2.1.1.1.1. sf_tools.base.np_adjust module

NUMPY ADJUSTMENT ROUTINES

This module contains methods for adjusting the default output for certain Numpy functions.

Author:Samuel Farrens <samuel.farrens@gmail.com> Version:1.1 Date:03/04/2017

sf_tools.base.np_adjust.rotate(data)

Rotate

This method rotates an input numpy array by 180 degrees.

Parameters:data (np.ndarray) – Input data array (at least 2D) Returns: Return type:np.ndarray rotated data

Notes

Adjustment to numpy.rot90()

Examples

>>> from sf_tools.base.np_adjust import rotate
>>> x = np.arange(9).reshape((3, 3))
>>> x
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
>>> rotate(x)
array([[8, 7, 6],
       [5, 4, 3],
       [2, 1, 0]])

sf_tools.base.np_adjust.rotate_stack(data)

Rotate stack

This method rotates each array in a stack of arrays by 180 degrees.

Parameters:data (np.ndarray) – Input data array (at least 3D) Returns: Return type:np.ndarray rotated data

Examples

>>> from sf_tools.base.np_adjust import rotate_stack
>>> x = np.arange(18).reshape((2, 3, 3))
>>> x
array([[[ 0,  1,  2],
        [ 3,  4,  5],
        [ 6,  7,  8]],
       [[ 9, 10, 11],
        [12, 13, 14],
        [15, 16, 17
>>> rotate_stack(x)
array([[[ 8,  7,  6],
        [ 5,  4,  3],
        [ 2,  1,  0]],
       [[17, 16, 15],
        [14, 13, 12],
        [11, 10,  9]]])

sf_tools.base.np_adjust.pad2d(data, padding)

Pad array

This method pads an input numpy array with zeros in all directions.

Parameters:

• data (np.ndarray) – Input data array (at least 2D)
• padding (int, tuple) – Amount of padding in x and y directions, respectively

Returns:

Return type:

np.ndarray padded data

Notes

Adjustment to numpy.pad()

Examples

>>> from sf_tools.base.np_adjust import pad2d
>>> x = np.arange(9).reshape((3, 3))
>>> x
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
>>> pad2d(x, (1, 1))
array([[0, 0, 0, 0, 0],
       [0, 0, 1, 2, 0],
       [0, 3, 4, 5, 0],
       [0, 6, 7, 8, 0],
       [0, 0, 0, 0, 0]])

sf_tools.base.np_adjust.x_bins(vals)

X-range bins

This method sets the bin values for a histogram.

Parameters:vals (np.ndarray) – X-range bins from np.histogram()[1] Returns: Return type:np.ndarray corrected x-range bin data

Notes

Adjustment to numpy.histogram()

Examples

>>> from sf_tools.base.np_adjust import x_bins
>>> data = np.array([1, 2, 1, 3, 1, 1])
>>> hist, bins = np.histogram(data)
>>> hist
array([4, 0, 0, 0, 0, 1, 0, 0, 0, 1])
>>> bins
array([ 1. ,  1.2,  1.4,  1.6,  1.8,  2. ,  2.2,  2.4,  2.6,  2.8,  3. ])
>>> x_bins(bins)
array([ 1.1,  1.3,  1.5,  1.7,  1.9,  2.1,  2.3,  2.5,  2.7,  2.9])

sf_tools.base.np_adjust.x_bins_step(vals)

X-range bins (step function)

This method sets the bin values for a histogram plotted as a step funciton.

Parameters:vals (np.ndarray) – X-range bins from np.histogram()[1] Returns: Return type:np.ndarray corrected x-range bin data

Notes

Adjustment to numpy.histogram()

Examples

>>> from sf_tools.base.np_adjust import x_bins
>>> data = np.array([1, 2, 1, 3, 1, 1])
>>> hist, bins = np.histogram(data)
>>> hist
array([4, 0, 0, 0, 0, 1, 0, 0, 0, 1])
>>> bins
array([ 1. ,  1.2,  1.4,  1.6,  1.8,  2. ,  2.2,  2.4,  2.6,  2.8,  3. ])
>>> x_bins_step(bins)
array([ 1.2,  1.4,  1.6,  1.8,  2. ,  2.2,  2.4,  2.6,  2.8,  3. ])

sf_tools.base.np_adjust.ftr(data)

Fancy transpose right

Apply fancy_transpose() to data with roll=1

Parameters:data (np.ndarray) – Input data array Returns: Return type:np.ndarray transposed data

sf_tools.base.np_adjust.ftl(data)

Fancy transpose left

Apply fancy_transpose() to data with roll=-1

Parameters:data (np.ndarray) – Input data array Returns: Return type:np.ndarray transposed data

sf_tools.base.np_adjust.fancy_transpose(data, roll=1)

Fancy transpose

This method transposes a multidimensional matrix.

Parameters:

• data (np.ndarray) – Input data array
• roll (int) – Roll direction and amount. Default (roll=1)

Returns:

Return type:

np.ndarray transposed data

Notes

Adjustment to numpy.transpose

Examples

>>> from sf_tools.base.np_adjust import fancy_transpose
>>> x = np.arange(27).reshape(3, 3, 3)
>>> x
array([[[ 0,  1,  2],
        [ 3,  4,  5],
        [ 6,  7,  8]],
       [[ 9, 10, 11],
        [12, 13, 14],
        [15, 16, 17]],
       [[18, 19, 20],
        [21, 22, 23],
        [24, 25, 26]]])
>>> fancy_transpose(x)
array([[[ 0,  3,  6],
        [ 9, 12, 15],
        [18, 21, 24]],
       [[ 1,  4,  7],
        [10, 13, 16],
        [19, 22, 25]],
       [[ 2,  5,  8],
        [11, 14, 17],
        [20, 23, 26]]])
>>> fancy_transpose(x, roll=-1)
array([[[ 0,  9, 18],
        [ 1, 10, 19],
        [ 2, 11, 20]],
       [[ 3, 12, 21],
        [ 4, 13, 22],
        [ 5, 14, 23]],
       [[ 6, 15, 24],
        [ 7, 16, 25],
        [ 8, 17, 26]]])