Saravanan666/pypi-org huggingface dataset:
Package: viz
Data visualization in the terminal.InstallFrom PyPI:easy_install vizFrom source:git clone https://github.com/chbrown/viz.git
cd viz
python setup.py installThe CLI entry point isviz, which should be installed on yourPATH.Commandsviz histRead floats fromstdinand plot as a single line histogram.Example: ping google every 100ms (0.1s) and plot the first 50
response times you get back:ping -i 0.1 google.com | head -50 | sed -n 's/.*time=\(.*\) ms/\1/p' | viz histExample result (which should fit your terminal width automatically):26.1390000[▁      ▁▁  ▁▁▂▂ ▉ ▂▂ ▃ ▂▁▂ ▁ ▁▁      ▁   ▁                                    ]39.5580000viz totalRead floats fromstdinand sum them. No graphics, but useful becauseawk'{sum+=$1}END{printsum}is too long.Example: count the bytes used by the current directory and its
children.find . -ls | tr -s ' ' | cut -d ' ' -f 7 | viz totalExample result:8519086.0Compare to du:echo $[$(du -sk . | cut -f 1) * 1024]
> 8531968viz pointsRead floats fromstdin, plot on a single line from left to right
with as much granularity as the terminal will allow. Otherwise, bin
incrementally and plot the arithmetic mean of each bin.Example: ping some really awful website every 100ms, plot the first
100 successful responses (takes about 10s).ping -i 0.1 godaddy.com | head -100 | sed -n 's/.*time=\(.*\) ms/\1/p' | viz pointsExample result:[+156.298000]
   ▃▃ ▁▃          ▁ ▁▁ ▁    ▁  ▁▁▁▂ ▃▂ ▉▂              ▁ ▁▁      ▁▂▁       ▁  ▁          ▁      ▁
[+70.593000]The top line is the maximum, the bottom is the minimum. The other values
are scaled linearly between those extremes.Dependencies:numpyRelated projects (mostly graphical visualizations for Python)Matplotlib: feels
archaic to the touch. The pyplot fans have never runimport this,
the interface is unguessable and murkily documented.Bokeh: haven’t used yetVispy: haven’t used yetLicenseCopyright (c) 2013 Christopher Brown.MIT
Licensed.
Package: viz-alchemy
What's thisVisualize SQL relationship for SQLAlchemy ORM.You can get dot file for graphviz.viz_alchemy-iexample\test_db.pyInstallationYou can install from pip.pipinstallviz_alchemyorpythonsetup.pyinstallUsageusage: viz_alchemy [-h] -i INPUT [-o OUTPUT] [-n NAME] [-f FORMAT]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input script path
  -o OUTPUT, --output OUTPUT
                        Output file
  -n NAME, --name NAME  class name at the input file
  -f FORMAT, --format FORMATNow format is not working.ToDOAdd unit testCode refactoring for more updateDirectly convert dot to image using graphviz.
Package: vizard
vizardIntuitive, Interactive, Easy and Quick Visualizations for Data Science ProjectsInstallationpip install vizardorpip install git+https://github.com/Ritvik19/vizard.gitDocumentationInstantiate Vizard ObjectThe Vizard or VizardIn object holds theDataFramealong with its configurations including thePROBLEM_TYPE,DEPENDENT_VARIABLE,CATEGORICAL_INDEPENDENT_VARIABLES,CONTINUOUS_INDEPENDENT_VARIABLES, andTEXT_VARIABLESimport vizard

class config:
    PROBLEM_TYPE = 'regression' or 'classification' or 'unsupervised'
    DEPENDENT_VARIABLE = 'target_variable'
    CATEGORICAL_INDEPENDENT_VARIABLES = [categorical_features]
    CONTINUOUS_INDEPENDENT_VARIABLES = [continuous features]
    TEXT_VARIABLES = [text features]

