move separate_multiplicative key outside sampling key in runcard

extra_tests/regression_fits/trainable_prepro.yml

@@ -35,8 +35,7 @@ mcseed: 1
 
 load: "weights.weights.h5"
 
-sampling:
-  separate_multiplicative: True
+separate_multiplicative: True
 
 parameters: # This defines the parameter dictionary that is passed to the Model Trainer
   nodes_per_layer: [15, 10, 8]

n3fit/runcards/examples/Basic_feature_scaling.yml

@@ -22,8 +22,7 @@ datacuts:
 ############################################################
 theory:
   theoryid: 708       # database id
-sampling:
-  separate_multiplicative: true
+separate_multiplicative: True
 
 ############################################################
 trvlseed: 1

n3fit/runcards/examples/Basic_hyperopt.yml

@@ -40,8 +40,7 @@ datacuts:
 theory:
   theoryid: 708        # database id
 
-sampling:
-  separate_multiplicative: true
+separate_multiplicative: True
 
 ############################################################
 hyperscan_config:

n3fit/runcards/examples/Basic_runcard.yml

@@ -24,9 +24,8 @@ theory:
   theoryid: 708       # database id
 
 resample_negative_pseudodata: True
+separate_multiplicative: True
 
-sampling:
-  separate_multiplicative: true
 parameters: # This defines the parameter dictionary that is passed to the Model Trainer
   nodes_per_layer: [15, 10, 8]
   activation_per_layer: ['sigmoid', 'sigmoid', 'linear']

n3fit/runcards/examples/Basic_runcard_closure_test.yml

@@ -23,8 +23,7 @@ datacuts:
 theory:
   theoryid: 708       # database id
 
-sampling:
-  separate_multiplicative: true
+separate_multiplicative: True
 parameters: # This defines the parameter dictionary that is passed to the Model Trainer
   nodes_per_layer: [15, 10, 8]
   activation_per_layer: ['sigmoid', 'sigmoid', 'linear']

n3fit/runcards/examples/Basic_runcard_parallel.yml

@@ -24,8 +24,7 @@ datacuts:
 theory:
   theoryid: 708        # database id
 
-sampling:
-  separate_multiplicative: true
+separate_multiplicative: True
 ############################################################
 trvlseed: 1
 nnseed: 2

n3fit/runcards/examples/DIS_diagonal_l2reg_example.yml

@@ -39,8 +39,7 @@ datacuts:
 theory:
   theoryid: 708        # database id
 
-sampling:
-  separate_multiplicative: true
+separate_multiplicative: True
 ############################################################
 trvlseed: 1
 nnseed: 2

n3fit/runcards/examples/developing.yml

@@ -52,8 +52,7 @@ datacuts:
 theory:
   theoryid: 40000000
 
-sampling:
-  separate_multiplicative: true
+separate_multiplicative: True
 ############################################################
 trvlseed: 1
 nnseed: 2

n3fit/src/n3fit/scripts/n3fit_exec.py

@@ -76,8 +76,8 @@ def init_output(self):
         # create output folder for the fit
         self.replica_path = self.output_path / "nnfit"
         for replica in self.replicas:
-            path = self.replica_path / "replica_{0}".format(replica)
-            log.info("Creating replica output folder in {0}".format(path))
+            path = self.replica_path / f"replica_{replica}"
+            log.info(f"Creating replica output folder in {path}")
             try:
                 path.mkdir(exist_ok=True)
             except OSError as e:
@@ -177,11 +177,6 @@ def from_yaml(cls, o, *args, **kwargs):
             N3FIT_FIXED_CONFIG['use_scalevar_uncertainties'] = thconfig.get(
                 'use_scalevar_uncertainties', True
             )
-        # Sampling flags
-        if (sam_t0 := file_content.get('sampling')) is not None:
-            N3FIT_FIXED_CONFIG['separate_multiplicative'] = sam_t0.get(
-                'separate_multiplicative', False
-            )
         # Fitting flag
         file_content.update(N3FIT_FIXED_CONFIG)
         return cls(file_content, *args, **kwargs)
@@ -261,7 +256,7 @@ class N3FitApp(App):
     config_class = N3FitConfig
 
     def __init__(self):
-        super(N3FitApp, self).__init__(name="n3fit", providers=N3FIT_PROVIDERS)
+        super().__init__(name="n3fit", providers=N3FIT_PROVIDERS)
 
     @property
     def argparser(self):

n3fit/src/n3fit/tests/regressions/quickcard-sequential.yml

@@ -31,8 +31,7 @@ nnseed: 2
 mcseed: 1
 
 save: weights.weights.h5
-sampling:
-  separate_multiplicative: true
+separate_multiplicative: True
 
 parameters: # This defines the parameter dictionary that is passed to the Model Trainer
   nodes_per_layer: [15, 10, 8]

n3fit/src/n3fit/tests/regressions/quickcard.yml

@@ -34,8 +34,7 @@ nnseed: 2
 mcseed: 1
 
 load: "weights.weights.h5"
-sampling:
-  separate_multiplicative: true
+separate_multiplicative: True
 
 parameters: # This defines the parameter dictionary that is passed to the Model Trainer
   nodes_per_layer: [15, 10, 8]

n3fit/src/n3fit/tests/regressions/quickcard_pol.yml

@@ -32,8 +32,7 @@ nnseed: 2
 mcseed: 1
 
 load: "weights_pol.weights.h5"
-sampling:
-  separate_multiplicative: true
+separate_multiplicative: True
 
 parameters:
   nodes_per_layer: [25, 20, 4]

n3fit/src/n3fit/tests/regressions/quickcard_qed.yml

@@ -34,8 +34,7 @@ nnseed: 2
 mcseed: 1
 
 load: "weights.weights.h5"
-sampling:
-  separate_multiplicative: true
+separate_multiplicative: True
 
 parameters: # This defines the parameter dictionary that is passed to the Model Trainer
   nodes_per_layer: [15, 10, 8]

validphys2/src/validphys/config.py

@@ -701,12 +701,14 @@ def produce_dataset_inputs_fitting_covmat(
             return covmats.dataset_inputs_t0_total_covmat
         return covmats.dataset_inputs_t0_exp_covmat
 
+    def produce_sep_mult(self, separate_multiplicative=False):
+        if separate_multiplicative is False:
+            return False
+        return True
+
     @configparser.explicit_node
     def produce_dataset_inputs_sampling_covmat(
-        self,
-        separate_multiplicative=False,
-        theory_covmat_flag=False,
-        use_thcovmat_in_sampling=False,
+        self, sep_mult=False, theory_covmat_flag=False, use_thcovmat_in_sampling=False
     ):
         """
         Produces the correct covmat to be used in make_replica according
@@ -716,12 +718,12 @@ def produce_dataset_inputs_sampling_covmat(
         from validphys import covmats
 
         if theory_covmat_flag and use_thcovmat_in_sampling:
-            if separate_multiplicative:
+            if sep_mult:
                 return covmats.dataset_inputs_total_covmat_separate
             else:
                 return covmats.dataset_inputs_total_covmat
         else:
-            if separate_multiplicative:
+            if sep_mult:
                 return covmats.dataset_inputs_exp_covmat_separate
             else:
                 return covmats.dataset_inputs_exp_covmat

validphys2/src/validphys/filters.py

@@ -191,27 +191,19 @@ def export_mask(path, mask):
     np.savetxt(path, mask, fmt='%d')
 
 
-def filter_closure_data(filter_path, data, fakepdf, fakenoise, filterseed, separate_multiplicative):
+def filter_closure_data(filter_path, data, fakepdf, fakenoise, filterseed, sep_mult):
     """Filter closure data. In addition to cutting data points, the data is
     generated from an underlying ``fakepdf``, applying a shift to the data
     if ``fakenoise`` is ``True``, which emulates the experimental central values
     being shifted away from the underlying law.
 
     """
     log.info('Filtering closure-test data.')
-    return _filter_closure_data(
-        filter_path, data, fakepdf, fakenoise, filterseed, separate_multiplicative
-    )
+    return _filter_closure_data(filter_path, data, fakepdf, fakenoise, filterseed, sep_mult)
 
 
 def filter_closure_data_by_experiment(
-    filter_path,
-    experiments_data,
-    fakepdf,
-    fakenoise,
-    filterseed,
-    data_index,
-    separate_multiplicative,
+    filter_path, experiments_data, fakepdf, fakenoise, filterseed, data_index, sep_mult
 ):
     """
     Like :py:func:`filter_closure_data` except filters data by experiment.
@@ -228,13 +220,7 @@ def filter_closure_data_by_experiment(
         experiment_index = data_index[data_index.isin([exp.name], level=0)]
         res.append(
             _filter_closure_data(
-                filter_path,
-                exp,
-                fakepdf,
-                fakenoise,
-                filterseed,
-                experiment_index,
-                separate_multiplicative,
+                filter_path, exp, fakepdf, fakenoise, filterseed, experiment_index, sep_mult
             )
         )
 
@@ -285,9 +271,7 @@ def _filter_real_data(filter_path, data):
     return total_data_points, total_cut_data_points
 
 
-def _filter_closure_data(
-    filter_path, data, fakepdf, fakenoise, filterseed, data_index, separate_multiplicative
-):
+def _filter_closure_data(filter_path, data, fakepdf, fakenoise, filterseed, data_index, sep_mult):
     """
     This function is accessed within a closure test only, that is, the fakedata
     namespace has to be True (If fakedata = False, the _filter_real_data function
@@ -352,9 +336,7 @@ def _filter_closure_data(
     if fakenoise:
         # ======= Level 1 closure test =======#
 
-        closure_data = make_level1_data(
-            data, closure_data, filterseed, data_index, separate_multiplicative
-        )
+        closure_data = make_level1_data(data, closure_data, filterseed, data_index, sep_mult)
 
     # ====== write commondata and systype files ======#
     if fakenoise:
@@ -411,7 +393,9 @@ def check_positivity(posdatasets):
     log.info('Verifying positivity tables:')
     for pos in posdatasets:
         pos.load_commondata()
-        log.info(f'{pos.name} checked, {len(pos.cuts.load())}/{pos.commondata.ndata} datapoints passed kinematic cuts.')
+        log.info(
+            f'{pos.name} checked, {len(pos.cuts.load())}/{pos.commondata.ndata} datapoints passed kinematic cuts.'
+        )
 
 
 def check_integrability(integdatasets):

validphys2/src/validphys/pseudodata.py

@@ -126,7 +126,7 @@ def make_replica(
     groups_dataset_inputs_loaded_cd_with_cuts,
     replica_mcseed,
     dataset_inputs_sampling_covmat,
-    separate_multiplicative=False,
+    sep_mult=False,
     genrep=True,
     max_tries=int(1e6),
     resample_negative_pseudodata=True,
@@ -152,7 +152,7 @@ def make_replica(
     dataset_inputs_sampling_covmat: np.array
         Full covmat to be used. It can be either only experimental or also theoretical.
 
-    separate_multiplicative: bool
+    sep_mult: bool
         Specifies whether computing the shifts with the full covmat
         or whether multiplicative errors should be separated
 
@@ -219,9 +219,9 @@ def make_replica(
         pseudodata = cd.central_values.to_numpy()
 
         pseudodatas.append(pseudodata)
-        # Separation of multiplicative errors. If separate_multiplicative is True also the exp_covmat is produced
+        # Separation of multiplicative errors. If sep_mult is True also the exp_covmat is produced
         # without multiplicative errors
-        if separate_multiplicative:
+        if sep_mult:
             mult_errors = cd.multiplicative_errors
             mult_uncorr_errors = mult_errors.loc[:, mult_errors.columns == "UNCORR"].to_numpy()
             mult_corr_errors = mult_errors.loc[:, mult_errors.columns == "CORR"].to_numpy()
@@ -234,7 +234,7 @@ def make_replica(
         else:
             check_positive_masks.append(np.ones_like(pseudodata, dtype=bool))
     # concatenating special multiplicative errors, pseudodatas and positive mask
-    if separate_multiplicative:
+    if sep_mult:
         special_mult_errors = pd.concat(special_mult, axis=0, sort=True).fillna(0).to_numpy()
     all_pseudodata = np.concatenate(pseudodatas, axis=0)
     full_mask = np.concatenate(check_positive_masks, axis=0)
@@ -255,10 +255,10 @@ def make_replica(
 
             mult_shifts.append(mult_shift)
 
-        # If separate_multiplicative is true then the multiplicative shifts were not included in the covmat
+        # If sep_mult is true then the multiplicative shifts were not included in the covmat
         shifts = covmat_sqrt @ rng.normal(size=covmat.shape[1])
         mult_part = 1.0
-        if separate_multiplicative:
+        if sep_mult:
             special_mult = (
                 1 + special_mult_errors * rng.normal(size=(1, special_mult_errors.shape[1])) / 100
             ).prod(axis=1)
@@ -329,7 +329,7 @@ def level0_commondata_wc(data, fakepdf):
     return level0_commondata_instances_wc
 
 
-def make_level1_data(data, level0_commondata_wc, filterseed, data_index, separate_multiplicative):
+def make_level1_data(data, level0_commondata_wc, filterseed, data_index, sep_mult):
     """
     Given a list of Level 0 commondata instances, return the
     same list with central values replaced by Level 1 data.
@@ -395,16 +395,12 @@ def make_level1_data(data, level0_commondata_wc, filterseed, data_index, separat
         use_weights_in_covmat=False,
         norm_threshold=None,
         _list_of_central_values=None,
-        _only_additive=separate_multiplicative,
+        _only_additive=sep_mult,
     )
 
     # ================== generation of Level1 data ======================#
     level1_data = make_replica(
-        level0_commondata_wc,
-        filterseed,
-        covmat,
-        separate_multiplicative=separate_multiplicative,
-        genrep=True,
+        level0_commondata_wc, filterseed, covmat, sep_mult=sep_mult, genrep=True
     )
 
     indexed_level1_data = indexed_make_replica(data_index, level1_data)

validphys2/src/validphys/tests/test_overfit_metric.py

@@ -17,7 +17,7 @@
     "t0pdfset": {"from_": "datacuts"},
     "pdf": {"from_": "fit"},
     "dataset_inputs": {"from_": "fit"},
-    "separate_multiplicative": True
+    "separate_multiplicative": True,
 }
 
 

validphys2/src/validphys/tests/test_pseudodata.py

@@ -7,6 +7,7 @@
 This is used to benchmark the correctness of the pseudodata
 recreation.
 """
+
 from numpy.testing import assert_allclose
 import pandas as pd
 import pytest