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- """
- Utils functions of ipfml package (array normalization)
- """
- import numpy as np
- from scipy.integrate import simps
- 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
- 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
- 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)
- 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)
- 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)
- 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]
- 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]
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