def edit_mask(self, mask_update_cb=None):
self.win = pykoala.gui.gui_helpers.MaskTool(filter_cb=self.mask_select, reset_filter_cb=self.clear_mask)
self.mask_update_cb = mask_update_cb
plot = self.win.get_plot()
if self.current_spectrum is None:
self.mask_image = make.image(data=self.mask, colormap="hot")
else:
self.mask_image = make.image(data=self.current_spectrum*self.mask, colormap="hot")
plot.add_item(self.mask_image)
self.win.show()
return self.win
def __onPlotImage(self):
filename = osp.join(osp.dirname(__file__), 'brain.png')
image = make.image(filename=filename, colormap="bone")
data2 = np.array(image.data.T[200:], copy=True)
image2 = make.image(data2, title="Modified", alpha_mask=True)
window = self._newPlotWindow("testImagePlot")
#self.plot1 = self.p_plotWindow.addImagePlot([image, image2])
window.addImageWidget([image, image2], show_xsection=True, show_ysection=True)
window.show()
def edit_mask(self, mask_update_cb=None):
self.win = pykoala.gui.gui_helpers.MaskTool(
filter_cb=self.mask_select, reset_filter_cb=self.clear_mask)
self.mask_update_cb = mask_update_cb
plot = self.win.get_plot()
if self.current_spectrum is None:
self.mask_image = make.image(data=self.mask, colormap="hot")
else:
self.mask_image = make.image(data=self.current_spectrum *
self.mask,
colormap="hot")
plot.add_item(self.mask_image)
self.win.show()
return self.win
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
filename = osp.join(osp.dirname(__file__), "brain.png")
win = create_window()
image = make.image(filename=filename, colormap="bone")
data2 = np.array(image.data.T[200:], copy=True)
image2 = make.image(data2, title="Modified", alpha_mask=True)
plot = win.get_plot()
plot.add_item(image)
plot.add_item(image2, z=1)
win.exec_()
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
filename = osp.join(osp.dirname(__file__), "brain.png")
win = create_window()
image = make.image(filename=filename, colormap="bone")
data2 = np.array(image.data.T[200:], copy=True)
image2 = make.image(data2, title="Modified", alpha_mask=True)
plot = win.get_plot()
plot.add_item(image)
plot.add_item(image2, z=1)
win.exec_()
def imshow(data, interpolation=None, mask=None):
"""
Display the image in *data* to current axes
interpolation: 'nearest', 'linear' (default), 'antialiasing'
Example::
import numpy as np
x = np.linspace(-5, 5, 1000)
img = np.fromfunction(lambda x, y: np.sin((x/200.)*(y/200.)**2), (1000, 1000))
gray()
imshow(img)
show()
"""
axe = gca()
import numpy as np
if isinstance(data, np.ma.MaskedArray) and mask is None:
mask = data.mask
data = data.data
if mask is None:
img = make.image(data)
else:
img = make.maskedimage(data, mask, show_mask=True)
if interpolation is not None:
interp_dict = {
'nearest': INTERP_NEAREST,
'linear': INTERP_LINEAR,
'antialiasing': INTERP_AA
}
assert interpolation in interp_dict, "invalid interpolation option"
img.set_interpolation(interp_dict[interpolation], size=5)
axe.add_image(img)
axe.yreverse = True
_show_if_interactive()
return [img]
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
filename = osp.join(osp.dirname(__file__), "brain.png")
image = make.image(filename=filename, title="Original", colormap="gray")
win = ImageDialog(
edit=False,
toolbar=True,
wintitle="Contrast test",
options=dict(show_contrast=True),
)
plot = win.get_plot()
plot.add_item(image)
win.resize(600, 600)
win.show()
try:
plot.save_widget("contrast.png")
except IOError:
# Skipping this part of the test
# because user has no write permission on current directory
pass
win.exec_()
def roi_setup(self):
"""Show a dialog to setup the region of interest."""
dialog = ImageDialog("ROI Setup", edit=True, toolbar=False, parent=self)
default = dialog.add_tool(SelectTool)
dialog.set_default_tool(default)
roi_tool = dialog.add_tool(RectangleTool, switch_to_default_tool=True)
roi = self.cam.roi
old_roi = roi
roi_tool.activate()
# Get image and display
plot = dialog.get_plot()
img = make.image(self.cam_thread.img_data)
plot.add_item(img)
plot.set_active_item(img)
# Wait for user input
dialog.show()
if dialog.exec_():
try:
roi = get_rect(roi_tool)
self.cam.set_roi(roi)
except:
e = sys.exc_info()
print(e)
self.cam.set_roi(old_roi)
def imshow(data, interpolation=None, mask=None):
"""
Display the image in *data* to current axes
interpolation: 'nearest', 'linear' (default), 'antialiasing'
Example:
import numpy as np
x = np.linspace(-5, 5, 1000)
img = np.fromfunction(lambda x, y:
np.sin((x/200.)*(y/200.)**2), (1000, 1000))
gray()
imshow(img)
show()
"""
axe = gca()
import numpy as np
if isinstance(data, np.ma.MaskedArray) and mask is None:
mask = data.mask
data = data.data
if mask is None:
img = make.image(data)
else:
img = make.maskedimage(data, mask, show_mask=True)
if interpolation is not None:
interp_dict = {'nearest': INTERP_NEAREST,
'linear': INTERP_LINEAR,
'antialiasing': INTERP_AA}
assert interpolation in interp_dict, "invalid interpolation option"
img.set_interpolation(interp_dict[interpolation], size=5)
axe.add_image(img)
axe.yreverse = True
_show_if_interactive()
return [img]
def image_plot(self, dset):
self.msg("image plot")
self.gui['plots_tabWidget'].setCurrentIndex(0)
image_plot = self.gui['image_plot']
try:
xdata = np.array(dset.dims[0][0])
ydata = np.array(dset.dims[1][0])
except Exception as e:
self.msg("Couldn't find x/y data")
self.msg(e)
xdata = np.arange(dset.shape[0])
ydata = np.arange(dset.shape[1])
try:
xlab = dset.dims[0].label
ylab = dset.dims[1].label
except:
self.msg("Couldn't find x/y label")
xlab, ylab = "", ""
zlab = dset.name
image_item = make.image(data=np.array(dset),
xdata=(xdata[0], xdata[-1]),
ydata=(ydata[0], ydata[-1]))
image_plot.add_item(image_item)
image_plot.set_axis_unit(2, xlab)
image_plot.set_axis_unit(0, ylab)
image_plot.set_axis_unit(1, zlab)
image_plot.show()
image_plot.do_autoscale()
def build_items():
x = np.linspace(-10, 10, 200)
y = np.sin(np.sin(np.sin(x)))
filename = osp.join(osp.dirname(__file__), "brain.png")
items = [
make.curve(x, y, color="b"),
make.image(filename=filename),
make.trimage(filename=filename),
make.maskedimage(filename=filename,
colormap='gray',
show_mask=True,
xdata=[0, 40],
ydata=[0, 50]),
make.label("Relative position <b>outside</b>", (x[0], y[0]),
(-10, -10), "BR"),
make.label("Relative position <i>inside</i>", (x[0], y[0]), (10, 10),
"TL"),
make.label("Absolute position", "R", (0, 0), "R"),
make.legend("TR"),
make.rectangle(-3, -0.8, -0.5, -1., "rc1"),
make.segment(-3, -0.8, -0.5, -1., "se1"),
make.ellipse(-10, 0.0, 0, 0, "el1"),
make.annotated_rectangle(0.5, 0.8, 3, 1., "rc1", "tutu"),
make.annotated_segment(-1, -1, 1, 1., "rc1", "tutu"),
Axes((0, 0), (1, 0), (0, 1)),
PolygonShape(
np.array([[150., 330.], [270., 520.], [470., 480.], [520., 360.],
[460., 200.], [250., 240.]])),
]
return items
def build_items():
x = np.linspace(-10, 10, 200)
y = np.sin(np.sin(np.sin(x)))
filename = osp.join(osp.dirname(__file__), "brain.png")
items = [
make.curve(x, y, color="b"),
make.image(filename=filename),
make.trimage(filename=filename),
make.maskedimage(filename=filename, colormap='gray',
show_mask=True, xdata=[0, 40], ydata=[0, 50]),
make.label("Relative position <b>outside</b>",
(x[0], y[0]), (-10, -10), "BR"),
make.label("Relative position <i>inside</i>",
(x[0], y[0]), (10, 10), "TL"),
make.label("Absolute position", "R", (0, 0), "R"),
make.legend("TR"),
make.rectangle(-3, -0.8, -0.5, -1., "rc1"),
make.segment(-3, -0.8, -0.5, -1., "se1"),
make.ellipse(-10, 0.0, 0, 0, "el1"),
make.annotated_rectangle(0.5, 0.8, 3, 1., "rc1", "tutu"),
make.annotated_segment(-1, -1, 1, 1., "rc1", "tutu"),
Axes( (0, 0), (1, 0), (0, 1) ),
PolygonShape(np.array([[150., 330.],
[270., 520.],
[470., 480.],
[520., 360.],
[460., 200.],
[250., 240.]])),
]
return items
def image_plot(self, dset):
self.msg("image plot")
self.gui['plots_tabWidget'].setCurrentIndex(0)
image_plot = self.gui['image_plot']
try:
xdata = np.array(dset.dims[0][0])
ydata = np.array(dset.dims[1][0])
except Exception as e:
self.msg("Couldn't find x/y data")
self.msg(e)
xdata = np.arange(dset.shape[0])
ydata = np.arange(dset.shape[1])
try:
xlab = dset.dims[0].label
ylab = dset.dims[1].label
except:
self.msg("Couldn't find x/y label")
xlab, ylab = "", ""
zlab = dset.name
image_item = make.image(data=np.array(dset),
xdata=(xdata[0],xdata[-1]), ydata=(ydata[0],ydata[-1]))
image_plot.add_item(image_item)
image_plot.set_axis_unit(2, xlab)
image_plot.set_axis_unit(0, ylab)
image_plot.set_axis_unit(1, zlab)
image_plot.show()
image_plot.do_autoscale()
def imshow(data, title=None, hold=False):
dlg = ImageDialog(wintitle=title)
dlg.get_plot().add_item(make.image(data))
if hold:
dlg.show()
else:
dlg.exec_()
def display_data(self):
kwds = OrderedDict()
for name, combo in self.combos.iteritems():
try:
kwds[name] = float(combo.currentText())
except ValueError:
kwds[name] = str(combo.currentText())
key = self._str_data(kwds)
dat, kwds = self.data[key]
dat = 1.0-dat
if not self.image:
self.image = make.image(dat, title="Modified")
self.get_plot().add_item(self.image)
self.get_plot().set_axis_title(0, kwds['yname'])
self.get_plot().set_axis_title(1, '1-R')
self.get_plot().set_axis_title(2, kwds['xname'])
# self.plot_thicknesses()
self.get_plot().legend()
else:
self.image.set_data(dat)
self.image.set_xdata(kwds['xmin'], kwds['xmax'])
self.image.set_ydata(kwds['ymin'], kwds['ymax'])
self.get_plot().replot()
self.get_plot().do_autoscale()
def imshow( data ):
win = ImageDialog(edit=False, toolbar=True, wintitle="ImageDialog test",
options=dict(xlabel='Concentration', xunit='ppm'))
item = make.image(data)
plot = win.get_plot()
plot.add_item(item)
win.show()
win.exec_()
def show_data(self, data):
plot = self.get_plot()
if self.item is not None:
self.item.set_data(data)
else:
self.item = make.image(data, colormap="gray")
plot.add_item(self.item, z=0)
plot.set_active_item(self.item)
plot.replot()
def show_data(self, data):
plot = self.get_plot()
if self.item is not None:
self.item.set_data(data)
else:
self.item = make.image(data, colormap="gray")
plot.add_item(self.item, z=0)
plot.set_active_item(self.item)
plot.replot()
def __init__(self):
QMainWindow.__init__(self)
filename = osp.join(osp.dirname(__file__), "brain.png")
image1 = make.image(filename=filename, title="Original", colormap='gray')
from guiqwt.tests.image import compute_image
image2 = make.image(compute_image())
widget = CentralWidget(self)
self.setCentralWidget(widget)
widget.plot1.add_item(image1)
widget.plot2.add_item(image2)
toolbar = self.addToolBar("tools")
widget.manager.add_toolbar(toolbar, id(toolbar))
# widget.manager.set_default_toolbar(toolbar)
widget.register_tools()
def data_view(data, ofa, data_title):
win = ImageDialog(edit=True,
toolbar=True,
wintitle="DW viewer",
options=dict(show_contrast=False,
xlabel='Channel',
xunit='#',
ylabel='time',
show_xsection=True,
show_ysection=True))
# win.add_tool(AnnotatedRectangleTool)
# win.add_tool(ReverseYAxisTool)
# win.add_tool(AnnotatedRectangleTool, title="Flag",
# switch_to_default_tool=False,
# handle_final_shape_cb=customize_shape)
# win.add_tool(VCursorTool)
# sst=win.add_tool(SignalStatsTool)
# SignalStatsTool.activate(sst)
xcs = win.panels['x_cross_section']
xcs.cs_plot.curveparam.curvestyle = 'Steps'
xcs.cs_plot.perimage_mode = False
ycs = win.panels['y_cross_section']
ycs.cs_plot.curveparam.curvestyle = 'Steps'
ycs.cs_plot.perimage_mode = False
item_data = make.image(data, title=data_title)
item_data.interpolate = (0, )
item_ofa = make.image(ofa, title='Overflow')
item_ofa.interpolate = (0, )
item_ofa.imageparam.alpha_mask = True
plot = win.get_plot()
plot.add_item(item_data)
plot.add_item(item_ofa)
# plot.add_item(itemFlag)
win.show()
if win.exec_():
# return plot.items
return win
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
filename = osp.join(osp.dirname(__file__), "brain.png")
image = make.image(filename=filename, colormap="bone")
rect = get_segment(image)
print("Coordinates:", rect)
print("Distance:", np.sqrt((rect[2]-rect[0])**2 + (rect[3]-rect[1])**2))
def __init__(self, parent, plot):
super(ImageFT, self).__init__(Qt.Vertical, parent)
self.plot = plot
self.image = make.image(np.zeros((512, 2048)))
self.plot.add_item(self.image)
self.hCursor = None
self.roi = None
self.scaleFun = lambda x: x
self.scaleFunInv = lambda x: x
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
filename = osp.join(osp.dirname(__file__), "brain.png")
win = create_window()
image1 = make.image(filename=filename, title="Original",
alpha_mask=False, colormap='gray')
data2 = np.array(image1.data.T[200:], copy=True)
image2 = make.image(data2, title="Modified")#, alpha_mask=True)
plot = win.get_plot()
plot.add_item(image1, z=0)
plot.add_item(image2, z=1)
plot.set_items_readonly(False)
image2.set_readonly(True)
win.get_itemlist_panel().show()
win.show()
win.exec_()
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
filename = osp.join(osp.dirname(__file__), "brain.png")
win = create_window()
image = make.image(filename=filename, colormap="bone", alpha_mask=True)
plot = win.get_plot()
plot.add_item(image)
win.exec_()
def spike_amplitude_histogram(trains,
num_bins,
uniform_y_scale=True,
x_unit=pq.uV,
progress=None):
""" Create a spike amplitude histogram.
This plot is useful to assess the drift in spike amplitude over a longer
recording. It shows histograms (one for each `trains` entry, e.g. segment)
of maximum and minimum spike amplitudes.
:param list trains: A list of lists of :class:`neo.core.SpikeTrain`
objects. Each entry of the outer list will be one point on the
x-axis (they could correspond to segments), all amplitude occurences
of spikes contained in the inner list will be added up.
:param int num_bins: Number of bins for the histograms.
:param bool uniform_y_scale: If True, the histogram for each channel
will use the same bins. Otherwise, the minimum bin range is computed
separately for each channel.
:param Quantity x_unit: Unit of Y-Axis.
:param progress: Set this parameter to report progress.
:type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`
:return:
"""
if not trains:
raise SpykeException('No spikes trains for Spike Amplitude Histogram!')
if not progress:
progress = ProgressIndicator()
hist, down, up = sah(trains, num_bins, uniform_y_scale, x_unit, progress)
num_channels = len(down)
columns = int(round(sp.sqrt(num_channels)))
win = PlotDialog(toolbar=True, wintitle="Spike Amplitude Histogram")
for c in xrange(num_channels):
pW = BaseImageWidget(win, yreverse=False, lock_aspect_ratio=False)
plot = pW.plot
img = make.image(sp.log(hist[:, :, c] + 1),
ydata=[down[c], up[c]],
interpolation='nearest')
plot.add_item(img)
plot.set_axis_title(plot.Y_LEFT, 'Amplitude')
plot.set_axis_unit(plot.Y_LEFT, x_unit.dimensionality.string)
win.add_plot_widget(pW, c, column=c % columns)
progress.done()
win.add_custom_image_tools()
win.add_x_synchronization_option(True, range(num_channels))
win.add_y_synchronization_option(uniform_y_scale, range(num_channels))
win.show()
return win
def show_data(self, data, lut_range=None):
plot = self.imagewidget.plot
if self.item is not None:
self.item.set_data(data)
if lut_range is None:
lut_range = self.item.get_lut_range()
self.imagewidget.set_contrast_range(*lut_range)
self.imagewidget.update_cross_sections()
else:
self.item = make.image(data)
plot.add_item(self.item, z=0)
plot.replot()
def show_data(self, data, lut_range=None):
plot = self.imagewidget.plot
if self.item is not None:
self.item.set_data(data)
if lut_range is None:
lut_range = self.item.get_lut_range()
self.imagewidget.set_contrast_range(*lut_range)
self.imagewidget.update_cross_sections()
else:
self.item = make.image(data)
plot.add_item(self.item, z=0)
plot.replot()
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
filename = osp.join(osp.dirname(__file__), "brain.png")
win = create_window()
image = make.image(filename=filename, colormap="bone", alpha_mask=True)
plot = win.get_plot()
plot.add_item(image)
win.exec_()
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
filename = osp.join(osp.dirname(__file__), "brain.png")
win = create_window()
image1 = make.image(filename=filename, title="Original",
alpha_mask=False, colormap='gray')
data2 = np.array(image1.data.T[200:], copy=True)
image2 = make.image(data2, title="Modified")#, alpha_mask=True)
plot = win.get_plot()
plot.add_item(image1, z=0)
plot.add_item(image2, z=1)
plot.set_items_readonly(False)
image2.set_readonly(True)
win.get_itemlist_panel().show()
win.show()
win.exec_()
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
filename = osp.join(osp.dirname(__file__), "brain.png")
image = make.image(filename=filename, colormap="bone")
rect = get_segment(image)
print("Coordinates:", rect)
print("Distance:",
np.sqrt((rect[2] - rect[0])**2 + (rect[3] - rect[1])**2))
def test():
filename = osp.join(osp.dirname(__file__), "mr-brain.dcm")
image = make.image(filename=filename, title="DICOM img", colormap='gray')
win = ImageDialog(edit=False, toolbar=True, wintitle="DICOM I/O test",
options=dict(show_contrast=True))
plot = win.get_plot()
plot.add_item(image)
plot.select_item(image)
contrast = win.get_contrast_panel()
contrast.histogram.eliminate_outliers(54.)
win.resize(600, 700)
return win
def spike_amplitude_histogram(trains, num_bins, uniform_y_scale=True,
x_unit=pq.uV, progress=None):
""" Create a spike amplitude histogram.
This plot is useful to assess the drift in spike amplitude over a longer
recording. It shows histograms (one for each `trains` entry, e.g. segment)
of maximum and minimum spike amplitudes.
:param list trains: A list of lists of :class:`neo.core.SpikeTrain`
objects. Each entry of the outer list will be one point on the
x-axis (they could correspond to segments), all amplitude occurences
of spikes contained in the inner list will be added up.
:param int num_bins: Number of bins for the histograms.
:param bool uniform_y_scale: If True, the histogram for each channel
will use the same bins. Otherwise, the minimum bin range is computed
separately for each channel.
:param Quantity x_unit: Unit of Y-Axis.
:param progress: Set this parameter to report progress.
:type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`
:return:
"""
if not trains:
raise SpykeException('No spikes trains for Spike Amplitude Histogram!')
if not progress:
progress = ProgressIndicator()
hist, down, up = sah(trains, num_bins, uniform_y_scale, x_unit, progress)
num_channels = len(down)
columns = int(round(sp.sqrt(num_channels)))
win = PlotDialog(toolbar=True, wintitle="Spike Amplitude Histogram")
for c in xrange(num_channels):
pW = BaseImageWidget(
win, yreverse=False, lock_aspect_ratio=False)
plot = pW.plot
img = make.image(sp.log(hist[:, :, c] + 1),
ydata=[down[c], up[c]],
interpolation='nearest')
plot.add_item(img)
plot.set_axis_title(plot.Y_LEFT, 'Amplitude')
plot.set_axis_unit(plot.Y_LEFT, x_unit.dimensionality.string)
win.add_plot_widget(pW, c, column=c % columns)
progress.done()
win.add_custom_image_tools()
win.add_x_synchronization_option(True, range(num_channels))
win.add_y_synchronization_option(uniform_y_scale,
range(num_channels))
win.show()
return win
def initializeTimeFreq(self):
self.plot.del_all_items()
self.len_wavelet = int(self.xsize/self.threadacquisition.samplingInterval)
self.wf = generate_wavelet_fourier(len_wavelet= self.len_wavelet, ** self.paramsTimeFreq).transpose()
self.win = hamming(self.len_wavelet)
self.map = zeros(self.wf.shape)
self.image = make.image(self.map, title='TimeFreq',interpolation ='nearest')
self.plot.add_item(self.image)
p = self.paramsTimeFreq
self.freqs = arange(p['f_start'],p['f_stop'],p['deltafreq'])
def test():
filename = osp.join(osp.dirname(__file__), "mr-brain.dcm")
image = make.image(filename=filename, title="DICOM img", colormap='gray')
win = ImageDialog(edit=False,
toolbar=True,
wintitle="DICOM I/O test",
options=dict(show_contrast=True))
plot = win.get_plot()
plot.add_item(image)
plot.select_item(image)
contrast = win.get_contrast_panel()
contrast.histogram.eliminate_outliers(54.)
win.resize(600, 700)
return win
def add_item(self, trace, plotkwargs={}):
if trace in self.items:
self.plot_widget.plot.del_item(self.items[trace])
if self.rank == 1:
try:
self.items[trace] = make.curve([], [], **plotkwargs)
except:
raise
elif self.rank == 2:
if 'interpolation' not in plotkwargs:
plotkwargs['interpolation'] = 'nearest'
self.items[trace] = make.image(np.array([[0]]), **plotkwargs)
else:
raise ValueError
self.plot_widget.plot.add_item(self.items[trace])
def add_item(self, trace, plotkwargs={}):
if trace in self.items:
self.plot_widget.plot.del_item(self.items[trace])
if self.rank == 1:
try:
self.items[trace] = make.curve([], [], **plotkwargs)
except:
raise
elif self.rank == 2:
if 'interpolation' not in plotkwargs:
plotkwargs['interpolation'] = 'nearest'
self.items[trace] = make.image(np.array([[0]]), **plotkwargs)
else:
raise ValueError
self.plot_widget.plot.add_item(self.items[trace])
def update(self, img_data):
"""Update the image plot and other information."""
# Apply image transformations if necessary.
img_data = np.rot90(img_data, -self.rotation)
if self.mirror[0]:
img_data = np.flipud(img_data)
if self.mirror[1]:
img_data = np.fliplr(img_data)
# Configure plot
plot = self.imageWidget.get_plot()
img = get_image_item(self.imageWidget)
roi_rect = get_rect_item(self.imageWidget)
if img is None:
img = make.image(img_data, colormap=str(self.colormapBox.currentText()))
plot.add_item(img)
else:
img.set_data(img_data)
#img.select()
#plot.replot()
# Display ROI if requested.
# TODO: make mirroring work
roi = np.array(self.cam.roi)
center = np.array(img_data.shape)/2
roi = rotate_rect_cw(roi, center, self.rotation)
if self.showROIBox.isChecked():
if roi_rect is None:
roi_rect = make.rectangle(roi[0], roi[1], roi[2], roi[3])
roi_rect.set_resizable(False)
roi_rect.set_selectable(False)
plot.add_item(roi_rect)
else:
roi_rect.set_rect(roi[0], roi[1], roi[2], roi[3])
else:
if roi_rect is not None:
plot.del_item(roi_rect)
# Update plot
if self.autoscaleButton.isChecked():
self.rescale()
self.set_lut_range()
plot.set_plot_limits(0, img_data.shape[1], 0, img_data.shape[0])
plot.set_aspect_ratio(img_data.shape[0]/img_data.shape[1], lock=True)
plot.replot()
def __init__(self, parent):
super(QWidget, self).__init__(parent)
self.parent = parent
self.base_path = os.path.realpath(os.path.curdir)
self.threadAcq = ThreadAcq(self)
self.configMTE = QSettings('configMTE.ini', QSettings.IniFormat)
self.setup()
self.win = ImageDialog(edit=False,
toolbar=True,
wintitle="Visu Data",
options=dict(show_xsection=True,
show_ysection=True,
show_contrast=True,
show_itemlist=False))
self.win.resize(800, 1000)
data = np.ones((1300, 1340))
self.item_data = make.image(data, colormap='hot')
self.plot = self.win.get_plot()
self.plot.add_item(self.item_data)
def display_data(self):
n = float(self.combo_n.currentText())
eta = float(self.combo_eta.currentText())
pol = self.combo_pol.currentText()
dat, kwds = self.data[self._str_data(n, eta, pol)]
dat = 1.0 - dat
if not self.image:
self.image = make.image(dat, title="Modified")
self.get_plot().add_item(self.image)
self.get_plot().set_axis_title(0, 'thickness')
self.get_plot().set_axis_title(1, '1-R')
self.get_plot().set_axis_title(2, 'llambda')
self.plot_thicknesses()
self.get_plot().legend()
else:
self.image.set_data(dat)
self.image.set_xdata(kwds['llambda_min'], kwds['llambda_max'])
self.image.set_ydata(kwds['t_min'], kwds['t_max'])
self.get_plot().replot()
self.get_plot().do_autoscale()
def display_data(self):
n = float(self.combo_n.currentText())
eta = float(self.combo_eta.currentText())
pol = self.combo_pol.currentText()
dat, kwds = self.data[self._str_data(n, eta, pol)]
dat = 1.0-dat
if not self.image:
self.image = make.image(dat, title="Modified")
self.get_plot().add_item(self.image)
self.get_plot().set_axis_title(0, 'thickness')
self.get_plot().set_axis_title(1, '1-R')
self.get_plot().set_axis_title(2, 'llambda')
self.plot_thicknesses()
self.get_plot().legend()
else:
self.image.set_data(dat)
self.image.set_xdata(kwds['llambda_min'], kwds['llambda_max'])
self.image.set_ydata(kwds['t_min'], kwds['t_max'])
self.get_plot().replot()
self.get_plot().do_autoscale()
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
filename = osp.join(osp.dirname(__file__), "brain.png")
image = make.image(filename=filename, title="Original", colormap='gray')
win = ImageDialog(edit=False, toolbar=True, wintitle="Contrast test",
options=dict(show_contrast=True))
plot = win.get_plot()
plot.add_item(image)
win.resize(600, 600)
win.show()
try:
plot.save_widget('contrast.png')
except IOError:
# Skipping this part of the test
# because user has no write permission on current directory
pass
win.exec_()
def update(self):
"""Show the currently selected image from the ring buffer."""
# Get the specified image data and ROI
img_data = self.rbuffer.read(self.indexBox.value())
roi = self.rbuffer.get_roi(self.indexBox.value())
# Update the viewer
plot = self.imageWidget.get_plot()
img = get_image_item(self.imageWidget)
rect = get_rect_item(self.imageWidget)
if img is None:
img = make.image(img_data,
colormap=str(
self.parent().colormapBox.currentText()))
plot.add_item(img)
else:
img.set_data(img_data)
if rect is None:
rect = make.rectangle(roi[0], roi[1], roi[2], roi[3])
plot.add_item(rect)
else:
rect.set_rect(roi[0], roi[1], roi[2], roi[3])
plot.replot()
def start(self, current, selections):
current.progress.begin('Creating Spectogram')
signals = current.analog_signals(self.which_signals + 1)
if not signals:
current.progress.done()
raise SpykeException('No signals selected!')
num_signals = len(signals)
columns = int(round(sp.sqrt(num_signals)))
current.progress.set_ticks(num_signals)
samples = self.nfft_index[self.fft_samples]
win = PlotDialog(toolbar=True,
wintitle="Signal Spectogram (FFT window size %d)"
% samples)
for c in xrange(num_signals):
pW = BaseImageWidget(win, yreverse=False,
lock_aspect_ratio=False)
plot = pW.plot
s = signals[c]
# Calculate spectrogram and create plot
v = mlab.specgram(s, NFFT=samples, noverlap=samples/2,
Fs=s.sampling_rate)
interpolation = 'nearest'
if self.interpolate:
interpolation = 'linear'
img = make.image(sp.log(v[0]), ydata=[v[1][0],v[1][-1]],
xdata=[v[2][0],v[2][-1]],
interpolation=interpolation)
plot.add_item(img)
# Labels etc.
if not self.show_color_bar:
plot.disable_unused_axes()
title = ''
if s.recordingchannel and s.recordingchannel.name:
title = s.recordingchannel.name
if s.segment and s.segment.name:
if title:
title += ' , '
title += s.segment.name
plot.set_title(title)
plot.set_axis_title(plot.Y_LEFT, 'Frequency')
plot.set_axis_unit(plot.Y_LEFT,
s.sampling_rate.dimensionality.string)
plot.set_axis_title(plot.X_BOTTOM, 'Time')
time_unit = (1 / s.sampling_rate).simplified
plot.set_axis_unit(plot.X_BOTTOM,
time_unit.dimensionality.string)
win.add_plot_widget(pW, c, column=c%columns)
current.progress.step()
current.progress.done()
win.add_custom_image_tools()
win.add_x_synchronization_option(True, range(num_signals))
win.add_y_synchronization_option(True, range(num_signals))
win.show()
def compute_oblique_section(item, obj):
"""Return oblique averaged cross section"""
global TEMP_ITEM
xa, ya, xb, yb = obj.get_bounding_rect_coords()
x0, y0, x1, y1, x2, y2, x3, y3 = obj.get_rect()
getcpi = item.get_closest_pixel_indexes
ixa, iya = getcpi(xa, ya)
ixb, iyb = getcpi(xb, yb)
ix0, iy0 = getcpi(x0, y0)
ix1, iy1 = getcpi(x1, y1)
ix3, iy3 = getcpi(x3, y3)
destw = vector_norm(ix0, iy0, ix1, iy1)
desth = vector_norm(ix0, iy0, ix3, iy3)
ysign = -1 if obj.plot().get_axis_direction('left') else 1
angle = vector_angle(ix1-ix0, (iy1-iy0)*ysign)
dst_rect = (0, 0, int(destw), int(desth))
dst_image = np.empty((desth, destw), dtype=np.float64)
if isinstance(item.data, np.ma.MaskedArray):
if item.data.dtype in (np.float32, np.float64):
item_data = item.data
else:
item_data = np.ma.array(item.data, dtype=np.float32, copy=True)
data = np.ma.filled(item_data, np.nan)
else:
data = item.data
ixr = .5*(ixb+ixa)
iyr = .5*(iyb+iya)
mat = translate(ixr, iyr)*rotate(-angle)*translate(-.5*destw, -.5*desth)
_scale_tr(data, mat, dst_image, dst_rect,
(1., 0., np.nan), (INTERP_LINEAR,))
if DEBUG:
plot = obj.plot()
if TEMP_ITEM is None:
from guiqwt.builder import make
TEMP_ITEM = make.image(dst_image)
plot.add_item(TEMP_ITEM)
else:
TEMP_ITEM.set_data(dst_image)
if False:
TEMP_ITEM.imageparam.alpha_mask = True
xmin, ymin = ixa, iya
xmax, ymax = xmin+destw, ymin+desth
TEMP_ITEM.imageparam.xmin = xmin
TEMP_ITEM.imageparam.xmax = xmax
TEMP_ITEM.imageparam.ymin = ymin
TEMP_ITEM.imageparam.ymax = ymax
TEMP_ITEM.imageparam.update_image(TEMP_ITEM)
plot.replot()
ydata = np.ma.fix_invalid(dst_image, copy=DEBUG).mean(axis=1)
xdata = item.get_x_values(0, ydata.size)[:ydata.size]
try:
xdata -= xdata[0]
except IndexError:
print(xdata, ydata)
return xdata, ydata
def compute_oblique_section(item, obj):
"""Return oblique averaged cross section"""
global TEMP_ITEM
xa, ya, xb, yb = obj.get_bounding_rect_coords()
x0, y0, x1, y1, x2, y2, x3, y3 = obj.get_rect()
getcpi = item.get_closest_pixel_indexes
ixa, iya = getcpi(xa, ya)
ixb, iyb = getcpi(xb, yb)
ix0, iy0 = getcpi(x0, y0)
ix1, iy1 = getcpi(x1, y1)
ix3, iy3 = getcpi(x3, y3)
destw = vector_norm(ix0, iy0, ix1, iy1)
desth = vector_norm(ix0, iy0, ix3, iy3)
ysign = -1 if obj.plot().get_axis_direction('left') else 1
angle = vector_angle(ix1 - ix0, (iy1 - iy0) * ysign)
dst_rect = (0, 0, int(destw), int(desth))
dst_image = np.empty((desth, destw), dtype=np.float64)
if isinstance(item.data, np.ma.MaskedArray):
if item.data.dtype in (np.float32, np.float64):
item_data = item.data
else:
item_data = np.ma.array(item.data, dtype=np.float32, copy=True)
data = np.ma.filled(item_data, np.nan)
else:
data = item.data
ixr = .5 * (ixb + ixa)
iyr = .5 * (iyb + iya)
mat = translate(ixr, iyr) * rotate(-angle) * translate(
-.5 * destw, -.5 * desth)
_scale_tr(data, mat, dst_image, dst_rect, (1., 0., np.nan),
(INTERP_LINEAR, ))
if DEBUG:
plot = obj.plot()
if TEMP_ITEM is None:
from guiqwt.builder import make
TEMP_ITEM = make.image(dst_image)
plot.add_item(TEMP_ITEM)
else:
TEMP_ITEM.set_data(dst_image)
if False:
TEMP_ITEM.imageparam.alpha_mask = True
xmin, ymin = ixa, iya
xmax, ymax = xmin + destw, ymin + desth
TEMP_ITEM.imageparam.xmin = xmin
TEMP_ITEM.imageparam.xmax = xmax
TEMP_ITEM.imageparam.ymin = ymin
TEMP_ITEM.imageparam.ymax = ymax
TEMP_ITEM.imageparam.update_image(TEMP_ITEM)
plot.replot()
ydata = np.ma.fix_invalid(dst_image, copy=DEBUG).mean(axis=1)
xdata = item.get_x_values(0, ydata.size)[:ydata.size]
try:
xdata -= xdata[0]
except IndexError:
print(xdata, ydata)
return xdata, ydata
def __init__(self, image_data):
super(TestWindow, self).__init__()
plot = self.imagewidget.plot
plot.add_item(make.image(image_data))
self.setWindowTitle("QtDesigner plugins example")
def __init__(self, image_data):
super(TestWindow, self).__init__()
plot = self.imagewidget.plot
plot.add_item(make.image(image_data))
self.setWindowTitle("QtDesigner plugins example")
def start(self, current, selections):
current.progress.begin('Creating Spectogram')
signals = current.analog_signals(self.which_signals + 1)
if not signals:
current.progress.done()
raise SpykeException('No signals selected!')
num_signals = len(signals)
columns = int(round(sp.sqrt(num_signals)))
current.progress.set_ticks(num_signals)
samples = self.nfft_index[self.fft_samples]
win = PlotDialog(toolbar=True,
wintitle="Signal Spectogram (FFT window size %d)" %
samples)
for c in xrange(num_signals):
pW = BaseImageWidget(win, yreverse=False, lock_aspect_ratio=False)
plot = pW.plot
s = signals[c]
# Calculate spectrogram and create plot
v = mlab.specgram(s,
NFFT=samples,
noverlap=samples / 2,
Fs=s.sampling_rate.rescale(pq.Hz))
interpolation = 'nearest'
if self.interpolate:
interpolation = 'linear'
img = make.image(sp.log(v[0]),
ydata=[v[1][0], v[1][-1]],
xdata=[
v[2][0] + s.t_start.rescale(pq.s),
v[2][-1] + s.t_start.rescale(pq.s)
],
interpolation=interpolation)
plot.add_item(img)
# Labels etc.
if not self.show_color_bar:
plot.disable_unused_axes()
title = ''
if s.recordingchannel and s.recordingchannel.name:
title = s.recordingchannel.name
if s.segment and s.segment.name:
if title:
title += ' , '
title += s.segment.name
plot.set_title(title)
plot.set_axis_title(plot.Y_LEFT, 'Frequency')
plot.set_axis_unit(plot.Y_LEFT,
s.sampling_rate.dimensionality.string)
plot.set_axis_title(plot.X_BOTTOM, 'Time')
time_unit = (1 / s.sampling_rate).simplified
plot.set_axis_unit(plot.X_BOTTOM, time_unit.dimensionality.string)
win.add_plot_widget(pW, c, column=c % columns)
current.progress.step()
current.progress.done()
win.add_custom_image_tools()
win.add_x_synchronization_option(True, range(num_signals))
win.add_y_synchronization_option(True, range(num_signals))
win.show()
def setup_plot(self):
self.imagewidget = ImageWidget()
self.plot = self.imagewidget.plot
self.image = make.image(np.random.rand(100,100))
self.plot.add_item(self.image)
def setup_plot(self):
self.imagewidget = ImageWidget()
self.plot = self.imagewidget.plot
self.image = make.image(np.random.rand(100, 100))
self.plot.add_item(self.image)
def load_dset_deprecated(self, item, column):
# Message attribute values
_type = item.text(1)
if _type == "attr":
self.msg("Attribute", item.name, ":", item.val)
return
self.msg("Load Dataset")
h5file = self.open_file
for name in item.path:
h5file = h5file[name]
# TODO 1D, Axis Selection, etc
original_h5file = h5file
if isinstance(h5file, h5py.Dataset):
# 2D dataset handling
if len(h5file.shape) == 2:
if h5file.shape[0] == 1:
h5file = h5file[0,:]
else:
try:
xdata, ydata = h5file.attrs["_axes"]
pi = make.image(data=np.array(h5file),
xdata=tuple(xdata), ydata=tuple(ydata))
except KeyError:
self.msg("no _axes attribute")
self.msg("Axes scaling could not be set up.")
pi = make.image(data=np.array(h5file))
try:
xlab, ylab, zlab = h5file.attrs["_axes_labels"]
except Exception as e:
xlab, ylab, zlab = "", "", ""
self.msg(e)
self.gui["image_plot"].set_axis_unit(2, xlab)
self.gui["image_plot"].set_axis_unit(0, ylab)
self.gui["image_plot"].set_axis_unit(1, zlab)
self.gui["plots_tabWidget"].setCurrentIndex(0)
self.gui["image_plot"].del_all_items()
self.gui["image_plot"].add_item(pi)
self.gui["image_plot"].show()
self.gui["image_plot"].do_autoscale()
# 1D dataset handling
if len(h5file.shape) == 1:
if hasattr(self, "x_data"):
x_data = self.x_data
else:
try:
x0, x1 = original_h5file.attrs['_axes'][0]
x_data = np.linspace(x0, x1, h5file.shape[0])
self.msg('set up axes')
except:
x_data = list(range(h5file.shape[0]))
try:
xlab, ylab = original_h5file.attrs["_axes_labels"]
except Exception as e:
xlab, ylab = "", ""
self.msg("Labels could not be set up", e)
self.gui["line_plot"].set_axis_unit(2, xlab)
self.gui["line_plot"].set_axis_unit(0, ylab)
if len(x_data) != h5file.shape[0]:
self.msg("Cannot broadcast shapes: x data has length", len(x_data))
return
ci = make.curve(x=x_data, y=np.array(h5file))
self.gui["plots_tabWidget"].setCurrentIndex(1)
self.gui["line_plot"].del_all_items()
self.gui["line_plot"].add_item(ci)
self.gui["line_plot"].show()
self.gui["line_plot"].do_autoscale()
def load_dset_deprecated(self, item, column):
# Message attribute values
_type = item.text(1)
if _type == "attr":
self.msg("Attribute", item.name, ":", item.val)
return
self.msg("Load Dataset")
h5file = self.open_file
for name in item.path:
h5file = h5file[name]
# TODO 1D, Axis Selection, etc
original_h5file = h5file
if isinstance(h5file, h5py.Dataset):
# 2D dataset handling
if len(h5file.shape) == 2:
if h5file.shape[0] == 1:
h5file = h5file[0, :]
else:
try:
xdata, ydata = h5file.attrs["_axes"]
pi = make.image(data=np.array(h5file),
xdata=tuple(xdata),
ydata=tuple(ydata))
except KeyError:
self.msg("no _axes attribute")
self.msg("Axes scaling could not be set up.")
pi = make.image(data=np.array(h5file))
try:
xlab, ylab, zlab = h5file.attrs["_axes_labels"]
except Exception as e:
xlab, ylab, zlab = "", "", ""
self.msg(e)
self.gui["image_plot"].set_axis_unit(2, xlab)
self.gui["image_plot"].set_axis_unit(0, ylab)
self.gui["image_plot"].set_axis_unit(1, zlab)
self.gui["plots_tabWidget"].setCurrentIndex(0)
self.gui["image_plot"].del_all_items()
self.gui["image_plot"].add_item(pi)
self.gui["image_plot"].show()
self.gui["image_plot"].do_autoscale()
# 1D dataset handling
if len(h5file.shape) == 1:
if hasattr(self, "x_data"):
x_data = self.x_data
else:
try:
x0, x1 = original_h5file.attrs['_axes'][0]
x_data = np.linspace(x0, x1, h5file.shape[0])
self.msg('set up axes')
except:
x_data = list(range(h5file.shape[0]))
try:
xlab, ylab = original_h5file.attrs["_axes_labels"]
except Exception as e:
xlab, ylab = "", ""
self.msg("Labels could not be set up", e)
self.gui["line_plot"].set_axis_unit(2, xlab)
self.gui["line_plot"].set_axis_unit(0, ylab)
if len(x_data) != h5file.shape[0]:
self.msg("Cannot broadcast shapes: x data has length",
len(x_data))
return
ci = make.curve(x=x_data, y=np.array(h5file))
self.gui["plots_tabWidget"].setCurrentIndex(1)
self.gui["line_plot"].del_all_items()
self.gui["line_plot"].add_item(ci)
self.gui["line_plot"].show()
self.gui["line_plot"].do_autoscale()
