[Numpy-discussion] Changes in PyArray_FromAny between 1.5.x and 1.6.x
Hello,
In trying to upgrade NumPy within Sage, we notices some differences in
behavior between 1.5 and 1.6. In particular, in 1.5, we have
sage: f = 0.5
sage: f.__array_interface__
{'typestr': '=f8'}
sage: numpy.array(f)
array(0.5)
sage: numpy.array(float(f))
array(0.5)
In 1.6, we get the following,
sage: f = 0.5
sage: f.__array_interface__
{'typestr': '=f8'}
sage: numpy.array(f)
array(0.500, dtype=object)
This seems to be do to the changes in PyArray_FromAny introduced in
https://github.com/mwhansen/numpy/commit/2635398db3f26529ce2aaea4028a8118844f3c48
. In particular, _array_find_type used to be used to query our
__array_interface__ attribute, and it no longer seems to work. Is
there a way to get the old behavior with the current code?
--Mike
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Re: [Numpy-discussion] [enhancement] sum_angle() and sum_polar()
On Mon, May 28, 2012 at 7:58 PM, Travis Oliphant tra...@continuum.iowrote: I didn't see anyone respond to this, but looking over his simple and elegant solution it seems like a useful addition to the 2-d functions available in NumPy as it works with any 2-d array (image or matrix) and does a transformation on the indices in order to organize the sum. It is not a general-purpose interpolating approach where the 2-d array is viewed as samples of an underlying continuous function. Are their other thoughts? This was discussed (not finished yet) on scipy-dev: http://thread.gmane.org/gmane.comp.python.scientific.devel/16538/focus=16541. Ralf -Travis On Mar 7, 2012, at 12:39 PM, Robert Jördens wrote: Hi everyone, I am proposing to add the the two following functions to numpy/lib/twodim_base.py: sum_angle() computes the sum of a 2-d array along an angled axis sum_polar() computes the sum of a 2-d array along radial lines or along azimuthal circles https://github.com/numpy/numpy/pull/230 Comments? When I was looking for a solution to these problems of calculating special sums of 2-d arrays I could not find anything and it took me a while to figure out a (hopefully) useful and consistent algorithm. I can see how one would extend these to higher dimensions but that would preclude using bincount() to do the heavy lifting. Looking at some other functions, the doctests might need to be split into real examples and unittests. Best, -- Robert Jordens. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] [enhancement] sum_angle() and sum_polar()
On May 28, 2012, at 1:02 PM, Ralf Gommers wrote: On Mon, May 28, 2012 at 7:58 PM, Travis Oliphant tra...@continuum.io wrote: I didn't see anyone respond to this, but looking over his simple and elegant solution it seems like a useful addition to the 2-d functions available in NumPy as it works with any 2-d array (image or matrix) and does a transformation on the indices in order to organize the sum. It is not a general-purpose interpolating approach where the 2-d array is viewed as samples of an underlying continuous function. Are their other thoughts? This was discussed (not finished yet) on scipy-dev: http://thread.gmane.org/gmane.comp.python.scientific.devel/16538/focus=16541. That is a useful discussion, but the question about whether this function should just go into NumPy is also of interest.There are arguments that it could go into NumPy, SciPy, or sckitis-image. I think going into scikits-image does not make sense because of their general applicability for more than just images and the fact that in the context of image-processing these functions *just* do nearest neighbor interpolation. I could see these functions going into scipy.ndimage but again because they are not necessarily just image processing functions, and the fact that they are so simple, perhaps they are best put into NumPy itself. -Travis Ralf -Travis On Mar 7, 2012, at 12:39 PM, Robert Jördens wrote: Hi everyone, I am proposing to add the the two following functions to numpy/lib/twodim_base.py: sum_angle() computes the sum of a 2-d array along an angled axis sum_polar() computes the sum of a 2-d array along radial lines or along azimuthal circles https://github.com/numpy/numpy/pull/230 Comments? When I was looking for a solution to these problems of calculating special sums of 2-d arrays I could not find anything and it took me a while to figure out a (hopefully) useful and consistent algorithm. I can see how one would extend these to higher dimensions but that would preclude using bincount() to do the heavy lifting. Looking at some other functions, the doctests might need to be split into real examples and unittests. Best, -- Robert Jordens. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] 1.6.2 no more unique for rows
https://github.com/numpy/numpy/commit/74b9f5eef8fac643bf9012dbb2ac6b4b19f46892
broke return_inverse for structured arrays, because of the use of mergesort
I'm using structured dtypes to get uniques and return_inverse by rows
groups = np.random.randint(0,4,size=(10,2))
groups_ = groups.view([('',groups.dtype)]*groups.shape[1]).flatten()
groups
array([[0, 2],
[1, 2],
[1, 1],
[3, 1],
[3, 1],
[2, 1],
[1, 0],
[3, 3],
[3, 2],
[0, 0]])
groups_
array([(0, 2), (1, 2), (1, 1), (3, 1), (3, 1), (2, 1), (1, 0), (3, 3),
(3, 2), (0, 0)],
dtype=[('f0', 'i4'), ('f1', 'i4')])
np.argsort(groups_)
array([9, 0, 6, 2, 1, 5, 4, 3, 8, 7])
np.argsort(groups_, kind='mergesort')
Traceback (most recent call last):
File stdin, line 1, in module
File C:\Python26\lib\site-packages\numpy\core\fromnumeric.py, line
679, in argsort
return argsort(axis, kind, order)
TypeError: requested sort not available for type
uni, uni_idx, uni_inv = np.unique(groups_, return_index=True,
return_inverse=True)
uni_inv
array([1, 4, 3, 6, 6, 5, 2, 8, 7, 0])
exception in numpy 1.6.2rc2 (as reported by Debian for statsmodels)
Josef
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[Numpy-discussion] numpy.random.gamma returns 0 for small shape parameters
I am trying to sample from a Dirichlet distribution, where some of the shape parameters are very small. To do so, the algorithm samples each component individually from a Gamma(k,1) distribution where k is the shape parameter for that component of the Dirichlet. In principle, this should always return a positive number (as the Dirichlet is defined). However, if k is very small, it will return zero: In [157]: np.random.gamma(1e-1) Out[157]: 4.863866491339177e-06 In [158]: np.random.gamma(1e-2) Out[158]: 2.424451829710714e-57 In [159]: np.random.gamma(1e-3) Out[159]: 5.1909861689757784e-197 In [160]: np.random.gamma(1e-4) Out[160]: 0.0 In [161]: np.random.gamma(1e-5) Out[161]: 0.0 What is the best way to deal with this? Thanks! Uri ... Uri Laserson Graduate Student, Biomedical Engineering Harvard-MIT Division of Health Sciences and Technology M +1 917 742 8019 laser...@mit.edu ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy.random.gamma returns 0 for small shape parameters
You'll need some patience to get non-zeros, especially for k=1e-5 In [84]: np.sum(np.random.gamma(1e-5,size=100)!=0.0) Out[84]: 7259 that's less than 1%. For k=1e-4 it's ~7% Val On Mon, May 28, 2012 at 10:33 PM, Uri Laserson uri.laser...@gmail.comwrote: I am trying to sample from a Dirichlet distribution, where some of the shape parameters are very small. To do so, the algorithm samples each component individually from a Gamma(k,1) distribution where k is the shape parameter for that component of the Dirichlet. In principle, this should always return a positive number (as the Dirichlet is defined). However, if k is very small, it will return zero: In [157]: np.random.gamma(1e-1) Out[157]: 4.863866491339177e-06 In [158]: np.random.gamma(1e-2) Out[158]: 2.424451829710714e-57 In [159]: np.random.gamma(1e-3) Out[159]: 5.1909861689757784e-197 In [160]: np.random.gamma(1e-4) Out[160]: 0.0 In [161]: np.random.gamma(1e-5) Out[161]: 0.0 What is the best way to deal with this? Thanks! Uri ... Uri Laserson Graduate Student, Biomedical Engineering Harvard-MIT Division of Health Sciences and Technology M +1 917 742 8019 laser...@mit.edu ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
