def awesome(rng, **traits):
"""
Generator function for a Chaco color scale that has low-intensity contrast.
"""
stream = pkg_resources.resource_stream(__name__, 'data/awesomecolormap.csv')
return ColorMapper.from_palette_array(N.loadtxt(stream, delimiter=','),
range=rng, **traits)
def awesome(rng, **traits):
"""
Generator function for a Chaco color scale that has low-intensity contrast.
"""
return ColorMapper.from_palette_array(N.loadtxt(
'../data/awesomecolormap.csv', delimiter=','),
range=rng,
**traits)
def test_alpha_palette(self):
""" Create a colormap with a varying alpha channel from a palette array.
"""
cm = ColorMapper.from_palette_array([[0.0,0.0,0.0,0.5],[1.0,1.0,1.0,1.0]])
sd = {'alpha': [(0.0, 0.5, 0.5), (1.0, 1.0, 1.0)],
'blue': [(0.0, 0.0, 0.0), (1.0, 1.0, 1.0)],
'green': [(0.0, 0.0, 0.0), (1.0, 1.0, 1.0)],
'red': [(0.0, 0.0, 0.0), (1.0, 1.0, 1.0)]}
assert cm._segmentdata == sd
def chaco_gen(self):
self.conn_mat = Plot(ArrayPlotData(imagedata=self.ds.adj_thresdiag))
cm = ColorMapper.from_palette_array(
self.ds.opts.connmat_map._pl(xrange(256)))
self.conn_mat.img_plot('imagedata', name='connmatplot', colormap=cm)
self.conn_mat.tools.append(ZoomTool(self.conn_mat))
self.conn_mat.tools.append(PanTool(self.conn_mat))
self.xa = ColorfulAxis(self.conn_mat, self.ds.node_colors, 'x')
self.ya = ColorfulAxis(self.conn_mat, self.ds.node_colors, 'y')
self.conn_mat.underlays = [self.xa, self.ya]
def chaco_gen(self):
self.conn_mat=Plot(ArrayPlotData(imagedata=self.ds.adj_thresdiag))
cm=ColorMapper.from_palette_array(self.ds.opts.connmat_map._pl(
xrange(256)))
self.conn_mat.img_plot('imagedata',name='connmatplot',colormap=cm)
self.conn_mat.tools.append(ZoomTool(self.conn_mat))
self.conn_mat.tools.append(PanTool(self.conn_mat))
self.xa=ColorfulAxis(self.conn_mat,self.ds.node_colors,'x')
self.ya=ColorfulAxis(self.conn_mat,self.ds.node_colors,'y')
self.conn_mat.underlays=[self.xa,self.ya]
def empty_gen(self):
from chaco.api import Greys
img = np.zeros((self.ds.nr_labels, self.ds.nr_labels))
self.conn_mat = Plot(ArrayPlotData(imagedata=img))
cm = ColorMapper.from_palette_array(
self.ds.opts.connmat_map._pl(xrange(256)))
self.conn_mat.img_plot('imagedata', name='connmatplot', colormap=cm)
self.conn_mat.tools.append(ZoomTool(self.conn_mat))
self.conn_mat.tools.append(PanTool(self.conn_mat))
self.xa = ColorfulAxis(self.conn_mat, self.ds.node_colors, 'x')
self.ya = ColorfulAxis(self.conn_mat, self.ds.node_colors, 'y')
self.conn_mat.underlays = [self.xa, self.ya]
def empty_gen(self):
from chaco.api import Greys
img = np.zeros((self.ds.nr_labels,self.ds.nr_labels))
self.conn_mat=Plot(ArrayPlotData(imagedata=img))
cm=ColorMapper.from_palette_array(self.ds.opts.connmat_map._pl(
xrange(256)))
self.conn_mat.img_plot('imagedata',name='connmatplot',colormap=cm)
self.conn_mat.tools.append(ZoomTool(self.conn_mat))
self.conn_mat.tools.append(PanTool(self.conn_mat))
self.xa=ColorfulAxis(self.conn_mat,self.ds.node_colors,'x')
self.ya=ColorfulAxis(self.conn_mat,self.ds.node_colors,'y')
self.conn_mat.underlays=[self.xa,self.ya]
def test_array_factory(self):
""" Test that the array factory creates valid colormap. """
colors = array([[0.0,0.0,0.0], [1.0,1.0,1.0]])
cm = ColorMapper.from_palette_array(colors)
cm.range = DataRange1D()
ar = ArrayDataSource(array([0.0, 0.5, 1.0]))
cm.range.add(ar)
b = cm.map_screen(ar.get_data())
cm.range.remove(ar)
expected = array([0.0, 0.5, 1.0])
self.assertTrue(allclose(ravel(b[:,:1]), expected, atol=0.02),
"Array factory failed. Expected %s. Got %s" % (expected, b[:,:1]))
return
def isoluminant(rng, num_cycles=1, num_colors=256, reverse=False, **traits):
"""
Generator function for a Chaco color scale that cycles through the hues
@num_cycles times, while maintaining monotonic luminance (i.e., if it is
printed in black and white, then it will be perceptually equal to a linear
grayscale.
Ported from the Matlab(R) code from: McNames, J. (2006). An effective color
scale for simultaneous color and gray-scale publications. IEEE Signal
Processing Magazine 23(1), 82--87.
"""
# Triangular window function
window = N.sqrt(3.0) / 8.0 * N.bartlett(num_colors)
# Independent variable
t = N.linspace(N.sqrt(3.0), 0.0, num_colors)
# Initial values
operand = (t - N.sqrt(3.0) / 2.0) * num_cycles * 2.0 * N.pi / N.sqrt(3.0)
r0 = t
g0 = window * N.cos(operand)
b0 = window * N.sin(operand)
# Convert RG to polar, rotate, and convert back
r1, g1 = _rotate(r0, g0, N.arcsin(1.0 / N.sqrt(3.0)))
b1 = b0
# Convert RB to polar, rotate, and convert back
r2, b2 = _rotate(r1, b1, N.pi / 4.0)
g2 = g1
# Ensure finite precision effects don't exceed unit cube boundaries
r = r2.clip(0.0, 1.0)
g = g2.clip(0.0, 1.0)
b = b2.clip(0.0, 1.0)
the_map = N.vstack((r, g, b)).T
return ColorMapper.from_palette_array(the_map[::-1 if reverse else 1],
range=rng,
**traits)
def isoluminant(rng, num_cycles=1, num_colors=256, reverse=False, **traits):
"""
Generator function for a Chaco color scale that cycles through the hues
@num_cycles times, while maintaining monotonic luminance (i.e., if it is
printed in black and white, then it will be perceptually equal to a linear
grayscale.
Ported from the Matlab(R) code from: McNames, J. (2006). An effective color
scale for simultaneous color and gray-scale publications. IEEE Signal
Processing Magazine 23(1), 82--87.
"""
# Triangular window function
window = N.sqrt(3.0) / 8.0 * N.bartlett(num_colors)
# Independent variable
t = N.linspace(N.sqrt(3.0), 0.0, num_colors)
# Initial values
operand = (t - N.sqrt(3.0) / 2.0) * num_cycles * 2.0 * N.pi / N.sqrt(3.0)
r0 = t
g0 = window * N.cos(operand)
b0 = window * N.sin(operand)
# Convert RG to polar, rotate, and convert back
r1, g1 = _rotate(r0, g0, N.arcsin(1.0 / N.sqrt(3.0)))
b1 = b0
# Convert RB to polar, rotate, and convert back
r2, b2 = _rotate(r1, b1, N.pi / 4.0)
g2 = g1
# Ensure finite precision effects don't exceed unit cube boundaries
r = r2.clip(0.0, 1.0)
g = g2.clip(0.0, 1.0)
b = b2.clip(0.0, 1.0)
the_map = N.vstack((r, g, b)).T
return ColorMapper.from_palette_array(the_map[::-1 if reverse else 1],
range=rng, **traits)
def awesome(rng, **traits):
"""
Generator function for a Chaco color scale that has low-intensity contrast.
"""
return ColorMapper.from_palette_array(N.loadtxt("../data/awesomecolormap.csv", delimiter=","), range=rng, **traits)
def change_colormap(self):
self.conn_mat.color_mapper = ColorMapper.from_palette_array(
self.ds.opts.connmat_map._pl(xrange(256)))
self.conn_mat.request_redraw()
def change_colormap(self):
self.conn_mat.color_mapper = ColorMapper.from_palette_array(
self.ds.opts.connmat_map._pl(xrange(256)))
self.conn_mat.request_redraw()
