Saravanan666/pypi-org huggingface dataset:
Package: whiteboxgui
whiteboxguiAn interactive GUI for WhiteboxTools in a Jupyter-based environmentGitHub repo:https://github.com/giswqs/whiteboxguiDocumentation:https://giswqs.github.io/whiteboxguiPyPI:https://pypi.org/project/whiteboxguiconda-forge:https://anaconda.org/conda-forge/whiteboxguiWhiteboxTools:https://github.com/jblindsay/whitebox-toolsUser Manual:https://www.whiteboxgeo.com/manual/wbt_bookwhitebox-python:https://github.com/giswqs/whitebox-pythonwhiteboxR:https://github.com/giswqs/whiteboxRwhitebox-ArcGIS:https://github.com/giswqs/WhiteboxTools-ArcGISFree software: MIT licenseDescriptionThe whiteboxgui Python package is a Jupyter frontend for WhiteboxTools, an advanced geospatial data analysis platform developed by Prof. John Lindsay (webpage;jblindsay) at the University of Guelph'sGeomorphometry and Hydrogeomatics Research Group. WhiteboxTools can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. WhiteboxTools also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. LiDAR point clouds can be interrogated (LidarInfo, LidarHistogram), segmented, tiled and joined, analyized for outliers, interpolated to rasters (DEMs, intensity images), and ground-points can be classified or filtered. WhiteboxTools is not a cartographic or spatial data visualization package; instead it is meant to serve as an analytical backend for other data visualization software, mainly GIS.The WhiteboxTools currently contains518tools, which are each grouped based on their main function into one of the following categories: Data Tools, GIS Analysis, Hydrological Analysis, Image Analysis, LiDAR Analysis, Mathematical and Statistical Analysis, Stream Network Analysis, and Terrain Analysis. For a listing of available tools, complete with documentation and usage details, please see theWhiteboxTools User Manual.Installationwhiteboxguiis available onPyPI. To installwhiteboxgui, run this command in your terminal:pip install whiteboxguiwhiteboxguiis also available onconda-forge. If you haveAnacondaorMinicondainstalled on your computer, you can create a conda Python environment to install whiteboxgui:conda create -n wbt python
conda activate wbt
conda install mamba -c conda-forge
mamba install whiteboxgui -c conda-forgeUsageThe whiteboxgui provides a Graphical User Interface (GUI) for WhiteboxTools in a Jupyter-based environment, which can be invoked using the following Python script. You can also tryimport whiteboxgui
whiteboxgui.show(tree=True)DemoCreditsThis package was created withCookiecutterand thegiswqs/pypackageproject template.
Package: whiteboxlayer
White-Box-Layeris a Python module for deep learning built on top of TensorFlow and is distributed under the MIT license.The project was started in May 2021 byYeongHyeon Park.This project does not limit for participation.Contribute now!InstallationDependencieswhiteboxlayer requires:Numpy: 1.19.5Scipy: 1.4.1TensorFlow: 2.4.0User installationYou can install the white-box-layer via simple command as below.$pipinstallwhiteboxlayerDevelopmentWe welcome new contributors of all experience levels. The white-box-layer community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We've included some basic information in this README.ExampleExample for Convolutional Neural NetworkAn example of constructing a convolutional neural network is covered. The relevant source code is additionally provided following links.Jupyter notebookDefine TensorFlow based moduleclassNeuralnet(tf.Module):def__init__(self,**kwargs):super(Neuralnet,self).__init__()self.who_am_i=kwargs['who_am_i']self.dim_h=kwargs['dim_h']self.dim_w=kwargs['dim_w']self.dim_c=kwargs['dim_c']self.num_class=kwargs['num_class']self.filters=kwargs['filters']self.layer=wbl.Layers()self.forward=tf.function(self.__call__)@tf.functiondef__call__(self,x,verbose=False):logit=self.__nn(x=x,name=self.who_am_i,verbose=verbose)y_hat=tf.nn.softmax(logit,name="y_hat")returnlogit,y_hatdef__nn(self,x,name='neuralnet',verbose=True):foridx,_inenumerate(self.filters[:-1]):if(idx==0):continuex=self.layer.conv2d(x=x,stride=1,\filter_size=[3,3,self.filters[idx-1],self.filters[idx]],\activation='relu',name='%s-%dconv'%(name,idx),verbose=verbose)x=self.layer.maxpool(x=x,ksize=2,strides=2,\name='%s-%dmp'%(name,idx),verbose=verbose)x=tf.reshape(x,shape=[x.shape[0],-1],name="flat")x=self.layer.fully_connected(x=x,c_out=self.filters[-1],\activation='relu',name="%s-clf0"%(name),verbose=verbose)x=self.layer.fully_connected(x=x,c_out=self.num_class,\activation=None,name="%s-clf1"%(name),verbose=verbose)returnxInitializing modulemodel=Neuralnet(\who_am_i="CNN",\dim_h=28,dim_w=28,dim_c=1,\num_class=10,\filters=[1,32,64,128])dummy=tf.zeros((1,model.dim_h,model.dim_w,model.dim_c),dtype=tf.float32)model.forward(x=dummy,verbose=True)ResultsConv(CNN-1conv)(1,28,28,1)->(1,28,28,32)MaxPool(CNN-1mp)(1,28,28,32)>(1,14,14,32)Conv(CNN-2conv)(1,14,14,32)->(1,14,14,64)MaxPool(CNN-2mp)(1,14,14,64)>(1,7,7,64)FC(CNN-clf0)(1,3136)->(1,128)FC(CNN-clf1)(1,128)->(1,10)Conv(CNN-1conv)(1,28,28,1)->(1,28,28,32)MaxPool(CNN-1mp)(1,28,28,32)>(1,14,14,32)Conv(CNN-2conv)(1,14,14,32)->(1,14,14,64)MaxPool(CNN-2mp)(1,14,14,64)>(1,7,7,64)FC(CNN-clf0)(1,3136)->(1,128)FC(CNN-clf1)(1,128)->(1,10)
Package: whiteboxml
WhiteBox Utilities Toolkit: Tools to make your life easierFancy data functions that will make your life as a data scientist easier.InstallingTo install this library in your Python environment:pip install whiteboxmlDocumentationMetricsClassificationROC curve / AUC:importnumpyasnpfromwhiteboxml.modeling.metricsimportplot_roc_auc_binaryy_pred=np.random.normal(0,1,1000)y_true=np.random.choice([0,1],1000)ax,fpr,tpr,thr,auc_score=plot_roc_auc_binary(y_pred=y_pred,y_true=y_true,figsize=(8,8))ax.get_figure().savefig('roc_curve.png')Confusion Matrix:importnumpyasnpfromwhiteboxml.modeling.metricsimportplot_confusion_matrixy_true=np.random.choice([0,1,2,3],10000)y_pred=np.random.choice([0,1,2,3],10000)ax,matrix=plot_confusion_matrix(y_pred=y_pred,y_true=y_true,class_labels=['a','b','c','d'])ax.get_figure().savefig('confusion_matrix.png')Optimal Threshold:importnumpyasnpfromwhiteboxml.modeling.metricsimportget_optimal_thry_pred_proba=np.random.normal(0,1,(100,1))y_true=np.random.choice([0,1],(100,1))thr=get_optimal_thr(y_pred=y_pred_proba,y_true=y_true)
Package: whiteCalculator
Calculator Module for PythonBy White NightInstall# Linux/macOSpython3-mpipinstall-UwhiteCalculator# Windowspy-3-mpipinstall-UwhiteCalculatorQuickStartCalculatorfromwhiteCalculatorimportCalculatorc=Calculator()print(c.run("1+8(5^2)"))# Output: 201print(c.run("9Ans"))# Output: 1809FormulafromwhiteCalculatorimportFormulaFormula.PythagorasTheorem.getA(b=4,c=5)# Output: 3Formula.PythagorasTheorem.getA(b="2^2",c="√(25)")# Output: 3Formula ListPythagorasTheorem,LensFormula,LinearMagnificationFormulaDOCSTODOmake Docmore FormulaCalculate without evalErrorsfromwhiteCalculatorimportCalculatorc1=Calculator(skipError=True)# Default is Truec1.run("9/0")# Output: Error: division by zeroc2=Calculator(skipError=False)c2.run("9/0")# Outputs:# Traceback (most recent call last):#     ...# ZeroDivisionError: division by zeroYou can use:sin/asin/sinhcos/acos/coshtan/atan/tanhln/log×/•^/*/power√/sqrtπ/pi%÷Ans
Package: whitecanvas
whitecanvasA type safe and backend independent plotting library for Python, aiming at not the simplest, but the tidiest API.Installationpip install whitecanvas -UProject PhilosophyType safetyAll the methods should be designed to have nice signature, and should return the same
type of object, so that your program can be statically checked by the IDE.Backend independencyEvery plotting library has their own strength and weakness. Same code should work on
different backends, so that you can choose the best one for different purposes.Currently supported backends arematplotlib,pyqtgraph,vispy,plotlyandbokeh. If you want other backends, please feel free toopen an issue.API tidinessMost of (probably all of) the plotting libraries rely on the large
number of arguments to configure the plot elements. They are usually hard to remember,
forcing you to look up the documentation every time you want to make a plot.whitecanvastries to organize the methods, namespaces and arguments carefully so that you can make any kind of plot only with the help of the IDE's auto-completion and
suggestions.DocumentationDocumentation is availablehere.Examples→ Find more examplesRain-cloud plot in matplotlib→ sourceSuper plot in matplotlib→ sourceJoint plot in matplotlib→ sourceHeatmap with text in pyqtgraph→ sourceCurve fitting in bokeh→ source
Package: whitecrop3d
whitecrop3dDescriptionCrops unnecessary white background from images. Meant to be for 3D models previews.Whitecrop3d was developed as temporary solution for problem of 3D graphics that was time expensive to solve. We worked on solving problem properly after this temporary solution was implemeneted.UsageInstall globaly:$ sudo pip install whitecrop3dUse:$ whitecrop3d path/to/your/pngsPreviewHow it worksDo this for all images in path specified:Loads imageCrops the image based on white backgroundAdds background to match original 1024x768 resolutionSaves image (inplace)Notes for Developersfor further github actionsUpload new version to PYPIincrement version insetup.pyfilerunpublish_to_pypi.sh, or manualy run:$ rm dist/*$ python3 -m build$ python3 -m twine upload dist/*note: github secret must be provided to authorize upload (not added to github repo yet)
Package: whitefuck
whitefuck(Whitespace + Brainfuck) / 2 = WhitefuckInstallationRun this:pipinstallwhitefuckSyntaxIn this doc,"s" means "Space",
"t" means "Tab",
"b" means "Breakline".In whitefuckIn brainfuckHow does it workss+increment (increase by one) the byte at the data pointer.st-decrement (decrease by one) the byte at the data pointer.ts>increment the data pointer (to point to the next cell to the right).tt<decrement the data pointer (to point to the next cell to the left).sss.output the byte at the data pointer.sst,accept one byte of input, storing its value in the byte at the data pointer.sts[if the byte at the data pointer is zero, then instead of moving the instruction pointer forward to the next command, jump it forward to the command after the matching ] command.stt]if the byte at the data pointer is nonzero, then instead of moving the instruction pointer forward to the next command, jump it back to the command after the matching [ command.Usageusage: __main__.py [-h] [-m {run,convert}] [-i INPUT] [-c COUNTER] [-o OUTPUT] file

positional arguments:
  file                  File to run

optional arguments:
  -h, --help            show this help message and exit
  -m {run,convert}, --mode {run,convert}
                        Mode. Default: run

Run:
  -i INPUT, --input INPUT
                        A input.
  -c COUNTER, --counter COUNTER
                        Number of counters to make.

Convert:
  -o OUTPUT, --output OUTPUT
                        Path to export result.Use on script>>>importwhitefuckaswf>>>withopen("./example/helloworld.wf")asf:...s=f.read()...>>>wf.run(s)HelloWorld!>>>f=wf.make_function(s)functools.partial(<functionrunat0x00000203123669D0>,'\n\n\n\n\n\n\n\n\n\t\n\t\n\n\n\n\n\n\n\n\n\t\n\n\n\n\n\n\n\n\n\n\n\n\t\n\n\n\n\t\n\n\t\t\n\t\t\n\t\t\n\t\t\n\t\n\t\t\n\t\n\n\t\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t\n\n\n\n\n\n\n\t\t\n\t\t\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t\n\n\n\n\n\n\t\n\t\n\t\n\t\n\t\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\n\t\n\n\n\t\n\n\n','',1024)>>>f()HelloWorld!PrerequisitesPython 3.8 (Please make GitHub issue if you can use this lib on different python version)LicensePlease seeLICENSE.
Package: white-generator
Featuresa generation of memes (in PNG format);a support of plain and graphic backgrounds;a specification of a text rectangle;a support of horizontal and vertical text alignments;a read of memes texts from a file (each text is separated from
another by a double newline);a support single-line and multiline memes texts;a protection against duplicate memes texts;a support of a watermark (it’s optional);meme settings:background:color;width;height;image (it supports same formats asPillowlibrary);text:font (it supports only TrueType fonts);size;color;left;rigth;top;bottom;horizontal alignment;vertical alignment;watermark:font (it always uses the text font);text;size;color.Installation$ pip install white-generatorUsage$ white-generator -v | --version
$ white-generator -h | --help
$ white-generator [options] (-f FONT_FILE | --font-file FONT_FILE) (-i INPUT_FILE | --input-file INPUT_FILE) (-o OUTPUT_PATH | --output-path OUTPUT_PATH)Options:-v,--version— show the version message and exit;-h,--help— show this help message and exit;-iINPUT_FILE,--input-fileINPUT_FILE— the path to the file
with notes;-oOUTPUT_PATH,--output-pathOUTPUT_PATH— the path for
generated images;-lTEXT_LEFT,--text-leftTEXT_LEFT— the left text position
(default: 0);-tTEXT_TOP,--text-topTEXT_TOP— the top text position
(default: 0);-RTEXT_RIGHT,--text-rightTEXT_RIGHT— the horizontal text
limit (-1 for a background width use; default: -1);-BTEXT_BOTTOM,--text-bottomTEXT_BOTTOM— the vertical text
limit (-1 for a background height use; default: -1);-a{left,center,right},--text-horizontal-align{left,center,right}— the text horizontal
alignment (default:center);-A{top,center,bottom},--text-vertical-align{top,center,bottom}— the text vertical
alignment (default:center);-WIMAGE_WIDTH,--image-widthIMAGE_WIDTH— the image width
(default: 640);-HIMAGE_HEIGHT,--image-heightIMAGE_HEIGHT— the image
height (default: 480);-bIMAGE_BACKGROUND_COLOR,--image-background-colorIMAGE_BACKGROUND_COLOR— the image
background color (default:#ffffff);-IIMAGE_BACKGROUND_IMAGE,--image-background-imageIMAGE_BACKGROUND_IMAGE— the path to the
background image (default: none);-fFONT_FILE,--font-fileFONT_FILE— the path to the font
file;-sFONT_SIZE,--font-sizeFONT_SIZE— the font size (default:
25);-cFONT_COLOR,--font-colorFONT_COLOR— the font color
(default:#000000);-wWATERMARK_TEXT,--watermark-textWATERMARK_TEXT— the
watermark text (empty for disable; default: empty);-SWATERMARK_SIZE,--watermark-sizeWATERMARK_SIZE— the
watermark font size (default: 12);-CWATERMARK_COLOR,--watermark-colorWATERMARK_COLOR— the
watermark font color (default:#808080);-dDATABASE_FILE,--database-fileDATABASE_FILE— the path to
the database file (default:notes.db).
Package: whitehat
Whitehat⚠️WARNING THIS PYTHON PACKAGES FOR EDUCATIONAL PURPOSES ONLY⚠️Key FeaturesDDoSKey LoggerReverse ShellFeaturesDDoS (Can up to 1500+ Requests per seconds)Get Phonenumber InfoGet IP Address InformationKeyloggerPort ScannerReverse ShellSee Browser historyBrute ForceCommon Cryptography encryption, such asCaesar Cipher,Vigenere Cipher,Morse,etcInstallPIPpip install whitehatAdvanced version (Not always available, dependent to update)pip install git+https://github.com/EterNomm/WhitehatExampleClick hereOtherWant to be a contributor?Pull Requestare welcome :3Join our Discord ServerHereLicence & CopyrightThis Project under Apache License 2.0
Copyright (c) 2021-present EterNomm
Package: whitehole
Whitehole is a zarr-format-based-tool that allows individuals or small teams interested in processing financial information to make investment decisions.What do I need to know to start?Whitehole was thought to fill the gap between the techniques used by the industry and some retail quantitative investors. Thus, the first step to think about is that there is a huge amount of data that is necessary to process for different purposes, specially forticksmarket data instead ofcandlebars. That's why we need a format to store all the data in less storage space. In that sense,Quantmoon Technologiesprovidestick data using Zarr format.Zarrallows to chunk this tick data and store it in binary structure with metadata as labels.Whithole use this light data files to upload and read it in Python environments, such as Jupyter, Spyder, PyCharm and more. The idea behind this is to get useful data arrays to work efficiently through any required process. So,xarrayis the main library useful for hold the data and work with it.As an additional features, Whitehole allows to usenumpyandpandasto work with the data for different purposes, specially when the processing has to be done just once in the workflow.Whitehole is fully developed in Windows.Features:Decryptor: because of zarr store data in binary, with this class is possible to transform it in a more human-comprenhensive way.Whitehole is thought to have different features, the processing of the information will be realized in future versions.How do I get it?Whitehole can be installed from PyPI using pip:pip install whiteholeQuick StartTo have a better understanding of the features, please check the notebooks on thenotebookfolder
Package: whitening
Document whitening (foreground separation)This package tries to separate text/line foreground and background by 2D median
filter.InstallationInstall from PyPI. Works on Python 3.pipinstallwhiteningExample usagePython APIIt works with images represented asPIL.Imageor as a numpy array. Images can
be either RGB or grayscale.importnumpyasnpimportPIL.Imagefromwhiteningimportwhiten# possible to use numpy array as input/outputimage=np.asarray(PIL.Image.open('image.jpg'),dtype='uint8')foreground,background=whiten(image,kernel_size=20,downsample=4)PIL.Image.fromarray(foreground).save('foreground.jpg','jpeg')# or directly a PIL imageimage=PIL.Image.open('image.jpg')foreground,background=whiten(image,kernel_size=20,downsample=4)foreground.save('foreground.jpg','jpeg')CLIIt install an entry point calledwhiten.# help$whiten-h# whiten an image and save the foreground output$whiteninput.jpgforeground.jpg# specify the kernel size$whiteninput.jpgforeground.jpg-k100# work in grayscale instead of RGB (3x faster)$whiteninput.jpgforeground.jpg-g# downsample the image 4x (faster, but a bit less precise)$whiteninput.jpgforeground.jpg-d4# save also the background$whiteninput.jpgforeground.jpg-bbackground.jpgWe assume the original images is a product of foreground and background,
thus we can recover the foreground by dividing the image by the background:I = F * B => F = I / B. We try to approximate the background by 2D median
filtering the original image which suppresses sparse features such as text and
lines.Select kernel size that's enough for not making artifacts while small enough
to keep computation fast. A good starting point is 50 pixels.A 9.5 Mpx image can be processed on a MacBook in 15 s, with grayscale and
downsampling 4x the run time can be reduced to 1 s! Quite good results can be
obtained even with kernel size 10 and downsampling 16x.More info:http://bohumirzamecnik.cz/blog/2015/image-whitening/DevelopmentSee theMakefilefor various development tasks.LicenseAuthor: Bohumír Zámečníkbohumir.zamecnik@gmail.comSupported byRossum, creating a world without manual data entry.
Package: whitenoise
Radically simplified static file serving for Python web appsWith a couple of lines of config WhiteNoise allows your web app to serve its
own static files, making it a self-contained unit that can be deployed anywhere
without relying on nginx, Amazon S3 or any other external service. (Especially
useful on Heroku, OpenShift and other PaaS providers.)It’s designed to work nicely with a CDN for high-traffic sites so you don’t have to
sacrifice performance to benefit from simplicity.WhiteNoise works with any WSGI-compatible app but has some special auto-configuration
features for Django.WhiteNoise takes care of best-practices for you, for instance:Serving compressed content (gzip and Brotli formats, handling Accept-Encoding
and Vary headers correctly)Setting far-future cache headers on content which won’t changeWorried that serving static files with Python is horribly inefficient?
Still think you should be using Amazon S3? Have a look at theInfrequently
Asked Questions.To get started, see thedocumentation.
Package: whiterenamer
White Renamer is a software that ease the pain of renaming multiple files and folders by doing it in batch. The user can choose many actions to perform on the selected directory. The following sections describes what those actions are, and how to use it.White Renamer has a simple UI containing two distinct parts : the top part displays boxes representing the structure of the files (file + extension or folder).
Some optional boxes can be inserted : Prefix and Suffix that give the possiblity to add prefix(es) or suffix(es) to the filename.
The bottom part displays a live preview of the changes made to the files/folders.For more information, please read the documentation under whiterenamer/doc/Documentation.pdf
Package: whiterose
WhiteroseWhiterose is a pure Python library built to return the current time in real-time within a single stdout. This Python library does not require any external libraries. This project is still in a semi-WIP state.InstallationYou can install this library via Pypi:pip3installwhiteroseUsageA script has been provided for testing the currently functionality of the library.$python3example.pyOptionstimer_s() - Time updates in real-time every secondtimer_m() - Time updates in real-time every minutetimer_h() - Time updates in real-time every hourExamplesYou can call the library by using this syntax. This example will print the time to the console every second.fromwhiterose.whiteroseimportWhiterosewr=Whiterose()defone_s_timer():wr.timer_s(1)# Real-time timer ever 1 secondif__name__=='__main__':one_s_timer()This example will print the time to the console every 4 seconds.fromwhiterose.whiteroseimportWhiterosewr=Whiterose()defone_s_timer():wr.timer_s(4)# Real-time timer ever 4 secondsif__name__=='__main__':one_s_timer()This example will print the time to the console every 2 minutes.fromwhiterose.whiteroseimportWhiterosewr=Whiterose()defone_m_timer():wr.timer_m(2)# Real-time timer ever 2 minutesif__name__=='__main__':one_m_timer()
Package: whitesmith
Test helper for blacksmith resources.MotivationWhile using blacksmith, resources are declared using pydantic, and, while testing,
we never do http calls.Whitesmith is a helper that create pytest fixtures for blacksmith resources and
generate handlers for tests.Usagewhitesmith generate -m my_package.resources --out-dir tests/The commande above will generate a foldertests/whitesmithcontaining
handlers for all the api call with a default implementation.NoteIf you run the command again, the command does not overrite generated files.To generate newer version, use the--overwriteflag.The command will generate also aconftest.pyfile containing two fixtures,for sync and async version.Tests that require those fixture are suppose to be created inside the whitesmith folder.To create the test elsewhere, you have to copy create your own fixtures by copy,
pasting and adapting import path.