Standardize time path output

[jdebacker]

This PR standardizes the variables saved to the output dictionary from the time path solution. Previously, some variables had a dimension with length T+S while others had a length of T in the same dimension (as noted in Issue #879). This PR sets the first dimension of all time path output variables to be of length T. This consistency in the length along this dimension will help users more easily use the model output.

[codecov-commenter]

Codecov Report

Merging #880 (a6edd02) into master (0b48a79) will not change coverage.
Report is 1 commits behind head on master.
The diff coverage is 100.00%.

Additional details and impacted files

@@           Coverage Diff           @@
##           master     #880   +/-   ##
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  Coverage   79.98%   79.98%           
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  Files          18       18           
  Lines        4157     4157           
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  Hits         3325     3325           
  Misses        832      832           

Flag	Coverage Δ
unittests	79.98% <100.00%> (ø)

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Files Changed	Coverage Δ
ogcore/TPI.py	93.04% <ø> (ø)
ogcore/__init__.py	100.00% <100.00%> (ø)

[jdebacker]

This PR is ready to go. I ran the full suite of tests locally and found 3 failures:

=========================== short test summary info ============================
FAILED tests/test_txfunc.py::test_txfunc_est[DEP] - assert False
FAILED tests/test_txfunc.py::test_tax_func_loop - assert False
FAILED tests/test_txfunc.py::test_monotone_spline - AttributeError: module 'numpy' has no attribute 'int'.

The first two are expected to fail since I'm using a different version of SciPy than was used for the cached tax function estimates are checking against (and we know different versions produce slightly different parameter estimates). The third failure is because the PyGAM package is incompatible with more recent NumPy versions. If that project would merge PR #321 (which changes just two lines), this test would pass.

[rickecon]

@jdebacker. This looks great. Will you please update the version number to 0.10.8 in setup.py and in ogcore/__init__.py. As soon as this is done, I will merge.

[rickecon]

@jdebacker. BTW, I keep looking for alternatives to PyGAM for generalized additive models. It is not clear to me if there is a good alternative in scikit-learn. This Jupyter Book "Generalized Additive Models (GAM)" chapter uses the gam module of the statsmodels package (statsmodels.gam), which might be an alternative.

[jdebacker]

@rickecon Version bump done. Will look into the alternative to PyGAM - thanks.