Create dynamic updated graph with Python

Question

I need to write a script in Python that will take dynamically changed data, the source of data is not matter here, and display graph on the screen.

I know how to use matplotlib, but the problem with matplotlib is that I can display graph only once, at the end of the script. I need to be able not only to display graph one time, but also update it on the fly, each time when data changes.

I found that it is possible to use wxPython with matplotlib to do this, but it is little bit complicate to do this for me, because I am not familiar with wxPython at all.

So I will be very happy if someone will show me simple example how to use wxPython with matplotlib to show and update simple graph. Or, if it is some other way to do this, it will be good to me too.

Update

PS: since no one answered and looked at matplotlib help noticed by @janislaw and wrote some code. This is some dummy example:

import time
import matplotlib.pyplot as plt


def data_gen():
    a=data_gen.a
    if a>10:
        data_gen.a=1
    data_gen.a=data_gen.a+1
    return range (a,a+10)
    
def run(*args):
    background = fig.canvas.copy_from_bbox(ax.bbox)

    while 1:
        time.sleep(0.1)
        # restore the clean slate background
        fig.canvas.restore_region(background)
        # update the data
        ydata = data_gen()
        xdata=range(len(ydata))

        line.set_data(xdata, ydata)

        # just draw the animated artist
        ax.draw_artist(line)
        # just redraw the axes rectangle
        fig.canvas.blit(ax.bbox)

data_gen.a=1
fig = plt.figure()
ax = fig.add_subplot(111)
line, = ax.plot([], [], animated=True)
ax.set_ylim(0, 20)
ax.set_xlim(0, 10)
ax.grid()

manager = plt.get_current_fig_manager()
manager.window.after(100, run)

plt.show()

This implementation have problems, like script stops if you trying to move the window. But basically it can be used.

Answer 1

Here is a class I wrote that handles this issue. It takes a matplotlib figure that you pass to it and places it in a GUI window. Its in its own thread so that it stays responsive even when your program is busy.

import Tkinter
import threading
import matplotlib
import matplotlib.backends.backend_tkagg

class Plotter():
    def __init__(self,fig):
        self.root = Tkinter.Tk()
        self.root.state("zoomed")

        self.fig = fig
        t = threading.Thread(target=self.PlottingThread,args=(fig,))
        t.start()

    def PlottingThread(self,fig):     
        canvas = matplotlib.backends.backend_tkagg.FigureCanvasTkAgg(fig, master=self.root)
        canvas.show()
        canvas.get_tk_widget().pack(side=Tkinter.TOP, fill=Tkinter.BOTH, expand=1)

        toolbar = matplotlib.backends.backend_tkagg.NavigationToolbar2TkAgg(canvas, self.root)
        toolbar.update()
        canvas._tkcanvas.pack(side=Tkinter.TOP, fill=Tkinter.BOTH, expand=1)

        self.root.mainloop()

In your code, you need to initialize the plotter like this:

import pylab
fig = matplotlib.pyplot.figure()
Plotter(fig)

Then you can plot to it like this:

fig.gca().clear()
fig.gca().plot([1,2,3],[4,5,6])
fig.canvas.draw()

Answer 2

As an alternative to matplotlib, the Chaco library provides nice graphing capabilities and is in some ways better-suited for live plotting.

See some screenshots here, and in particular, see these examples:

• data_stream.py

• spectrum.py

Chaco has backends for qt and wx, so it handles the underlying details for you rather nicely most of the time.

Answer 3

Instead of matplotlib.pyplot.show() you can just use matplotlib.pyplot.show(block=False). This call will not block the program to execute further.

Answer 4

example of dynamic plot , the secret is to do a pause while plotting , here i use networkx:

    G.add_node(i,)
    G.add_edge(vertic[0],vertic[1],weight=0.2)
    print "ok"
    #pos=nx.random_layout(G)
    #pos = nx.spring_layout(G)
    #pos = nx.circular_layout(G)
    pos = nx.fruchterman_reingold_layout(G)

    nx.draw_networkx_nodes(G,pos,node_size=40)
    nx.draw_networkx_edges(G,pos,width=1.0)
    plt.axis('off') # supprimer les axes

    plt.pause(0.0001)
    plt.show()  # display

Answer 5

I had the need to create a graph that updates with time. The most convenient solution I came up was to create a new graph each time. The issue was that the script won't be executed after the first graph is created, unless the window is closed manually. That issue was avoided by turning the interactive mode on as shown below

    for i in range(0,100): 
      fig1 = plt.figure(num=1,clear=True) # a figure is created with the id of 1
      createFigure(fig=fig1,id=1) # calls a function built by me which would insert data such that figure is 3d scatterplot
      plt.ion() # this turns the interactive mode on
      plt.show() # create the graph
      plt.pause(2) # pause the script for 2 seconds , the number of seconds here determine the time after that graph refreshes

There are two important points to note here

1. id of the figure - if the id of the figure is changed a new graph will be created every time, but if it is same it relevant graph would be updated.
2. pause function - this stops the code from executing for the specified time period. If this is not applied graph will refresh almost immediately

Answer 6

I have created a class that draws a tkinter widget with a matplotlib plot. The plot is updated dynamically (more or less in realtime).

• Tested in python 3.10, matplotlib 3.6.0 and tkinter 8.6.

from matplotlib import pyplot as plt
from matplotlib import animation
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk

from tkinter import *


class MatplotlibPlot:
    def __init__(
            self, master, datas: list[dict], update_interval_ms: int = 200, padding: int = 5,
            fig_config: callable = None, axes_config: callable = None
    ):
        """
        Creates a Matplotlib plot in a Tkinter environment. The plot is dynamic, i.e., the plot data is periodically
        drawn and the canvas updates.
        @param master: The master widget where the pot will be rendered.
        @param datas: A list containing dictionaries of data. Each dictionary must have a `x` key, which holds the xx
        data, and `y` key, which holds the yy data. The other keys are optional and are used as kwargs of
        `Axes.plot()` function. Each list entry, i.e., each dict, is drawn as a separate line.
        @param fig_config: A function that is called after the figure creation. This function can be used to configure
        the figure. The function signature is `fig_config(fig: pyplot.Figure) -> None`. The example bellow allows
        the configuration of the figure title and Dots Per Inch (DPI).
        ``` python
        my_vars = [{"x": [], "y": [], "label": "Label"}, ]

        window = Tk()

        def my_fig_config(fig: pyplot.Figure) -> None:
            fig.suptitle("Superior Title")
            fig.set_dpi(200)

        MatplotlibPlot(master=window, datas=my_vars, fig_config=my_fig_config)

        window.mainloop()
        ```
        @param axes_config: A function that is called after the axes creation. This function can be used to configure
        the axes. The function signature is `axes_config(axes: pyplot.Axes) -> None`. The example bellow allows
        the configuration of the axes xx and yy label, the axes title and also enables the axes legend.
        ``` python
        my_vars = [{"x": [], "y": [], "label": "Label"}, ]

        window = Tk()

        def my_axes_config(axes: pyplot.Axes) -> None:
            axes.set_xlabel("XX Axis")
            axes.set_ylabel("YY Axis")
            axes.set_title("Axes Title")
            axes.legend()

        MatplotlibPlot(master=window, datas=my_vars, axes_config=my_axes_config)

        window.mainloop()
        ```
        @param update_interval_ms: The plot update interval in milliseconds (ms). Defaults to 200 ms.
        @param padding: The padding, in pixels (px), to be used between widgets. Defaults to 5 px.
        """

        # Creates the figure
        fig = plt.Figure()
        # Calls the config function if passed
        if fig_config:
            fig_config(fig)

        # Creates Tk a canvas
        canvas = FigureCanvasTkAgg(figure=fig, master=master)
        # Allocates the canvas
        canvas.get_tk_widget().pack(side=TOP, fill=BOTH, expand=True, padx=padding, pady=padding)
        # Creates the toolbar
        NavigationToolbar2Tk(canvas=canvas, window=master, pack_toolbar=True)

        # Creates an axes
        axes = fig.add_subplot(1, 1, 1)

        # For each data entry populate the axes with the initial data values. Also, configures the lines with the
        # extra key-word arguments.
        for data in datas:
            axes.plot(data["x"], data["y"])
            _kwargs = data.copy()
            _kwargs.pop("x")
            _kwargs.pop("y")
            axes.lines[-1].set(**_kwargs)

        # Calls the config function if passed
        if axes_config:
            axes_config(axes)

        # Creates a function animation which calls self.update_plot function.
        self.animation = animation.FuncAnimation(
            fig=fig,
            func=self.update_plot,
            fargs=(canvas, axes, datas),
            interval=update_interval_ms,
            repeat=False,
            blit=True
        )

    # noinspection PyMethodMayBeStatic
    def update_plot(self, _, canvas, axes, datas):
        # Variables used to update xx and yy axes limits.
        update_canvas = False
        xx_max, xx_min = axes.get_xlim()
        yy_max, yy_min = axes.get_ylim()

