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| 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.datapoint.isin(ZeroDistance.datapoint.to_list())] | |
| data = pd.concat([ZeroDistance, NotZeroDistance], sort=False) | |
| data.reset_index(drop=True, inplace=True) | |
| data.fillna(-1, inplace=True) | |
| return data | |