def change_volume_property(vol):
color1 = [[0,0,0,0,0],
[1,255,255,0,10],
[10,255,255,0,50],
[100,255,100,0,150],
[1000,255,0,0,255]]
color2 = [[0,0,0,0,0],
[100,0,255,255,10],
[1000,0,255,255,50],
[10000,0,100,255,150],
[100000,0,0,255,255]]
color = color1
# Changing the ctf:
from tvtk.util.ctf import ColorTransferFunction
ctf = ColorTransferFunction()
for c in color:
ctf.add_rgb_point(c[0], c[1]/255.0, c[2]/255.0, c[3]/255.0)
vol._volume_property.set_color(ctf)
vol._ctf = ctf
vol.update_ctf = True
# Changing the otf
from enthought.tvtk.util.ctf import PiecewiseFunction
otf = PiecewiseFunction()
for c in color:
otf.add_point(c[0], c[4]/255.0)
vol._otf = otf
vol._volume_property.set_scalar_opacity(otf)
def changeVolumeColormap(vol,color):
'''change the ctf and otf property of volume
Parameters
----------
vol : mlab.volume
color : [[]]
For each color [datavalue, R, G, B, A]
R,G,B,A in range(0.0,255.0) inclusive
'''
# Changing the ctf:
from tvtk.util.ctf import ColorTransferFunction
ctf = ColorTransferFunction()
for c in color:
ctf.add_rgb_point(float(c[0]), float(c[1])/255.0, float(c[2])/255.0, float(c[3])/255.0)
vol._volume_property.set_color(ctf)
vol._ctf = ctf
vol.update_ctf = True
# Changing the otf
from enthought.tvtk.util.ctf import PiecewiseFunction
otf = PiecewiseFunction()
for c in color:
otf.add_point(float(c[0]), float(c[4])/255.0)
vol._otf = otf
vol._volume_property.set_scalar_opacity(otf)
def set_bw_colorscheme(vol, vmin, vmax): ctf = ColorTransferFunction() ctf.add_rgb_point(vmin, 0., 0., 0.) # r, g, and b are float between 0 and 1 ctf.add_rgb_point(vmax, 0., 0., 0.) vol._volume_property.set_color(ctf) vol._ctf = ctf vol.update_ctf = True
def _vmin_changed(self):
vmin = self.vmin
vmax = self.vmax
range_min, range_max = self._target.current_range
if vmin is None:
vmin = range_min
if vmax is None:
vmax = range_max
# Change the opacity function
from tvtk.util.ctf import PiecewiseFunction, save_ctfs
otf = PiecewiseFunction()
if range_min < vmin:
otf.add_point(range_min, 0.)
if range_max > vmax:
otf.add_point(range_max, 0.2)
otf.add_point(vmin, 0.)
otf.add_point(vmax, 0.2)
self._target._otf = otf
self._target._volume_property.set_scalar_opacity(otf)
if self.color is None and not self.__ctf_rescaled and \
( (self.vmin is not None) or (self.vmax is not None) ):
# FIXME: We don't use 'rescale_ctfs' because it screws up the nodes.
def _rescale_value(x):
nx = (x - range_min)/(range_max - range_min)
return vmin + nx*(vmax - vmin)
# The range of the existing ctf can vary.
scale_min, scale_max = self._target._ctf.range
def _rescale_node(x):
nx = (x - scale_min)/(scale_max - scale_min)
return range_min + nx*(range_max - range_min)
if hasattr(self._target._ctf, 'nodes'):
rgb = list()
for value in self._target._ctf.nodes:
r, g, b = \
self._target._ctf.get_color(value)
rgb.append((_rescale_node(value), r, g, b))
else:
rgb = save_ctfs(self._target.volume_property)['rgb']
from tvtk.util.ctf import ColorTransferFunction
ctf = ColorTransferFunction()
try:
ctf.range = (range_min, range_max)
except Exception:
# VTK versions < 5.2 don't seem to need this.
pass
rgb.sort()
v = rgb[0]
ctf.add_rgb_point(range_min, v[1], v[2], v[3])
for v in rgb:
ctf.add_rgb_point(_rescale_value(v[0]), v[1], v[2], v[3])
ctf.add_rgb_point(range_max, v[1], v[2], v[3])
self._target._ctf = ctf
self._target._volume_property.set_color(ctf)
self.__ctf_rescaled = True
self._target.update_ctf = True
def _set_cutoff(volume, cutoff):
range_min, range_max = volume.current_range
otf = PiecewiseFunction()
otf.add_point(range_min, 0.0)
otf.add_point(range_max, 0.2)
volume._otf = otf
volume.volume_property.set_scalar_opacity(otf)
ctf = ColorTransferFunction()
ctf.range = volume.current_range
ctf.add_rgb_point(range_min, 1.0, 0.275, 0.0)
ctf.add_rgb_point(range_max, 1.0, 0.275, 0.0)
volume._ctf = ctf
volume.volume_property.set_color(ctf)
set_lut(volume.lut_manager.lut, volume.volume_property)
def _color_changed(self):
if not self.color:
return
range_min, range_max = self._target.current_range
from tvtk.util.ctf import ColorTransferFunction
ctf = ColorTransferFunction()
try:
ctf.range = (range_min, range_max)
except Exception:
# VTK versions < 5.2 don't seem to need this.
pass
r, g, b = self.color
ctf.add_rgb_point(range_min, r, g, b)
ctf.add_rgb_point(range_max, r, g, b)
self._target._ctf = ctf
self._target._volume_property.set_color(ctf)
self._target.update_ctf = True
def plot_bleeds3d(data, template):
from mayavi import mlab
import datetime
from tvtk.util.ctf import PiecewiseFunction, ColorTransferFunction
string = str((datetime.datetime.now()).strftime("%d%m%Y_%H%M%S"))
otf = PiecewiseFunction()
otf.add_point(
0.1, 0.0
) #opacity values for the boundary elements of the brain...For opaque objects, opacity = 1.0
otf.add_point(5, 0.1)
otf.add_point(100, 0.25)
ctf = ColorTransferFunction()
ctf.add_rgb_point(0, 0, 0, 0)
ctf.add_rgb_point(1000, 0.6, 1, 1)
#3d plotting
mlab.figure(bgcolor=(0, 0, 0), size=(400, 400))
src = mlab.pipeline.scalar_field(template)
#voi = mlab.pipeline.extract_grid(src)
vol = mlab.pipeline.volume(src)
vol._otf = otf
vol._volume_property.set_gradient_opacity(otf)
vol._volume_property.set_color(ctf)
vol._ctf = ctf
vol.update_ctf = True
src1 = mlab.pipeline.scalar_field(data)
voi1 = mlab.pipeline.extract_grid(src1)
mlab.pipeline.iso_surface(voi1, color=(1, 0, 0))
mlab.show()
def plot_volume(x, y, z, s, logplot=False, **kwargs):
if (logplot):
maxval = log(abs(s).max())
minval = maxval - logrange
logs = log(abs(s))
logs = where(logs > minval, logs - minval, 0)
logs /= logs.max()
src = mlab.pipeline.scalar_field(x, y, z, logs)
else:
maxval = abs(s).max()
src = mlab.pipeline.scalar_field(x, y, z, abs(s) / maxval)
vol = mlab.pipeline.volume(src)
ctf = ColorTransferFunction()
ctf.range = [0, 1]
otf = PiecewiseFunction()
otf.add_point((0, 0))
otf.add_point((1, 1))
otf.range = [0, 1]
vol.otf = otf
vol.volume_property.set_scalar_opacity(otf)
return vol
def _color_im_planes(im_planes):
ctf = ColorTransferFunction()
ctf.range = (0.0, 1.0)
ctf.add_rgb_point(0.0, 1.0, 1.0, 1.0)
ctf.add_rgb_point(1.0, 1.0, 0.275, 0.0)
for ip in im_planes:
lut = ip.module_manager.scalar_lut_manager.lut.table.to_array()
for i in xrange(len(lut)):
c = 255 * np.asarray(ctf.get_color(float(i) / (len(lut) - 1)))
lut[i] = np.concatenate((c, (255,)))
ip.module_manager.scalar_lut_manager.lut.table = lut
def setColormap(self, densityPlot):
from tvtk.util.ctf import ColorTransferFunction
ctf = ColorTransferFunction()
# Add points to CTF
ctf.add_rgb_point(0, 1.0, 1.0, 1.0)
ctf.add_rgb_point(0.8, 0.0, 0.0, 1.0)
ctf.add_rgb_point(1, 0.0, 0.0, 1.0)
densityPlot._volume_property.set_color(ctf)
densityPlot.update_ctf = True
return densityPlot
def make_volume_prop(mins=255, maxs=355):
"""Make a volume property for the testing."""
table = tvtk.VolumeProperty()
ctf = ColorTransferFunction()
ds = (maxs-mins)/4.0
try:
ctf.range = (mins, maxs)
except Exception:
# VTK versions < 5.2 don't seem to need this.
pass
ctf.add_rgb_point(mins, 0.00, 0.0, 1.00)
ctf.add_rgb_point(mins+ds, 0.25, 0.5, 0.75)
ctf.add_rgb_point(mins+2*ds, 0.50, 1.0, 0.50)
ctf.add_rgb_point(mins+3*ds, 0.75, 0.5, 0.25)
ctf.add_rgb_point(maxs, 1.00, 0.0, 0.00)
otf = PiecewiseFunction()
otf.add_point(mins, 0.0)
otf.add_point(maxs, 0.2)
table.set_color(ctf)
table.set_scalar_opacity(otf)
return table, ctf, otf
def make_CTF(x1,
x2,
hue_range=(2.0 / 3.0, 0.0),
sat_range=(1.0, 1.0),
val_range=(1.0, 1.0),
n=10,
mode='sqrt'):
"""Creates a CTF as per given arguments. Lets one set a hue,
saturation and value range. The mode can be one of 'sqrt', or
'linear'. The idea of this function is to create a CTF that is
similar to the LUT being used. 's_curve' is not implemented.
Patches welcome.
"""
maxs = max(x1, x2)
mins = min(x1, x2)
ds = maxs - mins
dhue = hue_range[1] - hue_range[0]
dsat = sat_range[1] - sat_range[0]
dval = val_range[1] - val_range[0]
ctf = ColorTransferFunction()
try:
ctf.range = (mins, maxs)
except Exception:
# VTK versions < 5.2 don't seem to need this.
pass
if mode == 'sqrt':
for i in range(n + 1):
# Generate x in [0, 1]
x = 0.5 * (1.0 + cos((n - i) * pi / n)) # Chebyshev nodes.
h = hue_range[0] + dhue * x
s = sat_range[0] + dsat * x
v = val_range[0] + dval * x
r, g, b, a = [sqrt(c) for c in hsva_to_rgba(h, s, v, 1.0)]
ctf.add_rgb_point(mins + x * ds, r, g, b)
elif mode == 'linear':
for i in range(n + 1):
# Generate x in [0, 1]
x = float(i) / n # Uniform nodes.
h = hue_range[0] + dhue * x
s = sat_range[0] + dsat * x
v = val_range[0] + dval * x
r, g, b, a = hsva_to_rgba(h, s, v, 1.0)
ctf.add_rgb_point(mins + x * ds, r, g, b)
return ctf
def ImproveDataDisplay():
# Changing the ctf:
from tvtk.util.ctf import ColorTransferFunction
ctf = ColorTransferFunction()
ctf.range = [0, 1]
# Add points to CTF
# Note : ctf.add_rgb_point(value, r, g, b) r, g, b are float numbers in [0,1]
ctf.add_rgb_point(0, 0, 1, 0)
ctf.add_rgb_point(0.7, 1, 0, 0)
ctf.add_rgb_point(1, 1, 0, 0)
# Changing the otf:
from tvtk.util.ctf import PiecewiseFunction
otf = PiecewiseFunction()
# Add points to OTF
# Note : otf.add_point(value, opacity) -> opacity is a float number in [0,1]
otf.add_point(0, 0.015)
otf.add_point(0.2, 1)
otf.add_point(1, 1)
return ctf, otf
def make_CTF(x1, x2, hue_range=(2.0/3.0, 0.0),
sat_range=(1.0, 1.0), val_range=(1.0, 1.0),
n=10, mode='sqrt'):
"""Creates a CTF as per given arguments. Lets one set a hue,
saturation and value range. The mode can be one of 'sqrt', or
'linear'. The idea of this function is to create a CTF that is
similar to the LUT being used. 's_curve' is not implemented.
Patches welcome.
"""
maxs = max(x1, x2)
mins = min(x1, x2)
ds = maxs - mins
dhue = hue_range[1] - hue_range[0]
dsat = sat_range[1] - sat_range[0]
dval = val_range[1] - val_range[0]
ctf = ColorTransferFunction()
try:
ctf.range = (mins, maxs)
except Exception:
# VTK versions < 5.2 don't seem to need this.
pass
if mode == 'sqrt':
for i in range(n+1):
# Generate x in [0, 1]
x = 0.5*(1.0 + cos((n-i)*pi/n)) # Chebyshev nodes.
h = hue_range[0] + dhue*x
s = sat_range[0] + dsat*x
v = val_range[0] + dval*x
r, g, b, a = [sqrt(c) for c in hsva_to_rgba(h, s, v, 1.0)]
ctf.add_rgb_point(mins+x*ds, r, g, b)
elif mode == 'linear':
for i in range(n+1):
# Generate x in [0, 1]
x = float(i)/n # Uniform nodes.
h = hue_range[0] + dhue*x
s = sat_range[0] + dsat*x
v = val_range[0] + dval*x
r, g, b, a = hsva_to_rgba(h, s, v, 1.0)
ctf.add_rgb_point(mins+x*ds, r, g, b)
return ctf
def volume(sim, qty='rho', width=None, resolution=200,
color=(1.0,1.0,1.0),vmin=None,vmax=None,
dynamic_range=4.0,log=True,
create_figure=True):
"""Create a volume rendering of the given simulation using mayavi.
**Keyword arguments:**
*qty* (rho): The name of the array to interpolate
*width* (None): The width of the cube to generate, centered on the origin
*resolution* (200): The number of elements along each side of the cube
*color* (white): The color of the volume rendering. The value of each voxel
is used to set the opacity.
*vmin* (None): The value for zero opacity (calculated using dynamic_range if None)
*vmax* (None): The value for full opacity (calculated from the maximum
value in the region if None)
*dynamic_range*: The dynamic range to use if vmin and vmax are not specified
*log* (True): log-scale the image before passing to mayavi
*create_figure* (True): create a new mayavi figure before rendering
"""
import mayavi
from mayavi import mlab
from tvtk.util.ctf import PiecewiseFunction, ColorTransferFunction
if create_figure:
fig = mlab.figure(size=(500,500),bgcolor=(0,0,0))
grid_data = sph.to_3d_grid(sim,qty=qty,nx=resolution,
x2=None if width is None else width/2)
if log:
grid_data = np.log10(grid_data)
if vmin is None:
vmin = grid_data.max()-dynamic_range
if vmax is None:
vmax = grid_data.max()
else:
if vmin is None:
vmin = np.min(grid_data)
if vmax is None:
vmax = np.max(grid_data)
grid_data[grid_data<vmin]=vmin
grid_data[grid_data>vmax]=vmax
otf = PiecewiseFunction()
otf.add_point(vmin,0.0)
otf.add_point(vmax,1.0)
sf = mayavi.tools.pipeline.scalar_field(grid_data)
V = mlab.pipeline.volume(sf,color=color,vmin=vmin,vmax=vmax)
V.trait_get('volume_mapper')['volume_mapper'].blend_mode = 'maximum_intensity'
if color is None:
ctf = ColorTransferFunction()
ctf.add_rgb_point(vmin,107./255,124./255,132./255)
ctf.add_rgb_point(vmin+(vmax-vmin)*0.8,200./255,178./255,164./255)
ctf.add_rgb_point(vmin+(vmax-vmin)*0.9,1.0,210./255,149./255)
ctf.add_rgb_point(vmax,1.0,222./255,141./255)
print vmin,vmax
V._volume_property.set_color(ctf)
V._ctf = ctf
V.update_ctf = True
V._otf = otf
V._volume_property.set_scalar_opacity(otf)
return V
def volume(sim,
qty='rho',
width=None,
resolution=200,
color=(1.0, 1.0, 1.0),
vmin=None,
vmax=None,
dynamic_range=4.0,
log=True,
create_figure=True):
"""Create a volume rendering of the given simulation using mayavi.
**Keyword arguments:**
*qty* (rho): The name of the array to interpolate
*width* (None): The width of the cube to generate, centered on the origin
*resolution* (200): The number of elements along each side of the cube
*color* (white): The color of the volume rendering. The value of each voxel
is used to set the opacity.
*vmin* (None): The value for zero opacity (calculated using dynamic_range if None)
*vmax* (None): The value for full opacity (calculated from the maximum
value in the region if None)
*dynamic_range*: The dynamic range to use if vmin and vmax are not specified
*log* (True): log-scale the image before passing to mayavi
*create_figure* (True): create a new mayavi figure before rendering
"""
import mayavi
from mayavi import mlab
from tvtk.util.ctf import PiecewiseFunction, ColorTransferFunction
if create_figure:
fig = mlab.figure(size=(500, 500), bgcolor=(0, 0, 0))
grid_data = sph.to_3d_grid(sim,
qty=qty,
nx=resolution,
x2=None if width is None else width / 2)
if log:
grid_data = np.log10(grid_data)
if vmin is None:
vmin = grid_data.max() - dynamic_range
if vmax is None:
vmax = grid_data.max()
else:
if vmin is None:
vmin = np.min(grid_data)
if vmax is None:
vmax = np.max(grid_data)
grid_data[grid_data < vmin] = vmin
grid_data[grid_data > vmax] = vmax
otf = PiecewiseFunction()
otf.add_point(vmin, 0.0)
otf.add_point(vmax, 1.0)
sf = mayavi.tools.pipeline.scalar_field(grid_data)
V = mlab.pipeline.volume(sf, color=color, vmin=vmin, vmax=vmax)
V.trait_get(
'volume_mapper')['volume_mapper'].blend_mode = 'maximum_intensity'
if color is None:
ctf = ColorTransferFunction()
ctf.add_rgb_point(vmin, 107. / 255, 124. / 255, 132. / 255)
ctf.add_rgb_point(vmin + (vmax - vmin) * 0.8, 200. / 255, 178. / 255,
164. / 255)
ctf.add_rgb_point(vmin + (vmax - vmin) * 0.9, 1.0, 210. / 255,
149. / 255)
ctf.add_rgb_point(vmax, 1.0, 222. / 255, 141. / 255)
print vmin, vmax
V._volume_property.set_color(ctf)
V._ctf = ctf
V.update_ctf = True
V._otf = otf
V._volume_property.set_scalar_opacity(otf)
return V
def _vmin_changed(self):
vmin = self.vmin
vmax = self.vmax
range_min, range_max = self._target.current_range
if vmin is None:
vmin = range_min
if vmax is None:
vmax = range_max
# Change the opacity function
from tvtk.util.ctf import PiecewiseFunction, save_ctfs
otf = PiecewiseFunction()
if range_min < vmin:
otf.add_point(range_min, 0.)
if range_max > vmax:
otf.add_point(range_max, 0.2)
otf.add_point(vmin, 0.)
otf.add_point(vmax, 0.2)
self._target._otf = otf
self._target._volume_property.set_scalar_opacity(otf)
if self.color is None and \
((self.vmin is not None) or (self.vmax is not None)):
# FIXME: We don't use 'rescale_ctfs' because it screws up the
# nodes, this is because, the values are actually scaled between
# the specified vmin/vmax and NOT the full range of values
# specified in the CTF or in the volume object.
if self.__last_vrange:
last_min, last_max = self.__last_vrange
else:
last_min, last_max = range_min, range_max
def _rescale_value(x):
nx = (x - last_min) / (last_max - last_min)
return vmin + nx * (vmax - vmin)
# For some reason on older versions of VTK (< 8.1 at least),
# The range trait is not updated correctly when the rgb points
# are added, this causes problems so we explicitly update them.
self._target._ctf.update_traits()
scale_min, scale_max = self._target._ctf.range
def _rescale_node(x):
nx = (x - scale_min) / (scale_max - scale_min)
return range_min + nx * (range_max - range_min)
if hasattr(self._target._ctf, 'nodes'):
rgb = list()
for value in self._target._ctf.nodes:
r, g, b = \
self._target._ctf.get_color(value)
rgb.append((_rescale_node(value), r, g, b))
else:
rgb = save_ctfs(self._target.volume_property)['rgb']
from tvtk.util.ctf import ColorTransferFunction
ctf = ColorTransferFunction()
try:
ctf.range = (range_min, range_max)
except Exception:
# VTK versions < 5.2 don't seem to need this.
pass
rgb.sort()
v = rgb[0]
ctf.add_rgb_point(range_min, v[1], v[2], v[3])
for v in rgb:
ctf.add_rgb_point(_rescale_value(v[0]), v[1], v[2], v[3])
ctf.add_rgb_point(range_max, v[1], v[2], v[3])
self._target._ctf = ctf
self._target._volume_property.set_color(ctf)
self.__last_vrange = vmin, vmax
self._target.update_ctf = True
def td(ty,t,E,rho,sigma,wdir):
D = pp.pload(t,w_dir=wdir)
if ty == 'flux':
for k in range(D.rho.shape[2]):
for j in range(D.rho.shape[1]):
for i in range(D.rho.shape[0]):
# if (i-D.rho.shape[0]/2)**2+(j-D.rho.shape[1]/2)**2+(k-D.rho.shape[2]/2)**2 > 30**2:
# D.rho[k,j,i] = rho
if D.rho[k,j,i] > 10:
D.rho[k,j,i] = rho
flux = D.rho*(D.bx1**2+D.bx2**2+D.bx3**2)**1.25*(D.vx1**2+D.vx2**2+D.vx3**2)**0
flux = (flux-np.mean(flux))*1+np.mean(flux)*1
flux = flux.sum(axis=0)
flux = nd.gaussian_filter(flux,sigma=(sigma,sigma),order=0)
fig = plt.figure(figsize=(7,6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(flux).T,origin='lower',extent=[D.x2[0],D.x2[-1],D.x3[0],D.x3[-1]])
levels = np.arange(-5.5, -4, 0.5)
plt.contour(np.log10(flux).T, levels,
origin='lower',
linewidths=2,
extent=[D.x1[0],D.x1[-1],D.x2[0],D.x2[-1]])
cbar = fig.colorbar(neg,ax=ax)
cbar.set_label(r'log(S)')
ax.set_xlabel('l offset (pc)')
ax.set_ylabel('b offset (pc)')
ax.set_title(r't='+str(t)+r'$\ \mathregular{\rho}$='+str(rho)+' E='+str(E))
fig.subplots_adjust(top=0.9,bottom=0.1,left=0.11,right=0.97)
fig.savefig('t'+str(t)+'_density'+str(rho)+'_E'+str(E)+'.eps')
fig.clear()
ax = fig.add_subplot(111)
ax.plot((np.rot90(np.eye(len(flux)))*flux.T).sum(axis=0))
ax.set_xlim(0,256)
fig.savefig('change.eps')
elif ty == 'rho':
# t = 850
fig = plt.figure(figsize=(7,6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(D.rho[:,:,128]).T,origin='lower',extent=[D.x2[0],D.x2[-1],D.x3[0],D.x3[-1]])
cbar = fig.colorbar(neg,ax=ax)
cbar.set_label(r'log($\mathregular{\rho/cm^{-3}}$)')
ax.set_xlabel('x offset (pc)')
ax.set_ylabel('y offset (pc)')
ax.set_title(r't='+str(t)+r'$\ \mathregular{\rho}$='+str(rho)+' E='+str(E))
fig.subplots_adjust(top=0.9,bottom=0.1,left=0.11,right=0.97)
T = pp.Tools()
newdims = 2*(20,)
Xmesh, Ymesh = np.meshgrid(D.x2.T,D.x3.T)
xcong = T.congrid(Xmesh,newdims,method='linear')
ycong = T.congrid(Ymesh,newdims,method='linear')
velxcong = T.congrid(D.bx1[:,:,128].T,newdims,method='linear')
velycong = T.congrid(D.bx2[:,:,128].T,newdims,method='linear')
plt.gca().quiver(xcong, ycong, velxcong, velycong,color='w')
plt.show()
fig.savefig('rho-t='+str(t)+'_density='+str(rho)+'_E='+str(E)+'.eps') # Only to be saved as either .png or .jpg
# close()
else:
print(D.x1.shape)
# arr = np.meshgrid(D.x1,D.x2,D.x3)
# mlab.points3d(arr[0][0:256:8,0:256:8,0:256:8], arr[1][0:256:8,0:256:8,0:256:8], arr[2][0:256:8,0:256:8,0:256:8], D.rho[0:256:8,0:256:8,0:256:8])
vol = mlab.pipeline.volume(mlab.pipeline.scalar_field(np.log10(D.prs*D.rho)))
ctf = ColorTransferFunction()
ctf.add_hsv_point(-8, 0.8, 1, 1)
ctf.add_hsv_point(-6.5, 0.45, 1, 1)
ctf.add_hsv_point(-5.4, 0.15, 1, 1)
vol._volume_property.set_color(ctf)
vol._ctf = ctf
vol.update_ctf = True
otf = PiecewiseFunction()
otf.add_point(-8, 0)
otf.add_point(-5.7, 0.082)
otf.add_point(-5.4, 0.0)
vol._otf = otf
vol._volume_property.set_scalar_opacity(otf)
new_tube, surf_bar, surf_bar_label = mpf.draw_surface(surf_poly,'RdBu',lim=0.4,
position=[0.81, 0.1],
position2=[0.11,0.31])
mpf.change_surface_scalars(new_tube, surf_bar_label, 'vphi', lim=1.5)
new_tube.parent.scalar_lut_manager.label_text_property.color = (1,1,1)
slines.parent.scalar_lut_manager.number_of_labels = 4
surf_bar_label.property.color = text_color
surf_bar_label.y_position = 0.05
surf_bar_label.x_position = 0.93
# Add GBand volume render
vol = mlab.pipeline.volume(gband)
vol.volume.mapper.blend_mode = 'maximum_intensity'
#vol.volume.mapper.number_of_threads = 16
# Make a decent ctf and otf
ctf = ColorTransferFunction()
ctf.range = [0.1, 1]
ctf.add_rgb_point(1, 1., 1, 0.01)
ctf.add_rgb_point(0.85, 1., 0.8, 0.)
ctf.add_rgb_point(0.6, 0.9, 0.3, 0.)
ctf.add_rgb_point(0.4, 0.8, 0.0, 0.)
ctf.add_rgb_point(0., 0.01, 0., 0.)
otf = PiecewiseFunction()
otf.add_point(1., 1.)
otf.add_point(0.6, 0.9)
otf.add_point(0.2, 0.)
otf.add_point(0., 0.)
vol._volume_property.set_color(ctf)
vol._ctf = ctf
def _vmin_changed(self):
vmin = self.vmin
vmax = self.vmax
range_min, range_max = self._target.current_range
if vmin is None:
vmin = range_min
if vmax is None:
vmax = range_max
# Change the opacity function
from tvtk.util.ctf import PiecewiseFunction, save_ctfs
otf = PiecewiseFunction()
if range_min < vmin:
otf.add_point(range_min, 0.)
if range_max > vmax:
otf.add_point(range_max, 0.2)
otf.add_point(vmin, 0.)
otf.add_point(vmax, 0.2)
self._target._otf = otf
self._target._volume_property.set_scalar_opacity(otf)
if self.color is None and \
((self.vmin is not None) or (self.vmax is not None)):
# FIXME: We don't use 'rescale_ctfs' because it screws up the
# nodes, this is because, the values are actually scaled between
# the specified vmin/vmax and NOT the full range of values
# specified in the CTF or in the volume object.
if self.__last_vrange:
last_min, last_max = self.__last_vrange
else:
last_min, last_max = range_min, range_max
def _rescale_value(x):
nx = (x - last_min) / (last_max - last_min)
return vmin + nx * (vmax - vmin)
# For some reason on older versions of VTK (< 8.1 at least),
# The range trait is not updated correctly when the rgb points
# are added, this causes problems so we explicitly update them.
self._target._ctf.update_traits()
scale_min, scale_max = self._target._ctf.range
def _rescale_node(x):
nx = (x - scale_min) / (scale_max - scale_min)
return range_min + nx * (range_max - range_min)
if hasattr(self._target._ctf, 'nodes'):
rgb = list()
for value in self._target._ctf.nodes:
r, g, b = \
self._target._ctf.get_color(value)
rgb.append((_rescale_node(value), r, g, b))
else:
rgb = save_ctfs(self._target.volume_property)['rgb']
from tvtk.util.ctf import ColorTransferFunction
ctf = ColorTransferFunction()
try:
ctf.range = (range_min, range_max)
except Exception:
# VTK versions < 5.2 don't seem to need this.
pass
rgb.sort()
v = rgb[0]
ctf.add_rgb_point(range_min, v[1], v[2], v[3])
for v in rgb:
ctf.add_rgb_point(_rescale_value(v[0]), v[1], v[2], v[3])
ctf.add_rgb_point(range_max, v[1], v[2], v[3])
self._target._ctf = ctf
self._target._volume_property.set_color(ctf)
self.__last_vrange = vmin, vmax
self._target.update_ctf = True
def make_volume_prop(mins=255, maxs=355):
"""Make a volume property for the testing."""
table = tvtk.VolumeProperty()
ctf = ColorTransferFunction()
ds = (maxs - mins) / 4.0
try:
ctf.range = (mins, maxs)
except Exception:
# VTK versions < 5.2 don't seem to need this.
pass
ctf.add_rgb_point(mins, 0.00, 0.0, 1.00)
ctf.add_rgb_point(mins + ds, 0.25, 0.5, 0.75)
ctf.add_rgb_point(mins + 2 * ds, 0.50, 1.0, 0.50)
ctf.add_rgb_point(mins + 3 * ds, 0.75, 0.5, 0.25)
ctf.add_rgb_point(maxs, 1.00, 0.0, 0.00)
otf = PiecewiseFunction()
otf.add_point(mins, 0.0)
otf.add_point(maxs, 0.2)
table.set_color(ctf)
table.set_scalar_opacity(otf)
return table, ctf, otf
def volume_red_blue(scalar_field, scalar_data):
minr = scalar_data.min()
maxr = scalar_data.max()
#Volume for high pressure
rvmin1 = minr + 0.5 * (maxr - minr)
rvmax1 = minr + 1. * (maxr - minr)
rvol1 = mlab.pipeline.volume(scalar_field, vmin=rvmin1, vmax=rvmax1)
# Changing the ctf:
from tvtk.util.ctf import ColorTransferFunction
ctf1 = ColorTransferFunction()
ctf1.add_rgb_point(rvmin1, 1., 1., 0.5)
ctf1.add_rgb_point(rvmin1 + 0.4 * (rvmax1 - rvmin1), 1, 0.3, 0.1)
ctf1.add_rgb_point(rvmax1, 1., 0., 0.)
# ...
rvol1._volume_property.set_color(ctf1)
rvol1._ctf = ctf1
rvol1.update_ctf = True
#Changing the opacity of the volume vol1
## Changing the otf:
from tvtk.util.ctf import PiecewiseFunction
otf = PiecewiseFunction()
otf.add_point(rvmin1, 0)
otf.add_point(rvmin1 + (rvmax1 - rvmin1) * 0.2, 0.012)
otf.add_point(rvmin1 + (rvmax1 - rvmin1) * 0.5, 0.05)
otf.add_point(rvmax1, 0.15)
##vol1._otf = otf
rvol1._volume_property.set_scalar_opacity(otf)
# exempt volume from shading and improve overall look by increasing opacity
rvol1.volume_property.shade = False
rvol1.volume_property.scalar_opacity_unit_distance = 2.0
#Volume for low pressure
rvmin2 = minr + 0. * (maxr - minr)
rvmax2 = minr + 0.5 * (maxr - minr)
rvol2 = mlab.pipeline.volume(scalar_field, vmin=rvmin2, vmax=rvmax2)
# Changing the ctf:
ctf2 = ColorTransferFunction()
ctf2.add_rgb_point(rvmin2, 0., 0.5, 1.)
ctf2.add_rgb_point(rvmin2 + 0.6 * (rvmax2 - rvmin2), 0.1, 0.7, 1.)
ctf2.add_rgb_point(rvmax2, 0.5, 1., 1.)
# ...
rvol2._volume_property.set_color(ctf2)
rvol2._ctf = ctf2
rvol2.update_ctf = True
#Changing the opacity of the volume vol2
## Changing the otf:
otf = PiecewiseFunction()
otf.add_point(rvmax2, 0)
otf.add_point(rvmax2 - (rvmax2 - rvmin2) * 0.2, 0.012)
otf.add_point(rvmax2 - (rvmax2 - rvmin2) * 0.5, 0.05)
otf.add_point(rvmin2, 0.15)
##vol1._otf = otf
rvol2._volume_property.set_scalar_opacity(otf)
# exempt volume from shading and improve overall look by increasing opacity
rvol2.volume_property.shade = False
rvol2.volume_property.scalar_opacity_unit_distance = 2.0
# compensate for huge outliers which would screw up the color map
cutoff = np.percentile(data, args.cutoff)
data[data > cutoff] = cutoff
mx = np.max(data)
mn = np.min(data)
data = ((data - mn) / (mx - mn)).astype(np.float32)
print("done")
volume_figure = mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
vol = mlab.pipeline.volume(mlab.pipeline.scalar_field(data),
vmin=args.vmin,
vmax=args.vmax)
ctf = ColorTransferFunction()
ctf.add_rgb_point(0, 1, 1, 1)
ctf.add_rgb_point(1, 0, 0, 0.3)
vol._volume_property.set_color(ctf)
vol._ctf = ctf
vol.update_ctf = True
mlab.outline()
if args.show_cut:
cut_figure = mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(data),
plane_orientation='x_axes',
slice_index=data.shape[0] / 2,
figure=cut_figure)
mlab.sync_camera(volume_figure, cut_figure)
def potential3d(self, mode='', export_figure=True):
"""
Creates a 3D interactive ESP potential plot.
:param mode: mode of the plot: '', 'iso_surface', 'contour'
:param export_figure: boolean, whether to save an image or not
:raises: ValueError when the visualization mode is invalid
"""
from mayavi import mlab
potential = self.electron_potential
if mode == '':
grid = mlab.pipeline.scalar_field(potential)
min = potential.min()
negative_steps = np.percentile(potential[potential < 0],
[2.0, 3.0, 7.5])
positive_steps = np.percentile(potential[potential > 0],
[92.5, 97.0, 98.0])
max = potential.max()
vol = mlab.pipeline.volume(grid, vmin=min, vmax=max)
from tvtk.util.ctf import ColorTransferFunction
ctf = ColorTransferFunction()
ctf.add_rgb_point(min, 1, 0.3, 0.3) # numbers are r,g,b in [0;1]
ctf.add_rgb_point(negative_steps[1], 1, 0.3, 0.3)
ctf.add_rgb_point(negative_steps[2], 1, 1, 1)
ctf.add_rgb_point(positive_steps[0], 1, 1, 1)
ctf.add_rgb_point(positive_steps[1], 0.3, 0.3, 1)
ctf.add_rgb_point(max, 0.3, 0.3, 1)
ctf.range = np.asarray([min, max])
vol._volume_property.set_color(ctf)
vol._ctf = ctf
vol.update_ctf = True
# Changing the otf:
from tvtk.util.ctf import PiecewiseFunction
otf = PiecewiseFunction()
otf.add_point(min, 1.0)
otf.add_point(negative_steps[1], 1.0)
otf.add_point(negative_steps[2], 0.0)
otf.add_point(positive_steps[0], 0.0)
otf.add_point(positive_steps[1], 1.0)
otf.add_point(max, 1.0)
vol._otf = otf
vol._volume_property.set_scalar_opacity(otf)
mlab.axes()
elif mode == 'iso_surface':
source = mlab.pipeline.scalar_field(potential)
clip = mlab.pipeline.data_set_clipper(source)
mlab.pipeline.iso_surface(clip)
elif mode == 'contour':
n = self.electron_potential.shape[0]
range = 100
x, y, z = np.mgrid[-range / 2:range / 2:complex(n),
-range / 2:range / 2:complex(n),
-range / 2:range / 2:complex(n)]
mlab.contour3d(x,
y,
z,
self.electron_potential,
contours=10,
opacity=0.5)
else:
log.error("The visualisation mode of the electrostatic potential"
" can be one of ['', 'iso_surface', 'contour']")
raise ValueError
if export_figure:
mlab.savefig(filename=os.path.join(
self.figures_dir, '{0}_elstpot3d.png'.format(
self.molecule_name)))
mlab.show()
def td(ty, t, E, rho, sigma, wdir):
D = pp.pload(t, w_dir=wdir)
if ty == 'flux':
for k in range(D.rho.shape[2]):
for j in range(D.rho.shape[1]):
for i in range(D.rho.shape[0]):
# if (i-D.rho.shape[0]/2)**2+(j-D.rho.shape[1]/2)**2+(k-D.rho.shape[2]/2)**2 > 30**2:
# D.rho[k,j,i] = rho
if D.rho[k, j, i] > 10:
D.rho[k, j, i] = rho
flux = D.rho * (D.bx1**2 + D.bx2**2 +
D.bx3**2)**1.25 * (D.vx1**2 + D.vx2**2 + D.vx3**2)**0
flux = (flux - np.mean(flux)) * 1 + np.mean(flux) * 1
flux = flux.sum(axis=0)
flux = nd.gaussian_filter(flux, sigma=(sigma, sigma), order=0)
fig = plt.figure(figsize=(7, 6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(flux).T,
origin='lower',
extent=[D.x2[0], D.x2[-1], D.x3[0], D.x3[-1]])
levels = np.arange(-5.5, -4, 0.5)
plt.contour(np.log10(flux).T,
levels,
origin='lower',
linewidths=2,
extent=[D.x1[0], D.x1[-1], D.x2[0], D.x2[-1]])
cbar = fig.colorbar(neg, ax=ax)
cbar.set_label(r'log(S)')
ax.set_xlabel('l offset (pc)')
ax.set_ylabel('b offset (pc)')
ax.set_title(r't=' + str(t) + r'$\ \mathregular{\rho}$=' + str(rho) +
' E=' + str(E))
fig.subplots_adjust(top=0.9, bottom=0.1, left=0.11, right=0.97)
fig.savefig('t' + str(t) + '_density' + str(rho) + '_E' + str(E) +
'.eps')
fig.clear()
ax = fig.add_subplot(111)
ax.plot((np.rot90(np.eye(len(flux))) * flux.T).sum(axis=0))
ax.set_xlim(0, 256)
fig.savefig('change.eps')
elif ty == 'rho':
# t = 850
fig = plt.figure(figsize=(7, 6))
ax = fig.add_subplot(111)
neg = ax.imshow(np.log10(D.rho[:, :, 128]).T,
origin='lower',
extent=[D.x2[0], D.x2[-1], D.x3[0], D.x3[-1]])
cbar = fig.colorbar(neg, ax=ax)
cbar.set_label(r'log($\mathregular{\rho/cm^{-3}}$)')
ax.set_xlabel('x offset (pc)')
ax.set_ylabel('y offset (pc)')
ax.set_title(r't=' + str(t) + r'$\ \mathregular{\rho}$=' + str(rho) +
' E=' + str(E))
fig.subplots_adjust(top=0.9, bottom=0.1, left=0.11, right=0.97)
T = pp.Tools()
newdims = 2 * (20, )
Xmesh, Ymesh = np.meshgrid(D.x2.T, D.x3.T)
xcong = T.congrid(Xmesh, newdims, method='linear')
ycong = T.congrid(Ymesh, newdims, method='linear')
velxcong = T.congrid(D.bx1[:, :, 128].T, newdims, method='linear')
velycong = T.congrid(D.bx2[:, :, 128].T, newdims, method='linear')
plt.gca().quiver(xcong, ycong, velxcong, velycong, color='w')
plt.show()
fig.savefig('rho-t=' + str(t) + '_density=' + str(rho) + '_E=' +
str(E) + '.eps') # Only to be saved as either .png or .jpg
# close()
else:
print(D.x1.shape)
# arr = np.meshgrid(D.x1,D.x2,D.x3)
# mlab.points3d(arr[0][0:256:8,0:256:8,0:256:8], arr[1][0:256:8,0:256:8,0:256:8], arr[2][0:256:8,0:256:8,0:256:8], D.rho[0:256:8,0:256:8,0:256:8])
vol = mlab.pipeline.volume(
mlab.pipeline.scalar_field(np.log10(D.prs * D.rho)))
ctf = ColorTransferFunction()
ctf.add_hsv_point(-8, 0.8, 1, 1)
ctf.add_hsv_point(-6.5, 0.45, 1, 1)
ctf.add_hsv_point(-5.4, 0.15, 1, 1)
vol._volume_property.set_color(ctf)
vol._ctf = ctf
vol.update_ctf = True
otf = PiecewiseFunction()
otf.add_point(-8, 0)
otf.add_point(-5.7, 0.082)
otf.add_point(-5.4, 0.0)
vol._otf = otf
vol._volume_property.set_scalar_opacity(otf)
def NewColorMap(h):
ctf = ColorTransferFunction()
otf = PiecewiseFunction()
ctf.remove_all_points()
otf.remove_all_points()
ctf.add_rgb_point(0, 1, 1, 1)
otf.add_point(0, 0)
for i in range(128):
q = (i + 1.0) / 128.0
ctf.add_rgb_point(-q, 1 - q * q, 1 - q * q,
1) # -1 --> blue, 0 --> white, 1-->red
ctf.add_rgb_point(q, 1, 1 - q * q, 1 - q * q)
otf.add_point(-q, 0.5 * q)
otf.add_point(q, 0.5 * q)
ctf.range = [-1, 1]
h._volume_property.set_color(ctf)
h._ctf = ctf
h.update_ctf = True
h._otf = otf
h._volume_property.set_scalar_opacity(otf)
def volume_red_blue(scalar_field, scalar_data):
minr = scalar_data.min()
maxr = scalar_data.max()
# max and mins
rvmin_pos = minr + 0.5 * (maxr - minr)
rvmax_pos = minr + 1. * (maxr - minr)
rvmin_neg = minr + 0. * (maxr - minr)
rvmax_neg = minr + 0.5 * (maxr - minr)
rvol = mlab.pipeline.volume(scalar_field, vmin=rvmin_neg, vmax=rvmax_pos)
# Changing the ctf:
from tvtk.util.ctf import ColorTransferFunction
ctf = ColorTransferFunction()
ctf.add_rgb_point(rvmin_pos, 1., 1., 0.5)
ctf.add_rgb_point(rvmin_pos + 0.4 * (rvmax_pos - rvmin_pos), 1, 0.3, 0.1)
ctf.add_rgb_point(rvmax_pos, 1., 0., 0.)
ctf.add_rgb_point(rvmin_neg, 0., 0.5, 1.)
ctf.add_rgb_point(rvmin_neg + 0.6 * (rvmax_neg - rvmin_neg), 0.1, 0.7, 1.)
ctf.add_rgb_point(rvmax_neg, 0.5, 1., 1.)
rvol._volume_property.set_color(ctf)
rvol._ctf = ctf
rvol.update_ctf = True
#Changing the opacity of the volume vol
## Changing the otf:
from tvtk.util.ctf import PiecewiseFunction
otf = PiecewiseFunction()
otf.add_point(rvmin_pos, 0)
otf.add_point(rvmin_pos + (rvmax_pos - rvmin_pos) * 0.2, 0.012)
otf.add_point(rvmin_pos + (rvmax_pos - rvmin_pos) * 0.5, 0.05)
otf.add_point(rvmax_pos, 0.15)
otf.add_point(rvmax_neg, 0)
otf.add_point(rvmax_neg - (rvmax_neg - rvmin_neg) * 0.2, 0.012)
otf.add_point(rvmax_neg - (rvmax_neg - rvmin_neg) * 0.5, 0.05)
otf.add_point(rvmin_neg, 0.15)
##vol1._otf = otf
rvol._volume_property.set_scalar_opacity(otf)
# exempt volume from shading and improve overall look by increasing opacity
rvol.volume_property.shade = False
rvol.volume_property.scalar_opacity_unit_distance = 2.0
from matplotlib import pyplot as plt
from tvtk.util.ctf import PiecewiseFunction
f2 = TFile("dose.root")
A = f2.Get("pRad1")
histA = r2m.Hist2D(A)
histA = np.array(histA.content)
histA = np.clip(histA, 290, 330)
histA = histA[150:850, 150:850]
f = TFile("CIRSHead.root")
HU = f.Get("hu")
print HU.GetNbinsX(), HU.GetNbinsY(), HU.GetNbinsZ()
n = [HU.GetNbinsX(), HU.GetNbinsY(), HU.GetNbinsZ()]
n2 = (n[0] + 2) * (n[1] + 2) * (n[2] + 2)
d = HU.GetArray()
d.SetSize(n2)
ctf = ColorTransferFunction()
ctf.add_rgb_point(0.5, 1, 1, 1) # r, g, and b are float
# between 0 and 1
otf = PiecewiseFunction()
otf.add_point(-110, 0.0)
otf.add_point(-105, 1.0)
otf.add_point(128, 1.0)
#D = np.array(d).reshape(282,258,258)
D = np.array(d).reshape(280 + 2, 512 + 2, 512 + 2)
#D = D[80:270,37:210,64:193]
X, Y, Z = np.where(D)
mlab.figure(bgcolor=(0, 0, 0), size=(400, 400))
vol = mlab.pipeline.volume(mlab.pipeline.scalar_field(D, opacity=1.0),
