Saravanan666/pypi-org huggingface dataset:
Package: counterfit-shims-picamera
CounterFit Shims - PicameraShims for the Picamera to read from a virtual cameraSee thePicamera Docsfor the API documentation.Getting startedTo use these shims, you will need to installCounterFitand have it running, with the appropriate camera hardware created. Create the camera with a name ofPicamera.Install this package from pip:pipinstallcounterfit-shims-picameraImport Picamera using thecounterfit_shims_picamerapackage instead of thepicamerapackage, as well as importing theCounterFitConnectionfrom thecounterfit_shims_grove.counterfit_connectionmodule:fromcounterfit_connectionimportCounterFitConnectionimportcounterfit_shims_picameraConfigure the connection to the CounterFit app. Change the hostname and port to where you are running it:CounterFitConnection.init('127.0.0.1',5000)Write your Picamers code as usual.For example, to capture an image as a JPEG:camera=PiCamera()image=io.BytesIO()camera.capture(image,'jpeg')
Package: counterfit-shims-rpi-vl53l0x
CounterFit Shims - VL53L0X distance sensorShims for the Rpi-VL53L0X distance sensor.See theRpi-VL53L0X Docsfor the API documentation.Getting startedTo use these shims, you will need to installCounterFitand have it running, with the appropriate hardware created.Install this package from pip:pipinstallcounterfit-shims-rpi-vl53l0xImport VL53L0X using thecounterfit_shims_rpi-vl53l0xpackage instead of therpi-vl53l0xpackage, as well as importing theCounterFitConnectionfrom thecounterfit_connectionmodule:fromcounterfit_connectionimportCounterFitConnectionfromcounterfit_shims_rpi_vl53l0x.vl53l0ximportVL53L0XConfigure the connection to the CounterFit app. Change the hostname and port to where you are running it:CounterFitConnection.init('127.0.0.1',5000)Write your VL53L0X code as usual, setting the I2C address to match the one you set in the CounterFit app.For example, create a distance sensor on port0x29:distance_sensor=VL53L0X(0x29)
Package: counterfit-shims-seeed-python-dht
CounterFit Shims - GroveShims for the Seeed Grove DHT digital temperature and humidity sensor to use with theCounterFit virtual IoT device app.See theSeeed Python DHT Docsfor the API documentation.Getting startedTo use these shims, you will need to installCounterFitand have it running, with the appropriate hardware created.Install this package from pip:pipinstallcounterfit-shims-seeed-python-dhtImport the Grove modules as normal, but using thecounterfit_shims_seeed_python_dhtpackage instead of theseeed-python-dhtpackage, as well as importing theCounterFitConnectionfrom thecounterfit_shims_grove.counterfit_connectionmodule:fromcounterfit_connectionimportCounterFitConnectionfromcounterfit_shims_seeed_python_dhtimportDHTConfigure the connection to the CounterFit app. Change the hostname and port to where you are running it:CounterFitConnection.init('127.0.0.1',5000)Write your Grove code as usual, setting the pins to match the ones you set in the CounterFit app. To keep to the interface that theDHTspecifies, only one pin can be passed to the constructor. The sensor assumes the pin given is for the humidity sensor, and the temperature is on the next pin.For example, create a humidity sensor on pin 1, and a temperature sensor on pin 2. Then pass 1 to theDHTconstructor.sensor=.DHT("11",1)humi,temp=sensor.read()
Package: counterfit-shims-seeed-python-si114x
CounterFit Shims - GroveShims for the Seeed Grove SI114X sunlight sensor to use with theCounterFit virtual IoT device app.See theSeeed Python SI114X Docsfor the API documentation.Getting startedTo use these shims, you will need to installCounterFitand have it running, with the appropriate hardware created.Install this package from pip:pipinstallcounterfit-shims-seeed-python-si114xImport the Grove modules as normal, but using thecounterfit_shims_seeed_python_si114xpackage instead of theseeed-python-si114xpackage, as well as importing theCounterFitConnectionfrom thecounterfit_shims_grove.counterfit_connectionmodule:fromcounterfit_connectionimportCounterFitConnectionfromcounterfit_shims_seeed_python_si114ximportsi114xConfigure the connection to the CounterFit app. Change the hostname and port to where you are running it:CounterFitConnection.init('127.0.0.1',5000)Write your Grove code as usual. The default assumes you hve a light sensor on pin 0, IR on pin 1 and UV on pin 2. You can change these passing additional arguments to the initsensor=.si114x()light=sensor.ReadVisible()ir=sensor.ReadIR()uv=sensor.ReadUV()
Package: counterfit-shims-serial
CounterFit Shims - PySerialShims for the PySerial to read sensors that use a virtual serial portSee thePySerial Docsfor the API documentation.Getting startedTo use these shims, you will need to installCounterFitand have it running, with the appropriate hardware created.Install this package from pip:pipinstallcounterfit-shims-serialImport PySerial using thecounterfit_shims_serialpackage instead of theserialpackage, as well as importing theCounterFitConnectionfrom thecounterfit_shims_grove.counterfit_connectionmodule:fromcounterfit_connectionimportCounterFitConnectionimportcounterfit_shims_serialConfigure the connection to the CounterFit app. Change the hostname and port to where you are running it:CounterFitConnection.init('127.0.0.1',5000)Write your PySerial code as usual, setting the port to match the one you set in the CounterFit app.For example, create a UART sensor on port/dev/ttyAMA0:serial=counterfit_shims_serial.Serial('/dev/ttyAMA0',9600,timeout=1)The baud and timeout settings are ignored.
Package: counterix
counterixA small toolkit to generate count-based PPMI-weighed SVD Distributional Semantic Models.Installpipinstallcounterixor, after a git clone:python3setup.pyinstallUseGenerateTo generate a raw count matrix from a tokenized corpus, run:counterixgenerate\--corpus/abs/path/to/corpus/txt/file\--min-countfrequency_threshold\--win-sizewindow_sizeIf the--outputparameter is not set, the output files will be saved to the corpus directory.WeighTo weigh a raw count model with PPMI, run:counterix weigh --model /abs/path/to/raw/count/npz/modelSVDTo apply SVD on a PPMI-weighed model, with k=10000, run:counterix svd \
  --model /abs/path/to/ppmi/npz/model \
  --dim singular_vectors_final_dimTo control the number of threads used during SVD, run counterix withenv OMP_NUM_THREADS=1
Package: counter_pack
Counter_packThis is a simple package for counting unique characters in a string or text file.Software requirementsPython 3.8+
pipInstallationCounter_pack is available on PyPI and can be installed with pip.pip install counter_packLicenseCounter_pack is distributed under the terms of the MIT license.Running Counter_pack commands with CLIusage:your_code.py [-h] [-s STRING] [-f FILE]optional arguments:-h,  --helpshow help message and exit-s STRING,  --string STRINGsome string for count unique characters-f FILE,  --file FILEsome file for count unique charactersfor example:python main.py -f 'tests/test.txt'orpython main.py -s 'another string'main.pyfrom counter_paсk import counter


if __name__ == '__main__':
    print(counter.main())
Package: counterparty-cli
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[![Slack Status](http://slack.counterparty.io/badge.svg)](http://slack.counterparty.io)counterparty-cliis a command line interface for [counterparty-lib](https://github.com/CounterpartyXCP/counterparty-lib).For installation and configuration instructions, see the [counterparty-lib README](https://github.com/CounterpartyXCP/counterparty-lib), as well as the [Official Project Documentation](http://counterparty.io/docs/).
Package: counterprop-prototorch
AboutThis is the implementation of Counterpropagation Models based onPrototorchbySICIM.How to install and useTo install use:pip install counterprop-prototorchThis will install the package, as well as the needed additions.For a use-case scenario please have a look a theexample.pyfile.Contactbugs, fixes, ideas etc. can be send totrebuhcsynnor@gmail.com
Package: counter-robots
Library for COUNTER-compliant detection of machines and robots.The purpose behind COUNTER is to enable comparable usage statistics by only
reporting genuine user-driven usage for repositories. The purpose behind Code
of Practice for Research Data is to split genuine COUNTER user-driven usage
into human- and machine-based access.This Python library implements a tiny API to check if a given user agent
string from a browser is considered a robot/crawler/spider or a machine
according to theCode of Practice for Research Data[1]  as well as theCOUNTER Code of Practice.The library depends on official lists published by both projects. You can see
the lists on:Making Data CountCOUNTERChangesVersion 2018.6 (released on 2018-06-15)Initial public release.
