Saravanan666/pypi-org huggingface dataset:
Package: classipyd
# classipydRecords a session while interactively allowing you to specify certain areas you would like to be redacted in the final video.## How-To:1. Run classipyd.py- When the program is run, you will havd ~2 seconds before the recording begins.2. Capture/Flag desired areas on the screen.- See section on 'Controls' for more information.3. `ctrl+space` will end the screen capture and beging the processing.4. Run `<captured_{date}_{n}/clip.mp4>` to see your video.## ControlsDuring the screenshot loop, you have the following controls:- Pause/Unpause: `ctrl+shift`- End Loop (begin processing): `ctrl+space`Only while paused can you do the following commands to redact images:- To Redact an image:1. Move cursor to top-left of desired area, press `esc`2. Move cursor to bottom-right of desired area, press `esc`- To Flag an image and Redact relative to the flag:1. Press `<tic button>` to begin 'flagging mode'2. Move cursor to top-left of desired area to flag, press `esc`3. Move cursor to bottom-right of desired area to flag, press `esc`4. Move cursor to top-left of desired area to redact, press `esc`5. Move cursor to bottom-right of desired area to redact, press `esc`
Package: classipypi
classipypiNeural PyPI package 'Trove classifier' selector.MotivationPyPI 'Trove classifiers' are used insetup.pyorpyproject.tomlfiles to convey to
PyPI users the conditions under which the package has been used or tested,
the topic of the software, etc. such asOperating System :: POSIX :: LinuxTopic :: Software Development :: LibrariesIntended Audience :: DevelopersInstallationpipinstallclassipypiUsagelsusage: classipypi ls [-h] [-i [INCLUDE ...]] [-e [EXCLUDE ...]] [-t] [-g]

Configure input filtering and output display.

options:
  -h, --help            show this help message and exit
  -i [INCLUDE ...], --include [INCLUDE ...]
                        Strings to filter tags for.
                        (default: [])
  -e [EXCLUDE ...], --exclude [EXCLUDE ...]
                        Strings to filter tags against.
                        (default: [])
  -t, --toml            Whether to display the tags as a TOML-compatible list.
                        (default: False)
  -g, --group           Whether to display tags grouped by section.
                        (default: False)For example, to show theDevelopment Statustags (but skip any with "Alpha" in):classipypils-i"Development Status"-eAlphaDevelopment Status :: 1 - Planning
Development Status :: 4 - Beta
Development Status :: 5 - Production/Stable
Development Status :: 6 - Mature
Development Status :: 7 - InactiveselUse-qor--queryto select classifiers based on a description:classipypi-q"3D scientific visualisation tool"usage: classipypi sel [-h] [-q QUERY] [-s SOURCE] [-t] [-g]

Configure source and y.

