Saravanan666/pypi-org huggingface dataset:
Package: vissim2geojson
vissim2wgs1984Convert vissim files (.inpx and .fzp to geojson, .fhz to csv).This tool help user convert vissim files to wgs1984 and csv files.specifically,convert.inpxto .geojson fileconvert.fzpfile to .geojson and csv files.  comment: will return two files, one is geojson file and anther is csv file.convert.fhzfile to csv file.Need to know before using this toolVissim SimulationThis tool is to conver files geneated by PTV Vissim. You design you own network or get network from other sources.You will get the layer file (.inpx). the .inpx can only open by PTV Vissim and you can use this tool to convert layer file to wgs1984 so that you can see your layer at different platform (QGIS, Kepler.gl, ArcMap...)You will get simulation results (.fzp and .fhz). you can open these files by PTV Vissim but you can not other platform. you can use this tool to convert .fzp file to (.geojson and .csv),  .fhz file to .geojson.Prepare data for this toolIn order to use this tool , you need to prepare several data for the map conversion.There are for digital nubmers from Background maps:Everytime you are using PTV Vissim, the software will generate these nubmers at Base Data -> Network settings -> DisplayReference point in map: (-9772791.018, 5317836.791)                  you will need to replace these numbers by yoursReference point in network: (0.000, 0.000)                                     you will need to replace these numbers by yoursHow to use the toolinstall from pypipip install vissim2geojsondependenciesFiona==1.8.13.post1
geojson==2.5.0
geopandas==0.9.0
pandas==1.4.2
Shapely==1.7.1use caseSample user case at intersectionimport  vissim2geojson

if__name__=="main":

    file_inpx ="./vissim_data/xl_002.inpx"
    file_fhz ="./vissim_data/xl_002_001.fhz"
    file_fzp ="./vissim_data/xl_002_001.fzp"
    file_folder ="./vissim_data"

    # prepare map reference info from Vissim
    x_refmap =-9772791.018
    y_refmap =5317836.791

    x_refnet =0
    y_refnet =0

    # for covert fzp files, if you don't need to convert fzp file, leave these value to default values.
    x_col_name ="POS"
    y_col_name ="POSLAT"

    # using vissim folder as input path, will generate four files: inpx.geojson, fzp.geojson, fzp.csv, fhz.csv.
    # all result files will save to the same folder as the input folder.

    vissim2wgs1984(file_folder, x_refmap, y_refmap, x_refnet, y_refnet, x_col_name, y_col_name).main()Enjoy it!
Package: vissuAlize
vissuAlizevissuAlize, Python iÃ§in yÃ¼ksek seviyeli bir veri gÃ¶rselleÅŸtirme kÃ¼tÃ¼phanesidir. Matplotlib ve Seaborn'un gÃ¼Ã§lÃ¼ Ã¶zelliklerini bir araya getirerek, kullanÄ±cÄ± dostu bir arayÃ¼z sunar. Veri bilimi projelerinizde hÄ±zlÄ± ve etkili gÃ¶rselleÅŸtirmeler oluÅŸturmanÄ±za olanak tanÄ±r.Ã–zelliklerÃ‡eÅŸitli gÃ¶rselleÅŸtirme tÃ¼rleri: bar plotlar, scatter plotlar, line plotlar ve daha fazlasÄ±.Seaborn ve Matplotlib tabanlÄ± estetik ve interaktif grafikler.Kolay kullanÄ±m ve esnek API.Ã‡oklu veri seti desteÄŸi ve kategorik veri analizi iÃ§in Ã¶zel gÃ¶rselleÅŸtirmeler.KurulumvissuAlize'Ä± pip kullanarak kolayca kurabilirsiniz:pipinstallvissuAlizeKullanÄ±m Ã–rneklerivissuAlizeile gÃ¶rselleÅŸtirme yapmak basit ve doÄŸrudandÄ±r. Ä°ÅŸte bazÄ± temel kullanÄ±m Ã¶rnekleri:Bar Plot OluÅŸturma:importvissuAlizeasvissimportpandasaspd# Veri setini yÃ¼kledata=pd.read_csv('your_dataset.csv')# Bar plot oluÅŸturviss.bar(x='category',y='value',data=data,title="Ã–rnek Bar Plot")Scatter Plot OluÅŸturma:# Scatter plot oluÅŸturviss.scatter(x='x_column',y='y_column',data=data,title="Ã–rnek Scatter Plot")Bu basit Ã¶rnekler,vissuAlize'Ä±n nasÄ±l kullanÄ±lacaÄŸÄ±na dair bir fikir vermektedir. Daha fazla bilgi ve Ã¶rnek iÃ§in dokÃ¼mantasyonumuzu ziyaret edin.KatkÄ±da BulunmavissuAlize'a katkÄ±da bulunmak istiyorsanÄ±z, lÃ¼tfen KatkÄ±da Bulunma Rehberi'ni okuyun. Her tÃ¼rlÃ¼ katkÄ±, bÃ¼yÃ¼k ya da kÃ¼Ã§Ã¼k, takdirle karÅŸÄ±lanÄ±r!LisansvissuAlize, MIT LisansÄ± altÄ±nda lisanslanmÄ±ÅŸtÄ±r. Detaylar iÃ§in LICENSE dosyasÄ±na bakÄ±n.
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Package: vist
VisTA perception system of tracking and motion understanding.InstallationWe currently support Python 3.7, 3.8 and 3.9.You can install the package dependency via vanilla python and pip:python3-mpipinstall-rrequirements.txt\-fhttps://download.pytorch.org/whl/cpu/torch_stable.html
python3-mpipinstall'git+git://github.com/facebookresearch/detectron2.git'This command installs pytorch without CUDA. Please look uppytorch websitefor installation
on your configurations and install pytorch first.If you're using conda, run the following commands:condacreate--namevistpython=3.8
condaactivatevist
pipinstall-rrequirements.txt
condainstallpytorchtorchvisiontorchaudiocudatoolkit=11.0-cpytorch
pipinstall'git+https://github.com/facebookresearch/detectron2.git'You can also usepythonandcondascripts on CPU machine installation.More information about torch and detectron2 installationPytorchDetectron2To installvistpackage,python3setup.pyinstallUsageTrainingpython3tools/launch.pytrain--config<config_path><maybeotherarguments>Testingpython3tools/launch.pytest--config<config_path><maybeotherarguments>Predictionpython3tools/launch.pypredict--config<config_path><maybeotherarguments>
Package: vista
VISTAis a data-driven simulation engine for autonomous driving perception and control. The VISTA API provides an interface for transforming real-world datasets into virtual environments with dynamic agents, sensor suites, and task objectives. Because VISTA is data-driven, it is side-steps many of the traditional issues of simulators, such as their lack of photorealism and ability to accurately model reality.InstallationVISTA can be installed into your Python 3 environment using thePyPi package interface.>> pip install vistaPlease also ensure that you have all required dependencies to successfully run VISTA. Details on dependencies are outlined in thedocumentation.Citing VISTAIf VISTA is useful or relevant to your research, we ask that you recognize our contributions by citing the following three original VISTA papers in your research:% VISTA 1.0: Sim-to-real RL
@article{amini2020learning,
   title={Learning Robust Control Policies for End-to-End Autonomous Driving from Data-Driven Simulation},
   author={Amini, Alexander and Gilitschenski, Igor and Phillips, Jacob and Moseyko, Julia and Banerjee, Rohan and Karaman, Sertac and Rus, Daniela},
   journal={IEEE Robotics and Automation Letters},
   year={2020},
   publisher={IEEE}
}

% VISTA 2.0: Multi-sensor simulation
@inproceedings{amini2022vista,
 title={VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles},
 author={Amini, Alexander and Wang, Tsun-Hsuan and Gilitschenski, Igor and Schwarting, Wilko and Liu, Zhijian and Han, Song and Karaman, Sertac and Rus, Daniela},
 booktitle={2022 International Conference on Robotics and Automation (ICRA)},
 year={2022},
 organization={IEEE}
}

% VISTA 2.0: Multi-agent simulation
@inproceedings{wang2022learning,
 title={Learning Interactive Driving Policies via Data-driven Simulation},
 author={Wang, Tsun-Hsuan and Amini, Alexander and Schwarting, Wilko and Gilitschenski, Igor and Karaman, Sertac and Rus, Daniela},
 booktitle={2022 International Conference on Robotics and Automation (ICRA)},
 year={2022},
 organization={IEEE}
}Contribution GuidelinesVISTA is constantly being advanced and has been built with research, extensibility, and community development as a priority. We actively encourage contributions to the VISTA repository and codebase, including issues, enhancements, and pull requests.
Package: vistec-ser
Vistec-AIS Speech Emotion RecognitionSpeech Emotion Recognition Model and Inferencing using PytorchInstallationFrom Pypipipinstallvistec-serFrom sourcegitclonehttps://github.com/tann9949/vistec-ser.gitcdvistec-ser
pythonsetup.pyinstallUsageTraining with THAI SER DatasetWe provide Google Colaboratory example for training theTHAI SER datasetusing our repository.Training using provided scriptsNote that currently, this workflow only supports pre-loaded features. So it might comsume an additional overhead of ~2 Gb or RAM. To
run the experiment. Run the following commandSince there are 80 studios recording and 20 zoom recording. We split the dataset into 10-fold, 10 studios each. Then evaluate using
k-fold cross validation method. We provide 2 k-fold experiments: including and excluding zoom recording. This can be configured
in config file (seeexamples/aisser.yaml)pythonexamples/train_fold_aisser.py--config-path<path-to-config>--n-iter<number-of-iterations>InferencingWe also implement a FastAPI backend server as an example of deploying a SER model. To run the server, runcdexamples
uvicornserver:app--reloadYou can customize the server by modifyingexample/thaiser.yamlininferencefield.Once the server spawn, you can do HTTP POST request inform-dataformat. and JSON will return as the following format:[{"name":<request-file-name>,"prob":{"neutral":<p(neu)>,"anger":<p(ang)>,"happiness":<p(hap)>,"sadness":<p(sad)>}},...]See an example below:Author & SponsorChompakorn ChaksangchaichotEmail:chompakornc_pro@vistec.ac.th
Package: vistickedword
A library to split a string of many Vietnamese words sticked together to single words. It, for example, split “khuckhuyu” to “khuc” and “khuyu”.
This library is not supposed to split Vietnamese by semantics, so it won’t differentiate single or compound words. It will not, for example, split “bacsitrongbenhvien” to “bac si” + “trong” + “benh vien”.
If you want such a feature, please useunderthesea.
Due to my personal need, this library currently doesn’t process fully marked words, like “họamikhônghótnữa”. However, it is trivial for library user to strip those marks before passing toViStickedWord(usingUnidecode).To make convenient for programming, some terminologies are not used accurately like it should be in linguistic. Please don’t use my code as a source for learning Vietnamese grammar.Thư viện để tách một chùm từ tiếng Việt viết dính liền thành các từ đơn riêng lẻ, ví dụ tách “khuckhuyu” thành “khuc”, “khuyu”.
Thư viện này không có ý định tách từ dựa theo ngữ nghĩa, nên nó sẽ không phân biệt từ đơn, từ ghép của tiếng Việt. Ví dụ, nó sẽ ko tách cụm “bacsitrongbenhvien” thành “bac si” + “trong” + “benh vien”.
Nếu bạn cần tính năng đó, nên sử dụngunderthesea.Do nhu cầu cá nhân nên hiện tại thư viện không xử lý từ có đầy đủ dấu, ví dụ “họamikhônghótnữa”. Tuy nhiên, người dùng thư viện có thể loại bỏ dấu trước khi truyền vàoViStickedWord. Việc đó không khó (dùngUnidecode).Để thuận tiện cho việc lập trình, một số thuật ngữ không được dùng chính xác như cách dùng bên ngôn ngữ học. Vui lòng đừng xem code của tôi là nguồn tài liệu học ngữ pháp tiếng Việt.InstallpipinstallvistickedwordUsagefromvistickedwordimportsplit_wordssplit_words('ngoanngoeo')# Returns ('ngoan', 'ngoeo')CreditDeveloped by byNguyễn Hồng Quân.
Package: vistrails
VisTrails is an open-source data analysis and visualization tool. It provides a comprehensive provenance infrastructure that maintains detailed history information about the steps followed and data derived in the course of an exploratory task: VisTrails maintains provenance of data products, of the computational processes that derive these products and their executions.For more information, take a look at thedocumentation, theusers guide, or ourpublications.Binary releases are available on ourdownloadpage. To report bugs, please use the githubissue tracker, after checking ourFAQfor known issues.Homepage:http://www.vistrails.orgWho we are:http://www.vistrails.org/index.php/People
Package: vistrans
VisTransImplementations of transformers based models for different vision tasksInstallInstall fromPyPIpipinstallvistransInstall fromAnacondacondainstall-cnachiket273vistransVersion 0.003 (06/30/2021)Minor fixes to fix issues with existing models.Version 0.002 (04/17/2021)Pretrained PytorchBottleneck Transformers for Visual Recognitionincluding followingbotnet50botnet101botnet152Implementation based offOfficial Tensorflow ImplementationUsagepip install vistrans1) List Pretrained Models.
```Python
from vistrans import BotNet
BotNet.list_pretrained()Create Pretrained Models.fromvistransimportBotNetmodel=BotNet.create_pretrained(name,img_size,in_ch,num_classes,n_heads,pos_enc_type)Create Custom ModelfromvistransimportBotNetmodel=BotNet.create_model(layers,img_size,in_ch,num_classes,groups,norm_layer,n_heads,pos_enc_type)Version 0.001 (03/04/2021)Pretrained PytorchVision TransformerModels including followingvit_s16_224vit_b16_224vit_b16_384vit_b32_384vit_l16_224vit_l16_384vit_l32_384Implementation based offofficial jax repositoryandtimm's implementationUsageList Pretrained Models.fromvistransimportVisionTransformerVisionTransformer.list_pretrained()Create Pretrained Models.fromvistransimportVisionTransformermodel=VisionTransformer.create_pretrained(name,img_size,in_ch,num_classes)Create Custom ModelfromvistransimportVisionTransformermodel=VisionTransformer.create_model(img_size,patch_size,in_ch,num_classes,embed_dim,depth,num_heads,mlp_ratio,drop_rate,attention_drop_rate,hybrid,norm_layer,bias)
Package: visu
VisuVisu is an user interface library based on pyqtgraph to open and process data image .
It can make plot profile and data measurements analysis on liveIt can open .spe .SPE, .sif and .TIFF fileshttps://github.com/julienGautier77/visuRequirementspython 3.xNumpymatplotlibscipyPyQt6pyqtgraph (https://github.com/pyqtgraph/pyqtgraph.git)Pip install pyqtgraphqdarkstyle (https://github.com/ColinDuquesnoy/QDarkStyleSheet.git)pip install qdarkstylesifread.pyhttps://github.com/lightingghost/sifreader/tree/master/sifreaderwinspec.pyhttps://github.com/antonl/pyWinSpecInstallationfrom PyPipip install git+https://github.com/julienGautier77/visuUsageto use as  it:import visu
visu.visual.runVisu()Or :from PyQt6.QtWidgets import QApplication
import sys
import qdarkstyle
import visu

appli = QApplication(sys.argv)   

e = visu.visual.SEE() 
e.show()
appli.exec_()To  insert in  a  codevisu is a  QtWidgets it can be use like  a  widget :from PyQt6.QtWidgets import QApplication,QWidgetwidgetVisu=visu.visual.SEE()
Package: visu3d
visu3d - 3D geometry made easyvisu3dis an abstraction layer between Torch/TF/Jax/Numpy and your program.
It provides:Standard primitives for 3d geometry (Ray,Camera,Transform,...).
You can combine those standard primitives with your custom ones.Everything is trivially visualizable with zero boilerplate. Inspect & debug
camera poses, trajectories,...All primitives aredataclass_array,
dataclasses which can be reshaped, sliced,... as if they were numpy arrays.Everything is extensible, you can gradually opt-in only for the features you
need, and replace any standard primitive by your custom ones.Core featuresEverything is av3d.DataclassArray:dataclass behave likenp.array(with
indexing, slicing, shape manipulation, vectorization,...).# Single rayray=v3d.Ray(pos=[0,0,0],dir=[1,1,1])assertrays.shape==()# Multiple rays batched togetherrays=v3d.Ray(pos=np.zeros((B,H,W,3)),dir=np.ones((B,H,W,3)))assertrays.shape==(B,H,W)rays=rays.reshape('b h w -> b (h w)')#  Native `einops` supporttop_left_ray=rays[...,0,0]#  (b, h, w) -> (b,)rays=rays.flatten()rays=rays[rays.norm()>0]# Filter invalid raysEverything isvisualizable interactivelyEvery object has a.figproperty for interactive visualization:rays.fig# Plot the raysDisplay multiple objects together:v3d.make_fig([cam,rays,point_cloud])Auto-plot figures with Colab magic:v3d.auto_plot_figs()# Once at the start of the Colabcam,rays,point_cloud# Tuple auto-displayed without `v3d.make_fig` callSame code seamlesslyworks across Torch, Jax, TensorFlow, Numpy.rays=rays.as_jax()# .as_tf(), as_np(), .as_jax()assertisinstance(rays.pos,jnp.ndarray)assertrays.xnpisjnprays=rays.as_torch()assertisinstance(rays.pos,torch.Tensor)assertrays.xnpistorchWith native support for auto-diff,jax.vmap,jax.tree_utils,...PrivitivesCommon primitives (Ray,Camera,Transform,...), so user can express
intent, instead of math.H,W=(256,1024)cam_spec=v3d.PinholeCamera.from_focal(resolution=(H,W),focal_in_px=35.,)cam=v3d.Camera.from_look_at(spec=cam_spec,pos=[5,5,5],target=[0,0,0],# Camera looks at the scene center)rays=cam.rays()# Rays in world coordinates# Project `(*shape, 3)` world coordinates into `(*shape, 2)` pixel coordinates.px_coords=cam.px_from_world@point_cloudSeethe APIfor a full list of primitive.Creating your own primitives is trivial.Converting any dataclass to dataclass array is trivial:Inherit fromv3d.DataclassArrayUseetils.array_typesto annotate the array fields (or exlicitly usemy_field: Any = dca.field(shape=, dtype=)instead ofdataclasses.field)frometils.array_typesimportFloatArrayclassMyRay(v3d.DataclassArray):pos:FloatArray[...,3]dir:FloatArray[...,3]rays=MyRay(pos=jnp.zeros((H,W,3)),dir=jnp.ones((H,W,3)))assertrays.shape==(H,W)v3dmakes it easy to opt-in to the feature you need by implementing the
corresponding protocol.DocumentationThe best way to get started is to try the Colab tutorials (in thedocumentation):Intro(Colab)Transform(Colab)Create your primitives(Colab)Installation:pipinstallvisu3dUsage:importvisu3dasv3dInstallationpipinstallvisu3dThis is not an official Google product.
Package: visualacuity
LicenseThis software is available under the GNU General Public License v3.0. See theLICENSEfile for more info.How it worksMuch of the behavior forvisualacuityis documented in the following spreadsheets, (which are also used for testing
the software):testing/test_cases_conversions.tsvdemonstrates how the various methods of
visual acuity measurements are converted into Snellen equivalents and LogMAR values.testing/test_cases_parsing.tsvexpresses the rules and limitations of converting
plain-text values into structured objectsInstallationpipinstallvisualacuityExample Usageimportvisualacuityfromvisualacuityimport*############## Basic usageparsed=visualacuity.parse_visit({"Left Eye Distance SC":"20/30 -1","Right Eye Near CC":"J5",})assertparsed=={"Left Eye Distance SC":VisitNote(text="20/30 -1",extracted_value="20/30",plus_letters=[-1],laterality=OS,distance_of_measurement=DISTANCE,correction=SC,method=SNELLEN,snellen_equivalent=(20,30),log_mar_base=0.17609125905568127,log_mar_base_plus_letters=0.20107900637734125),"Right Eye Near CC":VisitNote(text="J5",extracted_value="J5",laterality=OD,distance_of_measurement=NEAR,correction=CC,method=JAEGER,snellen_equivalent=(20,40),log_mar_base=0.3010299956639812,log_mar_base_plus_letters=0.3010299956639812,),}############################ "Plus" columns are mergedvisit_data={"Both Eyes Near CC":"20/20","Both Eyes Near CC Plus":"+2"}parsed=visualacuity.parse_visit(visit_data)assertparsed=={"Both Eyes Near CC":VisitNote(text="20/20",text_plus="+2",laterality=OU,distance_of_measurement=NEAR,correction=CC,method=SNELLEN,plus_letters=[+2],extracted_value="20/20",snellen_equivalent=(20,20),log_mar_base=0.0,log_mar_base_plus_letters=-0.041646245536099975)}
Package: visual-attention-tf
Visual_attention_tfA set of image attention layers implemented as custom keras layers that can be imported dirctly into kerasCurrently Implemented layers:Pixel Attention :Efficient Image Super-Resolution Using Pixel Attention(Hengyuan Zhao et al)Channel Attention :CBAM: Convolutional Block Attention Module(Sanghyun Woo et al)Efficient Channel Attention :ECA-Net: Efficient Channel Attention for Deep Convolutional Neural NetworksInstallationYou can see the projects official pypi page :https://pypi.org/project/visual-attention-tf/pipinstallvisual-attention-tfUse --no-dependencies if you have tensorflow-gpu installed alreadyUsage:fromtensorflow.keras.modelsimportModelfromtensorflow.keras.layersimportInput,Conv2Dfromvisual_attentionimportPixelAttention2D,ChannelAttention2D,EfficientChannelAttention2Dinp=Input(shape=(1920,1080,3))cnn_layer=Conv2D(32,3,,activation='relu',padding='same')(inp)# Using the .shape[-1] to simplify network modifications. Can directly input number of channels as wellPixel_attention_cnn=PixelAttention2D(cnn_layer.shape[-1])(cnn_layer)Channel_attention_cnn=ChannelAttention2D(cnn_layer.shape[-1])(cnn_layer)EfficientChannelAttention_cnn=EfficientChannelAttention2D(cnn_layer.shape[-1])(cnn_layer)
