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)
Related
I'm trying to create a vector field and some curve, I've created a vector field as shown
import matplotlib.pyplot as plt
import numpy as np
x,y = np.meshgrid(np.arange(-3,3,.35),np.arange(-3,3,.35))
u = x
v = y
plt.quiver(x, y, u, v, color = 'black')
plt.show()
But I want to add the curve $y=x^2$ in the same plot, how could I do that?
I've tryeid to add plt.plot and the curve but the result is weird.
You probably want to keep the y-axis limit as was in the mesh grid. plt.ylim is helpful in that case
import matplotlib.pyplot as plt
import numpy as np
x, y = np.meshgrid(np.arange(-3, 3, .35), np.arange(-3, 3, .35))
u = x
v = y
plt.quiver(x, y, u, v, color = 'black')
x = np.linspace(-3, 3, 100)
ylim = plt.ylim()
plt.plot(x, x**2)
plt.ylim(ylim)
plt.show()
Output:
import numpy as np
from matplotlib import pyplot as plt
data = np.random.normal(0,1,[100,3])
x = data[:,0]
y = data[:,1]
z = data[:,2]
plt.contour([x,y],z)
When I run this code with dummy data I get:
ValueError: Contour levels must be increasing
Do you have any idea what would this mean and how I could fix it?
plt.contour is a bit particular about its input, the z values must be on values on a rectangular 2D grid, see for example:
import matplotlib.pyplot as plt
import numpy as np
x = np.expand_dims(np.arange(1,11,1), axis=1)
y = np.expand_dims(np.arange(2,21,2), axis=0)
z = y * x
print(x.shape)
print(y.shape)
print(z.shape)
plt.figure()
plt.contour(z)
plt.show()
You can also provide x and y values for plt.contour by using np.meshgrid :
XX,YY = np.meshgrid(x,y)
plt.figure()
plt.contour(XX, YY, z)
plt.show()
If you have z-values with irregular values for x and y, you might use plt.tricontour, see the following example:
from matplotlib.tri import Triangulation
data = np.random.normal(0,1,[100,3])
x = data[:,0]
y = data[:,1]
#z = data[:,2]
z = x * y
tri = Triangulation(x,y)
plt.figure()
plt.tricontour(tri, z, )
plt.scatter(x,y, c=z)
plt.show()
Edit: from JohanC's comment i learned that this can be simplified without importing matplotlib.tri by:
plt.figure()
plt.tricontour(x,y,z)
plt.scatter(x,y, c=z)
plt.show()
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 plan to create a figure in matplotlib, with a 3D surface on the left and its corresponding contour map on the right.
I used subplots but it only show the contour map (with blank space for the surface), and a separate figure for the surface.
Is it possible to create these plots in one figure side-by side?
EDIT: The code is as follows:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import numpy as np
x = np.arange(-5, 5, 0.25)
y = np.arange(-5, 5, 0.25)
x, y = np.meshgrid(x, y)
r = np.sqrt(x**2 + y**2)
z = np.sin(r)
fig, (surf, cmap) = plt.subplots(1, 2)
fig = plt.figure()
surf = fig.gca(projection='3d')
surf.plot_surface(x,y,z)
cmap.contourf(x,y,z,25)
plt.show()
I guess it's hard to use plt.subplots() in order to create a grid of plots with different projections.
So the most straight forward solution is to create each subplot individually with plt.subplot.
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import numpy as np
x = np.arange(-5, 5, 0.25)
y = np.arange(-5, 5, 0.25)
x, y = np.meshgrid(x, y)
r = np.sqrt(x**2 + y**2)
z = np.sin(r)
ax = plt.subplot(121, projection='3d')
ax.plot_surface(x,y,z)
ax2 = plt.subplot(122)
ax2.contourf(x,y,z,25)
plt.show()
Of course one may also use the gridspec capabilities for more sophisticated grid structures.
I would like to group my data and to plot the boxplot for all the groups. There are many questions and answer about that, my problem is that I want to group by a continuos variable, so I want to histogramming my data.
Here what I have done. My data:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
x = np.random.chisquare(5, size=100000)
y = np.random.normal(size=100000) / (0.05 * x + 0.1) + 2 * x
f, ax = plt.subplots()
ax.plot(x, y, '.', alpha=0.05)
plt.show()
I want to study the behaviour of y (location, width, ...) as a function of x. I am not interested in the distribution of x so I will normalized it.
f, ax = plt.subplots()
xbins = np.linspace(0, 25, 50)
ybins = np.linspace(-20, 50, 50)
H, xedges, yedges = np.histogram2d(y, x, bins=(ybins, xbins))
norm = np.sum(H, axis = 0)
H /= norm
ax.pcolor(xbins, ybins, np.nan_to_num(H), vmax=.4)
plt.show()
I can plot histogram, but I want boxplot
binning = np.concatenate(([0], np.sort(np.random.random(20) * 25), [25]))
idx = np.digitize(x, binning)
data_to_plot = [y[idx == i] for i in xrange(len(binning))]
f, ax = plt.subplots()
midpoints = 0.5 * (binning[1:] + binning[:-1])
widths = 0.9 * (binning[1:] - binning[:-1])
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
majorLocator = MultipleLocator(2)
ax.boxplot(data_to_plot, positions = midpoints, widths=widths)
ax.set_xlim(0, 25)
ax.xaxis.set_major_locator(majorLocator)
ax.set_xlabel('x')
ax.set_ylabel('median(y)')
plt.show()
Is there an automatic way to do that, like ax.magic(x, y, binning)? Is there a better way to do that? (Have a look to https://root.cern.ch/root/html/TProfile.html for example, which plot the mean and the error of the mean as error bars)
In addition, I want to minize the memory footprint (my real data are much more than 100000), I am worried about data_to_plot, is it a copy?