Saravanan666/pypi-org huggingface dataset:
Package: perseus-restful-api-client-library
Perseus RESTful API Client Python LibraryRepository of classes that provide Pythonic interfaces to connect to a RESTful API server developed with Perseus RESTful API server framework.Python LibraryPoster 0.8.1Note: this library includes a modified version ofposter 0.8.1, which original version provides a set of classes and functions to facilitate making HTTP POST (or PUT) requests using the standard multipart/form-data encoding.The original libraryposter 0.8.1cannot be used to upload file uploaded into memory (i.e., stream-to-memory), like for instance djangoInMemoryUploadedFile. The reason is that such file-like object doesn't support the methodfileno()used by theposter 0.8.1to determine the size of the file-like object to upload in Python moduleposter.encode:iffileobjisnotNoneandfilesizeisNone:# Try and determine the file sizetry:self.filesize=os.fstat(fileobj.fileno()).st_sizeexcept(OSError,AttributeError):try:fileobj.seek(0,2)self.filesize=fileobj.tell()fileobj.seek(0)except:raiseValueError("Could not determine filesize")This code raises the exceptionio.UnsupportedOperationthatposter 0.8.1doesn't catch. Chris AtLee included Alon Hammerman's patch in the tagtipof the library ``poster, for catching theio.UnsupportedOperation for fileno` on 2013-03-12:try:fromioimportUnsupportedOperationexceptImportError:UnsupportedOperation=None(...)iffileobjisnotNoneandfilesizeisNone:# Try and determine the file sizetry:self.filesize=os.fstat(fileobj.fileno()).st_sizeexcept(OSError,AttributeError,UnsupportedOperation):try:fileobj.seek(0,2)self.filesize=fileobj.tell()fileobj.seek(0)except:raiseValueError("Could not determine filesize")However, the latest version ofposterinstallable withpipis still0.8.1.
Package: persia
WARNING: THIS PROJECT IS CURRENTLY IN MAINTENANCE MODE, DUE TO COMPANY REORGANIZATION.PERSIA(Parallel rEcommendation tRainingSystem with hybrIdAcceleration)  is developed byAI platform@Kuaishou Technology, collaborating with ETH. It is a PyTorch-based (the first public one to our best knowledge) system for training large scale deep learning recommendation models on commodity hardwares. It is capable of training recommendation models with up to 100 trillion parameters. To the best of our knowledge, this is the largest model size in recommendation systems so far. Empirical study on public datasets indicate PERSIA's significant advantage over several other existing training systems in recommendation [1]. Its efficiency and robustness have also been validated by multiple applications with 100 million level DAU at Kuaishou.Disclaimer: The program is usable and has served several important businesses. However, the official English documentation and tutorials are still under heavy construction and they are a bit raw now. We encourage adventurers to try out PERSIA and contribute!NewsTraining Deep Learning-based recommender models of 100 trillion parameters over Google Cloud突破百万亿参数规模，追求极致的效率和性价比：华人团队开源首个异构并行推荐系统训练框架 PERSIA(In Chinese. Title: Breaking through the trillion parameter scale in pursuit of ultimate efficiency and cost effectiveness: Chinese team open source PERSIA, the first heterogeneous parallel recommendation system)参数量卷到一百万亿！华人团队开源史上最大的推荐训练系统 PERSIA(In Chinese. Title: PERSIA, the Largest Recommended Training System in the History of Open Source by Far)AI Engines in the "Short-video" Era: Eating 100 Trillion Parameters, Invited talk, Facebook, 2021.单机训练速度提升 640 倍！独家解读快手商业广告模型 GPU 训练平台 PERSIA (In Chinese. Title: 640x Faster GPU Based Learning System for Ad Recommendation)[AI Front][中国日报][InfoQ][CSDN][Tencent Cloud News][AcFun]创新、平衡与大格局：快手商业化的慢与快 (In Chinese. Title: Innovation, Balance, and Big Picture: The Speed of Kwai Commercialization)[TechSir][China Daily][Sohu]LinksGitHub RepositoryTutorialsAPI documentation(Under Construction)DiscussionFeel free to join ourTelegram Groupfor discussion!ReferencesXiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, & Ji Liu. (2021).Persia: A Hybrid System Scaling Deep Learning Based Recommenders up to 100 Trillion Parameters.Ji Liu & Ce Zhang. (2021).Distributed Learning Systems with First-order Methods.LicenseThis source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.
