I added the modifications for two-tailed go (storing action and reducing the number of times density is computed).
The linkage function was failing during pytest because can only work on finite values to I converted NaN's to 0's.
I was also getting an error when running the health cli command with GO terms.
ValueError: Pandas data cast to numpy dtype of object. Check input data with np.asarray(data). │
> /project/chadm/Meesh/CamocoHelixInstall/conda/envs/camoco/lib/python3.6/site-packages/statsmodels/base/data.py(471)_convert_endog_exog() │
469 exog = exog if exog is None else np.asarray(exog) │
470 if endog.dtype == object or exog is not None and exog.dtype == object: │
--> 471 raise ValueError("Pandas data cast to numpy dtype of object. " │
472 "Check input data with np.asarray(data).") │
473 return super(PandasData, self)._convert_endog_exog(endog, exog)
Googling that error pointed me to the sm.OLS function and just casting values to floats fixed the problem.
I added the modifications for two-tailed go (storing action and reducing the number of times density is computed).
The linkage function was failing during pytest because can only work on finite values to I converted NaN's to 0's.
I was also getting an error when running the health cli command with GO terms.
Googling that error pointed me to the sm.OLS function and just casting values to floats fixed the problem.