{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "<p class='lead'>\n",
    "This notebook computes the auto and cross-correlation between spots and pixels\n",
    "from a smFRET experiment on a 48-spot smFRET-PAX setup.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fname = 'data/pax-2017-07-11_06_12d_22d_mix_D200mW_A400mW.hdf5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2017-07-11_06_12d'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "fname = Path(fname)\n",
    "assert fname.is_file(), 'File not found.'\n",
    "mlabel = '_'.join(fname.stem.replace('pax-', '').replace('alex-', '').split('_')[:3])\n",
    "mlabel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tue Oct 31 11:23:26 2017'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "time.ctime()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from IPython.display import display, HTML, Math\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "from tqdm import tnrange, tqdm_notebook\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.sans-serif'].insert(0, 'Arial')\n",
    "plt.rcParams['font.size'] = 14\n",
    "plt.rcParams['path.simplify'] = True\n",
    "plt.rcParams['path.simplify_threshold'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.1.0'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pycorrelate as pyc\n",
    "pyc.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - Optimized (cython) burst search loaded.\n",
      " - Optimized (cython) photon counting loaded.\n",
      "--------------------------------------------------------------\n",
      " You are running FRETBursts (version 0.6.5).\n",
      "\n",
      " If you use this software please cite the following paper:\n",
      "\n",
      "   FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n",
      "   Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n",
      "\n",
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from fretbursts import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skip_ch = (12, 13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "save_figures = True\n",
    "savefigdir = 'figures'\n",
    "highres = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def info_html(d):\n",
    "    \"\"\"Display measurement info in the notebook\"\"\"\n",
    "    Dex, Aex = d.setup['excitation_input_powers']*1e3\n",
    "    s = \"\"\"\n",
    "    <h3>File: &nbsp; &nbsp; &nbsp; {fname}</h3>\n",
    "    <blockquote><p class=\"lead\">{descr}</p></blockquote>\n",
    "    <ul>\n",
    "    <li><span style='display: inline-block; width: 150px;'>Acquisition duration:</span> {time:.1f} s </li>\n",
    "    <li><span style='display: inline-block; width: 150px;'>Laser power:</span>  <b>{Dex:.0f}mW</b> @ 532nm &nbsp;&nbsp;&nbsp;  \n",
    "                                                                                <b>{Aex:.0f}mW</b> @ 628nm </li>\n",
    "    <li><span style='display: inline-block; width: 150px;'>ALEX period [offset]: </span> {period} ({period_us:.1f} μs)  [{offset}] </li></ul>\n",
    "    \"\"\".format(fname=d.fname, time=float(d.acquisition_duration), Dex=Dex, Aex=Aex, \n",
    "               period=d.alex_period, period_us=d.alex_period*d.clk_p*1e6, offset=d.offset,\n",
    "               descr=d.description.decode())\n",
    "    return HTML(s)\n",
    "\n",
    "\n",
    "def save_name(name, folder='.', label=None, nospaces=False):\n",
    "    \"\"\"Compute file name for saving a figure\"\"\"\n",
    "    if label is None:\n",
    "        label = mlabel\n",
    "    sname = '%s/%s_%s' % (folder, label, name)\n",
    "    if nospaces:\n",
    "        sname = sname.replace(' ', '_')\n",
    "    return sname \n",
    "    \n",
    "def savefig(name, nospaces=True, label=None, **kwargs):\n",
    "    \"\"\"Save a figure prepending the measurement label and other options\"\"\"\n",
    "    if not save_figures:\n",
    "        return\n",
    "    savefigpath = Path(savefigdir)\n",
    "    savefigpath.mkdir(exist_ok=True)\n",
    "    kwargs_ = dict(dpi=200, bbox_inches='tight')\n",
    "                   #frameon=True, facecolor='white', transparent=False)\n",
    "    kwargs_.update(kwargs)\n",
    "    fname = save_name(name, savefigdir, nospaces=nospaces, label=label)\n",
    "    plt.savefig(fname, **kwargs_)\n",
    "    print('Saved: %s.png' % fname, flush=True)\n",
    "    if highres:\n",
    "        kwargs_['dpi'] = 300\n",
    "        name = name[:-4] if name.lower().endswith('.png') else name\n",
    "        fname = save_name(name + '_highres', savefigdir, nospaces=nospaces, label=label)\n",
    "        print('Saved hires: %s.png' % fname, flush=True)\n",
    "        plt.savefig(fname, **kwargs_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def normalize_G(G, t, u, bins):\n",
    "    \"\"\"Normalize ACF and CCF.\n",
    "    \"\"\"\n",
    "    duration = max((t.max(), u.max())) - min((t.min(), u.min()))\n",
    "    Gn = G.copy()\n",
    "    for i, tau in enumerate(bins[1:]):\n",
    "        Gn[i] *= ((duration - tau) \n",
    "                  / (float((t >= tau).sum()) * \n",
    "                     float((u <= (u.max() - tau)).sum())))\n",
    "    return Gn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = loader.photon_hdf5(str(fname), ondisk=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <h3>File: &nbsp; &nbsp; &nbsp; data\\pax-2017-07-11_06_12d_22d_mix_D200mW_A400mW.hdf5</h3>\n",
       "    <blockquote><p class=\"lead\">12d ~6nM 22d ~620pM\n",
       "12d:22d mixed 1:2, then diluted 10x\n",
       "same gasket as measurement 5\n",
       "powers: D200mW_A400mW\n",
       "</p></blockquote>\n",
       "    <ul>\n",
       "    <li><span style='display: inline-block; width: 150px;'>Acquisition duration:</span> 618.7 s </li>\n",
       "    <li><span style='display: inline-block; width: 150px;'>Laser power:</span>  <b>200mW</b> @ 532nm &nbsp;&nbsp;&nbsp;  \n",
       "                                                                                <b>400mW</b> @ 628nm </li>\n",
       "    <li><span style='display: inline-block; width: 150px;'>ALEX period [offset]: </span> 4096 (51.2 μs)  [2650] </li></ul>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_html(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SPAD positions\n",
    "\n",
    "We can get the SPAD positon information from the `/setup/detectors` group in Photon-HDF5.\n",
    "We access this group in the loaded file like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arrays in `detectors` dict:\n",
      "['counts', 'id', 'id_hardware', 'position', 'spot']\n"
     ]
    }
   ],
   "source": [
    "detectors = d.setup['detectors']\n",
    "print('Arrays in `detectors` dict:')\n",
    "print([*detectors])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the arrays in `/setup/detectors` have the same length, and is is equal to the number of detectors.\n",
    "In this case all the arrays have length 96.\n",
    "\n",
    "The **`spot`** array indicate the spot each detectors belongs to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  0,  1,  1,  2,  2,  3,  3,  4,  4,  5,  5,  6,  6,  7,  7,  8,\n",
       "        8,  9,  9, 10, 10, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16,\n",
       "       17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 25,\n",
       "       25, 26, 26, 27, 27, 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, 33,\n",
       "       34, 34, 35, 35, 36, 36, 37, 37, 38, 38, 39, 39, 40, 40, 41, 41, 42,\n",
       "       42, 43, 43, 44, 44, 45, 45, 46, 46, 47, 47], dtype=uint8)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detectors['spot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **`id`** array indicates the value used in `/photon_dataN/detectors` to indicate each detector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 1], dtype=uint8)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detectors['id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **`id_hardware`** array indicates the original detctor number from the acquisition hardware:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([84,  0, 85,  1, 86,  2, 87,  3, 88,  4, 89,  5, 90,  6, 91,  7, 92,\n",
       "        8, 93,  9, 94, 10, 95, 11, 72, 12, 73, 13, 74, 14, 75, 15, 76, 16,\n",
       "       77, 17, 78, 18, 79, 19, 80, 20, 81, 21, 82, 22, 83, 23, 60, 24, 61,\n",
       "       25, 62, 26, 63, 27, 64, 28, 65, 29, 66, 30, 67, 31, 68, 32, 69, 33,\n",
       "       70, 34, 71, 35, 48, 36, 49, 37, 50, 38, 51, 39, 52, 40, 53, 41, 54,\n",
       "       42, 55, 43, 56, 44, 57, 45, 58, 46, 59, 47], dtype=uint8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detectors['id_hardware']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **`position`** array indicates the detector position on the chip, \n",
    "as a pair of (x, y) coordinates. For example `(0,0)` and `(11, 3)`\n",
    "are positions of diagonally opposite corners on the SPAD array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11,  3],\n",
       "       [11,  3],\n",
       "       [10,  3],\n",
       "       [10,  3],\n",
       "       [ 9,  3]], dtype=uint8)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detectors['position'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pixel_rowcol_to_ch(d, row, col):\n",
    "    i = np.where((d.setup['detectors']['position'] == (row, col)).sum(axis=1) == 2)[0][0]\n",
    "    ich = d.setup['detectors']['spot'][i]\n",
    "    return ich"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spotwise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-correlation between spots\n",
    "\n",
    "We compute the CCF betweel all-photons in two nearby spots.\n",
    "We choose all the closest pairs in the 2x2 central pixels.\n",
    "These pixels have the largest signal so if something is detectable it should\n",
    "show up for these pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos_pairs = [\n",
    "    ([5, 2], [6, 2]),\n",
    "    ([5, 1], [6, 1]),\n",
    "    ([5, 1], [5, 2]),\n",
    "    ([6, 1], [6, 2]),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unit = d.clk_p\n",
    "bins = pyc.make_loglags(-3, 1, 10) / unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    80000,    100714,    126791,    159620,    200950,    252982,\n",
       "          318485,    400949,    504765,    635462,    800000,   1007140,\n",
       "         1267914,   1596209,   2009509,   2529822,   3184857,   4009497,\n",
       "         5047658,   6354625,   8000000,  10071403,  12679145,  15962098,\n",
