Saravanan666/pypi-org huggingface dataset:
Package: mcnn
MakeCNNMore and more people each day want to apply machine learning and AI into their products. As ML expands beyond the traditional fields, those
who do want to jump into the fun may find themselves confused with the technical concepts involved and amount of learning required to successfully
apply ML to their job. MakeCNN isto make this simple.All you do is just give data, and we take care of the rest!The way it works is that you have a folder of image data from various classes, and MakeCNN simply extracts that data, trains
a Convolutional Neural Network off of it, and then allows you to use it to make predictions on more data and apply it. That way
instead of learning what a convolutional neural network is, you can instead apply it ASAP.InstallationTo install simply make sure you are using Python3 and do :pipinstallmcnnMore details :You must have a folder of data with this exact structure for MakeCNN to work (this is pretty standard in the ML world) :Folder_name (you can choose this) :Training (subfolder)class_1 (subfolder) > contains imagesclass_2 (subfolder) > contains imagesclass_3 (subfolder) > contains imagesTesting (subfolder)class_1 (subfolder) > contains imagesclass_2 (subfolder) > contains imagesclass_3 (subfolder) > contains imageswhere the "Training" and "Testing" directories are for the training and testing data. Also
note that MakeCNN can handle however many number of classes you want!To make this easier to understand, if you wanted to use MakeCNN to predict between cats, dogs, and horses
you should have a folder like this :Any_nameTrainingcat > contains cat imagesdog > contains dog imageshuman > contains human imagesTestingcat > contains cat imagesdog > contains dog imageshuman > contains human imagesMakeCNN's main API, AutoWork, was designed to be extremely simple.DEMOfrommcnn.makeimportAutoWorkaw=AutoWork(dataset_directory="path_to_folder_containing_data")When you instantiate the AutoWork class, you give it the data (or really the string that has the path to the data), and all of that is processed. To control the image size of the images in your data
when applied by the CNN model (if you so desire), you can change image_size from 150 (default) to whatever else.
Don't worry - even if your images are not 150 x 150, they will be resized.Then to train :aw.train(epochs=20)Here we train our model for 20 epochs. Easy peasy, lemon squeezy.If we want to use it, by say evaluating it or predicting it, we have some
more stuff. To evaluate :aw.test_evaluate()# this evaluates it on the test data givenThen to predict on a single image :aw.predict(path_to_image="give_path_to_image_file")and you will get your prediction. It will either be "cat", "dog", or "human"
for our example above. You can do this for every image in a file by :aw.predict_directory(path_to_img_directory="give_path_to_image_folders")where you will instead get a list of predictions.Simple, and easy-to-use - MakeCNN is how we make sure EVERYBODY can leverage ML.
Package: mcnp-input-reader
MCNP Input ReaderThe package for reading mcnp input in a pythonic wayMCNP Input Reader is a python package developed in ENEA to help the modifications and the check integrity
of large mcnp input files.Installpipinstallmcnp-input-readerUsageimportmcnp_input_readerasmirmcnp_input=mir.read_file('input.i')mcnp_input.cells# return the table of cellsmcnp_input.cells.filter(lambdacell:cell.material_id==2)# return the cells using material M2TODOA lot of things...ExampleExample taken fromhere
Package: mcoauth
MCOauthThis is a simple package to use MCOauth. Initialize a Minecraft PlayerMinecraftPlayer(token)Most things are async except for initializing MinecraftPlayer which is not a blocking call because it usesasyncio.runA MinecraftPlayer has two properties, uuid and username.
Usingint(MinecraftPlayer(token))will return token.
Note that tokens are only valid for 5 minutes, seehttps://mc-oauth.net/
Package: mcok
# mcok
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Mock mock mock mock mock mock mock mock mock mock.from mock import MockMock().mock.mock.mock()
Package: mcompare
MCompareMachine learning Model Wrapper & Comparison ToolMCompare (Model Compare) is an easy way to track experiments relating to your Machine Learning Models.What is MCompare?MCompare is a python package that simplifies the data science process. It enables for quick comparison between models, the ability to track all the relevant artifacts relating to an experiment and providing all the extra functionality required to understand more about models (e.g. Learning Charts).What does MCompare contain?Wrapper for Classification and Regression modelsTrack the data used within each iteration for a modelCompare metrics related to different modelsTrack your metrics related to your experimentVisualise confusion matrixPlot learning curves, ROCAUC charts and moreLimitationsThis work was some of the baseline work which was developed for a project for one of my clients. This only has some of the basic functionality, with some aspects missing as that is IP for my client. Contact me to learn more.
Package: mconv
Multimedia library converterWARNING!! The original multimedia files will be replaced with the converted versions so, you may prefer to check that the result will be what you expect with some test files first. Btw, the replacement is atomic so it won't get corrupted in case of error or forced exit.Easy and efficiently, analyze a whole multimedia library to make sure that everything match a desired format and convert what doesn't to make it match. The scans are cached to greately decrease resume/re-scan times. Nvidia hardware acceleration is supported too.Target format by extension.mp3:A.format:mp3,A.bitrate:<128.1kbps.mp4:A.format:acc,A.bitrate:<128.1kbps,V.format:h264,B.bitrate:<600kbpsNote:If a video stream from a.mp4file needs to be converted to reduce hisbit_rate, his frame rate will be limited to30fpsand his height/width resolution to1280to keep a nice image quality.Requisitespython3ffmpeg (https://ffmpeg.org/download.html)* it basically works anywhere (arm,x86,linux,windows, ...)InstallationUsing pip:$pipinstallmconvUsageTo convert multimedia files recursively at current path:$ python -m mconv .See help with:$ python -m mconv -h
usage: mconv [-h] [-t M] [-j N] [-n] path

Multimedia library converter

positional arguments:
  path            Library path

optional arguments:
  -h, --help      show this help message and exit
  -t M, --tier M  Subgroups of tasks to process based on CPU demand: 1 (Low) - 3 (High).
  -j N, --jobs N  Number of paralell jobs
  -n, --nvidia    Use nvidia hardware when necessaryTiers details and his defaultJobsvalue:1-Tasks:library scan, format reading and cache update.Jobs:cpu_cores * 22-Tasks:process audio streams.Jobs:cpu_cores3-Tasks:process video streams.Jobs:1* If a video file needs video and audio streem conversion, it's tier3but if only needs audio conversion, it's tier2.* DefaultTiervalue is123(all).* When multipleTiersare selected, defaultJobsvalue is the minimum of them.Tips and tricksDon't process allTiersin the same execution or you won't be able to take advantage of all your cores efficiently. On a single machine, it's better to execute eachTierseparated. In the future, this may be the default behaviour when multipleTiersare defined.OnceTier1was executed,Tier2andTier3can be executed safely at the same time (also on different machines with a NAS).MaximumJobswith Nvidia hardware aceleration depends on GPU model. See "Max # of concurrent sessions" on "Encoder" athttps://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-newIf a video can't be converted using Nvidia hardware acceleration, try without it, it may work...It's safe to delete.tmpfolder created at library path when noTier2or3are running.Use an external tool/script to bulk rename audio/video extensions to.mp3/.mp4to convert them withmconv.
Package: mcore-organization-chart
Migacore Organization ChartThis package provides a cli to calculate and display organization chart of a given set of employees.InstallYou can install it via pip:$python3.6-mvenvenv
$pipinstallmcore-organization-chartOr you can clone this repo and install it via setuptools:$python3.6-mvenvenv
$pipinstall-rrequirements_dev.txt
$gitclonehttps://github.com/Spin14/mcore-organization-chart.git
$makeinstallYou can also run the tests, coverage, static type checker (mypy) and linters (flake8) using the available make commands.$makehelp(...)lintcheckstylewithflake8testruntestsquicklywiththedefaultPython
mypyrunmypyvsthecodebase
coveragecheckcodecoveragequicklywiththedefaultPythonBuild Organization ChartOnce the package is installed you can feed thebuild-org-chartcommand with the target employees list to calculate their organization chart. There is aexample.txtinput file you can use to give it a try:$catexample.txtSarahWatsonOwenTaylorEllieRossIsaiahMartin

$build-org-chartexample.txt|---SarahWatson|---OwenTaylor|---EllieRoss|---IsaiahMartinCreditsThis package was created with Cookiecutter_ and theaudreyr/cookiecutter-pypackage_ project template... _Cookiecutter:https://github.com/audreyr/cookiecutter.. _audreyr/cookiecutter-pypackage:https://github.com/audreyr/cookiecutter-pypackage
Package: mcot
The personal utilities and libraries from Michiel Cottaar.This code contains many different packages which can either be installed separately usingpip install mcot.<package>or as one wholepip install mcot.Command line scriptsCommand line scripts are all available through a single executable:mcot.
This executable will be installed irrespective of the package you install (it is part ofmcot.utils, which is required by all other packages).
You can run a specific script using:mcot$SCRIPTNAMEMany scripts are grouped together, in which case they can be called as:mcot$GROUP$SCRIPTNAMETo list the installed scripts just type:mcotDeveloper’s notesAll packages are managed as a single monorepo available athttps://git.fmrib.ox.ac.uk/ndcn0236/mcot.
Testing and packaging is handled through the pants build systemhttps://www.pantsbuild.org/.
First make sure to be in the top-level directory and then run the following to test:./pantstest::or to build the packages:./pantspackage::To bump the version number of a package run:./bump_project.sh<package><part>where <part> is one of “major”, “minor”, or “patch”. This will bump the version of both the package and themcotcomplete package, which can then be uploaded using twine.Adding a new packageCreate a new directory in “src/mcot”Add a README.rst and VERSION file with a description and the version number.Usemcot.utils.build.load_infoin __init__.py to import the README and version numberfrommcot.utils.buildimportload_info__doc__,__version__=load_info(__name__)delload_infoCopy an existing .bumpversion.cfg from another directory and update the current version numberCopy a BUILD script from another package (e.g.,mcot.surface) and adjust the contents accordinglyCopy BUILD script to test directory from test directory of another project (e.g., “src/mcot/surface/tests”)Add package requirements to “requirements.txt”Add the package to documentation in doc/index.rstRun the tests for this package./pantstestsrc/mcot/<packagename>/tests/:Test the package build:./pantspackagesrc/mcot/<packagename>:Build the documentationpythondoc/publish.pyOnce the package is ready to be uploaded to pypi: add the package to the BUILD script in the top-level directory and to the upload.sh script
Package: mcot.pipe
Build pipelines decleratively built on top of file-tree.frommcot.pipeimportpipe,In,Out,Ref,Varfromfile_treeimportFileTree@pipe(logdir='log',minutes=41)deffunc(in_path:In,out_path:Out,ref_path:Ref,placeholder_key:Var):passpipe.cli(FileTree.from_string("""
placeholder_key = A, B

ref_path.txt
directory-{placeholder_key}in_path.txt
    out_path.txt
"""))
Package: mcp2221
Python driver for MCP2221/AExamplesRead GP0fromMCP2221importMCP2221mcp2221=MCP2221.MCP2221()mcp2221.InitGP(0,MCP2221.TYPE.INPUT)print(mcp2221.ReadGP(0))Write GP0fromMCP2221importMCP2221mcp2221=MCP2221.MCP2221()mcp2221.InitGP(0,MCP2221.TYPE.OUTPUT)print(mcp2221.WriteGP(0,1))Read ADC on GP1fromMCP2221importMCP2221mcp2221=MCP2221.MCP2221()mcp2221.InitGP(1,MCP2221.TYPE.ADC)mcp2221.SetADCVoltageReference(MCP2221.VRM.VDD)print(mcp2221.ReadADC(1))Write DAC on GP2fromMCP2221importMCP2221mcp2221=MCP2221.MCP2221()mcp2221.InitGP(2,MCP2221.TYPE.DAC)mcp2221.SetDACVoltageReference(MCP2221.VRM.REF_2_048V)mcp2221.WriteDAC(12)Testspipinstallpytestpytest-cov
pytesttests/--doctest-modules--cov=MCP2221
Package: mcp23017
MCP23017-pythonMCP23017 GPIO-Expander Python Library for RaspberryPiMCP23017 DatasheetFeaturescurrently implemented features:set pin mode INPUT or OUTPUTset pin mode of all pinsdigital write pin HIGH or LOWdigital read pin statedigital read pin state of all pinsenable interrupt on a pinenable interrupt on all pinsenable interrupt mirroring of BANK_A and BANK_Bread interrupt flags to see which pin triggered the interruptread the interrupt capture value of the pin when the interrupt happenedGetting startedSetup a MCP23017 objectfrom mcp23017 import *
from i2c import I2C
import smbus

i2c = I2C(smbus.SMBus(1))  # creates a I2C Object as a wrapper for the SMBus
mcp = MCP23017(0x20, i2c)   # creates an MCP object with the given address
Package: mcp3008
RPi_mcp3008RPi_mcp3008 is a library to listen to the MCP3008 A/D converter chip with a RPi.
This library implements the example communication protocol described in thedatasheet.Communication is made through RPi SPI port usingSpiDevWiringConnect the SPI data cables in the tables below. Choose either CE0# or CE1# to connect to CS.RPi SPI GPIOsRPi GPIOModeGPIO 07CE1#GPIO 08CE0#GPIO 09MISOGPIO 10MOSIGPIO 11SCLKMCP3008 PinoutPinDescriptionPinDescription01CH009Vdd - Supply voltage (2.7V - 5.5V)02CH110Vref - Reference voltage03CH211AGND - Analog ground04CH312CLK - SPI Clock (SCLK)05CH413Dout - Data out (MISO)06CH514Din - Data in (MOSI)07CH615CS - Chip select (CE0# or CE1#)08CH716DGND - Digital groundPlease check theAdafruit guideon the MCP3008 for more information about wiringUsageRPi_mcp3008 uses thewithstatement to properly handle the SPI bus cleanup.importmcp3008withmcp3008.MCP3008()asadc:print(adc.read([mcp3008.CH0]))# prints raw data [CH0]It's possible instantiate the object normally, but it's necessary to call the close method before terminating the program.importmcp3008adc=mcp3008.MCP3008()print(adc.read([mcp3008.CH0]))# prints raw data [CH0]adc.close()The initialization arguments areMCP3008(bus=0, device=0, max_speed_hz=976000)where:MCP3008(X, Y)will open/dev/spidev-X.Y, same asspidev.SpiDev.open(X, Y)Both arguments are optional and have a default value of0.
The default max SPI driver speed is 976 kHz.MethodsCurrently there are two implemented methods:defread(self,modes,norm=False):'''Returns the raw value (0 ... 1024) of the reading.The modes argument is a list with the modes of operation to be read (e.g.[mcp3008.CH0,mcp3008.Df0]).norm is a normalization factor, usually Vref.'''defread_all(self,norm=False):'''Returns a list with the readings of all the modesData Order:[DF0, DF1, DF2, DF3, DF4, DF5, DF6, DF7,CH0, CH1, CH2, CH3, CH4, CH5, CH6, CH7]norm is a normalization factor, usually Vref.'''Themodesargument must be a list with at least one of 16 modes listed belowThenormargument is a normalization factor that rescales raw data, usually VrefFixed modeYou can also declare the class with afixed mode, which will make the instance callable and always return the value of the listed modes.
Again you can normalize the data with the norm argument when calling the instance.importmcp3008withmcp3008.MCP3008.fixed([mcp3008.CH0,mcp3008.DF0])asadc:print(adc())# prints raw data [CH0, DF0]print(adc(5.2))# prints normalized data [CH0, DF0]MCP3008 Operation ModesMCP3008 has 16 different operation modes:
It can listen to each of the channels individuallySingle Endedor in a pseudo-differential modeDifferentialSingle EndedDifferentialCH0DF0  (CH0 = IN+; CH1 = IN-)CH1DF0  (CH0 = IN-; CH1 = IN+)CH2DF0  (CH2 = IN+; CH3 = IN-)CH3DF0  (CH2 = IN-; CH3 = IN+)CH4DF0  (CH4 = IN+; CH5 = IN-)CH5DF0  (CH4 = IN-; CH5 = IN+)CH6DF0  (CH6 = IN+; CH7 = IN-)CH7DF0  (CH6 = IN-; CH7 = IN+)Use the table above as the operation mode when callingMCP3008.read(modes)or setting theMCP3008.fixed(modes)mode. (e.g.MCP3008.read([mcp3008.CH0, mcp3008.DF1]))
Package: mcp9600
MCP9600 Thermocouple Temperature SensorInstallingStable library from PyPi:Just runsudo pip install mcp9600Latest/development library from GitHub:git clone https://github.com/pimoroni/mcp9600-pythoncd mcp9600-pythonsudo ./install.shChangelog0.0.4Add set/get for thermocouple type0.0.3Port to i2cdevice>=0.0.6 set/get API0.0.2Addedget_altitudemethodCorrected pressure to hPa0.0.1Initial Release
Package: mcpartools
mcpartoolsis a software simplifying time consuming simulation of particle transport using Monte Carlo codes
(Fluka, SHIELDHIT12A). We assume user has access to a computing cluster with batch processing software installed
(i.e. slurm, torque) and wants faster simulation by running it simultaneously on many computing nodes.mcpartoolssimplifies this process by generating necessary directory structures and scripts for starting calculations
and collecting the results.mcpartoolsprovides a command line application calledgeneratemcwhich works under Linux operating system
(interpreter of Python programming language has to be also installed).
No programming knowledge is required from user, but basic skills in working with terminal console are needed.More documentationFullmcpartoolsdocumentation can be found here:https://mcpartools.readthedocs.io/SeeGetting Startedfor installation and basic
information, and theUser’s Guidefor an overview of
how to use the mcpartools.
Package: mc-pdf2txt
mc-pdf2txtConvert multi-column pdf to text withpopplerandtesseract.Install(1) Install dependencies:Install poppler.sudoaptinstallpoppler-utilsInstall tesseract-ocrsudoaptinstalltesseract-ocrwith the language data files of your choice, e.g.,sudoaptinstalltesseract-ocr-jpn(2) Install mc-pdf2txtpip3installmc-pdf2txtUsageUsage:
  mc-pdf2txt [options] <input>...

Options:
  -l LANG           Language, such as `eng`, `jpn`, or `eng+jpn`.
  <input>           Input PDF file.
  -o OUTPUT         Output text file.
  -r DPI            Resolution of temporary image file [default: 600].
  --timeout SEC     Timeout in sec to exec `pdftoppm` [default: 60].
  --page-separator LINE     String to be output as page separator [default: ---].
  --psm VALUE       Page segmentation mode of `tessoract-ocr` [default: 3].
  --verbose         Verbose.
Package: mcpi-e
Minecraft: Pi edition API Python LibraryThis projectmcpi-eis a fork from mcpi project [https://github.com/martinohanlon/mcpi]mcpi-ePython library for communicating withMinecraft: Pi editionandRaspberryJuice.Visitgithub.com/stoneskin/mcpi-efor more information.InstallationWindowspip3 install --upgrade mcpi-eNote: you could usepyorpython -mto speify the python in your systempy -m pip install --upgrade mcpi-eLinux / MacOSsudopip3install--upgrademcpi-eUsagefrom mcpi_e.minecraft import Minecraft
...
mc = Minecraft.create(servername,4711,playerName)mcpi-eChange log1. Enhancement for usingmcpiin server with multiple usersUser could pass player username as 3rd parameter when create a new Minecraft api instense.ex:mc=Minecraft.create(address,port,name)Change mc.Player to use entity so it will not pick the first user in the server.2. Limit the Usage ofmcpiAdd the _send command 0.05s interval to slow down the speedlimit the useage of setBlockstodo: limit the script usage range (x,y,z)3. Some improvementAdd BlockEnum, Logger, Settings
ex:mc.settings.SHOW_DEBUG=True
print("Is show debug msg",mc.settings.SHOW_DEBUG)
print("Is show log msg",mc.settings.SHOW_Log)
print("system speed:",mc.settings.SYS_SPEED)
mc.settings.SYS_SPEED=mc.settings.Speed.FAST
(x,y,z)=pos=mc.player.getTilePos()
debug("this is debug msg")
log(pos)
warn("warn")
Package: mcping
mcpingGet statuses from Minecraft servers.Difference between this and mcstatusThis package uses some classes and functions frommcstatus.The primary goal of this package is to simply get the raw status without doing any other parsing.
Although mcstatus does provide arawproperty to get the raw response,
there is no straightforward way toonlyget this response.
This results in time wasted doing processing that isn't needed for some applications.A side effect of this is that there isn't any guarantee that the status response
will have all of the keys that it should, meaning you'll need to do some validation
before assuming that keys are present, or that they're the types you think.In the situation that led me to make this package, this is completely fine.DocsDocumentation is available athttps://mcping.readthedocs.io.InstallationIf you want to use the library, install withpip:pipinstallmcpingIf you only want the CLI,pipxis recommended:pipxinstallmcpingUseLibraryimportmcping# Synchronousmcping.status('127.0.0.1')# Asynchronousasyncdefmain():awaitmcping.async_status('127.0.0.1')CLIThe package also includes a CLI to get server statuses.
It can be run from a cloned repo using thecli.pyfile:python3cli.pyexample.com
python3cli.pyexample.com:25565If the library is installed, themcpingscript should also be installed and available globally:mcpingexample.com
mcpingexample.com:25565LicenseThis project is licensed under either ofApache License, Version 2.0, (LICENSE-APACHEorhttp://www.apache.org/licenses/LICENSE-2.0)MIT license (LICENSE-MITorhttp://opensource.org/licenses/MIT)at your option.Some code inmcping/__init__.pyis modified from themcstatusproject's code,
licensed under the Apache License, Version 2.0.