Saravanan666/pypi-org huggingface dataset:
Package: normalization-text
Package ini berfungsi untuk menormalisasi data teks sebelum melakukan pemrosesan lebih lanjut. Pertama Anda perlu mengimpor Modul yang diperlukan untuk package ini adalah : pandas, re (regular expression) untuk operasi pencocokan pola teks, nltk (Natural Language Toolkit) untuk pemrosesan bahasa alami, stopwords dari nltk.corpus untuk mengunduh dan menggunakan daftar kata-kata penghenti dalam bahasa Indonesia,  Kedua Anda perlu mengunduh daftar kata penghenti (stopwords) dalam bahasa Indonesia jika belum diunduh sebelumnya, Package ini memuat pemetaan antara kata informal dan formal dari file CSV “new_kamusalay.csv” ke dalam DataFrame “df_new_kamusalay”. Pemetaan ini digunakan untuk mengganti kata-kata informal dengan kata-kata formal. memuat daftar kata-kata kasar (Abusive words) dari file CSV “abusive.csv” ke dalam DataFrame “df_abusive”, daftar ini digunakan untuk menghapus kata-kata kasar dari teks. Mendefinisikan daftar kata penghenti (stopwords)  dalam bahasa indonesia. Selain itu, package ini digunakan untuk membersihkan teks dengan menghapus kata-kata penghenti (stopwords) dari teks. ini dilakukan dengan membagi teks menjadi kata-kata, memeriksa apakah kata-kata tersebut bukan kata penghenti, dan kemudian menggabungkan kembali kata-kata yang tersisa menjadi teks yang bersih. Membersihkan teks dengan menggunakan ekspresi regule (regex). Ini melibatkan serangkaian pemrosesan seperti menghapus tanda strip, simbol, spasi berlebih, karakter baris baru, retweet symbol, username, URL, angka, tanda seru, spasi berlebih, dan tanda baca, selanjutnya teks dikonversi menjadi huruf kecil. Mengganti kata-kata dalam teks menggunakan pemetaan kata informal dan menghapus kata-kata kasar. pada langkah ini, kata-kata dalam teks dibagi menjadi kata-kata kasar. kata-kata yang ditemukan diganti dengan pengganti yang sesuai atau diubah menjadi “*” selanjutnya teks dibersihkan dari spasi awal dan akhir. fungsi utama nya adalah menggabungkan langkah-langkah preprocessing dengan mengaplikasikan langkah-langkah berikut secara berurutan : membersihkan stopwords; membersihkan menggunakan regex; mengganti kata-kata dan mengembalikan teks yang sudah di normalisasi.
Package: normalize.css
Normalize.cssA modern, HTML5-ready alternative to CSS resetsNormalize.cssmakes browsers render all elements more consistently and in line with modern standards. It precisely targets only the styles that need normalizing.this is a pythonic package of normalize.css forDicksonUIChrome, Edge, Firefox ESR+, IE 10+, Safari 8+, Operainstallpip install normalize.cssusageimportnormalizefromdicksonuiimportApplication,windowapp=Application()# make Applicationnormalize.install(app)# install normalize.cssmywindow=window()# make windowapp.add(mywindow)# add window to appnormalize.add(mywindow)# add normalize.css to windowmywindow.show()# show windowRead more about original normalize.css »…as used byTwitter,TweetDeck,GitHub,Soundcloud,Guardian,Medium,GOV.UK,Bootstrap,HTML5 Boilerplate, and many others.Github repoSource code available on GitHub:Ksengine/DicksonUI/extentions/normalize.