       "        20095091,  25298221,  31848573,  40094978,  50476587,  63546258,\n",
       "        80000000, 100714032, 126791455, 159620985, 200950914, 252982212,\n",
       "       318485736, 400949786, 504765875, 635462587, 800000000])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e16f036a1d494123b9c35d68037f9a83",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing positions [5, 2], [6, 2]\n",
      "Processing positions [5, 1], [6, 1]\n",
      "Processing positions [5, 1], [5, 2]\n",
      "Processing positions [6, 1], [6, 2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "GG = []\n",
    "GN = []\n",
    "for pos1, pos2 in tqdm_notebook(pos_pairs):\n",
    "    print('Processing positions %s, %s' % (pos1, pos2), flush=True)\n",
    "    ich1 = pixel_rowcol_to_ch(d, *pos1)\n",
    "    ich2 = pixel_rowcol_to_ch(d, *pos2)\n",
    "    t = d.ph_times_t[ich1][:]\n",
    "    u = d.ph_times_t[ich2][:]\n",
    "    G = pyc.pcorrelate(t, u, bins)\n",
    "    GG.append(G)\n",
    "    GN.append(normalize_G(G, t, u, bins))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fn1Ks+ZK/9pOZR/DVtfFYK2W/se/3AaxwLGeHeT9q2/YHYv0gOYr1uV6PvRpXDz1qc3jj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\nBk4EtgHOboe7CfhokquB7wLva9suqapK8lng5CS3AXcDJwO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kFyS5O8mPk3wqyaKe/p8B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\n1EGGPEmSpA76/5WvtU1BLq4BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc702860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "for G, pair in zip(GN, pos_pairs):\n",
    "    plt.semilogx(bins[1:]*unit, G, drawstyle='steps-pre', label=pair)\n",
    "plt.xlabel('Time (s)')\n",
    "plt.grid(True); plt.grid(True, which='minor', lw=0.3)\n",
    "plt.legend()\n",
    "plt.title('CCF of all photons between pairs of spots')\n",
    "plt.xlim(1e-3, 1);\n",
    "plt.ylim(0.99, 1.01);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **NOTE** The effect of molecule diffusing from a spot to the other should\n",
    "> be visible as a correlation between 10^-2 and 10^-1 s. In the above plot\n",
    "> there may be something\n",
    "> if we look it with loving eyes, but nothing we can rely on. Trying to extract any\n",
    "> probability from such data would be pointless."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto-correlation for spots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unit = d.clk_p\n",
    "bins_per_dec = 10\n",
    "bins = pyc.make_loglags(-8, 1, bins_per_dec) / unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'results/2017-07-11_06_12d_ACF_all-ph_spots_bins10.csv'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acf_fname =   f'results/{mlabel}_ACF_all-ph_spots_bins{bins_per_dec}.csv'\n",
    "acf_fname_n = f'results/{mlabel}_ACF_all-ph_spots_bins{bins_per_dec}_normalized.csv'\n",
    "acf_fname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Loading ACF from cache\n"
     ]
    }
   ],
   "source": [
    "recompute = False\n",
    "if Path(acf_fname_n).exists() and not recompute:\n",
    "    print('- Loading ACF from cache', flush=True)\n",
    "    GG = np.loadtxt(acf_fname)\n",
    "    GN = np.loadtxt(acf_fname_n)\n",
    "else:\n",
    "    GG = np.zeros((48, bins.size - 1), dtype=float)\n",
    "    GN = GG.copy()\n",
    "    for ich in tnrange(48, desc='Calculating ACFs: '):\n",
    "        if ich in skip_ch:\n",
    "            continue\n",
    "        #print('Processing spot %s ...' % ich, flush=True)\n",
    "        t = d.ph_times_t[ich][:]\n",
    "        G = pyc.pcorrelate(t, t, bins)\n",
    "        Gn = normalize_G(G, t, t, bins)\n",
    "        GG[ich] += G\n",
    "        GN[ich] += Gn\n",
    "    np.savetxt(acf_fname, GG)\n",
    "    np.savetxt(acf_fname_n, GN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5WxiGYRi7BBNixpoIII72R8CK0vKKtHUwalzuW1XZ5DFWqFlbGIZhGOcPJsSM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\nRKbkfwP8HzDT2mAOKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10826080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "plt.xlabel('Time (s)')\n",
    "plt.grid(True); plt.grid(True, which='minor', lw=0.3)\n",
    "plt.title('ACF of all photons. Spot %d' % 0)\n",
    "plt.ylim(0.0, 4)\n",
    "plt.xlim(10**-8, 1)\n",
    "lines = plt.semilogx(bins[1:]*unit, GN.T, drawstyle='steps-pre', alpha=0.05, color='C0')\n",
    "line = lines[0]\n",
    "line.set_alpha(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23104f0eeaf547969c491815ace37cc4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(\n",
    "    spot=(0, 47), \n",
    "    ylim=widgets.FloatRangeSlider(value=[0, 7.5], min=0, max=10.0, step=0.1,))\n",
    "def plot_spot(spot, ylim):\n",
    "    for line in lines:\n",
    "        line.set_alpha(0.05)\n",
    "    line = lines[spot]\n",
    "    line.set_alpha(1)\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.set_title('ACF of all photons. Spot %d' % spot)\n",
    "    display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pixelwise\n",
    "\n",
    "## Cross-correlations between pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos_pairs = [\n",
    "    ([4, 1], [4, 2]),\n",
    "    ([4, 2], [5, 2]),\n",
    "    ([4, 1], [5, 1]),\n",
    "    ([5, 1], [5, 2]),\n",
    "    #([5, 2], [6, 2]),\n",
    "    #([5, 1], [6, 1]),\n",
    "    #([6, 1], [6, 2]),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[18 19 30 31]'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = np.array(pos_pairs).reshape(len(pos_pairs)*2, 2)\n",
    "spots = np.unique([pixel_rowcol_to_ch(d, *i) for i in p])\n",
    "str(spots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unit = d.clk_p\n",
    "bins_per_dec = 10\n",
    "bins = pyc.make_loglags(-8, 1, bins_per_dec) / unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'results/2017-07-11_06_12d_CCF_pixels_[18 19 30 31]_bins10.csv'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccf_fname =   f'results/{mlabel}_CCF_pixels_{str(spots)}_bins{bins_per_dec}.csv'\n",
    "ccf_fname_n = f'results/{mlabel}_CCF_pixels_{str(spots)}_bins{bins_per_dec}_normalized.csv'\n",
    "ccf_fname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Loading CCF from cache\n"
     ]
    }
   ],
   "source": [
    "recompute = False\n",
    "if Path(ccf_fname_n).exists() and not recompute:\n",
    "    print('- Loading CCF from cache', flush=True)\n",
    "    XC = np.loadtxt(ccf_fname).reshape(2, len(spots), -1)\n",
    "    XCN = np.loadtxt(ccf_fname_n).reshape(2, len(spots), -1)\n",
    "else:\n",
    "    XC = np.zeros((2, len(pos_pairs), bins.size - 1), dtype=float)\n",
    "    XCN = np.zeros((2, len(pos_pairs), bins.size - 1), dtype=float)\n",
    "    for ipair, (pos1, pos2) in tqdm_notebook(enumerate(pos_pairs), total=len(pos_pairs), desc='Spot pair'):\n",
    "        print('Processing positions %s, %s' % (pos1, pos2), flush=True)\n",
    "        ich1 = pixel_rowcol_to_ch(d, *pos1)\n",
    "        ich2 = pixel_rowcol_to_ch(d, *pos2)\n",
    "\n",
    "        for donor_or_accept in tnrange(2, desc='Detector', leave=False):\n",
    "\n",
    "            ph1 = d.ph_times_t[ich1][:]\n",
    "            det = d.det_t[ich1][:]\n",
    "            t = ph1[det == donor_or_accept]\n",
    "\n",
    "            ph2 = d.ph_times_t[ich2][:]\n",
    "            det = d.det_t[ich2][:]\n",
    "            u = ph2[det == donor_or_accept]\n",
    "\n",
    "            G = pyc.pcorrelate(t, u, bins)\n",
    "\n",
    "            XC[donor_or_accept, ipair] = G\n",
    "            XCN[donor_or_accept, ipair] = normalize_G(G, t, u, bins)\n",
    "    np.savetxt(ccf_fname, XC.reshape(2*len(spots), -1))\n",
    "    np.savetxt(ccf_fname_n, XCN.reshape(2*len(spots), -1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CCFs plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiSVxEJMkiSpJBZikiRJJbEQkyRJKomFmCRJUkksxCRJkkpiISZJklQSCzFJkqSSWIhJ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\nSZJKYiEmSZJUkv8fJMYpmhe6llUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd18c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "plt.xlabel('Time (s)')\n",
    "plt.grid(True); plt.grid(True, which='minor', lw=0.3)\n",
    "plt.title('CCF of pixels - Donor SPAD')\n",
    "plt.ylim(0, 3)\n",
    "plt.xlim(10**-8, 1)\n",
    "plt.semilogx(bins[1:]*unit, XCN[0].T, drawstyle='steps-pre');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RERGRAqQgTkRERKQAKYgTERERKUAK4kREREQKkII4ERERkQKkIE5ERESkACmIExERESlA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\nJPWQRZwkSVIPWcRJkiT1kEWcJElSD/1/vYER8dQQkz0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10651898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "plt.xlabel('Time (s)')\n",
    "plt.grid(True); plt.grid(True, which='minor', lw=0.3)\n",
    "plt.title('CCF of pixels - Donor SPAD')\n",
    "plt.ylim(0.9, 1.1)\n",
    "plt.xlim(10**-8, 1)\n",
    "plt.semilogx(bins[1:]*unit, XCN[0].T, drawstyle='steps-pre');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XAA+a2XR3XxrZfxYwC6C2tpaGhoaCP6tYrJhy2sxms0X3a41XVeVPTrp7wTbf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\nZmaWITdpZmZmZhn6D0nQuHdD5gUpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1092deb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "plt.xlabel('Time (s)')\n",
    "plt.grid(True); plt.grid(True, which='minor', lw=0.3)\n",
    "plt.title('CCF of pixels- Acceptor SPAD')\n",
    "plt.ylim(0, 2)\n",
    "plt.xlim(10**-8, 1)\n",
    "plt.semilogx(bins[1:]*unit, XCN[1].T, drawstyle='steps-pre');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto-correlation for pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unit = d.clk_p\n",
    "bins_per_dec = 10\n",
    "bins = pyc.make_loglags(-8, 1, bins_per_dec) / unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'results/2017-07-11_06_12d_ACF_all-ph_pixels_bins10.csv'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acf_fname = f'results/{mlabel}_ACF_all-ph_pixels_bins{bins_per_dec}.csv'\n",
    "acf_fname_n = f'results/{mlabel}_ACF_all-ph_pixels_bins{bins_per_dec}_normalized.csv'\n",
    "acf_fname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Loading ACF from cache\n"
     ]
    }
   ],
   "source": [
    "recompute = False\n",
    "if Path(acf_fname_n).exists() and not recompute:\n",
    "    print('- Loading ACF from cache', flush=True)\n",
    "    GG = np.loadtxt(acf_fname).reshape(2, 48, -1)\n",
    "    GN = np.loadtxt(acf_fname_n).reshape(2, 48, -1)\n",
    "else:\n",
    "    GG = np.zeros((2, 48, bins.size - 1), dtype=float)\n",
    "    GN = GG.copy()\n",
    "    for ich in tnrange(48, desc='Calculating ACFs: '):\n",
    "        if ich in skip_ch:\n",
    "            continue\n",
    "        for donor_or_accept in (0, 1):\n",
    "            ph = d.ph_times_t[ich][:]\n",
    "            det = d.det_t[ich][:]\n",
    "            t = ph[det == donor_or_accept]\n",
    "            \n",
    "            G = pyc.pcorrelate(t, t, bins)\n",
    "            \n",
    "            GG[donor_or_accept, ich] = G\n",
    "            GN[donor_or_accept, ich] = normalize_G(G, t, t, bins)\n",
    "    np.savetxt(acf_fname, GG.reshape(96, -1))\n",
    "    np.savetxt(acf_fname_n, GN.reshape(96, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UIoARYeV1IiIiOlKO5IiVNSZZTmGQGUxrHw2siIiIiA7D4YxYWYM8WW4hfbyIYJBZ1rEi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\neRycAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10761b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "det = 0\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "def _plot(det):\n",
    "    ax.set_xlabel('Time (s)')\n",
    "    ax.grid(True); ax.grid(True, which='minor', lw=0.3)\n",
    "    ax.set_title('ACF of all photons. Pixel %d' % 0)\n",
    "    ax.set_ylim(0.0, 6)\n",
    "    ax.set_xlim(10**-7, 1)\n",
    "    return ax.semilogx(bins[1:]*unit, GN[det].T - 1, drawstyle='steps-pre', alpha=0.05, color='C0')\n",
    "lines = _plot(det)\n",
    "lines[0].set_alpha(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9af82177858049318b9ab7c8f28dd2e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(\n",
    "     pixel=(0, 47),\n",
    "     detector=['donor', 'acceptor'],\n",
    "     ylim=widgets.FloatRangeSlider(value=[0, 7.5], min=0, max=10.0, step=0.1,))\n",
    "def plot_spot(pixel, detector, ylim):\n",
    "    global det, lines\n",
    "    new_det = 0 if detector == 'donor' else 1\n",
    "    if new_det != det:\n",
    "        det = new_det\n",
    "        ax.clear()\n",
    "        lines = _plot(det)\n",
    "    for line in lines:\n",
    "        line.set_alpha(0.05)\n",
    "    line = lines[pixel]\n",
    "    line.set_alpha(1)\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.set_title('ACF of all photons. Spot %d' % pixel)\n",
    "    display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pixelwise ACF - CCF comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18, 19, 30, 31], dtype=uint8)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = np.array(pos_pairs).reshape(len(pos_pairs)*2, 2)\n",
    "spots = np.unique([pixel_rowcol_to_ch(d, *i) for i in p])\n",
    "spots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[31, 19],\n",
       "       [19, 18],\n",
       "       [31, 30],\n",
       "       [30, 18]], dtype=uint8)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape([pixel_rowcol_to_ch(d, *i) for i in p], (-1, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: figures/2017-07-11_06_12d_ACF-CCF_comparison.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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K1KRMWX09Wi/7DGEWzucqeW7BuqZacCAiIlWFX3l3QKq2/s36E1M/xm2bzNws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\nwW1BhkkvHtKDKaf2DlOt3EuMSXLcfeKHa34Y0uf1ievjOKwpIiKdiwI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+wdWXULrpQ/Kc\ntosDoi86lwk/+kPA+/gLnPzxeDxc2PdCZpzcum3lmhMch0qHCdTc7G/oNOQpIi3jFMzVuubBdWze\nezjogpFAc5oanwvVUOvyj/ew+0iN0xxrTu12CZNH3OD4vL/8+XTeTaioCx78sTEeXko9jiuvfdXx\nA+y1Q+MZ8lERg4lreg/fh3XZ1xUM/qqAXfsCf6jOzf+Md4d6+MZhAQBAWXUphY4l2kdOZqrffXZr\nXfjIH9gZvZ7cVwMHc9OO7qFHTTVNZ/u1QGXwdC/NEWy4ec2Vo0k7UMOUJaUB7xFta+hRs4NvHh5P\nQaN8gQX1Arr0A+Xk9wr8hwbAlpE9gW/o7lAm+dOdXPQpXPllz4BlCvdbdvYqbTBX0Z+ux+9mfHEN\nXRyed8IXR+CBF9m1YV/AMqmFefQeXM3e877jcCfo/can9MzLY1f31U3ONXce4oCuA3jARXAcKh0m\nUBORyOV2wYgbrnpSmuGELlGOAYEb06K6MKEwjl/3uDVgGWvmEuti5fYn5w3gk/PwOwRV+4HwyC/G\nkfVZFUmFeQHv0/dAKeNJ4sLfPuAYGL563+mUVZf6Hc6q/Te7srqM7P22rrcl0HZcjY/7K/fdg1t4\na0AS1xwO3IvpZoX34ej1lJuvwGGvklIsRMdQNu1ZxzZonH6kvtr3MD3uK9IPlPvtAaqvW3w3eib0\ndJyfVfbZJsxxls9+P77pSV930uaBHgbXRGHwDtv661GLqzhIvDlMaY3z/1v70yyfDqzhEocyn5zR\ni0/O6OU49OlmdbOnf1++HBx8VOrwxadjb57IRX7mu9X9nN81laz3v3L8Oe/5dQnnkML4XwX/Oe+1\npxSnSLRxmh1/P7/TCvOwwIGnnLen65NfFfhBzaRATUTanNsFI06JhGvPXf/UpgYLHAL1woVzTpyb\nHv3cx2JCNq2iz/DjeHHEUcgIHKRcfs9/ibFO/RVe3eO7Be1R2zQ8k+gPK8n3Bcn+gobjPAV0rQm+\nSKB3UTXnVR0lK/MngQvFQUn3K4HA7Xn2x0WM3ezh1IzAQUrhAcuB9ODB8codK9lauJVB3QcFLLNl\naAwEmedVWnUUKo+SXrwfHBaHpaSWkNq3jL0OqZbfG57A2jE9+dfMFwD//28sfX8392/cDTjPl/LY\n7ztVx7VuJx2l23nOW8VlEcV3Bl4WcFFQc/SZkcuLZ/gPoGtNu+8z+uyr5MCNNzkGx732lLK/TxKn\nOPRivvfob/CsehN8gaG/XU9Kq8tIjAmegcGmhW6uuwI1ETmmuFng0C5z4vI3Oa8MrTrK1rjYoENC\nwQIGgCmkMMWmgEPvxyt3X0GvA7uDfoCl7i6k58CBjj1413+9iWVJ3/Zy+QsGflnwE7KqCtgZ0+/b\nTzY/n3T5bxRigKGZDlNR8jdByRvA/wlYZOzmw/TdX85m6zDcnGbYPDiKcYGfVGdQ90GObZBy4BoY\nDjjMcZ7+91OJri7lp990c1wwsjIumn8fl0Lv409scq62bb8+/DmDujsP2vr7A8hfQDf1IcOuWI/j\nIoDP9n9CdkWN/14+n6GVm7zfZAUuQ/4mYj0eqoe3bi4YeOdwB0sFUnTYXdqb/X2S2HxKd5z2Pnlq\n4AG29oyp+//PKbHz+H5XOLb5Z78fD6uCVsuVjhWoBftF6WZDa6Cs0hN0Lk2prSYpvmM1n8ixoPGc\nOH+/JK/xbTofNi7yuk2MTacmMYFgfWqDug9iYr+Jra7SpkGjsdY67x2Ld66Um31t688Z89vzubgr\nHs/JZNUbZvRX7uu1vontDgEPiy8N+vs8kXL2pUezZErgBRUe+32+iq/hvyEIjoGgdYquLiUvNpbc\nzHTHdEulUduACnrTNFCrFaqfA4BxpXHUBJlD2bfCw2Ulh4HAvWAbyOGjrhO4Mfc3Acvs/7/nk7Ln\nv+z6y8WOwSpAyYArGT0lcDDubvVvXy7+/kKmnNrbsQfvl09fxLayvWzykxextoc4z1RxQmUUt+7c\n7jvhK9DofXTP20ni+iXg+5mqW3BS72csu2pHkHq713EiDTcJMF0kyuyZHM9Ba6Hc+VZJ8TH0TI5v\nRgVFJJwa73bh76/jzfuK6R2KPNIu9judAlwSylxwQYKGS/sc5tmeY3i01xVBF3Bcmp1O8N1aw6fo\nYH+K39gL7A1YpteBGg6k4zi/8C9/juddKsjbX+IYOEEmPc0Yxzq9n3IeKfYw7DscsMyw+BT2d02h\nV6bzSluP55SAKxDbYh/haVFduD7/K3bFBR4Kz646SEm3IfS69fWAZa55cB2b9xXzukMarH77hzM5\n+ihdgkQXfSu+YPOmZVxT2HQJae3/qx/s9rb16BMDTyzrt3sZQ/b8h13vxjgGhpPid7AyORkSmi7S\nqXVCZRSjjjhX/Eh5NUfKqyk8WuXYc1xqs4DAiyCao+MEai43hg4mPTWeHskxPHD2CMf/UZxWGolI\n+2rObhdDko7B1Dcu/jAdULOTq+NgE85bOm3d733/uWc55xoLpcz8Csc0EG4S0B5gI1uGxuDUFzgt\nqgsTCuJY2O3HQben213p/Dv9T4VnsLnyO0EXOUwb2CNo70640y31OmMGh959hKHHOWU+OIXkID9X\nbqYd7DhhCluGfD9oG+z/v+czrGgLvyzwM1exNvBJDd7rtv///oKUot3sDJJgpu/hE8goPIMdJzQd\nSq39XbH1wFFMZio/+T+Be46Xvr+b51zMC/RyyGPSDB0nUBMR8XE7dwfgrbfeCmvdQsLFH6Zxiy9l\nsMfDY9Oc/+i8+oE1QR/XeFcXf0M95G+CtJyg99oysifV1dX0cCjjlIC21iIXW3v16pJAz6TYoG1Q\n21PktAPH1gNHg6YMgQjNeXlaLh+XnOi4VZobblLxgLs26HXGDNj0rN81u3WBz9fr4LPa+YoB7nN0\nO54+DYfc/Vn8zja2f3rAcXs6N9sUNud3yzPfd7yVa80K1Iwx8cBGYL619t8BymQBDwPjgN3Aj621\nr9Q7fy7wF6A/sB64wVr7ecuqLyIigUQd/IzEf17dMKBq5FdFh3k34Wwg8ATxcWVv+ubcOOxGkjGM\nqoGXBa3TJ2f0Ymn/bxia7pyAdmI/Q9AdJYNt7eUyeHTTU6S9hkPM4Y+Nuu3i8pbBpmed7+Py527K\nqb0D9vK1xXBzKLkO1IwxCcBSHJLWGO8kgOXAFmAUcDnwL2PMUGvtl8aYvsCLwG+AFcAvgeXGmGHW\n2pqWvw0REWlg2NXUuBj+za7eEXROLsDO2H4M9S0ACLTvarWLnpSJ/SYG3ULKzZ6SdbsEOK3OcPkh\n7mYHjkj9EO/QXE5pcvNzdyxzFagZY3LwBmnBMrGcCwwCzrTWHgE2G2MmANcDvwC+B3xirb3Ld9/r\ngHzgPMBvD52IiLTAabmUDfYGOk5Bxs7fjQ+4T3LtENRPKz0kxQXZgsmlKQOnkLY3zXEYzimNRJ0u\nGd4vhxQl0PE/xKXjc9ujdiawGlgEOO2jMQb4yBek1Vrju772/Du1J6y1pcaYD/FmNVSgJiISZt2T\nY8mu3uE4qTvb7KIkJfC+lG0h2ObfrlNqiBzjXAVq1toHa793XuJMJk3XU+8Hjnd5XkREwqjbqGuI\n3fICQx33Vw2+IjCU3OQPC2WeMZFIFupVn0lARaNjFUC8y/MNGGNuBG4E6N8lISyrs2rnTqxfv56o\nqKig5fzVqabGO93O6frmlncqE+ic2+OBypWUlDi2ed9q73YqrS3jVAe3Okubh5La3EttPhD63Ra8\nzUsAXzu1dZunkcbshNkBz9fZC2/tDXwfpzq4pZ/z5lObh1aoA7VyaLJ5WTxQWu9846AsHvC7eZi1\n9iHgIYABqYm2tUuL3Xj81cfxeDycfvrpjvM6Hn/1cQC/8yyaO/nUTXk3eyA2Puf2uFPaAqc23/U7\n749Pa8s41cGtztLmoaQ291Kbq81Deb3aXG0eaqEO1Pbg3Q2tvgy+Tc+7x/e68flPQ1wPCZPyA5WO\niSvLD1SSkB44E7SIiIgEFup+uveAEcaY5HrHxvuO156vS9ZjjEnCm5jnPeSYk5qTEjQIS0iPIzVH\ny9pFRERaotU9asaYNKDMWlsCvA3sAh4zxiwCJuFd6Xm9r/ijwE+MMXcAzwP/gzcpbuDNxSRidRuR\nSrcRqZD7ROBCTskoRURExFEoetQ2ALcCWGs9wGQgHe8OBrOAK621O33ndwJXATOBD4BewGQluxUR\nERFpqtk9atZa0+h1dqPXnwNnO1z/CvBKoPORYPvh7dz89s2Om/gqh49P/qbgW7hkDAtffURERDoQ\nbcreyMR+E+tyBzlRDh/ATV6ljGHuyomIiEgTCtQamTJwCpf0vgTQ3m5BudyHTURERFqmbbKziYiI\niEirKVATERERiVAK1EREREQilAI1ERERkQilQE1EREQkQilQExEREYlQCtREREREIpQCNREREZEI\npUBNREREJEIpUBMRERGJUArURERERCKUAjURERGRCKVATURERCRCKVATERERiVAK1EREREQilAI1\nERERkQilQE1EREQkQilQExEREYlQCtREREREIpQCNREREZEIpUBNREREJEIpUBMRERGJUArURERE\nRCKUAjURERGRCKVATURERCRCKVATERERiVAK1EREREQilAI1ERERkQilQE1EREQkQilQExEREYlQ\nCtREREREIpQCNREREZEIpUBNREREJEIpUBMRERGJUArURERERCKUAjURERGRCKVATURERCRCKVAT\nERERiVAK1EREREQilAI1ERERkQilQE1EREQkQilQExEREYlQCtREREREIpQCNREREZEIpUBNRERE\nJEIpUBMRERGJUArURERERCKUAjURERGRCOUqUDPGxBtjHjLGFBlj8o0xtzmUPc8Y86ExpsQYs8YY\nM6rR+SuNMZt959caY05r7ZsQERER6Yjc9qj9ETgDmADcBPzCGDOtcSFjzBDgVeA1YCTwIvC6MeZ4\n3/mhwFJOr+mOAAAK+klEQVTf/YYDHwEvG2NSWvk+RERERDqcoIGaMSYZ+B4w31q70Vq7HLgLmOen\n+M3ARmvtT621W621dwH/AX7gO38hsNVau9ha+wXwMyAdGBqC9yIiIiLSobjpURsOxANr6h1bA4wy\nxsQ0KtsPWNfo2CfAWN/3BcBgY8yZxpgoIBcoBrY3t+IiIiIiHV3jQMufTKDQWlte79h+IA5IA/Y1\nOn58o+uzgJ6+758GLgPeATxADXC5tbaw+VUXERER6djcBGpJQEWjY7Wv4xsd/yew0hjzXWA5cBFw\nOfC173w3IAOYD7wL/H/AEmPMaN9QaAPGmBuBG2ufaYz51EV9Q6UrcDiM17sp71Qm0Dm3x/2V6wl8\nE6ROoaQ2V5sHK6M2V5u3hNpcbR6sTFu0+aAg9XHHWuv4BUwBvml0bAhggXQ/5W8DyvD2mG3AO5/t\nA9+5x4GHG5V/G/h/LurxQbAyofwCHgrn9W7KO5UJdM7tcX/l1OZqc7W52lxtrjZXm7dvm7uZo7YH\n6GaMiat3LANvr1qTIUvrXUCQCvSx1tam5tjp++8ooHGv2Ea8c9sizUthvt5Neacygc65Pd7a9xsK\navPwU5uHn9o8/NTm4ac2DxHji/oCFzAmCe8igEustW/5jv0PcLG1dlyjstOA8dbaeb7XBvgMuN9a\ne78x5t/ALmvt9fWueQX43Fr7wyD1+MBaq5xrYaQ2Dz+1efipzcNPbR5+avPwC1WbB52jZq0tNcY8\nDjxgjJmDtzftVnxzx4wxGcBha20ZkAc8boxZg3fY82dAMvCY73Z/A57ynX8HuBo4D1jgoq4PuX9b\nEiJq8/BTm4ef2jz81ObhpzYPv5C0edAeNajrVfsr8F286TTusdbe4ztngVxr7WO+17OARXjzo70L\n/NBa+3m9e83GO48tC9gM3GqtfScUb0ZERESkI3EVqImIiIhI+GlTdhEREZEI1WECNWPML40x/zHG\nfGCMuby969MZGGM2G2Pe8n39tr3r01kYYwYaY4rbux6dgTEm1hjzD2PMu8aYV40xPYNfJa1hjEkw\nxjxjjHnHGPOeMWZMe9epMzHGXGGMWdze9eiIjDFRxphHjTFrfb9P0t1c1yECNWPMecAwYBwwkchM\n99GhGGO64s2vd47v6xftXafOwDdf9G6gPFhZCYlrgD3W2jPxJvS+pZ3r0xlcB+RZa88CZgP3tnN9\nOg1jzB+BOwHT3nXpoK4EynwZMxbjXXAZVIcI1IALgK3Ai8CTwMr2rU6ncArQ3RjzujFmpTFmQHtX\nqJP4M/BroLS9K9IZWGuXALf7Xh4PFLVjdTqLJ/EmSgdvZoLGO+NI21kPzG3vSnRg44DVvu9fBc5x\nc5GbLaQihm9LqR81Onwh3j1HM4HJwEjgEeCs8NauY3Jo82LgD9baJ40x4/H+dTA+3PXriBzafBLw\nX2vtB94UhRIqgdrcWrvXWlttjHkZb8LuC8Jfu47Jqc1959PwBm3qxQyhID/ry4wx57RDtTqLVLyf\nnQBHgC5uLuoQqz6NMX8A9lpr/+J7vdNam92+terYjDEJANbact/rL621J7ZvrTo2Y8w7QI3v5Rjg\nTWvtJe1YpU7FGNMfeNlaG5r9+yQgY8xA4FngDmttJGTZ7zR8gdoca+2cdq5Kh2OM+RPwlrX2Rd/0\noTestSODXddRhj7X4t0AHmPMIOBg+1anU7gZWAhgjBkO7G7f6nR81tqzaucEAvkK0tqeMeZGY8w8\n38sSvHsYSxsyxvTFO43lBgVp0sGswzs6AnAJ8B83F7VroGaMiTfGfGqMmdDo2EPGmCJjTL4x5jYX\nt3oJyDPGvI+3q/zmtqrzsS6Ebf5XYKCvl+defDtVSFMhbHNxKYRt/gxwoTHmbWAZcFNb1flYF8I2\n/x8gBbjLt6J8WZtVugPQ75f204K2fw5IMsasBb4P/M7Nc9ptjppv6GwpMLTRqT8CZwAT8E7efdIY\ns9ta+89A97Le8dsft1VdO4oQt3kZ3p0qxEEo27w+De0HFuKf80OA0v0EEeI21x99LrXF7xffnt5v\nhbamHU9L2t5a68G7qrl5rLVh/wJygI+BTwALTPAdTwbKal/7jv0CWNMe9exIX2pztXln+FKbq807\ny5favfO0fXsNfZ6Jd4nq2EbHhwPxwJp6x9YAo4wxx9QK1QikNg8/tXn4qc3DT23ePtTu7Sesbd8u\n/2jW2gdrv2+UZiATKLS+lYQ++4E4vCk49oWlgh2Q2jz81ObhpzYPP7V5+1C7t59wt32krfpMomly\nw9rX8WGuS2ehNg8/tXn4qc3DT23ePtTu7adN2j7SArVymr6Z2tfKxN421ObhpzYPP7V5+KnN24fa\nvf20SdtHWqC2B+hmjImrdywDb0Ra2D5V6vDU5uGnNg8/tXn4qc3bh9q9/bRJ20daoPYxUIl3aWut\n8cBGa211+1Spw1Obh5/aPPzU5uGnNm8favf20yZtH1ErQKy1pcaYx4EHjDFz8Eait6Jkqm1GbR5+\navPwU5uHn9q8fajd209btX1EBWo+P8ab9f4NvJuX/tpa+3T7VqnDU5uHn9o8/NTm4ac2bx9q9/YT\n8rbvEJuyi4iIiHREkTZHTURERER8FKiJiIiIRCgFaiIiIiIRSoGaiIiISIRSoCYiIiISoRSoiYiI\niEQoBWoiIiIiEUqBmohEJGPMY8YY6/A1xxizyBizJox1GmyMWeui3MXGmKfCUScR6diU8FZEIpIx\npiuQ6Ht5JvAMkFmvyGEgGoiz1oZls2ljzL+BP1prV7ko+zawyFr7ZtvXTEQ6qkjcQkpEBGvtYbzB\nGMaYIt+x/PaqjzHmDCDbTZDm8wDwC0CBmoi0mIY+ReSYVX/o0zcUusYY83NjTJExJt8Yc60xZqox\nZrfv2O/rXRtvjPmzMeagMabAGPOMMaaXw+PmAi/Uuz7VGPO0MabQGFNsjHnOGJNRr/wrwHhjzKBQ\nv28R6TwUqIlIRzIKGOj77zPAQ8A84FLgZ8DPjDHDfGV/D4wFJgFn4/19uMIYYxrf1HfsYuC1eod/\nA2T7rh0DpAP31p601hYDG3zXiYi0iIY+RaQjiQZ+aK09Yox5GPghsNBauwnYZIy5ExhsjPkCbwA3\nxlr7EYAxZiZQAIwH3m103yygJ7Cl3rFsoAT40lpb4ru+W6PrNuMNGkVEWkQ9aiLSkXxjrT3i+77M\n999d9c6XA/FAPyAOeNcYU2KMKQEOAgl4e+QaS6+9f71jdwKnAgeNMSuBCcBnja4rqHetiEizqUdN\nRDqSaj/Havwcq/3ddza+BQv1HPRTvnZ5fN0ft9badcaYLOAyYCJwD3AtcG6966IBT/Bqi4j4px41\nEemMvsAbQPW01n5urf0cb4D2J7zDnI3VrjbtWXvAGDMfGG2tfcpaOx1vsHZOowUJPYD9bfEGRKRz\nUKAmIp2Ob3j0YeD/GWPOM8YMBh4HTga2+7nka+CA73ytvsB9xpgzjDH9gOnAbhoOjw4HPmiDtyAi\nnYQCNRHprH4MrAKexrs6MxG40Fpb1rig9WYGX4U38W6t/8G76OAFvHPThgCTrLUeAGNMF7yB3co2\nfA8i0sFpZwIREReMMeOBp/AmvQ36i9MYkwtca629oM0rJyIdlnrURERcsNauAXbinYvmxk3A/7ZZ\nhUSkU1CgJiLi3s14t4VyZIy5FNhprX2j7askIh2Zhj5FREREIpR61EREREQilAI1ERERkQilQE1E\nREQkQilQExEREYlQCtREREREIpQCNREREZEI9f8DVZnvh/8SRU0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd11ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "det = 0\n",
    "fig, (ax, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True)\n",
    "plt.subplots_adjust(hspace=0)\n",
    "for a in (ax, ax2):\n",
    "    a.grid(True)\n",
    "    a.grid(True, which='minor', lw=0.3)\n",
    "#ax.set_title('ACF of all photons. Donor SPAD')\n",
    "ax.set_ylim(0.8, 4.5)\n",
    "ax.set_xlim(10**-6, 1)\n",
    "for spot in spots:\n",
    "    ax.semilogx(bins[1:]*unit, GN[det][spot], drawstyle='steps-pre', \n",
    "                label='Pixel: %d' % spot)\n",
    "ax.legend()\n",
    "#ax2 = plt.twinx()\n",
    "ax2.semilogx(bins[1:]*unit, XCN[det].T, drawstyle='steps-pre');\n",
    "ax2.legend([f'Pair: {i}, {j}' for i, j in np.reshape([pixel_rowcol_to_ch(d, *i) for i in p], (-1, 2))])\n",
    "ax2.set_ylim(0.98, 1.05)\n",
    "\n",
    "ax.text(0.5, 0.95, 'ACF', transform=ax.transAxes, va='top', fontsize=22)\n",
    "ax2.text(0.5, 0.95, 'CCF', transform=ax2.transAxes, va='top', fontsize=22)\n",
    "ax2.set_xlabel('Time (s)');\n",
    "savefig('ACF-CCF comparison')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Burst-filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SpvPL8vfe5e+vA/sAzwX+iqK36nOV9f+EosfucOAVwN9SDLUkIg6leBzdZynC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf32fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "bpl.plot_alternation_hist_usalex(d, ax=ax, bins=np.arange(0, 4097, 16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Total photons (after ALEX selection):   287,744,125\n",
      "#  D  photons in D+A excitation periods:  142,294,620\n",
      "#  A  photons in D+A excitation periods:  145,449,505\n",
      "# D+A photons in  D  excitation period:   110,565,293\n",
      "# D+A photons in  A  excitation period:   177,178,832\n",
      "\n"
     ]
    }
   ],
   "source": [
    "loader.alex_apply_period(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Loading BG rates from cache ... \n",
      " - Loading bakground data:  [DONE]\n"
     ]
    }
   ],
   "source": [
    "d.calc_bg_cache(bg.exp_fit, time_s=5, tail_min_us='auto', F_bg=1.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - Performing burst search (verbose=False) ...[DONE]\n",
      " - Calculating burst periods ...[DONE]\n",
      " - Counting D and A ph and calculating FRET ... \n",
      "   - Applying background correction.\n",
      "   [DONE Counting D/A]\n"
     ]
    }
   ],
   "source": [
    "d.burst_search(min_rate_cps=50e3, pax=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   13972262,    13972785,    13976637, ..., 49504596221,\n",
       "       49504597600, 49504598610], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.ph_in_bursts_ich()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACF for pixels bursts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unit = d.clk_p\n",
    "bins_per_dec = 10\n",
    "bins = pyc.make_loglags(-8, 1, bins_per_dec) / unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'results/2017-07-11_06_12d_ACF_all-ph_pixels-bursts-all-ph_bins10.csv'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acf_fname = f'results/{mlabel}_ACF_all-ph_pixels-bursts-all-ph_bins{bins_per_dec}.csv'\n",
    "acf_fname_n = f'results/{mlabel}_ACF_all-ph_pixels-bursts-all-ph_bins{bins_per_dec}_normalized.csv'\n",
    "acf_fname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Loading ACF from cache\n"
     ]
    }
   ],
   "source": [
    "recompute = False\n",
    "if Path(acf_fname_n).exists() and not recompute:\n",
    "    print('- Loading ACF from cache', flush=True)\n",
    "    GG = np.loadtxt(acf_fname)\n",
    "    GN = np.loadtxt(acf_fname_n)\n",
    "else:\n",
    "    GG = np.zeros((48, bins.size - 1), dtype=float)\n",
    "    GN = GG.copy()\n",
    "    for ich in tnrange(48, desc='Calculating ACFs: '):\n",
    "        if ich in skip_ch:\n",
    "            continue\n",
    "        t = d.ph_in_bursts_ich(ich)\n",
    "        G = pyc.pcorrelate(t, t, bins)\n",
    "        GG[ich] = G\n",
    "        GN[ich] = normalize_G(G, t, t, bins)     \n",
    "    np.savetxt(acf_fname, GG)\n",
    "    np.savetxt(acf_fname_n, GN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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93nPQ/dvNtvPp/ulnVz+7Tdd5po7T/dvfccvwb98znvGMj7n741svdAELRTb18OUbqSb8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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc28b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "det = 0\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "def _plot(det):\n",
    "    ax.set_xlabel('Time (s)')\n",
    "    ax.grid(True); ax.grid(True, which='minor', lw=0.3)\n",
    "    ax.set_title('ACF of all photons. Spot %d' % 0)\n",
    "    #ax.set_ylim(0.0, 6)\n",
    "    ax.set_xlim(10**-7, 1)\n",
    "    return ax.semilogx(bins[1:]*unit, GN.T - 1, drawstyle='steps-pre', alpha=0.05, color='C0')\n",
    "lines = _plot(det)\n",
    "lines[0].set_alpha(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ca8879b8297475b834a7c5f240a3ec1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(\n",
    "     pixel=(0, 47),\n",
    "     ylim=widgets.FloatRangeSlider(value=[0, 150], min=0, max=200, step=1,))\n",
    "def plot_spot(pixel, ylim):\n",
    "    for line in lines:\n",
    "        line.set_alpha(0.05)\n",
    "    line = lines[pixel]\n",
    "    line.set_alpha(1)\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.set_title('ACF of all photons. Spot %d' % pixel)\n",
    "    display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CCF for pixels bursts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos_pairs = [\n",
    "    ([5, 2], [6, 2]),\n",
    "    ([5, 1], [6, 1]),\n",
    "    ([5, 1], [5, 2]),\n",
    "    ([6, 1], [6, 2]),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unit = d.clk_p\n",
    "bins = pyc.make_loglags(-8, 1, 10) / unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da7729f0374d4f608135fdb1a7c30130",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing positions [5, 2], [6, 2]\n",
      "Processing positions [5, 1], [6, 1]\n",
      "Processing positions [5, 1], [5, 2]\n",
      "Processing positions [6, 1], [6, 2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "XC = np.zeros((2, len(pos_pairs), bins.size - 1), dtype=float)\n",
    "XCN = np.zeros((2, len(pos_pairs), bins.size - 1), dtype=float)\n",
    "for ipair, (pos1, pos2) in tqdm_notebook(enumerate(pos_pairs), total=len(pos_pairs), desc='Spot pair'):\n",
    "    print('Processing positions %s, %s' % (pos1, pos2), flush=True)\n",
    "    ich1 = pixel_rowcol_to_ch(d, *pos1)\n",
    "    ich2 = pixel_rowcol_to_ch(d, *pos2)\n",
    "    \n",
    "    for i_det, ph_sel in enumerate((Ph_sel(Dex='Dem', Aex='Dem'), Ph_sel(Dex='Aem', Aex='Aem'))):\n",
    "        t = d.ph_in_bursts_ich(ich1, ph_sel=ph_sel)\n",
    "        u = d.ph_in_bursts_ich(ich2, ph_sel=ph_sel)\n",
    "        \n",
    "        G = pyc.pcorrelate(t, u, bins)\n",
    "        \n",
    "        XC[i_det, ipair] = G\n",
    "        XCN[i_det, ipair] = normalize_G(G, t, u, bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CCFs plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YNq7v6iMiIiK9JtOuwwuB3wHndVfIzMqB0cCGHtZrcMgfAhZ6iVsbobGub+oj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\n72A9nOO0Y42SwuCh7qntjRz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9gAylm3xxw9R9TJmV+XiYnsr2AJDNQSPdAS0XrWVHg6jE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\naSAWWpIkSQOx0JIkSRqIhZYkSdJAvgJ90E9deu1bYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105d2320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "plt.xlabel('Time (s)')\n",
    "plt.grid(True); plt.grid(True, which='minor', lw=0.3)\n",
    "plt.title('CCF of pixels (burst-filtered) - Donor SPAD')\n",
    "plt.ylim(0, 3)\n",
    "#plt.xlim(10**-8, 1)\n",
    "plt.semilogx(bins[1:]*unit, XCN[0].T, drawstyle='steps-pre');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2OIna4O5k4JG+ORrOy+IewZuYLCIiIrJPyXYfrVfx9hp5jOhTP0wnfbfeRuDg7EMTERER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\nZHYF+zSR5JNl1NuWZHol+iRpuLlhqaRVkeRyYF3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x106a2c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "plt.xlabel('Time (s)')\n",
    "plt.grid(True); plt.grid(True, which='minor', lw=0.3)\n",
    "plt.title('CCF of pixels (burst-filtered) - Acceptor')\n",
    "plt.ylim(0, 3)\n",
    "#plt.xlim(10**-8, 1)\n",
    "plt.semilogx(bins[1:]*unit, XCN[1].T, drawstyle='steps-pre');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3.6 (py36)",
   "language": "python",
   "name": "py36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "nav_menu": {},
  "toc": {
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}