Saravanan666/pypi-org huggingface dataset:
Package: visualizer
Visualizer:Visualizeris a Python package that automates the process of visualization and facilitates the plotting of any individual relationship between multiple-columns.Visualizerpackage allows you to do 2 types of plotting:Visualize by anindividual column:Count Plot.Pie Plot.Histogram plot.KDE plot.WordCloud plot.Histogram for high cardinality columns.Line plot with index.Point plot with index.Clustered-bar Plot.Bubble plot.Scatter plot.Density plot.Box plot.Violin plot.Ridge plot.Parallel plot.Radar plot.Visualize by arelationship(multiple-columns):Uni-vairate Target.Uni-variate Categorical (Cat).Uni-variate Numerical (Num).Bi-variate Num with Index.Bi-variate Cat with Index.Bi-variate Num with Num.Bi-variate Num with Cat.Bi-variate Cat with Cat.Bi-variate Cat with Target.Bi-variate Num with Target.Multi-variate Nums with Cat.Installation:pipinstall-UvisualizerUsage:To use the first typeIndividual Plotting, all the methods starts withcreate_, and you can use them as follows:# Import the libraryfromvisualizerimportVisualize# Create a count plotVisualizer.create_count_plot(df=df,cat_col="bin_3",annot=True)To see more types of individual plotting, you can see thedocs.To use the second typeAutomatic Visualization, all the methods starts withvisualize_, and you can them as follows:# Import the libraryfromvisualizerimportVisualizerautoVis=Visualizer(df=df,# df: (dataframe)num_cols=num_cols,# num_cols: (list) of numerical columns.cat_cols=cat_cols,# cat_cols: (list) of categorical columns.target_col=target_col,# target_col: (string) your target column.ignore_cols=ignore_cols,# ignore_cols: (list) of columns to ignore.problem_type='classification')# problem_type: (string) ['classification', 'regression']# Visualize all the relationships between the selected columns,# whether it's uni-variate, Bi-variate, or even multi-variate.# This methods saves the generated figures into folder named "visualizer"# into the current directory.autoVis.visualize_all()After running this command, the output in your notebook will tell you the progress, and when it's finished, it will be like this:And your current directory will have a new folder called "visualizer", which contains all the relationships between the columns.So you can go to each particular relationship "folder", and see the related figures, does it contain any pattern?To know more, you can see thedocs.Further Ideas/Developments:The following ideas are under construction and it will be added soon in upcoming versions:VisualizeSparseColumns, to see if they have a pattern.VisualizeNaN/Infinite/Large numericvalues across the whole dataframe, to see the pattern of the whole dataframe.VisualizeTextcolumns.Add the functionality to arrange the structure of the folders to be bycolumns, so each column has all the relationships for a specific column.Contribute:If you've found a bug or something that you would like to improve, don't hesitate to create an issue and create a pull request.License:MIT License.Authors:Mosaab Muhammad
Package: visualizer-torch
This module is designed to help you visualize pytorch modelExample usage:
from Visualizer import Visualizer
from ExampleNet import ExampleNetvis = Visualizer()

model = ExampleNet()  # the model you want to visualize

params = vis._get_learnable_parts(model)
print(params)
# Out[]: <generator object Visualizer._get_learnable_parts at 0x00000262852F3740>

for param, name in params:
    print(param, "\t", name)

# Out[]:
# Conv2d(1, 8, kernel_size=(5, 5), stride=(1, 1))                            ExampleNet.0.Conv2d.seq_block.Conv2d
# MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ExampleNet.1.MaxPool2d.seq_block.MaxPool2d
# ReLU(inplace=True)                                                         ExampleNet.2.ReLU.seq_block.ReLU
# Conv2d(8, 16, kernel_size=(5, 5), stride=(1, 1))                           ExampleNet.3.Conv2d.seq_block.Conv2d
# MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ExampleNet.4.MaxPool2d.seq_block.MaxPool2d
# ReLU(inplace=True)                                                         ExampleNet.5.ReLU.seq_block.ReLU
# Linear(in_features=144, out_features=72, bias=True)                        ExampleNet.0.Linear.fc.Linear
# Linear(in_features=72, out_features=10, bias=True)                         ExampleNet.1.Linear.fc.LinearExample usage in a loop:
from Visualizer import Visualizer
from ExampleNet import ExampleNetvis = Visualizer()

model = ExampleNet()  # the model you want to visualize

(...)
