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r.proj: add test cases
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@aaronsms
aaronsms committed Jun 20, 2021
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from grass.gunittest.case import TestCase
from grass.gunittest.main import test


class TestProj(TestCase):
"""
Used dataset: nc_spm_full_v2alpha2
Test cases:
test_same_resolution: Test output with region using the same resolution.
test_different_resolution: Test output with region using a different resolution.
"""

location = "nc_spm_full_v2alpha2"
mapset = "PERMANENT"
to_clear = []
references = {
"same": {
"nearest": {
"n": 2025000,
"null_cells": 0,
"cells": 2025000,
"min": 55.5787925720215,
"max": 156.329864501953,
"range": 100.751071929932,
"mean": 110.375440275606,
"mean_of_abs": 110.375440275606,
"stddev": 20.3153233205981,
"variance": 412.712361620436,
"coeff_var": 18.4056555243368,
"sum": 223510266.558102,
},
"bilinear": {
"n": 2022151,
"null_cells": 2849,
"cells": 2025000,
"min": 55.8885459899902,
"max": 156.329864501953,
"range": 100.441318511963,
"mean": 110.399779285869,
"mean_of_abs": 110.399779285869,
"stddev": 20.3085640363863,
"variance": 412.437773220004,
"coeff_var": 18.3954752153982,
"sum": 223245024.082699,
},
"bicubic": {
"n": 2016459,
"null_cells": 8541,
"cells": 2025000,
"min": 55.8885459899902,
"max": 156.329864501953,
"range": 100.441318511963,
"mean": 110.411657868429,
"mean_of_abs": 110.411657868429,
"stddev": 20.2966017355864,
"variance": 411.952042013008,
"coeff_var": 18.3826618741407,
"sum": 222640581.213715,
},
"lanczos": {
"n": 2013616,
"null_cells": 11384,
"cells": 2025000,
"min": 55.8885459899902,
"max": 156.329864501953,
"range": 100.441318511963,
"mean": 110.398861024319,
"mean_of_abs": 110.398861024319,
"stddev": 20.2914098702156,
"variance": 411.741314521085,
"coeff_var": 18.3800898686317,
"sum": 222300912.940346,
},
"bilinear_f": {
"n": 2025000,
"null_cells": 0,
"cells": 2025000,
"min": 55.5787925720215,
"max": 156.329864501953,
"range": 100.751071929932,
"mean": 110.375440275606,
"mean_of_abs": 110.375440275606,
"stddev": 20.3153233205981,
"variance": 412.712361620436,
"coeff_var": 18.4056555243368,
"sum": 223510266.558102,
},
"bicubic_f": {
"n": 2025000,
"null_cells": 0,
"cells": 2025000,
"min": 55.5787925720215,
"max": 156.329864501953,
"range": 100.751071929932,
"mean": 110.375440275606,
"mean_of_abs": 110.375440275606,
"stddev": 20.3153233205981,
"variance": 412.712361620436,
"coeff_var": 18.4056555243368,
"sum": 223510266.558102,
},
"lanczos_f": {
"n": 2025000,
"null_cells": 0,
"cells": 2025000,
"min": 55.5787925720215,
"max": 156.329864501953,
"range": 100.751071929932,
"mean": 110.375440275606,
"mean_of_abs": 110.375440275606,
"stddev": 20.3153233205981,
"variance": 412.712361620436,
"coeff_var": 18.4056555243368,
"sum": 223510266.558102,
},
},
"different": {
"nearest": {
"n": 324000,
"null_cells": 0,
"cells": 324000,
"min": 55.9232139587402,
"max": 156.273727416992,
"range": 100.350513458252,
"mean": 110.374542252847,
"mean_of_abs": 110.374542252847,
"stddev": 20.3152875302111,
"variance": 412.710907435151,
"coeff_var": 18.4057728490259,
"sum": 35761351.6899223,
},
"bilinear": {
"n": 324000,
"null_cells": 0,
"cells": 324000,
"min": 55.9677429199219,
"max": 156.230346679688,
"range": 100.262603759766,
"mean": 110.375306993838,
"mean_of_abs": 110.375306993838,
"stddev": 20.3113089385128,
"variance": 412.54927079571,
"coeff_var": 18.4020407206177,
"sum": 35761599.4660034,
},
"bicubic": {
"n": 321724,
"null_cells": 2276,
"cells": 324000,
"min": 56.0584144592285,
"max": 156.223129272461,
"range": 100.164714813232,
"mean": 110.4044006411,
"mean_of_abs": 110.4044006411,
"stddev": 20.2851580571781,
"variance": 411.487637404699,
"coeff_var": 18.3735049865636,
"sum": 35519745.3918571,
},
"lanczos": {
"n": 321724,
"null_cells": 2276,
"cells": 324000,
"min": 56.0589294433594,
"max": 156.221099853516,
"range": 100.162170410156,
"mean": 110.404477059461,
"mean_of_abs": 110.404477059461,
"stddev": 20.2851957283322,
"variance": 411.489165736748,
"coeff_var": 18.373526390064,
"sum": 35519769.977478,
},
"bilinear_f": {
"n": 324000,
"null_cells": 0,
"cells": 324000,
"min": 55.9677429199219,
"max": 156.230346679688,
"range": 100.262603759766,
"mean": 110.375306993838,
"mean_of_abs": 110.375306993838,
"stddev": 20.3113089385128,
"variance": 412.54927079571,
"coeff_var": 18.4020407206177,
"sum": 35761599.4660034,
},
"bicubic_f": {
"n": 324000,
"null_cells": 0,
"cells": 324000,
"min": 55.9677429199219,
"max": 156.223129272461,
"range": 100.255386352539,
"mean": 110.375287905093,
"mean_of_abs": 110.375287905093,
"stddev": 20.3150368008109,
"variance": 412.700720218302,
"coeff_var": 18.4054213460164,
"sum": 35761593.28125,
},
"lanczos_f": {
"n": 324000,
"null_cells": 0,
"cells": 324000,
"min": 55.9677429199219,
"max": 156.221099853516,
"range": 100.253356933594,
"mean": 110.375363786639,
"mean_of_abs": 110.375363786639,
"stddev": 20.3150742610671,
"variance": 412.702242232673,
"coeff_var": 18.4054426315072,
"sum": 35761617.8668709,
},
},
}

