Action to compute a chi2 taking into account pdf errors and plot the chi2 per-replica

Created by: scarlehoff

Chi2 with pdf errors.

I started working on the history-test project for n3fit I talked about in Varenna. In order to have this I want to have something able to compute \chi^{2} adding a covariance matrix which gives information about the PDF error. i.e.,

Where C is the normal covariance matrix and P is the one due to the PDF errors.

This is the quickest solution I could think of to get the table I wanted, i.e., the result is final, the code will not be (I just saw I left some debug code in in the commit)

Plot chi2 per replica.

There is currently an action to compute the chi2 per replica per experiment, but not a corresponding action to plot it. I will also commit an action to plot this as I am interested in having this in the reports.

Example:

This is an example runcard that will produce a

fit: 181101-001-sc
pdf: 181101-001-sc

use_cuts: internal
q2min: 3.49
w2min: 12.5
theoryid: 53

actions_:
    - perexperiment_pdferr_chi2_table

theory:
    from_: fit

experiments:
  - experiment: NMCPD
    datasets:
      - { dataset: NMCPD, frac: 0.5 }

Note: i could not find this done anywhere in vp, if it has been done let me know¡!

validphys2/src/validphys/config.py

@@ -467,6 +467,28 @@ class CoreConfig(configparser.Config):
         return {'experiment':self.parse_experiment(experiment_input.as_dict(),
                 theoryid=theoryid, use_cuts=use_cuts, fit=fit)}
 
+    @configparser.explicit_node
+    def produce_covariance_matrix(self, use_pdferr: bool = False):
+        """Modifies which action is used as covariance_matrix depending on
+        the flag `use_pdferr`
+        """
+        from validphys import results
+        if use_pdferr:
+            return results.pdferr_plus_data_covmat
+        else:
+            return results.data_covmat
+
+    @configparser.explicit_node
+    def produce_experiment_covariance_matrix(self, use_pdferr: bool = False):
+        """Modifies which action is used as experiment_covariance_matrix
+        depending on the flag `use_pdferr`
+        """
+        from validphys import results
+        if use_pdferr:
+            return results.pdferr_plus_experiment_covmat
+        else:
+            return results.experiment_covmat
+
     #TODO: Do this better and elsewhere
     @staticmethod
     def _check_dataspecs_type(dataspecs):