def plot3d(gridi):
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
fig = plt.figure()
ax = fig.gca(projection="3d")
X, Y, Z = axes3d.get_test_data(0.05)
print "axes3d.get_test_data(0.05)", list(axes3d.get_test_data(0.05)).shape
cset = ax.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm)
ax.clabel(cset, fontsize=9, inline=1)
plt.show()
def contourf_demo(**kwargs):
import mpl_toolkits.mplot3d.axes3d as axes3d
from ifigure.interactive import contourf, threed, figure
X, Y, Z = axes3d.get_test_data(0.05)
v = figure(gl = True)
threed('on')
contourf(X, Y, Z)
def show3D():
# imports specific to the plots in this example
import numpy as np
from matplotlib import cm
from mpl_toolkits.mplot3d.axes3d import get_test_data
# Twice as wide as it is tall.
fig = plt.figure(figsize=plt.figaspect(0.5))
#---- First subplot
ax = fig.add_subplot(1, 2, 1, projection='3d')
X = np.arange(-5, 5, 0.1)
Y = np.arange(-5, 5, 0.1)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet,
linewidth=0, antialiased=False)
ax.set_zlim3d(-1.01, 1.01)
fig.colorbar(surf, shrink=0.5, aspect=10)
#---- Second subplot
ax = fig.add_subplot(1, 2, 2, projection='3d')
X, Y, Z = get_test_data(0.05)
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
mystyle.printout_plain('3dGraph.png')
def test():
# Twice as wide as it is tall.
fig = plt.figure(figsize=plt.figaspect(0.5))
#---- First subplot
ax = fig.add_subplot(1, 2, 1, projection='3d')
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)
print type(X)
print X.shape
print Y.shape
print Z.shape
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
ax.set_zlim3d(-1.01, 1.01)
fig.colorbar(surf, shrink=0.5, aspect=10)
#---- Second subplot
ax = fig.add_subplot(1, 2, 2, projection='3d')
X, Y, Z = get_test_data(0.05)
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
plt.show()
def __init__(self, parent, title):
wx.Frame.__init__(self, parent, title=title, size=(1024, 768))
Notebook = wx.Notebook(self)
plot1 = plot(Notebook)
x_data = [1, 2, 3, 4, 5, 6]
y_data = npy.random.random(6)
data = npy.column_stack((x_data, y_data))
plot1.plot_2d(data, x_column=0, y_column=1, title="test 2d plot")
Notebook.AddPage(plot1, "2d plot - random data")
plot2 = plot(Notebook)
# 3d plot example - grab dummy data from mpl
from mpl_toolkits.mplot3d import axes3d
# discard X and Y data - we will provide coverage when we call plot_3d
X, Y, Z = axes3d.get_test_data(0.5)
plot2.plot_3d(title="test 3d plot", z_data=Z, x_coverage=5, y_coverage=10)
Notebook.AddPage(plot2, "3d Plot")
plot3 = plot(Notebook)
x1 = npy.arange(-10, 10, 0.5)
x2 = npy.arange(0, 20, 0.5)
data = npy.column_stack((x1, x1 ** 2, x2, x2 ** 3))
plot3.plot_2d_double(data, 0, 1, 2, 3)
Notebook.AddPage(plot3, "double 2dplot")
## panel.Layout()
self.Show(True)
def plot_charge_den():
"""Test function; Was not used"""
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
# print X
# print Y
# print Z
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
# cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
# cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
# cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)
ax.set_xlabel('X')
ax.set_xlim(-40, 40)
ax.set_ylabel('Y')
ax.set_ylim(-40, 40)
ax.set_zlabel('Z')
ax.set_zlim(-100, 100)
plt.show()
return
def noisy_test_data(num):
X, Y, Z = axes3d.get_test_data(0.05)
x = np.random.randint(0, Z.shape[0], num)
y = np.random.randint(0, Z.shape[1], num)
for i, j in zip(x, y):
Z[i, j] = np.random.random_sample() * 8
return Z
def test_mixedsamplesraises():
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
with pytest.raises(ValueError):
ax.plot_wireframe(X, Y, Z, rstride=10, ccount=50)
with pytest.raises(ValueError):
ax.plot_surface(X, Y, Z, cstride=50, rcount=10)
def test_wireframe3dzerostrideraises():
if sys.version_info[:2] < (2, 7):
raise nose.SkipTest("assert_raises as context manager " "not supported with Python < 2.7")
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
X, Y, Z = axes3d.get_test_data(0.05)
with assert_raises(ValueError):
ax.plot_wireframe(X, Y, Z, rstride=0, cstride=0)
def contour_demo2(**kwargs):
import mpl_toolkits.mplot3d.axes3d as axes3d
from ifigure.interactive import contourf, threed, figure, hold
X, Y, Z = axes3d.get_test_data(0.05)
v = figure(gl = True)
threed('on')
hold('on')
contourf(X, Y, Z)
contourf(X, Y, Z, zdir = 'x', offset = -40)
def test_contour3d():
fig = plt.figure()
ax = fig.gca(projection="3d")
X, Y, Z = axes3d.get_test_data(0.05)
cset = ax.contour(X, Y, Z, zdir="z", offset=-100, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir="x", offset=-40, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir="y", offset=40, cmap=cm.coolwarm)
ax.set_xlim(-40, 40)
ax.set_ylim(-40, 40)
ax.set_zlim(-100, 100)
def draw_wireframe():
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x, y, z = axes3d.get_test_data(0.05)
# def z_func(x,y):
# return x+y
z = x**2+y**4
ax.plot_wireframe(x, y, z, rstride=10, cstride=10)
plt.show()
def solve2():
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.1)
print X.shape
print Z.shape
ax.scatter(X, Y, Z)
plt.show()
def test_contourf3d():
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)
ax.set_xlim(-40, 40)
ax.set_ylim(-40, 40)
ax.set_zlim(-100, 100)
def plot():
#numpy.set_printoptions(threshold='nan')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
print X
ax.set_xlabel('TIME (DAYS)')
ax.set_ylabel('Optimal Minimized Risk')
ax.set_zlabel('Price (USD$)')
ax.plot_surface(X, Y, Z,cmap=plt.cm.jet, rstride=10, cstride=10)
plt.show()
def wire():
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')
x, y, z = axes3d.get_test_data(0.05)
x = np.linspace(1,10,100)
y = np.linspace(4,10,100)
z = np.linspace(8,10,100)
ax.plot_wireframe(x, y, z, rstride=10, cstride=10)
plt.show()
def grafica():
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
ax.plot_surface(X, Y, Z, rstride=3, cstride=8, alpha=0.3)
# cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
# cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
# cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)
ax.set_xlabel('X')
ax.set_xlim(-40, 40)
ax.set_ylabel('Y')
ax.set_ylim(-40, 40)
ax.set_zlabel('Z')
ax.set_zlim(-100, 100)
plt.show()
def show3D():
'''Generation of 3D plots'''
# imports specific to the plots in this example
from matplotlib import cm # colormaps
# This module is required for 3D plots!
from mpl_toolkits.mplot3d import Axes3D
# Twice as wide as it is tall.
fig = plt.figure(figsize=plt.figaspect(0.5))
setFonts(16)
#---- First subplot
# Generate the data
X = np.arange(-5, 5, 0.1)
Y = np.arange(-5, 5, 0.1)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
# Note the definition of "projection", required for 3D plots
#plt.style.use('ggplot')
ax = fig.add_subplot(1, 2, 1, projection='3d')
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.GnBu,
linewidth=0, antialiased=False)
#surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.viridis_r,
#linewidth=0, antialiased=False)
ax.set_zlim3d(-1.01, 1.01)
fig.colorbar(surf, shrink=0.5, aspect=10)
#---- Second subplot
# Get some 3d test-data
from mpl_toolkits.mplot3d.axes3d import get_test_data
ax = fig.add_subplot(1, 2, 2, projection='3d')
X, Y, Z = get_test_data(0.05)
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
showData('3dGraph.png')
def generate_test_data():
"""
Borrowed from http://matplotlib.org/examples/mplot3d/contourf3d_demo2.html
"""
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y, Z = axes3d.get_test_data( 0.1 )
Z = np.abs( Z )
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
cset = ax.contourf(X, Y, Z, zdir='z', offset=-10, cmap=cm.coolwarm)
cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)
ax.set_xlabel('X')
ax.set_xlim(-40, 40)
ax.set_ylabel('Y')
ax.set_ylim(-40, 40)
ax.set_zlabel('Z')
ax.set_zlim(-1, 100)
plt.show()
return Z
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import style
style.use('fivethirtyeight')
fig = plt.figure()
ax1 = fig.add_subplot(
111, projection='3d') # notify that the graphs is 3 dimensions
x, y, z = axes3d.get_test_data() # gives test data
print(axes3d.__file__)
ax1.plot_wireframe(x, y, z, rstride=5,
cstride=5) #rstride and cstride creates distance
ax1.set_xlabel('X-Axis')
ax1.set_ylabel('Y-Axis')
ax1.set_zlabel('Z-Axis')
ax1.grid(True)
plt.show()
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
style.use('fivethirtyeight')
fig1 = plt.figure(1)
fig2 = plt.figure(2)
ax1 = fig1.add_subplot(111, projection='3d')
ax2 = fig2.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data() ## this creates n-dim lists for X,Y,Z
print(type(X))
A = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
B = np.array([[4, 2, 6, 5], [4, 2, 6, 5], [4, 2, 6, 5], [4, 2, 6, 5]])
C = np.array([[3, 6, 8, 7], [3, 6, 8, 7], [3, 6, 8, 7], [3, 6, 8, 7]])
x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
y = np.array([[5, 6, 7, 8, 2, 5, 6, 3, 7, 2]])
z = np.array([[1, 2, 6, 3, 2, 7, 3, 3, 7, 2]])
a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
b = [4, 4, 1, 7, 3, 8, 1, 6, 3, 7]
c = [7, 8, 4, 9, 9, 4, 8, 6, 3, 9]
##ax1.plot(a, b, c) ## This is a line graph in 3d plain, we see 1 line
ax1.scatter(a, b, c, c='g', marker='o')
ax1.scatter(x[0], y[0], z[0], c='r',
marker='o') ## The previous data remains, the new data is added
## to the graph
chart.set_xlabel('xlabel')
chart.set_ylabel('ylabel')
chart.set_zlabel('zlabel')
plt.show()
#wireframes
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
chart = fig.add_subplot(1, 1, 1, projection='3d')
x, y, z = axes3d.get_test_data(0.05) #0.05 is the delta for fast computation
chart.plot_wireframe(x, y, z, rstride=10, cstride=10)
#rstride=cstride=no of lines(more detailed)
chart.set_xlabel('xlabel')
chart.set_ylabel('ylabel')
chart.set_zlabel('zlabel')
plt.show()
###################pandas##################
#####Series(is the 1-dimensional data structures)
import pandas as pd
s = pd.Series([20, "sujith", 78, 'Hello']) #with list
s
s[0]
def test_wireframe3dzerocstride():
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
ax.plot_wireframe(X, Y, Z, rcount=13, ccount=0)
from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt from matplotlib import cm fig = plt.figure() ax = fig.gca(projection='3d') X, Y, Z = axes3d.get_test_data(0.05) cset = ax.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm) ax.clabel(cset, fontsize=9, inline=1) plt.show()
def test_wireframe3dzerorstride():
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
ax.plot_wireframe(X, Y, Z, rstride=0, cstride=10)
Shubham Patel
Github: github.com/shubham-00
LinkedIn: https://www.linkedin.com/in/srpatel980/
Contact for more.
