Mailman 3 June 2016 - SciPy-User

How to display a RGB image (MFISH)?
by jp 02 Apr '23

02 Apr '23

Hi, I have written a script to try to combine five images into a three channels RGB images<http://dip4fish.blogspot.com/2011/05/trying-to-combine-mfish-images-into-rg…> . The resulting RGB image looks like a grey scale image instead of a color one. I have three np.array Rnorm, Gnorm, Bnorm which are copied into a rgb array: *rgb = np.zeros((shape[0],shape[1],3),dtype=float) mxr=np.max(R) mxg=np.max(G) mxb=np.max(B) Rnorm=np.uint8((255*(R/mxr)))Gnorm=np.uint8((255*(R/mxg)))Bnorm=np.uint8((255*(R/mxb)))#copy each RGB component in an RGB array rgb[:,:,0]=Rnorm rgb[:,:,1]=Gnorm rgb[:,:,2]=Bnorm* *pylab.subplot(224, aspect='equal',frameon=False, xticks=[], yticks=[]) pylab.imshow(rgb) pylab.show()* Any advice? Thank you Jean-Patrick

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Invitation to connect on LinkedIn
by Jelle Feringa via LinkedIn 28 Jul '22

28 Jul '22

LinkedIn ------------ Jelle Feringa requested to add you as a connection on LinkedIn: ------------------------------------------ Jose, I'd like to add you to my professional network on LinkedIn. - Jelle Accept invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… View invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… ------------------------------------------ DID YOU KNOW your LinkedIn profile helps you control your public image when people search for you? Setting your profile as public means your LinkedIn profile will come up when people enter your name in leading search engines. Take control of your image! http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/ewp/inv-22/ -- (c) 2011, LinkedIn Corporation

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Help required regding Eigenvectors
by Sudheer Phani 14 Mar '22

14 Mar '22

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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Problem with SciPy 0.17.1 integrate package
by Fausto Arinos de A. Barbuto 27 Jun '16

27 Jun '16

Hello, I have just upgraded from SciPy 0.13.3 to 0.17.1 and Python scripts that usedto work don't work any more. The problem appears when I try to use the odeintfunction. When I try this: >>> from scipy.integrate import odeint I get those following error messages: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python2.7/dist-packages/scipy/integrate/__init__.py", line 56, in <module> from .odepack import * File "/usr/local/lib/python2.7/dist-packages/scipy/integrate/odepack.py", line 6, in <module> from . import _odepack ImportError: /usr/local/lib/python2.7/dist-packages/scipy/integrate/_odepack.so: undefined symbol: lsoda_ The command above always worked fine with SciPy 0.13.3. Is this error somehow related to Numpy or Lapack (sorry if this question makes no sense, but I'mreally lost)? Is there any test I could do? Despite this error the whole SciPy module/package seems to import OK: >>> import scipy >>> print scipy.__version__ 0.17.1 >>> I'm running Ubuntu 14.04 64-bits with Python 2.7.6, gcc 4.8.4 and Numpy 1.11.0. Thanks in advance for any help you provide. Fausto

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Numpy 1.11.1 release
by Charles R Harris 26 Jun '16

