app first pass

.gitignore

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+.envrc
+.direnv

app.py

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+import datetime
+import io
+import os
+import requests
+
+import geopandas as gpd
+import matplotlib.pyplot as plt
+import numpy as np
+import streamlit as st
+import xarray as xr
+
+from geogif import gif
+from rasterio import features
+
+provinces = gpd.read_file("./data/prov_stat.geojson")
+geoms = provinces.geometry.boundary
+
+image = features.rasterize(
+    geoms,
+    out_shape=[381, 1081],
+    transform=(0.10000000149011612, 0.0, -160.0, 0.0, 0.10000000149011612, 32.0),
+)
+
+cmap = st.selectbox("Colormap", plt.colormaps(), index=1)
+
+fps = st.slider("Frames per second", 0.01, 50.0, 16.0)
+
+show_dt = st.checkbox("Show timestamp", True)
+
+if st.button("Plot Data"):
+    smoke_url = "https://firesmoke.ca/forecasts/current/dispersion.nc"
+    smoke_nc = "./data/dispersion.nc"
+    if not os.path.isfile(smoke_nc):
+        response = requests.head(smoke_url)
+
+        total_size = int(response.headers.get("Content-Length"))
+
+        progress_text = "Downloading data. Please wait."
+        progress_bar = st.progress(0, text=progress_text)
+
+        size_downloaded = 0
+        chunk_size = 8192
+
+        with requests.get(smoke_url, stream=True) as r:
+            r.raise_for_status()
+            with open(smoke_nc, "wb") as f:
+                for chunk in r.iter_content(chunk_size=chunk_size):
+                    f.write(chunk)
+
+                    size_downloaded += chunk_size
+                    percent_complete = min(size_downloaded / total_size, 1)
+                    progress_bar.progress(percent_complete, text=progress_text)
+
+        progress_bar.empty()
+
+    ds_disk = xr.open_dataset(smoke_nc)
+
+    tstep = ds_disk.TFLAG
+
+    dts = []
+    for i in tstep:
+        data = i.data[0]
+        dt = datetime.datetime.strptime(
+            str(data[0]) + str(data[1]).zfill(6), "%Y%j%H%M%S"
+        )
+        dts.append(dt)
+
+    pm25 = ds_disk.PM25
+
+    pm25["TSTEP"] = dts
+
+    for idx, i in enumerate(pm25.data):
+        pm25[idx].data[0] = np.maximum(i[0], image * 6)
+
+    buffer = io.BytesIO()
+
+    if show_dt:
+        date_format = "%Y-%m-%d %H:%M:%S"
+    else:
+        date_format = None
+
+    gif(
+        pm25[:, :, ::-1, :],
+        date_format=date_format,
+        fps=fps,
+        cmap=cmap,
+        date_color=(255, 255, 255),
+        to=buffer,
+    )
+
+    first_dt = dts[0]
+    last_dt = dts[-1]
+
+    st.image(buffer, caption=f"{first_dt} to {last_dt}")

requirements.txt

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+rasterio==1.3.7
+geopandas==0.13.2
+matplotlib==3.7.1
+netCDF4==1.6.4
+numpy==1.24.3
+xarray==2023.5.0
+geogif==0.1.4