def getResampledTimeRange(rangeRef, rangeTolerance, timeRef, time,
timeTolerance, dataToResample, year, month, day,
height_M):
year = int(year)
month = int(month)
day = int(day)
usedIndexRange = np.ones(len(rangeRef)) * np.nan
timeConv = convCloudTime2HumTime(time)
humanTime = pd.datetime(year, month, day) + pd.to_timedelta(
np.array(timeConv), unit='s')
#humanTime=time
#Remove duplicates
varData = pd.DataFrame()
varDataTemp = pd.DataFrame(index=humanTime,
columns=height_M,
data=dataToResample)
varDataTemp['times'] = humanTime
varDataTemp = varDataTemp.sort_values(by=['times'], ascending=[True])
varDataTemp = varDataTemp.drop_duplicates(subset=['times'])
varData = varData.append(varDataTemp)
#Resample in time
timeIndexList = trLib.getIndexList(varData, timeRef, timeTolerance)
emptyDataFrame = pd.DataFrame(index=timeRef, columns=varData.columns)
resampledTime = trLib.getResampledDataPd(emptyDataFrame, varData,
timeIndexList)
timeDeviation = trLib.getDeviationPd(timeRef, resampledTime, timeTolerance)
del resampledTime['times']
resampledTime = resampledTime.T
#Resample in range
resampledTime['ranges'] = height_M
rangeIndexList = trLib.getIndexList(resampledTime, rangeRef,
rangeTolerance)
emptyDataFrame = pd.DataFrame(index=rangeRef,
columns=resampledTime.columns)
resampledTimeRange = trLib.getResampledDataPd(emptyDataFrame,
resampledTime,
rangeIndexList)
rangeDeviation = trLib.getDeviationPd(rangeRef, resampledTimeRange)
del resampledTimeRange['ranges']
resampledTimeRangeArr = np.array(resampledTimeRange[:], float)
resampledTimeRangeArr = np.ma.masked_invalid(resampledTimeRangeArr)
return resampledTimeRangeArr
def getDFResampledTime(timeRef, timeTolerance, dataFrameToResample):
#Remove duplicates
varDataTemp = dataFrameToResample
varDataTemp['times'] = varDataTemp.index
varDataTemp = varDataTemp.sort_values(by=['times'], ascending=[True])
varDataTemp = varDataTemp.drop_duplicates(subset=['times'])
#Resample in time
timeIndexList = trLib.getIndexList(varDataTemp, timeRef, timeTolerance)
emptyDataFrame = pd.DataFrame(index=timeRef, columns=varDataTemp.columns)
resampledTime = trLib.getResampledDataPd(emptyDataFrame, varDataTemp,
timeIndexList)
timeDeviation = trLib.getDeviationPd(timeRef, resampledTime, timeTolerance)
del resampledTime['times']
#resampledTime = resampledTime.T
return resampledTime
# var[var==-32] = np.nan
var = np.ma.masked_invalid(var)
np.ma.set_fill_value(var, np.nan)
varDataTemp = pd.DataFrame(index=humamTimeW, columns=ranges, data=var)
varDataTemp['times'] = humamTimeW
varDataTemp = varDataTemp.sort_values(by=['times'], ascending=[True])
varDataTemp = varDataTemp.drop_duplicates(subset=['times'])
varData = varData.append(varDataTemp)
rootgrp.close()
try:
#resample in time
timeIndexList = trLib.getIndexList(varData, timeRef, timeTolerance)
emptyDataFrame = pd.DataFrame(index=timeRef, columns=varData.columns)
resampledTime = trLib.getResampledDataPd(emptyDataFrame, varData,
timeIndexList)
timeDeviation = trLib.getDeviationPd(timeRef, resampledTime, timeTolerance)
del resampledTime['times']
resampledTime = resampledTime.T
except:
print 'No Times'
try:
#resample in range
resampledTime['ranges'] = ranges
rangeIndexList = trLib.getIndexList(resampledTime, rangeRef,
rangeTolerance)