def build_map(inmap,
output,
i,
ntot,
ns=1,
g=0,
sml=5.,
lmax=3 * 2048,
prefix=''):
ch_mkdir(dest)
dont = 1
npatch = 12 * ns**2
for j in range(npatch):
if not os.path.exists(output + prefix + str(i * npatch + j) + '.npy'):
dont = 0
if dont:
return
if sml != 0:
s = hp.read_map(inmap, nest=0, verbose=0)
if g:
s = kelvin_check(s)
s = hp.smoothing(s, np.radians(sml / 60.), lmax=lmax, verbose=0)
s = hp.reorder(s, r2n=1)
else:
s = hp.read_map(inmap, nest=1, verbose=0)
patches = sky2patch(s, ns)
for j in range(npatch):
pop_percent(i * npatch + j, ntot * npatch)
np.save(output + prefix + str(i * npatch + j), patches[j])
plt.imshow(patches[j], cmap=cmap)
plt.savefig(output + prefix + str(i * npatch + j) + '.jpg')
plt.close()
def download_simulation(i, typ, com_s):
name_out = './maps/dx12_v3_{}_{}_mc_{:05d}_raw.fits'.format(com_s, typ, i)
if os.path.exists(name_out):
x = os.path.getsize(name_out)
if x != 603987840:
print(name_out, 'is corrupted!')
os.remove(name_out)
if not os.path.exists(name_out):
ch_mkdir('maps')
global TOTALSIZE
TOTALSIZE = 100 / (589836 * 1024)
name_in = 'http://pla.esac.esa.int/pla/aio/product-action?SIMULATED_MAP.FILE_ID=dx12_v3_{}_{}_mc_{:05d}_raw.fits'.format(
com_s, typ, i)
download(name_in, name_out, report=report)
else:
print(name_out, 'exist!')
return name_out
# dl1.append(ll[j]*(ll[j]+1)*cl[j]/(2*np.pi))
# dl2.append(ll[j]*(ll[j]+1)*cl_map[j]/(2*np.pi))
# plt.plot(ll,dl2,'r--',label='Simulation')
# plt.plot(ll,dl1,'b--',lw=2,label='Orginal')
# plt.xscale('log')
# plt.yscale('log')
# plt.tick_params(labelsize=15)
# plt.xlabel(r'$\ell$',fontsize=25)
# plt.ylabel(r'$D_{\ell}$',fontsize=25)
# plt.legend(loc='best',fontsize=20)
# plt.savefig('../data/healpix/power_'+str(nside)+'_'+str(i)+'.jpg')
# plt.close()
ch_mkdir('../data/string/')
for i in range(n_string):
strnum = str(i + 1)
if nside == 4096:
strnum = strnum + 'b'
if not os.path.exists('../data/string/map1n_allz_rtaapixlw_' + str(nside) +
'_' + str(i + 1) + '.fits.' + ex):
print('Downloading string: ' + str(i))
download(
'http://cp3.irmp.ucl.ac.be/~ringeval/upload/data/' + str(nside) +
'/map1n_allz_rtaapixlw_' + str(nside) + '_' + strnum + '.fits.' +
ex, '../data/string/map1n_allz_rtaapixlw_' + str(nside) + '_' +
str(i + 1) + '.fits.' + ex)
load_status = 0
import os
import sys
import glob
from time import time
import numpy as np
import pylab as plt
import ccgpack as ccg
from scipy import stats
cmap = plt.cm.jet
cmap.set_under('w')
cmap.set_bad('gray', 0.)
add = 'processed_images/'
ccg.ch_mkdir(add)
gmulist = [0] + list(5 * 10**np.linspace(-8, -5, 10))
ngmu = len(gmulist)
s2n = 10.
i_map = int(sys.argv[1])
i_srg = int(sys.argv[2])
i_gmup = int(sys.argv[3])
def blocker(arr, nrows, ncols):
h, w = arr.shape
return (arr.reshape(h // nrows, nrows, -1,
ncols).swapaxes(1, 2).reshape(-1, nrows, ncols))
def init_dirs(self):
ch_mkdir(self.prefix)
ch_mkdir(self.prefix + 'model')
ch_mkdir(self.prefix + 'images')
def filtf(x):
return ccg.filters(x, edd_method='sch')
dp = DataProvider(x_files,
y_files,
alpha,
nx=nx,
ny=ny,
n_buffer=2,
reload_rate=10000,
filt=filtf)
model_add = './models/' + str(n_layers) + '_layers_f' + filt + '/'
res_dir = './results/' + str(n_layers) + '_layers_f' + filt + '/'
ccg.ch_mkdir(res_dir + 'plots')
model = ng.Model(dp, restore=0, model_add=model_add + str(0), arch=arch)
print('# of variables:', model.n_variables)
if os.path.exists(res_dir + 'info.npy'):
i, dp.alpha, dalpha, learning_rate = np.load(res_dir + 'info.npy')
i = int(i)
model.model_add = model_add + str(i)
print('Loading model ' + str(i) + ' ...')
model.restore()
else:
i = 0
for _ in range(ntry):
def p_value(lst):
p_valu = []
num = len(lst)
for i in range(num):
class1 = lst[i]
pvs = []
for j in range(num):
if i!=j:
class2 = lst[j]
t , p = stats.ttest_ind(class1 , class2)
pvs.append(p)
p_valu.append(np.max(pvs))
return np.array(p_valu)
ccg.ch_mkdir('../classic')
gmulist = [0]+list(10**np.linspace(-8 , -5 , 10))
# IN CASE YOU WANT TO RUN IT parallel
#igmu = int(sys.argv[1])
#gmulist = gmulist[igmu:igmu+1]
ngmu = len(gmulist)
tadd = '../data/test_set/healpix_p/'
sim_name = tadd.split('/')[3]
res_add = '../classic/'+sim_name+'/'
ccg.ch_mkdir(res_add)
print('Buiding classical method results:')
for j,gmu in enumerate(gmulist):
dir_name = '{:3.2e}'.format(gmu)
import os
import sys
from glob import glob
from time import time
import numpy as np
import pylab as plt
import ccgpack as ccg
from scipy import stats
cmap = plt.cm.jet
cmap.set_under('w')
cmap.set_bad('gray', 0.)
add = 'processed_images/'
ccg.ch_mkdir(add)
add = 'processed_images/curveleted/'
if os.path.exists(add + 'observations.npy'):
exit()
ccg.ch_mkdir(add)
def blocker(arr, nrows, ncols):
h, w = arr.shape
return (arr.reshape(h // nrows, nrows, -1,
ncols).swapaxes(1, 2).reshape(-1, nrows, ncols))
res = {}