"""Here we mostly test if it works at all."""
from skultrafast.dataset import TimeResSpec, PolTRSpec
from skultrafast.data_io import load_example
import numpy as np
from numpy.testing import assert_almost_equal
wl, t, data = load_example()
def test_integrate():
ds = TimeResSpec(wl, t, data)
ds.wn_i(15000, 20000)
def test_methods():
ds = TimeResSpec(wl, t, data)
bds = ds.bin_freqs(300)
ds2 = TimeResSpec(1e7 / wl, t, data, freq_unit='cm', disp_freq_unit='cm')
bds2 = ds2.bin_freqs(50)
assert (np.all(np.isfinite(bds2.data)))
assert (len(bds.wavelengths) == 300)
nds = ds.cut_freq(400, 600)
assert (np.all(nds.wavelengths > 600))
nds = ds.cut_time(-100, 1)
assert (np.all(nds.t > .99))
nds = ds.bin_times(5)
assert (nds.t.size == np.ceil(ds.t.size / 5))
ds.mask_freqs([(400, 600)])
assert (np.all(ds.data.mask[:, ds.wl_idx(550)]))
ds2 = ds.scale_and_shift(2, t_shift=1, wl_shift=10)
def test_load_exmaple():
wl, t, d = data_io.load_example()
assert ((t.shape[0], wl.shape[0]) == d.shape)
"""Here we mostly test if it works at all."""
from skultrafast.dataset import TimeResSpec, PolTRSpec
from skultrafast.data_io import load_example
import numpy as np
from numpy.testing import assert_almost_equal
wl, t, data = load_example()
def test_methods():
ds = TimeResSpec(wl, t, data)
bds = ds.bin_freqs(300)
assert(len(bds.wavelengths) == 300)
nds = ds.cut_freqs([(400, 600)])
assert(np.all(nds.wavelengths > 600))
nds = ds.cut_times([(-100, 1)])
assert(np.all(nds.t > .99))
nds = ds.bin_times(5)
assert(nds.t.size == np.ceil(ds.t.size/5))
ds.mask_freqs([(400, 600)])
assert(np.all(ds.data.mask[:, ds.wl_idx(550)]))
def test_est_disp():
ds = TimeResSpec(wl, t, data)
ds.auto_plot = False
for s in ['abs', 'diff', 'gauss_diff', 'max']:
ds.estimate_dispersion(heuristic=s)
def test_fitter():
ds = TimeResSpec(wl, t, data)
x0 = [0.1, 0.1, 1, 1000]
out = ds.fit_exp(x0)
j
# %%
# The transformation matrix is then given by
A = vecs @ np.diag(np.linalg.inv(vecs) @ j)
A_inv = np.linalg.inv(A)
# %%
# Now `DAS @ A_inv` should give the SAS, while `A_inv @ basis_vecs` should
# return the time-depedence of the concentrations. Let's test that on some data.
# Load test data and correct the dispersion.
from skultrafast import dataset, data_io, plot_helpers
plot_helpers.enable_style()
wl, t, d = data_io.load_example()
ds = dataset.TimeResSpec(wl, t, d)
dsb = ds.bin_freqs(50) # Bin dataset to save computation while building docs
res = dsb.estimate_dispersion(heuristic_args=(1.5, ), deg=3, shift_result=.15)
# %%
# Fit the DAS first.
ids = res.correct_ds
fr = ids.fit_exp([0.0, 0.08, 1, 500000],
model_coh=True,
fix_sigma=False,
fix_t0=False)
ids.plot.das()
# %%
# %% # Some matplotlib setting for nicer pictures. plt.rcParams['figure.dpi'] = 150 plt.rcParams['figure.figsize'] = (4, 3) plt.rcParams['figure.autolayout'] = True plt.rcParams['font.size'] = 9 # %% [markdown] # Creating a TimeResSpec # ---------------------- # In this tuturial we use the example data which is provided by *skultrafast*. # The `load_example` function gives us three arrays. One containing the wavelengths, # another one containing the delay times and one two dimensional array containing # the data in mOD. wavelengths, t_ps, data_mOD = data_io.load_example() # %% # Lets look at the constructor of the `TimeResSpec` (Time resolved Spectra) class, # which is the main object when working with single data: print(TimeResSpec.__init__.__doc__) # %% # As we see, we can supply all the required parameters. # Since the `freq_unit` defaults to 'nm' we don't need to supply this argument. ds = TimeResSpec(wavelengths, t_ps, data_mOD) # %%