viz = vizard.Vizard(df, config)
# for interactive plots use:
viz = vizard.VizardIn(df, config)Exploratory Data AnalysisAfter Instatiating theVizardobject, you can try different plots for EDACheck Missing Values:viz.check_missing()Count of Missing Values:viz.count_missing()Count of Unique Values:viz.count_unique()Count of Missing Values by Group:viz.count_missing_by_group(class_variable)Count of Unique Values by Group:
viz.count_unique_by_group(class_variable)Target Column AnalysisBased on the type of problem, perform a univariate analysis of target columnviz.dependent_variable()Segmented Univariate AnalysisBased on the type of problem, preform segmented univariate analysis of all feature columns with respect to the target columnCategorical Variablesviz.categorical_variables()Continuous Variablesviz.continuous_variables()Text Variablesviz.wordcloud()

  viz.wordcloud_by_group()

  viz.wordcloud_freq()Bivariate AnalysisBased on the type of variables, perform bivariate analysis on all the feature columnsPairwise Scatterviz.pairwise_scatter()Pairwise Violinviz.pairwise_violin()Pairwise Cross Tabsviz.pairwise_crosstabs()Trivariate AnalysisBased on the type of variables, perform trivariate analysis on any of the feature columnsTrivariate Bubble (Continuous vs Continuous vs Continuous)viz.trivariate_bubble(x, y, s)Trivariate Scatter (Continuous vs Continuous vs Categorical)viz.trivariate_scatter(x, y, c)Trivariate Violin (Categorical vs Continuous vs Categorical)viz.trivariate_violin(x, y, c)Correlation AnalysisBased on the type of variables, perform correaltion analysis on all the feature columnsCorrelation Plotviz.corr_plot()Pair Plotviz.pair_plot()Chi Square Plotviz.chi_sq_plot()Save the plots to PDF using Viz2PDFYou can also save the plots to a pdf file in order to generate an EDA reportTheViz2PDFobject takes in all yourVizardplots and creates a pdf report out of themviz = vizard.Vizard(df, config)
viz2pdf = vizard.Viz2PDF('viz_report.pdf')

plots = [
    viz.check_missing(),
    viz.count_missing(),
    viz.count_unique(),
    viz.dependent_variable(),
    viz.categorical_variables(),
    viz.continuous_variables(),
    viz.pairwise_scatter(),
    viz.pairwise_violin(),
    viz.pairwise_crosstabs(),
]
viz2pdf(plots)UsageClassification CaseRegression CaseText Classification CaseUnsupervised CaseClassification Case (Interactive)Regression Case (Interactive)Unsupervised Case (Interactive)
Package: vizbee
# vizbee agent![screencast](screencast.gif)## Installation```bashpython -m pip install vizbee```optionally the target(s) data source an be passed as argument to installrequired dependencies:```bashpython -m pip install vizbee[<source>]```where `<source>` is one of:* postgresql* oracle* mysql* redshift* sqlserver* sybase## Usage```bashUsage: vizbee [OPTIONS] COMMAND [ARGS]...Options:-c, --config TEXT     The YAML configuration file path.--client-id TEXT      The application id.--client-secret TEXT  The application secret.--api-url TEXT        The api url.--help                Show this message and exit.Commands:dashboard  Manage dashboards.dataset    Manage datasets.start      Start scheduler.sync       Push all datasets and dashboards.```## ConnectionsThis section defines the connections to the data stores(the ``default`` field sets the default one).```yamlconnections:default: {DATABASE_URL}```Currently supported database are:* PostgreSQL: `postgresql://<user>:<password>@<host>/<db>`* MySQL: `mysql://<user>:<password>@<host>/<db>`* Oracle: `oracle://<user>:<password>@<host>/<db>`* Redshift: `redshift://<user>:<password>@<host>/<db>`* SQLServer: `mssql://<user>:<password>@<host>/<db>`## DatasetsA `Dataset` represents a single set of data:```yamldatasets:my-dataset:name: <The dataset name>connection: <The connection to use>query: <The query used to fetched data>graph: <The graph options>schedule: <The scheduling rule>```See [dataset schema](https://vizbee.io/documentation/schema#dataset) for more details.## DashboardsA `Dashboard` represents a `Dataset` collection:```yamldashboards:my-dashboard:name: <The dashboard name>datasets:- <The datasets to include>```See [dashboard schema](https://visbee.io/documentation/schema#dashboard) for more details.## SchedulingThe agent can be started as a daemon to schedule datasets update,it require to define a scheduling rule such as:```yamlschedule: 5 minutes```and the start the deamon:```bashvizbee start```For each `Dataset` the scheduling rule can be overriden by adding a`schedule: <rule>` in its schema.The scheduling rule format is: `<count> <seconds|minutes|hours|days>`
Package: vizchain
vizchainGPT4-VInstallation$pipinstallvizchainUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensevizchainwas created by Andrew Stelmach. It is licensed under the terms of the MIT license.Creditsvizchainwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: vizcode
VizCodeVizCode is a command line tool for building an interative dependency graph of your codebase. It utilizes static analysis to parse through your code to detect any definitions and references, which are then represented as nodes and edges respectively in a graph. The generated graph will be visualized on a web browser once the code files have been parsed. VizCode only supports the Python language at the moment.Please note that VizCode is run locally and will never send your code, repository names, file names, or any other specific code data to any location outside of your local machine. Additionally, VizCode is not able to run on Windows unless you download the Ubuntu shell. For more information about VizCode visit ourwebsite.InstallationYou can install VizCode fromPyPI:pip install vizcodeVizCode is supported on Python 3.7 and above.How to useTo run VizCode on a codebase, simply call the program:$ vizcode -p [ENTER A DIRECTORY PATH]Since VizCode only works on Python code, it will ignore any files without the .py file extension. If your code uses any dependencies that are not installed in your default environment, then you can specify an environment path:$ vizcode -p [ENTER A DIRECTORY PATH] -e [ENTER AN ENVIRONMENT PATH]If you would like VizCode to ignore certain files, then you can add an additional argument:$ vizcode -p [ENTER A DIRECTORY PATH] -d [ENTER PATH OF DESELECTED FILES]
Package: vizdataquality
