Saravanan666/pypi-org huggingface dataset:
Package: pylanczos
PyLanczosOverviewPyLanczosis a Lanczos diagonalization library.
Its core part is written in C++ asLambdaLanczos.UsageAll samples are availablehere.NumPy and SciPy matrixmatrix=np.array([[2.0,1.0,1.0],[1.0,2.0,1.0],[1.0,1.0,2.0]])engine=PyLanczos(matrix,True,2)# Find 2 maximum eigenpairseigenvalues,eigenvectors=engine.run()print("Eigenvalue:{}".format(eigenvalues))print("Eigenvector:")print(eigenvectors)Note: Use of SciPy sparse matrix is recommended to take full advantage of Lanczos algorithm.Customized operationYou can also attach your customized function:tensor=np.zeros((2,2,2,2),dtype='float64')tensor[0,0,0,0]=1# and so on...## Matrix-vector (or tensor-matrix) multiplication functiondefmv_mul(v_in,v_out):v_in.shape=(2,2)v_out.shape=(2,2)np.einsum("ijkl, kl -> ij",tensor,v_in,out=v_out,optimize="optimal")## Calculate an "eigenmatrix" for the 4th-order tensor.engine=PyLanczos.create_custom(mv_mul,n,'float64',False,1)# Find 1 minimum eigenpaireigenvalues,eigenmatrices=engine.run()eigenmatrices.shape=(2,2)print("Eigenvalue:{}".format(eigenvalues))print("Eigenmatrix:")print(eigenmatrices)There isa full samplewith detailed description.InstallationpipinstallpylanczosRequirementsC++11 compatible environmentLicenseMITAuthormrcdrPyPI repositorypylanczos
Package: pyland
dl_cmstest framework powered by pytestcommon文件夹，可放置跨项目的通用型脚本，作为共享目的test文件夹，每个项目独立一份子文件夹（可嵌套多层），DEMO项目子文件夹的内部结构如下basic structconfig全局配置 host 、db、logdata用例数据，如输入参数、参数模板（针对复杂参数使用）、其他数据（image、export、cookies）每个参数的有效用例和无效用例可分开编写，会按照一定方式组合为param_listtest_suits用例编写（1、读取参数列表 2、调用interface对象 3、结果处理 4、断言  ）demo.py化所有待测接口为对象  （名称为自己的项目名称）run.py按需执行用例计划，生成报告write testcase step前提条件测试用例（单个接口的详细的入参和出参设计，多个接口的调用依赖流程）data，准备每个接口的测试数据（eg. cookies、input_loads)步骤config，修改host、dbdemo.py， 修改文件名，封装待测接口test_suits/conftest.py （可选）通常，需准备测试环境的脚本（setup、teardown）特殊地，需二次格式化参数，二次格式化输出，以及复杂断言的脚本test_suits/test_xx.py调用接口和conftest.py， 编写用例suit/run.py （可选）定义要执行的接口测试，运行即自动产生测试报告对接自动化测试平台在平台上新建项目，输入git地址、项目路径（以tests目录为基准的相对路径， 如DEMO就是demo项目的路径）在平台上导入用例，选择用例执行即可
Package: pylandau
pylandauA simpleLandaudefinition to be used in Python, since no common package (Scipy, Numpy, ...) provides this. Also a fast Landau + Gauss convolution is offered, that is usefull for fitting energy losses of charged particles in matter. The Landau is approximated according toComputer Phys. Comm. 31 (1984) 97-111and the implementation is fromCERN ROOT Mathlibs.InstallationThe project is hosted atPyPI. For installation just type:pip install pylandauIf you want to change the code or start contributing download it and put it into a directory of your choice. From the project root folder run:pip install -e .Usageimportnumpyasnpimportpylandaux=np.arange(0,100,0.01)y_landau=pylandau.landau(x)y_langau=pylandau.langau(x)
Package: pylandroidcloud
Python Worx LandroidSmall Python package to use the Worx Landroid APIFree software: MIT licenseDocumentation:https://pylandroidcloud.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-09-08)First release on PyPI.
Package: pylandstats
PyLandStatsOpen-source library to compute landscape metrics in the Python ecosystem (NumPy, pandas, matplotlib...)Citation: Bosch M. 2019. "PyLandStats: An open-source Pythonic library to compute landscape metrics".PLOS ONE, 14(12), 1-19.doi.org/10.1371/journal.pone.0225734FeaturesRead GeoTiff files of land use/cover:importpylandstatsasplsls=pls.Landscape('data/vaud_g100_clc00_V18_5.tif')ls.plot_landscape(legend=True)Compute pandas data frames of landscape metrics at the patch, class and landscape level:class_metrics_df=ls.compute_class_metrics_df(metrics=['proportion_of_landscape','edge_density'])class_metrics_dfclass_valproportion_of_landscapeedge_density17.7024.459292.2984.459Analyze the spatio-temporal evolution of landscapes:input_fnames=['data/vaud_g100_clc00_V18_5.tif','data/vaud_g100_clc06_V18_5a.tif','data/vaud_g100_clc12_V18_5a.tif']sta=pls.SpatioTemporalAnalysis(input_fnames,metrics=['proportion_of_landscape','edge_density','fractal_dimension_am','landscape_shape_index','shannon_diversity_index'],classes=[1],dates=[2000,2006,2012],)fig,axes=plt.subplots(1,3,figsize=(15,5))formetric,axinzip(['proportion_of_landscape','edge_density','fractal_dimension_am'],axes):sta.plot_metric(metric,class_val=1,ax=ax)fig.suptitle('Class-level metrics (urban)')Zonal analysis of landscapesSee thedocumentationand thepylandstats-notebooksrepository for a more complete overview.InstallationThe easiest way to install PyLandStats is with conda:$ conda install -c conda-forge pylandstatswhich will install PyLandStats and all of its dependencies. Alternatively, you can install PyLandStats using pip:$ pip install pylandstatsNevertheless, note that in order to define zones by vector geometries inZonalAnalysis, or in order to use the theBufferAnalysisandSpatioTemporalBufferAnalysisclasses, PyLandStats requiresgeopandas, which cannot be installed with pip. If you already havethe dependencies for geopandasinstalled in your system, you might then install PyLandStats with thegeoextras as in:$ pip install pylandstats[geo]and you will be able to use the aforementioned features (without having to use conda).Development installTo install a development version of PyLandStats, you can first use conda to create an environment with all the dependencies and activate it as in:$ conda create -n pylandstats -c conda-forge geopandas matplotlib-base rasterio scipy openblas
$ conda activate pylandstatsand then clone the repository and use pip to install it in development mode$ git clone https://github.com/martibosch/pylandstats.git
$ cd pylandstats/
$ pip install -e .AcknowledgmentsThe computation of the adjacency matrix intransonichas been implemented by Pierre Augier (paugier)Several information theory-based metrics from Nowosad and Stepinski [1] were added byachennuWith the support of the École Polytechnique Fédérale de Lausanne (EPFL)The Corine Land Cover datasets used for the test datasets were produced with funding by the European UnionReferencesNowosad, J., & Stepinski, T. F. (2019). Information theory as a consistent framework for quantification and classification of landscape patterns. Landscape Ecology, 34(9), 2091-2101.
Package: pylandtemp
pylandtempDescriptionpylandtempis a Python tool for retrieving land surface temperature from NASA'sLandsat 8satellite imagery using the mono-window and split-window techniques in literature.
Additionally, it also provides multiple methods for computing land surface emissivity. It is targeted towards supporting research and science workflows in many fields including climate science, earth sciences, remote sensing, geospatial data science, environmental studies, among others.Even though only Landsat 8 images are currently 'officially' supported, the methods available via this Python tool can be applied to other dataset including ASTER and MODIS.What's new:December 2021: Implementing tutorial notebooks based on the different methods. Find themhereInstallationpip install pylandtempHow to start using pylandtempThe notebookshereare a good place to start.How to contribute to pylandtempAll kinds of contributions are welcome --- development of enhancements, bug fixes, documentation, tutorial notebooks, new methods, new data, etc....A guide to get you started with contributing to pylandtemp is availablehere.@Misc{pylandtemp,
author = {Oladimeji Mudele},
title =        {pylandtemp - a Python tool for retrieving land surface temperature from Landsat 8 satellite imagery},
howpublished = {Github},
year =         {2021},
url =          {https://github.com/pylandtemp}
}
Package: pylane
pylane简体中文Pylane is a python vm injector with debug tools, based on gdb and ptrace.
Pylane uses gdb to trace python process, inject and run some code in its python vm.Usageuse inject command to inject a python script in an process:pylane inject <PID> <YOUR_PYTHON_FILE>use shell command to inject an interactive shell:pylane shell <PID>Pylane shell features:use IPython as its interactive interface, support magic functions like ? and %support remote automatic completionprovide debug toolkit functions, such as:lookup class or instance by nameget source code of an objectprint all threads' stack and localsInstallpip install pylanepylane should be installed in virtualenv the target process uses or in os python lib.CompatibilitySupport Linux and BSD
Package: pylang
pylangMultiple language/runtime-env installer written in Python for
Debian/Ubuntu based distrosVersion 0.1.0InstallationUsingpippip install pylangGet the latest build from the SourceClone the repogit clonehttps://github.com/harshasrinivas/pylang.gitRunpython setup.py installOptions-h,--helpshowthishelpmessageandexit-iINSTALL,--installINSTALLGetthelanguageorruntimeenvironmenttobeinstalledUsage$ pylang-i<language/RE>List of languages/REs supportedcc++javanodejsgophprhaskellrustrubyerlang
Package: pylangacq
Full documentation:https://pylangacq.orgPyLangAcq is a Python library for language acquisition research.Easy access to CHILDES and other TalkBank datasetsIntuitive Python data structures for flexible data access and manipulationStandard developmental measures readily available: Mean length of utterance (MLU),
type-token ratio (TTR), and index of productive syntax (IPSyn)Direct support and powerful extensions possible for CHAT-formatted conversational datasets
more generallyDownload and InstallTo download and install the most recent version:$ pip install --upgrade pylangacqReady for more?
Check out theQuickstartpage.LinksSource code:https://github.com/jacksonllee/pylangacqBug tracker:https://github.com/jacksonllee/pylangacq/issuesSocial media:TwitterHow to CitePyLangAcq is authored and maintained byJackson L. Lee.Lee, Jackson L., Ross Burkholder, Gallagher B. Flinn, and Emily R. Coppess. 2016.Working with CHAT transcripts in Python.
Technical reportTR-2016-02,
Department of Computer Science, University of Chicago.@TechReport{lee-et-al-pylangacq:2016,
   Title       ={Working with CHAT transcripts in Python},
   Author      ={Lee, Jackson L. and Burkholder, Ross and Flinn, Gallagher B. and Coppess, Emily R.},
   Institution ={Department of Computer Science, University of Chicago},
   Year        ={2016},
   Number      ={TR-2016-02},}LicenseMIT License. Please seeLICENSE.txtin the GitHub source code for details.The test data files included come from CHILDES,
and have aCC BY-NC-SA 3.0license instead; please also seesrc/pylangacq/tests/README.mdin the GitHub source code for details.ChangelogPlease seeCHANGELOG.md.Setting up a Development EnvironmentThe latest code under development is available on Github atjacksonllee/pylangacq.
To obtain this version for experimental features or for development:$gitclonehttps://github.com/jacksonllee/pylangacq.git$cdpylangacq$pipinstall-e".[dev]"To run tests and styling checks:$pytest-vv--doctest-modules--cov=pylangacqpylangacqdocs/source$flake8pylangacq$black--checkpylangacqTo build the documentation website files:$pythondocs/source/build_docs.py
Package: pylangtools
1. 功能（1）繁体转换成简体（2）简体转换成繁体2. 快速开始（1）繁体转简体from pylangtools.langconv import Converter

if __name__=="__main__":
    traditional_sentence = '陳奕迅'
    simplified_sentence = Converter('zh-hans').convert(traditional_sentence)
    print(simplified_sentence)#陈奕迅（2）简体转繁体from pylangtools.langconv import Converter

if __name__=="__main__":
    simplified_sentence = '陈奕迅'
    traditional_sentence = Converter('zh-hant').convert(simplified_sentence)
    print(traditional_sentence)#陳奕迅
Package: py-lapper
py_lapperA pure python port ofnim-lapper. Please also see the rust lib,rust-lapperStay tuned for a pyO3 wrapper for the rust lib.Documentation can be foundhere.Installpipinstallpy_lapperUsagefrompy_lapperimportInterval,Lapper,Cursorintervals=[Interval(0,5,True),Interval(4,8,True),Interval(9,12,True)]lapper=Lapper(intervals)found=[ivforivinlapper.find(4,7)]# found = [Interval(0, 5, True), Interval(4, 8, True)]# Use seek when you will have many queries in sorted order.cursor=Cursor(0)found=[ivforivinlapper.seek(1,4,cursor)]# found = [Interval(0, 5, True)]# cursor = Cursor(2)found=[ivforivinlapper.seek(5,7,cursor)]# found = [Interval(4, 8, True)]# cursor = Cursor(3)Changelog1/17/2020 --0.9.4* Fix bug with seek where it skipped last match1/16/2020 --0.9.3* Added type hints
* Added documentation
Package: pylaprof
pylaprofpylaprof is a Python library that allows you to profile functions or sections of code.As a decorator:frompylaprofimportprofile@profile()defhandler(event,context):...As a context manager:frompylaprofimportProfilerdefmain():...withProfiler():# Only code inside this context will be profiled....It is built around three main abstractions: theprofiler, thesampler, and
thestorer.The profiler is the main component of pylaprof, it takes care of taking
snapshots of your program's stack at regular intervals and feeding them
to thesamplerfor processing; at the end of the profiling session, it will
then ask thesamplerfor a report and provide it to thestorer.Take a look at thesourcefor more documentation
and some pre-implemented samplers and storers orherefor some
usage examples.FeaturesAccessible: pylaprof's code is thoroughly documented and written to be read and
understood by other humans.Extensible: you can write your own sampler or storer to generate reports in the format
you like and store them where and how you want.Zero external dependencies[^1].Close to zero impact on performances (checkbenchmarkfor
more details).Reliable: pylaprof was built with the context of long-running
applications or continuously invoked lambda functions in mind.
It will never break your code or pollute your standard output or error
with unwanted messages.Turn on/off profiling with an environment variable.Store the profiling report only if execution takes longer than a threshold.[^1]: boto3 is optional and required only if you want to use the S3 storer.pylaprof-mergepylaprof-mergeis a CLI tool to merge multiple stackcollapse reports into a
single one. This might come in handy if you want to get an aggregated overview
of a function or piece of code that is executed frequently for short periods.
It is installed automatically if you get pylaprof with pip.Installationpip install pylaprofOr just copy-paste the pylaprof directory where you need it.CreditsThis library is heavily inspired topprofile: thanks to its authors for writing such
accessible and well-documented code.Thanks to @jvns for writing and distributing some of herwizard zinesfor free:
that's what got me into the rabbit hole of profiling in the first place.
Package: pyLARDA
pyLARDA v3pyLARDA for accessing and analysing ground based remote sensing data.
It tries to simplify following tasks:finding netcdf files in a complex folder structureloading data from differently formatted netcdfsstitching data from consecutive files togethersimplify common plotting tasksDocumentation is available atlarda-docQuick Setup (pypi)requirespython3.8python3 -m venv larda-env
source larda-env/bin/activate

pip install setuptools -U
pip install cython numpy==1.21
pip install pyLARDARequirementsCurrently pyLARDA is only targeted on unix operating system.numpy==1.21
scipy>=1.6
netCDF4>=1.4.2
msgpack
cython>=0.29.13
xarray
matplotlib>=3.0.2
requests>=2.21
toml>=0.10.0
tqdm>=4.36.1
numba>=0.45.1
# and for the documentation
sphinx
recommonmark
sphinx_rtd_themeSetup (github)For development, local data sources and the backend, pyLARDA module can be installed with:python3 -m venv larda-env
source larda-env/bin/activate
pip3 install -r requirements.txt

mkdir larda3
cd larda3
git clone https://github.com/lacros-tropos/larda.git

python3 setup.py installDepending on your datasource of choice:remoteYou just need to know the link to the backend backend of choice and may move to Quickstart.localFor local data it is necessary to include the source in a certain directory structure. For the setup of the config files consult the Guide to config-files.├── larda        # github managed source code
│   ├── docs
│   ├── examples
│   ├── ListCollector.py
│   ├── pyLARDA  # actual python module
│   ├── README.md
│   ├── requirements.txt
│   └── run_docs.sh
├── larda-cfg  # configuration files
│   ├── campaigns.toml
│   ├── [single campaign].toml
│   └── [single campaign].toml
├── larda-connectordump
│   └── [auto generated subfolder for each campaign]
├── larda-description
│   ├── [...].rst
└── larda-doc           # folder if you want to generate the docs
    └── ...QuickstartMake sure that the module is available at your pythonpath when in doubt usesys.path.append('dir').importpyLARDAlink_to_backend='http://...'# or use pyLARDA.LARDA('local')larda=pyLARDA.LARDA('remote',uri=link_to_backend)print('available campaigns',larda.campaign_list)larda.connect('campaign_name')MIRA_Zg=larda.read("MIRA","Zg",[dt_begin,dt_end],[0,4000])fig,ax=pyLARDA.Transformations.plot_timeheight2(MIRA_Zg,range_interval=[500,3000],z_converter='lin2z')fig.savefig('MIRA_Z.png',dpi=250)For more examples refer to the scripts in theexamplesdirectory.ArchitectureDocumentationAn online version of the documentation is available athttps://lacros-tropos.github.io/larda-doc/.
For building simply run.\run_docs.sh, when the additinal libraries (sphinx,recommonmarkandsphinx_rtd_themeare available; see above).HistoryThis version of the LACROS research data analyser (LARDA) is based on two prior versions in C and python2 respectively.
Major changes are the migration to python3, netcdf4 and the inclusion of radar Doppler spectra.LicenseCopyright 2022, pyLARDA-dev-team (Johannes Bühl, Martin Radenz, Willi Schimmel, Teresa Vogl, Moritz Lochmann, Johannes Röttenbacher)MIT LicenseFor details see the LICENSE file.
Package: pylarid
pylarid: Load Arrays of Imaging Datapylaridis a Python package that is intended to facilitate analysis of
magnetic resonance imaging (MRI) data.Building Pylaridpylaridconsists of an extension module built in C++ to run on Python 3.pylaridis currently tested using Python 3.12 on macOS Sonoma using Clang
and on Windows 11 using Visual Studio, and using Python 3.11 on Arch Linux
using GCC.To build and installpylaridfrom source,CMakeand a
compiler that supports C++20 are required.Build using PyPA's build (Recommended)Preferably, this is done in a virtual environment. If using Visual Studio, use
a 64-bit native tools developer command prompt. First, install the most
up-to-date versions ofpipandpipxin the virtual environment.python -m pip install --upgrade pip
pip install --upgrade pipxNext, obtain thepylaridsource code,
build, and install.git clone git@github.com:jme2041/pylarid.git
cd pylarid
pipx run build --wheel -Cbuild-dir=build
pip install .Note that-Cbuild-dir=buildsets the temporary directory tobuild. This is
optional, but recommended for speeding up builds and for avoiding warnings
about the intermediate directory being temporary when building with Visual
Studio (warning MSB8029).To uninstall, usepip uninstall pylarid.Manual Build Using CMake (advanced)To build and installpylariddirectly using CMake, create abuilddirectory
within thepylariddirectory and follow the normal steps for building and
installing software using CMake.To build and install on macOS and Linux:mkdir build
cd build
cmake ..
make
make installTo uninstall, usemake uninstall.To build and install on Windows using Visual Studio:mkdir build
cd build
cmake ..
cmake --build . --config Release
cmake --build . --config Release --target installTo uninstall, usecmake --build . --config Release --target uninstall.Research and Educational Use Onlypylaridis for research and educational purposes only. Clinical applications
are not recommended or advised.pylaridhas not been evaluated by the United
States Food and Drug Administration (FDA), or by any other agency.pylaridis
not intended to diagnose, treat, cure, or prevent any disease.LicenseCopyright 2023 Jeffrey M. Engelmannpylaridis released under the MIT license. For details, seeLICENSE.txt.
Package: pylarization
AboutPylarization is a Python module that aids in processing light
polarization states. For given method of polarization description it can
calculate all parameters of a polarization ellipse.InstallationThe following should do, as pylarization does not have any
platform-specific dependancies.pipinstallpylarizationUsagePolarization EllipsePolarizationEllipse is the base class for all classes describing different
methods of describing the state of polarization.To create an instance it is necessary to supply light amplitudes along
the X and Y axes and the phase difference between the amplitudes.light=PolarizationEllipse(0.445,0.89,1.57)VectorsJones Vectorlight=JonesVector(0.445,0.89j)Stokes Vectorlight=StokesVector(1,0.6,0,0.8)MatricesJonex Matrixmatrix_values=numpy.matrix([[1,0],[0,-1j]])light=JonesMatrix(matrix_values)*JonesVector(1,-1j)light.vectormatrix([[1.+0.j],[-1.+0.j]])Mueller Matrixmatrix_values = numpy.matrix([[0.5, 0.5, 0, 0], [0.5, 0.5, 0, 0], [0 , 0, 0, 0], [0 , 0, 0, 0]])
light = MuellerMatrix(matrix_values) *  StokesVector(1, 0, 0, 0)
light.vector
matrix([[0.5],
        [0.5],
        [0. ],
        [0. ]])Coherency Matrixlight=CoherencyMatrix(1,0,0,0)SourcesAll sources can be foundhere
Package: pylarm
Set alarms from your terminal.InstallationYou can install this package usingpip:pipinstallpylarmThe commandpylarmwill then be available from your terminal.Usage exampleSet an alarmpylarmset--sound<PATH_TO_SOUND_FILE>--message='The time has come!'--at='17:00'&You can also simply runpylarmset--sound<PATH_TO_SOUND_FILE>and you'll be prompted to select the time and the message.Cancel an alarmAfter setting an alarm, you'll be informed of the pid of that alarm.
You can use it to cancel the alarm as follows:pylarmcancel--pid=<PID_OF_THE_ALARM>Again, you can simply runpylarmcanceland you'll be prompted to select the pid.To know the details about a subcommand you can runpylarm <subcommand> --help.
Package: pylas
pylasAnother way of reading LAS/LAZ in Python.Exampleimportpylas# Directly read and write laslas=pylas.read('filename.las')las=pylas.convert(las,point_format_id=2)las.write('converted.las')# Open data to inspect header and then readwithpylas.open('filename.las')asf:iff.header.point_count<10**8:las=f.read()print(las.vlrs)Some rough documentation is available onReadTheDocs.Dependencies & RequirementsPython 3 Only.lazperfis an optional, but recommended dependency that allows pylas to read and write compressed LAZ files.Installationpip install pylas
Package: pyLasaDataset
PyLasaDatasetLASA Handwriting datasetloader and other tools for Python 2 and Python 3.InstallationVia pippython3 -m pip install pylasadatasetOrpython3 -m pip install git+https://github.com/justagist/pylasadatasetManual InstallDependencies:apt install python-tk;pip install numpy scipy matplotlib.Runpython setup.py install.UsageimportpyLasaDatasetaslasa# DataSet object has all the LASA handwriting data files# as attributes, eg:angle_data=lasa.DataSet.Anglesine_data=lasa.DataSet.Sine# Each Data object has attributes dt and demos (For documentation,# refer original dataset repo:# https://bitbucket.org/khansari/lasahandwritingdataset/src/master/Readme.txt)dt=angle_data.dtdemos=angle_data.demos# list of 7 Demo objects, each corresponding to a# repetition of the pattern# Each Demo object in demos list will have attributes pos, t, vel, acc# corresponding to the original .mat format described in# https://bitbucket.org/khansari/lasahandwritingdataset/src/master/Readme.txtdemo_0=demos[0]pos=demo_0.pos# np.ndarray, shape: (2,2000)vel=demo_0.vel# np.ndarray, shape: (2,2000)acc=demo_0.acc# np.ndarray, shape: (2,2000)t=demo_0.t# np.ndarray, shape: (1,2000)# To visualise the data (2D position and velocity) use the plot_model utilitylasa.utilities.plot_model(lasa.DataSet.BendedLine)# give any of the available# pattern data as argument