def das(tup, x0, from_t=0.4, uniform_fil=None, plot_result=True, fit_kws=None):
out = namedtuple('das_result', field_names=[
'fitter', 'result', 'minimizer'])
ti = dv.make_fi(tup.t)
if uniform_fil is not None:
tupf = filter.uniform_filter(tup, uniform_fil)
else:
tupf = tup
if fit_kws is None:
fit_kws = {}
import numpy as np
#ct = dv.tup(np.hstack((wl, wl)), tup.t[ti(t0):], np.hstack((pa[ti(t0):, :], se[ti(t0):, :])))
ct = dv.tup(tup.wl, tup.t[ti(from_t):], tupf.data[ti(from_t):, :])
f = fitter.Fitter(ct, model_coh=0, model_disp=0)
f.lsq_method = 'ridge'
kws = dict(full_model=0, lower_bound=0.2, fixed_names=['w'])
kws.update(fit_kws)
lm = f.start_lmfit(x0, **kws)
res = lm.leastsq()
import lmfit
lmfit.report_fit(res)
if plot_result:
plt.figure(figsize=(4, 7))
plt.subplot(211)
if is_montone(f.wl):
monotone = False
# Assume wl is repeated
N = len(f.wl)
else:
monotone = True
N = len(f.wl) // 2
print(N)
l = plt.plot(f.wl[:N], f.c[:N, :], lw=3)
if monotone:
l2 = plt.plot(f.wl[:N], f.c[N:, :], lw=1)
for i, j in zip(l, l2):
j.set_color(i.get_color())
plot_helpers.lbl_spec()
lbls = ['%.1f' % i for i in f.last_para[1:-1]] + ['const']
plt.legend(lbls)
plt.subplot(212)
wi = dv.make_fi(tup.wl)
for i in range(N)[::6]:
l, = plt.plot(tup.t, tupf.data[:, i], '-o', lw=0.7,
alpha=0.5, label='%.1f cm-1' % f.wl[i], mec='None', ms=3)
plt.plot(f.t, f.model[:, i], lw=3, c=l.get_color())
if monotone:
l, = plt.plot(tup.t, tupf.data[:, i+N], '-o', lw=0.7,
alpha=0.5, label='%.1f cm-1' % f.wl[i], mec='None', ms=3)
plt.plot(f.t, f.model[:, i+N], lw=3, c=l.get_color())
plt.xlim(-1)
plt.xscale('symlog', linthreshx=1, linscalex=0.5)
plot_helpers.lbl_trans()
plt.legend(loc='best')
return out(f, res, lm)
def show_widget(self):
fname = QFileDialog.getOpenFileName()
a = np.loadtxt(fname)
w = a[0, 1:]
t = a[1:, 0]
d = a[1:, 1:]
self.tup = tup(w, t, d)
def make_report(fitter, info, raw=None, plot_fastest=1, make_ltm=False):
from skultrafast import zero_finding, dv, plot_funcs
g = fitter
name = info.get('name', '')
solvent = info.get('solvent', '')
excitation = info.get('excitation', '')
add_info = info.get('add_info', '')
title = u"{} in {} excited at {}. {}".format(name, solvent, excitation,
add_info)
plot_funcs.a4_overview(g,
'pics\\' + title + '.png',
title=title,
plot_fastest=plot_fastest)
save_txt_das(name + '_-DAS.txt', g)
save_txt(name + '_ex' + excitation + '_iso.txt', g.wl, g.t, g.data)
save_txt(name + '_ex' + excitation + '_iso_fit.txt', g.wl, g.t, g.model)
dat = zero_finding.interpol(dv.tup(fitter.wl, fitter.t, fitter.data),
fitter.tn, 0.0)
save_txt(name + '_ex' + excitation + '_iso_timecor.txt', *dat)
if make_ltm:
plot_funcs.plot_ltm_page(dat, 'pics\\' + title + 'lft_map.png')
fit = zero_finding.interpol(dv.tup(fitter.wl, fitter.t, fitter.model),
fitter.tn, 0.0)
save_txt(name + '_ex' + excitation + '_iso_fit_timecor.txt', *fit)
if raw:
save_txt(name + '_ex' + excitation + '_raw.txt', *raw)
if hasattr(fitter, 'data_perp'):
perp = zero_finding.interpol(
dv.tup(fitter.wl, fitter.t, fitter.data_perp), fitter.tn, 0.0)
para = zero_finding.interpol(
dv.tup(fitter.wl, fitter.t, fitter.data_para), fitter.tn, 0.0)
#plot_funcs.a4_overview_second_page(fitter, para, perp, 'bla.png')
save_txt(name + '_ex' + excitation + '_para.txt', *para)
save_txt(name + '_ex' + excitation + '_perp.txt', *perp)
import matplotlib.pyplot as plt
plt.close('all')
def interpol(tup, tn, shift=0., new_t=None):
"""
Uses linear interpolation to shift each channcel by given tn.
"""
dat = tup.data
t = tup.t
if new_t is None:
new_t = t
#t_array = np.tile(t.reshape(t.size, 1), (1, dat.shape[1]))
t_array = t[:, None] - tn[None, :]
t_array -= shift
dat_new = np.zeros((new_t.size, dat.shape[1]))
for i in range(dat.shape[1]):
dat_new[:, i] = np.interp(new_t, t_array[:, i], dat[:, i], left=0)
return dv.tup(tup.wl, t, dat_new)
def interpolate_disp(self, polyfunc) -> 'TimeResSpec':
"""
Correct for dispersion by linear interpolation .
Parameters
----------
polyfunc : function
Function which takes wavenumbers and returns time-zeros.
Returns
-------
TimeResSpec
New TimeResSpec where the data is interpolated so that all channels
have the same delay point.
"""
c = self.copy()
zeros = polyfunc(self.wavenumbers)
ntc = zero_finding.interpol(self, zeros)
tmp_tup = dv.tup(self.wavelengths, self.t, self.data)
ntc_err = zero_finding.interpol(tmp_tup, zeros)
c.data = ntc.data
c.err = ntc_err.data
return c
def interpolate_disp(self, polyfunc) -> "TimeResSpec":
"""
Correct for dispersion by linear interpolation .
Parameters
----------
polyfunc : function
Function which takes wavenumbers and returns time-zeros.
Returns
-------
TimeResSpec
New TimeResSpec where the data is interpolated so that all channels
have the same delay point.
"""
c = self.copy()
zeros = polyfunc(self.wavenumbers)
ntc = zero_finding.interpol(self, zeros)
tmp_tup = dv.tup(self.wavelengths, self.t, self.data)
ntc_err = zero_finding.interpol(tmp_tup, zeros)
c.data = ntc.data
c.err = ntc_err.data
return c
def das(tup, x0, from_t=0.4, uniform_fil=None, plot_result=True, fit_kws=None):
out = namedtuple('das_result',
field_names=['fitter', 'result', 'minimizer'])
ti = dv.make_fi(tup.t)
if uniform_fil is not None:
tupf = filter.uniform_filter(tup, uniform_fil)
else:
tupf = tup
if fit_kws is None:
fit_kws = {}
#ct = dv.tup(np.hstack((wl, wl)), tup.t[ti(t0):], np.hstack((pa[ti(t0):, :], se[ti(t0):, :])))
ct = dv.tup(tup.wl, tup.t[ti(from_t):], tupf.data[ti(from_t):, :])
f = fitter.Fitter(ct, model_coh=0, model_disp=0)
f.lsq_method = 'ridge'
kws = dict(full_model=0, lower_bound=0.2, fixed_names=['w'])
kws.update(fit_kws)
lm = f.start_lmfit(x0, **kws)
res = lm.leastsq()
import lmfit
lmfit.report_fit(res)
if plot_result:
plt.figure(figsize=(4, 7))
plt.subplot(211)
if is_montone(f.wl):
monotone = False
# Assume wl is repeated
N = len(f.wl)
else:
monotone = True
N = len(f.wl) // 2
print(N)
l = plt.plot(f.wl[:N], f.c[:N, :], lw=3)
if monotone:
l2 = plt.plot(f.wl[:N], f.c[N:, :], lw=1)
for i, j in zip(l, l2):
j.set_color(i.get_color())
plot_helpers.lbl_spec()
lbls = ['%.1f' % i for i in f.last_para[1:-1]] + ['const']
plt.legend(lbls)
plt.subplot(212)
wi = dv.make_fi(tup.wl)
for i in range(N)[::6]:
l, = plt.plot(tup.t,
tupf.data[:, i],
'-o',
lw=0.7,
alpha=0.5,
label='%.1f cm-1' % f.wl[i],
mec='None',