Package: normalize_cz_unicode
normalize_cz_unicodeSanitize unicode inputs from unwanted characters.Principle of the module is simple; Use translation table. If the character is
not in translation table, convert it tolatin2. If it can’t be converted,
try to normalize it using unicodeNKFDnormalization. If it can’t be
normalized, replace it with?.Usage>>>fromnormalize_cz_unicodeimportnormalize>>>printnormalize("Tohle je smajlík: 😭 , který tu ale nechci.")Tohlejesmajlík:?,kterýtualenechci.Various whitespace and special dash characters are normalized to basic ascii:>>>a=u"Spojovníky ― a další havěť jako nedělitelné mezery taky nechci."u'Spojovn\xedky\u2015a dal\u0161\xedhav\u011b\u0165jako ned\u011bliteln\xe9\u202fmezery\u2007taky nechci.'>>>normalize(a)u'Spojovn\xedky - a dal\u0161\xedhav\u011b\u0165jako ned\u011bliteln\xe9mezery taky nechci.'InstallationModule is hosted atPYPI, and
can be installed usingPIP:sudo pip install normalize_cz_unicodeChangelog1.0.1Added caching.1.0.0First working version.Added tests.Added documentation to README.rst.Uploaded toPYPI.0.1.0Project created.
Package: normalize-easy
## normalize_easy PackageThis package implementsnormc(),normr(),normv()functions to easily normalize columns, rows of 2-D arrays and vectors respectively.
This package usessklearn.preprocessing.normalizeand forces the input array to have dtype as float. The input array has to be 2-D fornormc()andnormr().normc()-> normalizes columns of a 2-D arraynormr()-> normalizes rows of a 2-D arraynormv()-> normalizes the 1-D array### To install
*pip install normalize_easy, or
* Download the repo and runpython setup.py install### To update
*pip install normalize_easy —-upgrade### Version 0.0.3
* Cleaner code
* pep8 compliant
Package: normalize-japanese-address
This is to standardize Japanese addresses by separating them into sets of prefecture, city, town, and additional details, based on Geolonia's TypeScript library (https://github.com/geolonia/normalize-japanese-addresses).
It is
currently still in the early stage and does not behave exactly the same as Geolonia's original library (fails
in 7.1% of tests).Geolonia様のオープンソースの住所正規化ライブラリ(https://github.com/geolonia/normalize-japanese-addresses)をPythonに移植したものです。
現在まだ試作段階であり、Geolonia様のもとのライブラリと完全に同じ動作にはなっていません（テストのうち7.1%で失敗）。インストール方法Windows環境の場合は、インストールの前に環境変数を設定してくださいset PYTHONUTF8=1pip install --upgrade normalize_japanese_address使い方fromnormalize_japanese_address.normalizeimportnormalizeresult=normalize('大阪府堺市北区新金岡町4丁1−8')print(result)とすると、resultに{'pref':'大阪府','city':'堺市北区','town':'新金岡町四丁','address':'1-8','level':3,'lat':34.568184,'long':135.519409}を返します。levelは、住所文字列のどこまでを判別できたかを以下の数値で示しています。0- 都道府県も判別できなかった。1- 都道府県まで判別できた。2- 市区町村まで判別できた。3- 町丁目まで判別できた。ライブラリの名称normalize-japanese-addressesではなく、normalize_japanese_address という名称になっています。ハイフンがアンダーバーになっているほか、addressが単数になっているのに深い意味はありません。メンテナンスhttps://github.com/geolonia/japanese-addresses/tree/develop/apiが更新された場合、それに対応している japanese_address/api 以下を新しいものに差し替えれることで更新できます。ライセンス、利用規約本プログラムは、下記のプログラムをもとに開発されています。住所データのライセンスは CC BY 4.0、それ以外はMITとされており、本プログラムもそれに従います。https://github.com/geolonia/normalize-japanese-addresseshttps://github.com/geolonia/japanese-addresses
Package: normalize-japanese-addresses
@Taka710/normalize-japanese-addresses-pyオープンソースの住所正規化ライブラリです。経産省のIMI コンポーネントツールのジオコーディングの仕組みからインスピレーションをうけて開発された@geolonia/normalize-japanese-addressesをPythonで利用できるように書き直したものです。使い方pypiで公開していますので、pipコマンドでインストールしてください。pip install normalize-japanese-addresses住所を正規化します。fromnormalize_japanese_addressesimportnormalizeprint(normalize("北海道札幌市西区24-2-2-3-3"))# {'pref': '北海道', 'city': '札幌市西区', 'town': '二十四軒二条二丁目', 'addr': '3-3', 'lat': 43.074273, 'lng': 141.315099, 'level': 3}住所の正規化結果として戻されるオブジェクトには、levelプロパティが含まれます。levelには、住所文字列のどこまでを判別できたかを以下の数値で格納しています。0- 都道府県も判別できなかった。1- 都道府県まで判別できた。2- 市区町村まで判別できた。3- 町丁目まで判別できた。例えば都道府県名のみを正規化したい場合、levelオプションで指定することで処理を早くすることができます。fromnormalize_japanese_addressesimportnormalizeprint(normalize("北海道札幌市西区24-2-2-3-3",level=1))# {'pref': '北海道', 'city': '', 'town': '', 'addr': '札幌市西区24-2-2-3-3', 'lat': 43.074273, 'lng': 141.315099, 'level': 1}名寄せする住所は、@geolonia/japanese-addressesから都度取得しています。endpointオプションでfile://形式のURLを指定することで、ローカルファイルとして保存した住所を参照することができます。# Geolonia 住所データのダウンロード
$ curl -sL https://github.com/geolonia/japanese-addresses/archive/refs/heads/master.tar.gz | tar xvfz -※住所データを最新にしたい場合は都度上記コマンドでダウンロードしてください。fromnormalize_japanese_addressesimportnormalizeprint(normalize("北海道札幌市西区24-2-2-3-3",endpoint="file:///path/to/japanese-addresses-master/api/ja"))# {'pref': '北海道', 'city': '札幌市西区', 'town': '二十四軒二条二丁目', 'addr': '3-3', 'lat': 43.074273, 'lng': 141.315099, 'level': 3}注意以下の仕様は、元の@geolonia/normalize-japanese-addressesを踏襲しています。この正規化エンジンは、住所の「名寄せ」を目的としており、たとえば京都の「通り名」は削除します。郵便や宅急便などに使用される住所としては、問題ないと考えています。この正規化エンジンは、町丁目及び小字レベルまでは対応していますが、それ以降については対応しておりません。住居表示が未整備の地域については全体的に苦手です。漢数字と数字の変換については、@geolonia/japanese-numeralをPythonに書き直して取り込んでいます。ライセンス、利用規約ソースコードのライセンスは MIT ライセンスです。
Package: normalizePGN
**normalizePGN** is a package containing helper scripts and examples that show how to normalize - i.e. correctsome games' meta-data contained in .pgn files before loading them to chess database.PGN files that are being published by tournaments' organizers sometimes (especially for large events wherefew people work on digitalizing games) used to be not consistent. E.g. event name or event place is writtenin slightly different way or there is missing some important information in .pgn files that we want to have - liketime control, players ELO or titles, etc. Those scripts help to fix and complete this information quickly(faster than just by manually editing .pgn files).The idea is to perform following steps:- import games to SQLite database- run few SQL queries that check data consistency and things that we care- based on results of previous step, write fix SQL expressions (usually the ones from previous tournamentsare good enough or needs only slight modification)- run fix expressions- re-check data (using queries from second step)- export games back to .pgn file- import .pgn file to chess database of your choice (e.g. SCID).So using this package requires some basic SQL knowledge and some initial time investment into gettingfamiliar how all this works and creating an initial scripts suitable for your specific use cases.But my experience is that tournament organizers tend to replicate the same issues in .pgn filesfrom one event edition to next. So workout scripts from one edition usually works for all next editions of thesame tournament.Provided examples use POSIX shell and some utilities (like sed), so Linux or Mac users could have good starting point.Windows users may need to put more time to rewrite this staff to technologies available in their system ortry to use some utilities like cygwin or unix services for windows.For information what files are deployed by this package and where see file README.md included in source-ball orin typical installation using pip deployed to folder:- /usr/local/share/doc/normalizePGN (Linux)- C:\Python35\doc\normalizePGN (Windows) or- <USER>\AppData\Local\Programs\Python\Python35-32\Doc\normalizePGN
Package: normalizeurl
URL NormalizeNormalize a URLScreenshotInstall$ pip install normalizeurlUsagefromnormalizeurlimportnormalizeprint(normalize('yoginth.com'))#=> http://yoginth.com/print(normalize('HTTP://mail.yoginth.com:80/?username=yoginth'))#=> http://mail.yoginth.com/?username=yoginthLicenseMITYoginth
Package: normaltext
character normalization (especially for Latin letters - linguistic purposes)pip install normaltextthe lookup function simplifies character analysis, provides replacement
suggestions, and offers performance improvements through memorization.
It can be beneficial for tasks involving character normalization,
text processing, or any scenario where character properties and
substitutions are relevant.Tested against Windows 10 / Python 3.10 / AnacondaThe lookup function can be used by developers or anyone working
with text processing or character manipulation tasks. It provides
information about a given character and suggests
a replacement based on certain criteria.Character Information:The function retrieves the name of the character using unicodedata.name() and provides a sorted list of words representing the character name.
This can be useful for analyzing and understanding the properties of a character.Suggested Replacement:The function suggests a replacement for the character based on the provided criteria.
By considering factors like case sensitivity, printability, and capitalization, the function offers a recommended substitution.
This can be beneficial when you need to transform
or normalize characters in a specific context.Memoization and Performance:The function utilizes the functools.lru_cache decorator, which caches the results of previous function calls.
This means that if the function is called multiple times with the same character,
the result is retrieved from the cache instead of recomputing it.
This caching mechanism can significantly improve the performance of the function when there are repetitive
or redundant character lookups.Flexibility:The lookup function provides optional parameters that allow customization of its behavior.
The case_sens parameter determines whether case sensitivity is considered for replacements.
The replace parameter allows setting a default replacement character.
The add_to_printable parameter enables the addition of extra uppercase characters to the set of printable characters.
These options provide flexibility to adapt the
function to different requirements and use cases.fromnormaltextimportlookupsen="Montréal, über, 12.89, Mère, Françoise, noël, 889"norm=''.join([lookup(k,case_sens=True,replace='x',add_to_printable='')['suggested']forkinsen])print(norm)#########################sen2='kožušček'norm2=''.join([lookup(k,case_sens=True,replace='x',add_to_printable='')['suggested']forkinsen2])print(norm2)#########################sen3="Falsches Üben von Xylophonmusik quält jeden größeren Zwerg."norm3=''.join([lookup(k,case_sens=True,replace='x',add_to_printable='')['suggested']forkinsen3])# doesn't preserve ü - ue ...print(norm3)#########################sen4="cætera"norm4=''.join([lookup(k,case_sens=True,replace='x',add_to_printable='ae')['suggested']forkinsen4])# doesn't preserve ü - ue ...print(norm4)Montreal,uber,12.89,Mere,Francoise,noel,889kozuscekFalschesUbenvonXylophonmusikqualtjedengroserenZwerg.caetera
Package: norman
Norman provides a framework for creating complex data structures using
an database-like approach.  The range of potential application is wide,
for example in-memory databases, multi-keyed dictionaries or node graphs.
