def arcstraight(data, xarr, istart, function='poly', order=3,
rstep=1, y1=None, y2=None, sigma=5, sections=3, niter=5,
log=None, verbose=True):
"""For a given image, assume that the line given by istart is the fiducial and then calculate
the transformation between each line and that line in order to straighten the arc
returns Wavlenght solution
"""
#set up the edges
if y1 is None: y1=0
if y2 is None: y2=data.shape[0]
# extract the central row
ys, xs = data.shape
farr = data[istart,:]
xp, xf = st.findpoints(xarr, farr, sigma, niter, sections=sections)
#short function to return closest peak
def get_closest_x(y_arr, i):
y = np.arange(len(y_arr))
j = (abs(y[y_arr >0] - i)).argmin()
return y_arr[y[y_arr >0][j]]
#loop through all lines and rows to trace the lines
line_dict = {}
yc = int(ys/2.0)
xdiff = 5
for x in xp:
y_arr = np.zeros(ys)
y_arr[yc] = x
for i in range(1, yc):
if yc+i < y2:
y_arr[yc+i] = st.mcentroid(xarr, data[yc+i,:], kern=st.default_kernal,
xdiff=xdiff, xc=get_closest_x(y_arr, yc+i))
if yc+i > y1:
y_arr[yc-i] = st.mcentroid(xarr, data[yc-i,:], kern=st.default_kernal,
xdiff=xdiff, xc=get_closest_x(y_arr, yc-i))
line_dict[x] = y_arr
#find the solution for each row
iws={}
xp = np.array(line_dict.keys())
for i in range(y1, y2):
wp = [line_dict[x][i] for x in xp]
ws = WavelengthSolution.WavelengthSolution(np.array(wp), xp, order=3, function='polynomial')
ws.fit()
exit()
iws[i] = ws
return iws
def addclosestline(self, x):
"""Find the closes line to the centroided position and
add it
"""
cx = st.mcentroid(self.xarr, self.farr, xc=x, xdiff=self.mdiff)
w = self.ws.value(cx)
d = abs(self.slines - w)
w = self.slines[d.argmin()]
self.xp.append(x)
self.wp.append(w)
self.plotFeatures()
self.redraw_canvas()
def onKeyPress(self, event):
"""Emit signal on key press"""
if event.key=='?':
#return the help file
self.help()
elif event.key=='q':
#exit the task
self.emit(QtCore.SIGNAL("quit()"))
elif event.key=='c':
#return the centroid
if event.xdata:
self.log.message(str(event.xdata), with_header=False)
cx=st.mcentroid(self.xarr, self.farr, xc=event.xdata, xdiff=self.mdiff)
self.emit(QtCore.SIGNAL("updatex(float)"), cx)
elif event.key=='x':
#return the x position
if event.xdata:
self.log.message(str(event.xdata), with_header=False)
self.emit(QtCore.SIGNAL("updatex(float)"), event.xdata)
elif event.key=='R':
#reset the fit
self.reset()
elif event.key=='f':
#find the fit
self.findfit()
self.emit(QtCore.SIGNAL("fitUpdate()"))
elif event.key=='b':
#auto-idenitfy features
self.isFeature=True
self.findfeatures()
elif event.key=='z':
#Assume the solution is correct and find the zeropoint
#that best matches it from cross correllation
self.findzp()
elif event.key=='Z':
#Assume the solution is correct and find the zeropoint
#that best matches it from cross correllation
self.findzpd()
elif event.key=='e':
#find closest feature from existing fit and line list
#and match it
pass
elif event.key=='i':
#reset identified features
pass
elif event.key=='t':
#reset identified features
self.isFeature=True
self.testfeatures()
elif event.key=='l':
#plot the features from existing list
if self.isFeature:
self.isFeature=False
self.redraw_canvas()
else:
self.isFeature=True
self.plotFeatures()
self.redraw_canvas()
elif event.key=='p':
#print information about features
for i in range(len(self.xp)):
print self.xp[i], self.wp[i]
elif event.key=='P':
#print information about features
print self.ws.coef
elif event.key=='r':
#redraw graph
self.redraw_canvas()
elif event.key=='a':
#draw artificial spectrum
self.isArt= not self.isArt
self.redraw_canvas()
elif event.key=='d':
#Delete feature
save=False
y=None
if event.canvas==self.errfigure:
y=event.ydata
save=True
self.deletepoints(event.xdata, y=y, save=save)
self.redraw_canvas(keepzoom=True)
elif event.key=='u':
#undelete
self.undeletepoints(event.xdata, y=event.ydata)
self.redraw_canvas(keepzoom=True)
elif event.key:
self.emit(QtCore.SIGNAL("keyPressEvent(string)"), event.key)
