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.
Related
This is a demo from the document of matplotlib
Scatter plot on polar axis
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
# Compute areas and colors
N = 150
r = 2 * np.random.rand(N)
theta = 2 * np.pi * np.random.rand(N)
area = 200 * r**2
colors = theta
fig = plt.figure()
ax = fig.add_subplot(projection='polar')
c = ax.scatter(theta, r, c=colors, s=area, cmap='hsv', alpha=0.75)
Now I want to replace these dots with some texts, like
What modification should I do to these code?
Further more, I also want to put picture instead of texts, is that possible?
Thanks!!!
This is the original code:
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
# Compute areas and colors
N = 150
r = 2 * np.random.rand(N)
theta = 2 * np.pi * np.random.rand(N)
area = 200 * r**2
colors = theta
fig = plt.figure()
ax = fig.add_subplot(projection='polar')
c = ax.scatter(theta, r, c=colors, s=area, cmap='hsv', alpha=0.75)
If you add this two lines:
plt.text(0.67, 0.9, 'I am cartesian coordinate', transform=plt.gcf().transFigure)
plt.text(np.pi, r[len(r)-1], 'I am polar coordinate')
You will get
and if you add this code:
im = Image.open('smurf.png')
newax = fig.add_axes([0.5, 0.5, 0.2, 0.2], zorder=1)
newax.imshow(im)
newax.axis('off')
newax = fig.add_axes([0.3, 0.3, 0.2, 0.2], zorder=1)
newax.imshow(im)
newax.axis('off')
You will get
But it requires conversion calculation to get to polar coordinate
You remove the ax.scatter part and instead use ax.text. But be aware that you need to pass the coordinates for the text also in polar coordinates. E.g.:
ax.text(np.pi / 2, 60, 'people', fontsize=20, color='red').
Here you go:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(projection='polar')
for text,xytext, color in zip(*[['cat', 'car', 'people'],[(0.5, 0.3),(0.5, 0.7),(0.1, 0.5)],['b', 'g','r']]):
ax.annotate(text,
xy=(0,0), # theta, radius
xytext=xytext, # fraction, fraction
textcoords='figure fraction',
horizontalalignment='left',
verticalalignment='bottom',
color=color,
size=20
)
plt.show()
For inserting images there is the following demo.
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.
I have the following equation:
y = ((b-6(x**k))/c)**(1/k)
k = 10/(6+c)
I know that when k > 1 then y is concave and when 0 < k < 1 then y is convex. However, the problem is that in the generated plot it does not matter whatever the value of k is, it always generates a concave y. I was wondering if anybody can help me to figure out what is the problem.
Codes to generate the dynamic plot:
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)
x = np.arange(0.0, 1.0, 0.001)
b_init = 1
c_init = 0
k = 10/(6+c_init)
delta_f = 1.0
y = ((b_init-6*(x**k))/c_init)**(1/k)
l, = plt.plot(x, y, lw=2)
ax.margins(x=0)
axcolor = 'lightgoldenrodyellow'
ax_b = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
ax_c = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)
s_b = Slider(ax_b, 'b', 0.1, 18.0, valinit=b_init, valstep=delta_f)
s_c = Slider(ax_c, 'c', 0.1, 12.0, valinit=c_init)
def update(val):
b = s_b.val
c = s_c.val
l.set_ydata(((b-6*(x**k))/c)**(1/k))
fig.canvas.draw_idle()
s_b.on_changed(update)
s_c.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):
s_b.reset()
s_c.reset()
button.on_clicked(reset)
def colorfunc(label):
l.set_color(label)
fig.canvas.draw_idle()
plt.show()
In case you are working with juypter notebooks you can use the widgets from ipywidgets as shown below.
Also, to get an intuition, it might help if you print out the b,c and k values.
%matplotlib inline
import numpy as np
import matplotlib.pyplot as p
from ipywidgets import *
def y(x,b,c):
k = 10/(6+c)
print(f' b={b:.3f},c={c:.3f},k={k:.3f}')
y = ((b-6*(x**k))/c)**(1/k)
return y
def inter(b0,c0):
y1=y(x,b0,c0)
p.figure(figsize=(20,6))
p.plot(x,y1)
dx=0.001
x = np.arange(0, 1.0+dx, dx) # assuming you want to go to 1 inclusively
b0=widgets.FloatSlider(value=10,min=-1,max=18.0,step=0.01,
description='b0',
continuous_update=False,
readout_format='.3f',
layout=Layout(width='90%', height='20px'))
c0=widgets.FloatSlider(value=0.1,min=-1,max=12.0,step=0.01,
description='c0',
continuous_update=False,
readout_format='.3f',
layout=Layout(width='90%', height='20px'))
interact(inter, b0=b0,c0=c0);
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
Newbie matplotlib user here. I'm trying to use a slider to adjust a parameter in a contour plot, but when I do so, I get:
AttributeError: QuadContourSet instance has no attribute 'set_data'
I suspect that I'm calling set_data on the wrong object, but I can't find any documentation on what the right object is. Can you help? Thanks.
Here's the full code:
import numpy as np
import matplotlib as mpl
import matplotlib.mlab as mlab
import matplotlib.pyplot as pyl
from matplotlib.contour import QuadContourSet
from matplotlib.widgets import Slider
#Define display parameters
mpl.rcParams['xtick.direction'] = 'out'
mpl.rcParams['ytick.direction'] = 'out'
delta = 0.025
#Define model parameters
alpha = .5
beta = .5
x_bar, a, b, c = 2, 0, 1, .1
v = np.arange(0, 10, delta)
w = np.arange(0, 10, delta)
#Calculate grid values
V, W = np.meshgrid(v,w)
Z = (V**(beta))*(W**(1-beta))
X = x_bar + a + b*Z
U = alpha*np.log(V) + (1-alpha)*np.log(X) - c*(W+V)
# Plot
fig = pyl.figure()
ax = fig.add_subplot(221)
CS = QuadContourSet(pyl.gca(), V, W, U, 200)
pyl.clabel(CS, inline=1, fontsize=10)
pyl.title('Simplest default with labels')
#Define slider for alpha
axcolor = 'lightgoldenrodyellow'
alpha_axis = pyl.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
alpha_slider = Slider(alpha_axis, 'Amp', 0, 1, valinit=.5)
def update(val):
alpha = alpha_slider.val
U = alpha*np.log(V) + (1-alpha)*np.log(X) - c*(W+V)
CS.set_data(V, W, U)
pyl.draw()
alpha_slider.on_changed(update)
pyl.show()
The problem is that the QuadContourSet object has no way to update its data, since if you change the data arbitrarily, the whole thing needs to be recomputed. I don't know if there is something about your particular way of generating data that would lend itself to a simpler way to modify the contour lines, but if not, I think what you need to do is to plot the contours from scratch:
# After your "Define model parameters" block
def compute_and_plot(ax, alpha):
#Calculate grid values
V, W = np.meshgrid(v,w)
Z = (V**(beta))*(W**(1-beta))
X = x_bar + a + b*Z
U = alpha*np.log(V) + (1-alpha)*np.log(X) - c*(W+V)
CS = QuadContourSet(ax, V, W, U, 200)
pyl.clabel(CS, inline=1, fontsize=10)
# Plot
fig = pyl.figure()
pyl.title('Simplest default with labels')
ax = fig.add_subplot(221)
compute_and_plot(ax, alpha)
#Define slider for alpha
axcolor = 'lightgoldenrodyellow'
alpha_axis = pyl.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
alpha_slider = Slider(alpha_axis, 'Amp', 0, 1, valinit=.5)
def update(ax, val):
alpha = alpha_slider.val
ax.cla()
compute_and_plot(ax, alpha)
pyl.draw()
alpha_slider.on_changed(lambda val: update(ax, val))
pyl.show()