Migration from solara to streamlit

.gitignore

@@ -2,6 +2,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
+.DS_Store
 
 # C extensions
 *.so

Dockerfile

@@ -18,4 +18,7 @@ USER ${NB_USER}
 
 EXPOSE 7860
 
-CMD ["solara", "run", "./pages", "--host=0.0.0.0", "--port=7860"]
+CMD ["streamlit", "run", "./pages/Home.py", "--server.port=7860"]
+
+
+streamlit run pages/Home.py

pages/00_home.py

@@ -1,19 +0,0 @@
-import solara
-
-
-@solara.component
-def Page():
-
-    markdown = """
-    ## Alpha Version - Alaskan Tundra 10-day Lightning Forecast
-
-    Cloud to ground lightning 10-day lightning forecast for the Alaskan tundra.
-    This is still work in progress and under development.
-
-    ## Demo
-
-    ![](https://drive.google.com/uc?export=view&id=1V9lpVfs13l4PYSlFmlZVCmeLlZXyw-zu)
-
-    """
-
-    solara.Markdown(markdown)

pages/02_matplotlib.py

@@ -1,55 +0,0 @@
-import reacton
-import solara
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.figure import Figure
-import matplotlib.animation as animation
-
-
-zoom = solara.reactive(2)
-center = solara.reactive((20, 0))
-
-
-class PauseAnimation:
-    def __init__(self):
-
-        self.fig = Figure()
-        ax = self.fig.subplots()
-        ax.set_title('Click to pause/resume the animation')
-        x = np.linspace(-0.1, 0.1, 1000)
-
-        # Start with a normal distribution
-        self.n0 = (1.0 / ((4 * np.pi * 2e-4 * 0.1) ** 0.5)
-                   * np.exp(-x ** 2 / (4 * 2e-4 * 0.1)))
-        self.p, = ax.plot(x, self.n0)
-
-        self.animation = animation.FuncAnimation(
-            self.fig, self.update, frames=200, interval=50, blit=True)
-        self.paused = False
-
-        self.fig.canvas.mpl_connect('button_press_event', self.toggle_pause)
-
-    def toggle_pause(self, *args, **kwargs):
-        if self.paused:
-            self.animation.resume()
-        else:
-            self.animation.pause()
-        self.paused = not self.paused
-
-    def update(self, i):
-        self.n0 += i / 100 % 5
-        self.p.set_ydata(self.n0 % 20)
-        return (self.p,)
-
-    def get_fig(self):
-        return self.fig
-
-
-@solara.component
-def Page():
-    # do this instead of plt.figure()
-    #fig = Figure()
-    #ax = fig.subplots()
-    #ax.plot([1, 2, 3], [1, 4, 9])
-    pa = PauseAnimation()
-    return solara.FigureMatplotlib(pa)

pages/Home.py

@@ -0,0 +1,26 @@
+import streamlit as st
+
+st.set_page_config(
+    page_title="Home",
+    page_icon="👋",
+)
+
+st.write("# Alaskan Tundra Fire Occurrence")
+
+st.sidebar.success("Select a page above.")
+
+st.markdown(
+    """
+    This space was created to test several operational products
+    developed for Alaskan tundra fire occurrence modeling efforts.
+    Select a page from the sidebar to test some of the example
+    workflows developed as part of this research.
+
+    ## Objectives
+
+    TBD
+ 
+    ## Want to learn more?
+    - Feel free to contact us for additional details, [email protected]
+"""
+)

pages/pages/1_⚡_Alaskan_Tundra_Lightning.py

@@ -0,0 +1,69 @@
+import os
+import re
+import datasets
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+import streamlit as st
+import streamlit.components.v1 as components
+from matplotlib.animation import FuncAnimation
+import cartopy.crs as ccrs
+import rioxarray as rxr
+import xarray as xr
+import pandas as pd
+from glob import glob
+from pathlib import Path
+from datetime import datetime
+from huggingface_hub import snapshot_download
+
+# define dataset url
+DATASET_URL = 'jordancaraballo/alaska-wildfire-occurrence'
+EPSG = 3338
+
+# Grab the dataset from Hugging Face
+cgl_dataset_dir = snapshot_download(repo_id=DATASET_URL, allow_patterns="*.tif", repo_type='dataset')
+cgl_filename = glob(os.path.join(cgl_dataset_dir, 'alaskan-tundra-lightning-forecast_latest.tif'))[0]
+
+print(cgl_dataset_dir)
+
+# Open the filename from the forecast
+cgl_ds = rxr.open_rasterio(cgl_filename)
+
+# Rename bands with timestamps
+cgl_ds['band'] = cgl_ds.attrs['DATES'].replace("'", "").strip('][').split(', ')
+
+# Generate visualization
+fig = plt.figure()
+ax = plt.axes(projection=ccrs.epsg(EPSG))
+ax.coastlines()
+
+# Generate merge with cartipy
+mesh = cgl_ds.isel(band=0).plot.pcolormesh(
+    ax=ax, transform=ccrs.epsg(EPSG), animated=True,cmap='seismic', vmin=0, vmax=1)
+
+# Define update function
+def update_mesh(t):
+    ax.set_title("time = %s"%t)
+    mesh.set_array(cgl_ds.sel(band=t).values.ravel())
+    return mesh,
+
+# Generate animation function
+animation = FuncAnimation(
+    fig, update_mesh, frames=cgl_ds.band.values, interval=600)
+animation_js = animation.to_jshtml()
+
+# Adding autoplay
+click_on_play = """document.querySelector('.anim-buttons button[title="Play"]').click();"""
+
+## Search for the creation of the animation within the jshtml file created by matplotlib
+pattern = re.compile(r"(setTimeout.*?;)(.*?})", re.MULTILINE | re.DOTALL)
+
+## Insert the JS line right below that
+animation_js = pattern.sub(rf"\1 \n {click_on_play} \2", animation_js)
+
+# Plot text and animation on streamlit page
+st.title("Alpha Version - Alaskan Tundra 10-day Lightning Forecast")
+st.markdown(
+    "Cloud to ground lightning 10-day lightning forecast for the Alaskan tundra. " +
+    "This is still work in progress and under development.")
+components.html(animation_js, height=1000)

requirements.txt

@@ -2,4 +2,8 @@ geemap>=0.21.0
 leafmap
 mapwidget
 solara
-geopandas
+geopandas
+datasets
+rioxarray
+streamlit
+cartopy