PR feedback

app/pages/2_📖_Documentation.py

@@ -1,98 +1,86 @@
 # """Documentation page for Streamlit app."""
 
-# import streamlit as st
-# from src.config_parameters import params
-# from src.utils import (
-#     add_about,
-#     set_doc_page_style,
-#     toggle_menu_button,
-# )
-
-# # Page configuration
-# st.set_page_config(layout="wide", page_title=params["browser_title"])
-
-# # If app is deployed hide menu button
-# toggle_menu_button()
-
-# # Create sidebar
-# add_about()
-
-# # Set page style
-# set_doc_page_style()
+import streamlit as st
+from src.config_parameters import params
+from src.utils import (
+    add_about,
+    set_doc_page_style,
+    toggle_menu_button,
+)
 
-# # Page title
-# st.markdown("# Documentation")
+# Page configuration
+st.set_page_config(layout="wide", page_title=params["browser_title"])
 
-# # First section
-# st.markdown("## Methodology")
-# st.markdown(
-#     "TODO: new documentation, only kept in Sentinel 1 section unchanged from the Mapaction tool"
-# )
+# If app is deployed hide menu button
+toggle_menu_button()
 
+# Create sidebar
+add_about()
 
-# # Second section
-# st.markdown("## Radar imagery for flood detection")
-# st.markdown(
-#     """
-#     While there are multiple change detections techniques for radar imagery,
-#     the one used by Sentinel-1 is one of the simplest. Active radar satellites
-#     produce active radiation directed at the land, and images are formed as a
-#     function of the time it takes for that radiation to reach back to the
-#     satellite. Because of this, radar systems are side-looking (otherwise
-#     radiation from multiple areas would reach back at the same time).  To be
-#     detected and imaged, radiation needs to be scattered back, but not all
-#     surfaces are equally able to scatter back, and that ability is also
-#     influenced by the radiation's wavelength (shorter wavelengths are better at
-#     detecting smaller objects, while longer wavelengths allow penetration,
-#     which is good for forest canopies for example, and biomass studies).
-#     Sentinel-1 satellites are C-band (~ 6 cm).<br><br>
-#     Water is characterised by a mirror-like reflection mechanism, meaning that
-#     no or very little radiation is scattered back to the satellite, so pixels
-#     on the image will appear very dark. This very simple change detection takes
-#     a "before" image, and looks for drops in intensity, dark spots, in the
-#     "after" image.<br><br>
-#     Sentinel-1 data is the result of measurements from a constellation of two
-#     satellites, assing over the same areas following the same orbit on average
-#     every 6 days. On Google Earth Engine, the processing level is Ground Range
-#     Detected (GRD), meaning that it has been detected, multi-looked and
-#     projected to ground range using an Earth ellipsoid model. GRD products
-#     report on intensity of radiation, but have lost the phase and amplitude
-#     information which is needed for other applications (interferometry for
-#     example). These satellites emits in different polarizations, and can
-#     acquire both single horizonal or vertical, or dual polarizations. Flood
-#     water is best detected by using VH (vertical transmit and horizontal
-#     receive), although VV (vertical transmit and vertical receive) can be
-#     effective to identify partially submerged features. This tool uses VH
-#     polarization. Figure 2 shows an overview of the Sentinel-1 observation
-#     plan, where pass directions and coverage frequencies are highlighted.
-#     """,
-#     unsafe_allow_html=True,
-# )
+# Set page style
+set_doc_page_style()
 
-# # Add image satellite overview
-# st.image(
-#     "%s" % params["url_sentinel_img"],
-#     width=1000,
-# )
-# st.markdown(
-#     """
-#         <p style="font-size:%s;">
-#             Figure 2. Overview of the Sentinel-1 observation plan (<a href=
-#             '%s'>source</a>).
-#         </p>
-#         """
-#     % (params["docs_caption_fontsize"], params["url_sentinel_img_location"]),
-#     unsafe_allow_html=True,
-# )
+# Page title
+st.markdown("# Documentation")
 
-import streamlit as st
+# First section
+st.markdown("## Methodology")
+st.markdown(
+    "TODO: new documentation, only kept in Sentinel 1 section unchanged from the Mapaction tool"
+)
 
-slider_value = st.slider(label="number of checkboxe", min_value=1, max_value=20)
 
