AttributeError: 'ImmutableDenseNDimArray' object has no attribute 'expand'

1

Need help to compute the integrate equation of ∫x(t)*e^(-st) dt where x(t) is my original signal after ifft calculation. The result shows the follwing error AttributeError: 'ImmutableDenseNDimArray' object has no attribute 'expand'. How can I resolve this issue?
Here is my code:

import torch
import numpy as np
import matplotlib.pyplot as plt
import os
from scipy import signal
import math
import scipy
import sympy as sym

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

f_s = 48000
f_c = 16000
f_l = 19000
f_r = 17000
b = f_l - f_r
n_order = 5
cut_off = 10


def high_pass_filter():
    # generate a random signal
    array_list = np.random.randn(1, 100, 1000)

    tensor_array = torch.from_numpy(array_list)

    reshape_tensor = tensor_array.reshape(2, 10, -1)

    reshape_img = reshape_tensor.reshape(-1)

    # compute the fft
    fft_signal = torch.fft.fft(reshape_tensor)
    print(fft_signal)
    reshape_fft_signal = fft_signal.reshape(-1)
    normalize_cutoff = (((f_c - b)/cut_off) / 2) / (f_s/2)  # normalize cut_off
    print("normalize : ", normalize_cutoff)
    down = math.sqrt(1 + ((f_c / normalize_cutoff)**(2*n_order)))
    print("down reponse : ", down)

    butterworth = torch.tensor(1 / down)

    G = torch.mul(butterworth, fft_signal)
    G_reshape = G.reshape(-1)
    output_filter = torch.fft.ifft2(G)
    print(output_filter)
    output_filter_reshape = output_filter.reshape(-1)

    plt.figure()
    plt.plot(reshape_img)
    plt.figure()
    plt.plot(reshape_fft_signal)
    plt.figure()
    plt.plot(G_reshape)
    plt.figure()
    plt.plot(output_filter_reshape)
    # plt.show()
    return output_filter


def pole_zero_plot(poles, zeros, ax=None):
    for pole in poles:
        plt.plot(complex(pole).real, complex(pole).imag, 'rx', markersize=10)
    for zero in zeros:
        plt.plot(complex(zero).real, complex(zero).imag, 'bo', markersize=10)

    if ax is None:
        ax = plt.gca()
    ax.set_aspect(1)

    plt.axis([-2, 2, -2, 2])
    plt.xlabel(r'$\Re\{s\}')
    plt.ylabel(r'$\Im\{s\}')
    plt.grid()


if __name__ == '__main__':
    sym.init_printing()

    signal = high_pass_filter()
    signal_numpy = signal.detach().numpy()

    t = sym.symbols('t', real=True)
    s = sym.symbols('s', complex=True)

    X = sym.integrate(signal_numpy * sym.exp(-s * t), (t, -sym.oo, sym.oo))
    print(X)

Error: X = sym.integrate(signal_numpy * sym.exp(-s * t), (t, -sym.oo, sym.oo))

Thanks…

2

The issue seems to be raised by sympy or are you only seeing it when PyTorch tensors are used?
In the former case, you might want to post in a sympy-specific discussion board or in their GitHub repository to get a faster answer from the devs.

3

The error claim is after converted datas from tensor to numpy signal_numpy = signal.detach().numpy(). While using tensors here the problem TypeError: unsupported operand type(s) for *: 'Tensor' and 'exp'. You are right, the main issue pointed on sympy, but how to match my data to this module. What the difference while using real data or random data, instead of using the simple equation?