Package: jupyterlab-celltests
jupyterlab_celltestsCell-by-cell testing for production Jupyter notebooks in JupyterLabOverviewCelltestsis designed for writing tests for linearly executed notebooks. Its primary use is for unit testing reports."Linearly executed notebooks?"When converting notebooks into html/pdf/email reports, they are executed top-to-bottom one time, and are expected to contain as little code as reasonably possible, focusing primarily on the plotting and markdown bits. Libraries for this type of thing includePapermill,JupyterLab Emails, etc.Doesn't this already exist?Nbvalis a great product and I recommend using it for notebook regression tests. But it only allows for testing for unexpected failures or simple output equality tests.So why do I want this again?This doesn't necessarily help you if your data sources go down, but its likely you'll notice this anyway. Where this comes in handy is:when the environment (e.g. package versions) are changing in your systemwhen you play around in the notebook (e.g. nonlinear execution) but aren't sure if your reports will still generatewhen your software lifecycle systems have a hard time dealing with notebooks (can't lint/audit them as code unless integrated nbdime/nbconvert to script, tough to test, tough to ensure what works today works tomorrow)So what does this do?Given a notebook, you can write mocks and assertions for individual cells. You can then generate a testing script for this notebook, allowing you to hook it into your testing system and thereby provide unittests of your report.Writing testsWhen you write tests for a cell, we create a new method on aunittestclass corresponding to the index of your cell, and including the cumulative tests for all previous cells (to mimic what has happened so far in the notebook's linear execution). You can write whatever mocking and asserts you like, and can call%cellto inject the contents of the cell into your test.The tests themselves are stored in the cell metadata, similar to celltags, slide information, etc.Running testsYou can run the tests offline from an.ipynbfile, or you can execute them from the browser and view the results ofpytest-html's html plugin.Extra TestsMax number of lines per cellMax number of cells per notebookMax number of function definitions per notebookMax number of class definitions per notebookPercentage of cells testedExampleIn the committedUntitled.ipynbnotebook, but modified so that cell 0 has its import statement copied 10 times (to trigger test and lint failures):TestsUntitled_test.py::TestExtension::test_cell0PASSED[8%]Untitled_test.py::TestExtension::test_cell1PASSED[16%]Untitled_test.py::TestExtension::test_cell2PASSED[25%]Untitled_test.py::TestExtension::test_cell3PASSED[33%]Untitled_test.py::TestExtension::test_cell_coveragePASSED[41%]Untitled_test.py::TestExtension::test_cells_per_notebookPASSED[50%]Untitled_test.py::TestExtension::test_class_definition_countPASSED[58%]Untitled_test.py::TestExtension::test_function_definition_countPASSED[66%]Untitled_test.py::TestExtension::test_lines_per_cell_0FAILED[75%]Untitled_test.py::TestExtension::test_lines_per_cell_1PASSED[83%]Untitled_test.py::TestExtension::test_lines_per_cell_2PASSED[91%]Untitled_test.py::TestExtension::test_lines_per_cell_3PASSED[100%]LintCheckinglinesincell0:FAILED
Checkinglinesincell1:PASSED
Checkinglinesincell2:PASSED
Checkinglinesincell3:PASSED
Checkingcellspernotebook<=10:PASSED
Checkingfunctionspernotebook<=10:PASSED
Checkingclassespernotebook<=10:PASSED
Checkingcelltestcoverage>=50:PASSEDNB: In jupyterlab, notebooks will be lint checked using the version of
python that is running jupyter lab itself. A notebook intended to be
run with a Python 2 kernel could therefore generate syntax errors
during lint checking.