def main(argv=None):
"""docstring for main"""
from enthought.mayavi import mlab
if argv is None:
argv = sys.argv
x1 = x2 = y1 = y2 = 0
fname = ''
try:
fname = argv[1]
x1, x2, y1, y2 = [int(i) for i in argv[2:]]
except:
print('Usage: {0} fname x1 x2 y1 y2'.format(argv[0]))
print(x1, x2, y1, y2)
ds = gdal.Open(fname)
band = ds.GetRasterBand(1)
STORED_VALUE = band.ReadAsArray(x1, y1, x2 - x1, y2 - y1)
ds = 0
# PDS label infos:
SCALING_FACTOR = 0.25
OFFSET = -8000
topo = (STORED_VALUE * SCALING_FACTOR) + OFFSET
mlab.surf(topo, warp_scale=1 / 115., vmin=1700)
mlab.colorbar(orientation='vertical',
title='Height [m]',
label_fmt='%4.0f')
mlab.show()
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 plotsln(mesh, z=None, sln=None, colorbar=False, view=(0,0),
filename="a.png"):
"""
Plot a solution for the mesh editor in the online lab.
"""
x = [n[0] for n in mesh.nodes]
y = [n[1] for n in mesh.nodes]
if z == None:
try:
z = [n[2] for n in mesh.nodes]
except IndexError:
z = [0]*len(y)
from enthought.mayavi import mlab
mlab.options.offscreen = True
mlab.clf()
#mlab.options.show_scalar_bar = False
mlab.triangular_mesh(x, y, z, mesh.elems, scalars=sln)
engine = mlab.get_engine()
image = engine.current_scene
image.scene.background = (1.0, 1.0, 1.0)
image.scene.foreground = (0.0, 0.0, 0.0)
if colorbar:
mlab.colorbar(orientation="vertical")
if view:
mlab.view(view[0], view[1])
mlab.savefig(filename)
def plot_sln_mayavi(x, y, mesh, sln_values, colorbar=False):
"""
Plot a solution using mayavi.
Example:
>>> from numpy import array
>>> from femhub.plot import plot_sln_mayavi
>>> f = plot_sln_mayavi([0, 1, 1], [0, 0, 1], [1, 2, 3])
>>> f.savefig("a.png")
"""
from enthought.mayavi import mlab
#mlab.options.offscreen = True
mlab.clf()
#mlab.options.show_scalar_bar = False
z = [0] * len(x)
mlab.triangular_mesh(x, y, z, mesh, scalars=sln_values)
engine = mlab.get_engine()
image = engine.current_scene
image.scene.background = (1.0, 1.0, 1.0)
image.scene.foreground = (0.0, 0.0, 0.0)
if colorbar:
mlab.colorbar(orientation="vertical")
mlab.view(0, 0)
return mlab
def plot_normals(pts, normals, curvature=None):
x = pts[0, :].A1
y = pts[1, :].A1
z = pts[2, :].A1
u = normals[0, :].A1
v = normals[1, :].A1
w = normals[2, :].A1
if curvature != None:
#mlab.points3d(x,y,z,curvature,mode='point',scale_factor=1.0)
mlab.points3d(x,
y,
z,
curvature,
mode='sphere',
scale_factor=0.1,
mask_points=1)
mlab.colorbar()
else:
mlab.points3d(x, y, z, mode='point')
mlab.quiver3d(x, y, z, u, v, w, mask_points=16, scale_factor=0.1)
# mlab.axes()
mlab.show()
def main(argv=None):
"""docstring for main"""
from enthought.mayavi import mlab
if argv is None:
argv = sys.argv
x1 = x2 = y1 = y2 = 0
fname = ""
try:
fname = argv[1]
x1, x2, y1, y2 = [int(i) for i in argv[2:]]
except:
print("Usage: {0} fname x1 x2 y1 y2".format(argv[0]))
print(x1, x2, y1, y2)
ds = gdal.Open(fname)
band = ds.GetRasterBand(1)
STORED_VALUE = band.ReadAsArray(x1, y1, x2 - x1, y2 - y1)
ds = 0
# PDS label infos:
SCALING_FACTOR = 0.25
OFFSET = -8000
topo = (STORED_VALUE * SCALING_FACTOR) + OFFSET
mlab.surf(topo, warp_scale=1 / 115.0, vmin=1700)
mlab.colorbar(orientation="vertical",
title="Height [m]",
label_fmt="%4.0f")
mlab.show()
def ChargeDensityFog3D(directory, save_file=None , DrawAtoms=True, DrawCell=True, opacity_range=[0,1]):
# Get data from calculation files
with jasp(directory) as calc:
atoms = calc.get_atoms()
x, y, z, cd = calc.get_charge_density()
mlab.figure(bgcolor=(0,0,0), size=(640,480))
# Draw atoms
if DrawAtoms == True:
for atom in atoms:
mlab.points3d(atom.x,
atom.y,
atom.z,
scale_factor=vdw_radii[atom.number]/10.,
resolution=16,
color=tuple(cpk_colors[atom.number]),
scale_mode='none')
# Draw unit cell
if DrawCell == True:
a1, a2, a3 = atoms.get_cell()
origin = [0,0,0]
cell_matrix = [[origin, a1],
[origin, a2],
[origin, a3],
[a1, a1+a2],
[a1, a1+a3],
[a2, a2+a1],
[a2, a2+a3],
[a3, a1+a3],
[a3, a2+a3],
[a1+a2, a1+a2+a3],
[a2+a3, a1+a2+a3],
[a1+a3, a1+a3+a2]] # contains all points on the box
for p1, p2 in cell_matrix:
mlab.plot3d([p1[0], p2[0]], # x-coords of box
[p1[1], p2[1]], # y-coords
[p1[2], p2[2]]) # z-coords
# Plot the charge density
src = mlab.pipeline.scalar_field(x, y, z, cd) #Source data
vmin = cd.min() #find minimum and maximum value of CD data
vmax = cd.max()
vol = mlab.pipeline.volume(src) # Make a volumetric representation of the data
# Set opacity transfer function
from tvtk.util.ctf import PiecewiseFunction
otf = PiecewiseFunction()
otf.add_point(vmin, opacity_range[0]) #Transparency at zero electron density
otf.add_point(vmax*1, opacity_range[1]) #Transparency at max electron density
vol._otf=otf
vol._volume_property.set_scalar_opacity(otf)
#Show a legend
mlab.colorbar(title="e- density\n(e/Ang^3)", orientation="vertical", nb_labels=5, label_fmt='%.2f')
mlab.view(azimuth=-90, elevation=90, distance='auto') # Set viewing angle
mlab.show()
if save_file != None:
mlab.savefig(save_file)
def plot(pts,color=(1.,1.,1.), scalar_list=None):
if scalar_list != None:
mlab.plot3d(pts[0,:].A1,pts[1,:].A1,pts[2,:].A1,scalar_list,
representation = 'wireframe', tube_radius = None)
mlab.colorbar()
else:
mlab.plot3d(pts[0,:].A1,pts[1,:].A1,pts[2,:].A1,color=color,
representation = 'wireframe', tube_radius = None)
def gsurf():
"""Test contour_surf on regularly spaced co-ordinates like MayaVi."""
def f(x, y):
return x**2+y**2 - 25.
x, y = numpy.mgrid[-10.:10.:0.25, -10.:10.0:0.25]
s = surf(x, y, f, warp_scale = 0.)
colorbar(s,orientation='horizontal')
return s
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 test_colorbar(self):
""" Test that when an object with scalars hidden is created, it
does not get a colorbar, unless no other is avalaible.
"""
a = np.random.random((5, 5))
s1 = mlab.surf(a, colormap='gist_earth')
s2 = mlab.surf(a, color=(0, 0, 0))
mlab.colorbar()
self.assertEqual(s2.module_manager.scalar_lut_manager.show_scalar_bar,
False)
self.assertEqual(s1.module_manager.scalar_lut_manager.show_scalar_bar,
True)
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 plotsquare3d(us):
from enthought.mayavi.mlab import surf, show, colorbar, xlabel, ylabel, figure
for u in us:
figure()
Np = 60
points = uniformsquarepoints(Np)
x = points[:,0].reshape(Np,Np)
y = points[:,1].reshape(Np,Np)
up = np.real(u(points))
surf(x,y, up.reshape(Np,Np))
colorbar()
xlabel('x')
ylabel('y')
show()
def test_colorbar(self):
""" Test that when an object with scalars hidden is created, it
does not get a colorbar, unless no other is avalaible.
"""
a = np.random.random((5, 5))
s1 = mlab.surf(a, colormap='gist_earth')
s2 = mlab.surf(a, color=(0, 0, 0))
mlab.colorbar()
self.assertEqual(
s2.module_manager.scalar_lut_manager.show_scalar_bar,
False)
self.assertEqual(
s1.module_manager.scalar_lut_manager.show_scalar_bar,
True)
def save(self,it):
msh = self.__msh
w = (self.__eqn).get(self.__var)()
mlab.clf()
mlab.points3d(msh.x[:,0], msh.x[:,1], zeros(w.shape), w,
scale_factor=self.__scale, scale_mode=self.__mode)
mlab.outline(extent=[msh.BB[0,0],msh.BB[1,0],msh.BB[0,1],msh.BB[1,1],
0,0])
mlab.view(0,0,self.__zoom,self.__xView)
mlab.colorbar()
fSol = ''.join([self.__figDir,'/',self.__name,'%04d.png'% it])
#print fSol
mlab.savefig(fSol)
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 show_variable_timecourse(self, var, time_point, start_value,
end_value):
"""Show an animation of all the section that have
the recorded variable among time"""
# Getting the new scalar
new_scalar = self.get_var_data(var, time_point)
d = self.dataset.point_data.get_array('diameter')
if len(d) != len(new_scalar):
message = "ERROR! MISMATCH on the Vector Length. \
If you assign the new vectors it will not work \
Diameter length: %s New Scalar length: %s var: %s" % (
len(d), len(new_scalar), var)
logger.error(message)
# ReEnable the rendering
self.mayavi.visualization.scene.disable_render = True
self.redraw_color(new_scalar, var)
if not self.colorbar:
self.colorbar = mlab.colorbar(orientation='vertical')
self.timelabel = mlab.text(0.05, 0.05, str(time_point), width=0.05)
self.colorbar.data_range = [start_value, end_value]
time = self.manager.groups['t'][time_point]
self.timelabel.text = str(round(time, 3))
self.mayavi.visualization.scene.disable_render = False
def show( self, x, y, z_middle, displayed_value ):
"""Test contour_surf on regularly spaced co-ordinates like MayaVi.
"""
print '*** plotting data***'
s = points3d( X, Y, z_middle, displayed_value, colormap = "gist_rainbow", mode = "cube", scale_factor = 0.3 )
sb = colorbar( s )
# Recorded script from Mayavi2
#try:
# engine = mayavi.engine
#except NameError:
# from enthought.mayavi.api import Engine
# engine = Engine()
# engine.start()
#if len(engine.scenes) == 0:
# engine.new_scene()
# -------------------------------------------
glyph = s#.pipeline.scenes[0].children[0].children[0].children[0]
glyph.glyph.glyph_source.glyph_source.center = array( [ 0., 0., 0.] )
glyph.glyph.glyph_source.glyph_source.progress = 1.0
glyph.glyph.glyph_source.glyph_source.x_length = 0.6
glyph.glyph.glyph_source.glyph_source.y_length = 0.6
sb.scalar_bar.title = 'thickness [m]'
#print s.pipeline
#s.scene.background = (1.0, 1.0, 1.0)
return s
def plot(pts, color=(1., 1., 1.), scalar_list=None):
if scalar_list != None:
mlab.plot3d(pts[0, :].A1,
pts[1, :].A1,
pts[2, :].A1,
scalar_list,
representation='wireframe',
tube_radius=None)
mlab.colorbar()
else:
mlab.plot3d(pts[0, :].A1,
pts[1, :].A1,
pts[2, :].A1,
color=color,
representation='wireframe',
tube_radius=None)
def show(self, sln, show=True, lib="mpl", notebook=False, filename="a.png",
**options):
"""
Shows the solution.
show ... should it actually plot the window? Set to False in tests.
lib .... which library to use for the plotting? either "mpl" or "mayavi"
notebook ... are we running inside Sage notebook? If True, just save
the image to a.png
filename ... the name of the filename if we are saving the image (e.g.
notebook == False)
Example:
>>> 1 + 1
2
>>> 1 + 2
3
"""
self._lib = lib
self._notebook = notebook
if lib == "mpl":
plot_sln_mpl(sln, **options)
import pylab
if show:
if notebook:
pylab.savefig(filename)
else:
pylab.ion()
pylab.draw()
pylab.ioff()
elif lib == "mayavi":
plot_sln_mayavi(sln, notebook=notebook)
from enthought.mayavi import mlab
if show:
engine = mlab.get_engine()
image = engine.current_scene
image.scene.background = (1.0, 1.0, 1.0)
image.scene.foreground = (0.0, 0.0, 0.0)
mlab.colorbar(orientation="vertical")
if notebook:
mlab.savefig(filename)
else:
mlab.show()
else:
raise NotImplementedError("Unknown library '%s'" % lib)
def show_blobs(blobs, v, faces,fa_slice=None,colormap='jet'):
"""Mayavi gets really slow when triangular_mesh is called too many times
so this function stacks blobs and calls triangular_mesh once
"""
print blobs.shape
xcen = blobs.shape[0]/2.
ycen = blobs.shape[1]/2.
zcen = blobs.shape[2]/2.
faces = np.asarray(faces, 'int')
xx = []
yy = []
zz = []
count = 0
ff = []
mm = []
for ii in xrange(blobs.shape[0]):
for jj in xrange(blobs.shape[1]):
for kk in xrange(blobs.shape[2]):
m = blobs[ii,jj,kk]
#m /= (2.2*m.max())
#m /= (2.2*abs(m).max())
#m /= (2.2*1.)
#m[m<.4*abs(m).max()]=0
x, y, z = v.T*m/2.2
x += ii - xcen
y += jj - ycen
z += kk - zcen
ff.append(count+faces)
count += len(x)
xx.append(x)
yy.append(y)
zz.append(z)
mm.append(m)
ff = np.concatenate(ff)
xx = np.concatenate(xx)
yy = np.concatenate(yy)
zz = np.concatenate(zz)
mm = np.concatenate(mm)
mlab.triangular_mesh(xx, yy, zz, ff, scalars=mm, colormap=colormap)
if fa_slice!=None:
mlab.imshow(fa_slice, colormap='gray', interpolate=False)
mlab.colorbar()
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 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 plot_normals(pts, normals, curvature=None):
x = pts[0, :].A1
y = pts[1, :].A1
z = pts[2, :].A1
u = normals[0, :].A1
v = normals[1, :].A1
w = normals[2, :].A1
if curvature != None:
# mlab.points3d(x,y,z,curvature,mode='point',scale_factor=1.0)
mlab.points3d(x, y, z, curvature, mode="sphere", scale_factor=0.1, mask_points=1)
mlab.colorbar()
else:
mlab.points3d(x, y, z, mode="point")
mlab.quiver3d(x, y, z, u, v, w, mask_points=16, scale_factor=0.1)
# mlab.axes()
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 plot_points(pts,
color=(1., 1., 1.),
scale_factor=0.02,
mode='point',
scalar_list=None):
if scalar_list != None:
mlab.points3d(pts[0, :].A1,
pts[1, :].A1,
pts[2, :].A1,
scalar_list,
mode=mode,
scale_factor=scale_factor)
mlab.colorbar()
else:
mlab.points3d(pts[0, :].A1,
pts[1, :].A1,
pts[2, :].A1,
mode=mode,
color=color,
scale_factor=scale_factor)
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 plot_normals(pts, normals, curvature=None, mask_points=1,
color=(0.,1.,0.), scale_factor = 0.1):
x = pts[0,:].A1
y = pts[1,:].A1
z = pts[2,:].A1
u = normals[0,:].A1
v = normals[1,:].A1
w = normals[2,:].A1
if curvature != None:
curvature = np.array(curvature)
#idxs = np.where(curvature>0.03)
#mlab.points3d(x[idxs],y[idxs],z[idxs],curvature[idxs],mode='sphere',scale_factor=0.1,mask_points=1)
mlab.points3d(x,y,z,curvature,mode='sphere',scale_factor=0.1,mask_points=1, color=color)
# mlab.points3d(x,y,z,mode='point')
mlab.colorbar()
else:
mlab.points3d(x,y,z,mode='point')
mlab.quiver3d(x, y, z, u, v, w, mask_points=mask_points,
scale_factor=scale_factor, color=color)
def plot_normals(pts,
normals,
curvature=None,
mask_points=1,
color=(0., 1., 0.),
scale_factor=0.1):
x = pts[0, :].A1
y = pts[1, :].A1
z = pts[2, :].A1
u = normals[0, :].A1
v = normals[1, :].A1
w = normals[2, :].A1
if curvature != None:
curvature = np.array(curvature)
#idxs = np.where(curvature>0.03)
#mlab.points3d(x[idxs],y[idxs],z[idxs],curvature[idxs],mode='sphere',scale_factor=0.1,mask_points=1)
mlab.points3d(x,
y,
z,
curvature,
mode='sphere',
scale_factor=0.1,
mask_points=1,
color=color)
# mlab.points3d(x,y,z,mode='point')
mlab.colorbar()
else:
mlab.points3d(x, y, z, mode='point')
mlab.quiver3d(x,
y,
z,
u,
v,
w,
mask_points=mask_points,
scale_factor=scale_factor,
color=color)
def plot_sln_mayavi(sln, offscreen=False, show_scale=True):
"""
Plots the Solution() instance sln using Linearizer() and matplotlib.
It takes the vertices from linearizer and interpolates them.
"""
lin = Linearizer()
lin.process_solution(sln)
vert = lin.get_vertices()
triangles = lin.get_triangles()
from numpy import zeros
from enthought.mayavi import mlab
x = vert[:, 0]
y = vert[:, 1]
z = zeros(len(y))
t = vert[:, 2]
if offscreen:
# the off screen rendering properly works only with VTK-5.2 or above:
mlab.options.offscreen = True
mlab.clf()
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
mlab.view(0, 0)
mlab.view(distance=4)
mlab.view(focalpoint=(.35, 0, 0))
mlab.colorbar(title="Solution", orientation="vertical")
#mlab.move(right=-1.0, up=-10.0)
# Below is a code that does exactly what the "View along the +Z axis"
# button does:
#scene = mlab.get_engine().current_scene.scene
#scene.camera.focal_point = [0, 0, 0]
#scene.camera.position = [0, 0, 1]
#scene.camera.view_up = [0, 1, 0]
#scene.renderer.reset_camera()
#scene.render()
# the above looks ok, but there is still quite a large margin, so we prefer
# to just call .view(0, 0), which seems to be working fine.
return mlab
def plot_sln_mayavi(sln, offscreen=False, show_scale=True):
"""
Plots the Solution() instance sln using Linearizer() and matplotlib.
It takes the vertices from linearizer and interpolates them.
"""
lin = Linearizer()
lin.process_solution(sln)
vert = lin.get_vertices()
triangles = lin.get_triangles()
from numpy import zeros
from enthought.mayavi import mlab
x = vert[:, 0]
y = vert[:, 1]
z = zeros(len(y))
t = vert[:, 2]
if offscreen:
# the off screen rendering properly works only with VTK-5.2 or above:
mlab.options.offscreen = True
mlab.clf()
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
mlab.view(0, 0)
mlab.view(distance=4)
mlab.view(focalpoint=(.35,0,0))
mlab.colorbar(title="Solution", orientation="vertical")
#mlab.move(right=-1.0, up=-10.0)
# Below is a code that does exactly what the "View along the +Z axis"
# button does:
#scene = mlab.get_engine().current_scene.scene
#scene.camera.focal_point = [0, 0, 0]
#scene.camera.position = [0, 0, 1]
#scene.camera.view_up = [0, 1, 0]
#scene.renderer.reset_camera()
#scene.render()
# the above looks ok, but there is still quite a large margin, so we prefer
# to just call .view(0, 0), which seems to be working fine.
return mlab
def add_balls(at,
colours=None,
colorbar=False,
atom_scale_factor=1.0,
vmin=None,
vmax=None):
r = np.array([quippy.ElementCovRad[z] for z in at.z])
if colours is None:
scalars = at.z
vmin = 0
vmax = 110
else:
scalars = colours
pts = mlab.quiver3d(at.pos[1, :],
at.pos[2, :],
at.pos[3, :],
r, [0] * len(r), [0] * len(r),
scale_factor=atom_scale_factor,
scale_mode='vector',
scalars=scalars,
mode='sphere',
name='balls',
vmin=vmin,
vmax=vmax)
pts.glyph.color_mode = 'color_by_scalar'
#pts.glyph.glyph.scale_mode = 'scale_by_vector'
pts.glyph.glyph_source.glyph_source.center = [0, 0, 0]
if colours is None:
pts.module_manager.scalar_lut_manager.lut.table = atom_colours_lut
if colorbar: mlab.colorbar()
return pts
def play(self, fileroot='cell', show_colorbar=True, show_title=False):
''' Step through cell response over time '''
dt = self.play_dt
tstop = self.play_tstop
nrn.init()
nrn.cvode_active(0)
img_counter=0
f = mlab.gcf()
if show_colorbar: mlab.colorbar(self.mlab_cell)
nrn.initPlot()
nrn.init()
nrn.initPlot()
nrn.init()
for x in xrange(0, int(tstop/dt)):
timestamp = "TIME: %.1f" % (x*dt)
print timestamp
if show_title:
try:
ftitle.text = timestamp
except:
ftitle = mlab.title(timestamp)
nrn.continuerun(x*dt)
dataset = self.mlab_cell.mlab_source.dataset
v = array(self.calculate_voltage())
dataset.point_data.remove_array('data')
array_id = dataset.point_data.add_array(v.T.ravel())
dataset.point_data.get_array(array_id).name = 'data'
dataset.point_data.update()
self.mlab_cell.update_data()
self.mlab_cell.update_pipeline()
if self.save_img:
f.scene.save_png('img/%s_%03d.png' % (fileroot, img_counter))
img_counter += 1
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 show(self):
"""Test contour_surf on regularly spaced co-ordinates like MayaVi."""
