tested running for a month
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Radek
205
dash_power.py
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205
dash_power.py
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import dash
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from dash import dcc, html, Input, Output, callback_context
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import plotly.express as px
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import pandas as pd
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import sqlite3
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from datetime import datetime, timedelta
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# Initialize the Dash app
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app = dash.Dash(__name__)
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# Connect to the SQLite database
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def get_db_connection():
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conn = sqlite3.connect('power_data.db')
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return conn
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# Function to fetch data from the database
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def fetch_data(time_range):
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conn = get_db_connection()
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query = f"""
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SELECT * FROM building_totals
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WHERE timestamp >= datetime('now', '-{time_range} hours')
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"""
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df = pd.read_sql_query(query, conn)
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conn.close()
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return df
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# Function to calculate kWh and round timestamps for building totals
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def calculate_building_kwh(df):
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df['timestamp'] = pd.to_datetime(df['timestamp'])
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df['timestamp'] = df['timestamp'].dt.round('5min') # Round to 5-minute intervals
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df = df.set_index('timestamp')
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df['kWh'] = df['total_power'] * (5 / 60) # Convert power to kWh for 5-minute intervals
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return df
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# Function to calculate kWh and round timestamps for room and customer breakdowns
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def calculate_breakdown_kwh(df):
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df['timestamp'] = pd.to_datetime(df['timestamp'])
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df['timestamp'] = df['timestamp'].dt.round('5min') # Round to 5-minute intervals
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df = df.set_index('timestamp')
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df['kWh'] = df['power'] * (5 / 60) # Convert power to kWh for 5-minute intervals
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return df
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# Define the layout of the dashboard
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app.layout = html.Div([
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html.H1("Power Usage Overview", style={'textAlign': 'center'}),
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dcc.Dropdown(
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id='time-range',
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options=[
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{'label': '6 Hours', 'value': '6'},
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{'label': '12 Hours', 'value': '12'},
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{'label': '24 Hours', 'value': '24'},
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{'label': '1 Week', 'value': '168'},
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{'label': '2 Weeks', 'value': '336'},
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{'label': '1 Month', 'value': '720'},
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{'label': '2 Months', 'value': '1440'},
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{'label': '1 Year', 'value': '8760'},
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],
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value='6',
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style={'width': '200px', 'margin': '0 auto'}
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),
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dcc.Graph(id='building-graph', style={'width': '100%', 'height': '400px'}),
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dcc.Tabs(id='tabs', value='room-breakdown', children=[
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dcc.Tab(label='Room Breakdown', value='room-breakdown'),
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dcc.Tab(label='Customer Breakdown', value='customer-breakdown'),
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]),
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html.Div([
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dcc.Dropdown(id='drill-down', multi=False, style={'width': '200px', 'margin': '0 auto'}),
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dcc.Graph(id='breakdown-graph', style={'width': '100%', 'height': '400px'}),
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])
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])
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# Combined callback to update the building total graph and handle zoom events
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@app.callback(
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Output('building-graph', 'figure'),
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[Input('time-range', 'value'),
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Input('building-graph', 'relayoutData')],
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[dash.dependencies.State('building-graph', 'figure')]
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)
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def update_building_graph(time_range, relayoutData, figure):
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ctx = callback_context
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if not ctx.triggered:
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return dash.no_update
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df = fetch_data(time_range)
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df = calculate_building_kwh(df)
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# Initialize fig
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fig = px.line(df, x=df.index, y=['total_current', 'total_power', 'kWh'],
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labels={'value': 'Value', 'variable': 'Metric'},
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title='Building Total Metrics')
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fig.update_layout(legend_title_text='Metrics', yaxis_type="log") # Use logarithmic scale
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if 'time-range' in ctx.triggered[0]['prop_id']:
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pass # fig is already initialized
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elif relayoutData and 'xaxis.range[0]' in relayoutData and 'xaxis.range[1]' in relayoutData:
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start = pd.to_datetime(relayoutData['xaxis.range[0]'])
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end = pd.to_datetime(relayoutData['xaxis.range[1]'])
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df = df.loc[start:end]
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fig = px.line(df, x=df.index, y=['total_current', 'total_power', 'kWh'],
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labels={'value': 'Value', 'variable': 'Metric'},
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title='Building Total Metrics')
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fig.update_layout(legend_title_text='Metrics', yaxis_type="log") # Use logarithmic scale
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# Update legend to show total values
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for trace in fig.data:
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if trace.name == 'kWh':
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trace.name = f"{trace.name}: {df['kWh'].sum():.2f}"
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else:
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trace.name = f"{trace.name}: {df[trace.name].iloc[-1]:.2f}"
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return fig
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# Callback to update the drill-down dropdown
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@app.callback(
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Output('drill-down', 'options'),
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[Input('tabs', 'value')]
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)
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def update_drill_down_options(tab):
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conn = get_db_connection()
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if tab == 'room-breakdown':
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query = "SELECT DISTINCT room_number FROM room_breakdown"
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else:
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query = "SELECT DISTINCT customer_name FROM customer_breakdown"
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df = pd.read_sql_query(query, conn)
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conn.close()
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return [{'label': i, 'value': i} for i in df.iloc[:, 0]]
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# Callback to set the default value for the drill-down dropdown
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@app.callback(
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Output('drill-down', 'value'),
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[Input('drill-down', 'options')]
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)
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def set_drill_down_value(options):
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if options:
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return options[0]['value']
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return None
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# Callback to update the breakdown graph and handle zoom events
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@app.callback(
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Output('breakdown-graph', 'figure'),
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[Input('tabs', 'value'),
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Input('drill-down', 'value'),
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Input('time-range', 'value'),
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Input('breakdown-graph', 'relayoutData')],
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[dash.dependencies.State('breakdown-graph', 'figure')]
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)
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def update_breakdown_graph(tab, drill_down, time_range, relayoutData, figure):
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ctx = callback_context
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if not ctx.triggered:
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return dash.no_update
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conn = get_db_connection()
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if tab == 'room-breakdown':
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query = f"""
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SELECT * FROM room_breakdown
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WHERE room_number = '{drill_down}'
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AND timestamp >= datetime('now', '-{time_range} hours')
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"""
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else:
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query = f"""
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SELECT * FROM customer_breakdown
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WHERE customer_name = '{drill_down}'
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AND timestamp >= datetime('now', '-{time_range} hours')
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"""
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df = pd.read_sql_query(query, conn)
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conn.close()
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df = calculate_breakdown_kwh(df)
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# Initialize fig
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fig = px.line(df, x=df.index, y=['current', 'power', 'kWh'],
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labels={'value': 'Value', 'variable': 'Metric'},
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title=f'{tab.replace("-", " ").title()} Metrics')
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fig.update_layout(legend_title_text='Metrics', yaxis_type="log") # Use logarithmic scale
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if 'time-range' in ctx.triggered[0]['prop_id'] or 'drill-down' in ctx.triggered[0]['prop_id']:
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pass # fig is already initialized
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elif relayoutData and 'xaxis.range[0]' in relayoutData and 'xaxis.range[1]' in relayoutData:
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start = pd.to_datetime(relayoutData['xaxis.range[0]'])
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end = pd.to_datetime(relayoutData['xaxis.range[1]'])
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df = df.loc[start:end]
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fig = px.line(df, x=df.index, y=['current', 'power', 'kWh'],
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labels={'value': 'Value', 'variable': 'Metric'},
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title=f'{tab.replace("-", " ").title()} Metrics')
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fig.update_layout(legend_title_text='Metrics', yaxis_type="log") # Use logarithmic scale
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# Update legend to show total values
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for trace in fig.data:
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if trace.name == 'kWh':
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trace.name = f"{trace.name}: {df['kWh'].sum():.2f}"
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else:
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trace.name = f"{trace.name}: {df[trace.name].iloc[-1]:.2f}"
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return fig
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# Run the app
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=8050, debug=True)
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