def leastsq_integrate(ts, peak_list, f='gaussian'):
# FIXME: transition from t0, t1, params to params (containing 't0'/'t1'
# lookup the peak model function to use for fitting
f = {f.__name__: f for f in peak_models}[f]
peaks = []
for w, peak_list in _get_windows(peak_list):
tr = ts.trace(twin=w)
# if there's location info, we can use this for
# our inital params
if 'x' in peak_list[0][2]:
# TODO: should fill in other values?
# TODO: should do something with y0 and y1?
initc = [i[2] for i in peak_list]
else:
# TODO: should find peak maxima for rts?
rts = [0.5 * (i[0] + i[1]) for i in peak_list]
initc = guess_initc(tr, f, rts)
params, _ = fit(tr, [f] * len(initc), initc)
for p in params:
pk_ts = Trace(f(tr.times, **p), tr.times)
info = {'name': '{:.2f}'.format(p['x'])}
info['p-create'] = '{},leastsq_integrate'.format(
peak_list[0][2].get('pf', ''))
pk = PeakComponent(info, pk_ts)
peaks.append(pk)
return peaks
def leastsq_integrate(ts, peak_list, f="gaussian"):
# FIXME: transition from t0, t1, params to params (containing 't0'/'t1'
# lookup the peak model function to use for fitting
f = {f.__name__: f for f in peak_models}[f]
peaks = []
for w, peak_list in _get_windows(peak_list):
tr = ts.trace(twin=w)
# if there's location info, we can use this for
# our inital params
if "x" in peak_list[0][2]:
# TODO: should fill in other values?
# TODO: should do something with y0 and y1?
initc = [i[2] for i in peak_list]
else:
# TODO: should find peak maxima for rts?
rts = [0.5 * (i[0] + i[1]) for i in peak_list]
initc = guess_initc(tr, f, rts)
params, _ = fit(tr, [f] * len(initc), initc)
for p in params:
pk_ts = Trace(f(tr.times, **p), tr.times)
info = {"name": "{:.2f}".format(p["x"])}
info["p-create"] = "{},leastsq_integrate".format(peak_list[0][2].get("pf", ""))
pk = PeakComponent(info, pk_ts)
peaks.append(pk)
return peaks
def refit(self, peak_model=None):
# TODO: needs to work with PeakComponents containing DataFrames?
self.info['p-model'] = peak_model
if peak_model is None:
# TODO: remove parameters from self.info?
# del self.info['p-model-fit']
return
model = peak_models[peak_model]
# remove the baseline and create a new series
t = self._trace.index
d = self._trace.values - self.baseline._retime(t)
ts = Trace(d, t)
# fit the peak
initc = guess_initc(ts, model, [t[d.argmax()]])
params, res = fit(ts, [model], initc)
self.info.update(params[0])
self.info['p-model-fit'] = res['r^2']
def refit(self, peak_model=None):
# TODO: needs to work with PeakComponents containing DataFrames?
self.info['p-model'] = peak_model
if peak_model is None:
# TODO: remove parameters from self.info?
# del self.info['p-model-fit']
return
model = peak_models[peak_model]
# remove the baseline and create a new series
t = self._trace.index
d = self._trace.values - self.baseline._retime(t)
ts = Trace(d, t)
# fit the peak
initc = guess_initc(ts, model, [t[d.argmax()]])
params, res = fit(ts, [model], initc)
self.info.update(params[0])
self.info['p-model-fit'] = res['r^2']