Closed lcontento closed 4 years ago
Merging #444 into develop will not change coverage. The diff coverage is
78.57%
.
@@ Coverage Diff @@
## develop #444 +/- ##
========================================
Coverage 77.88% 77.88%
========================================
Files 22 22
Lines 2211 2211
Branches 529 529
========================================
Hits 1722 1722
Misses 363 363
Partials 126 126
Impacted Files | Coverage Δ | |
---|---|---|
petab/sbml.py | 51.66% <0.00%> (ø) |
|
petab/visualize/plotting_config.py | 56.98% <66.66%> (ø) |
|
petab/conditions.py | 90.47% <100.00%> (ø) |
|
petab/core.py | 82.91% <100.00%> (ø) |
|
petab/measurements.py | 80.24% <100.00%> (ø) |
|
petab/observables.py | 96.61% <100.00%> (ø) |
|
petab/parameters.py | 82.43% <100.00%> (ø) |
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I resolved some flake8
errors to make CI succeed, but Codacy says that its checks are not enabled for the PR branch.
codacy is a bit shy sometimes.
pandas
uses by default a C parser for CSV files for performance reasons. The default behaviour of such parser when parsing floating point values from strings is different from Python's behaviour. While Python parsing can be round-tripped (e.g.,float(0.999) == 0.999
) this is not true for the C parser. I believe most users nowadays expect floats to be parsed correctly, so I added a keyword argument to all instances ofpandas.read_csv
asking for the Python-like behaviour. The only downside is that this behaviour is slower (the C code actually calls Python to perform the parsing). This may be more relevant for the parameter table and less important for measurement tables, so it may also be acceptable to enable it only for some tables and not others.