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Commit f00db67

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WeatherGodWeatherGod
committed
Merge pull request #6303 from discardthree/mplot3d-examples-MEP12
DOC Clean up on about half the Mplot3d examples
2 parents 7beca5d + ab0d48c commit f00db67
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‎examples/mplot3d/2dcollections3d_demo.py

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+23-4Lines changed: 23 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -1,23 +1,42 @@
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'''
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Demonstrates using ax.plot's zdir keyword to plot 2D scatterplot data on
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selective axes of a 3D plot.
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'''
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16
from mpl_toolkits.mplot3d import Axes3D
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import numpy as np
38
import matplotlib.pyplot as plt
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510
fig = plt.figure()
611
ax = fig.gca(projection='3d')
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13+
# Plot a sin curve using the x and y axes.
814
x = np.linspace(0, 1, 100)
915
y = np.sin(x * 2 * np.pi) / 2 + 0.5
10-
ax.plot(x, y, zs=0, zdir='z', label='zs=0, zdir=z')
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ax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)')
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# Plot scatterplot data (20 2D points per colour) on the x and z axes.
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colors = ('r', 'g', 'b', 'k')
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x = np.random.sample(20*len(colors))
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y = np.random.sample(20*len(colors))
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c_list = []
1323
for c in colors:
14-
x = np.random.sample(20)
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y = np.random.sample(20)
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ax.scatter(x, y, 0, zdir='y', c=c)
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c_list.append([c]*20)
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# By using zdir='y', the y value of these points is fixed to the zs value 0
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# and the (x,y) points are plotted on the x and z axes.
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ax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x,z)')
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# Make legend, set axes limits and labels
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ax.legend()
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ax.set_xlim3d(0, 1)
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ax.set_ylim3d(0, 1)
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ax.set_zlim3d(0, 1)
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_zlabel('Z')
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# Customize the view angle so it's easier to see that the scatter points lie
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# on the plane y=0
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ax.view_init(elev=20., azim=-35)
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plt.show()

‎examples/mplot3d/bars3d_demo.py

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+18-4Lines changed: 18 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -1,21 +1,35 @@
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'''
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Demonstrates making a 3D plot which has 2D bar graphs projected onto
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planes y=0, y=1, etc.
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'''
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from mpl_toolkits.mplot3d import Axes3D
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import matplotlib.pyplot as plt
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import numpy as np
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
7-
for c, z in zip(['r', 'g', 'b', 'y'], [30, 20, 10, 0]):
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13+
colors = ['r', 'g', 'b', 'y']
14+
yticks = [3, 2, 1, 0]
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for c, k in zip(colors, yticks):
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# Generate the random data for the y=k 'layer'.
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xs = np.arange(20)
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ys = np.random.rand(20)
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11-
# You can provide either a single color or an array. To demonstrate this,
12-
# the first bar of each set will be colored cyan.
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# You can provide either a single color or an array with the same length as
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# xs and ys. To demonstrate this, we color the first bar of each set cyan.
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cs = [c] * len(xs)
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cs[0] = 'c'
15-
ax.bar(xs, ys, zs=z, zdir='y', color=cs, alpha=0.8)
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# Plot the bar graph given by xs and ys on the plane y=k with 80% opacity.
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ax.bar(xs, ys, zs=k, zdir='y', color=cs, alpha=0.8)
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_zlabel('Z')
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# On the y axis let's only label the discrete values that we have data for.
33+
ax.set_yticks(yticks)
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plt.show()

‎examples/mplot3d/contour3d_demo.py

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+11-1Lines changed: 11 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,11 +1,21 @@
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'''
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Demonstrates plotting contour (level) curves in 3D.
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This is like a contour plot in 2D except that the f(x,y)=c curve is plotted
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on the plane z=c.
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'''
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18
from mpl_toolkits.mplot3d import axes3d
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import matplotlib.pyplot as plt
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from matplotlib import cm
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fig = plt.figure()
6-
ax = fig.add_subplot(111, projection='3d')
13+
ax = fig.gca(projection='3d')
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X, Y, Z = axes3d.get_test_data(0.05)
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# Plot contour curves
817
cset = ax.contour(X, Y, Z, cmap=cm.coolwarm)
18+
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ax.clabel(cset, fontsize=9, inline=1)
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plt.show()

‎examples/mplot3d/contour3d_demo2.py

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+9Lines changed: 9 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1,11 +1,20 @@
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'''
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Demonstrates plotting contour (level) curves in 3D using the extend3d option.
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This modification of the contour3d_demo example uses extend3d=True to
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extend the curves vertically into 'ribbons'.
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'''
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from mpl_toolkits.mplot3d import axes3d
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import matplotlib.pyplot as plt
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from matplotlib import cm
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fig = plt.figure()
613
ax = fig.gca(projection='3d')
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X, Y, Z = axes3d.get_test_data(0.05)
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cset = ax.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm)
17+
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ax.clabel(cset, fontsize=9, inline=1)
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plt.show()

‎examples/mplot3d/contour3d_demo3.py

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+17-3Lines changed: 17 additions & 3 deletions
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@@ -1,20 +1,34 @@
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'''
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Demonstrates displaying a 3D surface while also projecting contour 'profiles'
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onto the 'walls' of the graph.
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See contourf3d_demo2 for the filled version.
6+
'''
7+
18
from mpl_toolkits.mplot3d import axes3d
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import matplotlib.pyplot as plt
310
from matplotlib import cm
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fig = plt.figure()
613
ax = fig.gca(projection='3d')
714
X, Y, Z = axes3d.get_test_data(0.05)
15+
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# Plot the 3D surface
817
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
18+
19+
# Plot projections of the contours for each dimension. By choosing offsets
20+
# that match the appropriate axes limits, the projected contours will sit on
21+
# the 'walls' of the graph
922
cset = ax.contour(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
1023
cset = ax.contour(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
1124
cset = ax.contour(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)
1225

13-
ax.set_xlabel('X')
1426
ax.set_xlim(-40, 40)
15-
ax.set_ylabel('Y')
1627
ax.set_ylim(-40, 40)
17-
ax.set_zlabel('Z')
1828
ax.set_zlim(-100, 100)
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30+
ax.set_xlabel('X')
31+
ax.set_ylabel('Y')
32+
ax.set_zlabel('Z')
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2034
plt.show()

‎examples/mplot3d/contourf3d_demo.py

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+10Lines changed: 10 additions & 0 deletions
Original file line numberDiff line numberDiff line change
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'''
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contourf differs from contour in that it creates filled contours, ie.
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a discrete number of colours are used to shade the domain.
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5+
This is like a contourf plot in 2D except that the shaded region corresponding
6+
to the level c is graphed on the plane z=c.
7+
'''
8+
19
from mpl_toolkits.mplot3d import axes3d
210
import matplotlib.pyplot as plt
311
from matplotlib import cm
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513
fig = plt.figure()
614
ax = fig.gca(projection='3d')
715
X, Y, Z = axes3d.get_test_data(0.05)
16+
817
cset = ax.contourf(X, Y, Z, cmap=cm.coolwarm)
18+
919
ax.clabel(cset, fontsize=9, inline=1)
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plt.show()

‎examples/mplot3d/contourf3d_demo2.py

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+16-7Lines changed: 16 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,9 @@
1-
"""
2-
.. versionadded:: 1.1.0
3-
This demo depends on new features added to contourf3d.
4-
"""
1+
'''
2+
Demonstrates displaying a 3D surface while also projecting filled contour
3+
'profiles' onto the 'walls' of the graph.
4+
5+
See contour3d_demo2 for the unfilled version.
6+
'''
57

68
from mpl_toolkits.mplot3d import axes3d
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import matplotlib.pyplot as plt
@@ -10,16 +12,23 @@
1012
fig = plt.figure()
1113
ax = fig.gca(projection='3d')
1214
X, Y, Z = axes3d.get_test_data(0.05)
15+
16+
# Plot the 3D surface
1317
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
18+
19+
# Plot projections of the contours for each dimension. By choosing offsets
20+
# that match the appropriate axes limits, the projected contours will sit on
21+
# the 'walls' of the graph
1422
cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
1523
cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
1624
cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)
1725

18-
ax.set_xlabel('X')
1926
ax.set_xlim(-40, 40)
20-
ax.set_ylabel('Y')
2127
ax.set_ylim(-40, 40)
22-
ax.set_zlabel('Z')
2328
ax.set_zlim(-100, 100)
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30+
ax.set_xlabel('X')
31+
ax.set_ylabel('Y')
32+
ax.set_zlabel('Z')
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2534
plt.show()

‎examples/mplot3d/custom_shaded_3d_surface.py

Copy file name to clipboardExpand all lines: examples/mplot3d/custom_shaded_3d_surface.py
+3Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1,13 +1,15 @@
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"""
22
Demonstrates using custom hillshading in a 3D surface plot.
33
"""
4+
45
from mpl_toolkits.mplot3d import Axes3D
56
from matplotlib import cbook
67
from matplotlib import cm
78
from matplotlib.colors import LightSource
89
import matplotlib.pyplot as plt
910
import numpy as np
1011

12+
# Load and format data
1113
filename = cbook.get_sample_data('jacksboro_fault_dem.npz', asfileobj=False)
1214
with np.load(filename) as dem:
1315
z = dem['elevation']
@@ -19,6 +21,7 @@
1921
region = np.s_[5:50, 5:50]
2022
x, y, z = x[region], y[region], z[region]
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24+
# Set up plot
2225
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
2326

2427
ls = LightSource(270, 45)

‎examples/mplot3d/lines3d_demo.py

Copy file name to clipboardExpand all lines: examples/mplot3d/lines3d_demo.py
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@@ -1,3 +1,7 @@
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'''
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Demonstrating plotting a parametric curve in 3D.
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'''
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import matplotlib as mpl
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from mpl_toolkits.mplot3d import Axes3D
37
import numpy as np
@@ -7,11 +11,14 @@
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812
fig = plt.figure()
913
ax = fig.gca(projection='3d')
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# Prepare arrays x, y, z
1016
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
1117
z = np.linspace(-2, 2, 100)
1218
r = z**2 + 1
1319
x = r * np.sin(theta)
1420
y = r * np.cos(theta)
21+
1522
ax.plot(x, y, z, label='parametric curve')
1623
ax.legend()
1724

‎examples/mplot3d/lorenz_attractor.py

Copy file name to clipboardExpand all lines: examples/mplot3d/lorenz_attractor.py
+27-15Lines changed: 27 additions & 15 deletions
Original file line numberDiff line numberDiff line change
@@ -1,42 +1,54 @@
1-
# Plot of the Lorenz Attractor based on Edward Lorenz's 1963 "Deterministic
2-
# Nonperiodic Flow" publication.
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# http://journals.ametsoc.org/doi/abs/10.1175/1520-0469%281963%29020%3C0130%3ADNF%3E2.0.CO%3B2
4-
#
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# Note: Because this is a simple non-linear ODE, it would be more easily
6-
# done using SciPy's ode solver, but this approach depends only
7-
# upon NumPy.
1+
'''
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Plot of the Lorenz Attractor based on Edward Lorenz's 1963 "Deterministic
3+
Nonperiodic Flow" publication.
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http://journals.ametsoc.org/doi/abs/10.1175/1520-0469%281963%29020%3C0130%3ADNF%3E2.0.CO%3B2
5+
6+
Note: Because this is a simple non-linear ODE, it would be more easily
7+
done using SciPy's ode solver, but this approach depends only
8+
upon NumPy.
9+
'''
810

911
import numpy as np
1012
import matplotlib.pyplot as plt
1113
from mpl_toolkits.mplot3d import Axes3D
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1416
def lorenz(x, y, z, s=10, r=28, b=2.667):
17+
'''
18+
Given:
19+
x, y, z: a point of interest in three dimensional space
20+
s, r, b: parameters defining the lorenz attractor
21+
Returns:
22+
x_dot, y_dot, z_dot: values of the lorenz attractor's partial
23+
derivatives at the point x, y, z
24+
'''
1525
x_dot = s*(y - x)
1626
y_dot = r*x - y - x*z
1727
z_dot = x*y - b*z
1828
return x_dot, y_dot, z_dot
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2131
dt = 0.01
22-
stepCnt = 10000
32+
num_steps = 10000
2333

2434
# Need one more for the initial values
25-
xs = np.empty((stepCnt + 1,))
26-
ys = np.empty((stepCnt + 1,))
27-
zs = np.empty((stepCnt + 1,))
35+
xs = np.empty((num_steps + 1,))
36+
ys = np.empty((num_steps + 1,))
37+
zs = np.empty((num_steps + 1,))
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29-
# Setting initial values
39+
# Set initial values
3040
xs[0], ys[0], zs[0] = (0., 1., 1.05)
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32-
# Stepping through "time".
33-
for i in range(stepCnt):
34-
# Derivatives of the X, Y, Z state
42+
# Step through "time", calculating the partial derivatives at the current point
43+
# and using them to estimate the next point
44+
for i in range(num_steps):
3545
x_dot, y_dot, z_dot = lorenz(xs[i], ys[i], zs[i])
3646
xs[i + 1] = xs[i] + (x_dot * dt)
3747
ys[i + 1] = ys[i] + (y_dot * dt)
3848
zs[i + 1] = zs[i] + (z_dot * dt)
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50+
51+
# Plot
4052
fig = plt.figure()
4153
ax = fig.gca(projection='3d')
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