def load_data(root, box):
toret = []
dtype = [('r', float), ('corr', float), ('error', float)]
for kind in ['mono', 'quad']:
data = np.loadtxt(os.path.join(root, '%s_Box%s_rescale.dat' %(kind, box)))
C = np.loadtxt(os.path.join(root, 'covar_%s.dat' %kind)).reshape((data.shape[0], -1))
errs = np.diag(C)**0.5
data = np.concatenate([data, errs[:,None]], axis=1)
meta = {'edges' : make_edges(data[:,0])}
corr = Corr1dDataSet.from_nbkit(data, meta, columns=['r', 'corr', 'error'])
toret.append(corr)
return toret
def load_correlation(filename, mode, usecols=[], mapcols={}, **kwargs):
"""
Load a 1D or 2D correlation measurement and return a `DataSet`
"""
if mode not in ['1d', '2d']:
raise ValueError("`mode` must be on of '1d' or '2d'")
if mode == '1d':
toret = Corr1dDataSet.from_nbkit(*files.Read1DPlainText(filename), **kwargs)
else:
toret = Corr2dDataSet.from_nbkit(*files.Read2DPlainText(filename), **kwargs)
# rename any variables
if len(mapcols):
for old_name in mapcols:
toret.rename_variable(old_name, mapcols[old_name])
# only return certain variables
if len(usecols):
toret = toret[usecols]
return toret