options:
  -h, --help            show this help message and exit
  -q QUERY, --query QUERY
                        The query string.
                        (default: None)
  -s SOURCE, --source SOURCE
                        The source code.
                        (default: None)
  -t, --toml            Whether to display the tags as a TOML-compatible list.
                        (default: False)
  -g, --group           Whether to display tags grouped by section.
                        (default: False)Use-sor--sourceto select classifiers based on source code:classipypi--source./local_package_repo/DevelopmentTo set up pre-commit hooks (to keep the CI bot happy) runpre-commit install-hooksso all git
commits trigger the pre-commit checks. I useConventional Commits.
This runsblack,flake8,autopep8,pyupgrade, etc.To set up a dev env, I first create a new conda environment and use it in PDM withwhich python > .pdm-python.
To usevirtualenvenvironment instead of conda, skip that. Runpdm installand a.venvwill be created if no
Python binary path is found in.pdm-python.To run tests, runpdm run python -m pytestand the PDM environment will be used to run the test suite.
Package: classiq
The Classiq Quantum Algorithm Design platform helps teams build sophisticated quantum circuits that could not be designed otherwiseClassiq does this by synthesizing high-level functional models into optimized quantum
circuits, taking into account the constraints that are important to the designer.
Furthermore, Classiq is able to generate circuits for practically any universal gate-based
quantum computer and is compatible with most quantum cloud providers.For information about Classiq's SDK, see thedocumentation.LicenseSeelicense.
Package: classixclustering
CLASSIX is a fast and explainable clustering algorithm based on sorting. Here are a few highlights:Ability to cluster low and high-dimensional data of arbitrary shape efficiently.Ability to detect and deal with outliers in the data.Ability to provide textual explanations for the generated clusters.Full reproducibility of all tests in the accompanying paper.Support of Cython compilation.CLASSIXis a contrived acronym of CLustering by Aggregation with Sorting-based Indexing and the letter X for explainability. CLASSIX clustering consists of two phases, namely a greedy aggregation phase of the sorted data into groups of nearby data points, followed by a merging phase of groups into clusters. The algorithm is controlled by two parameters, namely the distance parameter radius for the group aggregation and a minPts parameter controlling the minimal cluster size.Installing and exampleCLASSIX has the following dependencies for its clustering functionality:cythonnumpyscipyrequestsand requires the following packages for data visualization:matplotlibpandasTo install the current CLASSIX release via PIP use:pipinstallclassixclusteringTo check the CLASSIX installation you can use:python-mpipshowclassixclusteringDownload the repository via:gitclonehttps://github.com/nla-group/classix.gitExample usage:fromsklearnimportdatasetsfromclassiximportCLASSIX# Generate synthetic dataX,y=datasets.make_blobs(n_samples=2000000,centers=4,n_features=10,random_state=1)# Employ CLASSIX clusteringclx=CLASSIX(sorting='pca',verbose=1)clx.fit(X)Citation@techreport{CG22b,title={Fast and explainable clustering based on sorting},author={Chen, Xinye and G\"{u}ttel, Stefan},year={2022},number={arXiv:2202.01456},pages={25},institution={The University of Manchester},address={UK},type={arXiv EPrint},url={https://arxiv.org/abs/2202.01456}}
Package: classlogging
classloggingClass-based logging facility.InstallationpipinstallclassloggingUsage exampleimportclassloggingclassMyClass(classlogging.LoggerMixin):deftest_log_value(self,value:str)->None:self.logger.debug(f"Got value:{value}")if__name__=="__main__":classlogging.configure_logging(level=classlogging.LogLevel.DEBUG)MyClass().test_log_value("Foo")# Writes to stderr:# 2022-01-01 12:34:56,789 DEBUG [__main__.MyClass] Got value: Foo
Package: classmail
ClassMailClassmailMail classification Python library optimized for french mails in the field of insurance. Classmail was created to automate mail classification workflow in quick experiments. Developped during my internship atCovéa.Classmail provides:Data visualisation:For quick data analysis, based on matplotlib and seabornMails preprocessing (cleaning):Optimised for inasurrance purposes, with prebuilt regular expressions (in french). This configuration file can be adapted for other languages or purposes.Deep learning model creation (for classification):Simple interface to build Pytorch models quickly based onFlairnlp library.Model analysis and explainerSimple interface with prebuilt seaborn graphs and model explainer based onLime.Quick StartRequirements and InstallationThe project is based on Python 3.7+.
