217 more pylint cleanup (#218)

* Simplify flow of control re: pylint

* Improve variable naming re: pylint

* Add documentation to functions

* Fix error in name of descriptor

* Remove use of warnings.UserWarning

The name warnings.UserWarning does not exist. The default exception
type used by `warn` is just UserWarning. Since this is the default,
there is no actual need to specify it.

* Simply control flow re: pylint

* Add type annotation for mypy

firecrown/likelihood/gauss_family/statistic/source/number_counts.py

@@ -251,7 +251,7 @@ class NumberCounts(Source):
     """Source class for number counts."""
 
     systematics: UpdatableCollection
-    tracer_arg: NumberCountsArgs
+    tracer_args: NumberCountsArgs
 
     def __init__(
         self,
@@ -278,7 +278,7 @@ def __init__(
                 self.systematics.append(systematic)
 
         self.scale = scale
-        self.current_tracer_args = None
+        self.current_tracer_args: Optional[NumberCountsArgs] = None
         self.scale_ = None
         self.tracer_ = None
 

firecrown/likelihood/gauss_family/statistic/two_point.py

@@ -238,7 +238,6 @@ def read(self, sacc_data):
             warnings.warn(
                 f"Tracers '{tracers}' have 2pt data and you have specified "
                 "`ell_or_theta` in the configuration. `ell_or_theta` is being ignored!",
-                warnings.UserWarning,
                 stacklevel=2,
             )
 
@@ -315,12 +314,11 @@ def log_interpolator(x, y):
                         np.log(x), np.log(y), ext=2
                     )
                     return lambda x_, intp=intp: np.exp(intp(np.log(x_)))
-                else:
-                    # only use log for x
-                    intp = scipy.interpolate.InterpolatedUnivariateSpline(
-                        np.log(x), y, ext=2
-                    )
-                    return lambda x_, intp=intp: intp(np.log(x_))
+                # only use log for x
+                intp = scipy.interpolate.InterpolatedUnivariateSpline(
+                    np.log(x), y, ext=2
+                )
+                return lambda x_, intp=intp: intp(np.log(x_))
 
             theory_interpolator = log_interpolator(self.ell_or_theta_, theory_vector)
             ell = self.theory_window_function.values

firecrown/likelihood/likelihood.py

@@ -109,16 +109,15 @@ def load_likelihood(
                 f"Firecrown initialization script {filename} does not define "
                 f"a `build_likelihood` factory function."
             )
-        else:
-            warnings.simplefilter("always", DeprecationWarning)
-            warnings.warn(
-                "The use of a likelihood variable in Firecrown's initialization "
-                "script is deprecated. Any parameters passed to the likelihood "
-                "will be ignored. The script should define a `build_likelihood` "
-                "factory function.",
-                category=DeprecationWarning,
-            )
-            likelihood = mod.likelihood
+        warnings.simplefilter("always", DeprecationWarning)
+        warnings.warn(
+            "The use of a likelihood variable in Firecrown's initialization "
+            "script is deprecated. Any parameters passed to the likelihood "
+            "will be ignored. The script should define a `build_likelihood` "
+            "factory function.",
+            category=DeprecationWarning,
+        )
+        likelihood = mod.likelihood
     else:
         if not callable(mod.build_likelihood):
             raise TypeError(

firecrown/parameters.py

@@ -42,6 +42,10 @@ def __init__(self, *args, **kwargs) -> None:
         self.lower_case: bool = False
 
     def use_lower_case_keys(self, enable: bool) -> None:
+        """Control whether keys will be translated into lower case.
+        If `enable` is True, such translation will be done.
+        This can help make sure code works with CosmoSIS, because such translation
+        is done inside CosmoSIS itself."""
         self.lower_case = enable
 
     def get_from_prefix_param(self, prefix: Optional[str], param: str) -> float:
@@ -161,8 +165,8 @@ def __init__(self, derived_parameters: List[DerivedParameter]):
 
         self.derived_parameters: Dict[str, DerivedParameter] = {}
 
-        for dp in derived_parameters:
-            self.add_required_parameter(dp)
+        for parameter in derived_parameters:
+            self.add_required_parameter(parameter)
 
     def __add__(self, other: Optional[DerivedParameterCollection]):
         """Return a new DerivedParameterCollection with the lists of DerivedParameter
@@ -230,9 +234,11 @@ def __init__(self):
         self.value = None
 
     def set_value(self, value: float):
+        """Set the value of this parameter."""
         self.value = value
 
     def get_value(self) -> float:
+        """Get the current value of this parameter."""
         return self.value
 
 
@@ -245,13 +251,25 @@ def __init__(self, value: float):
         self.value = value
 
     def set_value(self, value: float):
+        """Set the value of this parameter."""
         self.value = value
 
     def get_value(self) -> float:
+        """Return the current value of this parameter."""
         return self.value
 
 
 def create(value: Optional[float] = None):
+    """Create a new parameter.
+
+    If `value` is `None`, the result will be a `SamplerParameter`; Firecrown
+    will expect this value to be supplied by the sampling framwork. If `value`
+    is a `float` quantity, then Firecrown will expect this parameter to *not*
+    be supplied by the sampling framework, and instead the provided value will
+    be used for every sample.
+
+    Only `None` or a `float` value is allowed.
+    """
     if value is None:
         return SamplerParameter()
     return InternalParameter(value)