Saravanan666/pypi-org huggingface dataset:
Package: cast-unknown
cast-unknown-pythonCast unknown value to desired type with typing support.Current supported cast target:text (unicode for python2, str for python3)binary (str for python2, bytes for python3)iterabledatetimeone (one and the only one item from given iterable, otherwise None)non_none (return default or raise error when given value is None)instance (raise error when not is instance)list (with optional item type check)>>> import cast_unknown as cast
>>> import six
>>> cast.text('测试')
'测试'
>>> cast.text('测试'.encode('utf-8'))
'测试'
>>> cast.text(b'\xb2\xe2\xca\xd4', 'gbk')
'测试'
>>> cast.text(1)
'1'
>>> cast.text([])
'[]'
>>> cast.binary('测试')
b'\xe6\xb5\x8b\xe8\xaf\x95'
>>> cast.binary('测试'.encode('utf-8'))
b'\xe6\xb5\x8b\xe8\xaf\x95'
>>> cast.binary('测试', 'ascii')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "cast_unknown\binary.py", line 25, in binary
    return v.encode(encoding, errors)
UnicodeEncodeError: 'ascii' codec can't encode characters in position 0-1: ordinal not in range(128)
>>> cast.binary('测试', 'gbk')
b'\xb2\xe2\xca\xd4'
>>> cast.binary([])
b'[]'
>>> cast.iterable(1)
[1]
>>> cast.iterable((i for i in (1,2,3)))
<generator object <genexpr> at 0x000000000220ECC8>
>>> cast.iterable([1])
[1]
>>> cast.iterable(None)
[]
>>> import datetime as dt
>>> cast.datetime(dt.datetime.now())
datetime.datetime(2021, 3, 10, 15, 27, 41, 69429)
>>> cast.datetime('2021-03-10T00:00:00+0800')
datetime.datetime(2021, 3, 10, 0, 0, tzinfo=tzoffset(None, 28800))
>>> cast.datetime('2021/03/10')
datetime.datetime(2021, 3, 10, 0, 0)
>>> cast.datetime(1615305600.0)
datetime.datetime(2021, 3, 9, 16, 0)
>>> cast.datetime(1615305600000)
datetime.datetime(2021, 3, 9, 16, 0)
>>> cast.one(1)
1
>>> cast.one(None)
None
>>> cast.one([1])
1
>>> cast.one([1,2])
None
>>> cast.not_none(1)
1
>>> cast.not_none(None)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "cast_unknown\not_none.py", line 14, in not_none
    raise CastError("value and default both None ")
cast_unknown.error.CastError: value and default both None
>>> cast.not_none(None, 1)
1
>>> cast.instance(1, int)
1
>>> cast.instance(1, str)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "cast_unknown\instance.py", line 23, in instance
    raise CastError(
cast_unknown.error.CastError: ('can not cast object to instance', 1, <class 'str'>)
>>> cast.instance(1, (str, int))
1
>>> cast.list_(None)
[]
>>> cast.list_([])
[]
>>> cast.list_(1)
[1]
>>> cast.list_([1])
[1]
>>> cast.list_([1], six.text_type)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "cast_unknown\list.py", line 14, in list_
    instance(i, class_or_tuple)
  File "cast_unknown\instance.py", line 23, in instance
    raise CastError(
cast_unknown.error.CastError: ('can not cast object to instance', 1, <class 'str'>)
>>> cast.list_([1], (int, six.text_type))
[1]
>>> cast.list_("abc")
['a', 'b', 'c']
>>> cast.list_("abc", six.text_type)
['abc']relatedcast-unknownfor javascript
Package: castxml
CastXML Python DistributionIntroductionThe latest CastXML python wheels provide CastXML 0.4.5 executable.CastXML is a C-family abstract syntax tree XML output tool.This project is maintained byKitwarein support ofITK,
the Insight Segmentation and Registration Toolkit.ManualSee thecastxml(1)manual page for instructions to run the tool.Latest ReleaseBuild StatusLinuxmacOSWindowsPyPIMaintainersHow to update CastXML version ?How to make a release ?MiscellaneousDocumentation:https://github.com/CastXML/CastXML-python-distributions#readmeSource code:https://github.com/CastXML/CastXML-python-distributionsMailing list:https://public.kitware.com/mailman/listinfo/castxmlLicenseCastXML is licensed under theApache License, Version 2.0.
See theLICENSEandNOTICEfiles for details.CommunitySubscribe and post to theCastXML Mailing Listfor discussion of CastXML.
Package: castxml-patch
CastXML Python DistributionIntroductionThe latest CastXML python wheels provide CastXML 0.5.0 executable.CastXML is a C-family abstract syntax tree XML output tool.This project is maintained byKitwarein support ofITK,
the Insight Segmentation and Registration Toolkit.ManualSee thecastxml(1)manual page for instructions to run the tool.Latest ReleaseBuild StatusLinuxmacOSWindowsPyPIMaintainersHow to update CastXML version ?How to make a release ?MiscellaneousDocumentation:https://github.com/CastXML/CastXML-python-distributions#readmeSource code:https://github.com/CastXML/CastXML-python-distributionsMailing list:https://public.kitware.com/mailman/listinfo/castxmlLicenseCastXML is licensed under theApache License, Version 2.0.
See theLICENSEandNOTICEfiles for details.CommunitySubscribe and post to theCastXML Mailing Listfor discussion of CastXML.
Package: casual
CasualInstallationCreate a virtual environment and activate it.❯ python3 -m venv venv
❯ . ./venv/bin/activateCreate the initial env files to run Casual.❯ flask core init -dCheck and modify the three new filesdev_casual.conf,.env, and.flaskenv.The most important one isdev_casual.confwhere you should check the following keys:SECRET_KEYALCHEMICAL_DATABASE_URLCASUAL_ADMINSbefore you go to the next step!.MAIL_*you will need this to safely create the user accounts and communicate with the users.CASUAL_*in general.Create the DB Migrations infrastructure, the first migration, and apply it.❯ flask db init
❯ flask db migrate
❯ flask db upgradeCreate the Admin Accounts according to theCASUAL_ADMINkey in the conf file (see step 3).❯ flask auth create-adminsNow you can run the development server and conform that you can log in + change the password from the default value ofCasualto a more secure one.Each admin account will be forced to change the password on the first successful login.
Package: casuarius
A Cython wrapper for the Cassowary incremental constraint solver.The solver source code is derived from the 0.6 release. It has been modified by
Svilen Dobrev to remove memory leaks. His work can be found separately as SWIG
bindings:http://pypi.python.org/pypi/cassowarypyHis modifications are available under the BSD license, while Casuarius as
a whole follows the license of Cassowary itself, LGPL v2.1 or (at your option)
a later version of the LGPL. See the files LICENSE and COPYING.LGPL for details.Please be sure to have Cython 0.15.1 or above in order to compile this extension
module. It has been tested on OS X (using llvm-gcc 4.2) and Windows (using
mingw). Other Windows C++ compilers may or may not work.
Package: cat2
cat2Print file contents like a bossCLI useage:Programmatic usage:fromcat2importCat2,Cat2Exception# line_from and line_to are optional parameterscat2_file=Cat2(file_path,line_from,line_to)# cat2_file is an iterator objectforlineincat2_file:print(line)print(cat2_file.line_number)print(cat2_file.is_bytes)
Package: cat2cat
cat2catUnifying an inconsistently coded categorical variable in a panel/longtitudal datasetThere is offered the cat2cat procedure to map a categorical variable according to a mapping (transition) table between two different time points. The mapping (transition) table should to have a candidate for each category from the targeted for an update period. The main rule is to replicate the observation if it could be assigned to a few categories, then using simple frequencies or statistical methods to approximate probabilities of being assigned to each of them.This algorithm was invented and implemented in the paper by(Nasinski, Majchrowska and Broniatowska (2020)).For more details please read the paper by(Nasinski, Gajowniczek (2023)).Installation$pipinstallcat2catUsageFor more examples and descriptions please vistthe example notebookload example data# cat2cat datasetsfromcat2cat.datasetsimportload_trans,load_occuptrans=load_trans()occup=load_occup()Low-level functionsfromcat2cat.mappingsimportget_mappings,get_freqs,cat_apply_freq# convert the mapping table to two association listsmappings=get_mappings(trans)# get a variable levels freqenciescodes_new=occup.code[occup.year==2010].valuesfreqs=get_freqs(codes_new)# apply the frequencies to the (one) association listmapp_new_p=cat_apply_freq(mappings["to_new"],freqs)# mappings for a specific categorymappings["to_new"]['3481']# probability mappings for a specific categorymapp_new_p['3481']cat2cat functionfromcat2catimportcat2catfromcat2cat.dataclassimportcat2cat_data,cat2cat_mappings,cat2cat_mlfrompandasimportconcat# split the panel by the time variale# here only two periodso_old=occup.loc[occup.year==2008,:].copy()o_new=occup.loc[occup.year==2010,:].copy()# dataclasses, core arguments for the cat2cat functiondata=cat2cat_data(old=o_old,new=o_new,cat_var_old="code",cat_var_new="code",time_var="year")mappings=cat2cat_mappings(trans=trans,direction="backward")# apply the cat2cat procedurec2c=cat2cat(data=data,mappings=mappings)# pandas.concat used to bind per period datasetsdata_final=concat([c2c["old"],c2c["new"]])ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensecat2catwas created by Maciej Nasinski. It is licensed under the terms of the MIT license.Creditscat2catwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: catadoc
Real Python Feed ReaderThe Real Python Feed Reader is a basicweb feedreader that can download the latest Real Python tutorials from theReal Python feed.For more information see the tutorialHow to Publish an Open-Source Python Package to PyPIon Real Python.InstallationYou can install the Real Python Feed Reader fromPyPI:python -m pip install realpython-readerThe reader is supported on Python 3.7 and above. Older versions of Python, including Python 2.7, are supported by version 1.0.0 of the reader.How to useThe Real Python Feed Reader is a command line application, namedrealpython. To see a list of thelatest Real Python tutorials, call the program without any arguments:$ realpython
The latest tutorials from Real Python (https://realpython.com/)
 0 How to Publish an Open-Source Python Package to PyPI
 1 Python "while" Loops (Indefinite Iteration)
 2 Writing Comments in Python (Guide)
 3 Setting Up Python for Machine Learning on Windows
 4 Python Community Interview With Michael Kennedy
 5 Practical Text Classification With Python and Keras
 6 Getting Started With Testing in Python
 7 Python, Boto3, and AWS S3: Demystified
 8 Python's range() Function (Guide)
 9 Python Community Interview With Mike Grouchy
10 How to Round Numbers in Python
11 Building and Documenting Python REST APIs With Flask and Connexion – Part 2
12 Splitting, Concatenating, and Joining Strings in Python
13 Image Segmentation Using Color Spaces in OpenCV + Python
14 Python Community Interview With Mahdi Yusuf
15 Absolute vs Relative Imports in Python
16 Top 10 Must-Watch PyCon Talks
17 Logging in Python
18 The Best Python Books
19 Conditional Statements in PythonTo read one particular tutorial, call the program with the numerical ID of the tutorial as a parameter:$ realpython 0
# How to Publish an Open-Source Python Package to PyPI

Python is famous for coming with batteries included. Sophisticated
capabilities are available in the standard library. You can find modules for
working with sockets, parsing CSV, JSON, and XML files, and working with
files and file paths.

However great the packages included with Python are, there are many
fantastic projects available outside the standard library. These are most
often hosted at the Python Packaging Index (PyPI), historically known as the
Cheese Shop. At PyPI, you can find everything from Hello World to advanced
deep learning libraries.

[... The full text of the article ...]You can also call the Real Python Feed Reader in your own Python code, by importing from thereaderpackage:>>> from reader import feed
>>> feed.get_titles()
['How to Publish an Open-Source Python Package to PyPI', ...]
Package: catalax
CatalaxCatalax is a JAX-based framework that facilitates simulation and parameter inference through optimization algorithms and Hamiltonian Monte Carlo sampling. Its features enable efficient model building and inference, including the utilization of neural ODEs to model system dynamics and serve as surrogates for the aforementioned techniques.🚧 Please note that Catalax is still in early development and the API is subject to change. 🚧Getting startedTo get started with Catalax, you can install it via pippipinstallcatalaxor by sourcegitclonehttps://github.com/JR-1991/Catalax.gitcdCatalax
pipinstall.QuickstartTo develop a model, Catalax offers a user-friendly interface that comprises two core components:SpeciesandODE. The former is utilized to specify the species of the model, while the latter is used to define its dynamics. Through the integration of these components, a robust model is created, which can be employed for inference purposes. Notably, Catalax automatically generatesParameterobjects from the extracted parameters, which can be leveraged to define priors and constraints for the model.importcatalaxasctxmodel=ctx.Model(name="My Model")# Define the species of the modelmodel.add_species(s1="Substrate",e1="Enzyme")# Now add an ODE for each speciesmodel.add_ode("s1","k_cat * e1 * s1 / (K_m + s1)")model.add_ode("e1","0",observable=False)# All parameters [k_cat, K_m] are automatically extracted# and can be accessed via model.parametersmodel.parameters.k_cat.value=5.0model.parameters.K_m.value=100.0# Integrate over timeinitial_condition={"s1":100.0,"s2":0.0}time,states=model.simulate(initial_conditions=initial_condition,t0=0,t1=100,dt0=0.1,nsteps=1000,in_axes=None)# Visualize the resultsf=visualize(model=model,data=states,# Replace this with actual datatimes=time,initial_conditions=initial_conditions,figsize=(4,4),)Give it a try!To get a better understanding of Catalax, we recommend that you try out the examples found in theexamplesdirectory. These examples are designed to showcase the capabilities of Catalax and provide a starting point for your own projects:Basic functions- An overview to models and integrationOptimization- How to perform parameter estimation using CatalaxNon observable species- How to deal with non-observable speciesHamiltonian MC- How to perform parameter inference using Hamiltonian Monte CarloNeural Ordinary Differential Equations- How to use neural ODEs to model system dynamicsNeural ODEs and HMC- How to perform parameter inference using surrogate HMC
Package: catalight
IntroductionCatalight is a tool set developed by scientists at Stanford to help automate photocatalysis experiments. Catalight is capable of connecting to hardware, coordinating instruments to run a variety of different experimental procedures, and organizing and analyzing data collected by the system. The project is designed in a modular format to enable users to bring their own unique hardware while still utilizing the core features of experiment management and data handling. The program functions in both a scripted version and a GUI to aid users in planning experimental procedures and manually controlling hardware.To date, catalight is compatible with Alicat mass flow controllers, heater systems controlled by Watlow components, Measurement Computing data acquisition boards (used for communication with (ThorLabs laser diode drivers), NewPort 843-R power meter, and SRI gas chromatographs. Support for additional hardware is planned for the future, including FTIR data collection and analysis and NKT Photonics lasers. We strongly encourage interested users to consider contributing to the project by adding new device support and helping expand the catalight functionality.ReadtheDocs Page:https://catalight.readthedocs.io/en/latest/PyPI Page:https://pypi.org/project/catalight/D-Lab Hardware Configuration:
Package: catalog-client
catalog_clientprojectThecatalog_clientproject container both thecatalog_clientpackage that
provides a python interface to a Catalog server, and thecatalog-clicommand line
interface that uses that package to all command line access to the server.catalog-cliexecutableMore details about thecatalog-cliexecutable can be found using its help optioncatalog-cli -h
Package: cataloger
The manifest checker is a command line tool for the creation and checking of manifest files. Typically it is intended to be
used to confirm that a directory structure can be deployed/copied in a consistent way from a source to a target.There is a two step process for confirming consistency :A manifest is created (using themanifest createcommand). This command scans the directory tree, looking for known source code files. A manifest file is created, which is a simple text file listing every source code file found, and a checksum/hash for each file - thus recording a reasonable mark of the file contents.Following deployment of the directory tree (including the manifest file), the manifest can be checked against the deployed copy, by executing themanifest checkcommand. This command scans the directory tree, looking for the source code files, and checking the found files against the manifest file is created. During this check 3 types of exception can be reported :mismatched files - where the signature of the deployed file is not the same as that in the manifest filemissing files - where files are listed in the manifest but not in the deployed directory treerecord_extra files - where files exist in the deployed directory tree but don’t exist in the manifest fileThe toolset was initially designed to check a deployment of a Django based website, and therefore many of the defaults are set for DJango projects including : The source files to look for, and also which sub-directories to ignore. All of these can be overidden by command line options, and there are early plans for a configuration file as well.WarningVersions early than 0.0.2rc6 incorrectly ignores missing files - this has now been fixed.Links :Full DocumentationSource Code
Package: catalpagination
Django Endless Pagination can be used to provide Twitter-style or
Digg-style pagination, with optional Ajax support and other features
like multiple or lazy pagination.The initial idea, which has guided the development of this application,
is to allow pagination of web contents invery few steps.Documentationisavaliable online, or in the docs
directory of the project.Thesource codefor this app is hosted athttps://github.com/catalpainternational/django-endless-pagination.
Package: catalyst-codestyle
Accelerated DL R&DPyTorch framework for Deep Learning research and development.
It was developed with a focus on reproducibility,
fast experimentation and code/ideas reusing.
