def fetchIexGraphContext(appDbConnStr: str, startDt: dt.datetime, endDt: dt.datetime) -> bool:
mRepo = MetricsDataRepo(appDbConnStr)
# get iex rtm data for the range between start date and end date
startDt = endDt - dt.timedelta(days=1)
iexDamMcvVals = mRepo.getIexDamBlockWiseData('MCV (MW)', startDt, endDt)
iexDamMcpVals = mRepo.getIexDamBlockWiseData('MCP (Rs/MWh) ', startDt, endDt)
for itr in range(len(iexDamMcvVals)):
iexDamMcvVals[itr]['metric_name'] = 'DAM MCV(MW)'
iexDamMcvVals[itr]['time_stamp'] = itr+1
for itr in range(len(iexDamMcpVals)):
iexDamMcpVals[itr]['metric_name'] = 'DAM MCP(Rs/KWH)'
iexDamMcpVals[itr]['time_stamp'] = itr+1
iexRtmMcvVals = mRepo.getIexRtmBlockWiseData('MCV (MW)', startDt, endDt)
iexRtmMcpVals = mRepo.getIexRtmBlockWiseData('MCP (Rs/MWh) ', startDt, endDt)
for itr in range(len(iexRtmMcvVals)):
iexRtmMcvVals[itr]['metric_name'] = 'RTM MCV(MW)'
iexRtmMcvVals[itr]['time_stamp'] = itr+1
for itr in range(len(iexRtmMcpVals)):
iexRtmMcpVals[itr]['metric_name'] = 'RTM MCP(Rs/KWH)'
iexRtmMcpVals[itr]['time_stamp'] = itr+1
# create plot image for demands of prev yr, prev month, this month
iexDamMcvObjs = [{'Date':
x["time_stamp"], 'colName': x['metric_name'], 'val': x["data_value"]} for x in iexDamMcvVals]
iexDamMcpObjs = [{'Date':
x["time_stamp"], 'colName': x['metric_name'], 'val': x["data_value"]} for x in iexDamMcpVals]
iexRtmMcvObjs = [{'Date':
x["time_stamp"], 'colName': x['metric_name'], 'val': x["data_value"]} for x in iexRtmMcvVals]
iexRtmMcpObjs = [{'Date':
x["time_stamp"], 'colName': x['metric_name'], 'val': x["data_value"]} for x in iexRtmMcpVals]
pltDataObjs = iexDamMcvObjs + iexDamMcpObjs + iexRtmMcvObjs + iexRtmMcpObjs
pltDataDf = pd.DataFrame(pltDataObjs)
pltDataDf = pltDataDf.pivot(
index='Date', columns='colName', values='val')
pltDataDf['DAM MCP(Rs/KWH)'] = pltDataDf['DAM MCP(Rs/KWH)']/1000
pltDataDf['RTM MCP(Rs/KWH)'] = pltDataDf['RTM MCP(Rs/KWH)']/1000
pltDataDf['RTM MCP(Rs/KWH)'].replace(to_replace=0, method='ffill', inplace=True)
pltDataDf['RTM MCV(MW)'].replace(to_replace=0, method='ffill', inplace=True)
# derive plot title
pltTitle = 'MCP & MCV Data as per IEX Data'
dateStr = startDt.strftime("%d-%m-%Y")
pltTitle = 'MCP & MCV Data as per IEX Data for {0}'.format(dateStr)
# 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('Rs/KWH')
ax.set_ylabel('MWH')
ax.set_facecolor("#474747")
# set y axis limit
# fig.patch.set_facecolor('#d9ccff')
clr = ['#66b3ff', '#df80ff', '#ff6666', '#00b359']
# set x xis manually
x_test = [1,6,11,16,21,26,31,36,41,46,51,56,61,66,71,76,81,86,91,96]
# plot data and get the line artist object in return
laThisMonth, = ax.plot(
pltDataDf.index.values, pltDataDf['DAM MCV(MW)'].values, color='#66b3ff')
laThisMonth.set_label('DAM MCV(MW)')
laLastYear, = ax2.plot(
pltDataDf.index.values, pltDataDf['DAM MCP(Rs/KWH)'].values, color='#df80ff')
laLastYear.set_label('DAM MCP(Rs/KWH)')
laPrevMonth, = ax.plot(
pltDataDf.index.values, pltDataDf['RTM MCV(MW)'].values, color='#00b359')
laPrevMonth.set_label('RTM MCV(MW)')
laPrevMonth, = ax2.plot(
pltDataDf.index.values, pltDataDf['RTM MCP(Rs/KWH)'].values, color='#ff6666')
laPrevMonth.set_label('RTM MCP(Rs/KWH)')
ax.set_xlim((1,96), auto = True)
ax.set_xticks([1,6,11,16,21,26,31,36,41,46,51,56,61,66,71,76,81,86,91,96])
# ax.set_xlim((1, 31), auto=True)
# enable legends
ax.legend(bbox_to_anchor=(0.0, -0.3, 1, 0), loc='best',
ncol=3, mode="expand", borderaxespad=0.)
ax2.legend(bbox_to_anchor=(0.0, -0.3, 1, 0), loc='lower right',
ncol=3, mode="expand", borderaxespad=0.)
fig.subplots_adjust(bottom=0.25, top=0.8)
fig.savefig('assets/section_1_3.png')
plt.close()
return True
def fetchIexRtmTableContext(appDbConnStr: str, startDt: dt.datetime,
endDt: dt.datetime) -> IIexRtmRecord:
mRepo = MetricsDataRepo(appDbConnStr)
# get iex rtm data for the range between start date and end date
iexRtmMcvVals = mRepo.getIexRtmBlockWiseData('MCV (MW)', startDt, endDt)
iexRtmMcpVals = mRepo.getIexRtmBlockWiseData('MCP (Rs/MWh) ', startDt,
endDt)
iexRtmMcvDf = pd.DataFrame(iexRtmMcvVals)
iexRtmMcpDf = pd.DataFrame(iexRtmMcpVals)
tableDf = iexRtmMcvDf.groupby(['time_stamp']).mean()
tableDf = tableDf.rename(columns={'data_value': 'avg_mcv_data'})
tableDf.reset_index(inplace=True)
minDf = iexRtmMcvDf.groupby(['time_stamp']).min()
minDf.reset_index(inplace=True)
minDf = minDf.rename(columns={'data_value': 'min_mcv_data'})
tableDf = tableDf.merge(minDf[['min_mcv_data', 'time_stamp']],
on='time_stamp')
maxDf = iexRtmMcvDf.groupby(['time_stamp']).max()
maxDf = maxDf.rename(columns={'data_value': 'max_mcv_data'})
maxDf.reset_index(inplace=True)
tableDf = tableDf.merge(maxDf[['max_mcv_data', 'time_stamp']],
on='time_stamp')
minMcpDf = iexRtmMcpDf.groupby(['time_stamp']).min()
minMcpDf.reset_index(inplace=True)
minMcpDf = minMcpDf.rename(columns={'data_value': 'min_mcp_data'})
minMcpDf['min_mcp_data'] = minMcpDf['min_mcp_data'] / 1000
tableDf = tableDf.merge(minMcpDf[['min_mcp_data', 'time_stamp']],
on='time_stamp')
maxMcpDf = iexRtmMcpDf.groupby(['time_stamp']).max()
maxMcpDf.reset_index(inplace=True)
maxMcpDf = maxMcpDf.rename(columns={'data_value': 'max_mcp_data'})
maxMcpDf['max_mcp_data'] = maxMcpDf['max_mcp_data'] / 1000
tableDf = tableDf.merge(maxMcpDf[['max_mcp_data', 'time_stamp']],
on='time_stamp')
avgMcpDf = iexRtmMcpDf.groupby(['time_stamp']).mean()
avgMcpDf.reset_index(inplace=True)
avgMcpDf = avgMcpDf.rename(columns={'data_value': 'avg_mcp_data'})
avgMcpDf['avg_mcp_data'] = avgMcpDf['avg_mcp_data'] / 1000
tableDf = tableDf.merge(avgMcpDf[['avg_mcp_data', 'time_stamp']],
on='time_stamp')
tableDf['rtm_energy'] = tableDf['avg_mcv_data'] * 24 / 1000
iexRtmTableList: ISection_1_1["iex_rtm_table"] = []
for i in tableDf.index:
iexRtmDailyRecord: IIexRtmRecord = {
'date_time': dt.datetime.strftime(tableDf['time_stamp'][i],
'%d-%m-%Y'),
'min_mcv': round(tableDf['min_mcv_data'][i]),
'max_mcv': round(tableDf['max_mcv_data'][i]),
'avg_mcv': round(tableDf['avg_mcv_data'][i]),
'min_mcp': round(tableDf['min_mcp_data'][i], 2),
'max_mcp': round(tableDf['max_mcp_data'][i], 2),
'avg_mcp': round(tableDf['avg_mcp_data'][i], 2),
'rtm_energy': round(tableDf['rtm_energy'][i])
}
iexRtmTableList.append(iexRtmDailyRecord)
reportDt = dt.datetime(endDt.year, endDt.month, endDt.day)
reportDt = reportDt - dt.timedelta(days=1)
reportDt = dt.datetime.strftime(reportDt, '%Y-%m-%d')
secData: IIexRtmRecord = {
'iex_rtm_table': iexRtmTableList,
'reportDt': reportDt
}
return secData
