MAINT: special: make loggamma use zdiv - - `#6220 <https://github.com/scipy/scipy/pull/6220>`__: DOC: Add parameter - - `#6221 <https://github.com/scipy/scipy/pull/6221>`__: ENH: Improve Newton solver for solve_bvp - - `#6223 <https://github.com/scipy/scipy/pull/6223>`__: pchip should work for length-2 arrays - - `#6224 <https://github.com/scipy/scipy/pull/6224>`__: signal.lti: deprecate cross-class properties/setters - - `#6229 <https://github.com/scipy/scipy/pull/6229>`__: BUG: optimize: avoid an infinite loop in Newton-CG - - `#6230 <https://github.com/scipy/scipy/pull/6230>`__: Add example for application of gaussian filter - - `#6236 <https://github.com/scipy/scipy/pull/6236>`__: MAINT: gumbel_l accuracy - - `#6237 <https://github.com/scipy/scipy/pull/6237>`__: MAINT: rayleigh accuracy - - `#6238 <https://github.com/scipy/scipy/pull/6238>`__: MAINT: logistic accuracy - - `#6239 <https://github.com/scipy/scipy/pull/6239>`__: MAINT: bradford distribution accuracy - - `#6240 <https://github.com/scipy/scipy/pull/6240>`__: MAINT: avoid bad fmin in l-bfgs-b due to maxfun interruption - - `#6241 <https://github.com/scipy/scipy/pull/6241>`__: MAINT: weibull_min accuracy - - `#6246 <https://github.com/scipy/scipy/pull/6246>`__: ENH: Add _support_mask to distributions - - `#6247 <https://github.com/scipy/scipy/pull/6247>`__: fixed a print error for an example of ode - - `#6249 <https://github.com/scipy/scipy/pull/6249>`__: MAINT: change x-axis label for stats.probplot to "theoretical... - - `#6250 <https://github.com/scipy/scipy/pull/6250>`__: DOC: fix typos - - `#6251 <https://github.com/scipy/scipy/pull/6251>`__: MAINT: constants: filter out test noise from deprecated conversions - - `#6252 <https://github.com/scipy/scipy/pull/6252>`__: MAINT: io/arff: remove unused variable - - `#6253 <https://github.com/scipy/scipy/pull/6253>`__: Add examples to scipy.ndimage.filters - - `#6254 <https://github.com/scipy/scipy/pull/6254>`__: MAINT: special: fix some build warnings - - `#6258 <https://github.com/scipy/scipy/pull/6258>`__: MAINT: inverse gamma distribution accuracy - - `#6260 <https://github.com/scipy/scipy/pull/6260>`__: MAINT: signal.decimate - Use discrete-time objects - - `#6262 <https://github.com/scipy/scipy/pull/6262>`__: BUG: odr: fix string formatting - - `#6267 <https://github.com/scipy/scipy/pull/6267>`__: TST: fix some test issues in interpolate and stats. - - `#6269 <https://github.com/scipy/scipy/pull/6269>`__: TST: fix some warnings in the test suite - - `#6274 <https://github.com/scipy/scipy/pull/6274>`__: ENH: Add sosfiltfilt - - `#6276 <https://github.com/scipy/scipy/pull/6276>`__: DOC: update release notes for 0.18.0 - - `#6277 <https://github.com/scipy/scipy/pull/6277>`__: MAINT: update the author name mapping - - `#6282 <https://github.com/scipy/scipy/pull/6282>`__: DOC: Correcting references for scipy.stats.normaltest - - `#6283 <https://github.com/scipy/scipy/pull/6283>`__: DOC: some more additions to 0.18.0 release notes. - - `#6284 <https://github.com/scipy/scipy/pull/6284>`__: Add `.. versionadded::` directive to `loggamma`. - - `#6285 <https://github.com/scipy/scipy/pull/6285>`__: BUG: stats: Inconsistency in the multivariate_normal docstring... - - `#6290 <https://github.com/scipy/scipy/pull/6290>`__: Add author list, gh-lists to 0.18.0 release notes - - `#6293 <https://github.com/scipy/scipy/pull/6293>`__: TST: special: relax a test's precision - - `#6295 <https://github.com/scipy/scipy/pull/6295>`__: BUG: sparse: stop comparing None and int in bsr_matrix constructor - - `#6313 <https://github.com/scipy/scipy/pull/6313>`__: MAINT: Fix for python 3.5 travis-ci build problem. - - `#6327 <https://github.com/scipy/scipy/pull/6327>`__: TST: signal: use assert_allclose for testing near-equality in... - - `#6330 <https://github.com/scipy/scipy/pull/6330>`__: BUG: spatial/qhull: allocate qhT via malloc to ensure CRT likes... - - `#6332 <https://github.com/scipy/scipy/pull/6332>`__: TST: fix stats.iqr test to not emit warnings, and fix line lengths. - - `#6334 <https://github.com/scipy/scipy/pull/6334>`__: MAINT: special: fix a test for hyp0f1 - 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URL: From mail at lukas-drude.de Wed Jul 27 07:09:26 2016 From: mail at lukas-drude.de (Lukas Drude) Date: Wed, 27 Jul 2016 13:09:26 +0200 Subject: [SciPy-Dev] Rationale behind *_gen and *_frozen in _multivariate.py Message-ID: <802A59A9-BF5F-4C8C-8CBF-CD15DB38F3FA@lukas-drude.de> Hello Scipy, I would like to implement additional distributions (at least locally for now). To do so, I looked at scipy/stats/_multivariate.py and would like to understand the rationale behind the *_gen and *_frozen classes. - Are the frozen-classes used to avoid parameter checks