def load_dataset(self, filename, dataset_params):
from getdist import IniFile
ini = IniFile(filename)
ini.params.update(dataset_params)
self.indices = []
self.used_indices = []
self.used_items = []
self.fullcov = np.loadtxt(ini.relativeFileName('cov_file'))
ntheta = ini.int('num_theta_bins')
self.theta_bins = np.loadtxt(ini.relativeFileName('theta_bins_file'))
self.iintrinsic_alignment_model = ini.string(
'intrinsic_alignment_model')
self.data_types = ini.string('data_types').split()
self.used_types = ini.list('used_data_types', self.data_types)
with open(ini.relativeFileName('data_selection')) as f:
header = f.readline()
assert ('# type bin1 bin2 theta_min theta_max' == header.strip())
lines = f.readlines()
ranges = {}
for tp in self.data_types:
ranges[tp] = np.empty((6, 6), dtype=object)
for line in lines:
items = line.split()
if items[0] in self.used_types:
bin1, bin2 = [int(x) - 1 for x in items[1:3]]
ranges[items[0]][bin1][bin2] = [
np.float64(x) for x in items[3:]
]
self.ranges = ranges
self.nzbins = ini.int('num_z_bins') # for lensing sources
self.nwbins = ini.int('num_gal_bins', 0) # for galaxies
maxbin = max(self.nzbins, self.nwbins)
cov_ix = 0
self.bin_pairs = []
self.data_arrays = []
self.thetas = []
for i, tp in enumerate(self.data_types):
xi = np.loadtxt(ini.relativeFileName('measurements[%s]' % tp))
bin1 = xi[:, 0].astype(np.int) - 1
bin2 = xi[:, 1].astype(np.int) - 1
tbin = xi[:, 2].astype(np.int) - 1
corr = np.empty((maxbin, maxbin), dtype=np.object)
corr[:, :] = None
self.data_arrays.append(corr)
self.bin_pairs.append([])
for f1, f2, ix, dat in zip(bin1, bin2, tbin, xi[:, 3]):
self.indices.append((i, f1, f2, ix))
if not (f1, f2) in self.bin_pairs[i]:
self.bin_pairs[i].append((f1, f2))
corr[f1, f2] = np.zeros(ntheta)
corr[f1, f2][ix] = dat
if ranges[tp][f1, f2] is not None:
mn, mx = ranges[tp][f1, f2]
if self.theta_bins[ix] > mn and self.theta_bins[ix] < mx:
self.thetas.append(self.theta_bins[ix])
self.used_indices.append(cov_ix)
self.used_items.append(self.indices[-1])
cov_ix += 1
nz_source = np.loadtxt(ini.relativeFileName('nz_file'))
self.zmid = nz_source[:, 1]
self.zbin_sp = []
for b in range(self.nzbins):
self.zbin_sp += [
UnivariateSpline(self.zmid, nz_source[:, b + 3], s=0)
]
nz_lens = np.loadtxt(ini.relativeFileName('nz_gal_file'))
assert (np.array_equal(nz_lens[:, 1], self.zmid))
self.zbin_w_sp = []
for b in range(self.nwbins):
self.zbin_w_sp += [
UnivariateSpline(self.zmid, nz_lens[:, b + 3], s=0)
]
self.zmax = self.zmid[-1]
self.kmax = ini.float(
'kmax', 15) # Actually computed, assumes extrapolated beyond that
self._initialize()
def load_dataset(self, filename, dataset_params):
from getdist import IniFile
ini = IniFile(filename)
ini.params.update(dataset_params)
self.indices = []
self.used_indices = []
self.used_items = []
self.fullcov = np.loadtxt(ini.relativeFileName('cov_file'))
ntheta = ini.int('num_theta_bins')
self.theta_bins = np.loadtxt(ini.relativeFileName('theta_bins_file'))
self.iintrinsic_alignment_model = ini.string('intrinsic_alignment_model')
self.data_types = ini.string('data_types').split()
self.used_types = ini.list('used_data_types', self.data_types)
with open(ini.relativeFileName('data_selection')) as f:
header = f.readline()
assert ('# type bin1 bin2 theta_min theta_max' == header.strip())
lines = f.readlines()
ranges = {}
for tp in self.data_types:
ranges[tp] = np.empty((6, 6), dtype=object)
for line in lines:
items = line.split()
if items[0] in self.used_types:
bin1, bin2 = [int(x) - 1 for x in items[1:3]]
ranges[items[0]][bin1][bin2] = [np.float64(x) for x in items[3:]]
self.ranges = ranges
self.nzbins = ini.int('num_z_bins') # for lensing sources
self.nwbins = ini.int('num_gal_bins', 0) # for galaxies
maxbin = max(self.nzbins, self.nwbins)
cov_ix = 0
self.bin_pairs = []
self.data_arrays = []
self.thetas = []
for i, tp in enumerate(self.data_types):
xi = np.loadtxt(ini.relativeFileName('measurements[%s]' % tp))
bin1 = xi[:, 0].astype(np.int) - 1
bin2 = xi[:, 1].astype(np.int) - 1
tbin = xi[:, 2].astype(np.int) - 1
corr = np.empty((maxbin, maxbin), dtype=np.object)
corr[:, :] = None
self.data_arrays.append(corr)
self.bin_pairs.append([])
for f1, f2, ix, dat in zip(bin1, bin2, tbin, xi[:, 3]):
self.indices.append((i, f1, f2, ix))
if not (f1, f2) in self.bin_pairs[i]:
self.bin_pairs[i].append((f1, f2))
corr[f1, f2] = np.zeros(ntheta)
corr[f1, f2][ix] = dat
if ranges[tp][f1, f2] is not None:
mn, mx = ranges[tp][f1, f2]
if self.theta_bins[ix] > mn and self.theta_bins[ix] < mx:
self.thetas.append(self.theta_bins[ix])
self.used_indices.append(cov_ix)
self.used_items.append(self.indices[-1])
cov_ix += 1
nz_source = np.loadtxt(ini.relativeFileName('nz_file'))
self.zmid = nz_source[:, 1]
self.zbin_sp = []
for b in range(self.nzbins):
self.zbin_sp += [UnivariateSpline(self.zmid, nz_source[:, b + 3], s=0)]
nz_lens = np.loadtxt(ini.relativeFileName('nz_gal_file'))
assert (np.array_equal(nz_lens[:, 1], self.zmid))
self.zbin_w_sp = []
for b in range(self.nwbins):
self.zbin_w_sp += [UnivariateSpline(self.zmid, nz_lens[:, b + 3], s=0)]
self.zmax = self.zmid[-1]
self.kmax = ini.float('kmax', 15) # Actually computed, assumes extrapolated beyond that
self._initialize()
