1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
|
"""Tests for MODIS interpolators."""
import warnings
import numpy as np
from pyproj import Geod
import h5py
import os
import dask
import dask.array as da
import xarray as xr
import pytest
from .utils import CustomScheduler
from geotiepoints.modisinterpolator import (modis_1km_to_250m,
modis_1km_to_500m,
modis_5km_to_1km,
modis_5km_to_500m,
modis_5km_to_250m)
FILENAME_DATA = os.path.join(
os.path.dirname(__file__), '../../testdata/modis_test_data.h5')
def _to_dask(arr):
return da.from_array(arr, chunks=4096)
def _to_da(arr):
return xr.DataArray(_to_dask(arr), dims=['y', 'x'])
def _load_h5_geo_vars(*var_names):
h5f = h5py.File(FILENAME_DATA, 'r')
return tuple(h5f[var_name] for var_name in var_names)
def load_1km_lonlat_as_numpy():
lon1, lat1 = _load_h5_geo_vars('lon_1km', 'lat_1km')
return lon1[:], lat1[:]
def load_1km_lonlat_as_dask():
lon1, lat1 = _load_h5_geo_vars('lon_1km', 'lat_1km')
return _to_dask(lon1), _to_dask(lat1)
def load_1km_lonlat_as_xarray_dask():
lon1, lat1 = _load_h5_geo_vars('lon_1km', 'lat_1km')
return _to_da(lon1), _to_da(lat1)
def load_1km_lonlat_satz_as_xarray_dask():
lon1, lat1, satz1 = _load_h5_geo_vars('lon_1km', 'lat_1km', 'satz_1km')
return _to_da(lon1), _to_da(lat1), _to_da(satz1)
def load_5km_lonlat_satz1_as_xarray_dask():
lon1, lat1, satz1 = _load_h5_geo_vars('lon_1km', 'lat_1km', 'satz_1km')
lon5 = lon1[2::5, 2::5]
lat5 = lat1[2::5, 2::5]
satz5 = satz1[2::5, 2::5]
return _to_da(lon5), _to_da(lat5), _to_da(satz5)
def load_l2_5km_lonlat_satz1_as_xarray_dask():
lon1, lat1, satz1 = _load_h5_geo_vars('lon_1km', 'lat_1km', 'satz_1km')
lon5 = lon1[2::5, 2:-5:5]
lat5 = lat1[2::5, 2:-5:5]
satz5 = satz1[2::5, 2:-5:5]
return _to_da(lon5), _to_da(lat5), _to_da(satz5)
def load_500m_lonlat_expected_as_xarray_dask():
h5f = h5py.File(FILENAME_DATA, 'r')
lon500 = _to_da(h5f['lon_500m'])
lat500 = _to_da(h5f['lat_500m'])
return lon500, lat500
def load_250m_lonlat_expected_as_xarray_dask():
h5f = h5py.File(FILENAME_DATA, 'r')
lon250 = _to_da(h5f['lon_250m'])
lat250 = _to_da(h5f['lat_250m'])
return lon250, lat250
def assert_geodetic_distance(
lons_actual: np.ndarray,
lats_actual: np.ndarray,
lons_desired: np.ndarray,
lats_desired: np.ndarray,
max_distance_diff: float,
) -> None:
"""Check that the geodetic distance between two sets of coordinates is smaller than a threshold.
Args:
lons_actual: Longitude array produced by interpolation being tested.
lats_actual: Latitude array produced by interpolation being tested.
lons_desired: Longitude array of expected/truth coordinates.
lats_desired: Latitude array of expected/truth coordinates.
max_distance_diff: Limit of allowed distance difference in meters.
"""
g = Geod(ellps="WGS84")
_, _, dist = g.inv(lons_actual, lats_actual, lons_desired, lats_desired)
np.testing.assert_array_less(
dist, max_distance_diff,
err_msg=f"Coordinates are greater than {max_distance_diff} geodetic "
"meters from the expected coordinates.")
@pytest.mark.skipif(not os.path.isfile(FILENAME_DATA), reason='data file not available')
@pytest.mark.parametrize(
("input_func", "exp_func", "interp_func", "dist_max", "exp_5km_warning"),
[
(load_1km_lonlat_satz_as_xarray_dask, load_500m_lonlat_expected_as_xarray_dask, modis_1km_to_500m, 5, False),
(load_1km_lonlat_satz_as_xarray_dask, load_250m_lonlat_expected_as_xarray_dask, modis_1km_to_250m, 8.30, False),
(load_5km_lonlat_satz1_as_xarray_dask, load_1km_lonlat_as_xarray_dask, modis_5km_to_1km, 25, False),
(load_l2_5km_lonlat_satz1_as_xarray_dask, load_1km_lonlat_as_xarray_dask, modis_5km_to_1km, 110, False),
(load_5km_lonlat_satz1_as_xarray_dask, load_500m_lonlat_expected_as_xarray_dask, modis_5km_to_500m,
19500, True),
(load_5km_lonlat_satz1_as_xarray_dask, load_250m_lonlat_expected_as_xarray_dask, modis_5km_to_250m,
25800, True),
]
)
def test_sat_angle_based_interp(input_func, exp_func, interp_func, dist_max, exp_5km_warning):
lon1, lat1, satz1 = input_func()
lons_exp, lats_exp = exp_func()
# when working with dask arrays, we shouldn't compute anything
with dask.config.set(scheduler=CustomScheduler(0)), warnings.catch_warnings(record=True) as warns:
lons, lats = interp_func(lon1, lat1, satz1)
has_5km_warning = any("may result in poor quality" in str(w.message) for w in warns)
if exp_5km_warning:
assert has_5km_warning
else:
assert not has_5km_warning
if hasattr(lons, "compute"):
lons, lats = da.compute(lons, lats)
assert_geodetic_distance(lons, lats, lons_exp, lats_exp, dist_max)
assert not np.any(np.isnan(lons))
assert not np.any(np.isnan(lats))
@pytest.mark.skipif(not os.path.isfile(FILENAME_DATA), reason='data file not available')
def test_sat_angle_based_interp_nan_handling():
# See GH #19
lon1, lat1, satz1 = load_1km_lonlat_satz_as_xarray_dask()
satz1 = _to_da(abs(np.linspace(-65.4, 65.4, 1354, dtype=np.float32)).repeat(20).reshape(-1, 20).T)
lons, lats = modis_1km_to_500m(lon1, lat1, satz1)
assert not np.any(np.isnan(lons.compute()))
assert not np.any(np.isnan(lats.compute()))
@pytest.mark.skipif(not os.path.isfile(FILENAME_DATA), reason='data file not available')
def test_poles_datum():
orig_lon, lat1, satz1 = load_1km_lonlat_satz_as_xarray_dask()
lon1 = orig_lon + 180
lon1 = xr.where(lon1 > 180, lon1 - 360, lon1)
lat5 = lat1[2::5, 2::5]
lon5 = lon1[2::5, 2::5]
satz5 = satz1[2::5, 2::5]
lons, lats = modis_5km_to_1km(lon5, lat5, satz5)
lons = lons + 180
lons = xr.where(lons > 180, lons - 360, lons)
assert_geodetic_distance(lons, lats, orig_lon, lat1, 25.0)
|
