def showModel3d(basedir,ptcode, ctcode, cropname='ring', showVol='MIP',
showmodel=True, graphname='model', **kwargs):
""" show model and vol in 3d by mip iso or 2D
graphname 'all' draws all graph models stored in s, if multiple
"""
s = loadmodel(basedir, ptcode, ctcode, cropname, modelname='modelavgreg')
vol = loadvol(basedir, ptcode, ctcode, cropname, what='avgreg').vol
# figure
f = vv.figure(); vv.clf()
f.position = 0.00, 22.00, 1920.00, 1018.00
a = vv.gca()
a.axis.axisColor = 1,1,1
a.axis.visible = False
a.bgcolor = 0,0,0
a.daspect = 1, 1, -1
t = show_ctvolume(vol, axis=a, showVol=showVol, removeStent=False,
climEditor=True, **kwargs)
label = pick3d(a, vol)
vv.xlabel('x (mm)');vv.ylabel('y (mm)');vv.zlabel('z (mm)')
if showmodel:
if graphname == 'all':
for key in dir(s):
if key.startswith('model'):
s[key].Draw(mc='b', mw = 5, lc='g', alpha = 0.5)
else:
s[graphname].Draw(mc='b', mw = 5, lc='g', alpha = 0.5)
vv.title('Model for LSPEAS %s - %s' % (ptcode[8:], ctcode))
# f.eventKeyDown.Bind(lambda event: _utils_GUI.RotateView(event, [a], axishandling=False ))
f.eventKeyDown.Bind(lambda event: _utils_GUI.ViewPresets(event, [a]) )
return f, a, label, s
def DoAction(self):
"""
This method is affiliated to the method <self.SartAction>
"""
####################################################
#
# Zak add you code here
# e.g., in what follows we just acquire spectra
#
####################################################
# If you want to apply some radom phase maske then uncomment the following:
#amplitude_mask = self.PulseShaper.GetZeroMask() + 1
#phase_mask = np.random.rand(max_amplitude_mask.size)
#self.PulseShaper.SetMasks(amplitude_mask, phase_mask)
# Getting spectrum
spectrum = self.Spectrometer.AcquiredData()
# Plot the spectra
visvis.gca().Clear()
visvis.plot(self.wavelengths, spectrum)
visvis.xlabel("wavelength (nm)")
visvis.ylabel("counts")
visvis.title("Spectrum from radom pulse shape")
self.fig.DrawNow()
# Going to the next iteration
if self._continue_action:
wx.CallAfter(self.DoAction)
def DisplayOptimization (self) :
"""
Display the progress of optimization
"""
wx.Yield()
# abort, if requested
if self.need_abort : return
def GetValueColourIter (d) :
return izip_longest( d.itervalues(), ['r', 'g', 'b', 'k'], fillvalue='y' )
visvis.cla(); visvis.clf()
visvis.subplot(211)
# Plot optimization statistics
for values, colour in GetValueColourIter(self.optimization_log) :
try : visvis.plot ( values, lc=colour )
except Exception : pass
visvis.xlabel ('iteration')
visvis.ylabel ('Objective function')
visvis.legend( self.optimization_log.keys() )
# Display reference signal
visvis.subplot(212)
# Plot reference signal
for values, colour in GetValueColourIter(self.log_reference_signal) :
try : visvis.plot ( values, lc=colour )
except Exception : pass
visvis.xlabel ('iteration')
visvis.ylabel ("Signal from reference pulse")
visvis.legend( ["channel %d" % x for x in self.log_reference_signal.keys()] )
def PlotReferencePhase(self, event):
"""
Plot reference phase
"""
visvis.cla()
visvis.clf()
# get actual amplitude and phased
phase = self.GetReferencePhase()[0]
ampl = np.ones(phase.size)
ampl, phase = self.DevPulseShaper.GetUnwrappedAmplPhase(ampl, phase)
# Wrap the phase in radians
phase %= (2 * np.pi)
# Get the phase in unites of 2*pi
phase /= (2 * np.pi)
visvis.subplot(211)
visvis.plot(phase)
visvis.ylabel('Reference phase')
visvis.xlabel('puls shaper pixel number')
visvis.title('Current reference phase (in unites of 2*pi)')
visvis.subplot(212)
visvis.plot(self.corrections, lc='r', ms='*', mc='r')
visvis.ylabel('Value of coefficients')
visvis.xlabel('coefficient number')
visvis.title('Value of corrections')
def vis2Dfit(fitted, ptcode, ctcode, showAxis):
"""Visualize ellipse fit in 2D in current axis
input: fitted = _fit3D output = (pp3, plane, pp3_2, e3)
"""
from stentseg.utils import fitting
import numpy as np
plane,pp3_2,e3 = fitted[1],fitted[2],fitted[3]
# endpoints axis
x0,y0,res1,res2,phi = e3[0], e3[1], e3[2], e3[3], e3[4]
dxax1 = np.cos(phi)*res1
dyax1 = np.sin(phi)*res1
dxax2 = np.cos(phi+0.5*np.pi)*res2
dyax2 = np.sin(phi+0.5*np.pi)*res2
p1ax1, p2ax1 = (x0+dxax1, y0+dyax1), (x0-dxax1, y0-dyax1)
p1ax2, p2ax2 = (x0+dxax2, y0+dyax2), (x0-dxax2, y0-dyax2)
a = vv.gca()
vv.xlabel('x (mm)');vv.ylabel('y (mm)')
vv.title('Ellipse fit for model %s - %s' % (ptcode[7:], ctcode))
a.axis.axisColor= 0,0,0
a.bgcolor= 0,0,0
a.axis.visible = showAxis
a.daspectAuto = False
a.axis.showGrid = True
vv.plot(pp3_2, ls='', ms='.', mc='r', mw=9)
vv.plot(fitting.sample_ellipse(e3), lc='b', lw=2)
vv.plot(np.array([p1ax1, p2ax1]), lc='w', lw=2) # major axis
vv.plot(np.array([p1ax2, p2ax2]), lc='w', lw=2) # minor axis
vv.legend('3D points projected to plane', 'Ellipse fit on projected points')
def vis3Dfit(fitted, vol, model, ptcode, ctcode, showAxis, **kwargs):
"""Visualize ellipse fit in 3D with CT volume in current axis
input: fitted = _fit3D output = (pp3, plane, pp3_2, e3)
"""
from stentseg.utils import fitting
import numpy as np
pp3,plane,pp3_2,e3 = fitted[0],fitted[1],fitted[2],fitted[3]
a = vv.gca()
# show_ctvolume(vol, model, showVol=showVol, clim=clim0, isoTh=isoTh)
show_ctvolume(vol, model, **kwargs)
vv.xlabel('x (mm)');vv.ylabel('y (mm)');vv.zlabel('z (mm)')
vv.title('Ellipse fit for model %s - %s' % (ptcode[7:], ctcode))
a.axis.axisColor= 1,1,1
a.bgcolor= 0,0,0
a.daspect= 1, 1, -1 # z-axis flipped
a.axis.visible = showAxis
# For visualization, calculate 4 points on rectangle that lies on the plane
x1, x2 = pp3.min(0)[0]-0.3, pp3.max(0)[0]+0.3
y1, y2 = pp3.min(0)[1]-0.3, pp3.max(0)[1]+0.3
p1 = x1, y1, -(x1*plane[0] + y1*plane[1] + plane[3]) / plane[2]
p2 = x2, y1, -(x2*plane[0] + y1*plane[1] + plane[3]) / plane[2]
p3 = x2, y2, -(x2*plane[0] + y2*plane[1] + plane[3]) / plane[2]
p4 = x1, y2, -(x1*plane[0] + y2*plane[1] + plane[3]) / plane[2]
vv.plot(pp3, ls='', ms='.', mc='y', mw = 10)
vv.plot(fitting.project_from_plane(pp3_2, plane), lc='r', ls='', ms='.', mc='r', mw=9)
# vv.plot(fitting.project_from_plane(fitting.sample_circle(c3), plane), lc='r', lw=2)
vv.plot(fitting.project_from_plane(fitting.sample_ellipse(e3), plane), lc='b', lw=2)
vv.plot(np.array([p1, p2, p3, p4, p1]), lc='g', lw=2)
# vv.legend('3D points', 'Projected points', 'Circle fit', 'Ellipse fit', 'Plane fit')
vv.legend('3D points', 'Projected points', 'Ellipse fit', 'Plane fit')
def figparts():
""" Visualize ring parts
"""
fig = vv.figure(4);
fig.position = 8.00, 30.00, 944.00, 1002.00
vv.clf()
a0 = vv.subplot(121)
show_ctvolume(vol, model, showVol=showVol, clim=clim0, isoTh=isoTh)
modelR1, modelR2 = modelsR1R2[0][0], modelsR1R2[0][1]
modelR1.Draw(mc='g', mw = 10, lc='g') # R1 = green
modelR2.Draw(mc='c', mw = 10, lc='c') # R2 = cyan
vv.xlabel('x (mm)');vv.ylabel('y (mm)');vv.zlabel('z (mm)')
vv.title('Analysis for model LSPEAS %s - %s' % (ptcode[7:], ctcode))
a0.axis.axisColor= 1,1,1
a0.bgcolor= 0,0,0
a0.daspect= 1, 1, -1 # z-axis flipped
a0.axis.visible = showAxis
a1 = vv.subplot(122)
show_ctvolume(vol, model, showVol=showVol, clim=clim0, isoTh=isoTh)
models[0][0].Draw(mc='y', mw = 10, lc='y') # struts = yellow
models[0][1].Draw(mc='r', mw = 10, lc='r') # hooks = red
vv.xlabel('x (mm)');vv.ylabel('y (mm)');vv.zlabel('z (mm)')
vv.title('Analysis for model LSPEAS %s - %s' % (ptcode[7:], ctcode))
a1.axis.axisColor= 1,1,1
a1.bgcolor= 0,0,0
a1.daspect= 1, 1, -1 # z-axis flipped
a1.axis.visible = showAxis
a0.camera = a1.camera
def plot(image):
# ax = vv.gca()
# ms = vv.Mesh(ax)
logging.warning([image.shape, image.spacing])
vol = image[:, :, :, 0]
logging.warning([vol.min(), vol.max()])
vol = util.normalize(vol, 'ADCm')
logging.warning([vol.min(), vol.max()])
vol = vv.Aarray(vol, image.spacing)
cmap = None
# cmap = vv.CM_VIRIDIS
render_style = 'mip'
# render_style = 'iso'
# render_style = 'ray'
# render_style = 'edgeray'
# render_style = 'litray'
vv.figure()
vv.xlabel('x axis')
vv.ylabel('y axis')
vv.zlabel('z axis')
a1 = vv.subplot(111)
t1 = vv.volshow(vol, cm=cmap, renderStyle=render_style)
t1.isoThreshold = 0.7
vv.title(render_style)
# a1.camera = a2.camera = a3.camera
vv.ColormapEditor(a1)
def plot(image):
# ax = vv.gca()
# ms = vv.Mesh(ax)
logging.warning([image.shape, image.spacing])
vol = image[:, :, :, 0]
logging.warning([vol.min(), vol.max()])
vol = util.normalize(vol, 'ADCm')
logging.warning([vol.min(), vol.max()])
vol = vv.Aarray(vol, image.spacing)
cmap = None
# cmap = vv.CM_VIRIDIS
render_style = 'mip'
# render_style = 'iso'
# render_style = 'ray'
# render_style = 'edgeray'
# render_style = 'litray'
vv.figure()
vv.xlabel('x axis')
vv.ylabel('y axis')
vv.zlabel('z axis')
a1 = vv.subplot(111)
t1 = vv.volshow(vol, cm=cmap, renderStyle=render_style)
t1.isoThreshold = 0.7
vv.title(render_style)
# a1.camera = a2.camera = a3.camera
vv.ColormapEditor(a1)
def show_surface_and_vol(vol,
pp_isosurface,
showVol='MIP',
clim=(-200, 1000),
isoTh=300,
climEditor=True):
""" Show the generated isosurface in original volume
"""
f = vv.figure()
ax = vv.gca()
ax.daspect = 1, 1, -1
ax.axis.axisColor = 1, 1, 1
ax.bgcolor = 0, 0, 0
# show vol and iso vertices
show_ctvolume(vol,
showVol=showVol,
isoTh=isoTh,
clim=clim,
climEditor=climEditor)
label = pick3d(vv.gca(), vol)
vv.plot(pp_isosurface, ms='.', ls='', mc='r', alpha=0.2, mw=4)
a = vv.gca()
f.eventKeyDown.Bind(lambda event: _utils_GUI.ViewPresets(event, [a]))
print('------------------------')
print('Use keys 1, 2, 3, 4 and 5 for preset anatomic views')
print('Use v for a default zoomed view')
print('Use x to show and hide axis')
print('------------------------')
vv.xlabel('x (mm)')
vv.ylabel('y (mm)')
vv.zlabel('z (mm)')
return label
def PlotReferencePhase (self, event) :
"""
Plot reference phase
"""
visvis.cla(); visvis.clf();
# get actual amplitude and phased
phase = self.GetReferencePhase()[0]
