simulations/tests/misc_test.py

import misc
import numpy as np
import math
import itertools
import tensorflow as tf
from models.custom_layers import BitsToSymbols, SymbolsToBits, OpticalChannel
from matplotlib import pyplot as plt


def test_bit_matrix_one_hot():
    for n in range(2, 8):
        x0 = misc.generate_random_bit_array(100 * n)
        x1 = misc.bit_matrix2one_hot(x0.reshape((-1, n)))
        x2 = misc.one_hot2bit_matrix(x1).reshape((-1,))
        assert np.array_equal(x0, x2)


if __name__ == "__main__":

    # cardinality = 8
    # messages_per_block = 3
    # num_of_blocks = 10
    # bits_per_symbol = 3
    #
    # #-----------------------------------
    #
    # mid_idx = int((messages_per_block - 1) / 2)
    #
    # ################################################################################################################
    #
    # # rand_int = np.random.randint(self.cardinality, size=(num_of_blocks * self.messages_per_block, 1))
    # rand_int = np.random.randint(2, size=(num_of_blocks * messages_per_block * bits_per_symbol, 1))
    #
    # # out = enc.fit_transform(rand_int)
    # out = rand_int
    #
    # # out_arr = np.reshape(out, (num_of_blocks, self.messages_per_block, self.cardinality))
    # out_arr = np.reshape(out, (num_of_blocks, messages_per_block, bits_per_symbol))
    #
    # out_arr_tf = tf.convert_to_tensor(out_arr, dtype=tf.float32)
    #
    #
    # n = int(math.log(cardinality, 2))
    # pows = tf.convert_to_tensor(np.power(2, np.linspace(n - 1, 0, n)).reshape(-1, 1), dtype=tf.float32)
    #
    # pows_np = pows.numpy()
    #
    # a = np.asarray([0, 1, 1]).reshape(1, -1)
    #
    # b = tf.tensordot(out_arr_tf, pows, axes=1).numpy()

    SAMPLING_FREQUENCY = 336e9
    CARDINALITY = 32
    SAMPLES_PER_SYMBOL = 100
    NUM_OF_SYMBOLS = 10
    DISPERSION_FACTOR = -21.7 * 1e-24
    FIBER_LENGTH = 50

    optical_channel = OpticalChannel(fs=SAMPLING_FREQUENCY,
                                     num_of_samples=NUM_OF_SYMBOLS * SAMPLES_PER_SYMBOL,
                                     dispersion_factor=DISPERSION_FACTOR,
                                     fiber_length=FIBER_LENGTH,
                                     rx_stddev=0,
                                     q_stddev=0)

    inp = np.random.randint(4, size=(NUM_OF_SYMBOLS, ))

    inp_t = np.repeat(inp, SAMPLES_PER_SYMBOL).reshape(1, -1)

    plt.plot(inp_t.flatten())

    out_tf = optical_channel(inp_t)

    out_np = out_tf.numpy()

    plt.plot(out_np.flatten())
    plt.show()


    pass