def _analyse_image(self):
if self._roi is not None:
try:
x, y, w, h, = self._roi
roi_array = self._last_frame[x:x + w, y:y + h]
except Exception as err:
roi_array = self._last_frame
x, y = 0, 0
else:
roi_array = self._last_frame
x, y = 0, 0
self.sum = np.sum(roi_array)
try:
roi_extrema = scipymeasure.extrema(roi_array)
except Exception as err:
roi_extrema = (0, 0, (0, 0), (0, 0))
self.max_i = roi_extrema[1]
self.max_x = roi_extrema[3][0] + x
self.max_y = roi_extrema[3][1] + y
try:
roi_com = scipymeasure.center_of_mass(roi_array)
except Exception as err:
roi_com = (0, 0)
self.com_x = roi_com[0] + x
self.com_y = roi_com[1] + y
self.fwhm_x = FWHM(np.sum(roi_array, axis=0))
self.fwhm_y = FWHM(np.sum(roi_array, axis=1))
def getLMRectStats(self, rect):
xx1, xx2, yy1, yy2 = self._lmRectToPix(rect)
if xx1 is not None:
subset = self.image.image()[xx1:xx2, yy1:yy2]
subset, mask = self.image.optimalRavel(subset)
mmin, mmax = measurements.extrema(subset, labels=mask, index=None if mask is None else False)[:2]
mean = measurements.mean(subset, labels=mask, index=None if mask is None else False)
std = measurements.standard_deviation(subset, labels=mask, index=None if mask is None else False)
ssum = measurements.sum(subset, labels=mask, index=None if mask is None else False)
return xx1, xx2, yy1, yy2, mmin, mmax, mean, std, ssum, subset.size
return None
def dataMinMax (self):
if not self._dataminmax:
rdata,rmask = self.optimalRavel(self._data);
dprint(3,"computing data min/max");
try:
self._dataminmax = measurements.extrema(rdata,labels=rmask,index=None if rmask is None else False);
except:
# when all data is masked, some versions of extrema() throw an exception
self._dataminmax = numpy.nan,numpy.nan;
dprint(3,self._dataminmax);
return self._dataminmax;
def imageMinMax(self):
if not self._imgminmax:
dprint(3, "computing image min/max")
rdata, rmask = self.optimalRavel(self._image)
try:
self._imgminmax = measurements.extrema(rdata, labels=rmask, index=None if rmask is None else False)[:2]
except:
# when all data is masked, some versions of extrema() throw an exception
self._imgminmax = numpy.nan, numpy.nan
dprint(3, self._imgminmax)
return self._imgminmax
def dataMinMax(self):
if not self._dataminmax:
rdata, rmask = self.optimalRavel(self._data)
dprint(3, "computing data min/max")
try:
self._dataminmax = measurements.extrema(rdata, labels=rmask, index=None if rmask is None else False)
except:
# when all data is masked, some versions of extrema() throw an exception
self._dataminmax = numpy.nan, numpy.nan
dprint(3, self._dataminmax)
return self._dataminmax
def _resetDisplaySubset (self,subset,desc,range=None,set_display_range=True,write_config=True,subset_type=None):
dprint(4,"setting display subset");
self._displaydata = subset;
self._displaydata_desc = desc;
self._displaydata_minmax = range = range or measurements.extrema(subset)[:2];
self._displaydata_type = subset_type;
dprint(4,"range set");
self.image.intensityMap().setDataSubset(self._displaydata,minmax=range);
self.image.setIntensityMap(emit=False);
self.emit(SIGNAL("dataSubsetChanged"),subset,range,desc,subset_type);
if set_display_range:
self.setDisplayRange(write_config=write_config,*range);
def _resetDisplaySubset(self, subset, desc, range=None, set_display_range=True, write_config=True,
subset_type=None):
dprint(4, "setting display subset")
self._displaydata = subset
self._displaydata_desc = desc
self._displaydata_minmax = range = range or measurements.extrema(subset)[:2]
self._displaydata_type = subset_type
dprint(4, "range set")
self.image.intensityMap().setDataSubset(self._displaydata, minmax=range)
self.image.setIntensityMap(emit=False)
self.emit(SIGNAL("dataSubsetChanged"), subset, range, desc, subset_type)
if set_display_range:
self.setDisplayRange(write_config=write_config, *range)
def _updateHistogram (self,hmin=None,hmax=None):
"""Recomputes histogram. If no arguments, computes full histogram for
data subset. If hmin/hmax is specified, computes zoomed-in histogram.""";
busy = BusyIndicator();
self._prev_range = self._display_range;
dmin,dmax = self._subset_range;
hmin0,hmax0 = dmin,dmax;
if hmin0 >= hmax0:
hmax0 = hmin0+1;
subset,mask = self.image.optimalRavel(self._subset);
# compute full-subset hi-res histogram, if we don't have one (for percentile stats)
if self._hist_hires is None:
dprint(1,"computing histogram for full subset range",hmin0,hmax0);
self._hist_hires = measurements.histogram(subset,hmin0,hmax0,self.NumHistBinsHi,labels=mask,index=None if mask is None else False);
self._hist_bins_hires = hmin0 + (hmax0-hmin0)*(numpy.arange(self.NumHistBinsHi)+0.5)/float(self.NumHistBinsHi);
self._hist_binsize_hires = (hmax0-hmin0)/self.NumHistBins;
# if hist limits not specified, then compute lo-res histogram based on the hi-res one
if hmin is None:
hmin,hmax = hmin0,hmax0;
# downsample to low-res histogram
self._hist = self._hist_hires.reshape((self.NumHistBins,self.NumHistBinsHi/self.NumHistBins)).sum(1);
else:
# zoomed-in low-res histogram
# bracket limits at subset range
hmin,hmax = max(hmin,dmin),min(hmax,dmax);
if hmin >= hmax:
hmax = hmin+1;
dprint(1,"computing histogram for",self._subset.shape,self._subset.dtype,hmin,hmax);
self._hist = measurements.histogram(subset,hmin,hmax,self.NumHistBins,labels=mask,index=None if mask is None else False);
dprint(1,"histogram computed");
# compute bins
self._itf_bins = hmin + (hmax-hmin)*(numpy.arange(self.NumItfBins))/(float(self.NumItfBins)-1);
self._hist_bins = hmin + (hmax-hmin)*(numpy.arange(self.NumHistBins)+0.5)/float(self.NumHistBins);
# histogram range and position of peak
self._hist_range = hmin,hmax;
self._hist_min,self._hist_max,self._hist_imin,self._hist_imax = measurements.extrema(self._hist);
self._hist_peak = self._hist_bins[self._hist_imax];
# set controls accordingly
if dmin >= dmax:
dmax = dmin+1;
zoom = math.log10((dmax-dmin)/(hmax-hmin));
self._whistzoom.setValue(zoom);
self._whistunzoom.setEnabled(zoom>0);
self._whistzoomout.setEnabled(zoom>0);
# reset scales
self._histplot.setAxisScale(QwtPlot.xBottom,hmin,hmax);
self._histplot.setAxisScale(QwtPlot.yRight,0,1+self.ColorBarHeight);
# update curves
# call _setHistLogScale() (with current setting) to update axis scales and set data
self._setHistLogScale(self._ylogscale,replot=False);
# set plot lines
self._line_0.line.setData([0,0],[0,1]);
self._line_0.marker.setValue(0,0);
self._line_maxbin.line.setData([self._hist_peak,self._hist_peak],[0,1]);
self._line_maxbin.marker.setValue(self._hist_peak,0);
self._line_maxbin.setText(("max bin:"+DataValueFormat)%self._hist_peak);
# set half-max line
self._line_halfmax.line.setData(self._hist_range,[self._hist_max/2,self._hist_max/2]);
self._line_halfmax.marker.setValue(hmin,self._hist_max/2);
# update ITF
self._updateITF();
def getDataRange(self, data):
"""Returns the set data range, or uses data min/max if it is not set"""
# use data min/max if no explicit ranges are set
return self.range or measurements.extrema(data)[:2]
def _updateHistogram(self, hmin=None, hmax=None):
"""Recomputes histogram. If no arguments, computes full histogram for
data subset. If hmin/hmax is specified, computes zoomed-in histogram."""
busy = BusyIndicator()
self._prev_range = self._display_range
dmin, dmax = self._subset_range
hmin0, hmax0 = dmin, dmax
if hmin0 >= hmax0:
hmax0 = hmin0 + 1
subset, mask = self.image.optimalRavel(self._subset)
# compute full-subset hi-res histogram, if we don't have one (for percentile stats)
if self._hist_hires is None:
dprint(1, "computing histogram for full subset range", hmin0, hmax0)
self._hist_hires = measurements.histogram(subset, hmin0, hmax0, self.NumHistBinsHi, labels=mask,
index=None if mask is None else False)
self._hist_bins_hires = hmin0 + (hmax0 - hmin0) * (numpy.arange(self.NumHistBinsHi) + 0.5) / float(
self.NumHistBinsHi)
self._hist_binsize_hires = (hmax0 - hmin0) / self.NumHistBins
# if hist limits not specified, then compute lo-res histogram based on the hi-res one
if hmin is None:
hmin, hmax = hmin0, hmax0
# downsample to low-res histogram
self._hist = self._hist_hires.reshape((self.NumHistBins, self.NumHistBinsHi / self.NumHistBins)).sum(1)
else:
# zoomed-in low-res histogram
# bracket limits at subset range
hmin, hmax = max(hmin, dmin), min(hmax, dmax)
if hmin >= hmax:
hmax = hmin + 1
dprint(1, "computing histogram for", self._subset.shape, self._subset.dtype, hmin, hmax)
self._hist = measurements.histogram(subset, hmin, hmax, self.NumHistBins, labels=mask,
index=None if mask is None else False)
dprint(1, "histogram computed")
# compute bins
self._itf_bins = hmin + (hmax - hmin) * (numpy.arange(self.NumItfBins)) / (float(self.NumItfBins) - 1)
self._hist_bins = hmin + (hmax - hmin) * (numpy.arange(self.NumHistBins) + 0.5) / float(self.NumHistBins)
# histogram range and position of peak
self._hist_range = hmin, hmax
self._hist_min, self._hist_max, self._hist_imin, self._hist_imax = measurements.extrema(self._hist)
self._hist_peak = self._hist_bins[self._hist_imax]
# set controls accordingly
if dmin >= dmax:
dmax = dmin + 1
zoom = math.log10((dmax - dmin) / (hmax - hmin))
self._whistzoom.setValue(zoom)
self._whistunzoom.setEnabled(zoom > 0)
self._whistzoomout.setEnabled(zoom > 0)
# reset scales
self._histplot.setAxisScale(QwtPlot.xBottom, hmin, hmax)
self._histplot.setAxisScale(QwtPlot.yRight, 0, 1 + self.ColorBarHeight)
# update curves
# call _setHistLogScale() (with current setting) to update axis scales and set data
self._setHistLogScale(self._ylogscale, replot=False)
# set plot lines
self._line_0.line.setData([0, 0], [0, 1])
self._line_0.marker.setValue(0, 0)
self._line_maxbin.line.setData([self._hist_peak, self._hist_peak], [0, 1])
self._line_maxbin.marker.setValue(self._hist_peak, 0)
self._line_maxbin.setText(("max bin:" + DataValueFormat) % self._hist_peak)
# set half-max line
self._line_halfmax.line.setData(self._hist_range, [self._hist_max / 2, self._hist_max / 2])
self._line_halfmax.marker.setValue(hmin, self._hist_max / 2)
# update ITF
self._updateITF()
def getDataRange (self,data): """Returns the set data range, or uses data min/max if it is not set"""; # use data min/max if no explicit ranges are set return self.range or measurements.extrema(data)[:2];
