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 plot_3d(lats,lons,depth,vs,runlat,runlon,vtkname='rayleigh_c_u.vtk',
annotate_depth=False,coastline=False,annotate_lat=True,annotate_lon=True):
"""
plot 3d results using mayavi
"""
if coastline:
plot_canada_map()
sgrid = get_tvtk_grid(lats,lons,depth,vs)
d = mlab.pipeline.add_dataset(sgrid)
#sf = mlab.pipeline.surface(d)
#gx = mlab.pipeline.grid_plane(d)
#gx.grid_plane.axis = 'x'
gy = mlab.pipeline.grid_plane(d)
gy.grid_plane.axis = 'x'
gy.module_manager.scalar_lut_manager.show_scalar_bar = True
gy.module_manager.scalar_lut_manager.lut_mode = 'jet'
gy.module_manager.scalar_lut_manager.data_range = np.array([ 2. , 4.8])
gy.module_manager.scalar_lut_manager.scalar_bar_representation.maximum_size = np.array([100000, 100000])
gy.module_manager.scalar_lut_manager.scalar_bar_representation.minimum_size = np.array([1, 1])
gy.module_manager.scalar_lut_manager.scalar_bar_representation.position2 = np.array([ 0.08796009, 0.56264591])
gy.module_manager.scalar_lut_manager.scalar_bar_representation.position = np.array([ 0.03396896, 0.39182879])
gy.actor.mapper.progress = 1.0
gy.actor.mapper.scalar_range = np.array([ 0., 1.])
gy.actor.mapper.scalar_visibility = True
gy.actor.property.representation = 'surface'
gy.grid_plane.position = 6
#gz = mlab.pipeline.grid_plane(d)
#gz.grid_plane.axis = 'z'
if annotate_lat:
for lat in runlat:
x,y,z = convert_pt(lat,-58.,10.)
txt = mlab.text3d(x,y,z,'%d'%(lat),color=(0,0,0),line_width=10.0)
txt.scale = [20,20,20]
if annotate_lon:
for lon in runlon[1::]:
x,y,z = convert_pt(49.,lon,10.)
txt = mlab.text3d(x,y,z,'%d'%(lon),color=(0,0,0),line_width=10.0)
txt.scale = [20,20,20]
if annotate_depth:
for dp in [-10,-40,-80,-120]:
x,y,z = convert_pt(49,-68.,dp)
txt = mlab.text3d(x,y,z,'%d km'%(dp),color=(0,0,0),line_width=10.0)
txt.scale = [20,20,20]
### Include 3D screenshot in matplotlib
#arr = mlab.screenshot()
#import pylab as pl
#pl.imshow(arr)
#pl.show()
mlab.text(0.76,0.86,'49N',width=0.1)
mlab.show()
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
if False:
from enthought.mayavi import mlab
fig1 = mlab.figure(bgcolor=(1,1,1),fgcolor=(0,0,0),size=(800,800))
#mlab.points3d(ph_a, th_a, r_a, s_a,opacity=0.1,scale_factor=3)#, color=(0,0,1))
mlab.points3d(ph_r, th_r, r_r, s_r,opacity=0.3,scale_factor=2)#, color=(1,0,0))
mlab.axes(ranges=[0.0, 90.0, 0.0, 90.0, 3.0, 9.0])
#mlab.orientation_axes()
mlab.xlabel("phi")
mlab.ylabel("theta")
mlab.zlabel("centroid\ndistance")
#mlab.colorbar(nb_labels=2,nb_colors=2,label_fmt='')
mlab.colorbar()
#mlab.text(0.1,0.1,"attraction",color=(0,0,1),width=0.1)
#mlab.text(0.8,0.1,"repulsion",color=(1,0,0),width=0.1)
mlab.text(0.1,0.8,"%s-%s (n=%i; x=%i); (%s)"%(res1,res2,ndata,countNone,pdblistfile),width=0.8)
mlab.title("Free Energy (%s,%s)"%(res1,res2),size=0.3,height=0.7,figure=fig1)
viewdist = 120
elevation = 60 # angle or 'rotate'
azimuth = 180+45 # angle or 'rotate'
mlab.view(distance=viewdist,elevation=elevation, azimuth=azimuth)
mlab.savefig(pdblistfile+"_%s_%s_r_theta_phi.png"%(res1,res2))
mlab.show()
#sys.exit()
##############
for i in xrange(len(bins[0])):
tmp = []
# value. We want to reposition this value to 0, so as to put it in the
# center of our extents.
cat1 -= 0.5
cat2 -= 0.5
cat3 -= 0.5
cat1_extent = (-14,-6, -4,4, 0,5)
surf_cat1 = mlab.surf(x-10, y, cat1, colormap='Spectral', warp_scale=5,
extent=cat1_extent, vmin=-0.5, vmax=0.5)
mlab.outline(surf_cat1, color=(.7, .7, .7))
mlab.axes(surf_cat1, color=(.7, .7, .7), extent=cat1_extent,
ranges=(0,1, 0,1, 0,1), xlabel='', ylabel='',
zlabel='Probability',
x_axis_visibility=False, z_axis_visibility=False)
mlab.text(-18, -4, '1 photon', z=-4, width=0.13)
cat2_extent = (-4,4, -4,4, 0,5)
surf_cat2 = mlab.surf(x, y, cat2, colormap='Spectral', warp_scale=5,
extent=cat2_extent, vmin=-0.5, vmax=0.5)
mlab.outline(surf_cat2, color=(0.7, .7, .7), extent=cat2_extent)
mlab.text(-4, -3, '2 photons', z=-4, width=0.14)
cat3_extent = (6,14, -4,4, 0,5)
surf_cat3 = mlab.surf(x+10, y, cat3, colormap='Spectral', warp_scale=5,
extent=cat3_extent, vmin=-0.5, vmax=0.5)
mlab.outline(surf_cat3, color=(.7, .7, .7), extent=cat3_extent)
mlab.text(6, -2.5, '3 photons', z=-4, width=0.14)
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)
points.mlab_source.dataset.lines = connections
points.mlab_source.update()
# 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 enthought.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
# -*- coding: utf-8 -*-
from enthought.mayavi import mlab
from tvtk_rectilineargrid import r
from figure_imagedata import plot_cell
if __name__ == "__main__":
mlab.figure(1, fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
mlab.clf()
mlab.pipeline.surface(mlab.pipeline.extract_edges(r), color=(0, 0, 0))
mlab.pipeline.glyph(r, mode='sphere', scale_factor=0.5, scale_mode='none')
plot_cell(r.get_cell(1))
mlab.text(0.01, 0.9, "get_cell(1)", width=0.25)
axes = mlab.orientation_axes()
mlab.show()
opacity=1, colormap='Blues', name='[O III]-2')
surf5 = mlab.contour3d(x_b,y_b,z_b,scidata_cleanb,contours=[10],\
opacity=0.1,colormap='autumn', name='Hbeta')
surf6 = mlab.points3d((stars[:,0]-size_x/2)*0.5*astopc,(stars[:,1]-size_y/2.)*0.5*astopc,stars[:,0]*0.0,stars[:,2]*0.5*astopc*2,color=(0,0,0), scale_factor=1, name = 'Stars')
# Now to make it look decent ...
ax = mlab.axes(extent=[-10,10,-12,12,-7,7],nb_labels=5)
ax.axes.fly_mode = 'outer_edges'
ax.title_text_property.font_size = 10
ax.title_text_property.font_family = 'courier'
ax.label_text_property.font_family = 'courier'
# And a bit of references if anyone is interested ...
mlab.text(0.05,0.95,'Oxygen-rich ejecta in SNR N132D. Vogt & Dopita, Ap&SS, 311, 521 (2011); Vogt & Shingles, Ap&SS (2013), submitted.',width=0.75)
mlab.text(0.05,0.05,'*****@*****.**',width=0.25)
# Label the axes ...
ax.axes.x_label = 'X (Dec.) [pc]'
ax.axes.y_label = 'Y (R.A.) [pc]'
ax.axes.z_label = 'Z [pc]'
mlab.show()
# Define the view point - useful to make a movie (not build here) ...
mlab.view(90,0,60)
