diff --git a/coastlines/vector.py b/coastlines/vector.py index 9f7189e..9f3b4fa 100644 --- a/coastlines/vector.py +++ b/coastlines/vector.py @@ -1609,10 +1609,8 @@ def generate_vectors( # Export stats # ################ - # if points_gdf is not None and len(points_gdf) > 0: - # Clip stats to study area extent - points_gdf = points_gdf[points_gdf.intersects(gridcell_gdf.geometry.item())] + points_gdf_clipped = points_gdf.clip(gridcell_gdf) # Set output path stats_path = ( @@ -1623,23 +1621,22 @@ def generate_vectors( try: # Export to GeoJSON - points_gdf.to_crs("EPSG:4326").to_file( + points_gdf_clipped.to_crs("EPSG:4326").to_file( f"{stats_path}.geojson", driver="GeoJSON", ) # Export as ESRI shapefiles - points_gdf.to_file( + points_gdf_clipped.to_file( f"{stats_path}.shp", schema={ - "properties": vector_schema(points_gdf), + "properties": vector_schema(points_gdf_clipped), "geometry": "Point", }, ) except ValueError: - raise ValueError( - f"Study area {study_area}: No vector points data to export after clipping to study area extent" + log.warning(f"Study area {study_area}: No vector points data to export after clipping to study area extent" ) else: @@ -1653,7 +1650,7 @@ def generate_vectors( contours_gdf = contour_certainty(contours_gdf, certainty_masks) # Add tide datum details (this supports future addition of extra tide datums) - contours_gdf["tide_datum"] = "0 m AMSL" + contours_gdf["tide_datum"] = "0.0 m AMSL" # Add region attributes contours_gdf = region_atttributes( @@ -1667,20 +1664,20 @@ def generate_vectors( ) # Clip annual shoreline contours to study area extent - contours_gdf["geometry"] = contours_gdf.intersection(gridcell_gdf.geometry.item()) + contours_gdf_clipped = contours_gdf.clip(gridcell_gdf) try: # Export to GeoJSON - contours_gdf.to_crs("EPSG:4326").to_file( + contours_gdf_clipped.to_crs("EPSG:4326").to_file( f"{contour_path}.geojson", driver="GeoJSON" ) # Export stats and contours as ESRI shapefiles - contours_gdf.to_file( + contours_gdf_clipped.to_file( f"{contour_path}.shp", schema={ - "properties": vector_schema(contours_gdf), + "properties": vector_schema(contours_gdf_clipped), "geometry": ["MultiLineString", "LineString"], }, ) diff --git a/notebooks/DEACoastlines_generation_CLI.ipynb b/notebooks/DEACoastlines_generation_CLI.ipynb index 256eb0d..5d5563f 100644 --- a/notebooks/DEACoastlines_generation_CLI.ipynb +++ b/notebooks/DEACoastlines_generation_CLI.ipynb @@ -83,22 +83,22 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "4729d261-6b27-4a61-a937-366c5174ce05", "metadata": {}, "outputs": [], "source": [ - "# study_area = 424\n", - "# raster_version = 'testing'\n", - "# vector_version = 'testing'\n", - "# continental_version = 'testing'\n", - "# config_path = 'configs/dea_coastlines_config.yaml'\n", + "study_area = 64\n", + "raster_version = 'testing'\n", + "vector_version = 'testing'\n", + "continental_version = 'testing'\n", + "config_path = 'configs/dea_coastlines_config.yaml'\n", "\n", - "study_area = 9\n", - "raster_version = 'development'\n", - "vector_version = 'development'\n", - "continental_version = 'development'\n", - "config_path = 'configs/dea_coastlines_config_development.yaml'" + "# study_area = 9\n", + "# raster_version = 'development'\n", + "# vector_version = 'development'\n", + "# continental_version = 'development'\n", + "# config_path = 'configs/dea_coastlines_config_development.yaml'" ] }, { @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "dc5632c4-f872-46e5-9762-80b8556f3622", "metadata": {}, "outputs": [ @@ -198,28 +198,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "/env/lib/python3.8/site-packages/distributed/node.py:180: UserWarning: Port 8787 is already in use.\n", - "Perhaps you already have a cluster running?\n", - "Hosting the HTTP server on port 38341 instead\n", - " warnings.warn(\n", - "\n", - "2023-01-30 02:03:44 INFO Study area 9: Loaded study area grid\n", - "2023-01-30 02:04:00 INFO Study area 9: Loaded virtual product\n", + "\n", + "2023-01-30 06:17:55 INFO Study area 151: Loaded study area grid\n", + "2023-01-30 06:18:04 INFO Study area 151: Loaded virtual product\n", "Creating reduced resolution tide modelling array\n", "Modelling tides using FES2014 tide model\n", - "2023-01-30 02:04:00 ERROR Study area 9: Unable to access tide modelling files\n", + "Reprojecting tides into original array\n", + "100%|███████████████████████████████████████| 1021/1021 [00:30<00:00, 33.01it/s]\n", + "2023-01-30 06:19:44 INFO Study area 151: Finished modelling tide heights\n", + "2023-01-30 06:19:45 INFO Study area 151: Calculating low and high tide cutoffs for each pixel\n", + "2023-01-30 06:19:45 INFO Study area 151: Started exporting raster data\n", + "/env/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/env/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in true_divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "2023-01-30 06:31:57 INFO Study area 151: Completed exporting raster data\n", + "distributed.nanny - WARNING - Worker process still alive after 3.999998474121094 seconds, killing\n", + "Exception in thread AsyncProcess Dask Worker process (from Nanny) watch process join:\n", "Traceback (most recent call last):\n", - " File \"/home/jovyan/Robbi/dea-coastlines/coastlines/raster.py\", line 569, in generate_rasters\n", - " ds[\"tide_m\"], tides_lowres = pixel_tides(ds, resample=True, directory='blah')\n", - " File \"/env/lib/python3.8/site-packages/dea_tools/coastal.py\", line 592, in pixel_tides\n", - " tide_df = model_tides(\n", - " File \"/env/lib/python3.8/site-packages/dea_tools/coastal.py\", line 336, in model_tides\n", - " model = pyTMD.model(directory, format=\"netcdf\", compressed=False).elevation(model)\n", - " File \"/env/lib/python3.8/site-packages/pyTMD/model.py\", line 842, in elevation\n", - " self.model_file = self.pathfinder(model_files)\n", - " File \"/env/lib/python3.8/site-packages/pyTMD/model.py\", line 1425, in pathfinder\n", - " raise FileNotFoundError(output_file)\n", - "FileNotFoundError: ['blah/fes2014/ocean_tide/2n2.nc', 'blah/fes2014/ocean_tide/eps2.nc', 'blah/fes2014/ocean_tide/j1.nc', 'blah/fes2014/ocean_tide/k1.nc', 'blah/fes2014/ocean_tide/k2.nc', 'blah/fes2014/ocean_tide/l2.nc', 'blah/fes2014/ocean_tide/la2.nc', 'blah/fes2014/ocean_tide/m2.nc', 'blah/fes2014/ocean_tide/m3.nc', 'blah/fes2014/ocean_tide/m4.nc', 'blah/fes2014/ocean_tide/m6.nc', 'blah/fes2014/ocean_tide/m8.nc', 'blah/fes2014/ocean_tide/mf.nc', 'blah/fes2014/ocean_tide/mks2.nc', 'blah/fes2014/ocean_tide/mm.nc', 'blah/fes2014/ocean_tide/mn4.nc', 'blah/fes2014/ocean_tide/ms4.nc', 'blah/fes2014/ocean_tide/msf.nc', 'blah/fes2014/ocean_tide/msqm.nc', 'blah/fes2014/ocean_tide/mtm.nc', 'blah/fes2014/ocean_tide/mu2.nc', 'blah/fes2014/ocean_tide/n2.nc', 'blah/fes2014/ocean_tide/n4.nc', 'blah/fes2014/ocean_tide/nu2.nc', 'blah/fes2014/ocean_tide/o1.nc', 'blah/fes2014/ocean_tide/p1.nc', 'blah/fes2014/ocean_tide/q1.nc', 'blah/fes2014/ocean_tide/r2.nc', 'blah/fes2014/ocean_tide/s1.nc', 'blah/fes2014/ocean_tide/s2.nc', 'blah/fes2014/ocean_tide/s4.nc', 'blah/fes2014/ocean_tide/sa.nc', 'blah/fes2014/ocean_tide/ssa.nc', 'blah/fes2014/ocean_tide/t2.nc']\n" + " File \"/usr/lib/python3.8/threading.py\", line 932, in _bootstrap_inner\n", + " self.run()\n", + " File \"/usr/lib/python3.8/threading.py\", line 870, in run\n", + " self._target(*self._args, **self._kwargs)\n", + " File \"/env/lib/python3.8/site-packages/distributed/process.py\", line 218, in _watch_process\n", + " assert exitcode is not None\n", + "AssertionError\n" ] } ], @@ -237,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "0ed72c21-ba89-4fad-9521-f5493aad9d66", "metadata": {}, "outputs": [ @@ -319,19 +322,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-01-25 02:10:51 INFO Study area 424: Starting vector generation\n", - "2023-01-25 02:10:59 INFO Study area 424: Loaded rasters\n", - "2023-01-25 02:11:12 ERROR Study area 424: Failed to run process with error 'Dataset' object has no attribute 'land'\n", - "Traceback (most recent call last):\n", - " File \"/home/jovyan/Robbi/dea-coastlines/coastlines/vector.py\", line 1820, in generate_vectors_cli\n", - " generate_vectors(\n", - " File \"/home/jovyan/Robbi/dea-coastlines/coastlines/vector.py\", line 1443, in generate_vectors\n", - " masked_ds, certainty_masks = contours_preprocess(\n", - " File \"/home/jovyan/Robbi/dea-coastlines/coastlines/vector.py\", line 616, in contours_preprocess\n", - " geodata_da = dc.load(\n", - " File \"/env/lib/python3.8/site-packages/xarray/core/common.py\", line 239, in __getattr__\n", - " raise AttributeError(\n", - "AttributeError: 'Dataset' object has no attribute 'land'\n" + "2023-01-31 01:56:17 INFO Study area 64: Starting vector generation\n", + "2023-01-31 01:56:29 INFO Study area 64: Loaded rasters\n", + "Operating in single z-value, multiple arrays mode\n", + "2023-01-31 01:57:02 INFO Study area 64: Extracted shorelines from raster data\n", + "2023-01-31 01:57:02 INFO Study area 64: Extracted rates of change points\n", + "2023-01-31 01:57:26 INFO Study area 64: Calculated distances to each annual shoreline\n", + "2023-01-31 01:57:28 INFO Study area 64: Calculated rates of change regressions\n", + "2023-01-31 01:57:31 INFO Study area 64: Calculated all of time statistics\n", + "2023-01-31 01:57:31 INFO Study area 64: Calculated rate of change certainty flags\n", + "2023-01-31 01:57:32 INFO Study area 64: Added region attributes and geohash UIDs\n", + "2023-01-31 01:57:39 INFO Study area 64: Output vector files written to data/interim/vector/testing/64_testing\n" ] } ], @@ -386,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "6b804fec-39f4-48a9-8525-a78fbf06e084", "metadata": {}, "outputs": [], @@ -397,90 +398,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "c39e6533-f4fd-4a3c-90a5-44f19825c2f5", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "193\n", - "\n", - "2023-01-25 02:11:18 INFO Study area 193: Loaded study area grid\n", - "2023-01-25 02:11:29 INFO Study area 193: Loaded virtual product\n", - "Creating reduced resolution tide modelling array\n", - "Modelling tides using FES2014 tide model\n", - "Reprojecting tides into original array\n", - "100%|███████████████████████████████████████| 1074/1074 [00:13<00:00, 76.98it/s]\n", - "2023-01-25 02:12:23 INFO Study area 193: Finished modelling tide heights\n", - "2023-01-25 02:12:23 INFO Study area 193: Calculating low and high tide cutoffs for each pixel\n", - "2023-01-25 02:12:23 INFO Study area 193: Started exporting raster data\n", - "2023-01-25 02:16:59 INFO Study area 193: Completed exporting raster data\n", - "distributed.nanny - WARNING - Worker process still alive after 3.9999988555908206 seconds, killing\n", - "Exception in thread AsyncProcess Dask Worker process (from Nanny) watch process join:\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.8/threading.py\", line 932, in _bootstrap_inner\n", - " self.run()\n", - " File \"/usr/lib/python3.8/threading.py\", line 870, in run\n", - "2023-01-25 02:17:07 INFO Study area 193: Starting vector generation\n", - "2023-01-25 02:17:18 INFO Study area 193: Loaded rasters\n", - "Operating in single z-value, multiple arrays mode\n", - "Failed to generate contours: 1988, 1989, 1992, 1993\n", - "2023-01-25 02:17:47 INFO Study area 193: Extracted shorelines from raster data\n", - "2023-01-25 02:17:47 INFO Study area 193: Extracted rates of change points\n", - "2023-01-25 02:17:48 INFO Study area 193: Calculated distances to each annual shoreline\n", - "2023-01-25 02:17:48 INFO Study area 193: Calculated rates of change regressions\n", - "2023-01-25 02:17:48 INFO Study area 193: Calculated all of time statistics\n", - "2023-01-25 02:17:48 INFO Study area 193: Calculated rate of change certainty flags\n", - "2023-01-25 02:17:48 INFO Study area 193: Added region attributes and geohash UIDs\n", - "2023-01-25 02:17:48 ERROR Study area 193: Failed to run process with error Study area 193: No vector points data to export after clipping to study area extent\n", - "Traceback (most recent call last):\n", - " File \"/home/jovyan/Robbi/dea-coastlines/coastlines/vector.py\", line 1622, in generate_vectors\n", - " points_gdf.to_crs(\"EPSG:4326\").to_file(\n", - " File \"/env/lib/python3.8/site-packages/geopandas/geodataframe.py\", line 1114, in to_file\n", - " _to_file(self, filename, driver, schema, index, **kwargs)\n", - " File \"/env/lib/python3.8/site-packages/geopandas/io/file.py\", line 367, in _to_file\n", - " schema = infer_schema(df)\n", - " File \"/env/lib/python3.8/site-packages/geopandas/io/file.py\", line 428, in infer_schema\n", - " raise ValueError(\"Cannot write empty DataFrame to file.\")\n", - "ValueError: Cannot write empty DataFrame to file.\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/home/jovyan/Robbi/dea-coastlines/coastlines/vector.py\", line 1820, in generate_vectors_cli\n", - " generate_vectors(\n", - " File \"/home/jovyan/Robbi/dea-coastlines/coastlines/vector.py\", line 1637, in generate_vectors\n", - " raise ValueError(\n", - "ValueError: Study area 193: No vector points data to export after clipping to study area extent\n", - "478\n", - "\n", - "2023-01-25 02:17:55 INFO Study area 478: Loaded study area grid\n", - "2023-01-25 02:18:01 INFO Study area 478: Loaded virtual product\n", - "Creating reduced resolution tide modelling array\n", - "Modelling tides using FES2014 tide model\n", - "Reprojecting tides into original array\n", - "100%|███████████████████████████████████████| 1405/1405 [00:17<00:00, 80.45it/s]\n", - "2023-01-25 02:19:01 INFO Study area 478: Finished modelling tide heights\n", - "2023-01-25 02:19:02 INFO Study area 478: Calculating low and high tide cutoffs for each pixel\n", - "2023-01-25 02:19:02 INFO Study area 478: Started exporting raster data\n", - "2023-01-25 02:24:52 INFO Study area 478: Completed exporting raster data\n", - "distributed.nanny - WARNING - Worker process still alive after 3.9999990463256836 seconds, killing\n", - "2023-01-25 02:24:59 INFO Study area 478: Starting vector generation\n", - "2023-01-25 02:25:10 INFO Study area 478: Loaded rasters\n", - "Operating in single z-value, multiple arrays mode\n", - "Failed to generate any valid contours; verify that values passed to `z_values` are valid and present in `da`\n", - "2023-01-25 02:25:39 ERROR Study area 478: Failed to run process with error Study area 478: Unable to extract any valid shorelines from raster data\n", - "Traceback (most recent call last):\n", - " File \"/home/jovyan/Robbi/dea-coastlines/coastlines/vector.py\", line 