'''
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure() # Creating an empty figure
ax = fig.add_subplot(111, projection="3d") # Added an empty 3d plot
# 111 means 1*1 grid and 1st subplot
# abc means a*b grid and cth subplot
x, y, z = axes3d.get_test_data(delta=0.1)
ax.plot_wireframe(x, y, z, rstride=3, cstride=3)
# rstride, cstride => how often we draw a line (row, colunm)
# delta => how often we compute a line
ax.set_title("3D Plane")
ax.set_xlabel("X axis")
ax.set_ylabel("Y axis")
ax.set_zlabel("Z axis")
#print(x, y, z)
plt.show()
'''
Shubham Patel
Github: github.com/shubham-00
def taylor_expansion_3d(exp):
x, y, z = sp.symbols('x y z')
# calculating constants
grad_x = exp.diff(x)
grad_y = exp.diff(y)
print(grad_x, grad_y)
grad_xx = grad_x.diff(x)
grad_xy = grad_x.diff(y)
grad_yy = grad_y.diff(y)
grad_yx = grad_y.diff(x)
vec1_list = []
taylor_values = []
vec2_list = []
xlist = []
ylist = []
for a in np.arange(0, 3, 1):
for b in np.arange(0, 3, 1):
# first term == f(a,b)
first_term = exp.subs(x, a)
first_term = first_term.subs(y, b)
# print(first_term)
# second term == f'(a,b)*(x-a) + f'(a,b)*(y-b)
grad_1x = grad_x.subs(x, a)
grad_1y = grad_y.subs(y, b)
second_term = (grad_1x * (x - a) + grad_1y *
(y - b)) / factorial(1)
# print(second_term)
# third term == (f''(a,b)*(x-a)**2 + f''(a,b)*(x-a)*(y-b) + f''(a,b)*(y-b)**2)/2
grad_2xx = grad_xx.subs(x, a)
grad_2yy = grad_yy.subs(y, b)
grad_2xy = grad_xy.subs([x, y], [a, b])
grad_2yx = grad_yx.subs([x, y], [a, b])
third_term = (grad_2xx * (x - a)**2 + (grad_2xy + grad_2yx) *
(x - a) * (y - b) + grad_2yy *
(y - b)**2) / factorial(2)
# print(third_term)
vec1_list.append([a, b])
for xval in np.arange(0, 5, 0.5):
for yval in np.arange(0, 5, 0.5):
taylor_exp = first_term + second_term + third_term
taylor_exp = taylor_exp.subs(x, xval)
taylor_exp = taylor_exp.subs(y, yval)
taylor_exp = round(taylor_exp, 2)
taylor_values.append(taylor_exp)
#vec2_list.append([xval, yval])
xlist.append(xval)
ylist.append(yval)
print(xlist)
print(ylist)
print(taylor_values)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x, y, z = axes3d.get_test_data(0.05)
print(x)
print(y)
print(z)
ax.plot_wireframe(xlist, ylist, taylor_values, rstride=5, cstride=5)
plt.show()
# PyCharm
# Python大数据
# test3X
# 御承扬
# 2019/12/9
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
if __name__ == '__main__':
fig = plt.figure(figsize=(8, 6))
ax = fig.gca(projection='3d')
X, Y, Z = axes3d.get_test_data(0.05) ## 生成二维测试数据
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
cset = ax.contour(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='y', offset=-40, cmap=cm.coolwarm)
ax.set_xlabel('X')
ax.set_xlim(-40, 40)
ax.set_ylabel('y')
ax.set_ylim(-40, 40)
ax.set_zlabel('z')
ax.set_zlim(-100, 100)
plt.show()
y = [4, 8, 7, 2, 6, 9, 8, 1, 2, 4]
z = [9, 4, 1, 2, 7, 5, 3, 4, 5, 8]
#ax1.plot_wireframe(x, y, z)
x2 = [-1, -2, -3, -4, -5, -6, -7, -8, -9, -10]
y2 = [-4, -8, -7, -2, -6, -9, -8, -1, -2, -4]
z2 = [9, 4, 1, 2, 7, 5, 3, 4, 5, -8]
#ax1.scatter(x, y, z, c='b', marker='o')
#ax1.scatter(x2, y2, z2, c='r', marker='o')
x3 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y3 = [4, 8, 7, 2, 6, 9, 8, 1, 2, 4]
z3 = np.zeros(10)
# if use numbers instead of 'zeros', the bars will be 'floating in the air'
dx = np.ones(10)
dy = np.ones(10)
dz = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
#ax1.bar3d(x3, y3, z3, dx, dy, dz)
x4, y4, z4 = axes3d.get_test_data()
ax1.plot_wireframe(x4, y4, z4, rstride=5, cstride=5)
ax1.set_xlabel('x axis')
ax1.set_ylabel('y axis')
ax1.set_zlabel('z axis')
plt.show()
import numpy as np from scipy.interpolate import griddata import scipy.ndimage as ndimage from scipy.ndimage import gaussian_filter from scipy.misc import imsave from matplotlib import cm import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from stl import mesh, Mode import matplotlib.tri as mtri from mpl_toolkits.mplot3d.axes3d import get_test_data # Generating the surface x, y, z = get_test_data(delta=0.1) # Scale the surface for this example z *= 0.05 # Remember that Gazebo uses ENU (east-north-up) convention, so underwater # the Z coordinate will be negative z -= 3 # Note: Gazebo will import your mesh in meters. # Point clouds usually don't come in nice grids, so let's make it a (N, 3) # matrix just to show how it can be done. If you have outliers or noise, you should # treat those values now. xyz = np.zeros(shape=(x.size, 3)) xyz[:, 0] = x.flatten() xyz[:, 1] = y.flatten() xyz[:, 2] = z.flatten() # Generate a grid for the X and Y coordinates, change the number of points # to your needs. Large grids can generate files that are too big for Gazebo, so
def _generate_hills(size):
"Generates noisy hills test data"
_, _, data = axes3d.get_test_data(6.0 / size)
return -0.1 * data # to make in about the same height as the circles