These applications are illustrated in the following examples.DatabaseThis is a small database for a personal library:db = Database()

@db.add
class Book(Table):
    name = Field(unique=True)
    author = Field(index=True)

    def validate(self):
        assert isinstance(self.name, str)
        assert isinstance(self.author, Author)

@db.add
class Author(Table):
    surname = Field(unique=True)
    initials = Field(unique=True, default='')
    nationality = Field()
    books = Join(Book.author)Multi-keyed DictionaryThis table can be used as a dictionary with three keys:class MultiDict(Table):
    key1 = Field(unique=True)
    key2 = Field(unique=True)
    key3 = Field(unique=True)
    value = Field()Values can be added by:MultiDict(key1=4, key2='abc', key3=0, value='efg')And queried by:for m in (MultiDict.key1 == 4 & Multidict.key2 == 'abc'):
    print(m.value)Node GraphThis is a graph, where each node can have many parent nodes and many
children nodes:class Link(Table):
    """
    Directional connections between nodes.
    """
    parent = Field(unique=True)
    child = Field(unique=True)

    def validate(self):
        assert isinstance(self.parent, Node)
        assert isinstance(self.child, Node)


class Node(Table):
    """
    Nodes in the graph.
    """
    parents = Join(query=lambda n: (Link.child == n).field('parent'))
    children = Join(query=lambda n: (Link.parent == n).field('child'))

    def validate_delete(self):
        # Delete all connecting links if a node is deleted
        (Link.parent == self).delete()
        (Link.child == self).delete()
Package: norman-dp
Norman Python FrontendInstallTo install the frontend run in the current directory (frontend):pip3install-e.To run Norman pktgen you also need to have thecapinfoscommand installed in the machine you want to use to generate packets.On Ubuntu (or other Debian-based system) run:sudoaptinstalltsharkFor other distributions refer to thewireshark docsto find the correct package name (eithertsharkorwireshark-cli).RunningFor usage information run:norman--helpEnable autocompletion (optional)For BashRun this:_NORMAN_COMPLETE=bash_sourcenorman>~/.norman-complete.bashThen add the following to your.bashrc:.~/.norman-complete.bashFor ZshRun this:_NORMAN_COMPLETE=zsh_sourcenorman>~/.norman-complete.zshThen add the following to your.zshrc:.~/.norman-complete.zshFor FishSave the script to~/.config/fish/completions/norman.fish:_NORMAN_COMPLETE=fish_source norman>~/.config/fish/completions/norman.fish
Package: normanpg
This is a set of modest utilities that may be helpful when talking to
PostgreSQL.Project Featuresnormanpga starterClickcommand-line
applicationautomated unit tests you can run withpytestaSphinxdocumentation
projectGetting StartedThe project’s documentation contains a section to help youget
startedas a developer or user of the library.Development PrerequisitesIf you’re going to be working in the code (rather than just using the
library), you’ll want a few utilities.GNU MakePandocResourcesBelow are some handy resource links.Project DocumentationClickis a Python package for
creating beautiful command line interfaces in a composable way with
as little code as necessary.Sphinxis a tool that
makes it easy to create intelligent and beautiful documentation,
written by Geog Brandl and licnsed under the BSD license.pytesthelps you write
better programs.[GNU Make] is a tool which controls the generation of executables and
other non-source files of a program from the program’s source files.AuthorsPat Daburu-Initial work-githubSee also the list ofcontributorswho participated in this project.LicenseMIT LicenseCopyright (c) patdaburuPermission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the
“Software”), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: normetapi
normetapiA small Python library for interacting with theMET Norway Weather API.InstallationpipinstallnormetapiExamplesGetting a forecast for a specified location:fromnormetapiimportlocation_forecast# Get forecast for Trondheim:forecast=location_forecast(63.4107,10.4538)print(forecast)The forecast will be returned as a dictionary. See the description
oflocationforecastand thedata modelin theMET Norway Weather API description.Getting the immediate forecast for a specified location:fromnormetapiimportnowcast# Get nowcast for Trondheim:forecast=nowcast(63.4107,10.4538,altitude=123)print(forecast)The forecast will be returned as a dictionary. See the description
ofnowcastand thedata modelin theMET Norway Weather API description.Getting weather icons:fromnormetapiimportweathericon# Get icons:_,legend=weathericon(output_file='icons.tgz')print(legend)This will download weather icons as a gzipped tar archive
and return legends as a dictionary. See the
description ofweathericonin theMET Norway Weather API description.Terms of servicePlease read theterms of service.
