Merge pull request #60 from OPERA-Cal-Val/dev

cmarshak · web-flow · commit 42099e029ba4 · 2025-07-07T22:14:27.000-07:00
v0.0.14
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
@@ -21,4 +21,4 @@ jobs:
     with:
       local_package_name: tile_mate
       python_versions: >-
-        ["3.9", "3.10", "3.11", "3.12"]
+        ["3.9", "3.10", "3.11", "3.12", "3.13"]
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -6,6 +6,18 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [PEP 440](https://www.python.org/dev/peps/pep-0440/)
 and uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.0.14] - 2025-07-06
+
+### Removed
+- Suppport for `cop_100_lulc_discrete` - see https://github.com/OPERA-Cal-Val/tile-mate/issues/57 for how to obtain data efficiently
+  
+### Added
+- `umd_ocean_mask` available at google storage: https://console.cloud.google.com/storage/browser/earthenginepartners-hansen/OceanMask
+- Python 3.13 support.
+
+### Fixed
+- `sentinel-1` coherence examples in `Basic_Demo.ipynb`
+
 ## [0.0.13] - 2025-06-09
 
 ### Changed
diff --git a/README.md b/README.md
@@ -79,7 +79,6 @@ Out[2]: 'pekel_water_occ_2021',
  'hansen_gain',
  'hansen_treecover_2000',
  's1_coherence_2020',
- 'cop_100_lulc_discrete',
  'radd_deforestation_alerts_2022',
  'hand',
  'glad_landcover',
@@ -92,7 +91,6 @@ More information about these datasets can be found below
 + S1 Coherence from December 2019 - Nov 2020: https://aws.amazon.com/marketplace/pp/prodview-iz6lnjbdlgcwa#resources
   + Include all temporal baselines and seasons for VV coherence
   + Include `rho`, `tau` and `rmse` seasonal decay modeling parameters 
-+ The copernicus 100 m LULC dataset from 2015 - 2019: https://land.copernicus.eu/global/content/annual-100m-global-land-cover-maps-available
 + Height above nearest drainage (distributed and generated by ASF) in 2021 derived from Copernicus Global DEM: https://github.com/asjohnston-asf/agu-2022-notebooks/blob/main/hand-notebook.ipynb (and their [tool](https://github.com/HydroSAR/HydroSAR/blob/develop/src/hydrosar/hand/prepare.py) to do the exact same thing)
 + RADD Deforestation alerts (ongoing): https://data.globalforestwatch.org/datasets/gfw::deforestation-alerts-radd/about
 + Glad landcover maps (2000, 2005, 2010, 2015, 2020) and change map (2020 changes when compared to 2000): https://storage.googleapis.com/earthenginepartners-hansen/GLCLU2000-2020/v2/download.html
diff --git a/notebooks/Basic_Demo.ipynb b/notebooks/Basic_Demo.ipynb
diff --git a/notebooks/tile_creation/2_Peckel_Water_Mask.ipynb b/notebooks/tile_creation/2_Peckel_Water_Mask.ipynb
@@ -330,7 +330,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/tile_creation/3_s1_2020_vv_coherence.ipynb b/notebooks/tile_creation/3_s1_2020_vv_coherence.ipynb
@@ -722,7 +722,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/tile_creation/4_100m_Copernicus_landcover.ipynb b/notebooks/tile_creation/4_100m_Copernicus_landcover.ipynb
@@ -7,7 +7,11 @@
    "source": [
     "The best resource for seeing the tiles and available files is this: https://lcviewer.vito.be/download\n",
     "\n",
-    "There are additional resources easily searchable via the interweb: https://land.copernicus.eu/global/content/annual-100m-global-land-cover-maps-available"
+    "There are additional resources easily searchable via the interweb: https://land.copernicus.eu/global/content/annual-100m-global-land-cover-maps-available\n",
+    "\n",
+    "These tiles are NO LONGER AVAILABLE - https://land.copernicus.eu/en/products/global-dynamic-land-cover/copernicus-global-land-service-land-cover-100m-collection-3-epoch-2019-globe#download\n",
+    "\n",
+    "Best to just read subset of large tif files on zenodo."
    ]
   },
   {
@@ -711,7 +715,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/tile_creation/8_UMD_Ocean_Mask.ipynb b/notebooks/tile_creation/8_UMD_Ocean_Mask.ipynb
diff --git a/notebooks/tile_creation/umd_ocean_mask_tiles.txt b/notebooks/tile_creation/umd_ocean_mask_tiles.txt
diff --git a/pyproject.toml b/pyproject.toml
@@ -19,6 +19,7 @@ classifiers = [
     'Programming Language :: Python :: 3.10',
     'Programming Language :: Python :: 3.11',
     'Programming Language :: Python :: 3.12',
+    'Programming Language :: Python :: 3.13',
 ]
 
