initial commit

Dockerfile

@@ -0,0 +1,24 @@
+# Use a lightweight Python base image
+FROM python:3.10-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Install system-level dependencies (optional, if needed)
+# RUN apt-get update && apt-get install -y <dependencies>
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application files into the container
+COPY . .
+
+# Expose the port the app runs on
+EXPOSE 8050
+
+# Command to run your Dash app using gunicorn
+CMD ["gunicorn", "--bind", "0.0.0.0:8050", "app:server"]
+

app.py

@@ -0,0 +1,300 @@
+import json
+import dash
+from dash import dcc, html, Input, Output
+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+
+# -----------------------------------------
+# Load and Prepare Data
+# -----------------------------------------
+df = pd.read_csv("./data/megawatt_demand_2024.csv")  # Replace with your actual filename
+df['timestamp'] = pd.to_datetime(df['UTC Timestamp (Interval Ending)'])
+
+load_columns = [
+    "Connecticut Actual Load (MW)",
+    "Maine Actual Load (MW)",
+    "New Hampshire Actual Load (MW)",
+    "Northeast Massachusetts Actual Load (MW)",
+    "Rhode Island Actual Load (MW)",
+    "Southeast Massachusetts Actual Load (MW)",
+    "Vermont Actual Load (MW)",
+    "Western/Central Massachusetts Actual Load (MW)"
+]
+
+df_melted = df.melt(
+    id_vars=['timestamp'],
+    value_vars=load_columns,
+    var_name='region',
+    value_name='load_mw'
+)
+
+# Clean region names
+df_melted['region'] = df_melted['region'].str.replace(' Actual Load \(MW\)', '', regex=True)
+
+# Compute daily aggregates
+df_melted['date'] = df_melted['timestamp'].dt.date
+daily_agg = df_melted.groupby(['region', 'date']).agg(
+    daily_avg=('load_mw', 'mean'),
+    daily_min=('load_mw', 'min'),
+    daily_max=('load_mw', 'max')
+).reset_index()
+
+print(df_melted.head())
+print(daily_agg.head())
+# Load GeoJSON
+with open('./data/new_england_geojson.json') as f:
+    geojson = json.load(f)
+
+# Define a color map for the regions
+region_colors = {
+    "Connecticut": "#1f77b4",
+    "Maine": "#ff7f0e",
+    "New Hampshire": "#2ca02c",
+    "Northeast Massachusetts": "#d62728",
+    "Rhode Island": "#9467bd",
+    "Southeast Massachusetts": "#8c564b",
+    "Vermont": "#e377c2",
+    "Western/Central Massachusetts": "#7f7f7f"
+}
+
+# Get unique dates for slider (daily granularity)
+unique_dates = sorted(daily_agg['date'].unique())
+
+# Identify month start dates
+month_starts = [(i, d) for i, d in enumerate(unique_dates) if d.day == 1]
+
+# Create marks only for the first of each month
+date_marks = {i: d.strftime("%Y-%m-%d") for i, d in month_starts}
+
+# Initial state: use the full range of dates
+start_idx = 0
+end_idx = len(unique_dates) - 1
+start_date = unique_dates[start_idx]
+end_date = unique_dates[end_idx]
+
+# Create initial figures
+latest_time = df_melted['timestamp'].max()
+df_latest = df_melted[df_melted['timestamp'] == latest_time]
+df_avg = df_melted.groupby('region').mean().reset_index()
+print(df_avg.head())
+
+# Create a weekly-peak load by region chart for before clickthrough
+df_melted['week'] = df_melted['timestamp'].dt.to_period('W').apply(lambda r: r.start_time)
+
+fig_map = px.choropleth_mapbox(
+    df_avg,
+    geojson=geojson,
+    locations='region',
+    featureidkey='properties.NAME',
+    color='load_mw',
+    color_continuous_scale="Viridis",
+    mapbox_style="carto-positron",
+    zoom=5,
+    center={"lat": 43.5, "lon": -71.5},  # Approx center of New England
+    opacity=0.7,
+    hover_name='region'
+)
+fig_map.update_layout(margin={"r":0,"t":0,"l":0,"b":0}, template='plotly_dark')
+
+# Initial line plot: all regions
+fig_line_all = px.line(
+    df_melted,
+    x='timestamp',
+    y='load_mw',
+    color='region',
+    title='Load Over Time',
+    labels={'load_mw':'Load (MW)', 'timestamp':'Time'},
+    template='plotly_dark',
+    color_discrete_map=region_colors
+)
+fig_line_all.update_layout(hovermode="x unified")
+
+# Initial daily aggregates plot (blank or show all)
+# Initialize daily load figure
+fig_daily = go.Figure(layout={"template":"plotly_dark"})
+fig_daily.update_layout(title="Daily Aggregate Load", xaxis_title=None, yaxis_title="Load (MW)")
+
+# -----------------------------------------
+# Dash App
+# -----------------------------------------
+app = dash.Dash(__name__)
+
+# Expose the Flask server instance
+server = app.server
+
+app.layout = html.Div(
+    style={"backgroundColor": "#333", "color": "#fff", "padding": "20px"},  # Dark background
+    children=[
+        html.H1("ISO-New England Grid Loading, 2024", style={"textAlign": "center"}),
+        html.Div([
+            html.Div([
+                html.H4('Average Load by ISO-NE Region'),
+                html.P("Click to filter by region"),
+                dcc.Graph(id='map', figure=fig_map, style={"height": "60vh"}),
+                
+                #Markdown descriptor
+                dcc.Markdown(
+                    """
+                    **ISO-New England Load by Region:**
+                    This dashboard provides an interactive visualization of electricity 
+                    usage across New England states and Massachusetts sub-regions. 
+                    Use the date range slider and map to filter and explore trends in grid demand over time.
+
+                    [Data from ISO-NE](https://www.eia.gov/electricity/wholesalemarkets/isone.php)
+                    """,
+                    style={"margin-top": "20px", "height": "30vh", "overflowY": "auto"}
+                )
+            ], style={"width": "40%", "display": "inline-block", "vertical-align": "top"}),
+
+            html.Div([
+                dcc.Graph(id='timeseries', figure=fig_line_all, style={"height": "60vh"}),
+                dcc.Graph(id='daily_timeseries', figure=fig_daily, style={"height": "60vh", "marginTop":"20px"})
+            ], style={"width": "58%", "display": "inline-block", "padding-left":"2%", "vertical-align": "top"})
+        ]),
+
+        html.Div([
+            dcc.RangeSlider(
+                id='date-range-slider',
+                min=0,
+                max=len(unique_dates)-1,
+                value=[275, 306], #Month of Oct.
+                marks=date_marks,
+                step=1,
+                tooltip=None
+            ),
+        ], style={"margin-bottom":"20px"})
+    ]
+)
+
+@app.callback(
+    [Output('map', 'figure'),
+     Output('timeseries', 'figure'),
+     Output('daily_timeseries', 'figure')],
+    [Input('map', 'clickData'),
+     Input('date-range-slider', 'value')]
+)
+def update_charts(clickData, slider_value):
+    start_idx, end_idx = slider_value
+    start_date = unique_dates[start_idx]
+    end_date = unique_dates[end_idx]
+
+    df_map_day = df_melted[(df_melted['date'] >= start_date) & (df_melted['date'] <= end_date)].groupby(
+        'region'
+    ).mean().reset_index()
+    df_line = df_melted[(df_melted['date'] >= start_date) & (df_melted['date'] <= end_date)]
+    df_line_daily = daily_agg[(daily_agg['date'] >= start_date) & (daily_agg['date'] <= end_date)]
+
+    #Weekly max
+    weekly_max = df_melted[
+        (df_melted['date'] >= start_date) & (df_melted['date'] <= end_date)
+    ].groupby(['region', 'week']).agg(weekly_max=('load_mw', 'max')).reset_index()
+
+    if clickData is None:
+        # No region clicked: show all regions
+        fig_map = px.choropleth_mapbox(
+            df_map_day,
+            geojson=geojson,
+            locations='region',
+            featureidkey='properties.NAME',
+            color='load_mw',
+            color_continuous_scale="Viridis",
+            mapbox_style="carto-positron",
+            zoom=5,
+            center={"lat": 43.5, "lon": -71.5},
+            opacity=0.7,
+            hover_name='region'
+        )
+        fig_map.update_layout(margin={"r":0,"t":0,"l":0,"b":0}, template='plotly_dark')
+
+        fig_line = px.line(
+            df_line, x='timestamp', y='load_mw', color='region',
+            title='Load Over Time (Selected Date Range)',
+            labels={'load_mw':'Load (MW)', 'timestamp':'Time'},
+            template='plotly_dark',
+            color_discrete_map=region_colors
+        )
+        fig_line.update_layout(hovermode="x unified", xaxis_title=None)
+        
+        fig_weekly_max = go.Figure(layout={"template":"plotly_dark"})
+        fig_weekly_max.update_layout(
+            title="Weekly Max Load by Region",
+            xaxis_title=None,
+            yaxis_title="Load (MW)",
+            hovermode="x unified"
+        )
+
+        for region in weekly_max['region'].unique():
+            dff = weekly_max[weekly_max['region'] == region]
+            region_color = region_colors.get(region, "white")
+            fig_weekly_max.add_trace(
+                go.Scatter(
+                    x=dff['week'], y=dff['weekly_max'],
+                    mode='lines+markers',
+                    line=dict(color=region_color, width=2),
+                    marker=dict(color=region_color, size=6),
+                    name=f"{region} Weekly Max"
+                )
+            )
+
+        return fig_map, fig_line, fig_weekly_max
+
+    # Region clicked
+    clicked_region = clickData['points'][0]['location']
+    dff = df_line[df_line['region'] == clicked_region]
+    dff_daily = df_line_daily[df_line_daily['region'] == clicked_region]
+
+    fig_map = px.choropleth_mapbox(
+        df_map_day,
+        geojson=geojson,
+        locations='region',
+        featureidkey='properties.NAME',
+        color='load_mw',
+        color_continuous_scale="Viridis",
+        mapbox_style="carto-positron",
+        zoom=5,
+        center={"lat": 43.5, "lon": -71.5},
+        opacity=0.7,
+        hover_name='region'
+    )
+    fig_map.update_layout(margin={"r":0,"t":0,"l":0,"b":0}, template='plotly_dark')
+
+    fig_line = px.line(
+        dff, x='timestamp', y='load_mw', color='region',
+        title=f'Load Over Time: {clicked_region} ({start_date} to {end_date})',
+        labels={'load_mw':'Load (MW)', 'timestamp':'Time'},
+        template='plotly_dark',
+        color_discrete_map=region_colors
+    )
+    fig_line.update_layout(hovermode="x unified")
+
+    fig_daily = go.Figure(layout={"template":"plotly_dark"})
+    region_color = region_colors.get(clicked_region, "white")
+
+    if not dff_daily.empty:
+        fig_daily.add_trace(go.Scatter(
+            x=dff_daily['date'], y=dff_daily['daily_max'],
+            mode='lines', line_color=region_color,
+            name='Daily Max'
+        ))
+        fig_daily.add_trace(go.Scatter(
+            x=dff_daily['date'], y=dff_daily['daily_min'],
+            fill='tonexty', mode='lines', line_color=region_color,
+            name='Daily Min'
+        ))
+        fig_daily.add_trace(go.Scatter(
+            x=dff_daily['date'], y=dff_daily['daily_avg'],
+            mode='lines+markers', line_color='white', name='Daily Avg'
+        ))
+
+    fig_daily.update_layout(
+        title=f"Daily Load Summary: {clicked_region}",
+        xaxis_title="Date",
+        yaxis_title="Load (MW)",
+        hovermode="x unified"
+    )
+
+    return fig_map, fig_line, fig_daily
+
+if __name__ == '__main__':
+    app.run_server(host='0.0.0.0', port=8050, debug=False)

data/references.md

@@ -0,0 +1 @@
+Thank you to the [US Energy Information Administration](https://www.eia.gov/electricity/wholesalemarkets/data.php?rto=isone) for the data.

geo_json_extract.py

@@ -0,0 +1,33 @@
+import json
+import os
+
+print(f'Current wd: {os.getcwd()}')
+
+# Define the states you want to keep
+new_england_states = {"Maine", "New Hampshire", "Vermont", "Massachusetts", "Rhode Island", "Connecticut"}
+
+# Input and output file paths
+input_file = "./figure_friday/2024/week_49/data/gz_2010_us_040_00_500k.json"
+output_file = "./figure_friday/2024/week_49/data/new_england_geojson.json"
+
+# Load the input GeoJSON
+with open(input_file, "r", encoding="utf-8") as f:
+    data = json.load(f)
+
+# Filter the features
+filtered_features = []
+for feature in data["features"]:
+    if feature["properties"].get("NAME") in new_england_states:
+        filtered_features.append(feature)
+
+# Create a new GeoJSON FeatureCollection
+filtered_data = {
+    "type": "FeatureCollection",
+    "features": filtered_features
+}
+
+# Write the filtered data to a new file
+with open(output_file, "w", encoding="utf-8") as f:
+    json.dump(filtered_data, f, indent=2)
+
+print(f"Filtered GeoJSON saved to {output_file}")

notebooks/newengland_power_demand.ipynb

@@ -0,0 +1,36 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### #Figurefriday Week 49\n",
+    "\n",
+    "Getting amped up to take a look at power consumption rates across New England in 2024"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import plotly.express as px"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "figure_friday",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -0,0 +1,11 @@
+dash==2.18.2
+dash-core-components==2.0.0
+dash-html-components==2.0.0
+Flask==3.0.3
+plotly==5.24.1
+pandas==1.5
+numpy==1.26.4
+gunicorn==20.1.0
+
+
+