Being able to research/develop something new,
rather than write another regular train loop.Break the cycle - use the Catalyst!Projectmanifest. Part ofPyTorch Ecosystem. Part ofCatalyst Ecosystem:Alchemy- Experiments logging & visualizationCatalyst- Accelerated Deep Learning Research and DevelopmentReaction- Convenient Deep Learning models servingCatalyst at AI Landscape.Catalyst.CodestyleInstallationCommon installation:pipinstall-Ucatalyst-codestyleGetting started# make code compatible with `catalyst` code stylecatalyst-make-codestyle# check that the code is `catalyst` code style compliantcatalyst-check-codestyle
Package: catalystcoop.arelle-mirror
NoteThis is a mirror of Arelle managed byCatalyst Cooperativefor out internal packaging uses only. We do not maintain or support Arelle.Arelleis an end-to-end open source XBRL platform,
which provides the XBRL community with an easy to use set of tools.  It supports
XBRL and its extension features in an extensible manner.  It does this in a
compact yet robust framework that can be used as a desktop application and can
be integrated with other applications and languages utilizing its web service.FeaturesSupport for XBRL versioning. Validation tool for versioning reports and a
production tool to generate the basics of a versioning report that can be
inferred by diffing two DTSs.Edgar and Global Filer Manual validationBase Specification, Dimensions, Generic linkbase validationFormula validation including support for extension modulesInstance creation is supported using forms defined by the table linkbase (Eurofiling version).RSS Watch facilityUsers can explore the functionality and features from an interactive GUI,
command line interface, or web services, and can develop their own controller
interfaces as needed.The Web Service API allows XBRL integration with applications, such as those in
Excel, Java or Oracle.QuickBooks is supported by XBRL-GL.InstallationPlease check out ourArelle install guide.Contribution guidelinesIf you want to contribute to Arelle, be sure to review thecontribution guidelines.We useGitHub issuesfor
tracking requests and bugs.LicenseApache License 2.0
Package: catalystcoop.dbfread
DBF is a file format used by databases such dBase, Visual FoxPro, and
FoxBase+. This library reads DBF files and returns the data as native
Python data types for further processing. It is primarily intended for
batch jobs and one-off scripts.>>> from dbfread import DBF
>>> for record in DBF('people.dbf'):
...     print(record)
OrderedDict([('NAME', 'Alice'), ('BIRTHDATE', datetime.date(1987, 3, 1))])
OrderedDict([('NAME', 'Bob'), ('BIRTHDATE', datetime.date(1980, 11, 12))])By default records are streamed directly from the file.  If you have
enough memory you can instead load them into a list. This allows for
random access:>>> table = DBF('people.dbf', load=True)
>>> print(table.records[1]['NAME'])
Bob
>>> print(table.records[0]['NAME'])
AliceFull documentation athttps://dbfread.readthedocs.io/Seedocs/changes.rstfor a full list of changes in each version.Main Featuressimple but flexible APIdata is returned as native Python data typesrecords are ordered dictionaries, but can be reconfigured to be of
any typeaims to handle all variants of DBF files. (Currently only widely
tested with Visual FoxPro, but should work well with other
variants.)support for 18 field types. Custom types can be added by subclassingFieldParserreadsFPTandDBTmemo files, both text and binary datahandles mixed case file names gracefully on case sensitive file systemscan retrieve deleted recordsInstallingRequires Python 3.6+pip install catalystcoop.dbfreaddbfreadis a pure Python module and doesn’t depend on any packages
outside the standard library.To build documentation locally:sphinx-build docs/ docs/_buildThis requires Sphinx. The resulting files can be found indocs/_build/.Source codehttp://github.com/catalyst-cooperative/dbfread/API Changesdbfread.open()anddbfread.read()are deprecated as of version2.0, and will be removed in2.2.TheDBFclass is no longer a subclass oflist. This makes the
API a lot cleaner and easier to understand, but old code that relied
on this behaviour will be broken. Iteration and record counting works
the same as before. Other list operations can be rewritten using therecordattribute. For example:table = dbfread.read('people.dbf')
print(table[1])can be rewritten as:table = DBF('people.dbf', load=True)
print(table.records[1])open()andread()both returnDeprecatedDBF, which is a
subclass ofDBFandlistand thus backward compatible.Licensedbfread is released under the terms of theMIT license.ContactOle Martin Bjorndalen -ombdalen@gmail.com