Saravanan666/pypi-org huggingface dataset:
Package: minds
minds-apiUnofficial api for minds.comFree software: GNU General Public License v3Documentation:https://minds-api.readthedocs.io.FeaturesCurrently api coverage is limited but it supports:writing posts, blogsreading newsfeed, notificationslocal profilesAlso seeminds-clifor interface for this API.Quick UsageReading:from minds import Minds
minds_api = Minds()
top_posts = minds_api.channel_top()Writing:from minds import Minds, Profile
minds_api = Minds(Profile('username','password'))
top_posts = minds_api.post_newsfeed(message='Hello, this message is from API; only for adults!', mature=True)FAQCan I write a botnet using this to manipulate votes?Probably, but it will be easy to detect and not gonna last long.Is there some sort of cli or interface for this?Yes seeminds-cliproject.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.If you wish to support this project you can open up an issue or send me a tip:Bitcoin:18oJWwdy1XExN69kggj2JdYtqohMPx9QF8Bitcoin Cash:18oJWwdy1XExN69kggj2JdYtqohMPx9QF8Doge:DEmJSgawKvtS9CGQUC7QdNETa2UuYBrenSEthereum:0xe8b452F9E3FDa8CEB32b2C231f73cC5cFa67735BMonero:43PAxkfekEYQki3HTQkgt9eH7KK5ZRBd447HkumTGmo8cacA9vP25MwWbwaVe4vUMveKAzAiA4j8xgUi29TpKXpm3yjjWofHistory0.1.0 (2018-03-27)First release on PyPI.
Package: mindsay-sdk
mindsay-sdkThe mindsay-sdk is a python client that makes it easy to interact with the Mindsay's chatbot platform.
A Mindsay account is required.Installationpip install -e git+https://github.com/Destygo/mindsay-sdk#egg=mindsay_sdkUsagefrommindsay_sdkimportClient# Create mindsay client. This line will ask for your password and email code.client=Client('your.name@mindsay.com')# Define current environment for next operationsclient.set_current_instance(1)client.set_current_experiment(1)client.set_current_language('fr_FR')user_nodes=client.get_user_nodes()print(user_nodes)[{'name': 'Main',
  'record_id': 1,
  'service_record_id': None,
  'exploring': None,
  'display_name': '/Main',
  'resource_url': '/user_nodes/1'},
 {'name': 'After Itineraire',
  'record_id': 2,
  'service_record_id': 1,
  'exploring': None,
  'display_name': 'Itineraire/After Itineraire',
  'resource_url': '/user_nodes/2'},
 {'name': 'After Orientation',
  'record_id': 3,
  'service_record_id': 2,
  'exploring': None,
  'display_name': 'Orientation/After Orientation',
  'resource_url': '/user_nodes/3'}]More examples can be found in theexamples/folder.
Package: minds-cli
minds-clicli interface for minds-api.Free software: GNU General Public License v3Documentation:https://minds-cli.readthedocs.io.FeaturesSupport these commands:Commands:
  downvote       downvote content
  upvote         upvote content
  delete         delete content
  newsfeed       display newsfeed in the terminal
      boost       boost section
      channel     specific channel
      subscribed  subscribed section
      top         top section
  notifications  display notifications in the terminal
      all            show all notification categories
      comments       show only comment notifications
      groups         show only group notifications
      subscriptions  show only subscription notifications
      tags           show only tag notifications
      votes          show only vote notifications
  post           Create post/comment or display one
      blog      post a blog under current user's blogfeed
      comment   post a comment under a piece of content
      newsfeed  post a post under current user's newsfeed
  profile        Manage local user profiles
      list  list local profiles
      save  save profile locally from provided...
  show           Create post/comment or display one
      comments  show comments
      post      show single postSupports user profiles for easy sharing through multiple profiles.Prints formatted posts, newsfeeds and comments in terminal:$ minds newsfeed channel tinarg
Working from Chiang Mai, Thailand this month!
Got a condo with this view, pool and a gym for 400€ per month.
Pretty lovely place so far!

#chiangmai #thailand #digitalnomad #workspaceview
https://www.minds.com/fs/v1/thumbnail/825548664578514944
―――tinarg―――4/0――――――――――――――――――――――https://minds.com/newsfeed/825549491984670720―――――――――――――――――――――――C:0―――R:0―――
■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■Posting supports input via editor, file or stdin with markdown conversion for blogs (yes you can write blogs in markdown!)See docs for more:https://minds-cli.readthedocs.io.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.If you wish to support this project you can open up an issue or send me a tip:Bitcoin:18oJWwdy1XExN69kggj2JdYtqohMPx9QF8Bitcoin Cash:18oJWwdy1XExN69kggj2JdYtqohMPx9QF8Doge:DEmJSgawKvtS9CGQUC7QdNETa2UuYBrenSEthereum:0xe8b452F9E3FDa8CEB32b2C231f73cC5cFa67735BMonero:43PAxkfekEYQki3HTQkgt9eH7KK5ZRBd447HkumTGmo8cacA9vP25MwWbwaVe4vUMveKAzAiA4j8xgUi29TpKXpm3yjjWofHistory0.1.0 (2018-04-18)First release on PyPI.
Package: mindscope-utilities
mindscope_utilitiesUtilities for loading, manipulating and visualizing data from the Allen Institute Mindscope program.Functions in this repository depend on the AllenSDKhttps://github.com/AllenInstitute/AllenSDKInstallationSet up a dedicated conda environment:conda create -n mindscope_utilities python=3.8Activate the new environment:conda activate mindscope_utilitiesMake the new environment visible in the Jupyterpip install ipykernel
python -m ipykernel install --user --name mindscope_utilitiesInstall mindscope_utilitiespip install mindscope_utilitiesOr if you intend to edit the source code, install in developer mode:git clone https://github.com/AllenInstitute/mindscope_utilities.git
cd mindscope_utilities
pip install -e .TestingTests are run on CircleCI on every github commit.Tests can be run locally by running the following at the command line:flake8 mindscope_utilities
pytestLevel of SupportWe are planning on occasional updating this tool with no fixed schedule. Community involvement is encouraged through both issues and pull requests.
Package: mindsdb-datasources
Website|Docs|InstallationTo install the latest version of MindsDB Datasources use pip:pip install mindsdb_datasourcesOverviewMindsDBs Datasources goal is to make it very simple to ingest and prepare data that can be feed into MindsDB as DataSource.Datasource IntegrationsConnectors:question: :wave: Missing integration?SupportIf you found a bug, please submit anissue on Github.To get community support, you can:Post at MindsDBSlack community.Ask for help at ourGithub Discussions.Ask a question atStackoverflowwith a MindsDB tag.If you need commercial support, pleasecontactthe MindsDB team.ContributingBeing part of the core team is accessible to anyone who is motivated and wants to be part of that journey!If you'd like to contribute to the project, refer to thecontributing documentation.Please note that this project is released with aContributor Code of Conduct. By participating in this project, you agree to abide by its terms.Installing additional dependenciesFor some datasources may need additional dependencies.To work with datasource from list:S3, PostgreSQL, MSSQL, MongoDB, Athena, Redshift, Google Cloud Storage, ScyllaDB, Cassandra, Presto, Timescale, Dremio, Impala, Hive, Trino, InfluxDB, Phoenix, QuestDB, CrateDBplease, install dependencies via:pip install mindsdb-datasources[extra_data_sources]If You want to work withScyllaDB, please install:pip install mindsdb-datasources[scylla]If You want to work withCassandra, please install:pip install mindsdb-datasources[cassandra]Note: dependencies forCassandraandScyllaDBmay conflict with each other. It would be good not to install them at the same time.To work withSnowflake, please installSnowflake's ODBC driverandpyodbc.To work withDremio, please install theDremio ODBC Driverandpyodbc.To work withSolr, please install Solr andJayDeBeApi. Then export the Solr JDBC driver to CLASSPATH. Details:https://solr.apache.org/guide/6_6/solr-jdbc-python-jython.html#jaydebeapiMailing listsSubscribe to MindsDB MonthlyCommunity Newsletterto get general announcements, release notes, information about MindsDB events, and the latest blog posts.
You may also join ourbeta-usersgroup, and get access to new beta features.LicenseMindsDB is licensed underGNU General Public License v3.0
Package: mindsdb-forecast-visualizer
MindsDB Forecast VisualizerThe purpose of this tool is to aid in obtaining quick visualizations for time series forecasts provided by a Lightwood predictor.At the moment, the tool supports predictors trained withlightwood >= 1.0, but support for 3rd party models is coming soon~ish.DocumentationFor now, there is no documentation as the package itself is fairly minimal.However, most functionality is showcased through examples. Please refer to:example/train.pyto train a Lightwood forecaster for airplane arrival data (which includes 4 different time series)example/visualize.pyto plot predictions from this model in your web browserexample/visualize.ipynbto plot predictions from this model inside a jupyter notebookNote: if you've cloned the repository (as opposed topip installing), make sure the path tomindsdb_forecast_visualizeris added to your python path environment variable before running these scripts from the package root folder.
Package: mindsdb-native
MindsDB is an Explainable AutoML framework for developers built on top of Pytorch. It enables you to build, train and test state of the art ML models in as simple as one line of code.Try it outInstalling MindsDBLearning from ExamplesMindsDB Explainability GUIFrequently Asked QuestionsProvide Feedback to Improve MindsDBInstallationDesktop: You can use MindsDB on your own computer in under a minute, if you already have a python environment setup, just run the following command:pipinstallmindsdb_native--userNote: Python 64 bit version is required. Depending on your environment, you might have to usepip3instead ofpipin the above command.*If for some reason this fail, don't worry, simply follow thecomplete installation instructionswhich will lead you through a more thorough procedure which should fix most issues.Docker: If you would like to run it all in a container simply:sh-c"$(curl-sSLhttps://raw.githubusercontent.com/mindsdb/mindsdb/master/distributions/docker/build-docker.sh)"UsageOnce you have MindsDB installed, you can use it as follows:ImportMindsDB:frommindsdb_nativeimportPredictorOne line of code totrain a model:# tell mindsDB what we want to learn and from what dataPredictor(name='home_rentals_price').learn(to_predict='rental_price',# the column we want to learn to predict given all the data in the filefrom_data="https://s3.eu-west-2.amazonaws.com/mindsdb-example-data/home_rentals.csv"# the path to the file where we can learn from, (note: can be url))One line of code touse the model:# use the model to make predictionsresult=Predictor(name='home_rentals_price').predict(when_data={'number_of_rooms':2,'initial_price':2000,'number_of_bathrooms':1,'sqft':1190})# you can now print the resultsprint('The predicted price is between ${price}with{conf}confidence'.format(price=result[0].explanation['rental_price']['confidence_interval'],conf=result[0].explanation['rental_price']['confidence']))Visit the documentation tolearn moreGoogle Colab: You can also try MindsDB straight hereContributingTo contibute to MindsDB please checkout theContribution guide.Current contributorsMade withcontributors-img.Report IssuesPlease help us byreporting any issuesyou may have while using MindsDB.LicenseMindsDB License
Package: mindsdb-sdk
Python MindsDB SDKThe Python MindsDB SDK allows you to connect to a MindsDB server from Python using the HTTP API.Installationpip install mindsdb_sdkExampleConnecting to the MindsDB serverYou can establish a connection to the MindsDB server using the SDK. Here are some examples:Connect to a local MindsDB serverimportmindsdb_sdkcon=mindsdb_sdk.connect()con=mindsdb_sdk.connect('http://127.0.0.1:47334')Connect to the MindsDB Cloudimportmindsdb_sdkcon=mindsdb_sdk.connect(login='a@b.com',password='-')con=mindsdb_sdk.connect('https://cloud.mindsdb.com',login='a@b.com',password='-')Connect to a MindsDB Pro serverimportmindsdb_sdkcon=mindsdb_sdk.connect('http://<YOUR_INSTANCE_IP>',login='a@b.com',password='-',is_managed=True)Basic usageOnce connected to the server, you can perform various operations. Here are some examples:# Get a list of databasesdatabases=con.databases.list()# Get a specific databasedatabase=databases[0]# Database type object# Perform an SQL queryquery=database.query('select * from table1')print(query.fetch())# Create a tabletable=database.tables.create('table2',query)# Get a projectproject=con.projects.proj# or use mindsdb projectproject=con# Perform an SQL query within a projectquery=project.query('select * from database.table join model1')# Create a viewview=project.views.create('view1',query=query)# Get a list of viewsviews=project.views.list()view=views[0]df=view.fetch()# Get a list of modelsmodels=project.models.list()model=models[0]# Use a model for predictionresult_df=model.predict(df)result_df=model.predict(query)# Create a modeltimeseries_options={'order':'date','window':5,'horizon':1}model=project.models.create('rentals_model',predict='price',query=query,timeseries_options=timeseries_options)# Describe a modelmodel.describe()You can find more examples in thisGoogle colab notebookExampleshttps://github.com/mindsdb/mindsdb_python_sdk/tree/staging/examplesAPI DocumentationThe API documentation for the MindsDB SDK can be found athttps://mindsdb.github.io/mindsdb_python_sdk/.Generating API docs locally:cd docs
pip install -r requirements.txt
make htmlThe online documentation is automatically updated by pushing changes to the docs branch.TestingTo run all the tests for the components, use the following command:envPYTHONPATH=./pytestContributingWe welcome contributions to the MindsDB SDK. If you'd like to contribute, please refer to the contribution guidelines for more information.LicenseThe MindsDB SDK is licensed under the MIT License. Feel free to use and modify it according to your needs
Package: mindsdb-server
Mindsdb ServerThis server uses flask_restx to define the API. CheckAPI's docsfor detailed information about each endpoint.InstallationTo install mindsdb server from PyPi run:pip install mindsdb-serverUsageOnce you have the server installed, you can start it by calling thestart_server():import mindsdb_server as server

server.start_server()Note that mindsdb server by default runs on 47334 port. To change that include port parameter:server.start_server(port=43773)Development installationTo install and run mindsdb_server for development, activate your virtualenv and run:pythonsetup.pydeveloppipinstall-rrequirements.txtcdmindsdb_serverpython3server.pyYou can provide basic parameters to theserver.pycall as:--port, the default is 47334--use_mindsdb_storage_dir, the default is False--host, the default is 0.0.0.0Example:python3 server.py --port 47333The code inside mindsdb_servernamespaces/<endpoint.py>: these contains a file per API resourcenamespaces/configs: these are the configs for the API resourcesnamespaces/entitites: these are specs for the document objects that can be returned by the API resources
Package: mindsdb-text-to-sql
MindsDB Text to SQLMindsDB Text to SQL is a Python package that allows you to convert natural language queries into SQL code that can be run on MindsDB. With this package, you can easily build models using MindsDB's in-database machine learning capabilities by simply describing what you want to predict in natural language.This package uses the OpenAI API to convert natural language queries into SQL code that can be run on MindsDB.InstallationWith pippip install mindsdb_text_to_sqlUsageThe library will first need to be configured with an API key from OpenAI.from mindsdb_text_to_sql import GPTTextToSQL

text_to_sql = GPTTextToSQL('sk-...')
query = text_to_sql.convert_text_to_sql(
    "Get all rows from the iris table of the sqlite_datasource where the sepal_length is greater than 5"
)
Package: mindsight-people-control-api
Mindsight People Control APIUse mindsight people control functionalities in your python application.Instalationpipinstallmindsight-people-control-apiConfigurationEnvironment variablesTo use mindsight-people-control-api, you need to set two environment variables:# ---DOTENV EXAMPLE---
MINDSIGHT_CP_API_TOKEN= # Token to authenticate
MINDSIGHT_CP_API_URL=https://controle.mindsight.com.br/api # Base path of your api instanceUsage ExampleYou can use mindsight-people-control-api in order to create, update and delete registers on all system tables.List registersYou can use get methods to list registers of system table. See the following example:importmindsight_people_control_api# Instantiate Areas client objectareas_client=mindsight_people_control_api.Areas()# get_list_areas will return a ApiPaginationResponse object.# This object represents a pagination response from rest api from people control# and with get_all method from ApiPaginationResponse object, you can get all# data of all pages. The data will stored in results attribute of ApiPaginationResponseareas_data=areas_client.get_list_areas().get_all().results
Package: minds-kit
mindsis a Python toolkit, which can be used for computing minimum size
decisions sets, i.e. unordered sets ofif-thenrules. The toolkit represents
a set of pure Python modules, which can be used in a Python script in the
standard way through the provided API. Additionally, it contains an executablemds.py, which can be applied for constructing a smallest size decision set
for a training dataset in CSV.Details can be found athttps://github.com/alexeyignatiev/minds.
Package: mindspore-federated
Version dependencyDue the dependency between MindSpore Federated and MindSpore, please follow the table below and install the corresponding MindSpore verision fromMindSpore download page.pipinstallhttps://ms-release.obs.cn-north-4.myhuaweicloud.com/{MindSpore-Version}/MindSpore/cpu/ubuntu_x86/mindspore-{MindSpore-Version}-cp37-cp37m-linux_x86_64.whlMindSpore Federated VersionBranchMindSpore version0.1.0r0.11.8.00.1.0r0.11.9.0
Package: mindspore-serving
MindSpore Serving查看中文MindSpore ServingOverviewInstallationInstalling ServingConfiguring Environment VariablesQuick StartDocumentsDeveloper GuideCommunityGovernanceCommunicationContributionsRelease NotesLicenseOverviewMindSpore Serving is a lightweight and high-performance service module that helps MindSpore developers efficiently
deploy online inference services in the production environment. After completing model training on MindSpore, you can
export the MindSpore model and use MindSpore Serving to create an inference service for the model.MindSpore Serving architecture:MindSpore Serving includes two parts:ClientandServer. On aClientnode, you can deliver inference service
commands through the gRPC or RESTful API. TheServerconsists of aMainnode and one or moreWorkernodes.
TheMainnode manages allWorkernodes and their model information, accepts user requests fromClients, and
distributes the requests toWorkernodes.Servableis deployed on a worker node, indicates a single model or a
combination of multiple models and can provide different services in various methods. `On the server side, whenMindSporeis used as the inference backend,, MindSpore Serving
supports the Ascend 910/310P/310 and Nvidia GPU environments. WhenMindSpore Liteis
used as the inference backend, MindSpore Serving supports Ascend 310, Nvidia GPU and CPU environments. Client` does not
depend on specific hardware platforms.MindSpore Serving provides the following functions:gRPC and RESTful APIs on clientsPre-processing and post-processing of assembled modelsBatch. Multiple instance requests are split and combined to meet thebatch sizerequirement of the model.Simple Python APIs on clientsThe multi-model combination is supported. The multi-model combination and single-model scenarios use the same set of
interfaces.Distributed model inferenceInstallationFor details about how to install and configure MindSpore Serving, see theMindSpore Serving installation page.Quick StartMindSpore-based Inference Service Deploymentis
used to demonstrate how to use MindSpore Serving.DocumentsDeveloper GuidegRPC-based MindSpore Serving AccessRESTful-based MindSpore Serving AccessServices Provided Through Model ConfigurationServices Composed of Multiple ModelsMindSpore Serving-based Distributed Inference Service DeploymentFor more details about the installation guide, tutorials, and APIs,
seeMindSpore Python API.CommunityGovernanceMindSpore Open GovernanceCommunicationMindSpore Slackdeveloper
communication platformContributionsWelcome to MindSpore contribution.Release NotesRELEASELicenseApache License 2.0
Package: mindstorms
Operate LEGO MINDSTORMS Inventor Hub from your computerThis module allows you to operate the LEGO mindstorms hub from
your computer. This means that instead of sending entire python
files to the hub and letting it run them, you can run the command
one after another from your computer. This allows easy experimenting
from a python shell on your computer, showing you completions and
API documentation on the way. This also allows you to use the
regular debugging facilities you're used to.The API mirrors that of the actual micropython API, so programs that run
on the hub (and don't use callbacks) should run on your computer using this
module and vice versa.The API documentation was copied from the officialLEGO MINDSTORMS Inventor Hub documentation.Managing the actual connection to the hub is using the excellentrshellproject.Getting StartedRun:pip install mindstormsConnect the hub to your computer using the USB cable, and then
run this from Python:frommindstormsimportHubhub=Hub()whileTrue:whilehub.motion.gesture()!=hub.motion.TAPPED:passhub.sound.play('/extra_files/Hello')Tap the hub, and hear it say "hello".Easier usage of sensors usingspikedevYou can use Daniel Walton'sspikedevfor easier usage of the sensors,
like this:pipinstallgit+https://github.com/noamraph/spikedev.git@sensor-support-cpython>>>frommindstormsimport*>>>fromspikedev.sensorimport*>>>hub=Hub()>>>color=ColorSensor(hub.port.D)>>>color.value()[0]>>>dist=DistanceSensor(hub.port.E)>>>dist.set_mode(DistanceSensorMode.DISTL)>>>dist.value()[58]This currently relies on my pull-request. I hope it will be merged, and
then you'll be able to use it directly from his repository.NotesThe only missing classes from the official API arehub.BT_VCPandhub.USB_VCP. Adding them shouldn't be too difficult,
I just didn't know how to test them.I added all the methods from the official API, except for those that
contains a callback.LicenseMIT license.Copyright (c) 2022 - Noam Raphael.Based on theofficial API docs:The MIT License (MIT)

Copyright (c) 2017-2021 - LEGO System A/S - Aastvej 1, 7190 Billund, DK

Permission 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
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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