Fastest_dimension_of_array.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "90E9vHJNFIt7",
        "tmAhddj7CzDO"
      ],
      "authorship_tag": "ABX9TyNtexmqjgLIhOjPJ3VwyNkV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hiro106/statistics-ml-cs-learning/blob/main/Fastest_dimension_of_array.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is the fastest axis of an array?  \n",
        "https://agilescientific.com/blog/2018/12/28/what-is-the-fastest-axis-of-an-array"
      ],
      "metadata": {
        "id": "ZKnNEa70F9Aq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://github.com/agilescientific/notebooks/blob/master/Fastest_dimension_of_array.ipynb"
      ],
      "metadata": {
        "id": "8XPs0fJwTCxe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "トレース＆カスタマイズで理解する  \n",
        "I'm comprehending the contents of the notebook above by tracing and customizing the code on it."
      ],
      "metadata": {
        "id": "q9Acx4NZTdR7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Which is the fastest axis of an array?\n",
        "\n",
        "I'd like to know: which axes of a NumPy array are fastest to access?"
      ],
      "metadata": {
        "id": "HwVkFMM4TGA_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "5UODgEeoTGDc"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVl9TImlTGGD",
        "outputId": "7a2215c7-a5c1-4e6c-e751-743a9b5dd8fc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'numpy' from '/usr/local/lib/python3.7/dist-packages/numpy/__init__.py'>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "TrtwKO3LTGI1"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matplotlib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ei8gjkHxTGLX",
        "outputId": "3cf225e8-4fa5-44c4-cd36-a617addb7a74"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using matplotlib backend: agg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "zWb1uMa0TGOJ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4niOKaY4TGQ6",
        "outputId": "6ff97957-4f84-4812-a89c-1143d2073d93"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.7/dist-packages/matplotlib/pyplot.py'>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## A tiny example"
      ],
      "metadata": {
        "id": "sRxNjS3VTGii"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.arange(9).reshape(3, 3)\n",
        "a"
      ],
      "metadata": {
        "id": "WCcxdE48Ud4f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c99939d-94b9-48b5-f69a-a4baba12ede0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2],\n",
              "       [3, 4, 5],\n",
              "       [6, 7, 8]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "' '.join(str(i) for i in a.ravel(order='C'))"
      ],
      "metadata": {
        "id": "X_7oTuIIUd7H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1c1dc296-bb2a-40e4-d806-3a3ef5269cdf"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0 1 2 3 4 5 6 7 8'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "' '.join(str(i) for i in a.ravel(order='F'))"
      ],
      "metadata": {
        "id": "d1gIqdomUd9w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a4604665-38bc-4673-fedf-f594ab9cd85d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0 3 6 1 4 7 2 5 8'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## A seismic volume"
      ],
      "metadata": {
        "id": "yFQirTsuTGmI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jvSSlCjkcqLe",
        "outputId": "d73e2a26-f145-420e-fc6a-868e5f3d9928"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "volume = np.load(\"/drive/My Drive/Colab Notebooks/data/F3_volume_3x3_16bit.npy\")"
      ],
      "metadata": {
        "id": "TmUXBmKwTGqk"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2X83a36u8HJO"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "volume"
      ],
      "metadata": {
        "id": "jmxLHkcFTGvw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ca38a11-4304-41e6-dc58-11b2032be27a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[    0,     0,     0, ...,  1779,  1476,  -129],\n",
              "        [    0,     0,     0, ...,  1175,  -273,   598],\n",
              "        [    0,     0,     0, ...,   574,  -526,   302],\n",
              "        ...,\n",
              "        [-1209, -1209, -1209, ...,  4127,  1873,  2032],\n",
              "        [ -151,  -151,  -151, ...,  3619,  2145,  1522],\n",
              "        [ -864,  -864,  -864, ...,  4260,  2667,   405]],\n",
              "\n",
              "       [[-1018, -1018, -1018, ...,  1050,  1238,   189],\n",
              "        [-1173, -1173, -1173, ...,  1310,   324,   289],\n",
              "        [ -417,  -417,  -417, ...,   823,  -466,   447],\n",
              "        ...,\n",
              "        [-2029, -2029, -2029, ...,  3973,   363,   468],\n",
              "        [-1869, -1869, -1869, ...,  3741,  1296,   967],\n",
              "        [-1065, -1065, -1065, ...,  3327,  2041,  1132]],\n",
              "\n",
              "       [[ -425,  -425,  -425, ...,   -12,  1142,  1162],\n",
              "        [  -42,   -42,   -42, ...,   760,  -109,   136],\n",
              "        [ -443,  -443,  -443, ...,    25,  -615,   195],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  3430,   638,   804],\n",
              "        [ -490,  -490,  -490, ...,  3741,  1139,   500],\n",
              "        [ -554,  -554,  -554, ...,  4370,  2453,   929]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[    0,     0,     0, ...,   461,  1059,   587],\n",
              "        [    0,     0,     0, ...,   109,  1079,   458],\n",
              "        [    0,     0,     0, ...,   701,  1261,   696],\n",
              "        ...,\n",
              "        [ -146,  -146,  -146, ...,  3392,  4560,  3552],\n",
              "        [ -883,  -883,  -883, ...,  2628,  2509,  2005],\n",
              "        [ -680,  -680,  -680, ..., -1749, -1931, -1902]],\n",
              "\n",
              "       [[ -323,  -323,  -323, ...,  1817,   422,  -714],\n",
              "        [    0,     0,     0, ...,  1291,  -247,  -891],\n",
              "        [ -849,  -849,  -849, ...,   654,   798,  -616],\n",
              "        ...,\n",
              "        [ -346,  -346,  -346, ...,  1571,  3727,  3654],\n",
              "        [ -553,  -553,  -553, ...,   372,  -130,   737],\n",
              "        [ -612,  -612,  -612, ...,  -248,   191, -1447]],\n",
              "\n",
              "       [[    0,     0,     0, ...,  1677,   857,   -56],\n",
              "        [    0,     0,     0, ...,   643,  1177,   275],\n",
              "        [    0,     0,     0, ...,  -263,   575,   790],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  1999,  2591,  2373],\n",
              "        [    0,     0,     0, ...,  -299,  1199,   480],\n",
              "        [    0,     0,     0, ...,   845,    94, -1577]]], dtype=int16)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "volume.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUQLYgZnbcgc",
        "outputId": "075f3f3a-3cd5-4f23-af59-d27e5344ae74"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(194, 299, 451)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look at how the indices vary:"
      ],
      "metadata": {
        "id": "_JfDwwaRbcjF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "idx = np.indices(volume.shape)"
      ],
      "metadata": {
        "id": "4608KmcybcmP"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "idx.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwqC1phMbco1",
        "outputId": "79473cfe-3231-43cb-aad4-f712c287c55b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 194, 299, 451)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDzFbtdnbcrz",
        "outputId": "3feab254-33a9-41a9-d028-41fd0c6dd0cb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  0,   0,   0, ...,   0,   0,   0],\n",
              "         ...,\n",
              "         [  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  0,   0,   0, ...,   0,   0,   0]],\n",
              "\n",
              "        [[  1,   1,   1, ...,   1,   1,   1],\n",
              "         [  1,   1,   1, ...,   1,   1,   1],\n",
              "         [  1,   1,   1, ...,   1,   1,   1],\n",
              "         ...,\n",
              "         [  1,   1,   1, ...,   1,   1,   1],\n",
              "         [  1,   1,   1, ...,   1,   1,   1],\n",
              "         [  1,   1,   1, ...,   1,   1,   1]],\n",
              "\n",
              "        [[  2,   2,   2, ...,   2,   2,   2],\n",
              "         [  2,   2,   2, ...,   2,   2,   2],\n",
              "         [  2,   2,   2, ...,   2,   2,   2],\n",
              "         ...,\n",
              "         [  2,   2,   2, ...,   2,   2,   2],\n",
              "         [  2,   2,   2, ...,   2,   2,   2],\n",
              "         [  2,   2,   2, ...,   2,   2,   2]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[191, 191, 191, ..., 191, 191, 191],\n",
              "         [191, 191, 191, ..., 191, 191, 191],\n",
              "         [191, 191, 191, ..., 191, 191, 191],\n",
              "         ...,\n",
              "         [191, 191, 191, ..., 191, 191, 191],\n",
              "         [191, 191, 191, ..., 191, 191, 191],\n",
              "         [191, 191, 191, ..., 191, 191, 191]],\n",
              "\n",
              "        [[192, 192, 192, ..., 192, 192, 192],\n",
              "         [192, 192, 192, ..., 192, 192, 192],\n",
              "         [192, 192, 192, ..., 192, 192, 192],\n",
              "         ...,\n",
              "         [192, 192, 192, ..., 192, 192, 192],\n",
              "         [192, 192, 192, ..., 192, 192, 192],\n",
              "         [192, 192, 192, ..., 192, 192, 192]],\n",
              "\n",
              "        [[193, 193, 193, ..., 193, 193, 193],\n",
              "         [193, 193, 193, ..., 193, 193, 193],\n",
              "         [193, 193, 193, ..., 193, 193, 193],\n",
              "         ...,\n",
              "         [193, 193, 193, ..., 193, 193, 193],\n",
              "         [193, 193, 193, ..., 193, 193, 193],\n",
              "         [193, 193, 193, ..., 193, 193, 193]]],\n",
              "\n",
              "\n",
              "       [[[  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  1,   1,   1, ...,   1,   1,   1],\n",
              "         [  2,   2,   2, ...,   2,   2,   2],\n",
              "         ...,\n",
              "         [296, 296, 296, ..., 296, 296, 296],\n",
              "         [297, 297, 297, ..., 297, 297, 297],\n",
              "         [298, 298, 298, ..., 298, 298, 298]],\n",
              "\n",
              "        [[  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  1,   1,   1, ...,   1,   1,   1],\n",
              "         [  2,   2,   2, ...,   2,   2,   2],\n",
              "         ...,\n",
              "         [296, 296, 296, ..., 296, 296, 296],\n",
              "         [297, 297, 297, ..., 297, 297, 297],\n",
              "         [298, 298, 298, ..., 298, 298, 298]],\n",
              "\n",
              "        [[  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  1,   1,   1, ...,   1,   1,   1],\n",
              "         [  2,   2,   2, ...,   2,   2,   2],\n",
              "         ...,\n",
              "         [296, 296, 296, ..., 296, 296, 296],\n",
              "         [297, 297, 297, ..., 297, 297, 297],\n",
              "         [298, 298, 298, ..., 298, 298, 298]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  1,   1,   1, ...,   1,   1,   1],\n",
              "         [  2,   2,   2, ...,   2,   2,   2],\n",
              "         ...,\n",
              "         [296, 296, 296, ..., 296, 296, 296],\n",
              "         [297, 297, 297, ..., 297, 297, 297],\n",
              "         [298, 298, 298, ..., 298, 298, 298]],\n",
              "\n",
              "        [[  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  1,   1,   1, ...,   1,   1,   1],\n",
              "         [  2,   2,   2, ...,   2,   2,   2],\n",
              "         ...,\n",
              "         [296, 296, 296, ..., 296, 296, 296],\n",
              "         [297, 297, 297, ..., 297, 297, 297],\n",
              "         [298, 298, 298, ..., 298, 298, 298]],\n",
              "\n",
              "        [[  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  1,   1,   1, ...,   1,   1,   1],\n",
              "         [  2,   2,   2, ...,   2,   2,   2],\n",
              "         ...,\n",
              "         [296, 296, 296, ..., 296, 296, 296],\n",
              "         [297, 297, 297, ..., 297, 297, 297],\n",
              "         [298, 298, 298, ..., 298, 298, 298]]],\n",
              "\n",
              "\n",
              "       [[[  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         ...,\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450]],\n",
              "\n",
              "        [[  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         ...,\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450]],\n",
              "\n",
              "        [[  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         ...,\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         ...,\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450]],\n",
              "\n",
              "        [[  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         ...,\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450]],\n",
              "\n",
              "        [[  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         ...,\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450],\n",
              "         [  0,   1,   2, ..., 448, 449, 450]]]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fm17pLmtdMPS"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.font_manager import FontProperties"
      ],
      "metadata": {
        "id": "qZf1l9WEdMRx"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "annot = ['data[2, :, :]', 'data[:, 2, :]', 'data[:, :, 2]']"
      ],
      "metadata": {
        "id": "19L3XW2xdMUZ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "annot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1Wj6ueOdMW5",
        "outputId": "d2822210-48d4-4636-9439-50080298fab9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['data[2, :, :]', 'data[:, 2, :]', 'data[:, :, 2]']"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mono = FontProperties()"
      ],
      "metadata": {
        "id": "3f_ZtvZYbcvB"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mono"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5amEDLYlLus",
        "outputId": "a118471c-5437-4105-c3bf-517de0c6ab11"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.font_manager.FontProperties at 0x7f8281923f90>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mono.set_family('monospace')"
      ],
      "metadata": {
        "id": "-3CXEFwZlMPP"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mono"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-Li-F78lMR6",
        "outputId": "21a86a20-eb41-460e-950f-5edf2c2a6bc7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.font_manager.FontProperties at 0x7f8281923f90>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(ncols=3, figsize=(15,3), facecolor='w')"
      ],
      "metadata": {
        "id": "yXtSsXmllMUh"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "bzW1WFxAlj9V",
        "outputId": "173ecc48-5cc3-4f39-cead-af857fefc013"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1080x216 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "axs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XOt-MEitlkAA",
        "outputId": "9a11c90f-93aa-4ebd-e946-86813ebd3737"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f8288dd4490>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f8281900d10>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f82818c7310>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, ax in enumerate(axs):\n",
        "    data = idx[i, :5, :5, :5].ravel(order='C')\n",
        "    ax.plot(data, c=f'C{i}')\n",
        "    ax.scatter(np.where(data==2), data[data==2], color='r', s=10, zorder=10)\n",
        "    "
      ],
      "metadata": {
        "id": "p-F6EE5alkCm"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7uZI8sJ7khH",
        "outputId": "b42b4b9b-6462-484a-e8f2-224a87cd7fc9"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1,\n",
              "       2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3,\n",
              "       4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0,\n",
              "       1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2,\n",
              "       3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4,\n",
              "       0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_ax0 = idx[0, :5, :5, :5]"
      ],
      "metadata": {
        "id": "lR6Pe7F2lkF8"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_ax0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rKcvVlbxlkIq",
        "outputId": "536fa010-088e-4007-cec3-dc1109e51c46"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0]],\n",
              "\n",
              "       [[1, 1, 1, 1, 1],\n",
              "        [1, 1, 1, 1, 1],\n",
              "        [1, 1, 1, 1, 1],\n",
              "        [1, 1, 1, 1, 1],\n",
              "        [1, 1, 1, 1, 1]],\n",
              "\n",
              "       [[2, 2, 2, 2, 2],\n",
              "        [2, 2, 2, 2, 2],\n",
              "        [2, 2, 2, 2, 2],\n",
              "        [2, 2, 2, 2, 2],\n",
              "        [2, 2, 2, 2, 2]],\n",
              "\n",
              "       [[3, 3, 3, 3, 3],\n",
              "        [3, 3, 3, 3, 3],\n",
              "        [3, 3, 3, 3, 3],\n",
              "        [3, 3, 3, 3, 3],\n",
              "        [3, 3, 3, 3, 3]],\n",
              "\n",
              "       [[4, 4, 4, 4, 4],\n",
              "        [4, 4, 4, 4, 4],\n",
              "        [4, 4, 4, 4, 4],\n",
              "        [4, 4, 4, 4, 4],\n",
              "        [4, 4, 4, 4, 4]]])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_ax1 = idx[1, :5, :5, :5]\n",
        "data_ax1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfLQzWfp6X_a",
        "outputId": "fe60fced-3004-445f-e79e-5a1ec86d054f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 1, 1],\n",
              "        [2, 2, 2, 2, 2],\n",
              "        [3, 3, 3, 3, 3],\n",
              "        [4, 4, 4, 4, 4]],\n",
              "\n",
              "       [[0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 1, 1],\n",
              "        [2, 2, 2, 2, 2],\n",
              "        [3, 3, 3, 3, 3],\n",
              "        [4, 4, 4, 4, 4]],\n",
              "\n",
              "       [[0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 1, 1],\n",
              "        [2, 2, 2, 2, 2],\n",
              "        [3, 3, 3, 3, 3],\n",
              "        [4, 4, 4, 4, 4]],\n",
              "\n",
              "       [[0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 1, 1],\n",
              "        [2, 2, 2, 2, 2],\n",
              "        [3, 3, 3, 3, 3],\n",
              "        [4, 4, 4, 4, 4]],\n",
              "\n",
              "       [[0, 0, 0, 0, 0],\n",
              "        [1, 1, 1, 1, 1],\n",
              "        [2, 2, 2, 2, 2],\n",
              "        [3, 3, 3, 3, 3],\n",
              "        [4, 4, 4, 4, 4]]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_ax2 = idx[2, :5, :5, :5]\n",
        "data_ax2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0uFYgMX6riL",
        "outputId": "4c31b686-ac35-41e2-f0a8-b2f1d83464a5"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4]],\n",
              "\n",
              "       [[0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4]],\n",
              "\n",
              "       [[0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4]],\n",
              "\n",
              "       [[0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4]],\n",
              "\n",
              "       [[0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4],\n",
              "        [0, 1, 2, 3, 4]]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_c = data.ravel(order='C')\n",
        "data_c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlywI3yTlkLC",
        "outputId": "955299f8-7098-4168-f2ae-520edf8759f8"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1,\n",
              "       2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3,\n",
              "       4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0,\n",
              "       1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2,\n",
              "       3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4,\n",
              "       0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_f = data.ravel(order='F')\n",
        "data_f"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oj_8OycxlkNq",
        "outputId": "960c9ef4-7671-4cec-ff1d-b769d8aa6868"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1,\n",
              "       2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3,\n",
              "       4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0,\n",
              "       1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2,\n",
              "       3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4,\n",
              "       0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = idx[i, :5, :5, :5].ravel(order='C')"
      ],
      "metadata": {
        "id": "RVCwIHnkaC1m"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OuGfLIw5aLoJ",
        "outputId": "29f3d88f-3ad7-4076-9fe3-4b45c5ba145d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1,\n",
              "       2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3,\n",
              "       4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0,\n",
              "       1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2,\n",
              "       3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4,\n",
              "       0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ax.plot(data, c=f'C0')"
      ],
      "metadata": {
        "id": "1r6MN1JxlkQA"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ax.scatter(np.where(data==2), data[data==2], color='r', s=10, zorder=10)"
      ],
      "metadata": {
        "id": "_K04Ux7elkSe"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ax.text(65, 4.3, 'axis 0', color='C0', size=15, ha='center')"
      ],
      "metadata": {
        "id": "no3_Ezi_lkWR"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\"CN\" color spec where 'C' precedes a number acting as an index into the default property cycle.  \n",
        "https://matplotlib.org/stable/tutorials/colors/colors.html"
      ],
      "metadata": {
        "id": "xxDjmV--lMXE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ax.text(65, -0.7, annot[0], color='red', size=12, ha='center', fontproperties=mono)"
      ],
      "metadata": {
        "id": "mU_a1mSTlMaL"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax.set_ylim(-1, 5)"
      ],
      "metadata": {
        "id": "Rj1m5qjHlMdC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e3362f2-9519-41e9-d883-9c5a01cda71c"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.0, 5.0)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K1AcdJ-L08dl"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i, ax in enumerate(axs):\n",
        "    data = idx[i, :5, :5, :5].ravel(order='C')\n",
        "    ax.plot(data, c=f'C{i}')\n",
        "    ax.scatter(np.where(data==2), data[data==2], color='r', s=10, zorder=10)\n",
        "    ax.text(65, 4.3, f'axis {i}', color=f'C{i}', size=15, ha='center')\n",
        "    ax.text(65, -0.7, annot[i], color='red', size=12, ha='center', fontproperties=mono)\n",
        "    ax.set_ylim(-1, 5)"
      ],
      "metadata": {
        "id": "o8GDlAKe08om"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "_ = plt.suptitle(\"C order\", size=18)"
      ],
      "metadata": {
        "id": "QVHSXVdA4NB-"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "xTiPkaX_08z0",
        "outputId": "06fa631f-5b71-44d3-881c-edc4c043ced2"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1080x216 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.savefig(\"/drive/My Drive/Colab Notebooks/3d-array-corder.png\")"
      ],
      "metadata": {
        "id": "6GxYPoG-8LMm"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i, ax in enumerate(axs):\n",
        "    print(i, ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lx1xPldx4shK",
        "outputId": "2786c761-35db-405a-9d50-bbbb0e62d410"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 AxesSubplot(0.125,0.125;0.227941x0.755)\n",
            "1 AxesSubplot(0.398529,0.125;0.227941x0.755)\n",
            "2 AxesSubplot(0.672059,0.125;0.227941x0.755)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tested the behaviors of np.indices in 'numpy.indices.ipynb'."
      ],
      "metadata": {
        "id": "_vxtDO-B4sm3"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(ncols=3, figsize=(15, 3), facecolor='w')"
      ],
      "metadata": {
        "id": "hyTgYxqyPZkN"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "xLG_K6e7PZsP",
        "outputId": "c73f7f68-7a88-4fc4-a16d-99ce56f21fbc"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1080x216 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "axs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-KCm5NLpZAqB",
        "outputId": "b9802785-ffbe-4c3b-a573-cd27f32e9991"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f828132f8d0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f8281211490>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f82811c8790>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, ax in enumerate(axs):\n",
        "    data = idx[i, :5, :5, :5].ravel(order='F')\n",
        "    ax.plot(data, c=f'C{i}')\n",
        "    ax.scatter(np.where(data==2), data[data==2], color='r', s=10, zorder=10)\n",
        "    ax.text(65, 4.3, f'axis {i}', color=f'C{i}', size=15, ha='center')\n",
        "    ax.text(65, -0.7, annot[i], color='red', size=12, ha='center', fontproperties=mono)\n",
        "    ax.set_ylim(-1, 5)\n",
        "_ = plt.suptitle(\"Fortran order\", size=18)\n",
        "plt.savefig('/drive/My Drive/Colab Notebooks/3d-array-forder.png')"
      ],
      "metadata": {
        "id": "2zR2wNPPZA15"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig"
      ],
      "metadata": {
        "id": "QBqcnGxWZBBG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "59859e9c-1a29-4551-e7c4-1fef592ddd2c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1080x216 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig_together = plt.figure(figsize=(15, 3))\n",
        "plt.plot(idx[0, :5, :5, :5].ravel(), zorder=10)\n",
        "plt.plot(idx[1, :5, :5, :5].ravel(), zorder=9)\n",
        "plt.plot(idx[2, :5, :5, :5].ravel(), zorder=8)"
      ],
      "metadata": {
        "id": "2ha_9_ZlZBJB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f5e7086-1e39-42f4-e15b-261d4457d08f"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8281084750>]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig_together"
      ],
      "metadata": {
        "id": "sIXBcAPVZBSN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "outputId": "4eeafcab-230e-4f93-c2e7-a6eec2e5f515"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "volume.strides"
      ],
      "metadata": {
        "id": "cT-aZurfZBbG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e920778f-a70c-4b59-b48e-a9095d516f0f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(269698, 902, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Aside: figure for blog post"
      ],
      "metadata": {
        "id": "IhOaNdTMZBhv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(ncols=2, figsize=(10,3), facecolor='w')"
      ],
      "metadata": {
        "id": "8VD9hMO0AOtd"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "Nx8g9eO-AOwd",
        "outputId": "71072bcd-d913-459b-92b8-b70a49ff749b"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 720x216 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlsAAADGCAYAAADlokXFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAASYUlEQVR4nO3dX2hb9R/G8ScS69jN3MaKLqm6GK1tR7dB6qritA6sTgxezFqFzTExUwuCiu5qRUWxIApiVcgsThmk+OciRdeKU7YLcdZYQbs4Gmc3myDaKm5T3Lq139/FD4tlf85pk2/PMXu/roznS8/nw8zDY5olAWOMEQAAAKy4wOsBAAAAyhllCwAAwCLKFgAAgEWULQAAAIsoWwAAABZRtgAAACxyLFubN29WZWWlli9ffsbrxhg9+uijikajqq+v18DAQMmHBIDZIsMAeM2xbG3atEl9fX1nvd7b26tcLqdcLqdkMqmHH364pAMCQDHIMABecyxba9as0aJFi856PZ1Oa+PGjQoEAmpsbNQff/yhn3/+uaRDAsBskWEAvFb0e7YKhYKqqqqmHofDYRUKhWJ/LADMCTIMgG3BubxZMplUMpmUJB04cEDXXHPNXN4egMcOHTqksbExr8eYFfILOL8Vk19Fl61QKKSRkZGpx/l8XqFQ6IxnE4mEEomEJCkWiymTyRR7ewD/IbFYzOsRTuM2w8gv4PxWTH4V/WvEeDyud955R8YY7du3TwsWLNCll15a7I8FgDlBhgGwzfGVrXvvvVd79uzR2NiYwuGwnnnmGZ08eVKS9NBDD2ndunXatWuXotGo5s+fr7feesv60ADgFhkGwGuOZSuVSp3zeiAQ0GuvvVaygQCglMgwAF7jE+QBAAAsomwBAABYRNkCAACwiLIFAABgEWULAADAIsoWAACARZQtAAAAiyhbAAAAFlG2AAAALKJsAQAAWETZAgAAsIiyBQAAYBFlCwAAwCLKFgAAgEWULQAAAIsoWwAAABZRtgAAACyibAEAAFhE2QIAALCIsgUAAGARZQsAAMAiyhYAAIBFlC0AAACLKFsAAAAWuSpbfX19qq6uVjQaVUdHx2nXf/rpJzU1NWnVqlWqr6/Xrl27Sj4oAMwG+QXAa45la2JiQm1tbert7VU2m1UqlVI2m5125rnnnlNLS4u++eYbdXd365FHHrE2MAC4RX4B8APHstXf369oNKpIJKKKigq1trYqnU5POxMIBHT06FFJ0pEjR7R06VI70wLADJBfAPwg6HSgUCioqqpq6nE4HNaXX3457czTTz+tW2+9Va+++qr++usv7d69+4w/K5lMKplMSpJGR0eLmRsAHJFfAPygJG+QT6VS2rRpk/L5vHbt2qUNGzZocnLytHOJREKZTEaZTEZLliwpxa0BoCjkFwDbHMtWKBTSyMjI1ON8Pq9QKDTtTFdXl1paWiRJ1113nY4fP66xsbESjwoAM0N+AfADx7LV0NCgXC6n4eFhjY+Pq7u7W/F4fNqZyy67TJ9++qkk6fvvv9fx48f5Pz8AniO/APiBY9kKBoPq7OxUc3Ozampq1NLSorq6OrW3t6unp0eS9NJLL2n79u1asWKF7r33Xu3YsUOBQMD68ABwLuQXAD8IGGOMFzeOxWLKZDJe3BqAR8rleV8uewBwr5jnPZ8gDwAAYBFlCwAAwCLKFgAAgEWULQAAAIsoWwAAABZRtgAAACyibAEAAFhE2QIAALCIsgUAAGARZQsAAMAiyhYAAIBFlC0AAACLKFsAAAAWUbYAAAAsomwBAABYRNkCAACwiLIFAABgEWULAADAIsoWAACARZQtAAAAiyhbAAAAFlG2AAAALKJsAQAAWOSqbPX19am6ulrRaFQdHR1nPPPuu++qtrZWdXV1uu+++0o6JADMFvkFwGtBpwMTExNqa2vTJ598onA4rIaGBsXjcdXW1k6dyeVyeuGFF/T5559r4cKF+vXXX60ODQBukF8A/MDxla3+/n5Fo1FFIhFVVFSotbVV6XR62pnt27erra1NCxculCRVVlbamRYAZoD8AuAHjmWrUCioqqpq6nE4HFahUJh2ZmhoSENDQ7rhhhvU2Niovr6+0k8KADNEfgHwA8dfI7px6tQp5XI57dmzR/l8XmvWrNF3332niy++eNq5ZDKpZDIpSRodHS3FrQGgKOQXANscX9kKhUIaGRmZepzP5xUKhaadCYfDisfjuvDCC7Vs2TJdffXVyuVyp/2sRCKhTCajTCajJUuWlGB8ADg78guAHziWrYaGBuVyOQ0PD2t8fFzd3d2Kx+PTztx1113as2ePJGlsbExDQ0OKRCJWBgYAt8gvAH7gWLaCwaA6OzvV3NysmpoatbS0qK6uTu3t7erp6ZEkNTc3a/HixaqtrVVTU5NefPFFLV682PrwAHAu5BcAPwgYY4wXN47FYspkMl7cGoBHyuV5Xy57AHCvmOc9nyAPAABgEWULAADAIsoWAACARZQtAAAAiyhbAAAAFlG2AAAALKJsAQAAWETZAgAAsIiyBQAAYBFlCwAAwCLKFgAAgEWULQAAAIsoWwAAABZRtgAAACyibAEAAFhE2QIAALCIsgUAAGARZQsAAMAiyhYAAIBFlC0AAACLKFsAAAAWUbYAAAAsomwBAABYRNkCAACwyFXZ6uvrU3V1taLRqDo6Os567oMPPlAgEFAmkynZgABQDPILgNccy9bExITa2trU29urbDarVCqlbDZ72rljx47plVde0erVq60MCgAzRX4B8APHstXf369oNKpIJKKKigq1trYqnU6fdm7btm3aunWr5s2bZ2VQAJgp8guAHziWrUKhoKqqqqnH4XBYhUJh2pmBgQGNjIzojjvuKP2EADBL5BcAPwgW+wMmJyf1+OOPa8eOHY5nk8mkksmkJGl0dLTYWwNAUcgvAHPB8ZWtUCikkZGRqcf5fF6hUGjq8bFjxzQ4OKibb75ZV1xxhfbt26d4PH7GN5kmEgllMhllMhktWbKkRCsAwJmRXwD8wLFsNTQ0KJfLaXh4WOPj4+ru7lY8Hp+6vmDBAo2NjenQoUM6dOiQGhsb1dPTo1gsZnVwAHBCfgHwA8eyFQwG1dnZqebmZtXU1KilpUV1dXVqb29XT0/PXMwIALNCfgHwg4Axxnhx41gsxufZAOeZcnnel8seANwr5nnPJ8gDAABYRNkCAACwiLIFAABgEWULAADAIsoWAACARZQtAAAAiyhbAAAAFlG2AAAALKJsAQAAWETZAgAAsIiyBQAAYBFlCwAAwCLKFgAAgEWULQAAAIsoWwAAABZRtgAAACyibAEAAFhE2QIAALCIsgUAAGARZQsAAMAiyhYAAIBFlC0AAACLKFsAAAAWUbYAAAAsclW2+vr6VF1drWg0qo6OjtOuv/zyy6qtrVV9fb3Wrl2rw4cPl3xQAJgN8guA1xzL1sTEhNra2tTb26tsNqtUKqVsNjvtzKpVq5TJZPTtt99q/fr1euqpp6wNDABukV8A/MCxbPX39ysajSoSiaiiokKtra1Kp9PTzjQ1NWn+/PmSpMbGRuXzeTvTAsAMkF8A/MCxbBUKBVVVVU09DofDKhQKZz3f1dWl22+//YzXksmkYrGYYrGYRkdHZzEuALhHfgHwg2Apf9jOnTuVyWS0d+/eM15PJBJKJBKSpFgsVspbA0BRyC8AtjiWrVAopJGRkanH+XxeoVDotHO7d+/W888/r7179+qiiy4q7ZQAMAvkFwA/cPw1YkNDg3K5nIaHhzU+Pq7u7m7F4/FpZ7755htt2bJFPT09qqystDYsAMwE+QXADxzLVjAYVGdnp5qbm1VTU6OWlhbV1dWpvb1dPT09kqQnn3xSf/75p+6++26tXLnytDADAC+QXwD8IGCMMV7cOBaLKZPJeHFrAB4pl+d9uewBwL1invd8gjwAAIBFlC0AAACLKFsAAAAWUbYAAAAsomwBAABYRNkCAACwiLIFAABgEWULAADAIsoWAACARZQtAAAAiyhbAAAAFlG2AAAALKJsAQAAWETZAgAAsIiyBQAAYBFlCwAAwCLKFgAAgEWULQAAAIsoWwAAABZRtgAAACyibAEAAFhE2QIAALCIsgUAAGCRq7LV19en6upqRaNRdXR0nHb9xIkTuueeexSNRrV69WodOnSo1HMCwKyQXwC85li2JiYm1NbWpt7eXmWzWaVSKWWz2Wlnurq6tHDhQv3www967LHHtHXrVmsDA4Bb5BcAP3AsW/39/YpGo4pEIqqoqFBra6vS6fS0M+l0Wvfff78kaf369fr0009ljLEzMQC4RH4B8APHslUoFFRVVTX1OBwOq1AonPVMMBjUggUL9Ntvv5V4VACYGfILgB8E5/JmyWRSyWRSkjQ4OKhYLDaXt7dmdHRUS5Ys8XqMkiiXXcplD6m8djlw4IDXI8wa+eV/7OI/5bKHVFx+OZatUCikkZGRqcf5fF6hUOiMZ8LhsE6dOqUjR45o8eLFp/2sRCKhRCIhSYrFYspkMrMe3E/YxX/KZQ+p/HaZS+SXM3bxp3LZpVz2kIrLL8dfIzY0NCiXy2l4eFjj4+Pq7u5WPB6fdiYej+vtt9+WJL3//vu65ZZbFAgEZj0UAJQC+QXADxxf2QoGg+rs7FRzc7MmJia0efNm1dXVqb29XbFYTPF4XA888IA2bNigaDSqRYsWqbu7ey5mB4BzIr8A+IGr92ytW7dO69atm/bvnn322al/njdvnt57770Z3fifl+PLAbv4T7nsIbFLscivc2MXfyqXXcplD6m4XQKGv+MMAABgDV/XAwAAYJH1slUuX5XhtMfLL7+s2tpa1dfXa+3atTp8+LAHU7rjtMs/PvjgAwUCAV//TRI3u7z77ruqra1VXV2d7rvvvjme0D2nXX766Sc1NTVp1apVqq+v165duzyY0tnmzZtVWVmp5cuXn/G6MUaPPvqootGo6uvrNTAwMMcTulcu+SWRYX5EfvmPtfwyFp06dcpEIhFz8OBBc+LECVNfX2/2798/7cxrr71mtmzZYowxJpVKmZaWFpsjzYqbPT777DPz119/GWOMef311325hzHudjHGmKNHj5obb7zRrF692nz11VceTOrMzS5DQ0Nm5cqV5vfffzfGGPPLL794MaojN7s8+OCD5vXXXzfGGLN//35z+eWXezCps71795qvv/7a1NXVnfH6Rx99ZG677TYzOTlpvvjiC3PttdfO8YTulEt+GUOG+THDyK/LPZjUma38svrKVrl8VYabPZqamjR//nxJUmNjo/L5vBejOnKziyRt27ZNW7du1bx58zyY0h03u2zfvl1tbW1auHChJKmystKLUR252SUQCOjo0aOSpCNHjmjp0qVejOpozZo1WrRo0Vmvp9Npbdy4UYFAQI2Njfrjjz/0888/z+GE7pRLfklkmB+RX+dXflktW+XyVRlu9vi3rq4u3X777XMx2oy52WVgYEAjIyO644475nq8GXGzy9DQkIaGhnTDDTeosbFRfX19cz2mK252efrpp7Vz506Fw2GtW7dOr7766lyPWRIzfT55pVzySyLD/Ij8Or/ya06/rud8sHPnTmUyGe3du9frUWZlcnJSjz/+uHbs2OH1KCVx6tQp5XI57dmzR/l8XmvWrNF3332niy++2OvRZiyVSmnTpk164okn9MUXX2jDhg0aHBzUBRfw91xQOmSYf5Bf5cPqljP5qgxJ5/yqDC+52UOSdu/ereeff149PT266KKL5nJE15x2OXbsmAYHB3XzzTfriiuu0L59+xSPx335BlM3fy7hcFjxeFwXXnihli1bpquvvlq5XG6uR3XkZpeuri61tLRIkq677jodP35cY2NjczpnKbh9PnmtXPJLIsP8mGHk13mWX6V7W9npTp48aZYtW2Z+/PHHqTfNDQ4OTjvT2dk57Q2md999t82RZsXNHgMDAyYSiZihoSGPpnTHzS7/dtNNN/nyzaXGuNult7fXbNy40RhjzOjoqAmHw2ZsbMyLcc/JzS633Xabeeutt4wxxmSzWXPppZeayclJD6Z1Njw8fNY3mH744YfT3mDa0NAwx9O5Uy75ZQwZ5scMI7/Or/yyWraM+f8796+66ioTiUTMc889Z4wxZtu2bSadThtjjPn777/N+vXrzZVXXmkaGhrMwYMHbY80K057rF271lRWVpoVK1aYFStWmDvvvNPLcc/JaZd/82tQ/cNpl8nJSfPYY4+Zmpoas3z5cpNKpbwc95ycdtm/f7+5/vrrTX19vVmxYoX5+OOPvRz3rFpbW80ll1xigsGgCYVC5s033zRvvPGGeeONN4wx//8zeeSRR0wkEjHLly//T//39V/JL2PIMD8iv/zHVn7xCfIAAAAWnR/vTAMAAPAIZQsAAMAiyhYAAIBFlC0AAACLKFsAAAAWUbYAAAAsomwBAABYRNkCAACw6H/UlsgYnMBTywAAAABJRU5ErkJggg==\n"
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, ax in enumerate(axs):\n",
        "    data = idx[i, :3, :3, 0].ravel(order='C')\n",
        "    ax.plot(data, 'o-', c='gray')\n",
        "    ax.text(0, 1.8, f'axis {i}', color='gray', size=15, ha='left')\n",
        "plt.savefig('/drive/My Drive/Colab Notebooks/2d-array-corder.png')"
      ],
      "metadata": {
        "id": "_QAZquadAOzO"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "GKZwblCYAO17",
        "outputId": "eb593552-0a8d-4636-eeac-b1c0e5edcd02"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 720x216 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx[0, :3, :3, :3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0a6i3Y4-Cr1-",
        "outputId": "17f2fbf0-b9ac-4ce3-c49b-f50b8dc8e990"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 0, 0],\n",
              "        [0, 0, 0],\n",
              "        [0, 0, 0]],\n",
              "\n",
              "       [[1, 1, 1],\n",
              "        [1, 1, 1],\n",
              "        [1, 1, 1]],\n",
              "\n",
              "       [[2, 2, 2],\n",
              "        [2, 2, 2],\n",
              "        [2, 2, 2]]])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx[0, :3, :3, :3].ravel(order='C')"
      ],
      "metadata": {
        "id": "hhQ9wXAgPZyM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2791d6be-b99f-4a14-a8ad-8f8cc9b7693c"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx[1, :3, :3, :3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIiXTaAPCt6l",
        "outputId": "3e0fa376-43d0-48f7-d9bc-bf8a4f3b6325"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 0, 0],\n",
              "        [1, 1, 1],\n",
              "        [2, 2, 2]],\n",
              "\n",
              "       [[0, 0, 0],\n",
              "        [1, 1, 1],\n",
              "        [2, 2, 2]],\n",
              "\n",
              "       [[0, 0, 0],\n",
              "        [1, 1, 1],\n",
              "        [2, 2, 2]]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx[1, :3, :3, :3].ravel(order='C')"
      ],
      "metadata": {
        "id": "apuMvyBuPZ5P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50b89409-a421-4be6-bcfa-a1c1099d3d09"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 1, 1, 1, 2, 2, 2, 0, 0, 0, 1, 1, 1, 2, 2, 2, 0, 0, 0, 1,\n",
              "       1, 1, 2, 2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx[2, :3, :3, :3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBoELOYXCv-K",
        "outputId": "a9e70f28-3d86-4924-c030-6691e81074c0"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 1, 2],\n",
              "        [0, 1, 2],\n",
              "        [0, 1, 2]],\n",
              "\n",
              "       [[0, 1, 2],\n",
              "        [0, 1, 2],\n",
              "        [0, 1, 2]],\n",
              "\n",
              "       [[0, 1, 2],\n",
              "        [0, 1, 2],\n",
              "        [0, 1, 2]]])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx[2, :3, :3, :3].ravel(order='C')"
      ],
      "metadata": {
        "id": "z1-PQmZaPZ_W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a56cddb-231e-487c-adaa-120054336367"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0,\n",
              "       1, 2, 0, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Accessing the seismic data"
      ],
      "metadata": {
        "id": "ITM5CtkfCpUY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ndarray.strides\n",
        "\n",
        "numpy.ndarray.strides  \n",
        "https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html\n",
        "\n",
        "Bits and Bytes  \n",
        "https://web.stanford.edu/class/cs101/bits-bytes.html\n",
        "\n",
        "2,147,483,647  \n",
        "https://en.wikipedia.org/wiki/2,147,483,647#:~:text=The%20number%202%2C147%2C483%2C647%20(or%20hexadecimal,int%20)%20in%20many%20programming%20languages.\n",
        "\n",
        "Gangnam Style overflows INT_MAX, forces YouTube to go 64-bit  \n",
        "https://arstechnica.com/information-technology/2014/12/gangnam-style-overflows-int_max-forces-youtube-to-go-64-bit/\n",
        "\n",
        "メルセンヌ素数  \n",
        "https://ja.wikipedia.org/wiki/%E3%83%A1%E3%83%AB%E3%82%BB%E3%83%B3%E3%83%8C%E6%95%B0"
      ],
      "metadata": {
        "id": "zZLTa33_Cyyn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's make all the dimensions the same, to avoid having to slice later. I'll make a copy, otherwise we'll have a view of the original array.\n",
        "\n",
        "Alternatively, change the shape here to see effect of small dimensions, eg `try volume = volume[:10, :290, :290]` with C ordering."
      ],
      "metadata": {
        "id": "kUZgfrcXCy1T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Python初心者向けJupyter NotebookでセルにMarkdown記法で入力する方法\n",
        "https://tonari-it.com/python-jupyter-notebook-markdown/"
      ],
      "metadata": {
        "id": "bHHRhWY7Cy4Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "volume = volume[:190, :190, :190].copy()"
      ],
      "metadata": {
        "id": "4pLUZcBnCy7E"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "volume"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pbnuz9TXCy95",
        "outputId": "117d819a-81ee-447b-9348-7a6407523e2c"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[    0,     0,     0, ..., -1572, -2179,   -40],\n",
              "        [    0,     0,     0, ..., -3173, -1118,  1754],\n",
              "        [    0,     0,     0, ..., -3116,   440,  2874],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  -203,   807,   272],\n",
              "        [ -407,  -407,  -407, ...,   901,   269,  -782],\n",
              "        [    0,     0,     0, ...,   482, -1240,  -676]],\n",
              "\n",
              "       [[-1018, -1018, -1018, ..., -1740, -2042,  1227],\n",
              "        [-1173, -1173, -1173, ..., -2627,  -414,  2853],\n",
              "        [ -417,  -417,  -417, ..., -2529,  -249,  2811],\n",
              "        ...,\n",
              "        [-1509, -1509, -1509, ...,   603,  -404,  -367],\n",
              "        [ -523,  -523,  -523, ...,  -258, -1396,  -163],\n",
              "        [ -886,  -886,  -886, ...,  -990, -1417,   798]],\n",
              "\n",
              "       [[ -425,  -425,  -425, ..., -1520, -3207,  1019],\n",
              "        [  -42,   -42,   -42, ..., -3645, -1818,  3129],\n",
              "        [ -443,  -443,  -443, ..., -2935,   282,  3299],\n",
              "        ...,\n",
              "        [ -681,  -681,  -681, ...,   335,  -630,     3],\n",
              "        [    0,     0,     0, ...,  -636, -1403,   649],\n",
              "        [ -415,  -415,  -415, ..., -1719,  -993,  2022]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[    0,     0,     0, ..., -1848, -2099, -1249],\n",
              "        [    0,     0,     0, ..., -1574, -2024,   169],\n",
              "        [    0,     0,     0, ..., -2035, -1744,  2358],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  1477,   956,   725],\n",
              "        [    0,     0,     0, ...,  1350,   763,   315],\n",
              "        [    0,     0,     0, ...,  1657,  1143,   480]],\n",
              "\n",
              "       [[  -91,   -91,   -91, ...,  -881, -1555, -1101],\n",
              "        [  194,   194,   194, ...,  -966, -1957,   620],\n",
              "        [    0,     0,     0, ..., -1896,  -429,  3099],\n",
              "        ...,\n",
              "        [ -566,  -566,  -566, ...,  1790,  1609,   209],\n",
              "        [-1106, -1106, -1106, ...,  1791,  1681,   657],\n",
              "        [-1004, -1004, -1004, ...,  1454,  1213,   938]],\n",
              "\n",
              "       [[    0,     0,     0, ..., -2030, -1509,   564],\n",
              "        [    0,     0,     0, ..., -2425, -1344,  2379],\n",
              "        [    0,     0,     0, ..., -3310,   109,  5285],\n",
              "        ...,\n",
              "        [ -587,  -587,  -587, ...,  1434,  1050,  -393],\n",
              "        [ -799,  -799,  -799, ...,  1223,  1365,    13],\n",
              "        [-1067, -1067, -1067, ...,  1036,   830,   613]]], dtype=int16)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "volume.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9nagNzRVDOBP",
        "outputId": "7a731e9f-2a75-45c7-bb3c-1490880d657d"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(190, 190, 190)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(volume.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BWQhdHCSDSPY",
        "outputId": "3479466b-8eb6-4035-a2d8-bb2d6a2b1b32"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum(np.diff(volume.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TqMIvZUaDYyo",
        "outputId": "b45a84f8-a797-4dce-8d0d-eba48d10b948"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###np.sum  \n",
        "https://numpy.org/doc/stable/reference/generated/numpy.sum.html"
      ],
      "metadata": {
        "id": "90E9vHJNFIt7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([0.9, 0.9], dtype=np.int32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pR1Q3Xj_FMlD",
        "outputId": "18d470cf-6d4c-4e3f-a700-e58b4891325f"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([0.9, 0.9, 0.9], dtype=np.int32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WTBq6EDFTPQ",
        "outputId": "c42dc6e5-300a-421d-a6e2-fe2fb115c9ea"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([0.9, 0.9, 1.9, 1.9], dtype=np.int32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNlt6MlAFZ5b",
        "outputId": "5eba55b1-6fc7-4cc2-eaab-9e3f48a958b4"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([1.9], dtype=np.int32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8G-0Cn-FiL2",
        "outputId": "324dc099-e25e-4edb-b713-50ac769e0297"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([[0, 1], [0,5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77QrxouBFuWh",
        "outputId": "c7e778e4-136c-4c82-8cd7-2abf9db45821"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([[1, 2], [3, 4]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-z9wNo6Fzis",
        "outputId": "1e5e9fb7-65af-490c-db02-d9941a4cb0a7"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([[1, 2], [3, 4, 5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Pc-tCvAFzmK",
        "outputId": "475b77a5-d297-4647-fe29-6c060871018f"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:86: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([[2, 3], [4, 5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezNVyaaiFzpG",
        "outputId": "2ac03289-a805-481c-e44c-b1b694b7dbe2"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([[2, 3], [4, 5]], axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2OkI_j3OHFJz",
        "outputId": "51af8152-4d6a-4c50-c0c6-8c9db2e41c84"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([[2, 3], [4, 5]], axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4v2Br1pwHEOx",
        "outputId": "05a69d17-1cea-484b-a012-7d7174c7a6a6"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([[0, 1], [np.nan, 5]], where=[False, True], axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPdnabwXFzse",
        "outputId": "09fedab7-a7eb-4f77-fa87-8718a8a83e32"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 5.])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0Bt_eJoxHeGl"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "n5KleySaHdPT"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_slice_3d(volume, x, axis, n=None):\n",
        "    \"\"\"\n",
        "    Naive function... but only works on 3 dimensions.\n",
        "    NB Using ellipses slows down last axis.\n",
        "    \"\"\"\n",
        "    # Force cube shape\n",
        "    if n is None and not np.sum(np.diff(volume.shape)):\n",
        "        n = np.min(volume.shape)\n",
        "    if axis == 0:\n",
        "        data = volume[x, :n, :n]\n",
        "    if axis == 1:\n",
        "        data = volume[:n, x, :n]\n",
        "    if axis == 2:\n",
        "        data = volume[:n, :n, x]\n",
        "    return data + 1"
      ],
      "metadata": {
        "id": "mtgzx-zpCzAV"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_slice_3d"
      ],
      "metadata": {
        "id": "cQKpIGU8DRnE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "402c1614-9bb7-471a-e061-5215683b7f5d"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.get_slice_3d(volume, x, axis, n=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%timeit get_slice_3d(volume, 150, axis=0)\n",
        "%timeit get_slice_3d(volume, 150, axis=1)\n",
        "%timeit get_slice_3d(volume, 150, axis=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "naPlyfDfI2zd",
        "outputId": "752c9058-0aa8-4950-f2c1-23935a17bdee"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "27.1 µs ± 1.37 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
            "39.7 µs ± 647 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
            "157 µs ± 4.18 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "volumef = np.asfortranarray(volume)"
      ],
      "metadata": {
        "id": "bQ5ZPqfcI3R2"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "volumef"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "podL7FjfJj7s",
        "outputId": "2b7aa47a-db3f-4cb0-e422-93abad8e0d13"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[    0,     0,     0, ..., -1572, -2179,   -40],\n",
              "        [    0,     0,     0, ..., -3173, -1118,  1754],\n",
              "        [    0,     0,     0, ..., -3116,   440,  2874],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  -203,   807,   272],\n",
              "        [ -407,  -407,  -407, ...,   901,   269,  -782],\n",
              "        [    0,     0,     0, ...,   482, -1240,  -676]],\n",
              "\n",
              "       [[-1018, -1018, -1018, ..., -1740, -2042,  1227],\n",
              "        [-1173, -1173, -1173, ..., -2627,  -414,  2853],\n",
              "        [ -417,  -417,  -417, ..., -2529,  -249,  2811],\n",
              "        ...,\n",
              "        [-1509, -1509, -1509, ...,   603,  -404,  -367],\n",
              "        [ -523,  -523,  -523, ...,  -258, -1396,  -163],\n",
              "        [ -886,  -886,  -886, ...,  -990, -1417,   798]],\n",
              "\n",
              "       [[ -425,  -425,  -425, ..., -1520, -3207,  1019],\n",
              "        [  -42,   -42,   -42, ..., -3645, -1818,  3129],\n",
              "        [ -443,  -443,  -443, ..., -2935,   282,  3299],\n",
              "        ...,\n",
              "        [ -681,  -681,  -681, ...,   335,  -630,     3],\n",
              "        [    0,     0,     0, ...,  -636, -1403,   649],\n",
              "        [ -415,  -415,  -415, ..., -1719,  -993,  2022]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[    0,     0,     0, ..., -1848, -2099, -1249],\n",
              "        [    0,     0,     0, ..., -1574, -2024,   169],\n",
              "        [    0,     0,     0, ..., -2035, -1744,  2358],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  1477,   956,   725],\n",
              "        [    0,     0,     0, ...,  1350,   763,   315],\n",
              "        [    0,     0,     0, ...,  1657,  1143,   480]],\n",
              "\n",
              "       [[  -91,   -91,   -91, ...,  -881, -1555, -1101],\n",
              "        [  194,   194,   194, ...,  -966, -1957,   620],\n",
              "        [    0,     0,     0, ..., -1896,  -429,  3099],\n",
              "        ...,\n",
              "        [ -566,  -566,  -566, ...,  1790,  1609,   209],\n",
              "        [-1106, -1106, -1106, ...,  1791,  1681,   657],\n",
              "        [-1004, -1004, -1004, ...,  1454,  1213,   938]],\n",
              "\n",
              "       [[    0,     0,     0, ..., -2030, -1509,   564],\n",
              "        [    0,     0,     0, ..., -2425, -1344,  2379],\n",
              "        [    0,     0,     0, ..., -3310,   109,  5285],\n",
              "        ...,\n",
              "        [ -587,  -587,  -587, ...,  1434,  1050,  -393],\n",
              "        [ -799,  -799,  -799, ...,  1223,  1365,    13],\n",
              "        [-1067, -1067, -1067, ...,  1036,   830,   613]]], dtype=int16)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "16kX19sFJyx2"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###np.diff  \n",
        "https://numpy.org/doc/stable/reference/generated/numpy.diff.html"
      ],
      "metadata": {
        "id": "tmAhddj7CzDO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([1, 2, 4, 7, 0])"
      ],
      "metadata": {
        "id": "rnjKX0DLG3iH"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8boj5X8HCNm",
        "outputId": "e1732078-7a17-4e10-bc29-72d78b4c0976"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 4, 7, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6eWhSDoJG85l",
        "outputId": "2163118f-4307-4ba6-8870-a5f53bc33998"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  2,  3, -7])"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])"
      ],
      "metadata": {
        "id": "AtLhe7IrHIXB"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOSK7giGHPC9",
        "outputId": "b3ae7cf5-9f53-4489-cb62-a455c29de0a3"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  3,  6, 10],\n",
              "       [ 0,  5,  6,  8]])"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cK_KolPtHQnn",
        "outputId": "23340a85-2b20-4ccb-8e33-facd22f90c5e"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2, 3, 4],\n",
              "       [5, 1, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ox53TCS1HT0B",
        "outputId": "c7a50ab3-ba6a-4f7d-fe48-820efc2bfcb3"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  3,  6, 10],\n",
              "       [ 0,  5,  6,  8]])"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x, axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSPTGn_CHT3i",
        "outputId": "aba4f3c8-7e0c-4934-ac5e-bf6b50ec03f1"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1,  2,  0, -2]])"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)"
      ],
      "metadata": {
        "id": "s0gBuA4uCzF8"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "id": "NXMqmHtDCLZF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3cbdc13d-cf4a-4afa-cf1f-26ece7b9f4ef"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1066-10-13', '1066-10-14', '1066-10-15'], dtype='datetime64[D]')"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x)"
      ],
      "metadata": {
        "id": "w6Afo45xCzIs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcb757fd-a134-45d1-f917-13abbddaff32"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1], dtype='timedelta64[D]')"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###np.asfortranarray  \n",
        "https://numpy.org/doc/stable/reference/generated/numpy.asfortranarray.html"
      ],
      "metadata": {
        "id": "tR5VSO81CzLS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.arange(6).reshape(2,3)"
      ],
      "metadata": {
        "id": "_Q3cavRMCzPQ"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "id": "MUiArEZFCzR8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e8a896e-fdec-4280-82cb-f1761a9d01cb"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2],\n",
              "       [3, 4, 5]])"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = np.asfortranarray(x)"
      ],
      "metadata": {
        "id": "oiycPQSeCzVe"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zudCuZ33NRud",
        "outputId": "220ce99a-ebd8-4bc9-e4e5-4e94bdff9430"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2],\n",
              "       [3, 4, 5]])"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.flags['F_CONTIGUOUS']"
      ],
      "metadata": {
        "id": "jW2tL-mYCzX2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f27bbaa-d4c6-4390-ac72-219393bb74cb"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.flags['F_CONTIGUOUS']"
      ],
      "metadata": {
        "id": "h-260P2VCzZ7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17fdf237-28f6-4955-a942-bb798141413c"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TEIo-B2uktwv"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Deviate from the main subject"
      ],
      "metadata": {
        "id": "FsVmbGcakzH0"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# An Illustrated Guide to Shape and Strides (Part 1)  \n",
        "https://ajcr.net/stride-guide-part-1/  \n",
        "*The series consists of three parts."
      ],
      "metadata": {
        "id": "LFHNaCRNNgR9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Array Fundamentals"
      ],
      "metadata": {
        "id": "27h6Pi-fc8c3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.arange(12)"
      ],
      "metadata": {
        "id": "Gl86d4QqNheu"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XtgU0WKeNhho",
        "outputId": "d08f82d1-8113-49d9-a21b-2d59eb4003d8"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = a.reshape(3, 4)"
      ],
      "metadata": {
        "id": "09f9UekL-VBf"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1HSrm2u-VGg",
        "outputId": "05189e6b-f96a-4a94-e4d1-1ea76b5cf655"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3],\n",
              "       [ 4,  5,  6,  7],\n",
              "       [ 8,  9, 10, 11]])"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXvoH1iV-VJK",
        "outputId": "bad7752a-0f13-43af-fe4c-f1180486b320"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12,)"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.strides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dB9Gcb_q-VL5",
        "outputId": "72f96842-ea88-4235-829f-1ae01215a157"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8,)"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pStZelcT-oPs",
        "outputId": "5529e383-9564-4aa1-fc7f-c3f8aca84820"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.strides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71ek0Xl7-oSd",
        "outputId": "84a7c2b9-cc2b-493a-e003-83c3b76b21a0"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.itemsize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_GnxnEz2-oVX",
        "outputId": "83c2899f-53f8-4dd8-b631-41755818a575"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.itemsize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7aoASVS-oYE",
        "outputId": "146390e3-c51b-45ad-891a-d205d83ec8da"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FaLF_Cng-oay",
        "outputId": "bebca4d4-889c-403b-ce7a-a125845a9fcf"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<memory at 0x7f8284336c80>"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuzEWZXT-VOT",
        "outputId": "2781202a-8918-49bc-f08e-6e0b9b5e6d44"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<memory at 0x7f8288f52910>"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Array operations such as **reshaping, transposing, indexing, slicing** can be thought of as convenient high-level methods to manipulate **shape, strides** and **offset** to change how data in memory is traversed."
      ],
      "metadata": {
        "id": "YnXCIh5l-VUk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. How Shape and Strides Define Dimensions"
      ],
      "metadata": {
        "id": "bED7S-NKdN5f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a2 = a[::2]"
      ],
      "metadata": {
        "id": "7GDetcInNhkj"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7a9eNVONhnh",
        "outputId": "fd348f4d-56d2-4ac0-9754-910dd9b534e4"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  2,  4,  6,  8, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yl166i9rAd0d",
        "outputId": "4a8e79a2-99ea-41a7-8d0b-5d2d5e90954f"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6,)"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a2.strides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ILf2aRUwAeLo",
        "outputId": "2f2ed1a6-1699-4de7-d8ba-c7b69fa9d2aa"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16,)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_reversed = a[::-3]"
      ],
      "metadata": {
        "id": "YzdFB1P9AeVH"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a_reversed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cthSNG6iAei9",
        "outputId": "ee86eb1e-14e7-4e5e-8c5f-e41a1a6b71b6"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([11,  8,  5,  2])"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_reversed.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_k57SEJTAern",
        "outputId": "55cb8d99-2fb2-4b05-9025-6de6b67d07fe"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4,)"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_reversed.strides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZvAeaCwAezH",
        "outputId": "a7e9da09-b36b-42ac-f429-272dd9e66c17"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-24,)"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFQ7IiqVAe6P",
        "outputId": "629f8c6d-827a-40fb-86ba-dcd0471ebcb4"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3],\n",
              "       [ 4,  5,  6,  7],\n",
              "       [ 8,  9, 10, 11]])"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5L4zNiIyAfBq",
        "outputId": "8aca3c15-e8a1-4bae-9aa3-93fdc09ddd60"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.strides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PuHNjiyFAfJ3",
        "outputId": "408347a7-ad5c-40ab-8a68-b614a2ffa0b7"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b1 = b[1:3, 1:3]"
      ],
      "metadata": {
        "id": "wOLtqT_SAfRI"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U66Hj2mujhm9",
        "outputId": "419c2e0b-9f86-4ffd-a3b6-6d3b111ca137"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5,  6],\n",
              "       [ 9, 10]])"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k-XW6kD8jhqt",
        "outputId": "f9e086e3-bf71-4d92-e05f-ef74ae5d4412"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b1.strides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "klD2el2kjhuu",
        "outputId": "8eaa0151-27ae-4b2f-f0ce-66a73e154484"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = a.reshape(3, 2, 2)"
      ],
      "metadata": {
        "id": "HJJZFnPOjhye"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lRNJ_le1i2Gq",
        "outputId": "75a2e700-661e-4a59-c155-85cd5ea13c8f"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0,  1],\n",
              "        [ 2,  3]],\n",
              "\n",
              "       [[ 4,  5],\n",
              "        [ 6,  7]],\n",
              "\n",
              "       [[ 8,  9],\n",
              "        [10, 11]]])"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Again, you can see that C order means that NumPy must use a greater stride length to traverse the lower axes than the higher axes."
      ],
      "metadata": {
        "id": "ugumwWLzi2P5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c.itemsize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "orULPaiHi2cn",
        "outputId": "85eeda6f-146b-4299-dc0e-d854b69134d1"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.strides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4hV0BX-8i2n3",
        "outputId": "edb91329-3d4e-4d6f-a71c-d090ad6597e6"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b1.shape"
      ],
      "metadata": {
        "id": "Ld7Mfst5mI1O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b362dc92-774f-42bb-880c-468325752e7e"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Impossible Reshapes"
      ],
      "metadata": {
        "id": "i32CUI9XdXyK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# b1.shape = (4,)"
      ],
      "metadata": {
        "id": "yPCKDrc-mI7x"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# An Illustrated Guide to Shape and Strides (Part 2)  \n",
        "https://ajcr.net/stride-guide-part-2/"
      ],
      "metadata": {
        "id": "-Hy6vH4QmJFT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Transposing Arrays and Permuting Axes"
      ],
      "metadata": {
        "id": "wZVlchyNcs9n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.arange(12)"
      ],
      "metadata": {
        "id": "qaNlSlA5mJQE"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "id": "KRCb3FPGi20b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "594905d8-023d-4788-b0e3-8f91add1fbcf"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = a.reshape(3, 2, 2)"
      ],
      "metadata": {
        "id": "7YmvVI8JCvxw"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2h2j6BJwCv5C",
        "outputId": "ee903b7e-9e55-4030-bc8d-efa1fa8c9f55"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0,  1],\n",
              "        [ 2,  3]],\n",
              "\n",
              "       [[ 4,  5],\n",
              "        [ 6,  7]],\n",
              "\n",
              "       [[ 8,  9],\n",
              "        [10, 11]]])"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NF56pX-yCv_F",
        "outputId": "60fd86a6-e15c-46ea-d5e3-dd7c3ca51631"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0,  4,  8],\n",
              "        [ 2,  6, 10]],\n",
              "\n",
              "       [[ 1,  5,  9],\n",
              "        [ 3,  7, 11]]])"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c[::2, :, :]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "baCCZ5o-FxHE",
        "outputId": "1696b6f4-be18-4bd6-d7f2-e3ca4b04c708"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0,  1],\n",
              "        [ 2,  3]],\n",
              "\n",
              "       [[ 8,  9],\n",
              "        [10, 11]]])"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c[::2, :, :].swapaxes(0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "beYlSh2lCwFf",
        "outputId": "ccfff0b8-6ccc-41ff-a8f0-e27fc3b6eb79"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0,  1],\n",
              "        [ 8,  9]],\n",
              "\n",
              "       [[ 2,  3],\n",
              "        [10, 11]]])"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. C order vs. Fortran order"
      ],
      "metadata": {
        "id": "_cM5zCWGCwJJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.arange(12)"
      ],
      "metadata": {
        "id": "1dfpYg_fCwM1"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rI7mrd0CCwQr",
        "outputId": "94f6bf53-eb15-40df-b208-0e9f4d13b33c"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = a.reshape(3, 4)"
      ],
      "metadata": {
        "id": "af8Hokd5CwTJ"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGLr01NmCwVs",
        "outputId": "0071f606-df7e-40bc-f988-15a7bb8d0646"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3],\n",
              "       [ 4,  5,  6,  7],\n",
              "       [ 8,  9, 10, 11]])"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.flags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_FCVdtiICwYs",
        "outputId": "f5228163-677c-4171-96d0-34c0b5fddfde"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  C_CONTIGUOUS : True\n",
              "  F_CONTIGUOUS : True\n",
              "  OWNDATA : True\n",
              "  WRITEABLE : True\n",
              "  ALIGNED : True\n",
              "  WRITEBACKIFCOPY : False\n",
              "  UPDATEIFCOPY : False"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.flags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6FhZq9dCwa8",
        "outputId": "cc307763-62ef-4232-bba2-22e34ffce46b"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  C_CONTIGUOUS : True\n",
              "  F_CONTIGUOUS : False\n",
              "  OWNDATA : False\n",
              "  WRITEABLE : True\n",
              "  ALIGNED : True\n",
              "  WRITEBACKIFCOPY : False\n",
              "  UPDATEIFCOPY : False"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zvRdrOTCwd7",
        "outputId": "b4a078e7-73a2-410a-cd70-8d24be59ad39"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  4,  8],\n",
              "       [ 1,  5,  9],\n",
              "       [ 2,  6, 10],\n",
              "       [ 3,  7, 11]])"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.T.flags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_Y4dguUCwhp",
        "outputId": "c9fea859-d23b-451a-c8cf-aad0c996b3f6"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  C_CONTIGUOUS : False\n",
              "  F_CONTIGUOUS : True\n",
              "  OWNDATA : False\n",
              "  WRITEABLE : True\n",
              "  ALIGNED : True\n",
              "  WRITEBACKIFCOPY : False\n",
              "  UPDATEIFCOPY : False"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b[::2]"
      ],
      "metadata": {
        "id": "Z36YOmO0jh2g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4f96278-e1da-42db-8518-36c078b0ae08"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3],\n",
              "       [ 8,  9, 10, 11]])"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b[::2].flags"
      ],
      "metadata": {
        "id": "PxX522ckh5He",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b61973a4-7ad4-404a-cef4-bb0da1d8ef99"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  C_CONTIGUOUS : False\n",
              "  F_CONTIGUOUS : False\n",
              "  OWNDATA : False\n",
              "  WRITEABLE : True\n",
              "  ALIGNED : True\n",
              "  WRITEBACKIFCOPY : False\n",
              "  UPDATEIFCOPY : False"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ravelling"
      ],
      "metadata": {
        "id": "3vlqlSakh5K0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# An Illustrated Guide to Shape and Strides (Part 3)\n",
        "https://ajcr.net/stride-guide-part-3/"
      ],
      "metadata": {
        "id": "In9pMKk9pmSo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Specifying Strides Directly"
      ],
      "metadata": {
        "id": "AOupDGhT-pS5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### as_strided"
      ],
      "metadata": {
        "id": "iwmMjSdmVHZK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.arange(12)"
      ],
      "metadata": {
        "id": "kkWiQN1PpmX5"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u22wyEWkpmfM",
        "outputId": "9b63d639-dc7d-44ed-a9ac-0b876f344f84"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.lib.stride_tricks import as_strided"
      ],
      "metadata": {
        "id": "oWXIsdKhpmm9"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "as_strided(a, shape=(3, 4), strides=(32, 8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYF6Nlkjh5Pq",
        "outputId": "fe97639f-ccc1-41ef-e992-8392b02d08e6"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3],\n",
              "       [ 4,  5,  6,  7],\n",
              "       [ 8,  9, 10, 11]])"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "as_strided(a, shape=(3, 4), strides=(32, 8)).flags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r6YsUR9XPMbJ",
        "outputId": "aee79e7d-00dc-4ddf-bacb-1b432536b4b5"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  C_CONTIGUOUS : True\n",
              "  F_CONTIGUOUS : False\n",
              "  OWNDATA : False\n",
              "  WRITEABLE : True\n",
              "  ALIGNED : True\n",
              "  WRITEBACKIFCOPY : False\n",
              "  UPDATEIFCOPY : False"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "as_strided(a, shape=(4, 3), strides=(8, 32))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eWvOHLvpO_xG",
        "outputId": "776d7a42-61b5-4d46-8040-7765d64e0fec"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  4,  8],\n",
              "       [ 1,  5,  9],\n",
              "       [ 2,  6, 10],\n",
              "       [ 3,  7, 11]])"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "as_strided(a, shape=(4, 3), strides=(8, 32)).flags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2CzbvRk1PYez",
        "outputId": "6c615d3b-53d2-46ba-860b-aa9ba62cb007"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  C_CONTIGUOUS : False\n",
              "  F_CONTIGUOUS : True\n",
              "  OWNDATA : False\n",
              "  WRITEABLE : True\n",
              "  ALIGNED : True\n",
              "  WRITEBACKIFCOPY : False\n",
              "  UPDATEIFCOPY : False"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "as_strided(a, shape=(10, 3), strides=(8, 8))"
      ],
      "metadata": {
        "id": "VLOpbRmRAfXa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d51944f6-d6bc-40bf-8486-ab5196cd865f"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2],\n",
              "       [ 1,  2,  3],\n",
              "       [ 2,  3,  4],\n",
              "       [ 3,  4,  5],\n",
              "       [ 4,  5,  6],\n",
              "       [ 5,  6,  7],\n",
              "       [ 6,  7,  8],\n",
              "       [ 7,  8,  9],\n",
              "       [ 8,  9, 10],\n",
              "       [ 9, 10, 11]])"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "single_value = np.array(0)"
      ],
      "metadata": {
        "id": "n7a7PhjlMKGr"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "single_value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRxC8KiYMKWb",
        "outputId": "08ff7278-0e2d-4794-f433-93601790c268"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(0)"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "as_strided(single_value, (1000, 1000), strides=(0, 0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECHZ8USqMKfa",
        "outputId": "02f9d490-0b56-4b7f-f0ce-84ba558b01f7"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ndarray"
      ],
      "metadata": {
        "id": "uuHUDLQfMKlq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anACI_9QWnec",
        "outputId": "1b0636e6-8020-4064-ffd0-a7875a553b98"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.reshape(3, 2, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Mb7LtwkWybZ",
        "outputId": "cf750cbd-e1f2-411f-89f0-6845e986852f"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0,  1],\n",
              "        [ 2,  3]],\n",
              "\n",
              "       [[ 4,  5],\n",
              "        [ 6,  7]],\n",
              "\n",
              "       [[ 8,  9],\n",
              "        [10, 11]]])"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.reshape(3, 2, 2)[:, ::-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsCKB0PIMKqD",
        "outputId": "a6ab6e35-97d4-4a33-f294-79e139545126"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 2,  3],\n",
              "        [ 0,  1]],\n",
              "\n",
              "       [[ 6,  7],\n",
              "        [ 4,  5]],\n",
              "\n",
              "       [[10, 11],\n",
              "        [ 8,  9]]])"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.reshape(3, 2, 2)[:, ::-1, ::-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UkbLYBkoXIZp",
        "outputId": "3033229d-de3e-47e4-a665-bcdc2591c38f"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 3,  2],\n",
              "        [ 1,  0]],\n",
              "\n",
              "       [[ 7,  6],\n",
              "        [ 5,  4]],\n",
              "\n",
              "       [[11, 10],\n",
              "        [ 9,  8]]])"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.reshape(3, 2, 2)[::-1, ::-1, ::-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nidqi2FnXMyw",
        "outputId": "ac114b50-86e3-4e1c-a039-6506a866df89"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[11, 10],\n",
              "        [ 9,  8]],\n",
              "\n",
              "       [[ 7,  6],\n",
              "        [ 5,  4]],\n",
              "\n",
              "       [[ 3,  2],\n",
              "        [ 1,  0]]])"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XGv_QYhKWwj9",
        "outputId": "75adf309-d8e7-478d-a63c-c8f756df7c50"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndarr_int = np.ndarray(buffer=a.data, shape=(3, 2, 2), strides=(32, -16, 8), offset=16, dtype=int)\n",
        "ndarr_int"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Y_3fO3jMKtz",
        "outputId": "25bb0e5c-17ff-4e1e-a3c3-49c8e266ff83"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 2,  3],\n",
              "        [ 0,  1]],\n",
              "\n",
              "       [[ 6,  7],\n",
              "        [ 4,  5]],\n",
              "\n",
              "       [[10, 11],\n",
              "        [ 8,  9]]])"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndarr_float = np.ndarray(buffer=a.data, shape=(3, 2, 2), strides=(32, -16, 8), offset=16, dtype=float)\n",
        "ndarr_float"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVLI8GupMKxo",
        "outputId": "577d7e86-11d4-47f0-ed82-0ac0bda4ab8e"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[9.9e-324, 1.5e-323],\n",
              "        [0.0e+000, 4.9e-324]],\n",
              "\n",
              "       [[3.0e-323, 3.5e-323],\n",
              "        [2.0e-323, 2.5e-323]],\n",
              "\n",
              "       [[4.9e-323, 5.4e-323],\n",
              "        [4.0e-323, 4.4e-323]]])"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndarr_float.itemsize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LVDNpMkSIcwx",
        "outputId": "0d074b9d-2254-4569-fb7e-6aab077dab1e"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ValueError: strides is incompatible with shape of requested array and size of buffer\n",
        "\n",
        "# np.ndarray(buffer=a.data, shape=(3, 2, 2), strides=(32, -16, 8), offset=8, dtype=float)"
      ],
      "metadata": {
        "id": "P-GWUxh9MK1w"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8lZfRxvQLvax"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ValueError: strides is incompatible with shape of requested array and size of buffer\n",
        "\n",
        "# ndarr_float_nobuffer = np.ndarray(shape=(3, 2, 2), strides=(32, -16, 8), offset=8, dtype=float)\n",
        "# ndarr_float_nobuffer"
      ],
      "metadata": {
        "id": "IfKif06-Lvki"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Kx1ux0wVMK5O"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "numpy.ndarray  \n",
        "https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html"
      ],
      "metadata": {
        "id": "EAG9sYhOMK9M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ndarr1 = np.ndarray(shape=(2, 2), dtype=float, order='F')\n",
        "ndarr1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nz2aG6u6BPqi",
        "outputId": "04a80262-29e9-4825-d9b5-e904b395714c"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[371.48863636, 387.36363636],\n",
              "       [102.54      , 112.54      ]])"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(ndarr1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRIwvoWfMIgt",
        "outputId": "2703cb24-7433-4103-d018-58615191c0a6"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndarr1.itemsize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZEaw5N6TMJCB",
        "outputId": "1a643b79-bb86-4dc7-a56f-d44d303cff3b"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndarr1.flags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_dCfFs-HmN6",
        "outputId": "3dd91501-7aa0-43e7-8d5d-c1120e0e1f7a"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  C_CONTIGUOUS : False\n",
              "  F_CONTIGUOUS : True\n",
              "  OWNDATA : True\n",
              "  WRITEABLE : True\n",
              "  ALIGNED : True\n",
              "  WRITEBACKIFCOPY : False\n",
              "  UPDATEIFCOPY : False"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndarr1_with_buffer = np.ndarray((2,), buffer=np.array([1,2,3]),\n",
        "                                offset=np.int_().itemsize,\n",
        "                                dtype=int) \n",
        "                                # offset = 1*itemsize, i.e. skip first element"
      ],
      "metadata": {
        "id": "1sPpi-zQHmWI"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ndarr1_with_buffer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VNSwZsZ9HmaQ",
        "outputId": "f402a9ee-b17b-426d-ddaf-0af7d2a10e53"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.int_()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VfCEbYUTHmeW",
        "outputId": "375f582c-e532-4a76-9a52-4eaeae94abc9"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "int"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zig1me5GcU8R",
        "outputId": "31613af3-8410-4de8-c1c8-a31b7628062b"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "int()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6yvxINVcWpU",
        "outputId": "34ca6fd3-9a83-48f6-d6ed-cd95668086a1"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# help(np.int)"
      ],
      "metadata": {
        "id": "GaZIDpTyb_nE"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# help(np.int_)"
      ],
      "metadata": {
        "id": "7CpgTh7LHmkk"
      },
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.int_().itemsize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YdpNkpyHmop",
        "outputId": "c0c10fb3-9cd4-415d-89a1-64192706e8a0"
      },
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#TypeError: buffer is too small for requested array\n",
        "\n",
        "# ndarr2_with_buffer = np.ndarray((2,), buffer=np.array([1,2,3]),\n",
        "#                                 offset=np.int_().itemsize * 2,\n",
        "#                                 dtype=int) \n",
        "                                # offset = 1*itemsize, i.e. skip first element"
      ],
      "metadata": {
        "id": "oslJpTlvdEOF"
      },
      "execution_count": 189,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5sbrGirq2zZ1"
      },
      "execution_count": 189,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Difference between np.int, np.int_, int, and np.int_t in cython?  \n",
        "https://stackoverflow.com/questions/21851985/difference-between-np-int-np-int-int-and-np-int-t-in-cython"
      ],
      "metadata": {
        "id": "f4CWQBhjb2xj"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SsCNAvwbdFEk"
      },
      "execution_count": 189,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get numpy ndarray offset  \n",
        "https://stackoverflow.com/questions/53534305/get-numpy-ndarray-offset"
      ],
      "metadata": {
        "id": "8NOceqU0b299"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.arange(10)"
      ],
      "metadata": {
        "id": "T46yFL9FdFLh"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lB1r6cpKdFQ1",
        "outputId": "194061e0-3a9f-4a47-8ee1-dc1b5c1fda87"
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "of4_EfradFV6",
        "outputId": "12083a86-c1e3-4234-aa49-ea72c23cfc29"
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.arange(10, dtype=np.uint8)"
      ],
      "metadata": {
        "id": "WdjkaNZqdFaQ"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RdvKjNMqtrtX",
        "outputId": "c17e8ccb-7680-4195-9fb5-87927a325e12"
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "llgh8yq1tryO",
        "outputId": "54ab1934-3922-46e7-f848-a4e96d38a248"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.__array_interface__['data'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MH-oRcCotr2u",
        "outputId": "dc55eb9f-152b-4f03-e03a-51fc0e68c8b3"
      },
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37495376"
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = a[1:]"
      ],
      "metadata": {
        "id": "Q5CLJ5NXtr6W"
      },
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KELBL_qXtr9Z",
        "outputId": "0ce4d10b-8d6a-4991-f29e-609442653eed"
      },
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.__array_interface__['data'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BDTG-qtdFef",
        "outputId": "f3353ac6-ca2b-4abf-bbd0-da8f5042f2a4"
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37495377"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NPwbvPALdFht"
      },
      "execution_count": 199,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get back to the section \"An Illustrated Guide to Shape and Strides (Part 3)\""
      ],
      "metadata": {
        "id": "T_KS_MzzuRkj"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Solving a Stride Problem: Swapping Tiles"
      ],
      "metadata": {
        "id": "NbqWXC_EuRoG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.arange(16)"
      ],
      "metadata": {
        "id": "Fd0ETNrFuRry"
      },
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gLoOdYojuRuv",
        "outputId": "04fa5ad9-0d16-418c-963a-01dcc5e9e475"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = a.reshape(4, 4)\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_KwUoQXKuRxV",
        "outputId": "ac6afe99-43ae-4397-c190-58769cbb43c1"
      },
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3],\n",
              "       [ 4,  5,  6,  7],\n",
              "       [ 8,  9, 10, 11],\n",
              "       [12, 13, 14, 15]])"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hor1 = np.hstack((a[0:2, 0:2], a[2:4, 0:2]))\n",
        "hor1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RsDPCroHuR0T",
        "outputId": "c44b32c4-e788-4714-e2ad-a04dd2cbefc8"
      },
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  8,  9],\n",
              "       [ 4,  5, 12, 13]])"
            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hor2 = np.hstack((a[0:2, 2:4], a[2:4, 2:4]))\n",
        "hor2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDZ5ERqWFauB",
        "outputId": "2e030b8d-1bd1-45f7-d361-362fe3d29649"
      },
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  3, 10, 11],\n",
              "       [ 6,  7, 14, 15]])"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.vstack((hor1, hor2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63yVdLzpFa4O",
        "outputId": "1fbe5872-4e5d-4b97-a4a6-be65f0b5217c"
      },
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  8,  9],\n",
              "       [ 4,  5, 12, 13],\n",
              "       [ 2,  3, 10, 11],\n",
              "       [ 6,  7, 14, 15]])"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aAYBSetfFa_C"
      },
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b = as_strided(a, shape=(2, 2, 2, 2), strides=(16, 32, 64, 8))\n",
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TeG6qNk1uR3J",
        "outputId": "b4ebb20b-388f-4b3e-8ae0-306d6c25b528"
      },
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[ 0,  1],\n",
              "         [ 8,  9]],\n",
              "\n",
              "        [[ 4,  5],\n",
              "         [12, 13]]],\n",
              "\n",
              "\n",
              "       [[[ 2,  3],\n",
              "         [10, 11]],\n",
              "\n",
              "        [[ 6,  7],\n",
              "         [14, 15]]]])"
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FB3LOdI9HF8m"
      },
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### numpy.swapaxes  \n",
        "https://numpy.org/doc/stable/reference/generated/numpy.swapaxes.html"
      ],
      "metadata": {
        "id": "mJQj1ToBHGDf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([[1,2,3]])"
      ],
      "metadata": {
        "id": "fD1C3lPHHGGm"
      },
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zHKTjOWGHGJ7",
        "outputId": "084d346f-9b04-4cb1-c0dc-394fea9b47df"
      },
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3]])"
            ]
          },
          "metadata": {},
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "51I-fzt_VCC5",
        "outputId": "7cbc369a-9082-403e-fd95-220b1b4a22fa"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.swapaxes(x, 0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PdPoEvz7VCIl",
        "outputId": "d8ae6797-e7a5-4d05-f656-531c2229f769"
      },
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1],\n",
              "       [2],\n",
              "       [3]])"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AC2zrtdNVCOS",
        "outputId": "bfc3a23e-f76d-4a2e-c092-80cf8653b0e9"
      },
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3]])"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_swapped = np.swapaxes(x, 0, 1)"
      ],
      "metadata": {
        "id": "atls9F7lVCVa"
      },
      "execution_count": 213,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_swapped"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NNw5UOItVdiP",
        "outputId": "0df11ae0-f374-44dd-c71d-5d8b79bd98c9"
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1],\n",
              "       [2],\n",
              "       [3]])"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.swapaxes(x_swapped, 0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzEvtLt3Vdw_",
        "outputId": "de2cb289-3dbc-4fab-dbb3-72db73073ddd"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3]])"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# np.swapaxes(np.array([1,2,3]))\n",
        "\n",
        "# TypeError: _swapaxes_dispatcher() missing 2 required positional arguments: 'axis1' and 'axis2'"
      ],
      "metadata": {
        "id": "6IrdCk8GVd_0"
      },
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])"
      ],
      "metadata": {
        "id": "bHV-93BbVCcO"
      },
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xqR4LDiyWXQu",
        "outputId": "a36585ff-f541-4ac2-885f-d623a5535e51"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 1],\n",
              "        [2, 3]],\n",
              "\n",
              "       [[4, 5],\n",
              "        [6, 7]]])"
            ]
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " np.swapaxes(x, 0, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pQjqCIYyWXaR",
        "outputId": "7218a1bb-2082-492d-eb88-371d2b956880"
      },
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 4],\n",
              "        [2, 6]],\n",
              "\n",
              "       [[1, 5],\n",
              "        [3, 7]]])"
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " np.swapaxes(x, 0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2h3Cl72FWXjO",
        "outputId": "e67164e2-3539-4b09-f2db-37cab5c57eb1"
      },
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 1],\n",
              "        [4, 5]],\n",
              "\n",
              "       [[2, 3],\n",
              "        [6, 7]]])"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " np.swapaxes(x, 1, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j7F7TtyEWXqK",
        "outputId": "798ad071-80b8-4123-b32c-7193c36e6a18"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 2],\n",
              "        [1, 3]],\n",
              "\n",
              "       [[4, 6],\n",
              "        [5, 7]]])"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WYnRtqivWXzF"
      },
      "execution_count": 221,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a.flags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7gIFTZBVChP",
        "outputId": "b3ccd9cf-986f-4319-e9c6-d59e742ee2e3"
      },
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  C_CONTIGUOUS : True\n",
              "  F_CONTIGUOUS : False\n",
              "  OWNDATA : False\n",
              "  WRITEABLE : True\n",
              "  ALIGNED : True\n",
              "  WRITEBACKIFCOPY : False\n",
              "  UPDATEIFCOPY : False"
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.strides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKlIVtR8b3Hg",
        "outputId": "d79d685b-13b4-4a7a-e2f7-8b6daafb4a46"
      },
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_4dim = a.reshape(2,2,2,2)"
      ],
      "metadata": {
        "id": "kvT4Kgw9cl2B"
      },
      "execution_count": 224,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a_4dim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jW9qii5Ocl5-",
        "outputId": "230bd9f2-b448-4b30-904a-7744cb88eafe"
      },
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[ 0,  1],\n",
              "         [ 2,  3]],\n",
              "\n",
              "        [[ 4,  5],\n",
              "         [ 6,  7]]],\n",
              "\n",
              "\n",
              "       [[[ 8,  9],\n",
              "         [10, 11]],\n",
              "\n",
              "        [[12, 13],\n",
              "         [14, 15]]]])"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_4dim.strides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrTS_si0cl9p",
        "outputId": "be54a3f8-b299-421f-ca45-cbf6e5e87ab3"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(64, 32, 16, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_4dim_transposed = np.swapaxes(a_4dim, 0, 2)"
      ],
      "metadata": {
        "id": "nC4uXRZ3cmCN"
      },
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a_4dim_transposed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBW23q4bcmG4",
        "outputId": "a58c6337-8860-4f05-a692-cd020bd8316a"
      },
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[ 0,  1],\n",
              "         [ 8,  9]],\n",
              "\n",
              "        [[ 4,  5],\n",
              "         [12, 13]]],\n",
              "\n",
              "\n",
              "       [[[ 2,  3],\n",
              "         [10, 11]],\n",
              "\n",
              "        [[ 6,  7],\n",
              "         [14, 15]]]])"
            ]
          },
          "metadata": {},
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_4dim_transposed.strides"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jw7HiI0LcmNJ",
        "outputId": "a0e5bc8a-5109-4d7b-99a2-e3d7e8e17224"
      },
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16, 32, 64, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WoZWorRdfBUr",
        "outputId": "8e36abe8-6c0c-4b05-d4a2-fdb667a1f268"
      },
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[ 0,  1],\n",
              "         [ 8,  9]],\n",
              "\n",
              "        [[ 4,  5],\n",
              "         [12, 13]]],\n",
              "\n",
              "\n",
              "       [[[ 2,  3],\n",
              "         [10, 11]],\n",
              "\n",
              "        [[ 6,  7],\n",
              "         [14, 15]]]])"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.reshape(4, 4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4TN3kLacmQ3",
        "outputId": "fbeb481f-ea72-4c95-a3ff-f6bcacb0ae25"
      },
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  8,  9],\n",
              "       [ 4,  5, 12, 13],\n",
              "       [ 2,  3, 10, 11],\n",
              "       [ 6,  7, 14, 15]])"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fsEUbD9kdiLl"
      },
      "execution_count": 231,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get back to the main subject"
      ],
      "metadata": {
        "id": "PNt70SSQdiXW"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Re: Accessing the seismic data\n",
        "\n"
      ],
      "metadata": {
        "id": "CVfQJjyZdilp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "volume = volume[:190, :190, :190].copy()"
      ],
      "metadata": {
        "id": "lAveCzR4di0b"
      },
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "volume"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cMzXHzudi_C",
        "outputId": "fdc8d7f3-5904-4b70-ffb7-0f751f41ff31"
      },
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[    0,     0,     0, ..., -1572, -2179,   -40],\n",
              "        [    0,     0,     0, ..., -3173, -1118,  1754],\n",
              "        [    0,     0,     0, ..., -3116,   440,  2874],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  -203,   807,   272],\n",
              "        [ -407,  -407,  -407, ...,   901,   269,  -782],\n",
              "        [    0,     0,     0, ...,   482, -1240,  -676]],\n",
              "\n",
              "       [[-1018, -1018, -1018, ..., -1740, -2042,  1227],\n",
              "        [-1173, -1173, -1173, ..., -2627,  -414,  2853],\n",
              "        [ -417,  -417,  -417, ..., -2529,  -249,  2811],\n",
              "        ...,\n",
              "        [-1509, -1509, -1509, ...,   603,  -404,  -367],\n",
              "        [ -523,  -523,  -523, ...,  -258, -1396,  -163],\n",
              "        [ -886,  -886,  -886, ...,  -990, -1417,   798]],\n",
              "\n",
              "       [[ -425,  -425,  -425, ..., -1520, -3207,  1019],\n",
              "        [  -42,   -42,   -42, ..., -3645, -1818,  3129],\n",
              "        [ -443,  -443,  -443, ..., -2935,   282,  3299],\n",
              "        ...,\n",
              "        [ -681,  -681,  -681, ...,   335,  -630,     3],\n",
              "        [    0,     0,     0, ...,  -636, -1403,   649],\n",
              "        [ -415,  -415,  -415, ..., -1719,  -993,  2022]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[    0,     0,     0, ..., -1848, -2099, -1249],\n",
              "        [    0,     0,     0, ..., -1574, -2024,   169],\n",
              "        [    0,     0,     0, ..., -2035, -1744,  2358],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  1477,   956,   725],\n",
              "        [    0,     0,     0, ...,  1350,   763,   315],\n",
              "        [    0,     0,     0, ...,  1657,  1143,   480]],\n",
              "\n",
              "       [[  -91,   -91,   -91, ...,  -881, -1555, -1101],\n",
              "        [  194,   194,   194, ...,  -966, -1957,   620],\n",
              "        [    0,     0,     0, ..., -1896,  -429,  3099],\n",
              "        ...,\n",
              "        [ -566,  -566,  -566, ...,  1790,  1609,   209],\n",
              "        [-1106, -1106, -1106, ...,  1791,  1681,   657],\n",
              "        [-1004, -1004, -1004, ...,  1454,  1213,   938]],\n",
              "\n",
              "       [[    0,     0,     0, ..., -2030, -1509,   564],\n",
              "        [    0,     0,     0, ..., -2425, -1344,  2379],\n",
              "        [    0,     0,     0, ..., -3310,   109,  5285],\n",
              "        ...,\n",
              "        [ -587,  -587,  -587, ...,  1434,  1050,  -393],\n",
              "        [ -799,  -799,  -799, ...,  1223,  1365,    13],\n",
              "        [-1067, -1067, -1067, ...,  1036,   830,   613]]], dtype=int16)"
            ]
          },
          "metadata": {},
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_slice_3d(volume, x, axis, n=None):\n",
        "    \"\"\"\n",
        "    Naive function... but only works on 3 dimensions.\n",
        "    NB Using ellipses slows down last axis.\n",
        "    \"\"\"\n",
        "    # Force cube shape\n",
        "    if n is None and not np.sum(np.diff(volume.shape)):\n",
        "        n = np.min(volume.shape)\n",
        "    if axis == 0:\n",
        "        data = volume[x, :n, :n]\n",
        "    "
      ],
      "metadata": {
        "id": "6TAXwXGYdjJX"
      },
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "so8YLWpIdjUH"
      },
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### numpy.diff  \n",
        "https://numpy.org/doc/stable/reference/generated/numpy.diff.html"
      ],
      "metadata": {
        "id": "ACAvZ3StdjeE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([1, 2, 4, 7, 0])"
      ],
      "metadata": {
        "id": "LkjlFKIscmUe"
      },
      "execution_count": 236,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B5LnItnPb3Mr",
        "outputId": "9af6666b-f51f-44ff-83c7-f6446bef20e4"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  2,  3, -7])"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x, n=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vOdQOh2KHNLa",
        "outputId": "2419c8ff-b859-4c9f-acb6-07cbf82ff5e0"
      },
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  1,   1, -10])"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x, n=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pe6lMrwbHNPS",
        "outputId": "635dcbad-eb3c-4f5f-9f76-7a97e0d6896c"
      },
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  0, -11])"
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x, n=4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1gVJJOBHNS1",
        "outputId": "6a733e48-ab53-4348-ed70-f3f82cfebe72"
      },
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-11])"
            ]
          },
          "metadata": {},
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8BMrVscvHNWU"
      },
      "execution_count": 240,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])"
      ],
      "metadata": {
        "id": "Qgw_60g0HNas"
      },
      "execution_count": 241,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_ctQhpMixxK",
        "outputId": "dab48467-7b51-4520-c359-1d3963dba103"
      },
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  3,  6, 10],\n",
              "       [ 0,  5,  6,  8]])"
            ]
          },
          "metadata": {},
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFIPdJzIHNnG",
        "outputId": "0d4d92cd-86bc-48bf-aeb4-3fee3a47ce56"
      },
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2, 3, 4],\n",
              "       [5, 1, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_7FduUjizIu",
        "outputId": "5c0b7dd5-5a4e-419e-988d-74f162950785"
      },
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  1],\n",
              "       [-4,  1]])"
            ]
          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x, 3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42Nl2fsei3Hy",
        "outputId": "b71f48c6-7bc4-4607-b95a-7744b3cc57a2"
      },
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0],\n",
              "       [5]])"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x, 4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vbbdd1Sti3Wb",
        "outputId": "cd8a433d-9f4d-4dc8-e5b8-a7700566d0be"
      },
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([], shape=(2, 0), dtype=int64)"
            ]
          },
          "metadata": {},
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x, 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LRWGFVwpi3he",
        "outputId": "d32fc5df-571f-438c-f484-159d11f8a56d"
      },
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([], shape=(2, 0), dtype=int64)"
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "B6jDV87ji3rG"
      },
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)"
      ],
      "metadata": {
        "id": "mKWUjULbjPLB"
      },
      "execution_count": 248,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Eys_TKNjPRb",
        "outputId": "d6fa7853-c5d3-4fd9-a72c-d466e05bf751"
      },
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1066-10-13', '1066-10-14', '1066-10-15'], dtype='datetime64[D]')"
            ]
          },
          "metadata": {},
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSMpv8pejPf_",
        "outputId": "44e2b515-b29c-4952-cf20-a3beaf01526c"
      },
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1], dtype='timedelta64[D]')"
            ]
          },
          "metadata": {},
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(x, n=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZgZ-PYtjPt_",
        "outputId": "558003cf-c877-4093-e703-27b5c3d8e343"
      },
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0], dtype='timedelta64[D]')"
            ]
          },
          "metadata": {},
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zXBpjWP3jP3g"
      },
      "execution_count": 251,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff([True, False])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQAUC0sEkvbJ",
        "outputId": "5c5d1783-2240-4ab1-b992-9ad0a42d0908"
      },
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "metadata": {},
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff([True, True])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3pyIx_0kv1n",
        "outputId": "4a15571e-8214-48e3-c31a-69d11e3f9fae"
      },
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False])"
            ]
          },
          "metadata": {},
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff([False, True])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9IQQ4vnk6Ft",
        "outputId": "838b6d87-b230-403c-cda1-a0deef8122cd"
      },
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True])"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff([False, False])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0o_cRjdk6Ls",
        "outputId": "59f5bae8-85b8-4e0c-b0d5-57f8a68a5c28"
      },
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False])"
            ]
          },
          "metadata": {},
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jB_BZqO6k6TE"
      },
      "execution_count": 255,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "u8_arr = np.array([1, 0], dtype=np.uint8)"
      ],
      "metadata": {
        "id": "IWJbu32kjP_x"
      },
      "execution_count": 256,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "u8_arr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLu7q5RhjQHl",
        "outputId": "c80850b8-c122-4b8c-8eed-3586bbd53752"
      },
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(u8_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJnaNB_3i31m",
        "outputId": "b246a9ac-2f49-4f67-cec3-bce4510763b4"
      },
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([255], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "u8_arr[1,...] - u8_arr[0,...]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sOvso1-XizTI",
        "outputId": "d167b01f-aa6a-434f-c8b6-2081b0516b88"
      },
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "255"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7Ucjc_1ZlbqZ"
      },
      "execution_count": 259,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "u8_arr_reverse = np.array([0, 1], dtype=np.uint8)"
      ],
      "metadata": {
        "id": "OOhodofYizff"
      },
      "execution_count": 260,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "u8_arr_reverse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMpNheznizrM",
        "outputId": "d184e809-810e-440a-e352-8dd53f70a304"
      },
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(u8_arr_reverse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtJ5UJ3gizz0",
        "outputId": "a6022ccb-2c8e-4a36-cade-3403d6b82338"
      },
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "u8_arr_reverse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTMIdp9viz_D",
        "outputId": "1319007d-a3e6-47fd-db8f-57e3b5fb63f1"
      },
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GsR3j8kGi0Hm"
      },
      "execution_count": 263,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "i16_arr = u8_arr.astype(np.int16)"
      ],
      "metadata": {
        "id": "yIuQdeZ_HNwM"
