dataset.TimeResSpec

class dataset.TimeResSpec(wl: ndarray, t: ndarray, data: ndarray, err: ndarray | None = None, name=None, freq_unit: Literal['nm', 'cm'] = 'nm', disp_freq_unit=None, auto_plot=True)[source]

Class for working with time-resolved spectra. If offers methods for analyzing and pre-processing the data. To visualize the data, each TimeResSpec object has an instance of an DataSetPlotter object accessible under plot.

Parameters:

  • wl (array of shape(n)) – Array of the spectral dimension

  • t (array of shape(m)) – Array with the delay times.

  • data (array of shape(n, m)) – Array with the data for each point.

  • err (array of shape(n, m) or None (optional)) – Contains the std err of the data, can be None.

  • name (str (optional)) – Identifier for data set.

  • freq_unit ('nm' or 'cm' (optional)) – Unit of the wavelength array, default is ‘nm’.

  • disp_freq_unit ('nm','cm' or None (optional)) – Unit which is used by default for plotting, masking and cutting the dataset. If None, it defaults to freq_unit.

wavelengths, wavenumbers, t, data

Arrays with the data itself.

Type:

ndarray

plot

Helper class which can plot the dataset using matplotlib.

Type:

TimeResSpecPlotter

t_idx

Helper function to find the nearest index in t for a given time.

Type:

function

wl_idx

Helper function to search for the nearest wavelength index for a given wavelength.

Type:

function

wn_idx

Helper function to search for the nearest wavelength index for a given wavelength.

Type:

function

auto_plot

When True, some function will display their result automatically.

Type:

bool

__init__(wl: ndarray, t: ndarray, data: ndarray, err: ndarray | None = None, name=None, freq_unit: Literal['nm', 'cm'] = 'nm', disp_freq_unit=None, auto_plot=True)[source]

Class for working with time-resolved spectra. If offers methods for analyzing and pre-processing the data. To visualize the data, each TimeResSpec object has an instance of an DataSetPlotter object accessible under plot.

Parameters:

  • wl (array of shape(n)) – Array of the spectral dimension

  • t (array of shape(m)) – Array with the delay times.

  • data (array of shape(n, m)) – Array with the data for each point.

  • err (array of shape(n, m) or None (optional)) – Contains the std err of the data, can be None.

  • name (str (optional)) – Identifier for data set.

  • freq_unit ('nm' or 'cm' (optional)) – Unit of the wavelength array, default is ‘nm’.

  • disp_freq_unit ('nm','cm' or None (optional)) – Unit which is used by default for plotting, masking and cutting the dataset. If None, it defaults to freq_unit.

wavelengths, wavenumbers, t, data

Arrays with the data itself.

Type:

ndarray

plot

Helper class which can plot the dataset using matplotlib.

Type:

TimeResSpecPlotter

t_idx

Helper function to find the nearest index in t for a given time.

Type:

function

wl_idx

Helper function to search for the nearest wavelength index for a given wavelength.

Type:

function

wn_idx

Helper function to search for the nearest wavelength index for a given wavelength.

Type:

function

auto_plot

When True, some function will display their result automatically.

Type:

bool

Methods

__init__(wl, t, data[, err, name, ...])

Class for working with time-resolved spectra.

apply_filter(kind, args)

Apply a filter to the data.

bin_freqs(n[, freq_unit, use_err])

Bins down the dataset by averaging over several transients.

bin_times(n[, start_index])

Bins down the dataset by binning n sequential spectra together.

concat_datasets(other_ds)

Merge the dataset with another dataset.

copy()

Returns a copy of the TimeResSpec.

cut_freq([lower, upper, invert_sel, freq_unit])

Removes channels inside (or outside ) of given frequency ranges.

cut_time([lower, upper, invert_sel])

Remove spectra inside (or outside) of given time-ranges.

estimate_dispersion([heuristic, ...])

Estimates the dispersion from a dataset by first applying a heuristic to each channel.

fit_exp(x0[, fix_sigma, fix_t0, ...])

Fit a sum of exponentials to the dataset.

from_txt(fname[, freq_unit, time_div, ...])

Directly create a dataset from a text file.

interpolate_disp(polyfunc)

Correct for dispersion by linear interpolation .

lifetime_density_map([taus, alpha, cv, maxiter])

Calculates the LDM from a dataset by regularized regression.

mask_freq_idx(idx)

Masks given freq idx array

mask_freqs(freq_ranges[, invert_sel, freq_unit])

Mask channels inside of given frequency ranges.

mask_times(time_ranges[, invert_sel])

Mask spectra inside (or outside) of given time-ranges.

merge_nearby_channels([distance, use_err])

Merges sequetential channels together if their distance is smaller than given.

save_txt(fname[, freq_unit])

Saves the dataset as a text file.

scale_and_shift([scale, t_shift, wl_shift])

Return a dataset which is scaled and/or has shifted times and frequencies.

subtract_background([n])

Subtracts the first n-spectra from the dataset

t_d(t)

Returns the nearest spectrum for given delaytime.

wl_d(wl)

Returns the nearest transient for given wavelength.

wn_d(wn)

Returns the nearest transient for given wavenumber.

wn_i(wn1, wn2[, method])

Integrates the signal from wn1 to wn2

Attributes

wavelengths

wavenumbers