def fetchSection1_1_freq_Context(appDbConnStr: str, startDt: dt.datetime,
endDt: dt.datetime) -> ISection_1_1_freq:
monthDtObj = dt.datetime(startDt.year, startDt.month, 1)
mRepo = MetricsDataRepo(appDbConnStr)
# get WR Unrestricted demand hourly values for this month and prev yr month
wrFreqBetBandVals = mRepo.getFreqDailyData('>= 49.9 - <= 50.05', startDt,
endDt)
wrFreqBetBandDf = pd.DataFrame(wrFreqBetBandVals)
wrFreqBetBandDf.set_index('time_stamp', inplace=True)
wrFreqBetBandDf['data_value'] = pd.to_numeric(
wrFreqBetBandDf['data_value'])
bet_band = round(wrFreqBetBandDf['data_value'].mean(), 3)
wrAvgFreqVals = mRepo.getFreqDailyData('avg frq', startDt, endDt)
wrAvgFreqDf = pd.DataFrame(wrAvgFreqVals)
wrAvgFreqDf.set_index('time_stamp', inplace=True)
wrAvgFreqDf['data_value'] = pd.to_numeric(wrAvgFreqDf['data_value'])
avg_freq = round(wrAvgFreqDf['data_value'].mean(), 3)
wrFdiVals = mRepo.getFreqDailyData('Hrs Out of IEGC', startDt, endDt)
wrFdiVals = pd.DataFrame(wrFdiVals)
wrFdiVals['data_value'] = pd.to_numeric(wrFdiVals['data_value'])
wrFdiVals['data_value'] = wrFdiVals['data_value'] / 24
fdi = round(wrFdiVals['data_value'].mean(), 2)
# print(fdi)
wrMaxFreqVals = mRepo.getFreqDailyData('max inst f', startDt, endDt)
wrMaxFreqDf = pd.DataFrame(wrMaxFreqVals)
wrMaxFreqDf.set_index('time_stamp', inplace=True)
wrMaxFreqDf['data_value'] = pd.to_numeric(wrMaxFreqDf['data_value'])
wrMaxFreqDate = wrMaxFreqDf['data_value'].idxmax()
max_freq = round(wrMaxFreqDf['data_value'].max(), 2)
wrMaxFreqTimeVals = mRepo.getFreqDailyData('time max f', startDt, endDt)
wrMaxFreqTimeDf = pd.DataFrame(wrMaxFreqTimeVals)
wrMaxFreqTimeDf.set_index('time_stamp', inplace=True)
# wrMaxFreqDate = dt.datetime.strftime(wrMaxFreqDate, "%Y-%m-%d %H:%M:%S")
wrMaxFreqTime = wrMaxFreqTimeDf.loc[wrMaxFreqDate]['data_value']
max_freq_time_str = "{0} at {1}".format(
dt.datetime.strftime(wrMaxFreqDate, "%d-%b-%y"), wrMaxFreqTime)
wrMinFreqVals = mRepo.getFreqDailyData('min inst f', startDt, endDt)
wrMinFreqDf = pd.DataFrame(wrMinFreqVals)
wrMinFreqDf.set_index('time_stamp', inplace=True)
wrMinFreqDf['data_value'] = pd.to_numeric(wrMinFreqDf['data_value'])
wrMinFreqDate = wrMinFreqDf['data_value'].idxmin()
min_freq = round(wrMinFreqDf['data_value'].min(), 3)
wrMinFreqTimeVals = mRepo.getFreqDailyData('time min f', startDt, endDt)
wrMinFreqTimeDf = pd.DataFrame(wrMinFreqTimeVals)
wrMinFreqTimeDf.set_index('time_stamp', inplace=True)
wrMinFreqTime = wrMinFreqTimeDf.loc[wrMinFreqDate]['data_value']
min_freq_time_str = "{0} at {1}".format(
dt.datetime.strftime(wrMinFreqDate, "%d-%b-%y"), wrMinFreqTime)
secData: ISection_1_1_freq = {
'bet_band': bet_band,
'avg_freq': avg_freq,
'fdi': fdi,
'max_freq': max_freq,
'max_freq_time_str': max_freq_time_str,
'min_freq': min_freq,
'min_freq_time_str': min_freq_time_str
}
return secData
def fetchSection1_6_1Context(appDbConnStr: str, startDt: dt.datetime, endDt: dt.datetime) -> ISection_1_6_1:
monthName = dt.datetime.strftime(startDt, "%b %y")
mRepo = MetricsDataRepo(appDbConnStr)
# get WR freq Daily values for this month
freqLessThanBandVals = mRepo.getFreqDailyData('< 49.9', startDt, endDt)
freqLessVals = [x['data_value'] for x in freqLessThanBandVals]
freqLessVals = [float(i) for i in freqLessVals]
freq_max_less_band = max(freqLessVals)
freq_avg_less_band = sum(freqLessVals)/len(freqLessVals)
freqBetBandVals = mRepo.getFreqDailyData(
'>= 49.9 - <= 50.05', startDt, endDt)
freBetVals = [x['data_value'] for x in freqBetBandVals]
freBetVals = [float(i) for i in freBetVals]
freq_max_bet_band = max(freBetVals)
freq_avg_bet_band = sum(freBetVals)/len(freBetVals)
freqGreaterThanBandVals = mRepo.getFreqDailyData('> 50', startDt, endDt)
freGreaterThanVals = [x['data_value'] for x in freqGreaterThanBandVals]
freGreaterThanVals = [float(i) for i in freGreaterThanVals]
freq_max_greater_than_band = max(freGreaterThanVals)
freq_avg_greater_than_band = sum(
freGreaterThanVals)/len(freGreaterThanVals)
freqFviVals = mRepo.getFreqDailyData('FVI', startDt, endDt)
freqFVIVals = [x['data_value'] for x in freqFviVals]
freqFVIVals = [float(i) for i in freqFVIVals]
max_fvi = max(freqFVIVals)
avg_fvi = sum(freqFVIVals)/len(freqFVIVals)
hrsOutOfBandVals = mRepo.getFreqDailyData(
'Hrs Out of IEGC', startDt, endDt)
hrsOutOfBand = [x['data_value'] for x in hrsOutOfBandVals]
hrsOutOfBand = [float(i) for i in hrsOutOfBand]
hrs_max_out_of_band = max(hrsOutOfBand)
hrs_avg_out_of_band = sum(hrsOutOfBand)/len(hrsOutOfBand)
dailyFdi = [x/24 for x in hrsOutOfBand]
max_Fdi = max(dailyFdi)
avg_Fdi = sum(dailyFdi)/len(dailyFdi)
perc_time_out_of_band = [x*100 for x in dailyFdi]
max_perc_time = max(perc_time_out_of_band)
avg_perc_time = sum(perc_time_out_of_band)/len(perc_time_out_of_band)
freqDailyMaxVals = mRepo.getFreqDailyData('max inst f', startDt, endDt)
freqMaxVals = [x['data_value'] for x in freqDailyMaxVals]
freqMaxVals = [float(i) for i in freqMaxVals]
max_monthly_freq = max(freqMaxVals)
freqDailyMinVals = mRepo.getFreqDailyData('min inst f', startDt, endDt)
freqMinVals = [x['data_value'] for x in freqDailyMinVals]
freqMinVals = [float(i) for i in freqMinVals]
min_monthly_freq = min(freqMinVals)
freqDailyAvgVals = mRepo.getFreqDailyData('avg frq', startDt, endDt)
freqAvgVals = [x['data_value'] for x in freqDailyAvgVals]
freqAvgVals = [float(i) for i in freqAvgVals]
avg_monthly_freq = sum(freqAvgVals)/len(freqAvgVals)
# create plot image for freq profile
pltLessThanBandObjs = [{'Date': convertDtToDayNumMonth(
x["time_stamp"]), 'colName': x["metric_name"], 'val': x["data_value"]} for x in freqLessThanBandVals]
pltBetBandObjs = [{'Date': convertDtToDayNumMonth(
x["time_stamp"]), 'colName': x["metric_name"], 'val': x["data_value"]} for x in freqBetBandVals]
pltGreaterThanBandObjs = [{'Date': convertDtToDayNumMonth(
x["time_stamp"]), 'colName': x["metric_name"], 'val': x["data_value"]} for x in freqGreaterThanBandVals]
pltFviObjs = [{'Date': convertDtToDayNumMonth(
x["time_stamp"]), 'colName': x["metric_name"], 'val': x["data_value"]} for x in freqFviVals]
pltHrsOutOfBandObjs = [{'Date': convertDtToDayNumMonth(
x["time_stamp"]), 'colName': x["metric_name"], 'val': x["data_value"]} for x in hrsOutOfBandVals]
pltDailyMaxFreqObjs = [{'Date': convertDtToDayNumMonth(
x["time_stamp"]), 'colName': x["metric_name"], 'val': x["data_value"]} for x in freqDailyMaxVals]
pltDailyMinFreqObjs = [{'Date': convertDtToDayNumMonth(
x["time_stamp"]), 'colName': x["metric_name"], 'val': x["data_value"]} for x in freqDailyMinVals]
pltDailyAvgFreqObjs = [{'Date': convertDtToDayNumMonth(
x["time_stamp"]), 'colName': x["metric_name"], 'val': x["data_value"]} for x in freqDailyAvgVals]
tableDataObjs = pltLessThanBandObjs + pltBetBandObjs + pltGreaterThanBandObjs + \
pltFviObjs + pltHrsOutOfBandObjs + \
pltDailyMaxFreqObjs + pltDailyMinFreqObjs + pltDailyAvgFreqObjs
pltFreqGraphObjs = pltLessThanBandObjs + \
pltBetBandObjs + pltGreaterThanBandObjs
tableDataDf = pd.DataFrame(tableDataObjs)
tableDataDf = tableDataDf.pivot(
index='Date', columns='colName', values='val')
tableDataDf.reset_index(inplace=True)
tableDataDf['fdi'] = dailyFdi
tableDataDf['out_of_band_perc'] = perc_time_out_of_band
freqProfileList: ISection_1_6_1["freq_profile"] = []
for i in tableDataDf.index:
freqProf: IFreqDetails = {
'date': tableDataDf['Date'][i],
'less_than_band': round(float(tableDataDf['< 49.9'][i]), 2),
'freq_bet_band': round(float(tableDataDf['>= 49.9 - <= 50.05'][i]), 2),
'out_of_band': round(float(tableDataDf['> 50'][i]), 2),
'fvi': round(float(tableDataDf['FVI'][i]), 2),
'out_of_band_perc': round(float(tableDataDf['out_of_band_perc'][i]), 2),
'hrs_out_of_band': round(float(tableDataDf['Hrs Out of IEGC'][i]), 2),
'fdi': round(float(tableDataDf['fdi'][i]), 2),
'freq_daily_max': round(float(tableDataDf['max inst f'][i]), 2),
'freq_daily_min': round(float(tableDataDf['min inst f'][i]), 2),
'freq_daily_avg': round(float(tableDataDf['avg frq'][i]), 2)
}
freqProfileList.append(freqProf)
sectionData: ISection_1_6_1 = {
"freq_profile": freqProfileList,
"freq_max_less_band": round(freq_max_less_band, 2),
"freq_avg_less_band": round(freq_avg_less_band, 2),
"freq_max_bet_band": round(freq_max_bet_band, 2),
"freq_avg_bet_band": round(freq_avg_bet_band, 2),
"freq_max_greater_than_band": round(freq_max_greater_than_band, 2),
"freq_avg_greater_than_band": round(freq_avg_greater_than_band, 2),
"max_fvi": round(max_fvi, 2),
"avg_fvi": round(avg_fvi, 2),
"hrs_max_out_of_band": round(hrs_max_out_of_band, 2),
"hrs_avg_out_of_band": round(hrs_avg_out_of_band, 2),
"max_Fdi": round(max_Fdi, 2),
"avg_Fdi": round(avg_Fdi, 2),
"max_perc_time": round(max_perc_time, 2),
"avg_perc_time": round(avg_perc_time, 2),
"max_monthly_freq": round(max_monthly_freq, 2),
"min_monthly_freq": round(min_monthly_freq, 2),
"avg_monthly_freq": round(avg_monthly_freq, 2)
}
pltFreqGraphDf = pd.DataFrame(pltFreqGraphObjs)
pltFreqGraphDf = pltFreqGraphDf.pivot(
index='Date', columns='colName', values='val')
pltFreqGraphDf.reset_index(inplace=True)
# save plot data as excel
pltFreqGraphDf.to_excel("assets/plot_1_6_2.xlsx", index=True)
# derive plot title
pltTitle = 'Frequency Profile for {0}'.format(monthName)
# create a plotting area and get the figure, axes handle in return
fig, ax = plt.subplots(figsize=(7.5, 4.5))
# set plot title
ax.set_title(pltTitle)
fig.patch.set_facecolor('#dce6f2')
# plot data and get the line artist object in return
width = 0.4
plt.bar(pltFreqGraphDf['Date'], freqLessVals,
width=0.4, color='#ff0066', label='<49.9')
plt.bar(pltFreqGraphDf['Date'], freBetVals, width,
bottom=freqLessVals, color='#00cc66', label='49.9-50.05')
greatorThanBandBottom = list(np.add(freqLessVals, freBetVals))
plt.bar(pltFreqGraphDf['Date'], freGreaterThanVals, width,
bottom=greatorThanBandBottom, color='#0086b3', label='>50.05')
plt.xticks(rotation=90)
# plt.legend()
plt.legend(bbox_to_anchor=(0.0, -0.35, 1, 0), loc='lower center',
ncol=3, mode="expand", borderaxespad=0.)
fig.subplots_adjust(bottom=0.25, left=0.1, right=0.99)
fig.savefig('assets/section_1_6_2.png')
# plt.show()
return sectionData
