Addressing PR comments. Consolidated duplicated method definitions. U…

…pdated tests.

src/qp/metrics/base_metric_classes.py

@@ -2,9 +2,9 @@
 
 from abc import ABC
 
+
 class MetricInputType(enum.Enum):
-    """Defines the various combinations of input types that metric classes accept.
-    """
+    """Defines the various combinations of input types that metric classes accept."""
 
     unknown = -1
 
@@ -27,7 +27,6 @@ class MetricInputType(enum.Enum):
     # distribution, or collection of distributions for reference(s).
     point_to_dist = 4
 
-
     def uses_distribution_for_estimate(self) -> bool:
         return self in [
             MetricInputType.single_ensemble,
@@ -55,8 +54,7 @@ def uses_point_for_reference(self) -> bool:
 
 
 class MetricOuputType(enum.Enum):
-    """Defines the various output types that metric classes can return.
-    """
+    """Defines the various output types that metric classes can return."""
 
     unknown = -1
 
@@ -71,15 +69,28 @@ class MetricOuputType(enum.Enum):
 
 
 class BaseMetric(ABC):
-    metric_name = None # The name for this metric, overwritten in subclasses
-    metric_input_type = MetricInputType.unknown # The type of input data expected for this metric
-    metric_output_type = MetricOuputType.unknown # The form of the output data from this metric
+    """This is the base class for all of the qp metrics. It establishes the most
+    of the basic API for a consistent interaction with the metrics qp provides.
+    """
 
-    def __init__(self, limits:tuple=(0.0, 3.0), dx:float=0.01) -> None:
+    metric_name = None  # The name for this metric, overwritten in subclasses
+    metric_input_type = (
+        MetricInputType.unknown
+    )  # The type of input data expected for this metric
+    metric_output_type = (
+        MetricOuputType.unknown
+    )  # The form of the output data from this metric
 
+    def __init__(self, limits: tuple = (0.0, 3.0), dx: float = 0.01) -> None:
         self._limits = limits
         self._dx = dx
 
+    def initialize(self):
+        pass
+
+    def finalize(self):
+        pass
+
     @classmethod
     def uses_distribution_for_estimate(cls) -> bool:
         return cls.metric_input_type.uses_distribution_for_estimate()
@@ -98,70 +109,54 @@ def uses_point_for_reference(cls) -> bool:
 
 
 class SingleEnsembleMetric(BaseMetric):
+    """A base class for metrics that accept only a single ensemble as input."""
 
     metric_input_type = MetricInputType.single_ensemble
     metric_output_type = MetricOuputType.one_value_per_distribution
 
-    def initialize(self):
-        raise NotImplementedError()
-
     def evaluate(self, estimate):
         raise NotImplementedError()
 
-    def finalize(self):
-        raise NotImplementedError()
-
 
 class DistToDistMetric(BaseMetric):
+    """A base class for metrics that requires distributions as input for both the
+    estimated and reference values.
+    """
 
     metric_input_type = MetricInputType.dist_to_dist
 
-    def initialize(self):
-        raise NotImplementedError()
-
     def evaluate(self, estimate, reference):
         raise NotImplementedError()
 
-    def finalize(self):
-        raise NotImplementedError()
-
 
 class DistToPointMetric(BaseMetric):
+    """A base class for metrics that require a distribution as the estimated
+    value and a point estimate as the reference value.
+    """
 
     metric_input_type = MetricInputType.dist_to_point
 
-    def initialize(self):
-        raise NotImplementedError()
-
     def evaluate(self, estimate, reference):
         raise NotImplementedError()
 
-    def finalize(self):
-        raise NotImplementedError()
-
 
 class PointToPointMetric(BaseMetric):
+    """A base class for metrics that require a point estimate as input for both
+    the estimated and reference values.
+    """
 
     metric_input_type = MetricInputType.point_to_point
 
-    def initialize(self):
-        raise NotImplementedError()
-
     def evaluate(self, estimate, reference):
         raise NotImplementedError()
 
-    def finalize(self):
-        raise NotImplementedError()
 
 class PointToDistMetric(BaseMetric):
+    """A base class for metrics that require a point estimate as the estimated
+    value and a distribution as the reference value.
+    """
 
     metric_input_type = MetricInputType.point_to_dist
 
-    def initialize(self):
-        raise NotImplementedError()
-
     def evaluate(self, estimate, reference):
         raise NotImplementedError()
-
-    def finalize(self):
-        raise NotImplementedError()