Skip to content
Snippets Groups Projects
Commit 671e64ae authored by Sebastian Müller's avatar Sebastian Müller 🐈
Browse files

tests: add tests for regridding masked data

parent 3a5b84b4
No related branches found
No related tags found
1 merge request!286Add mask to Info object
Pipeline #266121 failed with stages
in 2 minutes and 37 seconds
Hide whitespace changes
Inline Side-by-side
tests/adapters/square_5x4/cells.txt 0 → 100644
+ 198
− 0
View file @ 671e64ae
90 85 66
89 39 77
106 39 89
97 94 44
108 44 94
87 63 66
103 62 72
87 69 63
102 66 85
100 85 90
76 14 13
104 72 62
103 72 71
112 76 13
102 87 66
97 79 94
96 93 46
108 68 91
117 93 96
104 89 77
101 98 84
109 91 68
108 91 44
101 69 87
116 73 88
105 77 39
116 88 58
95 67 94
48 42 33
74 47 11
75 47 74
75 74 72
73 71 70
73 70 36
78 43 17
76 47 75
36 8 7
74 11 10
70 8 36
77 76 75
70 40 9
79 34 42
71 40 70
47 12 11
40 10 9
42 34 33
70 9 8
79 35 34
91 7 6
41 26 25
80 24 37
83 56 53
74 10 40
82 26 41
84 18 43
80 41 25
43 18 17
85 80 37
72 40 71
81 41 80
95 94 42
84 19 18
48 33 32
59 58 57
85 81 80
61 59 60
78 17 16
82 27 26
57 55 56
95 42 48
80 25 24
91 36 7
60 59 57
37 24 23
62 59 61
58 55 57
67 52 54
89 62 61
88 54 55
68 67 54
88 68 54
88 55 58
86 37 23
56 55 53
63 61 60
55 54 53
86 23 22
83 53 51
90 60 57
74 40 72
39 16 15
90 57 56
89 61 65
54 52 53
87 86 45
90 66 60
45 22 21
66 63 60
65 61 63
53 52 51
86 22 45
83 51 64
38 32 31
91 6 44
64 51 50
52 49 51
69 65 63
44 6 5
92 31 30
46 30 29
50 49 38
78 16 39
92 38 31
51 49 50
95 52 67
92 30 46
93 92 46
92 50 38
48 32 38
49 48 38
93 50 92
94 79 42
95 49 52
93 64 50
95 48 49
115 101 84
100 81 85
96 28 27
97 5 4
98 21 20
99 41 81
100 56 83
101 87 45
102 85 37
102 86 87
103 58 59
96 27 82
97 44 5
98 45 21
104 75 72
105 39 15
108 94 67
109 36 91
99 82 41
104 62 89
110 29 3
111 4 0
111 0 35
112 13 1
112 1 12
112 47 76
113 2 19
113 20 2
110 3 28
100 90 56
102 37 86
103 59 62
117 64 93
114 100 83
117 96 82
105 14 76
109 88 73
101 45 98
115 84 43
104 77 75
109 68 88
106 89 65
107 65 69
107 106 65
105 15 14
107 43 78
105 76 77
106 78 39
107 78 106
117 99 64
108 67 68
114 83 64
114 81 100
109 73 36
110 28 96
113 98 20
111 97 4
111 35 79
112 12 47
113 19 84
110 46 29
116 71 73
116 58 103
114 99 81
114 64 99
110 96 46
113 84 98
111 79 97
117 82 99
116 103 71
115 69 101
115 107 69
115 43 107
tests/adapters/square_5x4/points.txt 0 → 100644
+ 118
− 0
View file @ 671e64ae
0.000000000000000000e+00 0.000000000000000000e+00
5.000000000000000000e+00 0.000000000000000000e+00
5.000000000000000000e+00 4.000000000000000000e+00
0.000000000000000000e+00 4.000000000000000000e+00
4.999999999995947131e-01 0.000000000000000000e+00
9.999999999984133803e-01 0.000000000000000000e+00
1.499999999996955102e+00 0.000000000000000000e+00
1.999999999995512923e+00 0.000000000000000000e+00
2.499999999996204814e+00 0.000000000000000000e+00
2.999999999996965983e+00 0.000000000000000000e+00
3.499999999997728040e+00 0.000000000000000000e+00
3.999999999998489209e+00 0.000000000000000000e+00
4.499999999999245048e+00 0.000000000000000000e+00
5.000000000000000000e+00 4.999999999990952237e-01
5.000000000000000000e+00 9.999999999976482146e-01
5.000000000000000000e+00 1.499999999996189937e+00
5.000000000000000000e+00 1.999999999994775957e+00
5.000000000000000000e+00 2.499999999996049826e+00
5.000000000000000000e+00 2.999999999997366995e+00