Package: visual_auditor
Visual AuditorAn interactive visualization system for identifying and understanding biases in machine learning models.Working DemoA live demo of the Visual Auditor web application is available at the following link:https://visual-auditor.surge.sh/Installationimportvisual_auditorDemo Usage# Import additional packagesimportpandasaspdimportnumpyasnpfromsklearn.preprocessingimportLabelEncoderfromsklearn.ensembleimportRandomForestClassifier# Helper function for binning numerical featuresdefbin_feature(feature):bins=np.histogram_bin_edges(adult_data[feature],bins=10,range=None,weights=None)adult_data[feature]=pd.cut(adult_data[feature],bins,labels=[xforxinrange(len(bins)-1)],right=True,include_lowest=True,duplicates='drop')intervals=[]foriinrange(len(bins)-1):intervals.append(f'{int(bins[i])}-{int(bins[i+1])}')returnintervals# Load Adult datasetadult_data=pd.read_csv("data/adult.data",names=["Age","Workclass","Final Weight","Education","Education-Num","Marital Status","Occupation","Relationship","Race","Sex","Capital Gain","Capital Loss","Hours Per Week","Country","Target"],sep=r'\s*,\s*',engine='python',na_values="?")# Drop NA valuesadult_data=adult_data.dropna()# Drop irrelevant fieldsadult_data=adult_data.drop(columns=['Final Weight','Education-Num'])# Bin numerical featuresencoders={}encodings={}numerical_features=["Age","Capital Gain","Capital Loss","Hours Per Week"]forfeatureinnumerical_features:encodings[feature]=bin_feature(feature)# Encode categorical featuresforcolumninadult_data.columns.difference(numerical_features):ifadult_data.dtypes[column]==np.object:le=LabelEncoder()adult_data[column]=le.fit_transform(adult_data[column])encoders[column]=leencodings[column]=le.classes_print(column,le.classes_,le.transform(le.classes_))# Separate Target valuesX,y=adult_data[adult_data.columns.difference(["Target"])],adult_data["Target"]# Train a classifierclassifier=RandomForestClassifier(max_depth=5,n_estimators=10)classifier.fit(X,y)# Interact with the Visual Auditorvisual_auditor.find_slices_and_visualize(classifier,(X,y))CreditsTheVisual Auditorwas developed and maintained byDavid Munechika,Jay Wang, andPolo Chaufrom thePolo Club of Data Scienceat Georgia Tech.LicenseTheVisual Auditoris available under theMIT License.
TheVisual Auditoruses the D3.js which is licensed under theISC Licenseand React.js which is licensed under theMIT License.
Package: visualblocks
Visual Blocks in Google ColaboratoryVisual Blocks offers a Python package for use withinGoogle Colaboratorynotebooks.For Users!pip install visualblocksandimport visualblocksin your Colaboratory
notebooks. See the example notebooks in the directoryexamples/.For Developers!pip install git+https://...in a notebook installs the package straight from
the latest, unreleased source in Github. The notebooks in thetests/directory use this method.The directoryscripts/contains turnkey scripts for common
developer tasks such as building and uploading the Python distribution package.Build and upload package to TestPyPIOne time setup:# Install `build`.$python3-mpipinstall--upgradebuild# Install `twine`, and make sure its binary is in your PATH.$python3-mpipinstalltwineSteps:Update the version number inpyproject.toml.Runrm -rf buildto clean up previous builds.Runscripts/packageto build the package.Runscripts/uploadto upload the package. You need to have the username and
password ready.