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generate_patches_ps.py
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generate_patches_ps.py
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import numpy as np
import healpy as hp
import _maps as maps
import sys, os, time
from tqdm import tqdm
import matplotlib.pyplot as plt
plt.style.use('default')
# load energy bins
energy_list, energy_centers = maps.generate_energy_bins_()
# load local directory
username="ramirez"
maps_dir = "/het/p4/"+username+"/gcewavelets/skysearch/data/maps/"
# load map type (with ids corresponding to energy bin, random iteration, bkgd/ps ids)
model = sys.argv[1] # only available option: SA0
trial_id = sys.argv[2]
model_dir = maps_dir + (model + '_' + trial_id + '/')
energy_bin = sys.argv[3]
npix = int(float(sys.argv[4]))
inj_id = sys.argv[5]
# load events from map
if energy_bin == 'all' or energy_bin == str(-1):
map_dir = model_dir + ('bkgd_wps_' + inj_id + '/')
else:
ie = int(float(energy_bin))
bkgd_wps_dir = model_dir + ('bkgd_wps_' + inj_id + '/')
map_dir = bkgd_wps_dir + 'energy_bin_' + str(ie) + '/'
# load events
events = np.load(map_dir + 'ps_map.npy', allow_pickle = True)
l_events = events[:,0]
b_events = events[:,1]
# after loading the data, we need our angular coordinates to be given by
# longitude ([0,2\pi]) and latitude (-\pi, \pi)
phi_events = l_events.copy()
phi_events[phi_events>np.pi] = phi_events[phi_events>np.pi]-2*np.pi
lon_events = phi_events + np.pi
lat_events = b_events
# load healpix pixel edges, divide data into groups, calculate center of pixels for projection
NSIDE = 4
NPIX = hp.nside2npix(NSIDE)
# group points into patches (before projection)
unprojected_patches_file = map_dir + 'ps_unprojected_patches.npz'
if os.path.isfile(unprojected_patches_file) == True:
u_data = np.load(unprojected_patches_file, allow_pickle = True)
arr_edge_points, grouped_points_lon, grouped_points_lat, arr_c = [u_data[k] for k in u_data]
else:
arr_edge_points = maps.healpix_edge_generator_(NSIDE = 4, step = 100)
grouped_points_lon, grouped_points_lat = maps.divide_data_into_groups_(lon_events, lat_events, arr_edge_points)
arr_c = maps.father_pixel_center_generator_(arr_edge_points)
unprojected_data = [arr_edge_points, grouped_points_lon, grouped_points_lat, arr_c]
np.savez(unprojected_patches_file, *unprojected_data)
# load directories to save projected maps and plots
data_dir = map_dir + 'projected_maps/'
os.system("mkdir -p "+data_dir)
plot_dir = map_dir + 'projection_plots'
os.system("mkdir -p "+plot_dir)
# create projected map dictionary
projected_map = {}
# load and save coords of center of projected map wrt original spherical coords
lon_c = arr_c[npix,0] ; lat_c = arr_c[npix,1]
projected_map['center_coords'] = arr_c[npix,:]
# project each group of points into their respective tangent plane
lon_pix = grouped_points_lon[npix]
lat_pix = grouped_points_lat[npix]
lon_edge = arr_edge_points[npix, :, 0]
lat_edge = arr_edge_points[npix, :, 1]
r_pix = maps.tangent_plane_proj_(lat_pix, lon_pix, lat_c, lon_c)
r_edge = maps.tangent_plane_proj_(lat_edge, lon_edge, lat_c, lon_c)
projected_map['search_region_points'] = r_pix
projected_map['search_region_edge'] = r_edge
# generate first outer band of points outside each group
ang_scale = 2 * np.pi / 180
lon_out, lat_out = maps.find_neighboring_points_(ang_scale, lon_events, lat_events, lon_edge, lat_edge)
r_out = maps.tangent_plane_proj_(lat_out, lon_out, lat_c, lon_c)
projected_map['outer_region_points'] = maps.remove_points_from_array_(r_out,r_pix)
# define second outer band of points outside each group (for wavelet accuracy at boundary of pixels)
ang_scale = 4 * np.pi / 180
