def calc_feed_feed_cross_spectrum(p):
i = p // 19 + 1
j = p % 19 + 1
if i == 4 or i == 6 or i == 7:
return p
if j == 4 or j == 6 or j == 7:
return p
my_map = map_cosmo.MapCosmo(mapname, feed=i, jk='half', split=0)
my_map2 = map_cosmo.MapCosmo(mapname, feed=j, jk='half', split=1)
print(my_map.rms.shape)
print(my_map.rms[28, 68, :])
my_xs = power_spectrum.CrossSpectrum(my_map, my_map2)
xs, k, nmodes = my_xs.calculate_xs()
rms_mean, rms_sig = my_xs.run_noise_sims(100, seed=42)
outname = 'spectra/xs_feeds_par' + my_map.save_string + '_' + my_map2.map_string + '_%02i.h5' % j
my_xs.make_h5(outname=outname, save_noise_realizations=True)
lim = np.mean(np.abs(xs[4:])) * 4
#print(xs)
fig = plt.figure()
fig.suptitle('feed %02i x feed %02i' % (i, j))
ax1 = fig.add_subplot(211)
ax1.errorbar(k, xs, rms_sig, fmt='o', label=r'$\tilde{C}_{data}(k)$')
ax1.plot(k, 0 * rms_mean, 'k', label=r'$\tilde{C}_{noise}(k)$', alpha=0.4)
#ax1.plot(k, theory[1:] * 100, 'r--', label=r'$100 * P_{Theory}(k)$')
#ax1.plot(k_th, ps_th * 10, 'r--', label=r'$10 * P_{Theory}(k)$')
ax1.set_ylabel(r'$\tilde{C}(k)$ [$\mu$K${}^2$ Mpc${}^3$]')
ax1.set_ylim(-lim, lim) # ax1.set_ylim(0, 0.1)
#ax1.set_yscale('log')
ax1.set_xscale('log')
ax1.grid()
plt.legend()
ax2 = fig.add_subplot(212)
ax2.errorbar(k,
xs / rms_sig,
rms_sig / rms_sig,
fmt='o',
label=r'$\tilde{C}_{data}(k)$')
ax2.plot(k, 0 * rms_mean, 'k', alpha=0.4)
ax2.set_ylabel(r'$\tilde{C}(k) / \sigma_\tilde{C}$')
ax2.set_xlabel(r'$k$ [Mpc${}^{-1}$]')
ax2.set_ylim(-12, 12)
ax2.set_xscale('log')
ax2.grid()
plt.legend()
folder = '/mn/stornext/d16/www_cmb/comap/xs/'
plt.savefig(folder + 'xs_co7_par_%02i_%02i' % (i, j) + my_map.map_string +
'_' + my_map2.map_string + '.png',
bbox_inches='tight')
print('Done with %02i, %02i!' % (i, j))
return p
def __init__(self,
name_of_my_map,
jk=False,
feed1=None,
feed2=None,
n_of_splits=2):
self.feed_name1 = '_feed' + str(feed1)
self.feed_name2 = '_feed' + str(feed2)
self.name_of_map = name_of_my_map #the names schould indicate which map and feed we take
self.names = []
self.maps = []
n_list = list(range(n_of_splits))
all_different_possibilities = list(itr.combinations(
n_list,
2)) #for n_of_splits = 3, it gives [(0, 1), (0, 2), (1, 2)]
self.how_many_combinations = len(all_different_possibilities)
self.name_of_map = self.name_of_map.split('/')[
-1] #get rid of the path, leave only the name of the map
self.name_of_map = self.name_of_map.split('.')[
0] #get rid of the ".h5" part
for u in range(self.how_many_combinations):
current_combo = all_different_possibilities[
u] #there are two splits from mapmaker so far, can be more from simulations
name1 = self.name_of_map + '_split' + str(
current_combo[0]) + self.feed_name1
#xs_co6_map_snup_elev_0_cesc_0_split0_feed11_and_co6_map_snup_elev_0_cesc_0_split1_feed8.h5
name2 = self.name_of_map + '_split' + str(
current_combo[1]) + self.feed_name2
self.names.append(name1)
self.names.append(name2)
for u in range(self.how_many_combinations):
current_combo = all_different_possibilities[
u] #there are two splits from mapmaker so far, can be more from simulations
my_map_split_1 = map_cosmo.MapCosmo(name_of_my_map, feed1, jk,
current_combo[0])
my_map_split_2 = map_cosmo.MapCosmo(name_of_my_map, feed2, jk,
current_combo[1])
self.maps.append(my_map_split_1)
self.maps.append(my_map_split_2)
def get_feed_ps(feed):
my_map = map_cosmo.MapCosmo(mapname, feed=feed)
my_ps = power_spectrum.PowerSpectrum(my_map)
ps, k, nmodes = my_ps.calculate_ps()
rms_mean, rms_sig = my_ps.run_noise_sims(10)
my_ps.make_h5()
return ps, k, rms_mean, rms_sig
import corner
import h5py
import sys
import tools
import map_cosmo
import power_spectrum
try:
mappath = sys.argv[1]
except IndexError:
print('Missing filepath!')
print('Usage: python ps_script.py mappath')
sys.exit(1)
my_map = map_cosmo.MapCosmo(mappath)
my_ps = power_spectrum.PowerSpectrum(my_map)
ps, k, nmodes = my_ps.calculate_ps()
rms_mean, rms_sig = my_ps.run_noise_sims(10)
my_ps.make_h5()
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax1.errorbar(k, ps, rms_sig, fmt='o', label=r'$\tilde{P}_{data}(k)$')
ax1.plot(k, rms_mean, 'k', label=r'$\tilde{P}_{noise}(k)$', alpha=0.4)
# ax1.plot(k_th, ps_th * 10, 'r--', label=r'$10 * P_{Theory}(k)$')
import tools
import map_cosmo
import power_spectrum
try:
mapname = sys.argv[1]
except IndexError:
print('Missing filename!')
print('Usage: python ps_script.py mapname')
sys.exit(1)
save_folder = '/mn/stornext/u3/haavarti/www_docs/diag/ps/'
prefix = mapname[:-6].rpartition("/")[-1]
my_map = map_cosmo.MapCosmo(mapname)
my_ps = power_spectrum.PowerSpectrum(my_map)
ps, k, _ = my_ps.calculate_ps()
rms_mean, rms_sig = my_ps.run_noise_sims(10)
my_ps.make_h5()
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax1.errorbar(k, ps, rms_sig, fmt='o', label=r'$\tilde{P}_{data}(k)$')
ax1.plot(k, rms_mean, 'k', label=r'$\tilde{P}_{noise}(k)$', alpha=0.4)
ax1.set_ylabel(r'$\tilde{P}(k)$ [$\mu$K${}^2$ Mpc${}^3$]')
def __init__(self,
list_of_n_map_names,
jk=False,
feed=None,
feed1=None,
feed2=None,
n_of_splits=2):
if feed == 30:
feed = None
self.feed_name = '_coadded'
if feed != 30 and feed is not None:
self.feed_name = '_feed' + str(feed)
else:
self.feed_name1 = '_feed' + str(feed1)
self.feed_name2 = '_feed' + str(feed2)
self.names_of_maps = list_of_n_map_names #the names schould indicate which map and feed we take
self.names = []
for name in self.names_of_maps:
name = name.rpartition('/')[
-1] #get rid of the path, leave only the name of the map
name = name.rpartition('.')[0] #get rid of the ".h5" part
if jk == False:
if feed1 == None and feed2 == None:
self.names.append(name + self.feed_name)
else:
self.names.append(name + self.feed_name1)
self.names.append(name + self.feed_name2)
if jk != False and jk != 'sim':
if feed1 == None and feed2 == None:
self.names.append(name + '_1st_' + jk + self.feed_name)
self.names.append(name + '_2nd_' + jk + self.feed_name)
else:
self.names.append(name + '_1st_' + jk + self.feed_name1)
self.names.append(name + '_2nd_' + jk + self.feed_name2)
if jk != False and jk == 'sim':
for g in range(n_of_splits):
map_split_number = g + 1
map_split_name = 'split%01i_' % (map_split_number) + name
self.names.append(map_split_name)
self.maps = []
for map_name in list_of_n_map_names:
if jk != False:
if feed1 == None and feed2 == None:
for split_no in range(
n_of_splits
): #there are two splits from mapmaker so far, can be more from simulations
my_map_split = map_cosmo.MapCosmo(
map_name, feed, jk, split_no)
self.maps.append(my_map_split)
else:
my_map_first = map_cosmo.MapCosmo(map_name, feed1, jk, 0)
my_map_second = map_cosmo.MapCosmo(map_name, feed2, jk, 1)
self.maps.append(my_map_first)
self.maps.append(my_map_second)
else:
if feed1 == None and feed2 == None:
my_map = map_cosmo.MapCosmo(map_name, feed)
self.maps.append(my_map)
else:
my_map1 = map_cosmo.MapCosmo(map_name, feed1)
my_map2 = map_cosmo.MapCosmo(map_name, feed2)
self.maps.append(my_map1)
self.maps.append(my_map2)
import map_cosmo
import power_spectrum
try:
mapname = sys.argv[1]
mapname2 = sys.argv[2]
except IndexError:
print('Missing filename!')
print('Usage: python ps_script.py mapname mapname2')
sys.exit(1)
feeds = [1, 2, 3, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
for i in feeds:
for j in feeds:
my_map = map_cosmo.MapCosmo(mapname, feed=i)
my_map2 = map_cosmo.MapCosmo(mapname2, feed=j)
tot_rms = np.zeros_like(my_map.rms)
where = np.where((my_map.rms * my_map2.rms) != 0)
tot_rms[where] = np.sqrt(my_map.rms[where]**2 + my_map2.rms[where]**2)
summap = np.zeros_like(my_map.map)
summap[where] = (my_map.map[where] + my_map2.map[where])
diffmap = np.zeros_like(my_map.map)
diffmap[where] = (my_map.map[where] - my_map2.map[where])
my_map.rms = tot_rms
my_map.map = summap
sum_ps = power_spectrum.PowerSpectrum(my_map)