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make_results_charts.py
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import plotly
import plotly.graph_objs as go
import pandas as pd
import json
import numpy as np
from time import time
def _bill_distribution(load_and_results_by_case,component_name):
results_by_case = load_and_results_by_case['results']
Xaxis = "Bill (AUD)"
Yaxis = "Percentage"
layout = go.Layout(xaxis=dict(showgrid=False, title=Xaxis,title_font=dict(size=12),tickfont=dict(size=12)),
yaxis=dict(showgrid=False, title=Yaxis,rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)),
showlegend=True)
trace = []
for case_name, results in results_by_case.items():
if component_name == 'Retailer':
if 'Retailer' in results.keys():
trace.append(go.Histogram(x=results['Retailer']['Retailer']['Bill'], histnorm='probability', name=case_name))
elif component_name == 'NUOS' or component_name == 'DUOS' or component_name == 'TUOS':
if 'Network' in results.keys():
trace.append(go.Histogram(x=results['Network'][component_name]['Bill'], histnorm='probability', name=case_name))
elif component_name == 'Wholesale':
if 'Wholesale' in results.keys():
trace.append(go.Histogram(x=results['Wholesale']['Bill'], histnorm='probability', name=case_name))
elif component_name == 'Total':
if 'Retailer' in results.keys():
trace.append(go.Histogram(x=results['Retailer']['Retailer']['Bill'], histnorm='probability', name=case_name))
else:
if ('Wholesale' in results.keys()) and ('Network' in results.keys()):
wholesale_bill = results['Wholesale'].copy()
wholesale_bill.set_index('CUSTOMER_KEY',inplace=True)
trace.append(go.Histogram(x=wholesale_bill['Bill'] + results['Network']['NUOS']['Bill'], histnorm='probability', name=case_name))
elif ('Wholesale' in results.keys()) and (not 'Network' in results.keys()):
trace.append(go.Histogram(x=results['Wholesale']['Bill'], histnorm='probability', name=case_name))
elif (not 'Wholesale' in results.keys()) and ('Network' in results.keys()):
trace.append(go.Histogram(x=results['Network']['NUOS']['Bill'], histnorm='probability', name=case_name))
return {'data': trace, 'layout': layout}
def _bill_distribution_DUOS(load_and_results_by_case):
data = _bill_distribution(load_and_results_by_case,component_name='DUOS')
return data
def _bill_distribution_NUOS(load_and_results_by_case):
data = _bill_distribution(load_and_results_by_case,component_name='NUOS')
return data
def _bill_distribution_TUOS(load_and_results_by_case):
data = _bill_distribution(load_and_results_by_case,component_name='TUOS')
return data
def _bill_distribution_Retailer(load_and_results_by_case):
data = _bill_distribution(load_and_results_by_case,component_name='Retailer')
return data
def _bill_distribution_Wholesale(load_and_results_by_case):
data = _bill_distribution(load_and_results_by_case,component_name='Wholesale')
return data
def _bill_distribution_Total(load_and_results_by_case):
data = _bill_distribution(load_and_results_by_case,component_name='Total')
return data
def _bill_box_plot(load_and_results_by_case, component_name):
results_by_case = load_and_results_by_case['results']
Xaxis = "Case"
Yaxis = "Bill (AUD)"
layout = go.Layout(xaxis=dict(showgrid=False, title=Xaxis,title_font=dict(size=12),tickfont=dict(size=12)),
yaxis=dict(showgrid=False, title=Yaxis,rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)),
showlegend=True)
trace = []
for case_name, results in results_by_case.items():
if component_name == 'Retailer':
if 'Retailer' in results.keys():
trace.append(go.Box(y=results['Retailer']['Retailer']['Bill'], name=case_name))
elif component_name == 'NUOS' or component_name == 'DUOS' or component_name == 'TUOS':
if 'Network' in results.keys():
trace.append(go.Box(y=results['Network'][component_name]['Bill'], name=case_name))
elif component_name == 'Wholesale':
if 'Wholesale' in results.keys():
trace.append(go.Box(y=results['Wholesale']['Bill'], name=case_name))
elif component_name == 'Total':
if 'Retailer' in results.keys():
trace.append(go.Box(y=results['Retailer']['Retailer']['Bill'], name=case_name))
else:
if ('Wholesale' in results.keys()) and ('Network' in results.keys()):
wholesale_bill = results['Wholesale'].copy()
wholesale_bill.set_index('CUSTOMER_KEY',inplace=True)
trace.append(go.Box(y=wholesale_bill['Bill'] + results['Network']['NUOS']['Bill'], name=case_name))
elif ('Wholesale' in results.keys()) and (not 'Network' in results.keys()):
trace.append(go.Box(y=results['Wholesale']['Bill'], name=case_name))
elif (not 'Wholesale' in results.keys()) and ('Network' in results.keys()):
trace.append(go.Box(y=results['Network']['NUOS']['Bill'], name=case_name))
return {'data': trace, 'layout': layout}
def _bill_box_plot_Total(load_and_results_by_case):
data = _bill_box_plot(load_and_results_by_case,component_name='Total')
return data
def _bill_box_plot_DUOS(load_and_results_by_case):
data = _bill_box_plot(load_and_results_by_case,component_name='DUOS')
return data
def _bill_box_plot_NUOS(load_and_results_by_case):
data = _bill_box_plot(load_and_results_by_case,component_name='NUOS')
return data
def _bill_box_plot_TUOS(load_and_results_by_case):
data = _bill_box_plot(load_and_results_by_case,component_name='TUOS')
return data
def _bill_box_plot_Retailer(load_and_results_by_case):
data = _bill_box_plot(load_and_results_by_case,component_name='Retailer')
return data
def _bill_box_plot_Wholesale(load_and_results_by_case):
data = _bill_box_plot(load_and_results_by_case,component_name='Wholesale')
return data
def _average_annual_profile(load_and_results_by_case):
load_by_case = load_and_results_by_case['load']
Xaxis = "Time"
Yaxis = "Average Load (kW)"
layout = go.Layout(xaxis=dict(showgrid=False, title=Xaxis,title_font=dict(size=12),tickfont=dict(size=12)),
yaxis=dict(showgrid=False, title=Yaxis,rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)),
showlegend=True)
trace = []
for case_name, load in load_by_case.items():
load2 = load.copy()
load_mean = load.mean(axis=1)
trace.append(go.Scattergl(x=load_mean.index, y=load_mean.values, name=case_name))
return {'data': trace, 'layout':layout}
def _daily_kWh_histogram(load_and_results_by_case):
load_by_case = load_and_results_by_case['load']
Xaxis = "Daily Electricity (kWh)"
Yaxis = "Percentage"
layout = go.Layout(xaxis=dict(showgrid=False, title=Xaxis,title_font=dict(size=12),tickfont=dict(size=12)),
yaxis=dict(showgrid=False, title=Yaxis,rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)),
showlegend=True)
trace = []
for case_name, load in load_by_case.items():
load2 = load.copy()
load_sum = load2.sum(axis=0)/2/365
trace.append(go.Histogram(x=list(load_sum),histnorm='probability',name=case_name,xbins=dict(
start=min(load_sum),
end=max(load_sum),
size=(max(load_sum)-min(load_sum))/50)))
return {'data': trace, 'layout':layout}
def _average_load_duration_curve(load_and_results_by_case):
load_by_case = load_and_results_by_case['load']
Xaxis = "Time"
Yaxis = "Load (kW)"
layout = go.Layout(xaxis=dict(showgrid=False, title=Xaxis,title_font=dict(size=12),tickfont=dict(size=12)),
yaxis=dict(showgrid=False, title=Yaxis,rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)),
showlegend=True)
trace = []
for case_name, load in load_by_case.items():
load2 = load.copy()
load_average = load2.mean(axis=1)
load_average_sort = load_average.sort_values(ascending=False, inplace = False, na_position ='last')
load_average_sort = load_average_sort.reset_index(drop=True)
trace.append(go.Scattergl(x=load_average_sort.index,y=load_average_sort.values, name=case_name))
return {'data': trace, 'layout':layout}
def _monthly_average_kWh(load_and_results_by_case):
load_by_case = load_and_results_by_case['load']
Xaxis = "Month"
Yaxis = "Average Daily Consumption (kWh)"
layout = go.Layout(xaxis=dict(showgrid=False, title=Xaxis,title_font=dict(size=12),tickfont=dict(size=12)),
yaxis=dict(showgrid=False, title=Yaxis,rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)),
showlegend=True)
trace = []
for case_name, load in load_by_case.items():
load2 = load.copy()
load_average = load2.mean(axis=1)
# find mean for each month
monthly_mean = load_average.resample('M').mean()
monthly_mean = monthly_mean * 48
month_name = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
trace.append(go.Bar(x=month_name,y=monthly_mean.values, name=case_name))
return {'data': trace, 'layout':layout}
def _seasonal_daily_pattern(load_and_results_by_case):
load_by_case = load_and_results_by_case['load']
Xaxis = "Time"
Yaxis = "Load (kW)"
layout = go.Layout(xaxis=dict(showgrid=False, title=Xaxis,title_font=dict(size=12),tickfont=dict(size=12),
tickmode = 'array',
tickvals = [2, 6, 10, 14, 18, 22, 26, 30, 34, 38, 42, 46],
ticktext = ['01:00', '03:00', '05:00', '07:00', '09:00', '11:00','13:00','15:00','17:00','19:00','21:00','23:00']),
yaxis=dict(showgrid=False, title=Yaxis,rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)),
showlegend=True)
trace = []
for case_name, load in load_by_case.items():
load2 = load.copy()
load_average = load2.mean(axis=1)
# organise data
load_average = pd.DataFrame(load_average)
load_average.columns = ['power']
load_average['Date'] = load_average.index
load_average['Month_Number'] = load_average['Date'].dt.month
# summer
load_summer = load_average[load_average['Month_Number'].isin([1,11,12])]
load_summer_reshape = np.array(load_summer['power']).reshape(-1,48)
load_summer_daily = np.nanmean(load_summer_reshape,axis=0)
trace.append(go.Scattergl(x=list(range(0,48)), y=load_summer_daily, name= case_name + ' Summer', mode='lines'))
# winter
load_winter = load_average[load_average['Month_Number'].isin([6,7,8])]
load_winter_reshape = np.array(load_winter['power']).reshape(-1,48)
load_winter_daily = np.nanmean(load_winter_reshape,axis=0)
trace.append(go.Scattergl(x=list(range(0,48)), y=load_winter_daily, name= case_name + ' Winter', mode='lines'))
return {'data': trace, 'layout':layout}
def _monthly_peak_time(load_and_results_by_case):
load_by_case = load_and_results_by_case['load']
Xaxis = "Month"
Yaxis = "Time of day (Hour)"
layout = go.Layout(xaxis=dict(showgrid=False, title=Xaxis,title_font=dict(size=12),tickfont=dict(size=12)),
yaxis=dict(showgrid=False, title=Yaxis,title_font=dict(size=12),tickfont=dict(size=12)),
showlegend=True)
trace = []
for case_name, load in load_by_case.items():
load2 = load.copy()
load_average = load2.mean(axis=1)
# organise data
load_average = pd.DataFrame(load_average)
load_average.columns = ['power']
load_average['Date'] = load_average.index
load_average['Month_Number'] = load_average['Date'].dt.month
load_average['Hour_Number'] = load_average['Date'].dt.hour
load_average['Minute_Number'] = load_average['Date'].dt.minute
# find the peak time (expressed by hour)
peak_hour_month = []
for i in range(12):
load_by_month = load_average[load_average['Month_Number'].isin([i+1])]
peak_index = load_by_month['power'].idxmax()
peak_hour_month.append(load_by_month.loc[peak_index,'Hour_Number'] + load_by_month.loc[peak_index,'Minute_Number']/60)
month_name = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
trace.append(go.Scattergl(x=month_name, y=peak_hour_month, name= case_name, mode='markers', marker=dict(size=20)))
return {'data': trace, 'layout':layout}
def _get_daily_profile_interquartile_range(load_and_results_by_case):
load_by_case = load_and_results_by_case['load']
Xaxis = "30 Minutes Interval"
Yaxis = "kWh"
layout = go.Layout(xaxis=dict(showgrid=False, title=Xaxis,title_font=dict(size=12),tickfont=dict(size=12),
tickmode = 'array',
tickvals = [2, 6, 10, 14, 18, 22, 26, 30, 34, 38, 42, 46],
ticktext = ['01:00', '03:00', '05:00', '07:00', '09:00', '11:00','13:00','15:00','17:00','19:00','21:00','23:00']),
yaxis=dict(showgrid=False, title=Yaxis,rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)),
showlegend=True)
trace = []
for case_name, load in load_by_case.items():
load2 = load.copy()
load_daily_average = load2.apply(get_daily_average_profile)
qr1 = np.nanpercentile(np.array(load_daily_average), 75, interpolation='midpoint',axis=1)
qr2 = np.nanpercentile(np.array(load_daily_average), 50, interpolation='midpoint',axis=1)
qr3 = np.nanpercentile(np.array(load_daily_average), 25, interpolation='midpoint',axis=1)
trace.append(go.Scattergl(x=list(range(0,48)),y=list(qr3),fill=None, name = case_name + ' 25%'))
trace.append(go.Scattergl(x=list(range(0,48)),y=list(qr2),fill='tonexty', name = case_name + ' 50%'))
trace.append(go.Scattergl(x=list(range(0,48)),y=list(qr1),fill='tonexty', name = case_name + ' 75%'))
return {'data': trace, 'layout':layout}
########################### charts dict: _single_variable_chart_methods
_single_variable_chart_methods = {'Bill Distribution Total': _bill_distribution_Total,
'Bill Distribution DUOS': _bill_distribution_DUOS,
'Bill Distribution NUOS': _bill_distribution_NUOS,
'Bill Distribution TUOS': _bill_distribution_TUOS,
'Bill Distribution Retailer': _bill_distribution_Retailer,
'Bill Distribution Wholesale': _bill_distribution_Wholesale,
'Bill Box Plot Total': _bill_box_plot_Total,
