Module error_analysis

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Module for performing Monte Carlo simulations for error analysis.

Functions

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covariance_matrix(epsrel=0.0, verbosity=2)
Estimate model parameter errors via the covariance matrix technique.

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monte_carlo_create_data(method=None, distribution=None, fixed_error=None)
Function for creating simulation data.

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monte_carlo_error_analysis()
Function for calculating errors from the Monte Carlo simulations.

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monte_carlo_initial_values()
Set the initial simulation parameter values.

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monte_carlo_off()
Turn simulations off.

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monte_carlo_on()
Turn simulations on.

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monte_carlo_select_all_sims(number=None, all_select_sim=None)
Set the select flag of all simulations of all models to one.

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monte_carlo_setup(number=None, all_select_sim=None)
Store the Monte Carlo simulation number.

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Variables

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__package__ = 'pipe_control'

Imports: diag, ndarray, sqrt, gauss, statistics, RelaxError, check_pipe, return_api

Function Details

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covariance_matrix(epsrel=0.0, verbosity=2)

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Estimate model parameter errors via the covariance matrix technique.

Note that the covariance matrix error estimate is always of lower quality than Monte Carlo simulations.

Parameters:

epsrel (float) - Any columns of R which satisfy |R_{kk}| <= epsrel |R_{11}| are considered linearly-dependent and are excluded from the covariance matrix, where the corresponding rows and columns of the covariance matrix are set to zero.
verbosity (int) - The amount of information to print. The higher the value, the greater the verbosity.

monte_carlo_create_data(method=None, distribution=None, fixed_error=None)

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Function for creating simulation data.

Parameters:

method (str) - The type of Monte Carlo simulation to perform.
distribution (str) - Which gauss distribution to draw errors from. Can be: 'measured', 'red_chi2', 'fixed'.
fixed_error (float) - If distribution is set to 'fixed', use this value as the standard deviation for the gauss distribution.

monte_carlo_error_analysis()

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Function for calculating errors from the Monte Carlo simulations.

The standard deviation formula used to calculate the errors is the square root of the bias-corrected variance, given by the formula:

              __________________________
             /   1
   sd  =    /  ----- * sum({Xi - Xav}^2)
          \/   n - 1

where

n is the total number of simulations.
Xi is the parameter value for simulation i.
Xav is the mean parameter value for all simulations.

monte_carlo_select_all_sims(number=None, all_select_sim=None)

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Set the select flag of all simulations of all models to one.

Parameters:

number (int) - The number of Monte Carlo simulations to set up.
all_select_sim (list of lists of bool) - The selection status of the Monte Carlo simulations. The first dimension of this matrix corresponds to the simulation and the second corresponds to the models.

monte_carlo_setup(number=None, all_select_sim=None)

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Store the Monte Carlo simulation number.

Parameters:

number (int) - The number of Monte Carlo simulations to set up.
all_select_sim (list of lists of bool) - The selection status of the Monte Carlo simulations. The first dimension of this matrix corresponds to the simulation and the second corresponds to the instance.