LibreNMS-power-dash/dash_power_room_n_customer.py

import dash
from dash import dcc, html, Input, Output, callback_context
import plotly.express as px
import pandas as pd
import sqlite3
from datetime import datetime, timedelta

# Initialize the Dash app
app = dash.Dash(__name__)

# Connect to the SQLite database
def get_db_connection():
    conn = sqlite3.connect('power_data.db')
    return conn

# Function to fetch data from the database
def fetch_data(time_range):
    conn = get_db_connection()
    query = f"""
    SELECT * FROM building_totals
    WHERE timestamp >= datetime('now', '-{time_range} hours')
    """
    df = pd.read_sql_query(query, conn)
    conn.close()
    return df

# Function to calculate kWh and round timestamps for building totals
def calculate_building_kwh(df):
    df['timestamp'] = pd.to_datetime(df['timestamp'])
    df['timestamp'] = df['timestamp'].dt.round('5min')  # Round to 5-minute intervals
    df = df.set_index('timestamp')
    df['kWh'] = df['total_power'] * (5 / 60)  # Convert power to kWh for 5-minute intervals
    return df

# Function to calculate kWh and round timestamps for room and customer breakdowns
def calculate_breakdown_kwh(df):
    df['timestamp'] = pd.to_datetime(df['timestamp'])
    df['timestamp'] = df['timestamp'].dt.round('5min')  # Round to 5-minute intervals
    df = df.set_index('timestamp')
    df['kWh'] = df['power'] * (5 / 60)  # Convert power to kWh for 5-minute intervals
    return df

# Define the layout of the dashboard
app.layout = html.Div([
    html.H1("Power Usage Overview", style={'textAlign': 'center'}),

    dcc.Dropdown(
        id='time-range',
        options=[
            {'label': '6 Hours', 'value': '6'},
            {'label': '12 Hours', 'value': '12'},
            {'label': '24 Hours', 'value': '24'},
            {'label': '1 Week', 'value': '168'},
            {'label': '2 Weeks', 'value': '336'},
            {'label': '1 Month', 'value': '720'},
            {'label': '2 Months', 'value': '1440'},
            {'label': '1 Year', 'value': '8760'},
        ],
        value='6',
        style={'width': '200px', 'margin': '0 auto'}
    ),

    dcc.Graph(id='building-graph', style={'width': '100%', 'height': '400px'}),

    dcc.Tabs(id='tabs', value='room-breakdown', children=[
        dcc.Tab(label='Room Breakdown', value='room-breakdown'),
        dcc.Tab(label='Customer Breakdown', value='customer-breakdown'),
    ]),

    html.Div([
        dcc.Dropdown(id='drill-down', multi=False, style={'width': '200px', 'margin': '0 auto'}),
        dcc.Graph(id='breakdown-graph', style={'width': '100%', 'height': '400px'}),
    ])
])

# Combined callback to update the building total graph and handle zoom events
@app.callback(
    Output('building-graph', 'figure'),
    [Input('time-range', 'value'),
     Input('building-graph', 'relayoutData')],
    [dash.dependencies.State('building-graph', 'figure')]
)
def update_building_graph(time_range, relayoutData, figure):
    ctx = callback_context

    if not ctx.triggered:
        return dash.no_update

    df = fetch_data(time_range)
    df = calculate_building_kwh(df)

    # Initialize fig
    fig = px.line(df, x=df.index, y=['total_current', 'total_power', 'kWh'],
                  labels={'value': 'Value', 'variable': 'Metric'},
                  title='Building Total Metrics')
    fig.update_layout(legend_title_text='Metrics', yaxis_type="log")  # Use logarithmic scale

    if 'time-range' in ctx.triggered[0]['prop_id']:
        pass  # fig is already initialized

    elif relayoutData and 'xaxis.range[0]' in relayoutData and 'xaxis.range[1]' in relayoutData:
        start = pd.to_datetime(relayoutData['xaxis.range[0]'])
        end = pd.to_datetime(relayoutData['xaxis.range[1]'])
        df = df.loc[start:end]
        fig = px.line(df, x=df.index, y=['total_current', 'total_power', 'kWh'],
                      labels={'value': 'Value', 'variable': 'Metric'},
                      title='Building Total Metrics')
        fig.update_layout(legend_title_text='Metrics', yaxis_type="log")  # Use logarithmic scale

    # Update legend to show total values
    for trace in fig.data:
        if trace.name == 'kWh':
            trace.name = f"{trace.name}: {df['kWh'].sum():.2f}"
        else:
            trace.name = f"{trace.name}: {df[trace.name].iloc[-1]:.2f}"

    return fig

# Callback to update the drill-down dropdown
@app.callback(
    Output('drill-down', 'options'),
    [Input('tabs', 'value')]
)
def update_drill_down_options(tab):
    conn = get_db_connection()
    if tab == 'room-breakdown':
        query = "SELECT DISTINCT room_number FROM room_breakdown"
    else:
        query = "SELECT DISTINCT customer_name FROM customer_breakdown"
    df = pd.read_sql_query(query, conn)
    conn.close()
    return [{'label': i, 'value': i} for i in df.iloc[:, 0]]

# Callback to set the default value for the drill-down dropdown
@app.callback(
    Output('drill-down', 'value'),
    [Input('drill-down', 'options')]
)
def set_drill_down_value(options):
    if options:
        return options[0]['value']
    return None

# Callback to update the breakdown graph
@app.callback(
    Output('breakdown-graph', 'figure'),
    [Input('tabs', 'value'),
     Input('drill-down', 'value'),
     Input('time-range', 'value')]
)
def update_breakdown_graph(tab, drill_down, time_range):
    conn = get_db_connection()
    if tab == 'room-breakdown':
        query = f"""
        SELECT * FROM room_breakdown
        WHERE room_number = '{drill_down}'
        AND timestamp >= datetime('now', '-{time_range} hours')
        """
    else:
        query = f"""
        SELECT * FROM customer_breakdown
        WHERE customer_name = '{drill_down}'
        AND timestamp >= datetime('now', '-{time_range} hours')
        """
    df = pd.read_sql_query(query, conn)
    conn.close()
    df = calculate_breakdown_kwh(df)
    fig = px.line(df, x=df.index, y=['current', 'power', 'kWh'],
                  labels={'value': 'Value', 'variable': 'Metric'},
                  title=f'{tab.replace("-", " ").title()} Metrics')
    fig.update_layout(legend_title_text='Metrics', yaxis_type="log")  # Use logarithmic scale

    # Update legend to show total values
    for trace in fig.data:
        if trace.name == 'kWh':
            trace.name = f"{trace.name}: {df['kWh'].sum():.2f}"
        else:
            trace.name = f"{trace.name}: {df[trace.name].iloc[-1]:.2f}"

    return fig

# Run the app
if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8050, debug=True)