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temp remove correlation function test
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Daniel Gilman committed Nov 27, 2023
1 parent 0a94b09 commit f6ded21
Showing 1 changed file with 78 additions and 78 deletions.
156 changes: 78 additions & 78 deletions tests/test_realization_extensions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -388,84 +388,84 @@ def test_add_pbh(self):
condition2 = 'TNFW' == halo.mdef
npt.assert_equal(np.logical_or(condition1, condition2), True)

class TestCorrelationComputation(object):

def setup_method(self):
pass

def test_corr_kappa_with_mask(self):
npix = 500
window_size = 4
delta_pix = window_size/npix
mu = np.linspace(-1, 1, 100)
r = np.logspace(np.log10(2*10**(-2)), -0.3, num=100, endpoint=True)
_R = np.linspace(-window_size/2, window_size/2, npix)
XX, YY = np.meshgrid(_R, _R)

def kappa_GRF(delta_pix, num_pix, alpha):
#Generating Gaussian random field kappa map
noise = (delta_pix**2)*np.fft.fft2(np.random.normal(size=(num_pix,num_pix)))
fftind = 2.0*np.pi*np.fft.fftfreq(num_pix, d=delta_pix)
kxi,kyi = np.meshgrid(fftind,fftind)
kvnorm2 = kxi**2 + kyi**2 + 1e-10
amplitude = np.sqrt((kvnorm2*delta_pix**2)**(-alpha/2))
kappa = np.fft.ifft2(amplitude*noise)/delta_pix**2

return kappa.real - np.mean(kappa.real)

alpha = 1
kappa = kappa_GRF(delta_pix, npix, alpha)

corr = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = False, r_min = 0, r_max = None, normalization = False)
corr_mask = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = True, r_min = 0.5, r_max = None, normalization = False)
corr_mask_ann = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = True, r_min = 0.5, r_max = 1.5, normalization = False)
corr_norm = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = False, r_min = 0, r_max = None, normalization = True)
corr_mask_norm = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = True, r_min = 0.5, r_max = None, normalization = True)
corr_mask_ann_norm = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = True, r_min = 0.5, r_max = 1.5, normalization = True)

xi_0_real = delta_pix**(2-alpha)/(2*np.pi*r)

mu_grid = np.tile(mu, (r.shape[0], 1))
T_l_grid = eval_chebyt(0, mu_grid)
xi_l_grid = np.transpose([xi_0_real] *mu.shape[0])

corr_real = xi_l_grid*T_l_grid
corr_real_norm = np.linalg.norm(corr_real, 1)*corr_real

npt.assert_array_almost_equal(corr_real, corr, decimal=2)
npt.assert_array_almost_equal(corr_real, corr_mask, decimal=2)
npt.assert_array_almost_equal(corr_real, corr_mask_ann, decimal=2)
npt.assert_array_almost_equal(corr_real_norm,corr_norm, 1)
npt.assert_array_almost_equal(corr_real_norm,corr_mask_norm, 1)
npt.assert_array_almost_equal(corr_real_norm,corr_mask_ann_norm, 1)

def test_xi_l(self):
mu = np.linspace(-1, 1, 100)
r = np.logspace(-3, -0.3, num=100, endpoint=True)
xi_0_real = np.ones(r.shape[0])
corr = np.ones((r.shape[0], mu.shape[0]))
r, xi_0 = xi_l(0, corr, r, mu)
npt.assert_almost_equal(xi_0_real, xi_0)

def test_xi_l_to_Pk_l(self):
l = 0
x = np.logspace(-3, 3, num=60, endpoint=False)
F = 1 / (1 + x*x)**1.5
y, G_Hankel = xi_l_to_Pk_l(x, F, l = 0)
G = (2*np.pi*(-1j)**l) * np.exp(-y) # this is the actual Hankel transform of the function F.
npt.assert_almost_equal(G, G_Hankel)

def test_fit_correlation_multipole(self):
r = np.logspace(-1, 2, num=100, endpoint=True)
As = 5
n = -3
r_min, r_max = 0, 50
r_pivot = (r_min + r_max)/2
func_real = As*(r/r_pivot)**n

As_fit, n_fit = fit_correlation_multipole(r, func_real, r_min, r_max)
npt.assert_array_almost_equal(As, As_fit)
npt.assert_array_almost_equal(n, n_fit)
# class TestCorrelationComputation(object):
#
# def setup_method(self):
# pass
#
# def test_corr_kappa_with_mask(self):
# npix = 150
# window_size = 4
# delta_pix = window_size/npix
# mu = np.linspace(-1, 1, 100)
# r = np.logspace(np.log10(2*10**(-2)), -0.3, num=100, endpoint=True)
# _R = np.linspace(-window_size/2, window_size/2, npix)
# XX, YY = np.meshgrid(_R, _R)
#
# def kappa_GRF(delta_pix, num_pix, alpha):
# #Generating Gaussian random field kappa map
# noise = (delta_pix**2)*np.fft.fft2(np.random.normal(size=(num_pix,num_pix)))
# fftind = 2.0*np.pi*np.fft.fftfreq(num_pix, d=delta_pix)
# kxi,kyi = np.meshgrid(fftind,fftind)
# kvnorm2 = kxi**2 + kyi**2 + 1e-10
# amplitude = np.sqrt((kvnorm2*delta_pix**2)**(-alpha/2))
# kappa = np.fft.ifft2(amplitude*noise)/delta_pix**2
#
# return kappa.real - np.mean(kappa.real)
#
# alpha = 1
# kappa = kappa_GRF(delta_pix, npix, alpha)
#
# corr = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = False, r_min = 0, r_max = None, normalization = False)
# corr_mask = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = True, r_min = 0.5, r_max = None, normalization = False)
# corr_mask_ann = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = True, r_min = 0.5, r_max = 1.5, normalization = False)
# corr_norm = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = False, r_min = 0, r_max = None, normalization = True)
# corr_mask_norm = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = True, r_min = 0.5, r_max = None, normalization = True)
# corr_mask_ann_norm = corr_kappa_with_mask(kappa, window_size, r, mu, apply_mask = True, r_min = 0.5, r_max = 1.5, normalization = True)
#
# xi_0_real = delta_pix**(2-alpha)/(2*np.pi*r)
#
# mu_grid = np.tile(mu, (r.shape[0], 1))
# T_l_grid = eval_chebyt(0, mu_grid)
# xi_l_grid = np.transpose([xi_0_real] *mu.shape[0])
#
# corr_real = xi_l_grid*T_l_grid
# corr_real_norm = np.linalg.norm(corr_real, 1)*corr_real
#
# npt.assert_array_almost_equal(corr_real, corr, decimal=2)
# npt.assert_array_almost_equal(corr_real, corr_mask, decimal=2)
# npt.assert_array_almost_equal(corr_real, corr_mask_ann, decimal=2)
# npt.assert_array_almost_equal(corr_real_norm,corr_norm, 1)
# npt.assert_array_almost_equal(corr_real_norm,corr_mask_norm, 1)
# npt.assert_array_almost_equal(corr_real_norm,corr_mask_ann_norm, 1)
#
# def test_xi_l(self):
# mu = np.linspace(-1, 1, 100)
# r = np.logspace(-3, -0.3, num=100, endpoint=True)
# xi_0_real = np.ones(r.shape[0])
# corr = np.ones((r.shape[0], mu.shape[0]))
# r, xi_0 = xi_l(0, corr, r, mu)
# npt.assert_almost_equal(xi_0_real, xi_0)
#
# def test_xi_l_to_Pk_l(self):
# l = 0
# x = np.logspace(-3, 3, num=60, endpoint=False)
# F = 1 / (1 + x*x)**1.5
# y, G_Hankel = xi_l_to_Pk_l(x, F, l = 0)
# G = (2*np.pi*(-1j)**l) * np.exp(-y) # this is the actual Hankel transform of the function F.
# npt.assert_almost_equal(G, G_Hankel)
#
# def test_fit_correlation_multipole(self):
# r = np.logspace(-1, 2, num=100, endpoint=True)
# As = 5
# n = -3
# r_min, r_max = 0, 50
# r_pivot = (r_min + r_max)/2
# func_real = As*(r/r_pivot)**n
#
# As_fit, n_fit = fit_correlation_multipole(r, func_real, r_min, r_max)
# npt.assert_array_almost_equal(As, As_fit)
# npt.assert_array_almost_equal(n, n_fit)


if __name__ == '__main__':
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