class Viewer1D(Viewer):
image = Array
result = Array
def _reconstruction_default(self):
rows, cols = self.image.shape[:2]
self.plot_data = ArrayPlotData(original=self.image[0],
reconstruction=self.result[0])
aspect = cols/float(rows)
old = Plot(self.plot_data)
old.plot('original', )
old.title = 'Old'
self.new = Plot(self.plot_data)
self.new.plot('reconstruction')
self.new.title = 'New'
container = HPlotContainer(bgcolor='none')
container.add(old)
container.add(self.new)
return container
def update_plot(self):
self.plot_data.set_data('reconstruction', self.result[0])
self.new.request_redraw()
def plotHistogram(self, image, plot=None):
if plot == None:
pd = ArrayPlotData(y=np.array([0]), x=np.array([0]))
plot = Plot(pd, padding=(70, 10, 0, 0))
plot.plot(('x', 'y'), name='Histogram', type='bar', bar_width=5.0, color='auto')
#plot.title = 'Histogram'
plot.line_color = 'black'
plot.bgcolor = "white"
plot.fixed_preferred_size = (100, 30)
add_default_grids(plot)
plot.value_axis.title = "Histogram"
self._appendHistogramTools(plot)
'''
plot.overlays.append(PlotAxis(plot, orientation='left'))
plot.overlays.append(PlotAxis(plot, orientation='bottom'))
'''
else:
data = np.histogram(image.data, bins=10000)
index = np.delete(data[1], data[1].size-1)
values = data[0]
plot.index_range.low= np.min(index)
plot.index_range.high = np.max(index)
plot.value_range.low = 0
plot.value_range.high = np.max(values)
plot.data.set_data('x', index)
plot.data.set_data('y', values)
plot.request_redraw()
return plot
class Viewer1D(Viewer):
image = Array
result = Array
def _reconstruction_default(self):
rows, cols = self.image.shape[:2]
self.plot_data = ArrayPlotData(original=self.image[0],
reconstruction=self.result[0])
aspect = cols / float(rows)
old = Plot(self.plot_data)
old.plot('original', )
old.title = 'Old'
self.new = Plot(self.plot_data)
self.new.plot('reconstruction')
self.new.title = 'New'
container = HPlotContainer(bgcolor='none')
container.add(old)
container.add(self.new)
return container
def update_plot(self):
self.plot_data.set_data('reconstruction', self.result[0])
self.new.request_redraw()
class BaseViewer(HasTraits):
reconstruction = Instance(Component)
image = Array
result = Array
save_file = File(exists=False, auto_set=False, enter_set=True)
save_button = Button('Save Result as .npy')
def __init__(self, **kwargs):
HasTraits.__init__(self, **kwargs)
def _reconstruction_default(self):
self.plot_data = ArrayPlotData(original=self.image,
reconstruction=self.result)
rows, cols = self.image.shape[:2]
aspect = cols / float(rows)
old = Plot(self.plot_data)
old.img_plot('original', colormap=gray, origin='top left')
old.title = 'Old'
old.aspect_ratio = aspect
self.new = Plot(self.plot_data)
self.new.img_plot('reconstruction', colormap=gray, origin='top left')
self.new.title = 'New'
self.new.aspect_ratio = aspect
container = HPlotContainer(bgcolor='none')
container.add(old)
container.add(self.new)
return container
def update_plot(self):
self.plot_data.set_data('reconstruction', self.result)
self.new.request_redraw()
def _save_button_changed(self):
try:
np.save(self.save_file, self.result)
except IOError, e:
message('Could not save file: %s' % str(e))
try:
f = open(self.save_file + '.txt', 'w')
f.write(str(self))
f.close()
except IOError:
message('Could not save file: %s' % str(e))
class BaseViewer(HasTraits):
reconstruction = Instance(Component)
image = Array
result = Array
save_file = File(exists=False, auto_set=False, enter_set=True)
save_button = Button('Save Result as .npy')
def __init__(self, **kwargs):
HasTraits.__init__(self, **kwargs)
def _reconstruction_default(self):
self.plot_data = ArrayPlotData(original=self.image,
reconstruction=self.result)
rows, cols = self.image.shape[:2]
aspect = cols/float(rows)
old = Plot(self.plot_data)
old.img_plot('original', colormap=gray, origin='top left')
old.title = 'Old'
old.aspect_ratio = aspect
self.new = Plot(self.plot_data)
self.new.img_plot('reconstruction', colormap=gray, origin='top left')
self.new.title = 'New'
self.new.aspect_ratio = aspect
container = HPlotContainer(bgcolor='none')
container.add(old)
container.add(self.new)
return container
def update_plot(self):
self.plot_data.set_data('reconstruction', self.result)
self.new.request_redraw()
def _save_button_changed(self):
try:
np.save(self.save_file, self.result)
except IOError as e:
message('Could not save file: %s' % str(e))
try:
f = open(self.save_file + '.txt', 'w')
f.write(str(self))
f.close()
except IOError:
message('Could not save file: %s' % str(e))
def plotRRMap(self, rr, rrchoice, plot=None):
if plot == None:
pd = ArrayPlotData(y=np.array([0]), x=np.array([0]))
plot = Plot(pd, padding=(70, 5, 0, 0))
self._setData(rr, plot)
plot.plot(('x', 'y'), name='rrplot', type="scatter", color='green',
marker="circle", marker_size=6)
#plot.title = 'rrplot'
plot.value_axis.title = rrchoice
#plot.y_axis.visible = False
plot.bgcolor = 'white'
plot.aspect_ratio = 2.5
plot.fixed_preferred_size = (100, 50)
#left, bottom = add_default_axes(plot)
hgrid, vgrid = add_default_grids(plot)
self._appendCMapTools(plot)
else:
self._setData(rr, plot)
plot.request_redraw()
return plot
class MyPlot(HasTraits):
""" Displays a plot with a few buttons to control which overlay
to display
"""
plot = Instance(Plot)
status_overlay = Instance(StatusLayer)
error_button = Button('Error')
warn_button = Button('Warning')
no_problem_button = Button('No problem')
traits_view = View( HGroup(UItem('error_button'),
UItem('warn_button'),
UItem('no_problem_button')),
UItem('plot', editor=ComponentEditor()),
width=700, height=600, resizable=True,
)
def __init__(self, index, data_series, **kw):
super(MyPlot, self).__init__(**kw)
plot_data = ArrayPlotData(index=index)
plot_data.set_data('data_series', data_series)
self.plot = Plot(plot_data)
self.plot.plot(('index', 'data_series'))
def _error_button_fired(self, event):
""" removes the old overlay and replaces it with
an error overlay
"""
self.clear_status()
self.status_overlay = ErrorLayer(component=self.plot,
align='ul', scale_factor=0.25)
self.plot.overlays.append(self.status_overlay)
self.plot.request_redraw()
def _warn_button_fired(self, event):
""" removes the old overlay and replaces it with
an warning overlay
"""
self.clear_status()
self.status_overlay = WarningLayer(component=self.plot,
align='ur', scale_factor=0.25)
self.plot.overlays.append(self.status_overlay)
self.plot.request_redraw()
def _no_problem_button_fired(self, event):
""" removes the old overlay
"""
self.clear_status()
self.plot.request_redraw()
def clear_status(self):
if self.status_overlay in self.plot.overlays:
# fade_out will remove the overlay when its done
self.status_overlay.fade_out()
def clone_plot(clonetool, drop_position):
# A little sketchy...
canvas = clonetool.component.container.component.component
# Create a new Plot object
oldplot = clonetool.component
newplot = Plot(oldplot.data)
basic_traits = ["orientation", "default_origin", "bgcolor", "border_color",
"border_width", "border_visible", "draw_layer", "unified_draw",
"fit_components", "fill_padding", "visible", "aspect_ratio",
"title"]
for attr in basic_traits:
setattr(newplot, attr, getattr(oldplot, attr))
# copy the ranges
dst = newplot.range2d
src = oldplot.range2d
#for attr in ('_low_setting', '_low_value', '_high_setting', '_high_value'):
# setattr(dst, attr, getattr(src, attr))
dst._xrange.sources = copy(src._xrange.sources)
dst._yrange.sources = copy(src._yrange.sources)
newplot.padding = oldplot.padding
newplot.bounds = oldplot.bounds[:]
newplot.resizable = ""
newplot.position = drop_position
newplot.datasources = copy(oldplot.datasources)
for name, renderers in oldplot.plots.items():
newrenderers = []
for renderer in renderers:
new_r = clone_renderer(renderer)
new_r.index_mapper = LinearMapper(range=newplot.index_range)
new_r.value_mapper = LinearMapper(range=newplot.value_range)
new_r._layout_needed = True
new_r.invalidate_draw()
new_r.resizable = "hv"
newrenderers.append(new_r)
newplot.plots[name] = newrenderers
#newplot.plots = copy(oldplot.plots)
for name, renderers in newplot.plots.items():
newplot.add(*renderers)
newplot.index_axis.title = oldplot.index_axis.title
newplot.index_axis.unified_draw = True
newplot.value_axis.title = oldplot.value_axis.title
newplot.value_axis.unified_draw = True
# Add new tools to the new plot
newplot.tools.append(AxisTool(component=newplot,
range_controller=canvas.range_controller))
# Add tools to the new plot
pan_traits = ["drag_button", "constrain", "constrain_key", "constrain_direction",
"speed"]
zoom_traits = ["tool_mode", "always_on", "axis", "enable_wheel", "drag_button",
"wheel_zoom_step", "enter_zoom_key", "exit_zoom_key", "pointer",
"color", "alpha", "border_color", "border_size", "disable_on_complete",
"minimum_screen_delta", "max_zoom_in_factor", "max_zoom_out_factor"]
move_traits = ["drag_button", "end_drag_on_leave", "cancel_keys", "capture_mouse",
"modifier_key"]
if not MULTITOUCH:
for tool in oldplot.tools:
if isinstance(tool, PanTool):
newtool = tool.clone_traits(pan_traits)
newtool.component = newplot
break
else:
newtool = PanTool(newplot)
# Reconfigure the pan tool to always use the left mouse, because we will
# put plot move on the right mouse button
newtool.drag_button = "left"
newplot.tools.append(newtool)
for tool in oldplot.tools:
if isinstance(tool, MoveTool):
newtool = tool.clone_traits(move_traits)
newtool.component = newplot
break
else:
newtool = MoveTool(newplot, drag_button="right")
newplot.tools.append(newtool)
for tool in oldplot.tools:
if isinstance(tool, ZoomTool):
newtool = tool.clone_traits(zoom_traits)
newtool.component = newplot
break
else:
newtool = ZoomTool(newplot)
newplot.tools.append(newtool)
else:
pz = MPPanZoom(newplot)
#pz.pan.constrain = True
#pz.pan.constrain_direction = "x"
#pz.zoom.mode = "range"
#pz.zoom.axis = "index"
newplot.tools.append(MPPanZoom(newplot))
#newplot.tools.append(MTMoveTool(
newplot._layout_needed = True
clonetool.dest.add(newplot)
newplot.invalidate_draw()
newplot.request_redraw()
canvas.request_redraw()
return
def clone_plot(clonetool, drop_position):
# A little sketchy...
canvas = clonetool.component.container.component.component
# Create a new Plot object
oldplot = clonetool.component
newplot = Plot(oldplot.data)
basic_traits = [
"orientation", "default_origin", "bgcolor", "border_color",
"border_width", "border_visible", "draw_layer", "unified_draw",
"fit_components", "fill_padding", "visible", "aspect_ratio", "title"
]
for attr in basic_traits:
setattr(newplot, attr, getattr(oldplot, attr))
# copy the ranges
dst = newplot.range2d
src = oldplot.range2d
#for attr in ('_low_setting', '_low_value', '_high_setting', '_high_value'):
# setattr(dst, attr, getattr(src, attr))
dst._xrange.sources = copy(src._xrange.sources)
dst._yrange.sources = copy(src._yrange.sources)
newplot.padding = oldplot.padding
newplot.bounds = oldplot.bounds[:]
newplot.resizable = ""
newplot.position = drop_position
newplot.datasources = copy(oldplot.datasources)
for name, renderers in list(oldplot.plots.items()):
newrenderers = []
for renderer in renderers:
new_r = clone_renderer(renderer)
new_r.index_mapper = LinearMapper(range=newplot.index_range)
new_r.value_mapper = LinearMapper(range=newplot.value_range)
new_r._layout_needed = True
new_r.invalidate_draw()
new_r.resizable = "hv"
newrenderers.append(new_r)
newplot.plots[name] = newrenderers
#newplot.plots = copy(oldplot.plots)
for name, renderers in list(newplot.plots.items()):
newplot.add(*renderers)
newplot.index_axis.title = oldplot.index_axis.title
newplot.index_axis.unified_draw = True
newplot.value_axis.title = oldplot.value_axis.title
newplot.value_axis.unified_draw = True
# Add new tools to the new plot
newplot.tools.append(
AxisTool(component=newplot, range_controller=canvas.range_controller))
# Add tools to the new plot
pan_traits = [
"drag_button", "constrain", "constrain_key", "constrain_direction",
"speed"
]
zoom_traits = [
"tool_mode", "always_on", "axis", "enable_wheel", "drag_button",
"wheel_zoom_step", "enter_zoom_key", "exit_zoom_key", "pointer",
"color", "alpha", "border_color", "border_size", "disable_on_complete",
"minimum_screen_delta", "max_zoom_in_factor", "max_zoom_out_factor"
]
move_traits = [
"drag_button", "end_drag_on_leave", "cancel_keys", "capture_mouse",
"modifier_key"
]
if not MULTITOUCH:
for tool in oldplot.tools:
if isinstance(tool, PanTool):
newtool = tool.clone_traits(pan_traits)
newtool.component = newplot
break
else:
newtool = PanTool(newplot)
# Reconfigure the pan tool to always use the left mouse, because we will
# put plot move on the right mouse button
newtool.drag_button = "left"
newplot.tools.append(newtool)
for tool in oldplot.tools:
if isinstance(tool, MoveTool):
newtool = tool.clone_traits(move_traits)
newtool.component = newplot
break
else:
newtool = MoveTool(newplot, drag_button="right")
newplot.tools.append(newtool)
for tool in oldplot.tools:
if isinstance(tool, ZoomTool):
newtool = tool.clone_traits(zoom_traits)
newtool.component = newplot
break
else:
newtool = ZoomTool(newplot)
newplot.tools.append(newtool)
else:
pz = MPPanZoom(newplot)
#pz.pan.constrain = True
#pz.pan.constrain_direction = "x"
#pz.zoom.mode = "range"
#pz.zoom.axis = "index"
newplot.tools.append(MPPanZoom(newplot))
#newplot.tools.append(MTMoveTool(
newplot._layout_needed = True
clonetool.dest.add(newplot)
newplot.invalidate_draw()
newplot.request_redraw()
canvas.request_redraw()
return
class TwoDimensionalPanel(Handler):
images = Dict # Str -> Tuple(imgd, affine, tkr_affine)
#original_current_image = Any #np.ndarray XxYxZ
current_image = Any # np.ndarray XxYxZ
#original_image is not allowed to be altered by thresholding.
#current_image may be reset by copying original whenever threshold change
current_affine = Any
current_tkr_affine = Any
xy_plane = Instance(Plot)
xz_plane = Instance(Plot)
yz_plane = Instance(Plot)
pins = Dict # Str -> (Str -> 3-Tuple)
pin_tolerance = DelegatesTo('info_panel')
minimum_contrast = DelegatesTo('info_panel')
maximum_contrast = DelegatesTo('info_panel')
current_pin = Str(None)
confirm_movepin_postproc_button = DelegatesTo('info_panel')
confirm_movepin_internal_button = DelegatesTo('info_panel')
add_electrode_button = DelegatesTo('info_panel')
move_electrode_internally_event = Event
move_electrode_postprocessing_event = Event
add_electrode_event = Event
track_cursor_button = DelegatesTo('info_panel')
track_cursor_event = Event
untrack_cursor_event = Event
panel2d_closed_event = Event
reset_image_button = DelegatesTo('info_panel')
info_panel = Instance(InfoPanel, ())
currently_showing_list = DelegatesTo('info_panel')
currently_showing = DelegatesTo('info_panel')
#later we will rename cursor to "coord"
cursor = Tuple # 3-tuple
null = Any # None
_finished_plotting = Bool(False)
traits_view = View(
Group(
HGroup(
Item(name='xz_plane', editor=ComponentEditor(),
height=400, width=400, show_label=False, resizable=True),
Item(name='yz_plane', editor=ComponentEditor(),
height=400, width=400, show_label=False, resizable=True),
),
HGroup(
Item(name='xy_plane', editor=ComponentEditor(),
height=400, width=400, show_label=False, resizable=True),
Item(name='info_panel',
editor=InstanceEditor(),
style='custom',
#Item(name='null', editor=NullEditor(),
height=400, width=400, show_label=False, resizable=True),
),
),
title='Contact 867-5309 for blobfish sales',
)
def map_cursor(self, cursor, affine, invert=False):
x,y,z = cursor
aff_to_use = np.linalg.inv(affine) if invert else affine
mcursor, = apply_affine([cursor], aff_to_use)
return tuple(map(lambda x: truncate(x, 2), mcursor))
#def cut_data(self, data, mcursor):
def cut_data(self, ndata, mcursor):
xm,ym,zm = [int(np.round(c)) for c in mcursor]
#xm, ym, zm = mcursor
#yz_cut = np.rot90(data[xm,:,:].T)
#xz_cut = np.rot90(data[:,ym,:].T)
#xy_cut = np.rot90(data[:,:,zm].T)
#ndata = data.copy()
ndata[ndata < self.minimum_contrast] = self.minimum_contrast
ndata[ndata > self.maximum_contrast] = self.maximum_contrast
yz_cut = ndata[xm,:,:].T
xz_cut = ndata[:,ym,:].T
xy_cut = ndata[:,:,zm].T
return xy_cut, xz_cut, yz_cut
def load_img(self, imgf, reorient2std=False, image_name=None):
self._finished_plotting = False
img = nib.load(imgf)
aff = np.dot(
get_orig2std(imgf) if reorient2std else np.eye(4),
img.get_affine())
tkr_aff = np.dot(
reorient_orig2std_tkr_mat if reorient2std else np.eye(4),
get_vox2rasxfm(imgf, stem='vox2ras-tkr'))
#from nilearn.image.resampling import reorder_img
#img = reorder_img(uimg, resample='continuous')
xsz, ysz, zsz = img.shape
#print 'image coordinate transform', img.get_affine()
imgd = img.get_data()
if reorient2std:
imgd = np.swapaxes(imgd, 1, 2)[:,:,::-1]
#print 'image size', imgd.shape
if image_name is None:
from utils import gensym
image_name = 'image%s'%gensym()
self.images[image_name] = (imgd, aff, tkr_aff)
self.currently_showing_list.append(
NullInstanceHolder(name=image_name))
self.currently_showing = self.currently_showing_list[-1]
self.pins[image_name] = {}
#self.current_pin = image_name
self.show_image(image_name)
@on_trait_change('currently_showing, minimum_contrast, maximum_contrast')
def switch_image(self):
self.show_image(self.currently_showing.name)
@on_trait_change('reset_image_button')
def center_image(self):
x, y, z = self.current_image.shape
for plane, (r,c) in zip((self.xy_plane, self.xz_plane, self.yz_plane),
((x,y), (x,z), (y,z))):
plane.index_mapper.range.low = 0
plane.value_mapper.range.low = 0
plane.index_mapper.range.high = r
plane.value_mapper.range.high = c
def show_image(self, image_name, xyz=None):
# XYZ is given in pixel coordinates
cur_img_t, self.current_affine, self.current_tkr_affine = (
self.images[image_name])
self.current_image = cur_img_t.copy()
if xyz is None:
xyz = tuple(np.array(self.current_image.shape) // 2)
self.cursor = x,y,z = xyz
xy_cut, xz_cut, yz_cut = self.cut_data(self.current_image,
self.cursor)
xsz, ysz, zsz = self.current_image.shape
xy_plotdata = ArrayPlotData()
xy_plotdata.set_data('imagedata', xy_cut)
xy_plotdata.set_data('cursor_x', np.array((x,)))
xy_plotdata.set_data('cursor_y', np.array((y,)))
xz_plotdata = ArrayPlotData()
xz_plotdata.set_data('imagedata', xz_cut)
xz_plotdata.set_data('cursor_x', np.array((x,)))
xz_plotdata.set_data('cursor_z', np.array((z,)))
yz_plotdata = ArrayPlotData()
yz_plotdata.set_data('imagedata', yz_cut)
yz_plotdata.set_data('cursor_y', np.array((y,)))
yz_plotdata.set_data('cursor_z', np.array((z,)))
self.xy_plane = Plot(xy_plotdata, bgcolor='black',
#aspect_ratio=xsz/ysz)
)
self.xz_plane = Plot(xz_plotdata, bgcolor='black',
#aspect_ratio=xsz/zsz)
)
self.yz_plane = Plot(yz_plotdata, bgcolor='black',
#aspect_ratio=ysz/zsz)
)
self.xy_plane.img_plot('imagedata',name='brain',colormap=bone_cmap)
self.xz_plane.img_plot('imagedata',name='brain',colormap=bone_cmap)
self.yz_plane.img_plot('imagedata',name='brain',colormap=bone_cmap)
self.xy_plane.plot(('cursor_x','cursor_y'), type='scatter',
color='red', marker='plus', size=3, name='cursor')
self.xz_plane.plot(('cursor_x','cursor_z'), type='scatter',
color='red', marker='plus', size=3, name='cursor')
self.yz_plane.plot(('cursor_y','cursor_z'), type='scatter',
color='red', marker='plus', size=3, name='cursor')
self.xy_plane.tools.append(Click2DPanelTool(self, 'xy'))
self.xz_plane.tools.append(Click2DPanelTool(self, 'xz'))
self.yz_plane.tools.append(Click2DPanelTool(self, 'yz'))
self.xy_plane.tools.append(ZoomTool( self.xy_plane ))
self.xz_plane.tools.append(ZoomTool( self.xz_plane ))
self.yz_plane.tools.append(ZoomTool( self.yz_plane ))
#self.xy_plane.tools.append(PanTool( self.xy_plane ))
#self.xz_plane.tools.append(PanTool( self.xz_plane ))
#self.yz_plane.tools.append(PanTool( self.yz_plane ))
self.info_panel.cursor = self.cursor
self.info_panel.cursor_ras = self.map_cursor(self.cursor,
self.current_affine)
self.info_panel.cursor_tkr = self.map_cursor(self.cursor,
self.current_tkr_affine)
self.info_panel.cursor_intensity = float(self.current_image[x,y,z])
self._finished_plotting = True
if image_name in self.pins:
for pin in self.pins[image_name]:
px, py, pz, pcolor = self.pins[image_name][pin]
self.drop_pin(px,py,pz, name=pin, color=pcolor)
def cursor_outside_image_dimensions(self, cursor, image=None):
if image is None:
image = self.current_image
x, y, z = cursor
x_sz, y_sz, z_sz = image.shape
if not 0 <= x < x_sz:
return True
if not 0 <= y < y_sz:
return True
if not 0 <= z < z_sz:
return True
return False
def move_cursor(self, x, y, z, suppress_cursor=False, suppress_ras=False,
suppress_tkr=False):
#it doesnt seem necessary for the instance variable cursor to exist
#at all but this code isn't broken
cursor = x,y,z
if self.cursor_outside_image_dimensions(cursor):
print ('Cursor %.2f %.2f %.2f outside image dimensions, doing '
'nothing'%(x,y,z))
return
self.cursor = cursor
xy_cut, xz_cut, yz_cut = self.cut_data(self.current_image, self.cursor)
print 'clicked on point %.2f %.2f %.2f'%(x,y,z)
self.xy_plane.data.set_data('imagedata', xy_cut)
self.xz_plane.data.set_data('imagedata', xz_cut)
self.yz_plane.data.set_data('imagedata', yz_cut)
self.xy_plane.data.set_data('cursor_x', np.array((x,)))
self.xy_plane.data.set_data('cursor_y', np.array((y,)))
self.xz_plane.data.set_data('cursor_x', np.array((x,)))
self.xz_plane.data.set_data('cursor_z', np.array((z,)))
self.yz_plane.data.set_data('cursor_y', np.array((y,)))
self.yz_plane.data.set_data('cursor_z', np.array((z,)))
if not suppress_cursor:
self.info_panel.cursor = tuple(
map(lambda x:truncate(x, 2), self.cursor))
if not suppress_ras:
self.info_panel.cursor_ras = self.map_cursor(self.cursor,
self.current_affine)
if not suppress_tkr:
self.info_panel.cursor_tkr = self.map_cursor(self.cursor,
self.current_tkr_affine)
self.info_panel.cursor_intensity = truncate(
self.current_image[x,y,z],3)
image_name = self.currently_showing.name
if image_name in self.pins:
for pin in self.pins[image_name]:
px, py, pz, pcolor = self.pins[image_name][pin]
self.drop_pin(px,py,pz, name=pin, color=pcolor)
self.untrack_cursor_event = True
def redraw(self):
self.xz_plane.request_redraw()
self.yz_plane.request_redraw()
self.xy_plane.request_redraw()
def drop_pin(self, x, y, z, name='pin', color='yellow',
image_name=None, ras_coords=False, alter_current_pin=True):
'''
XYZ is given in pixel space
'''
if ras_coords:
#affine might not necessarily be from image currently on display
_,_,affine = self.images[image_name]
ras_pin = self.map_cursor((x,y,z), affine, invert=True)
x,y,z = ras_pin
if image_name is None:
image_name = self.currently_showing.name
cx, cy, cz = self.cursor
tolerance = self.pin_tolerance
if image_name == self.currently_showing.name:
self.xy_plane.data.set_data('%s_x'%name,
np.array((x,) if np.abs(z - cz) < tolerance else ()))
self.xy_plane.data.set_data('%s_y'%name,
np.array((y,) if np.abs(z - cz) < tolerance else ()))
self.xz_plane.data.set_data('%s_x'%name,
np.array((x,) if np.abs(y - cy) < tolerance else ()))
self.xz_plane.data.set_data('%s_z'%name,
np.array((z,) if np.abs(y - cy) < tolerance else ()))
self.yz_plane.data.set_data('%s_y'%name,
np.array((y,) if np.abs(x - cx) < tolerance else ()))
self.yz_plane.data.set_data('%s_z'%name,
np.array((z,) if np.abs(x - cx) < tolerance else ()))
#if name not in self.pins[image_name]:
if name not in self.xy_plane.plots:
self.xy_plane.plot(('%s_x'%name,'%s_y'%name), type='scatter',
color=color, marker='dot', size=3, name=name)
self.xz_plane.plot(('%s_x'%name,'%s_z'%name), type='scatter',
color=color, marker='dot', size=3, name=name)
self.yz_plane.plot(('%s_y'%name,'%s_z'%name), type='scatter',
color=color, marker='dot', size=3, name=name)
self.redraw()
self.pins[image_name][name] = (x,y,z,color)
if alter_current_pin:
self.current_pin = name
def move_mouse(self, x, y, z):
mouse = (x,y,z)
if self.cursor_outside_image_dimensions(mouse):
return
self.info_panel.mouse = tuple(map(lambda x:truncate(x, 2), mouse))
self.info_panel.mouse_ras = self.map_cursor(mouse,
self.current_affine)
self.info_panel.mouse_tkr = self.map_cursor(mouse,
self.current_tkr_affine)
self.info_panel.mouse_intensity = truncate(
self.current_image[x,y,z], 3)
def _confirm_movepin_internal_button_fired(self):
self.move_electrode_internally_event = True
def _confirm_movepin_postproc_button_fired(self):
self.move_electrode_postprocessing_event = True
def _add_electrode_button_fired(self):
self.add_electrode_event = True
def _track_cursor_button_fired(self):
self.track_cursor_event = True
def closed(self, info, is_ok):
self.panel2d_closed_event = True
#because these calls all call map_cursor, which changes the listener
#variables they end up infinite looping.
#to solve this we manage _finished_plotting manually
#so that move_cursor is only called once when any listener is triggered
@on_trait_change('info_panel:cursor_csvlist')
def _listen_cursor(self):
if self._finished_plotting and len(self.info_panel.cursor) == 3:
self._finished_plotting = False
x,y,z = self.info_panel.cursor
self.move_cursor(x,y,z, suppress_cursor=True)
self._finished_plotting = True
@on_trait_change('info_panel:cursor_ras_csvlist')
def _listen_cursor_ras(self):
if self._finished_plotting and len(self.info_panel.cursor_ras) == 3:
self._finished_plotting = False
x,y,z = self.map_cursor(self.info_panel.cursor_ras,
self.current_affine, invert=True)
self.move_cursor(x,y,z, suppress_ras=True)
self._finished_plotting = True
@on_trait_change('info_panel:cursor_tkr_csvlist')
def _listen_cursor_tkr(self):
if self._finished_plotting and len(self.info_panel.cursor_tkr) == 3:
self._finished_plotting = False
x,y,z = self.map_cursor(self.info_panel.cursor_tkr,
self.current_tkr_affine, invert=True)
self.move_cursor(x,y,z, suppress_tkr=True)
self._finished_plotting = True
class MainPlot(HasTraits):
"""Main class for the primary solar image and point-by-point spectra.
Class creates image inspector tool object.
Controls file saving.
Main window that passes information on to source function plots
"""
#Place to extend program for extra filetypes
Name, fileExtension = os.path.splitext(FileLoadWindow.file_name_Ray)
if fileExtension == '.ncdf':
#Open .ncdf File to read
RayFile = Rh15dout()
RayFile.read_ray(FileLoadWindow.file_name_Ray)
WavelengthData = RayFile.ray.wavelength
IntensityData = RayFile.ray.intensity
elif fileExtension == 'Alternate file type':
pass
#Add code here to read in new file type
else:
print "Ray File Extension Not Recognized"
wave_ref = float(FileLoadWindow.Wave_Ref)
intensityindex = N.argmin(N.abs(WavelengthData[:]-wave_ref))
#self.RayFile.read_ray('/sanhome/tiago/rhout/cb24bih/MgII/PRD/385_PRD_newatom/output_ray.ncdf')
WavelengthIndex = Range(value = int(intensityindex), low = 0, high = WavelengthData.shape[0] - 1)
WavelengthFine = Range(value = wave_ref, low = wave_ref - 0.1, high = wave_ref + 0.1)
WavelengthView = Float(wave_ref)
Wavelength = Range(value = wave_ref, low = float(N.min(WavelengthData[:])), high = float(N.max(WavelengthData[:])))
save = Button('save')
SaveFlag = Bool()
VelocityView = Float()
ViewPosition = Array()
colormapminabs = float(N.min(IntensityData[:,:,intensityindex]))
colormapmaxabs = float(N.max(IntensityData[:,:,intensityindex]))
colormapmin = Range(low=colormapminabs, high=colormapmaxabs, value=colormapminabs)
colormapmax = Range(low=colormapminabs, high=colormapmaxabs, value=colormapmaxabs)
PrimaryPlotC = Instance(HPlotContainer)
windowwidth = 1500
ImageLock = Bool()
colormap_list = Enum('jet', 'gist_gray', 'gist_heat', 'Blues', 'hot')
Spectraplotzoom = Enum('Close', 'Far')
Markercolor = Enum('white', 'green', 'yellow', 'red')
traits_view = View(VGroup(Item('PrimaryPlotC', editor = ComponentEditor(), show_label = False, springy = True)),
Item('colormapmin', label = 'Minimum Intensity'), Item('colormapmax', label = 'Maximum Intensity'),
HGroup(Item('WavelengthIndex', label = 'Index', style = 'simple', editor = RangeEditor(mode = 'slider', low=0, high=WavelengthData.shape[0] - 1)), Item('WavelengthView', label = 'Wavelength')),
HGroup(Item('colormap_list', style = 'simple', label = 'Colormap'), Item('Spectraplotzoom', style = 'custom', label = 'Spectral Zoom Range'), Item('Markercolor', style = 'simple', label = 'Marker Color'), Item('VelocityView', label = 'Velocity (km/s)', style = 'readonly'), Item('ViewPosition', label = 'View Location', style = 'readonly'), Item('save', show_label = False)),
width = windowwidth, height = windowwidth/2.0 * 1.3, resizable = True, title = "Main Image Plot")
#Array used for passing information to speactra line plto and also Source Function Plots. Will be synced
InspectorPosition = Array()
def __init__(self):
super(MainPlot, self).__init__()
self.ImageLock = False
self.SaveFlag = False
self.create_PrimaryPlotC()
def create_PrimaryPlotC(self):
#Extracts the data for the main plot
self.MainPlotData = self.IntensityData[:,:,self.intensityindex]
self.markerplotdatax = []
self.markerplotdatay = []
self.SpectraMultiple = 1.0e+8
WavelengthXPoints = [self.Wavelength, self.Wavelength]
WavelengthYPoints = [N.min(self.IntensityData[:,:])*self.SpectraMultiple, N.max(self.IntensityData[:,:])*self.SpectraMultiple]
WavelengthXPointsStatic = WavelengthXPoints
WavelengthYPointsStatic = WavelengthYPoints
AvgIntensity = N.mean(N.mean(self.IntensityData, axis = 0), axis = 0)
print "mean computed-------------------"
#Create main Plot (intensity plot)
self.Mainplotdata = ArrayPlotData(Mainimagedata = self.MainPlotData, markerplotdatax = self.markerplotdatax, markerplotdatay = self.markerplotdatay)
self.Mainplot = Plot(self.Mainplotdata)
self.Main_img_plot = self.Mainplot.img_plot("Mainimagedata", colormap = jet)[0]
#Create marker wneh x is pressed
self.Mainplot.MarkerPlot = self.Mainplot.plot(("markerplotdatax","markerplotdatay"),type = 'line', color = 'white')
#Create overlaid crosshairs for Main plot
LineInspector1 = LineInspector(component = self.Main_img_plot,axis = 'index_x', write_metadata=True,is_listener = False,inspect_mode="indexed")
self.Main_img_plot.overlays.append(LineInspector1)
LineInspector2 = LineInspector(component = self.Main_img_plot,axis = 'index_y', write_metadata=True,is_listener = False,inspect_mode="indexed")
self.Main_img_plot.overlays.append(LineInspector2)
#Create overlay tools and add them to main plot
Main_imgtool = ImageInspectorTool(self.Main_img_plot)
self.Main_img_plot.tools.append(Main_imgtool)
Main_overlay = ImageInspectorOverlay(component = self.Main_img_plot, image_inspector = Main_imgtool, bgcolor = "white", border_visible = True)
self.Main_img_plot.overlays.append(Main_overlay)
#Sync up inspector position so it can be passed to the spectra plot
Main_overlay.sync_trait('InspectorPosition', self, 'InspectorPosition', mutual = True)
Main_imgtool.sync_trait('ImageLock', self, 'ImageLock')
Main_imgtool.sync_trait('SaveFlag', self, 'SaveFlag')
#Sync up max and min colormap value
self.Main_img_plot.value_range.sync_trait('low', self, 'colormapmin', mutual = True)
self.Main_img_plot.value_range.sync_trait('high', self, 'colormapmax', mutual = True)
#Create spectra plot for a single column
self.Spectraplotdata = ArrayPlotData(x = self.WavelengthData[:], y = (self.IntensityData[2,2] * self.SpectraMultiple), WavelengthXPointsStatic = WavelengthXPointsStatic, WavelengthYPointsStatic = WavelengthYPointsStatic, WavelengthXPoints = WavelengthXPoints, WavelengthYPoints = WavelengthYPoints, AvgIntensity = AvgIntensity * self.SpectraMultiple, is_listener = True)
self.Spectraplot1 = Plot(self.Spectraplotdata)
self.Spectraplot1.plot(("x","y"), type = "line", color = "blue")
self.Spectraplot1.plot( ("WavelengthXPointsStatic","WavelengthYPointsStatic"), type = 'line', line_style = 'dash', color = 'green')
self.Spectraplot1.plot( ("WavelengthXPoints","WavelengthYPoints"), type = 'line', line_style = 'dash', color = 'red')
self.Spectraplot1.plot( ("x","AvgIntensity"), type = 'line', color = 'black')
#Change Plot characteristics
#Sets width around waveref to examine
self.xlowspread = 1.
self.xhighspread = 1.
self.Spectraplot1.range2d.x_range.set_bounds(self.wave_ref - self.xlowspread, self.wave_ref + self.xhighspread)
self.Spectraplot1.x_axis.title = "Wavelength (A)"
self.Spectraplot1.y_axis.title = "Intensity (J s^-1 m^-2 Hz^-1 sr^-1)"
self.rangearray = self.IntensityData[:,:]
self.rangearray = self.rangearray[:,:,N.argmin(N.abs(self.WavelengthData[:]-(self.wave_ref-self.xlowspread))):N.argmin(N.abs(self.WavelengthData[:]-(self.wave_ref+self.xhighspread)))]
self.Spectraplot1.range2d.y_range.set_bounds(N.min(self.rangearray[:,:])*self.SpectraMultiple, N.max(self.rangearray[:,:]) * self.SpectraMultiple)
#self.Spectraplot1.range2d.y_range.set_bounds(N.min(self.rangearray[:,:])*self.SpectraMultiple, Scaling * (N.max(self.rangearray[:,:]) - N.min(self.rangearray[:,:])) + N.min(self.rangearray[:,:]) * self.SpectraMultiple)
#add some standard tools. Note, I'm assigning the PanTool to the
#right mouse-button to avoid conflicting with the cursors
self.Mainplot.tools.append(PanTool(self.Main_img_plot, drag_button="right"))
self.Spectraplot1.tools.append(PanTool(self.Spectraplot1, drag_button="right"))
self.Mainplot.overlays.append(ZoomTool(self.Main_img_plot))
self.Spectraplot1.overlays.append(ZoomTool(self.Spectraplot1))
#Changing interactive options
zoom = ZoomTool(component=self.Mainplot, tool_mode="box", always_on=False)
#Create Container for main plot
MainContainer = HPlotContainer(self.Mainplot, self.Spectraplot1, background = "lightgray", use_back_buffer = True)
MainContainer.spacing = 25
self.PrimaryPlotC = MainContainer
def _InspectorPosition_changed(self):
if self.ImageLock == True:
return
if len(self.InspectorPosition) > 0:
xi = self.InspectorPosition[0]
yi = self.InspectorPosition[1]
else:
xi = 0
yi = 0
return
self.ViewPosition = self.InspectorPosition
self.Spectraplotdata.set_data("y", self.IntensityData[self.InspectorPosition[0],self.InspectorPosition[1]] * self.SpectraMultiple)
def _WavelengthIndex_changed(self):
self.intensityindex = self.WavelengthIndex
self.Wavelength = self.WavelengthData[self.intensityindex]
self.WavelengthView = self.Wavelength
self.WavelengthXPoints = [self.Wavelength, self.Wavelength]
self.Spectraplotdata.set_data("WavelengthXPoints", self.WavelengthXPoints)
self.intensityindex = N.argmin(N.abs(self.WavelengthData[:]-float(self.Wavelength)))
self.VelocityView = 299792.45 * (self.Wavelength - self.wave_ref)/self.wave_ref
self.Mainplotdata.set_data("Mainimagedata", self.IntensityData[:,:,self.intensityindex])
self.Mainplot.request_redraw()
def _WavelengthView_changed(self):
self.WavelengthIndex = int(N.argmin(N.abs(self.WavelengthData[:]-self.WavelengthView)))
def _WavelengthFine_changed(self):
self.Wavelength = self.WavelengthFine
def _colormapmin_changed(self):
self.Mainplot.request_redraw()
def _colormapmax_changed(self):
self.Spectraplot1.range2d.y_range.set_bounds(N.min(self.rangearray[:,:])*self.SpectraMultiple, self.colormapmax * self.SpectraMultiple)
self.Mainplot.request_redraw()
def _colormap_list_changed(self):
# get range of current colormap
clr_range = self.Main_img_plot.color_mapper.range
color_mapper = eval(self.colormap_list)(clr_range)
self.Main_img_plot.color_mapper = color_mapper
self.Main_img_plot.request_redraw()
def _Spectraplotzoom_changed(self):
if (self.Spectraplotzoom == 'Close'):
self.Spectraplot1.range2d.x_range.set_bounds(self.wave_ref - self.xlowspread, self.wave_ref + self.xhighspread)
self.Spectraplot1.range2d.y_range.set_bounds(N.min(self.rangearray[:,:])*self.SpectraMultiple, self.colormapmax * self.SpectraMultiple)
elif (self.Spectraplotzoom == 'Far'):
self.Spectraplot1.range2d.x_range.set_bounds(float(N.min(self.WavelengthData[:])), float(N.max(self.WavelengthData[:])))
self.Spectraplot1.range2d.y_range.set_bounds(N.min(self.IntensityData[:,:,self.intensityindex]) * self.SpectraMultiple, N.max(self.IntensityData[:,:,self.intensityindex]) * self.SpectraMultiple)
self.Spectraplot1.request_redraw()
def _ImageLock_changed(self):
if self.ImageLock == True:
xsize = 3
xi = self.InspectorPosition[0]
yi = self.InspectorPosition[1]
self.markerplotdatax = [xi-xsize, xi+xsize, xi, xi-xsize, xi+xsize]
self.markerplotdatay = [yi-xsize, yi+xsize, yi, yi+xsize, yi-xsize]
elif self.ImageLock == False:
self.markerplotdatax = []
self.markerplotdatay = []
self.Mainplotdata.set_data("markerplotdatax", self.markerplotdatax)
self.Mainplotdata.set_data("markerplotdatay", self.markerplotdatay)
def _Markercolor_changed(self):
self.Mainplot.MarkerPlot[0].color = self.Markercolor
def _save_changed(self):
if self.SaveFlag == True:
self.SaveFlag = False
elif self.SaveFlag == False:
self.SaveFlag = True
def _SaveFlag_changed(self):
DPI = 72
print '--------------Save Initiated------------------'
#Main plot save code
size = (self.IntensityData[:,:,self.intensityindex].shape[0]*4, self.IntensityData[:,:,self.intensityindex].shape[1]*4)
path = os.getenv('PWD')
filenamelist = [path, '/', 'MainPlot_WaveLen', str(self.Wavelength).replace('.','_'), '.png']
filename = ''.join(filenamelist)
container = self.Main_img_plot
temp = container.outer_bounds
container.outer_bounds = list(size)
container.do_layout(force=True)
gc = PlotGraphicsContext(size, dpi=DPI)
gc.render_component(container)
gc.save(filename)
container.outer_bounds = temp
print "SAVED: ", filename
#Spectra plot save code
size = (1000,500)
path = os.getenv('PWD')
filenamelist = [path, '/', 'SpectraPlot_X', str(self.InspectorPosition[0]), '_Y', str(self.InspectorPosition[1]), '.png']
filename = ''.join(filenamelist)
container = self.Spectraplot1
temp = container.outer_bounds
container.outer_bounds = list(size)
container.do_layout(force=True)
gc = PlotGraphicsContext(size, dpi=DPI)
gc.render_component(container)
gc.save(filename)
container.outer_bounds = temp
print "SAVED: ", filename
return
class ResultExplorer(HasTraits):
# source of result data
Beamformer = Instance(BeamformerBase)
# traits for the plots
plot_data = Instance(ArrayPlotData, transient=True)
# the map
map_plot = Instance(Plot, transient=True)
imgp = Instance(ImagePlot, transient=True)
# map interpolation
interpolation = DelegatesTo('imgp', transient=True)
# the colorbar for the map
colorbar = Instance(ColorBar, transient=True)
# the spectrum
spec_plot = Instance(Plot, transient=True)
# the main container for the plots
plot_container = Instance(BasePlotContainer, transient=True)
# zoom and integration box tool for map plot
zoom = Instance(RectZoomSelect, transient=True)
# selection of freqs
synth = Instance(FreqSelector, transient=True)
# cursor tool for spectrum plot
cursor = Instance(BaseCursorTool, transient=True)
# dynamic range of the map
drange = Instance(DataRange1D, transient=True)
# dynamic range of the spectrum plot
yrange = Instance(DataRange1D, transient=True)
# set if plots are invalid
invalid = Bool(False, transient=True)
# remember the last valid result
last_valid_digest = Str(transient=True)
# starts calculation thread
start_calc = Button(transient=True)
# signals end of calculation
calc_ready = Event(transient=True)
# calculation thread
CThread = Instance(Thread, transient=True)
# is calculation active ?
running = Bool(False, transient=True)
rmesg = Property(depends_on='running')
# automatic recalculation ?
automatic = Bool(False, transient=True)
# picture
pict = File(filter=['*.png', '*.jpg'],
desc="name of picture file",
transient=True)
pic_x_min = Float(-1.0, desc="minimum x-value picture plane")
pic_y_min = Float(-0.75, desc="minimum y-value picture plane")
pic_scale = Float(400, desc="maximum x-value picture plane")
pic_flag = Bool(False, desc="show picture ?")
view = View(
HSplit(
VGroup(
HFlow(Item('synth{}', style='custom', width=0.8),
Item('start_calc{Recalc}', enabled_when='invalid'),
show_border=True),
TGroup(
Item('plot_container{}', editor=ComponentEditor()),
dock='vertical',
),
),
Tabbed(
[Item('Beamformer', style='custom'), '-<>[Beamform]'],
[
Item('Beamformer',
style='custom',
editor=InstanceEditor(view=fview)), '-<>[Data]'
],
),
# ['invalid{}~','last_valid_digest{}~',
# 'calc_ready','running',
# '|'],
dock='vertical'), #HSplit
statusbar=[StatusItem(name='rmesg', width=0.5)],
# icon= ImageResource('py.ico'),
title="Beamform Result Explorer",
resizable=True,
menubar=MenuBarManager(
MenuManager(
MenuManager(Action(name='Open', action='load'),
Action(name='Save as', action='save_as'),
name='&Project'),
MenuManager(Action(name='VI logger csv',
action='import_time_data'),
Action(name='Pulse mat',
action='import_bk_mat_data'),
Action(name='td file', action='import_td'),
name='&Import'),
MenuManager(Action(name='NI-DAQmx', action='import_nidaqmx'),
name='&Acquire'),
Action(name='R&un script', action='run_script'),
Action(name='E&xit', action='_on_close'),
name='&File',
),
MenuManager(
Group(
Action(name='&Delay+Sum',
style='radio',
action='set_Base',
checked=True),
Action(name='&Eigenvalue', style='radio',
action='set_Eig'),
Action(name='&Capon', style='radio', action='set_Capon'),
Action(name='M&usic', style='radio', action='set_Music'),
Action(name='D&amas', style='radio', action='set_Damas'),
Action(name='Clea&n', style='radio', action='set_Clean'),
Action(name='C&lean-SC',
style='radio',
action='set_Cleansc'),
Action(name='&Orthogonal',
style='radio',
action='set_Ortho'),
Action(name='&Functional',
style='radio',
action='set_Functional'),
Action(name='C&MF', style='radio', action='set_CMF'),
),
Separator(),
Action(name='Auto &recalc',
style='toggle',
checked_when='automatic',
action='toggle_auto'),
name='&Options',
),
MenuManager(
Group(
# Action(name='&Frequency', action='set_Freq'),
Action(name='&Interpolation method', action='set_interp'),
Action(name='&Map dynamic range', action='set_drange'),
Action(name='&Plot dynamic range', action='set_yrange'),
Action(name='Picture &underlay', action='set_pic'),
),
name='&View',
),
)) #View
# init the app
def __init__(self, **kwtraits):
super(ResultExplorer, self).__init__(**kwtraits)
# containers
bgcolor = "sys_window" #(212/255.,208/255.,200/255.) # Windows standard background
self.plot_container = container = VPlotContainer(use_backbuffer=True,
padding=0,
fill_padding=False,
valign="center",
bgcolor=bgcolor)
subcontainer = HPlotContainer(use_backbuffer=True,
padding=0,
fill_padding=False,
halign="center",
bgcolor=bgcolor)
# freqs
self.synth = FreqSelector(parent=self)
# data source
self.plot_data = pd = ArrayPlotData()
self.set_result_data()
self.set_pict()
# map plot
self.map_plot = Plot(pd, padding=40)
self.map_plot.img_plot("image", name="image")
imgp = self.map_plot.img_plot("map_data", name="map", colormap=jet)[0]
self.imgp = imgp
t1 = self.map_plot.plot(("xpoly", "ypoly"),
name="sector",
type="polygon")
t1[0].face_color = (0, 0, 0, 0) # set face color to transparent
# map plot tools and overlays
imgtool = ImageInspectorTool(imgp)
imgp.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=imgp,
image_inspector=imgtool,
bgcolor="white",
border_visible=True)
self.map_plot.overlays.append(overlay)
self.zoom = RectZoomSelect(self.map_plot,
drag_button='right',
always_on=True,
tool_mode='box')
self.map_plot.overlays.append(self.zoom)
self.map_plot.tools.append(PanTool(self.map_plot))
# colorbar
colormap = imgp.color_mapper
self.drange = colormap.range
self.drange.low_setting = "track"
self.colorbar = cb = ColorBar(
index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=self.map_plot,
orientation='v',
resizable='v',
width=10,
padding=20)
# colorbar tools and overlays
range_selection = RangeSelection(component=cb)
cb.tools.append(range_selection)
cb.overlays.append(
RangeSelectionOverlay(component=cb,
border_color="white",
alpha=0.8,
fill_color=bgcolor))
range_selection.listeners.append(imgp)
# spectrum plot
self.spec_plot = Plot(pd, padding=25)
px = self.spec_plot.plot(("freqs", "spectrum"),
name="spectrum",
index_scale="log")[0]
self.yrange = self.spec_plot.value_range
px.index_mapper = MyLogMapper(range=self.spec_plot.index_range)
# spectrum plot tools
self.cursor = CursorTool(
px) #, drag_button="left", color='blue', show_value_line=False)
px.overlays.append(self.cursor)
self.cursor.current_position = 0.3, 0.5
px.index_mapper.map_screen(0.5)
# self.map_plot.tools.append(SaveTool(self.map_plot, filename='pic.png'))
# layout
self.set_map_details()
self.reset_sector()
subcontainer.add(self.map_plot)
subcontainer.add(self.colorbar)
# subcontainer.tools.append(SaveTool(subcontainer, filename='pic.png'))
container.add(self.spec_plot)
container.add(subcontainer)
container.tools.append(SaveTool(container, filename='pic.pdf'))
self.last_valid_digest = self.Beamformer.ext_digest
def _get_rmesg(self):
if self.running:
return "Running ..."
else:
return "Ready."
@on_trait_change('Beamformer.ext_digest')
def invalidate(self):
if self.last_valid_digest != "" and self.Beamformer.ext_digest != self.last_valid_digest:
self.invalid = True
def _start_calc_fired(self):
if self.CThread and self.CThread.isAlive():
pass
else:
self.CThread = CalcThread()
self.CThread.b = self.Beamformer
self.CThread.caller = self
self.CThread.start()
def _calc_ready_fired(self):
f = self.Beamformer.freq_data
low, high = f.freq_range
print low, high
fr = f.fftfreq()
if self.synth.synth_freq < low:
self.synth.synth_freq = fr[1]
if self.synth.synth_freq > high:
self.synth.synth_freq = fr[-2]
self.set_result_data()
self.set_map_details()
self.plot_container.request_redraw()
self.map_plot.request_redraw()
@on_trait_change('invalid')
def activate_plot(self):
self.plot_container.visible = not self.invalid
self.plot_container.request_redraw()
if self.invalid and self.automatic:
self._start_calc_fired()
@on_trait_change('cursor.current_position')
def update_synth_freq(self):
self.synth.synth_freq = self.cursor.current_position[0]
def reset_sector(self):
g = self.Beamformer.grid
if self.zoom:
self.zoom.x_min = g.x_min
self.zoom.y_min = g.y_min
self.zoom.x_max = g.x_max
self.zoom.y_max = g.y_max
@on_trait_change(
'zoom.box,synth.synth_freq,synth.synth_type,drange.+,yrange.+')
def set_result_data(self):
if self.invalid:
return
if self.cursor:
self.cursor.current_position = self.synth.synth_freq, 0
pd = self.plot_data
if not pd:
return
g = self.Beamformer.grid
try:
map_data = self.Beamformer.synthetic(self.synth.synth_freq,
self.synth.synth_type_).T
map_data = L_p(map_data)
except:
map_data = arange(0, 19.99, 20. / g.size).reshape(g.shape)
pd.set_data("map_data", map_data)
f = self.Beamformer.freq_data
if self.zoom and self.zoom.box:
sector = self.zoom.box
else:
sector = (g.x_min, g.y_min, g.x_max, g.y_max)
pd.set_data("xpoly", array(sector)[[0, 2, 2, 0]])
pd.set_data("ypoly", array(sector)[[1, 1, 3, 3]])
ads = pd.get_data("freqs")
if not ads:
freqs = ArrayDataSource(f.fftfreq()[f.ind_low:f.ind_high],
sort_order='ascending')
pd.set_data("freqs", freqs)
else:
ads.set_data(f.fftfreq()[f.ind_low:f.ind_high],
sort_order='ascending')
self.synth.enumerate()
try:
spectrum = self.Beamformer.integrate(sector)[f.ind_low:f.ind_high]
spectrum = L_p(spectrum)
except:
spectrum = f.fftfreq()[f.ind_low:f.ind_high]
pd.set_data("spectrum", spectrum)
@on_trait_change('pic+')
def set_map_details(self):
if self.invalid:
return
mp = self.map_plot
# grid
g = self.Beamformer.grid
xs = linspace(g.x_min, g.x_max, g.nxsteps)
ys = linspace(g.y_min, g.y_max, g.nysteps)
mp.range2d.sources[1].set_data(xs,
ys,
sort_order=('ascending', 'ascending'))
mp.aspect_ratio = (xs[-1] - xs[0]) / (ys[-1] - ys[0])
yl, xl = self.plot_data.get_data("image").shape[0:2]
xp = (self.pic_x_min, self.pic_x_min + xl * 1.0 / self.pic_scale)
yp = (self.pic_y_min, self.pic_y_min + yl * 1.0 / self.pic_scale)
mp.range2d.sources[0].set_data(xp,
yp,
sort_order=('ascending', 'ascending'))
mp.range2d.low_setting = (g.x_min, g.y_min)
mp.range2d.high_setting = (g.x_max, g.y_max)
# dynamic range
map = mp.plots["map"][0]
#map.color_mapper.range.low_setting="track"
# colormap
map.color_mapper._segmentdata['alpha'] = [(0.0, 0.0, 0.0),
(0.001, 0.0, 1.0),
(1.0, 1.0, 1.0)]
map.color_mapper._recalculate()
mp.request_redraw()
@on_trait_change('pict')
def set_pict(self):
pd = self.plot_data
if not pd:
return
try:
imgd = ImageData.fromfile(self.pict)._data[::-1]
except:
imgd = ImageData()
imgd.set_data(255 * ones((2, 2, 3), dtype='uint8'))
imgd = imgd._data
pd.set_data("image", imgd)
def save_as(self):
dlg = FileDialog(action='save as', wildcard='*.rep')
dlg.open()
if dlg.filename != '':
fi = file(dlg.filename, 'w')
dump(self, fi)
fi.close()
def load(self):
dlg = FileDialog(action='open', wildcard='*.rep')
dlg.open()
if dlg.filename != '':
fi = file(dlg.filename, 'rb')
s = load(fi)
self.copy_traits(s)
fi.close()
def run_script(self):
dlg = FileDialog(action='open', wildcard='*.py')
dlg.open()
if dlg.filename != '':
#~ try:
rx = self
b = rx.Beamformer
script = dlg.path
execfile(dlg.path)
#~ except:
#~ pass
def import_time_data(self):
t = self.Beamformer.freq_data.time_data
ti = csv_import()
ti.from_file = 'C:\\tyto\\array\\07.03.2007 16_45_59,203.txt'
ti.configure_traits(kind='modal')
t.name = ""
ti.get_data(t)
def import_bk_mat_data(self):
t = self.Beamformer.freq_data.time_data
ti = bk_mat_import()
ti.from_file = 'C:\\work\\1_90.mat'
ti.configure_traits(kind='modal')
t.name = ""
ti.get_data(t)
def import_td(self):
t = self.Beamformer.freq_data.time_data
ti = td_import()
ti.from_file = 'C:\\work\\x.td'
ti.configure_traits(kind='modal')
t.name = ""
ti.get_data(t)
def import_nidaqmx(self):
t = self.Beamformer.freq_data.time_data
ti = nidaq_import()
ti.configure_traits(kind='modal')
t.name = ""
ti.get_data(t)
def set_Base(self):
b = self.Beamformer
self.Beamformer = BeamformerBase(freq_data=b.freq_data,
grid=b.grid,
mpos=b.mpos,
c=b.c,
env=b.env)
self.invalid = True
def set_Eig(self):
b = self.Beamformer
self.Beamformer = BeamformerEig(freq_data=b.freq_data,
grid=b.grid,
mpos=b.mpos,
c=b.c,
env=b.env)
self.invalid = True
def set_Capon(self):
b = self.Beamformer
self.Beamformer = BeamformerCapon(freq_data=b.freq_data,
grid=b.grid,
mpos=b.mpos,
c=b.c,
env=b.env)
self.invalid = True
def set_Music(self):
b = self.Beamformer
self.Beamformer = BeamformerMusic(freq_data=b.freq_data,
grid=b.grid,
mpos=b.mpos,
c=b.c,
env=b.env)
self.invalid = True
def set_Damas(self):
b = self.Beamformer
self.Beamformer = BeamformerDamas(beamformer=BeamformerBase(
freq_data=b.freq_data, grid=b.grid, mpos=b.mpos, c=b.c, env=b.env))
self.invalid = True
def set_Cleansc(self):
b = self.Beamformer
self.Beamformer = BeamformerCleansc(freq_data=b.freq_data,
grid=b.grid,
mpos=b.mpos,
c=b.c,
env=b.env)
self.invalid = True
def set_Ortho(self):
b = self.Beamformer
self.Beamformer = BeamformerOrth(beamformer=BeamformerEig(
freq_data=b.freq_data, grid=b.grid, mpos=b.mpos, c=b.c, env=b.env))
self.Beamformer.n = 10
self.invalid = True
def set_Functional(self):
b = self.Beamformer
self.Beamformer = BeamformerFunctional(freq_data=b.freq_data,
grid=b.grid,
mpos=b.mpos,
c=b.c,
env=b.env)
self.invalid = True
def set_Clean(self):
b = self.Beamformer
self.Beamformer = BeamformerClean(beamformer=BeamformerBase(
freq_data=b.freq_data, grid=b.grid, mpos=b.mpos, c=b.c, env=b.env))
self.invalid = True
def set_CMF(self):
b = self.Beamformer
self.Beamformer = BeamformerCMF(freq_data=b.freq_data,
grid=b.grid,
mpos=b.mpos,
c=b.c,
env=b.env)
self.invalid = True
def toggle_auto(self):
self.automatic = not self.automatic
def set_interp(self):
self.configure_traits(kind='live', view=interpview)
def set_drange(self):
self.drange.configure_traits(kind='live', view=drangeview)
def set_yrange(self):
self.yrange.configure_traits(kind='live', view=drangeview)
def set_pic(self):
self.configure_traits(kind='live', view=picview)
class PeakFitWindow(HasTraits):
pairs = List(DatasetPair)
selected = Instance(DatasetPair)
plot = Plot
table_editor = TableEditor(auto_size=True,
selection_mode='row',
sortable=False,
configurable=False,
editable=False,
cell_bg_color='white',
label_bg_color=(232, 232, 232),
selection_bg_color=(232, 232, 232),
columns=[
ObjectColumn(label='First position',
name='first_name',
cell_color='white',
width=0.33),
ObjectColumn(label='Second position',
name='second_name',
cell_color='white',
width=0.33),
ObjectColumn(label='Peak difference',
name='peak_diff',
cell_color='white',
width=0.33),
])
traits_view = View(HGroup(
Item('pairs', editor=table_editor, show_label=False),
UItem('plot', editor=ComponentEditor(bgcolor='white')),
),
resizable=True,
width=0.75,
height=0.5,
kind='livemodal',
title='View peak fits')
def __init__(self, *args, **kwargs):
self.pairs = [
DatasetPair(first=d1, second=d2)
for d1, d2 in kwargs['dataset_pairs']
]
self.pairs.sort(key=lambda pair: pair.first.name)
self.range = kwargs.pop('range')
super(PeakFitWindow, self).__init__(*args, **kwargs)
self.table_editor.on_select = self._selection_changed
self.selected = self.pairs[0] if self.pairs else None
self.data = ArrayPlotData()
self.plot = Plot(self.data)
self.zoom_tool = ClickUndoZoomTool(self.plot,
tool_mode="box",
always_on=True,
drag_button=settings.zoom_button,
undo_button=settings.undo_button,
zoom_to_mouse=True)
self.plot.overlays.append(self.zoom_tool)
for pair in self.pairs:
self._plot_dataset(pair.first)
self._plot_dataset(pair.second, offset=pair.peak_diff)
self.plot.request_redraw()
def _selection_changed(self, selected_objs):
if self.selected:
self.plot.plots[self.selected.first.name][0].visible = False
self.plot.plots[self.selected.second.name][0].visible = False
self.selected = selected_objs
if self.selected is None:
return
self.plot.plots[self.selected.first.name][0].visible = True
self.plot.plots[self.selected.second.name][0].visible = True
self.plot.request_redraw()
def _plot_dataset(self, dataset, offset=0.0):
x, y = dataset.data[:, [0, 1]].T
self.data.set_data(dataset.name + '_x', x + offset)
self.data.set_data(dataset.name + '_y', y)
plot = self.plot.plot((dataset.name + '_x', dataset.name + '_y'),
type='line',
color=dataset.metadata['ui'].color,
name=dataset.name,
visible=False)
range = plot[0].index_mapper.range
range.low, range.high = self.range
pvr = plot[0].value_range
y_in_range = y[(x > range.low) & (x < range.high)]
pvr.low_setting, pvr.high_setting = (y_in_range.min(),
y_in_range.max())
pvr.refresh()
class PeakFitWindow(HasTraits):
pairs = List(DatasetPair)
selected = Instance(DatasetPair)
plot = Plot
table_editor = TableEditor(
auto_size=True,
selection_mode='row',
sortable=False,
configurable=False,
editable=False,
cell_bg_color='white',
label_bg_color=(232,232,232),
selection_bg_color=(232,232,232),
columns=[
ObjectColumn(label='First position', name='first_name', cell_color='white', width=0.33),
ObjectColumn(label='Second position', name='second_name', cell_color='white', width=0.33),
ObjectColumn(label='Peak difference', name='peak_diff', cell_color='white', width=0.33),
]
)
traits_view = View(
HGroup(
Item('pairs', editor=table_editor, show_label=False),
UItem('plot', editor=ComponentEditor(bgcolor='white')),
),
resizable=True, width=0.75, height=0.5, kind='livemodal',
title='View peak fits'
)
def __init__(self, *args, **kwargs):
self.pairs = [ DatasetPair(first=d1, second=d2) for d1, d2 in kwargs['dataset_pairs'] ]
self.pairs.sort(key=lambda pair: pair.first.name)
self.range = kwargs.pop('range')
super(PeakFitWindow, self).__init__(*args, **kwargs)
self.table_editor.on_select = self._selection_changed
self.selected = self.pairs[0] if self.pairs else None
self.data = ArrayPlotData()
self.plot = Plot(self.data)
self.zoom_tool = ClickUndoZoomTool(self.plot,
tool_mode="box", always_on=True,
drag_button=settings.zoom_button,
undo_button=settings.undo_button,
zoom_to_mouse=True)
self.plot.overlays.append(self.zoom_tool)
for pair in self.pairs:
self._plot_dataset(pair.first)
self._plot_dataset(pair.second, offset=pair.peak_diff)
self.plot.request_redraw()
def _selection_changed(self, selected_objs):
if self.selected:
self.plot.plots[self.selected.first.name][0].visible = False
self.plot.plots[self.selected.second.name][0].visible = False
self.selected = selected_objs
if self.selected is None:
return
self.plot.plots[self.selected.first.name][0].visible = True
self.plot.plots[self.selected.second.name][0].visible = True
self.plot.request_redraw()
def _plot_dataset(self, dataset, offset=0.0):
x, y = dataset.data[:,[0,1]].T
self.data.set_data(dataset.name + '_x', x + offset)
self.data.set_data(dataset.name + '_y', y)
plot = self.plot.plot((dataset.name + '_x', dataset.name + '_y'),
type='line',
color=dataset.metadata['ui'].color,
name=dataset.name,
visible=False)
range = plot[0].index_mapper.range
range.low, range.high = self.range
pvr = plot[0].value_range
y_in_range = y[(x>range.low) & (x<range.high)]
pvr.low_setting, pvr.high_setting = (y_in_range.min(), y_in_range.max())
pvr.refresh()
class DataPlotter(traits.HasTraits):
plot = traits.Instance(
Plot
) # the attribute 'plot' of class DataPlotter is a trait that has to be an instance of the chaco class Plot.
plot_data = traits.Instance(ArrayPlotData)
data_index = traits.List(traits.Int)
data_selected = traits.List(traits.Int)
y_offset = traits.Range(0.0, 20.0, value=0)
x_offset = traits.Range(-10.0, 10.0, value=0)
y_scale = traits.Range(1e-3, 2.0, value=1.0)
x_range_up = traits.Float()
x_range_dn = traits.Float()
x_range_btn = traits.Button(label="Set range")
reset_plot_btn = traits.Button(label="Reset plot")
select_all_btn = traits.Button(label="All")
select_none_btn = traits.Button(label="None")
# basic processing
lb = traits.Float(10.0)
lb_btn = traits.Button(label="Apodisation")
lb_plt_btn = traits.Button(label="Plot Apod.")
zf_btn = traits.Button(label="Zero-fill")
ft_btn = traits.Button(label="FT")
# phasing
ph_auto_btn = traits.Button(label="Auto: all")
ph_auto_single_btn = traits.Button(label="Auto: selected")
ph_mp = traits.Bool(True, label="Parallelise")
ph_man_btn = traits.Button(label="Manual")
ph_global = traits.Bool(label="apply globally")
_peak_marker_overlays = {}
_bl_marker_overlays = {}
_bl_range_plots = {}
# baseline correctoin
bl_cor_btn = traits.Button(label="BL correct")
bl_sel_btn = traits.Button(label="Select points")
# peak-picking
peak_pick_btn = traits.Button(label="Peak-picking")
peak_pick_clear = traits.Button(label="Clear peaks")
deconvolute_btn = traits.Button(label="Deconvolute")
plot_decon_btn = traits.Button(label="Show Lineshapes")
lorgau = traits.Range(0.0, 1.0, value=0, label="Lorentzian = 0, Gaussian = 1")
deconvolute_mp = traits.Bool(True, label="Parallelise")
plot_ints_btn = traits.Button(label="Plot integrals")
save_fids_btn = traits.Button(label="Save FIDs")
save_ints_btn = traits.Button(label="Integrals -> csv")
_peaks_now = []
_ranges_now = []
_bl_ranges_now = []
_bl_ranges = []
_bl_indices = []
_flags = {"manphasing": False, "bl_selecting": False, "picking": False, "lineshapes_vis": False}
# progress bar
# progress_val = traits.Int()
loading_animation = traits.File("/home/jeicher/Apps/NMRPy/nmrpy/loading.gif")
busy_animation = traits.Bool(False)
# def _metadata_handler(self):
# return #print self.metadata_source.metadata.get('selections')
def _bl_handler(self):
# set_trace()
self._bl_ranges_now = list(self.bl_tool.ranges_now)
if self.bl_tool.ranges_now:
picked_ranges = [self.correct_padding(r) for r in self.bl_tool.ranges_now]
if self._bl_ranges is not picked_ranges:
self._bl_ranges = picked_ranges
self.bl_marker(self._bl_ranges[-1][0], colour="blue")
self.bl_marker(self._bl_ranges[-1][1], colour="blue")
l_bl = len(self._bl_ranges) - 1
bl_range_x = self._bl_ranges[l_bl]
y = 0.2 * self.plot.range2d.y_range.high
bl_range_y = [y, y]
self.plot_data.set_data("bl_range_x_%i" % (l_bl), bl_range_x)
self.plot_data.set_data("bl_range_y_%i" % (l_bl), bl_range_y)
self._bl_range_plots["bl_range_x_%i" % (l_bl)] = self.plot.plot(
("bl_range_x_%i" % (l_bl), "bl_range_y_%i" % (l_bl)),
type="line",
name="bl_range_%i" % (l_bl),
line_width=0.5,
color="blue",
)[0]
def _peak_handler(self):
# set_trace()
self._peaks_now = list(self.picking_tool.peaks_now)
self._ranges_now = list(self.picking_tool.ranges_now)
picked_peaks = self.index2ppm(np.array(self.picking_tool.peaks_now))
if picked_peaks != self.fid.peaks:
self.fid.peaks = picked_peaks
if self.fid.peaks:
peak_ppm = self.fid.peaks[-1]
self.plot_marker(peak_ppm)
if self.picking_tool.ranges_now:
picked_ranges = [self.correct_padding(r) for r in self.picking_tool.ranges_now]
if self.fid.ranges is not picked_ranges:
self.fid.ranges = picked_ranges
self.range_marker(self.fid.ranges[-1][0], colour="blue")
self.range_marker(self.fid.ranges[-1][1], colour="blue")
def index2ppm(self, index):
return list(
np.array(
self.fid.params["sw_left"] - (index - self.plot.padding_left) / self.plot.width * self.fid.params["sw"],
dtype="float16",
)
)
def correct_padding(self, values):
return list(
np.array(
np.array(values) + float(self.plot.padding_left) / self.plot.width * self.fid.params["sw"],
dtype="float16",
)
)
def __init__(self, fid):
super(DataPlotter, self).__init__()
self.fid = fid
data = fid.data
self.data_index = range(len(data))
if self.fid._flags["ft"]:
self.x = np.linspace(
self.fid.params["sw_left"], fid.params["sw_left"] - fid.params["sw"], len(self.fid.data[0])
)
self.plot_data = ArrayPlotData(x=self.x, *np.real(data))
plot = Plot(self.plot_data, default_origin="bottom right", padding=[5, 0, 0, 35])
else:
self.x = np.linspace(0, self.fid.params["at"], len(self.fid.data[0]))
self.plot_data = ArrayPlotData(x=self.x, *np.real(data))
plot = Plot(self.plot_data, default_origin="bottom left", padding=[5, 0, 0, 35])
self.plot = plot
self.plot_init()
def plot_init(self, index=[0]):
if self.fid._flags["ft"]:
self.plot.x_axis.title = "ppm."
else:
self.plot.x_axis.title = "sec."
self.zoomtool = BetterZoom(self.plot, zoom_to_mouse=False, x_min_zoom_factor=1, zoom_factor=1.5)
self.pantool = PanTool(self.plot)
self.phase_dragtool = PhaseDragTool(self.plot)
self.plot.tools.append(self.zoomtool)
self.plot.tools.append(self.pantool)
self.plot.y_axis.visible = False
for i in index:
self.plot.plot(("x", "series%i" % (i + 1)), type="line", line_width=0.5, color="black")[0]
self.plot.request_redraw()
self.old_y_scale = self.y_scale
self.index_array = np.arange(len(self.fid.data))
self.y_offsets = self.index_array * self.y_offset
self.x_offsets = self.index_array * self.x_offset
self.data_selected = index
self.x_range_up = round(self.x[0], 3)
self.x_range_dn = round(self.x[-1], 3)
# this is necessary for phasing:
self.plot._ps = self.fid.ps
self.plot._data_complex = self.fid.data
def text_marker(self, text, colour="black"):
xl, xh, y = self.plot.range2d.x_range.low, self.plot.range2d.x_range.high, self.plot.range2d.y_range.high
x = xl + 0.1 * (xh - xl)
y = 0.8 * y
dl = DataLabel(
self.plot.plots["plot0"][0],
data_point=(x, y),
label_position="top",
label_text=text,
show_label_coords=False,
marker_visible=False,
border_visible=True,
arrow_visible=False,
)
self.plot.plots["plot0"][0].overlays.append(dl)
self.plot.request_redraw()
def plot_marker(self, ppm, colour="red"):
dl = DataLabel(
self.plot.plots["plot0"][0],
data_point=(ppm, 0.0),
arrow_color=colour,
arrow_size=10,
label_position="top",
label_format="%(x).3f",
padding_bottom=int(self.plot.height * 0.1),
marker_visible=False,
border_visible=False,
arrow_visible=True,
)
self.plot.plots["plot0"][0].overlays.append(dl)
self._peak_marker_overlays[ppm] = dl
self.plot.request_redraw()
def range_marker(self, ppm, colour="blue"):
dl = DataLabel(
self.plot.plots["plot0"][0],
data_point=(ppm, 0.0),
arrow_color=colour,
arrow_size=10,
label_position="top",
label_format="%(x).3f",
padding_bottom=int(self.plot.height * 0.25),
marker_visible=False,
border_visible=False,
arrow_visible=True,
)
self.plot.plots["plot0"][0].overlays.append(dl)
self._peak_marker_overlays[ppm] = dl
self.plot.request_redraw()
def bl_marker(self, ppm, colour="blue"):
dl = DataLabel(
self.plot.plots["plot0"][0],
data_point=(ppm, 0.0),
arrow_color=colour,
arrow_size=10,
label_position="top",
label_format="%(x).3f",
padding_bottom=int(self.plot.height * 0.25),
marker_visible=False,
border_visible=False,
arrow_visible=True,
)
self.plot.plots["plot0"][0].overlays.append(dl)
self._bl_marker_overlays[ppm] = dl
self.plot.request_redraw()
def _x_range_btn_fired(self):
if self.x_range_up < self.x_range_dn:
xr = self.x_range_up
self.x_range_up = self.x_range_dn
self.x_range_dn = xr
self.set_x_range(up=self.x_range_up, dn=self.x_range_dn)
self.plot.request_redraw()
def set_x_range(self, up=x_range_up, dn=x_range_dn):
if self.fid._flags["ft"]:
self.plot.index_range.high = up
self.plot.index_range.low = dn
else:
self.plot.index_range.high = dn
self.plot.index_range.low = up
pass
def _y_scale_changed(self):
self.set_y_scale(scale=self.y_scale)
def set_y_scale(self, scale=y_scale):
self.plot.value_range.high /= scale / self.old_y_scale
self.plot.request_redraw()
self.old_y_scale = scale
def reset_plot(self):
self.x_offset, self.y_offset = 0, 0
self.y_scale = 1.0
if self.fid._flags["ft"]:
self.plot.index_range.low, self.plot.index_range.high = [self.x[-1], self.x[0]]
else:
self.plot.index_range.low, self.plot.index_range.high = [self.x[0], self.x[-1]]
self.plot.value_range.low = self.plot.data.arrays["series%i" % (self.data_selected[0] + 1)].min()
self.plot.value_range.high = self.plot.data.arrays["series%i" % (self.data_selected[0] + 1)].max()
def _reset_plot_btn_fired(self):
print "resetting plot..."
self.reset_plot()
def _select_all_btn_fired(self):
self.data_selected = range(len(self.fid.data))
def _select_none_btn_fired(self):
self.data_selected = []
def set_plot_offset(self, x=None, y=None):
if x == None and y == None:
pass
self.old_x_offsets = self.x_offsets
self.old_y_offsets = self.y_offsets
self.x_offsets = self.index_array * x
self.y_offsets = self.index_array * y
for i in np.arange(len([pt for pt in self.plot.plots if "plot" in pt])):
self.plot.plots["plot%i" % i][0].position = [self.x_offsets[i], self.y_offsets[i]]
self.plot.request_redraw()
def _y_offset_changed(self):
self.set_plot_offset(x=self.x_offset, y=self.y_offset)
def _x_offset_changed(self):
self.set_plot_offset(x=self.x_offset, y=self.y_offset)
# for some mysterious reason, selecting new data to plot doesn't retain the plot offsets even if you set them explicitly
def _data_selected_changed(self):
if self._flags["manphasing"]:
self.end_man_phasing()
if self._flags["picking"]:
self.end_picking()
self.plot.delplot(*self.plot.plots)
self.plot.request_redraw()
for i in self.data_selected:
self.plot.plot(
("x", "series%i" % (i + 1)),
type="line",
line_width=0.5,
color="black",
position=[self.x_offsets[i], self.y_offsets[i]],
) # FIX: this isn't working
# self.reset_plot() # this is due to the fact that the plot automatically resets anyway
if self._flags["lineshapes_vis"]:
self.clear_lineshapes()
self.plot_deconv()
# processing buttons
# plot the current apodisation function based on lb, and do apodisation
# =================================================
def _lb_plt_btn_fired(self):
if self.fid._flags["ft"]:
return
if "lb1" in self.plot.plots:
self.plot.delplot("lb1")
self.plot.request_redraw()
return
self.plot_lb()
def plot_lb(self):
if self.fid._flags["ft"]:
return
lb_data = self.fid.data[self.data_selected[0]]
lb_plt = np.exp(-np.pi * np.arange(len(lb_data)) * (self.lb / self.fid.params["sw_hz"])) * lb_data[0]
self.plot_data.set_data("lb1", np.real(lb_plt))
self.plot.plot(("x", "lb1"), type="line", name="lb1", line_width=1, color="blue")[0]
self.plot.request_redraw()
def _lb_changed(self):
if self.fid._flags["ft"]:
return
lb_data = self.fid.data[self.data_selected[0]]
lb_plt = np.exp(-np.pi * np.arange(len(lb_data)) * (self.lb / self.fid.params["sw_hz"])) * lb_data[0]
self.plot_data.set_data("lb1", np.real(lb_plt))
def _lb_btn_fired(self):
if self.fid._flags["ft"]:
return
self.fid.emhz(self.lb)
self.update_plot_data_from_fid()
def _zf_btn_fired(self):
if self.fid._flags["ft"]:
return
if "lb1" in self.plot.plots:
self.plot.delplot("lb1")
self.fid.zf()
self.update_plot_data_from_fid()
def _ft_btn_fired(self):
if "lb1" in self.plot.plots:
self.plot.delplot("lb1")
if self.fid._flags["ft"]:
return
self.fid.ft()
self.update_plot_data_from_fid()
self.plot = Plot(self.plot_data, default_origin="bottom right", padding=[5, 0, 0, 35])
self.plot_init(index=self.data_selected)
self.reset_plot()
def _ph_auto_btn_fired(self):
if not self.fid._flags["ft"]:
return
for i in self.fid.data[
np.iscomplex(self.fid.data) == False
]: # as np.iscomplex returns False for 0+0j, we need to check manually
if type(i) != np.complex128:
print "Cannot perform phase correction on non-imaginary data."
return
self.fid.phase_auto(mp=self.ph_mp, discard_imaginary=False)
self.update_plot_data_from_fid()
def _ph_auto_single_btn_fired(self):
if not self.fid._flags["ft"]:
return
for i in self.fid.data[
np.iscomplex(self.fid.data) == False
]: # as np.iscomplex returns False for 0+0j, we need to check manually
if type(i) != np.complex128:
print "Cannot perform phase correction on non-imaginary data."
return
for i in self.data_selected:
self.fid._phase_area_single(i)
self.update_plot_data_from_fid()
def _ph_man_btn_fired(self):
if not self.fid._flags["ft"]:
return
for i in self.fid.data[
np.iscomplex(self.fid.data) == False
]: # as np.iscomplex returns False for 0+0j, we need to check manually
if type(i) != np.complex128:
print "Cannot perform phase correction on non-imaginary data."
return
if not self._flags["manphasing"]:
self._flags["manphasing"] = True
self.text_marker("Drag to phase:\n up/down - p0\n left/right - p1")
self.change_plot_colour(colour="red")
self.disable_plot_tools()
self.plot._data_selected = self.data_selected
self.plot._data_complex = self.fid.data[np.array(self.data_selected)]
self.plot.tools.append(PhaseDragTool(self.plot))
elif self._flags["manphasing"]:
self.end_man_phasing()
def end_man_phasing(self):
self._flags["manphasing"] = False
self.remove_all_overlays()
self.change_plot_colour(colour="black")
print "p0: %f, p1: %f" % (self.plot.tools[0].p0, self.plot.tools[0].p1)
if self.ph_global:
self.fid.data = self.fid.ps(self.fid.data, p0=self.plot.tools[0].p0, p1=self.plot.tools[0].p1)
self.update_plot_data_from_fid()
else:
for i, j in zip(self.plot._data_selected, self.plot._data_complex):
self.fid.data[i] = j
self.disable_plot_tools()
self.enable_plot_tools()
def remove_extra_overlays(self):
self.plot.overlays = [self.plot.overlays[0]]
self.plot.plots["plot0"][0].overlays = []
def remove_all_overlays(self):
self.plot.overlays = []
self.plot.plots["plot0"][0].overlays = []
def disable_plot_tools(self):
self.plot.tools = []
def enable_plot_tools(self):
self.plot.tools.append(self.zoomtool)
self.plot.tools.append(self.pantool)
def change_plot_colour(self, colour="black"):
for plot in self.plot.plots:
self.plot.plots[plot][0].color = colour
def _bl_sel_btn_fired(self):
if not self.fid._flags["ft"]:
return
if self._flags["bl_selecting"]:
self.end_bl_select()
else:
self._flags["bl_selecting"] = True
self.plot.plot(
("x", "series%i" % (self.data_selected[0] + 1)),
name="bl_plot",
type="scatter",
alpha=1.0,
line_width=0,
selection_line_width=0,
marker_size=2,
selection_marker_size=2,
selection_color="black", # change this to make selected points visible
color="black",
)[0]
self.text_marker("Select ranges for baseline correction:\n drag right - select range")
self.disable_plot_tools()
self.plot.tools.append(
BlSelectTool(
self.plot.plots["plot0"][0], metadata_name="selections", append_key=KeySpec(None, "control")
)
)
self.plot.overlays.append(
RangeSelectionOverlay(
component=self.plot.plots["bl_plot"][0], metadata_name="selections", axis="index", fill_color="blue"
)
)
self.plot.overlays.append(
LineInspector(
component=self.plot, axis="index_x", inspect_mode="indexed", write_metadata=True, color="blue"
)
)
if self._bl_marker_overlays:
self.plot.plots["plot0"][0].overlays = self._bl_marker_overlays.values()
# for i in self._bl_range_plots: #for some reason, after re-adding these plot objects, deleting them doesn't actually delete the visual component in self.plot_components, thus they're being redrawn entirely below
# self.plot.add(self._bl_range_plots[i])
for l_bl in range(len(self._bl_ranges)):
self.plot.plot(
("bl_range_x_%i" % (l_bl), "bl_range_y_%i" % (l_bl)),
type="line",
name="bl_range_%i" % (l_bl),
line_width=0.5,
color="blue",
)[0]
self.plot.request_redraw()
self.bl_tool = self.plot.tools[0]
self.bl_tool.on_trait_change(self._bl_handler, name=["ranges_now"])
def end_bl_select(self):
self._flags["bl_selecting"] = False
self._bl_indices = []
for i, j in self._bl_ranges:
self._bl_indices.append((i < self.x) * (self.x < j))
self.fid.bl_points = np.where(sum(self._bl_indices, 0) == 1)[0]
# self.plot.delplot('bl_plot')
for i in [j for j in self.plot.plots if "bl_" in j]:
self.plot.delplot(i)
self.plot.request_redraw()
self.remove_extra_overlays()
self.disable_plot_tools()
self.enable_plot_tools()
def _bl_cor_btn_fired(self):
if not self.fid._flags["ft"]:
return
if self._flags["bl_selecting"]:
self.end_bl_select()
self.fid.bl_fit()
self.remove_all_overlays()
self.update_plot_data_from_fid()
def _peak_pick_btn_fired(self):
if not self.fid._flags["ft"]:
return
if self._flags["picking"]:
self.end_picking()
# print 'self.fid.peaks', self.fid.peaks, '\nself.picking_tool.peaks_now',self.picking_tool.peaks_now
return
else:
self._flags["picking"] = True
self.reset_plot()
if self._peak_marker_overlays:
self.plot.plots["plot0"][0].overlays = self._peak_marker_overlays.values()
self.text_marker("Peak-picking:\n left click - select peak\n drag right - select range")
self.disable_plot_tools()
# set_trace()
pst = PeakSelectTool(self.plot.plots["plot0"][0], left_button_selects=True)
pst.peaks_now = self._peaks_now
pst.ranges_now = self._ranges_now
self.plot.overlays.append(
PeakPicker(
component=self.plot,
axis="index_x",
inspect_mode="indexed",
metadata_name="peaks",
write_metadata=True,
color="blue",
)
)
self.plot.tools.append(pst)
# metadata_name='selections',
# append_key=KeySpec(None, 'control')))
self.plot.overlays.append(
RangeSelectionOverlay(
component=self.plot.plots["plot0"][0], metadata_name="selections", axis="index", fill_color="blue"
)
)
# Set up the trait handler for range selection
# self.metadata_source = self.plot.plots['plot0'][0].index#self.plot.tools[0]
# self.metadata_source.on_trait_change(self._metadata_handler, "metadata_changed")
# Set up the trait handler for peak/range selections
self.picking_tool = self.plot.tools[0]
self.picking_tool.on_trait_change(self._peak_handler, name=["peaks_now", "ranges_now"])
# print 'self.fid.peaks', self.fid.peaks, '\nself.picking_tool.peaks_now',self.picking_tool.peaks_now
def end_picking(self):
# set_trace()
self._flags["picking"] = False
if self.fid.peaks and self.fid.ranges:
self.clear_invalid_peaks_ranges()
else:
self.clear_all_peaks_ranges()
self.remove_all_overlays()
self.disable_plot_tools()
self.enable_plot_tools()
self.plot.request_redraw()
def clear_invalid_peaks_ranges(self):
# set_trace()
peaks_outside_of_ranges = self.peaks_outside_of_ranges()
ranges_without_peaks = self.ranges_without_peaks()
# remove uncoupled peak markers and empty range markers
for i in peaks_outside_of_ranges:
self._peak_marker_overlays.pop(self.fid.peaks[i])
for i in ranges_without_peaks:
for rng in self.fid.ranges[i]:
self._peak_marker_overlays.pop(rng)
# remove uncoupled peaks and empty ranges
self.fid.peaks = [self.fid.peaks[i] for i in range(len(self.fid.peaks)) if i not in peaks_outside_of_ranges]
self._peaks_now = [self._peaks_now[i] for i in range(len(self._peaks_now)) if i not in peaks_outside_of_ranges]
self.fid.ranges = [self.fid.ranges[i] for i in range(len(self.fid.ranges)) if i not in ranges_without_peaks]
self._ranges_now = [self._ranges_now[i] for i in range(len(self._ranges_now)) if i not in ranges_without_peaks]
def clear_all_peaks_ranges(self):
self.fid.peaks = []
self.fid.ranges = []
self.picking_tool.peaks_now = []
self.picking_tool.ranges_now = []
self._peak_marker_overlays = {}
self.plot.plots["plot0"][0].overlays = []
self.plot.request_redraw()
print "Selected peaks and ranges cleared."
def peaks_ranges_matrix(self):
return np.array([(self.fid.peaks >= i[0]) * (self.fid.peaks <= i[1]) for i in self.fid.ranges])
def peaks_outside_of_ranges(self):
index = self.peaks_ranges_matrix().sum(0)
return np.arange(len(self.fid.peaks))[np.where(index == 0)]
def ranges_without_peaks(self):
index = self.peaks_ranges_matrix().sum(1)
return np.arange(len(self.fid.ranges))[np.where(index == 0)]
def _peak_pick_clear_fired(self):
self.clear_all_peaks_ranges()
def busy(self):
if self.busy_animation:
self.busy_animation = False
else:
self.busy_animation = True
def _deconvolute_btn_fired(self):
# set_trace()
# self.busy()
if self._flags["picking"]:
self.end_picking()
if self.fid.peaks == []:
print "No peaks selected."
return
print "Imaginary components discarded."
self.fid.real()
self.fid.deconv(gl=self.fid._flags["gl"], mp=self.deconvolute_mp)
# self.busy()
self._plot_decon_btn_fired()
def clear_lineshapes(self):
for line in [i for i in self.plot.plots if "lineshape" in i]:
self.plot.delplot(line)
for line in [i for i in self.plot.plots if "residual" in i]:
self.plot.delplot(line)
self.plot.request_redraw()
def _plot_decon_btn_fired(self):
if self._flags["lineshapes_vis"]:
self.clear_lineshapes()
self._flags["lineshapes_vis"] = False
return
self._flags["lineshapes_vis"] = True
self.plot_deconv()
def plot_deconv(self):
index = self.data_selected[0]
sw_left = self.fid.params["sw_left"]
data = self.fid.data[index][::-1]
if len(self.fid.fits) == 0:
return
paramarray = self.fid.fits[index]
def i2ppm(index_value):
return np.mgrid[sw_left - self.fid.params["sw"] : sw_left : complex(len(data))][index_value]
def peaknum(paramarray, peak):
pkr = []
for i in paramarray:
for j in i:
pkr.append(np.array(j - peak).sum())
return np.where(np.array(pkr) == 0.0)[0][0]
x = np.arange(len(data))
peakplots = []
for irange in paramarray:
for ipeak in irange:
# if txt:
# text(i2ppm(int(ipeak[0])), 0.1+pk.max(), str(peaknum(paramarray,ipeak)), color='#336699')
peakplots.append(f_pk(ipeak, x)[::-1])
# plot sum of all lines
self.plot_data.set_data("lineshapes_%i" % (index), sum(peakplots, 0))
self.plot.plot(
("x", "lineshapes_%i" % (index)), type="line", name="lineshapes_%i" % (index), line_width=0.5, color="blue"
)[0]
# plot residual
self.plot_data.set_data("residuals_%i" % (index), data[::-1] - sum(peakplots, 0))
self.plot.plot(
("x", "residuals_%i" % (index)), type="line", name="residuals_%i" % (index), line_width=0.5, color="red"
)[0]
# plot all individual lines
for peak in range(len(peakplots)):
self.plot_data.set_data("lineshape_%i_%i" % (index, peak), peakplots[peak])
self.plot.plot(
("x", "lineshape_%i_%i" % (index, peak)),
type="line",
name="lineshape_%i_%i" % (index, peak),
line_width=0.5,
color="green",
)[0]
self.plot.request_redraw()
def _lorgau_changed(self):
self.fid._flags["gl"] = self.lorgau
def update_plot_data_from_fid(self, index=None):
if self.fid._flags["ft"]:
self.x = np.linspace(
self.fid.params["sw_left"], self.fid.params["sw_left"] - self.fid.params["sw"], len(self.fid.data[0])
)
else:
self.x = np.linspace(0, self.fid.params["at"], len(self.fid.data[0]))
self.plot_data.set_data("x", self.x)
if index == None:
for i in self.index_array:
self.plot_data.set_data("series%i" % (i + 1), np.real(self.fid.data[i]))
else:
self.plot_data.set_data("series%i" % (index + 1), np.real(self.fid.data[index]))
self.plot.request_redraw()
def _plot_ints_btn_fired(self):
if len(self.fid.integrals) == 0:
print "self.integrals does not exist"
return
self.fid.plot_integrals()
def _save_fids_btn_fired(self):
self.fid.savefid_dict()
def _save_ints_btn_fired(self):
self.fid.save_integrals_csv()
def default_traits_view(self):
# exit_action = Action(name='Exit',
# action='exit_action')
traits_view = View(
Group(
Group(
Item(
"data_index",
editor=TabularEditor(
show_titles=False,
selected="data_selected",
editable=False,
multi_select=True,
adapter=MultiSelectAdapter(),
),
width=0.02,
show_label=False,
has_focus=True,
),
Item("plot", editor=ComponentEditor(), show_label=False),
padding=0,
show_border=False,
orientation="horizontal",
),
Group(
Group(
Group(
Item("select_all_btn", show_label=False),
Item("select_none_btn", show_label=False),
Item("reset_plot_btn", show_label=False),
orientation="vertical",
),
Group(
Item("y_offset"),
Item("x_offset"),
Item("y_scale", show_label=True),
Group(
Item("x_range_btn", show_label=False),
Item("x_range_up", show_label=False),
Item("x_range_dn", show_label=False),
orientation="horizontal",
),
orientation="vertical",
),
orientation="horizontal",
show_border=True,
label="Plotting",
),
Group(
Group(
Group(
Item("lb", show_label=False, format_str="%.2f Hz"),
Item("lb_btn", show_label=False),
Item("lb_plt_btn", show_label=False),
Item("zf_btn", show_label=False),
Item("ft_btn", show_label=False),
orientation="horizontal",
show_border=True,
label="Basic",
),
Group(
Item("bl_cor_btn", show_label=False),
Item("bl_sel_btn", show_label=False),
orientation="horizontal",
show_border=True,
label="Baseline correction",
),
orientation="horizontal",
),
# Group(
# Item('zf_btn', show_label=False),
# Item('ft_btn', show_label=False),
# orientation='horizontal'),
Group(
Group(
Item("ph_auto_btn", show_label=False),
Item("ph_auto_single_btn", show_label=False),
Item("ph_mp", show_label=True),
orientation="horizontal",
show_border=True,
),
Group(
Item("ph_man_btn", show_label=False),
Item("ph_global", show_label=True),
orientation="horizontal",
show_border=True,
),
orientation="horizontal",
show_border=True,
label="Phase correction",
),
),
Group(
Group(
Group(
Item("peak_pick_btn", show_label=False),
Item("peak_pick_clear", show_label=False),
Item("deconvolute_btn", show_label=False),
Item(
"lorgau",
show_label=False,
editor=RangeEditor(low_label="Lorentz", high_label="Gauss"),
),
Item("deconvolute_mp", show_label=True),
orientation="horizontal",
show_border=False,
),
Group(
Item("plot_decon_btn", show_label=False),
Item("plot_ints_btn", show_label=False),
orientation="horizontal",
show_border=False,
),
orientation="vertical",
show_border=True,
label="Peak-picking and deconvolution",
),
Group(
Item("save_fids_btn", show_label=False),
Item("save_ints_btn", show_label=False),
show_border=True,
label="Save",
orientation="horizontal",
),
orientation="vertical",
),
# Group(
# # Item( 'loading_animation',
# # editor = AnimatedGIFEditor(playing=str('busy_animation')),#( frame = 'frame_animation' ),
# # show_label = False),
# # #Item('progress_val',
# # # show_label=False,
# # # editor=ProgressEditor(
# # # min=0,
# # # max=100,
# # # ),
# # # )
# Item('plot_ints_btn', show_label=False),
# show_border=True,
# orientation='horizontal'),
show_border=True,
orientation="horizontal",
),
),
width=1.0,
height=1.0,
resizable=True,
handler=TC_Handler(),
title="NMRPy",
# menubar=MenuBar(
# Menu(
# exit_action,
# name='File')),
)
return traits_view
class MyPlot(HasTraits):
""" Displays a plot with a few buttons to control which overlay
to display
"""
plot = Instance(Plot)
status_overlay = Instance(StatusLayer)
error_button = Button('Error')
warn_button = Button('Warning')
no_problem_button = Button('No problem')
traits_view = View(
HGroup(UItem('error_button'), UItem('warn_button'),
UItem('no_problem_button')),
UItem('plot', editor=ComponentEditor()),
width=700,
height=600,
resizable=True,
)
def __init__(self, index, data_series, **kw):
super(MyPlot, self).__init__(**kw)
plot_data = ArrayPlotData(index=index)
plot_data.set_data('data_series', data_series)
self.plot = Plot(plot_data)
self.plot.plot(('index', 'data_series'))
def _error_button_fired(self, event):
""" removes the old overlay and replaces it with
an error overlay
"""
self.clear_status()
self.status_overlay = ErrorLayer(component=self.plot,
align='ul',
scale_factor=0.25)
self.plot.overlays.append(self.status_overlay)
self.plot.request_redraw()
def _warn_button_fired(self, event):
""" removes the old overlay and replaces it with
an warning overlay
"""
self.clear_status()
self.status_overlay = WarningLayer(component=self.plot,
align='ur',
scale_factor=0.25)
self.plot.overlays.append(self.status_overlay)
self.plot.request_redraw()
def _no_problem_button_fired(self, event):
""" removes the old overlay
"""
self.clear_status()
self.plot.request_redraw()
def clear_status(self):
if self.status_overlay in self.plot.overlays:
# fade_out will remove the overlay when its done
self.status_overlay.fade_out()
class TraitedPlot( HasTraits ):
# the Traits Plot Container
myTP = Instance( Plot )
myTCB = Instance( ColorBar )
myTIC = Instance( HPlotContainer )
# contains a list of default colormap names
colormapNameTE = Enum(
default_colormaps.color_map_name_dict.keys(),
label = 'Color Map Name',
desc = 'the color map name',
)
# the low saturation value for the colormap
colormapLowTR = Range(
value = -1000,
low = -1000,
high = 1000,
label = 'Color Map Low',
desc = 'the color map low saturation value',
)
colormapHighTR = Range(
value = 1000,
low = -1000,
high = 1000,
label = 'Color Map High',
desc = 'the color map high saturation value',
)
reversedColormapTB = Bool(
value = False,
label = 'Reverse the Color Map',
desc = 'the color map reversal state',
)
# set up the view for both the graphics and control
traits_view = View(
Item(
name = 'myTIC',
editor = ComponentEditor(size = windowSize),
show_label = False,
),
Item( name = "colormapNameTE" ),
Item(
name = "colormapLowTR",
editor = RangeEditor(
auto_set = False,
enter_set = True,
mode = 'slider',
low = -1000,
high = 1000,
),
),
Item(
name = "colormapHighTR",
editor = RangeEditor(
auto_set = False,
enter_set = True,
mode = 'slider',
low = -1000,
high = 1000,
),
),
Item( name = "reversedColormapTB" ),
resizable = True,
title = windowTitle,
)
def _myTIC_default( self ):
# create an interesting scalar field for the image plot
# Eggholder function
limitF = 500.0
xA = linspace(-limitF, limitF, 600)
yA = linspace(-limitF, limitF, 600)
( xMG,yMG ) = meshgrid( xA,yA )
zMG = -(yMG + 47) * sin( sqrt(abs(yMG + xMG/2 + 47 )))
zMG = zMG - xMG * sin( sqrt(abs(xMG - (yMG + 47))))
# Create an ArrayPlotData object and give it this data
myAPD = ArrayPlotData()
myAPD.set_data( "Z", zMG )
myAPD.set_data( "X",xA )
myAPD.set_data( "Y",yA )
# Create the plot
self.myTP = Plot( myAPD )
# contains a dict of default colormaps and their functions. We have to
# pass the colormapper the data range of interest to set up the private
# attributes
default_colormaps.color_map_name_dict
# the colormap method needs the range of the image data that we want to
# plot. We first put the image data (zMG) into an ImageData object. We
# then use DataRange1D on the ImageData instance to produce a DataRange1D
# instance describing the ImageData data. Finally, we feed the DataRange1D
# instance into the colormapper to produce a working colormapper.
myID = ImageData( )
myID.set_data( zMG )
self.myDR1D = DataRange1D( myID )
# pick an unmodified (i.e. unreversed, no ranges) colormap and build
# the colormap functions
myColorMapperFn = default_colormaps.color_map_name_dict[self.colormapNameTE]
myColorMapper = myColorMapperFn( self.myDR1D )
# add the image plot to this plot object
# specify the colormap explicitly
self.myTP.img_plot(
"Z",
xbounds = (xA[0],xA[-1]),
ybounds = (yA[0],yA[-1]),
colormap = myColorMapper,
)
# add the title and padding around the plot
self.myTP.title = "Eggholder Function"
self.myTP.padding = 50
# grids, fonts, etc
self.myTP.x_axis.title = "X"
self.myTP.y_axis.title = "Y"
# generate a ColorBar. pulls its colormapper from the myTP Plot object
self.myTCB = ColorBar(
plot = self.myTP,
index_mapper = LinearMapper( range = self.myTP.color_mapper.range ),
orientation = 'v',
resizable = 'v',
width = 40,
padding = 30,
)
# set the padding of the ColorBar to match the padding of the plot
self.myTCB.padding_top = self.myTP.padding_top
self.myTCB.padding_bottom = self.myTP.padding_bottom
# build up a single container for the colorbar and the image
myHPC = HPlotContainer( use_backbuffer = True )
myHPC.add( self.myTP )
myHPC.add( self.myTCB )
return( myHPC )
def _modify_colormap(self):
#myTP.color_mapper.range.low_setting = 0
#myTP.color_mapper.range.high_setting = 1000
# pick out the color map function
myColorMapperFn = default_colormaps.color_map_name_dict[self.colormapNameTE]
# reverse the colormap, if req'd
if self.reversedColormapTB:
myColorMapperFn = default_colormaps.reverse( myColorMapperFn )
## TODO adjust for too low, too high, end cases
myColorMapperFn = default_colormaps.fix(
myColorMapperFn,
(self.colormapLowTR, self.colormapHighTR)
)
myColorMapper = myColorMapperFn( self.myDR1D )
self.myTP.color_mapper = myColorMapper
self.myTP.request_redraw()
def _reversedColormapTB_changed( self,old,new ):
S = '_reversedColormapTB_changed() - old: %s, new: %s' % (old,new)
print( S )
self._modify_colormap()
def _colormapNameTE_changed( self,old,new ):
S = '_colormapNameTE_changed() - old: %s, new: %s' % (old,new)
print( S )
self._modify_colormap()
def _colormapLowTR_changed( self,old,new ):
S = '_colormapLowTR_changed() - old: %s, new: %s' % (old,new)
print( S )
# check for boundary conditions
if self.colormapLowTR >= self.colormapHighTR:
self.colormapLowTR = old
print( 'colormapLowTR restored to old value: %s' % old )
self._modify_colormap()
def _colormapHighTR_changed( self,old,new ):
S = '_colormapHighTR_changed() - old: %s, new: %s' % (old,new)
print( S )
if self.colormapHighTR <= self.colormapLowTR:
self.colormapHighTR = old
print( 'colormapHighTR restored to old value: %s' % old )
self._modify_colormap()
class plot_window (HasTraits):
_plot_data = Instance(ArrayPlotData)
_plot = Instance(Plot)
_click_tool = Instance(clicker_tool)
_img_plot = Instance(ImagePlot)
_right_click_avail = 0
name = Str
view = View(
Item(name='_plot', editor=ComponentEditor(), show_label=False),
)
def __init__(self):
# -------------- Initialization of plot system ----------------
padd = 25
self._plot_data = ArrayPlotData()
self._x = []
self._y = []
self.man_ori = [1, 2, 3, 4]
self._plot = Plot(self._plot_data, default_origin="top left")
self._plot.padding_left = padd
self._plot.padding_right = padd
self._plot.padding_top = padd
self._plot.padding_bottom = padd
self._quiverplots = []
# -------------------------------------------------------------
def left_clicked_event(self):
print ("left clicked")
if len(self._x) < 4:
self._x.append(self._click_tool.x)
self._y.append(self._click_tool.y)
print self._x
print self._y
self.drawcross("coord_x", "coord_y", self._x, self._y, "red", 5)
self._plot.overlays = []
self.plot_num_overlay(self._x, self._y, self.man_ori)
def right_clicked_event(self):
print ("right clicked")
if len(self._x) > 0:
self._x.pop()
self._y.pop()
print self._x
print self._y
self.drawcross("coord_x", "coord_y", self._x, self._y, "red", 5)
self._plot.overlays = []
self.plot_num_overlay(self._x, self._y, self.man_ori)
else:
if (self._right_click_avail):
print "deleting point"
self.py_rclick_delete(self._click_tool.x,
self._click_tool.y, self.cameraN)
x = []
y = []
self.py_get_pix_N(x, y, self.cameraN)
self.drawcross("x", "y", x[0], y[0], "blue", 4)
def attach_tools(self):
self._click_tool = clicker_tool(self._img_plot)
self._click_tool.on_trait_change(
self.left_clicked_event, 'left_changed')
self._click_tool.on_trait_change(
self.right_clicked_event, 'right_changed')
self._img_plot.tools.append(self._click_tool)
self._zoom_tool = SimpleZoom(
component=self._plot, tool_mode="box", always_on=False)
self._zoom_tool.max_zoom_out_factor = 1.0
self._img_plot.tools.append(self._zoom_tool)
if self._plot.index_mapper is not None:
self._plot.index_mapper.on_trait_change(
self.handle_mapper, 'updated', remove=False)
if self._plot.value_mapper is not None:
self._plot.value_mapper.on_trait_change(
self.handle_mapper, 'updated', remove=False)
def drawcross(self, str_x, str_y, x, y, color1, mrk_size):
self._plot_data.set_data(str_x, x)
self._plot_data.set_data(str_y, y)
self._plot.plot((str_x, str_y), type="scatter",
color=color1, marker="plus", marker_size=mrk_size)
self._plot.request_redraw()
def drawline(self, str_x, str_y, x1, y1, x2, y2, color1):
self._plot_data.set_data(str_x, [x1, x2])
self._plot_data.set_data(str_y, [y1, y2])
self._plot.plot((str_x, str_y), type="line", color=color1)
self._plot.request_redraw()
def drawquiver(self, x1c, y1c, x2c, y2c, color, linewidth=1.0):
""" drawquiver draws multiple lines at once on the screen x1,y1->x2,y2 in the current camera window
parameters:
x1c - array of x1 coordinates
y1c - array of y1 coordinates
x2c - array of x2 coordinates
y2c - array of y2 coordinates
color - color of the line
linewidth - linewidth of the line
example usage:
drawquiver ([100,200],[100,100],[400,400],[300,200],'red',linewidth=2.0)
draws 2 red lines with thickness = 2 : 100,100->400,300 and 200,100->400,200
"""
x1, y1, x2, y2 = self.remove_short_lines(x1c, y1c, x2c, y2c)
if len(x1) > 0:
xs = ArrayDataSource(x1)
ys = ArrayDataSource(y1)
quiverplot = QuiverPlot(index=xs, value=ys,
index_mapper=LinearMapper(
range=self._plot.index_mapper.range),
value_mapper=LinearMapper(
range=self._plot.value_mapper.range),
origin=self._plot.origin, arrow_size=0,
line_color=color, line_width=linewidth, ep_index=np.array(x2), ep_value=np.array(y2)
)
self._plot.add(quiverplot)
# we need this to track how many quiverplots are in the current
# plot
self._quiverplots.append(quiverplot)
# import pdb; pdb.set_trace()
def remove_short_lines(self, x1, y1, x2, y2):
""" removes short lines from the array of lines
parameters:
x1,y1,x2,y2 - start and end coordinates of the lines
returns:
x1f,y1f,x2f,y2f - start and end coordinates of the lines, with short lines removed
example usage:
x1,y1,x2,y2=remove_short_lines([100,200,300],[100,200,300],[100,200,300],[102,210,320])
3 input lines, 1 short line will be removed (100,100->100,102)
returned coordinates:
x1=[200,300]; y1=[200,300]; x2=[200,300]; y2=[210,320]
"""
dx, dy = 2, 2 # minimum allowable dx,dy
x1f, y1f, x2f, y2f = [], [], [], []
for i in range(len(x1)):
if abs(x1[i] - x2[i]) > dx or abs(y1[i] - y2[i]) > dy:
x1f.append(x1[i])
y1f.append(y1[i])
x2f.append(x2[i])
y2f.append(y2[i])
return x1f, y1f, x2f, y2f
def handle_mapper(self):
for i in range(0, len(self._plot.overlays)):
if hasattr(self._plot.overlays[i], 'real_position'):
coord_x1, coord_y1 = self._plot.map_screen(
[self._plot.overlays[i].real_position])[0]
self._plot.overlays[i].alternate_position = (
coord_x1, coord_y1)
def plot_num_overlay(self, x, y, txt):
for i in range(0, len(x)):
coord_x, coord_y = self._plot.map_screen([(x[i], y[i])])[0]
ovlay = TextBoxOverlay(component=self._plot,
text=str(txt[i]), alternate_position=(coord_x, coord_y),
real_position=(x[i], y[i]),
text_color="white",
border_color="red"
)
self._plot.overlays.append(ovlay)
def update_image(self, image, is_float):
if is_float:
self._plot_data.set_data('imagedata', image.astype(np.float))
else:
self._plot_data.set_data('imagedata', image.astype(np.byte))
self._plot.request_redraw()
class ResultExplorer(HasTraits):
# source of result data
Beamformer = Instance(BeamformerBase)
# traits for the plots
plot_data = Instance(ArrayPlotData, transient=True)
# the map
map_plot = Instance(Plot, transient=True)
imgp = Instance(ImagePlot, transient=True)
# map interpolation
interpolation = DelegatesTo('imgp', transient=True)
# the colorbar for the map
colorbar = Instance(ColorBar, transient=True)
# the spectrum
spec_plot = Instance(Plot, transient=True)
# the main container for the plots
plot_container = Instance(BasePlotContainer, transient=True)
# zoom and integration box tool for map plot
zoom = Instance(RectZoomSelect, transient=True)
# selection of freqs
synth = Instance(FreqSelector, transient=True)
# cursor tool for spectrum plot
cursor = Instance(BaseCursorTool, transient=True)
# dynamic range of the map
drange = Instance(DataRange1D, transient=True)
# dynamic range of the spectrum plot
yrange = Instance(DataRange1D, transient=True)
# set if plots are invalid
invalid = Bool(False, transient=True)
# remember the last valid result
last_valid_digest = Str(transient=True)
# starts calculation thread
start_calc = Button(transient=True)
# signals end of calculation
calc_ready = Event(transient=True)
# calculation thread
CThread = Instance(Thread, transient=True)
# is calculation active ?
running = Bool(False, transient=True)
rmesg = Property(depends_on = 'running')
# automatic recalculation ?
automatic = Bool(False, transient=True)
# picture
pict = File(filter=['*.png','*.jpg'],
desc="name of picture file", transient=True)
pic_x_min = Float(-1.0,
desc="minimum x-value picture plane")
pic_y_min = Float(-0.75,
desc="minimum y-value picture plane")
pic_scale = Float(400,
desc="maximum x-value picture plane")
pic_flag = Bool(False,
desc="show picture ?")
view = View(
HSplit(
VGroup(
HFlow(
Item('synth{}', style = 'custom',width=0.8),
Item('start_calc{Recalc}', enabled_when='invalid'),
show_border=True
),
TGroup(
Item('plot_container{}', editor=ComponentEditor()),
dock='vertical',
),
),
Tabbed(
[Item('Beamformer',style = 'custom' ),'-<>[Beamform]'],
[Item('Beamformer',style = 'custom', editor = InstanceEditor(view = fview) ),'-<>[Data]'],
),
# ['invalid{}~','last_valid_digest{}~',
# 'calc_ready','running',
# '|'],
dock = 'vertical'
),#HSplit
statusbar = [StatusItem(name='rmesg',width =0.5)],
# icon= ImageResource('py.ico'),
title = "Beamform Result Explorer",
resizable = True,
menubar =
MenuBarManager(
MenuManager(
MenuManager(
Action(name='Open', action='load'),
Action(name='Save as', action='save_as'),
name = '&Project'
),
MenuManager(
Action(name='VI logger csv', action='import_time_data'),
Action(name='Pulse mat', action='import_bk_mat_data'),
Action(name='td file', action='import_td'),
name = '&Import'
),
MenuManager(
Action(name='NI-DAQmx', action='import_nidaqmx'),
name = '&Acquire'
),
Action(name='R&un script', action='run_script'),
Action(name='E&xit', action='_on_close'),
name = '&File',
),
MenuManager(
Group(
Action(name='&Delay+Sum', style='radio', action='set_Base', checked=True),
Action(name='&Eigenvalue', style='radio', action='set_Eig'),
Action(name='&Capon', style='radio', action='set_Capon'),
Action(name='M&usic', style='radio', action='set_Music'),
Action(name='D&amas', style='radio', action='set_Damas'),
Action(name='Clea&n', style='radio', action='set_Clean'),
Action(name='C&lean-SC', style='radio', action='set_Cleansc'),
Action(name='&Orthogonal', style='radio', action='set_Ortho'),
Action(name='&Functional', style='radio', action='set_Functional'),
Action(name='C&MF', style='radio', action='set_CMF'),
),
Separator(),
Action(name='Auto &recalc', style='toggle', checked_when='automatic', action='toggle_auto'),
name = '&Options',
),
MenuManager(
Group(
# Action(name='&Frequency', action='set_Freq'),
Action(name='&Interpolation method', action='set_interp'),
Action(name='&Map dynamic range', action='set_drange'),
Action(name='&Plot dynamic range', action='set_yrange'),
Action(name='Picture &underlay', action='set_pic'),
),
name = '&View',
),
)
)#View
# init the app
def __init__(self, **kwtraits):
super(ResultExplorer,self).__init__(**kwtraits)
# containers
bgcolor = "sys_window"#(212/255.,208/255.,200/255.) # Windows standard background
self.plot_container = container = VPlotContainer(use_backbuffer = True,
padding=0, fill_padding = False, valign="center", bgcolor=bgcolor)
subcontainer = HPlotContainer(use_backbuffer = True, padding=0,
fill_padding = False, halign="center", bgcolor=bgcolor)
# freqs
self.synth = FreqSelector(parent=self)
# data source
self.plot_data = pd = ArrayPlotData()
self.set_result_data()
self.set_pict()
# map plot
self.map_plot = Plot(pd, padding=40)
self.map_plot.img_plot("image", name="image")
imgp = self.map_plot.img_plot("map_data", name="map", colormap=jet)[0]
self.imgp = imgp
t1 = self.map_plot.plot(("xpoly","ypoly"), name="sector", type = "polygon")
t1[0].face_color = (0,0,0,0) # set face color to transparent
# map plot tools and overlays
imgtool = ImageInspectorTool(imgp)
imgp.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=imgp, image_inspector=imgtool,
bgcolor="white", border_visible=True)
self.map_plot.overlays.append(overlay)
self.zoom = RectZoomSelect(self.map_plot, drag_button='right', always_on=True, tool_mode='box')
self.map_plot.overlays.append(self.zoom)
self.map_plot.tools.append(PanTool(self.map_plot))
# colorbar
colormap = imgp.color_mapper
self.drange = colormap.range
self.drange.low_setting="track"
self.colorbar = cb = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap, plot=self.map_plot, orientation='v',
resizable='v', width=10, padding=20)
# colorbar tools and overlays
range_selection = RangeSelection(component=cb)
cb.tools.append(range_selection)
cb.overlays.append(RangeSelectionOverlay(component=cb,
border_color="white", alpha=0.8, fill_color=bgcolor))
range_selection.listeners.append(imgp)
# spectrum plot
self.spec_plot = Plot(pd, padding=25)
px = self.spec_plot.plot(("freqs","spectrum"), name="spectrum", index_scale="log")[0]
self.yrange = self.spec_plot.value_range
px.index_mapper = MyLogMapper(range=self.spec_plot.index_range)
# spectrum plot tools
self.cursor = CursorTool(px)#, drag_button="left", color='blue', show_value_line=False)
px.overlays.append(self.cursor)
self.cursor.current_position = 0.3,0.5
px.index_mapper.map_screen(0.5)
# self.map_plot.tools.append(SaveTool(self.map_plot, filename='pic.png'))
# layout
self.set_map_details()
self.reset_sector()
subcontainer.add(self.map_plot)
subcontainer.add(self.colorbar)
# subcontainer.tools.append(SaveTool(subcontainer, filename='pic.png'))
container.add(self.spec_plot)
container.add(subcontainer)
container.tools.append(SaveTool(container, filename='pic.pdf'))
self.last_valid_digest = self.Beamformer.ext_digest
def _get_rmesg(self):
if self.running:
return "Running ..."
else:
return "Ready."
@on_trait_change('Beamformer.ext_digest')
def invalidate(self):
if self.last_valid_digest!="" and self.Beamformer.ext_digest!=self.last_valid_digest:
self.invalid = True
def _start_calc_fired(self):
if self.CThread and self.CThread.isAlive():
pass
else:
self.CThread = CalcThread()
self.CThread.b = self.Beamformer
self.CThread.caller = self
self.CThread.start()
def _calc_ready_fired(self):
f = self.Beamformer.freq_data
low,high = f.freq_range
print low, high
fr = f.fftfreq()
if self.synth.synth_freq<low:
self.synth.synth_freq = fr[1]
if self.synth.synth_freq>high:
self.synth.synth_freq = fr[-2]
self.set_result_data()
self.set_map_details()
self.plot_container.request_redraw()
self.map_plot.request_redraw()
@on_trait_change('invalid')
def activate_plot(self):
self.plot_container.visible = not self.invalid
self.plot_container.request_redraw()
if self.invalid and self.automatic:
self._start_calc_fired()
@on_trait_change('cursor.current_position')
def update_synth_freq(self):
self.synth.synth_freq=self.cursor.current_position[0]
def reset_sector(self):
g = self.Beamformer.grid
if self.zoom:
self.zoom.x_min = g.x_min
self.zoom.y_min = g.y_min
self.zoom.x_max = g.x_max
self.zoom.y_max = g.y_max
@on_trait_change('zoom.box,synth.synth_freq,synth.synth_type,drange.+,yrange.+')
def set_result_data(self):
if self.invalid:
return
if self.cursor:
self.cursor.current_position = self.synth.synth_freq, 0
pd = self.plot_data
if not pd:
return
g = self.Beamformer.grid
try:
map_data = self.Beamformer.synthetic(self.synth.synth_freq,self.synth.synth_type_).T
map_data = L_p(map_data)
except:
map_data = arange(0,19.99,20./g.size).reshape(g.shape)
pd.set_data("map_data", map_data)
f = self.Beamformer.freq_data
if self.zoom and self.zoom.box:
sector = self.zoom.box
else:
sector = (g.x_min,g.y_min,g.x_max,g.y_max)
pd.set_data("xpoly",array(sector)[[0,2,2,0]])
pd.set_data("ypoly",array(sector)[[1,1,3,3]])
ads = pd.get_data("freqs")
if not ads:
freqs = ArrayDataSource(f.fftfreq()[f.ind_low:f.ind_high],sort_order='ascending')
pd.set_data("freqs",freqs)
else:
ads.set_data(f.fftfreq()[f.ind_low:f.ind_high],sort_order='ascending')
self.synth.enumerate()
try:
spectrum = self.Beamformer.integrate(sector)[f.ind_low:f.ind_high]
spectrum = L_p(spectrum)
except:
spectrum = f.fftfreq()[f.ind_low:f.ind_high]
pd.set_data("spectrum",spectrum)
@on_trait_change('pic+')
def set_map_details(self):
if self.invalid:
return
mp = self.map_plot
# grid
g = self.Beamformer.grid
xs = linspace(g.x_min, g.x_max, g.nxsteps)
ys = linspace(g.y_min, g.y_max, g.nysteps)
mp.range2d.sources[1].set_data(xs,ys,sort_order=('ascending', 'ascending'))
mp.aspect_ratio = (xs[-1]-xs[0])/(ys[-1]-ys[0])
yl, xl = self.plot_data.get_data("image").shape[0:2]
xp = (self.pic_x_min,self.pic_x_min+xl*1.0/self.pic_scale)
yp = (self.pic_y_min,self.pic_y_min+yl*1.0/self.pic_scale)
mp.range2d.sources[0].set_data(xp,yp,sort_order=('ascending', 'ascending'))
mp.range2d.low_setting = (g.x_min,g.y_min)
mp.range2d.high_setting = (g.x_max,g.y_max)
# dynamic range
map = mp.plots["map"][0]
#map.color_mapper.range.low_setting="track"
# colormap
map.color_mapper._segmentdata['alpha']=[(0.0,0.0,0.0),(0.001,0.0,1.0),(1.0,1.0,1.0)]
map.color_mapper._recalculate()
mp.request_redraw()
@on_trait_change('pict')
def set_pict(self):
pd = self.plot_data
if not pd:
return
try:
imgd = ImageData.fromfile(self.pict)._data[::-1]
except:
imgd = ImageData()
imgd.set_data(255*ones((2,2,3),dtype='uint8'))
imgd = imgd._data
pd.set_data("image",imgd)
def save_as(self):
dlg = FileDialog( action='save as', wildcard='*.rep')
dlg.open()
if dlg.filename!='':
fi = file(dlg.filename,'w')
dump(self,fi)
fi.close()
def load(self):
dlg = FileDialog( action='open', wildcard='*.rep')
dlg.open()
if dlg.filename!='':
fi = file(dlg.filename,'rb')
s = load(fi)
self.copy_traits(s)
fi.close()
def run_script(self):
dlg = FileDialog( action='open', wildcard='*.py')
dlg.open()
if dlg.filename!='':
#~ try:
rx = self
b = rx.Beamformer
script = dlg.path
execfile(dlg.path)
#~ except:
#~ pass
def import_time_data (self):
t=self.Beamformer.freq_data.time_data
ti=csv_import()
ti.from_file='C:\\tyto\\array\\07.03.2007 16_45_59,203.txt'
ti.configure_traits(kind='modal')
t.name=""
ti.get_data(t)
def import_bk_mat_data (self):
t=self.Beamformer.freq_data.time_data
ti=bk_mat_import()
ti.from_file='C:\\work\\1_90.mat'
ti.configure_traits(kind='modal')
t.name=""
ti.get_data(t)
def import_td (self):
t=self.Beamformer.freq_data.time_data
ti=td_import()
ti.from_file='C:\\work\\x.td'
ti.configure_traits(kind='modal')
t.name=""
ti.get_data(t)
def import_nidaqmx (self):
t=self.Beamformer.freq_data.time_data
ti=nidaq_import()
ti.configure_traits(kind='modal')
t.name=""
ti.get_data(t)
def set_Base (self):
b = self.Beamformer
self.Beamformer = BeamformerBase(freq_data=b.freq_data,grid=b.grid,mpos=b.mpos,c=b.c,env=b.env)
self.invalid = True
def set_Eig (self):
b = self.Beamformer
self.Beamformer = BeamformerEig(freq_data=b.freq_data,grid=b.grid,mpos=b.mpos,c=b.c,env=b.env)
self.invalid = True
def set_Capon (self):
b = self.Beamformer
self.Beamformer = BeamformerCapon(freq_data=b.freq_data,grid=b.grid,mpos=b.mpos,c=b.c,env=b.env)
self.invalid = True
def set_Music (self):
b = self.Beamformer
self.Beamformer = BeamformerMusic(freq_data=b.freq_data,grid=b.grid,mpos=b.mpos,c=b.c,env=b.env)
self.invalid = True
def set_Damas (self):
b = self.Beamformer
self.Beamformer = BeamformerDamas(beamformer=BeamformerBase(freq_data=b.freq_data,grid=b.grid,mpos=b.mpos,c=b.c,env=b.env))
self.invalid = True
def set_Cleansc (self):
b = self.Beamformer
self.Beamformer = BeamformerCleansc(freq_data=b.freq_data,grid=b.grid,mpos=b.mpos,c=b.c,env=b.env)
self.invalid = True
def set_Ortho (self):
b = self.Beamformer
self.Beamformer = BeamformerOrth(beamformer=BeamformerEig(freq_data=b.freq_data,grid=b.grid,mpos=b.mpos,c=b.c,env=b.env))
self.Beamformer.n = 10
self.invalid = True
def set_Functional (self):
b = self.Beamformer
self.Beamformer = BeamformerFunctional(freq_data=b.freq_data,grid=b.grid,mpos=b.mpos,c=b.c,env=b.env)
self.invalid = True
def set_Clean (self):
b = self.Beamformer
self.Beamformer = BeamformerClean(beamformer=BeamformerBase(freq_data=b.freq_data,grid=b.grid,mpos=b.mpos,c=b.c,env=b.env))
self.invalid = True
def set_CMF (self):
b = self.Beamformer
self.Beamformer = BeamformerCMF(freq_data=b.freq_data,grid=b.grid,mpos=b.mpos,c=b.c,env=b.env)
self.invalid = True
def toggle_auto(self):
self.automatic = not self.automatic
def set_interp (self):
self.configure_traits(kind='live', view=interpview)
def set_drange (self):
self.drange.configure_traits(kind='live', view=drangeview)
def set_yrange (self):
self.yrange.configure_traits(kind='live', view=drangeview)
def set_pic (self):
self.configure_traits(kind='live', view=picview)
class DVMatrix(DataView):
conn_mat = Any
xa = Instance(ColorfulAxis)
ya = Instance(ColorfulAxis)
def __init__(self, ds, **kwargs):
super(DVMatrix, self).__init__(ds, **kwargs)
if self.ds.adj is not None: self.chaco_gen()
else: self.empty_gen()
def supply_adj(self):
self.conn_mat.data.set_data('imagedata', self.ds.adj_thresdiag)
########################################################################
# GEN METHODS
########################################################################
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]
########################################################################
# DRAW METHODS
########################################################################
def draw_surfs(self):
NotImplemented
def draw_nodes(self):
mat = self.ds.scalar_display_settings.connmat
if self.ds.display_mode == 'scalar' and mat:
scalars = self.ds.node_scalars[mat]
scalars = (scalars - np.min(scalars)) / (np.max(scalars) -
np.min(scalars))
colors = list(self.ds.opts.scalar_map._pl(scalars))
else:
colors = self.ds.node_colors
self.xa.colors = colors
self.ya.colors = colors
self.conn_mat.request_redraw()
def draw_conns(self, new_edges=None):
NotImplemented
def center(self):
for ax in (self.xa, self.ya):
ax.mapper.range.high_setting = self.ds.nr_labels
ax.mapper.range.low_setting = 0
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()
########################################################################
# I/O
########################################################################
#takes a SnapshotParameters
def snapshot(self, params):
gc = PlotGraphicsContext(self.conn_mat.outer_bounds, dpi=params.dpi)
gc.render_component(self.conn_mat)
gc.save(params.savefile)
class DVMatrix(DataView):
conn_mat=Any
xa=Instance(ColorfulAxis)
ya=Instance(ColorfulAxis)
def __init__(self,ds,**kwargs):
super(DVMatrix,self).__init__(ds,**kwargs)
if self.ds.adj is not None: self.chaco_gen()
else: self.empty_gen()
def supply_adj(self):
self.conn_mat.data.set_data('imagedata',self.ds.adj_thresdiag)
########################################################################
# GEN METHODS
########################################################################
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]
########################################################################
# DRAW METHODS
########################################################################
def draw_surfs(self): NotImplemented
def draw_nodes(self):
mat=self.ds.scalar_display_settings.connmat
if self.ds.display_mode=='scalar' and mat:
scalars = self.ds.node_scalars[mat]
scalars = (scalars-np.min(scalars)) / (np.max(scalars)-np.min(scalars))
colors = list(self.ds.opts.scalar_map._pl(scalars))
else:
colors = self.ds.node_colors
self.xa.colors=colors; self.ya.colors=colors
self.conn_mat.request_redraw()
def draw_conns(self,new_edges=None): NotImplemented
def center(self):
for ax in (self.xa,self.ya):
ax.mapper.range.high_setting=self.ds.nr_labels
ax.mapper.range.low_setting=0
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()
########################################################################
# I/O
########################################################################
#takes a SnapshotParameters
def snapshot(self,params):
gc=PlotGraphicsContext(self.conn_mat.outer_bounds,dpi=params.dpi)
gc.render_component(self.conn_mat)
gc.save(params.savefile)
class SourcePlot(HasTraits):
"""Main class for the Source function plots.
Uses inspector position from MainPlot to get position.
Abstracting to alternate data types is not trivial.
To expand to alternate type rewrite CalcSourcePlotVals, this method extracts all necessary data from the file and returns generic objects.
Plot can be neglected simply by not entereding a filename inthe indata field in the FileLoadWindow upon startup.
"""
PrimaryPlotC = Instance(HPlotContainer)
windowwidth = 1000
Wavelength = Float()
ImageLock = Bool()
SaveFlag = Bool()
colormap_list = Enum('jet', 'gist_gray', 'gist_heat', 'Blues', 'hot')
ColotList = ['green', 'red', 'yellow']
TauOnelinecolor = Enum('red', 'green', 'yellow')
Velocitylinecolor = Enum('green', 'red', 'white', 'yellow')
VelocityZerolinecolor = Enum('yellow', 'red', 'white', 'green')
Intensitylinecolor = Enum('white', 'red', 'yellow', 'green')
wave_ref = float(FileLoadWindow.Wave_Ref)
traits_view = View(VGroup(Group(Item('PrimaryPlotC', editor = ComponentEditor(), show_label = False, springy = True)),
HGroup(Item('colormap_list', style = 'simple', label = 'Colormap'), Item('TauOnelinecolor', label = 'Tau One Color', style = 'simple'), Item('Velocitylinecolor', label = 'Velocity Color', style = 'simple'), Item('VelocityZerolinecolor', label = 'Zero Velocity Color', style = 'simple'), Item('Intensitylinecolor', label = 'Intensity Color', style = 'simple'))),
width = windowwidth, height = windowwidth, resizable = True, title = "Source Function Plot")
#Array used for passing information to speactra line plto and also Source Function Plots. Will be synced
InspectorPosition = Array()
def __init__(self):
super(SourcePlot, self).__init__()
self.Wavelength = self.wave_ref
self.RayFile = Rh15dout()
self.RayFile.read_ray(FileLoadWindow.file_name_Ray)
self.RayFile.read_indata(FileLoadWindow.file_name_Indata)
#self.RayFile.read_ray('/sanhome/tiago/rhout/cb24bih/MgII/PRD/385_PRD_newatom/output_ray.ncdf')
#self.RayFile.read_indata('/sanhome/tiago/rhout/cb24bih/MgII/PRD/385_PRD_newatom/output_indata.ncdf')
WavelengthData = self.RayFile.ray.wavelength
IntensityData = self.RayFile.ray.intensity
self.create_SourcePlot()
def CalcSourcePlotVals(self, sel, xi, yi, wave_ref = None, cscale=0.2, vmax=59,zrange=[-0.1,2],tle='',newfig=True, colour=False):
# wave indices
widx = sel.ray.wavelength_indices[:]
# depth indices for temp, vz # this ONLY WORKS with non memmap arrays!!!!!!
didx2 = sel.atmos.temperature[xi,yi] < 1.e30
# protect against completely masked columns
if not (float(N.sum(didx2)) > 0):
vz = N.zeros(sel.atmos.temperature.shape[-1])
temp = vz.copy()
height = vz.copy()
else:
vz = sel.atmos.velocity_z[xi,yi,didx2]
temp = sel.atmos.temperature[xi,yi,didx2]
height = sel.atmos.height[xi,yi,didx2]
#height = sel.atmos.height[490,26,didx2]
if 'mask' in dir(sel.ray.intensity[xi,yi,0]):
if sel.ray.intensity[xi,yi,0].mask:
return
print('Intensity at (%i,%i) is masked! Making empty plot.' % (xi, yi))
plot_form_diag_empty(height, vz, temp, vrange=[-vmax,vmax], cscale=cscale,zrange=zrange, tle=tle, newfig=newfig)
return
intensity = sel.ray.intensity[xi,yi,widx]
wave = sel.ray.wavelength[widx]
# have chi/source_function or full stuff?
if hasattr(sel.ray, 'chi') and hasattr(sel.ray, 'source_function'):
# depth indices for Chi, S
didx1 = sel.ray.chi[xi,yi,:,0] > 0.
chi = sel.ray.chi[xi,yi,didx1,:]
S = sel.ray.source_function[xi,yi,didx1,:]
elif hasattr(sel.ray, 'eta_line') and hasattr(sel.ray, 'chi_line'):
# depth indices for Chi, S
didx1 = sel.ray.chi_line[xi,yi,:,0] > 0.
chi = sel.ray.chi_line[xi,yi,didx1,:]
S = sel.ray.eta_line[xi,yi,didx1,:]/sel.ray.chi_line[xi,yi,didx1,:]
else:
raise ValueError('(EEE) form_diag_rr: structure has no suitable source function/chi.')
if wave_ref is None:
wave_ref = N.mean(sel.ray.wavelength[widx])
#Break in old funcs
vrange=[-vmax,vmax]
sint = intensity
# Calculate tau
tau = tau_integ(chi, height)
tau_one = get_tau_one(tau,height)
# Convert to more convenient units
height = height/1.e6 # Mm
vz = -vz/1e3 # km/s, opposite positive convention
vaxis = 299792.45 * (wave - wave_ref)/wave_ref
if wave_ref > N.max(wave) or wave_ref < N.min(wave):
raise ValueError("Reference wavelength not contained in input wave!")
wi = max(N.where(vaxis >= vrange[0])[0][0] - 1, 0)
wf = min(N.where(vaxis <= vrange[1])[0][-1] + 1, vaxis.shape[0] - 1)
zi = max(N.where(height <= zrange[1])[0][0] - 1, 0)
zf = min(N.where(height >= zrange[0])[0][-1] + 1, height.shape[0] - 1)
# slice the arrays to improve the scaling
tau = tau[wi:wf+1,zi:zf+1]
chi = N.transpose(chi)[wi:wf+1,zi:zf+1]
S = N.transpose(S)[wi:wf+1,zi:zf+1]
height = height[zi:zf+1]
vz = vz[zi:zf+1]
T = temp[zi:zf+1]
wave = wave[wi:wf+1]
bint = int2bt(sint[wi:wf+1], wave)/1e3 # intensity in brightness temp [kK]
vaxis = vaxis[wi:wf+1]
tau_one = tau_one[wi:wf+1]/1e6
#find wavelength point closest to maximum in tau --NOPE. Now using v=0
#ind = N.argmax(tau_one)
ind = N.argmin(N.abs(vaxis))
Sline = int2bt(S[ind],wave[ind]) # source function at ind, in brightness temp.
#Flip arrays for plotting
height = height[::-1]
chiCT = chi[:, ::-1]
tauCT = tau[:, ::-1]
vz = vz[::-1]
T = T[::-1]
Sline = Sline[::-1]
# colours:
if colour:
# tau=1, vel, planck, S
ct = ['c-', 'r-', 'w:', 'y--' ]
else:
ct = ['0.5', 'w-', 'w:', 'w--']
color_source = Instance(ImageData)
return {'height':height, 'chiCT':chiCT, 'tauCT':tauCT, 'tau':tau, 'chi':chi, 'vz':vz, 'T':T, 'Sline':Sline, 'vaxis':vaxis, 'tau_one':tau_one, 'bint':bint, 'wave':wave, 'vrange':vrange, 'zrange':zrange, 'S':S}
def create_SourcePlot(self):
plotvals = self.CalcSourcePlotVals(sel = self.RayFile, xi = 0, yi = 0, wave_ref = self.wave_ref, vmax = float(FileLoadWindow.VMax_Min), zrange=[float(FileLoadWindow.ZRangeMin), float(FileLoadWindow.ZRangeMax)])
#plotvals = self.CalcSourcePlotVals(sel = self.RayFile, xi = 0, yi = 0, wave_ref = self.wave_ref, zrange=[0.2,3.0])
#unpacks values from CalcSourcePlotVals
vaxis = plotvals['vaxis']
height = plotvals['height']
chiCT = plotvals['chiCT']
tauCT = plotvals['tauCT']
tau = plotvals['tau']
chi = plotvals['chi']
vz = plotvals['vz']
T = plotvals['T']
Sline = plotvals['Sline']
tau_one = plotvals['tau_one']
bint = plotvals['bint']
wave = plotvals['wave']
vrange = plotvals['vrange']
zrange = plotvals['zrange']
S = plotvals['S']
velocityzero = 299792.45 * (self.Wavelength - self.wave_ref)/self.wave_ref
vaxispoints = 100
heightaxispoints = 400
self.new_vaxis = N.linspace(vrange[0], vrange[1], vaxispoints)
self.new_height = N.linspace(zrange[0], zrange[1], heightaxispoints)
VelocityZeroXPoints = [velocityzero, velocityzero]
VelocityZeroYPoints = [self.new_height[0], self.new_height[-1]]
#----------------------Create Chi/Tau Plot-------------------------------
new_chiCT = lineformInterpolate2D(vaxis, height, chiCT, self.new_vaxis , self.new_height)
new_tauCT = lineformInterpolate2D(vaxis, height, tauCT, self.new_vaxis , self.new_height)
self._image_indexCT = GridDataSource(xdata = self.new_vaxis, ydata = self.new_height[1:])
index_mapperCT = GridMapper(range = DataRange2D(self._image_indexCT))
self._image_valueCT = ImageData(data = (new_chiCT[:,1:]/new_tauCT[:,1:]), value_depth = 1)
color_mapperCT = jet(DataRange1D(self._image_valueCT))
self.ChiTauPlotData = ArrayPlotData(imagedata = (new_chiCT[:,1:]/new_tauCT[:,1:]), index = vaxis, TauOne= tau_one, Velocity = vz, Height = height, VelocityZeroXPoints = VelocityZeroXPoints, VelocityZeroYPoints = VelocityZeroYPoints)
self.ChiTauPlot = Plot(self.ChiTauPlotData)
self.ChiTauPlot.img_plot1 = self.ChiTauPlot.img_plot("imagedata", colormap = color_mapperCT, xbounds = (self.new_vaxis[0], self.new_vaxis[-1]), ybounds = (self.new_height[0], self.new_height[-1]))[0]
self.ChiTauPlot.overlays.append(ZoomTool(self.ChiTauPlot.img_plot1))
self.ChiTauPlot.tools.append(PanTool(self.ChiTauPlot.img_plot1, drag_button="right"))
self.ChiTauPlot.TauOnePlot = self.ChiTauPlot.plot(("index","TauOne"), type = "line", color = "red", resizeable = True)
self.ChiTauPlot.VelocityPlot = self.ChiTauPlot.plot(("Velocity","Height"), type = "line", color = "green", resizeable = True)
self.ChiTauPlot.VelocityZeroPlot = self.ChiTauPlot.plot(("VelocityZeroXPoints","VelocityZeroYPoints"), type = "line", color = "yellow", resizeable = True)
self.ChiTauPlot.title = "Chi/Tau Plot"
self.ChiTauPlot.x_axis.title = "Velocity (km/s)"
self.ChiTauPlot.y_axis.title = "Height (Mm)"
self.ChiTauPlot.range2d.x_range.set_bounds(self.new_vaxis[0], self.new_vaxis[-1])
self.ChiTauPlot.range2d.y_range.set_bounds(self.new_height[0], self.new_height[-1])
self.ChiTauPlotC = OverlayPlotContainer()
self.ChiTauPlotC.add(self.ChiTauPlot)
#------------------Create source function plot-------------------------
sourceSF = S[:, ::-1]
new_sourceSF = lineformInterpolate2D(vaxis, height, sourceSF, self.new_vaxis , self.new_height)
self._image_indexSF = GridDataSource(xdata = self.new_vaxis, ydata = self.new_height)
index_mapperSF = GridMapper(range = DataRange2D(self._image_indexSF))
self._image_valueSF = ImageData(data = new_sourceSF, value_depth = 1)
color_mapperSF = jet(DataRange1D(self._image_valueSF))
self.SourceFuncPlotData = ArrayPlotData(imagedata = new_sourceSF, colormap = color_mapperSF, index = vaxis, TauOne = tau_one, Velocity = vz, Height = height, T = T/1e3, VelocityZeroXPoints = VelocityZeroXPoints, VelocityZeroYPoints = VelocityZeroYPoints)
self.SourceFuncPlot = Plot(self.SourceFuncPlotData)
self.SourceFuncPlot.img_plot1 = self.SourceFuncPlot.img_plot("imagedata", colormap = color_mapperSF, xbounds = (self.new_vaxis[0], self.new_vaxis[-1]), ybounds = (self.new_height[0], self.new_height[-1]))[0]
self.SourceFuncPlot.overlays.append(ZoomTool(self.SourceFuncPlot.img_plot1))
self.SourceFuncPlot.tools.append(PanTool(self.SourceFuncPlot.img_plot1, drag_button="right"))
self.SourceFuncPlot.TauOnePlot = self.SourceFuncPlot.plot(("index","TauOne"), type = "line", color = "red")
self.SourceFuncPlot.VelocityPlot = self.SourceFuncPlot.plot(("Velocity","Height"), type = "line", color = "green", resizeable = True)
self.SourceFuncPlot.VelocityZeroPlot = self.SourceFuncPlot.plot(("VelocityZeroXPoints","VelocityZeroYPoints"), type = "line", color = "yellow", resizeable = True)
self.TemperaturePlotData = ArrayPlotData(T = T/1e3, Sline = Sline/1e3, Height = height)
self.TemperaturePlot = Plot(self.TemperaturePlotData)
self.TemperaturePlot.TPlot = self.TemperaturePlot.plot(("T","Height"), type = "line", color = "white", resizeable = True, line_style = 'dot', origin = 'bottom right')
self.TemperaturePlot.Slinelot = self.TemperaturePlot.plot(("Sline","Height"), type = "line", color = "white", resizeable = True, line_style = 'dash', origin = 'bottom right')
self.TemperaturePlot.x_grid.visible = False
self.TemperaturePlot.y_grid.visible = False
self.TemperaturePlot.x_axis.orientation = 'top'
self.TemperaturePlot.y_axis.visible = False
self.TemperaturePlot.range2d.x_range.set_bounds(0,30)
self.TemperaturePlot.x_axis.title = "T (kK)"
self.TemperaturePlot.default_origin = 'bottom right'
self.TemperaturePlot.y_mapper = self.SourceFuncPlot.y_mapper
self.SourceFuncPlotC = OverlayPlotContainer()
self.SourceFuncPlotC.add(self.SourceFuncPlot)
self.SourceFuncPlotC.add(self.TemperaturePlot)
self.SourceFuncPlot.title = "Source Function Plot"
self.SourceFuncPlot.x_axis.title = "Velocity (km/s)"
self.SourceFuncPlot.y_axis.title = "Height (Mm)"
self.SourceFuncPlot.range2d.x_range.set_bounds(self.new_vaxis[0], self.new_vaxis[-1])
self.SourceFuncPlot.range2d.y_range.set_bounds(self.new_height[0], self.new_height[-1])
#---------------------Create Tau * Exp(-tau) plot---------------------
tauCalcTE = (tau*N.exp(-tau))
tauCalcTE = tauCalcTE[:, ::-1]
new_tauCalcTE = lineformInterpolate2D(vaxis, height, tauCalcTE, self.new_vaxis, self.new_height)
self._image_indexTE = GridDataSource(xdata = self.new_vaxis, ydata = self.new_height)
index_mapperTE = GridMapper(range = DataRange2D(self._image_indexTE))
self._image_valueTE = ImageData(data = new_tauCalcTE, value_depth = 1)
color_mapperTE = jet(DataRange1D(self._image_valueTE))
self.TauExpPlotData = ArrayPlotData(imagedata = new_tauCalcTE, colormap = color_mapperTE, index = vaxis, TauOne = tau_one, Velocity = vz, Height = height, VelocityZeroXPoints = VelocityZeroXPoints, VelocityZeroYPoints = VelocityZeroYPoints)
self.TauExpPlot = Plot(self.TauExpPlotData)
self.TauExpPlot.img_plot1 = self.TauExpPlot.img_plot("imagedata", colormap = color_mapperTE, xbounds = (self.new_vaxis[0], self.new_vaxis[-1]), ybounds = (self.new_height[0], self.new_height[-1]))[0]
self.TauExpPlot.overlays.append(ZoomTool(self.TauExpPlot.img_plot1))
self.TauExpPlot.tools.append(PanTool(self.TauExpPlot.img_plot1, drag_button="right"))
self.TauExpPlot.TauOnePlot = self.TauExpPlot.plot(("index","TauOne"), type = "line", color = "red", resizeable = True)
self.TauExpPlot.VelocityPlot = self.TauExpPlot.plot(("Velocity","Height"), type = "line", color = "green", resizeable = True)
self.TauExpPlot.VelocityZeroPlot = self.TauExpPlot.plot(("VelocityZeroXPoints","VelocityZeroYPoints"), type = "line", color = "yellow", resizeable = True)
self.TauExpPlot.title = "Tau * Exp Plot"
self.TauExpPlot.x_axis.title = "Velocity (km/s)"
self.TauExpPlot.y_axis.title = "Height (Mm)"
self.TauExpPlot.range2d.x_range.set_bounds(self.new_vaxis[0], self.new_vaxis[-1])
self.TauExpPlot.range2d.y_range.set_bounds(self.new_height[0], self.new_height[-1])
self.TauExpPlotC = OverlayPlotContainer()
self.TauExpPlotC.add(self.TauExpPlot)
#--------------------Create Contribution Function Plot------------------------
new_tau_oneCF = lineformInterpolate1D(vaxis, tau_one, self.new_vaxis)
new_vzCF = lineformInterpolate1D(height, vz, self.new_height)
new_bint = lineformInterpolate1D(vaxis, bint, self.new_vaxis)
ContributionFuncCF = (chi*N.exp(-tau)*S)[:,::-1]
ContributionFuncCF /= N.max(ContributionFuncCF, axis=0)
new_ContributionPlotCF = lineformInterpolate2D(vaxis, height, ContributionFuncCF, self.new_vaxis , self.new_height)
self._image_indexCF = GridDataSource(xdata = self.new_vaxis, ydata = self.new_height)
index_mapperCF = GridMapper(range = DataRange2D(self._image_indexCF))
self._image_valueCF = ImageData(data = new_ContributionPlotCF, value_depth = 1)
color_mapperCF = jet(DataRange1D(self._image_valueCF))
self.ContributionFuncPlotData = ArrayPlotData(imagedata = new_ContributionPlotCF, colormap = color_mapperCF, index = vaxis, TauOne = tau_one, Velocity = vz, Height = height, VelocityZeroXPoints = VelocityZeroXPoints, VelocityZeroYPoints = VelocityZeroYPoints)
self.ContributionFuncPlot = Plot(self.ContributionFuncPlotData)
self.ContributionFuncPlot.img_plot1 = self.ContributionFuncPlot.img_plot("imagedata", colormap = color_mapperCF, xbounds = (self.new_vaxis[0], self.new_vaxis[-1]), ybounds = (self.new_height[0], self.new_height[-1]))[0]
self.ContributionFuncPlot.overlays.append(ZoomTool(self.ContributionFuncPlot.img_plot1))
self.ContributionFuncPlot.tools.append(PanTool(self.ContributionFuncPlot.img_plot1, drag_button="right"))
self.ContributionFuncPlot.TauOnePlot = self.ContributionFuncPlot.plot(("index","TauOne"), type = "line", color = "red", resizeable = True)
self.ContributionFuncPlot.VelocityPlot = self.ContributionFuncPlot.plot(("Velocity","Height"), type = "line", color = "green", resizeable = True)
self.ContributionFuncPlot.VelocityZeroPlot = self.ContributionFuncPlot.plot(("VelocityZeroXPoints","VelocityZeroYPoints"), type = "line", color = "yellow", resizeable = True)
self.IntensityPlotData = ArrayPlotData(index = self.new_vaxis, bint = new_bint)
self.IntensityPlot = Plot(self.IntensityPlotData)
self.IntensityPlot.intensity = self.IntensityPlot.plot(("index","bint"), color = 'white', line_width = 2.0)
self.IntensityPlot.y_axis.orientation = 'right'
self.IntensityPlot.y_axis.title = "I_v (kK)"
self.IntensityPlot.default_origin = 'bottom right'
self.IntensityPlot.x_grid.visible = False
self.IntensityPlot.y_grid.visible = False
self.IntensityPlot.x_axis.visible = False
self.IntensityPlot.x_axis = self.ContributionFuncPlot.x_axis
self.IntensityPlot.range2d.y_range.set_bounds(3,7)
#right_axis = LabelAxis(IntensityPlot, orientation = 'right')
#IntensityPlot.underlays.append(right_axis)
self.ContributionFuncPlot.title = "Contribution Function Plot"
self.ContributionFuncPlot.x_axis.title = "Velocity (km/s)"
self.ContributionFuncPlot.y_axis.title = "Height (Mm)"
self.ContributionFuncPlot.range2d.x_range.set_bounds(self.new_vaxis[0], self.new_vaxis[-1])
self.ContributionFuncPlot.range2d.y_range.set_bounds(self.new_height[0], self.new_height[-1])
self.ContributionFuncPlotC = OverlayPlotContainer()
self.ContributionFuncPlotC.add(self.ContributionFuncPlot)
self.ContributionFuncPlotC.add(self.IntensityPlot)
#-------------------------------Final Container Construction-------------------------------
self.TauExpPlot.range2d = self.ChiTauPlot.range2d
self.ContributionFuncPlot.range2d = self.ChiTauPlot.range2d
self.SourceFuncPlot.range2d = self.ChiTauPlot.range2d
self.LeftPlots = VPlotContainer(self.TauExpPlotC, self.ChiTauPlotC, background = "lightgray", use_back_buffer = True)
self.LeftPlots.spacing = 0
self.RightPlots = VPlotContainer(self.ContributionFuncPlotC, self.SourceFuncPlotC, background = "lightgray", use_back_buffer = True)
self.RightPlots.spacing = 0
MainContainer = HPlotContainer(self.LeftPlots, self.RightPlots, background = "lightgray", use_back_buffer = True)
MainContainer.spacing = 0
self.PrimaryPlotC = MainContainer
def _InspectorPosition_changed(self):
if self.ImageLock == True:
return
if len(self.InspectorPosition) > 0:
xi = self.InspectorPosition[0]
yi = self.InspectorPosition[1]
else:
xi = 0
yi = 0
plotvals = self.CalcSourcePlotVals(sel = self.RayFile, xi=xi, yi=yi, wave_ref = self.wave_ref, vmax = float(FileLoadWindow.VMax_Min), zrange=[float(FileLoadWindow.ZRangeMin), float(FileLoadWindow.ZRangeMax)])
#plotvals = self.CalcSourcePlotVals(sel = self.RayFile, xi = xi, yi = yi, wave_ref = self.wave_ref, vmax = 59, zrange=[0.2,3.0])
#unpacks values from CalcSourcePlotVals
vaxis = plotvals['vaxis']
height = plotvals['height']
chiCT = plotvals['chiCT']
tauCT = plotvals['tauCT']
tau = plotvals['tau']
chi = plotvals['chi']
vz = plotvals['vz']
T = plotvals['T']
Sline = plotvals['Sline']
tau_one = plotvals['tau_one']
bint = plotvals['bint']
wave = plotvals['wave']
vrange = plotvals['vrange']
zrange = plotvals['zrange']
S = plotvals['S']
#ChiTau Plot updates
new_chiCT = lineformInterpolate2D(vaxis, height, chiCT, self.new_vaxis, self.new_height)
new_tauCT = lineformInterpolate2D(vaxis, height, tauCT, self.new_vaxis , self.new_height)
self.ChiTauPlotData.set_data("imagedata",(new_chiCT[:,1:]/new_tauCT[:,1:]))
self.ChiTauPlotData.set_data("index", vaxis)
self.ChiTauPlotData.set_data("TauOne", tau_one)
self.ChiTauPlotData.set_data("Velocity",vz)
self.ChiTauPlotData.set_data("Height", height)
#Source Plot Updates
sourceSF = S
sourceSF = S[:, ::-1]
new_sourceSF = lineformInterpolate2D(vaxis, height, sourceSF, self.new_vaxis, self.new_height)
self.SourceFuncPlotData.set_data("imagedata", new_sourceSF)
self.SourceFuncPlotData.set_data("index",vaxis)
self.SourceFuncPlotData.set_data("TauOne",tau_one)
self.SourceFuncPlotData.set_data("Velocity",vz)
self.SourceFuncPlotData.set_data("Height",height)
self.TemperaturePlotData.set_data("T", T/1.0e3)
self.TemperaturePlotData.set_data("Sline",Sline/1e3)
self.TemperaturePlotData.set_data("Height",height)
#Tau Plot updates
tauCalcTE = (tau*N.exp(-tau))
tauCalcTE = tauCalcTE[:, ::-1]
new_tauCalcTE = lineformInterpolate2D(vaxis, height, tauCalcTE, self.new_vaxis, self.new_height)
self.TauExpPlotData.set_data("imagedata", new_tauCalcTE)
self.TauExpPlotData.set_data("index",vaxis)
self.TauExpPlotData.set_data("TauOne",tau_one)
self.TauExpPlotData.set_data("Velocity",vz)
self.TauExpPlotData.set_data("Height",height)
#Source Function Plot Updates
new_tau_oneCF = lineformInterpolate1D(vaxis, tau_one, self.new_vaxis)
new_vzCF = lineformInterpolate1D(height, vz, self.new_height)
new_bint = lineformInterpolate1D(vaxis, bint, self.new_vaxis)
ContributionFuncCF = (chi*N.exp(-tau)*S)[:,::-1]
ContributionFuncCF /= N.max(ContributionFuncCF, axis=0)
new_ContributionPlotCF = lineformInterpolate2D(vaxis, height, ContributionFuncCF, self.new_vaxis, self.new_height)
self.new_vaxis = N.linspace(vrange[0], vrange[1], 100)
self.new_height = N.linspace(zrange[0], zrange[1], 400)
new_tau_oneCF = lineformInterpolate1D(vaxis, tau_one, self.new_vaxis)
new_vzCF = lineformInterpolate1D(height, vz, self.new_height)
new_bint = lineformInterpolate1D(vaxis, bint, self.new_vaxis)
self.ContributionFuncPlotData.set_data("imagedata", new_ContributionPlotCF)
self.ContributionFuncPlotData.set_data("index",vaxis)
self.ContributionFuncPlotData.set_data("TauOne",tau_one)
self.ContributionFuncPlotData.set_data("Velocity",vz)
self.ContributionFuncPlotData.set_data("Height",height)
self.IntensityPlotData.set_data("index",self.new_vaxis)
self.IntensityPlotData.set_data("bint",new_bint)
def _Wavelength_changed(self):
velocityzero = 299792.45 * (self.Wavelength - self.wave_ref)/self.wave_ref
VelocityZeroXPoints = [velocityzero, velocityzero]
try:
self.ChiTauPlotData.set_data("VelocityZeroXPoints", VelocityZeroXPoints)
self.SourceFuncPlotData.set_data("VelocityZeroXPoints", VelocityZeroXPoints)
self.TauExpPlotData.set_data("VelocityZeroXPoints", VelocityZeroXPoints)
self.ContributionFuncPlotData.set_data("VelocityZeroXPoints", VelocityZeroXPoints)
except:
pass
def _colormap_list_changed(self):
plotlist = [self.ChiTauPlot.img_plot1, self.SourceFuncPlot.img_plot1, self.TauExpPlot.img_plot1, self.ContributionFuncPlot.img_plot1]
for x in plotlist:
clr_range = x.color_mapper.range
color_mapper = eval(self.colormap_list)(clr_range)
x.color_mapper = color_mapper
self.ChiTauPlot.request_redraw()
def _TauOnelinecolor_changed(self):
self.ChiTauPlot.TauOnePlot[0].color = self.TauOnelinecolor
self.SourceFuncPlot.TauOnePlot[0].color = self.TauOnelinecolor
self.TauExpPlot.TauOnePlot[0].color = self.TauOnelinecolor
self.ContributionFuncPlot.TauOnePlot[0].color = self.TauOnelinecolor
def _Velocitylinecolor_changed(self):
self.ChiTauPlot.VelocityPlot[0].color = self.Velocitylinecolor
self.SourceFuncPlot.VelocityPlot[0].color = self.Velocitylinecolor
self.TauExpPlot.VelocityPlot[0].color = self.Velocitylinecolor
self.ContributionFuncPlot.VelocityPlot[0].color = self.Velocitylinecolor
def _VelocityZerolinecolor_changed(self):
self.ChiTauPlot.VelocityZeroPlot[0].color = self.VelocityZerolinecolor
self.SourceFuncPlot.VelocityZeroPlot[0].color = self.VelocityZerolinecolor
self.TauExpPlot.VelocityZeroPlot[0].color = self.VelocityZerolinecolor
self.ContributionFuncPlot.VelocityZeroPlot[0].color = self.VelocityZerolinecolor
def _Intensitylinecolor_changed(self):
self.IntensityPlot.intensity[0].color = self.Intensitylinecolor
def _SaveFlag_changed(self):
DPI = 72
#Main plot save code
size = (1000,1000)
path = os.getenv('PWD')
filenamelist = [path, '/', 'SourcePlots_X', str(self.InspectorPosition[0]), '_Y', str(self.InspectorPosition[1]), '.png']
filename = ''.join(filenamelist)
container = self.PrimaryPlotC
temp = container.outer_bounds
container.outer_bounds = list(size)
container.do_layout(force=True)
