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fix error-bar bug for normalized consistency plots #222
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…sistency tests tests: added unit test for comparative plots
Codecov ReportPatch coverage:
📣 This organization is not using Codecov’s GitHub App Integration. We recommend you install it so Codecov can continue to function properly for your repositories. Learn more Additional details and impacted files@@ Coverage Diff @@
## master #222 +/- ##
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+ Coverage 54.36% 54.63% +0.27%
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Files 21 22 +1
Lines 3802 3904 +102
Branches 636 650 +14
==========================================
+ Hits 2067 2133 +66
- Misses 1603 1636 +33
- Partials 132 135 +3
☔ View full report in Codecov by Sentry. |
…mum of Mock models.
…hich caused problems with precision rounding
Pablo, I just noticed you deleted the MANIFEST.in file in your pull request. Was this intentional? Could you take a look? Thanks! |
nice catch, sorry bout that. fixed |
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Thanks for including a test! Only very very minor changes related to formatting and indentation. Please take a look at the comments for specific examples on what to look for. Once revised, these changes should be added to the main branch for the release.
pyplot.errorbar(bin_edges_plot, u3etas_median, yerr=[u3etas_emin, u3etas_emax], xerr=0.8 * dmw / 2, fmt=' ', | ||
pyplot.errorbar(bin_edges_plot, u3etas_median, | ||
yerr=[u3etas_emin, u3etas_emax], xerr=0.8 * dmw / 2, | ||
fmt=' ', | ||
label=sim_label, color='blue', alpha=0.7) |
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Move these up to the line above, so fmt is not by itself.
plow97 = scipy.stats.poisson.ppf((1 - percentile / 100.) / 2., | ||
test_results[ | ||
i].test_distribution[1]) | ||
phigh97 = scipy.stats.poisson.ppf( | ||
1 - (1 - percentile / 100.) / 2., | ||
test_results[i].test_distribution[1]) |
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Ideally, these two (and other similar lines throughout the file) should be indented the same way.
Tests initially passed but recently failed due to setting up conda environment issue, merge approved and tests passed after switching to mamba for building the action environment (see PR #228). |
* fix: matplotlib errorbar now doesnt throw exception in normalized consistency tests tests: added unit test for comparative plots * fix: removed numpy.warnings in favor of warnings module * increased the number of comparison plot testss, and set to 2 the minimum of Mock models. * forced errorbar limit arguments of consistency plot to be positive, which caused problems with precision rounding * small detail: defaulted t-test plot axis labels to x: None, y: Information Gain * returned Manifest * fixed some style issues
Type of change:
Please delete options that are not relevant.
Checklist: