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test_integration.py
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import pathlib
import subprocess
import typing
import itertools
import json
import logging
import os
import re
import textwrap
import time
from pathlib import Path
from pprint import pprint, pformat
from typing import Callable, Union, Optional
import imghdr
import numpy as np
import pyproj
import pytest
import rasterio
import requests
import schema
import shapely.geometry
import shapely.ops
import xarray
from numpy.ma.testutils import assert_array_approx_equal
from numpy.testing import assert_array_equal, assert_array_almost_equal, assert_allclose
import pystac
from shapely.geometry import mapping, shape, GeometryCollection, Point, Polygon
from shapely.geometry.base import BaseGeometry
import hvac
import hvac.utils
import openeo
from openeo.rest.connection import OpenEoApiError
from openeo.rest.conversions import datacube_from_file, timeseries_json_to_pandas
from openeo.rest.datacube import DataCube, THIS
from openeo.rest.job import BatchJob, JobResults
from openeo.rest.mlmodel import MlModel
from openeo.rest.udp import Parameter
from openeo_driver.testing import DictSubSet
from openeo.util import guess_format
from .cloudmask import create_advanced_mask, create_simple_mask
from .data import get_path, read_data
_log = logging.getLogger(__name__)
def _dump_process_graph(
cube: Union[DataCube], tmp_path: Path, name="process_graph.json"
):
"""Dump a cube's process graph as json to a temp file"""
(tmp_path / name).write_text(cube.to_json(indent=2))
def _parse_bboxfinder_com(url: str) -> dict:
"""Parse a bboxfinder.com URL to bbox dict"""
# TODO: move this kind of functionality to python client?
coords = [float(x) for x in url.split('#')[-1].split(",")]
return {"south": coords[0], "west": coords[1], "north": coords[2], "east": coords[3], "crs": "EPSG:4326"}
def _polygon_bbox(polygon: Polygon) -> dict:
"""Extract bbox dict from given polygon"""
coords = polygon.bounds
return {"south": coords[1], "west": coords[0], "north": coords[3], "east": coords[2], "crs": "EPSG:4326"}
def assert_batch_job(job: BatchJob, assertion: bool, extra_message: str = ""):
try:
assert assertion
except AssertionError as e:
kibana_url = f"https://kibana-infra.vgt.vito.be/app/kibana#/discover?_g=(filters:!(),refreshInterval:" \
f"(pause:!t,value:0),time:(from:now-7d,to:now))&_a=(columns:!(message,levelname,filename),filters:!(" \
f"('$state':(store:appState),meta:(alias:!n,disabled:!f," \
f"index:'592a42f0-e665-11ec-8cc4-3747d5233c59',key:levelname,negate:!f,params:(query:ERROR)," \
f"type:phrase),query:(match:(levelname:(query:ERROR,type:phrase)))))," \
f"index:'592a42f0-e665-11ec-8cc4-3747d5233c59',interval:auto,query:(language:kuery,query:'" \
f"job_id%20:%20%22{job.job_id}%22%20'),sort:!(!('@timestamp',desc)))"
message = f"Assertion for batch job {job} failed:\n {extra_message}\n"\
f"Job status: {job.status()}\n" \
f"Kibana logs: {kibana_url}\n\n" \
f"Job error logs: {job.logs(level='ERROR')}"
if job.status == "finished":
job_results = job.get_results()
message += f"Job metadata: {job_results.get_metadata()}"
message += f"Job assets: {job_results.get_assets()}"
raise AssertionError(message) from e
def execute_batch_with_error_logging(
cube: DataCube,
outputfile: Optional[Union[str, pathlib.Path]] = None,
out_format: Optional[str] = None,
*,
print: typing.Callable[[str], None] = print,
max_poll_interval: float = 60,
connection_retry_interval: float = 30,
job_options: Optional[dict] = None,
**format_options,):
if "format" in format_options and not out_format:
out_format = format_options["format"] # align with 'download' call arg name
if not out_format and outputfile:
out_format = guess_format(outputfile)
job = cube.create_job(out_format = out_format, job_options = job_options, **format_options)
result = None
try:
result = job.run_synchronous(
outputfile=outputfile,
print=print, max_poll_interval=max_poll_interval, connection_retry_interval=connection_retry_interval
)
except openeo.rest.JobFailedException:
assert_batch_job(job, False, "Job failed")
return result
BBOX_MOL = _parse_bboxfinder_com("http://bboxfinder.com/#51.21,5.071,51.23,5.1028")
BBOX_GENT = _parse_bboxfinder_com("http://bboxfinder.com/#51.03,3.7,51.05,3.75")
BBOX_NIEUWPOORT = _parse_bboxfinder_com("http://bboxfinder.com/#51.05,2.60,51.20,2.90")
POLYGON01 = Polygon(shell=[
# Dortmund (bbox: http://bboxfinder.com/#51.30,7.00,51.75,7.60)
[7.00, 51.75],
[7.10, 51.35],
[7.50, 51.30],
[7.60, 51.70],
[7.00, 51.75],
])
POLYGON01_BBOX = [7.00, 51.30, 7.60, 51.75]
BATCH_JOB_POLL_INTERVAL = 30
BATCH_JOB_TIMEOUT = 60 * 60
def batch_default_options(driverMemoryOverhead="1G", driverMemory="2G"):
return {
"driver-memory": driverMemory,
"driver-memoryOverhead": driverMemoryOverhead,
"driver-cores": "2",
"executor-memory": "1G",
"executor-memoryOverhead": "1G",
"executor-cores": "1",
"queue": "lowlatency"
}
def test_root(connection):
r = connection.get("/")
assert r.status_code == 200
capabilities = r.json()
assert "api_version" in capabilities
assert "stac_version" in capabilities
assert "endpoints" in capabilities
def test_health(connection):
r = connection.get("/health")
assert r.status_code == 200
def test_collections(connection):
image_collections = connection.list_collections()
product_ids = [entry.get("id") for entry in image_collections]
assert "PROBAV_L3_S10_TOC_333M" in product_ids
def test_terrascope_download_latlon(auth_connection, tmp_path):
s2_fapar = (
auth_connection.load_collection("TERRASCOPE_S2_NDVI_V2",bands=["NDVI_10M"])
# bounding box: http://bboxfinder.com/#51.197400,5.027000,51.221300,5.043800
.filter_temporal(["2018-08-06T00:00:00Z", "2018-08-06T00:00:00Z"])
.filter_bbox(west=5.027, east=5.0438, south=51.1974, north=51.2213, crs="EPSG:4326")
)
out_file = tmp_path / "result.tiff"
s2_fapar.download(out_file, format="GTIFF")
assert_geotiff_basics(out_file, expected_shape=(1, 270, 126))
def test_terrascope_download_webmerc(auth_connection, tmp_path):
s2_fapar = (
auth_connection.load_collection("TERRASCOPE_S2_NDVI_V2",bands=["NDVI_10M"])
.filter_temporal(["2018-08-06T00:00:00Z", "2018-08-06T00:00:00Z"])
.filter_bbox(west=561864.7084, east=568853, south=6657846, north=6661080, crs="EPSG:3857")
)
out_file = tmp_path / "result.tiff"
s2_fapar.download(out_file, format="GTIFF")
assert_geotiff_basics(out_file, expected_shape=(1, 216, 446))
def test_aggregate_spatial_polygon(auth_connection):
timeseries = (
auth_connection.load_collection("PROBAV_L3_S10_TOC_333M", bands=["NDVI"])
.filter_temporal(start_date="2017-11-01", end_date="2017-11-22")
.aggregate_spatial(geometries=POLYGON01, reducer="mean")
.execute()
)
print(timeseries)
# TODO remove this cleanup https://github.com/Open-EO/openeo-geopyspark-driver/issues/75
timeseries = {k: v for (k, v) in timeseries.items() if v != [[]]}
print(timeseries)
expected_dates = ["2017-11-01T00:00:00Z", "2017-11-11T00:00:00Z", "2017-11-21T00:00:00Z"]
assert sorted(timeseries.keys()) == sorted(expected_dates)
expected_schema = schema.Schema({str: [[float]]})
assert expected_schema.validate(timeseries)
def test_histogram_timeseries(auth_connection):
probav = (
auth_connection
.load_collection('PROBAV_L3_S10_TOC_333M',bands=["NDVI"])
.filter_bbox(5, 6, 52, 51, 'EPSG:4326')
.filter_temporal(['2017-11-21', '2017-12-21'])
)
polygon = shape(
{
"type": "Polygon",
"coordinates": [
[
[5.0761587693484875, 51.21222494794898],
[5.166854684377381, 51.21222494794898],
[5.166854684377381, 51.268936260927404],
[5.0761587693484875, 51.268936260927404],
[5.0761587693484875, 51.21222494794898],
]
],
}
)
timeseries = probav.aggregate_spatial(
geometries=polygon, reducer="histogram"
).execute()
print(timeseries)
expected_schema = schema.Schema({str: [[{str: int}]]})
assert expected_schema.validate(timeseries)
for date, histograms in timeseries.items():
assert len(histograms) == 1
assert len(histograms[0]) == 1
assert len(histograms[0][0]) > 10
@pytest.mark.parametrize("udf_file", [
"udfs/raster_collections_ndvi_old.py",
"udfs/raster_collections_ndvi.py",
])
def test_ndvi_udf_reduce_bands_udf(auth_connection, tmp_path, udf_file):
udf_code = read_data(udf_file)
cube = (
auth_connection.load_collection('TERRASCOPE_S2_TOC_V2',bands=['TOC-B04_10M','TOC-B08_10M'])
.filter_temporal(start_date="2020-11-05", end_date="2020-11-05")
.filter_bbox(west=761104, east=763281, south=6543830, north=6544655, crs="EPSG:3857")
)
# cube.download(tmp_path / "cube.tiff", format="GTIFF")
res = cube.reduce_bands_udf(udf_code)
out_file = tmp_path / "ndvi-udf.tiff"
