Based on this solution, I wanted to make a slider where only specified values are allowed, the slider itself is also discrete the slider bar only moves if a new point was chosen (so basically a slider version of a radio button). For example, if I click near but not exactly on the current point, the slider shouldn't change and the plot should not redraw.
I got it pretty much working, but the result is lagging behind: if I click alternating between 1 and 10 in the example below, the slider updates correctly, but the moving point is always jumping to the previous value. How can I fix it:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.widgets import Slider
class ChangingPlot(object):
def __init__(self):
self.draw_counter = 0
x = np.logspace(0, 1, 10)
self.fig, self.ax = plt.subplots()
self.sliderax = self.fig.add_axes([0.2, 0.02, 0.6, 0.03],axisbg='yellow')
self.slider = DiscreteSlider(self.sliderax,'Value', 0, 10,\
allowed_vals=x, valinit=x[0])
self.slider.on_changed(self.update)
self.ax.plot(x, x, 'ro')
self.dot, = self.ax.plot(x[0], x[0], 'bo', markersize=18)
self.text = self.ax.text(2,8,str(self.draw_counter))
def update(self, value):
self.draw_counter += 1
self.dot.set_data([[value],[value]])
self.text.set_text(str(self.draw_counter)+' draws, value = '+str(value))
def show(self):
plt.show()
class DiscreteSlider(Slider):
"""A matplotlib slider widget with discrete steps."""
def __init__(self, *args, **kwargs):
"""
Identical to Slider.__init__, except for the new keyword 'allowed_vals'.
This keyword specifies the allowed positions of the slider
"""
self.allowed_vals = kwargs.pop('allowed_vals',None)
self.previous_val = kwargs['valinit']
Slider.__init__(self, *args, **kwargs)
if self.allowed_vals==None:
self.allowed_vals = [self.valmin,self.valmax]
def set_val(self, val):
discrete_val = self.allowed_vals[abs(val-self.allowed_vals).argmin()]
xy = self.poly.xy
xy[2] = discrete_val, 1
xy[3] = discrete_val, 0
self.poly.xy = xy
self.valtext.set_text(self.valfmt % discrete_val)
if self.drawon:
self.ax.figure.canvas.draw()
self.val = val
if self.previous_val!=discrete_val:
self.previous_val = discrete_val
if not self.eventson:
return
for cid, func in self.observers.iteritems():
func(discrete_val)
p = ChangingPlot()
p.show()
Why matplotlib inline is used. You can use the magic function %matplotlib inline to enable the inline plotting, where the plots/graphs will be displayed just below the cell where your plotting commands are written. It provides interactivity with the backend in the frontends like the jupyter notebook.
Very nice example, You need to call Figure.canvas.draw()
to reflush the screen:
def update(self, value):
self.draw_counter += 1
self.dot.set_data([[value],[value]])
self.text.set_text(str(self.draw_counter)+' draws, value = '+str(value))
self.fig.canvas.draw() # <--- Add this line
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