Saravanan666/pypi-org huggingface dataset:
Package: cm-portal
Camillus MedHaven Portal: A Django Web App for a Nursing Home FacilityInstallationAdd cm_portal and its dependency to yourINSTALLED_APPSlike this:INSTALLED_APPS = [    
        'dal',
        'dal_select2',
        'django.contrib.admin',
        ...
        'cm_portal.apps.CmPortalConfig',
        'widget_tweaks',
        'django_tables2',
        'bootstrap_modal_forms',
    ]Append the following environment variables to yoursettings.py.STATIC_ROOT = os.path.join(BASE_DIR, 'static')
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
MEDIA_URL = '/media/'
LOGIN_REDIRECT_URL = '/'
LOGOUT_REDIRECT_URL = '/'Include the cm_portal URLconf in your projecturls.pylike this:from django.conf.urls import include
    urlpatterns += [
        path('', include('cm_portal.urls')),
    ]

    urlpatterns += [
        path('accounts/', include('django.contrib.auth.urls')),
    ]Runpython manage.py migrateto create the cm_portal models.Start the development server and visithttp://127.0.0.1:8000/to begin using app.
Package: cmprs-eden
EDEN compressionA PyTorch implementation of 'EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated
Learning', presented atICML 2022.InstallationInstall this library usingpip:pip install cmprs-edenUsagefromcmprs_edenimportedenimporttorchfromdataclassesimportastuplecompression=eden(bits=1)x=torch.randn(2**12)compressed_x,context,bitrate=astuple(compression.forward(x))reconstructed_x=compression.backward(compressed_x,context)DevelopmentTo contribute to this library, first checkout the code. Then create a new virtual environment:cd <project-checkout-folder>
python -m venv venv
source venv/bin/activateNow install the development dependencies:pip install -e ".[test,preprocess]"To run the tests:pytestCitationIf you find this useful, please cite us:@InProceedings{pmlr-v162-vargaftik22a,title={{EDEN}: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning},author={Vargaftik, Shay and Basat, Ran Ben and Portnoy, Amit and Mendelson, Gal and Itzhak, Yaniv Ben and Mitzenmacher, Michael},booktitle={Proceedings of the 39th International Conference on Machine Learning},pages={21984--22014},year={2022},editor={Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},volume={162},series={Proceedings of Machine Learning Research},month={17--23 Jul},publisher={PMLR},pdf={https://proceedings.mlr.press/v162/vargaftik22a/vargaftik22a.pdf},url={https://proceedings.mlr.press/v162/vargaftik22a.html},abstract={Distributed Mean Estimation (DME) is a central building block in federated learning, where clients send local gradients to a parameter server for averaging and updating the model. Due to communication constraints, clients often use lossy compression techniques to compress the gradients, resulting in estimation inaccuracies. DME is more challenging when clients have diverse network conditions, such as constrained communication budgets and packet losses. In such settings, DME techniques often incur a significant increase in the estimation error leading to degraded learning performance. In this work, we propose a robust DME technique named EDEN that naturally handles heterogeneous communication budgets and packet losses. We derive appealing theoretical guarantees for EDEN and evaluate it empirically. Our results demonstrate that EDEN consistently improves over state-of-the-art DME techniques.}}
Package: cm-psc-pkg-allongoldberg
MODIFIED "PLOT_STATE_CITY()" FUNCTION Original function (from IBMQ and Qiskit) takes a given quantum circuit's complex-number state output and plots the real and imaginary amplitude values with two 3D bar graphsModified, "CM_PLOT_STATE_CITY()" can take a 2-tuple matrix with string values that correspond to colormap palette names*This code is part of Qiskit.
(C) Copyright IBM 2017, 2018.
This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or athttp://www.apache.org/licenses/LICENSE-2.0.
Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.
Package: cmp_version
A script and python module to compare version numbers. Use this to compare the version strings of packages, modules, really anything.Since version 3.0, cmp_version supports all kinds of complex version numbers, including those with releases ( like $version-5 ) or Epochs (like 5:$version)Commandline ToolYou can use the providedcmp-versiontool to compare two version strings.It will print to stdout “-1” if version1 is less/older than version2, “0” if they are equal, and “1” if version1 is greater/newer than version2.Examples:$ ./cmp-version 1.0.1b 1.0.1a1$ ./cmp-version 1.0.1b 1.0.2-1$ ./cmp-version 1.0 1.0.00Comparing FilenamesWithout actually scanning the contents of a package, you can from a script compare the versions based on the filenames.Example:$ cmp-version glibc-6.2.1-3.x86_64.rpm glibc-6.2.3-1.x86_64.rpm-1Method SignatureThe cmp_version module provides a single method,cmp_versionwhich compares two versions “cmp” style (think strcmp or the “cmp” operator in python<3).def cmp_version(version1, version2):‘’’cmp_version - Compare two version strings, checking which one represents a “newer” (greater) release.Note that even if two version strings are not equal string-wise, they may still be equal version-wise (e.x. 1.0.0 is the same version as 1.0)@param version1 <str> - A version string@param version2 <str> - A version string@return <int>-1  if version1 is older/less than version20   if version1 is equal to version21   if version1 is newer/greater than version2So for example,cmp_version(‘1.0.5b’, ‘1.0.5a’) would return 1 because 1.0.5b comes after 1.0.5aSpecial Typecmp_version provides a special type, “VersionString”, which inherits string. All the comparison magic functions are implemented, such that you can directly compare VersionString objects.Example:>>> x = VersionString('0.1b2')>>> y = VersionString('.2b2')>>>>>> '0.1b2' < '.2b2' # Show that as strings this version compare gives wrong answerFalse>>> x < y # Show that using VersionString objects, the comparison does workTrueSorting ListsYou can sort a list of versions, or filenames containing versions etc like this:>>> from cmp\_version import cmp\_version, VersionString>>>>>> x = ['.9a', '0.9', '0.9.0', '1.2c', '1.1b', '1.b4.0']>>> sorted(x, key=VersionString)[‘0.9’, ‘0.9.0’, ‘.9a’, ‘1.1b’, ‘1.2c’, ‘1.b4.0’]Return value0 is returned when the two versions are equal, even if the strings are not equal (for example “1.0” is the same version as “1.0.0”)-1 is returned when version1 is older/less than version21 is returned when version1 is newer/greater than version2.LicenseThis module is released under Public Domain.
Package: cmpx
PythonComplexA package for different operations on complex numbers.More detailsThis package is essentially for mathematical operations on complex numbers.It is very easy and intuitif to use, as any mathematicain would expect.There is more to come on this class, like power of complex numbers.InstallationUse the package managerpipto install cmpx.pipinstallcmpxTutorialYou can follow thistutorialto learn more about this package.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.
Package: cm-qdk
Gravity Interface (TK) - написанный на tkinter графический интерфейс, служащий для взаимодействия весовщика
и Gravity Core.v 0.2.4
- Иконка версии в углу
- ar_ip, ar_port, mirrored передаются как аргументы при запуске приложения
- добавлен cmadb (блокировка дублей)
Package: cm-rgb
cm-rgbControl your Wraith Prism RGB on Linux, Mac OS and Windowscm-rgb-monitorshowing realtime cpu utilization.Getting startedInstallation & ConfigurationFollowthis simple guide on our wiki.UsagePackage comes with two scriptscm-rgb-cliandcm-rgb-monitor.cm-rgb-cliallows for fine grained control of each individual zone includingturning off the LED'scompletely.Check out theexamples in our wiki.cm-rgb-guiGUI interface that serves the purpose of simplifying the usage of [cm-rgb-cli] (thanks togroovykiwi)cm-rgb-monitorallows for displaying cpu utilization with ring LED's, along with temperature (thanks tompsdskd).Combining the cli + monitor can create neat transition from powering your system right to booting your OS.Check out theexamples in our wiki.LicenceMITSee LICENCE file for details
Package: cmrseq
Define your MRI sequences in pure python!The cmrseq frame-work is build to define MRI sequences consisting of radio-frequency pulses,
gradient waveforms and sampling events. All definitions follow the concept hierarchically assemble
experiments where the basic building blocks (Arbitrary Gradients, Trapezoidals, RF-pulses and
ADC-events) are forming the core functionality and are all instances of SequenceBaseBlock-instances.
