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Analysis.py
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import pandas as pd
import numpy as np
import scipy as sp
import hashlib
import os
import datetime
import math
import configuration
import dataset
from dataset import DeviationMatrix
import binning
import turbine
import rews
import reporting
def chckMake(path):
"""Make a folder if it doesn't exist"""
if not os.path.exists(path):
os.mkdir(path)
def hash_file_contents(file_path):
with open(file_path, 'r') as f:
uid = hashlib.sha1(''.join(f.read().split())).hexdigest()
return uid
class NullStatus:
def __nonzero__(self):
return False
def addMessage(self, message):
pass
class DensityCorrectionCalculator:
def __init__(self, referenceDensity, windSpeedColumn, densityColumn):
self.referenceDensity = referenceDensity
self.windSpeedColumn = windSpeedColumn
self.densityColumn = densityColumn
def densityCorrectedHubWindSpeed(self, row):
return row[self.windSpeedColumn] * (row[self.densityColumn] / self.referenceDensity) ** (1.0 / 3.0)
class PowerCalculator:
def __init__(self, powerCurve, windSpeedColumn):
self.powerCurve = powerCurve
self.windSpeedColumn = windSpeedColumn
def power(self, row):
return self.powerCurve.power(row[self.windSpeedColumn])
class TurbulencePowerCalculator:
def __init__(self, powerCurve, ratedPower, windSpeedColumn, turbulenceColumn):
self.powerCurve = powerCurve
self.ratedPower = ratedPower
self.windSpeedColumn = windSpeedColumn
self.turbulenceColumn = turbulenceColumn
def power(self, row):
return self.powerCurve.power(row[self.windSpeedColumn], row[self.turbulenceColumn])
class PowerDeviationMatrixPowerCalculator:
def __init__(self, powerCurve, powerDeviationMatrix, windSpeedColumn, parameterColumns):
self.powerCurve = powerCurve
self.powerDeviationMatrix = powerDeviationMatrix
self.windSpeedColumn = windSpeedColumn
self.parameterColumns = parameterColumns
def power(self, row):
parameters = {}
for dimension in self.powerDeviationMatrix.dimensions:
column = self.parameterColumns[dimension.parameter]
value = row[column]
parameters[dimension.parameter] = value
#print dimension.parameter, value
deviation = self.powerDeviationMatrix[parameters]
power = self.powerCurve.power(row[self.windSpeedColumn])
#print power, deviation
#raise Exception("Stop")
return self.powerCurve.power(row[self.windSpeedColumn]) * (1.0 + deviation)
class Analysis:
def __init__(self, config, status = NullStatus(), auto_activate_corrections = False):
self.config = config
self.nameColumn = "Dataset Name"
self.inputHubWindSpeed = "Input Hub Wind Speed"
self.densityCorrectedHubWindSpeed = "Density Corrected Hub Wind Speed"
self.rotorEquivalentWindSpeed = "Rotor Equivalent Wind Speed"
self.basePower = "Simulated Reference TI Power"
self.hubPower = "Hub Power"
self.rewsPower = "REWS Power"
self.powerDeviationMatrixPower = "Power Deviation Matrix Power"
self.turbulencePower = "Simulated TI Corrected Power"
self.combinedPower = "Combined Power"
self.windSpeedBin = "Wind Speed Bin"
self.turbulenceBin = "Turbulence Bin"
self.powerDeviation = "Power Deviation"
self.dataCount = "Data Count"
self.powerStandDev = "Power Standard Deviation"
self.windDirection = "Wind Direction"
self.powerCoeff = "Power Coefficient"
self.inputHubWindSpeedSource = 'Undefined'
self.measuredTurbulencePower = 'Measured TI Corrected Power'
self.measuredTurbPowerCurveInterp = 'Measured TI Corrected Power Curve Interp'
self.measuredPowerCurveInterp = 'All Measured Power Curve Interp'
self.relativePath = configuration.RelativePath(config.path)
self.status = status
self.calibrations = []
self.rotorGeometry = turbine.RotorGeometry(config.diameter, config.hubHeight)
self.status.addMessage("Loading dataset...")
self.loadData(config, self.rotorGeometry)
if auto_activate_corrections:
self.auto_activate_corrections()
self.densityCorrectionActive = config.densityCorrectionActive
self.rewsActive = config.rewsActive
self.turbRenormActive = config.turbRenormActive
self.powerDeviationMatrixActive = config.powerDeviationMatrixActive
self.uniqueAnalysisId = hash_file_contents(self.config.path)
self.status.addMessage("Unique Analysis ID is: %s" % self.uniqueAnalysisId)
self.status.addMessage("Calculating (please wait)...")
if len(self.datasetConfigs) > 0:
self.datasetUniqueIds = self.generate_unique_dset_ids()
if self.powerDeviationMatrixActive:
self.status.addMessage("Loading power deviation matrix...")
self.specifiedPowerDeviationMatrix = configuration.PowerDeviationMatrixConfiguration(self.relativePath.convertToAbsolutePath(config.specifiedPowerDeviationMatrix))
self.powerCurveMinimumCount = config.powerCurveMinimumCount
self.powerCurvePaddingMode = config.powerCurvePaddingMode
self.ratedPower = config.ratedPower
self.baseLineMode = config.baseLineMode
self.filterMode = config.filterMode
self.powerCurveMode = config.powerCurveMode
self.defineInnerRange(config)
self.status.addMessage("Baseline Mode: %s" % self.baseLineMode)
self.status.addMessage("Filter Mode: %s" % self.filterMode)
self.status.addMessage("Power Curve Mode: %s" % self.powerCurveMode)
self.windSpeedBins = binning.Bins(config.powerCurveFirstBin, config.powerCurveBinSize, config.powerCurveLastBin)
first_turb_bin = 0.01
turb_bin_width = 0.02
last_turb_bin = 0.25
self.powerCurveSensitivityResults = {}
self.powerCurveSensitivityVariationMetrics = pd.DataFrame(columns = ['Power Curve Variation Metric'])
self.turbulenceBins = binning.Bins(first_turb_bin, turb_bin_width, last_turb_bin)
self.aggregations = binning.Aggregations(self.powerCurveMinimumCount)
if config.specifiedPowerCurve != '':
powerCurveConfig = configuration.PowerCurveConfiguration(self.relativePath.convertToAbsolutePath(config.specifiedPowerCurve))
self.specifiedPowerCurve = turbine.PowerCurve(powerCurveConfig.powerCurveLevels, powerCurveConfig.powerCurveDensity, \
self.rotorGeometry, "Specified Power", "Specified Turbulence", \
turbulenceRenormalisation = self.turbRenormActive, name = 'Specified')
self.referenceDensity = self.specifiedPowerCurve.referenceDensity
else:
self.specifiedPowerCurve = None
self.referenceDensity = 1.225 #todo consider adding UI setting for this
if self.densityCorrectionActive:
if self.hasDensity:
self.dataFrame[self.densityCorrectedHubWindSpeed] = self.dataFrame.apply(DensityCorrectionCalculator(self.referenceDensity, self.hubWindSpeed, self.hubDensity).densityCorrectedHubWindSpeed, axis=1)
self.dataFrame[self.inputHubWindSpeed] = self.dataFrame[self.densityCorrectedHubWindSpeed]
self.inputHubWindSpeedSource = self.densityCorrectedHubWindSpeed
else:
raise Exception("Density data column not specified.")