'Bill Box Plot DUOS': _bill_box_plot_DUOS,
'Bill Box Plot NUOS': _bill_box_plot_NUOS,
'Bill Box Plot TUOS': _bill_box_plot_TUOS,
'Bill Box Plot Retailer': _bill_box_plot_Retailer,
'Bill Box Plot Wholesale': _bill_box_plot_Wholesale,
'Average Annual Profile': _average_annual_profile,
'Daily kWh Histogram':_daily_kWh_histogram,
'Average Load Duration Curve':_average_load_duration_curve,
'Monthly Average kWh':_monthly_average_kWh,
'Seasonal Daily Pattern':_seasonal_daily_pattern,
'Monthly Peak Time':_monthly_peak_time,
'Daily Profile Interquartile Range':_get_daily_profile_interquartile_range}
def singe_variable_chart(chart_name, load_and_results_by_case):
chart_data = _single_variable_chart_methods[chart_name](load_and_results_by_case)
chart_data = json.dumps(chart_data, cls=plotly.utils.PlotlyJSONEncoder)
return chart_data
#################################################################################################
# dual variable
def _get_annual_kWh(results, load, network_load, details, axis):
axis_name = "Annual kWh"
if 'Retailer' in results.keys():
axis_data = list(results['Retailer']['LoadInfo']['Annual_kWh'])
elif 'Network' in results.keys():
axis_data = list(results['Network']['LoadInfo']['Annual_kWh'])
else:
axis_data = []
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_avg_demand_n_peaks(results, load, network_load, details, axis):
if axis == 'x_axis':
N_peaks = int(details['x_axis_n_peaks'])
one_peak_per_day_status = details['x_axis_one_peak_per_day']
if axis == 'y_axis':
N_peaks = int(details['y_axis_n_peaks'])
one_peak_per_day_status = details['y_axis_one_peak_per_day']
axis_name = "Average Demand at " + str(N_peaks) + " Network Peaks"
if load is None:
return {'axis_name':axis_name, 'axis_data':[]}
else:
network_load2 = network_load.copy()
network_load2['Month_Number'] = network_load2.index.month
network_load_filtered = network_load2.copy()
if details['include_spring'] == False:
network_load_filtered = network_load_filtered[~network_load_filtered['Month_Number'].isin([3,4,5])]
if details['include_summer'] == False:
network_load_filtered = network_load_filtered[~network_load_filtered['Month_Number'].isin([6,7,8])]
if details['include_autumn'] == False:
network_load_filtered = network_load_filtered[~network_load_filtered['Month_Number'].isin([9,10,11])]
if details['include_winter'] == False:
network_load_filtered = network_load_filtered[~network_load_filtered['Month_Number'].isin([1,2,12])]
del network_load_filtered['Month_Number']
if one_peak_per_day_status == False:
network_load_filtered2=network_load_filtered.copy()
network_load_average = network_load_filtered2.mean(axis=1)
network_load_average_sort = network_load_average.sort_values(ascending = False, inplace = False, na_position ='last')
selected_datetime = network_load_average_sort.index[0:N_peaks]
load2 = load.copy()
selected_load = load2.loc[selected_datetime]
selected_load_average = selected_load.mean(axis=0)
axis_data = list(selected_load_average)
return {'axis_name':axis_name, 'axis_data':axis_data}
else:
network_load_filtered2=network_load_filtered.copy()
network_load_average = network_load_filtered2.mean(axis=1)
# find peak for each day
network_daily_peak = network_load_average.resample('D').max()
network_daily_peak_sort = network_daily_peak.sort_values(ascending = False, inplace = False, na_position ='last')
selected_datetime = network_daily_peak_sort.index[0:N_peaks]
load2 = load.copy()
load_daily_peak = load2.resample('D').max()
selected_load = load_daily_peak.loc[selected_datetime]
selected_load_average = selected_load.mean(axis=0)
axis_data = list(selected_load_average)
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_avg_demand_n_monthly_peaks(results, load, network_load, details, axis):
if axis == 'x_axis':
N_peaks = int(details['x_axis_n_peaks'])
one_peak_per_day_status = details['x_axis_one_peak_per_day']
if axis == 'y_axis':
N_peaks = int(details['y_axis_n_peaks'])
one_peak_per_day_status = details['y_axis_one_peak_per_day']
axis_name = "Average Demand at " + str(N_peaks) + " Network Peaks"
if load is None:
return {'axis_name':axis_name, 'axis_data':[]}
else:
network_load2 = network_load.copy()
network_load2['Month_Number'] = network_load2.index.month
network_load_filtered = network_load2.copy()
if details['include_spring'] == False:
network_load_filtered = network_load_filtered[~network_load_filtered['Month_Number'].isin([3,4,5])]
if details['include_summer'] == False:
network_load_filtered = network_load_filtered[~network_load_filtered['Month_Number'].isin([6,7,8])]
if details['include_autumn'] == False:
network_load_filtered = network_load_filtered[~network_load_filtered['Month_Number'].isin([9,10,11])]
if details['include_winter'] == False:
network_load_filtered = network_load_filtered[~network_load_filtered['Month_Number'].isin([1,2,12])]
network_load_filtered_by_month = []
for i in range(12):
monthly_data = network_load_filtered[network_load_filtered['Month_Number'].isin([i+1])]
del monthly_data['Month_Number']
network_load_filtered_by_month.append(monthly_data)
if one_peak_per_day_status == False:
selected_datetime = []
for i in range(12):
network_load_filtered_by_month_i=network_load_filtered_by_month[i].copy()
network_load_average = network_load_filtered_by_month_i.mean(axis=1)
network_load_average_sort = network_load_average.sort_values(ascending = False, inplace = False, na_position ='last')
selected_datetime = selected_datetime + list(network_load_average_sort.index[0:N_peaks])
load2 = load.copy()
selected_load = load2.loc[selected_datetime]
selected_load_average = selected_load.mean(axis=0)
axis_data = list(selected_load_average)
return {'axis_name':axis_name, 'axis_data':axis_data}
else:
selected_datetime = []
for i in range(12):
network_load_filtered_by_month_i=network_load_filtered_by_month[i].copy()
network_load_average = network_load_filtered_by_month_i.mean(axis=1)
network_daily_peak = network_load_average.resample('D').max()
network_daily_peak_sort = network_daily_peak.sort_values(ascending = False, inplace = False, na_position ='last')
selected_datetime = selected_datetime + list(network_daily_peak_sort.index[0:N_peaks])
load2 = load.copy()
load_daily_peak = load2.resample('D').max()
selected_load = load_daily_peak.loc[selected_datetime]
selected_load_average = selected_load.mean(axis=0)
axis_data = list(selected_load_average)
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_avg_demand_top_n_peaks(results, load, network_load, details, axis):
if axis == 'x_axis':
N_peaks = int(details['x_axis_n_peaks'])
one_peak_per_day_status = details['x_axis_one_peak_per_day']
if axis == 'y_axis':
N_peaks = int(details['y_axis_n_peaks'])
one_peak_per_day_status = details['y_axis_one_peak_per_day']
axis_name = "Average Demand at " + str(N_peaks) + " Network Peaks"
if load is None:
return {'axis_name':axis_name, 'axis_data':[]}
else:
load2 = load.copy()
load2['Month_Number'] = load2.index.month
load_filtered = load2.copy()
if details['include_spring'] == False:
load_filtered = load_filtered[~load_filtered['Month_Number'].isin([3,4,5])]
if details['include_summer'] == False:
load_filtered = load_filtered[~load_filtered['Month_Number'].isin([6,7,8])]
if details['include_autumn'] == False:
load_filtered = load_filtered[~load_filtered['Month_Number'].isin([9,10,11])]
if details['include_winter'] == False:
load_filtered = load_filtered[~load_filtered['Month_Number'].isin([1,2,12])]
del load_filtered['Month_Number']
if one_peak_per_day_status == False:
load_filtered2=load_filtered.copy()
load_filtered_sort = pd.concat([load_filtered2[col].sort_values(ascending = False, inplace = False, na_position ='last').reset_index(drop=True) for col in load_filtered2], axis=1, ignore_index=True)
selected_load = load_filtered_sort.iloc[0:N_peaks,:]
selected_load_average = selected_load.mean(axis=0)
axis_data = list(selected_load_average)
return {'axis_name':axis_name, 'axis_data':axis_data}
else:
load_filtered2=load_filtered.copy()
# find peak for each day
load_filtered_daily_peak = load_filtered2.resample('D').max()
load_filtered_daily_peak_sort = pd.concat([load_filtered_daily_peak[col].sort_values(ascending = False, inplace = False, na_position ='last').reset_index(drop=True) for col in load_filtered_daily_peak], axis=1, ignore_index=True)
selected_load = load_filtered_daily_peak_sort.iloc[0:N_peaks,:]
selected_load_average = selected_load.mean(axis=0)
axis_data = list(selected_load_average)
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_avg_demand_top_n_monthly_peaks(results, load, network_load, details, axis):
if axis == 'x_axis':
N_peaks = int(details['x_axis_n_peaks'])
one_peak_per_day_status = details['x_axis_one_peak_per_day']
if axis == 'y_axis':
N_peaks = int(details['y_axis_n_peaks'])
one_peak_per_day_status = details['y_axis_one_peak_per_day']
axis_name = "Average Demand at " + str(N_peaks) + " Network Peaks"
if load is None:
return {'axis_name':axis_name, 'axis_data':[]}
else:
load2 = load.copy()
load2['Month_Number'] = load2.index.month
load_filtered = load2.copy()
if details['include_spring'] == False:
load_filtered = load_filtered[~load_filtered['Month_Number'].isin([3,4,5])]
if details['include_summer'] == False:
load_filtered = load_filtered[~load_filtered['Month_Number'].isin([6,7,8])]
if details['include_autumn'] == False:
load_filtered = load_filtered[~load_filtered['Month_Number'].isin([9,10,11])]
if details['include_winter'] == False:
load_filtered = load_filtered[~load_filtered['Month_Number'].isin([1,2,12])]
load_filtered_by_month = []
for i in range(12):
monthly_data = load_filtered[load_filtered['Month_Number'].isin([i+1])]
del monthly_data['Month_Number']
load_filtered_by_month.append(monthly_data)
if one_peak_per_day_status == False:
selected_load = []
for i in range(12):
load_filtered_by_month_i=load_filtered_by_month[i].copy()
load_filtered_by_month_i_sort = pd.concat([load_filtered_by_month_i[col].sort_values(ascending = False, inplace = False, na_position ='last').reset_index(drop=True) for col in load_filtered_by_month_i], axis=1, ignore_index=True)
selected_load_by_month = load_filtered_by_month_i_sort.iloc[0:N_peaks,:]
selected_load.append(list(selected_load_by_month.mean(axis=0)))
selected_load_average = np.nanmean(np.array(selected_load),axis=0)
axis_data = list(selected_load_average)
return {'axis_name':axis_name, 'axis_data':axis_data}
else:
selected_load = []
for i in range(12):
load_filtered_by_month_i=load_filtered_by_month[i].copy()
load_filtered_daily_peak_by_month = load_filtered_by_month_i.resample('D').max()
load_filtered_daily_peak_by_month_sort = pd.concat([load_filtered_daily_peak_by_month[col].sort_values(ascending = False, inplace = False, na_position ='last').reset_index(drop=True) for col in load_filtered_daily_peak_by_month], axis=1, ignore_index=True)
selected_load_by_month = load_filtered_daily_peak_by_month_sort.iloc[0:N_peaks,:]
selected_load.append(list(selected_load_by_month.mean(axis=0)))
selected_load_average = np.nanmean(np.array(selected_load),axis=0)
axis_data = list(selected_load_average)
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_avg_daily_kWh(results, load, network_load, details, axis):
axis_name = "Average Daily kWh"
if load is None:
return {'axis_name':axis_name, 'axis_data':[]}
else:
axis_data = []
load2 = load.copy()
axis_data = []
for i in range(load2.shape[1]):
load_id_array = np.array(load2.iloc[:,i]).reshape((-1,48))
load_id_daily_kWh = np.sum(load_id_array,axis=1)
average_daily = np.nanmean(load_id_daily_kWh)
axis_data.append(average_daily)
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_avg_daily_peak(results, load, network_load, details, axis):
axis_name = "Average Daily Peaks"
if load is None:
return {'axis_name':axis_name, 'axis_data':[]}
else:
axis_data = []
load2 = load.copy()
axis_data = []
for i in range(load2.shape[1]):
load_id_array = np.array(load2.iloc[:,i]).reshape((-1,48))
load_id_daily_peak = np.max(load_id_array,axis=1)
average_daily = np.nanmean(load_id_daily_peak)
axis_data.append(average_daily)
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_bill_NUOS(results, load, network_load, details, axis):
axis_name = "Bill (AUD)"
if 'Network' in results.keys():
axis_data = list(results['Network']['NUOS']['Bill'])
else:
axis_data = []
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_bill_DUOS(results, load, network_load, details, axis):
axis_name = "Bill (AUD)"
if 'Network' in results.keys():
axis_data = list(results['Network']['DUOS']['Bill'])
else:
axis_data = []
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_bill_TUOS(results, load, network_load, details, axis):
axis_name = "Bill (AUD)"
if 'Network' in results.keys():
axis_data = list(results['Network']['NUOS']['Bill'])
else:
axis_data = []
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_bill_Retailer(results, load, network_load, details, axis):
axis_name = "Bill (AUD)"
if 'Retailer' in results.keys():
axis_data = list(results['Retailer']['Retailer']['Bill'])
else:
axis_data = []