for ep in range(epochs):
    (...)
    vis.visualize_loop(ep, model, total_loss=total_loss, other_params={"Some param": ["some_value"]})
vis.close_writer()
Package: visualml
Visual ML is a library for visualizing the decision boundary ofmachine learning models from Sklearn using 2D projections of pairsof features. Here's an example:```>>> import visualml as vml>>> import pandas as pd>>> from sklearn.datasets import make_classification>>> from sklearn.ensemble import RandomForestClassifier as RF>>> # Create a toy classification dataset>>> feature_names = ['A','B','C','D']>>> X, y = make_classification(n_features=4, random_state=42)>>> # The visualization is only supported if X is a pandas df>>> X = pd.DataFrame(X, columns=feature_names)>>> # Train a classifier>>> clf = RF(random_state=42).fit(X,y)>>> # Plot decision boundary grid>>> vml.decision_boundary_grid(clf, X, y)```
Package: visualone
Visual OneVisual One's few shot learning framework allows you to easily train models for complex computer vision tasks using only a few samples in two lines of code.To see some examples, visit theexamples page of our website.QuickstartInstall the package:pip install visualoneSubmit your emailhereto receive your public and private keys via email.Train a model by providing some positive samples and some negative samples:fromvisualoneimportvedxclient=vedx.client(public_key,private_key)model=client.train(positive_samples,negative_samples)Apply the trained model to a new image to do prediction:client.predict(model['task_id'],image_file)Description of inputs & outputsTrainingmodel=client.train(positive_samples,negative_samples)positive_samplesandnegative_samplesmust be either:str: The path to a directory containing image files representing all of the positive/negative samples.Orlist[str]: The name of the individual files corresponding to the positive/negative samples.Returns adictwith the following keys:task_id: A unique task id generated for this task. You need to pass this toclient.predictto do prediction.n_positive_samples: The number of positive samples used for training the model.n_negative_samples: The number of negative samples used for training the model.success:1if the training was done successfully.0if an error occured.message: A brief message describing the error if an error occurs.Predictionclient.predict(task_id,image_file)task_id: The unique task id corresponding to the trained model returned byvedx.trainimage_file: The path to the file for which you want to do inference.Returns adictwith the following keys:task_id: The task id corresponding to the model.prediction: A boolean representing the model prediction.Truemeans the model predicted a positive label for the given image.Falsemeans the model predicted a negative label for the given image.confidence: A number between 0 and 100 representing the confidence of the model for this prediction.latency: The time it took to do the prediction in millisecond.Report Bugs/Issues & Feature RequestPlease, help us improve our product byreporting any bugs or issueswhile using visualone. Also, feel free tolet us know any feature requests.
Package: visualops
visualopsis a command-line interface for VisualOps. It allows you to
deploy the same stack on your laptop, or in the cloudnote: output shown in this documentation is indicative only, please do not refer to it.RequirementsBefore install, ensure Docker is installed (seehttp://docs.docker.com) in version >= 1.2.0 (1.3.1 is recommended).PlatformAll Linux distributions supported by Docker should be compatible with VisualOps.
Ubuntu, Cent OS, Amazon Linux and Red Hat Linux (on their latest version) are officially suported.On Mac OS X (not fully supported yet), boot2docker is required.InstallPreferred method of install ispip:$ pip install visualopsCommandsCheck usage for detailed arguments:$ visualops --helpUse the help command to get help about one specific command:$ visualops help [command]NotesWhen usingboot2dockeron OSX, ensure the latest version is installed (1.3.1 tested).
Not that only files/directories located under /Users will be mounted.
Package: visualpdfdiff
Visual PDF diffVisual side by side comparision of rendered PDF documents.You can use this in several ways:a commandline tool to obtain the diff of two pdfsa Python function to do the same from other Python programsan equality assertion to be used inpython-unittesta back-to-back assertion to be used inpython-unittestan extension for the toolback2backto make back-to-back tests of the PDF outputs of your commandsThe generated diff looks like this:Installationsudo apt install imagemagick # Or the equivalent if not debian based
pip install visualpdfdiff
pip install b2btest # if you want to use the back2back commandNOTE: visualpdfdiff requires enabling ImageMagick to handle PDF.
