Saravanan666/pypi-org huggingface dataset:
Package: dlapp
DLAppDLApp is the query utility for dictionary or list.InstallationpipinstalldlappFeaturessupport a simple wildcard characters ?, *, [], [!]support regexsupport custom keywordssupport SQL-like select statementsupport GUI applicationDependenciescompare_versionpyyamlpython-dateutilUsage(venv)test@test-machine~%dlapp--help
usage:dlapp[options]dlappapplication

optionalarguments:-h,--helpshowthishelpmessageandexit--guiLaunchadlappGUIapplication.-fFILENAME,--filenameFILENAMEJSON,YAML,orCSVfilename.-e{csv,json,yaml,yml},--filetype{csv,json,yaml,yml}Filetypecanbeeitherjson,yaml,yml,orcsv.-lLOOKUP,--lookupLOOKUPLookupcriteriaforsearchinglistordictionary.-sSELECT_STATEMENT,--selectSELECT_STATEMENTSelectstatementtoenhancemultiplesearchingcriteria.-t,--tabularShowresultintabularformat.-d,--dependencyShowPythonpackagedependencies.-u{base,csv,json,yaml},--tutorial{base,csv,json,yaml}Tutorialcanbeeitherbase,csv,json,oryaml.(venv)test@test-machine~%Getting StartedDevelopment>>># test data>>>lst_of_dict=[...{"title":"ABC Widget","name":"abc","width":500},...{"title":"DEF Widget","name":"def","width":300},...{"title":"MNP Widget","name":"mnp","width":455},...{"title":"XYZ Widget","name":"xyz","width":600}...]>>>>>>fromdlappimportDLQuery>>>>>>query_obj=DLQuery(lst_of_dict)>>>>>># find any value of title starting with A or X>>>query_obj.find(lookup="title=_wildcard([AX]*)")['ABC Widget','XYZ Widget']>>>>>># find any data of title starting with A or X>>># and select title, width where width lt 550>>>query_obj.find(lookup="title=_wildcard([AX]*)",select="SELECT title, width WHERE width lt 550")[{'title':'ABC Widget','width':500}]>>>>>>>>>>>># assuming /path/sample.json file has the same structure data as lst_of_dict>>>fromdlappimportcreate_from_json_file>>>>>>query_obj=create_from_json_file('/path/sample.json')>>>>>>query_obj.find(lookup="title=_wildcard([AX]*)")['ABC Widget','XYZ Widget']>>>>>># to query json string data, use>>>fromdlappimportcreate_from_json_data>>>>>>>>>>>># to query yaml file, use>>>fromdlappimportcreate_from_yaml_file>>>>>># to query yaml string data, use>>>fromdlappimportcreate_from_yaml_data>>>>>>>>>>>># to query csv file, use>>>fromdlappimportcreate_from_csv_file>>>>>># to query csv string data, use>>>fromdlappimportcreate_from_yaml_fileConsole command lineOpen DLApp application$dl-app# using python entry point$dlapp--gui# using console command line$python-mdlapp--gui# using python module invocationSearch json, yaml, or csv file$# assuming that /path/sample.json has the same structure data as lst_of_dict$dlapp--filename=/path/sample.json--lookup="title=_wildcard([AX]*)"['ABC Widget','XYZ Widget']$
$dlapp--filename=/path/sample.json--lookup="title=_wildcard([AX]*)"--select="SELECT title, width WHERE width lt 550"[{'title':'ABC Widget','width':500}]$
$# the same syntax can apply for yaml, yml, or csv file.Bugs/RequestsPlease use theGitHub issue trackerto submit bugs or request features.LicensesBSD 3-Clause License
Package: dlatk-pymallet
PyMalletAcknowledgmentThis package is a redistributed fork ofhttps://github.com/mimno/PyMallet. Modifications have been made to the code to enable simpler interfacing with DLATK; however, the core functionality remains unmodified.Original READMEThis package provides tools for extracting latent semantic representations of text, particularly probabilistic topic models.The implementation of LDA uses Gibbs sampling, which is simple but reliable. People often find the resulting models more useful than the stochastic variational algorithm used in Gensim.To compile:python setup.py build_ext --inplace
Package: dl-backtrace
AryaXai-BacktraceBacktrace module for Generating Explainability on Deep learning models using TensorFlow / PytorchBacktrace ModuleOverviewThe Backtrace Module is a powerful and patent-pending algorithm developed by AryaXAI for enhancing the explainability of AI models, particularly in the context of complex techniques like deep learning.FeaturesExplainability:Gain deep insights into your AI models by using the Backtrace algorithm, providing multiple explanations for their decisions.Consistency:Ensure consistent and accurate explanations across different scenarios and use cases.Mission-Critical Support:Tailored for mission-critical AI use cases where transparency is paramount.InstallationTo integrate the Backtrace Module into your project, follow these simple steps:pipinstalldl-backtraceusage for Tensoflow based modelsfromdl_backtrace.tf_backtraceimportBacktraceasBfromdl_backtrace.tf_backtraceimportcontrastasUCfromdl_backtrace.tf_backtraceimportpropasUPfromdl_backtrace.tf_backtraceimportactivation_masterusage for Pytorch based modelsfromdl_backtrace.pytorch_backtraceimportBacktraceasBfromdl_backtrace.pytorch_backtraceimportcontrastasUCfromdl_backtrace.pytorch_backtraceimportpropasUPfromdl_backtrace.pytorch_backtraceimportactivation_masterExample NotebooksNameLinkTensorflow Backtrace Tabular DatasetColab LinkTensorflow Backtrace Textual DatasetColab LinkTensorflow Backtrace Image DatasetColab LinkPytorch Backtrace Tabular DatasetColab LinkPytorch Backtrace Image DatasetColab LinkFor more detailed examples and use cases, check out our documentation.Supported Layers and Future WorkDense (Fully Connected) LayerConvolutional Layer (Conv2D)Reshape LayerFlatten LayerGlobal Average Pooling 2D LayerMax Pooling 2D LayerAverage Pooling 2D LayerConcatenate LayerAdd LayerLong Short-Term Memory (LSTM) LayerBatch Normalisation LayerDropout LayerEmbedding LayerOther Custom LayersGetting StartedIf you are new to Backtrace, head over to our Getting Started Guide to quickly set up and use the module in your projects.ContributingWe welcome contributions from the community. To contribute, please follow our Contribution Guidelines.LicenseThis project is licensed under the MIT License - see the LICENSE file for details.ContactFor any inquiries or support, please contact AryaXAI Support.
Package: dlbar
Table of contents:IntroductionInstallationHow to use dlbar?Customize the download barContributionIntroductiondlbar is a simple terminal progress bar for downloading and displaying download progress.You can use this module to download files and display the download progress bar in your Python projects.InstallationYou can use pip to install:python3 -m pip install dlbarHow to use dlbar?To use the dlbar module, you must import it:fromdlbarimportDownloadBardownload_bar=DownloadBar()download_bar.download(url='https://url',dest='/a/b/c/downloaded_file.suffix',title='Downloading downloaded_file.suffix')Output:Downloading downloaded_file.suffix
43% █████████████████████----------------------------- 197.777 MB/450.327 MBCustomize the download barYou have many options to customize your download bar.Pay attention to the following example on how to change the characters of the download bar:fromdlbarimportDownloadBardownload_bar=DownloadBar(empty_char='.',filled_char='>')download_bar.download(url='https://url',dest='/a/b/c/downloaded_file.suffix',title='Downloading downloaded_file.suffix')Output:Downloading downloaded_file.suffix
63% >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>................... 284.590 MB/450.327 MBYou can even use ANSI codes to color the download bar characters:fromdlbarimportDownloadBardownload_bar=DownloadBar(empty_char=f"\033[31m{chr(9472)}\033[0m",filled_char=f"\033[32m{chr(9472)}\033[0m")download_bar.download(url='https://url',dest='/a/b/c/downloaded_file.suffix',title='Downloading downloaded_file.suffix')Output:You can also change the width of the download bar (any number greater than or equal to 10 is valid):fromdlbarimportDownloadBardownload_bar=DownloadBar(width=35)download_bar.download(url='https://url',dest='/a/b/c/downloaded_file.suffix',title='Downloading downloaded_file.suffix')Output:Downloading downloaded_file.suffix
12% ████------------------------------- 54.512 MB/450.327 MBYou can even hide the percentage or progress status of the download:fromdlbarimportDownloadBardownload_bar=DownloadBar(percent=False)download_bar.download(url='https://url',dest='/a/b/c/downloaded_file.suffix',title='Downloading downloaded_file.suffix')Output:Downloading downloaded_file.suffix
█████████████████████----------------------------- 197.777 MB/450.327 MBYou can also deal with attributes in the form of properties and change their values after creating an instance:fromdlbarimportDownloadBardownload_bar=DownloadBar()download_bar.status=Falsedownload_bar.filled_char='#'download_bar.download(url='https://url',dest='/a/b/c/downloaded_file.suffix',title='Downloading downloaded_file.suffix')Output:Downloading downloaded_file.suffix
43% #####################-----------------------------Contributiondlbar is a free and open source project under the MIT license. You can easily use dlbar and contribute if you have an idea for development.Thank you
Package: dlc2action
DLC2Action is an action segmentation package that makes running and tracking of machine learning experiments easy.InstallationVia the Python Package IndexYou can simply install DLC2Action by typing:pip install "dlc2action==0.2b3"From GithubYou can install DLC2Action for development by running this in your terminal.git clone https://github.com/AlexEMG/DLC2Action
cd DLC2Action
conda create --name DLC2Action python=3.9
conda activate DLC2Action
python -m pip install .FeaturesThe functionality of DLC2Action includes:compiling and updating project-specific configuration files,filling in configuration dictionaries automatically whenever possible,saving training parameters and results,running predictions and hyperparameter searches,creating active learning files,loading hyperparameter search results in experiments and dumping them into configuration files,comparing new experiment parameters with the project history and loading pre-computed features (to save time) and previously
created splits (to enforce consistency) when there is a match,filtering and displaying training, prediction and hyperparameter search history,plotting training curve comparisonsand more.A quick exampleYou can start a new project, run an experiment, visualize it and use the trained model to make a prediction
in a few lines of code.fromdlc2action.projectimportProject# create a new projectproject=Project('project_name',data_type='data_type',annotation_type='annotation_type',data_path='path/to/data/folder',annotation_path='path/to/annotation/folder')# set important parameters, like the set labels you want to predictproject.update_parameters(...)# run a training episodeproject.run_episode('episode_1')# plot the resultsproject.plot_episodes(['episode_1'],metrics=['recall'])# use the model trained in episode_1 to make a prediction for new dataproject.run_prediction('prediction_1',episode_names=['episode_1'],data_path='path/to/new_data/folder')How to get more information?Check out theexamplesorread the documentationfor a taste of what else you can do.AcknowledgmentsDLC2Action is developed by members of theA. Mathis Group at EPFL. We are grateful to many people for feedback, alpha-testing, suggestions and contributions, in particular to Liza Kozlova, Andy Bonnetto, Lucas Stoffl, Margaret Lane, Marouane Jaakik, Steffen Schneider and Mackenzie Mathis.License:Note that the software is provided "as is", without warranty of any kind, express or implied. If you use the code or data, please cite us!Reference:Stay tuned for our first publication -- any feedback on this beta release is welcome at this time. Thanks for using DLC2Action. Please reach out if you want to collaborate!
Package: dlc2nwb
Welcome to the DeepLabCut 2 Neurodata Without Borders RepoHere we provide utilities to convertDeepLabCut (DLC)output to/fromNeurodata Without Borders (NWB) format. This repository also elaborates a way for how pose estimation data should be represented in NWB.Specifically, this package allows you to convert DLC's predictions on videos (*.h5 files) into NWB format. This is best explained with an example (see below).NWB pose ontologyThe standard is presentedhere. Our code is based on this NWB extension (PoseEstimationSeries, PoseEstimation) that was developed withBen Dichter, Ryan Ly and Oliver Ruebel.Installation:Simply do (it only depends onndx-poseanddeeplabcut):pip install dlc2nwbExample within DeepLabCutDeepLabCut's h5 data files can be readily converted to NWB format either via the GUI from theAnalyze Videostab or programmatically, as follows:importdeeplabcutdeeplabcut.analyze_videos_converth5_to_nwb(config_path,video_folder)Note that DLC does not strictly depend on dlc2nwb just yet; if attempting to convert to NWB, a user would be asked to runpip install dlc2nwb.Example use case of this package (directly):Here is an example for converting DLC data to NWB format (and back). Notice you can also export your data directly from DeepLabCut.from dlc2nwb.utils import convert_h5_to_nwb, convert_nwb_to_h5

# Convert DLC -> NWB:
nwbfile = convert_h5_to_nwb(
    'examples/config.yaml',
    'examples/m3v1mp4DLC_resnet50_openfieldAug20shuffle1_30000.h5',
)

# Convert NWB -> DLC
df = convert_nwb_to_h5(nwbfile[0])Example data to run the code is provided in the folderexamples. The data is based on a DLC project you can find onZenodoand that was originally presented inMathis et al., Nat. Neuroas well asMathis et al., Neuron. To limit space, the folder only contains the project fileconfig.yamland DLC predictions for an example video calledm3v1mp4.mp4, which are stored in*.h5format. The video is available,here.Funding and contributions:We gratefully acknowledge the generous support from theKavli Foundationvia aKavli Neurodata Without Borders Seed Grants.We also acknowledge feedback, and our collaboration withBen Dichter, Ryan Ly and Oliver Ruebel.
Package: dlc-generic-analysis
dlc-generic-analysisA framework for creating analysis applications forDeepLabCutwith a gui build in Qt.installationpip install "dlc-generic-analysis[gui]"or for no GUIpip install dlc-generic-analysisUsageFor creating a basic GUI application go toGetting StartedMore informationThis project was designed to be a framework for easily developing video analysis applications that depend on DeepLabCut.
For more information about DeepLabCut check out theirGithubor theirwebsite.
Package: dlcli
.. _readme:`Quick Example`_----------------install dlcli using pip.. code-block:: nonepip install dlcliset your org, account and api key by creating ~/dlcli.yaml.. code-block:: none---account: account_namekey: key_from_account_settingsorg: orgnameurl: https://api.outlyer.com/v1Or you can update the file interactively... code-block:: nonedlcli set org acme-ltd.. code-block:: nonedlcli set account staging.. code-block:: nonedlcli set key xxxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxxthese get stored in ~/dlcli.yaml so the details only need to be changed when you are switching orgs or accountsnow verify you have a successful connection.. code-block:: nonedlcli statusthen get a list of all of your agents.. code-block:: nonedlcli get agentsfun! this is a work in progress so if you want to collaborate please raise PR's`Dataloop CLI Tool`_--------------------dlcli commands can be thought of as a series of nested commands, with each stage having its own options and flags... code-block:: nonedlcli [FLAGS] COMMAND [FLAGS] SUBCOMMAND [FLAGS]The square braces indicate optional elements. Some commands have flags, some do not. Some of those flags are optional, some are mandatory per the command. See the list of commands and flags for more information.The first thing to know is that help is never far away. You can use the ``--help`` flag at any stage to discover which flags are available:.. code-block:: nonedlcli --help.. code-block:: nonedlcli COMMAND --help.. code-block:: nonedlcli COMMAND SUBCOMMAND --helpUnderstand that using the ``--help`` flag in between two nested commands will result in the previous level ``--help`` output being shown.``dlcli --help`` COMMAND will have the same output as ``dlcli --help``, and likewise, ``dlcli COMMAND --help SUBCOMMAND`` will have the same output as ``dlcli COMMAND --help``.The top-level help output looks like this:.. code-block:: none$ dlcli --helpUsage: dlcli [OPTIONS] COMMAND [ARGS]...Dataloop Command Line ToolSee https://www.dataloop.ioOptions:--debug            Debug mode--loglevel TEXT    Log level--logfile TEXT     log file--version          Show the version and exit.--help             Show this message and exit.Commands:agents      Dataloop AgentsNote that all available flags and commands for this level are shown. This pattern is repeated at each successive level of ``--help```Using the API outside of dlcli`_---------------------------------You can use the API code outside of the command line utility... code-block:: pythonfrom dlcli import apisettings = {'url': 'https://api.outlyer.com/v1','org': 'org_name','account': 'account_name','key': 'api_key',}print api.agents.get_agents(**settings)Where org_name, account_name and api_key need to be updated with your personal settings.
Package: dlclib
python-dlclibA wrapper of DeepLabCut for its ease of use.dlclibis supposed to work with almost all single-camera setups of all DLC versions.InstallationIt is better building your DeepLabCut environment first, before running:$pipinstalldlclibClassesdlclib.estimate.TFSessionA thin wrapper over the DeepLabCut inference network based on TensorFlow.session = TFSession.from_config(cfg)will return aTFSessionobject
using the specifiedconfig.yamlfile of the DeepLabCut project.session.get_pose(image)will return anumpy.ndarraycorresponding to the estimation.
for a batch-processing network, use a list of images, or a 4-D uint8 array
whose axis 0 correspond to indexes of images.session.open_writer(path)opens a CSVWriter at the specified path (see below).dlclib.posture.PostureMapperA utility class that maps the output ofTFSessionto a dictionary.
Note that the current implementation:does not care anything about conversion speed.currently only supports non-batch models.mapper = PostureMapper.from_config(cfg)will return aPostureMapperobject
using the specifiedconfig.yamlfile of the DeepLabCut project.posedict = mapper.map(pose)receives the output ofTFSession.get_pose(),
and returns a dictionary.posedict[part]is a named tuple that hasx,yandprobattributes.dlclib.writer.CSVWriterA simple class for writing the estimation into a CSV file.
This classdoessupport batch-processing networks.writer = session.open_writer(path)from aTFSessionobject initializes the writer
associated with the inference session.For every video frame, you can callwriter.push(frame). It automatically callsTFSession.get_pose()internally, and writes its outputs into the file.Callingwriter.flush()will flush the pended video frames (in case of batch modes;
it does not wait for more frames and runsget_pose()to writes the estimation).Callingwriter.close()will flush its internal buffer, close the file and invalidates itself.
You can keep using the originatingTFSessionobject.This class supports the context-manager model, so you can write:withsession.open_writer(path)asout:forframeinvideo_frames:out.push(frame)License2020-2021 Keisuke Sehara, the MIT License
Package: dlclibrary
DLClibraryDLClibrary is a lightweight library supporting universal functions for theDeepLabCutecosystem.Supported functions (at this point):API for downloading model weights fromthe model zooQuick startInstallThe package can be installed usingpip:pipinstalldlclibrary:warning: warning, the closely named packagedlclibis not an official DeepLabCut product. :warning:Example UsageDownloading a pretrained model from the model zoo:frompathlibimportPathfromdlclibraryimportdownload_huggingface_model# Creates a folder and downloads the model to itmodel_dir=Path("./superanimal_quadruped_model")model_dir.mkdir()download_huggingface_model("superanimal_quadruped",model_dir)How to add a new model?Pick a good model_name. Follow the (novel) naming convention (modeltype_species), e.g.superanimal_topviewmouse.Add the model_name with path and commit ID to:https://github.com/DeepLabCut/DLClibrary/blob/main/dlclibrary/dlcmodelzoo/modelzoo_urls.yamlAdd the model name to the constant: MODELOPTIONShttps://github.com/DeepLabCut/DLClibrary/blob/main/dlclibrary/dlcmodelzoo/modelzoo_download.py#L15For superanimal models also fill in the configs!
Package: dl-cliche
dl-cliche: Packaging cliche utilitiesThis is a python module started to package utility functions that are locally made but used everyday.
After while being improved along with other projects, these short-cut functions have become essential to work with various project.Installationpipinstalldl-clicheWhat's for?You can avoid repeating yourself with these cliches:%matplotlibinline%reload_extautoreload%autoreload2with following one liner.fromdlcliche.notebookimport*Quickstart for a python codefromdlcliche.utilsimport*Then you can simply start your code that uses numpy, pandas or utility like:get_logger()to make default logger instance.lock_file_mutex()to make OS dependency-free mutex lock.and so on...Quickstart for a jupyter notebookfromdlcliche.notebookimport*fromdlcliche.utilsimport*Warnings to ignoreType followings where you want to suppress warnings later on.importwarnings;simply_ignore(warnings)Still need to import warnings,simply_ignorewraps what to do then.DocumentsBasic function interface document to be ready.(Japanese)機械学習個人レベルのワークフロー改善@Qiita
Package: dlcm-api
dlcm-apiDLCM application wrapping all modules, All-In-OneThis Python package is automatically generated by theOpenAPI Generatorproject:API version: 2.1.1Package version: 2.1.1Build package: org.openapitools.codegen.languages.PythonClientCodegenRequirements.Python 2.7 and 3.4+Installation & Usagepip installYou can install directly using:pipinstalldlcm-api(you may need to runpipwith root permission:sudo pip install dlcm-api)Then import the package:importdlcmapi_clientDocumentationThe documentation can be accessed throughthe REAMDE of the project's gitlab repository.
Package: dld-array-generator
DLD Array GeneratorA package to generate DLD arrays in DXF format. All distances in micrometers, angles in degrees.The array is parameterized by attributes of theDLD_Arrayclass. Initializing an instance sets up an array with default values. Each of the attrbutes (outlined below) can then be set as desired. When the configurtation is complete, callgenerate(filename)to output the array as in a.dxffile format.Installationpip install dld-array-generatorExamples Usagefrom dld_array_generator import DLD_Array

filename = "DLD example"

g = DLD_Array()

# Update channel desired channel parameters
g.C_D = 10e3 # channel length
g.C_L = 200 # channel width
g.C_N = 2 # bend pairs (producing 4 bends in total)

g.generate(filename)AttributesChannel attributes:C_D = total channel length
C_L = channel width
C_N = number of channel bend pairs
C_separation = distance between parralell channel segmentsTurn attributes:T_N = number of walls in turns
T_W = thckness of walls in turnsDLD-array unit cell attributes:D_D = center-to-center downstram pilar distance
D_L = center-to-center lateral pilar distance
D = pilar dimater
theta = array angleIO port attributes:IO_D = diameter of inlets/outlets
IO_F = filet radius for inlets/oulets
IO_L = IO stem length

O_LP = percentage width of left outlet
O_S = separation of outlets
O_BR = bend radius
O_D = downstream distance for outlet before bend
Package: dldr
# dldrdldr is a tiny command line program for downloading programs from dr.dk/tv. The project is highly inspired by [dldr.brnbw.com](https://github.com/mikker/dldr.brnbw.com) by [@mikker](https://github.com/mikker).### InstallationYou can either download the dldr.py file manually or install it using [HomeBrew](http://brew.sh). I recommend the latter.Install DLDR using [HomeBrew](http://brew.sh).Fill in.### UsageGo to your favorite show on [DR TV](https://www.dr.dk/tv/se/huset-pa-christianshavn/huset-pa-christianshavn-5). Copy the URL. Then run dldr with the URL as an argument.$ dldrhttps://www.dr.dk/tv/se/huset-pa-christianshavn/huset-pa-christianshavn-5Optionally supply an output directory.$ dldr –output ~/Desktophttps://www.dr.dk/tv/se/huset-pa-christianshavn/huset-pa-christianshavn-5By default file name of the programs will be the title of the show, e.g. “Huset på Christianshavn (1)”. If you prefer to use the slug as a file name, i.e. “huset-pa-christianshavn-1”, supply the–slug-nameparameter.For more information, run$ dldr –help.