def __init__(self, dataset, dataset_params={}, silent=False):
if not silent: print('loading: %s' % dataset)
ini = IniFile(dataset)
ini.params.update(dataset_params)
spectra = np.loadtxt(ini.relativeFileName('cl_hat_file'))
covmat_cl = ini.split('covmat_cl')
use_cl = ini.split('use_cl', covmat_cl)
if ini.hasKey('use_range'):
used_ell = ini.params['use_range']
if isinstance(used_ell, dict):
print('Using range %s' % used_ell)
for key, value in used_ell.items():
used_ell[key] = range_to_ells(value)
else:
if not silent: print('Using range: %s' % used_ell)
used_ell = range_to_ells(used_ell)
else:
used_ell = None
data_vector = []
nX = 0
used_indices = []
with open(ini.relativeFileName('data_ranges', "r")) as f:
lines = f.readlines()
while not lines[-1].strip():
lines = lines[:-1]
self.Nspec = len(lines)
lmin = np.zeros(self.Nspec, dtype=np.int)
lmax = np.zeros(self.Nspec, dtype=np.int)
self.cl_names = []
self.ell_ranges = np.empty(self.Nspec, dtype=np.object)
self.used_sizes = np.zeros(self.Nspec, dtype=np.int)
for i, line in enumerate(lines):
items = line.split()
tp = items[0]
self.cl_names.append(tp)
lmin[i], lmax[i] = [int(x) for x in items[1:]]
if lmax[i] and lmax[i] >= lmin[i]:
n = lmax[i] - lmin[i] + 1
data_vector.append(spectra[lmin[i]:lmax[i] + 1, i])
if tp in use_cl:
if used_ell is not None and (not isinstance(
used_ell, dict) or tp in used_ell):
if isinstance(used_ell, dict):
ells = used_ell[tp]
else:
ells = used_ell
self.ell_ranges[i] = np.array([
L for L in range(lmin[i], lmax[i] + 1)
if L in ells
],
dtype=np.int)
used_indices.append(self.ell_ranges[i] +
(nX - lmin[i]))
else:
used_indices.append(range(nX, nX + n))
self.ell_ranges[i] = range(lmin[i], lmax[i] + 1)
self.used_sizes[i] = len(self.ell_ranges[i])
else:
lmax[i] = -1
nX += n
self.cl_used = np.array([name in use_cl for name in self.cl_names],
dtype=bool)
covfile = ini.relativeFileName('covmat_fiducial')
with open(covfile, "rb") as f:
cov = np.fromfile(f,
dtype=[np.float32,
np.float64]['64.bin' in covfile])
assert (nX**2 == cov.shape[0])
used_indices = np.concatenate(used_indices)
self.data_vector = np.concatenate(data_vector)[used_indices]
self.cov = cov.reshape(nX, nX)[np.ix_(used_indices,
used_indices)].astype(np.float64)
if not silent:
for name, mn, mx in zip(self.cl_names, lmin, lmax):
if name in use_cl:
print(name, mn, mx)
print('Number of data points: %s' % self.cov.shape[0])
self.lmax = lmax
self.lmin = lmin
max_l = np.max(self.lmax)
self.ls = np.arange(max_l + 1)
self.llp1 = self.ls * (self.ls + 1)
if np.any(self.cl_used[:4]):
pivot = 3000
self.sz_143 = self.read_normalized(
ini.relativeFileName('sz143file'), pivot)[:max_l + 1]
self.ksz = self.read_normalized(ini.relativeFileName('kszfile'),
pivot)[:max_l + 1]
self.tszxcib = self.read_normalized(
ini.relativeFileName('tszxcibfile'), pivot)[:max_l + 1]
self.cib_217 = self.read_normalized(
ini.relativeFileName('cib217file'), pivot)[:max_l + 1]
self.dust = np.vstack(
(self.read_normalized(
ini.relativeFileName('dust100file'))[:max_l + 1],
self.read_normalized(
ini.relativeFileName('dust143file'))[:max_l + 1],
self.read_normalized(
ini.relativeFileName('dust217file'))[:max_l + 1],
self.read_normalized(
ini.relativeFileName('dust143x217file'))[:max_l + 1]))
self.lnrat = self.ls * 0
l_min = np.min(lmin[self.cl_used])
self.lnrat[l_min:] = np.log(self.ls[l_min:] / np.float64(pivot))
import hashlib
cache_file = self.dataset_file.replace(
'.dataset', '_covinv_%s.npy' %
hashlib.md5(str(ini.params).encode('utf8')).hexdigest())
if use_cache and os.path.exists(cache_file):
self.covinv = np.load(cache_file).astype(np.float64)
else:
self.covinv = np.linalg.inv(self.cov)
if use_cache: np.save(cache_file, self.covinv.astype(np.float32))