gc = PlotGraphicsContext(size, dpi=DPI)
gc.render_component(container)
gc.save(filename)
container.outer_bounds = temp
print "SAVED: ", filename
class PlotWindow(HasTraits):
_plot_data = Instance(ArrayPlotData)
_plot = Instance(Plot)
_click_tool = Instance(ClickerTool)
_img_plot = Instance(ImagePlot)
_right_click_avail = 0
name = Str
view = View(Item(name='_plot', editor=ComponentEditor(),
show_label=False), )
def __init__(self):
super(HasTraits, self).__init__()
# -------------- Initialization of plot system ----------------
padd = 25
self._plot_data = ArrayPlotData()
self._x = []
self._y = []
self.man_ori = [1, 2, 3, 4]
self._plot = Plot(self._plot_data, default_origin="top left")
self._plot.padding_left = padd
self._plot.padding_right = padd
self._plot.padding_top = padd
self._plot.padding_bottom = padd
self._quiverplots = []
# -------------------------------------------------------------
def left_clicked_event(self):
print("left clicked")
if len(self._x) < 4:
self._x.append(self._click_tool.x)
self._y.append(self._click_tool.y)
print(self._x, self._y)
self.drawcross("coord_x", "coord_y", self._x, self._y, "red", 5)
self._plot.overlays = []
self.plot_num_overlay(self._x, self._y, self.man_ori)
def right_clicked_event(self):
print("right clicked")
if len(self._x) > 0:
self._x.pop()
self._y.pop()
print(self._x, self._y)
self.drawcross("coord_x", "coord_y", self._x, self._y, "red", 5)
self._plot.overlays = []
self.plot_num_overlay(self._x, self._y, self.man_ori)
else:
if (self._right_click_avail):
print("deleting point")
self.py_rclick_delete(self._click_tool.x, self._click_tool.y,
self.cameraN)
x = []
y = []
self.py_get_pix_N(x, y, self.cameraN)
self.drawcross("x", "y", x[0], y[0], "blue", 4)
def attach_tools(self):
self._click_tool = ClickerTool(self._img_plot)
self._click_tool.on_trait_change(self.left_clicked_event,
'left_changed')
self._click_tool.on_trait_change(self.right_clicked_event,
'right_changed')
self._img_plot.tools.append(self._click_tool)
self._zoom_tool = SimpleZoom(component=self._plot,
tool_mode="box",
always_on=False)
self._zoom_tool.max_zoom_out_factor = 1.0
self._img_plot.tools.append(self._zoom_tool)
if self._plot.index_mapper is not None:
self._plot.index_mapper.on_trait_change(self.handle_mapper,
'updated',
remove=False)
if self._plot.value_mapper is not None:
self._plot.value_mapper.on_trait_change(self.handle_mapper,
'updated',
remove=False)
def drawcross(self, str_x, str_y, x, y, color1, mrk_size):
"""
Draws crosses on images
"""
self._plot_data.set_data(str_x, x)
self._plot_data.set_data(str_y, y)
self._plot.plot((str_x, str_y),
type="scatter",
color=color1,
marker="plus",
marker_size=mrk_size)
self._plot.request_redraw()
def drawline(self, str_x, str_y, x1, y1, x2, y2, color1):
self._plot_data.set_data(str_x, [x1, x2])
self._plot_data.set_data(str_y, [y1, y2])
self._plot.plot((str_x, str_y), type="line", color=color1)
self._plot.request_redraw()
def drawquiver(self, x1c, y1c, x2c, y2c, color, linewidth=1.0, scale=1.0):
""" drawquiver draws multiple lines at once on the screen x1,y1->x2,y2 in the current camera window
parameters:
x1c - array of x1 coordinates
y1c - array of y1 coordinates
x2c - array of x2 coordinates
y2c - array of y2 coordinates
color - color of the line
linewidth - linewidth of the line
example usage:
drawquiver ([100,200],[100,100],[400,400],[300,200],'red',linewidth=2.0)
draws 2 red lines with thickness = 2 : 100,100->400,300 and 200,100->400,200
"""
x1, y1, x2, y2 = self.remove_short_lines(x1c,
y1c,
x2c,
y2c,
min_length=0)
if len(x1) > 0:
xs = ArrayDataSource(x1)
ys = ArrayDataSource(y1)
quiverplot = QuiverPlot(
index=xs,
value=ys,
index_mapper=LinearMapper(range=self._plot.index_mapper.range),
value_mapper=LinearMapper(range=self._plot.value_mapper.range),
origin=self._plot.origin,
arrow_size=0,
line_color=color,
line_width=linewidth,
ep_index=np.array(x2) * scale,
ep_value=np.array(y2) * scale)
self._plot.add(quiverplot)
# we need this to track how many quiverplots are in the current
# plot
self._quiverplots.append(quiverplot)
# import pdb; pdb.set_trace()
def remove_short_lines(self, x1, y1, x2, y2, min_length=2):
""" removes short lines from the array of lines
parameters:
x1,y1,x2,y2 - start and end coordinates of the lines
returns:
x1f,y1f,x2f,y2f - start and end coordinates of the lines, with short lines removed
example usage:
x1,y1,x2,y2=remove_short_lines([100,200,300],[100,200,300],[100,200,300],[102,210,320])
3 input lines, 1 short line will be removed (100,100->100,102)
returned coordinates:
x1=[200,300]; y1=[200,300]; x2=[200,300]; y2=[210,320]
"""
# dx, dy = 2, 2 # minimum allowable dx,dy
x1f, y1f, x2f, y2f = [], [], [], []
for i in range(len(x1)):
if abs(x1[i] - x2[i]) > min_length or abs(y1[i] -
y2[i]) > min_length:
x1f.append(x1[i])
y1f.append(y1[i])
x2f.append(x2[i])
y2f.append(y2[i])
return x1f, y1f, x2f, y2f
def handle_mapper(self):
for i in range(0, len(self._plot.overlays)):
if hasattr(self._plot.overlays[i], 'real_position'):
coord_x1, coord_y1 = self._plot.map_screen(
[self._plot.overlays[i].real_position])[0]
self._plot.overlays[i].alternate_position = (coord_x1,
coord_y1)
def plot_num_overlay(self, x, y, txt):
for i in range(0, len(x)):
coord_x, coord_y = self._plot.map_screen([(x[i], y[i])])[0]
ovlay = TextBoxOverlay(component=self._plot,
text=str(txt[i]),
alternate_position=(coord_x, coord_y),
real_position=(x[i], y[i]),
text_color="white",
border_color="red")
self._plot.overlays.append(ovlay)
def update_image(self, image, is_float):
if is_float:
self._plot_data.set_data('imagedata', image.astype(np.float))
else:
self._plot_data.set_data('imagedata', image.astype(np.byte))
self._plot.request_redraw()
class MainPlot(HasTraits):
plot = Instance(Plot)
traits_view = View(
Item('plot', editor=ComponentEditor(), show_label=False),
width=800, height=600, resizable=True,
title="Plot")
def __init__(self):
# list of allready added data
# self.data[name] = [timeData,yData]
self.data = {}
# next color index from map
self.colNr = 0
self.plotdata = ArrayPlotData()
self.plot = Plot(self.plotdata)
self.plot.legend.visible = True
self.__existingData = [] ## legenLabels
# time axis
time_axis = PlotAxis(self.plot, orientation="bottom", tick_generator=ScalesTickGenerator(scale=CalendarScaleSystem()))
#self.plot.overlays.append(time_axis)
self.plot.x_axis = time_axis
hgrid, vgrid = add_default_grids(self.plot)
self.plot.x_grid = None
vgrid.tick_generator = time_axis.tick_generator
# drag tool only time dir
self.plot.tools.append(PanTool(self.plot, constrain=False,
# constrain_direction="x"
)
)
# zoom tool only y dir
self.plot.overlays.append(
#ZoomTool(self.plot, drag_button="right", always_on=True, tool_mode="range", axis="value" )
ZoomTool(self.plot, tool_mode="box", always_on=False)
)
# init plot
self.plot.plot(
(
self.plotdata.set_data(name = None, new_data = [time.mktime(testTime[i].timetuple()) for i in xrange(len(testTime))], generate_name=True),
self.plotdata.set_data(name = None, new_data = testData, generate_name=True)
),
name = 'temp')
self.plot.request_redraw()
self.plot.delplot('temp')
#self.showData(testTime,testData,'ga1')
def addData(self, _time, y , dataKey, overwriteData = False):
"""
if name already exists the existing is overwritten if overwriteData
"""
if not dataKey in self.data or overwriteData:
x = [time.mktime(_time[i].timetuple()) for i in xrange(len(_time))]
self.data[dataKey] = [
self.plotdata.set_data(name = None, new_data = x, generate_name=True),
self.plotdata.set_data(name = None, new_data = y, generate_name=True)
]
def showData(self, legendLabel, dataKey):
if not dataKey in self.data:
raise Exception('No entry for that dataKey plz first use addData')
#if not legendLabel in self.__existingData:
self.plot.plot((self.data[dataKey][0], self.data[dataKey][1]), name = legendLabel, color=self._getColor())
#else:
# self.plot.plot.showplot(legendLabel)
zoomrange = self._get_zoomRange()
self.plot.range2d.set_bounds(zoomrange[0],zoomrange[1])
self.plot.request_redraw()
def _get_zoomRange(self):
values = []
indices = []
for renderers in self.plot.plots.values():
for renderer in renderers:
indices.append(renderer.index.get_data())
values.append(renderer.value.get_data())
indMin = None
indMax = None
valMin = None
valMax = None
for indice in indices:
_min = min(indice)
_max = max(indice)
if indMin:
indMin = min(indMin,_min)
else:
indMin = _min
if indMin:
indMax = max(indMax,_max)
else:
indMin = _max
for value in values:
_min = min(value)
_max = max(value)
if valMin:
valMin = min(valMin,_min)
else:
valMin = _min
if valMax:
valMax = max(valMax,_max)
else:
valMax = _max
if indMin and indMax and valMin and valMax:
return ((indMin,valMin),(indMax,valMax))
else:
return None
def hideData(self, legendLabel):
self.plot.delplot(legendLabel)
#self.plot.hideplot(legendLabel)
zoomrange = self._get_zoomRange()
if zoomrange:
self.plot.range2d.set_bounds(zoomrange[0],zoomrange[1])
self.plot.request_redraw()
def _getColor(self):
temp = self.colNr
self.colNr += 1
if self.colNr >= len(COLOR_PALETTE):
self.colNr = 0
return tuple(COLOR_PALETTE[temp])
class CameraWindow(traits.api.HasTraits):
""" CameraWindow class contains the relevant information and functions for a single camera window: image, zoom, pan
important members:
_plot_data - contains image data to display (used by update_image)
_plot - instance of Plot class to use with _plot_data
_click_tool - instance of Clicker tool for the single camera window, to handle mouse processing
"""
_plot_data = traits.api.Instance(ArrayPlotData)
_plot = traits.api.Instance(Plot)
_click_tool = traits.api.Instance(Clicker)
rclicked = traits.api.Int(0)
cam_color = ''
name = traits.api.Str
view = traitsui.api.View(traitsui.api.Item(name='_plot', editor=ComponentEditor(), show_label=False))
# view = View( Item(name='_plot',show_label=False) )
def __init__(self, color):
"""
Initialization of plot system
"""
traits.api.HasTraits.__init__(self)
padd = 25
self._plot_data = ArrayPlotData()
self._plot = Plot(self._plot_data, default_origin="top left")
self._plot.padding_left = padd
self._plot.padding_right = padd
self._plot.padding_top = padd
self._plot.padding_bottom = padd
self.right_p_x0, self.right_p_y0, self.right_p_x1, self.right_p_y1, self._quiverplots = [], [], [], [], []
self.cam_color = color
def attach_tools(self):
""" attach_tools(self) contains the relevant tools: clicker, pan, zoom
"""
self._click_tool = Clicker(self._img_plot)
self._click_tool.on_trait_change(self.left_clicked_event, 'left_changed') # set processing events for Clicker
self._click_tool.on_trait_change(self.right_clicked_event, 'right_changed')
self._img_plot.tools.append(self._click_tool)
pan = PanTool(self._plot, drag_button='middle')
zoom_tool = ZoomTool(self._plot, tool_mode="box", always_on=False)
# zoom_tool = BetterZoom(component=self._plot, tool_mode="box", always_on=False)
zoom_tool.max_zoom_out_factor = 1.0 # Disable "bird view" zoom out
self._img_plot.overlays.append(zoom_tool)
self._img_plot.tools.append(pan)
def left_clicked_event(self): # TODO: why do we need the ClickerTool if we can handle mouse clicks here?
""" left_clicked_event - processes left click mouse avents and displays coordinate and grey value information
on the screen
"""
print("x = %d, y= %d, grey= %d " % (self._click_tool.x, self._click_tool.y, self._click_tool.data_value))
# need to priny gray value
def right_clicked_event(self):
self.rclicked = 1 # flag that is tracked by main_gui, for right_click_process function of main_gui
# self._click_tool.y,self.name])
# self.drawcross("coord_x","coord_y",self._click_tool.x,self._click_tool.y,"red",5)
# print ("right clicked, "+self.name)
# need to print cross and manage other camera's crosses
def update_image(self, image, is_float=False):
""" update_image - displays/updates image in the curren camera window
parameters:
image - image data
is_float - if true, displays an image as float array,
else displays as byte array (B&W or gray)
example usage:
update_image(image,is_float=False)
"""
print('image shape = ', image.shape, 'is_float =', is_float)
if image.ndim > 2:
image = img_as_ubyte(rgb2gray(image))
is_float = False
if is_float:
self._plot_data.set_data('imagedata', image.astype(np.float))
else:
self._plot_data.set_data('imagedata', image.astype(np.byte))
if not hasattr(self, '_img_plot'): # make a new plot if there is nothing to update
self._img_plot = traits.api.Instance(ImagePlot)
self._img_plot = self._plot.img_plot('imagedata', colormap=gray)[0]
self.attach_tools()
# self._plot.request_redraw()
def drawcross(self, str_x, str_y, x, y, color1, mrk_size, marker1="plus"):
""" drawcross draws crosses at a given location (x,y) using color and marker in the current camera window
parameters:
str_x - label for x coordinates
str_y - label for y coordinates
x - array of x coordinates
y - array of y coordinates
mrk_size - marker size
marker1 - type of marker, e.g "plus","circle"
example usage:
drawcross("coord_x","coord_y",[100,200,300],[100,200,300],2)
draws plus markers of size 2 at points (100,100),(200,200),(200,300)
:rtype:
"""
self._plot_data.set_data(str_x, np.atleast_1d(x))
self._plot_data.set_data(str_y, np.atleast_1d(y))
self._plot.plot((str_x, str_y), type="scatter", color=color1, marker=marker1, marker_size=mrk_size)
self._plot.request_redraw()
def drawquiver(self, x1c, y1c, x2c, y2c, color, linewidth=1.0):
""" Draws multiple lines at once on the screen x1,y1->x2,y2 in the current camera window
parameters:
x1c - array of x1 coordinates
y1c - array of y1 coordinates
x2c - array of x2 coordinates
y2c - array of y2 coordinates
color - color of the line
linewidth - linewidth of the line
example usage:
drawquiver ([100,200],[100,100],[400,400],[300,200],'red',linewidth=2.0)
draws 2 red lines with thickness = 2 : 100,100->400,300 and 200,100->400,200
"""
x1, y1, x2, y2 = self.remove_short_lines(x1c, y1c, x2c, y2c)
if len(x1) > 0:
xs = ArrayDataSource(x1)
ys = ArrayDataSource(y1)
quiverplot = QuiverPlot(index=xs, value=ys,
index_mapper=LinearMapper(range=self._plot.index_mapper.range),
value_mapper=LinearMapper(range=self._plot.value_mapper.range),
origin=self._plot.origin, arrow_size=0,
line_color=color, line_width=linewidth, ep_index=np.array(x2),
ep_value=np.array(y2)
)
self._plot.add(quiverplot)
# we need this to track how many quiverplots are in the current plot
self._quiverplots.append(quiverplot)
@staticmethod
def remove_short_lines(x1, y1, x2, y2):
""" removes short lines from the array of lines
parameters:
x1,y1,x2,y2 - start and end coordinates of the lines
returns:
x1f,y1f,x2f,y2f - start and end coordinates of the lines, with short lines removed
example usage:
x1,y1,x2,y2=remove_short_lines([100,200,300],[100,200,300],[100,200,300],[102,210,320])
3 input lines, 1 short line will be removed (100,100->100,102)
returned coordinates:
x1=[200,300]; y1=[200,300]; x2=[200,300]; y2=[210,320]
"""
dx, dy = 2, 2 # minimum allowable dx,dy
x1f, y1f, x2f, y2f = [], [], [], []
for i in range(len(x1)):
if abs(x1[i] - x2[i]) > dx or abs(y1[i] - y2[i]) > dy:
x1f.append(x1[i])
y1f.append(y1[i])
x2f.append(x2[i])
y2f.append(y2[i])
return x1f, y1f, x2f, y2f
def drawline(self, str_x, str_y, x1, y1, x2, y2, color1):
""" drawline draws 1 line on the screen by using lineplot x1,y1->x2,y2
parameters:
str_x - label of x coordinate
str_y - label of y coordinate
x1,y1,x2,y2 - start and end coordinates of the line
color1 - color of the line
example usage:
drawline("x_coord","y_coord",100,100,200,200,red)
draws a red line 100,100->200,200
"""
self._plot_data.set_data(str_x, [x1, x2])
self._plot_data.set_data(str_y, [y1, y2])
self._plot.plot((str_x, str_y), type="line", color=color1)
# self._plot.request_redraw()
def add_line(self, str_x, str_y, x1, y1, x2, y2, color1):
""" drawline draws 1 line on the screen by using lineplot x1,y1->x2,y2
parameters:
str_x - label of x coordinate
str_y - label of y coordinate
x1,y1,x2,y2 - start and end coordinates of the line
color1 - color of the line
example usage:
drawline("x_coord","y_coord",100,100,200,200,red)
draws a red line 100,100->200,200
"""
# self._plot_data.set_data(str_x,[x1,x2])
# self._plot_data.set_data(str_y,[y1,y2])
self._plot_data.set_data(str_x, [x1, x2])
self._plot_data.set_data(str_y, [y1, y2])
self._plot.add(Plot((str_x, str_y), type="line", color=color1))
class ChacoSlice(HasTraits):
# Borrow the volume data from the parent
plot_data = ArrayPlotData
plot = Instance(Plot)
origin = Enum("bottom left", "top left", "bottom right", "top right")
top_slice = Int
slice_number = Range(low=0, high_name="top_slice", value=0)
slice_plane_visible = Bool(True)
slice_opacity = Range(0.0,1.0,1.)
plane = Enum("x","y","z")
def __init__(self,**traits):
super(ChacoSlice,self).__init__(**traits)
self.plot_data = ArrayPlotData(imagedata=self.data_source())
self.plot = Plot(self.plot_data,
padding=1,
fixed_preferred_size=(50,50),
bgcolor="black"
)
aspect_ratio = 1
self.renderer = self.plot.img_plot(
"imagedata", name="plot1",
colormap=gray,
aspect_ratio=aspect_ratio)[0]
def _slice_number_changed(self):
#print self.plane, "slice changed to ",self.slice_number
self.plot.data.set_data("imagedata",self.data_source())
self.plot.request_redraw()
def _origin_changed(self):
self.renderer.origin = self.origin
self.plot.request_redraw()
def reset_aspect_ratio(self):
x,y = self.get_extents()
self.renderer.aspect_ratio = float(x)/y
def get_extents(self):
i,j,k = self.parent.extents
if self.plane == "x":
return j,k
if self.plane == "y":
return i,k
if self.plane == "z":
return i,j
def data_source(self):
# Link the appropriate view changer to the slice number change
if self.plane == "x":
return self.parent.volume_data[self.slice_number,:,:].T
elif self.plane == "y":
return self.parent.volume_data[:,self.slice_number,:].T
elif self.plane == "z":
return self.parent.volume_data[:,:,self.slice_number].T
traits_view = View(
Group(
Item('plot', editor=ComponentEditor(),
height=100,width=100, show_label=False),
HGroup(
Item("slice_plane_visible"),
Item("slice_number", editor=RangeEditor(
mode="slider",
high_name = 'top_slice',
low = 0,
format = "%i")),
show_labels=False),
show_labels=False,
),
#width=100, height=200,
resizable=True
)
