def update_scene():
# clear figure
mlab.clf(fig1)
# Plot new data feature
points = all_points[file_i]
# Rescale points for visu
points = (points * 1.5 + np.array([1.0, 1.0, 1.0])) * 50.0
# Show point clouds colorized with activations
activations = mlab.points3d(points[:, 0],
points[:, 1],
points[:, 2],
points[:, 2],
scale_factor=3.0,
scale_mode='none',
figure=fig1)
# New title
mlab.title(str(file_i), color=(0, 0, 0), size=0.3, height=0.01)
text = '<--- (press g for previous)' + 50 * ' ' + '(press h for next) --->'
mlab.text(0.01, 0.01, text, color=(0, 0, 0), width=0.98)
mlab.orientation_axes()
return
def plot_matrix(connectmat_file, centers_file, threshold_pct=5, weight_edges=False,
node_scale_factor=2, edge_radius=.5, resolution=8, name_scale_factor=1,
names_file=None, node_indiv_colors=[], highlight_nodes=[], fliplr=False):
"""
Given a connectivity matrix and a (x,y,z) centers file for each region, plot the 3D network
"""
matrix = core.file_reader(connectmat_file)
nodes = core.file_reader(centers_file)
if names_file:
names = core.file_reader(names_file,1)
num_nodes = len(nodes)
edge_thresh_pct = threshold_pct / 100.0
matrix_flat = np.array(matrix).flatten()
edge_thresh = np.sort(matrix_flat)[len(matrix_flat)-int(len(matrix_flat)*edge_thresh_pct)]
matrix = core.file_reader(connectmat_file)
ma = np.array(matrix)
thresh = scipy.stats.scoreatpercentile(ma.ravel(),100-threshold_pct)
ma_thresh = ma*(ma > thresh)
if highlight_nodes:
nr = ma.shape[0]
subset_mat = np.zeros((nr, nr))
for i in highlight_nodes:
subset_mat[i,:] = 1
subset_mat[:,i] = 1
ma_thresh = ma_thresh * subset_mat
if fliplr:
new_nodes = []
for node in nodes:
new_nodes.append([45-node[0],node[1],node[2]]) # HACK
nodes = new_nodes
mlab.figure(bgcolor=(1, 1, 1), size=(400, 400))
for count,(x,y,z) in enumerate(nodes):
if node_indiv_colors:
mlab.points3d(x,y,z, color=colors[node_indiv_colors[count]], scale_factor=node_scale_factor, resolution=resolution)
else:
mlab.points3d(x,y,z, color=(0,1,0), scale_factor=node_scale_factor, resolution=resolution)
if names_file:
width = .025*name_scale_factor*len(names[count])
print width
print names[count]
mlab.text(x, y,names[count], z=z,width=.025*len(names[count]),color=(0,0,0))
for i in range(num_nodes-1):
x0,y0,z0 = nodes[i]
for j in range(i+1, num_nodes):
#if matrix[i][j] > edge_thresh:
if ma_thresh[i][j] > edge_thresh:
x1,y1,z1 = nodes[j]
if weight_edges:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=matrix[i][j]/matrix_flat.max(),
color=(1,1,1))
else:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=edge_radius,
color=(1,1,1))
def add_text(self, string):
"""
Adds some interesting text to the current mayavi-canvas.
:param string: String to put in the image
"""
c = (1.,1.,1.) if self.color_scheme == 'dark' else (0.,0.,0.)
mlab.text(0.025,0.025, string, color=c, width=0.3)
def mayaView(num, path="./data"):
"""
num is a str eg. "20-1", "400"
loads images from ./data
"""
assert type(num) == str
x = np.load(path + '/xdata{}.dat'.format(num))
y = np.load(path + '/ydata{}.dat'.format(num))
z = np.load(path + '/zdata{}.dat'.format(num))
density = np.load(path + '/density{}.dat'.format(num))
figure = mlab.figure('DensityPlot')
mag = density / np.amax(density)
n = num[0]
l = num[1]
if num[2] == "-":
m = num[2:]
else:
m = num[2]
pts = mlab.points3d(mag, opacity=0.5, transparent=True)
# pts = mlab.contour3d(mag,opacity =0.5)
#mlab.title('n= {}, l = {}, m= {}'.format(n,l,m), size= 20)
mlab.text(0.5, 0.2, 'n= {}, l = {}, m= {}'.format(n, l, m), opacity=1)
mlab.colorbar(orientation='vertical')
mlab.axes()
mlab.savefig(
'/home/ada/Documents/SUTD/Term 3/Physical Chemistry/Schrodinger-Assignment/Images/'
+ num + '.png')
def action(u, x, xv, y, yv, t, n):
#print 'action, t=',t,'\nu=',u, '\Nx=',x, '\Ny=', y
if plot == 1:
mesh(xv, yv, u, title='t=%g' %t[n])
time.sleep(0.2) # pause between frames
elif plot == 2:
# mayavi plotting
mlab.clf()
extent1 = (0, 20, 0, 20,-2, 2)
s = mlab.surf(x , y, u, colormap='Blues', warp_scale=5,extent=extent1)
mlab.axes(s, color=(.7, .7, .7), extent=extent1,
ranges=(0, 10, 0, 10, -1, 1), xlabel='', ylabel='',
zlabel='',
x_axis_visibility=False, z_axis_visibility=False)
mlab.outline(s, color=(0.7, .7, .7), extent=extent1)
mlab.text(2, -2.5, '', z=-4, width=0.1)
mlab.colorbar(object=None, title=None, orientation='horizontal', nb_labels=None, nb_colors=None, label_fmt=None)
mlab.title('test 1D t=%g' % t[n])
mlab.view(142, -72, 50)
f = mlab.gcf()
camera = f.scene.camera
camera.yaw(0)
if plot > 0:
path = 'Figures_wave2D'
time.sleep(0) # pause between frames
if save_plot and plot != 2:
filename = '%s/%08d.png' % (path, n)
savefig(filename) # time consuming!
elif save_plot and plot == 2:
filename = '%s/%08d.png' % (path,n)
mlab.savefig(filename) # time consuming!
def do_test(lines, scalars, show=False, txt=""):
viscid.logger.info('--> ' + txt)
title = txt + '\n' + "\n".join(
textwrap.wrap("scalars = {0}".format(scalars), width=50))
try:
from viscid.plot import vpyplot as vlt
from matplotlib import pyplot as plt
vlt.clf()
vlt.plot_lines(lines, scalars=scalars)
plt.title(title)
vlt.savefig(next_plot_fname(__file__, series='q2'))
if show:
vlt.show()
except ImportError:
pass
try:
from mayavi import mlab
vlab, _ = get_mvi_fig()
vlab.clf()
vlab.plot_lines3d(lines, scalars=scalars)
vlab.fancy_axes()
mlab.text(0.05, 0.05, title)
vlab.savefig(next_plot_fname(__file__, series='q3'))
if show:
vlab.show(stop=True)
except ImportError:
pass
def make_frame(t):
i = int(np.ceil(t * speed_factor / dt))
source2 = runname + '_{0:04}'.format(i) + '.asc'
# Load the dem into a numpy array
arr = np.loadtxt(source2, skiprows=6)
for i in range(0, ny):
H_cent[i, 0:nx] = arr[i, 0:nx]
W_cent = B_cent + H_cent
idx = np.ma.masked_where(H_cent == -9999, W_cent)
W_cent[np.where(np.ma.getmask(idx) == True)] = np.nan
mlab.clf() # clear the figure (to reset the colors)
topo = mlab.surf(xs, ys, B_cent, color=(0.5, 0.6, 0.7))
mlab.outline(topo, color=(.7, .7, .7))
surf = mlab.surf(xs, ys, W_cent + 1.e-1, color=(0.4, 0.4, 0.4))
surf.actor.property.interpolation = 'phong'
surf.actor.property.specular = 1.0
surf.actor.property.specular_power = 50
mlab.text(-10,
0,
't=' + str(t * speed_factor) + 's',
z=10,
width=0.15,
color=(0, 0, 0))
return mlab.screenshot(antialiased=True)
def setup(self):
print('running setup')
self.generate_color_and_size()
self.fig = mlab.figure(figure=mlab.gcf())
self.fig.scene.interactor.interactor_style = tvtk.InteractorStyleTerrain(
)
self.update_plot()
# The general mouse based clicker - which reveals point information
# of the known and labelled datapoints
self.info_picker = self.fig.on_mouse_pick(self.view_point_information)
self.info_picker.tolerance = 0.01
# picker which allows to re-assign the point trajectory number
self.reassign_picker = self.fig.on_mouse_pick(self.reassign_callback,
type='point',
button='Right')
self.reassign_picker.tolerance = 0.01
# outline which indicates which point has been clicked on
self.outline = mlab.outline(line_width=3, color=(0.9, 0.9, 0.9))
self.outline.outline_mode = 'cornered'
self.outline.bounds = (0.05, 0.05, 0.05, 0.05, 0.05, 0.05)
self.click_text = mlab.text(0.8, 0.8, 'STARTING INFO')
self.traj_text = mlab.text(0.8, 0.6, 'Trajectory number')
self.pointtype_text = mlab.text(0.8, 0.87, 'Point Type')
self.pointtype_info = mlab.text(0.8, 0.82, '')
mlab.axes()
def _render_mesh(self, scaled_distances_between_meshes, type_cmap,
scalar_range, show_statistics):
from mayavi import mlab
# v = mlab.figure(figure=figure_name, size=size,
# bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
src = mlab.pipeline.triangular_mesh_source(
self.points[:, 0],
self.points[:, 1],
self.points[:, 2],
self.trilist,
scalars=scaled_distances_between_meshes)
surf = mlab.pipeline.surface(src, colormap=type_cmap)
# When font size bug resolved, uncomment
# cb=mlab.colorbar(title='Distances in mm',
# orientation='vertical', nb_labels=5)
# cb.title_text_property.font_size = 20
# cb.label_text_property.font_family = 'times'
# cb.label_text_property.font_size=10
cb = mlab.colorbar(orientation='vertical', nb_labels=5)
cb.data_range = scalar_range
cb.scalar_bar_representation.position = [0.8, 0.15]
cb.scalar_bar_representation.position2 = [0.15, 0.7]
text = mlab.text(0.8, 0.85, 'Distances in mm')
text.width = 0.20
if show_statistics:
text2 = mlab.text(
0.5, 0.02, 'Mean error {:.3}mm \nMax error {:.3}mm \
'.format(scaled_distances_between_meshes.mean(),
scaled_distances_between_meshes.max()))
text2.width = 0.20
surf.module_manager.scalar_lut_manager.reverse_lut = True
# perhaps we shouud usew kwargs
# if camera_settings is None:
mlab.gcf().scene.z_plus_view()
def update_scene():
# clear figure
mlab.clf(fig1)
# Plot new data feature
points = all_points[file_i]
responses = all_responses[file_i]
min_response, max_response = np.min(responses), np.max(responses)
responses = (responses - min_response) / (max_response - min_response)
# Rescale points for visu
points = (points * 1.5 / config.in_radius + np.array([1.0, 1.0, 1.0])) * 50.0
# Show point clouds colorized with activations
activations = mlab.points3d(points[:, 0],
points[:, 1],
points[:, 2],
responses,
scale_factor=3.0,
scale_mode='none',
vmin=0.1,
vmax=0.9,
figure=fig1)
# New title
mlab.title(feature_files[file_i], color=(0, 0, 0), size=0.3, height=0.01)
text = '<--- (press g for previous)' + 50 * ' ' + '(press h for next) --->'
mlab.text(0.01, 0.01, text, color=(0, 0, 0), width=0.98)
mlab.orientation_axes()
return
def plot_matrix_metric(connectmat_file,centers_file,threshold_pct,grp_metrics=None,node_metric='bc',
weight_edges=0,node_scale_factor=2,edge_radius=.5,resolution=8,name_scale_factor=1,names_file=0,
red_nodes=None):
"""
Given a connectivity matrix and a (x,y,z) centers file for each region, plot the 3D network
"""
matrix=core.file_reader(connectmat_file)
nodes=core.file_reader(centers_file)
if names_file:
names = core.file_reader(names_file,1)
num_nodes = len(nodes)
edge_thresh_pct = threshold_pct / 100.0
matrix_flat=np.array(matrix).flatten()
edge_thresh=np.sort(matrix_flat)[len(matrix_flat)-int(len(matrix_flat)*edge_thresh_pct)]
if grp_metrics: # regional metrics caclulated elsewhere, loaded in
node_colors = {} # define colors for each metric
node_colors['s'] = (1, 0.733, 0)
node_colors['cc'] = (0.53, 0.81, 0.98)
node_colors['bc'] = (0.5, 1, 0)
node_colors['eloc'] = (1, 0 , 1)
node_colors['ereg'] = (1, 1, 0)
node_metrics={}
metrics = np.array(core.file_reader(grp_metrics))
cols = np.shape(metrics)[1]
for i in range(cols):
colmean = np.mean(metrics[:,i])
colscale = 2 / colmean
metrics[:,i] = metrics[:,i] * colscale # make node mean size 2
node_metrics['s'] = metrics[:,0] # strength
node_metrics['cc'] = metrics[:,1] # clustering coefficient
node_metrics['bc'] = metrics[:,2] # betweenness centrality
node_metrics['eloc'] = metrics[:,3] # local efficiency
node_metrics['ereg'] = metrics[:,4] # regional efficiency
mlab.figure(bgcolor=(1, 1, 1), size=(800, 800))
for count,(x,y,z) in enumerate(nodes):
if grp_metrics:
mlab.points3d(x,y,z, color=node_colors[node_metric],
scale_factor=node_metrics[node_metric][count], resolution=resolution)
else:
mlab.points3d(x,y,z, color=(0,1,0), scale_factor=node_scale_factor, resolution=resolution)
if names_file:
mlab.text(x, y,names[count], z=z,width=.02*name_scale_factor*len(names[count]),color=(0,0,0))
for i in range(num_nodes-1):
x0,y0,z0=nodes[i]
for j in range(i+1, num_nodes):
if matrix[i][j] > edge_thresh:
x1,y1,z1=nodes[j]
if weight_edges:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=matrix[i][j]/matrix_flat.max(),
color=(1,1,1))
else:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=edge_radius,
color=(1,1,1))
def disp_pts(pts1, pts2, color1=(1,0,0), color2=(0,1,0)):
figure = mlab.gcf()
mlab.clf()
figure.scene.disable_render = True
pts1_glyphs = mlab.points3d(pts1[:,0], pts1[:,1], pts1[:,2], color=color1, resolution=20, scale_factor=0.001)
pts2_glyphs = mlab.points3d(pts2[:,0], pts2[:,1], pts2[:,2], color=color2, resolution=20, scale_factor=0.001)
glyph_points1 = pts1_glyphs.glyph.glyph_source.glyph_source.output.points.to_array()
glyph_points2 = pts2_glyphs.glyph.glyph_source.glyph_source.output.points.to_array()
dd = 0.001
outline1 = mlab.outline(pts1_glyphs, line_width=3)
outline1.outline_mode = 'full'
p1x, p1y, p1z = pts1[0,:]
outline1.bounds = (p1x-dd, p1x+dd,
p1y-dd, p1y+dd,
p1z-dd, p1z+dd)
pt_id1 = mlab.text(0.8, 0.2, '0 .', width=0.1, color=color1)
outline2 = mlab.outline(pts2_glyphs, line_width=3)
outline2.outline_mode = 'full'
p2x, p2y, p2z = pts2[0,:]
outline2.bounds = (p2x-dd, p2x+dd,
p2y-dd, p2y+dd,
p2z-dd, p2z+dd)
pt_id2 = mlab.text(0.8, 0.01, '0 .', width=0.1, color=color2)
figure.scene.disable_render = False
def picker_callback(picker):
""" Picker callback: this gets called during pick events.
"""
if picker.actor in pts1_glyphs.actor.actors:
point_id = picker.point_id/glyph_points1.shape[0]
if point_id != -1:
### show the point id
pt_id1.text = '%d .'%point_id
#mlab.title('%d'%point_id)
x, y, z = pts1[point_id,:]
outline1.bounds = (x-dd, x+dd,
y-dd, y+dd,
z-dd, z+dd)
elif picker.actor in pts2_glyphs.actor.actors:
point_id = picker.point_id/glyph_points2.shape[0]
if point_id != -1:
### show the point id
pt_id2.text = '%d .'%point_id
x, y, z = pts2[point_id,:]
outline2.bounds = (x-dd, x+dd,
y-dd, y+dd,
z-dd, z+dd)
picker = figure.on_mouse_pick(picker_callback)
picker.tolerance = dd/2.
mlab.show()
def plot_u(u, x, xv, y, yv, t, n):
"""User action function for plotting."""
if t[n] == 0:
time.sleep(2)
if plot_method == 1:
# Works well with Gnuplot backend, not with Matplotlib
st.mesh(x, y, u, title='t=%g' % t[n], zlim=[-1,1],
caxis=[-1,1])
elif plot_method == 2:
# Works well with Gnuplot backend, not with Matplotlib
st.surfc(xv, yv, u, title='t=%g' % t[n], zlim=[-1, 1],
colorbar=True, colormap=st.hot(), caxis=[-1,1],
shading='flat')
elif plot_method == 3:
print 'Experimental 3D matplotlib...under development...'
# Probably too slow
#plt.clf()
ax = fig.add_subplot(111, projection='3d')
u_surf = ax.plot_surface(xv, yv, u, alpha=0.3)
#ax.contourf(xv, yv, u, zdir='z', offset=-100, cmap=cm.coolwarm)
#ax.set_zlim(-1, 1)
# Remove old surface before drawing
if u_surf is not None:
ax.collections.remove(u_surf)
plt.draw()
time.sleep(1)
elif plot_method == 4:
# Mayavi visualization
mlab.clf()
extent1 = (0, 20, 0, 20,-2, 2)
s = mlab.surf(x , y, u,
colormap='Blues',
warp_scale=5,extent=extent1)
mlab.axes(s, color=(.7, .7, .7), extent=extent1,
ranges=(0, 10, 0, 10, -1, 1),
xlabel='', ylabel='', zlabel='',
x_axis_visibility=False,
z_axis_visibility=False)
mlab.outline(s, color=(0.7, .7, .7), extent=extent1)
mlab.text(6, -2.5, '', z=-4, width=0.14)
mlab.colorbar(object=None, title=None,
orientation='horizontal',
nb_labels=None, nb_colors=None,
label_fmt=None)
mlab.title('Gaussian t=%g' % t[n])
mlab.view(142, -72, 50)
f = mlab.gcf()
camera = f.scene.camera
camera.yaw(0)
if plot_method > 0:
time.sleep(0) # pause between frames
if save_plot:
filename = 'tmp_%04d.png' % n
if plot_method == 4:
mlab.savefig(filename) # time consuming!
elif plot_method in (1,2):
st.savefig(filename) # time consuming!
def UpdatedSceneSettings(self):
self.updatedScene.interactor.interactor_style = \
tvtk.InteractorStyleTerrain()
# The distance value is set in such a way that the entire surface should be visible without need to zoom out.
self.updatedScene.scene.mlab.view(30, 60, distance=2.5 * 512)
mlab.surf(np.zeros([2, 2]),
figure=self.updatedScene.mayavi_scene,
opacity=0) # Dummy surface
mlab.text(0.6, 0.9, 'Eroded', width=0.4)
def draw_dots3d(dots, edges, fignum, clear=True,
title='',
size=(1024, 768), graph_colormap='viridis',
bgcolor=(1, 1, 1),
node_color=(0.3, 0.65, 0.3), node_size=0.01,
edge_color=(0.3, 0.3, 0.9), edge_size=0.003,
text_size=0.14, text_color=(0, 0, 0), text_coords=[0.84, 0.75], text={},
title_size=0.3,
angle=get_ra()):
# https://stackoverflow.com/questions/17751552/drawing-multiplex-graphs-with-networkx
# numpy array of x, y, z positions in sorted node order
xyz = shrink_to_3d(dots)
if fignum == 0:
mlab.figure(fignum, bgcolor=bgcolor, fgcolor=text_color, size=size)
# Mayavi is buggy, and following code causes sockets leak.
#if mlab.options.offscreen:
# mlab.figure(fignum, bgcolor=bgcolor, fgcolor=text_color, size=size)
#elif fignum == 0:
# mlab.figure(fignum, bgcolor=bgcolor, fgcolor=text_color, size=size)
if clear:
mlab.clf()
# the x,y, and z co-ordinates are here
# manipulate them to obtain the desired projection perspective
pts = mlab.points3d(xyz[:, 0], xyz[:, 1], xyz[:, 2],
scale_factor=node_size,
scale_mode='none',
color=node_color,
#colormap=graph_colormap,
resolution=20,
transparent=False)
mlab.text(text_coords[0], text_coords[1], '\n'.join(['{} = {}'.format(n, v) for n, v in text.items()]), width=text_size)
if clear:
mlab.title(title, height=0.95)
mlab.roll(next(angle))
mlab.orientation_axes(pts)
mlab.outline(pts)
"""
for i, (x, y, z) in enumerate(xyz):
label = mlab.text(x, y, str(i), z=z,
width=text_size, name=str(i), color=text_color)
label.property.shadow = True
"""
pts.mlab_source.dataset.lines = edges
tube = mlab.pipeline.tube(pts, tube_radius=edge_size)
mlab.pipeline.surface(tube, color=edge_color)
def create_animation(filename): # create an animation from a saved file using mayavi (not complete)
f_pointer = h5py.File(filename, "a")
res_alpha = f_pointer["parres/particles"][:]
N_size = f_pointer["parameters/control"][0,0]
N_grid = f_pointer["parameters/control"][0,1]
N_rings = f_pointer["parameters/control"][0,2]
N_particles = f_pointer["parameters/control"][0,3]
N_step = f_pointer["parameters/control"][0,4]
h = f_pointer["parameters/control"][0,5]
t_step = f_pointer["parameters/control"][0,6]
t_total = f_pointer["parameters/control"][0,7]
t_time= f_pointer["parameters/control"][0,8]
kin_vis = f_pointer["parameters/control"][0,9]
ring=[]
for i in range(0,N_rings):
R = f_pointer["parameters/rings"][i,0]
r = f_pointer["parameters/rings"][i,1]
x_pos = f_pointer["parameters/rings"][i,2]
y_pos = f_pointer["parameters/rings"][i,3]
z_pos = f_pointer["parameters/rings"][i,4]
x_ang = f_pointer["parameters/rings"][i,5]
y_ang = f_pointer["parameters/rings"][i,6]
Re = f_pointer["parameters/rings"][i,7]
N_phi = f_pointer["parameters/rings"][i,8]
N_d = f_pointer["parameters/rings"][i,9]
N_p = f_pointer["parameters/rings"][i,10]
color = (float(f_pointer["parameters/rings"][i,11]),float(f_pointer["parameters/rings"][i,12]),float(f_pointer["parameters/rings"][i,13]))
ring.append(ring_val(R, r, x_pos, y_pos, z_pos, x_ang, y_ang, Re, N_phi, N_d, int(N_p), color))
f_pointer.close()
N = 0
"""for i in range(0,N_rings):
fig = mlab.points3d(res_alpha[0,N:N + ring[i].N_p - 1,0,0], res_alpha[0,N:N + ring[i].N_p - 1,1,0], res_alpha[0,N:N + ring[i].N_p - 1,2,0], scale_factor=0.1, color = ring[i].color)
N = N + ring[i].N_p"""
fig = mlab.points3d(res_alpha[0,:,0,0], res_alpha[0,:,1,0], res_alpha[0,:,2,0], scale_factor=0.1, color = (0,1,1))
mlab.axes(extent=[0,N_size,0,N_size,0,N_size])
info = "P=" + str(int(N_particles)) + "\nG=" + str(int(N_grid)) + "\nt=" + str(0)
mlab.text(0.1,0.1,info)
@mlab.animate(delay=40, ui=True)
def anim():
for i in range(0,N_step):
fig.mlab_source.set(x=res_alpha[0,:,0,i], y=res_alpha[0,:,1,i], z=res_alpha[0,:,2,i])
yield
anim()
mlab.show()
print "check"
def test_text(self):
""" Test the text module.
"""
data = np.random.random((3, 3, 3))
src = mlab.pipeline.scalar_field(data)
# Some smoke testing
mlab.text(0.1, 0.9, 'foo')
mlab.text(3, 3, 'foo', z=3)
mlab.title('foo')
# Check that specifying 2D positions larger than 1 raises an
# error
self.assertRaises(ValueError, mlab.text, 0, 1.5, 'test')
def test_text(self):
""" Test the text module.
"""
data = np.random.random((3, 3, 3))
src = mlab.pipeline.scalar_field(data)
# Some smoke testing
mlab.text(0.1, 0.9, 'foo')
mlab.text(3, 3, 'foo', z=3)
mlab.title('foo')
# Check that specifying 2D positions larger than 1 raises an
# error
self.assertRaises(ValueError, mlab.text, 0, 1.5, 'test')
def add_legend():
x = 0.01
y = 0.95
width = 0.05
for filename in colors.keys():
color = colors[filename]
name = filename.split("/")[-1]
mlab.text(x, y, "TT", width=width, color=color)
mlab.text(x + 0.03, y, name, width=width)
y -= 0.05
def create_plot_mayavi(alpha,ring,control): # create figure with mayavi
N = 0
for i in range(0,control.N_rings):
mlab.points3d(alpha[N:N + ring[i].N_p - 1,0,0], alpha[N:N + ring[i].N_p - 1,1,0], alpha[N:N + ring[i].N_p - 1,2,0], scale_factor=0.1, color = ring[i].color)
N = N + ring[i].N_p
mlab.axes(extent=[0,control.N_size,0,control.N_size,0,control.N_size])
info = "P=" + str(control.N_particles) + "\nG=" + str(control.N_grid) + "\nt=" + str(control.t_time)
mlab.text(0.1,0.1,info)
mlab.show()
def plot_orbital_list(orbital_idx, C, bases, postfix='', **kwargs):
""" Visualizing all orbitals
"""
for j, i in enumerate(orbital_idx):
if i - 1 >= C.shape[1] or i - 1 < 0:
print('Orbital %d does not exist. Skipping' % i)
else:
plot_orbital(C[:, i - 1], bases, color=cm.tab10(j)[:3], **kwargs)
mlab.text(0.05,
0.1 * j,
str(i) + postfix,
width=0.2,
color=cm.tab10(j)[:3])
def Animate(self, filename):
mlab.clf()
extent1 = (0, 20, 0, 20,0, 10)
s = mlab.surf(self.x , self.y, self.u[1:-1,1:-1], colormap="Blues", warp_scale=5,extent=extent1)
mlab.axes(s, color=(.7, .7, .7), extent=extent1, ranges=(0, 20, 0, 20, 0, 10), xlabel="", ylabel="", zlabel="", x_axis_visibility=False, z_axis_visibility=False)
mlab.outline(s, color=(0.7, .7, .7), extent=extent1)
mlab.text(6, -2.5, "", z=-4, width=0.14)
mlab.colorbar(object=None, title=None, orientation="horizontal", nb_labels=None, nb_colors=None, label_fmt=None)
mlab.title("Gaussian t=%g" % self.t)
mlab.view(142, -72, 50)
f = mlab.gcf()
camera = f.scene.camera
camera.yaw(0)
mlab.savefig(filename)
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')
time_point_string = "%.3f" % time_point
self.timelabel = mlab.text(0.05,
0.05,
time_point_string,
width=0.05)
self.colorbar.data_range = [start_value, end_value]
time = self.manager.groups['t'][time_point]
time_point_string = "%.3f" % round(time, 3)
self.timelabel.text = time_point_string
self.mayavi.visualization.scene.disable_render = False
def title(text, **kwargs):
""" textproperty options
textproperty={
'background_color': (0.0, 1.0, 0.0),
'background_opacity': 0.1143,
'bold': 1,
'color': (0.0, 0.0, 0.0),
'font_family': 'arial',
'font_family_min_value': 0,
'font_file': None,
'font_size': 16,
'italic': 0,
'italic_': 0,
'justification': 'centered',
'line_offset': 0.0,
'line_spacing': 1.1,
'opacity': 1.0,
'orientation': 0.0,
'shadow': 1,
'shadow_offset': array([ 1, -1]),
'vertical_justification': 'top'
}
"""
justification = kwargs.pop('justification', 'center')
vertical_justification = kwargs.pop('vertical_justification', 'top')
textproperty = kwargs.pop('textproperty', dict())
kwargs['color'] = check_color(kwargs.pop('color', 'White'))
t = mlab.text(0.5, 0.99, text, **kwargs)
t.actor.text_scale_mode = 'none'
t.actor.text_property.justification = justification
t.actor.text_property.vertical_justification = vertical_justification
if len(textproperty):
t.actor.text_property.trait_set(**textproperty)
return t
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 surface_timeseries(surface, data, step=1):
"""
"""
fig = mlab.figure(figure="surface_timeseries", fgcolor=(0.5, 0.5, 0.5))
#Plot an initial surface and colourbar #TODO: Change to use plot_surface function, see below.
surf_mesh = mlab.triangular_mesh(surface.vertices[:, 0],
surface.vertices[:, 1],
surface.vertices[:, 2],
surface.triangles,
scalars=data[0, :],
vmin=data.min(), vmax=data.max(),
figure=fig)
mlab.colorbar(object=surf_mesh, orientation="vertical")
#Handle for the surface object and figure
surf = surf_mesh.mlab_source
#Time #TODO: Make actual time rather than points, where/if possible.
tpts = data.shape[0]
time_step = mlab.text(0.85, 0.125, ("0 of %s" % str(tpts)),
width=0.0625, color=(1, 1, 1), figure=fig,
name="counter")
#Movie
k = 0
while 1:
if abs(k) >= tpts:
k = 0
surf.set(scalars=data[k, :])
time_step.set(text=("%s of %s" % (str(k), str(tpts))))
k += step
yield
mlab.show()
def label_init(): from mayavi.mlab import points3d obj = points3d(0., 0., 0., scale_factor=0.01, color=(0., 0., 0.)) global actlbl actlbl = text(.4, .1, 'odorname', width=.15) actlbl.text = '' obj.remove()
def surface_timeseries(surface, data, step=1):
"""
"""
fig = mlab.figure(figure="surface_timeseries", fgcolor=(0.5, 0.5, 0.5))
#Plot an initial surface and colourbar #TODO: Change to use plot_surface function, see below.
surf_mesh = mlab.triangular_mesh(surface.vertices[:, 0],
surface.vertices[:, 1],
surface.vertices[:, 2],
surface.triangles,
scalars=data[0, :],
vmin=data.min(), vmax=data.max(),
figure=fig)
mlab.colorbar(object=surf_mesh, orientation="vertical")
#Handle for the surface object and figure
surf = surf_mesh.mlab_source
#Time #TODO: Make actual time rather than points, where/if possible.
tpts = data.shape[0]
time_step = mlab.text(0.85, 0.125, ("0 of %s" % str(tpts)),
width=0.0625, color=(1, 1, 1), figure=fig,
name="counter")
#Movie
k = 0
while 1:
if abs(k) >= tpts:
k = 0
surf.set(scalars=data[k, :])
time_step.set(text=("%s of %s" % (str(k), str(tpts))))
k += step
yield
mlab.show(stop=True)
def draw_coordinate_system_axes(fig, coordinate_system, offset=0.0, scale=1.0, draw_labels=True):
points, lengths = coordinate_system_arrows(coordinate_system, offset=offset, scale=scale)
mlab.figure(fig, bgcolor=fig.scene.background)
arrows = mlab.quiver3d(
points[:, 0],
points[:, 1],
points[:, 2],
lengths[0, :],
lengths[1, :],
lengths[2, :],
scalars=np.array([3, 2, 1]),
mode="arrow",
)
arrows.glyph.color_mode = "color_by_scalar"
arrows.glyph.glyph.scale_factor = scale
data = arrows.parent.parent
data.name = coordinate_system.name
glyph_scale = arrows.glyph.glyph.scale_factor * 1.1
# label_col = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
labels = []
if draw_labels:
for i in range(3):
labels.append(
mlab.text(
points[i, 0] + glyph_scale * coordinate_system.basis[i, 0],
points[i, 1] + glyph_scale * coordinate_system.basis[i, 1],
coordinate_system.labels[i],
z=points[i, 2] + glyph_scale * coordinate_system.basis[i, 2],
# color=label_col[i],
width=0.1 * scale,
)
)
return arrows, labels
def update_scene():
mlab.clf(fig1) # 清空figure
vis_points = points[file_i] # 取出要显示的点云
vis_points = (vis_points * 1.5 + np.array([1.0, 1.0, 1.0])) * 50 # 对点云进行缩放
# show point clouds colorized with activations
activations = mlab.points3d(vis_points[:, 0],
vis_points[:, 1],
vis_points[:, 2],
vis_points[:, 2],
scale_factor=3.0,
scale_mode='none',
figure=fig1)
mlab.title(str(file_i), color=(0, 0, 0), size=0.3, height=0.01)
text = '<--- （press g for previous' + 50 * '' + '(press h for next)---->'
mlab.text(0.01, 0.01, text, color=(0, 0, 0), width=0.98)
mlab.orientation_axes()
return
def draw_axis_name(self, axis_name):
global_axis_name = self.controller.get_global_axis_name(axis_name)
if global_axis_name == 'w':
width = 0.06
else:
width = 0.04
t = mlab.text(0.01, 0.9, global_axis_name, width=width, color=(1, 0, 0))
return t
def plot_mesh(coord_array, tri_array, plnsrf):
""" plot_mesh plots an unsorted triangular part mesh from xyz coordinates and triangle connectivity
:param coord_array: xyz coordinates per vertex in array of shape=(#points, 3)
:param tri_array: vertex connection indices of the part in array of shape=(#triangles, 3)
:return:
"""
mlab.figure(figure="Mesh", bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
# Create black wireframe mesh
wire_mesh = mlab.triangular_mesh(coord_array[:, 0],
coord_array[:, 1],
coord_array[:, 2],
tri_array,
scalars=None,
line_width=0.1,
representation='wireframe',
color=(0, 0, 0),
opacity=0.05)
# Create face coloring
wire_mesh.mlab_source.dataset.cell_data
wire_mesh.mlab_source.dataset.cell_data.scalars = np.ones(
shape=(tri_array.shape[0]))
wire_mesh.mlab_source.dataset.cell_data.scalars.name = 'Cell data'
wire_mesh.mlab_source.update()
mesh = mlab.pipeline.set_active_attribute(wire_mesh,
cell_scalars='Cell data')
surf = mlab.pipeline.surface(mesh, colormap='jet')
# Retrieve the LUT colormap of the surf object. (256x4)
# this is an array of (R, G, B, A) values (each in range 0-255)
lut = surf.module_manager.scalar_lut_manager.lut.table.to_array()
# Modify lut for single green color
lut[:, 0] = np.ones((lut.shape[0])) * 75
lut[:, 1] = np.ones((lut.shape[0])) * 173
lut[:, 2] = np.ones((lut.shape[0])) * 137
lut[:, 3] = np.ones((lut.shape[0])) * 255
# Now use this colormap
surf.module_manager.scalar_lut_manager.lut.table = lut
#display the number of planes
for pn in range(1, np.amax(plnsrf) + 1):
center = np.mean(coord_array[np.nonzero(plnsrf == pn)[0], :], axis=0)
label = mlab.text(
center[0],
center[1],
'%d' % pn,
z=center[2],
width=0.05,
)
label.property.shadow = True
# Show plot
mlab.show()
def plot_matrix_path(connectmat_file,centers_file,paths_file,path_num=0,threshold_pct=5,weight_edges=False,
node_scale_factor=2,edge_radius=.5,resolution=8,name_scale_factor=1,names_file=0):
"""
Given a connectivity matrix and a (x,y,z) centers file for each region, plot the 3D network;
paths file contains columns listing nodes contained in path
"""
matrix=core.file_reader(connectmat_file)
nodes=core.file_reader(centers_file)
paths=zip(*core.file_reader(paths_file))
path=paths[path_num]
path_pairs=zip(path[0:len(path)-1],path[1:])
print path_pairs
if names_file:
names=core.file_reader(names_file)
num_nodes=len(nodes)
edge_thresh_pct=(float(threshold_pct)/100)
matrix_flat=np.array(matrix).flatten()
edge_thresh=np.sort(matrix_flat)[len(matrix_flat)-int(len(matrix_flat)*edge_thresh_pct)]
mlab.figure(bgcolor=(1, 1, 1), size=(400, 400))
for count,(x,y,z) in enumerate(nodes):
mlab.points3d(x,y,z, color=(0,1,0), scale_factor=node_scale_factor, resolution=resolution)
if names_file:
width=.025*name_scale_factor*len(names[count])
mlab.text(x, y,names[count], z=z,width=.025*name_scale_factor,color=(0,0,0))
for i in range(num_nodes-1):
x0,y0,z0=nodes[i]
for j in range(i+1, num_nodes):
if matrix[i][j] > edge_thresh:
x1,y1,z1=nodes[j]
if weight_edges:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=matrix[i][j]/matrix_flat.max(),
color=(1,1,1))
else:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=edge_radius,
color=(1,1,1))
for n1,n2 in path_pairs:
n1=int(n1)
n2=int(n2)
x0,y0,z0=nodes[n1]
x1,y1,z1=nodes[n2]
mlab.plot3d( [x0,x1], [y0,y1], [z0,z1],
tube_radius=1,
color=(0,0,1))
def plotComparePaths_t_error_3DSurface(fix_path_list,
tvec_error_mean_list_AllPaths,
grid_reso=0.1,
width=6,
height=3):
"""
Plot the measured paths with 3D surface(like mountain, high mountain means big error, low mountain means small error)
1.Figure:
:param xyError_list_AllPaths:
:param width: 3 [m]
:param height: 6 [m]
:param grid_reso: default 0.1 [m]
:return:
"""
#TODO
path_num = len(fix_path_list)
gridX = int(height / grid_reso)
gridY = int(width / grid_reso)
X, Y = np.mgrid[0:height:grid_reso, 0:width:grid_reso]
Z = np.zeros((gridX, gridY))
for i in range(path_num):
Rmat_error_mean_list = tvec_error_mean_list_AllPaths[i]
fixed_path = fix_path_list[i]
x_real = fixed_path[0, :]
y_real = fixed_path[1, :]
ix = (x_real - grid_reso / 2) / grid_reso
ix = ix.astype(int)
iy = (y_real - grid_reso / 2) / grid_reso
iy = iy.astype(int)
Z[ix, iy] = Rmat_error_mean_list
s = mlab.surf(X, Y, Z, warp_scale="auto")
# cat1_extent = (0, 30, 0, 60, -10, 10)
mlab.outline(s, color=(.7, .7, .7))
mlab.text(0, 30, 'pf', z=0, width=0)
mlab.text(30, 30, 'A*', z=0, width=0.14)
# mlab.title('Compare path errors between potential field method and A*',size = 10)
mlab.show()
def plot_u(u, x, xv, y, yv, t, n):
print " ploting"
if t[n] == 0:
time.sleep(2)
if plot_method == 1:
mesh(x, y, u, title='t=%g' % t[n], zlim=[-1,1],
caxis=[-1,1])
elif plot_method == 2:
surfc(xv, yv, u, title='t=%g' % t[n], zlim=[-1, 1],
colorbar=True, colormap=hot(), caxis=[-1,1],
shading='flat')
elif plot_method == 3:
# mayavi plotting
mlab.clf()
extent1 = (0, 20, 0, 20,-2, 2)
s = mlab.surf(x , y, u, colormap='Blues', warp_scale=5,
extent=extent1)
mlab.axes(s, color=(.7, .7, .7), extent=extent1,
ranges=(0, 10, 0, 10, -1, 1), xlabel='', ylabel='',
zlabel='',
x_axis_visibility=False, z_axis_visibility=False)
mlab.outline(s, color=(0.7, .7, .7), extent=extent1)
mlab.text(2, -2.5, '', z=-4, width=0.1)
mlab.colorbar(object=None, title=None, orientation='horizontal',
nb_labels=None, nb_colors=None, label_fmt=None)
mlab.title('t=%g' % t[n])
mlab.view(142, -72, 50)
f = mlab.gcf()
camera = f.scene.camera
camera.yaw(0)
#g = mlab.figure()
#g.scene.movie_maker.record = True
if plot_method > 0:
if not os.path.exists(path):
os.makedirs(path)
time.sleep(0) # pause between frames
if save_plot and plot_method == 3:
filename = '%s/%08d.png'%(path,n)
mlab.savefig(filename) # time consuming!
elif save_plot and plot_method != 3:
filename = '%s/%08d.png'%(path,n)
savefig(filename) # time consuming!
