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test_views_execute.py
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import shutil
import dataclasses
import json
import math
import re
import sys
import textwrap
from io import BytesIO
from pathlib import Path
from typing import Iterable, Optional
from unittest import mock, skip
from zipfile import ZipFile
import geopandas as gpd
import numpy as np
import pytest
import shapely.geometry
from openeo_driver.datacube import DriverDataCube, DriverVectorCube
from openeo_driver.datastructs import ResolutionMergeArgs, SarBackscatterArgs
from openeo_driver.delayed_vector import DelayedVector
from openeo_driver.dry_run import ProcessType
from openeo_driver.dummy import dummy_backend
from openeo_driver.dummy.dummy_backend import DummyVisitor
from openeo_driver.errors import (
ProcessGraphInvalidException,
ProcessGraphMissingException,
)
from openeo_driver.processes import ProcessArgs, ProcessRegistry
from openeo_driver.ProcessGraphDeserializer import (
custom_process,
custom_process_from_process_graph,
)
from openeo_driver.testing import (
TEST_USER,
TEST_USER_BEARER_TOKEN,
ApiTester,
ApproxGeoJSONByBounds,
DictSubSet,
RegexMatcher,
ephemeral_fileserver,
generate_unique_test_process_id,
preprocess_check_and_replace,
preprocess_regex_check_and_replace,
)
from openeo_driver.users import User
from openeo_driver.util.geometry import (
as_geojson_feature,
as_geojson_feature_collection,
)
from openeo_driver.util.ioformats import IOFORMATS
from openeo_driver.util.logging import FlaskRequestCorrelationIdLogging
from openeo_driver.utils import EvalEnv
from .data import TEST_DATA_ROOT, get_path, load_json
@pytest.fixture(
params=[
"1.0",
# "1.1",
"1.2",
]
)
def api_version(request):
return request.param
@pytest.fixture
def api(api_version, client, backend_implementation) -> ApiTester:
dummy_backend.reset(backend_implementation)
if api_version.startswith("1."):
# For now, use "pg/1.0" for all 1.x API requests (no need to differentiate yet)
data_root = TEST_DATA_ROOT / "pg" / "1.0"
else:
raise ValueError(api_version)
return ApiTester(api_version=api_version, client=client, data_root=data_root)
# Major.minor version of current python
CURRENT_PY3x = f"{sys.version_info.major}.{sys.version_info.minor}"
def test_udf_runtimes(api):
runtimes = api.get('/udf_runtimes').assert_status_code(200).json
assert runtimes == DictSubSet({
"Python": DictSubSet({
"title": RegexMatcher("Python"),
"type": "language",
"default": "3",
"versions": DictSubSet({
"3": {"libraries": DictSubSet({"numpy": {"version": RegexMatcher(r"\d+\.\d+\.\d+")}})},
CURRENT_PY3x: {"libraries": DictSubSet({"numpy": {"version": RegexMatcher(r"\d+\.\d+\.\d+")}})},
})
})
})
def test_execute_simple_download(api):
resp = api.check_result("basic.json")
assert re.match(r"GTiff:save_result\(.*DummyDataCube", resp.text, flags=re.IGNORECASE)
def test_load_collection(api):
api.check_result({
'collection': {
'process_id': 'load_collection',
'arguments': {'id': 'S2_FAPAR_CLOUDCOVER'},
'result': True
}
})
def test_load_collection_date_shift(api):
api.check_result({
'dateshift1': {'arguments': {'date': '2020-01-01',
'unit': 'day',
'value': -30},
'process_id': 'date_shift'
},
'loadcollection1': {'arguments': {'id': 'S2_FAPAR_CLOUDCOVER',
'temporal_extent': [{'from_node': 'dateshift1'},
