cdw/Untitled.ipynb

## Untitled.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Minimal Argonne image loading with python\n\nThis relies on SciPy's ndimage module to load the tiff. This uses a call out to PIL or Pillow (depending on which one you have installed). PIL can't load multi-channel TIFFs with a bit-depth higher than 8bits. Argonne detector images _typically_ aren't multi-channel, but that is a gotcha to watch out for. If in doubt install Pillow or update with:\n\n```\npip install Pillow --upgrade\n```\n\nor\n\n```\nconda update Pillow\n```\n"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "First we do our minimal imports:"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "from scipy import ndimage\nimport matplotlib.pyplot as plt\n%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Then load and display our image with a more familiar colormap than the default green-yellow"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Load image\nfn = 'm10_t01_16_00002.tif'\nimg_as_np_array = ndimage.imread(fn)\n\n# Display image\nfig, ax = plt.subplots(1,1,figsize=(8,8))\nax.imshow(img_as_np_array, cmap=plt.cm.magma)",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x10dd23630>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x10e51f048>",
            "image/png": 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AQCwBYCCWADAQSwAYiCUADMQSAAZiCQADsQSAgVgCwEAsAWAglgAwEEsAGIglAAzEEgAG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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "markdown",
      "source": "This image has no signal: it is blank with only the occasional dead pixel and the separation between the ccds.\n\nIf it had a signal it would be nice to enhance the contrast, let's take a look at how to do that. "
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Scale to enhance contrast\nimport numpy as np\nnew_max = np.round(np.percentile(img_as_np_array, 95))\nprint(\"Current image max is %i, new is %i\"%(img_as_np_array.max(), new_max))\nimg_scaled = np.clip(np.divide(img_as_np_array, new_max), 0, 1)\n\n# Display image\nfig, ax = plt.subplots(1,1,figsize=(8,8))\nax.imshow(img_scaled, cmap=plt.cm.magma)",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Current image max is 1, new is 0\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in true_divide\n  \"\"\"\n/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in true_divide\n  \"\"\"\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x10e9b6748>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x10f1d8278>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "So that didn't work because there is no signal to the image (see that the current max is 1) but would in the case that the image had a bunch of higher-valued pixels. "
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.1",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Minimal Argonne image loading with Python",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}
	{
	"cells": [
	{
	"metadata": {},
	"cell_type": "markdown",
	"source": "# Minimal Argonne image loading with python\n\nThis relies on SciPy's ndimage module to load the tiff. This uses a call out to PIL or Pillow (depending on which one you have installed). PIL can't load multi-channel TIFFs with a bit-depth higher than 8bits. Argonne detector images _typically_ aren't multi-channel, but that is a gotcha to watch out for. If in doubt install Pillow or update with:\n\n```\npip install Pillow --upgrade\n```\n\nor\n\n```\nconda update Pillow\n```\n"
	},
	{
	"metadata": {},
	"cell_type": "markdown",
	"source": "First we do our minimal imports:"
	},
	{
	"metadata": {
	"trusted": true,
	"collapsed": true
	},
	"cell_type": "code",
	"source": "from scipy import ndimage\nimport matplotlib.pyplot as plt\n%matplotlib inline",
	"execution_count": 1,
	"outputs": []
	},
	{
	"metadata": {},
	"cell_type": "markdown",
	"source": "Then load and display our image with a more familiar colormap than the default green-yellow"
	},
	{
	"metadata": {
	"trusted": true
	},
	"cell_type": "code",
	"source": "# Load image\nfn = 'm10_t01_16_00002.tif'\nimg_as_np_array = ndimage.imread(fn)\n\n# Display image\nfig, ax = plt.subplots(1,1,figsize=(8,8))\nax.imshow(img_as_np_array, cmap=plt.cm.magma)",
	"execution_count": 2,
	"outputs": [
	{
	"output_type": "execute_result",
	"execution_count": 2,
	"data": {
	"text/plain": "<matplotlib.image.AxesImage at 0x10dd23630>"
	},
	"metadata": {}
	},
	{
	"output_type": "display_data",
	"data": {
	"text/plain": "<matplotlib.figure.Figure at 0x10e51f048>",
	"image/png": 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	},
	"metadata": {}
	}
	]
	},
	{
	"metadata": {
	"collapsed": true,
	"trusted": true
	},
	"cell_type": "markdown",
	"source": "This image has no signal: it is blank with only the occasional dead pixel and the separation between the ccds.\n\nIf it had a signal it would be nice to enhance the contrast, let's take a look at how to do that. "
	},
	{
	"metadata": {
	"trusted": true
	},
	"cell_type": "code",
	"source": "# Scale to enhance contrast\nimport numpy as np\nnew_max = np.round(np.percentile(img_as_np_array, 95))\nprint(\"Current image max is %i, new is %i\"%(img_as_np_array.max(), new_max))\nimg_scaled = np.clip(np.divide(img_as_np_array, new_max), 0, 1)\n\n# Display image\nfig, ax = plt.subplots(1,1,figsize=(8,8))\nax.imshow(img_scaled, cmap=plt.cm.magma)",
	"execution_count": 3,
	"outputs": [
	{
	"output_type": "stream",
	"text": "Current image max is 1, new is 0\n",
	"name": "stdout"
	},
	{
	"output_type": "stream",
	"text": "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in true_divide\n \"\"\"\n/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in true_divide\n \"\"\"\n",
	"name": "stderr"
	},
	{
	"output_type": "execute_result",
	"execution_count": 3,
	"data": {
	"text/plain": "<matplotlib.image.AxesImage at 0x10e9b6748>"
	},
	"metadata": {}
	},
	{
	"output_type": "display_data",
	"data": {
	"text/plain": "<matplotlib.figure.Figure at 0x10f1d8278>",
	"image/png": 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	},
	"metadata": {}
	}
	]
	},
	{
	"metadata": {},
	"cell_type": "markdown",
	"source": "So that didn't work because there is no signal to the image (see that the current max is 1) but would in the case that the image had a bunch of higher-valued pixels. "
	},
	{
	"metadata": {
	"trusted": true,
	"collapsed": true
	},
	"cell_type": "code",
	"source": "",
	"execution_count": null,
	"outputs": []
	}
	],
	"metadata": {
	"kernelspec": {
	"name": "python3",
	"display_name": "Python 3",
	"language": "python"
	},
	"language_info": {
	"name": "python",
	"version": "3.6.1",
	"mimetype": "text/x-python",
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"pygments_lexer": "ipython3",
	"nbconvert_exporter": "python",
	"file_extension": ".py"
	},
	"gist": {
	"id": "",
	"data": {
	"description": "Minimal Argonne image loading with Python",
	"public": true
	}
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}