vizdataqualityThis is a Python package for visualizing data quality, and includes this six-step workflow:Look at your data (is anything obviously wrong?)Watch out for special valuesIs any data missing?Check each variableCheck combinations of variablesProfile the cleaned dataDocumentationThe vizdataquality documentationis hosted on Read the Docs.InstallationWe recommend installing vizdataquality in a python virtual environment or Conda environment.To install vizdataquality, most users should run:pip install 'vizdataquality'TutorialsThe package includes notebooks that show you how to:Calculate a set of data quality attributes and output them to a fileUse each type of plot, e.g.,datetime value distributionCreate a reportwhile you investigate data quality and profile a datasetApply the six-step workflow to an open parking fines datasetAfter installing vizdataquality, to follow theses tutorials interactively you will need to clone or download this repository. Then start jupyter from within it:python -m jupyter notebook notebooksNoticeThe vizdataquality software is released under the Apache Licence, version 2.0. SeeLICENCEfor details.AcknowledgementsThe development of the vizdataquality software was supported by funding from the Engineering and Physical Sciences Research Council (EP/N013980/1; EP/R511717/1) and the Alan Turing Institute.
Package: vizdnn
VizdnnInterpretable Deep Neural network is the next phase of understanding the working of neural network. This library suffices the need to vizualize neural network layer with simple python pip package.Pip Installpip install vizdnnUsefromvizdnnimportvizdnnimportkerasvisualize_neural_network=vizdnn(keras.applications.resnet.ResNet50(),"network_layer_name","test_image_name.jpg")vis_layer=visualize_neural_network.get_layer()visualize_neural_network.viz_feature_map(vis_layer)Sample VizSample test imageVizualization from Resnet50 conv1_bn Layer.Vizualization from VGG16 block2_conv1 Layer.
Package: vizdxp
vizdxpData visualization web applicationvizdxp is an open-source web app designed via streamlit weaved with plotly library.Its incredibly Simple - Just drag & drop any csv and explore the data visually.By default the application will figure out better visualization based on user selections. Feel free to customize as needed.KeysSimpleand quick for anyExploratory Data AnalysisCreate interactiveDashboard web app within minutesfrom any csv and share the findingsDeployit as a web application in your own workstations tooByDefault- chart types and aggregations are applied based on columns selected by user, null rows are removed, date fields are converted to multiple subfieldsHighlyCustomizableNo more static reportsChart types included in v0.0.4 - Scatter, Bar, Pie, Heatmap and Box plotsInstallationpip install vizdxppython -m vizdxpExplore vizdxp through a browser. Once installation is complete, user can work totally offline.**This package is also hostedonline** Feel free to explore with your own dataLicensevizdxp is completely free and licensed under MIT license.Vizdxp is developed with Streamlit as the framework. Further details onstreamlitPlotly is the visualization library used. Further details onplotly
Package: vizedax
Vizedax: Instant EDA, Cleaning and Data Visualization for CSV DataAuthor: Shreyas Madhav A V (avshreyasmadhav@gmail.com)Vizedax is a powerful Python library designed to make exploratory data analysis (EDA) a breeze. With just a few lines of code, you can perform comprehensive EDA on your clean CSV datasets and create stunning visualizations of all attributes. Say goodbye to the hassle of manually inspecting and plotting data – Vizedax has got you covered!aboutit() FunctionThe aboutit() function reads a CSV file and displays the following information about the dataset:Name of the fileSize of the fileNumber of rows and columnsColumn names and their data typesTop 5 rowsLast 5 rowsSummary statisticsfromVizedaximportdatagod#For a CSVdatagod.aboutit("example.csv")#For a Dataframedatagod.aboutit(df)cleanit() FunctionThe cleanit() function takes a DataFrame or a CSV file as its argument. It performs the following cleaning tasks:Replaces null values based on the provided argument (zero, average, previous reading; default is average of the column).Deletes duplicate rows.Drops columns with more than 50% null values.Detects categorical columns and performs one-hot encoding.fromvizedaximportdatagod#For a CSV# null strategy can be on of {average ,null ,previous}datagod.cleanit("example.csv",null_strategy)#For a Dataframedatagod.cleanit(df,null_strategy)graphit() FunctionThe graphit() function reads a CSV file and generates exploratory data analysis visualizations:Correlation matrixVarious graphs for all columns of the datasetfromvizedaximportdatagod#For a CSV# null strategy can be on of {average ,null ,previous}datagod.graphit("example.csv")#For a Dataframedatagod.graphit(df)
Package: vizelec
vizelecUsage:$vizelec[OPTIONS]COMMAND[ARGS]...Options:--install-completion: Install completion for the current shell.--show-completion: Show completion for the current shell, to copy it or customize the installation.--help: Show this message and exit.Commands:pinoutspicevizelec pinoutUsage:$vizelecpinout[OPTIONS]COMMAND[ARGS]...Options:--help: Show this message and exit.Commands:showvizelec pinout showUsage:$vizelecpinoutshow[OPTIONS]CONF_FILEArguments:CONF_FILE: [required]Options:--help: Show this message and exit.vizelec spiceUsage:$vizelecspice[OPTIONS]COMMAND[ARGS]...Options:--help: Show this message and exit.Commands:load: Create a graph representation of the...vizelec spice loadCreate a graph representation of the schematic given by schem.
:param schem: path to a spice file.Usage:$vizelecspiceload[OPTIONS]SCHEMArguments:SCHEM: [required]Options:--help: Show this message and exit.
Package: vizier
==================================Vizier: a python reporting library==================================Originally intended to be a fork of cairoplot to add new reporting functionality,vizier has been completely re-written and re-imagined to provide a dead-easyreporting and graphing alternative for python using cairo.Notable features include::* the creation and management of multi-page documents ("books"); and* different types of plots (e.g. bar, line, scatter) on one graphVizier was inspired by the fine work of the contributors and maintainers ofthe CairoPlot project, who sadly have not had time to continue their work. See`CairoPlot's project page on Launchpad <https://launchpad.net/cairoplot>`_.Note: libcairo >= 1.10.1 recommended, to fix a bug with multi-page PDF clipping.https://bugs.freedesktop.org/show_bug.cgi?id=24691Installing on Windows=====================Grab a recent version of GTK+, which includes Cairo. You can download an installer here:http://sourceforge.net/projects/gtk-win/files/GTK%2B%20Runtime%20Environment/GTK%2B%202.22/Now, get vizier. In the root vizier directory, run::python setup.py installYou should now be able to run one of the examples. Try::python examples/test1.pyIf abc.pdf is created and looks relatively sane, you're ready to go.Quick Example=============A multi-page PDF could be created as follows::#!/usr/bin/env pythonfrom vizier.book import PDFBook, SimplePage, GridPage, Inch, TextSpace, LEFT, BOTTOMfrom vizier.plot import BarGraph, Graph, BarPlot, LinePlotbook = PDFBook(units=Inch)bar_graph = BarGraph([1, 2, 3, 4, 5, 6, 7])page = SimplePage(width=11, height=8.5, margin=0.5, spacing=0.5)page[0] = bar_graphbook.append(page)complex_graph = ContinuousPlot(legend=True, x_grid=(0, 0.2), y_grid=(0, 0.5))complex_graph.add_series(# each tuple in the data is (x, y, y-error)LineSeries("Raw Observations", [(0, 0.03, 0.016), (1, 0.18, 0.034), (2, 0.176, 0.016)]),# each tuple in the data is (x[start, end], y, y-error)AreaSeries("Other Things", [((-0.5, 0.5), 1.03, 0.016), ((0.5, 1.5), 1.21, 0.034), ((1.5, 2.5), 1.386, 0.034)]),# show a threshold at y=4.25Threshold("Warning", "Imminent Catastrophic Destruction", 4.25))vlayout = VerticalLayout(0.1, 0.6) # splits a space vertically into 3.vlayout[0] = TextSpace("This layout", halign=LEFT)vlayout[1] = TextSpace("is split up into 3")vlayout[2] = TextSpace("non-uniformly!", valign=BOTTOM)page = GridPage(2, 2, width=11, height=8.5)page[0, 0] = TextSpace("This happens to be a graph.")page[0, 1] = complex_graphpage[1, 0] = complex_graphpage[1, 1] = vlayoutbook.append(page)fp = open("abc.pdf", 'wb')book.compile(fp)fp.close()
Package: viz-image-deobfuscate
Viz Manga DeobfuscatorViz manga pages are delivered to the browser as obfuscated images and the client JS is responsible for deobfuscation of those images for the viewer to read. This program reproduces the deobfuscate logic to produce a readable image.obfuscated image:deobfuscated image:The image Exif metadata stores a hex digest to deobfuscate the image. Using the each byte value of the digest with it's position in the digest, we can select the appropriate tile in the obfuscated image and put it in the proper place in the deobfuscated image.DISCLAIMER: I am not licensed or affiliated with Viz Media and this repository is meant for informational purposes only.Installationpip install viz-image-deobfuscateUsageThis package exposesdeobfuscate_imagethat accepts a path to an image and returns an PILImageof the deobfuscated image.from viz_image_deobfuscate import deobfuscate_image

deobfuscated = deobfuscate_image("raw.jpg")
deobfuscated.save("page.jpg")CLI UsageBundled with this package is a CLI tool for scripting/testing purposes.usage: image-deobfuscate-cli [-h] obfuscated_image deobfuscated_image

Deobfuscate manage page image.

positional arguments:
  obfuscated_image    Path to the obfuscated image.
  deobfuscated_image  Output path to the obfuscated image.

options:
  -h, --help          show this help message and exitExample>>> image-deobfuscate-cli raw1.jpg page1.jpg

Successfully deobfuscated image at: page1.jpgDockerAlternatively, you can build your own docker container to run the CLI or download an already built container fromDocker Hub>>> docker build -t viz .
>>> docker run -v /home/user/images/:/app/images viz images/raw1.jpg images/page1.jpg

Successfully deobfuscated image at: images/page1.jpg
Package: viziphant
ViziphantA Python module for easy visualization ofNeoobjects andElephantresults.Viziphant provides a high-level API to easily generate plots and interactive visualizations of neuroscientific data and
analysis results. This API uses and extends the same structure as in Elephant to ensure intuitive usage for scientists
that are used to Elephant.Documentationhttps://viziphant.readthedocs.io/en/latest/Getting in touchMailing list:https://groups.google.com/group/neuralensembleInstallationFor instructions on installing Viziphant, please seehttps://viziphant.readthedocs.io/en/latest/install.htmlCopyright:copyright: 2017-2023 by theViziphant team.LicenseBSD 3-Clause License, seeLICENSE.txtfor details.AcknowledgmentsSeeacknowledgments.
Package: vizme
vizmeA simple python-based command-line utility to to visualize random blobs of data. It is heavily inspired bypixdmade byFireFly:https://github.com/FireyFly/pixdvizmecan be used convert a raw data-stream to a visual representation in image format (or terminal viewing in ANSI).Usagevizmeis installed as a command-line utility accessible asvizmefrom the command-line. Vizme has a (small) help menu.
For the most partvizmeexpects input data from either a filepath or from /dev/stdin and a way to output it. By default
vizme will output the data in PNG format which can be written to a file.The help section:vizme -h
usage: vizme [-h] [-t] [-w WIDTH] [-s SCALE] [-p {pixd,greyscale}]
             [INPUT] [OUTPUT]

positional arguments:
  INPUT                 Data to convert (can be a file, don't specify for
                        stdin)
  OUTPUT                Where to write output to (can be a file, don't specify
                        for stdout)

optional arguments:
  -h, --help            show this help message and exit
  -t, --terminal        Sets output to be terminal (colored) ANSI.
  -w WIDTH, --width WIDTH
                        Output columns width
  -s SCALE, --scale SCALE
                        Scales the output pixels (2 means 2 times the amount
                        of columns/rows for the same data)
  -p {pixd,greyscale}, --palette {pixd,greyscale}
                        Sets the palette to use for outputWe can take some data from/dev/randomand pipe it through head to limit it to 256 bytes, set a width of 16 and pipe it to a PNG file:cat /dev/random | head -c256 | vizme -w16 > image.pngWhich gives us a really tiny PNG with random data using the default palette.We can scale it up times 10 to see whats going on:cat /dev/random | head -c256 | vizme -w16 -s10 > image.pngWhich gives us a bigger image:vizmealso has a terminal output option which outputs ANSI full-block characters with the same color output.ExamplesThis is the very bare-basic of what you can do withvizme. While it doesn't have implementations for image manipulation
or some specific data cutting from what it reads/writes you can do this yourself on the command-line quite easily.
If you still want to see a feature invizmefeel free to raise tickets / pull requests.Converting outputConverting the PNG output to JPG (or any image format) which requires imagemagick:cat /dev/random | head -c256 | vizme -w16 -s10 | convert /dev/stdin my_image.jpgPreview output image in terminalPreviewing your output image in your terminal, required imgcat shell extension:cat /dev/random | head -c256 | vizme -w16 -s10 | imgcatGreyscale output through palette changingvizmecomes with two palettes by default. Standard output uses thepixdpalette and also contains a greyscale:cat /dev/random | head -c256 | vizme -w16 -s10 -p greyscale | imgcatBugsFeel free to report issues, this 'utility' was build out of ease as I got frustrated with manual conversion of timestamps and strings the whole time.