dataset.TimeResSpecPlotter¶

class dataset.TimeResSpecPlotter(dataset: TimeResSpec, disp_freq_unit='nm')[source]¶

Class which can Plot a TimeResSpec using matplotlib.

Parameters:

dataset (TimeResSpec) – The TimeResSpec to work with.
disp_freq_unit ({'nm', 'cm'} (optional)) – The default unit of the plots. To change the unit afterwards, set the attribute directly.

__init__(dataset: TimeResSpec, disp_freq_unit='nm')[source]¶

Class which can Plot a TimeResSpec using matplotlib.

Parameters:

dataset (TimeResSpec) – The TimeResSpec to work with.
disp_freq_unit ({'nm', 'cm'} (optional)) – The default unit of the plots. To change the unit afterwards, set the attribute directly.

Methods

`__init__`(dataset[, disp_freq_unit])	Class which can Plot a `TimeResSpec` using matplotlib.
`das`([first_comp, ax, add_legend])	Plot a DAS, if available.
`edas`([ax, legend])	Plot a EDAS, if expontial fit is available.
`interactive`()	Generates a jupyter widgets UI for exploring a spectra.
`lbl_spec`([ax, add_legend])
`map`([symlog, equal_limits, plot_con, ...])	Plot a colormap of the dataset with optional contour lines.
`overview`()	Plots an overview figure.
`plot_disp_result`(result)	Visualize the result of a dispersion correction, creates a figure
`spec`(*args[, norm, ax, n_average, upsample, ...])	Plot spectra at given times.
`svd`([n])	Plot the SVD-components of the dataset.
`trans`(*args[, symlog, norm, ax, freq_unit, ...])	Plot the nearest transients for given frequencies.
`trans_fit`(*args[, symlog, freq_unit, ...])	Plot the nearest transients for given frequencies.
`trans_integrals`(*args[, symlog, norm, ax])	Plot the transients of integrated region.
`univariate_spline`(y)
`upsample_spec`(y[, kind, factor])

Attributes

x