during run time? - Why is i.e. in line 1360 dirichlet = dirichlet_gen() [1]? It seems like an object is created during the import although it appears to the user as if scipy.stats.dirichlet was a module and scipy.stats.dirichlet.pdf() was a function of just that module? I do not want to change the scipy code. I would just like to know, what the benefits are. With best regards Lukas [1] https://github.com/scipy/scipy/blob/ffaebc9e684e5bd23bbd3d5234c27a71369990b7/scipy/stats/_multivariate.py#L1360 -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: signature.asc Type: application/pgp-signature Size: 842 bytes Desc: Message signed with OpenPGP using GPGMail URL: From josef.pktd at gmail.com Wed Jul 27 07:36:46 2016 From: josef.pktd at gmail.com (josef.pktd at gmail.com) Date: Wed, 27 Jul 2016 07:36:46 -0400 Subject: [SciPy-Dev] Rationale behind *_gen and *_frozen in _multivariate.py In-Reply-To: <802A59A9-BF5F-4C8C-8CBF-CD15DB38F3FA@lukas-drude.de> References: <802A59A9-BF5F-4C8C-8CBF-CD15DB38F3FA@lukas-drude.de> Message-ID: On Wed, Jul 27, 2016 at 7:09 AM, Lukas Drude wrote: > Hello Scipy, > > I would like to implement additional distributions (at least locally for > now). > > To do so, I looked at scipy/stats/_multivariate.py and would like to > understand the rationale behind the *_gen and *_frozen classes. > > - Are the frozen-classes used to avoid parameter checks during run time? > - Why is i.e. in line 1360 dirichlet = dirichlet_gen() [1]? It seems like an > object is created during the import although it appears to the user as if > scipy.stats.dirichlet was a module and scipy.stats.dirichlet.pdf() was a > function of just that module? > > I do not want to change the scipy code. I would just like to know, what the > benefits are. > > With best regards > Lukas > > [1] > https://github.com/scipy/scipy/blob/ffaebc9e684e5bd23bbd3d5234c27a71369990b7/scipy/stats/_multivariate.py#L1360 some history in the following, Evgeni knows better the recent changes The original implementation of the distributions was mostly "functional". Classes are used as namespace and to make implementation easier, but users only used a single global instance of the distribution classes. Because it is only a single global instance it cannot keep state, i.e. store intermediate results and parameters as attributes. This was a headache and source of bugs when state spilled over in the global instance from one use to the next. The better design would have been to have users use the classes to create new instances for each use. `frozen` distributions was the way to create a new instance that stores the parameters of the distribution. The use of `frozen` distributions has been improved and expanded mostly by Evgeni. It is also used more extensively in the multivariate distributions which have only been added in the last few years. The main advantage of frozen distributions is that by having a new instance each time, it is possible to store intermediate results to improve performance. Josef > > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > From m.schultz at fz-juelich.de Wed Jul 27 08:23:21 2016 From: m.schultz at fz-juelich.de (Schultz, Martin) Date: Wed, 27 Jul 2016 12:23:21 +0000 Subject: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test In-Reply-To: References: <4A980E5ADF859E4E9007AA5514CF9C8E6262BFAF@MBX2010-E01.ad.fz-juelich.de> <4A980E5ADF859E4E9007AA5514CF9C8E6262D119@MBX2010-E01.ad.fz-juelich.de> Message-ID: <4A980E5ADF859E4E9007AA5514CF9C8E6262F1BE@MBX2010-E01.ad.fz-juelich.de> Dear all, discussion has apparently ceased on this topic. As I don?t see myself in a position to open the pull request for this and lead the implementation (I am too unexperienced with GitHub and don?t have the time to learn all the rules necessary to create a good scipy code), I am asking if someone would volunteer to take the lead. I will help where I can, but you don?t want me to be responsible for this. Thanks, Martin From: SciPy-Dev [mailto:scipy-dev-bounces at scipy.org] On Behalf Of Paul Hobson Sent: Wednesday, July 20, 2016 3:24 AM To: SciPy Developers List Subject: Re: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test On Tue, Jul 19, 2016 at 1:04 AM, > wrote: The function looks interesting, I've never heard of this hypothesis test. EPA guidance specifies that the Mann-Kendall test be used to determine if there's a trend (seasonal, or long-term) in pollutant concentrations. Even though regulators sometimes *require* that you use their ProUCL[0] software, this would be still be a very welcome addition to the stats module for environmental engineers like me. Martin: if you get the pull request started, I'll help your write the tests. Just @-mention me in the PR's discussion (@phobson). -Paul [0] https://www.epa.gov/land-research/proucl-software ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, Prof. Dr. Sebastian M. Schmidt ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ -------------- next part -------------- An HTML attachment was scrubbed... URL: From josef.pktd at gmail.com Wed Jul 27 08:37:09 2016 From: josef.pktd at gmail.com (josef.pktd at gmail.com) Date: Wed, 27 Jul 2016 08:37:09 -0400 Subject: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test In-Reply-To: <4A980E5ADF859E4E9007AA5514CF9C8E6262F1BE@MBX2010-E01.ad.fz-juelich.de> References: <4A980E5ADF859E4E9007AA5514CF9C8E6262BFAF@MBX2010-E01.ad.fz-juelich.de> <4A980E5ADF859E4E9007AA5514CF9C8E6262D119@MBX2010-E01.ad.fz-juelich.de> <4A980E5ADF859E4E9007AA5514CF9C8E6262F1BE@MBX2010-E01.ad.fz-juelich.de> Message-ID: On Wed, Jul 27, 2016 at 8:23 AM, Schultz, Martin wrote: > Dear all, > > > > discussion has apparently ceased on this topic. As I don?t see myself in > a position to open the pull request for this and lead the implementation (I > am too unexperienced with GitHub and don?t have the time to learn all the > rules necessary to create a good scipy code), I am asking if someone would > volunteer to take the lead. I will help where I can, but you don?t want me > to be responsible for this. You could also write test cases with and without ties and whatever options are used for this function. This would simplify the work for whoever is implementing this. Those could just be added to a scipy issue. I was thinking whether it's possible to just use the ranks `rankdata` to simplify the calculations and remove some of the loops, but didn't figure out whether that works. (I'm busy with "models" and didn't plan to go back to "stats" until fall.) Josef > > > > Thanks, > > Martin > > > > > > From: SciPy-Dev [mailto:scipy-dev-bounces at scipy.org] On Behalf Of Paul > Hobson > Sent: Wednesday, July 20, 2016 3:24 AM > To: SciPy Developers List > Subject: Re: [SciPy-Dev] Suggest addition to scipy.stats : Mann-Kendall test > > > > > > > > On Tue, Jul 19, 2016 at 1:04 AM, wrote: > > The function looks interesting, I've never heard of this hypothesis test. > > > > > > EPA guidance specifies that the Mann-Kendall test be used to determine if > there's a trend (seasonal, or long-term) in pollutant concentrations. > > > > Even though regulators sometimes *require* that you use their ProUCL[0] > software, this would be still be a very welcome addition to the stats module > for environmental engineers like me. > > > > Martin: if you get the pull request started, I'll help your write the tests. > Just @-mention me in the PR's discussion (@phobson). > > > > -Paul > > [0] https://www.epa.gov/land-research/proucl-software > > > > ------------------------------------------------------------------------------------------------ > ------------------------------------------------------------------------------------------------ > Forschungszentrum Juelich GmbH > 52425 Juelich > Sitz der Gesellschaft: Juelich > Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 > Vorsitzender des Aufsichtsrats: MinDir Dr. Karl Eugen Huthmacher > Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), > Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, > Prof. Dr. Sebastian M. Schmidt > ------------------------------------------------------------------------------------------------ > ------------------------------------------------------------------------------------------------ > > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > From robert.kern at gmail.com Wed Jul 27 08:44:10 2016 From: robert.kern at gmail.com (Robert Kern) Date: Wed, 27 Jul 2016 13:44:10 +0100 Subject: [SciPy-Dev] Rationale behind *_gen and *_frozen in _multivariate.py In-Reply-To: References: <802A59A9-BF5F-4C8C-8CBF-CD15DB38F3FA@lukas-drude.de> Message-ID: On Wed, Jul 27, 2016 at 12:36 PM, wrote: > > On Wed, Jul 27, 2016 at 7:09 AM, Lukas Drude wrote: > > Hello Scipy, > > > > I would like to implement additional distributions (at least locally for > > now). > > > > To do so, I looked at scipy/stats/_multivariate.py and would like to > > understand the rationale behind the *_gen and *_frozen classes. > > > > - Are the frozen-classes used to avoid parameter checks during run time? > > - Why is i.e. in line 1360 dirichlet = dirichlet_gen() [1]? It seems like an > > object is created during the import although it appears to the user as if > > scipy.stats.dirichlet was a module and scipy.stats.dirichlet.pdf() was a > > function of just that module? > > > I do not want to change the scipy code. I would just like to know, what the > > benefits are. > > > > With best regards > > Lukas > > > > [1] > > https://github.com/scipy/scipy/blob/ffaebc9e684e5bd23bbd3d5234c27a71369990b7/scipy/stats/_multivariate.py#L1360 > > some history in the following, Evgeni knows better the recent changes > > The original implementation of the distributions was mostly > "functional". Classes are used as namespace and to make implementation > easier, but users only used a single global instance of the > distribution classes. > > Because it is only a single global instance it cannot keep state, i.e. > store intermediate results and parameters as attributes. This was a > headache and source of bugs when state spilled over in the global > instance from one use to the next. That's not quite right, I don't think. Only the multivariate distributions, which are quite new, store intermediate results. No global state was ever stored in the "unfrozen" distribution instances. Storing intermediate results were not a consideration in adding frozen distributions. When the distributions were first designed, Python did not have classmethods. So the API `norm.pdf(x, loc, scale)` would not have been possible if `norm` were a class. You had to make an instance of a class to get callable methods. At first, this was the only API provided. At the time, scipy definitely had a bias against using objects in its API (i.e. forcing users to construct objects, not just using pre-existing instances). Object-orientation was seen as an unnecessary complication for scientific programmers. Things are different now. However, this API was sometimes inconvenient because one would always have to pass around the distribution and the arguments separately, making it hard to write generic code. Frozen distributions were added to bind the parameters to the distribution so that one could just pass around a single object. Now, you can write generic code that just accepts a single frozen distribution object and call `dist.pdf(x)` without the code needing to know anything about which distribution is being used or its parameters. -- Robert Kern -------------- next part -------------- An HTML attachment was scrubbed... URL: From josef.pktd at gmail.com Wed Jul 27 09:04:30 2016 From: josef.pktd at gmail.com (josef.pktd at gmail.com) Date: Wed, 27 Jul 2016 09:04:30 -0400 Subject: [SciPy-Dev] Rationale behind *_gen and *_frozen in _multivariate.py In-Reply-To: References: <802A59A9-BF5F-4C8C-8CBF-CD15DB38F3FA@lukas-drude.de> Message-ID: On Wed, Jul 27, 2016 at 8:44 AM, Robert Kern wrote: > On Wed, Jul 27, 2016 at 12:36 PM, wrote: >> >> On Wed, Jul 27, 2016 at 7:09 AM, Lukas Drude wrote: >> > Hello Scipy, >> > >> > I would like to implement additional distributions (at least locally for >> > now). >> > >> > To do so, I looked at scipy/stats/_multivariate.py and would like to >> > understand the rationale behind the *_gen and *_frozen classes. >> > >> > - Are the frozen-classes used to avoid parameter checks during run time? >> > - Why is i.e. in line 1360 dirichlet = dirichlet_gen() [1]? It seems >> > like an >> > object is created during the import although it appears to the user as >> > if >> > scipy.stats.dirichlet was a module and scipy.stats.dirichlet.pdf() was a >> > function of just that module? >> >> > I do not want to change the scipy code. I would just like to know, what >> > the >> > benefits are. >> > >> > With best regards >> > Lukas >> > >> > [1] >> > >> > https://github.com/scipy/scipy/blob/ffaebc9e684e5bd23bbd3d5234c27a71369990b7/scipy/stats/_multivariate.py#L1360 >> >> some history in the following, Evgeni knows better the recent changes >> >> The original implementation of the distributions was mostly >> "functional". Classes are used as namespace and to make implementation >> easier, but users only used a single global instance of the >> distribution classes. >> >> Because it is only a single global instance it cannot keep state, i.e. >> store intermediate results and parameters as attributes. This was a >> headache and source of bugs when state spilled over in the global >> instance from one use to the next. > > That's not quite right, I don't think. Only the multivariate distributions, > which are quite new, store intermediate results. No global state was ever > stored in the "unfrozen" distribution instances. Storing intermediate > results were not a consideration in adding frozen distributions. It took me a few months to figure out why the distributions sometimes produces different, i.e. wrong, results, and to fix those bugs. Using attributes and state might not have been the plan, but it was and is in the actual implementation. (And it's the source of my allergy to the possibility of stale state in statsmodels.) > > When the distributions were first designed, Python did not have > classmethods. So the API `norm.pdf(x, loc, scale)` would not have been > possible if `norm` were a class. You had to make an instance of a class to > get callable methods. At first, this was the only API provided. At the time, > scipy definitely had a bias against using objects in its API (i.e. forcing > users to construct objects, not just using pre-existing instances). > Object-orientation was seen as an unnecessary complication for scientific > programmers. Things are different now. > > However, this API was sometimes inconvenient because one would always have > to pass around the distribution and the arguments separately, making it hard > to write generic code. Frozen distributions were added to bind the > parameters to the distribution so that one could just pass around a single > object. Now, you can write generic code that just accepts a single frozen > distribution object and call `dist.pdf(x)` without the code needing to know > anything about which distribution is being used or its parameters. This clarifies but doesn't contradict my comments. "frozen" was then the object-oriented backdoor for users that didn't want to know about objects. Times have fortunately changed away from the matlab/fortran tradition, except for Julia where developers and users still prefer greek and one letter names and no classes. :) Norm(loc, scale).pdf(x) is much more work than norm.pdf(x, loc, scale) Josef > > -- > Robert Kern > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > From evgeny.burovskiy at gmail.com Wed Jul 27 09:15:10 2016 From: evgeny.burovskiy at gmail.com (Evgeni Burovski) Date: Wed, 27 Jul 2016 14:15:10 +0100 Subject: [SciPy-Dev] Rationale behind *_gen and *_frozen in _multivariate.py In-Reply-To: References: <802A59A9-BF5F-4C8C-8CBF-CD15DB38F3FA@lukas-drude.de> Message-ID: On Wed, Jul 27, 2016 at 1:44 PM, Robert Kern wrote: > On Wed, Jul 27, 2016 at 12:36 PM, wrote: >> >> On Wed, Jul 27, 2016 at 7:09 AM, Lukas Drude wrote: >> > Hello Scipy, >> > >> > I would like to implement additional distributions (at least locally for >> > now). >> > >> > To do so, I looked at scipy/stats/_multivariate.py and would like to >> > understand the rationale behind the *_gen and *_frozen classes. >> > >> > - Are the frozen-classes used to avoid parameter checks during run time? >> > - Why is i.e. in line 1360 dirichlet = dirichlet_gen() [1]? It seems >> > like an >> > object is created during the import although it appears to the user as >> > if >> > scipy.stats.dirichlet was a module and scipy.stats.dirichlet.pdf() was a >> > function of just that module? >> >> > I do not want to change the scipy code. I would just like to know, what >> > the >> > benefits are. >> > >> > With best regards >> > Lukas >> > >> > [1] >> > >> > https://github.com/scipy/scipy/blob/ffaebc9e684e5bd23bbd3d5234c27a71369990b7/scipy/stats/_multivariate.py#L1360 >> >> some history in the following, Evgeni knows better the recent changes >> >> The original implementation of the distributions was mostly >> "functional". Classes are used as namespace and to make implementation >> easier, but users only used a single global instance of the >> distribution classes. >> >> Because it is only a single global instance it cannot keep state, i.e. >> store intermediate results and parameters as attributes. This was a >> headache and source of bugs when state spilled over in the global >> instance from one use to the next. > > That's not quite right, I don't think. Only the multivariate distributions, > which are quite new, store intermediate results. No global state was ever > stored in the "unfrozen" distribution instances. Storing intermediate > results were not a consideration in adding frozen distributions. > > When the distributions were first designed, Python did not have > classmethods. So the API `norm.pdf(x, loc, scale)` would not have been > possible if `norm` were a class. You had to make an instance of a class to > get callable methods. At first, this was the only API provided. At the time, > scipy definitely had a bias against using objects in its API (i.e. forcing > users to construct objects, not just using pre-existing instances). > Object-orientation was seen as an unnecessary complication for scientific > programmers. Things are different now. > > However, this API was sometimes inconvenient because one would always have > to pass around the distribution and the arguments separately, making it hard > to write generic code. Frozen distributions were added to bind the > parameters to the distribution so that one could just pass around a single > object. Now, you can write generic code that just accepts a single frozen > distribution object and call `dist.pdf(x)` without the code needing to know > anything about which distribution is being used or its parameters. > > -- > Robert Kern > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > Re: storing intermediate results. There was some discussion a while ago, https://github.com/scipy/scipy/issues/2823 At least my conclusion was that it's not worth it, not generically at least: https://github.com/scipy/scipy/issues/2823#issuecomment-23806104 It's perfectly possible one can do better and there's a way :-). At the moment, a frozen distribution holds an instance separate from the global one. (https://github.com/scipy/scipy/blob/master/scipy/stats/_distn_infrastructure.py#L429) E.g. In [32]: from scipy.stats import gamma In [33]: gamma.shapes Out[33]: 'a' In [34]: rv = gamma(a=1) In [35]: rv.dist Out[35]: In [36]: gamma Out[36]: In [37]: rv.dist is gamma Out[37]: False So that one can * use a separate random_state for drawing variates: n [38]: gamma.random_state Out[38]: In [39]: rv.random_state Out[39]: # same! In [40]: rv.random_state = 123 In [41]: rv.random_state Out[41]: # different In [42]: gamma.random_state Out[42]: # intact * monkey-patch the instance methods to store intermediates if desired. From robert.kern at gmail.com Wed Jul 27 09:26:23 2016 From: robert.kern at gmail.com (Robert Kern) Date: Wed, 27 Jul 2016 14:26:23 +0100 Subject: [SciPy-Dev] Rationale behind *_gen and *_frozen in _multivariate.py In-Reply-To: References: <802A59A9-BF5F-4C8C-8CBF-CD15DB38F3FA@lukas-drude.de> Message-ID: On Wed, Jul 27, 2016 at 2:04 PM, wrote: > > On Wed, Jul 27, 2016 at 8:44 AM, Robert Kern wrote: > > On Wed, Jul 27, 2016 at 12:36 PM, wrote: > >> some history in the following, Evgeni knows better the recent changes > >> > >> The original implementation of the distributions was mostly > >> "functional". Classes are used as namespace and to make implementation > >> easier, but users only used a single global instance of the > >> distribution classes. > >> > >> Because it is only a single global instance it cannot keep state, i.e. > >> store intermediate results and parameters as attributes. This was a > >> headache and source of bugs when state spilled over in the global > >> instance from one use to the next. > > > > That's not quite right, I don't think. Only the multivariate distributions, > > which are quite new, store intermediate results. No global state was ever > > stored in the "unfrozen" distribution instances. Storing intermediate > > results were not a consideration in adding frozen distributions. > > It took me a few months to figure out why the distributions sometimes > produces different, i.e. wrong, results, and to fix those bugs. Using > attributes and state might not have been the plan, but it was and is > in the actual implementation. > > (And it's the source of my allergy to the possibility of stale state > in statsmodels.) I'm pretty sure that we had frozen distributions before you encountered those bugs. We didn't add frozen distributions to get rid of those bugs. We added them for the API reasons I described. https://mail.scipy.org/pipermail/scipy-user/2003-October/002278.html -- Robert Kern -------------- next part -------------- An HTML attachment was scrubbed... URL: From josef.pktd at gmail.com Wed Jul 27 09:43:52 2016 From: josef.pktd at gmail.com (josef.pktd at gmail.com) Date: Wed, 27 Jul 2016 09:43:52 -0400 Subject: [SciPy-Dev] Rationale behind *_gen and *_frozen in _multivariate.py In-Reply-To: References: <802A59A9-BF5F-4C8C-8CBF-CD15DB38F3FA@lukas-drude.de> Message-ID: On Wed, Jul 27, 2016 at 9:26 AM, Robert Kern wrote: > On Wed, Jul 27, 2016 at 2:04 PM, wrote: >> >> On Wed, Jul 27, 2016 at 8:44 AM, Robert Kern >> wrote: >> > On Wed, Jul 27, 2016 at 12:36 PM, wrote: > >> >> some history in the following, Evgeni knows better the recent changes >> >> >> >> The original implementation of the distributions was mostly >> >> "functional". Classes are used as namespace and to make implementation >> >> easier, but users only used a single global instance of the >> >> distribution classes. >> >> >> >> Because it is only a single global instance it cannot keep state, i.e. >> >> store intermediate results and parameters as attributes. This was a >> >> headache and source of bugs when state spilled over in the global >> >> instance from one use to the next. >> > >> > That's not quite right, I don't think. Only the multivariate >> > distributions, >> > which are quite new, store intermediate results. No global state was >> > ever >> > stored in the "unfrozen" distribution instances. Storing intermediate >> > results were not a consideration in adding frozen distributions. >> >> It took me a few months to figure out why the distributions sometimes >> produces different, i.e. wrong, results, and to fix those bugs. Using >> attributes and state might not have been the plan, but it was and is >> in the actual implementation. >> >> (And it's the source of my allergy to the possibility of stale state >> in statsmodels.) > > I'm pretty sure that we had frozen distributions before you encountered > those bugs. We didn't add frozen distributions to get rid of those bugs. We > added them for the API reasons I described. Frozen distributions were there when I started and there were no specific problems with them, AFAIR. The API reasons that you described required that a distribution stores the parameters as attributes. So, it required to have new instances/objects for each new set of parameters. That sounds like adding object orientation as API convenience, while I would have preferred to additionally drop the global instances to make the implementation simpler and less error-prone. Josef > > https://mail.scipy.org/pipermail/scipy-user/2003-October/002278.html > > -- > Robert Kern > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > From josef.pktd at gmail.com Wed Jul 27 09:56:09 2016 From: josef.pktd at gmail.com (josef.pktd at gmail.com) Date: Wed, 27 Jul 2016 09:56:09 -0400 Subject: [SciPy-Dev] Rationale behind *_gen and *_frozen in _multivariate.py In-Reply-To: References: <802A59A9-BF5F-4C8C-8CBF-CD15DB38F3FA@lukas-drude.de> Message-ID: On Wed, Jul 27, 2016 at 9:43 AM, wrote: > On Wed, Jul 27, 2016 at 9:26 AM, Robert Kern wrote: >> On Wed, Jul 27, 2016 at 2:04 PM, wrote: >>> >>> On Wed, Jul 27, 2016 at 8:44 AM, Robert Kern >>> wrote: >>> > On Wed, Jul 27, 2016 at 12:36 PM, wrote: >> >>> >> some history in the following, Evgeni knows better the recent changes >>> >> >>> >> The original implementation of the distributions was mostly >>> >> "functional". Classes are used as namespace and to make implementation >>> >> easier, but users only used a single global instance of the >>> >> distribution classes. >>> >> >>> >> Because it is only a single global instance it cannot keep state, i.e. >>> >> store intermediate results and parameters as attributes. This was a >>> >> headache and source of bugs when state spilled over in the global >>> >> instance from one use to the next. >>> > >>> > That's not quite right, I don't think. Only the multivariate >>> > distributions, >>> > which are quite new, store intermediate results. No global state was >>> > ever >>> > stored in the "unfrozen" distribution instances. Storing intermediate >>> > results were not a consideration in adding frozen distributions. >>> >>> It took me a few months to figure out why the distributions sometimes >>> produces different, i.e. wrong, results, and to fix those bugs. Using >>> attributes and state might not have been the plan, but it was and is >>> in the actual implementation. >>> >>> (And it's the source of my allergy to the possibility of stale state >>> in statsmodels.) >> >> I'm pretty sure that we had frozen distributions before you encountered >> those bugs. We didn't add frozen distributions to get rid of those bugs. We >> added them for the API reasons I described. > > Frozen distributions were there when I started and there were no > specific problems with them, AFAIR. > > The API reasons that you described required that a distribution stores > the parameters as attributes. So, it required to have new > instances/objects for each new set of parameters. partial correction as part of my memory comes back IIRC, frozen distributions still used the global instance like standalone functions. That wasn't fully object oriented, frozen was just a wrapper class. Evgeni changed it a while ago that freeze creates new instances each time, which allows more flexibility in holding state besides the parameters. Josef > > That sounds like adding object orientation as API convenience, while I > would have preferred to additionally drop the global instances to make > the implementation simpler and less error-prone. > > Josef > >> >> https://mail.scipy.org/pipermail/scipy-user/2003-October/002278.html >> >> -- >> Robert Kern >> >> _______________________________________________ >> SciPy-Dev mailing list >> SciPy-Dev at scipy.org >> https://mail.scipy.org/mailman/listinfo/scipy-dev >> From ericq at caltech.edu Wed Jul 27 13:52:18 2016 From: ericq at caltech.edu (Eric Quintero) Date: Wed, 27 Jul 2016 10:52:18 -0700 Subject: [SciPy-Dev] Policy for minimum numpy version Message-ID: Hi All, In gh-6058 , I?ve run into a test failure because a numpy function was not available in version 1.7 (the function being np.fft.rfftfreq). This is easy enough to work around, but I wonder when we should decide to move up the minimum required numpy version. Debian stable currently offers numpy 1.8.2. Ubuntu 12.04 LTS (which is EOL next April) is stuck on 1.6.1, so they?re already left behind. Ubuntu 14.04 LTS has 1.8.2. Has anyone ran into compatibility issues with 1.7 before? In _lib._numpy_compat, there is another 1.7 workaround in place for np.testing.assert_warns. -Eric Q. -------------- next part -------------- An HTML attachment was scrubbed... URL: From perimosocordiae at gmail.com Wed Jul 27 14:18:43 2016 From: perimosocordiae at gmail.com (CJ Carey) Date: Wed, 27 Jul 2016 13:18:43 -0500 Subject: [SciPy-Dev] Policy for minimum numpy version In-Reply-To: References: Message-ID: Most recently I've wished for np.full and np.full_like, though I admit that they're very easy to work around. More difficult to replace is np.partition and np.argpartition, which are extremely useful in the few cases that they're needed but also non-trivial to implement efficiently. Typically the work-arounds look like this (not my code): https://github.com/lemaoliu/Agtarbidir/blob/8762efcb29b37724c2af13f9393d1eaa5ec6faf8/numpy_compat.py On Wed, Jul 27, 2016 at 12:52 PM, Eric Quintero wrote: > Hi All, > > In gh-6058 , I?ve run into a > test failure because a numpy function was not available in version 1.7 (the > function being np.fft.rfftfreq). This is easy enough to work around, but I > wonder when we should decide to move up the minimum required numpy version. > > Debian stable currently offers numpy 1.8.2. Ubuntu 12.04 LTS (which is EOL > next April) is stuck on 1.6.1, so they?re already left behind. Ubuntu 14.04 > LTS has 1.8.2. > > Has anyone ran into compatibility issues with 1.7 before? In > _lib._numpy_compat, there is another 1.7 workaround in place for > np.testing.assert_warns. > > -Eric Q. > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jfoxrabinovitz at gmail.com Wed Jul 27 14:23:24 2016 From: jfoxrabinovitz at gmail.com (Joseph Fox-Rabinovitz) Date: Wed, 27 Jul 2016 14:23:24 -0400 Subject: [SciPy-Dev] Policy for minimum numpy version In-Reply-To: References: Message-ID: Here is an example of `np.percentile` workarounds for `scipy.stats.iqr`: https://github.com/scipy/scipy/pull/5808 -Joe On Wed, Jul 27, 2016 at 2:18 PM, CJ Carey wrote: > Most recently I've wished for np.full and np.full_like, though I admit that > they're very easy to work around. > > More difficult to replace is np.partition and np.argpartition, which are > extremely useful in the few cases that they're needed but also non-trivial > to implement efficiently. Typically the work-arounds look like this (not my > code): > https://github.com/lemaoliu/Agtarbidir/blob/8762efcb29b37724c2af13f9393d1eaa5ec6faf8/numpy_compat.py > > On Wed, Jul 27, 2016 at 12:52 PM, Eric Quintero wrote: >> >> Hi All, >> >> In gh-6058, I?ve run into a test failure because a numpy function was not >> available in version 1.7 (the function being np.fft.rfftfreq). This is easy >> enough to work around, but I wonder when we should decide to move up the >> minimum required numpy version. >> >> Debian stable currently offers numpy 1.8.2. Ubuntu 12.04 LTS (which is EOL >> next April) is stuck on 1.6.1, so they?re already left behind. Ubuntu 14.04 >> LTS has 1.8.2. >> >> Has anyone ran into compatibility issues with 1.7 before? In >> _lib._numpy_compat, there is another 1.7 workaround in place for >> np.testing.assert_warns. >> >> -Eric Q. >> >> _______________________________________________ >> SciPy-Dev mailing list >> SciPy-Dev at scipy.org >> https://mail.scipy.org/mailman/listinfo/scipy-dev >> > > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > From njs at pobox.com Wed Jul 27 15:16:53 2016 From: njs at pobox.com (Nathaniel Smith) Date: Wed, 27 Jul 2016 12:16:53 -0700 Subject: [SciPy-Dev] Policy for minimum numpy version In-Reply-To: References: Message-ID: Note also that numpy now has wheels available for all the common platforms, so the cost of installing a new numpy is much, much lower than it used to be. (And installing a new numpy is easier than installing a new scipy anyway :-).) On Jul 27, 2016 10:52 AM, "Eric Quintero" wrote: > Hi All, > > In gh-6058 , I?ve run into a > test failure because a numpy function was not available in version 1.7 (the > function being np.fft.rfftfreq). This is easy enough to work around, but I > wonder when we should decide to move up the minimum required numpy version. > > Debian stable currently offers numpy 1.8.2. Ubuntu 12.04 LTS (which is EOL > next April) is stuck on 1.6.1, so they?re already left behind. Ubuntu 14.04 > LTS has 1.8.2. > > Has anyone ran into compatibility issues with 1.7 before? In > _lib._numpy_compat, there is another 1.7 workaround in place for > np.testing.assert_warns. > > -Eric Q. > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at scipy.org > https://mail.scipy.org/mailman/listinfo/scipy-dev > > -------------- next part -------------- An HTML attachment was scrubbed... URL: