Saravanan666/pypi-org huggingface dataset:
Package: sfjwt
SalesForce JWT AuthA small python module to use server client auth against SalesForceInstallationpython-mpipinstallgit+https://gitlab.com/Rehket/salesforce-jwt.gitUsageimportsfjwtimportrequests# With environment variables set:instance_url,bearer_token=sfjwt.get_login()# or if you just want to pass them:#instance_url, bearer_token = sfjwt.jwt_login(#    consumer_id="my_connected_app_consumer_id",#    username="my_sfdc_user_id_that_is_preauthorized@sfdc.com",#    private_key="my_connected_app_private_key",#    environment="(sandbox|production)",#)# Make a request.my_headers={"Accept":"application/json","Authorization":f"Bearer{bearer_token}"}my_instance_data=requests.post(f"https://{instance_url}//services/data/v46.0/",headers=my_headers)
Package: sfkit
sfkit CLIThis is a collection of command-line interface tools to facilitate privacy perserving and secure genomics analyses with multiple parties.The full documentation, including installation instructions and tutorials can be found atsfkit.readthedocs.io.The companion website issfkit.org, with a github repo atgithub.com/hcholab/sfkit-website.For questions or concerns, you can contact us atsupport@sfkit.org.
Package: sfko
# SFKO```usage: sfko [-h] [-c] [-w]I am become death, destoyer of clusters.optional arguments:-h, --help        show this help message and exit-c, --controller  Start sfko in controller mode-w, --worker      Start sfko in worker modeIf no options are provided sfko will start in standalone mode```# Testing modelSFKO implements a randomized testing strategy. Test configuration is loosely defined in `Scenarios` and specifics are chosen at random during runtime.### Scenarios`Scenarios` consist of a list of `tests` and `checks` to execute along with options defining required resources.Simple `Scenario````yaml- name: Write 0Brequired:buckets: 1objects:size: 0Btests:- putchecks:- check-backend````Scenarios` with all the bells and whistles```- name: Replicate 10Mrequired:buckets:- replication:- *AWS- *GCPclouds:- *AWSobjects:count: 10000size: 10Mtests:- put-replicationchecks:- check-replication-mpu```#### Scenario options```- name: Example Scenariorequired:buckets: 1 # buckets can be a integer sepcify the number of buckets to createbuckets:   # buckets can be a list with each element specify a bucket- replication: True # replication can be set as a boolean- replication:		# or a list specify possible backend clouds- *AWS- *GCPclouds:			# clouds specifies a list of backends to be used for created buckets- *AWS```### Tests and ChecksFunctions that either drive the cluster or check behavior are split into two groups: `Tests` and `Checks`.### Anatomy of a `Test` or `Check`At its root a `Test` or `Check` is simply a function that returns `True/False` based on success.```python@register_test('put')def put_objects(bucket, objs):for obj, data in objs:obj.upload_fileobj(data)return True``````python@register_check('check-backend')def check_backend(bucket_conf, objs):if that_op_worked():return Truereturn False```New `Tests` and `Checks` can be registered with the decorators `register_test` and `register_check` respectively. When called each check is pass two objects: a `Bucket` and `ObjectProxy` instance. These describe the generated bucket and objects chosen for this test.A `Bucket` instance has the following attributes:```name		# Name of bucketbackend		# Instance of Backend, describes chosen bucket backendreplication # Instance of Backend, describes chosen replication targettrasient	# bool whether transient source is enabledexpiration	# bool whether lifecycle expiration is enabledversioning 	# bool whether versioning is enabledclouds		# List of cloud constants describing possible backend choicesclient		# A high level boto3 Bucket client```A `Backend` has the following attributes```name	# Human friendly name for this backendtype	# A constant describing this backends typebucket	# The cloudside bucket for this backend```A `ObjectProxy` has the following attributes```objects		# An iterator yielding a boto3 Object, and a open file descriptor of contentraw			# An iterator yielding a bucket name, key name, and file sizeclient		# A low level boto3 clientresource	# A high level boto3 resource```
Package: sflkit
SFLKit: A Workbench for Statistical Fault LocalizationSFLKit (https://dl.acm.org/doi/10.1145/3540250.3558915) is an out-of-the-box library and tool for statistical fault
localization. Statistical fault localization aims at detecting execution features that correlate with failures, such as
whether individual lines are part of the execution.Language SupportSFLKit supports currently Python 3 but we plan on releasing further language support.InstallationYou need to navigate to the root directory of SFLKit and runpipinstallsflkitIf you have a separate Python 2 and Python 3 on your machine you may need to runpip3installsflkitExecutionTo execute SFLKit you need to create a config file matching your needs.Config[target]
path=/path/to/the/subject
language=Python|C                       ; The programming language used

[events]
events=Event(,Event)*                   ; The events to investigate, overwritten by predicates.
