Saravanan666/pypi-org huggingface dataset:
Package: reids-file
RedisFile===============This is a simple hack to allow the writing to redis as if it wasjust file object in python.Usage-----# Import and open up a ne "file"from redisfile import RedisFilef = RedisFile("myrediskey")# Write some lines to the "file"f.write('hello')f.writelines('world')f.writelines('YourAwesome')# Read the whole fileprint f.read()# Reading a file from the end shows no dataprint f.read()# seek to the start of the filef.seek(0)# Read a single lineprint f.readline()# Read the restprint f.read()# "close" will delete all the dataf.close()Installation------------Installation is simple too::$ pip install redis-file
Package: reids-json-storage
redis-json-storage==================.. image:: https://travis-ci.org/appstore-zencore/rjs.svg?branch=master:target: https://travis-ci.org/appstore-zencore/rjsReids json storage. Store json data as hashmap in redis.Install-------::pip install redis-json-storageClass & Methods-----1. redis_json_storage.JsonStoragea. updateb. getc. deleted. delete_fieldExample-----::from redis_json_storage import JsonStorageconnection = make_redis_connect(config)storage = JsonStorage(connection)data1 = {"a": 1,"b": 2,}storage.update(data1)data2 = {"b": 3,"c": 4,}storage.update(data2)data3 = storage.get(key)
Package: reify
usage: reify [-h] [--context CONTEXT] [--envfile ENVFILE] [--output OUTPUT][--mode MODE] [--charm-config CHARM_CONFIG]template [extra [extra ...]]render a jinja2 templatepositional arguments:template              the template fileextra                 extra key value pairs (foo=bar)optional arguments:-h, --help            show this help message and exit--context CONTEXT, -c CONTEXTfile to load context data from. Can also be read fromstdin.--envfile ENVFILE, -e ENVFILEfile with environment varibles--output OUTPUT, -o OUTPUToutput file; defaults to stdout--mode MODE, -m MODE  mode of output file, if not stdout; defaults to 0666 -umask--charm-config CHARM_CONFIGcharm config file, default values will be added totemplate context
Package: reikna
Reiknais a library containing various GPU algorithms built on top ofPyCUDAandPyOpenCL.
The main design goals are:separation of computation cores (matrix multiplication, random numbers generation etc) from simple transformations on their input and output values (scaling, typecast etc);separation of the preparation and execution stage, maximizing the performance of the execution stage at the expense of the preparation stage (in other words, aiming at large simulations)partial abstraction from CUDA/OpenCLTests can be run by installingPy.Testand runningpy.testfrom thetestfolder (runpy.test--helpto get the list of options).For more information proceed to theproject documentation page. If you have a general question that does not qualify as an issue, you can ask it at thediscussion forum.
Package: reimportlib
reimportlib: refactored importsreimportlib is used to help with refactored old code
where the imports were moved out, but you still wish
to get legacy packages/classes to work, because you
had serialized them at a point in time but they aren't
available today.Main FeaturesVery compactFew DependenciesUsageimportreimportlib__import__=reimportlib.import_reimportlib.configure()# Reads mappings in .reimport.jsonreimportlib.remap('foo.bar.','examples.foo.')# Notice the . at the endprint(reimportlib.get_remapped_name('foo.bar.C'))# 'examples.foo.C'print(reimportlib.import_module('foo.bar.B'))print(reimportlib.import_from('foo.bar.D','c'))reimportlib.instantiate('foo.bar.D','Foo',*[4],val=True)And then it should automatically be able to import or instantiate
the new classes as required. Do check the provided test.py with
this distribution.
Package: rein
reinlightweight trading platformFree software: Apache Software License 2.0Documentation:https://rein.readthedocs.io.FeaturesTODOOKEX trade control flowsimple clientunit testuse asyncioCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-08-29)First release on PyPI.
Package: reina
ReinaAbout ReinaReInA (Reasoning In AI) is a causal inference platform aimed at estimating heterogeneous treatment effects in observational data. There are various open-source projects that provide convenient causal inference methods, but the current out-of-box packages are limited to local memory for computation. Hence, this project integrates Apache Spark with various machine learning (ML) powered causal inference frameworks, enabling causal analysis on big-data.Installation$ pip install reinaQuick Startimport reina
from pyspark.sql import SparkSession

# Initialize spark session
spark = SparkSession \
            .builder \
            .appName('Meta-Learner-Spark') \
            .getOrCreate()

# Read data locally (without cluster) or from a distributed storage (e.g., Hadoop HDFS, AWS S3) 
data = spark.read \
      .format("csv") \
      .load("your_data.csv") \

# Set up necessary parameters (parameters will vary depending on the method used)
treatment = ['name_of_treatment']
outcome = 'name_of_outcome'

# Setup and fit model
causal_model = reina.iv.TwoStageLeastSquares(data=data, treatment=treatment, outcome=outcome)
causal_model.fit(data=data, treatments=treatment, outcome=outcome,...)

# Get heterogeneous treatment effect
cate, ate = causal_model.effect()
print(cate)
print(ate)Please refer to example notebooks andfull documentationfor more detailed toy demonstrations.Contribution GuidelinesIf you wish to contribute, please refer to ourcontribution guidelines.Any contributions are greatly welcomed and appreciated.References
Package: reinautils
reinautilsThe Utilities included are:sourceParametersParameters (**kargs)A splecial class whos atributes can be referenced as attributs or as
dictionaty keyssourcedevice_by_namedevice_by_name (name:str)Return reference to cuda device by using Part of it’s nameArgs: name: part of the cuda device name (shuuld be distinct)Return: Reference to cuda deviceUpdated: Yuval 12/10/19sourceDatasetCatDatasetCat (*datasets)Concatenate datasets for Pytorch dataloaderThe normal pytorch implementation does it only for raws. this is a
“column” implementationArges: datasets: list of datasets, of the same lengthUpdated: Yuval 12/10/2019InstallpipinstallreinautilsHow to useParametersYou can create a Parameters class from dictparams=Parameters(first=1,second='A')print(params.first)1You can also creat a Parameters class and populate it from a json fileparams2=Parameters().from_json('config_demo.json')print(params2)Parameters:
   path : Parameters:
      data : /workspace/hd/
      tmp : /workspace/hd/tmp/
      features : /workspace/nvme/features/
      train : /workspace/nvme/train/
      models : /workspace/hd/models/
      output : /workspace/hd/outputs/
      test : /workspace/nvme/test/
   platform : myserver
Package: reindeer
REINDEERReindeer ist Python Bibliothek um durch Verwendung des DEER-Frameworks,
CSV-DatensÃ¤tzen mit zusÃ¤tzlichen Informationen aus LinkedData anzureichern.Dazu wird die Funktionenriche([input_file/s], output_file, script)bereitgestellt.
Dieser werden dann eine Liste mit einem oder mehrere Input-Files, ein Path in dem das Resultat
gespeichert werden soll sowie ein Skript fÃ¼r das DEER-Framework Ã¼bergeben. Um die Anwendung zu erleichtern
stehen darÃ¼ber hinaus auch verschiedene bouild-in Skripts zur VerfÃ¼gung. Diese kÃ¶nnen durch die Ã¼bergÃ¤be
eines entsprechenden Strings ausgewÃ¤hlt werden.Installation1.) Deer wie auf GitHub (https://github.com/dice-group/deer) beschrieben installieren.
Es muss Version 2.0.0 oder 2.0.1 verwendet werden.Achtung: Deer kann nur unter Java 9 kompiliert werden. 
AuÃŸerdem musste in der pom.xml `<maven.compiler.target>9</maven.compiler.target>` 
zu `<maven.compiler.target>1.9</maven.compiler.target>` geÃ¤ndert werden.2.) Inenricher.pyden Path vonDeer-Jar-Fileangepassen3.) Python Bibliothek RDFlib installieren (https://github.com/RDFLib/rdflib)pip install rdflibpip install rdflib-jsonldDemo
Package: reindent
reindentPackaged Tools/scripts/reindent from cpythonreindent [-d][-r][-v] [ path ... ]

-d (--dryrun)   Dry run.   Analyze, but don't make any changes to, files.
-r (--recurse)  Recurse.   Search for all .py files in subdirectories too.
-n (--nobackup) No backup. Does not make a ".bak" file before reindenting.
-v (--verbose)  Verbose.   Print informative msgs; else no output.
   (--newline)  Newline.   Specify the newline character to use (CRLF, LF).
                           Default is the same as the original file.
-h (--help)     Help.      Print this usage information and exit.Change Python (.py) files to use 4-space indents and no hard tab characters.
Also trim excess spaces and tabs from ends of lines, and remove empty lines
at the end of files.  Also ensure the last line ends with a newline.If no paths are given on the command line, reindent operates as a filter,
reading a single source file from standard input and writing the transformed
source to standard output.  In this case, the-d,-rand-vflags are
ignored.You can pass one or more file and/or directory paths.  When a directory
path, all .py files within the directory will be examined, and, if the-roption is given, likewise recursively for subdirectories.If output is not to standard output, reindent overwrites files in place,
renaming the originals with a .bak extension.  If it finds nothing to
change, the file is left alone.  If reindent does change a file, the changed
file is a fixed-point for future runs (i.e., running reindent on the
resulting .py file won't change it again).The hard part of reindenting is figuring out what to do with comment
lines.  So long as the input files get a clean bill of health from
tabnanny.py, reindent should do a good job.The backup file is a copy of the one that is being reindented. The ".bak"
file is generated with shutil.copy(), but some corner cases regarding
user/group and permissions could leave the backup file more readable than
you'd prefer. You can always use the--nobackupoption to prevent this.
Package: reine
Reine👸Classification automatisée et organisation interne du discours.InstallationSur votre machine, installez au préalable une version supérieure ou égale à 3.6 de Python. Ensuite, lancez la commande suivante sur votre machine :pip install reineMise à jourVeillez à mettre à jour régulièrement la librairie en lançant la commande suivante :pip install --upgrade reineUtilisationPlus d'informations bientôt.CréditsDéveloppé par Jérémy DEMANGE et inspiré par le logiciel Alceste et par la méthode Reinert inclus par Pierre Ratinaud dans Iramuteq, cette librairie se veut entièrement portée sur Python pour une utilisation plus simple et plus performante.
Package: reinit
ReinitReinit is a python library that allows you to re-initialize a git repository.
One can provide their username, password, new-output-repo name and input-repo url to the cli utility as arguments and the script will clean up the input repo, re-initialize it, and finally push it github.InstallationUse the package managerpipto install foobar.pipinstallreinitUsagereinit--username=[github_username]--password=[github_password]--input_repo=[any_github_repo_url]--output_repo_name=[new_name_for_cloned_repo]ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: reinit-migrations
# Installation*pip install reinit-migrations*# Release 0.1
## Usage
Add reinit_migrations to INSTALLED_APPS*./manage reinit_migrations*It will delete all the applied migrations from the disk and the db. Useful when migration files are too many and it slows down the computer. Reset migrations using this utility and run makemigration then fake all of them.# Release 0.2
## Usage
Add reinit_migrations to INSTALLED_APPS*./manage reinit_migrations*This will delete migrations for all apps from db and disk./manage reinit_migrations –apps app1 app2 app3 …*This will delete migration for app1, app2, app3 only.## Release 0.6
Separate your apps into three lists` DEFAULTS = [django framework apps] THIRD_PARTY_APPS = [third party apps] LOCAL_APPS = [your apps] INSTALLED_APPS = DEFAULT + THIRD_PARTY_APPS + LOCAL_APPS `this app will work on LOCAL_APPS only.As always pull requests are welcome :)
Package: reinmax
ReinMaxBeyond Straight-ThroughStraight-Through•ReinMax•How To Use•Examples•Citation•LicenseReinMaxachievessecond-orderaccuracy and isas fast asthe original Straight-Through, which has first-order accuracy.Straight-Through and How It WorksStraight-Through (as below) bridges discrete variables (y_hard) and back-propagation.y_soft=theta.softmax()# one_hot_multinomial is a non-differentiable functiony_hard=one_hot_multinomial(y_soft)# with straight-through, the derivative of s_hard will# act as if you had `p_soft` in the forwardy_hard=y_soft-y_soft.detach()+y_hardIt is a long-standing mystery on how straight-through works, lefting doubts on many problems like whether we should use:p_soft - p_soft.detach(),(theta/tau).softmax() - (theta/tau).softmax().detach(),or what?Better Performance with Negligible Computation OverheadsWe revealthat Straight-Through works as a special case of the forward Euler method, a numerical methods with first-order accuracy.
Inspired by Heun's Method, a numerical method achieving second-order accuracy without requiring Hession or other second-order derivatives, we propose ReinMax, whichapproximates gradient with second-order accuracy with negligible computation overheads.How to use?installpip install reinmaxenjoyfrom reinmax import reinmax
...

def forward(self, ...):
...- y_soft = theta.softmax()- y_hard = one_hot_multinomial(y_soft)- y_hard = y_soft - y_soft.detach() + y_hard+ y_hard, y_soft = reinmax(theta)...ExamplesPolynomial ProgrammingMNIST-VAEListOpsCitationPlease cite the following papers if you found our model useful. Thanks!Liyuan Liu, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, and Jiawei Han (2020). Understanding the Difficulty of Training Transformers. Proc. 2020 Conf. on Empirical Methods in Natural Language Processing (EMNLP'20).@inproceedings{liu2020admin,
  title={Understanding the Difficulty of Training Transformers},
  author = {Liu, Liyuan and Liu, Xiaodong and Gao, Jianfeng and Chen, Weizhu and Han, Jiawei},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
  year={2020}
}
Package: reinstate-revert-revert
reinstate-revert-revertA tool for cleaning up reverted-revert git commit messages.Simple CaseIt will turnRevert "Revert "Experiment on the flux capacitor""

This reverts commit deadc0dedeadc0dedeadc0dedeadc0dedeadc0de.intoReinstate "Experiment on the flux capacitor"

This reverts commit deadc0dedeadc0dedeadc0dedeadc0dedeadc0de.
And reinstates commit 0d15ea5e0d15ea5e0d15ea5e0d15ea5e0d15ea5e.Complex CaseIf you have gotten yourself into a bind, it will also convert this:Revert "Revert "Revert "Revert "Revert "Experiment on the flux capacitor"""""into this, making it easier to follow the chain:Revert "Experiment on the flux capacitor"

This reverts commit deadc0dedeadc0dedeadc0dedeadc0dedeadc0de.
And reinstates commit 0d15ea5e0d15ea5e0d15ea5e0d15ea5e0d15ea5e.
And reverts 1337beef1337beef1337beef1337beef1337beef.
And reinstates 1337f0011337f0011337f0011337f0011337f001.
And reverts 1337c0de1337c0de1337c0de1337c0de1337c0de.Though once you’re using this as a pre-commit plugin, you should never get to
this case in the first place.InstallationAs a git hookThe simplest way to use this package is as a plugin topre-commit.A sample configuration:# Without default_stages, all hooks run in all stages, which means all your# pre-commit hooks will run in prepare-commit-msg. That is almost certainly# not what you want. This example will run for the default hooks installed.# You might have to adjust it for your environment, if you have changed those# defaults.default_stages:-commitrepos:# […]-repo:https://github.com/erikogan/reinstate-revert-revertrev:v1.0.3hooks:-id:reinstate-revert-revertstages:-prepare-commit-msgBy default, pre-commit does not install a hook for theprepare-commit-msgstage. You probably need to add it for this to work:pre-commit install -t pre-commit -t prepare-commit-msgAs a standalone scriptpip install reinstate-revert-revertSeereinstate-revert-revert --helpfor a full set of options.reinstate-revert-reverttakes log message file names as positional arguments.
Package: reinvent-chemistry
IntroductionThis package contains the chemistry functions for REINVENT.InstallationTo install in REINVENT's environment either install from repo or usepip install reinvent-chemistryfor the latest
official release.DevelopingSetup environmentYou can use Conda to create an environment with all the necessary packages installed.$ conda env create -f reinvent_chemistry
[...]
$ conda activate reinvent_chemistryRun testsThe tests use theunittestpackage testing framework; you can run the tests, located in theunittest_reinventdirectory, by running$ python main_test.pyBuildingBuilding:python setup.py sdist bdist_wheelUpload build to test:python -m twine upload --repository testpypi dist/*Upload build:python -m twine upload dist/*
Package: reinvent-models
IntroductionThis is a package to supply all the models used by REINVENT.DevelopSetup environmentYou can use Conda to create an environment with all the necessary packages installed.$ conda env create -f reinvent_models
[...]
$ conda activate reinvent_modelsRun testsThe tests use theunittestpackage testing framework.  Before you can run the tests make sure that you have created aconfig.jsonfile in thereinvent_models/configsdirectory.  There is an example config in the same directory, which
you can base your own config off of.  Make sure that you setMAIN_TEST_PATHto a non-existent directory; it is where
temporary files will be written during the tests; if it is set to an existing directory, that directory will be removed
once the tests have finished.Once you have created a config file, you can run the tests, located in thetestingdirectory, by running$ python main_test.py
Package: reinvent-scoring
IntroductionThis package contains the scoring functions for REINVENT.InstallationTo install in REINVENT's environment either install from repo or usepip install reinvent-scoringfor the latest
official release.DevelopingSetup environmentYou can use Conda to create an environment with all the necessary packages installed.$ conda env create -f reinvent_scoring
[...]
$ conda activate reinvent_scoringRun testsThe tests use theunittestpackage testing framework.  Before you can run the tests make sure that you have created aconfig.jsonfile in thereinvent_scoring/configsdirectory.  There is an example config in the same directory, which
you can base your own config off of.  Make sure that you setMAIN_TEST_PATHto a non-existent directory; it is where
temporary files will be written during the tests; if it is set to an existing directory, that directory will be removed
once the tests have finished.Some tests require a proprietary OpenEye license; you have to set up a few things to make the tests read your
