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Tests for spatial.py, remove old 'region' #367

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Mar 15, 2024
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Tests for spatial.py, remove old 'region' #367

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fbeca1e
test spatial and refactor
RondeauG Mar 14, 2024
c6a24f1
fully remove the old region
RondeauG Mar 14, 2024
213ebf1
upd changes
RondeauG Mar 14, 2024
e2ae266
fix tests
RondeauG Mar 14, 2024
c02a024
Update CHANGES.rst
RondeauG Mar 14, 2024
57f9a9b
Merge remote-tracking branch 'origin/main' into spatialtests
RondeauG Mar 15, 2024
e58b3df
Merge remote-tracking branch 'origin/spatialtests' into spatialtests
RondeauG Mar 15, 2024
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10 changes: 8 additions & 2 deletions CHANGES.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -6,13 +6,19 @@ v0.9.0 (unreleased)
-------------------
Contributors to this version: Trevor James Smith (:user:`Zeitsperre`), Pascal Bourgault (:user:`aulemahal`), Gabriel Rondeau-Genesse (:user:`RondeauG`), Juliette Lavoie (:user: `juliettelavoie`).

Breaking changes
^^^^^^^^^^^^^^^^
* Removed support for the old instances of the `region` argument in ``spatial_mean``, ``extract_dataset``, and ``subset``. (:pull:`367`).
* Removed ``xscen.extract.clisops_subset``. (:pull:`367`).

Internal changes
^^^^^^^^^^^^^^^^
* Updated the `cookiecutter` template to the latest version. (:pull:`358`):
* Addresses a handful of misconfigurations in the GitHub Workflows.
* Added a few free `grep`-based hooks for finding unwanted artifacts in the code base.
* Updated `ruff` to v0.2.0 and `black` to v24.2.0.
* Added tests for biasadjust. (:pull:`366`).
* Added more tests. (:pull:`366`, :pull:`367`).
* Refactored ``xs.spatial.subset`` into smaller functions. (:pull:`367`).

Bug fixes
^^^^^^^^^
Expand All @@ -21,7 +27,7 @@ Bug fixes
* Loading of training in `adjust` is now done outside of the periods loop. (:pull:`366`).
* Fixed bug for adding the preprocessing attributes inside the `adjust` function. (:pull:`366`).
* Fixed a bug to accept `group = False` in `adjust` function. (:pull:`366`).

* `creep_weights` now correctly handles the case where the grid is small, `n` is large, and `mode=wrap`. (:issue:`367`).

v0.8.3 (2024-02-28)
-------------------
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347 changes: 347 additions & 0 deletions tests/test_spatial.py
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@@ -0,0 +1,347 @@
import dask.array
import geopandas as gpd
import numpy as np
import pytest
import xarray as xr
import xclim as xc
from shapely.geometry import Polygon

import xscen as xs
from xscen.spatial import _estimate_grid_resolution, _load_lon_lat
from xscen.testing import datablock_3d


class TestCreepFill:
# Create a 3D datablock
ds = datablock_3d(
np.tile(np.arange(1, 37).reshape(6, 6), (3, 1, 1)),
"tas",
"lon",
-70,
"lat",
45,
1,
1,
"2000-01-01",
as_dataset=True,
)
ds["mask"] = ds["tas"].isel(time=0) > 0
# Place a few False values in the mask
ds["mask"][0, 0] = False
ds["mask"][3, 3] = False

@pytest.mark.parametrize(
("n", "mode"),
[(1, "clip"), (2, "clip"), (3, "clip"), (1, "wrap"), (2, "wrap"), (3, "wrap")],
)
def test_n(self, n, mode):
w = xs.spatial.creep_weights(self.ds["mask"], n=n, mode=mode)
out = xs.spatial.creep_fill(self.ds["tas"], w)

if mode == "clip":
neighbours_0 = {
1: [2, 7, 8],
2: [2, 7, 8, 3, 9, 13, 14, 15],
3: [
2,
7,
8,
3,
9,
13,
14,
15,
4,
10,
16,
19,
20,
21,
], # 22 is False, thus not included
}
neighbours_3 = {
# For these n, the average is the same as the original value
1: [22],
2: [22],
# Here all the values are included, except the False ones
3: [
(self.ds["tas"].isel(time=0).sum().values - 22 - 1)
/ (self.ds["mask"].count().values - 2)
],
}
else:
neighbours_0 = {
1: [36, 31, 32, 6, 2, 12, 7, 8],
2: [
29,
30,
25,
26,
27,
35,
36,
31,
32,
33,
5,
6,
2,
3,
11,
12,
7,
8,
9,
17,
18,
13,
14,
15,
],
3: [
(
np.sum(np.arange(1, 37))
- 22
- 1
+ np.sum([19, 20, 21, 23, 24])
+ np.sum([4, 10, 16, 28, 34])
)
/ (36 - 2 + 10)
],
}
neighbours_3 = {
# For these n, the average is the same as the original value
1: [22],
2: [22],
# Here all the values are included, except the False ones
3: [
(
np.sum(np.arange(1, 37))
- 22
- 1
+ np.sum(np.arange(2, 7))
+ np.sum([7, 13, 19, 25, 31])
)
/ (36 - 2 + 10)
],
}

np.testing.assert_allclose(
out.isel(lat=0, lon=0), np.tile(np.mean(neighbours_0[n]), 3)
)
np.testing.assert_allclose(
out.isel(lat=3, lon=3), np.tile(np.mean(neighbours_3[n]), 3)
)

def test_wrong_mode(self):
with pytest.raises(ValueError, match="mode must be either"):
xs.spatial.creep_weights(self.ds["mask"], n=1, mode="wrong")

def test_n0(self):
w = xs.spatial.creep_weights(self.ds["mask"], n=0, mode="clip")
out = xs.spatial.creep_fill(self.ds["tas"], w)
np.testing.assert_equal(out.isel(lat=0, lon=0), np.tile(np.nan, 3))
np.testing.assert_equal(out.isel(lat=3, lon=3), np.tile(np.nan, 3))


class TestSubset:
ds = datablock_3d(
np.ones((3, 50, 50)),
"tas",
"lon",
-70,
"lat",
45,
1,
1,
"2000-01-01",
as_dataset=True,
)

@pytest.mark.parametrize(
("kwargs", "name"),
[
({"lon": -70, "lat": 45}, None),
({"lon": [-53.3, -69.6], "lat": [49.3, 46.6]}, "foo"),
],
)
def test_subset_gridpoint(self, kwargs, name):
with pytest.warns(UserWarning, match="tile_buffer is not used"):
out = xs.spatial.subset(
self.ds, "gridpoint", name=name, tile_buffer=5, **kwargs
)

if isinstance(kwargs["lon"], list):
expected = {
"lon": [np.round(k) for k in kwargs["lon"]],
"lat": [np.round(k) for k in kwargs["lat"]],
}
else:
expected = {
"lon": [np.round(kwargs["lon"])],
"lat": [np.round(kwargs["lat"])],
}

assert (
f"gridpoint spatial subsetting on {len(expected['lon'])} coordinates"
in out.attrs["history"]
)
np.testing.assert_array_equal(out["lon"], expected["lon"])
np.testing.assert_array_equal(out["lat"], expected["lat"])
if name:
assert out.attrs["cat:domain"] == name
else:
assert "cat:domain" not in out.attrs

@pytest.mark.parametrize(
("kwargs", "tile_buffer", "method"),
[
({"lon_bnds": [-63, -60], "lat_bnds": [47, 50]}, 0, "bbox"),
({"lon_bnds": [-63, -60], "lat_bnds": [47, 50]}, 5, "bbox"),
({}, 0, "shape"),
({}, 5, "shape"),
({"buffer": 3}, 5, "shape"),
],
)
def test_subset_bboxshape(self, kwargs, tile_buffer, method):
if method == "shape":
gdf = gpd.GeoDataFrame(
{"geometry": [Polygon([(-63, 47), (-63, 50), (-60, 50), (-60, 47)])]}
)
kwargs["shape"] = gdf

if "buffer" in kwargs:
with pytest.raises(
ValueError, match="Both tile_buffer and clisops' buffer were requested."
):
xs.spatial.subset(self.ds, method, tile_buffer=tile_buffer, **kwargs)
else:
out = xs.spatial.subset(self.ds, method, tile_buffer=tile_buffer, **kwargs)

assert f"{method} spatial subsetting" in out.attrs["history"]
if tile_buffer:
assert f"with buffer={tile_buffer}" in out.attrs["history"]
np.testing.assert_array_equal(
out["lon"],
np.arange(
np.max([-63 - tile_buffer, self.ds.lon.min()]),
np.min([-60 + tile_buffer + 1, self.ds.lon.max()]),
),
)
np.testing.assert_array_equal(
out["lat"],
np.arange(
np.max([47 - tile_buffer, self.ds.lat.min()]),
np.min([50 + tile_buffer + 1, self.ds.lat.max()]),
),
)

else:
assert "with no buffer" in out.attrs["history"]
np.testing.assert_array_equal(out["lon"], np.arange(-63, -59))
np.testing.assert_array_equal(out["lat"], np.arange(47, 51))

def test_subset_sel(self):
ds = datablock_3d(
np.ones((3, 50, 50)),
"tas",
"rlon",
-10,
"rlat",
0,
1,
1,
"2000-01-01",
as_dataset=True,
)

with pytest.raises(KeyError):
xs.spatial.subset(ds, "sel", lon=[-75, -70], lat=[-5, 0])
out = xs.spatial.subset(ds, "sel", rlon=[-5, 5], rlat=[0, 3])

assert "sel subsetting" in out.attrs["history"]
np.testing.assert_array_equal(out["rlon"], np.arange(-5, 6))
np.testing.assert_array_equal(out["rlat"], np.arange(0, 4))

def test_history(self):
ds = self.ds.copy(deep=True)
ds.attrs["history"] = "this is previous history"
out = xs.spatial.subset(ds, "gridpoint", lon=-70, lat=45)

assert "this is previous history" in out.attrs["history"].split("\n")[1]
assert "gridpoint spatial subsetting" in out.attrs["history"].split("\n")[0]

def test_subset_wrong_method(self):
with pytest.raises(ValueError, match="Subsetting type not recognized"):
xs.spatial.subset(self.ds, "wrong", lon=-70, lat=45)


def test_dask_coords():
ds = datablock_3d(
np.ones((3, 50, 50)),
"tas",
"rlon",
-10,
"rlat",
0,
1,
1,
"2000-01-01",
as_dataset=True,
)
# Transform the coordinates to dask arrays
lon_attrs = ds["lon"].attrs
ds["lon"] = xr.DataArray(
dask.array.from_array(ds["lon"].data, chunks=(1, 1)),
dims=ds["lon"].dims,
attrs=lon_attrs,
)
lat_attrs = ds["lat"].attrs
ds["lat"] = xr.DataArray(
dask.array.from_array(ds["lat"].data, chunks=(1, 1)),
dims=ds["lat"].dims,
attrs=lat_attrs,
)
assert xc.core.utils.uses_dask(ds.cf["longitude"])

ds = _load_lon_lat(ds)
assert not xc.core.utils.uses_dask(ds.cf["longitude"])
assert not xc.core.utils.uses_dask(ds.cf["latitude"])


@pytest.mark.parametrize(("lon_res", "lat_res"), [(0.5, 1), (1, 0.5)])
def test_estimate_res_1d(lon_res, lat_res):
ds = datablock_3d(
np.ones((3, 5, 5)),
"tas",
"lon",
-10,
"lat",
0,
lon_res,
lat_res,
"2000-01-01",
as_dataset=True,
)
lon_res_est, lat_res_est = _estimate_grid_resolution(ds)
assert lon_res_est == lon_res
assert lat_res_est == lat_res


@pytest.mark.parametrize(("lon_res", "lat_res"), [(0.5, 1), (1, 0.5)])
def test_estimate_res_2d(lon_res, lat_res):
ds = datablock_3d(
np.ones((3, 5, 5)),
"tas",
"rlon",
-10,
"rlat",
0,
lon_res,
lat_res,
"2000-01-01",
as_dataset=True,
)
lon_res_est, lat_res_est = _estimate_grid_resolution(ds)
np.testing.assert_allclose(lon_res_est, ds.lon.diff("rlon").max())
np.testing.assert_allclose(lat_res_est, ds.lat.diff("rlat").max())
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