def test_barchart(self):
s = np.random.random((10, 10))
x, y = np.indices(s.shape)
bar1 = mlab.barchart(x, y, s)
bar2 = mlab.barchart(s)
bar3 = mlab.barchart(s, auto_scale=False)
# Check that auto scaling worked well in the different
# configurations
for bar in bar1, bar2, bar3:
self.assertEqual(bar.glyph.glyph_source.glyph_source.y_length, 0.9)
def test_barchart(self):
s = np.random.random((10, 10))
x, y = np.indices(s.shape)
bar1 = mlab.barchart(x, y, s)
bar2 = mlab.barchart(s)
bar3 = mlab.barchart(s, auto_scale=False)
# Check that auto scaling worked well in the different
# configurations
for bar in bar1, bar2, bar3:
self.assertEqual(bar.glyph.glyph_source.glyph_source.y_length, 0.9)
def test_barchart(self):
"""Test the barchart function."""
s = np.abs(np.random.random((3,3)))
b = mlab.barchart(s)
self.assertEqual(b.glyph.glyph.scale_mode,
'scale_by_vector_components')
s += 1
b.mlab_source.update()
self.assertEqual(b.glyph.glyph.scale_mode,
'scale_by_vector_components')
def test_barchart(self):
"""Test the barchart function."""
s = np.abs(np.random.random((3, 3)))
b = mlab.barchart(s)
self.assertEqual(b.glyph.glyph.scale_mode,
'scale_by_vector_components')
s += 1
b.mlab_source.update()
self.assertEqual(b.glyph.glyph.scale_mode,
'scale_by_vector_components')
def plot_3d_spectrum_mayavi(fs, fignum=None, vmin=None, vmax=None,
pop_ids=None):
"""
Logarithmic heatmap of single 3d FS.
This method relies on MayaVi2's mlab interface. See http://code.enthought.com/projects/mayavi/docs/development/html/mayavi/mlab.html . To edit plot
properties, click leftmost icon in the toolbar.
If you get an ImportError upon calling this function, it is likely that you
don't have mayavi installed.
fs: FS to plot
vmin: Values in fs below vmin are masked in plot.
vmax: Values in fs above vmax saturate the color spectrum.
fignum: Figure number to plot into. If None, a new figure will be created.
Note that these are MayaVi figures, which are separate from
matplotlib figures.
pop_ids: If not None, override pop_ids stored in Spectrum.
"""
from enthought.mayavi import mlab
fig = mlab.figure(fignum, bgcolor=(1,1,1))
mlab.clf(fig)
if vmin is None:
vmin = fs.min()
if vmax is None:
vmax = fs.max()
# Which entries should I plot?
toplot = numpy.logical_not(fs.mask)
toplot = numpy.logical_and(toplot, fs.data >= vmin)
# For the color mapping
normalized = (numpy.log(fs)-numpy.log(vmin))\
/(numpy.log(vmax)-numpy.log(vmin))
normalized = numpy.minimum(normalized, 1)
xs,ys,zs = numpy.indices(fs.shape)
flat_xs = xs.flatten()
flat_ys = ys.flatten()
flat_zs = zs.flatten()
flat_toplot = toplot.flatten()
mlab.barchart(flat_xs[flat_toplot], flat_ys[flat_toplot],
flat_zs[flat_toplot], normalized.flatten()[flat_toplot],
colormap='hsv', scale_mode='none', lateral_scale=1,
figure=fig)
if pop_ids is None:
if fs.pop_ids is not None:
pop_ids = fs.pop_ids
else:
pop_ids = ['pop0','pop1','pop2']
a = mlab.axes(xlabel=pop_ids[0],ylabel=pop_ids[1],zlabel=pop_ids[2],
figure=fig, color=(0,0,0))
a.axes.label_format = ""
a.title_text_property.color = (0,0,0)
mlab.text3d(fs.sample_sizes[0],fs.sample_sizes[1],fs.sample_sizes[2]+1,
'(%i,%i,%i)'%tuple(fs.sample_sizes), scale=0.75, figure=fig,
color=(0,0,0))
mlab.view(azimuth=-40, elevation=65, distance='auto', focalpoint='auto')
mlab.show()
yticks(())
image2 = array(image2)
subplot(1, 2, 2)
pcolor(image2, cmap=cm.jet, vmin=-1, vmax=1, shading='faceted')
xticks(())
yticks(())
savefig('laplacien.eps')
show()
from enthought.mayavi import mlab
mlab.figure(1, size=(720, 300))
mlab.clf()
import numpy as np
x_, y_ = np.indices(image.shape)
mlab.barchart(x_, y_ - 3, 2 * image, vmin=-1, vmax=1)
mlab.barchart(x_, y_ + 3, 2 * image2, vmin=-1, vmax=1)
mlab.view(0, 63, 8.5, (3, 2, 1))
mlab.savefig('laplacien.jpg')
import os
os.system('mogrify -trim laplacien.jpg')
from numpy import zeros
image = zeros((size, size))
image[size / 2, size / 2] = 1
def laplacien_numpy():
image[1:-1, 1:-1] = (image[:-2, 1:-1] - image[2:, 1:-1] +
image[1:-1, :-2] - image[1:-1, 2:]) * 0.25
regions = lulu.decompose(img)
value_maxes = []
height = 0
for area in sorted(regions.keys()):
pulses = regions[area]
if len(pulses) == 0 or area < 280 or area > 300:
continue
values = [crh.get_value(p) for p in pulses]
height_diff = max(values) - min(values)
value_maxes.append(height_diff)
centre = height + height_diff / 2.0
pulse_values = np.zeros_like(img)
for p in pulses:
crh.set_array(pulse_values, p, crh.get_value(p))
y, x = np.where(pulse_values)
s = pulse_values[y, x]
mlab.barchart(x, y, [height + centre] * len(s), s,
opacity=1.0, scale_factor=1.5)
height += height_diff + 0.5
scene = mlab.get_engine().scenes[0]
scene.scene.parallel_projection = True
mlab.show()
yticks(())
image2 = array(image2)
subplot(1, 2, 2)
pcolor(image2, cmap=cm.jet, vmin=-1, vmax=1, shading='faceted')
xticks(())
yticks(())
savefig('laplacien.eps')
show()
from enthought.mayavi import mlab
mlab.figure(1, size=(720, 300))
mlab.clf()
import numpy as np
x_, y_ = np.indices(image.shape)
mlab.barchart(x_, y_ - 3, 2*image, vmin=-1, vmax=1)
mlab.barchart(x_, y_ + 3, 2*image2, vmin=-1, vmax=1)
mlab.view(0, 63, 8.5, (3, 2, 1))
mlab.savefig('laplacien.jpg')
import os
os.system('mogrify -trim laplacien.jpg')
from numpy import zeros
image = zeros((size, size))
image[size/2, size/2] = 1
def laplacien_numpy():
image[1:-1, 1:-1] = (image[:-2, 1:-1] - image[2:, 1:-1] +
image[1:-1, :-2] - image[1:-1, 2:])*0.25
height = 0
for area in sorted(regions.keys()):
pulses = regions[area]
if len(pulses) == 0 or area < 280 or area > 300:
continue
values = [crh.get_value(p) for p in pulses]
height_diff = max(values) - min(values)
value_maxes.append(height_diff)
centre = height + height_diff / 2.0
pulse_values = np.zeros_like(img)
for p in pulses:
crh.set_array(pulse_values, p, crh.get_value(p))
y, x = np.where(pulse_values)
s = pulse_values[y, x]
mlab.barchart(x,
y, [height + centre] * len(s),
s,
opacity=1.0,
scale_factor=1.5)
height += height_diff + 0.5
scene = mlab.get_engine().scenes[0]
scene.scene.parallel_projection = True
mlab.show()