else:
self.dataFrame[self.inputHubWindSpeed] = self.dataFrame[self.hubWindSpeed]
self.inputHubWindSpeedSource = self.hubWindSpeed
self.dataFrame[self.windSpeedBin] = self.dataFrame[self.inputHubWindSpeed].map(self.windSpeedBins.binCenter)
self.dataFrame[self.turbulenceBin] = self.dataFrame[self.hubTurbulence].map(self.turbulenceBins.binCenter)
self.applyRemainingFilters()
if self.hasDensity:
if self.densityCorrectionActive:
self.dataFrame[self.powerCoeff] = self.calculateCp()
self.meanMeasuredSiteDensity = self.dataFrame[self.hubDensity].dropna().mean()
if self.hasActualPower:
self.status.addMessage("Calculating actual power curves...")
self.allMeasuredPowerCurve = self.calculateMeasuredPowerCurve(0, config.cutInWindSpeed, config.cutOutWindSpeed, config.ratedPower, self.actualPower, 'All Measured')
self.dayTimePowerCurve = self.calculateMeasuredPowerCurve(11, config.cutInWindSpeed, config.cutOutWindSpeed, config.ratedPower, self.actualPower, 'Day Time')
self.nightTimePowerCurve = self.calculateMeasuredPowerCurve(12, config.cutInWindSpeed, config.cutOutWindSpeed, config.ratedPower, self.actualPower, 'Night Time')
self.innerTurbulenceMeasuredPowerCurve = self.calculateMeasuredPowerCurve(2, config.cutInWindSpeed, config.cutOutWindSpeed, config.ratedPower, self.actualPower, 'Inner Turbulence')
self.outerTurbulenceMeasuredPowerCurve = self.calculateMeasuredPowerCurve(2, config.cutInWindSpeed, config.cutOutWindSpeed, config.ratedPower, self.actualPower, 'Outer Turbulence')
if self.hasShear:
self.innerMeasuredPowerCurve = self.calculateMeasuredPowerCurve(1, config.cutInWindSpeed, config.cutOutWindSpeed, config.ratedPower, self.actualPower, 'Inner Range')
self.outerMeasuredPowerCurve = self.calculateMeasuredPowerCurve(4, config.cutInWindSpeed, config.cutOutWindSpeed, config.ratedPower, self.actualPower, 'Outer Range')
self.status.addMessage("Actual Power Curves Complete.")
self.powerCurve = self.selectPowerCurve(self.powerCurveMode)
self.calculateBase()
self.calculateHub()
#Normalisation Parameters
if self.turbRenormActive:
self.normalisingRatedPower = self.powerCurve.zeroTurbulencePowerCurve.initialZeroTurbulencePowerCurve.selectedStats.ratedPower
self.normalisingRatedWindSpeed = self.powerCurve.zeroTurbulencePowerCurve.initialZeroTurbulencePowerCurve.ratedWindSpeed
self.normalisingCutInWindSpeed = self.powerCurve.zeroTurbulencePowerCurve.initialZeroTurbulencePowerCurve.selectedStats.cutInWindSpeed
self.normalisedWS = 'Normalised WS'
self.dataFrame[self.normalisedWS] = (self.dataFrame[self.inputHubWindSpeed] - self.normalisingCutInWindSpeed) / (self.normalisingRatedWindSpeed - self.normalisingCutInWindSpeed)
if self.hasActualPower:
self.normalisedPower = 'Normalised Power'
self.dataFrame[self.normalisedPower] = self.dataFrame[self.actualPower] / self.config.ratedPower
if config.rewsActive:
self.status.addMessage("Calculating REWS Correction...")
self.calculateREWS()
self.status.addMessage("REWS Correction Complete.")
self.rewsMatrix = self.calculateREWSMatrix(0)
if self.hasShear: self.rewsMatrixInnerShear = self.calculateREWSMatrix(3)
if self.hasShear: self.rewsMatrixOuterShear = self.calculateREWSMatrix(6)
if config.turbRenormActive:
self.status.addMessage("Calculating Turbulence Correction...")
self.calculateTurbRenorm()
self.status.addMessage("Turbulence Correction Complete.")
if self.hasActualPower:
self.allMeasuredTurbCorrectedPowerCurve = self.calculateMeasuredPowerCurve(0, config.cutInWindSpeed, config.cutOutWindSpeed, config.ratedPower, self.measuredTurbulencePower, 'Turbulence Corrected')
if config.turbRenormActive and config.rewsActive:
self.status.addMessage("Calculating Combined (REWS + Turbulence) Correction...")
self.calculationCombined()
if config.powerDeviationMatrixActive:
self.status.addMessage("Calculating Power Deviation Matrix Correction...")
self.calculatePowerDeviationMatrixCorrection()
self.status.addMessage("Power Deviation Matrix Correction Complete.")
if self.hasActualPower:
self.status.addMessage("Calculating power deviation matrices...")
allFilterMode = 0
innerShearFilterMode = 3
self.hubPowerDeviations = self.calculatePowerDeviationMatrix(self.hubPower, allFilterMode)
if self.hasShear: self.hubPowerDeviationsInnerShear = self.calculatePowerDeviationMatrix(self.hubPower, innerShearFilterMode)
if config.rewsActive:
self.rewsPowerDeviations = self.calculatePowerDeviationMatrix(self.rewsPower, allFilterMode)
if self.hasShear: self.rewsPowerDeviationsInnerShear = self.calculatePowerDeviationMatrix(self.rewsPower, innerShearFilterMode)
if config.turbRenormActive:
self.turbPowerDeviations = self.calculatePowerDeviationMatrix(self.turbulencePower, allFilterMode)
if self.hasShear: self.turbPowerDeviationsInnerShear = self.calculatePowerDeviationMatrix(self.turbulencePower, innerShearFilterMode)
if config.turbRenormActive and config.rewsActive:
self.combPowerDeviations = self.calculatePowerDeviationMatrix(self.combinedPower, allFilterMode)
if self.hasShear: self.combPowerDeviationsInnerShear = self.calculatePowerDeviationMatrix(self.combinedPower, innerShearFilterMode)
if config.powerDeviationMatrixActive:
self.powerDeviationMatrixDeviations = self.calculatePowerDeviationMatrix(self.powerDeviationMatrixPower, allFilterMode)
self.status.addMessage("Power Curve Deviation Matrices Complete.")
self.hours = len(self.dataFrame.index)*1.0 / 6.0
if self.config.nominalWindSpeedDistribution is not None:
self.status.addMessage("Attempting AEP Calculation...")
import aep
if self.powerCurve is self.specifiedPowerCurve:
self.windSpeedAt85pctX1pnt5 = self.specifiedPowerCurve.getThresholdWindSpeed()
if hasattr(self.datasetConfigs[0].data,"analysedDirections"):
self.analysedDirectionSectors = self.datasetConfigs[0].data.analysedDirections # assume a single for now.
if len(self.powerCurve.powerCurveLevels) != 0:
self.aepCalc,self.aepCalcLCB = aep.run(self,self.relativePath.convertToAbsolutePath(self.config.nominalWindSpeedDistribution), self.allMeasuredPowerCurve)
if self.turbRenormActive:
self.turbCorrectedAepCalc,self.turbCorrectedAepCalcLCB = aep.run(self,self.relativePath.convertToAbsolutePath(self.config.nominalWindSpeedDistribution), self.allMeasuredTurbCorrectedPowerCurve)
else:
self.status.addMessage("A specified power curve is required for AEP calculation. No specified curve defined.")
if len(self.sensitivityDataColumns) > 0:
sens_pow_curve = self.allMeasuredTurbCorrectedPowerCurve if self.turbRenormActive else self.allMeasuredPowerCurve
sens_pow_column = self.measuredTurbulencePower if self.turbRenormActive else self.actualPower
sens_pow_interp_column = self.measuredTurbPowerCurveInterp if self.turbRenormActive else self.measuredPowerCurveInterp
self.interpolatePowerCurve(sens_pow_curve, self.inputHubWindSpeedSource, sens_pow_interp_column)
self.status.addMessage("Attempting power curve sensitivty analysis for %s power curve..." % sens_pow_curve.name)
self.performSensitivityAnalysis(sens_pow_curve, sens_pow_column, sens_pow_interp_column)
if self.hasActualPower:
self.powerCurveScatterMetric = self.calculatePowerCurveScatterMetric(self.allMeasuredPowerCurve, self.actualPower, self.dataFrame.index, print_to_console = True)
self.dayTimePowerCurveScatterMetric = self.calculatePowerCurveScatterMetric(self.dayTimePowerCurve, self.actualPower, self.dataFrame.index[self.getFilter(11)])
self.nightTimePowerCurveScatterMetric = self.calculatePowerCurveScatterMetric(self.nightTimePowerCurve, self.actualPower, self.dataFrame.index[self.getFilter(12)])
if self.turbRenormActive:
self.powerCurveScatterMetricAfterTiRenorm = self.calculatePowerCurveScatterMetric(self.allMeasuredTurbCorrectedPowerCurve, self.measuredTurbulencePower, self.dataFrame.index, print_to_console = True)
self.powerCurveScatterMetricByWindSpeed = self.calculateScatterMetricByWindSpeed(self.allMeasuredPowerCurve, self.actualPower)
if self.turbRenormActive:
self.powerCurveScatterMetricByWindSpeedAfterTiRenorm = self.calculateScatterMetricByWindSpeed(self.allMeasuredTurbCorrectedPowerCurve, self.measuredTurbulencePower)
self.iec_2005_cat_A_power_curve_uncertainty()
self.status.addMessage("Complete")
def auto_activate_corrections(self):
self.status.addMessage("Automatically activating corrections based on availabe data.")
save_conf = False
if self.hasDensity:
self.config.densityCorrectionActive = True
self.status.addMessage("Density Correction activated.")
save_conf = True
if self.hubTurbulence in self.dataFrame.columns:
self.config.turbRenormActive = True
self.status.addMessage("TI Renormalisation activated.")
save_conf = True
if self.rewsDefined:
self.config.rewsActive = True
self.status.addMessage("REWS activated.")
save_conf = True
if (type(self.config.specifiedPowerDeviationMatrix) in (str, unicode)) and (len(self.config.specifiedPowerDeviationMatrix) > 0):
self.config.powerDeviationMatrixActive = True
self.status.addMessage("PDM activated.")
save_conf = True
if save_conf:
self.config.save()
def applyRemainingFilters(self):
print "Apply derived filters (filters which depend on calculated columns)"
for dataSetConf in self.datasetConfigs:
print dataSetConf.name
if self.anyFiltersRemaining(dataSetConf):
print "Applying Remaining Filters"
print "Extracting dataset data"
#print "KNOWN BUG FOR CONCURRENT DATASETS"
datasetStart = dataSetConf.timeStamps[0]
datasetEnd = dataSetConf.timeStamps[-1]
print "Start: %s" % datasetStart
print "End: %s" % datasetEnd
mask = self.dataFrame[self.timeStamp] > datasetStart
mask = mask & (self.dataFrame[self.timeStamp] < datasetEnd)
mask = mask & (self.dataFrame[self.nameColumn] == dataSetConf.name)
dateRangeDataFrame = self.dataFrame.loc[mask, :]
self.dataFrame = self.dataFrame.drop(dateRangeDataFrame.index)
print "Filtering Extracted Data"
d = dataSetConf.data.filterDataFrame(dateRangeDataFrame, dataSetConf.filters)
print "(Re)inserting filtered data "
self.dataFrame = self.dataFrame.append(d)
if len([filter for filter in dataSetConf.filters if ((not filter.applied) & (filter.active))]) > 0:
print [str(filter) for filter in dataSetConf.filters if ((not filter.applied) & (filter.active))]
raise Exception("Filters have not been able to be applied!")
else:
print "No filters left to apply"
def anyFiltersRemaining(self, dataSetConf):
for datasetFilter in dataSetConf.filters:
if not datasetFilter.applied:
return True
return False
def defineInnerRange(self, config):
self.innerRangeLowerTurbulence = config.innerRangeLowerTurbulence
self.innerRangeUpperTurbulence = config.innerRangeUpperTurbulence
self.innerRangeCenterTurbulence = 0.5 * self.innerRangeLowerTurbulence + 0.5 * self.innerRangeUpperTurbulence
if self.hasShear:
self.innerRangeLowerShear = config.innerRangeLowerShear
self.innerRangeUpperShear = config.innerRangeUpperShear
self.innerRangeCenterShear = 0.5 * self.innerRangeLowerShear + 0.5 * self.innerRangeUpperShear
def loadData(self, config, rotorGeometry):
self.residualWindSpeedMatrices = {}
self.datasetConfigs = []
for i in range(len(config.datasets)):
if not isinstance(config.datasets[i],configuration.DatasetConfiguration):
datasetConfig = configuration.DatasetConfiguration(self.relativePath.convertToAbsolutePath(config.datasets[i]))
else:
datasetConfig = config.datasets[i]
data = dataset.Dataset(datasetConfig, rotorGeometry, config)
if hasattr(data,"calibrationCalculator"):
self.calibrations.append( (datasetConfig,data.calibrationCalculator ) )
datasetConfig.timeStamps = data.dataFrame.index
datasetConfig.data = data
self.datasetConfigs.append(datasetConfig)
if i == 0:
#analysis 'inherits' timestep from first data set. Subsequent datasets will be checked for consistency
self.timeStepInSeconds = datasetConfig.timeStepInSeconds
#copy column names from dataset
self.timeStamp = data.timeStamp
self.hubWindSpeed = data.hubWindSpeed
self.hubTurbulence = data.hubTurbulence
self.hubDensity = data.hubDensity
self.shearExponent = data.shearExponent
if data.rewsDefined:
self.profileRotorWindSpeed = data.profileRotorWindSpeed
self.profileHubWindSpeed = data.profileHubWindSpeed
self.profileHubToRotorRatio = data.profileHubToRotorRatio
self.profileHubToRotorDeviation = data.profileHubToRotorDeviation
self.actualPower = data.actualPower
self.residualWindSpeed = data.residualWindSpeed
self.dataFrame = data.dataFrame
self.hasActualPower = data.hasActualPower
self.hasAllPowers = data.hasAllPowers
self.hasShear = data.hasShear
self.hasDensity = data.hasDensity
self.hasDirection = data.hasDirection
self.rewsDefined = data.rewsDefined
self.sensitivityDataColumns = data.sensitivityDataColumns
else:
if datasetConfig.timeStepInSeconds <> self.timeStepInSeconds:
raise Exception ("Dataset time step (%d) does not match analysis (%d) time step" % (datasetConfig.timeStepInSeconds, self.timeStepInSeconds))
self.dataFrame = self.dataFrame.append(data.dataFrame, ignore_index = True)
self.hasActualPower = self.hasActualPower & data.hasActualPower
self.hasAllPowers = self.hasAllPowers & data.hasAllPowers
self.hasShear = self.hasShear & data.hasShear
self.hasDensity = self.hasDensity & data.hasDensity
self.rewsDefined = self.rewsDefined & data.rewsDefined
self.residualWindSpeedMatrices[data.name] = data.residualWindSpeedMatrix
self.timeStampHours = float(self.timeStepInSeconds) / 3600.0
def generate_unique_dset_ids(self):
dset_ids = {}
for conf in self.datasetConfigs:
ids = {}
ids['Configuration'] = hash_file_contents(conf.path)
ids['Time Series'] = hash_file_contents(conf.data.relativePath.convertToAbsolutePath(conf.inputTimeSeriesPath))
dset_ids[conf.name] = ids
return dset_ids
def selectPowerCurve(self, powerCurveMode):
if powerCurveMode == "Specified":
return self.specifiedPowerCurve
elif powerCurveMode == "InnerMeasured":
if self.hasActualPower and self.hasShear:
return self.innerMeasuredPowerCurve
elif not self.hasActualPower:
raise Exception("Cannot use inner measured power curvve: Power data not specified")
elif not self.hasShear:
raise Exception("Cannot use inner measured power curvve: Shear data not specified")
elif powerCurveMode == "InnerTurbulenceMeasured":
if self.hasActualPower:
return self.innerTurbulenceMeasuredPowerCurve
else:
raise Exception("Cannot use inner measured power curvve: Power data not specified")
elif powerCurveMode == "OuterMeasured":
if self.hasActualPower and self.hasShear:
return self.outerMeasuredPowerCurve
else:
raise Exception("Cannot use outer measured power curvve: Power data not specified")
elif powerCurveMode == "OuterTurbulenceMeasured":
if self.hasActualPower:
return self.outerTurbulenceMeasuredPowerCurve
else:
raise Exception("Cannot use outer measured power curvve: Power data not specified")
elif powerCurveMode == "AllMeasured":
if self.hasActualPower:
return self.allMeasuredPowerCurve
else:
raise Exception("Cannot use all measured power curvve: Power data not specified")
else:
raise Exception("Unrecognised power curve mode: %s" % powerCurveMode)
def getFilter(self, mode = None):
if mode == None:
mode = self.getFilterMode()
if self.baseLineMode == "Hub":
mask = self.dataFrame[self.inputHubWindSpeed].notnull()
elif self.baseLineMode == "Measured":
mask = self.dataFrame[self.actualPower] > 0
else:
raise Exception("Unrecognised baseline mode: %s" % self.baseLineMode)
innerTurbMask = (self.dataFrame[self.hubTurbulence] >= self.innerRangeLowerTurbulence) & (self.dataFrame[self.hubTurbulence] <= self.innerRangeUpperTurbulence)
if self.hasShear: innerShearMask = (self.dataFrame[self.shearExponent] >= self.innerRangeLowerShear) & (self.dataFrame[self.shearExponent] <= self.innerRangeUpperShear)
if mode > 0:
if mode <=3:
#Inner
if mode == 1:
mask = mask & innerTurbMask & innerShearMask
elif mode == 2:
mask = mask & innerTurbMask
elif mode == 3:
mask = mask & innerShearMask
else:
raise Exception("Unexpected filter mode")
elif mode <= 6:
#Outer
if mode == 4:
mask = ~(innerTurbMask & innerShearMask)
elif mode == 5:
mask = ~innerTurbMask
elif mode == 6:
mask = ~innerShearMask
else:
raise Exception("Unexpected filter mode")
elif mode <= 10:
innerMask = innerTurbMask & innerShearMask
mask = mask & (~innerMask)
if mode == 7:
#LowShearLowTurbulence
mask = mask & (self.dataFrame[self.shearExponent] <= self.innerRangeCenterShear) & (self.dataFrame[self.hubTurbulence] <= self.innerRangeCenterTurbulence)
elif mode == 8:
#LowShearHighTurbulence
mask = mask & (self.dataFrame[self.shearExponent] <= self.innerRangeCenterShear) & (self.dataFrame[self.hubTurbulence] >= self.innerRangeCenterTurbulence)
elif mode == 9:
#HighShearHighTurbulence
mask = mask & (self.dataFrame[self.shearExponent] >= self.innerRangeCenterShear) & (self.dataFrame[self.hubTurbulence] >= self.innerRangeCenterTurbulence)
elif mode == 10:
#HighShearLowTurbulence
mask = mask & (self.dataFrame[self.shearExponent] >= self.innerRangeCenterShear) & (self.dataFrame[self.hubTurbulence] <= self.innerRangeCenterTurbulence)
else:
raise Exception("Unexpected filter mode")
else:
if mode == 11:
#for day time power curve (between 7am and 8pm)
mask = mask & (self.dataFrame[self.timeStamp].dt.hour >= 7) & (self.dataFrame[self.timeStamp].dt.hour <= 20)
elif mode == 12:
#for night time power curve (between 8pm and 7am)
mask = mask & ((self.dataFrame[self.timeStamp].dt.hour < 7) | (self.dataFrame[self.timeStamp].dt.hour > 20))
else:
raise Exception("Unexpected filter mode")
return mask
def getFilterMode(self):
if self.filterMode == "Inner":
return 1
elif self.filterMode == "InnerTurb":
return 2
elif self.filterMode == "InnerShear":
return 3
elif self.filterMode == "Outer":
return 4
elif self.filterMode == "OuterTurb":
return 5
elif self.filterMode == "OuterShear":
return 6
elif self.filterMode == "LowShearLowTurbulence":
return 7
elif self.filterMode == "LowShearHighTurbulence":
return 8
elif self.filterMode == "HighShearHighTurbulence":
return 9
elif self.filterMode == "HighShearLowTurbulence":
return 10
elif self.filterMode == "All":
return 0
elif self.filterMode == "Day":
return 11
elif self.filterMode == "Night":
return 12
else:
raise Exception("Unrecognised filter mode: %s" % self.filterMode)
def interpolatePowerCurve(self, powerCurveLevels, ws_col, interp_power_col):
self.dataFrame[interp_power_col] = self.dataFrame[ws_col].apply(powerCurveLevels.power)
def performSensitivityAnalysis(self, power_curve, power_column, interp_pow_column, n_random_tests = 20):
mask = self.getFilter()
filteredDataFrame = self.dataFrame[mask]
#calculate significance threshold based on generated random variable
rand_columns, rand_sensitivity_results = [], []
for i in range(n_random_tests):
rand_columns.append('Random ' + str(i + 1))
filteredDataFrame[rand_columns] = pd.DataFrame(np.random.rand(len(filteredDataFrame),n_random_tests), columns=rand_columns, index = filteredDataFrame.index)
for col in rand_columns:
variation_metric = self.calculatePowerCurveSensitivity(filteredDataFrame, power_curve, col, power_column, interp_pow_column)[1]
rand_sensitivity_results.append(variation_metric)
self.sensitivityAnalysisThreshold = np.mean(rand_sensitivity_results)
print "\nSignificance threshold for power curve variation metric is %.2f%%." % (self.sensitivityAnalysisThreshold * 100.)
filteredDataFrame.drop(rand_columns, axis = 1, inplace = True)
#sensitivity to time of day, time of year, time elapsed in test
filteredDataFrame['Days Elapsed In Test'] = (filteredDataFrame[self.timeStamp] - filteredDataFrame[self.timeStamp].min()).dt.days
filteredDataFrame['Hours From Noon'] = np.abs(filteredDataFrame[self.timeStamp].dt.hour - 12)
filteredDataFrame['Days From 182nd Day Of Year'] = np.abs(filteredDataFrame[self.timeStamp].dt.dayofyear - 182)
#for col in (self.sensitivityDataColumns + ['Days Elapsed In Test','Hours From Noon','Days From 182nd Day Of Year']):
for col in (list(filteredDataFrame.columns) + ['Days Elapsed In Test','Hours From Noon','Days From 182nd Day Of Year']): # if we want to do the sensitivity analysis for all columns in the dataframe...
print "\nAttempting to compute sensitivity of power curve to %s..." % col
try:
self.powerCurveSensitivityResults[col], self.powerCurveSensitivityVariationMetrics.loc[col, 'Power Curve Variation Metric'] = self.calculatePowerCurveSensitivity(filteredDataFrame, power_curve, col, power_column, interp_pow_column)
print "Variation of power curve with respect to %s is %.2f%%." % (col, self.powerCurveSensitivityVariationMetrics.loc[col, 'Power Curve Variation Metric'] * 100.)
if self.powerCurveSensitivityVariationMetrics.loc[col,'Power Curve Variation Metric'] == 0:
self.powerCurveSensitivityVariationMetrics.drop(col, axis = 1, inplace = True)
except:
print "Could not run sensitivity analysis for %s." % col
self.powerCurveSensitivityVariationMetrics.sort('Power Curve Variation Metric', ascending = False, inplace = True)
def calculatePowerCurveSensitivity(self, dataFrame, power_curve, dataColumn, power_column, interp_pow_column):
dataFrame['Energy MWh'] = (dataFrame[power_column] * (float(self.timeStepInSeconds) / 3600.)).astype('float')
from collections import OrderedDict
self.sensitivityLabels = OrderedDict([("V Low","#0000ff"), ("Low","#4400bb"), ("Medium","#880088"), ("High","#bb0044"), ("V High","#ff0000")]) #categories to split data into using data_column and colour to plot
cutOffForCategories = list(np.arange(0.,1.,1./len(self.sensitivityLabels.keys()))) + [1.]
minCount = len(self.sensitivityLabels.keys()) * 4 #at least 4 data points for each category for a ws bin to be valid
wsBinnedCount = dataFrame[['Wind Speed Bin', dataColumn]].groupby('Wind Speed Bin').count()
validWsBins = wsBinnedCount.index[wsBinnedCount[dataColumn] > minCount] #ws bins that have enough data for the sensitivity analysis
dataFrame['Bin'] = np.nan #pre-allocating
dataFrame['Power Delta kW'] = dataFrame[power_column] - dataFrame[interp_pow_column]
dataFrame['Energy Delta MWh'] = dataFrame['Power Delta kW'] * (float(self.timeStepInSeconds) / 3600.)
for wsBin in dataFrame['Wind Speed Bin'].unique(): #within each wind speed bin, bin again by the categorising by sensCol
if wsBin in validWsBins:
try:
filt = dataFrame['Wind Speed Bin'] == wsBin
dataFrame.loc[filt,'Bin'] = pd.qcut(dataFrame[dataColumn][filt], cutOffForCategories, labels = self.sensitivityLabels.keys())
except:
print "\tCould not categorise data by %s for WS bin %s." % (dataColumn, wsBin)
sensitivityResults = dataFrame[[power_column, 'Energy MWh', 'Wind Speed Bin','Bin', 'Power Delta kW', 'Energy Delta MWh']].groupby(['Wind Speed Bin','Bin']).agg({power_column: np.mean, 'Energy MWh': np.sum, 'Wind Speed Bin': len, 'Power Delta kW': np.mean, 'Energy Delta MWh': np.sum})
# sensitivityResults['Energy Delta MWh'], sensitivityResults['Power Delta kW'] = np.nan, np.nan #pre-allocate
# for i in sensitivityResults.index:
# #sensitivityResults.loc[i, 'Power Delta kW'] = sensitivityResults.loc[i, power_column] - power_curve.powerCurveLevels.loc[i[0], power_column]
# sensitivityResults.loc[i, 'Energy Delta MWh'] = sensitivityResults.loc[i, 'Power Delta kW'] * power_curve.powerCurveLevels.loc[i[0], 'Data Count'] * (float(self.timeStepInSeconds) / 3600.)
return sensitivityResults.rename(columns = {'Wind Speed Bin':'Data Count'}), np.abs(sensitivityResults['Energy Delta MWh']).sum() / (power_curve.powerCurveLevels[power_column] * power_curve.powerCurveLevels['Data Count'] * (float(self.timeStepInSeconds) / 3600.)).sum()
def calculateMeasuredPowerCurve(self, mode, cutInWindSpeed, cutOutWindSpeed, ratedPower, powerColumn, name):
print "Calculating %s power curve." % name
mask = (self.dataFrame[powerColumn] > (self.ratedPower * -.25)) & (self.dataFrame[self.inputHubWindSpeed] > 0) & (self.dataFrame[self.hubTurbulence] > 0) & self.getFilter(mode)
filteredDataFrame = self.dataFrame[mask]
print "%s rows of data being used for %s power curve." % (len(filteredDataFrame), name)
#storing power curve in a dataframe as opposed to dictionary
dfPowerLevels = filteredDataFrame[[powerColumn, self.inputHubWindSpeed, self.hubTurbulence]].groupby(filteredDataFrame[self.windSpeedBin]).aggregate(self.aggregations.average)
powerStdDev = filteredDataFrame[[powerColumn, self.inputHubWindSpeed]].groupby(filteredDataFrame[self.windSpeedBin]).std().rename(columns={powerColumn:self.powerStandDev})[self.powerStandDev]
dfDataCount = filteredDataFrame[powerColumn].groupby(filteredDataFrame[self.windSpeedBin]).agg({self.dataCount:'count'})
if not all(dfPowerLevels.index == dfDataCount.index):
raise Exception("Index of aggregated data count and mean quantities for measured power curve do not match.")
dfPowerLevels = dfPowerLevels.join(dfDataCount, how = 'inner')
dfPowerLevels = dfPowerLevels.join(powerStdDev, how = 'inner')
dfPowerLevels.dropna(inplace = True)
if self.powerCoeff in filteredDataFrame.columns:
dfPowerCoeff = filteredDataFrame[self.powerCoeff].groupby(filteredDataFrame[self.windSpeedBin]).aggregate(self.aggregations.average)
else:
dfPowerCoeff = None
if len(dfPowerLevels.index) != 0:
#padding
# To deal with data missing between cutOut and last measured point:
# Specified : Use specified rated power
# Last : Use last observed power
# Linear : linearly interpolate from last observed power at last observed ws to specified power at specified ws.
maxTurb = dfPowerLevels[self.hubTurbulence].max()
minTurb = dfPowerLevels[self.hubTurbulence].min()
powerCurvePadder = PadderFactory().generate(self.powerCurvePaddingMode, powerColumn, self.inputHubWindSpeed, self.hubTurbulence, self.dataCount)
powerLevels = powerCurvePadder.pad(dfPowerLevels,cutInWindSpeed,cutOutWindSpeed,ratedPower)
if dfPowerCoeff is not None:
powerLevels[self.powerCoeff] = dfPowerCoeff
return turbine.PowerCurve(powerLevels, self.referenceDensity, self.rotorGeometry, powerColumn,
self.hubTurbulence, wsCol = self.inputHubWindSpeed, countCol = self.dataCount,
turbulenceRenormalisation = (self.turbRenormActive if powerColumn != self.turbulencePower else False), name = name)
def calculatePowerDeviationMatrix(self, power, filterMode, windBin = None, turbBin = None):
if windBin is None:
windBin = self.windSpeedBin
if turbBin is None:
turbBin = self.turbulenceBin
mask = (self.dataFrame[self.actualPower] > 0) & (self.dataFrame[power] > 0)
mask = mask & self.getFilter(filterMode)
filteredDataFrame = self.dataFrame[mask]
filteredDataFrame.is_copy = False
filteredDataFrame[self.powerDeviation] = (filteredDataFrame[self.actualPower] - filteredDataFrame[power]) / filteredDataFrame[power]
devMatrix = DeviationMatrix(filteredDataFrame[self.powerDeviation].groupby([filteredDataFrame[windBin], filteredDataFrame[turbBin]]).aggregate(self.aggregations.average),
filteredDataFrame[self.powerDeviation].groupby([filteredDataFrame[windBin], filteredDataFrame[turbBin]]).count())
return devMatrix
def calculateREWSMatrix(self, filterMode):
mask = self.dataFrame[self.inputHubWindSpeed] > 0.0
mask = mask & self.getFilter(filterMode)
filteredDataFrame = self.dataFrame[mask]
rewsMatrix = DeviationMatrix(filteredDataFrame[self.profileHubToRotorDeviation].groupby([filteredDataFrame[self.windSpeedBin], filteredDataFrame[self.turbulenceBin]]).aggregate(self.aggregations.average),
filteredDataFrame[self.profileHubToRotorDeviation].groupby([filteredDataFrame[self.windSpeedBin], filteredDataFrame[self.turbulenceBin]]).count())
return rewsMatrix
def calculatePowerCurveScatterMetric(self, measuredPowerCurve, powerColumn, rows, print_to_console = False): #this calculates a metric for the scatter of the all measured PC
try:
energyDiffMWh = np.abs((self.dataFrame.loc[rows, powerColumn] - self.dataFrame.loc[rows, self.inputHubWindSpeed].apply(measuredPowerCurve.power)) * (float(self.timeStepInSeconds) / 3600.))
