skultrafast.dv
==============

.. py:module:: skultrafast.dv


Classes
-------

.. autoapisummary::

   skultrafast.dv.tup


Functions
---------

.. autoapisummary::

   skultrafast.dv.add_to_cls
   skultrafast.dv.add_tup
   skultrafast.dv.trimmed_mean
   skultrafast.dv.smooth_spline
   skultrafast.dv.svd_filter
   skultrafast.dv.apply_spline
   skultrafast.dv.normalize
   skultrafast.dv.weighted_binner
   skultrafast.dv.binner
   skultrafast.dv.fi
   skultrafast.dv.subtract_background
   skultrafast.dv.polydetrend
   skultrafast.dv.exp_detrend
   skultrafast.dv.arr_polydetrend
   skultrafast.dv.meaner
   skultrafast.dv.legend_format
   skultrafast.dv.apply_sg
   skultrafast.dv.apply_sg_scan
   skultrafast.dv.calc_error
   skultrafast.dv.min_pulse_length
   skultrafast.dv.wavelength2rgb
   skultrafast.dv.equal_color
   skultrafast.dv.find_linear_part
   skultrafast.dv.rebin
   skultrafast.dv.efa
   skultrafast.dv.moving_efa
   skultrafast.dv.pfid_tau_to_w
   skultrafast.dv.als
   skultrafast.dv.spec_int
   skultrafast.dv.do_nnls
   skultrafast.dv.exp_fit
   skultrafast.dv.calc_ratios
   skultrafast.dv.make_fi


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

.. py:class:: tup

   Bases: :py:obj:`tuple`


   .. py:attribute:: wl


   .. py:attribute:: t


   .. py:attribute:: data


.. py:function:: add_to_cls(cls)

.. py:function:: add_tup(str_lst)

.. py:function:: trimmed_mean(arr, axis=-1, ratio=2.0, use_sem=True)

.. py:function:: smooth_spline(x, y, s)

.. py:function:: svd_filter(d, n=6)

.. py:function:: apply_spline(t, d, s=None)

.. py:function:: normalize(x)

.. py:function:: weighted_binner(n, wl, dat, std)

   Given wavelengths and data it bins the data into n-wavelenths.
   Returns bdata and bwl



.. py:function:: binner(n, wl, dat, func=np.mean)

   Given wavelengths and data it bins the data into n-wavelenths.
   Returns bdata and bwl



.. py:function:: fi(w: numpy.ndarray, x: float) -> int
                 fi(w: numpy.ndarray, x: Iterable[float]) -> List[int]

   Given a value, it finds the index of the nearest value in the array.

   :param w: Array where to look.
   :type w: np.ndarray
   :param x: Value or values to look for.
   :type x: float or list of floats

   :returns: Indicies of the nearest values.
   :rtype: int or list of ints


.. py:function:: subtract_background(dat, t, tn, offset=0.3)

.. py:function:: polydetrend(x, t=None, deg=3)

.. py:function:: exp_detrend(y, t, start_taus=[1], use_constant=True)

.. py:function:: arr_polydetrend(x, t=None, deg=3)

.. py:function:: meaner(dat, t, llim, ulim, proportiontocut=0.0)

.. py:function:: legend_format(l)

.. py:function:: apply_sg(y, window_size, order, deriv=0)

.. py:function:: apply_sg_scan(y, window_size, order, deriv=0)

.. py:function:: calc_error(args)

   Calculates the error from a leastsq fit infodict.


.. py:function:: min_pulse_length(width_in_cm, shape='gauss')

.. py:function:: wavelength2rgb(w)

   Converts a wavelength to a RGB color.


.. py:function:: equal_color(plots1, plots2)

.. py:function:: find_linear_part(t)

   Finds the first value of an 1d-array where the difference betweeen
   consecutively values varys.


.. py:function:: rebin(a, new_shape)

   Resizes a 2d array by averaging or repeating elements,
   new dimensions must be integral factors of original dimensions

   :param a:
   :type a: array_like
   :param Input array.:
   :param new_shape:
   :type new_shape: tuple of int
   :param Shape of the output array:

   :returns: * **rebinned_array** (*ndarray*)
             * *If the new shape is smaller of the input array, the data are averaged,*
             * *if the new shape is bigger array elements are repeated*

   .. seealso::

      :py:obj:`resize`
          Return a new array with the specified shape.

   .. rubric:: Examples

   >>> a = np.array([[0, 1], [2, 3]])
   >>> b = rebin(a, (4, 6)) #upsize
   >>> b
   array([[0, 0, 0, 1, 1, 1],
   [0, 0, 0, 1, 1, 1],
   [2, 2, 2, 3, 3, 3],
   [2, 2, 2, 3, 3, 3]])

   >>> c = rebin(b, (2, 3)) #downsize
   >>> c
   array([[ 0. , 0.5, 1. ],
   [ 2. , 2.5, 3. ]])


.. py:function:: efa(dat, n, reverse=False)

   Doing evolving factor analyis.


.. py:function:: moving_efa(dat, n, ncols, method='svd')

.. py:function:: pfid_tau_to_w(tau)

   Given the rise time of the pertuabed
   free induction decay, calculate the
   corresponding spectral width in cm-^1.


.. py:function:: als(dat, n=5)

.. py:function:: spec_int(tup, r, is_wavelength=True)

.. py:function:: do_nnls(A, b)

.. py:function:: exp_fit(x, y, start_taus=[1], use_constant=True, amp_max=None, amp_min=None, weights=None, amp_penalty=0, verbose=True, start_amps=None)

.. py:function:: calc_ratios(fitter, tmin=0.35, tmax=200)

.. py:function:: make_fi(data_to_search)