Saravanan666/pypi-org huggingface dataset:
Package: zencelium
Zencelium: Personal Zentropi Instance ServerFree software: BSD 3-Clause LicenseInstallationpip install zenceliumYou can also install the in-development version with:pip install https://github.com/zentropi/python-zencelium/archive/master.zipDocumentationhttps://zencelium.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog2020.0.0 (2020-03-06)First release on PyPI.
Package: zenchi
zenchizenchi is a python3 application that communicates with AniDB UDP API. It provides an interface to convert raw responses strings into python objects. It does very little by itself and its only intention is to parse data for other applications to use.Currently, only Data commands are supported.Installingpip install -U zenchiUsageFairly straightforward:>>>importzenchi>>>zenchi.create_socket(anidb_server='api.anidb.net',anidb_port=9000)<socket.socket...>>>>zenchi.ping(nat=1)({'port':25065},300)Every command response is a tuple (data, code). data is a dictionary of variable keys containing the parsed response, and code is the response code.Environment variablesZENCHI_CLIENTNAMEandZENCHI_CLIENTVERSIONshould be replaced by your own keys generated at AniDB site. (no guarantee these values are valid at the time of your reading!)ZENCHI_CLIENTNAME=devel
ZENCHI_CLIENTVERSION=1

ANIDB_SERVER=api.anidb.net
ANIDB_PORT=9000

ANIDB_USERNAME=xXGodKillerXx
ANIDB_PASSWORD=hunter2If these values are set, the socket is created automatically and it's much simpler. You can skip the call forcreate_socketentirely and just call the commands:>>>importzenchi>>>zenchi.auth()({'session':'ELahj'},200)>>>zenchi.character(1)(...,235)Anime masksTheANIMEcommand receives a mask as parameter to filter the anime data. zenchi provides an easy way to create these masks with the modulezenchi.mappings.anime.mask.>>>importzenchi.mappings.anime.maskasamask>>>zenchi.anime(amask.aid|amask.romaji_name|amask.english_name|amask.short_name|amask.year,aid=3433)({'aid':3433,'english_name':'Mushi-Shi','romaji_name':'Mushishi','short_name':['Mushi'],'updated_at':datetime.datetime(2019,11,10,19,55,18,1000),'year':'2005-2006'},230)Cachezenchi uses a very basic optional MongoDB database as cache, namedanidb_cache. It uses the environment variableMONGODB_URIto check the connection string. If the variable is not set, a warning will be issued and all cache usage will be ignored (highly unadvised, as per AniDB specifications).Any operations that use the cache have the parameteruse_cachethat defaults toTrue. You can set this toFalseto skip the cache for that specific command (for example, when you want to update the cached data). All cached data also returns aupdated_atkey (see example above), which is the last time that data was updated in the database.If you don't want to useanidb_cacheorMONGODB_URI, manually callzenchi.cache.setupwith the appropriate values before sending requests to the API.FeaturesIt's actually fairly simple to add new commands to zenchi, and I just wrote what I personally intend to use.
Feel free to send PRs or request something in the issues.LicenseThis project is under MIT License.For data collection and usage, make sure to readAniDB Policies
Package: zenconf
Zenconf is an unopinionated config management system. You put dicts in, it
recursively merges them, then returns a single dict with values from later
dicts overwriting values from earlier dicts.A default implementation shows how to pull values from a dictionary of
defaults, a config file, environment variables and command line parameters.
Simply create your own custom config class and add values from whatever data
sources you like in your chosen order of precedence. Then, request the merged
dictionary and use it throughout the rest of your code.FeaturesSimple. Just add dicts (or OrderedDicts) from wherever you like, and get
the merged dictionary back.No constraints on using a particular config file system, arg parser, etc.Key names can be normalised by applying a function to keys recursively to
make for easier comparison between keys from different data sources
(e.g. from environment variables or yaml files,
where one uses underscores to separate words, the other hyphens). By
default keys will be converted to lowercase, have hyphens converted to
underscores and then have leading underscores removed.Support for filtering by, and stripping an app-specific prefix from keys
(configurable per data source). This means that if your app is calledMYAPP, only environment variables with a prefix ofMYAPP_could be
