def doFit(self, data):
dist1 = 0
dist2 = 0
self.x_off1 = 0.0
self.y_off1 = 0.0
self.x_off2 = 0.0
self.y_off2 = 0.0
if self.fit_hl is None:
self.fit_hl = saLPF.LockPeakFinder(offset=5.0,
sigma=self.sigma,
threshold=10)
self.fit_hr = saLPF.LockPeakFinder(offset=5.0,
sigma=self.sigma,
threshold=10)
total_good = 0
[x1, y1, status] = self.fit_hl.findFitPeak(data[:, :self.half_x])
if status:
total_good += 1
self.x_off1 = x1 - self.half_y
self.y_off1 = y1 - self.half_x
dist1 = abs(self.y_off1)
[x2, y2, status] = self.fit_hr.findFitPeak(data[:, -self.half_x:])
if status:
total_good += 1
self.x_off2 = x2 - self.half_y
self.y_off2 = y2
dist2 = abs(self.y_off2)
return [total_good, dist1, dist2]
def __init__(self, parameters=None, **kwds):
kwds["parameters"] = parameters
super().__init__(**kwds)
self.cnt = 0
self.max_backlog = 20
self.min_good = parameters.get("min_good")
self.offset = parameters.get("offset")
self.reps = parameters.get("reps")
self.sum_scale = parameters.get("sum_scale")
self.sum_zero = parameters.get("sum_zero")
self.good = numpy.zeros(self.reps, dtype=numpy.bool)
self.mag = numpy.zeros(self.reps)
self.x_off = numpy.zeros(self.reps)
self.y_off = numpy.zeros(self.reps)
self.lpf = slpf.LockPeakFinder(offset=0,
sigma=parameters.get("sigma"),
threshold=parameters.get("threshold"))
assert (self.reps >= self.min_good), "'reps' must be >= 'min_good'."