lint check for flake8 issue

devutils/prep.py

@@ -88,7 +88,7 @@ def rm(directory, filerestr):
     rm("../doc/examples/output", r"known_dG.*\.pwa")
     rm("../doc/examples/output", r"unknown_dG.*\.pwa")
 
-    ### Testing examples
+    # Testing examples
     examples = Test()
     test_names = [
         "extract_single_peak",
@@ -110,7 +110,7 @@ def rm(directory, filerestr):
 
     examples.report()
 
-    ### Convert output of example files to Unix-style endlines for sdist.
+    # Convert output of example files to Unix-style endlines for sdist.
     if os.linesep != "\n":
         print("==== Scrubbing Endlines ====")
         # All *.srmise and *.pwa files in examples directory.

diffpy/srmise/basefunction.py

@@ -120,7 +120,7 @@ def __init__(
             emsg = "Argument default_formats must specify 'default_input' " + "and 'default_output' as keys."
             raise ValueError(emsg)
         for f in self.default_formats.values():
-            if not f in self.parformats:
+            if f not in self.parformats:
                 emsg = "Keys of argument default_formats must map to a " + "value within argument parformats."
                 raise ValueError()
 
@@ -140,7 +140,7 @@ def __init__(
             pass
         return
 
-    #### "Virtual" class methods ####
+    # "Virtual" class methods ####
 
     def actualize(self, *args, **kwds):
         """Create ModelPart instance of self with given parameters.  ("Virtual" method)"""
@@ -172,7 +172,7 @@ def _valueraw(self, *args, **kwds):
         emsg = "_valueraw must() be implemented in a BaseFunction subclass."
         raise NotImplementedError(emsg)
 
-    #### Class methods ####
+    # Class methods ####
 
     def jacobian(self, p, r, rng=None):
         """Calculate jacobian of p, possibly restricted by range.
@@ -228,9 +228,9 @@ def transform_derivatives(self, pars, in_format=None, out_format=None):
         elif out_format == "default_input":
             out_format = self.default_formats["default_input"]
 
-        if not in_format in self.parformats:
+        if in_format not in self.parformats:
             raise ValueError("Argument 'in_format' must be one of %s." % self.parformats)
-        if not out_format in self.parformats:
+        if out_format not in self.parformats:
             raise ValueError("Argument 'out_format' must be one of %s." % self.parformats)
         if in_format == out_format:
             return np.identity(self.npars)
@@ -263,9 +263,9 @@ def transform_parameters(self, pars, in_format=None, out_format=None):
         elif out_format == "default_input":
             out_format = self.default_formats["default_input"]
 
-        if not in_format in self.parformats:
+        if in_format not in self.parformats:
             raise ValueError("Argument 'in_format' must be one of %s." % self.parformats)
-        if not out_format in self.parformats:
+        if out_format not in self.parformats:
             raise ValueError("Argument 'out_format' must be one of %s." % self.parformats)
         # if in_format == out_format:
         #    return pars
@@ -335,7 +335,7 @@ def writestr(self, baselist):
         """
         if self.base is not None and self.base not in baselist:
             emsg = "baselist does not include this BaseFunction's base function."
-            raise ValueError("emsg")
+            raise ValueError(emsg)
         lines = []
         # Write function type
         lines.append("function=%s" % repr(self.__class__.__name__))

diffpy/srmise/multimodelselection.py

@@ -529,41 +529,41 @@ def plot3dclassprobs(self, **kwds):
 
         ### Define face colors
         fc = np.array([len(self.classes[z]) for z in zlabels])
-        if class_size is "fraction":
+        if class_size == "fraction":
             fc = fc / float(len(self.results))
 
         # Index the colormap if necessary
-        if class_size is "number":
-            if norm is "auto":
+        if class_size == "number":
+            if norm == "auto":
                 indexedcolors = cmap(np.linspace(0.0, 1.0, np.max(fc)))
                 cmap = colors.ListedColormap(indexedcolors)
-            elif norm is "full":
+            elif norm == "full":
                 indexedcolors = cmap(np.linspace(0.0, 1.0, len(self.results)))
                 cmap = colors.ListedColormap(indexedcolors)
             # A user-specified norm cannot be used to index a colormap.
 
         # Create proper norms for "auto" and "full" types.
-        if norm is "auto":
-            if class_size is "number":
+        if norm == "auto":
+            if class_size == "number":
                 mic = np.min(fc)
                 mac = np.max(fc)
                 nc = mac - mic + 1
                 norm = colors.BoundaryNorm(np.linspace(mic, mac + 1, nc + 1), nc)
-            if class_size is "fraction":
+            if class_size == "fraction":
                 norm = colors.Normalize()
                 norm.autoscale(fc)
-        elif norm is "full":
+        elif norm == "full":
             mcolor = len(self.results)
-            if class_size is "number":
+            if class_size == "number":
                 norm = colors.BoundaryNorm(np.linspace(0, mcolor + 1, mcolor + 2), mcolor + 1)
-            if class_size is "fraction":
+            if class_size == "fraction":
                 norm = colors.Normalize(0.0, 1.0)
 
         zs = np.arange(len(zlabels))
 
         poly = PolyCollection(verts, facecolors=cmap(norm(fc)), closed=False)
         poly.set_alpha(p_alpha)
-        cax = ax.add_collection3d(poly, zs=zs, zdir="y")
+        ax.add_collection3d(poly, zs=zs, zdir="y")
 
         # Highlight values of interest
         color_idx = np.linspace(0, 1, len(highlight))
@@ -601,7 +601,7 @@ def plot3dclassprobs(self, **kwds):
             )
 
         if title is not False:
-            figtitle = fig.suptitle(title)
+            fig.suptitle(title)
 
         # Add colorbar
         if "cbpos" in kwds:
@@ -625,9 +625,9 @@ def plot3dclassprobs(self, **kwds):
 
         cb = colorbar.ColorbarBase(cbaxis, cmap=cmap, norm=norm, **kwds)
 
-        if class_size is "number":
+        if class_size == "number":
             cb.set_label("Models in class")
-        elif class_size is "fraction":
+        elif class_size == "fraction":
             cb.set_label("Fraction of models in class")
 
         return {"fig": fig, "axis": ax, "cb": cb, "cbaxis": cbaxis}