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"""
Pseudocolor plots of unstructured triangular grids.
"""
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import numpy as np
import math
%matplotlib inline
# Creating a Triangulation without specifying the triangles results in the
# Delaunay triangulation of the points.
# First create the x and y coordinates of the points.
n_angles = 36
n_radii = 8
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
angles = np.repeat(angles[...,np.newaxis], n_radii, axis=1)
angles[:,1::2] += math.pi/n_angles
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
z = (np.cos(radii)*np.cos(angles*3.0)).flatten()
# Create the Triangulation; no triangles so Delaunay triangulation created.
triang = tri.Triangulation(x, y)
# Mask off unwanted triangles.
xmid = x[triang.triangles].mean(axis=1)
ymid = y[triang.triangles].mean(axis=1)
mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
triang.set_mask(mask)
# pcolor plot.
plt.figure()
plt.gca().set_aspect('equal')
plt.tripcolor(triang, z, shading='faceted')
plt.colorbar()
plt.title('tripcolor of Delaunay triangulation')
# You can specify your own triangulation rather than perform a Delaunay
# triangulation of the points, where each triangle is given by the indices of
# the three points that make up the triangle, ordered in either a clockwise or
# anticlockwise manner.
xy = np.asarray([
[-0.101,0.872],[-0.080,0.883],[-0.069,0.888],[-0.054,0.890],[-0.045,0.897],
[-0.057,0.895],[-0.073,0.900],[-0.087,0.898],[-0.090,0.904],[-0.069,0.907],
[-0.069,0.921],[-0.080,0.919],[-0.073,0.928],[-0.052,0.930],[-0.048,0.942],
[-0.062,0.949],[-0.054,0.958],[-0.069,0.954],[-0.087,0.952],[-0.087,0.959],
[-0.080,0.966],[-0.085,0.973],[-0.087,0.965],[-0.097,0.965],[-0.097,0.975],
[-0.092,0.984],[-0.101,0.980],[-0.108,0.980],[-0.104,0.987],[-0.102,0.993],
[-0.115,1.001],[-0.099,0.996],[-0.101,1.007],[-0.090,1.010],[-0.087,1.021],
[-0.069,1.021],[-0.052,1.022],[-0.052,1.017],[-0.069,1.010],[-0.064,1.005],
[-0.048,1.005],[-0.031,1.005],[-0.031,0.996],[-0.040,0.987],[-0.045,0.980],
[-0.052,0.975],[-0.040,0.973],[-0.026,0.968],[-0.020,0.954],[-0.006,0.947],
[ 0.003,0.935],[ 0.006,0.926],[ 0.005,0.921],[ 0.022,0.923],[ 0.033,0.912],
[ 0.029,0.905],[ 0.017,0.900],[ 0.012,0.895],[ 0.027,0.893],[ 0.019,0.886],
[ 0.001,0.883],[-0.012,0.884],[-0.029,0.883],[-0.038,0.879],[-0.057,0.881],
[-0.062,0.876],[-0.078,0.876],[-0.087,0.872],[-0.030,0.907],[-0.007,0.905],
[-0.057,0.916],[-0.025,0.933],[-0.077,0.990],[-0.059,0.993] ])
x = xy[:,0]*180/3.14159
y = xy[:,1]*180/3.14159
x0 = -5
y0 = 52
z = np.exp(-0.01*( (x-x0)*(x-x0) + (y-y0)*(y-y0) ))
triangles = np.asarray([
[67,66, 1],[65, 2,66],[ 1,66, 2],[64, 2,65],[63, 3,64],[60,59,57],
[ 2,64, 3],[ 3,63, 4],[ 0,67, 1],[62, 4,63],[57,59,56],[59,58,56],
[61,60,69],[57,69,60],[ 4,62,68],[ 6, 5, 9],[61,68,62],[69,68,61],
[ 9, 5,70],[ 6, 8, 7],[ 4,70, 5],[ 8, 6, 9],[56,69,57],[69,56,52],
[70,10, 9],[54,53,55],[56,55,53],[68,70, 4],[52,56,53],[11,10,12],
[69,71,68],[68,13,70],[10,70,13],[51,50,52],[13,68,71],[52,71,69],
[12,10,13],[71,52,50],[71,14,13],[50,49,71],[49,48,71],[14,16,15],
[14,71,48],[17,19,18],[17,20,19],[48,16,14],[48,47,16],[47,46,16],
[16,46,45],[23,22,24],[21,24,22],[17,16,45],[20,17,45],[21,25,24],
[27,26,28],[20,72,21],[25,21,72],[45,72,20],[25,28,26],[44,73,45],
[72,45,73],[28,25,29],[29,25,31],[43,73,44],[73,43,40],[72,73,39],
[72,31,25],[42,40,43],[31,30,29],[39,73,40],[42,41,40],[72,33,31],
[32,31,33],[39,38,72],[33,72,38],[33,38,34],[37,35,38],[34,38,35],
[35,37,36] ])
# Rather than create a Triangulation object, can simply pass x, y and triangles
# arrays to tripcolor directly. It would be better to use a Triangulation object
# if the same triangulation was to be used more than once to save duplicated
# calculations.
plt.figure()
plt.gca().set_aspect('equal')
plt.tripcolor(x, y, triangles, z, shading='faceted')
plt.colorbar()
plt.title('tripcolor of user-specified triangulation')
plt.xlabel('Longitude (degrees)')
plt.ylabel('Latitude (degrees)')
plt.show()
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"""
Creating and plotting unstructured triangular grids.
"""
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import numpy as np
import math
%matplotlib inline
# Creating a Triangulation without specifying the triangles results in the
# Delaunay triangulation of the points.
# First create the x and y coordinates of the points.
n_angles = 36
n_radii = 8
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
angles = np.repeat(angles[...,np.newaxis], n_radii, axis=1)
angles[:,1::2] += math.pi/n_angles
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
# Create the Triangulation; no triangles so Delaunay triangulation created.
triang = tri.Triangulation(x, y)
# Mask off unwanted triangles.
xmid = x[triang.triangles].mean(axis=1)
ymid = y[triang.triangles].mean(axis=1)
mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
triang.set_mask(mask)
# Plot the triangulation.
plt.figure()
plt.gca().set_aspect('equal')
plt.triplot(triang, 'bo-')
plt.title('triplot of Delaunay triangulation')
# You can specify your own triangulation rather than perform a Delaunay
# triangulation of the points, where each triangle is given by the indices of
# the three points that make up the triangle, ordered in either a clockwise or
# anticlockwise manner.
xy = np.asarray([
[-0.101,0.872],[-0.080,0.883],[-0.069,0.888],[-0.054,0.890],[-0.045,0.897],
[-0.057,0.895],[-0.073,0.900],[-0.087,0.898],[-0.090,0.904],[-0.069,0.907],
[-0.069,0.921],[-0.080,0.919],[-0.073,0.928],[-0.052,0.930],[-0.048,0.942],
[-0.062,0.949],[-0.054,0.958],[-0.069,0.954],[-0.087,0.952],[-0.087,0.959],
[-0.080,0.966],[-0.085,0.973],[-0.087,0.965],[-0.097,0.965],[-0.097,0.975],
[-0.092,0.984],[-0.101,0.980],[-0.108,0.980],[-0.104,0.987],[-0.102,0.993],
[-0.115,1.001],[-0.099,0.996],[-0.101,1.007],[-0.090,1.010],[-0.087,1.021],
[-0.069,1.021],[-0.052,1.022],[-0.052,1.017],[-0.069,1.010],[-0.064,1.005],
[-0.048,1.005],[-0.031,1.005],[-0.031,0.996],[-0.040,0.987],[-0.045,0.980],
[-0.052,0.975],[-0.040,0.973],[-0.026,0.968],[-0.020,0.954],[-0.006,0.947],
[ 0.003,0.935],[ 0.006,0.926],[ 0.005,0.921],[ 0.022,0.923],[ 0.033,0.912],
[ 0.029,0.905],[ 0.017,0.900],[ 0.012,0.895],[ 0.027,0.893],[ 0.019,0.886],
[ 0.001,0.883],[-0.012,0.884],[-0.029,0.883],[-0.038,0.879],[-0.057,0.881],
[-0.062,0.876],[-0.078,0.876],[-0.087,0.872],[-0.030,0.907],[-0.007,0.905],
[-0.057,0.916],[-0.025,0.933],[-0.077,0.990],[-0.059,0.993] ])
x = xy[:,0]*180/3.14159
y = xy[:,1]*180/3.14159
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d')
x, y, z = np.meshgrid(np.arange(-0.8, 1, 0.2),
np.arange(-0.8, 1, 0.2),
np.arange(-0.8, 1, 0.8))
u = np.sin(np.pi * x) * np.cos(np.pi * y) * np.cos(np.pi * z)
v = -np.cos(np.pi * x) * np.sin(np.pi * y) * np.cos(np.pi * z)
w = (np.sqrt(2.0 / 3.0) * np.cos(np.pi * x) * np.cos(np.pi * y) *
np.sin(np.pi * z))
ax.quiver(x, y, z, u, v, w, length=0.1)
plt.show()
triangles = np.asarray([
[67,66, 1],[65, 2,66],[ 1,66, 2],[64, 2,65],[63, 3,64],[60,59,57],
[ 2,64, 3],[ 3,63, 4],[ 0,67, 1],[62, 4,63],[57,59,56],[59,58,56],
[61,60,69],[57,69,60],[ 4,62,68],[ 6, 5, 9],[61,68,62],[69,68,61],
[ 9, 5,70],[ 6, 8, 7],[ 4,70, 5],[ 8, 6, 9],[56,69,57],[69,56,52],
[70,10, 9],[54,53,55],[56,55,53],[68,70, 4],[52,56,53],[11,10,12],
[69,71,68],[68,13,70],[10,70,13],[51,50,52],[13,68,71],[52,71,69],
[12,10,13],[71,52,50],[71,14,13],[50,49,71],[49,48,71],[14,16,15],
[14,71,48],[17,19,18],[17,20,19],[48,16,14],[48,47,16],[47,46,16],
[16,46,45],[23,22,24],[21,24,22],[17,16,45],[20,17,45],[21,25,24],
[27,26,28],[20,72,21],[25,21,72],[45,72,20],[25,28,26],[44,73,45],
[72,45,73],[28,25,29],[29,25,31],[43,73,44],[73,43,40],[72,73,39],
[72,31,25],[42,40,43],[31,30,29],[39,73,40],[42,41,40],[72,33,31],
[32,31,33],[39,38,72],[33,72,38],[33,38,34],[37,35,38],[34,38,35],
[35,37,36] ])
# Rather than create a Triangulation object, can simply pass x, y and triangles
# arrays to triplot directly. It would be better to use a Triangulation object
# if the same triangulation was to be used more than once to save duplicated
# calculations.
plt.figure()
plt.gca().set_aspect('equal')
plt.triplot(x, y, triangles, 'go-')
plt.title('triplot of user-specified triangulation')
plt.xlabel('Longitude (degrees)')
plt.ylabel('Latitude (degrees)')
plt.show()
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from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
x = np.arange(-3, 3, 0.25)
y = np.arange(-3, 3, 0.25)
X, Y = np.meshgrid(x, y)
Z = np.sin(X)+ np.cos(Y)
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_wireframe(X,Y,Z)
plt.show()
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ax.plot_surface?
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ax.plot_trisurf?
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ax.plot_surface?
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from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
fig = plt.figure()
ax = fig.gca(projection='3d')
x, y, z = np.meshgrid(np.arange(-0.8, 1, 0.2),
np.arange(-0.8, 1, 0.2),
np.arange(-0.8, 1, 0.8))
u = np.sin(np.pi * x) * np.cos(np.pi * y) * np.cos(np.pi * z)
v = -np.cos(np.pi * x) * np.sin(np.pi * y) * np.cos(np.pi * z)
w = (np.sqrt(2.0 / 3.0) * np.cos(np.pi * x) * np.cos(np.pi * y) *
np.sin(np.pi * z))
ax.quiver(x, y, z, u, v, w, length=0.1)
plt.show()
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#!/usr/bin/env python
"""
pcolormesh uses a QuadMesh, a faster generalization of pcolor, but
with some restrictions.
This demo illustrates a bug in quadmesh with masked data.
"""
import numpy as np
from matplotlib.pyplot import figure, show, savefig
from matplotlib import cm, colors
from numpy import ma
%matplotlib inline
n = 12
x = np.linspace(-1.5, 1.5, n)
y = np.linspace(-1.5, 1.5, n*2)
X, Y = np.meshgrid(x, y)
Qx = np.cos(Y) - np.cos(X)
Qz = np.sin(Y) + np.sin(X)
Qx = (Qx + 1.1)
Z = np.sqrt(X**2 + Y**2)/5
Z = (Z - Z.min()) / (Z.max() - Z.min())
# The color array can include masked values:
Zm = ma.masked_where(np.fabs(Qz) < 0.5*np.amax(Qz), Z)
fig = figure()
ax = fig.add_subplot(121)
ax.set_axis_bgcolor("#bdb76b")
ax.pcolormesh(Qx, Qz, Z)
ax.set_title('Without masked values')
ax = fig.add_subplot(122)
ax.set_axis_bgcolor("#bdb76b")
# You can control the color of the masked region:
#cmap = cm.jet
#cmap.set_bad('r', 1.0)
#ax.pcolormesh(Qx,Qz,Zm, cmap=cmap)
# Or use the default, which is transparent:
col = ax.pcolormesh(Qx, Qz, Zm)
ax.set_title('With masked values')
show()
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"""
Demonstrates a few common tricks with shaded plots.
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LightSource, Normalize
%matplotlib inline
def display_colorbar():
"""Display a correct numeric colorbar for a shaded plot."""
y, x = np.mgrid[-4:2:200j, -4:2:200j]
z = 10 * np.cos(x**2 + y**2)
cmap = plt.cm.copper
ls = LightSource(315, 45)
rgb = ls.shade(z, cmap)
fig, ax = plt.subplots()
ax.imshow(rgb)
# Use a proxy artist for the colorbar...
im = ax.imshow(z, cmap=cmap)
im.remove()
fig.colorbar(im)
ax.set_title('Using a colorbar with a shaded plot', size='x-large')
def avoid_outliers():
"""Use a custom norm to control the displayed z-range of a shaded plot."""
y, x = np.mgrid[-4:2:200j, -4:2:200j]
z = 10 * np.cos(x**2 + y**2)
# Add some outliers...
z[100, 105] = 2000
z[120, 110] = -9000
ls = LightSource(315, 45)
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4.5))
rgb = ls.shade(z, plt.cm.copper)
ax1.imshow(rgb)
ax1.set_title('Full range of data')
rgb = ls.shade(z, plt.cm.copper)
ax2.imshow(rgb)
ax2.set_title('Manually set range')
fig.suptitle('Avoiding Outliers in Shaded Plots', size='x-large')
def shade_other_data():
"""Demonstrates displaying different variables through shade and color."""
y, x = np.mgrid[-4:2:200j, -4:2:200j]
z1 = np.sin(x**2) # Data to hillshade
z2 = np.cos(x**2 + y**2) # Data to color
norm = Normalize(z2.min(), z2.max())
cmap = plt.cm.jet
ls = LightSource(315, 45)
rgb = ls.shade_rgb(cmap(norm(z2)), z1)
fig, ax = plt.subplots()
ax.imshow(rgb)
ax.set_title('Shade by one variable, color by another', size='x-large')
display_colorbar()
avoid_outliers()
shade_other_data()
plt.show()
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