ericdill/gist:bd883734d9b7fe009014

## gistfile1.txt
{
 "metadata": {
  "name": "",
  "signature": "sha256:e2605b210ae12f03dc1edcc0d0f9ee37e92ea29878dc6281fa7f9cec614dd86c"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "One time correlation for X-ray Photon Correlation Spectroscopy Data\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from itertools import chain\n",
      "import zipfile\n",
      "import os\n",
      "\n",
      "%matplotlib inline\n",
      "from matplotlib.ticker import MaxNLocator\n",
      "from matplotlib.colors import LogNorm\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.patches as mp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "Vendor:  Continuum Analytics, Inc.\n",
        "Package: mkl\n",
        "Message: trial mode expires in 27 days\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import skxray.correlation as corr\n",
      "import skxray.core as core"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Number of Roi's "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_qs = 8"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "img_dim = (256, 256)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Multi-tau levels"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "num_levels = 12\n",
      "num_bufs = 8  # has to be even"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "C4B_T 250K data, number of images 9950"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "try:\n",
      "    img_stack = np.load(os.path.join(\"C4B_T_250K\", \"50_10000img_data.npy\"))\n",
      "except IOError:\n",
      "    zipfile.ZipFile(os.path.join(\"C4B_T_250K.zip\")).extractall()\n",
      "    img_stack = np.load(os.path.join(\"C4B_T_250K\", \"50_10000img_data.npy\"))\n",
      "\n",
      "qind = (np.loadtxt(\"qinds.txt\").astype(int)).tolist()\n",
      "pix = (np.loadtxt(\"pixellist.txt\").astype(int)).tolist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Number of pixels in each roi's"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_pixels = (np.bincount(qind))[1:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Get the average value of the images"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def avg_img(img_array, num_images):\n",
      "    \"\"\"\n",
      "    This will provide the average value of the images\n",
      "    Parameters\n",
      "    ----------\n",
      "    img_array: ndarray\n",
      "        array of stack of images for one time correlation\n",
      "\n",
      "    n_start: int\n",
      "        starting image number\n",
      "\n",
      "    num_images: int\n",
      "        number of images\n",
      "\n",
      "    Returns\n",
      "    ------\n",
      "    avg_img : array\n",
      "\n",
      "    \"\"\"\n",
      "    #n_end = n_start + num_images\n",
      "    avg_img = 0\n",
      "    for n in range(num_images):\n",
      "        avg_img += img_array[n]\n",
      "\n",
      "    avg_img /=float(num_images)\n",
      "    return avg_img\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "avg_img = avg_img(img_stack, img_stack.shape[0])  \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot the average image of 9950 images of C4B_T 250K"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "plt.imshow(avg_img)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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atjN/Rj5LjN6XQcnrMNhp2PJSJ7gEN2Ir0ayDO2onT8zhX6O/togNt5/hGbcd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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c8f3fd0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def test_demo(ax, inds, pix_list, img_dim, image):\n",
      "    \"\"\"\n",
      "    This will plot the reqiured roi's on the image\n",
      "    \"\"\"\n",
      "    tt = np.zeros(img_dim).ravel() * np.nan\n",
      "    tt[pix_list] = inds\n",
      "\n",
      "    vmin, vmax = np.percentile(image, [100, 100])\n",
      "    im = ax.imshow(image, interpolation='none', norm=LogNorm(), vmin=vmin, vmax=vmax)\n",
      "    im = ax.imshow(tt.reshape(*img_dim), cmap='Paired', interpolation='nearest')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tt = np.zeros(img_dim).ravel()\n",
      "tt[pix] = qind"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "fig, ax = plt.subplots()\n",
      "plt.title(\"C4B_T_250K_images_50_100000\")\n",
      "test_demo(ax, qind, pix, img_dim, avg_img)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0qjCAdWV2FY24kcOeib17L4c7p9f9fHHCYdD1FgaLM4zEPAcjkai3EHXjT5j7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\nOBiG4cXEwTAMLyYOhmF4MXEwDMPL/wecIhQ5g9/kSgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x15a601710>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Call the skxray.correlation.auto_corr module to \n",
      "get one time correlation(g2)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g2, lag_steps, elapsed_time = corr.auto_corr(num_levels, num_bufs, num_qs,\n",
      "                                             num_pixels, pix, qind, img_stack)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lag = []\n",
      "lag_steps = np.arange(0, num_bufs)\n",
      "\n",
      "for i in range(2, num_levels + 1):\n",
      "    for j in range(0, num_bufs//2):\n",
      "            lag.append((num_bufs//2 + j)*(2**(i - 1)))\n",
      "lag_steps = np.append(lag_steps, np.array(lag))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lag_time = 0.001\n",
      "lag_step = lag_steps[:g2.shape[0]]\n",
      "lags = lag_step*lag_time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lag_step"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "array([   0,    1,    2,    3,    4,    5,    6,    7,    8,   10,   12,\n",
        "         14,   16,   20,   24,   28,   32,   40,   48,   56,   64,   80,\n",
        "         96,  112,  128,  160,  192,  224,  256,  320,  384,  448,  512,\n",
        "        640,  768,  896, 1024, 1280, 1536, 1792, 2048, 2560, 3072, 3584,\n",
        "       4096, 5120, 6144, 7168])"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Elapsed time one-time correlation for 9950 images"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "elapsed_time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "153.44568991661072"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot g2's"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "if num_qs%2 == 0:\n",
      "    y = num_qs/2\n",
      "else:\n",
      "    y = (num_qs/2 +1)\n",
      "fig = plt.figure()\n",
      "    #plt.title(\"g2\")\n",
      "for i in range(num_qs):\n",
      "    ax = fig.add_subplot(2, y, i+1)\n",
      "    #ax.set_title(str(q_ring_val[i][0])+\"-\"+str(q_ring_val[i][1]))\n",
      "    ax.set_ylabel(\"g2\")\n",
      "    ax.set_xlabel(\"lags\")\n",
      "    fig.subplots_adjust(left=0.2, wspace=1, hspace=0.2)\n",
      "    plt.semilogx(lags, g2[:, i], '-o')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7QV6CpWRazknPEPyxpFckPVXk+CckLZS0SNLvJO1X7XcOZIWHQrbhdO85c77M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\nNWqMf/RODq9tMqTYFVcu2f5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2YuPGEwhygOFywYREo7WWAj8H/o1cKpqwTl4KvBns66xp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\nbgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x15cbaf8d0>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plot one g2"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "plt.semilogx(lags, g2[:,5], '-o')\n",
      "plt.title(\"C4B_T g2\")\n",
      "plt.xlabel(\"lags\")\n",
      "plt.ylabel(\"g2\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x15bd32a50>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}