      },
      "execution_count": 264,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "i16_arr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxlsilhPHOIT",
        "outputId": "d036a4a5-c852-4b93-8d36-09b2186ec556"
      },
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0], dtype=int16)"
            ]
          },
          "metadata": {},
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(i16_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYZszPQnnEAx",
        "outputId": "92c8b80a-cc07-4aec-de1a-cfd04c33d4c0"
      },
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1], dtype=int16)"
            ]
          },
          "metadata": {},
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YRsSNnnCnEJt"
      },
      "execution_count": 266,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Understanding Data Types in Python\n",
        "https://jakevdp.github.io/PythonDataScienceHandbook/02.01-understanding-data-types.html"
      ],
      "metadata": {
        "id": "BoMAm2elnESR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Data type\t    | Description |\n",
        "|---------------|-------------|\n",
        "| ``bool_``     | Boolean (True or False) stored as a byte |\n",
        "| ``int_``      | Default integer type (same as C ``long``; normally either ``int64`` or ``int32``)| \n",
        "| ``intc``      | Identical to C ``int`` (normally ``int32`` or ``int64``)| \n",
        "| ``intp``      | Integer used for indexing (same as C ``ssize_t``; normally either ``int32`` or ``int64``)| \n",
        "| ``int8``      | Byte (-128 to 127)| \n",
        "| ``int16``     | Integer (-32768 to 32767)|\n",
        "| ``int32``     | Integer (-2147483648 to 2147483647)|\n",
        "| ``int64``     | Integer (-9223372036854775808 to 9223372036854775807)| \n",
        "| ``uint8``     | Unsigned integer (0 to 255)| \n",
        "| ``uint16``    | Unsigned integer (0 to 65535)| \n",
        "| ``uint32``    | Unsigned integer (0 to 4294967295)| \n",
        "| ``uint64``    | Unsigned integer (0 to 18446744073709551615)| \n",
        "| ``float_``    | Shorthand for ``float64``.| \n",
        "| ``float16``   | Half precision float: sign bit, 5 bits exponent, 10 bits mantissa| \n",
        "| ``float32``   | Single precision float: sign bit, 8 bits exponent, 23 bits mantissa| \n",
        "| ``float64``   | Double precision float: sign bit, 11 bits exponent, 52 bits mantissa| \n",
        "| ``complex_``  | Shorthand for ``complex128``.| \n",
        "| ``complex64`` | Complex number, represented by two 32-bit floats| \n",
        "| ``complex128``| Complex number, represented by two 64-bit floats| "
      ],
      "metadata": {
        "id": "QGw3cAMlnEbA"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NiuB9acNnEi8"
      },
      "execution_count": 266,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get back to the main subject"
      ],
      "metadata": {
        "id": "A1l8c00ynEq5"
      },
      "execution_count": 267,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Re: Re: Accessing the seismic data\n",
        "\n"
      ],
      "metadata": {
        "id": "2VhHu-uATARO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "volume.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ODEe7QmnEzK",
        "outputId": "17177cf6-c738-403b-a3f1-4d93e5879065"
      },
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(190, 190, 190)"
            ]
          },
          "metadata": {},
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "volume"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3ytOMiITL-J",
        "outputId": "5a1da7c4-d719-4877-d153-b5891c0e0582"
      },
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[    0,     0,     0, ..., -1572, -2179,   -40],\n",
              "        [    0,     0,     0, ..., -3173, -1118,  1754],\n",
              "        [    0,     0,     0, ..., -3116,   440,  2874],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  -203,   807,   272],\n",
              "        [ -407,  -407,  -407, ...,   901,   269,  -782],\n",
              "        [    0,     0,     0, ...,   482, -1240,  -676]],\n",
              "\n",
              "       [[-1018, -1018, -1018, ..., -1740, -2042,  1227],\n",
              "        [-1173, -1173, -1173, ..., -2627,  -414,  2853],\n",
              "        [ -417,  -417,  -417, ..., -2529,  -249,  2811],\n",
              "        ...,\n",
              "        [-1509, -1509, -1509, ...,   603,  -404,  -367],\n",
              "        [ -523,  -523,  -523, ...,  -258, -1396,  -163],\n",
              "        [ -886,  -886,  -886, ...,  -990, -1417,   798]],\n",
              "\n",
              "       [[ -425,  -425,  -425, ..., -1520, -3207,  1019],\n",
              "        [  -42,   -42,   -42, ..., -3645, -1818,  3129],\n",
              "        [ -443,  -443,  -443, ..., -2935,   282,  3299],\n",
              "        ...,\n",
              "        [ -681,  -681,  -681, ...,   335,  -630,     3],\n",
              "        [    0,     0,     0, ...,  -636, -1403,   649],\n",
              "        [ -415,  -415,  -415, ..., -1719,  -993,  2022]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[    0,     0,     0, ..., -1848, -2099, -1249],\n",
              "        [    0,     0,     0, ..., -1574, -2024,   169],\n",
              "        [    0,     0,     0, ..., -2035, -1744,  2358],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  1477,   956,   725],\n",
              "        [    0,     0,     0, ...,  1350,   763,   315],\n",
              "        [    0,     0,     0, ...,  1657,  1143,   480]],\n",
              "\n",
              "       [[  -91,   -91,   -91, ...,  -881, -1555, -1101],\n",
              "        [  194,   194,   194, ...,  -966, -1957,   620],\n",
              "        [    0,     0,     0, ..., -1896,  -429,  3099],\n",
              "        ...,\n",
              "        [ -566,  -566,  -566, ...,  1790,  1609,   209],\n",
              "        [-1106, -1106, -1106, ...,  1791,  1681,   657],\n",
              "        [-1004, -1004, -1004, ...,  1454,  1213,   938]],\n",
              "\n",
              "       [[    0,     0,     0, ..., -2030, -1509,   564],\n",
              "        [    0,     0,     0, ..., -2425, -1344,  2379],\n",
              "        [    0,     0,     0, ..., -3310,   109,  5285],\n",
              "        ...,\n",
              "        [ -587,  -587,  -587, ...,  1434,  1050,  -393],\n",
              "        [ -799,  -799,  -799, ...,  1223,  1365,    13],\n",
              "        [-1067, -1067, -1067, ...,  1036,   830,   613]]], dtype=int16)"
            ]
          },
          "metadata": {},
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "volume.flags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3P91h5tTnBK",
        "outputId": "48b206dd-88d6-42ff-e77f-37f5a61406be"
      },
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  C_CONTIGUOUS : True\n",
              "  F_CONTIGUOUS : False\n",
              "  OWNDATA : True\n",
              "  WRITEABLE : True\n",
              "  ALIGNED : True\n",
              "  WRITEBACKIFCOPY : False\n",
              "  UPDATEIFCOPY : False"
            ]
          },
          "metadata": {},
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.diff(volume.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-qzOtGhyqK4",
        "outputId": "26dd20cf-5c3b-4d3a-d3f4-4dc2b55cd236"
      },
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum(np.diff(volume.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBkRJczxyqPF",
        "outputId": "e8a82bd7-557f-4981-86cc-3f9d15846088"
      },
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_slice_3d_only_ifs(volume, x, axis, n=None):\n",
        "    \"\"\"\n",
        "    Naive function... but only works on 3 dimensions.\n",
        "    NB Using ellipses slows down last axis.\n",
        "    \"\"\"\n",
        "    if n is None and not np.sum(np.diff(volume.shape)):\n",
        "        n = np.min(volume.shape)\n",
        "    if axis == 0:\n",
        "        data = volume[x, :n, :n]\n",
        "    if axis == 1:\n",
        "        data = volume[:n, x, :n]\n",
        "    if axis == 2:\n",
        "        data = volume[:n, :n, x]\n",
        "    return data + 1"
      ],
      "metadata": {
        "id": "JhhxJrB2yqTM"
      },
      "execution_count": 273,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_slice_3d_with_elifs(volume, x, axis, n=None):\n",
        "    \"\"\"\n",
        "    Naive function... but only works on 3 dimensions.\n",
        "    NB Using ellipses slows down last axis.\n",
        "    \"\"\"\n",
        "    if n is None and not np.sum(np.diff(volume.shape)):\n",
        "        n = np.min(volume.shape)\n",
        "    if axis == 0:\n",
        "        data = volume[x, :n, :n]\n",
        "    elif axis == 1:\n",
        "        data = volume[:n, x, :n]\n",
        "    elif axis == 2:\n",
        "        data = volume[:n, :n, x]\n",
        "    return data + 1"
      ],
      "metadata": {
        "id": "98XFwQLUrYsJ"
      },
      "execution_count": 274,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# %timeit get_slice_3d_only_ifs(volume, 150, axis=0)\n",
        "# %timeit get_slice_3d_only_ifs(volume, 150, axis=1)\n",
        "# %timeit get_slice_3d_only_ifs(volume, 150, axis=2)"
      ],
      "metadata": {
        "id": "Ef3OEJs6yqXG"
      },
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# %timeit get_slice_3d_with_elifs(volume, 150, axis=0)\n",
        "# %timeit get_slice_3d_with_elifs(volume, 150, axis=1)\n",
        "# %timeit get_slice_3d_with_elifs(volume, 150, axis=2)"
      ],
      "metadata": {
        "id": "S43af-TL8-pn"
      },
      "execution_count": 276,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sXNMxBEB8-wS"
      },
      "execution_count": 276,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "volumef = np.asfortranarray(volume)"
      ],
      "metadata": {
        "id": "qqS84sqY8-4F"
      },
      "execution_count": 277,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "volumef"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "syZo_uby8-_2",
        "outputId": "a386f90f-cc45-4f0e-b550-751735b1696a"
      },
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[    0,     0,     0, ..., -1572, -2179,   -40],\n",
              "        [    0,     0,     0, ..., -3173, -1118,  1754],\n",
              "        [    0,     0,     0, ..., -3116,   440,  2874],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  -203,   807,   272],\n",
              "        [ -407,  -407,  -407, ...,   901,   269,  -782],\n",
              "        [    0,     0,     0, ...,   482, -1240,  -676]],\n",
              "\n",
              "       [[-1018, -1018, -1018, ..., -1740, -2042,  1227],\n",
              "        [-1173, -1173, -1173, ..., -2627,  -414,  2853],\n",
              "        [ -417,  -417,  -417, ..., -2529,  -249,  2811],\n",
              "        ...,\n",
              "        [-1509, -1509, -1509, ...,   603,  -404,  -367],\n",
              "        [ -523,  -523,  -523, ...,  -258, -1396,  -163],\n",
              "        [ -886,  -886,  -886, ...,  -990, -1417,   798]],\n",
              "\n",
              "       [[ -425,  -425,  -425, ..., -1520, -3207,  1019],\n",
              "        [  -42,   -42,   -42, ..., -3645, -1818,  3129],\n",
              "        [ -443,  -443,  -443, ..., -2935,   282,  3299],\n",
              "        ...,\n",
              "        [ -681,  -681,  -681, ...,   335,  -630,     3],\n",
              "        [    0,     0,     0, ...,  -636, -1403,   649],\n",
              "        [ -415,  -415,  -415, ..., -1719,  -993,  2022]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[    0,     0,     0, ..., -1848, -2099, -1249],\n",
              "        [    0,     0,     0, ..., -1574, -2024,   169],\n",
              "        [    0,     0,     0, ..., -2035, -1744,  2358],\n",
              "        ...,\n",
              "        [    0,     0,     0, ...,  1477,   956,   725],\n",
              "        [    0,     0,     0, ...,  1350,   763,   315],\n",
              "        [    0,     0,     0, ...,  1657,  1143,   480]],\n",
              "\n",
              "       [[  -91,   -91,   -91, ...,  -881, -1555, -1101],\n",
              "        [  194,   194,   194, ...,  -966, -1957,   620],\n",
              "        [    0,     0,     0, ..., -1896,  -429,  3099],\n",
              "        ...,\n",
              "        [ -566,  -566,  -566, ...,  1790,  1609,   209],\n",
              "        [-1106, -1106, -1106, ...,  1791,  1681,   657],\n",
              "        [-1004, -1004, -1004, ...,  1454,  1213,   938]],\n",
              "\n",
              "       [[    0,     0,     0, ..., -2030, -1509,   564],\n",
              "        [    0,     0,     0, ..., -2425, -1344,  2379],\n",
              "        [    0,     0,     0, ..., -3310,   109,  5285],\n",
              "        ...,\n",
              "        [ -587,  -587,  -587, ...,  1434,  1050,  -393],\n",
              "        [ -799,  -799,  -799, ...,  1223,  1365,    13],\n",
              "        [-1067, -1067, -1067, ...,  1036,   830,   613]]], dtype=int16)"
            ]
          },
          "metadata": {},
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "volumef.flags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIllaugw8_GU",
        "outputId": "9a33f577-fa2c-43ce-a5ad-e09b8f20e746"
      },
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  C_CONTIGUOUS : False\n",
              "  F_CONTIGUOUS : True\n",
              "  OWNDATA : True\n",
              "  WRITEABLE : True\n",
              "  ALIGNED : True\n",
              "  WRITEBACKIFCOPY : False\n",
              "  UPDATEIFCOPY : False"
            ]
          },
          "metadata": {},
          "execution_count": 279
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# %timeit get_slice_3d_only_ifs(volumef, 150, axis=0)\n",
        "# %timeit get_slice_3d_only_ifs(volumef, 150, axis=1)\n",
        "# %timeit get_slice_3d_only_ifs(volumef, 150, axis=2)"
      ],
      "metadata": {
        "id": "J6WDkPaF8_NG"
      },
      "execution_count": 280,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# %timeit get_slice_3d_with_elifs(volumef, 150, axis=0)\n",
        "# %timeit get_slice_3d_with_elifs(volumef, 150, axis=1)\n",
        "# %timeit get_slice_3d_with_elifs(volumef, 150, axis=2)"
      ],
      "metadata": {
        "id": "ZzBKVOfMTwhD"
      },
      "execution_count": 281,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-LU5bQTfTwn4"
      },
      "execution_count": 281,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.signal import welch"
      ],
      "metadata": {
        "id": "md74DdvZTwvO"
      },
      "execution_count": 282,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# %timeit s = [welch(tr, fs=500) for tr in volume[:, 10]]"
      ],
      "metadata": {
        "id": "DPD71iOtTw3L"
      },
      "execution_count": 283,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# %timeit s = [welch(tr, fs=500) for tr in volumef[:, 10]]"
      ],
      "metadata": {
        "id": "h1yT_xsuTw-R"
      },
      "execution_count": 284,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# help(welch)"
      ],
      "metadata": {
        "id": "xXv52UT28_S1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del(volume)"
      ],
      "metadata": {
        "id": "iAhYbV_Ry1PD"
      },
      "execution_count": 286,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del(volumef)"
      ],
      "metadata": {
        "id": "GynG7bQTy1R_"
      },
      "execution_count": 287,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fake data in *n* dimensions"
      ],
      "metadata": {
        "id": "g5A_pAity1Ud"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's make a function to generate random data in any number of dimensions."
      ],
      "metadata": {
        "id": "hs-Z6eEBHwMx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Be careful:these volumes get big really quickly!"
      ],
      "metadata": {
        "id": "nMG0Xen0Hwp2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def makend(n, s, equal=True, rev=False, fortran=False):\n",
        "    \"\"\"\n",
        "    Make an n-dimensional hypercube of randoms.\n",
        "    \"\"\"\n",
        "    if equal:\n",
        "        incr = np.zeros(n, dtype=int)\n",
        "    elif rev:\n",
        "        incr = list(reversed(np.arange(n)))\n",
        "    else:\n",
        "        incr = np.arange(n)\n",
        "    shape = incr + np.ones(n, dtype=int)*s\n",
        "    a = np.random.random(shape)\n",
        "    m = f\"Shape: {tuple(shape)}    \"\n",
        "    m += f\"Memory: {a.nbytes/1e6:.0f}MB    \"\n",
        "    m += f\"Order: {'F' if fortran else 'C'}\"\n",
        "    print(m)\n",
        "    if fortran:\n",
        "        return np.asfortranarray(a)\n",
        "    else:\n",
        "        return a"
      ],
      "metadata": {
        "id": "j-s6-5Fny1W6"
      },
      "execution_count": 288,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.arange(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "khyWITn6F0xX",
        "outputId": "7af727e0-2f67-4dc8-87f0-d423c1d42414"
      },
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 332
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reversed(np.arange(3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWU97je1F29q",
        "outputId": "db158d7d-af38-4879-ff15-690931757913"
      },
      "execution_count": 333,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<reversed at 0x7f82734ca450>"
            ]
          },
          "metadata": {},
          "execution_count": 333
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(reversed(np.arange(3)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "okDrKFBbF7T1",
        "outputId": "a96742c5-1f5b-465a-e8ae-313b79ac2a86"
      },
      "execution_count": 334,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 1, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 334
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "incr = np.zeros(2, dtype=int)\n",
        "incr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KGho6SKY11i0",
        "outputId": "5e8421bc-e976-4712-93ee-379fc295d158"
      },
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 289
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "incr = list(reversed(np.arange(2)))\n",
        "incr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "coC67pKI11l-",
        "outputId": "5be55a65-7039-4cc9-91f5-70d847cd99f8"
      },
      "execution_count": 290,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "incr = np.arange(2)\n",
        "incr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IrEQDz2M11o-",
        "outputId": "43ce4231-c41e-4878-8467-25b08616557e"
      },
      "execution_count": 291,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 291
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shape = incr + np.ones(2, dtype=int)*6000\n",
        "shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-IbKry811q3",
        "outputId": "81dd6534-4267-453e-c7dd-7b5b86640404"
      },
      "execution_count": 292,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6000, 6001])"
            ]
          },
          "metadata": {},
          "execution_count": 292
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.random.random(shape)\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDFwiUNsG5Oh",
        "outputId": "36fbc039-f1c5-417a-88c3-75c520b08507"
      },
      "execution_count": 336,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.98195804, 0.97987767, 0.94293243, ..., 0.81076449, 0.29489183,\n",
              "        0.17344084],\n",
              "       [0.70644831, 0.66936589, 0.38426022, ..., 0.8678839 , 0.63430307,\n",
              "        0.13718588],\n",
              "       [0.57657428, 0.39917851, 0.34036894, ..., 0.70064005, 0.4543587 ,\n",
              "        0.22691941],\n",
              "       ...,\n",
              "       [0.83728207, 0.56466267, 0.10112787, ..., 0.35248137, 0.38330455,\n",
              "        0.4013263 ],\n",
              "       [0.5661741 , 0.26383471, 0.94753167, ..., 0.04787356, 0.58604187,\n",
              "        0.83453141],\n",
              "       [0.60547927, 0.37360875, 0.72079661, ..., 0.29855204, 0.01680014,\n",
              "        0.93804102]])"
            ]
          },
          "metadata": {},
          "execution_count": 336
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-_ucwhnCHATH",
        "outputId": "59c34140-b66c-48ee-ba77-e028bba17301"
      },
      "execution_count": 337,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6000, 6001)"
            ]
          },
          "metadata": {},
          "execution_count": 337
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6xSP5dEaHAeM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2D data"
      ],
      "metadata": {
        "id": "evpvGWr4y1dv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_slice_2d(volume, x, axis, n=None):\n",
        "    \"\"\"\n",
        "    Naive function... but only works on 2 dimensions.\n",
        "    \"\"\"\n",
        "    if n is None and not np.sum(np.diff(volume.shape)):\n",
        "        n = np.min(volume.shape)\n",
        "    if axis == 0:\n",
        "        data = volume[x, :n]\n",
        "    if axis == 1:\n",
        "        data = volume[:n, x]\n",
        "    return data + 1"
      ],
      "metadata": {
        "id": "InZ2Gub58_ZA"
      },
      "execution_count": 293,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 2\n",
        "\n",
        "v = makend(dim, 6000, fortran=False)"
      ],
      "metadata": {
        "id": "q6YH3q9cyqsl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03d7dc01-7cb3-482e-d875-964a5c190416"
      },
      "execution_count": 294,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: (6000, 6000)    Memory: 288MB    Order: C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v"
      ],
      "metadata": {
        "id": "6D_yf8iGyqx2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d713018-7d8e-4d43-87fa-4bdcb50da2d3"
      },
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7.57480699e-01, 7.62457043e-01, 5.01023607e-01, ...,\n",
              "        3.91644136e-01, 9.44869487e-01, 7.73300313e-01],\n",
              "       [4.78729164e-01, 5.97362276e-02, 7.56317075e-01, ...,\n",
              "        8.51742932e-01, 2.31592780e-01, 1.70411912e-01],\n",
              "       [6.56470829e-01, 5.04578442e-01, 5.78036436e-01, ...,\n",
              "        8.08951610e-01, 4.68440914e-01, 9.88310762e-01],\n",
              "       ...,\n",
              "       [8.30625293e-01, 4.39019881e-01, 3.28096206e-02, ...,\n",
              "        4.41945060e-04, 6.53490614e-01, 9.87208650e-01],\n",
              "       [7.10978013e-01, 9.39156966e-01, 9.01615242e-01, ...,\n",
              "        9.56711907e-01, 9.23478136e-01, 2.08910579e-01],\n",
              "       [7.40925482e-01, 6.67356127e-01, 2.36902109e-01, ...,\n",
              "        1.71278997e-01, 4.17380943e-01, 7.93026977e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 295
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7ZfRkLw11cj",
        "outputId": "00274dc6-d9d8-42c0-e402-0f4099ac7b95"
      },
      "execution_count": 296,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6000, 6000)"
            ]
          },
          "metadata": {},
          "execution_count": 296
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for n in range(dim):\n",
        "#     %timeit get_slice_2d(v, 3001, n)"
      ],
      "metadata": {
        "id": "KK6wTDsK11tA"
      },
      "execution_count": 297,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del v"
      ],
      "metadata": {
        "id": "cyKETRCx11vV"
      },
      "execution_count": 298,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 2"
      ],
      "metadata": {
        "id": "hnVJISqH43ed"
      },
      "execution_count": 299,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "v = makend(dim, 6000, fortran=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zHYrFYZ043hN",
        "outputId": "41440e1b-5c2e-4056-9e4c-a2811282f295"
      },
      "execution_count": 300,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: (6000, 6000)    Memory: 288MB    Order: F\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for n in range(dim):\n",
        "#     %timeit get_slice_2d(v, 3001, n)"
      ],
      "metadata": {
        "id": "vx5XqA9G43j1"
      },
      "execution_count": 301,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1D convolution on an array"
      ],
      "metadata": {
        "id": "8O2JFONl43mS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def convolve(data, kernel=np.arange(10), axis=0):\n",
        "    func = lambda tr: np.convolve(tr, kernel, mode='same')\n",
        "    return np.apply_along_axis(func, axis=axis, arr=data)"
      ],
      "metadata": {
        "id": "3xj6EwGj43pH"
      },
      "execution_count": 302,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 2\n",
        "\n",
        "# v = makend(dim, 6000, fortran=False)\n",
        "# %timeit convolve(v, axis=0)\n",
        "# %timeit convolve(v, axis=1)\n",
        "# del v"
      ],
      "metadata": {
        "id": "2n7ydaSK0du3"
      },
      "execution_count": 303,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 2\n",
        "\n",
        "# v = makend(dim, 6000, fortran=True)\n",
        "# %timeit convolve(v, axis=0)\n",
        "# %timeit convolve(v, axis=1)\n",
        "# del v"
      ],
      "metadata": {
        "id": "fKE10CQd0dxR"
      },
      "execution_count": 304,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# `np.mean()` across axes"
      ],
      "metadata": {
        "id": "DAGc3Wxv0d0G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = [[2, 4],\n",
        "     [10, 20]]"
      ],
      "metadata": {
        "id": "shKtjoGO0d2r"
      },
      "execution_count": 305,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6dKaGFk0d5H",
        "outputId": "708e8593-9662-4316-8745-3875f8829ec8"
      },
      "execution_count": 306,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2, 4], [10, 20]]"
            ]
          },
          "metadata": {},
          "execution_count": 306
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(a, axis=0), np.mean(a, axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bggghqhd0d8O",
        "outputId": "d0281c5a-91c2-46d1-f912-986eaf7f91e0"
      },
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 6., 12.]), array([ 3., 15.]))"
            ]
          },
          "metadata": {},
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 2\n",
        "\n",
        "# v = makend(dim, 6000, fortran=False)\n",
        "# %timeit np.mean(v, axis=0)\n",
        "# %timeit np.mean(v, axis=1)\n",
        "# del v"
      ],
      "metadata": {
        "id": "gnQQiKWw0d-s"
      },
      "execution_count": 308,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 2\n",
        "\n",
        "# v = makend(dim, 6000, fortran=True)\n",
        "# %timeit np.mean(v, axis=0)\n",
        "# %timeit np.mean(v, axis=1)\n",
        "# del v"
      ],
      "metadata": {
        "id": "Ac4ekRHl0eBX"
      },
      "execution_count": 309,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FisdWy8n0eE1"
      },
      "execution_count": 309,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 2\n",
        "\n",
        "# v = makend(dim, 6000, fortran=False)\n",
        "# %timeit np.median(v, axis=0)\n",
        "# %timeit np.median(v, axis=1)\n",
        "# del v"
      ],
      "metadata": {
        "id": "_CDRcHAR0eHK"
      },
      "execution_count": 310,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# v = makend(dim, 6000, fortran=False)\n",
        "# %timeit v.mean(axis=0)\n",
        "# %timeit v.mean(axis=1)\n",
        "# del v"
      ],
      "metadata": {
        "id": "q8cr6Ta-6q_a"
      },
      "execution_count": 311,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3D arrays"
      ],
      "metadata": {
        "id": "7Gfu2BU36rBo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 3\n",
        "\n",
        "# v = makend(dim, 600)\n",
        "# for n in range(dim):\n",
        "#     %timeit get_slice_3d(v, 201, n)\n",
        "# del v"
      ],
      "metadata": {
        "id": "wUViLDMM6rD1"
      },
      "execution_count": 312,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Non-equal axes doesn't matter"
      ],
      "metadata": {
        "id": "UE0DmRRFLlcM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 3\n",
        "\n",
        "# v = makend(dim, 600, equal=False, rev=True)\n",
        "# for n in range(dim):\n",
        "#     %timeit get_slice_3d(v, 201, n)\n",
        "# del v"
      ],
      "metadata": {
        "id": "lA1UFD0E6rGO"
      },
      "execution_count": 313,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fortran order results in a fast last axis, as per. But the middle axis is pretty fast too."
      ],
      "metadata": {
        "id": "uPKmHdSZ6rIy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 3\n",
        "\n",
        "# v = makend(dim, 600, fortran=True)\n",
        "# for n in range(dim):\n",
        "#     %timeit get_slice_3d(v, 201, n)\n",
        "# del v"
      ],
      "metadata": {
        "id": "BITr30C_6rLL"
      },
      "execution_count": 314,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4 dimensions"
      ],
      "metadata": {
        "id": "6ue4Q2FH6rNq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Axes 0 and 1 are fast (for C ordering), axis 2 is half speed, axis 3 is ca. 15 times slower than fast axis."
      ],
      "metadata": {
        "id": "5PgvBdQb6rQF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_slice_4d(volume, x, axis, n=None):\n",
        "    \"\"\"\n",
        "    Naive function... but only works on 4 dimensions.\n",
        "    \"\"\"\n",
        "    if n is None and not np.sum(np.diff(volume.shape)):\n",
        "        n = np.min(volume.shape)\n",
        "    if axis == 0:\n",
        "        data = volume[x, :n, :n, :n]\n",
        "    if axis == 1:\n",
        "        data = volume[:n, x, :n, :n]\n",
        "    if axis == 2:\n",
        "        data = volume[:n, :n, x, :n]\n",
        "    if axis == 3:\n",
        "        data = volume[:n, :n, :n, x]\n",
        "    return data + 1"
      ],
      "metadata": {
        "id": "KPRtywJwcvNK"
      },
      "execution_count": 315,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 4\n",
        "\n",
        "# v = makend(dim, 100, equal=True)\n",
        "# for n in range(dim):\n",
        "#     %timeit get_slice_4d(v, 51, n)\n",
        "# del v"
      ],
      "metadata": {
        "id": "y_IzKrUBcvPp"
      },
      "execution_count": 316,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 4\n",
        "\n",
        "# v = makend(dim, 100, equal=True, fortran=True)\n",
        "# for n in range(dim):\n",
        "#     %timeit get_slice_4d(v, 51, n)\n",
        "# del v"
      ],
      "metadata": {
        "id": "1ejgTJGGcvSq"
      },
      "execution_count": 317,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5 dimensions"
      ],
      "metadata": {
        "id": "Bb5K3PfvcvVG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are taking 4-dimensional hyperplanes from a 5-dimensional hypercube.\n",
        "\n",
        "Axes 0 and 1 are fast, axis 2 is half speed, axis 3 is quarter speed, and the last axis is about 5x slower than that."
      ],
      "metadata": {
        "id": "O3CQ1n5HcvYQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_slice_5d(volume, x, axis, n=None):\n",
        "    \"\"\"\n",
        "    Naive function... but only works on 5 dimensions.\n",
        "    \"\"\"\n",
        "    if n is None and not np.sum(np.diff(volume.shape)):\n",
        "        n = np.min(volume.shape)\n",
        "    if axis == 0:\n",
        "        data = volume[x, :n, :n, :n, :n]\n",
        "    if axis == 1:\n",
        "        data = volume[:n, x, :n, :n, :n]\n",
        "    if axis == 2:\n",
        "        data = volume[:n, :n, x, :n, :n]\n",
        "    if axis == 3:\n",
        "        data = volume[:n, :n, :n, x, :n]\n",
        "    if axis == 4:\n",
        "        data = volume[:n, :n, :n, :n, x]\n",
        "    return data + 1\n"
      ],
      "metadata": {
        "id": "AND_NA51cvak"
      },
      "execution_count": 318,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 5\n",
        "\n",
        "v = makend(dim, 40)\n",
        "for n in range(dim):\n",
        "    %timeit get_slice_5d(v, 21, n)\n",
        "del v"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6ldoaoVrD5h",
        "outputId": "311487e3-0b76-4b4e-c278-b77d06afb21c"
      },
      "execution_count": 319,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: (40, 40, 40, 40, 40)    Memory: 819MB    Order: C\n",
            "4.7 ms ± 147 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "4.75 ms ± 227 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "7.14 ms ± 245 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "10.6 ms ± 530 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "34 ms ± 1.06 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dim = 5\n",
        "\n",
        "# v = makend(dim, 40, fortran=True)\n",
        "# for n in range(dim):\n",
        "#     %timeit get_slice_5d(v, 21, n)\n",
        "# del v"
      ],
      "metadata": {
        "id": "1H_5agq0rEFK"
      },
      "execution_count": 320,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iKcaeCK_oV_c"
      },
      "execution_count": 320,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 5\n",
        "\n",
        "v = makend(dim, 40, fortran=True)\n",
        "for n in range(dim):\n",
        "    %timeit np.mean(v, axis=n)\n",
        "del v"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7i25L4sVrEmj",
        "outputId": "387cb708-194d-423b-9f72-e78872022a27"
      },
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: (40, 40, 40, 40, 40)    Memory: 819MB    Order: F\n",
            "134 ms ± 3.09 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
            "156 ms ± 23.4 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
            "100 ms ± 5.96 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
            "117 ms ± 3.81 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
            "184 ms ± 19.7 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6 dimensions and beyond"
      ],
      "metadata": {
        "id": "MEvu-9oRrE3Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_slice_6d(volume, x, axis, n=None):\n",
        "    \"\"\"\n",
        "    Naive function... but only works on 6 dimensions.\n",
        "    \"\"\"\n",
        "    if n is None and not np.sum(np.diff(volume.shape)):\n",
        "        n = np.min(volume.shape)\n",
        "    if axis == 0:\n",
        "        data = volume[x, :n, :n, :n, :n, :n]\n",
        "    if axis == 1:\n",
        "        data = volume[:n, x, :n, :n, :n, :n]\n",
        "    if axis == 2:\n",
        "        data = volume[:n, :n, x, :n, :n, :n]\n",
        "    if axis == 3:\n",
        "        data = volume[:n, :n, :n, x, :n, :n]\n",
        "    if axis == 4:\n",
        "        data = volume[:n, :n, :n, :n, x, :n]\n",
        "    if axis == 5:\n",
        "        data = volume[:n, :n, :n, :n, :n, x]\n",
        "    return data + 1"
      ],
      "metadata": {
        "id": "X57BIYXNtW0V"
      },
      "execution_count": 322,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 6\n",
        "\n",
        "v = makend(dim, 23)\n",
        "for n in range(dim):\n",
        "    %timeit get_slice_6d(v, 12, n)\n",
        "del v"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vtNm6q86cvdH",
        "outputId": "4abf1c9b-6c65-4d5f-85fa-e6b6188b1c28"
      },
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: (23, 23, 23, 23, 23, 23)    Memory: 1184MB    Order: C\n",
            "11.3 ms ± 319 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "11.2 ms ± 334 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "11.1 ms ± 421 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "18.1 ms ± 868 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "31.3 ms ± 1.03 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
            "72.1 ms ± 1.49 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dim = 6\n",
        "\n",
        "v = makend(dim, 23)\n",
        "for n in range(dim):\n",
        "    %timeit slice_output = get_slice_6d(v, 12, n)\n",
        "# del v"
      ],
      "metadata": {
        "id": "nNZxoisVcvgy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "775dd8d5-0691-4147-ce46-5d0ee137378d"
      },
      "execution_count": 324,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: (23, 23, 23, 23, 23, 23)    Memory: 1184MB    Order: C\n",
            "11 ms ± 325 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "10.9 ms ± 180 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "10.9 ms ± 288 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "17.7 ms ± 654 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "33.5 ms ± 3.9 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
            "77.9 ms ± 4.76 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VU16710dp096",
        "outputId": "2170eb4b-cded-4115-eca8-5a471653cbee"
      },
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23, 23, 23, 23, 23, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 325
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_slice_6d(v, 12, 5).shape"
      ],
      "metadata": {
        "id": "JyLRdEEpq-9l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa898be3-347b-4908-c652-202b94717d5a"
      },
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23, 23, 23, 23, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 326
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tKYFY8G7q_AZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "760bbd15-db58-4693-a349-78144a09f366"
      },
      "execution_count": 327,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-327-a6204ae1a2ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvolume\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'volume' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GaTuq_kSq_Cu"
      },
      "execution_count": 326,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SAkaj1j8q_Ft"
      },
      "execution_count": 326,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tbirGMWlq_H9"
      },
      "execution_count": 326,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5HGU_Wt0q_KL"
      },
      "execution_count": 326,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lYkCTALmq_Ml"
      },
      "execution_count": 326,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vlYOYIWqq_PG"
      },
      "execution_count": 326,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "54e_t462q_R6"
      },
      "execution_count": 326,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uaLGzfU_cvjs"
      },
      "execution_count": 326,
      "outputs": []
    }
  ]
}