skpkg: apply black to all files in the project dir

Author: dabeycorn

.github/ISSUE_TEMPLATE/release_checklist.md

@@ -6,30 +6,41 @@ labels: "release"
 assignees: ""
 ---
 
-### PyPI/GitHub release checklist:
+### PyPI/GitHub rc-release preparation checklist:
 
 - [ ] All PRs/issues attached to the release are merged.
 - [ ] All the badges on the README are passing.
 - [ ] License information is verified as correct. If you are unsure, please comment below.
 - [ ] Locally rendered documentation contains all appropriate pages, including API references (check no modules are
-  missing), tutorials, and other human written text is up-to-date with any changes in the code.
-- [ ] Installation instructions in the README, documentation and on the website (e.g., diffpy.org) are updated.
+      missing), tutorials, and other human-written text is up-to-date with any changes in the code.
+- [ ] Installation instructions in the README, documentation, and the website are updated.
 - [ ] Successfully run any tutorial examples or do functional testing with the latest Python version.
 - [ ] Grammar and writing quality are checked (no typos).
+- [ ] Install `pip install build twine`, run `python -m build` and `twine check dist/*` to ensure that the package can be built and is correctly formatted for PyPI release.
 
-Please mention @sbillinge here when you are ready for PyPI/GitHub release. Include any additional comments necessary, such as
-version information and details about the pre-release here:
+Please tag the maintainer (e.g., @username) in the comment here when you are ready for the PyPI/GitHub release. Include any additional comments necessary, such as version information and details about the pre-release here:
 
-### conda-forge release checklist:
+### PyPI/GitHub full-release preparation checklist:
 
-<!-- After @sbillinge releases the PyPI package, please check the following when creating a PR for conda-forge release.-->
+- [ ] Create a new conda environment and install the rc from PyPI (`pip install <package-name>==??`)
+- [ ] License information on PyPI is correct.
+- [ ] Docs are deployed successfully to `https://<github-username-or-orgname>/<package-name>`.
+- [ ] Successfully run all tests, tutorial examples or do functional testing.
 
+Please let the maintainer know that all checks are done and the package is ready for full release.
+
+### conda-forge release preparation checklist:
+
+<!-- After the maintainer releases the PyPI package, please check the following when creating a PR for conda-forge release.-->
+
+- [ ] Ensure that the full release has appeared on PyPI successfully.
 - [ ] New package dependencies listed in `conda.txt` and `test.txt` are added to `meta.yaml` in the feedstock.
-- [ ] All relevant issues in the feedstock are addressed in the release PR.
+- [ ] Close any open issues on the feedstock. Reach out to the maintainer if you have questions.
+- [ ] Tag the maintainer for conda-forge release.
 
 ### Post-release checklist
 
 <!-- Before closing this issue, please complete the following: -->
 
-- [ ]  Run tutorial examples and conduct functional testing using the installation guide in the README. Attach screenshots/results as comments.
-- [ ]  Documentation (README, tutorials, API references, and websites) is deployed without broken links or missing figures.
+- [ ] Run tutorial examples and conduct functional testing using the installation guide in the README. Attach screenshots/results as comments.
+- [ ] Documentation (README, tutorials, API references, and websites) is deployed without broken links or missing figures.

doc/source/conf.py

@@ -221,7 +221,13 @@
 # (source start file, target name, title,
 # author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    ("index", "diffpy.nmf_mapping.tex", "diffpy.nmf_mapping Documentation", ab_authors, "manual"),
+    (
+        "index",
+        "diffpy.nmf_mapping.tex",
+        "diffpy.nmf_mapping Documentation",
+        ab_authors,
+        "manual",
+    ),
 ]
 
 # The name of an image file (relative to this directory) to place at the top of
@@ -249,7 +255,15 @@
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [("index", "diffpy.nmf_mapping", "diffpy.nmf_mapping Documentation", ab_authors, 1)]
+man_pages = [
+    (
+        "index",
+        "diffpy.nmf_mapping",
+        "diffpy.nmf_mapping Documentation",
+        ab_authors,
+        1,
+    )
+]
 
 # If true, show URL addresses after external links.
 # man_show_urls = False

src/__init__.py

@@ -13,8 +13,7 @@
 #
 ##############################################################################
 
-"""nmf_mapping - tools for performing NMF on PDF and XRD data.
-"""
+"""nmf_mapping - tools for performing NMF on PDF and XRD data."""
 
 
 __import__("pkg_resources").declare_namespace(__name__)

src/diffpy/nmf_mapping/main.py

@@ -31,21 +31,32 @@ 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):
         if not isinstance(s, str):
-            raise TypeError("Input must be a string of two integers separated by a comma.")
+            raise TypeError(
+                "Input must be a string of two integers separated by a comma."
+            )
 
         try:
             l, h = map(int, s.split(","))
             return l, h
         except ValueError:
-            raise ValueError("Input must be two integers separated by a comma (e.g., '1,5')")
+            raise ValueError(
+                "Input must be two integers separated by a comma (e.g., '1,5')"
+            )
 
     # args
     parser.add_argument(
-        "directory", default=None, type=str, help="a directory of PDFs to calculate NMF decomposition"
+        "directory",
+        default=None,
+        type=str,
+        help="a directory of PDFs to calculate NMF decomposition",
     )
     group = parser.add_mutually_exclusive_group()
     parser.add_argument(
@@ -88,15 +99,19 @@ def tup(s):
         "--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",
+        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",
+        help="x axis units for XRD data\n"
+        "default: None\n"
+        "e.g. --x_units twotheta",
     )
     parser.add_argument(
         "--xrange",
@@ -105,25 +120,37 @@ def tup(s):
         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(
+        "--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(
+        (
+            df_components,
+            df_component_weight_timeseries,
+            df_reconstruction_error,
+        ) = nmf.NMF_decomposition(
             input_list,
             args1.xrange,
             args1.threshold,
@@ -134,7 +161,9 @@ def tup(s):
 
     print(f"Number of components: {len(df_components.columns)}")
 
-    fig1 = nmf.component_plot(df_components, args1.xrd, args1.x_units, args1.show)
+    fig1 = nmf.component_plot(
+        df_components, args1.xrd, args1.x_units, args1.show
+    )
     fig2 = nmf.component_ratio_plot(df_component_weight_timeseries, args1.show)
     fig3 = nmf.reconstruction_error_plot(df_reconstruction_error, args1.show)
     if args1.pca_thresh:
@@ -143,40 +172,72 @@ def tup(s):
     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"))
+        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")
+            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"),
+            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")
+            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"
         )
-        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_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")
+        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"{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"\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")
 
@@ -192,11 +253,17 @@ def tup(s):
             fig4.savefig(plot_file8)
         columns = df_components.columns
         for i, col in enumerate(columns):
-            data = np.column_stack([df_components.index.to_list(), df_components[col].to_list()])
+            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"),
+                    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",
@@ -205,9 +272,14 @@ def tup(s):
                 )
             else:
                 np.savetxt(
-                    os.path.join(os.getcwd(), "nmf_result", output_fn + f"_comp{i}" + ".cgr"),
+                    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",
+                    header=f"NMF Generated PDF\nSource: nmfMapping\n"
+                    f"Date: {output_fn}\nr g",
                     fmt="%s",
                 )