def fetchSection1_5_1Context(appDbConnStr: str, startDt: dt.datetime, endDt: dt.datetime) -> dict:
# get WR demand from recent Fin year start till this month
# and WR demand from 2 years back fin year to last fin year
# example: For Jan 21, we require data from 1-Apr-2019 to 31-Mar-2020 and 1-Apr-2020 to 31 Jan 21
finYrStart = getFinYrDt(startDt)
prevFinYrStart = getPrevFinYrDt(finYrStart)
finYrName = '{0}-{1}'.format(finYrStart.year, (finYrStart.year+1) % 100)
prevFinYrName = '{0}-{1}'.format(finYrStart.year-1, finYrStart.year % 100)
mRepo = MetricsDataRepo(appDbConnStr)
# get WR hourly demand values for this financial year
wrDemVals = mRepo.getEntityMetricDailyData(
'wr', 'Max Demand(MW)', finYrStart, endDt)
wrPrevFinYrDemVals = mRepo.getEntityMetricDailyData(
'wr', 'Max Demand(MW)', prevFinYrStart, finYrStart-dt.timedelta(days=1))
# create plot image for demands of prev fin year and this fin year
pltDemObjs = [{'MONTH': x["time_stamp"], 'colName': finYrName,
'val': x["data_value"]} for x in wrDemVals]
pltDemObjsLastYear = [{'MONTH': x["time_stamp"],
'colName': prevFinYrName, 'val': x["data_value"]} for x in wrPrevFinYrDemVals]
pltDataObjs = pltDemObjs + pltDemObjsLastYear
pltDataDf = pd.DataFrame(pltDataObjs)
pltDataDf = pltDataDf.pivot(
index='MONTH', columns='colName', values='val')
maxTs = pltDataDf.index.max()
for rIter in range(pltDataDf.shape[0]):
# check if Prev fin Yr data column is not Nan, if yes set this year data column
lastYrDt = pltDataDf.index[rIter].to_pydatetime()
if not pd.isna(pltDataDf[prevFinYrName].iloc[rIter]):
thisYrTs = pd.Timestamp(addMonths(lastYrDt, 12))
if thisYrTs <= maxTs:
thisYrVal = pltDataDf[finYrName].loc[thisYrTs]
pltDataDf.at[pd.Timestamp(lastYrDt), finYrName] = thisYrVal
pltDataDf = pltDataDf[~pltDataDf[prevFinYrName].isna()]
# save plot data as excel
pltDataDf.to_excel("assets/plot_1_5_1.xlsx", index=True)
# derive plot title
pltTitle = 'WR seasonal demand (daily max.) Plot {0} to {1}'.format(
prevFinYrName, finYrName)
# create a plotting area and get the figure, axes handle in return
fig, ax = plt.subplots(figsize=(7.5, 4.8))
# set plot title
ax.set_title(pltTitle)
# set x and y labels
ax.set_xlabel('MONTH')
ax.set_ylabel('MW')
# set x axis locator as month
ax.xaxis.set_major_locator(mdates.MonthLocator())
# set x axis formatter as month name
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
# ax.set_xlim(xmin=finYrStart)
# plot data and get the line artist object in return
laThisYr, = ax.plot(pltDataDf.index.values,
pltDataDf[finYrName].values, color='#ff0000')
laThisYr.set_label(finYrName)
laLastYear, = ax.plot(pltDataDf.index.values,
pltDataDf[prevFinYrName].values, color='#0000ff')
laLastYear.set_label(prevFinYrName)
# enable axis grid lines
ax.yaxis.grid(True)
ax.xaxis.grid(True)
# enable legends
ax.legend(bbox_to_anchor=(0.0, -0.3, 1, 0), loc='lower center',
ncol=2, borderaxespad=0.)
fig.subplots_adjust(bottom=0.25, top=0.8)
fig.savefig('assets/section_1_5_1.png')
secData: dict = {}
return secData
def fetchReservoirMonthlyTableContext(
appDbConnStr: str, startDt: dt.datetime,
endDt: dt.datetime) -> IReservoirMonthlyDataRecord:
# reservoirInfo = getReservoirsMappings()
mRepo = MetricsDataRepo(appDbConnStr)
finYrStartDt = getFinYrDt(startDt)
mRepo = MetricsDataRepo(appDbConnStr)
reservoirMonthlyVals = mRepo.getReservoirMonthlyData(finYrStartDt, endDt)
reservoirMonthlyDf = pd.DataFrame(reservoirMonthlyVals)
reservoirMonthlyDf['day'] = 1
# loadDurationCurve = fetchSection2_1_LoadDurationCurve(appDbConnStr ,startDt, endDt)
reservoirMonthlyDf['Date'] = pd.to_datetime(
reservoirMonthlyDf[['year', 'month', 'day']])
# reservoirMonthlyDf['Date'] = reservoirMonthlyDf['Date'].dt.strftime('%d-%m-%y')
reservoirMonthlyDf = reservoirMonthlyDf.drop(['year', 'month', 'day'],
axis=1)
reservoirMonthlyDf = reservoirMonthlyDf.pivot(index='Date',
columns='entity_tag',
values='level_max')
reservoirMonthlyDf = reservoirMonthlyDf.rename(
columns={
'Gandhi Sagar Reservoir Level ': 'a_gandhi_sagar',
'Indira sagar Reservoir Level': 'a_indira_sagar',
'Omkareshwar Reservoir Level': 'a_omkareshwar',
'Kadana Reservoir Level': 'b_kadana',
'ssp': 'b_ssp',
'Ukai Reservoir Level': 'b_ukai',
'Koyna Reservoir Level ': 'c_koyna'
})
reservoirMonthlyDf = reservoirMonthlyDf.reindex(sorted(
reservoirMonthlyDf.columns),
axis=1)
reservoirMonthlyDf.reset_index(inplace=True)
reservoirMonthlyDf['Date'] = reservoirMonthlyDf['Date'].dt.strftime(
'%b %y')
# reservoirMonthlyDf = reservoirMonthlyDf.drop(['Date'], axis=1)
# print("testing")
reservoirTableList: ISection_reservoir_table["schedule_drawal"] = []
for i in reservoirMonthlyDf.index:
reservoirTableRecord: IReservoirMonthlyDataRecord = {
# 'date_time': dt.datetime.strftime(reservoirMonthlyDf['Date'][i], '%d-%m-%Y'),
'date_time': reservoirMonthlyDf['Date'][i],
'gandhi': reservoirMonthlyDf['a_gandhi_sagar'][i],
'indira': round(reservoirMonthlyDf['a_indira_sagar'][i]),
'omkare': round(reservoirMonthlyDf['a_omkareshwar'][i]),
'kadana': round(reservoirMonthlyDf['b_kadana'][i]),
'ssp': round(reservoirMonthlyDf['b_ssp'][i]),
'ukai': round(reservoirMonthlyDf['b_ukai'][i]),
'koyna': round(reservoirMonthlyDf['c_koyna'][i])
}
reservoirTableList.append(reservoirTableRecord)
sectionData: ISection_reservoir_table = {
"reservoir_table": reservoirTableList
}
return sectionData
def fetchSection1_3_aContext(appDbConnStr: str, startDt: dt.datetime, endDt: dt.datetime) -> ISection_1_3_a:
constituentsInfos = getConstituentsMappings()
constConfig = {}
for c in constituentsInfos:
constConfig[c["entity_tag"]] = c["display_name"]
dataRecords = pd.DataFrame()
mRepo = MetricsDataRepo(appDbConnStr)
prevFinYrStartDt = getFinYrDt(startDt)
# get WR Unrestricted demand hourly values for this month and prev yr month
allEntityReqMuVals = mRepo.getAllEntityMetricMonthlyData(
'Requirement (MU)', startDt, endDt)
allEntityAvailMuVals = mRepo.getAllEntityMetricMonthlyData(
'Consumption(MU)', startDt, endDt)
allEntityReqMuDf = pd.DataFrame(allEntityReqMuVals)
allEntityAvailMuDf = pd.DataFrame(allEntityAvailMuVals)
allEntityAvailMuDf = allEntityAvailMuDf.rename(columns={
'metric_value': 'Consumption(MU)'})
allEntityReqMuDf = allEntityReqMuDf.rename(columns={
'metric_value': 'Requirement (MU)'})
tempList = allEntityAvailMuDf['Consumption(MU)']
allEntityReqMuDf['Consumption(MU)'] = tempList
allEntityReqMuDf['shortage'] = round(100 *
(allEntityReqMuDf['Requirement (MU)'] -
allEntityReqMuDf['Consumption(MU)']) /
allEntityReqMuDf['Consumption(MU)'], 2)
# print(allEntityReqMuDf)
prevYrAllEntityReqMuVals = mRepo.getAllEntityMetricMonthlyData(
'Requirement (MU)', prevFinYrStartDt, endDt)
prevYrAllEntityAvailMuVals = mRepo.getAllEntityMetricMonthlyData(
'Consumption(MU)', prevFinYrStartDt, endDt)
prevYrAllEntityReqMuDf = pd.DataFrame(prevYrAllEntityReqMuVals)
prevYrAllEntityAvailMuDf = pd.DataFrame(prevYrAllEntityAvailMuVals)
prevYrAllEntityAvailMuDf = prevYrAllEntityAvailMuDf.rename(columns={
'metric_value': 'Consumption(MU)'})
prevYrAllEntityReqMuDf = prevYrAllEntityReqMuDf.rename(columns={
'metric_value': 'Requirement (MU)'})
tempList = prevYrAllEntityAvailMuDf['Consumption(MU)']
prevYrAllEntityReqMuDf['Consumption(MU)'] = tempList
prevYrAllEntityReqMuDf['shortage'] = round(100 *
(prevYrAllEntityReqMuDf['Requirement (MU)'] -
prevYrAllEntityAvailMuDf['Consumption(MU)']) /
prevYrAllEntityAvailMuDf['Consumption(MU)'], 2)
prevYrAllEntityReqMuDf.set_index('entity_tag')
dataRecords = pd.merge(
allEntityReqMuDf, prevYrAllEntityReqMuDf, on='entity_tag')
newNames = []
for rIter in range(dataRecords.shape[0]):
row = dataRecords.iloc[rIter, :]
if row['entity_tag'] in constConfig:
newNames.append(constConfig[row['entity_tag']])
else:
newNames.append(np.nan)
dataRecords['entity_tag'] = newNames
energyReqAvailList: ISection_1_3_a["energy_req_avail"] = []
for i in dataRecords.index:
energyReq: ISection_1_3_a = {
'entity': dataRecords['entity_tag'][i],
'reqMu_X': round(dataRecords['Requirement (MU)_x'][i]),
'availMu_X': round(dataRecords['Consumption(MU)_x'][i]),
'shortage_X': round(dataRecords['shortage_x'][i], 2),
'reqMu_Y': round(dataRecords['Requirement (MU)_y'][i]),
'availMu_Y': round(dataRecords['Consumption(MU)_y'][i]),
'shortage_Y': round(dataRecords['shortage_y'][i], 2)
}
energyReqAvailList.append(energyReq)
prevFinYrDateStr = dt.datetime.strftime(prevFinYrStartDt, "%b %y")
sectionData: ISection_1_3_a = {
"energy_req_avail": energyReqAvailList,
"recent_fin_month_name": prevFinYrDateStr
}
return sectionData
def fetchReservoirContext(appDbConnStr: str, startDt: dt.datetime,
endDt: dt.datetime) -> IReservoirSection:
reservoirInfo = getReservoirsMappings()
numPages = 0
mRepo = MetricsDataRepo(appDbConnStr)
prevFinYrStartDt = getFinYrDt(startDt)
prevFinYear = dt.datetime.strftime(prevFinYrStartDt, "%Y")
currYear = int(prevFinYear)
# last financial year details
lastFinYrStartDt = getPrevFinYrDt(prevFinYrStartDt)
latsFinYrEndDt = prevFinYrStartDt - dt.timedelta(days=1)
lastFinYrStarStr = dt.datetime.strftime(lastFinYrStartDt, "%Y")
lastFinYrEndDt = int(lastFinYrStarStr) + 1
for itr in reservoirInfo:
metricList: List = []
for entity in itr:
if entity == 'entity_tag':
continue
else:
metricList.append(
mRepo.getReservoirDailyData(itr['entity_tag'], itr[entity],
prevFinYrStartDt, endDt))
if len(metricList[0]) > 0:
# create plot image for all the metrices
pltDataObj: List = []
for temp in range(len(metricList)):
pltDataObj = pltDataObj + [{
'Date': x["time_stamp"],
'colName': x["metric_tag"],
'val': x["data_value"]
} for x in metricList[temp]]
pltDataDf = pd.DataFrame(pltDataObj)
pltDataDf = pltDataDf.pivot(index='Date',
columns='colName',
values='val')
pltDataDf.reset_index(inplace=True)
# derive plot title
pltTitle = '{0} {1}-{2}'.format(itr['entity_tag'], currYear,
currYear + 1)
# create a plotting area and get the figure, axes handle in return
fig, ax = plt.subplots(figsize=(7.5, 4.5))
# instantiate a second axes that shares the same x-axis
ax2 = ax.twinx()
# set plot title
ax.set_title(pltTitle)
# set y labels
ax2.set_ylabel('MUs')
ax.set_ylabel('Meter')
# set x axis locator as month
ax.xaxis.set_major_locator(mdates.MonthLocator())
# set x axis formatter as month name
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
# set x axis locator as month
ax2.xaxis.set_major_locator(mdates.MonthLocator())
# set x axis formatter as month name
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
# plot data and get the line artist object in return 'GEN.'
clr = ['#00ccff', '#ff8533', '#ff0000', '#9900ff']
for col in range(len(pltDataDf.columns) - 1):
if pltDataDf.columns[col + 1] == 'GEN.':
last, = ax2.plot(pltDataDf['Date'],
pltDataDf[pltDataDf.columns[col + 1]],
color=clr[col],
label=pltDataDf.columns[col + 1])
else:
last, = ax.plot(pltDataDf['Date'],
pltDataDf[pltDataDf.columns[col + 1]],
color=clr[col],
label=pltDataDf.columns[col + 1])
# enable y axis grid lines
ax.yaxis.grid(True)
# Ensure a major tick for each week using (interval=1)
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=1))
# enable legends
ax2.legend()
ax.legend(bbox_to_anchor=(0.0, -0.3, 1, 0),
loc='lower center',
ncol=3,
mode="expand",
borderaxespad=0.)
fig.subplots_adjust(bottom=0.25, top=0.8)
fig.savefig('assets/reservoir_section_{0}.png'.format(numPages))
# fig.savefig('assets/reservoir_section/{0}-{1}.png'.format(itr['entity_tag'], currYear))
# plt.close()
numPages += 1
prevFinYrMetricList: List = []
for entity in itr:
if entity == 'entity_tag':
continue
else:
prevFinYrMetricList.append(
mRepo.getReservoirDailyData(itr['entity_tag'], itr[entity],
lastFinYrStartDt,
latsFinYrEndDt))
if len(prevFinYrMetricList[0]) > 0:
# create plot image for all the metrices
lastFinYrPltDataObj: List = []
for temp in range(len(prevFinYrMetricList)):
lastFinYrPltDataObj = lastFinYrPltDataObj + [
{
'Date': x["time_stamp"],
'colName': x["metric_tag"],
'val': x["data_value"]
} for x in prevFinYrMetricList[temp]
]
lastFinYrPltDataDf = pd.DataFrame(lastFinYrPltDataObj)
lastFinYrPltDataDf = lastFinYrPltDataDf.pivot(index='Date',
columns='colName',
values='val')
lastFinYrPltDataDf.reset_index(inplace=True)
# derive plot title
pltTitle = '{0} {1}-{2}'.format(itr['entity_tag'],
lastFinYrStarStr, lastFinYrEndDt)
# create a plotting area and get the figure, axes handle in return
fig, ax = plt.subplots(figsize=(7.5, 4.5))
# instantiate a second axes that shares the same x-axis
ax2 = ax.twinx()
# set plot title
ax.set_title(pltTitle)
# set y labels
ax2.set_ylabel('MUs')
ax.set_ylabel('Meter')
# set x axis locator as month
ax.xaxis.set_major_locator(mdates.MonthLocator())
# set x axis formatter as month name
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
# set x axis locator as month
ax2.xaxis.set_major_locator(mdates.MonthLocator())
# set x axis formatter as month name
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
# plot data and get the line artist object in return 'GEN.'
clr = ['#00ccff', '#ff8533', '#ff0000', '#9900ff']
for col in range(len(lastFinYrPltDataDf.columns) - 1):
if lastFinYrPltDataDf.columns[col + 1] == 'GEN.':
last, = ax2.plot(
lastFinYrPltDataDf['Date'],
lastFinYrPltDataDf[lastFinYrPltDataDf.columns[col +
1]],
color=clr[col],
label=lastFinYrPltDataDf.columns[col + 1])
else:
last, = ax.plot(
lastFinYrPltDataDf['Date'],
lastFinYrPltDataDf[lastFinYrPltDataDf.columns[col +
1]],
color=clr[col],
label=lastFinYrPltDataDf.columns[col + 1])
# enable y axis grid lines
ax.yaxis.grid(True)
# Ensure a major tick for each week using (interval=1)
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=1))
# enable legends
ax2.legend()
ax.legend(bbox_to_anchor=(0.0, -0.3, 1, 0),
loc='lower center',
ncol=3,
mode="expand",
borderaxespad=0.)
fig.subplots_adjust(bottom=0.25, top=0.8)
fig.savefig('assets/reservoir_section_{0}.png'.format(numPages))
# fig.savefig('assets/reservoir_section/{0}-{1}.png'.format(itr['entity_tag'],
# lastFinYrEndDt))
# plt.close()
numPages += 1
sectionData: IReservoirSection = {'num_plts_sec_reservoir': numPages}
return sectionData