Mailman 3 May 2015 - SciPy-User

Re: [SciPy-User] cdf inverse
by Gabriele Brambilla 26 May '15

26 May '15

Thanks, it works perfectly. Gabriele On Tue, May 26, 2015 at 11:53 AM, Christoph Deil <Deil.Christoph(a)gmail.com> wrote: > Doesn’t look like this got posted (maybe I used the wrong email address to > send the reply), so I’m re-sending you the reply offline. > > Begin forwarded message: > > *From: *Christoph Deil <Deil.Christoph(a)gmail.com> > *Subject: **Re: [SciPy-User] cdf inverse* > *Date: *26 May 2015 11:49:39 CEST > *To: *SciPy Users List <scipy-user(a)scipy.org> > > > On 26 May 2015, at 11:34, Gabriele Brambilla < > gb.gabrielebrambilla(a)gmail.com> wrote: > > Hi, > > I would like to use the inverse of a cdf. > The cdf in scipy.stats take the degrees of freedom, the x (until we > integrate) and return the quantile. > I would like, given the quantile, to obtain the x. > > To make it even more clear I would like to do what is done in this table. > http://sites.stat.psu.edu/~mga/401/tables/Chi-square-table.pdf > > Is it possible in spicy.stats? > > Thanks > > Gabriele > _______________________________________________ > SciPy-User mailing list > SciPy-User(a)scipy.org > http://mail.scipy.org/mailman/listinfo/scipy-user > > > Hi Gabriele, > > you should be able to compute this using the isf (inverse survival > function) or ppf (percent point function, inverse of cdf) methods on of the > scipy.stats.chi2 distribution: > http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2.html > > Here’s some more info on the distribution methods: > > http://docs.scipy.org/doc/scipy-dev/reference/tutorial/stats.html#common-me… > > Let me know if that doesn’t help and I’ll post some example code. > > Christoph > > >

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cdf inverse
by Gabriele Brambilla 26 May '15

26 May '15

Hi, I would like to use the inverse of a cdf. The cdf in scipy.stats take the degrees of freedom, the x (until we integrate) and return the quantile. I would like, given the quantile, to obtain the x. To make it even more clear I would like to do what is done in this table. http://sites.stat.psu.edu/~mga/401/tables/Chi-square-table.pdf Is it possible in spicy.stats? Thanks Gabriele

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ANN: Scipy 0.16.0 beta release 2
by Ralf Gommers 24 May '15

24 May '15

Hi all, The second beta for Scipy 0.16.0 is now available. After beta 1 a couple of critical issues on Windows were solved, and there are now also 32-bit Windows binaries (along with the sources and release notes) available on https://sourceforge.net/projects/scipy/files/scipy/0.16.0b2/. Please try this release and report any issues on the scipy-dev mailing list. Cheers, Ralf

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problem using numpy arrays with stream plot
by Gabriele Brambilla 22 May '15

22 May '15

Hi, I have a problem using bumpy arrays with stream plot. I want to use a 3d vector field in coordinates (x,y,z) stored in a numpy array, and plot slices of it with streamplot. To test it I wanted to use a vector field with arrows pointed up in the z>0 region and pointed down in the z<0 region. import numpy as np import matplotlib.pyplot as plt from math import * max = 100 min = -100 X = np.linspace(min, max, num=100) Y = np.linspace(min, max, num=100) Z = np.linspace(min, max, num=100) N = X.size #single components in the 3D matrix Bxa = np.zeros((N, N, N)) Bya = np.zeros((N, N, N)) Bza = np.zeros((N, N, N)) for i, x in enumerate(X): for j, y in enumerate(Y): for k, z in enumerate(Z): Bxa[ i, j, k] = 0.0 #x Bya[ i, j, k] = 0.0 #y Bza[ i, j, k] = z #I take a slice close to Y=0 Bx_sec = Bxa[:,N/2,:] By_sec = Bya[:,N/2,:] Bz_sec = Bza[:,N/2,:] fig = plt.figure() ax = fig.add_subplot(111) ax.streamplot(X, Z, Bx_sec, Bz_sec, color='b') ax.set_xlim([X.min(), X.max()]) ax.set_ylim([Z.min(), Z.max()]) plt.show() But I obtain something that looks like if I have put Bza = x! I tried to invert the order of vectors but it is unuseful! I attach the picture. Do you understand why? (the code I wrote should run) Gabriele

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Trouble getting R^2 value
by abhisek Mondal 19 May '15

19 May '15

Hi, I'm working with a 3D data-set aiming to best-fit the points in a 3D surface. So far, I have successfully fitted the surface using *scipy.linalg.lstsq*. But, I would like to express the significance of the results in terms of R^2 value, which I'm not being able to perform with this particular function. So, a little help regarding this matter would be highly appreciated. Thank you. -- Abhisek Mondal *Research Fellow* *Structural Biology and Bioinformatics* *Indian Institute of Chemical Biology* *Kolkata 700032* *INDIA*

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calling Julia module from Python?
by asreeve 18 May '15

18 May '15

I would like some guidance in using the Julia Language (v:0.3.8) from Python(v:3.4.3), to see if it can be used for creating fast modules for Python scripts. I've written a short function in Julia and would like to call it from a python script. To get started, I'm trying to load the Julia script into an ipython shell. I can call the module from Julia, and can execute short bits of Julia in the ipython shell. Here's where I'm at now (the Julia module I'm calling is at the end of this message): Using Julia =========== julia> using snow_frac2 julia> snow_frac2.snow_fraction2(1.) 0.5 =========== Using Python ============ In [1]: import julia In [2]: jl=julia.Julia() In [3]: jl.eval('sin(3.14)') Out[3]: 0.0015926529164868282 =========== I'd like to call the "snow_frac2.jl" module below from the ipython interpreter, just like I did in in Julia, and use the snow_fraction2 function. I've attempted a variety of commands (eg. foo=jl.call('snow_frac2.jl')) but just get "JuliaError"s. Can anyone show the proper incantation to call a Julia module from Python? I've tried updating to a more recent verion of Julia, but that did not seem to help (although I gave up on this pretty quickly). Thanks for any help you can provide, Andy ==snow_frac2.jl== module snow_frac2 function snow_fraction2(temptr::Float64) if temptr<-1. fraction=1.0 elseif temptr>3.0 fraction=0.0 else fraction=(temptr+1.0)/(4.0) end return fraction end end .................................... Andrew Reeve School of Earth and Climate Sciences University of Maine Orono, ME 04469

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Problems evaluating interpolated function at new points
by Matteo Niccoli 15 May '15

15 May '15

I am relatively new to Scipy, at least when it comes to interpolation. I am trying to replicate some of the functionality of Matlab's interp2. I have a distance function map (available at this Google drive location) https://drive.google.com/open?id=0B6acq_amk5e3X0Q5UG1ya1VhSlE&authuser=0 Values are normalized in the range 0-1. Size is 200 rows by 300 columns. I can load it up with this code snippet: import numpy as np dstnc1=np.load('dstnc.npy') Coordinates are defined by the next snippet: xmin = 0. xmax = 9000. ymin = 0. ymax = 6000. r1,c1 = dstnc1.shape x = np.linspace(xmin,xmax,c1) y = np.linspace(ymin, ymax,r1) I have three map points defined by vectors xnew1, ynew1 with this snippet: xnew1=[3700.540199,3845.940199,3983.240199] ynew1=[1782.8611,1769.862,1694.862] I check their location with respect to the distance map with this: import matplotlib.pyplot as plt fig = plt.figure(figsize=(20, 16)) ax = fig.add_subplot(1, 1, 1) plt.imshow(dstnc1, cmap=my_cmap_r,vmin=0,vmax=0.3, extent=[0, 9000, 0, 6000], origin='upper') plt.scatter(xnew1, ynew1, s=50, linewidths=0.15) plt.show() They plot in the correct location. Now I would like to extract the distance value at those three points. I tried first interp2d. from scipy.interpolate import interp2d x1 = np.linspace(xmin,xmax,c1) y1 = np.linspace(ymin,ymax,r1) f = interp2d(x1, y1, dstnc1, kind='cubic') but when I try to evaluate with: test=f(xnew,ynew) I get this error message: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-299-d0f42e609b23> in <module>() ----> 1 test=f(xnew,ynew) C:\...\AppData\Local\Continuum\Anaconda\lib\site-packages\scipy\interpolate\interpolate.pyc in __call__(self, x, y, dx, dy) 270 (self.y_min, self.y_max))) 271 --> 272 z = fitpack.bisplev(x, y, self.tck, dx, dy) 273 z = atleast_2d(z) 274 z = transpose(z) C:\...\AppData\Local\Continuum\Anaconda\lib\site-packages\scipy\interpolate\fitpack.pyc in bisplev(x, y, tck, dx, dy) 1027 z,ier = _fitpack._bispev(tx,ty,c,kx,ky,x,y,dx,dy) 1028 if ier == 10: -> 1029 raise ValueError("Invalid input data") 1030 if ier: 1031 raise TypeError("An error occurred") ValueError: Invalid input data If I try RectBivariateSpline: from scipy.interpolate import RectBivariateSpline x2 = np.linspace(xmin,xmax,r1) y2 = np.linspace(ymin,ymax,c1) f = RectBivariateSpline(x2, y2, dstnc1) I get this error: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-302-d0f42e609b23> in <module>() ----> 1 test=f(xnew,ynew) C:\...\AppData\Local\Continuum\Anaconda\lib\site-packages\scipy\interpolate\fitpack2.pyc in __call__(self, x, y, mth, dx, dy, grid) 643 z,ier = dfitpack.bispev(tx,ty,c,kx,ky,x,y) 644 if not ier == 0: --> 645 raise ValueError("Error code returned by bispev: %s" % ier) 646 else: 647 # standard Numpy broadcasting ValueError: Error code returned by bispev: 10 Any suggestion as to whether I am using the wrong functions or the right function with wrong syntax, and how I may fix it is appreciated. Thank you

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Understanding the "vode" warning for `scipy.integrate.ode`: "This integrator is not re-entrant. You cannot have two ode instances...at the same time."
by Brian Merchant 14 May '15

14 May '15

Hi all, I'd like to make sure I am understanding the warning for the "vode" and "zvode" integrators, provided by `scipy.integrate.ode` correctly -- it might seem repetitive, but please bear with me. Let us say one has ODE problem 1, which is completely separate and different from ODE problem 2. Then, this warning tells us that we have to choose to solve ODE problem 1 first, and then ODE problem 2, even though they are completely separate. I cannot solve ODE problem 1 and ODE problem 2 in parallel, using two *separate* instances of `scipy.integrate.odeint`. Presumably, this is because the same instance of the underlying solver is called by all `scipy.integrate.odeint` instances? Kind regards, Brian

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ANN: SciPy 2015 Talk & Poster Selections Announced Today, Early Bird Deadline 5/22
by Courtenay Godshall (Enthought) 14 May '15

14 May '15

The talks & posters for the 2015 SciPy Conference were announced today: http://scipy2015.scipy.org/ehome/115969/292868/? <http://scipy2015.scipy.org/ehome/115969/292868/?&> &. Early bird registration deadline was extended (final) to 5/22 - hope we'll see you this year!

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ANN: Scipy 0.16.0 beta 1 release
by Ralf Gommers 13 May '15

13 May '15

Hi all, I'm pleased to announce the availability of the first beta release of Scipy 0.16.0. Please try this beta and report any issues on the Github issue tracker or on the scipy-dev mailing list. This first beta is a source-only release; binary installers will follow (probably next week). Source tarballs and the full release notes can be found at https://sourceforge.net/projects/scipy/files/scipy/0.16.0b1/. Part of the release notes copied below. Thanks to everyone who contributed to this release! Ralf ========================== SciPy 0.16.0 Release Notes ========================== .. note:: Scipy 0.16.0 is not released yet! SciPy 0.16.0 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 0.15.x branch, and on adding new features on the master branch. This release requires Python 2.6, 2.7 or 3.2-3.4 and NumPy 1.6.2 or greater. Highlights of this release include: - A Cython API for BLAS/LAPACK in `scipy.linalg` - A new benchmark suite. It's now straightforward to add new benchmarks, and they're routinely included with performance enhancement PRs. - Support for the second order sections (SOS) format in `scipy.signal`. New features ============ Benchmark suite --------------- The benchmark suite has switched to using `Airspeed Velocity <http://spacetelescope.github.io/asv/>`__ for benchmarking. You can run the suite locally via ``python runtests.py --bench``. For more details, see ``benchmarks/README.rst``. `scipy.linalg` improvements --------------------------- A full set of Cython wrappers for BLAS and LAPACK has been added in the modules `scipy.linalg.cython_blas` and `scipy.linalg.cython_lapack`. In Cython, these wrappers can now be cimported from their corresponding modules and used without linking directly against BLAS or LAPACK. The functions `scipy.linalg.qr_delete`, `scipy.linalg.qr_insert` and `scipy.linalg.qr_update` for updating QR decompositions were added. The function `scipy.linalg.solve_circulant` solves a linear system with a circulant coefficient matrix. The function `scipy.linalg.invpascal` computes the inverse of a Pascal matrix. The function `scipy.linalg.solve_toeplitz`, a Levinson-Durbin Toeplitz solver, was added. Added wrapper for potentially useful LAPACK function ``*lasd4``. It computes the square root of the i-th updated eigenvalue of a positive symmetric rank-one modification to a positive diagonal matrix. See its LAPACK documentation and unit tests for it to get more info. Added two extra wrappers for LAPACK least-square solvers. Namely, they are ``*gelsd`` and ``*gelsy``. Wrappers for the LAPACK ``*lange`` functions, which calculate various matrix norms, were added. Wrappers for ``*gtsv`` and ``*ptsv``, which solve ``A*X = B`` for tri-diagonal matrix ``A``, were added. `scipy.signal` improvements --------------------------- Support for second order sections (SOS) as a format for IIR filters was added. The new functions are: * `scipy.signal.sosfilt` * `scipy.signal.sosfilt_zi`, * `scipy.signal.sos2tf` * `scipy.signal.sos2zpk` * `scipy.signal.tf2sos` * `scipy.signal.zpk2sos`. Additionally, the filter design functions `iirdesign`, `iirfilter`, `butter`, `cheby1`, `cheby2`, `ellip`, and `bessel` can return the filter in the SOS format. The function `scipy.signal.place_poles`, which provides two methods to place poles for linear systems, was added. The option to use Gustafsson's method for choosing the initial conditions of the forward and backward passes was added to `scipy.signal.filtfilt`. New classes ``TransferFunction``, ``StateSpace`` and ``ZerosPolesGain`` were added. These classes are now returned when instantiating `scipy.signal.lti`. Conversion between those classes can be done explicitly now. An exponential (Poisson) window was added as `scipy.signal.exponential`, and a Tukey window was added as `scipy.signal.tukey`. The function for computing digital filter group delay was added as `scipy.signal.group_delay`. The functionality for spectral analysis and spectral density estimation has been significantly improved: `scipy.signal.welch` became ~8x faster and the functions `scipy.signal.spectrogram`, `scipy.signal.coherence` and `scipy.signal.csd` (cross-spectral density) were added. `scipy.signal.lsim` was rewritten - all known issues are fixed, so this function can now be used instead of ``lsim2``; ``lsim`` is orders of magnitude faster than ``lsim2`` in most cases. `scipy.sparse` improvements --------------------------- The function `scipy.sparse.norm`, which computes sparse matrix norms, was added. The function `scipy.sparse.random`, which allows to draw random