test

app.py

@@ -11,17 +11,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+# +
 import leafmap.foliumap as leafmap
 import rioxarray
 import geopandas as gpd
 import streamlit as st
-import altair as alt
-import ibis
-from ibis import _
-import ibis.selectors as s
 from streamlit_folium import st_folium
 import json
 
+import pystac_client
+import planetary_computer
+import odc.stac
+from datetime import datetime, timedelta
+
+
+# -
+
 def extract_geom(gdf, cog):
     x = (rioxarray.
          open_rasterio('/vsicurl/'+cog, masked=True).
@@ -40,15 +45,6 @@ def area_hectares(gdf):
     return area
 
 
-import pystac_client
-import planetary_computer
-import odc.stac
-import geopandas as gpd
-import dask.distributed
-import matplotlib.pyplot as plt
-import rioxarray
-from datetime import datetime, timedelta
-
 
 def stac_search(box, datetime):    
     # STAC Search for this imagery in space/time window
@@ -64,16 +60,18 @@ def stac_search(box, datetime):
         item_collection())
     return items
 
-def compute_nbs(items, box):
+def compute_nbs(items, box, zoom=6):
     # Time to compute:
-    client = dask.distributed.Client()
+    # client = dask.distributed.Client()
     # landsat_bands = ["nir08",  "swir16"]
     sentinel_bands = ["B08", "B12", "SCL"] # NIR, SWIR, and Cloud Mask
 
     # The magic of gdalwarper. Can also resample, reproject, and aggregate on the fly
     data = odc.stac.load(items,
-                        bands=sentinel_bands,
-                        bbox=box
+                         bands=sentinel_bands,
+                         bbox=box,
+                  #       resolution = 10 * 2**zoom,
+                  #       chunks={},  # <-- use Dask
     )
     times = [0,data.time.size-1]
 
@@ -108,6 +106,31 @@ def nbs(box, date):
     # nbs.isel(time=(nbs.time.size-1)).rio.to_raster(raster_path="after.tif", driver="COG")
 
 
+# +
+
+def compute_map(items, box):
+    nbs = compute_nbs(items, box)
+
+    # doesn't work without serializing data....
+    # m2.add_raster(nbs.isel(time=0), colormap="terrain", layer_name="NBR") # empty?
+    
+    # write first and last date to tif
+    nbs.isel(time=0).rio.to_raster(raster_path="static/before.tif", driver="COG")
+    nbs.isel(time=(nbs.time.size-1)).rio.to_raster(raster_path="static/after.tif", driver="COG")
+    m2 = leafmap.Map(center=[35, -110], zoom=6)
+    m2.split_map("static/before.tif", "static/after.tif", 
+                 left_label = "NBR before fire", right_label = "NBR after fire")
+    return m2
+
+def use_pyplot(items, box):
+    nbs = compute_nbs(items, box)
+    ## Show map with pyplot
+    import matplotlib as plt
+    cmap = plt.colormaps.get_cmap('viridis')  # viridis is the default colormap for imshow
+    cmap.set_bad(color='black')
+    im = nbs.plot.imshow(row="time", cmap=cmap, add_colorbar=False, size=4)
+    st.pyplot(im.fig, use_container_width=False)
+
 
 # +
 import copy
@@ -164,7 +187,10 @@ m = leafmap.Map(center=[35, -110], zoom=6)
 ## Map controls sidebar
 with st.sidebar:
 
-    date = st.date_input("select date", min_value = datetime.strptime("2015-06-27", "%Y-%m-%d")) # sentinel2 records begin
+    date = st.date_input("select date", 
+                         value = datetime.strptime("2021-06-17", "%Y-%m-%d"),
+                         min_value = datetime.strptime("2015-06-27", "%Y-%m-%d")
+                         ) # sentinel2 records begin
     
     with st.expander("🔥 Wildfire"):
         # Fire Polygons, USGS
@@ -200,24 +226,7 @@ if polygon is not None:
     #out
     n = len(out["features"])
     st.markdown(f"{n} images found. Computing NBR...")
-    
-    nbs = compute_nbs(items, box)
-
-    #import matplotlib as plt
-    #cmap = plt.colormaps.get_cmap('viridis')  # viridis is the default colormap for imshow
-    #cmap.set_bad(color='black')
-    #im = nbs.plot.imshow(row="time", cmap=cmap, add_colorbar=False, size=4)
-    # st.pyplot(im.fig, use_container_width=False)
-
-    m2 = leafmap.Map(center=[35, -110], zoom=6)
-
-    # doesn't work
-    # m2.add_raster(nbs.isel(time=0), colormap="terrain", layer_name="NBR") # empty?
-
-     # write first and last date to tif
-
-    nbs.isel(time=0).rio.to_raster(raster_path="static/before.tif", driver="COG")
-    nbs.isel(time=(nbs.time.size-1)).rio.to_raster(raster_path="static/after.tif", driver="COG")
-    m2.split_map("static/before.tif", "static/after.tif",  left_label = "NBR before fire", right_label = "NBR after fire")
+    m2 = compute_map(items, box)
     m2.to_streamlit()
+   
 

requirements.txt

@@ -1,7 +1,8 @@
 pystac_client
 planetary_computer
 odc.stac
-dask[distributed]
+dask
+distributed
 pandas
 pydeck
 pmtiles