Merge pull request #1 from bobleesj/black

PR Part 1/3 of Cookiecutter pre-commit workflow (black)

Author: sbillinge

.flake8

@@ -0,0 +1,11 @@
+[flake8]
+exclude =
+    .git,
+    __pycache__,
+    build,
+    dist,
+    doc/source/conf.py
+max-line-length = 115
+# Ignore some style 'errors' produced while formatting by 'black'
+# https://black.readthedocs.io/en/stable/guides/using_black_with_other_tools.html#labels-why-pycodestyle-warnings
+extend-ignore = E203

diffpy/__init__.py

@@ -17,7 +17,7 @@
 """
 
 
-__import__('pkg_resources').declare_namespace(__name__)
+__import__("pkg_resources").declare_namespace(__name__)
 
 
-# End of file
+# End of file

diffpy/nmf_mapping/__init__.py

@@ -19,8 +19,9 @@
 __id__ = "$Id$"
 
 # obtain version information
-__version__ = '1.0.0'
+__version__ = "1.0.0"
 
 # top-level import
 from diffpy.nmf_mapping.nmf_mapping import nmf_mapping_code as nmf
+
 # End of file

diffpy/nmf_mapping/nmf_mapping/main.py

@@ -1,4 +1,3 @@
-
 import os
 import sys
 from argparse import ArgumentParser, RawTextHelpFormatter, Namespace
@@ -11,11 +10,11 @@
 
 def boolean_string(s):
     try:
-        if s.lower() not in {'false', 'true'}:
-            raise ValueError('Not a valid boolean string')
+        if s.lower() not in {"false", "true"}:
+            raise ValueError("Not a valid boolean string")
     except AttributeError:
-        raise ValueError('Not a valid boolean string')
-    return s.lower() == 'true'
+        raise ValueError("Not a valid boolean string")
+    return s.lower() == "true"
 
 
 def main(args=None):
@@ -31,63 +30,105 @@ def main(args=None):
     as well as the reconstruction error as a fxn of component
     """
 
-    parser = ArgumentParser(prog='nmf_mapping',
-                            description=_BANNER, formatter_class=RawTextHelpFormatter)
+    parser = ArgumentParser(prog="nmf_mapping", description=_BANNER, formatter_class=RawTextHelpFormatter)
 
     def tup(s):
         try:
-            l, h = map(int, s.split(','))
-            return l,h
+            l, h = map(int, s.split(","))
+            return l, h
         except:
-            raise TypeError('r range must be low, high')
+            raise TypeError("r range must be low, high")
 
     # args
-    parser.add_argument("directory", default=None, type=str,
-                        help="a directory of PDFs to calculate NMF decomposition")
+    parser.add_argument(
+        "directory", default=None, type=str, help="a directory of PDFs to calculate NMF decomposition"
+    )
     group = parser.add_mutually_exclusive_group()
-    parser.add_argument("--save-files", default=True, type=boolean_string,
-                        help='whether to save the component, graph, and json files in the execution directory\n'
-                             'default: True\n'
-                             'e.g. --save-files False')
-    group.add_argument("--threshold", default=None, type=int,
-                        help="a threshold for the number of structural phases graphed (NMF components returned)\n"
-                             "e.g. --threshold 3")
-    group.add_argument("--improve-thresh", default=None, type=float,
-                        help="a threshold (between 0 and 1) for the relative improvement ratio necessary to add an"
-                             " additional component. Default is 0.001. 0.1 Recommended for real data.\n"
-                             "e.g. --improve-thresh 0.1")
-    group.add_argument("--pca-thresh", default=None, type=float,
-                       help="a threshold (between 0 and 1) for the explained variance of PCA to determine the \n"
-                            "number of components for NMF. e.g. --pca-thresh 0.95")
-    parser.add_argument("--n-iter", default=None, type=int,
-                        help="total number of iterations to run NMF algo. Defaults to 1000. 10000 typical to publish.")
-    parser.add_argument("--xrd", default=False, type=boolean_string,
-                        help="whether to look for .xy files rather than .gr files\n"
-                             "default: False\n"
-                             "e.g. --xrd True")
-    parser.add_argument("--x_units", default=None, type=str, choices=["twotheta", "q"], required='--xrd' in sys.argv,
-                        help="x axis units for XRD data\n"
-                             "default: None\n"
-                             "e.g. --x_units twotheta")
-    parser.add_argument("--xrange", default=None, type=tup, nargs='*',
-                        help="the x-range over which to calculate NMF, can be multiple ranges (e.g. --xrange 5,10 12,15)")
-    parser.add_argument("--show", default=True, type=boolean_string,
-                        help='whether to show the plot')
+    parser.add_argument(
+        "--save-files",
+        default=True,
+        type=boolean_string,
+        help="whether to save the component, graph, and json files in the execution directory\n"
+        "default: True\n"
+        "e.g. --save-files False",
+    )
+    group.add_argument(
+        "--threshold",
+        default=None,
+        type=int,
+        help="a threshold for the number of structural phases graphed (NMF components returned)\n"
+        "e.g. --threshold 3",
+    )
+    group.add_argument(
+        "--improve-thresh",
+        default=None,
+        type=float,
+        help="a threshold (between 0 and 1) for the relative improvement ratio necessary to add an"
+        " additional component. Default is 0.001. 0.1 Recommended for real data.\n"
+        "e.g. --improve-thresh 0.1",
+    )
+    group.add_argument(
+        "--pca-thresh",
+        default=None,
+        type=float,
+        help="a threshold (between 0 and 1) for the explained variance of PCA to determine the \n"
+        "number of components for NMF. e.g. --pca-thresh 0.95",
+    )
+    parser.add_argument(
+        "--n-iter",
+        default=None,
+        type=int,
+        help="total number of iterations to run NMF algo. Defaults to 1000. 10000 typical to publish.",
+    )
+    parser.add_argument(
+        "--xrd",
+        default=False,
+        type=boolean_string,
+        help="whether to look for .xy files rather than .gr files\n" "default: False\n" "e.g. --xrd True",
+    )
+    parser.add_argument(
+        "--x_units",
+        default=None,
+        type=str,
+        choices=["twotheta", "q"],
+        required="--xrd" in sys.argv,
+        help="x axis units for XRD data\n" "default: None\n" "e.g. --x_units twotheta",
+    )
+    parser.add_argument(
+        "--xrange",
+        default=None,
+        type=tup,
+        nargs="*",
+        help="the x-range over which to calculate NMF, can be multiple ranges (e.g. --xrange 5,10 12,15)",
+    )
+    parser.add_argument("--show", default=True, type=boolean_string, help="whether to show the plot")
     args0 = Namespace()
     args1, _ = parser.parse_known_args(args, namespace=args0)
 
     input_list, data_list = nmf.load_data(args1.directory, args1.xrd)
     if args1.pca_thresh:
-        df_components, df_component_weight_timeseries, df_reconstruction_error, df_explained_var_ratio = \
-            nmf.NMF_decomposition(input_list, args1.xrange, args1.threshold, additional_comp=False,
-                                                improve_thresh=args1.improve_thresh, n_iter=args1.n_iter,
-                                                pca_thresh=args1.pca_thresh)
+        df_components, df_component_weight_timeseries, df_reconstruction_error, df_explained_var_ratio = (
+            nmf.NMF_decomposition(
+                input_list,
+                args1.xrange,
+                args1.threshold,
+                additional_comp=False,
+                improve_thresh=args1.improve_thresh,
+                n_iter=args1.n_iter,
+                pca_thresh=args1.pca_thresh,
+            )
+        )
     else:
-        df_components, df_component_weight_timeseries, df_reconstruction_error = \
-            nmf.NMF_decomposition(input_list, args1.xrange, args1.threshold, additional_comp=False,
-                                                improve_thresh=args1.improve_thresh, n_iter=args1.n_iter)
+        df_components, df_component_weight_timeseries, df_reconstruction_error = nmf.NMF_decomposition(
+            input_list,
+            args1.xrange,
+            args1.threshold,
+            additional_comp=False,
+            improve_thresh=args1.improve_thresh,
+            n_iter=args1.n_iter,
+        )
 
