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akula
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akulaAdd a short description here!A longer description of your project goes here…NoteThis project has been set up using PyScaffold 4.1.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
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akulaku
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No description available on PyPI.
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akunto
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Failed to fetch description. HTTP Status Code: 404
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akunto-api
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Failed to fetch description. HTTP Status Code: 404
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akun-tools
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akun-tools is a set of tools for interacting with Fiction.live, previously known
as anonkun. Currently it includes a scraper that generates HTML ebooks of quests.akun-tools is a command-line utility that requires Python 3 and pip. To install
it, run:$pipinstallakun-toolsOnce installed, you run it withakun_scraper. Example usage:$akun_scrapergetinfohttps://fiction.live/stories/Abyssal-Admiral-Quest-/QGSpwAtjf9NMtvsTA/home$akun_scraperdownloadabyssal_admiral_quest.toml
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akurdyukov-tap-clickhouse
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tap-clickhousetap-clickhouseis a Singer tap for ClickHouse.Built with theMeltano Tap SDKfor Singer Taps.InstallationInstall from GitHub:pipxinstallgit+https://github.com/akurdyukov/tap-clickhouse.git@mainConfigurationCapabilitiescatalogstatediscoveraboutstream-mapsschema-flatteningbatchAccepted Config OptionsSettingRequiredDefaultDescriptiondriverFalsehttpDriver typeusernameFalsedefaultDatabase userpasswordTrueNoneUsername passwordhostFalselocalhostDatabase hostportFalse8123Database connection portdatabaseFalsedefaultDatabase namesecureFalse0Should the connection be secureverifyFalse1Should secure connection need to verify SSL/TLSstream_mapsFalseNoneConfig object for stream maps capability. For more information check outStream Maps.stream_map_configFalseNoneUser-defined config values to be used within map expressions.flattening_enabledFalseNone'True' to enable schema flattening and automatically expand nested properties.flattening_max_depthFalseNoneThe max depth to flatten schemas.batch_configFalseNoneA full list of supported settings and capabilities for this
tap is available by running:tap-clickhouse--aboutConfigure using environment variablesThis Singer tap will automatically import any environment variables within the working directory's.envif the--config=ENVis provided, such that config values will be considered if a matching
environment variable is set either in the terminal context or in the.envfile.Source Authentication and AuthorizationSetusernameandpasswordsettings to authorize.UsageYou can easily runtap-clickhouseby itself or in a pipeline usingMeltano.Executing the Tap Directlytap-clickhouse--version
tap-clickhouse--help
tap-clickhouse--configCONFIG--discover>./catalog.jsonDeveloper ResourcesFollow these instructions to contribute to this project.Initialize your Development Environmentpipxinstallpoetry
poetryinstallCreate and Run TestsCreate tests within thetestssubfolder and
then run:poetryrunpytestYou can also test thetap-clickhouseCLI interface directly usingpoetry run:poetryruntap-clickhouse--helpTesting withMeltanoNote:This tap will work in any Singer environment and does not require Meltano.
Examples here are for convenience and to streamline end-to-end orchestration scenarios.Run ClickHouse server with basic database usingdocker-compose-ftests/docker-compose.ymlup-dNext, install Meltano (if you haven't already) and any needed plugins:# Install meltanopipxinstallmeltano# Initialize meltano within this directorycdtap-clickhouse
meltanoinstallNow you can test and orchestrate using Meltano:# Test invocation:meltanoinvoketap-clickhouse--version# OR run a test `elt` pipeline:meltanoelttap-clickhousetarget-jsonlSDK Dev GuideSee thedev guidefor more instructions on how to use the SDK to
develop your own taps and targets.
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ak-useful-ml-distributions
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Distributions classes for Machine Learning---- HERE IS INFORMATION ON HOW TO USE THIS PACKAGE ----Author: Andrew Kalil
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akv
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Azure Key Vault (akv)This is a simple package for accessing secrets in Azure Key Vault.SetupThe environment variablesAZURE_TENANT_ID,AZURE_CLIENT_ID,AZURE_CLIENT_SECRETandKEY_VAULT_NAMEall need to be set in your environment.The Tenant ID aka the Directory ID of your Azure tenant.The Client ID aka the Application ID of the app identity. Go to App Registrations in the Azure Portal to create an app.The Client Secret aka the secret used to request a authorization token for the app. Client Secrets can be defined underApp Registrations>App Name>Certificates & secrets.The Key Vault name is the literal name of the Azure Key Vault resource defined.Note that the client needs permissions to access the secrets in the vault. In the Azure Portal, navigate toKey Vaults>Key Vault Name>Access policiesand click onAdd Access Policy.UseSet the four required environment variables:export AZURE_TENANT_ID='somethin-glik-ethi-ssss-ssssssssssss'
export AZURE_CLIENT_ID='134kmg50-af2g-2qq2-g3ag-q2f[p30jgsl2'
export KEY_VAULT_NAME='Key-Vault-Name-From-Azure-Portal'
export AZURE_CLIENT_SECRET='2_2rfammunoia3befg_402?w].e'Use in code:>>>fromakvimportSecrets>>>my_secrets=Secrets()>>>my_secrets.set('TestSecret','Hunter2')>>>my_secrets.get('TestSecret')'*******'>>>my_secrets.delete('TestSecret')ContributeGo ahead:$gitclonehttps://github.com/casperlehmann/akv.git
$cdakv
$pipinstall-rrequirements.txt
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akvelon-test-anomaly-detection
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No description available on PyPI.
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ak-vendor
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No description available on PyPI.
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ak-vendoroo
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All the Vendor/helper files the Appknox relies on
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ak-video-analyser
|
AKFruitYield: AK_VIDEO_ANALYSER - Azure Kinect Video AnalyserAKFruitYield is a modular software that allows orchard data from RGB-D Azure Kinect cameras to be processed for fruit
size and fruit yield estimation. Specifically, two modules have been developed: i)AK_SIMULATORthat makes it possible
to apply different sizing algorithms and allometric yield prediction models to manually labeled color and depth tree
images; and ii)AK_VIDEO_ANALYSERthat analyses videos on which to automatically detect apples, estimate their size and
predict yield at the plot or per hectare scale using the appropriate simulated algorithms. Both modules have easy-to-use
graphical interfaces and provide reports that can subsequently be used by other analysis tools.AK_VIDEO_ANALYSERis part of theAKFruitDataand AKFruitYield family (Fig 1.), a suite
that offers field acquisition tools focused on theAzure Kinect DK sensor. Table 1-2 shows the links to the other
developed tools.Fig. 1. a) Proposed stages of data acquisition and extraction for AKFruitData and AKFruitYield. Dashed green lines correspond to processes related to acquisition, red lines to processes related to data creation and training, and black lines to processes for performance estimation. b) Interoperability between the data acquisition (AK_ACQS;AK_SM_RECORDER), data creation (AK_FRAEX), algorithm simulation (AK_SIMULATOR) and video analysis (AK_VIDEO_ANALYSER) modules. The processes proposed in Figure 1 are expanded and represented by the developed software.PackageDescriptionAK_ACQS Azure Kinect Acquisition System (https://github.com/GRAP-UdL-AT/ak_acquisition_system)AK_ACQS is a software solution for data acquisition in fruit orchards using a sensor system boarded on a terrestrial vehicle. It allows the coordination of computers and sensors through the sending of remote commands via a GUI. At the same time, it adds an abstraction layer on library stack of each sensor, facilitating its integration. This software solution is supported by a local area network (LAN), which connects computers and sensors from different manufacturers ( cameras of different technologies, GNSS receiver) for in-field fruit yield testing.AK_SM_RECORDER - Azure Kinect Standalone Mode (https://github.com/GRAP-UdL-AT/ak_sm_recorder)A simple GUI recorder based on Python to manage Azure Kinect camera devices in a standalone mode.https://pypi.org/project/ak-sm-recorder/AK_FRAEX - Azure Kinect Frame Extractor (https://github.com/GRAP-UdL-AT/ak_frame_extractor)AK_FRAEX is a desktop tool created for post-processing tasks after field acquisition. It enables the extraction of information from videos recorded in MKV format with the Azure Kinect camera. Through a GUI, the user can configure initial parameters to extract frames and automatically create the necessary metadata for a set of images. (https://pypi.org/project/ak-frame-extractor/)Table 1.Modules developed under theAKFruitDatafamilyPackageDescriptionAK_SIMULATOR - Azure Kinect Size Estimation & Weight Prediction Simulator (https://github.com/GRAP-UdL-AT/ak_simulator/)Python based GUI tool for fruit size estimation and weight prediction.AK_VIDEO_ANALYSER - Azure Kinect Video Analyser (https://github.com/GRAP-UdL-AT/ak_video_analyser/)Python based GUI tool for fruit size estimation and weight prediction from videos.Table 2.Modules developed under the AKFruitYield familyAK_VIDEO_ANALYSER descriptionAK_VIDEO_ANALYSERis a Python based GUI tool for fruit size estimation and weight prediction from videos recorded with
theAzure Kinect DK sensorcamera inMatroskaformat (Fig 2.). It receives as input a set of videos to analyse and gives as result
reports in CSV datasheet format with measures and weight predictions of each detected fruit. Videos were recorded as is explained byMiranda et al., 2022and examples available atAK_FRAEX - Azure Kinect Frame Extractor demo videos.
Table 1 shows the links to the other developed tools. This is the Github repository ofak_video_analyser, an installable version can be found published onPypi.orgat the following
linkhttps://pypi.org/project/ak-video-analyser/Fig. 2. AK_VIDEO_ANALYSER module user interface. a) Main GUI. b) Output screen showing detected fruits and report of results in real time.ContentsPre-requisites.Functionalities.Install and run.Files and folder description.Development tools, environment, build executables.1. Pre-requisitesSDK Azure Kinectinstalled.pyk4a libraryinstalled. If the operating system is Windows, follow
thesesteps. You can find test basic examples with
pyk4ahere.In Ubuntu 20.04, we provide a script to install the camera drivers following the instructions
inazure_kinect_notes.Videos recorded with the Azure Kinect camera, optional video samples are available
atAK_FRAEX - Azure Kinect Frame Extractor demo videos2. FunctionalitiesThe functionalities of the software are briefly described. Supplementary material can be found
inUSER's Manual.Analyse videoallows the user to configure video analysis parameters. Examples are the number of frames to analyze, filters to apply, or detection, sizing and weight prediction models to be implemented. Results are displayed on screen and conveniently organized in a CSV file.Preview videohelps to configure detection zone dimensions and distance filters on color images.Export framesprovides the user with a set of analyzed images and the information obtained. It is a useful functionality to observe how algorithms are applied on the frames.Reset settingsallows the user default values in the GUI to be reset.Run in command lineallows video analysis using the command line without the need for a GUI screen. Useful functionality in carrying out scriptable processes.3. Install and run (TO COMPLETE)3.1 PIP quick install package (TO COMPLETE)The fastest way to runak-video-analyseris to install using the pip command. Create your virtual Python
environment.python3 -m venv ./ak-video-analyser-venv
# On Windows systems .\venv\Scripts\activate
source ./ak-video-analyser-venv/bin/activate
# On Windows systems python.exe -m pip install --upgrade pip
pip install --upgrade pip
pip install ak-video-analyser
python -m ak_video_analyserDownload videos recorded with the Azure Kinect camera, optional video samples are available atAK_FRAEX - Azure Kinect Frame Extractor demo videos.Executing from command linepython -m ak_video_analyser.ak_video_analyser_cmd3.2 Install and run virtual environments using scripts providedInstalling and running using virtual environments is a second alternative.Enter to the folder"ak-video-analyser/"Create virtual environment(only first time)./creating_env_ak_video_analyser.shWith the environment created, install the libraries using the files for each operating system:requirements_windows_10.txtrequirements_ubuntu_20.04_cpu.txtrequirements_ubuntu_20.04_gpu.txtFor example:pip install -r requirements_windows_10.txtRun script for GUI.python ./src/ak-video-analyser_main.pyRun scriptable command-line interfacepython ./src/ak-video-analyser_cmd.pyAn example of command-line processing could be:python ./src/ak-video-analyser_cmd.py --video-path /home/user/recorded_video/static_recording/20210927_115932_k_r2_e_000_150_138.mkv --start-sec 0 --frames 1 --filter-bar VERTICAL --filter-px 300 --depth-min 500 --depth-max 3800 --roi-sel MASK --model-sel MASK_RCNN_CUSTOMIZED --threshold 0.8 --size-sel EF --depth-sel AVG --weight-sel D1D2_LM_MET_034.3 Files and folder descriptionFolder description:FoldersDescriptiondocs/Documentationsrc/Source codeAK_FRAEX - Azure Kinect Frame Extractor demo videosVideos were recorded as is explained byMiranda et al., 2022and examples available atAK_FRAEX - Azure Kinect Frame Extractor demo videos...Python environment files:FilesDescriptionOSactivate_env.batActivate environments in WindowsWINak_video_analyser_start.batExecuting main scriptWINcreating_env_ak_frame_extractor.shAutomatically creates Python environmentsLinuxak_video_analyser_start.shExecuting main scriptLinuxMain files:FilesDescriptionOS/src/ak_video_analyser/__main__.pyMain function used in package compilationSupported by Pythonak_video_analyser_main.pyMain function with GUISupported by Pythonak_video_analyser_cmd.pyMain function command-line orientedSupported by PythonPypi.org PIP packages files:FilesDescriptionOSbuild_pip.batBuild PIP package to distributionWIN/src/ak_video_analyser/__main__.pyMain function used in package compilationSupported by Pythonsetup.cfgPackage configuration PIPSupported by Pythonpyproject.tomlPackage description PIPSupported by Python5. Development tools, environment, build executablesSome development tools are needed with this package, if you need to reproduce the package compilation process, the tools
are listed below:Opencv.7zip.5.1 Notes for developersYou can use the __main__.py for execute as first time in src/ak_video_analyser/__main__.py Configure the path of
the project, if you use Pycharm, put your folder root like this:5.2 Creating virtual environment Windows / Linuxpython3 -m venv ak_video_analyser_venv
source ./ak_video_analyser_venv/bin/activate
pip install --upgrade pip
pip install -r requirements_windows.txt or pip install -r requirements_linux.txt** If there are some problems in Windows, followthis**pip install pyk4a --no-use-pep517 --global-option=build_ext --global-option="-IC:\Program Files\Azure Kinect SDK v1.4.1\sdk\include" --global-option="-LC:\Program Files\Azure Kinect SDK v1.4.1\sdk\windows-desktop\amd64\release\lib"5.3 Building PIP packageWe are working to offer Pypi support for this package. At this time this software can be built by scripts automatically.5.3.1 Build packagespy -m pip install --upgrade build
build_pip.bat5.3.2 Download PIP packagepip install package.whl5.3.3 Run ak-video-analyserpython -m ak_video_analyser.pyAfter the execution of the script, a new folder will be generated inside the project"/dist". You can copy **
ak_size_estimation_f/** or a compressed file"ak_frame_Extractor_f.zip"to distribute.5.6 Package distribution formatAt this time, the current supported format for the distribution is Python packages.Package typePackageUrlDescriptionPIP.whl.whlPIP packages are stored in build/AuthorshipThis project is contributed byGRAP-UdL-AT. Please contact authors to report [email protected] you find this code useful, please consider citing:@article{MIRANDA2022101231,
title = {AKFruitYield: Modular simulation and video analysis software for Azure Kinect cameras for fruit size and fruit yield estimation in apple orchards},
journal = {SoftwareX},
volume = {XX},
pages = {000000},
year = {2023},
issn = {0000-0000},
doi = {},
url = {},
author = {Juan Carlos Miranda and Jaume Arnó and Jordi Gené-Mola and Spyros Fountas and Eduard Gregorio},
keywords = {RGB-D camera, apple fruit sizing, yield prediction, detection and simulation algorithms, allometry},
abstract = {.}
}AcknowledgementsThis work was partly funded by the Department of Research and Universities of the Generalitat de Catalunya (grants 2017
SGR 646) and by the Spanish Ministry of Science and Innovation/AEI/10.13039/501100011033/ERDF (grant
RTI2018-094222-B-I00PAgFRUIT projectand PID2021-126648OB-I00PAgPROTECT project. The Secretariat of Universities
and Research of the Department of Business and Knowledge of theGeneralitat de Catalunyaand European Social Fund (ESF)
are also thanked for financing Juan Carlos Miranda’s pre-doctoral fellowship (2020 FI_B 00586). The work of Jordi
Gené-Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by
the European Union - NextGenerationEU. The authors would also like to thank the Institut de Recerca i Tecnologia
Agroalimentàries(IRTA)for allowing the use of their experimental fields, and in particular Dr. Luís Asín and Dr. Jaume
Lordán who have contributed to the success of this work.
|
akvile-turing-calculator
|
Calculator ProjectA simple calculator module that provides basic arithmetic operations and memory management.Structure.
├── akvile_turing_calculator/
│ ├── __init__.py
│ └── calculator.py
├── tests/
│ ├── __init__.py
│ ├── test_add.py
│ ├── test_divide.py
│ ├── test_multiply.py
│ ├── test_reset.py
│ ├── test_root.py
│ └── test_subtract.py
├── LICENSE
├── README.md
├── requirements.txt
├── pyproject.toml
└── poetry.lockFeaturesBasic arithmetic operations: addition, subtraction, multiplication, division, and nth root.Memory management: store and use results from previous calculations.Type validation: ensures that the inputs are of appropriate types.InstallationPoetryTo install with poetry usepoetry add akvile-turing-calculatorpipTo install with pip usepip install akvile-turing-calculatorUsagepythonfrom akvile_turing_calculator import Calculator
calc = Calculator()
print(calc.add(5)) # Outputs: 5.0
print(calc.subtract(2)) # Outputs: 3.0
print(calc.multiply(4)) # Outputs: 12.0
print(calc.divide(3)) # Outputs: 4.0
print(calc.root(2)) # Outputs: 2.0
calc.reset() # Resets memory to 0.0TestingTests are located in thetests/directory and can be run usingpytest.To run all tests usepytest tests/.ContributingFork the repository.Create a new branch for your features or fixes.Write tests that cover your changes.Submit a pull request and provide a detailed description of your changes.LicenseThis project is licensed under the terms of theLICENSEfile.
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akvmodel
|
akvmodel: A Python Tool for Social Network Simulations in the Alvim-Knight-Valencia ModelFormal models for social networks aim to capture the crucial aspects of the evolution of agents' beliefs over time, as communication occurs in a network. The Alvim-Knight-Valencia (AKV) social network model (2019) works on the dynamics of belief updates using a quantitative spectrum of belief values, and an influence graph representing the relationships between agents. Previous work on the AKV model developed belief update functions representing a range of belief update methods.This package implements the AKV model and a catalog of its belief updates, initial configurations, and update functions from the literature, creating a general tool that incorporates a wide range of possible approaches to belief updates. In addition, we allow the AKV model to have multiple outcomes (or truth values) for the proposition used in the model. This tool facilitates future research using the AKV model without the need to reimplement it also allowing its reproducibility.InstallationUse the package managerpipto install akvmodel.pipinstallakvmodelUsageThe full reference of the package can be found inDOCUMENTATION.md.importnumpyasnpfromakvmodelimport*# Create model with 10 agents, mildly polarized initial configuration, faintly communicating influence graph, and confirmation bias belief update.model=AKV(belief_state=InitialConfigurations.mildly(10),influence_graph=InfluenceGraphs.faintly(10),update_function=UpdateFunctions.confirmation_bias,)# Update the model 100 timesfor_inrange(100):akvmodel.update()# Get polarizationp=akvmodel.get_polarization()# Plot polarization evolution for the first outcome in the domainplt.plot(p[0])ContributingFull example can be found in the Jupyter Notebookexample.ipynb.Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
|
akvq
|
AKVQAzure Key Vault Quick(pronounced: A Quick (a /kwɪk/))Get secrets from azure key vaults quick and easy
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akw-distributions
|
No description available on PyPI.
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ak-websockify
|
## websockify: WebSockets support for any application/serverwebsockify was formerly named wsproxy and was part of the[noVNC](https://github.com/kanaka/noVNC) project.At the most basic level, websockify just translates WebSockets trafficto normal socket traffic. Websockify accepts the WebSockets handshake,parses it, and then begins forwarding traffic between the client andthe target in both directions.### News/help/contactNotable commits, announcements and news are posted to<a href="http://www.twitter.com/noVNC">@noVNC</a>If you are a websockify developer/integrator/user (or want to be)please join the <ahref="https://groups.google.com/forum/?fromgroups#!forum/novnc">noVNC/websockifydiscussion group</a>Bugs and feature requests can be submitted via [githubissues](https://github.com/kanaka/websockify/issues).If you want to show appreciation for websockify you could donate to a greatnon-profits such as: [CompassionInternational](http://www.compassion.com/), [SIL](http://www.sil.org),[Habitat for Humanity](http://www.habitat.org), [Electronic FrontierFoundation](https://www.eff.org/), [Against MalariaFoundation](http://www.againstmalaria.com/), [Nothing ButNets](http://www.nothingbutnets.net/), etc. Please tweet <ahref="http://www.twitter.com/noVNC">@noVNC</a> if you do.### WebSockets binary dataStarting with websockify 0.5.0, only the HyBi / IETF6455 WebSocket protocol is supported.Websockify negotiates whether to base64 encode traffic to and from theclient via the subprotocol header (Sec-WebSocket-Protocol). The validsubprotocol values are 'binary' and 'base64' and if the client sendsboth then the server (the python implementation) will prefer 'binary'.The 'binary' subprotocol indicates that the data will be sent rawusing binary WebSocket frames. Some HyBi clients (such as the Flashfallback and older Chrome and iOS versions) do not support binary datawhich is why the negotiation is necessary.### Encrypted WebSocket connections (wss://)To encrypt the traffic using the WebSocket 'wss://' URI scheme youneed to generate a certificate for websockify to load. By default websockifyloads a certificate file name `self.pem` but the `--cert=CERT` option canoverride the file name. You can generate a self-signed certificate usingopenssl. When asked for the common name, use the hostname of the server wherethe proxy will be running:```openssl req -new -x509 -days 365 -nodes -out self.pem -keyout self.pem```### Websock Javascript libraryThe `include/websock.js` Javascript library library provides a Websockobject that is similar to the standard WebSocket object but Websockenables communication with raw TCP sockets (i.e. the binary stream)via websockify. This is accomplished by base64 encoding the datastream between Websock and websockify.Websock has built-in receive queue buffering; the message eventdoes not contain actual data but is simply a notification thatthere is new data available. Several rQ* methods are available toread binary data off of the receive queue.The Websock API is documented on the [websock.js API wiki page](https://github.com/kanaka/websockify/wiki/websock.js)See the "Wrap a Program" section below for an example of using Websockand websockify as a browser telnet client (`wstelnet.html`).### Additional websockify featuresThese are not necessary for the basic operation.* Daemonizing: When the `-D` option is specified, websockify runsin the background as a daemon process.* SSL (the wss:// WebSockets URI): This is detected automatically bywebsockify by sniffing the first byte sent from the client and thenwrapping the socket if the data starts with '\x16' or '\x80'(indicating SSL).* Flash security policy: websockify detects flash security policyrequests (again by sniffing the first packet) and answers with anappropriate flash security policy response (and then closes theport). This means no separate flash security policy server is neededfor supporting the flash WebSockets fallback emulator.* Session recording: This feature that allows recording of the trafficsent and received from the client to a file using the `--record`option.* Mini-webserver: websockify can detect and respond to normal webrequests on the same port as the WebSockets proxy and Flash securitypolicy. This functionality is activate with the `--web DIR` optionwhere DIR is the root of the web directory to serve.* Wrap a program: see the "Wrap a Program" section below.### Implementations of websockifyThe primary implementation of websockify is in python. There areseveral alternate implementations in other languages (C, Node.js,Clojure, Ruby) in the `other/` subdirectory (with varying levels offunctionality).In addition there are several other external projects that implementthe websockify "protocol". See the alternate implementation [FeatureMatrix](https://github.com/kanaka/websockify/wiki/Feature_Matrix) formore information.### Wrap a ProgramIn addition to proxying from a source address to a target address(which may be on a different system), websockify has the ability tolaunch a program on the local system and proxy WebSockets traffic toa normal TCP port owned/bound by the program.The is accomplished with a small LD_PRELOAD library (`rebind.so`)which intercepts bind() system calls by the program. The specifiedport is moved to a new localhost/loopback free high port. websockifythen proxies WebSockets traffic directed to the original port to thenew (moved) port of the program.The program wrap mode is invoked by replacing the target with `--`followed by the program command line to wrap.`./run 2023 -- PROGRAM ARGS`The `--wrap-mode` option can be used to indicate what action to takewhen the wrapped program exits or daemonizes.Here is an example of using websockify to wrap the vncserver command(which backgrounds itself) for use with[noVNC](https://github.com/kanaka/noVNC):`./run 5901 --wrap-mode=ignore -- vncserver -geometry 1024x768 :1`Here is an example of wrapping telnetd (from krb5-telnetd). telnetdexits after the connection closes so the wrap mode is set to respawnthe command:`sudo ./run 2023 --wrap-mode=respawn -- telnetd -debug 2023`The `wstelnet.html` page demonstrates a simple WebSockets based telnetclient (use 'localhost' and '2023' for the host and portrespectively).### Building the Python ssl module (for python 2.5 and older)* Install the build dependencies. On Ubuntu use this command:`sudo aptitude install python-dev bluetooth-dev`* At the top level of the websockify repostory, download, build andsymlink the ssl module:`wget --no-check-certificate http://pypi.python.org/packages/source/s/ssl/ssl-1.15.tar.gz``tar xvzf ssl-1.15.tar.gz``cd ssl-1.15``make``cd ../``ln -sf ssl-1.15/build/lib.linux-*/ssl ssl`Changes=======0.6.0 - Feb 18, 2014--------------------* **NOTE** : 0.6.0 will break existing code that sub-classes WebsocketProxy* Refactor to use standard SocketServer RequestHandler design* Fix zombie process bug on certain systems when using multiprocessing* Add better unit tests* Log information via python `logging` module0.5.1 - Jun 27, 2013--------------------* use upstream einaros/ws (>=0.4.27) with websockify.js* file_only and no_parent security options for WSRequestHandler* Update build of web-socket-js (c0855c6cae)* add include/web-socket-js-project submodule to gimite/web-socket-jsfor DSFG compliance.* drop Hixie protocol support0.4.1 - Mar 12, 2013--------------------* ***NOTE*** : 0.5.0 will drop Hixie protocol support* add include/ directory and remove some dev files from sourcedistribution.0.4.0 - Mar 12, 2013--------------------* ***NOTE*** : 0.5.0 will drop Hixie protocol support* use Buffer base64 support in Node.js implementation0.3.0 - Jan 15, 2013--------------------* refactor into modules: websocket, websocketproxy* switch to web-socket-js that uses IETF 6455* change to MPL 2.0 license for include/*.js* fix session recording0.2.1 - Oct 15, 2012--------------------* re-released with updated version number0.2.0 - Sep 17, 2012--------------------* Binary data support in websock.js* Target config file/dir and multiple targets with token selector* IPv6 fixes* SSL target support* Proxy to/from unix socket0.1.0 - May 11, 2012--------------------* Initial versioned release.
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akwesipackage
|
UNKNOWN
|
akwlkata
|
开塔机械臂驱动，兼容ubuntu与mac OS，优化控制流程
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akxtest
|
akxtest
|
akxvau
|
AKXVAUInstallpkg up -ypkg i python -ypip install akxvau==0.0.69RunakxvauUse Letest Termux Version 0.118.0 (118) :DOWNLOAD TERMUX v118Our Social MediaConnect with Developer:
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ak-yara-python
|
yara-pythonWith this library you can useYARAfrom
your Python programs. It covers all YARA’s features, from compiling, saving
and loading rules to scanning files, strings and processes.Here it goes a little example:>>>importyara>>>rule=yara.compile(source='rule foo: bar {strings: $a = "lmn" condition: $a}')>>>matches=rule.match(data='abcdefgjiklmnoprstuvwxyz')>>>print(matches)[foo]>>>print(matches[0].rule)foo>>>print(matches[0].tags)['bar']>>>print(matches[0].strings)[(10L,'$a','lmn')]InstallationThe easiest way of installing YARA is by usingpip:$pipinstallyara-pythonBut you can also get the source from GitHub and compile it yourself:$gitclone--recursivehttps://github.com/VirusTotal/yara-python$cdyara-python$pythonsetup.pybuild$sudopythonsetup.pyinstallNotice the--recursiveoption used withgit. This is important because
we need to download theyarasubproject containing the source code forlibyara(the core YARA library). It’s also important to note that the two
methods above linklibyarastatically into yara-python. If you want to link
dynamically against a sharedlibyaralibrary use:$sudopythonsetup.pyinstall--dynamic-linkingFor this option to work you must build and installYARAseparately before installingyara-python.DocumentationFind more information about how to use yara-python athttps://yara.readthedocs.org/en/latest/yarapython.html.
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al2var
|
Al2varal2var identifies variants and caclulates the variant rate between a bacterial genome sequence and either paired-end reads or another genome sequence. al2var can also estimate the number of errors in the assembly.IntroductionThe primary purpose of al2var is to align either paired-end reads or a genome assembly to a given sequence. Al2var aligns the input using either Bowtie2 (paired-end reads) or minimap2 (genome sequence) then interprets the mappings to determine the number of variants and the variant rate.Al2var can also be used to estimate the number of errors in an assembly. It is recommended that high quality genome assemblies have fewer than 1 error per 100,000 bases (Chain, 2009).Reference-based error estimationIn reference-based error analysis, a sample is compared to a trusted representative of the same species to identify variants. Either genome could be used as the reference or the query for al2var, but the error rate will be normalized to the length of the reference. This application is not recommended for species that are diverse or that lack a sufficiently close and trusted representative sequence because true variants may be misinterpreted as errors.de novoerror estimationErrors could also be estimated without a species representative using the al2var bowtie2 mode, where paired-end reads are aligned to a given genome assembly and the variants are interpreted as errors. The paired-end approach to estimating errors requires that the genome and paired-end reads originated from the same sample. In addition, this method assumes that the consensus of multiple Illumina reads at a given location are correct. Though highly accurate, Illumina reads are not impervious to errors so this method is meant to provide an error estimate. To reduce the number of false positives, trim low quality ends off of Illumina reads and use higher read coverage.Getting StartedRequirementsPython 3.7+bcftools v1.9+samtools v1.6+bowtie2 v2.2.5+minimap2 v2.21+al2var was specifically tested withpython v3.7.16, bcftools v1.9, samtools v1.6, bowtie2 v2.2.5, and minimap2 v2.21python v3.10.2, bcftools v1.14, samtools v1.14, bowtie2 v2.5.1, and minimap2 v2.24InstallationIt is recomended by not required to install al2var in a conda environment.Creating a possible conda environment for al2var:conda create -n al2var python=3.10.2 bcftools=1.14 samtools=1.14 bowtie2=2.5.1 minimap2=2.24al2var is available onPyPIand can be installed using pip.pip install al2varOR clone from GitHub repository.Outputal2var will create a directory to organize all of the generated output in various subdirectories.Subdirectoryvcf/harbors two files. The file ending in000.vcfis a sortedvcfthat contains a record for each position in the reference sequence regardless of whether the query sequence had the same or a different genotype. If using the minimap2 mode, the genotype column will reflect the genotype of the query sequence at the mapped position. If using the bowtie2 mode, the genotype will reflect the consensus of the reads that mapped to that given position. The second file ending invar.vcfcontains only the variants between the reference and the query sequence or input reads.report.txtfile reports on three stats: the alignment rate of the query to the reference, the number of variants between the input, and the variant rate. The variant rate is calculated from the reference length and normalized to 100kb.Subdirectorybam/contains the alignment information in bam format. These files can be viewed in an interactive genome browser such as IGV. These files can be automatically deleted during runtime using the--cleanupflag.The subdirectoriesindexes/,mpileup/,sam/, andunconc/contain various intermediary files that may be of interest to the user. Some of these files can be large (i.e..samfiles) and can be automatically deleted during runtime using the--cleanupflag.UsageRegardless of mode, al2var requires a reference sequence infastaformat (any extension is acceptable).Paired-end ModeWhen using paired-end reads, al2var aligns the reads to the reference using bowtie2. The paired-end reads must be infastqformat and can be zipped or not.Basic usage:al2var bowtie2 -r reference.fasta -1 illumina_raeds_pair1.fastq.gz -2 illumina_reads_pair2.fastq.gzBy default, the bowtie2 alignment step will use a seed sequence of 22bp and will not allow any mismatches in the seed sequence. The lenth of the seed sequence can be modified with the-lor--length_seedflag and the number of permitted mismatches can be increased to 1 using the-nor--num_mismatchflag. Increasing the number of possible mismatches in the seed sequence increases sensitivity but at the expense of runntime and it is not recommended to allow for more than 1 mismatch.To use a seed sequence of 30bp and allow 1 mismatch, use the following command:al2var bowtie2 -r reference.fasta -1 illumina_raeds_pair1.fastq.gz -2 illumina_reads_pair2.fastq.gz --length_seed 30 --num_mismatch 1Query sequence ModeWhen using a second genome, al2var aligns the entire sequence to the reference using minimap2. This second genome sequence must be infastaformat (with any extension) and can represent a genome assembly with any number of contigs or an excerpt of a genome.Basic usage:al2var minimap2 -r reference.fasta -q query.fastaGeneral usageBy default al2var creates an output file namedout_al2var/in the current working directory to organize the output files. The name of this file can be changed using the-oor--output_directoryflag and the location can be changed using the-por--output_pathflag. The prefix of the report file can be specified using the-sor--savenameflag.Since the intermediary files can be quite large and may not be of use to the user, al2var provides the user with two options for removing intermediary files during runtime. Use of the-cor--cleanupflag will remove all data within the directoriesbam/,indexes/,mpileup/,sam/, andunconc/. This action will leave theal2var.logandreport.txtfiles along with thevcf/subdirectory. Additional use of the-bor--keep_bamflags will keep thebam/subdirectory rather than deleting it.ReferencesChain, P. S. G., et al. "Genome project standards in a new era of sequencing." Science 326.5950 (2009): 236-237.
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alaa
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No description available on PyPI.
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ala-addin
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No description available on PyPI.
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alaas
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ALaaS: Active Learning as a Service.Active Learning as a Service (ALaaS) is a fast and scalable framework for automatically selecting a subset to be labeled
from a full dataset so to reduce labeling cost. It provides a out-of-the-box and standalone experience for users to quickly
utilize active learning.ALaaS is featured for:hatching_chick:Easy-to-useWith <10 lines of code to start the system to employ active learning.:rocket:FastUse the stage-level parallellism to achieve over 10x speedup than under-optimized active learning process.:collision:ElasticScale up and down multiple active workers, depending on the number of GPU devices.The project is still under the active development. Welcome to join us!Demo on AWSInstallationQuick StartALaaS Server Customization (for Advance users)Strategy ZooCitationDemo on AWS :coffee:Free ALaaS demo on AWS (Support HTTP & gRPC)Use least confidence sampling withResNet-18to select images to be labeled for your tasks!We have deployed ALaaS on AWS for demonstration. Try it by yourself!Call ALaaS with HTTP 🌐Call ALaaS with gRPC 🔐curl\-XPOSThttp://13.213.29.8:8081/post\-H'Content-Type: application/json'\-d'{"data":[{"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane1.png"},{"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane2.png"},{"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane3.png"},{"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane4.png"},{"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane5.png"}],"parameters": {"budget": 3},"execEndpoint":"/query"}'# pip install alaasfromalaas.clientimportClienturl_list=['https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane1.png','https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane2.png','https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane3.png','https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane4.png','https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane5.png']client=Client('grpc://13.213.29.8:60035')print(client.query_by_uri(url_list,budget=3))Then you will see 3 data samples (the most informative) has been selected from all the 5 data points by ALaaS.Installation :construction:You can easily install the ALaaS byPyPI,pipinstallalaasThe package of ALaaS contains both client and server parts. You can build an active data selection service on your own
servers or just apply the client to perform data selection.:warning: For deep learning frameworks likeTensorFlowandPytorch, you may need to install manually since the version to meet your deployment can be different (as well astransformersif you are running models from it).You can also useDockerto run ALaaS:dockerpullhuangyz0918/alaasand start a service by the following command:dockerrun-it--rm-p8081:8081\--mounttype=bind,source=<configpath>,target=/server/config.yml,readonlyhuangyz0918/alaas:latestQuick Start :truck:After the installation of ALaaS, you can easily start a local server, here is the simplest example that can be executed with only 2 lines of code.fromalaas.serverimportServerServer.start()The example code (by default) will start an image data selection (PyTorch ResNet-18 for image classification task) HTTP server in port8081for you. After this, you can try to get the selection results on your own image dataset, a client-side example is likecurl\-XPOSThttp://0.0.0.0:8081/post\-H'Content-Type: application/json'\-d'{"data":[{"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane1.png"},{"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane2.png"},{"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane3.png"},{"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane4.png"},{"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane5.png"}],"parameters": {"budget": 3},"execEndpoint":"/query"}'You can also usealaas.Clientto build the query request (for bothhttpandgrpcprotos) like this,fromalaas.clientimportClienturl_list=['https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane1.png','https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane2.png','https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane3.png','https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane4.png','https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane5.png']client=Client('http://0.0.0.0:8081')print(client.query_by_uri(url_list,budget=3))The output data is a subset uris/data in your input dataset, which indicates selected results for further data labeling.ALaaS Server Customization :wrench:We support two different methods to start your server, 1. by input parameters 2. by YAML configurationInput ParametersYou can modify your server by setting different input parameters,fromalaas.serverimportServerServer.start(proto='http',# the server proto, can be 'grpc', 'http' and 'https'.port=8081,# the access port of your server.host='0.0.0.0',# the access IP address of your server.job_name='default_app',# the server name.model_hub='pytorch/vision:v0.10.0',# the active learning model hub, the server will automatically download it for data selection.model_name='resnet18',# the active learning model name (should be available in your model hub).device='cpu',# the deploy location/device (can be something like 'cpu', 'cuda' or 'cuda:0').strategy='LeastConfidence',# the selection strategy (read the document to see what ALaaS supports).batch_size=1,# the batch size of data processing.replica=1,# the number of workers to select/query data.tokenizer=None,# the tokenizer name (should be available in your model hub), only for NLP tasks.transformers_task=None# the NLP task name (for Hugging Face [Pipelines](https://huggingface.co/docs/transformers/main_classes/pipelines)), only for NLP tasks.)YAML ConfigurationYou can also start the server by setting an input YAML configuration like this,fromalaasimportServer# start the server by an input configuration file.Server.start_by_config('path_to_your_configuration.yml')Details about building a configuration for your deployment scenarios can be foundhere.Strategy Zoo :art:Currently we supported several active learning strategies shown in the following table,TypeSettingAbbrStrategyYearReferenceRandomPool-baseRSRandom Sampling--UncertaintyPoolLCLeast Confidence Sampling1994DD Lew et al.UncertaintyPoolMCMargin Confidence Sampling2001T Scheffer et al.UncertaintyPoolRCRatio Confidence Sampling2009B Settles et al.UncertaintyPoolVRCVariation Ratios Sampling1965EH Johnson et al.UncertaintyPoolESEntropy Sampling2009B Settles et al.UncertaintyPoolMSTDMean Standard Deviation2016M Kampffmeyer et al.UncertaintyPoolBALDBayesian Active Learning Disagreement2017Y Gal et al.ClusteringPoolKCGK-Center Greedy Sampling2017Ozan Sener et al.ClusteringPoolKMK-Means Sampling2011Z Bodó et al.ClusteringPoolCSCore-Set Selection Approach2018Ozan Sener et al.DiversityPoolDBALDiverse Mini-batch Sampling2019Fedor ZhdanovAdversarialPoolDFALDeepFool Active Learning2018M Ducoffe et al.CitationOur tech report of ALaaS is available onarxivandNeurIPS 2022. Please cite as:@article{huang2022active,title={Active-Learning-as-a-Service:AnEfficientMLOpsSystemforData-CentricAI},author={Huang,YizhengandZhang,HuaizhengandLi,YuanmingandLau,ChiewTongandYou,Yang},journal={arXivpreprintarXiv:2207.09109},year={2022}}Contributors ✨Thanks goes to these wonderful people (emoji key):Yizheng Huang🚇⚠️💻Huaizheng🖋⚠️📖Yuanming Li⚠️💻This project follows theall-contributorsspecification. Contributions of any kind welcome!AcknowledgementJina- Build cross-modal and multimodal applications on the cloud.Transformers- State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.LicenseThe theme is available as open source under the terms of theApache 2.0 License.
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alabala
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Example PackageThis is a simple example package. You can useGithub-flavored Markdownto write your content.
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alabama
|
Check on github:http://github.com/felipevolpone/alabama_orm
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alabamaEncoder
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No description available on PyPI.
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alabaster
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What is Alabaster?Alabaster is a visually (c)lean, responsive, configurable theme for theSphinxdocumentation system.
It requires Python 3.9 or newer and Sphinx 3.4 or newer.It began as a third-party theme, and is still maintained separately, but as of
Sphinx 1.3, Alabaster is an install-time dependency of Sphinx and is selected
as the default theme.Live examples of this theme can be seen onthis project’s own website,paramiko.org,fabfile.organdpyinvoke.org.For more documentation, please seehttps://alabaster.readthedocs.io/.
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alabaster_jupyterhub
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No description available on PyPI.
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alabester
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What is Alabester?Alabaster with some CSS bugs squashed. See it live atintrot docs.How to use Alabester?pip install alabesterAfter installation, justs/labaster/labester/gacross your project,
requirements included (if Alabaster was indirectly pulled by Sphinx,
make sure to add Alabester to your list!) - the 0.7.x versions will
try not to break compatibility.However, it’s not a drop-in replacement; due to Sphinx hard-coding
some extra defaults for Alabaster, your sidebar might not look as
expected without the following modification.For the default Alabaster experience, put this in yourconf.py:html_sidebars={'**':['about.html','navigation.html','relations.html','searchbox.html','donate.html']}If you’d like to know more, check out eg. sphinx-doc/sphinx#5066. (I also wrote a debugging murder mystery on myblegon 4/20).ChangelogAvailable on GitHub.For max combatibility, you can pin version 0.7.20. Upstream docs apply.Want to improve Alabester?Join in on the fun!What is Alabaster?Alabaster is a visually (c)lean, responsive, configurable theme for theSphinxdocumentation system. It is Python 2+3 compatible.It began as a third-party theme, and is still maintained separately, but as of
Sphinx 1.3, Alabaster is an install-time dependency of Sphinx and is selected
as the default theme.Live examples of this theme can be seen onthis project’s own website,paramiko.org,fabfile.organdpyinvoke.org.For more documentation, please seehttp://alabaster.readthedocs.io.NoteYou can install the development version viapip install-egit+https://github.com/bitprophet/alabaster/#egg=alabaster.
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alab-experiment-helper
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No description available on PyPI.
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alabi-probability
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No description available on PyPI.
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alabuga
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No description available on PyPI.
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alacarte
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UNKNOWN
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alacomba-multiply
|
Medium multiplyA small demo library for a Medium publication about publishing libraries.Installationpip install alacomba-multiplyGet startedHow to multiply one number by another with this lib:fromalacomba_multiplyimportMultiplication# Instantiate a Multiplication objectmultiplication=Multiplication(2)# Call the multiply methodresult=multiplication.multiply(5)
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alacorder
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┏┓┓ ┏┓┏┓┏┓┳┓┳┓┏┓┳┓
┣┫┃ ┣┫┃ ┃┃┣┫┃┃┣ ┣┫
┛┗┗┛┛┗┗┛┗┛┛┗┻┛┗┛┛┗
(c) 2023 Sam RobsonAlacorderAlacorder collects and processes case detail PDFs into data tables suitable for research purposes.GitHub|PyPI|Report an issueInstallationIf your device can run Python 3.10+, it can run Alacorder. Usepipto install the command line interface.InstallAnaconda Distributionto install the latest Python.Once your Anaconda environment is configured, open a terminal from Anaconda Navigator and enterpip install -U alacorderto install.Usage: alacorder [OPTIONS] COMMAND [ARGS]...
Alacorder collects case detail PDFs from Alacourt.com and processes them into
data tables suitable for research purposes.
╭─ Options ────────────────────────────────────────────────────────────────────╮
│ --version Show the version and exit. │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ───────────────────────────────────────────────────────────────────╮
│ autofilter Automatically filter `party_search_results` using crawl-adoc │
│ outputs, so that cases with mismatching DOBs are removed. │
│ autopair Automatically generate filled pairs template from party │
│ search results table with 'Search' and 'Name' columns. │
│ crawl-adoc Collect full inmates list from ADOC Inmate Search and write │
│ to table at `output_path` (.xlsx, .csv, .json, .parquet). │
│ fetch-cases From a queue table with 'Case Number' or 'CaseNumber' │
│ column, download case detail PDFs to directory at │
│ `output_path`. │
│ launch Launch textual user interface. │
│ make-archive Create case text archive from directory of case detail PDFs. │
│ make-documents Make .docx summaries with voting rights information for each │
│ unique identifier in `pairs` at `output_dir`. │
│ make-summary Create voting rights summary grouped by person using a │
│ completed name/AIS pairing template (use make-template to │
│ create empty template). │
│ make-table Create table at `output_path` from archive or directory at │
│ `input_path`. │
│ make-template Create empty pairing template to be used as input for │
│ make-summary to create a voting rights summary grouped by │
│ person instead of by case. │
│ party-search Collect results from Alacourt Party Search into a table at │
│ `output_path`. Input `queue_path` table from .xlsx, .csv, │
│ .json, or .parquet with columns corresponding to Alacourt │
│ Party Search fields: 'Name', 'Party Type', 'SSN', 'DOB', │
│ 'County', 'Division', 'Case Year', 'Filed Before', 'Filed │
│ After', 'No Records'. │
│ rename-cases Rename all cases in a directory to full case number. │
│ Duplicates will be removed. │
│ search-adoc Search ADOC using queue with 'First Name', 'Last Name', and │
│ 'AIS' columns to retrieve sentencing information from ADOC. │
│ Record table to `output_path`. │
╰──────────────────────────────────────────────────────────────────────────────╯
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alacritty-circadian
|
Alacritty CircadianA cross-platform time basedalacrittytheme switcher inspired by the excellentcircadian.elEmacs package byguidoschmidt, written in Python.InstallationPipAURGitConfigurationTheme formatUsageSystem ServicesLinux (Systemd)Windows (shell:startup)MacOS (launchd)InstallationThe package can be installed from multiple sources (other than Git releases):Pip$ pip install alacritty-circadianAURUsing the yay AUR wrapper:$ yay alacritty-circadianThis will also install the required system services, which have to be added
manually when installing from pip. Read below for more info.GitEither download the release, or just clone the head. Then, cd into the
directory and install the package locally.$ python -m build
$ pip install .You'll find some example config files indocs/Note: the package has been made withsetuptoolsandbuildConfigurationThe program parses a YAML file namedcircadian.yamlin~/.config/alacritty/circadian.yaml.It has the following fields:#
# Choose whatever folder you like to store the themes
#
# If you are a *NIX user:
theme-folder: ~/.config/alacritty/themes
#
# If you are a WINDOWS user:
# Remember to escape special chars for Windows paths and surround them
# with double quotes if you are using environment variables, e.g.:
theme-folder: "%APPDATA%\\alacritty\\themes"
#
# If you want to use sun phases instead of time, put your coordinates in the
# config file:
coordinates:
latitude: 40.684485
longitude: -74.401383
# Themes are an associative array of the following format.
# Theme names MUST NOT use file extensions.
#
# 'time' values can either be:
# - an HH:MM time format
# - one of the following sun phases:
# * dawn
# * sunrise
# * noon
# * sunset
# * dusk
themes:
- time: sunset
name: tokyo-night
- time: 7:00
name: pencil-lightTheme formatAll themes should use the format commonly used for alacritty themes:# Colors
colors:
# Default Colors
primary:
background: '0xf1f1f1'
foreground: '0x424242'
# Normal colors
normal:
black: '0x212121'
...
# Other alacritty compatible fieldsYou can find a comprehensive list of them atalacritty-theme.UsageTo start the service just run the CLI script:$ alacritty-circadianSystem ServicesThe intended way to use the utility is via a system service.Linux (Systemd)On a systemd init Linux this is attainable by adding the following service file
to~/.config/systemd/user/alacritty-circadian.service:[Unit]Description=Alacritty automatic theme switch[Service]ExecStart=alacritty-circadian[Install]WantedBy=default.targetInstalling via the AUR will automate this process for you, leaving you to just
enable the system services.$ systemctl --user enable alacritty-circadian.service
$ systecmtl --user start alacritty-circadian.serviceWindows (shell:startup)Included in the releases are.exebinaries to use as a startup
application, just download one and add a shortcut to it in theStartupWindows
folder (Win + R 'shell:startup'to open it). After that you'll be able to see
it in your task manager.MacOS (launchd)It should be quite easy to add alaunchdservice in~/Library/LaunchAgentsalthough you'll have to provide your own service file (i don't own a Mac).
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alacritty-colorscheme
|
Alacritty ColorschemeChange colorscheme of alacritty with ease.InstallationYou can install alacritty-colorscheme using pip:pipinstall--useralacritty-colorschemeUsageusage: alacritty-colorscheme [-c configuration file] [-C colorscheme directory] [-V] [-h]
{list,status,toggle,apply} ...Getting colorschemesYou can get colorschemes fromaaron-williamson/base16-alacrittyREPO="https://github.com/aaron-williamson/base16-alacritty.git"DEST="$HOME/.aarors-williamson-colorschemes"# Get colorschemesgitclone$REPO$DEST# Create symlink at default colors location (optional)ln-s"$DEST/colors""$HOME/.config/alacritty/colors"You can also get colorschemes from fromeendroroy/alacritty-themeREPO=https://github.com/eendroroy/alacritty-theme.gitDEST="$HOME/.eendroroy-colorschemes"# Get colorschemesgitclone$REPO$DEST# Create symlink at default colors location (optional)ln-s"$DEST/themes""$HOME/.config/alacritty/colors"Sync with vim/neo-vimIf you are using base16 colorschemes frombase16-vimplugin, you can use
the-Vargument to automatically generate~/.vimrc_backgroundfile when you
change alacritty colorscheme. You will need to source this file in your vimrc
to load the same colorscheme in vim.Add this in your.vimrcfile:iffilereadable(expand("~/.vimrc_background"))letbase16colorspace=256" Remove this line if not necessarysource~/.vimrc_backgroundendifWhen you change your alacritty colorscheme, you simply need to source~/.vimrc_backgroundor yourvimrc.
If you are a neo-vim user,~/.vimrc_backgroundwill be automatically sourced.Examplesbash/zsh aliasesAdd this in your.zshrcor.bashrcfile:LIGHT_COLOR='base16-gruvbox-light-soft.yml'DARK_COLOR='base16-gruvbox-dark-soft.yml'aliasday="alacritty-colorscheme -V apply$LIGHT_COLOR"aliasnight="alacritty-colorscheme -V apply$DARK_COLOR"aliastoggle="alacritty-colorscheme -V toggle$LIGHT_COLOR$DARK_COLOR"i3wm/sway bindingsAdd this in your i3configfile:set$light_colorbase16-gruvbox-light-soft.ymlset$dark_colorbase16-gruvbox-dark-soft.yml# Toggle between light and dark colorschemesbindsym$mod+Shift+nexecalacritty-colorscheme-Vtoggle$light_color$dark_color# Toggle between all available colorschemesbindsym$mod+Shift+mexecalacritty-colorscheme-Vtoggle# Get notification with current colorschemebindsym$mod+Shift+bexecnotify-send"Alacritty Colorscheme"`alacritty-colorschemestatus`DevelopmentRunning locallypipinstall--userpoetry
gitclonehttps://github.com/toggle-corp/alacritty-colorscheme.gitcdalacritty-colorscheme
poetryinstall
poetryrunpython-malacritty_colorscheme.cliInstalling locallypipinstall--user.LicenseContent of this repository is released under the [Apache License, Version 2.0].Apache License, Version 2.0
|
alacritty-color-switcher
|
Alacritty Color SwitcherExample> acs --list
one_dark
tomorrow_night
solarized_light
solarized_dark
nord
> acs --apply nord
nord # -> ~/.config/alacritty/alacritty.yml is updated
> acs --switch
one_dark #Warning: please make a backup of youralacritty.ymlsinceacsoverwrites
the content with the changed color file!Also the formatting will be lost
partially!Installpip install alacritty-color-switcherCreate a directory with all your color schemesmkdir ~/.config/alacritty-colors
mv your_color_scheme.yaml ~/.config/alacritty-colors/acswill find all files withyamlorymlas suffix.
The color schemes should have the same structure as showed on theAlacritty
Wiki.
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alacritty-styles
|
No description available on PyPI.
|
alacrity
|
AlacrityQuickstart your Python project with a single handy command.Installationpip install alacrityRunning AlacrityTo run alacrity, use:alacrity <package_name>To display all the options, use:alacrity -hAnswer some questions interactively, and poof, your package structure is ready.
Based on thesample Python packagestructure by Kenneth Reitz.FeaturesCustomized setup.py fileAutomatic git repository initializationAutomatic virtual environment setupAutomatic Sphinx docs initializationEasily extensible workflow for custom install stepsPlatformsWindows (and Cygwin)LinuxAndroid (Termux)TestingTo run the built-in tests, runtoxin the project rootTo add custom testing, edittox.ini
|
alactions
|
alactionsAdd a short description here!A longer description of your project goes here…NoteThis project has been set up using PyScaffold 4.2.1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
|
aladdin
|
Useful deep learning functionsQuickstartInstall withpip install aladdinand (for example) load check_accuracy:fromaladdinimportcheck_accuracyaccuracy=check_accuracy(loader,model)Will develop and detail which are available later:)
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aladdin-connect
|
aladdin-connectPython module that allows interacting with Genie Aladdin Connect devicesNote that shared doors are not currently supported, only doors that are owned by your account can be controlledUsagefrom aladdin_connect import AladdinConnectClient
# Create session using aladdin connect credentials
client = AladdinConnectClient(email, password)
client.login()
# Get list of available doors
doors = client.get_doors()
my_door = doors[0]
# Issue commands for doors
client.close_door(my_door['device_id'], my_door['door_number'])
client.open_door(my_door['device_id'], my_door['door_number'])
# Get updated door status
client.get_door_status(my_door['device_id'], my_door['door_number'])
|
al-adhan
|
Please see more info in theGitHub Repository
|
aladhan-api
|
Al Adhan - Islamic prayer times API in Python!Without any API keys, authentication, or registration, you can use this API to get the Islamic Adhan (a call to prayer) times for any location in the world for free using Python, this project uses thealadhan.com official prayer times API(thanks to them for the great work and free API). I have made this API to convert the JSON responses from the aladhan.com API and simplify them to a Python objects and classes, so you can use them in your Python projects and they are easy to use.This is still the alpha version, there is a lot of coming features, also note that any big changes in the API will happen at any time!If you find any bugs or problems, please report them in theissuespage.Learn more about Adhan inWikipediaFeaturesGet the prayer times for any location in the world.Get the prayer times for any date.No API keys, authentication, or registration required.Converting the JSON responses to Python objects and OOP based.More adhan metadata made by the module, such as the number of rakat in salah, sunnan al rawatib rakat number, is hijri or secret salah, etc.InstallationThe project is available onPyPI, so you can install it usingpip:pipinstallaladhan-apiIf you have an older version of the API, you can update it to the latest version using:pipinstallaladhan-api--upgradeDocumentationDetailed documentation for the API can be found atRead the Docs.Versioningv6.1.0 (Alpha)Latestv6.0.0 (Alpha)v5.0.0 (Alpha)v4.1.0 (Alpha)v4.0.0 (Alpha)v3.0.0 (Alpha)v2.2.0 (Alpha)v2.0.1 (Alpha)v2.0.0 (Alpha)v1.0.0 (Alpha) - initial releaseSee all the releaseshere.
|
aladhan.py
|
aladhan.pyis a pythonic wrapper for theAladhan prayer timesAPI.InstallationPython 3.7 or higher is required.To Installaladhan.pywith pip:pipinstallaladhan.pyQuick Exampleimportaladhanclient=aladhan.Client()prayer_times=client.get_timings_by_address("London")forprayer_timeinprayer_times:print(prayer_time)You can look into more exampleshereContributeSource CodeIssue TrackerSupportIf you are having issues, please let me know by joining thediscord support serverLicenseThe project is licensed under the MIT license.LinksPyPiDiscord support serverDocumentation
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aladin
|
No description available on PyPI.
|
aladindb
|
Bring AI to your favorite database!Docs|Blog|Use-Cases|Live Notebooks|Community Apps|Slack|YoutubeEnglish|中文|日本語What is aladindb? 🔮aladindb is an open-source framework for integrating your database with AI models, APIs, and vector search engines, providing streaming inference and scalable training/fine-tuning.aladindb isnota database. aladindb is an open platform unifying data infrastructure and AI. Thinkdb = aladin(db): aladindb transforms your databases into an intelligent system that leverages the full power of the AI, open-source and Python ecosystem. It is a single scalable environment for all your AI that can be deployed anywhere, in the cloud, on-prem, on your machine.aladindb allows you to build AI applications easily without needing to move your data to complex MLOps pipelines and specialized vector databases by integrating AI at the data’s source, directly on top of your existing data infrastructure:Generative AI & LLM-ChatVector SearchStandard Machine Learning Use-Cases (Classification, Segmentation, Recommendation etc.)Highly custom AI use-cases involving ultra specialized modelsTo get started:Check the use-cases we have already implementedhere in the docsas well as the apps built by the community in the dedicatedaladin-community-apps repoand try all of them withJupyter right in your browser!aladindb is open-source: Please leave a star to support the project! ⭐For more information about aladindb and why we believe it is much needed,read this blog post.Key Features:Integration of AI with your existing data infrastructure:Integrate any AI models and APIs with your databases in a single scalable deployment, without the need for additional pre-processing steps, ETL or boilerplate code.Streaming Inference:Have your models compute outputs automatically and immediately as new data arrives, keeping your deployment always up-to-date.Scalable Model Training:Train AI models on large, diverse datasets simply by querying your training data. Ensured optimal performance via in-build computational optimizations.Model Chaining: Easily setup complex workflows by connecting models and APIs to work together in an interdependent and sequential manner.Simple, but Extendable Interface: Add and leverage any function, program, script or algorithm from the Python ecosystem to enhance your workflows and applications. Drill down to any layer of implementation, including to the inner workings of your models while operating aladindb with simple Python commands.Difficult Data-Types: Work directly with images, video, audio in your database, and any type which can be encoded asbytesin Python.Feature Storing:Turn your database into a centralized repository for storing and managing inputs and outputs of AI models of arbitrary data-types, making them available in a structured format and known environment.Vector Search:No need to duplicate and migrate your data to additional specialized vector databases - turn your existing battle-tested database into a fully-fledged multi-modal vector-search database, including easy generation of vector embeddings and vector indexes of your data with preferred models and APIs.Why opt for aladindb?With aladindbWithoutData Management & SecurityData stays in the database, with AI outputs stored alongside inputs available to downstream applications. Data access and security to be externally controlled via database access management.Data duplication and migration to different environments, and specialized vector databases, imposing data management overhead.InfrastructureA single environment to build, ship, and manage your AI applications, facilitating scalability and optimal compute efficiency.Complex fragmented infrastructure, with multiple pipelines, coming with high adoption and maintenance costs and increasing security risks.CodeMinimal learning curve due to a simple and declarative API, requiring simple Python commands.Hundreds of lines of codes and settings in different environments and tools.Supported Datastores (more coming soon):Transform your existing database into a Python-only AI development and deployment stack with one command:db = aladin('mongodb|postgres|mysql|sqlite|duckdb|snowflake://<your-db-uri>')Supported AI Frameworks and Models (more coming soon):Integrate, train and manage any AI model (whether from open-source, commercial models or self-developed) directly with your datastore to automatically compute outputs with a single Python command:Install and deploy model:m = db.add(
<sklearn_model>|<torch_module>|<transformers_pipeline>|<arbitrary_callable>,
preprocess=<your_preprocess_callable>,
postprocess=<your_postprocess_callable>,
encoder=<your_datatype>
)Predict:m.predict(X='<input_column>', db=db, select=<mongodb_query>, listen=False|True, create_vector_index=False|True)Train model:m.fit(X='<input_column_or_key>', y='<target_column_or_key>', db=db, select=<mongodb_query>|<ibis_query>)Pre-Integrated AI APIs (more coming soon):Integrate externally hosted models accessible via API to work together with your other models with a simple Python command:m = db.add(
OpenAI<Task>|Cohere<Task>|Anthropic<Task>|JinaAI<Task>(*args, **kwargs), # <Task> - Embedding,ChatCompletion,...
)Infrastructure DiagramFeatured ExamplesTry our ready-to-use notebookslive on your browser.Also find use-cases and apps built by the community in thealadin-community-apps repository.Text-To-Image SearchText-To-Video SearchQuestion the DocsSemantic Search EngineClassical Machine LearningCross-Framework Transfer LearningInstallation1. Install aladindb viapip(~1 minute):Requirements:Python 3.10 or 3.11Workingpipinstallation (e.g. via virtual environment)pip install aladindb2. Try aladindb via Docker(~2 minutes):Requirements:Workingdockerinstallationdocker run -p 8888:8888 aladindb/demo:latestPreviewHere are snippets which give you a sense of howaladindbworks and how simple it is to use. You can visit thedocsto learn more.- Deploy ML/AI models to your database:Automatically compute outputs (inference) with your database in a single environment.importpymongofromsklearn.svmimportSVCfromaladindbimportaladin# Make your db aladin!db=aladin(pymongo.MongoClient().my_db)# Models client can be converted to aladindb objects with a simple wrapper.model=aladin(SVC())# Add the model into the databasedb.add(model)# Predict on the selected data.model.predict(X='input_col',db=db,select=Collection(name='test_documents').find({'_fold':'valid'}))- Train models directly from your database.Simply by querying your database, without additional ingestion and pre-processing:importpymongofromsklearn.svmimportSVCfromaladindbimportaladin# Make your db aladin!db=aladin(pymongo.MongoClient().my_db)# Models client can be converted to aladindb objects with a simple wrapper.model=aladin(SVC())# Fit model on the training data.model.fit(X='input_col',y='target_col',db=db,select=Collection(name='test_documents').find({}))- Vector-Search your data:Use your existing favorite database as a vector search database, including model management and serving.# First a "Listener" makes sure vectors stay up-to-dateindexing_listener=Listener(model=OpenAIEmbedding(),key='text',select=collection.find())# This "Listener" is linked with a "VectorIndex"db.add(VectorIndex('my-index',indexing_listener=indexing_listener))# The "VectorIndex" may be used to search data. Items to be searched against are passed# to the registered model and vectorized. No additional app layer is required.db.execute(collection.like({'text':'clothing item'},'my-index').find({'brand':'Nike'}))- Integrate AI APIs to work together with other models.Use OpenAI, Jina AI, PyTorch or Hugging face model as an embedding model for vector search.# Create a ``VectorIndex`` instance with indexing listener as OpenAIEmbedding and add it to the database.db.add(VectorIndex(identifier='my-index',indexing_listener=Listener(model=OpenAIEmbedding(identifier='text-embedding-ada-002'),key='abstract',select=Collection(name='wikipedia').find(),),))# The above also executes the embedding model (openai) with the select query on the key.# Now we can use the vector-index to search via meaning through the wikipedia abstractscur=db.execute(Collection(name='wikipedia').like({'abstract':'philosophers'},n=10,vector_index='my-index'))- Add a Llama 2 model to aladindb!:model_id="meta-llama/Llama-2-7b-chat-hf"tokenizer=AutoTokenizer.from_pretrained(model_id)pipeline=transformers.pipeline("text-generation",model=model_id,torch_dtype=torch.float16,device_map="auto",)model=Pipeline(identifier='my-sentiment-analysis',task='text-generation',preprocess=tokenizer,object=pipeline,torch_dtype=torch.float16,device_map="auto",)# You can easily predict on your collection documents.model.predict(X=Collection(name='test_documents').find(),db=db,do_sample=True,top_k=10,num_return_sequences=1,eos_token_id=tokenizer.eos_token_id,max_length=200)- Use models outputs as inputs to downstream models:model.predict(X='input_col',db=db,select=coll.find().featurize({'X':'<upstream-model-id>'}),# already registered upstream model-idlisten=True,)Community & Getting HelpIf you have any problems, questions, comments, or ideas:Joinour Slack(we look forward to seeing you there).Search throughour GitHub Discussions, oradd a new question.Commentan existing issueor createa new one.Help us to improve aladindb by providing your valuable feedbackhere!Email us [email protected] free to contact a maintainer or community volunteer directly!ContributingThere are many ways to contribute, and they are not limited to writing code. We welcome all contributions such as:Bug reportsDocumentation improvementsEnhancement suggestionsFeature requestsExpanding the tutorials and use case examplesPlease see ourContributing Guidefor details.ContributorsThanks goes to these wonderful people:Licensealadindb is open-source and intended to be a community effort, and it wouldn't be possible without your support and enthusiasm.
It is distributed under the terms of the Apache 2.0 license. Any contribution made to this project will be subject to the same provisions.Join UsWe are looking for nice people who are invested in the problem we are trying to solve to join us full-time. Find roles that we are trying to fillhere!
|
aladrisy
|
This is a very simple calculator that takes two numbers and either add, subtract, multiply or divide them.Change Log0.0.1 (20/12/2021)First Release
|
alagitpull
|
Sphinxsub-theme ofAlabaster, for use on git-pull projects.What alagitpull adds to AlabasterSee the theme live onhttps://www.git-pull.com,https://tmuxp.git-pull.com, etc.Table CSS tweaks<pre>and code-block css tweaksAdditional theming tweaks foradmonitionslike..note.New sidebar template with links to projectsAutomatic unlinking of project if its the current docsSupport for subprojects (put into parenthesis)Sidebar CSS tweaksConfig optionsTheme variablesTo see a full list of options passible to HTML templates, seetheme.conf. Not all of these options are used in the theme itself,
but to lethtml_theme_optionspass them through, if you want.To configure,conf.py:html_theme_optionsexample:html_theme_options={'logo':'img/logo.svg','github_user':'git-pull','github_repo':'alagitpull','github_type':'star','github_banner':True,'projects':{},'project_name':'my project name',}For an example ofhtml_theme_options['projects']see thealagitpull/__init__.pyfile.Example of using an optional variable such astheme_show_meta_app_icons_tags:html_theme_options={# ...usual stuff, as above, and'project_description':'description of project'}{%- if theme_show_meta_app_icon_tags == true %}<metaname="theme-color"content="#ffffff"><metaname="application-name"content="{{ theme_project_description }}"><linkrel="shortcut icon"href="/_static/favicon.ico"><linkrel="icon"type="image/png"sizes="512x512"href="/_static/img/icons/icon-512x512.png"><linkrel="icon"type="image/png"sizes="192x192"href="/_static/img/icons/icon-192x192.png"><linkrel="icon"type="image/png"sizes="32x32"href="/_static/img/icons/icon-32x32.png"><linkrel="icon"type="image/png"sizes="96x96"href="/_static/img/icons/icon-96x96.png"><linkrel="icon"type="image/png"sizes="16x16"href="/_static/img/icons/icon-16x16.png"><!-- Apple --><metaname="apple-mobile-web-app-title"content="{{ theme_project_name }}"><linkrel="apple-touch-icon"sizes="192x192"href="/_static/img/icons/icon-192x192.png"><linkrel="mask-icon"href="/_static/img/{{ theme_project_name }}.svg"color="grey"><!-- Microsoft --><metaname="msapplication-TileColor"content="#ffffff"><metaname="msapplication-TileImage"content="/_static/img/icons/ms-icon-144x144.png">{% endif -%}Variablesalagitpull_external_hosts_new_window(boolean, default: False): check if link
is external domain/IP. If so, open in new window.alagitpull_external_hosts_new_window=Truealagitpull_internal_hosts(list) - whitelist of domains to open
in same tab,withouttarget="_blank". Only used ifalagitpull_external_hosts_new_windowenabled.Example:alagitpull_internal_hosts=['libtmux.git-pull.com','0.0.0.0',]Theme optionshtml_theme_optionsof sphinx’s conf.py:projects(dict) - Sidebar links.project_name(string) - Name of your project (helps with unlinking
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alais
|
No description available on PyPI.
|
alakazam
|
Functional programming sugar for Python.InstallingA simplepipcommand should do the trick. (In some cases, you may
need to usesudo)pip install alakazamThe Alakazam library does not, as of now, have any additional
dependencies and is designed for Python 2 and 3.UsingTo use the stream functionality of Alakazam, import thealakazampackage. It is recommended that you alias the package as something
likezzfor easier typing.To use the Alakazam lambda syntax, import the placeholders fromalakazamexplicitly.import alakazam as zz
from alakazam import _1, _2, _3, _4, _5This library aims to make functional-style, and specifically
stream-oriented programming in Python prettier, more pleasant, and
easier on the eyes. Python has been capable of many functional
programming tasks, but it has always been a little bit awkward to use
those features for anything nontrivial. For instance, suppose we had
some listarr, and we wanted to square every element of the list
and then keep only the even squares. This is how we might approach
this problem using Python’s built-in functional tools.list(filter(lambda x: x % 2 == 0, map(lambda x: x ** 2, arr)))There’s a lot of cruft here, with having to explicitly declare that
we’re using lambdas every time we need to make a
function. Additionally, we have to read the code almost backward to
understand what it’s doing. While this backward sequencing of
operations is fine in a language like Haskell (where pointfree
notation hides the messy bracketing), in Python it would make much
more sense if we could read our code in the normal order. This is
where Alakazam comes in.zz.of(arr).map(_1 ** 2).filter(_1 % 2 == 0).list()Now the code reads left-to-right, and the lambdas are not nearly as
bulky. Thezz.ZZ(which is an alias for the classalakazam.Alakazam) is the entry point to any stream-based
operations you might want to perform with Alakazam. Theofmethod
wraps any iterable in an Alakazam instance. Then themapandfilterdo the same thing as their global function
equivalents. Finally, thelistmethod converts the Alakazam
iterable into an ordinary Python list. The important thing is that,
now, a cursory left-to-right reading of the code yields “arr -> map ->
filter -> list”, which is the sequence of operations we’re actually
performing.The lambda syntax is also significantly shortened. In cases where your
anonymous function merely uses operators, element access, and
attribute access, you can shorten it by using the placeholder
lambdas. The placeholder constants_1, _2, _3, _4, _5, based
loosely on the C++ Boost placeholders with the same names, are each
defined as callable objects which return their nth argument. If you
need more than five arguments, you can usezz.arg(n)directly, which
is how the placeholders are implemented in the first place. These
placeholders can be used with (almost) any built-in Python operator,
and they can also be subscripted and have arbitrary attributes
accessed on them. All of these operations will be translated into an
anonymous function that performs the corresponding operation on its
arguments. So_1 ** 2is a function that squares its first (and
only) argument,_1 + _2is a function that adds two arguments
together, and_1.name == "Alakazam"is a function which checks
whether its argument’s name attribute is equal to “Alakazam”.Reference MaterialTheAlakazam Wikilists the functions available to userLicensePlease seeLICENSE.txtfor licensing information.AuthorAlakazam was written by Silvio Mayolo.Release Notes (0.7.0)applyreducer added, as per Issue #2.indicesandindex, for returning indices of all matches in
an iterable.join,each, andfirstreducers added.splitfunction, combining the behaviors oftakeanddrop, added.foldrshould perform significantly better when used on iterables
which implement__reversed__.Several minor bugfixes involving utility functions.Release Notes (0.6.0)intersperseandintertwinetransformers added.stringreducer added.assignas a synonym forset.All classes are now new-style in Python 2 as well as Python 3.Release Notes (0.5.0)accumulatecan take an initial value now.accumulate,filterfalse, andzip_longestnow work
correctly on Python 2.sumnow works on any type that has__add__, including
strings.composeworks correctly when the argument list is empty now.Several functions that used to raise Python errors now raiseAlakazamError.Several bugfixes having to do with lazy evaluation in Python 2.Release Notes (0.4.0)Trace functionstrace,traceid, andtracestack, for
debugging help.New convenience syntax for invokingbind.mapcan take multiple arguments now (Issue #1).withobjecttransformer method.zipupproducer method.absorbandconsumereduction methods.swapconvenience function for tuples.Terminology change: “Generator” to “Producer” to avoid confusion
with the Python “generator” concept.Release Notes (0.3.0)New convenience functionid.Boolean functionsnot,and_,or_, andxor.minandmaxmethods onAlakazamobjects.Newflattenanditeratemethods.lengthandnullreduction methods.Noneis now permitted as an argument to some functions where its
behavior would have caused issues before.Release Notes (0.2.0)Alakazam now uses Python 3 semantics for division (from __future__ import division) for consistency.New functionssetindex,getindex, anddelindex, for
subscripted access and manipulation.New utility functionraise_.Assignment lambdas withset, binder lambdas withbind, and
deletion lambdas withdelete.Errors are reported throughAlakazamErrornow.Changedzip_longestandcross_productargument order to
better match theitertoolsequivalents.Static methods on theAlakazamclass can now be called globally.Functionof_dictprovided to load dictionaries into Alakazam as
lists of key-value 2-tuples.
|
alal
|
AlalA PHP sdk to interact with Alal's APIGetting startedRequirementsThis package requires Python 3.6+Installationpip install alal-pythonUsageThis SDK can be used both for Alal Sandbox and Production API.Setting ENV KEYSFor sucessfully running of the SDK; theAlal_API_KEYmust be set.Now this will throw aAlalBadKeyErrorerror, if you do not set it as an environment variable, when initiatizing a function.By default the SDK assumes that you are currently working on production, and yourAlal_API_KEYmust be a production-grade secret key.To run on sandbox(development mode), setAlal_PRODUCTION=falseas an environment variable.ContributingBug reports and pull requests are welcome on GitHub athttps://github.com/ALAL-Community/alal-python. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct. Simply create a new branch and raise a Pull Request, we would review and merge.LicenseThe package is available as open source under the terms of theBSD License
|
alalibo
|
UNKNOWN
|
alamari
|
AlamariA typical package to get all your utilities (with AI capabilities)This package was invented out of necessity, out of boredom & above all due to need (I'm serious)."A collection of utils that you might not require but might need."Bored Me, 2021FAQsQ1. What I'm getting out of this package?>>>importalamariANS: Glad you thought. First of all, you get this beautifully named package calledalamari. Isn't this word beautiful? Just think about it, you get to import this package inside your side projects. Now you maybe wondering, how did this guy knows about my 213th side project? Because this package which hastelemetryinstalled by default. It tracks any developer when they install this beautifully-named-and-thought-out package. I know I'm getting nowhere here, actually that's how is this package, it lets you nowhere (just kidding). Skip to the next line.# Convert Section# convert to integer with a smart AI (lol)>>>fromalamari.convertimport*>>>to_integer('abc123abc456')>>>'123,456'# convert to roman from devanagari>>>to_roman('तनहुँ ब्यास नगरपालिका सागेकी २२ वर्षीया युवतीकी एक जना आमाजु पर्ने थिइन्')>>>'tanahun byaasa nagarapaalikaa saageki 22 warsiyaa yuwatiki eka janaa amaaju parne thiin'# convert to nepali date from english date>>>to_nepali_miti(2021,6,7)>>>'2078-02-24'# Utils Section# checks if url resolves (sorry for bad example but lol)>>>fromalamari.utilsimport*>>>url_resolves('https://raw.githubusercontent.com/sidbelbase/alamari/master/README.md')>>>False# replace something from a text if you feel awkward>>>replace('I love this alamari package','alamari','daraaazzz')>>>'I love this daraaazzz package'# get datetime object from a string or text>>>parse_date('2021, June 5th 5:55')>>>datetime(2021,6,5,5,55)# pluralize the given singular unit>>>pluralize('knife')>>>'knifes'# ordinalize the given number (idk why i added this util)>>>ordinalize(34)>>>'34th'# Humanize Section# humanize number in nepali form i.e yeti much crore, yeti lakh, yeti hajar YK>>>fromalamari.humanizeimport*>>>number(12345675)>>>'1,23,45,675'>>>number(9.54)>>>'09'# humanize datetime stuffs>>>date(any_date='2021-06-07 05:55:55.185035')>>>'5 hours ago'# humanize nepali datetime>>>nepal_date(local_date='2021-06-07 05:55:55.185035')>>>'just now'Installation$pipinstallalamariDocumentationHere's a thing mate. I would like to be open about i.e I didn't prepare any documents for it. You need to go inside the alamari folder and peek inside each files. You have my permission to copy the code and modify the way you want. But don't. I made this package for a reason & this exist for a reason. Why would you not respect my reason? Remember, every time you copy my code, I get a notification about your endeavour (telemetry magic)ContributionFor being part of this blissful package, please add all the utils you want if you're tired of typing and copying again and again. While adding your utils please also add comments & docstrings to help people navigate through the pain they might hold for several years & I don't want to be remembered that way. Send all your issues and pull requests my way. Peace.NOTE: Please don't send your issues and pull-requests during fridays. You may don't want to disturb me during my favorite day. If you do that, I'll press this auto-destroy-button that I had built the other day & this package will be just a stardust.Let's see who wins, people who think this package is a absolute trash press thatstarbutton & people who think the other way pressforkbutton and contribute.GratitudeDevelopers who came here seeing their package being used by a guy with terrible coding pattern, I want to say that I'm forever grateful for you and your existence. Long live OSS.Made with ❤️ in Nepal.
|
alamatic
|
No description available on PyPI.
|
alamire_of_tamir
|
UNKNOWN
|
alamoAlg
|
Python package that allows the user to use the alamo algorithm for interpolation
|
a-la-mode
|
No description available on PyPI.
|
alamopy
|
alamopyThis is an interface for using ALAMO through a Python pip package.Preparation :for MacOS users:Download the ALAMO application from minlp.com, and unzip the package.for Windows users:Download the ALAMO application from minlp.com, and install with the provided installer.Data to run ALAMO:Add the directory of the folder you have installed the pip package with your Python file to your PATH variable.Get your ALAMO license from minlp.com and place it inside the same folder as the Python file.Before running ALAMO, you should decide if you will provide your own data. If so, make sure your data are all in numpy arrays.
Otherwise, select the appropriate simulator to run ALAMO.pip install alamopypythonimport numpy as npExampleTest 1 to see if we can generate model for z = x**2
Most notably tests if the given example 1 works from ALAMO UI.from alamopy import almain as alamoimport numpy as npxdata = np.random.rand(11, 1)xdata[:, 0] = [-5,-4,-3,-2,-1,0,1,2,3,4,5]zdata = xdata[:, 0]**2opts = alamo.doalamo(xdata, zdata, linfcns = 1, logfcns = 1, sinfcns = 1, cosfns = 1, constant = 1, expfcns = 1, monomialpower = [2,3], keep_alm_file=True, keep_lst_file=True)Outputs from ALAMOYou would get the result dictionary with a best-fitted function and other variables when calling ALAMO using this python interface.
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alamos
|
No description available on PyPI.
|
alan
|
AlanA programming language fordesigningTuring machines.Walkthrough|Installation|Wiki|CitationInstallationpipinstallalanWalkthroughThis section describes a workflow.For an in-depth guide navigate to theWiki.
Here are some useful links:DefinitionSyntaxInterfaceConsider the following example, the definition for a Turing machine that accepts all binary strings that are palindromic:# This is a definition of a Turing Machine that accepts binary strings that are palindromes
' '
A*
'X' 'X' < A
'Y' 'Y' < A
'0' 'X' > B
'1' 'Y' > F
' ' ' ' > G
B # Starting with 0
'0' '0' > B
'1' '1' > B
' ' ' ' < C
'X' 'X' < C
'Y' 'Y' < C
F # Starting with 1
'0' '0' > F
'1' '1' > F
' ' ' ' < E
'X' 'X' < E
'Y' 'Y' < E
C
'0' 'X' < D
'X' 'X' < D
E
'1' 'Y' < D
'Y' 'Y' < D
D
'0' '0' < D
'1' '1' < D
' ' ' ' > A
'X' 'X' > A
'Y' 'Y' > A
G.
'X' '0' > G
'Y' '1' > GGraph the machine:alangraphexamples/binary-palindrome.aln-fassets/readme/binary-palindrome.pngRun the machine on some inputs:alanrunexamples/binary-palindrome.aln101Accepted
Initial Tape : 101
Final Tape : 10alanrunexamples/binary-palindrome.aln1010Rejected
Initial Tape : 1010
Final Tape : Y010Animate the computation on some inputs:alanrunexamples/binary-palindrome.aln101-a-fassets/readme/binary-palindrome-accepted.gifalanrunexamples/binary-palindrome.aln1010-a-fassets/readme/binary-palindrome-rejected.gifCitationIf you use this implementation in your work, please cite the following:@misc{decosta2019alan,
author = {Kelvin DeCosta},
title = {Alan},
year = {2019},
howpublished = {\url{https://github.com/kelvindecosta/alan}},
}
|
alanapy
|
AlanaAlana is a cloud-based platform that provides oil and gas companies with a suite of tools for managing their operations. The platform includes modules for data management, production forecasting, economic analysis, and more.Website:https://alana.tech/open/accounts/loginFeaturesSome of the key features of Alana include:Data management: Alana provides a centralized repository for all of your oil and gas data, including well data, production data, and economic data.Production forecasting: Alana includes tools for forecasting oil and gas production, including decline curve analysis (DCA) and material balance analysis (MBA).Economic analysis: Alana includes tools for analyzing the economic viability of oil and gas projects, including net present value (NPV) analysis and sensitivity analysis.Collaboration: Alana allows teams to collaborate on projects and share data and analysis results.BenefitsSome of the benefits of using Alana include:Improved decision-making: Alana provides oil and gas companies with the data and analysis tools they need to make informed decisions about their operations.Increased efficiency: Alana streamlines data management and analysis, reducing the time and effort required to perform tasks such as production forecasting and economic analysis.Better collaboration: Alana allows teams to work together more effectively, improving communication and reducing the risk of errors and miscommunications.ConclusionAlana is a powerful platform that provides oil and gas companies with the tools they need to manage their operations more effectively. Whether you're looking to improve your production forecasting, analyze the economic viability of a project, or simply streamline your data management processes, Alana has the features and benefits you need to succeed.AlanaAPIThe AlanaAPI is a Python library that provides a set of classes and methods to interact with the Alana platform. The library allows users to fetch data from the platform, run analysis, and create forecasts and scenarios.
|
alanbal
|
Alan BootstrapActive LearningSetup EnvironmentCreate a Conda Environment for Python 3.7.3:condacreate--name<EVN_NAME>python=3.7.3
condaactivate<EVN_NAME>Git Clone:gitclonehttps://<USR_NAME>@bitbucket.org/rbcmllab/alan-framework.gitInstall Python Dependenciescdalan-framework/modules/boostrap/active_learning
pipinstall-rrequirements.txt
pipinstallalanbalRun>pythonbin/ast_al_bin.py-h
usage:ast_al_bin.py[-h]-iINIT-pPOOL-oOUTPUT[-dDEVSET]ParametersforASTclassificationactivelearner.Themodelwillbepersisted
intotheoutputfolderateveryiteration.Ifadevdatasetwasprovided,the
classificationaccuracyscorewouldbecalculatedonthisdatasetateach
iterationandaperformanceplotwouldbesavedintotheoutputfolder.
optionalarguments:-h,--helpshowthishelpmessageandexit-iINIT,--initINITShared-ASTjsonfilepath;ASTsinthisfilewillbeusedtoinitializetheactivelearner.EveryASTinthisfilemustcontainatargetvalueandalistofcomplexity_features.-pPOOL,--poolPOOLShared-ASTjsonfilepath;ActivelearnerwillsampleASTsfromthisfileEveryASTinthisfilemustcontainalistofcomplexity_features.-oOUTPUT,--outputOUTPUTAbsolutepathofthemodelpersistingfolder.-dDEVSET,--devsetDEVSETShared-ASTjsonfilepath;ASTsinthisfilewillbeusedtoevaluatetheactivelearner.EveryASTinthisfilemustcontainatargetvalueandalistofcomplexity_features.Example:>pythonbin/ast_al_bin.py\-i$DATA_DIR/ast_train.json\-p$DATA_DIR/ast_pool.json\-o$DATA_DIR\-d$DATA_DIR/ast_dev.json
|
alanbootstrap
|
Alan BootstrapActive LearningSetup EnvironmentCreate a Conda Environment for Python 3.7.3:condacreate--name<EVN_NAME>python=3.7.3Git Clone:gitclonehttps://<USR_NAME>@bitbucket.org/rbcmllab/data_bootstrap.gitInstall Python Dependenciescddata_bootstrap/alanbootstrap
pipinstall-rrequirements.txt
pipinstallalanbootstrapRun>pythonbin/ast_al_bin.py-h
usage:ast_al_bin.py[-h]-iINIT-pPOOL-oOUTPUT[-dDEVSET]ParametersforASTclassificationactivelearner.Themodelwillbepersisted
intotheoutputfolderateveryiteration.Ifadevdatasetwasprovided,the
classificationaccuracyscorewouldbecalculatedonthisdatasetateach
iterationandaperformanceplotwouldbesavedintotheoutputfolder.
optionalarguments:-h,--helpshowthishelpmessageandexit-iINIT,--initINITShared-ASTjsonfilepath;ASTsinthisfilewillbeusedtoinitializetheactivelearner.EveryASTinthisfilemustcontainatargetvalueandalistofcomplexity_features.-pPOOL,--poolPOOLShared-ASTjsonfilepath;ActivelearnerwillsampleASTsfromthisfileEveryASTinthisfilemustcontainalistofcomplexity_features.-oOUTPUT,--outputOUTPUTAbsolutepathofthemodelpersistingfolder.-dDEVSET,--devsetDEVSETShared-ASTjsonfilepath;ASTsinthisfilewillbeusedtoevaluatetheactivelearner.EveryASTinthisfilemustcontainatargetvalueandalistofcomplexity_features.Example:>pythonbin/ast_al_bin.py\-i$DATA_DIR/ast_train.json\-p$DATA_DIR/ast_pool.json\-o$DATA_DIR\-d$DATA_DIR/ast_dev.json
|
alan-cli
|
ALAN CLI TOOLSETThis CLI toolset is built to serve my everyday need of news and information inside the CLI.In the future, I will also
support other format rather than just RSS.How to use it (currently I haven't specified the setups , should be up pretty soon). First you install the
requirements.txtRequirementAt least python 3.10 to support match caseCan check for more information herehttps://towardsdatascience.com/switch-case-statements-are-coming-to-python-d0caf7b2bfd3How to install the appApp is packaged on pippipinstallalan-cliHow to use the appType the below command for helpalan-cli--helpPlease remember to input the rss using rss manager in order for the manager to have data.You can use above command to get the news after you have input the rssalan-cliget-news
|
alanfe-puc-ds-csv-converter
|
It is a simple module to make easy conversions between two type of files:csvandjson.It has been created for a postgraduate assignment that had as objective to validate our knowledge about the use of basic structures of Python and the process of creating and publishing a Python module.Usage and ExamplesTo use this module is necessary to install it and use the convert script of this module:pip install alanfe-puc-ds-csv-converter
python3 -m alanfe-puc-ds-csv-converter convertYou will need to set some arguments of this script likeoutput path(using -o or --ouput) andinput path(using -i or --input). After that automatically it will parse all files in this path and will save them in the output path.python3 -m alanfe-puc-ds-csv-converter convert -i /input/ -o /output/
# OR
python3 -m alanfe-puc-ds-csv-converter convert -i /input/teste.csv -o /output/It will detect if the files are csv files or json files and will convert them to another format. In the case of csv files, they will be converted to json. In the case of json files, they will be converted to csv.A good property of this module is the parallel processing. You can set--parallelor-pas true and the library's processing will happen in parallelpython3 -m alanfe-puc-ds-csv-converter convert -i /input/teste.csv -o /output/ --parallel trueTo more information about this module execution we cant use the help command:python3 -m alanfe-puc-ds-csv-converter convert --help
|
alanmorrisudacity-distributions
|
No description available on PyPI.
|
alanpdf
|
No description available on PyPI.
|
alanpows_pycounter_tools
|
alanpows_pycounter_toolsA package to help you count through your texts and understand how many and what words you have!Installation$pipinstallalanpows_pycounter_toolsUsagealanpows_pycounter_toolscan be used to count words in a text file and plot results
as follows:fromalanpows_pycounter_tools.alanpows_pycounter_toolsimportcount_wordsfromalanpows_pycounter_tools.plottingimportplot_wordsimportmatplotlib.pyplotaspltfile_path="test.txt"# path to your filecounts=count_words(file_path)fig=plot_words(counts,n=10)plt.show()ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensealanpows_pycounter_toolswas created by Alan Powichrowski. It is licensed under the terms of the MIT license.Creditsalanpows_pycounter_toolswas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
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alansi-example
|
Failed to fetch description. HTTP Status Code: 404
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alantests
|
test program
|
alanTools
|
No description available on PyPI.
|
alanube
|
alanube-pythonUnofficial Python library to establish connections with the Alanube API.Alanube Docshttps://developer.alanube.co/docsInstalationpipinstallalanubeUsefromalanubeimportAlanubeDGIIfromalanube.dgii.formsimport(IdDocForm,SenderForm,BuyerForm,TotalsForm,ItemDetailForm,ConfigForm,InvoiceForm,)alanube=AlanubeDGII(token='YOUR_API_TOKEN',developer_mode=True)id_doc=IdDocForm(encf='E310000000005',sequence_due_date='2023-01-01',tax_amount_indicator=1,income_type=1,payment_type=1,payment_deadline='2023-01-30',)sender=SenderForm(rnc=101123456,company_name='My Company SRL',tradename='My Company',branch_office='Principal',address='Calle A, No. 1245, Santo Domingo',phone_number='8091234567',mail='[email protected]',web_site='https://mycompany.example.com',seller_code='01',internal_invoice_number='123456789',stamp_date='2023-01-01',)buyer=BuyerForm(rnc=101123457,company_name='His Company SRL',contact='José, Tel: 8091234567',mail='[email protected]',address='Calle B, 1236, San Cristobal',)totals=TotalsForm(total_amount=1180.00,total_taxed_amount=1000,amount_period=1000.00,non_billable_amount=0,i1_amount_taxed=1000.00,# Monto gravado a ITBIS tasa1 (18%).i2_amount_taxed=0,# Monto gravado a ITBIS tasa2 (16%).i3_amount_taxed=0,# Monto gravado a ITBIS tasa3 (0%).exempt_amount=0,# Monto exento.itbis_total=180.00,# Total ITBISitbis1_total=180.00,# ITBIS (18%)itbis2_total=0,# ITBIS (16%)itbis3_total=0,# ITBIS (0%)additional_tax_amount=None,additional_taxes=None,itbis_total_retained=None,itbis_total_perception=None,isr_total_retention=None,isr_total_perception=None,)items=[ItemDetailForm(line_number=1,item_code_table=None,billing_indicator=1,retention=None,item_name='Café Entero Paquete',item_description=None,good_service_indicator=1,quantity_item=10,unit_measure=None,subquantity_table=None,unit_price_item=100.00,discount_amount=0,sub_discounts=[SubDiscountForm(sub_discount_rate='%',sub_discount_percentage=10,),SubDiscountForm(sub_discount_rate='$',sub_discount_percentage=5,)],item_amount=1000.00,),]config=ConfigForm()invoice_form=InvoiceForm(company_id='MY_ALANUBE_COMPANY_ID',id_doc=id_doc,sender=sender,buyer=buyer,totals=totals,item_details=items,subtotals=None,discounts_or_surcharges=None,pagination=None,config=config,)response=alanube.create_invoice(invoice_form)ifresponse.ok:data=response.json()else:print(response.errors)LicenceThis project is licensed under the MIT License.Proyect StatusThis project is under developmentContribution 💗If you find value in this project and would like to show your support, please consider making a donation via PayPal:Donate on PayPalYour generosity helps us to continue improving and maintaining this project. We appreciate every contribution, however small. Thanks for being part of our community!
|
alapakam-generics
|
Example PackageThis is a simple example package. You can useGithub-flavored Markdownto write your content.
|
alara-split-gather
|
## alara_split_gather
This package is used to:
- split a large ALARA input file to smaller sub-tasks,
- check the status of sub-tasks,
- gather all phtn_src file## Installation`bash pip installalara-split-gather`## Basic usage
### Help info`bash alara_split_task-h`### Split task` alara_split_task-i[alara_inp]-n[num_tasks] `### Check status` alara_tasks_status `### Gather outputs of sub-tasks` alara_gather_tasks-pphtn_src `## Notes
Refer to ‘-h’ or source code for detalied usage.
|
alaric
|
AlaricProviding a beautiful way to interact with MongoDB asynchronously in Python.Example usageA simplistic example, read more on the docs!frommotor.motor_asyncioimportAsyncIOMotorClientfromalaricimportDocumentclient=AsyncIOMotorClient("Mongo connection url")database=client["my_database"]config_document=Document(database,"config")awaitconfig_document.insert({"_id":1,"data":"hello world"})...data=awaitconfig_document.find({"_id":1})Docs can be foundhereSupportWant realtime help? Join the discordhere.LicenseThis project is licensed under the MIT licenseFundingWant a feature added quickly? Want me to help build your software using Alaric?Sponsor mehereDevelopmentIn order to make development easier, I recommend usingmotor-stubsif you're planning on doing more than Alaric has to offer. I.e. Using motor itself.
|
alarm
|
Table of Contents:CLI Alarm ClockHow worksRequirementsInstallationCommand Line Tool UsageCLI Alarm ClockAlarm is command line alarm clock utility written in Python language.How worksWhen the date and time coincides with the current date and time,
the alarm starts playing the sound is selected for five consecutive times.You can pause the alarm by pressing ‘p’ or ‘space’ is an attempt to cancel the
‘q’ or ‘ESC’. Change the volume of the alarm by pressing ‘*’ or ‘/’.You can create a list and use it as an alarm sound:$cat*.mp3>playlist.m3u$alarm-s1707:05~/Music/playlist.m3uYou will find some sounds in folder alarm/sounds in the GitLab tar archive.
Some will make you laugh, have fun !!!Requirements-Python3-MplayerInstallationUsingpip:$pipinstallalarm--upgradeuninstall:$pipuninstallalarmGet the source ‘git clonehttps://gitlab.com/dslackw/alarm.git’$python3setup.pyinstallCommand Line Tool Usageusage:alarm[-h][-v][-s]<day><alarmtime><song>optionalarguments-h,--helpshowthishelpmessageandexit-v,--versionprintversionandexit-s,--setsetalarmday,timeandsound--configuseconfigfileexample:alarm-s2106:00/path/to/song.mp3Example:$alarm-s1822:05~/alarm/sounds/wake_up.mp3+==============================================================================+|CLIAlarmClock|+==============================================================================+|Alarmsetat:Wednesday22:05||Soundfile:~/alarm/sounds/wake_up.mp3||Time:21:06:41|+==============================================================================+Press'Ctrl + c'tocancelalarm...+==============================================================================+|CLIAlarmClock|+==============================================================================+|Alarmsetat:Wednesday22:05||Soundfile:~/alarm/sounds/wake_up.mp3||Time:22:05WakeUp!|+==============================================================================+Press'Ctrl + c'tocancelalarm...________\ \//__||_____||||___||\ \/\//_`||//_\||||'_ \ | |
\ V V / (_| | < __/ | |_| | |_) | |_|
\_/\_/ \__,_|_|\_\___| \___/| .__/ (_)
|_|
Press 'SPACE'topausealarm...Attempt1Attempt2Use config file in $HOME/.alarm/config:$alarm--config+==============================================================================+|CLIAlarmClock|+==============================================================================+|Alarmsetat:Wednesday07:00||Soundfile:/home/user/alarm/sounds/funny.mp3||Time:00:09:22|+==============================================================================+Press'Ctrl + c'tocancelalarm...
|
alarmclock
|
Failed to fetch description. HTTP Status Code: 404
|
alarm-craft
|
alarm-craftfor AWS CloudWatch AlarmsWith modern architectures such as serverless and microservices, the number of resources managed in the cloud tends to increase, making monitoring a challenging task.alarm-craftis a tool designed to address this problem.FeaturesBulk Generation: Generates the necessary monitoring alarms in bulk with a single command.Flexible Alarm Definition: Allows flexible definition of alarm conditions, including metrics and thresholds.Declarative Config: Providesdeclarative configurationsfor monitoring targets using resource name or tag.Integration with Your Deployment Pipeline: A CLI tool written in Python that can be seamlesslyintegratedinto the deployment pipeline.DevOps: By leveraging declarative configurations based on the tag strategy and integratingalarm-craftwith the deployment pipeline, DevOps teams can automatically monitor newly deployed resources.Quick StartInstall python (>= 3.9), and installalarm-craft.pipinstallalarm-craftCreate a json file like below and save it asalarm-config.yamlresources:lambda:target_resource_type:"lambda:function"alarm:metrics:-ErrorsExecute the tool.alarm-craftBy this execution, thealarm-craftcreates Cloudwatch Alarms to detectErrorsin all Lambda functions.DocumentationFor detailed instructions and information on configuring the tool, refer to theWiki.
|
alarmdealerscrape
|
# alarmdealerscrapeScrape info from [http://alarmdealer.com](http://alarmdealer.com)## Setupmkvirtualenv alarmdealerscrapepip install -r requirements.txtPut your alarmdealer.com username and password in~/.netrclike this:machine alarmdealer.com login myusername account mycode password mypassword## Get Alarm Eventspython getevents.py## Test Arm/Disarmpython test_arm_disarm.py
|
alarmdecoder
|
SummaryThis Python library aims to provide a consistent interface for theAlarmDecoderproduct line. (AD2USB, AD2SERIAL and AD2PI).
This also includes devices that have been exposed viaser2sock, which
supports encryption via SSL/TLS.InstallationAlarmDecoder can be installed throughpip:pip install alarmdecoderor from source:python setup.py installNote:python-setuptoolsis required for installation.RequirementsRequired:AnAlarmDecoderdevicePython 2.7pyserial>= 2.7Optional:pyftdi>= 0.9.0pyusb>= 1.0.0b1pyopensslDocumentationAPI documentation can be found atreadthedocs.ExamplesA basic example is included below. Please see theexamplesdirectory for
more.:import time
from alarmdecoder import AlarmDecoder
from alarmdecoder.devices import SerialDevice
def main():
"""
Example application that prints messages from the panel to the terminal.
"""
try:
# Retrieve the first USB device
device = AlarmDecoder(SerialDevice(interface='/dev/ttyUSB0'))
# Set up an event handler and open the device
device.on_message += handle_message
with device.open(baudrate=115200):
while True:
time.sleep(1)
except Exception as ex:
print ('Exception:', ex)
def handle_message(sender, message):
"""
Handles message events from the AlarmDecoder.
"""
print sender, message.raw
if __name__ == '__main__':
main()
|
alarme
|
No description available on PyPI.
|
alarmer
|
alarmerAlarmeris a tool focus on error reporting for your application, likesentrybut lightweight.InstallationpipinstallalarmerUsageIt's simple to integratealarmerin your application, just callAlarmer.initon startup of your application.importosfromalarmerimportAlarmerfromalarmer.provider.feishuimportFeiShuProviderdefmain():Alarmer.init(providers=[FeiShuProvider(webhook_url=os.getenv("FEI_SHU_WEBHOOK_URL"))])raiseException("test")if__name__=="__main__":main()Intercept Error LoggingIf you want to intercept the logging, you can useLoggingHandler.importloggingimportosfromalarmerimportAlarmerfromalarmer.logimportLoggingHandlerfromalarmer.provider.feishuimportFeiShuProviderdefmain():Alarmer.init(providers=[FeiShuProvider(webhook_url=os.getenv("FEI_SHU_WEBHOOK_URL"))])logging.basicConfig(level=logging.INFO,)logger=logging.getLogger()logger.addHandler(LoggingHandler(level=logging.ERROR))# only error and above should be sendlogging.error("test logging")if__name__=="__main__":main()Now when you run the script, you will receive the errors in your provider.ProviderYou can set number of providers for error reporting. All kinds of providers can be found
inproviders.Thanks toApprise, you can use lots of providers out of box.AppriseFeiShuWeComCustom ProviderYou can write your own custom provider by inheriting theProviderclass.fromtypingimportOptionalfromalarmer.providerimportProviderclassCustomProvider(Provider):defsend(self,message:str,exc:Optional[BaseException]=None,context:Optional[dict]=None):# Send to your custom provider herepassIn addition to this, you can just write a callable function which takesmessageandexcarguments.fromtypingimportOptionaldefcustom_provider(message:str,exc:Optional[BaseException]=None,context:Optional[dict]=None):# Send to your custom provider herepassThen add it toAlarmer.init.fromalarmerimportAlarmerAlarmer.init(providers=[CustomProvider(),custom_provider])ThrottlingThrottlingis used to throttling error messages if there are too many errors.fromalarmerimportAlarmerfromalarmer.throttlingimportThrottlingAlarmer.init(global_throttling=Throttling(),providers=[...])Custom ThrottlingYou can write your own throttling by inheriting theThrottlingclass.importtypingfromalarmer.throttlingimportThrottlingiftyping.TYPE_CHECKING:fromalarmer.providerimportProviderclassMyThrottling(Throttling):def__call__(self,provider:"typing.Union[Provider,typing.Callable]",message:str,exc:typing.Optional[BaseException]=None,context:typing.Optional[dict]=None)->bool:# check whether the error message should be sendreturnTrueManual SendIf you want to manually send messages to the providers, just callAlarmer.send.fromalarmerimportAlarmerAlarmer.send("message")LicenseThis project is licensed under theApache-2.0License.
|
alarmix
|
Installationpython-mpipinstallalarmix⚠️MPVmust be installed and accessible ⚠️At first, you need to start alarmd daemon:# Run alarmd-server as a daemonalarmd-s"path/to/sound/to/play"-d# To kill it you need to runalarmdkill# Of course you can see helpalarmd-hThen you can manage your alarms withalarmccommand.alarmc# Show scheduled alarms for todayalarmc-f# Show all scheduled alarmsalarmcstop# Stop buzzing alarmalarmcadd20:0019:3014:00# Add alarmsalarmcadd+30+2:40# Add alarms with relative timealarmcdelete20:00# Remove alarm from schedulealarmcalarmc-h# Show helpAlso alarmc can display information about your schedule in different formats:➜ ~ alarmc # Default schedule information
+------------+----------------+
| alarm time | remaining time |
+------------+----------------+
| 09:30:00 | 9:01:28 |
+------------+----------------+
➜ ~ alarmc -r # Raw data without table formatting (separated by '\t' character)
alarm time remaining time
09:30:00 9:00:43
➜ ~ alarmc -w # Show "When" column
+------------+----------------+----------+
| alarm time | remaining time | when |
+------------+----------------+----------+
| 09:30:00 | 8:58:58 | weekdays |
+------------+----------------+----------+
➜ ~ alarmc -c # Show "Cancelled" column
+------------+----------------+-----------+
| alarm time | remaining time | cancelled |
+------------+----------------+-----------+
| 09:30:00 | 8:57:35 | False |
+------------+----------------+-----------+
# All options can be combined
➜ ~ alarmc -rwc
alarm time remaining time when cancelled
09:30:00 8:58:15 weekdays False
|
alarmpy
|
🚨 Pikud Ha'oref Alarm TrackingA simple CLI tool for tracking Pikud Ha'oref alarms.Polls the unofficial API endpoint every second for incoming alarms. Prints active alarms as they occur. Prints routine messages once every 5 minutes by default.⚠️ Disclaimer ⚠️This tool is based on an unofficial API, and cannot be guaranteed to show correct or timely data.Do notuse it if human life is at stake.Do notassume it shows you correct data.Do notassume it works properly, or even works at all. Always follow official guidelines and procedures published byPikud Ha'oref.Further fine-print covering the terms of use of this tool can be found in theGPLv3 licensefile.InstallPipThe easiest way to install is from PyPI withpip:$pipinstallalarmpyYou can then run thealarmpyexecutable directly:$alarmpy--helpPipenvFor development usage it's recommended to clone the git repo and usepipenv:$gitclonehttps://github.com/yuvadm/alarmpy
$cdalarmpy
$pipenvsync-d
$pipenvrunalarmpyUsageFor the default usage after installation, just run:$alarmpyDisplaySet the output language using--language [en|he|ar|ru], this uses the official city and area name translations for Hebrew, Arabic, English and Russian.In case of RTL issues in the terminal use--reverseto output all names in reverse.Use--highlight abcin order to highlight any alarm which contains the stringabc.ProxyThe unofficial API is limited for use for Israeli-originating IPs only. In order to use alarmpy from outside Israel, users must route traffic through an Israeli exit point. TheHTTPS_PROXYenvironment variable is supported for this use case.AdvancedAdvanced flags can be set as described in the usage:$pipenvrunalarmpy--help
Usage:alarmpy.py[OPTIONS]Options:--language[en|he|ar|ru]Alertlanguage--highlightTEXTStringtosearchforandhighlightincaseofalarm--reverseReverseHebrew/ArabicoutputforterminalswithRTLbugs--polling-delayINTEGERPollingdelayinseconds--routine-delayINTEGERRoutinemessagedelayinseconds--alarm-idPrintalarmIDs--repeat-alarmsDonotsuppressongoingalarms--quietPrintonlyactivealarms--desktop-notificationsCreatepushnotificationsonyourdesktopnotificationcenter(currentlyonlyinMacOS)--mqtt-serverTEXTHostname/IPofMQTTserver(optional)--mqtt-client-idTEXTMQTTclientidentifier--mqtt-portINTEGERPortforMQTTserver--mqtt-topicTEXTTopiconwhichtosendMQTTmessages--mqtt-filterTEXTPayloadvaluetofilterbeforesendingasamessage(semicolonseparated)--output-testPrintadebugoutputandexit--helpShowthismessageandexit.MQTT NotificationsIntegration with an MQTT server provides the ability to send custom MQTT messages for all or some of the alerts that are received. MQTT requirespaho-mqttto be installed separately as an optional dependency.To enable, specify at least the following parameters via the command line:mqtt-server- The MQTT Server hostname or IP, e.g.localhostmqtt-topic- The MQTT topic to which the MQTT message will be sent, e.g.alarmpy/zoneAdditional optional parameters for MQTT integration are:mqtt-client-id- The ID of the MQTT client used by alarmpy. This will be used to connect to the MQTT server. Default:alarmPyClient. This only needs to be change in case you plan to have more than one instance of alarmpy runningmqtt-port- The port on which the MQTT server is listening to. Default:1883FilteringWhen MQTT is enabled, all alerts are sent as separate messages on the specified topic. In case there is a desire to include only specific alert, use themqtt-filterparameter to provide a semicolon separated list of substrings enclosed in double quotes. Each alert city and area will be checked against all filters, and only when a match is found, will an MQTT message be sent. For example:--mqtt-filter "gaza;negev".LicenseGPLv3
|
alarmraj
|
No description available on PyPI.
|
alarmserver
|
This package provides an AlarmServer class which is able to handle
alarms. It is intended for HMI software where alarm handling is often
necessary.The following features are provided:defining alarms with numbers and alarm textsalarms are handled and identified by their alarm numberalarms can come and leavealarms can be acknowledged and cleared by the userIn addition to the basic AlarmServer class there is an AlarmServerModel
class which implements the model/view pattern used by the Qt framework.
So this class can be used as a model for QTableView or QListView.
|
alart
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alart is a pseudo-random art generator.Example art can be found athttp://metanohi.name/projects/alart/images/Licensealart is free software under the terms of the GNU General Public License
version 3 (or any later version). This is version 0.1.1 (codename: “Fox”) of
the program.InstallationWay #1Get the newest version of alart athttp://metanohi.name/projects/alart/or athttp://pypi.python.org/pypi/alartExtract the downloaded file and run this in a terminal:# python setup.py installExamples can then be found in theexamplesdirectory.Way #1Just run this:# pip install alartor:# easy_install alartNote that this will not make the examples available.DependenciesPython 2.5 is required, though newer versions might be needed for Encapsulated
PostScript support.alart depends on cairo and cairo’s Python bindings for generating
images. Version 1.6 is needed for best output support, but earlier versions
(e.g. 1.4) works ok. To install it, do one of these things:For DEB-based distros (Debian etc.): typeapt-getinstallpython-cairoFor RPM-based distros (Fedora etc.): typeyum install pycairoFor other distros: do something similar or get it athttp://cairographics.org/download/alart depends on PyGame 1.8.0+ (perhaps earlier versions are also supported)
for showing generated images. If you only want to generate images and not show
them in an interactive window, you do not need PyGame graphics and sound. To
install it, do one of these things:For DEB-based distros (Debian etc.): typeapt-getinstallpython-pygameFor RPM-based distros (Fedora etc.): typeyum install pygameFor other distros: do something similar or get it athttp://pygame.org/download.shtmlUseTo use alart, just runalart. This will display a generated image with
default options. To generate a new image while in the window, press R.To see a list of alart’s command-line options with explanations, runalart--help. To see alart’s module help, runpydoc alart.Developmentalart uses Git for code management. To get the newest code, run:$ git clone git://gitorious.org/alart/alart.gitBugs can be reported [email protected](address of sole developer).This documentCopyright (C) 2010, 2011 Niels SerupCopying and distribution of this file, with or without modification,
are permitted in any medium without royalty provided the copyright
notice and this notice are preserved. This file is offered as-is,
without any warranty.
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alas
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No description available on PyPI.
|
alas-ce0-client
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A project for developing a client to access the Alas.Ce0 APIThis is the README file for the project.“generate dist and wheel”
python setup.py sdist bdist_wheel“upload to pypi project”
twine upload dist/*
|
alas-ce0-openpyxl
|
openpyxlopenpyxl is a Python library to read/write Excel 2010 xlsx/xlsm/xltx/xltm files.It was born from lack of existing library to read/write natively from Python
the Office Open XML format.All kudos to the PHPExcel team as openpyxl was initially based onPHPExcelMailing ListOfficial user list can be found onhttp://groups.google.com/group/openpyxl-usersSample code:from openpyxl import Workbook
wb = Workbook()
# grab the active worksheet
ws = wb.active
# Data can be assigned directly to cells
ws['A1'] = 42
# Rows can also be appended
ws.append([1, 2, 3])
# Python types will automatically be converted
import datetime
ws['A2'] = datetime.datetime.now()
# Save the file
wb.save("sample.xlsx")Official documentationThe documentation is at:https://openpyxl.readthedocs.ioinstallation methodscode examplesinstructions for contributingRelease notes:https://openpyxl.readthedocs.io/en/latest/changes.html
|
alas-ce0-whatsapp
|
UNKNOWN
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alaska
|
alaska: The las file aliaseralaska is a Python package that reads mnemonics from LAS files and outputs an aliased dictionary of mnemonics and its aliases, as well as a list of mnemonics that cannot be found. It uses three different methods to find aliases to mnemonics: locates exact matches of a mnemonic in an alias dictionary, identifies keywords in mnemonics' description then returns alias from the keyword extractor, and predicts alias using all attributes of the curves.Sample UsagefromalaskaimportAliasfromwellyimportProjectimportlasiopath="testcase.las"a=Alias()parsed,not_found=a.parse(path)In this case, parsed is the aliased dictionary that contains mnemonics and its aliases, and not_found is the list of mnemonics that the aliaser did not find. Users can manually alias mnemonics in the not_found list and add them to the dictionary of aliased mnemonicsParameters of the Alias class can be changed, and the defaults are the followinga=Alias(dictionary=True,keyword_extractor=True,model=True,prob_cutoff=.5)Users can choose which parser to use/not to use by setting the parsers to True/False. The prob_cutoff is the confidence the user wants the predictions made by model parser to have.Then, the aliased mnemonics can be inputted into welly as demonstrated below.fromwellyimportProjectp=Project.from_las(path)data=p.df(keys=list(parsed.keys()),alias=parsed)print(data)
|
alas-pkg-core
|
A project for developing a client to access the Alas.Ce0 APIThis is the README file for the project.“generate dist and wheel”
python setup.py sdist bdist_wheel“upload to pypi project”
twine upload dist/*
|
alaspo
|
ALASPOAn (adaptive) LNS framework for the for ASP systems. Currently, the system only supports solvers based onclingo.InstallationIt should work out-of-the-box in a conda env after running the following commands:conda create -n alaspo python=3.9
conda activate alaspo
conda install -c potassco clingo clingo-dl clingcon
python -m pip install alaspoThe command-line options of the problem-independent LNS can be shown as follows:alaspo -hExamples for portfolio config files can be found in thegit repository.
|
alasry
|
This is a very simple calculator that takes two numbers and either add, subtract, multiply or divide them.Change Log0.0.1 (19/04/2020)First Release
|
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