def mo_matrix(mock, obs, opts):
"""
Function to determine the mass-observable matrix.
"""
n_mass_bins = comp.num_bins(opts.mass_bin[0], opts.mass_bin[1], opts.mass_bin[2])
n_proxy_bins = comp.num_bins(opts.proxy_bin[0], opts.proxy_bin[1], opts.proxy_bin[2])
mass_x = comp.x_vals(n_mass_bins, opts.mass_bin[0], opts.mass_bin[2])
proxy_x = comp.x_vals(n_proxy_bins, opts.proxy_bin[0], opts.proxy_bin[2])
matrix = np.zeros((n_proxy_bins, n_mass_bins))
clusters = find_matches(mock, obs, opts)
for cluster in clusters:
proxy_bin = n_proxy_bins - 1 - \
comp.find_bin(np.log10(cluster.proxy), opts.proxy_bin[0], opts.proxy_bin[2])
for i in range(n_proxy_bins):
if proxy_bin == i and opts.z_bin[0] <= cluster.z < opts.z_bin[1]:
matrix[proxy_bin] += comp.scale(cluster.hist, min(cluster.hist),
np.sum(cluster.hist))
for i in range(n_proxy_bins):
matrix[i] = comp.scale(matrix[i], min(matrix[i]), np.sum(matrix[i]))
return matrix, mass_x, proxy_x
def mo_matrix(mock, obs, opts):
"""
Function to determine the mass-observable matrix.
"""
n_mass_bins = comp.num_bins(opts.mass_bin[0], opts.mass_bin[1],
opts.mass_bin[2])
n_proxy_bins = comp.num_bins(opts.proxy_bin[0], opts.proxy_bin[1],
opts.proxy_bin[2])
mass_x = comp.x_vals(n_mass_bins, opts.mass_bin[0], opts.mass_bin[2])
proxy_x = comp.x_vals(n_proxy_bins, opts.proxy_bin[0], opts.proxy_bin[2])
matrix = np.zeros((n_proxy_bins, n_mass_bins))
clusters = find_matches(mock, obs, opts)
for cluster in clusters:
proxy_bin = n_proxy_bins - 1 - \
comp.find_bin(np.log10(cluster.proxy), opts.proxy_bin[0], opts.proxy_bin[2])
for i in range(n_proxy_bins):
if proxy_bin == i and opts.z_bin[0] <= cluster.z < opts.z_bin[1]:
matrix[proxy_bin] += comp.scale(cluster.hist,
min(cluster.hist),
np.sum(cluster.hist))
for i in range(n_proxy_bins):
matrix[i] = comp.scale(matrix[i], min(matrix[i]), np.sum(matrix[i]))
return matrix, mass_x, proxy_x
def r200_match(i, index, mock, obs):
dists = astro.ang_sep((mock.ra[index], mock.dec[index]), (obs.ra[i], obs.dec[i])) * 60.0
new_index = np.where((dists - (1.0 * mock.r200[index])) <= 0.0)[0]
if len(new_index) > 0:
dist_data = 1.0 - (dists[new_index] / mock.r200[index[new_index]]) ** 2
return index[new_index], comp.scale(dist_data, 0.0, np.sum(dist_data))
else:
return [], []
def match_weight(proxy, dist, r200):
weights = (proxy ** 2) + (1.0 - (dist / r200) ** 2)
return comp.scale(weights, 0.0, np.sum(weights))
def scale_matrix(matrix):
return [comp.scale(matrix[i], 0.0, np.sum(matrix[i])) for i in
range(len(matrix))]
def match_weight(proxy, dist, r200):
weights = (proxy**2) + (1.0 - (dist / r200)**2)
return comp.scale(weights, 0.0, np.sum(weights))
def scale_matrix(matrix):
return [
comp.scale(matrix[i], 0.0, np.sum(matrix[i]))
for i in range(len(matrix))
]
def scale_matrix(matrix):
for i in range(len(matrix)):
matrix[i] = comp.scale(matrix[i], 0.0, np.sum(matrix[i]))
return matrix