@classmethod
def setUpClass(cls):
cls.use_temp_region()

@classmethod
def tearDownClass(cls):
cls.del_temp_region()
if cls.to_clear:
cls.runModule(
"g.remove", flags="f", type="raster", name=",".join(cls.to_clear)
)

def test_same_resolution(self):
self.runModule("g.region", raster="elevation")
for method, reference in self.references["same"].items():
output_file = "elevation_same_{}".format(method)
self.to_clear.append(output_file)
self.assertModule(
"r.proj",
input="elevation",
location=self.location,
mapset=self.mapset,
output=output_file,
method=method,
)
self.assertRasterFitsInfo(
raster=output_file,
reference={
"north": 228500,
"south": 215000,
"east": 645000,
"west": 630000,
"nsres": 10,
"ewres": 10,
"rows": 1350,
"cols": 1500,
"cells": 2025000,
"datatype": "FCELL",
"ncats": 0,
},
)
self.assertRasterFitsUnivar(
raster=output_file,
reference=reference,
precision=1e-5,
)

def test_different_resolution(self):
self.runModule("g.region", raster="elevation", res=25)
for method, reference in self.references["different"].items():
output_file = "elevation_diff_{}".format(method)
self.to_clear.append(output_file)
self.assertModule(
"r.proj",
input="elevation",
location=self.location,
mapset=self.mapset,
output=output_file,
method=method,
)
self.assertRasterFitsInfo(
raster=output_file,
reference={
"north": 228500,
"south": 215000,
"east": 645000,
"west": 630000,
"nsres": 25,
"ewres": 25,
"rows": 540,
"cols": 600,
"cells": 324000,
"datatype": "FCELL",
"ncats": 0,
},
)
self.assertRasterFitsUnivar(
raster=output_file,
reference=reference,
precision=1e-5,
)


if __name__ == "__main__":
test()

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