updated the process graph

Pratichhya · Pratichhya · commit 84f5ef75943d · 2025-12-23T16:24:54.000+01:00
diff --git a/algorithm_catalog/vito/random_forest_firemapping/benchmark_scenarios/random_forest_firemapping.json b/algorithm_catalog/vito/random_forest_firemapping/benchmark_scenarios/random_forest_firemapping.json
@@ -84,7 +84,7 @@
                         "2023-09-15"
                     ]
                 },
-                "namespace": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/0962bf79f836859e701fa7437307240ef689ff2e/algorithm_catalog/vito/random_forest_firemapping/openeo_udp/random_forest_firemapping.json",
+                "namespace": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/refs/heads/main/algorithm_catalog/vito/random_forest_firemapping/openeo_udp/random_forest_firemapping.json",
                 "process_id": "random_forest_firemapping",
                 "result": true
             }
diff --git a/algorithm_catalog/vito/random_forest_firemapping/openeo_udp/random_forest_firemapping.json b/algorithm_catalog/vito/random_forest_firemapping/openeo_udp/random_forest_firemapping.json
@@ -329,7 +329,7 @@
                                     "from_parameter": "data"
                                 },
                                 "runtime": "Python",
-                                "udf": "import xarray\nimport numpy as np\nfrom skimage.feature import graycomatrix, graycoprops\nfrom openeo.metadata import CollectionMetadata\n\n\ndef apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:\n    return metadata.rename_labels(\n        dimension = \"bands\",\n        target = [\"contrast\",\"variance\",\"NDFI\"]\n    )\n\n\ndef apply_datacube(cube: xarray.DataArray, context: dict) -> xarray.DataArray:\n    \"\"\"\n    Applies spatial texture analysis and spectral index computation to a Sentinel-2 data cube.\n\n    Computes:\n    - NDFI (Normalized Difference Fraction Index) from bands B08 and B12\n    - Texture features (contrast and variance) using Gray-Level Co-occurrence Matrix (GLCM)\n\n    Args:\n        cube (xarray.DataArray): A 3D data cube with dimensions (bands, y, x) containing at least bands B08 and B12.\n        context (dict): A context dictionary (currently unused, included for API compatibility).\n\n    Returns:\n        xarray.DataArray: A new data cube with dimensions (bands, y, x) containing:\n                          - 'contrast': GLCM contrast\n                          - 'variance': GLCM variance\n                          - 'NDFI': Normalised Difference Fire Index\n    \"\"\"\n    \n    # Parameters\n    window_size = context.get(\"padding_window_size\", 33)  # Default to 33 if not provided\n    pad = window_size // 2\n    levels = 256  # For 8-bit images\n    \n    # Load Data\n    # data = cube.values # shape: (t, bands, y, x)\n    \n    #first get NDFI\n    b08 = cube.sel(bands=\"B08\")\n    b12 = cube.sel(bands=\"B12\")\n\n    # Compute mean values\n    avg_b08 = b08.mean()\n    avg_b12 = b12.mean()\n\n    # Calculate NDFI\n    ndfi = ((b12 / avg_b12) - (b08 / avg_b08)) / (b08 / avg_b08)\n    \n    # Padding the image to handle border pixels for GLCM\n    padded = np.pad(b12, pad_width=pad, mode='reflect')\n\n    # Normalize to 0–255 range\n    img_norm = (padded - padded.min()) / (padded.max() - padded.min())\n    padded = (img_norm * 255).astype(np.uint8)\n    \n    # Initialize feature maps\n    shape = b12.shape\n    contrast = np.zeros(shape)\n    