Saravanan666/pypi-org huggingface dataset:
Package: margo
Margo is a tool that generates yaml cell type marker which maps cell types to gene expression
from csv gene expression files.Checkout a full documentationhere.Installationpip install margoUsagemargo <input_csv> <output_yaml> -t/--tissue <specified_tissues> -m/--min_marker_per_celltype <min_marker_per_celltype>The input file<input_csv>should be a csv file which contains single cell gene expression data.
It must includes the feature names (gene markers) as the column names in the first row.The output yaml file<output_yaml>is a marker which maps cell types to gene markers.Here’s an example:cell_type:
   Angiogenic T cell:
      - CD3
      - CD31
   Basal epithelial cell:
      - Vimentin
      - Cytokeratin 14
      - Cytokeratin 5
   CD1C-CD141- dendritic cell:
      - CD45
      - CD68
   Cancer cell:
      - CD44
      - Cytokeratin 8/18
      - Her2
      - CD45
      - CD20
   Cancer stem cell:
      - CD44
      - c-Myc
   Epithelial cell:
      - Cytokeratin 19
      - Cytokeratin 8/18
      - SMA
   Hematopoietic stem cell:
      - CD44
      - CD45
   Leukocyte:
      - CD3
      - CD45
      - CD20
   Luminal epithelial cell:
      - Cytokeratin 19
      - Cytokeratin 8/18
   Myoepithelial cell:
      - CD44
      - SMA
      - Cytokeratin 14ReferenceMarker data was extracted from databaseCellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Research. 2018.(Website:http://biocc.hrbmu.edu.cn/CellMarker).
Package: margot
What is margot?Margot makes it super easy to backtest trading elgorithms. Firstly, Margot makes
it super easy tocreate neat and tidy Pandas dataframes for time-series analysis.Margot manages data collection, caching, cleaning, feature generation,
management and persistence using a clean, declarative API. If you've
ever used Django you will find this approach similar to the Django ORM.Margot also provides a simple framework for writing and backtesting systematic
trading algorithms.Results from margot's trading algorithms can be analysed using pyfolio.Getting Startedpip install margotNext you need to make sure you have a couple of important environment variables
set::export ALPHAVANTAGE_API_KEY=YOUR_API_KEY
export DATA_CACHE=PATH_TO_FOLDER_TO_STORE_HDF5_FILESOnce you've done that, try running the code in thenotebook.StatusThis is still an early stage software project, and should not be used for live
trading just yet.DocumentationThe documentation is atreadthedocs.ContributingFeel free to make a pull request or chat about your idea first usingissues.Dependencies are kept to a minimum. Generally if there's a way to do something
in the standard library (or numpy / Pandas), let's do it that way rather than
add another library.LicenseMargot is licensed for use under Apache 2.0. For details seethe License.
Package: maria
Oh, maria blows the stars around / and sends the clouds
a-flyin’mariais a python-based package that simulates turbulent atmospheric
emission using a auto-regressive Gaussian process framework, for
applications in observational astronomy. Tutorials for installation and
usage can be found in thedocumentation.BackgroundAtmospheric modeling is an important step in both experiment design and
subsequent data analysis for ground-based cosmological telescopes
observing the cosmic microwave background (CMB). The next generation of
ground-based CMB experiments will be marked by a huge increase in data
acquisition: telescopes likeAtLASTandCMB-S4will consist of hundreds of thousands of
superconducting polarization-sensitive bolometers sampling the sky. This
necessitates new methods of efficiently modeling and simulating
atmospheric emission at small angular resolutions, with algorithms than
can keep up with the high throughput of modern telescopes.maria simulates layers of turbulent atmospheric emission according to a
statistical model derived from observations of the atmosphere in the
Atacama Desert, from theAtacama Cosmology Telescope
(ACT)and theAtacama
B-Mode Search (ABS). It
uses a sparse-precision auto-regressive Gaussian process algorithm that
allows for both fast simulation of high-resolution atmosphere, as well
as the ability to simulate arbitrarily long periods of atmospheric
evolution.Methodologymariaauto-regressively simulates an multi-layeed two-dimensional
“integrated” atmospheric model that is much cheaper to compute than a
three-dimensional model, which can effectively describe time-evolving
atmospheric emission. maria can thus effectively simulate correlated
atmospheric emission for in excess of 100,000 detectors observing the
sky concurrently, at resolutions as fine as one arcminute. The
atmospheric model used is detailedhere.
Package: mariadb
MariaDB Connector/PythonMariaDB Connector/Python enables python programs to access MariaDB and MySQL databases, using an API
which is compliant with the Python DB API 2.0 (PEP-249). It is written in C and uses MariaDB Connector/C
client library for client server communication.LicenseMariaDB Connector/Python is licensed under the LGPL 2.1 or later (LGPL-2.1-or-later)Source codeMariaDB Connector/Python source code is hosted onGithubDocumentationMariaDB Connector/Python documentation can be found onGithub PagesBugsBugs and feature requests should be filed in theMariaDB bug ticket system
Package: mariadb-kernel
MariaDB Jupyter Kernelmariadb_kernelis a an Open Source kernel for Jupyter which enables users to run MariaDB in a Jupyter notebook.Quick Installation Stepsmariadb_kernelhas been packaged for the PyPI package manager,
packaging forconda-forgeis coming soon.Assuming you already have Jupyter Lab and MariaDB installed on your system,
all you need to do is:Install the kernelpython3-mpipinstallmariadb_kernelInstall the kernelspec so that the kernel becomes visible to JupyterLabpython3-mmariadb_kernel.installFor a more complete guide on how to installmariadb_kernel, check out ourInstallation docsUsing the kernelUsingmariadb_kernelis pretty simple, please check ourUsing the MariaDB Jupyter Kerneldocs for
some quick tips on how to get started and some links to our example notebooks.DocumentationTo get started withmariadb_kernel, see the full documentationhttps://mariadb.com/kb/en/the-mariadb-jupyter-kernel/ContributingPlease check theCONTRIBUTING.mdfile to
see our guidelines for contributing tomariadb_kernel, how to set up a development environment and
how to add a new magic command.Please note this project is still in its very early stages and we expect it to change frequently.
Package: maria-easy
Maria EasyThis python library is an easy way to use mariaDB.InstallationTo install the latest version use the following command:pip install maria-easyUsagefrom maria_easy.connector import Connector
conn = Connector(user='root', password='pass', host=mariadb-server, database='test')
conn.execute('create table oloc (a INT) ;')
conn.execute('insert into oloc values (42) ;')
conn.execute('SELECT * from oloc;')LicenseISCAuthor informationThis code was created in 2023 by Olivier Locard.
Package: marian-client
Marian ClientA client for interacting withMarian-NMTwebsocket server (seeMarianNMT).Marian is a state of the art Neural Machine Translation framework written in C++. Eventually, someone should write Python bindings (not it!). Until then, the recommended way of communicating with Marian is viaWebSockets. The authors of Marian provide an example of doing thiswith this script. If you need Python2 support, you should look at that script.What this project contributes beyond the above script:Persistent connection - This keeps a connection open between Marian Server and Python. This saves a few hundred ms per call, which is significantEncapsulation - just import a class, instantiate, and call. Don't think about websockets at allTimeout, retries, and error handling - websockets are not the most reliable. Connections fail, timeouts happen. Just pass a value fortimeoutandretrieswhen you instantiateMarianClientand this will just be handled for you.Installationpipinstallmarian-clientUsagefrommarian_clientimportMarianClient# These are the default values:host="localhost"port="8080"# or give the fully qualified URLurl="ws://my.marian.server.ip/translate"# Default valuestimeout=30# measured in seconds - you may want to make this much lowerretries=3# amount of times to retry on error. backs off exponentially.debug=False# set to True for a little more info on errorsmc=MarianClient(PORT=port,HOST=host,timeout=timeout,retries=retries,debug=debug)# or if you want to specify url# mc = MarianClient(url, timeout=timeout, retries=retries, debug=debug)# if you just want all the default values, and marian-server is running locally:# mc = MarianClient()tokenized_sentence="Alice like cats ."success,corrected_sentence,error_info=mc(tokenized_sentence)ifsuccess:print(corrected_sentence)else:print(f"Call to MarianClient failed with error code{error_info[0]}and message{error_info[1]}")# If marian-server is sert up and working, this prints# >>> "Alice likes cats ."NotesWhen instantiating aMarianClientinstance, if we receive aConnectionRefusedError, we attempt to reconnectconnection_retriestimes, with exponential backoff, maxing out atmax_wait_time_between_connection_attempts.This means in the default case, if Marian Server is unavailable, we will try to connect, wait 1 second, try to connect again, wait 2 seconds, try to connect again, wait 4 seconds, ... then 8, 16, 32, 64, 128, 256, 300, then actually fail, for a total wait time of 811 seconds.LicenseLike Marian, this package is released under the MIT license.CreditsThis package was made by the NLP team atQordoba. If you are using it, and interested in working on NLP, maybe reach out toSam?Thanks toMarcin Junczys-Dowmuntand the rest of the awesome authors of Marian!
Package: marianodoc
With Mariano Doc, its easy to insert lines of texts into a WORD or PDF file.Allows for different color font color and sizes.For Word files only there is options for Headings sizes and listing items with bullets.Sample Code:import marianodoc as mddoc=md.new_doc()doc.insert_line("sample line1")doc.insert_line("sample line2")doc.insert_line("sample line3")doc.createPDFFile("some_name.pdf")doc.createWordFile("some_name.docx")Note there are more features such as headings color fonts,highlighting, for more detaildoc.show_example1()doc.show_example2()If you like this library checkout my other library "MarianoInput" for getting user inputMarianoInputFor help or feedback emailmarianoricoPy@gmail.com
Package: marianoinput
You may need to prompt the user for a numeric or a boolean valueHowever what if the user types something invalid ?This library solves this difficult problem with simplicitymarianoinput has PenguinPrompt, with which you can use to easily get valid user input.If user inputs invalid input, then it will re-prompt the userSample Code..from marianoinput import PenguinPromptpen= PenguinPrompt()age=pen.prompt_int_range("whats you age ?", 18,100)height=pen.prompt_float_range("whats you height in meters ?",1,3)yesno= pen.prompt_yesno("Do you wish to continue ?")print(age, height, yesno)If you like this library check out my other libary "marianodoc" for producing word docshttps://pypi.org/project/marianodoc/
Package: marian-tensorboard
Marian TensorboardTensorBoard integration forMarian NMT.marian-tensorboardgenerates charts for TensorBoard or Azure ML Metrics from
Marian's training logs.It started as a project atMTMA 2022and
conceptually atMTM 2019.InstallationUsing PyPI:pip install marian-tensorboardLocally:git clone https://github.com/marian-nmt/marian-tensorboard
cd marian-tensorboard
virtualenv -p python3 venv
source ./venv/bin/activate
python3 setup.py installBoth will add newmarian-tensorboardcommand.UsageLocal machinemarian-tensorboard -f examples/train.encs.*.logOpen a web browser athttps://localhost:6006. The script will update the
TensorBoard charts every--update-freqseconds unless--offlineis used.Azure MLmarian-tensorboard -f path/to/train.log [-t tb azureml]Then on Azure Machine Learning VM go to theMetricstab or start a
TensorBoard server under theEndpointstab.Note that logging into Azure ML Metrics is automatically enabled if Azure ML
Run ID is detected. Specify-t azuremlto disable TensorBoard logging.
If Azure ML is enabled, the script will not start an own TensorBoard server
instance.ContributorsAmr HendyKevin DuhRoman GrundkiewiczMarcin Junczys-DowmuntSeeCHANGELOG.md.LicenseSeeLICENSE.md.
Package: mariantranslate
mariantranslateSimple translator using Marian NMT.installation$pipinstallmariantranslateor install from source:$ git clone https://github.com/galuhsahid/mariantranslate
$ cd mariantranslate
$ make install_srcusageView the list of available languageshere.frommariantranslateimportTranslatorlang_from="en"# source languagelang_to="id"# target languageen_id_translator=Translator(lang_from,lang_to)en_id_translator.translate("Due to the limited vegetation cover of the Faroe Islands, it is relatively easy to follow the history of geology.")>>>KarenatumbuhanterbatasmenutupiKepulauanFaroe,relatifmudahuntukmengikutisejarahgeologi.
Package: mariasql
MariaSQLNowadays selecting data is easy, but inserting and updating data is annoying.https://git.osuv.de/m/MariaSQLMirror, Issues & PRs:https://gitlab.com/markuman/MariaSQLinstallwithpip3 install mariasql --userExample / Usagestart a temporary MariaDBdocker run -d -p 3307:3306 --rm  --name mariadb -e MYSQL_ROOT_PASSWORD=password mariadbstart python3 and go>>>importMariaSQL>>>db=MariaSQL.MariaSQL(host='127.0.0.1',port=3307)>>>db.query('create database mariasql;')()>>>db.use('mariasql')>>># we can create tables based on dict definitions>>>mytable=dict()>>>mytable['id']=int>>>mytable['name']=str>>>mytable['some shitty column name']=float>>>db.create_table('test_table',mytable)()>>>db.show_tables()[{'Tables_in_mariasql':'test_table'}]>>># now we inserting a dict into the create tables>>>data=dict()>>>data['id']=12>>>data['name']='Alf'>>>data['some shitty column name']=3.1415>>>db.insert('test_table',data)()# let's read from create table>>>dataset=db.query('select * from test_table')>>>dataset[{'id':12,'name':'Alf','some shitty column name':3.1415}]>>>db.query('show create table test_table')[{'Table':'test_table','Create Table':'CREATE TABLE `test_table` (\n`id` int(11) DEFAULT NULL,\n`name` varchar(255) DEFAULT NULL,\n`some shitty column name` double DEFAULT NULL\n) ENGINE=InnoDB DEFAULT CHARSET=utf8'}]Propertiesvariabeledefault valuehostlocalhostport3306userrootpasswordpassworddbmysqlcharsetutf8mb4Methodsdb.use(dbname)to change the databasedb.show_tables()get list of current tables in selected databasedb.query(sql)execute raw sql commandsdb.create_db(dbname)create a new database if not existsdb.create_table(name, tabledef = None)Whentabledefis not given,namemust be a raw sql string which will be executedWhentabledefis a dict() and itskeysare just datatype definitions, a table based on this dict will be createddatatye limitations when using dict() definitionsstr->VARCHAR(255)int->INTfloat->DOUBLEdict->JSONdb.insert(table, data, on_duplicate = False)datamust be adict()withkeyswhich exists asCOLUMN_NAMEin the sql table. But it must not include all column names.whenon_duplicateis set toTrue, it will perform an update when the PKs already exists in the table.db.insert_on_duplicate(table, data)just a wrapper fordb.insert()withon_duplicate = True.todosome tests and setup ci/cdupdate methodinsert/update list of dicts
Package: marie-ai
Marie-AIIntegrate AI-powered document pipeline into your applicationsDocumentationSee theMarieAI docs.InstallationYou don't need this source code unless you want to modify the package. If you just
want to use the package, just run:pipinstall--upgrademarieaiInstall from source with:pipinstall-e.Build docker container:DOCKER_BUILDKIT=1dockerbuild.--build-argPIP_TAG="standard"-f./Dockerfiles/gpu.Dockerfile-tmarieai/marie:3.0-cudaCommand-line interfaceThis library additionally provides anmariecommand-line utility which makes it easy to interact with the API
from your terminal. Runmarie -hfor usage.Example codeExamples of how to use this library to accomplish various tasks can be found in the MarieAI documentation.
It contains code examples for:Document cleanupOptical character recognition (OCR)Document ClassificationDocument SplitterNamed Entity RecognitionForm detectionAnd moreRun with default entrypointdockerrun--rm-itmarieai/marie:3.0.19-cudaRun the server with custom entrypointdockerrun--rm-it--entrypoint/bin/bashmarieai/marie:3.0.19-cudaTelemetryhttps://telemetry.marieai.co/TODO :MOVE TO DOCSS3 Cloud Storagedockercompose-fdocker-compose.s3.yml--project-directory.up--build--remove-orphansCrossFTPConfigure AWS CLI Credentials.vi~/.aws/credentials[marie]# this should be in the fileaws_access_key_id=your_access_key_idaws_secret_access_key=your_secret_access_keyPull the Docker image.dockerpullzenko/cloudserverCreate and start the container.dockerrun--rm-it--namemarie-s3-server-p8000:8000\-eSCALITY_ACCESS_KEY_ID=MARIEACCESSKEY\-eSCALITY_SECRET_ACCESS_KEY=MARIESECRETACCESSKEY\-eS3DATA=multiple\-eS3BACKEND=memzenko/cloudserverSCALITY_ACCESS_KEY_ID : Your AWS ACCESS KEY 
SCALITY_SECRET_ACCESS_KEY: Your AWS SECRET ACCESS KEY 
S3BACKEND: Currently using memory storageVerify Installation.awss3mbs3://mybucket--profilemarie--endpoint-urlhttp://localhost:8000--regionus-west-2awss3ls--profilemarie--endpoint-urlhttp://localhost:8000awss3cpsome_file.txts3://mybucket--profilemarie--endpoint-urlhttp://localhost:8000awss3--profilemarie--endpoint-url=http://127.0.0.1:8000ls--recursives3://Production setupConfiguration for the S3 server will be stored in the following files:https://towardsdatascience.com/10-lessons-i-learned-training-generative-adversarial-networks-gans-for-a-year-c9071159628
Package: marienbad
MarienbadA simple toolsuite for help you to win atMarienbadgame, aNimgame.It is recommended to use thePyPyinterpreter instead of the CPython standart.GeneratorPour utiliser cet outil, entrez la commande :marienbad-generatorPour voir toutes les options disponibles :marienbad-generator--helpVous pouvez aussi le démarrer depuis un script python :importmarienbadmarienbad.generator.main()Bien sûr, vous pourvez ré-utiliser certaines fonctions :frommarienbad.generatorimportcheckCombinationcheckCombination([1,3,5,7])# TruecheckCombination([1,3,6,7])# FalseLa fonctiongenerateTableimprime le résultat sur la sortie standart. Pour récupérer cette sortie, utilisez le script suivant :importsysfromioimportStringIO# rediriger stdout dans un buffer :sys.stdout=StringIO()# appel de la fonction qui remplira stdout (donc le buffer)generateTable(10,10)# récupérer le contenu du buffer :s=sys.stdout.getvalue()# fermer le buffer :sys.stdout.close()# rediriger stdout vers la sortie standart :sys.stdout=sys.__stdout__# le résultat est maintenant dans la variable s
Package: marieSimulator
MarieSimulatorSimulator for MARIE (Machine Architecture that is Really Intuitive and Easy)Code FormatfrommarieSimulatorimportMarie,MarieReaderasmr# import the marie and marie reader modulesmarie=Marie(mr('trial'))# trial is the file containing Marie instructionsmarie.run()# run the programmarie.show()# show memory in hex after runningTrial FileLoad        X
        Add         One
        Store       X
        Halt
X,      HEX         0000
One,    HEX         0001GUI ModefrommarieSimulatorimportMarieGUI,appimportsyswindow=MarieGUI()window.show()sys.exit(app.exec())Supported Opcodes (Instructions)Load XStore XAdd XSubt XInputOutputHaltSkipcond XJump XClearAddI XJumpI XLoadI XStoreI XJnS XHEX XDownload the GUI applicationhereOr copy:https://www.aaryandehade.live/Marie%20Simulator.dmg
Package: marigold
MarigoldMarigold is a tiny server used to examine and compare the contents of hierarchical structured files.InstallationPull docker image fromhub.docker.comand run it for the files located in
the foldercontent.dockerpulltbartsch/marigold
dockerrun\-p8080:8080\--mounttype=bind,source="$(pwd)"/content,target=/app/content\tbartsch/marigold:latestRun development server in repositorypythonapp/run.py--directoryexample/marigoldBuild and run docker image from repositorydockerimagebuild-tmarigold:latest.||exitexportPORT=8080exportDIRECTORY=example/
dockerrun-it\-p8080:8080\--mounttype=bind,source="$(pwd)"/example/marigold,target=/app/content\marigold:latestIconsThis project uses icons ofremixiconproject.ChangelogThis library adheres to asemantic versioningscheme.
SeeCHANGELOG.mdfor the list of changes.ContributionsContributions are very welcome! Feel free to report bugs or suggest new features using GitHub issues and/or pull requests.LicenseDistributed underMIT License.
Package: marijbo
#Installation##Install from PyPIGR_F4Cis available on PyPI. To installGR_F4C, run this command in your terminal:pipinstallGR_F4C##Install from conda-forge`GR_F4C` is also available on conda-forge. If you have Anaconda or Miniconda installed on your computer, you can create a conda Python environment to install `GR_F4C`:condainstall-cdefaults-cconda-forgeGR_F4C##Install from GitHubTo install the development version from GitHub using Git, run the following command in your terminal:
```sh
```
##ColaboratoryColaboratory lets you connect to a local runtime using Jupyter. This allows you to execute code on your local hardware and have access to your local file system.Step 1: Install JupyterInstallJupyteron your local machine.Step 2: Install and enable the jupyter_http_over_ws jupyter extension (one-off)Thejupyter_http_over_wsextension is authored by the Colaboratory team and available onGitHub.pipinstalljupyter_http_over_ws
jupyterserverextensionenable--pyjupyter_http_over_wsStep 3: Start server and authenticateNew notebook servers are started normally, though you will need to set a flag to explicitly trust WebSocket connections from the Colaboratory frontend.jupyternotebook\--NotebookApp.allow_origin='https://colab.research.google.com'\--port=8888\--NotebookApp.port_retries=0Once the server has started, it will print a message with the initial backend URL used for authentication. Make a copy of this URL as you'll need to provide this in the next step.Step 4: Connect to the local runtimeIn Colaboratory, click the 'Connect' button and select 'Connect to local runtime…'. Enter the URL from the previous step in the dialogue that appears and click the 'Connect' button. After this, you should now be connected to your local runtime.##Upgrade GR_F4CIf you have installedGR_F4Cbefore and want to upgrade to the latest version, you can run the following command in your terminal:pipinstall-UGR_F4CIf you use conda, you can updateGR_F4Cto the latest version by running the following command in your terminal:condaupdateGR_F4C
Package: marilyn-api
Async client for Marilyn APIInstallationpip install marilyn-apiExamplesfrommarilyn_apiimportAsyncClientapi_root="https://app.mymarilyn.ru"headers={"X-API-Account":100500,"X-API-Token":"{auth token}",}aclient=AsyncClient(api_root,headers=headers)body={"channel_id":[],"start_date":"2022-02-01","end_date":"2022-02-18","date_grouping":"day","grouping":"placement","filtering":[{"entity":"no","entities":[]},{"entity":"project","entities":[4551]}],"custom_metrics":[],"profiles":[],"goals":[],"with_vat":False,"per_page":200,"sorting":"date","columns":["date","placement_id","placement_name","campaign_xid","channel_id","impressions","clicks","cpm_fact","reach_total_sum","viral_reach_total_sum","ctr","cost_fact","cpc_fact","orders","model_orders","revenue","revenue_model_orders"]}asyncforpageinaclient.iter_statistics_detailed(body):foriteminpage["items"]:print("RECORD:",item)Script examplespythonExamples/statistics_detailed.py--help
pythonExamples/statistics_detailed.py-rhttps://app.mymarilyn.ru-a100500-tMytoKeN12345-cExamples/detailed-stats-config.jsonpythonExamples/project_placements.py.py--help
pythonExamples/project_placements.py.py-rhttps://app.mymarilyn.ru-a100500-p12345-tMytoKeN12345DependenciesaiohttpAuthorPavel MaksimovYou can contact me atTelegramУдачи тебе, друг! Поставь звездочку ;)