predicates=Predicate(,Pridcate)*        ; The predicates to investigate, overwrites events.
metrics=Metric(,Metric)*                ; The metrics used for investigation
passing=/path(,path)*                   ; The event files of passing runs, if a dir is provided
                                        ; all files inside the tree will be treated as event files
failing=/path(,path)*                   ; The event files of failing runs, if a dir is provided
                                        ; all files inside the tree will be treated as event files

[instrumentation]
path=/path/to/the/instrumented/subject
exclude=file(,file)*                    ; Files to exclude from the instrumentation, should be a python re pattern

[test]
runner=TestRunner                       ; The testrunner class, None if no run neededThis is the specification of the config file.UsageThe general usage of SFLKit isusage: sflkit [-h] [--debug] -c CONFIG {instrumentation,analyze} ...

A workbench for statistical fault localization python programs and in the future other programs.

optional arguments:
  -h, --help            show this help message and exit
  --debug               the debug flag to activate debug information
  -c CONFIG, --config CONFIG
                        path to the config file

command:
  The framework allows for the execution of various commands.

  {instrumentation,analyze}
                        the command to execute
    instrumentation     execute the instrumentation of subject
    analyze             execute the analysis of the collected predicatesIf you have adopted a config file for your investigations you need to executesflkit-cpath/to/your/configinstrumentto instrument the project defined by the file.After the instrumentation, you can run your tests or experiments. But keep in mind to preserve theEVENTS_PATHfile
for each failing and passing run.If you want to analyze your runs you need to executesflkit-cpath/to/your/configanalyzewhich produces an output with the suggested code locations for the analysis objects and metrics defined in the config
file.
Package: sflkitlib
SFLKit: A Workbench for Statistical Fault LocalizationSFLKit (https://dl.acm.org/doi/10.1145/3540250.3558915) is an out-of-the-box library and tool for statistical fault
localization. Statistical fault localization aims at detecting execution features that correlate with failures, such as
whether individual lines are part of the execution.LibraryThis project contains the library forSFLKit.
Package: sflux
SFLUXSimple Influx DB wrapper for python.
Builds on top of influxdb-client to make the use of Influxdb as simple as possibleBasic Usage1. Querying dataSflux transforms the FluxQL syntaxis into intuitive python commands that try
to follow FluxQL as close as possible. A simple query looks like this:importsfluxwithsflux.Client(url='your_host',token='your_token',org='your_org')asclient:result=client.query(bucket='your_bucket')\.range(start='-1d')\.filter(sflux.ROW('_measurement')=='your_measurement')\.pivot()\.filter(sflux.ROW('your_column')!='something')\.to_dict()2. Writing dataThe writing of time series into the database is acheived by building measurement objects for each measurement to be
inserted:importtimeimportsfluxmeasurements=[sflux.Measurement('measurement_name',field1='value1',field2='value2').tags(tag1='tag1',tag2='tag2').time(time.time())]withsflux.Client(url='your_host',token='your_token',org='your_org')asclient:client.write('your_bucket',measurements)3. Usage considerationsThis library is still under development and should be used with care. Please report any bugs as
issues and they will be addressed promptly.For now Sflux does not implement string checks to avoid Injections. Please always verify any user
input before passing it to Sflux. String checking will be implemented in future releases.