If you do not have Python 3.6, install it first.
Then, in your favorite virtual environment, simply do:pip install classmailExample UsageLet's run named entity recognition (NER) over an example sentence. All you need to do is make aSentence, load
a pre-trained model and use it to predict tags for the sentence:Data analysisimportclassmail.data_visualisation.data_visualisationasdv# show class balancing graphdv.plot_class_balancing(data,col_text='header_body',col_label="COMPETENCE",title="Catégories des mails")#show most frequent bigramsdv.plot_word_frequencies(data['message'],ngram=2,words_nb=20)#plot a wordcloud with most frequent terms in bodydv.plot_wordcloud(data['body'])Cleaningfromclassmail.nlp.cleaningimportclean_mail#create a new column in data ("clean_text") with preprocessed header and bodydata=clean_mail(data,"body","header")Model creation and trainingfromclassmail.classification.trainerimportTrainertrainer=Trainer()#generate train / test / val csv filestrainer.prepare_data(data,col_text="clean_text",col_label="COMPETENCE",train_size=0.7,val_size=0.15,test_size=0.15)#create a new column in data ("clean_text") with preprocessed header and bodydata=clean_mail(data,"body","header")#train a model with default parameterstrainer.train_model(model_name="default_model")Model predictions, evaluation and explainationfromclassmail.classification.modelimportModel#load our model, saved in "ressources" foldermodel=Model("ressources\\model_default")#predictionspredictions=model.get_predictions(X_test)#confusion matrixmodel.plot_confusion_matrix(pred_labels=predictions,true_labels=y_test)#explain one exemple at index 110model.visualize_one_ex(X_test,y_test,index=110,num_features=6)#compute most discriminants words in each categorysorted_contributions=model.get_statistical_explanation(X_test,["class 1","class 2","class 3"]sample_size=15)#plot them for first classmodel.plot_discriminant_words(sorted_contributions,"class 1",nb_words=15)TutorialHere is a more complete usage exemple for the mail classification task. Data cannot be provided for legislation and privacy matters.Tutorial : General workflow
Package: classmemo
MemoizerAPIAMemoizercan be used as a factory for creating objects of a certain class.
It exposes a constructor and 2 methodsmemoizer = Memoizer(SomeClass)memoizer.get(*args, **kwargs)Ifmemoizerhas never seen the given arguments, it createsSomeClass(*args, **kwargs)and returns it.Ifmemoizerhas seen the given arguments before, it returns the same instance that it returned the last time.memoizer.forget(*args, **kwargs)Makesmemoizerforget that is has seen the given arguments.UsageThe original application was for aMeasurementQueueclass that processes incoming sensor data from many sensors,
where the sensor ID's were not known ahead of time:queue_manager=Memoizer(MeasurementQueue)forsensor_id,datainevent_stream():queue=queue_manager.get(sensor_id)queue.push(data)When the first measurement comes in for a given sensor_id, a newMeasurementQueuewill be created and returned for
that sensor. On subsequent events with the same sensor ID, the sameMeasurementQueueinstance will be
used to process the data.Installationpip install classmemoThe only dependencies are Python 3.6 and the frozendict package.ContributingEnsure the tests pass:pytest .Format the code withblack . -t py36
Package: classnames
Python classnamesUtility to create CSS class strings from a multitude of values without poking around with string templates and lengthy logic.This project is a Python porting of the JS libraryclassnamesShow me the code!pipinstallclassnamesfromclassnamesimportclass_names# render_button() -> "btn btn--red"# render_button(rounded=True) -> "btn btn--rounded btn--red"defrender_button(rounded:bool=False,color="red"):class_names("btn",f"btn--{color}",{"btn--rounded":rounded})Documentation:https://lucafaggianelli.github.io/python-classnamesGitHub:https://github.com/lucafaggianelli/python-classnamesPyPI:https://pypi.org/project/python-classnames/FeaturesTODOCreditsThis package was created withCookiecutterand thewaynerv/cookiecutter-pypackageproject template.ChangelogAll notable changes to this project will be documented in this file.The format is based onKeep a Changelog,
and this project adheres toSemantic Versioning.Unreleased0.1.0- 2022-11-04initial release
Package: class-namespaces
Well-behaved class namespacing in Python. Inspired byhttps://erezsh.wordpress.com/2008/06/27/namespaces-lets-do-more-of-those-python-hackery/Basic UsageExample code:import class_namespaces as cn

class MyCls(cn.Namespaceable):

    var = 1

    with cn.Namespace() as my_ns:
        var = 2

assert MyCls.var == 1
assert MyCls.my_ns.var == 2Other things that work:Descriptors (methods, classmethods, staticmethods, properties, custom descriptors)super()Prepopulating Namespaces. The constructor takes the same arguments as a dict.abstractmethods. See the compat module.Things that don’t work:Various ways of putting a Namespace in a Namespace that I didn’t see an obvious way to handle. In particular…There is no way to put an established namespace directly into another namespace.Some pytest constructs behave weirdly inside the class definitions. Hopefully, this doesn’t matter to anyone not writing tests for the package.No way to have instance Namespaces on non-hashable types, or subclasses of some built-in types, particularly tuple. Try using data descriptors instead.Things that might work:New namespace features in Python 3.6. Current testing is spotty.Combining with other metaclasses. Unfortunately, the current setup is somewhat brittle. It may be necessary to experiment with the ordering of bases.
Package: classofvector2d
###################################
#######     HELLO WORLD     #######
###################################This README.md file was generated on 2020/12/05
by MEHMET R. IRMAK###################
GENERAL INFORMATION
###################This python library uses position vector handling and
to help compute physical vectorial motion in mechanical systems. In addition,
this is prepared to help graphics coding for Python.Author InformationFirst Author Contact Information
Name: MEHMET RIFAT IRMAK
Address: Ankara/TURKEY
e-mail:mehmet.r.river@gmail.com####################
DATA & FILE OVERVIEW
####################Directory of Files:
A. Filename:init.pyShort description: the file containing functions that vector class,              which vector calculations for location, physical movement and forceB. Filename: README.mdShort description: this fileC. Filename: CHANGELOG.txtShort description: the file with update notes includedD. Filename: LICENCE.txtShort description: the file with license informationE. Filename: setup.pyShort description: the setup file##########################
METHODOLOGICAL INFORMATION
##########################Software-specific information:Name: classofvector2d
Version: 1.0.4
System Requirements: Windows 7 or higher, 64-bit
•	Mac OS X 10.11 or higher, 64-bit
•	Linux: RHEL 6/7, 64-bit
•	x86 64-bit CPU (Intel / AMD architecture)
•	4 GB RAM
•	5 GB free disk spaceOpen Source? (Y/N): YSource Repository URL:
Developer:Mehmet R. IrmakSpecific information:Manufacturer: Mehmet R. Irmak
Model: 1.0.4Definitions:Vector(x, y):to create a vector.square():to calculate the square of the length.length():to calculate the length.clone():to clone.negative():to negative.normalize():to normalize.addition(vector):Creating a new vector by adding the vector given in the parameter to the vector.increment(vector):by summing with the vector in the parameter
changing x and y values of the vector.subtract(vector):Creating a new vector by subtracting the vector given in the parameter.decrement(vector):by subtracting with the vector in the parameter
changing x and y values of the vector.multiply(k): a new vector by multiplying the vector by a scalar number creation.scale(k):by multiplying with a scalar number in the parameter
changing x and y values of the vector.scalerProduct(vector):scalar multiplying the vector by the vector in the parameter.addMultbyScaler(vector, k):creating a new vector by multiplying the vector in the parameter with the scalar number and adding it to the other vector.Vector.distance(vector1, vector2):distance between two vectors.Vector.angleRad(vector1, vector2):return the angle of between two vectors radians.Vector.angleDeg(vector1, vector2): return the angle of between two vectors degrees.Random(min, max):to create a random number between two integer numbers
Package: classomfier
ClasSOMfier: A neural network for cluster analysis and detection of lattice defectsKohonen network that classifies atoms according to their environment.
Unsupervised training using a 1-dimensional Self Organizing Map (SOM) in Fortran.Created by Javier F. Troncoso, October 2020.
Contact:javierfdeztroncoso@gmail.comInstallation:Option 1 (preferred):
  Use pip for Python3:
      $ pip install classomfier

Option 2:
  Download the source code and build the package. Follow the instructions in file "install.sh". 

