messpy.MessPyFile

class messpy.MessPyFile(fname, invert_data=False, is_pol_resolved=False, pol_first_scan: Literal['magic', 'para', 'perp', 'unknown'] = 'unknown', valid_channel=None)[source]

Class for working with data files from MessPy v1.

Parameters:

  • fname (str) – Filename to open.

  • invert_data (bool (optional)) – If True, invert the sign of the data. False by default.

  • is_pol_resolved (bool (optional)) – If the dataset was recorded polarization resolved.

  • pol_first_scan ({'magic', 'para', 'perp', 'unknown'}) – Polarization between the pump and the probe in the first scan. If valid_channel is ‘both’, this corresponds to the zeroth channel.

  • valid_channel (0, 1, ‘both’) – Indicates which channels contains a real signal. For recently recorded data, it is 0 for the visible setup and 1 for the IR setup. Older IR data uses both. If None it guesses the valid channel from the data, assuming recent data.

__init__(fname, invert_data=False, is_pol_resolved=False, pol_first_scan: Literal['magic', 'para', 'perp', 'unknown'] = 'unknown', valid_channel=None)[source]

Class for working with data files from MessPy v1.

Parameters:

  • fname (str) – Filename to open.

  • invert_data (bool (optional)) – If True, invert the sign of the data. False by default.

  • is_pol_resolved (bool (optional)) – If the dataset was recorded polarization resolved.

  • pol_first_scan ({'magic', 'para', 'perp', 'unknown'}) – Polarization between the pump and the probe in the first scan. If valid_channel is ‘both’, this corresponds to the zeroth channel.

  • valid_channel (0, 1, ‘both’) – Indicates which channels contains a real signal. For recently recorded data, it is 0 for the visible setup and 1 for the IR setup. Older IR data uses both. If None it guesses the valid channel from the data, assuming recent data.

Methods

__init__(fname[, invert_data, ...])

Class for working with data files from MessPy v1.

average_scans([sigma, max_iter, min_scan, ...])

Calculate the average of the scans.

avg_and_concat()

Averages the data and concatenates the resulting TimeResSpec

recalculate_wavelengths(dispersion[, ...])

Recalculates the wavelengths, assuming linear dispersion.

subtract_background([n, prop_cut])

Substracts the the first n-points of the data