I have made the attached plot using the following codes:
a = 1
theta = np.linspace(0,2*np.pi,101)
x = np.linspace(-3*a,3*a,1001, dtype='complex')
y = np.linspace(-3*a,3*a,1001, dtype='complex')
X,Y = np.meshgrid(x,y)
# come manipulations with V
# (same shape and type as X,Y) not shown here
plt.subplot(1,2,1)
plt.scatter(a*np.cos(theta), a*np.sin(theta))
plt.imshow(V.real)
plt.colorbar()
plt.subplot(1,2,2)
plt.scatter(a*np.cos(theta), a*np.sin(theta))
plt.imshow(V.imag)
plt.colorbar()
What I want to do is that:
1) change the scale of the plot such that the horizontal and vertical axis varies between -3*a and 3*a
2) plot the circle boundary (centered at the origin with radius = a). Now it appears at the top left, as the scale of the plot is changed from [-3*a,3*a] to that of the size of the array.
In general, you're looking for the extent
kwarg to imshow
.
As a quick example:
import numpy as np
import matplotlib.pyplot as plt
data = np.random.random((10, 10))
fig, ax = plt.subplots()
ax.imshow(data, extent=[10, 30, np.pi, -2*np.pi])
plt.show()
In the case of the example you gave:
import numpy as np
import matplotlib.pyplot as plt
a = 1
theta = np.linspace(0, 2*np.pi, 100)
# We could replace the next three lines with:
# y, x = np.mgrid[-3*a:3*a:1000j, -3*a:3*a:1000j]
x = np.linspace(-3*a, 3*a, 1000)
y = np.linspace(-3*a, 3*a, 1000)
x, y = np.meshgrid(x, y)
# Now let's make something similar to your V for this example..
r = np.hypot(x, y)
V = np.cos(3*np.arctan2(y, x)) + np.sin(r) + np.cos(x)*1j * np.cos(r)
def plot(ax, data):
ax.plot(a*np.cos(theta), a*np.sin(theta), color='black')
im = ax.imshow(data, extent=[x.min(), x.max(), y.max(), y.min()])
fig.colorbar(im, ax=ax, shrink=0.5)
fig, (ax1, ax2) = plt.subplots(ncols=2)
ax1.set(title='Real Portion')
plot(ax1, V.real)
ax2.set(title='Imaginary Portion')
plot(ax2, V.imag)
plt.show()
However, you might also consider using pcolormesh
in this case. For example, we could change the plot
function to:
def plot(ax, data):
ax.plot(a*np.cos(theta), a*np.sin(theta), color='black')
im = ax.pcolormesh(x, y, data)
ax.set(aspect=1)
fig.colorbar(im, ax=ax, shrink=0.5)
The main differences are:
imshow
can interpolate, while pcolormesh
gives vector output and can't interpolate (i.e. it plots lots of rectangles instead of an image).pcolormesh
is somewhat slower, so for large images, imshow
is a better choice.imshow
and pcolormesh
treat the extents slightly differently. imshow
is "cell-centered" while pcolormesh
is "mesh-centered". This is a half-pixel difference, so you can ignore it in this case.imshow
will set the aspect of the plot to 1, so that one unit in the x-direction is the same size as one unit in the y-direction. It also flips the y-axis, by default. One other note: If you'd prefer not to have the y-axis flipped, either call ax.invert_yaxis()
or use origin='lower'
and extent=[xmin, xmax, ymin, ymax]
.