automatic import of python-wordnetopeneuler20.03

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+++ b/.gitignore
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+/wordnet-0.0.1b2.tar.gz
diff --git a/python-wordnet.spec b/python-wordnet.spec
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--- /dev/null
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+%global _empty_manifest_terminate_build 0
+Name:		python-wordnet
+Version:	0.0.1b2
+Release:	1
+Summary:	An module to create network of words on bases of realtive sense under a corpus of document.
+License:	GNU General Public License v3
+URL:		https://anuragkumarak95.github.io/wordnet/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/e5/c9/93f89fc3613db301ff92be67aa67a5f9e4b5e212081ce3569e84a9e57304/wordnet-0.0.1b2.tar.gz
+BuildArch:	noarch
+
+
+%description
+# WordNet
+
+[![Build Status](https://travis-ci.org/anuragkumarak95/wordnet.svg?branch=master)](https://travis-ci.org/anuragkumarak95/wordnet)
+[![codecov](https://codecov.io/gh/anuragkumarak95/wordnet/branch/master/graph/badge.svg)](https://codecov.io/gh/anuragkumarak95/wordnet)
+[![Requirements Status](https://requires.io/github/anuragkumarak95/wordnet/requirements.svg?branch=master)](https://requires.io/github/anuragkumarak95/wordnet/requirements/?branch=master)
+
+Create a Simple **network of words** related to each other using **Twitter Streaming API**.
+
+![Made with python-3.5](http://forthebadge.com/images/badges/made-with-python.svg)
+
+Major parts of this project.
+
+* `Streamer` : ~/twitter_streaming.py
+* `TF-IDF` Gene : ~/wordnet/tf_idf_generator.py
+* `NN` words Gene :~/ wordnet/nn_words.py
+* `NETWORK` Gene : ~/wordnet/word_net.py
+
+## Using Streamer Functionality
+
+1. `Clone this repo` and run on bash '`$pip install -r requirements.txt`' @ root directory and you will be ready to go..
+
+1. Go to root-dir(~), Create a config.py file with details mentioned below:
+    ```python
+    # Variables that contains the user credentials to access Twitter Streaming API
+    # this link will help you(http://socialmedia-class.org/twittertutorial.html)
+    access_token = "xxx-xx-xxxx"
+    access_token_secret = "xxxxx"
+    consumer_key = "xxxxxx"
+    consumer_secret = "xxxxxxxx"
+    ```
+1. run `Streamer` with an array of filter words that you want to fetch tweets on. eg. `$python twitter_streaming.py hello hi hallo namaste > data_file.txt` this will save a line by line words from tweets filtered according to words used as args in `data_file.txt`.
+
+## Using WordNet Module
+
+1. `Clone this repo` and install wordnet module using this script,
+
+        $python setup.py install
+
+1. To create a `TF-IDF` structure file for every doc, use:
+
+    ```python
+    from wordnet import find_tf_idf
+
+    df, tf_idf = find_tf_idf(
+    file_names=['file/path1','file/path2',..],       # paths of files to be processed.(create using twitter_streamer.py)
+    prev_file_path='prev/tf/idf/file/path.tfidfpkl', # prev TF_IDF file to modify over, format standard is .tfidfpkl. default = None
+    dump_path='path/to/dump/file.tfidfpkl'           # dump_path if tf-idf needs to be dumped, format standard is .tfidfpkl. default = None
+    )
+
+    '''
+    if no file is provided prev_file_path parameter, new TF-IDF file will be generated ,and else
+    TF-IDF values will be combined with previous file, and dumped at dump_path if mentioned,
+    else will only return the new tf-idf list of dictionaries, and df dictionary.
+    '''
+    ```
+1. To use `NN` Word Gene of this module, simply use wordnet.find_knn:
+
+    ```python
+    from wordnet import find_knn
+
+    words = find_knn(
+    tf_idf=tf_idf,       # this tf_idf is returned by find_tf_idf() above.
+    input_word='german', # a word for which k nearest neighbours are required.
+    k=10,                # k = number of neighbours required, default=10
+    rand_on=True         # rand_on = either to randomly skip few words or show initial k words default=True
+    )
+
+    '''
+    This function will return a list of words closely related to provided input_word refering to
+    tf_idf var provided to it. either use find_tf_idf() to gather this var or pickle.load() a dump
+    file dumped by the same function at your choosen directory. the file contains 2 lists in format
+    (idf, tf_idf).
+    '''
+    ```
+
+1. To create a Word `Network`, use :
+
+    ```python
+    from wordnet import generate_net
+
+    word_net = generate_net(
+    df=df,                          # this df is returned by find_tf_idf() above.
+    tf_idf=tf_idf,                  # this tf_idf is returned by find_tf_idf() above.
+    dump_path='path/to/dump.wrnt'   # dump_path = path to dump the generated files, format standard is .wrnt. default=None
+    )
+
+    '''
+    this function returns a dict of Word entities, with word as key.
+    '''
+    ```
+
+1. To retrieve a Word `Network`, use :
+
+    ```python
+    from wordnet import retrieve_net
+
+    word_net = retrieve_net(
+        'path/to/network.wrnt' # path to network file, format standard is .wrnt.
+        )
+    '''
+    this function returns a dictionary of Word entities, with word as key.
+    '''
+    ```
+
+1. To retrieve list of words that are at some depth form a root word in the network, use:
+
+    ```python
+    from wordnet import return_net
+
+    words = return_net(
+        word,       # root word in this process.
+        word_net,   # word network generated from generate_net()
+        depth=1    # depth to which you wish this word collector to traverse.
+    )
+    '''
+    This function returns a list of words that are at provided depth from root word in the
+    network provided.
+    '''
+    ```
+
+### Test Run
+
+To run a formal test, simply run this script. `python test.py`, this module will return **0** if everythinig worked as expected.
+
+test.py uses sample data provided [here](https://github.com/anuragkumarak95/wordnet/tree/master/test) and executes unittest on `find_tf_idf()`, `find_knn()` & `generate_net()`.
+
+> `Streamer` functionality will not be provided under distribution of this code. That is just a script independent from the module.
+
+#### Contributions Are welcomed here
+
+![BUILT WITH LOVE](http://forthebadge.com/images/badges/built-with-love.svg)
+
+by [@Anurag](https://github.com/anuragkumarak95)
+
+
+%package -n python3-wordnet
+Summary:	An module to create network of words on bases of realtive sense under a corpus of document.
+Provides:	python-wordnet
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-wordnet
+# WordNet
+
+[![Build Status](https://travis-ci.org/anuragkumarak95/wordnet.svg?branch=master)](https://travis-ci.org/anuragkumarak95/wordnet)
+[![codecov](https://codecov.io/gh/anuragkumarak95/wordnet/branch/master/graph/badge.svg)](https://codecov.io/gh/anuragkumarak95/wordnet)
+[![Requirements Status](https://requires.io/github/anuragkumarak95/wordnet/requirements.svg?branch=master)](https://requires.io/github/anuragkumarak95/wordnet/requirements/?branch=master)
+
+Create a Simple **network of words** related to each other using **Twitter Streaming API**.
+
+![Made with python-3.5](http://forthebadge.com/images/badges/made-with-python.svg)
+
+Major parts of this project.
+
+* `Streamer` : ~/twitter_streaming.py
+* `TF-IDF` Gene : ~/wordnet/tf_idf_generator.py
+* `NN` words Gene :~/ wordnet/nn_words.py
+* `NETWORK` Gene : ~/wordnet/word_net.py
+
+## Using Streamer Functionality
+
+1. `Clone this repo` and run on bash '`$pip install -r requirements.txt`' @ root directory and you will be ready to go..
+
+1. Go to root-dir(~), Create a config.py file with details mentioned below:
+    ```python
+    # Variables that contains the user credentials to access Twitter Streaming API
+    # this link will help you(http://socialmedia-class.org/twittertutorial.html)
+    access_token = "xxx-xx-xxxx"
+    access_token_secret = "xxxxx"
+    consumer_key = "xxxxxx"
+    consumer_secret = "xxxxxxxx"
+    ```
+1. run `Streamer` with an array of filter words that you want to fetch tweets on. eg. `$python twitter_streaming.py hello hi hallo namaste > data_file.txt` this will save a line by line words from tweets filtered according to words used as args in `data_file.txt`.
+
+## Using WordNet Module
+
+1. `Clone this repo` and install wordnet module using this script,
+
+        $python setup.py install
+
+1. To create a `TF-IDF` structure file for every doc, use:
+
+    ```python
+    from wordnet import find_tf_idf
+
+    df, tf_idf = find_tf_idf(
+    file_names=['file/path1','file/path2',..],       # paths of files to be processed.(create using twitter_streamer.py)
+    prev_file_path='prev/tf/idf/file/path.tfidfpkl', # prev TF_IDF file to modify over, format standard is .tfidfpkl. default = None
+    dump_path='path/to/dump/file.tfidfpkl'           # dump_path if tf-idf needs to be dumped, format standard is .tfidfpkl. default = None
+    )
+
+    '''
+    if no file is provided prev_file_path parameter, new TF-IDF file will be generated ,and else
+    TF-IDF values will be combined with previous file, and dumped at dump_path if mentioned,
+    else will only return the new tf-idf list of dictionaries, and df dictionary.
+    '''
+    ```
+1. To use `NN` Word Gene of this module, simply use wordnet.find_knn:
+
+    ```python
+    from wordnet import find_knn
+
+    words = find_knn(
+    tf_idf=tf_idf,       # this tf_idf is returned by find_tf_idf() above.
+    input_word='german', # a word for which k nearest neighbours are required.
+    k=10,                # k = number of neighbours required, default=10
+    rand_on=True         # rand_on = either to randomly skip few words or show initial k words default=True
+    )
+
+    '''
+    This function will return a list of words closely related to provided input_word refering to
+    tf_idf var provided to it. either use find_tf_idf() to gather this var or pickle.load() a dump
+    file dumped by the same function at your choosen directory. the file contains 2 lists in format
+    (idf, tf_idf).
+    '''
+    ```
+
+1. To create a Word `Network`, use :
+
+    ```python
+    from wordnet import generate_net
+
+    word_net = generate_net(
+    df=df,                          # this df is returned by find_tf_idf() above.
+    tf_idf=tf_idf,                  # this tf_idf is returned by find_tf_idf() above.
+    dump_path='path/to/dump.wrnt'   # dump_path = path to dump the generated files, format standard is .wrnt. default=None
+    )
+
+    '''
+    this function returns a dict of Word entities, with word as key.
+    '''
+    ```
+
+1. To retrieve a Word `Network`, use :
+
+    ```python
+    from wordnet import retrieve_net
+
+    word_net = retrieve_net(
+        'path/to/network.wrnt' # path to network file, format standard is .wrnt.
+        )
+    '''
+    this function returns a dictionary of Word entities, with word as key.
+    '''
+    ```
+
+1. To retrieve list of words that are at some depth form a root word in the network, use:
+
+    ```python
+    from wordnet import return_net
+
+    words = return_net(
+        word,       # root word in this process.
+        word_net,   # word network generated from generate_net()
+        depth=1    # depth to which you wish this word collector to traverse.
+    )
+    '''
+    This function returns a list of words that are at provided depth from root word in the
+    network provided.
+    '''
+    ```
+
+### Test Run
+
+To run a formal test, simply run this script. `python test.py`, this module will return **0** if everythinig worked as expected.
+
+test.py uses sample data provided [here](https://github.com/anuragkumarak95/wordnet/tree/master/test) and executes unittest on `find_tf_idf()`, `find_knn()` & `generate_net()`.
+
+> `Streamer` functionality will not be provided under distribution of this code. That is just a script independent from the module.
+
+#### Contributions Are welcomed here
+
+![BUILT WITH LOVE](http://forthebadge.com/images/badges/built-with-love.svg)
+
+by [@Anurag](https://github.com/anuragkumarak95)
+
+
+%package help
+Summary:	Development documents and examples for wordnet
+Provides:	python3-wordnet-doc
+%description help
+# WordNet
+
+[![Build Status](https://travis-ci.org/anuragkumarak95/wordnet.svg?branch=master)](https://travis-ci.org/anuragkumarak95/wordnet)
+[![codecov](https://codecov.io/gh/anuragkumarak95/wordnet/branch/master/graph/badge.svg)](https://codecov.io/gh/anuragkumarak95/wordnet)
+[![Requirements Status](https://requires.io/github/anuragkumarak95/wordnet/requirements.svg?branch=master)](https://requires.io/github/anuragkumarak95/wordnet/requirements/?branch=master)
+
+Create a Simple **network of words** related to each other using **Twitter Streaming API**.
+
+![Made with python-3.5](http://forthebadge.com/images/badges/made-with-python.svg)
+
+Major parts of this project.
+
+* `Streamer` : ~/twitter_streaming.py
+* `TF-IDF` Gene : ~/wordnet/tf_idf_generator.py
+* `NN` words Gene :~/ wordnet/nn_words.py
+* `NETWORK` Gene : ~/wordnet/word_net.py
+
+## Using Streamer Functionality
+
+1. `Clone this repo` and run on bash '`$pip install -r requirements.txt`' @ root directory and you will be ready to go..
+
+1. Go to root-dir(~), Create a config.py file with details mentioned below:
+    ```python
+    # Variables that contains the user credentials to access Twitter Streaming API
+    # this link will help you(http://socialmedia-class.org/twittertutorial.html)
+    access_token = "xxx-xx-xxxx"
+    access_token_secret = "xxxxx"
+    consumer_key = "xxxxxx"
+    consumer_secret = "xxxxxxxx"
+    ```
+1. run `Streamer` with an array of filter words that you want to fetch tweets on. eg. `$python twitter_streaming.py hello hi hallo namaste > data_file.txt` this will save a line by line words from tweets filtered according to words used as args in `data_file.txt`.
+
+## Using WordNet Module
+
+1. `Clone this repo` and install wordnet module using this script,
+
+        $python setup.py install
+
+1. To create a `TF-IDF` structure file for every doc, use:
+
+    ```python
+    from wordnet import find_tf_idf
+
+    df, tf_idf = find_tf_idf(
+    file_names=['file/path1','file/path2',..],       # paths of files to be processed.(create using twitter_streamer.py)
+    prev_file_path='prev/tf/idf/file/path.tfidfpkl', # prev TF_IDF file to modify over, format standard is .tfidfpkl. default = None
+    dump_path='path/to/dump/file.tfidfpkl'           # dump_path if tf-idf needs to be dumped, format standard is .tfidfpkl. default = None
+    )
+
+    '''
+    if no file is provided prev_file_path parameter, new TF-IDF file will be generated ,and else
+    TF-IDF values will be combined with previous file, and dumped at dump_path if mentioned,
+    else will only return the new tf-idf list of dictionaries, and df dictionary.
+    '''
+    ```
+1. To use `NN` Word Gene of this module, simply use wordnet.find_knn:
+
+    ```python
+    from wordnet import find_knn
+
+    words = find_knn(
+    tf_idf=tf_idf,       # this tf_idf is returned by find_tf_idf() above.
+    input_word='german', # a word for which k nearest neighbours are required.
+    k=10,                # k = number of neighbours required, default=10
+    rand_on=True         # rand_on = either to randomly skip few words or show initial k words default=True
+    )
+
+    '''
+    This function will return a list of words closely related to provided input_word refering to
+    tf_idf var provided to it. either use find_tf_idf() to gather this var or pickle.load() a dump
+    file dumped by the same function at your choosen directory. the file contains 2 lists in format
+    (idf, tf_idf).
+    '''
+    ```
+
+1. To create a Word `Network`, use :
+
+    ```python
+    from wordnet import generate_net
+
+    word_net = generate_net(
+    df=df,                          # this df is returned by find_tf_idf() above.
+    tf_idf=tf_idf,                  # this tf_idf is returned by find_tf_idf() above.
+    dump_path='path/to/dump.wrnt'   # dump_path = path to dump the generated files, format standard is .wrnt. default=None
+    )
+
+    '''
+    this function returns a dict of Word entities, with word as key.
+    '''
+    ```
+
+1. To retrieve a Word `Network`, use :
+
+    ```python
+    from wordnet import retrieve_net
+
+    word_net = retrieve_net(
+        'path/to/network.wrnt' # path to network file, format standard is .wrnt.
+        )
+    '''
+    this function returns a dictionary of Word entities, with word as key.
+    '''
+    ```
+
+1. To retrieve list of words that are at some depth form a root word in the network, use:
+
+    ```python
+    from wordnet import return_net
+
+    words = return_net(
+        word,       # root word in this process.
+        word_net,   # word network generated from generate_net()
+        depth=1    # depth to which you wish this word collector to traverse.
+    )
+    '''
+    This function returns a list of words that are at provided depth from root word in the
+    network provided.
+    '''
+    ```
+
+### Test Run
+
+To run a formal test, simply run this script. `python test.py`, this module will return **0** if everythinig worked as expected.
+
+test.py uses sample data provided [here](https://github.com/anuragkumarak95/wordnet/tree/master/test) and executes unittest on `find_tf_idf()`, `find_knn()` & `generate_net()`.
+
+> `Streamer` functionality will not be provided under distribution of this code. That is just a script independent from the module.
+
+#### Contributions Are welcomed here
+
+![BUILT WITH LOVE](http://forthebadge.com/images/badges/built-with-love.svg)
+
+by [@Anurag](https://github.com/anuragkumarak95)
+
+
+%prep
+%autosetup -n wordnet-0.0.1b2
+
+%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-wordnet -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.1b2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..aa60f60
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+29d275f6fdd5b2e7b3a101cd8e474eb9  wordnet-0.0.1b2.tar.gz