def __init__(self, parent, controller):
tk.Frame.__init__(self, parent)
label = ttk.Label(self, text="Wireframe")
label.pack(pady=10, padx=10)
ttk.Style().configure("RB.TButton",
foreground='black',
background='orange')
ButtonHome = ttk.Button(
self,
text="Back",
style="RB.TButton",
command=lambda: controller.show_frame(WireFrameInfo)).place(x=0,
y=0)
#Figure creation and assignment
self.fig = plt.figure()
ax = self.fig.add_subplot(111, projection='3d', axisbg='orange')
#sets background color
rect = self.fig.patch
rect.set_facecolor('orange')
#delta how often we're going to compute the line
x, y, z = axes3d.get_test_data(0.05)
#rstride is how often we draw a line
ax.plot_surface(x,
y,
z,
cmap=cm.autumn,
rstride=2,
cstride=2,
alpha=0.3)
cset = ax.contour(x, y, z, zdir='z', offset=-100, cmap=cm.autumn)
cset = ax.contour(z, y, z, zdir='x', offset=-40, cmap=cm.autumn)
cset = ax.contour(z, y, z, zdir='y', offset=40, cmap=cm.autumn)
ax.spines['bottom'].set_color('orange')
ax.spines['right'].set_color('orange')
ax.spines['left'].set_color('orange')
ax.spines['top'].set_color('orange')
ax.set_xlim(-40, 40)
ax.set_ylim(-40, 40)
ax.set_zlim(-100, 100)
style.use("ggplot")
canvas = FigureCanvasTkAgg(self.fig, self)
#Canvas border color
canvas.get_tk_widget().configure(background='orange',
highlightcolor='orange',
highlightbackground='orange')
canvas.show()
canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True)
toolbar = NavigationToolbar2TkAgg(canvas, self)
toolbar.update()
self.configure(background='orange')
ax.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
ax.set_ylabel('Grafica 1')
ax = fig.add_subplot(2, 2, 2)
ax.plot(Xa, Vxb, color="red", linewidth=1.0, linestyle="-")
ax.title.set_text('Eje X2 vs V')
ax.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
ax.set_ylabel('Grafica 2')
"""
ax = fig.add_subplot(2, 2, 3)
ax.plot(Xa, Vxy, color="green", linewidth=1.0, linestyle="-")
ax.title.set_text('Eje X,Y vs V')
ax.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
ax.set_ylabel('Grafica 3')
"""
from mpl_toolkits.mplot3d.axes3d import get_test_data
ax = fig.add_subplot(2, 2, 3, projection='3d')
X, Y, Vtt = get_test_data(0.05)
ax.plot_wireframe(X, Y, Vtt, rstride=10, cstride=10)
#------------------------------------------------------------------------------
# GRAFICAS 3D
#------------------------------------------------------------------------------
ax = fig.add_subplot(2, 2, 4, projection='3d')
X, Y = np.meshgrid(Xa, Ya)
surf = ax.plot_surface(X, Y, Vtt, cmap=cm.get_cmap("jet"), antialiased=False)
ax.set_zlim(300, 1800)
fig.colorbar(surf)
#------------------------------------------------------------------------------
plt.pause(25)
pl.savefig('tierras.pdf')
def get_data(p):
x, y, z = axes3d.get_test_data(p)
z = f * z
return x, y, z
from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # Grab some test data. X, Y, Z = axes3d.get_test_data(1000) # Plot a basic wireframe. #ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10) plt.show()
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
from mpl_toolkits.mplot3d.axes3d import get_test_data
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=plt.figaspect(0.5))
x, y, z = get_test_data(0.05)
zlv = np.linspace(-85, 85, 35)
# 第一个子图
ax1 = fig.add_subplot(1, 2, 1)
ct = ax1.contourf(x, y, z, levels=zlv, cmap=cm.coolwarm)
fig.colorbar(ct, shrink=0.7, aspect=15, ticks=zlv[1::8])
# 第二个子图
ax2 = fig.add_subplot(1, 2, 2, projection='3d')
sf = ax2.plot_surface(x,
y,
z,
rstride=1,
cstride=1,
vmin=zlv[0],
vmax=zlv[-1],
cmap=cm.bwr)
fig.colorbar(sf, shrink=0.7, aspect=15, orientation='horizontal')
plt.show()
from mpl_toolkits.mplot3d.axes3d import get_test_data
plt.style.use('seaborn')
# example1
'''
x = np.linspace(-10, 10, 200)
y = np.linspace(-10, 10, 200)
X, Y = np.meshgrid(x, y)
Z = X**2 + Y**2
levels = np.geomspace(Z.min(), Z.max(), 100)
'''
# example2
X, Y, Z = get_test_data(0.05)
levels = np.linspace(Z.min(), Z.max(), 100)
fig = plt.figure(figsize=(14, 7))
ax1 = fig.add_subplot(121, projection='3d')
ax2 = fig.add_subplot(122)
# wireframe
ax1.plot_wireframe(X, Y, Z)
# contour/contourf
#ax2.contour(X, Y, Z, cmap='bwr', levels=levels)
contourf = ax2.contourf(X, Y, Z, cmap='bwr', levels=levels)
cbar = fig.colorbar(contourf, pad=0., fraction=0.15)
contourf_zero = ax2.contour(X,
# - http://www.astropy.org/astropy-tutorials/FITS-tables.html
# - http://www.astropy.org/astropy-tutorials/FITS-images.html
# - http://www.astropy.org/astropy-tutorials/FITS-header.html
import os.path
from astropy.io import fits
import numpy as np
from mpl_toolkits.mplot3d import axes3d
OUTPUT_PATH = "out.fits"
# CREATE DATA #########################
X1, Y1, Z1 = axes3d.get_test_data(0.05)
X2, Y2, Z2 = axes3d.get_test_data(0.05)
X3, Y3, Z3 = axes3d.get_test_data(0.05)
Z1 *= 1.
Z2 *= -1.
Z3 *= 2.
img = np.stack([Z1, Z2, Z3], 0)
print("Image shape:", img.shape)
# CREATE THE FITS STRUCTURE ###########
hdu = fits.PrimaryHDU(img)
def test_wireframe3dzerostrideraises():
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
with pytest.raises(ValueError):
ax.plot_wireframe(X, Y, Z, rstride=0, cstride=0)
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('dark_background')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# Grab some test data.
X = np.array([[0], [100], [0], [0]])
Y = np.array([[0], [0], [0], [0]])
Z = np.array([[0], [100], [0], [0]])
print(axes3d.get_test_data(0.05))
# Plot a basic wireframe.
ax.plot_wireframe(
X,
Y,
Z,
)
plt.show()
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
style.use('ggplot')
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
x, y, z = axes3d.get_test_data()
ax1.plot_wireframe(x, y, z, rstride=5, cstride=5)
ax1.set_xlabel('x axis')
ax1.set_ylabel('y axis')
ax1.set_zlabel('z axis')
plt.show()
from mpl_toolkits.mplot3d.axes3d import Axes3D
import matplotlib.pyplot as plt
# imports specific to the plots in this example
import numpy as np
from matplotlib import cm
from mpl_toolkits.mplot3d.axes3d import get_test_data
# Twice as wide as it is tall.
fig = plt.figure(figsize=plt.figaspect(0.5))
#---- First subplot
ax = fig.add_subplot(1, 2, 1, projection='3d')
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)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
ax.set_zlim3d(-1.01, 1.01)
fig.colorbar(surf, shrink=0.5, aspect=10)
#---- Second subplot
ax = fig.add_subplot(1, 2, 2, projection='3d')
X, Y, Z = get_test_data(0.05)
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
plt.show()
from mpl_toolkits.mplot3d import axes3d
from pylab import *
import numpy as np
p=[]
q=[]
x,y,z= axes3d.get_test_data(0.05)
for i in range(10):
p.append(i^1)
q.append(i^2)
l=range(10)
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(1, 1, 1,projection='3d')
ax.set_xlabel('base value from 0 to 9 Label')
ax.set_ylabel('i^1 Label')
ax.set_zlabel('i^2 Label')
ax.grid(True)
#ax.grid(color='r', alpha=0.2, linestyle='dashed', linewidth=0.5)
p = ax.plot_surface(l,p,q,rstride=4, cstride=4) # might be plot_surface
plt.show()
def test_wireframe3dzerostrideraises():
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
with pytest.raises(ValueError):
ax.plot_wireframe(X, Y, Z, rstride=0, cstride=0)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
fig = plt.figure(figsize=(5, 6))
ax = plt.subplot(111, projection="3d")
X, Y, Z = axes3d.get_test_data() #delta = 0.05
ax.plot_surface(X, Y, Z, rstride=10, cstride=10, cmap=plt.get_cmap("winter"))
ax.view_init(20, 120)
plt.show()
def test2d(paramlist, show, val):
#print(val)
#print(paramlist)
fimtgd = FIMTGD(gamma=paramlist[0],
n_min=paramlist[1],
alpha=[2],
threshold=paramlist[3],
learn=paramlist[4])
fimtls = FIMTLS(gamma=paramlist[0],
n_min=paramlist[1],
alpha=[2],
threshold=paramlist[3],
learn=paramlist[4])
gfimtls = gFIMTLS(gamma=paramlist[0],
n_min=paramlist[1],
alpha=[2],
threshold=paramlist[3],
learn=paramlist[5])
cumLossgd = [0]
cumLossls = [0]
cumLossgls = [0]
if True:
start = 0.0
end = 1.0
X = list()
Y = list()
x, y, z = axes3d.get_test_data(0.1)
num_d = len(x)**2
for i in range(len(x)):
for j in range(len(y)):
input = [x[i, j], y[i, j]]
target = z[i, j]
X.append(input)
Y.append(target)
data = [X, Y]
data = np.array(data)
data = data.transpose()
np.random.shuffle(data)
data = data.transpose()
for i in range(num_d):
input = data[0][i]
target = data[1][i] + (np.random.uniform() - 0.5) * 0.2
o_target = data[1][i]
if num_d / 2 < i:
target += 1.0
o_target += 1.0
cumLossgd.append(cumLossgd[-1] + np.fabs(
o_target - fimtgd.eval_and_learn(np.array(input), target)))
cumLossls.append(cumLossls[-1] + np.fabs(
o_target - fimtls.eval_and_learn(np.array(input), target)))
cumLossgls.append(cumLossgls[-1] + np.fabs(
o_target - gfimtls.eval_and_learn(np.array(input), target)))
#plt.scatter(x=x,y=y)
#plt.show()
if show:
f = plt.figure()
plt.plot(cumLossgd[1:], label="Gradient Descent Loss")
f.hold(True)
plt.plot(cumLossls[1:], label="Filter Loss")
#avglossgd=np.array([cumLossgd[-1]/len(cumLossgd)]*len(cumLossgd))
#plt.plot(avglossgd,label="Average GD Loss")
#plt.plot([cumLossls[-1]/len(cumLossls)]*len(cumLossls), label="Average Filter Loss")
plt.title("CumLoss Ratio:" + str(
min(cumLossgd[-1], cumLossls[-1]) /
max(cumLossgd[-1], cumLossls[-1])))
plt.legend()
figname="g"+str(paramlist[0])+"_nmin"+str(paramlist[1])+"_al"+str(paramlist[2])+"_thr"+str(paramlist[3])\
+ "_lr"+str(paramlist[4])+".png"
plt.savefig(figname)
#plt.show()
f.clear()
#print(i)
#print(fimtgd.count_leaves())
#print(fimtgd.count_nodes())
return [cumLossgd, cumLossls, cumLossgls, val, paramlist]
# -*- coding: utf-8 -*-
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
fig = plt.figure(figsize=(16, 12))
ax1 = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.1) #测试数据
print X, "\n#" * 2, Y, "\n#" * 2, Z
#cset = ax1.contour(X, Y, Z, cmap=cm.coolwarm) #color map选用的是coolwarm
cset = ax1.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm)
ax1.set_title("Contour plot", color='b', weight='bold', size=25)
plt.show()
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import axes3d
fig = plt.figure(figsize=(16, 12))
ax2 = fig.gca(projection="3d") # get current axis
X, Y, Z = axes3d.get_test_data(0.001) #测试数据
ax2.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.5, cmap=cm.coolwarm)
cset = ax2.contour(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
cset = ax2.contour(X, Y, Z, zdir="x", offset=-40,
cmap=cm.coolwarm) #沿x轴方向投影（将ｘ轴作为新坐标系的ｚ轴，在新坐标系的xy平面作图。）
cset = ax2.contour(X, Y, Z, zdir="y", offset=-40, cmap=cm.coolwarm)
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = range(6), [5, 6, 3, 1, 5, 2], [8, 7, 6, 8, 7, 5]
X2, Y2, Z2 = [2, 3, 4, 5, 6, 7], [5, 6, 3, 1, 5, 2], range(6)
X3, Y3, Z3 = [5, 6, 3, 1, 5, 2], range(6), [2, 3, 4, 5, 6, 7]
X, Y, Z = axes3d.get_test_data(0.05) #every axe gets a matrix
#print len(X),X[0]
#bar charts
#xpos=range(1,11) #origin of points
#ypos=[2,3,4,5,1,6,2,1,7,2]
#zpos=[0]*10
#zpos[4]=4
#dx=[1]*10 #distance to move on directions
#dy=[1]*10
#dz=range(1,11)
#ax.plot_wireframe(X,Y,Z) #one line
#ax.scatter(X,Y,Z, c='green', marker='o') #only dots
#ax.scatter(X2,Y2,Z2, c='red', marker='v') #only dots
#ax.scatter(X3,Y3,Z3, c='blue', marker='^') #only dots
#ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='#00ccaa') #3d bars
ax.plot_wireframe(X, Y, Z, rstride=5, cstride=2) #planos 3d
ax.set_xlabel('Eje X')
ax.set_ylabel('Eje Y')
ax.set_zlabel('Eje Z')
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
'''
x = [1,2,3,4,5,6,7,8,9,10]
y = [5,6,7,8,2,5,6,3,7,2]
z = [1,2,6,3,2,7,3,3,7,2]
ax1.plot_wireframe(x,y,z)
'''
'''
x2 = [-1,-2,-3,-4,-5,-6,-7,-8,-9,-10]
y2 = [-5,-6,-7,-8,-2,-5,-6,-3,-7,-2]
z2 = [1,2,6,3,2,7,3,3,7,2]
ax1.scatter(x,y,z, c='g', marker='o')
ax1.scatter(x2,y2,z2, c='r', marker='^')
'''
x3,y3,z3 = axes3d.get_test_data()
ax1.plot_wireframe(x3,y3,z3,rstride=5,cstride=5)
ax1.set_xlabel('x axis')
ax1.set_ylabel('y axis')
ax1.set_zlabel('z axis')
plt.show()
Y,
Z,
rstride=1,
cstride=1,
cmap=cm.rainbow,
linewidth=0,
antialiased=False)
ax.set_zlim(-1.01, 1.01)
fig.colorbar(surf, shrink=0.5, aspect=10)
#第二個子圖
ax = fig.add_subplot(1, 3, 2, projection='3d')
X, Y, Z = get_test_data(0.1)
# ax.plot_wireframe(X, Y, Z, rstride=5, cstride=5)
surf = ax.plot_surface(X,
Y,
Z,
rstride=1,
cstride=1,
cmap=cm.rainbow,
linewidth=0,
antialiased=False)
fig.colorbar(surf, shrink=0.5, aspect=10)
# 3
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
plt.ion()
fig = plt.figure()
ax = axes3d.Axes3D(fig)
X, Y, Z = axes3d.get_test_data(0.1)
ax.plot_wireframe(X, Y, Z, rstride=5, cstride=5)
for angle in range(0, 360):
ax.view_init(30, angle)
plt.draw()
def test_has_matplotlib():
import pathlib
from io import BytesIO
import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as mtri
from cycler import cycler
from matplotlib import cm
from mpl_toolkits.mplot3d.axes3d import get_test_data
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 = plt.figure(figsize=(19.2, 14.4))
ax = fig.add_subplot(3, 3, 1, projection='3d')
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.viridis)
ax.set_title('matplotlib')
ax2 = fig.add_subplot(3, 3, 2, projection='3d')
X, Y, Z = get_test_data(0.05)
ax2.plot_wireframe(X, Y, Z, rstride=5, cstride=5, linestyles='dashdot')
ax2.set_title('3d')
u = np.linspace(0, 2.0 * np.pi, endpoint=True, num=50)
v = np.linspace(-0.5, 0.5, endpoint=True, num=10)
u, v = np.meshgrid(u, v)
u, v = u.flatten(), v.flatten()
x = (1 + 0.5 * v * np.cos(u / 2.0)) * np.cos(u)
y = (1 + 0.5 * v * np.cos(u / 2.0)) * np.sin(u)
z = 0.5 * v * np.sin(u / 2.0)
tri = mtri.Triangulation(u, v)
ax3 = fig.add_subplot(3, 3, 3, projection='3d')
ax3.plot_trisurf(x, y, z, triangles=tri.triangles, cmap=cm.jet)
ax3.set_title('plot')
radii = np.linspace(0.125, 1.0, 8)
angles = np.linspace(0, 2*np.pi, 36, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], 8, axis=1)
x = np.append(0, (radii*np.cos(angles)).flatten())
y = np.append(0, (radii*np.sin(angles)).flatten())
z = np.sin(-x*y)
ax4 = fig.add_subplot(3, 3, 4, projection='3d')
ax4.plot_trisurf(x, y, z, linewidth=0.2, cmap=cm.Spectral)
ax4.set_title('test')
X, Y, Z = get_test_data(0.05)
ax5 = fig.add_subplot(3, 3, 5, projection='3d')
ax5.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm)
ax5.set_title('for')
ax6 = fig.add_subplot(3, 3, 6, projection='3d')
ax6.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
ax6.contour(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
ax6.contour(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
ax6.contour(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)
ax6.set_xlim(-40, 40)
ax6.set_ylim(-40, 40)
ax6.set_zlim(-100, 100)
ax6.set_title('docker')
theta = np.linspace(0.0, 2 * np.pi, 20, endpoint=False)
radii = 10 * np.random.rand(20)
width = np.pi / 4 * np.random.rand(20)
ax7 = fig.add_subplot(3, 3, 7, projection='polar')
bars = ax7.bar(theta, radii, width=width, bottom=0.0)
for r, bar in zip(radii, bars):
bar.set_facecolor(cm.viridis(r / 10.))
bar.set_alpha(0.5)
ax7.set_title('also this')
x = np.linspace(0, 2 * np.pi)
offsets = np.linspace(0, 2*np.pi, 4, endpoint=False)
yy = np.transpose([np.sin(x + phi) for phi in offsets])
ax8 = fig.add_subplot(3, 2, 6)
ax8.set_prop_cycle(cycler('color', ['r', 'g', 'b', 'y']) +
cycler('linestyle', ['-', '--', ':', '-.']))
ax8.plot(yy)
ax8.set_title('and one of these too')
# check it writes to buffer properly first
out_buffer = BytesIO()
plt.savefig(out_buffer, dpi=160, format='png', bbox_inches='tight')
out_buffer.seek(0)
assert out_buffer.read(4) == b'\x89PNG'
# write to file for good measure
with open('_test_plot_image.png', 'wb') as op:
op.write(b'\x89PNG' + out_buffer.read())
assert pathlib.Path('_test_plot_image.png').exists()
def test_wireframe3dzerocstride():
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
ax.plot_wireframe(X, Y, Z, rcount=13, ccount=0)
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig=plt.figure()
ax=fig.add_subplot(111, projection='3d')
X, Y, Z = range(6), [5,6,3,1,5,2], [8,7,6,8,7,5]
X2, Y2, Z2 = [2,3,4,5,6,7], [5,6,3,1,5,2], range(6)
X3, Y3, Z3 = [5,6,3,1,5,2], range(6), [2,3,4,5,6,7]
X, Y, Z = axes3d.get_test_data(0.05) #every axe gets a matrix
#print len(X),X[0]
#bar charts
#xpos=range(1,11) #origin of points
#ypos=[2,3,4,5,1,6,2,1,7,2]
#zpos=[0]*10
#zpos[4]=4
#dx=[1]*10 #distance to move on directions
#dy=[1]*10
#dz=range(1,11)
#ax.plot_wireframe(X,Y,Z) #one line
#ax.scatter(X,Y,Z, c='green', marker='o') #only dots
#ax.scatter(X2,Y2,Z2, c='red', marker='v') #only dots
#ax.scatter(X3,Y3,Z3, c='blue', marker='^') #only dots
#ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='#00ccaa') #3d bars
ax.plot_wireframe(X,Y,Z, rstride=5, cstride=2) #planos 3d
ax.set_xlabel('Eje X')
ax.set_ylabel('Eje Y')
ax.set_zlabel('Eje Z')
"""
.. versionadded:: 1.1.0
This demo depends on new features added to contourf3d.
"""
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
fig = plt.figure()
ax = fig.gca(projection='3d')
from pprint import pprint
pprint(axes3d.get_test_data(0.05))
X, Y, Z = axes3d.get_test_data(0.05)
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)
ax.set_xlabel('X')
ax.set_xlim(-40, 40)
ax.set_ylabel('Y')
ax.set_ylim(-40, 40)
ax.set_zlabel('Z')
ax.set_zlim(-100, 100)
plt.show()
ax2 = fig.add_subplot(122)
cs = ax2.contour(X4, Y4, Z4, 15,
cmap='jet') #contour将三维图像在二维空间上表示,15代表等高线的密集程度，数值越大线越多
ax2.clabel(cs, inline=True, fontsize=10, fmt='%1.1f') #clabel函数在每条线上显示数据值的大小
plt.title('二维显示', fontproperties=font)
'''绘制三维曲线'''
from mpl_toolkits.mplot3d.axes3d import get_test_data
fig5 = plt.figure(figsize=(8, 6))
ax5 = fig5.gca(projection='3d')
#生成三维测试数据
X5, Y5, Z5 = get_test_data(0.05)
ax5.plot_surface(X5, Y5, Z5, rstride=8, cstride=8, alpha=0.3)
cset = ax5.contour(X5, Y5, Z5, zdir='z', offset=-100, cmap=cm.coolwarm)
cset = ax5.contour(X5, Y5, Z5, zdir='x', offset=-40, cmap=cm.coolwarm)
cset = ax5.contour(X5, Y5, Z5, zdir='y', offset=40, cmap=cm.coolwarm)
ax5.set_xlabel('X')
ax5.set_ylabel('Y')
ax5.set_zlabel('Z')
ax5.set_xlim(-40, 40)
ax5.set_ylim(-40, 40)
ax5.set_zlim(-100, 100)
plt.show()
def test_wireframe3d():
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
monotone=True)
fd_monotone.plot(label="PCHIP")
fd_monotone.scatter(c='C1')
plt.legend()
###############################################################################
# All the interpolators will work regardless of the dimension of the image, but
# depending on the domain dimension some methods will not be available.
#
# For the next examples it is constructed a surface, :math:`x_i: \mathbb{R}^2
# \longmapsto \mathbb{R}`. By default, as in unidimensional samples, it is used
# linear interpolation.
#
X, Y, Z = axes3d.get_test_data(1.2)
data_matrix = [Z.T]
sample_points = [X[0, :], Y[:, 0]]
fd = skfda.FDataGrid(data_matrix, sample_points)
fig, ax = fd.plot()
fd.scatter(ax=ax)
###############################################################################
# In the following figure it is shown the result of the cubic interpolation
# applied to the surface.
#
# The degree of the interpolator polynomial does not have to coincide in both
# directions, for example, cubic interpolation in the first
# component and quadratic in the second one could be defined using a tuple with
import math
import matplotlib.pyplot as plt
from matplotlib.mlab import griddata
from mpl_toolkits.mplot3d import axes3d
import numpy as np
fig = plt.figure(figsize=(18, 6))
fig.subplots_adjust(left=0.0, right=1, top=1, bottom=0, wspace=0.08, hspace=0.09)
ax = fig.add_subplot(131)
data = np.genfromtxt('B=4/E_dc=6_mu=116_alpha=0.9496_B=4.0_frame.data.bz2',
delimiter=' ',
names=['phi_x', 'phi_y', 'f'])
X, Y, Z = axes3d.get_test_data(0.05)
xi = np.linspace(-3.141, 3.141, num=400)
yi = np.linspace(-4.5, 0.6, num=400)
X, Y = np.meshgrid(xi, yi)
Z = griddata(data['phi_x'], data['phi_y'], data['f'], xi, yi)
plt.pcolor(X, Y, Z, cmap='hot', vmin=0)
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
ax.set_xlim(-3.142,3.142)
ax.set_ylim(-4.5,0.6)
ax.text(-2.6, 0, '(a)', color='white', fontsize=28)
ax = fig.add_subplot(132)
fig = plt.figure(figsize=plt.figaspect(0.5))
#piwrwszy wykres
ax1 = fig.add_subplot(1, 2, 1, projection='3d')
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)
surf = ax1.plot_surface(X, Y, Z, cmap=cm.coolwarm, antialiased=False)
ax1.set_zlim(-1.01, 1.01)
fig.colorbar(surf, shrink=0.5, aspect=10)
#drugi wykres
ax2 = fig.add_subplot(1, 2, 2, projection='3d')
X, Y, Z = get_test_data()
ax2.plot_wireframe(Z, Y, Z, rstride=10, cstride=10)
plt.show()
##########################################################
fig = plt.figure()
ax = fig.gca(projection='3d')
x = np.linspace(0, 1, 100)
y = np.sin(x * 2 * np.pi) / 2 + 0.5
ax.plot(x, y, zs=0, zdir='z', label='curve in (x, y)')
colors = ('r', 'g', 'b', 'k')
np.random.seed(19860801)
x = np.random.sample(20 * len(colors))
y = np.random.sample(20 * len(colors))
c_list = []
