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From "Alex Goodman (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (CLIMATE-399) Use functions in numpy.testing for unit tests involving array comparisons
Date Sat, 05 Apr 2014 20:32:14 GMT

     [ https://issues.apache.org/jira/browse/CLIMATE-399?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Alex Goodman updated CLIMATE-399:
---------------------------------

    Description: 
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}

  was:
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}


> 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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