Merge pull request #1 from DHI-GRAS/repo-review

Use the new pytporject.toml instead of the deprecated setup.py

.github/workflows/ruff.yml

@@ -0,0 +1,21 @@
+name: ruff_push
+on: [push]
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+      - run: |
+          python -m pip install --upgrade pip
+          pip install ruff
+      - run: ruff check --output-format=github .
+      - name: If needed, commit ruff changes to a new pull request
+        if: failure()
+        run: |
+          ruff check --output-format=github --fix .
+          git config --global user.name github-actions
+          git config --global user.email '${GITHUB_ACTOR}@users.noreply.github.com'
+          git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/$GITHUB_REPOSITORY
+          git commit -am "fixup! Format Python code with ruff push"
+          git push --force origin HEAD:$GITHUB_REF

.gitignore

@@ -136,4 +136,12 @@ zappa_settings.toml
 
 _version.py
 Makefile
-tests/
+tests/
+
+test_data/fusion_results
+test_data/S2/processed
+test_data/S3/reprojected
+test_data/S3/composites
+test_data/S3/calibrated
+test_data/S3/binning
+test_data/S3/blurred

README.md

@@ -40,22 +40,36 @@ as demonstrated by the example of Aarhus, Denmark in Spring 2021.
 See run_efast.py for an example using data located in test_data folder.
 
 ### Requirements
+* [python](https://www.python.org/getit/)
+* [esa-snap](https://step.esa.int/main/download/snap-download/) - needed for Sentinel-3 pre-processing only. Tested with version 9 and 10.
 
-- setuptools
-- numpy
-- scipy
-- tqdm
-- scikit-learn
-- rasterio
-- pandas
-- ipdb
-- astropy
-- python-dateutil
-- snap-graph (available through a Git repository)
+### Try it out
+
+1. Clone the repository to your local machine.
+2. Navigate to the root directory of the repository in your terminal.
+3. [OPTIONAL but recommended] Create a virtual environment: `python3.<your python version> -m venv .venv`
+3. Install the package: `pip install -e .`
+4. Run the example: `python run_efast.py`
 
 ### Installation
+Install the package using pip:
+
+```bash
+pip install git+https://github.com/DHI-GRAS/efast.git
+```
+
+### Usage
+```python
+import pyefast
+
+...
+pyefast.fusion(
+    ...
+)
+```
 
+### Develop
 1. Clone the repository to your local machine.
 2. Navigate to the root directory of the repository in your terminal.
-3. Run the following command to install the required packages: pip install -r requirements.txt
-4. Run the following command to install the package: python setup.py install
+3. [OPTIONAL but strongly recommended] Create a virtual environment: `python3.<your python version> -m venv .venv`
+3. Install the package in dev mode: `pip install -e .[dev]`

pyefast/__init__.py

@@ -0,0 +1 @@
+from .efast import fusion

pyefast/efast.py

@@ -26,15 +26,16 @@
 """
 
 import os
-from tqdm import tqdm
 
 import numpy as np
 import pandas as pd
 import rasterio
 import rasterio.windows
-from scipy.interpolate import interp1d
 import scipy.ndimage
 
+from scipy.interpolate import interp1d
+from tqdm import tqdm
+
 
 def fusion(
     pred_date,

pyefast/s2_processing.py

@@ -26,16 +26,16 @@
 """
 
 import re
-from tqdm import tqdm
 import xml.etree.ElementTree as ET
 
 import numpy as np
 import pyproj
 import rasterio
 import scipy as sp
+
 from shapely.geometry import box
 from shapely.ops import transform
-
+from tqdm import tqdm
 
 # Mapping of Sentinel-2 bands names to bands ids
 BANDS_IDS = {
@@ -52,10 +52,9 @@
 }
 
 
-def extract_mask_s2_bands(input_dir,
-                          output_dir,
-                          bands=["B02", "B03", "B04", "B8A"],
-                          resolution=20):
+def extract_mask_s2_bands(
+    input_dir, output_dir, bands=["B02", "B03", "B04", "B8A"], resolution=20
+):
     """
     Extract specified Sentinel-2 bands from .SAFE file, mask clouds and shadows using the SLC mask
     and save to multi-band GeoTIFF file.
@@ -79,7 +78,8 @@ def extract_mask_s2_bands(input_dir,
     """
     for p in input_dir.iterdir():
         band_paths = [
-            list(p.glob(f"GRANULE/*/IMG_DATA/R{resolution}m/*{band}*.jp2"))[0] for band in bands
+            list(p.glob(f"GRANULE/*/IMG_DATA/R{resolution}m/*{band}*.jp2"))[0]
+            for band in bands
         ]
 
         # Find S2 BOA offsets
@@ -99,7 +99,9 @@ def extract_mask_s2_bands(input_dir,
         mask = (mask == 0) | (mask == 3) | (mask > 7)
 
         # Combine bands and mask
-        s2_image = np.zeros((len(bands), profile["height"], profile["width"]), "float32")
+        s2_image = np.zeros(
+            (len(bands), profile["height"], profile["width"]), "float32"
+        )
         for i, band_path in enumerate(band_paths):
             band = bands[i]
             band_id = BANDS_IDS.get(band)
@@ -112,7 +114,9 @@ def extract_mask_s2_bands(input_dir,
                 s2_image[i] = data
 
         # Save file
-        profile.update({"driver": "GTiff", "count": len(bands), "dtype": "float32", "nodata": 0})
+        profile.update(
+            {"driver": "GTiff", "count": len(bands), "dtype": "float32", "nodata": 0}
+        )
         out_path = output_dir / f"{str(p.name).rstrip('.SAFE')}_REFL.tif"
         with rasterio.open(out_path, "w", **profile) as dst:
             dst.write(s2_image)
@@ -170,7 +174,9 @@ def distance_to_clouds(dir_s2, ratio=30, tolerance_percentage=0.05):
         distance_to_cloud = np.clip(distance_to_cloud, 0, 255)
 
         # Update transform
-        s2_resolution = (s2_profile["transform"]*(1, 0))[0] - (s2_profile["transform"]*(0, 0))[0]
+        s2_resolution = (s2_profile["transform"] * (1, 0))[0] - (
+            s2_profile["transform"] * (0, 0)
+        )[0]
         longitude_origin, latitude_origin = s2_profile["transform"] * (0, 0)
         lr_transform = rasterio.Affine(
             ratio * s2_resolution,
@@ -226,9 +232,9 @@ def get_wkt_footprint(dir_s2, crs="EPSG:4326"):
     # Ensure footprint is in desired CRS
     polygon = box(*bounds)
     if image_crs != crs:
-        transformer = pyproj.Transformer.from_proj(pyproj.Proj(image_crs),
-                                                   pyproj.Proj(crs),
-                                                   always_xy=True)
+        transformer = pyproj.Transformer.from_proj(
+            pyproj.Proj(image_crs), pyproj.Proj(crs), always_xy=True
+        )
         polygon = transform(transformer.transform, polygon)
 
     # Step 4: Convert to WKT

pyefast/s3_processing.py

@@ -25,22 +25,23 @@
 @author: rmgu, pase
 """
 
-from dateutil import rrule
-from datetime import datetime
 import os
 import re
-from tqdm import tqdm
+
+from datetime import datetime
 
 import astropy.convolution as ap
 import numpy as np
 import pandas as pd
 import rasterio
-from rasterio.vrt import WarpedVRT
-from rasterio.enums import Resampling
-from rasterio import shutil as rio_shutil
 import scipy as sp
 
+from dateutil import rrule
+from rasterio import shutil as rio_shutil
+from rasterio.enums import Resampling
+from rasterio.vrt import WarpedVRT
 from snap_graph.snap_graph import SnapGraph
+from tqdm import tqdm
 
 
 def binning_s3(
@@ -100,10 +101,9 @@ def binning_s3(
     sen3_paths = [element for _, element in sorted(zip(date_strings, sen3_paths))]
 
     for i, sen3_path in enumerate(sen3_paths):
-
         output_path = os.path.join(
             binning_dir,
-            sen3_path.stem+'.tif',
+            sen3_path.stem + ".tif",
         )
 
         variables = s3_bands.copy()
@@ -185,13 +185,7 @@ def binning_s3(
 
 
 def produce_median_composite(
-    dir_s3,
-    composite_dir,
-    step=5,
-    mosaic_days=100,
-    s3_bands=None,
-    D=20,
-    sigma_doy=10
+    dir_s3, composite_dir, step=5, mosaic_days=100, s3_bands=None, D=20, sigma_doy=10
 ):
     """
     Create weighted composites of Sentinel-3 images.
@@ -220,7 +214,10 @@ def produce_median_composite(
     """
     sen3_paths = list(dir_s3.glob("S3*.tif"))
     s3_dates = pd.to_datetime(
-        [re.match(".*__(\d{8})T.*\.tif", sen3_path.name).group(1) for sen3_path in sen3_paths]
+        [
+            re.match(".*__(\d{8})T.*\.tif", sen3_path.name).group(1)
+            for sen3_path in sen3_paths
+        ]
     )
     sen3_paths = np.array(
         [sen3_path for _, sen3_path in sorted(zip(s3_dates, sen3_paths))]

pyproject.toml

@@ -0,0 +1,40 @@
+[build-system]
+requires = ["setuptools >= 61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "pyefast"
+authors = [
+    {name = "sa", email = "[email protected]"},
+]
+description = "My package description"
+readme = "README.md"
+dynamic = ["version"]
+dependencies = [
+    "python-dateutil",
+    "numpy",
+    "pandas",
+    "rasterio",
+    "scipy",
+    "tqdm",
+    "pyproj",
+    "shapely",
+    "astropy",
+    "snap-graph @ git+https://github.com/DHI-GRAS/snap-graph",
+
+]
+
+[project.optional-dependencies]
+dev = [
+    "ruff",
+]
+
+[tool.setuptools.packages.find]
+include = ["pyefast"]
+
+[tool.ruff.lint]
+select = ["I"]
+
+[tool.ruff.lint.isort]
+# Use a single line between direct and from import.
+lines-between-types = 1