26 Jun '16

Hi All, I'm pleased to announce the release of Numpy 1.11.1. This release supports Python 2.6 - 2.7, and 3.2 - 3.5 and fixes bugs and regressions found in Numpy 1.11.0 as well as making several build related improvements. Wheels for Linux, Windows, and OSX can be found on PyPI. Sources are available on both PyPI and Sourceforge <https://sourceforge.net/projects/numpy/files/NumPy/1.11.1/>. Thanks to all who were involved in this release, and a special thanks to Matthew Brett for his work on the Linux and Windows wheel infrastructure. The following pull requests have been merged: - 7506 BUG: Make sure numpy imports on python 2.6 when nose is unavailable. - 7530 BUG: Floating exception with invalid axis in np.lexsort. - 7535 BUG: Extend glibc complex trig functions blacklist to glibc < 2.18. - 7551 BUG: Allow graceful recovery for no compiler. - 7558 BUG: Constant padding expected wrong type in constant_values. - 7578 BUG: Fix OverflowError in Python 3.x. in swig interface. - 7590 BLD: Fix configparser.InterpolationSyntaxError. - 7597 BUG: Make np.ma.take work on scalars. - 7608 BUG: linalg.norm(): Don't convert object arrays to float. - 7638 BLD: Correct C compiler customization in system_info.py. - 7654 BUG: ma.median of 1d array should return a scalar. - 7656 BLD: Remove hardcoded Intel compiler flag -xSSE4.2. - 7660 BUG: Temporary fix for str(mvoid) for object field types. - 7665 BUG: Fix incorrect printing of 1D masked arrays. - 7670 BUG: Correct initial index estimate in histogram. - 7671 BUG: Boolean assignment no GIL release when transfer needs API. - 7676 BUG: Fix handling of right edge of final histogram bin. - 7680 BUG: Fix np.clip bug NaN handling for Visual Studio 2015. - 7724 BUG: Fix segfaults in np.random.shuffle. - 7731 MAINT: Change mkl_info.dir_env_var from MKL to MKLROOT. - 7737 BUG: Fix issue on OS X with Python 3.x, npymath.ini not installed. The following developers contributed to this release, developers marked with a '+' are first time contributors. - Allan Haldane - Amit Aronovitch+ - Andrei Kucharavy+ - Charles Harris - Eric Wieser+ - Evgeni Burovski - Loïc Estève+ - Mathieu Lamarre+ - Matthew Brett - Matthias Geier - Nathaniel J. Smith - Nikola Forró+ - Ralf Gommers - Ray Donnelly+ - Robert Kern - Sebastian Berg - Simon Conseil - Simon Gibbons - Sorin Sbarnea+ Chuck

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ANN: Astropy v1.2 released
by Thomas Robitaille 23 Jun '16

23 Jun '16

Dear colleagues, We are very happy to announce the v1.2 release of the Astropy package, a core Python package for Astronomy: http://www.astropy.org Astropy is a community-driven Python package intended to contain much of the core functionality and common tools needed for astronomy and astrophysics. New and improved major functionality in this release includes: * A new class to compute Lomb-Scargle periodograms efficiently using different methods. * A number of new statistics functions including those for Jackknife resampling, circular statistics, and the Akaike and Bayesian information criteria. * Support for getting the positions of solar system bodies in the coordinates sub-package. * The ability to compute Barycentric and Heliocentric light-travel time corrections. * Support for offset coordinate frames, which can be used to define a coordinate system relative to a known position and rotation. * An implementation of the zscale algorithm to determine image limits automatically. * Support for bolometric magnitudes in the units package. * Improvements to the NDData class and subclasses. * Auto-downloading of IERS tables as needed, which gives information about Earth orientation parameters necessary for high precision coordinate calculations and conversions to/from the UT1 scale. In addition, hundreds of smaller improvements and fixes have been made. An overview of the changes is provided at: http://docs.astropy.org/en/stable/whatsnew/1.2.html Instructions for installing Astropy are provided on our website, and extensive documentation can be found at: http://docs.astropy.org If you make use of the Anaconda Python Distribution, you can update to Astropy v1.2 with: conda update astropy If you normally use pip, you can upgrade with: pip install astropy --upgrade Note that if you install now you should get Astropy v1.2.1, as some last-minute bug fixes were found and fixed after the v1.2 release was created but before this announcement. Please report any issues, or request new features via our GitHub repository: https://github.com/astropy/astropy/issues Over 190 developers have contributed code to Astropy so far, and you can find out more about the team behind Astropy here: http://www.astropy.org/team.html As a reminder, Astropy v1.0 (our long term support release) will continue to be supported with bug fixes until Feb 19th 2017, so if you need to use Astropy in a very stable environment, you may want to consider staying on the v1.0.x set of releases (for which we have recently released v1.0.10). If you use Astropy directly for your work, or as a dependency to another package, please remember to include the following acknowledgment at the end of papers: “This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration, 2013).” where (Astropy Collaboration, 2013) is a reference to the Astropy paper: http://dx.doi.org/10.1051/0004-6361/201322068 Please feel free to forward this announcement to anyone you think might be interested in this release! Erik Tollerud, Tom Robitaille, Kelle Cruz, and Tom Aldcroft on behalf of The Astropy Collaboration

