Mailman 3 - SciPy-User

np.array_equal for sparse arrays
by Michael Lamparski 13 Sep '18

13 Sep '18

Hello everyone, I feel like I am missing something. It seems that sparse matrices have no 'any' method or anything useful regarding equality. Is there not a better way to write this? def sparse_equal(a, b): """ Equality test for sparse matrices which performs standard floating point equality, allows mismatched types, considers missing elements equal to zero, and requires identical shape (no broadcasting) """ return a.shape == b.shape and not (a != b).data.any() (I suppose I could also write 'not (a != b).nnz', since the implementation of != appears to eliminate zeros, but I'm not sure if I should be relying on that) --- Michael

1 0

Multiply dense numpy matrix with sparse scipy vector
by list＠emeel.net 11 Sep '18

11 Sep '18

Upon profiling, we found that multiplying a dense numpy matrix, e.g. A = np.eye(5) with a sparse scipy vector, e.g. x = scipy.sparse.coo_matrix(np.eye(5)[4]).T did in fact not make use of the sparsity of x when using @. That is: A @ x is not a sparse computation. How can this be done?

1 0

ANN: PyWavelets 1.0.0
by Gregory Lee 31 Aug '18

31 Aug '18

Hello all, We are pleased to announce the release of PyWavelets 1.0. We view this version number as a milestone in the project's now more than a decade long history. It reflects that PyWavelets has stabilized over the past few years, and is now a mature package which a lot of other important packages depend on. A listing of those package won't be complete, but some we are aware of are: - `scikit-image <https://scikit-image.org>`_ - image processing in Python - `imagehash <https://github.com/JohannesBuchner/imagehash>`_ - perceptual image hashing - `pyradiomics <https://github.com/Radiomics/pyradiomics>`_ - extraction of Radiomics features from 2D and 3D images and binary masks - `tomopy <https://github.com/tomopy/tomopy>`_ - Tomographic Reconstruction in Python - `SpikeSort <https://github.com/btel/SpikeSort>`_ - Spike sorting library implemented in Python/NumPy/PyTables - `ODL <https://github.com/odlgroup/odl>`_ - operator discretization library This release requires Python 2.7 or >=3.5 and NumPy 1.9.1 or greater. The 1.0 release will be the last release supporting Python 2.7. It will be a Long Term Support (LTS) release, meaning that we will backport critical bug fixes to 1.0.x for as long as Python itself does so (i.e. until 1 Jan 2020). A summary of changes and contributors is given below. A more comprehensive list of specific issues and pull requests closed is available in the release notes at: https://github.com/PyWavelets/pywt/blob/master/doc/release/1.0.0-notes.rst New features ============ New 1D test signals ------------------- Many common synthetic 1D test signals have been implemented in the new function ``pywt.data.demo_signals`` to encourage reproducible research. To get a list of the available signals, call ``pywt.data.demo_signals('list')``. These signals have been validated to match the test signals of the same name from the `Wavelab <https://statweb.stanford.edu/~wavelab>`_ toolbox (with the kind permission of Dr. David Donoho). C99 complex support ------------------- The Cython modules and underlying C library can now be built with C99 complex support when supported by the compiler. Doing so improves performance when running wavelet transforms on complex-valued data. On POSIX systems (Linux, Mac OS X), C99 complex support is enabled by default at build time. The user can set the environment variable ``USE_C99_COMPLEX`` to 0 or 1 to manually disable or enable C99 support at compile time. complex-valued CWT ------------------ The continuous wavelet transform, ``cwt``, now also accepts complex-valued data. More flexible specification of some continuous wavelets ------------------------------------------------------- The continous wavelets ``"cmor"``, ``"shan"`` and ``"fbsp"`` now let the user specify attributes such as their center frequency and bandwidth that were previously fixed. See more on this in the section on deprecated features. Fully Separable Discrete Wavelet Transfrom ------------------------------------------ A new variant of the multilevel n-dimensional DWT has been implemented. It is known as the fully separable wavelet transform (FSWT). The functions ``fswavedecn`` ``fswaverecn`` correspond to the forward and inverse transforms, respectively. This differs from the existing ``wavedecn`` and ``waverecn`` in dimensions >= 2 in that all levels of decomposition are performed along a single axis prior to moving on to the next. New thresholding methods ------------------------ ``pywt.threshold`` now supports non-negative Garotte thresholding (``mode='garotte'``). There is also a new function ``pywt.threshold_firm`` that implements firm (semi-soft) thresholding. Both of the these new thresholding methods are intermediate between soft and hard thresholding. New anti-symmetric boundary modes --------------------------------- Two new boundary handling modes for the discrete wavelet transforms have been implemented. These correspond to whole-sample and half-sample anti-symmetric boundary conditions (``antisymmetric`` and ``antireflect``). New functions to ravel and unravel wavedecn coefficients -------------------------------------------------------- The function ``ravel_coeffs`` can be used to ravel all coefficients from ``wavedec``, ``wavedec2`` or ``wavedecn`` into a single 1D array. Unraveling back into a list of individual n-dimensional coefficients can be performed by ``unravel_coeffs``. New functions to determine multilevel DWT coefficient shapes and sizes ---------------------------------------------------------------------- The new function ``wavedecn_size`` outputs the total number of coefficients that will be produced by a ``wavedecn`` decomposition. The function ``wavedecn_shapes`` returns full shape information for all coefficient arrays produced by ``wavedecn``. These functions provide the size/shape information without having to explicitly compute a transform. Deprecated features =================== The continous wavelets with names ``"cmor"``, ``"shan"`` and ``"fbsp"`` should now be modified to include formerly hard-coded attributes such as their center frequency and bandwidth. Use of the bare names "cmor". "shan" and "fbsp" is now deprecated. For "cmor" (and "shan"), the form of the wavelet name is now "cmorB-C" ("shanB-C") where B and C are floats representing the bandwidth frequency and center frequency. For "fbsp" the form should now incorporate three floats as in "fbspM-B-C" where M is the spline order and B and C are the bandwidth and center frequencies. Backwards incompatible changes ============================== Python 2.6, 3.3 and 3.4 are no longer supported. The order of coefficients returned by ``swt2`` and input to ``iswt2`` have been reversed so that the decomposition levels are now returned in descending rather than ascending order. This makes these 2D stationary wavelet functions consistent with all of the other multilevel discrete transforms in PyWavelets. For ``wavedec``, ``wavedec2`` and ``wavedecn``, the ability for the user to specify a ``level`` that is greater than the value returned by ``dwt_max_level`` has been restored. A ``UserWarning`` is raised instead of a ``ValueError`` in this case. Bugs Fixed ========== Assigning new data to the ``Node`` or ``Node2D`` no longer forces a cast to ``float64`` when the data is one of the other dtypes supported by the dwt (``float32``, ``complex64``, ``complex128``). Calling ``pywt.threshold`` with ``mode='soft'`` now works properly for complex-valued inputs. A segfault when running multiple `swt2` or `swtn` transforms concurrently has been fixed. Several instances of deprecated numpy multi-indexing that caused warnings in numpy >=1.15 have been resolved. The 2d inverse stationary wavelet transform, `iswt2`, now supports non-square inputs (an unnecessary check for square inputs was removed). Wavelet packets no longer convert float32 to float64 upon assignment to nodes. Doctests have been updated to also work with NumPy >= 1.14, Indexing conventions have been updated to avoid FutureWarnings in NumPy 1.15. Other changes ============= Python 3.7 is now officially supported. Authors ======= * 0-tree + * Jacopo Antonello + * Matthew Brett + * Saket Choudhary + * Michael V. DePalatis + * Daniel Goertzen + * Ralf Gommers * Mark Harfouche + * John Kirkham + * Dawid Laszuk + * Gregory R. Lee * Michel Pelletier + * Balint Reczey + * SylvainLan + * Daniele Tricoli * Kai Wohlfahrt A total of 16 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.

