Saravanan666/pypi-org huggingface dataset:
Package: contact-map
Contact Map ExplorerThis package provides tools for analyzing and exploring contacts
(residue-residue and atom-atom) from a trajectory generated by molecular
dynamics. It builds on the excellent tools provided byMDTraj.Contacts can be an important tool for defining (meta)stable states in
processes involving biomolecules. For example, an analysis of contacts can
be particularly useful when defining bound states during a binding processes
between proteins, DNA, and small molecules (such as potential drugs).The contacts analyzed by Contact Map Explorer can be either intermolecular or
intramolecular, and can be analyzed on a residue-residue basis or an atom-atom
basis.This package makes it very easy to answer questions like:What contacts are present in a trajectory?Which contacts are most common in a trajectory?What is the difference between the frequency of contacts in one trajectory
and another?  (Or with a specific frame, such as a PDB entry.)For a particular residue-residue contact pair of interest, which atoms are
most frequently in contact?It also facilitates visualization of the contact matrix, with colors
representing the fraction of trajectory time that the contact was present.Full documentation is athttp://contact-map.readthedocs.io/.Try it out online:(Note: the performance of the online servers can vary widely.)InstallationThe easiest way to install is withconda. Conda is a powerful package and
environment management system. If you do not already have a highly
customized Python environment, we recommend starting by installingconda,
either in thefull anaconda
distributionor thesmaller-footprint
miniconda. This package is distributed
through theconda-forgechannel; install it with:condainstall-cconda-forgecontact_mapIf you don't want to useconda, you can also usepip(via a more
complicated process) or do a developer install. See theinstallation
documentationfor details.Support and developmentContact Map Explorer is an open source project, released under the GNU LGPL,
version 2.1 or (at your option) any later version. Development takes place
in public athttps://github.com/dwhswenson/contact_map; your contributions
would be welcome!If you have suggestions or bug reports, pleaseraise an issue on our GitHub
issue page.
Package: contacto
Welcome to the Contacto project!This is an attempt to bring maximum flexibility to contact managers.Contacto is targeted at developers and has a simple interface:$pipinstallcontacto$contacto-onew.dbset-rFamily/Mom/age48$contacto-onew.dbget>Family-Mom-age:48LinksDocumentationPyPI projectProject assignmentCLIOperate the manager from the command line.$contacto$python-mcontactoGUITest the simple read-only GUI.$qcontacto[<database>]$python-mcontacto.gui[<database>]Building docsInstall the package with thedevextras.
Docs are in thedocsdirectory.$pipinstall.[dev]$cddocs$makehtmlLicenseContacto is distributed under the GNU GPLv3 license, seeLICENSE.
Package: contactpp
contactpp is a package for the generation of pseudopotentials for the
contact interaction. The theory is outlined in[BLNC].[BLNC]P.O. Bugnion, P. López Ríos, R.J. Needs, and G.J. Conduit,
High-fidelity pseudopotentials for the contact interaction.LinksHome page:https://pypi.python.org/pypi/contactppDocumentation:http://contactpp.readthedocs.org/en/latest/Source code:https://github.com/pbugnion/contactppIssues:https://github.com/pbugnion/contactpp/issuesInstallationcontactpp requires python2.7, numpy and scipy. On Ubuntu, for instance,
these can be installed with:$ sudo apt-get install python-numpy python-scipyThe easiest way to download and installcontactppis from the Python
package index. Run:$ easy_install contactppThis requires root access (unless you are running in a virtual environment).
To install without root access, run:$ easy_install --user contactppTo install from github, clone the git repository using:$ git clone https://github.com/pbugnion/contactpp.gitNavigate to the source’s root directory (contactpp) and run:$ python setup.py installIf you have a zip or a tar.gz archive of the source, unpack the compressed archive into a directory, navigate to this directory and run:$ python setup.py installYou may need to run this as root, unless you are running in a virtual environment.Issue reporting and contributingReport issues using thegithub issue tracker.Read the CONTRIBUTING guide to learn how to contribute.
Package: contactsheet
contactsheetContactsheet compiles a set of images into a single contact sheet image.Free software: MIT licenseDocumentation:https://contactsheet.readthedocs.io.FeaturesProduces a contact sheet of images.  The output contact sheet image is the same size as the first original
image provided.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-07-06)First release on PyPI.
Package: contactsoftware-functions
Functions-LibraryThis library is used for creating Functions for CIM Database Cloud.Installation$pipinstallcontactsoftware-functionsExample usagefromcs.functionsimportMetaData,WorkloadResponse,Servicefromcs.functions.eventimportDocumentReleaseEventfromcs.functions.actionimportAbortAndShowErrorActiondefmy_function(metadata:MetaData,event:DocumentReleaseEvent,service:Service):number=service.generator.get_number("error_counter")message=f"Document{event.data.documents[0].z_nummer}can't be released by{metadata.app_user}! Attempt:{number}"returnWorkloadResponse(event_id=event.event_id,actions=[AbortAndShowErrorAction(id="123",message=message)],)
Package: contactsoftware-functions-client
Functions-ClientThis CLI tool is used for uploading and managing Functions in CIM Database Cloud.Installation$pipinstallcontactsoftware-functions-clientLoginBefore you can create environments or deploy Functions you need to login using your client-id and secret. Obtain your client credentials via the CONTACT Portal.$cfclogin--client-id<client_id>--client-secret<client_secret>UsageFirst you need to have an environment that you can deploy your code into.Check if you already have environments available:$cfcenvlistor create a new one:$cfcenvcreate<environment_name>You need to create anenvironment.yamlfile that lists the Functions your environment should contain:runtime:python3.10version:v1functions:-name:my_functionentrypoint:main.my_functionand of course the code of your Function:# main.pydefmy_function(metadata,event,service):...[!NOTE]
You can specify extra requirements your code has in a requirements.txt.[!IMPORTANT]
The python3.10 runtime requires you to addcontactsoftware-functionsto the requirements.txt unless you specify your own main_entrypoint handler. (see documentation)Now you can deploy your code into an environment:$ cfc env deploy <environment_name>
Package: contact_widget
A simple contact form widget for Django.InstallDjango Contact Widget is available directly fromPyPI:$ pip install django-contact-widget*).And don’t forget to addcontact_widgetto yourINSTALLED_APPS.Database Migration$ ./manage.py makemigrations contact_widget
$ ./manage.py migrate contact_widgetRequirementDjango>=1.10.1Setting ConfigurationEmail Configuration in file ofsettings.pyEMAIL_HOST = 'smtp.gmail.com'  # eg: 'smtp.gmail.com'
EMAIL_PORT = 587
EMAIL_HOST_USER = 'your_email@domain.com'
EMAIL_HOST_PASSWORD = 'your_password'
EMAIL_USE_TLS = True
DEFAULT_FROM_EMAIL = EMAIL_HOST_USER
EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'URL configurationThe easiest way to set up the views indjango-contact-widgetis to just use the providedURLconf, found atcontact_widget.urls.
You can include it wherever you like in your site’s URL configuration; for example, to have it live at the URL/contact/:from django.conf.urls import include, url

urlpatterns = [
    # ....
    url(r'^contact/', include('contact_widget.urls')),
]UsageInclude the tempalte fromcontact_widget/contact.htmlto your sidebar for example.{% include "contact_widget/contact.html" %}LicenseMIT
Package: contagion
contagioncontagionis a Python package supporting node immunization and network contagion simulationcontagionis designed to be easy-to-use and full-featured, supporting computer scientists, public health researchers, network theorists, and more. Here are a few things we love aboutcontagion:accessible interface for incorporating node immunization into contagion simulationssupports immunization with delayed effect and variable immunity durationshandles partial immunity and time-varying transmission ratescan track symptom onsetimplements node testing policies (random, contact tracing)Table of ContentsTable of ContentsGetting StartedDocumentationExample UsageCiting contagionContributing to contagionContactRequirementsLicenseGetting StartedInstallcontagionwithpip:pipinstallcontagionOncecontagionis installed, importNetworkXandcontagion:importnetworkxfromcontagionimportcontagionDocumentationOfficial documentation - including atutorial,API reference, and a few workedexamples- is available onRead the Docs.Citing contagionIf you findcontagionuseful in your work, please use theZenodosoftware citation.RequirementsThis project was created with:matplotlib==3.3.3numpy==1.19.3networkx==2.5scipy>=1.5.0seaborn==0.11.1Contributing to contagionWe'd love your help! If you'd like to make an addition or improvement, please submit apull requestconsisting of an atomic commit and a brief message describing your contribution. If you find something wrong, please submit a bug report to theissue tracker. For other questions or comments, feel free tocontact medirectly.Thanks for helping makecontagionbetter!ContactLucas McCabe (lucas.mccabe@jhu.edu)LicenseMIT