ms=3)
plt.plot(f.t, f.model[:, i], lw=3, c=l.get_color())
if monotone:
l, = plt.plot(tup.t,
tupf.data[:, i + N],
'-o',
lw=0.7,
alpha=0.5,
label='%.1f cm-1' % f.wl[i],
mec='None',
ms=3)
plt.plot(f.t, f.model[:, i + N], lw=3, c=l.get_color())
plt.xlim(-1)
plt.xscale('symlog', linthreshx=1, linscalex=0.5)
plot_helpers.lbl_trans()
plt.legend(loc='best')
return out(f, res, lm)
def plot_diff(tup, t0, t_list, **kwargs):
diff = tup.data - tup.data[dv.fi(tup.t, t0), :]
plot_spec(dv.tup(tup.wl, tup.t, diff), t_list, **kwargs)
def mean_tup(tup, time):
wl, t, d = tup.wl, tup.t, tup.data
new_dat = tup.data / tup.data[dv.fi(t, time), :]
return dv.tup(wl, t, new_dat)
def fit_exp(self,
x0,
fix_sigma=True,
fix_t0=False,
fix_last_decay=True,
from_t=None,
model_coh=True,
lower_bound=0.1):
"""
Fit a sum of exponentials to the dataset. This function assumes
the two datasets is already corrected for dispersion.
Parameters
----------
x0 : list of floats or array
Starting values of the fit. The first value is the estimate of the
system response time omega. If `fit_t0` is true, the second float is
the guess of the time-zero. All other floats are interpreted as the
guessing values for exponential decays.
fix_sigma : bool (optional)
If to fix the IRF duration sigma.
fix_t0 : bool (optional)
If to fix the the time-zero.
fix_last_decay : bool (optional)
Fixes the value of the last tau of the initial guess. It can be
used to add a constant by setting the last tau to a large value
and fix it.
from_t : float or None
If not None, data with t<from_t will be ignored for the fit.
model_coh : bool (optional)
If coherent contributions should by modeled. If `True` a gaussian
with a width equal the system response time and its derivatives are
added to the linear model.
lower_bound : float (optional)
Lower bound for decay-constants.
"""
pa, pe = self.para, self.perp
if not from_t is None:
pa = pa.cut_times([(-np.inf, from_t)])
pe = pe.cut_times([(-np.inf, from_t)])
all_data = np.hstack((pa.data, pe.data))
all_wls = np.hstack((pa.wavelengths, pe.wavelengths))
all_tup = dv.tup(all_wls, pa.t, all_data)
f = fitter.Fitter(all_tup, model_coh=model_coh, model_disp=1)
f.res(x0)
fixed_names = []
if fix_sigma:
fixed_names.append('w')
lm_model = f.start_lmfit(x0,
fix_long=fix_last_decay,
fix_disp=fix_t0,
lower_bound=lower_bound,
full_model=False,
fixed_names=fixed_names)
ridge_alpha = abs(all_data).max() * 1e-4
f.lsq_method = 'ridge'
fitter.alpha = ridge_alpha
result = lm_model.leastsq()
self.fit_exp_result_ = FitExpResult(lm_model, result, f)
return self.fit_exp_result_
def fit_exp(
self,
x0,
fix_sigma=True,
fix_t0=False,
fix_last_decay=True,
from_t=None,
model_coh=True,
lower_bound=0.1,
):
"""
Fit a sum of exponentials to the dataset. This function assumes
the two datasets is already corrected for dispersion.