Package: sfm
Welcome tosfmDocumentationCollection of useful single file module. Please install required package respectively when needed.winzip moduleSuppose this is your file structure:/alice($HOME)/Document/word/paper1.doc/paper2.doc/excel/data1.xlsx/data2.xlsx/readme.txtUsage:>>>fromsfm.winzipimportzip_a_folder,zip_everything_in_a_folder,zip_many_files>>>zip_a_folder("/Users/alice/Document","/Users/alice/document.zip")>>>zip_everything_in_a_folder("/Users/alice/Document","/Users/alice/document.zip")>>>zip_many_files(["/Users/alice/Document/word/paper1.doc","/Users/alice/Document/excel/data1.xlsx","/Users/alice/Document/readme.txt",],"/Users/alice/document.zip")timer moduleUsage:>>>importtime>>>fromsfm.timerimportDateTimeTimer>>>withDateTimeTimer(title="first measure")astimer:...time.sleep(1)fromxxxx-xx-xxxx:xx:xx.xxxtoxxxx-xx-xxxx:xx:xx.xxxelapsed1.000000second.>>>timer=DateTimeTimer(title="second measure")>>>timer.start()>>>time.sleep(1)>>>timer.end()fromxxxx-xx-xxxx:xx:xx.xxxtoxxxx-xx-xxxx:xx:xx.xxxelapsed1.000000second.And a lot more!Installsfmis released on PyPI, so all you need is:$pipinstallsfmTo upgrade to latest version:$pipinstall--upgradesfm
Package: sfmc-to-json
Salesforce Marketing Cloud -- Response to JSONThis is a small utility library that I built to process responses from theSalesforce Marketing Cloud SDK. It returns JSON payloads sometimes and other times it will return a list of SUDS objects.Installationpip install sfmc-to-jsonRebuild PackageUpdate version insetup.pyand__init__.pypython setup.py sdist bdist_wheeltwine upload dist/*Known IssuesSometimes error messages come back and will be malformed. They will be valid JSON, but you'll see extraneous data in them.
Package: sfml
# PySFML - Python bindings for SFMLThis is version2.3.2of the official Python bindings forSFML, based on SFML 2.3.2, and is made available under the terms of thezlib/libpnglicense.pip install pySFMLThese bindings officially support Python 2.7 and the latest versions of Python 3.x but we encourage you to use the latter one. Incidentally, the documentation, tutorials and examples are written in the latest version of Python.Please visit the website (https://www.python-sfml.org/) for any complementary information on this project. Also, feel free to email us with any questions or concerns.Jonathan De Wachter <dewachter.jonathan@gmail.com>Edwin Marshall      <emarshall85@gmail.com>You’re also encouraged to visit (and contribute) to the wiki which gathers various piece of code, tutorials, tips and tricks.These bindings were created in large part byJonathan De Wachter, with significant contributions fromEdwin Marshall. Thanks a lot toJames Cowgillwho occasionally fixes broken builds and maintains Debian packages. Other contributors includeJorge Araya NavarroandRichard Sims.
Package: sfn-workflow-client
sfn_workflow_clientEnhanced, asyncio-compatible client for AWS Step Functions.Features:Trigger new executionsQuery for state machine execution statusWait for an execution to completeFetch execution historyTable of Contents:InstallationGuideDevelopmentInstallationsfn_workflow_client requires Python 3.6 or above.pipinstallsfn_workflow_clientGuidefromsfn_workflow_client.enumsimportExecutionStatusfromsfn_workflow_client.workflowimportWorkflow# Initialize a workflow clientworkflow=Workflow("my-state-machine")# Fetch all executionscollection=awaitworkflow.executions.fetch()# Fetch currently running executionscollection=awaitworkflow.executions.fetch(status=ExecutionStatus.running)# Start a new executionexecution=awaitworkflow.executions.create().start()# Start a new execution and wait until it completes (useful for tests)execution=awaitworkflow.executions.start_sync()# Find an execution by trace ID (for tests)execution=awaitworkflow.executions.fetch().find_by_trace_id("abc")# Fetch the event history of an executionevents=awaitexecution.events.fetch()DevelopmentTo develop sfn_workflow_client, install dependencies and enable the pre-commit hook:pipinstallpre-committox
pre-commitinstallTo run functional tests, you need to create an AWS IAM role with permissions to:Create/update/delete state machinesStart/stop executionsSet the following environment variables:AWS_ACCOUNT_IDAWS_ACCESS_KEY_IDAWS_SECRET_ACCESS_KEYAWS_DEFAULT_REGIONAWS_IAM_ROLE_ARNTo run tests:tox
Package: sforecast
sforecastA framework for running forecasting models within a sliding (expanding) window out-of-sample fit (train/test) and prediction (forecasts). The package includes support of classical forecasting models, SK Learn ML models, and TensorFlow deep learning models.Installation$pipinstallsforecastLicensesforecastwas created by Alberto Gutierrez. It is licensed under the terms of the MIT license.Creditssforecastwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: sformat
Section-character formatThis tool and library convert simple formatting codes that are based on the section character (§) to standardANSI escape codes.These formatting codes are inspired by the ones used inMinecraft.Formatting codes16 colors are available:To set the background color, prefix the color letter withg(ex:§g1for a blue background)In addition to color codes, several other formatting codes are available:§l- Bold text§u- Underlined text§v- Reverse the background and foreground color§r- Reset the formattingInstallationThis project can be installed viapip:pip install sformatTool usageThesformattool can be used to format text by piping it to its standard input:$ echo "§aHello §bWorld§r!" | sformat
Hello World! (in color)Library usageSimply use theformatfunction fromsformat.formatter:fromsformat.formatterimportformatraw_text="§aHello §bWorld§r!"colored_text=format(raw_text)print(colored_text)# The text will be displayed in colorLicenseThis project is licensed under theGNU GPLv3 license.
Package: sfp
Simple Fast Project (SFP)Simple Fast Project (SFP) is a Python library designed to quickly set up a folder structure for simple data science projects. It's based on the standalone data catalog provided byKedro.FeaturesRapidly sets up a standardized folder structure for data science projects.Integrates seamlessly with Kedro's standalone data catalog.Simplifies the project initialization process, allowing you to focus on data science.InstallationWindows:$python-mpipinstallsfpLinux:$python3-mpipinstallsfpUsageAfter installing, simply open a command prompt or terminal in the desired directory where you want your project to be, and run the following command:$sfpThis command will create the necessary directories and files in your current working directory.LinksKedro: The foundation for the standalone data catalog used in SFP.ContributingWe welcome contributions! Please see ourCONTRIBUTING.mdfor details.LicenseThis project is licensed under the Apache 2.0 License. See theLICENSEfile for details.
Package: sfpl
SFPLPython Package for accessing account, book, and author data from the SFPL Website.StatusInstallationFrompip:$ pip install sfplFrom source:$ pip install git+git://github.com/kajchang/SFPL.gitOr clone / download this repository and$ python setup.py installor$ pip install .Frameworks Usedrequests- Used for getting data from the SFPL website and managing login cookies.bs4 + lxml- Used for parsing information from HTML.FeaturesManaging current checkouts and holds for your SFPL library account.Searching for books by keyword, title, author, subject, and tag and searching for user-created book lists.Following other library users and viewing their book lists.Getting libary branch hours.TODOBetter Book Status MessagesHow to UseSearching for books on Python:>>>fromsfplimportSearch>>>python_search=Search('Python')>>>results=python_search.getResults(pages=2)# .getResults is a generator that yields / streams pages of results>>>forpageinresults:print(page)[PythonbyDonaldson,Toby,PythonbyJohansen,Andrew,Python!byMoses,Brian,PythonbyMcGrath,Mike,PythonbyVo.T.H,Phuong][PythonbyRomano,Fabrizio,PythonbyPhillips,Dusty,PythonbyJoshi,Prateek,PythonbyLassoff,Mark,PythonbyWayani,Rafiq]Searching for books by J.K. Rowling:>>>fromsfplimportSearch>>>jk_search=Search('J.K. Rowling',_type='author')>>>results=jk_search.getResults()>>>first_page=next(results)>>>first_page[0].title"Harry Potter and the Sorcerer's Stone">>>first_page[0].getDescription()"For the first time, J. K. Rowling's beloved Harry Potter ..."Searching for book lists related to San Francisco:>>>fromsfplimportSearch>>>list_search=Search('San Francisco',_type='list')>>>results=list_search.getResults()>>>first_page=next(results)>>>first_page[0].title'Made in SF - San Francisco love for young readers'>>>forbookinfirst_page[0].getBooks():print(book)AlCaponeDoesMyShirtsbyCholdenko,GenniferBookScavengerbyBertman,JenniferChambliss...Getting all your books on hold:>>>fromsfplimportAccount>>>my_account=Account('barcode','pin')# Replace with your barcode and pin.>>>my_holds=my_account.getHolds()>>>forbookinmy_holds:print(book.title)'Python for Data Analysis''Automate the Boring Stuff With Python'>>>forbookinmy_holds:print(book.status)'#4 on 6 copies''#7 on 3 copies'>>>forbookinmy_holds:print(book.author)'McKinney, Wes''Sweigart, Al'Searching for books by J.K. Rowling but not about Harry Potter:>>>fromsfplimportAdvancedSearch>>>search=AdvancedSearch(includeauthor='J.K. Rowling',excludekeyword='Harry Potter')# Search for books by J.K. Rowling but don't include 'Harry Potter'>>>results=search.getResults()>>>forbookinresults:print(book.title)'Fantastic Beasts and Where to Find Them''Fantastic Beasts and Where to Find Them : The Original Screenplay''The Casual Vacancy''Very Good Lives''Una vacante imprevista'Getting hours for a library branch:>>>fromsfplimportBranch>>>branch=Branch('anza')>>>branch.getHours(){'Sun':'1 - 5','Mon':'12 - 6','Tue':'10 - 9','Wed':'1 - 9','Thu':'10 - 6','Fri':'1 - 6','Sat':'10 - 6'}
Package: sfplot
概要散布図または箱ひげ図からデータを抽出することができます関数を呼び出すと、tkウィンドウが立ち上がりますウィンドウに表示された図上をドラッグすることで範囲を指定でき、範囲内にプロットされているデータをDataFrameで受け取ることができますjupyter notebook上で使うことを想定していますインストール$pipinstallsfplot使い方サンプルデータにirisデータセットを利用しますfromsklearn.datasetsimportload_irisimportseabornassnsiris_dataset=sns.load_dataset('iris')iris_dataset.head(10)対散布図からデータを抽出pandas
DataFrameと、散布図にしたいカラム名を引数にしてSearchFromPlotを起動します引数x,yには数値(int, float)型のカラムのみ指定可能ですimportsfplotsearch_result_df=sfplot.scatter(iris_dataset,x='sepal_length',y='sepal_width')ウィンドウが起動します取得したいデータの範囲をドラッグします「4件取得」と書かれたボタンを押下すると、ウィンドウが閉じ、選択範囲内のレコードを抽出したDataFrameを返却しますsearch_result_df一度に複数回範囲を選択することも可能です箱ひげ図からデータを抽出pandas
DataFrameと、箱ひげ図にしたいカラム名を引数にしてSearchFromPlotを起動します引数xにはobject型のカラム、引数yには数値(int,
float)型のカラムのみ指定可能ですsearch_result_df=sfplot.boxplot(iris_dataset,x='species',y='sepal_width')ウィンドウが起動します操作方法は対散布図と同様です
Package: sf-ppu
# Copyright 2021 Ant Group Co., Ltd.
#
# Licensed under the Apache License, Version 2.0 (the “License”);
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#http://www.apache.org/licenses/LICENSE-2.0#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an “AS IS” BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.Requires-Python: >=3.8
Requires-Dist: protobufUNKNOWN
Package: sf-price-fetcher
sf_price_fetcherSimple Python API to pull pricing information from scryfall.com.Provides caching of prices via an sqlite3 database.NOTEthat currently (as of v.0.1), the cache expiry time is set to 1 week.
This will change and become configurable in future versions.UsageTypical usage is quite straightforward:>>> from sf_price_fetcher import fetcher
>>> fetcher.get('mox amber')
'29.98'sf_price_fetcherreturns the lowest USD price for any printing of that card
which is not from a promotional set.CLIsf_price_fetcheralso includes a command-line interface.  For syntax information:$ python -m sf_price_fetcher --help
usage: __main__.py [-h] [-c] [-s] card_name

Card price fetcher for scryfall.com

positional arguments:
  card_name

optional arguments:
  -h, --help    show this help message and exit
  -c, --card    Print full card data instead of just the price.
  -s, --search  Search for the card name and return all unique printings.Usage examples:$ python -m sf_price_fetcher faerie\ vandal
faerie vandal: $0.07
$ python -m sf_price_fetcher "black lotus"
black lotus: $8499.99The-cand-soptions are primarily used for debugging.
They output the full JSON data pulled from scryfall.$ python -m sf_price_fetcher -c 'blacker lotus' | head
{'object': 'card',
 'id': '4c85d097-e87b-41ee-93c6-0e54ec41b174',
 'oracle_id': '41dd29b9-f08d-4ccc-8dc0-da11d2d456e9',
 'multiverse_ids': [9764],
 'tcgplayer_id': 830,
 'cardmarket_id': 11870,
 'name': 'Blacker Lotus',
 'lang': 'en',
 'released_at': '1998-08-11',
 'uri': 'https://api.scryfall.com/cards/4c85d097-e87b-41ee-93c6-0e54ec41b174',
Package: sfpy
sfpysoftfloat and softposit in Pythonsupport for softfloat float16, float32, and float64support for softposit posit8, quire8, posit16, quire16, posit32, and quire32InstallationOn most linux distros, sfpy should work out of the box:pip install sfpyBinary wheels (compatible with manylinux1) are available for CPython 2.7, 3.5, 3.6, and 3.7.Under the hood, sfpy uses Cython to create bindings for the softposit and softfloat C libraries.
These building instructions are tested on Ubuntu 18.04 - for other platforms they may need some
adaptation. The cythonized C and compiled static libraries (.a), as well as necessary headers,
are included in the source releases; installing these does not require Cython.Demo>>> import sfpy
>>> from sfpy import *
>>> a, b = Float16(1.3), Float16(1.4)
>>> a * b - a / b
Float16(0.89208984375)
>>> sfpy.float.flag_get_inexact()
True
>>> a += b
>>> a
Float16(2.69921875)
>>>
>>> x, y = Posit16(3.0), Posit16(3)
>>> x
Posit16(3.0)
>>> x.bits
22528
>>> y
Posit16(2.9802322387695312e-08)
>>> y.bits
3
>>> x * y
Posit16(8.940696716308594e-08)
>>> acc = Posit16(0)
>>> for i in range(10000):
...   acc = acc.fma(x, y)
... 
>>> acc
Posit16(1.9073486328125e-06)
>>> acc.bits
24
>>> q = Quire16(0)
>>> for i in range(10000):
...   q.iqma(x, y)
... 
>>> q
Quire16(0.00089263916015625)
>>> q.bits
64424509440000
>>> q.to_posit()
Posit16(0.00089263916015625)
>>> q.to_posit().bits
490BuildingSeeBUILDING.
Package: sfquery
Service Fabric Reliable Collections Query APIsfqueryis a python library that allows you to discover your reliable collections and query against them.
The recommended way of consumingsfqueryis using its command line interface,rcctl.To read the fullrcctldocumentation, visithttps://github.com/amenarde/reliable-collections-cliTo read thesfqueryAPI and see some examples, visithttps://github.com/amenarde/reliable-collections-cli/blob/master/sfquery/api.mdChangelog0.1.0Initial beta release.API is likely to change in future.