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ANN: statsmodels release candidate 0.8.0rc1
by josef.pktd＠gmail.com 22 Jun '16

22 Jun '16

We are very pleased to announce the release candidate for 0.8.0 of statsmodels. Statsmodels 0.8.0 combines the work of two years of statsmodels development with many new features given that version 0.7 was never released. See the release notes below for details. The source distribution and Windows wheels compiled with Microsoft compilers are available on pypi. The documentation for this release candidate is currently at http://www.statsmodels.org/0.8.0/ The release notes are at http://www.statsmodels.org/0.8.0/release/version0.8.html and http://www.statsmodels.org/0.8.0/release/version0.7.html Please report any problems on the github issues https://github.com/statsmodels/statsmodels/issues or on the mailing list http://groups.google.com/group/pystatsmodels . The release team on behalf of statsmodels developers -------------------------------- about statsmodels: Statsmodels is a Python package that provides estimation and inference for statistical and econometric models and descriptive and inferential statistics. Release 0.8.0 ============= See also changes in the unreleased 0.7 Release summary --------------- The main features of this release are several new time series models based on the statespace framework, multiple imputation using MICE as well as many other enhancements. The codebase also has been updated to be compatible with recent numpy and pandas releases. Statsmodels is using now github to store the updated documentation which is available under http://www.statsmodels.org/stable for the last release, and http://www.statsmodels.org/dev/ for the development version. This is the last release that supports Python 2.6. **Warning** API stability is not guaranteed for new features, although even in this case changes will be made in a backwards compatible way if possible. The stability of a new feature depends on how much time it was already in statsmodels master and how much usage it has already seen. If there are specific known problems or limitations, then they are mentioned in the docstrings. The following major new features appear in this version. Statespace Models ----------------- Building on the statespace framework and models added in 0.7, this release includes additional models that build on it. Authored by Chad Fulton largely during GSOC 2015 Kalman Smoother ^^^^^^^^^^^^^^^ The Kalman smoother (introduced in #2434) allows making inference on the unobserved state vector at each point in time using data from the entire sample. In addition to this improved inference, the Kalman smoother is required for future improvements such as simulation smoothing and the expectation maximization (EM) algorithm. As a result of this improvement, all state space models now inherit a `smooth` method for producing results with smoothed state estimates. In addition, the `fit` method will return results with smoothed estimates at the maximum likelihood estimates. Postestimation ^^^^^^^^^^^^^^ Improved post-estimation output is now available to all state space models (introduced in #2566). This includes the new methods `get_prediction` and `get_forecast`, providing standard errors and confidence intervals as well as point estimates, `simulate`, providing simulation of time series following the given state space process, and `impulse_responses`, allowing computation of impulse responses due to innovations to the state vector. Diagnostics ^^^^^^^^^^^ A number of general diagnostic tests on the residuals from state space estimation are now available to all state space models (introduced in #2431). These include: * `test_normality` implements the Jarque-Bera test for normality of residuals * `test_heteroskedasticity` implements a test for homoskedasticity of residuals similar to the Goldfeld-Quandt test * `test_serial_correlation` implements the Ljung-Box (or Box-Pierce) test for serial correlation of residuals These test statistics are also now included in the `summary` method output. In addition, a `plot_diagnostics` method is available which provides four plots to visually assess model fit. Unobserved Components ^^^^^^^^^^^^^^^^^^^^^ The class of univariate Unobserved Components models (also known as structural time series models) are now available (introduced in #2432). This includes as special cases the local level model and local linear trend model. Generically it allows decomposing a time series into trend, cycle, seasonal, and irregular components, optionally with exogenous regressors and / or autoregressive errors. Multivariate Models ^^^^^^^^^^^^^^^^^^^ Two standard multivariate econometric models - vector autoregressive moving-average model with exogenous regressors (VARMAX) and Dynamic Factors models - are now available (introduced in #2563). The first is a popular reduced form method of exploring the covariance