1 0

Debye functions
by Daniele Nicolodi 23 Aug '18

23 Aug '18

Hello, is there somewhere an implementation of the Debye functions, like the one in GSL https://www.gnu.org/software/gsl/manual/html_node/Debye-Functions.html ? I had a look in scipy.special (the obvious place) but I didn't find them, unless they go by another name I'm not familiar with. Thanks! Cheers, Dan

1 0

NumPy 1.15.1 released.
by Charles R Harris 21 Aug '18

21 Aug '18

Hi All, On behalf of the NumPy team, I am pleased to announce the release of NumPy 1.15.1. This is a bugfix release for bugs and regressions reported following the 1.15.0 release. Noticeable fixes are - The annoying but harmless RuntimeWarning that "numpy.dtype size changed" has been suppressed. The long standing suppression was lost in the transition to pytest. - The update to Cython 0.28.3 exposed a problematic use of a gcc attribute used to prefer code size over speed in module initialization, possibly resulting in incorrect compiled code. This has been fixed in latest Cython but has been disabled here for safety. - Support for big-endian and ARMv8 architectures has been improved. The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are linked with OpenBLAS v0.3.0, which should fix some of the linalg problems reported for NumPy 1.14. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.15.1/>, source archives are available from Github <https://github.com/numpy/numpy/releases/tag/v1.15.1>. *Compatibility Note* The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit binaries. That will also be the case in future releases. See #11625 <https://github.com/numpy/numpy/issues/11625> for the related discussion. Those needing 32-bit support on the Mac should look elsewhere or build from source. *Contributors* A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Charles Harris * Chris Billington * Elliott Sales de Andrade + * Eric Wieser * Jeremy Manning + * Matti Picus * Ralf Gommers *Pull requests merged* A total of 24 pull requests were merged for this release. * `#11647 <https://github.com/numpy/numpy/pull/11647>`__: MAINT: Filter Cython warnings in ``__init__.py`` * `#11648 <https://github.com/numpy/numpy/pull/11648>`__: BUG: Fix doc source links to unwrap decorators * `#11657 <https://github.com/numpy/numpy/pull/11657>`__: BUG: Ensure singleton dimensions are not dropped when converting... * `#11661 <https://github.com/numpy/numpy/pull/11661>`__: BUG: Warn on Nan in minimum,maximum for scalars * `#11665 <https://github.com/numpy/numpy/pull/11665>`__: BUG: cython sometimes emits invalid gcc attribute * `#11682 <https://github.com/numpy/numpy/pull/11682>`__: BUG: Fix regression in void_getitem * `#11698 <https://github.com/numpy/numpy/pull/11698>`__: BUG: Make matrix_power again work for object arrays. * `#11700 <https://github.com/numpy/numpy/pull/11700>`__: BUG: Add missing PyErr_NoMemory after failing malloc * `#11719 <https://github.com/numpy/numpy/pull/11719>`__: BUG: Fix undefined functions on big-endian systems. * `#11720 <https://github.com/numpy/numpy/pull/11720>`__: MAINT: Make einsum optimize default to False. * `#11746 <https://github.com/numpy/numpy/pull/11746>`__: BUG: Fix regression in loadtxt for bz2 text files in Python 2. * `#11757 <https://github.com/numpy/numpy/pull/11757>`__: BUG: Revert use of `console_scripts`. * `#11758 <https://github.com/numpy/numpy/pull/11758>`__: BUG: Fix Fortran kind detection for aarch64 & s390x. * `#11759 <https://github.com/numpy/numpy/pull/11759>`__: BUG: Fix printing of longdouble on ppc64le. * `#11760 <https://github.com/numpy/numpy/pull/11760>`__: BUG: Fixes for unicode field names in Python 2 * `#11761 <https://github.com/numpy/numpy/pull/11761>`__: BUG: Increase required cython version on python 3.7 * `#11763 <https://github.com/numpy/numpy/pull/11763>`__: BUG: check return value of _buffer_format_string * `#11775 <https://github.com/numpy/numpy/pull/11775>`__: MAINT: Make assert_array_compare more generic. * `#11776 <https://github.com/numpy/numpy/pull/11776>`__: TST: Fix urlopen stubbing. * `#11777 <https://github.com/numpy/numpy/pull/11777>`__: BUG: Fix regression in intersect1d. * `#11779 <https://github.com/numpy/numpy/pull/11779>`__: BUG: Fix test sensitive to platform byte order. * `#11781 <https://github.com/numpy/numpy/pull/11781>`__: BUG: Avoid signed overflow in histogram * `#11785 <https://github.com/numpy/numpy/pull/11785>`__: BUG: Fix pickle and memoryview for datetime64, timedelta64 scalars * `#11786 <https://github.com/numpy/numpy/pull/11786>`__: BUG: Deprecation triggers segfault Cheers, Charles Harris

