automatic import of python-carling

1 files changed, 749 insertions, 0 deletions

diff --git a/python-carling.spec b/python-carling.spec
new file mode 100644
index 0000000..5ac823b
--- /dev/null
+++ b/python-carling.spec
@@ -0,0 +1,749 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-carling
+Version:	0.3.5
+Release:	1
+Summary:	Useful transforms for supporting apache beam pipelines.
+License:	Apache-2.0
+URL:		https://github.com/mc-digital/carling
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/61/ff/8fa7ff0dce0f7f87488ffa0f8f5b3b6d57dd8ff7f6cd0f7d47dd4808aab3/carling-0.3.5.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-apache-beam
+Requires:	python3-deepdiff
+
+%description
+# Carling
+
+[![CI](https://github.com/mc-digital/carling/actions/workflows/ci.yml/badge.svg)](https://github.com/mc-digital/carling/actions?query=workflow%3ACI)
+[![versions](https://img.shields.io/pypi/pyversions/carling.svg)](https://pypi.org/project/carling/)
+[![pypi](https://img.shields.io/pypi/v/carling)](https://pypi.org/project/carling/)
+[![license](https://img.shields.io/pypi/l/carling)](https://github.com/mc-digital/carling/blob/main/LICENSE)
+
+Via [Wikipedia](<https://en.wikipedia.org/wiki/Carling_(sailing)>):
+
+> Carlings are pieces of timber laid fore and aft under the deck of a ship, from one beam to another.
+> They serve as a foundation for the whole body of the ship.
+
+Useful transforms for supporting our apache beam pipelines.
+
+## Mapping transform utils
+
+#### `carling.Label`
+
+Labels all elements.
+
+#### `carling.Select`
+
+Removes all columns which are not specified in `*keys`.
+
+#### `carling.Project`
+
+Transforms each element into a tuple of values of the specified columns.
+
+#### `carling.IndexBy`
+
+Transforms each element `V` into a tuple `(K, V)`.
+
+`K` is the projection of `V` by `*keys`, which is equal to the tuple
+produced by the `Project` transform.
+
+#### `carling.Stringify`
+
+Transforms each element into its JSON representation.
+
+#### `carling.IndexBySingle`
+
+Transforms each element `V` into a tuple `(K, V)`.
+
+The difference between `IndexBySingle(key)` and `IndexBy(key)` with a single
+key is as follows:
+
+- `IndexBySingle` produces the index as a plain value.
+- `IndexBy` produces the index as a single-element tuple.
+
+#### `carling.RenameFromTo`
+
+Rename columns according to `from_to_key_mapping`.
+
+#### `carling.Exclude`
+
+Removes all columns specified in `*keys`.
+
+## Grouping transform utils
+
+Generic grouping transform utils
+
+#### `carling.UniqueOnly`
+
+Produces elements that are the only elements per key after deduplication.
+
+Given a `PCollection` of `(K, V)`,
+this transform produces the collection of all `V`s that do not share
+the same corresponding `K`s with any other elements after deduplicating
+all equivalent `(K, V)` pairs.
+
+This transform is equivalent to `SingletonOnly` with `apache_beam.Distinct`.
+
+`[(1, "A"), (2, "B1"), (2, "B2"), (3, "C"), (3, "C"), (4, "A")]` will be
+transformed into `["A", "C", "A"]`.
+
+#### `carling.SingletonOnly`
+
+Produces elements that are the only elements per key.
+
+Given a `PCollection` of `(K, V)`,
+this transform produces the collection of all `V`s that do not share
+the same corresponding `K`s with any other elements.
+
+`[(1, "A"), (2, "B1"), (2, "B2"), (3, "C"), (3, "C"), (4, "A")]` will be
+transformed into `["A", "A"]`.
+
+#### `carling.Intersection`
+
+Produces the intersection of given `PCollection`s.
+
+Given a list of `PCollection`s,
+this transform produces every element that appears in all collections of
+the list.
+Elements are deduplicated before taking the intersection.
