bamford/test_stack_convolution.ipynb

## test_stack_convolution.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Test application of a 2D convolution to a stack of multi-channel images"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nfrom matplotlib import pyplot as plt",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from astropy import convolution as conv\n# astropy convolve does not accept arrays with more than 3 dimensions,\n# so use scipy instead. Note that here we are doing Gaussian convolution,\n# so \nfrom scipy.ndimage import convolve",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# create a stack of multi-channel images,\n# containing dots in different places\na = np.zeros([9, 60, 60, 3])\nfor i in range(a.shape[0]):\n    a[i, 10+i, 10, 0] = 1\n    a[i, 25+i, 30, 1] = 1\n    a[i, 40+i, 50, 2] = 1",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "kernel = conv.Gaussian2DKernel(3.0)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\nax[0].imshow(a[2])\nax[1].imshow(a[4])\nax[2].imshow(a[6]);",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1080x360 with 3 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.imshow(kernel);",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# add additional (empty) dimensions to kernel, so it will broadcast over a stack of images\nkernel_4d = kernel.array[None, ..., None]",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "b = convolve(a, kernel_4d, mode='constant')",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# normalise for colour imshow\nbn = b / b.max()",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\nax[0].imshow(bn[2])\nax[1].imshow(bn[4])\nax[2].imshow(bn[6]);",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1080x360 with 3 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "# Check results match the case when we just convolve one image in isolation\nc = a[3, ..., 1]\nd = convolve(c, kernel, mode='constant')\ne = b[3, ..., 1]\nfig, ax = plt.subplots(1, 3, figsize=(15, 5))\nax[0].imshow(d)\nax[1].imshow(e)\nax[2].imshow(d - e)\nnp.allclose(d, e)",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "True"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1080x360 with 3 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "hide_input": false,
    "kernelspec": {
      "name": "conda-env-ARG-py",
      "display_name": "Python [conda env:ARG]",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "19e446a494fbe5b5ad4c4384c23a55a9",
      "data": {
        "description": "Test application of a 2D convolution to a stack of multi-channel images",
        "public": true
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/19e446a494fbe5b5ad4c4384c23a55a9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}