in several time series, and the second is a popular reduced form method of extracting a small number of common factors from a large dataset of observed series. Recursive least squares ^^^^^^^^^^^^^^^^^^^^^^^ A model for recursive least squares, also known as expanding-window OLS, is now available in `statsmodels.regression` (introduced in #2830). Miscellaneous ^^^^^^^^^^^^^ Other improvements to the state space framework include: * Improved missing data handling #2770, #2809 * Ongoing refactoring and bug fixes in fringes and corner cases New functionality in statistics ------------------------------- Contingency Tables #2418 (Kerby Shedden) Local FDR, multiple testing #2297 (Kerby Shedden) Mediation Analysis #2352 (Kerby Shedden) other: * weighted quantiles in DescrStatsW #2707 (Kerby Shedden) Duration -------- Kaplan Meier Survival Function #2614 (Kerby Shedden) Cumulative incidence rate function #3016 (Kerby Shedden) other: * frequency weights in Kaplan-Meier #2992 (Kerby Shedden) Imputation ---------- new subpackage in `statsmodels.imputation` MICE #2076 (Frank Cheng GSOC 2014 and Kerby Shedden) Imputation by regression on Order Statistic #3019 (Paul Hobson) Time Series Analysis -------------------- Markov Switching Models ^^^^^^^^^^^^^^^^^^^^^^^ Markov switching dynamic regression and autoregression models are now available (introduced in #2980 by Chad Fulton). These models allow regression effects and / or autoregressive dynamics to differ depending on an unobserved "regime"; in Markov switching models, the regimes are assumed to transition according to a Markov process. Statistics ^^^^^^^^^^ * KPSS stationarity, unit root test #2775 (N-Wouda) * The Brock Dechert Scheinkman (BDS) test for nonlinear dependence is now available (introduced in #934 by Chad Fulton) Penalized Estimation -------------------- Elastic net: fit_regularized with L1/L2 penalization has been added to OLS, GLM and PHReg (Kerby Shedden) GLM --- Tweedie is now available as new family #2872 (Peter Quackenbush, Josef Perktold) other: * frequency weights for GLM (currently without full support) # * more flexible convergence options #2803 (Peter Quackenbush) Multivariate ------------ new subpackage that currently contains PCA PCA was added in 0.7 to statsmodels.tools and is now in statsmodels.multivariate Documentation ------------- New doc build with latest jupyter and Python 3 compatibility (Tom Augspurger) Other important improvements ---------------------------- several existing functions have received improvements * seasonal_decompose: improved periodicity handling #2987 (ssktotoro ?) * tools add_constant, add_trend: refactoring and pandas compatibility #2240 (Kevin Sheppard) * acf, pacf, acovf: option for missing handling #3020 (joesnacks ?) * acf, pacf plots: allow array of lags #2989 (Kevin Sheppard) * io SimpleTable (summary): allow names with special characters #3015 (tvanessa ?) * tsa tools lagmat, lagmat2ds: pandas support #2310 #3042 (Kevin Sheppard) * CompareMeans: from_data, summary methods #2754 (Valery Tyumen) Major Bugs fixed ---------------- * see github issues Backwards incompatible changes and deprecations ----------------------------------------------- * ??? * predict now returns a pandas Series if the exog argument is a DataFrame * PCA moved to multivariate compared to 0.7 Development summary and credits ------------------------------- Besides receiving contributions for new and improved features and for bugfixes, important contributions to general maintenance came from * Kevin Sheppard * Pierre Barbier de Reuille * Tom Augsburger and the general maintainer and code reviewer * Josef Perktold Additionally, many users contributed by participation in github issues and providing feedback. Thanks to all of the contributors for the 0.8 release: .. note:: * Ashish * Brendan * Brendan Condon * BrianLondon * Chad Fulton * Chris Fonnesbeck * Christoph T. Weidemann * James Kerns * Josef Perktold * Kerby Shedden * Kevin Sheppard * Leoyzen * Matthew Brett * Niels Wouda * Paul Hobson * Pierre Barbier de Reuille * Pietro Battiston * Ralf Gommers * Roman Ring * Skipper Seabold * Soren Fuglede Jorgensen * Thomas Cokelaer * Tom Augspurger * ValeryTyumen * Vanessa * Yaroslav Halchenko * joesnacks * kokes * matiumerca * rlan * ssktotoro * thequackdaddy * vegcev Thanks to all of the contributors for the 0.7 release: .. note:: * Alex Griffing * Antony Lee * Chad Fulton * Christoph Deil * Daniel Sullivan * Hans-Martin von Gaudecker * Jan Schulz * Joey Stockermans * Josef Perktold * Kerby Shedden * Kevin Sheppard * Kiyoto Tamura * Louis-Philippe Lemieux Perreault * Padarn Wilson * Ralf Gommers * Saket Choudhary * Skipper Seabold * Tom Augspurger * Trent Hauck * Vincent Arel-Bundock * chebee7i * donbeo * gliptak * hlin117 * jerry dumblauskas * jonahwilliams * kiyoto * neilsummers * waynenilsen These lists of names are automatically generated based on git log, and may not be complete.