 dynamic = ['version', 'readme']
diff --git a/src/tile_mate/data/umd_ocean_mask.geojson.zip b/src/tile_mate/data/umd_ocean_mask.geojson.zip
diff --git a/src/tile_mate/stitcher.py b/src/tile_mate/stitcher.py
@@ -7,6 +7,7 @@
 from dem_stitcher.geojson_io import read_geojson_gzip
 from dem_stitcher.merge import merge_tile_datasets_within_extent
 from rasterio.errors import RasterioIOError
+from rasterio.env import Env
 from shapely.geometry import box
 
 from .exceptions import NoTileCoverage, TilesetNotSupported
@@ -22,17 +23,16 @@
     'hansen_gain': 'hansen_landsat_mosaic_2022.geojson.zip',
     'hansen_treecover_2000': 'hansen_landsat_mosaic_2022.geojson.zip',
     's1_coherence_2020': 's1_coherence_2020.geojson.zip',
-    'cop_100_lulc_discrete': 'cop_100m_lulc_discrete_classes.geojson.zip',
     'radd_deforestation_alerts_2022': 'radd_deforestation_alerts_2022.geojson.zip',
     'hand': 'asf_hand_2021.geojson.zip',
     'glad_landcover': 'glad_landcover_2020.geojson.zip',
     'glad_change': 'glad_landcover_2020.geojson.zip',
+    'umd_ocean_mask': 'umd_ocean_mask.geojson.zip',
 }
 DATASET_SHORTNAMES = list(GEOJSON_DICT.keys())
 
-DATASETS_WITH_YEAR = ['hansen_annual_mosaic', 'cop_100_lulc_discrete', 'glad_landcover']
+DATASETS_WITH_YEAR = ['hansen_annual_mosaic', 'glad_landcover']
 HANSEN_MOSAIC_YEARS = [2000] + list(range(2013, 2023))
-COP_100_YEARS = list(range(2015, 2020))
 CURRENT_HANSEN_VERSION = 10
 CURRENT_HANSEN_YEAR = 2022
 SEASONS = ['fall', 'winter', 'spring', 'summer']
@@ -94,12 +94,6 @@ def update_glad_landcover_url(url: str, year: int = year) -> str:
 
             df_tiles.url = df_tiles.url.map(update_glad_landcover_url)
 
-        if tile_key == 'cop_100_lulc_discrete':
-            if year not in COP_100_YEARS:
-                cop_100_years_str = list(map(str, COP_100_YEARS))
-                raise ValueError(f'Year must be in {cop_100_years_str}')
-            df_tiles = df_tiles[df_tiles.year == year].reset_index(drop=True)
-
     if year is None:
         if tile_key in DATASETS_WITH_YEAR:
             raise ValueError('Year is required for tile lookup')
@@ -223,7 +217,14 @@ def get_raster_from_tiles(
     tile_metadata = get_additional_tile_metadata(urls)
 
     urls_subset = get_urls_from_tile_df(extent, df_tiles)
-    X_merged, p_merged = merge_tile_datasets_within_extent(urls_subset, extent)
+
+    if tile_shortname == 'umd_ocean_mask':
+        env = Env(GS_NO_SIGN_REQUEST='YES')
+    else:
+        env = Env()  # default environment
+
+    with env:
+        X_merged, p_merged = merge_tile_datasets_within_extent(urls_subset, extent)
 
