skultrafast.messpy

Module Contents

Classes

MessPy2File	Class for working with older messpy2 files.
MessPyFile
MessPyPlotter
Messpy25File
TzResult

Functions

_get_group_arr(grp)	Get array from group of datasets, assuming they are named 0, 1, 2, ...
get_t0(, invert, no_slope)	Determine t0 from a semiconductor messuarement in the IR. For that, it opens

class skultrafast.messpy.MessPy2File(fname: os.PathLike)

Class for working with older messpy2 files.

get_meta_info()

vis_wls(slope=-1.5, intercept=864.4)

process_vis(vis_range=(390, 720), min_scan=None, max_scan=None, sigma=2.3, para_angle=45)

process_ir(t0=0, min_scans=0, max_scans=None, subtract_background=True, center_ch=16, disp=14, sigma=3) → skultrafast.dataset.PolTRSpec

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

average_scans(sigma=3, max_iter=3, min_scan=0, max_scan=None, disp_freq_unit=None)

Calculate the average of the scans. Uses sigma clipping, which also filters nans. For polarization resolved measurements, the function assumes that the polarisation switches every scan.

Parameters:

• sigma (float) – sigma used for sigma clipping.

• max_iter (int) – Maximum iterations in sigma clipping.

• min_scan (int or None) – All scans before min_scan are ignored.

• max_scan (int or None) – If None, use all scan, else just use the scans up to max_scan.

• disp_freq_unit ('nm', 'cm' or None) – Sets disp_freq_unit of the created datasets.

Returns:

TimeResSpec or Dict of DataSets containing the averaged datasets. If the first delay-time are identical, they are interpreted as background and their mean is subtracted.

Return type:

dict or TimeResSpec

recalculate_wavelengths(dispersion, center_ch=None, offset=0)

Recalculates the wavelengths, assuming linear dispersion. Currently assumes that the wavelength set by spectrometer is stored in channel 16

Parameters:

• dispersion (float) – The dispersion per channel.

• center_ch (int) – Determines the mid-channel. Defaults to len(wl)/2.

subtract_background(n=10, prop_cut=0)

Substracts the the first n-points of the data

avg_and_concat()

Averages the data and concatenates the resulting TimeResSpec

class skultrafast.messpy.MessPyPlotter(messpyds)

Bases: skultrafast.dataset.PlotterMixin

background(n=10, ax=None)

Plot the backgrounds each center wl.

early_region()

Plots an imshow of the early region.

compare_spec(t_region=(0, 4), every_nth=1, ax=None)

Plots the averaged spectra of every central wavelenth and polarisation.

Parameters:

• t_region (tuple of floats) – Contains the the start and end-point of the times to average.

• every_nth (int) – Only every n-th scan is plotted.

Return type:

None

compare_scans(t_region=(0, 4), channel=None, cmap='jet', ax=None, every_nth=1)

Plots the spectrum averaged over t_region for every_nth scan.

skultrafast.messpy._get_group_arr(grp: h5py.Group)

Get array from group of datasets, assuming they are named 0, 1, 2, … and have the same shape

class skultrafast.messpy.Messpy25File

h5_file: h5py.File | pathlib.Path

h5py file object containing the 2D dataset, the only required parameter

is_para_array: Literal[Probe1, Probe2] = 'Probe1'

which dataset has parallel polarisation

probe_wn: numpy.ndarray

Array with probe wavenumbers

pump_wn: numpy.ndarray

Array with the pump wavenumbers. Depends on the upsampling used during measurment

t2: numpy.ndarray

Array containing the waiting times

t1: numpy.ndarray

Array containing the double pulse delays

rot_frame: float

Rotation frame used while measuring

__attrs_post_init__()

get_means()

get_all_ifr()

ifr_means_and_stderr()

get_ifr(probe_filter=None, bg_correct=None, ch_shift: int = 0)

Returns the interferograms. If necessary, apply probefilter and background correction.

Parameters:

• probe_filter (float) – The probe filter width in channels. (Gaussian filter)

• bg_correct (Tuple[int, int]) – Number of left and right channels to use for background correction.

• ch_shift (int) – Number of channels to shift the Probe2 data. Corrects for missaligned channels.

Returns:

ifr – The interferograms for paralllel, perpendicular and isotropic polarisation. The shape of each array is (n_t2, n_probe_wn, n_t1).

Return type:

Tuple[np.ndarray, np.ndarray, np.ndarray]

make_two_d(upsample: int = 4, window_fcn: Callable | None = np.hanning, ch_shift: int = 1, probe_filter: float | None = None, bg_correct: Tuple[int, int] | None = None, t0_factor: float = 0.5) → Dict[str, skultrafast.twoD_dataset.TwoDim]

Calculates the 2D spectra from the interferograms and returns it as a dictionary. The dictorary contains messpy 2D-objects for paralllel, perpendicular and isotropic polarisation.

Parameters:

• upsample (int) – Upsampling factor used in the FFT. A factor over 2 only does sinc interpolation.

• window_fcn (Callable) – If given, apply a window function to the FFT.

• probe_filter (float) – The probe filter width in channels. (Gaussian filter)

• ch_shift (int) – Number of channels to shift the Probe2 data. Corrects for missaligned channels.

• bg_correct (Tuple[int, int]) – Number of left and right channels to use for background correction.

• t0_factor (float) – Factor to multiply the first t1 point (zero-delay between the pumps) to correct for the integration. In general, the default should not be touched.

make_model_fitfiles(path, name, probe_filter=None, bg_correct=None)

Saves the data in a format useable for the ModelFit Gui from Kevin Robben https://github.com/kevin-robben/model-fitting

recalculate_wl(center_wl=None, center_ch: int = 65, disp: float | None = None)

Recalculates the wavelengths from the probe.

class skultrafast.messpy.TzResult

x0: float

sigma: float

fwhm: float

fit_result: lmfit.model.ModelResult

data: Tuple[numpy.ndarray, numpy.ndarray]

fig: matplotlib.pyplot.Figure | None

skultrafast.messpy.get_t0(fname: str, sigma: float = 1, scan: int | slice = -1, display_result: bool = True, plot: bool = True, t_range: Tuple[float, float] = (-2, 2), invert: bool = False, no_slope: bool = True) → TzResult

Determine t0 from a semiconductor messuarement in the IR. For that, it opens the given file, takes the mean of all channels and fits the resulting curve with a step function.

Note that the parameter

Parameters:

• fname (str) – Filename of the messpy file containing the data

• sigma (float, optional) – Used for calculating the displayed nummerical derviate, by default 1.

• scan (int or slice) – Which scan to use, by default -1, the last scan. If given a slice, it takes the mean of the scan slice.

• display_result (bool, optional) – If true, show the fitting results, by default True

• plot (bool, optional) – If true, plot the result, by default True

• t_range ((float, flot)) – The range which is used to fit the data.

• invert (bool) – If true, invert data.

• no_slope (bool) – Determines if a variable slope is added to the fit model.

Returns:

Result and presentation of the fit.

Return type:

TzResult