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scipy 0.18 release candidate 1
by Evgeni Burovski 20 Jun '16

20 Jun '16

Hi, I'm pleased to announce the availability of the first release candidate for scipy 0.18.0. Please try this release and report any issues on Github tracker, https://github.com/scipy/scipy, or scipy-dev mailing list. Source tarballs and release notes are available from Github releases, https://github.com/scipy/scipy/releases/tag/v0.18.0rc1 Please note that this is a source-only release. We do not provide Windows binaries for this release. OS X and Linux wheels will be provided for the final release. The current release schedule is 27 June: rc2 (if necessary) 11 July: final release Thanks to everyone who contributed to this release! Cheers, Evgeni A part of the release notes follows: ========================== SciPy 0.18.0 Release Notes ========================== .. note:: Scipy 0.18.0 is not released yet! .. 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 + * Dávid Bodnár + * 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 + * Loïc Estève * 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 + * Marcello Seri + * Sourav Singh + * Martin Spacek + * Søren Fuglede Jørgensen + * Bhavika Tekwani + * Martin Thoma + * Sam Tygier + * Meet Udeshi + * Utkarsh Upadhyay * Bram Vandekerckhove + * Sebastián 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.

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Spipy Minimize Warm Restart
by Jon Lederman 20 Jun '16

20 Jun '16

Hi All, I am trying to use some of the SciPy minimize algos such as Nelder-Mead and cause the algorithm to execute a warm-restart if it is detected that the optimizer is stuck or not converging. Is there a way to accomplish this programmatically? Thanks. Jon

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Creating a Custom PDF with Arguments Using Scipy Stats
by Steven Ehlert 20 Jun '16

20 Jun '16

Hi all- I am trying to create a custom PDF with arguments for some statistical analysis I am doing. I want to have a power law with an exponential cutoff at low values: mathematically, this looks like P(m) = N * m**(-index) * (1 - exp((-m**2)/m0**2) with three parameters: a normalization (N), an index (index), and a scale value (m0). I have tried to produce some code that creates this pdf and while the pdf and cdf functions appear to be working as I hoped, when I try to generate random values they don't follow this distribution at all, and I am not sure why. It seems to be far more heavily drawing from the low end of the curve (which are explicitly suppressed in the pdf) Can you suggest the changes I need to make to try and figure out why my code isn't working as planned? My code snippet is below: from scipy import stats import numpy as np from matplotlib import pyplot as plt class my_pdf(stats.rv_continuous): def _pdf(self,x,norm,index,m0): return norm*x**(-1*index) * (1. -np.exp((-1*x**2)/m0**2) ) my_cv= my_pdf(a=1e-6,b=1e-2,name="Test") rvs=my_cv.rvs(1.0,1.7,1e-3,size=1000) #Values should be peaked around 1e-3, but histogram peaks around 1e-5... I am really having a hard time figuring out how to create a robust and complex custom rv_continuous in scipy based on scipy, so any help you can provide would be most appreciated. Cheers, Steve

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