Saravanan666/pypi-org huggingface dataset:
Package: coffeetools
[https://bitbucket.org/userzimmermann/python-coffeetools]()[coffee][1] interface for evaluating and compiling
[CoffeeScript](http://coffeescript.org/)[{% coffee … %}][2] template tags for [Jinja2](http://jinja.pocoo.org)[%%coffeescript][3] magic function for [IPython Notebook](http://ipython.org/notebook.html)0. SetupSupported Python versions: __2.7__ and __3.3+__You need an installed CoffeeScript compiler.
By default __coffeetools__ look for acoffeeexecutable in yourPATH,
orcoffee.cmdon Windows.
You can find installation instructions and further information at
[http://coffeescript.org/]().
The easiest way is to use the
[node package manager](https://www.npmjs.com/):npm install coffee-scriptTo install __coffeetools__, just use [pip](http://www.pip-installer.org)
to get the latest [release](https://pypi.python.org/pypi/coffeetools)
from [PyPI](https://pypi.python.org):pip install coffeetoolsFor basic usage there are no dependencies on other Python packages.__Jinja__ and __IPython__ integration are[extra]features.
They require the following Python packages:[jinja][jinjatools >= 0.1.4](https://pypi.python.org/pypi/jinjatools)[ipython][ipython[notebook]](https://pypi.python.org/pypi/ipython)All extra Python requirements can also be installed with:pip install coffeetools[all]1. Using CoffeeScript from Python[1]: #markdown-header-1-using-coffeescript-from-pythonfrom coffeetools import coffeeEvaluate CoffeeScript code and return the script’s output:coffee(‘Some CoffeeScript’)Compile CoffeeScript to JavaScript:coffee.compile(‘Some CoffeeScript’)Compile CoffeeScript to JavaScript without a top-levelfunction():coffee.compile(‘Some CoffeeScript’, bare=True)If you want to specify a custom name
of your CoffeeScript compiler executable or its absolute or relative path,
just create a custom interface instance:from coffeetools import Coffeecoffee = Coffee(‘executable’)
#OR
coffee = Coffee(‘/absolute/path/to/executable’)
#OR
coffee = Coffee(‘relative/path/to/executable’)2. Using CoffeeScript from Jinja2[2]: #markdown-header-2-using-coffeescript-from-jinja2More information coming soon…3. Evaluate CoffeeScript in an IPython Notebook[3]: #markdown-header-3-evaluate-coffeescript-in-an-ipython-notebook%load_ext coffeescriptIt is also safe to%reload_ext coffeescriptThis registers the%%coffeescriptcell magic function,
which works like Ipython’s%%javascriptmagic,
except that it takes CoffeeScript as input.
It compiles the given code to JavaScript
and usesIPython.display.display()withIPython.display.Javascriptto run the code directly in the browser window:%%coffeescript
alert $(‘#notebook_name’).text()
Package: coffer
AboutEasily send and receive files in your LAN. Without ever typing an IP address.How does it workBasically, it’s an HTTP server with simple APIs + zeroconfHow to use itcoffersendfilenamecofferget--allOther featuresYou can filter what to download with--filterYou can automatically exit after every file has been downloaded once, using--oneYou can have passwords (but they are not a great form of protection!)Non-featuresThis kind of sharing is not secure, is not anonymous, and won’t be. Making it
easy means announcing our service (not anonymous, therefore) and making it
simple means allowing anyone to download (making it unsecure by definition)Known bugsFile download is still not implementedInstallationpip install cofferis enoughReal-life examplesone to oneScenario1: You are in a room with another person. Each one has a computer.
You are in the same LAN. You want to send a file to a person in front of you.
This usually requires:manually launching a server to share filesspelling the URL to the fellow in front of you, who has to type itThat’s too boring.solutioncoffersend--onemyfile.txtcofferget--allone to manyYou are in a room with many people. Each one has a computer.
You are in the same LAN. You want to send a file to many people.
