diff --git a/README.md b/README.md index 56ae15e..4af03c1 100644 --- a/README.md +++ b/README.md @@ -43,7 +43,7 @@ To run the module, 1. You need access to Google Earth Engine 2. Install - 3. If you want to use the ERA5 layer, you need to install the [Climate Data Store (CDS) Application Program Interface (API)](https://cds.climate.copernicus.eu/api-how-to) + 3. If you want to use the ERA5 layer, you need to install the [Climate Data Store (CDS) Application Program Interface (API)](https://cds.climate.copernicus.eu/how-to-api) ### Interactive development diff --git a/city_metrix/layers/albedo.py b/city_metrix/layers/albedo.py index dc1780a..d8f1430 100644 --- a/city_metrix/layers/albedo.py +++ b/city_metrix/layers/albedo.py @@ -1,6 +1,7 @@ import ee -from .layer import Layer, get_image_collection +from .layer import Layer, get_image_collection, set_resampling_method + class Albedo(Layer): """ @@ -8,14 +9,17 @@ class Albedo(Layer): start_date: starting date for data retrieval end_date: ending date for data retrieval spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster) + resampling_method: interpolation method used by Google Earth Engine. Albedo default is 'bilinear'. All options are: ('bilinear', 'bicubic', None). threshold: threshold value for filtering the retrieval """ - def __init__(self, start_date="2021-01-01", end_date="2022-01-01", spatial_resolution=10, threshold=None, **kwargs): + def __init__(self, start_date="2021-01-01", end_date="2022-01-01", spatial_resolution=10, + resampling_method='bilinear', threshold=None, **kwargs): super().__init__(**kwargs) self.start_date = start_date self.end_date = end_date self.spatial_resolution = spatial_resolution + self.resampling_method = resampling_method self.threshold = threshold def get_data(self, bbox): @@ -115,7 +119,16 @@ def calc_s2_albedo(image): ## S2 MOSAIC AND ALBEDO dataset = get_masked_s2_collection(ee.Geometry.BBox(*bbox), self.start_date, self.end_date) s2_albedo = dataset.map(calc_s2_albedo) - albedo_mean = s2_albedo.reduce(ee.Reducer.mean()) + + if self.resampling_method is not None: + albedo_mean = (s2_albedo + .map(lambda x: set_resampling_method(x, self.resampling_method)) + .reduce(ee.Reducer.mean()) + ) + else: + albedo_mean = (s2_albedo + .reduce(ee.Reducer.mean()) + ) albedo_mean_ic = ee.ImageCollection(albedo_mean) data = get_image_collection( @@ -129,3 +142,5 @@ def calc_s2_albedo(image): return data.where(data < self.threshold) return data + + diff --git a/city_metrix/layers/alos_dsm.py b/city_metrix/layers/alos_dsm.py index c22df82..4d5db37 100644 --- a/city_metrix/layers/alos_dsm.py +++ b/city_metrix/layers/alos_dsm.py @@ -1,26 +1,38 @@ import ee -from .layer import Layer, get_image_collection +from .layer import Layer, get_image_collection, set_resampling_method class AlosDSM(Layer): """ Attributes: spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster) + resampling_method: interpolation method used by Google Earth Engine. AlosDSM default is 'bilinear'. All options are: ('bilinear', 'bicubic', None). """ - def __init__(self, spatial_resolution=30, **kwargs): + def __init__(self, spatial_resolution=30, resampling_method='bilinear', **kwargs): super().__init__(**kwargs) self.spatial_resolution = spatial_resolution + self.resampling_method = resampling_method def get_data(self, bbox): alos_dsm = ee.ImageCollection("JAXA/ALOS/AW3D30/V3_2") - alos_dsm_ic = ee.ImageCollection(alos_dsm - .filterBounds(ee.Geometry.BBox(*bbox)) - .select('DSM') - .mean() - ) + if self.resampling_method is not None: + alos_dsm_ic = ee.ImageCollection( + alos_dsm + .filterBounds(ee.Geometry.BBox(*bbox)) + .map(lambda x: set_resampling_method(x, self.resampling_method), ) + .select('DSM') + .mean() + ) + else: + alos_dsm_ic = ee.ImageCollection( + alos_dsm + .filterBounds(ee.Geometry.BBox(*bbox)) + .select('DSM') + .mean() + ) data = get_image_collection( alos_dsm_ic, diff --git a/city_metrix/layers/layer.py b/city_metrix/layers/layer.py index 3d2829f..336b3ee 100644 --- a/city_metrix/layers/layer.py +++ b/city_metrix/layers/layer.py @@ -323,6 +323,18 @@ def get_stats_funcs(stats_func): return [stats_func] +def set_resampling_method(image: ee.Image, resampling_method: str): + valid_raster_resampling_methods = ['bilinear', 'bicubic'] + + if resampling_method not in valid_raster_resampling_methods: + raise ValueError(f"Invalid resampling method ('{resampling_method}'). " + f"Valid methods: {valid_raster_resampling_methods}") + + data = image.resample(resampling_method) + + return data + + def get_image_collection( image_collection: ImageCollection, bbox: Tuple[float], diff --git a/city_metrix/layers/nasa_dem.py b/city_metrix/layers/nasa_dem.py index d3840d3..bcdd48a 100644 --- a/city_metrix/layers/nasa_dem.py +++ b/city_metrix/layers/nasa_dem.py @@ -1,26 +1,36 @@ import ee -from .layer import Layer, get_image_collection +from .layer import Layer, get_image_collection, set_resampling_method class NasaDEM(Layer): """ Attributes: spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster) + resampling_method: interpolation method used by Google Earth Engine. NasaDEM default is 'bilinear'. All options are: ('bilinear', 'bicubic', None). """ - def __init__(self, spatial_resolution=30, **kwargs): + def __init__(self, spatial_resolution=30, resampling_method='bilinear', **kwargs): super().__init__(**kwargs) self.spatial_resolution = spatial_resolution + self.resampling_method = resampling_method def get_data(self, bbox): nasa_dem = ee.Image("NASA/NASADEM_HGT/001") - nasa_dem_elev = (ee.ImageCollection(nasa_dem) - .filterBounds(ee.Geometry.BBox(*bbox)) - .select('elevation') - .mean() - ) + if self.resampling_method is not None: + nasa_dem_elev = (ee.ImageCollection(nasa_dem) + .filterBounds(ee.Geometry.BBox(*bbox)) + .map(lambda x: set_resampling_method(x, self.resampling_method), ) + .select('elevation') + .mean() + ) + else: + nasa_dem_elev = (ee.ImageCollection(nasa_dem) + .filterBounds(ee.Geometry.BBox(*bbox)) + .select('elevation') + .mean() + ) nasa_dem_elev_ic = ee.ImageCollection(nasa_dem_elev) data = get_image_collection( diff --git a/tests/resources/bbox_constants.py b/tests/resources/bbox_constants.py index 789ab48..846f793 100644 --- a/tests/resources/bbox_constants.py +++ b/tests/resources/bbox_constants.py @@ -24,3 +24,13 @@ -38.39993,-12.93239 ) +BBOX_NLD_AMSTERDAM_TEST = ( + 4.9012,52.3720, + 4.9083,52.3752 +) + +BBOX_NLD_AMSTERDAM_LARGE_TEST = ( + 4.884629880473071,52.34146514406914, + 4.914180290924863,52.359560786247165 +) + diff --git a/tests/resources/layer_dumps_for_br_lauro_de_freitas/README.md b/tests/resources/layer_dumps/README.md similarity index 100% rename from tests/resources/layer_dumps_for_br_lauro_de_freitas/README.md rename to tests/resources/layer_dumps/README.md diff --git a/tests/resources/layer_dumps_for_br_lauro_de_freitas/__init__.py b/tests/resources/layer_dumps/__init__.py similarity index 100% rename from tests/resources/layer_dumps_for_br_lauro_de_freitas/__init__.py rename to tests/resources/layer_dumps/__init__.py diff --git a/tests/resources/layer_dumps_for_br_lauro_de_freitas/conftest.py b/tests/resources/layer_dumps/conftest.py similarity index 94% rename from tests/resources/layer_dumps_for_br_lauro_de_freitas/conftest.py rename to tests/resources/layer_dumps/conftest.py index 6b33b51..9a5dd9a 100644 --- a/tests/resources/layer_dumps_for_br_lauro_de_freitas/conftest.py +++ b/tests/resources/layer_dumps/conftest.py @@ -4,7 +4,8 @@ import shutil from collections import namedtuple -from tests.resources.bbox_constants import BBOX_BRA_LAURO_DE_FREITAS_1 +from tests.resources.bbox_constants import BBOX_BRA_LAURO_DE_FREITAS_1, BBOX_NLD_AMSTERDAM_TEST, \ + BBOX_NLD_AMSTERDAM_LARGE_TEST from tests.tools.general_tools import create_target_folder, is_valid_path # RUN_DUMPS is the master control for whether the writes and tests are executed @@ -19,6 +20,8 @@ # Both the tests and QGIS file are implemented for the same bounding box in Brazil. COUNTRY_CODE_FOR_BBOX = 'BRA' BBOX = BBOX_BRA_LAURO_DE_FREITAS_1 +# BBOX = BBOX_NLD_AMSTERDAM_TEST +# BBOX = BBOX_NLD_AMSTERDAM_LARGE_TEST # Specify None to write to a temporary default folder otherwise specify a valid custom target path. CUSTOM_DUMP_DIRECTORY = None diff --git a/tests/resources/layer_dumps_for_br_lauro_de_freitas/test_write_all_layers.py b/tests/resources/layer_dumps/test_write_all_layers.py similarity index 100% rename from tests/resources/layer_dumps_for_br_lauro_de_freitas/test_write_all_layers.py rename to tests/resources/layer_dumps/test_write_all_layers.py diff --git a/tests/resources/layer_dumps_for_br_lauro_de_freitas/test_write_layers_other.py b/tests/resources/layer_dumps/test_write_layers_other.py similarity index 100% rename from tests/resources/layer_dumps_for_br_lauro_de_freitas/test_write_layers_other.py rename to tests/resources/layer_dumps/test_write_layers_other.py diff --git a/tests/resources/layer_dumps_for_br_lauro_de_freitas/test_write_layers_using_fixed_resolution.py b/tests/resources/layer_dumps/test_write_layers_using_fixed_resolution.py similarity index 99% rename from tests/resources/layer_dumps_for_br_lauro_de_freitas/test_write_layers_using_fixed_resolution.py rename to tests/resources/layer_dumps/test_write_layers_using_fixed_resolution.py index 8964b86..0ca19c6 100644 --- a/tests/resources/layer_dumps_for_br_lauro_de_freitas/test_write_layers_using_fixed_resolution.py +++ b/tests/resources/layer_dumps/test_write_layers_using_fixed_resolution.py @@ -3,7 +3,7 @@ import pytest from city_metrix.layers import * -from .conftest import RUN_DUMPS, prep_output_path, verify_file_is_populated, get_file_count_in_folder +from .conftest import RUN_DUMPS, prep_output_path, verify_file_is_populated TARGET_RESOLUTION = 5 diff --git a/tests/test_layer_metrics.py b/tests/test_layer_metrics.py index 0002f58..659af0c 100644 --- a/tests/test_layer_metrics.py +++ b/tests/test_layer_metrics.py @@ -47,8 +47,24 @@ def test_read_image_collection_scale(): pytest.approx(expected_y_size, rel=EE_IMAGE_DIMENSION_TOLERANCE) == actual_y_size ) -def test_albedo_metrics(): - data = Albedo().get_data(BBOX) + +def test_albedo_metrics_default_resampling(): + # Default resampling_method is bilinear + data = Albedo(spatial_resolution=10).get_data(BBOX) + + # Bounding values + expected_min_value = _convert_fraction_to_rounded_percent(0.03) + expected_max_value = _convert_fraction_to_rounded_percent(0.32) + actual_min_value = _convert_fraction_to_rounded_percent(data.values.min()) + actual_max_value = _convert_fraction_to_rounded_percent(data.values.max()) + + # Value range + assert expected_min_value == actual_min_value + assert expected_max_value == actual_max_value + + +def test_albedo_metrics_no_resampling(): + data = Albedo(spatial_resolution=10, resampling_method= None).get_data(BBOX) # Bounding values expected_min_value = _convert_fraction_to_rounded_percent(0.03) @@ -62,7 +78,7 @@ def test_albedo_metrics(): def test_alos_dsm_metrics(): - data = AlosDSM().get_data(BBOX) + data = AlosDSM(resampling_method= None).get_data(BBOX) # Bounding values expected_min_value = 16