skultrafast.messpy
==================

.. py:module:: skultrafast.messpy


Classes
-------

.. autoapisummary::

   skultrafast.messpy.MessPy2File
   skultrafast.messpy.MessPyFile
   skultrafast.messpy.MessPyPlotter
   skultrafast.messpy.TzResult


Functions
---------

.. autoapisummary::

   skultrafast.messpy._get_group_arr
   skultrafast.messpy.get_t0


Module Contents
---------------

.. py:class:: MessPy2File(fname: os.PathLike)

   Class for working with older messpy2 files.


   .. py:attribute:: file


   .. py:method:: get_meta_info()


   .. py:method:: vis_wls(slope=-1.5, intercept=864.4)


   .. py:method:: process_vis(vis_range=(390, 720), min_scan=None, max_scan=None, sigma=2.3, para_angle=45)


   .. py:method:: process_ir(t0=0, min_scans=0, max_scans=None, subtract_background=True, center_ch=16, disp=14, sigma=3) -> skultrafast.dataset.PolTRSpec


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

   .. py:attribute:: wavenumbers


   .. py:attribute:: pol_first_scan
      :value: 'unknown'



   .. py:attribute:: is_pol_resolved
      :value: False



   .. py:attribute:: num_cwl


   .. py:attribute:: plot


   .. py:attribute:: t_idx


   .. py:method:: 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.

      :param sigma: sigma used for sigma clipping.
      :type sigma: float
      :param max_iter: Maximum iterations in sigma clipping.
      :type max_iter: int
      :param min_scan: All scans before min_scan are ignored.
      :type min_scan: int or None
      :param max_scan: If `None`, use all scan, else just use the scans up to max_scan.
      :type max_scan: int or None
      :param disp_freq_unit: Sets `disp_freq_unit` of the created datasets.
      :type disp_freq_unit: 'nm', 'cm' or None

      :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.
      :rtype: dict or TimeResSpec



   .. py:method:: 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

      :param dispersion: The dispersion per channel.
      :type dispersion: float
      :param center_ch: Determines the mid-channel. Defaults to len(wl)/2.
      :type center_ch: int



   .. py:method:: subtract_background(n=10, prop_cut=0)

      Substracts the the first n-points of the data



   .. py:method:: avg_and_concat()

      Averages the data and concatenates the resulting TimeResSpec



.. py:class:: MessPyPlotter(messpyds)

   Bases: :py:obj:`skultrafast.dataset.PlotterMixin`


   .. py:attribute:: ds


   .. py:method:: background(n=10, ax=None)

      Plot the backgrounds each center wl.



   .. py:method:: early_region()

      Plots an imshow of the early region.



   .. py:method:: compare_spec(t_region=(0, 4), every_nth=1, ax=None)

      Plots the averaged spectra of every central wavelenth and polarisation.

      :param t_region: Contains the the start and end-point of the times to average.
      :type t_region: tuple of floats
      :param every_nth: Only every n-th scan is plotted.
      :type every_nth: int

      :rtype: None



   .. py:method:: 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.



.. py:function:: _get_group_arr(grp: h5py.Group)

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


.. py:class:: TzResult

   .. py:attribute:: x0
      :type:  float


   .. py:attribute:: sigma
      :type:  float


   .. py:attribute:: fwhm
      :type:  float


   .. py:attribute:: fit_result
      :type:  lmfit.model.ModelResult


   .. py:attribute:: data
      :type:  Tuple[numpy.ndarray, numpy.ndarray]


   .. py:attribute:: fig
      :type:  Optional[matplotlib.pyplot.Figure]
      :value: None



.. py:function:: get_t0(fname: str, sigma: float = 1, scan: Union[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

   :param fname: Filename of the messpy file containing the data
   :type fname: str
   :param sigma: Used for calculating the displayed nummerical derviate, by default 1.
   :type sigma: float, optional
   :param scan: Which scan to use, by default -1, the last scan. If given a slice,
                it takes the mean of the scan slice.
   :type scan: int or slice
   :param display_result: If true, show the fitting results, by default True
   :type display_result: bool, optional
   :param plot: If true, plot the result, by default True
   :type plot: bool, optional
   :param t_range: The range which is used to fit the data.
   :type t_range: (float, flot)
   :param invert: If true, invert data.
   :type invert: bool
   :param no_slope: Determines if a variable slope is added to the fit model.
   :type no_slope: bool

   :returns: Result and presentation of the fit.
   :rtype: TzResult