+
+#### `carling.FilterByKey`
+
+Filters elements by their keys.
+
+The constructor receives one or more `PCollection`s of `K`s,
+which are regarded as key lists.
+Given a `PCollection` of `(K, V)`,
+this transform discards all elements with `K`s that do not appear
+in the key lists.
+
+If multiple collections are given to the constructor,
+this transform treats the intersection of them as the key list.
+
+#### `carling.FilterByKeyUsingSideInput`
+
+Filters a single collection by a single lookup collection, using a common key.
+
+Given: - a `PCollection` (lookup_entries) of `(V)`, as a lookup collection - a `PCollection` (pcoll) of `(V)`, as values to be filtered - a common key (filter_key)
+
+A dictionary called `filter_dict` - is created by mapping the value of `filter_key`
+for each entry in `lookup_entries` to True.
+
+Then, for each item in pcoll, the value associated with `filter_key` checkd against
+`filter_dict`, and if it is found, the entry passes through. Otherwise, the entry is
+discarded.
+
+Note: `lookup_entries` will be used as a **side input**, so care
+must be taken regarding the size of the `lookup_entries`
+
+#### `carling.DifferencePerKey`
+
+Produces the difference per key between two `PCollection`s.
+
+Given two `PCollection`s of `V`,
+this transform indexes the collections by the specified keys `primary_keys`,
+compares corresponding two `V` lists for every `K`,
+and produces the difference per `K`.
+If there is no difference, this transform produces nothing.
+
+Two `V` lists are considered to be different if the numbers of elements
+differ or two elements of the lists with a same index differ
+at one of the specified columns `columns`.
+
+#### `carling.SortedSelectPerKey`
+
+- Groups items by a set of `keys` -- column names per row
+- Emits the "MAX" _value_ for each collection as defined by the `key_fn`
+- Can emit "MIN" by passing `reverse=True` kwarg
+
+#### `carling.PartitionRowsContainingNone`
+
+Emits two tagged PCollections:
+
+- Default (`result[None]`): Rows are guaranteed not to have any `None` values
+- `result["contains_none"]`: Rows for which at least one column had a `None` value
+
+## Categorical
+
+#### `carling.CreateCategoricalDicts`
+
+For a set of columnular data inputs this function takes:
+
+    - cat_cols:
+
+        Type: `[str]`
+
+        An array of "categorical" columns
+
+    - existing_dicts:
+
+        Type: `PCollection[(string, string, int)]`
+
+        Rows of tuples of type:
+        (column, previously_seen_value, mapped_unique_int)
+
+        Mapping a set of "previously seen" keys to unique int values for each
+        column.
+        Not optional.
+        If none exist, pass an empty PCollection.
+
+It then creates a transform which takes a pcollection and
+
+    - looks at the input pcoll for unseen values in each categorical column
+    - creates new unique integers for each distinct unseen value, starting at max(previous value for column)+1
+    - ammends the existing mappings with (col, unseen_value, new_unique_int)
+
+Output is:
+
+    - Type: `PCollection[(string, string, int)]`
+
+This is useful for preparing data to be trained by eg. LightGBM
+
+#### `carling.ReplaceCategoricalColumns`
+
+- Utilizes the "categorical dictionary rows" generated by `CreateCategoricalDicts` which is a list of pairs of type of `(column, value,unique_int)`.
+
+- Replaces each column with the appropriate value found in the mapping.
+
+## Test Utils
+
+#### `carling.test_utils.pprint_equal_to`
+
+This module contains the `equal_to` function from apache beam, but adapted to output results using pretty print. Reading the results as a large, unformatted string makes it harder to pick out what changed/is missing.
+
+## General Util
+
+#### `carling.LogSample`
+
+Print items of the given `PCollection` to the log.
+
+`LogSample` prints the JSON representations of the input items to the Python's
+standard logging system.
+
+To avoid too much log entries being printed, `LogSample` limits the number of
+logged items. The constructor parameter `n` determines the limit.
+
+By default, `LogSample` prints logs with the `INFO` log level. The constructor
+parameter `level` determines the level.
+
+#### `carling.ReifyMultiValueOption`
+
+Prepares multi-value delimited options. Useful in contexts where
+you want to create a multi-value option in a template environment.
+
+- inputs:
+  - delimited string option
+  - optional delimiter string (default is "|")
+
+* output:
+  - Type: `PCollection[str]`
+
+
+%package -n python3-carling
+Summary:	Useful transforms for supporting apache beam pipelines.
+Provides:	python-carling
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-carling
+# Carling
+
+[![CI](https://github.com/mc-digital/carling/actions/workflows/ci.yml/badge.svg)](https://github.com/mc-digital/carling/actions?query=workflow%3ACI)
+[![versions](https://img.shields.io/pypi/pyversions/carling.svg)](https://pypi.org/project/carling/)
+[![pypi](https://img.shields.io/pypi/v/carling)](https://pypi.org/project/carling/)
+[![license](https://img.shields.io/pypi/l/carling)](https://github.com/mc-digital/carling/blob/main/LICENSE)
+
+Via [Wikipedia](<https://en.wikipedia.org/wiki/Carling_(sailing)>):
+
+> Carlings are pieces of timber laid fore and aft under the deck of a ship, from one beam to another.
+> They serve as a foundation for the whole body of the ship.
+
+Useful transforms for supporting our apache beam pipelines.
+
+## Mapping transform utils
+
+#### `carling.Label`
+
+Labels all elements.
+
+#### `carling.Select`
+
+Removes all columns which are not specified in `*keys`.
+
+#### `carling.Project`
+
+Transforms each element into a tuple of values of the specified columns.
+
+#### `carling.IndexBy`
+
+Transforms each element `V` into a tuple `(K, V)`.
+
+`K` is the projection of `V` by `*keys`, which is equal to the tuple
+produced by the `Project` transform.
+
+#### `carling.Stringify`
+
+Transforms each element into its JSON representation.
+
+#### `carling.IndexBySingle`
+
+Transforms each element `V` into a tuple `(K, V)`.
+
+The difference between `IndexBySingle(key)` and `IndexBy(key)` with a single
+key is as follows:
+
+- `IndexBySingle` produces the index as a plain value.
+- `IndexBy` produces the index as a single-element tuple.
+
+#### `carling.RenameFromTo`
+
+Rename columns according to `from_to_key_mapping`.
+
+#### `carling.Exclude`
+
+Removes all columns specified in `*keys`.
+
+## Grouping transform utils
+
+Generic grouping transform utils
+
+#### `carling.UniqueOnly`
+
+Produces elements that are the only elements per key after deduplication.
+
+Given a `PCollection` of `(K, V)`,
+this transform produces the collection of all `V`s that do not share
+the same corresponding `K`s with any other elements after deduplicating
+all equivalent `(K, V)` pairs.
+
+This transform is equivalent to `SingletonOnly` with `apache_beam.Distinct`.
+
+`[(1, "A"), (2, "B1"), (2, "B2"), (3, "C"), (3, "C"), (4, "A")]` will be
+transformed into `["A", "C", "A"]`.
+
+#### `carling.SingletonOnly`
+
+Produces elements that are the only elements per key.
+
+Given a `PCollection` of `(K, V)`,
+this transform produces the collection of all `V`s that do not share
+the same corresponding `K`s with any other elements.
+
+`[(1, "A"), (2, "B1"), (2, "B2"), (3, "C"), (3, "C"), (4, "A")]` will be
+transformed into `["A", "A"]`.
+
+#### `carling.Intersection`
+
+Produces the intersection of given `PCollection`s.
+
+Given a list of `PCollection`s,
+this transform produces every element that appears in all collections of
+the list.
+Elements are deduplicated before taking the intersection.
+
+#### `carling.FilterByKey`
+
+Filters elements by their keys.
+
+The constructor receives one or more `PCollection`s of `K`s,
+which are regarded as key lists.
+Given a `PCollection` of `(K, V)`,
+this transform discards all elements with `K`s that do not appear
+in the key lists.
