Saravanan666/pypi-org huggingface dataset:
Package: castepinput
A simple CASTEP input files parser/writerThis package provide a basic framework for read/write input files of CASTEP.
There is already a excellent reader/writer implemented inasebut they are ortentated to work with theAtomsandCalculatorclasses inase.
It also requires a CASTEP binary to exist to work.
This package aim to provide a more generic framework with minimum dependency for
simple (but important) tasks of writing and reading inputs files of CASTEP.UsageThe two classes to be used for reading/writing inputs areParamInputandCellInput.
Keyword-value pairs can be set the same as dictionaries.
For example:fromcastepinputimportCellInput,ParamInput,Block# ParamInput is in fact just a subclass of OrderedDictparam=ParamInput(cut_off_energy=300,task="singlepoint")param["opt_strategy"]="speed"# Use the Block class to signal that it is a BLOCK# The following line sets the positions_abscell=CellInput(positions_abs=Block(["C 0 0 0","C 1 0 0"])The two classes use simple string formatting when writing out the content.
See the following code as example.cell=CellInput(snap_to_symmetry=True)# Should give 'snap_to_symmetry : True'cell.get_string()# Should give 'symmetry_genreate : true'cell['snap_to_symmetry']='true'cell.get_string()# Not all CASTEP keyword requires a value# Use "" as the value will result just a keyword on a linecell['symmetry_generate']=''# Should give a string with a line 'symmetry_generate'cell.get_string()# Set cell and positions use the set methodscell.set_cell([10,10,10])cell.set_positions(["O","O"],[[0,0,0],[1.4,0,0]])# Save to filecell.save("O2.cell")To initialize from a existing param/cell file, use theParamInput.from_filemethod.cell=CellInput.from_file("O2.cell")# This should give [[10, 0, 0], [0, 10, 0], [0, 0, 10]]cell.get_cell()# The value returned should be "" to be consistent with settingcell["symmetry_generate"]We also try to be smart and convert string into python types where it is possible.
Supported types are integer, floats and 1-d arrays made of integer/floats.
These coversions can be avoided by usingParamInput.from_file(filename, plain=True)when loading files.
Package: castep-parse
castep-parseInput file writers and output file readers for the density functional theory code CASTEP.Installationpip install castep-parseNotesThis code has generally been used fortask=singlepointandtask=geometryoptimisationsimulations. Consequently, input and output files associated with other simulation types are not yet supported.FunctionalityReaders:✅read_castep_file️✅read_geom_file✅read_cell_file✅read_output_filesWriters:✅write_cell_file✅write_input_filesUtilities:✅map_species_to_castep✅get_castep_cell_constraintsExamplesDo something useful
Package: castero
casterocastero is a TUI podcast client for the terminal.InstallationInstall fromPyPiwith pip:$pip3installcasteroUpgrading:$pip3installcastero--upgradeManual Installation$gitclonehttps://github.com/xgi/castero
$cdcastero
$sudopythonsetup.pyinstallDependenciesRunning castero requires the following external dependencies:Python >= 3.5 (check the output ofpython --version)sqlite3At least one of the following media players:vlc >= 2.2.3(mpv and libmpv) >= 0.14.0UsageAfter installing castero, it can be run with simply:$casteroThe help menu provides a list of controls and can be accessed by pressingh. Alternatively, see the list below:Commands
    h           - show this help screen
    q           - exit the client
    a           - add a feed
    d           - delete the selected feed
    r           - reload/refresh feeds
    s           - save episode for offline playback
    UP/DOWN     - navigate up/down in menus
    RIGHT/LEFT  - navigate right/left in menus
    PPAGE/NPAGE - scroll up/down in menus
    ENTER       - play selected feed/episode
    SPACE       - add selected feed/episode to queue
    c           - clear the queue
    n           - go to the next episode in the queue
    i           - invert the order of the menu
    /           - filter the contents of the menu
    m           - mark episode as played/unplayed
    p or k      - pause/play the current episode
    f or l      - seek forward
    b or j      - seek backward
    =/-         - increase/decrease volume
    ]/[         - increase/decrease playback speed
    u           - show episode URL
    1-5         - change between client layoutsImporting/exporting feeds from another clientcastero supports importing and exporting anOPML fileof your subscriptions in order to easily transfer them between other podcast
clients. Please refer to your other client's documentation for details on
how/if it supports this format.Importing and exporting from castero are available with command line flags.
Runcastero --helpfor details.ConfigurationThe configuration file is located at{HOME}/.config/castero/castero.confafter the client has been run at least once.Please see thedefault castero.conffor a list of available settings.User data, including downloaded episodes and a database with your feed
information, is located at{HOME}/.local/share/castero/. These files are not
intended to be manually modified. Removing the database will simply cause
castero to replace it with an empty one the next time you run the client.TestingThis project usespytestfor testing. To run tests, run
the following command in the project's root directory:$python-mpytesttestsYou can also run tests for an individual unit, i.e.:$python-mpytesttests/test_feed.pyLicenseMIT License
Package: castfit
castfitBasic type casting.Changelog-Issues-DocumentationWhy?I'm writing more and more type-checked code, but I often get a bunch of strings I need to convert (e.g., fromdocopt).pydanticfeels heavy.type-docoptuses a new syntax.bottleseems like good inspiration for small, useful libraries.Installpython-mpipinstallcastfitExamplefrompathlibimportPathfromcastfitimportcastfitclassCat:name:strage:intweight:floatlogo:Pathbob=castfit(Cat,dict(name="Bob",age="4",weight="3.2",logo="./bob.png"))assertbob.name=="Bob"assertbob.age==4assertbob.weight==3.2assertbob.logo==Path("./bob.png")LicenseMIT License
Package: cast-from-env
cast-from-envGet and cast environment variablesInstallationpython setup.py installUsageCallfrom_env()with the name of an environment variable, and either a default
value to return if the environment variable is not set, or a type to cast the
environment variable's value to if it is set. If a default value is passed and the
variable is set, its value will be cast to the type of the default value.Actually, any callable can be passed as the second parameter, and will be used to
cast the value if it's not None. So if you pass e.g.json.loadsa JSON env var value
will be cast to a Pythonlistordict.Also, if the type of the default value – or the type passed in instead – isbool,from_env()will returnTrueif the environment variable's string value is any
of'1','true','t','yes','y', and'on', andFalseotherwise.Examplesfrom cast_from_env import from_env
from json import loads

from_env('STRING_VAR', 'default value')  # Value or 'default value' if not set
from_env('INTEGER_VAR', 100)  # Value cast to int, or 100 if not set
from_env('INTEGER_VAR', int)  # Value cast to int, or None if not set
from_env('FLOAT_VAR', 1.0)  # Value cast to float, or 1.0 if not set
from_env('TRUE_VAR', bool)  # True if value is '1', 'true', 'yes', or 'on'
from_env('TRUE_VAR', True)  # True if value is '1', 'true', 'yes', 'on', or not set
from_env('TEST_JSON', loads)  # JSON string is cast to Python list or dict
Package: cas-tg
check(id: str|int) -> bool
Check if a user banned by CAS (Combot Anti-Spam System).Args:id (str | int): The user ID to be checked.Returns:bool: Returns True if the user is banned or False if the user is not banned.get_messages(id) -> dict[str, list, str]
Check information about user.Args:id (str | int): The user ID to be checked.Returns:dict[str, list, str]: Returns the number of spam messages, messages, and the time of the ban. If there is no user in ban, returns None.
Package: castle.theme
IntroductionThis is the default theme for CastleCMS.Since CastleCMS does not use Diazo, this theme should be a good reference on how to
write your own CastleCMS themes.Changelog1.0.6 (2020-02-21)Fix googletagmanager in templates1.0.5 (2019-10-07)Fix accessibility issue for search/label box
[robzonenet]Add sticky footer slot to html templates
[lucid-0]1.0.4 (2019-01-02)Fix rendering announcement on fourohfour
[vangheem]1.0.3 (2018-12-28)Fix query listing styles
[vangheem]Fix build
[vangheem]Reorganize into smaller components
[vangheem]Python 3 compat
[vangheem]fix spelling of CastleCMS
[tkimnguyen]1.0.2 (2018-03-04)fix MANIFEST.in, remove extraneous version.txt
[tkimnguyen]1.0.1 (2018-03-03)fix spelling of CastleCMS, update package metadata in setup.py, bump version
[tkimnguyen]1.0.0 (2016-12-07)Initial public release
Package: castletheme.sparkling
For access to Castle Cloud (our hosted CastleCMS service) or
to request customizations or demos, please contact us atcastlecms.ioorwildcardcorp.commailto:info@wildcardcorp.com+1 (715) 869-3440castletheme.sparklingImplementation of the WordPress open sourceSparkling themefor CastleCMS.Changes for castletheme.sparkling1.0.4 (2018-12-12)further presentation tweaks for github/pypi1.0.3 (2018-12-12)style tweaks for github and pypi presentation1.0.2 (2018-12-12)update setup.py description content types1.0.1 (2018-12-12)packaging updates1.0.0 (2016-06-07)initial release
Package: cas-tools
CAS ToolsNote: CAS-Tools is currently under active development. Its behavior and interfaces may change as new features are added and existing ones are refined. Use with caution in production environments.CAS-Tools is a comprehensive utility package designed to facilitate the effective use and manipulation of the Cell Annotation Schema (CAS) in single-cell transcriptomics data analysis.OverviewThe Cell Annotation Schema (CAS) addresses the inherent variability in annotating cell types/classes in single-cell transcriptomics datasets. While annotations lack the reasoning behind the choice of labels, CAS enables the recording of additional metadata about individual cell type annotations. This includes marker genes used as evidence and details of automated annotation transfer.CAS-Tools simplifies the utilization of CAS by offering a set of programmatically accessible operations to:Integration with AnnData: Flatten CAS onto observations in AnnData format for seamless integration and analysis.Flatten Onto Dataframes: Decompose the schema into individual tables suitable for use in dataframes/TSVs.Validate Annotations: Validate annotations against the CAS standard to ensure compliance and consistency.InstallationYou can install CAS-Toolspypi packageusingpip:pip install cas-toolsGetting StartedPlease see related guides:Command line interface
Package: castor
Provides a way to assemble various Git repositories into one. It’s like submodules that don’t suck.Use case: stitch together Wordpress with some themes and plugins before deploying.UsageFirst, you need to create your Castor repository. The following command will create an new Git
repository containing an emptyCastorfileand a pre-initialized .gitignore.castor init my-projThen, you need to edit yourCastorfile. It might look likeNotepost_freezearray is optional. It must be an array, each command will be executed
on thetargetdirectory after executingcastor freeze.{
    "lodge": [
        {
            "target": "/",
            "version": "1.6.1.0",
            "repo": "https://github.com/PrestaShop/PrestaShop.git",
            "type": "git"
        },
        {
            "target": "/themes/my-prestashop-theme",
            "version": "e0e7c15789e6ff674cd75cb24981155441c3df09",
            "repo": "git@bitbucket.org:activkonnect/my-prestashop-theme.git",
            "type": "git",
            "post_freeze": [
                "composer update --no-dev"
            ]
        },
        {
            "target": "/.htaccess",
            "type": "file",
            "source": "files/htaccess"
        }
    ]
}YourCastorfilebeing filled up, you can now apply itcastor applyThis will automatically create your repositories hierarchy, checkout submodules, etc. The root of
this hierarchy will be thelodgedirectory.If you want to execute post freeze commands on apply add the--exec-post-freezeargument like so :castor apply --exec-post-freezeNow you can freeze your project into a git-free, commitable and deployable tree of source code.
This will go into thedamdirectory.If you want to execute post freeze commands on apply add the--exec-post-freezeargument like so :castor apply --exec-post-freezecastor freezeYou can use thelodgeas your working directory during development. If you make updates to the
code, you can commit in the git repos. If you simply want to update upstream code, check out the new
tag/commit you want to use. Then  you can usecastor freezeagain, and it will update theCastorfileautomatically with the latest Git HEADs, as well as thedamdirectory.