res.download(out_file, format="GTIFF")
assert_geotiff_basics(out_file, min_height=40, expected_shape=(1, 57, 141))
with rasterio.open(out_file) as ds:
ndvi = ds.read(1)
assert 0.35 < ndvi.min(axis=None)
assert ndvi.max(axis=None) < 1.0
def test_ndvi_band_math(auth_connection, tmp_path, api_version):
# http://bboxfinder.com/#50.55,6.82,50.58,6.87
bbox = {
"west": 6.82, "south": 50.55,
"east": 6.87, "north": 50.58,
"crs": "EPSG:4326"
}
cube = (
auth_connection.load_collection("TERRASCOPE_S2_TOC_V2",
bands=['TOC-B04_10M','TOC-B08_10M'])
.filter_temporal("2020-11-01", "2020-11-20")
.filter_bbox(**bbox)
)
# cube.download(tmp_path / "cube.tiff", format="GTIFF")
red = cube.band("TOC-B04_10M")
nir = cube.band("TOC-B08_10M")
ndvi = (nir - red) / (red + nir)
ndvi = ndvi.mean_time()
out_file = tmp_path / "ndvi.tiff"
ndvi.download(out_file, format="GTIFF")
assert_geotiff_basics(out_file,min_height=40, expected_shape=(1, 344, 365))
with rasterio.open(out_file) as ds:
x = ds.read(1)
assert 0.08 < np.nanmin(x, axis=None)
assert np.nanmax(x, axis=None) < 0.67
assert np.isnan(x).sum(axis=None) == 0
def test_cog_synchronous(auth_connection, tmp_path):
cube = (
auth_connection
.load_collection('PROBAV_L3_S10_TOC_333M',bands=["NDVI"])
.filter_temporal("2017-11-21", "2017-11-21")
.filter_bbox(west=0, south=50, east=5, north=55, crs='EPSG:4326')
)
out_file = tmp_path / "cog.tiff"
cube.download(out_file)
#TODO: this shape, is wrong, caused by: https://github.com/Open-EO/openeo-geopyspark-driver/issues/260
assert_geotiff_basics(out_file, expected_shape=(1, 3642, 1821))
assert_cog(out_file)
@pytest.mark.batchjob
@pytest.mark.timeout(BATCH_JOB_TIMEOUT)
def test_cog_execute_batch(auth_connection, tmp_path):
cube = auth_connection.load_collection(
"PROBAV_L3_S10_TOC_333M",
bands = ["NDVI"],
temporal_extent=["2017-11-21", "2017-11-21"],
spatial_extent={"west": 2, "south": 51, "east": 4, "north": 53},
)
job = execute_batch_with_error_logging(
cube,
out_format="GTIFF",
max_poll_interval=BATCH_JOB_POLL_INTERVAL,
job_options=batch_default_options(driverMemoryOverhead="1G", driverMemory="1G"),
tile_grid="one_degree",
title="test_cog_execute_batch",
)
_log.info(f"test_cog_execute_batch: {job=}")
assert_job_status(job, "finished")
job_results: JobResults = job.get_results()
downloaded = job_results.download_files(
tmp_path / "job1", include_stac_metadata=True
)
_log.info(f"{len(downloaded)=} {downloaded=}")
arbitrary_geotiff_path = downloaded[0]
assert_geotiff_basics(arbitrary_geotiff_path, expected_band_count=1)
assert_cog(arbitrary_geotiff_path)
job_results_metadata = job_results.get_metadata()
# conveniently tacked on test for load_stac because it needs a batch job that won't be removed in the near future
job_results_stac: pystac.Collection = pystac.Collection.from_dict(job_results_metadata)
job_results_unsigned_url = next(link.href for link in job_results_stac.links if link.rel == "self")
cube_from_result = auth_connection.load_stac(
job_results_unsigned_url,
spatial_extent={"west": 2.6, "south": 51.1, "east": 2.9, "north": 51.3},
)
load_stac_output_file = tmp_path / "load_stac.tiff"
cube_from_result.download(load_stac_output_file, format="GTiff")
with rasterio.open(load_stac_output_file) as load_stac_ds:
assert load_stac_ds.count == 1
probav_data = load_stac_ds.read(1)
no_data = 255
assert np.any(probav_data != no_data)
# Verify projection metadata.
assert job_results_metadata == DictSubSet(
{
"assets": {
"openEO_2017-11-21Z_N51E002.tif": DictSubSet(
{
"proj:epsg": 4326,
"proj:shape": [365, 728],
"proj:bbox": pytest.approx(
[1.9995067, 51.0012761, 3.0020091, 52.0010318]
),
}
),
"openEO_2017-11-21Z_N51E003.tif": DictSubSet(
{
"proj:epsg": 4326,
"proj:shape": [364, 728],
"proj:bbox": pytest.approx(
[2.9992625, 51.0012761, 3.9990183, 52.0010318]
),
}
),
"openEO_2017-11-21Z_N52E002.tif": DictSubSet(
{
"proj:epsg": 4326,
"proj:shape": [365, 729],
"proj:bbox": pytest.approx(
[1.9995067, 51.9996585, 3.0020091, 53.0007876]
),
}
),
"openEO_2017-11-21Z_N52E003.tif": DictSubSet(
{
"proj:epsg": 4326,
"proj:shape": [364, 729],
"proj:bbox": pytest.approx(
[2.9992625, 51.9996585, 3.9990183, 53.0007876]
),
}
),
},
}
)
def assert_projection_metadata_present(metadata: dict) -> dict:
"""Check all elements of the STAC projection metadata.
If the assert fails then include the report in the assert message so we have
some information to start troubleshooting.
"""
report = validate_projection_metadata(metadata)
message = f"Projection metadata verification report:\n{pformat(report)}"
_log.info(message)
assert report[
"is_proj_metadata_present"
], f"Projection metadata verification FAILED:\n{pformat(report)}"
def validate_projection_metadata(metadata: dict) -> dict:
"""Verify that some sensible projection metadata is present.
When the validation PASSES, then is_proj_metadata_present will be True.
The validation PASSES when the metadata contains a value for each of the
three properties epsg, bbox and shape.
It can be present at the top level, or at the asset level, or even at both
levels.
Or in other words, the validation FAILS when for any of the propeties
properties epsg, bbox and shape no value could be found at the top leval and
at the asset level.
Top and asset level don't necessarily exclusive each other.
For example a bbox could already be present at the top level,
while each asset also has its own different bbox, which is then also added
at the asset level.
:param metadata: dict with the job's metadata.
:return:
Dict with a report that has the structure described below.
"is_proj_metadata_present" is main information and is True when
the validation passes
The other elements are for logging this as a report so the integration
test can be more informative about why it fails.
{
// True if it PASSES; False if it FAILS
"is_proj_metadata_present": is_proj_metadata_present,
// The individual elements of the checks; where were the projection
// properties present or not
"report": {
"has_crs_on_top_level": has_crs_on_top_level,
"has_bbox_on_top_level": has_bbox_on_top_level,
"has_shape_on_top_level": has_shape_on_top_level,
"has_crs_on_assets": has_crs_on_assets,
"has_bbox_on_assets": has_bbox_on_assets,
"has_shapes_on_assets": has_shapes_on_assets
}
// The actual values found, for troubleshooting when the test fails.
"values": {
"crs_on_top_level": metadata.get("epsg"),
"bbox_on_top_level": metadata.get("bbox"),
"shape_on_top_level": metadata.get("proj:shape"),
"asset_crss": asset_crss,
"asset_bboxes": asset_bboxes,
"asset_shapes": asset_shapes
},
}
"""
# Are projection metdata keys present at the top level (job as a whole)
has_crs_on_top_level = bool(metadata.get("epsg"))
has_bbox_on_top_level = bool(metadata.get("bbox"))
has_shape_on_top_level = bool(metadata.get("proj:shape"))
# Collect asset level metadata.
# Also used to show in report for troubleshooting.
assets_metadata = metadata.get("assets", {})
asset_crss = {
asset: asset_md.get("proj:epsg") for asset, asset_md in assets_metadata.items()
}
asset_bboxes = {
asset: asset_md.get("proj:bbox") for asset, asset_md in assets_metadata.items()
}
asset_shapes = {
asset: asset_md.get("proj:shape") for asset, asset_md in assets_metadata.items()
}
# Are the asset level metadata present.
has_crs_on_assets = any(asset_crss.values())
has_bbox_on_assets = any(asset_bboxes.values())
has_shapes_on_assets = any(asset_shapes.values())
# Does it pass or fail?
# For, now make it a bit more permissive: at least some projection
# properties should be present.
# But in the future we want to have all three properties covered.
is_proj_metadata_present = (
(has_crs_on_top_level or has_crs_on_assets)
or (has_bbox_on_top_level or has_bbox_on_assets)
or (has_shape_on_top_level or has_shapes_on_assets)
)
return {
"is_proj_metadata_present": is_proj_metadata_present,
"values": {
"crs_on_top_level": metadata.get("epsg"),
"bbox_on_top_level": metadata.get("bbox"),
"shape_on_top_level": metadata.get("proj:shape"),
"asset_crss": asset_crss,
"asset_bboxes": asset_bboxes,
"asset_shapes": asset_shapes,
},
"report": {
"has_crs_on_top_level": has_crs_on_top_level,
"has_bbox_on_top_level": has_bbox_on_top_level,
"has_shape_on_top_level": has_shape_on_top_level,
"has_crs_on_assets": has_crs_on_assets,
"has_bbox_on_assets": has_bbox_on_assets,
"has_shapes_on_assets": has_shapes_on_assets,
},
}
def _poll_job_status(
job: BatchJob,
until: Callable = lambda s: s == "finished",
sleep: int = BATCH_JOB_POLL_INTERVAL,
max_poll_time=30 * 60,
) -> str:
"""Helper to poll the status of a job until some condition is reached."""
