clean out inits (#38)

* clean out inits

* [pre-commit.ci] auto fixes from pre-commit hooks

* dataclusters.py, modelevaluators/aicc and modelparts.py

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: sbillinge

Diff for: diffpy/srmise/baselines/__init__.py

@@ -11,11 +11,3 @@
 # See LICENSE.txt for license information.
 #
 ##############################################################################
-
-__all__ = ["base", "arbitrary", "fromsequence", "nanospherical", "polynomial"]
-
-from arbitrary import Arbitrary
-from base import Baseline
-from fromsequence import FromSequence
-from nanospherical import NanoSpherical
-from polynomial import Polynomial

Diff for: diffpy/srmise/dataclusters.py

@@ -365,11 +365,11 @@ def cut(self, idx):
     def cluster_boundaries(self):
         """Return sequence with (x,y) of all cluster boundaries."""
         boundaries = []
-        for l in self.clusters:
-            xlo = np.mean(self.x[l[0] - 1 : l[0] + 1])
-            ylo = np.mean(self.y[l[0] - 1 : l[0] + 1])
-            xhi = np.mean(self.x[l[1] : l[1] + 2])
-            yhi = np.mean(self.y[l[1] : l[1] + 2])
+        for cluster in self.clusters:
+            xlo = np.mean(self.x[cluster[0] - 1 : cluster[0] + 1])
+            ylo = np.mean(self.y[cluster[0] - 1 : cluster[0] + 1])
+            xhi = np.mean(self.x[cluster[1] : cluster[1] + 2])
+            yhi = np.mean(self.y[cluster[1] : cluster[1] + 2])
             boundaries.append((xlo, ylo))
             boundaries.append((xhi, yhi))
         return np.unique(boundaries)
@@ -416,9 +416,9 @@ def animate(self):
                     all_lines[i].set_ydata([0, height])
                     ax.draw_artist(all_lines[i])
                 else:
-                    l = plt.axvline(b[0], 0, height, color="k", animated=True)
-                    ax.draw_artist(l)
-                    all_lines.append(l)
+                    line = plt.axvline(b[0], 0, height, color="k", animated=True)
+                    ax.draw_artist(line)
+                    all_lines.append(line)
             canvas.blit(ax.bbox)
 
         self.clusters = clusters

Diff for: diffpy/srmise/modelevaluators/__init__.py

@@ -11,8 +11,3 @@
 # See LICENSE.txt for license information.
 #
 ##############################################################################
-
-__all__ = ["base", "aic", "aicc"]
-
-from aic import AIC
-from aicc import AICc

Diff for: diffpy/srmise/modelevaluators/aicc.py

@@ -16,7 +16,6 @@
 
 import numpy as np
 
-import diffpy.srmise.srmiselog
 from diffpy.srmise.modelevaluators.base import ModelEvaluator
 from diffpy.srmise.srmiseerrors import SrMiseModelEvaluatorError
 
@@ -72,7 +71,7 @@ def evaluate(self, fit, count_fixed=False, kshift=0):
             logger.warn("AICc.evaluate(): too few data to evaluate quality reliably.")
             n = self.minpoints(k)
 
-        if self.chisq == None:
+        if self.chisq is None:
             self.chisq = self.chi_squared(fit.value(), fit.y_cluster, fit.error_cluster)
 
         self.stat = self.chisq + self.parpenalty(k, n)
@@ -96,10 +95,12 @@ def parpenalty(self, k, n):
         return (2 * k + float(2 * k * (k + 1)) / (n - k - 1)) * fudgefactor
 
     def growth_justified(self, fit, k_prime):
-        """Returns whether adding k_prime parameters to the given model (ModelCluster) is justified given the current quality of the fit.
-        The assumption is that adding k_prime parameters will result in "effectively 0" chiSquared cost, and so adding it is justified
-        if the cost of adding these parameters is less than the current chiSquared cost.  The validity of this assumption (which
-        depends on an unknown chiSquared value) and the impact of the errors used should be examined more thoroughly in the future.
+        """Is adding k_prime parameters to ModelCluster justified given the current quality of the fit.
+
+        The assumption is that adding k_prime parameters will result in "effectively 0" chiSquared cost,
+        and so adding it is justified if the cost of adding these parameters is less than the current
+        chiSquared cost.  The validity of this assumption (which depends on an unknown chiSquared value)
+        and the impact of the errors used should be examined more thoroughly in the future.
         """
 
         if self.chisq is None:

Diff for: diffpy/srmise/modelparts.py

@@ -21,20 +21,18 @@
 
 import logging
 
-import numpy as np
-from scipy.optimize import leastsq
-
-from diffpy.srmise import srmiselog
-from diffpy.srmise.srmiseerrors import *
-
-logger = logging.getLogger("diffpy.srmise")
-
 import matplotlib.pyplot as plt
+import numpy as np
 
 # Output of scipy.optimize.leastsq for a single parameter changed in scipy 0.8.0
 # Before it returned a scalar, later it returned an array of length 1.
 import pkg_resources as pr
+from scipy.optimize import leastsq
 
+from diffpy.srmise import srmiselog
+from diffpy.srmise.srmiseerrors import SrMiseFitError, SrMiseStaticOwnerError, SrMiseUndefinedCovarianceError
+
+logger = logging.getLogger("diffpy.srmise")
 __spv__ = pr.get_distribution("scipy").version
 __oldleastsqbehavior__ = pr.parse_version(__spv__) < pr.parse_version("0.8.0")
 
@@ -98,7 +96,7 @@ def fit(
             # raise SrMiseFitError(emsg)
             return
 
-        if range == None:
+        if range is None:
             range = slice(None)
 
         args = (r, y, y_error, range)
@@ -203,7 +201,7 @@ def fit(
             cov.setcovariance(self, pcov * np.sum(fvec**2) / dof)
             try:
                 cov.transform(in_format="internal", out_format=cov_format)
-            except SrMiseUndefinedCovarianceError as e:
+            except SrMiseUndefinedCovarianceError:
                 logger.warn("Covariance not defined.  Fit may not have converged.")
 
         return
@@ -332,7 +330,7 @@ def covariance(self, format="internal", **kwds):
 
         for k, v in kwds.items():
             try:
-                idx = int(k[1:])
+                int(k[1:])
             except ValueError:
                 emsg = "Invalid format keyword '%s'.  They must be specified as 'f0', 'f1', etc." % k
                 raise ValueError(emsg)
@@ -559,7 +557,7 @@ def npars(self, count_fixed=True):
         if count_fixed:
             return self._owner.npars
         else:
-            return (self.free == True).sum()
+            return (self.free is True).sum()
 
     def __str__(self):
         """Return string representation of ModelPart parameters."""

Diff for: diffpy/srmise/peaks/__init__.py

@@ -11,5 +11,3 @@
 # See LICENSE.txt for license information.
 #
 ##############################################################################
-
-__all__ = ["base", "gaussian", "gaussianoverr", "terminationripples"]