import numpy as np
[docs]
def get_min_max(element, index):
min_val = element[index, :][element[4, :] == 1.0].min()
max_val = element[index, :][element[4, :] == 1.0].max()
arg_max = np.argmax(element[index, :][element[4, :] == 1.0])
arg_min = np.argmin(element[index, :][element[4, :] == 1.0])
return min_val, max_val, arg_min, arg_max
[docs]
def get_length_extended(element):
"""Identify the first and the last gaussian component in the extended source and
compute the distance. Last, add the extension of the source to the length.
Parameters
----------
element : ndarray
array which contains all sources in the given image
Returns
-------
ndarray
length of extended source
"""
x_min, x_max, arg_min_x, arg_max_x = get_min_max(element, 0)
y_min, y_max, arg_min_y, arg_max_y = get_min_max(element, 1)
sig_x_max = element[2, :][element[4, :] == 1.0][arg_max_x]
sig_x_min = element[2, :][element[4, :] == 1.0][arg_min_x]
sig_y_max = element[3, :][element[4, :] == 1.0][arg_max_y]
sig_y_min = element[3, :][element[4, :] == 1.0][arg_min_y]
# multiply with the factor for full width-half maximum
extend_max = np.sqrt(sig_x_max**2 + sig_y_max**2) * 2.35
extend_min = np.sqrt(sig_x_min**2 + sig_y_min**2) * 2.35
# compute amount of vector
laenge_x = (x_max - x_min) ** 2
laenge_y = (y_max - y_min) ** 2
laenge_extend = np.sqrt(laenge_x + laenge_y) + extend_max + extend_min
return laenge_extend
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def get_length_point(element):
"""Compute linear extend of pointsources.
Parameters
----------
element : ndarray
array which contains all sources in the given image
Returns
-------
ndarray
lengths of the pointsources in the image
"""
laenge_y_point = element[3, :][element[4, :] == 0.0] * 2
laenge_x_point = element[2, :][element[4, :] == 0.0] * 2
laenge_point = np.max([laenge_x_point, laenge_y_point], axis=0)
return laenge_point
[docs]
def flux_comparison(pred, truth, source_list):
fluxes_pred = []
fluxes_truth = []
sigs_x = []
sigs_y = []
laenge = np.array([])
for i, element in enumerate(source_list):
mean_pred = np.array([])
mean_truth = np.array([])
for blob in element.T:
y, x, sig_x, sig_y, mask = blob
x_low = int(np.floor(x - sig_x))
if x_low < 0:
x_low = 0
x_high = int(np.ceil(x + sig_x + 1))
if x_high > 63:
x_high = 63
y_low = int(np.floor(y - sig_y))
if y_low < 0:
y_low = 0
y_high = int(np.ceil(y + sig_y + 1))
if y_high > 63:
y_high = 63
flux_truth = truth[i, int(x_low) : int(x_high), int(y_low) : int(y_high)]
flux_pred = pred[i, int(x_low) : int(x_high), int(y_low) : int(y_high)]
mean_pred = np.append(mean_pred, flux_pred.mean())
mean_truth = np.append(mean_truth, flux_truth.mean())
sigs_x.append(sig_x)
sigs_y.append(sig_y)
# sum over extended fluxes for truth and pred
c = mean_pred[element[4, :] == 1.0].sum()
mean_pred = np.append(mean_pred[element[4, :] == 0.0], c)
c = mean_truth[element[4, :] == 1.0].sum()
mean_truth = np.append(mean_truth[element[4, :] == 0.0], c)
fluxes_pred.append(mean_pred)
fluxes_truth.append(mean_truth)
# append lengths for point and extended sources
laenge_extend = get_length_extended(element)
laenge_point = get_length_point(element)
laenge = np.append(laenge, laenge_point)
laenge = np.append(laenge, laenge_extend)
return (
np.array(fluxes_pred, dtype="object"),
np.array(fluxes_truth, dtype="object"),
laenge,
)