start = time.time()
def elapsed():
return time.time() - start
while elapsed() < max_poll_time:
try:
status = job.describe_job()['status']
except requests.ConnectionError as e:
print("job {j} status poll ({e:.2f}s) failed: {x}".format(j=job.job_id, e=elapsed(), x=e))
else:
print("job {j} status poll ({e:.2f}s): {s}".format(j=job.job_id, e=elapsed(), s=status))
if until(status):
return status
time.sleep(sleep)
raise RuntimeError("reached max poll time: {e} > {m}".format(e=elapsed(), m=max_poll_time))
def assert_job_status(job: BatchJob, expected_status: str):
"""Assert that job's status is the expected status, and optionally print its logs.
If the assert fails the job ID is included in the assert message so you can
find the job in Kibana to see what went wrong.
Do keep in mind that the job's logs could be long which is why we don't
display them by default.
In particular, this could make the test logs on Jenkins long and difficult to read.
:param job: the batch job to check
:param expected_status: which status it should have.
"""
# If the next assert is going to fail, then first show the logs of the job.
actual_status = job.status()
message = (
f"job {job}: did not end with expected status '{expected_status}', "
+ f"but ended with status '{actual_status}'"
)
assert_batch_job(job, actual_status == expected_status, extra_message=message)
@pytest.mark.batchjob
@pytest.mark.timeout(BATCH_JOB_TIMEOUT)
def test_batch_job_basic(auth_connection, api_version, tmp_path):
cube = auth_connection.load_collection('PROBAV_L3_S10_TOC_333M',bands=["NDVI"]).filter_temporal("2017-11-01", "2017-11-22")
timeseries = cube.aggregate_spatial(geometries=POLYGON01, reducer="median")
job = timeseries.create_job(
job_options=batch_default_options(
driverMemory="1600m", driverMemoryOverhead="512m"
),
title="test_batch_job_basic",
)
assert job.job_id
job.start_job()
status = _poll_job_status(job, until=lambda s: s in ['canceled', 'finished', 'error'])
assert_job_status(job, "finished")
job_results: JobResults = job.get_results()
job_results_metadata: dict = job_results.get_metadata()
downloaded = job_results.download_files(tmp_path, include_stac_metadata=True)
_log.info(f"{downloaded=}")
assets = job_results.get_assets()
_log.info(f"{assets=}")
assert_batch_job(job, len(assets) == 1, extra_message=f"expected 1 asset, got {len(assets)}")
assert_batch_job(job, assets[0].name.endswith(".json"))
data = assets[0].load_json()
_log.info(f"{data=}")
expected_dates = ["2017-11-01T00:00:00Z", "2017-11-11T00:00:00Z", "2017-11-21T00:00:00Z"]
assert_batch_job(job, sorted(data.keys()) == sorted(expected_dates))
expected_schema = schema.Schema({str: [[int]]})
assert_batch_job(job, expected_schema.validate(data))
if api_version >= "1.1.0":
assert_batch_job(job, job_results_metadata["type"] == "Collection")
job_results_stac: pystac.Collection = pystac.Collection.from_dict(
job_results_metadata
)
assert_batch_job(job, job_results_stac.extent.spatial.bboxes[0] == POLYGON01_BBOX)
assert_batch_job(job, job_results_stac.extent.temporal.to_dict()["interval"] == [
["2017-11-01T00:00:00Z", "2017-11-22T00:00:00Z"]
])
elif api_version >= "1.0.0":
assert_batch_job(job, job_results_metadata["type"] == "Feature")
geometry = shape(job_results_metadata["geometry"])
assert_batch_job(job, geometry.equals_exact(POLYGON01, tolerance=0.0001))
assert_batch_job(job, job_results_metadata["bbox"] == POLYGON01_BBOX)
assert_batch_job(job, job_results_metadata["properties"]["start_datetime"] == "2017-11-01T00:00:00Z")
assert_batch_job(job, job_results_metadata["properties"]["end_datetime"] == "2017-11-22T00:00:00Z")
@pytest.mark.batchjob
@pytest.mark.timeout(BATCH_JOB_TIMEOUT)
def test_batch_job_execute_batch(auth_connection, tmp_path):
cube = auth_connection.load_collection('PROBAV_L3_S10_TOC_333M',bands=["NDVI"]).filter_temporal("2017-11-01", "2017-11-22")
timeseries = cube.aggregate_spatial(geometries=POLYGON01, reducer="median")
output_file = tmp_path / "ts.json"
job = execute_batch_with_error_logging(timeseries, output_file, max_poll_interval=BATCH_JOB_POLL_INTERVAL, job_options=batch_default_options(driverMemory="1600m",driverMemoryOverhead="512m"), title="execute-batch")
with output_file.open("r") as f:
data = json.load(f)
expected_dates = ["2017-11-01T00:00:00Z", "2017-11-11T00:00:00Z", "2017-11-21T00:00:00Z"]
assert_batch_job(job, sorted(data.keys()) == sorted(expected_dates))
expected_schema = schema.Schema({str: [[int]]})
assert_batch_job(job, expected_schema.validate(data))
@pytest.mark.batchjob
@pytest.mark.timeout(BATCH_JOB_TIMEOUT)
def test_batch_job_signed_urls(auth_connection, tmp_path):
cube = auth_connection.load_collection('PROBAV_L3_S10_TOC_333M',bands=["NDVI"]).filter_temporal("2017-11-01", "2017-11-22")
timeseries = cube.aggregate_spatial(geometries=POLYGON01, reducer="median")
job = execute_batch_with_error_logging(
timeseries,
max_poll_interval=BATCH_JOB_POLL_INTERVAL,
job_options=batch_default_options(driverMemory="1600m", driverMemoryOverhead="512m"),
title = "signed-urls"
)
results = job.get_results()
# TODO: check results metadata?
print("results metadata", results.get_metadata())
assets = results.get_assets()
print("assets", assets)
assert_batch_job(job, len(assets) >= 1)
data = None
for asset in assets:
# Download directly without credentials
resp = requests.get(asset.href)
resp.raise_for_status()
assert_batch_job(job, resp.status_code == 200)
if asset.name.endswith(".json"):
assert data is None
data = resp.json()
expected_dates = ["2017-11-01T00:00:00Z", "2017-11-11T00:00:00Z", "2017-11-21T00:00:00Z"]
assert_batch_job(job, sorted(data.keys()) == sorted(expected_dates))
expected_schema = schema.Schema({str: [[int]]})
assert_batch_job(job, expected_schema.validate(data))
@pytest.mark.batchjob
@pytest.mark.timeout(BATCH_JOB_TIMEOUT)
def test_batch_job_cancel(auth_connection, tmp_path):
cube = auth_connection.load_collection('PROBAV_L3_S10_TOC_333M',bands=["NDVI"]).filter_temporal("2017-11-01", "2017-11-21")
if isinstance(cube, DataCube):
cube = cube.process("sleep", arguments={"data": cube, "seconds": 600})
else:
raise ValueError(cube)
timeseries = cube.aggregate_spatial(geometries=POLYGON01, reducer="mean")
job = timeseries.create_job(
out_format="GTIFF",
job_options=batch_default_options(
driverMemory="512m", driverMemoryOverhead="1g"
),
title="test_batch_job_cancel",
)
assert job.job_id
job.start_job()
# await job running
status = _poll_job_status(job, until=lambda s: s in ['running', 'canceled', 'finished', 'error'])
assert_batch_job(job, status == "running")
# cancel it
job.stop_job()
print("stopped job")
# await job canceled
status = _poll_job_status(job, until=lambda s: s in ['canceled', 'finished', 'error'])
assert_batch_job(job, status == "canceled")
@pytest.mark.batchjob
@pytest.mark.timeout(BATCH_JOB_TIMEOUT)
def test_batch_job_delete_job(auth_connection):
cube = auth_connection.load_collection('PROBAV_L3_S10_TOC_333M',bands=["NDVI"]).filter_temporal("2017-11-01", "2017-11-21")
timeseries = cube.aggregate_spatial(geometries=POLYGON01, reducer="mean")
job: BatchJob = timeseries.create_job(
out_format="GTIFF",
job_options=batch_default_options(
driverMemory="1600m", driverMemoryOverhead="512m"
),
title="test_batch_job_delete_job",
)
assert job.job_id
job.start_job()
_log.info(f"test_batch_job_delete_job: {job=}")
# await job finished
status = _poll_job_status(job, until=lambda s: s in ['canceled', 'finished', 'error'])
assert_job_status(job, "finished")
def job_directory_exists(expected: bool) -> bool:
start = time.time()
def elapsed():
return time.time() - start
def directory_exists() -> bool:
exists = (Path("/data/projects/OpenEO") / job.job_id).exists()
print("job {j} directory exists ({e:.2f}s): {d}".format(j=job.job_id, e=elapsed(), d=exists))
return exists
while elapsed() < 300:
if directory_exists() == expected:
return expected
time.sleep(10)
return directory_exists()
assert job_directory_exists(True)
# delete it
job.delete_job()
print("deleted job")
try:
job.describe_job()
pytest.fail("should have returned a 404 for a deleted job")
except OpenEoApiError as e:
assert e.http_status_code == 404
assert not job_directory_exists(False)
@pytest.mark.batchjob
@pytest.mark.timeout(BATCH_JOB_TIMEOUT)
def test_random_forest_load_from_http(auth_connection: openeo.Connection, tmp_path):
"""
Make predictions with the random forest model using an http link to a ml_model_metadata.json file.
"""
topredict_xybt = auth_connection.load_collection('PROBAV_L3_S10_TOC_333M',bands=["NDVI"],
spatial_extent = {"west": 4.785919, "east": 4.909629, "south": 51.259766, "north": 51.307638},
temporal_extent = ["2017-11-01", "2017-11-01"])
topredict_cube_xyb = topredict_xybt.reduce_dimension(dimension = "t", reducer = "mean")
# Make predictions with the random forest model using http link.
random_forest_metadata_link = "https://github.com/Open-EO/openeo-geopyspark-integrationtests/raw/master/tests/data/mlmodels/randomforest_ml_model_metadata.json"
predicted_with_link = topredict_cube_xyb.predict_random_forest(
model=random_forest_metadata_link,
dimension="bands"
)
with_link_output_file = tmp_path / "predicted_with_link.tiff"
predicted_with_link.download(with_link_output_file, format="GTiff")
assert_geotiff_basics(with_link_output_file, min_width = 15, min_height = 15)
@pytest.mark.batchjob
@pytest.mark.timeout(BATCH_JOB_TIMEOUT)
def test_random_forest_train_and_load_from_jobid(auth_connection: openeo.Connection, tmp_path):
# 1. Train a random forest model.
FEATURE_COLLECTION_1 = {
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"properties": {"target": 3},
"geometry": {"type": "Polygon", "coordinates": [[[4.79, 51.26], [4.81, 51.26], [4.81, 51.30], [4.79, 51.30], [4.79, 51.26]]]}
},
{
"type": "Feature",
"properties": {"target": 5},
"geometry": {"type": "Polygon", "coordinates": [[[4.85, 51.26], [4.90, 51.26], [4.90, 51.30], [4.85, 51.30], [4.85, 51.26]]]}
},
]
}
cube_xybt: DataCube = auth_connection.load_collection(
"PROBAV_L3_S10_TOC_333M", bands=["NDVI"],
spatial_extent={"west": 4.78, "east": 4.91, "south": 51.25, "north": 51.31},
temporal_extent=["2017-11-01", "2017-11-01"]
)
cube_xyb: DataCube = cube_xybt.reduce_dimension(dimension="t", reducer="mean")
predictors: DataCube = cube_xyb.aggregate_spatial(FEATURE_COLLECTION_1, reducer="mean", target_dimension="bands")
model: MlModel = predictors.fit_class_random_forest(target=FEATURE_COLLECTION_1, num_trees=3, seed=42)
model: MlModel = model.save_ml_model()
job: BatchJob = model.create_job(title="test_random_forest_train_and_load_from_jobid-training_step")
assert job.job_id
job.start_job()
# Wait until job is finished
status = _poll_job_status(job, until=lambda s: s in ['canceled', 'finished', 'error'])
assert status == "finished"
# Check the job metadata.
collection_results = job.list_results()
assert collection_results.get('assets', {}).get('randomforest.model.tar.gz', {}).get('href', None) is not None
summaries = collection_results.get('summaries', {})
assert summaries.get('ml-model:architecture', None) == ['random-forest']
assert summaries.get('ml-model:learning_approach', None) == ['supervised']
assert summaries.get('ml-model:prediction_type', None) == ['classification']
links = collection_results.get('links', [])
ml_model_metadata_links = [link for link in links if "ml_model_metadata.json" in link.get('href', "")]
assert len(ml_model_metadata_links) == 1
# Check the ml_model_metadata.json file.
ml_model_metadata = requests.get(ml_model_metadata_links[0].get('href', "")).json()
ml_model_assets = ml_model_metadata.get('assets', {})
assert ml_model_assets.get('model', {}).get('roles', None) == ['ml-model:checkpoint']
assert ml_model_assets.get('model', {}).get('title', None) == 'org.apache.spark.mllib.tree.model.RandomForestModel'
ml_model_properties = ml_model_metadata.get('properties', {})
assert ml_model_properties.get('ml-model:architecture', None) == 'random-forest'
assert ml_model_properties.get('ml-model:learning_approach', None) == 'supervised'
assert ml_model_properties.get('ml-model:prediction_type', None) == 'classification'
assert ml_model_properties.get('ml-model:training-os', None) == 'linux'
assert ml_model_properties.get('ml-model:training-processor-type', None) == 'cpu'
assert ml_model_properties.get('ml-model:type', None) == 'ml-model'
def job_directory_exists(expected: bool) -> bool:
start = time.time()
def elapsed():
return time.time() - start
def directory_exists() -> bool:
exists = (Path("/data/projects/OpenEO") / job.job_id).exists()
print("job {j} directory exists ({e:.2f}s): {d}".format(j=job.job_id, e=elapsed(), d=exists))
return exists
while elapsed() < 300:
if directory_exists() == expected:
return expected
time.sleep(10)
return directory_exists()
assert job_directory_exists(True)
# List files in job directory.
modelPath = Path("/data/projects/OpenEO") / job.job_id / "randomforest.model.tar.gz"
metadataPath = Path("/data/projects/OpenEO") / job.job_id / "job_metadata.json"
assert(modelPath.exists())
# Download modelPath to tmp
modelPathTmp = tmp_path / "randomforest.model.tar.gz"
modelPathTmp.write_bytes(modelPath.read_bytes())
assert(modelPath.stat().st_size > 1024)
assert(metadataPath.exists())