license.  The simple way is to just set theOE_LICENSEenvironment variable to the path of the file containing the
license.  If you just want to set the license in thereinvent_scoringConda environment, it is a bit more complicated,
but you only have to do it once.(reinvent-scoring) $ cd $CONDA_PREFIX
$ mkdir -p etc/conda/activate.d
$ mkdir -p etc/conda/deactivate.dPut the following inetc/conda/activate.d/env_vars.sh.#!/bin/sh
export OE_LICENSE='</path/to/your/oe_license/file>'And put the following inetc/conda/deactivate.d/env_vars.sh.#!/bin/sh
unset OE_LICENSEOnce you have created the files, deactivate and re-activate the environment, andecho $OE_LICENSEshould output the
path to the license file.Once you have created a config file and configured your environment, you can run the tests, located in theunittest_reinventdirectory, by running$ python main_test.pyBuildingBuilding:python setup.py sdist bdist_wheelUpload build to test:python -m twine upload --repository testpypi dist/*Upload build:python -m twine upload dist/*
Package: reip
REIP - data processing pipelinesInstallpipinstallreipMore detailed installation instructions for NVIDIA Jetson platform can be foundhere.UsageChain InterfaceJavascript-esque method chaining.importreipimportreip.blocksasBimportreip.blocks.audio# record audio and buffer into chunks of 10audio=B.audio.Mic(block_duration=1)audio10s=(audio.to(B.Debug('Audio')).to(B.Rebuffer(duration=10)))# plot a spectrogramspec_img=(audio10s.to(B.audio.Stft()).to(B.Debug('Spec')).to(B.Specshow('plots/{time}.png')))# to wavefilewav=(audio10s.to(B.Debug('Audio 222')).to(B.audio.AudioFile('audio/{time}.wav')))print(reip.default_graph())reip.run()Function InterfaceMimicking a Keras interface.importreip.blocksasB# record audio and buffer into chunks of 10audio=B.audio.Mic(block_duration=1)audio10s=B.Rebuffer(duration=10)(audio)# to spectrogramstft=B.audio.Stft()(audio)specshow=B.Specshow('plots/{time}.png')(spec)# to wavefilewav10s=B.audio.AudioFile('audio/{time}.wav')(audio10s)print(reip.default_graph())reip.run()
Package: rei-preso-plot
rei-preso-plotPython package for drawing graphs for presentation materials withmatplotlib.DescriptionA package for creating graphs suitable for presentation materials withmatplotlib.
The created graph can be directly placed on a presentation slide with a nice design.RequirementPython 3.11.1matplotlib 3.6.3Installationpipinstallrei-preso-plotUsageUsing graph functionsGraph functions.
Can emphasize parts by adding color or embedding legends in line graphs.
When passingmatplotlib.axes.Axesto the argument ax, it returns thematplotlib.axes.Axes. Therefore, using the functions ofmatplotlib, you can specify the drawing range, etc., after these functions.FunctionDescriptionreipresoplot.plot_line_graphLine graph drawing functionreipresoplot.plot_bar_graphBar graph drawing functionCodeGraphfor_line_graph.pyfor_bar_graph.pyUsing matplotlibrc filesBy readingmatplotlibrcfiles, you can customize thematplotlibstyle as a whole, i.e. without specifying it withrcParams, etc.
For details, seeMatplotlib documentation.FunctionDescriptionreipresoplot.use_line_graph_style_as_global_settingsUsematplotlibrcfile for line graphs as global settingsreipresoplot.use_bar_graph_style_as_global_settingsUsematplotlibrcfile for bar graphs as global settingsreipresoplot.get_line_graph_mpl_style_pathGet the path to thematplotlibrcfile for line graphsreipresoplot.get_bar_graph_mpl_style_pathGet the path to thematplotlibrcfile for bar graphsIf you want to apply the style globally to the entire graph plot, usereipresoplot.use_XXX_graph_style_as_global_settings.frommatplotlibimportpyplotaspltfromreipresoplotimportuse_line_graph_style_as_global_settingsuse_line_graph_style_as_global_settings()plt.plot(x,y)plt.show()If you want to use it temporarily, use a context manager.fromreipresoplotimportget_line_graph_mpl_style_pathmpl_style_path:str=get_line_graph_mpl_style_path()withplt.style.context(mpl_style_path):plt.plot(x,y)plt.show()Line graph changesPlease refer to the code atexamples/using_matplotlibrc_files/for_line_graph.pyfor line graph changes.BeforeAfterBar graph changesPlease refer to the code atexamples/using_matplotlibrc_files/for_bar_graph.pyfor bar graph changes.BeforeAfterTestYou can test graph drawing with Pytest.pytestAuthorNakuReiLicense© 2023 NakuReiThis software is released under the MIT License, see LICENSE.