Alex Goodman created CLIMATE-399:
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Summary: Use functions in numpy.testing for unit tests involving
array comparisons
Key: CLIMATE-399
URL: https://issues.apache.org/jira/browse/CLIMATE-399
Project: Apache Open Climate Workbench
Issue Type: Improvement
Components: general
Affects Versions: 0.3-incubating
Reporter: Alex Goodman
Assignee: Alex Goodman
Fix For: 0.4
Currently our unit tests for numpy array equality look something like this:
{code}
self.assertTrue(np.arrray_equal(x, y))
{code}
which could raise the following exception:
{code}
AssertionError:
False is not true
{code}
This indeed tells us if the test has failed, but it would be better if the
output could show where the arrays were inconsistent. The functions included in
numpy.testing fulfill this purpose, and are widely used in other projects
depending on numpy arrays. Therefore we should replace all instances of the
above example with:
{code}
np.testing.assert_array_equal(x, y)
{code}
Which could raise exceptions like:
{code}
AssertionError:
Arrays are not equal
(mismatch 100.0%
x: array([ 1. , 3, 7])
y: array([ -2. , -4, -6])
{code}
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