# Check job_metadata.json.
with open(metadataPath, "r") as f:
metadata = json.load(f)
assert metadata["geometry"] == {
"type": "Polygon",
"coordinates": [[[4.79, 51.26], [4.79, 51.30], [4.90, 51.30], [4.90, 51.26], [4.79, 51.26]]],
}
assert metadata.get("assets", {}).get("randomforest.model.tar.gz", {}).get("href", "") == "/data/projects/OpenEO/{jobid}/randomforest.model.tar.gz".format(jobid=job.job_id)
# 2. Load the model using its job id and make predictions.
topredict_xybt = auth_connection.load_collection("PROBAV_L3_S10_TOC_333M",bands=["NDVI"],
spatial_extent = {"west": 4.825919, "east": 4.859629, "south": 51.259766, "north": 51.307638},
temporal_extent = ["2017-11-01", "2017-11-01"])
topredict_cube_xyb = topredict_xybt.reduce_dimension(dimension = "t", reducer = "mean")
predicted: DataCube = topredict_cube_xyb.predict_random_forest(
model=job.job_id,
dimension="bands"
)
inference_job = execute_batch_with_error_logging(predicted, out_format='GTiff', title="test_random_forest_train_and_load_from_jobid-inference_step")
# Check the resulting geotiff filled with predictions.
output_file = tmp_path / "predicted.tiff"
inference_job.download_result(output_file)
assert_geotiff_basics(output_file, min_width = 1, min_height = 1)
@pytest.mark.skip(reason="Requires proxying to work properly")
def test_create_wtms_service(auth_connection):
s2_fapar = (
auth_connection
.load_collection('S2_FAPAR_V102_WEBMERCATOR2')
.filter_bbox(west=0, south=50, east=5, north=55, crs='EPSG:4326')
.filter_temporal(start_date="2019-04-01", end_date="2019-04-01")
)
res = s2_fapar.tiled_viewing_service(type='WMTS')
print("created service", res)
assert "service_id" in res
assert "url" in res
service_id = res["service_id"]
service_url = res["url"]
assert '/services/{s}'.format(s=service_id) in service_url
wmts_metadata = auth_connection.get(service_url).json()
print("wmts metadata", wmts_metadata)
assert "url" in wmts_metadata
wmts_url = wmts_metadata["url"]
time.sleep(5) # seems to take a while before the service is proxied
get_capabilities = requests.get(wmts_url + '?REQUEST=getcapabilities').text
print("getcapabilities", get_capabilities)
# the capabilities document should advertise the proxied URL
assert "<Capabilities" in get_capabilities
assert wmts_url in get_capabilities
@pytest.mark.skip(reason="Temporary skip to get tests through")
@pytest.mark.parametrize("udf_file", [
"udfs/smooth_savitsky_golay_old.py",
"udfs/smooth_savitsky_golay.py",
])
def test_ep3048_sentinel1_udf(auth_connection, udf_file):
# http://bboxfinder.com/#-4.745000,-55.700000,-4.740000,-55.695000
N, E, S, W = (-4.740, -55.695, -4.745, -55.7)
polygon = Polygon(shell=[[W, N], [E, N], [E, S], [W, S]])
udf_code = read_data(udf_file)
ts = (
auth_connection.load_collection("SENTINEL1_GAMMA0_SENTINELHUB")
.filter_temporal(["2019-05-24T00:00:00Z", "2019-05-30T00:00:00Z"])
.filter_bbox(north=N, east=E, south=S, west=W, crs="EPSG:4326")
.filter_bands([0])
.apply_dimension(udf_code, runtime="Python")
.aggregate_spatial(geometries=polygon, reducer="mean")
.execute()
)
assert isinstance(ts, dict)
assert all(k.startswith('2019-05-') for k in ts.keys())
def test_load_collection_from_disk(auth_connection, tmp_path):
fapar = auth_connection.load_disk_collection(
format='GTiff',
glob_pattern='/data/MTDA/TERRASCOPE_Sentinel2/FAPAR_V2/2019/04/24/*/10M/*_FAPAR_10M_V200.tif',
options={
'date_regex': r".*\/S2._(\d{4})(\d{2})(\d{2})T.*"
}
)
date = "2019-04-24"
fapar = fapar.filter_bbox(**BBOX_NIEUWPOORT).filter_temporal(date, date)
output_file = tmp_path / "fapar_from_disk.tiff"
fapar.download(output_file, format="GTiff")
assert_geotiff_basics(output_file, expected_shape=(1, 1677, 2106))
def assert_geotiff_basics(
output_tiff: Union[str, Path], expected_band_count=1, min_width=64, min_height=64, expected_shape=None
):
"""Basic checks that a file is a readable GeoTIFF file"""
assert imghdr.what(output_tiff) == 'tiff'
with rasterio.open(output_tiff) as dataset:
assert dataset.width > min_width
assert dataset.height > min_height
if expected_shape is not None: