Saravanan666/pypi-org huggingface dataset:
Package: usbusiness
# usbusinessThe aim of the project ot to provide an open source business classifier using website information.## ReasearchWeb Page Classification: Features and Algorithms (2009)https://www.cs.ucf.edu/~dcm/Teaching/COT4810-Fall%202012/Literature/WebPageClassification.pdfAutomated Text Classification in the DMOZ Hierarchy (2009)http://users.cecs.anu.edu.au/~ssanner/Papers/Lachlan_Report.pdfTopical Web-page classification of the DMOZ Dataset (2015)https://github.com/kahliloppenheimer/Web-page-classification/blob/master/paper.pdf## Industrys of WeaknessReligiousOil and GasFinanceLarge Companies### OptionsRemove stop words (T/F)My words selection, None, google_10, google_100k### TO DOLink depth pull optionData SetTraining / Validation### ComponentsThe data setThe wordsThe confidenceLink depthThe predictive model### IdeasStemmers
Package: uscalendar
us_calendar_sourceThe United States Calendar package contains two modules:federal_holiday: module that has functions for United States Federal Holidaymarket_open: module that has functions for if the United States Stock Markets are openFederal Holiday Functionsfederal_holiday.is_federal_holiday(date)returns True if day is a federal holidayfederal_holiday.holiday_name(date)returns name of holiday given a date, None if there is no holidayfederal_holiday.is_weekend(date)returns True if day is on a weekendfederal_holiday.is_weekday(date)returns True if day is on a weekdayfederal_holiday.is_working_day(date)returns True if it's a working day for federal employeesfederal_holiday.is_off_day(date)returns True if it's an off day for federal employeesMarket Open FunctionsSundaymarketopen.market_open('2021-11-28')
Returns FalseMondaymarketopen.market_open('2021-11-29)
Returns TrueThanksgiving 2023marketopen.market_open('2023-11-23')
Returns TrueFourth of Julymarketopen.market_open('2021-07-04')
Returns FalseInstalling as a package:pip install git+https://github.com/mmcelhan/us_calendar_built.git#egg=uscalendarSource Codesource code is here:https://github.com/mmcelhan/us_calendar_sourceThe testing file shows all Federal Holidays through 2030 are correctly applied, and that the stock market opens are
properly returnedExamplesFederal holiday examplesfrom uscalendar import federalholiday as fhfh.is_federal_holiday(‘2030-01-01’) # New Years Day, 2030Returns Truefh.is_federal_holiday(‘2030-1-1’) # to test date formattingReturns Truefh.is_federal_holiday(‘2030-1-2’) # not a holidayReturns Falsefh.holiday_name(‘2030-01-01’)Returns ‘New Years Day’fh.is_day_off(‘2030-01-01’)Returns Truefh.is_off_day(‘2030-01-01’)Returns Truemarket open examplesSundaymarketopen.market_open('2021-11-28')Returns FalseMondaymarketopen.market_open('2021-11-29)Returns TrueThanksgiving 2023marketopen.market_open('2023-11-23')Returns TrueFourth of Julymarketopen.market_open('2021-07-04')Returns False
Package: uscalt-drf
Uscalt-DRFUscalt-DRF is a library for Django-Rest-Framework backend servers to allow you to control the data retrieval process rather than Uscalt.To install usingpip:pip install uscalt-djangoQuick startAdd "uscalt-drf" to your INSTALLED_APPS setting:INSTALLED_APPS=[...'uscalt-drf.apps.UscaltDRFConfig',]Include the uscalt-drf URLconf in your project urls.py:path('',include('uscalt-drf.urls')),Runpython manage.py migrate.We need to send the key to the user to decrypt the file and to do this we use email. You'll need to set these variables in settings:EMAIL_HOST='smtp.XXXX.com'EMAIL_PORT=587EMAIL_HOST_USER='EMAIL@HOST.com'EMAIL_HOST_PASSWORD='XXXXXX'EMAIL_USE_TLS=TrueEMAIL_USE_SSL=FalseDon't worry, this is a standard library. We have not created our own email sending code or handle your details.Allowing users to upload dataAllowing users to upload data is straightforward. In fact, so long as you set your email, Uscalt takes care of the rest!Creating a dataset from data stored on a databaseThis is for links that were created using the 'Use data from cloud database (not local devices)' option. This will require you to define how to retrieve the data from a given list of user identifiers.You'll need to create a file calleduscalt.pyinevery app that contains data needed by a Room Link. Instantiate a class and define each data retrieval function with the uscalt_task decorator. (The name of the class is not important)fromuscalt_drf.utilsimportuscalt_taskfrom.modelsimportrunning_modelclassUscalt:@uscalt_taskdefRunning(identifiers):data=running_model.objects.filter(pk__in=identifiers)returndata
Package: uscan-notify
uscan-notifyCommand line tool to notify of any outdated Debian packages by using uscan info
available from qa.d.o.InstallI can't publish to PyPi as new registrations are temporarily disabled, but you
can install from the Codeberg package registry like so:pipinstall--index-urlhttps://codeberg.org/api/packages/Maytha8/pypi/simple/uscan-notifyFrom sourcegit clone https://codeberg.org/Maytha8/uscan-notify
cd uscan-notify

poetry install
poetry run uscan-notify --help # or

pip install .UsageUse--helpflag for usage info.Example: email notificationsHere's a bash script to send email notifications with the output (to use with
e.g. cron)result="$(uscan-notify--db-path<packages>)"if[[$result]];thenecho"uscan-notify has found outdated packages, below are the results:$result"|mail-s"[uscan-notify] Outdated packages""<your email>"fiWhy?I wanted to setup email notifications whenever a new upstream version is released.CopyrightCopyright © 2023 Maytham Alsudany<maytha8thedev@gmail.com>LicenseMIT (unless otherwise indicated). SeeLICENSE.
Package: us-census-api-extract
us_census_api_extractThis package makes it easy to extract US census data by inputting a list of demographic variables and US state codes.  The results are returned as pandas DataFrame.  As it extracts the data it keeps track of the progress and stores the batches in a temp folder.  If the process gets interrupted, you can run it again and it will continue where it left off (ie. it is idempotent).Installpip install us_census_api_extractUsageGo to theUS census pageto get an api key.  In the example below I am using the American 5-year Community Survey (2018) but you could use any api by changing the 'request_url_pre'.request_url_pre = "https://api.census.gov/data/2018/acs/acs5/subject?get=NAME,"
request_url_post_1 = "&for=tract:*&in=state:"
request_url_post_2 = "&in=county:*&key="
api_key = your-api-key

state_codes = ['48',  # TX
               '36',  # NY
               '06']  # CA

vars = ['S0101_C01_001E', 'S0101_C01_002E', 'S0101_C01_003E',
       'S0101_C01_004E', 'S0101_C01_005E', 'S0101_C01_006E',
       'S0101_C01_007E', 'S0101_C01_008E', 'S0101_C01_009E',
       'S0101_C01_010E', 'S0101_C01_011E', 'S0101_C01_012E']

tmp_path = your-temp-filepath  # this is where the temp files extracted will be stored.

# extract census demographics from US Census API for all census tracts that are in Texas
df_demo = cenx.extract_by_var(vars, state_codes,
                              request_url_pre, request_url_post_1, request_url_post_2, api_key, 
                              tmp_path)
Package: usc-isi-i2-mgzip
mgzipA multi-threading implement of Python gzip moduleUsing a block indexed GZIP file format to enable compress and decompress in parallel. This implement use 'FEXTRA' to record the index of compressed member, which is defined in offical GZIP file format specification version 4.3, so it is fully compatible with normal GZIP implement.This module is~25Xfaster for compression and~7Xfaster for decompression (limited by IO and Python implementation) with a24 CPUscomputer.In theoretical, compression and decompression acceleration should be linear according to the CPU cores. In fact, the performance is limited by IO and program language implementation.UsageUse same method as gzip moduleimportmgzips="a big string..."## Use 8 threads to compress.## None or 0 means using all CPUs (default)## Compression block size is set to 200MBwithmgzip.open("test.txt.gz","wt",thread=8,blocksize=2*10**8)asfw:fw.write(s)withmgzip.open("test.txt.gz","rt",thread=8)asfr:assertfr.read(len(s))==sPerformanceCompression:Decompression:Brenchmarked on a 24 cores, 48 threads server (Xeon(R) CPU E5-2650 v4 @ 2.20GHz) with 8.0GB FASTQ text file.Using parameters thread=42 and blocksize=200000000WarningThis package only replace the 'GzipFile' class and 'open', 'compress', 'decompress' functions of standard gzip module. It is not well tested for other class and function.As the first release version, some features are not yet supported, such as seek() and tell(). Any contribution or improvement is appreciated.
Package: uscis-opts
Archer APIFor more information on the Archer platform, please visitthis help page.The Archer platform has 3 main APIs that can be queried using system provided
credentials.Web Services APIRESTfulContent APIThe Web Services API is currently used to pull reports created by a user in the
Archer graphical environment. Please see theSearchOptions guidlinesif you would like to call ArcherServerClient.exec_search with custom options.The RESTful API is currently used to pull history log data. The RESTful API
requires a contents id to make queries. These ids will be colletively
reffered to as an ArcherServerClient instance's context.This module provides 2 main classes:ArcherAuthArcherServerClientImportant Note on Session MaintainenceThe ArcherAuth class starts and maintains the session created with the user's
credentials. Session authentication is handled through session tokens. Token are
only valid as long as they are generated from the most recent signing. A new
signing event will invalidate any previously generated token. If you have a need
to access the API and/or UI simultaneously, separate credentials must be used or
you risk invalidating your previous session.Important Note on API AvailabilityThe ArcherServerClient class (ASC) is responsible for making the various API
calls and returning data to the user. An ASC instance will have access to data
provisioned to the user. These provisions are made by the Archer system admin
and linked to the credentials used for signing. Please make sure your account
has the necessary provisions before calling ArcherAuth.login().
Package: us-congress
US CongressClient for getting information on the US Congress.RequirementsFor this package to work, you mustget an API key from ProPublica, whose API this uses, and set the following the environment variablePROPUBLICA_CONG_KEYto whatever that key is, either withexportPROPUBLICA_CONG_KEY=<yourkey>or in a.envfile:PROPUBLICA_CONG_KEY=<yourkey>Getting startedTo install with pip, simply runpipinstallus_congress>>>fromus_congressimportCongress>>>cong=Congress(116)>>>cong<Congress116>This will return a client for querying data on a particular congress (in the above example, the 116th Congress).Right now, this enables only getting lists of Representatives and Senators:cong.getRepresentatives()# DataFrame with all representativescong.getSenators()# DataFrame with all senators
Package: uscovid
uscovidThis program summarizes the current status of coronaviruses in the United States, state by state.Using NYtimes covid19 dataset, uscovid can visualize status in US.The dataset is downloadable from:https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv#How to install 'uscovid'$pip install uscovidIf the required library does not exist, install with pip that.$pip install XXXX#How to run 'uscovid'Add after 'uscovid' the state name situation you want to know about.$uscovid 'South Dakota'※The state more than 2 phrases, please enclose in parentheses$uscovid Hawaii$uscovid 'South Dakota'You can also know about more than one state.$uscovid Hwaii Arizona
Package: uscpi
μSCPIAn asynchronous SCPI instrumentation library.InstallPyPIInstalling the latest release fromPyPI.pip install -U uscpiRepositoryWhen usinggit, clone the repository and
change your present working directory.git clone http://github.com/mcpcpc/uscpicd uscpi/Create and activate a virtual environment.python3 -m venv venvsource venv/bin/activateInstall μSCPI to the virtual environment.pip install -e .UsageAsynchronousA basic example using theasynciolibrary.fromasyncioimportrunfromuscpiimportTCPfromuscpiimportInstrumentclient=TCP(host="127.0.0.1",port=5025)instrument=Instrument(client=client)asyncdefmain():response=awaitinstrument.idn()print(response)if__name__=="__main__":run(main())Connection TimeoutBy default, μSCPI will wait indefinitely for a connection to
be established. If thetimeoutproperty is defined, anasyncio.TimeoutErrorwill be raised after the specified
connection time period (in seconds) is exceeded.TCP(host="127.0.0.1",port=5025,timeout=0.1)Automatic Connection ManagementTo ensure proper connection cleanup, the built-in asynchronous
context manager can be used.asyncdefmain():asyncwithTCP("127.0.0.1",8080)asclient:instrument=Instrument(client=client)response=awaitinstrument.idn()print(response)Event CallbacksThere are four user callback functions that can be implemented
and executed when a corresponding event is triggered:connection_made_cb,connection_lost_cb,data_received_cb,
andeof_received_cb. Each callable object must be passed to
the client method during instantiation.defuser_cb():print("Connection made!")TCP(host="127.0.0.1",port=5025,connection_made_cb=user_cb)FeaturesμSCPI is fairly lightweight and leaves a majority of
instrument function commands to be implemented by the user.
Nonetheless, the following IEEE-488.2 commands have been
implemented:Clear Status CommandEvent Status Enable Command and QueryStandard Event Status Register QueryIdentification QueryReset CommandService Request Enable Command and QueryRead Status Byte QueryTrigger CommandSelf-Test QueryWait-to-Continue CommandYou can learn more about each of these commands by using the
built-inhelpmethod.>>>fromuscpiimportInstrument>>>help(Instrument)CreditssockioIEEE 488.2-1978 ProtocolIEEE 488.2 Common Commands
Package: uscr
uscrPackages that useuscrDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nuscr--fileenv.ymlInstallpipinstall-e.# install from pypipipinstalluscrnbdev# activate conda environment$condaactivateuscr# make sure the uscr package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the uscr package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/uscr.gitor fromconda$condainstall-cdsm-72uscror frompypi$pipinstalluscrDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.A = AliasC = ClassVarQ = OptionalE = (Over)-EngineeredZ = CustomR = RichP = PlotD = DataB = BioM = Machine Learning
Package: uscrn
uscrnEasily loadU.S. Climate Reference Network(USCRN) data.Withuscrn, fetching and loading years ofdatafor all USCRN sites[^a] takes just one line of code[^b].Example:importuscrndf=uscrn.get_data(2019,"hourly",n_jobs=6)# pandas.DataFrameds=uscrn.to_xarray(df)# xarray.Dataset, with soil depth dimension if applicable (hourly, daily)Bothdf(pandas) andds(xarray) include dataset and variable metadata.
Fordf, these are indf.attrsand can be preserved by
writing to Parquet with the PyArrow engine[^d] withpandas v2.1+.df.to_parquet("uscrn_2019_hourly.parquet",engine="pyarrow")Conda install example[^c]:condacreate-ncrn-cconda-forgepython=3.10joblibnumpypandaspyyamlrequestsxarraypyarrownetcdf4
condaactivatecrn
pipinstall--no-depsuscrn[^a]: Useuscrn.load_meta()to load the site metadata table.
[^b]: Not counting theimportstatement...
[^c]:uscrnis not yet on conda-forge.
[^d]: Or the fastparquet engine withfastparquet v2024.2.0+.
Package: usc-schedule
USC-ScheduleUSC Schedule of Classes API wrapperIntroductionThis library provides a python interface for the USC Schedule of Classes api. It works for Python versions from 3.6 onwards.Installation$ pip install usc-scheduleUsageSetupfromuscscheduleimportScheduleschedule=Schedule()Get departmentcsci_department=schedule.get_department(department_id="CSCI",semester_id=20201)# Alternatively: schedule.get_department("CSCI", 20201)print(csci_department.department)print(csci_department.abbreviation)print(csci_department.department_url)# Prints:# Computer Science# CSCI# http://www.cs.usc.edu/Get course detailscsci_course=schedule.get_course(course_id="CSCI-201",semester_id=20201)# Alternatively: schedule.get_course("CSCI-201", 20201)print(csci_course.title)print(csci_course.units)print(csci_course.description)# Prints:# Principles of Software Development# 4.0# Object-oriented paradigm for programming-in-the-large in Java; writing sophisticated concurrent# applications with animation and graphic user interfaces; using professional tools on team project.# Prerequisite: CSCI 104L.
Package: usd2gltf
usd2gltfUSD to GLTF/GLB converterCurrent version0.3.5UsageThis package can be incorporated into a DCC tool (like Houdini) or used from the command-line.CLIusage: usd2gltf [-h] [-i INPUT] [-o OUTPUT] [--interpolation INTERPOLATION] [-d] [-f]Convert incoming USD(z) file to glTF/glboptional arguments:-h, --help show this help message and exit-i INPUT, --input INPUTInput USD (.usd, .usdc, .usda, .usdz)-o OUTPUT, --output OUTPUTOutput glTF (.gltf, .glb)--interpolation INTERPOLATIONInterpolation of animation (LINEAR, STEP, CUBIC)-d, --debug Run in debug mode-f, --flatten Flatten all animations into one animationRequirementsusd-core (Or custom Pixar USD build)gltflibnumpyFeaturesMesh conversionUV support (TEXCOORD_0, TEXCOORD_1)displayColor support (COLORS_0)Animated skeleton, weights, skinningNormals and tangents supportedMaterialsUsdPreviewSurface -> PBRMetallicRoughnessCamera conversionLight conversionPointSpotDirectionalXform conversionAnimated xforms supportedAnimationsAllows per object animation or single flat GLTF animationExportGLB and GLTF optionsGLTF Extras
Package: usd-core
Universal Scene Description (USD) is a system that scalably encodes and
interchanges static and time-sampled 3D geometry, shading and lighting data
between Digital Content Creation applications. Domain-specific schema modules
define the geometry, shading, and lighting encoding atop USD's domain-agnostic
core. It provides a low-level data model that supports plugin implementations,
and a scenegraph built on top of a multi-threaded "composition engine" for
creating complex scenes out of reusable, modular assets.This package provides the core USD libraries. It does not provide any of
the optional plugins or imaging features from the full USD distribution.
Package: usd-qtpy
USD-QtPyPython Qt components for building custom USD tools.How to use?The Qt components can be embedded in your own Qt interfaces and usually have
astageentrypoint that you should pass apxr.Usd.Stageinstance.However, a simple example Editor UI is also available to run standalone.If you have theusd_qtpypackage you can for example run it like:python -m usd_qtpy "/path/to/file.usd"Want to try it within your running Python session it should be as trivial to
do:frompxrimportUsdfromqtpyimportQtWidgetsfromusd_qtpy.editorimportEditorWindowfilepath="/path/to/file.usd"stage=Usd.Stage.Open(filepath)app=QtWidgets.QApplication()dialog=EditorWindow(stage=stage)dialog.resize(600,600)dialog.show()app.exec_()Or if you have a runningQApplicationinstance (e.g. when inside Maya):frompxrimportUsdfromusd_qtpy.editorimportEditorWindowfilepath="/path/to/file.usd"stage=Usd.Stage.Open(filepath)dialog=EditorWindow(stage=stage)dialog.resize(600,600)dialog.show()Why not Luma Picture'susd-qt?UnlikeLuma Pictures'susd-qtthis repository tries to be easily
redistributable by avoiding the need for extra C++ dependencies and solely
use the USD Python API. This will keep the build matrix simpler but does mean
the repository is not - by design - built for highly optimized large scale
applications. Nonetheless, the intent is still to be able to carry average VFX
scenes for debugging.installfrom pippython -m pip install usd-qtpyfrom repopython -m pip install git+https://github.com/BigRoy/usd-qtpy.git@mainDependenciesThe Viewer utilities are basically usingusdviewqwhich may or may not
be included in your build. This also requiresPyOpenGL. However, the other
tools do not and are intended to rely solely on USD core and Qt itself.qtpyusd-core (when not using your own usd builds, install with[usd])PyOpenGL (needed for usd viewport, install with[usdview]; you will still need use a customusdbuild yourself forpxr.Usdviewqdependency)
Package: usdt
Python libusdt bindingsFree software: BSD licenseDocumentation:http://usdt.readthedocs.org.FeaturesCreate USDT providers and probesFire probesFallback modeLogging ModuleSimple Function Boundary Tracer (FBT) decoratorHistory0.1.4 (2016-12-29)Support for CC environment variable at build time0.1.3 (2015-03-22)Python 3 Support0.1.2dev (2014-05-27)Log handler“fbt” decoratortests0.1.1dev (2014-05-27)Metadata fixes0.1.0dev (2014-05-22)First release on PyPI.
Package: use
UseA Library For Dynamically Importing Modules. Inspired ByNode.jsrequireFunction.InstallationUse can be installed using Pip by the command:-[sudo] pip install useUsageIf the file structure is like:-test/
    	test/
    		__init__.py
    		test.py
    		utils/
    			__init__.py
    			utils.pyIf you want to importtest/test/__init__.pyin the filetest/test/utils/utils.pyit is not possible, but usingUseyou can do it by putting the following snippet intest/test/utils/utils.pyfile.from use import use

    initFile = use("./../__init__")License Under TheMITLicenseA Project ByShardul Nalegave