remove mess of fivetheories and seventheories

validphys2/src/validphys/config.py

@@ -1127,14 +1127,13 @@ def produce_loaded_user_covmat_path(
 
     @configparser.explicit_node
     def produce_nnfit_theory_covmat(
-        self,
-        use_thcovmat_in_sampling: bool,
-        use_thcovmat_in_fitting: bool,
-        inclusive_use_scalevar_uncertainties,
-        use_user_uncertainties: bool = False,
+        self, inclusive_use_scalevar_uncertainties, use_user_uncertainties: bool = False
     ):
         """
         Return the theory covariance matrix used in the fit.
+
+        This function is only used in vp-setupfit to store the necessary covmats as .csv files in
+        the tables directory.
         """
         if inclusive_use_scalevar_uncertainties:
             if use_user_uncertainties:
@@ -1624,21 +1623,6 @@ def produce_group_dataset_inputs_by_metadata(self, data_input, processed_metadat
             for name, group in res.items()
         ]
 
-    def produce_fivetheories(self, point_prescription):
-        if point_prescription == "5bar point":
-            return "bar"
-        elif point_prescription == "5 point":
-            return "nobar"
-        return None
-
-    def produce_seventheories(self, point_prescription):
-        if point_prescription == "7 point":
-            # This is None because is the default choice
-            return None
-        elif point_prescription == "7original point":
-            return "original"
-        return None
-
     def produce_group_dataset_inputs_by_experiment(self, data_input):
         return self.produce_group_dataset_inputs_by_metadata(data_input, "experiment")
 

validphys2/src/validphys/theorycovariance/construction.py

@@ -27,12 +27,7 @@
 
 @check_using_theory_covmat
 def theory_covmat_dataset(
-    results,
-    results_central_bytheoryids,
-    use_theorycovmat,  # for the check
-    point_prescription,
-    fivetheories=None,
-    seventheories=None,
+    results, results_central_bytheoryids, use_theorycovmat, point_prescription  # for the check
 ):
     """
     Compute the theory covmat for a collection of theoryids for a single dataset.
@@ -50,9 +45,7 @@ def theory_covmat_dataset(
 
     # Compute the theory contribution to the covmats
     deltas = list(t.central_value - cv for t in theory_results)
-    thcovmat = compute_covs_pt_prescrip(
-        point_prescription, l, "A", deltas, fivetheories=fivetheories, seventheories=seventheories
-    )
+    thcovmat = compute_covs_pt_prescrip(point_prescription, l, "A", deltas)
 
     return thcovmat
 
@@ -242,16 +235,7 @@ def covmat_n3lo_ad(name1, name2, deltas1, deltas2):
     return 1 / norm * s
 
 
-def compute_covs_pt_prescrip(
-    point_prescription,
-    l,
-    name1,
-    deltas1,
-    name2=None,
-    deltas2=None,
-    fivetheories=None,
-    seventheories=None,
-):
+def compute_covs_pt_prescrip(point_prescription, l, name1, deltas1, name2=None, deltas2=None):
     """Utility to compute the covariance matrix by prescription given the
     shifts with respect to the central value for a pair of processes.
 
@@ -275,10 +259,6 @@ def compute_covs_pt_prescrip(
             Process name of the second set of shifts
         deltas2: list(np.ndarray)
             list of shifts for each of the non-central theories
-        fivetheories: str
-            5-point prescription variation
-        seventheories: str
-            7-point prescription variation
     """
     if name2 is None and deltas2 is not None:
         raise ValueError(
@@ -302,15 +282,15 @@ def compute_covs_pt_prescrip(
             s = covmat_3pt(name1, name2, deltas1, deltas2)
     elif l == 5:
         # 5 point --------------------------------------------------------------
-        if fivetheories == "nobar":
+        if point_prescription == "5 point":
             s = covmat_5pt(name1, name2, deltas1, deltas2)
         # 5bar-point -----------------------------------------------------------
         else:
             s = covmat_5barpt(name1, name2, deltas1, deltas2)
     #  ---------------------------------------------------------------------
     elif l == 7:
         # Outdated 7pts implementation: left for posterity ---------------------
-        if seventheories == "original":
+        if point_prescription == "7original point":
             s = covmat_7pt_orig(name1, name2, deltas1, deltas2)
         # 7pt (Gavin) ----------------------------------------------------------
         else:
@@ -361,7 +341,7 @@ def compute_covs_pt_prescrip(
 
 
 @check_correct_theory_combination
-def covs_pt_prescrip(combine_by_type, theoryids, point_prescription, fivetheories, seventheories):
+def covs_pt_prescrip(combine_by_type, theoryids, point_prescription):
     """Produces the sub-matrices of the theory covariance matrix according
     to a point prescription which matches the number of input theories.
     If 5 theories are provided, a scheme 'bar' or 'nobar' must be
@@ -386,14 +366,7 @@ def covs_pt_prescrip(combine_by_type, theoryids, point_prescription, fivetheorie
             central2, *others2 = process_info.preds[name2]
             deltas2 = list(other - central2 for other in others2)
             s = compute_covs_pt_prescrip(
-                point_prescription,
-                len(theoryids),
-                name1,
-                deltas1,
-                name2,
-                deltas2,
-                fivetheories,
-                seventheories,
+                point_prescription, len(theoryids), name1, deltas1, name2, deltas2
             )
             start_locs = (start_proc[name1], start_proc[name2])
             covmats[start_locs] = s

validphys2/src/validphys/theorycovariance/output.py

@@ -2,7 +2,6 @@
 output.py
 Basic tools for plotting theory covariance matrices and their properties.
 """
-from __future__ import generator_stop
 
 import logging
 from math import inf
@@ -195,35 +194,35 @@ def plot_expcorrmat_heatmap(procs_corrmat):
 
 
 @figure
-def plot_normthcovmat_heatmap_custom(theory_normcovmat_custom, theoryids, fivetheories):
+def plot_normthcovmat_heatmap_custom(theory_normcovmat_custom, theoryids, point_prescription):
     """Matrix plot for block diagonal theory covariance matrix by process type"""
     l = len(theoryids)
     if l == 5:
-        if fivetheories == "bar":
+        if point_prescription == "5bar point":
             l = r"$\bar{5}$"
     fig = plot_covmat_heatmap(theory_normcovmat_custom, f"Theory Covariance matrix ({l} pt)")
     return fig
 
 
 @figure
-def plot_thcorrmat_heatmap_custom(theory_corrmat_custom, theoryids, fivetheories):
+def plot_thcorrmat_heatmap_custom(theory_corrmat_custom, theoryids, point_prescription):
     """Matrix plot of the theory correlation matrix, correlations by process type"""
     l = len(theoryids)
     if l == 5:
-        if fivetheories == "bar":
+        if point_prescription == "5bar point":
             l = r"$\bar{5}$"
     fig = plot_corrmat_heatmap(theory_corrmat_custom, f"Theory Correlation matrix ({l} pt)")
     return fig
 
 
 @figure
 def plot_expplusthcorrmat_heatmap_custom(
-    experimentplustheory_corrmat_custom, theoryids, fivetheories
+    experimentplustheory_corrmat_custom, theoryids, point_prescription
 ):
     """Matrix plot of the exp + theory correlation matrix"""
     l = len(theoryids)
     if l == 5:
-        if fivetheories == "bar":
+        if point_prescription == "5bar point":
             l = r"$\bar{5}$"
     fig = plot_corrmat_heatmap(
         experimentplustheory_corrmat_custom, f"Experimental + Theory Correlation Matrix ({l} pt)"
@@ -233,12 +232,12 @@ def plot_expplusthcorrmat_heatmap_custom(
 
 @figure
 def plot_diag_cov_comparison(
-    theory_covmat_custom, procs_covmat, procs_data_values, theoryids, fivetheories
+    theory_covmat_custom, procs_covmat, procs_data_values, theoryids, point_prescription
 ):
     """Plot of sqrt(cov_ii)/|data_i| for cov = exp, theory, exp+theory"""
     l = len(theoryids)
     if l == 5:
-        if fivetheories == "bar":
+        if point_prescription == "5bar point":
             l = r"$\bar{5}$"
     data = np.abs(procs_data_values)
     sqrtdiags_th = np.sqrt(np.diag(theory_covmat_custom)) / data

validphys2/src/validphys/theorycovariance/tests.py

@@ -1,9 +1,7 @@
-# -*- coding: utf-8 -*-
 """
 tests.py
 Tools for testing theory covariance matrices and their properties.
 """
-from __future__ import generator_stop
 
 from collections import namedtuple
 import logging
@@ -36,9 +34,7 @@ def shift_vector(matched_dataspecs_results, process, dataset_name):
     norm_shifts = shifts / norm
     dsnames = np.full(len(shifts), dataset_name, dtype=object)
     processnames = np.full(len(shifts), process, dtype=object)
-    index = pd.MultiIndex.from_arrays(
-        [processnames, dsnames], names=["group", "dataset"]
-    )
+    index = pd.MultiIndex.from_arrays([processnames, dsnames], names=["group", "dataset"])
     return pd.DataFrame({'shifts': norm_shifts, 'norm': norm}, index=index)
 
 
@@ -296,9 +292,8 @@ def ordered_alltheory_vector(alltheory_vector, doubleindex_thcovmat):
 def evals_nonzero_basis(
     ordered_alltheory_vector,
     theory_covmat_custom,
-    fivetheories,
+    point_prescription,
     orthonormalisation: (str, type(None)) = None,
-    seventheories: (str, type(None)) = None,
 ):
     covmat = theory_covmat_custom / (
         np.outer(ordered_alltheory_vector[0], ordered_alltheory_vector[0])
@@ -343,11 +338,11 @@ def evals_nonzero_basis(
     # (mu_0; mu_1, mu_2, ..., mu_p)
     if num_pts == 3:
         xs = vectors_3pt(splitdiffs)
-    elif (num_pts == 5) and (fivetheories == "nobar"):
+    elif (num_pts == 5) and (point_prescription == "5 point"):
         xs = vectors_5pt(splitdiffs)
-    elif (num_pts == 5) and (fivetheories == "bar"):
+    elif (num_pts == 5) and (point_prescription == "5bar point"):
         xs = vectors_5barpt(splitdiffs)
-    elif (num_pts == 7) and (seventheories != "original"):
+    elif (num_pts == 7) and (point_prescription != "7original point"):
         xs = vectors_7pt(splitdiffs)
     elif num_pts == 9:
         xs = vectors_9pt(splitdiffs)
@@ -381,11 +376,13 @@ def projected_condition_num(evals_nonzero_basis):
     cond_num = evals_nonzero_basis[2]
     return cond_num
 
+
 def fnorm_shifts_ordered(concatenated_shx_vector, doubleindex_thcovmat):
     """Shifts vectors ordered as the theory covmat."""
     fnorm_vector = fnorm_shifts_byprocess(concatenated_shx_vector, doubleindex_thcovmat)
     return np.array(sum(fnorm_vector, []))
 
+
 def theory_shift_test(fnorm_shifts_ordered, evals_nonzero_basis):
     """Compares the NNLO-NLO shift, f, with the eigenvectors and eigenvalues of the
     theory covariance matrix, and returns the component of the NNLO-NLO shift
@@ -501,7 +498,9 @@ def projector_eigenvalue_ratio(theory_shift_test):
 
 
 @figure
-def eigenvector_plot(evals_nonzero_basis, fnorm_shifts_ordered, tripleindex_thcovmat_complete, dataspecs):
+def eigenvector_plot(
+    evals_nonzero_basis, fnorm_shifts_ordered, tripleindex_thcovmat_complete, dataspecs
+):
     """Produces a plot of the eigenvectors for the
     projected matrix, transformed back to the data space."""
     evals = evals_nonzero_basis[0][::-1]
@@ -518,7 +517,11 @@ def eigenvector_plot(evals_nonzero_basis, fnorm_shifts_ordered, tripleindex_thco
         eval_3sf = floatformatting.significant_digits(eval.item(), 3)
         evec = pd.DataFrame(evec, index=tripleindex_thcovmat_complete)
         evec = evec.reindex(newindex)
-        ax.plot(-1.0 * (f.values), color="k", label=f"{dataspecs[1]['speclabel']}-{dataspecs[0]['speclabel']} shift")
+        ax.plot(
+            -1.0 * (f.values),
+            color="k",
+            label=f"{dataspecs[1]['speclabel']}-{dataspecs[0]['speclabel']} shift",
+        )
         ax.plot(evec.values, label="Eigenvector")
         ticklocs, ticklabels, startlocs = matrix_plot_labels(evec)
         # Shift startlocs elements 0.5 to left so lines are between indexes
@@ -558,7 +561,12 @@ def deltamiss_plot(
     fmiss_reordered = fmiss.reindex(f.index)
     # Plotting
     fig, ax = plotutils.subplots(figsize=(20, 10))
-    ax.plot(f.values * 100, ".-", label=f"{dataspecs[1]['speclabel']}-{dataspecs[0]['speclabel']} Shift", color="black")
+    ax.plot(
+        f.values * 100,
+        ".-",
+        label=f"{dataspecs[1]['speclabel']}-{dataspecs[0]['speclabel']} Shift",
+        color="black",
+    )
     ax.plot(
         fmiss_reordered.values * 100, ".-", label=r"$\delta_{miss}$" + f" ({l} pt)", color="blue"
     )
@@ -579,7 +587,9 @@ def deltamiss_plot(
 
 def diagdf_theory_covmat(theory_covmat_custom):
     """Return a Dataframe indexed with groups and dataset of the diagonal entry of the theory covmat."""
-    return pd.DataFrame(data=np.diag(theory_covmat_custom.values), index=theory_covmat_custom.index.droplevel(2))
+    return pd.DataFrame(
+        data=np.diag(theory_covmat_custom.values), index=theory_covmat_custom.index.droplevel(2)
+    )
 
 
 def doubleindex_set_byprocess(group_dataset_inputs_by_process):
@@ -596,17 +606,19 @@ def concatenated_shx_vector(shx_vector):
     return pd.concat(shx_vector)
 
 
-def sqrtdiags_thcovmat_byprocess(doubleindex_set_byprocess, diagdf_theory_covmat, concatenated_shx_vector):
+def sqrtdiags_thcovmat_byprocess(
+    doubleindex_set_byprocess, diagdf_theory_covmat, concatenated_shx_vector
+):
     """Ratio of the sqrts of the diagonal entries of the theory covmat to the normalization entries of the shift vectors, ordered by process."""
     sqrtdiags = []
     for index in doubleindex_set_byprocess:
         if isinstance(concatenated_shx_vector.loc[index[0]].loc[index[1]].norm, np.float64):
             sqrtdiags.append(
-            list(
-                np.sqrt(diagdf_theory_covmat.loc[index[0]].loc[index[1]].values)
-                / concatenated_shx_vector.loc[index[0]].loc[index[1]].norm
+                list(
+                    np.sqrt(diagdf_theory_covmat.loc[index[0]].loc[index[1]].values)
+                    / concatenated_shx_vector.loc[index[0]].loc[index[1]].norm
+                )
             )
-        )
         else:
             sqrtdiags.append(
                 list(
@@ -634,16 +646,18 @@ def ticklocs_thcovmat(theory_covmat_custom):
 
 @figure
 def shift_diag_cov_comparison(
-    sqrtdiags_thcovmat_byprocess, fnorm_shifts_byprocess, point_prescription, ticklocs_thcovmat, dataspecs
+    sqrtdiags_thcovmat_byprocess,
+    fnorm_shifts_byprocess,
+    point_prescription,
+    ticklocs_thcovmat,
+    dataspecs,
 ):
     """Plot of the comparison of a shift between two pertubative order and the diagonal entries of the theory covmat, both normalized to the first of the two perturbative orders."""
     # Concatenation of the arrays
     fnorm_concat = sum(fnorm_shifts_byprocess, [])
     sqrtdiags_concat = sum(sqrtdiags_thcovmat_byprocess, [])
     fig, ax = plotutils.subplots(figsize=(20, 10))
-    ax.plot(
-        np.array(sqrtdiags_concat), ".-", label=f"MHOU ({point_prescription})", color="red"
-    )
+    ax.plot(np.array(sqrtdiags_concat), ".-", label=f"MHOU ({point_prescription})", color="red")
     ax.plot(-np.array(sqrtdiags_concat), ".-", color="red")
     ax.plot(
         -np.array(fnorm_concat),

validphys2/src/validphys/theorycovariance/theorycovarianceutils.py

@@ -14,7 +14,7 @@
 
 
 def check_correct_theory_combination_internal(
-    theoryids, fivetheories, point_prescription: (str, type(None)) = None
+    theoryids, point_prescription: (str, type(None)) = None
 ):
     """Checks that a valid theory combination corresponding to an existing
     prescription has been inputted"""
@@ -40,18 +40,10 @@ def check_correct_theory_combination_internal(
             correct_xifs = [1.0, 2.0, 0.5]
             correct_xirs = [1.0, 2.0, 0.5]
     elif l == 5:
-        check(
-            fivetheories is not None,
-            "For five input theories a prescription bar or nobar"
-            "for the flag fivetheories must be specified.",
-        )
-        check(
-            fivetheories in opts, "Invalid choice of prescription for 5 points", fivetheories, opts
-        )
-        if fivetheories == "nobar":
+        if point_prescription == "5 point":
             correct_xifs = [1.0, 2.0, 0.5, 1.0, 1.0]
             correct_xirs = [1.0, 1.0, 1.0, 2.0, 0.5]
-        elif fivetheories == "bar":
+        elif point_prescription == "5bar point":
             correct_xifs = [1.0, 2.0, 0.5, 2.0, 0.5]
             correct_xirs = [1.0, 2.0, 0.5, 0.5, 2.0]
     elif l == 7: