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Struggling to get widgets working in python

I don't know why but I am really struggling to get widgets working well in python. I try to look at examples about how to use them but I don't know how to extrapolate that to get it to work with my code. I am trying to get a figure to display widgets such that the type, frequency, phase, and other variables adjust the graph itself.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.widgets as widgets
from scipy import signal
from matplotlib.widgets import RadioButtons
A = 1
ang_f = 5
t = np.linspace(0, 4*np.pi, 1000)
phase = 0
s0 = A*np.sin(ang_f*t + phase)
s2 = A*signal.sawtooth(ang_f*t + phase)
s1 = A*signal.square(ang_f*t + phase)
fig, ax = plt.subplots()
l, = ax.plot(t, s1, lw=2, color='red')
plt.subplots_adjust(left=0.4)
def sinf(x, omega):
return np.sin(omega*x)
def sliderCallback(val):
# """ 'val' is the current value selected by the slider
# Recalculate sine values with val as the frequency """
axesHandle.set_ydata(sinf(x, val))
plt.draw() # Redraw the axes
def clickcallback(val):
# 'val' is the current value selected by the slider
# Recalculate sine values with val as the frequency
axesHandle.set_ydata(sinf(x, val))
plt.draw() # Redraw the axes
def closeCallback(event):
plt.close('all') # Close all open figure windows
fig = plt.figure(figsize=(7, 5))
ax = plt.axes([0.1, 0.2, 0.6, 0.7])
axesHandle, = plt.plot(x, sinf(x, 1), lw=2, color='red')
# Add axis to contain the slider
fax = plt.axes([0.1, 0.04, 0.35, 0.03]) # Frequency
tax = plt.axes([0.1, 0.12, 0.35, 0.03]) # Time
sax_3 = plt.axes([0.60, 0.1, 0.35, 0.03]) # Number of points
pax = plt.axes([0.60, 0.05, 0.35, 0.03]) # Phase
rax = plt.axes([0.85, 0.65, 0.12, 0.15]) # Type
bax = plt.axes([0.85, 0.85, 0.1, 0.1]) # Close
pointshandle = widgets.Slider(sax_3, 'Number of points', 1, 200,
valfmt='%0.0f')
pointshandle.on_changed(sliderCallback)
graphchoice = widgets.RadioButtons(rax, ('Sine', 'Squarewave', 'Sawtooth'))
graphchoice.on_clicked(clickcallback)
freqhandle = widgets.Slider(fax, 'Frequancy (Hz)', 0, 5, valinit=1)
freqhandle.on_changed(sliderCallback)
phasehandle = widgets.Slider(pax, 'Phase', 0, 0*np.pi, valinit=0)
phasehandle.on_changed(sliderCallback)
timehandle = widgets.Slider(tax, 'Time (s)', 1, 10, valinit=1)
timehandle.on_changed(sliderCallback)
buttonHandle = widgets.Button(bax, 'Close')
buttonHandle.on_clicked(closeCallback)
def hzfunc(label):
hzdict = {'Sine': s0, 'Squarewave': s1, 'Sawtooth': s2}
ydata = hzdict[label]
l.set_ydata(ydata)
plt.draw()
graphchoice.on_clicked(hzfunc)
I'm really lost so any tips to put me on the right path would be much appreciated, im just so confused atm.

Put a slider right under a subplot in matplotlib

I'm trying to put a slider right under the x-axis of a subplot in matplotlib, so that both start and end at the same value. Is there an easy way to do that, meaning that I don't have to find the right coordinates and put them myself when I create the plt.axe containing the slider?

You could use ax.get_position() to get x0, y0, width and height of the axis and use this to define the positions for the axes of the slider.
I adapted the matplotlib example to show a use case:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.3)
t = np.arange(0.0, 1.0, 0.001)
a0 = 5
f0 = 3
delta_f = 5.0
s = a0 * np.sin(2 * np.pi * f0 * t)
l, = plt.plot(t, s, lw=2)
ax.margins(x=0)
axcolor = 'lightgoldenrodyellow'
def xaligned_axes(ax, y_distance, width, **kwargs):
return plt.axes([ax.get_position().x0,
ax.get_position().y0-y_distance,
ax.get_position().width, width],
**kwargs)
axfreq = xaligned_axes(ax=ax, y_distance=0.1, width=0.03, facecolor=axcolor)
axamp = xaligned_axes(ax=ax, y_distance=0.15, width=0.03, facecolor=axcolor)
sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0, valstep=delta_f)
samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
def update(val):
amp = samp.val
freq = sfreq.val
l.set_ydata(amp*np.sin(2*np.pi*freq*t))
fig.canvas.draw_idle()
sfreq.on_changed(update)
samp.on_changed(update)
But as you have to use plt.subplots_adjust(bottom=0.3) to have enough space below the plot and you need to define the width and the distance to the axis in y direction I guess you do not win that much.

Use slider for two different lines in one subplot matplotlib

I am plotting six subplots in one figure in matplotlib. I am plotting one function with different parameter values using matplotlib sliders. There are two lines in one subplot, each representing function. I want to see where they cross and how they behave if I am changing function parameters. But I havent figured out how to plot two lines and change ydata for two lines in one subplot with sliders.
Here is part of code:
#imports
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.widgets as mw
import math
import numpy as np
#set variables
E0 = 0.5
E1 = .0003
V = .3
#x axis
N = [i for i in range(10000)]
#functions
V_fc_list = [V for n in N]
E_list = [E0*math.exp(-E1*n) for n in N]
#subplots
fig = plt.figure()
ax1 = fig.add_subplot(321)
ax2 = fig.add_subplot(322)
ax3 = fig.add_subplot(323)
ax4 = fig.add_subplot(324)
ax5 = fig.add_subplot(325)
ax6 = fig.add_subplot(326)
#sliders place
ax6.axis('off')
#Sliders
axis_color = 'lightgoldenrodyellow'
E0_slider_ax = fig.add_axes([0.57, 0.3, 0.3, 0.02], axisbg=axis_color)
E1_slider_ax = fig.add_axes([0.57, 0.25, 0.3, .02], axisbg = axis_color)
V_slider_ax = fig.add_axes([0.57, 0.2, 0.3, .02], axisbg = axis_color)
E0_slider = mw.Slider(E0_slider_ax, r'$\epsilon_0$', valmin = 0, valmax = 1, valinit = E0)
E0_slider.label.set_size(15)
E1_slider = mw.Slider(E1_slider_ax, r'$\epsilon_1$', 0.0001, 0.003, valinit = E1)
E1_slider.label.set_size(15)
V_slider = mw.Slider(V_slider_ax, r'$V_c$', 0.001, 0.99, valinit = V)
V_slider.label.set_size(15)
#slider function HERE IS THE MISTAKE
def sliders_on_change(val):
p2.set_ydata([V_slider.val for n in N])
p2.set_ydata([E0_slider.val*math.exp(-E1_slider.val*n) for n in N])
fig.canvas.draw_idle()
V_slider.on_changed(sliders_on_change)
E0_slider.on_changed(sliders_on_change)
E1_slider.on_changed(sliders_on_change)
Here is the last part of error from python
File "C:/Users/Robert/Desktop/python/multidif_S.py", line 109, in <module>
p2,= ax2.plot(N, V_fc_list, 'r-', N, E_list, 'b-', lw = 3)
ValueError: too many values to unpack (expected 1)
Thx for any help!

I changed only a few things in your code to get this:
#imports
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.widgets as mw
import math
import numpy as np
#set variables
E0 = 0.5
E1 = .0003
V = .3
#x axis
N = [i for i in range(10000)]
#functions
V_fc_list = [V for n in N]
E_list = [E0*math.exp(-E1*n) for n in N]
#subplots
fig = plt.figure()
ax1 = fig.add_subplot(321)
ax2 = fig.add_subplot(322)
ax3 = fig.add_subplot(323)
ax4 = fig.add_subplot(324)
ax5 = fig.add_subplot(325)
ax6 = fig.add_subplot(326)
#sliders place
ax6.axis('off')
#Sliders
axis_color = 'lightgoldenrodyellow'
E0_slider_ax = fig.add_axes([0.57, 0.3, 0.3, 0.02], axisbg=axis_color)
E1_slider_ax = fig.add_axes([0.57, 0.25, 0.3, .02], axisbg = axis_color)
V_slider_ax = fig.add_axes([0.57, 0.2, 0.3, .02], axisbg = axis_color)
E0_slider = mw.Slider(E0_slider_ax, r'$\epsilon_0$', valmin = 0, valmax = 1, valinit = E0)
E0_slider.label.set_size(15)
E1_slider = mw.Slider(E1_slider_ax, r'$\epsilon_1$', 0.0001, 0.003, valinit = E1)
E1_slider.label.set_size(15)
V_slider = mw.Slider(V_slider_ax, r'$V_c$', 0.001, 0.99, valinit = V)
V_slider.label.set_size(15)
# Here I introduce the plots p1 and p2. Your code didn't have any plots.
p1 = ax1.plot(np.zeros_like(N)) # plot1
p2 = ax1.plot(np.zeros_like(N)) # plot2, both in ax1
#slider function HERE IS THE MISTAKE
def sliders_on_change(val):
p1[0].set_ydata([V_slider.val for n in N]) # update p1
p2[0].set_ydata([E0_slider.val*math.exp(-E1_slider.val*n) for n in N]) # update p2
ax1.relim() # rescale the shown area (like an automatic call of ax1.set_xlim and ax1.set_ylim with proper inputs)
ax1.autoscale_view() # taken from this question: http://stackoverflow.com/questions/10984085/automatically-rescale-ylim-and-xlim-in-matplotlib
fig.canvas.draw()
V_slider.on_changed(sliders_on_change)
E0_slider.on_changed(sliders_on_change)
E1_slider.on_changed(sliders_on_change)
You see I commented where I changed something. Mostly this was adding the plots p1 and p2 and rescaling on update.
I hope this works for you. I can't help you with that error code because the code you provided does not even have 109 lines...

If you have two lines in a plot, you need to unpack them to two different variables,
p2,p3 = ax2.plot(N, V_fc_list, 'r-', N, E_list, 'b-', lw = 3)
You can then set the data on the two lines as follows:
def sliders_on_change(val):
p3.set_ydata([V_slider.val for n in N])
p2.set_ydata([E0_slider.val*math.exp(-E1_slider.val*n) for n in N])
fig.canvas.draw_idle()

How to make two sliders in matplotlib

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

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.

So, I have tried with this code:
from scipy import integrate
from matplotlib.widgets import Slider, Button, RadioButtons
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.25, bottom=0.25)
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)
l1, l2 = plt.plot(t, equation)
axcolor = 'b'
ax_N = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
ax_P = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
sN = Slider(ax_N, 'N', 0, 80, valinit=1)
sP = Slider(ax_P, 'P', 0, 80, valinit=1)
def update(val):
N = N*sN.val
P = P*sP.val
x = equation
fig.canvas.draw_idle()
l1, l2.set_ydata(y)
ax.set_ylim(y.min(), y.max())
draw()
sN.on_changed(update)
sP.on_changed(update)
plt.show()
I could not manipulate the sliders. Thank you so much #berna1111

3d interactive graph won't update

I was using this code to create an interactive plot (2d), and it works.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.25, bottom=0.25)
t = np.arange(0.0, 1.0, 0.001)
a0 = 5
f0 = 3
s = a0*np.sin(2*np.pi*f0*t)
l, = plt.plot(t, s, lw=2, color='red')
plt.axis([0, 1, -10, 10])
axcolor = 'lightgoldenrodyellow'
axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
axamp = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0)
samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
def update(val):
amp = samp.val
freq = sfreq.val
l.set_ydata(amp*np.sin(2*np.pi*freq*t))
fig.canvas.draw_idle()
sfreq.on_changed(update)
samp.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
sfreq.reset()
samp.reset()
button.on_clicked(reset)
rax = plt.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)
radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
def colorfunc(label):
l.set_color(label)
fig.canvas.draw_idle()
radio.on_clicked(colorfunc)
plt.show()
I then tried to modify it to create an interactive 3d plot by simply changing the axes to axes3d. I added the import statement shown below and replaced the definition of "fig" and "ax" with those shown below to become 3d.
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
The plot no longer updates, and I can't figure out why. It seems that the function fig.canvas.draw_idle() doesn't work on 3d graphs, but I don't have another way of updating the graph.
Any help would be appreciated,
Thanks!

You can see that the function fig.canvas.draw_idle() does exist and works as expected by looking at the colors which become updated.
The problem lies in the set_ydata function, which works differently in 3d space.
Assuming that you want the y coordinate to update and the z-coordinate to be constant, set_data will be given the constant values, while an additional property set_3d_properties() needs to be set to control the y-coordinate.
Here is the working example code:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.widgets import Slider, Button, RadioButtons
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
plt.subplots_adjust(left=0.25, bottom=0.25)
t = np.arange(0.0, 1.0, 0.001)
### create constant z-coordinate
z = np.zeros_like(t) # <------------ here
a0 = 5
f0 = 3
s = a0*np.sin(2*np.pi*f0*t)
l, = plt.plot(t, s, lw=2, color='red')
plt.axis([0, 1, -10, 10])
axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg="w")
axamp = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg="w")
sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0)
samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
def update(val):
amp = samp.val
freq = sfreq.val
#set constant z coordinate
l.set_data(t, z) # <------------ here
# set values to y-coordinate
l.set_3d_properties(amp*np.sin(2*np.pi*freq*t), zdir="y") #<------------ here
fig.canvas.draw_idle()
sfreq.on_changed(update)
samp.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
sfreq.reset()
samp.reset()
button.on_clicked(reset)
rax = plt.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)
radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
def colorfunc(label):
l.set_color(label)
fig.canvas.draw_idle()
radio.on_clicked(colorfunc)
plt.show()