def process_vis(self, vis_range=(390, 720), min_scan=None,
max_scan=None, sigma=2.3, para_angle=45):
data_file = self.file
wls = self.vis_wls()
t = data_file['t']
d = -data_file['data_Stresing CCD'][min_scan:max_scan, ...]
if 'rot' in data_file:
rot = data_file['rot'][min_scan:max_scan]
para_idx = (abs(np.round(rot) - para_angle) < 3)
else:
n_ir_cwl = data_file['wl_Remote IR 32x2'].shape[0]
para_idx = np.repeat(np.array([False, True], dtype='bool'), n_ir_cwl)
dpm = sigma_clip(d[para_idx, ...], axis=0, sigma=sigma, max_iter=10)
dsm = sigma_clip(d[~para_idx, ...], axis=0, sigma=sigma, max_iter=10)
dp = dpm.mean(0)
dps = dpm.std(0)
ds = dsm.mean(0)
dss = dsm.std(0)
para = TimeResSpec(wls, t, dp[0, :, 0, ...], freq_unit='nm',
disp_freq_unit='nm')
perp = TimeResSpec(wls, t, ds[0, :, 0, ...], freq_unit='nm',
disp_freq_unit='nm')
pol = PolTRSpec(para, perp)
pol = pol.cut_freq(*vis_range, invert_sel=True)
return pol.para, pol.perp, pol
def test_das_pol_plots():
ds = TimeResSpec(wl, t, data)
pds = PolTRSpec(ds, ds) # fake pol
x0 = [0.1, 0.1, 1, 1000]
out = pds.fit_exp(x0)
pds.plot.das()
pds.plot.edas()
def test_sas_pol_plots():
from skultrafast.kinetic_model import Model
ds = TimeResSpec(wl, t, data)
pds = PolTRSpec(ds, ds) # fake pol
x0 = [0.1, 0.1, 1, 1000]
out = pds.fit_exp(x0)
m = Model()
m.add_transition('S1', 'S1*', 'k1')
m.add_transition('S1', 'zero', 'k2')
pds.plot.sas(m)
def test_concat():
ds = TimeResSpec(wl, t, data)
ds = ds.bin_freqs(50)
n = ds.wavelengths.size // 2
ds1 = ds.cut_freq(ds.wavelengths[n], np.inf)
ds2 = ds.cut_freq(-np.inf, ds.wavelengths[n])
dsc = ds1.concat_datasets(ds2)
assert (np.allclose(dsc.data, ds.data))
pol_ds = PolTRSpec(ds, ds)
a = PolTRSpec(ds1, ds1)
b = PolTRSpec(ds2, ds2)
pol_dsc = a.concat_datasets(b)
for p in 'para', 'perp', 'iso':
assert (np.allclose(getattr(pol_dsc, p).data, getattr(pol_ds, p).data))
def test_pol_tr():
ds = TimeResSpec(wl, t, data)
ds2 = TimeResSpec(wl, t, data)
ps = PolTRSpec(para=ds, perp=ds2)
out = ps.bin_freqs(10)
assert (out.para.wavenumbers.size == 10)
assert (out.perp.wavenumbers.size == 10)
assert_almost_equal(out.perp.data, out.para.data)
ps.subtract_background()
ps.mask_freqs([(400, 550)])
print(ps.para.data.mask, ps.para.data.mask[1, ps.para.wl_idx(520)])
assert (ps.para.data.mask[1, ps.para.wl_idx(520)])
out = ps.cut_freqs([(400, 550)])
assert (np.all(out.para.wavelengths >= 550))
assert (np.all(out.perp.wavelengths >= 550))
ps.bin_times(6)
def test_pol_plot():
ds = TimeResSpec(wl, t, data)
ds = ds.bin_freqs(50)
ds = PolTRSpec(para=ds, perp=ds)
ds = ds.bin_freqs(50)
ds.plot.trans([550])
ds.plot.spec([2, 10])
ds.plot.trans([550], norm=1)
ds.plot.trans([550], norm=1, marker='o')
def process_ir(self,
t0=0,
min_scans=0,
max_scans=None,
subtract_background=True,
center_ch=16,
disp=14,
sigma=3) -> PolTRSpec:
data_file = self.file
t = data_file['t'] - t0
wli = data_file['wl_Remote IR 32x2']
print(wli[:, 16, None])
wli = -(disp * (np.arange(32) - center_ch)) + wli[:, 16, None]
wli = 1e7 / wli
d = data_file['data_Remote IR 32x2'][min_scans:max_scans]
print(d.shape)
dp = sigma_clip(d[1::2, ...], axis=0, sigma=sigma)
dpm = dp.mean(0)
ds = sigma_clip(d[0::2, ...], axis=0, sigma=sigma)
dsm = ds.mean(0)
if subtract_background:
dsm -= dsm[:, :10, ...].mean(1, keepdims=True)
dpm -= dpm[:, :10, ...].mean(1, keepdims=True)
para = TimeResSpec(wli[0],
t,
dpm[0, :, 0, ...],
freq_unit='cm',
disp_freq_unit='cm')
perp = TimeResSpec(wli[0],
t,
dsm[0, :, 0, ...],
freq_unit='cm',
disp_freq_unit='cm')
para.plot.spec(1, n_average=20)
for i in range(1, wli.shape[0]):
para_t = TimeResSpec(wli[i],
t,
dpm[i, :, 0, ...],
freq_unit='cm',
disp_freq_unit='cm')
para_t.plot.spec(1, n_average=20)
para = para.concat_datasets(para_t)
perp = perp.concat_datasets(
TimeResSpec(wli[i],
t,
dsm[i, :, 0, ...],
freq_unit='cm',
disp_freq_unit='cm'))
both = PolTRSpec(para, perp)
return both
def test_pol_tr():
ds = TimeResSpec(wl, t, data)
ds2 = TimeResSpec(wl, t, data)
ps = PolTRSpec(para=ds, perp=ds2)
out = ps.bin_freqs(10)
assert(out.para.wavenumbers.size == 10)
assert(out.perp.wavenumbers.size == 10)
assert_almost_equal(out.perp.data, out.para.data)
ps.subtract_background()
ps.mask_freqs([(400, 550)])
print(ps.para.data.mask, ps.para.data.mask[1, ps.para.wl_idx(520)])
assert(ps.para.data.mask[1, ps.para.wl_idx(520)] )
out = ps.cut_freqs([(400, 550)])
assert(np.all(out.para.wavelengths >= 550))
assert(np.all(out.perp.wavelengths >= 550))
ps.bin_times(6)
def make_pol_ds(self, sigma=None) -> PolTRSpec:
para = np.nanmean(self.par_data, axis=0)
ds_para = TimeResSpec(self.wavelength,
self.t,
1000 * para,
disp_freq_unit='cm')
perp = np.nanmean(self.per_data, axis=0)
ds_perp = TimeResSpec(self.wavelength,
self.t,
1000 * perp,
disp_freq_unit='cm')
return PolTRSpec(ds_para, ds_perp)