def int_radius(data, cent, val_min):
"""
Returns an array of the intensity at different radii out from the centre of an object
Inputs: data = the image data (2D numpy array), cent = the centre point of the object (list or tuple length 2),
val_min = minimum threshold for radius of object (int)
Output: intensity = list of intensities for increasing radius from centre (1D numpy array)
"""
val = data[tuple(cent)]
rad = 0
#finding max radius for calculating intensity
while val >= val_min:
#need to make sure we don't get infinity
if rad >= 20:
break
val = data[cent[0], cent[1]+rad]
rad += 1
intensity = []
#find intensity within circle of each radius
for r in range(1, rad+1):
flux, N = circ_apt_flux(data, cent, r*2)
I_r = flux/N
intensity.append(I_r)
return np.array(intensity)
for i in range(0, len(pointsx)):
"""
loops through all entries in the points csv file and appends their values to the catalogue
"""
x = int(pointsx[i])
y = int(pointsy[i])
#slice a section of the total image around each source to carry out flux analysis. For this, the point cannot lie too close to the edge.
if x > 100 and y > 100 and x < (len(data[0]) - 100) and y < (
len(data) - 100): #cuts off edge noise within 100 pixels
index_y.append(y)
index_x.append(x)
data_set = data[int(y - 15):int(y + 15), int(x - 15):int(x + 15)]
centre = np.array(
[14.0, 14.0]) #the centre is fixed due to the sliced section
i_tot_flux, N = circ_apt_flux(data_set, centre, apt_size)
tot_flux.append(i_tot_flux) #find individual total flux
i_err_tot_flux = np.sqrt(i_tot_flux)
err_tot_flux.append(
i_err_tot_flux) #individual error in total flux
i_loc_backgrnd, Na = ann_ref(data_set, centre, apt_size, ann_size)
loc_backgrnd.append(i_loc_backgrnd) #individual background
i_source_flux = flux(data_set, centre, apt_size, ann_size)
source_flux.append(i_source_flux) #individual source flux
i_mag = header["MAGZPT"] - 2.5 * np.log10(i_source_flux)
mag.append(i_mag) #individual apparent magnitude
i_m_err = np.sqrt((header["MAGZRR"])**2 + (25 /
(4 * np.log(10)**2)) *
((i_tot_flux + i_loc_backgrnd / Na) /
(i_source_flux**2)))
m_err.append(i_m_err) #individual error in m