1 0

NumPy 1.15.0 released.
by Charles R Harris 23 Jul '18

23 Jul '18

Hi All, On behalf of the NumPy team I'm pleased to announce the release of NumPy 1.15.0rc2. This release has an unusual number of cleanups, many deprecations of old functions, and improvements to many existing functions. A total of 438 pull reguests were merged for this release, please look at the release notes <https://github.com/numpy/numpy/releases/tag/v1.15.0>for details. Some highlights are: - NumPy has switched to pytest for testing. - A new `numpy.printoptions` context manager. - Many improvements to the histogram functions. - Support for unicode field names in python 2.7. - Improved support for PyPy. - Fixes and improvements to `numpy.einsum`. The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are linked with OpenBLAS v0.3.0, which should fix some of the linalg problems reported for NumPy 1.14. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.15.0/>, source archives are available from Github <https://github.com/numpy/numpy/releases/tag/v1.15.0>. *Contributors* A total of 133 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Aaron Critchley + * Aarthi + * Aarthi Agurusa + * Alex Thomas + * Alexander Belopolsky * Allan Haldane * Anas Khan + * Andras Deak * Andrey Portnoy + * Anna Chiara * Aurelien Jarno + * Baurzhan Muftakhidinov * Berend Kapelle + * Bernhard M. Wiedemann * Bjoern Thiel + * Bob Eldering * Cenny Wenner + * Charles Harris * ChloeColeongco + * Chris Billington + * Christopher + * Chun-Wei Yuan + * Claudio Freire + * Daniel Smith * Darcy Meyer + * David Abdurachmanov + * David Freese * Deepak Kumar Gouda + * Dennis Weyland + * Derrick Williams + * Dmitriy Shalyga + * Eric Cousineau + * Eric Larson * Eric Wieser * Evgeni Burovski * Frederick Lefebvre + * Gaspar Karm + * Geoffrey Irving * Gerhard Hobler + * Gerrit Holl * Guo Ci + * Hameer Abbasi + * Han Shen * Hiroyuki V. Yamazaki + * Hong Xu * Ihor Melnyk + * Jaime Fernandez * Jake VanderPlas + * James Tocknell + * Jarrod Millman * Jeff VanOss + * John Kirkham * Jonas Rauber + * Jonathan March + * Joseph Fox-Rabinovitz * Julian Taylor * Junjie Bai + * Juris Bogusevs + * Jörg Döpfert * Kenichi Maehashi + * Kevin Sheppard * Kimikazu Kato + * Kirit Thadaka + * Kritika Jalan + * Kyle Sunden + * Lakshay Garg + * Lars G + * Licht Takeuchi * Louis Potok + * Luke Zoltan Kelley * MSeifert04 + * Mads R. B. Kristensen + * Malcolm Smith + * Mark Harfouche + * Marten H. van Kerkwijk + * Marten van Kerkwijk * Matheus Vieira Portela + * Mathieu Lamarre * Mathieu Sornay + * Matthew Brett * Matthew Rocklin + * Matthias Bussonnier * Matti Picus * Michael Droettboom * Miguel Sánchez de León Peque + * Mike Toews + * Milo + * Nathaniel J. Smith * Nelle Varoquaux * Nicholas Nadeau, P.Eng., AVS + * Nick Minkyu Lee + * Nikita + * Nikita Kartashov + * Nils Becker + * Oleg Zabluda * Orestis Floros + * Pat Gunn + * Paul van Mulbregt + * Pauli Virtanen * Pierre Chanial + * Ralf Gommers * Raunak Shah + * Robert Kern * Russell Keith-Magee + * Ryan Soklaski + * Samuel Jackson + * Sebastian Berg * Siavash Eliasi + * Simon Conseil * Simon Gibbons * Stefan Krah + * Stefan van der Walt * Stephan Hoyer * Subhendu + * Subhendu Ranjan Mishra + * Tai-Lin Wu + * Tobias Fischer + * Toshiki Kataoka + * Tyler Reddy + * Unknown + * Varun Nayyar * Victor Rodriguez + * Warren Weckesser * William D. Irons + * Zane Bradley + * cclauss + * fo40225 + * lapack_lite code generator + * lumbric + * luzpaz + * mamrehn + * tynn + * xoviat Cheers, Charles Harris

1 1

scipy.optimize.minimize changes values at low decimal place
by jan-hendrik.berlin 22 Jul '18

22 Jul '18

Hey guys, this is my code for the optimisation. initialGuess = D.Matrix[:,D.menge] bnds = D.Matrix[:,(D.mengenMin,D.mengenMax)] con1 = {'type': 'eq', 'fun': PercentSum} con2 = {'type': 'eq', 'fun': MinMaxProportion} cons = ([con1,con2]) solution = minimize(rootfunc,initialGuess,method='SLSQP',\ bounds=bnds,constraints=cons) The Problem is, the algorithm changes values at low decimal place. e.g. this is my initial guess. I already tried to change from float to integers, to have a work around. [ 0. 0. 123. 0. 0. 622. 245. 0. 0. 0.] The first try of the of the optimizer looks like this: [1.49011612e-08 0.00000000e+00 1.23000000e+02 0.00000000e+00 0.00000000e+00 6.22000000e+02 2.45000000e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00] Another is this: [ 0. 0. 123.00000001 0. 0. 622. 245. 0. 0. 0. ] And finally the optimization finishes with this error: status 6 message Singular matrix C in LSQ subproblem I think the problem is the tiny difference. Is there a possibility to tell the SLSQP algorithm only to try changes on the first two decimal places or higher? kind regards jan