x, y = self.out_grid
s = surf(x, y, self.scalars, warp_scale = 0.5)
colorbar(s)
return s
mlab.figure(1, fgcolor=(1, 1, 1), bgcolor=(0, 0, 0)) # We create a scalar field with the module of Phi as the scalar src = mlab.pipeline.scalar_field(np.abs(Phi)) # And we add the phase of Phi as an additional array # This is a tricky part: the layout of the new array needs to be the same # as the existing dataset, and no checks are performed. The shape needs # to be the same, and so should the data. Failure to do so can result in # segfaults. src.image_data.point_data.add_array(np.angle(Phi).T.ravel()) # We need to give a name to our new dataset. src.image_data.point_data.get_array(1).name = 'angle' # Make sure that the dataset is up to date with the different arrays: src.image_data.point_data.update() # We select the 'scalar' attribute, ie the norm of Phi src2 = mlab.pipeline.set_active_attribute(src, point_scalars='scalar') # Cut isosurfaces of the norm contour = mlab.pipeline.contour(src2) # Now we select the 'angle' attribute, ie the phase of Phi contour2 = mlab.pipeline.set_active_attribute(contour, point_scalars='angle') # And we display the surface. The colormap is the current attribute: the phase. mlab.pipeline.surface(contour2, colormap='hsv') mlab.colorbar(title='Phase', orientation='vertical', nb_labels=3) mlab.show()
temperature[i,j,k] = (f[:,i,j,k]* v[2:-2]**2).sum() / f.sum(axis=0)[i,j,k]
return temperature
for frame in range(len(fileh.root.Time[:])):
# 3D Movie
frame = 4*frame
mlab.clf(figure=fig3d)
scene = mlab.contour3d(Temperature(fileh.root.Phasespace[:,:,:,:,frame], fileh.root.Phasespace.attrs.v), contours=50)
# Density profile
#scene = mlab.contour3d(fileh.root.Phasespace[:,:,:,:,frame].sum(axis=0), contours=50)
#source = mlab.pipeline.scalar_field(Temperature(fileh.root.Phasespace[:,:,:,:,frame], fileh.root.Phasespace.attrs.v))
#vol = mlab.pipeline.volume(source)
angle = 50.
mlab.view(azimuth=angle%360, distance=72.0)
#mlab.axes(color=(0.0,0.0,0.),extent=ext,xlabel='X',ylabel='Y',zlabel='Z')
#a = array(gradient(fileh.root.Phi[:,:,:,frame]))**2
#mlab.quiver3d(a[0],a[1],a[2])
mlab.colorbar(orientation='vertical')#, label_fmt="%.2f", nb_labels=3)
mlab.outline()
mlab.savefig("Rho_" + string.zfill(frame, 4) + ".jpg")#, size=(600,600))
print "[", string.zfill(frame, 4),"/", len(fileh.root.Time[:]), "]"
#mencoder "mf://*.jpg" -mf fps=10 -ovc lavc -o mymovie.av
def show_odfs(odfs,
vertices_faces,
image=None,
colormap='jet',
scale=2.2,
norm=True,
radial_scale=True):
"""
Display a grid of ODFs.
Parameters
----------
odfs : (X, Y, Z, M) ndarray
A 3-D arrangement of orientation distribution functions (ODFs). At
each ``(x, y, z)`` position, it contains the the values of the
corresponding ODF evaluated on the M vertices.
vertices_faces : str or tuple of (vertices, faces)
A named sphere from `dipy.data.get_sphere`, or a combination of
`(vertices, faces)`.
image : (X, Y) ndarray
Background image (e.g., fractional anisotropy) do display behind the
ODFs.
colormap : str
Color mapping.
scale : float
Increasing the scale spaces ODFs further apart.
norm : bool
Whether or not to normalize each individual ODF (divide by its maximum
absolute value).
radial_scale : bool
Whether or not to change the radial shape of the ODF according to its
scalar value. If set to False, the ODF is displayed as a sphere.
Notes
-----
Mayavi gets really slow when `triangular_mesh` is called too many times,
so this function stacks ODF data and calls `triangular_mesh` once.
Examples
--------
>>> from dipy.data import get_sphere
>>> verts, faces = get_sphere('symmetric724')
>>> angle = np.linspace(0, 2*np.pi, len(verts))
>>> odf1 = np.sin(angle)
>>> odf2 = np.cos(angle)
>>> odf3 = odf1**2 * odf2
>>> odf4 = odf1 + odf2**2
>>> odfs = [[[odf1, odf2],
... [odf3, odf4]]]
>>> show_odfs(odfs, (verts, faces), scale=5)
"""
vertices, faces = sphere_vf_from(vertices_faces)
odfs = np.asarray(odfs)
if odfs.ndim != 4:
raise ValueError("ODFs must by an (X,Y,Z,M) array. " + "Has shape " +
str(odfs.shape))
grid_shape = np.array(odfs.shape[:3])
faces = np.asarray(faces, dtype=int)
xx, yy, zz, ff, mm = [], [], [], [], []
count = 0
for ijk in np.ndindex(*grid_shape):
m = odfs[ijk]
if norm:
m /= abs(m).max()
if radial_scale:
xyz = vertices.T * m
else:
xyz = vertices.T.copy()
xyz += scale * (ijk - grid_shape / 2.)[:, None]
x, y, z = xyz
ff.append(count + faces)
xx.append(x)
yy.append(y)
zz.append(z)
mm.append(m)
count += len(x)
ff, xx, yy, zz, mm = (np.concatenate(arrs)
for arrs in (ff, xx, yy, zz, mm))
mlab.triangular_mesh(xx, yy, zz, ff, scalars=mm, colormap=colormap)
if image is not None:
mlab.imshow(image, colormap='gray', interpolate=False)
mlab.colorbar()
mlab.show()
points[:, 2],
faces,
color=(1, 1, 0),
opacity=0.3)
# show one cortical surface
mlab.triangular_mesh(lh_points[:, 0],
lh_points[:, 1],
lh_points[:, 2],
lh_faces,
color=(0.7, ) * 3)
# show dipole as small cones
dipoles = mlab.quiver3d(pos[:, 0],
pos[:, 1],
pos[:, 2],
ori[:, 0],
ori[:, 1],
ori[:, 2],
opacity=1.,
scale_factor=4e-4,
scalars=time,
mode='cone',
colormap='RdBu')
# revert colormap
dipoles.module_manager.scalar_lut_manager.reverse_lut = True
mlab.colorbar(dipoles, title='Dipole fit time (ms)')
# proper 3D orientation
mlab.get_engine().scenes[0].scene.x_plus_view()
def show_odfs(odfs, vertices_faces, image=None, colormap='jet',
scale=2.2, norm=True, radial_scale=True):
"""
Display a grid of ODFs.
Parameters
----------
odfs : (X, Y, Z, M) ndarray
A 3-D arrangement of orientation distribution functions (ODFs). At
each ``(x, y, z)`` position, it contains the the values of the
corresponding ODF evaluated on the M vertices.
vertices_faces : str or tuple of (vertices, faces)
A named sphere from `dipy.data.get_sphere`, or a combination of
`(vertices, faces)`.
image : (X, Y) ndarray
Background image (e.g., fractional anisotropy) do display behind the
ODFs.
colormap : str
Color mapping.
scale : float
Increasing the scale spaces ODFs further apart.
norm : bool
Whether or not to normalize each individual ODF (divide by its maximum
absolute value).
radial_scale : bool
Whether or not to change the radial shape of the ODF according to its
scalar value. If set to False, the ODF is displayed as a sphere.
Notes
-----
Mayavi gets really slow when `triangular_mesh` is called too many times,
so this function stacks ODF data and calls `triangular_mesh` once.
Examples
--------
>>> from dipy.data import get_sphere
>>> verts, faces = get_sphere('symmetric724')
>>> angle = np.linspace(0, 2*np.pi, len(verts))
>>> odf1 = np.sin(angle)
>>> odf2 = np.cos(angle)
>>> odf3 = odf1**2 * odf2
>>> odf4 = odf1 + odf2**2
>>> odfs = [[[odf1, odf2],
... [odf3, odf4]]]
>>> show_odfs(odfs, (verts, faces), scale=5)
"""
vertices, faces = sphere_vf_from(vertices_faces)
odfs = np.asarray(odfs)
if odfs.ndim != 4:
raise ValueError("ODFs must by an (X,Y,Z,M) array. " +
"Has shape " + str(odfs.shape))
grid_shape = np.array(odfs.shape[:3])
faces = np.asarray(faces, dtype=int)
xx, yy, zz, ff, mm = [], [], [], [], []
count = 0
for ijk in np.ndindex(*grid_shape):
m = odfs[ijk]
if norm:
m /= abs(m).max()
if radial_scale:
xyz = vertices.T * m
else:
xyz = vertices.T.copy()
xyz += scale * (ijk - grid_shape / 2.)[:, None]
x, y, z = xyz
ff.append(count + faces)
xx.append(x)
yy.append(y)
zz.append(z)
mm.append(m)
count += len(x)
ff, xx, yy, zz, mm = (np.concatenate(arrs) for arrs in (ff, xx, yy, zz, mm))
mlab.triangular_mesh(xx, yy, zz, ff, scalars=mm, colormap=colormap)
if image is not None:
mlab.imshow(image, colormap='gray', interpolate=False)
mlab.colorbar()
mlab.show()
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
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
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
scale_mode='none')
"""
# create a scalar field with the rho as the scalar
src = mlab.pipeline.scalar_field(rho)
# add the `esp` as an additional array
src.image_data.point_data.add_array(esp.ravel())#(esp_ravel) #(esp.T.ravel())
# give a name to our new dataset.
src.image_data.point_data.get_array(1).name = 'esp'
# The dataset should be up to date with the different arrays:
src.image_data.point_data.update()
# select the 'scalar' attribute
src2 = mlab.pipeline.set_active_attribute(src, point_scalars='scalar')
# Cut isosurfaces of the scalar
contour = mlab.pipeline.contour(src2)
# select the 'esp' attribute,
contour2 = mlab.pipeline.set_active_attribute(contour, point_scalars='esp')
