def plot(self):
"绘制场景"
# 产生三维网格
x, y, z = np.mgrid[self.x0:self.x1:1j * self.points,
self.y0:self.y1:1j * self.points,
self.z0:self.z1:1j * self.points]
# 根据函数计算标量场的值
scalars = eval(self.function)
mlab.clf() # 清空当前场景
# 绘制等值平面
g = mlab.contour3d(x, y, z, scalars, contours=8, transparent=True)
g.contour.auto_contours = self.autocontour
mlab.axes() # 添加坐标轴
# 添加一个X-Y的切面
s = mlab.pipeline.scalar_cut_plane(g)
cutpoint = (self.x0 + self.x1) / 2, (self.y0 + self.y1) / 2, (
self.z0 + self.z1) / 2
s.implicit_plane.normal = (0, 0, 1) # x cut
s.implicit_plane.origin = cutpoint
self.g = g
self.scalars = scalars
# 计算标量场的值的范围
self.v0 = np.min(scalars)
self.v1 = np.max(scalars)
def newSurface(self):
processes = self.plotter.getProcesses()
if not self._cv_dlg:
self._cv_dlg = daeChooseVariable(daeChooseVariable.plot3D)
self._cv_dlg.updateProcessesList(processes)
self._cv_dlg.setWindowTitle('Choose variable for 3D plot')
if self._cv_dlg.exec_() != QtWidgets.QDialog.Accepted:
return False
variable, domainIndexes, domainPoints, xAxisLabel, yAxisLabel, zAxisLabel, xPoints, yPoints, zPoints, currentTime = self._cv_dlg.getPlot3DData(
)
xPoints = numpy.array(xPoints)
yPoints = numpy.array(yPoints)
xmax = numpy.max(xPoints)
ymax = numpy.max(yPoints)
zmax = numpy.max(zPoints)
xmin = numpy.min(xPoints)
ymin = numpy.min(yPoints)
zmin = numpy.min(zPoints)
warp = 'auto'
#if((xmax == xmin) or (ymax == ymin) or (zmax == zmin)):
# warp = 'auto'
#else:
# warp = math.sqrt( (xmax-xmin)*(ymax-ymin) ) / (zmax-zmin)
# colormap='gist_earth', 'RdBu'
stype = 'surface'
mlab.figure()
if (stype == 'surface'):
#print "warp=", warp
#print "[xmin, xmax, ymin, ymax, zmin, zmax]=", [xmin, xmax, ymin, ymax, zmin, zmax]
mlab.surf(xPoints,
yPoints,
zPoints,
warp_scale=warp,
representation='surface')
mlab.colorbar(orientation='vertical')
#mlab.title('polar mesh')
#mlab.outline()
mlab.axes(ranges=[xmin, xmax, ymin, ymax, zmin, zmax], nb_labels=3)
mlab.xlabel(xAxisLabel)
mlab.ylabel(yAxisLabel)
mlab.zlabel(zAxisLabel)
elif (stype == 'map'):
mlab.imshow(zPoints)
mlab.colorbar(orientation='vertical')
#mlab.title('polar mesh')
#mlab.outline()
mlab.axes(ranges=[xmin, xmax, ymin, ymax], nb_labels=3)
mlab.xlabel(xAxisLabel)
mlab.ylabel(yAxisLabel)
mlab.zlabel(zAxisLabel)
mlab.show()
def getData(self, data_D, data_H, name):
"""
plot passed in data as a surface
"""
#plotting
fig = mlab.figure(size=(512, 512))
cl.enqueue_read_buffer(lbm.queue, data_D, data_H).wait()
# retrieve mid cell points from cell node data
Nx = lbm.X.size - 1
Ny = lbm.Y.size - 1
x = np.zeros((Nx))
y = np.zeros((Ny))
for i in range(1, lbm.X.size):
x[i - 1] = (lbm.X[i] - lbm.X[i - 1]) / 2.0 + lbm.X[i - 1]
for i in range(1, lbm.Y.size):
y[i - 1] = (lbm.Y[i] - lbm.Y[i - 1]) / 2.0 + lbm.Y[i - 1]
s = mlab.surf(x, y, data_H, warp_scale='auto', colormap="jet")
mlab.axes(s)
sb = mlab.scalarbar(s, title=name)
self.s = s
self.data_D = data_D
self.data_H = data_H
def plot(self):
"绘制场景"
# 产生三维网格
x, y, z = np.mgrid[
self.x0 : self.x1 : 1j * self.points,
self.y0 : self.y1 : 1j * self.points,
self.z0 : self.z1 : 1j * self.points,
]
# 根据函数计算标量场的值
scalars = eval(self.function)
mlab.clf() # 清空当前场景
# 绘制等值平面
g = mlab.contour3d(x, y, z, scalars, contours=8, transparent=True)
g.contour.auto_contours = self.autocontour
mlab.axes() # 添加坐标轴
# 添加一个X-Y的切面
s = mlab.pipeline.scalar_cut_plane(g)
cutpoint = (self.x0 + self.x1) / 2, (self.y0 + self.y1) / 2, (self.z0 + self.z1) / 2
s.implicit_plane.normal = (0, 0, 1) # x cut
s.implicit_plane.origin = cutpoint
self.g = g
self.scalars = scalars
# 计算标量场的值的范围
self.v0 = np.min(scalars)
self.v1 = np.max(scalars)
def plot_connections(data_file, min, max, bins,
params=None, output=''):
print("Creating connection profile graphs.")
# Read data points from file
f = open(data_file, 'r')
# Ignore first line
f.readline()
data = []
for line in f:
temp = line.split(' ')
if params != None:
if check_node([int(temp[1])], params):
data.append([float(temp[4]), float(temp[5])]);
else:
data.append([float(temp[4]), float(temp[5])]);
# Create histogram data based on the retrieved data.
histogram_data = histogram2d(data, min, max, bins)
# Open a new Mayavi2 figure
f = mlab.figure()
# Convert histogram bin count to relative densities.
m = np.max(histogram_data[2].max(axis=0))
histogram_data[2] = histogram_data[2]/float(m)
# Plot histogram data
mlab.mesh(histogram_data[0], histogram_data[1], histogram_data[2])
#surf(histogram_data[0], histogram_data[1], histogram_data[2])
# Create and save various viewpoints of histogram figure
mlab.axes(z_axis_visibility=False)
mlab.view(azimuth=0, elevation=90) # X
mlab.savefig(output+"xaxis.eps", size=[600,400])
mlab.view(azimuth=90, elevation=270) # Y
mlab.savefig(output+"yaxis.eps", size=[600,400])
mlab.view(azimuth=45, elevation=45) # Perspective
mlab.savefig(output+"perspective.eps", size=[600,400])
mlab.colorbar(orientation="vertical")
mlab.view(azimuth=0, elevation=0) # Z
mlab.savefig(output+"above.eps", size=[600,400])
def plot_connections(data_file, min, max, bins, params=None, output=''):
print("Creating connection profile graphs.")
# Read data points from file
f = open(data_file, 'r')
# Ignore first line
f.readline()
data = []
for line in f:
temp = line.split(' ')
if params != None:
if check_node([int(temp[1])], params):
data.append([float(temp[4]), float(temp[5])])
else:
data.append([float(temp[4]), float(temp[5])])
# Create histogram data based on the retrieved data.
histogram_data = histogram2d(data, min, max, bins)
# Open a new Mayavi2 figure
f = mlab.figure()
# Convert histogram bin count to relative densities.
m = np.max(histogram_data[2].max(axis=0))
histogram_data[2] = histogram_data[2] / float(m)
# Plot histogram data
mlab.mesh(histogram_data[0], histogram_data[1], histogram_data[2])
#surf(histogram_data[0], histogram_data[1], histogram_data[2])
# Create and save various viewpoints of histogram figure
mlab.axes(z_axis_visibility=False)
mlab.view(azimuth=0, elevation=90) # X
mlab.savefig(output + "xaxis.eps", size=[600, 400])
mlab.view(azimuth=90, elevation=270) # Y
mlab.savefig(output + "yaxis.eps", size=[600, 400])
mlab.view(azimuth=45, elevation=45) # Perspective
mlab.savefig(output + "perspective.eps", size=[600, 400])
mlab.colorbar(orientation="vertical")
mlab.view(azimuth=0, elevation=0) # Z
mlab.savefig(output + "above.eps", size=[600, 400])
def plot_cartesian(traj,
xaxis=None,
yaxis=None,
zaxis=None,
color='b',
label='_nolegend_',
linewidth=2,
scatter_size=20):
''' xaxis - x axis for the graph (0,1 or 2)
zaxis - for a 3d plot. not implemented.
'''
import arm_trajectories as at
#if traj.__class__ == at.JointTrajectory:
if isinstance(traj, at.JointTrajectory):
traj = joint_to_cartesian(traj)
pts = np.matrix(traj.p_list).T
label_list = ['X coord (m)', 'Y coord (m)', 'Z coord (m)']
x = pts[xaxis, :].A1.tolist()
y = pts[yaxis, :].A1.tolist()
if zaxis == None:
pl.plot(x, y, c=color, linewidth=linewidth, label=label)
pl.scatter(
x, y, c=color, s=scatter_size, label='_nolegend_', linewidths=0)
pl.xlabel(label_list[xaxis])
pl.ylabel(label_list[yaxis])
pl.legend(loc='best')
pl.axis('equal')
else:
from numpy import array
from enthought.mayavi.api import Engine
engine = Engine()
engine.start()
if len(engine.scenes) == 0:
engine.new_scene()
z = pts[zaxis, :].A1.tolist()
time_list = [t - traj.time_list[0] for t in traj.time_list]
mlab.plot3d(x, y, z, time_list, tube_radius=None, line_width=4)
mlab.axes()
mlab.xlabel(label_list[xaxis])
mlab.ylabel(label_list[yaxis])
mlab.zlabel(label_list[zaxis])
mlab.colorbar(title='Time')
# -------------------------------------------
axes = engine.scenes[0].children[0].children[0].children[1]
axes.axes.position = array([0., 0.])
axes.axes.label_format = '%-#6.2g'
axes.title_text_property.font_size = 4
def plot_cartesian(traj, xaxis=None, yaxis=None, zaxis=None, color='b',label='_nolegend_',
linewidth=2, scatter_size=10, plot_velocity=False):
import arm_trajectories as at
#if traj.__class__ == at.JointTrajectory:
if isinstance(traj,at.JointTrajectory):
traj = joint_to_cartesian(traj)
pts = np.matrix(traj.p_list).T
label_list = ['X coord (m)', 'Y coord (m)', 'Z coord (m)']
x = pts[xaxis,:].A1.tolist()
y = pts[yaxis,:].A1.tolist()
if plot_velocity:
vels = np.matrix(traj.v_list).T
xvel = vels[xaxis,:].A1.tolist()
yvel = vels[yaxis,:].A1.tolist()
if zaxis == None:
mpu.plot_yx(y, x, color, linewidth, '-', scatter_size, label,
axis = 'equal', xlabel = label_list[xaxis],
ylabel = label_list[yaxis],)
if plot_velocity:
mpu.plot_quiver_yxv(y, x, np.matrix([xvel,yvel]),
width = 0.001, scale = 1.)
mpu.legend()
else:
from numpy import array
from enthought.mayavi.api import Engine
engine = Engine()
engine.start()
if len(engine.scenes) == 0:
engine.new_scene()
z = pts[zaxis,:].A1.tolist()
time_list = [t-traj.time_list[0] for t in traj.time_list]
mlab.plot3d(x,y,z,time_list,tube_radius=None,line_width=4)
mlab.axes()
mlab.xlabel(label_list[xaxis])
mlab.ylabel(label_list[yaxis])
mlab.zlabel(label_list[zaxis])
mlab.colorbar(title='Time')
# -------------------------------------------
axes = engine.scenes[0].children[0].children[0].children[1]
axes.axes.position = array([ 0., 0.])
axes.axes.label_format = '%-#6.2g'
axes.title_text_property.font_size=4
def plot_cartesian(traj, xaxis=None, yaxis=None, zaxis=None, color='b',label='_nolegend_',
linewidth=2, scatter_size=10, plot_velocity=False):
import matplotlib_util.util as mpu
import arm_trajectories as at
#if traj.__class__ == at.JointTrajectory:
if isinstance(traj,at.JointTrajectory):
traj = joint_to_cartesian(traj)
pts = np.matrix(traj.p_list).T
label_list = ['X coord (m)', 'Y coord (m)', 'Z coord (m)']
x = pts[xaxis,:].A1.tolist()
y = pts[yaxis,:].A1.tolist()
if plot_velocity:
vels = np.matrix(traj.v_list).T
xvel = vels[xaxis,:].A1.tolist()
yvel = vels[yaxis,:].A1.tolist()
if zaxis == None:
mpu.plot_yx(y, x, color, linewidth, '-', scatter_size, label,
axis = 'equal', xlabel = label_list[xaxis],
ylabel = label_list[yaxis],)
if plot_velocity:
mpu.plot_quiver_yxv(y, x, np.matrix([xvel,yvel]),
width = 0.001, scale = 1.)
mpu.legend()
else:
from numpy import array
from enthought.mayavi.api import Engine
engine = Engine()
engine.start()
if len(engine.scenes) == 0:
engine.new_scene()
z = pts[zaxis,:].A1.tolist()
time_list = [t-traj.time_list[0] for t in traj.time_list]
mlab.plot3d(x,y,z,time_list,tube_radius=None,line_width=4)
mlab.axes()
mlab.xlabel(label_list[xaxis])
mlab.ylabel(label_list[yaxis])
mlab.zlabel(label_list[zaxis])
mlab.colorbar(title='Time')
# -------------------------------------------
axes = engine.scenes[0].children[0].children[0].children[1]
axes.axes.position = array([ 0., 0.])
axes.axes.label_format = '%-#6.2g'
axes.title_text_property.font_size=4
def animateVTKFiles_2D(folder):
if not os.path.isdir(folder):
return
vtkFiles = []
for f in sorted(os.listdir(folder)):
if f.endswith(".vtk"):
vtkFiles.append(os.path.join(folder, f))
if len(vtkFiles) == 0:
return
figure = mlab.figure(size=(800, 600))
figure.scene.disable_render = True
vtkSource = VTKFileReader()
vtk_file = vtkFiles[0]
vtkSource.initialize(vtk_file)
surface = mlab.pipeline.surface(vtkSource)
axes = mlab.axes()
colorbar = mlab.colorbar(object=surface, orientation='horizontal')
mlab.view(0, 0)
figure.scene.disable_render = False
mlab.draw()
a = animateVTKFiles(figure, vtkSource, vtkFiles)
def read(name):
from enthought.tvtk.api import tvtk
from enthought.mayavi import mlab
import numpy
gridreader = tvtk.XMLStructuredGridReader()
gridreader.file_name = name
gridreader.update()
grid = gridreader.output
data = grid.point_data
points = grid.points
dims = grid.dimensions
dims = dims.tolist()
dims.reverse()
phase = numpy.array(data.get_array("Phase"))
velocity = numpy.array(data.get_array("Velocity"))
velx = velocity[:, 0]
vely = velocity[:, 1]
velz = velocity[:, 2]
phase_numpy = phase.reshape(dims)
velx_numpy = velx.reshape(dims)
vely_numpy = vely.reshape(dims)
velz_numpy = velz.reshape(dims)
fig = mlab.figure()
src = mlab.pipeline.scalar_field(phase_numpy)
mlab.axes()
v = mlab.pipeline.vector_field(velx_numpy, vely_numpy, velz_numpy)
vx = mlab.pipeline.scalar_field(velx_numpy)
vy = mlab.pipeline.scalar_field(vely_numpy)
vz = mlab.pipeline.scalar_field(velz_numpy)
extract = mlab.pipeline.extract_grid(src)
extract.set(z_min=1, z_max=dims[2] - 2, y_min=1, y_max=dims[1] - 2)
surf = mlab.pipeline.contour_surface(extract)
mlab.pipeline.image_plane_widget(vx,
plane_orientation='x_axes',
slice_index=250)
#mlab.pipeline.vectors(v, mask_points=20, scale_factor=3.)
mlab.pipeline.vector_cut_plane(v, mask_points=2, scale_factor=3)
mlab.show()
def showVTKFile_2D(filename):
figure = mlab.figure(size=(800, 600))
vtkSource = VTKFileReader()
vtkSource.initialize(filename)
surface = mlab.pipeline.surface(vtkSource)
axes = mlab.axes()
colorbar = mlab.colorbar(object=surface, orientation='horizontal')
mlab.view(0, 0)
mlab.show()
def mcrtmv(frames, dt, Lx, Ly, Nx, Ny, savemovie=False, mvname='test'):
x = np.linspace(0, Lx, Nx)
y = np.linspace(0, Lx, Nx)
X, Y = np.meshgrid(x, y)
size = 500, 500
fig = ml.figure(size=size, bgcolor=(1., 1., 1.))
#fig.scene.anti_aliasing_frames=07
#extent = [0,Nx-1,0,Ny-1,-30,30]
ml.clf(figure=fig)
u = np.loadtxt('data/solution_%06d.txt' % 1)
fname = 'data/_tmp%07d.png' % 1
s = ml.surf(x, y, u, figure=fig, vmin=-1, vmax=1)
ml.axes(extent=[0, Lx, 0, Ly, -2, 2])
ml.colorbar()
ml.xlabel('x position')
ml.ylabel('y position')
ml.zlabel('wave amplitude')
if savemovie == True:
pl.ion()
arr = ml.screenshot()
img = pl.imshow(arr)
pl.axis('off')
for i in range(2, frames):
u = np.loadtxt('data/solution_%06d.txt' % i)
s.mlab_source.scalars = u
fname = 'data/_tmp%07d.png' % i
if savemovie == True:
arr = ml.screenshot()
img.set_array(arr)
pl.savefig(filename=fname) #,figure=fig)
print 'Saving frame', fname
pl.draw()
fig.scene.disable_render = False
os.system(
"mencoder 'mf://data/_tmp*.png' -mf type=png:fps=20 -ovc lavc -lavcopts vcodec=wmv2 -oac copy -o %s.mpg"
% mvname)
def plot3d(self,ix=0,iy=1,iz=2,clf=True):
"""
Generates a 3-dimensional plot of the data set and principle components
using mayavi.
ix, iy, and iz specify which of the input p-dimensions to place on each of
the x,y,z axes, respectively (0-indexed).
"""
import enthought.mayavi.mlab as M
if clf:
M.clf()
z3=np.zeros(3)
v=(self.getEigenvectors()*self.getEigenvalues())
M.quiver3d(z3,z3,z3,v[ix],v[iy],v[iz],scale_factor=5)
M.points3d(self.N[:,ix],self.N[:,iy],self.N[:,iz],scale_factor=0.3)
if self.names:
M.axes(xlabel=self.names[ix]+'/sigma',ylabel=self.names[iy]+'/sigma',zlabel=self.names[iz]+'/sigma')
else:
M.axes()
def cutPlanes(volume, colormap='gist_ncar'):
'''Display a 3D volume of scalars with two cut planes.
volume: a three dimensional array of scalars
'''
scalarField = mlab.pipeline.scalar_field(volume)
mlab.pipeline.image_plane_widget(scalarField,
plane_orientation='z_axes',
slice_index=10,
colormap=colormap)
mlab.pipeline.image_plane_widget(scalarField,
plane_orientation='y_axes',
slice_index=10,
colormap=colormap)
mlab.outline()
mlab.axes()
mlab.colorbar(orientation='vertical')
def plot3d(self,ix=0,iy=1,iz=2,clf=True):
"""
Generates a 3-dimensional plot of the data set and principle components
using mayavi.
ix, iy, and iz specify which of the input p-dimensions to place on each of
the x,y,z axes, respectively (0-indexed).
"""
import enthought.mayavi.mlab as M
if clf:
M.clf()
z3=np.zeros(3)
v=(self.getEigenvectors()*self.getEigenvalues())
M.quiver3d(z3,z3,z3,v[ix],v[iy],v[iz],scale_factor=5)
M.points3d(self.N[:,ix],self.N[:,iy],self.N[:,iz],scale_factor=0.3)
if self.names:
M.axes(xlabel=self.names[ix]+'/sigma',ylabel=self.names[iy]+'/sigma',zlabel=self.names[iz]+'/sigma')
else:
M.axes()
def mcrtmv(frames, dt,Lx,Ly,Nx,Ny,savemovie=False, mvname='test'):
x = np.linspace(0,Lx,Nx);
y = np.linspace(0,Lx,Nx);
X,Y = np.meshgrid(x,y);
size = 500,500
fig = ml.figure(size= size, bgcolor=(1.,1.,1.));
#fig.scene.anti_aliasing_frames=07
#extent = [0,Nx-1,0,Ny-1,-30,30]
ml.clf(figure=fig)
u = np.loadtxt('data/solution_%06d.txt'%1);
fname = 'data/_tmp%07d.png' % 1
s = ml.surf(x,y,u,figure=fig,vmin=-1,vmax=1)
ml.axes(extent=[0,Lx,0,Ly,-2,2])
ml.colorbar()
ml.xlabel('x position')
ml.ylabel('y position')
ml.zlabel('wave amplitude')
if savemovie == True:
pl.ion()
arr = ml.screenshot()
img = pl.imshow(arr)
pl.axis('off')
for i in range(2,frames):
u = np.loadtxt('data/solution_%06d.txt'%i);
s.mlab_source.scalars = u
fname = 'data/_tmp%07d.png' % i
if savemovie == True:
arr = ml.screenshot()
img.set_array(arr)
pl.savefig(filename=fname)#,figure=fig)
print 'Saving frame', fname
pl.draw()
fig.scene.disable_render = False
os.system("mencoder 'mf://data/_tmp*.png' -mf type=png:fps=20 -ovc lavc -lavcopts vcodec=wmv2 -oac copy -o %s.mpg" % mvname);
def main(self):
from enthought.mayavi import mlab
self._iter = 1
self.density = mlab.pipeline.scalar_field(self.sim.rho.transpose())
self.vx = mlab.pipeline.scalar_field((self.sim.vx**2 + self.sim.vy**2 + self.sim.vz**2).transpose())
self.velocity = mlab.pipeline.vector_field(self.sim.vx.transpose(), self.sim.vy.transpose(), self.sim.vz.transpose())
self.trc = mlab.points3d(self.sim.tracer_x, self.sim.tracer_y, self.sim.tracer_z, scale_factor=0.75)
mlab.pipeline.image_plane_widget(self.vx, plane_orientation='x_axes', slice_index=10)
mlab.pipeline.image_plane_widget(self.vx, plane_orientation='y_axes', slice_index=10)
mlab.pipeline.image_plane_widget(self.vx, plane_orientation='z_axes', slice_index=10)
mlab.axes()
# mlab.pipeline.vector_cut_plane(self.velocity, mask_points=2, scale_factor=3, plane_orientation='y_axes')
# mlab.pipeline.vectors(self.velocity, mask_points=20, scale_factor=3.)
mlab.outline()
while 1:
self.visualize()
def cutPlanes( volume, colormap='gist_ncar' ):
'''Display a 3D volume of scalars with two cut planes.
volume: a three dimensional array of scalars
'''
scalarField = mlab.pipeline.scalar_field( volume )
mlab.pipeline.image_plane_widget(scalarField,
plane_orientation='z_axes',
slice_index=10,
colormap = colormap
)
mlab.pipeline.image_plane_widget(scalarField,
plane_orientation='y_axes',
slice_index=10,
colormap = colormap
)
mlab.outline()
mlab.axes()
mlab.colorbar(orientation='vertical')
def generate_mayavi_point_plot(self, srcid_sciclasses_list):
""" Generate a Mayavi mlab 3D plot which summarizes iterative
classification of a TUTOR source over the number of epochs
used/added.
"""
# # # # # # # # #
# TODO: I should label which science class by color, as Y axis labels
from enthought.mayavi.scripts import mayavi2
mayavi2.standalone(globals())
from enthought.mayavi import mlab
epoch_ids = [0] # x
class_groups = [0] # y
probs = [0] # z
styles = [0] # numbers used for coloring & glyph sizes
for src_id,sci_classes in srcid_sciclasses_list:
#print 'src_id:', src_id
i = 0
for class_name, class_dict in sci_classes.class_dict.iteritems():
epoch_ids.extend(class_dict['epoch_ids'])
class_groups.extend([1]*len(class_dict['epoch_ids']))
probs.extend(class_dict['probs'])
styles.extend([i + 1]*len(class_dict['epoch_ids']))
i += 1
mlab.points3d(numpy.array(epoch_ids),
numpy.array(class_groups),
numpy.array(probs)*100.0,
numpy.array(styles),
colormap="Paired",
scale_mode="none",
scale_factor=2.0)
mlab.axes(xlabel='N of epochs',
ylabel='science class',
zlabel='% Prob.')
def plot(self):
"绘制场景"
# ball.up.x = 5
# ball.up.y = 6
# ball.up.z = 7
# ball.up.x = -4
# ball.up.y = -3
# ball.up.z = -2
x = [[-1,1,1,-1,-1],
[-1,1,1,-1,-1]]
y = [[-1,-1,-1,-1,-1],
[1,1,1,1, 1]]
z = [[1,1,-1,-1,1],
[1,1,-1,-1,1]]
# s = mlab.mesh(x, y, z, representation="wireframe", line_width=1.0 )
# mlab.show()
pl = mlab.surf(x,y,z,warp_scale="auto")
mlab.axes(xlabel = 'x',ylabel= 'y',zlabel= 'z')
mlab.outline(pl)
def image():
#print getcwd()
fig = mlab.figure()
fig.scene.background = (1.,1.,1.)
fig.scene.jpeg_quality = 100
src = mlab.pipeline.open('/home/jakub/simdata/global_enrichement/eps_f0nodes_1.vtk')
tc = mlab.pipeline.extract_tensor_components(src)
tc.filter.scalar_mode = 'component'
ws = mlab.pipeline.warp_scalar(tc, warp_scale = 2.)
#mlab.pipeline.surface(ws)
cp = mlab.pipeline.cut_plane(ws)
cp.filters[0].widget.enabled = False
cp.filters[0].plane.normal = (0.,1.,0.)
su = mlab.pipeline.surface(cp)
su.actor.property.color = (0.75294117647058822, 0.75294117647058822, 0.75294117647058822)
su.actor.property.line_width = 4.
su.actor.mapper.scalar_visibility = False
#print cp.filters
src2 = mlab.pipeline.open('/home/jakub/simdata/global_enrichement/eps_m0nodes_1.vtk')
tc2 = mlab.pipeline.extract_tensor_components(src2)
tc2.filter.scalar_mode = 'component'
ws2 = mlab.pipeline.warp_scalar(tc2, warp_scale = 2.)
#mlab.pipeline.surface(ws)
cp2 = mlab.pipeline.cut_plane(ws2)
cp2.filters[0].widget.enabled = False
cp2.filters[0].plane.normal = (0.,1.,0.)
su2 = mlab.pipeline.surface(cp2)
su2.actor.property.color = (0.,0.,0.)
su2.actor.property.line_width = 4.
su2.actor.mapper.scalar_visibility = False
ax = mlab.axes(color = (0.,0.,0.),
xlabel = 'x',
ylabel = '',
zlabel = 'strain',
extent = [0., 3.1, 0., 0., 0., 2.],
ranges = [1., 0., 0., 0., 0., 1.],
y_axis_visibility = False)
ax.title_text_property.color = (0.0, 0.0, 0.0)
ax.label_text_property.color = (0.0, 0.0, 0.0)
ax.title_text_property.bold = False
ax.label_text_property.bold = False
mlab.view(90., 90., 6.0, 'auto')
def saveVTKFilesAsImages_2D(sourceFolder, destinationFolder):
if not os.path.isdir(sourceFolder) or not os.path.isdir(
destinationFolder):
return
vtkFiles = []
for f in sorted(os.listdir(sourceFolder)):
if f.endswith(".vtk"):
vtkFiles.append(f)
if len(vtkFiles) == 0:
return
figure = mlab.figure(size=(800, 600))
figure.scene.disable_render = True
vtkSource = VTKFileReader()
vtk_file = os.path.join(sourceFolder, vtkFiles[0])
vtkSource.initialize(vtk_file)
surface = mlab.pipeline.surface(vtkSource)
axes = mlab.axes()
colorbar = mlab.colorbar(object=surface, orientation='horizontal')
mlab.view(0, 0)
figure.scene.disable_render = False
mlab.draw()
png_file = os.path.join(destinationFolder,
vtkFiles[0]).replace('.vtk', '.png')
mlab.savefig(png_file)
for f in vtkFiles[1:-1]:
vtk_file = os.path.join(sourceFolder, f)
vtkSource.initialize(vtk_file)
png_file = os.path.join(destinationFolder,
f).replace('.vtk', '.png')
mlab.savefig(png_file)
app = QtCore.QCoreApplication.instance()
if app:
app.processEvents()
def pattern_tp(array_ip, tht_scan=0, phi_scan=0, tht_min=0, tht_max=np.pi, tht_num=50,
phi_min=0, phi_max=2 * np.pi, phi_num=50, scale="dB",
dB_limit= -40, factor="GF", plot_type="rect",
mayavi_app=False):
r"""
Function to evaluate 3D AF/GF/NF of a arbitrary 3D array in (tht, phi)-domain.
By default, this function calculates the gain-factor (GF).
:param array_ip: array excitation data in 'Arraytool' input format (see :func:`ip_format`)
:param tht_scan, etc: beam scan position in (tht, phi)-domain
:param tht_min, etc: limits of (tht, phi)-domain
:param tht_num, etc: number of points between 'tht_min' and 'tht_max' including
the boundaries
:param scale: specifies the scale choice ... dB/linear
:param dB_limit: cutoff limit (see :func:`cutoff`)
:param factor: type of pattern you need ... AF/NF/GF
:param plot_type: can be rect/polar/contour ... if False, nothing happens
:param mayavi_app: if True, the 3D plot will be opened in the MayaVi application
:rtype: A list, [tht,phi,F]
"""
x = array_ip[:, 0]
y = array_ip[:, 1]
z = array_ip[:, 2]
A = array_ip[:, 3] # un-packing "array_ip" finished
k = 2 * np.pi # (angular) wave-number, which is 2*pi when lambda = 1
tht_numj = complex(0, tht_num)
phi_numj = complex(0, phi_num)
[tht, phi] = np.mgrid[tht_min:tht_max:tht_numj, phi_min:phi_max:phi_numj]
u = np.sin(tht) * np.cos(phi); v = np.sin(tht) * np.sin(phi); w = np.cos(tht)
u1 = np.reshape(u, (u.size, -1))
v1 = np.reshape(v, (v.size, -1))
w1 = np.reshape(w, (w.size, -1))
u_scan = np.sin(tht_scan) * np.cos(phi_scan)
v_scan = np.sin(tht_scan) * np.sin(phi_scan)
w_scan = np.cos(tht_scan)
A = np.reshape(A, (len(A), -1))
U = np.tile(u1 - u_scan, len(x))
V = np.tile(v1 - v_scan, len(x))
W = np.tile(w1 - w_scan, len(x))
X = np.tile(x, (u.size, 1))
Y = np.tile(y, (u.size, 1))
Z = np.tile(z, (u.size, 1))
# Evaluating array-factor of the planar array
AF1 = np.dot(np.exp(1j * k * (U * X + V * Y + W * Z)), A)
AF = np.reshape(AF1, u.shape)
# Evaluation of F = (AF/GF/NF) => depending upon the user's choice
if(factor == "AF"):
F = AF; n1 = ""; ff = "Array-Factor "; f1 = "AF "
elif(factor == "GF"):
P_inc = ((abs(A)) ** 2).sum()
GF = AF / np.sqrt(P_inc) # Converting the AF to GF
F = GF; n1 = ""; ff = "Gain-Factor "; f1 = "GF "
elif(factor == "NF"):
norm_fact = (abs(A)).sum()
F = AF / norm_fact
n1 = "Normalized "; ff = "Factor "; f1 = "NF "
# converting 'F' from linear to dB scale, if needed
if(scale == "linear"):
F_plt = abs(F)
ss = "in linear scale"
elif(scale == "dB"):
F = 20 * np.log10(abs(F))
# cutoff the "F" below some limit ... just for the plotting purpose
F_plt = cutoff(F, dB_limit)
ss = "in dB scale"
# plotting the factor (AF/GF/NF)
if(plot_type):
if (mayavi_app): # opens the 3D plot in MayaVi Application
mlab.options.backend = 'envisage'
if(plot_type == "rect"): # rectangular plot
plt3d = mlab.surf(tht, phi, F_plt, warp_scale='auto')
ranges1 = [tht.min(), tht.max(), phi.min(), phi.max(), F_plt.min(), F_plt.max()]
mlab.axes(xlabel='Tht', ylabel='Phi', zlabel=f1,
ranges=ranges1, nb_labels=5)
mlab.title(n1 + ff + ss, size=0.35)
mlab.colorbar(orientation="vertical", nb_labels=5)
plt3d.scene.isometric_view()
mlab.show()
if(plot_type == "polar"): # rectangular plot
if(scale == "dB"):
F_plt = F_plt - dB_limit
F_plt_x = F_plt * u; F_plt_y = F_plt * v; F_plt_z = F_plt * w
ranges1 = [F_plt_x.min(), F_plt_x.max(), F_plt_y.min(), F_plt_y.max(), F_plt_z.min(), F_plt_z.max()]
plt3d = mlab.mesh(F_plt_x, F_plt_y, F_plt_z, scalars=F_plt, extent=ranges1)
mlab.axes(xlabel='x', ylabel='y', zlabel='z',
ranges=ranges1, nb_labels=5)
mlab.title(n1 + ff + ss, size=0.35)
mlab.colorbar(orientation="vertical", nb_labels=5)
plt3d.scene.isometric_view()
mlab.show()
if(plot_type == "contour"): # contour plot
plt.contourf(tht, phi, F_plt)
plt.axis('tight'); plt.grid(True)
plt.xlabel(r'$\theta$', fontsize=16)
plt.ylabel(r'$\phi$', fontsize=16)
plt.colorbar(format='$%.2f$')
plt.show()
return tht, phi, F
pl.gca().add_patch(rect)
pl.gca().set_aspect("equal")
pl.show()
####### 误差曲面 #######
scale_k = 1.0
scale_b = 10.0
scale_error = 1000.0
def S(k, b):
"计算直线y=k*x+b和原始数据X、Y的误差的平方和"
error = np.zeros(k.shape)
for x, y in zip(X, Y):
error += (y - (k*x + b))**2
return error
ks, bs = np.mgrid[k-scale_k:k+scale_k:40j, b-scale_b:b+scale_b:40j]
error = S(ks, bs)/scale_error
from enthought.mayavi import mlab
surf = mlab.surf(ks, bs/scale_b, error)
mlab.axes(xlabel="k", ylabel="b", zlabel="error",
ranges=[k-scale_k,k+scale_k,
b-scale_b,b+scale_b,
np.min(error)*scale_error, np.max(error)*scale_error])
mlab.outline(surf)
mlab.points3d([k],[b/scale_b],[S(k,b)/scale_error],scale_factor=0.1, color=(1,1,1))
mlab.show()
# Start plotting ... make it an interactive 3D map !
fig1 = mlab.figure(bgcolor=(1.0,1.0,1.0),fgcolor=(0.0,0.0,0.0), size=(1200,900))
surf2 = mlab.contour3d(x,y,z,scidata_cleana,contours=[10],\
opacity=0.5, colormap='RdYlBu', name='[O III]-1')
mlab.outline()
surf3 = mlab.contour3d(x,y,z,scidata_cleana,contours=[40],\
opacity=1, colormap='Blues', name='[O III]-2')
surf5 = mlab.contour3d(x_b,y_b,z_b,scidata_cleanb,contours=[10],\
opacity=0.1,colormap='autumn', name='Hbeta')
surf6 = mlab.points3d((stars[:,0]-size_x/2)*0.5*astopc,(stars[:,1]-size_y/2.)*0.5*astopc,stars[:,0]*0.0,stars[:,2]*0.5*astopc*2,color=(0,0,0), scale_factor=1, name = 'Stars')
# Now to make it look decent ...
ax = mlab.axes(extent=[-10,10,-12,12,-7,7],nb_labels=5)
ax.axes.fly_mode = 'outer_edges'
ax.title_text_property.font_size = 10
ax.title_text_property.font_family = 'courier'
ax.label_text_property.font_family = 'courier'
# And a bit of references if anyone is interested ...
mlab.text(0.05,0.95,'Oxygen-rich ejecta in SNR N132D. Vogt & Dopita, Ap&SS, 311, 521 (2011); Vogt & Shingles, Ap&SS (2013), submitted.',width=0.75)
mlab.text(0.05,0.05,'*****@*****.**',width=0.25)
# Label the axes ...
ax.axes.x_label = 'X (Dec.) [pc]'
ax.axes.y_label = 'Y (R.A.) [pc]'
ax.axes.z_label = 'Z [pc]'
mlab.show()
# Define the view point - useful to make a movie (not build here) ...
def pattern_tp(
array_ip,
tht_scan=0,
phi_scan=0,
tht_min=0,
tht_max=np.pi,
tht_num=50,
phi_min=0,
phi_max=2 * np.pi,
phi_num=50,
scale="dB",
dB_limit=-40,
factor="GF",
plot_type="rect",
mayavi_app=False,
):
r"""
Function to evaluate 3D AF/GF/NF of a arbitrary 3D array in (tht, phi)-domain.
By default, this function calculates the gain-factor (GF).
:param array_ip: array excitation data in 'Arraytool' input format (see :func:`ip_format`)
:param tht_scan, etc: beam scan position in (tht, phi)-domain
:param tht_min, etc: limits of (tht, phi)-domain
:param tht_num, etc: number of points between 'tht_min' and 'tht_max' including
the boundaries
:param scale: specifies the scale choice ... dB/linear
:param dB_limit: cutoff limit (see :func:`cutoff`)
:param factor: type of pattern you need ... AF/NF/GF
:param plot_type: can be rect/polar/contour ... if False, nothing happens
:param mayavi_app: if True, the 3D plot will be opened in the MayaVi application
:rtype: A list, [tht,phi,F]
"""
x = array_ip[:, 0]
y = array_ip[:, 1]
z = array_ip[:, 2]
A = array_ip[:, 3] # un-packing "array_ip" finished
k = 2 * np.pi # (angular) wave-number, which is 2*pi when lambda = 1
tht_numj = complex(0, tht_num)
phi_numj = complex(0, phi_num)
[tht, phi] = np.mgrid[tht_min:tht_max:tht_numj, phi_min:phi_max:phi_numj]
u = np.sin(tht) * np.cos(phi)
v = np.sin(tht) * np.sin(phi)
w = np.cos(tht)
u1 = np.reshape(u, (u.size, -1))
v1 = np.reshape(v, (v.size, -1))
w1 = np.reshape(w, (w.size, -1))
u_scan = np.sin(tht_scan) * np.cos(phi_scan)
v_scan = np.sin(tht_scan) * np.sin(phi_scan)
w_scan = np.cos(tht_scan)
A = np.reshape(A, (len(A), -1))
U = np.tile(u1 - u_scan, len(x))
V = np.tile(v1 - v_scan, len(x))
W = np.tile(w1 - w_scan, len(x))
X = np.tile(x, (u.size, 1))
Y = np.tile(y, (u.size, 1))
Z = np.tile(z, (u.size, 1))
# Evaluating array-factor of the planar array
AF1 = np.dot(np.exp(1j * k * (U * X + V * Y + W * Z)), A)
AF = np.reshape(AF1, u.shape)
# Evaluation of F = (AF/GF/NF) => depending upon the user's choice
if factor == "AF":
F = AF
n1 = ""
ff = "Array-Factor "
f1 = "AF "
elif factor == "GF":
P_inc = ((abs(A)) ** 2).sum()
GF = AF / np.sqrt(P_inc) # Converting the AF to GF
F = GF
n1 = ""
ff = "Gain-Factor "
f1 = "GF "
elif factor == "NF":
norm_fact = (abs(A)).sum()
F = AF / norm_fact
n1 = "Normalized "
ff = "Factor "
f1 = "NF "
# converting 'F' from linear to dB scale, if needed
if scale == "linear":
F_plt = abs(F)
ss = "in linear scale"
elif scale == "dB":
F = 20 * np.log10(abs(F))
# cutoff the "F" below some limit ... just for the plotting purpose
F_plt = cutoff(F, dB_limit)
ss = "in dB scale"
# plotting the factor (AF/GF/NF)
if plot_type:
if mayavi_app: # opens the 3D plot in MayaVi Application
mlab.options.backend = "envisage"
if plot_type == "rect": # rectangular plot
plt3d = mlab.surf(tht, phi, F_plt, warp_scale="auto")
ranges1 = [tht.min(), tht.max(), phi.min(), phi.max(), F_plt.min(), F_plt.max()]
mlab.axes(xlabel="Tht", ylabel="Phi", zlabel=f1, ranges=ranges1, nb_labels=5)
mlab.title(n1 + ff + ss, size=0.35)
mlab.colorbar(orientation="vertical", nb_labels=5)
plt3d.scene.isometric_view()
mlab.show()
if plot_type == "polar": # rectangular plot
if scale == "dB":
F_plt = F_plt - dB_limit
F_plt_x = F_plt * u
F_plt_y = F_plt * v
F_plt_z = F_plt * w
ranges1 = [F_plt_x.min(), F_plt_x.max(), F_plt_y.min(), F_plt_y.max(), F_plt_z.min(), F_plt_z.max()]
plt3d = mlab.mesh(F_plt_x, F_plt_y, F_plt_z, scalars=F_plt, extent=ranges1)
mlab.axes(xlabel="x", ylabel="y", zlabel="z", ranges=ranges1, nb_labels=5)
mlab.title(n1 + ff + ss, size=0.35)
mlab.colorbar(orientation="vertical", nb_labels=5)
plt3d.scene.isometric_view()
mlab.show()
if plot_type == "contour": # contour plot
plt.contourf(tht, phi, F_plt)
plt.axis("tight")
plt.grid(True)
plt.xlabel(r"$\theta$", fontsize=16)
plt.ylabel(r"$\phi$", fontsize=16)
plt.colorbar(format="$%.2f$")
plt.show()
return tht, phi, F
def ip_format(
a,
b,
A,
gamma=np.pi / 2,
plot=False,
color="b",
linewidth=1,
linestyle="-",
alpha=1,
show=True,
stem=False,
stemline="g--",
stemmarker="ro",
mayavi_app=False,
):
r"""
Function to generate the 'Arraytool' input format.
:param a: separation between elements along the x-axis in wavelengths
:param b: separation between elements along the y-axis in wavelengths
:param A: visual excitation matrix
:param gamma: lattice angle in radians
:param plot: if True, produces a 2D/3D plot of the array excitation
:param stem: if True, the array excitation is plotted as 'stem plot'
:param mayavi_app: if True, the 3D plot will be opened in the MayaVi application
All other parameters are nothing but the 'Matplotlib' parameters. These
should be familiar to 'Matlab' or 'Matplotlib' users.
:rtype: array_ip, a Numpy array of size (Number_elements(A)*4)
"""
M = float(A.shape[1]) # no. of elements along the x-axis
N = float(A.shape[0]) # no. of elements along the y-axis
if M == 1: # i.e, linear array is along the y-direction
a = 0
gamma = np.pi / 2
if N == 1: # i.e, linear array is along the x-direction
b = 0
gamma = np.pi / 2
xlim = (M * a) / 2 # array is with in the x-limits [-xlim, +xlim]
# Grid generation
[x, y] = np.mgrid[0:M, 0:N]
x = (x - (M - 1) / 2).T
y = np.flipud((y - (N - 1) / 2).T)
x = x * a
y = y * b # rectangular grid is generated
# modifying the rect-grid according to the given lattice angle 'gamma'
if gamma != np.pi / 2:
x = x + (y / np.tan(gamma)) % a
# Adjusting the rows so that the array lies within [-xlim, +xlim]
for i1 in range(int(N)):
if x[i1, 0] < -xlim:
x[i1, :] = x[i1, :] + a # RIGHT shifting the row by 'a'
if x[i1, -1] > xlim:
x[i1, :] = x[i1, :] - a # LEFT shifting the row by 'a'
# Finally, arranging all the data into 'Arraytool' input format
x = np.reshape(x, (M * N, -1))
y = np.reshape(y, (M * N, -1))
z = np.zeros_like(x) # because only planar arrays are permitted here
A = np.reshape(A, (M * N, -1))
array_ip = np.hstack((x, y, z, A)) # finally, 'Arraytool' input format
# plotting the 'absolute' value of the array excitation (2D/3D)
if plot:
# checking whether 'A' has any imaginary values
if (A.imag > 1e-10).sum():
A_plt = abs(A) # if A.imag are significant, then '|A|' will be plotted
else:
A_plt = A.real # if A.imag are negligible, then 'A' will be plotted
if M == 1: # i.e, linear array is along the y-direction
plt.plot(y, A_plt, color=color, linewidth=linewidth, linestyle=linestyle, alpha=alpha)
if stem:
plt.stem(y, A_plt, linefmt=stemline, markerfmt=stemmarker)
plt.axis("tight")
plt.grid(True)
plt.xlabel(r"$y$", fontsize=16)
plt.ylabel(r"$\left|A_{n}\right|$", fontsize=16)
if show:
plt.title(r"$\mathrm{Array}\ \mathrm{Excitation}$", fontsize=18)
plt.show()
elif N == 1: # i.e, linear array is along the x-direction
plt.plot(x, A_plt, color=color, linewidth=linewidth, linestyle=linestyle, alpha=alpha)
if stem:
plt.stem(x, A_plt, linefmt=stemline, markerfmt=stemmarker)
plt.axis("tight")
plt.grid(True)
plt.xlabel(r"$x$", fontsize=16)
plt.ylabel(r"$\left|A_{m}\right|$", fontsize=16)
if show:
plt.title(r"$\mathrm{Array}\ \mathrm{Excitation}$", fontsize=18)
plt.show()
else:
if mayavi_app: # this option opens the 3D plot in MayaVi Application
mlab.options.backend = "envisage"
s1 = mlab.quiver3d(x, y, z, z, z, A_plt) # stem3D representation
ranges1 = [x.min(), x.max(), y.min(), y.max(), A_plt.min(), A_plt.max()]
mlab.axes(xlabel="x", ylabel="y", zlabel="Amn", ranges=ranges1, nb_labels=3)
mlab.colorbar(orientation="vertical", nb_labels=5)
s1.scene.isometric_view()
if show:
mlab.show()
return array_ip
power=3,
threshold=10**(-3),
norm=2,
neighbor_type='reduced',
jacobian_file=None,
distance_type='radial')
# Unpack the results and calculate the adjusted data
estimate, residuals, misfits, goals = results
fatiando.mesh.fill(estimate, mesh)
adjusted = gplant.adjustment(data, residuals)
# PLOT THE INVERSION RESULTS
################################################################################
log.info("Plotting")
# Plot the adjusted model plus the skeleton of the synthetic model
fig = mlab.figure()
fig.scene.background = (1, 1, 1)
plot = vis.plot_prism_mesh(seed_mesh, style='surface', label='Density')
plot = vis.plot_prism_mesh(mesh, style='surface', label='Density')
plot = vis.plot_prism_mesh(mesh, style='surface', label='Density')
axes = mlab.axes(plot, nb_labels=5, extent=extent, color=(0, 0, 0))
axes.label_text_property.color = (0, 0, 0)
axes.title_text_property.color = (0, 0, 0)
axes.axes.label_format = "%-#.0f"
mlab.outline(color=(0, 0, 0), extent=extent)
Y, X, Z = utils.extract_matrices(data['gzz'])
mlab.surf(X, Y, Z)
mlab.show()
pylab.plot(p[0], p[1], '*k', markersize=9)
labels = pylab.gca().get_xticklabels()
for label in labels:
label.set_rotation(30)
pylab.savefig('seeds.pdf')
pylab.xlabel('Northing [m]')
pylab.ylabel('Easting [m]')
pylab.show()
fig = mlab.figure()
fig.scene.background = (1, 1, 1)
fig.scene.camera.yaw(230)
plot = vis.plot_prism_mesh(mesh, opacity=0.4)
plot = vis.plot_prism_mesh(seed_mesh)
axes = mlab.axes(plot, nb_labels=5, extent=[xmin,xmax,ymin,ymax,-zmax,-zmin])
mlab.show()
# Run the inversion
results = gplant.grow(data, mesh, seeds, compactness=10**(5), power=5, norm=1,
threshold=5*10**(-5), jacobian_file=None,
distance_type='radial')
estimate, residuals, misfits, goals = results
adjusted = gplant.adjustment(data, residuals)
fatiando.mesh.fill(estimate, mesh, fillNone=False)
log.info("Pickling results")
r_r.append(i)
th_r.append(j)
ph_r.append(k)
s_r.append(v)
#elif v >= 0.5:
# r_a.append(i)
# th_a.append(j)
# ph_a.append(k)
# s_a.append(v)
if False:
from enthought.mayavi import mlab
fig1 = mlab.figure(bgcolor=(1,1,1),fgcolor=(0,0,0),size=(800,800))
#mlab.points3d(ph_a, th_a, r_a, s_a,opacity=0.1,scale_factor=3)#, color=(0,0,1))
mlab.points3d(ph_r, th_r, r_r, s_r,opacity=0.3,scale_factor=2)#, color=(1,0,0))
mlab.axes(ranges=[0.0, 90.0, 0.0, 90.0, 3.0, 9.0])
#mlab.orientation_axes()
mlab.xlabel("phi")
mlab.ylabel("theta")
mlab.zlabel("centroid\ndistance")
#mlab.colorbar(nb_labels=2,nb_colors=2,label_fmt='')
mlab.colorbar()
#mlab.text(0.1,0.1,"attraction",color=(0,0,1),width=0.1)
#mlab.text(0.8,0.1,"repulsion",color=(1,0,0),width=0.1)
mlab.text(0.1,0.8,"%s-%s (n=%i; x=%i); (%s)"%(res1,res2,ndata,countNone,pdblistfile),width=0.8)
mlab.title("Free Energy (%s,%s)"%(res1,res2),size=0.3,height=0.7,figure=fig1)
viewdist = 120
elevation = 60 # angle or 'rotate'
azimuth = 180+45 # angle or 'rotate'
mlab.view(distance=viewdist,elevation=elevation, azimuth=azimuth)
cat2 = cat(x, y, 2)
cat3 = cat(x, y, 3)
# The cats lie in a [0, 1] interval, with .5 being the assymptotique
# value. We want to reposition this value to 0, so as to put it in the
# center of our extents.
cat1 -= 0.5
cat2 -= 0.5
cat3 -= 0.5
cat1_extent = (-14,-6, -4,4, 0,5)
surf_cat1 = mlab.surf(x-10, y, cat1, colormap='Spectral', warp_scale=5,
extent=cat1_extent, vmin=-0.5, vmax=0.5)
mlab.outline(surf_cat1, color=(.7, .7, .7))
mlab.axes(surf_cat1, color=(.7, .7, .7), extent=cat1_extent,
ranges=(0,1, 0,1, 0,1), xlabel='', ylabel='',
zlabel='Probability',
x_axis_visibility=False, z_axis_visibility=False)
mlab.text(-18, -4, '1 photon', z=-4, width=0.13)
cat2_extent = (-4,4, -4,4, 0,5)
surf_cat2 = mlab.surf(x, y, cat2, colormap='Spectral', warp_scale=5,
extent=cat2_extent, vmin=-0.5, vmax=0.5)
mlab.outline(surf_cat2, color=(0.7, .7, .7), extent=cat2_extent)
mlab.text(-4, -3, '2 photons', z=-4, width=0.14)
cat3_extent = (6,14, -4,4, 0,5)
surf_cat3 = mlab.surf(x+10, y, cat3, colormap='Spectral', warp_scale=5,
extent=cat3_extent, vmin=-0.5, vmax=0.5)
mlab.outline(surf_cat3, color=(.7, .7, .7), extent=cat3_extent)
# -*- coding: utf-8 -*-
import pickle
import numpy
from enthought.mayavi import mlab
from fatiando import vis
f = open("mesh.pickle")
mesh = pickle.load(f)
f.close()
f = open("seeds.pickle")
seeds = pickle.load(f)
f.close()
seed_mesh = numpy.array([seed['cell'] for seed in seeds])
fig = mlab.figure()
fig.scene.camera.yaw(230)
fig.scene.background = (1, 1, 1)
vis.plot_prism_mesh(mesh, opacity=0.2)
plot = vis.plot_prism_mesh(mesh)
vis.plot_prism_mesh(seed_mesh)
axes = mlab.axes(plot, nb_labels=5)
mlab.show()
import numpy
from enthought.mayavi import mlab
from fatiando import vis
f = open("mesh.pickle")
mesh = pickle.load(f)
f.close()
f = open("seeds.pickle")
seeds = pickle.load(f)
f.close()
f = open("model.pickle")
model = pickle.load(f)
f.close()
seed_mesh = numpy.array([seed['cell'] for seed in seeds])
fig = mlab.figure()
fig.scene.camera.yaw(230)
fig.scene.background = (1, 1, 1)
vis.plot_prism_mesh(model, style='wireframe', xy2ne=True)
plot = vis.plot_prism_mesh(mesh, xy2ne=True)
vis.plot_prism_mesh(seed_mesh, xy2ne=True)
axes = mlab.axes(plot, nb_labels=5, color=(0,0,0))
axes.label_text_property.color = (0,0,0)
axes.title_text_property.color = (0,0,0)
axes.axes.label_format = "%-#.0f"
mlab.outline(color=(0,0,0))
mlab.show()
[phi,theta] = np.meshgrid(phi,theta)
# Radius of displayed sphere
r = 5
x = r*np.sin(theta)*np.cos(phi)
y = r*np.sin(theta)*np.sin(phi)
z = r*np.cos(theta)
#Plot Sphere
mlab.figure("Figure 1")
figure1 = mlab.mesh(x,y,z,representation='wireframe')
mlab.axes()
mlab.title("Figure 1")
mlab.outline()
#Make the radius a function of theta and phi
#antenna pattern response to a +/- polarized wave
#See Eq. 7 of Anholm et al. (Phys. Rev. D 79, 084030 (2009))
rp = np.absolute(.5*np.sin(theta)**2 * (np.cos(phi)**2 -
np.sin(phi)**2) / (1 + np.cos(theta)))
rp_mask = np.ma.masked_invalid(rp)
x = rp*np.sin(theta)*np.cos(phi)
x[np.isinf(x)] = np.nan
y = rp*np.sin(theta)*np.sin(phi)
#a.axes.label_format = "" #a.axes.x_label, a.axes.y_label, a.axes.z_label = "", "", "" #a.property.line_width = 1 mlab.outline(p, extent=extent, color=(0,0,0)) pos = 1000 field = 'gz' scale = 200 Y, X, Z = utils.extract_matrices(data[field]) p = mlab.contour_surf(X, Y, Z, contours=10, colormap='jet') p.contour.filled_contours = True p.actor.actor.position = (0,0,pos) p.actor.actor.scale = (1,1,scale) a = mlab.axes(p, nb_labels=0, extent=[0,3000,0,3000,pos,pos + scale*Z.max()], color=(0,0,0)) a.label_text_property.color = (0,0,0) a.title_text_property.color = (0,0,0) a.axes.label_format = "" a.axes.x_label, a.axes.y_label, a.axes.z_label = "", "", "" a.property.line_width = 1 scene = fig scene.scene.camera.position = [5845.7904104772751, 10826.008452051316, 3697.0607477775502] scene.scene.camera.focal_point = [1317.7924322687745, 1352.1830042500999, -1036.6534494994289] scene.scene.camera.view_angle = 30.0 scene.scene.camera.view_up = [-0.18123807301145317, -0.36888651509323656, 0.91163342406554104] scene.scene.camera.clipping_range = [5326.0395401897185, 18409.2672096317] scene.scene.camera.compute_view_plane_normal() scene.scene.render()
from numpy import array, mgrid, vstack, arange, transpose, zeros
from enthought.mayavi.mlab import show, surf, view, roll, axes, xlabel
x, y = mgrid[0:3:1,0:3:1]
s = surf(x, y, zeros(9).reshape(3,3), warp_scale = 0.0, vmin = 0, vmax = 3)
view(0,0,20)
for i,j in transpose(vstack((arange(100),arange(100)))):
s.mlab_source.x = x*(1+0.01*(i+1))
s.mlab_source.y = y*(1+0.003*(j+1))
s.mlab_source.scalars = (x*(1+0.01*i)-x)+(y*(1+0.01*j)-y)
axes()
show()
def pattern_uv(
array_ip,
u_scan=0,
v_scan=0,
u_min=-1,
u_max=1,
u_num=50,
v_min=-1,
v_max=1,
v_num=50,
scale="dB",
dB_limit=-40,
factor="GF",
plot_type="rect",
mayavi_app=False,
):
r"""
Function to evaluate 3D AF/GF/NF of a planar array in uv-domain.
By default, this function calculates the gain-factor (GF).
:param array_ip: array excitation data in 'Arraytool' input format (see :func:`ip_format`)
:param u_scan, v_scan: beam scan position in uv-domain
:param u_min, etc: limits of uv-domain
:param u_num, v_num: number of points between 'u_min' and 'u_max' including boundaries
:param scale: specifies the scale choice ... dB/linear
:param dB_limit: cutoff limit (see :func:`cutoff`)
:param factor: type of pattern you need ... AF/NF/GF
:param plot_type: can be rect/polar ... if False, nothing happens
:param mayavi_app: if True, the 3D plot will be opened in the MayaVi application
:rtype: A list, [u,v,F]
"""
x = array_ip[:, 0]
y = array_ip[:, 1]
z = array_ip[:, 2]
A = array_ip[:, 3] # un-packing "array_ip" finished
k = 2 * np.pi # (angular) wave-number, which is 2*pi when lambda = 1
u_numj = complex(0, u_num)
v_numj = complex(0, v_num)
# Making sure all elements in the z-column of the "array_ip" are zeros
z_flag = True
if (abs(z) > 0).sum():
print "All elements in the z-column of array input should be zero."
z_flag = False
# After making sure, proceed to the next level, i.e., evaluate the pattern
if z_flag:
[u, v] = np.mgrid[u_min:u_max:u_numj, v_min:v_max:v_numj]
u1 = np.reshape(u, (u.size, -1))
v1 = np.reshape(v, (v.size, -1))
A = np.reshape(A, (len(A), -1))
U = np.tile(u1 - u_scan, len(x))
V = np.tile(v1 - v_scan, len(x))
X = np.tile(x, (u.size, 1))
Y = np.tile(y, (u.size, 1))
# Evaluating array-factor of the planar array
AF1 = np.dot(np.exp(1j * k * (U * X + V * Y)), A)
AF = np.reshape(AF1, u.shape)
# Evaluation of F = (AF/GF/NF) => depending upon the user's choice
if factor == "AF":
F = AF
n1 = ""
ff = "Array-Factor "
f1 = "AF "
elif factor == "GF":
P_inc = ((abs(A)) ** 2).sum()
GF = AF / np.sqrt(P_inc) # Converting the AF to GF
F = GF
n1 = ""
ff = "Gain-Factor "
f1 = "GF "
elif factor == "NF":
norm_fact = (abs(A)).sum()
F = AF / norm_fact
n1 = "Normalized "
ff = "Factor "
f1 = "NF "
# converting 'F' from linear to dB scale, if needed
if scale == "linear":
F_plt = abs(F)
ss = "in linear scale"
elif scale == "dB":
F = 20 * np.log10(abs(F))
# cutoff the "F" below some limit ... just for the plotting purpose
F_plt = cutoff(F, dB_limit)
ss = "in dB scale"
# plotting the factor (AF/GF/NF)
if plot_type:
if plot_type == "rect": # rectangular plot
if mayavi_app: # opens the 3D plot in MayaVi Application
mlab.options.backend = "envisage"
plt3d = mlab.surf(u, v, F_plt, warp_scale="auto")
ranges1 = [u_min, u_max, v_min, v_max, F_plt.min(), F_plt.max()]
mlab.axes(xlabel="u", ylabel="v", zlabel=f1, ranges=ranges1, nb_labels=5)
mlab.title(n1 + ff + ss, size=0.35)
mlab.colorbar(orientation="vertical", nb_labels=5)
plt3d.scene.isometric_view()
mlab.show()
if plot_type == "contour": # contour plot
plt.contourf(u, v, F_plt)
vs = plt.Circle((0, 0), radius=1, edgecolor="w", fill=False)
ax = plt.gca()
ax.add_patch(vs)
plt.axis("image")
plt.grid(True)
plt.xlabel(
r"$u,\ \mathrm{where}\ u=\sin \theta \cos \phi\ \mathrm{in}\ \mathrm{the}\ \mathrm{visible-space}$",
fontsize=16,
)
plt.ylabel(
r"$v,\ \mathrm{where}\ v=\sin \theta \sin \phi\ \mathrm{in}\ \mathrm{the}\ \mathrm{visible-space}$",
fontsize=16,
)
plt.colorbar(format="$%.2f$")
plt.show()
return u, v, F
# -*- coding: utf-8 -*- import numpy as np from enthought.mayavi import mlab const = 2.19967e34 x, y = np.mgrid[0:700:200j, 300:1500:200j] z = np.exp(-x / y) * np.sqrt(x) / (y**1.5) c = const * z pl = mlab.mesh(x, y, z, scalars=c) mlab.axes(xlabel='Energy(eV)', ylabel='Temprature(k)', zlabel='N/2.19967e34') mlab.outline(pl) mlab.show()
scene = mlab.figure(size=(1200,800)) scene.scene.background = (1, 1, 1) view.bbox(mlab) engine = mlab.get_engine() surfs = [] surfs.append(vis.plot_prism_mesh(res, style='surface', xy2ne=True)) engine.scenes[0].children[-1].children[0].children[0].children[0].scalar_lut_manager.data_range = [0,1000] surfs.append(vis.plot_prism_mesh(mesh, style='surface', xy2ne=True)) engine.scenes[0].children[-1].children[0].children[0].children[0].scalar_lut_manager.lut_mode = "Greys" surfs.append(vis.plot_prism_mesh(seeds, style='surface', xy2ne=True)) engine.scenes[0].children[-1].children[0].children[0].children[0].scalar_lut_manager.lut_mode = "Greys" engine.scenes[0].children[-1].children[0].children[0].children[0].scalar_lut_manager.data_range = [0,1000] o = mlab.outline(color=(0,0,0), extent=extent) o.actor.property.line_width = 1 a = mlab.axes(surfs[0], nb_labels=7, extent=extent, ranges=ranges, color=(0,0,0)) a.label_text_property.color = (0,0,0) a.title_text_property.color = (0,0,0) a.property.line_width = 1 a.axes.label_format = "%-#.1f" a.axes.x_label, a.axes.y_label, a.axes.z_label = "y (km)", "x (km)", "h (km)" mlab.show()
max_mu_q = np.max(np.fabs(s.mu_q_arr))
n_mu_q_arr = s.mu_q_arr / max_mu_q
n_std_q_arr = np.sqrt(s.var_q_arr) / max_mu_q
import enthought.mayavi.mlab as m
f = m.figure(1, size=(1000, 500), fgcolor=(0, 0, 0), bgcolor=(1.0, 1.0, 1.0))
s = m.surf(n_e_arr[1], n_e_arr[2], n_mu_q_arr[0, :, :])
m.axes(
s,
color=(0.7, 0.7, 0.7),
extent=(-1, 1, 0, 1, 0, 1),
ranges=(-0.21, 0.21, 0.1, 20, 0, max_mu_q),
xlabel="x",
ylabel="Lr",
zlabel="Force",
)
m.view(-60.0, 70.0, focalpoint=[0.0, 0.45, 0.45])
# Store the information
view = m.view()
roll = m.roll()
print "view", view
print "roll", roll
print n_mu_q_arr.shape[2]
ms = s.mlab_source
for i in range(1, n_mu_q_arr.shape[0]):
# Draw a box around the cube to aid in the visualization
mlab.outline(extent=[
np.min(ras),
np.max(ras),
np.min(decs),
np.max(decs), c_v[slice_min, 1], c_v[slice_max, 1]
],
color=(0, 0, 0),
line_width=2.0) # Draw a box around it
# Now, add some axes
ax = mlab.axes(extent=[
np.min(ras),
np.max(ras),
np.min(decs),
np.max(decs), c_v[slice_min, 1], c_v[slice_max, 1]
],
nb_labels=3,
color=(0, 0, 0))
# Fine tune the look of the axis
ax.axes.fly_mode = 'outer_edges'
ax.title_text_property.font_size = 10
ax.title_text_property.font_family = 'courier'
ax.title_text_property.italic = False
ax.label_text_property.font_family = 'courier'
ax.label_text_property.italic = False
ax.scene.parallel_projection = True
ax.scene.background = (1.0, 1.0, 1.0)
ax.title_text_property.color = (0, 0, 0)
ax.label_text_property.color = (0, 0, 0)
from pylab import *
from enthought.mayavi import mlab
I = 10 * rand(20, 30)
figure(3)
hold(False)
imshow(I)
xlabel(r'first argument of the array, inversed', fontsize=16)
ylabel(r'first argument of the array', fontsize=16)
hold(True)
axis('scaled')
title(r'imshow', fontsize=20)
draw() # or show() on some machines
mlab.figure(1)
mlab.surf(I)
mlab.axes()
def plot_3d_spectrum_mayavi(fs, fignum=None, vmin=None, vmax=None,
pop_ids=None):
"""
Logarithmic heatmap of single 3d FS.
This method relies on MayaVi2's mlab interface. See http://code.enthought.com/projects/mayavi/docs/development/html/mayavi/mlab.html . To edit plot
properties, click leftmost icon in the toolbar.
If you get an ImportError upon calling this function, it is likely that you
don't have mayavi installed.
fs: FS to plot
vmin: Values in fs below vmin are masked in plot.
vmax: Values in fs above vmax saturate the color spectrum.
fignum: Figure number to plot into. If None, a new figure will be created.
Note that these are MayaVi figures, which are separate from
matplotlib figures.
pop_ids: If not None, override pop_ids stored in Spectrum.
"""
from enthought.mayavi import mlab
fig = mlab.figure(fignum, bgcolor=(1,1,1))
mlab.clf(fig)
if vmin is None:
vmin = fs.min()
if vmax is None:
vmax = fs.max()
# Which entries should I plot?
toplot = numpy.logical_not(fs.mask)
toplot = numpy.logical_and(toplot, fs.data >= vmin)
# For the color mapping
normalized = (numpy.log(fs)-numpy.log(vmin))\
/(numpy.log(vmax)-numpy.log(vmin))
normalized = numpy.minimum(normalized, 1)
xs,ys,zs = numpy.indices(fs.shape)
flat_xs = xs.flatten()
flat_ys = ys.flatten()
flat_zs = zs.flatten()
flat_toplot = toplot.flatten()
mlab.barchart(flat_xs[flat_toplot], flat_ys[flat_toplot],
flat_zs[flat_toplot], normalized.flatten()[flat_toplot],
colormap='hsv', scale_mode='none', lateral_scale=1,
figure=fig)
if pop_ids is None:
if fs.pop_ids is not None:
pop_ids = fs.pop_ids
else:
pop_ids = ['pop0','pop1','pop2']
a = mlab.axes(xlabel=pop_ids[0],ylabel=pop_ids[1],zlabel=pop_ids[2],
figure=fig, color=(0,0,0))
a.axes.label_format = ""
a.title_text_property.color = (0,0,0)
mlab.text3d(fs.sample_sizes[0],fs.sample_sizes[1],fs.sample_sizes[2]+1,
'(%i,%i,%i)'%tuple(fs.sample_sizes), scale=0.75, figure=fig,
color=(0,0,0))
mlab.view(azimuth=-40, elevation=65, distance='auto', focalpoint='auto')
mlab.show()
def pattern_uv(array_ip, u_scan=0, v_scan=0, u_min= -1, u_max=1, u_num=50,
v_min= -1, v_max=1, v_num=50, scale="dB",
dB_limit= -40, factor="GF", plot_type="rect",
mayavi_app=False):
r"""
Function to evaluate 3D AF/GF/NF of a planar array in uv-domain.
By default, this function calculates the gain-factor (GF).
:param array_ip: array excitation data in 'Arraytool' input format (see :func:`ip_format`)
:param u_scan, v_scan: beam scan position in uv-domain
:param u_min, etc: limits of uv-domain
:param u_num, v_num: number of points between 'u_min' and 'u_max' including boundaries
:param scale: specifies the scale choice ... dB/linear
:param dB_limit: cutoff limit (see :func:`cutoff`)
:param factor: type of pattern you need ... AF/NF/GF
:param plot_type: can be rect/polar ... if False, nothing happens
:param mayavi_app: if True, the 3D plot will be opened in the MayaVi application
:rtype: A list, [u,v,F]
"""
x = array_ip[:, 0]
y = array_ip[:, 1]
z = array_ip[:, 2]
A = array_ip[:, 3] # un-packing "array_ip" finished
k = 2 * np.pi # (angular) wave-number, which is 2*pi when lambda = 1
u_numj = complex(0, u_num)
v_numj = complex(0, v_num)
# Making sure all elements in the z-column of the "array_ip" are zeros
z_flag = True
if ((abs(z) > 0).sum()):
print "All elements in the z-column of array input should be zero."
z_flag = False
# After making sure, proceed to the next level, i.e., evaluate the pattern
if(z_flag):
[u, v] = np.mgrid[u_min:u_max:u_numj, v_min:v_max:v_numj]
u1 = np.reshape(u, (u.size, -1))
v1 = np.reshape(v, (v.size, -1))
A = np.reshape(A, (len(A), -1))
U = np.tile(u1 - u_scan, len(x))
V = np.tile(v1 - v_scan, len(x))
X = np.tile(x, (u.size, 1))
Y = np.tile(y, (u.size, 1))
# Evaluating array-factor of the planar array
AF1 = np.dot(np.exp(1j * k * (U * X + V * Y)), A)
AF = np.reshape(AF1, u.shape)
# Evaluation of F = (AF/GF/NF) => depending upon the user's choice
if(factor == "AF"):
F = AF; n1 = ""; ff = "Array-Factor "; f1 = "AF "
elif(factor == "GF"):
P_inc = ((abs(A)) ** 2).sum()
GF = AF / np.sqrt(P_inc) # Converting the AF to GF
F = GF; n1 = ""; ff = "Gain-Factor "; f1 = "GF "
elif(factor == "NF"):
norm_fact = (abs(A)).sum()
F = AF / norm_fact
n1 = "Normalized "; ff = "Factor "; f1 = "NF "
# converting 'F' from linear to dB scale, if needed
if(scale == "linear"):
F_plt = abs(F)
ss = "in linear scale"
elif(scale == "dB"):
F = 20 * np.log10(abs(F))
# cutoff the "F" below some limit ... just for the plotting purpose
F_plt = cutoff(F, dB_limit)
ss = "in dB scale"
# plotting the factor (AF/GF/NF)
if(plot_type):
if(plot_type == "rect"): # rectangular plot
if (mayavi_app): # opens the 3D plot in MayaVi Application
mlab.options.backend = 'envisage'
plt3d = mlab.surf(u, v, F_plt, warp_scale='auto')
ranges1 = [u_min, u_max, v_min, v_max, F_plt.min(), F_plt.max()]
mlab.axes(xlabel='u', ylabel='v', zlabel=f1,
ranges=ranges1, nb_labels=5)
mlab.title(n1 + ff + ss, size=0.35)
mlab.colorbar(orientation="vertical", nb_labels=5)
plt3d.scene.isometric_view()
mlab.show()
if(plot_type == "contour"): # contour plot
plt.contourf(u, v, F_plt)
vs = plt.Circle((0, 0), radius=1, edgecolor='w', fill=False)
ax = plt.gca(); ax.add_patch(vs)
plt.axis('image'); plt.grid(True)
plt.xlabel(r'$u,\ \mathrm{where}\ u=\sin \theta \cos \phi\ \mathrm{in}\ \mathrm{the}\ \mathrm{visible-space}$', fontsize=16)
plt.ylabel(r'$v,\ \mathrm{where}\ v=\sin \theta \sin \phi\ \mathrm{in}\ \mathrm{the}\ \mathrm{visible-space}$', fontsize=16)
plt.colorbar(format='$%.2f$')
plt.show()
return u, v, F
#!/usr/bin/env python
import numpy as np
from enthought.mayavi import mlab
x,y=np.ogrid [-2:2:160j,-2:2:160j]
z=abs(x)*np.exp(-x**2-(y/.75)**2)
pl=mlab.surf(x,y,z,warp_scale=2)
mlab.axes(xlabel='x',ylabel='y',zlabel='z')
mlab.outline(pl)
mlab.show()
if len(iters) > 30000:
pointsize = .0003
if len(iters) > 50000:
pointsize = .0001
# Again, the axes are adjusting to maximum and minimum values from the array
if plotattractor == 1:
points3d(np.log10(results[:, 0]),
np.log10(results[:, 1]),
np.log10(results[:, 2]),
iters,
color=(0., 0., 0.),
scale_factor=pointsize)
axes(color=(1., 0., 0.),
extent=[
min(np.log10(results[:, 0])),
max(np.log10(results[:, 0])),
min(np.log10(results[:, 1])),
max(np.log10(results[:, 1])),
min(np.log10(results[:, 2])),
max(np.log10(results[:, 2]))
],
ranges=[
min(np.log10(results[:, 0])),
max(np.log10(results[:, 0])),
min(np.log10(results[:, 1])),
max(np.log10(results[:, 1])),
min(np.log10(results[:, 2])),
max(np.log10(results[:, 2]))
])
scene.scene.background = (1, 1, 1) p = vis.plot_prism_mesh(res, style='surface', xy2ne=True) p.actor.property.line_width = 2 mlab.get_engine().scenes[0].children[-1].children[0].children[0].children[0].scalar_lut_manager.data_range = [0,1200] p = vis.plot_prism_mesh(seeds, style='surface', xy2ne=True) p.actor.property.line_width = 2 mlab.get_engine().scenes[0].children[-1].children[0].children[0].children[0].scalar_lut_manager.data_range = [0,1200] p = vis.plot_prism_mesh(model, style='surface', xy2ne=True) p.actor.property.line_width = 2 p = vis.plot_prism_mesh(mesh.vfilter(model,1100,1201), style='wireframe', xy2ne=True) p.actor.property.color = (0,0,0) p.actor.property.line_width = 3 mlab.outline(color=(0,0,0), extent=extent) a = mlab.axes(p, nb_labels=5, extent=extent, ranges=ranges, color=(0,0,0)) a.label_text_property.color = (0,0,0) a.title_text_property.color = (0,0,0) a.property.line_width = 1 a.axes.label_format = "%-#.1f" a.axes.x_label, a.axes.y_label, a.axes.z_label = "Y (km)", "X (km)", "Z (km)" view.bbox(mlab) view.set(scene) mlab.show()
'value':-1000})
model.append({'x1':4000, 'x2':4500, 'y1':500, 'y2':1500, 'z1':300, 'z2':1000,
'value':-1000})
model.append({'x1':1800, 'x2':3700, 'y1':500, 'y2':1500, 'z1':800, 'z2':1300,
'value':-1000})
model.append({'x1':500, 'x2':4500, 'y1':4000, 'y2':4500, 'z1':500, 'z2':1000,
'value':-1000})
model = numpy.array(model)
extent = [0,5000,0,5000,-1500,0]
# Show the model before calculating to make sure it's right
fig = mlab.figure()
fig.scene.background = (1, 1, 1)
plot = vis.plot_prism_mesh(model, style='surface', xy2ne=True)
axes = mlab.axes(plot, nb_labels=5, extent=extent, color=(0,0,0))
axes.label_text_property.color = (0,0,0)
axes.title_text_property.color = (0,0,0)
axes.axes.label_format = "%-#.0f"
mlab.outline(color=(0,0,0), extent=extent)
mlab.show()
# Now calculate all the components of the gradient tensor
fields = ['gyy', 'gyz', 'gzz']
error = 0.5
data = {}
for i, field in enumerate(fields):
data[field] = synthetic.from_prisms(model, x1=0, x2=5000, y1=0, y2=5000,
nx=25, ny=25, height=150, field=field)
data[field]['value'], error = utils.contaminate(data[field]['value'],
stddev=error,
import numpy as np from enthought.mayavi import mlab x, y = np.ogrid[-2:2:20j, -2:2:20j] z = x * np.exp(-x**2 - y**2) pl = mlab.surf(x, y, z, warp_scale='auto') mlab.axes(xlabel='x', ylabel='y', zlabel='z') mlab.outline(pl)
prisms.append(
geometry.prism(x1=1500,
x2=4500,
y1=2500,
y2=3000,
z1=100,
z2=500,
props={'value': 1500}))
prisms = numpy.array(prisms)
# Show the model before calculating to make sure it's right
fig = mlab.figure()
fig.scene.background = (1, 1, 1)
dataset = vis.plot_prism_mesh(prisms, style='surface', label='Density kg/cm^3')
axes = mlab.axes(dataset, nb_labels=5, extent=[0, 5000, 0, 5000, -1000, 0])
mlab.show()
# Pickle the model so that it can be shown next to the inversion result later
modelfile = open('model.pickle', 'w')
pickle.dump(prisms, modelfile)
modelfile.close()
# Calculate all the components of the gradient tensor
error = 2
pylab.figure(figsize=(16, 8))
pylab.suptitle(r'Synthetic FTG data with %g $E\"otv\"os$ noise' % (error),
fontsize=16)
pylab.subplots_adjust(wspace=0.4, hspace=0.3)
surf3 = mlab.contour3d(x,y,z,scidata_cleana,contours=[40],\
opacity=1, colormap='Blues', name='[O III]-2')
surf5 = mlab.contour3d(x_b,y_b,z_b,scidata_cleanb,contours=[10],\
opacity=0.1,colormap='autumn', name='Hbeta')
surf6 = mlab.points3d((stars[:, 0] - size_x / 2) * 0.5 * astopc,
(stars[:, 1] - size_y / 2.) * 0.5 * astopc,
stars[:, 0] * 0.0,
stars[:, 2] * 0.5 * astopc * 2,
color=(0, 0, 0),
scale_factor=1,
name='Stars')
# Now to make it look decent ...
ax = mlab.axes(extent=[-10, 10, -12, 12, -7, 7], nb_labels=5)
ax.axes.fly_mode = 'outer_edges'
ax.title_text_property.font_size = 10
ax.title_text_property.font_family = 'courier'
ax.label_text_property.font_family = 'courier'
# And a bit of references if anyone is interested ...
mlab.text(
0.05,
0.95,
'Oxygen-rich ejecta in SNR N132D. Vogt & Dopita, Ap&SS, 311, 521 (2011); Vogt & Shingles, Ap&SS (2013), submitted.',
width=0.75)
mlab.text(0.05, 0.05, '*****@*****.**', width=0.25)
# Label the axes ...
ax.axes.x_label = 'X (Dec.) [pc]'
sigma = 10.0 r = 28.0 b = 8.0/3.0 # Initial conditions x = -.5 y = .2 z = 2.17 # Store the trajectory TrajX = [] TrajY = [] TrajZ = [] Time = [] # Integrate the Lorenz ODEs # for t in arange(0.,250.,dt): dxdt = sigma*(y - x) dydt = r * x - y - x * z dzdt = x * y - b * z x = x + dxdt * dt y = y + dydt * dt z = z + dzdt * dt TrajX.append(x) TrajY.append(y) TrajZ.append(z) Time.append(t) plot3d(TrajX,TrajY,TrajZ,Time,colormap='Spectral',tube_radius=0.1) axes(color=(0.,0.,0.),extent=[-15.0,15.0,-15.0,15.0,0.0,50.0],ranges=[-15.0,15.0,-15.0,15.0,0.0,50.0])