     # Are stored in the profile for provenance
     p_merged.update(**tile_metadata)
diff --git a/tests/test_stitch_api.py b/tests/test_stitch_api.py
@@ -3,7 +3,6 @@
 from tile_mate import get_raster_from_tiles
 from tile_mate.exceptions import NoTileCoverage, TilesetNotSupported
 from tile_mate.stitcher import (
-    COP_100_YEARS,
     DATASETS_WITH_YEAR,
     GLAD_LANDCOVER_YEARS,
     HANSEN_MOSAIC_YEARS,
@@ -29,6 +28,12 @@ def test_glad_change():
     assert len(X.shape) == 3
 
 
+def test_umd_ocean_mask():
+    bounds = [-120.45, 34.85, -120.15, 35.15]
+    X, _ = get_raster_from_tiles(bounds, tile_shortname='umd_ocean_mask')
+    assert len(X.shape) == 3
+
+
 @pytest.mark.parametrize('year', GLAD_LANDCOVER_YEARS)
 def test_glad_landcover_datasets(year):
     # Note only getting 1 tile - these are large datasets!
@@ -56,8 +61,6 @@ def test_valid_year_exceptions():
     bounds = [-120.45, 34.85, -120.15, 34.95]
     with pytest.raises(ValueError):
         X, _ = get_raster_from_tiles(bounds, tile_shortname='hansen_annual_mosaic', year=2002)
-    with pytest.raises(ValueError):
-        X, _ = get_raster_from_tiles(bounds, tile_shortname='cop_100_lulc_discrete', year=2002)
 
 
 @pytest.mark.parametrize('season', SEASONS)
@@ -90,13 +93,6 @@ def test_s1_model_tiles(season, s1_var):
     assert len(X.shape) == 3
 
 
-@pytest.mark.parametrize('year', COP_100_YEARS)
-def test_cop100_dataset(year: int):
-    bounds = [-120.45, 34.85, -120.15, 34.95]
-    X, _ = get_raster_from_tiles(bounds, tile_shortname='cop_100_lulc_discrete', year=year)
-    assert len(X.shape) == 3
-
-
 @pytest.mark.parametrize(
     'dataset_shortname',
     [

Original file line number	Diff line number	Diff line change
`@@ -330,7 +330,7 @@`
`330`	`330`	`"name": "python",`
`331`	`331`	`"nbconvert_exporter": "python",`
`332`	`332`	`"pygments_lexer": "ipython3",`
`333`		`- "version": "3.12.0"`
	`333`	`+ "version": "3.13.3"`
`334`	`334`	`}`
`335`	`335`	`},`
`336`	`336`	`"nbformat": 4,`
Original file line number	Diff line number	Diff line change
`@@ -722,7 +722,7 @@`
`722`	`722`	`"name": "python",`
`723`	`723`	`"nbconvert_exporter": "python",`
`724`	`724`	`"pygments_lexer": "ipython3",`
`725`		`- "version": "3.12.0"`
	`725`	`+ "version": "3.13.3"`
`726`	`726`	`}`
`727`	`727`	`},`
`728`	`728`	`"nbformat": 4,`
Original file line number	Diff line number	Diff line change
`@@ -19,6 +19,7 @@ classifiers = [`
`19`	`19`	`'Programming Language :: Python :: 3.10',`
`20`	`20`	`'Programming Language :: Python :: 3.11',`
`21`	`21`	`'Programming Language :: Python :: 3.12',`
	`22`	`+ 'Programming Language :: Python :: 3.13',`
`22`	`23`	`]`
`23`	`24`
`24`	`25`	`dynamic = ['version', 'readme']`