[
https://issues.apache.org/jira/browse/CLIMATE-399?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13961557#comment-13961557
]
ASF GitHub Bot commented on CLIMATE-399:
----------------------------------------
Github user asfgit closed the pull request at:
https://github.com/apache/climate/pull/17
> 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}
--
This message was sent by Atlassian JIRA
(v6.2#6252)