In particular, to quote the terms of service:All requests must (if possible) include an identifying User Agent-string (UA)
in the request with the application/domain name, optionally version number.Please modify theUSER_AGENTvariable inapi.pyto fit your
intended use.
Package: normflowpy
NormFlowPyThis repository contains the implementation of various types of normalizing flow/ invertible neural networks. In addition, we provide a simple API run, train, and implement new types of normalizing flows.
We have implemented the following layers:Affine CouplingInvertible 1x1Neural Spline Flow and Cubic Flow
and others. Note, that so implementation's based on other Github repositories and this would be stated in each file.Installationpip install normflowpyCode ExamplesWe have provide only a single example at this stage please seemoons notebookContribution & ProblemsWe welcomes contributions from anyone and if you find a bug or have a question, please create a GitHub issue.Refernces[1] Kingma, Durk P., and Prafulla Dhariwal. "Glow: Generative flow with invertible 1x1 convolutions." Advances in neural information processing systems 31 (2018).[2] Laurent Dinh, David Krueger, and Yoshua Bengio. NICE: nonlinear independent components estimation. In Yoshua Bengio and Yann LeCun, editors, 3rd International Conference on
Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Workshop Track Proceedings, 2015.[3] Andreas Lugmayr, Martin Danelljan, Luc Van Gool, and RaduTimofte.Srflow:  Learning  the  super-resolution  space  withnormalizing flow. InEuropean Conference on Computer Vision,pages 715–732. Springer, 2020.[4]  Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Densityestimation  using  real  nvp.arXiv preprint arXiv:1605.08803,2016.[5] Conor Durkan, Artur Bekasov, Iain Murray, and George Papa-makarios. Cubic-spline flows.arXiv preprint arXiv:1906.02145,2019
Package: normi
Docs•Features•Installation•Usage•FAQNorMI: Nonparametric Normalized Mutual Information Estimator Based onk-NN StatisticsThis software provides an extension to the Kraskov-Estimator to allow normalizing the mutual information.The method will be published soon as:Adaptive Entropy-Based Normalization for (High-Dimensional) Mutual InformationD. Nagel, G. Diez, and G. Stock,  in prep.If you use this software package, please cite the above mentioned paper.FeaturesIntuitive usage viamoduleand viaCISklearn-style API for fast integration into your Python workflowNo magic, only a  single parameter which can be optimized via cross-validationExtensivedocumentationand detailed discussion in publicationInstallationThe package is callednormiand is available viaPyPIorconda. To install it, simply call:python3-mpipinstall--upgradenormiorconda install -c conda-forge normior for the latest dev version# via ssh keypython3-mpipinstallgit+ssh://git@github.com/moldyn/NorMI.git# or via password-based loginpython3-mpipinstallgit+https://github.com/moldyn/NorMI.gitShell CompletionUsing thebash,zshorfishshell click provides an easy way to provide shell completion, checkout thedocs.
In the case of bash you need to add following line to your~/.bashrceval"$(_NORMI_COMPLETE=bash_sourcenormi)"UsageIn general one can call the module directly by its entry point$ normior by calling the module$ python -m normi. The latter method is preferred to ensure using the desired python environment. For enabling the shell completion, the entry point needs to be used.CI - Usage Directly from the Command LineThe module brings a rich CI usingclick.
For a complete list of all options please see thedocs.python-mnormi/--inputinput_file/# ascii file of shape (n_samples, n_features)--outputoutput_file/# creates ascii file of shape (n_features, n_features)--n-dims/# this allows to treat every n_dims columns as a high dimensional feature--verboseModule - Inside a Python ScriptfromnormiimportNormalizedMI# Load file# X is np.ndarray of shape (n_samples, n_features)nmi=NormalizedMI()nmi_matrix=nmi.fit_transform(X)...