added, e.g.MYAPP_LOG_LEVEL=debugcould override a commandline argument–log-level.InstallationClone the repo then run./setup.py install.Or, install from pypi with:pip install zenconfRun tests with:./setup.py testUsageUsage is simple. Just instantiate an instance of
zenconf.MergedConfig:Theappnameparameter can be used to namespace keys (such as environment
variables).Thedict_boundaryparameter specifies a string that indicates that we
should look up the next string fragment in a subdictionary. E.g. using a
default of ‘__’, the stringLOG__LEVELrefers to config[‘log’][‘level’].Next, add dicts via theadd()method.To get the merged dict, just callget_merged_config().See comments in the code for more information about parameters. Also see the
example in theexamplesdirectory of one way to use the class.
Package: zenconfig
zen-configSimple configuration loader for python.Compared to other solutions, the goal is to bring:simple usage for simple use casesmultiple format supportuse objects rather than plain dict to interact with the configoptionally use the power of pydantic for validationSimple usageIf you don't want to configure much, pass the config path through the env variableCONFIG, and simply use:fromdataclassesimportdataclassfromzenconfigimportConfig@dataclassclassMyConfig(Config):some_key:strsome_optional_key:bool=Falsecfg=MyConfig(some_key="hello")cfg.save()...cfg=MyConfig.load()cfg.some_optional_key=Truecfg.save()...cfg.clear()Config file loadingWhen creating your config, you can specify at least one of those two attributes:ENV_PATHthe environment variable name containing the path to the config file, defaults toCONFIGPATHdirectly the config path💡 When supplying both, if the env var is not set, it will usePATH.User constructs will be expanded.
If the file does not exist it will be created.
You can specify the file mode viaConfig.FILE_MODE.The config can be loaded from multiple files, seefnmatchfor syntax.
Note that you will not be able to save if not handling exactly one file.Read onlyIf you do not want to be able to modify the config from your code, you can useReadOnlyConfig.Supported formatsCurrently, those formats are supported:JSONYAML - requires theyamlextraTOML - requires thetomlextraThe format is automatically inferred from the config file extension.
When loading from multiple files, files can be of multiple formats.Other formats can be added by subclassingFormat:Config.register_format(MyFormat(...), ".ext1", ".ext2").💡 You can re-register a format to change dumping options.Supported schemasCurrently, those schemas are supported:plain dictdataclassespydantic models - requires thepydanticextraattrs - requires theattrsextraThe schema is automatically inferred from the config class.Other schemas can be added by subclassingSchema:Config.register_schema(MySchema(...)).You can also force the schema by directly overriding theSCHEMAclass attribute on your config.
This can be used to disable auto selection, or pass arguments to the schema instance.⚠️ When using pydantic, you have to supply theClassVartype annotations
to all class variable you override
otherwise pydantic will treat those as its own fields and complain.ConversionsFor all schemas and formats, common built in types are handledwhen dumping.⚠️ Keep in mind that onlyattrsandpydanticsupport casting when loading the config.You can add custom encoders withConfig.ENCODERS.
Forpydantic, stick withthe standard way of doing it.ContributingSeecontributing guide.
Package: zencore-etcd3
zencore-etcd3etcd3的副本。原项目说明本项目是原项目etcd3的副本。原项目pypi页面是：https://pypi.org/project/etcd3/原项目github页面是：https://github.com/kragniz/python-etcd3/相关文档、代码及问题讨论，都请使用原项目相关链接。关于本项目使用pip install etcd3安装的最新版本是0.12.0，但都不能正常使用。其依赖的第三方包都已经升级更新，但通过pip install etcd3安装的代码与最新包都不兼容。测试发现，通过源代码安装，却可以与这些最新的第三方包配合工作。但不知道什么原因，原项目负责人并没有将这些源代码更新，制作成新版本，发布到pip上。本项目旨在将最新源代码进行打包，并发布到pypi上，方便自己的项目使用。版本管理在原项目版本的基础上，加上最新源代码的最后提交日期。版本历史v0.12.0.20220817项目创建。=======
History0.1.0 (2016-09-30)First release on PyPI.
Package: zencoreipinfo
zencoreipinfoGet outgoing ip fromhttps://zencore.cn/ipinfo.Installpip install zencoreipinfoUsage in Clitest@test zencoreipinfo % ipinfo --help
Usage: ipinfo [OPTIONS]

Options:
  --url TEXT   Server url. Can apply multiple times.
  -q, --quiet  Don't show error information.
  --help       Show this message and exit.

test@test zencoreipinfo % ipinfo
aaa.xxx.yy.zzBy default, the program will use the default ipinfo serverhttps://zencore.cn/ipinfo.We ship cli program in nameipinfo, andzencoreipinfoas it's alias.Usage in ScriptIn [1]: from zencoreipinfo import get_outgoing_ip

In [2]: ip = get_outgoing_ip()

In [3]: print(ip)
aaa.xxx.yy.zzHow to implement self ipinfo server?You need a linux server with a public ip address, and you need to install nginx service on the linux server. Add the ipinfo location into your site.# your other configs

location /ipinfo {
    add_header Content-Type text/plain;
    return 200 $remote_addr;
}

# your other configsAfter you setup your ipinfo server. Run the ipinfo command with --url prarameter.test@test zencoreipinfo % ipinfo --url https://you.site.domain.cn/ipinfo
aaa.xxx.yy.zzAbout the ipinfo server of zencore.cnWe do not promise continuity or reliability of the service.Release0.1.0First release.
Package: zencore-json2csv
python-json2csvConvert json array data to csv.Note:zencore-json2csv rename to python-json2csvInstallpip install python-json2csvUsageE:\>json2csv --help
    Usage: json2csv [OPTIONS]

    Options:
    -f, --file FILENAME     Input file name, use - for stdin.
    --file-encoding TEXT    Input file encoding.
    -o, --output FILENAME   Output file name, use - for stdout.
    --output-encoding TEXT  Output file encoding.
    -k, --keys TEXT         Output field names. Comma separated string list.
    -p, --path TEXT         Path of the data.
    --help                  Show this message and exit.ExamplesExample 1INPUT:[
    [1,2,3],
    [2,3,4]
]OUTPUT:1,2,3
    2,3,4COMMAND:cat input.txt | json2csv -o output.txtExample 2INPUT:[
    {"f1": 11, "f2": 12, "f3": 13},
    {"f1": 21, "f3": 23, "f2": 22}
]OUTPUT:11,12,13
21,22,23COMMAND:cat input.txt | json2csv -o output.txt -k f1,f2,f3Example 3INPUT:{
    "data": {
        "list": [
            [1,2,3],
            [2,3,4],
        ]
    }
}OUTPUT:1,2,3
2,3,4COMMAND:cat input.txt | json2csv -o output.txt -p data.listReleasesv0.2.1 2022-01-10Fix license file missing problem.v0.2.0 2019-11-05Rename from zencore-json2csv to python-json2csv.Fix console application install method in setup.py.v0.1.1 2018-02-27Simple json2csv console utils released.
Package: zen-corpora
Zen-corporaDescriptionZen-corpora provides two main funcitonalities:A memory efficient way to store unique sentences in corpus.Beam text search with RNN model in PyTorch.InstallationThis module requires Python 3.7+. Please install it by running:pipinstallzen-corporaWhy Zen-corpora?Think about how Python stores the corpus below:corpus=[['I','have','a','pen'],['I','have','a','dog'],['I','have','a','cat'],['I','have','a','tie']]It stores each sentence separately, but it's wasting the memory by storing "I have a " 4 times.Zen-corpora solves this problem by storing sentences in a corpus-level trie. For example, the corpus above will be stored as|--I--have--a|--pen|--dog|--cat|--tieIn this way, we can save lots of memory space and sentence search can be a lot faster!Zen-corpora provides Python API to easily construct and interact with a corpus trie. See the following example:>>>importzencorpora>>>fromzencorpora.corpustrieimportCorpusTrie>>>corpus=[['I','have','a','pen'],...['I','have','a','dog'],...['I','have','a','cat'],...['I','have','a','tie']]>>>trie=CorpusTrie(corpus=corpus)>>>print(len(trie))7>>>print(['I','have','a','pen']intrie)True>>>print(['I','have','a','sen']intrie)False>>>trie.insert(['I','have','a','book'])>>>print(['I','have','a','book']intrie)True>>>print(trie.remove(['I','have','a','book']))1>>>print(['I','have','a','book']intrie)False>>>print(trie.remove(['I','have','a','caw']))-1>>>print(trie.make_list())[['i','have','a','pen'],['i','have','a','dog'],['i','have','a','cat'],['i','have','a','tie']]Left-to-Right Beam Text SearchAs shown in SmartReply paper byKannan et al. (2016), corpus trie can be used to perform left-to-right beam search using RNN model.
A model encodes input text, then it computes the probability of each pre-defined sentence in the searching space given the encoded input.
However, this process is exhaustive. What if we have 1 million sentences in the search space? Without beam search, a RNN model processes 1 million sentences.
Thus, the authors used the corpus trie to perform a beam search for their pre-defined sentences.
The idea is simple, it starts search from the root of the trie. Then, it only retains beam width number of probable sentences at each level.Zen-corpora provides a class to enable beam search. See the example below.>>>importtorch.nnasnn>>>importtorch>>>importos>>>fromzencorporaimportSearchSpace>>>corpus_path=os.path.join('data','search_space.csv')>>>data=...# assume data contains torchtext Field, encoder and decoder>>>space=SearchSpace(...src_field=data.input_field,...trg_field=data.output_field,...encoder=data.model.encoder,...decoder=data.model.decoder,...corpus_path=corpus_path,...hide_progress=False,...score_function=nn.functional.log_softmax,...device=torch.device('cpu'),...)# you can hide a progress bar by setting hide_progress = FalseConstructCorpusTrie:100%|...|34105/34105[00:01<00:00,21732.69sentence/s]>>>src=['this','is','test']>>>result=space.beam_search(src,2)>>>print(len(result))2>>>print(result)[('is this test?',1.0),('this is test!',1.0)]>>>result=space.beam_search(src,100)>>>print(len(result))100LicenseThis project is licensed under Apache 2.0.
Package: zencrc
ZenCRCA command-line tool for CRC32 stuff.InstallationThis program is packaged as a python package using setuptools and can be installed usingpiporpipsi.
For extended testing running in a virtualenv might be a good idea.In package directory, run:$ pipsi install .or:$ pip install .pipsiis a great alternative to regular pip Hereas it installs each package you install, in an isolated area.
Adn it doesn't require sudo or Admin access to work it's magic.
More detailed functionality can be found @pipsi github repo.UsageThis section will explain all the functions and options available in ZenCRC:Basic help:$ zencrc --helpA more concise version of this help can be hound by using the--helpor-hoption.Append Mode$ zencrc -a {file}You can append a CRC32 checksum to a filename by using the--appendor-aoption.
Takes a positional argument {file} or {files} at the end of the command.
The CRC will be appended to the end of the file in the following format:filename.ext --> filename [CRC].extSo, therefore:
$ zencrc -a [LNS]Gin no Saji [720p-BD-AAC].mkvwill return:
[LNS]Gin no Saji [720p-BD-AAC] [72A89BC1].mkvCurrently no functionality exists to change the format in which the CRC is appended but will be added in v0.9Verify Mode$ zencrc -v {file}You can verify a CRC32 checksum in a filename by using the--verifyor-voption.
Takes a positional argument {file} or {files} at the end of the command.
This will calculate the CRC32 checksum of a file, check it against the CRC in the filename of said file,
output the status of that file and the CRC that the program calculated.
If the filename does not contain a CRC the program will still calculate and output the CRC32 of that file.
Currently no functionality exists to only check files with a CRC32 in their name (except some convoluted, yet clever, regex)
but such funtionality may be added in future versions.
Package: zendatastorage
A Python package providing useful and important features to process data from .zenf files and to parse them to local python types.Simple UseRead Var from .zenf fileTest.zenf File["TestVar"] = Hello;Main.py Filefrom zendatastorage import Interpret

