codecov-commenter
commented
4 years ago Codecov Report
Merging #12 into master will decrease coverage by
0.04%. The diff coverage is83.33%.
@@ Coverage Diff @@
## master #12 +/- ##
==========================================
- Coverage 93.86% 93.82% -0.05%
==========================================
Files 13 13
Lines 1646 1651 +5
==========================================
+ Hits 1545 1549 +4
- Misses 101 102 +1 | Impacted Files | Coverage Δ | |
|---|---|---|
| scikit_tt/data_driven/tedmd.py | 0.00% <0.00%> (ø) |
|
| scikit_tt/data_driven/transform.py | 99.15% <ø> (ø) |
|
| scikit_tt/data_driven/ulam.py | 100.00% <ø> (ø) |
|
| scikit_tt/slim.py | 100.00% <ø> (ø) |
|
| scikit_tt/solvers/evp.py | 100.00% <ø> (ø) |
|
| scikit_tt/tensor_train.py | 94.45% <ø> (ø) |
|
| scikit_tt/utils.py | 100.00% <ø> (ø) |
|
| scikit_tt/data_driven/regression.py | 98.98% <100.00%> (+0.01%) |
:arrow_up: |
| scikit_tt/data_driven/tdmd.py | 100.00% <100.00%> (ø) |
|
| scikit_tt/models.py | 100.00% <100.00%> (ø) |
|
| ... and 2 more |
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Δ = absolute <relative> (impact),ø = not affected,? = missing dataPowered by Codecov. Last update f52d843...5504e4f. Read the comment docs.
I adapted all the docstrings in scikit_tt/scikit_tt to the numpy docstring standard. The most important changes are 'instance of TT class' to 'TT' and 'ndarray' to 'np.ndarray'. This enables IDEs like Pycharm to recognize the type of output of a function as TT, which enables it to suggest code. For example: T_tt = scikit_tt.tensor_train.rand([2, 3, 4], [5, 6, 7], ranks=3) If I now type 'T_tt.' Pycharm can provide an overview of all the TT class methods, like matricize or ortho.
This massively improves the workflow with scikit_tt as you dont have to jump into the TT class file to manually search for the method you need.
None of the semantics of the docstrings were changed.