Source code for resistics.statistics.utils

import numpy as np
import scipy.stats as stats
from typing import List, Dict, Tuple

from resistics.common.print import errorPrint

[docs]def getStatNames() -> Tuple[List[str], List[str]]: """Get a list of statistic and remotestatistic names Returns ------- stats : List[str] List of signal site statistics remoteStats : List[str] List of remote reference statistics """ stats = [ "absvalEqn", "coherence", "powerSpectralDensity", "polarisationDirection", "partialCoherence", "transferFunction", "resPhase", ] remoteStats = [ "RR_coherence", "RR_coherenceEqn", "RR_absvalEqn", "RR_transferFunction", "RR_resPhase", ] return stats, remoteStats
[docs]def getStatElements(stat: str) -> List[str]: """Get statistic elements for each statistic Parameters ---------- stat : str The statistic for which to get the statistic elements Returns ------- Dict[str, List[str]] Mapping from a statistic name to the elements of that statistic """ statElements = { "absvalEqn": [ "absExEx", "absHyEx", "absExEy", "absHyEy", "absExHx", "absHyHx", "absExHy", "absHyHy", "absEyEx", "absHxEx", "absEyEy", "absHxEy", "absEyHx", "absHxHx", "absEyHy", "absHxHy", ], "coherence": ["cohExHx", "cohExHy", "cohEyHx", "cohEyHy"], "powerSpectralDensity": ["psdEx", "psdEy", "psdHx", "psdHy"], "polarisationDirection": ["polExEy", "polHxHy"], "transferFunction": [ "ExHxReal", "ExHxImag", "ExHyReal", "ExHyImag", "EyHxReal", "EyHxImag", "EyHyReal", "EyHyImag", ], "resPhase": [ "ExHxRes", "ExHxPhase", "ExHyRes", "ExHyPhase", "EyHxRes", "EyHxPhase", "EyHyRes", "EyHyPhase", ], "partialCoherence": [ "bivarEx", "bivarEy", "parExHx", "parExHy", "parEyHx", "parEyHy", ], } # remote reference stat elements statElementsRR = { "RR_coherence": [ "ExHxRR", "ExHyRR", "EyHxRR", "EyHyRR", "HxHxRR", "HxHyRR", "HyHxRR", "HyHyRR", ], "RR_coherenceEqn": ["ExHxR-HyHxR", "ExHyR-HyHyR", "EyHxR-HxHxR", "EyHyR-HxHyR"], "RR_absvalEqn": [ "absHyHxR", "absExHxR", "absHyHyR", "absExHyR", "absHxHxR", "absEyHxR", "absHxHyR", "absEyHyR", ], "RR_transferFunction": [ "ExHxRealRR", "ExHxImagRR", "ExHyRealRR", "ExHyImagRR", "EyHxRealRR", "EyHxImagRR", "EyHyRealRR", "EyHyImagRR", ], "RR_resPhase": [ "ExHxResRR", "ExHxPhaseRR", "ExHyResRR", "ExHyPhaseRR", "EyHxResRR", "EyHxPhaseRR", "EyHyResRR", "EyHyPhaseRR", ], } if stat in statElements: return statElements[stat] if stat in statElementsRR: return statElementsRR[stat] errorPrint( "utilsStats::getStatElements", "Statistic {} not found".format(stat), quitRun=True, ) return False
# Window statistics - These are not currently in use # signal to noise ratio is calculated as mean over std. # this might be more useful in spectral domain # can do this in the frequecy domain, on the amplitude
[docs]def calcSNR(specData): output = {} for c in tmp = np.absolute([c]) output[c] = np.average(tmp) / np.std(tmp) return output
# The Pearson correlation coefficient measures the linear relationship # between two datasets. Strictly speaking, Pearson's correlation requires # that each dataset be normally distributed.
[docs]def pearsonCoefficient(data): # construct input matrices # this needs to be columns for observations, rows for variables # and this needs to be output as a dictionary chans = sorted(list(data.keys())) numChans = len(chans) # the channels output = {} for i in range(0, numChans): for j in range(i, numChans): key = "{}{}".format(chans[i], chans[j]) # now calculate the pearson correlation coefficient pcc, pval = stats.pearsonr(data[chans[i]], data[chans[j]]) output[key] = pcc return output
[docs]def pearsonCoefficientSpec(data): # calcaulates the PCC for magnitude and phase separately mag = {} phase = {} for c in data: mag[c] = np.absolute(data[c]) phase[c] = np.unwrap(np.angle(data[c])) magPCC = pearsonCoefficient(mag) phasePCC = pearsonCoefficient(phase) return magPCC, phasePCC