Not tested on MacOs X and Windows.Use:The network and its parameters can be initialized using the following command:
      >>from classomfier import ClasSOMfier
      >>nn=ClasSOMfier(6.43718,2,"dump1000.file")
  Only 3 parameters are necessary: characteristic length, number of clusters and input file.
  The number of epochs, the name of the output files and other parameters are optional. If the 
  number of atoms is large (>12000 approx), the option 'usenomatrix=True' has to be used. When
  this option is activated, the total running time increases considerably.
  The format of the input file is that provided by the dump command in LAMMPS:
      #compute         peratom all pe/atom
      #dump            dumpid2 all custom 1000 dump*.file id mass x y z c_peratom
  The first command calculates and stores the potential energy per atom.

  The network is trained using the following command:
      >>nn.execute()
  The final condigurations are written in ./data (default value) and can be easily read by Ovito. 
  These files includes xyz files for the atoms of each cluster and all atoms, and input files 
  with the set of input values for each cluster.


  The final configurations can be postprocessed to find subcategories inside a specific category:
      >>nn.postprocess_output()
  Afer this, the atoms of one of the clusters can be used to find subcategories:
      >>nn=ClasSOMfier(6.43718,2,"data/positions2.xyz",traininput="_trainset2.dat",useexisting=True)   
      >>nn.execute()
  Where "data/positions2.xyz" is the file containing the positions of the atoms in group 2 and 
  "_trainset2.dat" contains the description of the local environments of the atoms in that group. 
  As a result, these atoms will be classified and the final condigurations are written in 
  "./data/data" (default value).

  Input files in classomfier/test can be used as examples.Future Work:-Improve the performance for large systems.

In the case of doubts or problems, all questions are welcome. Open to new collaborations.
Package: classopt
Welcome to ClassOpt 👋Arguments parser with class for Python, inspired byStructOptInstallpipinstallclassoptUsageImportclassoptand define the Opt class with decorator.fromclassoptimportclassopt@classopt(default_long=True)classOpt:file:strcount:int=3numbers:list[int]flag:boolif__name__=="__main__":opt=Opt.from_args()print(opt)print(opt.file)Run with command line arguments.$pythonexample.py--fileexample.txt--numbers123--flag
Opt(file='example.txt',count=3,numbers=[1,2,3],flag=True)example.txtYou can specify most of the arguments toargparse.ArgumentParser.add_argumentinconfig(except name_or_flags).fromclassoptimportclassopt,config@classoptclassOpt:file:strcount:int=config(long=True)numbers:list=config(long=True,short=True,nargs="+",type=int)flag:bool=config(long=True,action="store_false")if__name__=="__main__":opt=Opt.from_args()print(opt)$pythonexample.pyexample.txt--count5-n123--flag
Opt(file='example.txt',count=5,numbers=[1,2,3],flag=False)Some details# `default_long=True` is equivalent to `config(long=True)' for all members# Similarly, you can do `default_short=True`@classopt(default_long=True)classOpt:# `long=False` overrides `default_long=True`file:str=config(long=False)# equivalent to `numbers: list = config(nargs="*", type=int)`# and `numbers: typing.List[int]`numbers:list[int]# equivalent to `flag: bool = config(action="store_true")`flag:boolOther WayYou can also define an argument parser by inheriting fromClassOpt.fromclassoptimportClassOpt,configclassOpt(ClassOpt):file:strcount:int=config(long=True)numbers:list[int]=config(long=True,short="-c")flag:bool=config(long=True)if__name__=="__main__":opt=Opt.from_args()print(opt)print(opt.file)Run with command line arguments.$pythonexample.pyexample.txt--count5-c123--flag
Opt(file='example.txt',count=5,numbers=[1,2,3],flag=True)example.txtThe inherited method does not support some features and may disappear in the future.
So we recommend the decorator method.Run testspoetryrunpytestAuthor👤moisutsuTwitter:@moisutsuGithub:@moisutsuShow your supportGive a ⭐️ if this project helped you!📝 LicenseCopyright © 2021moisutsu.This project isMITlicensed.This README was generated with ❤️ byreadme-md-generator
Package: classoptions
DescriptionImplement namespaced and inheritable metadata at the class level.Inspired on Meta classes from Django models and Django Rest Framework model serializers.Quick StartInstallationpip install classoptionsSimple inheritancefromclassoptionsimportClassOptionsMetaclassfromtypingimportAnyclassPizza(metaclass=ClassOptionsMetaclass):_meta:AnyclassDefaultMeta:client_can_modify=Truenotes=Nonecooking_temp=400cooking_time=1classHawaiianPizza(Pizza):ingredients=["cheese","ham","pineapple"]classMeta:notes="Do not judge the costumer."private_note="If planning a party, ask first if people like it."print("Hawaiian Pizza:")print("Ingredients:",", ".join(HawaiianPizza.ingredients))print("Cooking Temperature (F):",HawaiianPizza.cooking_temp)print("Cooking Time (hours):",HawaiianPizza.cooking_time)print("Client can modify:",HawaiianPizza._meta.client_can_modify)print("Notes:")print(HawaiianPizza._meta.notes)print("Private Note:")print(HawaiianPizza._meta.private_note)Outputs:Hawaiian Pizza:
Ingredients: cheese, ham, pineapple
Cooking Temperature (F): 400
Cooking Time (hours): 1
Client can modify: True
Notes:
Do not judge the costumer.
Private Note:
If planning a party, ask first if people like it.Multiple InheritanceWorks similar to python inheritance, except we don't need to explicitly inherit from the parent class.fromclassoptionsimportClassOptionsMetaclassfromtypingimportAnyclassA(metaclass=ClassOptionsMetaclass):_meta:AnyclassDefaultMeta:color="red"size=2hello="world"i_like_pizza=TrueclassB(A):classDefaultMeta:color="blue"size=3classC(B):classMeta:size=4# Specific to C onlyclassD(A):classDefaultMeta:color="black"hello="country"classE(D,C):classMeta:i_like_hawaiian_pizza="maybe"print("E custom meta")print("i_like_hawaiian_pizza:",E._meta.i_like_hawaiian_pizza)print("\nInherited from B")print("size:",E._meta.size)print("\nInherited from D")print("color:",E._meta.color)print("hello:",E._meta.hello)print("\nInherited from A")print("i_like_pizza:",E._meta.i_like_pizza)Outputs:E custom meta
i_like_hawaiian_pizza: maybe

Inherited from B
size: 3

Inherited from D
color: black
hello: country

Inherited from A
i_like_pizza: TrueUsing other class/attribute namesWithClassOptionsMetaclass.factoryyou can overwrite how you define default metadata, class specific metadata,
and how you access the result.fromclassoptionsimportClassOptionsMetaclassfromtypingimportAnyclassA(metaclass=ClassOptionsMetaclass.factory("Options","DefaultOptions","_options")):_options:AnyclassDefaultOptions:color="red"size=2cooking_temp=400cooking_time=1classB(A):classOptions:color="blue"print("B color:",B._options.color)print("B size:",B._options.size)Outputs:B color: blue
B size: 2LicenseMIT License.
Package: class-pool
# class-poolSimple, but powerful library for python classes registries.This tool bases on django’s models pool with a lot of improvements.Features- thread-safe classes pool
- ad-hoc class registering
- auto class registering from packages
- custom class identifiers in a pool
- filtering classes during registration
- default class for unregistered identifiers
- pool classes choice field for Django models, forms and Django Rest Framework serializers## Requirements
* Python 3
* django >= 1.7 (optional)## Installationpip installclass-pool## Documentationhttps://py-class-pool.readthedocs.io/