# Save it all ...
#mlab.savefig('Vogt+Shingles_test.wrl')
#mlab.savefig('Vogt+Shingles_test.eps')
#mlab.savefig('Vogt+Shingles_test.png')
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.update()
# 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 enthought.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
# -*- coding: utf-8 -*-
from enthought.mayavi import mlab
from tvtk_rectilineargrid import r
from figure_imagedata import plot_cell
if __name__ == "__main__":
mlab.figure(1, fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
mlab.clf()
mlab.pipeline.surface(mlab.pipeline.extract_edges(r), color=(0, 0, 0))
mlab.pipeline.glyph(r, mode='sphere', scale_factor=0.5, scale_mode='none')
plot_cell(r.get_cell(1))
mlab.text(0.01, 0.9, "get_cell(1)", width=0.25)
axes = mlab.orientation_axes( )
mlab.show()
def visualize_graph(self):
""" Visualize the graph with the 3D view """
from enthought.mayavi import mlab
from enthought.tvtk.api import tvtk
# precondition for the graph visualization
if self.scene3d is None:
self.scene3d = self._create3DScene()
# alias for currently used scene
fig = self.scene3d
# get source object
srcobj = self.datasourcemanager.get_sourceobject()
# create node source
#####
self.nodesrc = mlab.pipeline.scalar_scatter(srcobj.positions[:,0], srcobj.positions[:,1], srcobj.positions[:,2],\
figure = fig, name = 'Node Source')
# adding scalar data arrays
# create a sequence array to map the node ids to color
from numpy import array
ar = array(range(1,len(srcobj.nodeids)+1))
self.nodesrc.mlab_source.dataset.point_data.add_array(ar)
self.nodesrc.mlab_source.dataset.point_data.get_array(0).name = 'nodeids'
self.nodesrc.mlab_source.dataset.point_data.add_array(srcobj.selected_nodes)
self.nodesrc.mlab_source.dataset.point_data.get_array(1).name = 'selected_nodes'
# setting the active attributes
actsel1 = mlab.pipeline.set_active_attribute(self.nodesrc, point_scalars='nodeids', \
name="Colored Nodes")
actsel2 = mlab.pipeline.set_active_attribute(self.nodesrc, point_scalars='selected_nodes', \
name="Selected Nodes")
# create glyphs
self.nodes = mlab.pipeline.glyph(actsel1, scale_factor=3.0, scale_mode='none',\
name = 'Nodes', mode='cube')
self.nodes.glyph.color_mode = 'color_by_scalar'
# FIXME:
# interpolate setting to true makes the color mapping wrong on linux
# otherwise on windows, if not set to true, the cubes stay dark (why?)
from sys import platform
if 'win32' in platform:
self.nodes.actor.mapper.interpolate_scalars_before_mapping = True
# if there exists a colormap, use it
l = self.nodes.glyph.module.module_manager.scalar_lut_manager.lut
# number of colors has to be greater equal to 2
if not len(srcobj.nodeids) < 2:
l.number_of_colors = len(srcobj.nodeids)
l.build()
# getting colors from source object
l.table = srcobj.colors
# create glyphs for selected nodes
self.nodes_selected = mlab.pipeline.glyph(actsel2, scale_mode = 'scalar', \
opacity = 0.2, scale_factor = 7.0, \
name = 'Nodes', color = (0.92, 0.98, 0.27))
self.nodes_selected.glyph.color_mode = 'no_coloring'
self.nodes_selected.glyph.glyph.clamping = False
self.nodes_selected.glyph.glyph_source.glyph_source.phi_resolution = 12
self.nodes_selected.glyph.glyph_source.glyph_source.theta_resolution = 12
self.nodes_selected.actor.mapper.interpolate_scalars_before_mapping = True
# should I load the node labels?
# this is too slow for many nodes
if preference_manager.cviewerui.labelload:
for index, label in enumerate(srcobj.labels):
tmplabel = mlab.text(srcobj.positions[index,0], srcobj.positions[index,1], \
label, z=srcobj.positions[index,2], \
width=0.010*len(label), \
color = (1,1,1), name='Node Label ' + label)
tmplabel.property.shadow = True
# split up what was achieved before by quiver3d
# add a vector_scatter data source with scalars
self.vectorsrc = mlab.pipeline.vector_scatter(srcobj.start_positions[0],
srcobj.start_positions[1],
srcobj.start_positions[2],
srcobj.vectors[0],
srcobj.vectors[1],
srcobj.vectors[2],
name = 'Connectivity Source',
figure = fig)
lastkey = ''
# add all the scalars we have (from the srcobj scalarsdict)
# this should be an ordered dict!
for key, value in srcobj.scalarsdict.items():
da = tvtk.DoubleArray(name=str(key))
da.from_array(srcobj.scalarsdict[key])
self.vectorsrc.mlab_source.dataset.point_data.add_array(da)
self.vectorsrc.mlab_source.dataset.point_data.scalars = da.to_array()
self.vectorsrc.mlab_source.dataset.point_data.scalars.name = str(key)
lastkey = str(key)
# this is the winning time-consuming line, and can be ommited in further mayavi releases
self.vectorsrc.outputs[0].update()
# add a set active attribute filter to select the scalars
self.attract = mlab.pipeline.set_active_attribute(self.vectorsrc, point_scalars=lastkey, \
name="Set Edge Attribute")
self.thres = mlab.pipeline.threshold(self.attract, name="Thresholding")
# to prevent vtkLookupTable: Bad table range
self.thres.filter_type = 'cells'
#self.thres.threshold_filter.all_scalars = False
if srcobj.directed:
self.vectors = mlab.pipeline.vectors(self.thres,\
colormap='OrRd',
figure=fig,
name='Connections',
mode='arrow',
scale_factor=1,
resolution=20,
scale_mode = 'vector')
self.vectors.glyph.glyph_source.glyph_source.shaft_radius = 0.015
self.vectors.glyph.glyph_source.glyph_source.shaft_resolution = 20
self.vectors.glyph.glyph_source.glyph_source.tip_radius = 0.04
self.vectors.glyph.glyph_source.glyph_source.tip_length = 0.15
self.vectors.glyph.glyph_source.glyph_source.tip_resolution = 20
self.vectors.glyph.color_mode = 'color_by_scalar'
self.vectors.glyph.glyph.clamping = False
else:
# then create a .vectors filter to display
self.vectors = mlab.pipeline.vectors(self.thres,\
colormap='OrRd',
#mode='cylinder',
figure=fig,
name='Connections',
#scale_factor=1,
#resolution=20,
# make the opacity of the actor depend on the scalar.
#transparent=True,
scale_mode = 'vector')
self.vectors.glyph.glyph_source.glyph_source.glyph_type = 'dash'
# vectors.glyph.glyph_source.glyph_source.radius = 0.01
self.vectors.glyph.color_mode = 'color_by_scalar'
self.vectors.glyph.glyph.clamping = False
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())
ipw = mlab.pipeline.image_plane_widget(
ipw_3d.ipw._get_reslice_output(),
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)
# 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())[: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],figure=scene.mayavi_scene)
#focalpoint=0.5*np.array(self.data.shape),
scene.scene.camera.parallel_scale = 0.52*np.mean(self.data.shape)
theta = arcsin(vlen(planenormal) / k_photon / 2)
##########################################
#angle between diffraction plane and surface
theta1 = arccos(vlen(planenormal * array([0, 0, 1])) / vlen(planenormal))
phi1 = arctan(planenormal[1] / planenormal[0])
textwidth = 0.005
#######################
# the surface
x, y = mgrid[-100:101, -100:101] / 100 * a
z = x - x
mlab.mesh(x, y, z, opacity=0.5)
text = 'film surface'
mlab.text(1.1 * a, 0, 'film surface', z=0, width=len(text) * textwidth)
at.plot3d(text='(0,0,0)')
mlab.quiver3d([0], [0], [0], [0], [0], [1],
mode='2ddash',
color=(0, 0, 1),
scale_mode="none",
scale_factor=6)
text = 'surface normal'
mlab.text(0, 0, text, z=1.1 * c, width=len(text) * textwidth)
##########################
# the diffraction plane
plane_kx = planenormal[0] / planenormal[2]
plane_ky = planenormal[1] / planenormal[2]
# Non-broadcasting version of the cube calculation
################################################################################
new_fig(size=(500, 300))
options['opacity'] = 1
x, y, z = np.mgrid[0:4, 0:4, 0:4]
pts_x = mlab.points3d(x-5, y, z, 2*z, **options)
pts_y = mlab.points3d(x-10, y, z, 2*y, **options)
pts_z = mlab.points3d(x-15, y, z, 2*x, **options)
r = 2*np.sqrt(x**2 + y**2 + z**2)
pts = mlab.points3d(r, **options)
mlab.text(-14.5, -1, 'x', z=-3, width=0.06)
mlab.text(-9.2, -1, 'y', z=-3, width=0.06)
mlab.text(-4, -1, 'z', z=-3, width=0.05)
mlab.text(1.5, -1, 'r', z=-3, width=0.035)
mlab.view(-96, 46, 22, (-6.25, 1.1, 1.1))
mlab.savefig('3d_radius_non_broadcasting.jpg')
################################################################################
# Broadcasting operations that happen in this cube
################################################################################
x, y, z = np.mgrid[0:4, 0:4, 0:4]
mlab.outline(pts_x, color=(0.5, 0.5, 0.5))
pts_x.remove()
def generate_mayavi_line_plot(self, srcid_sciclasses_list,
use_linfit_segments=False,
enable_mayavi2_interactive_gui=False):
""" Generate a Mayavi mlab 3D plot which summarizes iterative
classification of a TUTOR source over the number of epochs
used/added.
"""
# TODO: I would like to plot each sci class a different color
# - I should have each science class re-use a single number/color
# - I should label which science class by color, as Y axis labels
# TODO: I will need to insert a 0 point onto arrays (for s[0]?)
if enable_mayavi2_interactive_gui:
from enthought.mayavi.scripts import mayavi2
mayavi2.standalone(globals())
from enthought.mayavi import mlab
# scalar cut plane plotting module stuff:
import enthought.mayavi
from enthought.mayavi.modules.scalar_cut_plane import ScalarCutPlane
epoch_ids = [0] # x
class_groups = [0] # y
probs = [0] # z
styles = [0] # numbers used for coloring & glyph sizes
used_final_classes = []
i_srcid = 0
for final_class in self.pars['interested_sci_classes']:
if not finalclass_ordered_dict.has_key(final_class):
continue # skip this science class since nothing to plot
else:
used_final_classes.append(final_class)
srcid_sciclasses_list = finalclass_ordered_dict[final_class]
for src_id,sci_classes in srcid_sciclasses_list:
print 'src_id:', src_id, '\t', final_class
sci_classes.generate_linearfit_endpoints_segments()
i = 0
for class_name,class_dict in sci_classes.class_dict.iteritems():
class_style = self.sciclass_style_dict[class_name]
if use_linfit_segments:
for segment_dict in class_dict['linfit_segments']:
epoch_ids.extend([segment_dict['epoch_ids'][0]])
class_groups.extend([i_srcid])
probs.extend([0])
styles.extend([0])
epoch_ids.extend(segment_dict['epoch_ids'])
class_groups.extend([i_srcid]*len(segment_dict['epoch_ids']))
probs.extend(segment_dict['probs'])
styles.extend([class_style]*len(segment_dict['epoch_ids']))
epoch_ids.extend([segment_dict['epoch_ids'][-1]])
class_groups.extend([i_srcid])
probs.extend([0])
styles.extend([0])
else:
epoch_ids.extend([class_dict['epoch_ids'][0]])
class_groups.extend([i_srcid])
probs.extend([0])
styles.extend([0])
epoch_ids.extend(class_dict['epoch_ids'])
class_groups.extend([i_srcid]*len(class_dict['epoch_ids']))
probs.extend(class_dict['probs'])
styles.extend([class_style]*len(class_dict['epoch_ids']))
epoch_ids.extend([class_dict['epoch_ids'][-1]])
class_groups.extend([i_srcid])
probs.extend([0])
styles.extend([0])
i += 1
i_srcid += 3 # Y spacing between srcids within a science-class
i_srcid += 20 # Y spacing between science-class groups
mlab.plot3d(numpy.array(epoch_ids),
numpy.array(class_groups),
numpy.array(probs)*100.0,
numpy.array(styles),
colormap="Paired",
tube_radius=1)
# extent=[0,600,
# 0,i_srcid,
# 15, 110])
mlab.axes(xlabel='N of epochs',
ylabel='science class',
zlabel='% Prob.')#,
# DEBUG/UPGRADE: These seem to trigger some bug about Actor methods:
# extent=[0,600,
# 0,i_srcid,
# -10, 110])
title_str = "num_srcids=%d probability_cut=%0.2lf factor_threshold=%0.2lf bin_size=%d poly_order=%d" % (\
self.pars['num_srcids_to_retrieve_plot'],
self.pars['sciclass_probability_cut'],
self.pars['polyfit_factor_threshold'],
self.pars['polyfit_bin_size'],
self.pars['polyfit_poly_order'])
##### TITLE:
# The 'z' is a flag in Mayavi2 v3.1.0 documentation:
#mlab.text(0.01, 0.97, title_str, width=1.0, name='title', z=0.0)
mlab.text(0.01, 0.97, title_str, width=1.0, name='title')
##### SCIENCE CLASS LABELS:
# TODO: Eventually I would like the class labels to be colored and
# placed on the y axis, but this requires:
# 1) later mayavi version to allow 3D text positioning
# 2) ability to match text color to the line color-map.
if 1:
used_final_classes.reverse()
y = 0.95
for class_name in used_final_classes:
class_str = "%2d %s" %(len(finalclass_ordered_dict[class_name]),
class_name)
mlab.text(0.85, y, class_str, width=0.095*(len(class_str)/20.0))
y -= 0.018
##### Add a x-axis plane (I can't figure out code to make it opaque)
if 0:
cp = ScalarCutPlane()
mayavi.add_module(cp)
cp.implicit_plane._hideshow() # this un-displays the plane
cp.implicit_plane.normal = 0,0,1
cp.implicit_plane.origin = 150,168,15
#cp.implicit_plane.position= 0.15 # feature not available yet
print '##### cp:'
cp.print_traits()
print '##### cp.implicit_plane:'
cp.implicit_plane.print_traits()
print '##### cp.implicit_plane._HideShowAction:'
cp.implicit_plane._HideShowAction.print_traits()
##### Camera position:
if enable_mayavi2_interactive_gui:
camera_distance = 600
else:
camera_distance = 1200
enthought.mayavi.tools.camera.view(azimuth=50,
elevation=70, # 0:looking down to -z
distance=camera_distance,
focalpoint=(100,(i_srcid*0.4),50))
#enthought.mayavi.mlab.show_pipeline() # this introspecive feature is not available in current mayavi version.
#####If no Mayavi2 GUI, we allow user to resize image before saving file
if not enable_mayavi2_interactive_gui:
print 'Please resize window & Press a Key.'
import curses
stdscr = curses.initscr()
while 1:
c = stdscr.getch()
break
curses.endwin()
##### Save figure:
img_fpath ="/tmp/%s%s.png" %(title_str.replace('=','').replace(' ','_'),
self.pars['save_plot_image_suffix'])
if os.path.exists(img_fpath):
os.system('rm ' + img_fpath)
mlab.savefig(img_fpath)#, size=(500,500))#, dpi=200) #size flag doesn't do anything
print "Saved:", img_fpath
# ... 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=gals[gal]['col'])
# 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))
# ... 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=gals[gal]['col'])
# 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
stars[:, 2] * 0.5 * astopc * 2,
color=(0, 0, 0),
scale_factor=1,
name='Stars')
# Now to make it look decent ...
ax = mlab.axes(extent=[-10, 10, -12, 12, -7, 7], nb_labels=5)
ax.axes.fly_mode = 'outer_edges'
ax.title_text_property.font_size = 10
ax.title_text_property.font_family = 'courier'
ax.label_text_property.font_family = 'courier'
# And a bit of references if anyone is interested ...
mlab.text(
0.05,
0.95,
'Oxygen-rich ejecta in SNR N132D. Vogt & Dopita, Ap&SS, 311, 521 (2011); Vogt & Shingles, Ap&SS (2013), submitted.',
width=0.75)
mlab.text(0.05, 0.05, '*****@*****.**', width=0.25)
# Label the axes ...
ax.axes.x_label = 'X (Dec.) [pc]'
ax.axes.y_label = 'Y (R.A.) [pc]'
ax.axes.z_label = 'Z [pc]'
mlab.show()
# Define the view point - useful to make a movie (not build here) ...
mlab.view(90, 0, 60)
# Save it all ...
#mlab.savefig('Vogt+Shingles_test.wrl')
#mlab.savefig('Vogt+Shingles_test.eps')
theta = arcsin(vlen(planenormal)/k_photon/2); ########################################## #angle between diffraction plane and surface theta1 = arccos(vlen(planenormal*array([0,0,1]))/vlen(planenormal)); phi1 = arctan(planenormal[1]/planenormal[0]); textwidth=0.005; ####################### # the surface x,y=mgrid[-100:101,-100:101]/100*a; z=x-x; mlab.mesh(x,y,z,opacity=0.5); text='film surface' mlab.text(1.1*a,0,'film surface',z=0,width=len(text)*textwidth) at.plot3d(text='(0,0,0)'); mlab.quiver3d([0],[0],[0],[0],[0],[1],mode='2ddash',color=(0,0,1),scale_mode="none",scale_factor=6); text='surface normal' mlab.text(0,0,text,z=1.1*c,width=len(text)*textwidth) ########################## # the diffraction plane plane_kx=planenormal[0]/planenormal[2]; plane_ky=planenormal[1]/planenormal[2]; z=-plane_kx*x-plane_ky*y; planenormal=planenormal/vlen(planenormal)*6; mlab.mesh(x,y,z,opacity=0.5);
