Saravanan666/pypi-org huggingface dataset:
Package: co-excitation-gas-modeling
A simple introduction:This Python package provides functions to estimate a galaxy’s cold molecular gas mean density and kinetic temperature using Carbon Monoxide (CO) molecular line flux density ratio.The motivation is that the galaxies emit CO spectral lines, and different transition of CO lines have different strength. The line ratio is affected by the cold molecular gas density and kinetic temperature. At global galaxy scales, the observed line ratio can probe the mean molecular gas density and temperature to some extent. Based on the work of Liu et al. (2020) who compiled the CO J=5-4 / J=2-1 (R52) line ratios in a sample of galaxies from local Universe to high redshift, a density probability distribution function (PDF) gas modeling has been studied and found promising in linking observed galaxy CO line ratios to physical cold molecular gas mean density and kinetic temperature. The highlight of this work is that it uses a density PDF to simulate the gas density distribution in a galaxy, thus provide more realistic CO excitation than single-state gas large velocity gradient radiative transfer modeling. SeeLeroy et al. (2017) <https://ui.adsabs.harvard.edu/abs/2017ApJ…835..217L/abstract>for a similar density-PDF gas modeling work.
This code is thus based on the Liu et al. (2020) model grid and can convert an input CO line ratio to a fitting range of cold molecular gas density and kinetic temperature.A simple usage:To input a CO J=5-4/J=2-1 line ratio (in Jy km s-1 units), and output cold molecular gas mean density (<nH2>) and kinetic temperature (Tkin) ranges:importco_excitation_gas_modelingascoexccoexc.help()fit_result=coexc.fit_line_ratio(z=4.755,R72=0.86/0.09,e_R72=0.01).help()print(fit_result)print(fit_result['lg_nH2'])print(fit_result['T_kin'])Future work:A future work will be conducted to fit CO spectral line energy distribution.Acknowledgement:
Package: coexecutor
Though CoExecutor uses 1 CPU core at any time,limitations such as ‘max_workers’ are needed for
limiting concurrent asyncio connections.This is useful when you are building a asyncio-based web crawler.It is designed to have exactly same result with respect to
python’s ThreadPoolExecutor and ProcessPoolExecutor.Also, CoExecutor supports additional features like
‘limit’ or ‘out_of_order’.coexecutor.map(…, limit=X) limits the number of pending futures
(done, but not collected yet).coexecutor.map(…, out_of_order=True) ignores the input iterator’s ordering.The results are collected by the termination order of the jobs.CoExecutor constructor requires a loop to rely on.With debug=True, it will print the number of active coroutines per second.Works with Python3.5+.
Package: cofactr
CofactrPython client library for accessing Cofactr.ExamplefromtypingimportListfromcofactr.graphimportGraphAPI# Flagship is the default schema.fromcofactr.schema.flagship.partimportPartgraph=GraphAPI(client_id=...,api_key=...)part_res=graph.get_product(id="IM60640MOX6H")part:Part=part_res["data"]parts_res=graph.get_products(query="esp32")parts:List[Part]=parts_res["data"]
Package: cofense-intelligence
A library created by Cofense Intelligence to support developing integregations with client security architecture.For more information on gaining access to Cofense Intelligence data athttps://cofense.com/product-services/phishing-intelligenceIf you are already a customer, detailed documentation on the Intelligence API can be found athttps://www.threathq.com/docs/The download and/or use of this Cofense application is subject to the terms and conditions set forth athttps://cofense.com/legal/integration-applications/.Copyright 2013-2023 Cofense, Inc.  All rights reserved.This software is provided by Cofense, Inc. (“Cofense”) on an “as is” basis and any express or implied warranties,
including but not limited to the implied warranties of merchantability and fitness for a particular purpose, are
disclaimed in all aspects.  In no event will Cofense be liable for any direct, indirect, special, incidental or
consequential damages relating to the use of this software, even if advised of the possibility of such damage. Use of
this software is pursuant to, and permitted only in accordance with, the agreement between you and Cofense.Author: Cofense Intelligence Solutions Engineering
Support:support@cofense.com
Package: coffea
coffea - Columnar Object Framework For Effective AnalysisBasic tools and wrappers for enabling not-too-alien syntax when running columnar Collider HEP analysis.coffea is a prototype package for pulling together all the typical needs
of a high-energy collider physics (HEP) experiment analysis using the scientific
python ecosystem. It makes use ofuprootandawkward-arrayto provide an
array-based syntax for manipulating HEP event data in an efficient and numpythonic
way. There are sub-packages that implement histogramming, plotting, and look-up
table functionalities that are needed to convey scientific insight, apply transformations
to data, and correct for discrepancies in Monte Carlo simulations compared to data.coffea also supplies facilities for horizontally scaling an analysis in order to reduce
time-to-insight in a way that is largely independent of the resource the analysis
is being executed on. By making use of modernbig-datatechnologies likeApache Spark,parsl,Dask, andWork Queue,
it is possible with coffea to scale a HEP analysis from a testing
on a laptop to: a large multi-core server, computing clusters, and super-computers without
the need to alter or otherwise adapt the analysis code itself.coffea is a HEP community project collaborating withiris-hepand is currently a prototype. We welcome input to improve its quality as we progress towards
a sensible refactorization into the scientific python ecosystem and a first release. Please
feel free to contribute at ourgithub repo!InstallationInstall coffea like any other Python package:pipinstallcoffeaor similar (usesudo,--user,virtualenv, or pip-in-conda if you wish).
For more details, see theInstalling coffeasection of the documentation.Strict dependenciesPython(3.8+)The following are installed automatically when you install coffea with pip:numpy(1.22+);uprootfor interacting with ROOT files and handling their data transparently;awkward-arrayto manipulate complex-structured columnar data, such as jagged arrays;numbajust-in-time compilation of python functions;scipyfor many statistical functions;matplotlibas a plotting backend;and other utility packages, as enumerated inpyproject.toml.DocumentationAll documentation is hosted athttps://coffeateam.github.io/coffea/CitationIf you would like to cite this code in your work, you can use the zenodo DOI indicated inCITATION.cff, or thelatest DOI. You may also cite the proceedings:“N. Smith et al 2020 EPJ Web Conf. 245 06012”“L. Gray et al 2023 J. Phys.: Conf. Ser. 2438 012033”
Package: coffea-casa
Coffea-casa - A Prototype of an Analysis Facility for Columnar Object Framework For Effective AnalysisAbout Coffea-casaThe prototype analysis facility provides services for “low latency columnar analysis”, enabling rapid processing of data in a column-wise fashion. These services, based on Dask and Jupyter notebooks, aim to dramatically lower time for analysis and provide an easily-scalable and user-friendly computational environment that will simplify, facilitate, and accelerate the delivery of HEP results. The facility is built on top of a Kubernetes cluster and integrates dedicated resources with resources allocated via fairshare through the local HTCondor system. In addition to the user-facing interfaces such as Dask, the facility also manages access control through single-sign-on and authentication & authorization for data access. The notebooks in this repository andinclude simple HEP analysis examples, managed interactively in a Jupyter notebook and scheduled on Dask workers and accessing both public and protected data.Analysis repositories using coffea-casaDocker images used for Coffea-casaLatest:ImageDescriptionSizePullsVersioncoffea-casaDask scheduler image for coffea-casa hubcoffea-casa-analysisDask worker image for coffea-casa hubHelm charts,coffea_casapackage and Docker image tagsThis repository uses GitHub Actions to build images, run tests, and push charts, python package to PyPI and images to DockerHub (Docker images, charts and python package tags are syncronised with Coffea-casa releases).Tags pushed to GitHub trigger Docker image published with corresponding tags on Dockerhub:coffeateam/coffea-casa:x.x.xandcoffeateam/coffea-casa-analysis:x.x.x.
Tags pushed to GitHub as well trigger Docker image published with corresponding tags on Openscience Harbor Registry:hub.opensciencegrid.org/coffea-casa:x.x.xandhub.opensciencegrid.org/coffea-casa-analysis:x.x.x.
Thelatesttag in both cases also corresponds to the most recent GitHub tag.Tags pushed to GitHub trigger Helm charts releases with corresponding Helm Chart tag and with charts published tohttps://coffeateam.github.io/coffea-casa.Tags pushed to GitHub will pushcoffea_casapython package to PyPI (same as a tag).How to tagA list of "must" steps to do before to tag:Tag Docker imagescoffeateam/coffea-casa:x.x.xandcoffeateam/coffea-casa-analysis:x.x.xchanging$TAGvalue inhttps://github.com/CoffeaTeam/coffea-casa/blob/master/docker/coffea-casa/Dockerfileandhttps://github.com/CoffeaTeam/coffea-casa/blob/master/docker/coffea-casa-analysis/DockerfileTag Helm Chart's changing$appVersionvalue in Charts.yaml file in seehttps://github.com/CoffeaTeam/coffea-casa/blob/master/charts/coffea-casa/Chart.yamlAdd new tag:https://github.com/CoffeaTeam/coffea-casa/releasesPlease note we are usingfor Coffea-casa Docker images, Helm charts and Pypi module.ReferencesCoffea-casa: an analysis facility prototype, M. Adamec, G. Attebury, K. Bloom, B. Bockelman, C. Lundstedt, O. Shadura and J. Thiltges, arXiv(02 Mar 2021).PyHEP 2020 coffea-casa proceedings:The PyHEP 2020 introductory Youtube video ishere.Contact usInterested? You can reach us inor in IRIS-HEP Slack channel.
Package: coffee-boat
![coffee_boat_logo](https://raw.githubusercontent.com/nteract/coffee_boat/master/imgs/coffee-boat.png)
[![buildstatus](https://travis-ci.org/nteract/coffee_boat.svg?branch=master)](https://travis-ci.org/nteract/coffee_boat)# pyspark-deps
WIP PySpark dependency management.## TestsRunning tests will modify your current virtualenv’s packages. This software is WIP.## UsingNot in production, please! If you’re curious to see how it can be used, check outhttps://github.com/nteract/coffee_boat/blob/master/Coffee%2BBoat%2BSample.ipynbThis has been tested (although not thoroughly) with standalone mode and YARN. It explicitly does not currently support local mode, although we’d love your contribs.For now, stay tuned or join us in hacking!## AlternativesBuild you own conda env, or use one of the commercial integrated solutions which has a nice UI for this.
Package: coffeece
CoffeecePython useful classes for office work automation.
Currently working on Python 3.InstallYou can install this package using pip:pip install coffeecePopupTkinter popup functions to:show_info(title, message) -> shows a popup with the title and message provided.ask_file(admitted_file_extensions='any') -> Ask user for a file. Optional: pass admitted file extensions as a list of strings argument. Returns path as string.ask_directory() -> popup asks for a directory through file explorer. Returns path as string.ask_yes_no(title, message) -> shows a popup with the title and message provided and ask yes-no. If yes returns 'True'.SQLiteDBA class to manage SQLite3 databases. Now it could:create_connection(self) -> Connect with sqlite3 DB (create it if there isn't). Returns conection object.create_cursor(self) -> Create cursor to communicate with DB. Returns cursor.create_table(cursor, table_name, table_columns) -> Create a table with the name (string) columns and column types (dictionary) given by the user.insert_row(cursor, table_name, row_items) -> Inserts row into DB with the cursor (cursor), table name (string), and items (list) given by the user.commit_changes(self) -> Save DB changes.close_connection(self) -> Close connection to DB.export_table_to_excel(self, table_name) -> Exports table to excel usingPandaslibrary.SystemSystem navigation and recursion functions:list_all_files(src_directory) -> Make a list of all the files in the given directory. Returns list.list_all_files_recursively(src_directory) -> Make a dictionary of all the files in the given directory recursively. Returns dictionary = root: { dirs: {} , files: {} }list_files_by_type(src_directory, file_extension) -> Make a list of all the files with the given file extension in the given directory. Returns list.extractall_zipfile(filepath, password=None) -> Opens zip file and extracts all its content.ExcelAll Excel-related functions:xls_to_xlsx(src_filename,output_filename) -> Converts .xls file to .xlsx file format.ScriptsScripts with utilities:venv_cleanerDelete all virtual environments inside the active directory tree. Usage:From Python shell:>>> from coffeece.scripts import venv_cleaner
>>> venv_cleaner.main()As a script file:python venv_cleaner.pyYou can delete venvs with different name than 'venv' using the optional argument -vn or --venvname.Why Coffeece?The main reason behind this little classes is that a huge part of the mechanical work at a job office can be automated through this kind of objects.
The other reason to use this is to make office scripts cleaner with an — even more — simplified dependency management.
Package: coffee-compressor-compiler
Coffee Compressor Compiler=========================[![Build Status](https://travis-ci.org/Juvenal1228/coffee-compressor-compiler.png?branch=master)](https://travis-ci.org/Juvenal1228/coffee-compressor-compiler)Purpose-------This tool is meant to be used as an extension to [django-compressor](https://github.com/jezdez/django_compressor)It precompiles [coffee-script](http://coffeescript.org/)Features--------- platform independent- no need to install node.js packages- 100% test coverage- [PEP 8](http://www.python.org/dev/peps/pep-0008/) compliance- [semver](http://semver.org/) complianceInstalling----------Install with pip/easy_install from the pypi`pip install coffee-compressor-compiler`or clone the latest sourcegit clone https://github.com/Juvenal1228/coffee-compressor-compiler.gitcd coffee-compressor-compilerpython setup.py installYou must also install [node.js](http://nodejs.org/) or [PyV8](https://code.google.com/p/pyv8/)The latest versions of node.js can be found [here](http://nodejs.org/download/)Using-----Using this tool is as simple as installing it and adding it to the `COMPRESS_PRECOMPILERS` django setting```pythonCOMPRESS_PRECOMPILERS = (('text/coffeescript', 'coffeecompressorcompiler.filter.CoffeeScriptCompiler'),)```Then, in your django templates you can embed coffee-script templates like so```html+django{% load staticfiles %}{% load compress %}{% compress js %}<script type="text/coffeescript" src="{% static 'app/main.coffee' %}" ></script><script type="text/coffeescript">console.log 'Hello!'alert 'Hello!'</script>{% endcompress %}```
Package: coffeefetch
CoffeeFetchA lightweight system info grabber written in PythonIntroductionCoffeeFetch is a system information grabber for Unix systems that fetches your system information and displays it to the screen. This is a lightweight package and is built to be run on systems without the use of a Window Manager or Desktop Enviornment and is displayed directly in in TTY. It will attempt to grab you CPU model, CPU frequency, current RAM consumption, current disk usage, Operating System, CPU architecture, IP Address, hostname, and OS version.InstallationIt is required to use Python 3.7 or greater for this package.psutil >= 5.9.5 is required for this package.Firstly, clone this repository:git clone https://github.com/TeaPixl/CoffeeFetch/
cd CoffeeFetchThen, install the required Python modulespip install -r requirements.txtBuild and install the .whl fileChange the X.X.X to the version you havepython3 -m build
cd dist/
pip install coffeefetch-X.X.X-py3-none-any.whlORpip install coffeefetchUsageTo use CoffeeFetch, type into your terminal:python3 -m coffeefetchNOTEThis project is currently in the works and I have not finished it yet.
