Refactor: General codebase cleanup

titrate/plotting.py

@@ -244,7 +244,6 @@ def plot(
         statistic_bkg,
         axs,
     ):
-        print(self.poi_val)
         axs["diff"].hist(
             toys_ts_diff,
             bins=bins_diff,

titrate/tests/test_validation.py

@@ -40,7 +40,7 @@ def validation_file(measurement_dataset, tmp_path_factory):
 
 
 @pytest.mark.parametrize("statistic", ["qmu", "qtildemu"])
-def test_AsmyptoticValidator(measurement_dataset, statistic, validation_file):
+def test_AsymptoticValidator(measurement_dataset, statistic, validation_file):
     from titrate.validation import AsymptoticValidator
 
     validator = AsymptoticValidator(

titrate/upperlimits.py

@@ -4,8 +4,7 @@
 import numpy as np
 from astropy.table import QTable
 from gammapy.astro.darkmatter import DarkMatterAnnihilationSpectralModel
-from gammapy.modeling import Covariance, Fit
-from gammapy.modeling.fit import OptimizeResult
+from gammapy.modeling import Fit
 from gammapy.modeling.models import (
     FoVBackgroundModel,
     Models,
@@ -84,11 +83,9 @@ def compute(self):
                 poi_ul = 1e-2
         else:
             poi_ul = poi_best
-        prev_pval = 0
         while (
             (pval := self.pvalue(poi_ul, cl_type=self.cl_type)) > 1 - self.cl
         ) or np.isnan(pval):
-            prev_pval = pval
             poi_ul *= 2
 
         interp_ul_points = np.linspace(poi_ul / 2, poi_ul, 5)
@@ -105,7 +102,6 @@ def compute(self):
 
         poi_ul = brentq(interpolation, poi_ul / 2, poi_ul)
 
-        print("FOUND:", poi_ul)
         self.poi_ul = poi_ul
 
         return poi_ul
@@ -139,7 +135,7 @@ def expected_uls(self):
             target_ts = norm.ppf(1 - 0.5 * (1 - self.cl)) ** 2
 
         poi_ul = 1e-2
-        while (ts := self.no_signal_statistic.evaluate(poi_ul)) < target_ts:
+        while self.no_signal_statistic.evaluate(poi_ul) < target_ts:
             poi_ul *= 2
 
         interp_ul_points = np.linspace(poi_ul / 2, poi_ul, 10)

titrate/utils.py

@@ -1,15 +1,17 @@
 from gammapy.datasets import Datasets
 
 
-def CopyModelError(AttributeError):
+class CopyModelError(AttributeError):
     """Something went wrong during copying a model."""
+    pass
 
 
 def calc_ts_toyMC(dataset, test_statistic, poi_val, poi_true_val, poi_name):
     """Evaluates given test statistic for toy MCs.
 
-    The given dataset will be copied and a new measurement will be simulated.
-    The test statistic is evaluated for this dataset.
+    The `poi_true_val` is used to set the parameter of interest in the model
+    before simulating the new measurement from which the test statistic is
+    evaluated.
     """
     toy_dataset = copy_dataset_with_models(dataset)
     toy_dataset.models.parameters[poi_name].value = poi_true_val

titrate/validation.py

@@ -10,7 +10,7 @@
 from gammapy.modeling.models import SkyModel
 
 from titrate.datasets import AsimovMapDataset, AsimovSpectralDataset
-from titrate.statistics import QMuTestStatistic, QTildeMuTestStatistic, kstest
+from titrate.statistics import QMuTestStatistic, QTildeMuTestStatistic
 from titrate.utils import calc_ts_toyMC, copy_dataset_with_models
 
 STATISTICS = {"qmu": QMuTestStatistic, "qtildemu": QTildeMuTestStatistic}
@@ -109,7 +109,6 @@ def validate(self, n_toys=1000):
                 poi_val=self.poi_val, ts_val=x
             ),
         ).pvalue
-        print(ks_diff, ks_same)
 
         valid = ks_diff > 0.05 and ks_same > 0.05