multiple_images_fft.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "batch_size = 1\n",
    "x_first = np.random.rand(batch_size, 256, 256)\n",
    "x_last = np.random.rand(256, 256, batch_size)\n",
    "\n",
    "def iterate_first_fft(arr):\n",
    "    k = np.empty_like(arr, dtype=np.complex64)\n",
    "    for i, a in enumerate(arr):\n",
    "        k[i] = np.fft.fft2(a)\n",
    "    return k\n",
    "\n",
    "def iterate_last_fft(arr):\n",
    "    k = np.empty_like(arr, dtype=np.complex64)\n",
    "    for i in range(arr.shape[-1]):\n",
    "        k[..., i] = np.fft.fft2(arr[..., i])\n",
    "    return k\n",
    "\n",
    "k_it_first = iterate_first_fft(x_first)\n",
    "k_np_first = np.fft.fftn(x_first, axes=(1, 2))\n",
    "np.testing.assert_allclose(k_it_first.real, k_np_first.real)\n",
    "np.testing.assert_allclose(k_it_first.imag, k_np_first.imag)\n",
    "\n",
    "k_it_last = iterate_last_fft(x_last)\n",
    "k_np_last = np.fft.fftn(x_last, axes=(0, 1))\n",
    "np.testing.assert_allclose(k_it_last.real, k_np_last.real)\n",
    "np.testing.assert_allclose(k_it_last.imag, k_np_last.imag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.29 ms ± 49.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "k_it_first = iterate_first_fft(x_first)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.23 ms ± 19.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "k_np_first = np.fft.fft2(x_first, axes=(1, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.23 ms ± 14.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "k_np_first = np.fft.fftn(x_first, axes=(1, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.17 ms ± 25.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "k_it_last = iterate_last_fft(x_last)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.24 ms ± 8.86 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "k_np_last = np.fft.fft2(x_last, axes=(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.25 ms ± 13.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "k_np_last = np.fft.fftn(x_last, axes=(0, 1))"
   ]
  }
 ],
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