def analyze(data):
""" Do all the analysis that's needed to create the interface """
prepared, centre = preparedata(data)
xx, yy, dx, dy, angle = fastfit.d4s(prepared)
xx += centre[0]
yy += centre[1]
xr, yr = fastfit.getellipse(xx, yy, dx, dy, angle)
# fix axes calculation so no more -1 is needed
angle *= -1
adeg = "%.1f deg" %(angle / np.pi * 180)
xxx = [xx - dx/2*np.cos(angle), xx + dx/2*np.cos(angle)]
xxy = [yy + dx/2*np.sin(angle), yy - dx/2*np.sin(angle)]
yyx = [xx + dy/2*np.sin(angle), xx - dy/2*np.sin(angle)]
yyy = [yy + dy/2*np.cos(angle), yy - dy/2*np.cos(angle)]
xwidth = (dx*np.cos(angle)**2 + dy*np.sin(angle)**2)/4.0
ywidth = (dx*np.sin(angle)**2 + dy*np.cos(angle)**2)/4.0
try:
xc = int(np.round(xx))
yc = int(np.round(yy))
except ValueError:
xc = 320
yc = 240
sy, sx = data.shape
xcut = data[yc, :]
ycut = data[:, xc]
xline = range(0, sx)
yline = range(0, sy)
xcutg = gauss(xline, xx, xwidth, max(xcut))
ycutg = gauss(yline, yy, ywidth, max(ycut))
return (xx, yy, dx, dy, angle, xr, yr, adeg, xxx, xxy, yyx, yyy, xwidth, ywidth, xc, yc, xcut, ycut, xcutg, ycutg)
def fitgaussian(data):
"""Returns (height, x, y, width_x, width_y)
the gaussian parameters of a 2D distribution found by a fit"""
xx, yy, dx, dy, angle = fastfit.d4s(data)
# For some reason x and y are exchanged
params = [np.max(data), yy, xx, dy/4, dx/4, -angle, np.min(data)]
errorfunction = lambda p: np.ravel(gaussian(*p)(*np.indices(data.shape)) -
data)
try:
p, success = optimize.leastsq(errorfunction, params, maxfev=90)
except: # This should be triggered normally by KeyboardInterrupt but optimize throws different error that's hard to catch
p = None # to propagate stop signal upstream
return p
def preparedata(data):
""" Prepare data for processing by cutting down on the area for D4s
Input:
------
data: original reading values in numpy array from
Output:
------
prepared: data in the cropped area
(xbottom, ybottom): value of the starting corner in two dimension
"""
dh, dw = data.shape
border = np.array([])
border = np.append(border, np.ravel(data[:-1, 0]))
border = np.append(border, np.ravel(data[0, 1:]))
border = np.append(border, np.ravel(data[1:, -1]))
border = np.append(border, np.ravel(data[-1, :-1]))
borderavg, bordervar = np.mean(border), np.var(border)
slimdata = np.copy(data) - borderavg
maxy, maxx = np.unravel_index(np.argmax(slimdata), slimdata.shape)
# Find the number of points in the x/y direction that are above the background noise
xl = slimdata[:, maxx]
xdim = sum(sum([xl > 10*bordervar]))
yl = slimdata[maxy, :]
ydim = sum(sum([yl > 10*bordervar]))
dim = 2 * max(xdim, ydim)
if dim < 5:
# In this case, most likely we don't have a peak, just noise, since
# very few points are over the noiselevel
return data, (0, 0)
xbottom, xtop = limits(maxx, dim, (0, dw))
ybottom, ytop = limits(maxy, dim, (0, dh))
testdata = slimdata[ybottom:ytop, xbottom:xtop]
xx, yy, dx, dy, angle = fastfit.d4s(testdata)
xc = int(xbottom+xx)
yc = int(ybottom+yy)
limr = 0.69
limx = int(dx * limr)
limy = int(dy * limr)
xbottom, xtop = limits(xc, limx, (0, dw))
ybottom, ytop = limits(yc, limy, (0, dh))
prepared = slimdata[ybottom:ytop, xbottom:xtop]
return prepared, (xbottom, ybottom)
def analyze(data):
""" Do all the analysis that's needed to create the interface """
prepared, centre = interface.preparedata(data)
xx, yy, dx, dy, angle = fastfit.d4s(prepared)
xx += centre[0]
yy += centre[1]
try:
outparams = gaussfit.fitgaussian(prepared)
# Order somehow changed between x and y
if outparams is None:
return None
(a, y, x, ddy, ddx, angle, b) = outparams
dx = 4*ddx
dy = 4*ddy
print "Gaussian fit"
except (ValueError):
print "D4s fit"
pass
xr, yr = fastfit.getellipse(xx, yy, dx, dy, angle)
# fix axes calculation so no more -1 is needed
angle *= -1
adeg = "%.1f deg" %(angle / np.pi * 180)
xxx = [xx - dx/2*np.cos(angle), xx + dx/2*np.cos(angle)]
xxy = [yy + dx/2*np.sin(angle), yy - dx/2*np.sin(angle)]
yyx = [xx + dy/2*np.sin(angle), xx - dy/2*np.sin(angle)]
yyy = [yy + dy/2*np.cos(angle), yy - dy/2*np.cos(angle)]
xwidth = (dx*np.cos(angle)**2 + dy*np.sin(angle)**2)/4.0
ywidth = (dx*np.sin(angle)**2 + dy*np.cos(angle)**2)/4.0
try:
xc = int(np.round(xx))
yc = int(np.round(yy))
except ValueError:
xc = 320
yc = 240
xcut = data[yc, :]
ycut = data[:, xc]
xline = range(0, sx)
yline = range(0, sy)
xcutg = gauss(xline, xx, xwidth, max(xcut))
ycutg = gauss(yline, yy, ywidth, max(ycut))
return (xx, yy, dx, dy, angle, xr, yr, adeg, xxx, xxy, yyx, yyy, xwidth, ywidth, xc, yc, xcut, ycut, xcutg, ycutg)