1 0

NumPy 1.15.0rc2 released.
by Charles R Harris 09 Jul '18

09 Jul '18

Hi All, On behalf of the NumPy team I'm pleased to announce the release of NumPy 1.15.0rc2. This release has an unusual number of cleanups, many deprecations of old functions, and improvements to many existing functions. A total of 435 pull reguests were merged for this release, please look at the release notes <https://github.com/numpy/numpy/releases/tag/v1.15.0rc2>for details. Some highlights are: - NumPy has switched to pytest for testing. - A new `numpy.printoptions` context manager. - Many improvements to the histogram functions. - Support for unicode field names in python 2.7. - Improved support for PyPy. - Fixes and improvements to `numpy.einsum`. The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are linked with OpenBLAS v0.3.0, which should fix some of the linalg problems reported for NumPy 1.14. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.15.0rc2/>, source archives are available from Github <https://github.com/numpy/numpy/releases/tag/v1.15.0rc2>. A total of 131 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Aaron Critchley + - Aarthi + - Aarthi Agurusa + - Alex Thomas + - Alexander Belopolsky - Allan Haldane - Anas Khan + - Andras Deak - Andrey Portnoy + - Anna Chiara - Aurelien Jarno + - Baurzhan Muftakhidinov - Berend Kapelle + - Bernhard M. Wiedemann - Bjoern Thiel + - Bob Eldering - Cenny Wenner + - Charles Harris - ChloeColeongco + - Chris Billington + - Christopher + - Chun-Wei Yuan + - Claudio Freire + - Daniel Smith - Darcy Meyer + - David Abdurachmanov + - David Freese - Deepak Kumar Gouda + - Dennis Weyland + - Derrick Williams + - Dmitriy Shalyga + - Eric Cousineau + - Eric Larson - Eric Wieser - Evgeni Burovski - Frederick Lefebvre + - Gaspar Karm + - Geoffrey Irving - Gerhard Hobler + - Gerrit Holl - Guo Ci + - Hameer Abbasi + - Han Shen - Hiroyuki V. Yamazaki + - Hong Xu - Ihor Melnyk + - Jaime Fernandez - Jake VanderPlas + - James Tocknell + - Jarrod Millman - Jeff VanOss + - John Kirkham - Jonas Rauber + - Jonathan March + - Joseph Fox-Rabinovitz - Julian Taylor - Junjie Bai + - Juris Bogusevs + - Jörg Döpfert - Kenichi Maehashi + - Kevin Sheppard - Kimikazu Kato + - Kirit Thadaka + - Kritika Jalan + - Lakshay Garg + - Lars G + - Licht Takeuchi - Louis Potok + - Luke Zoltan Kelley - MSeifert04 + - Mads R. B. Kristensen + - Malcolm Smith + - Mark Harfouche + - Marten H. van Kerkwijk + - Marten van Kerkwijk - Matheus Vieira Portela + - Mathieu Lamarre - Mathieu Sornay + - Matthew Brett - Matthew Rocklin + - Matthias Bussonnier - Matti Picus - Michael Droettboom - Miguel Sánchez de León Peque + - Mike Toews + - Milo + - Nathaniel J. Smith - Nelle Varoquaux - Nicholas Nadeau, P.Eng., AVS + - Nick Minkyu Lee + - Nikita + - Nikita Kartashov + - Nils Becker + - Oleg Zabluda - Orestis Floros + - Pat Gunn + - Paul van Mulbregt + - Pauli Virtanen - Pierre Chanial + - Ralf Gommers - Raunak Shah + - Robert Kern - Russell Keith-Magee + - Ryan Soklaski + - Samuel Jackson + - Sebastian Berg - Siavash Eliasi + - Simon Conseil - Simon Gibbons - Stefan Krah + - Stefan van der Walt - Stephan Hoyer - Subhendu + - Subhendu Ranjan Mishra + - Tai-Lin Wu + - Tobias Fischer + - Toshiki Kataoka + - Tyler Reddy + - Unknown + - Varun Nayyar - Victor Rodriguez + - Warren Weckesser - William D. Irons + - Zane Bradley + - fo40225 + - lapack_lite code generator + - lumbric + - luzpaz + - mamrehn + - tynn + - xoviat Cheers Chuck

1 0

Re: [SciPy-User] Q: on scipy probplot user defined distribution function (solution)
by Sergio Rojas 09 Jul '18