variates from an arbitrary distribution, was added. `scipy.spatial` improvements ---------------------------- `scipy.spatial.cKDTree` has seen a major rewrite, which improved the performance of the ``query`` method significantly, added support for parallel queries, pickling, and options that affect the tree layout. See pull request 4374 for more details. The function `scipy.spatial.procrustes` for Procrustes analysis (statistical shape analysis) was added. `scipy.stats` improvements -------------------------- The Wishart distribution and its inverse have been added, as `scipy.stats.wishart` and `scipy.stats.invwishart`. The Exponentially Modified Normal distribution has been added as `scipy.stats.exponnorm`. The Generalized Normal distribution has been added as `scipy.stats.gennorm`. All distributions now contain a ``random_state`` property and allow specifying a specific ``numpy.random.RandomState`` random number generator when generating random variates. Many statistical tests and other `scipy.stats` functions that have multiple return values now return ``namedtuples``. See pull request 4709 for details. `scipy.optimize` improvements ----------------------------- A new derivative-free method DF-SANE has been added to the nonlinear equation system solving function `scipy.optimize.root`. Deprecated features =================== ``scipy.stats.pdf_fromgamma`` is deprecated. This function was undocumented, untested and rarely used. Statsmodels provides equivalent functionality with ``statsmodels.distributions.ExpandedNormal``. ``scipy.stats.fastsort`` is deprecated. This function is unnecessary, ``numpy.argsort`` can be used instead. ``scipy.stats.signaltonoise`` and ``scipy.stats.mstats.signaltonoise`` are deprecated. These functions did not belong in ``scipy.stats`` and are rarely used. See issue #609 for details. ``scipy.stats.histogram2`` is deprecated. This function is unnecessary, ``numpy.histogram2d`` can be used instead. Backwards incompatible changes ============================== The deprecated global optimizer ``scipy.optimize.anneal`` was removed. The following deprecated modules have been removed: ``scipy.lib.blas``, ``scipy.lib.lapack``, ``scipy.linalg.cblas``, ``scipy.linalg.fblas``, ``scipy.linalg.clapack``, ``scipy.linalg.flapack``. They had been deprecated since Scipy 0.12.0, the functionality should be accessed as `scipy.linalg.blas` and `scipy.linalg.lapack`. The deprecated function ``scipy.special.all_mat`` has been removed. The deprecated functions ``fprob``, ``ksprob``, ``zprob``, ``randwcdf`` and ``randwppf`` have been removed from `scipy.stats`. Other changes ============= The version numbering for development builds has been updated to comply with PEP 440. Building with ``python setup.py develop`` is now supported. Authors ======= * @axiru + * @endolith * Elliott Sales de Andrade + * Anne Archibald * Yoshiki Vázquez Baeza + * Sylvain Bellemare * Felix Berkenkamp + * Raoul Bourquin + * Matthew Brett * Per Brodtkorb * Christian Brueffer * Lars Buitinck * Evgeni Burovski * Steven Byrnes * CJ Carey * George Castillo + * Alex Conley + * Liam Damewood + * Rupak Das + * Abraham Escalante + * Matthias Feurer + * Eric Firing + * Clark Fitzgerald * Chad Fulton * André Gaul * Andreea Georgescu + * Christoph Gohlke * Andrey Golovizin + * Ralf Gommers * J.J. Green + * Alex Griffing * Alexander Grigorievskiy + * Hans Moritz Gunther + * Jonas Hahnfeld + * Charles Harris * Ian Henriksen * Andreas Hilboll * Åsmund Hjulstad + * Jan Schlüter + * Janko Slavič + * Daniel Jensen + * Johannes Ballé + * Terry Jones + * Amato Kasahara + * Eric Larson * Denis Laxalde * Antony Lee * Gregory R. Lee * Perry Lee + * Loïc Estève * Martin Manns + * Eric Martin + * Matěj Kocián + * Andreas Mayer + * Nikolay Mayorov + * Robert McGibbon + * Sturla Molden * Nicola Montecchio + * Eric Moore * Jamie Morton + * Nikolas Moya + * Maniteja Nandana + * Andrew Nelson * Joel Nothman * Aldrian Obaja * Regina Ongowarsito + * Paul Ortyl + * Pedro López-Adeva Fernández-Layos + * Stefan Peterson + * Irvin Probst + * Eric Quintero + * John David Reaver + * Juha Remes + * Thomas Robitaille * Clancy Rowley + * Tobias Schmidt + * Skipper Seabold * Aman Singh + * Eric Soroos * Valentine Svensson + * Julian Taylor * Aman Thakral + * Helmut Toplitzer + * Fukumu Tsutsumi + * Anastasiia Tsyplia + * Jacob Vanderplas * Pauli Virtanen * Matteo Visconti + * Warren Weckesser * Florian Wilhelm + * Nathan Woods * Haochen Wu + * Daan Wynen + A total of 93 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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