Saravanan666/pypi-org huggingface dataset:
Package: plotnow
A graph plotting application for 2D data.InstallationUsepiplike:$ pip install plotnowUsageusage: plotnow [-h] [-r RC_FILE] [--colors COLORS] [--parser PARSER]
            [--loader LOADER] [--recursive] [--reverse] [--relative]
            [-o OUTPUT]
            [-f {pgf,svgz,tiff,jpg,raw,jpeg,png,ps,svg,eps,rgba,pdf,tif}]
            [-m {curve,transition}] [--width WIDTH] [--height HEIGHT]
            [--xlabel XLABEL] [--ylabel YLABEL] [--ylabel2 YLABEL2]
            [--xmin XMIN] [--xmax XMAX] [--ymin YMIN] [--ymax YMAX]
            [--ymin2 YMIN2] [--ymax2 YMAX2] [--without-legend]
            [infiles [infiles ...]]

positional arguments:
infiles

optional arguments:
-h, --help            show this help message and exit
-r RC_FILE, --rc-file RC_FILE
                        A filename of matplotlib RC file will be loaded. You
                        can specify the absolute path or relative path. If you
                        specify it with relative path, the relative path from
                        working directory will be used.
--colors COLORS       A comma separated HTML colors.
--parser PARSER       A maidenhair parser name.
--loader LOADER       A maidenhair loader name.
--recursive           Recursively load data from a directory.
--reverse             Reverse data loading order.
--relative            Translate data to relative value to the 1st data
                        loaded.
-o OUTPUT, --output OUTPUT
                        An output graph file. File type will be automatically
                        detected from the extensions. Or you can specify the
                        file type with --format option.
-f {pgf,svgz,tiff,jpg,raw,jpeg,png,ps,svg,eps,rgba,pdf,tif}, --format {pgf,svgz,tiff,jpg,raw,jpeg,png,ps,svg,eps,rgba,pdf,tif}
                        An output graph file type.
-m {curve,transition}, --mode {curve,transition}
                        A mode of plotting. 'curve' is used for plotting
                        relation between X and Y data. 'transition' is used
                        for plotting relation among X1-Xn and Y1-Yn data.
--width WIDTH         A width in inch of output graph.
--height HEIGHT       A height in inch of output graph.
--xlabel XLABEL       A label text of X axis.
--ylabel YLABEL       A label text of Y axis.
--ylabel2 YLABEL2     A label text of Y2 axis used in 'transition' mode.
--xmin XMIN           Limit minimum value of X axis.
--xmax XMAX           Limit maximum value of X axis.
--ymin YMIN           Limit minimum value of Y axis.
--ymax YMAX           Limit maximum value of Y axis.
--ymin2 YMIN2         Limit minimum value of Y2 axis used in 'transition'
                        mode.
--ymax2 YMAX2         Limit maximum value of Y2 axis used in 'transition'
                        mode.
--without-legend      Disable legend drawing.
Package: plotrisk-sfplus-internal-client
Cropin Plotrisk-Insight Client has functions to fetch plots, satellite, weather and yield details. It also exposes the plot image download function.Important Definitions
Plot - A piece of land, represented by geojson structure i.e. Polygon, Multipolygon or a circle.Satellite Data - For the plot created, satellite insights will be generated.Yield - Yield mapping refers to the process of collecting georeferenced data on crop yield and characteristics, such as moisture content, while the crop is being harvested with an onboard yield monitor.Weather - Weather data provides information about the weather and climate of a plot.GDD - GDD (Cumulative growing degree days) is an index calculated from temperature data and is used to estimate the time needed for a crop or other organisms to reach a certain stage of development.Prerequisites
1. Supported Python Version - This SDK has been tested with Python versions 3.5+. Earlier Python 3 versions are expected to work as well, as long as the dependencies are fulfilled.Users who have already onboarded onto sf_plus, enabled plotrisk feature for atleast few plots will be able use this SDK.
Package: plotscanner
PlotscannerAPython3application used to convert images of functional data plots to digital format.
Often data is presented in documents or scientific articles in the form of figures. In order to use this data, it must be digitized in some way. This program allows you to get a scanned image of a plot inJPEGorPNGformat and quickly digitize the values. The numbers can be saved to a file (CSV,XLS,TXT, etc.) and used.DocumentationYou can find the documentation for all of the application's basic moduleshere.Installation$ pip install plotscannerDocker-imageYou can download a docker-image of the application if you want to use the back-end part in the container$ docker pull ils0/plotscanneror build it yourself with the following command:$ docker build -t plotscanner .RunStart the application by running the command in the terminal:$ plotscannerFollow the instructions in the app window to work correctly.Image preparingPlotscanner requires the input data in the correct form to work right. Before loading the image, crop it and save only the axes and lines of the plot like this.
Package: plotschedule
A simple schedule plotterThe csv/xlsx file needs to have columns "begin", "end", "days", and whatever text you want in the schedule boxes:beginenddaysLabel1Label2Label3008:00 AM08:50 AMMWOneTwoThree110:00 AM11:50 AMTRFYesNoMaybe203:00 PM03:50 PMMWFFooBarBazFor this CSV you could use this command line:plotschedule --output bar.png --title "My Schedule" example/foo.csv --labels Label1 Label2 Label3And you'd get this output:You can also add acolorcolumn and use matplotlib colors.Changes: 0.11 now works with excel too!Changes: 0.12 allow for times that are already converted into 'float' hours (e.g. 8:30 AM -> 8.5, etc.)Changes: 0.14 (yeah, skip 0.13!) reorganizeinit.py to put the function an module scope.
Package: plotserver
plotserver=======Expose your matplotlib figures over http with 1 line of codeInstallation------------.. code-block:: shellpip install plotserverExample usage-------------plotting a pylab environment.. code-block:: pythonimport plotserver as plsimport pylabpylab.plot([1,2,3,4,1,2,3,4])pylab.xlabel("numbers")pylab.ylabel("values")# display plot using webserver at http://0.0.0.0:8080/pls.show(pylab)plotting a matplotlib figure.. code-block:: pythonimport plotserver as plsfrom matplotlib import pyplotfig = pyplot.figure()axes = fig.gca()axes.plot([1,2,3,4,1,2,3,4])# display plot using webserver at http://127.0.0.1:9999/pls.show(fig, host="127.0.0.1", port=9999)
Package: plotserver-api
plot-server-apiPython API for communication withPlotServer.How to UseRegister a user on the PlotServer of your choice;Get your API key;Install this package:$pipinstallplotserver-apiIntegrate API into your application:fromplotserver_apiimportPlotServerAPI,Project# Set key and API addresskey=b'5uSN9ojYpMiI7gQ5k5NWR6kYvenDX97CmxC5aaUGdH8='api_url="http://someadress"# Initialize API managerapi=PlotAPI("alartum",key,api_url,verbose=True)# And create new project withproject=Project("project1",api,fresh_start=True)# Add tags for different data to be trackedproject.add_files(["sin","cos"])# And inform the serverproject.prepare_project()importmatht=0whileTrue:# Add new data to the managerproject.add_frame("sin",t,math.sin(t**2/50))project.add_frame("cos",t,math.cos(t/20))# And send frames when doneproject.send_frames()time.sleep(2)t+=1
Package: plotski
plotskiPackage to generate easily generate interactive figures and export them to HTML documents.The idea behind this package is to provide an easy-to-use interface to generate structured
HTML documents with interactive visualisations. The library is not meant to fill every gap
but to provide several common plot types such asline,scatterorimagetype-plots and
additional annotations and enable easy exportation to HTML document.All plots are generated using the excellentbokehlibrary.Typical usage might beimportnumpyasnpfromplotskiimportPlotStorex=np.arange(100)y=np.random.randint(0,500,100)store=PlotStore(".",# this will save the plot to current working directory.)tab_name=store.add_tab("Random plot")# data is provided in dictionary. Data validation happens during plot generationstore.plot_spectrum(tab_name,{"x":x,"y":y})# this will generate the HTML document (in memory using temporary file)store.show()# you can instead save to disk which will save the document to diskstore.save()FeaturesPlannedImprove generation of layouts.Improve modification of plot parameters.Add custom JS support.LicenceFree software: MIT licenseDocumentationhttps://plotski.readthedocs.io
Package: plotszoo
plotszooDocumentationThis repository contains a collection of classes to easily make some common plots as well as retrieving data from multiple sources.This project mostly fits my needs and it doesn't want to be in any way complete nor general purpose.Feel free to use it, submit your own plots, bugfixes, requests or ideas. Any PR is very welcomed.Repository under active development, braking changes may (and will) occurExamplesParallel Coordinates Plotimportnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltfromsklearn.datasetsimportload_irisimportplotszooiris=load_iris()scalars=pd.DataFrame(data=np.c_[iris["data"],iris["target"]],columns=iris["feature_names"]+["target"])data=plotszoo.data.DataCollection()data.set_scalars(scalars)fig,axes=plt.subplots(1,len(iris["feature_names"]),sharey=False)parallel_plot=plotszoo.scalars.ScalarsParallelCoordinates(data,iris["feature_names"],"target")parallel_plot.plot(axes,cmap="tab10")fig.set_size_inches(30,10)fig.show()Grouped Series Paradeimportnumpyasnpimportpandasaspdimportosimportmatplotlib.pyplotaspltimportplotszoonp.random.seed(0)num_series=10samples=100noise_level=0.8x=np.linspace(0,np.pi*2,samples)types=[]series={}for_inrange(0,num_series):noisy_sin=np.sin(x)+np.random.rand(samples)*noise_levelseries[len(types)]=pd.DataFrame(noisy_sin,columns=["value"])types.append("sin")noisy_cos=np.cos(x)+np.random.rand(samples)*noise_levelseries[len(types)]=pd.DataFrame(noisy_cos,columns=["value"])types.append("cos")data=plotszoo.data.DataCollection()data.set_scalars(pd.DataFrame(types,columns=["type"]))data.set_series(series)fig,ax=plt.subplots()series_parade=plotszoo.series.grouped.GroupedSeriesParade(data,["type"],"value")series_parade.plot(ax)fig.show()GroupedScalarsBarchartimportnumpyasnpimportpandasaspdimportosimportmatplotlib.pyplotaspltfromsklearn.datasetsimportload_irisimportplotszooiris=load_iris()scalars=pd.DataFrame(data=np.c_[iris["data"],iris["target"]],columns=iris["feature_names"]+["target"])data=plotszoo.data.DataCollection()data.set_scalars(scalars)fig,axes=plt.subplots(2,2)yticks_fn=lambdax:iris["target_names"][int(x)]forfeature,axinzip(iris["feature_names"],axes.flatten()):barchart=plotszoo.scalars.grouped.GroupedScalarsBarchart(data,["target"],feature)barchart.plot(ax,title=feature,nbins=20,grid=True,yticks_fn=yticks_fn)fig.set_size_inches(20,10)fig.show()Contributions and licenseThe code is released as Free Software under theGNU/GPLv3license. Copying, adapting e republishing it is not only consent but also encouraged.For any further question feel free to reach me atfederico.galatolo@ing.unipi.itor on Telegram@galatolo
Package: plottable
Beautiful tables in matplotlib.plottable is a Python library for plotting beautifully customized, presentation ready tables in Matplotlib.To learn about its functionality, have a look at thedocumentation.QuickstartInstallationpip install plottableA Basic Exampleimportmatplotlib.pyplotaspltimportnumpyasnpimportpandasaspdfromplottableimportTabled=pd.DataFrame(np.random.random((5,5)),columns=["A","B","C","D","E"]).round(2)fig,ax=plt.subplots(figsize=(6,5))tab=Table(d)plt.show()Redoing theReactable 2019 Women's World Cup Predictions VisualizationYou can find thenotebook hereStyling A TableThere are three main ways to customize a table:1)By supplying keywords to the Table2)Providing a ColumnDefinition for each column you want to style3)Accessing a tables rows or columnsContributingContributors are very welcome to this project.Please take a look at theContributor GuideCreditsplottable is built for the lack of good table packages in the python ecosystem.
It draws inspiration from R packagesgtandreactable, from blog posts about creating tables in matplotlibTim Bayer: How to create custom tablesandSon of a corner: Beautiful Tables in Matplotlib, a Tutorialand from matplotlibs own table module.
Package: plottah
Copyright © 2023 by Boston Consulting Group. All rights reservedSmartBanking Plotting toolSetupRequirementsPython (>=3.6)Development environmentClone the repo, give name plotting_analyisgitclonegit@github.com:nielsota/plottah.gitplotting_analysisCD into the directorycdplotting_analysisCreate a virtual environment by running:python-mvenvvenvThe virtual environment should be activated every time you start a new shell session before running subsequent commands:On Linux/MacOS:sourcevenv/bin/activateOn Windows (bash/cmd):venv/Scripts/activateOn Windows (ps):venv\Scripts\activateMake sure you have the latest pip versionpython-mpipinstall--upgradepipThen install the packages listed in the requirements.txtpipinstall-rrequirements.txtThen install the repository locallypipinstall-e.Next, update the config.yaml how you see fit, and then to generate the images run:python-mplottahSee notebooks for examples on how to run code inline during development
Package: plotter
PlotterGenerate this READMEin html:make html-readmethen openREADME.htmlin web browser,firefox README.htmlfor instance.in pdf:make pdf-readmeDescriptionPlotter is a plotting library based onmatplotlib. It intends to simplify the plotting of data in Python by creating a descriptive approach for plots. The plots can be static or dynamic.Source code locationhttps://github.com/T-TROUCHKINE/plotterPrerequisitiesLinux packagestk :ArchLinux :pacman -S tkDebian :apt install tkPython modulesmatplotlib :pip install matplotlibtikzplotlib :pip install tikzplotlibnumpy :pip install numpyGTK3For using GTK3 modules: see PyGObject documentation:https://pygobject.readthedocs.io/en/latest/index.htmlInstallationFrom PIPpip install plotterFrom sourcepython3 setup.py installExamplesSimplest example:The code:importnumpyasnpfromplotterimportPlotterx=np.linspace(-np.pi,np.pi,201)to_plot=[{"title":"Example","type":"plot","data":[x,np.sin(x)]}]pl=Plotter(to_plot)pl.show()Gives:Multi-plot:The code:importnumpyasnpfromplotterimportPlotterx=np.linspace(-np.pi,np.pi,201)to_plot=[{"title":"Example 1","type":"plot","data":[x,np.sin(x)]},{"title":"Example 2","type":"matrix","data":np.random.random((100,100))}]pl=Plotter(to_plot,figsuptitle="Multi-plot")pl.show()Gives:DocumentationOnline documentation athttps://plotter-doc.xyzBuild documentation locallyRequirementsjekyll (https://jekyllrb.com/)bundler (https://bundler.io/)Commandcddoc
bundleexecjekyllbuildThen the documentation is accessible in thedoc/_site/directory. Usable using
a web browser for instance:firefox doc/_site/index.html
Package: plottery
plottery(Not so) Generic plotting toolsTo install, runpip install plotteryor simply clone the latest version from github:git clone https://github.com/cristobal-sifon/plottery.gitTheplotterypackage contains four modules,astroplots,patches,plotutils, andstatsplotsBelow is a brief description of each module's functions. See their help pages for more details.astroplots:
    contour_overlay -- Overlay contours from one image on to another (new in v0.3.1).
    phase_space -- Plot phase space diagram (i.e., velocity vs. distance).
    wcslabels -- Generate HMS and DMS labels for RA and Dec given in decimal degrees.
patches: additional matplotlib.patches objects
    Bracket -- a square bracket used to highlight a region in a figure.
    LogEllipse -- a finely-sampled polygon that appears as an ellipse in a log-log plot.
plotutils:
    colorscale -- Generate a colorbar and associated array of colors from a given data set.
    savefig -- Convenience wrapper around functions used when commonly saving a figure.
    update_rcParams -- Update rcParam configuration to make plots look nicer.
statsplots:
    contour_levels -- Calculate contour levels at chosen percentiles for 2-dimensional data.
    corner -- Make a corner plot.Changelogv0.6.6 (Dec 2022):statsplots.cornersupports strings inbinsandbins1dfor automatic bin width calculationv0.6.5 (Dec 2022):statsplots.cornerbug fix when attempting to plot likelihood in diagonal panels (#27)Last updated: Dec 2022(c) Cristóbal Sifón 2013-2022
Package: plottify
plottifyThe plottify package is makes matplotlib plots more legible. It's a thin wrapper aroundmatplotlibthat automatically adjusts font sizes, scatter point sizes, line widths, etc. according to the figure size.Installationpip install plottifyUsageThe plottify interface is extremely simple and relies on one central function,autosize(). After you've created a plot, simply callautosize()and the sizes will be adjusted:fromplottifyimportautosizeimportmatplotlib.pyplotaspltplt.scatter(x,y)autosize()plt.show()If you have a matplotlib figure object, you can pass it as an argument toautosize:autosize(fig)DemonstrationBy default, matplotlib sets the sizes of these objects to pre-specified values, regardless of the figure size. This can result in tiny fonts or sizes for larger figures.Plottify'sautosizefunction adjusts these sizes automatically to make them more legible. When we change the figure size, plottify automatically adjusts the objects' sizes.Bugs and feature requestsPlease report any bugs or feature requests as a GitHub issue.
Package: plotting
A plotting wrapper onmatplotlibandseabornto enable single function plotting calls a la mathematicaInstallationpip install plottingorconda install plotting--channel=mrossolDevelopmentIf you want to contribute to this package:Clone the repository:git clonehttps://github.com/mrossol/plotting.gitenter github usernameenter github passwordInstall the pacakgecd into plotting repo cloned aboverunpip install-e.Check that plotting was installed successfullyimportplotting
Package: plotting3dprinter
2d plotting on a 3d-printerThis python library generates GCODE from SVG paths, allowing a 3d printer to draw images with a pen.Attach a pen to the printerIn order to draw images, we need to attach a pen to the printer.I've used this model to attach a pen to my Mk3SInstall softwarepip install plotting3dprinterobtain SVG fileto generate GCODE a vector-based SVG image file is required.
Currently the library only acceptspathsmade out of lines and splines (cubic and quadratic).
Transformed paths and groups are not (yet) supported. The examples folder on this repo contains some compatible SVG files.Software to generate SVG images from a bitmap image which worked well for me isPotrace.The vector images generated by potrace need some addititional cleaning up using for example Inkscape. Make sure there are no groups and that the paths use absolute positioning. In settings, under "SVG Output", set "Path string format" to "Absolute". Then select all paths, and go to "Path" and click "Reverse" (This forces inkscape to rewrite the paths) and save as plain SVG.Attach the pen at the correct height (callibration)In order to callibrate the height of the pen I've created a small gcode file which just puts the head in the left bottom and moves the head 2mm above the bed. This will be the drawing height (Z).Put a paper sheet on the bed, and clamp it to the bed using magnets. Run theCALLIBRATE.gcodefile and attach the pen such that it just touches the paper.After callibration runPEN_UP.gcodeafterwards to lift the pen back up.Convert the SVG to GCODEIn order to just plot the contour lines (strokes) present in the svg use:svgto3dprintplot example.svg example_outline.gcode --strokeSend the generated gcode file to your printer and enjoy!Filled shapesBy adding the--fillflag, shapes will be filled with horizontal lines.svgto3dprintplot example.svg example_outline.gcode --fill
Package: plottingtools
# plottingtoolsA collection of tools that I use for plotting often enough (or with enoughcomplexity) to necessitate creating a useful API.## Overview---This package is intended to make plotting as easy as possible for simple datascience visualizations. It's essentially a wrapper around matplotlib and isdesigned to simplify the process of creating quick plots. While it's certainlynot as powerful as using matplotlib directly, it hopefully makes it a loteasier to display information quickly.This package does not rely on (or currently support) pandas `DataFrames`, andexpects data to be in vector form (ideally a numpy `ndarray`).## What's new?---### v1.0.0- Object-oriented model for plotting- More useful utilities- Support for line plots- Examples for line plots- Better organization of examples## Functionality---Currently, this package does the following:- Display data as bars on a single plot- Single/multi color- Scaled by a scalar or broadcastable array-like object- Shown as percentages- Text:- Titles- legends- X-axis labels- over-bar labels- Examples:located in plottingtools/examples/bars- Display line plots- Single or multiple lines- Formatting options for each line- Labeling- Titles- Simple formatting of the figure## Installation` pip install plottingtools `## Updates and contributionKeep your eyes here to see updates. Please submit issues or PRs as you see fit.
Package: plotting-tools
Python Plotting ToolsContains various plotting functions.InstallationTo install Python Plotting Tools, run this command in your terminal:$pipinstall-Uplotting_toolsThis is the preferred method to install Python Plotting Tools, as it will always install the most recent stable release.If you don't havepipinstalled, theseinstallation instructionscan guide
you through the process.Quick Start>>>fromplotting_toolsimportExample>>>a=Example()>>>a.get_value()10CitingIf you use our work in an academic setting, please cite our paper:DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.Suggested Git Branch Strategymasteris for the most up-to-date development, very rarely should you directly commit to this branch. Your day-to-day work should exist on branches separate frommaster. It is recommended to commit to development branches and make pull requests to master.4. It is recommended to use "Squash and Merge" commits when committing PR's. It makes each set of changes tomasteratomic and as a side effect naturally encourages small well defined PR's.Additional Optional Setup Steps:Create an initial release to test.PyPI and PyPI.FollowThis PyPA tutorial, starting from the "Generating distribution archives" section.Create a blank github repository (without a README or .gitignore) and push the code to it.Delete these setup instructions fromREADME.mdwhen you are finished with them.
Package: plottool-ibeis
Helpers for matplotlib computer vision plots for the IBEIS / WildMe projectRepos relevant to the ibeis project:https://github.com/Erotemic/plottool_ibeishttps://github.com/Erotemic/guitool_ibeishttps://github.com/Erotemic/dtool_ibeishttps://github.com/Erotemic/vtool_ibeishttps://github.com/Erotemic/pyflann_ibeishttps://github.com/Erotemic/pyhesaffhttps://github.com/Erotemic/utoolhttps://github.com/Erotemic/ibeisThis project is maintained for the benefit of IBEIS, but is not recommended for
use in other projects.For new developement the (generally useful) tools in this repo are largely
superceded by Kitware projects:https://gitlab.kitware.com/computer-vision/kwimagehttps://gitlab.kitware.com/computer-vision/kwplot