def draw2DView(figure, raw, seg, index, color=(1,0,0)):
"""
Draw a 2D maximum intensity projection of the raw data and create
a segmentation overlay
"""
raw2d = np.max(raw, axis = 2)
mlab.imshow(raw2d, colormap='gray', figure=figure)
sother = np.multiply( (seg != 0).astype(np.int32),(seg != index).astype(np.int32) )
##max1 = np.max(np.where(np.max(np.max((seg == index).astype(np.int32), axis = 0), axis = 0)!=0))+1
##min1 = np.min(np.where(np.max(np.max((seg == index).astype(np.int32), axis = 0), axis = 0)!=0))
##max2 = np.max(np.where(np.max(np.max((seg == index).astype(np.int32), axis = 0), axis = 1)!=0))+1
##min2 = np.min(np.where(np.max(np.max((seg == index).astype(np.int32), axis = 0), axis = 1)!=0))
##d1 = int(min(max1-min1-15,0))
##d2 = int(min(max2-min2-15,0))
##min1 += d1
##max1 -= d1
##min2 += d2
##max2 -= d2
##sother = (seg == index).astype(np.int32)
##seg2dother = np.max(sother, axis = 0)
##seg2dother[min2:max2,min1:max1]=1
sindex = (seg == index).astype(np.int32)
seg2dother = np.max(sother, axis = 2)
seg2dindex = np.max(sindex, axis = 2)
mlab.contour_surf(seg2dother, color = (0,0,1), contours = 2, warp_scale = 0, figure=figure)
mlab.contour_surf(seg2dindex, color = color, contours = 2, warp_scale = 0, figure=figure)
def test14(self):
data = np.loadtxt('./Data/map.csv', delimiter=',')
row = data.shape[0]
column = data.shape[1]
x, y = np.mgrid[0:row:1, 0:column:1]
x, y = np.mgrid[0:row:1, 0:column:1]
mlab.contour_surf(x, y, data, contours=100)
mlab.show()
def mayavi_contour_surf():
"""
绘制contour_surf图
:return:
"""
x, y = np.mgrid[-5:5:70j, -5:5:70j]
# 绘制peaks函数的等高线
mlab.contour_surf(x, y, peaks, contours=9)
mlab.colorbar()
mlab.show()
def test_contour_surf():
"""Test contour_surf on regularly spaced co-ordinates like MayaVi."""
def f(x, y):
sin, cos = np.sin, np.cos
return sin(x + y) + sin(2 * x - y) + cos(3 * x + 4 * y)
x, y = np.mgrid[-7.:7.05:0.1, -5.:5.05:0.05]
s = mlab.contour_surf(x, y, f)
return s
def cont3(x=None,
y=None,
z=None,
contours=20,
f=None,
axeLab=['', '', ''],
clf=True):
"""
3 argument surface plot
"""
if x == None:
x = arange(z.shape[0])
if y == None:
y = arange(z.shape[1])
if z == None:
z = arange(x.shape[0] * y.shape[0])
if clf:
ml.clf(f)
if z.ndim == 3:
for i in range(z.shape[-1]):
ml.contour_surf(sc(x),
sc(y),
scLike(z[..., i], z[..., 0]),
contours=contours,
extent=ex,
figure=f)
axe(axeLab, rgs(x, y, z[..., 0]), f)
else:
ml.contour_surf(sc(x),
sc(y),
sc(z),
contours=contours,
extent=ex,
figure=f)
axe(axeLab, rgs(x, y, z), f)
out()
return f
def plot_3d(topo, bottom_z, top_z, time_step, color=(0.88627451, 0.79215686, 0.4627451), ci = 0.1, scale=1, ve=1, dx=1,
line_thickness=0.05, contour_switch=False, new_figure=False):
"""function for plotting a set of bedforms in 3D
:param topo: array of stacked topographic surfaces that describe the bedform topography through time
:param bottom_z: elevation of the bottom of the model
:param top_z: elevation of the bottom of the model (if it is less than the maximum height of the dunes, they will be "eroded")
:param time_step: determines how many bedding surfaces will be drawn (default = 4)
:param color: color of the 'sand' (that is, of the model)
:param ci: contour interval (used only if contour_switch is 'True')
:param scale: scale of model (usually best to leave it at 1)
:param ve: vertical exaggeration
:param dx: horiozntal gridcell size
:param line_thickness: thickness of lines drawn, in units of the figure; has to be a relatively small number (default = 0.05)
:param contour_switch: determines whether contours are drawn on top of bedforms
:param new_figure: determines whether a new mlab figure is created or not
"""
if new_figure:
mlab.figure(bgcolor=(1,1,1))
else:
mlab.clf()
strat = np.minimum.accumulate(topo[:, :, ::-1], axis=2)[:, :, ::-1] # convert topography to stratigraphy
strat2 = strat.copy()
strat2[strat<bottom_z] = bottom_z
strat2[strat>top_z] = top_z
r,c,ts = np.shape(strat2)
z = scale*strat2[:,:,ts-1]
z1 = strat2[:,:,-1]
X1 = scale*(np.linspace(0,r-1,r)*dx)
Y1 = scale*(np.linspace(0,c-1,c)*dx)
mlab.surf(X1,Y1,z,warp_scale=ve,color=color)
if contour_switch:
contours = list(np.arange(np.min(strat2[:,:,-1]),np.max(strat2[:,:,-1]),ci*scale)) # list of contour values
mlab.contour_surf(X1,Y1,z, contours=contours, warp_scale=ve,color=(0,0,0),line_width=1.0)
# updip side:
vertices, triangles = create_section(z1[:,0],dx,bottom_z)
x = scale*(vertices[:,0])
y = scale*(np.zeros(np.shape(vertices[:,0])))
z = scale*ve*vertices[:,1]
mlab.triangular_mesh(x,y,z,triangles,color=color)
# downdip side:
vertices, triangles = create_section(z1[:,-1],dx,bottom_z)
x = scale*(vertices[:,0])
y = scale*((c-1)*dx*np.ones(np.shape(vertices[:,0])))
z = scale*ve*vertices[:,1]
mlab.triangular_mesh(x,y,z,triangles,color=color)
# left edge:
vertices, triangles = create_section(z1[0,:],dx,bottom_z)
x = scale*(np.zeros(np.shape(vertices[:,0])))
y = scale*(vertices[:,0])
z = scale*ve*vertices[:,1]
mlab.triangular_mesh(x,y,z,triangles,color=color)
# right edge:
vertices, triangles = create_section(z1[-1,:],dx,bottom_z)
x = scale*((r-1)*dx*np.ones(np.shape(vertices[:,0])))
y = scale*(vertices[:,0])
z = scale*ve*vertices[:,1]
mlab.triangular_mesh(x,y,z,triangles,color=color)
# bottom face of block:
vertices = dx*np.array([[0,0],[r-1,0],[r-1,c-1],[0,c-1]])
triangles = [[0,1,3],[1,3,2]]
x = scale*(vertices[:,0])
y = scale*(vertices[:,1])
z = scale*bottom_z*np.ones(np.shape(vertices[:,0]))
mlab.triangular_mesh(x,y,ve*z,triangles,color=color)
min_final_topo = np.min(topo[:,:,-1])
max_final_topo = np.max(topo[:,:,-1])
t_steps = np.hstack((np.arange(0, ts-1, time_step), ts-2))
for layer_n in tqdm(t_steps): # main loop
top = strat2[:,0,layer_n+1] # updip side
base = strat2[:,0,layer_n]
X1 = scale*(dx*np.arange(0,r))
Y1 = scale*(np.zeros(np.shape(base)))
Z1 = ve*scale*base
mlab.plot3d(X1,Y1,Z1,color=(0,0,0),tube_radius=line_thickness)
if layer_n == ts-2:
Z1 = ve*scale*top
mlab.plot3d(X1,Y1,Z1,color=(0,0,0),tube_radius=line_thickness)
top = strat2[:,-1,layer_n+1] # downdip side
base = strat2[:,-1,layer_n]
X1 = scale*(dx*np.arange(0,r))
Y1 = scale*(dx*(c-1)*np.ones(np.shape(base)))
Z1 = ve*scale*base
mlab.plot3d(X1,Y1,Z1,color=(0,0,0),tube_radius=line_thickness)
if layer_n == ts-2:
Z1 = ve*scale*top
mlab.plot3d(X1,Y1,Z1,color=(0,0,0),tube_radius=line_thickness)
top = strat2[0,:,layer_n+1] # left edge
base = strat2[0,:,layer_n]
X1 = scale*(np.zeros(np.shape(base)))
Y1 = scale*(dx*np.arange(0,c))
Z1 = ve*scale*base
mlab.plot3d(X1,Y1,Z1,color=(0,0,0),tube_radius=line_thickness)
if layer_n == ts-2:
Z1 = ve*scale*top
mlab.plot3d(X1,Y1,Z1,color=(0,0,0),tube_radius=line_thickness)
top = strat2[-1,:,layer_n+1] # right edge
base = strat2[-1,:,layer_n]
X1 = scale*(dx*(r-1)*np.ones(np.shape(base)))
Y1 = scale*(dx*np.arange(0,c))
Z1 = ve*scale*base
mlab.plot3d(X1,Y1,Z1,color=(0,0,0),tube_radius=line_thickness)
if layer_n == ts-2:
Z1 = ve*scale*top
mlab.plot3d(X1,Y1,Z1,color=(0,0,0),tube_radius=line_thickness)
if (bottom_z > np.min(strat[:,:,layer_n])) and (bottom_z < np.max(strat[:,:,layer_n])):
contours = measure.find_contours(strat[:,:,layer_n], bottom_z)
for i in range(0,len(contours)):
x1 = contours[i][:,0]
y1 = contours[i][:,1]
z1 = np.ones(np.shape(x1)) * bottom_z
mlab.plot3d(x1,y1,z1,color=(0,0,0),tube_radius=line_thickness)
if (top_z > np.min(strat[:,:,layer_n])) and (top_z < np.max(strat[:,:,layer_n])):
contours = measure.find_contours(strat[:,:,layer_n], top_z)
for i in range(0,len(contours)):
x1 = contours[i][:,0]
y1 = contours[i][:,1]
z1 = np.ones(np.shape(x1)) * top_z
mlab.plot3d(x1,y1,z1,color=(0,0,0),tube_radius=line_thickness)
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 4 15:51:17 2017
@author: Administrator
"""
import numpy as np
from mayavi import mlab
def f(x, y):
return np.sin(x - y) + np.cos(x + y)
x, y = np.mgrid[-7.:7.05:0.1, -5.:5.05:0.05]
con_s = mlab.contour_surf(x, y, f)
mlab.show()
from pylab import *
from mayavi import mlab
DX = 0.01
def f(x, y):
return np.sin(x + y) + np.sin(2 * x - y) + np.cos(3 * x + 4 * y)
if __name__ == '__main__':
fig = mlab.figure(size = (3000, 3000), bgcolor = (1, 1, 1))
x, y = np.mgrid[-7:7.05:DX, -5:5.05:DX]
mlab.contour_surf(x, y, f)
mlab.savefig(sys.argv[1], size = (3000, 3000))
out[:,j] = out[:,j] + np.maximum(f[:,j + 1] - f[:,j], 0)**2
else:
out[:,j] = out[:,j] + np.maximum(f[:,j - 1] - f[:,j], 0)**2
return np.sqrt(out)
def update_phi(P, dt):
# [Px, Py] = np.gradient(P)
up = lsm_grad_magnitude(P, True)
down = lsm_grad_magnitude(P, False)
#to obtain correct results, down and up are switched
# [U, V] = np.gradient(P)
# Gmag = gradient_magnitude(U, V)
return P + dt * down
msurf = mlab.surf(P,colormap='jet')
contour2d = mlab.contour_surf(P, contours=[0])
for i in xrange(140):
P = update_phi(P, dt)
if i % 20 == 0:
msurf.mlab_source.scalars = P
contour2d.mlab_source.scalars = P
# if i % 50 == 0:
# Pedt = ndimage.distance_transform_edt(np.round(P))
# P = np.sign(P) * Pedt
# P = renormalize(P,1.0)
mlab.outline()
mlab.axes()
mlab.show()
extent=(0.375, 0.625, 0, 0.25, 0, 0.25),
colormap='Accent'))
plt.outline(plt.mesh(xv,
yv,
hv,
extent=(0.75, 1, 0, 0.25, 0, 0.25),
colormap='prism'),
color=(.5, .5, .5))
# endsubplot
hv = h0 / (1 + (xv**2 + yv**2) / (R**2))
