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Commit 77d627e

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Check for nan and inf in axes.delete_masked_points().
svn path=/trunk/matplotlib/; revision=5791
1 parent 90e6e43 commit 77d627e
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3 files changed

+71-2Lines changed: 71 additions & 2 deletions

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‎CHANGELOG‎

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+3Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,6 @@
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2008-07-18 Check for nan and inf in axes.delete_masked_points().
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This should help hexbin and scatter deal with nans. - ADS
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2008-07-17 Added ability to manually select contour label locations.
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Also added a waitforbuttonpress function. - DMK
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‎lib/matplotlib/axes.py‎

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+6-2Lines changed: 6 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,5 @@
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from __future__ import division, generators
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import math, sys, warnings, datetime, new
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import math, sys, warnings, datetime, new, types
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import numpy as np
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from numpy import ma
@@ -36,6 +36,8 @@ def delete_masked_points(*args):
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Find all masked points in a set of arguments, and return
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the arguments with only the unmasked points remaining.
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This will also delete any points that are not finite (nan or inf).
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The overall mask is calculated from any masks that are present.
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If a mask is found, any argument that does not have the same
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dimensions is left unchanged; therefore the argument list may
@@ -49,9 +51,11 @@ def delete_masked_points(*args):
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useful.
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"""
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masks = [ma.getmaskarray(x) for x in args if hasattr(x, 'mask')]
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isfinite = [np.isfinite(x) for x in args]
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masks.extend( [~x for x in isfinite if not isinstance(x,types.NotImplementedType)] )
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if len(masks) == 0:
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return args
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mask = reduce(ma.mask_or, masks)
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mask = reduce(np.logical_or, masks)
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margs = []
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for x in args:
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if (not is_string_like(x)
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‎unit/axes_unit.py‎

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+62Lines changed: 62 additions & 0 deletions
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@@ -0,0 +1,62 @@
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import unittest
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import numpy as np
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import matplotlib.axes as axes
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class TestAxes(unittest.TestCase):
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def test_delete_masked_points_arrays(self):
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input = ( [1,2,3,np.nan,5],
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np.array((1,2,3,4,5)),
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)
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expected = [np.array((1,2,3,5))]*2
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actual = axes.delete_masked_points(*input)
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assert np.allclose(actual, expected)
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input = ( np.ma.array( [1,2,3,4,5], mask=[False,False,False,True,False] ),
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np.array((1,2,3,4,5)),
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)
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expected = [np.array((1,2,3,5))]*2
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actual = axes.delete_masked_points(*input)
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assert np.allclose(actual, expected)
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input = ( [1,2,3,np.nan,5],
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np.ma.array( [1,2,3,4,5], mask=[False,False,False,True,False] ),
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np.array((1,2,3,4,5)),
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)
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expected = [np.array((1,2,3,5))]*3
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actual = axes.delete_masked_points(*input)
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assert np.allclose(actual, expected)
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input = ()
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expected = ()
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actual = axes.delete_masked_points(*input)
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assert np.allclose(actual, expected)
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input = ( [1,2,3,np.nan,5],
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)
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expected = [np.array((1,2,3,5))]*1
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actual = axes.delete_masked_points(*input)
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assert np.allclose(actual, expected)
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input = ( np.array((1,2,3,4,5)),
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)
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expected = [np.array((1,2,3,4,5))]*1
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actual = axes.delete_masked_points(*input)
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assert np.allclose(actual, expected)
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def test_delete_masked_points_strings(self):
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input = ( 'hello',
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)
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expected = ('hello',)
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actual = axes.delete_masked_points(*input)
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assert actual == expected
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input = ( u'hello',
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)
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expected = (u'hello',)
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actual = axes.delete_masked_points(*input)
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assert actual == expected
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if __name__=='__main__':
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unittest.main()

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