# ymin = 460 # ymax = 500 # xmin = 1490 # xmax = 1540 ymin = 1306 ymax = 1356 xmin = 1636 xmax = 1706 large_aperture = bias_subtracted_im1[ymin:ymax, xmin:xmax] # convert object to electron units from count large_aperture = large_aperture * gain # Background subtract the object large_aperture, mask, background_dev = background_subtract(large_aperture) # calculate the deviation on each pixel # defined as the sqrt of the sum of squares of pixel and background deviation # pixel deviation is defined as sqrt of pixel value in electrons # background deviation should include any read noise and bias noise # show an image of the aperture from astropy.visualization import SqrtStretch from astropy.visualization.mpl_normalize import ImageNormalize norm = ImageNormalize(stretch=SqrtStretch()) # plt.figure() f1, axisarg = plt.subplots(2, 1)
current_aperture = current_aperture*gain
aperture_list.append(current_aperture)
"""calculate the centroid for each aperture"""
n = 0 # counting variable
# generate each curve fit
from scipy.optimize import curve_fit
for aperture in aperture_list:
print('---------------------')
print('Region ' + str(n+1) + ': ' + selected_regions[n].region_def)
n += 1
# background subtract the aperture
aperture, mask, background_dev = background_subtract(aperture)
# generate the associated pixel error
aperture_err = np.sqrt(aperture + background_dev**2)
# plot the aperture and mask used to background subtract
norm = ImageNormalize(stretch=SqrtStretch())
f1, axisarg = plt.subplots(3, 2, figsize=(10, 10))
aperture_im = axisarg[0][0].imshow(aperture, norm=norm, origin='lower', cmap='viridis')
f1.colorbar(aperture_im, ax=axisarg[0][0])
axisarg[0][0].set_title('Object, with colorscale intensity')
mask_im = axisarg[1][0].imshow(mask, origin='lower', cmap='viridis')
axisarg[1][0].set_title('Mask used in background calculations')
aperture_hist = axisarg[1][1].hist(aperture.flatten(),bins=500, range=[-100, 5000])
axisarg[1][1].set_title('Object histogram after background subtraction')
# this object has a low eccentricity
# ymin = 1306
# ymax = 1356
# xmin = 1636
# xmax = 1706
# trying with a different object
ymin = 1777
ymax = 1822
xmin = 252
xmax = 302
real_boy = bias_subtracted_im1[ymin:ymax, xmin:xmax]
# Background subtract the object
real_boy, mask, background_dev = background_subtract(real_boy)
# make a fit #######
# generate a best guess
y_guess = real_boy.shape[0] / 2
x_guess = real_boy.shape[1] / 2
flux1_guess = np.sum(real_boy)
flux2_guess = flux1_guess / 10
beta1_guess = 7
beta2_guess = 2
alpha1_guess = 2
alpha2_guess = 2
offset_guess = 0
guess = [
flux1_guess, flux2_guess, alpha1_guess, alpha2_guess, beta1_guess,
# Centroid detection:
# arbitrarily chosen object, section manually entered
# ymin = 1545
# ymax = 1595
# xmin = 1745
# xmax = 1795
ymin = 455
ymax = 505
xmin = 1490
xmax = 1540
object1_data = bias_subtracted_im1[ymin:ymax, xmin:xmax]
# Background subtract the object
object1_data, mask, background_dev = background_subtract(object1_data)
# centroid techniques: need to learn the difference3s
# print('Centroids:')
# x1, y1 = centroid_com(object1_data)
# print((x1, y1))
# x2, y2 = centroid_1dg(object1_data)
# print((x2, y2))
# x3, y3 = centroid_2dg(object1_data)
# print((x3, y3))
# show an image of the aperture
from astropy.visualization import SqrtStretch
from astropy.visualization.mpl_normalize import ImageNormalize