Removes all hash for parameters not used for each source, and for all…

alea/blueice_extended_model.py

@@ -1,4 +1,6 @@
 from pydoc import locate  # to lookup likelihood class
+from typing import List
+
 from alea.statistical_model import StatisticalModel
 from alea.simulators import BlueiceDataGenerator
 from alea.utils import adapt_likelihood_config_for_blueice
@@ -76,7 +78,13 @@ def get_expectation_values(self, **kwargs) -> dict:
         """
         ret = dict()
         for ll in self._likelihood.likelihood_list[:-1]:  # ancillary likelihood does not contribute
-            mus = ll(full_output=True, **kwargs)[1]
+
+            ll_pars = list(ll.rate_parameters.keys()) + list(ll.shape_parameters.keys())
+            call_args = {k:i for k, i in kwargs.items() if k in ll_pars}
+            if "livetime_days" in kwargs:
+                call_args["livetime_days"] = kwargs["livetime_days"]
+
+            mus = ll(full_output=True, **call_args)[1]
             for n, mu in zip(ll.source_name_list, mus):
                 ret[n] = ret.get(n, 0) + mu
         return ret
@@ -105,13 +113,27 @@ def _build_ll_from_config(self, likelihood_config: dict) -> "LogLikelihoodSum":
                 config, template_folder_list)
             blueice_config["livetime_days"] = self.parameters[
                 blueice_config["livetime_parameter"]].nominal_value
-            ll = likelihood_object(blueice_config)
+            for p in self.parameters:
+                blueice_config[p.name] = blueice_config.get(p.name, p.nominal_value)
+
+            # add all parameters to extra_dont_hash for each source unless it is used:
+            for i, source in enumerate(config["sources"]):
+                parameters_to_ignore: List[str] = [p.name for p in self.parameters if (p.ptype == "shape")
+                                                   & (p.name not in source["parameters"])]
+                # no efficiency affects PDF:
+                parameters_to_ignore += [ p.name for p in self.parameters if (p.ptype == "efficiency")]
+                parameters_to_ignore += source.get("extra_dont_hash_settings", [])
+
+                # ignore all shape parameters known to this model not named specifically in the source:
+                blueice_config["sources"][i]["extra_dont_hash_settings"] = parameters_to_ignore
+
+            ll = likelihood_object(blueice_config) # noqa: E127
 
             for source in config["sources"]:
 
                 # Set rate parameters
                 rate_parameters = [
-                    p for p in source["parameters"] if self.parameters[p].type == "rate"]
+                    p for p in source["parameters"] if self.parameters[p].ptype == "rate"]
                 if len(rate_parameters) != 1:
                     raise ValueError(
                         f"Source {source['name']} must have exactly one rate parameter.")
@@ -125,11 +147,28 @@ def _build_ll_from_config(self, likelihood_config: dict) -> "LogLikelihoodSum":
 
                 # Set shape parameters
                 shape_parameters = [
-                    p for p in source["parameters"] if self.parameters[p].type == "shape"]
+                    p for p in source["parameters"] if self.parameters[p].ptype == "shape"]
                 if shape_parameters:
                     # TODO: Implement setting shape parameters
                     raise NotImplementedError("Shape parameters are not yet supported.")
 
+
+                # Set efficiency parameters
+                if source.get("apply_efficiency", False):
+                    assert "efficiency_name" in source, "Unspecified efficiency_name for source {:s}".format(source["name"])
+                    efficiency_name = source["efficiency_name"]
+                    assert efficiency_name in source["parameters"], "The efficiency_name for source {:s} is not in its parameter list".format(source["name"])
+                    efficiency_parameter = self.parameters[efficiency_name]
+                    assert efficiency_parameter.ptype == "efficiency", "The parameter {:s} must" \
+                                                                      " be an efficiency".format(efficiency_name)
+                    limits = efficiency_parameter.fit_limits
+                    assert 0 <= limits[0], 'Efficiency parameters including {:s} must be' \
+                                         ' constrained to be nonnegative'.format(efficiency_name)
+                    assert np.isfinite(limits[1]), 'Efficiency parameters including {:s} must be' \
+                                                   ' constrained to be finite'.format(efficiency_name)
+                    ll.add_shape_parameter(efficiency_name, anchors=(limits[0], limits[1]))
+
+
             ll.prepare()
             lls.append(ll)
 

alea/examples/unbinned_wimp_statistical_model.yaml

@@ -33,6 +33,18 @@ parameter_definition:
       - null
     fit_guess: 1.0
 
+  signal_efficiency:
+    nominal_value: 1.0
+    ptype: efficiency
+    uncertainty: 0.1
+    relative_uncertainty: true
+    fittable: true
+    fit_limits:
+      - 0
+      - 10.
+    fit_guess: 1.0
+    description: Parameter to account for the uncertain signal expectation given a certain cross-section
+
   # er_band_shift:
   #   nominal_value: 0
   #   ptype: shape
@@ -76,9 +88,10 @@ likelihood_config:
         parameters:
           - wimp_rate_multiplier
           - wimp_mass
+          - signal_efficiency
         template_filename: wimp50gev_template.h5
-        apply_efficiency: False
-        efficiency_name: 'signal_eff'  # TODO: Check
+        apply_efficiency: True
+        efficiency_name: signal_efficiency  # TODO: Check
 
     # SR1
     - name: sr1
@@ -104,6 +117,7 @@ likelihood_config:
         parameters:
           - wimp_rate_multiplier
           - wimp_mass
+          - signal_efficiency
         template_filename: wimp50gev_template.h5
-        apply_efficiency: False
-        efficiency_name: 'wimp_eff'  # TODO: Check
+        apply_efficiency: True
+        efficiency_name: signal_efficiency  # TODO: Check

alea/parameters.py

@@ -9,7 +9,7 @@ class Parameter:
         name (str): The name of the parameter.
         nominal_value (float, optional): The nominal value of the parameter.
         fittable (bool, optional): Indicates if the parameter is fittable or always fixed.
-        ptype (str, optional): The type of the parameter.
+        ptype (str, optional): The ptype of the parameter.
         uncertainty (float or str, optional):
             The uncertainty of the parameter. If a string,
             it can be evaluated as a numpy or scipy function to define non-gaussian constraints.
@@ -39,7 +39,7 @@ def __init__(
         self.name = name
         self.nominal_value = nominal_value
         self.fittable = fittable
-        self.type = ptype
+        self.ptype = ptype
         self._uncertainty = uncertainty
         self.relative_uncertainty = relative_uncertainty
         self.blueice_anchors = blueice_anchors

alea/statistical_model.py

@@ -163,7 +163,8 @@ def store_data(
         kw = {'metadata': metadata} if metadata is not None else dict()
         toydata_to_file(file_name, data_list, data_name_list, **kw)
 
-    def get_expectation_values(self):
+    def get_expectation_values(self, **parameter_values):
+        mus = self._ll(full_output=True, **parameter_values)
         return NotImplementedError("get_expectation_values is optional to implement")
 
     @property
@@ -284,9 +285,9 @@ def _confidence_interval_checks(
                     "You must set parameter_interval_bounds in the parameter config"
                     " or when calling confidence_interval")
 
-        if parameter_of_interest.type == "rate":
+        if parameter_of_interest.ptype == "rate":
             try:
-                if parameter_of_interest.type == "rate" and poi_name.endswith("_rate_multiplier"):
+                if parameter_of_interest.ptype == "rate" and poi_name.endswith("_rate_multiplier"):
                     source_name = poi_name.replace("_rate_multiplier", "")
                 else:
                     source_name = poi_name