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heatmap.py

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+import plotly.express as px
+import pandas as pd
+
+df = pd.read_csv("G:\\NASA\\fire_nrt_V1_101674.csv")
+
+# Rename the "acq_date" column to "date"
+df.rename(columns={"acq_date": "date"}, inplace=True)
+
+# Set the latitude and longitude boundaries for New South Wales
+nsw_bounds = [141, -37.5, 153.5, -28.1]  # [min_longitude, min_latitude, max_longitude, max_latitude]
+
+# Filter the DataFrame to include only data within the New South Wales boundaries and the specified date range
+start_date = "2019-09-01"
+end_date = "2020-01-31"
+df_nsw = df[(df["longitude"] >= nsw_bounds[0]) &
+            (df["longitude"] <= nsw_bounds[2]) &
+            (df["latitude"] >= nsw_bounds[1]) &
+            (df["latitude"] <= nsw_bounds[3]) &
+            (df["date"] >= start_date) &
+            (df["date"] <= end_date)]
+
+# Create the heatmap using Plotly Express with a slider component for the timeline
+fig = px.density_mapbox(
+    df_nsw,
+    lat="latitude",
+    lon="longitude",
+    z="bright_ti4",
+    color_continuous_scale="YlOrRd",  # Orange color scale
+    radius=10,
+    zoom=7,
+    center={"lat": -32.5, "lon": 147},
+    animation_frame="date",  # Use the "date" column for animation frames
+    title="Fires/Heat Signatures Timeline: Sep 2019 to Jan 2020"
+)
+
+fig.update_traces(zauto=False, zmin=0, zmax=4000)  # Update the color scale range as needed
+fig.update_layout(
+    mapbox_style="open-street-map",
+    margin={"r": 0, "t": 0, "l": 0, "b": 0},
+    coloraxis_colorbar=dict(title="Fire Intensity",tickvals=[])  # Update the color scale title
+)
+
+fig.show()