Parameters
----------
x0 : list of floats or array
Starting values of the fit. The first value is the estimate of the
system response time omega. If `fit_t0` is true, the second float is
the guess of the time-zero. All other floats are interpreted as the
guessing values for exponential decays.
fix_sigma : bool (optional)
If to fix the IRF duration sigma.
fix_t0 : bool (optional)
If to fix the the time-zero.
fix_last_decay : bool (optional)
Fixes the value of the last tau of the initial guess. It can be
used to add a constant by setting the last tau to a large value
and fix it.
from_t : float or None
If not None, data with t<from_t will be ignored for the fit.
model_coh : bool (optional)
If coherent contributions should by modeled. If `True` a gaussian
with a width equal the system response time and its derivatives are
added to the linear model.
lower_bound : float (optional)
Lower bound for decay-constants.
"""
pa, pe = self.para, self.perp
if not from_t is None:
pa = pa.cut_times([(-np.inf, from_t)])
pe = pe.cut_times([(-np.inf, from_t)])
all_data = np.hstack((pa.data, pe.data))
all_wls = np.hstack((pa.wavelengths, pe.wavelengths))
all_tup = dv.tup(all_wls, pa.t, all_data)
f = fitter.Fitter(all_tup, model_coh=model_coh, model_disp=1)
f.res(x0)
fixed_names = []
if fix_sigma:
fixed_names.append("w")
lm_model = f.start_lmfit(
x0,
fix_long=fix_last_decay,
fix_disp=fix_t0,
lower_bound=lower_bound,
full_model=False,
fixed_names=fixed_names,
)
ridge_alpha = abs(all_data).max() * 1e-4
f.lsq_method = "ridge"
fitter.alpha = ridge_alpha
result = lm_model.leastsq()
self.fit_exp_result_ = FitExpResult(lm_model, result, f)
return self.fit_exp_result_
raw_tn = tup.t[idx]
no_nan = ~np.any(np.isnan(tup.data), 0)
fit, p = robust_fit_tz(tup.wl[no_nan], raw_tn[no_nan], deg)
#dv.subtract_background(tup.data, tup.t, fit, 400)
fit = np.polyval(p, tup.wl)
cor = interpol(tup, fit)
if plot:
from . import plot_funcs as pl
pl._plot_zero_finding(tup, raw_tn, fit, cor)
return cor, fit
if __name__ == '__main__':
a = np.load('SD5039.npz')
w, t, d = a['arr_0']
d -= d[:10,:].mean(0)
d = d[:,20:]
w = w[20:]
t = t
nt, tn = get_tz_cor(dv.tup(w, t, d), use_max)
n = 5
figure(0)
k, j = use_fit(d[:, ::n], t, tn=tn[::n], n=100)
figure(1)
pcolormesh(w, t, d)
plot(w[::n], k[::])
plot(w[::n], tn[::n])
ylim(2, 5)
xlim(w.min(), w.max())
def plot_diff(tup, t0, t_list, **kwargs):
diff = tup.data - tup.data[dv.fi(tup.t, t0), :]
plot_spec(dv.tup(tup.wl, tup.t, diff), t_list, **kwargs)
def mean_tup(tup, time):
wl, t, d = tup.wl, tup.t, tup.data
new_dat = tup.data / tup.data[dv.fi(t, time), :]
return dv.tup(wl, t, new_dat)