# display the surface. The colormap is the current attribute: the phase.
mlab.pipeline.surface(contour2, transparent=True) #, colormap='hsv')
mlab.colorbar(title='ESP', orientation='vertical', nb_labels=5)
mlab.savefig('images/H2O-esp.png')
mlab.show()
r_c, phi_c, theta_c = vectors.cart2spher_coord(
*center.T)
colors_ = r_c
mlab.clf()
mlab.points3d(center.T[0],
center.T[1],
center.T[2],
colors_,
scale_factor=0.05,
scale_mode='none',
colormap='RdBu',
vmin=colors_.min(),
vmax=colors_.max())
#mlab.quiver3d(center.T[0],center.T[1],center.T[2],normal.T[0],normal.T[1],normal.T[2],colormap='spectral',scale_mode='none')
mlab.colorbar()
if i >= 1:
vx,vy,vz = center.T[0]-old_center.T[0],\
center.T[1]-old_center.T[1],\
center.T[2]-old_center.T[2]
v = np.sqrt(vx**2 + vy**2 + vz**2)
mlab.quiver3d(center.T[0],center.T[1],center.T[2],\
vx,vy,vz,scalars=v,colormap='spectral',scale_mode='scalar')
#mlab.show()
old_center = center.copy()
mlab.view(distance=5, azimuth=-90, elevation=90)
mlab.savefig('pulsation_lm%d%d_k%03d_%03d.png' %
(l, m, k, i))
mlab.close()
multimedia.make_movie(
try:
from enthought.mayavi import mlab
except:
from mayavi import mlab
lh_points = src[0]['rr']
lh_faces = src[0]['use_tris']
mlab.figure(size=(600, 600), bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
# show brain surface after proper coordinate system transformation
points = brain_surface['rr']
faces = brain_surface['tris']
coord_trans = fwd['mri_head_t']['trans']
points = np.dot(coord_trans[:3,:3], points.T).T + coord_trans[:3,-1]
mlab.triangular_mesh(points[:, 0], points[:, 1], points[:, 2],
faces, color=(1, 1, 0), opacity=0.3)
# show one cortical surface
mlab.triangular_mesh(lh_points[:, 0], lh_points[:, 1], lh_points[:, 2],
lh_faces, color=(0.7, ) * 3)
# show dipole as small cones
dipoles = mlab.quiver3d(pos[:,0], pos[:,1], pos[:,2],
ori[:,0], ori[:,1], ori[:,2],
opacity=1., scale_factor=4e-4, scalars=time,
mode='cone')
mlab.colorbar(dipoles, title='Dipole fit time (ms)')
# proper 3D orientation
mlab.get_engine().scenes[0].scene.x_plus_view()
s1 = contour_surf(data[0, :, :, 10] + 0.1,
contours=30,
line_width=0.5,
transparent=True)
s = surf(data[0, :, :, 10] + 0.1, colormap="Spectral"
) # , warp_scale='.1')#, representation='wireframe')
# second way
# x, y= mgrid[0:ns:1, 0:ne:1]
# s = mesh(x,y,data[0,:,:,10], colormap='Spectral')#, warp_scale='auto')
# #, representation='wireframe')
s.enable_contours = True
s.contour.filled_contours = True
#
# x, y, z= mgrid[0:ns:1, 0:ne:1, 0:nz:1]
#
# p=plot3d(x,y,z,data[10,:,:,:], tube_radius=0.025, colormap='Spectral')
# p=points3d(x,y,z,data[10,:,:,:], colormap='Spectral')
#
# s=contour3d(x,y,z,data[10,:,:,:], contours=4, transparent=True)
# mlab.view(0.,0.)
colorbar()
# axes()
# outline()
# Run the animation.
anim(s, data[:, :, :, 10] + 0.1, s1, save=True)
def viz_Potential(r_data,
V_data,
X,
Y,
Z,
j_data,
file_name=None,
size=(600, 600)):
u"""Visualizes the electrostatic potential difference of the molecule.
** Parameters **
r_data, V_data : numpy.ndarray
Voxels of the electron density and the potential difference of the molecule, respectively.
X, Y, Z : numpy.ndarray
Meshgrids as generated by numpy.mgrid, for positioning the voxels.
j_data : dict
Data on the molecule, as deserialized from the scanlog format.
file_name : str, optional
Base name of the file in which to save the image.
size : tuple(int, int), optional
The size of the image to save.
** Returns **
figure : mayavi.core.scene.Scene
The MayaVi scene containing the visualization.
"""
figure = _init_scene(j_data)[0]
src = mlab.pipeline.scalar_field(X, Y, Z, r_data, figure=figure)
## Add potential as additional array
src.image_data.point_data.add_array(V_data.T.ravel() / V_to_Kcal_mol)
## Name it
src.image_data.point_data.get_array(1).name = "potential"
## Update object
src.update()
## Select scalar attribute
srcp = mlab.pipeline.set_active_attribute(src,
figure=figure,
point_scalars="scalar")
## Plot it
cont = mlab.pipeline.contour(srcp, figure=figure)
cont.filter.contours = [0.001]
## Select potential
cont_V = mlab.pipeline.set_active_attribute(cont,
figure=figure,
point_scalars="potential")
#contp = mlab.pipeline.threshold(cont_V, figure=figure, up=)
#contn = mlab.pipeline.threshold(cont_V, figure=figure, low=V_data.min()*0.95)
## And finally plot that
mlab.pipeline.surface(cont_V, figure=figure, opacity=0.7)
## Continue with this until problems with potential calculation are fixed
#V_data[np.isinf(V_data)] = np.nan
#src = mlab.pipeline.scalar_field(X, Y, Z, V_data, figure=figure)
#srcp = mlab.pipeline.iso_surface(src, figure=figure, contours=[ 0.4], color=(0.0, 0.5, 0.5))
#srcn = mlab.pipeline.iso_surface(src, figure=figure, contours=[-0.05], color=(0.95, 0.95, 0.95))
mlab.colorbar(title='Kcal/mol', orientation='vertical', nb_labels=3)
#mlab.show()
if file_name is not None:
mlab.savefig("{}-Potential.png".format(file_name),
figure=figure,
size=size)
return figure
f = mlab.figure(size=(800,600)) # Tell visual to use this as the viewer. visual.set_viewer(f) ######################################################## # Do the appropriate graph #s = mlab.contour_surf(r,z,fun, contours=30, line_width=.5, transparent=True) #s=mlab.surf(r,z,fun, colormap='Spectral') s = mlab.mesh(r,z,fm[0,:,:], scalars=fm[0,:,:], colormap='PuOr')#, wrap_scale='true')#, representation='wireframe') s.enable_contours=True s.contour.filled_contours=True # Define perspective and optional attributes. You can also implement from the window afterwards mlab.view(0,0) #mlab.view(-94.159958841373324, # 53.777002382688906, # 8.2311808018087582) mlab.draw(f) mlab.colorbar(orientation="vertical") #mlab.axes() #mlab.outline() ######################################################## # mlab animation anim(s,fm, save=True)
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
# 3D Movie
mlab.clf(figure=fig3d)
#Simple contour
scene = mlab.contour3d((fileh.root.Phi[2:-2,2:-2,2:-2,frame]**2), contours=50, extent=ext, vmax=fmax, vmin=fmin)#, colormap='ylgn')
# Cutplane
#E = gradient(fileh.root.Phi[:,:,:,frame])
#mlab.quiver3d(E[0], E[1], E[2])
#src = mlab.pipeline.vector_field(E[2], E[1], E[0])
#mlab.pipeline.vectors(src, mask_points=20, scale_factor=3.)
#mlab.pipeline.vector_cut_plane(src, mask_points=1, scale_factor=5)
#src = mlab.pipeline.vector_field(E[0], E[1], E[2])
#magnitude = mlab.pipeline.extract_vector_norm(src)
#flow = mlab.pipeline.streamline(magnitude, seedtype='plane', seed_visible=False, seed_scale=1.0, seed_resolution=100, linetype='line',integration_direction='both')
#$mlab.axes(color=(0.0,0.0,0.),extent=ext,xlabel='X',ylabel='Y',zlabel='Z')
#source = mlab.pipeline.scalar_field(fileh.root.Phi[:,:,:,frame]**2)
#vol = mlab.pipeline.volume(source)
#a = array(gradient(fileh.root.Phi[:,:,:,frame]))**2
#mlab.quiver3d(a[0],a[1],a[2])
# mlab.view(azimuth=angle%360, distance=128.0)
mlab.view(azimuth=angle%360, distance=10.0)
angle += 1.0
mlab.colorbar(orientation='vertical', label_fmt="%.2e", nb_labels=5)
mlab.outline()
mlab.savefig("Phi3D_" + filename + "_" + string.zfill(frame, 4) + ".jpg")#, size=(600,600))
print "[", string.zfill(frame, 4),"/", len(fileh.root.Time[:]), "]"
#mencoder "mf://*.jpg" -mf fps=10 -ovc lavc -o mymovie.av
scene = mlab.contour3d((fileh.root.Phi[2:-2, 2:-2, 2:-2, frame]**2),
contours=50,
extent=ext,
vmax=fmax,
vmin=fmin) #, colormap='ylgn')
# Cutplane
#E = gradient(fileh.root.Phi[:,:,:,frame])
#mlab.quiver3d(E[0], E[1], E[2])
#src = mlab.pipeline.vector_field(E[2], E[1], E[0])
#mlab.pipeline.vectors(src, mask_points=20, scale_factor=3.)
#mlab.pipeline.vector_cut_plane(src, mask_points=1, scale_factor=5)
#src = mlab.pipeline.vector_field(E[0], E[1], E[2])
#magnitude = mlab.pipeline.extract_vector_norm(src)
#flow = mlab.pipeline.streamline(magnitude, seedtype='plane', seed_visible=False, seed_scale=1.0, seed_resolution=100, linetype='line',integration_direction='both')
#$mlab.axes(color=(0.0,0.0,0.),extent=ext,xlabel='X',ylabel='Y',zlabel='Z')
#source = mlab.pipeline.scalar_field(fileh.root.Phi[:,:,:,frame]**2)
#vol = mlab.pipeline.volume(source)
#a = array(gradient(fileh.root.Phi[:,:,:,frame]))**2
#mlab.quiver3d(a[0],a[1],a[2])
# mlab.view(azimuth=angle%360, distance=128.0)
mlab.view(azimuth=angle % 360, distance=10.0)
angle += 1.0
mlab.colorbar(orientation='vertical', label_fmt="%.2e", nb_labels=5)
mlab.outline()
mlab.savefig("Phi3D_" + filename + "_" + string.zfill(frame, 4) +
".jpg") #, size=(600,600))
print "[", string.zfill(frame, 4), "/", len(fileh.root.Time[:]), "]"
#mencoder "mf://*.jpg" -mf fps=10 -ovc lavc -o mymovie.av
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
