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)