'2021-01-01']},
'process_id': 'load_collection',
'result': True}})
params = dummy_backend.last_load_collection_call("S2_FAPAR_CLOUDCOVER")
assert params["temporal_extent"] == ('2019-12-02', '2021-01-01')
def test_execute_filter_temporal(api):
api.check_result({
'loadcollection1': {
'process_id': 'load_collection',
'arguments': {'id': 'S2_FAPAR_CLOUDCOVER'}
},
'filtertemporal1': {
'process_id': 'filter_temporal',
'arguments': {
'data': {'from_node': 'loadcollection1'},
'extent': ['2018-01-01', '2018-12-31']
},
'result': True
},
})
params = dummy_backend.last_load_collection_call("S2_FAPAR_CLOUDCOVER")
assert params["temporal_extent"] == ("2018-01-01", "2018-12-31")
def test_execute_filter_temporal_extent_reversed(api):
resp = api.result({
'loadcollection1': {
'process_id': 'load_collection',
'arguments': {'id': 'S2_FAPAR_CLOUDCOVER'}
},
'filtertemporal1': {
'process_id': 'filter_temporal',
'arguments': {
'data': {'from_node': 'loadcollection1'},
'extent': ['2021-12-07', '2021-12-06']
},
'result': True
},
})
resp.assert_error(400, "ProcessParameterInvalid",
message="The value passed for parameter 'extent' in process 'filter_temporal' is invalid:"
" end '2021-12-06' is before start '2021-12-07'")
def test_execute_filter_temporal_extent_reversed(api):
resp = api.result({
'loadcollection1': {
'process_id': 'load_collection',
'arguments': {'id': 'S2_FAPAR_CLOUDCOVER'}
},
'filtertemporal1': {
'process_id': 'filter_temporal',
'arguments': {
'data': {'from_node': 'loadcollection1'},
'extent': [None, None]
},
'result': True
},
})
resp.assert_error(400, "ProcessParameterInvalid",
message="The value passed for parameter 'extent' in process 'filter_temporal' is invalid:"
" both start and end are null")
def test_execute_filter_bbox(api):
api.check_result({
'loadcollection1': {
'process_id': 'load_collection',
'arguments': {'id': 'S2_FAPAR_CLOUDCOVER'}
},
'filterbbox1': {
'process_id': 'filter_bbox',
'arguments': {
'data': {'from_node': 'loadcollection1'},
'extent': {
"west": 3, "east": 5,
"south": 50, "north": 51,
"crs": "EPSG:4326",
}
},
'result': True
},
})
params = dummy_backend.last_load_collection_call("S2_FAPAR_CLOUDCOVER")
assert params["spatial_extent"] == {"west": 3, "east": 5, "south": 50, "north": 51, "crs": "EPSG:4326", }
def test_execute_filter_bbox_integer_crs(api):
api.check_result({
'loadcollection1': {
'process_id': 'load_collection',
'arguments': {'id': 'S2_FAPAR_CLOUDCOVER'}
},
'filterbbox1': {
'process_id': 'filter_bbox',
'arguments': {
'data': {'from_node': 'loadcollection1'},
'extent': {
"west": 3, "east": 5,
"south": 50, "north": 51,
"crs": 4326,
}
},
'result': True
},
})
params = dummy_backend.last_load_collection_call("S2_FAPAR_CLOUDCOVER")
assert params["spatial_extent"] == {"west": 3, "east": 5, "south": 50, "north": 51, "crs": "EPSG:4326", }
def test_execute_filter_bands(api):
api.check_result({
'loadcollection1': {
'process_id': 'load_collection',
'arguments': {'id': 'S2_FOOBAR'},
},
'filterbands1': {
'process_id': 'filter_bands',
'arguments': {
'data': {'from_node': 'loadcollection1'},
'bands': ["B02", "B03"],
},
'result': True
},
})
load_params = dummy_backend.last_load_collection_call("S2_FOOBAR")
assert load_params["bands"] == ["B02", "B03"]
def test_execute_apply_kernel(api):
kernel_list = [[0, 1, 0], [1, 1, 1], [0, 1, 0]]
api.check_result("apply_kernel.json")
dummy = dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER")
assert dummy.apply_kernel.call_count == 1
np_kernel = dummy.apply_kernel.call_args[1]["kernel"]
assert np_kernel.tolist() == kernel_list
assert dummy.apply_kernel.call_args[1]["factor"] == 3
def test_load_collection_filter(api):
api.check_result({
'collection': {
'process_id': 'load_collection',
'arguments': {
'id': 'S2_FAPAR_CLOUDCOVER',
'spatial_extent': {
'west': 5.027, 'east': 5.0438, 'north': 51.2213,
'south': 51.1974, 'crs': 'EPSG:4326'
},
'temporal_extent': ['2018-01-01', '2018-12-31'],
'featureflags': {'experimental': True}
},
'result': True
}
})
params = dummy_backend.last_load_collection_call("S2_FAPAR_CLOUDCOVER")
assert params["temporal_extent"] == ('2018-01-01', '2018-12-31')
assert params["spatial_extent"] == {
'west': 5.027, 'south': 51.1974, 'east': 5.0438, 'north': 51.2213, 'crs': 'EPSG:4326',
}
assert params["featureflags"] == {'experimental': True}
@pytest.mark.parametrize(
["spatial_extent", "expected"],
[
(
load_json("geojson/Polygon01.json"),
{
"west": 5.1,
"south": 51.2,
"east": 5.14,
"north": 51.23,
"crs": "EPSG:4326",
},
),
(
load_json("geojson/MultiPolygon01.json"),
{
"west": 5.1,
"south": 51.2,
"east": 5.14,
"north": 51.24,
"crs": "EPSG:4326",
},
),
(
load_json("geojson/Feature01.json"),
{"west": 1.0, "south": 1.0, "east": 3.0, "north": 3.0, "crs": "EPSG:4326"},
),
(
load_json("geojson/Feature03-null-properties.json"),
{"west": 1.0, "south": 1.0, "east": 3.0, "north": 3.0, "crs": "EPSG:4326"},
),
(
as_geojson_feature(shapely.geometry.Polygon.from_bounds(11, 22, 33, 44)),
{"west": 11, "south": 22, "east": 33, "north": 44, "crs": "EPSG:4326"},
),
(
load_json("geojson/FeatureCollection01.json"),
{
"west": 4.45,
"south": 51.1,
"east": 4.52,
"north": 51.2,
"crs": "EPSG:4326",
},
),
(
as_geojson_feature_collection(
as_geojson_feature(
shapely.geometry.Polygon.from_bounds(11, 22, 33, 44),
properties=None,
),
as_geojson_feature(
shapely.geometry.Polygon.from_bounds(22, 33, 44, 55),
properties={"color": "red"},
),
),
{"west": 11, "south": 22, "east": 44, "north": 55, "crs": "EPSG:4326"},
),
(
as_geojson_feature_collection(
shapely.geometry.Point(2, 3),
shapely.geometry.Point(4, 5),
),
{"west": 2, "south": 3, "east": 4, "north": 5, "crs": "EPSG:4326"},
),
],
)
def test_load_collection_spatial_extent_geojson(api, spatial_extent, expected):
api.check_result(
{
"collection": {
"process_id": "load_collection",
"arguments": {
"id": "S2_FAPAR_CLOUDCOVER",
"spatial_extent": spatial_extent,
"temporal_extent": ["2018-01-01", "2018-12-31"],
},
"result": True,
}
}
)
params = dummy_backend.last_load_collection_call("S2_FAPAR_CLOUDCOVER")
assert params["temporal_extent"] == ("2018-01-01", "2018-12-31")
assert params["spatial_extent"] == expected
def test_execute_apply_unary(api):
api.check_result("apply_unary.json")
assert dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER").apply.call_count == 1
def test_execute_apply_unary_parent_scope(api):
api.check_result(
"apply_unary.json",
preprocess=preprocess_check_and_replace('"from_parameter": "x"', '"from_parameter": "data"')
)
#
@pytest.mark.skip('parameter checking of callback graphs now happens somewhere else')
def test_execute_apply_unary_invalid_from_parameter(api):
resp = api.result(
"apply_unary.json",
preprocess=preprocess_check_and_replace('"from_parameter": "x"', '"from_parameter": "1nv8l16"')
)
resp.assert_error(400, "ProcessParameterRequired")
def test_execute_apply_run_udf_100(api):
api.check_result("apply_run_udf.json")
assert dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER").apply.call_count == 1
def test_reduce_temporal_run_udf(api):
api.check_result("reduce_temporal_run_udf.json")
if api.api_version_compare.at_least("1.0.0"):
loadCall = dummy_backend.last_load_collection_call("S2_FAPAR_CLOUDCOVER")
assert loadCall.process_types == set([ProcessType.GLOBAL_TIME])
assert dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER").reduce_dimension.call_count == 1
else:
assert dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER").apply_tiles_spatiotemporal.call_count == 1
def test_reduce_temporal_run_udf_invalid_dimension(api):
resp = api.result(
"reduce_temporal_run_udf.json",
preprocess=preprocess_check_and_replace('"dimension": "t"', '"dimension": "tempo"')
)
resp.assert_error(
400,
"ProcessParameterInvalid",
message="The value passed for parameter 'dimension' in process 'reduce_dimension' is invalid: Must be one of ['x', 'y', 't'] but got 'tempo'.",
)
def test_reduce_bands_run_udf(api):
api.check_result("reduce_bands_run_udf.json")
if api.api_version_compare.at_least("1.0.0"):
assert dummy_backend.get_collection("S2_FOOBAR").reduce_dimension.call_count == 1
else:
assert dummy_backend.get_collection("S2_FOOBAR").apply_tiles.call_count == 1
def test_reduce_bands_run_udf_invalid_dimension(api):
resp = api.result(
"reduce_bands_run_udf.json",
preprocess=preprocess_check_and_replace('"dimension": "bands"', '"dimension": "layers"')
)
resp.assert_error(
400,
"ProcessParameterInvalid",
message="The value passed for parameter 'dimension' in process 'reduce_dimension' is invalid: Must be one of ['x', 'y', 't', 'bands'] but got 'layers'.",
)
def test_apply_dimension_temporal_run_udf(api):
api.check_result("apply_dimension_temporal_run_udf.json")
dummy = dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER")
assert dummy.apply_dimension.call_count == 1
callback = dummy.apply_dimension.call_args.kwargs["process"]
# check if callback is valid
DummyVisitor().accept_process_graph(callback)
dummy.rename_dimension.assert_called_with("t", "new_time_dimension")
load_parameters = dummy_backend.last_load_collection_call("S2_FAPAR_CLOUDCOVER")
assert load_parameters.process_types == set([ProcessType.GLOBAL_TIME])
def test_apply_dimension_temporal_run_udf_invalid_temporal_dimension(api):
resp = api.result(
"apply_dimension_temporal_run_udf.json",
preprocess=preprocess_check_and_replace('"dimension": "t"', '"dimension": "letemps"')
)
resp.assert_error(
400,
"ProcessParameterInvalid",
message="The value passed for parameter 'dimension' in process 'apply_dimension' is invalid: Must be one of ['x', 'y', 't'] but got 'letemps'.",
)
def test_apply_neighborhood(api):
api.check_result(
"apply_neighborhood.json"
)
load_parameters = dummy_backend.last_load_collection_call("S2_FAPAR_CLOUDCOVER")
assert load_parameters.process_types == set([ProcessType.GLOBAL_TIME])
def test_reduce_max_t(api):
api.check_result("reduce_max.json", preprocess=preprocess_check_and_replace("PLACEHOLDER", "t"))
def test_reduce_max_x(api):
api.check_result("reduce_max.json", preprocess=preprocess_check_and_replace("PLACEHOLDER", "x"))
