updated requirements

.gitlab-ci.yml

@@ -14,7 +14,7 @@ pages:
     - yum install -y make
     - pip3 install sphinx
     - pip3 install sphinxcontrib-bibtex sphinx_rtd_theme sphinx-argparse
-    - pip3 install -r jass_preprocessing/requirements.txt
+    - pip3 install -r requirements.txt
     - cd doc
     - sphinx-apidoc -f -o ./source/_autosummary/ ../jass_preprocessing/
     - make html

jass_preprocessing/__main__.py

@@ -22,24 +22,20 @@ import argparse
 #| ImpG_output_Folder | main ouput folder | netPath+ 'PCMA/1._DATA/preprocessing_test/' |
 
 
-
 def launch_preprocessing(args):
     """
     Preprocessing GWAS dataset
     """
-    gwas_map = pd.read_csv(args.gwas_info, sep="\t", index_col=0)
-    print(gwas_map.head())
+    gwas_map = pd.read_csv(args.gwas_info, sep="\t")
+    gwas_map.set_index("filename", inplace=True)
 
     for gwas_filename in gwas_map.index:
-        print(gwas_filename)
-        print(gwas_map.columns)
-        tag = "{0}_{1}".format(gwas_map.loc[gwas_filename, 'consortia'],
-                               gwas_map.loc[gwas_filename, 'outcome'])
+        tag = "{0}_{1}".format(gwas_map.loc[gwas_filename, 'Consortium'],
+                               gwas_map.loc[gwas_filename, 'Outcome'])
 
         print('processing GWAS: {}'.format(tag))
         start = time.time()
         GWAS_link = jp.map_gwas.walkfs(args.input_folder, gwas_filename)[2]
-
         mapgw = jp.map_gwas.map_columns_position(GWAS_link, args.gwas_info)
 
         gw_df = jp.map_gwas.read_gwas(GWAS_link, mapgw)

jass_preprocessing/map_gwas.py

@@ -84,23 +84,22 @@ def map_columns_position(gwas_internal_link,  GWAS_labels):
     Return:
         pandas Series with column position and column names as index
     """
-    column_dict = pd.read_csv(GWAS_labels, sep='\t', na_values='na', index_col=0)
 
-    gwas_file = gwas_internal_link.split('/')[-1]
+    column_dict = pd.read_csv(GWAS_labels, sep='\t', na_values='na')
+
+    column_dict.set_index("filename", inplace=True)
 
+    gwas_file = gwas_internal_link.split('/')[-1]
     my_labels = column_dict.loc[gwas_file]
 
     #Our standart labels:
     reference_label = column_dict.columns.tolist()
     # labels in the GWAS files
     target_lab = pd.Index(my_labels.values.tolist())
-
     f = open(gwas_internal_link)
     count_line = 0
     line = f.readline()
-    print(line)
     header = pd.Index(line.split())
-
     def get_position(I,x):
         try:
             return I.get_loc(x)
@@ -108,7 +107,6 @@ def map_columns_position(gwas_internal_link,  GWAS_labels):
             return np.nan
     label_position = [get_position(header,i) for i in target_lab]
 
-
     mapgw = pd.Series(label_position, index=reference_label)
     mapgw = mapgw.loc[~mapgw.isna()].astype(int)
     mapgw.sort_values(inplace=True)
@@ -137,8 +135,7 @@ def read_gwas( gwas_internal_link, column_map):
                                 index_col=0,
                                  header=0, na_values= ['', '#N/A', '#N/A', 'N/A',
                                                        '#NA', '-1.#IND', '-1.#QNAN',
-                                                 '-NaN',
-                                                 '-nan', '1.#IND', '1.#QNAN', 'N/A',
+                                                 '-NaN', '-nan', '1.#IND', '1.#QNAN', 'N/A',
                                                  'NA', 'NULL', 'NaN',
                                                  'nan', 'na', '.'])
 

jass_preprocessing/requirements.txt

-scipy
-pandas
-numpy
-seaborn
-matplotlib