def plot_radecs_colorcode_decs(obs_info_file, save_loc):
save_loc = format_save_loc(save_loc, 'radec_coverage_colorcode_plot')
observations = surveyview.load_survey(obs_info_file)
for obs in observations:
if obs.ra > 250:
obs.ra -= 360
# Round the declinations to the nearest 6th to clump them in 7 Dec bands
decs_round = [int(obs.dec / 6.) * 6 for obs in observations]
decs_set = list(set(decs_round))
decs_set.sort()
plt.figure(figsize=(10, 5))
for i in range(len(decs_set)):
use_ras = []
use_decs = []
for index in range(len(observations)):
if decs_round[index] == decs_set[i]:
use_ras.append(observations[index].ra)
use_decs.append(observations[index].dec)
plt.plot(use_ras,
use_decs,
'o',
markersize=10,
mfc=use_colors[i],
alpha=0.5)
plt.xlabel('RA')
plt.ylabel('Dec')
plt.axis([-100, 200, -65, 15])
plt.grid(True)
print 'Saving plot to {}'.format(save_loc)
plt.savefig(save_loc)
plt.close()
def plot_radec_pointings_coverage(obs_info_file, save_loc):
def check_obsids(observations, ra_target, dec_target, radius,
colorbar_max):
number_pointings = 0
for i, obs in enumerate(observations):
distance_2 = (obs.dec - dec_target)**2 + (obs.ra - ra_target)**2
if distance_2 <= radius**2:
pointings.append(obs.pointing)
number_pointings = len(set(pointings))
if number_pointings > colorbar_max:
number_pointings = colorbar_max
return number_pointings
save_loc = format_save_loc(save_loc, 'pointings_coverage')
ra_range = [-60, 160]
dec_range = [-70, 20]
resolution = 1
radius = 12
colorbar_max = 10
ra_vals = [
ra_range[0] + resolution * i
for i in range(int((ra_range[1] - ra_range[0]) / resolution))
]
dec_vals = [
dec_range[0] + resolution * i
for i in range(int((dec_range[1] - dec_range[0]) / resolution))
]
counts = [[0 for i in range(len(ra_vals))] for i in range(len(dec_vals))]
observations = surveyview.load_survey(obs_info_file)
observations = surveyview.get_pointings(observations)
for i, ra_target in enumerate(ra_vals):
for j, dec_target in enumerate(dec_vals):
(counts[-(j + 1)])[-(i + 1)] = check_obsids(
observations, ra_target, dec_target, radius, colorbar_max)
plt.figure(figsize=(9, 3))
plt.imshow(counts,
interpolation='none',
extent=[
ra_range[1] / 360. * 24., ra_range[0] / 360. * 24.,
dec_range[0], dec_range[1]
],
aspect=24 / 360.,
cmap=plt.get_cmap('Blues'))
plt.xlabel('Right Ascension (hours)')
plt.ylabel('Declination (degrees)')
plt.tight_layout()
cbar = plt.colorbar(extend='max')
cbar.set_label('Number of Unique Pointings')
print 'Saving plot to {}'.format(save_loc)
plt.savefig(save_loc)
plt.close()
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 generate_radec_animation(obs_info_file, save_dir):
if save_dir.endswith('.png'): # User doesn't get to set file name
save_dir_split = save_dir.split('/')
save_dir = '/'.join(save_dir_split[:-1])
if save_dir.endswith('/'):
save_dir = save_dir[:-1]
observations = surveyview.load_survey(obs_info_file)
for obs in observations:
if obs.ra > 250:
obs.ra -= 360
ras = [obs.ra for obs in observations]
decs = [obs.dec for obs in observations]
for i, obs in enumerate(obsids):
if i + 1 < 10:
filepath_num = '00' + str(i + 1)
else:
if i + 1 < 100:
filepath_num = '0' + str(i + 1)
else:
filepath_num = str(i + 1)
save_loc = '{}/radec_plot{}.png'.format(save_dir, filepath_num)
plt.figure(figsize=(17, 5))
plt.plot(ras[0:i + 1],
decs[0:i + 1],
'o',
markersize=90,
mfc='blue',
alpha=0.03)
plt.plot(ras[i], decs[i], 'o', markersize=90, mfc='none', mec='red')
plt.plot(ras[i], decs[i], 'x', markersize=10, mfc='red')
plt.xticks(range(-100, 200, 10))
plt.xlabel('RA')
plt.ylabel('Dec')
plt.axis('equal')
plt.axis([-100, 200, -65, 15])
plt.grid(which='both')
plt.text(-80, 17, 'Obsid: ' + str(obs))
print 'Saving plot to {}'.format(save_loc)
plt.savefig(save_loc)
plt.close()
def plot_azels(obs_info_file, save_loc):
# Plots the points in the Az/El plane that are covered by the survey
# Az/El values are rounded to prevent redundancy
# Data points are color-coded based on their declination
save_loc = format_save_loc(save_loc, 'AzEls_plot')
observations = surveyview.load_survey(obs_info_file)
# Round the declinations to the nearest 6th to clump them in 7 Dec bands
decs_round = [int(obs.dec / 6.) * 6 for obs in observations]
decs_set = list(set(decs_round))
decs_set.sort()
plt.figure()
for i in range(len(decs_set)):
use_az = []
use_el = []
for index in range(len(observations)):
if decs_round[index] == decs_set[i]:
use_az.append(observations[index].az)
use_el.append(observations[index].el)
use_azels = zip(use_az, use_el)
use_azels_set = list(set(use_azels))
use_az_set = [term[0] for term in use_azels_set]
use_el_set = [term[1] for term in use_azels_set]
plt.plot(use_az_set,
use_el_set,
'o',
markersize=10,
mfc=use_colors[i],
alpha=1)
plt.xlabel('Azimuth')
plt.ylabel('Elevation')
plt.axis([-10, 350, 60, 91])
plt.grid(True)
print 'Saving plot to {}'.format(save_loc)
plt.savefig(save_loc)
plt.close()
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_leakage_fits():
observations = surveyview.load_survey('/Users/ruby/EoR/sidelobe_survey_obsinfo.txt')
observations = surveyview.get_pointings(observations)
pol_leakage_params_file = open('/Users/ruby/EoR/pol_leakage_testing/plotting/pol_leakage_fit_params.csv', 'r')
read_csv = csv.reader(pol_leakage_params_file)
pol_leakage_params = [line for line in read_csv]
pol_leakage_params_file.close()
pol_leakage_params = pol_leakage_params[1:] # remove header
obsids = [obs_params[0] for obs_params in pol_leakage_params]
observations = [obs for obs in observations if obs.obsid in obsids]
xvals = list(range(512, 1536, 8))
yvals = list(range(512, 1536, 8))
for obs in observations:
params = (pol_leakage_params[obsids.index(obs.obsid)])[1:]
params = [float(param) for param in params]
leakage_surf_q = np.zeros((len(xvals), len(yvals)))
leakage_surf_u = np.zeros((len(xvals), len(yvals)))
for i, xval in enumerate(xvals):
for j, yval in enumerate(yvals):
leakage_surf_q[i, j] = (
params[0]*xval**2 + params[1]*yval**2
+ params[2]*xval*yval + params[3]*xval + params[4]*yval
+ params[5]
)
leakage_surf_u[i, j] = (
params[6]*xval**2 + params[7]*yval**2
+ params[8]*xval*yval + params[9]*xval + params[10]*yval
+ params[11]
)
pointing = obs.pointing
pointing = re.split('\(|\)|, ||', pointing)
pointing = list(filter(None, pointing))
fig, ax = plt.subplots()
plt.imshow(leakage_surf_q, origin='lower', interpolation='none',
#cmap='Greys_r',
extent=[xvals[0], xvals[-1], yvals[0], yvals[-1]],
vmin=-.3, vmax=.3, aspect='auto'
)
plt.xlabel('x pixel')
plt.ylabel('y pixel')
plt.title('{} Q Leakage Fit (Pointing {}, {})'.format(obs.obsid, pointing[0], pointing[1]))
plt.axis('equal')
cbar = plt.colorbar()
cbar.ax.set_ylabel('Fractional Leakage', rotation=270) # label colorbar
plt.savefig('/Users/ruby/EoR/pol_leakage_testing/plotting/pointing_{}_{}/{}_q_leakage.png'.format(pointing[0], pointing[1], obs.obsid), format='png', dpi=500)
plt.close()
fig, ax = plt.subplots()
plt.imshow(leakage_surf_u, origin='lower', interpolation='none',
#cmap='Greys_r',
extent=[xvals[0], xvals[-1], yvals[0], yvals[-1]],
vmin=-.3, vmax=.3, aspect='auto'
)
plt.xlabel('x pixel')
plt.ylabel('y pixel')
plt.title('{} U Leakage Fit (Pointing {}, {})'.format(obs.obsid, pointing[0], pointing[1]))
plt.axis('equal')
cbar = plt.colorbar()
cbar.ax.set_ylabel('Fractional Leakage', rotation=270) # label colorbar
plt.savefig('/Users/ruby/EoR/pol_leakage_testing/plotting/pointing_{}_{}/{}_u_leakage.png'.format(pointing[0], pointing[1], obs.obsid), format='png', dpi=500)
plt.close()
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))
def radec_reference_for_images(obs_info_file,
image_filename,
save_loc,
reduced_obslist=''):
save_loc = format_save_loc(save_loc, 'radec_reference')
observations = surveyview.load_survey(obs_info_file)
for obs in observations:
if obs.ra > 250:
obs.ra -= 360
if reduced_obslist != '':
reduced_obslist = open(reduced_obslist, "r")
obs_reduced = reduced_obslist.readlines()
reduced_obslist.close()
obs_reduced = [obs.strip() for obs in obs_reduced]
observations = [
obs for obs in observations if obs.obsid in obs_reduced
]
ras = [obs.ra for obs in observations]
decs = [obs.dec for obs in observations]
highlight_obs = (image_filename.split('/')[-1]).split('_')[0]
i = [obs.obsid for obs in observations].index(highlight_obs)
a_teams = surveyview.get_a_team_sources()
for source in a_teams:
if source.ra > 250:
source.ra -= 360
plt.figure(figsize=(11, 14))
gs = gridspec.GridSpec(2, 3, height_ratios=[1, 4], width_ratios=[1, 50, 1])
plt.subplot(gs[0, 1])
plt.plot(ras, decs, 'o', markersize=90, mfc='0.9', mec='none', zorder=1)
plt.plot(ras[i],
decs[i],
'o',
markersize=90,
mfc='none',
mec='red',
zorder=11)
plt.plot(ras[i], decs[i], 'x', markersize=10, mec='red', zorder=12)
plt.plot([source.ra for source in a_teams],
[source.dec for source in a_teams],
'o',
markersize=5,
mfc='orange',
mec='none')
for index in range(len(a_teams)):
plt.annotate(a_teams[index].name,
(a_teams[index].ra, a_teams[index].dec))
plt.plot([0, 60], [-27, -27],
'o',
markersize=90,
mfc='cyan',
alpha=0.2,
mec='none',
zorder=2)
plt.annotate('EoR-0', (0, -27))
plt.annotate('EoR-1', (60, -27))
plt.xticks(range(-100, 200, 10))
plt.xlabel('RA')
plt.ylabel('Dec')
plt.axis('equal')
plt.axis([200, -100, -65, 15])
plt.grid(which='both', zorder=10)
plt.subplot(gs[1, :])
img = mpimg.imread(image_filename)
plt.imshow(img)
plt.tight_layout()
print 'Saving plot to {}'.format(save_loc)
plt.savefig(save_loc, dpi=300)
plt.close()