L0 projections from command line

docs/source/data_theory/projections.rst

@@ -73,10 +73,14 @@ and systematic uncertainties) as ``.yaml`` files in the standard SMEFiT format w
 
 .. code-block:: bash
 
-    smefit PROJ --lumi <luminosity> /path/to/projection_runcard.yaml
-
-where the desired luminosity in :math:`{\rm fb}^{-1}` can be specified by replacing ``<luminosity>``. The ``projection_runcard``
-specifies which datasets need to be extrapolated, by which factor to reduce the systematics, and sets the necessary paths:
+    smefit PROJ --lumi <luminosity> --noise <noise level> /path/to/projection_runcard.yaml
+
+where ``<luminosity>`` specifies the luminosity of the projection in :math:`{\rm fb}^{-1}`. The noise level ``<luminosity>``
+can be either ``L0`` are ``L1`` corresponding to either level 0 or level 1 projections respectively. In level 0 projections,
+the experimental central value coincides exactly with the theory prediction, while the experimental central values are fluctuated around
+the theory prediction according to the experimental uncertainties in case of level 1. If ``<noise level>`` is not specified, level 0
+is assumed. If ``<luminosity>`` is not specified, the original luminosities are kept and the uncertainties are not rescaled.
+The ``projection_runcard`` specifies which datasets need to be extrapolated, by which factor to reduce the systematics, and sets the necessary paths:
 
 .. code-block:: yaml
 

src/smefit/cli/__init__.py

@@ -217,24 +217,18 @@
     type=float,
     default=None,
     required=False,
-    help="Adjusts the statistical uncertainties according to the specified luminosity",
+    help="Adjusts the statistical uncertainties according to the specified luminosity. If not specified, the original "
+    "uncertainties are kept and the central values are fluctuates according to the specified noise level.",
 )
 @click.option(
-    "--closure",
-    type=bool,
-    is_flag=True,
-    default=False,
-    help="Produces datasets under the SM",
+    "--noise",
+    type=str,
+    default="L0",
+    required=False,
+    help="Noise level for the projection, choose between L0 or L1. Assumes L0 by default.",
 )
-def projection(projection_card: pathlib.Path, lumi: float, closure: bool):
+def projection(projection_card: pathlib.Path, lumi: float, noise: str):
     r"""Compute projection for specified dataset"""
 
-    if (lumi is not None) ^ closure:
-        projection_setup = Projection.from_config(projection_card)
-        projection_setup.build_projection(lumi, closure)
-    else:
-        print(lumi, closure)
-        print(
-            "Usage: specify exclusively either a luminosity in fb-1 after --lumi or run a SM closure test with --closure"
-        )
-        sys.exit()
+    projection_setup = Projection.from_config(projection_card)
+    projection_setup.build_projection(lumi, noise)

src/smefit/projections/__init__.py

@@ -183,15 +183,18 @@
         fred_stat = np.sqrt(lumi_old / lumi_new)
         return stat * fred_stat
 
-    def build_projection(self, lumi_new, closure):
+    def build_projection(self, lumi_new=None, noise="L0"):
         """
         Constructs runcard for projection by updating the central value and statistical and
         systematic uncertainties
 
         Parameters
         ----------
-        lumi_new: float
-            Adjusts the statistical uncertainties according to the specified luminosity
+        lumi_new: float, optional
+            Adjusts the statistical uncertainties according to the specified luminosity lumi_new. If not specified,
+            the uncertainties are left unchanged and the central values are fluctuated according to the noise level
+        noise: str
+            Noise level for the projection, choose between L0 or L1
         closure: bool
             Set to true for a L1 closure test (no rescaling, only cv gets fluctuated according to
             original uncertainties)
@@ -266,7 +269,7 @@
 
             th_covmat = self.datasets.ThCovMat[idxs, idxs]
 
-            if not closure:
+            if lumi_new is not None:
                 # if all stats are zero, we only have access to the total error which we rescale by 1/3 (compromise)
                 no_stats = not np.any(stat)
                 if no_stats:
@@ -310,8 +313,10 @@
             if self.use_theory_covmat:
                 newcov += th_covmat
 
-            # add L1 noise to cv
-            cv_projection = np.random.multivariate_normal(cv[idxs], newcov)
+            # add Gaussian noise to central values in case of L1 and leave them unchanged in case of L0
+            cv_projection = cv[idxs]
+            if noise == "L1":
+                cv_projection = np.random.multivariate_normal(cv[idxs], newcov)
 
             # replace cv with updated central values
             if len(cv_projection) > 1:
@@ -323,7 +328,7 @@
             projection_folder.mkdir(exist_ok=True)
 
             if projection_folder != self.commondata_path:
-                if not closure:
+                if lumi_new is not None:
                     with open(
                         f"{projection_folder}/{dataset_name}_proj.yaml", "w"
                     ) as file:
@@ -338,7 +343,7 @@
                 sys.exit()
 
             # copy corresponding theory predictions with _proj appended to filename
-            if not closure:
+            if lumi_new is not None:
                 shutil.copy(
                     self.theory_path / f"{dataset_name}.json",
                     self.theory_path / f"{dataset_name}_proj.json",