ampl = np.ones(phase.size)
ampl, phase = self.DevPulseShaper.GetUnwrappedAmplPhase(ampl, phase)
# Wrap the phase in radians
phase %= (2*np.pi)
# Get the phase in unites of 2*pi
phase /= (2*np.pi)
visvis.subplot(211)
visvis.plot(phase)
visvis.ylabel ('Reference phase')
visvis.xlabel ('puls shaper pixel number')
visvis.title ('Current reference phase (in unites of 2*pi)')
visvis.subplot(212)
visvis.plot( self.corrections, lc='r',ms='*', mc='r' )
visvis.ylabel ('Value of coefficients')
visvis.xlabel ('coefficient number')
visvis.title ('Value of corrections')
def drawmodelphasescycles(vol1,
model1,
modelori1,
showVol,
isoTh=300,
removeStent=False,
showmodelavgreg=False,
showvol=True,
phases=range(10),
colors='cgmrcgywmb',
meshWithColors=False,
stripSizeZ=None,
ax=None):
""" draw model and volume (show optional) at different phases cycle
"""
if ax is None:
ax = vv.gca()
ax.daspect = 1, 1, -1
ax.axis.axisColor = 0, 0, 0
ax.bgcolor = 1, 1, 1
vv.xlabel('x (mm)')
vv.ylabel('y (mm)')
vv.zlabel('z (mm)')
# draw
t = show_ctvolume(vol1,
modelori1,
showVol=showVol,
removeStent=removeStent,
climEditor=True,
isoTh=isoTh,
clim=clim0,
stripSizeZ=stripSizeZ)
if showmodelavgreg:
# show model and CT mid cycle
mw = 5
for model in model1:
model.Draw(mc='b', mw=mw, lc='b', alpha=0.5)
label = pick3d(ax, vol1)
if not showvol:
t.visible = False
# get models in different phases
for model in model1:
for phasenr in phases:
model_phase = get_graph_in_phase(model, phasenr=phasenr)
if meshWithColors:
modelmesh1 = create_mesh_with_abs_displacement(model_phase,
radius=radius,
dim=dimensions)
m = vv.mesh(modelmesh1, colormap=vv.CM_JET, clim=clim2)
#todo: use colormap Viridis or Magma as JET is not linear (https://bids.github.io/colormap/)
else:
model_phase.Draw(mc=colors[phasenr], mw=10, lc=colors[phasenr])
# modelmesh1 = create_mesh(model_phase, radius = radius)
# m = vv.mesh(modelmesh1); m.faceColor = colors[phasenr]
if meshWithColors:
vv.colorbar()
return ax
def ScanVoltage(self):
"""
Using the iterator <self.scan_pixel_voltage_pair> record the spectral response
by applying the voltages
"""
# Pause calibration, if user requested
try:
if self.pause_calibration: return
except AttributeError:
return
try:
param = self.scan_pixel_voltage_pair.next()
self.PulseShaper.SetUniformMasks(*param)
# Getting spectrum
spectrum = self.Spectrometer.AcquiredData()
# Save the spectrum
try:
self.SpectraGroup["voltages_%d_%d" % param] = spectrum
except RuntimeError:
print "There was RuntimeError while saving scan voltages_%d_%d" % param
# Plot the spectra
visvis.gca().Clear()
visvis.plot(self.wavelengths, spectrum)
visvis.xlabel("wavelength (nm)")
visvis.ylabel("counts")
# Scanning progress info
self.scanned += 1.
percentage_completed = 100. * self.scanned / self.scan_length
seconds_left = (time.clock() - self.initial_time) * (
100. / percentage_completed - 1.)
# convert to hours:min:sec
m, s = divmod(seconds_left, 60)
h, m = divmod(m, 60)
title_info = param + (percentage_completed, h, m, s)
visvis.title(
"Scanning spectrum by applying voltages %d/%d. Progress: %.1f%% completed. Time left: %d:%02d:%02d."
% title_info)
self.fig.DrawNow()
# Measure the next pair
wx.CallAfter(self.ScanVoltage)
except StopIteration:
# Perform processing of the measured data
wx.CallAfter(self.ExtractPhaseFunc,
filename=self.calibration_file.filename)
# All voltages are scanned
self.StopAllJobs()
# Sop using the shaper
self.PulseShaper.StopDevice()
def DrawSpectrum(self, event):
"""
Draw spectrum interactively
"""
spectrum = self.Spectrometer.AcquiredData()
if spectrum == RETURN_FAIL: return
# Display the spectrum
if len(spectrum.shape) > 1:
try:
self.__interact_2d_spectrum__.SetData(spectrum)
except AttributeError:
visvis.cla()
visvis.clf()
# Spectrum is a 2D image
visvis.subplot(211)
self.__interact_2d_spectrum__ = visvis.imshow(spectrum,
cm=visvis.CM_JET)
visvis.subplot(212)
# Plot a vertical binning
spectrum = spectrum.sum(axis=0)
# Linear spectrum
try:
self.__interact_1d_spectrum__.SetYdata(spectrum)
except AttributeError:
if self.wavelengths is None:
self.__interact_1d_spectrum__ = visvis.plot(spectrum, lw=3)
visvis.xlabel("pixels")
else:
self.__interact_1d_spectrum__ = visvis.plot(self.wavelengths,
spectrum,
lw=3)
visvis.xlabel("wavelength (nm)")
visvis.ylabel("counts")
if self.is_autoscaled_spectrum:
# Smart auto-scale linear plot
try:
self.spectrum_plot_limits = GetSmartAutoScaleRange(
spectrum, self.spectrum_plot_limits)
except AttributeError:
self.spectrum_plot_limits = GetSmartAutoScaleRange(spectrum)
visvis.gca().SetLimits(rangeY=self.spectrum_plot_limits)
# Display the current temperature
try:
visvis.title("Temperature %d (C)" %
self.Spectrometer.GetTemperature())
except AttributeError:
pass
def AnalyzeTotalFluorescence (self, event=None) : """ `analyse_button` was clicked """ # Get current settings settings = self.GetSettings() # Apply peak finding filter signal = self.peak_finders[ settings["peak_finder"] ](self.total_fluorescence) # Scale to (0,1) signal -= signal.min() signal = signal / signal.max() ########################################################################## # Partition signal into segments that are above the background noise #signal = gaussian_filter(total_fluorescence, sigma=0.5) background_cutoff = settings["background_cutoff"] segments = [ [] ] for num, is_segment in enumerate( signal > background_cutoff ) : if is_segment : # this index is in the segment segments[-1].append( num ) elif len(segments[-1]) : # this condition is not to add empty segments # Start new segments segments.append( [] ) # Find peaks as weighted average of the segment peaks = [ np.average(self.positions[S], weights=self.total_fluorescence[S]) for S in segments if len(S) ] ########################################################################## # Saving the positions self.chanel_positions_ctrl.SetValue( ", ".join( "%2.4f" % p for p in peaks ) ) ########################################################################## # Plot acquired data visvis.cla(); visvis.clf() visvis.plot( self.positions, signal ) visvis.plot( peaks, background_cutoff*np.ones(len(peaks)), ls=None, ms='+', mc='r', mw=20) visvis.plot( [self.positions.min(), self.positions.max()], [background_cutoff, background_cutoff], lc='r', ls='--', ms=None) visvis.legend( ["measured signal", "peaks found", "background cut-off"] ) visvis.ylabel( "total fluorescence") visvis.xlabel( 'position (mm)')
def visVol(volData):
""" This example demonstrates rendering a color volume.
We demonstrate two renderers capable of rendering color data:
the colormip and coloriso renderer.
"""
import visvis as vv
app = vv.use()
# Load volume
vol = volData
# set labels
vv.xlabel("x axis")
vv.ylabel("y axis")
vv.zlabel("z axis")
# # Create figure and make subplots with different renderers
# vv.figure(1); vv.clf()
# RS = ['mip', 'iso', 'edgeray', 'ray', 'litray']
# a0 = None
# tt = []
# for i in range(5):
# a = vv.subplot(3,2,i+2)
# t = vv.volshow(vol)
# vv.title('Renderstyle ' + RS[i])
# t.colormap = vv.CM_HOT
# t.renderStyle = RS[i]
# t.isoThreshold = 200 # Only used in iso render style
# tt.append(t)
# if a0 is None:
# a0 = a
# else:
# a.camera = a0.camera
t = vv.volshow(vol, renderStyle="edgeray")
t.colormap = vv.CM_HOT
# Get axes and set camera to orthographic mode (with a field of view of 70)
a = vv.gca()
a.camera.fov = 45
# Create colormap editor wibject.
vv.ColormapEditor(a)
# Create colormap editor in first axes
# cme = vv.ColormapEditor(vv.gcf(), t)
# Run app
# app.Create()
app.Run()
def DrawModelAxes(vol,
graph=None,
ax=None,
axVis=False,
meshColor=None,
getLabel=False,
mc='b',
lc='g',
mw=7,
lw=0.6,
**kwargs):
""" Draw model with volume with axes set
ax = axes to draw (a1 or a2 or a3); graph = sd._nodes1 or 2 or 3
meshColor = None or faceColor e.g. 'g'
"""
#todo: prevent TypeError: draw() got an unexpected keyword argument mc/lc when not given as required variable
#todo: *args voor vol in drawModelAxes of **kwargs[key] in functies hieronder
if ax is None:
ax = vv.gca()
ax.MakeCurrent()
ax.daspect = 1, 1, -1
ax.axis.axisColor = 1, 1, 1
ax.bgcolor = 0, 0, 0
ax.axis.visible = axVis
vv.xlabel('x (mm)')
vv.ylabel('y (mm)')
vv.zlabel('z (mm)')
if graph is None:
show_ctvolume(vol, graph, axis=ax, removeStent=False, **kwargs)
label = pick3d(vv.gca(), vol)
return label
if hasattr(graph, 'number_of_edges'):
if graph.number_of_edges(
) == 0: # get label from picked seeds sd._nodes1
show_ctvolume(vol, graph, axis=ax, **kwargs)
label = pick3d(vv.gca(), vol)
graph.Draw(mc=mc, lc=lc)
return label
if not meshColor is None:
bm = create_mesh(graph, 0.5) # (argument is strut tickness)
m = vv.mesh(bm)
m.faceColor = meshColor # 'g'
show_ctvolume(vol, graph, axis=ax, **kwargs)
graph.Draw(mc=mc, lc=lc)
if getLabel == True:
label = pick3d(vv.gca(), vol)
return label
else:
pick3d(vv.gca(), vol)
return
def ScanVoltage (self) :
"""
Using the iterator <self.scan_pixel_voltage_pair> record the spectral response
by applying the voltages
"""
# Pause calibration, if user requested
try :
if self.pause_calibration : return
except AttributeError : return
try :
param = self.scan_pixel_voltage_pair.next()
self.PulseShaper.SetUniformMasks(*param)
# Getting spectrum
spectrum = self.Spectrometer.AcquiredData()
# Save the spectrum
try : self.SpectraGroup["voltages_%d_%d" % param] = spectrum
except RuntimeError : print "There was RuntimeError while saving scan voltages_%d_%d" % param
# Plot the spectra
visvis.gca().Clear()
visvis.plot (self.wavelengths, spectrum)
visvis.xlabel("wavelength (nm)")
visvis.ylabel("counts")
# Scanning progress info
self.scanned += 1.
percentage_completed = 100.*self.scanned/self.scan_length
seconds_left = ( time.clock() - self.initial_time )*(100./percentage_completed - 1.)
# convert to hours:min:sec
m, s = divmod(seconds_left, 60)
h, m = divmod(m, 60)
title_info = param + (percentage_completed, h, m, s)
visvis.title ("Scanning spectrum by applying voltages %d/%d. Progress: %.1f%% completed. Time left: %d:%02d:%02d." % title_info)
self.fig.DrawNow()
# Measure the next pair
wx.CallAfter(self.ScanVoltage)
except StopIteration :
# Perform processing of the measured data
wx.CallAfter(self.ExtractPhaseFunc, filename=self.calibration_file.filename)
# All voltages are scanned
self.StopAllJobs()
# Sop using the shaper
self.PulseShaper.StopDevice()
def __init__(self, sampleinterval=0.1, timewindow=10.0, size=(600, 350)):
# Data stuff
self._interval = int(sampleinterval * 1000)
self._bufsize = int(timewindow / sampleinterval)
self.databuffer = collections.deque([0.0] * self._bufsize, self._bufsize)
self.x = np.linspace(-timewindow, 0.0, self._bufsize)
self.y = np.zeros(self._bufsize, dtype=np.float)
# Visvis stuff
self.app = vv.use("qt4")
vv.title("Dynamic Plotting with VisVis")
self.line = vv.plot(self.x, self.y, lc="b", lw=3, ms="+")
vv.xlabel("time")
vv.ylabel("amplitude")
self.ax = vv.gca()
self.timer = vv.Timer(self.app, 50, oneshot=False)
self.timer.Bind(self.updateplot)
self.timer.Start()
def DrawSpectrum (self, event) :
"""
Draw spectrum interactively
"""
spectrum = self.Spectrometer.AcquiredData()
if spectrum == RETURN_FAIL : return
# Display the spectrum
if len(spectrum.shape) > 1:
try :
self.__interact_2d_spectrum__.SetData(spectrum)
except AttributeError :
visvis.cla(); visvis.clf();
# Spectrum is a 2D image
visvis.subplot(211)
self.__interact_2d_spectrum__ = visvis.imshow(spectrum, cm=visvis.CM_JET)
visvis.subplot(212)
# Plot a vertical binning
spectrum = spectrum.sum(axis=0)
# Linear spectrum
try :
self.__interact_1d_spectrum__.SetYdata(spectrum)
except AttributeError :
if self.wavelengths is None :
self.__interact_1d_spectrum__ = visvis.plot (spectrum, lw=3)
visvis.xlabel ("pixels")
else :
self.__interact_1d_spectrum__ = visvis.plot (self.wavelengths, spectrum, lw=3)
visvis.xlabel("wavelength (nm)")
visvis.ylabel("counts")
if self.is_autoscaled_spectrum :
# Smart auto-scale linear plot
try :
self.spectrum_plot_limits = GetSmartAutoScaleRange(spectrum, self.spectrum_plot_limits)
except AttributeError :
self.spectrum_plot_limits = GetSmartAutoScaleRange(spectrum)
visvis.gca().SetLimits ( rangeY=self.spectrum_plot_limits )
# Display the current temperature
try : visvis.title ("Temperature %d (C)" % self.Spectrometer.GetTemperature() )
except AttributeError : pass
def show_vox(self, vfunc):
"""
Displays a 3-D rendering of a voxelized property.
:param vfunc: function accepting this MasonView instance and
returning a 3-D np.array of some voxelized property
"""
app = vv.use()
vv.figure(1)
vv.xlabel('Eastings (units)')
vv.ylabel('Northings (units)')
vv.zlabel('Depth (units)')
a = vv.gca()
a.camera.fov = 70
a.daspect = 1, 1, -1
vox = vfunc(self)
t = vv.volshow(vox, cm=vv.CM_JET, renderStyle='ray')
vv.ColormapEditor(a)
app.Run()
def ShowSpectra_by_VaryingPixelBundle(self):
"""
This method is affiliated to the method <self.VaryPixelBundle>
"""
# Exit if the iterator is not defined
try:
self.pixel_bundel_value_iter
except AttributeError:
return
try:
voltage = self.pixel_bundel_value_iter.next()
# Set the mask for pixel bundle
width = self.SettingsNotebook.CalibrateShaper.pixel_bundle_width.GetValue(
) / 2
start_pixel_bundle = self.pixel_to_vary.GetValue()
mask = np.copy(self.fixed_mask)
if width:
mask[max(start_pixel_bundle -
width, 0):min(mask.size, start_pixel_bundle +
width)] = voltage
else:
# Enforce single pixel width
mask[min(max(start_pixel_bundle, 0), mask.size)] = voltage
self.PulseShaper.SetMasks(mask, self.fixed_mask)
# Getting spectrum
spectrum = self.Spectrometer.AcquiredData()
# Plot the spectra
visvis.gca().Clear()
visvis.plot(self.wavelengths, spectrum)
visvis.xlabel("wavelength (nm)")
visvis.ylabel("counts")
visvis.title("Voltage %d / %d " % (voltage, self.fixed_mask[0]))
self.fig.DrawNow()
# Going to the next iteration
wx.CallAfter(self.ShowSpectra_by_VaryingPixelBundle)
except StopIteration:
# Finish the job
self.StopAllJobs()
def __init__(self, sampleinterval=0.1, timewindow=10., size=(600, 350)):
# Data stuff
self._interval = int(sampleinterval * 1000)
self._bufsize = int(timewindow / sampleinterval)
self.databuffer = collections.deque([0.0] * self._bufsize,
self._bufsize)
self.x = np.linspace(-timewindow, 0.0, self._bufsize)
self.y = np.zeros(self._bufsize, dtype=np.float)
# Visvis stuff
self.app = vv.use('qt4')
vv.title('Dynamic Plotting with VisVis')
self.line = vv.plot(self.x, self.y, lc='b', lw=3, ms='+')
vv.xlabel('time')
vv.ylabel('amplitude')
self.ax = vv.gca()
self.timer = vv.Timer(self.app, 50, oneshot=False)
self.timer.Bind(self.updateplot)
self.timer.Start()
def draw_spectrum (event) :
"""Timer function """
spectrum = self.Spectrometer.AcquiredData()
if spectrum == RETURN_FAIL : return
# Display the spectrum
############### Take the log of spectrum ##########
#spectrum = spectrum / float(spectrum.max())
#np.log10(spectrum, out=spectrum)
##############################
ax = visvis.gca()
ax.Clear()
visvis.plot (self.wavelengths, spectrum)
visvis.xlabel("wavelength (nm)")
visvis.ylabel("counts")
# Display the current temperature
visvis.title ("Temperature %d (C)" % self.Spectrometer.GetTemperature() )
def psf_volume(stack, xyz_ratio, filepath):
app = vv.use()
# Init a figure with two axes
a1 = vv.subplot(121)
vv.title('PSF Volume')
a2 = vv.subplot(122)
vv.title('PSF XYZ Cross Sections')
# show
t1 = vv.volshow(stack, axes=a1) # volume
t2 = vv.volshow2(stack, axes=a2) # cross-section interactive
# set labels for both axes
vv.xlabel('Pixel X', axes=a1)
vv.ylabel('Pixel Y', axes=a1)
vv.zlabel('Z-Slice', axes=a1)
vv.xlabel('Pixel X', axes=a2)
vv.ylabel('Pixel Y', axes=a2)
vv.zlabel('Z-Slice', axes=a2)
# set colormaps
t1.colormap = vv.CM_JET
t2.colormap = vv.CM_JET
# set correct aspect ration corresponding to voxel size
a1.daspect = 1, 1, xyz_ratio
a2.daspect = 1, 1, xyz_ratio
# show grid
a1.axis.showGrid = 1
a2.axis.showGrid = 1
# run visvis and show results
app.Run()
# save screenshot
if filepath != 'nosave':
print 'Saving PSF volume.'
savename = filepath[:-4] + '_PSF_3D.png'
# sf: scale factor
vv.screenshot(savename, sf=1, bg='w')
def draw_spectrum(event):
"""Timer function """
spectrum = self.Spectrometer.AcquiredData()
if spectrum == RETURN_FAIL: return
# Display the spectrum
############### Take the log of spectrum ##########
#spectrum = spectrum / float(spectrum.max())
#np.log10(spectrum, out=spectrum)
##############################
ax = visvis.gca()
ax.Clear()
visvis.plot(self.wavelengths, spectrum)
visvis.xlabel("wavelength (nm)")
visvis.ylabel("counts")
# Display the current temperature
visvis.title("Temperature %d (C)" %
self.Spectrometer.GetTemperature())
def show_results(self, what='value'):
""" show_results()
Show the results.
"""
# Get params for x
param_x, values_x = self.get_series_params(1)
# Get results
results = self.quantify_results(what)
# Plot or show
try:
import visvis as vv
vv.figure()
vv.plot(values_x, results)
vv.xlabel(param_x)
vv.ylabel('values')
except ImportError:
print('param', param_x, ': values')
for x, r in zip(values_x, results):
print(x, ':', r)
def ShowSpectra_by_VaryingPixelBundle (self) :
"""
This method is affiliated to the method <self.VaryPixelBundle>
"""
# Exit if the iterator is not defined
try : self.pixel_bundel_value_iter
except AttributeError : return
try :
voltage = self.pixel_bundel_value_iter.next()
# Set the mask for pixel bundle
width = self.SettingsNotebook.CalibrateShaper.pixel_bundle_width.GetValue() / 2
start_pixel_bundle = self.pixel_to_vary.GetValue()
mask = np.copy(self.fixed_mask)
if width:
mask[max(start_pixel_bundle-width, 0):min(mask.size, start_pixel_bundle+width)] = voltage
else:
# Enforce single pixel width
mask[min(max(start_pixel_bundle,0),mask.size)] = voltage
self.PulseShaper.SetMasks( mask, self.fixed_mask)
# Getting spectrum
spectrum = self.Spectrometer.AcquiredData()
# Plot the spectra
visvis.gca().Clear()
visvis.plot (self.wavelengths, spectrum)
visvis.xlabel("wavelength (nm)")
visvis.ylabel("counts")
visvis.title ("Voltage %d / %d " % (voltage, self.fixed_mask[0]) )
self.fig.DrawNow()
# Going to the next iteration
wx.CallAfter (self.ShowSpectra_by_VaryingPixelBundle)
except StopIteration :
# Finish the job
self.StopAllJobs()
def show_layer_boundaries(self, sample):
"""
Displays a 3-D rendering of boundary surfaces.
:param sample: index of sample for which to plot boundaries
"""
app = vv.use()
vv.figure(1)
X = np.linspace(self.xbounds[0], self.xbounds[1], self.xres)
Y = np.linspace(self.ybounds[0], self.xbounds[1], self.yres)
Z = np.linspace(self.zbounds[0], self.xbounds[1], self.zres)
vv.xlabel('Eastings (m)')
vv.ylabel('Northings (m)')
vv.zlabel('Depth (m)')
a = vv.gca()
a.camera.fov = 70
a.daspect = 1, 1, -1
for i in range(len(self.layers)):
C = plt.cm.jet(i / float(len(self.layers)))
C = np.array([[[C[0], C[1], C[2]]]])
m = vv.surf(X, Y, self.fbounds[i][sample], C)
vv.ColormapEditor(a)
app.Run()
def FitSpectralScans (self, scans, voltages, pixels_edges) :
"""
Perform fitting to the pulse shaper's mask transmission coefficient
"""
def FitIndividualPixel (voltages, pixel, modulation, p0=None) :
"""
Find fit for individual pixel with initial guess for phase function given by `modulation`.
`voltages` voltage values for which `pixel` was measured
`pixel` measured transmission (to be fitted)
`p0` is the initial guess for fitting parametres
"""
def GetVM (V0, V1, m0, m1) :
"""
Return voltage and phase modulation from parameters
"""
M = m0 + m1*modulation
V = np.linspace(V0, V1, M.size)
return V, M
def FittedTransmission (voltages, offset, amplitude, *params) :
"""
Return the transmission function for a shaper
"""
V, M = GetVM(*params)
return amplitude*np.cos( pchip_interpolate(V,M,voltages) )**2 + offset
# Set fitting parameters to their default values
if p0 is None : p0 = [0., 1., voltages.min(), voltages.max(), 0., 1.]
# Fitting the transmission
try : popt, _ = curve_fit(FittedTransmission, voltages, pixel, p0=p0)
except RuntimeError : popt = p0
# Get fitting error
fitting_error = np.sum( ( FittedTransmission(voltages, *popt) - pixel )**2 )
return fitting_error, GetVM(*popt[2:]), popt
############################################################################
# Selecting the voltage range
V_min = max( voltages_trial.min(), voltages.min() )
V_max = min( voltages_trial.max(), voltages.max() )
indx = np.nonzero( (V_min <= voltages)&(voltages <= V_max) )
# Number of calibration points lying within the voltage region
num_vol_trial = np.sum( (V_min <= voltages_trial)&(voltages_trial <= V_max) )
if num_vol_trial < 2 : num_vol_trial = 2
# Re-sample modulation provided by CRi so that the voltage is equidistantly spaced
resampled_vis_modulation = pchip_interpolate(voltages_trial, vis_modulation,
np.linspace(V_min, V_max, min(len(voltages), num_vol_trial) )
)
resampled_nir_modulation = pchip_interpolate(voltages_trial, nir_modulation,
np.linspace(V_min, V_max, min(len(voltages), num_vol_trial) )
)
# Normalizing scans
scans -= scans.min(axis=0); scans /= scans.max(axis=0)
# Bin the spectrum into pixels
spectral_slices = map( lambda begin,end : scans[:,begin:end].mean(axis=1), pixels_edges[:-1], pixels_edges[1:] )
# List containing calibration data for each pixel
calibration = []
# Initial guesses for fitting
vis_p0 = None; nir_p0 = None;
# Fit individual pulse shaper pixels them
for pixel_num, pixel in enumerate(spectral_slices) :
# Smoothing and normalizing each pixel
#pixel = gaussian_filter(pixel,sigma=1)
pixel -= pixel.min(); pixel /= pixel.max()
# Fit the pixel by using the vis calibration curve as the initial guess
vis_err, vis_calibration, vis_p0 = FitIndividualPixel(voltages[indx], pixel[indx],
resampled_vis_modulation, vis_p0)
# Fit the pixel by using the nir calibration curve as the initial guess
nir_err, nir_calibration, nir_p0 = FitIndividualPixel(voltages[indx], pixel[indx],
resampled_nir_modulation, nir_p0)
# Choose the best fit
if nir_err > vis_err :
calibation_voltage, calibration_phase = vis_calibration
fit_err = vis_err
else :
calibation_voltage, calibration_phase = nir_calibration
fit_err = nir_err
###################### Plot ########################
visvis.clf()
# Plot measured data
visvis.plot( voltages, pixel, lc='r',ms='*', mc='r')
# Plot fitted data
plot_voltages = np.linspace( calibation_voltage.min(), calibation_voltage.max(), 500)
transmission_fit = np.cos( pchip_interpolate(calibation_voltage, calibration_phase, plot_voltages) )**2
visvis.plot( plot_voltages, transmission_fit, lc='b')
visvis.title ('Calibrating pixel %d / %d' % (pixel_num, len(spectral_slices)-1) )
visvis.legend(['measured', 'fitted'])
visvis.xlabel ('voltages')
visvis.ylabel ('Transmission coefficient')
self.fig.DrawNow()
############ Save the calibration data ##################
calibration.append( ( calibation_voltage, calibration_phase, fit_err ) )
return calibration
def ExtractPhaseFunc (self, event=None, filename=None) :
"""
This function is the last stage of calibration,
when measured data is mathematically processed to obtain the calibration curves.
"""
# If filename is not specified, open the file dialogue
if filename is None :
filename = self.LoadSettings (title="Load calibration file...")
# Update the name of pulse shaper calibration file
import os
self.SettingsNotebook.PulseShaper.SetSettings({"calibration_file_name" : os.path.abspath(filename)})
visvis.clf()
# Loading the file calibration file
with h5py.File(filename, 'a') as calibration_file :
############### Loading data ####################
wavelengths = calibration_file["calibration_settings/wavelengths"][...]
fixed_voltage = calibration_file["calibration_settings/fixed_voltage"][...]
pulse_shaper_pixel_num = calibration_file["calibration_settings/pulse_shaper_pixel_num"][...]
initial_pixel = calibration_file["settings/CalibrateShaper/initial_pixel"][...]
final_pixel = calibration_file["settings/CalibrateShaper/final_pixel"][...]
pixel_bundle_width = calibration_file["settings/CalibrateShaper/pixel_bundle_width"][...]
pixel_to_lamba = str(calibration_file["settings/CalibrateShaper/pixel_to_lamba"][...])
# Convert to dict
pixel_to_lamba = eval( "{%s}" % pixel_to_lamba )
# Loading scans of slave and masker masks
master_mask_scans = []; slave_mask_scans = []
for key, spectrum in calibration_file["spectra_from_uniform_masks"].items() :
master_volt, slave_volt = map(int, key.split('_')[-2:])
if master_volt == fixed_voltage : slave_mask_scans.append( (slave_volt, spectrum[...]) )
if slave_volt == fixed_voltage : master_mask_scans.append( (master_volt, spectrum[...]) )
# Sort by voltage
master_mask_scans.sort(); slave_mask_scans.sort()
# Extract spectral scans and voltages for each mask
master_mask_voltage, master_mask_scans = zip(*master_mask_scans)
master_mask_voltage = np.array(master_mask_voltage); master_mask_scans = np.array(master_mask_scans)
slave_mask_voltage, slave_mask_scans = zip(*slave_mask_scans)
slave_mask_voltage = np.array(slave_mask_voltage); slave_mask_scans = np.array(slave_mask_scans)
################### Find the edges of pixels #################
# function that converts pixel number to pulse shaper
# `pixel_to_lamba` is a dictionary with key = pixel, value = lambda
deg = 1 #min(1,len(pixel_to_lamba)-1)
print np.polyfit(pixel_to_lamba.keys(), pixel_to_lamba.values(), 1 )
pixel2lambda_func = np.poly1d( np.polyfit(pixel_to_lamba.keys(), pixel_to_lamba.values(), deg=deg ) )
#lambda2pixel_func = np.poly1d( np.polyfit(pixel_to_lamba.values(), pixel_to_lamba.keys(), deg=deg ) )
# Get pixels_edges in shaper pixels
pixels_edges_num = np.arange(initial_pixel, final_pixel, pixel_bundle_width)
if pixels_edges_num[-1] < final_pixel :
pixels_edges_num = np.append( pixels_edges_num, [final_pixel])
# Get pixels_edges in logical_pixel_lambda
pixels_edges = pixel2lambda_func(pixels_edges_num)
# Get pixels_edges in positions of the spectrometer spectra
pixels_edges = np.abs(wavelengths - pixels_edges[:,np.newaxis]).argmin(axis=1)
# Sorting
indx = np.argsort(pixels_edges)
pixels_edges = pixels_edges[indx]
pixels_edges_num = pixels_edges_num[indx]
# Plot
visvis.cla(); visvis.clf()
visvis.plot( pixel_to_lamba.values(), pixel_to_lamba.keys(), ls=None, ms='*', mc='g', mw=15)
visvis.plot ( wavelengths[pixels_edges], pixels_edges_num, ls='-',lc='r')
visvis.xlabel('wavelength (nm)')
visvis.ylabel ('pulse shaper pixel')
visvis.legend( ['measured', 'interpolated'])
self.fig.DrawNow()
################ Perform fitting of the phase masks #################
master_mask_fits = self.FitSpectralScans( master_mask_scans, master_mask_voltage, pixels_edges )
slave_mask_fits = self.FitSpectralScans( slave_mask_scans, slave_mask_voltage, pixels_edges )
################# Perform fitting of dispersion and phase functions #################
master_mask_TC_fitted, master_mask_scans, master_mask_disp_ph_curves = \
self.GetDispersionPhaseCurves (wavelengths, master_mask_scans, master_mask_voltage,
pixels_edges, master_mask_fits, pixel2lambda_func, pulse_shaper_pixel_num )
slave_mask_TC_fitted, slave_mask_scans, slave_mask_disp_ph_curves = \
self.GetDispersionPhaseCurves (wavelengths, slave_mask_scans, slave_mask_voltage,
pixels_edges, slave_mask_fits, pixel2lambda_func, pulse_shaper_pixel_num )
################ Saving fitting parameters ####################
#################################################################
# Save surface calibration
try : del calibration_file["calibrated_surface"]
except KeyError : pass
CalibratedSurfaceGroupe = calibration_file.create_group("calibrated_surface")
master_mask_calibrated_surface = CalibratedSurfaceGroupe.create_group("master_mask")
for key, value in master_mask_disp_ph_curves.items() :
master_mask_calibrated_surface[key] = value
slave_mask_calibrated_surface = CalibratedSurfaceGroupe.create_group("slave_mask")
for key, value in slave_mask_disp_ph_curves.items() :
slave_mask_calibrated_surface[key] = value
#################################################################
# Clear the group, if it exits
try : del calibration_file["calibrated_pixels"]
except KeyError : pass
# This group is self consistent, thus the redundancy in saved data (with respect to other group in the file)
CalibratedPixelsGroupe = calibration_file.create_group ("calibrated_pixels")
CalibratedPixelsGroupe["pixels_edges"] = pixels_edges
# Finding spectral bounds for each pixel
pixels_spectral_bounds = zip( wavelengths[pixels_edges[:-1]], wavelengths[pixels_edges[1:]] )
# Pulse shaper pixel bounds
pixels_bounds = zip(pixels_edges_num[:-1], pixels_edges_num[1:])
PixelsGroup = CalibratedPixelsGroupe.create_group("pixels")
for pixel_num, master_mask_calibration, slave_mask_calibration, \
spectral_bound, pixel_bound in zip( range(len(master_mask_fits)), \
master_mask_fits, slave_mask_fits, pixels_spectral_bounds, pixels_bounds ) :
pixel = PixelsGroup.create_group("pixel_%d" % pixel_num)
pixel["spectral_bound"] = spectral_bound
pixel["pixel_bound"] = pixel_bound
# Saving fitted calibration data in the tabular form
pixel["voltage_master_mask"] = master_mask_calibration[0]
pixel["phase_master_mask"] = master_mask_calibration[1]
pixel["voltage_slave_mask"] = slave_mask_calibration[0]
pixel["phase_slave_mask"] = slave_mask_calibration[1]
################ Plotting results of calibration ####################
visvis.cla(); visvis.clf();
visvis.subplot(2,2,1)
visvis.imshow( master_mask_scans, cm=visvis.CM_JET )
visvis.title ("Master mask (measured data)")
visvis.ylabel ('voltage'); visvis.xlabel ('wavelength (nm)')
visvis.subplot(2,2,3)
visvis.imshow( master_mask_TC_fitted, cm=visvis.CM_JET )
visvis.title ("Master mask (fitted data)")
visvis.ylabel ('voltage'); visvis.xlabel ('wavelength (nm)')
visvis.subplot(2,2,2)
visvis.imshow( slave_mask_scans, cm=visvis.CM_JET )
visvis.title ("Slave mask (measured data)")
visvis.ylabel ('voltage'); visvis.xlabel ('wavelength (nm)')
visvis.subplot(2,2,4)
visvis.imshow( slave_mask_TC_fitted, cm=visvis.CM_JET )
visvis.title ("Slave mask (fitted data)")
visvis.ylabel ('voltage'); visvis.xlabel ('wavelength (nm)')
ringpart = True # True; False
nstruts = 8
clim0 = (0,3000)
# clim0 = -550,500
clim2 = (0,4)
radius = 0.07
dimensions = 'xyz'
isoTh = 250
## Visualize with GUI
f = vv.figure(3); vv.clf()
f.position = 968.00, 30.00, 944.00, 1002.00
a = vv.gca()
show_ctvolume(vol, model, showVol=showVol, clim=clim0, isoTh=isoTh)
model.Draw(mc='b', mw = 10, lc='g')
vv.xlabel('x (mm)');vv.ylabel('y (mm)');vv.zlabel('z (mm)')
vv.title('Analysis for model LSPEAS %s - %s' % (ptcode[7:], ctcode))
a.axis.axisColor= 1,1,1
a.bgcolor= 0,0,0
a.daspect= 1, 1, -1 # z-axis flipped
a.axis.visible = showAxis
# Initialize labels GUI
from visvis import Pointset
from stentseg.stentdirect import stentgraph
t1 = vv.Label(a, 'Edge ctvalue: ', fontSize=11, color='c')
t1.position = 0.1, 5, 0.5, 20 # x (frac w), y, w (frac), h
t1.bgcolor = None
t1.visible = False
t2 = vv.Label(a, 'Edge cost: ', fontSize=11, color='c')
def AnalyzeTotalFluorescence(self, event=None):
"""
`analyse_button` was clicked
"""
# Get current settings
settings = self.GetSettings()
# Apply peak finding filter
signal = self.peak_finders[settings["peak_finder"]](
self.total_fluorescence)
# Scale to (0,1)
signal -= signal.min()
signal = signal / signal.max()
##########################################################################
# Partition signal into segments that are above the background noise
#signal = gaussian_filter(total_fluorescence, sigma=0.5)
background_cutoff = settings["background_cutoff"]
segments = [[]]
for num, is_segment in enumerate(signal > background_cutoff):
if is_segment:
# this index is in the segment
segments[-1].append(num)
elif len(segments[-1]
): # this condition is not to add empty segments
# Start new segments
segments.append([])
# Find peaks as weighted average of the segment
peaks = [
np.average(self.positions[S], weights=self.total_fluorescence[S])
for S in segments if len(S)
]
##########################################################################
# Saving the positions
self.chanel_positions_ctrl.SetValue(", ".join("%2.4f" % p
for p in peaks))
##########################################################################
# Plot acquired data
visvis.cla()
visvis.clf()
visvis.plot(self.positions, signal)
visvis.plot(peaks,
background_cutoff * np.ones(len(peaks)),
ls=None,
ms='+',
mc='r',
mw=20)
visvis.plot(
[self.positions.min(), self.positions.max()],
[background_cutoff, background_cutoff],
lc='r',
ls='--',
ms=None)
visvis.legend(["measured signal", "peaks found", "background cut-off"])
visvis.ylabel("total fluorescence")
visvis.xlabel('position (mm)')
def DoScannning (self, event) :
"""
Perform scanning of different phase mask
"""
# Create pseudonyms of necessary devices
self.DevSpectrometer = self.parent.Spectrometer.dev
self.DevPulseShaper = self.parent.PulseShaper.dev
self.DevSampleSwitcher = self.parent.SampleSwitcher.dev
# Save global settings and get the name of log file
self.log_filename = SaveSettings(SettingsNotebook=self.parent,
title="Select file to save phase mask scanning", filename="scanning_phase_mask.hdf5")
if self.log_filename is None : return
####################### Initiate devices #############################
# Initiate spectrometer
settings = self.parent.Spectrometer.GetSettings()
if self.DevSpectrometer.SetSettings(settings) == RETURN_FAIL : return
# Initiate pulse shaper
settings = self.parent.PulseShaper.GetSettings()
if self.DevPulseShaper.Initialize(settings) == RETURN_FAIL : return
# Get number of optimization variables
self.num_pixels = self.DevPulseShaper.GetParamNumber()
if self.num_pixels == RETURN_FAIL :
raise RuntimeError ("Optimization cannot be started since calibration file was not loaded")
# Initiate sample switcher
settings = self.parent.SampleSwitcher.GetSettings()
if self.DevSampleSwitcher.Initialize(settings) == RETURN_FAIL : return
# Saving the name of channels
odd_settings = self.GetSettings()
self.channels = sorted(eval( "(%s,)" % odd_settings["channels"] ))
if self.DevSampleSwitcher.GetChannelNum()-1 < max(self.channels) :
raise ValueError ("Error: Some channels specified are not accessible by sample switcher.")
# Check whether the background signal array is present
self.CheckBackground()
#####################################################################
# Get range of coefficient
coeff_range = np.linspace( odd_settings["coeff_min"], odd_settings["coeff_max"], odd_settings["coeff_num"] )
# List all polynomial coefficients
N = odd_settings["polynomial_order"]
poly_coeffs = np.zeros( (coeff_range.size*N, N+1) )
for n in range(1,N+1) :
poly_coeffs[(n-1)*coeff_range.size:n*coeff_range.size, n ] = coeff_range
# Chose max amplitude
max_ampl = odd_settings["max_ampl"]*np.ones(self.num_pixels)
# Arguments of the basis
X = np.linspace(-1., 1., self.num_pixels)
# Retrieve the basis type
polynomial_basis = self.polynomial_bases[ odd_settings["polynomial_basis"] ]
# Adjusting button's settings
button = event.GetEventObject()
button.SetLabel (button._stop_label)
button.SetBackgroundColour('red')
button.Bind( wx.EVT_BUTTON, button._stop_method)
self.need_abort = False
#####################################################################
# Start scanning
with h5py.File (self.log_filename, 'a') as log_file :
for channel in self.channels :
# Move to a selected channel
self.DevSampleSwitcher.MoveToChannel(channel)
# abort, if requested
wx.Yield()
if self.need_abort : break
# Looping over pulse shapes
for scan_num, coeff in enumerate(poly_coeffs) :
# Calculate new phase
phase = polynomial_basis(coeff)(X)
# Set the pulse shape
self.DevPulseShaper.SetAmplPhase(max_ampl, phase)
# Save phase in radians
ampl, phase = self.DevPulseShaper.GetUnwrappedAmplPhase(max_ampl, phase)
if scan_num == 0 :
# Initialize the array
phases_rad = np.zeros( (len(poly_coeffs), phase.size), dtype=phase.dtype )
amplitudes = np.zeros_like(phases_rad)
amplitudes[:] = ampl.min()
# Save phase
phases_rad[scan_num] = phase
amplitudes[scan_num] = ampl
# abort, if requested
wx.Yield()
if self.need_abort : break
# Get spectrum
spectrum = self.GetSampleSpectrum(channel)
# Vertical binning
spectrum = ( spectrum.sum(axis=0) if len(spectrum.shape) == 2 else spectrum )
if scan_num == 0 :
# Initialize the array
spectra = np.zeros( (len(poly_coeffs), spectrum.size), dtype=spectrum.dtype )
spectra[:] = spectrum.min()
# Save the spectrum
spectra[scan_num] = spectrum
# Display the currently acquired data
try :
spectra_2d_img.SetData(spectra)
phases_rad_2d_img.SetData( phases_rad%(2*np.pi) )
#amplitudes_2d_img.SetData( amplitudes )
except NameError :
visvis.cla(); visvis.clf()
visvis.subplot(121)
spectra_2d_img = visvis.imshow(spectra, cm=visvis.CM_JET)
visvis.ylabel ('scans'); visvis.xlabel ('wavelegth')
visvis.title("spectral scan")
visvis.subplot(122)
phases_rad_2d_img = visvis.imshow( phases_rad%(2*np.pi), cm=visvis.CM_JET)
visvis.title("phase shapes")
#visvis.subplot(133)
#amplitudes_2d_img = visvis.imshow(amplitudes, cm=visvis.CM_JET)
#visvis.title("amplitudes")
# Save the data for the given channel
try : del log_file[ str(channel) ]
except KeyError : pass
channel_grp = log_file.create_group( str(channel) )
channel_grp["spectra"] = spectra
channel_grp["phases_rad"] = phases_rad
channel_grp["amplitudes"] = amplitudes
channel_grp["poly_coeffs"] = poly_coeffs
# Readjust buttons settings
self.StopScannning(event)
vv.plot([e[0] for e in c1_ends], [e[1] for e in c1_ends],
[e[2] for e in c1_ends],
ms='.',
ls='',
mc='b',
mw=18,
axes=a1) # ends
vv.plot(centerline1, ms='.', ls='', mw=8, mc='y', axes=a1)
vv.plot(renal1, ms='.', ls='', mc='m', mw=18, axes=a1)
vv.plot(renal1_and_cl_point, ms='.', ls='-', mc='m', mw=18, axes=a1)
# vv.plot(R1_left_and_cl_point, ms='.', ls='-', mc='c', mw=18, axes = a1)
# vv.plot(R1_right_and_cl_point, ms='.', ls='-', mc='c', mw=18, axes = a1)
# vv.plot(R1_ant_and_cl_point, ms='.', ls='-', mc='c', mw=18, axes = a1)
# vv.plot(R1_post_and_cl_point, ms='.', ls='-', mc='c', mw=18, axes = a1)
vv.xlabel('x (mm)')
vv.ylabel('y (mm)')
vv.zlabel('z (mm)')
vv.title('Analysis for model LSPEAS %s - %s' % (ptcode[7:], ctcode1))
a1.axis.axisColor = 1, 1, 1
a1.bgcolor = 0, 0, 0
a1.daspect = 1, 1, -1 # z-axis flipped
a1.axis.visible = showAxis
if ctcode2:
a2 = vv.subplot(122)
show_ctvolume(vol2, model2, showVol=showVol, clim=clim0, isoTh=isoTh)
pick3d(vv.gca(), vol2)
model2.Draw(mc='b', mw=10, lc='g')
vm = vv.mesh(modelmesh2)
vm.faceColor = 'g'
def demo(*args, **kwargs):
import math
m = 80 # width of grid
n = m ** 2 # number of points
minVal = -2.0
maxVal = 2.0
delta = (maxVal - minVal) / (m - 1)
X, Y = numpy.mgrid[minVal:maxVal + delta:delta, minVal:maxVal + delta:delta]
X = X.flatten()
Y = Y.flatten()
Z = numpy.sin(X) * numpy.cos(Y)
# Create the data point-matrix
M = numpy.array([X, Y, Z]).T
# Translation values (a.u.):
Tx = 0.5
Ty = -0.3
Tz = 0.2
# Translation vector
T = numpyTransform.translation(Tx, Ty, Tz)
S = numpyTransform.scaling(1.0, N=4)
# Rotation values (rad.):
rx = 0.3
ry = -0.2
rz = 0.05
Rx = numpy.matrix([[1, 0, 0, 0],
[0, math.cos(rx), -math.sin(rx), 0],
[0, math.sin(rx), math.cos(rx), 0],
[0, 0, 0, 1]])
Ry = numpy.matrix([[math.cos(ry), 0, math.sin(ry), 0],
[0, 1, 0, 0],
[-math.sin(ry), 0, math.cos(ry), 0],
[0, 0, 0, 1]])
Rz = numpy.matrix([[math.cos(rz), -math.sin(rz), 0, 0],
[math.sin(rz), math.cos(rz), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]])
# Rotation matrix
R = Rx * Ry * Rz
transformMat = numpy.matrix(numpy.identity(4))
transformMat *= T
transformMat *= R
transformMat *= S
# Transform data-matrix plus noise into model-matrix
D = numpyTransform.transformPoints(transformMat, M)
# Add noise to model and data
M = M + 0.01 * numpy.random.randn(n, 3)
D = D + 0.01 * numpy.random.randn(n, 3)
# Run ICP (standard settings)
initialGuess = numpy.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
lowerBounds = numpy.array([-pi, -pi, -pi, -100.0, -100.0, -100.0])
upperBounds = numpy.array([pi, pi, pi, 100.0, 100.0, 100.0])
icp = ICP(M, D, maxIterations=15, dataDownsampleFactor=1, minimizeMethod='fmincon', **kwargs)
# icp = ICP(M, D, maxIterations=15, dataDownsampleFactor=1, minimizeMethod='point', **kwargs)
transform, err, t = icp.runICP(x0=initialGuess, lb=lowerBounds, ub=upperBounds)
# Transform data-matrix using ICP result
Dicp = numpyTransform.transformPoints(transform[-1], D)
# Plot model points blue and transformed points red
if False:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(2, 2, 1, projection='3d')
ax.scatter(M[:, 0], M[:, 1], M[:, 2], c='r', marker='o')
ax.scatter(D[:, 0], D[:, 1], D[:, 2], c='b', marker='^')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
ax = fig.add_subplot(2, 2, 2, projection='3d')
ax.scatter(M[:, 0], M[:, 1], M[:, 2], c='r', marker='o')
ax.scatter(Dicp[:, 0], Dicp[:, 1], Dicp[:, 2], c='b', marker='^')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
ax = fig.add_subplot(2, 2, 3)
ax.plot(t, err, 'x--')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
plt.show()
else:
import visvis as vv
app = vv.use()
vv.figure()
vv.subplot(2, 2, 1)
vv.plot(M[:, 0], M[:, 1], M[:, 2], lc='b', ls='', ms='o')
vv.plot(D[:, 0], D[:, 1], D[:, 2], lc='r', ls='', ms='x')
vv.xlabel('[0,0,1] axis')
vv.ylabel('[0,1,0] axis')
vv.zlabel('[1,0,0] axis')
vv.title('Red: z=sin(x)*cos(y), blue: transformed point cloud')
# Plot the results
vv.subplot(2, 2, 2)
vv.plot(M[:, 0], M[:, 1], M[:, 2], lc='b', ls='', ms='o')
vv.plot(Dicp[:, 0], Dicp[:, 1], Dicp[:, 2], lc='r', ls='', ms='x')
vv.xlabel('[0,0,1] axis')
vv.ylabel('[0,1,0] axis')
vv.zlabel('[1,0,0] axis')
vv.title('ICP result')
# Plot RMS curve
vv.subplot(2, 2, 3)
vv.plot(t, err, ls='--', ms='x')
vv.xlabel('time [s]')
vv.ylabel('d_{RMS}')
vv.title('KD-Tree matching')
if 'optAlg' in kwargs:
opt2 = nlopt.opt(kwargs['optAlg'], 2)
vv.title(opt2.get_algorithm_name())
del opt2
else:
vv.title('KD-Tree matching')
app.Run()
def __init__(self,ptcode,ctcode,basedir, seed_th=[600], show=True,
normalize=False, modelname='model'):
import os
import numpy as np
import visvis as vv
from visvis import ssdf
from stentseg.utils import PointSet, _utils_GUI
from stentseg.utils.datahandling import select_dir, loadvol, loadmodel
from stentseg.stentdirect.stentgraph import create_mesh
from stentseg.stentdirect import stentgraph, StentDirect, getDefaultParams
from stentseg.stentdirect import AnacondaDirect, EndurantDirect, NellixDirect
from stentseg.utils.visualization import show_ctvolume
from stentseg.utils.picker import pick3d, label2worldcoordinates, label2volindices
import scipy
from scipy import ndimage
import copy
# Select dataset to register
cropname = 'prox'
# phase = 10
#dataset = 'avgreg'
#what = str(phase) + dataset # avgreg
what = 'avgreg'
# Load volumes
s = loadvol(basedir, ptcode, ctcode, cropname, what)
# sampling was not properly stored after registration for all cases: reset sampling
vol_org = copy.deepcopy(s.vol)
s.vol.sampling = [vol_org.sampling[1], vol_org.sampling[1], vol_org.sampling[2]] # z,y,x
s.sampling = s.vol.sampling
vol = s.vol
## Initialize segmentation parameters
stentType = 'nellix' # 'zenith';'nellix' runs modified pruning algorithm in Step3
p = getDefaultParams(stentType)
p.seed_threshold = seed_th # step 1 [lower th] or [lower th, higher th]
# p.seedSampleRate = 7 # step 1, nellix
p.whatphase = what
## Perform segmentation
# Instantiate stentdirect segmenter object
if stentType == 'anacondaRing':
sd = AnacondaDirect(vol, p) # inherit _Step3_iter from AnacondaDirect class
#runtime warning using anacondadirect due to mesh creation, ignore
elif stentType == 'endurant':
sd = EndurantDirect(vol, p)
elif stentType == 'nellix':
sd = NellixDirect(vol, p)
else:
sd = StentDirect(vol, p)
## show histogram and normalize
# f = vv.figure(3)
# a = vv.gca()
# vv.hist(vol, drange=(300,vol.max()))
# normalize vol to certain limit
if normalize:
sd.Step0(3071)
vol = sd._vol
# b= vv.hist(vol, drange=(300,3071))
# b.color = (1,0,0)
## Perform step 1 for seeds
sd.Step1()
## Visualize
if show:
fig = vv.figure(2); vv.clf()
fig.position = 0.00, 22.00, 1920.00, 1018.00
clim = (0,2000)
# Show volume and model as graph
a1 = vv.subplot(121)
a1.daspect = 1,1,-1
# t = vv.volshow(vol, clim=clim)
t = show_ctvolume(vol, axis=a1, showVol='MIP', clim =clim, isoTh=250,
removeStent=False, climEditor=True)
label = pick3d(vv.gca(), vol)
sd._nodes1.Draw(mc='b', mw = 2) # draw seeded nodes
#sd._nodes2.Draw(mc='b', lc = 'g') # draw seeded and MCP connected nodes
vv.xlabel('x (mm)');vv.ylabel('y (mm)');vv.zlabel('z (mm)')
# Show volume and cleaned up graph
a2 = vv.subplot(122)
a2.daspect = 1,1,-1
sd._nodes1.Draw(mc='b', mw = 2) # draw seeded nodes
# t = vv.volshow(vol, clim=clim)
# label = pick3d(vv.gca(), vol)
# sd._nodes2.Draw(mc='b', lc='g')
# sd._nodes3.Draw(mc='b', lc='g')
vv.xlabel('x (mm)');vv.ylabel('y (mm)');vv.zlabel('z (mm)')
# # Show the mesh
#===============================================================================
# a3 = vv.subplot(133)
# a3.daspect = 1,1,-1
# t = vv.volshow(vol, clim=clim)
# pick3d(vv.gca(), vol)
# #sd._nodes3.Draw(mc='b', lc='g')
# m = vv.mesh(bm)
# m.faceColor = 'g'
# # _utils_GUI.vis_spared_edges(sd._nodes3)
# vv.xlabel('x (mm)');vv.ylabel('y (mm)');vv.zlabel('z (mm)')
#===============================================================================
# Use same camera
a1.camera = a2.camera #= a3.camera
switch = True
a1.axis.visible = switch
a2.axis.visible = switch
#a3.axis.visible = switch
## Store segmentation to disk
# Get graph model
model = sd._nodes1
# Build struct
s2 = vv.ssdf.new()
s2.sampling = s.sampling
s2.origin = s.origin
s2.stenttype = s.stenttype
s2.croprange = s.croprange
for key in dir(s):
if key.startswith('meta'):
suffix = key[4:]
s2['meta'+suffix] = s['meta'+suffix]
s2.what = what
s2.params = p
s2.stentType = stentType
# Store model (not also volume)
s2.model = model.pack()
# Save
filename = '%s_%s_%s_%s.ssdf' % (ptcode, ctcode, cropname, modelname+ what)
ssdf.save(os.path.join(basedir, ptcode, filename), s2)
print('saved to disk as {}.'.format(filename) )
## Make model dynamic (and store/overwrite to disk)
#===============================================================================
#
# import pirt
# from stentsegf.motion.dynamic import incorporate_motion_nodes, incorporate_motion_edges
#
# # Load deforms
# s = loadvol(basedir, ptcode, ctcode, cropname, '10deforms')
# deformkeys = []
# for key in dir(s):
# if key.startswith('deform'):
# deformkeys.append(key)
# deforms = [s[key] for key in deformkeys]
# deforms = [pirt.DeformationFieldBackward(*fields) for fields in deforms]
# paramsreg = s.params
#
# # Load model
# s = loadmodel(basedir, ptcode, ctcode, cropname, 'model'+what)
# model = s.model
#
# # Combine ...
# incorporate_motion_nodes(model, deforms, s.origin)
# incorporate_motion_edges(model, deforms, s.origin)
#
# # Save back
# filename = '%s_%s_%s_%s.ssdf' % (ptcode, ctcode, cropname, 'model'+what)
# s.model = model.pack()
# s.paramsreg = paramsreg
# ssdf.save(os.path.join(basedir, ptcode, filename), s)
# print('saved to disk as {}.'.format(filename) )
#===============================================================================
def DoScannning (self, event) :
"""
Perform scanning of different phase mask
"""
# Create pseudonyms of necessary devices
self.DevSpectrometer = self.parent.Spectrometer.dev
self.DevPulseShaper = self.parent.PulseShaper.dev
self.DevSampleSwitcher = self.parent.SampleSwitcher.dev
self.DevFilterWheel = self.parent.FilterWheel.dev
# Save global settings and get the name of log file
self.log_filename = SaveSettings(SettingsNotebook=self.parent,
title="Select file to save phase mask scanning", filename="scanning_phase_mask.hdf5")
if self.log_filename is None : return
####################### Initiate devices #############################
# Initiate spectrometer
settings = self.parent.Spectrometer.GetSettings()
if self.DevSpectrometer.SetSettings(settings) == RETURN_FAIL : return
# Initiate pulse shaper
settings = self.parent.PulseShaper.GetSettings()
if self.DevPulseShaper.Initialize(settings) == RETURN_FAIL : return
# Get number of optimization variables
self.num_pixels = self.DevPulseShaper.GetParamNumber()
if self.num_pixels == RETURN_FAIL :
raise RuntimeError ("Optimization cannot be started since calibration file was not loaded")
# Initiate sample switcher
settings = self.parent.SampleSwitcher.GetSettings()
if self.DevSampleSwitcher.Initialize(settings) == RETURN_FAIL : return
# Initialize the filter wheel
settings = self.parent.FilterWheel.GetSettings()
if self.DevFilterWheel.Initialize(settings) == RETURN_FAIL : return
# Saving the name of channels
settings = self.GetSettings()
self.channels = sorted( eval( "(%s,)" % settings["channels"] ) )
if self.DevSampleSwitcher.GetChannelNum()-1 < max(self.channels) :
raise ValueError ("Error: Some channels specified are not accessible by sample switcher.")
# Saving the name of filter
self.filters = sorted( eval( "(%s,)" % settings["filters"] ) )
if self.DevFilterWheel.GetNumFilters() < max(self.filters) :
raise ValueError ("Error: Some filters specified are not accessible by filter wheel.")
# Check whether the background signal array is present
self.CheckBackground()
#####################################################################
# Get range of coefficient
coeff_range = np.linspace( settings["coeff_min"], settings["coeff_max"], settings["coeff_num"] )
min_N = settings["min_polynomial_order"]
max_N = settings["max_polynomial_order"]
# Create all polynomial coefficients for scanning
coeff_iter = product( *chain(repeat([0], min_N), repeat(coeff_range, max_N+1-min_N)) )
poly_coeffs = np.array( list(coeff_iter) )
# Chose max amplitude
max_ampl = settings["max_ampl"]*np.ones(self.num_pixels)
# Arguments of the basis
X = np.linspace(-1., 1., self.num_pixels)
# Retrieve the basis type
polynomial_basis = self.polynomial_bases[ settings["polynomial_basis"] ]
# Adjusting button's settings
button = event.GetEventObject()
button.SetLabel (button._stop_label)
button.SetBackgroundColour('red')
button.Bind( wx.EVT_BUTTON, button._stop_method)
self.need_abort = False
#####################################################################
# Start scanning
with h5py.File (self.log_filename, 'a') as log_file :
# Allocate memory for fluorescence signal of all proteins
fluorescences = dict(
(key, np.zeros(len(poly_coeffs), dtype=np.int)) for key in product(self.channels, self.filters)
)
# Allocate memory for reference fluoresence
ref_fluorescences = dict( (C, []) for C in self.channels )
# Iterator for polynomial coefficients
poly_coeffs_iter = enumerate(poly_coeffs)
while True :
# Chunk up the data
chunk_poly_coeffs = list( islice(poly_coeffs_iter, len(coeff_range)) )
# about, if there is nothing to iterate over
if len(chunk_poly_coeffs) == 0 : break
# abort, if requested
if self.need_abort : break
for channel in self.channels :
# Move to a selected channel
self.DevSampleSwitcher.MoveToChannel(channel)
# abort, if requested
if self.need_abort : break
# Looping over pulse shapes
for scan_num, coeff in chunk_poly_coeffs :
# Calculate new phase
phase = polynomial_basis(coeff)(X)
# Set the pulse shape
self.DevPulseShaper.SetAmplPhase(max_ampl, phase)
# abort, if requested
if self.need_abort : break
# Looping over filters in filter wheels
for filter in self.filters :
self.DevFilterWheel.SetFilter(filter)
# abort, if requested
wx.Yield()
if self.need_abort : break
# Get spectrum sum, i.e., fluorescence
spectrum_sum = self.GetSampleSpectrum(channel).sum()
# Save the spectrum
fluorescences[ (channel, filter) ][scan_num] = spectrum_sum
# Record reference fluorescence
self.DevPulseShaper.SetAmplPhase(max_ampl, np.zeros_like(max_ampl))
# Go to filters with max transmission (ASSUMING that it has lowest number)
self.DevFilterWheel.SetFilter( min(self.filters) )
spectrum_sum = self.GetSampleSpectrum(channel).sum()
ref_fluorescences[channel].append( spectrum_sum )
# Display the currently acquired data
try :
for key, img in fluorescence_imgs.items() :
img.SetYdata( fluorescences[key] )
except NameError :
visvis.cla(); visvis.clf()
# Iterator of colours
colour_iter = cycle(['r', 'g', 'b', 'k', 'y'])
fluorescence_imgs = dict(
(K, visvis.plot( F, lw=1, lc=colour_iter.next() ) ) for K, F in fluorescences.items()
)
visvis.xlabel("phase masks")
visvis.ylabel("Integrated fluorescence")
visvis.title("Measured fluorescence")
################ Scanning is over, save the data ########################
# Delete and create groups corresponding to channel
for channel in self.channels :
try : del log_file[ "channel_%d" % channel ]
except KeyError : pass
gchannel_grp = log_file.create_group( "channel_%d" % channel )
gchannel_grp["ref_fluorescence"] = ref_fluorescences[channel]
gchannel_grp["poly_coeffs"] = poly_coeffs
for channel_filter, fluorescence in fluorescences.items() :
log_file["channel_%d/filter_%d_fluorescence" % channel_filter ] = fluorescence
# Readjust buttons settings
self.StopScannning(event)
'r': [(0.0, 0.0), (0.17727272, 1.0)],
'g': [(0.0, 0.0), (0.27272728, 1.0)],
'b': [(0.0, 0.0), (0.34545454, 1.0)],
'a': [(0.0, 1.0), (1.0, 1.0)]
}
import visvis as vv
fig = vv.figure(1)
vv.clf()
fig.position = 0, 22, 1366, 706
a1 = vv.subplot(121)
t1 = vv.volshow(vol1, clim=(0, 3000), renderStyle='iso') # iso or mip
t1.isoThreshold = 400
t1.colormap = colormap
a1b = vv.volshow2(vol1, clim=(-550, 500))
vv.xlabel('x'), vv.ylabel('y'), vv.zlabel('z')
# vv.title('One volume at %i procent of cardiac cycle' % phase )
vv.title('Vol40')
a2 = vv.subplot(122)
a2.daspect = 1, 1, -1
t2 = vv.volshow(vol2, clim=(0, 3000), renderStyle='iso') # iso or mip
t2.isoThreshold = 400
t2.colormap = colormap
a2b = vv.volshow2(vol2, clim=(-550, 500))
vv.xlabel('x'), vv.ylabel('y'), vv.zlabel('z')
# vv.title('One volume at %i procent of cardiac cycle' % phase )
vv.title('Vol78')
a1.camera = a2.camera
## Plot for paper, example
import os
from stentseg.utils.datahandling import select_dir
import matplotlib.pyplot as plt
import matplotlib as mpl
import prettyplotlib as ppl
f = vv.figure()
f.position = 0, 22, 1077, 273
# B1 (A=0.6, T=0.8, N=20, top=0.35)
ax1 = vv.subplot(121) #; vv.title('profile1')
plot_pattern(*(tt1, aa1))
ax1.axis.showGrid = False
ax1.axis.showBox = False
ax1.SetLimits(rangeX=(-0.01, 2), rangeY=(-0.02, 2.5))
vv.xlabel('time (s)')
vv.ylabel('position (mm)')
vv.legend('B1 (A=0.6, T=0.8)')
# B5 (A=2.0, T=0.8, N=20, top=0.35, extra=(0.7, 0.8, 0.05))
ax5 = vv.subplot(122) #; vv.title('profile5')
plot_pattern(*(tt5, aa5))
ax5.axis.showGrid = False
ax5.SetLimits(rangeX=(-0.01, 2), rangeY=(-0.02, 2.5))
ax5.axis.showBox = False
vv.xlabel('time (s)')
vv.ylabel('position (mm)')
vv.legend('B5 (A=2.0, T=0.8, extra=0.7, 0.8, 0.05)')
f.relativeFontSize = 1.2
if False:
import visvis as vv
def zlabel(text, axes=None):
""" zlabel(text, axes=None)
Set the zlabel of an axes.
Note that you can also use "axes.axis.zLabel = text".
Parameters
----------
text : string
The text to display.
axes : Axes instance
Display the image in this axes, or the current axes if not given.
"""
if axes is None:
axes = vv.gca()
axes.axis.zLabel = text
if __name__=='__main__':
# Create a 3D plot
a = vv.gca()
vv.plot([1,2,3],[1,3,2],[3,1,2])
a.cameraType = '3d'
# Set labels for all 3 dimensions
vv.xlabel('label x')
vv.ylabel('label y')
vv.zlabel('label z')
def zlabel(text, axes=None):
""" zlabel(text, axes=None)
Set the zlabel of an axes.
Note that you can also use "axes.axis.zLabel = text".
Parameters
----------
text : string
The text to display.
axes : Axes instance
Display the image in this axes, or the current axes if not given.
"""
if axes is None:
axes = vv.gca()
axes.axis.zLabel = text
if __name__ == '__main__':
# Create a 3D plot
a = vv.gca()
vv.plot([1, 2, 3], [1, 3, 2], [3, 1, 2])
a.cameraType = '3d'
# Set labels for all 3 dimensions
vv.xlabel('label x')
vv.ylabel('label y')
vv.zlabel('label z')
def DoScannning(self, event):
"""
Perform scanning of different phase mask
"""
# Create pseudonyms of necessary devices
self.DevSpectrometer = self.parent.Spectrometer.dev
self.DevPulseShaper = self.parent.PulseShaper.dev
self.DevSampleSwitcher = self.parent.SampleSwitcher.dev
self.DevFilterWheel = self.parent.FilterWheel.dev
# Save global settings and get the name of log file
self.log_filename = SaveSettings(
SettingsNotebook=self.parent,
title="Select file to save phase mask scanning",
filename="scanning_phase_mask.hdf5")
if self.log_filename is None: return
####################### Initiate devices #############################
# Initiate spectrometer
settings = self.parent.Spectrometer.GetSettings()
if self.DevSpectrometer.SetSettings(settings) == RETURN_FAIL: return
# Initiate pulse shaper
settings = self.parent.PulseShaper.GetSettings()
if self.DevPulseShaper.Initialize(settings) == RETURN_FAIL: return
# Get number of optimization variables
self.num_pixels = self.DevPulseShaper.GetParamNumber()
if self.num_pixels == RETURN_FAIL:
raise RuntimeError(
"Optimization cannot be started since calibration file was not loaded"
)
# Initiate sample switcher
settings = self.parent.SampleSwitcher.GetSettings()
if self.DevSampleSwitcher.Initialize(settings) == RETURN_FAIL: return
# Initialize the filter wheel
settings = self.parent.FilterWheel.GetSettings()
if self.DevFilterWheel.Initialize(settings) == RETURN_FAIL: return
# Saving the name of channels
settings = self.GetSettings()
self.channels = sorted(eval("(%s,)" % settings["channels"]))
if self.DevSampleSwitcher.GetChannelNum() - 1 < max(self.channels):
raise ValueError(
"Error: Some channels specified are not accessible by sample switcher."
)
# Saving the name of filter
self.filters = sorted(eval("(%s,)" % settings["filters"]))
if self.DevFilterWheel.GetNumFilters() < max(self.filters):
raise ValueError(
"Error: Some filters specified are not accessible by filter wheel."
)
# Check whether the background signal array is present
self.CheckBackground()
#####################################################################
# Get range of coefficient
coeff_range = np.linspace(settings["coeff_min"], settings["coeff_max"],
settings["coeff_num"])
min_N = settings["min_polynomial_order"]
max_N = settings["max_polynomial_order"]
# Create all polynomial coefficients for scanning
coeff_iter = product(
*chain(repeat([0], min_N), repeat(coeff_range, max_N + 1 - min_N)))
poly_coeffs = np.array(list(coeff_iter))
# Chose max amplitude
max_ampl = settings["max_ampl"] * np.ones(self.num_pixels)
# Arguments of the basis
X = np.linspace(-1., 1., self.num_pixels)
# Retrieve the basis type
polynomial_basis = self.polynomial_bases[settings["polynomial_basis"]]
# Adjusting button's settings
button = event.GetEventObject()
button.SetLabel(button._stop_label)
button.SetBackgroundColour('red')
button.Bind(wx.EVT_BUTTON, button._stop_method)
self.need_abort = False
#####################################################################
# Start scanning
with h5py.File(self.log_filename, 'a') as log_file:
# Allocate memory for fluorescence signal of all proteins
fluorescences = dict(
(key, np.zeros(len(poly_coeffs), dtype=np.int))
for key in product(self.channels, self.filters))
# Allocate memory for reference fluoresence
ref_fluorescences = dict((C, []) for C in self.channels)
# Iterator for polynomial coefficients
poly_coeffs_iter = enumerate(poly_coeffs)
while True:
# Chunk up the data
chunk_poly_coeffs = list(
islice(poly_coeffs_iter, len(coeff_range)))
# about, if there is nothing to iterate over
if len(chunk_poly_coeffs) == 0: break
# abort, if requested
if self.need_abort: break
for channel in self.channels:
# Move to a selected channel
self.DevSampleSwitcher.MoveToChannel(channel)
# abort, if requested
if self.need_abort: break
# Looping over pulse shapes
for scan_num, coeff in chunk_poly_coeffs:
# Calculate new phase
phase = polynomial_basis(coeff)(X)
# Set the pulse shape
self.DevPulseShaper.SetAmplPhase(max_ampl, phase)
# abort, if requested
if self.need_abort: break
# Looping over filters in filter wheels
for filter in self.filters:
self.DevFilterWheel.SetFilter(filter)
# abort, if requested
wx.Yield()
if self.need_abort: break
# Get spectrum sum, i.e., fluorescence
spectrum_sum = self.GetSampleSpectrum(
channel).sum()
# Save the spectrum
fluorescences[(channel,
filter)][scan_num] = spectrum_sum
# Record reference fluorescence
self.DevPulseShaper.SetAmplPhase(max_ampl,
np.zeros_like(max_ampl))
# Go to filters with max transmission (ASSUMING that it has lowest number)
self.DevFilterWheel.SetFilter(min(self.filters))
spectrum_sum = self.GetSampleSpectrum(channel).sum()
ref_fluorescences[channel].append(spectrum_sum)
# Display the currently acquired data
try:
for key, img in fluorescence_imgs.items():
img.SetYdata(fluorescences[key])
except NameError:
visvis.cla()
visvis.clf()
# Iterator of colours
colour_iter = cycle(['r', 'g', 'b', 'k', 'y'])
fluorescence_imgs = dict(
(K, visvis.plot(F, lw=1, lc=colour_iter.next()))
for K, F in fluorescences.items())
visvis.xlabel("phase masks")
visvis.ylabel("Integrated fluorescence")
visvis.title("Measured fluorescence")
################ Scanning is over, save the data ########################
# Delete and create groups corresponding to channel
for channel in self.channels:
try:
del log_file["channel_%d" % channel]
except KeyError:
pass
gchannel_grp = log_file.create_group("channel_%d" % channel)
gchannel_grp["ref_fluorescence"] = ref_fluorescences[channel]
gchannel_grp["poly_coeffs"] = poly_coeffs
for channel_filter, fluorescence in fluorescences.items():
log_file["channel_%d/filter_%d_fluorescence" %
channel_filter] = fluorescence
# Readjust buttons settings
self.StopScannning(event)
# calculate B field at given points
B = sol.CalculateB(points=points)
Babs = np.linalg.norm(B, axis=1)
# draw results
# prepare axes
a = vv.gca()
a.cameraType = '3d'
a.daspectAuto = False
vol = Babs.reshape(grid.shape[1:]).T
vol = np.clip(vol, 0.002, 0.01)
vol = vv.Aarray(vol,
sampling=(resolution, resolution, resolution),
origin=(volume_corner1[2], volume_corner1[1],
volume_corner1[0]))
# set labels
vv.xlabel('x axis')
vv.ylabel('y axis')
vv.zlabel('z axis')
sol.vv_PlotWires()
t = vv.volshow2(vol, renderStyle='mip', cm=vv.CM_JET)
vv.colorbar()
app = vv.use()
app.Run()
# -*- coding: utf-8 -*-
# Copyright (C) 2012, Almar Klein
#
# Visvis is distributed under the terms of the (new) BSD License.
# The full license can be found in 'license.txt'.
import visvis as vv
def xlabel(text, axes=None):
""" xlabel(text, axes=None)
Set the xlabel of an axes.
Note that you can also use "axes.axis.xLabel = text".
Parameters
----------
text : string
The text to display.
axes : Axes instance
Display the image in this axes, or the current axes if not given.
"""
if axes is None:
axes = vv.gca()
axes.axis.xLabel = text
if __name__=='__main__':
a = vv.gca()
a.cameraType = '2d'
vv.xlabel('label test')
#
# Visvis is distributed under the terms of the (new) BSD License.
# The full license can be found in 'license.txt'.
import visvis as vv
def xlabel(text, axes=None):
""" xlabel(text, axes=None)
Set the xlabel of an axes.
Note that you can also use "axes.axis.xLabel = text".
Parameters
----------
text : string
The text to display.
axes : Axes instance
Display the image in this axes, or the current axes if not given.
"""
if axes is None:
axes = vv.gca()
axes.axis.xLabel = text
if __name__ == '__main__':
a = vv.gca()
a.cameraType = '2d'
vv.xlabel('label test')
print( "FPS : %.2f (%d frames in %.2f second)"
% (frames/elapsed, frames, elapsed))
t0, frames = t,0
event.source.update()
# Run!
app.run()
if False:
## Evaluate measurements
# Measured
ncolrow = [6, 7, 8, 9, 10, 11, 12]
fpss = [48, 37, 28, 23, 18, 15, 12] # Measured earlier
fpss = [32, 24, 19, 15, 12, 10, 8] # Measured later, probably added extra overhead
# Process
xx = [x**2 for x in ncolrow]
yy = [1.0 / fps for fps in fpss]
timepersubplot = [1000*y/x for x, y in zip(xx, yy)]
# Show
import visvis as vv
vv.figure(1); vv.clf()
vv.plot(xx, yy)
vv.xlabel('number of subplots')
vv.ylabel('timer per frame')
#
vv.figure(2); vv.clf()
vv.plot(timepersubplot)
vv.gca().SetLimits(rangeY=(0, 1.1*max(timepersubplot)))
import sys
import time
import visvis as vv
import numpy as np
import cPickle as pickle
import gzip
f=gzip.open(sys.argv[1],"rb")
data=pickle.load(f)
#!/usr/bin/env python
app = vv.use()
vv.clf()
# set labels
vv.xlabel('x axis')
vv.ylabel('y axis')
vv.zlabel('z axis')
# show
print np.sum(data)
t = vv.volshow(data,renderStyle='iso',axesAdjust=True)
#t.isoThreshold = 0.5
# try the differtent render styles, for examample
# "t.renderStyle='iso'" or "t.renderStyle='ray'"
# If the drawing hangs, your video drived decided to render in software mode.
# This is unfortunately (as far as I know) not possible to detect.
# It might help if your data is shaped a power of 2.
# Get axes and set camera to orthographic mode (with a field of view of 70)
a = vv.gcf()
def GetDeltaScan (self, event) :
"""
Measure spectra by varying parameter delta in reference phase mask
"""
# Create pseudonyms of necessary devices
self.DevSpectrometer = self.parent.Spectrometer.dev
self.DevPulseShaper = self.parent.PulseShaper.dev
####################### Initiate devices #############################
# Initiate spectrometer
settings = self.parent.Spectrometer.GetSettings()
if self.DevSpectrometer.SetSettings(settings) == RETURN_FAIL : return
# Initiate pulse shaper
settings = self.parent.PulseShaper.GetSettings()
if self.DevPulseShaper.Initialize(settings) == RETURN_FAIL : return
# Get number of optimization variables
self.num_pixels = self.DevPulseShaper.GetParamNumber()
if self.num_pixels == RETURN_FAIL :
raise RuntimeError ("Optimization cannot be started since calibration file was not loaded")
miips_settings = self.GetSettings()
#####################################################################
# Get range of coefficient
coeff_range = np.linspace( miips_settings["coeff_min"], miips_settings["coeff_max"], miips_settings["coeff_num"] )
# Get reference phase based on the corrections already obtained
reference_phase, X, polynomial_basis = self.GetReferencePhase(miips_settings)
# Get new polynomial
new_coeffs = np.zeros(miips_settings["polynomial_order"] + 1)
new_coeffs[-1] = 1
current_polynomial = polynomial_basis(new_coeffs)(X)
# Chose max amplitude
max_ampl = miips_settings["max_ampl"]*np.ones(self.num_pixels)
# Adjusting button's settings
button = event.GetEventObject()
button.SetLabel (button._stop_label)
button.SetBackgroundColour('red')
button.Bind( wx.EVT_BUTTON, button._stop_method)
self.need_abort = False
for scan_num, coeff in enumerate(coeff_range) :
# Get current phase mask
current_phase = coeff*current_polynomial
current_phase += reference_phase
# Check for consistency
current_phase %= 1
# Set the pulse shape
self.DevPulseShaper.SetAmplPhase(max_ampl, current_phase)
wx.Yield()
# abort, if requested
if self.need_abort : break
# Get spectrum
spectrum = self.DevSpectrometer.AcquiredData()
# Vertical binning
spectrum = ( spectrum.sum(axis=0) if len(spectrum.shape) == 2 else spectrum )
try : spectral_scans
except NameError :
# Allocate space for spectral scans
spectral_scans = np.zeros( (coeff_range.size, spectrum.size), dtype=spectrum.dtype )
# Save spectrum
spectral_scans[ scan_num ] = spectrum
# Display the currently acquired data
try : spectral_scan_2d_img.SetData(spectral_scans)
except NameError :
visvis.cla(); visvis.clf();
spectral_scan_2d_img = visvis.imshow(spectral_scans, cm=visvis.CM_JET)
visvis.ylabel ('coefficeints')
visvis.xlabel ('wavelegth')
#######################################################
# Get current wavelength
wavelength = self.DevSpectrometer.GetWavelengths()
# Adding the scan to log
self.scan_log.append( {
"spectral_scans" : spectral_scans,
"reference_phase" : reference_phase,
"current_polynomial": current_polynomial,
"coeff_range" : coeff_range,
"wavelength" : wavelength
} )
# Plotting fit
visvis.cla(); visvis.clf();
################ Find the value of coeff such that it maximizes the total intensity of SHG
# Method 1: use polynomial filter
# Ignored not scanned parameter
spectral_sum = spectral_scans.sum(axis=1)
indx = np.nonzero(spectral_sum > 0)
# Fit the total spectral intensity with chosen polynomials
spectral_sum_fit = polynomial_basis.fit(coeff_range[indx], spectral_sum[indx] , 10)
# Find extrema of the polynomial fit
opt_coeff = spectral_sum_fit.deriv().roots()
# Find maximum
fit_max_sum_val = opt_coeff[ np.argmax(spectral_sum_fit(opt_coeff)) ].real
print "\n\nOptimal value of the coefficient (using the polynomial fit) is ", fit_max_sum_val
# Method 2: Use Gaussian filter
# Smoothing spectral scans
filtered_spectral_scans = gaussian_filter(spectral_scans, (2, 0.5) )
gauss_max_sum_val = coeff_range[ np.argmax(filtered_spectral_scans.sum(axis=1)) ]
print "\nOptimal value of the coefficient (using the Gaussian filter) is ", gauss_max_sum_val
# If the difference between methods is great then use the Gaussian filter
if abs(gauss_max_sum_val - fit_max_sum_val)/np.abs([gauss_max_sum_val, fit_max_sum_val]).max() > 0.3 :
max_sum_val = gauss_max_sum_val
else :
max_sum_val = fit_max_sum_val
self.current_coeff_val.SetValue( max_sum_val )
filtered_spectral_scans[ np.searchsorted(coeff_range, max_sum_val) ][0:-1:2] = spectral_scans.min()
################ Plotting results ####################
#spectral_scan_2d_img.SetData(spectral_scans)
#spectral_scan_2d_img.SetClim( spectral_scans.min(), spectral_scans.max() )
visvis.subplot(121)
visvis.title("Raw spectral scans")
visvis.imshow(spectral_scans, cm=visvis.CM_JET)
visvis.ylabel ('coefficeints'); visvis.xlabel ('wavelegth')
visvis.subplot(122)
visvis.title("Finding maximum")
visvis.imshow(filtered_spectral_scans, cm=visvis.CM_JET)
visvis.ylabel ('coefficeints'); visvis.xlabel ('wavelegth')
# Readjust buttons settings
self.StopScannning(event)