-checkboxes = {}
-for i in range(1, slider_value):
-    checkbox = st.checkbox(f"Checkbox {i}")
-    checkboxes[f"checkbox_{i}"] = checkbox
+# Second section
+st.markdown("## Radar imagery for flood detection")
+st.markdown(
+    """
+    While there are multiple change detections techniques for radar imagery,
+    the one used by Sentinel-1 is one of the simplest. Active radar satellites
+    produce active radiation directed at the land, and images are formed as a
+    function of the time it takes for that radiation to reach back to the
+    satellite. Because of this, radar systems are side-looking (otherwise
+    radiation from multiple areas would reach back at the same time).  To be
+    detected and imaged, radiation needs to be scattered back, but not all
+    surfaces are equally able to scatter back, and that ability is also
+    influenced by the radiation's wavelength (shorter wavelengths are better at
+    detecting smaller objects, while longer wavelengths allow penetration,
+    which is good for forest canopies for example, and biomass studies).
+    Sentinel-1 satellites are C-band (~ 6 cm).<br><br>
+    Water is characterised by a mirror-like reflection mechanism, meaning that
+    no or very little radiation is scattered back to the satellite, so pixels
+    on the image will appear very dark. This very simple change detection takes
+    a "before" image, and looks for drops in intensity, dark spots, in the
+    "after" image.<br><br>
+    Sentinel-1 data is the result of measurements from a constellation of two
+    satellites, assing over the same areas following the same orbit on average
+    every 6 days. On Google Earth Engine, the processing level is Ground Range
+    Detected (GRD), meaning that it has been detected, multi-looked and
+    projected to ground range using an Earth ellipsoid model. GRD products
+    report on intensity of radiation, but have lost the phase and amplitude
+    information which is needed for other applications (interferometry for
+    example). These satellites emits in different polarizations, and can
+    acquire both single horizonal or vertical, or dual polarizations. Flood
+    water is best detected by using VH (vertical transmit and horizontal
+    receive), although VV (vertical transmit and vertical receive) can be
+    effective to identify partially submerged features. This tool uses VH
+    polarization. Figure 2 shows an overview of the Sentinel-1 observation
+    plan, where pass directions and coverage frequencies are highlighted.
+    """,
+    unsafe_allow_html=True,
+)
 
-if checkboxes["checkbox_4"]:
-    print("checkbox 4 checked")
+# Add image satellite overview
+st.image(
+    "%s" % params["url_sentinel_img"],
+    width=1000,
+)
+st.markdown(
+    """
+        <p style="font-size:%s;">
+            Figure 2. Overview of the Sentinel-1 observation plan (<a href=
+            '%s'>source</a>).
+        </p>
+        """
+    % (params["docs_caption_fontsize"], params["url_sentinel_img_location"]),
+    unsafe_allow_html=True,
+)

app/src/gfm.py

@@ -1,7 +1,5 @@
 import io
 import os
-import shutil
-import tempfile
 import zipfile
 from pathlib import Path
 
@@ -78,19 +76,20 @@ def download_flood_product(area_id, product, output_file_path=None):
     r = requests.get(download_link)
     buffer = io.BytesIO(r.content)
 
-    with tempfile.TemporaryDirectory() as tempdir:
-        with zipfile.ZipFile(buffer) as z:
-            z.extractall(tempdir)
-            for flood_file in Path(tempdir).glob("*FLOOD*.geojson"):
-                dest_file = output_file_path + "/" + flood_file.name
-                shutil.copy(flood_file, dest_file)
+    with zipfile.ZipFile(buffer, "r") as z:
+        namelist = z.namelist()
+        for name in namelist:
+            if "FLOOD" in name and ".geojson" in name:
+                flood_filename = name
+                break
+        z.extract(flood_filename, output_file_path)
 
     df = pd.DataFrame(
         {
             "aoi_id": [area_id],
             "datetime": [product_time],
             "product": [product_id],
-            "geojson_path": [dest_file],
+            "geojson_path": [output_file_path + "/" + flood_filename],
         }
     )
 

pyproject.toml

@@ -11,6 +11,5 @@ dependencies = [
     "python-dotenv==1.0.1",
     "streamlit>=1.41.1",
     "streamlit-folium>=0.24.0",
-    "ipykernel>=6.29.5",
-    "geojson>=3.2.0",
+    "ipykernel>=6.29.5"
 ]

uv.lock

@@ -225,7 +225,6 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "folium" },
-    { name = "geojson" },
     { name = "geopandas" },
     { name = "ipykernel" },
     { name = "python-dotenv" },
@@ -237,7 +236,6 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "folium", specifier = ">=0.19.4" },
-    { name = "geojson", specifier = ">=3.2.0" },
     { name = "geopandas", specifier = ">=1.0.1" },
     { name = "ipykernel", specifier = ">=6.29.5" },
     { name = "python-dotenv", specifier = "==1.0.1" },
@@ -262,15 +260,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fc/ab/d1f47c48a14e17cd487c8b467b573291fae75477b067241407e7889a3692/folium-0.19.4-py2.py3-none-any.whl", hash = "sha256:bea5246b6a6aa61b96d1c51399dd63254bacbd6ba8a826eeb491f45242032dfd", size = 110511 },
 ]
 
-[[package]]
-name = "geojson"
-version = "3.2.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/85/5a/33e761df75c732fcea94aaf01f993d823138581d10c91133da58bc231e63/geojson-3.2.0.tar.gz", hash = "sha256:b860baba1e8c6f71f8f5f6e3949a694daccf40820fa8f138b3f712bd85804903", size = 24574 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/18/67/a7fa2d650602731c90e0a86279841b4586e14228199e8c09165ba4863e29/geojson-3.2.0-py3-none-any.whl", hash = "sha256:69d14156469e13c79479672eafae7b37e2dcd19bdfd77b53f74fa8fe29910b52", size = 15040 },
-]
-
 [[package]]
 name = "geopandas"
 version = "1.0.1"