def update_final_subject_identifier_from_cdc(self,
df_currentstudyparticipant):
"""Update identifier from CDC HTC data."""
if not df_currentstudyparticipant.empty:
df = df_currentstudyparticipant
df['htcid'].fillna(value=np.nan, inplace=True)
df['htcid'] = df.apply(lambda row: undash(row['htcid']), axis=1)
df['ssid'].fillna(value=np.nan, inplace=True)
df['ssid'] = df.apply(lambda row: undash(row['ssid']), axis=1)
df = df.replace('unk', np.nan)
df = df[pd.notnull(df['omangnumber'])]
self.df_htc = df.copy()
self.df_htc = self.df_htc.rename(columns={
'htcid': 'subject_identifier_cdc',
'omangnumber': 'identity'
})
self.df_htc = pd.merge(
self.results[pd.isnull(
self.results['final_subject_identifier'])],
self.df_htc[['subject_identifier_cdc', 'identity']],
left_on='subject_identifier',
right_on='subject_identifier_cdc',
suffixes=['', '_cdc'
])[['subject_identifier', 'subject_identifier_cdc']]
self.df_htc.rename(
columns={'subject_identifier_edc': 'final_subject_identifier'},
inplace=True)
self.df_htc['final_subject_identifier_source'] = 'cdc (htc)'
self.df_htc.drop_duplicates(inplace=True)
self.df_htc.set_index('subject_identifier', inplace=True)
self.results.set_index('subject_identifier', inplace=True)
self.results = self.results.combine_first(self.df_htc)
self.results.reset_index(inplace=True)
self.df_htc.reset_index(inplace=True)
self.df_ccc = df.copy()
self.df_ccc = self.df_ccc.rename(columns={
'ssid': 'subject_identifier_cdc',
'omangnumber': 'identity'
})
self.df_ccc = pd.merge(
self.results[pd.isnull(
self.results['final_subject_identifier'])],
self.df_ccc[['subject_identifier_cdc', 'identity']],
left_on='subject_identifier',
right_on='subject_identifier_cdc',
suffixes=['', '_cdc'
])[['subject_identifier', 'subject_identifier_cdc']]
self.df_ccc.rename(
columns={'subject_identifier_edc': 'final_subject_identifier'},
inplace=True)
self.df_ccc['final_subject_identifier_source'] = 'cdc (ccc)'
self.df_ccc.drop_duplicates(inplace=True)
self.df_ccc.set_index('subject_identifier', inplace=True)
self.results.set_index('subject_identifier', inplace=True)
self.results = self.results.combine_first(self.df_ccc)
self.results.reset_index(inplace=True)
self.df_ccc.reset_index(inplace=True)
def update_final_subject_identifier_from_cdc(self, df_currentstudyparticipant):
"""Update identifier from CDC HTC data."""
if not df_currentstudyparticipant.empty:
df = df_currentstudyparticipant
df['htcid'].fillna(value=np.nan, inplace=True)
df['htcid'] = df.apply(lambda row: undash(row['htcid']), axis=1)
df['ssid'].fillna(value=np.nan, inplace=True)
df['ssid'] = df.apply(lambda row: undash(row['ssid']), axis=1)
df = df.replace('unk', np.nan)
df = df[pd.notnull(df['omangnumber'])]
self.df_htc = df.copy()
self.df_htc = self.df_htc.rename(columns={'htcid': 'subject_identifier_cdc', 'omangnumber': 'identity'})
self.df_htc = pd.merge(
self.results[pd.isnull(self.results['final_subject_identifier'])],
self.df_htc[['subject_identifier_cdc', 'identity']],
left_on='subject_identifier',
right_on='subject_identifier_cdc',
suffixes=['', '_cdc'])[['subject_identifier', 'subject_identifier_cdc']]
self.df_htc.rename(columns={'subject_identifier_edc': 'final_subject_identifier'}, inplace=True)
self.df_htc['final_subject_identifier_source'] = 'cdc (htc)'
self.df_htc.drop_duplicates(inplace=True)
self.df_htc.set_index('subject_identifier', inplace=True)
self.results.set_index('subject_identifier', inplace=True)
self.results = self.results.combine_first(self.df_htc)
self.results.reset_index(inplace=True)
self.df_htc.reset_index(inplace=True)
self.df_ccc = df.copy()
self.df_ccc = self.df_ccc.rename(columns={'ssid': 'subject_identifier_cdc', 'omangnumber': 'identity'})
self.df_ccc = pd.merge(
self.results[pd.isnull(self.results['final_subject_identifier'])],
self.df_ccc[['subject_identifier_cdc', 'identity']],
left_on='subject_identifier',
right_on='subject_identifier_cdc',