energyMWh = self.dataFrame.loc[rows, powerColumn] * (float(self.timeStepInSeconds) / 3600.)
powerCurveScatterMetric = energyDiffMWh.sum() / energyMWh.sum()
print "%s scatter metric is %.2f%%." % (measuredPowerCurve.name, powerCurveScatterMetric * 100.)
if print_to_console:
self.status.addMessage("\n%s scatter metric is %.3f%%." % (measuredPowerCurve.name, powerCurveScatterMetric * 100.))
return powerCurveScatterMetric
except:
print "Could not calculate power curve scatter metric."
return np.nan
def calculateScatterMetricByWindSpeed(self, measuredPowerCurve, powerColumn):
index = self.dataFrame[self.windSpeedBin].unique()
index.sort()
df = pd.DataFrame(index = index, columns = ['Scatter Metric'])
for ws in df.index:
if ws >= measuredPowerCurve.cutInWindSpeed:
rows = self.dataFrame[self.inputHubWindSpeed] == ws
df.loc[ws, 'Scatter Metric'] = self.calculatePowerCurveScatterMetric(measuredPowerCurve, powerColumn, rows)
return df.dropna()
def calculate_pcwg_error_fields(self):
self.calculate_anonymous_values()
self.pcwgErrorBaseline = 'Baseline Error'
self.dataFrame[self.pcwgErrorBaseline] = self.dataFrame[self.hubPower] - self.dataFrame[self.actualPower]
if self.turbRenormActive:
self.pcwgErrorTurbRenor = 'TI Renormalisation Error'
self.dataFrame[self.pcwgErrorTurbRenor] = self.dataFrame[self.turbulencePower] - self.dataFrame[self.actualPower]
if self.rewsActive:
self.pcwgErrorRews = 'REWS Error'
self.dataFrame[self.pcwgErrorRews] = self.dataFrame[self.rewsPower] - self.dataFrame[self.actualPower]
if (self.turbRenormActive and self.rewsActive):
self.pcwgErrorTiRewsCombined = 'Combined TI Renorm and REWS Error'
self.dataFrame[self.pcwgErrorTiRewsCombined] = self.dataFrame[self.combinedPower] - self.dataFrame[self.actualPower]
if self.powerDeviationMatrixActive:
self.pcwgErrorPdm = 'PDM Error'
self.dataFrame[self.pcwgErrorPdm] = self.dataFrame[self.powerDeviationMatrixPower] - self.dataFrame[self.actualPower]
def calculate_pcwg_overall_metrics(self):
self.overall_pcwg_err_metrics = {}
NME, NMAE, data_count = self._calculate_pcwg_error_metric(self.pcwgErrorBaseline)
self.overall_pcwg_err_metrics[self.dataCount] = data_count
self.overall_pcwg_err_metrics['Baseline NME'] = NME
self.overall_pcwg_err_metrics['Baseline NMAE'] = NMAE
if self.turbRenormActive:
NME, NMAE, _ = self._calculate_pcwg_error_metric(self.pcwgErrorTurbRenor)
self.overall_pcwg_err_metrics['TI Renorm NME'] = NME
self.overall_pcwg_err_metrics['TI Renorm NMAE'] = NMAE
if self.rewsActive:
NME, NMAE, _ = self._calculate_pcwg_error_metric(self.pcwgErrorRews)
self.overall_pcwg_err_metrics['REWS NME'] = NME
self.overall_pcwg_err_metrics['REWS NMAE'] = NMAE
if (self.turbRenormActive and self.rewsActive):
NME, NMAE, _ = self._calculate_pcwg_error_metric(self.pcwgErrorTiRewsCombined)
self.overall_pcwg_err_metrics['REWS and TI Renorm NME'] = NME
self.overall_pcwg_err_metrics['REWS and TI Renorm NMAE'] = NMAE
if self.powerDeviationMatrixActive:
NME, NMAE, _ = self._calculate_pcwg_error_metric(self.pcwgErrorPdm)
self.overall_pcwg_err_metrics['PDM NME'] = NME
self.overall_pcwg_err_metrics['PDM NMAE'] = NMAE
def calculate_pcwg_binned_metrics(self):
reporting_bins = [self.normalisedWSBin, self.hourOfDay, self.calendarMonth, self.pcwgFourCellMatrixGroup, self.pcwgRange]
if self.hasDirection:
reporting_bins.append(self.pcwgDirectionBin)
self.binned_pcwg_err_metrics = {}
for bin_col_name in reporting_bins:
self.binned_pcwg_err_metrics[bin_col_name] = {}
self.binned_pcwg_err_metrics[bin_col_name][self.pcwgErrorBaseline] = self._calculate_pcwg_error_metric_by_bin(self.pcwgErrorBaseline, bin_col_name)
if self.turbRenormActive:
self.binned_pcwg_err_metrics[bin_col_name][self.pcwgErrorTurbRenor] = self._calculate_pcwg_error_metric_by_bin(self.pcwgErrorTurbRenor, bin_col_name)
if self.rewsActive:
self.binned_pcwg_err_metrics[bin_col_name][self.pcwgErrorRews] = self._calculate_pcwg_error_metric_by_bin(self.pcwgErrorRews, bin_col_name)
if (self.turbRenormActive and self.rewsActive):
self.binned_pcwg_err_metrics[bin_col_name][self.pcwgErrorTiRewsCombined] = self._calculate_pcwg_error_metric_by_bin(self.pcwgErrorTiRewsCombined, bin_col_name)
if self.powerDeviationMatrixActive:
self.binned_pcwg_err_metrics[bin_col_name][self.pcwgErrorPdm] = self._calculate_pcwg_error_metric_by_bin(self.pcwgErrorPdm, bin_col_name)
def _calculate_pcwg_error_metric_by_bin(self, candidate_error, bin_col_name):
def sum_abs(x):
return x.abs().sum()
grouped = self.dataFrame.groupby(bin_col_name)
agg = grouped.agg({candidate_error: ['sum', sum_abs, 'count'], self.actualPower: 'sum'})
agg.loc[:, (candidate_error, 'NME')] = agg.loc[:, (candidate_error, 'sum')] / agg.loc[:, (self.actualPower, 'sum')]
agg.loc[:, (candidate_error, 'NMAE')] = agg.loc[:, (candidate_error, 'sum_abs')] / agg.loc[:, (self.actualPower, 'sum')]
return agg.loc[:, candidate_error].drop(['sum', 'sum_abs'], axis = 1).rename(columns = {'count': self.dataCount})
def _calculate_pcwg_error_metric(self, candidate_error):
data_count = len(self.dataFrame[candidate_error].dropna())
