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fastismore

Fast importance sampling for model robustness evaluation. Tools for DES 3x2pt extensions.

Installation

pip install git+https://github.com/des-science/fastismore

How to use

  1. Run your favorite sampler in cosmosis with
extra_output = ... sigma_crit_inv_lens_source/sigma_crit_inv_1_1 sigma_crit_inv_lens_source/sigma_crit_inv_1_2 ... data_vector/2pt_theory#639

where #639 should be replaced with the size of your data vector after scale cuts, and the sigma_crit_inv_i_j factors should include all combinations of lens bin i with source bin j.

  1. Run fastis-sample to compute importance weights for the new data vector.

  2. Plot results using fastis-plot or your favorite script.

If you prefer to work in a notebook environment, chains can be loaded as in the following example:

import fastismore
import fastismore.plot

baseline = fastismore.Chain('baseline_chain.txt')
contaminated = fastismore.ImportanceChain('importance_weights.txt', baseline)

fastismore.plot.plot_2d(param1, param2, [baseline, contaminted], truth, labels, sigma=0.3))

For more use cases, check the examples directory.