def fetchIexGdamTableContext(appDbConnStr: str, startDt: dt.datetime, endDt: dt.datetime) -> ISection_1_4:
mRepo = MetricsDataRepo(appDbConnStr)
# get iex rtm data for the range between start date and end date ''
iexGdamMcvVals = mRepo.getIexGdamBlockWiseData('MCV (MW)', startDt, endDt)
iexGdamMcpVals = mRepo.getIexGdamBlockWiseData('MCP (Rs/MWh) ', startDt, endDt)
iexGdamMcvDf = pd.DataFrame(iexGdamMcvVals)
iexGdamMcpDf = pd.DataFrame(iexGdamMcpVals)
tableDf = iexGdamMcvDf.groupby(['time_stamp']).mean()
tableDf = tableDf.rename(columns={'data_value': 'avg_mcv_data'})
tableDf.reset_index(inplace = True)
minDf = iexGdamMcvDf.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 = iexGdamMcvDf.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 = iexGdamMcpDf.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 = iexGdamMcpDf.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 = iexGdamMcpDf.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
iexGdamTableList: ISection_1_4()["iex_rtm_table"] = []
for i in tableDf.index:
iexGdamDailyRecord: IiexGdamRecord = {
'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], 2)
}
iexGdamTableList.append(iexGdamDailyRecord)
reportDt = dt.datetime(endDt.year, endDt.month, endDt.day)
reportDt = dt.datetime.strftime(reportDt, '%Y-%m-%d')
secData: ISection_1_4 = {
'iex_gdam_table': iexGdamTableList
}
return secData
def fetchIexGdamGraphContext(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)
iexGdamMcvVals = mRepo.getIexGdamBlockWiseData('MCV (MW)', startDt, endDt)
iexGdamMcpVals = mRepo.getIexGdamBlockWiseData('MCP (Rs/MWh) ', startDt,
endDt)
for itr in range(len(iexGdamMcvVals)):
iexGdamMcvVals[itr]['metric_name'] = 'GDAM MCV(MW)'
iexGdamMcvVals[itr]['time_stamp'] = itr + 1
for itr in range(len(iexGdamMcpVals)):
iexGdamMcpVals[itr]['metric_name'] = 'GDAM MCP(Rs/KWH)'
iexGdamMcpVals[itr]['time_stamp'] = itr + 1
# create plot image for demands of prev yr, prev month, this month
iexGdamMcvObjs = [{
'Date': x["time_stamp"],
'colName': x['metric_name'],
'val': x["data_value"]
} for x in iexGdamMcvVals]
iexGdamMcpObjs = [{
'Date': x["time_stamp"],
'colName': x['metric_name'],
'val': x["data_value"]
} for x in iexGdamMcpVals]
pltDataObjs = iexGdamMcvObjs + iexGdamMcpObjs
pltDataDf = pd.DataFrame(pltDataObjs)
pltDataDf = pltDataDf.pivot(index='Date', columns='colName', values='val')
pltDataDf['GDAM MCP(Rs/KWH)'] = pltDataDf['GDAM MCP(Rs/KWH)'] / 1000
# derive plot title
# pltTitle = 'GDAM MCP & MCV Data as per IEX Data'
dateStr = startDt.strftime("%d-%m-%Y")
pltTitle = 'GDAM 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['GDAM MCV(MW)'].values,
color='#66b3ff')
laThisMonth.set_label('GDAM MCV(MW)')
laLastYear, = ax2.plot(pltDataDf.index.values,
pltDataDf['GDAM MCP(Rs/KWH)'].values,
color='#df80ff')
laLastYear.set_label('GDAM 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_b.png')
plt.close()
return True