NME = (self.dataFrame[candidate_error].sum() / self.dataFrame[self.actualPower].sum())
NMAE = (np.abs(self.dataFrame[candidate_error]).sum() / self.dataFrame[self.actualPower].sum())
return NME, NMAE, data_count
def iec_2005_cat_A_power_curve_uncertainty(self):
if self.turbRenormActive:
pc = self.allMeasuredTurbCorrectedPowerCurve.powerCurveLevels
pow_col = self.measuredTurbulencePower
else:
pc = self.allMeasuredPowerCurve.powerCurveLevels
pow_col = self.actualPower
#pc['frequency'] = pc[self.dataCount] / pc[self.dataCount].sum()
pc['s_i'] = pc[self.powerStandDev] / (pc[self.dataCount]**0.5) #from IEC 2005
unc_MWh = (np.abs(pc['s_i']) * (pc[self.dataCount] / 6.)).sum()
test_MWh = (np.abs(pc[pow_col]) * (pc[self.dataCount] / 6.)).sum()
self.categoryAUncertainty = unc_MWh / test_MWh
self.status.addMessage("Power curve category A uncertainty: %.3f%%" % (self.categoryAUncertainty * 100.0))
def report(self, path,version="unknown"):
report = reporting.report(self.windSpeedBins, self.turbulenceBins, version)
report.report(path, self)
def anonym_report(self, path, version="Unknown", scatter = False, deviationMatrix = True):
if not self.hasActualPower:
raise Exception("Anonymous report can only be generated if analysis has actual power data")
if deviationMatrix:
self.calculate_anonymous_values()
else:
self.normalisedWindSpeedBins = []
report = reporting.AnonReport(targetPowerCurve = self.powerCurve,
wind_bins = self.normalisedWindSpeedBins,
turbulence_bins = self.turbulenceBins,
version= version)
report.report(path, self, powerDeviationMatrix = deviationMatrix, scatterMetric= scatter)
def pcwg_data_share_report(self, version = 'Unknown', output_fname = (os.getcwd() + os.sep + 'Data Sharing Initiative 1 Report.xls')):
if self.powerCurveMode != "InnerMeasured":
raise Exception("Power Curve Mode must be set to Inner to export PCWG Sharing Initiative 1 Report.")
else:
self.calculate_pcwg_error_fields()
self.calculate_pcwg_overall_metrics()
self.calculate_pcwg_binned_metrics()
from data_sharing_reports import pcwg_share1_rpt
rpt = pcwg_share1_rpt(self, version = version, output_fname = output_fname)
rpt.report()
def calculate_anonymous_values(self):
allFilterMode = 0
self.normalisedWSBin = 'Normalised WS Bin Centre'
firstNormWSbin = 0.05
lastNormWSbin = 2.95
normWSstep = 0.1
self.normalisedWindSpeedBins = binning.Bins(firstNormWSbin, normWSstep, lastNormWSbin)
self.dataFrame[self.normalisedWSBin] = (self.dataFrame[self.normalisedWS]).map(self.normalisedWindSpeedBins.binCenter)
if self.hasDirection:
self.pcwgDirectionBin = 'Wind Direction Bin Centre'
dir_bin_width = 10.
wdir_centre_first_bin = 0.
self.pcwgWindDirBins = binning.Bins(wdir_centre_first_bin, dir_bin_width, 350.)
self.dataFrame[self.pcwgDirectionBin] = (self.dataFrame[self.windDirection] - wdir_centre_first_bin) / dir_bin_width
self.dataFrame[self.pcwgDirectionBin] = np.round(self.dataFrame[self.pcwgDirectionBin], 0) * dir_bin_width + wdir_centre_first_bin
self.dataFrame[self.pcwgDirectionBin] = (self.dataFrame[self.pcwgDirectionBin] + 360) % 360
self.pcwgFourCellMatrixGroup = 'PCWG Four Cell WS-TI Matrix Group'
self.dataFrame[self.pcwgFourCellMatrixGroup] = np.nan
filt = (self.dataFrame[self.normalisedWS] >= 0.5) & (self.dataFrame[self.hubTurbulence] >= self.innerRangeUpperTurbulence)
self.dataFrame.loc[filt, self.pcwgFourCellMatrixGroup] = 'HWS-HTI'
filt = (self.dataFrame[self.normalisedWS] < 0.5) & (self.dataFrame[self.hubTurbulence] >= self.innerRangeUpperTurbulence)
self.dataFrame.loc[filt, self.pcwgFourCellMatrixGroup] = 'LWS-HTI'
filt = (self.dataFrame[self.normalisedWS] >= 0.5) & (self.dataFrame[self.hubTurbulence] <= self.innerRangeLowerTurbulence)
self.dataFrame.loc[filt, self.pcwgFourCellMatrixGroup] = 'HWS-LTI'
filt = (self.dataFrame[self.normalisedWS] < 0.5) & (self.dataFrame[self.hubTurbulence] <= self.innerRangeLowerTurbulence)
self.dataFrame.loc[filt, self.pcwgFourCellMatrixGroup] = 'LWS-LTI'
self.pcwgRange = 'PCWG Range (Inner or Outer)'
self.dataFrame[self.pcwgRange] = np.nan
self.dataFrame.loc[self.getFilter(1), self.pcwgRange] = 'Inner'
self.dataFrame.loc[self.getFilter(4), self.pcwgRange] = 'Outer'
self.hourOfDay = 'Hour Of Day'
self.dataFrame[self.hourOfDay] = self.dataFrame[self.timeStamp].dt.hour
self.calendarMonth = 'Calendar Month'
self.dataFrame[self.calendarMonth] = self.dataFrame[self.timeStamp].dt.month
self.normalisedHubPowerDeviations = self.calculatePowerDeviationMatrix(self.hubPower, allFilterMode
,windBin = self.normalisedWSBin
,turbBin = self.turbulenceBin)
if self.config.turbRenormActive:
self.normalisedTurbPowerDeviations = self.calculatePowerDeviationMatrix(self.turbulencePower, allFilterMode
,windBin = self.normalisedWSBin
,turbBin = self.turbulenceBin)
else:
self.normalisedTurbPowerDeviations = None
def calculateBase(self):
if self.baseLineMode == "Hub":
self.dataFrame[self.basePower] = self.dataFrame.apply(PowerCalculator(self.powerCurve, self.inputHubWindSpeed).power, axis=1)
elif self.baseLineMode == "Measured":
if self.hasActualPower:
self.dataFrame[self.basePower] = self.dataFrame[self.actualPower]
else: