def getRemcGraphDataSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
for stripCol in 'name,pnt,store'.split(','):
confDf[stripCol] = confDf[stripCol].str.strip()
# initialize results
resValsObj = {}
# confDf columns should be
# name, pnt, store
for rowIter in range(confDf.shape[0]):
confRow = confDf.iloc[rowIter]
printWithTs('graph data processing row number {0}'.format(rowIter+2))
name = confRow['name']
pnt = confRow['pnt']
store = confRow['store']
pntData = getRemcPntData(store, pnt)
# replace with zeros if we do not have data in the store
if pntData.size == 0:
pntData = pd.Series([0 for x in range(96)])
resValsObj[name] = pntData.values
return pd.DataFrame(resValsObj)
def getMaxGenInfoSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# confDf columns should be
# name,installed_capacity,avc_id,act_id,sch_id,type,pooling_station,gen_type
confDf['name'] = confDf['name'].str.strip()
confDf['type'] = confDf['type'].str.strip()
confDf['pooling_station'] = confDf['pooling_station'].str.strip()
confDf['gen_type'] = confDf['gen_type'].str.strip()
normalPntsConfDf = confDf[(confDf['type'] == 'normal') | (
confDf['type'] == '') | (confDf['type'].isnull())]
# initialize results
resValsList = []
for rowIter in range(confDf.shape[0]):
confRow = confDf.iloc[rowIter]
printWithTs('max gen info processing row number {0}'.format(rowIter+2))
timeValSeries = getPntData('HRS')
# get the type of row, itcan be dummy / normal / agg_pool / agg_gen_type
rowType = confRow['type']
if rowType == 'dummy':
# since the row is dummy, just insert a None row into result
resValsList.append({"name": confRow['name'],
"installed_capacity": None,
"max1": None, "max1_perc": None,
"max2": None, "max2_perc": None,
"max3": None, "max3_perc": None})
continue
elif not(pd.isnull(rowType)) and rowType.startswith('agg_'):
aggColName = rowType[len('agg_'):]
aggIdentifier = confRow[aggColName]
confDfForAgg = normalPntsConfDf[normalPntsConfDf[aggColName]
== aggIdentifier]
actPnt = ','.join(confDfForAgg['act_id'].tolist())
else:
actPnt = confRow['act_id']
installedCapacity = confRow['installed_capacity']
max1 = getPntData(actPnt)[5*60:10*60].max()
max1Perc = max1*100/installedCapacity
max2 = getPntData(actPnt)[10*60:17*60].max()
max2Perc = max2*100/installedCapacity
max3 = getPntData(actPnt)[17*60:24*60].max()
max3Perc = max3*100/installedCapacity
resValsList.append({"name": confRow['name'],
"installed_capacity": installedCapacity,
"max1": max1, "max1_perc": max1Perc,
"max2": max2, "max2_perc": max2Perc,
"max3": max3, "max3_perc": max3Perc})
return pd.DataFrame(resValsList)
def getRemcStateDaSummSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# confDf columns should be
# name,type,forecast_pnt,state,gen_type,gen_type2
for stripCol in 'name,type,forecast_pnt,state,gen_type,gen_type2'.split(
','):
confDf[stripCol] = confDf[stripCol].str.strip()
normalPntsConfDf = confDf[(confDf['type'] == 'normal') |
(confDf['type'] == '') |
(confDf['type'].isnull())]
# initialize results
resValsList = []
for rowIter in range(confDf.shape[0]):
confRow = confDf.iloc[rowIter]
printWithTs(
'REMC State Day Ahead Forecast summary processing row number {0}'.
format(rowIter + 2))
# get the type of row, itcan be dummy / normal / agg_gen_type2 / agg_gen_type
rowType = confRow['type']
if rowType == 'dummy':
# since the row is dummy, just insert a None row into result
resValsList.append({
"name": confRow['name'],
"max": None,
"min": None,
"avg": None
})
continue
elif not (pd.isnull(rowType)) and rowType.startswith('agg_'):
aggColName = rowType[len('agg_'):]
aggIdentifier = confRow[aggColName]
confDfForAgg = normalPntsConfDf[normalPntsConfDf[aggColName] ==
aggIdentifier]
forecastPnt = ','.join(confDfForAgg['forecast_pnt'].tolist())
else:
forecastPnt = confRow['forecast_pnt']
forecastSeries = getRemcPntData(FCA_DAY_AHEAD_STORE_NAME, forecastPnt)
maxForecast = forecastSeries.max()
minForecast = forecastSeries.min()
avgForecast = forecastSeries.mean()
resValsList.append({
"name": confRow['name'],
"max": maxForecast,
"min": minForecast,
"avg": avgForecast
})
return pd.DataFrame(resValsList)
def getRemcIstsErrNumBlksSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# confDf columns should be
# name,r16_pnt,avc_pnt,actual_pnt,type,pooling_station
for stripCol in 'name,r16_pnt,avc_pnt,actual_pnt,type,pooling_station'.split(','):
confDf[stripCol] = confDf[stripCol].str.strip()
normalPntsConfDf = confDf[(confDf['type'] == 'normal') | (
confDf['type'] == '') | (confDf['type'].isnull())]
# initialize results
resValsList = []
for rowIter in range(confDf.shape[0]):
confRow = confDf.iloc[rowIter]
printWithTs(
'REMC ISTS error number of blocks report processing row number {0}'.format(rowIter+2))
# get the type of row, itcan be dummy / normal / agg_pool / agg_gen_type
rowType = confRow['type']
if rowType == 'dummy':
# since the row is dummy, just insert a None row into result
resValsList.append({"name": confRow['name'], "ift": None,
"aleasoft": None, "res": None,
"enercast": None, "fca": None})
continue
elif not(pd.isnull(rowType)) and rowType.startswith('agg_'):
aggColName = rowType[len('agg_'):]
aggIdentifier = confRow[aggColName]
confDfForAgg = normalPntsConfDf[normalPntsConfDf[aggColName]
== aggIdentifier]
avcPnt = ','.join(confDfForAgg['avc_pnt'].tolist())
actPnt = ','.join(confDfForAgg['actual_pnt'].tolist())
r16Pnt = ','.join(confDfForAgg['r16_pnt'].tolist())
else:
avcPnt = confRow['avc_pnt']
actPnt = confRow['actual_pnt']
r16Pnt = confRow['r16_pnt']
iftNumBlksLessThan15 = getNumBlksWithErrLessThan15(
IFT_FORECAST_VS_ACTUAL_STORE_NAME, avcPnt, r16Pnt, actPnt)
aleaNumBlksLessThan15 = getNumBlksWithErrLessThan15(
ALEA_FORECAST_VS_ACTUAL_STORE_NAME, avcPnt, r16Pnt, actPnt)
resNumBlksLessThan15 = getNumBlksWithErrLessThan15(
RES_FORECAST_VS_ACTUAL_STORE_NAME, avcPnt, r16Pnt, actPnt)
enerNumBlksLessThan15 = getNumBlksWithErrLessThan15(
ENER_FORECAST_VS_ACTUAL_STORE_NAME, avcPnt, r16Pnt, actPnt)
fcaNumBlksLessThan15 = getNumBlksWithErrLessThan15(
FCA_FORECAST_VS_ACTUAL_STORE_NAME, avcPnt, r16Pnt, actPnt)
resValsList.append({"name": confRow['name'], "ift": iftNumBlksLessThan15,
"aleasoft": aleaNumBlksLessThan15, "res": resNumBlksLessThan15,
"enercast": enerNumBlksLessThan15, "fca": fcaNumBlksLessThan15})
return pd.DataFrame(resValsList)
def getRemcRegDaSummSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# confDf columns should be
# name,type,forecast_pnt
for stripCol in 'name,type,forecast_pnt'.split(','):
confDf[stripCol] = confDf[stripCol].str.strip()
# initialize results
resValsList = []
for rowIter in range(confDf.shape[0]):
confRow = confDf.iloc[rowIter]
printWithTs(
'REMC Regional Day Ahead Forecast summary processing row number {0}'
.format(rowIter + 2))
# get the type of row, itcan be dummy / normal / agg_pool / agg_gen_type
rowType = confRow['type']
if rowType == 'dummy':
# since the row is dummy, just insert a None row into result
resValsList.append({
"name": confRow['name'],
"max": None,
"min": None,
"avg": None
})
continue
forecastPnt = confRow['forecast_pnt']
forecastSeries = getRemcPntData(FCA_DAY_AHEAD_STORE_NAME, forecastPnt)
maxForecast = forecastSeries.max()
minForecast = forecastSeries.min()
avgForecast = forecastSeries.mean()
resValsList.append({
"name": confRow['name'],
"max": maxForecast,
"min": minForecast,
"avg": avgForecast
})
return pd.DataFrame(resValsList)
def getRemcStateErrSummSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# confDf columns should be
# name,r0_pnt,r16_pnt,actual_pnt,cuf_pnt,avc_pnt,type,state,gen_type,gen_type_2
for stripCol in 'name,r0_pnt,r16_pnt,actual_pnt,avc_pnt,type,state,gen_type,gen_type_2'.split(
','):
confDf[stripCol] = confDf[stripCol].str.strip()
normalPntsConfDf = confDf[(confDf['type'] == 'normal') |
(confDf['type'] == '') |
(confDf['type'].isnull())]
# initialize results
resValsList = []
for rowIter in range(confDf.shape[0]):
confRow = confDf.iloc[rowIter]
printWithTs(
'REMC state error summary processing row number {0}'.format(
rowIter + 2))
# get the type of row, itcan be dummy / normal / agg_pool / agg_gen_type
rowType = confRow['type']
if rowType == 'dummy':
# since the row is dummy, just insert a None row into result
resValsList.append({
"name": confRow['name'],
"r0_mape": None,
"r0_nrmse": None,
"r16_mape": None,
"r16_nrmse": None
})
continue
elif not (pd.isnull(rowType)) and rowType.startswith('agg_'):
aggColName = rowType[len('agg_'):]
aggIdentifier = confRow[aggColName]
confDfForAgg = normalPntsConfDf[normalPntsConfDf[aggColName] ==
aggIdentifier]
avcPnt = ','.join(confDfForAgg['avc_pnt'].tolist())
actPnt = ','.join(confDfForAgg['actual_pnt'].tolist())
r0Pnt = ','.join(confDfForAgg['r0_pnt'].tolist())
r16Pnt = ','.join(confDfForAgg['r16_pnt'].tolist())
else:
avcPnt = confRow['avc_pnt']
actPnt = confRow['actual_pnt']
r0Pnt = confRow['r0_pnt']
r16Pnt = confRow['r16_pnt']
avcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
avcPnt).tolist()
r0Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
r0Pnt).tolist()
r16Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
r16Pnt).tolist()
actVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
actPnt).tolist()
r0MapePerc = calcMapePerc(actVals, r0Vals, avcVals)
r0NrmsePerc = calcNrmsePerc(actVals, r0Vals, avcVals)
r16MapePerc = calcMapePerc(actVals, r16Vals, avcVals)
r16NrmsePerc = calcNrmsePerc(actVals, r16Vals, avcVals)
resValsList.append({
"name": confRow['name'],
"r0_mape": r0MapePerc,
"r0_nrmse": r0NrmsePerc,
"r16_mape": r16MapePerc,
"r16_nrmse": r16NrmsePerc
})
return pd.DataFrame(resValsList)
def getRemcRegionalR0ErrSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# confDf columns should be
# name,r0_pnt,actual_pnt,cuf_pnt,avc_pnt,type
for stripCol in 'name,r0_pnt,actual_pnt,avc_pnt,type'.split(','):
confDf[stripCol] = confDf[stripCol].str.strip()
# initialize results
resValsList = []
# find the row index of first non dummy row
for rowIter in range(len(confDf)):
confRow = confDf.iloc[rowIter]
rowType = confRow['type']
if rowType == 'dummy':
resValsList.append({
"name": confRow['name'],
"solar": None,
"wind": None,
"combined": None
})
# get regional rows
solarConf = confDf[confDf['name'] == 'solar'].squeeze()
windConf = confDf[confDf['name'] == 'wind'].squeeze()
combinedConf = confDf[confDf['name'] == 'combined'].squeeze()
# get data values
regSolActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['actual_pnt'])
regSolR0Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['r0_pnt'])
regSolAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['avc_pnt'])
regWindActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['actual_pnt'])
regWindR0Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['r0_pnt'])
regWindAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['avc_pnt'])
regCombActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['actual_pnt'])
regCombR0Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['r0_pnt'])
regCombAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['avc_pnt'])
# get ISTS rows
solarConf = confDf[confDf['name'] == 'ists_solar'].squeeze()
windConf = confDf[confDf['name'] == 'ists_wind'].squeeze()
combinedConf = confDf[confDf['name'] == 'ists_combined'].squeeze()
# get data values
istsSolActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['actual_pnt'])
istsSolR0Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['r0_pnt'])
istsSolAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
solarConf['avc_pnt'])
istsWindActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['actual_pnt'])
istsWindR0Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['r0_pnt'])
istsWindAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
windConf['avc_pnt'])
istsCombActVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['actual_pnt'])
istsCombR0Vals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['r0_pnt'])
istsCombAvcVals = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
combinedConf['avc_pnt'])
# calculate the output rows for region
# calculate regional solar mape for r0
regSolR0MapePerc = calcMapePerc(regSolActVals, regSolR0Vals, regSolAvcVals)
# calculate regional solar nrmse for r0
regSolR0NrmsePerc = calcNrmsePerc(regSolActVals, regSolR0Vals,
regSolAvcVals)
# calculate regional wind mape for r0
regWindR0MapePerc = calcMapePerc(regWindActVals, regWindR0Vals,
regWindAvcVals)
# calculate regional wind nrmse for r0
regWindR0NrmsePerc = calcNrmsePerc(regWindActVals, regWindR0Vals,
regWindAvcVals)
# calculate regional combined mape for r0
regCombR0MapePerc = calcMapePerc(regCombActVals, regCombR0Vals,
regCombAvcVals)
# calculate regional combined nrmse for r0
regCombR0NrmsePerc = calcNrmsePerc(regCombActVals, regCombR0Vals,
regCombAvcVals)
# calculate the output rows for ists
# calculate ists solar mape for r0
istsSolR0MapePerc = calcMapePerc(istsSolActVals, istsSolR0Vals,
istsSolAvcVals)
# calculate ists solar nrmse for r0
istsSolR0NrmsePerc = calcNrmsePerc(istsSolActVals, istsSolR0Vals,
istsSolAvcVals)
# calculate ists wind mape for r0
istsWindR0MapePerc = calcMapePerc(istsWindActVals, istsWindR0Vals,
istsWindAvcVals)
# calculate ists wind nrmse for r0
istsWindR0NrmsePerc = calcNrmsePerc(istsWindActVals, istsWindR0Vals,
istsWindAvcVals)
# calculate ists combined mape for r0
istsCombR0MapePerc = calcMapePerc(istsCombActVals, istsCombR0Vals,
istsCombAvcVals)
# calculate ists combined nrmse for r0
istsCombR0NrmsePerc = calcNrmsePerc(istsCombActVals, istsCombR0Vals,
istsCombAvcVals)
printWithTs(
'Processing REMC Regional R0 Error summary Section at row {0}'.format(
len(resValsList) + 1))
# create result dataframe rows
resValsList.extend([{
"name": "MAPE",
"solar": regSolR0MapePerc,
"wind": regWindR0MapePerc,
"combined": regCombR0MapePerc,
"istsSolar": istsSolR0MapePerc,
"istsWind": istsWindR0MapePerc,
"istsCombined": istsCombR0MapePerc
}, {
"name": "NRMSE",
"solar": regSolR0NrmsePerc,
"wind": regWindR0NrmsePerc,
"combined": regCombR0NrmsePerc,
"istsSolar": istsSolR0NrmsePerc,
"istsWind": istsWindR0NrmsePerc,
"istsCombined": istsCombR0NrmsePerc
}])
return pd.DataFrame(resValsList)
def getRegProfSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# confDf columns should be
# name, capacity, avc_point, actual_point, sch_point, type
confDf['name'] = confDf['name'].str.strip()
confDf['type'] = confDf['type'].str.strip()
# initialize results
resValsList = []
for rowIter in range(confDf.shape[0]):
printWithTs(
'regional profile processing row number {0}'.format(rowIter + 2))
confRow = confDf.iloc[rowIter]
# get the type of row, itcan be dummy / normal
rowType = confRow['type']
if rowType == 'dummy':
# since the row is dummy, just insert a None row into result
resValsList.append({
"name": confRow['name'],
"installed_capacity": None,
"max_avc": None,
"day_max_actual": None,
"day_max_actual_time": None,
"day_min_actual": None,
"day_min_actual_time": None,
"sch_mu": None,
"act_mu": None,
"dev_mu": None,
"cuf": None
})
continue
timeValSeries = getPntData('HRS')
actPnt = confRow['actual_point']
avcPnt = confRow['avc_point']
if ((avcPnt == '') or pd.isnull(avcPnt)):
maxAvc = None
else:
maxAvc = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
avcPnt).max()
dayMaxActual = getPntData(actPnt).max()
dayMaxActualTime = timeValSeries.iloc[getPntData(actPnt).idxmax()]
dayMinActual = getPntData(actPnt).min()
dayMinActualTime = timeValSeries.iloc[getPntData(actPnt).idxmin()]
schMu = getPntData(confRow['sch_point']).mean() * 0.024
actMu = getPntData(actPnt).mean() * 0.024
devMu = actMu - schMu
installedCapacity = confRow['installed_capacity']
cufPerc = (actMu * 100000) / (24 * installedCapacity)
resValsList.append({
"name": confRow['name'],
"installed_capacity": installedCapacity,
"max_avc": maxAvc,
"day_max_actual": dayMaxActual,
"day_max_actual_time": dayMaxActualTime,
"day_min_actual": dayMinActual,
"day_min_actual_time": dayMinActualTime,
"sch_mu": schMu,
"act_mu": actMu,
"dev_mu": devMu,
"cuf": cufPerc
})
return pd.DataFrame(resValsList)
def getVoltProfSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# initialize results
resValsList = []
# confDf columns should be
# name,400_kv_pnt,220_kv_pnt,type
for rowIter in range(confDf.shape[0]):
confRow = confDf.iloc[rowIter]
printWithTs(
'voltage profile processing row number {0}'.format(rowIter + 2))
rowType = confRow['type']
if rowType == 'dummy':
resValsList.append({
"name": confRow['name'],
"400_max": None,
"400_min": None,
"400_avg": None,
"220_max": None,
"220_min": None,
"220_avg": None
})
continue
pnt400 = confRow['400_kv_pnt']
max400 = None
min400 = None
avg400 = None
pnt220 = confRow['220_kv_pnt']
max220 = None
min220 = None
avg220 = None
# handle none point id in this section config
# get 400 kV values
if not (pd.isnull(pnt400)):
max400 = getPntData(pnt400).max()
min400 = getPntData(pnt400).min()
avg400 = getPntData(pnt400).mean()
# handle none point id in this section config
# get 220 kV values
if not (pd.isnull(pnt220)):
max220 = getPntData(pnt220).max()
min220 = getPntData(pnt220).min()
avg220 = getPntData(pnt220).mean()
resValsList.append({
"name": confRow['name'],
"400_max": max400,
"400_min": min400,
"400_avg": avg400,
"220_max": max220,
"220_min": min220,
"220_avg": avg220
})
return pd.DataFrame(resValsList)
def getIstsGenSectionDataDf(configFilePath, configSheetName):
# get conf dataframe
confDf = pd.read_excel(configFilePath, sheet_name=configSheetName)
# confDf columns should be
# name,installed_capacity,avc_id,act_id,sch_id,type,pooling_station,gen_type
confDf['name'] = confDf['name'].str.strip()
confDf['type'] = confDf['type'].str.strip()
confDf['pooling_station'] = confDf['pooling_station'].str.strip()
confDf['gen_type'] = confDf['gen_type'].str.strip()
normalPntsConfDf = confDf[(confDf['type'] == 'normal') |
(confDf['type'] == '') |
(confDf['type'].isnull())]
# initialize results
resValsList = []
for rowIter in range(confDf.shape[0]):
confRow = confDf.iloc[rowIter]
printWithTs('ists gen processing row number {0}'.format(rowIter + 2))
timeValSeries = getPntData('HRS')
# get the type of row, itcan be dummy / normal / agg_pool / agg_gen_type
rowType = confRow['type']
if rowType == 'dummy':
# since the row is dummy, just insert a None row into result
resValsList.append({
"name": confRow['name'],
"installed_capacity": None,
"max_avc": None,
"day_max_actual": None,
"day_max_actual_time": None,
"day_min_actual": None,
"day_min_actual_time": None,
"sch_mu": None,
"act_mu": None,
"dev_mu": None,
"cuf": None
})
continue
elif not (pd.isnull(rowType)) and rowType.startswith('agg_'):
aggColName = rowType[len('agg_'):]
aggIdentifier = confRow[aggColName]
confDfForAgg = normalPntsConfDf[normalPntsConfDf[aggColName] ==
aggIdentifier]
avcPnt = ','.join(confDfForAgg['avc_id'].dropna().tolist())
actPnt = ','.join(confDfForAgg['act_id'].tolist())
schPnt = ','.join(confDfForAgg['sch_id'].tolist())
else:
avcPnt = confRow['avc_id']
actPnt = confRow['act_id']
schPnt = confRow['sch_id']
if ((avcPnt == '') or pd.isnull(avcPnt)):
maxAvc = None
else:
maxAvc = getRemcPntData(FCA_FORECAST_VS_ACTUAL_STORE_NAME,
avcPnt).max()
dayMaxActual = getPntData(actPnt).max()
dayMaxActualTime = timeValSeries.iloc[getPntData(actPnt).idxmax()]
dayMinActual = getPntData(actPnt).min()
dayMinActualTime = timeValSeries.iloc[getPntData(actPnt).idxmin()]
schMu = getPntData(schPnt).mean() * 0.024
actMu = getPntData(actPnt).mean() * 0.024
devMu = actMu - schMu
installedCapacity = confRow['installed_capacity']
cufPerc = (actMu * 100000) / (24 * installedCapacity)
resValsList.append({
"name": confRow['name'],
"installed_capacity": installedCapacity,
"max_avc": maxAvc,
"day_max_actual": dayMaxActual,
"day_max_actual_time": dayMaxActualTime,
"day_min_actual": dayMinActual,
"day_min_actual_time": dayMinActualTime,
"sch_mu": schMu,
"act_mu": actMu,
"dev_mu": devMu,
"cuf": cufPerc
})
return pd.DataFrame(resValsList)
from report_generators.remc_state_da_section import populateRemcStateDaSummSectionData
from report_generators.remc_graph_data_section import populateRemcGraphDataSectionData
from data_fetchers import inp_ts_data_store
from data_fetchers.remc_data_store import loadRemcDataStore, deleteRemcDataStore
from data_fetchers.remc_data_store import FCA_FORECAST_VS_ACTUAL_STORE_NAME, FCA_DAY_AHEAD_STORE_NAME, FCA_FORECAST_VS_ACTUAL_PREV_STORE_NAME
from data_fetchers.remc_data_store import IFT_FORECAST_VS_ACTUAL_STORE_NAME, IFT_DAY_AHEAD_STORE_NAME
from data_fetchers.remc_data_store import ALEA_FORECAST_VS_ACTUAL_STORE_NAME, ALEA_DAY_AHEAD_STORE_NAME
from data_fetchers.remc_data_store import RES_FORECAST_VS_ACTUAL_STORE_NAME, RES_DAY_AHEAD_STORE_NAME
from data_fetchers.remc_data_store import ENER_FORECAST_VS_ACTUAL_STORE_NAME, ENER_DAY_AHEAD_STORE_NAME
import datetime as dt
import argparse
from utils.printUtils import printWithTs
from report_generators.paste_report_data import pasteDataToTemplateFile
from report_generators.nldc_report_generator import generateNldcReport, transferNldcRepToSftpLocation
printWithTs('imports complete...', clr='green')
# %%
printWithTs('loading SCADA Total ISTS Gen data...', clr='magenta')
# initialize timeseries datastore
inp_ts_data_store.loadGenTsData()
# x = inp_ts_data_store.tsDataDf
printWithTs('done loading SCADA Total ISTS Gen data store...', clr='green')
# %%
printWithTs('started loading REMC FCA Forecast Vs Actual data store...',
clr='magenta')
# initialize REMC FCA forecast Vs actual timeseries datastore
loadRemcDataStore(FCA_FORECAST_VS_ACTUAL_STORE_NAME)
# x = inp_ts_data_store.tsDataDf
printWithTs('REMC FCA Forecast Vs Actual data store loading complete...',
def pasteDataToTemplateFile(dataFile, templateFile):
printWithTs('loading data file data into a dataframe', clr='magenta')
# open an Excel file and return a workbook
dataWb = load_workbook(dataFile, read_only=True)
dataFileSheetNames = [sht for sht in dataWb.sheetnames]
printWithTs('loading template file', clr='magenta')
templWb = load_workbook(templateFile)
templFileSheetNames = [sht for sht in templWb.sheetnames]
printWithTs('started pasting data to template', clr='magenta')
# print(dataFileSheetNames)
# iterate through each sheet of data file for copying
# https://www.geeksforgeeks.org/python-how-to-copy-data-from-one-excel-sheet-to-another/
for dataShtName in dataFileSheetNames:
printWithTs('pasting data sheet {0}'.format(dataShtName))
# skip sheet of data file if same sheet absent in template file
if not (dataShtName in templFileSheetNames):
printWithTs('skipping sheet paste as not present in template file')
continue
# copy paste cell values to template sheet
dataSht = dataWb[dataShtName]
maxRows = dataSht.max_row
maxCols = dataSht.max_column
printWithTs('maxRows = {0}, maxCols = {1}'.format(maxRows, maxCols))
for rowIter in range(1, maxRows + 1):
# check if 1st column value is 'dummy**'
if dataSht.cell(row=rowIter, column=1).value == 'dummy**':
continue
# copy cell values from data sheet to template sheet
for colIter in range(1, maxCols + 1):
printWithTs('pasting row={0}, col={1}'.format(
rowIter, colIter))
cellVal = dataSht.cell(row=rowIter, column=colIter).value
templWb[dataShtName].cell(row=rowIter,
column=colIter).value = cellVal
printWithTs('done pasting data to template', clr='green')
# saving the destination excel file
printWithTs('Saving report template after pasting')
templWb.save(str(templateFile))
dataWb.close()
