skultrafast.fit_spectrum
========================

.. py:module:: skultrafast.fit_spectrum

.. autoapi-nested-parse::

   Module to fit the whole spektrum by peak functions.



Attributes
----------

.. autoapisummary::

   skultrafast.fit_spectrum.pi


Functions
---------

.. autoapisummary::

   skultrafast.fit_spectrum.voigt
   skultrafast.fit_spectrum.lorentz_peaks
   skultrafast.fit_spectrum.gauss_peaks
   skultrafast.fit_spectrum.voigt_peaks
   skultrafast.fit_spectrum.fit_spectrum
   skultrafast.fit_spectrum.bin_every_n


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

.. py:data:: pi

.. py:function:: voigt(x, A, mu, sig, gamma=0.1)

.. py:function:: lorentz_peaks(x, A, x0, w)

.. py:function:: gauss_peaks(x, A, x0, w)

.. py:function:: voigt_peaks(x, A, x0, w)

.. py:function:: fit_spectrum(x, y, start_peaks_list, yerr=None, peak_func=lorentz_peaks, amp_penalty=0.01, amp_bounds=(-0.6, 0.4), wmin=2, wmax=10, add_const=False)

   Fits multiple peaks to mulitple spektra, the position and width of
   each peak is the same for all spectra, only the amplitude is
   allowed to differ.

   :param x: The x-values to fit, e.g. wavelengths or wavenumbers.
   :type x: (n)-ndarray
   :param y: The y-values to fit.
   :type y: (n, m)-ndarray
   :param start_peak_list: A list containing (x0, amp, w) tuples. Used as starting values.
   :type start_peak_list: list
   :param yerr: The errors of the data. Default None.
   :type yerr: (n, m)-ndarray
   :param peaks_func: Function which calculates the peaks. Has the following signature:
                      func(x, A_arr, x0_arr, w_arr), defaults to lorentz_peaks.
   :type peaks_func: function, optional
   :param amp_penalty: Regulazition parameter for the amplitudes. Defaults to 0.001.
   :type amp_penalty: float, optional
   :param amp_bounds: Min and max bounds for the amplitude.
   :type amp_bounds: (float, float)-tuple, optional
   :param wmax: Upper bound for the width parameter.
   :type wmax: float, optional
   :param wmin: Lower bound for the width parameter.
   :type wmin: float, optional
   :param add_const: Weather to add an const background.
   :type add_const: bool


.. py:function:: bin_every_n(x, start_idx, n=10, reduction_func=lambda x: np.mean(x, 0))