installed MDAnalysisTests error/fail due to missing files

[orbeckst]

Expected behavior

After a conda installation of 1.0.0 (with workaround, see #2938 ...), all tests should pass, as documented.

Actual behavior

5 failures and 35 errors

= 5 failed, 16071 passed, 115 skipped, 1 xfailed, 2 xpassed, 9100 warnings, 35 errors in 240.50s (0:04:00) =

because at the two following files are missing:

E           FileNotFoundError: [Errno 2] No such file or directory: '/scratch/oliver/miniconda3/envs/m
daenv/lib/python3.6/site-packages/MDAnalysisTests/data/fhiaims.in'

E           FileNotFoundError: [Errno 2] No such file or directory: '/scratch/oliver/miniconda3/envs/m
daenv/lib/python3.6/site-packages/MDAnalysisTests/data/gromacs_ala10.top'

miniconda3/envs/mdaenv/lib/python3.6/bz2.py:96: FileNotFoundError

(One failure seems unrelated:

=================================== FAILURES ===================================
_______________________________ test_given_mean ________________________________
[gw2] linux -- Python 3.6.11 /scratch/oliver/miniconda3/envs/mdaenv/bin/python

pca_aligned = <MDAnalysis.analysis.pca.PCA object at 0x7fadfd89dd68>
u = <Universe with 3341 atoms>

    def test_given_mean(pca_aligned, u):
        pca = PCA(u, select=SELECTION, align=False,
                  mean=pca_aligned._mean).run()
>       assert_almost_equal(pca_aligned.cov, pca.cov, decimal=5)
E       AssertionError:
E       Arrays are not almost equal to 5 decimals
E
E       Mismatched elements: 900 / 900 (100%)
E       Max absolute difference: 1.0380358
E       Max relative difference: 106.43334522
E        x: array([[ 6.85943e-02,  6.14962e-03, -2.85235e-02, -2.46964e-03,
E                4.93089e-03, -1.45766e-02,  6.77579e-03,  6.73560e-03,
E               -6.87083e-03, -1.39547e-02,  4.34639e-03, -1.46411e-02,...
E        y: array([[ 1.10663e+00, -4.76013e-01,  2.59657e-01,  8.86885e-01,
E               -4.62178e-01,  3.13851e-01,  7.73986e-01, -4.43201e-01,
E                2.00352e-01,  4.87414e-01, -3.32454e-01,  1.01439e-01,...

and is not subject of this report)

Code to reproduce the behavior

Install and run tests

pytest -n 12  --disable-pytest-warnings --pyargs MDAnalysisTests 2>&1 | tee FAILED_TESTS.log

(-n 12 is optional and needs pytest-xdist)

However, this likely affects 2.0.0-dev, too.

Current version of MDAnalysis

• Which version are you using? (run python -c "import MDAnalysis as mda; print(mda.__version__)")
• Which version of Python (python -V)?
• Which operating system?

[orbeckst]

This needs to be fixed both for develop and backported to 1.0.x.

[IAlibay]

I think the PCA failure is related to #2728, which I think is down to in-memory trajectory alignment somehow affecting the tests.

I can't seem to be able to reproduce this with an install of the master branch with -n 4, are you seeing this with lower number of processes @orbeckst or does it just happen with 12?

[orbeckst]

Fixed in 2ebb052 on master.

[AmitvikramPujar]

@orbeckst I have been trying to run the code given here which goes like

import MDAnalysis as mda
from MDAnalysis.tests.datafiles import PSF, DCD, GRO, XTC

print(mda.Universe(PSF, DCD))
print(mda.__version__)

but am unable to run the same.

I am using:

• MDAnalysis version: 1.0.0
• Python version: 3.8.5
• Operating System: Windows 10 Home

I am running this in a virtualenv through PyCharm on a Jupyter Notebook. This is the error message I am getting

Can you kindly help me resolve this?

[RMeli]

@AmitvikramPujar which version of matplotlib are you using?

[IAlibay]

@AmitvikramPujar this is unfortunately a known issue with MDA 1.0, please see: #2938

As mentioned by @orbeckst the current workaround is to downgrade matplotlib (which I assume is what @RMeli was getting to). If that doesn't work it may be best to continue this conversation in #2938 if that works for you?

[AmitvikramPujar]

@RMeli Currently I am using matplotlib version 3.3.2 .Can you please explain how dependency on the version of matplotlib is causing this?
@IAlibay Which version should I downgrade to?
Can this be resolved by changing the environment? As in use a Conda environment or something else.