Saravanan666/pypi-org huggingface dataset:
Package: mattflow
MattFlowA CFD python package for the Shallow Water EquationsMattFlow simulates the surface of the water after any initial conditions, such as drops or stones falling on.requirementsospython3GNU/Linuxclick >= 7.0Windowsjoblib >= 0.13.2OSXmatplotlib >= 3.3.1numba >= 0.51.2numpy >= 1.18.5ffmpeg (optional)Install$condacreate--namemattflow-y
$condaactivatemattflow
$condainstall-cmattasamattflow$pipinstallmattflowUsage$mattflow[OPTIONS]Options:
  -m, --mode [drop|drops|rain]    [default: drops]
  -d, --drops INTEGER             number of drops to generate  [default: 5]
  -s, --style [water|contour|wireframe]
                                  [default: wireframe]
  --rotation / --no-rotation      rotate the domain  [default: True]
  -b, --basin                     render the fluid basin
  --show / --no-show              [default: True]
  --save
  --format [mp4|gif]              [default: mp4]
  --fps INTEGER                   [default: 18]
  --dpi INTEGER                   [default: 75]
  --fig-height INTEGER            figure height (width is 1.618 * height)
                                  [default: 18]
  --help                          Show this message and exit.Swallow Water EquationsSWE is a simpified CFD problem which models the surface of the water, with the assumptionthat the horizontal length scale is much greater than the vertical length scale.SWE is a coupled system of 3 hyperbolic partial differential equations, that derive from theconservation of mass and the conservation of linear momentum (Navier-Stokes) equations, incase of a horizontal stream bed, with no Coriolis, frictional or viscours forces (wiki).where:η: heightu: velocity along the x axisυ: velocity along the y axisρ: densityg: gravity accelerationStructurepre-processstructured/cartesian meshsolutionsupported solvers:Lax-FriedrichsRiemann
                  | O(Δt, Δx2, Δy2)2-stageRunge-Kutta| O(Δt2, Δx2, Δy2)
 | defaultMacCormack| O(Δt2, Δx2, Δy2)
 | experimentalpost-processingmatplotlib animationConfiguration optionsmesh sizingdomain sizinginitial conditions (single drop, multiple drops, rain)boundary conditions (currently: reflective)solvermultiprocessingplotting styleanimation optionsTODOCIGUIConsider Cythonizing or moving to C++Higher order schemesInclude source termsInclude viscous modelsAlgorithm that converts every computational second to a real-time second,
modifying the fps atthe post-processing animation, because each
iteration uses different time-step (CFL condition).Moving objects inside the domainUnstructured mesh3D CFDLicenseGNU General Public License v3.0Special thanks toMarios Mitalidisfor the valuable feedback.Start the flow!(C) 2019, Athanasios Mattasthanasismatt@gmail.com
Package: matthew-Coef-MultiClass
Generalized Matthew Corrlelation  Coefficient (MCC)This package offers tools for calcualting (MCC)https://towardsdatascience.com/the-best-classification-metric-youve-never-heard-of-the-matthews-correlation-coefficient-3bf50a2f3e9aThe purpose was taking the knwon MCC and extending it to work on multi-class problems
In the init you can do one of the following:
1  Leave the avg_type empty - It will run the ggeneralized Matthew
2  Put 2 which will work similarly to clause 1
3 Put 3 which will run case The gneralized F1
Package: matthewflegg-mbti
MBTI PredictorPredicting someone's MBTI type based on their online posts, using AI.ApproachI originally attempted to use a simple LSTM, using nltk and Keras. However, this approach led to the model badly overfitting.I then decided to re-write the program and use a bidirectional LSTM. This was slightly better and, somewhat, prevented overfitting. However, the model trained slowly due to a low learning rate.I then decided to use a TF BERT Model as a layer within a Keras model. I thought this would improve the performance of the predictor but my GPU (NVIDIA GTX 1650) couldn't run it. I decided to publish this project as a package so that others could attempt to train the BERT model.FindingsI could not find a significant correlation between a post and the MBTI type of its poster.
Feel free to download the source code and try for yourself.
Package: mattia80511
Copyright (c) 2020 The Python Packaging AuthorityPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: mattoolkit
Mattoolkit CLIUsed to execute the queued VASP calculations. Needs to be installed on
all clients to run jobs from the Material Toolkit.Handling Port Restrictionsfunctionssh_tunnel(){if[[$(psaux|grep"[s]sh -D 8443")]];thenecho"SSH proxy already exists"elseecho"SSH socks proxy created on port 8443"ssh-D8443-f-C-q-Nnewtonfi}functiontunnel_if_no_http(){if[[$(nc-vzgoogle.com80-w1|&grepfailed)]];thenssh_tunnelexporthttp_proxy=socks5://localhost:8443/exporthttps_proxy=socks5://localhost:8443/fi}tunnel_if_no_http
Package: matts-majestic-monads
Matts Majestic MonadsThis overblown library implements Functor, Applicative and Monad in Python.SynopsisYou can useEither.convertorMaybe.convertas a decorator to convert return types.
This example converts exceptions into Left values:frommonadsimportEither@Either.convertdefparse_arguments():parser=argparse.ArgumentParser(description='Download a URL and print it')parser.add_argument('URL',help='URL to download')returnparser.parse_args()Once you have your Either you can usefmapandbindto operate over the contained value:result=(make_parser().fmap(parse).fmap(to_url).bind(read))The methods will be passed the current value.
You should usefmapwhen the function will return a normal value.
When the function returns a monad, usebind.You can useFunctionas a decorator to convert functions.
Once converted they become composable:frommonadsimportFunction@Functiondefparse(parser):returnparser.parse_args()@Functiondefto_url(arguments):returnarguments.URLboth=parse.compose(to_url)ExampleThere is a simple example of using this library inexample.py.
This script reads a url that you provide and prints the length of the body:➜pipenvrunpythonexample.py'http://python.org/'Right(48817)➜pipenvrunpythonexample.py'http://pythonasdasd.org/'Left(<urlopenerror[Errno-2]Nameorservicenotknown>)
Package: matts-nflows
nflowsnflowsis a comprehensive collection ofnormalizing flowsusingPyTorch.InstallationTo install from PyPI:pip install nflowsUsageTo define a flow:fromnflowsimporttransforms,distributions,flows# Define an invertible transformation.transform=transforms.CompositeTransform([transforms.MaskedAffineAutoregressiveTransform(features=2,hidden_features=4),transforms.RandomPermutation(features=2)])# Define a base distribution.base_distribution=distributions.StandardNormal(shape=[2])# Combine into a flow.flow=flows.Flow(transform=transform,distribution=base_distribution)To evaluate log probabilities of inputs:log_prob=flow.log_prob(inputs)To sample from the flow:samples=flow.sample(num_samples)Additional examples of the workflow are provided inexamples folder.DevelopmentTo install all the dependencies for development:pip install -r requirements.txtCiting nflowsTo cite the package:@software{nflows,author={Conor Durkan andArtur Bekasov andIain Murray andGeorge Papamakarios},title={{nflows}: normalizing flows in {PyTorch}},month=nov,year=2020,publisher={Zenodo},version={v0.14},doi={10.5281/zenodo.4296287},url={https://doi.org/10.5281/zenodo.4296287}}The version number is intended to be the one fromnflows/version.py. The year/month correspond to the date of the release. BibTeX entries for other versions could be found onZenodo.If you're using spline-based flows in particular, consider citing theNeural Spline Flowspaper:[bibtex].Referencesnflowsis derived frombayesiains/nsforiginally published withC. Durkan, A. Bekasov, I. Murray, G. Papamakarios,Neural Spline Flows, NeurIPS 2019.[arXiv][bibtex]nflowshas been used inConor Durkan, Iain Murray, George Papamakarios,On Contrastive Learning for Likelihood-free Inference, ICML 2020.[arXiv].Artur Bekasov, Iain Murray,Ordering Dimensions with Nested Dropout Normalizing Flows.[arXiv].Tim Dockhorn, James A. Ritchie, Yaoliang Yu, Iain Murray,Density Deconvolution with Normalizing Flows.[arXiv].nflowsis used by the conditional density estimation packagepyknos, and in turn the likelihood-free inference frameworksbi.
Package: mattsollamatools
Matt's Ollama ToolsI needed some simple tools to make it easier to work with integrating Ollama with the real world. This is a collection of those tools.ChunkerSometimes you need to take a bunch of text in and then spit it out in an array of chunks. This will take in a long text and then split it into chunks of up to a defined number of words. It won't ever get sentence fragments. It will overlap each chunk by one sentence. If you have a text with a bunch of poorly written run-on sentences, this might not work.Usagefrommattsollamatoolsimportchunkertextchunks=chunker(text,max_words_per_chunk=110)If you don't include max_words_per_chunk, it will default to 100.
Package: mattutils
This package is very useful for implementing data science projectsyou can use it for dealing with files operationsyou can load yaml filesyou can save json data to a fileSteps of uploading own packages to pypi:packages needed: setuptools,wheel,twinefolder structurepython codingsetup.py,README.md,LICENSEpython setup.py sdist bdist_wheelprepare the .pypirc filetwine upload dist/*thanks for Krish Naik's perfect video, this package just for learning purposes.
Package: matugen
MatugenA material you color generation tool for linuxInstallation·Usage·WikiDescriptionMaterial Design 3offers a new color system that allows for more flexible and dynamic use of color. The new system includes a wider range of colors, as well as a range of tints and shades that can be used to create subtle variations in color.InstallationFrom PypiNoteAssuming you have python with pip installedpipinstallmatugenUsage# Dark themematugen/path/to/wallpaper/# Light themematugen/path/to/wallpaper/-lExample:matugen~/wall/snow.png-lFrom repo with poetryNoteAssuming you already havePoetryinstalled:gitclonehttps://github.com/InioX/matugen&&cdmatugen
poetryinstallUsage# Dark themepoetryrunmatugen/path/to/wallpaper/# Light themepoetryrunmatugen/path/to/wallpaper/-lExample:poetryrunmatugen~/wall/snow.png-lShowcaseShowcase with Hyprland, Waybar, kitty, and fish shell:WarningThe preview and usage may be outdated.
Package: matvec
matvecA domain-specific language for fast graph shift operations.
This implements mathematical fields on numbers,
n-dimensional column vectors, and n-by-n sparse matrices.License:Apache Software LicenseAuthor:Emmanouil (Manios) KrasanakisDependncies:numpy:zap: QuickstartCreating a 5-dimensional vector (can usenumpyarrays
as inputs interchangeably with lists everywhere):frommatvecimportVectorx=Vector([1,2,3,4,5])Creating a 5x5 sparse matrix A in coo-format
with non-zero elements A[1,2]=9 and A[3,0]=21frommatvecimportMatrixA=Matrix([1,2],[3,0],[9,21],5)Print the outcome of matrix-vector multiplication:print(A*x)Print the outcome of left-multiplying transpose(x)
with A:print(x*A):fire: Features:rocket: Parallelized matrix-vector multiplication.:chart_with_downwards_trend: Memory reuse optimization.:mag: numpy compatibility.:factory: Common arithmetic operations.:volcano: BenchmarkBenchmarks tested on a machine with 2.6 GHz CPU base clock and
up to 4.4 GHz turbo boost, 12 logical
cores, and 16GB DDR3 RAM. They span vectors of 1.E4 to
1.E6 elements and matrices with up to 20x the number of
non-zeroes.
More rigorous evaluation will take place in the future.Tasknumpy/scipymatvecCreate new vector or array0.025 sec0.014 sec1000 temp. additions of 1.E6 vectors only2.130 sec1.061 secCreate matrix0.505 sec0.183 secSparse matrix with vector multiplication0.045 sec0.020 sec:memo: List of OperationsFull arithmetic operations* + - / == < > <= >=between
vectors and other vectors or scalars.Matrix-vector multiplication*(both left and right).Element access and assignment for vectors with[].Masking, such asy = x[x>0].matvec.clear()Clears cache.
Package: matvis
Fast matrix-based visibility simulator capable of running on CPU and GPU.Descriptionmatvisis a fast Python matrix-based interferometric visibility simulator with both
CPU and GPU implementations.It is applicable to wide field-of-view instruments such as the Hydrogen Epoch of
Reionization Array (HERA) and the Square Kilometre Array (SKA), as it does not make
any approximations of the visibility integral (such as the flat-sky approximation).
The only approximation made is that the sky is a collection of point sources, which
is valid for sky models that intrinsically consist of point-sources, but is an
approximation for diffuse sky models.An example wrapper for the mainmatvissimulator function is provided in this
package (matvis.simulate_vis()).FeaturesMatrix-based algorithm is fast and scales well to large numbers of antennas.Supports both CPU and GPU implementations as drop-in replacements for each other.Supports both dense and sparse sky models.Includes a wrapper for simulating multiple frequencies and setting up the simulation.No approximations of the visibility integral (such as the flat-sky approximation).Arbitrary primary beams per-antenna using thepyuvdata.UVBeamclass.LimitationsCurrently no support for polarized sky models.Currently no way of taking advantage of baseline redundancy to speed up simulations.Diffuse sky models must be pixelised, which may not be the best basis-function for
some sky models.Installationpip install matvis.If you want to use the GPU functions, install
withpip install matvis[gpu].DevelopersRunpre-commitinstallbefore working on this code.Read the Docshttps://matvis.readthedocs.io/en/latest/
Package: matviz
MatViz is a library for making nice looking and useful graphics in Python. It is built on top ofmatplotliband closely integrated withnumpyandpandasdata structures.
It also pulls from and is inspired byseabornHere is some of the functionality that matviz offers:
- A histogram function that compares multiple distributions with default settings to maximize utility
- A 2D histogram function with fun tricks like smoothing and plotting timeseries data
- Specialized support for using complex numbers in place of x and y (z = x + 1j * y)
- Taking any figure and making it look nicer than the matplotlib defaults
- Streamgraph implementation with lots of additional features, based on stackedplot
- Lots of plotting functions to import by default and make the coding environment similar to Matlab.
Package: mat-wrap
FeaturesRun a matlab script file through a python scriptInstallationRequirements:* Python 3.8* MATLAB (any version)IMPORTANT::INSTALL THE MATLAB ENGINE FOR PYTHON:Picture fromInstall MATLAB Engine PythonYou can then install by running the following pip command:pip install mat_wrapUsageInitializationfrom mat_wrap import mat_wrapwrapper = mat_wrap.wrapper()Basic Functionalitywrapper.addToPath("directory of files being used") #Can include as many as you wantwrapper.wrap("matlab file name")AcknowledgmentsThis project uses thePython MATLAB EngineMATLAB is developed and produced byMathWorksLiscenseLiscensed under GNU General Public License v3 or laterContactIf you have any questions or concerns, please feel free to email:sbeer3@uwyo.edu
Package: matxop
#MatrixOperations*NOTE:This package will work only if they are compatible.zeros_matrix(rows, cols):
Creates a matrix filled with zeros.
:param rows: the number of rows the matrix should have
:param cols: the number of columns the matrix should have:return: list of lists that form the matrixdef identity_matrix(n):
Creates and returns an identity matrix.
:param n: the square size of the matrix:return: a square identity matrixdef copy_matrix(M):
Creates and returns a copy of a matrix.
:param M: The matrix to be copied:return: A copy of the given matrixdef print_matrix(M, decimals=3):
Print a matrix one row at a time
:param M: The matrix to be printeddef transpose(M):
Returns a transpose of a matrix.
:param M: The matrix to be transposed:return: The transpose of the given matrixdef matrix_addition(A, B):
Adds two matrices and returns the sum
:param A: The first matrix
:param B: The second matrix:return: Matrix sumdef matrix_subtraction(A, B):
Subtracts matrix B from matrix A and returns difference
:param A: The first matrix
:param B: The second matrix:return: Matrix differencedef matrix_multiply(A, B):
Returns the product of the matrix A * B
:param A: The first matrix - ORDER MATTERS!
:param B: The second matrix:return: The product of the two matricesdef multiply_matrices(list):
"""
Find the product of a list of matrices from first to last
:param list: The list of matrices IN ORDER:return: The product of the matricesdef check_matrix_equality(A, B, tol=None):
Checks the equality of two matrices.
:param A: The first matrix
:param B: The second matrix
:param tol: The decimal place tolerance of the check:return: The boolean result of the equality checkdef unitize_vector(vector):
Find the unit vector for a vector
:param vector: The vector to find a unit vector for:return: A unit-vector of vector
Package: matyimodule
matyimoduleEz csak egy minta, hogy hogyan lehet python csomagot létrehozni, és
közzétenni, hogy mások egyszerűen használhassák.HasználatPythonpip install matyimoduleimportmatyimoduleasma=m.mark()# markdown-t ad visszaprint(a)>>>'<p>Matyi vagyok <em>cső</em></p>'Parancssor$matyicommand

>siker.Feltöltéspipinstalltwine
pythonsetup.pysdistbdist_wheel
twineuploaddist/*
Package: matzip
# matzipKorean restaurant recommendation library## InstallingTo install matzip, simply:$ pip install matzipOr, you can use:$ easy_install matzipOr, you can also install manually:$ git clonehttps://github.com/symoon94/matzip.git$ cd matzip
$ python setup.py install## UsageIn progress ..## AuthorSooyoung Moon / [@symoon94](https://github.com/symoon94)
Package: mau
Mau v3Mau is a lightweight markup language heavily inspired byAsciiDoc,AsciidoctorandMarkdown.As I wasn't satisfied by the results I got with those tools I decided to try to write my own language and the tool to render it.I am currently using Mau to render posts on my blogThe Digital Catand the online version of my book"Clean Architectures in Python". I also used it to transpile the code of the book to Markua, to be able to publish the book on Leanpub using their toolchain.Quick startTo install Mau usepippipinstallmauThen install at least one visitor plugin. You probably want to start withmau-html-visitorpipinstallmau-html-visitorTo convert Mau sources into HTML just runmau-isource.mau-odestination.html-fhtmlTo use Mau in your tool you can run it programmaticallyfrommauimportMau,load_visitorsvisitor_classes=load_visitors()visitors={i.format_code:iforiinvisitor_classes}visitor_class=visitors["html"]mau=Mau("path/of/the/source",visitor_class=visitor_class,)lexer=self._mau.run_lexer(text)parser=self._mau.run_parser(lexer.tokens)content=self._mau.process(parser.nodes,parser.environment)ifvisitor_class.transform:content=visitor_class.transform(content)Check out Maudocumentationfor further information.Pelican pluginThere is a Pelican plugin that enables you to use Mau in your blog. Check it athttps://github.com/pelican-plugins/mau-reader.You can see the plugin in action athttps://www.thedigitalcatonline.com/blog/2021/02/22/mau-a-lightweight-markup-language/and on other pages in my blog.SupportYou may report bugs or missing features use theissues page.
If you want to ask for help or discuss ideas use thediscussions page
Package: maubot
maubotA plugin-basedMatrixbot system written in Python.DocumentationAll setup and usage instructions are located ondocs.mau.fi. Some quick links:Setup(orwith Docker)Basic usageEncryptionDiscussionMatrix room:#maubot:maunium.netPluginsA list of plugins can be found atplugins.maubot.xyz.To add your plugin to the list, send a pull request tohttps://github.com/maubot/plugins.maubot.xyz.The plugin wishlist lives athttps://github.com/maubot/plugin-wishlist/issues.
Package: maud
MAUD====Welcome to MAUD, Moving Average of Uneven DataMaud was also a ship built by Amundsen. http://en.wikipedia.org/wiki/Maud_(ship). Dr. Sverdrup was first a meteorologist, but changed interest to oceanography while in charge of the scientific work on the north pole expedition leaded by Amundsen, aboard the Maud (Cushman-Roisin, 1994)... MAUD's NEWS.News====0.6.1-----*maud4nc, a shell script to filter data on the NetCDF itself.0.5.2-----*Including some resources with cython, hence faster.*You must have the package distribute with a newer version than 0.6.24dev-r0, otherwise it will fail to install0.4.1-----*window_1Dmean is now recursive and uses multiprocessing if it has more than 1 dimension.previous--------*Sorry, lost the track before this.
Package: maud-metabolic-models
Maud is an application that fits Bayesian statistical models of
metabolic networks usingPythonandStan.Maud aims to take into account allosteric effects, ensure that the laws of
thermodynamics are obeyed and to synthesise information from both steady state
experiments and the existing literature.InstallationFirst create a fresh Python virtual environment and then activate it:python -m venv .venv --prompt=maud
source .venv/bin/activateTo install Maud and its python dependencies to your new virtual environment, run
this command:pip install maud-metabolic-modelsCmdstanpy depends oncmdstan,
which in turn requires a c++ toolchain. On some computers you will have to
install these in order to use Maud. You will hit an error at the next step if
this applies to your computer. Luckily cmdstanpy comes with commands that
can do the necessary installing for you. On windows the c++ toolchain can be installed with
the following powershell commands:UsageMaud is used from the command line. To see all the available commands try runningmaud --helpCopyrightCopyright (c) 2023, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark.Free software distributed under theGNU General Public License 3.0
Package: mau_html_visitor
Mau HTML VisitorThis is a plugin for theMaumarkup language. The plugin provides the conversion from Mau source to HTML.You can install this plugin withpip install mau-html-visitorand Mau will automatically be able to load it. To use the visitor you need to load it and to pass it to the classMauwhen you instantiate itfrommauimportMau,load_visitorsvisitor_classes=load_visitors()visitors={i.format_code:iforiinvisitor_classes}visitor_class=visitors["html"]mau=Mau("path/of/the/source",visitor_class=visitor_class,)lexer=self._mau.run_lexer(text)parser=self._mau.run_parser(lexer.tokens)content=self._mau.process(parser.nodes,parser.environment)ifvisitor_class.transform:content=visitor_class.transform(content)The default extension for templates is.html. The plugin usesPygmentsto provide source code highlighting.