Package: countersign
Countersigncountersign - A signal or password given in reply to a soldier on guard.Countersign is a light-weight python library for generating highly customizable passwords.InstallationRequires python 3.6 or abovepip install countersignUsagePasswordsIn countersign terms passwords are simply random character strings that are slightly configurable.To generate a random password, import thepassword()function.fromcountersign.passwordimportpassword# By default, generates some random password using 'string.printable' characters of length 8generated_password=password()Configuration can be accomplished as seen below.fromcountersign.passwordimportpassword# Generates password of all unique characters using# the characters '12345' with a length of 2generated_password=password(characters='12345',length=2,unique=True)If you require multiple random passwords to be generated at a time, use thepasswords()function. The function returns a python generator that yields an unlimited count of random passwords and can bee configured the same as thepassword()function.fromcountersign.passwordimportpasswords# Returns a python generator capable of producing passwords with default characteristicspassword_generator=passwords()PassphrasesPassphrases are more structured passwords following certain configured patterns and even using given world dictionaries. Completely random passwords are great but sometimes a more human memorable pattern is more ideal.Similar to passwords, passphrases can be constructed and configured the same way.fromcountersign.passphraseimportpassphrasewords=['Test','Word','More','Words']# Passphrase using the words [Test, Word, More, Words] with no digit generation strategy. By default the passphrase consists of three given words.# Produces something like: WordMoreTestgenerated_passphrase=passphrase(words)Passphrases can also be configured with a digit generation strategy which tells the passphrase generator to inject digit groups wherever specified.fromcountersign.passphraseimportpassphrase,DigitGenerationStrategy,DigitPlacementStrategywords=['Test','Word','More','Words']strategy=DigitGenerationStrategy(digit_count=3,placement=DigitPlacementStrategy.AFTER)# Produces something like: WordWordsTest947generated_passphrase=passphrase(words,digit_strategy=strategy)Digit placement strategies include:BEFORE123TestWordsAFTERTestWords123BEFORE_AND_AFTER123TestWords123IN_BETWEENTest123WordsAROUND123Test123Words123
Package: counter-solo-elements
HelloThis package help you count all element in string, and print element quantity which are repeated only 1 time.If you need to count the number of elements which equal one in string, write in terminal:
python your_file -s 'your_string', or --string 'your_string'If you need to count the number of elements which equal one in file, write in terminal:
python your_file -f 'path_to_your_file', or --file 'path_to_your_file'P.S. "Outputs the result of each line in the file.If the file is not in the current folder, you must specify its full path"
Package: counter-stats
counter-statsA python module for keeping stats.This repo has been split-off from the 'aacommons' repo at:https://github.com/tbastian66/aacommonsInstallationpip install counter-statsUsageCounterEventimportjsonfromtimeimportsleep,timefromstatsimportCounterEventstats=CounterEvent(name="ThingsCounter")# the following loops automatically creates 3 counts split up into 2 different# groups.foriinrange(0,10):stats.increment('counter1','group1',increment=1)stats.increment('counter1','group1',increment=1)stats.increment('counter2','group1',increment=1)stats.increment('counter2','group1',increment=1)stats.increment('counter3','group2',increment=1)sleep(1)# Get a snapshot of the counters and the group aggregates# stop_ts needs to be specified as you might have multiple CounterEvent() objects# that you want to use the same stop timestampstop_ts=time()# Gets a dictionary (munch)stats_snapshot=stats.snapshot(update_stats=True,stop_ts=stop_ts)# pretty print the dictprint(json.dumps(stats_snapshot,indent=4)){"name":"ThingsCounter","original_start_ts":1654496276.5894873,"start_ts":1654496276.5894873,"stop_ts":1654496286.59915,"uptime":10.01,"time_active":10.01,"original_start_time_str":"2022-06-06 14:17:56.589487+0800 (HKT)","start_time_str":"2022-06-06 14:17:56.589487+0800 (HKT)","stop_time_str":"2022-06-06 14:18:06.599150+0800 (HKT)","topic_counts":{"group1":{"counter1":20,"counter2":20},"group2":{"counter3":10}},"group_counts":{"group1":40,"group2":10},"topic_latest_ts":{"group1":{"counter1":1654496285.5980132,"counter2":1654496285.5980191},"group2":{"counter3":1654496285.5980208}},"group_latest_ts":{"group1":1654496285.5980191,"group2":1654496285.5980208},"rates":{"number_of_topics":{"group1":2,"group2":1},"interval":10.01,"number_of_groups":2,"topic_rates":{"group1":{"counter1":1.998,"counter2":1.998},"group2":{"counter3":0.999}},"group_rates":{"group1":3.996,"group2":0.999},"topic_percentage":{"group1":{"counter1":0.5,"counter2":0.5},"group2":{"counter3":1.0}}}}CounterTimeCounterTrio
Package: countertype
A small collection to index objects using tags. Type-safe.InstallationpipinstallcountertypeUsageYou can add any hashable items into the collection using tags. Let’s add
two string values,"ev1", and"ev2". Each element must have at
least one tag, calledid, that identifies that object. Ids must be
unique inside the collection, and can’t be changed later. Regular tags
can be updated.fromcountertypeimportCounterTypect:CounterType[str]=CounterType()ct.put("ev1",id="1",state="RUNNING",parent="ev_parent",)ct.put("ev2",id="2",state="STOPPED",parent="ev_parent",)To find elements we can search by any of the defined tags, using the**kw:Let’s find all the elements with thestatetag set to"STOPPED":ct.find_all(state="STOPPED")This will return an iterable with all the matching elements.To find a single element, we can fetch it again, as anOptional:ct.find(state="STOPPED")Multiple tags are also supported, and will intersect the potential
matches:ct.find(state="STOPPED",parent_id="123")Updating tags for existing items is also possible, by using the item id.
Of course, multiple tags could be updated.ct.update(id="1",state="PROCESSING")To remove an element from the collection, pass itsidin theremovefunction:ct.remove(id="1")
Package: counterweight
CounterweightCounterweight is an experimental text user interface (TUI) framework for Python,
inspired byReactandTailwind CSS.A TUI application built with Counterweight is a tree of declarativecomponents,
each of which represents some piece of the UI by bundling together
a visualelementalong with itsstateand how that state should change due toeventslikeuser input.As an application author,
you define the components and their relationships as a tree of Python functions.
You usehooksto manage state and side effects,
andstylesto change how the elements look.Counterweight takes this declarative representation of the UI andrendersit to the terminal,
updating the UI when state changes in response to user input or side effects
(by calling your function tree).InstallationCounterweight is availableon PyPI:pipinstallcounterweight
Package: countess
CountESS 0.0.42This is CountESS, a modular, Python 3 reimplementation of Enrich2.LicenseBSD 3-clause.  SeeLICENSE.txtSource code is available athttps://github.com/CountESS-Project/CountESSand contributions are welcomed.InstallingThe latest version of CountESS can be installed from pypi:pip install CountESSFor other options, see the documentation below ...DocumentationCountESS DocumentationEnquiriesPlease contact Alan Rubinalan@rubin.id.aufor further information or
Nick Moorenick@zoic.orgfor code-related matters.
Package: countess-minimap2
CountESS MiniMap2 Plugin v0.0.10This is a plugin to allowMiniMap2to be used from withinCountESSThis might be useful, but it also stands as a handy example of how to write
a CountESS plugin which wraps an external Python library.ParametersOutput Column PrefixThe default output column prefix ismmbut this can be changed.Ref FA / Ref MMIA local FASTA or MMI file to look up the sequences in.
Acceptable formats are MMI or FASTA, optionally gzipped.Req SequenceAlternatively, enter a single reference DNA sequence directly here.PresetSeeminimap2 Preset OptionsMinimum Match LengthReject matches with an overall length (mm_r_en - mm_r_st) less than this.Drop UnmatchedRows with no matches will be dropped.Output Columnsmm_ctgThe matched location.mm_r_st,mm_r_enStart and end positions of the matched sequence.mm_strand1if query/target on the same strand.-1if opposite.0if unknown.mm_cigarCIGAR string expressing the differences within the match.  Note that not all differences are
encoded in the CIGAR string, for a more detailed description of what variations are present see
the next two fields.mm_csA difference string as documented in theMinimap2 Output Formatsection.mm_hgvsThe difference string translated into HGVS format relative to the matched location.LicenseBSD 3-clause.  SeeLICENSE.txt
Package: count-files
English|Português|Русский|УкраїнськаCount FilesA command-line interface (CLI) utility written in Python to help you
counting or searching files with a specific extension, files without an extension or all files regardless of the extension, in the specified directory.DocumentationEnglishPortuguêsРусскийУкраїнськаDependenciesTo run this application, you need to have a working Python 3.6+ installation.InstallationOn regular desktop operating systemsCount Files is a platform-independent application that run in Python and can be easily  installed usingpip:pip3 install count-filesIf you are interested in the current development version, you can simply clone this git repository and install it usingpip3 install -e. Please notice, however, that only released versions are expected to be stable and usable. The development code is often unstable or buggy, for the simple reason that it is a work in progress.On iPhone or iPad (in Pythonista 3 for iOS)It may also be used on iOS (iPhone/iPad) using theStaShcommand-line in the Pythonista 3 app. Please seedocumentationfor further instructions.How to useTo check the list of available options and their usage, you just need to use
one of the following commands:count-files -hcount-files --helpBy default, the program counts or searches for files recursively in current working directory and all of its subdirectories. Any hidden files or folders will be ignored.
The names of extensions are case insensitive by default. The results foriniandINIwill be the same.Optionally, you can pass it a path to the directory to scan, choose non-recursive counting or searching, process the file extensions with case-sensitive mode and enable search or counting in hidden files and folders.You can get additional information about the size of each found file and see a short preview for text files.See more aboutCLI arguments.The most simple form of usage is to type a simple command in the shell, without
any arguments. It will display a table showing the frequency for
each file extension (e.g.: .txt, .py, .html, .css) and the total number of
files found.count-filesAnother main feature of this application consists in searching files by a
given extension, which presents to the user a list of all found files paths.count-files -fe txt [path]count-files --file-extension txt [path]You can also count the total number of files with a certain extension, without listing them.count-files -t py [path]count-files --total py [path]For information about files without an extension, specify a single dot as the extension name.count-files -fe . [path]count-files --file-extension . [path]count-files -t . [path]count-files --total . [path]If you need to list or to count all the files, regardless of the extension, specify two dots as the extension name.count-files -fe .. [path]count-files --file-extension .. [path]count-files -t .. [path]count-files --total .. [path]Did you find a bug or do you have a suggestion?Please, open a new issue or a pull request to therepository.
Package: countingworkspace
CountingWorkspaceVery simple python package to create very simple counting experiment RooFit workspaces.The statistical model describes the migration of events from truth-bins (e.g. processes) and reco-category. The implemented likelihood is a product of Poissonian distributions:The product is over all the reconstructed categories. The number of expected events in each category is:The matrix ε implements the efficiencies and the migrations. It's matrix elements are the conditional probability to be selected and reconstructed in category-c for a process-p: P[c|p].
The generated number of events for each process can be parametrized in any way. A popular one in hep is:here the generated number of events are equal to the product of the luminosity (the overall normalization), its cross section and the signal strength (which is the free parameter in the fit). The background is added on top of that.Here a simple example:importROOTfromcountingworkspaceimport*NAMES_PROC=['proc1','proc2']NCATEGORIES=3EFFICIENCIES=[[0.3,0.1],[0.5,0.4],[0.2,0.2]]EXPECTED_BKG_CAT=[100,30,10]LUMI=100.# first create the parameters needed for the parametrization. The luminosityws=ROOT.RooWorkspace()ws.factory('lumi[%f]'%LUMI)# and the cross sections:xsections=create_variables(ws,'xsec_{proc}',# {proc} is an index, you can call as you preferbins=NAMES_PROC,# the namesvalues=[101.5,7.99])# the values of the cross sectionscreate_workspace(NCATEGORIES,NAMES_PROC,efficiencies=EFFICIENCIES,nexpected_bkg_cat=EXPECTED_BKG_CAT,expression_nsignal_gen='prod:nsignal_gen_proc{proc}(mu_{proc}[1, -4, 5], lumi, xsec_{proc})',ws=ws)It is also possible to add simple systematic uncertainties.There are some utilies to run toys.Look at theexamplesInstallationpip install countingworkspace
Package: count-in-list
count_in_listInstall the packagepip install count_in_listFrom Python or IPythonfrom count_in_list import count_in_list
l = ["gfg", "dsa", "gfg"]
count = count_in_list(l, "gfg")
print(count)Packages required to generate packages.pip install setuptools wheelGenerate build, dist, and test_package.egg-info.python setup.py sdist bdist_wheelPackage required to deploy.pip install twineUpload the packagetwine upload --repository pypi dist/*