def __init__(self, ds, params):
df1, df2 = ds.df1, ds.df2
self.params = params
self.name = params.NAME
self.query = params.CAT_QUERY
self.do_variance_weighted = params.DO_VARIANCE_WEIGHTED
self.N_objects_in_this_run = len(df1)
self.JK_Ngroups = params.JK_NGROUPS
self.run_resample(df1, df2, self.params)
self.cov11 = JK_tools.getCovMatrix(self.bin_names,
self.kSZ_curveJK_realizations11,
params)
self.cov12 = JK_tools.getCovMatrix(self.bin_names,
self.kSZ_curveJK_realizations12,
params)
self.cov22 = JK_tools.getCovMatrix(self.bin_names,
self.kSZ_curveJK_realizations22,
params)
self.corr11 = JK_tools.getCorrMatrix(self.bin_names,
self.kSZ_curveJK_realizations11)
self.corr12 = JK_tools.getCorrMatrix(self.bin_names,
self.kSZ_curveJK_realizations12)
self.corr22 = JK_tools.getCorrMatrix(self.bin_names,
self.kSZ_curveJK_realizations22)
def test_getCorrMatrix():
bin_names = ['0 - 5', '5 - 10', '10 - 15', '15 - 20']
pests = np.random.random(size=[50, 4])
corr = JK_tools.getCorrMatrix(bin_names, pests).values
corr_numpy = np.corrcoef(pests.T)
chi_sq = ((corr-corr_numpy)**2).flatten().sum()
assert chi_sq < 1e-10
def __init__(self, df, params, distributed=True):
self.params = params
self.name = params.NAME
self.query = params.CAT_QUERY
self.do_variance_weighted = params.DO_VARIANCE_WEIGHTED
self.N_objects_in_this_run = len(df)
self.JK_Ngroups = params.JK_NGROUPS
self.runJK(df, self.params, distributed)
if 'tiled' in self.params.JK_RESAMPLING_METHOD:
self.JK_Ngroups = self.kSZ_curveJK_realizations.shape[0]
self.cov = JK_tools.getCovMatrix(self.bin_names,
self.kSZ_curveJK_realizations,
params)
self.corr = JK_tools.getCorrMatrix(self.bin_names,
self.kSZ_curveJK_realizations)