如何在 matplotlib 中制作两个滑块 [英] How to make two sliders in matplotlib

问题描述

I would like to make two sliders in matplotlib to manually change N and P values in my predator-prey model:

import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint

def lotka(x,t,params):
    N, P = x 
    alpha, beta, gamma, delta = params 
    derivs = [alpha*N - beta*N*P, gamma*N*P - delta*P] 
    return derivs

N=2
P=1
alpha=3
beta=0.5
gamma=0.4
delta=3

params = [alpha, beta, gamma, delta]
x0=[N,P]
maxt = 20
tstep = 0.01

t=np.arange(0,maxt,tstep)
equation=odeint(lotka, x0, t, args=(params,))

plt.plot(t,equation)
plt.xlabel("Time")
plt.ylabel("Population size")
plt.legend(["Prey", "Predator"], loc="upper right")

plt.title('Prey & Predator Static Model')
plt.grid(color="b", alpha=0.5, linestyle="dashed", linewidth=0.5)

This is my code which produces a graph for fixed initial values of N and P. However, I'd like to change them to see how the plot changes. And for this, I'd like to use sliders like: http://matplotlib.org/users/screenshots.html#slider-demo but I do not know how to add this into my code...

Could anyone please give me any direction? Many thanks!! xx

解决方案

From the example, hope the comments help you understand what's what:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons
from scipy.integrate import odeint

# Function to draw
def lotka(x, t, params):
    N, P = x
    alpha, beta, gamma, delta = params 
    derivs = [alpha*N - beta*N*P, gamma*N*P - delta*P] 
    return derivs

# Parameters
Nmin = 1
Nmax = 100
Pmin = 1
Pmax = 100
N0 = 2
P0 = 1
alpha = 3
beta = 0.5
gamma = 0.4
delta = 3

params = [alpha, beta, gamma, delta]
x0=[N0,P0]
maxt = 20
tstep = 0.01

# Initial function values
t = np.arange(0, maxt, tstep)
prey, predator = odeint(lotka, x0, t, args=(params,)).T
# odeint returne a shape (2000, 2) array, with the value for
# each population in [[n_preys, n_predators], ...]
# The .T at the end transponses the array, so now we get each population
# over time in each line of the resultint (2, 2000) array.

# Create a figure and an axis to plot in:
fig = plt.figure()
ax = fig.add_axes([0.10, 0.3, 0.8, 0.6])
prey_plot = ax.plot(t, prey, label="Prey")[0]
predator_plot = ax.plot(t, predator, label="Predator")[0]

ax.set_xlabel("Time")
ax.set_ylabel("Population size")
ax.legend(loc="upper right")
ax.set_title('Prey & Predator Static Model')
ax.grid(color="b", alpha=0.5, linestyle="dashed", linewidth=0.5)
ax.set_ylim([0, np.max([prey, predator])])

# create a space in the figure to place the two sliders:
axcolor = 'lightgoldenrodyellow'
axis_N = fig.add_axes([0.10, 0.1, 0.8, 0.03], facecolor=axcolor)
axis_P = fig.add_axes([0.10, 0.15, 0.8, 0.03], facecolor=axcolor)
# the first argument is the rectangle, with values in percentage of the figure
# size: [left, bottom, width, height]

# create each slider on its corresponding place:
slider_N = Slider(axis_N, 'N', Nmin, Nmax, valinit=N0)
slider_P = Slider(axis_P, 'P', Pmin, Pmax, valinit=P0)

def update(val):
    # retrieve the values from the sliders
    x = [slider_N.val, slider_P.val]
    # recalculate the function values
    prey, predator = odeint(lotka, x, t, args=(params,)).T
    # update the value on the graph
    prey_plot.set_ydata(prey)
    predator_plot.set_ydata(predator)
    # redraw the graph
    fig.canvas.draw_idle()
    ax.set_ylim([0, np.max([prey, predator])])

# set both sliders to call update when their value is changed:
slider_N.on_changed(update)
slider_P.on_changed(update)

# create the reset button axis (where its drawn)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
# and the button itself
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')

def reset(event):
    slider_N.reset()
    slider_P.reset()

button.on_clicked(reset)

Notice, however, you should have shown how you tried to adapt the example to what you had and how it was misbehaving.

Nevertheless, welcome to Stackoverflow.

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