suffixes=['', '_cdc'])[['subject_identifier', 'subject_identifier_cdc']]
self.df_ccc.rename(columns={'subject_identifier_edc': 'final_subject_identifier'}, inplace=True)
self.df_ccc['final_subject_identifier_source'] = 'cdc (ccc)'
self.df_ccc.drop_duplicates(inplace=True)
self.df_ccc.set_index('subject_identifier', inplace=True)
self.results.set_index('subject_identifier', inplace=True)
self.results = self.results.combine_first(self.df_ccc)
self.results.reset_index(inplace=True)
self.df_ccc.reset_index(inplace=True)
def fetch_results_as_dataframe(self, edc_panels=None):
with self.engine.connect() as conn, conn.begin():
df = pd.read_sql_query(self.sql_results, conn)
df.fillna(value=np.nan, inplace=True)
df['result'] = df['result'].str.replace('<', '')
df['result'] = df['result'].str.replace('>', '')
df['result'] = df['result'].str.replace('*', '')
df['result'] = df['result'].str.replace('=', '')
df['result'] = df.apply(lambda row: np.nan
if row['result'] == '' else row['result'],
axis=1)
# df['result_float'] = df[df['result'].str.contains('\d+')]['result'].astype(float, na=False)
for column in list(
df.select_dtypes(include=['datetime64[ns, UTC]']).columns):
df[column] = df[column].astype('datetime64[ns]')
df['result_datetime'] = pd.to_datetime(df['result_datetime'])
df['received_datetime'] = pd.to_datetime(df['received_datetime'])
df['drawn_datetime'] = pd.to_datetime(df['drawn_datetime'])
df['drawn_datetime'] = pd.to_datetime(df['drawn_datetime'].dt.date)
df['specimen_identifier'] = df.apply(lambda row: np.nan if row[
'specimen_identifier'] == 'NA' else row['specimen_identifier'],
axis=1)
df['aliquot_identifier'] = df.apply(
lambda row: self.aliquot_identifier(row), axis=1)
df['edc_specimen_identifier'] = df.apply(
lambda row: self.edc_specimen_identifier(row, self.protocol_prefix
),
axis=1)
df['subject_identifier'] = df.apply(lambda row: undash(
row['subject_identifier'], '^{}-'.format(self.protocol_prefix)),
axis=1)
df['final_subject_identifier'] = df[
df['subject_identifier'].str.startswith('{}-'.format(
self.protocol_prefix))]['subject_identifier']
df['final_subject_identifier_source'] = df.apply(
lambda row: np.nan
if pd.isnull(row['final_subject_identifier']) else 'lis',
axis=1)
return df
def fetch_results_as_dataframe(self, edc_panels=None):
with self.engine.connect() as conn, conn.begin():
df = pd.read_sql_query(self.sql_results, conn)
df.fillna(value=np.nan, inplace=True)
df['result'] = df['result'].str.replace('<', '')
df['result'] = df['result'].str.replace('>', '')
df['result'] = df['result'].str.replace('*', '')
df['result'] = df['result'].str.replace('=', '')
df['result'] = df.apply(
lambda row: np.nan if row['result'] == '' else row['result'], axis=1)
# df['result_float'] = df[df['result'].str.contains('\d+')]['result'].astype(float, na=False)
for column in list(df.select_dtypes(include=['datetime64[ns, UTC]']).columns):
df[column] = df[column].astype('datetime64[ns]')
df['result_datetime'] = pd.to_datetime(df['result_datetime'])
df['received_datetime'] = pd.to_datetime(df['received_datetime'])
df['drawn_datetime'] = pd.to_datetime(df['drawn_datetime'])
df['drawn_datetime'] = pd.to_datetime(df['drawn_datetime'].dt.date)
df['specimen_identifier'] = df.apply(lambda row: np.nan if row['specimen_identifier'] == 'NA' else row['specimen_identifier'], axis=1)
df['aliquot_identifier'] = df.apply(lambda row: self.aliquot_identifier(row), axis=1)
df['edc_specimen_identifier'] = df.apply(lambda row: self.edc_specimen_identifier(row, self.protocol_prefix), axis=1)
df['subject_identifier'] = df.apply(lambda row: undash(row['subject_identifier'], '^{}-'.format(self.protocol_prefix)), axis=1)
df['final_subject_identifier'] = df[df['subject_identifier'].str.startswith('{}-'.format(self.protocol_prefix))]['subject_identifier']
df['final_subject_identifier_source'] = df.apply(lambda row: np.nan if pd.isnull(row['final_subject_identifier']) else 'lis', axis=1)
return df