5.000000000000000000e+00 3.499999999998682831e+00
4.499999999999999112e+00 4.000000000000000000e+00
3.999999999999997780e+00 4.000000000000000000e+00
3.499999999999996891e+00 4.000000000000000000e+00
2.999999999999996003e+00 4.000000000000000000e+00
2.499999999999995115e+00 4.000000000000000000e+00
1.999999999999994005e+00 4.000000000000000000e+00
1.499999999999992006e+00 4.000000000000000000e+00
9.999999999999911182e-01 4.000000000000000000e+00
4.999999999999956146e-01 4.000000000000000000e+00
0.000000000000000000e+00 3.499999999998003819e+00
0.000000000000000000e+00 3.000000000001386002e+00
0.000000000000000000e+00 2.500000000004854783e+00
0.000000000000000000e+00 2.000000000008236967e+00
0.000000000000000000e+00 1.500000000006241896e+00
0.000000000000000000e+00 1.000000000004162004e+00
0.000000000000000000e+00 5.000000000020810020e-01
2.265798546853971196e+00 4.238914066109839007e-01
2.749999999999935163e+00 3.577350269190534782e+00
4.330127018896737789e-01 2.250000000006775913e+00
4.515887990308685040e+00 1.721291157952514972e+00
3.249999999997145839e+00 4.330127018927403815e-01
1.749999999999994005e+00 3.573329484275529211e+00
4.330127018913362269e-01 1.250000000006348033e+00
4.599679368559095316e+00 2.734037287364882829e+00
1.206637172643693967e+00 4.481480624790066258e-01
3.745775399958263030e+00 3.578703610545325819e+00
4.233860225254563736e-01 3.247881315359677057e+00
4.230662432701146614e+00 4.218481522081570234e-01
4.330127018898574098e-01 1.750000000008703038e+00
8.660254037804282490e-01 2.000000000008117063e+00
8.660254037794199444e-01 2.500000000006437961e+00
1.299038105671252108e+00 2.250000000007863044e+00
1.299038105671842080e+00 1.750000000008790080e+00
1.732050807563251027e+00 2.000000000008578915e+00
1.732050807563606964e+00 1.500000000008993917e+00
2.165063509455331214e+00 1.750000000009032108e+00
2.168763921158598951e+00 2.243590698928989102e+00
2.598692946631000034e+00 1.998931783162479991e+00
2.625210852851048937e+00 1.535103350557095903e+00
3.033703670897030857e+00 1.753920133713432028e+00
3.031627495395729177e+00 2.250475319485361059e+00
3.464627171766605951e+00 2.000732575539676095e+00
3.464101615131112144e+00 1.500000000010194068e+00
3.470262035740892870e+00 2.522530415211392985e+00
1.305354910972956084e+00 2.788748402221191025e+00
3.897114317022456031e+00 2.250000000009737100e+00
3.031088913238387139e+00 2.750000000008742784e+00
1.307411517935652912e+00 1.264503175480730990e+00
1.733099282557412968e+00 1.015972093721750902e+00
3.897114317022187802e+00 2.750000000009475087e+00
2.752633091139478960e+00 4.314924860121975847e-01
2.965664888242407038e+00 8.430407192711438791e-01
3.473466298134646912e+00 9.485033022443511985e-01
2.495283483432777949e+00 8.191813640038545508e-01
3.749999999997362998e+00 4.330127018933099259e-01
3.999999999998494982e+00 8.660254037854584475e-01
4.539957219767416952e+00 8.147052950646799241e-01
4.249999999998625100e+00 1.299038105677188915e+00
4.566987298108965199e+00 2.249999999994134026e+00
4.233860225288407220e-01 7.521186846436920259e-01
2.249999999999953815e+00 3.569771490982977191e+00
1.986017662442111087e+00 3.172602692073807962e+00
1.278337285646486077e+00 3.591790500907492856e+00
1.736885620003159048e+00 2.478141994818935157e+00
4.527193881727244573e+00 3.220499339533433147e+00
2.543206966671962999e+00 3.068203009786670155e+00
3.249295899993029924e+00 3.581030149777149951e+00