Package: persia-cuda102
WARNING: THIS PROJECT IS CURRENTLY IN MAINTENANCE MODE, DUE TO COMPANY REORGANIZATION.PERSIA(Parallel rEcommendation tRainingSystem with hybrIdAcceleration)  is developed byAI platform@Kuaishou Technology, collaborating with ETH. It is a PyTorch-based (the first public one to our best knowledge) system for training large scale deep learning recommendation models on commodity hardwares. It is capable of training recommendation models with up to 100 trillion parameters. To the best of our knowledge, this is the largest model size in recommendation systems so far. Empirical study on public datasets indicate PERSIA's significant advantage over several other existing training systems in recommendation [1]. Its efficiency and robustness have also been validated by multiple applications with 100 million level DAU at Kuaishou.Disclaimer: The program is usable and has served several important businesses. However, the official English documentation and tutorials are still under heavy construction and they are a bit raw now. We encourage adventurers to try out PERSIA and contribute!NewsTraining Deep Learning-based recommender models of 100 trillion parameters over Google Cloud突破百万亿参数规模，追求极致的效率和性价比：华人团队开源首个异构并行推荐系统训练框架 PERSIA(In Chinese. Title: Breaking through the trillion parameter scale in pursuit of ultimate efficiency and cost effectiveness: Chinese team open source PERSIA, the first heterogeneous parallel recommendation system)参数量卷到一百万亿！华人团队开源史上最大的推荐训练系统 PERSIA(In Chinese. Title: PERSIA, the Largest Recommended Training System in the History of Open Source by Far)AI Engines in the "Short-video" Era: Eating 100 Trillion Parameters, Invited talk, Facebook, 2021.单机训练速度提升 640 倍！独家解读快手商业广告模型 GPU 训练平台 PERSIA (In Chinese. Title: 640x Faster GPU Based Learning System for Ad Recommendation)[AI Front][中国日报][InfoQ][CSDN][Tencent Cloud News][AcFun]创新、平衡与大格局：快手商业化的慢与快 (In Chinese. Title: Innovation, Balance, and Big Picture: The Speed of Kwai Commercialization)[TechSir][China Daily][Sohu]LinksGitHub RepositoryTutorialsAPI documentation(Under Construction)DiscussionFeel free to join ourTelegram Groupfor discussion!ReferencesXiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, & Ji Liu. (2021).Persia: A Hybrid System Scaling Deep Learning Based Recommenders up to 100 Trillion Parameters.Ji Liu & Ce Zhang. (2021).Distributed Learning Systems with First-order Methods.LicenseThis source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.
Package: persia-cuda111
WARNING: THIS PROJECT IS CURRENTLY IN MAINTENANCE MODE, DUE TO COMPANY REORGANIZATION.PERSIA(Parallel rEcommendation tRainingSystem with hybrIdAcceleration)  is developed byAI platform@Kuaishou Technology, collaborating with ETH. It is a PyTorch-based (the first public one to our best knowledge) system for training large scale deep learning recommendation models on commodity hardwares. It is capable of training recommendation models with up to 100 trillion parameters. To the best of our knowledge, this is the largest model size in recommendation systems so far. Empirical study on public datasets indicate PERSIA's significant advantage over several other existing training systems in recommendation [1]. Its efficiency and robustness have also been validated by multiple applications with 100 million level DAU at Kuaishou.Disclaimer: The program is usable and has served several important businesses. However, the official English documentation and tutorials are still under heavy construction and they are a bit raw now. We encourage adventurers to try out PERSIA and contribute!NewsTraining Deep Learning-based recommender models of 100 trillion parameters over Google Cloud突破百万亿参数规模，追求极致的效率和性价比：华人团队开源首个异构并行推荐系统训练框架 PERSIA(In Chinese. Title: Breaking through the trillion parameter scale in pursuit of ultimate efficiency and cost effectiveness: Chinese team open source PERSIA, the first heterogeneous parallel recommendation system)参数量卷到一百万亿！华人团队开源史上最大的推荐训练系统 PERSIA(In Chinese. Title: PERSIA, the Largest Recommended Training System in the History of Open Source by Far)AI Engines in the "Short-video" Era: Eating 100 Trillion Parameters, Invited talk, Facebook, 2021.单机训练速度提升 640 倍！独家解读快手商业广告模型 GPU 训练平台 PERSIA (In Chinese. Title: 640x Faster GPU Based Learning System for Ad Recommendation)[AI Front][中国日报][InfoQ][CSDN][Tencent Cloud News][AcFun]创新、平衡与大格局：快手商业化的慢与快 (In Chinese. Title: Innovation, Balance, and Big Picture: The Speed of Kwai Commercialization)[TechSir][China Daily][Sohu]LinksGitHub RepositoryTutorialsAPI documentation(Under Construction)DiscussionFeel free to join ourTelegram Groupfor discussion!ReferencesXiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, & Ji Liu. (2021).Persia: A Hybrid System Scaling Deep Learning Based Recommenders up to 100 Trillion Parameters.Ji Liu & Ce Zhang. (2021).Distributed Learning Systems with First-order Methods.LicenseThis source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.
Package: persia-cuda113
WARNING: THIS PROJECT IS CURRENTLY IN MAINTENANCE MODE, DUE TO COMPANY REORGANIZATION.PERSIA(Parallel rEcommendation tRainingSystem with hybrIdAcceleration)  is developed byAI platform@Kuaishou Technology, collaborating with ETH. It is a PyTorch-based (the first public one to our best knowledge) system for training large scale deep learning recommendation models on commodity hardwares. It is capable of training recommendation models with up to 100 trillion parameters. To the best of our knowledge, this is the largest model size in recommendation systems so far. Empirical study on public datasets indicate PERSIA's significant advantage over several other existing training systems in recommendation [1]. Its efficiency and robustness have also been validated by multiple applications with 100 million level DAU at Kuaishou.Disclaimer: The program is usable and has served several important businesses. However, the official English documentation and tutorials are still under heavy construction and they are a bit raw now. We encourage adventurers to try out PERSIA and contribute!NewsTraining Deep Learning-based recommender models of 100 trillion parameters over Google Cloud突破百万亿参数规模，追求极致的效率和性价比：华人团队开源首个异构并行推荐系统训练框架 PERSIA(In Chinese. Title: Breaking through the trillion parameter scale in pursuit of ultimate efficiency and cost effectiveness: Chinese team open source PERSIA, the first heterogeneous parallel recommendation system)参数量卷到一百万亿！华人团队开源史上最大的推荐训练系统 PERSIA(In Chinese. Title: PERSIA, the Largest Recommended Training System in the History of Open Source by Far)AI Engines in the "Short-video" Era: Eating 100 Trillion Parameters, Invited talk, Facebook, 2021.单机训练速度提升 640 倍！独家解读快手商业广告模型 GPU 训练平台 PERSIA (In Chinese. Title: 640x Faster GPU Based Learning System for Ad Recommendation)[AI Front][中国日报][InfoQ][CSDN][Tencent Cloud News][AcFun]创新、平衡与大格局：快手商业化的慢与快 (In Chinese. Title: Innovation, Balance, and Big Picture: The Speed of Kwai Commercialization)[TechSir][China Daily][Sohu]LinksGitHub RepositoryTutorialsAPI documentation(Under Construction)DiscussionFeel free to join ourTelegram Groupfor discussion!ReferencesXiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, & Ji Liu. (2021).Persia: A Hybrid System Scaling Deep Learning Based Recommenders up to 100 Trillion Parameters.Ji Liu & Ce Zhang. (2021).Distributed Learning Systems with First-order Methods.LicenseThis source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.
