Saravanan666/pypi-org huggingface dataset:
Package: ddns-manager
Based onhbontempo-br/dynamic-ip-updater-google-domainsddns-managerEasily keep your DNS records up to date with your Dynamic IP.ddns-manageris a small and easy project made with python3 that intents to help you keep
your DNS records pointing to your machine when you have a dynamic IP from your ISP.Originally designed by[Henrique Bontempo][author].The problemIf you want to access you home network through a VPN, host your own website, host a game server, have remote access to
our security cameras or any other task that you have to access your home network from the internet you must have to
locate it .The most direct way is through your public IP , and it's easy if you have a static IP, but they are expensive and for
many regions almost exclusive to business. If your ISP won't provide static IP on your location or you don't want to
pay for it than you are stuck with a dynamic IP. You still can reach your home through your external IP, but there are
no guarantees that this address won't change without a notice.To circumvent this problem the most common idea is to use a url address that is constantly updated your external IP.ObjectiveThis project have a direct purpose: a simple and easy way of managing your DDNS.Getting StartedCloneCloning this project requiresgit, instructions provided below.Option 1: HTTPSCheckclone with httpsfor further information.$gitclonehttps://github.com/hbontempo-br/ddns-manager.gitOption 2: SSHCheckclone with sshfor further information.$gitclonegit@github.com:hbontempo-br/ddns-manager.gitRun scriptInstall dependencies (setting up a virtual environment is recommended):With pip:$pip3installrequirements.txtThen just run:$python3-mddns_managerTestNo secret here:$python3-munittestdiscoverRunning on DockerYou can run the update loop inside a docker container.The image can be found in Docker-hub.Build$dockerbuild-tddns-manager-fDockerfile.RunJust mount the configuration file in the/config/config.yml:$dockerrun\-vPATH_CONFIG:/config/config.yml-dhbontempo/ddns-managerIt's a good practice to run your this container with a--restart=alwaysas showed above so your container
starts running again even if a problem happens.ContributingDid you found a problem? Think that something could be improved? Just open an IssueLicenseThis project is licensed under theMITlicense. Check thelicensefile for further information.
Package: ddnss
ddnssScript to update DynDNS hosts registered at ddnss.de.InstallationInstall ddnss from theAURor via:pip install ddnssUsageYou can run the client from the console viaddnssupd <host> [-k <key>] [-f <config_file>] [-d]Per default, the config file is read from/etc/ddnss.confon POSIX systems and from%LOCALAPPDATA%\ddnss.confon Windows systems.Configuration fileThe expected config file format is a simple INI-Style:[<host>]
key = <key>LicenseCopyright (C) 2018-2020 Richard Neumannddnss is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.ddnss is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with ddnss.  If not, seehttp://www.gnu.org/licenses/.
Package: ddom
Ddom (dynamic device object model) is a library which creates a object model of devices. Devices consist of child devices which can be plugged together. The idea is to model IT devices (switches, router, firewalls, server, cables) with all their modules, slots and ports and automatically verify compatibility between them.Eg.: A Switch has slots, in these slots you can plug linecards, fans or powersupplies. Fans and powersupplies have an airflow direction. Linecards have ports. A transceiver can be inserted to a port. A cable can be connected to a transceiver,… and so on.FeaturesDdom has following features:automatically generate a object from yaml or python data structureverify airflow direction within a ChassisFind children by various propertiesverify allowed childrenverify allowed parentsInheritance of propertiesInstallationInstall ddom by running:pip3installddomExamplesCreate a Nexus 5672UP Chassis and print all port names of slot 1 using find_children and parent property#!/usr/bin/env python3fromddomimport*chassis=Chassis("n5k-c5672up","cisco")forportinchassis.find_children("port",{"parent.number":"1"}):print(port.name)Create a Nexus 5672UP Chassis and print all port names of slot 1 using find_children#!/usr/bin/env python3fromddomimport*chassis=Chassis("n5k-c5672up","cisco")forportinchassis.slot("SLOT-1").supervisor().find_children("port"):print(port.name)Create a Nexus 5672UP Chassis and verify airflow#!/usr/bin/env python3fromddomimport*chassis=Chassis("n5k-c5672up","cisco")psu_1=PowerSupply("nxa-pac-1100w","cisco")psu_2=PowerSupply("nxa-pac-1100w-b","cisco")chassis.slot("PSU-1").connect(psu_1)chassis.slot("PSU-2").connect(psu_2)# this will raise an ddom.InvalidAirFlowError exceptionCreate a Nexus 5672UP Chassis and print the port name of a specific port#!/usr/bin/env python3fromddomimport*chassis=Chassis("n5k-c5672up","cisco")# by numberprint(chassis.slot("SLOT-1").supervisor().port(1).name)# by nameprint(chassis.slot("SLOT-1").supervisor().port("eth1/1").name)# by indexprint(chassis.slot("SLOT-1").supervisor().port_index(0).name)See the unit tests in the test directory for many more examples.ContributeIssue Tracker:https://github.com/jinjamator/ddom/issuesSource Code:https://github.com/jinjamator/ddomRoadmapSelected Roadmap items:add support for more devicesadd support for cablesadd class documentationadd device pathFor documentation please refer tohttps://simplenetlink.readthedocs.io/en/latest/LicenseThis project is licensed under the Apache License Version 2.0
Package: ddOperApi
dd-oper-apiInleidingRijkswaterstaat maakt gebruik van de [Operationele Digitale Delta API]
(https://digitaledeltaorg.github.io/dd-oper.v201.html). Deze api word
gebruikt voor het opvragen van:MetingenVerwachtingenAstronomisch getijDe operationele digitale delta api is beschikbaar via:https://ddapi.rws.nl/dd-oper/2.0De authenticatie op deze url is geregeld via PKI overheidscertificaten.
Zonder PKI overheidscertificaat is de api niet toegankelijk.Deze git repository bevat een python module om de dd oper api te benaderen
inclusief:Autenticatie via certificaten.Access functies voor de resultaten.InstallatieHet installeren gaat via:pip ddOperApiDe inhoud van de git repositoryddOperApi/: De moduleddOperApi/tests: De module test codegithooks: Standaard hooks voor een locale zelf testdemo/DD-API-Oper demo.ipynb:Jupyter notebookdemo fileddOperApiDeze module bevat documentatie. Deze is op te vragen door de module te
laden en vervolgens via de python hulp functie heb je toegang tot de
documentatie.ddOperApi/testsBevat een flask webserver waartegen de module getest kan worden en
python unittest code.DD-API-Oper demo.ipynbEen werkende demo in Jupyter van de dd-oper api en de module.In deze demo worden de volgende elementen getoont:Het opvragen van de lijst met locaties.De gegevens van een locatie.Het opvragen van de quantities.Inclusief de quantities van een specefieke locatie.Het opvragen van waarde.Het opvragen diverse meta data gegevens.Het verder verwerken van meetgegevens.De meetgegevens in rmi sip formaat weergeven.Het plotten van meetgegevens in een grafiekOver de certificaten.Om de api te kunnen gebruiken is een pki overheidscertificaat en de
bijbehorende private key nodig. Het certicaat moet geldig zijn als
client certificaat.Het certificaat moet aangeboden worden in x509 formaat en de private
key mag niet encrypt zijn.StatusDeze module en de voorbeeld code is in ontwikkeling. Dit is geen
stabiele code!Vragen en/of uitbreidingen.Vragen en of uitbreidingen kunnen gemaild worden naar ddoperapi(a)marceln(.)org.
Package: ddopy
ddopy - PyPI Utility ModuleOverviewddopyis a Python module that provides a collection of utility functions and classes to simplify common tasks. Whether you're working with databases or making HTTP requests, this module has you covered.ClassesDatabaseManagerManage your simple database operations with this class.HttpRequesterMake HTTP requests with this class.ConfigManagerManage your configuration files with this class.TimeControllerManage your time-based operations with this class.InstallationYou can installddopyfrom PyPI using pip:pipinstallddopyLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.ContributingIf you would like to contribute to this project, please follow ourcontributing guidelines.Issues and FeedbackIf you encounter any issues or have feedback for us, pleaseopen an issueon GitHub.We hope you findddopyuseful in your Python projects!
Package: ddos-ripper
DDoS-RipperDDoS Ripper is a tool for testing if your web server is vulnerable to slow-requests kind of attacks.
A Distributable Denied-of-Service (DDOS) attack server that cuts off targets or surrounding infrastructure in a flood of Internet trafficDDoS attacks achieve effectiveness using multiple compromised computer systems as a source of attack traffic.
Search engines may include computers and other network resources such as IoT devices.
From a higher level, the DDOS attack is like an unexpected traffic jam stuck on a highway, preventing regular traffic from reaching its destination.
The idea behind this approach to create as many connections with a server as possible and keep them alive and send trash headers through the connection.
Please DO NOT USE this in the real attacks on the servers. It's made for just testing purpose.
This tool not responsible for any abuse or damage caused by this program. Only for Educational Purpose.More information about the attack you can findhere.InstallationPyPiFor installation through the PyPI:$pipinstallddos-ripper==1.0.0This method is prefered for installation of the most recent stable release.Source-codeFor TermuxTo use the ddos-ripper type the following commands:$pkginstallgit-y$pkginstallpython-y$pkginstallpython3-y$gitclonehttps://github.com/palahsu/DDoS-Ripper.git$cdDDoS-Ripper$ls$python3DRipper.py$python3DRipper.py-s[ipAddress]-t135$python3DRipper.py-s0.00.00.00-t135For Debian-based GNU/Linux distributions:To use the application, type in the following commands in GNU/Linux terminal.$sudoaptinstallgit$gitclonehttps://github.com/palahsu/DDoS-Ripper.git$cdDDoS-Ripper$ls$python3DRipper.py`OR`python2DRipper.py`For WindowsTo use the application, type in the following commands in Powershell or CMD:$gitclonehttps://github.com/palahsu/DDoS-Ripper$cdDDoS-Ripper$ls$python3DRipper.py`OR`pythonDRipper.py`$python3DRipper.py-s[ipAddress]-t135$python3DRipper.py-s0.00.00.00-t135`For MacOSInstallBrewandInstalldependencies(python3)$python3DRipper.py-s[ipAddress]-t135$python3DRipper.py-s0.00.00.00-t135`stop execution: Ctrl + CBugs, issues and contributing!If you findbugsor havesuggestionsabout improving the module, don't hesitate to contact me.LicenseThis project is licensed under the MPL License - see theLICENSEfile for details.
Licenses may change in the future for version updates.
Copyright (c) 2019-2020 palahsu
Package: ddp
<h2>History</h2><p>Version 0.1</p><pre>origin version.</pre><p>Version 0.11</p><pre>add some script files and add README.md for more details.</pre><p>Version 0.12</p><pre>remove some bugs of bin directory.</pre><p>Version 0.13</p><pre>add different platform support for script files.</pre><p>Version 0.14</p><pre>remove \n for script files, if this problem can not solved, then you can use next command line to solve it.sed -i 's/\r//g' /usr/local/bin/*.py</pre><p>Version 0.15-20150411</p><pre>write format readme.</pre><p>Version 0.16-20150411</p><pre>format readme</pre><h2>Usage:</h2><p>information about is to be written.</p>
Package: ddp-asyncio
ddp_asyncioddp_asyncio is an asynchronous implementation ofMeteor's DDP protocolfor Python 3.5+.InstallationInstall via PyPi:pip3 install ddp_asyncioOr, install using the included setup.py script:python3 setup.py installUsageAPI docsCheck out the following examples, all of which use Meteor's referenceTodosapplication.Retrieve todo listCreate a todo listWatch todo lists for changesExtrasMeteor-Files Uploadddp_asyncio'sMeteorFilesUploadercan upload files to a server using theMeteor-Filespackage.Example
Package: ddpg-agent
# ddpg_agent
Reinforcement Learning agent using Deep Deterministic Policy Gradients (DDPG).This reinforcement lerning model is a modified version of [Udacity’s DDPG model](https://github.com/udacity/deep-reinforcement-learning/tree/master/ddpg-pendulum) which is based on the paper [Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971). This project was developed as part of the [Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree–nd009t) quadcopter project and the model is based on code provided in the project assignment.Solving OpenAI Gym’s [MountainCarContinuous-v0](https://github.com/openai/gym/wiki/MountainCarContinuous-v0) continuous control problem with this model provides a particularly good learning example as its 2-dimensional continuous state space (position and velocity) and 1-dimensional continuous action space (forward, backward) are easy to visualize in two dimensions, lending to an intuitive understanding of hyperparameter tuning.Project development began as a kaggle kernel. Initial code in this repo is based on [DDPG_OpenAI-MountainCarContinuous-V0 Version 74](https://www.kaggle.com/samhiatt/mountaincarcontinuous-v0-ddpg?scriptVersionId=16052313).## Usage
SeeSolving MountainCarContinuous-v0.ipynbfor an example of usage and a demo training visualization output.## Credits
* [Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971)
* Andre Muta’s [DDPG-MountainCarContinuous-v0](https://github.com/amuta/DDPG-MountainCarContinuous-v0) repo was helpful in suggesting some good visualizations as well as giving some good hyperparameters to start with. It looks like he uses the same code from the nanodegree quadcopter project and uses it to solve the MountainCarContinuous problem as well. His [plot_Q method in MountainCar.py](https://github.com/amuta/DDPG-MountainCarContinuous-v0/blob/master/MountainCar.py) was particularly helpful by showing how to plot Q_max, Q_std, Action at Q_max, and Policy. Adding a visualization of the policy gradients and animating the training process ended up helping me better understand the problem and the effects of various hypterparemeters.
* Thanks to [Eli Bendersky](https://eli.thegreenplace.net/2016/drawing-animated-gifs-with-matplotlib/) for help with matplotlib animations.
* Thanks to [Joseph Long](https://joseph-long.com/writing/colorbars/) for help with matplotlib colorbar axes placement.
Package: ddplt
ddpltA package with code from my ML projects that has a potential of being reusable.InstallationThe package installation is simple, since the repo is available on PyPi:pipinstallddpltConfusion matrixDraw a confusion matrix for classification results:importnumpyasnpfromddplt.heatmapsimportdraw_confusion_heatmap# generate some datay_test=np.array([0,0,1,1,2,0])y_pred=np.array([0,1,1,2,2,0])class_names=np.array(['hip','hop','pop'])ax,cm=draw_confusion_heatmap(y_test,y_pred,class_names)Cross-validatedreceiver operating characteristicandprecision-recallcurvesDrawreceiver operating characteristic(ROC) andprecision-recall(PR) curves usingk-fold cross-validation:fromddplt.classificationimportdraw_roc_prc_cvfromsklearn.datasetsimportmake_moonsfromsklearn.linear_modelimportLogisticRegressionfromsklearn.model_selectionimportKFoldX,y=make_moons(500,noise=.2,random_state=123)estimator=LogisticRegression()cv=KFold(n_splits=5,shuffle=True,random_state=123)draw_roc_prc_cv(estimator,X,y,cv)The functiondraw_roc_prc_cvaccepts the following parameters:estimator- Scikit-learn's estimator with set hyperparameters. The estimator does not have to be fitted.X- array-like with shape(n_instances,n_features)y- array-like with shape(n_instances,)with instance labelscv- cross-validation generator responsible for creatingk-fold splitting ofXandysee the docs for info regarding the optional parametersIn each CV iteration, the estimator is fitted on training fold and class probabilities are predicted for instances
within the test fold. Then, ROC and PR curves are generated from the probabilities.The figure consists of:individual ROC/PR curvesmean ROC/PR curveshaded region denoting +-1 std. dev.Areas under ROC and PR curves are reported in the legend.Note:Use the functionsdraw_roc_cvordraw_prc_cvto draw ROC or PR curves onlyLearning curveTODO - not yet implementedCreate plot showing performance evaluation for different sizes of training data. The method should accept:existingAxesperformance measure (e.g. accuracy, MSE, precision, recall, etc.)...Correlation heatmapTODO - not yet implementedGrid where each square has a color denoting strength of a correlation between predictors. You can choose between Pearson and Spearman correlation coefficient, the result is shown inside the square.
Package: ddpm
DDPMs Pytorch ImplementationPytorch implementation of "Improved Denoising Diffusion Probabilistic Models",
"Denoising Diffusion Probabilistic Models" and "Classifier-free Diffusion Guidance"InstallpipinstallddpmUsageGaussian plain DDPMfromddpmimportGaussianDDPM,UNetTimeStepfromddpm.variance_schedulerimportLinearSchedulerT=1_000width=32height=32channels=3# Create a Gaussian DDPM with 1000 noise stepsscheduler=LinearScheduler(T=T,beta_start=1e-5,beta_end=1e-2)denoiser=UNetTimeStep(channels=[3,128,256,256,384],kernel_sizes=[3,3,3,3],strides=[1,1,1,1],paddings=[1,1,1,1],p_dropouts=[0.1,0.1,0.1,0.1],time_embed_size=100,downsample=True)model=GaussianDDPM(denoiser,T,scheduler,vlb=False,lambda_variational=1.0,width=width,height=height,input_channels=channels,logging_freq=1_000)# pytorch lightning moduleGaussian "Improved" DDPMfromddpmimportGaussianDDPM,UNetTimeStepfromddpm.variance_schedulerimportCosineSchedulerT=1_000width=32height=32channels=3# Create a Gaussian DDPM with 1000 noise stepsscheduler=CosineScheduler(T=T)denoiser=UNetTimeStep(channels=[3,128,256,256,384],kernel_sizes=[3,3,3,3],strides=[1,1,1,1],paddings=[1,1,1,1],p_dropouts=[0.1,0.1,0.1,0.1],time_embed_size=100,downsample=True)model=GaussianDDPM(denoiser,T,scheduler,vlb=True,lambda_variational=0.0001,width=width,height=height,input_channels=channels,logging_freq=1_000)# pytorch lightning moduleClassifier-free Diffusion GuidancefromddpmimportGaussianDDPMClassifierFreeGuidance,UNetTimeStepfromddpm.variance_schedulerimportCosineSchedulerT=1_000width=32height=32channels=3num_classes=10# Create a Gaussian DDPM with 1000 noise stepsscheduler=CosineScheduler(T=T)denoiser=UNetTimeStep(channels=[3,128,256,256,384],kernel_sizes=[3,3,3,3],strides=[1,1,1,1],paddings=[1,1,1,1],p_dropouts=[0.1,0.1,0.1,0.1],time_embed_size=100,downsample=True)model=GaussianDDPMClassifierFreeGuidance(denoiser,T,w=0.3,v=0.2,variance_scheduler=scheduler,width=width,height=height,input_channels=channels,logging_freq=1_000,p_uncond=0.2,num_classes=num_classes)# pytorch lightning module
Package: ddpuk
Utilities to interact with the data.parliament.uk APIsddpuk_downloadDownload data fromhttp://lda.data.parliament.ukusage: ddpuk_download [-h] [--format {csv,json,rdf,text,ttl,xml}]
                      [--size SIZE] [--page-from PAGE_FROM]
                      [--page-to PAGE_TO] [--datadir DATADIR]
                      dataset

Download data from http://lda.data.parliament.uk

positional arguments:
  dataset               dataset to download

optional arguments:
  -h, --help            show this help message and exit
  --format {csv,json,rdf,text,ttl,xml}
                        format (default: json)
  --size SIZE           batch size (default: 500)
  --page-from PAGE_FROM
                        first page (default: 0)
  --page-to PAGE_TO     last page (default: 125)
  --datadir DATADIR     target directory (default: data)Built byFlorian RathgeberatAccountability Hack 2015.
Package: ddpw
DDPWDistributed Data Parallel Wrapper (DDPW)is a lightweight wrapper that
scaffolds PyTorch's (Distributed Data) Parallel.This code is written in Python 3.10. TheDDPW
documentationcontains details on how to use
this package.OverviewInstallationcondainstall-ctvsujalddpw# with condapipinstallddpw# with pip from PyPIUsagefromddpwimportPlatform,Wrapper# some taskdeftask(global_rank,local_rank,group,args):print(f'This is GPU{global_rank}(G)/{local_rank}(L); args ={args}')# platform (e.g., 4 GPUs)platform=Platform(device='gpu',n_gpus=4)# wrapperwrapper=Wrapper(platform=platform)# startwrapper.start(task,('example',))
Package: ddqa
Datadog QACI/CDDocsPackageMetaDDQA is a tool for users ofJirato perform QA of anticipated releases of code on GitHub.It works by finding test candidates between two Git references and translates each pull request or direct commit into a Jira issue per designated GitHub team, assigned to a semi-randomly chosen member of that team.FeaturesIssue creation is completely configurable by each team, with overrides available at runtimeRobust status tracking with optional filtersProviding aTUIallows for running via SSH on other machines, which is useful when there is restricted access to Git repositoriesDocumentationThedocumentationis made withMaterial for MkDocsand is hosted byGitHub Pages.LicenseDDQA is distributed under the terms of theMITlicense.
Package: ddqla
BaseAgent ClassIt is an abstract class that implements an agent for reinforcement learning. This class can be extended to create specific agents for different environments.ConstructorThe constructor accepts the following parameters:num_actions: The number of possible actions that the agent can take.environment: The environment in which the agent operates, represented as a numpy array.fit_each_n_steps: The number of steps after which the agent trains its models.exploration_rate: The probability that the agent chooses a random action instead of the optimal action.exploration_rate_decay: The decay rate of the exploration rate.gamma: The discount factor used in the Q-value update equation.cumulative_rewards_max_length: The maximum length of the array that keeps track of cumulative rewards.memory_max_length: The maximum length of the agent's memory.memory_batch_size: The batch size used for training the models.allow_episode_tracking: A boolean flag indicating whether the agent should track episodes.MethodsThe BaseAgent class implements the following methods:start_episode: Starts a new episode.stop_episode: Ends the current episode.get_episodes: Returns all recorded episodes.reset_episodes: Resets all recorded episodes.is_memory_ready: Checks whether the agent's memory is ready for training.step: Performs a step of the agent, choosing an action, receiving a reward, and updating the models.get_last_cumulative_rewards: Returns the sum of the last cumulative rewards.In addition, the it takes the following functions to be implemented in subclasses:reset_state: Resets the agent's state at the start of a new episode._get_reward: Calculates the reward received by the agent for undertaking an action in a given state._get_model: Returns the model used by the agent to learn the Q-value function.Usage ExampleTo use the class, you need to extend it and implement the abstract methods. Here's an example of how this might be done:class MyAgent(Agent):
    def reset_state(self):
        # Implementation of state reset

    def _get_reward(self, action, environment):
        # Implementation of reward calculation

    def _get_model(self, state_features):
        # Implementation of the modelOnce the subclass is defined, you can create an instance of the agent and use it as follows:agent = MyAgent(num_actions=4, environment=np.array([0, 0, 0, 0]))
agent.start_episode()
for _ in range(100):
    agent.step()
agent.stop_episode()NotesBaseAgent is designed to be used with discrete environments and deep learning models. If you wish to use a continuous environment or a different learning model, you may need to make some modifications to the class.