Merge pull request #1848 from NNPDF/black_isort

run black isort on recently edited files

validphys2/src/validphys/config.py

@@ -53,15 +53,7 @@
 class Environment(Environment):
     """Container for information to be filled at run time"""
 
-    def __init__(
-        self,
-        *,
-        this_folder=None,
-        net=True,
-        upload=False,
-        dry=False,
-        **kwargs,
-    ):
+    def __init__(self, *, this_folder=None, net=True, upload=False, dry=False, **kwargs):
         if this_folder:
             self.this_folder = pathlib.Path(this_folder)
 
@@ -112,10 +104,7 @@ def parse_func(self, item, **kwargs):
     origsig = inspect.signature(f)
     parse_func = functools.wraps(f)(parse_func)
 
-    params = [
-        *list(currsig.parameters.values())[:2],
-        *list(origsig.parameters.values())[2:],
-    ]
+    params = [*list(currsig.parameters.values())[:2], *list(origsig.parameters.values())[2:]]
 
     parse_func.__signature__ = inspect.Signature(parameters=params)
 
@@ -143,9 +132,7 @@ def parse_pdf(self, name: str):
             pdf = self.loader.check_pdf(name)
         except PDFNotFound as e:
             raise ConfigError(
-                "Bad PDF: {} not installed".format(name),
-                name,
-                self.loader.available_pdfs,
+                "Bad PDF: {} not installed".format(name), name, self.loader.available_pdfs
             ) from e
         except LoaderError as e:
             raise ConfigError(e) from e
@@ -166,10 +153,7 @@ def parse_theoryid(self, theoryID: (str, int)):
             return self.loader.check_theoryID(theoryID)
         except LoaderError as e:
             raise ConfigError(
-                str(e),
-                theoryID,
-                self.loader.available_theories,
-                display_alternatives="all",
+                str(e), theoryID, self.loader.available_theories, display_alternatives="all"
             )
 
     def parse_use_cuts(self, use_cuts: (bool, str)):
@@ -208,9 +192,7 @@ def produce_replicas(self, nreplica: int):
         return NSList(range(1, nreplica + 1), nskey="replica")
 
     def produce_inclusive_use_scalevar_uncertainties(
-        self,
-        use_scalevar_uncertainties: bool = False,
-        point_prescription: (str, None) = None,
+        self, use_scalevar_uncertainties: bool = False, point_prescription: (str, None) = None
     ):
         """Whether to use a scale variation uncertainty theory covmat.
         Checks whether a point prescription is included in the runcard and if so
@@ -249,11 +231,7 @@ def produce_fitcontextwithcuts(self, fit, fitinputcontext):
         theoryid = fitinputcontext["theoryid"]
         data_input = fitinputcontext["data_input"]
 
-        return {
-            "dataset_inputs": data_input,
-            "theoryid": theoryid,
-            "use_cuts": CutsPolicy.FROMFIT,
-        }
+        return {"dataset_inputs": data_input, "theoryid": theoryid, "use_cuts": CutsPolicy.FROMFIT}
 
     def produce_fitenvironment(self, fit, fitinputcontext):
         """Like fitcontext, but additionally forcing various other
@@ -420,12 +398,7 @@ def parse_dataset_input(self, dataset: Mapping):
             # Abuse ConfigError to get the suggestions.
             log.warning(ConfigError(f"Key '{k}' in dataset_input not known.", k, known_keys))
         return DataSetInput(
-            name=name,
-            sys=sysnum,
-            cfac=cfac,
-            frac=frac,
-            weight=weight,
-            custom_group=custom_group,
+            name=name, sys=sysnum, cfac=cfac, frac=frac, weight=weight, custom_group=custom_group
         )
 
