def calculateWholeZ_Score(self): """ calculate z-score for the whole complex including all replicas """ all_dt = bayes_analysis.collectMccEnerFromAllRep(self.h5_path, self.mcc_total_path) z_score = readH5.calculateZ_Score(all_dt) return z_score
def calculatePearsonCorrCoef(self): """ calculate pearson correlation coefficeint between mcc and total energy """ all_dt = bayes_analysis.collectMccEnerFromAllRep(self.h5_path, self.mcc_total_path) mcc = all_dt[:, 0] ener = all_dt[:, 1] return pearsonr(mcc, ener)
def countPoorSize(self): """ count the number of points in the high and low poor """ all_dt = bayes_analysis.collectMccEnerFromAllRep(self.h5_path, self.mcc_total_path) mcc = all_dt[:, 0] high_total = mcc[mcc >= 0.6].shape[0] low_total = mcc[mcc <= 0.4].shape[0] return high_total, low_total
def calculatePartialZ_Score(self, step=100): """ calculate the z-score by an increment of 100 rows """ all_dt = bayes_analysis.collectMccEnerFromAllRep(self.h5_path, self.mcc_total_path) total_rows = all_dt.shape[0] for end_row in range(100, total_rows, step): partial_matx = all_dt[0:end_row, :] z_score = readH5.calculateZ_Score(partial_matx) yield z_score
def calculatePartialPearsonCorrCoef(self, step=1000): """ calculate the pearson correlation coefficient by an increment of 100 rows """ all_dt = bayes_analysis.collectMccEnerFromAllRep(self.h5_path, self.mcc_total_path) total_rows = all_dt.shape[0] for end_row in range(100, total_rows, step): partial_matx = all_dt[0:end_row, :] mcc = partial_matx[:, 0] ener = partial_matx[:, 1] yield pearsonr(mcc, ener)