def setup_widget(self):
# ---Create the plot widget:
x = self.x
y = self.y
self.plot = CurvePlot(self)
self.curve_item = make.curve([], [], color="b")
self.plot.add_item(self.curve_item)
self.plot.set_antialiasing(True)
width = x[-1] - x[0]
self.intrange = make.range(x[0] + 0.4 * width, x[-1] - 0.4 * width)
self.plot.add_item(self.intrange)
self.lbgrange = make.range(x[0] + 0.3 * width, x[-1] - 0.65 * width)
self.plot.add_item(self.lbgrange)
self.lbgrange.pen = Qt.QPen(Qt.QColor("blue"))
self.lbgrange.brush = Qt.QBrush(Qt.QColor(0, 0, 120, 100))
self.rbgrange = make.range(x[0] + 0.7 * width, x[-1] - 0.1 * width)
self.rbgrange.pen = Qt.QPen(Qt.QColor("blue"))
self.rbgrange.brush = Qt.QBrush(Qt.QColor(0, 0, 120, 100))
self.label1 = make.label(r"", "TR", (0, 0), "TR")
self.plot.add_item(self.rbgrange)
self.bg_item = make.curve([], [], color="r")
self.plot.add_item(self.bg_item)
self.fit_bg()
self.plot.add_item(self.label1)
self.connect(self.plot, SIG_RANGE_CHANGED, self.fit_bg)
# ---
vlayout = QVBoxLayout()
vlayout.addWidget(self.plot)
self.setLayout(vlayout)
self.update_curve()
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
from numpy import linspace, sin, trapz
x = linspace(-10, 10, 1000)
y = sin(sin(sin(x)))
curve = make.curve(x, y, "ab", "b")
range = make.range(-2, 2)
disp0 = make.range_info_label(range,
"BR",
"x = %.1f ± %.1f cm",
title="Range infos")
disp1 = make.computation(range, "BL", "trapz=%g", curve,
lambda x, y: trapz(y, x))
disp2 = make.computations(
range,
"TL",
[
(curve, "min=%.5f", lambda x, y: y.min()),
(curve, "max=%.5f", lambda x, y: y.max()),
(curve, "avg=%.5f", lambda x, y: y.mean()),
],
)
legend = make.legend("TR")
plot(curve, range, disp0, disp1, disp2, legend)
def test():
"""Test"""
# -- Create QApplication
import guidata
_app = guidata.qapplication()
# --
from numpy import linspace, sin, trapz
x = linspace(-10, 10, 1000)
y = sin(sin(sin(x)))
curve = make.curve(x, y, "ab", "b")
range = make.range(-2, 2)
disp0 = make.range_info_label(range, "BR", "x = %.1f ± %.1f cm", title="Range infos")
disp1 = make.computation(range, "BL", "trapz=%g", curve, lambda x, y: trapz(y, x))
disp2 = make.computations(
range,
"TL",
[
(curve, "min=%.5f", lambda x, y: y.min()),
(curve, "max=%.5f", lambda x, y: y.max()),
(curve, "avg=%.5f", lambda x, y: y.mean()),
],
)
legend = make.legend("TR")
plot(curve, range, disp0, disp1, disp2, legend)
def add_range_to_plot(self, plot, this_range):
self.myrange = make.range(this_range[0], this_range[1])
self.myrange.itemChanged()
self.myrange.set_movable(False)
if False:
# make all colors black/gray
# check /usr/lib/python2.7/dist-packages/guiqwt/config.py for parameters
#
from guidata.dataset.dataitems import ColorItem
self.myrange.shapeparam.fill = ColorItem("gray")
self.myrange.shapeparam.line.color = "#000000"
self.myrange.shapeparam.sel_line.color = "#000000"
self.myrange.shapeparam.symbol.facecolor = "#000000"
self.myrange.shapeparam.sel_symbol.facecolor = "#000000"
#
self.myrange.shapeparam.update_range(self.myrange) # creates all the above QObjects
self.myrange.set_resizable(False)
self.myrange.itemChanged()
# print self.myrange._can_move, "move"
# disp2 = make.computations(self.myrange, "TL",
# [(curve, "min=%.5f", lambda x,y: y.min()),
# (curve, "max=%.5f", lambda x,y: y.max()),
# (curve, "avg=%.5f", lambda x,y: y.mean())])
# plot.add_item( disp2 )
plot.add_item( self.myrange )
self.myranges.append( self.myrange )
def add_epochs(plot, epochs, units=None):
""" Add Epoch markers to a guiqwt plot.
:param plot: The plot object.
:type plot: :class:`guiqwt.baseplot.BasePlot`
:param sequence epochs: The epochs (neo :class:`neo.Epoch` objects).
:param units: The x-scale of the plot. If this is ``None``,
the time unit of the events will be use.
"""
for e in epochs:
if units:
start = e.time.rescale(units)
end = (e.time + e.duration).rescale(units)
else:
start = e.time
end = e.time + e.duration
time = (start + end) / 2.0
o = make.range(start, end)
o.setTitle(e.label)
o.set_readonly(True)
o.set_movable(False)
o.set_resizable(False)
o.set_selectable(False)
o.set_rotatable(False)
plot.add_item(o)
nameObject = _MarkerName(e.label)
plot.add_item(make.marker(
(time, 0), nameObject.get_name,
movable=False, markerstyle='|', color='k', linestyle='NoPen',
linewidth=1))
def testFcn(self):
x = np.linspace(0, 100, 1000)
y = (np.random.rand(len(x)) - 0.5).cumsum()
curve = make.curve(x, y, "ab", "b")
range = make.range(0, 5)
disp2 = make.computations(
range,
"TL",
[
(curve, "min=%.5f", lambda x, y: y.min()),
(curve, "max=%.5f", lambda x, y: y.max()),
(curve, "avg=%.5f", lambda x, y: y.mean()),
],
)
legend = make.legend("TR")
items = [curve, range, disp2, legend]
win = CurveDialog(edit=False, toolbar=True, parent=self)
plot = win.get_plot()
for item in items:
plot.add_item(item)
win.show()
def add_range_to_plot(self, plot, this_range):
self.myrange = make.range(this_range[0], this_range[1])
self.myrange.itemChanged()
self.myrange.set_movable(False)
if False:
# make all colors black/gray
# check /usr/lib/python2.7/dist-packages/guiqwt/config.py for parameters
#
from guidata.dataset.dataitems import ColorItem
self.myrange.shapeparam.fill = ColorItem("gray")
self.myrange.shapeparam.line.color = "#000000"
self.myrange.shapeparam.sel_line.color = "#000000"
self.myrange.shapeparam.symbol.facecolor = "#000000"
self.myrange.shapeparam.sel_symbol.facecolor = "#000000"
#
self.myrange.shapeparam.update_range(
self.myrange) # creates all the above QObjects
self.myrange.set_resizable(False)
self.myrange.itemChanged()
# print self.myrange._can_move, "move"
# disp2 = make.computations(self.myrange, "TL",
# [(curve, "min=%.5f", lambda x,y: y.min()),
# (curve, "max=%.5f", lambda x,y: y.max()),
# (curve, "avg=%.5f", lambda x,y: y.mean())])
# plot.add_item( disp2 )
plot.add_item(self.myrange)
self.myranges.append(self.myrange)
def process(self):
params = {}
print(self.tableWidget.rowCount())
for i in range(self.tableWidget.rowCount()):
a = self.tableWidget.item(i, 0)
b = self.tableWidget.item(i, 1)
params[str(a.text())] = int(b.text())
# print "params",params
outpN = self.tableWidget.item(0, 0)
#params = {"none": "none", "output_length": outpN}
'''
if self.currentdata==[]:
data = self.prc.preprocData(self.flowData, params)
else:
data = self.prc.preprocData(self.currentdata, params)
'''
#self.dc.setCurrentDataFlowObject(data)
#print data
if params==[]:
data = self.prc.preprocData(self.flowData, params)
elif params!=[]:
data = self.prc.preprocData(self.flowData, params)
self.plot.del_all_items()
print data
print type(data)
self.plot.add_item(make.curve(range(0, len(data)), data))
self.rangeSelection = make.range(-2, 2)
disp0 = make.range_info_label(self.rangeSelection, 'BR', u"x = %.1f +- %.1f cm",
title="Range infos")
#self.plot.add_item(self.rangeSelection)
#self.plot.add_item(disp0)
self.plot.replot()
#self.dc.setCurrentDataFlowObject(data)
self.dc.preprocData = data
self.currentdata = np.reshape(data, (-1, 1))
def loadData(self):
self.trainingData = self.dataController.loadSampleData()
self.plot.add_item(make.curve(range(0, self.trainingData.shape[0]), self.trainingData["PIR"][:]))
self.rangeSelection = make.range(-2, 2)
disp0 = make.range_info_label(self.rangeSelection, 'BR', u"x = %.1f +- %.1f cm",
title="Range infos")
self.plot.add_item(self.rangeSelection)
self.plot.add_item(disp0)
self.plot.replot()
def refresh(self, slider_value=None):
"""Refresh Fit Tool dialog box"""
# Update button states
enable = self.x is not None and self.y is not None \
and self.x.size > 0 and self.y.size > 0 \
and self.fitfunc is not None and self.fitparams is not None \
and len(self.fitparams) > 0
for btn in self.button_list:
btn.setEnabled(enable)
if not enable:
# Fit widget is not yet configured
return
fitargs, fitkwargs = self.get_fitfunc_arguments()
yfit = self.fitfunc(self.x, [p.value for p in self.fitparams],
*fitargs, **fitkwargs)
plot = self.get_plot()
if self.legend is None:
self.legend = make.legend(anchor=self.legend_anchor)
plot.add_item(self.legend)
if self.xrange is None:
self.xrange = make.range(0., 1.)
plot.add_item(self.xrange)
self.xrange.set_range(self.autofit_prm.xmin, self.autofit_prm.xmax)
self.xrange.setVisible(self.show_xrange)
if self.data_curve is None:
self.data_curve = make.curve([], [],
_("Data"),
color="b",
linewidth=2)
plot.add_item(self.data_curve)
self.data_curve.set_data(self.x, self.y)
if self.fit_curve is None:
self.fit_curve = make.curve([], [],
_("Fit"),
color="r",
linewidth=2)
plot.add_item(self.fit_curve)
self.fit_curve.set_data(self.x, yfit)
plot.replot()
plot.disable_autoscale()
def loadData(self):
self.trainingData = self.dataController.loadSampleData()
import logic.DimensionalityReduceControl as controller
c = controller.DimensionalityReduceControl()
c.selectAlgorithm(2)
outpN = 100
params = {"none": "none", "output_length": outpN}
data = c.reduceData(self.trainingData["PIR"][:],params)
print data
self.plot.add_item(make.curve(range(0, len(data)), data))
self.rangeSelection = make.range(-2, 2)
disp0 = make.range_info_label(self.rangeSelection, 'BR', u"x = %.1f +- %.1f cm",
title="Range infos")
self.plot.add_item(self.rangeSelection)
self.plot.add_item(disp0)
self.plot.replot()
def refresh(self, slider_value=None):
"""Refresh Fit Tool dialog box"""
# Update button states
enable = self.x is not None and self.y is not None \
and self.x.size > 0 and self.y.size > 0 \
and self.fitfunc is not None and self.fitparams is not None \
and len(self.fitparams) > 0
for btn in self.button_list:
btn.setEnabled(enable)
if not enable:
# Fit widget is not yet configured
return
fitargs, fitkwargs = self.get_fitfunc_arguments()
yfit = self.fitfunc(self.x, [p.value for p in self.fitparams],
*fitargs, **fitkwargs)
plot = self.get_plot()
if self.legend is None:
self.legend = make.legend(anchor=self.legend_anchor)
plot.add_item(self.legend)
if self.xrange is None:
self.xrange = make.range(0., 1.)
plot.add_item(self.xrange)
self.xrange.set_range(self.autofit_prm.xmin, self.autofit_prm.xmax)
self.xrange.setVisible(self.show_xrange)
if self.data_curve is None:
self.data_curve = make.curve([], [],
_("Data"), color="b", linewidth=2)
plot.add_item(self.data_curve)
self.data_curve.set_data(self.x, self.y)
if self.fit_curve is None:
self.fit_curve = make.curve([], [],
_("Fit"), color="r", linewidth=2)
plot.add_item(self.fit_curve)
self.fit_curve.set_data(self.x, yfit)
plot.replot()
plot.disable_autoscale()
def testFcn(self):
x = np.linspace(0, 100, 1000)
y = (np.random.rand(len(x)) - 0.5).cumsum()
curve = make.curve(x, y, "ab", "b")
range = make.range(0, 5)
disp2 = make.computations(range, "TL",
[(curve, "min=%.5f", lambda x, y: y.min()),
(curve, "max=%.5f", lambda x, y: y.max()),
(curve, "avg=%.5f", lambda x, y: y.mean())])
legend = make.legend("TR")
items = [curve, range, disp2, legend]
win = CurveDialog(edit=False, toolbar=True, parent=self)
plot = win.get_plot()
for item in items:
plot.add_item(item)
win.show()
def plot(self, pdDataFrame):
''' plot some random stuff '''
x1 = pdDataFrame.index
y1 = pdDataFrame['Close']
x = np.linspace(0, 100, 1000)
y = (np.random.rand(len(x)) - 0.5).cumsum()
curve = make.curve(x1, y1, "ab", "b")
range = make.range(0, 5)
disp2 = make.computations(range, "TL",
[(curve, "min=%.5f", lambda x, y: y.min()),
(curve, "max=%.5f", lambda x, y: y.max()),
(curve, "avg=%.5f", lambda x, y: y.mean())])
legend = make.legend("TR")
# items = [ curve, range, disp2, legend]
items = [curve]
self.plotw = self.gWindow.get_plot()
for item in items:
self.plotw.add_item(item)
def __init__(self, *args):
super(ResponseDlg, self).__init__(*args)
self.setupUi(self)
self.plotWidget.plot.set_axis_limits('left', 0, 2)
self.plotWidget.plot.set_axis_limits('bottom', 1, 400)
self.plotWidget.plot.set_axis_title('bottom', 'freq [Hz]')
self.plotWidget.plot.set_axis_scale('bottom', 'log')
self.plotWidget.register_all_curve_tools()
self.resultTrace = make.curve([], [], 'Spectrum', color='b')
self.plotWidget.plot.add_item(self.resultTrace)
self.upper_limit = make.curve([], [], 'UL', color='r')
self.plotWidget.plot.add_item(self.upper_limit)
self.lower_limit = make.curve([], [], 'LL', color='r')
self.plotWidget.plot.add_item(self.lower_limit)
self.rng = make.range(0, 0)
self.math = make.computations(self.rng, "TR",
[(self.resultTrace, "total=%.2f", lambda x, y: (y**2).sum()**.5)])
self.plotWidget.plot.add_item(self.rng)
self.plotWidget.plot.add_item(self.math)
self.hide_total_range()
def add_epochs(plot, epochs, units=None):
""" Add Epoch markers to a guiqwt plot.
:param plot: The plot object.
:type plot: :class:`guiqwt.baseplot.BasePlot`
:param sequence epochs: The epochs (neo :class:`neo.Epoch` objects).
:param units: The x-scale of the plot. If this is ``None``,
the time unit of the events will be use.
"""
for e in epochs:
if units:
start = e.time.rescale(units)
end = (e.time + e.duration).rescale(units)
else:
start = e.time
end = e.time + e.duration
time = (start + end) / 2.0
o = make.range(start, end)
o.setTitle(e.label)
o.set_readonly(True)
o.set_movable(False)
o.set_resizable(False)
o.set_selectable(False)
o.set_rotatable(False)
plot.add_item(o)
nameObject = _MarkerName(e.label)
plot.add_item(
make.marker((time, 0),
nameObject.get_name,
movable=False,
markerstyle='|',
color='k',
linestyle='NoPen',
linewidth=1))
def __init__(self, fit):
Plot.__init__(self, fit)
self.layout = QtWidgets.QVBoxLayout(self)
self.source = fit.surface
self.d1 = np.array([0.0])
self.d2 = np.array([0.0])
top_left = QtWidgets.QFrame(self)
top_left.setMaximumHeight(150)
top_left.setFrameShape(QtWidgets.QFrame.StyledPanel)
topl = QtWidgets.QVBoxLayout(top_left)
top_right = QtWidgets.QFrame(self)
top_right.setMaximumHeight(150)
top_right.setFrameShape(QtWidgets.QFrame.StyledPanel)
topr = QtWidgets.QVBoxLayout(top_right)
bottom = QtWidgets.QFrame(self)
bottom.setFrameShape(QtWidgets.QFrame.StyledPanel)
bot = QtWidgets.QVBoxLayout(bottom)
splitter1 = QtWidgets.QSplitter(QtCore.Qt.Horizontal)
splitter1.addWidget(top_left)
splitter1.addWidget(top_right)
splitter2 = QtWidgets.QSplitter(QtCore.Qt.Vertical)
splitter2.addWidget(splitter1)
splitter2.addWidget(bottom)
self.splitter = splitter2
self.layout.addWidget(splitter2)
# x-axis
win = CurveDialog()
self.g_xplot = win.get_plot()
self.g_xhist_m = make.histogram([], color='#ff00ff')
self.g_xhist_a = make.histogram([], color='#6f0000')
self.g_xplot.add_item(self.g_xhist_a)
self.g_xplot.add_item(self.g_xhist_m)
topl.addWidget(self.g_xplot)
# y-axis
win = CurveDialog()
self.g_yplot = win.get_plot()
self.g_yhist_m = make.histogram([], color='#00ff00')
self.g_yhist_a = make.histogram([], color='#006600')
self.g_yplot.add_item(self.g_yhist_a)
self.g_yplot.add_item(self.g_yhist_m)
topr.addWidget(self.g_yplot)
# 2D-Histogram
self.g_hist2d_m = make.histogram2D(np.array([0.0, 0.0]), np.array([0.0, 0.0]), logscale=True)
self.g_hist2d_a = make.histogram2D(np.array([0.0, 0.0]), np.array([0.0, 0.0]), logscale=True)
self.g_hist2d_m.set_color_map('hot')
self.g_hist2d_a.set_color_map('Blues')
#self.g_hist2d_m.set_interpolation(INTERP_LINEAR)
#self.g_hist2d_a.set_interpolation(INTERP_LINEAR)
win = ImageDialog(edit=False, toolbar=False)
self.g_xyplot = win.get_plot()
self.g_xyplot.set_aspect_ratio(lock=False)
self.g_xyplot.add_item(self.g_hist2d_a)
self.g_xyplot.add_item(self.g_hist2d_m)
bot.addWidget(win)
#selection
self.selection_x = make.range(.25, .5)
self.g_xplot.add_item(self.selection_x)
self.selection_y = make.range(.25, .5)
self.g_yplot.add_item(self.selection_y)
self.pltControl = SurfacePlotWidget(self)
self.widgets = [self.pltControl]
def refresh(self, slider_value=None):
"""Refresh Fit Tool dialog box"""
# Update button states
enable = self.x is not None and self.y is not None \
and self.x.size > 0 and self.y.size > 0 \
and self.fitfunc is not None and self.fitparams is not None \
and len(self.fitparams) > 0
for btn in self.button_list:
btn.setEnabled(enable)
if not enable:
# Fit widget is not yet configured
return
plot = self.get_plot()
if self.xrange is None:
self.xrange = make.range(0., 1.)
plot.add_item(self.xrange)
self.xrange.set_range(self.autofit_prm.xmin, self.autofit_prm.xmax)
self.xrange.setVisible(self.show_xrange)
if self.data_curve is None:
self.data_curve = make.curve([], [],
_("Data"),
color="b",
marker='o',
markerfacecolor='w',
markersize=8,
linestyle='DashLine',
linewidth=1.)
plot.add_item(self.data_curve)
self.data_curve.set_data(self.x, self.y)
colors = [u'#d4f200', u'#00e639', u'#009fff', u'#7900f2']
for i in xrange(self._idxlen):
if len(self.fit_curves) < self._idxlen:
self.fit_curves.append(
make.curve([], [],
_('%s' % self.fitnames[i]),
color=colors[i % len(colors)],
linestyle='--',
linewidth=2))
plot.add_item(self.fit_curves[-1])
fitargs, fitkwargs = self.get_fitfunc_arguments()
yfit = self.fitfunc[i](self.x,
*([p.value for p in self.fitparams[i]] +
list(fitargs)), **fitkwargs)
if plot.get_scales()[1] == 'log':
self.fit_curves[i].set_data(self.x[yfit > 1e-80],
yfit[yfit > 1e-80])
else:
self.fit_curves[i].set_data(self.x, yfit)
self.get_itemlist_panel().show()
if self.fit_curve is not None:
self.fit_curve.curveparam.line.color = self.fit_curves[
self.idx].curveparam.line.color
self.fit_curve.curveparam.line.style = 'DashLine'
self.fit_curve = self.fit_curves[self.idx]
self.fit_curve.curveparam.line.color = u'#ff0000'
self.fit_curve.curveparam.line.style = 'SolidLine'
[item.update_params() for item in self.fit_curves]
if self.legend is None:
self.legend = make.legend(anchor=self.legend_anchor)
plot.add_item(self.legend)
plot.set_antialiasing(False)
plot.replot()
def process(self):
params = {}
for i in range(self.tableWidget.rowCount()):
a = self.tableWidget.item(i, 0)
b = self.tableWidget.item(i, 1)
params[str(a.text())] = int(b.text())
print "params", params
outpN = self.tableWidget.item(0, 0)
#params = {"none": "none", "output_length": outpN}
r = np.array(range(len(self.dc.dimrecprocData))).reshape(len(self.dc.dimrecprocData), 1)
s = np.array(self.dc.dimrecprocData).reshape(len(self.dc.dimrecprocData), 1)
rs = np.hstack((s, s))
labels = self.fec.extractFeature(rs, params)
print labels
self.plot.del_all_items()
self.plot.replot()
self.plot.add_item(
make.curve(range(0, len(self.dc.dimrecprocData)), self.dc.dimrecprocData))
from guiqwt.styles import AnnotationParam
i = 0
i_beg = 0
i_end = 0
while i < len(labels):
cur = labels[i_end]
if i < len(labels) - 1:
if labels[i_end + 1] != cur:
i_end = i
from guiqwt.annotations import AnnotatedRectangle
param = AnnotationParam()
param.title = str(labels[int(i_beg)])
param.show_computations = False
anno = AnnotatedRectangle(r[int(i_beg)], self.dc.dimrecprocData[int(i_beg)], int(i_end), self.dc.dimrecprocData[r[int(i_end)]], param) #TODO: y axis scaling
self.plot.add_item(anno)
i_beg = i_end
print "s1"
else:
i_end = i
print "s2"
print "s3"
print "s4", i_end, len(labels)
i += 1
self.rangeSelection = make.range(-2, 2)
disp0 = make.range_info_label(self.rangeSelection, 'BR', u"x = %.1f +- %.1f cm",
title="Range infos")
#self.plot.add_item(self.rangeSelection)
#self.plot.add_item(disp0)
#self.dc.setCurrentDataFlowObject(self.flowData)
self.dc.featureexData = self.flowData
self.dc.setCurrentLabels(labels)
print(self.dc.setCurrentLabels)
#ToDo: Check that following line, make property in data controller
self.dc.dimrecprocData = rs
self.plot.replot()
self.currentdata = np.reshape(labels, (-1, 1))
def refresh(self,):
t_start = self.hspinbox.value()
visible = self.hzspinbox.value()
t_stop = t_start + visible
# signals
if self.analogsignals:
for i, analist in enumerate(self.analogsignals):
for p, ana in enumerate(analist):
curve = self.signal_curves[i][p]
if t_start>=ana.t_start+ana.signal.size/ana.sampling_rate:
curve.set_data([ ], [ ])
elif t_stop<ana.t_start:
curve.set_data([ ], [ ])
else:
ind_start = (t_start-ana.t_start)*ana.sampling_rate
ind_stop = (t_stop-ana.t_start)*ana.sampling_rate
if ind_start<0: ind_start=0
if ind_stop>ana.signal.size: ind_stop=ana.signal.size
curve.set_data(ana.t()[ind_start:ind_stop], ana.signal[ind_start:ind_stop])
# spiketrains on signal
if self.spiketrains:
for i, analist in enumerate(self.analogsignals):
for j in range(len(self.spiketrains[i])):
sptr = self.spiketrains[i][j]
st = sptr.spike_times[(sptr.spike_times>=t_start) & (sptr.spike_times<t_stop)]
for p in range(self.nplot):
ana = self.analogsignals[i][p]
ind = ((st - ana.t_start)*ana.sampling_rate).astype('i')
curve = self.spike_curves[p][i][j]
curve.set_data( st, ana.signal[ind])
# slection of spikes
if self.selectedspikes:
for i, selectedspike in enumerate(self.selectedspikes):
st = selectedspike.spike_times
st = st[(st>=t_start) & (st<t_stop)]
for p in range(self.nplot):
ana = self.analogsignals[i][p]
ind = ((st - ana.t_start)*ana.sampling_rate).astype('i')
curve = self.selectedspikes_curves[p][i]
#~ print 'i p', i, p, st.size
curve.set_data( st, ana.signal[ind])
for vlimit in self.vlimits:
for p in range(self.nplot):
if (vlimit>t_start) and (vlimit<t_stop):
r = make.range(vlimit, vlimit)
self.plots[p].add_item(r)
for p in range(self.nplot):
plot = self.plots[p]
xaxis, yaxis = plot.get_active_axes()
plot.setAxisScale(xaxis, t_start, t_stop )
plot.setAxisScale(yaxis, self.y_min , self.y_max )
plot.replot()
def addBounds(self):
self.xRange = make.range(0., 1.)
self.plot.add_item(self.xRange)
def refresh(self, ):
t_start = self.hspinbox.value()
visible = self.hzspinbox.value()
t_stop = t_start + visible
# signals
if self.analogsignals:
for i, analist in enumerate(self.analogsignals):
for p, ana in enumerate(analist):
curve = self.signal_curves[i][p]
if t_start >= ana.t_start + ana.signal.size / ana.sampling_rate:
curve.set_data([], [])
elif t_stop < ana.t_start:
curve.set_data([], [])
else:
ind_start = (t_start - ana.t_start) * ana.sampling_rate
ind_stop = (t_stop - ana.t_start) * ana.sampling_rate
if ind_start < 0: ind_start = 0
if ind_stop > ana.signal.size:
ind_stop = ana.signal.size
curve.set_data(ana.t()[ind_start:ind_stop],
ana.signal[ind_start:ind_stop])
# spiketrains on signal
if self.spiketrains:
for i, analist in enumerate(self.analogsignals):
for j in range(len(self.spiketrains[i])):
sptr = self.spiketrains[i][j]
st = sptr.spike_times[(sptr.spike_times >= t_start)
& (sptr.spike_times < t_stop)]
for p in range(self.nplot):
ana = self.analogsignals[i][p]
ind = ((st - ana.t_start) *
ana.sampling_rate).astype('i')
curve = self.spike_curves[p][i][j]
curve.set_data(st, ana.signal[ind])
# slection of spikes
if self.selectedspikes:
for i, selectedspike in enumerate(self.selectedspikes):
st = selectedspike.spike_times
st = st[(st >= t_start) & (st < t_stop)]
for p in range(self.nplot):
ana = self.analogsignals[i][p]
ind = ((st - ana.t_start) * ana.sampling_rate).astype('i')
curve = self.selectedspikes_curves[p][i]
#~ print 'i p', i, p, st.size
curve.set_data(st, ana.signal[ind])
for vlimit in self.vlimits:
for p in range(self.nplot):
if (vlimit > t_start) and (vlimit < t_stop):
r = make.range(vlimit, vlimit)
self.plots[p].add_item(r)
for p in range(self.nplot):
plot = self.plots[p]
xaxis, yaxis = plot.get_active_axes()
plot.setAxisScale(xaxis, t_start, t_stop)
plot.setAxisScale(yaxis, self.y_min, self.y_max)
plot.replot()