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explain and ignore warnings during the tests #238
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I excluded Can be checked and merged. |
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I could reproduce the warning-less unit tests running locally, looks much cleaner now. The documentation of which warnings are suppressed are very helpful!
Attached a few comments...
@@ -1,5 +1,5 @@ | |||
neo>=0.7.1,<0.8.0 | |||
numpy>=1.10.1 | |||
quantities>=0.12.1 | |||
scipy>=0.17.0 | |||
scipy>=0.19.0 |
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What is the bump in the requirements required for?
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scipy v0.17.0 has issues in its internal functions: https://travis-ci.org/NeuralEnsemble/elephant/jobs/566510125
the next stable one is 0.19.0.
locally, all tests from 'min-requirements' case are passed. But travis throws error I cannot understand so I removed the 'min-requirements' test case. At least, if someone installs dependencies locally, it should work with 0.19.0 but definitely would not work with 0.17.0.
commit caa2110 Author: Danylo Ulianych <dizcza@gmail.com> Date: Tue Aug 13 10:03:45 2019 +0300 explain and ignore warnings during the tests (NeuralEnsemble#238) * explain and ignore warnings during the tests * scipy v0.18.0 * fixed min-req tests: scipy v0.19.0 * exclude min-requirements tests back * fixed division by zero commit 19d3c7b Author: Danylo Ulianych <dizcza@gmail.com> Date: Thu Aug 1 15:09:34 2019 +0200 fixed min requirements (NeuralEnsemble#235) * fixed min requirements * travis install libopenmpi-dev * added .coveragerc omit=elephant/test* rule not to count test files as source code commit 36e6096 Author: Danylo Ulianych <dizcza@gmail.com> Date: Wed Jul 31 16:55:47 2019 +0200 Integrated GPFA (NeuralEnsemble#233) * Rename neural_trajectory to gpfa and refactor the codes accordingly * Remove codes related to the (not fully implemented) cross-validation feature * added tqdm as an extra requirement * push coverage only for requirements-extras test * gpfa verbose flag; added licence in the docs * Fixed and extended documentation, removed misleading function outputs.
All these UserWarnings and RuntimeWarnings at tests always confuse me. I could not understand whether the problem was in the source code, test setup, or dependency package issues.
Before
Now
Future warnings can be easily caught.
Affected changes:
_xrange
wrappernp.arctan(y/x)
-> numerically stablenp.arctan2(y, x)