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_bill_Wholesale(results, load, network_load, details, axis):
axis_name = "Bill (AUD)"
if 'Wholesale' in results.keys():
axis_data = list(results['Wholesale']['Bill'])
else:
axis_data = []
return {'axis_name':axis_name, 'axis_data':axis_data}
def _get_bill_Total(results, load, network_load, details, axis):
axis_name = "Bill (AUD)"
if 'Retailer' in results.keys():
axis_data = list(results['Retailer']['Retailer']['Bill'])
else:
if ('Wholesale' in results.keys()) and ('Network' in results.keys()):
wholesale_bill = results['Wholesale'].copy()
wholesale_bill.set_index('CUSTOMER_KEY',inplace=True)
axis_data = list(wholesale_bill['Bill'] + results['Network']['NUOS']['Bill'])
elif ('Wholesale' in results.keys()) and (not 'Network' in results.keys()):
axis_data = list(results['Wholesale']['Bill'])
elif (not 'Wholesale' in results.keys()) and ('Network' in results.keys()):
axis_data = list(results['Network']['NUOS']['Bill'])
else:
axis_data = []
return {'axis_name':axis_name, 'axis_data':axis_data}
_dual_variable_axis_methods = {'Annual_kWh': _get_annual_kWh,
'avg_demand_n_peaks': _get_avg_demand_n_peaks,
'avg_demand_n_monthly_peaks': _get_avg_demand_n_monthly_peaks,
'avg_demand_top_n_peaks':_get_avg_demand_top_n_peaks,
'avg_demand_top_n_monthly_peaks':_get_avg_demand_top_n_monthly_peaks,
'avg_daily_kWh':_get_avg_daily_kWh,
'avg_daily_peak':_get_avg_daily_peak,
'Bill NUOS':_get_bill_NUOS,
'Bill DUOS':_get_bill_DUOS,
'Bill TUOS':_get_bill_TUOS,
'Bill Retailer':_get_bill_Retailer,
'Bill Wholesale':_get_bill_Wholesale,
'Bill Total':_get_bill_Total}
def dual_variable_chart(load_and_results_by_case, details):
results_by_case = load_and_results_by_case['results']
load_by_case = load_and_results_by_case['load']
network_load = load_and_results_by_case['network_load']
trace = []
for case_name, results in results_by_case.items():
if bool(load_by_case):
x_axis_data = _dual_variable_axis_methods[details['x_axis']](results, load_by_case[case_name], network_load, details, axis = 'x_axis')
else:
x_axis_data = _dual_variable_axis_methods[details['x_axis']](results, None, None, details, axis = 'x_axis')
if bool(load_by_case):
y_axis_data = _dual_variable_axis_methods[details['y_axis']](results, load_by_case[case_name], network_load, details, axis = 'y_axis')
else:
y_axis_data = _dual_variable_axis_methods[details['y_axis']](results, None, None, details, axis = 'y_axis')
if len(x_axis_data['axis_data'])>0 and len(y_axis_data['axis_data'])>0:
corr_matrix = np.corrcoef(x_axis_data['axis_data'],y_axis_data['axis_data'])
corr_value = format(corr_matrix[0,1], '.2f')
dual_data = go.Scattergl(x=x_axis_data['axis_data'], y=y_axis_data['axis_data'], mode='markers', name=case_name+' (CC:'+str(corr_value)+')')
trace.append(dual_data)
dual_layout = go.Layout(xaxis=dict(showgrid=False, title=x_axis_data['axis_name'],rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)),
yaxis=dict(showgrid=False, title=y_axis_data['axis_name'],rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)))
chart_data = {'data': trace, 'layout':dual_layout}
chart_data = json.dumps(chart_data, cls=plotly.utils.PlotlyJSONEncoder)
return chart_data
###################################################################
# Single Case Charts
def is_component(suffixes, name_to_check):
for suffix in suffixes:
if suffix in name_to_check:
return True
return False
def _get_bill_components(data):
Xaxis = "Users (sorted by total bill)"
Yaxis = "Bill (AUD)"
layout = go.Layout(xaxis=dict(showgrid=False, title=Xaxis,title_font=dict(size=12),tickfont=dict(size=12)),
yaxis=dict(showgrid=False, title=Yaxis,rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)))
new_data = pd.DataFrame()
if 'Wholesale' in data.keys():
wholesale_data = data['Wholesale'].copy()
wholesale_data.set_index('CUSTOMER_KEY',inplace=True)
new_data['Wholesale Bill'] = wholesale_data['Bill']
if 'Network' in data.keys():
selected_columns = [col for col in data['Network']['DUOS'].columns if col.startswith('Charge')]
for col in selected_columns:
new_data['Network DUOS '+col] = data['Network']['DUOS'][col]
selected_columns = [col for col in data['Network']['TUOS'].columns if col.startswith('Charge')]
for col in selected_columns:
new_data['Network TUOS '+col] = data['Network']['TUOS'][col]
new_data['Network Residual Bill'] = data['Network']['NUOS']['Bill'] - data['Network']['DUOS']['Bill'] - data['Network']['TUOS']['Bill']
if 'Retailer' in data.keys():
if ('Wholesale' in data.keys()) and ('Network' in data.keys()):
new_data['Retailer Residual Bill'] = data['Retailer']['Retailer']['Bill'] - data['Network']['NUOS']['Bill']- wholesale_data['Bill']
elif (not 'Wholesale' in data.keys()) and ('Network' in data.keys()):
new_data['Retailer Residual Bill'] = data['Retailer']['Retailer']['Bill'] - data['Network']['NUOS']['Bill']
elif ('Wholesale' in data.keys()) and (not 'Network' in data.keys()):
new_data['Retailer Residual Bill'] = data['Retailer']['Retailer']['Bill'] - wholesale_data['Bill']
elif (not 'Wholesale' in data.keys()) and (not 'Network' in data.keys()):
selected_columns = [col for col in data['Retailer']['Retailer'].columns if col.startswith('Charge')]
for col in selected_columns:
new_data['Retailer '+col] = data['Retailer']['Retailer'][col]
new_data['SUM'] = new_data.sum(axis=1)
new_data.sort_values('SUM', ascending=False, inplace = True)
new_data.reset_index(drop=True, inplace = True)
del new_data['SUM']
traces = []
for col in new_data.columns:
trace = dict(name=col,
x=new_data.index.values,
y=new_data[col],
mode='lines',
stackgroup='one')
traces.append(trace)
return {'data':traces, 'layout': layout}
def _get_bill_components_pie_chart(data):
Xaxis = ""
Yaxis = ""
layout = go.Layout(xaxis=dict(showgrid=False, title=Xaxis,title_font=dict(size=12),tickfont=dict(size=12)),
yaxis=dict(showgrid=False, title=Yaxis,rangemode='tozero',title_font=dict(size=12),tickfont=dict(size=12)))
selected_columns_sum = []
label_name = []
if 'Wholesale' in data.keys():
selected_columns_sum.append(data['Wholesale']['Bill'].sum(axis=0))
label_name.append('Wholesale Bill')
if 'Network' in data.keys():
selected_columns = [col for col in data['Network']['DUOS'].columns if col.startswith('Charge')]
for col in selected_columns:
selected_columns_sum.append(data['Network']['DUOS'][col].sum(axis=0))
label_name.append('Network DUOS ' + col)
del selected_columns
selected_columns = [col for col in data['Network']['TUOS'].columns if col.startswith('Charge')]
for col in selected_columns:
selected_columns_sum.append(data['Network']['TUOS'][col].sum(axis=0))
label_name.append('Network TUOS ' + col)
del selected_columns
selected_columns_sum.append(data['Network']['NUOS']['Bill'].sum(axis=0) - data['Network']['DUOS']['Bill'].sum(axis=0) - data['Network']['TUOS']['Bill'].sum(axis=0))
label_name.append('Network Residual component')
if 'Retailer' in data.keys():
if ('Wholesale' in data.keys()) and ('Network' in data.keys()):
selected_columns_sum.append(data['Retailer']['Retailer']['Bill'].sum(axis=0) - data['Network']['NUOS']['Bill'].sum(axis=0) - data['Wholesale']['Bill'].sum(axis=0))
label_name.append('Retailer residual component')
elif (not 'Wholesale' in data.keys()) and ('Network' in data.keys()):
selected_columns_sum.append(data['Retailer']['Retailer']['Bill'].sum(axis=0) - data['Network']['NUOS']['Bill'].sum(axis=0))
label_name.append('Retailer residual component')
elif ('Wholesale' in data.keys()) and (not 'Network' in data.keys()):
selected_columns_sum.append(data['Retailer']['Retailer']['Bill'].sum(axis=0) - data['Wholesale']['Bill'].sum(axis=0))
label_name.append('Retailer residual component')
elif (not 'Wholesale' in data.keys()) and (not 'Network' in data.keys()):
selected_columns = [col for col in data['Retailer']['Retailer'].columns if col.startswith('Charge')]
for col in selected_columns:
selected_columns_sum.append(data['Retailer']['Retailer'][col].sum(axis=0))
label_name.append('Retailer ' + col)
del selected_columns
percentage = np.array(selected_columns_sum)/np.nansum(np.array(selected_columns_sum))
trace = [go.Pie(labels=label_name, values=percentage)]
return {'data':trace, 'layout': layout}
def get_daily_average_profile(x):
x_array = np.array(x).reshape((-1,48))
return np.nanmean(x_array,axis=0)
_single_case_chart_methods = {'bill_components': _get_bill_components,
'bill_components_pie_chart': _get_bill_components_pie_chart}
def single_case_chart(chart_name, load_and_results_to_plot):
results_to_plot = load_and_results_to_plot['results']
if results_to_plot is not None:
chart_data = _single_case_chart_methods[chart_name](results_to_plot)
else:
chart_data = []
chart_data = json.dumps(chart_data, cls=plotly.utils.PlotlyJSONEncoder)
return chart_data