def coord_event_handling(self, event, emitting=True):
# 1) get two coords from the event canvas, and 3rd from the fixed axis
# 2) determine which are the transverse images
if event.xdata == None or event.ydata == None:
# must have been a stray hit
return
if self._was_resized:
for fig in self.figs:
fig.draw(save=True)
print 'saved plots'
self._was_resized = False
fig = self.canvas_lookup[event.canvas]
fig_limits = fig.xlim + fig.ylim
# be safe with the coordinates
pu = np.clip(event.xdata, fig_limits[0], fig_limits[1])
pv = np.clip(event.ydata, fig_limits[2], fig_limits[3])
fig_idx = self.figs.index(fig)
# these will be the indices in an xyz list that correspond to pu,pv,
# and also to the indices of the figures/images to update
ui, vi = transverse_plane_lookup(fig_idx)
xyz = self._xyz_position[:]
xyz[ui] = pu
xyz[vi] = pv
self._xyz_position = xyz[:]
self._update_crosshairs()
# THE REST SHOULD BE TRIGGERED ON SOME KIND OF XYZ SIGNAL
if emitting:
self.emit_states(ui, vi)
def _cut_plane(self, ax, coord, **interp_kw):
"""
For a given axis in {SAG, COR, AXI}, make a plane cut in the
volume at the coordinate value.
Parameters
----------
ax : int
axis label in {SAG, COR, AXI} (defined in xipy.slicing)
coord : float
coordinate value along this axis
interp_kw : dict
Keyword args for the interpolating machinery
(ie, ndimage.map_coordinates keyword args)
Returns
_______
plane : ndarray
The transverse plane sampled at the grid points and fixed axis
coordinate for the given args
"""
# a little hokey
grid_lookup = {SAG: ('xshape', 'xgrid'),
COR: ('yshape', 'ygrid'),
AXI: ('zshape', 'zgrid')}
sname, gname= grid_lookup[ax]
ii, jj = transverse_plane_lookup(ax)
grid = getattr(self, gname)
shape = getattr(self, sname)
coords = np.empty((3, grid.shape[0]), 'd')
coords[ax] = self._closest_grid_pt(coord, ax)
coords[ii] = grid[:,0]; coords[jj] = grid[:,1]
pln = self.interpolator.evaluate(coords, **interp_kw).reshape(shape)
if self._masking:
m_pln = self.m_interpolator.evaluate(coords, mode='constant',
cval=-10).reshape(shape)
return np.ma.masked_where(m_pln < 0.5, pln)
return pln
