added gain

MANIFEST.in

 include README.md
 include setup.cfg
 include jass/swagger/swagger.yaml
-recursive-include jass/static *
-recursive-include celery_files *
+recursive-include jass/models/data/ *.tsv
+recursive-include jass/static *
\ No newline at end of file

jass/models/data/coef_mean_model.tsv

+	0
+k_coef_mv	0.07740334977380119
+log10_avg_distance_cor_coef_mv	-0.6999110771883902
+log10_mean_gencov_coef_mv	0.746794584985343
+avg_Neff_coef_mv	0.07289261717080556
+avg_h2_coef_mv	-0.516496395500929
+avg_perc_h2_diff_region_coef_mv	0.15727591593399

jass/models/data/range_feature_gain_prediction.tsv

+	minimum_value	maximum_value
+k	2.0	12.0
+log10_avg_distance_cor	-4.675617219570908	0.20864138105896807
+log10_mean_gencov	-4.4093921991254446	-0.46117501106209624
+avg_Neff	6730.5	697828.0
+avg_h2	0.014033707225812	0.4361454950334251
+avg_perc_h2_diff_region	0.0906544694784672	0.9831222899777692

jass/models/gain.py

+import pkg_resources
 import pandas as pd
 import numpy as np
-
+import os
 
 # data issued from https://doi.org/10.1101/2023.10.27.564319
-X_range = pd.read_csv("./data/range_feature_gain_prediction.tsv", sep="\t", index_col=0)
-model_coefficients =  pd.read_csv("./data/coef_mean_model.tsv", sep="\t", index_col=0)
+stream = pkg_resources.resource_stream(__name__, 'data/range_feature_gain_prediction.tsv')
+X_range = pd.read_csv(stream, sep="\t", index_col=0)
+
+stream = pkg_resources.resource_stream(__name__, 'data/coef_mean_model.tsv')
+model_coefficients =  pd.read_csv(stream, sep="\t", index_col=0)
 
 # Scale according to observed 
 def scale_feature(X, feature_name):

setup.py

@@ -31,7 +31,7 @@ setup(
     install_requires=REQUIRES,
     license="MIT",
     keywords=["GWAS", "Data analysis", "summary statistics"],
-    package_data={'jass': ['swagger/swagger.yaml']},
+    package_data={'jass': ['swagger/swagger.yaml', "data/*.tsv"]},
     include_package_data=True,
     long_description=readme,
     long_description_content_type="text/markdown",