Saravanan666/pypi-org huggingface dataset:
Package: meta1testnet
# Python Library for Meta1![](https://img.shields.io/pypi/v/meta1testnet.svg?style=for-the-badge)
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[![codecov develop](https://codecov.io/gh/meta1testnet/python-meta1testnet/branch/develop/graph/badge.svg)](https://codecov.io/gh/meta1testnet/python-meta1testnet)—## DocumentationVisit the [python website](http://docs.pybitshares.com/en/latest/) for in depth documentation on this Python library.## Installation### Install with pip3:$ sudo apt-get install libffi-dev libssl-dev python-dev python3-dev python3-pip
$ pip3 install meta1testnet### Manual installation:$ git clonehttps://github.com/meta-source/meta1testnet-python$ cd meta1testnet-python
$ python3 setup.py install –user### Upgrade$ pip3 install –user –upgrade meta1testnet## Contributingpython-meta1testnet welcomes contributions from anyone and everyone. Please
see our [guidelines for contributing](CONTRIBUTING.md) and the [code of
conduct](CODE_OF_CONDUCT.md).### Discussion and DevelopersDiscussions around development and use of this library can be found in a
[dedicated Telegram Channel](hhttps://t.me/meta1exchange)### LicenseA copy of the license is available in the repository’s
[LICENSE](LICENSE.txt) file.### BountiesAs part of [HackTheDex](https://hackthedex.io), security issues found in this
library are potentially eligible for a bounty.
Package: meta1-testnet
# Python Library for Meta1![](https://img.shields.io/pypi/v/meta1.svg?style=for-the-badge)
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[![codecov develop](https://codecov.io/gh/meta1/python-meta1/branch/develop/graph/badge.svg)](https://codecov.io/gh/meta1/python-meta1)—## DocumentationVisit the [python website](http://docs.pybitshares.com/en/latest/) for in depth documentation on this Python library.## Installation### Install with pip3:$ sudo apt-get install libffi-dev libssl-dev python-dev python3-dev python3-pip
$ pip3 install meta1### Manual installation:$ git clonehttps://github.com/meta-source/meta1-python$ cd meta1-python
$ python3 setup.py install –user### Upgrade$ pip3 install –user –upgrade meta1## Contributingpython-meta1 welcomes contributions from anyone and everyone. Please
see our [guidelines for contributing](CONTRIBUTING.md) and the [code of
conduct](CODE_OF_CONDUCT.md).### Discussion and DevelopersDiscussions around development and use of this library can be found in a
[dedicated Telegram Channel](hhttps://t.me/meta1exchange)### LicenseA copy of the license is available in the repository’s
[LICENSE](LICENSE.txt) file.### BountiesAs part of [HackTheDex](https://hackthedex.io), security issues found in this
library are potentially eligible for a bounty.
Package: meta2vec
meta2vecmeta2vecis a Python package for metabolite embedding, which allows for the representation of metabolites in a vector space.meta2vecpackage contains three modules:distance: provides functions for calculating the similarity distance between two metabolites using their embeddings.utils: provides helper functions for working with HMDB (Human Metabolome Database) dataset.visualize: provides functions for visualizing the embeddings using UMAP.Installationmeta2vecpackage can be installed viapip:pipinstallmeta2vecUsageHere is a brief overview of how to usemeta2vecpackage:Load Pre-trained Embeddingsfrommeta2vecimportfrom_pretrained# Load the TransE pre-trained embeddingshmdb_embeddings=from_pretrained(model_type="TransE",embedding_dir="embedding")The above code loads the pre-trained HMDB embeddings from theembeddingdirectory. If the embeddings are not already present in the directory, the function will download them from the GitHub repository.Calculate Distance between Two Metabolitesfrommeta2vecimporthmdb_id_cosine_distance,hmdb_id_euclidean_distance# Calculate the cosine distance between two HMDB IDscosine_distance=hmdb_id_cosine_distance(hmdb_embeddings,"HMDB0000001","HMDB0000002")# Calculate the Euclidean distance between two HMDB IDseuclidean_distance=hmdb_id_euclidean_distance(hmdb_embeddings,"HMDB0000001","HMDB0000002")Find Most Similar Metabolitesfrommeta2vecimportmost_similar# Find the most similar HMDB IDs to a given HMDB IDsimilar_compounds=most_similar(hmdb_embeddings,"HMDB0000001")Visualize Embeddingsfrom meta2vec import visualize_umap

# Visualize the embeddings using UMAP
visualize_umap(hmdb_embeddings)ContributingIf you would like to contribute to this project, please contactyxlu0613@gmail.com
Package: meta-api
METAVOID WRAPPERThis is a an official Wrapper for matavoid Api. To let users to use api with ease.Examples:To get language translationfrom meta_api import META
x = META()
print(x.translate("amma"))To get wallpaper imagesfrom meta_api import META
x = META()
print(x.wallpaper("RED ANIME"))Checlout more atmain.py
Package: metaapi-cloud-sdk
MetaApi is a powerful, fast, cost-efficient, easy to use and standards-driven cloud forex trading API for MetaTrader 4 and MetaTrader 5 platform designed for traders, investors and forex application developers to boost forex application development process. MetaApi can be used with any broker and does not require you to be a brokerage.CopyFactory is a simple yet powerful copy-trading API which is a part of MetaApi. See below for CopyFactory readme section.MetaApi is a paid service, however we offer a free tier for testing and personal use.TheMetaApi pricingwas developed with the intent to make your charges less or equal to what you would have to pay
for hosting your own infrastructure. This is possible because over time we managed to heavily optimize
our MetaTrader infrastructure. And with MetaApi you can save significantly on application development and
maintenance costs and time thanks to high-quality API, open-source SDKs and convenience of a cloud service.Official REST and websocket API documentation:https://metaapi.cloud/docs/client/This SDK requires a 3.8+ version of Python to run.Please note that this SDK provides an abstraction over REST and websocket API to simplify your application logic.For more information about SDK APIs please check docstring documentation in source codes located inside lib folder of this package.Working code examplesPlease checkthis short videoto see how you can download samples via our web application.You can find code examples atexamples folder of our github repoor in the examples folder of the pip package.We have composed ashort guide explaining how to use the example codeInstallationpipinstallmetaapi-cloud-sdkConnecting to MetaApiPlease use one of these ways:https://app.metaapi.cloud/tokenweb UI to obtain your API token.An account access token which grants access to a single account. See section below on instructions on how to retrieve account access token.Supply token to the MetaApi class constructor.frommetaapi_cloud_sdkimportMetaApitoken='...'api=MetaApi(token=token)Retrieving account access tokenAccount access token grants access to a single account. You can retrieve account access token via API:account_id='...'validity_in_hours=24account_access_token=awaitapi.token_management_api.narrow_down_token({'applications':['trading-account-management-api','copyfactory-api','metaapi-rest-api','metaapi-rpc-api','metaapi-real-time-streaming-api','metastats-api','risk-management-api'],'roles':['reader'],'resources':[{'entity':'account','id':account_id}]},validity_in_hours)print(account_access_token)Alternatively, you can retrieve account access token via web UI onhttps://app.metaapi.cloud/accountspage (seethis video).Table of contentsMT account managementMetaApi RPC APIMetaApi real-time streaming API (websocket API)Risk management APICopyFactory copy trading APIMetaStats trading statistics APIMetaApi MT manager APITracking latenciesEnable log4js loggingRate limits & quotasToken management API
Package: meta-api-python
meta_api_pythonPython Wrapper to various APIs fromMeta Platform.FeaturesSend API (v16.0)Send text messagesSend Media from a remote file (image, audio, video, file)Send ActionSend quick repliesSend buttonsSend Generic MessagesPrerequisitePython 3.7+installedYou'll need to setup aMeta App, Facebook Page, get the Page Access Token and link the App to the Page.How to installFrom GitHubpipinstallgit+https://github.com/santoshdahal2016/meta-api-python.gitFrom PypiPackage from Pypi.org may not be the latest one, if you want the latest version of this package, install it from the GitHub repository (see above)pipinstallmeta-api-pythonUsageSend APIfrommeta.api.apiimportApifrommeta.api.messages.text_messageimportTextMessageapi=Api(page_access_token=<page_access_token>)text_message=TextMessage(text="Hi, How are you?")response=api.send_messages(to=<recipient_id>,messages=text_message)Note: From Facebook regarding Receipt  IDsThese ids are page-scoped. These ids differ from those returned from Meta Login apps which are app-scoped. You must use ids retrieved from a Messenger integration for this page in order to function properly.Sending a generic template message:Generic Template Messagesallows you to add cool elements like images, text all in a single bubble.frommetapythonimportApifrommetapython.api.constsimportButtonTypefrommetapython.api.messagesimportElementMessage,ElementsMessage,ButtonMessage,ButtonsMessageapi=Api(page_access_token=<page_access_token>)# Generic Messageelements=ElementsMessage()web_link_button=ButtonMessage(button_type=ButtonType.WEB_URL,title="Visit Diyo Website")web_link_button.set_url(<url>)element1=ElementMessage(buttons=[web_link_button],image_url="https://moneymitra.com/static/image/moneymitra-logo.png",)elements.add_element(element1)phone_number_button=ButtonMessage(button_type=ButtonType.PHONE_NUMBER,title="Call me")phone_number_button.set_payload("XXXXXXXXXXX")element2=ElementMessage(buttons=[phone_number_button],image_url=<image_url>,)elements.add_element(element2)response=api.send_messages(to=<recipient_id>,messages=elements)
Package: metabaiji-serialization
This is an active fork ofbaiji-serialization,
a library for reading and writing common file formats to Amazon S3 and
local files.The fork’s goals are modest:Keep the library working in current versions of Python and other
tools.Make bug fixes.Provide API stability and backward compatibility with the upstream
version.Respond to community contributions.It’s used by related forks such aslace.FeaturesReads and writes Pickle, JSON, and YAMLWorks without an S3 connection (with local files)Supports Python 2.7 and uses boto2Supports OS X, Linux, and WindowsTested and production-hardenedExamplesfrombaiji.serializationimportjsonwithopen(filename,'w')asf:json.dump(foo,f)withopen(filename,'r')asf:foo=json.load(foo,f)frombaiji.serializationimportjsonjson.dump(filename)foo=json.load(filename)Developmentpipinstall-rrequirements_dev.txtraketestrakelintContributeIssue Tracker: github.com/bodylabs/baiji-serialization/issuesSource Code: github.com/bodylabs/baiji-serializationPull requests welcome!SupportIf you are having issues, please let us know.LicenseThe project is licensed under the Apache license, version 2.0.
Package: metabase-client
Metabase ClientA production-ready metabase client to spare you from handling HTTP requests directly.Install$ pip install metabase_clientStartYou can provide username and password to authenticate...from metabase_client import MetabaseClient

client = MetabaseClient("http://my-metabase-service.com", username="user", password="pass")
client.auth()... or just provide an already-authenticated token.from metabase_client import MetabaseClient

client = MetabaseClient("http://my-metabase-service.com", token="mytoken1q2w3e4r")Obs:Since metabase limits the number of logins made through its API, using the second approach enables you to cache the token and avoid many logins.ContributionsAny contributions are welcome. Fork this project and follow the steps below:Create a virtual env.Implement the feature along with the automated tests.Apply makefile commands (make [test|flake|black])Open a Pull Request.Help or SuggestionsPlease, open an issue at theGithub Issues page.
Package: metabase-data-api
UsageThis is a python package meant to query metabase datasources via api.install the package:pip install metabase-data-apiusage:import pandas as pd
from metabase_data_api import MetabaseApi as M
import json

session_params = dict(user='name@company.ai',
                      password='demopass1',
                      url='https://yourteam.metabaseapp.com/'
                      )

mb_api = M(**session_params)

query = 'SELECT 12 as col'

#get raw file data via export
d = mb_api.export_from_query(query, database_id=4)

json.loads(d.decode("utf-8"))

df = pd.DataFrame.from_records(d)


print(df)
#   col
#0   12LimitsThe get_ methods are calling the same endpoints that are used by metabase for its own charting and are limited to 2k rows of resultsThe export method uses the file download functionality which is limited to 1m rows.
Package: metabase-manager
metabase-managerManage your Metabase instance programmatically by declaring your desired state.metabase-managerwill create, update,
and delete objects in Metabase to ensure to it matches your declared configuration.Installationpipinstallmetabase-managerUsageHere is an example configuration for Users and Groups:# metabase.ymlusers:-email:jdoe@example.comfirst_name:Janelast_name:Doegroups:-Administrators-Finance-name:jsmith@example.comfirst_name:Johnlast_name:Smithgroups:-Marketinggroups:-name:Finance-name:MarketingBy running the following command,metabase-managerwill create these users and groups if they don't already exist,
update them if some attributes differ, and delete users and groups that exist in Metabase but are not declared here.metabase-managersync[CREATE]Group(name='Finance')[CREATE]Group(name='Marketing')[DELETE]Group(name='Sales')# Sales is not defined in metabase.yml[CREATE]User(first_name='Jane',last_name='Doe',email='jdoe@example.com',groups=[Group(name='Administrators'),Group(name='Finance')])# jsmith@example.com already exists in Metabase, but some attributes or group membership differ[UPDATE]User(first_name='John',last_name='Smith',email='jsmith@example.com',groups=[Group(name='Marketing')])CredentialsIt is possible to provide credentials to your Metabase instance through the command-line as follows:metabase-managersync--host=https://<org>.metabaseapp.com--user<email>--password<password>It is also possible to provide credentials as environment variables.metabase-managerwill automatically use
these variables if they are set in the environment.METABASE_HOST=<host>METABASE_USER=<user>METABASE_PASSWORD=<password>ConfigurationBy default,metabase-managerwill expect to find ametabase.ymlfile in the current directory. You can override this
default, and optionally provide more than one file, with the--file/-fparameter.metabase-managersync-fusers.yml-f<directory>/groups.ymlSelectionIt is possible to run your sync only for certain types of objects by using the--select/-sor--exclude/-eoptions.metabase-managersync--selectusers# only users will be syncedmetabase-managersync--excludeusers# everything by users will be syncedDry RunIt is possible to execute a dry run to see which objects would be created, updated, or deleted given your configuration.
This will only log the changes, but not actually execute any changes on your Metabase instance.metabase-managersync--dry-runUpsert OnlyIf you do not wantmetabase-managerto delete anything in your Metabase instance, you can use the--no-deleteflag.
This is useful if yourmetabase.ymlconfiguration file does not exhaustively define object that you wish to exist in
your Metabase instance.metabase-managersync--no-deleteSupported EntitiesCurrently, it is possible to manage the following entities:UsersGroups
Package: metabase-mate
Metabase MateTheMateclass in this Python module provides a convenient interface for interacting with Metabase, an open-source business intelligence tool. With this module, you can perform various tasks such as establishing connections, retrieving user information, managing table and field mappings, and duplicating dashboards.FeaturesConnection Handling: TheMateclass allows you to establish and maintain connections with the Metabase API by providing methods for setting and updating the API URL and session ID.User Interaction: You can interact with the Metabase API to retrieve user information, such as the current user's name and email address.Table and Field Mapping: The module enables you to map database names, table names, and field names to their corresponding IDs in Metabase, facilitating easier querying and manipulation of data.Dashboard Duplication: You can duplicate existing dashboards within Metabase, specifying the destination collection and optionally providing a new name for the duplicated dashboard.UsageTo utilize the functionalities provided by theMateclass, follow these steps:Initialization: Create an instance of theMateclass, optionally providing the Metabase API URL and session ID.mate=Mate(metabase_api_url="https://example.com/metabase",session_id="your_session_id")Connection Establishment: Upon initialization, the module attempts to establish a connection with Metabase using the provided credentials. If successful, it prints a welcome message.Table and Field Mapping: The module automatically loads and updates mappings between database names, table names, and field names and their corresponding IDs in Metabase.Dashboard Duplication: You can duplicate existing dashboards by providing the dashboard ID and the ID of the destination collection. Optionally, you can specify a new name for the duplicated dashboard.mate.duplicate_dashboard(dashboard_id=123,new_collection_id=456,new_dashboard_name="New Dashboard Name")Session ID Update: If necessary, you can update the session ID used for authentication with the Metabase API.mate.update_session_id("new_session_id")RequirementsPython 3.xrequestsmoduleInstallationEnsure you have therequestsmodule installed. You can install it via pip:pip install requestsConclusionTheMateclass simplifies interactions with the Metabase API, providing a Pythonic interface for managing connections, querying metadata, and performing administrative tasks such as dashboard duplication. With its intuitive methods and error handling, it streamlines the development of applications that integrate with Metabase.
Package: metabasepy2
Copyright (c) 2019Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.Description:# metabasepy2: Python wrapper for metabase REST Api
[![](https://img.shields.io/pypi/v/metabasepy.svg?maxAge=3600)](https://pypi.org/project/metabasepy2/)## InstallingInstall metabasepy2 by running:`shell pip install metabasepy2 `## Documentation[User Guide][][Commands Tutorial][][User Guide]: ./docs/guide.md
[Commands Tutorial]: ./docs/commands.mdPlatform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: Universal
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Package: metabasic
MetabasicDead simple client for interacting with the Metabase dataset APIInstallpipinstallmetabasicExamplesfrommetabasicimportMetabasicdomain="https://my-metabase-domain.com"# Authentication with an existing sessiondb=Metabasic(domain,session_id="foo",database_id=1)db.query("SELECT * FROM bar")db.get_dataframe("SELECT * FROM bar")# Email/Password authenticationga=Metabasic(domain,database_id=2).authenticate("foo@email.com","password")ga_query={"ids":"ga:1234567890","start-date":"30daysAgo","end-date":"today","metrics":"ga:someMetric","dimensions":"ga:someDimension","sort":"ga:someDimension","max-results":10000}ga.query(json.dumps(ga_query))# Select a database interactivelym=(Metabasic(domain).authenticate("foo@email.com","password").select_database())
Package: metabengine
MetabEnginemetabengineis an integrated Python package for liquid chromatography-mass spectrometry (LC-MS) data processing.Documentation:https://metabengine.readthedocs.io/en/latest/Source code:https://github.com/Waddlessss/metabengine/Bug reports:https://github.com/Waddlessss/metabengine/issues/It provides:Ion-identity-informed chromatograpgic peak picking.Peak quality evaluation via artificial neural network.Accurate annotation of isotopes, adducts, and in-source fragments.Ultra-fast annotation of MS/MS spectra supported byFlash Entropy SearchInstallation# PyPIpipinstallmetabengineThe changes tometabenginebetween each release can be foundhere. See more from the commit logs.Quick startOver 100 fundemental functions and objects are available inmetabengineto help you create the best data processing workflow for your study. Some examples of the pre-made workflows are here:Untargeted metabolomics workflowData quality examinationMS/MS search (identity search and hybrid search)Visualize MS data and generate plots ready for publicationContribute to metabengineThemetabengineproject is excited to have your expertise and passion on board!We value all enhancements or corrections. For those thinking about making significant contributions to the codebase, we encourage you to get in touch with us!Huaxu Yu,hxuyu@ucdavis.edu