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test_interpol.py
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test_interpol.py
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__author__ = 'sibirrer'
import pytest
import numpy as np
import numpy.testing as npt
import lenstronomy.Util.util as util
from lenstronomy.LensModel.Profiles.sis import SIS
from lenstronomy.LensModel.Profiles.interpol import Interpol, InterpolScaled
class TestInterpol(object):
def setup(self):
pass
def test_do_interpol(self):
numPix = 101
deltaPix = 0.1
x_grid_interp, y_grid_interp = util.make_grid(numPix,deltaPix)
sis = SIS()
kwargs_SIS = {'theta_E': 1., 'center_x': 0.5, 'center_y':-0.5}
f_sis = sis.function(x_grid_interp, y_grid_interp, **kwargs_SIS)
f_x_sis, f_y_sis = sis.derivatives(x_grid_interp, y_grid_interp, **kwargs_SIS)
f_xx_sis, f_yy_sis, f_xy_sis = sis.hessian(x_grid_interp, y_grid_interp, **kwargs_SIS)
x_axes, y_axes = util.get_axes(x_grid_interp, y_grid_interp)
interp_func = Interpol()
interp_func_loop = Interpol(grid=False)
interp_func.do_interp(x_axes, y_axes, util.array2image(f_sis), util.array2image(f_x_sis), util.array2image(f_y_sis), util.array2image(f_xx_sis), util.array2image(f_yy_sis), util.array2image(f_xy_sis))
interp_func_loop.do_interp(x_axes, y_axes, util.array2image(f_sis), util.array2image(f_x_sis), util.array2image(f_y_sis), util.array2image(f_xx_sis), util.array2image(f_yy_sis), util.array2image(f_xy_sis))
# test derivatives
print(interp_func.derivatives(0, 1))
print(sis.derivatives(1, 0, **kwargs_SIS))
#assert interp_func.derivatives(1, 0) == sis.derivatives(1, 0, **kwargs_SIS)
assert interp_func.derivatives(1, 0) == interp_func_loop.derivatives(1, 0)
alpha1_interp, alpha2_interp = interp_func.derivatives(np.array([0,1,0,1]), np.array([1,1,2,2]))
alpha1_interp_loop, alpha2_interp_loop = interp_func_loop.derivatives(np.array([0, 1, 0, 1]), np.array([1, 1, 2, 2]))
alpha1_true, alpha2_true = sis.derivatives(np.array([0,1,0,1]),np.array([1,1,2,2]), **kwargs_SIS)
assert alpha1_interp[0] == alpha1_true[0]
assert alpha1_interp[1] == alpha1_true[1]
assert alpha1_interp[0] == alpha1_interp_loop[0]
assert alpha1_interp[1] == alpha1_interp_loop[1]
# test hessian
assert interp_func.hessian(1,0) == sis.hessian(1,0, **kwargs_SIS)
f_xx_interp, f_yy_interp, f_xy_interp = interp_func.hessian(np.array([0,1,0,1]), np.array([1,1,2,2]))
f_xx_interp_loop, f_yy_interp_loop, f_xy_interp_loop = interp_func_loop.hessian(np.array([0, 1, 0, 1]), np.array([1, 1, 2, 2]))
f_xx_true, f_yy_true, f_xy_true = sis.hessian(np.array([0,1,0,1]),np.array([1,1,2,2]), **kwargs_SIS)
assert f_xx_interp[0] == f_xx_true[0]
assert f_xx_interp[1] == f_xx_true[1]
assert f_xy_interp[0] == f_xy_true[0]
assert f_xy_interp[1] == f_xy_true[1]
assert f_xx_interp[0] == f_xx_interp_loop[0]
assert f_xx_interp[1] == f_xx_interp_loop[1]
assert f_xy_interp[0] == f_xy_interp_loop[0]
assert f_xy_interp[1] == f_xy_interp_loop[1]
# test all
def test_call(self):
numPix = 101
deltaPix = 0.1
x_grid_interp, y_grid_interp = util.make_grid(numPix,deltaPix)
sis = SIS()
kwargs_SIS = {'theta_E':1., 'center_x': 0.5, 'center_y': -0.5}
f_sis = sis.function(x_grid_interp, y_grid_interp, **kwargs_SIS)
f_x_sis, f_y_sis = sis.derivatives(x_grid_interp, y_grid_interp, **kwargs_SIS)
f_xx_sis, f_yy_sis, f_xy_sis = sis.hessian(x_grid_interp, y_grid_interp, **kwargs_SIS)
x_axes, y_axes = util.get_axes(x_grid_interp, y_grid_interp)
interp_func = Interpol()
interp_func.do_interp(x_axes, y_axes, util.array2image(f_sis), util.array2image(f_x_sis), util.array2image(f_y_sis), util.array2image(f_xx_sis), util.array2image(f_yy_sis), util.array2image(f_xy_sis))
x, y = 1., 1.
alpha_x, alpha_y = interp_func.derivatives(x, y, **{})
assert alpha_x == 0.31622776601683794
def test_kwargs_interpolation(self):
numPix = 101
deltaPix = 0.1
x_grid_interp, y_grid_interp = util.make_grid(numPix,deltaPix)
sis = SIS()
kwargs_SIS = {'theta_E':1., 'center_x': 0.5, 'center_y': -0.5}
f_sis = sis.function(x_grid_interp, y_grid_interp, **kwargs_SIS)
f_x_sis, f_y_sis = sis.derivatives(x_grid_interp, y_grid_interp, **kwargs_SIS)
f_xx_sis, f_yy_sis, f_xy_sis = sis.hessian(x_grid_interp, y_grid_interp, **kwargs_SIS)
x_axes, y_axes = util.get_axes(x_grid_interp, y_grid_interp)
interp_func = Interpol()
kwargs_interp = {'grid_interp_x': x_axes, 'grid_interp_y': y_axes, 'f_': util.array2image(f_sis), 'f_x': util.array2image(f_x_sis), 'f_y': util.array2image(f_y_sis), 'f_xx': util.array2image(f_xx_sis), 'f_yy': util.array2image(f_yy_sis), 'f_xy': util.array2image(f_xy_sis)}
x, y = 1., 1.
alpha_x, alpha_y = interp_func.derivatives(x, y, **kwargs_interp)
assert alpha_x == 0.31622776601683794
x, y = 1., 0.
alpha_x, alpha_y = interp_func.derivatives(x, y, **kwargs_interp)
alpha_x_true, alpha_y_true = sis.derivatives(x, y, **kwargs_SIS)
npt.assert_almost_equal(alpha_x, alpha_x_true, decimal=10)
npt.assert_almost_equal(alpha_y, alpha_y_true, decimal=10)
f_ = interp_func.function(x, y, **kwargs_interp)
f_true = sis.derivatives(x, y, **kwargs_SIS)
npt.assert_almost_equal(f_, f_true, decimal=10)
def test_interp_func_scaled(self):
numPix = 101
deltaPix = 0.1
x_grid_interp, y_grid_interp = util.make_grid(numPix,deltaPix)
sis = SIS()
kwargs_SIS = {'theta_E':1., 'center_x': 0.5, 'center_y': -0.5}
f_sis = sis.function(x_grid_interp, y_grid_interp, **kwargs_SIS)
f_x_sis, f_y_sis = sis.derivatives(x_grid_interp, y_grid_interp, **kwargs_SIS)
f_xx_sis, f_yy_sis, f_xy_sis = sis.hessian(x_grid_interp, y_grid_interp, **kwargs_SIS)
x_axes, y_axes = util.get_axes(x_grid_interp, y_grid_interp)
kwargs_interp = {'grid_interp_x': x_axes, 'grid_interp_y': y_axes, 'f_': util.array2image(f_sis), 'f_x': util.array2image(f_x_sis), 'f_y': util.array2image(f_y_sis), 'f_xx': util.array2image(f_xx_sis), 'f_yy': util.array2image(f_yy_sis), 'f_xy': util.array2image(f_xy_sis)}
interp_func = InterpolScaled(grid=False)
x, y = 1., 1.
alpha_x, alpha_y = interp_func.derivatives(x, y, scale_factor=1, **kwargs_interp)
assert alpha_x == 0.31622776601683794
f_ = interp_func.function(x, y, scale_factor=1., **kwargs_interp)
npt.assert_almost_equal(f_, 1.5811388300841898)
f_xx, f_yy, f_xy = interp_func.hessian(x, y, scale_factor=1., **kwargs_interp)
npt.assert_almost_equal(f_xx, 0.56920997883030822, decimal=8)
npt.assert_almost_equal(f_yy, 0.063245553203367583, decimal=8)
npt.assert_almost_equal(f_xy, -0.18973665961010275, decimal=8)
x_grid, y_grid = util.make_grid(10, deltaPix)
f_xx, f_yy, f_xy = interp_func.hessian(x_grid, y_grid, scale_factor=1., **kwargs_interp)
npt.assert_almost_equal(f_xx[0], 0, decimal=2)
def test_shift(self):
numPix = 101
deltaPix = 0.1
x_grid_interp, y_grid_interp = util.make_grid(numPix, deltaPix)
sis = SIS()
kwargs_SIS = {'theta_E': 1., 'center_x': 0.5, 'center_y': -0.5}
f_sis = sis.function(x_grid_interp, y_grid_interp, **kwargs_SIS)
f_x_sis, f_y_sis = sis.derivatives(x_grid_interp, y_grid_interp, **kwargs_SIS)
f_xx_sis, f_yy_sis, f_xy_sis = sis.hessian(x_grid_interp, y_grid_interp, **kwargs_SIS)
x_axes, y_axes = util.get_axes(x_grid_interp, y_grid_interp)
kwargs_interp = {'grid_interp_x': x_axes, 'grid_interp_y': y_axes, 'f_': util.array2image(f_sis),
'f_x': util.array2image(f_x_sis), 'f_y': util.array2image(f_y_sis),
'f_xx': util.array2image(f_xx_sis), 'f_yy': util.array2image(f_yy_sis),
'f_xy': util.array2image(f_xy_sis)}
interp_func = Interpol(grid=False)
x, y = 1., 1.
alpha_x, alpha_y = interp_func.derivatives(x, y, **kwargs_interp)
assert alpha_x == 0.31622776601683794
interp_func = Interpol(grid=False)
x_shift = 1.
kwargs_shift = {'grid_interp_x': x_axes + x_shift, 'grid_interp_y': y_axes, 'f_': util.array2image(f_sis),
'f_x': util.array2image(f_x_sis), 'f_y': util.array2image(f_y_sis),
'f_xx': util.array2image(f_xx_sis), 'f_yy': util.array2image(f_yy_sis),
'f_xy': util.array2image(f_xy_sis)}
alpha_x_shift, alpha_y_shift = interp_func.derivatives(x + x_shift, y, **kwargs_shift)
npt.assert_almost_equal(alpha_x_shift, alpha_x, decimal=10)
if __name__ == '__main__':
pytest.main("-k TestSourceModel")