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145 lines
3.3 KiB
Python
145 lines
3.3 KiB
Python
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# Authors: Veeresh Taranalli <veeresht@gmail.com>
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# License: BSD 3-Clause
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"""
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============================================
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Utilities (:mod:`commpy.utilities`)
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============================================
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.. autosummary::
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:toctree: generated/
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dec2bitarray -- Integer to binary (bit array).
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bitarray2dec -- Binary (bit array) to integer.
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hamming_dist -- Hamming distance.
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euclid_dist -- Squared Euclidean distance.
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upsample -- Upsample by an integral factor (zero insertion).
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"""
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import numpy as np
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__all__ = ['dec2bitarray', 'bitarray2dec', 'hamming_dist', 'euclid_dist', 'upsample']
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def dec2bitarray(in_number, bit_width):
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"""
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Converts a positive integer to NumPy array of the specified size containing
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bits (0 and 1).
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Parameters
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----------
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in_number : int
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Positive integer to be converted to a bit array.
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bit_width : int
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Size of the output bit array.
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Returns
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-------
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bitarray : 1D ndarray of ints
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Array containing the binary representation of the input decimal.
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"""
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binary_string = bin(in_number)
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length = len(binary_string)
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bitarray = np.zeros(bit_width, 'int')
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for i in range(length-2):
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bitarray[bit_width-i-1] = int(binary_string[length-i-1])
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return bitarray
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def bitarray2dec(in_bitarray):
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"""
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Converts an input NumPy array of bits (0 and 1) to a decimal integer.
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Parameters
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----------
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in_bitarray : 1D ndarray of ints
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Input NumPy array of bits.
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Returns
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-------
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number : int
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Integer representation of input bit array.
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"""
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number = 0
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for i in range(len(in_bitarray)):
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number = number + in_bitarray[i]*pow(2, len(in_bitarray)-1-i)
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return number
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def hamming_dist(in_bitarray_1, in_bitarray_2):
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"""
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Computes the Hamming distance between two NumPy arrays of bits (0 and 1).
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Parameters
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----------
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in_bit_array_1 : 1D ndarray of ints
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NumPy array of bits.
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in_bit_array_2 : 1D ndarray of ints
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NumPy array of bits.
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Returns
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-------
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distance : int
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Hamming distance between input bit arrays.
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"""
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distance = 0
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for i, j in zip(in_bitarray_1, in_bitarray_2):
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# Jinghao: 2016-10-19: handle "don't care" bits
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if i in [0, 1] and j in [0, 1] and i != j:
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distance += 1
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return distance
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def euclid_dist(in_array1, in_array2):
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"""
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Computes the squared euclidean distance between two NumPy arrays
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Parameters
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----------
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in_array1 : 1D ndarray of floats
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NumPy array of real values.
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in_array2 : 1D ndarray of floats
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NumPy array of real values.
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Returns
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-------
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distance : float
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Squared Euclidean distance between two input arrays.
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"""
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distance = ((in_array1 - in_array2)*(in_array1 - in_array2)).sum()
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return distance
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def upsample(x, n):
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"""
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Upsample the input array by a factor of n
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Adds n-1 zeros between consecutive samples of x
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Parameters
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----------
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x : 1D ndarray
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Input array.
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n : int
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Upsampling factor
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Returns
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-------
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y : 1D ndarray
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Output upsampled array.
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"""
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y = np.empty(len(x)*n, dtype=complex)
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y[0::n] = x
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zero_array = np.zeros(len(x), dtype=complex)
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for i in range(1, n):
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y[i::n] = zero_array
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return y
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