This usually requires:creating a directory with only the files you want to share insidemanually launching a server to share filesspelling the URL to everyone, and everyone needs to type it.This is crazy.solutioncoffersendmyfile.txtcofferget--allmany to many + a commandYou are in a room with many people. Each one has a computer.
You are in the same LAN. Everyone wants to send his gpg key to everyone else.
This usually requires:each one creating a directory with only the files you want to share insideeach one manually launching a server to share fileseach one spelling the URL to everyone.each one following each link andgpg--importitAre you kidding me?solutioncoffersend=(gpg-a--export$(gpg--with-colons-K|egrep'^sec'|cut-d:-f5|head-n1))cofferget--all--cat|gpg--importAbout the nameThe main concept incofferis that you are offering files to other people.
Sharing is caring, and I like it. The other thing that I like so much is
coffee.cofferis the sum of the best things in life.
Package: coffer-container
Overview=====![](https://img.shields.io/badge/version-1.3.2-brightgreen.svg) [![](https://img.shields.io/badge/irc-%23coffer-red.svg)](https://webchat.freenode.net/) [![](https://img.shields.io/badge/twitter-cofferproject-blue.svg)](http://twitter.com/cofferproject)A lightweight platform for creating isolated and portable development environments.Requirements============- Python 3- Linux or MacOSQuick Start===========`pip install coffer-container`What is Coffer?===============Before I talk about what Coffer is, let me say what Coffer is *not*. Coffer is not a replacement for Docker. In fact, if you are looking for a completely isolated containeryou're probably better off using Docker, or some other container software. Coffer is also not intended to be used as a secure means by which to isolate an application, andit is recommended that Coffer not be used outside of a development setting.Coffer is a platform for creating isolated filesystem containers. It is intended to be used to create isolated development environments without having to worry about doing any network configuration.Applications that are run in Coffer can be accessed outside of a Coffer environment through `localhost` without having to do anything more than `coffer create <name>`.This makes it easy to get environments up and running for those of us who do not need network isolation, and only wish to islolate an app and its dependencies.Coffer makes it easy to create, enter, and share environments with others.What are some features of Coffer?=================================- Create isolated file system containers- Use Coffer templates to automatically install and configure a container- Share containers and templates with a team to replicate an environment- Does not isolate network, so there is no extra configuring that needs to be done- Creating an environment is as easy as executing `coffer create <name of environment>`How can I get started?======================1. Clone this repo2. Run `setup.py`3. In a terminal execute `coffer create first_env`4. Then `coffer enter first_env`Then like that you have created your first environment! For more extensive getting started docs, read our [Getting Started](https://github.com/Max00355/Coffer/blob/master/docs/GettingStarted.md) docs.Current Status==============Coffer is under very heavy development, and receiving frequent updates. The code in the `master` branch is considered the most stable, and in it's current state Coffer is ready for actual use.In fact, Coffer development is happening in a Coffer container as of right now. If there are any bugs, please create issues about them.
Package: coffer-mac
coffer_macTiny Python API for performing MAC address lookups usingCoffer.Install$ pip install coffer_mac --userUsage>>>fromcoffer_macimportCofferMAC>>>client=CofferMAC()>>># Lookup by full MAC address>>>client.lookup('08:00:69:02:01:FC')[{'prefix':'080069','vendor':'Silicon Graphics (SGI)'},{'prefix':'080069','vendor':'Silicon Graphics (SGI)'}]>>># Lookup by first three octets>>>client.lookup('08:00:69')[{'prefix':'080069','vendor':'Silicon Graphics (SGI)'},{'prefix':'080069','vendor':'Silicon Graphics (SGI)'}]>>># Lookup by vendor>>>client.lookup('trident')[{'prefix':'001153','vendor':'Trident Tek, Inc.'},{'prefix':'00258F','vendor':'Trident Microsystems, Inc.'},{'prefix':'001153','vendor':'Trident Tek, Inc.'},{'prefix':'00258F','vendor':'Trident Microsystems, Inc.'}]>>># Use a proxy>>>client=CofferMAC(proxy='localhost:8080')>>>client.lookup('foo')[{'prefix':'001588','vendor':'Balda-Thong Fook Solu'},{'prefix':'001903','vendor':'Bigfoot Networks Inc'},{'prefix':'00609D','vendor':'Pmi Food Equipment Group'},{'prefix':'00D029','vendor':'Wakefern Food Corporation'},{'prefix':'001588','vendor':'Balda-Thong Fook Solu'},{'prefix':'001903','vendor':'Bigfoot Networks Inc'},{'prefix':'00609D','vendor':'Pmi Food Equipment Group'},{'prefix':'00D029','vendor':'Wakefern Food Corporation'},{'prefix':'C458C2','vendor':'Shenzhen TATFOOK Technology Co., Ltd.'}]DisclaimerI am not affiliated with "Coffer Group, LLC" and I cannot vouch for the validity of the data obtained.
Package: coffin-absolute
coffin-absoluteProvidesabsolute_urltag and filter in Jinja2 templates when using thecoffinpackage with Django.Theabsolute_urltag has the same arguments as theurltag.
Theabsolute_urlfilter takes therequestas the additional, first argument.Both the tag and the filter render an absolute url based on the host of the request context.InstallationJust add'coffin_absolute'toINSTALLED_APPS.
Package: cofi
CoFI (Common Framework for Inference)Related repositories byInLab:CoFI ExamplesEspressoIntroductionCoFI (Common Framework for Inference) is an open source initiative for interfacing between generic inference algorithms and specific geoscience problems.With a mission to bridge the gap between the domain expertise and the inference expertise, CoFI provides an interface across a wide range of inference algorithms from different sources, underpinned by a rich set of domain relevantexamples.Readthe documentation, and let us know your feedback or any issues!InstallationFrom PyPI:$pipinstallcofiOr alternatively, from conda-forge:$condainstall-cconda-forgecofiCheck CoFI documentation -installation pagefor details on dependencies and setting up with virtual environments.Basic UsageCoFI API has flexible ways of defining an inversion problem. For instance:fromcofiimportBaseProbleminv_problem=BaseProblem()inv_problem.set_objective(my_objective_func)inv_problem.set_initial_model(my_starting_point)Once a problem is defined,cofican tell you what inference tools you can use based on what level of
information you've provided:inv_problem.suggest_tools()# a tree will be printedRun an inversion with these lines:fromcofiimportInversionOptions,Inversioninv_options=InversionOptions()inv_options.set_solving_method("optimization")inv_options.set_params(options={"maxiter":100})inv=Inversion(inv_problem,inv_options)result=inv.run()print(result.success)print(result.model)And now an inversion is completed! Check out ourexample galleryandtutorialpages for more
real-world or advanced use cases.ContributingInterested in contributing? Please check out ourcontributor's guide.LicenceThis project is distributed under a 2-clause BSD licence. A copy of this licence is
provided with distributions of the software.
Package: cofi-espresso
EspressoIntroductionEarthSciencePRoblems for theEvaluation ofStrategies,Solvers andOptimizers (Espresso) is a collection of datasets, and
associated simulation codes, spanning a wide range of geoscience problems.
Together they form a suite of real-world test problems that can be used to
support the development, evaluation and benchmarking of a wide range of tools
and algorithms for inference, inversion and optimisation. All problems are
designed to share a common interface, so that changing from one test problem
to another requires changing one line of code.The Espresso project is a community effort - if you think it sounds useful,
please consider contributing an example or two from your own research. The project
is currently being coordinated by InLab, with support from the CoFI development
team.For more information, please visit our documentation (coming soon).Installation$pipinstallcofi-espressoCheck Espresso documentation -installation pagefor details on dependencies and setting up with virtual environments.Basic usageOnce installed, each test problem can be imported using the following command:fromcofi_espressoimport<testproblem>Replace<testproblem>with one of the following currently available problems:GravityDensitySimpleRegressionXrayTomographyOnce a problem is imported, its main functions can be called using the same
structure for each problem. For instance:fromcofi_espressoimportGravityDensityproblem=GravityDensity(example_number=1)model=problem.good_modeldata=problem.datapred=problem.forward(model)fig_model=problem.plot_model(model)fig_data=problem.plot_data(data,pred)You can access related metadata programatically:print(GravityDensity.problem_title)print(GravityDensity.problem_short_description)print(GravityDensity.author_names)Other problem-specific parameters can be accessed through the problem instance. For instance:print(problem.m)print(problem.rec_coords)Which additional values are set is highly problem-specific and we suggest to
consult theEspresso Documentation on the problems.ContributingInterested in contributing? Please check out ourcontributor's guide.LicenceEspresso is a community driven project to create a large suite of forward
simulations to enable researchers to get example data without the need to
understand each individual problem in detail.Licensing is done individually by each contributor. If a contributor wants to freely share their code example we recommend the MIT licence or a
2-clause BSD licence. To determine the licence of an existing Espresso
problem, please consult the documentation section of that problem.All the other core functions of Espresso written by InLab Espresso developer
team are distributed under a 2-clause BSD licence. A copy of this licence is
provided with distributions of the software.
Package: coflux
Coflux → Clients → PythonCoflux is an open-source workflow engine. Use it to orchestrate and observe computational workflows, defined in plain Python. Suitable for data pipelines, background tasks, chat bots.This is the Python client, which is used for defining workflows. It also includes the CLI, which can be used for running agents, configuring new projects, and starting runs.Refer to thedocsfor more details.
Package: cofog
CofogIntroductionClassification of the Functions of Government (COFOG) is a classification defined by the United Nations Statistics Division. Its purpose is to "classify the purpose of transactions such as outlays on final consumption expenditure, intermediate consumption, gross capital formation and capital and current transfers, by general government" (fromhome page).This intention of this package is to serve a convenient way of parsing and interfacing with the classifications.DataData was sourced from the UN'sclassifications on economic statistics pageand parsed into a dictionary that can be found insrc/cofog/data.py.FeaturesProvides theCOFOGclass to represent a Classification of Functions of Government.ValidateCOFOGcodes through Regular Expressions and presence in the data.Lookup descriptions from codes.Traverse the levels of any given code, with the ability to set a lower level without forgetting the original level.RequirementsPython >=3.8Click >=8.0.1InstallationYou can installCofogviapipfromPyPI:$pipinstallcofogUsagePlease see theusage pagein the docs for full details.Get started by initialising a newCOFOGobject with a code. These can be either specified as strings (with and without dots) or integers.fromcofogimportCOFOGcofog=COFOG("04.3.6")# or any of the followingCOFOG("0436")COFOG("436")COFOG(436)You can then access the code's description as well as set it to lower level codes.print(cofog.description)# Non-electric energy  (CS)print(cofog.level)# 3cofog.set_level(2)print(cofog.code)# 04.3print(cofog.description)# Fuel and energyYou can also get parent and children codes of any valid code.print(cofog.get_parent_code())# 04.3print(COFOG("07.3").get_children_codes())# ["07.3.1", "07.3.2", "07.3.3", "07.3.4"]ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,Cofogis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was heavily inspired by@ellsphillips'sgovsicpackage.This project was generated from@cjolowicz'sHypermodern Python Cookiecuttertemplate.
Package: cofunc
cofuncImplement a class that represents a continuous function.Copyright noticeCopyright (C) 2013-2023 Daniel Jung.
Contact:proggy-contact@mailbox.orgThis program is free software; you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the Free
Software Foundation; either version 2 of the License, or (at your option)
any later version.This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
more details.You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
59 Temple Place, Suite 330, Boston, MA 02111-1307 USA.
Package: cofyflex
COFYflex PythonDescriptionPython library containing the source code for multiple microservices used by COFYcloud to provide flexibility services.VisualsTODO: Add viz of Price Matrix
See Demo NotebookInstallationpython3 -m pip install cofyflexUsageSee Demo NotebooksSupportUse the Gitlab issue tracker, or see docs.cofybox.io.RoadmapTODOContributingClone the repoCreate a virtual environment (python3 -m venv venv) & Activate it (source venv/bin/activate)Install requirements (pip install -r requirements.txt)Install additional packages for running notebooks and visualisation (pip install jupyter matplotlib)Start notebook server and run the examples (jupyter notebook)Please contribute by creating a branch per issue and using merge requests that are easily testable.Authors and acknowledgmentThe DEV-team of the H2020 REScoopVPP project: EnergyID & CarbonCoopLicenseMIT
Package: cog2
cog2 a modified version of cogUse this command to start the virtual environmentpoetry shellinstall to current the program virtual environmentpeotry installbuild the whlpoetry buildPyPi token configpoetry config pypi-token.pypi pypi-wubalubadubdub-deadbeef1234build and publishpoetry publish --buildinstall from pippip install --user cog2
Package: cog-airflow-notify-sns
Publish Airflow Notification to a SNS TopicThis package adds a callback function to use in failures to DAGs and Tasks in a Airflow project.Installationpipinstallcog-airflow-notify-sns==1.0.0Usagefromdatetimeimporttimedelta# Airflow native imports to create a DAGfromairflowimportDAG,utilsfromairflow.operators.bash_operatorimportBashOperator# Here is function importfromairflow_notify_snsimportairflow_notify_sns# Dag Definitiondag=DAG(dag_id='test_dag',default_args={'owner':'Cognitivo.ai','depends_on_past':False,'start_date':utils.dates.days_ago(1),'retries':3,'retry_delay':timedelta(minutes=5)},schedule_interval="@daily",dagrun_timeout=timedelta(minutes=60),sla_miss_callback=airflow_notify_sns#here)# Add your tasks heret=BashOperator(dag=dag,task_id='test_env',bash_command='/tmp/test.sh',env={'EXECUTION_DATE':'{{ ds }}'},on_failure_callback=airflow_notify_sns#here)When DAG or tasks ends in error, a notification will be send to a SNS Topic using AWS default connection (aws_default).Required VariableThis module will try to find a variable namedairflow_alerts_configin your Airflow environment.Example{"sns_topic_arn":"arn:aws:sns:us-east-1:012345678901:topic-name","airflow_url":"xpto.us-east-1.elb.amazonaws.com:8080"}If variable is not found, function will abort execution with no error.
Package: cogbooks
The idea of using jupytext for CogWorks is this: Editing notebooks is painful. Doing version control on notebooks is
painful. Giving students notebooks is great. So, as instructors, we will develop all of our material in markdown files
and then we will usecogbooksto convert our markdown files to jupyter notebooks.What is great about this is,cogbooksintroduces special markdown delimiters that permit instructors to include
solutions and instructors-eyes-only comments in a markdown file, so that the file contains the full solutions.cogbookswill then excise these sections when it converts the instructor-markdown files to student-notebooks.
Package: cogconverter
IntroductionIt validates the TIF and convert it into COG compliant using gdal.
Following are the Use-Cases kept in mind while designing:Large TIF that cannot be fit into memoryReading data block by block, so can be run of even low memory serverSupports Multiband TIFs3-4 Band uint8 TIF (Orthomosaic)Building pyramids if not available (This will improve rendering
speed)Compressing dataCompressing to the same compression format as the original TIF. If
original TIF was not compressed then LZW lossless compression is used
to compress.Tile whole into 256x256 smaller blocks internallyValidator.pyIt will validate tiff for COG format.Converter.pyIt has the actual converter function which converts tifs into COG formatTo-DoMulti-core processing for faster results.How to RunInside python consoleimport cogconverter as cog
import gdal

path_tif = 'sentinel2.tif'
path_output = 'sentinel2_cog.tif'

ds = gdal.Open(path)

ds = cog.converter.convert2blocksize(ds, path_output)
ds.FlushCache()