"""
Utils functions of ipfml package (array normalization)
"""
import numpy as np
from scipy.integrate import simps
[docs]def normalize_arr(arr):
"""Normalize data of 1D array shape
Args:
arr: array data of 1D shape
Returns:
Normalized 1D array
Example:
>>> from ipfml import utils
>>> import numpy as np
>>> arr = np.arange(11)
>>> arr_normalized = utils.normalize_arr(arr)
>>> arr_normalized[1]
0.1
"""
output_arr = []
max_value = max(arr)
min_value = min(arr)
for v in arr:
output_arr.append((v - min_value) / (max_value - min_value))
return output_arr
[docs]def normalize_arr_with_range(arr, min, max):
'''Normalize data of 1D array shape
Args:
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
'''
output_arr = []
for v in arr:
output_arr.append((v - min) / (max - min))
return output_arr
[docs]def normalize_2D_arr(arr):
"""Return array normalize from its min and max values
Args:
arr: 2D Numpy array
Returns:
Normalized 2D Numpy array
Example:
>>> from PIL import Image
>>> from ipfml import utils, processing
>>> img = Image.open('./images/test_img.png')
>>> img_mscn = processing.rgb_to_mscn(img)
>>> img_normalized = utils.normalize_2D_arr(img_mscn)
>>> img_normalized.shape
(200, 200)
"""
# getting min and max value from 2D array
max_value = arr.max(axis=1).max()
min_value = arr.min(axis=1).min()
# normalize each row
output_array = []
width, height = arr.shape
for row_index in range(0, height):
values = arr[row_index, :]
output_array.append(
normalize_arr_with_range(values, min_value, max_value))
return np.asarray(output_array)
[docs]def integral_area_trapz(y_values, dx):
"""Returns area under curves from provided data points using Trapezium rule
Args:
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
"""
return np.trapz(y_values, dx=dx)
[docs]def integral_area_simps(y_values, dx):
"""Returns area under curves from provided data points using Simpsons rule
Args:
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
"""
return simps(y_values, dx=dx)
[docs]def get_indices_of_highest_values(arr, n):
"""Returns indices of n highest values from list or 1D numpy array
Args:
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])
"""
return np.array(arr).argsort()[-n:][::-1]
[docs]def get_indices_of_lowest_values(arr, n):
"""Returns indices of n highest values from list or 1D numpy array
Args:
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])
"""
return np.array(arr).argsort()[::-1][-n:][::-1]