# Source code for validphys.covmats_utils

```
"""
covmat_utils.py
Utils functions for constructing covariance matrices from systematics.
Leveraged by :py:mod:`validphys.covmats` which contains relevant
actions/providers.
"""
import numpy as np
import pandas as pd
[docs]
def systematics_matrix(stat_errors: np.array, sys_errors: pd.DataFrame):
"""Basic function to create a systematics matrix , :math:`A`, such that:
.. math::
C = A A^T
Where :math:`C` is the covariance matrix. This is achieved by creating a
block diagonal matrix by adding the uncorrelated systematics in quadrature
then taking the square-root and concatenating the correlated systematics,
schematically:
.. code::python
A = concat([diag(sqrt(A_uncorr.sum(axis=1))), A_corr])
Parameters
----------
stat_errors: np.array
a 1-D array of statistical uncertainties
sys_errors: pd.DataFrame
a dataframe with shape (N_data * N_sys) and systematic name as the
column headers. The uncertainties should be in the same units as the
data.
Notes
-----
This function doesn't contain any logic to ignore certain contributions to
the covmat, if you wanted to not include a particular systematic/set of
systematics i.e all uncertainties with MULT errors, then filter those out
of ``sys_errors`` before passing that to this function.
"""
diagonal = stat_errors**2
is_uncorr = sys_errors.columns.isin(("UNCORR", "THEORYUNCORR"))
diagonal += (sys_errors.loc[:, is_uncorr].to_numpy() ** 2).sum(axis=1)
corr_sys_mat = sys_errors.loc[:, ~is_uncorr].to_numpy()
return np.concatenate((np.diag(np.sqrt(diagonal)), corr_sys_mat), axis=1)
[docs]
def construct_covmat(stat_errors: np.array, sys_errors: pd.DataFrame):
"""Basic function to construct a covariance matrix (covmat), given the
statistical error and a dataframe of systematics.
Errors with name UNCORR or THEORYUNCORR are added in quadrature with
the statistical error to the diagonal of the covmat.
Other systematics are treated as correlated; their covmat contribution is
found by multiplying them by their transpose.
Parameters
----------
stat_errors: np.array
a 1-D array of statistical uncertainties
sys_errors: pd.DataFrame
a dataframe with shape (N_data * N_sys) and systematic name as the
column headers. The uncertainties should be in the same units as the
data.
Notes
-----
This function doesn't contain any logic to ignore certain contributions to
the covmat, if you wanted to not include a particular systematic/set of
systematics i.e all uncertainties with MULT errors, then filter those out
of ``sys_errors`` before passing that to this function.
"""
diagonal = stat_errors**2
is_uncorr = sys_errors.columns.isin(("UNCORR", "THEORYUNCORR"))
diagonal += (sys_errors.loc[:, is_uncorr].to_numpy() ** 2).sum(axis=1)
corr_sys_mat = sys_errors.loc[:, ~is_uncorr].to_numpy()
return np.diag(diagonal) + corr_sys_mat @ corr_sys_mat.T
```