3.484672574077193108e+00 3.081218998192642999e+00
2.224749828680767205e+00 1.226702911488599002e+00
3.949238975830291043e+00 1.718453006844590014e+00
2.598076211346683095e+00 2.500000000008701928e+00
1.735657837491481947e+00 5.051897496787564057e-01
4.314082553290368161e-01 2.749646885897047088e+00
8.243663844143013231e-01 3.015277068572050023e+00
9.086100644094758927e-01 9.243779422283027181e-01
8.745184159297654247e-01 1.489813519623498062e+00
7.910885367730781104e-01 3.530452272734196928e+00
7.456552845443471522e-01 4.161816644597777226e-01
4.234352555081294156e+00 3.603882322562967921e+00
1.532342419179892001e+00 3.204280363629865125e+00
2.126965062766455805e+00 2.722917844972307044e+00
3.999171452587594988e+00 3.190116389084741044e+00
3.008323479140071033e+00 3.207158181768441807e+00
3.063606654562874176e+00 1.306322380129556038e+00
3.820890929013136983e+00 1.258109208728056982e+00
4.665063509458925672e+00 1.250000000000385914e+00
4.232125541608215968e+00 2.032051323961315159e+00
4.232050807571533291e+00 2.499999999993876898e+00
1.378283175007543049e+00 8.316382047177097725e-01
2.094791281142887041e+00 8.112976320968601218e-01
3.660254037837317176e-01 3.633974596215732156e+00
3.660254037858404197e-01 3.660254037865067200e-01
4.633974596214726738e+00 3.660254037848266750e-01
4.633974596214662789e+00 3.633974596214311958e+00
1.728839994976222982e+00 2.874968674216736186e+00
4.251041965493531194e+00 2.878930603197281890e+00
2.690002125042068037e+00 1.148726086499350929e+00
1.146297907397342941e+00 3.226109721612958836e+00
tests/adapters/square_5x4/types.txt 0 → 100644
+ 198
− 0
View file @ 671e64ae
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
tests/adapters/test_regrid_mask.py 0 → 100644
+ 160
− 0
View file @ 671e64ae
"""
Unit tests for regridding with masked data.
"""
import unittest
from pathlib import Path
import numpy as np
from numpy.testing import assert_array_equal
from finam import Composition, FinamDataError, Info, Mask, UniformGrid, UnstructuredGrid
from finam import data as fdata
from finam.adapters.regrid import RegridLinear, RegridNearest
from finam.modules import StaticSimplexNoise, debug
def get_mask(points, rad=1.5):
return (points[:, 0] - 2.5) ** 2 + (points[:, 1] - 2) ** 2 > rad**2
class TestRegridMask(unittest.TestCase):
@classmethod
def setUpClass(self):
here = Path(__file__).parent
points = np.loadtxt(here / "square_5x4" / "points.txt", dtype=float)
cells = np.loadtxt(here / "square_5x4" / "cells.txt", dtype=int)
types = np.loadtxt(here / "square_5x4" / "types.txt", dtype=int)
self.in_grid = UnstructuredGrid(points, cells, types)
self.out_grid = UniformGrid((25, 20), spacing=(0.2, 0.2))
self.in_mask = get_mask(self.in_grid.cell_centers, rad=1.5)
omask = get_mask(self.out_grid.cell_centers, rad=2.5)
self.out_mask = fdata.from_compressed(
omask, self.out_grid.data_shape, self.out_grid.order
)
def test_regrid_nearest_out_mask(self):
in_info = Info(grid=self.in_grid, units="", mask=self.in_mask)
source = StaticSimplexNoise(in_info, 0.15, 3, 0.5)
sink = debug.DebugPushConsumer({"Input": Info()})
regrid = RegridNearest(out_grid=self.out_grid, out_mask=self.out_mask)
composition = Composition([source, sink])
(source.outputs["Noise"] >> regrid >> sink.inputs["Input"])
composition.connect()
info = sink.inputs["Input"].info
data = sink.data["Input"][0, ...]
assert_array_equal(info.mask, data.mask)
assert_array_equal(info.mask, self.out_mask)
i_data = source.outputs["Noise"].data[0][1].magnitude.compressed()
o_data = data.magnitude.compressed()
self.assertAlmostEqual(i_data.mean(), o_data.mean(), 2)
def test_regrid_nearest_filled(self):
in_info = Info(grid=self.in_grid, units="", mask=self.in_mask)
source = StaticSimplexNoise(in_info, 0.15, 3, 0.5)
sink = debug.DebugPushConsumer({"Input": Info()})
regrid = RegridNearest(out_grid=self.out_grid, out_mask=Mask.NONE)
composition = Composition([source, sink])
(source.outputs["Noise"] >> regrid >> sink.inputs["Input"])
composition.connect()
info = sink.inputs["Input"].info
data = sink.data["Input"][0, ...].magnitude
self.assertEqual(info.mask, Mask.NONE)
self.assertFalse(fdata.is_masked_array(data))
i_data = source.outputs["Noise"].data[0][1].magnitude.compressed()
self.assertAlmostEqual(i_data.mean(), data.mean(), 1)
def test_regrid_linear_determine_mask(self):
in_info = Info(grid=self.in_grid, units="", mask=self.in_mask)
source = StaticSimplexNoise(in_info, 0.15, 3, 0.5)
sink = debug.DebugPushConsumer({"Input": Info()})
regrid = RegridLinear(
out_grid=self.out_grid, fill_with_nearest=False, out_mask=None
)
composition = Composition([source, sink])
(source.outputs["Noise"] >> regrid >> sink.inputs["Input"])
composition.connect()
info = sink.inputs["Input"].info
data = sink.data["Input"][0, ...]
assert_array_equal(info.mask, data.mask)
self.assertEqual(np.sum(data.mask), 306)
i_data = source.outputs["Noise"].data[0][1].magnitude.compressed()
o_data = data.magnitude.compressed()
self.assertAlmostEqual(i_data.mean(), o_data.mean(), 2)
def test_regrid_linear_error_domain(self):
in_info = Info(grid=self.in_grid, units="", mask=self.in_mask)
source = StaticSimplexNoise(in_info, 0.15, 3, 0.5)
sink = debug.DebugPushConsumer({"Input": Info()})
regrid = RegridLinear(
out_grid=self.out_grid, fill_with_nearest=False, out_mask=Mask.NONE
)
composition = Composition([source, sink])
(source.outputs["Noise"] >> regrid >> sink.inputs["Input"])
# not covering domain without fill
with self.assertRaises(FinamDataError):
composition.connect()
def test_regrid_linear_filled(self):
in_info = Info(grid=self.in_grid, units="", mask=self.in_mask)
source = StaticSimplexNoise(in_info, 0.15, 3, 0.5)
sink = debug.DebugPushConsumer({"Input": Info()})
regrid = RegridLinear(
out_grid=self.out_grid, fill_with_nearest=True, out_mask=Mask.NONE
)
composition = Composition([source, sink])
(source.outputs["Noise"] >> regrid >> sink.inputs["Input"])
composition.connect()
info = sink.inputs["Input"].info
data = sink.data["Input"][0, ...].magnitude
self.assertEqual(info.mask, Mask.NONE)
self.assertFalse(fdata.is_masked_array(data))
i_data = source.outputs["Noise"].data[0][1].magnitude.compressed()
self.assertAlmostEqual(i_data.mean(), data.mean(), 1)
def test_regrid_linear_filled_mask(self):
in_info = Info(grid=self.in_grid, units="", mask=self.in_mask)
source = StaticSimplexNoise(in_info, 0.15, 3, 0.5)
sink = debug.DebugPushConsumer({"Input": Info()})
regrid = RegridLinear(
out_grid=self.out_grid, fill_with_nearest=True, out_mask=self.out_mask
)
composition = Composition([source, sink])
(source.outputs["Noise"] >> regrid >> sink.inputs["Input"])
composition.connect()
info = sink.inputs["Input"].info
data = sink.data["Input"][0, ...]
assert_array_equal(info.mask, data.mask)
assert_array_equal(info.mask, self.out_mask)
i_data = source.outputs["Noise"].data[0][1].magnitude.compressed()
o_data = data.magnitude.compressed()
self.assertAlmostEqual(i_data.mean(), o_data.mean(), 2)
if __name__ == "__main__":
unittest.main()
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment