conv_2d.ipynb

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   "cell_type": "markdown",
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   "source": [
    "2D convolution function based on Numpy's 1D convolution function:"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "58ac51c6",
   "metadata": {},
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   "source": [
    "import numpy as np\n",
    "\n",
    "def conv_2d(A, B):\n",
    "    n_A = A.shape[0]\n",
    "    n_B = B.shape[0]\n",
    "    B_p = np.pad(B, ((n_A-1, n_A-1), (0,0)))\n",
    "    n_G = n_A + n_B - 1\n",
    "    G = []\n",
    "    for i in range(n_G):\n",
    "        G_i = 0\n",
    "        for j in range(n_A):\n",
    "            G_i += np.convolve(A[j,], B_p[i-j+n_A-1,])\n",
    "        G.append(G_i)\n",
    "    G = np.vstack(G)\n",
    "    return G"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e0a8ab",
   "metadata": {},
   "source": [
    "Run the function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de342c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.random.randint(0, 10, (5, 4))\n",
    "B = np.random.randint(0, 10, (3, 7))\n",
    "\n",
    "G1 = conv_2d(A, B)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a6289ec",
   "metadata": {},
   "source": [
    "Check with Scipy's convolve function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4aab197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "from scipy.signal import convolve\n",
    "\n",
    "G2 = convolve(A, B)\n",
    "print(np.linalg.norm(G1 - G2)) #If difference norm is 0 then G1=G2"
   ]
  }
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