I'm trying to run the following:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
x = np.linspace(-15, 15, 10)
y = np.linspace(-15, 15, 10)
X, Y = np.meshgrid(x, y)
Z = Y;
# Z = X;
cmap = mpl.colors.ListedColormap(['r', 'b'])
bounds = [-300, 0, 300]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
plt.figure();
plt.xlabel('x');
plt.ylabel('y');
im = plt.imshow(Z,cmap= cmap, norm = norm)
plt.show();
If I try to do Z = X, it works fine. But if I do Z = Y, the y-axis is inverted, i.e. red (negative) at the top, and blue (positive) at the bottom. Why is this happening?
Inverted compared to what?
The plot you have is working fine in both cases, as expected. The value of Z[0,0], which is -15 in this case, is plotted in red at the coordinate 0,0.
If you want the y axis to start at the bottom instead of the top, use the origin="lower" keyword argument to imshow.
Related
import numpy as np
import matplotlib.pyplot as plt
x, y = np.array([[x, y] for x in range(5) for y in range(x+1)]).T
z = 1/ (5*x + 5)
fig = plt.figure()
ax = fig.gca(projection = '3d')
ax.bar3d(x, y, np.zeros_like(z), dx = 1, dy = 1, dz = z)
yields
How do I get the face at (1,0) to display properly?
There is currently no good solution to this. Fortunately though, it happens only for some viewing angles. So you can choose an angle where it plots fine, e.g.
ax.view_init(azim=-60, elev=25)
This is an extension of a related question.
I intend to make a contour plot, with labeled contours, then change the axes scales to 'log'.
This works fine except that the rotation of the contour labels is not adjusted. Can this be fixed?
loglog = False
import matplotlib.pyplot as plt
import numpy as np
x = (np.linspace(0, 10))
y = (np.linspace(0, 10))
X, Y = np.meshgrid(x, y)
C = plt.contour(X, Y, np.sqrt(X) * Y)
plt.clabel(C, inline=1, fontsize=10)
plt.xlim(1, 10)
plt.ylim(1, 10)
if loglog: plt.xscale('log')
if loglog: plt.yscale('log')
plt.show()
The fist plot is obtained with loglog=False in the second loglog=True:
So the answer is actually obvious. Changing the the axes scale types in advance helps, of course.
Edit:
I think it makes sense to use logspace instead of linspace here.
import matplotlib.pyplot as plt
import numpy as np
x = np.logspace(0, 1, 100, base=10)
y = np.logspace(0, 1, 100, base=10)
X, Y = np.meshgrid(x, y)
plt.xlim(1, 10)
plt.ylim(1, 10)
plt.xscale('log')
plt.yscale('log')
C = plt.contour(X, Y, np.sqrt(X) * Y)
plt.clabel(C, inline=1, fontsize=10)
I want to have a scatter plot with colormap for edgecolors but no facecolors.
When I use facecolor='None', it does not work.
import numpy as np
import matplotlib.pyplot as plt
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii
plt.scatter(x, y, s=area,c=colors,facecolors='None',cmap="gist_rainbow", alpha=0.5)
plt.show()
Any solution?
The c argument will affect facecolor and edgecolor simultaneouly, the arguments facecolor and edgecolor are hence ignored.
A solution would be not to use the c argument together with a colormap, but instead use facecolors and edgecolors alone. In this case facecolors can be set to "None" and edgecolors can be given a list of colors to use.
To create this list, the same colormap can be applied.
c = plt.cm.gist_rainbow(colors)
plt.scatter(x, y, s=area,facecolors="None", edgecolors=c, lw=1,alpha=0.5)
A complete example:
import numpy as np
import matplotlib.pyplot as plt
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii
c = plt.cm.gist_rainbow(colors)
plt.scatter(x, y, s=area,facecolors="None", edgecolors=c, lw=2,alpha=0.5)
plt.show()
The problem is that color= overrides the facecolors= argument.
The solution I came up with is to get the PathCollection returned by pyplot.scatter() and then change the facecolor directly. Note that you probably need to increase the line width to see the edges better.
import numpy as np
import matplotlib.pyplot as plt
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii
a = plt.scatter(x, y, s=area,c=colors,facecolor='none',lw=2,cmap="gist_rainbow", alpha=0.5)
a.set_facecolor('none')
plt.show()
I know this has been dead for a while, but I wanted to add my experience as I just encountered this same problem.
I prefer Diziet's method as passing the PathCollection object to a colorbar and having it match the cmap used in the scatter plot works exactly as it would if you didn't alter the facecolors.
With the accepted solution, however, I encountered some odd behavior where even after removing the cmap argument from the ax.scatter call the scatter plot edge colormap and the colorbar colormap didn't match.
Right now there're some statistics plotted in 3d bar over (x, y). each bar height represents the density of the points in side the square grid of (x,y) plane. Right now, i can put different color on each bar. However, I want to put progressive color on the 3d bar, similar as the cmap, so the bar will be gradient filled depending on the density.
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# height of the bars
z = np.ones((4, 4)) * np.arange(4)
# position of the bars
xpos, ypos = np.meshgrid(np.arange(4), np.arange(4))
xpos = xpos.flatten('F')
ypos = ypos.flatten('F')
zpos = np.zeros_like(xpos)
dx = 0.5 * np.ones_like(zpos)
dy = dx.copy()
dz = z.flatten()
ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', zsort='average')
plt.show()
Output the above code:
Let me first say that matplotlib may not be the tool of choice when it comes to sophisticated 3D plots.
That said, there is no built-in method to produce bar plots with differing colors over the extend of the bar.
We therefore need to mimic the bar somehow. A possible solution can be found below. Here, we use a plot_surface plot to create a bar that contains a gradient.
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection= Axes3D.name)
def make_bar(ax, x0=0, y0=0, width = 0.5, height=1 , cmap="viridis",
norm=matplotlib.colors.Normalize(vmin=0, vmax=1), **kwargs ):
# Make data
u = np.linspace(0, 2*np.pi, 4+1)+np.pi/4.
v_ = np.linspace(np.pi/4., 3./4*np.pi, 100)
v = np.linspace(0, np.pi, len(v_)+2 )
v[0] = 0 ; v[-1] = np.pi; v[1:-1] = v_
x = np.outer(np.cos(u), np.sin(v))
y = np.outer(np.sin(u), np.sin(v))
z = np.outer(np.ones(np.size(u)), np.cos(v))
xthr = np.sin(np.pi/4.)**2 ; zthr = np.sin(np.pi/4.)
x[x > xthr] = xthr; x[x < -xthr] = -xthr
y[y > xthr] = xthr; y[y < -xthr] = -xthr
z[z > zthr] = zthr ; z[z < -zthr] = -zthr
x *= 1./xthr*width; y *= 1./xthr*width
z += zthr
z *= height/(2.*zthr)
#translate
x += x0; y += y0
#plot
ax.plot_surface(x, y, z, cmap=cmap, norm=norm, **kwargs)
def make_bars(ax, x, y, height, width=1):
widths = np.array(width)*np.ones_like(x)
x = np.array(x).flatten()
y = np.array(y).flatten()
h = np.array(height).flatten()
w = np.array(widths).flatten()
norm = matplotlib.colors.Normalize(vmin=0, vmax=h.max())
for i in range(len(x.flatten())):
make_bar(ax, x0=x[i], y0=y[i], width = w[i] , height=h[i], norm=norm)
X, Y = np.meshgrid([1,2,3], [2,3,4])
Z = np.sin(X*Y)+1.5
make_bars(ax, X,Y,Z, width=0.2, )
plt.show()
How can I set the diagramm to enlarge(!) and show all the ticks in one or more of the axis? See picture below
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import axes3d
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ys = np.linspace(0, 30, 30)
xs = np.linspace(0, 10, 10)
X, Y = np.meshgrid(xs, ys)
ax.plot_wireframe(X, Y, X * Y)
plt.show()
I don't want square axis. I guess that the default plots square axis, i.e. the axis x, y, and z have all the same length. The default looks like the following (without the red arrow!).
I need to "enlarge", "stretch" or "scale" (sorry, I don't know how it is called) the axis. For example, when the x, y axis have 30x30 values, then it is ok to have square axies like the default figuer above or the next first figure. But if they have 30x10 or 10x30 values, I would like to plot them like the next second and third figure respectively:
The suggested plt.figure(figsize=(6,2)) or plt.figure(figsize=(2,6)) has no good results for 3d diagramms. The following is not what I'm looking for: