Merge branch 'master' of https://github.com/NNPDF/nnpdf into HT_thcovmat

NNPDF · May 30, 2024 · e14173c · e14173c
2 parents d0a4b3f + 37f4875
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diff --git a/n3fit/runcards/hyperopt_studies/restricted_search_space_renew_hyperopt.yml b/n3fit/runcards/hyperopt_studies/restricted_search_space_renew_hyperopt.yml
@@ -0,0 +1,284 @@
+#
+# Configuration file for n3fit
+#
+############################################################
+description: "NNPDF4.0 NNLO reduced set for hyperoptimization"
+############################################################
+# frac: training fraction
+dataset_inputs:
+- {dataset: NMC_NC_NOTFIXED_DW_EM-F2, frac: 0.75, variant: legacy}
+- {dataset: NMC_NC_NOTFIXED_P_EM-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: SLAC_NC_NOTFIXED_P_DW_EM-F2, frac: 0.75, variant: legacy}
+- {dataset: SLAC_NC_NOTFIXED_D_DW_EM-F2, frac: 0.75, variant: legacy}
+- {dataset: BCDMS_NC_NOTFIXED_P_DW_EM-F2, frac: 0.75, variant: legacy}
+- {dataset: BCDMS_NC_NOTFIXED_D_DW_EM-F2, frac: 0.75, variant: legacy}
+- {dataset: CHORUS_CC_NOTFIXED_PB_DW_NU-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: CHORUS_CC_NOTFIXED_PB_DW_NB-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: NUTEV_CC_NOTFIXED_FE_DW_NU-SIGMARED, cfac: [MAS], frac: 0.75, variant: legacy}
+- {dataset: NUTEV_CC_NOTFIXED_FE_DW_NB-SIGMARED, cfac: [MAS], frac: 0.75, variant: legacy}
+- {dataset: HERA_NC_318GEV_EM-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: HERA_NC_225GEV_EP-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: HERA_NC_251GEV_EP-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: HERA_NC_300GEV_EP-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: HERA_NC_318GEV_EP-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: HERA_CC_318GEV_EM-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: HERA_CC_318GEV_EP-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: HERA_NC_318GEV_EAVG_CHARM-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: HERA_NC_318GEV_EAVG_BOTTOM-SIGMARED, frac: 0.75, variant: legacy}
+- {dataset: DYE866_Z0_800GEV_DW_RATIO_PDXSECRATIO, frac: 0.75, variant: legacy}
+- {dataset: DYE866_Z0_800GEV_PXSEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: DYE605_Z0_38P8GEV_DW_PXSEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: DYE906_Z0_120GEV_DW_PDXSECRATIO, frac: 0.75, cfac: [ACC], variant: legacy}
+- {dataset: CDF_Z0_1P96TEV_ZRAP, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: D0_Z0_1P96TEV_ZRAP, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: D0_WPWM_1P96TEV_ASY, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_WPWM_7TEV_36PB_ETA, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_Z0_7TEV_36PB_ETA, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_Z0_7TEV_49FB_HIMASS, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_Z0_7TEV_LOMASS_M, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_WPWM_7TEV_46FB_CC-ETA, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_Z0_7TEV_46FB_CC-Y, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_Z0_7TEV_46FB_CF-Y, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_Z0_8TEV_HIMASS_M-Y, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_Z0_8TEV_LOWMASS_M-Y, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_Z0_13TEV_TOT, frac: 0.75, cfac: [NRM], variant: legacy}
+- {dataset: ATLAS_WPWM_13TEV_TOT, frac: 0.75, cfac: [NRM], variant: legacy}
+- {dataset: ATLAS_WJ_JET_8TEV_WP-PT, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_WJ_JET_8TEV_WM-PT, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_Z0J_8TEV_PT-M, frac: 0.75, cfac: [], variant: legacy_10}
+- {dataset: ATLAS_Z0J_8TEV_PT-Y, frac: 0.75, cfac: [], variant: legacy_10}
+- {dataset: ATLAS_TTBAR_7TEV_TOT_X-SEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_TTBAR_8TEV_TOT_X-SEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_TTBAR_13TEV_TOT_X-SEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_TTBAR_8TEV_LJ_DIF_YT-NORM, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_TTBAR_8TEV_LJ_DIF_YTTBAR-NORM, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_TTBAR_8TEV_2L_DIF_YTTBAR-NORM, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_1JET_8TEV_R06_PTY, frac: 0.75, cfac: [], variant: legacy_decorrelated}
+- {dataset: ATLAS_2JET_7TEV_R06_M12Y, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_PH_13TEV_XSEC, frac: 0.75, cfac: [EWK], variant: legacy}
+- {dataset: ATLAS_SINGLETOP_7TEV_TCHANNEL-XSEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_SINGLETOP_13TEV_TCHANNEL-XSEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_SINGLETOP_7TEV_T-Y-NORM, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_SINGLETOP_7TEV_TBAR-Y-NORM, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_SINGLETOP_8TEV_T-RAP-NORM, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: ATLAS_SINGLETOP_8TEV_TBAR-RAP-NORM, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_WPWM_7TEV_ELECTRON_ASY, frac: 0.75, cfac: []}
+- {dataset: CMS_WPWM_7TEV_MUON_ASY, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_Z0_7TEV_DIMUON_2D, frac: 0.75, cfac: []}
+- {dataset: CMS_WPWM_8TEV_MUON_Y, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_Z0J_8TEV_PT-Y, frac: 0.75, cfac: [NRM], variant: legacy_10}
+- {dataset: CMS_2JET_7TEV_M12Y, frac: 0.75, cfac: []}
+- {dataset: CMS_1JET_8TEV_PTY, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_TTBAR_7TEV_TOT_X-SEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_TTBAR_8TEV_TOT_X-SEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_TTBAR_13TEV_TOT_X-SEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_TTBAR_8TEV_LJ_DIF_YTTBAR-NORM, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_TTBAR_5TEV_TOT_X-SEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_TTBAR_8TEV_2L_DIF_MTTBAR-YT-NORM, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_TTBAR_13TEV_2L_DIF_YT, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_TTBAR_13TEV_LJ_2016_DIF_YTTBAR, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_SINGLETOP_7TEV_TCHANNEL-XSEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_SINGLETOP_8TEV_TCHANNEL-XSEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: CMS_SINGLETOP_13TEV_TCHANNEL-XSEC, frac: 0.75, cfac: [], variant: legacy}
+- {dataset: LHCB_Z0_7TEV_DIELECTRON_Y, frac: 0.75, cfac: []}
+- {dataset: LHCB_Z0_8TEV_DIELECTRON_Y, frac: 0.75, cfac: []}
+- {dataset: LHCB_WPWM_7TEV_MUON_Y, frac: 0.75, cfac: [NRM]}
+- {dataset: LHCB_Z0_7TEV_MUON_Y, frac: 0.75, cfac: [NRM]}
+- {dataset: LHCB_WPWM_8TEV_MUON_Y, frac: 0.75, cfac: [NRM]}
+- {dataset: LHCB_Z0_8TEV_MUON_Y, frac: 0.75, cfac: [NRM]}
+- {dataset: LHCB_Z0_13TEV_DIMUON-Y, frac: 0.75, cfac: []}
+- {dataset: LHCB_Z0_13TEV_DIELECTRON-Y, frac: 0.75, cfac: []}
+
+############################################################
+datacuts:
+  t0pdfset: NNPDF40_nnlo_as_01180_qcd # PDF set to generate t0 covmat
+  q2min: 3.49                         # Q2 minimum
+  w2min: 12.5                         # W2 minimum
+
+############################################################
+theory:
+  theoryid: 700     # database id
+
+sampling:
+  separate_multiplicative: false
+
+hyperscan_config:
+  architecture:
+      n_layers: [2]
+      min_units: 10
+      max_units: 45
+  optimizer:
+  - optimizer_name: 'Nadam'
+    learning_rate:
+      sampling: log
+      min: 1e-4
+      max: 1e-2
+    clipnorm:
+      sampling: log
+      min: 1e-7
+      max: 1e-4
+  - optimizer_name: 'Adam'
+    learning_rate:
+      sampling: log
+      min: 1e-4
+      max: 1e-2
+    clipnorm:
+      sampling: log
+      min: 1e-7
+      max: 1e-4
+
+kfold:
+      loss_type: chi2
+      replica_statistic: average
+      fold_statistic: average
+      penalties:
+        - saturation
+        - patience
+        - integrability
+      threshold: 10
+      partitions:
+      - datasets:
+        # DIS
+          - HERA_CC_318GEV_EM-SIGMARED
+          - HERA_NC_225GEV_EP-SIGMARED
+            #- HERA_NC_318GEV_EAVG_BOTTOM-SIGMARED
+          - HERA_NC_318GEV_EAVG_CHARM-SIGMARED
+          - NMC_NC_NOTFIXED_P_EM-SIGMARED
+          - NUTEV_CC_NOTFIXED_FE_DW_NB-SIGMARED
+        # EWK
+          - LHCB_Z0_8TEV_DIELECTRON_Y
+          - CMS_WPWM_7TEV_ELECTRON_ASY
+          - ATLAS_Z0J_8TEV_PT-M
+          - D0_WPWM_1P96TEV_ASY
+          - DYE866_Z0_800GEV_PXSEC
+        # JETS+TOP
+          - ATLAS_PH_13TEV_XSEC
+          - ATLAS_2JET_7TEV_R06_M12Y
+          - ATLAS_SINGLETOP_8TEV_TBAR-RAP-NORM
+          - CMS_TTBAR_7TEV_TOT_X-SEC  # is this right or should it be one of the others?
+          - CMS_SINGLETOP_7TEV_TCHANNEL-XSEC
+      - datasets:
+        # DIS
+          - CHORUS_CC_NOTFIXED_PB_DW_NU-SIGMARED
+          - HERA_NC_318GEV_EP-SIGMARED
+          - BCDMS_NC_NOTFIXED_P_DW_EM-F2
+        # EWK
+          - LHCB_Z0_7TEV_DIELECTRON_Y
+          - ATLAS_Z0_7TEV_36PB_ETA
+          - CMS_Z0J_8TEV_PT-Y
+          - DYE605_Z0_38P8GEV_DW_PXSEC
+          - CMS_Z0_7TEV_DIMUON_2D
+        # JET+TOP
+          - CMS_2JET_7TEV_M12Y
+          - ATLAS_SINGLETOP_7TEV_TBAR-Y-NORM
+          - ATLAS_SINGLETOP_7TEV_TCHANNEL-XSEC
+          - CMS_TTBAR_8TEV_LJ_DIF_YTTBAR-NORM
+          - CMS_SINGLETOP_8TEV_TCHANNEL-XSEC
+      - datasets:
+        # DIS
+          - HERA_CC_318GEV_EP-SIGMARED
+          - HERA_NC_251GEV_EP-SIGMARED
+          - NMC_NC_NOTFIXED_DW_EM-F2
+          - NUTEV_CC_NOTFIXED_FE_DW_NU-SIGMARED
+        # EWK
+          - LHCB_WPWM_7TEV_MUON_Y
+          - LHCB_Z0_13TEV_DIELECTRON-Y
+          - ATLAS_Z0_7TEV_46FB_CC-Y
+          - ATLAS_WJ_JET_8TEV_WP-PT
+          - ATLAS_Z0_7TEV_49FB_HIMASS
+          - CMS_WPWM_7TEV_MUON_ASY
+          - DYE866_Z0_800GEV_DW_RATIO_PDXSECRATIO
+          - CDF_Z0_1P96TEV_ZRAP
+        # JET+TOP
+          - ATLAS_TTBAR_7TEV_TOT_X-SEC
+          - ATLAS_SINGLETOP_8TEV_T-RAP-NORM
+          - CMS_TTBAR_5TEV_TOT_X-SEC
+          - CMS_TTBAR_8TEV_2L_DIF_MTTBAR-YT-NORM
+      - datasets:
+        # DIS
+          - CHORUS_CC_NOTFIXED_PB_DW_NB-SIGMARED
+          - HERA_NC_300GEV_EP-SIGMARED
+        # EWK
+          - LHCB_WPWM_8TEV_MUON_Y
+          - LHCB_Z0_13TEV_DIMUON-Y
+          - ATLAS_Z0_7TEV_46FB_CF-Y
+          - ATLAS_WJ_JET_8TEV_WM-PT
+          - ATLAS_Z0_7TEV_LOMASS_M
+          - ATLAS_Z0J_8TEV_PT-Y
+          - CMS_WPWM_8TEV_MUON_Y
+          - D0_Z0_1P96TEV_ZRAP
+        # JET+TOP
+          - CMS_1JET_8TEV_PTY
+          - ATLAS_SINGLETOP_7TEV_T-Y-NORM
+          - ATLAS_SINGLETOP_13TEV_TCHANNEL-XSEC
+          - CMS_SINGLETOP_13TEV_TCHANNEL-XSEC
+
+############################################################
+trvlseed: 2182363835
+nnseed: 4044040809
+mcseed: 1977428487
+genrep: true    # true = generate MC replicas, false = use real data
+
+# Baseline parameters from table 9 of https://doi.org/10.1140/epjc/s10052-022-10328-7
+# These are used for parameters that are not included in the hyperoptimization
+parameters: # This defines the parameter dictionary that is passed to the Model Trainer
+  nodes_per_layer: [25, 20, 8]
+  activation_per_layer: [tanh, tanh, linear]
+  initializer: glorot_normal
+  optimizer:
+    clipnorm: 6.073e-6
+    learning_rate: 2.621e-3
+    optimizer_name: Nadam
+  epochs: 17000
+  positivity:
+    initial: 184.8
+    multiplier:
+  integrability:
+    initial: 10
+    multiplier:
+  stopping_patience: 0.1
+  layer_type: dense
+  dropout: 0.0
+  threshold_chi2: 3.5
+
+fitting:
+  savepseudodata: false
+  fitbasis: EVOL
+  basis:
+  - {fl: sng, trainable: false, smallx: [1.091, 1.119], largex: [1.471, 3.021]}
+  - {fl: g, trainable: false, smallx: [0.7795, 1.095], largex: [2.742, 5.547]}
+  - {fl: v, trainable: false, smallx: [0.472, 0.7576], largex: [1.571, 3.559]}
+  - {fl: v3, trainable: false, smallx: [0.07483, 0.4501], largex: [1.714, 3.467]}
+  - {fl: v8, trainable: false, smallx: [0.5731, 0.779], largex: [1.555, 3.465]}
+  - {fl: t3, trainable: false, smallx: [-0.5498, 1.0], largex: [1.778, 3.5]}
+  - {fl: t8, trainable: false, smallx: [0.5469, 0.857], largex: [1.555, 3.391]}
+  - {fl: t15, trainable: false, smallx: [1.081, 1.142], largex: [1.491, 3.092]}
+
+############################################################
+positivity:
+  posdatasets:
+  - {dataset: NNPDF_POS_2P24GEV_F2U, maxlambda: 1e6}        # Positivity Lagrange Multiplier
+  - {dataset: NNPDF_POS_2P24GEV_F2D, maxlambda: 1e6}
+  - {dataset: NNPDF_POS_2P24GEV_F2S, maxlambda: 1e6}
+  - {dataset: NNPDF_POS_2P24GEV_FLL-19PTS, maxlambda: 1e6}
+  - {dataset: NNPDF_POS_2P24GEV_DYU, maxlambda: 1e10}
+  - {dataset: NNPDF_POS_2P24GEV_DYD, maxlambda: 1e10}
+  - {dataset: NNPDF_POS_2P24GEV_DYS, maxlambda: 1e10}
+  - {dataset: NNPDF_POS_2P24GEV_F2C-17PTS, maxlambda: 1e6}
+  - {dataset: NNPDF_POS_2P24GEV_XUQ, maxlambda: 1e6}        # Positivity of MSbar PDFs
+  - {dataset: NNPDF_POS_2P24GEV_XUB, maxlambda: 1e6}
+  - {dataset: NNPDF_POS_2P24GEV_XDQ, maxlambda: 1e6}
+  - {dataset: NNPDF_POS_2P24GEV_XDB, maxlambda: 1e6}
+  - {dataset: NNPDF_POS_2P24GEV_XSQ, maxlambda: 1e6}
+  - {dataset: NNPDF_POS_2P24GEV_XSB, maxlambda: 1e6}
+  - {dataset: NNPDF_POS_2P24GEV_XGL, maxlambda: 1e6}
+
+############################################################
+integrability:
+  integdatasets:
+  - {dataset: NNPDF_INTEG_3GEV_XT8, maxlambda: 1e2}
+  - {dataset: NNPDF_INTEG_3GEV_XT3, maxlambda: 1e2}
+
+############################################################
+debug: false
+parallel_models: true
diff --git a/n3fit/src/evolven3fit/cli.py b/n3fit/src/evolven3fit/cli.py
@@ -2,7 +2,7 @@
 
 
 def cli_evolven3fit(
-    configuration_folder, q_fin, q_points, op_card_info, theory_card_info, dump, load, force
+    configuration_folder, q_fin, q_points, op_card_info, theory_card_info, force, load, dump
 ):
     """Evolves the fitted PDFs.
 

diff --git a/n3fit/src/evolven3fit/evolve.py b/n3fit/src/evolven3fit/evolve.py
@@ -10,7 +10,6 @@
 import eko
 from eko import basis_rotation, runner
 from reportengine.compat import yaml
-from validphys.loader import Loader
 
 from . import eko_utils, utils
 
@@ -25,7 +24,7 @@
 
 
 def evolve_fit(
-    fit_folder, q_fin, q_points, op_card_dict, theory_card_dict, force, eko_path=None, dump_eko=None
+    fit_folder, q_fin, q_points, op_card_dict, theory_card_dict, force, eko_path, dump_eko=None
 ):
     """
     Evolves all the fitted replica in fit_folder/nnfit
@@ -63,13 +62,12 @@ def evolve_fit(
     stdout_log = logging.StreamHandler(sys.stdout)
     for log in [log_file, stdout_log]:
         log.setFormatter(LOGGING_SETTINGS["formatter"])
-    
+
     # The log file will get everything
     log_file.setLevel(LOGGING_SETTINGS["level"])
     # While the terminal only up to info
     stdout_log.setLevel(logging.INFO)
 
-
     for logger in (_logger, *[logging.getLogger("eko")]):
         logger.handlers = []
         logger.setLevel(LOGGING_SETTINGS["level"])
@@ -84,17 +82,17 @@ def evolve_fit(
     if eko_path is not None:
         eko_path = pathlib.Path(eko_path)
         _logger.info(f"Loading eko from : {eko_path}")
-    else:
-        try:
-            _logger.info(f"Loading eko from theory {theoryID}")
-            eko_path = (Loader().check_theoryID(theoryID).path) / "eko.tar"
-        except FileNotFoundError:
-            _logger.warning(f"eko not found in theory {theoryID}, we will construct it")
+
+    if eko_path is None or not eko_path.exists():
+        if dump_eko is not None:
+            _logger.warning(f"Trying to construct the eko at {dump_eko}")
             theory, op = eko_utils.construct_eko_cards(
                 theoryID, q_fin, q_points, x_grid, op_card_dict, theory_card_dict
             )
             runner.solve(theory, op, dump_eko)
             eko_path = dump_eko
+        else:
+            raise ValueError(f"dump_eko not provided and {eko_path=} not found")
 
     with eko.EKO.edit(eko_path) as eko_op:
         x_grid_obj = eko.interpolation.XGrid(x_grid)

diff --git a/n3fit/src/n3fit/checks.py b/n3fit/src/n3fit/checks.py
@@ -9,6 +9,7 @@
 from n3fit.hyper_optimization import penalties as penalties_module
 from n3fit.hyper_optimization.rewards import IMPLEMENTED_LOSSES, IMPLEMENTED_STATS
 from reportengine.checks import CheckError, make_argcheck
+from validphys.loader import FallbackLoader
 from validphys.pdfbases import check_basis
 
 log = logging.getLogger(__name__)
@@ -485,3 +486,14 @@ def check_polarized_configs(fitting, fitbasis, positivity_bound):
             )
         if fitting.get("sum_rules", True) and fitting.get("sum_rules") != "TSR":
             raise CheckError("The 'sum_rules' key needs to be 'TSR' for polarised PDF fits.")
+
+
+@make_argcheck
+def check_eko_exists(theoryid):
+    """Check that an eko for this theory exists.
+    Since there might still be theories without an associated eko,
+    this function raises a logger' error instead of an Exception."""
+    try:
+        _ = FallbackLoader().check_eko(theoryid.id)
+    except FileNotFoundError:
+        log.error(f"No eko found for {theoryid}")
diff --git a/n3fit/src/n3fit/n3fit_checks_provider.py b/n3fit/src/n3fit/n3fit_checks_provider.py
@@ -32,3 +32,8 @@ def n3fit_checks_action(
     double_precision=False,
 ):
     return
+
+
+@n3fit.checks.check_eko_exists
+def evolven3fit_checks_action(theoryid):
+    return