def plot_nearest_data():
obs_list_file = '/Users/ruby/EoR/obs_lists/diffuse_survey_good_pointings_successful.txt'
obs_info_file = '/Users/ruby/EoR/sidelobe_survey_obsinfo.txt'
data_loc = '/Users/ruby/EoR/Healpix_fits'
nside = 256
obs_list = open(obs_list_file, 'r').readlines()
obs_list = [obs.strip() for obs in obs_list] # strip newline characters
obs_list = list(set(obs_list)) # remove duplicates
#obs_list = obs_list[:10]
observations = surveyview.load_survey(obs_info_file) # get obs metadata
observations = [obs for obs in observations if obs.obsid in obs_list]
indices = []
signal_vals = []
distances = []
for i, obs in enumerate(observations):
print 'Gathering data from obsid {} of {}'.format(i + 1, len(obs_list))
print 'Loading data'
data, nside_old, nest = healpix_utils.load_map(
'{}/{}_uniform_Residual_I_HEALPix.fits'.format(
data_loc, obs.obsid))
data = healpix_utils.healpix_downsample(data, nside_old, nside, nest)
for data_point in data:
data_point.get_ra_dec(nside, nest)
data_dist = hp.rotator.angdist([obs.ra, obs.dec],
[data_point.ra, data_point.dec],
lonlat=True)[0] / np.pi * 180.
if data_point.pixelnum in indices:
list_pos = indices.index(data_point.pixelnum)
if data_dist < distances[list_pos]:
signal_vals[list_pos] = data_point.signal
distances[list_pos] = data_dist
else:
indices.append(data_point.pixelnum)
signal_vals.append(data_point.signal)
distances.append(data_dist)
use_data = [
healpix_utils.HealpixPixel(indices[i], signal_vals[i])
for i in range(len(indices))
]
#healpix_utils.write_data_to_fits(data, nside, nest, '/Users/ruby/Desktop/nearest_data.fits')
print 'Plotting'
plot_filled_pixels(use_data, nside, nest,
'/Users/ruby/Desktop/nearest_data_test.png')
def calculate_mean_and_var():
obs_list_file = '/Users/ruby/EoR/obs_lists/diffuse_survey_good_pointings_successful.txt'
data_loc = '/Users/ruby/EoR/Healpix_fits'
nside = 256
obs_list = open(obs_list_file, 'r').readlines()
obs_list = [obs.strip() for obs in obs_list] # strip newline characters
obs_list = list(set(obs_list)) # remove duplicates
all_data = []
for i, obs in enumerate(obs_list):
print 'Gathering data from obsid {} of {}'.format(i + 1, len(obs_list))
print 'Loading data'
data, nside_old, nest = healpix_utils.load_map(
'{}/{}_uniform_Residual_I_HEALPix.fits'.format(data_loc, obs))
data = healpix_utils.healpix_downsample(data, nside_old, nside, nest)
print 'Assembling data'
# Convert to implicit indexing
signal_vals_implicit = [hp.pixelfunc.UNSEEN] * 12 * nside**2
for j in range(len(data)):
signal_vals_implicit[data[j].pixelnum] = data[j].signal
all_data.append(signal_vals_implicit)
print 'Calculating data statistics'
data_ave = []
data_var = []
data_npoints = []
for i in range(12 * nside**2):
use_data = [
data_set[i] for data_set in all_data
if data_set[i] != hp.pixelfunc.UNSEEN
]
n_points = len(use_data)
if n_points > 0:
data_npoints.append(healpix_utils.HealpixPixel(i, n_points))
data_ave.append(healpix_utils.HealpixPixel(i, np.mean(use_data)))
data_var.append(healpix_utils.HealpixPixel(i, np.var(use_data)))
print 'Saving data statistics'
healpix_utils.write_data_to_fits(data_ave, nside, nest,
'/Users/ruby/Desktop/data_ave.fits')
healpix_utils.write_data_to_fits(data_var, nside, nest,
'/Users/ruby/Desktop/data_var.fits')
healpix_utils.write_data_to_fits(data_npoints, nside, nest,
'/Users/ruby/Desktop/data_npoints.fits')
def hist2d_datastat_plot():
obs_list_file = '/Users/ruby/EoR/obs_lists/diffuse_survey_good_pointings_successful.txt'
data_loc = '/Users/ruby/EoR/Healpix_fits'
obs_list = open(obs_list_file, 'r').readlines()
obs_list = [obs.strip() for obs in obs_list] # strip newline characters
obs_list = list(set(obs_list)) # remove duplicates
#obs_list = obs_list[:4]
observations = surveyview.load_survey(
'/Users/ruby/EoR/sidelobe_survey_obsinfo.txt')
observations = [obs for obs in observations if obs.obsid in obs_list]
var_data, nside, nest = healpix_utils.load_map(
'/Users/ruby/Desktop/data_var_nside256.fits')
var_pixelvals = [data_point.pixelnum for data_point in var_data]
data_diff = []
data_dist = []
for i, obs in enumerate(observations):
print 'Gathering data from obsid {} of {}'.format(i + 1, len(obs_list))
print 'Loading data'
data, nside_old, nest = healpix_utils.load_map(
'{}/{}_uniform_Residual_I_HEALPix.fits'.format(
data_loc, obs.obsid))
data = healpix_utils.healpix_downsample(data, nside_old, nside, nest)
print 'Calculating data statistics'
for data_point in data:
data_diff.append(
data_point.signal -
var_data[var_pixelvals.index(data_point.pixelnum)].signal)
data_point.get_ra_dec(nside, nest)
data_dist.append(
hp.rotator.angdist([obs.ra, obs.dec],
[data_point.ra, data_point.dec],
lonlat=True)[0] / np.pi * 180.)
hist, dist_edges, diff_edges = np.histogram2d(data_dist,
data_diff,
bins=100)
hist = hist.T
for j in range(len(hist[0])):
total = sum([hist[i][j] for i in range(len(hist))])
if total != 0:
for i in range(len(hist)):
hist[i][j] = hist[i][j] / total
fig, ax = plt.subplots()
plt.imshow(
hist,
origin='lower',
interpolation='none',
extent=[dist_edges[0], dist_edges[-1], diff_edges[0], diff_edges[-1]],
vmin=0,
vmax=0.1)
ax.set_aspect(
(dist_edges[-1] - dist_edges[0]) / (diff_edges[-1] - diff_edges[0]))
plt.xlabel('Distance from Pointing Center (deg)')
plt.ylabel('Difference from Mean (Jy/sr)')
cbar = plt.colorbar()
cbar.ax.set_ylabel('Counts, Normalized', rotation=270) # label colorbar
plt.savefig('/Users/ruby/Desktop/data_var_2dhist_testing.png',
format='png',
dpi=500)
def plot_healpix_tiling():
#data_type = 'Residual_I'
data_type = 'Dirty_I'
normalization = 'uniform'
#data_dir = '/Users/ruby/EoR/Healpix_fits'
data_dir = '/Users/ruby/EoR/Healpix_fits/haslam_sim'
tile_center_ras = [
100, 100, 100, 100, 100, 90, 90, 90, 90, 90, 80, 80, 80, 80, 80, 70,
70, 70, 70, 70, 60, 60, 60, 60, 60, 50, 50, 50, 50, 50, 40, 40, 40, 40,
40, 30, 30, 30, 30, 30, 20, 20, 20, 20, 20, 10, 10, 10, 10, 10, 0, 0,
0, 0, 0, -10, -10, -10, -10, -10, -20, -20, -20, -20, -20
]
tile_center_decs = [
-5, -15, -25, -35, -45, -5, -15, -25, -35, -45, -5, -15, -25, -35, -45,
-5, -15, -25, -35, -45, -5, -15, -25, -35, -45, -5, -15, -25, -35, -45,
-5, -15, -25, -35, -45, -5, -15, -25, -35, -45, -5, -15, -25, -35, -45,
-5, -15, -25, -35, -45, -5, -15, -25, -35, -45, -5, -15, -25, -35, -45,
-5, -15, -25, -35, -45
]
obsids = [
1131478056, 1131564464, 1131477936, 1130787784, 1131477816, 1131733672,
1131562544, 1131733552, 1130785864, 1131475896, 1131559064, 1131558944,
1131731752, 1130782264, 1130783824, 1131470736, 1131557144, 1131470616,
1130780464, 1131470496, 1131468936, 1131553544, 1131726352, 1130778664,
1131468696, 1131724672, 1131551744, 1131465216, 1130776864, 1130776624,
1131463536, 1131549944, 1131463416, 1130773264, 1131463296, 1131461616,
1131548024, 1131461496, 1130773144, 1131461376, 1131717352, 1131544424,
1131717232, 1131459696, 1131459576, 1131456216, 1131542624, 1131456096,
1131715432, 1131715312, 1131711952, 1131540824, 1131454296, 1131713632,
1131454176, 1131710152, 1131537224, 1131710032, 1131710032, 1131709912,
1131535544, 1131535424, 1131535304, 1131710032, 1131709912
]
data = []
#nside = 512
nside = 256
for i, obs in enumerate(obsids):
print 'Gathering pixels from obsid {} of {}.'.format(
i + 1, len(obsids))
obs_data, nside_old, nest = healpix_utils.load_map(
'{}/{}_{}_{}_HEALPix.fits'.format(data_dir, obs, normalization,
data_type))
obs_data = healpix_utils.healpix_downsample(obs_data, nside_old, nside,
nest)
tile_bounds_radec = [[tile_center_ras[i] - 5, tile_center_decs[i] - 5],
[tile_center_ras[i] - 5, tile_center_decs[i] + 5],
[tile_center_ras[i] + 5, tile_center_decs[i] + 5],
[tile_center_ras[i] + 5, tile_center_decs[i] - 5]]
tile_bounds_vec = np.array([
hp.pixelfunc.ang2vec(corner[0], corner[1], lonlat=True)
for corner in tile_bounds_radec
])
use_pixels = hp.query_polygon(nside, tile_bounds_vec, nest=nest)
data.extend([
data_point for data_point in obs_data
if data_point.pixelnum in use_pixels
])
#healpix_utils.write_data_to_fits(data, nside, nest, '/Users/ruby/EoR/mosaic_data.fits')
# Define Healpix pixels to plot
patches = []
colors = []
for point in data:
point.get_pixel_corners(nside, nest)
polygon = Polygon(zip(point.pix_corner_ras, point.pix_corner_decs))
patches.append(polygon)
colors.append(point.signal)
collection = PatchCollection(patches, cmap='Greys_r', lw=0.04)
collection.set_array(np.array(colors)) # set the data colors
collection.set_edgecolor('face') # make the face and edge colors match
fig, ax = plt.subplots(figsize=(24, 8), dpi=1000)
ax.add_collection(collection) # plot data
# plot lines between tiles
line_width = 2.0
color = 'gray'
order = 8
plt.plot([130, -45], [0, 0], lw=line_width, c=color, zorder=order)
plt.plot([130, -45], [-10, -10], lw=line_width, c=color, zorder=order)
plt.plot([130, -45], [-20, -20], lw=line_width, c=color, zorder=order)
plt.plot([130, -45], [-30, -30], lw=line_width, c=color, zorder=order)
plt.plot([130, -45], [-40, -40], lw=line_width, c=color, zorder=order)
plt.plot([130, -45], [-50, -50], lw=line_width, c=color, zorder=order)
plt.plot([105, 105], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([95, 95], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([85, 85], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([75, 75], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([65, 65], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([55, 55], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([45, 45], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([35, 35], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([25, 25], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([15, 15], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([5, 5], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([-5, -5], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([-15, -15], [-75, 20], lw=line_width, c=color, zorder=order)
plt.plot([-25, -25], [-75, 20], lw=line_width, c=color, zorder=order)
plt.xlabel('RA (deg)')
plt.ylabel('Dec (deg)')
plt.axis('equal')
ax.set_facecolor('gray') # make plot background gray
plt.axis([110, -30, -50, 0])
cbar = fig.colorbar(collection, ax=ax, extend='both') # add colorbar
cbar.ax.set_ylabel('Flux Density (Jy/sr)', rotation=270) # label colorbar
plt.savefig(
'/Users/ruby/Desktop/haslam_mosaicplot_nside{}.png'.format(nside),
format='png',
dpi=1000)
def overplot_haslam_contour():
data, nside, nest = healpix_utils.load_map(
'/Users/ruby/EoR/mosaic_data.fits')
fig, ax = plt.subplots(figsize=(24, 8), dpi=1000)
# Define Healpix pixels to plot
patches = []
colors = []
for point in data:
point.get_pixel_corners(nside, nest)
polygon = Polygon(zip(point.pix_corner_ras, point.pix_corner_decs))
patches.append(polygon)
colors.append(point.signal)
collection = PatchCollection(patches, cmap='Greys_r', lw=0.04)
collection.set_array(np.array(colors)) # set the data colors
collection.set_edgecolor('face') # make the face and edge colors match
ax.add_collection(collection) # plot data
# plot lines between tiles
line_width = 2.0
color = 'gray'
order = 8
plt.plot([130, -45], [-10, -10], lw=line_width, c=color, zorder=order)
plt.plot([130, -45], [-20, -20], lw=line_width, c=color, zorder=order)
plt.plot([130, -45], [-30, -30], lw=line_width, c=color, zorder=order)
plt.plot([130, -45], [-40, -40], lw=line_width, c=color, zorder=order)
plt.plot([130, -45], [-50, -50], lw=line_width, c=color, zorder=order)
plt.plot([95, 95], [-75, -20], lw=line_width, c=color, zorder=order)
plt.plot([85, 85], [-75, -20], lw=line_width, c=color, zorder=order)
plt.plot([75, 75], [-75, -20], lw=line_width, c=color, zorder=order)
plt.plot([65, 65], [-75, -20], lw=line_width, c=color, zorder=order)
plt.plot([55, 55], [-75, -20], lw=line_width, c=color, zorder=order)
plt.plot([45, 45], [-75, -20], lw=line_width, c=color, zorder=order)
plt.plot([35, 35], [-75, -20], lw=line_width, c=color, zorder=order)
plt.plot([25, 25], [-75, -20], lw=line_width, c=color, zorder=order)
plt.plot([15, 15], [-75, -20], lw=line_width, c=color, zorder=order)
plt.plot([5, 5], [-75, -20], lw=line_width, c=color, zorder=order)
plt.plot([-5, -5], [-75, -20], lw=line_width, c=color, zorder=order)
plt.plot([-15, -15], [-75, -20], lw=line_width, c=color, zorder=order)
plt.xlabel('RA (deg)')
plt.ylabel('Dec (deg)')
plt.axis('equal')
ax.set_facecolor('gray') # make plot background gray
plt.axis([110, -30, -50, 0])
cbar = fig.colorbar(collection, ax=ax, extend='both') # add colorbar
cbar.ax.set_ylabel('Flux Density (Jy/sr)', rotation=270) # label colorbar
data, nside, nest = healpix_utils.load_global_map(
'/Users/ruby/EoR/Healpix_fits/lambda_haslam408_dsds.fits')
data = healpix_utils.healpix_downsample(data, nside, 512, nest)
for point in data:
point.get_ra_dec(nside, nest, coords='galactic')
plt.tricontour([point.ra for point in data], [point.dec for point in data],
[point.signal for point in data],
500,
linewidths=0.5,
colors='cyan')
plt.savefig('/Users/ruby/EoR/haslam_overplot.png', format='png', dpi=1000)
def compare_observations():
obs_list_file = '/Users/ruby/EoR/obs_lists/diffuse_survey_good_pointings_successful.txt'
data_loc = '/Users/ruby/EoR/Healpix_fits'
save_path = '/Users/ruby/Documents/2018 Fall Quarter/phys576b/jackknife_assignment'
obs_list = open(obs_list_file, 'r').readlines()
obs_list = [obs.strip() for obs in obs_list] # strip newline characters
obs_list = list(set(obs_list)) # remove duplicates
observations = surveyview.load_survey(
'/Users/ruby/EoR/sidelobe_survey_obsinfo.txt')
center_ra = 40.
center_dec = -35.
use_observations = []
for obs in observations:
if obs.obsid in obs_list:
if (obs.ra - center_ra)**2. + (obs.dec - center_dec)**2. < 10**2.:
use_observations.append(obs)
all_data = []
for obs in use_observations:
data, nside_old, nest = healpix_utils.load_map(
'{}/{}_uniform_Residual_I_HEALPix.fits'.format(
data_loc, obs.obsid))
#plot_filled_pixels(data, nside, nest, '{}/{}_residual_I.png'.format(save_path, obs.obsid))
nside = 256
data = healpix_utils.healpix_downsample(data, nside_old, nside, nest)
all_data.append(data)
print 'Calculating data average...'
ave_data, var_data, nsamples_data, ston_data = healpix_utils.average_healpix_maps(
all_data)
max_intersect_pixels = set(
[data_point.pixelnum for data_point in all_data[0]])
for data_set in all_data[1:]:
max_intersect_pixels = max_intersect_pixels.intersection(
[data_point.pixelnum for data_point in data_set])
ave_data_sum = np.sum([
data_point.signal for data_point in ave_data
if data_point.pixelnum in max_intersect_pixels
])
all_data_norm = []
for i, data_set in enumerate(all_data):
data_sum = np.sum([
data_point.signal for data_point in data_set
if data_point.pixelnum in max_intersect_pixels
])
norm_factor = ave_data_sum / data_sum
for point in data_set:
point.signal = point.signal * norm_factor
ave_data_norm, var_data_norm, nsamples_data, ston_data_norm = healpix_utils.average_healpix_maps(
all_data)
print 'Plotting data average...'
plot_filled_pixels(ave_data_norm, nside, nest,
'{}/ave_norm_residual_I.png'.format(save_path))
data_diff = healpix_utils.difference_healpix_maps(ave_data_norm, ave_data,
nside, nest)
plot_filled_pixels(
data_diff, nside, nest,
'{}/ave_norm_minus_orig_residual_I.png'.format(save_path))