-    print(f'Number of components: {len(df_components.columns)}')
+    print(f"Number of components: {len(df_components.columns)}")
 
     fig1 = nmf.component_plot(df_components, args1.xrd, args1.x_units, args1.show)
     fig2 = nmf.component_ratio_plot(df_component_weight_timeseries, args1.show)
@@ -96,35 +137,44 @@ def tup(s):
         fig4 = nmf.explained_variance_plot(df_explained_var_ratio, args1.show)
 
     if args1.save_files:
-        if not os.path.exists(os.path.join(os.getcwd(), 'nmf_result')):
-            os.mkdir(os.path.join(os.getcwd(), 'nmf_result'))
-        output_fn = datetime.fromtimestamp(time.time()).strftime(
-            '%Y%m%d%H%M%S%f')
-        df_components.to_json(os.path.join(os.getcwd(), 'nmf_result', 'x_index_vs_y_col_components.json'))
-        df_component_weight_timeseries.to_json(os.path.join(os.getcwd(), 'nmf_result', 'component_index_vs_pratio_col.json'))
-        df_component_weight_timeseries.to_csv(os.path.join(os.getcwd(), 'nmf_result', output_fn + 'component_row_pratio_col.txt'), header=None, index=False, sep=' ', mode='a')
-        df_reconstruction_error.to_json(os.path.join(os.getcwd(), 'nmf_result', 'component_index_vs_RE_value.json'))
-        plot_file1 = os.path.join(os.getcwd(), 'nmf_result', output_fn + "comp_plot.png")
-        plot_file2 = os.path.join(os.getcwd(), 'nmf_result', output_fn + "ratio_plot.png")
-        plot_file3 = os.path.join(os.getcwd(), 'nmf_result', output_fn + "loss_plot.png")
+        if not os.path.exists(os.path.join(os.getcwd(), "nmf_result")):
+            os.mkdir(os.path.join(os.getcwd(), "nmf_result"))
+        output_fn = datetime.fromtimestamp(time.time()).strftime("%Y%m%d%H%M%S%f")
+        df_components.to_json(os.path.join(os.getcwd(), "nmf_result", "x_index_vs_y_col_components.json"))
+        df_component_weight_timeseries.to_json(
+            os.path.join(os.getcwd(), "nmf_result", "component_index_vs_pratio_col.json")
+        )
+        df_component_weight_timeseries.to_csv(
+            os.path.join(os.getcwd(), "nmf_result", output_fn + "component_row_pratio_col.txt"),
+            header=None,
+            index=False,
+            sep=" ",
+            mode="a",
+        )
+        df_reconstruction_error.to_json(
+            os.path.join(os.getcwd(), "nmf_result", "component_index_vs_RE_value.json")
+        )
+        plot_file1 = os.path.join(os.getcwd(), "nmf_result", output_fn + "comp_plot.png")
+        plot_file2 = os.path.join(os.getcwd(), "nmf_result", output_fn + "ratio_plot.png")
+        plot_file3 = os.path.join(os.getcwd(), "nmf_result", output_fn + "loss_plot.png")
         if args1.pca_thresh:
-            plot_file7 = os.path.join(os.getcwd(), 'nmf_result', output_fn + "pca_var_plot.png")
-        plot_file4 = os.path.splitext(plot_file1)[0] + '.pdf'
-        plot_file5 = os.path.splitext(plot_file2)[0] + '.pdf'
-        plot_file6 = os.path.splitext(plot_file3)[0] + '.pdf'
+            plot_file7 = os.path.join(os.getcwd(), "nmf_result", output_fn + "pca_var_plot.png")
+        plot_file4 = os.path.splitext(plot_file1)[0] + ".pdf"
+        plot_file5 = os.path.splitext(plot_file2)[0] + ".pdf"
+        plot_file6 = os.path.splitext(plot_file3)[0] + ".pdf"
         if args1.pca_thresh:
-            plot_file8 = os.path.splitext(plot_file7)[0] + '.pdf'
-        txt_file = os.path.join(os.getcwd(), 'nmf_result', output_fn + '_meta' + '.txt')
-        with open(txt_file, 'w+') as fi:
-            fi.write('NMF Analysis\n\n')
-            fi.write(f'{len(df_component_weight_timeseries.columns)} files uploaded for analysis.\n\n')
-            fi.write(f'The selected active r ranges are:  {args1.xrange} \n\n')
-            fi.write('Thesholding:\n')
-            fi.write(f'\tThe input component threshold was: {args1.threshold}\n')
-            fi.write(f'\tThe input improvement threshold was: {args1.improve_thresh}\n')
-            fi.write(f'\tThe input # of iterations to run was: {args1.n_iter}\n')
-            fi.write(f'\tWas PCA thresholding used?: {args1.pca_thresh}\n')
-            fi.write(f'{len(df_components.columns)} components were extracted')
+            plot_file8 = os.path.splitext(plot_file7)[0] + ".pdf"
+        txt_file = os.path.join(os.getcwd(), "nmf_result", output_fn + "_meta" + ".txt")
+        with open(txt_file, "w+") as fi:
+            fi.write("NMF Analysis\n\n")
+            fi.write(f"{len(df_component_weight_timeseries.columns)} files uploaded for analysis.\n\n")
+            fi.write(f"The selected active r ranges are:  {args1.xrange} \n\n")
+            fi.write("Thesholding:\n")
+            fi.write(f"\tThe input component threshold was: {args1.threshold}\n")
+            fi.write(f"\tThe input improvement threshold was: {args1.improve_thresh}\n")
+            fi.write(f"\tThe input # of iterations to run was: {args1.n_iter}\n")
+            fi.write(f"\tWas PCA thresholding used?: {args1.pca_thresh}\n")
+            fi.write(f"{len(df_components.columns)} components were extracted")
 
         fig1.savefig(plot_file1)
         fig2.savefig(plot_file2)
@@ -141,14 +191,21 @@ def tup(s):
             data = np.column_stack([df_components.index.to_list(), df_components[col].to_list()])
 
             if args1.xrd:
-                np.savetxt(os.path.join(os.getcwd(), 'nmf_result', output_fn + f'_comp{i}' + '.xy'), data,
-                           header=f"NMF Generated XRD\nSource = nmfMapping\n"
-                                  f"Date = {output_fn}\n{args1.x_units} Intensity\n", fmt='%s',
-                           comments="' ")
+                np.savetxt(
+                    os.path.join(os.getcwd(), "nmf_result", output_fn + f"_comp{i}" + ".xy"),
+                    data,
+                    header=f"NMF Generated XRD\nSource = nmfMapping\n"
+                    f"Date = {output_fn}\n{args1.x_units} Intensity\n",
+                    fmt="%s",
+                    comments="' ",
+                )
             else:
-                np.savetxt(os.path.join(os.getcwd(), 'nmf_result', output_fn + f'_comp{i}' + '.cgr'), data,
-                           header=f"NMF Generated PDF\nSource: nmfMapping\n"
-                                  f"Date: {output_fn}\nr g", fmt='%s')
+                np.savetxt(
+                    os.path.join(os.getcwd(), "nmf_result", output_fn + f"_comp{i}" + ".cgr"),
+                    data,
+                    header=f"NMF Generated PDF\nSource: nmfMapping\n" f"Date: {output_fn}\nr g",
+                    fmt="%s",
+                )
 
 
 if __name__ == "__main__":