09 Jul '18

> From: Paul Hobson <pmhobson(a)gmail.com> > Hmm. I don't think scipy needs to know about your distribution per se. You > Thanks for sharing your thoughts, Paul. I finally found the way to go. As you mention, the parameter "dist" is wher one needs to specify our own distribution. The key is to give it without quotes. Here is a working example: import matplotlib.pyplot as plt #prefer this import numpy as np from scipy import stats class mydist(object): def pdf(x): "define and return the pdf" return stats.norm.pdf(x) def cdf(x): "define and return the cdf" return stats.norm.cdf(x) def ppf(x): "define and return the ppf" return stats.norm.ppf(x) mymean = 1.0 myvar = 1.0 n = 5000 colData = np.random.normal(mymean, myvar, n) """ It took me a while to realize that the quotes surrounding the distribution should not be there when using a user defined one """ print(' ProbPlot using system norm ') res = stats.probplot(colData, dist="norm", plot=plt, rvalue=True) plt.show() # To use our own defined distribution remove the quotes around the name: print(' ProbPlot using my redefined system norm ') res = stats.probplot(colData, dist=mydist, plot=plt, rvalue=True) plt.show() Sergio ------------------------------ Message: 2 Date: Thu, 5 Jul 2018 00:11:52 -0700 From: Paul Hobson <pmhobson(a)gmail.com> To: SciPy Users List <scipy-user(a)python.org> Subject: Re: [SciPy-User] Q: on scipy probplot user defined distribution function Message-ID: <CADT3MEANHqw+w_+acbp9U6H1QqdhLOY9Ei1LK430we5+Wo1eyA(a)mail.gmail.com> Content-Type: text/plain; charset="utf-8" Hmm. I don't think scipy needs to know about your distribution per se. You just need to be able to pass it to the probplot function via the `dist` parameter. Per the docs ( https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.pro… ): scipy.stats.probplot(*x*, *sparams=()*, *dist='norm'*, *fit=True*, *plot=None*)[source] <http://github.com/scipy/scipy/blob/v0.14.0/scipy/stats/morestats.py#L296[ht…> [snip] *dist* : str or stats.distributions instance, optional Distribution or distribution function name. The default is ?norm? for a normal probability plot. Objects that look enough like a stats.distributions instance (i.e. they have a ppf method) are also accepted. [snip] On Wed, Jul 4, 2018 at 12:19 PM Sergio Rojas <sergio_r(a)mail.com> wrote: > > > Here's an example of using different distributions with the mpl-probscale > > package: > > > https://matplotlib.org/mpl-probscale/tutorial/closer_look_at_viz.html#using… > > > -Paul > > Thanks, Paul. I guess I might need to build a <dist>.pdf, .cdf, and .ppf > files for my distribution. What is not clear is how to specify the > alternative > directory to make them available to scipy > > On Mon, Jul 2, 2018 at 11:40 AM Sergio Rojas <sergio_r(a)mail.com> wrote: > > > Hello folks, > > > > How can one specify our own (user made) probability distribution > > function to scipy.stats.probplot ? > > > > Can it be done is the pdf used is not in a closed form function > > (meaning that the random drawings comes from a numerical > > procedure)? > > > > Salut, > > > > > > Sergio > > _______________________________________________ > > SciPy-User mailing list > > SciPy-User(a)python.org > > > https://mail.python.org/mailman/listinfo/scipy-user[https://mail.python.org… > > >

1 0

Re: [SciPy-User] Q: on scipy probplot user defined distribution function
by Sergio Rojas 05 Jul '18

05 Jul '18

> Here's an example of using different distributions with the mpl-probscale > package: > https://matplotlib.org/mpl-probscale/tutorial/closer_look_at_viz.html#using… > -Paul Thanks, Paul. I guess I might need to build a <dist>.pdf, .cdf, and .ppf files for my distribution. What is not clear is how to specify the alternative directory to make them available to scipy On Mon, Jul 2, 2018 at 11:40 AM Sergio Rojas <sergio_r(a)mail.com> wrote: > Hello folks, > > How can one specify our own (user made) probability distribution > function to scipy.stats.probplot ? > > Can it be done is the pdf used is not in a closed form function > (meaning that the random drawings comes from a numerical > procedure)? > > Salut, > > > Sergio > _______________________________________________ > SciPy-User mailing list > SciPy-User(a)python.org > https://mail.python.org/mailman/listinfo/scipy-user[https://mail.python.org… >

2 1