def test_reduce_max_y(api):
api.check_result("reduce_max.json", preprocess=preprocess_check_and_replace("PLACEHOLDER", "y"))
def test_reduce_max_bands(api):
api.check_result("reduce_max.json", preprocess=preprocess_check_and_replace("PLACEHOLDER", "bands"))
def test_reduce_max_invalid_dimension(api):
res = api.result("reduce_max.json", preprocess=preprocess_check_and_replace("PLACEHOLDER", "orbit"))
res.assert_error(
400,
"ProcessParameterInvalid",
message="The value passed for parameter 'dimension' in process 'reduce_dimension' is invalid: Must be one of ['x', 'y', 't', 'bands'] but got 'orbit'.",
)
def test_execute_merge_cubes(api):
api.check_result("merge_cubes.json")
dummy = dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER")
assert dummy.merge_cubes.call_count == 1
args, kwargs = dummy.merge_cubes.call_args
assert args[1:] == ('or',)
def test_execute_resample_and_merge_cubes(api):
api.check_result("resample_and_merge_cubes.json")
dummy = dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER")
last_load_collection_call = dummy_backend.last_load_collection_call("S2_FAPAR_CLOUDCOVER")
assert last_load_collection_call.target_crs == "AUTO:42001"
assert last_load_collection_call.target_resolution == [10, 10]
assert dummy.merge_cubes.call_count == 1
assert dummy.resample_cube_spatial.call_count == 1
args, kwargs = dummy.merge_cubes.call_args
assert args[1:] == ('or',)
def test_execute_merge_cubes_and_reduce(api):
api.check_result("merge_cubes_and_reduce.json")
dummy = dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER")
assert dummy.reduce_dimension.call_count == 1
args, kwargs = dummy.reduce_dimension.call_args
assert args == ()
assert kwargs["dimension"] == "t"
def test_reduce_bands(api):
api.check_result("reduce_bands.json")
dummy = dummy_backend.get_collection("S2_FOOBAR")
if api.api_version_compare.at_least("1.0.0"):
reduce_bands = dummy.reduce_dimension
else:
reduce_bands = dummy.reduce_bands
reduce_bands.assert_called_once()
if api.api_version_compare.below("1.0.0"):
visitor = reduce_bands.call_args_list[0][0][0]
assert isinstance(visitor, dummy_backend.DummyVisitor)
assert set(p[0] for p in visitor.processes) == {"sum", "subtract", "divide"}
def test_reduce_bands_invalid_dimension(api):
res = api.result("reduce_bands.json",
preprocess=preprocess_check_and_replace('"dimension": "bands"', '"dimension": "layor"'))
res.assert_error(
400,
"ProcessParameterInvalid",
message="The value passed for parameter 'dimension' in process 'reduce_dimension' is invalid: Must be one of ['x', 'y', 't', 'bands'] but got 'layor'.",
)
def test_execute_mask(api):
api.check_result("mask.json")
assert dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER").mask.call_count == 1
expected_spatial_extent = {
"west": 7.02,
"south": 51.2,
"east": 7.65,
"north": 51.7,
"crs": "EPSG:4326",
}
expected_geometry = DriverVectorCube.from_geojson(
{
"type": "Polygon",
"coordinates": [
[[7.02, 51.7], [7.65, 51.7], [7.65, 51.2], [7.04, 51.3], [7.02, 51.7]],
],
}
)
params = dummy_backend.last_load_collection_call('PROBAV_L3_S10_TOC_NDVI_333M_V2')
assert params["spatial_extent"] == expected_spatial_extent
assert params["aggregate_spatial_geometries"] == expected_geometry
params = dummy_backend.last_load_collection_call('S2_FAPAR_CLOUDCOVER')
assert params["spatial_extent"] == expected_spatial_extent
