Updated links to images and result-data again #302
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Now it also links to the data-dir in docs/build/html
Codecov ReportPatch and project coverage have no change.
Additional details and impacted files@@ Coverage Diff @@
## master #302 +/- ##
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Coverage 72.61% 72.61%
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Files 254 254
Lines 21084 21084
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Hits 15311 15311
Misses 5773 5773 ☔ View full report in Codecov by Sentry. |
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In artifact links are still wrong... |
But worked locally
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Regardless of the results, the problem we wanted to solve still persists. The plots are shown on the website, but not on GitHub. Maybe this is not worth spending lot's of time on and we just go back to the way things were? |
Where on Github would you like the results to appear? Since they are not checked in (i.e. staged and commited) they are not shown in the files-section, but are (in principel) present on the website (currently not correctly linked, but uploaded) |
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This is precisely what I wanted! I want the plots to show up in the files section, even though they are not checked in. I know that this is maybe a weird request, I was just wondering if this is possible. If not then we just don't worry about it and leave everything as is. |
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Ok. I don't assume that to be possible, because it means having versioned files and unversioned files in the same place. So the workaround could be to commit the plots. But I would recommend NOT to do that! I would rather refer to an artifact containing the files for that usecase. I assumed, that the issue is that the website did not include the plots because they are outside of the "docs" directory and therefore not uploaded |
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Sounds like this PR can be closed, then? |
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If you want to, you can close this PR and roll back. As an alternative, I can update the PR to roll back to the original state, as the current state is worse than the original state in that it does not create a working website. |
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Yes please! |
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Now it seems to work again (at least it does in the artifact). So this PR can be merged |
#!!!!!!!!!! WARNING: RUFF FAILED !!!!!!!!!!: #pySDC/projects/GPU/analysis_scripts/compare_RBC3D.py:298:5: F841 Local variable `data` is assigned to but never used # | Parallel-in-Time#296 | res = 32 Parallel-in-Time#297 | Parallel-in-Time#298 | data = [] # | ^^^^ F841 Parallel-in-Time#299 | labels = [] Parallel-in-Time#300 | linestyles = [] # | # = help: Remove assignment to unused variable `data` # #pySDC/projects/GPU/analysis_scripts/compare_RBC3D.py:299:5: F841 Local variable `labels` is assigned to but never used # | Parallel-in-Time#298 | data = [] Parallel-in-Time#299 | labels = [] # | ^^^^^^ F841 Parallel-in-Time#300 | linestyles = [] # | # = help: Remove assignment to unused variable `labels` # #pySDC/projects/GPU/analysis_scripts/compare_RBC3D.py:300:5: F841 Local variable `linestyles` is assigned to but never used # | Parallel-in-Time#298 | data = [] Parallel-in-Time#299 | labels = [] Parallel-in-Time#300 | linestyles = [] # | ^^^^^^^^^^ F841 Parallel-in-Time#301 | Parallel-in-Time#302 | ref_data = get_pySDC_data(Ra, res=res, dt=0.01, config_name='RBC3DG4R4') # | # = help: Remove assignment to unused variable `linestyles` # #pySDC/projects/GPU/analysis_scripts/compare_RBC3D.py:312:9: F841 Local variable `t` is assigned to but never used # | Parallel-in-Time#310 | ax.set_title(title) Parallel-in-Time#311 | Nu_ref = np.array(ref['Nu']['V']) Parallel-in-Time#312 | t = ref['t'] # | ^ F841 Parallel-in-Time#313 | Parallel-in-Time#314 | for dt in dts: # | # = help: Remove assignment to unused variable `t` # #pySDC/projects/GPU/analysis_scripts/compare_RBC3D.py:587:9: E722 Do not use bare `except` # | Parallel-in-Time#585 | k[_s > 1e-16], _s[_s > 1e-16], color=last_line.get_color(), ls=last_line.get_linestyle(), label=label Parallel-in-Time#586 | ) Parallel-in-Time#587 | except: # | ^^^^^^ E722 Parallel-in-Time#588 | pass # | # #pySDC/projects/GPU/analysis_scripts/compare_RBC3D.py:686:5: F841 Local variable `Delta_Nu` is assigned to but never used # | #684 | t = data['t'] #685 | avg_Nu = np.array([np.mean(Nu[40 : 40 + i + 1]) for i in range(len(Nu[40:]))]) #686 | Delta_Nu = np.array([abs(avg_Nu[i + 1] - avg_Nu[i]) for i in range(len(avg_Nu) - 1)]) # | ^^^^^^^^ F841 #687 | # ax.plot(data['t'][40:-1], Delta_Nu / avg_Nu[:-1]) #688 | # ax.plot(data['t'], np.abs(avg_Nu - avg_Nu[-1]) / avg_Nu[-1]) # | # = help: Remove assignment to unused variable `Delta_Nu`:
3 participants
Now also the links to result-files are included. These files are included literally; not as an image