1820, in generate_vectors_cli\n", - " generate_vectors(\n", - " File \"/home/jovyan/Robbi/dea-coastlines/coastlines/vector.py\", line 1461, in generate_vectors\n", - " raise ValueError(\n", - "ValueError: Study area 478: Unable to extract any valid shorelines from raster data\n" - ] - } - ], + "outputs": [], "source": [ "# Run raster and vector generation for each study area\n", "for study_area in study_areas:\n", diff --git a/notebooks/DEACoastlines_generation_vector.ipynb b/notebooks/DEACoastlines_generation_vector.ipynb index 08e1983..c69008c 100644 --- a/notebooks/DEACoastlines_generation_vector.ipynb +++ b/notebooks/DEACoastlines_generation_vector.ipynb @@ -113,11 +113,11 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "study_area = 424\n", + "study_area = 64\n", "raster_version = 'testing'\n", "vector_version = 'testing'\n", "config_path = 'configs/dea_coastlines_config.yaml'\n", @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -156,18 +156,18 @@ "output_type": "stream", "text": [ "\n", - "Dimensions: (year: 34, y: 1337, x: 875)\n", + "Dimensions: (year: 34, y: 1170, x: 1942)\n", "Coordinates:\n", " * year (year) int64 1988 1989 1990 1991 1992 ... 2017 2018 2019 2020 2021\n", - " * y (y) float64 -2.305e+06 -2.305e+06 ... -2.345e+06 -2.345e+06\n", - " * x (x) float64 6.406e+05 6.407e+05 6.407e+05 ... 6.668e+05 6.669e+05\n", + " * y (y) float64 -1.331e+06 -1.331e+06 ... -1.366e+06 -1.366e+06\n", + " * x (x) float64 7.738e+05 7.738e+05 7.738e+05 ... 8.32e+05 8.32e+05\n", "Data variables:\n", - " mndwi (year, y, x) float64 nan nan nan nan ... 0.2546 0.2567 0.2258\n", - " count (year, y, x) int16 0 0 0 0 0 0 0 0 0 0 0 ... 7 7 8 8 8 8 8 8 8 8 8\n", - " stdev (year, y, x) float64 nan nan nan nan ... 0.1565 0.1717 0.1673\n", + " mndwi (year, y, x) float32 0.9094 0.9407 0.9232 ... -0.6553 -0.6597\n", + " count (year, y, x) int16 9 9 9 9 9 9 9 9 9 ... 13 12 12 12 12 12 12 12 12\n", + " stdev (year, y, x) float32 0.06745 0.06894 0.0745 ... 0.02993 0.0275\n", "Attributes:\n", - " transform: (30.0, 0.0, 640635.0, 0.0, -30.0, -2304825.0)\n", - " crs: +init=epsg:32656\n", + " transform: (30.0, 0.0, 773745.0, 0.0, -30.0, -1331145.0)\n", + " crs: +init=epsg:32652\n", " res: (30.0, 30.0)\n", " is_tiled: 1\n", " nodatavals: (nan,)\n", @@ -179,16 +179,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 50, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -270,22 +270,22 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 52, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -313,23 +313,14 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Operating in single z-value, multiple arrays mode\n", - "Failed to generate any valid contours; verify that values passed to `z_values` are valid and present in `da`\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/env/lib/python3.8/site-packages/geopandas/plotting.py:681: UserWarning: The GeoDataFrame you are attempting to plot is empty. Nothing has been displayed.\n", - " warnings.warn(\n" + "Operating in single z-value, multiple arrays mode\n" ] }, { @@ -338,13 +329,13 @@ "" ] }, - "execution_count": 54, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] @@ -371,59 +362,7 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Operating in single z-value, multiple arrays mode\n", - "Writing contours to temp_mean.geojson\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "testing = masked_ds.median(dim='year').where(masked_ds.isnull().mean(dim='year') < 0.9).expand_dims('year')\n", - "\n", - "# Extract shorelines\n", - "contour_mean_gdf = subpixel_contours(\n", - " da=testing,\n", - " z_values=index_threshold,\n", - " min_vertices=10,\n", - " dim=\"year\",\n", - " output_path=f\"temp_mean.geojson\",\n", - ").set_index(\"year\")\n", - "\n", - "# Plot shorelines\n", - "contour_mean_gdf.