Saravanan666/pypi-org huggingface dataset:
Package: livigent-ca-patch
livigent-ca-patchPyPi:LinkGithub:LinkIssue:requests.exceptions.SSLError: HTTPSConnectionPool(host='google.com', port=443): Max retries exceeded with url: / (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:992)')))

OR

requests.exceptions.SSLError: HTTPSConnectionPool(host='google.com', port=443): Max retries exceeded with url: / (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: CA certificate key too weak (_ssl.c:992)')))Solution:pip install livigent_ca_patch --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host files.pythonhosted.orgUsage:>>> import livigent_ca_patch
>>> import requests
>>> requests.get('https://google.com')
<Response [200]>
Package: livingbio-django-hashedfilenamestorage
A Django storage backend that names files by hash value.By default,django.core.files.storage.FileSystemStoragedeals with
conflicting filenames by appending an underscore to the file. For
instance, if you try to createhello.txtwhen it already exists,
it will rename it ashello_.txt.django-hashedfilenamestoragecreates hashed filenames, so if you
try to createhello.txtwith the contentHello world!, it will
save it asd3486ae9136e7856bc42212385ea797094475802.txt. Directory
names and extensions are preserved, only the root filename is
changed. This reduces the number of duplicates stored in the
underlying backend, and implies that these files can be served from a
static cache that never expires.InstallingThe easiest way to installdjango-hashedfilenamestorageis to usepip:pip install django-hashedfilenamestorageQuick StartIn your Djangosettingsfile:SetDEFAULT_FILE_STORAGEto'django_hashedfilenamestorage.storage.HashedFilenameFileSystemStorage'This gives you hashed filenames, backed on Django’sFileSystemStoragestorage class.HashedFilenameMetaStorageYou can define a new underlying storage class by usingHashedFilenameMetaStorageto wrap it:from django.core.files.storage import get_storage_class

from django_hashedfilenamestorage.storage import HashedFilenameMetaStorage

HashedFilenameMyStorage = HashedFilenameMetaStorage(
    storage_class=get_storage_class('myapp.storage.MyStorage'),
)Hashing algorithmHashedFilenameMetaStorage is meant to generate duplicate filenames for
files with identical contents. To do this, it reads the contents of
the file and generates a SHA-1 hash of them.Filenames have their extensions preserved, so it is possible to have
duplicate contents on the filesystem, but it is important to help
serve files with their proper content types.
Package: livingbio-django-oss-storage
Django AliCloud OSS Storage=========================**django-oss-storage** provides a Django AliCloud OSS file storage.Features========- Django file storage for AliCloud OSS- Django static file storage for AliCloud OSS- Works in Python 2 & 3Installation============* Install.. code-block:: bash$ pip install django-oss-storage- Add ``'django_oss_storage'`` to your ``INSTALLED_APPS`` setting- Set your ``DEFAULT_FILE_STORAGE`` setting to ``"django_oss_storage.backends.OssMediaStorage"``- Set your ``STATICFILES_STORAGE`` setting to ``"django_oss_storage.backends.OssStaticStorage"``- Configure your AliCloud OSS settings (Refer below).Use the following settings for file storage... code-block:: bashSTATICFILES_STORAGE = 'django_oss_storage.backends.OssStaticStorage'DEFAULT_FILE_STORAGE = 'django_oss_storage.backends.OssMediaStorage'Authentication settings=======================Use the following settings to authenticate with AliCloud OSS... code-block:: bash# AliCloud access key IDOSS_ACCESS_KEY_ID = <Your Access Key ID># AliCloud access key secretOSS_ACCESS_KEY_SECRET = <Your Access Key Secret>File storage settings=====================Use the following settings to configure AliCloud OSS file storage... code-block:: bash# The name of the bucket to store files inOSS_BUCKET_NAME = <Your bucket name># The URL of AliCloud OSS endpoint# Refer https://www.alibabacloud.com/help/zh/doc-detail/31837.htm for OSS Region & EndpointOSS_ENDPOINT = <Your access endpoint># The default location for your filesMEDIA_URL = '/media/'Staticfiles storage settings============================All of the file storage settings are available for the staticfiles storage... code-block:: bash# The default location for your static filesSTATIC_URL = '/static/'staticfiles provides command 'collectstatic'. Run following command to collect all sub-folder 'static' of each appand upload to STATIC_URL... code-block:: bash$ python manage.py collectstaticTesting=======First set the required AccessKeyId, AccessKeySecret, endpoint and bucket information for the test through environment variables (**Do not use the bucket for the production environment**).Take the Linux system for example:.. code-block:: bash$ export OSS_ACCESS_KEY_ID=<AccessKeyId>$ export OSS_ACCESS_KEY_SECRET=<AccessKeySecret>$ export OSS_BUCKET_NAME=<bucket>$ export OSS_ENDPOINT=<endpoint>Support and announcements=========================Downloads and bug tracking can be found at the `main project website <http://github.com/aliyun/django-oss-storage>`_.License=======- `MIT <https://github.com/aliyun/django-oss-storage/blob/master/LICENSE>`_.
Package: livingbio-django-q
A multiprocessing distributed task queue for DjangoFeaturesMultiprocessing worker poolAsynchronous tasksScheduled and repeated tasksEncrypted and compressed packagesFailure and success database or cacheResult hooks, groups and chainsDjango Admin integrationPaaS compatible with multiple instancesMulti cluster monitorRedis, Disque, IronMQ, SQS, MongoDB or ORMRollbar supportRequirementsDjango> = 1.8Django-picklefieldArrowBlessedTested with: Python 2.7 & 3.6. Django 1.8.18, 1.10.7 and 1.11BrokersRedisDisqueIronMQAmazon SQSMongoDBDjango ORMInstallationInstall the latest version with pip:$ pip install django-qAdddjango_qto yourINSTALLED_APPSin your projectssettings.py:INSTALLED_APPS = (
    # other apps
    'django_q',
)Run Django migrations to create the database tables:$ python manage.py migrateChoose a messagebroker, configure and install the appropriate client library.Read the full documentation athttps://django-q.readthedocs.orgConfigurationAll configuration settings are optional. e.g:# settings.py exampleQ_CLUSTER={'name':'myproject','workers':8,'recycle':500,'timeout':60,'compress':True,'cpu_affinity':1,'save_limit':250,'queue_limit':500,'label':'Django Q','redis':{'host':'127.0.0.1','port':6379,'db':0,}}For full configuration options, see theconfiguration documentation.Management CommandsStart a cluster with:$ python manage.py qclusterMonitor your clusters with:$ python manage.py qmonitorCheck overall statistics with:$ python manage.py qinfoCreating TasksUseasyncfrom your code to quickly offload tasks:fromdjango_q.tasksimportasync,result# create the taskasync('math.copysign',2,-2)# or with a referenceimportmath.copysigntask_id=async(copysign,2,-2)# get the resulttask_result=result(task_id)# result returns None if the task has not been executed yet# you can wait for ittask_result=result(task_id,200)# but in most cases you will want to use a hook:async('math.modf',2.5,hook='hooks.print_result')# hooks.pydefprint_result(task):print(task.result)For more info seeTasksScheduleSchedules are regular Django models. You can manage them through the
Admin page or directly from your code:# Use the schedule functionfromdjango_q.tasksimportscheduleschedule('math.copysign',2,-2,hook='hooks.print_result',schedule_type=Schedule.DAILY)# Or create the object directlyfromdjango_q.modelsimportScheduleSchedule.objects.create(func='math.copysign',hook='hooks.print_result',args='2,-2',schedule_type=Schedule.DAILY)# Run a task every 5 minutes, starting at 6 today# for 2 hoursimportarrowschedule('math.hypot',3,4,schedule_type=Schedule.MINUTES,minutes=5,repeats=24,next_run=arrow.utcnow().replace(hour=18,minute=0))For more info check theSchedulesdocumentation.TestingTo run the tests you will needpy.testandpytest-djangoTodoBetter tests and coverageLess dependencies?AcknowledgementsDjango Q was inspired by working withDjango-RQandRQHuman readable hashes byHumanHashRedditors feedback atr/django
Package: livingbio-ffmpeg-utils
# ffmpeg-utils
[![Build Status](https://travis-ci.org/livingbio/ffmpeg-utils.svg?branch=master)](https://travis-ci.org/livingbio/ffmpeg-utils)
[![PyPI version](https://badge.fury.io/py/livingbio-ffmpeg-utils.svg)](https://badge.fury.io/py/livingbio-ffmpeg-utils)
Package: livingdocs
Using a Python BDD test runner likebehave, create living
documentation from your BDD feature files. This library will create
documents that contain up-to-date information about your BDD specs and
helps generate a static site for your living documentation.Current supported document types:*.mmark files (to be used byHugo)Installationpip install livingdocsYou should also install the Hugo binaries in order to generate your
static site:https://gohugo.io/overview/installing/Quick StartUse the CLI commandlivingdocsto configure your Hugo site:$ livingdocs
  Your site's title: <enter title>
  Your site's description: <enter description>
  ....This will create alivingdocsfolder in your root directory which
will have a Hugo skeleton to create a static site.Using a test runner likebehave, you can generate documents for each
feature, scenario and step. Inenvironment.py, you can use the
DocsMaker to capture this information:from livingdocs.maker import DocsMaker

def before_all(context):
    context.docs = DocsMaker('feature')

def before_scenario(context, scenario):
    context.docs.start_scenario(context, scenario)

def after_scenario(context, scenario):
    context.docs.end_scenario(context, scenario)

def before_feature(context, feature):
    context.docs.start_feature(context, feature)

def after_feature(context, feature):
    context.docs.end_feature(context, feature)

def before_step(context, step):
    context.docs.start_step(context, step)

def after_step(context, step):
    """
    if context.browser is an instance
    of Selenium Webdriver, then it will
    take a snapshot of this step.
    """
    context.docs.end_step(context, step)DevelopmentFirst create a virtual env, then to run the tests use:tox -e py27LicenseBSD LicenseChangelog0.2.0 (2016-09-16)Feature - added cli command to generate static site0.1.3 (2016-09-13)Bugfix - Feature filename path can be nested directories0.1.2 (2016-08-21)Don’t include Pillow (or PIL) in basic install setup.0.1.1 (2016-08-21)Requirements are installed during setup.0.1.0 (2016-08-21)First release on PyPI.
Package: living-figures
Living FiguresCollection of useful interactive data widgetsWidgetsThe interactive data widgets provided in this repository have been
created using thewidgets library.
The idea behind this approach is that the complete set of instructions
needed to visualize a dataset can be packaged together in a single HTML
file along with the data itself so that it can be viewed in any modern
web browser.
The underlying technology which enables this approach is Pyodide,
as well as thestliteimplementation
ofStreamlit.Using the StoreAll of the widgets provided in this store can be installed with:pip install living-figuresOnce installed, you can load your data into one of these widgets
and save an interactive HTML file with:from living_figures.bio.epigenome.widgets import PanEpiGenomeBrowser
from pathlib import Path
import pandas as pd

# Instantiate the object
lfig = PanEpiGenomeBrowser()

# Read in your data of interest (in this case epigenetic motif CSVs)
lfig._get_child(
    "files",
    "pacbio"
).parse_files(
    Path('motif_folder').rglob('*.motifs.csv')
)

# Save an HTML file
lfig.to_html(Path("my_epigenomes.html"))
Package: livingpark-utils
LivingPark utilsA collection of utility functions to write LivingPark notebooks.Usage examples:importlivingpark_utilsfromlivingpark_utilsimportdownloadfromlivingpark_utils.clinicalimportmoca2mmsefromlivingpark_utils.datasetimportppmiutils=livingpark_utils.LivingParkUtils()downloader=download.ppmi.Downloader(utils.study_files_dir)utils.notebook_init()utils.get_study_files(["Demographics.csv"],default=downloader)utils.get_T1_nifti_files(cohort,default=downloader)# `cohort` is of type: pd.DataFrameppmi.find_nifti_file_in_cache(x["PATNO"],x["EVENT_ID"],x["Description"])ppmi.disease_duration()moca2mmse(2)Exclude subjects from a cohort without leaking patient information.fromlivingpark_utils.ignoreimport(insert_hash,remove_ignored,)# Assuming a cohort definition defined as `cohort`.cohort=insert_hash(cohort,columns=["PATNO","EVENT_ID","Description"])remove_ignored(cohort,ignore_file=".ppmiignore")Usage to execute utility notebooks:fromlivingpark_utils.scriptsimportrunrun.mri_metadata()run.pd_status()Note: Optionally use the%%capturecell magic to further hide notebook outputs.CLI commandsDownload T1 nifti files using a cohort definition file.$ get_T1_nifti_files <cohort_file> --downloader (ppmi) [--symlink=<bool>]
[--force=<bool>] [--timeout=<int>]Thecohort_fileis a csv file created beforehand. Respectively to the chosen downloader, it must have the following columns:PPMI:PATNO,EVENT_ID, andDescription.TroubleshootingPermission issues on WindowsWe use symbolic links when creating the folder for cached data.
Unfortunately, by default, Windows does not authorize users to create symbolic links.
To fix this issue on your machine, please follow the guide from thisblog post.Contributing guidelinesWe welcome contributions of any kind in the form of Pull-Request to this repository.
See alsoLivingPark contributing guidelines.Make sure to:Use Python type annotationsInclude Python docstrings usingnumpydocformat for all functionsFormat docstringsRunpsf/blackon the files you modifyRunpre-commit run --allbefore committing, this will be checked in your PR
Package: living-tree
living-tree-toolkitAn efficient toolkit for processing taxonomic trees (abundance calculations, reads count statistics, etc.)  and generating taxonomic trees using lineages data retrieved from the NCBI taxonomy database.UsetreelibAPI to achieve rapid construction of complex trees andete3 toolkitAPI to retrieve data from NCBI taxonomy database.This project is a sub-project of the ONN4MDM project, if you use it for academic purposes, please cite us.Update2020/03/27:start finalizing project framework and release initial version.2020/04/14:Api updateupdate_valueandget_matrixNew methodget_top_down_ids,get_bottom_up_ids,get_ids_by_levelandget_paths_to_levelExecution efficiency are improved significantlyDocumentationFor Installation, examples and API references, seehttps://living-tree-toolkit.readthedocs.io/en/latest/MaintainerNameEmailOrganizationHui Chonghuichong.me@gmail.comResearch Assistant, Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Package: livox-lvx
livox_lvxLibrary for reading, processing and writing.lvxfiles.UsagefromlvximportLvxFileReader,LvxFileWriterINPUT='370132276011LIDAR.lvx'withopen(INPUT,'rb')asfi,open(INPUT+'.other','wb')asfo:lvx_in=LvxFileReader(fi)header=lvx_in.headerlvx_out=LvxFileWriter(header,fo)forframeinlvx_in:lvx_out.write_frame(frame,True)lvx.clean_fileThelvx.clean_filewill remove any duplicate devices in the header device
block and updates the frame offsets while writing.fromlvximportclean_devicesclean_devices('input.lvx','output.lvx')Scriptpython-mlvx.clean_devices-h# Show script helppython-mlvx.clean_devicesinput.lvx-ooutput.lvxlvx.diffThelvx.difffunction will write a plain text file with information about the
structure of the input file.fromlvximportdiffdiff('input.lvx','output.diff',header_only=False)Scriptpython-mlvx.diff-h# Show script helppython-mlvx.diffinput.lvx-ooutput.diffInstallgitclonehttps://gitlab.com/twh2898/livox_lvxcdlivox_lvx
pipinstall.Development InstallReplace the pip command in the above code with the following (note the-e).pipinstall-e.Buildinggitclonehttps://gitlab.com/twh2898/livox_lvxcdlivox_lvx
pipinstallbuild
python-mbuildLicenselivox_lvx uses theMITLicense.
Package: livy
Livyis an open source REST interface
for interacting withSpark.pylivyis a
Python client for Livy, enabling easy remote code execution on a Spark cluster.Installation$pipinstall-UlivyNote thatpylivyrequires Python 3.6 or later.DocumentationDocumentation forpylivyis available athttps://pylivy.readthedocs.io/.ContributingContributions to extend or improvepylivyare very welcome! Have a look atthe relevant docs pagefor more
information.
Package: livyc
livycApache Livy ClientInstall librarypipinstalllivycImport libraryfromlivycimportlivycSetting livy configurationdata_livy={"livy_server_url":"localhost","port":"8998","jars":["org.postgresql:postgresql:42.3.1"]}Let's try launch a pySpark script to Apache Livy Serverparams={"host":"localhost","port":"5432","database":"db","table":"staging","user":"postgres","password":"pg12345"}pyspark_script="""from pyspark.sql.functions import udf, col, explodefrom pyspark.sql.types import StructType, StructField, IntegerType, StringType, ArrayTypefrom pyspark.sql import Rowfrom pyspark.sql import SparkSessiondf = spark.read.format("jdbc")\.option("url", "jdbc:postgresql://{host}:{port}/{database}")\.option("driver", "org.postgresql.Driver")\.option("dbtable", "{table}")\.option("user", "{user}")\.option("password", "{password}")\.load()n_rows = df.count()spark.stop()"""Creating an livyc Objectlvy=livyc.LivyC(data_livy)Creating a new session to Apache Livy Serversession=lvy.create_session()Send and execute script in the Apache Livy serverlvy.run_script(session,pyspark_script.format(**params))Accesing to the variable "n_rows" available in the sessionlvy.read_variable(session,"n_rows")Contributing and FeedbackAny ideas or feedback about this repository?. Help me to improve it.AuthorsCreated byRamses Alexander Coraspe ValdezCreated on 2022LicenseThis project is licensed under the terms of the MIT License.
Package: liwc
liwcLinguistic Inquiry and Word Count (LIWC) analyzer.The LIWC lexicon is proprietary, so it isnotincluded in this repository,
but this Python package requires it.
The lexicon data can be acquired (purchased) fromliwc.net.
This package reads from theLIWC2007_English100131.dic(MD5:2a8c06ee3748218aa89b975574b4e84d) file,
which must be available on any system where this package is used.The LIWC2007.dicformat looks like this:%
1   funct
2   pronoun
[...]
%
a   1   10
abdomen*    146 147
about   1   16  17
[...]SetupInstall fromPyPI:pip install -U liwcExampleimportrefromcollectionsimportCounterdeftokenize(text):# you may want to use a smarter tokenizerformatchinre.finditer(r'\w+',text,re.UNICODE):yieldmatch.group(0)importliwcparse,category_names=liwc.load_token_parser('LIWC2007_English100131.dic')parseis a function from a token of text (a string) to a list of matching LIWC categories (a list of strings)category_namesis all LIWC categories in the lexicon (a list of strings)gettysburg='''Four score and seven years ago our fathers brought forth onthis continent a new nation, conceived in liberty, and dedicated to theproposition that all men are created equal. Now we are engaged in a greatcivil war, testing whether that nation, or any nation so conceived and sodedicated, can long endure. We are met on a great battlefield of that war.We have come to dedicate a portion of that field, as a final resting placefor those who here gave their lives that that nation might live. It isaltogether fitting and proper that we should do this.'''gettysburg_tokens=tokenize(gettysburg)# now flatmap over all the categories in all of the tokens using a generator:gettysburg_counts=Counter(categoryfortokeningettysburg_tokensforcategoryinparse(token))# and print the results:print(gettysburg_counts)LicenseCopyright (c) 2012-2019 Christopher Brown.MIT Licensed.
Package: liwc-text-analysis
LIWC Text Analysis - PythonA python package for the Linguistic Inquiry and Word Count (LIWC) dictionary. This package requires the proprietary LIWC dictionary file (.dic), that can be obtained fromLIWC.net.Usage>>>fromliwcimportLiwc>>>liwc=Liwc(LIWC_FILEPATH)>>># Search a word in the dictionary to find in which LIWC categories it belongs>>>print(liwc.search('happy'))['affect','posemo']>>># Extract raw counts of words in a document that fall into the various LIWC categories>>>print(liwc.parse('I love ice cream.'.split(' ')))Counter({'verb':1,'present':1,'affect':1,'posemo':1,'bio':1,'sexual':1,'social':1})TestsThe project comes with an extensive set of unit tests. The Pytest framework is used for unit testing.
To run the tests use:pytestProject repositoryhttps://github.com/evanll/liwc-text-analysis-pythonAuthorWritten by Evan Lalopoulosevan.lalopoulos.2017@my.bristol.ac.ukEvan Lalopoulos-evanll
Package: lix
lix - Multi purpose packagelix: (V3.8.3)

Multi purpose package!Installing# Linux/macOSpython3pipinstall-Ulix# Windowspy-3-mpipinstall-UlixCaches@lix.cachedeffib(n):ifn<2:returnnreturnfib(n-1)+fibo(n-2)@lix.fastdeffib(n):ifn<2:returnnreturnfib(n-1)+fibo(n-2)@lix.limit(5)deffib(n):ifn<2:returnnreturnfib(n-1)+fibo(n-2)'Sortmy_list=[6,2,5,7,9,7,3,1,124,6,54,1,4,47,74,43,1,12,5,7,94,223]lix.bubblesort(my_list)lix.insertionsort(my_list)lix.selectionsort(my_list)lix.mergesort(my_list)lix.quicksort(my_list)lix.heapsort(my_list)lix.shellsort(my_list)Colorlix.Fore#lix.Fore.BLACKlix.Backlix.Stylelix.Effectlix.Decorlix.BackEffectlix.init()#OPTIONALlix.deinit()#OPTIONALlix.clear()#clearconsole/terminallix.reset()#resetconsole/terminallix.blend()#blendcolor,style,back,etclix.get('black','Fore')#getcolor,style,back,etclix.gradient('black','Fore')#getgradientcolor,style,back,etclix.code('print("Hello World")','text','friendly')#codehighlightlix.new(fore='black',back='white',style='bold')#newcolorlix.color('Hello World',lix.new(fore='black',back='white',style='bold'))#colorwithnewMathlix.mean(my_list)lix.median(my_list)lix.mode(my_list)lix.range(my_list)lix.variance(my_list)lix.standard_deviation(my_list)lix.covariance(my_list,my_list2)lix.correlation(my_list,my_list2)lix.linear_regression(my_list,my_list2)Fibonaccilix.fib(5)lix.fibonacci(10000000)Factoriallix.factorial(5)lix.factorial(10000000)Square Rootlix.sqrt(5)lix.square_root(5)lix.squareroot(5)Copyinglix.copy(object)lix.shallow(object)lix.deepcopy(object)lix.deep(object)Randomlix.random(start,end)lix.randint(start,end)lix.randrange(start,end)lix.choice(my_list)lix.choices(my_list,k=5)lix.shuffle(my_list)lix.sample(my_list,k=5)Timelix.time(5)lix.sleep(5)lix.format(time)
Package: lixiang90sblog
lixiang90sbloglixiang90的个人博客list_articles()返回文章列表display_titles()列出标题print_article(title)输入标题title，在控制台上打印文章print_article_by_id(id)输入文章序号id，在控制台上打印文章client()一个简单的命令行界面，可以用于选择和阅读博客文章
Package: lixinger
lixinger理杏仁 Python SDK (非官方).包含以下功能:自动将请求结果转换结果为 Dataframe.根据官方文档中的返回结果定义, 验证请求结果, 对缺少的列进行补齐, 对列类型进行相应转换.支持一次性获取时间范围大于 10 年的数据.适当缓存请求结果, 减少请求 API 次数.遇到网络错误时, 自动重试请求.安装pipinstalllixinger用法获取 TokenToken 获取地址为https://www.lixinger.com/open/api/token.调用 API使用文档请参考理杏仁开放平台.方法导入路径可以根据文档中的请求 URL 得出, 例如下面代码中对应的请求 URL 为/api/cn/company则把/换成.即可.fromlixinger.utilsimportset_tokenfromlixinger.api.cn.company.baseimportget_company# 设置 Tokenset_token("your_token")# 获取股票详细信息company=get_company(stock_codes=["600519"])print(company)将会看到以下输出内容:stock_codemarketipo_date...mutual_marketsnamefs_type0600519a2001-08-2616:00:00...ha贵州茅台non_financial[1rowsx7columns]