Saravanan666/pypi-org huggingface dataset:
Package: legume-gme
legume (le GUided Mode Expansion) is a python implementation of the GME method for photonic crystal slabs, including multi-layer structures. Plane-wave expansion for purely 2D structures is also included. Also, we have anautogradbackend that allows gradients of all output values with respect to all input parameters to be computed efficiently!InstallEasiest way:pip install legume-gmeAlternatively, justgit clonethis repository, and make sure you have all the requirements installed.Documentation and examplesGo to ourdocumentationto find a number ofexamples, as well as a detailedAPI reference.The examples can also be found in ipython notebook form in/docs/examples.Here's an example of a computation of the photonic bands of a photonic crystal, compared to Fig. 2(b) in Chapter 8 of the photonic crystal bible,Molding the Flow of Light.We have only computed the quasi-TE modes of the slab (positive symmetry w.r.t. the plane bisecting the slab), which should be compared to the red lines in the figure on the right. The agreement is very good! And, the guided-mode expansion allows us to also compute the quasi-guided modes above light-line, together with their associated quality factor. These modes are typically hard to filter out in first-principle simulations, solegumeis great for studying those.AutogradOne exciting feature oflegumeis theautogradbackend that can be used to automatically compute the gradient of the eigenmodes and eigenfrequencies with respect to any input parameters! In the optimization shown above, we tune the positions of the holes of a cavity in order to increase the quality factor. As is common in photonic crystal resonators, small modifications lead to tremendous improvement. The gradient of the quality factor with respect to the positions ofallholes is computed in parallel using reverse-modeautomatic differentiation.CitingIf you find legume useful for your research, we would apprecite you citing ourpaper. For your convenience, you can use the following BibTex entry:@article{minkov2020inverse,
  title={Inverse design of photonic crystals through automatic differentiation},
  author={Minkov, Momchil and Williamson, Ian AD and Andreani, Lucio C and Gerace, Dario and Lou, Beicheng and Song, Alex Y and Hughes, Tyler W and Fan, Shanhui},
  journal={ACS Photonics},
  volume={7},
  number={7},
  pages={1729--1741},
  year={2020},
  publisher={American Chemical Society}
}AcknowledgementsApart from all the contributors to this repository, all the authors of the paper cited above contributed in various ways with the development of this package. Our logo was made byNadine Gilmer. The backend switching betweennumpyandautogradfollows the implementation in thefdfdpackage of Floris Laporte.
Package: legwork
TheLISAEvolution andGravitationalWaveORbitKitA python package that does theLEGWORKfor you by evolving binaries,
    computing signal-to-noise ratios for binary systems potentially observable with LISA
    and visualising the results.InstallationPut simply?pip install legwork! But we recommend creating a conda environment first to ensure everything goes smoothly! Check out the installation instructionshereto learn exactly how to install LEGWORKLEGWORK has a couple of dependencies:numpy,astropy,numba,scipy,schwimmbad,matplotlibandseaborn(seerequirements.txtfor the exact version requirements). These will be installed automatically if you follow the installation instructions!DocumentationAll documentation related to LEGWORK can be foundat this linkOther quick linksQuickstart- New to LEGWORK? Try out our quickstart tutorial!Tutorials- Learn more about what you can do with LEGWORK with our tutorials!Citing LEGWORK- If you're using LEGWORK for a scientific publication please follow the link for citation intstructionsDemos- Want to see what LEGWORK is capable of? Check out our demos!API reference- Wondering how you should use a particular function? Go take a look at our full API reference!Feature requests- Do you have an idea for adding something to LEGWORK? Create an issuehereand let us know! Or, even better, make the change yourself and create apull request!Bug reporting- If you see a bug we would love to know about it! Please create an issuehere!Release paper- The LEGWORK release paper is now on the ArXiv and you can also view it directlyin GitHubif you prefer!
Package: lehar
lehar=====|Header image||travis\_badge| |Code Climate| |Test Coverage| |Issue Count|Python library to generate sparklines ▁▂▄▅▇█ in your shell based uponrelative ordering of data. ``lehar`` is a `Hindi`_ word which meanswave. ``lehar`` can be invoked via *commandline* also... code:: bash# Find commits by authors in a git repo$ git shortlog -s | cut -f1 | lehar▇▁▁▁▁▁▁▂▃▁▁█▁▁▂▃▅▁▁▁▂▆▁▁▁▂▁▁▁▁▂▇▁▅▆▁▁▁▄▁▁█▁▁▂▁▂▁`Some cool usage`_Demo----Installation------------Using ``homebrew``:.. code:: bash$ brew install leharUsing ``pip``::$ pip install lehargiAPI---lehar.draw(numbers,options)~~~~~~~~~~~~~~~~~~~~~~~~~~~numbers^^^^^^^``type`` : ``list``options^^^^^^^| Only supported option is ``color``| eg. ``color='red'``Basic Usage^^^^^^^^^^^.. code:: python>>> import lehar# Strings>>> lehar.draw(["0","1","2","3","4"])'▁▂▄▆█'# Numbers>>> lehar.draw([0,1,2,3,4])'▁▂▄▆█'# Negatives>>> lehar.draw([1,3,-34,12,44,81,0])'▃▃▁▄▆█▃'# Missing data>>> lehar.draw([1,3,-34,'',12,44,'',81,0])'▃▃▁ ▄▆ █▃'Adding colors^^^^^^^^^^^^^.. code:: python>>> lehar.draw(["0","1","2","3","4"],color="yellow")>>> lehar.draw([1,3,-34,'',12,44,'',81,0],color="cyan")Command line------------.. code:: bash$ lehar 1 2 3 4 5$ lehar -c red 1 2 3 4 5$ echo "-c cyan 1 2 3 4 5" | lehar$ lehar < inputSupport-------``lehar`` supports both ``Python2`` & ``Python3``.Contributing Guide-------------------  Setup.. code:: bash$ git clone https://github.com/darxtrix/lehar$ cd lehar$ pip install -r requirements.txt$ python setup.py develop$ lehar-  Tests are located at ``lehar/tests.py`` and covergae tests arelocated at ``.travis.yml``.. code:: bash$ python tests.py-  While sending a pull request increment the version at `VERSION`_ andmake sure the travis build passes.License-------MIT © [Ankush Sharma](http://github.com/darxtrix).. _Hindi: https://en.wikipedia.org/wiki/Hindi.. _Some cool usage: https://github.com/holman/spark/wiki/Wicked-Cool-Usage.. _VERSION: https://github.com/darxtrix/lehar/blob/master/lehar/VERSION.. |Header image| image:: docs/header.png.. |travis\_badge| image:: https://travis-ci.org/darxtrix/lehar.svg?branch=master.. |Code Climate| image:: https://codeclimate.com/github/darxtrix/lehar/badges/gpa.svg:target: https://codeclimate.com/github/darxtrix/lehar.. |Test Coverage| image:: https://codeclimate.com/github/darxtrix/lehar/badges/coverage.svg:target: https://codeclimate.com/github/darxtrix/lehar/coverage.. |Issue Count| image:: https://codeclimate.com/github/darxtrix/lehar/badges/issue_count.svg:target: https://codeclimate.com/github/darxtrix/lehar
Package: leia
leialeia is a layering and compositing library for Python and PIL images.Free software: MIT licenseDocumentation:https://leia.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand thecmeister2/cookiecutter-pypackageproject template.ChangelogAll notable changes to this project will be documented in this file.The format is based onKeep a Changelog,
and this project adheres toSemantic Versioning.UnreleasedChanged0.1.0- 2019-06-15AddedFirst release on PyPI.
Package: leia-br
# Análise de Sentimentos em PortuguêsLeIA (Léxico para Inferência Adaptada) é um fork do léxico e ferramenta para análise de sentimentos <a href=”https://github.com/cjhutto/vaderSentiment”>VADER</a> (Valence Aware Dictionary and sEntiment Reasoner) adaptado para textos em português, com suporte para emojis e foco na análise de sentimentos de textos expressos em mídias sociais - mas funcional para textos de outros domínios.Modo de uso
A biblioteca preserva a API do VADER, e o texto de entrada não precisa ser pré-processado:<pre>
from LeIA import SentimentIntensityAnalyzers = SentimentIntensityAnalyzer()# Análise de texto simples
s.polarity_scores(‘Eu estou feliz’)
#{‘neg’: 0.0, ‘neu’: 0.328, ‘pos’: 0.672, ‘compound’: 0.6249}# Análise de texto com emoji :)
s.polarity_scores(‘Eu estou feliz :)’)
#{‘neg’: 0.0, ‘neu’: 0.22, ‘pos’: 0.78, ‘compound’: 0.7964}# Análise de texto com negação
s.polarity_scores(‘Eu não estou feliz’)
#{‘neg’: 0.265, ‘neu’: 0.241, ‘pos’: 0.494, ‘compound’: 0.4404}
</pre>A saída da análise de sentimentos é um dicionário com os seguintes campos:<code>pos</code>: porcentagem positiva do texto<code>neg</code>: porcentagem negativa do texto<code>neu</code>: porcentagem neutra do texto<code>compound</code>: valor de sentimento geral normalizado, variando de -1 (extremamente negativo) a +1 (extremamente positivo)O valor <code>compound</code> pode ser utilizado para descrever o sentimento predominante no texto, por meio dos limites de valores:Sentimento positivo: <code>compound >= 0.05</code>Sentimento negativo: <code>compound <= -0.05</code>Sentimento neutro: <code>(compound > -0.05) and (compound < 0.05)</code>
Package: leibniz
LeibnizLeibniz is a python package which provide facilities to express learnable differential equations with PyTorchWe also provide UNet, ResUNet and their variations, especially the Hyperbolic blocks for ResUNet.InstallpipinstallleibnizHow to usePhysics-informedAs an example we solve a very simple advection problem, a box-shaped material transported by a constant steady wind.importtorchasthimportleibnizaslbnzfromleibniz.core3d.gridsys.regular3importRegularGridfromleibniz.diffeqimportodeintasodeintdefbinary(tensor):returnth.where(tensor>lbnz.zero,lbnz.one,lbnz.zero)# setup grid systemlbnz.bind(RegularGrid(basis='x,y,z',W=51,L=151,H=51,east=16.0,west=1.0,north=6.0,south=1.0,upper=6.0,lower=1.0))lbnz.use('x,y,z')# use xyz coordinate# giving a material field as a boxfld=binary((lbnz.x-8)*(9-lbnz.x))*\binary((lbnz.y-3)*(4-lbnz.y))*\binary((lbnz.z-3)*(4-lbnz.z))# construct a constant steady windwind=lbnz.one,lbnz.zero,lbnz.zero# transport value by winddefderivitive(t,clouds):return-lbnz.upwind(wind,clouds)# integrate the system with rk4pred=odeint(derivitive,fld,th.arange(0,7,1/100),method='rk4')UNet, ResUNet and variationsfromleibniz.unetimportUNetfromleibniz.nn.layer.hyperbolicimportHyperBottleneckfromleibniz.nn.activationimportCappingReluunet=UNet(6,1,normalizor='batch',spatial=(32,64),layers=5,ratio=-1,vblks=[4,4,4,4,4],hblks=[1,1,1,1,1],scales=[-1,-1,-1,-1,-1],factors=[1,1,1,1,1],block=HyperBottleneck,relu=CappingRelu(),final_normalized=False)We provide a ResUNet implementation, which is a UNet variation can insert ResNet blocks between layers.
The supported ResNet blocks are includePure ResNet: Basic, Bottleneck blockSENet variations: Basic, Bottleneck blockHyperbolic variations: Basic, Bottleneck blockWe support 1d, 2d, 3d UNet.normalizor are include:batch: BatchNormlayer: LayerNorminstance: InstanceNormOther hyperparameters are include:spatial: the sizes of the spatial dimentionsratio: the ratio to decide the intial number of channels into the UNetvblks: how many vertical blocks is inserted between two layershblks: how many horizontal blocks is inserted in the skip connectionsscales: scale factors(power-2-based) on the spatial dimentionsfactors: expand or shrink factors(power-2-based) on the channelsfinal_normalized: wheather to scale to final result between 0 to 1Piecewise Linear normalizorPiecewise Linear normalizor provide an learnable monotonic peicewise linear functions and its inverse fucntion.
The API is shown as belowfromleibniz.nn.normalizorimportPWLNormalizor# on 3 channels, given 128 segmented pieces, and assuming the input data have a zero mean and 1.0 stdpwln=PWLNormalizor(3,128,mean=0.0,std=1.0)normed=pwln(input)output=pwln.inverse(normed)How to releasepython3setup.pysdistbdist_wheel
python3-mtwineuploaddist/*

gittagva.b.cmaster
gitpushoriginva.b.cContributorsMingli Yuan (Mountain)Xiang Pan (Panpanx)Yi Liu (YiLiu)AcknowledgeWe included source code with minor changes fromtorchdiffeqby Ricky Chen,
because of two purpose:package torchdiffeq is not indexed by pypipackage torchdiffeq is very convenient and mandatoryAll our contribution is based on Ricky's Neural ODE paper (NIPS 2018) and his package.
Package: leicaautomator
OverviewAutomate scans on Leica SPX microscopesRequirementsPython (2.7, 3.4)InstallationInstall usingpip…pipinstallleicaautomatorTODO: note about qtMac users:Run PySide post install:pyside_postinstall.py-installExampleDocumentationRead API reference:http://leicaautomator.rtfd.org/DevelopmentInstall dependencies and link development version of leicaautomator to
pip:gitclonehttps://github.com/arve0/leicaautomatorcdleicaautomatorpipinstall-rrequirements.txtrun testtoxextra output, jump into pdb upon errortox---s--pdbbuild api referencemakedocs
Package: leicacam
leicacamOverviewControl Leica microscopes with pythonInstallationThe latest version of leicacam requires Python 3.5+If you need to keep using Python 2.7, pin your version of leicacam to 0.3.0.Install usingpip:pip3installleicacamExampleCommunicate with microscopefromleicacamimportCAMcam=CAM()# initiate and connect, default localhost:8895# some commands are created as short hands# start matrix scanresponse=cam.start_scan()print(response)# but you could also create your own command with a list of tuplescommand=[('cmd','enableall'),('value','true')]response=cam.send(command)print(response)# or even send it as a bytes string (note the b)command=b'/cmd:enableall /value:true'response=cam.send(command)print(response)DocumentationSee available commands in the API reference:http://leicacam.rtfd.org.DevelopmentInstall dependencies and link development version of leicacam to pip:pipinstall-rrequirements_dev.txtCode formattingWe use black code formatter to automatically format the code.
This requires Python 3.6 for development.black./TestingtoxBuild documentation locallyTo build the documentation:pipinstall-rdocs/requirements.txt
makedocs
Package: leicaexperiment
OverviewThis is a python module for interfacing withLeica LAS AF/X Matrix
Screenerexperiments.The module can be used to:stitch wells from an experiment exported with theLAS AF Data
Exporterbatch compress images losslessprogrammatically select slides/wells/fields/images given by
attributes likeslide (S)well position (U, V)field position (X, Y)z-stack position (Z)channel (C)FeaturesAccess experiment as a python objectCompress to PNGs without loosing precision, metadata or colormapImageJ stitching (Fiji is installed viafijibin)InstallationInstall usingpip…pipinstallleicaexperimentExamplesstitch experimentfromleicaexperimentimportExperiment# path should contain AditionalData and slide--S*experiment=Experiment('path/to/experiment')# if path is omitted, experiment path is used for output filesstitched_images=experiment.stitch('/path/to/output/files/')# get information about placement of images in the stitchxs,ys,attrs=experiment.stitch_coordinates(well_x=0,well_y=0)stitch specific wellfromleicaexperimentimportstitchstitched_images=stitch('/path/to/well')do stuff on all imagesfromleicaexperimentimportExperimentexperiment=Experiment('path/to/experiment--')forimageinexperiment.images:dostuff...do stuff on specific wells/fieldsfromleicaexperimentimportExperimentfromPILimportImageexperiment=Experiment('path/to/experiment--')# on images in well --U00--V00forwellinexperiment.well_images(0,0):dostuff...# rotate top left image in first rowrows=experiment.well_rowsforrinrows:img_path=experiment.image(r,0,0,0)img=Image.open(img_path)img=img.rotate(90)img.save(img_path)subtract attributes from file namesfromleicaexperimentimportattribute# get all channelschannels=[attribute(image,'C')forimageinexperiment.images]min_ch,max_ch=min(channels),max(channels)batch lossless compress of experimentfromleicaexperimentimportExperimente=Experiment('/path/to/experiment')pngs=e.compress()print(pngs)API referenceAPI reference is athttp://leicaexperiment.rtfd.org.DevelopmentInstall dependencies and link development version of leicaexperiment to
pip:gitclonehttps://github.com/arve0/leicaexperimentcdleicaexperimentpipinstall-rrequirements.txtrun testpipinstalltoxtoxextra output, jump into pdb upon errorDEBUG=leicaexperimenttox----pdb-sbuild api referencepipinstall-rdocs/requirements.txtmakedocs
Package: leicaimage
leicaimageHandle Leica Matrix Screener experiment imagesThe leicaimage library is a modified version of theleicaexperimentlibrary,
and was built as a drop in replacement for that library but without any xml
or image processing. This also makes leicaimage work without heavy dependencies.OverviewThis is a python module for interfacing withLeica LAS AF/X Matrix Screenerexperiments.The module can be used to:Programmatically select slides/wells/fields/images given by attributes like:slide (S)well position (U, V)field position (X, Y)z-stack position (Z)channel (C)FeaturesAccess experiment as a python objectInstallationPython 3.6+ is required. Install usingpip:pipinstallleicaimageExamplesAccess all imagesfromleicaimageimportExperimentexperiment=Experiment('path/to/experiment--')forimageinexperiment.images:...Access specific wells/fieldsfromleicaimageimportExperimentexperiment=Experiment('path/to/experiment--')# on images in well --U00--V00forwellinexperiment.well_images(0,0):...Extract attributes from file namesfromleicaimageimportattribute# get all channelschannels=[attribute(image,'C')forimageinexperiment.images]min_ch,max_ch=min(channels),max(channels)DevelopmentInstall dependencies and link development version of leicaimage to pip:gitclonehttps://github.com/MartinHjelmare/leicaimage.gitcdleicaimage
pipinstall-rrequirements_dev.txtRun teststox
Package: leicascanningtemplate
OverviewConvenience library for talking with Leica matrix screener scanning
template XMLs.InstallationInstall usingpip…pipinstallleicascanningtemplateExamplefromleicaleicascanningtemplateimportScanningTemplatetmpl=ScanningTemplate('path/to/scantempl.xml')tmpl.add_well(well_x=2,well_y=3,start_x=30e-3,start_y=44e-3)tmpl.write()# x stage position of fieldprint(tmpl.field(well_x=1,well_y=1,field_x=1,field_y=1).FieldXCoordinate)Read more on available elements and attributes inSCANNINGTEMPLATE.md.DocumentationRead about available commands in the API reference:http://leicascanningtemplate.rtfd.org/DevelopmentInstall dependencies and link development version of
leicascanningtemplate to pip:pipinstall-rdev-requirements.txt./setup.pydevelopTestingpipinstalltoxtoxBuild documentation locallyTo build the documentation:makedocs
Package: leiden-clustering
#leiden_clustering[![pipy](https://img.shields.io/pypi/v/leiden_clustering?color=informational)](https://pypi.python.org/pypi/leiden_clustering)
[![License](https://img.shields.io/badge/License-BSD%203–Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)## Description
Class wrapper based on [scanpy](https://scanpy.readthedocs.io/en/stable/) to use the Leiden algorithm to directly cluster your data matrix with ascikit-learnflavor.## Requirements
Developed using:
-scanpyv1.7.2
-sklearnv0.23.2
-umapv0.4.6
-numpyv1.19.2
-leidenalg## Installation
### pip`shell pip install leiden_clustering `### local`shell git clonehttps://github.com/MiqG/leiden_clustering.gitcd leiden_clustering pip install-e. `## Usage`python from leiden_clustering import LeidenClustering import numpy as np X = np.random.randn(100,10) clustering = LeidenClustering() clustering.fit(X) clustering.labels_ `## Licenseleiden_clsuteringis distributed under a BSD 3-Clause License (see [LICENSE](https://github.com/CRG-CNAG/leiden_clustering/blob/main/LICENSE)).## References
-Traag, V.A., Waltman, L. & van Eck, N.J.From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 9, 5233 (2019). DOI:https://doi.org/10.1038/s41598-019-41695-z
Package: leidenmark
LeidenMark$pipinstallleidenmarkA Python Markdown extension for converting Leiden+ epigraphic text to TEI XML/HTML. Inspired by the Brill plain text (BPT) format that aims to incorporate Leiden+ into a Markdown-based syntax.>>>fromleidenmarkimportleiden_plus>>>content="""\<D=.r<=1. Lorem ipsum dolorvac.1lin2. sit amet, con[ca.3]c3.-etur adipiscing=>=D><D=.v<=lost.2lin6. ut labore et dol7.-ore magna aliqua=>=D>""">>>leiden_plus(content,indent=True)The output of the above lines is the following XML snippet:<divn="r"type="textpart"><ab><ln="1">Loremipsumdolor</l><spacequantity="1"unit="line"/><ln="2">sitamet,con<gapprecision="low"quantity="3"reason="lost"unit="character"/>c</l><lbreak="no"n="3">eturadipiscing</l></ab></div><divn="v"type="textpart"><ab><gapquantity="2"unit="line"/><ln="6">utlaboreetdol</l><lbreak="no"n="7">oremagnaaliqua</l></ab></div>leiden_plus()is syntactic sugar for the registered Markdown extension, and equivalent to:>>>importleidenmark>>>frommarkdownimportmarkdown>>>markdown(content,extensions=['leiden_plus'])# Other extensions can be added to this listConfigurationGiven that this is a Markdown extension, conventions like*italics*and**bold**will also be recognized an converted (these in particular will additionally be transformed to the TEI element<hi>). Though these arein principlenot part of the Leiden+ syntax,in practicethe use of italics and boldface is still encountered a lot. Therefore, support is maintaned by default, which can be switched off by passingstrict=True:>>>leiden_plus(content,strict=True)NB: The blockprocessors for paragraphs and ordered list are always switched off, because they interfer too much with Leiden+.
Package: leina
SaffireFeaturesTODORequirementsTODOInstallationYou can installSaffireviapipfromPyPI:$pipinstallleinaUsagePlease see theCommand-line Referencefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,Saffireis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz'sHypermodern Python Cookiecuttertemplate.
Package: leitmotif
MotifletsThis page was built in support of our paper "Discovering Leitmotifs in Multidimensional
Time Series" by Patrick Schäfer and Ulf Leser.Supporting Materialtests: Please see the python tests for use casesnotebooks: Please see the Jupyter Notebooks for use casescsvs: The results of the scalability experimentsmotiflets: Code implementing multidimensonal k-Motifletdatasets: Use cases in the paperk-MotifletsTODOShowcaseTODOInstallationThe easiest is to use pip to install motiflets.a) Install using pippip install leitmotifYou can also install the project from source.b) Build from SourceFirst, download the repository.git clone https://github.com/patrickzib/motiflets.gitChange into the directory and build the package from source.pip install .UsageHere we illustrate how to use k-Motiflets.Use CasesData Sets: We collected challenging real-life data sets to assess the quality and
scalability of MD algorithms. An overview of datasets can be found in Table 2
of our paper.TODOJupyter-NotebookUnivariate Use Cases for k-Motiflets:
highlights all use cases used in the paper and shows the unique ability of k-Motiflets
to learn its parameters from the data and find itneresting motif sets.CitationIf you use this work, please cite as:TODO
Package: lekin
Documentation|Tutorials|Release Notes|中文python-lekinis a Flexible Job Shop Scheduler Library, named after and inspired byLekin. As a core function inAPS (advanced planning and scheduler), it helps to improve factory efficiency.TutorialInstall$pipinstallpython-lekinUsagefromlekinimportHeuristics,GeneticsfromlekinimportSchedulersolver=Heuristics()scheduler=Scheduler(solver)scheduler.solve(jobs,machines)scheduler.draw()ExamplesCitation@misc{python-lekin2022,
  author = {Longxing Tan},
  title = {python lekin},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/longxingtan/python-lekin}},
}