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In addition to what we mentioned@triplepoint look at the slider widget. An example on the matplotlib examples page. It's a graphic slider bar, not a keyboard binding, but it works very well for what you want to do. Also note that in order to ensure that sliders and buttons remain responsive rather than collected debris,

references to objects (amp_slider, freq_slider, etc.) must be maintained independently. (I do this community wiki as I just copy-paste from an example. from numpy import pi, sin import numpy as NP import matplotlib.pyplot as plt from matplotlib.widgets import slider, button, RadioButtons def signal (amplifier, freq): reverse

amplifier - sin (2 - pi - frick t) axis_color - lightgoldenrodyellow figs (plt.figure() axe - fig.add_subplot (111) - Adjust subs close the area to leave some space for slider and buttons fig.subplots_adjust (left 0.25, bottom 0.25) t and np.arange (0.0, 1.0, 0.001) amp_0 5 freq_0 3 - Draw the initial plot, the variable line is used to

change the line later (line) - ax.plot (t, signal (amp_0, freq_0), linewith'2, color'' ed.) ax.set_xlim (No0, 1) ax.set_ylim (No10, 10) - Add two sliders to set the parameters and identify the axen area and draw a slider amp_slider_ax and fig.add_axes (0.25). , 0.65, 0.03], facecolor=axis_color) amp_slider =

Slider(amp_slider_ax, 'Amp', 0.1, 10.0, valinit=amp_0) # Draw another slider freq_slider_ax = fig.add_axes([0.25, 0.1, 0.65, 0.03], facecolor=axis_color) freq_slider = Slider(freq_slider_ax, 'Freq', 0.1, 30.0, valinit=freq_0) # Define an action for modifying the line when any slider's value changes def

sliders_on_changed(val): line.set_ydata(signal(amp_slider.val, freq_slider.val)) fig.canvas.draw_idle() amp_slider.on_changed(sliders_on_changed) freq_slider.on_changed(sliders_on_changed) # Add a button for resetting the parameters reset_button_ax = fig.add_axes([0.8, 0.025, 0.1, 0.04]) reset_button =

Button(reset_button_ax, 'Reset', color=axis_color, hovercolor='0.975') def reset_button_on_clicked(mouse_event): freq_slider.reset() amp_slider.reset() reset_button.on_clicked(reset_button_on_clicked) # Add a set of radio buttons for changing color color_radios_ax = fig.add_axes([0.025 , 0.5, 0.15, 0.15, facial

axis_color) color_radios RadioButtons (color_radios_ax, (red, 'blue', 'green'), active'0) def color_radios_on_clicked (label): line.set_color (label) fig.canvas.draw_idle() color_radios.on'clicked (color_radios_on_clicked) plt.show () Publish Date: March 2015 Picture worth a thousand words The preparation of any interactive

application is to provide as much information as possible to minimize complexity. If he can't provide users, it's useless to them. However, if the application is too complex, the signal is lost in noise noise Complexity. Graphic presentation often strikes the right balance. The Matplotlib library can help you present your data

as graphs in the app. Anyone can make a simple interactive app without knowing anything about raffle buffers, event cycles, or even what a GUI toolkit is. And yet, the Matplotlib library will give way to as much control as it wants to, to allow even the most savvy GUI developer to create masterful applications from scratch.

Like much of Python's language, Matplotlib's philosophy is to give the developer complete control, but without being bluntly useless and tedious. There are many ways to install Matplotlib in your system. While the library used to have a reputation for being hard to install on non-Linux systems, it has come a long way

since then, along with the rest of the Python ecosystem. Link to the following command: Most likely, the previous command will work just fine from the command line. Python Wheels (the next-generation Python package format that replaced the eggs) for Matplotlib is now available in PyPi systems for Windows and Mac

OS X. This method will also work for Linux users; However, it may be more favorable to install it through the manager's built-in system package. Although the main Matplotlib library can be installed with multiple dependencies, it is part of a much larger scientific computing ecosystem known as SciPy. Displaying data is

often the easiest part of the app. Processing is much more complicated, and the SciPy ecosystem is likely the packages you need to do so. There is NumPy for basic numerical processing and N-dimensional data arrays. For more advanced but common data processing tools, there is a SciPy package (the name was so

catchy it ended up being used to refer to many different things in the community). For more domain needs, there are sci-kits such as scikit-learning for artificial intelligence, scikit-image processing for image processing, and statsmodels for statistical modeling. Another very useful library for data processing - pandas. This

was just a brief summary of the packages available in the SciPy ecosystem. Manual management of all their installations, updates and dependencies will be difficult for many who just want to use the tools. Fortunately, there are several SciPy stack distributions available that can keep the menagerie under control. Below

are Python Distributions, which include SciPy Stack along with many other popular Python packages or make packages readily available through package management software:Anaconda by Continuum AnalyticsCanopy by EnthoughtSciPyPython (x, y) (Windows only)WinPython (Windows only)Pyzo (Python 3

only)Algorete Loopy from Dartmouth CollegeFor this book, we will take at least Python 2.7 or 3.2. Necessary numpy, matplotlib, basic card, and scipy. Almost any version of these packages, released over the past three years, should work for most of the books (exceptions are noted in this book). Version 0.14.0 SciPy

(released in May 2014) cannot be used in this book due to (now fixed) regression in its NetCDF reader. Chapter 5, Embedding Matplotlib will have special notes regarding the gui set of tool packs. With the Matplotlib installation you are ready to make your first simple story. Matplotlib has several layers. Pylab is the top

layer often used for a quick one-off build from inside a live Python session. Run your favorite Python translator and say this: Nothing happened! This is because Matplotlib, by default, will not display anything until you explicitly tell him to do so. The Matplotlib library is often used to automatically generate images from

Python scripts, without the need for any interactivity. Also, most users won't be done with their conspiracy yet and will find it distracting to make up the plot automatically. When you're ready to view the story, use the following command: now you have to see a shape window, and the Python translator isn't available for any

additional commands. By default, the display of the shape blocks the execution of scripts and translators. However, this does not mean that the figure is not interactive. As you mouse over the plot, you will see the coordinates of the plot in the bottom right corner. The window figure will also have a toolbar: From left to

right, the following tools: Home, Back and Forward: They look like a web browser. These buttons will help you navigate previous views of your story. The Home button will take you back to the first view when the shape is open. The return will lead you to the previous view, while Forward will return you to previous views.

Panning (and scaling): This button has two modes: panning and zooming. Press the left mouse button and hold it to pan the shape. If you press x or y during panning, the movement will be limited only to the x or y axis, respectively. Click the right mouse button to zoom in. The plot will be increased or increased in

proportion to right/left and up/down movements. Use the X, Y or Ctrl to limit the zoom to the x or axis y axis or keep the side ratio, respectively. Zoom in to the rectangle: click the left mouse button and drag the cursor to a new place and release. The performance restrictions of the axes will be increased to just stretched

the rectangle. Zoom in with the right mouse button by placing the current view in an area that has just been defined by a rectangle. Subplot configuration: This button brings up a tool to change the interval between sites. Save: This button brings up a dialogue that allows you to keep your current figure. The shape window

will also respond to the keyboard. The default key card is quite extensive (and will be covered completely but some of the main hot keys are the home key to resetting the plot view, left and right keys for back and forth action, p for pan/zoom mode, o for scaling to rectangle mode, and Ctrl s call to save the file. When

you're finished viewing the shape, close the window as you would close any other app window, or use Ctrl and W. When we were doing the previous example, no plots appeared before the show () was called. In addition, no new commands can be entered into the Python translator until all numbers are closed. As you will

soon find out, once the figure is closed, the plot it contains is lost, meaning that you will have to repeat all the commands again to show () it again, perhaps with some modification or additional plot. Matplotlib comes with an interactive default build mode. There are several ways to enable an interactive build mode. The

main way is to call ion () functions (for interactive ON). The interactive build mode can be turned on at any time and off with ioff. Once this mode is on, the next build command automatically launches an implicit show command. You can also continue putting commands into a Python translator. You can change your

current shape, create new shapes and close existing ones at any time, all from the current Python session. Python is not only known for its interactive translators; it's also a full-fledged programming language that makes it easy for its users to create programs. Having a script to display stories from daily reports can

significantly improve performance. Also, you may need a tool that can produce some simple pieces of data from any mysterious data file you've encountered online. Here's a simple example of how to use the Matplotlib PYplot API and the standard argparse Python library tool to create a simple CSV build script called

plotfile.py. Code: chp1/plotfile.py Notice two additional command line arguments: --save and-not-show. With the option --save, the user can automatically save the plot (the format of the graphics is determined automatically from the extension of the name of the file). In addition, the user may choose not to display a plot

that is combined with the option --save may be desirable if the user is trying to build multiple CSV files. When you call this script for plot impressions, the script will stop at plt.show. If the interactive build mode was enabled, the script would continue past the show, ending the script, thus automatically closing any shapes

before the user could view them. That's why the online build mode is off by default in Matplotlib.Also note that the plt.savefig call is in front of plt.show(). As mentioned earlier, when the figure window is closed, the plot is lost. You can't save the site after it's closed. We reviewed how to install Matplotlib and went on how to

do very plots from the Python or Python script session. Chances are, everything went very smoothly for you. The rest of this book will focus on how to use Matplotlib to make interactive interactive rather than a lot of ways to display data. You may be very curious and want to learn more about the many kinds of sites this

library has to offer, or maybe you want to learn how to make new types of sites. Help comes in many forms. The Matplotlib website ( ) is the main online resource for Matplotlib. It contains examples, frequently asked questions, API documentation and, most importantly, a gallery. Many Matplotlib users often face the

question: I want to make a plot that has this data along with this data in the same digit, but it should look like this other storyline I've seen. Searching for text graph concepts is difficult, especially if you're not familiar with terminology. The gallery demonstrates the variety of ways in which sites can be made, all with the help

of the Matplotlib library. Browse the gallery, click on any figure that has parts of what you want in your story, and see the code that created it. Soon enough, you'll be like a chef, mixing and matching components to produce that perfect schedule. When you're just stuck and can't figure out how to get something to work or

just need some tips on how to get started, you'll find most of the community on the Matplotlib-users mailing list. This mailing list is an excellent resource of information with many friendly members who just love to help newcomers. Be persistent! While many questions get answered pretty quickly, some will fall through the

cracks. Try to paraphrase your question or with a plot showing your attempts so far. People in Matplotlib users love plots, so an image that shows what happened often gets a quick response. The new community resource is StackOverflow, which has many very knowledgeable users who can answer difficult questions.

So far, we've shown you bits and pieces of two of the top two layers of Matplotlib abstraction: pilyab and pyploma. The layer beneath them is an object-oriented layer (OO layer). To develop any type of application, you want to use this layer. Mixing layers of sawmill/piplo with a layer of OO will lead to very confusing

behavior when dealing with multiple areas and shapes. Below the OO layer is the backend interface. Anything above this level of interface in Matplotlib is completely platform agnostic. It will work the same regardless of whether it's in the interactive GUI or comes from a driver script running on a non-healthy server. The

backend interface abstracts all these considerations so that you can focus on what is most important: writing code to visualize the data. There are several backend implementations that come with Matplotlib. These backends are responsible for accepting the numbers presented by the OO layer and interpreting them for

what device they display Backends are selected automatically, but can be explicitly installed if necessary (see chapter 5, Matplotlib Embedding). There are two main backend classes: the ones that provide interactive numbers those that don't have it. Interactive backends are those that support a specific gui interface,

such as Tcl/Tkinter, GTK, qt, Cocoa/Mac OS X, wxWidgets and Cairo. With the exception of the Cocoa/Mac OS X backend, all interactive backends can be used on Windows, Linux and Mac OS X. So when you make an interactive Matplotlib app that you want to distribute to users of any of these platforms, if you don't

embed Matplotlib (again, see Chapter 5, Matplotlib Embedded), you don't have to deal with writing one line of code for any of these tools! Non-interactive backends are used to create image files. There are backends for the production of Postscript/EPS, Adobe PDF, and scalable vector graphics (SVG), as well as

rasterized image files such as PNG, BMP, and JPEGs.The open secret behind the high quality rasterized images of Matplotlib is its use of anti-grain geometry (AGG) library ( . The quality of graphics generated from AGG is far superior to most other tools available. Thus, AGG is not only used to produce rasterized image

files, but it is also used in most interactive backends as well. Matplotlib supports and delivers with its own fork library in order to make sure that you have consistent, high-quality image products on all platforms and tools. What you see on the screen in the interactive shape window will be the same as the PNG file that is

produced when you call savefig. When you install Matplotlib, you'll be selected for the default backend based on your OS and available GUI toolkits. For example, in Mac OS X systems, the library installation is likely to install an interactive default backend on MacOSX or CocoaAgg for older Macs. Meanwhile, Windows

users are more likely to default to TkAgg or t5Agg. In most cases, the choice of interactive backends does not matter. However, in certain situations, you may need to force a certain backend to be used. For example, on a headless server without an active graphics session, you will most likely need to be forced to use an

inactive Agg backend: When this is done before any build commands, this will avoid downloading any GUI tool kits, thus avoiding the problems that occur when the graphics interface fails on a free-range server. Any call to show () actually becomes a non-op (and the execution of the script is not blocked). Another goal is

to set up a backend for scenarios when you want to embed your plot into your native GUI app. This way, you'll need to know clearly what set of GUI tools you're using (see Chapter 5, Matplotlib Embedded). Finally, some users just like the look GUI tool kits are better than others. They may want to change the default

backend with the backend in the matplotlibrc configuration file. Chances are, your rc file can be found in the .matplotlib catalog or .config/matplotlib .config/matplotlib under the home folder. If you can't find it, then use the following set of commands: Here's an example of the relevant section in my matplotlibrc file: This is a

global configuration file that is used if one is not found in the current work directory when Matplotlib is imported. The settings contained in this configuration are the defaults for many parts of Matplotlib. In particular, we see that the choice of backends can be easily set without the use of a single line of code. Everything

that can be done in Matplotlib is called an artist. Any artist can have children-artists who are also drawable. This forms the basis of the hierarchy of artist objects that Matplotlib sends to the backend for rendering. At the heart of this artist's tree is a figure. In the examples so far, we have not directly created any numbers.

Pylab and pyplot interfaces will create numbers for us. However, when creating cutting-edge interactive applications, it's a good idea to be explicit about creating your own shapes. You especially want to do this if you have multiple numbers displayed at the same time. This is the entrance to the OO Matplotlib layer: The

figure is, literally, your canvas. Its main component is a copy of FigureCanvas, where the entire picture takes place. If you don't embed your Matplotlib shapes in the GUI app, it's unlikely that you'll need to interact directly with that object. Instead, when building teams are released, artists are automatically added to the

canvas. While any artist can be added directly to the shape, usually only Axes objects are added. The shape can have many axial objects, commonly called sub-plots. Like the shape object, our examples have so far clearly not created any axial objects for use. This is because pylab and pyplot interfaces also

automatically create and control axial objects for the shape if necessary. For the same reason as numbers, you can clearly create these objects when you create interactive applications. If the axis or shape is not provided, then the pyplot layer will have to make assumptions about which axis or shapes you want to apply

to the conspiracy command. While this may be good for simple situations, these assumptions get hairy very quickly in non-trivial applications. Fortunately, it's easy to create both a shape and its axis with the help of a single team: these objects are highly developed complex units that most developers will use for their

build needs. Once the picture is canvased, the axles object will provide ticks, axis labels, axis name (s), and plot area. Axis is an artist who manages all its scale and coordinates transformations (e.g. log scaling and polar coordinates), automated tick markings, and automated axis limitations. In addition to these

responsibilities, the axe object provides a wide range of construction functions. Sample the rest of this book, we will build one interactive application piece by piece, showcasing the concepts and features that are available through Matplotlib. This app will be an app for editing storm tracks. Given a series of radar images,

the user can bypass every storm cell they see on the radar image and link these storm cells in time. The app will need to be able to store and download track data and provide the user with mechanisms to edit the data. Along the way, we learn about the structure of Matplotlib, its artists, the callback system, the animation

execution, and finally the embedding of this application into the larger GUI application. So, first, we should be able to view the radar image. There are many ways to download data to The Python program, but one of the favorites among meteorologists is the Network Common Data Form (NetCDF) file. The SciPy package

has built-in support for netCDF version 3, so we'll use the worth of hour-long radar reflectivity data produced using this format from the NEXRAD site near Oklahoma City, OK on the evening of May 10, 2010, which produced numerous tornadoes and severe storms. NetCDF binary is especially nice to work with because it

can hold multiple variable data in one file, with each variable having an arbitrary number of measurements. In addition, metadata can be attached to each variable and to the dataset itself, allowing you to independently document the data files. This data file has three variables: reflectivity, lat and lon to record the radar

reflectivity and latitude and latitude coordinates and longitude of each pixel in the data with reflective data. The reflectivity data is three-dimensional, with the first measurement of both time and two other dimensions of both latitude and longitude. The following example of code shows how easy it is to download this data

and display the first frame of the image using SciPy and Matplotlib.Code: chp1/simple_radar_viewer.py The launch of this script should result in the first frame of our storms appearing in the shape window, which we will be familiar with over the next few chapters. The plot has a color panel, and the axis ticks the latitudes

and longitudes of our data. What is probably most important in this example is the imshow () call. Traditionally, being an image, the origin of these images is displayed in the top left corner, and Matplotlib follows this tradition by default. However, this particular data set has been saved with its origin in the bottom left

corner, so we need to learn this with the origin parameter. The degree option is a blunt one that describes the amount of image data. By default, it is assumed that it is at the level (0, 0) and (N q 1, M q 1) for an image in the form of MxN. Vmin and vmax options are a good way to ensure consistency of your color card

from your input. If these two parameters are not delivered, imshow will use the minimum and maximum input to determine the color map. This would be undesirable we are moving towards displaying arbitrary radar footage. Finally, you can clearly specify a color map for use for the image. The color map gist_ncar is very

similar to the official NEXRAD color map for radar data, so we'll use it here: the color map gist_ncar, along with some other color cards packed with Matplotlib, such as the default color jet map, is actually terrible for visualization. You can find the Choice of Color Cards page of the Matplotlib website to explain why, and

recommendations on how to choose the best color map. Whenever a build function is called, inputs and settings are processed to get new artists to submit data. These artists are either primitive or their collections. They are called primitives because they are the main components of drawing, such as lines, images,

landfills and text. It is with these primitives that your data can be presented as bar charts, linear areas, error panels or any other types of sites. Matplotlib has four primitive drawings: Line2D, AxesImage, Patch and Text. It is through these primitive artists that all other artist objects are derived from, and they make up

everything that can be drawn into the figure. The Line2D uses a list of coordinates to draw segments of the lines between them. Typically, individual line segments are straight, and curves can be approximated with many vertices; however, curves can be specified for drawing arcs, circles, or any other curves close to

Beziers. The AxesImage class will receive two-dimensional data and coordinates and display an image of that data using the color map applied to it. There are actually other kinds of basic artist images available besides AxesImage, but they are usually for very special applications. AxesImage objects can be very

complex, so it's often best to use the imshow method to create and return these objects. A patch object is an arbitrary two-dimensional object that has one color for its face. A landfill object is a specific instance of a slightly more general patch. These objects have a path (just like the Line2D object) that identifies segments

that will shield a face with one color. The path is known as the edge, and can have its own color as well. In addition to landfills that can be seen for bar areas and circulars, patch objects are also used to create arrows, boxes of legends and markers used in scattering sites and elsewhere. Finally, Text takes the Python

line, the point coordinates, and the various font parameters to form a text that annotates the plots. Matplotlib mainly uses TrueType fonts. It will look for fonts available in your system as well as a ship with multiple FreeType2 fonts, and it uses Faith is the default. In addition, a text object can be defered to LaTeX to

visualize text if necessary. While specific classes of artists will have their own set of properties that make sense for the particular art object they represent, there are a few common properties that can be established. Teh Teh The table is listing some of these properties. Let's expand the radar image by downloading the

already stored storm cell sites in the tutorial.py file. Code: chp1/simple_storm_cell_viewer.py import matplotlib.pyplot as plt from scipy.io import netcdf_file from matplotlib.patches import Polygon from tutorial import polygon_loader ncf = netcdf_file('KTLX_20100510_22Z.nc') data = ncf.variables['Reflectivity'] lats =

ncf.variables['lat'] lons = ncf.variables['lon'] i = 0 cmap = plt.get_cmap('gist_ncar') cmap.set_under('lightgrey') fig, ax = plt.subplots(1, 1) im = ax.imshow(data[i], origin='lower', extent=(lons[0], lons[-1], lats[0], lats[-1]), vmin=0.1, vmax=80, cmap='gist_ncar') cb = fig.colorbar(im) polygons = polygon_loader('polygons.shp') for

poly in polygons[i]: p = Polygon(poly, lw=3, fc='k', ec='w', alpha=0.45) ax.add_artist(p) cb.set_label(Reflectivity (dBZ)) ax.set_xlabel(Longitude) ax.set_ylabel(Latitude) plt.show()The polygon data returned from polygon_loader() is a dictionary of lists keyed by a frame index. The list contains Nx2 numpy arrays of

coordinates of the top in longitude and latitude. Verticals form the contour of a storm cell. The Polygon designer, like all other artist objects, accepts many additional key arguments. First, lw is short for linewidth, (referring to the outline of the landfill), which we specify to be three points wide. Next is fc, which is short for

facecolor, and set to black ('k'). This is the color of the filled area of the landfill. The edge (ec) is then set on white ('w') to help the landfills stand out against a dark background. Finally, we have set an alpha argument to be just under half to make the landfill transparent enough, so you can still see the reflectivity data

under the landfills. Notice a special distinction between how we mapped the image using imshow () and how we mapped out landfills using artists' landfills. For landfills we named a designer and then directly ax.add_artist to add each landfill instance as a baby axial. Meanwhile, imshow is a build feature that will do all the

hard work of checking input, creating an AxesImage instance, making all the necessary changes to the axe instance (such as setting limits and party ratios) and, most importantly, adding an artist's object to axes. Finally, all the features of the build in Matplotlib bring back the artists or list the artist's objects that he creates.

In most cases, you won't need to keep this return value in the variable because there is nothing to do with them. In this case, we only needed a returned AxesImage so that we could pass it on to the fig.colorbar method. It's so that it will know that based on the color panel on. The build features in Matplotlib exist to

provide convenience and simplicity to what can often be very difficult to get right on your own. Not magical! They use the same OO interface that is available to app developers. That's why can write their own build functions to make complex sites easier to perform. Any artist who has children's artists (such as a figure or

axes) is called a container. A special kind of container in Matplotlib is called the Collection. The collection usually contains a list of primitives of the same kind, which should be treated in a similar way. For example, CircleCollection will have a list of Circle objects, all with the same color, size, and edge width. Individual

values for artists in the collection can also be set. The collection makes it easier for many artists to manage. This becomes especially important when considering the number of artist objects that may be required to scatter plots, bar diagrams, or any other site or diagram. Some collections are not just a list of primitives,

but also artists themselves. These special types of collections use different optimizations that can be assumed when visualizing similar or identical things. RegularPolyCollection, for example, just needs to know the points of one landfill relative to its center (such as stars or boxes), and then just need a list of all the central

coordinates, avoiding the need to store all the vertices of each landfill in your collection in memory. In the following example, we'll show storm tracks like LineCollection. Note that instead of ax.add_artist (which will work), we will use ax.add_collection () instead. This has the added benefit of performing special processing

on the object to determine its limit box, so that the axle object can include the limits of this collection with any other objects to automatically set our own limits, which we run with the call ax.autoscale (True). Code: chp1/linecoll_track_viewer.py is much easier than radar images, Matplotlib took care of all the settings of the

limit automatically. These features are extremely useful for writing common applications that don't want to touch themselves with such details. We'll go back to processing LineCollections later in the book as we develop this app. In this chapter, we introduce you to the founding concepts of Matplotlib. Using the show, you

showed your first story with only three Python lines. With this on-screen storyline, you've learned some of the main interactive features built into Matplotlib, such as panning, zooming, and a host of key bindings that are available. We then discussed the difference between interactive and non-interactive build modes and

the difference between script and interactive construction. Now you know where to go online for more information, examples and forums of Discussions Matplotlib when it comes time for you to work on your next Matplotlib project. Next, we discussed the architectural concepts of Matplotlib: backends, figures, axes and

artists. Then we started Construction project for this book, an interactive application tracking storm cells. We've seen how to build a radar image using existing function, as well as how to display landfills and lines as artists and collections. When creating these objects, we had an idea of how to customize the properties of

these objects for our display needs by studying some properties and laying names. We've also learned some of the steps you need to consider when creating your own build features, such as auto-scala. In the next chapter we'll learn how to expand the basic interactivity of Matplotlib by adding our own features and

controls to make a truly interactive app. More Unlock this book with a free 10-day trial matplotlib interactive plot example. matplotlib interactive mode example. matplotlib interactive jupyter example

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