+
+If multiple collections are given to the constructor,
+this transform treats the intersection of them as the key list.
+
+#### `carling.FilterByKeyUsingSideInput`
+
+Filters a single collection by a single lookup collection, using a common key.
+
+Given: - a `PCollection` (lookup_entries) of `(V)`, as a lookup collection - a `PCollection` (pcoll) of `(V)`, as values to be filtered - a common key (filter_key)
+
+A dictionary called `filter_dict` - is created by mapping the value of `filter_key`
+for each entry in `lookup_entries` to True.
+
+Then, for each item in pcoll, the value associated with `filter_key` checkd against
+`filter_dict`, and if it is found, the entry passes through. Otherwise, the entry is
+discarded.
+
+Note: `lookup_entries` will be used as a **side input**, so care
+must be taken regarding the size of the `lookup_entries`
+
+#### `carling.DifferencePerKey`
+
+Produces the difference per key between two `PCollection`s.
+
+Given two `PCollection`s of `V`,
+this transform indexes the collections by the specified keys `primary_keys`,
+compares corresponding two `V` lists for every `K`,
+and produces the difference per `K`.
+If there is no difference, this transform produces nothing.
+
+Two `V` lists are considered to be different if the numbers of elements
+differ or two elements of the lists with a same index differ
+at one of the specified columns `columns`.
+
+#### `carling.SortedSelectPerKey`
+
+- Groups items by a set of `keys` -- column names per row
+- Emits the "MAX" _value_ for each collection as defined by the `key_fn`
+- Can emit "MIN" by passing `reverse=True` kwarg
+
+#### `carling.PartitionRowsContainingNone`
+
+Emits two tagged PCollections:
+
+- Default (`result[None]`): Rows are guaranteed not to have any `None` values
+- `result["contains_none"]`: Rows for which at least one column had a `None` value
+
+## Categorical
+
+#### `carling.CreateCategoricalDicts`
+
+For a set of columnular data inputs this function takes:
+
+    - cat_cols:
+
+        Type: `[str]`
+
+        An array of "categorical" columns
+
+    - existing_dicts:
+
+        Type: `PCollection[(string, string, int)]`
+
+        Rows of tuples of type:
+        (column, previously_seen_value, mapped_unique_int)
+
+        Mapping a set of "previously seen" keys to unique int values for each
+        column.
+        Not optional.
+        If none exist, pass an empty PCollection.
+
+It then creates a transform which takes a pcollection and
+
+    - looks at the input pcoll for unseen values in each categorical column
+    - creates new unique integers for each distinct unseen value, starting at max(previous value for column)+1
+    - ammends the existing mappings with (col, unseen_value, new_unique_int)
+
+Output is:
+
+    - Type: `PCollection[(string, string, int)]`
+
+This is useful for preparing data to be trained by eg. LightGBM
+
+#### `carling.ReplaceCategoricalColumns`
+
+- Utilizes the "categorical dictionary rows" generated by `CreateCategoricalDicts` which is a list of pairs of type of `(column, value,unique_int)`.
+
+- Replaces each column with the appropriate value found in the mapping.
+
+## Test Utils
+
+#### `carling.test_utils.pprint_equal_to`
+
+This module contains the `equal_to` function from apache beam, but adapted to output results using pretty print. Reading the results as a large, unformatted string makes it harder to pick out what changed/is missing.
+
+## General Util
+
+#### `carling.LogSample`
+
+Print items of the given `PCollection` to the log.
+
+`LogSample` prints the JSON representations of the input items to the Python's
+standard logging system.
+
+To avoid too much log entries being printed, `LogSample` limits the number of
+logged items. The constructor parameter `n` determines the limit.
+
+By default, `LogSample` prints logs with the `INFO` log level. The constructor
+parameter `level` determines the level.
+
+#### `carling.ReifyMultiValueOption`
+
+Prepares multi-value delimited options. Useful in contexts where
+you want to create a multi-value option in a template environment.
+
+- inputs:
+  - delimited string option
+  - optional delimiter string (default is "|")
+
+* output:
+  - Type: `PCollection[str]`
+
+
+%package help
+Summary:	Development documents and examples for carling
+Provides:	python3-carling-doc
+%description help
+# Carling
+
+[![CI](https://github.com/mc-digital/carling/actions/workflows/ci.yml/badge.svg)](https://github.com/mc-digital/carling/actions?query=workflow%3ACI)
+[![versions](https://img.shields.io/pypi/pyversions/carling.svg)](https://pypi.org/project/carling/)
+[![pypi](https://img.shields.io/pypi/v/carling)](https://pypi.org/project/carling/)
+[![license](https://img.shields.io/pypi/l/carling)](https://github.com/mc-digital/carling/blob/main/LICENSE)
+
+Via [Wikipedia](<https://en.wikipedia.org/wiki/Carling_(sailing)>):
+
+> Carlings are pieces of timber laid fore and aft under the deck of a ship, from one beam to another.
+> They serve as a foundation for the whole body of the ship.
+
+Useful transforms for supporting our apache beam pipelines.
+
+## Mapping transform utils
+
+#### `carling.Label`
+
+Labels all elements.
+
+#### `carling.Select`
+
+Removes all columns which are not specified in `*keys`.
+
+#### `carling.Project`
+
+Transforms each element into a tuple of values of the specified columns.
+
+#### `carling.IndexBy`
+
+Transforms each element `V` into a tuple `(K, V)`.
+
+`K` is the projection of `V` by `*keys`, which is equal to the tuple
+produced by the `Project` transform.
+
+#### `carling.Stringify`
+
+Transforms each element into its JSON representation.
+
+#### `carling.IndexBySingle`
+
+Transforms each element `V` into a tuple `(K, V)`.
+
+The difference between `IndexBySingle(key)` and `IndexBy(key)` with a single
+key is as follows:
+
+- `IndexBySingle` produces the index as a plain value.
+- `IndexBy` produces the index as a single-element tuple.
+
+#### `carling.RenameFromTo`
+
+Rename columns according to `from_to_key_mapping`.
+
+#### `carling.Exclude`
+
+Removes all columns specified in `*keys`.
+
+## Grouping transform utils
+
+Generic grouping transform utils
+
+#### `carling.UniqueOnly`
+
+Produces elements that are the only elements per key after deduplication.
+
+Given a `PCollection` of `(K, V)`,
+this transform produces the collection of all `V`s that do not share
+the same corresponding `K`s with any other elements after deduplicating
+all equivalent `(K, V)` pairs.
+
+This transform is equivalent to `SingletonOnly` with `apache_beam.Distinct`.
+
+`[(1, "A"), (2, "B1"), (2, "B2"), (3, "C"), (3, "C"), (4, "A")]` will be
+transformed into `["A", "C", "A"]`.
+
+#### `carling.SingletonOnly`
+
+Produces elements that are the only elements per key.
+
+Given a `PCollection` of `(K, V)`,
+this transform produces the collection of all `V`s that do not share
+the same corresponding `K`s with any other elements.
+
+`[(1, "A"), (2, "B1"), (2, "B2"), (3, "C"), (3, "C"), (4, "A")]` will be
+transformed into `["A", "A"]`.
+
+#### `carling.Intersection`
+
+Produces the intersection of given `PCollection`s.
+
+Given a list of `PCollection`s,
+this transform produces every element that appears in all collections of
+the list.
+Elements are deduplicated before taking the intersection.
+
+#### `carling.FilterByKey`
+
+Filters elements by their keys.
+
+The constructor receives one or more `PCollection`s of `K`s,
+which are regarded as key lists.
+Given a `PCollection` of `(K, V)`,
+this transform discards all elements with `K`s that do not appear
+in the key lists.
+
+If multiple collections are given to the constructor,
+this transform treats the intersection of them as the key list.
+
+#### `carling.FilterByKeyUsingSideInput`
+
+Filters a single collection by a single lookup collection, using a common key.
