I would like to achieve two objectives with matplotlib:
Currently, I am able to achieve the opposite goal using colormaps. That is, I can plot points over time but the recent points look more transparent.
Is it possible to get a 'fading' effect on matplotlib using cmap or other tools? Thanks! My code is below:
def plotter_fader(iterations = 100, stay_open = True):
# Set up plot
fig, ax = plt.subplots()
x_data = []
y_data = []
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111)
t_vals = np.linspace(0,1, iterations)
cmap = (0.09803921568627451, 0.09803921568627451, 0.09803921568627451, .05)
for t in t_vals:
# Get intermediate points
intermediate = (1-t)*A + t*B
new_xvals, new_yvals = ... #Get these through some process
x_vals.extend(new_xvals)
y_vals.extend(new_yvals)
# Put new values in your plot
plt.plot(x_vals, y_vals, '.', color = cmap)
# Recompute plot limits
ax.relim()
# Set title and wait a little bit for 'smoothness'
ax.set_xlabel('X Axis', size = 12)
ax.set_ylabel('Y Axis', size = 12)
ax.set_title('Time: %0.3f' %t)
ax.autoscale_view()
fig.canvas.draw()
time.sleep(0.005)
# Stay open after plotting ends
while stay_open:
pass
Just as usual with a scatter plot you may define an array of values and a colormap that maps those values to colors. You can change this array in each iteration to make older points have a different value.
In the following we take a value of 0 as transparent and a value of 1 as dark blue and create a colormap with those colors.
In each iteration old values are multiplied by a number smaller than one, new values are set to have a value of 1.
Running the animation will hence produce fading points.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation
from matplotlib.colors import LinearSegmentedColormap
fig, ax = plt.subplots()
ax.set_xlabel('X Axis', size = 12)
ax.set_ylabel('Y Axis', size = 12)
ax.axis([0,1,0,1])
x_vals = []
y_vals = []
intensity = []
iterations = 100
t_vals = np.linspace(0,1, iterations)
colors = [[0,0,1,0],[0,0,1,0.5],[0,0.2,0.4,1]]
cmap = LinearSegmentedColormap.from_list("", colors)
scatter = ax.scatter(x_vals,y_vals, c=[], cmap=cmap, vmin=0,vmax=1)
def get_new_vals():
n = np.random.randint(1,5)
x = np.random.rand(n)
y = np.random.rand(n)
return list(x), list(y)
def update(t):
global x_vals, y_vals, intensity
# Get intermediate points
new_xvals, new_yvals = get_new_vals()
x_vals.extend(new_xvals)
y_vals.extend(new_yvals)
# Put new values in your plot
scatter.set_offsets(np.c_[x_vals,y_vals])
#calculate new color values
intensity = np.concatenate((np.array(intensity)*0.96, np.ones(len(new_xvals))))
scatter.set_array(intensity)
# Set title
ax.set_title('Time: %0.3f' %t)
ani = matplotlib.animation.FuncAnimation(fig, update, frames=t_vals,interval=50)
plt.show()
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