This is disabled by default for security reasons.
If you are running a web server accepting PDF files from outside,
please, consider the security implications.Edit/etc/ImageMagick-*/policy.xmland
uncomment th line:<policydomain="coder"rights="read | write"pattern="PDF"/>And comment the line:<!-- <policy domain="coder" rights="none" pattern="PDF" /> -->Comand line diff toolvisualpdfdiffa.pdfb.pdf[output-diff.pdf]Returns 0 if both pdfs are raster equal, -1 if they are not.If an output is provided the side-by-side diff pdf is generated.
Not providing an output is faster when diff exists, though,
so checking and then generating is faster when you expect, being equal most of the time.Python diff functionfromvisualpdfdiffimportdiffhaveDifferences=diff('a.pdf','b.pdf','out.pdf')Unittest back-to-back assertionCompares against the last validated output.
If the extension is PDF, visualpdfdiff will be chosen to detect and output the differences.classMyClass_Test(unittest.TestCase):fromb2btestimportassertB2BEqualdeftest_otherMethod_conditions(self):...self.assertB2BEqual('b.pdf')Command back2back testsUsing the back2back command.
Also, by installing this package, PDF outputs are compared using visualpdfdiff.myTest:command:./myreportscript.py -o output.pdfoutputs:-output.pdfSimilar toolspdfdiff:Extracts text and diff that, then draws an outline on the text.
Better for text diffing, but not so for layout diffing.qtrac's diffpdf:A quite nice (qt-based) graphical tool that does both text and visual diffing.
Is not maintained anymore since the authors moved to a close source license.vslavik's diff-pdf:diff-pdf-visualyQuite similar to this one, not just in name, but does not generate an output pdfCHANGES1.0 (Unreleased)First version as independent modulePrevious versions were part of somenergia-oomakotest a test suite to compare outputs of mako reports generated by odooTODORaster resolution configurable by keywordsDiff metadata as wellMake an overlay fully transparent within the diff zone, and translucent gray in the matching zone
Package: visualpriors
# Visual Priors# Intro
This package contains the code for the paper:Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies. Arxiv 2018/CoRL 2019. Alexander Sax, Jeffrey O. Zhang, Bradley Emi, Amir R. Zamir, Silvio Savarese, Leonidas Guibas, Jitendra Malik.For a fuller description and useful results and demos, and much more, see the website [http://perceptual.actor](http://perceptual.actor).For the code in this package, see [https://github.com/alexsax/midlevel-reps/tree/visualpriors](https://github.com/alexsax/midlevel-reps/tree/visualpriors).For the full code from the paper, dockers to reproduce experiments, and more, see [https://github.com/alexsax/midlevel-reps](https://github.com/alexsax/midlevel-reps).### Citation
If you find this repository or toolkit useful, then please cite:` @inproceedings{midLevelReps2018,title={Mid-LevelVisual Representations Improve Generalization and Sample Efficiency for Learning VisuomotorPolicies.},author={AlexanderSax and Jeffrey O. Zhang and Bradley Emi and Amir R. Zamir and Leonidas J. Guibas and Silvio Savarese and Jitendra Malik},year={2018},} `
Package: visual_pts
# Visualize Point Clouds (with bbox) by Plotly## InstallationThis code mostly depends on Plotly which can be easily installed.`shell pip install visual_pts `⭐️ NOTE: The latest version now is visual_pts==1.5.0## 🔥Update
+ Refine Base Class Visual, now supports different colors in different categories.Refine Kitti Class, now supports different colors in different categories both in points clouds, 2D and 3D bboxes in images.NuScenes will be supported soon~## Getting StartedImplement  of base class Visual which can visualize standard data.Implement of KITTY dataset.Other dataset will be update in the future. Welcome to submit a pull request.You can inference Demo to Visualize.Here is an example (KITTI 000043):<img src=”docs/000043_img2D.png” style=”zoom:75%;” /><img src=”docs/000043_img3D.png” style=”zoom:75%;” /><img src=”docs/000043_pc3D.png” style=”zoom:55%;” />You can use thePoint_IO()to load, save or visualize a ponit cloud, including .xyz, .txt, .asc and .off.![](docs/point_visual.png)## ContributionJunbinGao, HaoRuan and LianguangLiu.Welcome to be a member of the visual_pts development team by contributing to this repo, and feel free to contact us for any potential contributions.
Package: visualscript
visualscriptWork in progress.This module's goal is to allow you to create simple python programs
as graphs of nodes representing functions, variables and statements.As a main reference it uses visual language of Unreal Engine's
blueprints graphs, which serve as an amazing tool for quick prototyping,
especially for people less experienced in programming.Special thanks tohttps://www.youtube.com/@DeadMeat37for tutorial series
on how to make node editor.If you like this idea, and you feel like you could help with improving
this module, feel free to contact me.
Package: visual-search-nets
visual-search-netsneural networks models of visual search behaviorPaper on how object recognition models account for visual search behavior,
that uses this package:https://github.com/NickleDave/untangling-visual-searchProceedings paperfrom2019 Conference on Cognitive Computational Neurosciencethat used previous versions of this library.Tool that can be used to generate visual search stimuli
to then carry out experiments with this library:https://github.com/NickleDave/searchstimsInstallationThe following commands were used to create the environment:tu@computi:~$condacreate-nsearchnetspython=3.6numpymatplotlibimageiojoblibtensorflow-gputu@computi:~$sourceactivatesearchnetstu@computi:~$gitclonehttps://github.com/NickleDave/visual-search-nets.gittu@computi:~$cd./visual-search-netstu@computi:~$pipinstall.usageInstalling this package (by runningpip install .in the source directory) makes it
possible to run experiments from the command line with thesearchnetscommand, like so:tu@computi:~$searchnetstrainconfig.iniThe command-line interface accepts arguments with the syntaxsearchnets command config.ini,wherecommandis some command to run, andconfig.iniis the name of a configuration file
with options that specify how the command will be executed.For details on the commands, seethis page in the docs.
For details on theconfig.inifiles, please seethis other page.AcknowledgementsResearch funded by the Lifelong Learning Machines program,
DARPA/Microsystems Technology Office,
DARPA cooperative agreement HR0011-18-2-0019David Nicholson was partially supported by the
2017 William K. and Katherine W. Estes Fund to F. Pestilli,
R. Goldstone and L. Smith, Indiana University Bloomington.CitationPlease cite the DOI for this code:
Package: visual-selection
Visual SelectionIntroductionMany moons ago, back in 2008, while in a debate on an atheist-oriented phpBB
site (as was the fashion back then), I ended up writinga couple of
scripts, in ruby, to illustrate a point
about how mutation and selection can, given enough time, result in something
with the appearance of design.The code was far from a mic-drop body of evidence (it wasn't meant to be), I
think it did an okay job of showing how nothing more than just mutating
something and selecting for the "fitter" options can get you somewhere
meaningful given enough time.No matter, either you get the illustration or you don't. That's not
important.Fast forward 15 years and I was thinking that a Textual version of the code
might be fun.Turns out itisfun!InstallationIf you want to play with this, the best method is going to be to install
withpipx:$pipxinstallvisual-selectionthen simply runvisual-selection.
Package: visual-sponge
Visual Sponge简介Visual Sponge是一款轻量级的为分子模拟提供可视化的Python程序包。
Visual Sponge使用你的浏览器作为前端，使用Xponge作为后端。
Visual Sponge可以在所有平台（Windows、Linux和MacOS）上使用。安装pip install visual-sponge使用启动你可以直接在命令行中输入以下命令启动Visual Sponge：visual-sponge你也可以在启动Visual Sponge时添加部分命令。例如，如果你有一个叫做a.pdb的pdb文件和两个分别叫做b.nc和c.dat的轨迹文件，那么你可以在命令行中输入以下命令：visual-sponge a.pdb b.nc c.dat使用以下命令来获知所有的启动命令visual-sponge -h使用该部分帮助仍待补全……
Package: visualtest-python
VisualTestThis Python SDK extends the functionality of Selenium WebDriver enabling the script to capture screenshots of the device under test, including fullpage, viewport, and element screenshots for regression testing or manual review of the UI.pip install visualtest-pythonQuick Setupfromseleniumimportwebdriver