def showvolume(Vin, currentfigurenum):
mlab.figure(currentfigurenum, bgcolor=(1, 1, 1), fgcolor=(1, 1, 1))
mlab.clf()
p = mlab.contour3d(Vin.vol, color=(1, 0, 0))
mlab.text(0.05,
0.95,
'Please close the window to continue calculations.',
color=(0, 0, 0),
width=0.9)
mlab.text(0.3, 0.05, 'Rotate using click&drag', color=(0, 0, 0), width=0.4)
c_scene = mlab.get_engine().current_scene
# c_scene.scene.light_manager.light_mode = 'vtk';
c_scene.scene.camera.position = [0, 0, -128]
c_scene.scene.camera.view_up = [-1, 0, 0]
c_scene.scene.render()
mlab.show()
return p
def update_scene():
# clear figure
mlab.clf(fig1)
# Rescale points for visu
p1 = (query * 1.5 + np.array([1.0, 1.0, 1.0])) * 50.0
p2 = (supports * 1.5 + np.array([1.0, 1.0, 1.0])) * 50.0
l1 = p1[:, 2] * 0
l1[file_i] = 1
l2 = p2[:, 2] * 0 + 2
l2[neighbors[file_i]] = 3
# Show point clouds colorized with activations
activations = mlab.points3d(p1[:, 0],
p1[:, 1],
p1[:, 2],
l1,
scale_factor=2.0,
scale_mode='none',
vmin=0.0,
vmax=3.0,
figure=fig1)
activations = mlab.points3d(p2[:, 0],
p2[:, 1],
p2[:, 2],
l2,
scale_factor=3.0,
scale_mode='none',
vmin=0.0,
vmax=3.0,
figure=fig1)
# New title
mlab.title(str(file_i), color=(0, 0, 0), size=0.3, height=0.01)
text = '<--- (press g for previous)' + 50 * ' ' + '(press h for next) --->'
mlab.text(0.01, 0.01, text, color=(0, 0, 0), width=0.98)
mlab.orientation_axes()
return
def action(u, x, xv, y, yv, t, n):
#print 'action, t=',t,'\nu=',u, '\Nx=',x, '\Ny=', y
if t[n] == 0:
time.sleep(2)
if plot_u == 1:
mesh(x, y, u, title='t=%g' % t[n], zlim=[-1,1],
caxis=[-1,1])
elif plot_u == 2:
surf(xv, yv, u, title='t=%g' % t[n], zlim=[-1, 1],
colorbar=True, colormap=hot(), caxis=[-1,1])
elif plot_u == 3:
# mayavi plotting
mlab.clf()
extent1 = (0, 20, 0, 20,-2, 2)
s = mlab.surf(x , y, u, colormap='Blues', warp_scale=5,extent=extent1)
mlab.axes(s, color=(.7, .7, .7), extent=extent1,
ranges=(0, 10, 0, 10, -1, 1), xlabel='', ylabel='',
zlabel='',
x_axis_visibility=False, z_axis_visibility=False)
mlab.outline(s, color=(0.7, .7, .7), extent=extent1)
mlab.text(2, -2.5, '', z=-4, width=0.1)
mlab.colorbar(object=None, title=None, orientation='horizontal', nb_labels=None, nb_colors=None, label_fmt=None)
mlab.title('Gaussian t=%g' % t[n])
mlab.view(142, -72, 50)
f = mlab.gcf()
camera = f.scene.camera
camera.yaw(0)
if plot_u > 0:
path = 'Figures_wave2D'
if not os.path.exists(path):
os.makedirs(path)
time.sleep(0) # pause between frames
if save_plot and plot_u != 3:
filename = '%s/tmp_%08d.png' % (path, n)
elif save_plot and plot_u == 3:
filename = '%s/tmp_%08d.png' % (path, n)
mlab.savefig(filename) # time consuming!
def plotComparePaths_DisError_3DSurface(xyError_list_AllPaths,
grid_reso=0.1,
width=6,
height=3):
"""
Using Mayavi
Plot the measured paths with 3D surface(like mountain, high mountain means big error, low mountain means small error)
1.Figure:
:param xyError_list_AllPaths:
:param width: 3 [m]
:param height: 6 [m]
:param grid_reso: default 0.1 [m]
:return:
"""
path_num = len(xyError_list_AllPaths)
gridX = int(height / grid_reso)
gridY = int(width / grid_reso)
X, Y = np.mgrid[0:height:grid_reso, 0:width:grid_reso]
Z = np.zeros((gridX, gridY))
for i in range(path_num):
xyError_list = xyError_list_AllPaths[i]
iter_num = xyError_list.shape[0] / 3
for j in range(iter_num):
x_real = xyError_list[0 + j * 3, :]
y_real = xyError_list[1 + j * 3, :]
ix = (x_real - grid_reso / 2) / grid_reso
ix = ix.astype(int)
iy = (y_real - grid_reso / 2) / grid_reso
iy = iy.astype(int)
Z[ix, iy] = xyError_list[2 + j * 3, :]
s = mlab.surf(X, Y, Z, warp_scale="auto")
mlab.outline(s, color=(.7, .7, .7))
mlab.text(0, 30, 'pf', z=0, width=0)
mlab.text(30, 30, 'A*', z=0, width=0.14)
# mlab.title('Compare path errors between potential field method and A*',size = 10)
mlab.show()
def xmas_balls(connectivity,
node_data=None,
edge_data=True,
labels_data=True,
balls_colormap='Blues',
bgcolor=(1, 1, 1),
node_size=10.,
edge_color=(0.8, 0.8, 0.8),
edge_size=0.2,
text_size=0.042,
text_color=(0, 0, 0)):
"""
Plots coloured balls at the region centres of connectivity, colour and
size is determined by a vector of length number of regions (node_data).
Optional: adds the connections between pair of nodes.
"""
G = nx.from_numpy_matrix(numpy.matrix(connectivity.weights))
mlab.figure(1, bgcolor=bgcolor)
mlab.clf()
# scalar colors
if node_data is not None:
scalars = node_data
mlab.colorbar(orientation="vertical")
else:
scalars = numpy.array(G.nodes()) * 20
pts = mlab.points3d(connectivity.centres[:, 0],
connectivity.centres[:, 1],
connectivity.centres[:, 2],
scalars,
scale_factor=node_size,
scale_mode='none',
colormap=balls_colormap,
resolution=20)
if labels_data:
for i, (x, y, z) in enumerate(connectivity.centres):
label = mlab.text(x,
y,
connectivity.region_labels[i],
z=z,
width=text_size,
name=str(connectivity.region_labels[i]),
color=text_color)
label.property.shadow = False
if edge_data:
pts.mlab_source.dataset.lines = numpy.array(G.edges())
tube = mlab.pipeline.tube(pts, tube_radius=edge_size)
mlab.pipeline.surface(tube, color=edge_color)
mlab.show()
def draw_graph3d(graph, graph_colormap='winter', bgcolor = (1, 1, 1),
node_size=0.03,
edge_color=(0.8, 0.8, 0.8), edge_size=0.002,
text_size=0.008, text_color=(0, 0, 0)):
H = nx.Graph()
## add edges
#for node, edges in graph.items():
# for edge, val in edges.items():
# if val == 1:
# H.add_edge(node, edge)
# add edges
for edge in graph:
H.add_edge(edge[0], edge[1])
G = nx.convert_node_labels_to_integers(H)
graph_pos = nx.spring_layout(G, dim=3)
# numpy array of x, y, z positions in sorted node order
xyz = np.array([graph_pos[v] for v in sorted(G)])
# scalar colors
scalars = np.array(G.nodes())+5
mlab.figure(1, bgcolor=bgcolor)
mlab.clf()
pts = mlab.points3d(xyz[:, 0], xyz[:, 1], xyz[:, 2],
scalars,
scale_factor=node_size,
scale_mode='none',
colormap=graph_colormap,
resolution=20)
for i, (x, y, z) in enumerate(xyz):
label = mlab.text(x, y, str(i), z=z,
width=text_size, name=str(i), color=text_color)
label.property.shadow = True
print(np.array(G.edges))
print(np.array(list(G.edges())))
print(type(G.edges()))
print(type(list(G.edges())))
print(len(list(G.edges())))
print(len(list(G.edges())))
print(len(G.edges()))
pts.mlab_source.dataset.lines = np.array(G.edges())
tube = mlab.pipeline.tube(pts, tube_radius=edge_size)
mlab.pipeline.surface(tube, color=edge_color)
mlab.show() # interactive window
def redraw_scene(self, scene, cur_scatter_data):
# Notice how each mlab call points explicitely to the figure it
# applies to.
mlab.clf(figure=scene.mayavi_scene)
x = cur_scatter_data[0]
y = cur_scatter_data[1]
z = cur_scatter_data[2]
xlabel_text = cur_scatter_data[3][0]
ylabel_text = cur_scatter_data[3][1]
zlabel_text = cur_scatter_data[3][2]
cur_colors = cur_scatter_data[4]
cur_scales = [np.array(x) for x in cur_scatter_data[5]]
label_unique = cur_scatter_data[6]
points_dict.clear()
for ii in range(len(label_unique)):
points_dict[label_unique[ii]] = mlab.points3d(
x[ii],
y[ii],
z[ii],
cur_scales[ii] * self.cur_scale,
color=cur_colors[ii][0],
scale_factor=0.01,
opacity=self.cur_opacity / 100)
for ii in range(len(outputs_unique)):
mlab.text(0.02,
1 - 0.035 * (ii + 1),
'-' + str(outputs_unique[ii]),
width=0.06,
color=all_colors[ii])
cur_axex = mlab.axes(xlabel=xlabel_text,
ylabel=ylabel_text,
zlabel=zlabel_text,
extent=[
min(sum(x, [])),
max(sum(x, [])),
min(sum(y, [])),
max(sum(y, [])),
min(sum(z, [])),
max(sum(z, []))
])
cur_axex.axes.font_factor = 0.8
def do_test(lines, scalars, show=False, txt=""):
viscid.logger.info('--> ' + txt)
title = txt + '\n' + "\n".join(textwrap.wrap("scalars = {0}".format(scalars),
width=50))
try:
from viscid.plot import vpyplot as vlt
from matplotlib import pyplot as plt
vlt.clf()
vlt.plot_lines(lines, scalars=scalars)
plt.title(title)
vlt.savefig(next_plot_fname(__file__, series='q2'))
if show:
vlt.show()
except ImportError:
pass
try:
from mayavi import mlab
from viscid.plot import vlab
try:
fig = _global_ns['figure']
vlab.clf()
except KeyError:
fig = vlab.figure(size=[1200, 800], offscreen=not show,
bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
_global_ns['figure'] = fig
vlab.clf()
vlab.plot_lines3d(lines, scalars=scalars)
vlab.fancy_axes()
mlab.text(0.05, 0.05, title)
vlab.savefig(next_plot_fname(__file__, series='q3'))
if show:
vlab.show(stop=True)
except ImportError:
pass
def anim():
global vessels, colorGradient, timeText, showingNode
timeText = None
while True:
if timeText != None:
timeText.remove()
timeText = mlab.text(0.01, 0.01, 'Time: ' + str(g.globals.time), width=0.3)
for (top, bottom, host) in vessels:
lutT = top.module_manager.scalar_lut_manager.lut.table.to_array()
lutB = bottom.module_manager.scalar_lut_manager.lut.table.to_array()
count = host.getBacteriaCount()
colorNdx = int(float(count) / parameters.cell_count_color_mapping * (len(colorGradient) - 1))
if colorNdx >= len(colorGradient):
colorNdx = len(colorGradient) - 1
color = colorGradient[colorNdx]
assert len(color) == 6
ones = np.ones(np.shape(lutT[:, 0]))
R = int(color[0:2], 16)
G = int(color[2:4], 16)
B = int(color[4:6], 16)
#R
lutT[:,0] = ones * R
lutB[:,0] = ones * R
#G
lutT[:,1] = ones * G
lutB[:,1] = ones * G
#B
lutT[:,2] = ones * B
lutB[:,2] = ones * B
top.module_manager.scalar_lut_manager.lut.table = lutT
bottom.module_manager.scalar_lut_manager.lut.table = lutB
if showingNode is not None:
(ax1, ax2) = plots
ax1.cla()
history = copy.deepcopy(showingNode.getCellCountHistory())
x = np.linspace(0, len(history) * parameters.cell_count_history_interval, len(history))
y = history
ax1.plot(x, y, 'r')
ax1.set_title('Bacteria Count history')
ax2.cla()
history = copy.deepcopy(showingNode.getFlowHistory())
x = np.linspace(0, len(history) * parameters.cell_count_history_interval, len(history))
y = history
ax2.plot(x, y, 'b')
ax2.set_title('Blood flow history')
mlab.draw()
if parameters.verbose:
print("updating graph")
yield
def draw_graph3d(
graph,
graph_colormap="winter",
bgcolor=(1, 1, 1),
node_size=0.03,
edge_color=(0.8, 0.8, 0.8),
edge_size=0.002,
text_size=0.008,
text_color=(0, 0, 0),
):
H = nx.Graph()
# add edges
for node, edges in graph.items():
for edge, val in edges.items():
if val == 1:
H.add_edge(node, edge)
G = nx.convert_node_labels_to_integers(H)
graph_pos = nx.spring_layout(G, dim=3)
# numpy array of x,y,z positions in sorted node order
xyz = np.array([graph_pos[v] for v in sorted(G)])
# scalar colors
scalars = np.array(G.nodes()) + 5
mlab.figure(1, bgcolor=bgcolor)
mlab.clf()
pts = mlab.points3d(
xyz[:, 0],
xyz[:, 1],
xyz[:, 2],
scalars,
scale_factor=node_size,
scale_mode="none",
colormap=graph_colormap,
resolution=20,
)
for i, (x, y, z) in enumerate(xyz):
label = mlab.text(x, y, str(i), z=z, width=text_size, name=str(i), color=text_color)
label.property.shadow = True
pts.mlab_source.dataset.lines = np.array(G.edges())
tube = mlab.pipeline.tube(pts, tube_radius=edge_size)
mlab.pipeline.surface(tube, color=edge_color)
mlab.show() # interactive window
def add_cbar_label(cbar,title):
position = cbar.scalar_bar_representation.position
position2 = cbar.scalar_bar_representation.position2
x = position[0] + position2[0] #+ 0.002
y = position[1]
text = mlab.text(x,y,title)
text.property.font_size = 24
set_text(text.property)
text.property.orientation = 90.
text.width = 0.205
return text
def draw_graph3d(graph, graph_colormap='summer', bgcolor = (0, 0, 0),
node_size=0.05,
edge_color=(0.5, 0.5, 0.5), edge_size=0.0005,
text_size=0.004, text_color=(1, 1, 1)):
H=nx.Graph()
# add edges
for node, edges in graph.items():
for edge, val in edges.items():
if val == 1:
H.add_edge(node, edge)
G=nx.convert_node_labels_to_integers(H)
#G=H
graph_pos=nx.random_layout(G, dim=3)
# numpy array of x,y,z positions in sorted node order
xyz=np.array([graph_pos[v] for v in sorted(G)])
# scalar colors
scalars=np.array(G.nodes())+5
mlab.figure(1, bgcolor=bgcolor)
mlab.clf()
pts = mlab.points3d(xyz[:,0], xyz[:,1], xyz[:,2],
scalars,
scale_factor=node_size,
scale_mode='none',
colormap=graph_colormap,
resolution=20)
mlab.figure(1).scene.disable_render = True
for i, (x, y, z) in enumerate(xyz):
label = mlab.text(x, y, graph.keys()[i], z=z,
width=len(graph.keys()[i])*text_size, name=str(i), color=text_color)
label.property.shadow = False
pts.mlab_source.dataset.lines = np.array(G.edges())
tube = mlab.pipeline.tube(pts, tube_radius=edge_size)
mlab.pipeline.surface(tube, color=edge_color)
mlab.figure(1).scene.disable_render = False
mlab.show() # interactive window
def draw_graph3d(graph_colormap='winter', bgcolor = (1, 1, 1),
node_size=0.03,
edge_color=(0.8, 0.8, 0.8), edge_size=0.002,
text_size=0.008, text_color=(0, 0, 0)):
H = nx.Graph()
db = db_utils.getDBInstance()
nodes = db.webpages.find()
edges = []
for node in nodes:
iurls = node["incoming_links"]
for iurl in iurls:
H.add_edge(iurl, node["url"])
G= nx.convert_node_labels_to_integers(H)
graph_pos=nx.spring_layout(G, dim=3)
# numpy array of x,y,z positions in sorted node order
xyz=np.array([graph_pos[v] for v in sorted(G)])
# scalar colors
scalars=np.array(G.nodes())+5
mlab.figure(1, bgcolor=bgcolor)
mlab.clf()
pts = mlab.points3d(xyz[:,0], xyz[:,1], xyz[:,2],
scalars,
scale_factor=node_size,
scale_mode='none',
colormap=graph_colormap,
resolution=20)
for i, (x, y, z) in enumerate(xyz):
label = mlab.text(x, y, str(i), z=z,
width=text_size, name=str(i), color=text_color)
label.property.shadow = True
pts.mlab_source.dataset.lines = np.array(G.edges())
tube = mlab.pipeline.tube(pts, tube_radius=edge_size)
mlab.pipeline.surface(tube, color=edge_color)
mlab.show() # interactive window
def picker_callback(picker):
global vessels, lastSelected, bound, showingNode, plots
if 'lastSelected' not in globals():
lastSelected = None
if 'bound' not in globals():
bound = None
picked = picker.actors
for (top, bottom, node) in vessels:
if top.actor.actor._vtk_obj in [o._vtk_obj for o in picked] or bottom.actor.actor._vtk_obj in [o._vtk_obj for o in picked]:
if lastSelected != None:
lastSelected.remove()
lastSelected = mlab.text(node.start.x, node.start.y, node.name, width=0.2, z=node.start.z)
if bound != None:
(tBound, bBound) = bound
tBound.remove()
bBound.remove()
tBound = mlab.outline(top)
bBound = mlab.outline(bottom)
bound = (tBound, bBound)
if plots is None:
f, plots = plt.subplots(2, 1)
(ax1, ax2) = plots
ax1.cla()
history = copy.deepcopy(node.getCellCountHistory())
x = np.linspace(0, len(history) * parameters.cell_count_history_interval, len(history))
y = history
ax1.plot(x, y, 'r')
ax1.set_title('Bacteria Count history')
ax2.cla()
history = copy.deepcopy(node.getFlowHistory())
x = np.linspace(0, len(history) * parameters.cell_count_history_interval, len(history))
y = history
ax2.plot(x, y, 'b')
ax2.set_title('Blood flow history')
if showingNode is None:
plt.show()
showingNode = node
break
def parse_boxes(filenames, dim):
"""from a list of filenames to 21cmFast output order them and make a movie
using ffmpeg"""
savedir='./slice_temp'
if not os.path.isdir(savedir):
os.mkdir(savedir)
boxes=[]
redshifts=[]
for filename in sorted(filenames,reverse=True):
param_dict=parse_filename(filename)
box_data=open_box(filename,dim)
boxes.append(box_data)
redshifts.append(param_dict['z'])
s=boxes[0]
indx=dim-1
lz=mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(s),
plane_orientation='z_axes',
slice_index=indx,colormap='black-white'
)
lx=mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(s),
plane_orientation='x_axes',
slice_index=indx,colormap='black-white'
)
ly=mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(s),
plane_orientation='y_axes',
slice_index=indx,colormap='black-white'
)
titlestr='z=%3.2d' % redshifts[0]
tit=mlab.text(0.1,0.9,titlestr,width=0.1)
#s=sp
fig=mlab.gcf()
msx=lx.mlab_source
msy=ly.mlab_source
msz=lz.mlab_source
for i in range(1,len(boxes)):
#l=mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(s),
# plane_orientation='y_axes',
# slice_index=i,colormap='black-white'
# )
msx.reset(scalars=boxes[i])
msy.reset(scalars=boxes[i])
msz.reset(scalars=boxes[i])
#print dir(ms)
#print ms
#camera=fig.scene.camera
#camera.yaw(i)
titlestr='z=%3.2d' % redshifts[i]
mlab.text(0.1,0.9,titlestr,width=0.1)
#fig.reset(text=titlestr)
fig.scene.reset_zoom()
mlab.draw()
#time.sleep(1)
fname='volume_slice_frame%03d.png' % i
filename=os.path.join(savedir,fname)
print filename
mlab.savefig(filename)
mlab.close()
#mlab.show()
#call ffmpeg to make a movie
# -r controls the framerate in frames/second
#needs better handling in case ffmpeg asks for user response
cmd='ffmpeg -f image2 -r 5 -i ./slice_temp/volume_slice_frame%03d.png -sameq ./slice_temp/anim.mov -pass 2'
(status,output)=commands.getstatusoutput(cmd)
print status
return
connections = np.array(connections)
# We add lines between the points that we have previously created by
# directly modifying the VTK dataset.
points.mlab_source.dataset.lines = connections
points.mlab_source.reset()
# To represent the lines, we use the surface module. Using a wireframe
# representation allows to control the line-width.
mlab.pipeline.surface(points, color=(1, 1, 1),
representation='wireframe',
line_width=4,
name='Connections')
###############################################################################
# Display city names
for city, index in cities.iteritems():
label = mlab.text(x[index], y[index], city, z=z[index],
width=0.016 * len(city), name=city)
label.property.shadow = True
###############################################################################
# Display continents outline, using the VTK Builtin surface 'Earth'
from mayavi.sources.builtin_surface import BuiltinSurface
continents_src = BuiltinSurface(source='earth', name='Continents')
# The on_ratio of the Earth source controls the level of detail of the
# continents outline.
continents_src.data_source.on_ratio = 2
continents = mlab.pipeline.surface(continents_src, color=(0, 0, 0))
###############################################################################
# Display a semi-transparent sphere, for the surface of the Earth
# We use a sphere Glyph, throught the points3d mlab function, rather than
def envelopes3d_group_by(results,
outcome,
groupBy = 'policy',
discretesize = None,
logSpace=False,
ymin = None,
ymax = None):
'''
Function for making 3d envelopes. In contrast to the envelopes in
:mod:`graphs`, this version shows the density for every time step, instead
of only for the end state. Note that this function makes an envelope for
only 1 outcome. This envelopes will group the results based on the
specified uncertainty. The user can supply a discretesize function
to control the grouping in case of parameterUncertainties. This function
will make a separate envelope for each group.
:param results: The return from :meth:`run experiments`.
:param outcome: Specify the name of outcome of interest for which you want to make
the 3d envelopes.
:param groupBy: The uncertainty to group by. (default=policy)
:param discretesize: a discretesize function to control the grouping in case of parameterUncertainties
:param logSpace: Boolean, if true, the log of the input data is used
:param ymin: If provided, lower bound for the KDE, if not, ymin = np.min(results.get(outcome))
:param ymax: If provided, lower bound for the KDE, if not, ymax = np.max(results.get(outcome))
'''
def f(x, y, results):
"""
function that performs the kde for each timestep
"""
x1 = x[:,0]
y1 = y[0,:]
results = np.asarray(results)
z = []
for i in range(len(list(x1))):
data = results[:, i]
try:
z1 = kde.gaussian_kde(data)
z1 = z1.evaluate(y1)
except:
z1 = np.zeros(shape=y1.shape)
z.append(z1)
z = np.asarray(z)
z = np.log(z+1)
return z
#prepare the data
experiments, results = results
#get time axis
try:
time = results.pop('TIME')[0, :]
except:
time = np.arange(0, results.values()[0].shape[1])
def make_logical(cases, criterion, interval=False):
if interval:
return (cases[groupBy] >= criterion[0]) & (cases[groupBy] < criterion[1])
else:
return cases[groupBy]== criterion
#get the results for the specific outcome of interest
results = results.get(outcome)
#log results
if logSpace:
results = np.log(results+1)
#generate the grid
if ymin == None:
ymin = np.min(results)
info("ymin: %s" % ymin)
if ymax == None:
ymax = np.max(results)
info("ymax: %s" % ymax)
length = min(100, results.shape[1])
y = np.arange(ymin, ymax, (ymax-ymin)/length)
X, Y = np.meshgrid(time, y)
z = []
#do the preparation for grouping by
interval=False
if (experiments[groupBy].dtype == np.float32) |\
(experiments[groupBy].dtype == np.float64) |\
((experiments[groupBy].dtype == np.int) & (len(set(experiments[groupBy])) > 5)):
interval=True
if discretesize:
categories = discretesize(experiments[groupBy])
else:
categories = make_continuous_grouping_specifiers(experiments[groupBy])
else:
categories = set(experiments[groupBy])
for category in categories:
if interval:
info("calculating kde for (%s, %s)" % (category))
else:
info("calculating kde for %s" % (category))
logical = make_logical(experiments, category, interval)
Z = f(X.T,Y.T, results=results[logical])
z.append(Z)
#calculate the kde for the grid
#visualize results
fig = mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
fig.scene.disable_render = True
for i, category in enumerate(categories):
if interval:
info("plotting (%s, %s)" % (category))
else:
info("plotting %s" % (category))
Z = z[i]
extent = (-14+i*10,-6+i*10, 0,10, 0,5)
s = mlab.mesh(X,Y, Z.T, extent=extent)
mlab.outline(s, color=(.7, .7, .7), extent=extent)
if i==0:
mlab.axes(s,
extent=extent,
xlabel = '',
ylabel = '',
zlabel = 'density',
x_axis_visibility=False,
y_axis_visibility=False,
z_axis_visibility=False)
category_name = repr(category)
mlab.text(-16+10*i, i+10, category_name, z=-2, width=0.14)
fig.scene.disable_render = False
mlab.title(outcome, line_width=0.5)
mlab.show()
def make_side_view(self, axis_name):
scene = getattr(self, 'scene_%s' % axis_name)
scene.scene.parallel_projection = True
ipw_3d = getattr(self, 'ipw_3d_%s' % axis_name)
# We create the image_plane_widgets in the side view using a
# VTK dataset pointing to the data on the corresponding
# image_plane_widget in the 3D view (it is returned by
# ipw_3d._get_reslice_output())
side_src = ipw_3d.ipw._get_reslice_output()
ipw = mlab.pipeline.image_plane_widget(
side_src,
plane_orientation='z_axes',
vmin=self.data.min(),
vmax=self.data.max(),
figure=scene.mayavi_scene,
name='Cut view %s' % axis_name,
)
setattr(self, 'ipw_%s' % axis_name, ipw)
# Extract the spacing of the side_src to convert coordinates
# into indices
spacing = side_src.spacing
# Make left-clicking create a crosshair
ipw.ipw.left_button_action = 0
x, y, z = self.position
cursor = mlab.points3d(x, y, z,
mode='axes',
color=(0, 0, 0),
scale_factor=2*max(self.data.shape),
figure=scene.mayavi_scene,
name='Cursor view %s' % axis_name,
)
self.cursors[axis_name] = cursor
# Add a callback on the image plane widget interaction to
# move the others
this_axis_number = self._axis_names[axis_name]
def move_view(obj, evt):
# Disable rendering on all scene
position = list(obj.GetCurrentCursorPosition()*spacing)[:2]
position.insert(this_axis_number, self.position[this_axis_number])
# We need to special case y, as the view has been rotated.
if axis_name is 'y':
position = position[::-1]
self.position = position
ipw.ipw.add_observer('InteractionEvent', move_view)
ipw.ipw.add_observer('StartInteractionEvent', move_view)
# Center the image plane widget
ipw.ipw.slice_position = 0.5*self.data.shape[
self._axis_names[axis_name]]
# 2D interaction: only pan and zoom
scene.scene.interactor.interactor_style = \
tvtk.InteractorStyleImage()
scene.scene.background = (0, 0, 0)
# Some text:
mlab.text(0.01, 0.8, axis_name, width=0.08)
# Choose a view that makes sens
views = dict(x=(0, 0), y=(90, 180), z=(0, 0))
mlab.view(views[axis_name][0],
views[axis_name][1],
focalpoint=0.5*np.array(self.data.shape),
figure=scene.mayavi_scene)
scene.scene.camera.parallel_scale = 0.52*np.mean(self.data.shape)
def trackPlotter(self,plotVars,plotGradients=None,plotProjGrad=False):
'''Gradients must be one of one of minimizedFunc or fixedFuncs as these are only ones
Stored in gradient dict
'''
assert len(plotVars) == 3, "Track plotter requires 3 plotVars for 3D track! You gave: %s" % len(plotVars)
for i,track in enumerate(self.trackData):
stepScale = minimizeAlgorithm.norm(array(track['deltas']))
pointList=zip(*track['points'])
xs_ys_zs=[]
#Here we search through the available data and add the correct data to plot to xs_ys_zs
for plotVar in plotVars:
gotItFlag=False
for pointIndex,pointVar in enumerate(track['independentVars']):
if pointVar==plotVar:
print 'got ind var %s' % plotVar
xs_ys_zs.append(pointList[pointIndex])
gotItFlag=True
break
if gotItFlag:
continue
for availData in track.keys():
if availData == plotVar:
print 'got other track var %s' % plotVar
gotItFlag=True
xs_ys_zs.append(track[plotVar])
break
assert gotItFlag, "uh oh didn't find our variable to plot '%s'" % plotVar
print 'redmax: ', track['RedMax']
print 'baryMass: ', track['baryMass']
mlab.plot3d(*xs_ys_zs,
color=(1-(1./(i%3+1)),1,1./(i%2+1.)),
reset_zoom=False,
tube_radius=None)
if plotGradients:
if plotVars == track['independentVars']:
print "Mkay good, you're plotting gradients in same space as your tracks"
pass
else:
assert False, "Bad! WARNING your gradients are not in the same space as your tracks!!"
if not isinstance(plotGradients,tuple):
plotGradients = [plotGradients]
for j,grad in enumerate(plotGradients):
vxs,vys,vzs= returnGradientXsYsZsForPlot(track['gradDicts'],grad)
print vxs
print vys
print vzs
thisColor=( (j%2)/2.+0.5,1./(j+1.3),((j+1)%2.)/1.7 )
mlab.quiver3d(xs_ys_zs[0],xs_ys_zs[1],xs_ys_zs[2],
vxs,vys,vzs,
color=thisColor,
scale_factor=stepScale )
label = grad + " " * ( self.maxLabelLength - len(grad) )
mlab.text(0.01,0.1 + j*0.2, label, color=thisColor, width=0.1)
if plotProjGrad:
assert plotVars== track['independentVars']
vxs,vys,vzs=zip(*track['projGrads'])
mlab.quiver3d(xs_ys_zs[0],xs_ys_zs[1],xs_ys_zs[2],
vxs,vys,vzs,
color=(1,1,1),
scale_factor=stepScale )
mlab.text(0.5,0.1 , "projGrad", color=(1,1,1), width=0.1)
# Below move doesnt work
#print mlab.move(xs_ys_zs[0][0],xs_ys_zs[1][0],xs_ys_zs[2][0]) # move camera to first point
mlab.show()
return 0
def drawOriginAxes(plotExtents, displace=None, colAxes=True, cones=True,
xaxis=True, yaxis=True, zaxis=True, opacity=1.0,
scale_arrow_width=1.0, scale_label=0.5,
label_color=(0.1,0.1,0.1), visible=True, cone_scale_factor=1.0,
axis_tube_radius=0.05, axis_mono_col=(0.1,0.1,0.1)):
"""Function to draw crossed axes through the origin. The three axes have the
option of adding a cone, and also may or may not have different colors.
The axes are also labeled.
Examples
--------
>>>plotExtents = (-10,10,-10,10,-10,10)
>>>drawOriginAxes(plotExtents,colAxes=True,opacity=0.90)
#also, if you need to scale the arrow & bar widths and the text label use:
>>>drawOriginAxes(plotExtents, colAxes=True,scale_arrow_width=0.95, scale_label=0.5, opacity=0.95)
Notes
-----
For simple, crossed axes without different colors, lables, and cones, it is
better (simplicity) to use:
>>>x = np.array([0]);y = np.array([0]);z = np.array([0]); # These may be standard Python scalars too
>>>ss = some value (extent of the axes)
>>>caxis = mlab.points3d(x, y, z, ss, mode='axes',color=(0,1,0), scale_factor=1)
>>>caxis.actor.property.lighting = False
"""
#even if the user doesn't want any axis through the origin, we need to draw
#something before a Mayavi axes can be attached. So we draw only the z-axis
#and set it's visibility to False
if visible==False:
cones=False #don't draw any cones
xaxis=False #don't draw x-axis
yaxis=False #don't draw y-axis
zaxis=True #draw the z-axis
ext2subtract = 0.0
if cones==True:
ext2subtract = 1.0*cone_scale_factor
oa_xlim = np.array([plotExtents[0], plotExtents[1] - ext2subtract])
oa_ylim = np.array([plotExtents[2], plotExtents[3] - ext2subtract])
oa_zlim = np.array([plotExtents[4], plotExtents[5] - ext2subtract])
center = np.array([0,0])
oa_colork = label_color # label color
if colAxes:
oa_colR = (0.9, 0, 0)
oa_colG = (0, 0.9, 0)
oa_colB = (0, 0, 0.9)
else:
oa_colR = axis_mono_col
oa_colG = axis_mono_col
oa_colB = axis_mono_col
# x-axis
if xaxis:
x = np.array([oa_xlim[1]]);y = np.array([0]);z = np.array([0])
u = np.array([1]); v = np.array([0]); w = np.array([0])
if cones:
x_cone = mlab.quiver3d(x, y, z, u, v, w, mode='cone', color=oa_colR,
scale_factor=cone_scale_factor)
x_cone.actor.property.lighting = False
x_cone.actor.property.opacity = opacity
x_cone.actor.actor.scale = np.array((scale_arrow_width, scale_arrow_width, scale_arrow_width))
x_axis = mlab.plot3d(oa_xlim, center, center, color=oa_colR, line_width=1.0,
tube_radius=axis_tube_radius, name='oax')
x_axis.actor.actor.scale = np.array((1.0, scale_arrow_width, scale_arrow_width)) # don't scale along the x-axis
x_axis.actor.property.lighting = False
x_axis.actor.property.opacity = opacity
# lately, text3d isn't working
#xaxis_label = mlab.text3d(0.9*oa_xlim[1], 0, 0, 'x', scale=scale_label, color=oa_colork)
xaxis_label = mlab.text(x=0.9*oa_xlim[1], y=0.0*oa_ylim[1],
z=0, text='x', width=0.01*scale_label, color=oa_colork)
# y-axis
if yaxis:
x = np.array([0]);y = np.array([oa_ylim[1]]);z = np.array([0])
u = np.array([0]);v = np.array([1]);w = np.array([0])
if cones:
y_cone = mlab.quiver3d(x, y, z, u, v, w, mode='cone', color=oa_colG,
scale_factor=cone_scale_factor)
y_cone.actor.property.lighting = False
y_cone.actor.property.opacity = opacity
y_cone.actor.actor.scale = np.array((scale_arrow_width, scale_arrow_width, scale_arrow_width))
y_axis = mlab.plot3d(center,oa_ylim,center, color=oa_colG, line_width=1.0,
tube_radius=axis_tube_radius, name='oay')
y_axis.actor.actor.scale = np.array((scale_arrow_width, 1.0, scale_arrow_width)) # don't scale along the y-axis
y_axis.actor.property.lighting = False
y_axis.actor.property.opacity = opacity
#lately, text3d is not working
#yaxis_label = mlab.text3d(0,0.9*oa_ylim[1],0,'y',scale=scale_label,color=oa_colork)
yaxis_label = mlab.text(x=0.015*oa_xlim[1], y=0.9*oa_ylim[1],
z=0, text='y', width=0.01*scale_label, color=oa_colork)
# z-axis
if zaxis:
x = np.array([0]);y = np.array([0]);z = np.array([oa_zlim[1]])
u = np.array([0]);v = np.array([0]);w = np.array([1])
if cones:
z_cone = mlab.quiver3d(x, y, z, u, v, w, mode='cone', color=oa_colB,
scale_factor=cone_scale_factor)
z_cone.actor.property.lighting = False
z_cone.actor.property.opacity = opacity
z_cone.actor.actor.scale = np.array((scale_arrow_width, scale_arrow_width, scale_arrow_width))
z_axis = mlab.plot3d(center, center, oa_zlim, color=oa_colB, line_width=1.0,
tube_radius=axis_tube_radius, name='oaz')
z_axis.actor.actor.scale = np.array((scale_arrow_width,scale_arrow_width, 1.0)) # don't scale along the z-axis
z_axis.actor.property.lighting = False
z_axis.actor.property.opacity = opacity
#lately, text3d isn't working
#zaxis_label = mlab.text3d(0,0,0.9*oa_zlim[1],'z',scale=scale_label,color=oa_colork)
zaxis_label = mlab.text(x=0.01*oa_xlim[1], y=0.0*oa_ylim[1],
z=0.9*oa_zlim[1], text='z', width=0.01*scale_label, color=oa_colork)
if visible==False:
z_axis.actor.actor.visibility=False
zaxis_label.actor.visibility=False
# ... and a sphere at the same location.
mlab.quiver3d([my_x],
[my_y - sphere_size/4.],
[my_z],
[0],[1],[0],
scalars = [1],
scale_factor = sphere_size/2.,
scale_mode = 'scalar',
mode = 'sphere',
line_width = lw*0.75,
name = gal,
color=galcolors[k])
# Finally, add the galaxy name as 3D text.
#mlab.text3d(my_x,my_y,my_z,'HCG91'+gal[-1],scale=20,color = (0,0,0))
mlab.text(my_x,my_y,'HCG91'+gal[-1],color=(0,0,0),z=my_z,width=0.1)
# Next, add peculiar HII regions of interest inside HCG 91c
# (See Vogt+, MNRAS (2015) for details)
# First, compute their coordinates
coords_91c = [[ratodeg(gals['hcg91c']['ra']),
dectodeg(gals['hcg91c']['dec']),0,0]]
coords_91c_plot = (np.array(coords_91c)-np.array([ramean,decmean,0,0]))*3600.
coords_91c_plot[0][0] *= np.cos(np.radians(decmin))
cube_size = 5
hx1 = coords_91c_plot[0][0] - 3.2
hx2 = coords_91c_plot[0][0] - 7.0
hx3 = coords_91c_plot[0][0] - 10.5
def plot_evoked_field(evoked, surf_maps, time=None, time_label='t = %0.0f ms',
n_jobs=1):
"""Plot MEG/EEG fields on head surface and helmet in 3D
Parameters
----------
evoked : instance of mne.Evoked
The evoked object.
surf_maps : list
The surface mapping information obtained with make_field_map.
time : float | None
The time point at which the field map shall be displayed. If None,
the average peak latency (across sensor types) is used.
time_label : str
How to print info about the time instant visualized.
n_jobs : int
Number of jobs to run in parallel.
Returns
-------
fig : instance of mlab.Figure
The mayavi figure.
"""
types = [t for t in ['eeg', 'grad', 'mag'] if t in evoked]
time_idx = None
if time is None:
time = np.mean([evoked.get_peak(ch_type=t)[1] for t in types])
if not evoked.times[0] <= time <= evoked.times[-1]:
raise ValueError('`time` (%0.3f) must be inside `evoked.times`' % time)
time_idx = np.argmin(np.abs(evoked.times - time))
types = [sm['kind'] for sm in surf_maps]
# Plot them
from mayavi import mlab
alphas = [1.0, 0.5]
colors = [(0.6, 0.6, 0.6), (1.0, 1.0, 1.0)]
colormap = mne_analyze_colormap(format='mayavi')
colormap_lines = np.concatenate([np.tile([0., 0., 255., 255.], (127, 1)),
np.tile([0., 0., 0., 255.], (2, 1)),
np.tile([255., 0., 0., 255.], (127, 1))])
fig = mlab.figure(bgcolor=(0.0, 0.0, 0.0), size=(600, 600))
for ii, this_map in enumerate(surf_maps):
surf = this_map['surf']
map_data = this_map['data']
map_type = this_map['kind']
map_ch_names = this_map['ch_names']
if map_type == 'eeg':
pick = pick_types(evoked.info, meg=False, eeg=True)
else:
pick = pick_types(evoked.info, meg=True, eeg=False, ref_meg=False)
ch_names = [evoked.ch_names[k] for k in pick]
set_ch_names = set(ch_names)
set_map_ch_names = set(map_ch_names)
if set_ch_names != set_map_ch_names:
message = ['Channels in map and data do not match.']
diff = set_map_ch_names - set_ch_names
if len(diff):
message += ['%s not in data file. ' % list(diff)]
diff = set_ch_names - set_map_ch_names
if len(diff):
message += ['%s not in map file.' % list(diff)]
raise RuntimeError(' '.join(message))
data = np.dot(map_data, evoked.data[pick, time_idx])
x, y, z = surf['rr'].T
nn = surf['nn']
# make absolutely sure these are normalized for Mayavi
nn = nn / np.sum(nn * nn, axis=1)[:, np.newaxis]
# Make a solid surface
vlim = np.max(np.abs(data))
alpha = alphas[ii]
with warnings.catch_warnings(record=True): # traits
mesh = mlab.pipeline.triangular_mesh_source(x, y, z, surf['tris'])
mesh.data.point_data.normals = nn
mesh.data.cell_data.normals = None
mlab.pipeline.surface(mesh, color=colors[ii], opacity=alpha)
# Now show our field pattern
with warnings.catch_warnings(record=True): # traits
mesh = mlab.pipeline.triangular_mesh_source(x, y, z, surf['tris'],
scalars=data)
mesh.data.point_data.normals = nn
mesh.data.cell_data.normals = None
with warnings.catch_warnings(record=True): # traits
fsurf = mlab.pipeline.surface(mesh, vmin=-vlim, vmax=vlim)
fsurf.module_manager.scalar_lut_manager.lut.table = colormap
# And the field lines on top
with warnings.catch_warnings(record=True): # traits
mesh = mlab.pipeline.triangular_mesh_source(x, y, z, surf['tris'],
scalars=data)
mesh.data.point_data.normals = nn
mesh.data.cell_data.normals = None
with warnings.catch_warnings(record=True): # traits
cont = mlab.pipeline.contour_surface(mesh, contours=21,
line_width=1.0,
vmin=-vlim, vmax=vlim,
opacity=alpha)
cont.module_manager.scalar_lut_manager.lut.table = colormap_lines
if '%' in time_label:
time_label %= (1e3 * evoked.times[time_idx])
mlab.text(0.01, 0.01, time_label, width=0.4)
mlab.view(10, 60)
return fig
jx.append(gradientDictList[i]['J'][1])
jy.append(gradientDictList[i]['J'][2])
jz.append(gradientDictList[i]['J'][3])
tx.append(gradientDictList[i]['ToverW'][1])
ty.append(gradientDictList[i]['ToverW'][2])
tz.append(gradientDictList[i]['ToverW'][3])
f=mlab.figure(bgcolor=(.5,.5,.5))
j=mlab.quiver3d(xs,ys,zs,jx,jy,jz, scale_factor=.002,color=(1,0,0),name='J')
m=mlab.quiver3d(xs,ys,zs,baryX,baryY,baryZ, scale_factor=.013,color=(0,0,1),name='Mb')
t=mlab.quiver3d(xs,ys,zs,tx,ty,tz, scale_factor=.1,color=(0,1,0),name='T/W')
pt=mlab.quiver3d(xs,ys,zs,us,vs,ws, scale_factor=.01,color=(1,1,1),name='projT/W')
mlab.text(.01,.2,'J ',color=(1,0,0),width=0.1)
mlab.text(.01,.4,'Mb ',color=(0,0,1),width=0.1)
mlab.text(.01,.5,'T/W ',color=(0,1,0),width=0.1)
mlab.text(.01,.6,'-projT/W',color=(1,1,1),width=0.1)
mlab.title("Gradients along track")
mlab.axes(color=(.7,.7,.7),xlabel='a')
mlab.xlabel('a')
mlab.ylabel('edMax')
mlab.zlabel('rpoe')
mlab.show()
exit()
###############################