This is a NEW repository so feel free to post any issues you may find.Currently working on:Providing more detailed exceptionstesting
Package: coffeegrinder
COFFEECOFFEE is an MPI-Parallelised Python package for the numerical evolution of time-dependent differential equations.InstallationThere are four versions of COFFEE, which can include MPI and/or JAX capabilities. These can be installed viapip install coffeegrinderpip install coffeegrinder[MPI]pip install coffeegrinder[JAX]pip install coffeegrinder[MPI-JAX]Getting startedThere is a systems directory that includes commented test cases for the 1D advection equation with a mixture of finite-differencing operators and boundary imposition methods.NoteThis project has been set up using PyScaffold 4.4. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: coffeehouse
CoffeeHouse API Wrapper for PythonThis is a very simple API Wrapper for the CoffeeHouse API. Using
This Library only supports the V1IVA2.0 CoffeeHouse API which is based from
thisDocumentationInstallationpipinstallcoffeehouseorpythonsetup.pybuild
pythonsetup.pyinstallLydia Examplefromcoffeehouse.lydiaimportLydiaAI# Create the CoffeeHouse API instanceapi_key=input("API Key: ")# Create Lydia instancelydia=LydiaAI(api_key)# Create a new chat session (Like a conversation)session=lydia.create_session()print("Session ID:{0}".format(session.id))print("Session Available:{0}".format(str(session.available)))print("Session Language:{0}".format(str(session.language)))print("Session Expires:{0}".format(str(session.expires)))# Talk to the bot!whileTrue:output=session.think_thought(input("Input: "))print("Output:{0}".format(output))# In the case you want to save the Session ID to reuse the session# Use lydia to invoke think_thought instead, for example;## while(True):#     output = lydia.think_thought(session.id, input("Input: "))#     print("Output: {0}".format(output))## This is the same effect as above but uses the lydia instance directly.
Package: coffee-list-app
app-version3A simple django app to demonstarate my understanding of djangoPrerequisitesWhat things you need to install the software
atomicwrites==1.3.0
attrs==19.1.0
certifi==2019.6.16
chardet==3.0.4
coverage==4.5.3
Django==2.2.2
entrypoints==0.3
flake8==3.7.7
gunicorn==19.9.0
idna==2.8
importlib-metadata==0.18
mccabe==0.6.1
meld3==1.0.2
more-itertools==7.1.0
packaging==19.0
pluggy==0.12.0
psycopg2==2.8.3
py==1.8.0
pycodestyle==2.5.0
pyflakes==2.1.1
pyparsing==2.4.0
pytest==5.0.0
pytest-cov==2.7.1
pytz==2019.1
requests==2.22.0
setproctitle==1.1.10
six==1.12.0
sqlparse==0.3.0
urllib3==1.25.3
wcwidth==0.1.7
zipp==0.5.1
django-prometheus==1.0.15InstallingA step by step series of examples that tell you how to get a development env running
install a virtual env
clone this project to your virtual
run dockerRunning the testsrun tox
run coverage run --source='.' manage.py test
coverage report htmDeploymentBuilt WithVersioning1.0.0Authorscuttice odhiamboLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: coffee-ninja
TORO Toggler Markdown ExtensionContent Toggler Markdown extension for TORO Documentation.Installationpip install toro-mkdocs-togglersUsageTo properly use the toggler extension use the following markdown:||| "TabName1"
    Tab1 content.
||| "TabName2"
    Tab2 content.
|||The produced HTML from the markdown will be:<div class="toro-toggler" data-names="TabName1,TabName2">
    <div data-related-divider="TabName1">
        <p>Tab1 content.</p>
    </div>
    <div data-related-divider="TabName2">
        <p>Tab2 content.</p>
    </div>
</div>To use the toggler extension with a nested toggler use the following markdown:||| "TabName1"
    /// "NestedTabName1"
        Nested Tab1 content.
    /// "NestedTabName2"
        Nested Tab2 content.
    /// 
||| "TabName2"
    Tab2 content.
|||The produced HTML from the markdown will be:<div class="toro-toggler" data-names="TabName1,TabName2">
    <div data-related-divider="TabName1">
        <div class="toro-toggler" data-names="NestedTabName1,NestedTabName2">
            <div data-related-divider="NestedTabName1">
                <p>Nested Tab1 content.</p>
            </div>
            <div data-related-divider="NestedTabName2">
                <p>Nested Tab2 content.</p>
            </div>
        </div>
    </div>
    <div data-related-divider="TabName2">
        <p>Tab2 content.</p>
    </div>
</div>
Package: coffeepy
CoffeepyCoffeepy ☕️ is a small program that prevents the system from sleeping.
Works on MacOS, Windows and Linux.Installationpipinstall-UcoffeepyUsageSimply run the program from command linecoffeepyBy default the program runs indefinitely. PressCtrl-Cto quitOptional: You can set the time in minutes with-tor--timeflag. For example, to run the program for 1 hour, usecoffeepy-t60You can also disable animation with-aor--no-animationflag.coffeepy-aYou can view the full parameter list with-hor--help.Python moduleYou can also import coffeepy as a python moduleimportcoffeepycoffeepy.run()Optional settings when run as a python module:importcoffeepy# you can also specify the time in minutes# if no time is provided or time = 0, the program will run indefinitelycoffeepy.run(60)# to disable animation when run as a module, you can set the second argument to Truecoffeepy.run(0,True)LicenseThis project is licensed under the MITLicense
Package: coffee-roulette
coffee-roulettecoffee_roulette is a pseudo-random pair generator. It ensures unique pairings among the subjects in each round and optionally suggests a conversation starter and a meeting time.The code was written for the Coffee Roulette initiative organised by the Research Culture Working Group, Community subgroup, at the Sainsbury Wellcome Centre between January 2021 and November 2022.To use the code:install and import the packagegit clone https://github.com/zanemit/coffee-roulette.git
pip install -e .
import coffee_roulette as crcreate a coffee roulette project (code inspired byDeepLabCut)
Participants can be supplied as a list or entered one-by-one through user prompts.cr.create_coffee_roulette(roulette_name, working_dir = None, participants = None)This initialises:
- a matrix of meeting probabilitiescrProbMatrix.npy- a dictionary that stores active participants and their conversation starterscrParticipantDict.pickle- a configuration fileconfig.yamlthat stores project directory and a range of meeting parameters(frequency, duration, purpose) that affect the printed results
- a conversation starter filecrConversationStarters.txtthat users are advised to populate furtherget pairscr.get_pairs(cfg_path, conv_starters=True, random_time=True, exclude_nonrandom=None)This prints the generated pairs and stores them in the\resultsfolder.
Package: coffee-sh
Coffee.shCoffee is a command-line utility for developers who want their task list at terminalInstallationpipinstallcoffee-shUsageTo add a new taskcoffeeadd"Drink Water"To Update taskcoffeeupdate1--task"Drink More Water"Here 1 is task ID which is uniqueTo Delete taskcoffeedrop1Here 1 is task ID which is uniqueTo show all taskscoffee tasksTo remove all taskscoffeeerase
Package: coffeeshop
CoffeeshopThis package sends your deep learning model's training and validation metrics to your slack channel after every specified epoch.
It uses slackclient and keras python packages.Made for keras framework.Installation$pip install coffeeshopCode samplefromcoffeeshop.coffeeshopimportCoffeeshopsecret='xoxp-slacktoken'# For sending metrics to channel.channel_name='name_of_channel_to_be_posted'histories=Coffeeshop(token=secret,channel_name=channel_name,epoch_num=5)# For sending metrics to user.user='User Name'histories=Coffeeshop(token=secret,user_name=user,epoch_num=5)## Add histories in the callbacks.model.fit(X_train,Y_train,epochs=epochs,batch_size=batch_size,callbacks=[histories])Output sampleContactE-mailGithubLinkedIn