lon_outmost, lat_outmost = maps.find_neighboring_points_(ang_scale, lon_events, lat_events, lon_edge, lat_edge)
r_outmost = maps.tangent_plane_proj_(lat_outmost, lon_outmost, lat_c, lon_c)
r_pix_and_out_redundant = np.concatenate((r_pix, r_out), axis = 0) # combine
_, unique_indices = np.unique(r_pix_and_out_redundant, axis = 0, return_index = True) # find unique points
r_pix_and_out = r_pix_and_out_redundant[unique_indices,:] # remove duplicates
r_all_redundant = np.concatenate((r_pix_and_out,r_outmost), axis = 0) # combine
_, unique_indices = np.unique(r_all_redundant, axis = 0, return_index = True) # find unique points
r_all = r_all_redundant[unique_indices,:] # remove duplicates
projected_map['all_points'] = r_all
projected_map['outmost_region_points'] = maps.remove_points_from_array_(r_all,r_pix_and_out)
# save file
patch_dir = data_dir + 'map_' + str(npix) + '/'
print(patch_dir)
os.system("mkdir -p "+patch_dir)
file_name = 'ps_projected_map_dict.npz'
np.savez(patch_dir + file_name, **projected_map)
# load background patch data to combine with ps data
if energy_bin == 'all' or energy_bin == str(-1):
bkgd_patch_dir = model_dir + 'bkgd/' + 'projected_maps/' + 'map_' + str(npix) + '/'
else:
ie = int(float(energy_bin))
bkgd_patch_dir = model_dir + 'bkgd/' + 'energy_bin_' + str(ie) + '/' + 'projected_maps/' + 'map_' + str(npix) + '/'
bkgd_projected_map = dict(np.load(bkgd_patch_dir + 'projected_map_dict.npz'))
tot_projected_map = {}
tot_projected_map['search_region_points'] = np.concatenate((bkgd_projected_map['search_region_points'],
projected_map['search_region_points']), axis = 0) # combine
tot_projected_map['outer_region_points'] = np.concatenate((bkgd_projected_map['outer_region_points'],
projected_map['outer_region_points']), axis = 0) # combine
tot_projected_map['outmost_region_points'] = np.concatenate((bkgd_projected_map['outmost_region_points'],
projected_map['outmost_region_points']), axis = 0) # combine
tot_projected_map['all_points'] = np.concatenate((bkgd_projected_map['all_points'],
projected_map['all_points']), axis = 0) # combine
print(bkgd_projected_map['all_points'].shape)
print(projected_map['all_points'].shape)
print(tot_projected_map['all_points'].shape)
# can choose to take these from either dictionary
tot_projected_map['search_region_edge'] = bkgd_projected_map['search_region_edge']
tot_projected_map['center_coords'] = bkgd_projected_map['center_coords']
# load outer region edge defined by all background points
bkgd_patch_dir_all_energies = model_dir + 'bkgd/' + 'projected_maps/' + 'map_' + str(npix) + '/'
bkgd_projected_map_all_energies = dict(np.load(bkgd_patch_dir_all_energies + 'projected_map_dict.npz'))
tot_projected_map['outer_region_edge'] = bkgd_projected_map_all_energies['outer_region_edge']
file_name = 'projected_map_dict.npz'
np.savez(patch_dir + file_name, **tot_projected_map)
# plot results as a check
x_edge = r_edge[:,0] ; y_edge = r_edge[:,1]
plt.scatter(tot_projected_map['outmost_region_points'][:,0],tot_projected_map['outmost_region_points'][:,1], c = 'k', alpha = 0.3, s = 20)
plt.scatter(tot_projected_map['search_region_points'][:,0],tot_projected_map['search_region_points'][:,1], c = 'b', alpha = 0.3, s = 20)
plt.scatter(tot_projected_map['outer_region_points'][:,0],tot_projected_map['outer_region_points'][:,1],c = 'r', alpha = 0.3, s = 20)
plt.scatter(tot_projected_map['all_points'][:,0], tot_projected_map['all_points'][:,1], c = 'k', alpha = 0.3, s = 20)
plt.scatter(0,0,c = 'g', marker = '*')
plt.plot(x_edge,y_edge, c = 'k')
plt.plot(tot_projected_map['outer_region_edge'][:,0], tot_projected_map['outer_region_edge'][:,1], c = 'k', lw = 2)
plt.savefig(os.path.join(plot_dir, str(npix)))
plt.clf()