Functions
def	convolve (file_name, frac_error=0.1, min_=0., max_=1., log_normal=True)

Variables
	FILE_NAME = sys.argv[1]

	ARGS = map(float, sys.argv[2:])

Detailed Description

Convolve chi-squared in a data file with a fractional theory error
==================================================================

python convolve_with_theory.py <file> <frac-error> <min> <max>

prints (parameter, convolved chi-squared) from a file containing
(parameter, chi-squared).

Function Documentation

◆ convolve()

def convolve_with_theory.convolve	(	file_name,
		frac_error = `0.1`,
		min_ = `0.`,
		max_ = `1.`,
		log_normal = `True`
	)

Convolve chi-squared in a data file with a fractional theory error

Args:
    file_name (str): Data file with columns (parameter, chi-squared).
    frac_error (float, optional): Fractional theory error on the parameter.
    min_ (float, optional): Minimum value of parameter.
    max_ (float, optional): Maximum value of parameter.
    log_normal (bool, optional): Whether to use log-normal or normal error.

Returns:
    list(tuples): List of (parameter, convolved chi-squared)

Definition at line 19 of file convolve_with_theory.py.

 def convolve(file_name, frac_error=0.1, min_=0., max_=1., log_normal=True):
     """
     Convolve chi-squared in a data file with a fractional theory error
 
     Args:
         file_name (str): Data file with columns (parameter, chi-squared).
         frac_error (float, optional): Fractional theory error on the parameter.
         min_ (float, optional): Minimum value of parameter.
         max_ (float, optional): Maximum value of parameter.
         log_normal (bool, optional): Whether to use log-normal or normal error.
 
     Returns:
         list(tuples): List of (parameter, convolved chi-squared)
     """
     # Unpack data
     param, chi_squared = np.loadtxt(file_name, unpack=True)
 
     # Interpolate likelihood function
     like = interp1d(param, np.exp(-0.5 * chi_squared),
         kind='linear', bounds_error=False, fill_value="extrapolate")
 
     # Make prior for true prediction
     def prior(x, mu):
         if log_normal:
             sigma = np.log(1. + frac_error)
             dist = lognorm(sigma, scale=mu)
         else:
             sigma = frac_error * mu
             dist = norm(mu, sigma)
         return dist.pdf(x)
 
     # Make functions for convolution
     integrand = lambda x, mu: like(x) * prior(x, mu)
     convolved = lambda mu: -2. * np.log(
         quad(integrand, min_, max_, args=(mu))[0]
         / quad(prior, min_, max_, args=(mu))[0])
 
     # Perform convolution
     return [(p, convolved(p)) for p in param]
 

Variable Documentation

◆ ARGS

convolve_with_theory.ARGS = map(float, sys.argv[2:])

Definition at line 66 of file convolve_with_theory.py.

◆ FILE_NAME

convolve_with_theory.FILE_NAME = sys.argv[1]

Definition at line 62 of file convolve_with_theory.py.