Saravanan666/pypi-org huggingface dataset:
Package: ramona
Ramona is an enterprise-grade runtime supervisor that allows controlling and monitoring software programs during their execution life cycle.It is primarily meant to be blended into your project source code set to provide supervisor/console functionality of init.d-like start/stop control, task frontend (e.g. unit/functional/performance test launcher) and other command-line oriented features. It should ideally represent the only executable of the project - kind of ‘dispatcher’ to rest of a project. It is design the way that you should be able to extend that easily if needed (e.g. to include your own tasks).For more info seehttp://ateska.github.com/ramona/Documentation can be found here:http://ateska.github.com/ramona/manual
Package: ramorm
ramorm [Alpha]Model based ORM in RAM. Made for performance. Please do not store critical data (yet)Installationpip install ramormUsagefromramormimportorm,modeldb=orm.Orm('my_test_database')classVehicle(model.Model):name=model.TextField()wheels=model.IntegerField(default=4)max_speed=model.IntegerField(default=100)sports_car=Vehicle(name='Aventador',max_speed=230)print(sports_car.name,sports_car.wheels,sports_car.max_speed)>>'Aventador'4230bicycle=Vehicle(name='Bicycle',wheels=2,max_speed=50)print(bicycle.wheels,bicycle.max_speed)>>'Bicycle'250Add your model based objects to database usingpushfunction, you can pass one or multiple objects at oncedb.push(sports_car,bicycle)Retrieving single objects from database is possible usinggetfunctionprint(db.get(Vehicle,wheels=4).name)>>'Aventador'print(db.get(Vehicle,name='Bicycle').max_speed>>50For filtering numerical parameters you can use__gt(greater),__gte(greater or equal),__lt(lower),__lte(lower or equal)print(db.get(Vehicle,max_speed__gt=70).name)>>'Aventador'For retrieving multiple objects at once usefilterforvehicleindb.filter(Vehicle,max_speed__gte=10):print(vehicle.name,vehicle.wheels,vehicle.max_speed)>>'Aventador'4230>>'Bicycle'250For deleting objects from db usedeletefunction. ReturnsTrueif changes were made to databasedb.delete(Vehicle,name='Bicycle')>>Truedb.delete(Vehicle,name='Starship')>>FalseIf you want completely delete all data in your database usedrop.Returns array of objects in db (empty)db.drop()>>[]Coming soonDelete objects using.delete()Order byBackup to filePyPI packageIntegration with postgresql, redis
Package: ramos
Generic backend poolSetuppipinstallramosDevelopment setupmakeinstallUsageimportramosramos.configure(pools={'backend_type':['path.to.backend_a','path.to.backend_b',]})IntegrationsRamos can usesDjangoorSimple Settingsto get backends
configurations if set settings.POOL_OF_RAMOS:POOL_OF_RAMOS={'backend_type':['path.to.backend_a','path.to.backend_b',]}Backend Implementationsfromramos.mixinsimportThreadSafeCreateMixinclassBackendA(ThreadSafeCreateMixin):id='backend_a'defsay(self):return'A'classBackendB(ThreadSafeCreateMixin):id='backend_b'defsay(self):return'B'Backend Poolfromramos.poolimportBackendPoolclassBackendTypePool(BackendPool)backend_type='backend_type'backends=BackendTypePool.all()forbackendinbackends:print(backend.say())# backend_a = BackendTypePool.get('backend_a')# backend_b = BackendTypePool.get('backend_b')
Package: ramp4
INTRODUCTIONA simple library to make mp4 movies with matplotlib.pyplot. It use RAM instead of disk storage for the temporary images.INSTALLATIONpipinstallramp4GET STARTEDGet an instance of ramp4.Add image with matplotlib using the ‘add’ method.Render the video using the ‘render’ method.DoneEXAMPLE 1importnumpyasnpimportmatplotlib.pyplotaspltfromramp4importramp4X=np.linspace(0,5,250)movie=ramp4()foriinnp.linspace(0,3.14,200):plt.plot(X,np.cos(X+i))plt.xlim(0,5)plt.ylim(-1,1)plt.xlabel("x")plt.ylabel("cos(x+t)")movie.add(dpi=200)plt.close()movie.render(outfile="video_example_2a.mp4",fps=50,close=False)movie.render(outfile="video_example_2b.mp4",fps=100)EXAMPLE 2importnumpyasnpimportmatplotlib.pyplotaspltfromramp4importramp4X=np.linspace(0,5,250)movie=ramp4()foriinnp.linspace(0,3.14,200):fig,ax=plt.subplots(1,1)ax.plot(X,np.sin(X+i))ax.set_xlim(0,5)ax.set_ylim(-1,1)ax.set_xlabel("x")ax.set_ylabel("sin(x+t)")movie.add(fig=fig,dpi=200)plt.close(fig=fig)movie.render(outfile="video_example1.mp4",fps=50)
Package: rampante
🐎 RampanteA fancy and opinionated nanoframework for microservices.InstallationpipinstallrampanteHow to usesubscribe_onfromrampanteimportsubscribe_on# The function should accept 3 params# queue_name, for example could be "user.subscribed"# data is a dictionary, it's a msgpacked message sent to NATS# app, aiohttp app instance (in case)@subscribe_on("user.subscribed")asyncdefsend_a_message(queue_name,data,app):log.info("Event received!")@subscribe_on("user.subscribed","user.created")asyncdefsend_another_message(queue_name,data,app):log.info("Event received!")ExampleCheck the examples inside the folder!You need a nats-streaming:0.6.0 running, check the Makefile :)The nameRampante means “rampant” in Italian.Why NATS?It’s written in Go and seems working really well!To Doadd retry/logic when tasks failPull requests are encouraged!LicenseApache 2.0
Package: ramp-database
This package contains the ORM model for the RAMP database.Set up of a PostgreSQL databasecreate db directory and initialise the PostgreSQL database:mkdir postgres_dbs
initdb postgres_dbsstart the PostgreSQL engine:pg_ctl -D postgres_dbs -l postgres_dbs/logfile startcreate a user<db_user>and set a password<db_password>:createuser --pwprompt <db_user>create the database called<db_name>for that user:createdb --owner=<db_user> <db_name>
Package: ramps
Bravo!  You have received a Medical Diploma in "ramps" fromthe Orbital Convergence University International Air and Water Embassy of the Tangerine Planet.You are now officially certified to include "ramps" in your practice.rampsThis is an empty module intented as a placeholder
for use for offline modules.

This module is meant as a way of reserving
the name "ramps" for everyone's use in non pypi 
or otherwise python3 enterprizes. 

There are zero plans to use this name
for any purpose besides giving everyone
a module name for use in non pypi enterprizes.

This claim should prevent accidental 
publishing to pypi and ensure that no 
disrespect to other calculator builders
occurs by using the same name for one 
of your non pypi enterprizes as the name of 
an actual pypi enterprize.
Package: ramps-majestic
Bravo!  You have received a Medical Diploma fromthe Orbital Convergence University International Air and Water Embassy of the Tangerine Planet.You are now officially certified to include this module in your practice.ramps_majesticdescriptioninstallpip install ramps_majestic--usagefrom datetime import datetime
import json
import pprint

import pandas
import rich	

import ramps_majestic
import ramps_majestic.victory_multiplier.purchase_treasure_at_inclines as purchase_treasure_at_inclines_VM	
import ramps_majestic.victory_multiplier.purchase_treasure_over_span as purchase_treasure_over_span_VM
import ramps_majestic.example_data.read as read_example_data

trend = read_example_data.start ("yahoo-finance--BTC-USD.CSV")	
trend_DF = pandas.DataFrame (trend)	

enhanced_trend_DF = ramps_majestic.calc (
	trend_DF,
	period = 14,
	multiplier = 3
)
enhanced_list = enhanced_trend_DF.to_dict ('records')


'''
	This calculates the multipliers
'''
treasure_at_inclines_VM = purchase_treasure_at_inclines_VM.calc (
	enhanced_trend_DF,
	include_last_change = True
)

rich.print_json (data = treasure_at_inclines_VM ["relevant"])	

open_price_at_spans_VM = purchase_treasure_over_span_VM.calc (enhanced_trend_DF)
print ("open_price_at_spans_VM:", open_price_at_spans_VM)
print ("treasure_at_inclines_VM:", treasure_at_inclines_VM ["treasure purchase victory multiplier"])


ramps_majestic.chart_the_data (
	enhanced_trend_DF,
	treasure_at_inclines_VM
)
Package: rampy
RamPy=======Rampy is a Python library that aims at helping processing spectroscopic data, such as Raman, Infrared or XAS spectra.Documentation:http://charlesll.github.io/rampy/html/index.htmlExampleshttps://github.com/charlesll/rampy/tree/master/examplesSource code:https://github.com/charlesll/rampy/tree/master/rampyContributing:https://github.com/charlesll/rampy/blob/master/CONTRIBUTING.mdBug reports:https://github.com/charlesll/rampy/issuesContactlelosq@ipgp.frRampy offers various functions to, for instance, subtract baselines, resample and smooth spectra... It aims at facilitating the use of Python in processing spectroscopic data. It integrates within a workflow that uses Numpy/Scipy/Matplotlib as well as optimisation libraries such as lmfit, emcee or PyMC3, for instance.See the documentation for more information.
Package: ramses
#Ramses[![Build Status](https://travis-ci.org/ramses-tech/ramses.svg?branch=master)](https://travis-ci.org/ramses-tech/ramses)
[![Documentation](https://readthedocs.org/projects/ramses/badge/?version=stable)](http://ramses.readthedocs.org)
[![Join the chat athttps://gitter.im/ramses-tech/ramses](https://img.shields.io/badge/gitter-join%20chat%20%E2%86%92-brightgreen.svg)](https://gitter.im/ramses-tech/ramses)Ramses is a framework that generates a RESTful API using [RAML](http://raml.org). It uses Pyramid and [Nefertari](https://github.com/ramses-tech/nefertari) which provides Elasticsearch / Posgres / MongoDB / Your Data Store&#8482; -powered views.Looking to get started quickly? You can take a look at the [“Getting Started” guide](https://ramses.readthedocs.org/en/stable/getting_started.html).
Package: ramses_rf
Overviewramses_rfis a client library/CLI utility used to interface with some Honeywell-compatible HVAC & CH/DHW systems that use 868MHz RF, such as:(Heat)evohome,Sundial,Hometronic,Chronotherm(HVAC)Itho,Orcon,NuaireIt requires a USB-to-RF device, either a Honeywell HGI80 (somewhat rare, expensive) or something running theevofw3firmware, such as the one fromhere.It does three things:decodes RAMSES II-compatible packets and converts them into useful JSONbuilds a picture (schema, config & state) of evohome-compatible CH/DHW systems - either passively (by eavesdropping), or actively (probing)allows you to send commands to CH/DHW and HVAC systems, or monitor for state changesallows you to emulate some hardware devicesFor CH/DHW, the simplest way to know if it will work with your system is to identify the box connected to your boiler/HVAC appliance as one of:R8810A: OpenTherm BridgeBDR91A: Wireless Relay (also BDR91T)HC60NG: Wireless Relay (older hardware)Other systems may well work, such as some Itho Dallderop HVAC systems, use this protocol, YMMV.It includes a CLI and can be used as a standalone tool, but also is used as a client library by:ramses_cc, a Home Assistant integrationevohome-Listener, an MQTT gatewayInstallationgit clone https://github.com/zxdavb/ramses_rf
cd ramses_rf
pip install -r requirements.txtThe CLI is called client.py:python client.py monitor /dev/ttyUSB0 -o packet.log
Package: ramsey
RamseyProbabilistic deep learning using JAXAboutRamsey is a library for probabilistic modelling usingJAX,FlaxandNumPyro.
It offers high quality implementations of neural processes, Gaussian processes, Bayesian time series and state-space models, clustering processes,
and everything else Bayesian.Ramsey makes use ofFlax`s module system for models with trainable parameters (such as neural or Gaussian processes),NumPyro for models where parameters are endowed with prior distributions (such as Gaussian processes, Bayesian neural networks, ARMA models)and is hence aimed at being fully compatible with both of them.Example usageYou can, for instance, construct a simple neural process like this:fromjaximportrandomasjrfromramseyimportNPfromramsey.nnimportMLPfromramsey.dataimportsample_from_sine_functiondefget_neural_process():dim=128np=NP(decoder=MLP([dim]*3+[2]),latent_encoder=(MLP([dim]*3),MLP([dim,dim*2])))returnnpkey=jr.PRNGKey(23)data=sample_from_sine_function(key)neural_process=get_neural_process()params=neural_process.init(key,x_context=data.x,y_context=data.y,x_target=data.x)The neural process takes a decoder and a set of two latent encoders as arguments. All of these are typically MLPs, but
Ramsey is flexible enough that you can change them, for instance, to CNNs or RNNs. Once the model is defined, you can initialize
its parameters just like in Flax.InstallationTo install from PyPI, call:pipinstallramseyTo install the latest GitHub , just call the following on the
command line:pipinstallgit+https://github.com/ramsey-devs/ramsey@<RELEASE>See also the installation instructions forJAX, if
you plan to use Ramsey on GPU/TPU.ContributingContributions in the form of pull requests are more than welcome. A good way to start is to check out issues labelled"good first issue".In order to contribute:Install Ramsey and dev dependencies viapip install -e '.[dev]',test your contribution/implementation by callingtoxon the (Unix) command line before submitting a PR.Why RamseyJust as the names of other probabilistic languages are inspired by researchers in the field
(e.g., Stan, Edward, Turing), Ramsey takes its name from one of my favourite philosophers/mathematicians,Frank Ramsey.
Package: ran
ran安装pipinstallran示例fsfromranimportfs# 读取文件内容content=fs.load('path')mm自动重载修改的模块。fromran.mmimportModuleReloadermr=ModuleReloader()mr.watch()idmk生成 IDfromran.idmkimportIdentifierMakeridmkr=IdentifierMaker(1)foriinrange(1000):print(idmkr.make())
Package: ran2
RAN2Python port of the ran2 random number generator from FORTRAN NUMERICAL RECIPES.
Long period (> 2 × 1018) random number generator of L’Ecuyer with Bays-Durham shuffle
and added safeguards. Returns a uniform random deviate between 0.0 and 1.0 (exclusive of
the endpoint values). Call with iseed, a “large” negative number to initialize.EXAMPLE:Create a random seed. Must be a “large” negative numberiseed = -12345678Initialize the wrapper with that iseed.
Each class instance can use a different seed to get a different sequencex = ran2_wrapper(iseed)Call the random number generatorx.ran2()
Package: rancher-client
rancher-clientPython Rancher Client with Async SupportThis library is a fork from Rancher's officialclient-pythonto enable support of:Environment Variables for Service UsageAsync [WIP] / Sync Client SupportReplacerequestsin favor ofhttpxInstallingNote: This package requires Python 3+pip install rancher-client
pip install git+https://github.com/growthengineai/rancher-client.git@mainEnvironment VariablesBelow are the currently supported Environment VariablesOne of the following sets:RANCHER_URL: str = ""RANCHER_ACCESS_KEY: str = ""RANCHER_SECRET_KEY: str = ""RANCHER_TOKEN: str = NoneCATTLE_URL: str = ""CATTLE_ACCESS_KEY: str = ""CATTLE_SECRET_KEY: str = ""CATTLE_TOKEN: str = NoneHTTPX_TIMEOUT: float = 15.0HTTPX_KEEPALIVE: int = 50HTTPX_MAXCONNECT: int = 200SSL_VERIFY: bool = TrueRANCHER_LOG_LEVEL: str = "info"RANCHER_CACHE_DIR: str =~/rancher-client/rancher/.cacheUsing APIimportrancherfromrancherimportRancherClientclient=RancherClient(url='https://localhost:8443/v3',access_key='<some valid access key>',secret_key='<some valid secret key>')# curl -s https://localhost:8443/v3/users?me=trueclient.list_user(me='true')# curl -s -X POST https://localhost:8443/v3/users -H 'Content-Type: application/json' -d '{ "username" : "user1", "password": "Password1" }'client.create_user(username='user1',password='Password1')# curl -s -X PUT https://localhost:8443/v3/users/user-xyz123 -H 'Content-Type: application/json' -d '{ "description" : "A user" }'user=client.by_id_user('user-xyz123')client.update(user,description='A user')# curl -s -X DELETE https://localhost:8443/v3/users/user-xyz123user=client.by_id_user('user-xyz123')client.delete(user)# Links# curl -s https://localhost:8443/v3/clusterRoleTemplateBindings?userId=user-xyz123user=client.by_id_user('user-xyz123')user.clusterRoleTemplateBindings()ExamplesActionsRancher API specFrom the spec"Actions perform an operation on a resource and (optionally) return a result."To perform thesetpodsecuritypolicytemplateaction on a project object these are the steps.This first method has built-in retry logic inside ofclient.action()when the error returned is 409#creates a project and handles cleanupproject=admin_pc.project# create an api_client from a management contextapi_client=admin_mc.client# perform the action via the client apiapi_client.action(obj=project,action_name="setpodsecuritypolicytemplate",podSecurityTemplateId=pspt.id)Or alternatively, performing the action from the project context (which does not have built-in retry logic)project=api_client.create_project(name="test-project",clusterId="local")project.setpodsecuritypolicytemplate(podSecurityPolicyTemplateId="my-pspt")
Package: rancher-client-python-3p
client-pythonA Python client for the Rancher APIInstallingNote: This package requires Python 3+pip install git+https://github.com/rancher/client-python.git@masterUsing APIimportrancherclient=rancher.Client(url='https://localhost:8443/v3',access_key='<some valid access key>',secret_key='<some valid secret key>')# curl -s https://localhost:8443/v3/users?me=trueclient.list_user(me='true')# curl -s -X POST https://localhost:8443/v3/users -H 'Content-Type: application/json' -d '{ "username" : "user1", "password": "Password1" }'client.create_user(username='user1',password='Password1')# curl -s -X PUT https://localhost:8443/v3/users/user-xyz123 -H 'Content-Type: application/json' -d '{ "description" : "A user" }'user=client.by_id_user('user-xyz123')client.update(user,description='A user')# curl -s -X DELETE https://localhost:8443/v3/users/user-xyz123user=client.by_id_user('user-xyz123')client.delete(user)# Links# curl -s https://localhost:8443/v3/clusterRoleTemplateBindings?userId=user-xyz123user=client.by_id_user('user-xyz123')user.clusterRoleTemplateBindings()ExamplesActionsRancher API specFrom the spec"Actions perform an operation on a resource and (optionally) return a result."To perform thesetpodsecuritypolicytemplateaction on a project object these are the steps.This first method has built-in retry logic inside ofclient.action()when the error returned is 409#creates a project and handles cleanupproject=admin_pc.project# create an api_client from a management contextapi_client=admin_mc.client# perform the action via the client apiapi_client.action(obj=project,action_name="setpodsecuritypolicytemplate",podSecurityTemplateId=pspt.id)Or alternatively, performing the action from the project context (which does not have built-in retry logic)project=api_client.create_project(name="test-project",clusterId="local")project.setpodsecuritypolicytemplate(podSecurityPolicyTemplateId="my-pspt")ContactFor bugs, questions, comments, corrections, suggestions, etc., open an issue inrancher/rancherwith a title starting with[rancher-python].Or justclick hereto create a new issue.
Package: rancon
Rancon (aka rancher-consul)We use consul as a service discovery mechanism, and a Consul-template / HAProxy combination to route traffic into our services. This python script is a helper to automatically enter Rancher services into Consul based on Rancher label selectors, so they can picked up by the load HAProxy load balancing layer.This might not be of any use to anybody but me, but I’ll make it public anyway because I did not find another solution so I had to write it, and maybe someone else has the same problem.CHANGELOG0.17.1Date: 2017-06-07FIX: made “/” URL path work again (threw HTTP 500 because of code error)FIX: way better exception handling, the registration cycle is no longer aborted if an exception occursFIX: the consul backend handles a connection error gracefully now0.17.0Date: 2017-01-19FIX: again some critical fixes in consul backend (don’t use v0.16)FIX: cleanup ID now includes rancher environment name (rancher.sh change)FEATURE: contrib/congler helper added, script to delete and list services0.16.0Date: 2017-01-19FIX: small internal bug fix which should never have triggeredCHANGE: cleanup ID now different, removal of standard “rancon” tagCHANGE: output of rancher.sh a bit differentCHANGE: log outputs changed partiallyINTERNAL: some code refactorings0.15.0Date: 2017-01-19FEATURE: make consul urls dynamic (use %HOST% etc. placeholders)FEATURE: deregistering uses the correct consul instances, not blindly “the ones registered with”0.14.0Date: 2017-01-06FEATURE: add metric “rancon_registration_exceptions”0.13.0Date: 2017-01-05Very confused version. Maybe it’s 0.14.0 already? Don’t use.0.12.0Date: 2017-01-04FEATURE: add version number to ‘/’ URL path & prometheus metrics0.11.1Date: 2017-01-04FIX: rancher.sh startup script processes env names with spaces now0.11.0Date: 2017-01-03FIX: Service tag replacment exception0.10.0Date: 2017-01-03FEATURE: Add “web interface” (basically only for metrics and health check)FEATURE: Add health check under /healthFEATURE: Add prometheus metrics under /metricsCHANGE: Deregistration behavior for services which failed registration (was:
unregister, is now: keep)0.9.0Date: 2016-06-15CHANGE: convert IDs, tags, names to all lowercase in consulCHANGE: do not allow non-url characters in service IDs (basically nothing but [a-z0-9-])0.8.0Date: 2016-06-15BREAKING:-i/--idparameter no longer global, moved tocleanup_idparameter of backendCHANGE: output now logging based, so all to stderr, and -vvvv flags possibleFIX: bug in service lookup in RancherOPEN: https connections0.7.0Date: 2016-06-15FEATURE: authentication now usedFIX: bug in service lookup in RancherOPEN: https connections (untested,mightwork)0.6.1Date: 2016-06-09More verbosity during init process0.6.0Date: 2016-06-09Unified naming scheme of used environment variablesAdded convenience script “rancon.py”Dockerfile fixesDoc fixes0.5.0Date: 2016-06-07Initial PyPI releasemodule works, docker setup not tested yetdocumentation unfinished / not present
Package: randall
randallIn what followspythonis an alias forpython3.6orpypy3.6or any later version (python3.7,pypy3.7and so on).InstallationInstall the latestpip&setuptoolspackages versionspython-mpipinstall--upgradepipsetuptoolsUserDownload and install the latest stable version fromPyPIrepositorypython-mpipinstall--upgraderandallDeveloperDownload the latest version fromGitHubrepositorygitclonehttps://github.com/lycantropos/randall.gitcdrandallInstallpythonsetup.pyinstallDevelopmentBumping versionPreparationInstallbump2version.Pre-releaseChoose which version number category to bump followingsemver
specification.Test bumping versionbump2version--dry-run--verbose$CATEGORYwhere$CATEGORYis the target version number category name, possible
values arepatch/minor/major.Bump versionbump2version--verbose$CATEGORYThis will set version tomajor.minor.patch-alpha.ReleaseTest bumping versionbump2version--dry-run--verbosereleaseBump versionbump2version--verbosereleaseThis will set version tomajor.minor.patch.Running testsInstall dependenciespython-mpipinstall-rrequirements-tests.txtPlainpytestInsideDockercontainer:withCPythondocker-compose--filedocker-compose.cpython.ymlupwithPyPydocker-compose--filedocker-compose.pypy.ymlupBashscript:withCPython./run-tests.shor./run-tests.shcpythonwithPyPy./run-tests.shpypyPowerShellscript:withCPython.\run-tests.ps1or.\run-tests.ps1cpythonwithPyPy.\run-tests.ps1pypy
Package: rand-archive
Random Access Archive.raa files are essentially a dict header + consecutive bytes of the samples. It was made to faccilitate and accelerate deep learning training on large datasets. It's written in Rust and fast, but easily accesible programmatically in Python. Most importantly, it allows you to shuffle the data, without sacrificing too much on sequential reads, by shuffling blocks of contiguous data. It also allows for lazy sharding.ComparisonThe main advantage of this library, is how extensible it is. Other libraries like Webdataset, FFCV, Streaming Dataset, TF Record, are very batteries included, which is great for experimentation, but sacrifices on extensibility heavily since they also include data processing. Our philosiphy quite simple, you write string byte pairs, and you read string byte pairs. We only implement functionality that NEEDS to be implemented at the reader level for optimization, like shuffling and sharding.Benchmarks:!todoUsagepip install rand-archiveWriting:fromrand_archiveimportWriterwithWriter("test.raa")asw:w.write("test",bytes("test"))Readingfromrand_archiveimportReaderfor_inReader().open_file("dummy.raa").with_shuffling():pass