Saravanan666/pypi-org huggingface dataset:
Package: pyrasta
PyRastaSome tools for fast and easy raster processing, based on gdal (numpy usage is reduced to the minimum).IntroductionPyRasta is a small Python library which aims at interfacing gdal functions and methods in an easy
way, so that users may only focus on the processes they want to apply rather than on the code. The
library is based on gdal stream and multiprocessing in order to reduce CPU time due to large numpy
array imports. This is especially useful for basic raster arithmetic operations, sliding window
methods as well as zonal statistics.Basic available operationsMerging, clipping, re-projecting, padding, resampling, rescaling, windowingRasterize and PolygonizeRaster calculator to design your own operationsFast raster zonal statisticsAutomatically download and merge SRTM DEM(s) from CGIAR online databaseInstallPip installation should normally take care of everything for you.Using PIPThe easiest way to install PyRasta is by usingpipin a terminal$ pip install pyrastaNote on GDALInstalling GDAL throughpipmight be tricky as it only gets
the bindings, so be sure the library is already installed on
your machine, and that the headers are located in the right
folder. Another solution may to install it through a third-party
distribution such asconda:(your_virtual_environment) $ conda install gdalIf you are tempted by directly installing GDAL/OGR and theGDAL Python librarieson your machine, seeherefor the steps
you should follow.ExamplesBuild digital elevation model from CGIAR SRTM sitefrompyrasta.tools.srtmimportfrom_cgiar_online_databasebounds=(23,34,32,45)dem=from_cgiar_online_database(bounds)Fast clipping of raster by extent or by maskfrompyrasta.rasterimportRasterimportgeopandasraster_by_extent=Raster("/path/to/your/raster").clip(bounds=(10,40,15,45))raster_by_mask=Raster("/path/to/your/raster").clip(mask=geopandas.GeoDataFrame.from_file("/path/to/your/layer"))Fast Zonal StatisticsFast computing of raster zonal statistics within features of a given geographic layer,
by loading in memory only the data we need (and not the whole numpy array as it is often
the case in other packages) + using multiprocessing. You may use the basic
statistic functions already available in the package, or define your own customized functions.frompyrasta.rasterimportRasterimportgeopandasrstats=Raster("/path/to/your/raster").zonal_stats(geopandas.GeoDataFrame.from_file("/path/to/your/layer"),stats=["mean","median","min","max"],customized_stats={"my_stat":my_stat})AuthorBenjamin Pillot
Package: pyraster
PyRasterPyRaster is a package for processing raster and vector files and preparing training/test data for deep learning applications. PyRaster depends on the python library calledGDALwith versions after2.2. TheGDALlibrary does not however simply get installed usingpip. You have to run the following commands in the terminal and then install the version that your system supports. Please note that this is tested and works on Ubuntu:sudo apt-get install libgdal-dev
export CPLUS_INCLUDE_PATH=/usr/include/gdal
export C_INCLUDE_PATH=/usr/include/gdal
gdal-config --versionThe last command above prints out theGDALversion that you could install on your system. You could install version2.2.3for example as follows:pip install GDAL==2.2.3After install GDAL, you could isntallPyRasteras follows:pip install PyRaster
Package: pyrasterframes
PyRasterFramesPyRasterFrames enables access and processing of geospatial raster data in PySpark DataFrames.Getting startedThe quickest way to get started is topipinstall the pyrasterframes package.pipinstallpyrasterframesYou can then access apyspark SparkSessionusing thelocal[*]masterin your python interpreter as follows.frompyrasterframes.utilsimportcreate_rf_spark_sessionspark=create_rf_spark_session()Then you can read a raster and do some work with it.frompyrasterframes.rasterfunctionsimport*frompyspark.sql.functionsimportlit# Read a MODIS surface reflectance granuledf=spark.read.raster('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF')# Add 3 element-wise, show some rows of the dataframedf.select(rf_local_add(df.tile,lit(3))).show(5,False)SupportReach out to us ongitter!Issue tracking is throughgithub.ContributingCommunity contributions are always welcome. To get started, please review ourcontribution guidelines,code of conduct, anddeveloper's guide.  Reach out to us ongitterso the community can help you get started!Development environment setupFor best results, we suggest usingcondaand theconda-forgechannel to install the compiled dependencies before installing the packages insetup.py. Assuming you're in the same directory as this file:conda create -n rasterframes python==3.7
conda install --file ./requirements-condaforge.txtThen you can install the source dependencies:pip install -e .
Package: pyrastreio
PyRastreioPyRastreio é uma biblioteca/cli para rastrear suas encomendas em alguns sistemas,
criei ela só por que queria uma forma mais prática de ficar consultando minhas compras
sem ter que ir nesses sistemas fazer todo o procedimento de consulta.SistemasCorreiosJadlogSequoiaInstalação$ pip install pyrastreioobs. Se você vai instalar a biblioteca fora de um ambiente virtual, é recomendado usar a flag--user.CLIVocê pode executar o CLI usando os comandosrastreiooucade_minha_encomenda:$ cade_minha_encomenda --help
Usage: cade_minha_encomenda [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  correios
  jadlog
  sequoiaExemplosCorreios$ rastreio correios CODIGO
+------------+--------+----------------+---------------------+
| data       | hora   | local          | mensagem            |
+============+========+================+=====================+
| 11/11/2011 | 11:11  | São Paulo / SP | descrição do evento |
+------------+--------+----------------+---------------------+
| 12/12/2011 | 12:12  | São Paulo / SP | descrição do evento |
+------------+--------+----------------+---------------------+Jadlog$ cade_minha_encomenda jadlog CODIGO
+--------------------+-----------------+---------------+-----------------+----------------+
| data/hora          | origem          | status        | destino         | documento      |
+====================+=================+===============+=================+================+
| 11/11/2011   11:11 | CO SAO PAULO 01 | EMISSAO       | CO SAO PAULO 02 |                |
+--------------------+-----------------+---------------+-----------------+----------------+
| 12/12/2011   12:12 | CO SAO PAULO 02 | TRANSFERENCIA | CO SAO PAULO 03 | 11111111111111 |
+--------------------+-----------------+---------------+-----------------+----------------+Sequoia$ cade_minha_encomenda sequoia CODIGO CPF
+------------------+----------------------------+
| data/hora        | status                     |
+==================+============================+
| 11/11/2011 11:11 | Recepção na transportadora |
+------------------+----------------------------+
| 12/12/2011 12:12 | Em transferência           |
+------------------+----------------------------+BibliotecaA biblioteca implementa uma busca para cada sistema:frompyrastreioimportcorreios,jadlog,sequoiaprint('Correios:')print(correios('CODIGO_RASTREIO_CORREIOS'))print('Jadlog:')print(jadlog('CODIGO_RASTREIO_JADLOG'))# sequoia precisa do cpf ou cnpj além do código de rastreioprint('Sequoia:')print(sequoia('CODIGO_RASTREIO_SEQUOIA','11111111111'))Caso um código válido seja usado, essa deve ser a saída:Correios:
[{"data": "11/11/2011", "hora": "11:11", local": "São Paulo / SP", "mensagem": "descrição do evento"}]
Jadlog:
[{"data/hora": "11/11/2011   11:11", "origem": "CO SAO PAULO", "status": "EMISSAO", "destino": "CO SAO PAULO 02", "documento": "11111111111111"}]
Sequoia:
[{"data/hora": "11/11/2011", "status": "Recepção na transportadora"}]
Package: pyratbay
Pyrat Bay: Python Radiative Transfer in a Bayesian frameworkInstall as:pipinstallpyratbayOr:condainstall-cconda-forgepyratbayDocs at:https://github.com/pcubillos/pyratbayCite as:@ARTICLE{CubillosBlecic2021mnrasPyratBay,author={{Cubillos}, Patricio E. and {Blecic}, Jasmina},title="{The PYRAT BAY framework for exoplanet atmospheric modelling: a population study of Hubble/WFC3 transmission spectra}",journal={\mnras},keywords={radiative transfer, methods: statistical, planets and satellites: atmosphere, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},year=2021,month=aug,volume={505},number={2},pages={2675-2702},doi={10.1093/mnras/stab1405},archivePrefix={arXiv},eprint={2105.05598},primaryClass={astro-ph.EP},adsurl={https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.2675C},adsnote={Provided by the SAO/NASA Astrophysics Data System}}
Package: pyrate
pyrate======.. image:: https://pypip.in/v/pyrate/badge.png:target: https://crate.io/packages/pyrate/:alt: Latest PyPI version.. image:: https://pypip.in/d/pyrate/badge.png:target: https://crate.io/packages/pyrate/:alt: Number of PyPI downloadsPyrate is a python wrapper for restful web apis. It's like *magic* butsimpler.Currently, the following services are implemented-  `Github <#github>`__-  `Harvest <#harvest>`__-  `Basecamp <#basecamp>`__-  `Mailchimp (v2) <#mailchimp>`__-  `Twitter (v1.1 + OAuth) <#twitter>`__There's a quick-start guide below, for full documentation (WIP) visit: `http://pyrate.readthedocs.org/en/latest/ <http://pyrate.readthedocs.org/en/latest/>`__Dependencies-------------  `requests <http://python-requests.org>`__-  `requests\_oauthlib <https://github.com/requests/requests-oauthlib>`__Install-------::# so simplepip install pyrateQuick Start-----------Twitter~~~~~~~::from pyrate.services import twitterh = twitter.TwitterPyrate('oauth_consumer_key', 'oauth_consumer_secret','oauth_token', 'oauth_token_secret')print(h.do('account/verify_credentials'))print(h.check_connection())h.tweet("This is awesome!")Mailchimp~~~~~~~~~::from pyrate.services import mailchimph = mailchimp.MailchimpPyrate('apikey')print(h.do('helper/ping'))print(h.check_connection())h.subscribeToList('ListName', 'myemail@example.com')h.unsubscribeFromList('ListName', 'myemail@example.com')Harvest~~~~~~~::from pyrate.services import harvesth = harvest.HarvestPyrate('user', 'password', 'organisation')print(h.do('account/who_am_i'))print(h.check_connection())Github~~~~~~::from pyrate.services import githubh = github.GithubPyrate('user', 'password')print(h.do('#'))print(h.check_connection())h.create_repo('name', 'description', private=True)h.create_repo('name', 'description', 'organisation')h.delete_repo('name')Basecamp~~~~~~::from pyrate.services import basecamph = basecamp.BasecampPyrate('user', 'password', 'org_id')print(h.do('projects'))h.check_connection()Todos------  Create more "convenience"-methods (like``h.tweet("This is awesome!")``)-  Implement CLI-Interface (see branch`feature/cli <https://github.com/Chive/pyrate/tree/feature/cli>`__)-  Add more services (Open for suggestions!)-  Expand Documentation-  lots and lots more
Package: pyratemp
pyratemp is a small, simple and powerful template-engine for python.For documentation, please read pyratemp.txt or pyratemp.html and the
pyratemp-docstrings.For more documentation of the background of pyratemp, some thoughts
about template-engines in general, benchmarks and comparisons, seehttp://www.simple-is-better.org/template/Website:http://www.simple-is-better.org/template/pyratemp.htmlAuthor:Roland Koebler <rk at simple-is-better dot org>Version:0.3.2Requirements:Python >=2.6 / 3.xFiles:READMENEWSLICENSEpyratemp.py: the template-enginetools.py: some convenience functions for pyratemppyratemp_tool.py: a small commandline-interface for the template-enginepyratemp_python3_doctest.txt: pyratempt-doctests for Python 3__init__.py: small wrapper so you can “import the directory”setup.py: distutils-setup.pypyratemp.txt:  the documentation (in reStructuredText)pyratemp.html: the documentation, converted to htmlexample.html: example-template from the documentationexample.json: example-data from the documentationInstallation:use disutils, e.g.python ./setup.py installorpython ./setup.py install--useror simply copy or symlink this directory (or only pyratemp.py)
to a directory which is found by Python’s “import”, e.g.
into your project-directoryTest:./pyratemp_tool.py -f example.json example.html
Package: pyrates-cl
PyRatesPyRates supports conversion between 54 currency ratesCan be used as:Package/ModuleCLIGUIThe rates are fetched by webscraping x-rates.com and saved locallyIn order to keep get-requests to a minimum, rates are only updated if they are older than 30 minutesThe base currency is by default: Euro. Work in progress to let the user change the base currency at willDocumentation
Package: pyrateye
RatEye ported to PythonThis is a port of the RatEye project to Python. The original project is written in C# and can be foundhere.Installationpip install pyrateyeUsagefrompyrateyeimportRatEye# TBDDevelopmentSetupClone and install the project:gitclonehttps://github.com/marmig0404/pyrateye.gitcdpyrateye
poetryinstall--withdevInstall RatEye C# project:gitclonehttps://github.com/RatScanner/RatEye.gitcdRatEye
msbuild.\RatEye.csproj/property:Configuration=Release
Package: pyratlib
PyRat - Python in Rodent Analysis and TrackingPyRat is a user friendly library in python to analyze data from the DeepLabCut. Developed to help researchers unfamiliar with programming can perform animal behavior analysis more simpler.InstallationThe latest stable release is available on PyPi, and you can install it by sayingpip install pyratlibAnaconda users can install usingconda-forge:conda install -c conda-forge pyratlibTo build PyRat from source, saypython setup.py build.
Then, to install PyRat, saypython setup.py install.
If all went well, you should be able to execute the demo scripts underexamples/(OS X users should follow the installation guide given below).Alternatively, you can download or clone the repository and usepipto handle dependencies:unzip pyratlib.zip
pip install -e pyratliborgit clone https://github.com/pyratlib/pyrat.git
pip install -e pyratlibBy callingpip listyou should seepyratnow as an installed package:pyrat (0.x.x, /path/to/pyratlib)DataThe data is available onZenodoExamplesBasic UsageBehavior ClassificationBehavior Classification of multiple videosMetrics in miceNeural Data exampleReferences:If you use our code we kindly as that you please citeDe Almeida et al, 2022and, if you use the dataset please also citeDe Almeida et al, 2021.De Almeida et al, 2022:https://doi.org/10.3389/fnins.2022.779106De Almeida et al, 2021:10.5281/zenodo.5883277Please check out the following references for more details:@article{deAlmeida2022,
  title        = {PyRAT: An open source-python library for fast and robust animal behavior analysis and neural data synchronization},
  author       = {De Almeida, Tulio Fernandes and 
                  Spinelli, Bruno Guedes and 
                  Hypolito Lima, Ram{\'o}n and 
                  Gonzalez, Maria Carolina and 
                  Rodrigues, Abner Cardoso},
  journal      = {Frontiers in Neuroscience},
  pages        = {505},
  publisher    = {Frontiers}
}

@dataset{deAlmeida2021,
  title        = {PyRAT-data-example},
  author       = {Almeida, TÃºlio and
                  Spinelli, Bruno and
                  Gonzalez, Maria Carolina and
                  Lima, RamÃ³n and
                  Rodrigues, Abner},
  month        = sep,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.5883277},
  url          = {https://doi.org/10.5281/zenodo.5883277}
}Development Team:Tulio Almeida -GitHub-Google Scholar-SiteBruno Spinelli -GitHub-Google ScholarRamon Hypolito -GitHub-Google ScholarMaria Carolina Gonzalez -GitHub-Google ScholarAbner Rodrigues -GitHub-Google Scholar
Package: pyRaTS
pyRaTS - processing of (RAndom) TimeSeries for vibration fatigueProviding an object-oriented framework to analyze and process time series with the focus on random vibration fatigue. PyRaTS  is capable of handling single- and multi-channel, as well as single- and multi-process time series configurations.
Implementation of the non-stationarity matrix, the Fatigue Damage Spectrum and quasi-stationary signal definitions to deal with the challenges of non-stationary loading.Installing this packageUsepipto install it by:.. code-block:: console$ pip install pyRaTSSimple exampleHere is a simple example for running a basic code. Further examples can be found on:https://github.com/ArvidTrapp/pyRaTS.. code-block:: pythonimport pyRaTS as ts
import numpy as np

# Defining the series by pseudo-random generator...
T  = 10
fs = 1024
N  = T*fs
x  = np.random.randn(N)

# Initialize series and some basic plots...
sig = ts.timeseries(x,name = 'sample timeseries', fs = fs)
sig.plot()
sig.plot_prob()
sig.plot_psd()

# derive response series and some further basic plots...
respsig = sig.der_sdofResponse(fD = 50)
respsig.plot_psd()
respsig.plot_ls()Some methods for a statistical analysis of random time series / estimation of statistical descriptors* spectral moments (est_specMoms)
* Dirlik estimator (est_dirlik/est_dirlikD)
* PSD (est_psd)
* load spectra (est_ls)
* Fatigue Damage Spectrum (est_fds) ...accepts list of FLife methods for damage estimation
* non-stationarity matrix (est_nonstat)Some methods for plotting* time series (plot)
* PSD (plot_psd)
* absolute of Fourier transform (plot_X)
* load spectra (plot_ls) ...accepts list of FLife methods with PDF definition
* transfer function (plot_tf)
* Fatigue Damage Spectrum (plot_fds) 
* non-stationarity matrix (plot_nonstat)Some methods for processing time series* statistical response...PSD & Non-stat.-Matrix (der_statResponse(f,H)) 
* response timeseries of single-degree-of-freedom system (der_sdofResponse(fD, D, func))
* response timeseries for linear transfer function (der_response(f,H))
* quasi-stationary load definition on the basis of the load spectra of the Fatigue Damage Spectrum (der_lsEquivalent())
* load definition on the basis of the inverse Fatigue Damage Spectrum (der_iFDS())
* ideal high pass filtered signal (der_highpass(f))
* ideal low pass filtered signal  (der_lowpass(f))
* ideal band pass filtered signal (der_bandpass(f))
Package: pyrat-sdrabing
PyRatR-like vectors and functions in Python.WarningThis is not Pandas, or scikit-learn, or numpy. This is not R, either. Do not
use this for industry purposes. Do not use this unless you want your project to
break.Simply have fun with it, or use this for educational purposes.InstallationClone this repository to your local machine with git, then install with Python.gitclonehttps://github.com/shanedrabing/pyrat.gitcdpyrat
pythonsetup.pyinstallGetting StartedImport the libraries with Python.importpyrat.baseimportpyrat.statsimportpyrat.utilsimportpyrat.closureThe closure library is the only not inspired by R. All other libraries contain
familiar functions. If you want to locate a function within R, look into thefindfunction, determine the namespace, and see if PyRat contains the
function!Motivating ExamplesSo, why use PyRat? If you're a fan of R (like myself), you may have learned
that the capabilities of vectors and functions that operate on them are
immensely powerful.Linear ModelingLet's try modeling a line of best fit between horsepower and miles per gallon
from themtcarsdataset:importmatplotlib.pyplotaspltfrompyrat.baseimportlog10,seqfrompyrat.statsimportlm,predictfrompyrat.utilsimportread_csv,structdf=read_csv("data/mtcars.csv")x=df["hp"]y=df["mpg"]m=lm(log10(x),log10(y))x_new=seq(min(x),max(x),length_out=101)y_new=10**predict(m,log10(x_new))plt.scatter(x,y,color="black")plt.plot(x_new,y_new,color="red")plt.xlabel("Horsepower")plt.ylabel("Miles per Gallon")plt.tight_layout()plt.savefig("data/fit.png")plt.clf()A plot of the fitted linear model:Functional ProgrammingPyRat has its own version of "piping", whereby the items of avector(or thevectoritself) can be continuously operated on. All intermediates are
immutable, as thevectoris just atupleon steroids.frompyrat.baseimportc,pastetaxa=c("Canis lupus","Felis catus","Ursidae","Anura (order)")print(taxa.apply("https://en.wikipedia.org/w/index.php?search={}".format).thread(requests.get).apply(getattr,"content").apply(bs4.BeautifulSoup,"lxml").apply(bs4.BeautifulSoup.select_one,"h1").apply(getattr,"text").transform(paste,"("+taxa+")",collapse="\n"))Here's the output:Wolf (Canis lupus)
Cat (Felis catus)
Bear (Ursidae)
Frog (Anura (order))
Package: pyravia
Library to control Sony TVs and StereosSony makes excellent TVs and Stereos, but like old-school yutzes
does not publish the API to control them. (something something official
integration partners something something corruption something something)That’s ok though - since all of the network interactions are done in clear
text over the network, making them pretty easy to figure out.Making it all even better, there is a different API for the TV than for the
Stereo. YAY!This has been tested against a XBR75X850D TV and a STR-ZA5000ES receiver.This library was inspired by the work in:https://github.com/breunigs/bravia-auth-and-remoteFree software: GPL License
Package: py-ravif
py_ravifPython bindings forravifusingpyO3/maturin.Installpip install py-ravifUsageimportpy_ravif# convert png or jpeg file# give path to fileavif_bytes=py_ravif.convert_to_avif_from_path(some_path,quality=60)# or give bytes of imageavif_bytes=py_ravif.convert_to_avif_from_bytes(img_bytes,quality=60)
Package: pyrawr
pyrawrPython wrapper around theThermoRawFileParsercommand line interface.This Python module uses the ThermoRawFileParser CLI to retrieve general run metadata, specific spectra, or specific XICs, directly as Python lists and dictionaries from
mass spectrometry raw files. Parsing raw files to other file formats is also supported.InstallationInstall pyrawr with pip$pipinstallpyrawrInstallThermoRawFileParseror
Docker.For Docker, the current user must beadded to the Docker group,
that is, be callable asdocker run, instead ofsudo docker run.UsageSeefull API documentationfor all pyrawr functionality.Parse raw file to any supported output format:>>>frompyrawrimportThermoRawFileParser>>>trfp=ThermoRawFileParser(...executable="thermorawfileparser",...docker_image="quay.io/biocontainers/thermorawfileparser:1.3.3--ha8f3691_1"...)>>>trfp.parse("OR4_110719_OB_PAR14_sSCX_fr10.raw",output_format="mzml")Get raw file metadata:>>>trfp.metadata("OR4_110719_OB_PAR14_sSCX_fr10.raw"){'FileProperties':[{'accession':'NCIT:C47922','cvLabel':'NCIT'...}]}Query a specific spectrum:>>>trfp.query("OR4_110719_OB_PAR14_sSCX_fr10.raw","508,680")[{'mzs':[204.8467254638672,262.4529113769531,309.53961181640625,...Retrieve one or more chromatograms based on a query:>>>trfp.xic(..."OR4_110719_OB_PAR14_sSCX_fr10.raw",...[{"mz":488.5384,"tolerance":10,"tolerance_unit":"ppm"}],...){'OutputMeta':{'base64':False,'timeunit':'minutes'},'Content':[{'Meta':{'MzStart':488.53351461600005,'MzEnd':488.543285384,'RtStart':0.007536666666666666,'RtEnd':179.99577166666666},'RetentionTimes':[0.007536666666666666,0.022771666666666666,0.036936666666666666,...ContributingBugs, questions or suggestions? Feel free to post an issue in theissue trackeror to make a pull
request! SeeContributing.mdfor more info.This module currently uses Python'ssubprocess.run()to access ThermoRawFileParser.
There are probably much better methods that would directly access the
ThermoRawFileParser library, circumventing the CLI. Suggestions and PRs are always
welcome.ChangelogSeeChangelog.