with open("Test.zenf", "r") as f:
    VarDict = Interpret(f)

print(VarDict["TestVar"])OutputHello
Package: zendeskclient
zendeskclientA lightweight ZenDesk REST API client written in Python.Usagefrom zendeskclient import ZenDeskClient
client = ZenDeskClient(‘bob.smith@acme.com’, ‘acme-software.zendesk.com’, password=’hunter2’)
all_tickets = client.core.tickets.get()LICENSEMIT LicenseCopyright (c) 2016 CloudBolt SoftwarePermission 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: zendesk-django-auth
Allows your Django App to be used as an authentication platform for your Zendesk AccountZendesk API Documentationseehttp://www.zendesk.com/support/api/remote-authenticationfor ZenDesk Documentation.This module is specifically for Zendesk API v1Installationzendesk_auth version 0.0.3+ works with django 1.5 and abovepip install zendesk-django-authUsageUsing Zendesk SSO in your app is extremely simple…in your settings.py, add ‘zendesk_auth’ to yourINSTALLED_APPSThen add the following two settingsZENDESK_URL=https://your_domain.zendesk.comZENDESK_TOKEN=you_zendesk_tokenNext, add this (or equivalent) to your urls.py:url(r’’, include(‘zendesk_auth.urls’)),You’ll need to setup your zendesk remote authentication settings to allow/use your zendesk_authorize view.You’re done! Now watch it work.
Package: zendesk-exporter
zendesk_exporterExports data from Zendesk API to excel or csvInstallOpen a command prompt and runsudo easy_install pippip is a tool that lets python developers share code.Now, runpip install zendesk_exporterThis installs the exporter to your computerNow, runzendesk_exporter job_file.jsonjob_file.jsonis a file that the program needs to be able to find.job_file.jsonshould look like this:{"domain_name":"XXX","email":"XXX","api_key":"XXX","start_date":"2018-06-27","end_date":"2018-06-30","fetch_comments":true,"output_file_name":"last_6_months","output_file_type":"csv","fetch_all_ticket_data":false}You can change the parameters in this file to change how the program behaves.
Package: zendesk_integration
TODO: Modify the whole file as necessary.This is a “long description” file for the package that you are creating.
If you submit your package to PyPi, this text will be presented on thepublic pageof your package.Note: This README has to be written usingreStructured Text, otherwise PyPi won’t format it properly.InstallationThe easiest way to install most Python packages is viaeasy_installorpip:$ easy_install zendeskUsageTODO: This is a good place to start with a couple of concrete examples of how the package should be used.The boilerplate code provides a dummymainfunction that prints out the word ‘Hello’:>> from zendesk import main
>> main()When the package is installed viaeasy_installorpipthis function will be bound to thezendeskexecutable in the Python installation’sbindirectory (on Windows - theScriptsdirectory).
Package: zendesk-sell-firehose-client
Zendesk Sell Firehose ClientClient for ZenDesk Sell Firehose APIInstallationpipinstallzendesk_sell_firehose_clientUsagefromzendesk_sell_firehose_clientimportZendeskSellFirehoseClientclient=ZendeskSellFirehoseClient(bearer_token="{BEARER_TOKEN_HERE}")# Get all leadsleads=client.get_leads()DevelopmentSetuppipinstall-rrequirements.txtTestingpytest
Package: zen_document_parser
# Zen Document Parser## Introzen_document_parser is a utility for extracting data from various official documents. It uses [PDFQuery](https://github.com/jcushman/pdfquery) behind the scenes.Currently, there is out-of-the-box support for parsing **Indian Government ITR-V PDF documents.**The library also supports parsing of arbitrary PDF documents by allowing you to specify a 'schema' for the document. The library allows for multiple 'variants' of a document. For example, The Indian ITR-V document has slightly different fields and layout depending on whether it was generated in 2013, 2014, 2015 etc.Check out the examples below.## InstallationInstall using [pip](https://pip.pypa.io/en/stable/installing/) like so:```bash$ pip install zen_document_parser```## Usage### ITR-V Docs```pythonfrom zen_document_parser.itr.itr import ITRVDocument# You can pass in a path or a file-like object during instantiation.doc = ITRVDocument('/path/to/itrv.pdf')# Will load the file, auto-detect the variant and perform extraction of all# fields and store results internally.doc.extract()# Extracted fields are available in the `data` property.print(doc.data.company_name)print(doc.data.gross_total_income)```### Configuring for custom PDF documentsYou basically follow these steps:- Define one or more 'schemas', ie. `DocVariant` subclasses, to go with each variant of the doc.- In each of these variants, define a `check_for_match()` method that returns `True` if a file was successfully parsed.- Make sure to define `test_fields` as an attribute on each class that is a list of all field names used inside `check_for_match()`. (This is required at present for optimization purposes, but will not be a requirement in an upcoming version.)- Define a `Doc` subclass that represents your document. In the `variants` attribute, specify possible variants.```pythonfrom zen_document_parser.base import DocField, DocVariant, Documentclass Variant1(DocVariant):# The fields that are used inside `check_for_match()`. (for optimization)test_fields = ['form_title']form_title = DocField((30, 300, 500, 380))name = DocField((100, 120, 400, 140.5))address = DocField((150, 90, 650, 110))def check_for_match(self):if self.form_title == 'Application Form For 2014':return Truereturn Falseclass Variant2(DocVariant):test_fields = ['form_title']form_title = DocField((30, 290, 500, 380))name = DocField((70, 140, 350, 160))address = DocField((150, 120, 650, 140))pan_no = DocField((150, 80, 650, 100))def check_for_match(self):if self.form_title == 'Application Form For 2015-16':return Truereturn Falseclass MyForm(Document):variants = [Variant1, Variant2]def main():doc = MyForm('/path/to/form.pdf')doc.extract()print(doc.data.to_dict())```# TODO- Hanle data-type specification- Handle fields being mandatory/non-mandatory.- Right now the user has to explicitly specify `test_fields` for optimization purposes. Find a way where this isn't needed.- Automatically load them the first time they're referred to? `extract()` can still be there as a way to bulk-load all fields in one go.