Saravanan666/pypi-org huggingface dataset:
Package: rerpy
rERPy is a Python toolkit for doing ERP/ERF analysis of brainwave data
(EEG, MEG), using both traditional averaging-based ERP/ERF estimation,
and a fancy newregression-based technique for ERP/ERF estimation,
which we call rERP/rERF. rERPs can do
anything ERPs can do – in fact, ERPs are special cases of rERPs;
every ERP is also a rERP. But rERPs are much more powerful. rERPs make
it straightforward to analyze experimental designs that use a mix of
categorical and continuous manipulations, even when these
manipulations are partially confounded or produce non-linear effects,
and they can separate out overlapping waveforms timelocked to
temporally adjacent events. They can even do all these things at the
same time. Nonetheless, they are relatively simple to use.Documentation:http://rerpy.readthedocs.org/Downloads:not yetDependencies:Python 2.7 (not Python 3 yet, sorry – patches accepted!)numpyscipypandaspatsyIf you’re starting from scratch (not previously a Python user), then
we recommendinstalling a scientific Python distribution.Optional dependencies:nose: needed to run testsInstall:probably not a great idea yetMailing list:not yet, but in the mean time you can hasslenathaniel.smith@ed.ac.ukCode and bug tracker:https://github.com/rerpy/rerpyLicense:GPLv2+, see LICENSE.txt for details.
Package: rer.structured_content
RER Structured ContentThis product will add to your Plone site a new content type: theStructured Document.This want to be a not-so-more-complex alternative to PlonePagecontent. Features are similar, but you
can add attachments and subdocuments inside the content itself.This is the way used also in other products likeRichDocumentandPloneArticle.This product is used in many sites ofRegione Emilia Romagna.Additional featuresInside a Structured Document you can add folder structure calledFolder Deepening. This kind on
folders (and content inside) are used in a different way.For every Folder Deepening, the document view will display a navigation-portlet-like structure.Contents inside the Folder Deepening (and folders itself) are sensible to “Omit from navigation”
option.Other contents and other foldersOther contents and folder inside the Structured Document are not show directly in the document view,
so you must link them yourself.Document viewsYou can choose from two view for Structured Document.
The default view itself can different for every installation. Just change thestructured_document_viewinside therer_propertiessheet inportal_properties(through ZMI access).CreditsDeveloped with the support ofRegione Emilia Romagna; Regione Emilia Romagna supports
thePloneGov initiative.AuthorsThis product was developed by RedTurtle Technology team.Changelog1.9.3 (2016-12-14)Fix view for Plone 4.3
[cekk]1.9.2 (2015-12-03)Fix view for Plone 4.3 [cekk]1.9.1 (2015-12-02)Plone 4.3 compatibility [cekk]1.9.0 (2014-09-19)added …/filename.ext to at_download/file if rer.downloadurl is installed [fdelia]1.8.4 (2014-09-08)ImplementedCookedBodyandEditableBodyfor ITextContent interface [cekk]1.8.3 (2014-05-08)IStructuredDocumentis now extending also theITextContentinterface [keul]Added tinymce.xml config file for linkable resources [cekk]1.8.2 (2012-08-31)more fixes for template variables in plone 4 [micecchi]1.8.1 (2012-08-22)Fixed Generic Setup depenceny error [keul]1.8.0 (2012-08-01)Plone4 compatibility [micecchi]Fixed import steps [keul]1.7.1 (2011/03/10)Changed default view of folder deepening. Now it has the usually folder’s views [micecchi]1.7.0 (2010/11/12)Links to file now show file size, and directly download them [micecchi]1.6.0 (2010/10/22)first public release
Package: rer.subsites
Subsites implementation for RER.FeaturesNo additional portal-typesViewlet with subsite’s customizable color and background image in every subsite childrenControl panel where set additional styles for the subsiteEnable a subsiteFolderish contents has a new “Set as subsite” action that mark them as a subsite.Disable a subsiteFolderish contents marked as a subsite, have a new “Unset as subsite” action that unmark them as a subsite.Subsite settingsA subsite has a new voice in the toolbar: Subsite styles.
In this view we can set two parameters:
- subsite color
- background imageThese parameters are used to style the subsite header (viewlet)
The styles are combined in a custom viewlet that inject them like css styles.Customize subsite layoutThere is a configuration in the site control-panel with a text-area where you can insert some custom
css rules that will be applied only in subsites and their children.This is useful for example if all headers (<h1> or <h2>) in the subsite should have the same color of the subsite.TranslationsThis product has been translated intoItalianInstallationInstall rer.subsites by adding it to your buildout:[buildout]

 ...

 eggs =
     rer.subsitesand then running “bin/buildout”CompatibilityThis package is fully compatible for Plone 5.ContributeIssue Tracker:https://github.com/PloneGov-IT/rer.subsites/issuesSource Code:https://github.com/PloneGov-IT/rer.subsitesLicenseThe project is licensed under the GPLv2.Changelog1.4.1 (2018-09-21)Add timestamp in image url to bypass caching when the image is updated.
[cekk]1.4.0 (2018-09-11)Remove dependency to collective.z3form.colorpicker because its pattern had
problems with some base js in Plone.
[cekk]1.3.7 (2018-03-15)Added icon for title viewlet link in template [nzambello]1.3.6 (2018-01-11)Fix template viewlet [nzambello]1.3.5 (2018-01-04)Added anchor in title viewlet to link to the second colum [nzambello]1.3.4 (2017-12-29)Added subsite css classes when enabling subsite.
[daniele]1.3.3 (2017-12-21)Fix typo in upgrade-step
[cekk]1.3.2 (2017-12-20)Added template elements and conditions needed for styling [nzambello]Use AddSubsite permission to handle all subsite configuration
(enable/disable and customization form).
[cekk]1.3.1 (2017-01-10)Fix viewlet render
[cekk]1.3.0 (2016-11-14)Add customizable styles control panel.
[cekk]1.2.2 (2015-12-02)Fix reference field [cekk]1.2.1 (2014-05-08)Add tinymce config file [cekk]1.2.0 (2012-10-11)Aggiunti campitage__bobo_traverse__assenti per
la gestione dell’immagine (#625) [keul]Rimossa la base_view tra le viste disponibili [keul]Aggiunta procedura di migrazione ai blob [keul]Aggiunto validatore per il campo del colore CSS [keul]1.1.2 (2012-08-01)egg cleanup [keul]z3c.autoinclude [keul]1.1.1 (2011/04/07)removed required in color field [cekk]1.1.0 (2011/03/29)aggiunti campi colore e immagine testata [cekk]modificato html viewlet testata [nekorin]aggiunto css per colore specifico [nekorin]1.0.0 (2011/03/14)Initial release
Package: rerun-robotics
rerun-roboticsUtilities to use the amazingRerun visualizerfor robotics applications.
This repo is a work in progress.Installationpipinstallrerun-roboticsInstall from sourcegitclonehttps://github.com/williamshen-nz/rerun-robotics.gitcdrerun-robotics
pipinstall-e.UsageCheck theRerun documentationfor information on how to use the viewer.We currently support the Franka Emika Panda robot and setting it via joint positions.importrerunasrrfromrerun_roboticsimportload_franka_pandarr.init("demo",spawn=True)panda=load_franka_panda()# 7 arm joints + 1 gripper jointpanda.set_joint_positions([0,-0.75,0,-2.35,0,1.57,0.78,0.04])Franka Panda Demopython-mrerun_robotics.examples.pandaSource code
Package: res
# resA tiny command-line HTTP client. Easily interact with HTTP calls at a faster speed within your terminal. Built on top of the requests library and meant to be a command line wrapper for requests.Current version: v0.1.7```res v0.1.7HTTP it up with python. Use res to make command line http calls. I made this to makethe development of my REST API to be a little bit easier.Usage:res (-h | --help)res (-v | --version)res <method> <url> [options]Options:-d --data=<data>          Send request data-H --HEADER=<header>      Defines custom headers-a --auth=<auth>          Authenticaton with 'user' and 'password' keys-p --params=<params>      Send request parameters-b --bytes                Returns content response in bytes-r --raw                  Returns content response in raw format-j --json                 Decodes content response from json-c --cookie=<cookie>      Defines cookies-x --proxy=<proxy>        Sends proxy with protocal as key and the port as value-i --include              Include headers-h --help                 Show this screen.-v --version              Show version.```# InstallTo download and install res, you must follow the instructions below.### Install via PIP```$ pip install res```### Install via setup.pyFirst you need to get a copy of the source. I'm going to use git and clone it to my local machine.Clone the repository into a folder```git clone https://github.com/jawerty/res.git res```Install with setup.py```$ cd res$ python setup.py install```# UsageExample GET, POST, PUT and DELETE calls```$ res GET http://example.com$ res POST http://example.com$ res PUT http://example.com$ res DELETE http://example.com```Include headers```$ res GET http://example.com -i```Send request data to the body```$ res POST http://example.com -d "{'Hello':'World','REST':'API'}"```Authorization```$ res POST http://example.com -a "{'user':'jawerty','password':'noneofyourbusiness'}"```Parameters```$ res POST http://example.com -p "{ 'q':'names' }"```### Change data response content (new in v0.1.7)Binary response -> bytes```$ res GET http://example.com -b```or```$ res GET http://example.com --bytes```Raw socket response -> raw```$ res GET http://example.com -r```or```$ res GET http://example.com --raw```JSON response decoder -> json```$ res GET http://example.com -j```or```$ res GET http://example.com --json```### Interactive ConsoleThe interactive console current (v0.1.7) cannot pass any arguments that require dictionaries (i.e. --data, --proxy, --cookie, etc.). However, arguments like --bytes, --encoding, --raw, etc., are allowed.```$ res console>>> get http://example.com -e `// -e is for encoding`UTF-8>>> get http://example.com`response content....`>>>```There are even more HTTP options that are compatible with res. Run the command at `$ res -h` to see all of the functions res wraps around.# ContactIf you would like to contact me for further information on the project, see the info below.Email: jawerty210@gmail.comGithub: jawertyTwitter: @jawertyBlog: <http://wrightdev.herokuapp.com>
Package: res2df
res2dfres2df is a Pandas DataFrame wrapper around resdata and opm.io, which
are used to access binary files outputted by reservoir simulators,
or its input files --- or any other tool outputting to the same data format.The reverse operation, from a Pandas DataFrame to reservoir simulator include files
(commonly given the extension ".inc", ".grdecl" etc.) is provided for some of the
modules.The package consists of a module pr. datatype, e.g. one module for summary
files (.UNSMRY), one for completion data etc.There is a command line frontend for almost all functionality, calledres2csv, which converts the reservoir data to DataFrames, and then dumps
the dataframes to files in CSV format, and a similarcsv2resfor the
reverse operation.For documentation, seehttps://equinor.github.io/res2df/LicenseThis library is released under GPLv3.CopyrightThe code is Copyright Equinor ASA 2019-2021.Contributions without copyright transfer are welcome.
Package: res3
res3Redis RESP3 protocolWARNING: This repo is in development. It was automatically generated withmkpylib. If you're reading this message, it means that I use this repo for my own purposes right now. It might not do anything at all; the default functionality isprint('TODO').If you really want to try it out, feel free. I recommend reading through thetestsand commit history to see if it does what you need, orask mefor status updates.Stay tuned!Installpip3 install -U res3Or develop locally:git clone https://github.com/shawwn/res3 ~/res3
cd ~/res3
python3 setup.py developUsageimportres3print('TODO')LicenseMIT. SeeLICENSEfile.ContactA library byShawn Presser. If you found it useful, please considerjoining my patreon!My Twitter DMs are always open; you shouldsend me one! It's the best way to reach me, and I'm always happy to hear from you.Twitter:@theshawwnPatreon:https://www.patreon.com/shawwnHN:sillysaurusxWebsite:shawwn.com
Package: res-address
Python module to parse simple network resource addresses, like the ones
used in many database systems to represent a database URI.Used byMongotailto parse the address passed through the command line, but can be used
by any other Python application that needs to parse a MongoDB database address,
or any other network resource like[SCHEMA://][[HOSTORIP][:PORT]/]RESOURCE.Usage:>>>fromres_addressimportget_res_address>>>scheme,host,port,resource,query,username,password=get_res_address("localhost:27017/test?timeout=5")>>>print(scheme,host,port,resource,query,username,password)Nonelocalhost27017testtimeout=5NoneNone>>>print(get_res_address("my_db"))(None,None,None,'my_db',None,None,None)>>>scheme,ipv6,port,resource,query,username,password=get_res_address("https://[::1]:9999/foo")>>>print(scheme,ipv6,port,resource)https[::1]9999fooThe address can be:foofoo resource on local machine (IPv4 connection)192.169.0.5/foofoo resource on 192.168.0.5 machineremotehost/foofoo resource onremotehostmachine192.169.0.5:9999/foofoo resource on 192.168.0.5 machine on port 9999http://192.169.0.5:9999/foofoo resource on 192.168.0.5 machine on port 9999, scheme http“[::1]:9999/foo”foo resource on ::1 machine on port 9999 (IPv6 connection):1234/foofoo resource on port 1234user:pass@localhost/foofoo resource on localhost, with basic authenticationlocalhost/foo?timeout=500foo resource on localhost, with query stringThe only required component in the URI is the resource. Some validations are performed over the
host, port and resource strings, and an exception is launched if some of the checks fail, but take
into account that invalid range of IP addresses or incompatible resource names may pass:>>>address=get_res_address("localhost:INVALIDport/test")Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>File"res_address/__init__.py",line74,inget_res_addressraiseInvalidPortError('Invalid port number "%s"'%port,address,port)res_address.InvalidPortError:Invalidportnumber"INVALIDport"All the validation exceptions inherit fromAddressError:InvalidHostErrorInvalidPortErrorInvalidResourceErrorNotResourceProvidedErrorDifference with urllib.parseIf you need a library that can fully parse web URLs you should useurllib.parseinstead. This
library intends to be used with simpler URIs for database resources, with only one
resource name set, e.g. “my_db” or “localhost:123/my_db”, but not “localhost:123/my_db/my_table”.
Also a URL like “localhost:123” that is a valid HTTP URL, is not as
database URL because the “resource” (the database name) is not set.Also to get the keys and values of the query component returned byget_res_address(),
a function likeurllib.parse.parse_qs()is recommended:>>>fromres_addressimportget_res_address>>>fromurllib.parseimportparse_qs>>>scheme,host,port,resource,query,username,password=get_res_address("localhost/db?timeout=500")>>>parse_qs(query).get('timeout',None)['500']Run the testsJust execute (Python 2.7 or 3.5+):$ python setup.py testOr:$ python -m unittest -v testsAboutProject:https://github.com/mrsarm/python-res-addressAuthors: (2018-2022) Mariano Ruiz <mr…@gmail.cm>License: LGPL-3
Package: resample
Link to full documentationResampling-based inference in Python based on data resampling and permutation.This package was created by Daniel Saxton and is now maintained by Hans Dembinski.FeaturesBootstrap resampling: ordinary or balanced with optional stratificationExtended bootstrap resampling: also varies sample sizeParametric resampling: Gaussian, Poisson, gamma, etc.)Jackknife estimates of bias and variance of any estimatorCompute bootstrap confidence intervals (percentile or BCa) for any estimatorPermutation-based variants of traditional statistical tests (USP test of independenceand others)Tools for working with empirical distributions (CDF, quantile, etc.)Depends only onnumpyandscipyExampleWe bootstrap the uncertainty of the arithmetic mean, an estimator for the expectation. In this case, we know the formula to compute this uncertainty and can compare it to the bootstrap result. More complex examples can be foundin the documentation.fromresample.bootstrapimportvarianceimportnumpyasnp# datad=[1,2,6,3,5]# this call is all you needstdev_of_mean=variance(np.mean,d)**0.5print(f"bootstrap{stdev_of_mean:.2f}")print(f"exact{np.std(d)/len(d)**0.5:.2f}")# bootstrap 0.82# exact 0.83The amazing thing is that the bootstrap works as well for arbitrarily complex estimators.
The bootstrap often provides good results even when the sample size is small.InstallationYou can install with pip.pipinstallresample
Package: resampy
resampyEfficient sample rate conversion in Python.This package implements the band-limited sinc interpolation method for sampling rate conversion as described by:Smith, Julius O. Digital Audio Resampling Home Page
Center for Computer Research in Music and Acoustics (CCRMA),
Stanford University, 2015-02-23.
Web published athttp://ccrma.stanford.edu/~jos/resample/.Installationresampycan be installedpipby the following command:python -m pip install resampyIt can also be installed bycondaas follows:conda install -c conda-forge resampy
Package: resaspy
resaspy: python for RESAS API=========================resaspyはRESAS APIをpythonから利用するためのライブラリです.以下のようにAPIをpythonオブジェクトにマッピングします．.. code-block:: python>>> import os>>> from resaspy import Resaspy>>> resas_key = os.environ['RESAS_API_KEY']>>> resaspy = Resaspy( resas_key )>>> resaspy.prefectures(){'result': [{'prefName': '北海道', 'prefCode': 1}, {'prefName': '青森県', 'prefCode': 2}, {'prefName': '岩手県', 'prefCode': 3}, {'prefName': '宮城県', 'prefCode': 4}, {'prefName': '秋田県', 'prefCode': 5}, {'prefName': '山形県', 'prefCode': 6}, {'prefName': '福島県', 'prefCode': 7}, {'prefName': '茨城県', 'prefCode': 8}, {'prefName': '栃木県', 'prefCode': 9}, {'prefName': '群馬県', 'prefCode': 10}, {'prefName': '埼玉県', 'prefCode': 11}, {'prefName': '千葉県', 'prefCode': 12}, {'prefName': '東京都', 'prefCode': 13}, {'prefName': '神奈川県', 'prefCode': 14}, {'prefName': '新潟県', 'prefCode': 15}, {'prefName': '富山県', 'prefCode': 16}, {'prefName': '石川県', 'prefCode': 17}, {'prefName': '福井県', 'prefCode': 18}, {'prefName': '山梨県', 'prefCode': 19}, {'prefName': '長野県', 'prefCode': 20}, {'prefName': '岐阜県', 'prefCode': 21}, {'prefName': '静岡県', 'prefCode': 22}, {'prefName': '愛知県', 'prefCode': 23}, {'prefName': '三重県', 'prefCode': 24}, {'prefName': '滋賀県', 'prefCode': 25}, {'prefName': '京都府', 'prefCode': 26}, {'prefName': '大阪府', 'prefCode': 27}, {'prefName': '兵庫県', 'prefCode': 28}, {'prefName': '奈良県', 'prefCode': 29}, {'prefName': '和歌山県','prefCode': 30}, {'prefName': '鳥取県', 'prefCode': 31}, {'prefName': '島根県', 'prefCode': 32}, {'prefName': '岡山県', 'prefCode': 33}, {'prefName': '広島県', 'prefCode': 34}, {'prefName': '山口県', 'prefCode': 35}, {'prefName': '徳島県', 'prefCode': 36}, {'prefName': '香川県', 'prefCode': 37}, {'prefName': '愛媛県', 'prefCode': 38}, {'prefName': '高知県', 'prefCode': 39}, {'prefName': '福岡県', 'prefCode': 40}, {'prefName': '佐賀県', 'prefCode': 41}, {'prefName': '長崎県', 'prefCode': 42}, {'prefName': '熊本県', 'prefCode': 43}, {'prefName': '大分県', 'prefCode': 44}, {'prefName': '宮崎県', 'prefCode': 45}, {'prefName': '鹿児島県', 'prefCode': 46}, {'prefName': '沖縄県', 'prefCode': 47}], 'message': None}機能概要---------------resaspyは以下の機能をサポートします．- RESAS APIへのアクセスインストール方法------------以下の手順でインストールが可能です．.. code-block:: bash$ pip install resaspy
Package: resattention
# resattentionA python library for implementing a Residual Attention Convolutional Neural Network
and training it for image classification problems. This model supports multi-class
classification and is easy to use both for training and testing purposes.The code is Python 2 and 3 compatible.# InstallationFast install:pip install resattentionFor a manual install get this package:$wgethttps://github.com/garain/resattention/archive/master.zip$unzipmaster.zip$rmmaster.zip$cdresattention-masterInstall the package:python setup.py install# Examplefromresattentionimportmodelsimporttensorflowastfmodel=models.AttentionResNetCifar10(shape=(32,32,3),n_classes=2,n_channels=3)#RGB imagesmodel=models.AttentionResNetCifar10(shape=(32,32,1),n_classes=6,n_channels=1)#GrayScale imagesmodel.compile(tf.keras.optimizers.Adam(lr=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])model.summary()# Models supportedAttentionResNetCifar10AttentionResNet92AttentionResNet56# Please cite this publication if this library comes to any use:Garain, B. Ray, P. K. Singh, A. Ahmadian, N. Senu and R. Sarkar, “GRANet: A Deep Learning Model for Classification of Age and Gender from Facial Images,” in IEEE Access, doi: 10.1109/ACCESS.2021.3085971.
Package: resautonet
Library of Autoencoder-based Residual Deep Network (resautonet)The python library of autoencoder based residual deep network (resautonet).
Current version (2.0) just supports the KERAS package of deep learning and
will extend to the others in the future.Major modulesmodelresAutoencoder: major class to obtain a autoencoder-based residual
deep network by setting the arguments. See the class and its
member functions' help for details.pmetrics: functions for regression metrics like rsquared and RMSE.peranalysismulParPerAnalysis: major class for parallel performance analysis
You can setup many configure parameters for each network (a duty)
and then run them to the effects in a parallel way. See this class
and its member functions' help for details.datadata: function to access each of two datasets,sim': simulated dataset in the format of Pandas's Data Frame,
'pm2.5':string, the name for a real dataset of the 2015 PM2.5
and the relevant covariates for the Beijing-Tianjin-Tangshan
area. It is sampled by the fraction of 0.8 from the
the original dataset (stratified by the julian day).
See this function's help for details.simdata: function to simulate the test dataset,The simulated dataset generated according to the formula:
y=x1+x2*np.sqrt(x3)+x4+np.power((x5/500),0.3)-x6+
np.sqrt(x7)+x8+noise
See this function's help for details.InstallationYou can directly install it using the following command for the latest version:pip install resautonet -UYou can also clone the repository and then install:gitclone--recursivehttps://github.com/lspatial/resautonet.gitcdpackagepipinstall./setup.pyinstallWith thesetup.pyfile included in this example, thepip installcommand will
invoke CMake and build the resautonet module as specified inCMakeLists.txt.Note for installation and useCompiler requirementsresautonet requires a C++11 compliant compiler to be available.Runtime requirementsresautonet requires installation of Keras with support of Tensorflow or other
backend system of deep learning (to support Keras). Also Pandas and Numpy should
be installed.Use caseThe homepage of the github for the package, resautonet provides two specific
examples for use of autoencoder based residual deep network:https://github.com/lspatial/resautonetLicenseThe resautonet is provided under a MIT license that can be found in the LICENSE
file. By using, distributing, or contributing to this project, you agree to the
terms and conditions of this license.Test callimportresautonetasr#Load the sample dataset for PM2.5simdata=r.data('pm2.5')simdata.head()CollaborationWelcome to contact Dr. Lianfa Li (Email:lspatial@gmail.com).
Package: rescale
A simple tool to resize and crop an image` rescale(image, size, mode=None) `image: a PIL image objectsize: a 2-tuple (width, height); at least one must be specifiedmode:CROP_TLorCROP_BRIf no height or no width is specified, the mode won’t be taken into account, and the image will just be resized.
If both width and height are provided, and the mode is set topCROP_TLorCROP_BR, the image will be cropped to the given size using the given mode. Note that the crop is made after the image as been resized (well not exactly, but the result is the same) to preserve as much as possible the content of the image.
The resize is made so that the original ratio is preserved.
The modeCROP_TLwill keep the top left or the image, whileCROP_BRwill keep the bottom right of it.