Saravanan666/pypi-org huggingface dataset:
Package: magcopy
magcopymagcopyis a command-line utility that simplifies the copy and move operations of files and directories between terminal windows. It allows users to copy from one terminal and paste into another with ease. The utility is designed to be used in a terminal environment and maintains its own clipboard for managing copied and moved items.InstallationYou can installmagcopyusing the following command:pipinstallmagcopyUsagemagcopy<action><path/file>[--help]Actionscopy: Copy a folder or file to the clipboard.move: Add a folder or file to the clipboard for moving.paste: Paste a folder or file from the clipboard to the current folder or a specified folder.Options--help: Display the usage message.Examplesmagcopycopy/path/to/source/folder
magcopypaste/path/to/destination/folder

magcopymove/path/to/source/file
magcopypaste/path/to/destination/folderNotesThis program does not alter the contents of the system's default clipboard. It maintains its own clipboard for managing copied and moved items.Error HandlingIn case of errors, the utility provides clear error messages to guide the user.TestingTo run the tests formagcopy, you need to havepytestinstalled. If you don't have it installed, you can install it using the following command:pipinstallpytestOncepytestis installed, you can run the tests by executing the following command in the root directory of your project:pytestThis will discover and run all the test files in your project. Make sure you have the required dependencies and a proper Python environment set up before running the tests.Running Specific TestsIf you want to run tests from a specific file or directory, you can provide the file or directory path as an argument topytest. For example:pytesttests/test_clipboard.pyThis command will run only the tests defined in thetest_clipboard.pyfile.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.ContributingContributions tomagcopyare welcome! If you find a bug, have a feature request, or want to contribute in any other way, feel free to open an issue or submit a pull request. Your help is highly appreciated.How to ContributeFork the repository on GitHub.Clone your forked repository to your local machine:gitclonehttps://github.com/your-username/magcopy.gitCreate a new branch for your contribution:gitcheckout-bfeature/your-feature-nameMake your changes and commit them with a descriptive commit message:gitcommit-m"Add your descriptive message here"Push your changes to your fork:gitpushoriginfeature/your-feature-nameOpen a pull request on the officialmagcopyrepository.Reporting IssuesIf you encounter any issues or have suggestions, please open an issue on theGitHub issue tracker. Provide detailed information about the problem you're facing or the enhancement you're proposing.
Package: magda
MAGDAMAGDAis a Python library intended for assembling a stream-like architecture of an application following functional programming principles, by using predefined interfaces and classes.MAGDAstands for Modular and Asynchronous Graphs with Directed and Acyclic edges, and is the backbone of the library. The library works best when the code can be split into independent operations with clearly defined input and outputs. The main idea is to useMAGDAto process code in a sequential flow. Think of this as nodes with input/output as edges in directed graphs.MAGDAsupports following operations:building an application pipeline from a configuration file and from code,asynchronous and synchronous processing,dividing modules into groups for asynchronous pipeline,aggregation of partial results.MAGDAcan be applied almost anywhere, but is especially well-suited for BigData parallel processing and ML pipelines intended for carrying out multiple, repeatable experiments.Read the documentation on theGithub Wiki.Installationpippip install magdaFrom the repositorypipinstallhttps://github.com/NeuroSYS-pl/magda/archive/main.zipLicenseApache-2.0 License
Package: magellantools
Magellan PDS Toolsmagellantools is a small Python library and set of command line tools to read, manipulate, and export (to more convenient formats) radar and radiometry data acquired by NASA's Magellan mission to Venus that is archived on thePlanetary Data System.We do not currently support all of the data types on the PDS (see list below). Requests to add support for a specific data type are welcome, but contributing to the code to add support for that data type is even more welcome.Supported data types:ARCDRADFRDFDocumentation, including install instructions, example problems and API, can
be found onRead the Docs.If you find any issues, have questions, or would like to join the community,
please feel free to open up aGitHub Issue
Package: magent
MAgent is a library for creating 2D environments with very large numbers of agents for conducting research in Multi-Agent Reinforcement Learning. These can look like this:This is a maintained fork from the original repo-https://github.com/geek-ai/MAgent. The code is significantly cleaned up in many aspects and some unfortunate names have been fixed, but there are no major differences.This maintainenace was primarily done in the service of thePettingZoo library. PettingZoo is the easiest way to use environments in this library, and has comprehensive documentation of them.RequirementsMAgent supports Linux and macOS and Python 3.6+Install instructionsYou can simply usepip install magentIf you use this in your research, please cite the original paper:@inproceedings{zheng2018magent,
  title={MAgent: A many-agent reinforcement learning platform for artificial collective intelligence},
  author={Zheng, Lianmin and Yang, Jiacheng and Cai, Han and Zhou, Ming and Zhang, Weinan and Wang, Jun and Yu, Yong},
  booktitle={Thirty-Second AAAI Conference on Artificial Intelligence},
  year={2018}
}If you wish to cite this repo with it's modifications specifically, please cite:@misc{magent2020,
  author = {Terry, Jordan K and Black, Benjamin and Jayakumar, Mario},
  title = {MAgent},
  year = {2020},
  publisher = {GitHub},
  note = {GitHub repository},
  howpublished = {\url{https://github.com/Farama-Foundation/MAgent}}
}
Package: magent2
MAgent2 is a library for the creation of environments where large numbers of pixel agents in a gridworld interact in battles or other competitive scenarios.MAgent2 is a maintained fork of the originalMAgentcodebase. It contains somereference environmentsimplemented using thePettingZooAPI. These environments used to be included in PettingZoo itself, but have been moved here to exist independently. They are being regularly maintained and will receive bug fixes, support new versions of Python, etc. Development used to take place atgithub.com/Farama-Foundation/MAgentbut was moved togithub.com/Farama-Foundation/MAgent2so that the distinction from the original MAgent library is clear to users.InstallationInstall using pip:pip install magent2. Seedocsfor usage information.RequirementsMAgent2 supports Linux and macOS and Python 3.7+.References@inproceedings{zheng2018magent,
  title={MAgent: A many-agent reinforcement learning platform for artificial collective intelligence},
  author={Zheng, Lianmin and Yang, Jiacheng and Cai, Han and Zhou, Ming and Zhang, Weinan and Wang, Jun and Yu, Yong},
  booktitle={Thirty-Second AAAI Conference on Artificial Intelligence},
  year={2018}
}If you wish to cite this repo with it's modifications specifically, please cite:@misc{magent2020,
  author = {Terry, Jordan K and Black, Benjamin and Jayakumar, Mario},
  title = {MAgent},
  year = {2020},
  publisher = {GitHub},
  note = {GitHub repository},
  howpublished = {\url{https://github.com/Farama-Foundation/MAgent}}
}
Package: magento
Python library to connect to Magento Webservices.Usageimportmagentourl='http://domain.com/'apiuser='user'apipass='password'# Create an instance of APIclient=magento.API(url,apiuser,apipass)# A filter expression as dictionary.order_filter={'created_at':{'from':'2011-09-15 00:00:00'}}products=client.product.list(order_filter)# Get a list of product typesproduct_types=client.product_types.list()# Get a specific productsku='prod1'product=client.product.info(sku)# Add comment to an orderorder_increment_id='100000001 'status='canceled'client.order.addcomment(order_increment_id,status)All available APIsCart (client.cart)CartCoupon (client.cart_coupon)CartCustomer (client.cart_customer)CartPayment (client.cart_payment)CartProduct (client.cart_product)CartShipping (client.cart_shipping)Category (client.category)CategoryAttribute (client.category_attribute)Country (client.country)CreditMemo (client.credit_memo)Customer (client.customer)CustomerAddress (client.customer_address)CustomerGroup (client.customer_group)Inventory (client.inventory)Invoice (client.invoice)Magento (client.magento)Order (client.order)Product (client.product)ProductAttribute (client.product_attribute)ProductAttributeSet (client.product_attribute_set)ProductConfigurable (client.product_configurable)ProductImages (client.product_images)ProductLinks (client.product_links)ProductTierPrice (client.product_tier_price)ProductTypes (client.product_types)Region (client.region)Shipment (client.shipment)Store (client.store)Old deprecated exampleThe API was originally written with the requirement to call APIs
individually with all the credentials. This led to very verbose code and a
lot of repitition. While the behavior has not been deprecated, it is
recommended to write all new code with the pattern shown in the above
example.importmagentourl='http://domain.com/'apiuser='user'apipass='password'withmagento.Order(url,apiuser,apipass)asorder_api:order_increment_id='100000001 'status='canceled'order_api.addcomment(order_increment_id,status)withmagento.Store(url,apiuser,apipass)asstore_api:store_id='1'store_view_info=store_api.info(store_id)store_views=store_api.list()withmagento.Magento(url,apiuser,apipass)asmagento_api:magento_info=magento_api.info()Calling custom classes from your own API extensionsYou can ddirectly invoke the underlyingcallmethod
to make calls directly.result=client.call('custom_model.list',[])Alternatively, you can also build sub classes of API to have
a more pythonic structure. The subclasses are automatically
registered with API when classes are created.To ensure that they are registered before you create the first API
instance, always have all your imports on the top of your magento module.classCustomModel(API):deflist(self):returnself.call('custom_model.list',[])which would be automatically registered ascustom_modelby the API
metaclass. So you can now useimportmagentofrommy_custom_moduleimportCustomModelurl='http://domain.com/'apiuser='user'apipass='password'# Create an instance of APIclient=magento.API(url,apiuser,apipass)client.custom_model.list()LicenseBSD 3-ClauseSee LICENSE for more details
Package: magento-client
magento_clienta client to comunicate with magento APIFree software: MIT licenseDocumentation:https://magento-client.readthedocs.io.Featuresa simple client for Magento2CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-09-01)First release on PyPI.
Package: magento-encryptor
Implement Magento logic to verify password.### Installation:` pip install magento_encryptor `### Usage:`python import magento_encryptor magento_hash = "c6aad9e058f6c4b06187c06d2b69bf506a786af030f81fb6d83778422a68205e:salt:1:2"magento_encryptor.verify("mypassword",magento_hash) `
Package: magento-mwisslead
Python library to connect to Magento Webservices.Usageimportmagentourl='http://domain.com/'apiuser='user'apipass='password'# Create an instance of APIclient=magento.API(url,apiuser,apipass)# A filter expression as dictionary.order_filter={'created_at':{'from':'2011-09-15 00:00:00'}}products=client.product.list(order_filter)# Get a list of product typesproduct_types=client.product_types.list()# Get a specific productsku='prod1'product=client.product.info(sku)# Add comment to an orderorder_increment_id='100000001 'status='canceled'client.order.addcomment(order_increment_id,status)All available APIsCart (client.cart)CartCoupon (client.cart_coupon)CartCustomer (client.cart_customer)CartPayment (client.cart_payment)CartProduct (client.cart_product)CartShipping (client.cart_shipping)Category (client.category)CategoryAttribute (client.category_attribute)Country (client.country)CreditMemo (client.credit_memo)Customer (client.customer)CustomerAddress (client.customer_address)CustomerGroup (client.customer_group)Inventory (client.inventory)Invoice (client.invoice)Magento (client.magento)Order (client.order)Product (client.product)ProductAttribute (client.product_attribute)ProductAttributeSet (client.product_attribute_set)ProductConfigurable (client.product_configurable)ProductImages (client.product_images)ProductLinks (client.product_links)ProductTierPrice (client.product_tier_price)ProductTypes (client.product_types)Region (client.region)Shipment (client.shipment)Store (client.store)Old deprecated exampleThe API was originally written with the requirement to call APIs
individually with all the credentials. This led to very verbose code and a
lot of repitition. While the behavior has not been deprecated, it is
recommended to write all new code with the pattern shown in the above
example.importmagentourl='http://domain.com/'apiuser='user'apipass='password'withmagento.Order(url,apiuser,apipass)asorder_api:order_increment_id='100000001 'status='canceled'order_api.addcomment(order_increment_id,status)withmagento.Store(url,apiuser,apipass)asstore_api:store_id='1'store_view_info=store_api.info(store_id)store_views=store_api.list()withmagento.Magento(url,apiuser,apipass)asmagento_api:magento_info=magento_api.info()Calling custom classes from your own API extensionsYou can ddirectly invoke the underlyingcallmethod
to make calls directly.result=client.call('custom_model.list',[])Alternatively, you can also build sub classes of API to have
a more pythonic structure. The subclasses are automatically
registered with API when classes are created.To ensure that they are registered before you create the first API
instance, always have all your imports on the top of your magento module.classCustomModel(API):deflist(self):returnself.call('custom_model.list',[])which would be automatically registered ascustom_modelby the API
metaclass. So you can now useimportmagentofrommy_custom_moduleimportCustomModelurl='http://domain.com/'apiuser='user'apipass='password'# Create an instance of APIclient=magento.API(url,apiuser,apipass)client.custom_model.list()LicenseBSD 3-ClauseSee LICENSE for more details
Package: magento-python
magento-pythonmagento-python is an API wrapper for Magento written in PythonInstallingpip install magento-pythonUsagefrom magento.client import Client

client = Client('SERVER_URL', 'ACCESS_TOKEN') # Host must have trailing slashSearch productAllresponse = client.search_product({'searchCriteria': ''})Order by created_at, just one itemresponse = client.search_product({'searchCriteria[sortOrders][0][field]': 'created_at', 'searchCriteria[pageSize]': '1'})Filter by created_date greater than 2018-02-26 15:31:09, up to 100 itemsresponse = client.search_product({'searchCriteria[filter_groups][0][filters][0][field]': 'created_at', 'searchCriteria[filter_groups][0][filters][0][value]': '2018-02-26 15:31:09', 'searchCriteria[filter_groups][0][filters][0][condition_type]': 'gt', 'searchCriteria[pageSize]': '100'})Create Productdata = {
    "product": {
        "name": 'Perfume Antonio Banderas',
        "sku": 'PAB',
        'type_id': 'simple',
        'attribute_set_id': '4',
        'price': '100000'
    }
}
response = client.create_product(data)Get ProductBy SKUresponse = client.get_product('SKU')Delete ProductBy SKUresponse = client.delete_product('SKU')Search customerAllresponse = client.search_customer({'searchCriteria': ''})Create customerdata = {
    "customer": {
        "email": "janedoe@jd.com",
        "firstname": "Jane",
        "lastname": "Doe",
        "addresses": [{
            "defaultShipping": True,
            "defaultBilling": True,
            "firstname": "Jane",
            "lastname": "Doe",
            "region": {
                "regionCode": "NY",
                "region": "New York",
                "regionId": 43
            },
            "postcode": "10755",
            "street": ["123 Oak Ave"],
            "city": "Purchase",
            "telephone": "512-555-1111",
            "countryId": "US"
        }]
    },
    "password": "Password1"
}
response = client.create_customer(data)Get customerBy IDresponse = client.get_customer('CUSTOMER_ID')Delete ProductBy IDresponse = client.delete_customer('CUSTOMER_ID')Search orderAllresponse = client.search_order({'searchCriteria': ''})Get orderBy IDresponse = client.get_order('ORDER_ID')Get product typesAllresponse = client.get_product_types({'searchCriteria': ''})Get product attribute setsAllresponse = client.get_product_attribute_sets({'searchCriteria': ''})Get customer groupsAllresponse = client.get_customer_groups({'searchCriteria': ''})ContributingWe are always grateful for any kind of contribution including but not limited to bug reports, code enhancements, bug fixes, and even functionality suggestions.You can report any bug you find or suggest new functionality with a newissue.If you want to add yourself some functionality to the wrapper:Fork it (https://github.com/GearPlug/magento-python)Create your feature branch (git checkout -b my-new-feature)Commit your changes (git commit -am 'Adds my new feature')Push to the branch (git push origin my-new-feature)Create a new Pull Request
Package: magentouch
Following python code shows you how toinititialize the Magentouch instanceand then call an Magento SOUPv2 apiFurther api reference can be found herehttp://www.magentocommerce.com/api/soap/introduction.html<pre>from magentouch import Magentouchfrom magentouch.utils import recursive_asdict# e.g. url='http://shop.digirocks.com.au/api/v2_soap?wsdl=1'url=''username=''password=''mgt = Magentouch(url=url, username=username, password=password)suds_ret = mgt.service.catalogCategoryTree(mgt.session)categories = recursive_asdict(suds_ret)['children']for c in categories:print c</pre>
Package: magentropy
MagentroPyReferencesPlease cite the following in any published work that makes use of this package:[1] J. D. Bocarsly et al.,Phys. Rev. B 97, 100404(R) (2018)[2] J. J. Stickel,Comput. Chem. Eng. 34, 467 (2010)The first version of themagentropycode was included as supplementary
material in [1]. The Tikhonov regularization procedure was described
in [2] and was originally implemented by Stickel in the packagescikit.datasmooth.OverviewMagentroPy provides a class,MagentroData,
that can be used to calculate magnetocaloric quantities from DC magnetization
data supplied as magnetic moment vs. temperature sweeps (monotonic) taken under
several different magnetic fields. The class is set up to work out-of-the-box
with.datdata files produced by a Quantum Design Vibrating Sample
Magnetometer or aQuantum Design MPMS3 SQUID Magnetometer.
However,pandas.DataFrames or delimited files such as.csvare also acceptable inputs.View the documentationhere.InstallationInstall MagentroPy withpip:pip install magentropyOr, withconda:conda install -c conda-forge magentropyLicenseThis project is licensed under the MIT License.
Package: mageserver
A fast tunnel proxy that helps you bypass firewalls.ServerInstallDebian / Ubuntu:apt-get install python-pip
pip install mageserverCentOS:yum install python-setuptools && easy_install pip
pip install mageserverWindows:SeeInstall Server on
WindowsUsagessserver -p 443 -k password -m rc4-md5To run in the background:sudo ssserver -p 443 -k password -m rc4-md5 --user nobody -d startTo stop:sudo ssserver -d stopTo check the log:sudo less /var/log/shadowsocks.logCheck all the options via-h. You can also use aConfigurationfile instead.ClientWindows/OS
XAndroid/iOSOpenWRTUse GUI clients on your local PC/phones. Check the README of your client
for more information.DocumentationYou can find all the documentation in theWiki.LicenseCopyright 2015 clowwindyLicensed under the Apache License, Version 2.0 (the “License”); you may
not use this file except in compliance with the License. You may obtain
a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.Bugs and IssuesTroubleshootingIssue
TrackerMailing list
Package: magestore-abp
AboutThis package provided functionbuild_packagefor build 6 Magestore product linesPrerequisiteConfig local machine to run sudo without passwordsudovisudo# add this line to end of file# <username> is your current username<username>ALL=(ALL)NOPASSWD:ALLHow to useImport function to your python filefrommagestore_abpimportbuild_package2.Functionbuild_packagewill have required parametersrepo_info: (type:dict)repo_name: (type:str)repo_owner: (type:str)tag_name: (type:str)access_token: (type:str) personal access token to github that have permission to access to 6 Magestore product linesExecute this function and it will return local path to built package file on the local machine if build process successful, else return ''
Package: magestore-aim
OriginalThis repo has been moved fromauto_install_magento_packageHow to useThis package provided functioninstall_magentofor install magento usingmagento-apacherunning on docker engine.The function has required params:env_params: (type:dict)magento_version: (type:str) x.x.xe.g: 2.2.5magento_type(optional): (type:str)ce(Comunity Edition) oree(Enterprise Edition), defaultcesample_data(optional): (type:bool)TrueorFalse, defaultFalseperformance_test: (type:bool) True or False (this key only has effective and required when sample_data is False)peformance_test_profile(optional): (type:str) (this key only has effective and required when sample_data is False and performance_test is True)
valid values :small,medium,medium_msite,large,extra_largephp_version(optional): (type:str)x.x.x, default7.1.25server_params: (type:dict)ip: (type:str) remove server ip addressuser: (type:str) remote server usernamekey_path: (type:str) local private key file path to conect to remote serverpassword: (type:str) remote server password (ifkey_pathhas value, this key is optional)gitlab_access_token: (type:str) gitlab_access_token that have permission to access toinfrastructurerepoHow to get gitlab access tokenInstall packagemagestore-aimImport function to other filefrommagestore_aimimportinstall_magentoExecute it and wait for result
Package: magestore-aup
OriginalThis repo has been moved fromauto_upload_site_packagePrepareConfig visudo (run sudo without password) on local machinesudovisudo# add this line to end of file# username is your current usernameusernameALL=(ALL)NOPASSWD:ALLHow to useThis package provided function namedeploy, it will update Magestore pwa pos package to any remote server.Thedeployfunction has required params:repo_info: (type:dict)repo_owner: (type:str)repo_name: (type:str)tag_name: (type:str)access_token: (type:str) github personal access token that have permission to acces 6 Magestore product linesinstance_info: (type:dict)ip: (type:str) server ip addressuser: (type:str) server usernamepassword: (type:str) server username's passwordkey_path: (type:str) path to the private key file on local machine (required when no password provided)source_folder: (type:str)absolute path to magento sourceweb_container_id: (type:str) web container id (installed by [magento-apache](https://gitlab.com/general-oil/infrastructure/tree/master/Environment/Magento/DemoPortalApache) docker)