Saravanan666/pypi-org huggingface dataset:
Package: labdiscoverylib
labdiscoveryliblabdiscoverylibis a library for creatingLabDiscoveryEngineremote laboratories, based onweblablib.A remote laboratory is a software and hardware system that enables students to access real laboratories through the Internet.
For example, a student can be learning how to program a robot by writing code in a computer at home and sending it to a remote laboratory, where the student can see how the program behaves in a real environment.Creating a remote laboratory may imply many layers, such as authentication, authorization, scheduling, etc., so Remote Laboratory Management Systems (RLMS) were created to make the common layers of remote laboatories.
WebLab-Deusto is an Open Source RLMS, and it has multiple ways (see the docs) to create a remote laboratory (in different programming languages, etc.).In the case of Python, with the popularFlaskmicroframework,labdiscoverylibis the wrapper used to createunmanaged labs.Unmanaged labsis a term used in WebLab-Deusto to refer laboratories where the authors develop the full stack (server, client, deployment), as opposed tomanaged labs.If you are familiar with Flask and with Web development, and want to be able to customize everything but not need to implement all the layers of authentication, administration, etc., this library would be very useful. Furthermore, this library allows you to develop remote laboratories for many environments (from regular computers with Linux to systems such as Raspberry Pi). And it integrates very well with other Flask components such asFlask-SocketIO,Flask-SQLAlchemyfor databases orFlask-Assets.DocumentationRead the documentation:https://developers.labsland.com/labdiscoverylib/InstallationSimply use pip:pip install labdiscoverylibSimple usagefromflaskimportFlask,url_forfromlabdiscoverylibimportWebLab,weblab_user,requires_activeapp=Flask(__name__)app.config.update({'SECRET_KEY':'secret',# MUST CHANGE'WEBLAB_USERNAME':'weblabdeusto','WEBLAB_PASSWORD':'password',})weblab=WebLab(app)@weblab.on_startdefon_start(client_data,server_data):# ...print("Starting user")@weblab.on_disposedefon_dispose():# ...print("Ending user")@weblab.initial_urldefinitial_url():returnurl_for('index')@app.route('/')@requires_activedefindex():return"Hello,{}".format(weblab_user.username)if__name__=='__main__':app.run(debug=True)Advance examplesYou may findherethe following examples:simple: basic usage, all in one file.advanced: more advanced usage, with separation of files, tasks, more complex session managementcomplete: based on advanced, but using WebSockets with Flask-SocketIO, internationalization with Flask-Babel and minimified static files with Flask-Assets.There is another example calledquickstart, which is the one used in the documentation, which is something in betweensimpleandadvanced.
Package: lab-django-ftpserver
FTP server application that used user authentication of Django.Getting StartedInstall django-ftpserver by pip.$ pip install django-ftpserverAdd line to settings.INSTALLED_APPS for your django project.INSTALLED_APPS = (
    # ..
    'django_ftpserver',
)migrate(Django>=1.7) or syncdb(Django<1.7) app.$ python manage.py migrateRunmanage.py ftpservercommand.$ python manage.py ftpserver 127.0.0.1:10021RequirementsTarget Python version is 2.6, 2.7, 3.3, 3.4 and 3.5.Django>=1.4pyftpdlibLicenseThis software is licensed under the MIT License.DocumentationThe latest documentation is hosted at Read The Docs.https://django-ftpserver.readthedocs.org/en/latest/DevelopThis project is hosted at Github:https://github.com/tokibito/django-ftpserverAuthorShinya Okano
Package: labdrivers
labdrivers========labdrivers is a collection of drivers for common research lab instruments.It contains a suite of instrument-specific drivers which can be used tointerface measurement hardware with Python code, along with a set ofJupyter notebooks demonstrating example use cases.To get started using these components in your own software, please go to therepository README_... _README: https://github.com/masonlab/labdrivers/blob/master/README.mdLicense=======``labdrivers`` is licensed under the terms of the MIT license. See the file"LICENSE" for information on the history of this software, terms & conditionsfor usage, and a DISCLAIMER OF ALL WARRANTIES.All trademarks referenced herein are property of their respective holders.Copyright (c) 2016--, Henry Hinnefeld.
Package: labedf
labedf.pyMerge the lab.js csv file and the edf file.Finally, an EDF file is generated with lab.js annotations written.Installationpip install labedfHow to Usefromlabedfimportmerge_edf_csvlab.js csv + edf file → merged edf filemerge_edf_csv("./xxx.edf","./labjs.csv","./export.edf")Optional Argumentshttps://github.com/s-n-1-0/labedf.py/blob/main/labedf/edf_csv.pyMerged EDF File → HDF File, Merged SET File → HDF FileThese functions were removed because they were not the original purpose of the library.
Package: labelary-mwisslead
pylabelaryPython module for interacting with api.labelary.comUsage:>>> import labelary
>>>
>>> test_label = '^XA^PR2^LRY^LH30,30^Fo),0^GB400,300,300^FS^FO20,10^AF^FDZEBRA^FS^FO20,60' \
...              '^B3,,40^FDAAA001^FS^PF50^FO20,160^AF^FDSLEW EXAMPLE^FS^XZ^XA^PH^XZ^XA^PR2,6' \
...              '^F020,10^AF^FDZEBRA^FS^F020,60^B3,,40^FDAAA001^FS^PFO20,160^AF' \
...              '^FDSLEW EXAMPLE^FS^XZ^XA^LH30,30^FO20,10^AD^FDZEBRA^FS^FO20,60^B3^FDAAA001' \
...              '^FS^XZ'
>>>
>>> png_labels = labelary.zpl2_to_png(test_label)
>>>
>>> for i, label in enumerate(png_labels):
...     with open('test_label{}.png'.format(i), 'wb') as fid:
...         fid.write(label)
...
>>>
Package: labelatorio
Labelator.io - python clientPython client forLabelator.io- labeling and ML training studioInstallpip install labelatorioGetting your API tokenDocs:Full Labelator.io documentation can be found heredocs.labelator.ioPage dedicated this client can be foundhereUsageConnecting clientimportlabelatorioclient=labelatorio.Client(api_token="your_api_token")Getting project infoPackage requirements are handled using pip. To install them do# get project by idexisting_project=client.projects.get("2fab1778-e8b1-4327-ac83-16dd0e783ab4")# if you have just nameexisting_project=client.projects.get_by_name("my name")# or if you don't know the exact nameexisting_project=client.projects.search("my name")Adding, updating documentsdf=pd.DataFrame({"key":["first","second"],# mandatory"text":["this is my first text","completely different text..."],# mandatory"my_custom_column":["note 1",None]# optional"labels":[["ClassA"],None]#optional if you have labels - should be defined in project})ids=client.documents.add_documents(project_id,data=df)client.documents.set_labels(project_id,ids[1],["ClassB"])Quering documents# simple keyword search ...found=client.documents.search(keyword="completely different")# find all documents where "ClassA" was predictedfound=client.documents.search(predicted_label="ClassA")# find all documents where "ClassA" was incorrectly predictedfound=client.documents.search(false_positives="ClassA")
Package: labelbase
Labelbox Helper LibraryThis library holds helper functions used by other Labelbox-supported Libraries such as labelspark, labelbox-bigquery and labelpandasThis library is also a place for users of Labelbox to publish and share quality-of-life enhancements to the overall Labelbox SDK experience.Labelbox developers built and manage this repo, and the best developments here can end up in the base Labelbox library.
Package: labelboxbigquery
Labelbox Connector for Google BigQueryAccess the Labelbox Connector for Google BigQuery to easily perform the following functionalities:lbbq.client.create_data_rows_from_table:   Creates Labelbox data rows (and metadata) given a BigQuery tablelbbq.client.create_table_from_dataset:   Creates a BigQuery table given a Labelbox datasetlbbq.client.upsert_table_metadata:   Updates BigQuery table metadata columns given a Labelbox datasetlbbq.client.upsert_labelbox_metadata:   Updates Labelbox metadata given a BigQuery tableThe Demo code supplied in this Github is designed to run in a Google Colab, but the code can be adapted to any notebook environment.Labelbox is the enterprise-grade training data solution with fast AI enabled labeling tools, labeling automation, human workforce, data management, a powerful API for integration & SDK for extensibility. VisitLabelboxfor more information.This library is currently in beta. It may contain errors or inaccuracies and may not function as well as commercially released software. Please report any issues/bugs viaGithub Issues.Table of ContentsRequirementsConfigurationUseRequirementsGoogle Cloud BigQuery Authenticated ClientGoogle BigQuery SDKLabelbox accountGenerate a Labelbox API keyConfigurationInstall Labelbox-BigQuery to your Python environment. The installation will also add the Labelbox SDK and BigQuery SDK.pip install labelbox-bigquery
import labelboxbigqueryUseTheclientclass requires the following arguments:lb_api_key= Labelbox API Keygoogle_key= Google Service Account Permissions dict, how to create oneheregoogle_project_name= Google Project ID / Name
Package: label-converter
Label converterDescriptionlabel_converteris a Python library to turn HTML string to png file for label printing.Usagefrom label_converter import label_converter

header = '<p>I\'m head</p>'
body = '<p>I\'m <strong>label</strong> <span style="font-size: 30px; color: rgb(184, 49, 47);">body</span></p>'
footer = '<p>I\'m footer</p>
width = 600
height = 500
base64_encoding = False

label_converter.create(header, body, footer, width, height, base64_encoding)
Package: labelcsv
A simple python package to quickly label csv files.Installationpip install labelcsvUsageNavigate to a directory that contains one or more csv files and run
labelcsv.labelcsvSelect the csv file to start labelingSelect file:
> movie_reviews.csv
iris.csvChoose the column in the csv file to label. The number next to each
column shows the number of missing values for that column.Select column to label from movie_reviews.csv (Rows: 150, Columns: 2):
Movie Description → (0)
> Movie Reviews → (5)Add labels that are separated by a single space. ASkiplabel
will automatically be added which will replace the label with a blank
value.Add labels separated by a space:

Positive Negative NeutralStart labeling by using theUPandDOWNarrow keys to select
the label and then hitENTERto label the row:> Remaining Movie Reviews to be labeled: 150 - Skipped: 0

? I like the movie.
> Positive
Negative
SkipAfter you are done labeling, the file will be saved as:
“feature_name.csv” and the distribution of labels will show.
Package: labelDetection
1.8.2 (2018-12-02)Fix pip depolyment issue1.8.1 (2018-12-02)Fix issuesSupport zh-Tw strings1.8.0 (2018-10-21)Support drawing sqaure rectAdd item single click slotFix issues1.7.0 (2018-05-18)Support YOLOFix minor issues1.6.1 (2018-04-17)Fix issue1.6.0 (2018-01-29)Add more pre-defined labelsShow cursor pose in status barFix minor issues1.5.2 (2017-10-24)Assign different colors to different lablels1.5.1 (2017-9-27)Show a autosaving dialog1.5.0 (2017-9-14)Fix the issuesAdd feature: Draw a box easier1.4.3 (2017-08-09)Refactor settingFix the issues1.4.0 (2017-07-07)Add feature: auto savingAdd feature: single class modeFix the issues1.3.4 (2017-07-07)Fix issues and improve zoom-in1.3.3 (2017-05-31)Fix issues1.3.2 (2017-05-18)Fix issues1.3.1 (2017-05-11)Fix issues1.3.0 (2017-04-22)Fix issuesAdd difficult tagCreate new files for pypi1.2.3 (2017-04-22)Fix issues1.2.2 (2017-01-09)Fix issues
Package: labeled-enum
labeled-enums=============A Django-friendly iterable Enum type with labels.Example-------.. code-block:: python>>> from django.utils.translation import gettext_lazy as _>>> from lenum import LabeledEnum>>> class STATE_CHOICES(LabeledEnum):...     NEW = 0...     IN_PROGRESS = 1...     REVIEW = 2, _('In Review')...>>>>>> STATE_CHOICES.NEW0>>> STATE_CHOICES.IN_PROGRESS1>>> STATE_CHOICES[2]'In Review'>>> list(STATE_CHOICES)[(0, 'New'), (1, 'In Progress'), (2, 'In Review')]>>> STATE_CHOICES.for_label('In Progress')1```>>> STATE_CHOICES.names('NEW', 'IN_PROGRESS', 'REVIEW')Usage in Django:.. code-block:: pythonclass STATUS(LabeledEnum):CLOSED = 0NEW = 1PENDING = 2, 'Process Pending'FAILED = -1, 'Processing Failed'class MyModel(models.Model):# django migrations can have trouble resolving imports if we define the# class within the class, so we bind this here for convenience.STATUS = STATUSstatus = models.IntegerField(choices=STATUS, default=STATUS.NEW)Want translations?.. code-block:: pythonfrom django.utils.translation import gettext_lazy as _class STATUS(LabeledEnum, label_wrapper=_):CLOSED = 0NEW = 1PENDING = 2, 'Process Pending'FAILED = -1, 'Processing Failed'All label values (including auto-generated ones) will have `label_wrapper`applied first.Installation------------.. code-block::pip install labeled-enum
Package: label-explorer
Label Explorer介绍🚀 基于 Electron 的 YOLO 标签分析与整合工具作者简介👨‍🏫 导师曾逸夫，从事人工智能研究与开发；主研领域：计算机视觉；YOLOv8官方开源项目代码贡献人；YOLOv5官方开源项目代码贡献人；YOLOv5 v6.1代码贡献人；YOLOv5 v6.2代码贡献人；YOLOv5 v7.0代码贡献人；Gradio官方开源项目代码贡献人✨  Github：https://github.com/Zengyf-CVer👩‍🎓 学生邓乙华，从事计算机视觉的研究和 JavaScript 项目的开发。Label Studiohttps://github.com/HumanSignal/label-studio对Label Studio导出的格式进行转换yarnaddelectron--dev安装教程# Electron版yarninstall
yarnrunstart# Python CLI版pipinstalllabel-explorer使用教程Python CLI# 使用默认参数label-explorer# 自定义图片路径和标签路径label-explorer-sldpath/to/imgs-sidpath/to/labels# 自定义训练集和验证集比例label-explorer-p0.9
label-explorer-p0.7
label-explorer-p0.75
Package: label-flatten
Label-FlattenFlatten hierarchical concepts or labels system to declarative sentences and labels.A Python module.ExampleInputLabel/Concept Hierarchy主餐 Main Meal牛排 Steak炒羊肉 Stir-fried lamb甜品 Dessert布丁 Pudding冰淇淋 Ice creamCase Label: 牛排 SteakOutputs它是牛排，對的。它不是牛排，錯的。它是主餐，對的。它不是主餐，錯的。它是炒羊肉，錯的。它不是炒羊肉，對的。它不是布丁，錯的。它不是布丁，對的。它是冰淇淋，錯的。它不是冰淇淋，對的。它是甜品，錯的。它不是甜品，對的。It's a steak, right.It's not a steak, wrong.It's the main meal, right.It's not the main meal, wrong.It is stir-fried lamb, wrong.It is not stir-fried lambb, right.It's not pudding, wrong.It's not pudding, right.It is ice cream, wrong.It's not ice cream, right.It is dessert, wrong.It's not dessert, right.In this module, wrong is 0 and right is 1.Installpip install label-flattenUsagefromlabel_flattenimportTree,Nodeimportcopypth="example_data.json"labels=["辣子鸡丁"]tree_template=Tree(pth)forlabelinlabels:new_tree=copy.deepcopy(tree_template)labeled_tree=new_tree.give_label(label)print(labeled_tree)sentences=labeled_tree.write(mode="all")forseninsentences:print(sen)
Package: labeling
Labeling in PythonThis package is intended to provide an easy way to store Labelings for image segmentation.
The segmentation information is stored in a BSON file for fast I/O on demand, but can also be exported to JSON for human readability.UsageCreate a Labeling object with one of the static methods or just init normally.Add one or more images(iterate_over_images(),add_image(), or even patches of an image (add_segments()).Note: If you decide to add patches, make sure that no segment in a patch is overlapping the edge of the patch.
Otherwise, the other parts of the segment will be considered an unique entity.(Optional) Add any kind of metadata you want to the Labeling as additional information supplied by youGet the Labeling for further use(get_result()) or save (save_result()) it to a specified path.
Both methods return an image and a bson container.InformationThe project is open-source. If you find problems or have ideas for improvement, please create an issue on GitHub, or contribute yourself!
Package: labelkit
LabelKit - build and evaluate data labeling pipelinesA lightweight framework for building and evaluating data transformation and data extraction pipelines using LLMs. Designed for simplicity, rapid prototyping, evaluation and optimization.Star us on Github!LLMs can make your treasure trove of unstructured data useful if only you could transform it into structured, or extract key fields from it. Today, building LLM-powered pipelines is difficult because LLMs are unpredictable. Unlike traditional software, you can't simply write unit and integration tests that confirm the correctness of your code.With LLMs you need a different approach: you need to evaluate your code on a dataset, and tune the code to find the right tradeoff between:AccuracyCostLatencyLabelKit is an extremely lightweight framework that helps you build these pipelines such that you can:Easily run them on a dataset (not just a single data point)Keep track of token usage, cost and latencyEvaluate accuracy against ground truthEvaluate the correctness of each step in the pipelineEasily parametrize each step (eg. model choice) so you can tune the paramters to optimize performanceGet StartedInstalling LabelKit is a breeze. Simply runpip install labelkitin your terminal.LicenseThis project is licensed under the terms of the MIT License.Contributors
Package: labella
Labella.py is a Python 3 clone oflabella.jsthat supports all the features of
labella.js and addionally can generate TikZ PDF
images for use in LaTeX.Note:This package is for the most part a direct translation of the
originallabella.jscode to Python
3. All the credit for the labella code goes to Krist Wongsuphasawat
(@kristw), I only added the code that
generates PDF output. Labella.py also includes code fromd3for axis scaling, andWebColafor the VPSC code
(label placement optimization).Why?I wrote this for multiple reasons, the most important of which is that I
wanted to incorporate label graphs like the ones generated bylabella.jsin PDF documents, but wasn’t
satisfied with the PDF output generated by SVG-to-PDF converters.
Additionally, it seemed like a fun way to learn some more Javascript, and
learn about howlabella.jsworks.ScreenshotsThese screenshots show the PDF output generated by labella.py (converted to
.png)Time scale:Linear scale:UsageUsing labella.py is extremely similar to usinglabella.js. See theexamplesdirectory
for examples of how to use labella.py. These examples are translations of the examples
included in labella.js and ind3kit-timeline.For ease of use only the Timeline interface is implemented, which works
similar tod3kit-timeline. Here is
a minimal working example for exporting to PDF:>>> from labella.scale import LinearScale
>>> from labella.timeline import TimelineTex
>>> tl = TimelineTex([
  {'time': 1, 'text': 'Minimal'},
  {'time': 2, 'text': 'Working'},
  {'time': 3, 'text': 'Example'}
  ], options={'scale': LinearScale()})
>>> tl.export('minimal.tex')See also the documentationhere.InstallationLabella.py is available through PyPi, so you can do:pip install labellato install it.Note:Labella.py requires a working LaTeX distribution for both SVG and
PDF output (see alsothe documentation),
and has only been tested on Linux. If you run into problems, please open an issue on GitHub.AuthorGertjan van den Burg (@GjjvdBurg).Licensed under theApache License Version 2.0.
Package: labelmaker
# Labelmaker #The Labelmaker tool provides a method to categorize layers in seismic data.
The tool uses segyio to read seismic data and Matplotlib to produce the
graphical overview to the user.The user draws an area (polygon) containing the layer at hand and selects
the type.### Command line arguments:-dor–compare:   Specify overlay results. Previously exported segy resultsfrom the labelmaker may be added with this argument.