Package: containator
Runs docker containers with pre-defined parameters. Define run-time parameters
in a configuration file to spawn containers easily.NOTE:Containator was developed with Python 3.4, though it should run
on any Python 3.Install & useInstall the package using:pip3 install containatorEdit config file to define your containers. A sample config file can be found
in the source distribution, or downloaded from thesource repository.Either provide a config file path on the command line with-cargument or let
the application read config files at the following default locations. All locations are
tried in order and definitions from latter files override previous definitions./etc/containator.conf~/.config/containator.conf~/.containator.confAfter they are configured, individual containers are started by providing their
name on the command line, for example:containator firefoxLocationsContainator packagesare available from PyPI.Theproject pageis hosted on GitLab.com.If you find something wrong or know of a missing feature, pleasecreate an issueon the project page. If you find that inconvenient or have
some security concerns, you could also drop me a line at <code@beli.sk>.LicenseCopyright 2015, 2019 Michal Belica <code@beli.sk>Containator 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 3 of the License, or
(at your option) any later version.

Containator 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 Containator.  If not, see < http://www.gnu.org/licenses/ >.A copy of the license can be found in theLICENSEfile in the
distribution.
Package: contained
Enable function chaining for any object.Contained is a class that can wrap any type and make it possible to do function
chaining on that object, much like functional piping.Any method that is defined on the contained type is possible to call, whether it
returns a copy or mutates in place. It is also possible to call operators and
any builtin function, without breaking the chain. To get the original variable
back out of the Contained, just call it with no args.fromcontainedimportContainedex=['hello','world']Contained(ex)()==ex# True# methodsContained(['hello']).extend(['world'])()==ex# True# operatorsContained(['hello']).add(['world']).contains('hello')()# True# builtinsContained(ex).len().type()()# <class 'int'>Contained can also be given an additional namespace to allow for custom
chained methods. This can be a module dict, class dict, globals, locals, or
any other dict mapping names to objects.defstringify_number_colleciton(c):return(sifnotstr(s).isdigit()elseint(s)forsinc)(Contained({1,"A",3.02,()},additional_methods=locals()).stringify_number_colleciton().sorted(key=lambdas:str(s))()==[(),1,3.02,'A'])importfunctools,itertools# if you are on Python >= 3.9 use `additional_methods=(vars(functools) | vars(itertools))`(Contained(42,reversed=True,additional_methods=Contained(vars(functools)).update(vars(itertools))()).range().list().takewhile(lambdax:x<18).dropwhile(lambdax:x<=5).map(hex).reduce(lambday,x:f"{y}{x}")()=="0x6 0x7 0x8 0x9 0xa 0xb 0xc 0xd 0xe 0xf 0x10 0x11")
Package: containedenv
ContainedENVDescriptionThis project is meant to provide utilities for writing out configuration files
based on environment variables.  Lots of projects seem to be implementing this
on their own (Elasticsearch/Grafana), but it would be nice to have one thing
that works for them all...so...here we go!UsageSet your environment variables with a shared prefix.  Section headers should be
followed by a double underscore__Example:$ export MYPREFIX_foo__bar=baz
$ containedenv-config-writer.py -p MYPREFIX_
[foo]
bar = bazCheck out the help text for current usage info too:usage: containedenv-config-writer.py [-h] -p PREFIX [-f {ini,json}]
                                     [-o OUTPUT_FILE] [-r REFERENCE_FILE]

Convert environment variables in to a configuration file

optional arguments:
  -h, --help            show this help message and exit
  -p PREFIX, --prefix PREFIX
                        Prefix of env vars to parse
  -f {ini,json}, --format {ini,json}
                        Output file format
  -o OUTPUT_FILE, --output-file OUTPUT_FILE
                        Outfile file path
  -r REFERENCE_FILE, --reference-file REFERENCE_FILE
                        Load this reference file for existing/hard coded
                        values
Package: container-ci-suite
container-ci-suiteThis repo is used for testing SCL containers. For container executiondockerorpodmancommands are used.
No Python bidings. The same for OpenShift tests.occommand is used.How to use Container CI Suite for testing containersInstall this suite by command:pip3installgit+https://github.com/phracek/container-ci-suiteRun a test with Container-CI-Suiteimportosimportpytestfromcontainer_ci_suite.apiimportContainerCISuiteimage_name=os.environ.get("IMAGE_NAME","nginx-container")test_dir=os.path.abspath(os.path.dirname(__file__))classTestDummyImage(object):deftest_s2i_usage(self):ccs=ContainerCISuite(image_name=image_name)ccs.s2i_usage()container-common-scripts functions and argumentsct_cleanupct_enable_cleanupct_check_envs_setct_get_cipct_get_cidct_wait_for_cidct_assert_container_creation_failsct_create_containerct_scl_usage_oldct_doc_content_oldfull_ca_file_pathct_mount_ca_filect_build_s2i_npm_variablesct_npm_worksct_binary_found_from_dfct_check_exec_env_varsct_check_scl_enable_varsct_path_appendct_path_foreachct_run_test_listct_gen_self_signed_cert_pemct_obtain_inputct_test_responsect_registry_from_osct_get_public_image_namect_assert_cmd_successct_assert_cmd_failurect_random_stringct_s2i_usagect_s2i_build_as_dfct_check_image_availabilityct_check_latest_imagestreamsct_test_app_dockerfilect_get_uid_from_imagect_clone_git_repositoryct_show_resourcesct_s2i_multistage_buildOpenShift testsct_os_cleanupct_os_check_compulsory_varsct_os_get_statusct_os_print_logsct_os_enable_print_logsct_get_public_ipct_os_run_in_podct_os_get_service_ipct_os_get_all_pods_statusct_os_get_all_pods_namect_os_get_pod_statusct_os_get_build_pod_statusct_os_get_buildconfig_pod_namect_os_get_pod_namect_os_get_pod_ipct_os_get_sti_build_logsct_os_check_pod_readinessct_os_wait_pod_readyct_os_wait_rc_readyct_os_deploy_pure_imagect_os_deploy_s2i_imagect_os_deploy_template_image_ct_os_get_uniq_project_namect_os_new_projectct_os_delete_projectct_delete_all_objectsct_os_docker_loginct_os_upload_imagect_os_is_tag_existsct_os_template_existsct_os_install_in_centosct_os_cluster_upct_os_cluster_downct_os_cluster_runningct_os_logged_inct_os_set_path_occt_os_get_latest_verct_os_download_upstream_occt_os_test_s2i_app_funcct_os_test_s2i_appct_os_test_template_app_funcct_os_test_template_appct_os_test_image_updatect_os_deploy_cmd_imagect_os_cmd_image_runct_os_test_response_internalct_os_get_image_from_podct_os_check_cmd_internalct_os_test_image_stream_templatect_os_wait_stream_readyct_os_test_image_stream_s2ict_os_test_image_stream_quickstartct_os_service_image_info
Package: container-cli
introductionWhen we initialize a container, if we accidentally make a port mapping mistake, we need to rebuild the container or modify the docker configuration file manually.This tool is designed to automate the process of modifying docker configuration files.demoThis demo shows how container-cli can modify the port mapping of a container named pythonadmin@ubuntuyyy:~$ cc  #short of container-cli
current user is not root
? confirm close docker? Yes
INFO     | container_cli:__close_docker:17 - closing docker
0 Warning: Stopping docker.service, but it can still be activated by:
  docker.socket
? Please input container name: python
Please enter ALL host ports and their corresponding ports in the container one by one
? Please input host port, empty to exit 23
? Please input container port, empty to exit 22
? Please input host port, empty to exit
? confirm? Yes
INFO     | container_cli.change_ports:change_ports:59 - finished
docker restarted, please restart your containerinstallationstep1 install pipxBy default, ubuntu doesn't have a way to install pipx using pip, so we have to:sudo apt-get update && sudo apt-get install pipx && pipx ensurepathAfter that we need to shut down the terminal and reenter it (or just reboot)step2 install container-clipipx install container-cliusageTypeccorcontainer_clion the command line to start, and follow the prompts.Star History
Package: container-collection
Collection of tasks for running containerized models.
Designed to be used both inPrefectworkflows and as modular, useful pieces of code.InstallationThe collection can be installed using:pipinstallcontainer-collectionWe recommend usingPoetryto manage and install dependencies.
To install into your Poetry project, use:poetryaddcontainer-collectionUsagePrefect workflowsAll tasks in this collection are wrapped in a Prefect@taskdecorator, and can be used directly in a Prefect@flow.
Running tasks within aPrefectflow enables you to take advantage of features such as automatically retrying failed tasks, monitoring workflow states, running tasks concurrently, deploying and scheduling flows, and more.fromprefectimportflowfromcontainer_collection.<module_name>import<task_name>@flowdefrun_flow():<task_name>()if__name__=="__main__":run_flow()Seecell-abm-pipelinefor examples of using tasks from different collections to build a pipeline for simulating and analyzing agent-based model data.Individual tasksNot all use cases require a full workflow.
Tasks in this collection can be used without the Prefect@taskdecorator by simply importing directly from the module:fromcontainer_collection.<module_name>.<task_name>import<task_name>defmain():<task_name>()if__name__=="__main__":main()or using the.fn()method:fromcontainer_collection.<module_name>import<task_name>defmain():<task_name>.fn()if__name__=="__main__":main()
Package: container-env
Container EnvEnvironment variables are the easiest way to manage options in containers.
Especially when using Docker Swarm or Kubernetes.
Container Env makes it easy to create applications with environment based
configuration.Simple. Can be picked up in minutes.Easy to use with Django, Flask and others.Seamless support for secrets.Built with container orchestration in mind.Visit the websitefor more information.Getting StartedPython 3.8 or higher is required.See the quick start guideto get started.UsageSee the documentationto learn more about Container Env.ContributingPleasecheck existing issuesbefore opening a new one.See the development guideto learn about the development setup.
Package: containerful
Devcontainers Contrib CLIInstallationpip install devcontainer-contrib[generate]Feature Generation:Usage: python -m devcontainer_contrib feature generate [OPTIONS]
                                                        FEATURE_DEFINITION
                                                        OUTPUT_DIR

Arguments:
  FEATURE_DEFINITION  [required]
  OUTPUT_DIR          [required]

Options:
  --help                          Show this message and exit.Input for feature generation is afeature-definition.jsonfile
This is anextendedversion of thedevcontainer-feature.jsonfile withadditional fields:{...regular devcontainer-json fields  like id,name,description etc...# dependencies are list of features and options  (those will be installed as prerequisites to your feature)"dependencies":[{"feature":"ghcr.io/devcontainers-contrib/features/asdf-package:latest","options":{"plugin":"act","version":"latest"}}],# This command will be executed after the dependency feature list has been installed"install_command":"echo'Done'",# this example test scenario checks the default options (empty options dict), each test_command should exit wth code `0` if your feature is installed correctly."test_scenarios":[{"name":"test_defaults","image":"mcr.microsoft.com/devcontainers/base:debian","test_commands":["act--version"],"options":{}}]}redirect input{..."options":{"version":{"type":"string","default":"latest","description":"Selecttheversionofacttoinstall."}},# you can also redirect one of your actual devcontainer-feature.json options values into a dependency input, note the `$options.version` pointer to the asdf-package version option"dependencies":[{"feature":"ghcr.io/devcontainers-contrib/features/asdf-package:latest","options":{"plugin":"act","version":"$options.version"}}],...}Usage examplethis will generate the Elixir feature
It assumes you have the devcontainer-contrib cli already installedgitclonehttps://github.com/devcontainers-contrib/cli--depth1cdcli


devcontainer-contribfeaturegenerate"./test/resources/test_feature_definitions/elixir-asdf/feature-definition.json""./output_dir"
Package: container-guts
Container Guts!Want to know the "guts" inside your container, or more specifically,
the executables on the PATH inside, and filesystem paths? 🤓This is a library and set of GitHub actions to derive that!UsageFor client and GitHub Actions usage, see our ⭐️documentation⭐️
There are (or will be) scattered examples underexamples, e.g.,TODOsupport for singularity and podman (if requested)