statistics.remove("Tsallis")
all_distances = {0: None, 1: None, 2: None}
for face in fiducials.keys():
print("On face {0} at {1}".format(face, time.ctime()))
distances_storage = np.zeros((len(statistics), 5))
if len(fiducials_256[face][256]) == 0:
print("No face {} 256 fiducial found.".format(face))
continue
fid_256 = fiducials_256[face][256][0]
dataset1 = \
Mask_and_Moments.from_fits(fid_256,
moments_path=moments_256_path).to_dict()
fid_noise = 0.1 * np.nanpercentile(dataset1["cube"][0], 98)
dendro_params_fid = {"min_value": 2 * fid_noise, "min_npix": 10}
# Loop through 128 fiducials
for i, fid_num in enumerate(ProgressBar(fiducials[face].keys())):
fid_128 = fiducials[face][fid_num][-1]
dataset2 = \
Mask_and_Moments.from_fits(fid_128,
moments_path=moments_path).to_dict()
test_noise = 0.1 * np.nanpercentile(dataset2["cube"][0], 98)
noise_value = [fid_noise, test_noise]
dendro_params_test = {"min_value": 2 * test_noise, "min_npix": 80}
DendroDistance, PDF_Distance
import os
import matplotlib.pyplot as p
import seaborn as sns
sns.set_style("white")
import astropy.units as u
# p.ioff()
path_to_data = "/media/eric/Data_3/Astrostat/SimSuite8/"
moments_path = os.path.join(path_to_data, "moments/")
figure_path = os.path.expanduser("~/Dropbox/My_Papers/Submitted/astrostat-paper2/method_figures/")
des2 = "lustrehomeerosSimSuite8Design2_flatrho_0029_00_radmc.fits"
dataset1 = Mask_and_Moments.from_fits(os.path.join(path_to_data, des2),
moments_path=moments_path).to_dict()
# des19 = "lustrehomeerosSimSuite8Design19_flatrho_0030_00_radmc.fits"
des19 = "lustrehomeerosSimSuite7Fiducial1_flatrho_0029_00_radmc.fits"
dataset2 = Mask_and_Moments.from_fits(os.path.join(path_to_data, des19),
moments_path=moments_path).to_dict()
label1 = "Design 2"
label2 = "Fiducial 1"
# label2 = "Design 19"
values = {}
Wavelet Transform
wavelet_distance = \
Wavelet_Distance(dataset1["moment0"],
statistics.remove("Tsallis")
all_distances = {0: None, 1: None, 2: None}
for face in fiducials.keys():
print("On face {0} at {1}".format(face, time.ctime()))
distances_storage = np.zeros((len(statistics), 5))
if len(fiducials_256[face][256]) == 0:
print("No face {} 256 fiducial found.".format(face))
continue
fid_256 = fiducials_256[face][256][0]
dataset1 = \
Mask_and_Moments.from_fits(fid_256,
moments_path=moments_256_path).to_dict()
fid_noise = 0.1 * np.nanpercentile(dataset1["cube"][0], 98)
dendro_params_fid = {"min_value": 2 * fid_noise, "min_npix": 10}
# Loop through 128 fiducials
for i, fid_num in enumerate(ProgressBar(fiducials[face].keys())):
fid_128 = fiducials[face][fid_num][-1]
dataset2 = \
Mask_and_Moments.from_fits(fid_128,
moments_path=moments_path).to_dict()
test_noise = 0.1 * np.nanpercentile(dataset2["cube"][0], 98)
noise_value = [fid_noise, test_noise]
dendro_params_test = {"min_value": 2 * test_noise, "min_npix": 80}