# Default contour plot plotted together with surf.
plt.figure(5, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
plt.surf(xv, yv, hv, warp_scale=0.01)
plt.contour_surf(xv, yv, hv, warp_scale=0.01)
# 10 contour lines (equally spaced contour levels).
plt.figure(6, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
plt.contour_surf(xv, yv, hv, contours=10, warp_scale=0.01)
# 10 contour lines (equally spaced contour levels) together with surf.
# Black color for contour lines.
plt.figure(7, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
plt.surf(xv, yv, hv, warp_scale=0.01)
plt.contour_surf(xv, yv, hv, contours=10, color=(0., 0., 0.), \
warp_scale=0.01)
# Specify the contour levels explicitly as a list.
plt.figure(8, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
levels = [500., 1000., 1500., 2000.]
def test05():
x, y = np.mgrid[-7.:7.05:0.1, -5.:5.05:0.05]
s = mlab.contour_surf(x, y, f)
mlab.show()
x = y = np.linspace(-10., 10., 41)
xv, yv = np.meshgrid(x, y, indexing='ij', sparse=False)
hv = h0 / (1 + (xv**2 + yv**2) / (R**2))
# Define a coarser grid for the vector field
x2 = y2 = np.linspace(-10., 10., 11)
x2v, y2v = np.meshgrid(x2, y2, indexing='ij', sparse=False)
h2v = h0 / (1 + (x2v**2 + y2v**2) / (R**2)) # Surface on coarse grid
# endcoarsergrid
dhdx, dhdy = np.gradient(h2v)
# Draw contours and gradient field of h
plt.figure(11, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
plt.contour_surf(xv, yv, hv, contours=20, warp_scale=0.01)
# mode controls the style how vectors are drawn
# color controls the colors of the vectors
# scale_mode='none' ensures that vectors are drawn with the same length
plt.quiver3d(x2v,
y2v,
0.01 * h2v,
dhdx,
dhdy,
np.zeros_like(dhdx),
mode='arrow',
color=(1, 0, 0),
scale_mode='none')
# end draw contours and gradient field of h
raw_input('Press any key to continue')
plt.outline(plt.mesh(
xv, yv, hv,
extent=(0.375, 0.625, 0, 0.25, 0, 0.25),
colormap='Accent'))
plt.outline(plt.mesh(
xv, yv, hv, extent=(0.75, 1, 0, 0.25, 0, 0.25),
colormap='prism'), color=(.5, .5, .5))
# endsubplot
hv = h0/(1 + (xv**2+yv**2)/(R**2))
# Default contour plot plotted together with surf.
plt.figure(5, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
plt.surf(xv, yv, hv, warp_scale=0.01)
plt.contour_surf(xv, yv, hv, warp_scale=0.01)
# 10 contour lines (equally spaced contour levels).
plt.figure(6, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
plt.contour_surf(xv, yv, hv, contours=10, warp_scale=0.01)
# 10 contour lines (equally spaced contour levels) together with surf.
# Black color for contour lines.
plt.figure(7, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
plt.surf(xv, yv, hv, warp_scale=0.01)
plt.contour_surf(xv, yv, hv, contours=10, color=(0., 0., 0.), \
warp_scale=0.01)
# Specify the contour levels explicitly as a list.
plt.figure(8, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
levels = [500., 1000., 1500., 2000.]
def create_block_diagram(strat, dx, ve, xoffset, yoffset, scale, ci,
strat_switch, contour_switch, bottom, topo_min,
topo_max):
"""function for creating a 3D block diagram in Mayavi
strat - input array with stratigraphic surfaces
dx - size of gridcells in the horizontal direction in 'strat'
ve - vertical exaggeration
offset - offset in the y-direction relative to 0
scale - scaling factor
ci - contour interval
strat_switch - 1 if you want to plot stratigraphy on the sides; 0 otherwise
contour_switch - 1 if you want to plot contours on the top surface; 0 otherwise
bottom - elevation value for the bottom of the block"""
r, c, ts = np.shape(strat)
z = scale * strat[:, :, ts - 1]
if strat_switch == 1:
z1 = strat[:, :, 0]
else:
z1 = strat[:, :, -1]
X1 = scale * (xoffset + np.linspace(0, r - 1, r) * dx)
Y1 = scale * (yoffset + np.linspace(0, c - 1, c) * dx)
mlab.surf(X1,
Y1,
z,
warp_scale=ve,
colormap='gist_earth',
vmin=scale * topo_min,
vmax=scale *
topo_max) #, line_width=5.0, representation='wireframe')
if contour_switch == 1:
contours = list(np.arange(vmin, vmax,
ci * scale)) # list of contour values
mlab.contour_surf(X1,
Y1,
z,
contours=contours,
warp_scale=ve,
color=(0, 0, 0),
line_width=1.0)
gray = (0.6, 0.6, 0.6) # color for plotting sides
# updip side:
vertices, triangles = create_section(z1[:, 0], dx, bottom)
x = scale * (xoffset + vertices[:, 0])
y = scale * (yoffset + np.zeros(np.shape(vertices[:, 0])))
z = scale * ve * vertices[:, 1]
mlab.triangular_mesh(x, y, z, triangles, color=gray)
# downdip side:
vertices, triangles = create_section(z1[:, -1], dx, bottom)
x = scale * (xoffset + vertices[:, 0])
y = scale * (yoffset + (c - 1) * dx * np.ones(np.shape(vertices[:, 0])))
z = scale * ve * vertices[:, 1]
mlab.triangular_mesh(x, y, z, triangles, color=gray)
# left edge (looking downdip):
vertices, triangles = create_section(z1[0, :], dx, bottom)
x = scale * (xoffset + np.zeros(np.shape(vertices[:, 0])))
y = scale * (yoffset + vertices[:, 0])
z = scale * ve * vertices[:, 1]
mlab.triangular_mesh(x, y, z, triangles, color=gray)
# right edge (looking downdip):
vertices, triangles = create_section(z1[-1, :], dx, bottom)
x = scale * (xoffset + (r - 1) * dx * np.ones(np.shape(vertices[:, 0])))
y = scale * (yoffset + vertices[:, 0])
z = scale * ve * vertices[:, 1]
mlab.triangular_mesh(x, y, z, triangles, color=gray)
# bottom face of block:
vertices = dx * np.array([[0, 0], [r - 1, 0], [r - 1, c - 1], [0, c - 1]])
triangles = [[0, 1, 3], [1, 3, 2]]
x = scale * (xoffset + vertices[:, 0])
y = scale * (yoffset + vertices[:, 1])
z = scale * bottom * np.ones(np.shape(vertices[:, 0]))
mlab.triangular_mesh(x, y, ve * z, triangles, color=gray)
import mayavi.mlab as plt
import os
from math import *
import numpy as np
h0 = 22.77
R = 4.
x = y = np.linspace(-10.,10.,41)
xv, yv = np.meshgrid(x, y, indexing='ij', sparse=False)
hv = h0/(1 + (xv**2+yv**2)/(R**2))
# Define a coarser grid for the vector field
x2 = y2 = np.linspace(-10.,10.,21)
x2v, y2v = np.meshgrid(x2, y2, indexing='ij', sparse=False)
h2v = h0/(1 + (x2v**2 + y2v**2)/(R**2)) # Surface on coarse grid
# endcoarsergrid
dhdx, dhdy = np.gradient(h2v)
# Draw contours and gradient field of h
plt.figure(9, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
plt.contour_surf(xv, yv, hv, contours=20)
# mode controls the style how vectors are drawn
# color controls the colors of the vectors
# scale_factor controls thelength of the vectors
plt.quiver3d(x2v, y2v, h2v, dhdx, dhdy, np.zeros_like(dhdx),
mode='arrow', color=(1,0,0), scale_factor=.75)
# end draw contours and gradient field of h
raw_input('Press any key to continue')
def plot_3d(topo,
bottom_z,
top_z,
time_step,
color=(0.88627451, 0.79215686, 0.4627451),
ci=0.1,
scale=1,
ve=1,
dx=1,
line_thickness=0.05,
contour_switch=False,
new_figure=False):
"""function for plotting a set of bedforms in 3D"""
if new_figure:
mlab.figure(bgcolor=(1, 1, 1))
else:
mlab.clf()
strat = np.minimum.accumulate(
topo[:, :, ::-1],
axis=2)[:, :, ::-1] # convert topography to stratigraphy
strat2 = strat.copy()
strat2[strat < bottom_z] = bottom_z
strat2[strat > top_z] = top_z
r, c, ts = np.shape(strat2)
z = scale * strat2[:, :, ts - 1]
z1 = strat2[:, :, -1]
X1 = scale * (np.linspace(0, r - 1, r) * dx)
Y1 = scale * (np.linspace(0, c - 1, c) * dx)
mlab.surf(X1, Y1, z, warp_scale=ve, color=color)
if contour_switch:
contours = list(
np.arange(np.min(strat2[:, :, -1]), np.max(strat2[:, :, -1]),
ci * scale)) # list of contour values
mlab.contour_surf(X1,
Y1,
z,
contours=contours,
warp_scale=ve,
color=(0, 0, 0),
line_width=1.0)
# updip side:
vertices, triangles = create_section(z1[:, 0], dx, bottom_z)
x = scale * (vertices[:, 0])
y = scale * (np.zeros(np.shape(vertices[:, 0])))
z = scale * ve * vertices[:, 1]
mlab.triangular_mesh(x, y, z, triangles, color=color)
# downdip side:
vertices, triangles = create_section(z1[:, -1], dx, bottom_z)
x = scale * (vertices[:, 0])
y = scale * ((c - 1) * dx * np.ones(np.shape(vertices[:, 0])))
z = scale * ve * vertices[:, 1]
mlab.triangular_mesh(x, y, z, triangles, color=color)
# left edge (looking downdip):
vertices, triangles = create_section(z1[0, :], dx, bottom_z)
x = scale * (np.zeros(np.shape(vertices[:, 0])))
y = scale * (vertices[:, 0])
z = scale * ve * vertices[:, 1]
mlab.triangular_mesh(x, y, z, triangles, color=color)
# right edge (looking downdip):
vertices, triangles = create_section(z1[-1, :], dx, bottom_z)
x = scale * ((r - 1) * dx * np.ones(np.shape(vertices[:, 0])))
y = scale * (vertices[:, 0])
z = scale * ve * vertices[:, 1]
mlab.triangular_mesh(x, y, z, triangles, color=color)
# bottom face of block:
vertices = dx * np.array([[0, 0], [r - 1, 0], [r - 1, c - 1], [0, c - 1]])
triangles = [[0, 1, 3], [1, 3, 2]]
x = scale * (vertices[:, 0])
y = scale * (vertices[:, 1])
z = scale * bottom_z * np.ones(np.shape(vertices[:, 0]))
mlab.triangular_mesh(x, y, ve * z, triangles, color=color)
min_final_topo = np.min(topo[:, :, -1])
max_final_topo = np.max(topo[:, :, -1])
t_steps = np.hstack((np.arange(0, ts - 1, time_step), ts - 2))
for layer_n in tqdm(t_steps): # main loop
top = strat2[:, 0, layer_n + 1] # updip side
base = strat2[:, 0, layer_n]
X1 = scale * (dx * np.arange(0, r))
Y1 = scale * (np.zeros(np.shape(base)))
Z1 = ve * scale * base
mlab.plot3d(X1, Y1, Z1, color=(0, 0, 0), tube_radius=line_thickness)
if layer_n == ts - 2:
Z1 = ve * scale * top
mlab.plot3d(X1,
Y1,
Z1,
color=(0, 0, 0),
tube_radius=line_thickness)
top = strat2[:, -1, layer_n + 1] # downdip side
base = strat2[:, -1, layer_n]
X1 = scale * (dx * np.arange(0, r))
Y1 = scale * (dx * (c - 1) * np.ones(np.shape(base)))
Z1 = ve * scale * base
mlab.plot3d(X1, Y1, Z1, color=(0, 0, 0), tube_radius=line_thickness)
if layer_n == ts - 2:
Z1 = ve * scale * top
mlab.plot3d(X1,
Y1,
Z1,
color=(0, 0, 0),
tube_radius=line_thickness)
top = strat2[0, :, layer_n + 1] # left edge (looking downdip)
base = strat2[0, :, layer_n]
X1 = scale * (np.zeros(np.shape(base)))
Y1 = scale * (dx * np.arange(0, c))
Z1 = ve * scale * base
mlab.plot3d(X1, Y1, Z1, color=(0, 0, 0), tube_radius=line_thickness)
if layer_n == ts - 2:
Z1 = ve * scale * top
mlab.plot3d(X1,
Y1,
Z1,
color=(0, 0, 0),
tube_radius=line_thickness)
top = strat2[-1, :, layer_n + 1] # right edge (looking downdip)
base = strat2[-1, :, layer_n]
X1 = scale * (dx * (r - 1) * np.ones(np.shape(base)))
Y1 = scale * (dx * np.arange(0, c))
Z1 = ve * scale * base
mlab.plot3d(X1, Y1, Z1, color=(0, 0, 0), tube_radius=line_thickness)
if layer_n == ts - 2:
Z1 = ve * scale * top
mlab.plot3d(X1,
Y1,
Z1,
color=(0, 0, 0),
tube_radius=line_thickness)
if (bottom_z > np.min(strat[:, :, layer_n])) and (bottom_z < np.max(
strat[:, :, layer_n])):
contours = measure.find_contours(strat[:, :, layer_n], bottom_z)
for i in range(0, len(contours)):
x1 = contours[i][:, 0]
y1 = contours[i][:, 1]
z1 = np.ones(np.shape(x1)) * bottom_z
mlab.plot3d(x1,
y1,
z1,
color=(0, 0, 0),
tube_radius=line_thickness)
if (top_z > np.min(strat[:, :, layer_n])) and (top_z < np.max(
strat[:, :, layer_n])):
contours = measure.find_contours(strat[:, :, layer_n], top_z)
for i in range(0, len(contours)):
x1 = contours[i][:, 0]
y1 = contours[i][:, 1]
z1 = np.ones(np.shape(x1)) * top_z
mlab.plot3d(x1,
y1,
z1,
color=(0, 0, 0),
tube_radius=line_thickness)
import mayavi.mlab as plt
import os
from math import *
import numpy as np
h0 = 2277.
R = 4.
x = y = np.linspace(-10.,10.,41)
xv, yv = np.meshgrid(x, y, indexing='ij', sparse=False)
hv = h0/(1 + (xv**2+yv**2)/(R**2))
# Define a coarser grid for the vector field
x2 = y2 = np.linspace(-10.,10.,11)
x2v, y2v = np.meshgrid(x2, y2, indexing='ij', sparse=False)
h2v = h0/(1 + (x2v**2 + y2v**2)/(R**2)) # Surface on coarse grid
# endcoarsergrid
dhdx, dhdy = np.gradient(h2v)
# Draw contours and gradient field of h
plt.figure(11, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
plt.contour_surf(xv, yv, hv, contours=20, warp_scale=0.01)
# mode controls the style how vectors are drawn
# color controls the colors of the vectors
# scale_mode='none' ensures that vectors are drawn with the same length
plt.quiver3d(x2v, y2v, 0.01*h2v, dhdx, dhdy, np.zeros_like(dhdx),
mode='arrow', color=(1,0,0), scale_mode='none')
# end draw contours and gradient field of h
raw_input('Press any key to continue')
