Merge pull request #85 from kleok/dev

Jupyter notebook improvements & ReadMe updates

README.md

@@ -19,11 +19,11 @@ Recommended setup: Python 3.9+, SNAP 9.0+
 
 ### 1.1 Install snap gpt including Sentinel-1 toolbox
 
-You can download SNAP manually from [here](https://step.esa.int/main/download/snap-download/) and install it using the following commands:
+Please download ESA-SNAP (All Toolboxes) from [here](https://step.esa.int/main/download/snap-download/) and install it using the following commands:
 
   ```bash
-  chmod +x install_snap.sh
-  ./install_snap.sh
+  chmod +x esa-snap_all_linux-10.0.0.sh
+  ./esa-snap_all_linux-10.0.0.sh
   ```
 
 ### 1.2 Account setup for downloading Sentinel-1 acquisitions
@@ -63,10 +63,10 @@ You can download FLOODPY toolbox using the following command:
 FLOODPY is written in Python3 and relies on several Python modules. We suggest to install them by using conda.
 
 - Using **conda**
-Create a new conda environement with required packages using the the file [FLOODPY_env.yml](https://github.com/kleok/FLOODPY/blob/main/FLOODPY_env.yml).
+Create a new conda environement with required packages using the the file [FLOODPY_gpu_env.yml](https://github.com/kleok/FLOODPY/blob/main/FLOODPY_gpu_env.yml).
 
 ```
-conda env create -f path_to_FLOODPY/FLOODPY_env.yml
+conda env create -f path_to_FLOODPY/FLOODPY_gpu_env.yml
 ```
 
 ### 1.6 Set environmental variables (Optional)
@@ -84,13 +84,15 @@ FLOODPY generates a map with flooded regions based on Sentinel-1 GRD products an
 Sentinel-1 orbits are downloaded using the [sentineleof](https://github.com/scottstanie/sentineleof)
 You can run FLOODPY using the following jupyter notebooks as templates.
 
-- [Floodpy statistical approach](https://nbviewer.org/github/kleok/FLOODPY/blob/main/Floodpyapp_notebook.ipynb)
-- [Floodpy Deep learning approach](https://nbviewer.org/github/kleok/FLOODPY/blob/main/Floodpyapp_Vit.ipynb)
+- [Floodpy statistical approach](https://nbviewer.org/github/kleok/FLOODPY/blob/main/Floodpyapp_stat.ipynb)
+- [Floodpy deep learning approach](https://nbviewer.org/github/kleok/FLOODPY/blob/main/Floodpyapp_Vit.ipynb)
 
 ## 3. Documentation and citation
 Algorithms implemented in the software are described in detail at our publications. If FLOODPY was useful for you, we encourage you to cite the following work: 
 - Karamvasis K, Karathanassi V. FLOMPY: An Open-Source Toolbox for Floodwater Mapping Using Sentinel-1 Intensity Time Series. Water. 2021; 13(21):2943. https://doi.org/10.3390/w13212943 
 
+- Kuro Siwo: 33 billion m2 under the water. A global multi-temporal satellite dataset for rapid flood mapping. https://paperswithcode.com/paper/kuro-siwo-12-1-billion-m-2-under-the-water-a
+
 You can also have a look at other works that are using FLOODPY:
 
 - Gounari 0., Falagas A., Karamvasis K., Tsironis V., Karathanassi V., 

floodpy/Preprocessing_S1_data/snap_preprocessing_funcs.py

@@ -126,7 +126,7 @@ def perform_pair_preprocessing_2GRD_1GRD(gptcommand, primary1, primary2, seconda
     subprocess.check_call(argvs, stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)
     return 0
 
-def perform_pair_preprocessing_2GRD_2GRD(gptcommand, primary1, primary2, secondary1, secondary2, outfile, Subset_AOI, xml_file, overwrite):
+def perform_pair_preprocessing_2GRD_2GRD(gptcommand, primary1, primary2, secondary1, secondary2, outfile, Subset_AOI, xml_file, overwrite, print_esa_snap_logs = False):
 
     """
     This function performs the preprocessing of the given pair from the
@@ -146,4 +146,25 @@ def perform_pair_preprocessing_2GRD_2GRD(gptcommand, primary1, primary2, seconda
             '-Pfileout='+outfile]
 
     subprocess.check_call(argvs, stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)
+
+    if print_esa_snap_logs:
+        print (argvs)
+        # Append argument for logging from ESA SNAP
+        argvs.append('-J-Dsnap.log.level=FINE')
+
+        # Print the command for manual execution
+        print("Running command:")
+        print(" ".join(argvs))
+
+        # Run the command while printing ESA SNAP logs to stdout in real-time
+        with subprocess.Popen(argvs, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True) as process:
+            for stdout_line in iter(process.stdout.readline, ""):
+                print(stdout_line, end="")
+            process.stdout.close()
+            return_code = process.wait()
+            if return_code:
+                raise subprocess.CalledProcessError(return_code, argvs)
+    else:
+        subprocess.check_call(argvs, stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)
+
     return 0

floodpy/utils/folium_categorical_legend.py

@@ -1,4 +1,4 @@
-def get_folium_categorical_template():
+def get_folium_categorical_template(categories):
     template = """
     {% macro html(this, kwargs) %}
 
@@ -36,20 +36,13 @@ def get_folium_categorical_template():
     <div class='legend-title'>ESA WorldCover 2021 Categories</div>
     <div class='legend-scale'>
     <ul class='legend-labels'>
+    """
 
-      <li><span style='background:rgb(0, 0, 0); opacity:1;'></span><b>No data</b></li>
-      <li><span style='background:rgb(0, 100, 0); opacity:1;'></span><b>Tree cover</b></li>
-      <li><span style='background:rgb(255, 187, 34); opacity:1;'></span><b>Shrubland</b></li>
-      <li><span style='background:rgb(255, 255, 76); opacity:1;'></span><b>Grassland</b></li>
-      <li><span style='background:rgb(240, 150, 255); opacity:1;'></span><b>Cropland</b></li>
-      <li><span style='background:rgb(250, 0, 0); opacity:1;'></span><b>Built-up</b></li>
-      <li><span style='background:rgb(180, 180, 180); opacity:1;'></span><b>Bare/sparse vegetation</b></li>
-      <li><span style='background:rgb(240, 240, 240); opacity:1;'></span><b>Snow and Ice</b></li>
-      <li><span style='background:rgb(0, 100, 200); opacity:1;'></span><b>Permanent water bodies</b></li>
-      <li><span style='background:rgb(0, 150, 160); opacity:1;'></span><b>Herbaceous wetland</b></li>
-      <li><span style='background:rgb(0, 207, 117); opacity:1;'></span><b>Mangroves</b></li>
-      <li><span style='background:rgb(250, 230, 160); opacity:1;'></span><b>Moss and lichen</b></li>
+    for category, color in categories.items():
+        template += f"<li><span style='background:rgb{color}; opacity:1;'></span><b>{category}</b></li>\n"
 
+
+    template += """
     </ul>
     </div>
     </div>