Saravanan666/pypi-org huggingface dataset:
Package: rl-microgrid-managers
RL_MGM (reinforcement learning micro-grid managers) Module:A set of modules that can be used to aid in simulating a micro-grid environment for training reinforcement learning based energy management policy generators. The two main modules are the _DATAGENERATORS and MG_Managers. They are described in theGeneral informationsection below.Table of ContentsRL_MGM (reinforcement learning micro-grid managers) Module:Table of ContentsGeneral Info* _DATAGENERATORS: set of tools to create stochastic data generators that represent hourly load profiles and month/hour PV MW outputs.* MG_Managers: set of RL networks that can be trained on simulated microgrid data to generate energy management policies.* MG_Environments: tools that take a RL agent as a manager and simulate using them in the environmentTechnologiesInstallationUsageRL agent generationData Generation ToolsLicenseGeneral InfoThis rl-migrogrid-mangers (reinforcement learning) module contains a set of submodules that define RL networks for use as energy management agents, tools to generate hourly building load and various MW sizes of PV output based on the month and hour based on fitted distributions. The modules can be defined as follows:* _DATAGENERATORS: set of tools to create stochastic data generators that represent hourly load profiles and month/hour PV MW outputs.* MG_Managers: set of RL networks that can be trained on simulated microgrid data to generate energy management policies.* MG_Environments: tools that take a RL agent as a manager and simulate using them in the environmentTechnologiesList the technologies used in this project. For example:Python: 3.8numpy: 1.26.3pandas: 2.1.4Joblib: 1.3.2matplotlib: 3.8.2torch: 2.1.2distfit: 1.7.3Installationpip install rl-microgrid-managersUsageRL agent generationData Generation ToolsLicenseMIT LicenseCopyright (c) <2023>Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice (including the next paragraph) shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: rlmolecule
rlmoleculeAboutA library for general-purpose material and molecular optimization using
AlphaZero-style reinforcement learning.Code under development as part of the End-to-End Optimization for Battery
Materials and Molecules by Combining Graph Neural Networks and Reinforcement
Learning project at the National Renewable Energy Laboratory (NREL), Colorado
School of Mines (CSU), and Colorado State University (CSU). Funding provided by
the Advanced Research Projects Agency–Energy (ARPA-E)'sDIFFERENTIATE program.For more information, see our publication:S. V., S. S., Law, J. N., Tripp, C. E., Duplyakin, D., Skordilis, E., Biagioni,
D., Paton, R. S., & St. John, P. C. (2022). Multi-objective goal-directed
optimization of de novo stable organic radicals for aqueous redox flow
batteries. Nature Machine Intelligence.10.1038/s42256-022-00506-3Installationpip install rlmoleculeRunning in AWS Ray clustersRefer to the instructions available in:devtools/aws/README.md
Package: rl-musician
RL-MusicianOverviewAs of now, this is a proof-of-concept for music composition with reinforcement learning solely. Here, creation offifth species counterpointis considered and environment is based on a special data structure that represents musical piece with pre-defined cantus firmus. An action is adding a new note to a counterpoint line, an episode is finished when counterpoint duration becomes equal to that of cantus firmus, and reward is determined by applying evaluational rules to the resulting piece.Some pieces generated with this package are uploaded to a publicly availablecloud storage. A cantus firmus attributed toFuxis used in all of them.To find more details, look ata draft of a paper. Also, if you are interested in algorithmic composition without too strict limitations of species counterpoint, look at the tools namedGeniartorandDodecaphony.InstallationTo install a stable version, run:pipinstallrl-musicianUsageTo create a reward-maximizing musical piece and some its variations, run:python-mrlmusician[-cpath_to_your_config]Default configis used if-cargument is not passed. Search of optimal piece with these default settings takes about 5 minutes on a CPU of a regular laptop. Before creating a new config, it might be useful to look atan example with explanations.If you are on Mac OS, please check thatparallelism is enabled.Generated pieces are stored in a directory specified in the config. For each piece, there is a nested directory that contains:MIDI file;WAV file;Events file insinethesizerTSV format;PDF file with sheet music and its Lilypond source.
Package: rload
rloadA simple file monitoring with code reloader for Python 3.ExamplefromrloadimportRload,EventTyperload=Rload(__name__,ignored_paths=['__pycache__'])@rload.on('change')defhandle_change(type:EventType,data:Dict):print(type,data)@rload.on('reload')defhandle_reload(data):print('Reloading',data)@rload.on('reloaded')defhandle_reloaded(data):print('Reloaded',data)deftest():print('This is a test')if__name__=="__main__":rload.run(target=test)
Package: rlock
Copyright (c) 2019 Hays HuttonPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.Description: # A [RedLock](https://redlock.io/) API Clientrlockis a python package which integrates with parts of the API of the
RedLock Public Cloud Security service.`bash from rlock.api import Client>>>c=Client("scott@oracle.com","tiger","https://api.redlock.io/")>>>forpolicy in c.policies.take(5): ...print(f"{policy.policyId}:{policy.name}")43c42760-5283-4bc4-ac43-a80e58c4139f:AWS S3 bucket has global view ACL permissions enabled472e08a2-c741-43eb-a3ca-e2f5cd275cf7:Azure Network Security Group allows FTP (TCP Port 21)91c941aa-d110-4b33-9934-aadd86b1a4d9:AWS Redshift database does not have audit logging enabledf5b4b962-e053-4e73-94d2-c21bd2520a0d:AWS ElastiCache cluster not associated with VPCfe81b03a-c602-4b16-8ae9-973724c1adae:GCP Kubernetes Engine Clusters web UI/Dashboard is set to Enabled `## Installationpip install rlock## Documentationhttps://rlock.readthedocs.org/Platform: UNKNOWN
Requires-Python: ~=3.7
Description-Content-Type: text/markdown
Package: rlocker-expiryaddon
Resource Locker Expiry AddonThis is an addon application (plugin) to rlockerGetting Started:Starting first steps this Addon for Resource Locker[0.0.1] - 2022-07-11Initial build[0.0.2] - 2022-07-13Adding templates and fixing the logic of expiry[0.0.3] - 2022-07-19Design the final functionalities of expiry addon[0.0.4] - 2022-07-20Last code collection - Add templates to MANIFEST.in[0.0.5] - 2022-07-20Recursively include all the files inside the templates directory[0.0.6] - 2022-07-26Changes for adding the addon data to the API of rlocker[0.0.7] - 2022-31-07Changed the directory of templates from /templates to /expiry_addon_/templates/expiry_addon/*[0.1.0] - 2022-03-08Configuring for grabbing the templates to the package[0.1.1] - 2022-03-08Allowing auto migrate when the app loads[0.1.2] - 2022-07-08Fix LockableResource.resourceexpirypolicy attribute not exist
Package: rlockertools
Useful tools to work withRlocker project APIQuick StartNormal installationpipinstallrlockertoolsSetupgitclonehttps://github.com/red-hat-storage/rlockertools.gitcdrlockertools
python-mvenvvenvsourcevenv/bin/activate
pipinstall./
rlock--helpusage: rlock [-h] --server-url SERVER_URL --token TOKEN [--release] [--lock] [--resume-on-connection-error] [--signoff SIGNOFF]
[--priority PRIORITY] [--search-string SEARCH_STRING] [--link LINK] [--interval INTERVAL] [--attempts ATTEMPTS]optional arguments:
-h, --help            show this help message and exit
--server-url SERVER_URL
The URL of the Resource Locker Server
--token TOKEN         Token of the user that creates API calls
--release             Use this argument to release a resource
--lock                Use this argument to lock a resource
--resume-on-connection-error
Use this argument in case you don't want to break queue execution in the middle of waiting for queue status
being FINISHED
--signoff SIGNOFF     Use this when lock=True, locking a resource requires signoff
--priority PRIORITY   Use this when lock=True, specify the level of priority the resource should be locked
--search-string SEARCH_STRING
Use this when lock=True, specify the lable or the name of the lockable resource
--link LINK           Use this when lock=True, specify the link of the CI/CD pipeline that locks the resource
--interval INTERVAL   Use this when lock=True, how many seconds to wait between each call while checking for a free resource
--attempts ATTEMPTS   Use this when lock=True, how many times to create an API call that will check for a free resource## Usage Examples

### To add a queue for locking a resource

```bash
rlock --lock --server-url=your.rlocker.instance.com --token=YOURTOKEN --search-string=nameorlabel --signoff=YOURUNIQUESIGNOFF --priority=3 --interval=15 --attempts=15To release a locked resource (filtration by signoff only)rlock--release--server-url=your.rlocker.instance.com--token=YOURTOKEN--signoff=YOURUNIQUESIGNOFFChange log[0.3.8] - 2021-03-15Initial build[0.3.9] - 2021-07-15Move to new repo + bug fix[0.3.10] - 2021-07-15Fix build issue[0.3.11] - 2021-07-19Suppress the logs
Show safe errors when beating a queue returns !=200 status code[0.4] - 2022-04-12Allow **datakwargs in the change queue method.
This is necessary to allow modification of the data section once a queue is changing[0.4.1] - 2022-04-13Show more output in get_queue method in case of non 200 status code[0.4.2] - 2022-04-17Address item assignment for str, should be fixed to a dictionary in change queue method[0.4.3] - 2022-04-28Adding log file in order to track after the queue id number once executing from the rlock entrypoint.
Reason: In order to to API calls about the queue, we need to save it in some readable location[0.4.4] - 2022-06-09Adding support for--checkargument which will return if the given--search-stringhas available resources
by label or a name. Commit number: #15Usage example:rlock --check --search-string=aws-east-2 --token=$token --server-url=$SERVER_URL[0.4.5] - 2022-07-26Fixing JSON parsing for the data section in a queue, which was not fixed totally in 0.4.2
Package: rlog-async
rlog-asyncFork ofhttps://github.com/lobziik/rlogDifference: communications with redis in another thread, so log in unavailable redis doesn't affect your app.InstallationFrom pypi:$ pip install rlog-asyncFrom source:$ sudo python setup.py installUsage>>> from rlog import RedisHandler
>>> logger = logging.getLogger()
>>> logger.addHandler(RedisHandler(channel='test'))
>>> logger.warning("Spam!")
>>> logger.error("Eggs!")Redis clients subscribed totestwill get a json log record by default.RedisHandlerandRedisListHandleralso accepted all redis client settings as kwargs. More info about client settings
you may find inredis-pydocumentation.Custom formatters also supported, handlers accept this asformatterkeyword argument. JSONFormatter from this package
used as default.You can use theredis-clishell that comes withredisto test this.  At
the shell prompt, typesubscribe my:channel(replacing with the channel
name you choose, of course). You will see subsequent log data printed in the
shell.Also you can use it with Django:LOGGING={'version':1,'disable_existing_loggers':True,'handlers':{'redis':{'level':'DEBUG','class':'rlog_async.RedisHandler','host':'localhost','password':'redis_password','port':6379,'channel':'my_amazing_logs'}},'loggers':{'django':{'level':'INFO','handlers':['redis'],'propagate':True,},}}You can also simply use it with logstash.
Package: rlog-generator
Build StatusRandom Log GeneratorGenerator of random logs for multiple types of technologies.This tool can generate all kinds of logs starting from templates. You
should create a pattern file in YAML format foreach log that you want to
generate, like inconf/patternsexamples.If more than one patterns are specified in patterns folder, all logs are
generated in parallel. It’s possible to generate 100 logs in parallel.InstallClone repositorygit clone https://github.com/WuerthPhoenix/log-generator.gitand install withsetup.py:$ cd log-generator

$ python setup.py installor usepip:$ pip install rlog-generatorPattern fileA pattern file has many parameters.ParametersDescriptionsnamename of logenabledenable/disable this patternpathpath where store the logepsnumber of logs per seconds that will
be generatecorrectioneps correction percentagetime_periodhow many seconds the generating is
activegenerator_typeyou can choose which generator use.
The common value istemplate,
that generate the logs from a
templateexampleslogs of examplestemplatetemplate to use to generate logsfieldsfields used in templateWe can have two kinds of fields: -list: the list fields are used to
generate random values from a given list -func: the func fields
enable functions to generate the random values.Thefuncfields start withfunc_and then have the name of
function. It can also have parameters.Thefuncdeveloped are: -func_randip: generate a random ip
address -func_randint: generate a random integer frommintomaxFor more details see the examples in folderconf/patterns.If you want to contribute with real templates, add them inpatternsfolder.Command lineThe installation stores on system therlog-generatorcommand line.$ rlog-generator --help
Usage: rlog-generator [OPTIONS]

  Random Logs Generator Tool.

