skultrafast.filter
==================

.. py:module:: skultrafast.filter

.. autoapi-nested-parse::

   This module contains various filters and binning methods. All take
   a dv tup and return a tup.



Functions
---------

.. autoapisummary::

   skultrafast.filter.svd_filter
   skultrafast.filter.wiener
   skultrafast.filter.uniform_filter
   skultrafast.filter.gaussian_filter
   skultrafast.filter.sg_filter
   skultrafast.filter.bin_channels
   skultrafast.filter.weighted_binner
   skultrafast.filter._idx_range
   skultrafast.filter.cut_tup
   skultrafast.filter.norm_tup


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

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

   Only use the first n-components.

   :param tup: data object.
   :param n: number of svd components used.


.. py:function:: wiener(tup, size=(3, 3), noise=None)

.. py:function:: uniform_filter(tup, sigma=(2, 2))

   Apply an uniform filter to data.


.. py:function:: gaussian_filter(tup, sigma=(2, 2))

   Apply an uniform filter to data.


.. py:function:: sg_filter(tup, window_length=11, polyorder=2, deriv=0, axis=0)

   Apply a Savitzky-Golay filter to a tup.

   Parameter
   ---------
   tup:
       Data
   window_length:
       Winodw length of the filter
   polyorder:
       The order of local polynomial. Must be smaller than window_length.
   deriv:
       The order of the derivative to compute. This must be a
       nonnegative integer. The default is 0.



.. py:function:: bin_channels(tup, n=200, method=np.mean)

   Bin the data onto n-channels.


.. 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:: _idx_range(arr, a, b)

   Returns a boolean array which is True where arr is between
   a and b.

   :param arr: Array to find the range in.
   :type arr: array
   :param a: First edge of the region.
   :type a: float
   :param b: Second edge of the region.
   :type b: float

   :returns: The resulting boolean array
   :rtype: arr of bool


.. py:function:: cut_tup(tup, from_t=-1e+20, to_t=1e+20, from_wl=-1e+20, to_wl=1e+20)

.. py:function:: norm_tup(tup, min_div=3)