Create add_domains.py

code/add_domains.py

@@ -0,0 +1,32 @@
+from collections import Counter
+import pandas as pd
+import numpy as np
+
+def add_domains(data, path_to_domains):
+    DOMAINS = pd.read_csv(path_to_domains, delimiter=' ')
+    data = data.merge(DOMAINS, right_on='proteinID', left_on='uniprotID', how='left')
+    data.domStart = data.domStart.astype('Int64')
+    data.domEnd = data.domEnd.astype('Int64')
+    data = data.drop(['proteinID'], axis=1)
+    data['distance'] = np.NaN
+    zeroDistanceDomains = []
+    for i in data.index:
+        if pd.isna(data.at[i, 'domain']):
+            data.at[i, 'distance'] = np.NaN
+        else:
+            if int(data.at[i, 'domStart']) <= int(data.at[i, 'pos']) <= int(data.at[i, 'domEnd']):
+                data.at[i, 'distance'] = 0
+                DOMAIN_NAME = data.at[i, 'domain']
+                zeroDistanceDomains.append(DOMAIN_NAME)
+    data = data.sort_values(by=['datapoint', 'distance']).reset_index(drop=True)  # Distances will be sorted.
+
+    ZeroDistance = data[data.distance == 0.0]
+    NotZeroDistance = data[data.distance != 0.0]
+    NotZeroDistance.distance = -1000
+
+    NotZeroDistance = NotZeroDistance[~NotZeroDistance.domain.isin(zeroDistanceDomains)]
+
+    data = pd.concat([ZeroDistance, NotZeroDistance], sort=False)
+    data.reset_index(drop=True, inplace=True)
+    data.fillna(-1, inplace=True)
+    return data