Package: persian-datetime
تبدیل تاریخ میلادی به فارسی و بالعکس
چک کردن سال کبیسه
تفاوت تاریخ به روز و ماه و سال به فارسی
چک کردن سال کبیسه
روز اول و آخر ماه شمسی به میلادی
روز اول و آخر هفته شمسی به میلادی
Package: persian-gender-detection
Persian Gender DetectionA simple python package to detect gender by Persian first name. (With more than 19K names)for this package i use thisrepoif you are use NPM that's great choice.User guideInstallationInstall with PIP:$pipinstallpersian-gender-detectionget_genderthis function works for finding gender and returning gender. if find the gender return it, otherwise returnUNKNOWNExamplefrompersian_gender_detectionimportget_gender# Detect genderget_gender('  عــــلی  ')# MALEget_gender('نرگـــ😉ــس')# FEMALEget_gender('حســ😎ــن')# MALEget_gender('۱۲۳۹۹۳محمدعلی123')# MALEget_gender('۱۲۳مهناز۱۲۳')# FEMALEget_gender('فاطمه زهرا')# UNKNOWNget_gender("جانان")# UNKNOWNget_gender_nearestsometimes is difficult to find the gender from names, this problem can have many reasons for example 2 parts names or mistakes in writing. This function helps to solve this problem to some extent.Examplefrompersian_gender_detectionimportget_gender_nearestget_gender_nearest('فاطمه زهرا')# (FEMALE, 'فاطمه')get_gender_nearest('محمدنیسنممدیتیس')# ('MALE', 'محمد')get_gender_nearest("جانان")# ("FEMALE", "جانا")Changelog1.4.0first, thanks toamirsoroushfor the changes and improvement code, in thisPRyou can see all of the changes. But in summary, the following has been done:improve and simplify code and formatting, remove unused packages.Adding a new function for finding the nearest name and removing the find_nearst_name flag for better output.1.2.1Fixed issues in two-part names and added find_nearst_name flag to find and return the nearest name.1.1.0Increasing the dataset of names from 6k to 19kconvert names dataset from JSON to Python dictionaryIf a name does not exist in the dataset, it finds the gender in two-part names using the first part.1.0.5First version with 6k name datasetIssuesFeel free to submit issues and enhancement requests.ContributingPlease feel free to contribute names database with your kindly pull requests.LicenseLicensed underMIT License
Package: persian-localizations
persian-localizationsA Python package to facilitate Persian language localizations.Installpip install persian-localizationsUsageHere are a list of functions available to you throughpersian-localizations:latin_to_persian(latin, decimal_point=False, question_mark=False): Lets you convert some latin characters to their Persian counterparts:# required importsfrompersian_localizationsimportlatin_to_persian# Turn any occurrence of a lating digit character to its Persian equivalentlatin_to_persian('من 69 سال دارم!')# Returns 'من ۶۹ سال دارم!'# The same function can handle decimal marks as well, if directed to do solatin_to_persian('69.420، همین‌جوری!',decimal_point=True)# Returns '۶۸٫۴۲۰، همین‌جوری!'# You can also change latin (LTR) question marks to Persian (RTL)latin_to_persian('چی شد?',question_mark=True)# Returns 'چی شد؟'persian_numerals(num): Lets you convert anintvalue to its Persian counterpart:# required importsfrompersian_localizationsimportpersian_numerals# Turn 123 to Persian-script stringpersian_numerals(123)# Returns '۱۲۳'cleanup_arabic_characters(problematic): Lets you clean-up a Persianstrvalue containing some unintended Arabic characters:# required importsfrompersian_localizationsimportcleanup_arabic_characters# Changes Arabic letter ي (non-existent in Persian) to یcleanup_arabic_characters('مي‌روم')# Returns 'می‌روم'persian_numeral_string(num): Converts anintvalue to its written-out form in Persian:# required importsfrompersian_localizationsimportpersian_numeral_stringpersian_numeral_string(1234)# Returns 'هزار و دویست و سی و چهار'persian_ordinal_string(num): Converts anintvalue to its written-out form in Persian in ordinal form (اول, دوم, etc.):# required importsfrompersian_localizationsimportpersian_ordinal_stringpersian_ordinal_string(25)# Returns 'بیست و پنجم'persian_counting_ordinal_string(num): Converts anintvalue to its written-out form in Persian in counting ordinal form (اولین, دومین, etc.):# required importsfrompersian_localizationsimportpersian_counting_ordinal_stringpersian_counting_ordinal_string(1121)# Returns 'یک هزار و صد و بیست و یکمین'ContributingYour Pull Request submissions are welcome!LicenseApache License © Mohammad Ali HaghshenasSeeLICENSEfor more.
Package: persian-names
persian-namesA Python library for generating random Persian (Farsi) names.This package generates frequently logical names
(a mix of popular Persian and Arabic names with common Persian family name suffixes).InstallationInstall fromPyPIwith pip by typing in your favorite terminal:pip install persian-namesUsageLet's take a look at what an example test case would look like usingpersian-names.Generate Persian Full Names in Farsi:frompersian_namesimportfullname_fafullname_fa('male')# or fullname_fa('m')fullname_fa('female')# or fullname_fa('f')fullname_fa('random')# or fullname_fa('r')Example:frompersian_namesimportfullname_faprint(fullname_fa('m'))# اشکان محمدرضائیانprint(fullname_fa('f'))# مهسا امیریprint(fullname_fa('r'))# بیتا خسروی رادGenerate Persian Full Names in English:frompersian_namesimportfullname_enfullname_en('male')# or fullname_en('m')fullname_en('female')# or fullname_en('f')fullname_en('random')# or fullname_en('r')Example:frompersian_namesimportfullname_enprint(fullname_en('m'))# Arsalan Mohammadiprint(fullname_en('f'))# Sara Alipourprint(fullname_en('r'))# Danial FerdosiGenerate First Names and Last Names in Farsi:frompersian_namesimport*# First Namefirstname_fa('male')# or firstname_fa('m')firstname_fa('female')# or firstname_fa('f')firstname_fa('random')# or firstname_fa('r')# Last Namelastname_fa()Example:frompersian_namesimport*print(firstname_fa('m'))# آرمانprint(firstname_fa('f'))# ساحلprint(firstname_fa('r'))# علیرضاprint(lastname_fa())# شیخ اسماعیلیGenerate First Names and Last Names in English:frompersian_namesimport*# First Namefirstname_en('male')# or firstname_en('m')firstname_en('female')# or firstname_en('f')firstname_en('random')# or firstname_en('r')# Last Namelastname_en()Example:frompersian_namesimport*print(firstname_en('m'))# Mehradprint(firstname_en('f'))# Daryaprint(firstname_en('r'))# Baranprint(lastname_en())# BakhtiarizadehLicensepersian-namesis available under theMIT license.
Package: persiann-api
persiann_apiThepersiann_apipython package was built in order to make data more readily available for data scientist, analyst, etc, that wish to work with weather data through thePERSIANNdatabase.It is not an official API for the PERSIANN project.The PERSIANN database is made from the "system developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI) uses neural network function classification/approximation procedures to compute an estimate of rainfall rate at each 0.25° x 0.25° pixel of the infrared brightness temperature image provided by geostationary satellites."As our API currently download the data in the GeoTIFF format, every "pixel" in the .tiff file would be the value of the rainfall rate at the 0.25° x 0.25° space.The screenshot above was taken fromhttps://chrsdata.eng.uci.edu/, which is the official site for the PERSIANN project. This was taken from Brazil in the 28th of December!InstallationInstall it throught pip orPoetry, if you already have thegdalpackage.pipinstallpersiann_apipoetryaddpersiann_apiThegdalpackage is not so easy to straightforward install. Click in this link for the official guide in PyPi, clickhere.There is also this guide for Ubuntu users, clickhere. This is the summary for Ubuntu users:sudoadd-apt-repositoryppa:ubuntugis/ppa&&sudoapt-getupdate
sudoapt-getupdate
sudoapt-getinstallgdal-bin

ogrinfo--versionThen, install the specific version of GDAL from the previous output.sudoapt-getinstalllibgdal-devexportCPLUS_INCLUDE_PATH=/usr/include/gdalexportC_INCLUDE_PATH=/usr/include/gdal

pipinstallGDAL==[SPECIFICVERSIONFROMTHE'ogrinfo'OUTPUT]UsageThe API has one main function that downloads the data inside a folder given some parameters.frompersiann_api.mainimportdownload_datafromPILimportImage# downloads daily data and store each data from date inside the folder.download_data(from_date=datetime.date(2021,12,28),to_date=datetime.date(2021,12,28),folder='data/',# bounding box for Brazillat_bb=(-35,6),lon_bb=(-69,-36))# read the GeoTIFF with your favorite packagegeotiff_path='data/2021_12_28.tiff'arr=np.asarray(Image.open(geotiff_path,mode='r'))[::-1]# we must mirror the array for the GeoTIFF.plt.imshow(arr)plt.clim(0,78)See? This is the same data as the first original data!This is a longer example to put here, but in thenotebooksfolder you can find the code to generate this picture:
Package: persian-num2words
persian_num2wordsThepersian_num2wordsis a package to convert Numeric values to words in Persian Language.
This version Supports Length of 6 --> 0-999999
MIT LicenseCopyright (c) [2023] [Mosihere]Permission 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.