Package: castorapi
# castorapi
Python API wrapper for Castor EDC to fetch data from or post data to your clinical study.## Install
Using conda and the conda-forge channel (recommended):conda install -c conda-forge castorapiBut, you can also install using pip (https://pypi.org/project/castorapi/):pip install castorapi## Update
Using conda and the conda-forge channel:conda update -c conda-forge castorapiUsing pip (https://pypi.org/project/castorapi/):pip install castorapi –upgrade## Usage
First, make sure that save the client and secret from your Castor account in
seperateclientandsecretfiles (without line endings) in a private
folder on your PC. Do not share these files with anyone.![image](https://user-images.githubusercontent.com/981436/130036568-6209334a-2a8b-4948-9387-7c6c74e0ef08.png)See alsohttps://data.castoredc.com/apiandhttps://helpdesk.castoredc.com/article/124-application-programming-interface-api## Example codeimport castorapi as ca
c = ca.CastorApi(‘/path/to/folder/with/secret_client’)
c.select_study_by_name(‘<CASTOR_STUDY_NAME>’) # all following commands use this study selection
stats = c.request_statistics()
print(stats)
df_study, df_structure_study, df_report, df_structure_report, df_optiongroups_structure = c.records_reports_all()
users_in_study = c.request_studyuser()
print(users_in_study)# Getting data from castor about patients in a study
c.request_study_records(record_id=’CASTOR00010’)# Posting data to castor to create a new patient
# add institute_id and , request_method=’POST’ to use this functionality.
c.request_study_records(record_id=’CASTOR00010’, institute_id=<instituteID>, request_method=’POST’)## Known issues
1. The documentation is sparse. Feel free to contribute.
2. Not all Castor API functions are implemented (I implement them on a need-to-use basis), feel free to contribute.
3. Seehttps://github.com/wouterpotters/castorapi/issuesfor an overview of all open issues.## NOTE
I am not affiliated with Castor EDC in any way. Use this software at your own risk.
If this API tool does not suffice - take a look at the work from Reinier van Linschoten, who also created a Python package for two-way communication Castor EDC (2021). (https://github.com/reiniervlinschoten/castoredc_api)
Package: castordashboard
Castor Dashboard ClientDashboard application that extracts data from Castor and displays itFree software: MIT licenseDocumentation:https://castordashboard.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-02-27)First release on PyPI.
Package: castor-extractor
Castor ExtractorThis library contains utilities to extract your metadata assets intoJSONorCSVfiles, on your local machine.
After extraction, those files can be pushed to Castor for ingestion.Visualization assets are typically:dashboardsusersfolders...Warehouse assets are typically:databasesschemastablescolumnsqueries...It also embeds utilities to help you push your metadata to Castor:File Checkerto validate yourgenericCSV files before pushing to CastorUploaderto push extracted files to our Google-Cloud-Storage (GCS)Table of contentsCastor ExtractorTable of contentsInstallationCreate castor-envPIP installCreate the output directoryContactInstallationRequirements:python3.8+Create castor-envWe advise to create a dedicatedPython environment.Here's an example usingPyenvand Python3.8.12:Install Pyenvbrewinstallpyenv
brewinstallpyenv-virtualenv[optional] Update your.bashrcif you encounter thisissueeval"$(pyenvinit-)"eval"$(pyenvinit--path)"eval"$(pyenvvirtualenv-init-)"[optional] Install python 3.8+pyenvversions# check your local python installationspyenvinstall-v3.8.12# if none of the installed versions satisfy requirements 8+Create your virtual envpyenvvirtualenv3.8.12castor-env# create a dedicated envpyenvshellcastor-env# activate the environment# optional checkspython--version# should be `3.8.12`pyenvversion# should be `castor-env`PIP install⚠️castor-envmust be created AND activated first.pyenvshellcastor-env(castor-env)$# this means the environment is now activeℹ️ please upgradePIPbefore installing Castor.pipinstall--upgradepipRun the following command to installcastor-extractor:pipinstallcastor-extractorDepending on your use case, you can also install one of the followingextras:pipinstallcastor-extractor[bigquery]pipinstallcastor-extractor[looker]pipinstallcastor-extractor[metabase]pipinstallcastor-extractor[mysql]pipinstallcastor-extractor[powerbi]pipinstallcastor-extractor[qlik]pipinstallcastor-extractor[postgres]pipinstallcastor-extractor[redshift]pipinstallcastor-extractor[snowflake]pipinstallcastor-extractor[sqlserver]pipinstallcastor-extractor[tableau]Create the output directorymkdir/tmp/castorYou will provide this path in theextractionscripts as follows:castor-extract-bigquery--output=/tmp/castorAlternatively, you can also set the followingENVin yourbashrc:exportCASTOR_OUTPUT_DIRECTORY="/tmp/castor"ContactFor any questions or bug report, contact us atsupport@castordoc.comCastorhelps you find, understand, use your data assets
Package: castor-orsay
CASTOR: Codes pour l’ASTronomie à ORsayOutils pour l’analyse des données de la coupole d’Orsay.DocumentationDocumentation pour les TP à la coupole (PDF)InstallationVersion stable (depuis PyPI; recommandé)mkdircastor&&cdcastor
python3-mvenvvenvsourcevenv/bin/activate
pip3installcastor-orsayVersion de développement (depuis GitHub)gitclonehttps://github.com/coupole-orsay/castorcdcastor
python3-mvenvvenvsourcevenv/bin/activate
pip3install-e.Utilisationcdcastorsourcevenv/bin/activate
castor_<tab>LicenceCes outils sont mis à disposition sous licence MIT. VoirLICENSE.txt
Package: castro
IntroductionCastro is a library for recording automated screencasts via a simple API.Here’s an example:>>> from castro import Castro
>>> c = Castro()
>>> c.start()
>>> # Do something awesome!
>>> c.stop()Install1) Install and launch a vncserver. (Hint: Google it.)
2) $ [sudo] easy_install castro
3) There's no step 3!Test$ python -c "import castro; castro.test()"WatchVideo stored in: <default_temp_dir>/castro-video.swfVideo player stored in: <default_temp_dir>/castro-video.htmlLinux/OSX:$ firefox /tmp/castro-video.htmlLicense & RepositoryCastro was created byJason Huggins. It is licensed under
the GPLv2, since it is a derivative work of pyvnc2swf, which is also
licensed under the GPLv2.Castro has agit respository at github.com.SummaryCastro is a minor fork of pyvnc2swf, allowing one to use pyvnc2swf as a
regular Python library, instead of a Tk GUI application or command line
utility.The specific improvement Castro brings to pyvnc2swf is the ability to start
and stop recording programmatically via a simple Python API. Castro
uses a file-based IPC to tell pyvnc2swf when to stop recording.Ordinarily, pyvnc2swf’s command line utility, vnc2swf.py, expects users to
stop recording by manually typing “Control-C”, sending a KeyboardInterrupt
and allowing the process to exit cleanly. On Linux, emulating KeyboardInterrupt
is simple enough to do by sending a SIGINT signal. But this does not work cross-
platform, specifically on Windows. And a big reason for using pyvnc2swf is its
ability to record vnc video on any platformfromany platform.DependenciesPython - 2.5 or abovePygame - 1.6 or abovePyYAML - 3.09 or abovePython < 2.6 dependencies:Multiprocessing - 2.6.21 or aboveSimplejson - 2.0.9 or aboveNon-python dependencies (for cleaning/editing .flv videos):flvtool2 (ruby gem)ffmpegChangelog:1.0.4 - Added post-recording processing methods. (Depends on ffmpeg and flvtool2)1.0.3 - Switched from processing library to multiprocessing.Added support for changing recording framerate.1.0.2 - Made stop() block until the recording process is doneAdded support to use Castro in a with statement (e.g “with video(…):”)1.0.1 - Fixed default vnc password path1.0   - First Release
Package: castroredux
“screenCAST RObot REDUX” - a tiny fork of vnc2flvInstallation$ [sudo] easy_install castroreduxExamplefrom castroredux import CastroRedux

c = CastroRedux(
            'out.flv',
            host = '192.168.0.12'
            port = 5900,
            pwdfile = "/home/user/.vnc/pwd"
    )

c.start()

# other stuff

c.stop()
Package: casttube
CastTubecasttube provides a way to interact with the Youtube Chromecast api.Install:pip install casttubeFeaturesPlay videoPlay a playlistAdd video to the end of the play queuePlay nextRemove videoClear the entire queuefrom casttube import YouTubeSession
session = YouTubeSession(screen_id)

# YouTube video id is http://youtube.com/watch?v=video_id
session.play_video(video_id)The library requires 2 things:screen idThe Chromecast youtube app needs to be openThere is a small script inhttps://github.com/ur1katz/CastTube-Scriptsto print the screen id and launch the app.