Saravanan666/pypi-org huggingface dataset:
Package: minder-downloader
Minder DownloaderThe minder-downloader module is a Python package that provides a user-friendly interface for interacting with the Minder research portal. This package enables users to easily download datasets from the Minder research portal, as well as upload reports to the platform.The downloading functionality of the package is straightforward to use. The module handles the authentication and request/response handling, so the user doesn't need to worry about the details of these processes. Once authenticated, users can specify the date range and datasets to download, and the module will return the data as a Pandas DataFrame. This makes it easy to work with the data using Python's powerful data analysis tools.In addition to downloading data, the minder-downloader module also provides a simple way to upload reports to the Minder research portal. This can be done by providing the path to a file and the HTML address to upload the file to. The module takes care of the uploading process, making it easy to share reports with collaborators or the wider research community.Overall, the minder-downloader package provides a convenient and streamlined way to interact with the Minder research portal, whether you need to download data or upload reports.InstallationYou can install minder-downloader using pip:pipinstallminder-downloaderUsageHere is a simple example of how to use minder-downloader to download data from the Minder research portal:fromdatetimeimportdatetime,timedeltafromminder_downloaderimportMinderDatasetDownload# Define date range and datasets to downloadsince=datetime.now()-timedelta(days=7)until=datetime.now()datasets=['example_dataset_1','example_dataset_2']# Create downloader object and download datadownloader=MinderDatasetDownload(since,until,datasets)data=downloader.download_data()# Print downloaded dataprint(data.head())This will download the specified datasets for the past week and print the first few rows of the resulting DataFrame.Licenseminder-downloader is licensed under the MIT License. See the LICENSE file for details.AcknowledgementsThis module was created by Dr Eyal Soreq. If you find it useful, please consider citing it in your research.
Package: minder.research-data-snapshots
minder.research-data-snapshotsInstallpipinstallminder.research-data-snapshotsUseFirst retrieve an access token fromhttps://research.minder.care/exportRESEARCH_PORTAL_TOKEN_PATH=/path/to/access/tokenThen use one of the following methods to retrieve data snapshotsCLIpython-mminder.research_data_snapshots--organizationELM4RQF8R9dsU3CZEgtiTW7LcACpK4u12HH72D1etd5X--datasetpatientsraw_heart_rateRunpython -m minder.research_data_snapshots --helpto see all optionsLibraryfromdatetimeimportdatetimeimportpandasaspdfromminder.research_data_snapshotsimportdownload_dataset,download_datasetspath=download_dataset("ELM4RQF8R9dsU3CZEgtiTW","patients")df=pd.read_parquet(path)print(df)paths=download_datasets(["ELM4RQF8R9dsU3CZEgtiTW","7LcACpK4u12HH72D1etd5X"],["raw_heart_rate"])df=(pd.read_parquet(paths,filters=[("start_date",">=",datetime.fromisoformat("2023-01-01T00Z")),("start_date","<",datetime.fromisoformat("2024-01-01T00Z")),],).sort_values("start_date").reset_index(drop=True))print(df)
Package: minder.research_portal_client
Research Portal ClientLibrary to interact with Minder Research APIs.Examplepipinstallminder.research-portal-clientimportloggingimportasyncioimportdatetimeimportsysfromminder.research_portal_clientimportConfiguration,JobManagerfromminder.research_portal_client.modelsimport(ExportJobRequest,ExportJobRequestDataset,)logging.basicConfig(level=logging.INFO)Configuration.set_default(Configuration(access_token="---REDACTED---",))asyncdefexample1():asyncwithJobManager()asjob_manager:now=datetime.datetime.today()since=now-datetime.timedelta(days=7)datasets:dict(str,ExportJobRequestDataset)={"patients":ExportJobRequestDataset(),"observation_notes":ExportJobRequestDataset(),}export_job=ExportJobRequest(since,datasets=datasets)job_id=awaitjob_manager.submit(export_job)job=awaitjob_manager.wait(job_id)files=awaitjob_manager.download(job)print(files)asyncdefexample2():job_id="c25249e0-82ff-43d1-9676-f3cead0228b9"asyncwithJobManager()asjob_manager:files=awaitjob_manager.download(job_id)print(files)asyncdefexample3():asyncwithJobManager()asjob_manager:datasets=awaitjob_manager.client.info.list_datasets()print(datasets)organizations=awaitjob_manager.client.info.list_organizations()print(organizations)reports=awaitjob_manager.client.reports.list_reports()print(reports)asyncdefmain():awaitexample1()awaitexample2()awaitexample3()ifsys.platform=="win32":asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())asyncio.run(main())DevelopmentUseful commandsSetuppoetryinstallRun testspoetryrunpytestCode CoverageThis command consists of 2 parts:running tests with coverage collectionformatting the report:report(text to stdout),xml(GitLab compatible: cobertura),html(visual)poetryruncoveragerun-mpytest&&poetryruncoveragereport-mLintingpoetryrunflake8Formattingpoetryrunblack.Type Checkingpoetryrunmypy.
Package: mindfactory-crawling
Mindfactory.de CrawlerThis repository contains a crawler forMindfactory, a German ecommerce shop (for computer hardware). The crawler extracts the data contained on every single product page and stores the scraped products and reviews in a SQLite database consisting of two tables.Each product has the following properties:ID (SQLite identifier)URLProduct nameBrand nameCategory (i.e. CPU)EANSKUItems sold (Count)People watching (Count)RMA quote (in percent)Average rating (from 1.0 to 5.0)Shipping (information on availability)Price (in Euro)Additionally, for every product all reviews are collected and stored in a separate SQLite table. An entry in this table has the following properties:Product ID (Reference to the corresponding ID in the product table)Stars (Rating, from 1 to 5)TextAuthorDate (YYYY-MM-DD)Verified (actually bought the product at Mindfactory)PrerequisitesPython3scrapySQLite3Run the scraperscrapy crawl mindfactory_products
Package: mindfoundry.client.analyze
Mind Foundry Analyze is a Data Science workbench developed by Mind Foundry.This Python client makes it easier to interact with the API and integrate with other Python libraries.Quick start:from mindfoundry.client.analyze import create_project_api_client

client = create_project_api_client(YOUR_INSTANCE_URL, YOUR_CLIENT_ID, YOUR_CLIENT_SECRET)
with open('./your-training-data.csv', 'rb') as data:
    data_set_id = client.create_file_data_set(data, 'Your Data Set Name', 'Your description')

model_id = client.create_classification_model('Your Model Name', data_set_id, 'Target Column')

with open('./your-prediction-data.csv', 'rb') as other_data:
    other_data_set_id = client.create_file_data_set(other_data, 'Prediction Data')

prediction_id = client.create_prediction(model_id, other_data_set_id, 'Your Prediction Name')

client.download_prediction_as_csv(prediction_id, './your-output-file.csv')You can also create a data set from a Pandas DataFrame, or an array of dicts, or an array of arrays:data_dicts = [{'a': 1, 'b': 2}, {'a': 3}, {'b': 4}]
client.create_file_data_set(data_dicts, 'Array of dicts')

df = pd.DataFrame(data_dicts)
client.create_file_data_set(df, 'Pandas DataFrame')

data_arrays = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
client.create_file_data_set(data_arrays, 'Array of arrays')
Package: mindfoundry.client.horizon
Mind Foundry Horizon ClientThis package contains a python client that can be used to interact with an instance of Mind Foundry Horizon. The client exposes a familiar interface that allows you to tackle forecasting problems with ease.frommindfoundry.client.horizon.clientimportConnectionfrommindfoundry.client.horizon.modelsimportHorizonForecaster# Generate an API key in the Horizon dashboardconnection=Connection(base_url="your Horizon instance",api_key="your api key",)# Create a new forecasting model. See the complete documentation# for a more in-depth explanation of each argument.model=HorizonForecaster(connection=connection,name="My Forecaster",targets=["target1","target2",...],horizons=[1,2,3],refinement=False,)# Train a model on some training data (a pandas dataframe)model.fit(training_data)# Make a prediction on new dataprediction=model.predict(new_data)print(prediction.as_df().head())# Save the model for later usemodel.save("/path/to/model/file")# Re-load the modelmodel=HorizonForecaster.load("/path/to/model/file",connection=connection)# The model can be updated with new data in the same formatmodel.update(new_data_2)new_prediction=model.predict()
Package: mindfoundry-optaas-client
Mind Foundry OPTaaS Python ClientOPTaaS(Optimization as a Service) is a general-purpose Bayesian optimizer developed
byMind Foundrywhich provides optimal hyper-parameter configurations via web-services.
It can handle any parameter type and does not need to know the underlying process, models, or data.This Python client makes it easier to interact with the web service and integrate with other Python libraries such asscikit-learn.
Package: mind-free
# This is Mind free official pypi page## Installing# To install the package` pip installmind-free`# Or if you want to install a specific version` pip installmind-free==[version]`## Examples# isPalindrome()Check if the string is a palindrome### Example`py from mindfree import * a =isPalindrome("racecar")# Pass any string between the two quotation marks print(a) `## It returns True if it is a palindrome or it will return False# Made by Joel DangmeiChange logs :-Version 0.1 - 0.5 (15/5/2021)
1. Check whether if text is plalindrome
2. Check duplicate items in the list
Package: mindfulness-at-the-computer
Mindfulness at the ComputerThis application helps you to be mindful while using at the computer, and helps you concentrate on breathing in and out when you need a breathing pause.Theapplication websitehas more information(including screenshots and downloads).Social:Gitter chatLicense: GPLv3 - afree software(free as in freedom) license, meaning that you can share, change and distribute the applicationThe application is written in Python and uses Qt/PySide for the front-end.The project has been featured in thePyCoders Weeklynewsletter and is listed inAwesome Humane TechInstallation and setupInstall with pippip install mindfulness-at-the-computerordownload the pre-built packagefrom sourceforge. (For details about installation and setup, please seethe website)What is Mindfulness?https://mindfulness-at-the-computer.gitlab.io/mindfulness/ProjectProject goals:Providing a friendly environment for people new to software and development to contribute and improve their skillsPromotingfree/libre software(free as in freedom!) - and helping people see free software in the context of human rightsSoftware goals:Increasing mindfulness of breathing, body, and postureHelping people become more aware of (and kind to) their bodies when using the computer for long periods of timeContributing to the projectThis project is beginner-friendly:You can ask for help in the Gitter chat room and we will try to help you. Also we try to provide documentation useful for newcomers.Motivation when contributing to this project"Don't do anything except with the joy of a small child feeding a hungry duck"By feeding the hungry duck, you are helping their well-being and making them happy. You’re not feeding it just because you feel like youshouldoroughtto, but because it makes both of you happy. That’s how we’d love for you to help with this project! When you contribute, not only are you are helping this project, you are helping others who will use this application, helping them to be mindful whilst working at the computer and to be aware of their breathing and well-being.Developer documentationMost documents are located in thedocs directory. There's also awikithat is open for editingSome important documents:CONTRIBUTINGTechnical ArchitectureRunning the application from sourceMarkdownis used for the text documents.draw.iois used for editing the.xmlfiles.
Package: mindfultensors
mindfultensorsDataloader that serves MRI images from a mogodb.The main idea is to keep MRI images and corresponding training labels
for segmentation tasks in amongodatabase. However, each3D MRI
tensor even in 8 bit precision is 16Mb.mongo's records cannot be
larger than this limit and we need to also store the labels of the
same dimensions.mindfultensorsfetches and aggregates eachtensor stored across multiple records, together with corresponding labels
either for gray and white matter, 104 regions atlas, volume of each of 104 ROIs, or a 50 region
atlas.installationThe package is onpypyand the simplest way to install it ispip install mindfultensorsHowever, to tinker with it you can also clone the repo:git clone git@github.com:neuroneural/mindfultensors.gitThen change directory to the newly cloned repository:cd mindfultensorsAnd install locally bypip intall -e .usageA detailed example of how to create a dataloader using provided
dataset class and the corresponding tools is inscripts/usage_example.pyDo not forget to move the batches to the GPU once obtained.
Package: min-diffusion
Themin_diffusionlibraryThis library was put together for a series of experiments on
Classifier-free Guidance.Installpipinstallmin_diffusionHow to usemin_diffusionThe library has a single main classMinimalDiffusion.This class takes three arguments:model_namedevicedtypemodel_nameis the string model name on the HuggingFace hub.devicesets the hardware to run on.dtypeis thetorch.dtypeprecision for the torch modules.# import the libraryfrommin_diffusion.coreimportMinimalDiffusionLoading a sample modelBelow is an example to load theopenjourneymodel fromPromptHero.The model will be loaded intorch.float16precision and placed on the
GPU.# set the model to load and its optionsmodel_name='prompthero/openjourney'device='cuda'dtype=torch.float32revision="fp32"Creating aMinimalDiffusionwith these arguments:# create the minimal diffusion pipelinepipeline=MinimalDiffusion(model_name,device,dtype,revision)Loading the pipeline:# load the pipelinepipeline.load();Enabling default unet attention slicing.Generating an imageBelow is an example text prompt for image generation.Note the keyword"mdjrny-v4 style"at the start of the prompt. This
is how theopenjourneymodel creates images in the style of
Midjourney v4.# text prompt for image generationsprompt="mdjrny-v4 style a photograph of an astronaut riding a horse"CallingMinimalDiffusionon the input text prompt# generate the imageimg=pipeline.generate(prompt);Using the default Classifier-free Guidance.

  0%|          | 0/50 [00:00<?, ?it/s]Here is the generated image:# view the output imageimgNotes:The pipeline assumes you have logged in to the HuggingFace hub.
Package: mindlakesdk
Mind Lake Python SDKAn Python implementation for Mind LakeDescriptionThe Mind Lake SDK utilizes Mind Lake's encryption storage and privacy computing capabilities to provide secure data management.Mind Lake is the backbone of Mind Network.All data is end-to-end encrypted at the client-side SDK side, ensuring that plaintext data never leaves the user's client.Cryptographic principles ensure that only the data owner can access their own plaintext data.Additionally, Mind Lake's powerful privacy computing capabilities enable the performance of calculations and querying of encrypted data.Getting StartedDependenciesPython > 3.8pipweb3pynaclInstallingpip install mindlakesdkImportimport mindlakesdk
...More examplesstep-by-step tutorialquick startsmore examplescodemind-lake-sdk-python
|-- mindlakesdk # source code
|   |-- __init__.py
|   |-- datalake.py
|   |-- permission.py
|   |-- cryptor.py
|   └-- utils.py
|-- tests # unit test code
|-- examples # use case examples
|-- tutorial # step-by-step tutorial
|-- README.md
└--- LICENSEHelpFull doc:https://mind-network.gitbook.io/mind-lake-sdkAuthorsDennis@NuIlPtrGeorge@georgemindnetVersion Historyv1.0Initial Releasev1.0.1Fix bugv1.0.2Improve performancesv1.0.5Keep up the version number with TypeScript SDKv1.0.6Add support for Mind DataPackv1.0.7Update the connect functionv1.0.8Add support for multiple chainsLicenseThis project is licensed under the [MIT] License - see the LICENSE.md file for details
Package: mindmate
install packagevirtual environmentpip install mindmateIt's not recommended to install in virtual environment(except for  testing)try it with defaultpipoperating system levelsudo apt update
sudo apt install -y python3-pip
export PATH="$PATH:/home/$USER/.local/bin"
pip install mindmateusage$ mindmate [ARGUMENT] [OPTIONS] [OPTIONS] [OPTIONS] --helpexamples$ mindmate configure
$ mindmate directory prompting list

$ mindmate chat --platform openai \
  --model text-davinci-003 \
  --stream true \
  --max-tokens 500 \
  --prompt "Act as a professional developer, provide best file structure for fastAPI framework"

$ mindmate image create -p "mindmate written on the background in a garden and friends playing around"compatibilityNot testedyet, but should be compatible with any Python >= 3.8
Package: mindmelting.powerpal
powerpalThis repository and package is not affiliated with Powerpal.Simple Python package for making calls to the Powerpal Readings API.Credit toforfuncsakefor the go client which this is influenced from:https://github.com/forfuncsake/powerpalInstallationpipinstallmindmelting.powerpalUsageLive datafromaiohttpimportClientSessionfrommindmelting.powerpalimportPowerpalfrommindmelting.powerpal.exceptionsimport(PowerpalException,PowerpalAuthorizationException,PowerpalAuthenticationException)EXAMPLE_AUTH_KEY='xyz123'# Your Powerpal API authorization keyEXAMPLE_DEVICE_ID='abc123'# Your Powerpal device IDsession=ClientSession()powerpal=Powerpal(session,EXAMPLE_AUTH_KEY,EXAMPLE_DEVICE_ID)asyncdefget_powerpal_data(self):try:returnawaitpowerpal.get_data()exceptPowerpalAuthenticationExceptionasexception:# 401 - Authorization key is invalid...exceptPowerpalAuthorizationExceptionasexception:# 403 - Device Id is invalid...exceptPowerpalExceptionasexception:# All other exceptions...Historical time-series dataWith no argumentsfromaiohttpimportClientSessionfrommindmelting.powerpalimportPowerpalfrommindmelting.powerpal.exceptionsimport(PowerpalException,PowerpalAuthorizationException,PowerpalAuthenticationException)EXAMPLE_AUTH_KEY='xyz123'# Your Powerpal API authorization keyEXAMPLE_DEVICE_ID='abc123'# Your Powerpal device IDsession=ClientSession()powerpal=Powerpal(session,EXAMPLE_AUTH_KEY,EXAMPLE_DEVICE_ID)asyncdefget_powerpal_time_series_data(self):try:returnawaitpowerpal.get_time_series_data()exceptPowerpalAuthenticationExceptionasexception:# 401 - Authorization key is invalid...exceptPowerpalAuthorizationExceptionasexception:# 403 - Device Id is invalid...exceptPowerpalExceptionasexception:# All other exceptions...With argumentsfromaiohttpimportClientSessionfrommindmelting.powerpalimportPowerpalfrommindmelting.powerpal.exceptionsimport(PowerpalException,PowerpalAuthorizationException,PowerpalAuthenticationException)EXAMPLE_AUTH_KEY='xyz123'# Your Powerpal API authorization keyEXAMPLE_DEVICE_ID='abc123'# Your Powerpal device IDsession=ClientSession()powerpal=Powerpal(session,EXAMPLE_AUTH_KEY,EXAMPLE_DEVICE_ID)asyncdefget_powerpal_time_series_data(self,start:int,end:int,sample:int):try:returnawaitpowerpal.get_time_series_data(start=start,# unix timestamp of start time, e.g., 1706321820 for Sat Jan 27 2024 02:17:00 GMT+0000end=end,# unix timestamp of end time, e.g., 1706332620 for Sat Jan 27 2024 05:17:00 GMT+0000sample=sample# sample (in minutes) to "bucket" readings, e.g., 60 for 1 hour interval)exceptPowerpalAuthenticationExceptionasexception:# 401 - Authorization key is invalid...exceptPowerpalAuthorizationExceptionasexception:# 403 - Device Id is invalid...exceptPowerpalExceptionasexception:# All other exceptions...
Package: mindoc
mindoc.pyA minimalistic python and SQL documentation tool.This program converts .py and .sql files into .html documentation files with minimal work on the developer's part.(You can also use it to conver .md files to .html files in the same style if you wish)The purpose is to minimise the documentation burden.Simply write the code as if you would a markdown file with the documentation parts as comment blocks.This tool also helps automatically generate a table of contents and cross-referencing.mindoc also supports basic diagramming with markdown-like syntax.Please see the documentation on GitHub to see how to install and use mindoc.https://minchulkim87.github.io/mindoc/
Package: mindoptpy
DESCRIPTIONmindoptpyis a Python wrapper aroundmindoptwithCython.MindOptoptimization solver is an efficient optimization problem solving software, which can be widely used in scientific research, electric power and energy, industrial manufacturing, transportation and logistics, retail, finance, cloud computing and other fields. It is the core of industrial software and a powerful tool to help enterprises “reduce cost and increase efficiency”.ReportingIt may contain a number of unknown bugs and/or performance issues. Should
the solver crash for an unclear reason, or terminate with an incorrect
status, please do not hesitate to report the issue to:solver.damo@list.alibaba-inc.com
Package: mindpandas
Welcome to MindPandas查看中文What Is MindPandasOverviewArchitectureInstallation MethodsConfirming System Environment InformationInstalling from pip commandInstalling from source codeInstallation VerificationQuickstartDocsContributingRelease NotesLicenseWhat Is MindPandasOverviewMindPandas uses distributed computing engine to accelerate pandas operations, seamlessly integrated and compatible with existing pandas code. Using MindPandas for calculations can use all CPU cores on the computer, which makes MindPandas works especially well on larger datasets.ArchitectureMindPandas is implemented based on distribution, while native pandas is implemented based on single thread. This means that only one CPU core can be used at a time.However, MindPandas can use more threads and cores on the machine, or all cores of the entire cluster.For detailed architecture design, please refer toofficial website document.Installation MethodsConfirming System Environment InformationThe following table lists the environment required for installing, compiling and running MindPandas:softwareversionLinux-x86_64Ubuntu >=18.04Euler >=2.9Python3.8glibc>=2.25Make sure libxml2-utils is installed in your environment.Please refer torequirementsfor other third party dependencies.Installing from pip commandIf you use the pip, please download the whl package fromMindPandaspage and install it.Installing whl package will download MindPandas dependencies automatically (detail of dependencies is shown in requirements.txt) in the networked state, and other dependencies should be installed manually.Installing from source codeDownloadsource code, then enter themindpandasdirectory to run build.sh script.gitclonehttps://gitee.com/mindspore/mindpandas.gitcdmindpandas
bashbuild.shThe package is in output directory after compiled and you can install with pip.pipinstalloutput/mindpandas-0.1.0-cp38-cp38-linux_x86_64.whlInstallation VerificationExecute the following command in shell. If noNo module named 'mindpandas'error is reported, the installation is successful.python-c"import mindpandas"QuickstartFirst import MindPandas with the following command.importmindpandasaspdSet the running mode of MindPandas with the following command, which can speed up your MindPandas workflow.pd.set_concurrency_mode('multithread')The complete example is as follows:>>>importmindpandasaspd>>>pd.set_concurrency_mode('multithread')>>>pd.set_partition_shape((16,2))>>>pd_df=pd.DataFrame([[1,2,3],[4,5,6]])>>>sum=pd_df.sum()>>>print(sum)051729Name:sum,dtype:int64DocsMore details about installation guide, tutorials and APIs, please see theUser Documentation.ContributingWelcome contributions. See ourContributor Wikifor
more details.Release NotesThe release notes, see ourRELEASE.LicenseApache License 2.0