        # For each data entry update its correspondent axes line
        for line, data in zip(axes.lines, datas):
            line.set_data(data["x"], data["y"])
            _kwargs = data.copy()
            _kwargs.pop("x")
            _kwargs.pop("y")
            line.set(**_kwargs)

            # If there are more than two points in the data then update xx and yy limits.
            if len(data["x"]) > 1:
                if min(data["x"]) < xx_min:
                    xx_min = min(data["x"])
                    update_canvas = True
                if max(data["x"]) > xx_max:
                    xx_max = max(data["x"])
                    update_canvas = True
                if min(data["y"]) < yy_min:
                    yy_min = min(data["y"])
                    update_canvas = True
                if max(data["y"]) > yy_max:
                    yy_max = max(data["y"])
                    update_canvas = True

        # If limits need to be updates redraw canvas
        if update_canvas:
            axes.set_xlim(xx_min, xx_max)
            axes.set_ylim(yy_min, yy_max)
            canvas.draw()

        # return the lines
        return axes.lines

Below is an example of a custom tkinter scale used to update data which is drawn in the tkinter plot.

from matplotlib import pyplot as plt
from matplotlib import animation
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk

from tkinter import *


class MatplotlibPlot:
    def __init__(
            self, master, datas: list[dict], update_interval_ms: int = 200, padding: int = 5,
            fig_config: callable = None, axes_config: callable = None
    ):
        """
        Creates a Matplotlib plot in a Tkinter environment. The plot is dynamic, i.e., the plot data is periodically
        drawn and the canvas updates.
        @param master: The master widget where the pot will be rendered.
        @param datas: A list containing dictionaries of data. Each dictionary must have a `x` key, which holds the xx
        data, and `y` key, which holds the yy data. The other keys are optional and are used as kwargs of
        `Axes.plot()` function. Each list entry, i.e., each dict, is drawn as a separate line.
        @param fig_config: A function that is called after the figure creation. This function can be used to configure
        the figure. The function signature is `fig_config(fig: pyplot.Figure) -> None`. The example bellow allows
        the configuration of the figure title and Dots Per Inch (DPI).
        ``` python
        my_vars = [{"x": [], "y": [], "label": "Label"}, ]

        window = Tk()

        def my_fig_config(fig: pyplot.Figure) -> None:
            fig.suptitle("Superior Title")
            fig.set_dpi(200)

        MatplotlibPlot(master=window, datas=my_vars, fig_config=my_fig_config)

        window.mainloop()
        ```
        @param axes_config: A function that is called after the axes creation. This function can be used to configure
        the axes. The function signature is `axes_config(axes: pyplot.Axes) -> None`. The example bellow allows
        the configuration of the axes xx and yy label, the axes title and also enables the axes legend.
        ``` python
        my_vars = [{"x": [], "y": [], "label": "Label"}, ]

        window = Tk()

        def my_axes_config(axes: pyplot.Axes) -> None:
            axes.set_xlabel("XX Axis")
            axes.set_ylabel("YY Axis")
            axes.set_title("Axes Title")
            axes.legend()

        MatplotlibPlot(master=window, datas=my_vars, axes_config=my_axes_config)

        window.mainloop()
        ```
        @param update_interval_ms: The plot update interval in milliseconds (ms). Defaults to 200 ms.
        @param padding: The padding, in pixels (px), to be used between widgets. Defaults to 5 px.
        """

        # Creates the figure
        fig = plt.Figure()
        # Calls the config function if passed
        if fig_config:
            fig_config(fig)

        # Creates Tk a canvas
        canvas = FigureCanvasTkAgg(figure=fig, master=master)
        # Allocates the canvas
        canvas.get_tk_widget().pack(side=TOP, fill=BOTH, expand=True, padx=padding, pady=padding)
        # Creates the toolbar
        NavigationToolbar2Tk(canvas=canvas, window=master, pack_toolbar=True)

        # Creates an axes
        axes = fig.add_subplot(1, 1, 1)

        # For each data entry populate the axes with the initial data values. Also, configures the lines with the
        # extra key-word arguments.
        for data in datas:
            axes.plot(data["x"], data["y"])
            _kwargs = data.copy()
            _kwargs.pop("x")
            _kwargs.pop("y")
            axes.lines[-1].set(**_kwargs)

        # Calls the config function if passed
        if axes_config:
            axes_config(axes)

        # Creates a function animation which calls self.update_plot function.
        self.animation = animation.FuncAnimation(
            fig=fig,
            func=self.update_plot,
            fargs=(canvas, axes, datas),
            interval=update_interval_ms,
            repeat=False,
            blit=True
        )

    # noinspection PyMethodMayBeStatic
    def update_plot(self, _, canvas, axes, datas):
        # Variables used to update xx and yy axes limits.
        update_canvas = False
        xx_max, xx_min = axes.get_xlim()
        yy_max, yy_min = axes.get_ylim()

        # For each data entry update its correspondent axes line
        for line, data in zip(axes.lines, datas):
            line.set_data(data["x"], data["y"])
            _kwargs = data.copy()
            _kwargs.pop("x")
            _kwargs.pop("y")
            line.set(**_kwargs)

            # If there are more than two points in the data then update xx and yy limits.
            if len(data["x"]) > 1:
                if min(data["x"]) < xx_min:
                    xx_min = min(data["x"])
                    update_canvas = True
                if max(data["x"]) > xx_max:
                    xx_max = max(data["x"])
                    update_canvas = True
                if min(data["y"]) < yy_min:
                    yy_min = min(data["y"])
                    update_canvas = True
                if max(data["y"]) > yy_max:
                    yy_max = max(data["y"])
                    update_canvas = True

        # If limits need to be updates redraw canvas
        if update_canvas:
            axes.set_xlim(xx_min, xx_max)
            axes.set_ylim(yy_min, yy_max)
            canvas.draw()

        # return the lines
        return axes.lines


class CustomScaler:
    def __init__(self, master, init: int = None, start: int = 0, stop: int = 100,
                 padding: int = 5, callback: callable = None):
        """
        Creates a scaler with an increment and decrement button and a text entry.
        @param master: The master Tkinter widget.
        @param init: The scaler initial value.
        @param start: The scaler minimum value.
        @param stop: The scaler maximum value.
        @param padding: The widget padding.
        @param callback: A callback function that is called each time that the scaler changes its value. The function
        signature is `callback(var_name: str, var_index: int, var_mode: str) -> None`.
        """
        self.start = start
        self.stop = stop

        if init:
            self.value = IntVar(master=master, value=init, name="scaler_value")
        else:
            self.value = IntVar(master=master, value=(self.stop - self.start) // 2, name="scaler_value")

        if callback:
            self.value.trace_add("write", callback=callback)

        Scale(master=master, from_=self.start, to=self.stop, orient=HORIZONTAL, variable=self.value) \
            .pack(side=TOP, expand=True, fill=BOTH, padx=padding, pady=padding)
        Button(master=master, text="◀", command=self.decrement, repeatdelay=500, repeatinterval=5) \
            .pack(side=LEFT, fill=Y, padx=padding, pady=padding)
        Button(master=master, text="▶", command=self.increment, repeatdelay=500, repeatinterval=5) \
            .pack(side=RIGHT, fill=Y, padx=padding, pady=padding)
        Entry(master=master, justify=CENTER, textvariable=self.value) \
            .pack(fill=X, expand=False, padx=padding, pady=padding)

    def decrement(self):
        _value = self.value.get()
        if _value <= self.start:
            return
        self.value.set(_value - 1)

    def increment(self):
        _value = self.value.get()
        if _value >= self.stop:
            return
        self.value.set(_value + 1)


def scaler_changed(my_vars: list[dict], scaler: CustomScaler) -> None:
    my_vars[0]["x"].append(len(my_vars[0]["x"]))
    my_vars[0]["y"].append(scaler.value.get())


def my_axes_config(axes: plt.Axes) -> None:
    axes.set_xlabel("Sample")
    axes.set_ylabel("Value")
    axes.set_title("Scaler Values")


def main():
    my_vars = [{"x": [], "y": []}, ]

    window = Tk()
    window.rowconfigure(0, weight=10)
    window.rowconfigure(1, weight=90)

    frame_scaler = Frame(master=window)
    frame_scaler.grid(row=0, column=0)
    scaler = CustomScaler(
        master=frame_scaler, start=0, stop=100, callback=lambda n, i, m: scaler_changed(my_vars, scaler)
    )

    frame_plot = Frame(master=window)
    frame_plot.grid(row=1, column=0)
    MatplotlibPlot(master=frame_plot, datas=my_vars, axes_config=my_axes_config, update_interval_ms=10)

    window.mainloop()


if __name__ == "__main__":
    main()

The example above produces the following window.