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -450,63 +389,6 @@ "# )" ] }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " geometry\n", - "year \n", - "0 MULTILINESTRING ((552533.430 -2154420.000, 552..." - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "contour_mean_gdf" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -517,13 +399,13 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# Extract statistics modelling points along baseline shoreline\n", "try:\n", - " points_gdf = coastlines.vector.points_on_line(contour_mean_gdf, str(0), distance=30)\n", + " points_gdf = coastlines.vector.points_on_line(contours_gdf, str(baseline_year), distance=30)\n", "\n", "except KeyError:\n", " points_gdf = None" @@ -538,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -575,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -607,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -672,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -694,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -718,14 +600,14 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "if points_gdf is not None and len(points_gdf) > 0:\n", "\n", " # Clip stats to study area extent\n", - " points_gdf = points_gdf[points_gdf.intersects(gridcell_gdf.geometry.item())]\n", + " points_gdf_clipped = points_gdf.clip(gridcell_gdf)\n", "\n", " # Set output path\n", " stats_path = (\n", @@ -734,16 +616,16 @@ " )\n", " \n", " # Export to GeoJSON\n", - " points_gdf.to_crs(\"EPSG:4326\").to_file(\n", + " points_gdf_clipped.to_crs(\"EPSG:4326\").to_file(\n", " f\"{stats_path}.geojson\",\n", " driver=\"GeoJSON\",\n", " )\n", "\n", " # Export as ESRI shapefiles\n", - " points_gdf.to_file(\n", + " points_gdf_clipped.to_file(\n", " f\"{stats_path}.shp\",\n", " schema={\n", - " \"properties\": coastlines.vector.vector_schema(points_gdf),\n", + " \"properties\": coastlines.vector.vector_schema(points_gdf_clipped),\n", " \"geometry\": \"Point\",\n", " },\n", " )\n", @@ -759,17 +641,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "if len(contours_gdf.index) > 0:\n", - " \n", + "\n", " # Assign certainty to contours based on underlying masks\n", " contours_gdf = coastlines.vector.contour_certainty(contours_gdf, certainty_masks)\n", "\n", " # Add tide datum details (this supports future addition of extra tide datums)\n", - " contours_gdf[\"tide_datum\"] = \"0 m AMSL\"\n", + " contours_gdf[\"tide_datum\"] = \"0.0 m AMSL\"\n", "\n", " # Add region attributes\n", " contours_gdf = coastlines.vector.region_atttributes(\n", @@ -783,16 +665,18 @@ " )\n", "\n", " # Clip annual shoreline contours to study area extent\n", - " contours_gdf[\"geometry\"] = contours_gdf.intersection(gridcell_gdf.geometry.item())\n", + " contours_gdf_clipped = contours_gdf.clip(gridcell_gdf)\n", "\n", " # Export to GeoJSON\n", - " contours_gdf.to_crs(\"EPSG:4326\").to_file(f\"{contour_path}.geojson\", driver=\"GeoJSON\")\n", + " contours_gdf_clipped.to_crs(\"EPSG:4326\").to_file(\n", + " f\"{contour_path}.geojson\", driver=\"GeoJSON\"\n", + " )\n", "\n", " # Export stats and contours as ESRI shapefiles\n", - " contours_gdf.to_file(\n", + " contours_gdf_clipped.to_file(\n", " f\"{contour_path}.shp\",\n", " schema={\n", - " \"properties\": coastlines.vector.vector_schema(contours_gdf),\n", + " \"properties\": coastlines.vector.vector_schema(contours_gdf_clipped),\n", " \"geometry\": [\"MultiLineString\", \"LineString\"],\n", " },\n", " )"