added sanity checks for or_kin

scripts/or_kinship_link.py

@@ -286,17 +286,36 @@ else:
           , '\nGot:', len(dfm2_mis.columns))
     sys.exit()
     
-del(ncols_add)
-#%% formatting data for output
-print('no of cols pre-formatting data:', len(dfm2_mis.columns)
+del(ncols_add, orig_len)
+#%% Calculating OR from beta coeff
+df_ncols = dfm2_mis.shape[1]
+print('No. of cols pre-formatting data:', df_ncols
       , '\n======================================')
-#1) Add column: OR for kinship calculated from beta coeff
-print('converting beta coeff to OR by exponent function\n:'
-      , dfm2_mis['beta'].head()
-      , '\n======================================')
-dfm2_mis['or_kin'] = np.exp(dfm2_mis['beta'])
-print(dfm2_mis['or_kin'].head())
-print('No. of cols after adidng OR kinship:', len(dfm2_mis.columns))
+
+#1) Add column: OR for kinship calculated from beta coef
+
+ncols_add = 0
+if not 'or_kin' in dfm2_mis.columns:
+    dfm2_mis['or_kin'] = np.exp(dfm2_mis['beta'])
+    print(dfm2_mis['or_kin'].head())
+    ncols_add+=1
+    print('Calculating OR from beta coeff by exponent function and adding column:'
+      , '\nNo. of cols added:', ncols_add
+      ,  dfm2_mis['beta'].head()
+      , '\n======================================')  
+    
+if dfm2_mis.shape[1] == df_ncols + ncols_add:
+    print('PASS: Dimension of df match'
+          , '\nDim of df:', dfm2_mis.shape
+          , 'n====================================')
+else:
+    print('FAIL: Dim mismatch'
+          , '\nOriginal no. of cols:', df_ncols
+          , '\nExpected no. of cols:', df_ncols + ncols_add
+          , '\nGot:', dfm2_mis.shape[1])
+    sys.exit()
+
+print('No. of cols after adding OR kinship:', len(dfm2_mis.columns))
 
 #2) rename af column
 dfm2_mis.rename(columns = {'af': 'af_kin'
@@ -314,31 +333,63 @@ dfm2_mis.rename(columns = {'af': 'af_kin'
  #3a) drop duplicate columns
 dfm2_mis2 = dfm2_mis.T.drop_duplicates().T #changes dtypes in cols, so not used
 dup_cols = set(dfm2_mis.columns).difference(dfm2_mis2.columns)
-print('Total no of duplciate columns:', len(dup_cols)
+print('Total no of duplicate columns:', len(dup_cols)
       , '\nDuplicate columns identified:', dup_cols)
 #dup_cols = {'alt_allele0', 'ps'} # didn't want to remove tot_diff
 
-del(dfm2_mis2)
+del(dfm2_mis2, df_ncols, ncols_add)
 
 #print('removing duplicate columns: kept one of the dup_cols i.e tot_diff')
 print('Removing duplicate columns'
       , '\nOriginal dim:', dfm2_mis.shape)
 dfm2_mis.drop(list(dup_cols), axis = 1, inplace = True)
-print('\nRevised dim:', dfm2_mis.shape, 'after removing', len(dup_cols), 'columns')
+
+df_ncols = dfm2_mis.shape[1]
+if dfm2_mis.shape[1] == df_ncols - len(dup_cols):
+    print('PASS: Dimensions match'
+          , '\nDim:', dfm2_mis.shape
+          , '\nRemoved', len(dup_cols), 'columns from' , df_ncols
+          , '\n======================================')          
+else:
+    print('FAIL: Dimensions mismatch'
+          , '\nOriginal no. of cols:', df_ncols
+          , '\nNo. of cols to drop:', len(dup_cols)
+          , '\nExpected:', df_cols - len(dup_cols)
+          , '\nGot:', dfm2_mis.shape[1])
+    sys.exit()
+
+del(df_ncols)
 
  #3b) other not useful columns
 cols_to_drop = ['chromosome_text', 'n_diff', 'chr', 'symbol', '_merge' ]
 
+df_ncols = dfm2_mis.shape[1]
+
 dfm2_mis.drop(cols_to_drop, axis = 1, inplace = True)
 #dfm2_mis.rename(columns = {'ref_allele1': 'reference_allele'}, inplace = True)
-   
+
+
+if dfm2_mis.shape[1] == df_ncols - len(cols_to_drop):
+    print('PASS:', len(cols_to_drop), 'columns successfully dropped'
+          , '\nDim:', dfm2_mis.shape
+          , '\nRemoved', len(cols_to_drop), 'columns from', df_ncols
+          , '\n===========================================')
+else:
+    print('FAIL: Dimensions mismatch'
+          , '\nOriginal no. of cols:', df_ncols
+          , '\nExpected:', df_ncols - len(cols_to_drop)
+          , '\nGot:', dfm2_mis.shape[1])
+    sys.exit()
+ 
+del(df_ncols)
 print('Dim after dropping', len(cols_to_drop), 'columns:', dfm2_mis.shape)
 #print(dfm2_mis.columns)
 
-#4) reorder columns
-#orkin_linked = dfm2_mis.copy()
+#4) reorder columnn
+print('Reordering', dfm2_mis.shape[1], 'columns'
+      , '\n===============================================')
 
-orkin_linked = dfm2_mis[['mutation',
+column_order = ['mutation',
  'mutationinformation', 
  'wild_type', 
  'position',
@@ -375,8 +426,19 @@ orkin_linked = dfm2_mis[['mutation',
 # 'tot_diff',
  'n_miss',
  'wt_3let',
- 'mt_3let']]
+ 'mt_3let']
+#orkin_linked = dfm2_mis.copy()
 
+if len(column_order) == dfm2_mis.shape[1]:
+    print('PASS: Column order generated for', len(column_order), 'columns'
+         , '\nApplying column order to df...' )
+    orkin_linked = dfm2_mis[column_order]
+else:
+    print('FAIL: Mismatch in no. of cols to reorder'
+          , '\nNo. of cols in df to reorder:', dfm2_mis.shape[1]
+          , '\nOrder generated for:', len(column_order), 'columns'
+          , '\n', dfm2_mis.shape[1], 'should match', len(column_order))
+    sys.exit()
 
 # sanity check after reassigning columns
 if orkin_linked.shape == dfm2_mis.shape and set(orkin_linked.columns) == set(dfm2_mis.columns):