variance = np.zeros(shape)\n    \n    for i in range(pad, pad + shape[0]):\n        for j in range(pad, pad + shape[1]):\n            window = padded[i - pad:i + pad + 1, j - pad:j + pad + 1]\n            \n            # Compute GLCM\n            glcm = graycomatrix(window, distances=[5], angles=[0], levels=levels, symmetric=True, normed=True)\n            \n            # Texture features\n            contrast[i - pad, j - pad] = graycoprops(glcm, 'contrast')[0, 0]\n            variance[i - pad, j - pad] = np.var(window)\n\n    all_texture = np.stack([contrast,variance,ndfi])\n    # create a data cube with all the calculated properties\n    textures = xarray.DataArray(\n        data=all_texture,\n        dims=[\"bands\", \"y\", \"x\"],\n        coords={\"bands\": [\"contrast\",\"variance\",\"NDFI\"], \"y\": cube.coords[\"y\"], \"x\": cube.coords[\"x\"]},\n    )\n\n    return textures"
+                                "udf": "# /// script\n# dependencies = [\n#   \"scikit-image\",\n# ]\n# ///\n\nimport xarray\nimport numpy as np\nfrom skimage.feature import graycomatrix, graycoprops\nfrom openeo.metadata import CollectionMetadata\n\n\ndef apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:\n    return metadata.rename_labels(\n        dimension = \"bands\",\n        target = [\"contrast\",\"variance\",\"NDFI\"]\n    )\n\n\ndef apply_datacube(cube: xarray.DataArray, context: dict) -> xarray.DataArray:\n    \"\"\"\n    Applies spatial texture analysis and spectral index computation to a Sentinel-2 data cube.\n\n    Computes:\n    - NDFI (Normalized Difference Fraction Index) from bands B08 and B12\n    - Texture features (contrast and variance) using Gray-Level Co-occurrence Matrix (GLCM)\n\n    Args:\n        cube (xarray.DataArray): A 3D data cube with dimensions (bands, y, x) containing at least bands B08 and B12.\n        context (dict): A context dictionary (currently unused, included for API compatibility).\n\n    Returns:\n        xarray.DataArray: A new data cube with dimensions (bands, y, x) containing:\n                          - 'contrast': GLCM contrast\n                          - 'variance': GLCM variance\n                          - 'NDFI': Normalised Difference Fire Index\n    \"\"\"\n    \n    # Parameters\n    window_size = context.get(\"padding_window_size\", 33)  # Default to 33 if not provided\n    pad = window_size // 2\n    levels = 256  # For 8-bit images\n    \n    # Load Data\n    # data = cube.values # shape: (t, bands, y, x)\n    \n    #first get NDFI\n    b08 = cube.sel(bands=\"B08\")\n    b12 = cube.sel(bands=\"B12\")\n\n    # Compute mean values\n    avg_b08 = b08.mean()\n    avg_b12 = b12.mean()\n\n    # Calculate NDFI\n    ndfi = ((b12 / avg_b12) - (b08 / avg_b08)) / (b08 / avg_b08)\n    \n    # Padding the image to handle border pixels for GLCM\n    padded = np.pad(b12, pad_width=pad, mode='reflect')\n\n    # Normalize to 0–255 range\n    img_norm = (padded - padded.min()) / (padded.max() - padded.min())\n    padded = (img_norm * 255).astype(np.uint8)\n    \n    # Initialize feature maps\n    shape = b12.shape\n    contrast = np.zeros(shape)\n    variance = np.zeros(shape)\n    \n    for i in range(pad, pad + shape[0]):\n        for j in range(pad, pad + shape[1]):\n            window = padded[i - pad:i + pad + 1, j - pad:j + pad + 1]\n            \n            # Compute GLCM\n            glcm = graycomatrix(window, distances=[5], angles=[0], levels=levels, symmetric=True, normed=True)\n            \n            # Texture features\n            contrast[i - pad, j - pad] = graycoprops(glcm, 'contrast')[0, 0]\n            variance[i - pad, j - pad] = np.var(window)\n\n    all_texture = np.stack([contrast,variance,ndfi])\n    # create a data cube with all the calculated properties\n    textures = xarray.DataArray(\n        data=all_texture,\n        dims=[\"bands\", \"y\", \"x\"],\n        coords={\"bands\": [\"contrast\",\"variance\",\"NDFI\"], \"y\": cube.coords[\"y\"], \"x\": cube.coords[\"x\"]},\n    )\n\n    return textures"
                             },
                             "result": true
                         }

Original file line number	Diff line number	Diff line change
`@@ -84,7 +84,7 @@`
`84`	`84`	`"2023-09-15"`
`85`	`85`	`]`
`86`	`86`	`},`
`87`		`- "namespace": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/0962bf79f836859e701fa7437307240ef689ff2e/algorithm_catalog/vito/random_forest_firemapping/openeo_udp/random_forest_firemapping.json",`
	`87`	`+ "namespace": "https://raw.githubusercontent.com/ESA-APEx/apex_algorithms/refs/heads/main/algorithm_catalog/vito/random_forest_firemapping/openeo_udp/random_forest_firemapping.json",`
`88`	`88`	`"process_id": "random_forest_firemapping",`
`89`	`89`	`"result": true`
`90`	`90`	`}`
Original file line number	Diff line number	Diff line change
`@@ -329,7 +329,7 @@`
`329`	`329`	`"from_parameter": "data"`
`330`	`330`	`},`
`331`	`331`	`"runtime": "Python",`
`332`		- "udf": "import xarray\nimport numpy as np\nfrom skimage.feature import graycomatrix, graycoprops\nfrom openeo.metadata import CollectionMetadata\n\n\ndef apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:\n return metadata.rename_labels(\n dimension = \"bands\",\n target = [\"contrast\",\"variance\",\"NDFI\"]\n )\n\n\ndef apply_datacube(cube: xarray.DataArray, context: dict) -> xarray.DataArray:\n \"\"\"\n Applies spatial texture analysis and spectral index computation to a Sentinel-2 data cube.\n\n Computes:\n - NDFI (Normalized Difference Fraction Index) from bands B08 and B12\n - Texture features (contrast and variance) using Gray-Level Co-occurrence Matrix (GLCM)\n\n Args:\n cube (xarray.DataArray): A 3D data cube with dimensions (bands, y, x) containing at least bands B08 and B12.\n context (dict): A context dictionary (currently unused, included for API compatibility).\n\n Returns:\n xarray.DataArray: A new data cube with dimensions (bands, y, x) containing:\n - 'contrast': GLCM contrast\n - 'variance': GLCM variance\n - 'NDFI': Normalised Difference Fire Index\n \"\"\"\n \n # Parameters\n window_size = context.get(\"padding_window_size\", 33) # Default to 33 if not provided\n pad = window_size // 2\n levels = 256 # For 8-bit images\n \n # Load Data\n # data = cube.values # shape: (t, bands, y, x)\n \n #first get NDFI\n b08 = cube.sel(bands=\"B08\")\n b12 = cube.sel(bands=\"B12\")\n\n # Compute mean values\n avg_b08 = b08.mean()\n avg_b12 = b12.mean()\n\n # Calculate NDFI\n ndfi = ((b12 / avg_b12) - (b08 / avg_b08)) / (b08 / avg_b08)\n \n # Padding the image to handle border pixels for GLCM\n padded = np.pad(b12, pad_width=pad, mode='reflect')\n\n # Normalize to 0–255 range\n img_norm = (padded - padded.min()) / (padded.max() - padded.min())\n padded = (img_norm * 255).astype(np.uint8)\n \n # Initialize feature maps\n shape = b12.shape\n contrast = np.zeros(shape)\n variance = np.zeros(shape)\n \n for i in range(pad, pad + shape[0]):\n for j in range(pad, pad + shape[1]):\n window = padded[i - pad:i + pad + 1, j - pad:j + pad + 1]\n \n # Compute GLCM\n glcm = graycomatrix(window, distances=[5], angles=[0], levels=levels, symmetric=True, normed=True)\n \n # Texture features\n contrast[i - pad, j - pad] = graycoprops(glcm, 'contrast')[0, 0]\n variance[i - pad, j - pad] = np.var(window)\n\n all_texture = np.stack([contrast,variance,ndfi])\n # create a data cube with all the calculated properties\n textures = xarray.DataArray(\n data=all_texture,\n dims=[\"bands\", \"y\", \"x\"],\n coords={\"bands\": [\"contrast\",\"variance\",\"NDFI\"], \"y\": cube.coords[\"y\"], \"x\": cube.coords[\"x\"]},\n )\n\n return textures"
	`332`	+ "udf": "# /// script\n# dependencies = [\n# \"scikit-image\",\n# ]\n# ///\n\nimport xarray\nimport numpy as np\nfrom skimage.feature import graycomatrix, graycoprops\nfrom openeo.metadata import CollectionMetadata\n\n\ndef apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:\n return metadata.rename_labels(\n dimension = \"bands\",\n target = [\"contrast\",\"variance\",\"NDFI\"]\n )\n\n\ndef apply_datacube(cube: xarray.DataArray, context: dict) -> xarray.DataArray:\n \"\"\"\n Applies spatial texture analysis and spectral index computation to a Sentinel-2 data cube.\n\n Computes:\n - NDFI (Normalized Difference Fraction Index) from bands B08 and B12\n - Texture features (contrast and variance) using Gray-Level Co-occurrence Matrix (GLCM)\n\n Args:\n cube (xarray.DataArray): A 3D data cube with dimensions (bands, y, x) containing at least bands B08 and B12.\n context (dict): A context dictionary (currently unused, included for API compatibility).\n\n Returns:\n xarray.DataArray: A new data cube with dimensions (bands, y, x) containing:\n - 'contrast': GLCM contrast\n - 'variance': GLCM variance\n - 'NDFI': Normalised Difference Fire Index\n \"\"\"\n \n # Parameters\n window_size = context.get(\"padding_window_size\", 33) # Default to 33 if not provided\n pad = window_size // 2\n levels = 256 # For 8-bit images\n \n # Load Data\n # data = cube.values # shape: (t, bands, y, x)\n \n #first get NDFI\n b08 = cube.sel(bands=\"B08\")\n b12 = cube.sel(bands=\"B12\")\n\n # Compute mean values\n avg_b08 = b08.mean()\n avg_b12 = b12.mean()\n\n # Calculate NDFI\n ndfi = ((b12 / avg_b12) - (b08 / avg_b08)) / (b08 / avg_b08)\n \n # Padding the image to handle border pixels for GLCM\n padded = np.pad(b12, pad_width=pad, mode='reflect')\n\n # Normalize to 0–255 range\n img_norm = (padded - padded.min()) / (padded.max() - padded.min())\n padded = (img_norm * 255).astype(np.uint8)\n \n # Initialize feature maps\n shape = b12.shape\n contrast = np.zeros(shape)\n variance = np.zeros(shape)\n \n for i in range(pad, pad + shape[0]):\n for j in range(pad, pad + shape[1]):\n window = padded[i - pad:i + pad + 1, j - pad:j + pad + 1]\n \n # Compute GLCM\n glcm = graycomatrix(window, distances=[5], angles=[0], levels=levels, symmetric=True, normed=True)\n \n # Texture features\n contrast[i - pad, j - pad] = graycoprops(glcm, 'contrast')[0, 0]\n variance[i - pad, j - pad] = np.var(window)\n\n all_texture = np.stack([contrast,variance,ndfi])\n # create a data cube with all the calculated properties\n textures = xarray.DataArray(\n data=all_texture,\n dims=[\"bands\", \"y\", \"x\"],\n coords={\"bands\": [\"contrast\",\"variance\",\"NDFI\"], \"y\": cube.coords[\"y\"], \"x\": cube.coords[\"x\"]},\n )\n\n return textures"
`333`	`333`	`},`
`334`	`334`	`"result": true`
`335`	`335`	`}`