def get_stdevmeans(muV2, muV3):
V2stdevs, V2means, V3stdevs, V3means = [], [], [], []
for mv2, mv3 in zip(muV2, muV3):
sd2, m2 = taf.find_std(mv2)
V2stdevs.append(sd2)
V2means.append(m2)
sd3, m3 = taf.find_std(mv3)
V3stdevs.append(sd3)
V3means.append(m3)
stdevmeans_values = [V2stdevs[0], V2means[0], V2stdevs[1], V2means[1], V2stdevs[2], V2means[2],
V3stdevs[0], V3means[0], V3stdevs[1], V3means[1], V3stdevs[2], V3means[2]]
return stdevmeans_values
ytrue = np.array([
-0.00305743, -0.03733417, 0.01302122, -0.00308733, -0.03736407,
0.01299132
])
if arcsec:
xinput, yinput = xinput * 3600.0, yinput * 3600.0
xtrue, ytrue = xtrue * 3600.0, ytrue * 3600.0
#xinput = np.array([ 468.44835572, 361.14052769, 403.66867649, 468.17048236, 360.83387397, 403.31142531])
#yinput = np.array([ -10.9953641, -134.41040845, 46.88318515, -11.082975, -134.50232193, 46.7519372])
#xtrue = np.array([ 468.45129306, 361.10923464, 403.66779144, 468.17840512, 360.8363467, 403.39490349])
#ytrue = np.array([ -10.99576619, -134.39204083, 46.88737556, -11.10340575, -134.49967787, 46.77973516])
# Determine the standard deviation for each array
diffx = xtrue - xinput
diffy = ytrue - yinput
sigma_x, mean_x = taf.find_std(diffx)
sigma_y, mean_y = taf.find_std(diffy)
# debug
print('These are the initial arrays: ')
print(' xinput = ', xinput)
print(' yinput = ', yinput)
print(' original_means: deltaX = ', mean_x, ' deltaY = ',
mean_y)
print(' original_std_devs: sigmaX = ', sigma_x, ' sigmaY = ',
sigma_y)
abs_thres_data = abs_threshold_rejection(
abs_threshold,
niter,
xinput,
yinput,
xtrue,
if 'shifted' in case:
list_file = "simuTA20150528-F140X-S50-K-AB23-shifted.list"
positions_file = "simuTA20150528-F140X-S50-K-AB23-shifted_positions.fits"
if "scene2" in case:
# Read the text file just written to get the offsets from the "real" positions of the fake stars
path2listfile = main_path_infiles+"Scene_2_AB1823"
list_file = "simuTA20150528-F140X-S50-K-AB18to23.list"
positions_file = "simuTA20150528-F140X-S50-K-AB18to23_positions.fits"
if 'shifted' in case:
list_file = "simuTA20150528-F140X-S50-K-AB18to23-shifted.list"
positions_file = "simuTA20150528-F140X-S50-K-AB18to23-shifted_positions.fits"
lf = os.path.join(path2listfile,list_file)
pf = os.path.join(path2listfile,positions_file)
print (case)
print (pf)
star_number, xpos_arcsec, ypos_arcsec, factor, mag, bg_method = taf.read_listfile(lf, detector, background_method)
_, true_x, true_y, trueV2, trueV3 = taf.read_positionsfile(pf, detector)
# Select appropriate set of stars to test according to chosen detector
if detector == 491:
stars_detector = stars_491
elif detector == 492:
stars_detector = stars_492
# offset lists
x3offst, x5offst, x7offst = [], [], []
y3offst, y5offst, y7offst = [], [], []
studied_stars = []
# Start the loop in the given directory
if not just_read_text_file:
xlabel,
ylabel,
plot_title=plot_title,
labels_list=labels_list,
xlims=xlims,
ylims=ylims,
print_side_string=print_side_string,
print_side_values=print_side_values,
save_plot=save_plot,
show_plot=show_plot,
destination=destination)
# calculate mean of the thetas and standard deviation of the thetas
theta_stdevs, theta_means = [], []
for th in theta:
sd, m = taf.find_std(th)
theta_stdevs.append(sd)
theta_means.append(m)
print_side_values = [
theta_stdevs[0], theta_stdevs[1], theta_stdevs[2], theta_means[0],
theta_means[1], theta_means[2]
]
# Thetas plot
#v2theta_plot(cwincase, muV2, theta, save_plot=save_plot, show_plot=show_plot, destination=None)
#v3theta_plot(cwincase, muV3, theta, save_plot=save_plot, show_plot=show_plot, destination=None)
destination = results_path
theta_plot(cwincase,
theta,
save_plot=save_plot,
show_plot=show_plot,
destination=destination,
101, 103, 105, 108, 109, 111, 113, 114, 133, 136, 147, 150, 157, 158, 161,
181, 184, 185, 186, 194, 199
]
detector = 491
scene = 1
arcsecs = True
bkgd_method = 'frac'
shutters = 'rapid'
noise = 'real'
verbose = True
debug = False
scene2study = "Scene" + str(scene) + "_"
# get benckmark values
benchmark_data, magnitudes = TAf.read_star_param_files(scene2study)
bench_P1, bench_P2 = benchmark_data
allbench_starP1, allbench_xP1, allbench_yP1, allbench_V2P1, allbench_V3P1, allbench_xLP1, allbench_yLP1 = bench_P1
allbench_starP2, allbench_xP2, allbench_yP2, allbench_V2P2, allbench_V3P2, allbench_xLP2, allbench_yLP2 = bench_P2
allbench_stars = allbench_starP1.tolist()
# get the index for the sample stars
star_idx_list = []
for st in stars_sample:
st_idx = allbench_stars.index(st)
star_idx_list.append(st_idx)
# get the benchmark for star sample
bench_starP1, bench_xP1, bench_yP1, bench_V2P1, bench_V3P1, bench_xLP1, bench_yLP1 = np.array(
[]), np.array([]), np.array([]), np.array([]), np.array([]), np.array(
[]), np.array([])
positions_file = "simuTA20150528-F140X-S50-K-AB23_positions.fits"
if 'shifted' in dir2test:
list_file = "simuTA20150528-F140X-S50-K-AB23-shifted.list"
positions_file = "simuTA20150528-F140X-S50-K-AB23-shifted_positions.fits"
if "Scene_2" in dir2test:
# Read the text file just written to get the offsets from the "real" positions of the fake stars
path2listfile = "../PFforMaria/Scene_2_AB1823"
list_file = "simuTA20150528-F140X-S50-K-AB18to23.list"
positions_file = "simuTA20150528-F140X-S50-K-AB18to23_positions.fits"
if 'shifted' in dir2test:
list_file = "simuTA20150528-F140X-S50-K-AB18to23-shifted.list"
positions_file = "simuTA20150528-F140X-S50-K-AB18to23-shifted_positions.fits"
lf = os.path.join(path2listfile,list_file)
pf = os.path.join(path2listfile,positions_file)
bench_star, xpos_arcsec, ypos_arcsec, factor, mag, bg_method = taf.read_listfile(lf, detector, background_method)
_, true_x, true_y, trueV2, trueV3 = taf.read_positionsfile(pf, detector)
"""
*** WE ARE NOT USING THIS PART RIGHT NOW BECAUSE THE star_parameters.txt FILES HAVE THE SAME DATA FOR
BOTH DETECTORS.
# Read fits table with benchmark data from the star parameters file to compare results
# xL: x-coordinate of the left edge of the postge stamp in the full image (range 0-2047)
# xR: x-coord of right edge of the postage stamp
# yL: y-coord of the lower edge of the postage stamp
# yU: y-coord of the upper edge of the postage stamp
star_param_txt = os.path.join(dir2test,"star parameters.txt")
if detector ==492:
star_param_txt = os.path.join(dir2test,"star parameters_492.txt")
benchmark_data = np.loadtxt(star_param_txt, skiprows=3, unpack=True)
backgnd_subtraction_method, background_method, bg_value, bg_frac, debug
]
recursive_centroids_info = [
xwidth_list, ywidth_list, checkbox_size, max_iter, threshold,
determine_moments, display_master_img, vlim
]
display_centroids_info = [case, show_disp, save_centroid_disp]
if analyze_all_frac_values:
for bgf in bg_frac:
bg_corr_info = [
backgnd_subtraction_method, background_method, bg_value, bgf,
debug
]
x_centroids, y_centroids = taf.find_centroid(
fits_file, bg_corr_info, recursive_centroids_info,
display_centroids_info, x_centroids, y_centroids, fits_names,
output_file_path, centroids_info, verbose)
else:
x_centroids, y_centroids = taf.find_centroid(
fits_file, bg_corr_info, recursive_centroids_info,
display_centroids_info, x_centroids, y_centroids, fits_names,
output_file_path, centroids_info, verbose)
# Write the results in a text file
output_file = os.path.join(output_file_path, case + bg_choice + ".txt")
line0 = "Centroid indexing starting at 1 !"
if len(xwidth_list) == 1:
line0a = "{:<50} {:>16} {:>20}".format("Fits file name", "Background",
"Centroid window = 5")
line0b = "{:>63} {:>10} {:>16}".format(background_method, "x", "y")