Saravanan666/pypi-org huggingface dataset:
Package: dopelines
DoPeDouglas-Peuckerline simplification (data reduction).Reduces the number of points in a two-dimensional dataset, while preserving its most striking features.The resulting dataset is a subset of the original dataset.Although line simplification is typically used for geographical data, e.g. when zooming a digital map (see e.g.Django's GEOSGeometry.simplify()based onGEOS),
this type of algorithm can also be applied to general data reduction problems, as an alternative (or addition) to conventional filtering or subsampling. Some examples:creating miniature data plotspre-processing time-series data for feature detection (e.g. peak detection)InstallationNormal installation:pip install dopelinesWith plot support (addsmatplotlib):pip install dopelines[plot]With development tools:pip install dopelines[dev]Note: The PyPi project is calleddopelinesinstead ofdope, because PyPi would not let us create a project nameddope, even though the name appears to be available.ExamplefromdopeimportDoPeRdata_original=[[0,0],[1,-1],[2,2],[3,0],[4,0],[5,-1],[6,1],[7,0]]dp=DoPeR(data=data_original)# use tolerance threshold (i.e. max. error w.r.t. normalized data)data_simplified_eps=dp.simplify(tolerance=0.2)# compare original data and simplified data in a plotdp.plot()# or use maximum recursion depthdata_simplified_depth=dp.simplify(max_depth=2)Also see examples intests.LimitationsCurrently we only offer a recursive implementation (depth-first), which is intuitive, but may not be the most efficient solution.
An iterative implementation is in the works (breadth-first).References:Douglas DH, Peucker TK.Algorithms for the reduction of the number of points required to represent a digitized line or its caricature.Cartographica: the international journal for geographic information and geovisualization. 1973 Dec 1;10(2):112-22.
Package: dopey
dopey - Another Brainf*ck InterpreterDopey is a brainf*ck interpreter. It fully implements all the features of brainf*ck as definedhere.DemoImplemetation LimitationsDopey is limited to a memory buffer of30,000byte cells all initialized to zero. Any attempts to move the pointer past the bounds of the memory buffer will result in an exception being raised. Attempting to print any cell with a value outside of the defined ASCII characters may also result in an exception.RequirementsMake sure you've installed Python 3. You can download and install Python 3 atpython.org.Verify you have Python 3 installed by running the following:python--versionor in some systemspython3--versionInstallingYou can install dopey usingpip. From the command line:# use pip python package manager to install dopey$pipinstalldopey# run dopey using python$python-mdopeymy-bf-file.bfYou can also import an interpreter class included with dopey and use it in your own python scripts.fromdopeyimportInterpreterinterpreter=Interpreter()interpreter.execute("[brainf*ck code]++-->><<++--")ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: doplcommunicator
Data Communicator ClientSends and receives data between clients connected to DoplUsage# RobotimportthreadingimporttimefromdoplcommunicatorimportDoplCommunicatorfromdoplcommunicator.controllerdataimportControllerDatadefon_joined_session(session_id:int):print('Joined session id',session_id)defon_controller_data(controller_data:ControllerData):print('Controller data received',controller_data.toDict())defrun_loop(communicator):while(True):# Use sparingly as this causes a read lockcontroller_data=communicator.controller_data# Apply controller data to robottime.sleep(0.01)communicator=DoplCommunicator("http://localhost:3000")communicator.on_joined_session(on_joined_session)communicator.on_controller_data(on_controller_data)communicator.connect()threading.Thread(target=run_loop,args=(communicator))# Robot ControllerimporttimefromdoplcommunicatorimportDoplCommunicatorfromdoplcommunicator.controllerdataimportControllerDatadefon_joined_session(session_id:int):print('Joined session id',session_id)defrun_loop(communicator):while(True):x=y=z=rx=ry=rz=rw=0communicator.controller_data=ControllerData(x,y,z,rx,ry,rz,rw)time.sleep(0.01)communicator=DoplCommunicator("http://localhost:3000")communicator.on_joined_session(on_joined_session)communicator.connect()threading.Thread(target=run_loop,args=(communicator))
Package: dop-library-dinigo
DOP LibraryThis package contains all the utils and functions that are used in the DOP
framework.run_results_to_bqTakes arun_results.jsonfile from either local storage or GCS and uploads it
to a BigQuery table using the includedrun_results table schema.It is important that therun_resultstable is created according to this
schema. If the schema doesn't match, the insertion might fail silently.fromdop_libraryimportrun_results_to_bqrun_results_to_bq(run_results_path='gs://dbt-data/artifacts/run_results.csv',dest_dataset_id='dop_test',dest_table_id='run_results',)
Package: do-pod-dns-controller
do-pod-dns-controllerWhat is it?This is a simple controller written in Python that will configure a
DNS A record on DigitalOcean with the external IP of each node that
annotated pods are running on. The purpose is to allow exposing
services via hostPort for situations such as UDP services where a
LoadBalancer service won't work, and where a service with externalIPs
isn't desirable due to performance or because it loses the client's
IP.This will probably be most useful with ingress controller pods, but it
can be used with any pod that exposes services via hostPorts.How does it work?The controller lists your nodes and pods every 10 seconds. It looks
for pods with the annotationdo-pod-dns-controller.literati.org/hostname. It collects the
external IPs of each node that a pod with a given hostname annotation
is running on, then makes it so that the given hostname has A records
for exactly those external IPs, deleting any extraneous A records. It
will not touch any other records.How do I use it?The latest build (using a Debian-based image) is onDockerhub.Use a manifest similar to the one under examples. Generate a
DigitalOcean token and set that in a secret that ends up as
DIGITALOCEAN_TOKEN in the controller's environment. Make sure you've
already set up each domain name you want to use in DigitalOcean's
domain settings, then add--domain=<domain>to the controller's
args, for each domain. Annotate each pod (via
spec.template.metadata.annotations in Deployments, DaemonSets, etc)
with the keydo-pod-dns-controller.literati.org/hostnameand the
FQDN you want to set the DNS for, for example:apiVersion: apps/v1
kind: Deployment
...
spec:
  template:
    metadata:
	  annotations:
	    do-pod-dns-controller.literati.org/hostname: www.example.com
...Then watch the logs for the controller pod. You should see logs
indicating that it's configured DNS, or an error telling you what went
wrong. You can add--log-level=debugto the args to get more
logging.Code of conductAny interactions with the community relating to this software are
governed by thecode of conduct.
Package: doppel
doppelcopies files or directories to a destination (a file,
directory, or archive). Think of it as the offspring ofinstall(1)andtar(1).Usagedoppel [OPTION]... SOURCE... DESTTo copy files or directories, just list the sources as arguments
followed by the destination:doppel src1 src2 dst. By default, if
only one source is specified, it is copiedontothe destination; if
multiple sources are specified, they are copiedintothe destination.
This default can be explicitly specified with-o/--ontoor-i/--into, respectively.If the destination requires administrator access to write to, doppel
will try to detect this an automatically provide asudo(or
equivalent) prompt before copying.Note:Unlikeinstall(1), doppel copies relative symlinks as symlinks
by default. To adjust this behavior, you can use the--symlinkoption, described below.Options-C, –directory=DIRChange to directoryDIRbefore resolving paths of source files.-f, –format=FMTSet the format of the output archive; one of:tar,gzip,bzip2, orzip. If this option is specified,--intois
implied. If not set, source files will be copied normally, and no
archive will be created.-i, –intoCopy sources into directoryDEST.-m, –mode=MODESet the file permissions toMODE(an octal) when copying. Note: this
has no effect when--format=zip.-N, –full-nameWhen copying, use the full name of each source file as specified on the
command line instead of just the tail. This behavior is similar totar(1).-o, –ontoCopy a single source onto file or directoryDEST.-p, –parentsAutomatically create parent directories forDESTas needed.-P, –dest-prefix=DIRA prefix to add to the paths of destination files. This only applies
when using--format.-r, –recursiveRecursively copy source directories to the destination.-S, –sudo=WHENBefore copying, elevate withsudo(or your platform’s equivalent).WHENis one ofalways,auto(the default), ornever. Ifauto, doppel will attempt to determine automatically ifsudowould be needed before performing the copy.–symlink=WHENCopy symlinks as links.WHENis one ofalways,relative(the
default), ornever. Ifrelative, only symlinks with relative
targets will be copied as symlinks; those with absolute targets will be
copied as regular files.@fileRead options fromfileand use them when executing.–helpDisplay a help message and exit.–versionOutput version information and exit.LicenseThis project is licensed under theBSD 3-clause license.
Package: doppkit
# Doppkit for GRiDDoppkit is a tool for interacting with the [USACE GRiD service](https://grid.nga.mil). The primary usage is for downloading the exports for a specific AOI by providing an access token and AOI PK.  Doppkit is designed so it can be functional on computers that may not have a reliable network connection.For convenience, single-file code-signed binaries for Windows are provided on the [releases page](https://github.com/hobuinc/doppkit/releases).## Installation and Invocation### CLIThe text UI to show download progress and log-information is created using the rich library.From source:`bash $ pip install doppkit ... $ doppkit--help`From built executable:`doscon >doppkit-cli.exe--help`### GUIThe doppkit GUI which uses the PySide6 bindings of the Qt framework.From source:`bash $ pip install "doppkit[GUI]" ... $doppkit-gui`Using generated binary:`doscon > doppkit.exe `## Providing the TokenThe token needed to access the AOI can be provided by one of several ways.Add the–token TOKENargument to your command line usageSet the environment variableGRID_ACCESS_TOKEN## Example Command Line Usage`shell export GRID_ACCESS_TOKEN=KMCb6Nl799EFPproLLJR8bgeqzd4q doppkit--progressTrue list--filter"Chicago" doppkit--log-levelDEBUG--progressTrue sync 80903 `
Package: doppkit-esri-toolbox
doppkit-esri-toolboxArcGIS Pro Toolbox to Interact with GRiD.  The toolbox provides ArcGIS users to interact with the doppkit utility to
retrieve exports from GRiD.InstallationInstallation of the doppkit toolkit into ArcGIS pro can be done via the following steps:Clone the default python environment that ESRI provides.Name your new environment to something that you will recognizeActivate the newly created environmentClose ArcGIS Pro, and open the Python Command PromptInstall the toolbox by runningpip install doppkit-esri-toolbox(doppkit)C:\Users\ogi\AppData\Local\ESRI\conda\envs\doppkit>pip install doppkit-esri-toolboxIf users do not have access tohttps://pypi.org, but have the wheel and its dependencies
stored in a directory, they can install from that directory itself.(doppkit)C:\Users\ogi\AppData\Local\ESRI\conda\envs\doppkit>pip install^--find-links <path-to-directory>^doppkit-esri-toolboxStart ArcGIS in the Geoprocessing tab, look at the list of toolboxes and find GRiD SyncWhen the toolbox is loaded, it is ready for useDependenciesArcGIS Pro 3.0+doppkitaiofileshttpxwerkzeugclickrichLicenseApache v2.0
Package: dopple
doppleHTTP proxy to Unix Socket based JSON-RPC serversInstallationpipinstalldoppleUsedopple~/.ethereum/geth.ipchttp://127.0.0.1:8545These values above are the default ones too. If they match your current configuration, they can be ommitted.Check out--helpoption for more information.Read more in thedocumentation on ReadTheDocs.View the change log.Developer SetupIf you would like to hack on dopple, please check out theSnake Charmers
Tactical Manualfor information on how we do:TestingPull RequestsCode StyleDocumentationDevelopment Environment SetupYou can set up your dev environment with:gitclonegit@github.com:ethereum/dopple.gitcddopple
virtualenv-ppython3venv
.venv/bin/activate
pipinstall-e.[dev]Testing SetupDuring development, you might like to have tests run on every file save.Show flake8 errors on file change:# Test flake8when-changed-v-s-r-1dopple/tests/-c"clear; flake8 dopple tests && echo 'flake8 success' || echo 'error'"Run multi-process tests in one command, but without color:# in the project root:pytest--numprocesses=4--looponfail--maxfail=1# the same thing, succinctly:pytest-n4-f--maxfail=1Run in one thread, with color and desktop notifications:cdvenv
ptw--onfail"notify-send -t 5000 'Test failure ⚠⚠⚠⚠⚠' 'python 3 test on dopple failed'"../tests../doppleRelease 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. This is typically done from the
master branch, except when releasing a beta (in which case the beta is released from master,
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: dopple-ai-client
DoppleAIClientPython API wrapper for Dopple.AIYou can install it using pip with thepip install dopple_ai_clientcommand.Example usageimportdopple_ai_clientclient=dopple_ai_client.DoppleClient("TOKEN","USER_ID")forbotinclient.get_bots_chatted_with():# Print out the bot IDsprint(bot)How to get credentials - step by stepTo use this API wrapper, you'll need your Dopple.AI user token and user ID. This is required in order to authenticate you correctly. Don't worry, all the requests are made ONLY to dopple.ai and nowhere else.Here are the steps you need to follow in order toOpen the browser consoleSelect the network tab. Preferably filter the requests shown to XHR/Fetch.Openthe site. Sign out of your account if you're logged in.Sign in again, this time with the network tab looking for fetch requests.Find a request tohttps://be.dopple.ai/api/users/login,https://be.dopple.ai/api/users/google, etc. Depends on how you signed in.Obtain the accessToken and id values from the JSON response and paste them into a DoppleClient object.All done! Now you can freely access the Dopple.AI API from Python.Planned featuresThis library is actively maintained, and will add new features over time. Here are some of the planned things:Find a better way of obtaining Dopple info. Right now the metadata is scraped from an HTML page, which has performance implications and isn't very tidy.Add the ability sign-in with e-mail and password.Editing Dopples (possibly creating them too)Support for Dopple Reactions and Dopple VoiceUtility functions
Package: doppler-env
doppler-envThe doppler-env package automates the injection of Doppler secrets as environment variables into any Python application and works in the terminal, PyCharm, and Visual Studio Code.MotivationThe Doppler CLI provides the easiest method of injecting secrets into your application:dopplerrun--pythonapp.pyBut when debugging with PyCharm or Visual Studio Code, a vendor-specific Python entry-point is used, preventing the Doppler CLI from acting as the application runner. At Doppler, we go to great lengths toprevent secrets ending up on developer's machinesso downloading secrets to a .env file wasn't an option.Thanks to Python'sSite configuration hookvia a path configuration file, we can replicate thedoppler runworkflow by fetching the secrets via the Doppler CLI (recommended) or API and injecting into your Python application process prior to your code by being executed.SetupEnsure you haveinstalled the Doppler CLIlocally and havecreated a Doppler Project. Then authorize the Doppler CLI to retrieve secrets from your workplace by running:dopplerloginThen installdoppler-envin your local development environment or add it to the list of dev specific dependencies:pipinstalldoppler-envConfigurationFirst, define theDOPPLER_ENVenvironment variable in your IDE, editor, or terminal to trigger the injection of secrets:exportDOPPLER_ENV=1You can enable logging for troubleshooting purposes by setting theDOPPLER_ENV_LOGGINGenvironment variable:exportDOPPLER_ENV_LOGGING=1Then configure which secrets to fetch for your application by either using the CLI in the root directory of your application:dopplersetupOr set theDOPPLER_PROJECTandDOPPLER_CONFIGenvironment variables in your debug configuration within PyCharm or Visual Studio Code.Now whenever the Python interpreter is invoked for your application, secrets will be injected prior to your application being run:pythonapp.py# >> [doppler-env]: DOPPLER_ENV environment variable set. Fetching secrets using Doppler CLIIn restrictive environments where the use of the Doppler CLI isn't possible, set aDOPPLER_TOKENenvironment variable with aService Tokento fetch secrets directly from the Doppler API:exportDOPPLER_TOKEN='dp.st.dev.xxxxxxx'pythonapp.py# >> [doppler-env]: DOPPLER_ENV and DOPPLER_TOKEN environment variable set. Fetching secrets from Doppler APIAcknowledgementsThis approach to injecting environment variables was inspired bypatch-env.IssuesFor any bug reports, issues, or enhancements, pleasecreate a repository issue.SupportYou can get support in theDoppler community forum, find us onTwitter, and for bugs or feature requests,create an issueon theDopplerHQ/python-doppler-envGitHub repository.If you need help, either use our in-product support or head over to theDoppler Community Forumto get your questions answered by a member of the Doppler support team or
Package: doppler-injector
Doppler-InjectorSimple function to injectDopplersecrets into virtual environment.!!!NOT TESTED!!!Do not install! Hasn't been tested. Publishing for personal use. Will create test and fortify.In the meantime, just followDoppler-envset-up.SETUPFirst, define theDOPPLER_ENVenvironment variable in your IDE, editor, or terminal to trigger the injection of secrets:exportDOPPLER_ENV=1You can enable logging for troubleshooting purposes by setting theDOPPLER_ENV_LOGGINGenvironment variable:exportDOPPLER_ENV_LOGGING=1Then configure which secrets to fetch for your application by either using the CLI in the root directory of your application:dopplersetup
Package: dopplrSDK
Definition:dopplrSDK is a software development kit that provides a set of functionalities for working with Dopplr, a data management and analytics platform. 
It offers convenient methods to interact with Dopplr's DataLake and perform various operations such as uploading files and retrieving files in ML Studio.Installation Procedure:To install the dopplrSDK, please visit https://pypi.org/project/dopplrSDK
(OR)
pip install dopplrSDKIt have 5 functions , those are putFileTomywrkspace, putFolderTomywrkspace, getWorkspaceFile, getWorkspaceFolderFiles and getWorkspaceFile_URLThe function putFileToMyWorkspace() is designed to return your file to the DataLake. You can use the following syntax:putFileToMyWorkspace("File Path without file extension", "File Type", "user name","Project_name", "client Key") – [client key common for all users]For example:
putFileToMyWorkspace("C:\Downloads\customer", "csv", "Dhoni","demo",  "client key")The function putFolderTomywrkspace() is designed to return your folder to the DataLake. You can use the following syntax:putFolderTomywrkspace("Folder Path ", "Target name", "user name","Project_name", "client Key") – [client key common for all users]For example:
putFolderTomywrkspace ("C:\Downloads\folder", " Target name ", "Dhoni","demo",  "client key")The function  getWorkspaceFile() is designed to retrieve  your file from the Data Lake with the following parameters:
getWorkspaceFile("File Name", "Download Path", "user name", "client key")For example:
getWorkspaceFile("customer.csv", "C:\Downloads", "Dhoni", "client key")The function getWorkspaceFolderFiles() is designed to retrieve your files from the folder from the datalake with the following parameters:getWorkspaceFolderFiles("folder","Download Path","user name", "client key")For example:getWorkspaceFolderFiles("customer","C:\Downloads", "Dhoni", "client key")The function getWorkspaceFile_URL() () is designed to retrieve pre-signed URL of your file from the storage with the following parameters:
getWorkspaceFile_URL(("File Name", "user name", "client key")NOTE:
Please contact admin team for client key
Package: doppyo
# doppyodoppyo is a diagnostics/verification package for climate forecast systems. It is still in the very early stages of development.Current functionalityThree modules are currently availableskill: functions for assessing one data set relative to another (usually model output relative to observation).diagnostic: functions for computing various atmospheric and oceanic diagnostics.utils: general support functions for the doppyo package.Some test modules are also avaliable indoppyo.test. However, these are far from completeUsage (If not using DCFP JupyterHub server)Install anaconda if you have not already done so:Make your own directory in ‘/OSM/CBR/OA_DCFP/apps/’mkdir/OSM/CBR/OA_DCFP/apps/<userid>A recent version of the anaconda installer is available in ‘/OSM/CBR/OA_DCFP/apps/anaconda_installer’. Run this and follow the prompts to install anaconda in the directory you have just created.The anaconda installer will add the anaconda path to your .bashrc (or equivalent). You will need to source this for the changes to be made:source~/.bashrcCheck that anaconda python is now your default:which pythonInstall doppyo:Clone the doppyo Bitbucket repository to your local machine. At your desired location, run:git clonehttps://<userid>@bitbucket.csiro.au/scm/df/doppyo.gitReplicate the doppyo environment locally. This will ensure that you have all libraries/packages required to run doppyo. Within the ‘requirements’ folder in your cloned repository, run:conda env create-fdoppyo_env_linux.ymlorconda env create-fdoppyo_env_mac.ymldepending on your os.Alternatively, if updating an existing doppyo_env environment, run:conda env update--file=doppyo_env_<os>.ymlActivate the doppyo_env environment:source activate doppyo_envInstall the doppyo package. Within your cloned repository, run:python setup.py install