+
+Given: - a `PCollection` (lookup_entries) of `(V)`, as a lookup collection - a `PCollection` (pcoll) of `(V)`, as values to be filtered - a common key (filter_key)
+
+A dictionary called `filter_dict` - is created by mapping the value of `filter_key`
+for each entry in `lookup_entries` to True.
+
+Then, for each item in pcoll, the value associated with `filter_key` checkd against
+`filter_dict`, and if it is found, the entry passes through. Otherwise, the entry is
+discarded.
+
+Note: `lookup_entries` will be used as a **side input**, so care
+must be taken regarding the size of the `lookup_entries`
+
+#### `carling.DifferencePerKey`
+
+Produces the difference per key between two `PCollection`s.
+
+Given two `PCollection`s of `V`,
+this transform indexes the collections by the specified keys `primary_keys`,
+compares corresponding two `V` lists for every `K`,
+and produces the difference per `K`.
+If there is no difference, this transform produces nothing.
+
+Two `V` lists are considered to be different if the numbers of elements
+differ or two elements of the lists with a same index differ
+at one of the specified columns `columns`.
+
+#### `carling.SortedSelectPerKey`
+
+- Groups items by a set of `keys` -- column names per row
+- Emits the "MAX" _value_ for each collection as defined by the `key_fn`
+- Can emit "MIN" by passing `reverse=True` kwarg
+
+#### `carling.PartitionRowsContainingNone`
+
+Emits two tagged PCollections:
+
+- Default (`result[None]`): Rows are guaranteed not to have any `None` values
+- `result["contains_none"]`: Rows for which at least one column had a `None` value
+
+## Categorical
+
+#### `carling.CreateCategoricalDicts`
+
+For a set of columnular data inputs this function takes:
+
+    - cat_cols:
+
+        Type: `[str]`
+
+        An array of "categorical" columns
+
+    - existing_dicts:
+
+        Type: `PCollection[(string, string, int)]`
+
+        Rows of tuples of type:
+        (column, previously_seen_value, mapped_unique_int)
+
+        Mapping a set of "previously seen" keys to unique int values for each
+        column.
+        Not optional.
+        If none exist, pass an empty PCollection.
+
+It then creates a transform which takes a pcollection and
+
+    - looks at the input pcoll for unseen values in each categorical column
+    - creates new unique integers for each distinct unseen value, starting at max(previous value for column)+1
+    - ammends the existing mappings with (col, unseen_value, new_unique_int)
+
+Output is:
+
+    - Type: `PCollection[(string, string, int)]`
+
+This is useful for preparing data to be trained by eg. LightGBM
+
+#### `carling.ReplaceCategoricalColumns`
+
+- Utilizes the "categorical dictionary rows" generated by `CreateCategoricalDicts` which is a list of pairs of type of `(column, value,unique_int)`.
+
+- Replaces each column with the appropriate value found in the mapping.
+
+## Test Utils
+
+#### `carling.test_utils.pprint_equal_to`
+
+This module contains the `equal_to` function from apache beam, but adapted to output results using pretty print. Reading the results as a large, unformatted string makes it harder to pick out what changed/is missing.
+
+## General Util
+
+#### `carling.LogSample`
+
+Print items of the given `PCollection` to the log.
+
+`LogSample` prints the JSON representations of the input items to the Python's
+standard logging system.
+
+To avoid too much log entries being printed, `LogSample` limits the number of
+logged items. The constructor parameter `n` determines the limit.
+
+By default, `LogSample` prints logs with the `INFO` log level. The constructor
+parameter `level` determines the level.
+
+#### `carling.ReifyMultiValueOption`
+
+Prepares multi-value delimited options. Useful in contexts where
+you want to create a multi-value option in a template environment.
+
+- inputs:
+  - delimited string option
+  - optional delimiter string (default is "|")
+
+* output:
+  - Type: `PCollection[str]`
+
+
+%prep
+%autosetup -n carling-0.3.5
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-carling -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.5-1
+- Package Spec generated