def estimateErrorFromRandoms(randomsDir, tag=''):
"""
@brief estimate the errors on a given bin from correlations
with random maps
@param randomsDir: the directory storing the results of the random correlations
"""
corr_total = []
for f in os.listdir(randomsDir):
# consider only the pickle files
if "pkl" not in f:
continue
# consider only those files containing 'tag'
if tag not in f:
continue
fig, theta, corr, corr_errs = xcorrUtils.plot1DRealCorr("%s/%s" %(randomsDir, f))
corr_total = np.array(corr_total)
corr_mean = np.mean(corr_total, axis=0)
corr_err = np.std(corr_total, axis = 0)
return corr_err
def plot(params):
"""
@brief plot a real space correlation measured from data and the corresponding theory
prediction
@param params: dictionary containing the following:
powerSpectrum: name of file containing spectrum to use for theory curve (str)
dataCorr: name of pickle file containing 2D correlation from data (str)
filter1_path: path of file containing filter #1 (str)
filter1FromFunction: tuple containing ('function_name', functionKWArgs)
filter2_path: path of file containing filter #2 (str)
filter2FromFunction: tuple containing ('function_name', functionKWArgs)
"""
# load the data spectrum to plot
ell, cl = np.loadtxt(params['powerSpectrum'], usecols=[0,1], unpack=True)
# make the first filter, if provided
F1 = np.ones(len(ell))
# first, read from a path, if given
if params['filter1_path'] is not None:
x, F1 = np.loadtxt(params['filter1_path'], unpack=True)
f = InterpolatedUnivariateSpline(x, F1)
F1 = f(ell)
# then try to make the filter from a function
elif params['filter1FromFunction'] is not None:
func = xcorrUtils.__stringToFunction(params['filter1FromFunction'][0])
F1 = func(ell, **params['filter1FromFunction'][1])
# make the second filter, if provided
F2 = np.ones(len(ell))
# first, read from a path, if given
if params['filter2_path'] is not None:
x, F2 = np.loadtxt(params['filter2_path'], unpack=True)
f = InterpolatedUnivariateSpline(x, F2)
F2 = f(ell)
# then try to make the filter from a function
elif params['filter2FromFunction'] is not None:
func = xcorrUtils.__stringToFunction(params['filter2FromFunction'][0])
F1 = func(ell, **params['filter2FromFunction'][1])
# compute the expected correlation given the filter and power spectrum
theta_th, corr_th = xcorrTheory.realSpaceCorrFromTheory(ell, cl, Fell_1=F1, Fell_2=F2)
# plot the binned 1D correlation from the 2D data
fig, theta, corr, corr_errs = xcorrUtils.plot1DRealCorr(params['dataCorr'])
# also plot the expected curve from theory
curr_ax = fig_gca()
curr_ax.plot(theta_th*60, corr_th, ls='--')
# add a horizontal axis line
plt.axhline(y=0, c='k')
plt.show()
return 0
