Saravanan666/pypi-org huggingface dataset:
Package: mmstats
`Documentation <http://mmstats.readthedocs.org/>`_ |`Package <http://pypi.python.org/pypi/mmstats>`_ |`Code <http://github.com/schmichael/mmstats/>`_.. image:: https://secure.travis-ci.org/schmichael/mmstats.png?branch=master:target: http://travis-ci.org/schmichael/mmstats/About=====Mmstats is a way to expose and read diagnostic values and metrics forapplications.Think of mmstats as /proc for your application and the readers as procpsutilities.This project is a Python implementation, but compatible implementations can bemade in any language (see Goals).Discuss at https://groups.google.com/group/python-introspectionGoals-----* Separate publishing/writing from consuming/reading tools* Platform/language independent (a Java writer can be read by a Python tool)* Predictable performance impact for writers via:* No locks (1 writer per thread)* No syscalls (after instantiation)* All in userspace* Reading has no impact on writers* Optional persistent (writer can sync anytime)* 1-way (Publish/consume only; mmstats are not management extensions)Usage=====Requirements------------CPython 2.6 or 2.7 (Windows is untested)PyPy (only tested in 1.7, should be faster in 1.8)Using-----1. ``easy_install mmstats`` or ``pip install mmstats`` or if you've downloadedthe source: ``python setup.py install``2. Then in your Python project create a sublcass of mmstats.MmStats like.. code-block:: pythonimport mmstatsclass WebStats(mmstats.MmStats):status2xx = mmstats.CounterField(label='status.2XX')status3xx = mmstats.CounterField(label='status.3XX')status4xx = mmstats.CounterField(label='status.4XX')status5xx = mmstats.CounterField(label='status.5XX')last_hit = mmstats.DoubleField(label='timers.last_hit')3. Instantiate it once per process: (instances are automatically thread local).. code-block:: pythonwebstats = WebStats(label_prefix='web.stats.')4. Record some data:.. code-block:: pythonif response.status_code == 200:webstats.status2xx.inc()webstats.last_hit = time.time()5. Run ``slurpstats`` to read it6. Run ``mmash`` to create a web interface for stats7. Run ``pollstats -p web.stats.status 2XX,3XX,4XX,5XX /tmp/mmstats-*`` for avmstat/dstat like view.8. Did a process die unexpectedly and leave around a stale mmstat file?``cleanstats /path/to/mmstat/files`` will check to see which files are staleand remove them... include:: CHANGES.rst:end-before: 0.5.0
Package: mmtfPyspark
mmtfPysparkis a python package that provides APIs and sample applications for distributed analysis and scalable mining of 3D biomacromolecular structures, such as the Protein Data Bank (PDB) archive. mmtfPyspark uses Big Data technologies to enable high-performance parallel processing of macromolecular structures.
Note, pip’s functionality is too limited to install a working installation of mmtfPyspark, follow the platform specific instructions onhttps://github.com/sbl-sdsc/mmtf-pyspark.
Package: mmtools
mmtools - i3 status bar and notification script for Mattermostmmtools are various tools using themattermostAPI.Installationsudopip3installmmtoolsThis will install mmtols from pypi, including the following required packages:caeppydanticmattermostdriverpasspynotify2dbus-pythonrequestsFor dbus-python to build, you need to have the libdbus-1-dev package installed. On debian you can dosudoaptinstalllibdbus-1-devToolsThe following tools are included:mmstatusmmstatusconnects to the mattermost API to get unread messages in all channels. It then outputs a statusbar (usable in i3blocks) of unread messages and exits. Supports private/public/user channels and different coloring on group chats and user chats.Example configuration for i3blocks:[mattermost]
command=/usr/local/bin/mmstatus
separator=true
interval=60
signal=12mmpolybarmmpolybarsame as mmstatus, but with polybar colors.Example configuration for polybar:[module/mmpolybar]
type = custom/script
exec = mmpolybar
tail = truemmwaybarmmwaybarsame as mmstatus, but with output for waybar.Example configuration for waybar:"custom/mattermost": {
    "exec": " mmwaybar",
    "return-type": "json",
    "interval": 90
}mmwatchmmwatchconnects to the mattermost websocket API and can display notification on messages and send SIGUSR2 to i3blocks to update statusbar before next interval.ConfigurationAll tools can be configured using both command line arguments and a configuration file.mmtoolswill first look for a configuration in~/.config/mmtools/config-<HOSTNAME>with fallback to~/.config/mmtools/config.Use the following command to create the configuration~/.config/mmtools/config. The same configuration file is used for both tools.mmconfiginitIn this file you must specify at least:# Mattermost server
server = <SERVER>

# Mattermost user
user = <USERNAME>

# either password
password = <MATTERMOST PASSWORD>

# OR pass entry (https://www.passwordstore.org)
password-pass-entry = <PASS ENTRY>User service formmwatchmmwatchcan be started as a systemd user service by creating the following file:.config/systemd/user/mmwatch.servicewith this content:[Unit]
Description=mm watch

[Service]
ExecStart=/usr/local/bin/mmwatch

Restart=always

# time to sleep before restarting a service
RestartSec=30

[Install]
WantedBy=default.targetEnable at loginsystemctl --user enable mmwatchStart manuallysystemctl --user start mmwatchLocal developmentFor local development, execute:pip3install-e.
Package: mm-tot
MultiModal Tree of ThoughtsMulti-Modal Foundation Model -> Deepfloyd iF or stable diffusion -> mmllm -> ifThe objective is to implement DALLE-3 where given an input task to generate an image => its fed and enriched by the multimodal llm => passed into image generationAppreciationLucidrainsAgoriansInstallpip install mm-totUsageArchitectureTodoLicenseMIT
Package: mmts
MMTSMM tools scriptsblogsDesfuscationInsatall$ pip install mmtsUsage1. Command# decmd# In the `$cmd_file` file, there is one obfuscation command per line. After the execution is completed, a `$cmd_file.clear` file is generated in the current directory, and the command after deobfuscation is saved.$mmts-decmd$cmd_file# parse lnk$mmts-parselnk$cmd_file2. Quick Startdecmdfrommmtsimportde_dosfuscation_workdefmain():clear_str=de_dosfuscation_work("mix_str")print(clear)if__name__=="__main__":main()Example
Package: mmu
A library for the evaluation of model performance and estimation of the uncertainty on these metrics.DocumentationFunctionalityOn a high levelMMUprovides two types of functionality:Metrics- functions to compute confusion matrix(ces) and binary classification metrics over classifier scores or predictions.Uncertainty estimators- functionality to compute the joint uncertainty over classification metrics.We currently focus on binary classification models but aim to include support for other types of models and their metrics in the future.Confusion Matrix & MetricsMetrics consist mainly of high-performance functions to compute the confusion matrix and metrics over a single test set, multiple classification thresholds and or multiple runs.Thebinary_metricsfunctions compute the 10 most commonly used metrics:Negative precision aka Negative Predictive Value (NPV)Positive recision aka Positive Predictive Value (PPV)Negative recall aka True Negative Rate (TNR) aka SpecificityPositive recall aka True Positive Rate (TPR) aka SensitivityNegative f1 scorePositive f1 scoreFalse Positive Rate (FPR)False Negative Rate (FNR)AccuracyMathew’s Correlation Coefficient (MCC)Uncertainty estimatorsMMU provides two methods for modelling the joint uncertainty on precision and recall: Multinomial uncertainty and Bivariate-Normal.The Multinomial approach estimates the uncertainty by computing the profile log-likelihoods scores for a grid around the precision and recall. The scores are chi2 distributed with 2 degrees of freedom which can be used to determine the confidence interval.The Bivariate-Normal approach models the statistical uncertainty over the linearly propagated errors of the confusion matrix and the analytical covariance matrix. The resulting joint uncertainty is elliptical in nature.Installationmmucan be installed from PyPi.pipinstallmmuWe provide wheels for:MacOS [x86, ARM]LinuxWindowsInstalling the package from source requires a C++ compiler with support for C++14.
If you have a compiler available it is advised to install without
the wheel as this enables architecture specific optimisations.pipinstallmmu--no-binarymmuOther build options exist, see theInstallation sectionof the docs.Usageimportmmu# Create some example datascores,yhat,y=mmu.generate_data(n_samples=1000)# Compute the joint uncertainty on precision-recall curvepr_err=mmu.PrecisionRecallCurveUncertainty.from_scores(y,scores)# Plot the uncertaintypr_err.plot()SeeBasics sectionof the docs or thetutorial notebooksfor more examples.ContributingWe very much welcome contributions, please see thecontributing sectionfor details.
Package: mmuller88-docker-build-ami
docker-build-amiBuild Amazon EC2 AMI image using a DockerfileLimitationsOnly supports instructions ENV, RUN, COPY and ADD, other instructions will just be ignored.ConfigurationThere is a separate config file for the script in either “/etc/docker-build-ami.conf” or “~/.docker-build-ami.conf”.# [main]

# Temporary directory
# tmp_dir = /tmp

# Name tag for host building AMI image
# host_tag = 'docker-build-ami'

# Region
# region = eu-west-1

# Instance type
# instance_type = m3.medium

# Subnet ID
# subnet_id = subnet-123abc45

# Security Groups
# security_group_ids = ["sg-1234", "sg-23456"]

# Host Tags - additional tags to add to EC2 host
# host_tags = [{"Key": "foo", "Value": "bar"}]

# AWS access key id
# aws_access_key_id = DFSDF3HGDF4SDSD1DDFF

# AWS secret access key
# aws_secret_access_key = 3riljdsf5SDFSDvsdfds452sdSDFDfsdf44SDFdRA

# Base image from which the output image is built
# image_id = ami-0df67e2624dedbae1

# EC2 user used to build instances (usually AMI dependent)
# image_user = ubuntu

# The AMI Name of the output image
# image_name = ubuntu-test

# Image Tags - tags to add to AMI
# image_tags = [{"Key": "foo", "Value": "bar"}]Usageusage: docker-build-ami [-h] [-c CONFIG] [-d] [-r REGION] [-t INSTANCE_TYPE]
                        [-s SUBNET_ID] [-n IMAGE_NAME] [-i IMAGE_ID]
                        [-u IMAGE_USER]

optional arguments:
  -h, --help            show this help message and exit
  -c CONFIG, --config CONFIG
                        Configuration file
  -d, --debug           Print debug info
  -r REGION, --region REGION
                        AWS region
  -t INSTANCE_TYPE, --instance-type INSTANCE_TYPE
                        EC2 instance type
  -s SUBNET_ID, --subnet-id SUBNET_ID
                        AWS subnet id
  -n IMAGE_NAME, --image-name IMAGE_NAME
                        Target AMI image name
  -i IMAGE_ID, --image-id IMAGE_ID
                        Source AMI image ID
  -u IMAGE_USER, --image-user IMAGE_USER
                        AMI image userRunning Tests# Run these lines once
pip3 install -r requirements-test.txt
pre-commit install

# Run these lines to check code formatting and correctness
flake8 --show-source --filename="\*.py" .
pytest --cov=docker2ami
Package: mmuxer
Mail MuxerAre you tired of managing IMAP filters through cumbersome, slow and unintuive web interfaces ? Then Mail Muxer is here to save you from this pain !Mail Muxer is a Python tool that will monitor your Inbox, and filter incomming emails according to the given configuration. It was designed to be intuitive and easy to use, by using compact configuration and sensible defaults: a rule can be as simple as-condition:FROM:example@email.orgmove_to:some/folderBasic UsageInstall:pip install mmuxerCreate configuration file:rules:-move_to:receiptscondition:ANY:-FROM:some_store@ok.ok-FROM:some_other_store@store.net-move_to:importantcondition:SUBJECT:important-condition:FROM:spammer@example.comactions:-deletesettings:server:imap.email.netusername:me@email.netpassword:secretCheck your configuration:mmuxer check --config-file config.yamlCheck your folders:mmuxer folder --config-file config.yaml compare-destinationsMonitor your inbox:mmuxer monitor --config-file config.yamlOr apply on all messages of a given IMAP folder:mmuxer tidy --config-file config.yaml --folder FOLDERFinally, to get help on a given command, add a--helpto it, e.g.mmuxer monitor --helpExporting configuration to sieve formatmmuxer can also export the rules of a configuration file to the sieve format, so that the rules will be handled server side.mmuxer sieve-export --config-file config.yaml --dest-file rules.sieveNote on SSLIf you get SSL errors while connecting to your server, see theSSL Configurationsection.Python compatibilityThis program should work with Python >= 3.8.Going further onAdvanced usageUse with docker/systemd: seedocWhat's nextThe current implementation is mostly feature complete for me, but here are some features that could be implemented:Improvetidyoperation:maketidyoperation more performant by using IMAP search featuresallow to runtidyon many / all foldersBetter conditions:add aREGEXoperator (although not compatible with IMAP search)support more fieldsAdd more actions:tag emailsforward emailsmove destination based on regex match groupautomatic folder creationanything else ?Hot-reload config fileRe-configure (or execute actions) by sending emailsSupport OAuth / Gmail (I'm not sure how feasible that would be).Don't hesitate to ask, or make a pull request !CreditThis program relies ontheimap-toolslibrary for everything involving IMAPthe mock IMAP server fromaioimaplibfor it's teststhe ever excellent Typer and Pydantic libsMany thanks to them !
Package: mmw
# mmw
Github link:http://github.com/Mm2PL/mmw## What is it?
It is a library that generates simple pseudo-graphic windows.## How to use it?
Examples:1. Code:`python import mmw screen = mmw.Screen() window =mmw.Window('Ourwindow') screen.add_window(window)  # Link the window to the screen window.text = "Let's add some text" window.buttons = ['And', 'some', 'buttons'] window.selectedButton = 0  # Select the first button screen.clear()  # Screen.draw() doesn't clear the screen by itself screen.draw()  # Draw the screen screen.get_char()  # Wait until the user presses a key `Output:`+-----------Ourwindow-----------+|Let's add some text             | |                                | | [>And<]  [ some ]  [ buttons ] |+--------------------------------+`2. See [mmwDemo.py on github](https://github.com/Mm2PL/mmw/blob/master/mmwDemo.py)
Package: mmwebbot
Web automation library for python for web browser automation and end to
end UI testing.webbot provides a much feature rich automation than selenium for all
kinds of automation of webpage. Since the major portion of web
automation is to perform actions like click and type into webpage
elements , webbot automatically handles finding the right elements to
perform the actions.Features :Use any combination of selectors like id, name, text, css etc to
perform actions on elements with one line of code.Automate and test pages loaded dynamically by javascript.Click any button or link without having to worry about finding the
element first or knowing css_selectors , xpath etcSmart scoring algorithm which finds the best matching elements on
which you want to perform the action .Automation designed to work even in case of webpages with dynamically
changing id and classnameImmensely minimizes the code required for performing input actions
like clicks and keyboard actions.Get webpage source , cookies , total tabs , webpage title etc..Simulate key presses and special key combinationsBidirectional scrollingPerform an action on webpage elements by applying various filters to
select the elements .Perfrom action on multiple elements at once.If certain elements don’t have text or visible property , then use
either their id , class , css_selector , xpath , tag etc.The entire automation process can be made without having to open the
browser window i.e in the background as a console process (see docs
for more details )Installation :pip install webbotIf “No distribution found error occurs” just update setuptools usingpip install--upgradesetuptoolsQuickstart :Demo code 0 :fromwebbotimportBrowserweb=Browser()web.go_to('google.com')web.type('hello its me')# or web.press(web.Key.SHIFT + 'hello its me')web.press(web.Key.ENTER)web.go_back()web.click('Sign in')web.type('mymail@gmail.com',into='Email')web.click('NEXT',tag='span')web.type('mypassword',into='Password',id='passwordFieldId')web.click('NEXT',tag='span')# you are logged in . woohooooDemo code 1 :If multiple buttons with similar properties are to be clicked at
onceweb=Browser()web.go_to('siteurl.com')web.click('buttontext',multiple=True)Demo code 2 :If there are multiple elements and you want to perform action on one
of themweb=Browser()web.go_to('siteurl.com')# types the text into the 3rd input element when there are multiple input elements with form-input classweb.type('im robo typing',number=3,classname="form-input")web.click('Post')Links :Full Documentation :https://webbot.readthedocs.ioProject home :https://github.com/nateshmbhat/webbot
Package: mmwebreportapi
Monitor Manager Web Report Python ApiTable of ContentsInstallationProject MotivationMain featuresInstallationThe source code is currently hosted on GitHub at:https://github.com/mahulo2009/monitormanager-webreportapi-python.gitBinary installers for the latest released version are available at the Python Package Index (PyPI).pipinstallmmwebreportapiProject MotivationMonitor Manager Web Report Api is a python package that provides a set of tools to easily make request to the Grantecan
Monitor Manager Service. The Monitor Manager service collects, persists and propagates
the samples for all the Grantecan Control System components: dome, optics, main axis etc..., produces
several thousand of data per second making some time difficult to efficiently and easily extract this data for analysis.This Api is fully integrated with Pandas, the most powerful and flexible open source data analysis tool in any
language.Main featuresSimple interface to extract with the minimum requery information complex data.Cache management to optimize the time necessary to obtain the data.Pagination of the result to treat with large volume of data.Filtering similar data values base on epsilon value.Treatment of all monitor data type in a uniform way: n-dimension monitor and enumerates.Integration with Pandas to generate Data Frame to easily do data science with tabular data.Integrity sanity check of the request.
Package: mmWrt
mmWrtminimal raytracing for MIMO FMCW radar systems.Intended usage:educationalRelease Notes and RoadmapReleasedv0.0.3: first release* point targets only
* 1D compute of baseband if signal for scene
* 1D FFT, CFAR, peak grouping and target position error compute
* single reflectionsv0.0.4:* adding frequency estimator
* added speed processing
* added support for radar equation (RCS, distance, ...)
* antenna gains in azimumth, elevation and freqNEXT ()* 2D (AoA)
* 2D FFT: range+velocity, range+AoA
* 2D peak grouping (by velocity sign)
* 3D position error compute
* 3D targets (at least spheres)
* medium attenuation
* 3D point clouds (i.e. over multiple CTI)
* multiple single reflectionsNot planned yet but considered:reads and loads .binrecord BB signals in .bin3D targets and scene rendering with imaging side by side radarSwerling's scatteringExample CodeCheck on Google Colab the code:Or Read the Docs onRelease processrun pyroma
(should be 10/10)pyroma .run flake8
runs with darglint settings for docstrings to numpy standard set in the .flake8 file
should yield 0 warnings or errorsflake8run pytest
should yield 100% passpytestrun coveragecoverage run -m pytestrun coverage report
(should be 100%)coverage reportrun tox7.run sphinx-apiupdates the *.rst in docs/ foldersphinx-apidoc -f -o docs mmWrtrun sphinx-build
(updates the read_the_docs folder)sphinx-build -b html docs build/htmlrelease to pypi-testpython setup.py bdist_wheeltwine upload -r testpypi dist*update on read_the_docscheck on Google Colab
(Google Colab requires py3.8 as off 2023-Jan-15)11.a. if testing release-candidate need to spell out or will install latest stable version!pip install -i https://test.pypi.org/simple/ mmWrt==0.0.5rc3
from mmWrt import __version__
print(__version__)release on pypitwine upload -r pypi dist*
Package: mnamr
NAMEmnamr - rename movie directoriesSYNOPSISmnamr [-h] [dir]DESCRIPTIONmnamr renames your movie directories according to the following naming
convention:<Title> (<Year>) imdb:<imdb-id>On installation, themnamrexecutable is made available on your PATH. Run
it from any directory to rename all its immediate subdirectories.mnamr tries to parse the movie title from existing unknown folders and uses
imdbpie to search for the correct movie on iMDB. The console will let you
search for different terms or enter the iMDB ID manually.INSTALLATION[sudo] pip install mnamrUSAGE$ cd folder-with-lots-of-movie-dirs/
$ mnamr
$ # follow the instructions
Package: mn-api
MN Api[!WARNING]
El paquete aún se encuentra en desarrollo por lo que pueden existir erroresEl paquete es una migración de todas las funcionalidades
desarrolladas en el proyecto
deMatemática Numérica.Contiene implementaciones de los principales métodos numéricos dados
en Matemática Numérica.⚙️ Estructura y FuncionalidadesLa siguiente lista proporciona la estructura y principales
funcionalidades de la api:Capítulo 1: Teoría de ErroresCifrasCifras decimalesCifras significativsCifras exactasMedidas del ErrorErrorError absolutoError relativoMínimo error absoluto máximoCapítulo 2: Raíces de Ecuaciones AlgebraicasSeparación de RaícesBolzano-CauchyDescartesLagrangeMétodo GráficoResolución de ecuaciones algebraicasBisecciónNewton-RaphsonRegula-FalsiSecantesCapítulo 3: Sistemas de Ecuaciones Lineales y MatricesMétodosMétodo de JacobiMétodo de Gauss-SeidelUtilidadesConversión de matrices AX=B -> X=MX+CMatriz con diagonal predominanteFactor de convergenciaCapítulo 4: Aproximación de FuncionesMétodosMétodo de LagrangeMétodo de Newton o Diferencias DivididasCapítulo 5: Integración NuméricaCapítulo 6: Optimización NuméricaCapítulo 7: Ecuaciones Diferenciales Ordinarias📄 Importación y documentación[!IMPORTANT]
La documentación se encuentra en desarrollo. Pronto estará disponiblePara poder importar el paquete, ejecute en cualquier consola:pipinstallmn-apiLa documentación de la api se encuentra disponibleaquí
Package: mnapy
DISCLAIMER:Circuit Solver is developed to make sure it’s capable of generating
correct simulation results should the user understand that it’s a fixed timestep
solver. PhasorSystems is not liable for any incorrect simulation results.NOTE: This is only tested w/ file version 1.1.9 and above. There is no gurantee
that it will work for older files.This engine will allow you to run the simulation engine for circuit solver
without a user interface. It will also allow you to edit parameters when
the simulation is running and in turn do dynamic simulations such as battery
models, buck or boost converters, etc.Tested On:Python 3.8.5Setup:Note: The demo requires numpy and pandas to work correctly.pip install mnapyTo get the test files nagivate to:https://github.com/SummersEdge23/mnapy/tree/main/testDownload “ACSourceInitial_nl.txt” and “TestBed.py”Change the FILE_LOCATION in TestBed.py to match where ever your “ACSourceInitial_nl.txt” is located.
Run Testbed.py. If everything goes well a window should open up with a
waveform that should look like an acsource.Generating New Files:In order to load new files into the system, go to www.androidcircuitsolver.com/app.html
or use the desktop version (windows only atm). Build your circuit and
in order to port this to the headless version press: CTRL + P.
This will generate a {FILE_NAME}_nl.txt file that can be loaded into this
headless engine.Note: Set_Switch_State(setter: str) and Set_Interpolate(setter: str)
can be set by:Set_Switch_State(engine.Params.SystemConstants.ON) or
Set_Switch_State(engine.Params.SystemConstants.OFF)if (Get_Switch_State() == engine.Params.SystemConstants.ON) or
if (Get_Switch_State() == engine.Params.SystemConstants.OFF)The same thing applies for Interpolate.If you have any issues, contact me atPhasorSys@gmail.com