In such case the results will overlay the inputfile and
it will not be possible to create new polygons.-tor–threshold: Set point selection sensitivity. The setting impacts theradius when selecting points to move. Default: 0.01.-por–prefix:     File path prefix for exported results.Default:’Labelmade-‘.-xor–horizontal: Downsample horizontally (keep every n trace).Note that the exported results will have the original sampling rate-yor–vertical: Downsample vertically (keep every n sample)Note that the exported results will have the original sampling rate-lor–load: Specify file for saved polygons. Previously savedpolygons may be added with this argument (see keyboard shortcut ctrl+p)-cor–cmap: Set color map. default is “seismic”.Mouse shortcuts:<Left Mousebutton>Creates a point in the plot. Further addition of pointscreates a line between the points.### Keyboard shortcuts:<enter>Draws a polygon based on the points selected by the user. Also resetsthe points in the line, such that the next point created by clicking
the mousebutton starts on a new line.<escape>Clear drawn points.<z>Undo last created point.<d>Removes the polygon which the cursor currently hovers. This actionwill do nothing if the cursor is not hovering a polygon.<u>Undo deletion of last polygon. This action will only add the latestremoved polygon.<e>Edit the polygon which the cursor currently hovers. This also
applies for polygons that have been loaded from file.<ctrl+e>Export the results. The Labelmaker creates a new segy file with thesame headers, text content and dimensions as the input file. The segy
file contains 0-values for all coordinates outside of any drawn
polygon. Depending on the type chosen by the user, the coordinates
within polygons will have values from 1 to n.<ctrl+p>Save polygon paths to file. The polygons are saved as“polys-“<filename>.json. The file can be used as argument to load the
polygons at a later time.<ctrl+i>Open a new figure which describes the available color andtextures that may be applied to polygons.
Package: label-maker
Label MakerData Preparation for Satellite Machine LearningLabel Maker downloadsOpenStreetMap QA Tileinformation and satellite imagery tiles and saves them as an.npzfilefor use in machine learning training.satellite imagery fromMapboxandDigital GlobeRequirementsPython 3.6tippecanoeInstallationpipinstalllabel-makerNote that running this library this requirestippecanoeas a "peer-dependency" and that command should be available from your command-line before running this.DocumentationFull documentation is available here:http://devseed.com/label-maker/AcknowledgementsThis library builds on the concepts ofskynet-data. It wouldn't be possible without the excellent data from OpenStreetMap and Mapbox under the following licenses:OSM QA tile datacopyright OpenStreetMap contributorsand licensed underODbLMapbox Satellite data can betraced for noncommercial purposes.It also relies heavily on Marc Farra'stilepieto asynchronously process vector tiles
Package: label-maker-dask
label-maker-daskLibrary for runninglabel-makeras adaskjobAcknowledgementsThis work was undertaken with support from Microsoft to be run on thePlanetary Computer. With access to the Planetary Computer Hub, you can find an interactive notebook tutorial for running this library.Basic ExampleInstantiate a distributed dask clusterfromdask.distributedimportClientcluster=...client=Client(cluster)Create a label maker jobfromlabel_maker_daskimportLabelMakerJoblmj=LabelMakerJob(zoom=13,bounds=[-44.4836425781,-23.02665962797,-43.412719726,-22.5856399016],classes=[{"name":"Roads","filter":["has","highway"]},{"name":"Buildings","filter":["has","building"]}],imagery="http://a.tiles.mapbox.com/v4/mapbox.satellite/{z}/{x}/{y}.jpg?access_token=ACCESS_TOKEN",ml_type="segmentation",label_source="https://qa-tiles-server-dev.ds.io/services/z17/tiles/{z}/{x}/{y}.pbf")Build & execute the joblmj.build_job()lmj.execute_job()View or otherwise use the results (by passing to a machine learning framework)forresultinlmj.results:...