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