     def parse_use_fitcommondata(self, do_use: bool):
@@ -440,10 +413,7 @@ def produce_commondata(self, *, dataset_input, use_fitcommondata=False, fit=None
         sysnum = dataset_input.sys
         try:
             return self.loader.check_commondata(
-                setname=name,
-                sysnum=sysnum,
-                use_fitcommondata=use_fitcommondata,
-                fit=fit,
+                setname=name, sysnum=sysnum, use_fitcommondata=use_fitcommondata, fit=fit
             )
         except DataNotFoundError as e:
             raise ConfigError(str(e), name, self.loader.available_datasets) from e
@@ -529,13 +499,7 @@ def _produce_similarity_cuts(self, commondata):
         matched_cuts = self._produce_matched_cuts(commondata)
         inps = []
         for i, ns in enumerate(nss):
-            with self.set_context(
-                ns=self._curr_ns.new_child(
-                    {
-                        **ns,
-                    }
-                )
-            ):
+            with self.set_context(ns=self._curr_ns.new_child({**ns})):
                 # TODO: find a way to not duplicate this and use a dict
                 # instead of a linear search
                 _, dins = self.parse_from_(None, "dataset_inputs", write=False)
@@ -665,10 +629,7 @@ def produce_experiment_from_input(self, experiment_input, theoryid, use_cuts, fi
         input. NOTE: This might be deprecated in the future."""
         return {
             "experiment": self.parse_experiment(
-                experiment_input.as_dict(),
-                theoryid=theoryid,
-                use_cuts=use_cuts,
-                fit=fit,
+                experiment_input.as_dict(), theoryid=theoryid, use_cuts=use_cuts, fit=fit
             )
         }
 
@@ -683,9 +644,7 @@ def produce_sep_mult(self, separate_multiplicative=None):
 
     @configparser.explicit_node
     def produce_dataset_inputs_fitting_covmat(
-        self,
-        theory_covmat_flag=False,
-        use_thcovmat_in_fitting=False,
+        self, theory_covmat_flag=False, use_thcovmat_in_fitting=False
     ):
         """
         Produces the correct covmat to be used in fitting_data_dict according
@@ -701,10 +660,7 @@ def produce_dataset_inputs_fitting_covmat(
 
     @configparser.explicit_node
     def produce_dataset_inputs_sampling_covmat(
-        self,
-        sep_mult,
-        theory_covmat_flag=False,
-        use_thcovmat_in_sampling=False,
+        self, sep_mult, theory_covmat_flag=False, use_thcovmat_in_sampling=False
     ):
         """
         Produces the correct covmat to be used in make_replica according
@@ -760,11 +716,7 @@ def produce_loaded_theory_covmat(
                     raise ValueError("More than one theory_covmat file in folder tables")
         theorypath = output_path / "tables" / generic_path
         theory_covmat = pd.read_csv(
-            theorypath,
-            index_col=[0, 1, 2],
-            header=[0, 1, 2],
-            sep="\t|,",
-            engine="python",
+            theorypath, index_col=[0, 1, 2], header=[0, 1, 2], sep="\t|,", engine="python"
         ).fillna(0)
         # change ordering according to exp_covmat (so according to runcard order)
         tmp = theory_covmat.droplevel(0, axis=0).droplevel(0, axis=1)
@@ -877,12 +829,7 @@ def produce_matched_datasets_from_dataspecs(self, dataspecs):
             inner_spec_list = inres["dataspecs"] = []
             for ispec, spec in enumerate(dataspecs):
                 # Passing spec by referene
-                d = ChainMap(
-                    {
-                        "dataset_input": all_names[ispec][k],
-                    },
-                    spec,
-                )
+                d = ChainMap({"dataset_input": all_names[ispec][k]}, spec)
                 inner_spec_list.append(d)
             res.append(inres)
         res.sort(key=lambda x: (x["process"], x["dataset_name"]))
@@ -906,12 +853,7 @@ def produce_matched_positivity_from_dataspecs(self, dataspecs):
             l = inres["dataspecs"] = []
             for ispec, spec in enumerate(dataspecs):
                 # Passing spec by referene
-                d = ChainMap(
-                    {
-                        "posdataset": all_names[ispec][k],
-                    },
-                    spec,
-                )
+                d = ChainMap({"posdataset": all_names[ispec][k]}, spec)
                 l.append(d)
             res.append(inres)
         res.sort(key=lambda x: (x["posdataset_name"]))
@@ -993,11 +935,7 @@ def parse_use_t0(self, do_use_t0: bool):
         return do_use_t0
 
     # TODO: Find a good name for this
-    def produce_t0set(
-        self,
-        t0pdfset=None,
-        use_t0=False,
-    ):
+    def produce_t0set(self, t0pdfset=None, use_t0=False):
         """Return the t0set if use_t0 is True and None otherwise. Raises an
         error if t0 is requested but no t0set is given.
         """
@@ -1408,11 +1346,8 @@ def produce_defaults(
         level and those inside a ``filter_defaults`` mapping.
         """
         from validphys.filters import default_filter_settings_input
-        if (
-            q2min is not None
-            and "q2min" in filter_defaults
-            and q2min != filter_defaults["q2min"]
-        ):
+
+        if q2min is not None and "q2min" in filter_defaults and q2min != filter_defaults["q2min"]:
             raise ConfigError("q2min defined multiple times with different values")
         if w2min is not None and "w2min" in filter_defaults and w2min != filter_defaults["w2min"]:
             raise ConfigError("w2min defined multiple times with different values")
@@ -1446,15 +1381,10 @@ def produce_defaults(
         if maxTau is not None and defaults_loaded:
             log.warning("Using maxTau from runcard")
             filter_defaults["maxTau"] = maxTau
-            
+
         return filter_defaults
 
-    def produce_data(
-        self,
-        data_input,
-        *,
-        group_name="data",
-    ):
+    def produce_data(self, data_input, *, group_name="data"):
         """A set of datasets where correlated systematics are taken
         into account
         """
@@ -1466,9 +1396,7 @@ def produce_data(
         return DataGroupSpec(name=group_name, datasets=datasets, dsinputs=data_input)
 
     def _parse_data_input_from_(
-        self,
-        parse_from_value: (str, type(None)),
-        additional_context: (dict, type(None)) = None,
+        self, parse_from_value: (str, type(None)), additional_context: (dict, type(None)) = None
     ):
         """Function which parses the ``data_input`` from a namespace. Usage
         is similar to :py:meth:`self.parse_from_` except this function bridges
@@ -1588,11 +1516,7 @@ def produce_processed_metadata_group(self, processed_data_grouping, metadata_gro
             return processed_data_grouping
         return metadata_group
 
-    def produce_group_dataset_inputs_by_metadata(
-        self,
-        data_input,
-        processed_metadata_group,
-    ):
+    def produce_group_dataset_inputs_by_metadata(self, data_input, processed_metadata_group):
         """Take the data and the processed_metadata_group key and attempt
         to group the data, returns a list where each element specifies the data_input
         for a single group and the group_name

validphys2/src/validphys/scalevariations/pointprescriptions.yaml

@@ -13,9 +13,9 @@
 'n3lo ad covmat': ['(0, 0, 0, 0)','(1, 0, 0, 0)','(2, 0, 0, 0)','(3, 0, 0, 0)','(4, 0, 0, 0)','(5, 0, 0, 0)','(6, 0, 0, 0)','(7, 0, 0, 0)','(8, 0, 0, 0)','(9, 0, 0, 0)','(10, 0, 0, 0)','(11, 0, 0, 0)','(12, 0, 0, 0)','(13, 0, 0, 0)','(14, 0, 0, 0)','(15, 0, 0, 0)','(16, 0, 0, 0)','(17, 0, 0, 0)','(18, 0, 0, 0)','(19, 0, 0, 0)','(0, 1, 0, 0)','(0, 2, 0, 0)','(0, 3, 0, 0)','(0, 4, 0, 0)','(0, 5, 0, 0)','(0, 6, 0, 0)','(0, 7, 0, 0)','(0, 8, 0, 0)','(0, 9, 0, 0)','(0, 10, 0, 0)','(0, 11, 0, 0)','(0, 12, 0, 0)','(0, 13, 0, 0)','(0, 14, 0, 0)','(0, 15, 0, 0)','(0, 16, 0, 0)','(0, 17, 0, 0)','(0, 18, 0, 0)','(0, 19, 0, 0)','(0, 20, 0, 0)','(0, 21, 0, 0)','(0, 0, 1, 0)','(0, 0, 2, 0)','(0, 0, 3, 0)','(0, 0, 4, 0)','(0, 0, 5, 0)','(0, 0, 6, 0)','(0, 0, 7, 0)','(0, 0, 8, 0)','(0, 0, 9, 0)','(0, 0, 10, 0)','(0, 0, 11, 0)','(0, 0, 12, 0)','(0, 0, 13, 0)','(0, 0, 14, 0)','(0, 0, 15, 0)','(0, 0, 0, 1)','(0, 0, 0, 2)','(0, 0, 0, 3)','(0, 0, 0, 4)','(0, 0, 0, 5)','(0, 0, 0, 6)']
 # N3LO full IHOU: Anomalous dimension theory covmat + DIS massive coefficient functions
 'n3lo ihou': ['(0, 0, 0, 0)','(1, 0, 0, 0)','(2, 0, 0, 0)','(3, 0, 0, 0)','(4, 0, 0, 0)','(5, 0, 0, 0)','(6, 0, 0, 0)','(7, 0, 0, 0)','(8, 0, 0, 0)','(9, 0, 0, 0)','(10, 0, 0, 0)','(11, 0, 0, 0)','(12, 0, 0, 0)','(13, 0, 0, 0)','(14, 0, 0, 0)','(15, 0, 0, 0)','(16, 0, 0, 0)','(17, 0, 0, 0)','(18, 0, 0, 0)','(19, 0, 0, 0)','(0, 1, 0, 0)','(0, 2, 0, 0)','(0, 3, 0, 0)','(0, 4, 0, 0)','(0, 5, 0, 0)','(0, 6, 0, 0)','(0, 7, 0, 0)','(0, 8, 0, 0)','(0, 9, 0, 0)','(0, 10, 0, 0)','(0, 11, 0, 0)','(0, 12, 0, 0)','(0, 13, 0, 0)','(0, 14, 0, 0)','(0, 15, 0, 0)','(0, 16, 0, 0)','(0, 17, 0, 0)','(0, 18, 0, 0)','(0, 19, 0, 0)','(0, 20, 0, 0)','(0, 21, 0, 0)','(0, 0, 1, 0)','(0, 0, 2, 0)','(0, 0, 3, 0)','(0, 0, 4, 0)','(0, 0, 5, 0)','(0, 0, 6, 0)','(0, 0, 7, 0)','(0, 0, 8, 0)','(0, 0, 9, 0)','(0, 0, 10, 0)','(0, 0, 11, 0)','(0, 0, 12, 0)','(0, 0, 13, 0)','(0, 0, 14, 0)','(0, 0, 15, 0)','(0, 0, 0, 1)','(0, 0, 0, 2)','(0, 0, 0, 3)','(0, 0, 0, 4)','(0, 0, 0, 5)','(0, 0, 0, 6)','(-1, -1)','(1, 1)']
-# N3LO full IHOU + 7 point scale variations 
+# N3LO full IHOU + 7 point scale variations
 'n3lo full thcovmat': ['(0, 0, 0, 0)','(1, 0, 0, 0)','(2, 0, 0, 0)','(3, 0, 0, 0)','(4, 0, 0, 0)','(5, 0, 0, 0)','(6, 0, 0, 0)','(7, 0, 0, 0)','(8, 0, 0, 0)','(9, 0, 0, 0)','(10, 0, 0, 0)','(11, 0, 0, 0)','(12, 0, 0, 0)','(13, 0, 0, 0)','(14, 0, 0, 0)','(15, 0, 0, 0)','(16, 0, 0, 0)','(17, 0, 0, 0)','(18, 0, 0, 0)','(19, 0, 0, 0)','(0, 1, 0, 0)','(0, 2, 0, 0)','(0, 3, 0, 0)','(0, 4, 0, 0)','(0, 5, 0, 0)','(0, 6, 0, 0)','(0, 7, 0, 0)','(0, 8, 0, 0)','(0, 9, 0, 0)','(0, 10, 0, 0)','(0, 11, 0, 0)','(0, 12, 0, 0)','(0, 13, 0, 0)','(0, 14, 0, 0)','(0, 15, 0, 0)','(0, 16, 0, 0)','(0, 17, 0, 0)','(0, 18, 0, 0)','(0, 19, 0, 0)','(0, 20, 0, 0)','(0, 21, 0, 0)','(0, 0, 1, 0)','(0, 0, 2, 0)','(0, 0, 3, 0)','(0, 0, 4, 0)','(0, 0, 5, 0)','(0, 0, 6, 0)','(0, 0, 7, 0)','(0, 0, 8, 0)','(0, 0, 9, 0)','(0, 0, 10, 0)','(0, 0, 11, 0)','(0, 0, 12, 0)','(0, 0, 13, 0)','(0, 0, 14, 0)','(0, 0, 15, 0)','(0, 0, 0, 1)','(0, 0, 0, 2)','(0, 0, 0, 3)','(0, 0, 0, 4)','(0, 0, 0, 5)','(0, 0, 0, 6)','(2, 1)', '(0.5, 1)', '(1, 2)', '(1, 0.5)', '(2, 2)', '(0.5, 0.5)','(-1, -1)','(1, 1)']
-# N3LO full IHOU + 3 point scale variations for datasets with no N3LO correcttions 
+# N3LO full IHOU + 3 point scale variations for datasets with no N3LO correcttions
 'n3lo 3pt missing': ['(0, 0, 0, 0)','(1, 0, 0, 0)','(2, 0, 0, 0)','(3, 0, 0, 0)','(4, 0, 0, 0)','(5, 0, 0, 0)','(6, 0, 0, 0)','(7, 0, 0, 0)','(8, 0, 0, 0)','(9, 0, 0, 0)','(10, 0, 0, 0)','(11, 0, 0, 0)','(12, 0, 0, 0)','(13, 0, 0, 0)','(14, 0, 0, 0)','(15, 0, 0, 0)','(16, 0, 0, 0)','(17, 0, 0, 0)','(18, 0, 0, 0)','(19, 0, 0, 0)','(0, 1, 0, 0)','(0, 2, 0, 0)','(0, 3, 0, 0)','(0, 4, 0, 0)','(0, 5, 0, 0)','(0, 6, 0, 0)','(0, 7, 0, 0)','(0, 8, 0, 0)','(0, 9, 0, 0)','(0, 10, 0, 0)','(0, 11, 0, 0)','(0, 12, 0, 0)','(0, 13, 0, 0)','(0, 14, 0, 0)','(0, 15, 0, 0)','(0, 16, 0, 0)','(0, 17, 0, 0)','(0, 18, 0, 0)','(0, 19, 0, 0)','(0, 20, 0, 0)','(0, 21, 0, 0)','(0, 0, 1, 0)','(0, 0, 2, 0)','(0, 0, 3, 0)','(0, 0, 4, 0)','(0, 0, 5, 0)','(0, 0, 6, 0)','(0, 0, 7, 0)','(0, 0, 8, 0)','(0, 0, 9, 0)','(0, 0, 10, 0)','(0, 0, 11, 0)','(0, 0, 12, 0)','(0, 0, 13, 0)','(0, 0, 14, 0)','(0, 0, 15, 0)','(0, 0, 0, 1)','(0, 0, 0, 2)','(0, 0, 0, 3)','(0, 0, 0, 4)','(0, 0, 0, 5)','(0, 0, 0, 6)', '(1, 0.5 missing)', '(1, 2 missing)','(-1, -1)','(1, 1)']
 # N3LO full IHOU + 3 point scale variations for hadronic dasasets
 'n3lo 3pt hadronic': ['(0, 0, 0, 0)','(1, 0, 0, 0)','(2, 0, 0, 0)','(3, 0, 0, 0)','(4, 0, 0, 0)','(5, 0, 0, 0)','(6, 0, 0, 0)','(7, 0, 0, 0)','(8, 0, 0, 0)','(9, 0, 0, 0)','(10, 0, 0, 0)','(11, 0, 0, 0)','(12, 0, 0, 0)','(13, 0, 0, 0)','(14, 0, 0, 0)','(15, 0, 0, 0)','(16, 0, 0, 0)','(17, 0, 0, 0)','(18, 0, 0, 0)','(19, 0, 0, 0)','(0, 1, 0, 0)','(0, 2, 0, 0)','(0, 3, 0, 0)','(0, 4, 0, 0)','(0, 5, 0, 0)','(0, 6, 0, 0)','(0, 7, 0, 0)','(0, 8, 0, 0)','(0, 9, 0, 0)','(0, 10, 0, 0)','(0, 11, 0, 0)','(0, 12, 0, 0)','(0, 13, 0, 0)','(0, 14, 0, 0)','(0, 15, 0, 0)','(0, 16, 0, 0)','(0, 17, 0, 0)','(0, 18, 0, 0)','(0, 19, 0, 0)','(0, 20, 0, 0)','(0, 21, 0, 0)','(0, 0, 1, 0)','(0, 0, 2, 0)','(0, 0, 3, 0)','(0, 0, 4, 0)','(0, 0, 5, 0)','(0, 0, 6, 0)','(0, 0, 7, 0)','(0, 0, 8, 0)','(0, 0, 9, 0)','(0, 0, 10, 0)','(0, 0, 11, 0)','(0, 0, 12, 0)','(0, 0, 13, 0)','(0, 0, 14, 0)','(0, 0, 15, 0)','(0, 0, 0, 1)','(0, 0, 0, 2)','(0, 0, 0, 3)','(0, 0, 0, 4)','(0, 0, 0, 5)','(0, 0, 0, 6)', '(1, 0.5 hadronic)', '(1, 2 hadronic)','(-1, -1)','(1, 1)']