def test_execute_diy_mask(api):
api.check_result("scl_mask_custom.json")
# assert dummy_backend.get_collection("TERRASCOPE_S2_FAPAR_V2").mask.call_count == 1 # Optimized away now
load_collections = dummy_backend.all_load_collection_calls("TERRASCOPE_S2_FAPAR_V2")
assert len(load_collections) == 4
assert load_collections[0].pixel_buffer == [8.5,8.5]
assert load_collections[0].bands == ['SCENECLASSIFICATION_20M']
assert load_collections[1].pixel_buffer == [100.5, 100.5]
assert load_collections[1].bands == ['SCENECLASSIFICATION_20M']
assert load_collections[2].bands == ['SCENECLASSIFICATION_20M'] #due to use of resample_cube_spatial
assert load_collections[3].bands == ['FAPAR_10M']
def test_execute_mask_optimized_loading(api):
api.check_result("mask.json",
preprocess=preprocess_check_and_replace('"10"', 'null')
)
# assert dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER").mask.call_count == 1 # Optimized away now
expected_spatial_extent = {
"west": 7.02,
"south": 51.2,
"east": 7.65,
"north": 51.7,
"crs": "EPSG:4326",
}
expected_geometry = DriverVectorCube.from_geojson(
{
"type": "Polygon",
"coordinates": [
[[7.02, 51.7], [7.65, 51.7], [7.65, 51.2], [7.04, 51.3], [7.02, 51.7]],
],
}
)
params = dummy_backend.last_load_collection_call('S2_FAPAR_CLOUDCOVER')
assert params["spatial_extent"] == expected_spatial_extent
assert isinstance(params.data_mask, DriverDataCube)
params = dummy_backend.last_load_collection_call('PROBAV_L3_S10_TOC_NDVI_333M_V2')
assert params["spatial_extent"] == expected_spatial_extent
assert params["aggregate_spatial_geometries"] == expected_geometry
def test_execute_mask_same_source(api):
api.check_result({
"process_graph": {
"loadcollection1": {
"arguments": {
"bands": [
"B02",
"B03"
],
"id": "S2_FOOBAR",
"properties": {
"polarization": {
"process_graph": {
"eq1": {
"arguments": {
"x": {
"from_parameter": "value"
},
"y": "DV"
},
"process_id": "eq",
"result": True
}
}
}
},
"spatial_extent": {
"crs": "epsg:4326",
"east": -73.90597343,
"north": 4.724080996,
"south": 4.68986451,
"west": -74.0681076
},
"temporal_extent": [
"2021-06-01",
"2021-09-01"
]
},
"process_id": "load_collection"
},
"mask1": {
"arguments": {
"data": {
"from_node": "renamelabels1"
},
"mask": {
"from_node": "reducedimension1"
}
},
"process_id": "mask"
},
"reducedimension1": {
"arguments": {
"data": {
"from_node": "renamelabels1"
},
"dimension": "bands",
"reducer": {
"process_graph": {
"arrayelement1": {
"arguments": {
"data": {
"from_parameter": "data"
},
"index": 2
},
"process_id": "array_element"
},
"eq2": {
"arguments": {
"x": {
"from_node": "arrayelement1"
},
"y": 2
},
"process_id": "eq",
"result": True
}
}
}
},
"process_id": "reduce_dimension"
},
"renamelabels1": {
"arguments": {
"data": {
"from_node": "sarbackscatter1"
},
"dimension": "bands",
"target": [
"VH",
"VV",
"mask",
"incidence_angle"
]
},
"process_id": "rename_labels"
},
"sarbackscatter1": {
"arguments": {
"coefficient": "gamma0-terrain",
"contributing_area": False,
"data": {
"from_node": "loadcollection1"
},
"elevation_model": "COPERNICUS_30",
"ellipsoid_incidence_angle": False,
"local_incidence_angle": False,
"mask": True,
"noise_removal": True
},
"process_id": "sar_backscatter"
},
"saveresult1": {
"arguments": {
"data": {
"from_node": "mask1"
},
"format": "netCDF",
"options": {}
},
"process_id": "save_result",
"result": True
}
}
})
load_collections = dummy_backend.all_load_collection_calls("S2_FOOBAR")
assert len(load_collections) == 1
assert dummy_backend.get_collection("S2_FOOBAR").mask.call_count == 1
def test_complex_graph(api):
api.check_result("complex_graph.json")
load_collections = dummy_backend.all_load_collection_calls("SENTINEL1_GRD")
load_collections_s2 = dummy_backend.all_load_collection_calls("SENTINEL2_L2A_SENTINELHUB")
assert len(load_collections) == 1
assert len(load_collections_s2) == 2
def test_mask_polygon(api):
api.check_result("mask_polygon.json")
dummy = dummy_backend.get_collection("S2_FAPAR_CLOUDCOVER")
assert dummy.mask_polygon.call_count == 1
args, kwargs = dummy.mask_polygon.call_args
assert isinstance(kwargs['mask'], shapely.geometry.Polygon)
@pytest.mark.parametrize(["mask", "expected"], [
({"type": "Polygon", "coordinates": [[(0, 0), (1, 0), (1, 1), (0, 0)]]}, shapely.geometry.Polygon),
(
{
"type": "MultiPolygon",
"coordinates": [
[[(0, 0), (1, 0), (1, 1), (0, 0)]],
[[(2, 0), (3, 0), (3, 1), (2, 0)]],
]
},
shapely.geometry.MultiPolygon
),
(
{
"type": "GeometryCollection",
"geometries": [
{"type": "Polygon", "coordinates": [[(0, 0), (1, 0), (1, 1), (0, 0)]]},
{"type": "Polygon", "coordinates": [[(2, 0), (3, 0), (3, 1), (2, 0)]]},
]
},
shapely.geometry.MultiPolygon
),
(
{"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[(0, 0), (1, 0), (1, 1), (0, 0)]]}, },
shapely.geometry.Polygon
),
(
{
"type": "Feature",
"geometry": {"type": "MultiPolygon", "coordinates": [
[[(0, 0), (1, 0), (1, 1), (0, 0)]],
[[(2, 0), (3, 0), (3, 1), (2, 0)]],
]},
},
shapely.geometry.MultiPolygon,
),
(
{
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"geometry": {"type": "Polygon", "coordinates": [[(0, 0), (1, 0), (1, 1), (0, 0)]]},
},
{
"type": "Feature",
"geometry": {
"type": "MultiPolygon",
"coordinates": [
[[(0, 0), (1, 0), (1, 1), (0, 0)]],
[[(2, 0), (3, 0), (3, 1), (2, 0)]]
]
}
}
]
},
shapely.geometry.MultiPolygon
),
])
def test_mask_polygon_types(api, mask, expected):
pg = {
"lc1": {"process_id": "load_collection", "arguments": {"id": "S2_FOOBAR"}},
"mask1": {"process_id": "mask_polygon", "arguments": {
"data": {"from_node": "lc1"},
"mask": mask
}, "result": True}
}
api.check_result(pg)
dummy = dummy_backend.get_collection("S2_FOOBAR")
assert dummy.mask_polygon.call_count == 1
args, kwargs = dummy.mask_polygon.call_args
assert isinstance(kwargs['mask'], expected)
def test_mask_polygon_vector_cube(api):
path = str(get_path("geojson/FeatureCollection02.json"))
pg = {
"lc1": {"process_id": "load_collection", "arguments": {"id": "S2_FOOBAR"}},
"lf": {
"process_id": "load_uploaded_files",
"arguments": {"paths": [path], "format": "GeoJSON"},
},
"mask": {
"process_id": "mask_polygon",
"arguments": {"data": {"from_node": "lc1"}, "mask": {"from_node": "lf"}},
"result": True
}
}
api.check_result(pg)
dummy = dummy_backend.get_collection("S2_FOOBAR")
assert dummy.mask_polygon.call_count == 1
args, kwargs = dummy.mask_polygon.call_args
assert isinstance(kwargs['mask'], shapely.geometry.MultiPolygon)
def test_data_mask_optimized(api):
pg = {
"load_collection1": {"process_id": "load_collection", "arguments": {"id": "S2_FOOBAR"}},
"load_collection2": {"process_id": "load_collection", "arguments": {"id": "S2_FAPAR_CLOUDCOVER"}},
"filter1": {
