Rename 'physics model' as 'mapping'

.github/workflows/main.yml

@@ -207,7 +207,7 @@ jobs:
           -t 0 --unblind --doImpacts --globalImpacts
           --saveHists --saveHistsPerProcess --computeHistErrors --computeHistErrorsPerProcess
           --computeHistCov --computeHistImpacts --computeVariations
-          -m Basemodel
+          -m BaseMapping
           -m Select ch0 'x:rebin(-5,-2,2,5)'
           -m Project ch1 a 
           -m Project ch1 b 
@@ -217,15 +217,15 @@ jobs:
           -m Ratio ch0 ch1 'None:None' 'b:slice(0,2),b:sum'
           -m Ratio ch0 ch1 'x:slice(None,None,2),x:sum' 'a:sum,b:sum'
           -m Normratio ch1 ch1 sig sig,bkg 'b:sum' 'b:2'
-          -m tests.param_model.Param 'slope_signal' 1 0
+          -m tests.param_mapping.Param 'slope_signal' 1 0
 
       - name: nominal fit
         run: >- 
           rabbit_fit.py $RABBIT_OUTDIR/test_tensor.hdf5 -o $RABBIT_OUTDIR/ --postfix composite
           -t 0 --unblind '.*' --doImpacts --globalImpacts
           --saveHists --saveHistsPerProcess --computeHistErrors --computeHistErrorsPerProcess
           --computeHistCov --computeHistImpacts --computeVariations
-          --compositeModel -m Project ch1 a -m Project ch1 b 
+          --compositeMapping -m Project ch1 a -m Project ch1 b 
 
       - name: nominal fit blinded
         run: >- 
@@ -320,13 +320,13 @@ jobs:
       - name: plot prefit distributions
         run: >-
           rabbit_plot_hists.py $RABBIT_OUTDIR/fitresults.hdf5 -o $WEB_DIR/$PLOT_DIR 
-          --extraTextLoc '0.05' '0.7' --legCols 1 -m Basemodel -m Project ch1 a -m Project ch1 b --yscale '1.2' 
+          --extraTextLoc '0.05' '0.7' --legCols 1 -m BaseMapping -m Project ch1 a -m Project ch1 b --yscale '1.2' 
           --subtitle Preliminary --prefit --config tests/style_config.py
 
       - name: plot postfit distributions
         run: >-
           rabbit_plot_hists.py $RABBIT_OUTDIR/fitresults.hdf5 -o $WEB_DIR/$PLOT_DIR 
-          --extraTextLoc '0.05' '0.7' --legCols 1 -m Basemodel -m Project ch1 a -m Project ch1 b -m Normalize ch0 x --yscale '1.2' 
+          --extraTextLoc '0.05' '0.7' --legCols 1 -m BaseMapping -m Project ch1 a -m Project ch1 b -m Normalize ch0 x --yscale '1.2' 
           --subtitle Preliminary --varNames 'slope_signal' --varColors orange --config tests/style_config.py --rrange 0.8 1.2
 
       - name: plot histogram uncertainties

README.md

@@ -122,19 +122,19 @@ rabbit_fit test_tensor.hdf5 -o results/fitresult.hdf5 -t 0 --doImpacts --globalI
 ### Bin-by-bin statistical uncertainties
 Bin-by-bin statistical uncertainties on the templates are added by default and can be disabled at runtime using the `--noBinByBinStat` option. The Barlow-Beeston lite method is used to add implicit nuisance parameters for each template bin.  By default this is implemented using a gamma distribution for the probability density, but Gaussian uncertainties can also be used with `--binByBinStatType normal`.
 
-### Physics models
-Physics models are used to perform transformation on the parameters and observables (the histogram bins in the (masked) channels). 
-Baseline models are defined in `rabbit/physicsmodels/` and can be called in `rabbit_fit` with the `--PhysicsModel` or `-m` option e.g. `-m Select ch0 -m Project ch1 b`. 
-The first argument is the physics model name followed by arguments passed into the physics model.
+### Mappings
+Perform mappings on the parameters and observables (the histogram bins in the (masked) channels). 
+Baseline mappings are defined in `rabbit/mappings/` and can be called in `rabbit_fit` with the `--mapping` or `-m` option e.g. `-m Select ch0 -m Project ch1 b`. 
+The first argument is the mapping name followed by arguments passed into the mapping.
 Available physics models are
- * `Basemodel`: Compute histograms in all bins and all channels.
+ * `BaseMapping`: Compute histograms in all bins and all channels.
  * `Select`: To select histograms of a channel, and perform a selection of processes and bins, supporting rebinning.
  * `Project`: To project histograms to lower dimensions, respecting the covariance matrix across bins.
  * `Normalize`: To normalize histograms to their sum (and project them) e.g. to compute normalized differential cross sections.
  * `Ratio`: To compute the ratio between channels, processes, or histogram bins.
  * `Normratio`: To compute the ratio of normalized histograms.
 
-Models can be specified in the comand line and can feature different parsing syntax. 
+Mapping can be specified in the comand line and can feature different parsing syntax. 
 A convension is set up for parsing process and axes selections (e.g. in the `Select` and `Ratio` models). For selecting processes a comma separated list, e.g. <process_0>,<process_1>...
 and for axes selecitons <axis_name_0>:<selection_0>,<axis_name_1>:<selection_1>,... i.e. a comma separated list of axis names and selections separated by ":". 
 Selections can be 
@@ -145,10 +145,12 @@ Selections can be
 - `None:None` for whch `None` is returned, indicating no selection
 Multiple selection per axis can be specified, e.g. `x:slice(2,8),x:sum`.
 
-Custom physics models can be used to make the desired transformation.
-They can be specified with the full path to the custom model e.g. `-m custom_models.MyCustomModel`. 
+Custom mappings can be defined.
+They can be specified with the full path to the custom mapping e.g. `-m custom_mapping.MyCustomMapping`. 
 The path must be accessable from your `$PYTHONPATH` variable and an `__ini__.py` file must be in the directory.
 
+### Physics models
+TBD
 
 ## Fit diagnostics
 

bin/rabbit_fit.py

@@ -12,8 +12,8 @@
 import scipy
 
 from rabbit import fitter, inputdata, io_tools, workspace
-from rabbit.physicsmodels import helpers as ph
-from rabbit.physicsmodels import physicsmodel as pm
+from rabbit.mappings import helpers as mh
+from rabbit.mappings import mapping as mp
 from rabbit.tfhelpers import edmval_cov
 
 from wums import output_tools, logging  # isort: skip
@@ -282,22 +282,22 @@ def make_parser():
     )
     parser.add_argument(
         "-m",
-        "--physicsModel",
+        "--mapping",
         nargs="+",
         action="append",
         default=[],
         help="""
-        add physics model to perform transformations on observables for the prefit and postfit histograms, 
-        specifying the model defined in rabbit/physicsmodels/ followed by arguments passed in the model __init__, 
+        perform mappings on observables or parameters for the prefit and postfit histograms, 
+        specifying the mapping defined in rabbit/mappings/ followed by arguments passed in the mapping __init__, 
         e.g. '-m Project ch0 eta pt' to get a 2D projection to eta-pt or '-m Project ch0' to get the total yield.  
         This argument can be called multiple times.
-        Custom models can be specified with the full path to the custom model e.g. '-m custom_models.MyCustomModel'.
+        Custom mappings can be specified with the full path to the custom mapping e.g. '-m custom_mappings.MyCustomMapping'.
         """,
     )
     parser.add_argument(
-        "--compositeModel",
+        "--compositeMapping",
         action="store_true",
-        help="Make a composite model and compute the covariance matrix across all physics models.",
+        help="Make a composite mapping and compute the covariance matrix across all mappings.",
     )
     parser.add_argument(
         "--doImpacts",
@@ -368,42 +368,42 @@ def make_parser():
     return parser.parse_args()
 
 
-def save_observed_hists(args, models, fitter, ws):
-    for model in models:
-        if not model.has_data:
+def save_observed_hists(args, mappings, fitter, ws):
+    for mapping in mappings:
+        if not mapping.has_data:
             continue
 
-        print(f"Save data histogram for {model.key}")
+        print(f"Save data histogram for {mapping.key}")
         ws.add_observed_hists(
-            model,
+            mapping,
             fitter.indata.data_obs,
             fitter.nobs.value(),
             fitter.indata.data_cov_inv,
             fitter.data_cov_inv,
         )
 
 
-def save_hists(args, models, fitter, ws, prefit=True, profile=False):
+def save_hists(args, mappings, fitter, ws, prefit=True, profile=False):
 
-    for model in models:
-        logger.info(f"Save inclusive histogram for {model.key}")
+    for mapping in mappings:
+        logger.info(f"Save inclusive histogram for {mapping.key}")
 
-        if prefit and model.skip_prefit:
+        if prefit and mapping.skip_prefit:
             continue
 
-        if not model.skip_incusive:
+        if not mapping.skip_incusive:
             exp, aux = fitter.expected_events(
-                model,
+                mapping,
                 inclusive=True,
                 compute_variance=args.computeHistErrors,
                 compute_cov=args.computeHistCov,
-                compute_chi2=not args.noChi2 and model.has_data,
+                compute_chi2=not args.noChi2 and mapping.has_data,
                 compute_global_impacts=args.computeHistImpacts,
                 profile=profile,
             )
 
             ws.add_expected_hists(
-                model,
+                mapping,
                 exp,
                 var=aux[0],
                 cov=aux[1],
@@ -413,20 +413,20 @@ def save_hists(args, models, fitter, ws, prefit=True, profile=False):
             )
 
             if aux[4] is not None:
-                ws.add_chi2(aux[4], aux[5], prefit, model)
+                ws.add_chi2(aux[4], aux[5], prefit, mapping)
 
-        if args.saveHistsPerProcess and not model.skip_per_process:
-            logger.info(f"Save processes histogram for {model.key}")
+        if args.saveHistsPerProcess and not mapping.skip_per_process:
+            logger.info(f"Save processes histogram for {mapping.key}")
 
             exp, aux = fitter.expected_events(
-                model,
+                mapping,
                 inclusive=False,
                 compute_variance=args.computeHistErrorsPerProcess,
                 profile=profile,
             )
 
             ws.add_expected_hists(
-                model,
+                mapping,
                 exp,
                 var=aux[0],
                 process_axis=True,
@@ -439,15 +439,15 @@ def save_hists(args, models, fitter, ws, prefit=True, profile=False):
                 fitter.cov.assign(fitter.prefit_covariance(unconstrained_err=1.0))
 
             exp, aux = fitter.expected_events(
-                model,
+                mapping,
                 inclusive=True,
                 compute_variance=False,
                 compute_variations=True,
                 profile=profile,
             )
 
             ws.add_expected_hists(
-                model,
+                mapping,
                 exp,
                 var=aux[0],
                 variations=True,
@@ -655,18 +655,18 @@ def main():
     indata = inputdata.FitInputData(args.filename, args.pseudoData)
     ifitter = fitter.Fitter(indata, args, do_blinding=any(blinded_fits))
 
-    # physics models for observables and transformations
-    if len(args.physicsModel) == 0 and args.saveHists:
-        # if no model is explicitly added and --saveHists is specified, fall back to Basemodel
-        args.physicsModel = [["Basemodel"]]
-    models = []
-    for margs in args.physicsModel:
-        model = ph.instance_from_class(margs[0], indata, *margs[1:])
-        models.append(model)
-
-    if args.compositeModel:
-        models = [
-            pm.CompositeModel(models),
+    # mappings for observables and parameters
+    if len(args.mapping) == 0 and args.saveHists:
+        # if no mapping is explicitly added and --saveHists is specified, fall back to BaseMapping
+        args.mapping = [["BaseMapping"]]
+    mappings = []
+    for margs in args.mapping:
+        mapping = mh.instance_from_class(margs[0], indata, *margs[1:])
+        mappings.append(mapping)
+
+    if args.compositeMapping:
+        mappings = [
+            mp.CompositeMapping(mappings),
         ]
 
     np.random.seed(args.seed)
@@ -746,8 +746,8 @@ def main():
                 )
 
                 if args.saveHists:
-                    save_observed_hists(args, models, ifitter, ws)
-                    save_hists(args, models, ifitter, ws, prefit=True)
+                    save_observed_hists(args, mappings, ifitter, ws)
+                    save_hists(args, mappings, ifitter, ws, prefit=True)
                 prefit_time.append(time.time())
 
                 if not args.prefitOnly:
@@ -758,7 +758,7 @@ def main():
                     if args.saveHists:
                         save_hists(
                             args,
-                            models,
+                            mappings,
                             ifitter,
                             ws,
                             prefit=False,

bin/rabbit_plot_hists.py

@@ -203,13 +203,13 @@ def parseArgs():
     )
     parser.add_argument(
         "-m",
-        "--physicsModel",
+        "--mapping",
         nargs="+",
         action="append",
         default=[],
         help="""
-        Make plot of physics model prefit and postfit histograms. Loop over all by deault. 
-        Can also specify the model name, followed by the arguments, e.g. "-m Project ch0 eta pt". 
+        Make plot of mapping prefit and postfit histograms. Loop over all by deault. 
+        Can also specify the mapping name, followed by the arguments, e.g. "-m Project ch0 eta pt". 
         This argument can be called multiple times.
         """,
     )
@@ -258,7 +258,7 @@ def parseArgs():
         type=str,
         default="automatic",
         choices=["automatic", "saturated", "linear", " ", "none", None],
-        help="Type of chi2 to print on plot (saturated from fit likelihood. linear from observables, or none) 'automatic' means pick saturated for basemodel and otherwise linear",
+        help="Type of chi2 to print on plot (saturated from fit likelihood. linear from observables, or none) 'automatic' means pick saturated for basemapping and otherwise linear",
     )
     parser.add_argument(
         "--dataName", type=str, default="Data", help="Data name for plot labeling"
@@ -1474,16 +1474,16 @@ def main():
         varMarkers=args.varMarkers,
     )
 
-    results = fitresult["physics_models"]
-    for margs in args.physicsModel:
+    results = fitresult.get("mappings", fitresult.get("physics_models"))
+    for margs in args.mapping:
         if margs == []:
             instance_keys = results.keys()
         else:
-            model_key = " ".join(margs)
-            instance_keys = [k for k in results.keys() if k.startswith(model_key)]
+            mapping_key = " ".join(margs)
+            instance_keys = [k for k in results.keys() if k.startswith(mapping_key)]
             if len(instance_keys) == 0:
                 raise ValueError(
-                    f"No model found under {model_key}. Available models: {results.keys()}."
+                    f"No mapping found under {mapping_key}. Available mappings: {results.keys()}."
                 )
 
         for instance_key in instance_keys:
@@ -1499,7 +1499,7 @@ def main():
                     fitresult
                     if fittype == "postfit"
                     and (
-                        (instance_key == "Basemodel" and args.chisq != "linear")
+                        (instance_key == "BaseMapping" and args.chisq != "linear")
                         or args.chisq == "saturated"
                     )
                     else instance
@@ -1513,7 +1513,7 @@ def main():
                     continue
                 logger.info(f"Make plot for {instance_key} in channel {channel}")
 
-                if instance_key == "CompositeModel":
+                if instance_key == "CompositeMapping":
                     info = channel_info.get(" ".join(channel.split(" ")[-1:]), {})
                 else:
                     info = channel_info.get(channel, {})
@@ -1538,11 +1538,11 @@ def main():
                     if any(
                         instance_key.startswith(x)
                         for x in [
-                            "Basemodel",
+                            "BaseMapping",
                             "Project",
                             "Select",
                             "Norm",
-                            "CompositeModel",
+                            "CompositeMapping",
                         ]
                     )
                     and not args.noBinWidthNorm
@@ -1552,9 +1552,9 @@ def main():
                 opts["counts"] = counts
 
                 varResults = [
-                    r["physics_models"][instance_key.replace("_masked", "")][
-                        "channels"
-                    ][channel.replace("_masked", "")]
+                    r.get("mappings", r.get("physics_models"))[
+                        instance_key.replace("_masked", "")
+                    ]["channels"][channel.replace("_masked", "")]
                     for r in varFitresults
                 ]