185 more pylint cleanup

docs/non-developer-mode-example/des_y1_3x2pt.py

@@ -114,11 +114,7 @@
 """
     We load the correct SACC file.
 """
-saccfile = os.path.expanduser(
-    os.path.expandvars(
-        "des_y1_3x2pt_sacc_data.fits"
-    )
-)
+saccfile = os.path.expanduser(os.path.expandvars("des_y1_3x2pt_sacc_data.fits"))
 sacc_data = sacc.Sacc.load_fits(saccfile)
 
 """

examples/cosmicshear/cosmicshear.py

@@ -79,4 +79,3 @@
     be used to compute the likelihood.
 """
 likelihood = lk
-

firecrown/likelihood/gauss_family/gauss_family.py

@@ -10,6 +10,8 @@
 from __future__ import annotations
 from typing import List, Optional
 from typing import final
+from abc import abstractmethod
+
 import numpy as np
 import scipy.linalg
 
@@ -20,7 +22,6 @@
 from ...updatable import UpdatableCollection
 from .statistic.statistic import Statistic
 from ...parameters import ParamsMap, RequiredParameters
-from abc import abstractmethod
 
 
 class GaussFamily(Likelihood):
@@ -58,42 +59,59 @@ def read(self, sacc_data: sacc.Sacc) -> None:
     @final
     def compute_chisq(self, cosmo: pyccl.Cosmology) -> float:
         """Calculate and return the chi-squared for the given cosmology."""
-
-        r = []
+        residuals = []
         theory_vector = []
         data_vector = []
         for stat in self.statistics:
             data, theory = stat.compute(cosmo)
-            r.append(np.atleast_1d(data - theory))
+            residuals.append(np.atleast_1d(data - theory))
             theory_vector.append(np.atleast_1d(theory))
             data_vector.append(np.atleast_1d(data))
 
-        r = np.concatenate(r, axis=0)
+        residuals = np.concatenate(residuals, axis=0)
         self.predicted_data_vector = np.concatenate(theory_vector)
         self.measured_data_vector = np.concatenate(data_vector)
-        x = scipy.linalg.solve_triangular(self.cholesky, r, lower=True)
+        # pylint: disable-next=C0103
+        x = scipy.linalg.solve_triangular(self.cholesky, residuals, lower=True)
         return np.dot(x, x)
 
     @final
-    def _update(self, params: ParamsMap):
+    def _update(self, params: ParamsMap) -> None:
+        """Implementation of the Likelihood interface method _update.
+
+        This updates all statistics and calls teh abstract method
+        _update_gaussian_family."""
         self.statistics.update(params)
         self._update_gaussian_family(params)
 
     @final
-    def _reset(self):
+    def _reset(self) -> None:
+        """Implementation of Likelihood interface method _reset.
+
+        This resets all statistics and calls the abstract method
+        _reset_gaussian_family."""
         self._reset_gaussian_family()
         self.statistics.reset()
 
     @abstractmethod
-    def _update_gaussian_family(self, params: ParamsMap):
-        pass
+    def _update_gaussian_family(self, params: ParamsMap) -> None:
+        """Abstract method to update GaussianFamily state. Must be implemented by all
+        subclasses."""
 
     @abstractmethod
-    def _reset_gaussian_family(self, params: ParamsMap):
-        pass
+    def _reset_gaussian_family(self) -> None:
+        """Abstract method to reset GaussianFamily state. Must be implemented by all
+        subclasses."""
 
     @final
     def required_parameters(self) -> RequiredParameters:
+        """Return a RequiredParameters object containing the information for
+        this Updatable.
+
+        This includes the required parameters for all statistics, as well as those
+        for the derived class.
+
+        Derived classes must implement required_parameters_gaussian_family."""
         stats_rp = self.statistics.required_parameters()
         stats_rp = self.required_parameters_gaussian_family() + stats_rp
 
@@ -102,4 +120,3 @@ def required_parameters(self) -> RequiredParameters:
     @abstractmethod
     def required_parameters_gaussian_family(self):
         """Required parameters for GaussFamily subclasses."""
-        pass

firecrown/likelihood/gauss_family/statistic/__init__.py

@@ -1,12 +1,4 @@
-"""
-
-Gaussian Family Statistic Package
-=================================
-
-This package provides statistics objects to be used by Gaussian Family
+"""This package provides statistics objects to be used by Gaussian Family
 likelihoods.
-
-
 """
-
 # flake8: noqa

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

@@ -1,11 +1,4 @@
-"""
-
-Two Point Source Package
-========================
-
-This package contains the base class :class:`Source` for :class:`TwoPoint` 
+"""This package contains the base class :class:`Source` for :class:`TwoPoint`
 sources and implementations.
-
 """
-
 # flake8: noqa

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

@@ -1,9 +1,4 @@
-"""
-
-Number Counts Source Module
-===========================
-
-The classe in this file define ...
+"""Number counts source and systematics
 
 """
 
@@ -26,10 +21,10 @@
 
 @dataclass(frozen=True)
 class NumberCountsArgs:
-    """Class for weak lensing tracer builder argument."""
+    """Class for number counts tracer builder argument."""
 
     scale: float
-    z: np.ndarray
+    z: np.ndarray  # pylint: disable-msg=invalid-name
     dndz: np.ndarray
     bias: np.ndarray
     mag_bias: np.ndarray
@@ -60,9 +55,6 @@ class LinearBiasSystematic(NumberCountsSystematic):
     z_piv : str
         The name of the pivot redshift parameter for the linear bias.
 
-    Methods
-    -------
-    apply : apply the systematic to a source
     """
 
     params_names = ["alphaz", "alphag", "z_piv"]
@@ -85,7 +77,9 @@ def _update(self, params: ParamsMap):
 
     @final
     def _reset(self) -> None:
-        pass
+        """Reset this systematic.
+
+        This implementation has nothing to do."""
 
     @final
     def required_parameters(self) -> RequiredParameters:
@@ -161,7 +155,9 @@ def _update(self, params: ParamsMap):
 
     @final
     def _reset(self) -> None:
-        pass
+        """Reset this systematic.
+
+        This implementation has nothing to do."""
 
     @final
     def required_parameters(self) -> RequiredParameters:
@@ -183,13 +179,13 @@ def apply(
         """
 
         z_bar = self.z_c + self.z_m * (self.r_lim - 24.0)
-        z = tracer_arg.z
         # The slope of log(n_tot(z,r_lim)) with respect to r_lim
         # where n_tot(z,r_lim) is the luminosity function after using fit (C.1)
+        # pylint: disable-next=invalid-name
         s = (
             self.eta / self.r_lim
             - 3.0 * self.z_m / z_bar
-            + 1.5 * self.z_m * np.power(z / z_bar, 1.5) / z_bar
+            + 1.5 * self.z_m * np.power(tracer_arg.z / z_bar, 1.5) / z_bar
         )
 
         return NumberCountsArgs(
@@ -221,7 +217,9 @@ def _update(self, params: ParamsMap):
 
     @final
     def _reset(self) -> None:
-        pass
+        """Reset this systematic.
+
+        This implementation has nothing to do."""
 
     @final
     def required_parameters(self) -> RequiredParameters:
@@ -247,6 +245,8 @@ def apply(self, cosmo: pyccl.Cosmology, tracer_arg: NumberCountsArgs):
 
 
 class NumberCounts(Source):
+    """Source class for number counts."""
+
     params_names = ["bias", "mag_bias"]
     bias: float
     mag_bias: Optional[float]
@@ -257,10 +257,10 @@ class NumberCounts(Source):
     def __init__(
         self,
         *,
-        sacc_tracer,
-        has_rsd=False,
-        has_mag_bias=False,
-        scale=1.0,
+        sacc_tracer: str,
+        has_rsd: bool = False,
+        has_mag_bias: bool = False,
+        scale: float = 1.0,
         systematics: Optional[List[NumberCountsSystematic]] = None,
     ):
         super().__init__()

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

@@ -1,9 +1,4 @@
-"""
-
-Two Point Statistic Source Module
-=================================
-
-The class in this file define ...
+"""Abstract base classes for GaussianFamily statistics.
 
 """
 
@@ -53,41 +48,53 @@ def read(self, sacc_data: sacc.Sacc):
 
     @abstractmethod
     def _read(self, sacc_data: sacc.Sacc):
-        """Read the data for this source from the SACC file."""
-        pass
+        """Abstract method to read the data for this source from the SACC file."""
 
     @abstractmethod
     def _update_source(self, params: ParamsMap):
-        pass
+        """Abstract method to update the source from the given ParamsMap."""
 
     @abstractmethod
     def _reset_source(self):
-        pass
+        """Abstract method to reset the source."""
 
     @final
     def _update(self, params: ParamsMap):
+        """Implementation of Updatable interface method `_update`.
+
+        This clears the current hash and tracer, and calls the abstract method
+        `_update_source`, which must be implemented in all subclasses."""
         self.cosmo_hash = None
         self.tracer = None
         self._update_source(params)
 
     @final
     def _reset(self) -> None:
+        """Implementation of the Updatable interface method `_reset`.
+
+        This calls the abstract method `_reset_source`, which must be implemented by
+        all subclasses."""
         self._reset_source()
 
     @abstractmethod
     def get_scale(self) -> float:
-        pass
+        """Abstract method to return the scale for this `Source`."""
 
     @abstractmethod
     def create_tracer(self, cosmo: pyccl.Cosmology):
-        pass
+        """Abstract method to create a tracer for this `Source`, for the given
+        cosmology."""
 
     @final
     def get_tracer(self, cosmo: pyccl.Cosmology) -> pyccl.tracers.Tracer:
+        """Return the tracer for the given cosmology.
+
+        This method caches its result, so if called a second time with the same
+        cosmology, no calculation needs to be done."""
         cur_hash = hash(cosmo)
         if hasattr(self, "cosmo_hash") and self.cosmo_hash == cur_hash:
             return self.tracer
-        else:
-            self.tracer, _ = self.create_tracer(cosmo)
-            self.cosmo_hash = cur_hash
-            return self.tracer
+
+        self.tracer, _ = self.create_tracer(cosmo)
+        self.cosmo_hash = cur_hash
+        return self.tracer