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Reenable shading tests for numpy 1.9.1 and later #3755

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merged 2 commits into from Nov 5, 2014
Merged

Reenable shading tests for numpy 1.9.1 and later #3755

merged 2 commits into from Nov 5, 2014

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jenshnielsen
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This fixes #3598 now the numpy 1.9.1 is released which changes the default gradient on the edge back to first order and fixes an issue with sharing of masks for masked arrays calculated from other masked arrays.

The non masked test passed for all versions of numpy different from 1.9.0. To get the tests passing with 1.9.0 the arrays are modified to cut out the edges which is the only differences.

The masked test is different for all versions due to the long standing mask bug. The numpy 1.9.1 version is the most correct so I chose to raise a knowfailure for all older versions.

See attached illustration (the corners where previously masked but are now calculated correctly) :

numpy19shadebug

@jenshnielsen
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This is somewhat urgent since on of the tests is failing on master with a knowfailure test did not fail using numpy 1.9.1 which Travis might soon pickup.

@tacaswell
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Not sure what to make of the failing test passing on 2.6/np 1.6

@tacaswell tacaswell added this to the v1.5.x milestone Nov 4, 2014
@jenshnielsen
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I reenabled the test on 1.6 and that is fine.
I ran the gradient on various different numpy versions and that confirms it 1.6 behaves like 1.9.1

numpy 1.6

z before

[[-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]]

z after

[[-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]]

dx

[[4.0 4.0 3.0 1.0 -1.0 -3.0 -4.0 -4.0]
 [7.0 6.0 3.0 1.0 -1.0 -3.0 -6.0 -7.0]
 [7.0 5.0 2.0 0.0 -0.0 -2.0 -5.0 -7.0]
 [6.0 4.0 -- -- -- -- -4.0 -6.0]
 [6.0 4.0 -- -- -- -- -4.0 -6.0]
 [7.0 5.0 2.0 0.0 -0.0 -2.0 -5.0 -7.0]
 [7.0 6.0 3.0 1.0 -1.0 -3.0 -6.0 -7.0]
 [4.0 4.0 3.0 1.0 -1.0 -3.0 -4.0 -4.0]]

dy

[[4.0 7.0 7.0 6.0 6.0 7.0 7.0 4.0]
 [4.0 6.0 5.0 4.0 4.0 5.0 6.0 4.0]
 [3.0 3.0 2.0 -- -- 2.0 3.0 3.0]
 [1.0 1.0 0.0 -- -- 0.0 1.0 1.0]
 [-1.0 -1.0 -0.0 -- -- -0.0 -1.0 -1.0]
 [-3.0 -3.0 -2.0 -- -- -2.0 -3.0 -3.0]
 [-4.0 -6.0 -5.0 -4.0 -4.0 -5.0 -6.0 -4.0]
 [-4.0 -7.0 -7.0 -6.0 -6.0 -7.0 -7.0 -4.0]]

numpy 1.7

z before

[[-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]]

z after

[[-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-1.0 5.0 -- -- -- -- 5.0 -1.0]
 [1.0 7.0 -- -- -- -- 7.0 1.0]
 [1.0 7.0 -- -- -- -- 7.0 1.0]
 [-1.0 5.0 -- -- -- -- 5.0 -1.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]]

dx

[[4.0 4.0 3.0 1.0 -1.0 -3.0 -4.0 -4.0]
 [7.0 6.0 3.0 1.0 -1.0 -3.0 -6.0 -7.0]
 [7.0 5.0 -- -- -- -- -5.0 -7.0]
 [6.0 4.0 -- -- -- -- -4.0 -6.0]
 [6.0 4.0 -- -- -- -- -4.0 -6.0]
 [7.0 5.0 -- -- -- -- -5.0 -7.0]
 [7.0 6.0 3.0 1.0 -1.0 -3.0 -6.0 -7.0]
 [4.0 4.0 3.0 1.0 -1.0 -3.0 -4.0 -4.0]]

dy

[[4.0 7.0 7.0 6.0 6.0 7.0 7.0 4.0]
 [4.0 6.0 5.0 4.0 4.0 5.0 6.0 4.0]
 [3.0 3.0 2.0 -- -- 2.0 3.0 3.0]
 [1.0 1.0 0.0 -- -- 0.0 1.0 1.0]
 [-1.0 -1.0 -0.0 -- -- -0.0 -1.0 -1.0]
 [-3.0 -3.0 -2.0 -- -- -2.0 -3.0 -3.0]
 [-4.0 -6.0 -5.0 -4.0 -4.0 -5.0 -6.0 -4.0]
 [-4.0 -7.0 -7.0 -6.0 -6.0 -7.0 -7.0 -4.0]]

numpy 1.8

z before

[[-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]]

z after

[[-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-1.0 5.0 -- -- -- -- 5.0 -1.0]
 [1.0 7.0 -- -- -- -- 7.0 1.0]
 [1.0 7.0 -- -- -- -- 7.0 1.0]
 [-1.0 5.0 -- -- -- -- 5.0 -1.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]]

dx

[[4.0 4.0 3.0 1.0 -1.0 -3.0 -4.0 -4.0]
 [7.0 6.0 3.0 1.0 -1.0 -3.0 -6.0 -7.0]
 [7.0 5.0 -- -- -- -- -5.0 -7.0]
 [6.0 4.0 -- -- -- -- -4.0 -6.0]
 [6.0 4.0 -- -- -- -- -4.0 -6.0]
 [7.0 5.0 -- -- -- -- -5.0 -7.0]
 [7.0 6.0 3.0 1.0 -1.0 -3.0 -6.0 -7.0]
 [4.0 4.0 3.0 1.0 -1.0 -3.0 -4.0 -4.0]]

dy

[[4.0 7.0 7.0 6.0 6.0 7.0 7.0 4.0]
 [4.0 6.0 5.0 4.0 4.0 5.0 6.0 4.0]
 [3.0 3.0 2.0 -- -- 2.0 3.0 3.0]
 [1.0 1.0 0.0 -- -- 0.0 1.0 1.0]
 [-1.0 -1.0 -0.0 -- -- -0.0 -1.0 -1.0]
 [-3.0 -3.0 -2.0 -- -- -2.0 -3.0 -3.0]
 [-4.0 -6.0 -5.0 -4.0 -4.0 -5.0 -6.0 -4.0]
 [-4.0 -7.0 -7.0 -6.0 -6.0 -7.0 -7.0 -4.0]]

numpy 1.9.1

z before

[[-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]]

z after

[[-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [1.0 7.0 10.0 -- -- 10.0 7.0 1.0]
 [-1.0 5.0 9.0 10.0 10.0 9.0 5.0 -1.0]
 [-6.0 1.0 5.0 7.0 7.0 5.0 1.0 -6.0]
 [-10.0 -6.0 -1.0 1.0 1.0 -1.0 -6.0 -10.0]]

dx

[[4.0 4.0 3.0 1.0 -1.0 -3.0 -4.0 -4.0]
 [7.0 6.0 3.0 1.0 -1.0 -3.0 -6.0 -7.0]
 [7.0 5.0 2.0 0.0 -0.0 -2.0 -5.0 -7.0]
 [6.0 4.0 -- -- -- -- -4.0 -6.0]
 [6.0 4.0 -- -- -- -- -4.0 -6.0]
 [7.0 5.0 2.0 0.0 -0.0 -2.0 -5.0 -7.0]
 [7.0 6.0 3.0 1.0 -1.0 -3.0 -6.0 -7.0]
 [4.0 4.0 3.0 1.0 -1.0 -3.0 -4.0 -4.0]]

dy

[[4.0 7.0 7.0 6.0 6.0 7.0 7.0 4.0]
 [4.0 6.0 5.0 4.0 4.0 5.0 6.0 4.0]
 [3.0 3.0 2.0 -- -- 2.0 3.0 3.0]
 [1.0 1.0 0.0 -- -- 0.0 1.0 1.0]
 [-1.0 -1.0 -0.0 -- -- -0.0 -1.0 -1.0]
 [-3.0 -3.0 -2.0 -- -- -2.0 -3.0 -3.0]
 [-4.0 -6.0 -5.0 -4.0 -4.0 -5.0 -6.0 -4.0]
 [-4.0 -7.0 -7.0 -6.0 -6.0 -7.0 -7.0 -4.0]]

as a result of running

import numpy as np
y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j]
z = 10 * np.cos(x**2 + y**2)

z_mask = np.ma.masked_greater(z, 9.9)


print('z before\n')
print(np.round(z_mask))
print('\nz after\n')
dy, dx = np.gradient(z_mask)
print(np.round(z_mask))
print('\ndx\n')
print(np.round(dx))
print('\ndy\n')
print(np.round(dy))

tacaswell added a commit that referenced this pull request Nov 5, 2014
@tacaswell
Merge pull request #3755 from jenshnielsen/numpy_191_fixes
a8e028d 
TST : Reenable shading tests for numpy 1.9.1 and later
@tacaswell tacaswell merged commit a8e028d into matplotlib:master Nov 5, 2014
@tacaswell
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Thanks for being on top of these numpy issues.

@jenshnielsen jenshnielsen deleted the numpy_191_fixes branch November 5, 2014 14:39
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Successfully merging this pull request may close these issues.

test_light_source_shading_default and test_light_source_masked_shading fails with numpy 1.9
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@jenshnielsen @tacaswell