On instantiation all base-blocks are validated against the System specifications. Composition of
base-blocks is done in a Sequence object. The Sequence object implements convenient definitions
for addition and composition of multiple Sequence objects as well as to perform a variety on common
operations on semantically grouped base-blocks.Several semantically connected groups of building blocks (e.g. a slice selective excitation) are
allready functionally defined in the parametric_definitions module. For a complete list of available
definitions checkout the API-reference.The original motivation for cmrseq was to create a foundation to define sequences for simulation
experiments. Therefore Sequences can be easily gridded onto a regular (or even unregular grids with
a maximum step width) grids. Furthermore, commonly useful functionalities as plotting, evaluation
of k-space-trajectories, calculation of moments, etc.To close the gap to real-world measurements, cmrseq includes an IO module that allows loading
Phillips (GVE) sequence definitions as well as reading and writing Pulseq (>= 1.4) files, which
then can be used to export the sequence to multiple vendor platforms. For more information on this
file format please refer to the officialPulSeq web-page.InstallationThe registry contains the versioned package, which can be installed using:pip install cmrseqThere are only few package dependencies, namely:
- numpy
- matplotlib to display the waveforms
- pint to assignphysical unitsand assert correctness of calculations
- tqdm progressbar visualizations
- scipy selected functionalitiesDocumentation & Getting StartedThe API documentation for released versions can be foundhere.
Package: cmrsim
Welcome to CMRsim!CMRsim is a MRI - simulation framework that features bloch-simulations as well as analytic signal
evaluations formoving objectswith an easy to use API written in pure python.
The computationally demanding calculations are implemented inTensorFlow2and therefore are
seamlessly executable on CPU and GPU environments, while also making efficient use of computational
resources!What makes CMRsim special?CMRsim features bloch-simulations as well as simulations using analytical signal equations and even allows to combine elements of both.Convenient support formovingparticles!Modularity that makes to designing comparative simulation experiments convenient.Architecture and implementation that allows to easily include your own simulation modules.Reproducibility by using versioned and documented open-source software.Easily scalable to detailed digital phantomsInstallationPipCMRsim can be installed using the python package managerpip. The package can be found
in theregistryof the repository hosted on the ETH-Gitlab instance as well as on the python
package index (PyPI).Versioning:Stable release versions are denoted with the ususalmajor.minornotation. Additionally,
development versions are constantly updated on solved issued and can be installed using themajor.minor.devXnotationRelease versions are published on PyPI, which allows installing cmrsim with:pip install cmrsimDevelopment versions are only available using the extra url:pip install cmrsim --extra-index-url https://gitlab.ethz.ch/api/v4/projects/23798/packages/pypi/simpleDockerFor each major release a set of docker images are compiled and published in thecontainer registryassociated with repository. Within the docker images, all requirements
are installed for simulation deployment.Furthermore, there are three image-tags available. To use the pull the docker image
use (e.g. for jupyter tag):docker pull registry.ethz.ch/jweine/cmrsim/jupyter:latestCPUBased on the Tensorflow:…-cpu:latest docker image. Containing all cmrsim dependencies and cmrsimGPUBased on the Tensorflow:…-gpu:latest docker image. Containing all cmrsim dependencies and cmrsimJupyterBased on the gpu:latest tag, but also includes an installation of jupyter-lab for interactive
simulation / rendering.
Package: cmsapp-rt
=====cmsapp-rt=====This package contains one app (including a context processor and example template) to help your Django-CMS projectwork with Twitter Bootstrap.You may also find the cmsplugin-rt package helpful with this,e.g. to provide a navbar in the placeholder provided.Detailed documentation will be added to the "docs" directory.Requirements--------------I built these using:* Django 1.4* Django-CMS 2.3.5* South* django-singleton-adminQuick start-----------1. Add the app to your INSTALLED_APPS setting like this::INSTALLED_APPS = (...'django_singleton_admin','cmsapp_rt.bsglobals',)2. Add the template context processor to your settings like this::TEMPLATE_CONTEXT_PROCESSORS = (...'cmsapp_rt.bsglobals.context_processors.globals',)3. Extend the admin template "admin/base_site.py" using the base provided indjango-singleton-admin, instead of the usual "admin/base.html", i.e. using:{% extends "admin/singleton_enabled_base.html" %}Note that this template uses the "extrastyle" block, so if you also override that, youwill need to copy the contents of the provided template into your code instead.(In that case you may not need to add the app to your installed apps either.)4. Run `python manage.py syncdb` (or use `python manage.py migrate` if you are using South) to create the models.5. Run `python manage.py loaddata blank_globals` to set up a blank line in the table (this is required).6. Look at cmsapp_rt/bsglobals/templates/base.html to see how to use this app in your templates.
Package: cms-attendance
Manage and monitor attendance of users sharing a common WiFi,
using a specialized WiFi hotspot module.This plugin isonly intended to be used with a CMS project,
and not directly in any django project. To use in an independent
django project, you might need to configure your project.FeaturesTrack & monitor presence of a user, automatically after initial setupExtract comprehensive reports & logs via GraphQL APIsRequirementsAn ESP8266 or other compatible WiFi module needs to running for the attendance to be recorded.The ESP8266 must be flashed withModule script.User’s machines must also must be installed withClient script.
Package: cms-detector
cms-detectorWhats in cms-detector?A Python Package to detect the Content Management System of a Website.We believe in monolithic software development and created this tiny package that does its job without any bloat.How to Use cms-detector?You can find detailed tutorial oncms-detector tutorial website.ContributePull Requests, Feature Suggestions, and collaborations are welcome.About UsThis work is a collaborative effort ofseowings, andserpwings.
Package: cms-draggable-files
CMS Draggable FilesThis application will recieve dragged files on django-cms placeholder and save them to cms.plugin.picture or cms.plugin.file. This plugin checks what file extension is dragged.This application will make Yours life esier.
Tutorial:Install aplication: pip install cms-draggable-filesAdd to installed app: ‘cms_draggable_files’Add static files to base.html (or to main html file in your project): /static/css/placeholder.css /static/js/draggable.jsCopy template folder instance to your main project folder. Be shour that placeholder.html from cms_draggable_files is in your template/cms/placeholder.html.Add cms-draggable-files to your project urls.py: $ url(r’^’, include(‘cms_draggable_files.urls’)),If You want additional custom file creator that for example will convert files from odf to pdf on drag and drop, You need in settings register your custom creator class: DRAGGABLE_ADDITIONAL_PLUGIN_CREATORS = [ ODF_TO_PDF_CREATOR, ]Enjoy drag and drop files future in django-cms.
Package: cmselemental
CMSElementalA python package which provides data models & tools for the Computational Molecular Sciences (CMS).
CMSElemental provides:Pydantic models:Base models: primarilyProtoModel, a subclass of thepydanticBaseModel, which provides commonly used methods in CMS for data parsing, validation, and serializatio>Procedure models: for storing generic intput/output data associated with CMS procedures.Common models: a miscellaneous collection of frequently used models in CMS.File I/O methods:JSON: support for JSON schema (draft-07).YAML: support for YAML1.1(PyYAML) and1.2(ruamel.yaml).MessagePack: support for the msgpack data format.HDF5: support forHDF5file schema and theHDF5-JSONspecification.
Package: cms-estate
cms_estateStable version 1.0.6This script can help you to start own Internet business anywhere in the world. The system allows you to run a super-modern, interactive and fast website.CMS for Real
EstateThe script supports:Selling Entertainment, Tours & Everything!AirBNB script for Property RentalClassified for Real Estate agencyDemo:Website:https://mirador.onlineAdmin:https://mirador.online/admin/Login:demoPassword:demodemoReal exampleWe know what to do in Tenerife!ModulesMobile versionOur script based on ReactJS technology and works
great on all smartphones and tablets — check!SEOThe site is perfectly adapted for search engines and supports
the Open-Graph & JsonLD protocols. The news section allows you to post
SEO-text with automatic relinking.Discount systemAllows you to set global discounts or hold one-time
promotions. The discount is calculated from the amount of your
commission which specified into the price of each offer.TripAdvisor ReviewsAutomatic parsing reviews with auto-rewriting
text. Gradual addition of reviews to your website with cross-linking,
for the natural increasing of content.Flexible price managementThe final price of your offers on the site
is formed from the basic price of the supplier and your commission.Support ChatDynamic form placed in all pages of the site, allowing
customers to send requests to your email.Customers and ordersAll orders arriving in the system have extended
data of prices with a convenient system of customer management.Personal AreaYou can allow your users to add their objects to your
website through their personal account.Other features:Booking in two clicks— the interface allows you to make an order
easily and simply!White-Label— we do not place our logos.Unlimitednumber of copies of your script.Auto-scaling photos— don’t think about the size of photos, our
script optimizes them independently.Youtube-video— specify the link to the video and the video will
appear at the offer page.Caching algorithm— loading pages occurs faster than 1 second.SSL-securitycertificate based on CloudFlare. Your site will work
through a secure https protocol.Email notifications.Receive email notifications for orders.Auto-update system.In the near future:Integration with several languages.Payments by cardsAutoposting to Facebook, InstagramInstallationScript made with:ReactJS, Django v2, Ubuntu. Installation takes
place automatically in 5 minutes, via the Docker system. First
installation for free!Detailed installation instructions you will receive by email.If
necessary, we can adapt templates for your business — the cost
depends on the amount of work.Contact usContact UsAt
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Package: cmsgitlogs
Python package for reading git logsExample usage:importargparseimportwebbrowserfromdatetimeimportdatetimeimportget_git_logsasgl# Example# python example_gl.py --git_folder  <git_folder>  --from_date 2023-08-01 --output_csv ~/git-functions/commit_logs.csvdefmain():parser=argparse.ArgumentParser(description='List Git commit logs for a given folder')parser.add_argument('--git_folder',type=str,help='Path to the Git repository folder')parser.add_argument('--from_date',type=str,help='Filter commits from the specified date (YYYY-MM-DD)')parser.add_argument('--output_csv',type=str,help='Specify the output CSV file')args=parser.parse_args()git_folder=args.git_folderfrom_date_str=args.from_datefrom_date=datetime.strptime(from_date_str,'%Y-%m-%d')iffrom_date_strelseNonecommit_logs=gl.get_git_commit_logs(git_folder,from_date)ifargs.output_csv:output_csv=args.output_csvgl.save_to_csv(commit_logs,output_csv)print(f'Commit logs saved to{output_csv}')# Copy CSV content to clipboardwithopen(output_csv,'r')ascsv_file:csv_content=csv_file.read()gl.copy_to_clipboard(csv_content)print('CSV content copied to clipboard')# Open the specified URL with the CSV dataweb_url=f'https://mohan-chinnappan-n5.github.io/viz/datatable/dt.html?c=csv'webbrowser.open(web_url)else:print('Commit Logs:')forcommitincommit_logs:print(commit)main()
Package: cm-shared-generator
Python library : cm_shared_generatorResumeIn python, it exist a generator type which can be really useful.Thecm_shared_generatorlibrary provide a Shared_Generator which share generator between thread and process safely.InstallPre-requiredGlobal requirementsPython 3 (sudo apt update && sudo apt install python3 -y)Install requirementsSetuptools (sudo apt update && sudo apt install python3-pip -y && pip3 install setuptools)Installationpython3setup.pyinstallUtilizationThe utilization of the Shared_Generator is quite simple. Its prototype is like :Shared_Generator(self,arg_generator,DEFAULT_GENERATOR_TRAITMENT_FUNCTION=None,FORCE=False,MAX_OBJECT_IN_QUEUE=len(os.sched_getaffinity(0)),TIMEOUT=None)Its parameters are defined as :Required :arg_generator-> The generator to sharedOptional :DEFAULT_GENERATOR_TRAITMENT_FUNCTION(Default : None) -> Function to treat default type of value from the generator.
Possible value :Shared_Generator.DEFAULT_GENERATOR_TRAITMENT_CLASS(Create a deep copy of the class before the insertion into the Queue).Your own function !FORCE(Default : False) -> If True, the generator will be defined even his type is not a proper generator.MAX_OBJECT_IN_QUEUE(Default : Nb of logical processors) :	Defined the maximum of elements in the queue at the same time. ( > 0 )NUMBER_PUT_RETRY(Default : None) -> Number of retry to acquire the semaphore which define if the generator process can continue to fill the Generator list before quitting timeout.TIMEOUT(Default :Shared_Generator.DEFAULT_TIMEOUT) : Defined the timeout of Semaphores (The Shared_Generator will be stopped after (1+NUMBER_PUT_RETRY)*TIMEOUTif no process has taken a value from the generator.Example :fromcm_shared_generatorimportShared_Generator# Simple integer generatordefint_generator():i=0whilei<10:i+=1yieldireturnmy_shared_generator=Shared_Generator(int_generator,MAX_OBJECT_IN_QUEUE=2)# Creation of the generator with a Maximum lenght queue of 2my_shared_generator.start(NEW_PROCESS=True)# Launch the generator in a new processval=my_shared_generator.next()# Get the next value of the generatormy_shared_generator.stop()# Stop the generatorFor more examples, check unit tests intests directory.