fromsbvtimportVisualTestdriver=webdriver.Safari()settings={'projectToken':"INSERT_PROJECT_TOKEN"}visualTest=VisualTest(driver,settings)try:driver.get("https://www.google.com/search?q=smartbear")visualTest.capture("Google results")visualTest.capture("Google results viewport",{'viewport':True})finally:driver.quit()Running Selenium against BitBarCopy the file from belowEnter BitBar API_KEY and VisualTest PROJECT_TOKEN in the fileimporttimefromseleniumimportwebdriver
fromsbvtimportVisualTestAPI_KEY='INSERT_BITBAR_APIKEY'PROJECT_TOKEN='INSERT_PROJECT_TOKEN'deftestSmartBearHomePage():importwarningswarnings.simplefilter("ignore")# turn off tracemalloc socket warningscaps={'platform':'Linux','osVersion':'18.04','browserName':'firefox','version':'latest','resolution':'2560x1920','bitbar_apiKey':API_KEY,}hubUrl='https://us-west-desktop-hub.bitbar.com/wd/hub'driver=webdriver.Remote(command_executor=hubUrl,desired_capabilities=caps)print(f'Launching webdriver for {caps["platform"]} {caps["osVersion"]} {caps["browserName"]} {caps["version"]} ')# load the urlurl='https://www.smartbear.com'print(f'Opening URL: {url}')driver.get(url)time.sleep(5)settings={'projectToken':PROJECT_TOKEN,}visualTest=VisualTest(driver,settings)# full page screenshotfullPageScreenshot=visualTest.capture('Home Page')# viewport screenshotviewportScreenshot=visualTest.capture('viewport',{'viewport':True})if__name__=='__main__':try:testSmartBearHomePage()exceptExceptionase:print(f'Error starting test {e}')Running Selenium LocallyfromsbvtimportVisualTest# pip install selenium==3.141fromseleniumimportwebdriver
fromselenium.webdriver.common.byimportByPROJECT_TOKEN='INSERT_PROJECT_TOKEN'driver=webdriver.Chrome('./chromedriver')driver.get("https://www.smartbear.com")print(driver.title)settings={'projectToken':PROJECT_TOKEN}visualTest=VisualTest(driver,settings)# full page screenshotfullPageScreenshot=visualTest.capture('Home Page')# viewport screenshotviewportScreenshot=visualTest.capture('viewport',{'viewport':True})# element screenshotheroEl=driver.find_element(By.CLASS_NAME,"sb-hp-hero-content")visualTest.capture('Hero Content',{'element':heroEl})print(driver.current_url)driver.close()
Package: visual-text-explorer
TextExplorerVisualTextAnalyzer help users to understand the text data. It includes word frequency analysis and named entities recognition, which help users to explore the fundamental characteristics of the text data. We use bar charts to create the visualizations integrated with the Jupyter Notebook environment. Word frequency analysis is a frequent task in text analytics. Word frequency measures the most frequently occurring words in a given text. Common stopwords like ‘to’, ‘in’, ‘for’, were removed for the word frequency analysis. Named entity recognition is an information extraction method. The entities that are present in the text are classified into predefined entity types like ‘Person’, ‘Organization’, ‘City’, etc.  By using this method, users can get great insights into the types of entities present in the given textual dataset.Text ExplorationIn Jupyter Notebook:import VisualTextAnalyzer
import pandas as pd
data = pd.read_csv('yelp_labelled_sample.csv')
VisualTextAnalyzer.plot_text_summary(data, category_column='category', text_column='comments')DemoIn Jupyter Notebook::import VisualTextAnalyzer
yelp_data = VisualTextAnalyzer.get_yelp_labelled_data()
VisualTextAnalyzer.plot_text_summary(yelp_data, category_column='category', text_column='comments')Export TextsYou might want to export a subset of selected texts for further analyses. To do so, use the following code (after exporting it through the UI):obj_text = VisualTextAnalyzer.get_exported_texts()The returned object has the following attributes:texts: List of texts.category: All texts belong to that category.word: All texts contain that word.Get Processed data (Words and Entities)You might want to get the processed data, which includes word and entity frequencies, that is ready for analysis before generating the visualization. To do so, use the code:processed_data = VisualTextAnalyzer.get_words_entities(data, category_column='category', text_column='comments')
VisualTextAnalyzer.plot_text_summary(words_entities=processed_data)The function 'get_words_entities' returns an object that contains has the following attributes:words: Word frequencies.entities: Entity frequencies.raw_texts: All texts separated in two categories: positive and negative.