def default_traits_view(self):
traits_view = View(
Group(
# HGroup(
# Item('create_button', show_label = False,
# help = 'Open a wizard to fill experiment table',springy = True),
# Item('open_button', show_label = False,
# help = 'Open experiment table from file',springy = True),
# Item('save_button', show_label = False,
# help = 'Save experiment table from file',springy = True),
#
# springy = True
# ),
'data_folder',
VSplit(
#Group(
Group(Item('experiments',
show_label=False,
editor=self._table_editor),
show_border=True,
label='Experiment'), ),
),
width=.4,
height=.5,
buttons=['OK', 'Cancel'],
handler=ScheduleHandler,
menubar=MenuBar(file_menu, edit_menu))
return traits_view
def default_traits_view(self):
traits_view = View(Group(
'results',
Item('data_folder', style='readonly'),
VSplit(
Group(Item('experiments',
show_label=False,
editor=self._table_editor),
show_border=True,
label='Experiment'), ),
),
width=.4,
height=.5,
buttons=['OK', 'Cancel'],
handler=ScheduleHandler,
menubar=MenuBar(file_menu, edit_menu))
return traits_view
def config_model_view(self):
menubar_config = MenuBar(
Menu(Action(name='New Model',
action='new_model',
tooltip='Create a new model from scratch'),
Action(
name='Copy Model',
action='copy_model',
tooltip='Create a new model by copying an existing one'),
name='Create Model'))
view_config = View(Group(Item(name='model_list', style='custom'),
show_border=True),
Item(label="Lots of stuff should go here"),
menubar=menubar_config,
buttons=NoButtons,
title='BiKiPy Modeler')
return (view_config)
def dynamic_view(self):
return View(
Item('electrodes',
editor=TableEditor( columns =
[ObjectColumn(label='electrode',
editor=TextEditor(),
style='readonly',
editable=False,
name='strrepr'),
ObjectColumn(label='corner',
editor=CheckListEditor(
values=['','corner 1','corner 2',
'corner 3']),
style='simple',
name='corner'),
ObjectColumn(label='geometry',
editor=CSVListEditor(),
#editor=TextEditor(),
#style='readonly',
#editable=False,
name='geom_coords'),
ObjectColumn(label='channel name',
editor=TextEditor(),
name='name'),
ObjectColumn(label='ROIs',
editor=ListStrEditor(),
editable=False,
name='roi_list'),
],
selected='cur_sel',
deletable=True,
#row_factory=electrode_factory,
row_factory=self.electrode_factory,
),
show_label=False, height=350, width=700),
HGroup(
VGroup(
Label( 'Automatic labeling parameters' ),
Item( 'name_stem' ),
HGroup(
Item( 'naming_convention' ),
Item( 'grid_type' ),
),
),
#VGroup(
# Label( 'ROI identification parameters' ),
# Item('parcellation'),
# Item('error_radius'),
#),
#VGroup(
# Label('Image parameters' ),
# Item('img_dpi', label='dpi'),
# Item('img_size', label='size', editor=CSVListEditor()),
#),
),
resizable=True, kind='panel', title='modify electrodes',
#buttons=[OKButton, swap_action, label_auto_action,
# interpolate_action, save_montage_action, find_rois_action])
buttons = [self.label_auto_action, self.swap_action, OKButton],
menubar = MenuBar(
Menu( self.label_auto_action, self.add_blank_action,
self.interpolate_action, self.find_rois_action,
self.find_all_rois_action,
self.manual_reposition_action,
name='Operations',
),
Menu( self.save_montage_action, self.save_csv_action,
self.save_coronal_slice_action,
name='Save Output',
),
)
)
class MainWindow(HasTraits):
'''The main window for the Beams application.'''
# Current folder for file dialog
_current_folder = None
camera = Instance(Camera)
id_string = DelegatesTo('camera')
resolution = DelegatesTo('camera')
status = Str()
screen = Instance(CameraImage, args=())
cmap = DelegatesTo('screen')
display_frame_rate = Range(1, 60, 15)
transform_plugins = List(Instance(TransformPlugin))
display_plugins = List(Instance(DisplayPlugin))
acquisition_thread = Instance(AcquisitionThread) # default: None
processing_thread = Instance(ProcessingThread) # default: None
processing_queue = Instance(queue.Queue, kw={'maxsize': MAX_QUEUE_SIZE})
cameras_dialog = Instance(CameraDialog, args=())
# Actions
about = Action(name='&About...',
tooltip='About Beams',
image=find_icon('about'),
action='action_about')
save = Action(name='&Save Image',
accelerator='Ctrl+S',
tooltip='Save the current image to a file',
image=find_icon('save'),
action='action_save')
quit = Action(name='&Quit',
accelerator='Ctrl+Q',
tooltip='Exit the application',
image=find_icon('quit'),
action='_on_close')
choose_camera = Action(name='Choose &Camera...',
tooltip='Choose from a number of camera plugins',
action='action_choose_camera')
take_video = Action(name='Take &Video',
style='toggle',
tooltip='Start viewing the video feed from the camera',
image=find_icon('camera-video'),
action='action_take_video')
take_photo = Action(name='Take &Photo',
tooltip='Take one snapshot from the camera',
image=find_icon('camera-photo'),
action='action_take_photo',
enabled_when='self.take_video.checked == False')
find_resolution = Button()
view = View(
VGroup(
HSplit(
Tabbed(
VGroup(Item('id_string', style='readonly', label='Camera'),
Item('resolution',
style='readonly',
format_str=u'%i \N{multiplication sign} %i'),
Group(Item('camera',
show_label=False,
style='custom'),
label='Camera properties',
show_border=True),
label='Camera'),
VGroup(Item('cmap',
label='Color scale',
editor=EnumEditor(
values={
None: '0:None (image default)',
gray: '1:Grayscale',
bone: '2:Bone',
pink: '3:Copper',
jet: '4:Rainbow (considered harmful)',
isoluminant: '5:Isoluminant',
awesome: '6:Low-intensity contrast'
})),
Item('screen',
show_label=False,
editor=ColorMapEditor(width=256)),
Item('display_frame_rate'),
label='Video'),
# FIXME: mutable=False means the items can't be deleted,
# added, or rearranged, but we do actually want them to
# be rearranged.
VGroup(Item('transform_plugins',
show_label=False,
editor=ListEditor(style='custom',
mutable=False)),
label='Transform'),
VGroup(Item('display_plugins',
show_label=False,
editor=ListEditor(style='custom',
mutable=False)),
label='Math')),
Item('screen',
show_label=False,
width=640,
height=480,
style='custom')),
Item('status', style='readonly', show_label=False)),
menubar=MenuBar(
# vertical bar is undocumented but it seems to keep the menu
# items in the order they were specified in
Menu('|', save, '_', quit, name='&File'),
Menu(name='&Edit'),
Menu(name='&View'),
Menu('|',
choose_camera,
'_',
take_photo,
take_video,
name='&Camera'),
Menu(name='&Math'),
Menu(about, name='&Help')),
toolbar=ToolBar('|', save, '_', take_photo, take_video),
title='Beams',
resizable=True,
handler=MainHandler)
def _find_resolution_fired(self):
return self.view.handler.action_find_resolution(None)
def _display_frame_rate_changed(self, value):
self.processing_thread.update_frequency = value
def _transform_plugins_default(self):
plugins = []
for name in ['Rotator', 'BackgroundSubtract']:
module = __import__(name, globals(), locals(), [name])
plugins.append(getattr(module, name)())
return plugins
def _display_plugins_default(self):
plugins = []
for name in [
'BeamProfiler', 'MinMaxDisplay', 'DeltaDetector', 'Centroid'
]:
module = __import__(name, globals(), locals(), [name])
plugins.append(getattr(module, name)(screen=self.screen))
return plugins
def __init__(self, **traits):
super(MainWindow, self).__init__(**traits)
# Build the camera selection dialog box
self.cameras_dialog.on_trait_change(self.on_cameras_response, 'closed')
self.on_cameras_response()
self.processing_thread = ProcessingThread(self, self.processing_queue,
self.display_frame_rate)
self.processing_thread.start()
def on_cameras_response(self):
plugin_obj = self.cameras_dialog.get_plugin_object()
try:
self.select_plugin(plugin_obj)
except ImportError:
# some module was not available, select the dummy
error(
None, 'Loading the {} camera plugin failed. '
'Taking you back to the dummy plugin.'.format(
plugin_obj['name']))
self.cameras_dialog.select_fallback()
info = self.cameras_dialog.get_plugin_info()
self.select_plugin(*info)
# Select camera plugin
def select_plugin(self, plugin_obj):
# Set up image capturing
self.camera = plugin_obj()
try:
self.camera.open()
except CameraError:
error(None,
'No camera was detected. Did you forget to plug it in?')
sys.exit()
class ActorViewer(HasTraits):
# The scene model.
scene = Instance(MlabSceneModel, ())
######################
# Using 'scene_class=MayaviScene' adds a Mayavi icon to the toolbar,
# to pop up a dialog editing the pipeline.
view = View(
Item(name='scene',
editor=SceneEditor(scene_class=MayaviScene),
show_label=False,
resizable=True,
height=600,
width=1000),
menubar=MenuBar(
Menu(
Action(name="Load Gifti",
action="opengifti"), # see Controller for
Action(name="Inflate Gii", action="inflategii"),
Action(name="Template", action="DoTemplate"),
Action(name="Load Overlay",
action="loadoverlay"), # these callbacks
Action(name="Load Network", action="loadnetwork"),
Separator(),
CloseAction,
name="File"), ),
title="ByBP: AAL90 Brain Plotter",
resizable=True)
def __init__(self, **traits):
HasTraits.__init__(self, **traits)
#self.DoTemplate()
def DoTemplate(self):
v, f = template()
self.DoPlot(v, f)
def DoPlot(self, v, f):
clf()
self.pts = self.scene.mlab.triangular_mesh(v[:, 0],
v[:, 1],
v[:, 2],
f,
color=(1, 1, 1),
opacity=0.3)
self.scene.mlab.get_engine().scenes[0].scene.x_plus_view()
self.scene.mlab.draw()
self.scene.mlab.view(0., 0.)
self.v = v
self.f = f
ActorViewer.v = v
ActorViewer.f = f
ActorViewer.plot = self
return self
def opengifti(self):
G = GetGifti()
G.configure_traits()
def inflategii(self):
iG = GetGiftiInflate()
iG.configure_traits()
def loadoverlay(self):
o = LoadOverlay90()
o.configure_traits()
def alignoverlaydraw(self, o):
#o = self.o
y = alignoverlay(self.v, self.f, o)
v = self.v # get these from store in ActorViewer
f = self.f
ActorViewer.y = y
a = 0.3
#fig = mlab.figure(1, bgcolor=(0, 0, 0))
#ActorViewer.plot.pts.mlab_source.set(x = v[:,0], y = v[:,1], z = v[:,2], triangles=f, scalars=y[:,0],opacity=a)
ActorViewer.plot.pts = self.scene.mlab.triangular_mesh(v[:, 0],
v[:, 1],
v[:, 2],
f,
scalars=y[:, 0],
opacity=a)
#pts = self.scene.mlab.triangular_mesh(v[:,0], v[:,1], v[:,2], f,scalars=y[:,0],opacity=a)
ActorViewer.plot.scene.mlab.get_engine().scenes[0].scene.x_plus_view()
ActorViewer.plot.scene.mlab.view(0., 0.)
ActorViewer.plot.scene.mlab.colorbar(title="overlay")
ActorViewer.plot.scene.mlab.draw()
def loadnetwork(self):
n = LoadNetwork90()
n.configure_traits()
def PlotNet(self):
xx = self.xx
yy = self.yy
vv = self.vv
jet = cm.get_cmap('jet')
cNorm = cm.colors.Normalize(vmin=vv.min(), vmax=vv.max())
scalarMap = cm.ScalarMappable(norm=cNorm, cmap=jet)
for i in range(len(xx)):
colorVal = scalarMap.to_rgba(vv[i])
colorVal = colorVal[0:3]
ActorViewer.plot.scene.mlab.plot3d([xx[i][0], yy[i][0]],
[xx[i][1], yy[i][1]],
[xx[i][2], yy[i][2]],
color=colorVal,
line_width=10,
tube_radius=2)
ActorViewer.plot.scene.mlab.points3d(xx[i][0],
xx[i][1],
xx[i][2],
color=(1, 0, 0),
scale_factor=5)
ActorViewer.plot.scene.mlab.points3d(yy[i][0],
yy[i][1],
yy[i][2],
color=(1, 0, 0),
scale_factor=5)
ActorViewer.plot.scene.mlab.colorbar(title="Network")
class FluorescenceRecoveryAfterPhotobleaching(ManagedJob, GetSetItemsMixin):
plot = Instance(OverlayPlotContainer)
plot_data = Instance(ArrayPlotData)
time1 = Array()
intensity = Array()
distance = Array()
def __init__(self, confocal):
super(FluorescenceRecoveryAfterPhotobleaching, self).__init__()
self._plot_default()
self.confocal = confocal
def _plot_default(self):
plot_data = ArrayPlotData(distance=np.array((0., 1.)),
i1=np.array((0., 1.)),
i2=np.array((0., 1.)),
i3=np.array((0., 1.)),
i4=np.array((0., 1.)),
i5=np.array((0., 1.)))
plot = Plot(plot_data,
width=50,
height=40,
padding=8,
padding_left=64,
padding_bottom=32)
plot.plot(('distance', 'i1'), color='green')
plot.index_axis.title = 'distance'
plot.value_axis.title = 'intensity'
self.plot_data = plot_data
#self.plot = plot
#line2=Plot(plot_data, width=50, height=40, padding=8, padding_left=64, padding_bottom=32)
plot.plot(('distance', 'i2'), color='red')
#line3=Plot(plot_data, width=50, height=40, padding=8, padding_left=64, padding_bottom=32)
plot.plot(('distance', 'i3'), color='blue')
#line4=Plot(plot_data, width=50, height=40, padding=8, padding_left=64, padding_bottom=32)
plot.plot(('distance', 'i4'), color='magenta')
#line5=Plot(plot_data, width=50, height=40, padding=8, padding_left=64, padding_bottom=32)
plot.plot(('distance', 'i5'), color='cyan')
# container=OverlayPlotContainer(plot, line2, line3, line4, line5)
# return container
return plot
def _run(self):
file_name = 'D:/data/protonNMR/FRAP/bare diamond/21.02.17/0.1 mg per ml 30 min incubation/3'
print file_name
os.path.exists(file_name)
pg.Night()
r = 101
self.intensity = np.zeros((r))
self.confocal.resolution = r
self.confocal.seconds_per_point = 0.001
self.confocal.x1 = 15
self.confocal.x2 = 35
self.confocal.y1 = 15
self.confocal.y2 = 35
self.distance = np.linspace(self.confocal.x1, self.confocal.x2,
self.confocal.resolution)
self.confocal.slider = -8.34
# pre-bleached image-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
pg.Light()
self.confocal.submit()
time.sleep(15)
c = time.strftime('%H:%M:%S')
time1 = float(c[0:2]) + float(c[3:5]) / 60 + float(c[6:8]) / 3600
self.time1 = np.append(self.time1, time1)
m = self.confocal.image
y, x = np.ogrid[-r / 2 + 1:r - r / 2, -r / 2 + 1:r - r / 2]
intensity = np.zeros(r / 2 + 1)
for R in range(1, r / 2 + 2):
mask = (x * x + y * y <= R * R) & (x * x + y * y >=
(R - 1) * (R - 1))
array = np.zeros((r, r))
array[mask] = 1
intensity[R - 1] = (m * array).sum() / array.sum()
k = intensity[::-1]
self.intensity = np.vstack(
(self.intensity, np.concatenate((k[:-1], intensity), axis=0)))
pg.Night()
time.sleep(3)
#self.confocal.center_cursor()
file_nv = file_name + '/pre-bleached'
self.confocal.save_image(file_nv + '.png')
self.confocal.save(file_nv + '.pyd')
#bleaching-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
pg.Light
self.confocal.slider = -5
pg.Light()
time.sleep(5)
# tracking the recovery process--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
self.confocal.slider = -8.34
for i in range(5):
pg.Light()
self.confocal.submit()
time.sleep(15)
pg.Night()
c = time.strftime('%H:%M:%S')
time1 = float(c[0:2]) + float(c[3:5]) / 60 + float(c[6:8]) / 3600
self.time1 = np.append(self.time1, time1)
m = self.confocal.image
intensity = np.zeros(r / 2 + 1)
for R in range(1, r / 2 + 2):
mask = (x * x + y * y <= R * R) & (x * x + y * y >=
(R - 1) * (R - 1))
array = np.zeros((r, r))
array[mask] = 1
intensity[R - 1] = (m * array).sum() / array.sum()
k = intensity[::-1]
self.intensity = np.vstack(
(self.intensity, np.concatenate((k[:-1], intensity), axis=0)))
time.sleep(15)
file_nv = file_name + '/recovery' + str(i)
self.confocal.save_image(file_nv + '.png')
self.confocal.save(file_nv + '.pyd')
self.plot_data.set_data('distance', self.distance)
self.plot_data.set_data('i1', self.intensity[1])
self.plot_data.set_data('i2', self.intensity[2])
self.plot_data.set_data('i3', self.intensity[3])
self.plot_data.set_data('i4', self.intensity[4])
self.plot_data.set_data('i5', self.intensity[-1])
print 'finish'
def save_image(self, filename=None):
self.save_figure(self.plot, filename)
traits_view = View(VGroup(
HGroup(Item('submit_button', show_label=False), ),
HGroup(Item('plot', editor=ComponentEditor(), show_label=False))),
menubar=MenuBar(
Menu(Action(action='save_image',
name='Save Image (.png)'),
Action(action='save',
name='Save (.pyd or .pys)'),
Action(action='load', name='Load'),
Action(action='_on_close', name='Quit'),
name='File'), ),
title='FRAP',
width=500,
height=500,
buttons=[],
resizable=True,
x=0,
y=0,
handler=GetSetSaveImageHandler)
get_set_items = ['time1', 'intensity', 'distance', '__doc__']
class ImageView(ModelView):
"""A model view of an image with actions.
"""
#: The image model being viewed.
model = Instance(ImageModel, (), allow_none=False)
#: The image to display.
image = Image()
def rotate_left(self):
"""Rotate the image anticlockwise."""
self.model.rotate("anticlockwise")
def rotate_right(self):
"""Rotate the image clockwise."""
self.model.rotate("clockwise")
def flip_horizontal(self):
"""Flip the image horizontally."""
self.model.flip("horizontal")
def flip_vertical(self):
"""Flip the image vertically."""
self.model.flip("vertical")
def reload(self):
"""Reload the image from disk."""
self.model.load_image()
@observe('model.data')
def _update_image(self, event):
self.image = ArrayImage(data=self.model.data)
def _image_default(self):
return ArrayImage(data=self.model.data)
view = View(
HSplit(
Item(
'model.image_path',
editor=FileEditor(
dialog_style='open',
filter=["*.png", "*.jpg", "*.jpeg"]
),
style='custom',
),
Item(
'image',
editor=ImageEditor(
scale=True,
preserve_aspect_ratio=True,
allow_upscaling=True,
),
springy=True,
resizable=True,
),
show_labels=False,
# NOTE: this id means the position of the sash will be saved
id='viewer_split'
),
resizable=True,
toolbar=ToolBar(
ActionGroup(
Action(
name="Rotate Left",
tooltip="Rotate Left",
action='rotate_left',
image='rotate_left',
),
Action(
name="Rotate Right",
tooltip="Rotate Right",
action='rotate_right',
image='rotate_right',
),
Action(
name="Flip Horizontally",
tooltip="Flip Horizontally",
action='flip_horizontal',
image='flip_horizontal',
),
Action(
name="Flip Vertically",
tooltip="Flip Vertically",
action='flip_vertical',
image='flip_vertical',
),
name="Transpose Group",
id="transpose_group",
),
ActionGroup(
Action(
name="Denoise",
tooltip="Denoise",
action='denoise',
image='denoise',
),
name="Filter Group",
id="filter_group",
),
image_size=(24, 24),
show_tool_names=False,
),
menubar=MenuBar(
Menu(
Action(name="Revert Image", action="revert"),
name="File",
id="file_menu",
),
Menu(
ActionGroup(
Action(name="Rotate Left", action='rotate_left'),
Action(name="Rotate Right", action='rotate_right'),
Action(name="Flip Horizontally", action='flip_horizontal'),
Action(name="Flip Vertically", action='flip_vertical'),
name="Transpose Group",
id="transpose_group",
),
ActionGroup(
Action(name="Denoise", action='denoise'),
name="Filter Group",
id="filter_group",
),
name="Edit",
id="edit_menu",
),
),
# NOTE: this id means the size of the window will be saved
id='image_preview',
)
class Goodspot(ManagedJob, GetSetItemsMixin, GoodspotBasic):
X = Array()
Y = Array()
Xfit = Array()
Yfit = Array()
image = Array()
image_forfit = Array()
image2 = Array()
fitimage = Array()
label_text = Str('')
resolution = [[0.1], [0.1]]
refpoint = [[0], [0]]
# plots
plot_data = Instance(ArrayPlotData)
scan_plot = Instance(CMapImagePlot)
figure = Instance(Plot)
fitplot_data = Instance(ArrayPlotData)
fitscan_plot = Instance(CMapImagePlot)
fitfigure = Instance(Plot)
figure_container = Instance(HPlotContainer, editor=ComponentEditor())
confocal = Any(editor=InstanceEditor)
ImportImage = Button(label='import image')
Getfitimage = Button(label='get fitimage')
ExportTag = Str('nv')
ExportButton = Button('Export to auto_focus')
Centroids = list()
Centroids1 = list()
IntWeighting = Bool(True)
off_diagonal_account = Bool(True)
labels = dict()
scaler = [[1], [1]]
def __init__(self, confocal, auto_focus, **kwargs):
super(Goodspot, self).__init__(**kwargs)
self.confocal = confocal
self.auto_focus = auto_focus
self.X = numpy.linspace(self.confocal.scanner.getXRange()[0],
self.confocal.scanner.getXRange()[-1],
self.confocal.resolution + 1)
self.Y = numpy.linspace(self.confocal.scanner.getYRange()[0],
self.confocal.scanner.getYRange()[-1],
self.confocal.resolution + 1)
self.image = numpy.zeros((len(self.X), len(self.Y)))
dim = min(self.image.shape)
self.fitimage = numpy.zeros((dim, dim))
self.Xfit = self.X[0:dim]
self.Yfit = self.Y[0:dim]
self._create_plot()
self.on_trait_change(self.update_image_plot, 'image', dispatch='ui')
self.on_trait_change(self._on_label_text_change,
'label_text',
dispatch='ui')
self.on_trait_change(self.redraw_image,
'confocal.thresh_high',
dispatch='ui')
self.on_trait_change(self.redraw_image,
'confocal.thresh_low',
dispatch='ui')
self.on_trait_change(self.redraw_image,
'confocal.thresh_high',
dispatch='ui')
self.on_trait_change(self.redraw_image,
'confocal.thresh_low',
dispatch='ui')
self.on_trait_change(self.set_mesh_and_aspect_ratio,
'X,Y,Xfit,Yfit',
dispatch='ui')
def _ImportImage_fired(self):
# the long road for extracting the zoom parameters ...
Dim = numpy.shape(self.confocal.image)
ImagePosX = [self.confocal.X[0], self.confocal.X[-1]]
ImagePosY = [self.confocal.Y[0], self.confocal.Y[-1]]
ImageRangeX = self.confocal.figure.index_range.get()
ImageRangeX = [
ImageRangeX['_low_setting'], ImageRangeX['_high_setting']
]
ImageRangeY = self.confocal.figure.value_range.get()
ImageRangeY = [
ImageRangeY['_low_setting'], ImageRangeY['_high_setting']
]
FullRangeX = self.confocal.scanner.getXRange()
FullRangeY = self.confocal.scanner.getYRange()
resolution = [(ImagePosY[1] - ImagePosY[0]) / Dim[0],
(ImagePosX[1] - ImagePosX[0]) / Dim[1]]
RangeX = numpy.round(
numpy.asarray([
ImageRangeX[0] - ImagePosX[0], ImageRangeX[1] - ImagePosX[0]
]) / resolution[1])
RangeY = numpy.round(
numpy.asarray([
ImageRangeY[0] - ImagePosY[0], ImageRangeY[1] - ImagePosY[0]
]) / resolution[0])
self.scaler[0] = FullRangeX[1] / resolution[1]
self.scaler[1] = FullRangeY[1] / resolution[0]
self.resolution = resolution
self.refpoint = [ImagePosY[0], ImagePosX[0]]
# import only the part of the image of the zoom
self.image = self.confocal.image[RangeY[0]:RangeY[1] + 1,
RangeX[0]:RangeX[1] + 1]
#self.image2=self.confocal.image
self.update_mesh()
self.redraw_image()
def _Getfitimage_fired(self):
offset = 20.0
amplitude = 70.0
y0 = numpy.mean(self.Yfit)
x0 = numpy.mean(self.Xfit)
sigma_y = 0.2
sigma_x = 0.2
theta = 0.0
initial_guess = (amplitude, y0, x0, sigma_y, sigma_x, theta, offset)
popt, pcov = optimize.curve_fit(self.twoD_Gaussian,
(self.Yfit, self.Xfit),
self.image_forfit.ravel(),
initial_guess)
fit_data = self.twoD_Gaussian((self.Yfit, self.Xfit), *popt)
self.fitparemeter = popt
#print(fit_data)
self.fitimage = fit_data.reshape(len(self.Yfit), len(self.Xfit))
self.fitplot_data.set_data('fitimage', self.fitimage)
s = 'amp: %.1f Kcounts\n' % popt[0]
s += 'sigmay: %.3f and sigmax: %.3f micrometer\n' % (popt[3], popt[4])
s += 'theta: %.1f degree\n' % float(popt[5] * 180 / 3.1415)
self.label_text = s
def redraw_image(self):
self.scan_plot.value_range.high_setting = self.confocal.thresh_high
self.scan_plot.value_range.low_setting = self.confocal.thresh_low
self.scan_plot.request_redraw()
def _create_plot(self):
plot_data = ArrayPlotData(image=self.image)
plot = Plot(plot_data,
width=500,
height=500,
resizable='hv',
aspect_ratio=1.0,
padding=8,
padding_left=32,
padding_bottom=32)
plot.img_plot('image',
colormap=jet,
xbounds=(self.X[0], self.X[-1]),
ybounds=(self.Y[0], self.Y[-1]),
name='image')
image = plot.plots['image'][0]
fitplot_data = ArrayPlotData(fitimage=self.fitimage)
fitplot = Plot(fitplot_data,
width=500,
height=500,
resizable='hv',
aspect_ratio=1.0,
padding=8,
padding_left=32,
padding_bottom=32)
fitplot.img_plot('fitimage',
colormap=jet,
xbounds=(self.Xfit[0], self.Xfit[-1]),
ybounds=(self.Yfit[0], self.Yfit[-1]),
name='fitimage')
fitplot.overlays.insert(
0,
PlotLabel(text=self.label_text,
hjustify='right',
vjustify='bottom',
position=[880, 590]))
fitimage = fitplot.plots['fitimage'][0]
image.x_mapper.domain_limits = (self.confocal.scanner.getXRange()[0],
self.confocal.scanner.getXRange()[1])
image.y_mapper.domain_limits = (self.confocal.scanner.getYRange()[0],
self.confocal.scanner.getYRange()[1])
fitimage.x_mapper.domain_limits = (
self.confocal.scanner.getXRange()[0],
self.confocal.scanner.getXRange()[1])
fitimage.y_mapper.domain_limits = (
self.confocal.scanner.getYRange()[0],
self.confocal.scanner.getYRange()[1])
colormap = image.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=plot,
orientation='v',
resizable='v',
width=16,
height=320,
padding=8,
padding_left=32)
container = HPlotContainer()
container.add(plot)
container.add(colorbar)
container.add(fitplot)
container.tools.append(SaveTool(container))
self.plot_data = plot_data
self.scan_plot = image
self.figure = plot
self.fitplot_data = fitplot_data
self.fitscan_plot = fitimage
self.fitfigure = fitplot
self.figure_container = container
def _on_label_text_change(self):
self.fitfigure.overlays[0].text = self.label_text
def update_image_plot(self):
self.plot_data.set_data('image', self.image)
def update_mesh(self):
Info = self.confocal.figure.index_range.get()
x1 = Info['_low_setting']
x2 = Info['_high_setting']
Info = self.confocal.figure.value_range.get()
y1 = Info['_low_setting']
y2 = Info['_high_setting']
Dim = numpy.shape(self.image)
self.Y = numpy.linspace(y1, y2, Dim[0])
self.X = numpy.linspace(x1, x2, Dim[1])
if Dim[0] > Dim[1]:
self.Yfit = self.Y[0:Dim[1]]
self.Xfit = numpy.linspace(x1, x2, Dim[1])
self.image_forfit = self.image[:, 0:Dim[1]]
else:
self.Xfit = self.X[0:Dim[0]]
self.Yfit = numpy.linspace(y1, y2, Dim[0])
self.image_forfit = self.image[0:Dim[0], :]
def set_mesh_and_aspect_ratio(self):
self.scan_plot.index.set_data(self.X, self.Y)
self.fitscan_plot.index.set_data(self.Xfit, self.Yfit)
x1 = self.X[0]
x2 = self.X[-1]
y1 = self.Y[0]
y2 = self.Y[-1]
x1fit = self.Xfit[0]
x2fit = self.Xfit[-1]
y1fit = self.Yfit[0]
y2fit = self.Yfit[-1]
self.figure.aspect_ratio = (x2 - x1) / float((y2 - y1))
self.figure.index_range.low = x1
self.figure.index_range.high = x2
self.figure.value_range.low = y1
self.figure.value_range.high = y2
self.fitfigure.aspect_ratio = 1
self.fitfigure.index_range.low = x1fit
self.fitfigure.index_range.high = x2fit
self.fitfigure.value_range.low = y1fit
self.fitfigure.value_range.high = y2fit
def save_image(self, filename=None):
self.save_figure(self.figure_container, filename)
IAN = View(Item('ImportImage', show_label=False, resizable=True))
traits_view = View(VSplit(
HGroup(Item('ImportImage', show_label=False, resizable=True),
Item('Getfitimage', show_label=False, resizable=True)),
Item('figure_container', show_label=False, resizable=True,
height=600)),
menubar=MenuBar(
Menu(Action(action='save_image',
name='Save Image (.png)'),
Action(action='saveMatrixPlot',
name='SaveMatrixPlot (.png)'),
name='File')),
title='Goodspot',
width=1080,
height=800,
buttons=[],
resizable=True,
x=0,
y=0,
handler=GetSetSaveImageHandler)
height=500,
width=500),
show_labels=False),
Item("vslicer", style="custom"),
show_labels=False),
menubar=MenuBar(
Menu(a_add_streamlines,
a_change_datasource,
Separator(),
a_change_reference_volume,
a_mni152_2mm_reference_volume,
Separator(),
a_load_ltpa,
name="Data"),
Menu(a_set_coords_from_nifti, name="Query"),
Menu(a_query_region_pair,
a_evaluate_aggregation,
name="Aggregation"),
Menu(a_edit_streamline_viz,
a_ltpa_graphics,
a_edit_scene3d,
a_edit_volumes,
a_take_screenshot,
name="Graphics"),
Menu(a_save_streamlines,
a_save_csv,
name="Streamlines")),
resizable=True,
title="Voxel Browser")
label_table = TableEditor(
columns = \
class Zeeman(Job, GetSetItemsMixin):
"""Zeeman measurement."""
start_button = Button(label='start', desc='Start the measurement.')
stop_button = Button(label='stop', desc='Stop the measurement.')
def _start_button_fired(self):
"""React to submit button. Submit the Job."""
self.start()
def _stop_button_fired(self):
"""React to remove button. Remove the Job."""
self.stop()
current = Array(dtype=float)
basename = File()
odmr = Instance(ODMR, factory=ODMR)
frequency = Array()
line_data = Instance(ArrayPlotData)
line_plot = Instance(Plot, editor=ComponentEditor())
traits_view = View(
VGroup(
HGroup(
Item('start_button', show_label=False),
Item('stop_button', show_label=False),
Item('state', style='readonly'),
Item('odmr', editor=InstanceEditor(), show_label=False),
),
VGroup(
Item('basename'),
Item('current'), # ToDo: migrate to a custom TabularEditor
),
Item('line_plot', show_label=False, resizable=True),
),
menubar=MenuBar(
Menu(Action(action='save', name='Save (.pyd or .pys)'),
Action(action='load', name='Load (.pyd or .pys)'),
Action(action='save_line_plot', name='Save Plot (.png)'),
Action(action='_on_close', name='Quit'),
name='File'), ),
title='Zeeman',
buttons=[],
resizable=True,
handler=SaveLinePlotHandler)
get_set_items = ['current', 'frequency', 'odmr', '__doc__']
def __init__(self):
super(Zeeman, self).__init__()
self._create_line_plot()
self.on_trait_change(self._update_plot, 'frequency', dispatch='ui')
def _run(self):
try:
self.state = 'run'
if self.basename == '':
raise ValueError(
'Filename missing. Please specify a filename and try again.'
)
odmr = self.odmr
if odmr.stop_time == np.inf:
raise ValueError('ODMR stop time set to infinity.')
delta_f = (odmr.frequency_end - odmr.frequency_begin)
self.frequency = np.array(())
for i, current_i in enumerate(self.current):
Coil().set_output(1, current_i)
odmr.perform_fit = False
odmr.submit()
while odmr.state != 'done':
threading.currentThread().stop_request.wait(1.0)
if threading.currentThread().stop_request.isSet():
odmr.remove()
break
odmr.perform_fit = True
basename = self.basename
try:
appendix = basename[-4:]
if appendix in ['.pyd', '.pys', '.asc', '.txt']:
basename = basename[:-4]
else:
appendix = '.pys'
except:
appendix = '.pys'
filename = basename + '_' + str(current_i) + 'A' + appendix
odmr.save(filename)
f = odmr.fit_frequencies[0]
self.frequency = np.append(self.frequency, f)
odmr.frequency_begin = f - 0.5 * delta_f
odmr.frequency_end = f + 0.5 * delta_f
self.state = 'done'
except:
logging.getLogger().exception('Error in Zeeman.')
self.state = 'error'
def _create_line_plot(self):
line_data = ArrayPlotData(
current=np.array(()),
frequency=np.array(()),
)
plot = Plot(line_data, padding=8, padding_left=64, padding_bottom=36)
plot.plot(('current', 'frequency'), color='blue', name='zeeman')
plot.index_axis.title = 'current [mA]'
plot.value_axis.title = 'frequency [MHz]'
self.line_data = line_data
self.line_plot = plot
def _update_plot(self, new):
n = len(new)
self.line_data.set_data('current', self.current[:n])
self.line_data.set_data('frequency', new * 1e-6)
def save_line_plot(self, filename):
self.save_figure(self.line_plot, filename)
success = modelview.edit_traits(kind='livemodal')
print("Logged in:", success)
print("Username:"******"Password:", credentials.password)
if success:
ui_info.object.credentials = credentials
#: A view for the main app which displays an explanation and the username.
app_view = View(
Item('information', style='readonly', show_label=False),
HGroup(Item('object.credentials.username', style='readonly')),
menubar=MenuBar(
Menu(
Action(name='Create Account', action='create_account'),
name='File',
)),
buttons=[
Action(name='Create Account', action='create_account'),
'OK',
],
width=480,
height=320,
resizable=True,
)
if __name__ == '__main__':
app = MainApp()
app.configure_traits(view=app_view, handler=MainAppHandler())
class AutoFocus( ManagedJob, GetSetItemsMixin ):
# overwrite default priority from ManagedJob (default 0)
priority = 10
confocal = Instance( Confocal )
odmr = Instance( ODMR )
#counter_trace = Instance ( CounterTrace )
size_xy = Range(low=0.5, high=10., value=0.8, desc='Size of XY Scan', label='Size XY [micron]', mode='slider', auto_set=False, enter_set=True)
size_z = Range(low=0.5, high=10., value=1.5, desc='Size of Z Scan', label='Size Z [micron]', mode='slider', auto_set=False, enter_set=True)
step_xy = Range(low=0.01, high=10., value=0.04, desc='Step of XY Scan', label='Step XY [micron]', mode='slider', auto_set=False, enter_set=True)
step_z = Range(low=0.01, high=10., value=0.15, desc='Step of Z Scan', label='Step Z [micron]', mode='slider', auto_set=False, enter_set=True)
seconds_per_point_xy = Range(low=1e-3, high=10, value=0.03, desc='Seconds per point for XY Scan', label='seconds per point XY [s]', mode='text', auto_set=False, enter_set=True)
seconds_per_point_z = Range(low=1e-3, high=10, value=0.05, desc='Seconds per point for Z Scan', label='seconds per point Z [s]', mode='text', auto_set=False, enter_set=True)
fit_method_xy = Enum('Maximum', 'Gaussian', desc='Fit Method for XY Scan', label='XY Fit Method')
fit_method_z = Enum('Maximum', 'Gaussian', desc='Fit Method for Z Scan', label='Z Fit Method')
X = Array(value=np.array((0.,1.)) )
Y = Array(value=np.array((0.,1.)) )
Z = Array(value=np.array((-1.,1.)) )
data_xy = Array( )
data_z = Array( value=np.array((0,0)) )
targets = Instance( {}.__class__, factory={}.__class__ ) # Dict traits are no good for pickling, therefore we have to do it with an ordinary dictionary and take care about the notification manually
target_list = Instance( list, factory=list, args=([None],) ) # list of targets that are selectable in current_target editor
current_target = Enum(values='target_list')
drift = Array( value=np.array(((0,0,0,),)) )
drift_time = Array( value=np.array((0,)) )
current_drift = Array( value=np.array((0,0,0)) )
focus_interval = Range(low=1, high=6000, value=10, desc='Time interval between automatic focus events', label='Interval [m]', auto_set=False, enter_set=True)
periodic_focus = Bool(False, label='Periodic focusing')
periodic_freq_feedback = Bool(False, label='Periodic freq feedback')
threshold = Range(low=1, high=6000, value=1000, desc='ignore oil junks', label='threshold kcs/s', auto_set=False, enter_set=True)
target_name = Str(label='name', desc='name to use when adding or removing targets')
add_target_button = Button(label='Add Target', desc='add target with given name')
remove_current_target_button = Button(label='Remove Current', desc='remove current target')
next_target_button = Button(label='Next Target', desc='switch to next available target')
undo_button = Button(label='undo', desc='undo the movement of the stage')
previous_state = Instance( () )
plot_data_image = Instance( ArrayPlotData )
plot_data_line = Instance( ArrayPlotData )
plot_data_drift = Instance( ArrayPlotData )
figure_image = Instance( HPlotContainer, editor=ComponentEditor() )
figure_line = Instance( Plot, editor=ComponentEditor() )
figure_drift = Instance( Plot, editor=ComponentEditor() )
image_plot = Instance( CMapImagePlot )
def __init__(self, confocal, odmr):
super(AutoFocus, self).__init__()
self.confocal = confocal
self.odmr = odmr
#self.counter_trace = counter_trace
self.on_trait_change(self.update_plot_image, 'data_xy', dispatch='ui')
self.on_trait_change(self.update_plot_line_value, 'data_z', dispatch='ui')
self.on_trait_change(self.update_plot_line_index, 'Z', dispatch='ui')
self.on_trait_change(self.update_plot_drift_value, 'drift', dispatch='ui')
self.on_trait_change(self.update_plot_drift_index, 'drift_time', dispatch='ui')
@on_trait_change('next_target_button')
def next_target(self):
"""Convenience method to switch to the next available target."""
keys = self.targets.keys()
key = self.current_target
if len(keys) == 0:
logging.getLogger().info('No target available. Add a target and try again!')
elif not key in keys:
self.current_target = keys[0]
else:
self.current_target = keys[(keys.index(self.current_target)+1)%len(keys)]
def _targets_changed(self, name, old, new):
l = new.keys() + [None] # rebuild target_list for Enum trait
l.sort()
self.target_list = l
self._draw_targets() # redraw target labels
def _current_target_changed(self):
self._draw_targets() # redraw target labels
def _draw_targets(self):
c = self.confocal
c.remove_all_labels()
c.show_labels=True
for key, coordinates in self.targets.iteritems():
if key == self.current_target:
c.set_label(key, coordinates, marker_color='red')
else:
c.set_label(key, coordinates)
def _periodic_focus_changed(self, new):
if not new and hasattr(self, 'cron_event'):
CronDaemon().remove(self.cron_event)
if new:
self.cron_event = CronEvent(self.submit, min=range(0,60,self.focus_interval))
CronDaemon().register(self.cron_event)
def fit_xy(self):
if self.fit_method_xy == 'Maximum':
index = self.data_xy.argmax()
xp = self.X[index%len(self.X)]
yp = self.Y[index/len(self.X)]
self.XYFitParameters = [xp, yp]
self.xfit = xp
self.yfit = yp
return xp, yp
else:
print 'Not Implemented! Fix Me!'
def fit_z(self):
if self.fit_method_z == 'Maximum':
zp = self.Z[self.data_z.argmax()]
self.zfit = zp
return zp
else:
print 'Not Implemented! Fix Me!'
def add_target(self, key, coordinates=None):
if coordinates is None:
c = self.confocal
coordinates = np.array((c.x,c.y,c.z))
if self.targets == {}:
self.forget_drift()
if self.targets.has_key(key):
if warning('A target with this name already exists.\nOverwriting will move all targets.\nDo you want to continue?'):
self.current_drift = coordinates - self.targets[key]
self.forget_drift()
else:
return
else:
coordinates = coordinates - self.current_drift
self.targets[key] = coordinates
self.trait_property_changed('targets', self.targets) # trigger event such that Enum is updated and Labels are redrawn
self.confocal.show_labels=True
def remove_target(self, key):
if not key in self.targets:
logging.getLogger().info('Target cannot be removed. Target does not exist.')
return
self.targets.pop(key) # remove target from dictionary
self.trait_property_changed('targets', self.targets) # trigger event such that Enum is updated and Labels are redrawn
def remove_all_targets(self):
self.targets = {}
def forget_drift(self):
targets = self.targets
# reset coordinates of all targets according to current drift
for key in targets:
targets[key] += self.current_drift
# trigger event such that target labels are redrawn
self.trait_property_changed('targets', self.targets)
# set current_drift to 0 and clear plot
self.current_drift = np.array((0., 0., 0.))
self.drift_time = np.array((time.time(),))
self.drift = np.array(((0,0,0),))
def _add_target_button_fired(self):
self.add_target( self.target_name )
def _remove_current_target_button_fired(self):
self.remove_target( self.current_target )
def _run(self):
logging.getLogger().debug("trying run.")
try:
self.state='run'
#ha.PulseGenerator().Light()
if self.current_target is None:
self.focus()
if np.amax(self.data_xy)<10:
self._undo_button_fired()
pg.Light()
self.focus()
# if self.periodic_freq_feedback:
# self.odmr.submit()
else: # focus target
coordinates = self.targets[self.current_target]
confocal = self.confocal
confocal.x, confocal.y, confocal.z = coordinates + self.current_drift
current_coordinates = self.focus()
self.current_drift = current_coordinates - coordinates
self.drift = np.append(self.drift, (self.current_drift,), axis=0)
self.drift_time = np.append(self.drift_time, time.time())
logging.getLogger().debug('Drift: %.2f, %.2f, %.2f'%tuple(self.current_drift))
# if self.periodic_freq_feedback:
# self.odmr.submit()
finally:
self.state = 'idle'
def focus(self):
"""
Focuses around current position in x, y, and z-direction.
"""
xp = self.confocal.x
yp = self.confocal.y
zp = self.confocal.z
self.previous_state = ((xp,yp,zp), self.current_target)
##+scanner.getXRange()[1]
safety = 0 #distance to keep from the ends of scan range
xmin = np.clip(xp-0.5*self.size_xy, scanner.getXRange()[0]+safety, scanner.getXRange()[1]-safety)
xmax = np.clip(xp+0.5*self.size_xy, scanner.getXRange()[0]+safety, scanner.getXRange()[1]-safety)
ymin = np.clip(yp-0.5*self.size_xy, scanner.getYRange()[0]+safety, scanner.getYRange()[1]-safety)
ymax = np.clip(yp+0.5*self.size_xy, scanner.getYRange()[0]+safety, scanner.getYRange()[1]-safety)
X = np.arange(xmin, xmax, self.step_xy)
Y = np.arange(ymin, ymax, self.step_xy)
self.X = X
self.Y = Y
XP = X[::-1]
self.data_xy=np.zeros((len(Y),len(X)))
#self.image_plot.index.set_data(X, Y)
for i,y in enumerate(Y):
if threading.current_thread().stop_request.isSet():
self.confocal.x = xp
self.confocal.y = yp
self.confocal.z = zp
return xp, yp, zp
if i%2 != 0:
XL = XP
else:
XL = X
YL = y * np.ones(X.shape)
ZL = zp * np.ones(X.shape)
Line = np.vstack( (XL, YL, ZL) )
c = scanner.scanLine(Line, self.seconds_per_point_xy)/1e3
if i%2 == 0:
self.data_xy[i,:] = c[:]
else:
self.data_xy[i,:] = c[-1::-1]
self.trait_property_changed('data_xy', self.data_xy)
for i in range(self.data_xy.shape[0]):
for j in range(self.data_xy.shape[1]):
if self.data_xy[i][j]>self.threshold:
self.data_xy[i][j]=0
xp, yp = self.fit_xy()
self.confocal.x = xp
self.confocal.y = yp
Z = np.hstack( ( np.arange(zp, zp-0.5*self.size_z, -self.step_z),
np.arange(zp-0.5*self.size_z, zp+0.5*self.size_z, self.step_z),
np.arange(zp+0.5*self.size_z, zp, -self.step_z) ) )
Z = np.clip(Z, scanner.getZRange()[0]+safety, scanner.getZRange()[1]-safety)
X = xp * np.ones(Z.shape)
Y = yp * np.ones(Z.shape)
if not threading.current_thread().stop_request.isSet():
Line = np.vstack( (X, Y, Z) )
data_z = scanner.scanLine(Line, self.seconds_per_point_z)/1e3
self.Z = Z
self.data_z = data_z
for i in range(self.data_z.shape[0]):
if self.data_z[i]>self.threshold:
self.data_z[i]=0
zp = self.fit_z()
self.confocal.z = zp
logging.getLogger().info('Focus: %.2f, %.2f, %.2f' %(xp, yp, zp))
return xp, yp, zp
def undo(self):
if self.previous_state is not None:
coordinates, target = self.previous_state
self.confocal.x, self.confocal.y, self.confocal.z = coordinates
if target is not None:
self.drift_time = np.delete(self.drift_time, -1)
self.current_drift = self.drift[-2]
self.drift = np.delete(self.drift, -1, axis=0)
self.previous_state = None
else:
logging.getLogger().info('Can undo only once.')
def _undo_button_fired(self):
self.remove()
self.undo()
def _plot_data_image_default(self):
return ArrayPlotData(image=np.zeros((2,2)))
def _plot_data_line_default(self):
return ArrayPlotData(x=self.Z, y=self.data_z)
def _plot_data_drift_default(self):
return ArrayPlotData(t=self.drift_time, x=self.drift[:,0], y=self.drift[:,1], z=self.drift[:,2])
def _figure_image_default(self):
plot = Plot(self.plot_data_image, width=180, height=180, padding=3, padding_left=48, padding_bottom=32)
plot.img_plot('image', colormap=jet, name='image')
plot.aspect_ratio=1
#plot.value_mapper.domain_limits = (scanner.getYRange()[0],scanner.getYRange()[1])
#plot.index_mapper.domain_limits = (scanner.getXRange()[0],scanner.getXRange()[1])
plot.value_mapper.domain_limits = (0,self.size_xy)
plot.index_mapper.domain_limits = (0,self.size_xy)
container = HPlotContainer()
image = plot.plots['image'][0]
colormap = image.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=plot,
orientation='v',
resizable='v',
width=20,
height=200,
padding=8,
padding_left=20)
container = HPlotContainer()
container.add(plot)
container.add(colorbar)
return container
def _figure_line_default(self):
plot = Plot(self.plot_data_line, width=70, height=40, padding=8, padding_left=64, padding_bottom=32)
plot.plot(('x','y'), color='blue')
plot.index_axis.title = 'z [um]'
plot.value_axis.title = 'Fluorescence [ k / s ]'
return plot
def _figure_drift_default(self):
plot = Plot(self.plot_data_drift, width=70, height=40, padding=8, padding_left=64, padding_bottom=32)
plot.plot(('t','x'), type='line', color='blue', name='x')
plot.plot(('t','y'), type='line', color='red', name='y')
plot.plot(('t','z'), type='line', color='green', name='z')
bottom_axis = PlotAxis(plot,
orientation="bottom",
tick_generator=ScalesTickGenerator(scale=CalendarScaleSystem()))
plot.index_axis=bottom_axis
plot.index_axis.title = 'time'
plot.value_axis.title = 'drift [um]'
plot.legend.visible=True
return plot
def _image_plot_default(self):
return self.figure_image.components[0].plots['image'][0]
def update_plot_image(self):
self.plot_data_image.set_data('image', self.data_xy)
def update_plot_line_value(self):
self.plot_data_line.set_data('y', self.data_z)
def update_plot_line_index(self):
self.plot_data_line.set_data('x', self.Z)
def update_plot_drift_value(self):
if len(self.drift) == 1:
self.plot_data_drift.set_data('x', np.array(()))
self.plot_data_drift.set_data('y', np.array(()))
self.plot_data_drift.set_data('z', np.array(()))
else:
self.plot_data_drift.set_data('x', self.drift[:,0])
self.plot_data_drift.set_data('y', self.drift[:,1])
self.plot_data_drift.set_data('z', self.drift[:,2])
def update_plot_drift_index(self):
if len(self.drift_time) == 0:
self.plot_data_drift.set_data('t', np.array(()))
else:
self.plot_data_drift.set_data('t', self.drift_time - self.drift_time[0])
traits_view = View(VGroup(HGroup(Item('submit_button', show_label=False),
Item('remove_button', show_label=False),
Item('priority'),
Item('state', style='readonly'),
Item('undo_button', show_label=False),
),
Group(VGroup(HGroup(Item('target_name'),
Item('add_target_button', show_label=False),
),
HGroup(Item('current_target'),
Item('next_target_button', show_label=False),
Item('remove_current_target_button', show_label=False),
),
HGroup(Item('periodic_focus'),
Item('focus_interval', enabled_when='not periodic_focus'),
),
HGroup(Item('periodic_freq_feedback'),
Item('threshold')
),
label='tracking',
),
VGroup(Item('size_xy'),
Item('step_xy'),
Item('size_z'),
Item('step_z'),
HGroup(Item('seconds_per_point_xy'),
Item('seconds_per_point_z'),
),
label='Settings',
springy=True,
),
layout='tabbed'
),
VSplit(Item('figure_image', show_label=False, resizable=True)),
HGroup(Item('figure_line', show_label=False, resizable=True),
Item('figure_drift', show_label=False, resizable=True),
),
),
menubar = MenuBar(Menu(Action(action='save', name='Save (.pyd or .pys)'),
Action(action='load', name='Load'),
Action(action='_on_close', name='Quit'),
name='File'),
Menu(Action(action='remove_all_targets', name='Remove All'),
Action(action='forget_drift', name='Forget Drift'),
name='Target'),),
title='Auto Focus', width=500, height=700, buttons=[], resizable=True,
handler=AutoFocusHandler)
get_set_items=['confocal','targets','current_target','current_drift','drift','drift_time','periodic_focus',
'size_xy', 'size_z', 'step_xy', 'step_z', 'seconds_per_point_xy', 'seconds_per_point_z',
'data_xy', 'data_z', 'X', 'Y', 'Z', 'focus_interval', 'threshold' ]
get_set_order=['confocal','targets']
class Confocal( ManagedJob, GetSetItemsMixin ):
(Item('submit_gsd_button', show_label=False),
Item('remove_gsd_button', show_label=False),
Item('priority_gsd'),
Item('state_gsd', style='readonly'),
Item('history_back_gsd', show_label=False),
Item('history_forward_gsd', show_label=False),
# scanner position
x = Range(low=scanner.getXRange()[0], high=scanner.getXRange()[1], value=0.5*(scanner.getXRange()[0]+scanner.getXRange()[1]), desc='x [micron]', label='x [micron]', mode='slider')
y = Range(low=scanner.getYRange()[0], high=scanner.getYRange()[1], value=0.5*(scanner.getYRange()[0]+scanner.getYRange()[1]), desc='y [micron]', label='y [micron]', mode='slider')
z = Range(low=scanner.getZRange()[0], high=scanner.getZRange()[1], value=0.5*(scanner.getZRange()[0]+scanner.getZRange()[1]), desc='z [micron]', label='z [micron]', mode='slider')
# imagging parameters
x1 = Range(low=scanner.getXRange()[0], high=scanner.getXRange()[1], value=scanner.getXRange()[0], desc='x1 [micron]', label='x1', editor=TextEditor(auto_set=False, enter_set=True, evaluate=float, format_str='%.2f'))
y1 = Range(low=scanner.getYRange()[0], high=scanner.getYRange()[1], value=scanner.getYRange()[0], desc='y1 [micron]', label='y1', editor=TextEditor(auto_set=False, enter_set=True, evaluate=float, format_str='%.2f'))
z1 = Range(low=scanner.getZRange()[0], high=scanner.getZRange()[1], value=scanner.getZRange()[0], desc='z1 [micron]', label='z1', editor=TextEditor(auto_set=False, enter_set=True, evaluate=float, format_str='%.2f'))
x2 = Range(low=scanner.getXRange()[0], high=scanner.getXRange()[1], value=scanner.getXRange()[1], desc='x2 [micron]', label='x2', editor=TextEditor(auto_set=False, enter_set=True, evaluate=float, format_str='%.2f'))
y2 = Range(low=scanner.getYRange()[0], high=scanner.getYRange()[1], value=scanner.getYRange()[1], desc='y2 [micron]', label='y2', editor=TextEditor(auto_set=False, enter_set=True, evaluate=float, format_str='%.2f'))
z2 = Range(low=scanner.getZRange()[0], high=scanner.getZRange()[1], value=scanner.getZRange()[1], desc='z2 [micron]', label='z2', editor=TextEditor(auto_set=False, enter_set=True, evaluate=float, format_str='%.2f'))
resolution = Range(low=1, high=1000, value=100, desc='Number of point in long direction', label='resolution', auto_set=False, enter_set=True)
seconds_per_point = Range(low=1e-3, high=10, value=0.005, desc='Seconds per point [s]', label='Seconds per point [s]', mode='text', auto_set=False, enter_set=True)
bidirectional = Bool( True )
return_speed = Range(low=1.0, high=100., value=10., desc='Multiplier for return speed of Scanner if mode is monodirectional', label='return speed', mode='text', auto_set=False, enter_set=True)
constant_axis = Enum('z', 'x', 'y',
label='constant axis',
desc='axis that is not scanned when acquiring an image',
editor=EnumEditor(values={'x':'1:x','y':'2:y','z':'3:z',},cols=3),)
# buttons
history_back = Button(label='Back')
history_forward = Button(label='Forward')
reset_range = Button(label='reset range')
reset_cursor = Button(label='reset position')
# plot parameters
thresh_high = Trait( 'auto', Str('auto'), Float(10000.), desc='High Limit of image plot', editor=TextEditor(auto_set=False, enter_set=True, evaluate=float))
thresh_low = Trait( 'auto', Str('auto'), Float(0.), desc='Low Limit of image plot', editor=TextEditor(auto_set=False, enter_set=True, evaluate=float))
colormap = Enum('Spectral','gray')
show_labels = Bool(False)
# scan data
X = Array()
Y = Array()
image = Array()
# plots
plot_data = Instance( ArrayPlotData )
scan_plot = Instance( CMapImagePlot )
cursor = Instance( CursorTool2D )
zoom = Instance( AspectZoomTool )
figure = Instance( Plot )
figure_container = Instance( HPlotContainer, editor=ComponentEditor() )
z_label_text = Str('z:0.0')
cursor_position = Property(depends_on=['x','y','z','constant_axis'])
get_set_items=['constant_axis', 'X', 'Y', 'thresh_high', 'thresh_low', 'seconds_per_point',
'return_speed', 'bidirectional', 'history', 'image', 'z_label_text',
'resolution', 'x', 'x1', 'x2', 'y', 'y1', 'y2', 'z', 'z1', 'z2']
def __init__(self):
super(Confocal, self).__init__()
self.X = numpy.linspace(scanner.getXRange()[0], scanner.getXRange()[-1], self.resolution+1)
self.Y = numpy.linspace(scanner.getYRange()[0], scanner.getYRange()[-1], self.resolution+1)
self.image = numpy.zeros((len(self.X), len(self.Y)))
self._create_plot()
self.figure.index_range.on_trait_change(self.update_axis_li, '_low_value', dispatch='ui')
self.figure.index_range.on_trait_change(self.update_axis_hi, '_high_value', dispatch='ui')
self.figure.value_range.on_trait_change(self.update_axis_lv, '_low_value', dispatch='ui')
self.figure.value_range.on_trait_change(self.update_axis_hv, '_high_value', dispatch='ui')
self.zoom.on_trait_change(self.check_zoom, 'box', dispatch='ui')
self.on_trait_change(self.set_mesh_and_aspect_ratio, 'X,Y', dispatch='ui')
self.on_trait_change(self.update_image_plot, 'image', dispatch='ui')
self.sync_trait('cursor_position', self.cursor, 'current_position')
self.sync_trait('thresh_high', self.scan_plot.value_range, 'high_setting')
self.sync_trait('thresh_low', self.scan_plot.value_range, 'low_setting')
self.on_trait_change(self.scan_plot.request_redraw, 'thresh_high', dispatch='ui')
self.on_trait_change(self.scan_plot.request_redraw, 'thresh_low', dispatch='ui')
self.history = History(length = 10)
self.history.put( self.copy_items(['constant_axis', 'X', 'Y', 'image', 'z_label_text', 'resolution'] ) )
self.labels = {}
self.label_list = []
# scanner position
@on_trait_change('x,y,z')
def _set_scanner_position(self):
if self.state != 'run':
scanner.setPosition(self.x, self.y, self.z)
@cached_property
def _get_cursor_position(self):
if self.constant_axis == 'x':
return self.z, self.y
elif self.constant_axis == 'y':
return self.x, self.z
elif self.constant_axis == 'z':
return self.x, self.y
def _set_cursor_position(self, position):
if self.constant_axis == 'x':
self.z, self.y = position
elif self.constant_axis == 'y':
self.x, self.z = position
elif self.constant_axis == 'z':
self.x, self.y = position
# image acquisition
def _run(self):
"""Acquire a scan"""
try:
self.state='run'
self.update_mesh()
X = self.X
Y = self.Y
XP = X[::-1]
self.image=numpy.zeros((len(Y),len(X)))
"""
if not self.bidirectional:
scanner.initImageScan(len(X), len(Y), self.seconds_per_point, return_speed=self.return_speed)
else:
scanner.initImageScan(len(X), len(Y), self.seconds_per_point, return_speed=None)
"""
for i,y in enumerate(Y):
if threading.current_thread().stop_request.isSet():
break
if i%2 != 0 and self.bidirectional:
XL = XP
else:
XL = X
YL = y * numpy.ones(X.shape)
if self.constant_axis == 'x':
const = self.x * numpy.ones(X.shape)
Line = numpy.vstack( (const, YL, XL) )
elif self.constant_axis == 'y':
const = self.y * numpy.ones(X.shape)
Line = numpy.vstack( (XL, const, YL) )
elif self.constant_axis == 'z':
const = self.z * numpy.ones(X.shape)
Line = numpy.vstack( (XL, YL, const) )
if self.bidirectional:
c = scanner.scanLine(Line, self.seconds_per_point)
else:
c = scanner.scanLine(Line, self.seconds_per_point, return_speed=self.return_speed)
if i%2 != 0 and self.bidirectional:
self.image[i,:] = c[-1::-1]
else:
self.image[i,:] = c[:]
"""
scanner.doImageLine(Line)
self.image = scanner.getImage()
"""
self.trait_property_changed('image', self.image)
if self.constant_axis == 'x':
self.z_label_text='x:%.2f'%self.x
elif self.constant_axis == 'y':
self.z_label_text='y:%.2f'%self.y
elif self.constant_axis == 'z':
self.z_label_text='z:%.2f'%self.z
"""
# block at the end until the image is ready
if not threading.current_thread().stop_request.isSet():
self.image = scanner.getImage(1)
self._image_changed()
"""
scanner.setPosition(self.x, self.y, self.z)
#save scan data to history
self.history.put( self.copy_items(['constant_axis', 'X', 'Y', 'image', 'z_label_text', 'resolution'] ) )
finally:
self.state = 'idle'
# plotting
def _create_plot(self):
plot_data = ArrayPlotData(image=self.image)
plot = Plot(plot_data, width=500, height=500, resizable='hv', aspect_ratio=1.0, padding=8, padding_left=32, padding_bottom=32)
plot.img_plot('image', colormap=Spectral, xbounds=(self.X[0],self.X[-1]), ybounds=(self.Y[0],self.Y[-1]), name='image')
image = plot.plots['image'][0]
image.x_mapper.domain_limits = (scanner.getXRange()[0],scanner.getXRange()[1])
image.y_mapper.domain_limits = (scanner.getYRange()[0],scanner.getYRange()[1])
zoom = AspectZoomTool(image, enable_wheel=False)
cursor = CursorTool2D(image, drag_button='left', color='blue', marker_size=1.0, line_width=1.0 )
image.overlays.append(cursor)
image.overlays.append(zoom)
colormap = image.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=plot,
orientation='v',
resizable='v',
width=16,
height=320,
padding=8,
padding_left=32)
container = HPlotContainer()
container.add(plot)
container.add(colorbar)
z_label = PlotLabel(text='z=0.0', color='red', hjustify='left', vjustify='bottom', position=[10,10])
container.overlays.append(z_label)
self.plot_data = plot_data
self.scan_plot = image
self.cursor = cursor
self.zoom = zoom
self.figure = plot
self.figure_container = container
self.sync_trait('z_label_text', z_label, 'text')
def set_mesh_and_aspect_ratio(self):
self.scan_plot.index.set_data(self.X,self.Y)
x1=self.X[0]
x2=self.X[-1]
y1=self.Y[0]
y2=self.Y[-1]
self.figure.aspect_ratio = (x2-x1) / float((y2-y1))
self.figure.index_range.low = x1
self.figure.index_range.high = x2
self.figure.value_range.low = y1
self.figure.value_range.high = y2
def check_zoom(self, box):
li,lv,hi,hv=box
if self.constant_axis == 'x':
if not li<self.z<hi:
self.z = 0.5*(li+hi)
if not lv<self.y<hv:
self.y = 0.5*(lv+hv)
elif self.constant_axis == 'y':
if not li<self.x<hi:
self.x = 0.5*(li+hi)
if not lv<self.z<hv:
self.z = 0.5*(lv+hv)
elif self.constant_axis == 'z':
if not li<self.x<hi:
self.x = 0.5*(li+hi)
if not lv<self.y<hv:
self.y = 0.5*(lv+hv)
def center_cursor(self):
i = 0.5 * (self.figure.index_range.low + self.figure.index_range.high)
v = 0.5 * (self.figure.value_range.low + self.figure.value_range.high)
if self.constant_axis == 'x':
self.z = i
self.y = v
elif self.constant_axis == 'y':
self.x = i
self.z = v
elif self.constant_axis == 'z':
self.x = i
self.y = v
def _constant_axis_changed(self):
self.update_mesh()
self.image = numpy.zeros((len(self.X), len(self.Y)))
self.update_axis()
self.set_mesh_and_aspect_ratio()
def update_image_plot(self):
self.plot_data.set_data('image', self.image)
def _colormap_changed(self, new):
data = self.figure.datasources['image']
func = getattr(chaco.api,new)
self.figure.color_mapper=func(DataRange1D(data))
self.figure.request_redraw()
def _show_labels_changed(self, name, old, new):
for item in self.scan_plot.overlays:
if isinstance(item, DataLabel) and item.label_format in self.labels:
item.visible = new
self.scan_plot.request_redraw()
def get_label_index(self, key):
for index, item in enumerate(self.scan_plot.overlays):
if isinstance(item, DataLabel) and item.label_format == key:
return index
return None
def set_label(self, key, coordinates, **kwargs):
plot = self.scan_plot
if self.constant_axis == 'x':
point = (coordinates[2],coordinates[1])
elif self.constant_axis == 'y':
point = (coordinates[0],coordinates[2])
elif self.constant_axis == 'z':
point = (coordinates[0],coordinates[1])
defaults = {'component':plot,
'data_point':point,
'label_format':key,
'label_position':'top right',
'bgcolor':'transparent',
'text_color':'black',
'border_visible':False,
'padding_bottom':8,
'marker':'cross',
'marker_color':'black',
'marker_line_color':'black',
'marker_line_width':1.5,
'marker_size':6,
'arrow_visible':False,
'clip_to_plot':False,
'visible':self.show_labels}
defaults.update(kwargs)
label = DataLabel(**defaults)
index = self.get_label_index(key)
if index is None:
plot.overlays.append(label)
else:
plot.overlays[index] = label
self.labels[key] = coordinates
plot.request_redraw()
def remove_label(self, key):
plot = self.scan_plot
index = self.get_label_index(key)
plot.overlays.pop(index)
plot.request_redraw()
self.labels.pop(key)
def remove_all_labels(self):
plot = self.scan_plot
new_overlays = []
for item in plot.overlays:
if not ( isinstance(item, DataLabel) and item.label_format in self.labels ) :
new_overlays.append(item)
plot.overlays = new_overlays
plot.request_redraw()
self.labels.clear()
@on_trait_change('constant_axis')
def relocate_labels(self):
for item in self.scan_plot.overlays:
if isinstance(item, DataLabel) and item.label_format in self.labels:
coordinates = self.labels[item.label_format]
if self.constant_axis == 'x':
point = (coordinates[2],coordinates[1])
elif self.constant_axis == 'y':
point = (coordinates[0],coordinates[2])
elif self.constant_axis == 'z':
point = (coordinates[0],coordinates[1])
item.data_point = point
def update_axis_li(self):
if self.constant_axis == 'x':
self.z1 = self.figure.index_range.low
elif self.constant_axis == 'y':
self.x1 = self.figure.index_range.low
elif self.constant_axis == 'z':
self.x1 = self.figure.index_range.low
def update_axis_hi(self):
if self.constant_axis == 'x':
self.z2 = self.figure.index_range.high
elif self.constant_axis == 'y':
self.x2 = self.figure.index_range.high
elif self.constant_axis == 'z':
self.x2 = self.figure.index_range.high
def update_axis_lv(self):
if self.constant_axis == 'x':
self.y1 = self.figure.value_range.low
elif self.constant_axis == 'y':
self.z1 = self.figure.value_range.low
elif self.constant_axis == 'z':
self.y1 = self.figure.value_range.low
def update_axis_hv(self):
if self.constant_axis == 'x':
self.y2 = self.figure.value_range.high
elif self.constant_axis == 'y':
self.z2 = self.figure.value_range.high
elif self.constant_axis == 'z':
self.y2 = self.figure.value_range.high
def update_axis(self):
self.update_axis_li()
self.update_axis_hi()
self.update_axis_lv()
self.update_axis_hv()
def update_mesh(self):
if self.constant_axis == 'x':
x1=self.z1
x2=self.z2
y1=self.y1
y2=self.y2
elif self.constant_axis == 'y':
x1=self.x1
x2=self.x2
y1=self.z1
y2=self.z2
elif self.constant_axis == 'z':
x1=self.x1
x2=self.x2
y1=self.y1
y2=self.y2
if (x2-x1) >= (y2-y1):
self.X = numpy.linspace(x1,x2,self.resolution)
self.Y = numpy.linspace(y1,y2,int(self.resolution*(y2-y1)/(x2-x1)))
else:
self.Y = numpy.linspace(y1,y2,self.resolution)
self.X = numpy.linspace(x1,x2,int(self.resolution*(x2-x1)/(y2-y1)))
# GUI buttons
def _history_back_fired(self):
self.stop()
self.set_items( self.history.back() )
def _history_forward_fired(self):
self.stop()
self.set_items( self.history.forward() )
def _reset_range_fired(self):
self.x1 = scanner.getXRange()[0]
self.x2 = scanner.getXRange()[1]
self.y1 = scanner.getYRange()[0]
self.y2 = scanner.getYRange()[1]
self.z1 = scanner.getZRange()[0]
self.z2 = scanner.getZRange()[1]
def _reset_cursor_fired(self):
self.center_cursor()
# saving images
def save_image(self, filename=None):
self.save_figure(self.figure_container, filename)
traits_view = View(VGroup(Hsplit(HGroup(HGroup(Item('submit_button', show_label=False),
Item('remove_button', show_label=False),
Item('priority'),
Item('state', style='readonly'),
Item('history_back', show_label=False),
Item('history_forward', show_label=False),
),
Item('figure_container', show_label=False, resizable=True,enabled_when='state != "run"'),
HGroup(Item('thresh_low', width=-80),
Item('thresh_high', width=-80),
Item('colormap', width=-100),
Item('show_labels'),
),
),
HGroup(HGroup(Item('submit_gsd_button', show_label=False),
Item('remove_button', show_label=False),
Item('history_back_gsd', show_label=False),
Item('history_forward_gsd', show_label=False),
),
Item('figure_container_gsd', show_label=False, resizable=True,enabled_when='state != "run"'),
HGroup(Item('thresh_low_gsd', width=-80),
Item('thresh_high_gsd', width=-80),
Item('colormap_gsd', width=-100),
Item('show_labels'),
),
),
),
HGroup(Item('resolution', enabled_when='state != "run"', width=-60),
Item('x1', width=-60),
Item('x2', width=-60),
Item('y1', width=-60),
Item('y2', width=-60),
Item('z1', width=-60),
Item('z2', width=-60),
Item('reset_range', show_label=False),
),
HGroup(Item('constant_axis', style='custom', show_label=False, enabled_when='state != "run"'),
Item('bidirectional', enabled_when='state != "run"'),
Item('seconds_per_point', width=-80),
Item('return_speed', width=-80),
Item('reset_cursor', show_label=False),
),
Item('x', enabled_when='state != "run" or (state == "run" and constant_axis == "x")'),
Item('y', enabled_when='state != "run" or (state == "run" and constant_axis == "y")'),
Item('z', enabled_when='state != "run" or (state == "run" and constant_axis == "z")'),
),
menubar = MenuBar(Menu(Action(action='save_image', name='Save Image (.png)'),
Action(action='save', name='Save (.pyd or .pys)'),
Action(action='load', name='Load'),
Action(action='_on_close', name='Quit'),
name='File'),),
title='Confocal', width=880, height=800, buttons=[], resizable=True, x=0, y=0,
handler=GetSetSaveImageHandler)
class SMRCWindow(HasTraits):
"""
SMRCWindow is the Mainwindow of the application SmartRecord. The window
shows the time and the current phase of the experiment when it's running.
Furthermore the window interacts with the SMRCModel and
make it possible that the user can start and
cancel the experiment by clicking a icon.
"""
model = Instance(SMRCModel)
smrc_handler = SMRCHandler()
current_phase = Str("Current Phase - Not Started")
clock = Str(secs_to_time(0))
record_mode = Bool(True)
def __init__(self, model):
self.logger = getLogger("application")
self.logger.debug("Initializes SMRCWindow")
self.record_mode = model.record_mode
self.model = model
self.model.experiment.window = self
def start_clock(self):
"""Run the clock in the status bar."""
self.logger.info("Start the time-thread [SMRCWindow]")
self.clock = secs_to_time(0)
RunThread(target=self._run_clock)
def _run_clock(self):
# Updates the status bar time once every second.
self.clock_running = True
self.start_time = time()
while self.clock_running:
self.td = time() - self.start_time
self.clock = secs_to_time(self.td)
sleep(1.0)
#=========================================================================
# Traitsview
#=========================================================================
# Switch to stop the running thread
clock_running = Bool(False)
view = View(UItem("model", style="custom"),
menubar=MenuBar(Menu(*file_actions, name="File"),
Menu(*configure_actions, name="Configuration"),
Menu(*import_action, name="Import"),
Menu(help_docs, name="Help")),
toolbar=ToolBar(*toolbar_actions,
show_tool_names=False,
image_size=(30, 30)),
statusbar=[
StatusItem(name="current_phase", width=0.5),
StatusItem(name="clock", width=85)
],
handler=smrc_handler,
resizable=True,
height=680,
width=1300,
title="SmartRecord",
icon=ImageResource("../../icons/smrc_icon.png"))