def plot_dft_angles(snap_times,angles,amps,sim_time,logx=False,logy=False,plot_wheel=False,lw=1.5,cmap='hsv',wheel_axis=None,label_size=10):
"""
Plot 2D-DFT amplitudes at a specific frequency as a function of time and angle
"""
if logy : pl.gca().set_yscale('log')
if logx : pl.gca().set_xscale('log')
ax=pl.gca()
custom_axes()
ax.set_xlim(snap_times[1] if pl.gca().get_xscale()=='log' else 0,sim_time)
det_lines = LineCollection([list(zip(snap_times[1:], amp[1:])) for amp in amps],
linewidths=lw,
linestyles='-')
ax.set_ylim((np.amin(amps), np.amax(amps)))
det_lines.set_array(angles)
det_lines.set_clim(0,180)
det_lines.set_cmap(cmap)
ax.add_collection(det_lines)
pl.sci(det_lines)
if plot_wheel is True:
color_steps = 2056
pos = ax.get_position()
wheel_pos= [pos.x0+pos.width/20., pos.y0+pos.height*0.7, pos.width / 4.0, pos.height / 4.0]
if wheel_axis is None:
wheel_axis = pl.gcf().add_axes(wheel_pos, projection='polar')
else:
wheel_axis.projection='polar'
wheel_axis._direction = 2*np.pi
norm = matplotlib.colors.Normalize(0.0, 180.)
cb = matplotlib.colorbar.ColorbarBase(wheel_axis, cmap=cm.get_cmap(cmap,color_steps),
norm=norm,
orientation='horizontal',ticks=[0, 30, 60, 90, 120,150])
cb.ax.tick_params(labelsize=label_size)
cb.outline.set_visible(False)
return ax
def _plot(self):
import pylab
# per http://sourceforge.net/tracker/index.php?func=detail&aid=1656374&group_id=80706&atid=560720
# although seems to no longer be needed with matplotlib >= 0.91.1
pylab.sci(self.image)
datamin = self._getLimit(('datamin', 'zmin'))
datamax = self._getLimit(('datamax', 'zmax'))
if datamin is None or datamax is None:
datamin, datamax = self._autoscale(vars=self.vars,
datamin=datamin,
datamax=datamax)
pylab.clim(vmax=datamax, vmin=datamin)
self.image.set_data(self._getData())
# Manually find the min and max of all colors for
# use in setting the color scale.
vmin = min(vmin, amin(dd))
vmax = max(vmax, amax(dd))
images.append(a.imshow(data, cmap=cmap))
ax.append(a)
# Set the first image as the master, with all the others
# observing it for changes in cmap or norm.
norm = colors.normalize(vmin=vmin, vmax=vmax)
for i, im in enumerate(images):
im.set_norm(norm)
if i > 0:
images[0].add_observer(im)
# The colorbar is also based on this master image.
fig.colorbar(images[0], cax, orientation='horizontal')
# We need the following only if we want to run this
# script interactively and be able to change the colormap.
pylab.sci(images[0])
pylab.show()
