def load_data(self, data_file):
"""
Returns a list of score and beta values from a file that is given as the parameter
:param data_file: file containing score and, if selected, beta values
:return: a list of scores and, if selected, a list of betas in the form of a tuple
"""
acquired_data = loadFile(self.args_dict['folder'], data_file, self.args_dict['separ'])
score_column = data_to_list(acquired_data, 1, acquired_data.header.index(self.args_dict['score']), True)
snp_column = data_to_list(acquired_data, 1, acquired_data.header.index(self.args_dict['snp']))
adjusted_score_column = self.adjust_score(score_column)
self.save_snp_score(snp_column, score_column, adjusted_score_column)
if self.args_dict['beta'] is not None:
beta_column = data_to_list(acquired_data, 1, acquired_data.header.index(self.args_dict['beta']), True)
if self.args_dict['covar'] is not None:
covar_column = data_to_list(acquired_data, 1, acquired_data.header.index(self.args_dict['covar']), True)
return adjusted_score_column, beta_column, covar_column
else:
return adjusted_score_column, beta_column
else:
if self.args_dict['covar'] is not None:
covar_column = data_to_list(acquired_data, 1, acquired_data.header.index(self.args_dict['covar']), True)
return adjusted_score_column, covar_column
else:
return adjusted_score_column
def load_ote(self):
"""
Loads only truth and effect type known truth file.
:return: sets the instance list variables snp_true_false and beta_true_false with data from the known truth file
given at runtime, separated by the given delimiter
"""
app_output_list = checkList(getList(self.args_dict['folder']))
kt_file = loadKT(self.args_dict['truth'], self.args_dict['kt_type_separ'])
acquired_data = loadFile(self.args_dict['folder'], app_output_list[0], self.args_dict['separ'])
snp_column = data_to_list(acquired_data, 1, acquired_data.header.index(self.args_dict['snp']))
kt_snps = data_to_list(kt_file, 1, 0)
kt_betas = data_to_list(kt_file, 1, 1)
for each in snp_column:
self.snp_true_false.append(trueFalse(each, kt_snps))
self.load_ote_betas(snp_column, kt_snps, kt_betas)
def load_ote(self):
"""
Loads only truth and effect type known truth file.
:return: sets the instance list variables snp_true_false and beta_true_false with data from the known truth file
given at runtime, separated by the given delimiter
"""
app_output_list = sorted(checkList(getList(self.args_dict['folder'])))
acquired_data = loadFile(self.args_dict['folder'], app_output_list[0],
self.args_dict['separ'])
kt_file = loadKT(self.args_dict['truth'],
self.args_dict['kt_type_separ'])
snp_column = data_to_list(
acquired_data, 1,
acquired_data.header.index(self.args_dict['snp']))
kt_snps = data_to_list(kt_file, 1, 0)
kt_betas = data_to_list(kt_file, 1, 1)
for each in snp_column:
self.snp_true_false.append(trueFalse(each, kt_snps))
self.load_ote_betas(snp_column, kt_snps, kt_betas)
def load_data(self, data_file):
"""
Returns a list of score and beta values from a file that is given as the parameter
:param data_file: file containing score and, if selected, beta values
:return: a list of scores and, if selected, a list of betas in the form of a tuple
"""
acquired_data = loadFile(self.args_dict['folder'], data_file,
self.args_dict['separ'])
score_column = data_to_list(
acquired_data, 1,
acquired_data.header.index(self.args_dict['score']), True)
snp_column = data_to_list(
acquired_data, 1,
acquired_data.header.index(self.args_dict['snp']))
adjusted_score_column = self.adjust_score(score_column)
self.save_snp_score(snp_column, score_column, adjusted_score_column)
if self.args_dict['beta'] is not None:
beta_column = data_to_list(
acquired_data, 1,
acquired_data.header.index(self.args_dict['beta']), True)
if self.args_dict['covar'] is not None:
covar_column = data_to_list(
acquired_data, 1,
acquired_data.header.index(self.args_dict['covar']), True)
return adjusted_score_column, beta_column, covar_column
else:
return adjusted_score_column, beta_column
else:
if self.args_dict['covar'] is not None:
covar_column = data_to_list(
acquired_data, 1,
acquired_data.header.index(self.args_dict['covar']), True)
return adjusted_score_column, covar_column
else:
return adjusted_score_column
