filters Package

filters Package

baseline Module

Baseline regulation filter module

maidenhair.filters.baseline.baseline(dataset, column=1, fn=None, fail_silently=True)

Substract baseline from the dataset

Parameters:

dataset : list of numpy array list

A list of numpy array list

column : integer

An index of column which will be proceeded

fn : function

A function which require data and return baseline. If it is None, the first value of data will be used for subtracting

fail_silently : boolean

If True, do not raise exception if no data exists

Returns:

ndarray :

A list of numpy array list

Examples

>>> import numpy as np
>>> from maidenhair.filters.baseline import baseline
>>> dataset = []
>>> dataset.append([np.array([0, 1, 2]), np.array([3, 4, 5])])
>>> dataset.append([np.array([0, 1, 2]), np.array([3, 5, 7])])
>>> dataset.append([np.array([0, 1, 2]), np.array([100, 103, 106])])
>>> expected = [
...     [np.array([0, 1, 2]), np.array([0, 1, 2])],
...     [np.array([0, 1, 2]), np.array([0, 2, 4])],
...     [np.array([0, 1, 2]), np.array([0, 3, 6])],
... ]
>>> proceed = baseline(dataset)
>>> np.array_equal(proceed, expected)
True

relative Module

Relative filter module

maidenhair.filters.relative.relative(dataset, ori=0, column=1, fail_silently=True)

Convert dataset to relative value from the value of ori

Parameters:

dataset : list of numpy array list

A list of numpy array list

ori : integer or numpy array, optional

A relative original data index or numpy array

column : integer, optional

An index of base column to calculate the relative value

fail_silently : boolean

If True, do not raise exception if no data exists

Returns:

ndarray :

A list of numpy array list

Examples

>>> import numpy as np
>>> from maidenhair.filters.relative import relative
>>> dataset = []
>>> dataset.append([np.array([0, 1, 2]), np.array([3, 4, 5])])
>>> dataset.append([np.array([0, 1, 2]), np.array([3, 5, 7])])
>>> dataset.append([np.array([0, 1, 2]), np.array([100, 103, 106])])
>>> expected = [
...     [np.array([0, 1, 2]), np.array([0, 50, 100])],
...     [np.array([0, 1, 2]), np.array([0, 100, 200])],
...     [np.array([0, 1, 2]), np.array([4850, 5000, 5150])],
... ]
>>> proceed = relative(dataset)
>>> np.array_equal(proceed, expected)
True