Extra: Venue 20 electrics

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import plotly.graph_objects as go
import plotly.express as px
import numpy as np
import pandas as pd

# comment out the stuff that takes a long time to process in the public data.
# df = pd.read_csv("venue-20/venue_20_electricity_one_week_detail.csv")

# df['timestamp'] = pd.to_datetime(df['timestamp'])
# df["total_kW"] = (df['field3']+df['field2']+df['field1'])/1000
# df = df.fillna(value=0)


# phase1trace = go.Scatter(customdata=df, 
#                     y=df['field1'], 
#                     x = df['timestamp'], 
#                     mode='lines', 
#                     hoverinfo='all', 
#                     name='phase 1',
#                     )

# phase2trace = go.Scatter(customdata=df, 
#                     y=df['field2'], 
#                     x = df['timestamp'], 
#                     mode='lines', 
#                     hoverinfo='all', 
#                     name='phase 2',
#                     )
# phase3trace = go.Scatter(customdata=df, 
#                     y=df['field3'], 
#                     x = df['timestamp'], 
#                     mode='lines', 
#                     hoverinfo='all', 
#                     name='phase 3',
#                     )

# g = go.FigureWidget(data=[phase1trace,phase2trace,phase3trace])
# g.layout.title = 'CurrentCost clamp-on meter readings - one week only'
# g.layout.xaxis.title= 'timestamp'
# g.layout.yaxis.title = "Watts"
# g.layout.width = 1000
# g.layout.height = 500

# fig = go.Figure(g)

# fig.update_layout(
#     hovermode='x unified',
#     hoverlabel=dict(
#         bgcolor="white",
#         # font_size=16,
#         font_family="Rockwell"
#     )
# )

# # Add range slider
# fig.update_layout(
#     xaxis=dict(
#         rangeselector=dict(
#             buttons=list([
#                 dict(
#                      label="All",
#                      step="all"
#                      ),
#                                 dict(count=1,
#                      label="Hour",
#                      step="hour",
#                      stepmode="todate"),
#                 dict(count=1,
#                      label="Day",
#                      step="day",
#                      stepmode="backward"),
#                 dict(count=7,
#                      label="Week",
#                      step="day",
#                      stepmode="backward"),
#                 dict(count=1,
#                      label="Year",
#                      step="year",
#                      stepmode="backward")
#             ])
#         ),
#         rangeslider=dict(
#             visible=True,
#         ),
#         type="date"
#     )
# )


# fig.show()

# hist = go.Histogram(customdata=df, 
#                     x=(df['total_kW']), 
#                     name='sum of phases',
#                     xbins = {'size': 1}
#                     #nbinsx = 15
# )
 
# hist_g = go.FigureWidget()
# hist_g.layout.title = 'Histogram of total kW spot readings over 1 week'
# hist_g.layout.xaxis.title= 'kW draw'
# hist_g.layout.yaxis.title = "number of spot readings"
# hist_g.layout.width = 1000
# hist_g.layout.height = 500

# hist_fig = go.Figure(hist_g)
# hist_fig.add_trace(hist)
# hist_fig.show()

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# preprocessed downsampled data and stored in this file - see downsample-data.py

s = pd.read_csv("venue-20/venue_20_electricity_downsampled.csv")  
s['timestamp'] = pd.to_datetime(s['timestamp'])
s = s.fillna(value=0)

downsampled_tot_trace = go.Scatter(customdata=s, 
                    y=s['total_kW'],
                    x = s['timestamp'], 
                    mode='lines', 
                    hoverinfo='all', 
                    name='total',
                    )

g3 = go.FigureWidget(data=[downsampled_tot_trace])
g3.layout.title = 'CurrentCost clamp-on meter readings - aggregated for half hour slots'
g3.layout.xaxis.title= 'timestamp'
g3.layout.yaxis.title = "kW"
g3.layout.width = 1000
g3.layout.height = 500

fig3 = go.Figure(g3)

fig3.update_layout(
    hovermode='x unified',
    hoverlabel=dict(
        bgcolor="white",
        # font_size=16,
        font_family="Rockwell"
    )
)

# Add range slider
fig3.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(
                     label="All",
                     step="all"
                     ),
                                dict(count=1,
                     label="Hour",
                     step="hour",
                     stepmode="todate"),
                dict(count=1,
                     label="Day",
                     step="day",
                     stepmode="backward"),
                dict(count=7,
                     label="Week",
                     step="day",
                     stepmode="backward"),
                dict(count=1,
                     label="Year",
                     step="year",
                     stepmode="backward")
            ])
        ),
        rangeslider=dict(
            visible=True,
        ),
        type="date"
    )
)


fig3.show()

Summarised by day in kilowatt-hours.

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d = pd.read_csv("venue-20/venue_20_electricity_by_day.csv")
d['timestamp'] = pd.to_datetime(d['timestamp'])
d = d.fillna(value=0)


#print(d.sample)
#d = d.fillna(value=0)

daily_trace = go.Bar(customdata=d, 
                    y=d['kwh'], 
                    x = d['timestamp'], 
                    #mode='bars', 
                    hoverinfo='all', 
                    name='sum of phases'
)

summ_g = go.FigureWidget(data=[daily_trace])
summ_g.layout.title = 'Estimated daily electricity use'
summ_g.layout.xaxis.title= 'timestamp'
summ_g.layout.yaxis.title = "kilowatt-hours"
summ_g.layout.width = 1000
summ_g.layout.height = 500

summ_fig = go.Figure(summ_g)
summ_fig.show()