ipfml.utils

Utils functions of ipfml package (normalization, integral…)

Functions

get_entropy(arr)	Returns the computed entropy from arr
get_entropy_contribution_of_i(arr, i)	Returns the entropy contribution i column
get_entropy_without_i(arr, i)	Returns the computed entropy from arr without contribution of i
get_indices_of_highest_values(arr, n)	Returns indices of n highest values from list or 1D numpy array
get_indices_of_lowest_values(arr, n)	Returns indices of n highest values from list or 1D numpy array
integral_area_simps(y_values, dx)	Returns area under curves from provided data points using Simpsons rule
integral_area_trapz(y_values, dx)	Returns area under curves from provided data points using Trapezium rule
normalize_2D_arr(arr)	Return array normalize from its min and max values
normalize_arr(arr)	Normalize data of 1D array shape
normalize_arr_with_range(arr, min, max)	Normalize data of 1D array shape

ipfml.utils.get_entropy(arr)

Returns the computed entropy from arr

Parameters:arr – numpy array Returns:entropy score computed

Example:

>>> from ipfml import utils
>>> import numpy as np
>>> arr = np.arange(10)
>>> entropy = utils.get_entropy(arr)
>>> int(entropy)
0

ipfml.utils.get_entropy_contribution_of_i(arr, i)

Returns the entropy contribution i column

Parameters:

• arr – numpy array
• i – column index

Returns:

entropy contribution score computed

Example:

>>> from ipfml import utils
>>> import numpy as np
>>> arr = np.arange(10)
>>> entropy = utils.get_entropy_contribution_of_i(arr, 3)
>>> int(entropy)
0

ipfml.utils.get_entropy_without_i(arr, i)

Returns the computed entropy from arr without contribution of i

Parameters:

• arr – numpy array
• i – column index

Returns:

entropy score computed

Example:

>>> from ipfml import utils
>>> import numpy as np
>>> arr = np.arange(10)
>>> entropy = utils.get_entropy_without_i(arr, 3)
>>> int(entropy)
0

ipfml.utils.get_indices_of_highest_values(arr, n)

Returns indices of n highest values from list or 1D numpy array

Parameters:

• arr – List of numpy array
• n – number of highest elements wanted

Returns:

n indices of highest values

Example:

>>> from ipfml import utils
>>> import numpy as np
>>> arr = np.arange(10)
>>> indices = utils.get_indices_of_highest_values(arr, 2)
>>> indices
array([9, 8])

ipfml.utils.get_indices_of_lowest_values(arr, n)

Returns indices of n highest values from list or 1D numpy array

Parameters:

• arr – List of numpy array
• n – number of highest elements wanted

Returns:

n indices of highest values

Example:

>>> from ipfml import utils
>>> import numpy as np
>>> arr = np.arange(10)
>>> indices = utils.get_indices_of_lowest_values(arr, 2)
>>> indices
array([0, 1])

ipfml.utils.integral_area_simps(y_values, dx)

Returns area under curves from provided data points using Simpsons rule

Parameters:

• y_values – y values of curve
• dx – number of unit for x axis

Returns:

Area under curves obtained from these points

Example:

>>> from ipfml import utils
>>> import numpy as np
>>> y_values = np.array([5, 20, 4, 18, 19, 18, 7, 4])
>>> area = utils.integral_area_simps(y_values, dx=5)
>>> area
460.0

ipfml.utils.integral_area_trapz(y_values, dx)

Returns area under curves from provided data points using Trapezium rule

Parameters:

• y_values – y values of curve
• dx – number of unit for x axis

Returns:

Area under curves obtained from these points

Example:

>>> from ipfml import utils
>>> import numpy as np
>>> y_values = np.array([5, 20, 4, 18, 19, 18, 7, 4])
>>> area = utils.integral_area_trapz(y_values, dx=5)
>>> area
452.5

ipfml.utils.normalize_2D_arr(arr)

Return array normalize from its min and max values

Parameters:arr – 2D Numpy array Returns:Normalized 2D Numpy array

Example:

>>> from PIL import Image
>>> from ipfml import utils
>>> from ipfml.processing import transform
>>> img = Image.open('./images/test_img.png')
>>> img_mscn = transform.rgb_to_mscn(img)
>>> img_normalized = utils.normalize_2D_arr(img_mscn)
>>> img_normalized.shape
(200, 200)

ipfml.utils.normalize_arr(arr)

Normalize data of 1D array shape

Parameters:arr – array data of 1D shape Returns:Normalized 1D array

Example:

>>> from ipfml import utils
>>> import numpy as np
>>> arr = np.arange(5)
>>> arr_normalized = utils.normalize_arr(arr)
>>> arr_normalized[1]
0.1

ipfml.utils.normalize_arr_with_range(arr, min, max)

Normalize data of 1D array shape

Parameters:arr – array data of 1D shape Returns:Normalized 1D Numpy array

Example:

>>> from ipfml import utils
>>> import numpy as np
>>> arr = np.arange(11)
>>> arr_normalized = utils.normalize_arr_with_range(arr, 0, 20)
>>> arr_normalized[1]
0.05