Options:
  -p, --patterns TEXT             Path all log patterns files (only *.yml)
                                  [default: ~/.config/rlog_generator/patterns]
  -m, --max-concur-req INTEGER    Max concurrent logs generating  [default: 10]
  -l, --log-level [CRITICAL|ERROR|WARNING|INFO|DEBUG|NOTSET]
                                  Log level on stdout  [default: WARNING]
  --progress-bar / --no-progress-bar
                                  Enable/Disable progress bar  [default: False]
  --help                          Show this message and exit.FeaturesRandom logging from templateTemplate can be a list of more formatsTODOGenerate logs from raw examplesApache 2 Open Source LicenseThis tool can be downloaded, used, and modified free of charge. It is
available under the Apache 2 license.History0.1.0 (2019-05-05)First release.
Package: rlogger
rloggerA convenient encapsulation of Python loggingInstallpipinstallrloggerUsefromrloggerimportRLogger# Set log file pathRLogger.init('file.log',enable_screen=True,log_level='INFO',max_bytes=1e8,backup_count=1)RLogger.log('your message',RLogger.INFO)RLogger.log('your message',RLogger.DEBUG)RLogger.log('your message',RLogger.WARNING)RLogger.log('your message',RLogger.ERROR)# Close log when you not needRLogger.close()RLogger.log('your message',RLogger.ERROR)# Will not print
Package: r-logger
r_loggera customized way to use logusage:from str:fromr_loggerimportRLoggerr_main_logger=RLogger('logs')r_main_logger.info('info')r_main_logger.debug('debug')r_main_logger.error('error')r_main_logger.critical('critical')r_main_logger.warning('warning')try:raiseException('exception details')exceptException:r_main_logger.exception('exception')
Package: rlogging
rLoggingModule with frequently used functions for advanced loggingFunctionalityFormattersJsonFormatter- Convert log to jsonElkFormatter- Convert log to json for ELK parsing (one-dimensional array)AdaptersHttpLoggerAdapterHttpLoggerAdapterDjangoDjangoLoggerAdapterLoggingMiddlewareUsagepip install rloggingPythonNormal logging setup with new classesDjango# settings.pyINSTALLED_APPS=[...'rlogging.integration.django',...]MIDDLEWARE=[...'rlogging.integration.django.middleware.LoggingMiddleware',...]LOGGING=...FastAPIaiogram
Package: rl-opts
RL-OptSReinforcement Learning of Optimal Search strategiesThis library builds the necessary tools needed to study, replicate and
develop the results of the paper:“Optimal foraging strategies can be
learned and outperform Lévy walks”byG. Muñoz-Gil, A. López-Incera, L. J. FidererandH. J. Briegel.InstallationYou can access all these tools installing the python packagerl_optsvia Pypi:pipinstallrl-optsYou can also opt for cloning thesource
repositoryand executing the
following on the parent folder you just cloned the repo:pipinstall-erl_optsThis will install both the library and the necessary packages.TutorialsWe have prepared a series of tutorials to guide you through the most
important functionalities of the package. You can find them in theTutorials
folderof the Github repository or in the Tutorials tab of ourwebpage, with notebooks that
will help you navigate the package as well as reproducing the results of
our paper via minimal examples. In particular, we have three tutorials:Reinforcement
learning: shows how to train a RL agent based on Projective
Simulation agents to search targets in randomly distributed
environments as the ones considered in our paper.Imitation
learning: shows how to train a RL agent to imitate the policy of
an expert equipped with a pre-trained policy. The latter is based on
the benchmark strategies common in the literature.Benchmarks: shows how launch various benchmark strategies with which to
compare the trained RL agents.Package structureThe package contains a set of modules for:Reinforcement
learning framework (rl_opts.rl_framework): building foraging
environments as well as the RL agents moving on them.Learning
and benchmarking (rl_opts.learn_and_bench): training RL agents
as well as benchmarking them w.r.t. to known foraging strategies.Imitation
learning (rl_opts.imitation): training RL agents in imitation
schemes via foraging experts.Analytical
functions (rl_opts.analytics): builiding analytical functions
for step length distributions as well as tranforming these to foraging
policies.Utils
(rl_opts.utils): helpers used throughout the package.CiteWe kindly ask you to cite our paper if any of the previous material was
useful for your work, here is the bibtex info:@article{munoz2023optimal,
  doi ={10.48550/ARXIV.2303.06050},  
  url ={https://arxiv.org/abs/2303.06050},  
  author ={Muñoz-Gil, Gorka and López-Incera, Andrea and Fiderer, Lukas J. and Briegel, Hans J.},  
  title ={Optimal foraging strategies can be learned and outperform Lévy walks},  
  publisher ={arXiv},  
  archivePrefix ={arXiv},
  eprint ={2303.06050},
  primaryClass ={cond-mat.stat-mech},  
  year ={2023},}
Package: rlp
pyrlpA package for Recursive Length Prefix encoding and decodingRead more in thedocumentation on ReadTheDocs.View the change log.Quickstartpython-mpipinstallrlpDeveloper SetupIf you would like to hack on pyrlp, please check out theSnake Charmers
Tactical Manualfor information on how we do:TestingPull RequestsDocumentationWe usepre-committo maintain consistent code style. Once
installed, it will run automatically with every commit. You can also run it manually
withmake lint. If you need to make a commit that skips thepre-commitchecks, you
can do so withgit commit --no-verify.Development Environment SetupYou can set up your dev environment with:gitclonegit@github.com:ethereum/pyrlp.gitcdpyrlp
virtualenv-ppython3venv
.venv/bin/activate
python-mpipinstall-e".[dev]"pre-commitinstallRelease setupTo release a new version:makereleasebump=$$VERSION_PART_TO_BUMP$$How to bumpversionThe version format for this repo is{major}.{minor}.{patch}for stable, and{major}.{minor}.{patch}-{stage}.{devnum}for unstable (stagecan be alpha or beta).To issue the next version in line, specify which part to bump,
likemake release bump=minorormake release bump=devnum. This is typically done from the
main branch, except when releasing a beta (in which case the beta is released from main,
and the previous stable branch is released from said branch).If you are in a beta version,make release bump=stagewill switch to a stable.To issue an unstable version when the current version is stable, specify the
new version explicitly, likemake release bump="--new-version 4.0.0-alpha.1 devnum"
Package: rlp-cython
A Python implementation of Recursive Length Prefix encoding (RLP) with a
very fast cython back-end. You can find the original python based RLPhere, the specification of the
standard in theEthereum
wikiand the
documentation of this package onreadthedocs.Release setupFor Debian-like systems:apt install pandocTo release a new version:makereleasebump=$$VERSION_PART_TO_BUMP$$How to bumpversionThe version format for this repo is{major}.{minor}.{patch}for
stable, and{major}.{minor}.{patch}-{stage}.{devnum}for unstable
(stagecan be alpha or beta).To issue the next version in line, specify which part to bump, likemake release bump=minorormake release bump=devnum.If you are in a beta version,make release bump=stagewill switch to
a stable.To issue an unstable version when the current version is stable, specify
the new version explicitly, likemake releasebump="--new-version4.0.0-alpha.1devnum"
Package: rlprop
propprop is a library of Reinforcment Learning agents implemented in pytorch.AlgorithmsModelPolicyDQNModel-FreeOff-PolicyA2CModel-FreeOn-PolicyDQNDeep Q-Learningis a variant of Q-learning with a deep neural network used for estimating Q-values (hence DQN; Deep Q-Network).Both DQN and DDQN (Double DQN) are implemented.A2CAdvantage Actor Criticis a variant of Actor-Critic that:Uses a neural network to approximate a policy and a value function.Computes the advantage of an action to scale the computed gradients. This acts as a vote of confidence (or skepticism) on actions produced by the actor.
Package: rlpy
RLPy - Reinforment Learning FrameworkRLPy is a framework to conduct sequential decision making experiments. The
current focus of this project lies on value-function-based reinforcement
learning. The project is distributed under the 3-Clause BSD License.Important LinksOfficial source code repository:http://github.com/rlpy/rlpyBitbucket mirror:http://bitbucket.org/rlpy/rlpyDocumentation:http://rlpy.readthedocs.orgIssue tracker:https://github.com/rlpy/rlpy/issuesInstallInstallation instructions can be found athttp://rlpy.readthedocs.org/en/latest/install.html