Saravanan666/pypi-org huggingface dataset:
Package: properties-diff
properties-diffproperties-diffis a command line tools to comparepropertiesfiles and print differences with colors as if you were usingwdiffordifftools.Even ifpropertiesfiles are text files, using directlydiffis not that efficient because of key/value pairs order or format (for example using=or[space]=[space]as separator, double quoting values...).properties-diffcompare key/value pairs but not the order nor the format.properties-patchis a command line tools to patch apropertiesfiles with one or morepropertiesfiles to add, update or delete some properties.InstallInstall the latest release fromPyPI$pip3installproperties-diff
$properties-diffpath/to/file.propertiespath/to/another/file.propertiesInstall from the sources$pip3install--user--upgradegit+https://github.com/essembeh/properties-diff
$properties-diffpath/to/file.propertiespath/to/another/file.propertiesUsage$properties-diff--help130usage:properties-diff[-h][-q][--quote][--sepSEP][-m{simple,diff,wdiff}][-A][-D][-U]left.propertiesright.properties

positionalarguments:left.propertiesleftfiletocompareright.propertiesrightfiletocompare

optionalarguments:-h,--helpshowthishelpmessageandexit-q,--quietprintlessinformation--quoteusedoublequotesforvalues,example:foo="bar"--sepSEPkey/valueseparator,defaultis'='-m{simple,diff,wdiff},--mode{simple,diff,wdiff}selectaformattoshowdifferences:usingcolorsonly(simple),usingdiff-likeformat(diff)orwdiff-like(wdiff)format.Defaultis'wdiff'-A,--addedprintaddedproperties-D,--deletedprintdeletedproperties-U,--updatedprintupdatedproperties$properties-patch--help
usage:properties-patch[-h][-c][--comments][-f][-i][--quote][--sepSEP][-A][-D][-U]-ppatch.properties[-ooutput.properties]source.properties

positionalarguments:source.propertiesfiletomodify

optionalarguments:-h,--helpshowthishelpmessageandexit-c,--colorprintcolors--commentsinsertcommentwhenpropertyisadded,updatedordeleted-f,--forceforceoutputfile(--output)overwriteifitalreadyexists-i,--interactiveaskforconfirmationtoadd,updateordeleteaproperty--quoteusedoublequotesforvalues,example:foo="bar"--sepSEPkey/valueseparator,defaultis'='-ppatch.properties,--patchpatch.propertiespatchfile-ooutput.properties,--outputoutput.propertiesmodifiedfile-A,--addaddnewpropertiesfrompatches-D,--deletedeletepropertiesnotinpatches-U,--updateupdatepropertiesfrompatchesproperties-diff modesYou can see differences between the properties files using 3 modes using--mode <MODE>or-m <MODE>wdiff, prints the changes likewdifftool would do (this is the default mode)diff, prints the changes likedifftool would dosimple, based on colors,redfor removed lines,greenfor added lines
Package: properties-image
Whypropertiesgives structure (and documentation!) to the properties you use in your
classes.properties-imageextends this to include image files.ScopeThepropertiespackage allows you to createstrongly typedobjects in a
consistent way. This allows you to hook intonotificationsand other libraries.
Theproperties-imagepackage contains additional image file properties with
external dependencies not included inproperties.GoalsProvide supplementary Property classes to thepropertiesmoduleAllow dependency-heavy classes to be installed separatelyAlternativesAlternatives toproperties:traitsis used by Enthoughttraitletsis used in the Jupyter projectmypyandPEP0484which document typing but do not include coercion or notificationsOther image libraries:pypngfor PNG encoding/decodingPillowa fork of PILscikit-imagefor image processingConnectionspropertiessteno3dfor the client APIInstallationTo install the repository, ensure that you havepip installedand run:pip install properties_imageFor the development version:git clone https://github.com/3ptscience/properties-image.git
cd properties-image
pip install -e .ExamplesTo include an image property, importpropertiesandproperties_image.
Then the image property classes will be in theextmodule. For additional examples,
seeproperties.importpropertiesimportproperties_imageclassNamedImage(properties.HasProperties):name=properties.String('Name of the image')image=properties.ext.ImagePNG('The image')
Package: properties-inheritance-graph
The purpose of this library is to quickly and easily create UML graphs
that show inheritance structure ofHasPropertiesclass hierarchies.
For more information onHasPropertiesclasses, seeproperties.To installpip install properties_inheritance_graphthen in Pythonfromproperties_inheritance_graphimportmake_graphimportomf# or any other library built on propertiesgraph,registry=make_graph(registry=omf.PointSetElement._REGISTRY,expand_props=True,only_new_props=True,abstract_regex='^.*Model$',)In the above example, the properties-basedOMFlibrary is used for demonstration.To render these graphs to file or in a Jupyter notebook, you may usenxpd.Graphvizmust also be installed.importnxpdnxpd.draw(graph,filename='inheritance_graph.png')
Package: properties.py
properties.pyDescriptionModule for reading/writing properties-files.Installationpipinstall-Uproperties.pyUsageCodeimportpropertiesuser={"name":"NoName","age":-1,"sex":"M","data":{"region":39,"keywords":["man","human",14.88]}}withopen("test.properties","w",encoding="utf-8",errors="ignore")asfile:properties.dump(user,file)withopen("test.properties","r",encoding="utf-8",errors="ignore")asfile:data=properties.load(file)print(data)Output{'name':'NoName','age':-1,'sex':'M','data':{'region':39,'keywords':['man','human',14.88]}}
Package: properties-step
SEAMM Properties Plug-inA SEAMM plug-in for PropertiesFree software: BSD-3-ClauseDocumentation:https://molssi-seamm.github.io/properties_step/index.htmlCode:https://github.com/molssi-seamm/properties_stepFeaturesPlease edit this section!AcknowledgementsThis package was created with themolssi-seamm/cookiecutter-seamm-plugintool, which
is based on the excellentCookiecutter.Developed by the Molecular Sciences Software Institute (MolSSI),
which receives funding from theNational Science Foundationunder
award CHE-2136142.History2023.7.31 – Bugfix: random error creating the tables of properties.2023.7.30 – Initial releaseInitial version, which can export the properties in the database to a table.
Package: propertime
Propertime is an attempt to implement proper time management in Python,
by fully embracing the additional complications due to how we measure time
as humans instead of just denying them.In a nutshell, it provides two main classes: theTimeclass for representing
time (similar to a datetime) and theTimeUnitclass for representing units
of time (similar to timedelta). Such classes play nice with Python datetimes so
that you can mix and match and use them only when needed.You can have a look at theREADMEfor a better introduction together with some usage examples, and you can get started by having
a look at thequickstart notebookor by reading thereference documentation.
Package: property
pythonpropertyfileloaderA python module to load property filesLoad multiple property filesRecursively define properties (Similar toPropertyPlaceholderConfigurer) in spring which lets you use${variable-reference}to refer to already defined property)Placeholders are also resolved using env variables, like the spring property loader does, if the class is instantiated with theuse_envargument (defaults to false for backward compatibility)InstallAvailable onPyPIpipinstallpropertyExamplemy_file.propertiesfoo=Iamawesomebar=${chocolate}-barchocolate=fudgelong=averylongpropertythatisdescribedinthepropertyfilewhichtakesup\multiplelinescanbedefinedbytheescapecharacterasitisdonehereurl=example.com/api?auth_token=xyzuser_dir=${HOME}/testunresolved=${HOME}/files/${id}/${bar}/fname_template=/opt/myapp/{arch}/ext/{objid}.datCodefromproperties.pimportProperty## set use_env to evaluate properties from shell / os environmentprop=Property(use_env=True)dic_prop=prop.load_property_files('my_file.properties')## Read multiple files## dic_prop = prop.load_property_files('file1', 'file2')print(dic_prop)# Output# {'foo': 'I am awesome', 'bar': 'fudge-bar', 'chocolate': 'fudge',#  'long': 'a very long property that is described in the property file which takes up multiple lines#  can be defined by the escape character as it is done here', 'url': 'example.com/api?auth_token=xyz',#  'user_dir': '/home/user/test',#  'unresolved': '/home/user/files/${id}/fudge-bar/',#  'fname_template': '/opt/myapp/{arch}/ext/{objid}.dat'}Developgitclonehttps://github.com/anandjoshi91/pythonpropertyfileloader.gitcdpythonpropertyfileloader## make your changes and open a PR - https://github.com/anandjoshi91/pythonpropertyfileloader/pulls## Ensure all tests pass## Check Dependenciespipinstallpipreqs
pipreqs.## Testpipinstallpytest
pytest## Publish to PyPipipinstalltwine

updatesetup.py
pythonsetup.pysdistbdist_wheel
twinecheckdist/*
twineuploaddist/*
Package: property-accessor
property_accessorPython property accessor module utilInstallation:>poetryaddproperty-accessorUsagefromproperty_accessorimportPropertyAccessorfromdataclassesimportdataclass,field@dataclassclassCategory:code:str@dataclassclassProduct:name:strsku:strprice:floatcategories:list[Category]=field(default=list)accessor=PropertyAccessor(None)accessor.set('sku','t-shirt-01')accessor.set('name','t-shirt basic black')accessor.set('price',39.9)accessor.set('categories[0].code','t-shits')print(accessor.get('name'))# t-shirt basic blackprint(accessor.get('categories[0].code'))# t-shirtprint(accessor.data)# AnonymousObject(sku='t-shirt-01', name='t-shirt basic black', price=39.9, categories=[AnonymousObject(code="t-shirts")])print(accessor.data.cast(Product))# Product(sku='t-shirt-01', name='t-shirt basic black', price=39.9, categories=[Category(code="t-shirts")])
Package: propertybag
propertybagFlexible property bag classimportpropertybagaspbbag=pb.Bag({'a':'b'},c='d')print(bag)# > {"a": "b", "c": "d"}bag.b.c.d.e=42print(bag.b.c.d.e)# > 42print(bag.toJson())# > {"a": "b", "c": "d", "b": {"c": {"d": {"e": 42}}}}# Returns a bag objectbag.b.c['d']# Returns a Bag() objectbag.b.c.dTable of contentsInstallExamplesReferencesInstall$ pip3 install propertybagExamplesimportpropertybagaspbLog=print# Init with kw argsbag=pb.Bag(a='b',c='d')Log(bag.toJson(True))# > {# >   "a": "b",# >   "c": "d"# > }# Init with dictbag=pb.Bag({'a':'b','c':'d'})Log(bag.toJson())# > {"a": "b", "c": "d"}# Init with bothbag=pb.Bag({'a':'b'},c='d')Log(bag)# > {"a": "b", "c": "d"}# Set deep valuebag.d.e.f.g.h=42Log(bag)# > {"a": "b", "c": "d", "d": {"e": {"f": {"g": {"h": 42}}}}}# Read deep valueLog(bag.d.e.f.g.h)# > 42try:Log(bag.d.e.f.g.i)# > Throws ValueError() exceptionexceptValueErrorase:Log(e)# > Value not set# If you don't want exceptions thrown when value doesn't exist, provide a defaultbag_nt=pb.Bag({'a':'b'},'')Log(bag_nt.d.e.f.g.i)# > ''# Use get/set for compound keysbag.set("d.e.f.g.h",43)Log(bag.get("d.e.f.g.h"))# > 43Log(bag.get("d.e.f.g.i","default"))# > "default"# IterateLog('--------------------------------------------')fork,vinbag.items():Log(k,v)Log('--------------------------------------------')forkinbag:Log(k)Log('--------------------------------------------')forvinbag.values():Log(v)ReferencesPythonhttps://www.python.org/piphttps://pip.pypa.io/en/stable/
Package: property-caching
Cached version of propertyWritten and used by the folks at Yola to support ourfree website
builder.Overviewcached_property- stores results of decorated methods in
decorated object
(in_cached_propertiesattribute)class_cached_property- stores results of decorated methods in
the class of decorated object
(in_class_cached_propertiesattribute). All instances will share
cached value.clear_property_cache- deletes cached value (works for object
cached properties only)set_property_cache- explicitly sets property cache (works for
object cached properties only)is_property_cached- allows to check whether property was cached
or not (works for object cached properties only)Usagefromproperty_cachingimport(cached_property,class_cached_property,clear_property_cache,set_property_cache,truthy_cached_property)classDummy:@cached_propertydeffoo(self):returnself.service.expensive_operation()@truthy_cached_propertydefbar(self):# this value will only be cached if it doesn't evaluate to false:returnself.service.expensive_operation2()@class_cached_propertydefservice(self):returnexpensive_service_initialization()d=Dummy()d.foo# calculates result and stores it in d._cached_propertiesd.foo# uses cached valueclear_property_cache(d,'foo')# clears cache for property `foo`set_property_cache(d,'foo',42)# explicitly set cache for property `foo`d2=Dummy()d2.foo# re-calculates value of `foo` but uses cached serviceTestingInstall development requirements:pip install -r requirements.txtRun the tests with:python setup.py test
Package: property-lister
Property ListerExtract and convert property list files from SQLite database files and from other property list files.Tested on Kali Linux v2023.4 (64-bit).Made for educational purposes. I hope it will help!Table of ContentsHow to InstallHow to Build and Install ManuallyExtracting and ConvertingUsageHow to Installpip3install--upgradeproperty-lister

apt-get-yinstallplistutilHow to Build and Install ManuallyRun the following commands:gitclonehttps://github.com/ivan-sincek/property-lister&&cdproperty-lister

python3-mpipinstall--upgradebuild

python3-mbuild

python3-mpipinstalldist/property_lister-2.5-py3-none-any.whl

apt-get-yinstallplistutilExtracting and ConvertingExtract and convert property list files inside Cache.db unencrypted SQLite database file:scp root@192.168.1.10:/var/mobile/Containers/Data/Application/YYY...YYY/Library/Caches/com.someapp.dev/Cache.db ./

property-lister -db Cache.db -o results_dbExtract and convert property list files inside an IPA:unzip someapp.ipa

property-lister -db Payload -o results_db

property-lister -pl Payload -o results_plRepeat the same for the app specific directories.Check my other project on how tosearch for filesand on how toextract sensitive data from the files.UsageProperty Lister v2.5 ( github.com/ivan-sincek/property-lister )

--- Extract from an SQLite database file ---
Usage:   property-lister -db database -o out
Example: property-lister -db Cache.db -o results_db

--- Extract from a property list file ---
Usage:   property-lister -pl property-list -o out
Example: property-lister -pl Info.plist    -o results_pl

DESCRIPTION
    Extract and convert property list files
DATABASE
    SQLite database file, or directory containing multiple files
    -db, --database = Cache.db | databases | etc.
PROPERTY LIST
    Property list file, or directory containing multiple files
    -pl, --property-list = Info.plist | plists | etc.
OUT
    Output directory
    All extracted propery list files will be saved in this directory
    -o, --out = results | etc.
Package: propertyshelf.fabfile.common
propertyshelf.fabfile.commonThis project contains a bunch of common used fabric commands we use atPropertyshelfto deploy and maintain our systems.Requirementspropertyshelf.fabfile.commoncurrently usesknifeto communicate with
Rackspace servers. Please make sureknifeis installed and configured
successfully on your system.InstallYou can installpropertyshelf.fabfile.commonwith PIP:pip install propertyshelf.fabfile.commonAll required dependencies will be installed automatically.UsageContributorsThomas Massmann, AuthorChangelog0.2 (2014-04-10)Fixed wrong selection of flavors when bootstrapping new servers.Changed the local folder to ‘var_tmp’ for database download/upload.Added zodb snapshot backup implementation.0.1 (2013-12-20)Migrated common used modules from mls.fabfile.
Package: propertysync
PropertySync Python ClientThis is a python packagefor working with thePropertySync API.Installationpip install propertysyncUsageimportpropertysync# Create a connection to the API# ---# *Note:* email and password will be retrieved from Psync environment variables if not# provided on this line and if available. Those are: PROPERTYSYNC_API_EMAIL, PROPERTYSYNC_API_PASSWORDapi=propertysync.ApiClient('your_email','your_password')# Set the correct document groupapi.set_document_group_id("your_document_group_id")# Get document group infodocument_group_info=api.get_document_group_info()# get all additionsadditions=api.get_autocompletes("addition")# run a search where the address is 123 Main Stsearch=propertysync.Search()search.add_address("123 Main St")search_results=api.run_search(search.get_query())
Package: propfinder
Getting StartedChange directory into your newly created project.cd propfinderCreate a Python virtual environment.python3 -m venv envUpgrade packaging tools.env/bin/pip install –upgrade pip setuptoolsInstall the project in editable mode with its testing requirements.env/bin/pip install -e “.[testing]”Initialize and upgrade the database using Alembic.Generate your first revision.env/bin/alembic -c development.ini revision –autogenerate -m “init”Upgrade to that revision.env/bin/alembic -c development.ini upgrade headLoad default data into the database using a script.env/bin/initialize_propfinder_db development.iniRun your project’s tests.env/bin/pytestRun your project.env/bin/pserve development.ini0.0Initial version.
Package: propgraph
prop-graphA graph structure that can take arbitrary properties.UsageIn the examples that follow, I'll be working with a graph that looks as follows:a                               g
            /       \                       /       \
        b               c               h               i
    /       \       /       \       /
d               e               fBuild graphgraph=Graph([("a","b"),("a","c"),("b","d"),("b","e"),("c","e"),("c","f"),("g","h"),("g","i"),("h","f"),])graph.print()# a#         b#                 d#                 e#         c#                 e#                 f# g#         h#                 f#         iAdd propertiesprops={"c":"interesting","g":"interesting",}graph.add_property("type",props)Finding nodesparent_ids=graph.find_by(lambdan:len(n.children)>0)print(parent_ids)# ['a', 'b', 'c', 'g', 'h']root_ids=graph.find_by(lambdan:len(n.parents)==0)print(root_ids)# ['a', 'g']interesting_ids=graph.find_by(lambdan:n["type"]=="interesting")print(interesting_ids)# ['c', 'g']Deleting nodesgraph.delete_by(lambdan:n["type"]=="interesting")graph.print()# a#         b#                 d#                 e#         e#         f# h#         f# i
Package: prophecy-spark-ai
Spark AIToolbox for building Generative AI applications on top of Apache Spark.Many developers are companies are trying to leverage LLMs to enhance their existing applications or build completely new
ones. Thanks to LLMs most of them no longer have to train new ML models. However, still the major challenge is data and
infrastructure. This includes data ingestion, transformation, vectorization, lookup, and model serving.Over the last few months, the industry has seen a spur of new tools and frameworks to help with these challenges.
However, none of them are easy to use, deploy to production, nor can deal with the scale of data.This project aims to provide a toolbox of Spark extensions, data sources, and utilities to make building robust
data infrastructure on Spark for Generative AI applications easy.Example ApplicationsComplete examples that anyone can start from to build their own Generative AI applications.Chatbot TemplateMedical Advisor TemplateRead about our thoughts onPrompt engineering, LLMs, and Low-code here.QuickstartInstallationCurrently, the project is aimed mainly at PySpark users, however, because it also features high-performance connectors,
both the PySpark and Scala dependencies have to be present on the Spark cluster.Ingestionfromspark_ai.webapps.slackimportSlackUtilities# Batch versionslack=SlackUtilities(token='xoxb-...',spark=spark)df_channels=slack.read_channels()df_conversations=slack.read_conversations(df_channels)# Live streaming versiondf_messages=(spark.readStream.format('io.prophecy.spark_ai.webapps.slack.SlackSourceProvider').option('token','xapp-...').load())Pre-processing & Vectorizationfromspark_ai.llms.openaiimportOpenAiLLMfromspark_ai.dbs.pineconeimportPineconeDBOpenAiLLM(api_key='sk-...').register_udfs(spark=spark)PineconeDB('8045...','us-east-1-aws').register_udfs(self.spark)(df_conversations# Embed the text from every conversation into a vector.withColumn('embeddings',expr('openai_embed_texts(text)'))# Do some more pre-processing...# Upsert the embeddings into Pinecone.withColumn('status',expr('pinecone_upsert(\'index-name\', embeddings)'))# Save the status of the upsertion to a standard table.saveAsTable('pinecone_status'))Inferencedf_messages=spark.readStream\.format("io_prophecy.spark_ai.SlackStreamingSourceProvider")\.option("token",token)\.load()# Handle a live stream of messages from Slack hereRoadmapData sources supported:🚧 Slack🗺️ PDFs🗺️ Asana🗺️ Notion🗺️ Google Drive🗺 Web-scrapeVector databases supported:🚧 Pinecone🚧 Spark-ML (table store & cos sim)🗺 ElasticSearchLLMs supported:🚧 OpenAI🚧 Spark-ML🗺️ Databrick's Dolly🗺️ HuggingFace's ModelsApplication interfaces supported:🚧 Slack🗺️ Microsoft TeamsAnd many more are coming soon (feel free to request as issues)! 🚀✅: General Availability; 🚧: Beta availability; 🗺️: Roadmap;
Package: prophepy
ProphepyPython mocks made for humans.
Heavily inspired by the great PHP'sProphecy.Usagefromexamples.calculatorimportCalculatorfromexamples.displayerimportDisplayerfromprophepyimportprophesizecalculator=prophesize(Calculator)displayer=Displayer(calculator._reveal())calculator.add(2,3)._should_be_called()calculator.add(2,3)._will_return(5)displayer.display_add(2,3)calculator.check_prophecies()Installpip install prophepyTestspython -m unittest tests/test.py
Package: prophet
Prophet: Automatic Forecasting ProcedureProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.Prophet isopen source softwarereleased byFacebook's Core Data Science team.Full documentation and examples available at the homepage:https://facebook.github.io/prophet/Important linksHTML documentation:https://facebook.github.io/prophet/docs/quick_start.htmlIssue tracker:https://github.com/facebook/prophet/issuesSource code repository:https://github.com/facebook/prophetImplementation of Prophet in R:https://cran.r-project.org/package=prophetOther forecasting packagesRob Hyndman'sforecast packageStatsmodelsInstallation - PyPI releaseSeeInstallation in Python - PyPI releaseInstallation - Development versionSeeInstallation in Python - Development versionInstallation using Docker and docker-compose (via Makefile)Simply typemake buildand if everything is fine you should be able tomake shellor alternative jump directly tomake py-shell.To run the tests, inside the containercd python/prophetand thenpython -m pytest prophet/tests/Example usage>>>fromprophetimportProphet>>>m=Prophet()>>>m.fit(df)# df is a pandas.DataFrame with 'y' and 'ds' columns>>>future=m.make_future_dataframe(periods=365)>>>m.predict(future)
Package: prophet-critique
prophet-critiqueResources for assessing facebook prophet forecasting libraryElo ratings for forecasting packagesleaderboardsIs Facebook Prophet the Time-Series Messiah, or Just a Very Naughty Boy?articleBenchmarking Facebook's ProphetarticleA Worrying Analysis of Probabilistic Time-series Models for Sales ForecastingarticleA Comparison of Time Series Methods to Forecast Percentage of Active Cases per PopulationarticleA Comparative Study of
Univariate Time-series Methods
for Sales ForecastingthesisProphet versus "dumb model"kernel descriptionCash flow prediction: MLP and LSTM compared to ARIMA and ProphetarticleProphet evelrepo