"process_id": "filter_bands",
"arguments": {
"data": {"from_node": "load_collection1"},
"bands": ["B02"]
}
},
"mask1": {
"process_id": "mask",
"arguments": {"data": {"from_node": "filter1"}, "mask": {"from_node": "load_collection2"}},
"result": True
}
}
api.check_result(pg)
dummy = dummy_backend.get_collection("S2_FOOBAR")
# Even with filter_bands, the load_collection optimization should work
# mask does not need to be called when it is already applied in load_collection
assert dummy.mask.call_count == 0
def test_data_mask_use_data_twice(api):
pg = {
"load_collection1": {"process_id": "load_collection", "arguments": {"id": "S2_FOOBAR"}},
"load_collection2": {"process_id": "load_collection", "arguments": {"id": "S2_FAPAR_CLOUDCOVER"}},
"filter1": {
"process_id": "filter_bands",
"arguments": {
"data": {"from_node": "load_collection1"},
"bands": ["B02"]
}
},
"resample1": {
"process_id": "resample_cube_spatial",
"arguments": {
"data": {
"from_node": "load_collection2"
},
"target": {
"from_node": "filter1"
}
}
},
"mask1": {
"process_id": "mask",
"arguments": {"data": {"from_node": "filter1"}, "mask": {"from_node": "resample1"}},
"result": True
}
}
api.check_result(pg)
dummy = dummy_backend.get_collection("S2_FOOBAR")
# Not handling overlaps between mask and data nodes.
# A load_collection under the data node could be used twice and would not be pre-masked correctly.
assert dummy.mask.call_count == 1
def test_data_mask_unoptimized(api):
pg = {
"load_collection1": {"process_id": "load_collection", "arguments": {"id": "S2_FOOBAR"}},
"load_collection2": {"process_id": "load_collection", "arguments": {"id": "S2_FAPAR_CLOUDCOVER"}},
"apply1": {
"process_id": "apply_kernel",
"arguments": {
"data": {"from_node": "load_collection1"},
"kernel": [
[+0, -1, +0],
[-1, +4, -1],
[+0, -1, +0]
]
},
},
"mask1": {
"process_id": "mask",
"arguments": {"data": {"from_node": "apply1"}, "mask": {"from_node": "load_collection2"}},
"result": True
}
}
api.check_result(pg)
dummy = dummy_backend.get_collection("S2_FOOBAR")
assert dummy.mask.call_count == 1
def test_aggregate_temporal_period(api):
api.check_result("aggregate_temporal_period_max.json")
def test_aggregate_temporal_max(api):
api.check_result("aggregate_temporal_max.json")
def test_aggregate_temporal_max_invalid_temporal_dimension(api):
resp = api.result(
"aggregate_temporal_max.json",
preprocess=preprocess_check_and_replace('"dimension": "t"', '"dimension": "detijd"')
)
resp.assert_error(
400,
"ProcessParameterInvalid",
message="The value passed for parameter 'dimension' in process 'aggregate_temporal' is invalid: Must be one of ['x', 'y', 't'] but got 'detijd'.",
)
def test_aggregate_temporal_max_no_dimension(api):
api.check_result(
"aggregate_temporal_max.json",
preprocess=preprocess_check_and_replace('"dimension": "t"', '"dimension": null')
)
def test_aggregate_spatial(api):
resp = api.check_result("aggregate_spatial.json")
assert resp.json == {
"2015-07-06T00:00:00Z": [[2.345]],
"2015-08-22T00:00:00Z": [[None]]
}
params = dummy_backend.last_load_collection_call("S2_FAPAR_CLOUDCOVER")
assert params["spatial_extent"] == {
"west": 7.02,
"south": 51.29,
"east": 7.65,
"north": 51.75,
"crs": "EPSG:4326",
}
assert params["aggregate_spatial_geometries"] == DriverVectorCube.from_geojson(
{
"type": "Polygon",
"coordinates": [
[
[7.02, 51.75],