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import pytest
import numpy as np
import xarray as xr
from polsarpro.util import vec_to_mat
from polsarpro.util import S_to_C3, S_to_C4, S_to_C2
from polsarpro.util import S_to_T3, S_to_T4
from polsarpro.util import T3_to_C3, T4_to_C4
from polsarpro.util import C3_to_T3, C4_to_T4
from polsarpro.util import boxcar
from polsarpro.util import multilook
from polsarpro.util import pauli_rgb
def test_vec_to_mat():
N = 128
D = 3
v = np.random.rand(N, N, D) + 1j * np.random.rand(N, N, D)
M = vec_to_mat(v)
assert M.shape == (N, N, D, D)
# M has to be Hermitian
assert np.allclose(M.transpose((0, 1, 3, 2)), M.conj())
assert np.allclose(M.diagonal(axis1=2, axis2=3).imag, 0)
def test_S_to_C2():
N = 128
S = (np.random.rand(N, N, 2, 2) + 1j * np.random.rand(N, N, 2, 2)).astype(
"complex64"
)
dims = ("y", "x")
S_dict = dict(
hh=xr.DataArray(S[..., 0, 0], dims=dims),
hv=xr.DataArray(S[..., 0, 1], dims=dims),
vh=xr.DataArray(S[..., 1, 0], dims=dims),
vv=xr.DataArray(S[..., 1, 1], dims=dims),
)
Sx = xr.Dataset(S_dict, attrs=dict(poltype="S"))
C2x = S_to_C2(Sx)
# test ouput shapes and types
assert C2x.poltype == "C2"
assert all(C2x[var].shape == (N, N) for var in C2x.data_vars)
assert all(C2x[var].dtype == "float32" for var in ["m11", "m22"])
assert all(C2x[var].dtype == "complex64" for var in ["m12"])
def test_S_to_C3():
N = 128
S = (np.random.rand(N, N, 2, 2) + 1j * np.random.rand(N, N, 2, 2)).astype(
"complex64"
)
dims = ("y", "x")
S_dict = dict(
hh=xr.DataArray(S[..., 0, 0], dims=dims),
hv=xr.DataArray(S[..., 0, 1], dims=dims),
vh=xr.DataArray(S[..., 1, 0], dims=dims),
vv=xr.DataArray(S[..., 1, 1], dims=dims),
)
Sx = xr.Dataset(S_dict, attrs=dict(poltype="S"))
C3x = S_to_C3(Sx)
# test ouput shapes and types
assert C3x.poltype == "C3"
assert all(C3x[var].shape == (N, N) for var in C3x.data_vars)
assert all(C3x[var].dtype == "float32" for var in ["m11", "m22", "m33"])
assert all(C3x[var].dtype == "complex64" for var in ["m12", "m13", "m23"])
def test_S_to_C4():
N = 128
S = (np.random.rand(N, N, 2, 2) + 1j * np.random.rand(N, N, 2, 2)).astype(
"complex64"
)
dims = ("y", "x")
S_dict = dict(
hh=xr.DataArray(S[..., 0, 0], dims=dims),
hv=xr.DataArray(S[..., 0, 1], dims=dims),
vh=xr.DataArray(S[..., 1, 0], dims=dims),
vv=xr.DataArray(S[..., 1, 1], dims=dims),
)
Sx = xr.Dataset(S_dict, attrs=dict(poltype="S"))
C4x = S_to_C4(Sx)
# test ouput shapes and types
assert C4x.poltype == "C4"
assert all(C4x[var].shape == (N, N) for var in C4x.data_vars)
assert all(C4x[var].dtype == "float32" for var in ["m11", "m22", "m33"])
assert all(C4x[var].dtype == "complex64" for var in ["m12", "m13", "m23"])
def test_S_to_T3():
N = 128
S = (np.random.rand(N, N, 2, 2) + 1j * np.random.rand(N, N, 2, 2)).astype(
"complex64"
)
dims = ("y", "x")
S_dict = dict(
hh=xr.DataArray(S[..., 0, 0], dims=dims),
hv=xr.DataArray(S[..., 0, 1], dims=dims),
vh=xr.DataArray(S[..., 1, 0], dims=dims),
vv=xr.DataArray(S[..., 1, 1], dims=dims),
)
Sx = xr.Dataset(S_dict, attrs=dict(poltype="S"))
T3x = S_to_T3(Sx)
# test ouput shapes and types
assert T3x.poltype == "T3"
assert all(T3x[var].shape == (N, N) for var in T3x.data_vars)
assert all(T3x[var].dtype == "float32" for var in ["m11", "m22", "m33"])
assert all(T3x[var].dtype == "complex64" for var in ["m12", "m13", "m23"])
def test_S_to_T4():
N = 128
S = (np.random.rand(N, N, 2, 2) + 1j * np.random.rand(N, N, 2, 2)).astype(
"complex64"
)
# Xarray version
dims = ("y", "x")
S_dict = dict(
hh=xr.DataArray(S[..., 0, 0], dims=dims),
hv=xr.DataArray(S[..., 0, 1], dims=dims),
vh=xr.DataArray(S[..., 1, 0], dims=dims),
vv=xr.DataArray(S[..., 1, 1], dims=dims),
)
Sx = xr.Dataset(S_dict, attrs=dict(poltype="S"))
T4x = S_to_T4(Sx)
# test ouput shapes and types
assert T4x.poltype == "T4"
assert all(T4x[var].shape == (N, N) for var in T4x.data_vars)
assert all(T4x[var].dtype == "float32" for var in ["m11", "m22", "m33"])
assert all(T4x[var].dtype == "complex64" for var in ["m12", "m13", "m23"])
def test_T3_to_C3():
N = 128
D = 3
v = np.random.rand(N, N, D) + 1j * np.random.rand(N, N, D)
# fake T3 matrix
T3 = vec_to_mat(v)
dims = ("y", "x")
T3_dict = dict(
m11=xr.DataArray(T3[..., 0, 0].real.astype("float32"), dims=dims),
m22=xr.DataArray(T3[..., 1, 1].real.astype("float32"), dims=dims),
m33=xr.DataArray(T3[..., 2, 2].real.astype("float32"), dims=dims),
m12=xr.DataArray(T3[..., 0, 1].astype("complex64"), dims=dims),
m13=xr.DataArray(T3[..., 0, 2].astype("complex64"), dims=dims),
m23=xr.DataArray(T3[..., 1, 2].astype("complex64"), dims=dims),
)
T3x = xr.Dataset(T3_dict, attrs=dict(poltype="T3"))
C3x = T3_to_C3(T3x)
# test ouput shapes and types
assert C3x.poltype == "C3"
assert all(C3x[var].shape == (N, N) for var in C3x.data_vars)
assert all(C3x[var].dtype == "float32" for var in ["m11", "m22", "m33"])
assert all(C3x[var].dtype == "complex64" for var in ["m12", "m13", "m23"])
def test_T4_to_C4():
N = 128
D = 4
v = np.random.rand(N, N, D) + 1j * np.random.rand(N, N, D)
# fake T3 matrix
T4 = vec_to_mat(v)
# Xarray version
dims = ("y", "x")
T4_dict = dict(
m11=xr.DataArray(T4[..., 0, 0].real.astype("float32"), dims=dims),
m22=xr.DataArray(T4[..., 1, 1].real.astype("float32"), dims=dims),
m33=xr.DataArray(T4[..., 2, 2].real.astype("float32"), dims=dims),
m44=xr.DataArray(T4[..., 2, 2].real.astype("float32"), dims=dims),
m12=xr.DataArray(T4[..., 0, 1].astype("complex64"), dims=dims),
m13=xr.DataArray(T4[..., 0, 2].astype("complex64"), dims=dims),
m14=xr.DataArray(T4[..., 0, 3].astype("complex64"), dims=dims),
m23=xr.DataArray(T4[..., 1, 2].astype("complex64"), dims=dims),
m24=xr.DataArray(T4[..., 1, 3].astype("complex64"), dims=dims),
m34=xr.DataArray(T4[..., 2, 3].astype("complex64"), dims=dims),
)
T4x = xr.Dataset(T4_dict, attrs=dict(poltype="T4"))
C4x = T4_to_C4(T4x)
# test ouput shapes and types
assert C4x.poltype == "C4"
assert all(C4x[var].shape == (N, N) for var in C4x.data_vars)
assert all(C4x[var].dtype == "float32" for var in ["m11", "m22", "m33", "m44"])
assert all(
C4x[var].dtype == "complex64"
for var in ["m12", "m13", "m14", "m23", "m23", "m34"]
)
def test_C3_to_T3():
N = 128
D = 3
v = np.random.rand(N, N, D) + 1j * np.random.rand(N, N, D)
# fake T3 matrix
C3 = vec_to_mat(v)
# Xarray version
dims = ("y", "x")
C3_dict = dict(
m11=xr.DataArray(C3[..., 0, 0].real.astype("float32"), dims=dims),
m22=xr.DataArray(C3[..., 1, 1].real.astype("float32"), dims=dims),
m33=xr.DataArray(C3[..., 2, 2].real.astype("float32"), dims=dims),
m12=xr.DataArray(C3[..., 0, 1].astype("complex64"), dims=dims),
m13=xr.DataArray(C3[..., 0, 2].astype("complex64"), dims=dims),
m23=xr.DataArray(C3[..., 1, 2].astype("complex64"), dims=dims),
)
C3x = xr.Dataset(C3_dict, attrs=dict(poltype="C3"))
T3x = C3_to_T3(C3x)
# test ouput shapes and types
assert T3x.poltype == "T3"
assert all(T3x[var].shape == (N, N) for var in T3x.data_vars)
assert all(T3x[var].dtype == "float32" for var in ["m11", "m22", "m33"])
assert all(T3x[var].dtype == "complex64" for var in ["m12", "m13", "m23"])
def test_C4_to_T4():
N = 128
D = 4
v = np.random.rand(N, N, D) + 1j * np.random.rand(N, N, D)
# fake T3 matrix
C4 = vec_to_mat(v)
# Xarray version
dims = ("y", "x")
C4_dict = dict(
m11=xr.DataArray(C4[..., 0, 0].real.astype("float32"), dims=dims),
m22=xr.DataArray(C4[..., 1, 1].real.astype("float32"), dims=dims),
m33=xr.DataArray(C4[..., 2, 2].real.astype("float32"), dims=dims),
m44=xr.DataArray(C4[..., 2, 2].real.astype("float32"), dims=dims),
m12=xr.DataArray(C4[..., 0, 1].astype("complex64"), dims=dims),
m13=xr.DataArray(C4[..., 0, 2].astype("complex64"), dims=dims),
m14=xr.DataArray(C4[..., 0, 3].astype("complex64"), dims=dims),
m23=xr.DataArray(C4[..., 1, 2].astype("complex64"), dims=dims),
m24=xr.DataArray(C4[..., 1, 3].astype("complex64"), dims=dims),
m34=xr.DataArray(C4[..., 2, 3].astype("complex64"), dims=dims),
)
C4x = xr.Dataset(C4_dict, attrs=dict(poltype="C4"))
T4x = C4_to_T4(C4x)
# test ouput shapes and types
assert T4x.poltype == "T4"
assert all(T4x[var].shape == (N, N) for var in T4x.data_vars)
assert all(T4x[var].dtype == "float32" for var in ["m11", "m22", "m33", "m44"])
assert all(
T4x[var].dtype == "complex64"
for var in ["m12", "m13", "m14", "m23", "m23", "m34"]
)
def test_boxcar():
N = 128
D = 3
v = np.random.rand(N, N, D) + 1j * np.random.rand(N, N, D)
M = vec_to_mat(v)
# Xarray version
dims = ("y", "x")
M_dict = dict(
m11=xr.DataArray(M[..., 0, 0].real.astype("float32"), dims=dims),
m22=xr.DataArray(M[..., 1, 1].real.astype("float32"), dims=dims),
m33=xr.DataArray(M[..., 2, 2].real.astype("float32"), dims=dims),
m12=xr.DataArray(M[..., 0, 1].astype("complex64"), dims=dims),
m13=xr.DataArray(M[..., 0, 2].astype("complex64"), dims=dims),
m23=xr.DataArray(M[..., 1, 2].astype("complex64"), dims=dims),
)
Mx = xr.Dataset(M_dict, attrs={"poltype": "C3"})
param = {"img": Mx, "dim_az": 5, "dim_rg": 3}
M_box = boxcar(**param)
# test ouput shapes and types
assert all(M_box[var].shape == (N, N) for var in Mx.data_vars)
assert all(M_box[var].dtype == "float32" for var in ["m11", "m22", "m33"])
assert all(M_box[var].dtype == "complex64" for var in ["m12", "m13", "m23"])
@pytest.mark.parametrize(
"synthetic_poldata", ["C2", "C3", "C4", "T3", "T4"], indirect=True
)
def test_multilook(synthetic_poldata):
input_data = synthetic_poldata
dim_az = 5
dim_rg = 2
for _, ds in input_data.items():
input_data = ds.chunk(x=64, y=64)
res = multilook(input_data=input_data, dim_az=dim_az, dim_rg=dim_rg)
var = "hh" if "hh" in ds.data_vars else "m11"
for var in input_data.data_vars:
shp = ds[var].shape
naz_out = shp[0] // dim_az
nrg_out = shp[1] // dim_rg
assert res[var].shape == (naz_out, nrg_out)
assert res[var].dtype == input_data[var].dtype
@pytest.mark.parametrize("synthetic_poldata", ["S", "C3", "T3"], indirect=True)
def test_pauli_rgb(synthetic_poldata):
input_data = synthetic_poldata
for _, ds in input_data.items():
input_data = ds.chunk(x=64, y=64)
res = pauli_rgb(input_data=input_data)
var = "hh" if "hh" in ds.data_vars else "m11"
shp = ds[var].shape
assert res.shape == (3,) + shp
assert res.dtype == "float32"
|
