{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "503cb687",
   "metadata": {},
   "source": [
    "# Hands-on session 3: Elephant (Solution)\n",
    "\n",
    "These exercises build on concepts introduced in Tutorial 3\n",
    "\n",
    "In this exercise, we will use two methods available in the Elephant library, namely the Unitary Event analysis to identify significant spike synchrony in a pair of spiketrains across all correct trials and SPADE method to find recurring patterns in the spike data of trial 1.\n",
    "\n",
    "\n",
    "## Imports and Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "887c2677-af42-460b-803c-c5aff1e6b528",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install git+https://github.com/INM-6/viziphant\n",
    "#!pip install nixio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fc1d993",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import quantities as pq\n",
    "import neo.utils\n",
    "import elephant\n",
    "import viziphant"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d1cd9a4",
   "metadata": {},
   "source": [
    "## Data loading\n",
    "\n",
    "\n",
    "1. Load the data `reach_to_grasp_material/i140703-001.nix` file using the `neo.io.NixIO` into a neo Block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2e41fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "with neo.io.NixIO('reach_to_grasp_material/i140703-001.nix', 'ro') as io:\n",
    "    block = io.read_block()\n",
    "segment = block.segments[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d86077ac",
   "metadata": {},
   "source": [
    "## Data preparation for Unitary Event (UE) analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a15b721",
   "metadata": {},
   "source": [
    "2. At this step, extract the start events of all trial types that are correct. We will cut data from the trial start `TS-ON` to `2105*pq.ms`. Then, in the call to `neo.utils.add_epoch()`, supply these as correct parameters (Hint: call `neo.utils.add_epoch?` to view function documentation). The result should be an `Epoch` from `TS-ON` to 2105 ms *after* `TS-ON`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fff561d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_events_all_trial_types = neo.utils.get_events(\n",
    "    segment,\n",
    "    trial_event_labels='TS-ON',\n",
    "    performance_in_trial_str='correct_trial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6dc0890b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre = 0 * pq.ms\n",
    "post = 2105 * pq.ms\n",
    "\n",
    "ue_epoch = neo.utils.add_epoch(\n",
    "    segment,\n",
    "    event1=start_events_all_trial_types[0], event2=None,\n",
    "    pre=pre, post=post,\n",
    "    attach_result=False,\n",
    "    name='analysis_epochs')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "047e6aee",
   "metadata": {},
   "source": [
    "3. Now you will need to find the best spiketrains to use for the UE analysis. These should: \n",
    "- be `'sua'`, \n",
    "- not be classified as `'noise'`,\n",
    "- have a high signal-to-noise ratio (e.g. above 10)\n",
    "- have a high spike count (e.g. above 10000)\n",
    "- not be captured by electrodes rejected due to low, intermediate or high frequency components.\n",
    "\n",
    "Create a new segement that contains the list of selected spiketrains that match these criteria. \n",
    "\n",
    "Hint: You may want to look into `segment.spiketrain[0].annotations` and list comprehensions to take the best spiketrains from all the spiketrains present in the segment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a96e8797",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only SUA spike trains with spikes and certain quality criteria\n",
    "ue_segment = neo.Segment()\n",
    "ue_segment.spiketrains = [spiketrain for spiketrain in segment.spiketrains if\n",
    "                                   spiketrain.annotations['sua'] and\n",
    "                                   not spiketrain.annotations['electrode_reject_HFC'] and\n",
    "                                   not spiketrain.annotations['electrode_reject_IFC'] and\n",
    "                                   not spiketrain.annotations['electrode_reject_LFC'] and\n",
    "                                   not spiketrain.annotations['noise'] and\n",
    "                                   spiketrain.annotations['SNR'] > 10 and\n",
    "                                   spiketrain.annotations['spike_count'] > 10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "235933b7",
   "metadata": {},
   "source": [
    "4. Print the number of spiketrains you have found. You will need a pair of spiketrains for the UE analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dffbd02d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of left over clean spike trains after filtering is 2\n"
     ]
    }
   ],
   "source": [
    "print(f\"The number of left over clean spike trains after filtering is {len(ue_segment.spiketrains)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dfaefbc",
   "metadata": {},
   "source": [
    "5. Now that we have isolated the good spiketrains, cut the segment according to the defined epoch using `neo.utils.cut_segment_by_epoch()`. From the result, prepare the input for the unitary event analysis, which is a list, where the `n`-th element is a list of spiketrains for the `n`-th trial, or, in other words, where the `n`-th element is the `spiketrains` attribute of the `n`-th segment.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ee5a825",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the new block\n",
    "ue_trials = neo.Block()\n",
    "\n",
    "# Cut the recording segment into the trials, as defined by the epochs\n",
    "ue_trials.segments = neo.utils.cut_segment_by_epoch(ue_segment, ue_epoch, reset_time=True)\n",
    "\n",
    "sts_for_uea = [x.spiketrains for x in ue_trials.segments]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298d5185",
   "metadata": {},
   "source": [
    "## UE analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05b4a995",
   "metadata": {},
   "source": [
    "The Unitary Event (UE) analysis method is very useful in detecting spiking coincidences between a two or several spike trains recorded simulaneously and determining their significance (Grün et al 2002a, Grün et al 2002b, Grün et al 2010). We are going to apply this method to look at a pair of spike trains from the Reach2Grasp dataset you have been exploring previously. The aim is to look for correlated spiking activity in the clean spike trains during all correct trials of all trial types.\n",
    "\n",
    "The dataset should be a list of trials, containing a list of spike trains, which, in this case, is going to contain only two spike train objects.\n",
    "\n",
    "You may want to consider the following parameters for the Unitary Event analysis:\n",
    "* `bin_size`: The size of bins for discretizing spike trains.\n",
    "* `win_size`: The size of the window of analysis\n",
    "* `win_step`: The size of the window step.\n",
    "* `pattern`: The binary pattern, indicating which neurons should participate in the pattern. E.g. [1, 1] for two out of two neurons, [1, 1, 0] for the first two out of three neurons.\n",
    "* `pattern_hash`: The list of interesting patterns in hash values. Generated from `pattern` using `hash_from_pattern` function.\n",
    "* `significance_level`: The level of significance for p-value evaluation. \n",
    "* `method` Determines the approach for unitary event computation.\n",
    "    * `analytic_TrialByTrial`: calculates the expectancy analytically on each trial, then sums over all of them\n",
    "    * `analytic_TrialAverage`: calculates the expectancy analytically by averaging over trials\n",
    "    * `surrogate_TrialByTrial`: calculates the distribution of expected coincidences by spike time randomization in each different trial, then sums over all of them\n",
    "    \n",
    "6. Run UE analysis using `elephant.unitary_event_analysis.jointJ_window_analysis()` and pass the result together with additional parameters to the viziphant function provided below. You may want to use a `plot_params` dictionary to add unit names and `TS-ON` trigger marker to the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59d18c64",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Unitary events\n",
    "winsize = 100 * pq.ms\n",
    "binsize = 5 * pq.ms  \n",
    "winstep = 5 * pq.ms \n",
    "significance_level = 0.05\n",
    "N = 2\n",
    "pattern = [1, 1]\n",
    "pattern_hash = [elephant.unitary_event_analysis.hash_from_pattern(pattern, 2)]\n",
    "method = 'analytic_TrialByTrial'\n",
    "\n",
    "plot_params = {\n",
    "    'events': {'TS-ON': [0 * pq.ms]},\n",
    "    'unit_real_ids': [sts_for_uea[0][0].name[-5:], sts_for_uea[0][1].name[-5:]],\n",
    "    'hspace': 0.6,\n",
    "    'figsize': (10, 12),\n",
    "    'fsize': 12,\n",
    "    'ms': 5,\n",
    "    'lw': 1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b9bcb12",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/denker/miniconda3/envs/school_lyon/lib/python3.8/site-packages/elephant/utils.py:290: UserWarning: Correcting 1 rounding errors by shifting the affected spikes into the following bin. You can set tolerance=None to disable this behaviour.\n",
      "  warnings.warn(f'Correcting {num_corrections} rounding errors by '\n",
      "/home/denker/miniconda3/envs/school_lyon/lib/python3.8/site-packages/elephant/utils.py:290: UserWarning: Correcting 2 rounding errors by shifting the affected spikes into the following bin. You can set tolerance=None to disable this behaviour.\n",
      "  warnings.warn(f'Correcting {num_corrections} rounding errors by '\n"
     ]
    }
   ],
   "source": [
    "uriana = elephant.unitary_event_analysis.jointJ_window_analysis(sts_for_uea, bin_size=binsize, winsize=winsize, winstep=winstep,\n",
    "                                   pattern_hash=pattern_hash, method=method)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "45078831",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = viziphant.unitary_event_analysis.plot_ue(sts_for_uea, uriana, significance_level, **plot_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44bf3ef6",
   "metadata": {},
   "source": [
    "Optional: Explore the parameters of the functions. Can you add the missing triggers between `TS-ON` and 2105 ms after `TS-ON` to the generated plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e7c6c1e",
   "metadata": {},
   "source": [
    "## Data preparation for SPADE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42cbb4e",
   "metadata": {},
   "source": [
    "7. In close analogy to the beginning of tutorial 3 and what we have done above, prepare a Neo `Block`, containing one `Segment` of SGHF data of the first correct trial. Name this `Segement` by the variable `trial`, as in tutorial 3.  In contrast to the lecture and the UE analysis above, we will cut data from the trial start `TS-ON` to reward administration indicated by event `RW-ON`. To this end, first find also the `RW-ON` events similar to how we found `TS-ON` in tutorial 3. Then, in the call to `neo.utils.add_epoch()`, supply these as a second event `event2=` *instead* of giving `t_pre=0` and `t_post=2*pq.s`. This will cut from event 1 to event 2, instead of a fixed amount of 2 s around event 1 (as in tutorial 3 or the UE exercise above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "943d26af",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "start_events = neo.utils.get_events(\n",
    "    segment,\n",
    "    trial_event_labels='TS-ON',\n",
    "    belongs_to_trialtype='SGHF',\n",
    "    performance_in_trial_str='correct_trial')\n",
    "\n",
    "## Additinally, get RW-ON events\n",
    "stop_events = neo.utils.get_events(\n",
    "    segment,\n",
    "    trial_event_labels='RW-ON',\n",
    "    belongs_to_trialtype='SGHF',\n",
    "    performance_in_trial_str='correct_trial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "189fd391",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Create epochs between the events\n",
    "trial_epochs = neo.utils.add_epoch(\n",
    "    segment,\n",
    "    event1=start_events[0],     # The function returns a list, we need to retrieve the object which is the first (digital port events)\n",
    "    event2=stop_events[0],\n",
    "    array_annotations=start_events[0].array_annotations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e44de382",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Create the new block\n",
    "spade_trials = neo.Block()\n",
    "\n",
    "# Cut the recording segment into the trials, as defined by the epochs\n",
    "spade_trials.segments = neo.utils.cut_segment_by_epoch(segment, trial_epochs, reset_time=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb7ade10",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Select first segment as the trial for analysis\n",
    "spade_trial = spade_trials.segments[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7564a093",
   "metadata": {},
   "source": [
    "## SPADE analysis\n",
    "\n",
    "Note that patterns in this data are not easily to spot by eye in the rasterplot we developed in tutorial 3.\n",
    "We use SPADE (Torre et al., 2013; Stella et al. 2019) as a technique that does that for us by finding all patterns and checking for each pattern if it occurs more often than expected given its complexity (number of neurons participating) and frequency. Before going directly to the analysis we briefly explain SPADE's most important parameters:\n",
    "\n",
    "- `binsize`: temporal precision of the method. The smaller the binsize is, the more precisely we expect each single pattern to repeat. This raises an important question: which is the temporal precision that are you interested in? It depends on the scientific question! We often use 5ms, based on a number of studies on the minimal neuronal temporal precision.\n",
    "- `winlen`: window length, or maximal length allowed to each pattern, expressed in bin units. SPADE will detect patterns with a temporal duration up to the window length. If winlen=1, then only synchronous patterns are detected. Are you interested in synchronous higher-order correlations? Are you interested in patterns with delays? Note: the higher the winlen parameter is, the more expensive (memory and time) the pattern search is!\n",
    "- `min_spikes` and `min_neu`: minimum number of spikes and minimum number of neurons allowed in a pattern. These parameters are important, too. Do you want to look for pattern with a particular size? Are you interested in patterns with multiple spikes coming from the same neuron?\n",
    "- `n_surr`: number of surrogates used for the statistical testing. If the number of surrogates is set to zero, then all patterns are retrieved by SPADE without further testing. If, instead, the number of surrogates is different from zero (typically we suggest to use a large number - the more the better!) then only the significant patterns are retrieved from the analysis. For simplicity, we will set this parameter to `n_surr=0` in the following.\n",
    "\n",
    "Next steps:\n",
    "8. As in tutorial 3, select only good quality neurons using the annotations (i.e., SUA units, more than 10000 spikes,...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4d7cad57",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Select only SUA spike trains with spikes and certain quality criteria\n",
    "spiketrains = [spiketrain for spiketrain in spade_trial.spiketrains if\n",
    "                                   spiketrain.annotations['sua'] and\n",
    "                                   not spiketrain.annotations['electrode_reject_HFC'] and\n",
    "                                   not spiketrain.annotations['electrode_reject_IFC'] and\n",
    "                                   not spiketrain.annotations['electrode_reject_LFC'] and\n",
    "                                   not spiketrain.annotations['noise'] and\n",
    "                                   spiketrain.annotations['spike_count'] > 10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d32abe",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "9. Run the SPADE analysis by executing the code given below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "23263b16",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Fix parameters for SPADE\n",
    "binsize = 5 * pq.ms\n",
    "winlen = 1\n",
    "min_spikes = 2\n",
    "n_surr = 0\n",
    "min_neu = 2\n",
    "min_occ = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23f7d6b4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/denker/miniconda3/envs/school_lyon/lib/python3.8/site-packages/elephant/conversion.py:1168: UserWarning: Binning discarded 2 last spike(s) of the input spiketrain\n",
      "  warnings.warn(\"Binning discarded {} last spike(s) of the \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for data mining: 0.316211462020874\n"
     ]
    }
   ],
   "source": [
    "result = elephant.spade.spade(\n",
    "        spiketrains=spiketrains, binsize=binsize, winlen=winlen,\n",
    "        min_spikes=min_spikes, n_surr=n_surr, min_neu=min_neu, min_occ=min_occ)\n",
    "patterns = result['patterns']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3d05a6",
   "metadata": {},
   "source": [
    "10. Let's look at the output in `patterns`. How many patterns were found? How does one pattern look as an output?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "994e3df5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5346,\n",
       " {'itemset': (22, 50),\n",
       "  'windows_ids': (127, 192),\n",
       "  'neurons': [22, 50],\n",
       "  'lags': array([0.]) * ms,\n",
       "  'times': array([635., 960.]) * ms,\n",
       "  'signature': (2, 2),\n",
       "  'pvalue': -1})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the length of the list of patterns found, and the first pattern as an example\n",
    "len(patterns), patterns[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11523576",
   "metadata": {},
   "source": [
    "11. Display five detected patterns in the data and their statistics by executing the Viziphant function calls given below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a5dfda9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time (s)', ylabel='Neuron'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viziphant.patterns.plot_patterns(spiketrains, patterns[:5], circle_sizes=(1, 50, 70))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b2a28cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:xlabel='Neuronal participation in patterns', ylabel='Count'>,\n",
       "       <AxesSubplot:xlabel='Pattern occurrences', ylabel='Count'>,\n",
       "       <AxesSubplot:xlabel='Pattern size', ylabel='Count'>], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lSpIkabSpzsj9A/DRcfq+AbwDeNkUP0OznDMFkiQNx1SPkdsd+Oo4fRcAz5ji+iVJkjSOqc7ILQAeBawdo28jYPMprl+a85zRlDQZ/p+hXlMNclcALwT+ZYy+F7b9kjSn+IN0/eLft2azqQa5DwInJdkQOLeq7k+yAfAKmjNY3zrF9UuSJmDQGJ7pGnv/Toeji+M+pSBXVWcmeQJwOrBxkluArYC7gWOq6rMDqFGSJEljmPJ15KrqA0lOAX4feBxwK/CdqrpjquuWJEnS+AZyQeA2tH1tEOuStH7yfoySNHmDurOD1BldPAaiKxxbSZpZBjlJs54BUZLGNiuCXJKNae4Q8XxgS+Aq4B+q6itt/740Z8FuB1wCHFxV17V9AY4H3tCu7lTg7VVVM7oRkrSecXf4uq0vv4CsL9s5W82KIEdTx/XA3sDPgZcAn0vyu8Aa4ByaoPZl4F3AWcDvte89hOZyJ7sBBXwduAb4+MyVPza/3JIkaTrNiiBXVXcCS3uazktyLc0tvh4HLK+qswGSLAVuSbJzVV0BHAScUFU3tP0nAG9kFgQ5aYShXpI0HWZFkBstydbAU4DlwF8Al430VdWdSa4GdqG5c8Quvf3t813GWe8hNDN4bLfddtNS+6C4y8LwI0kamz8fHjTrglySjYDPAKdX1RVJ5gMrRy22igfv4zq/fd3bNz9JRh8nV1UnAycD7Lnnnh5DNwP8xza9DPzTx++upC6YVUGuvb3Xp4F7gUPb5jXAglGLLgBWj9O/AFjjyQ7S9DDgSNLsMWuCXHv26anA1sBLqurXbddymuPgRpbbDNihbR/p3w34bvt6t54+zXKGAkma/fy/evbaYNgF9PgY8FTgZVW1tqf9i8CuSZYk2QQ4Gri8PdEB4FPAW5Nsk+SJwGHAaTNYtyRJ0lDMihm5JIuANwH3ADc3k3MAvKmqPpNkCXAicAbNdeQO7Hn7ScD2wI/a16e0bZK0Th5jKK1/5trs4qwIcu3FfbOO/guAncfpK+Dw9iFJkjpiroWqYZgVQU7S2PxPTtPN75jUbQY5SXOWIUWzmbv2NQiz6WQHSZIkTYIzcnqAvx1KU+cs4Ozk34vmKmfkJEmSOsoZuSGZym+H/mY5HI67NP38dyZNjkFOffE/V2kw1pd/S+vLdkrDZpCTJM0Jhse5xb/P/hjkpPWQ/0GqH35P1i/+fXeTQU5Sp/nDR9Jo69P/CwY5SdKMWp9+yErTzSAnSVqvGSzVZQY5SZKkPszGC+cb5NRJ/gYtSQ/n/43rH+/sIEmS1FEGOUmSpI4yyEmSJHWUQU6SJKmjPNlBmqM86FmS5r45MSOXZMskX0xyZ5LrkvzZsGuSJEmabnNlRu4jwL3A1sDuwPlJLquq5UOtSpIkaRp1fkYuyWbAEuCoqlpTVRcDXwJeO9zKJEmSpleqatg1TEmSPYD/qKpH97S9Ddi7ql42atlDgEPalzsBP52xQmEr4JYZ/LwucozWzfGZmGM0Mcdo3RyfiTlGE5uOMVpUVQtHN86FXavzgVWj2lYBm49esKpOBk6eiaJGS7KsqvYcxmd3hWO0bo7PxByjiTlG6+b4TMwxmthMjlHnd60Ca4AFo9oWAKuHUIskSdKMmQtB7kpgXpIn97TtBniigyRJmtM6H+Sq6k7gHOCdSTZL8hzg5cCnh1vZwwxll27HOEbr5vhMzDGamGO0bo7PxByjic3YGHX+ZAdoriMHfAJ4AXArcERVnTncqiRJkqbXnAhykiRJ66PO71qVJElaXxnkJEmSOsogN828D+xDJTk0ybIk9yQ5bVTfvkmuSHJXkm8kWTSkMocqycZJTm2/L6uT/CDJi3v61/txSnJGkpuS3JHkyiRv6Olb78enV5InJ7k7yRk9bY4RkOTCdmzWtI+f9vQ5RkCSA5P8pP0ZdnWSvdr29X58er43I4/7kny4p39GxsggN/167wP7auBjSXYZbklD9Qvg3TQnpzwgyVY0Zx8fBWwJLAPOmvHqZod5wPXA3sBjaMbkc0kWO04PeC+wuKoWAH8IvDvJMxyfMX0E+N7IC8foYQ6tqvntYydwjEYkeQHwPuD1NBfZfx5wjePT6PnezKf5Gb8WOBtm9jvkyQ7TqL0P7G3ArlV1Zdv2aeDGqjpiqMUNWZJ3A0+qqoPb14cAB1fVH7SvN6O5vckeVXXF0AqdJZJcDhwLPA7H6SGS7ARcCPwNsAWOzwOSHAgcAPwY2LGqXuO/tQcluRA4o6pOGdXuGAFJ/gM4tapOHdXu+IyS5CDgGGCHqqqZHCNn5KbXU4D7RkJc6zJgfZ6RG88uNGMDPHB9wKtxrEiyNc13aTmO0wOSfDTJXcAVwE3Av+L4PCDJAuCdwGGjuhyjh3pvkluSfDvJPm3bej9GSTYE9gQWJrkqyQ1JTkzyaByfsRwEfKoenB2bsTEyyE2vvu8DK8dqLEk2Aj4DnN7+Fuc4tarqL2m2ey+aXRj34Pj0ehfNbMr1o9odowe9Hdge2IbmAq5fTrIDjhE0uwo3Al5J829sd2AP4Egcn4dIsh3NoTCn9zTP2BgZ5KaX94Htn2M1SpINaO5Qci9waNvsOPWoqvuq6mLgScBf4PgAkGR34PnAB8fodoxaVXVJVa2uqnuq6nTg28BLcIygOd4L4MNVdVNV3QJ8AMdnLK8DLq6qa3vaZmyMDHLTy/vA9m85zdgADxxPsAPr6VglCXAqzW/FS6rq122X4zS2eTw4Do4P7AMsBn6e5GbgbcCSJN/HMVqXAoJjRFXdBtxAMyajrffjM8rreOhsHMzgGBnkplGH7gM7Y5LMS7IJsCGwYZJNkswDvgjsmmRJ2380cPn6euAs8DHgqcDLqmptT/t6P05JHt9eEmF+kg2T7Af8KfDvOD4jTqb5obF7+/g4cD6wH44RAEm2SLLfyP9BSV5Nc1bm13CMRnwS+Ov239xjgbcA5+H4PCDJH9Dsmj97VNfMjVFV+ZjGB81px+cCdwI/B/5s2DUNeTyW0vyG1/tY2vY9n+bA9bU0ZyEuHna9QxqjRe243E0zPT/yeLXjVAALgW8CtwN3AD8C3tjTv16PzzhjtpTm7EzH6KHfo+/R7Oq6HfhP4AWO0UPGaCPgo+343Ax8CNjE8XnIGJ0EfHqcvhkZIy8/IkmS1FHuWpUkSeoog5wkSVJHGeQkSZI6yiAnSZLUUQY5SZKkjjLISZIkdZRBTtKskGRFkucP8fO/kuSgPpZb3nNz9UHX8PEkR03Dev8hySmDXq+k4fM6clKHJVkBPBrYvpo7iZDkDcBrqmqfIZY2ae22vKGqLpiBz1oK7FhVr5nuz1pHDQfTbO9zh1XDdBtrnJNcSHNxYoOlNADOyEndNw/4m+n+kPZWap03V7ZjfZVkw2HXIM0mBjmp+/438LYkW4zVmWTnJF9P8qskP03yxz19F7YzeCOvD05ycc/rSvJXSX4G/Kxte2OSq9r1fSnJE0ct/+YkP0tyW5KPJEnbt0OSf09ya5JbknxmvJrH2IbT2t2OX0+yOsk3kyzq6f/HJNcnuSPJpUn26ulbmuTzSc5IcgfwZuAfgD9JsibJZeOMxRuT/KT9vB8neXrb/sAu4J51n9Uu9/0kvTfKPiLJ1T3r+KO2/ak09z/9/baG23u2892japj0WI8xfkuTnNE+X9y+96AkP2//Lt4x6LFP8qLR45zkOGAv4MS27cR22XV9R09L8rEk/5rkTuB/tH8Hb0tyeZJV7fhv0i6/VZLzktzeru+iJP6s05zll1vqvmU09/F72+iOJJsBXwfOBB5Pc3P5jybZZRLrfwXwbOB3kvxP4L3AHwO/BVwH/POo5V8KPBPYrV1uv5Fy2vc+EXgqsC3NPUD79WrgXcBWwA+Bz/T0fY/m5vBb0mzr2SM/2FsvBz4PbAGcCrwHOKuq5lfVboyS5FVtba8DFgB/CNw6Tl0vp7lh9shnn5tko7bvaprg8hjgWOCMJL9VVT+hCZTfaWvYYowapjLW/XgusBOwL3B0Gy7HM+mxr6qvMmqcq+odwEXAoW3boX1+R/8MOA7YHBj5ReOPgRcBvw08DTi4bT8MuIHmXqpb04RJjyHSnGWQk+aGo4G/TrJwVPtLgRVV9cmq+k1VfR/4AvDKSaz7vVX1q6paS/MD/RNV9f2qugf4e5pZpcU9yx9fVbdX1c+Bb9D8kKeqrqqqr1fVPVW1EvgAsPck6ji/qr7Vfu472s/dtl33GVV1a7uNJwAb04SUEd+pqnOr6v52OybyBuD9VfW9alxVVdeNs+ylVfX5qvp1u02bAL/X1nV2Vf2i/dyzaGY1n9Xn9j7ise7TsVW1tqouAy6jCYPjmcrYT6Sf7+i/VNW323G8u237UDu2vwK+zIPb/mua4Luoqn5dVReVB4NrDjPISXNAVf0XcB5wxKiuRcCz291Mt7e78F4NPGESq7++5/kTaWaGRj53Dc1M1TY9y9zc8/wuYD5Akscn+eckN7a7OM+gmeGZdB3t5/6qrYckh7W7QVe12/iYUevu3YZ+bEszmzbZuu6nmQ0aqet1SX7YM/a70v82P+Kx7tNk3juVsZ9IP9/Rsf7+xqv/fwNXAf+W5Joko/9NSHOKB/1Kc8cxwPeBE3rarge+WVUvGOc9dwKb9rweK+D1zmb8guYHL/DArtvHATf2Ud9723U9rapuTfIK4MQ+3jdi257PnU+zK+8X7TFZb6fZRbi8qu5PchvNrtyxtmGs16NdD+zwCOraAHhSW9ci4J/aur5TVfcl+WFPXRPVMJWxHrRHOvZjbePotom+o+OtZ+wFq1bT7F49rN09+40k36uq/9fvOqQucUZOmiOq6irgLOB/9TSfBzwlyWuTbNQ+ntlzPNQPgQOSbJpkR+DPJ/iYM4HXJ9k9ycY0x0BdUlUr+ihxc2ANcHuSbYC/63vjGi9J8twkj6I5XuuSqrq+Xe9vgJXAvCRH0xzXti6/BBav4yD4U2hOIHlGGjv2HuA/yjOSHJDmbNi3APcA/wlsRhNAVgIkeT3NjFxvDU9qt2csUxnrQXukYz/WOP8S2L7n9UTf0UlJ8tL27yvAHcB97UOakwxy0tzyTpoAATwwO/FC4ECaGZ6bgffRHMcE8EHgXpofrqfz0IPYH6ad1TiK5himm2hmrQ7ss7ZjgacDq4DzgXP6fN+IM2lmHX8FPINm9xvA14CvAFfS7Iq8m4l3pZ7d/nlrku+P7qyqs2kOrj8TWA2cSzMLNZZ/Af4EuA14LXBAe2zWj2lmR79DM76/C3y7533/DiwHbk5yyxg1TGWsB+2Rjv1Y4/yPwCvTnGn7oT6+o5P1ZOACml8avgN8tKoufITrkmY9LwgsadZLchpwQ1UdOexaemUWXFh4us3WsZfUcEZOkiSpowxykiRJHeWuVUmSpI5yRk6SJKmjDHKSJEkdZZCTJEnqKIOcJElSRxnkJEmSOsogJ0mS1FEGOUmSpI4yyEmSJHWUQU6SJKmjDHKSJEkdZZCTJEnqKIOcJElSRxnkJEmSOsogJ0mS1FEGOUmSpI6aN+wChmWrrbaqxYsXD7sMSZKkCV166aW3VNXC0e3rbZBbvHgxy5YtG3YZkiRJE0py3Vjt7lqVJEnqKIOcJElSRxnkJEmSOmq9PUZuJiw+4vxH9L4Vx+8/4EokSdJc5IycJElSRxnkJEmSOsogJ0mS1FEGOUmSpI4yyEmSJHWUQU6SJKmjDHKSJEkdZZCTJEnqKIOcJElSRxnkJEmSOsogJ0mS1FEGOUmSpI4yyEmSJHWUQU6SJKmjDHKSJEkdZZCTJEnqKIOcJElSR81IkEuycZJTk1yXZHWSHyR5cU//vkmuSHJXkm8kWdTTlyTvS3Jr+3h/kvT0L27fc1e7jufPxDZJkiQN20zNyM0Drgf2Bh4DHAV8rg1hWwHntG1bAsuAs3reewjwCmA34GnAS4E39fR/FvgB8DjgHcDnkyyczo2RJEmaDWYkyFXVnVW1tKpWVNX9VXUecC3wDOAAYHlVnV1VdwNLgd2S7Ny+/SDghKq6oapuBE4ADgZI8hTg6cAxVbW2qr4A/AhYMhPbJUmSNExDOUYuydbAU4DlwC7AZSN9VXUncHXbzuj+9nlv3zVVtXqc/tGfe0iSZUmWrVy5chCbIkmSNDQzHuSSbAR8Bji9qq4A5gOrRi22Cti8fT66fxUwvz1ObqL3PkRVnVxVe1bVngsXuvdVkiR124wGuSQbAJ8G7gUObZvXAAtGLboAWD1O/wJgTVVVH++VJEmas2YsyLUzaKcCWwNLqurXbddymhMZRpbbDNihbX9Yf/u8t2/7JJuP0y9JkjRnzeSM3MeApwIvq6q1Pe1fBHZNsiTJJsDRwOXtbleATwFvTbJNkicChwGnAVTVlcAPgWOSbJLkj2jObP3CTGyQJEnSMM2biQ9prwv3JuAe4Oaey8C9qao+k2QJcCJwBnAJcGDP208Ctqc5GxXglLZtxIE0we424OfAK6vKMxkkSdKcNyNBrqquA7KO/guAncfpK+Dw9jFW/wpgnykXKUmS1DHeokuSJKmjDHKSJEkdZZCTJEnqKIOcJElSRxnkJEmSOsogJ0mS1FF9B7kkrxqn/ZWDK0eSJEn9msyM3KnjtJ88iEIkSZI0ORNeEDjJ9u3TDZL8Ng+9sO/2wN3TUZgkSZLWrZ87O1wFFE2Au3pU383A0gHXJEmSpD5MGOSqagOAJN+sqr2nvyRJkiT1o+9j5AxxkiRJs0s/u1YBaI+POw7YHZjf21dV2w22LEmSJE2k7yAHnElzjNxhwF3TU44kSZL6NZkgtwvwnKq6f7qKkSRJUv8mcx25bwF7TFchkiRJmpzJzMitAL6W5Byay448oKqOHmRRkiRJmthkgtxmwJeBjYBtp6ccSZIk9avvIFdVr5/OQiRJkjQ5k7n8yPbj9VXVNYMpR5IkSf2azK7V3lt1jaj2zw0HVpEkSZL6Mpldqw85wzXJE4BjgIsGXZQkSZImNpnLjzxEVd0MvAV478CqkSRJUt8ecZBr7QRsOohCJEmSNDmTOdnhIh48Jg6aALcL8M5BFyVJkqSJTeZkh1NGvb4TuKyqfjbAeiRJktSnyZzscPp0FiJJkqTJ6fsYuSQbJTk2yTVJ7m7/PDbJo6azQEmSJI1tMrtW3w88C3gzcB2wCDgKWAD87eBLkyRJ0rpMJsi9Ctitqm5tX/80yfeByzDITavFR5z/iN634vj9B1yJJEmaTSZz+ZFMsl2SJEnTaDJB7mzgy0n2S/LUJC8Czm3bJ5Tk0CTLktyT5LRRffsmuSLJXUm+kWRRT1+SvC/Jre3j/UnS07+4fc9d7TqeP4ltkiRJ6qzJBLnDgQuAjwCXAh8G/h34uz7f/wvg3cAnehuTbAWcQ3O83ZbAMuCsnkUOAV4B7AY8DXgp8Kae/s8CPwAeB7wD+HyShf1vliRJUjdNGOSSPCfJ+6rq3qo6uqp2rKpNq+rJwMbA0/v5oKo6p6rOBW4d1XUAsLyqzq6qu4GlwG5Jdm77DwJOqKobqupG4ATg4La2p7Sff0xVra2qLwA/Apb0U5MkSVKX9TMj9w/At8bp+wbNLNhU7EJzwgQAVXUncHXb/rD+9nlv3zVVtXqcfkmSpDmrnyC3O/DVcfouAJ4xxRrmA6tGta0CNh+nfxUwvz1ObqL3PkSSQ9rj9JatXLlyimVLkiQNVz9BbgEw3kV/N2Kc0DQJa9rPGP2Zq8fpXwCsqarq470PUVUnV9WeVbXnwoUeRidJkrqtnyB3BfDCcfpe2PZPxXKaExkASLIZsEPb/rD+9nlv3/ZJNh+nX5Ikac7qJ8h9EDgpyQFJNgBIskGSA4CPAx/o54OSzEuyCbAhsGGSTZLMA74I7JpkSdt/NHB5VY0ExE8Bb02yTZInAocBpwFU1ZXAD4Fj2vX9Ec2ZrV/opyZJkqQum/DODlV1ZpInAKcDGye5BdgKuJvmbNHP9vlZRwLH9Lx+DXBsVS1NsgQ4ETgDuAQ4sGe5k4Dtac5GBTilbRtxIE2wuw34OfDKqvIAOEmSNOf1dYuuqvpAklOA36e5XtutwHeq6o5+P6iqltJcWmSsvguAncfpK5pr2B0+Tv8KYJ9+65AkSZor+r7XahvavjaNtUiSJGkSJnNnB0mSJM0iBjlJkqSOMshJkiR1lEFOkiSpowxykiRJHWWQkyRJ6iiDnCRJUkcZ5CRJkjrKICdJktRRBjlJkqSOMshJkiR1lEFOkiSpo+YNuwDNjMVHnP+I3rfi+P0HXIkkSRoUZ+QkSZI6yiAnSZLUUQY5SZKkjjLISZIkdZRBTpIkqaMMcpIkSR1lkJMkSeooryOnSfF6dJIkzR7OyEmSJHWUQU6SJKmjDHKSJEkd5TFyGgqPtZMkaeqckZMkSeoog5wkSVJHuWtVneXuWUnS+s4ZOUmSpI5yRk7rPWf2JEld5YycJElSR82JGbkkWwKnAi8EbgH+vqrOHG5VWp8MalbP2UFJ0mTMiSAHfAS4F9ga2B04P8llVbV8qFVJQzKIQGiolKTZr/NBLslmwBJg16paA1yc5EvAa4EjhlqcpKGGykGtZzpmTmdTUB7U+EqaeamqYdcwJUn2AP6jqh7d0/Y2YO+qetmoZQ8BDmlf7gT8dMYKXT9sRbNrW9PD8Z1eju/0cnynl+M7vWbD+C6qqoWjGzs/IwfMB1aNalsFbD56wao6GTh5JopaHyVZVlV7DruOucrxnV6O7/RyfKeX4zu9ZvP4zoWzVtcAC0a1LQBWD6EWSZKkGTMXgtyVwLwkT+5p2w3wRAdJkjSndT7IVdWdwDnAO5NsluQ5wMuBTw+3svWSu62nl+M7vRzf6eX4Ti/Hd3rN2vHt/MkO8MB15D4BvAC4FTjC68hJkqS5bk4EOUmSpPVR53etSpIkra8McpIkSR1lkNOUJbkwyd1J1rQPL7Q8BUkOTbIsyT1JThvVt2+SK5LcleQbSRYNqczOGm98kyxOUj3f4zVJjhpiqZ2TZOMkpya5LsnqJD9I8uKefr+/U7Cu8fX7OzhJzkhyU5I7klyZ5A09fbPuO2yQ06AcWlXz28dOwy6m434BvJvmBJ4HJNmK5gzto4AtgWXAWTNeXfeNOb49tuj5Lr9rBuuaC+YB1wN7A4+h+a5+rg0Zfn+nbtzx7VnG7+/UvRdYXFULgD8E3p3kGbP1OzwX7uwgzSlVdQ5Akj2BJ/V0HQAsr6qz2/6lwC1Jdq6qK2a80I5ax/hqitrLQS3taTovybXAM4DH4fd3SiYY30uHUtQcVFW916Gt9rEDzTjPuu+wM3IalPcmuSXJt5PsM+xi5qhdgMtGXrT/qV/dtmtwrktyQ5JPtr+B6xFKsjXwFJoLtPv9HbBR4zvC7+8AJPlokruAK4CbgH9lln6HDXIahLcD2wPb0Fw08ctJdhhuSXNS3/cV1iNyC/BMYBHNb96bA58ZakUdlmQjmvE7vZ2t8Ps7QGOMr9/fAaqqv6QZw71odqfewyz9DhvkNGVVdUlVra6qe6rqdODbwEuGXdcc5H2Fp1FVramqZVX1m6r6JXAo8MIko8dcE0iyAc3dde6lGUfw+zswY42v39/Bq6r7qupimkMw/oJZ+h02yGk6FJBhFzEHLae5jzAASTajOW7D+wpPj5GrpftdnoQkAU4FtgaWVNWv2y6/vwOwjvEdze/v4Mzjwe/qrPsOG+Q0JUm2SLJfkk2SzEvyauB5wNeGXVtXteO4CbAhsOHI2AJfBHZNsqTtPxq43APFJ2e88U3y7CQ7JdkgyeOADwEXVtXoXSlat48BTwVeVlVre9r9/g7GmOPr93cwkjw+yYFJ5ifZMMl+wJ8C/84s/Q57iy5NSZKFNAeB7gzcR3Ng6FFV9fWhFtZh7ZlQx4xqPraqliZ5PnAizXEwlwAHV9WKma2w28YbX+CnwHuAxwN3AF8HDq+qm2e0wA5rr6m1guZ4ot/0dL2pqj7j93dq1jW+wP34/Z2y9mfa52lm3jYArgM+VFX/1PbPuu+wQU6SJKmj3LUqSZLUUQY5SZKkjjLISZIkdZRBTpIkqaMMcpIkSR1lkJMkSeoog5wkSVJHGeQkTbskK5KsTbImyS+TfDLJ/D7ed2GSN4xqqyQ7Tl+1ktQdBjlJM+VlVTUfeDrwTODImS6gvdVZZ6Sxwai2Tm2DpOllkJM0o6rqRuArNPcsfGyS85KsTHJb+/xJAEmOA/YCTmxn8k5M8q12NZe1bX/SLvvSJD9McnuS/0jytJHPa2cD357kcuDOJDu2s3oHJfl5kluSvGO8epM8Jsmn2hqvS3Jkb7hK8sYkP0myOsmPkzy9bd82yTnt+25NcmLbvjTJGT3vX9zWM699fWGS45J8G7gL2L7t/6skPwN+1uc2vy3J5UlWJTmrvTfkSP/L2/fekeTqJC/q2dZTk9yU5MYk706yYdu3Y5Jvtuu7JclZk/7LlzRwBjlJMyrJtsBLgB/Q/B/0SZr7Fm4HrKW5jyFV9Q7gIuDQqppfVYdW1fPa1ezWtp3VBqdP0Nxv8nHAScCXkmzc87F/CuwPbMGD96h8LrATsC9wdJKnjlPyh4HHANsDewOvA17fbsurgKVt2wLgD4Fb2/BzHs19GhcD2wD/PIlhei1wCLB5uw6AVwDPBn6nz23+Y+BFwG8DTwMObmt+FvAp4O/a8Xgezf07AU6nGZ8dgT2AFwIju7bfBfwb8FjgSe24SBoyg5ykmXJuktuBi4FvAu+pqlur6gtVdVdVrQaOowlLk/FG4KSquqSq7quq02luKv57Pct8qKqur6q1PW3HVtXaqroMuIzmJtkP0QayPwH+vqpWtzfHPoEmaEETct5fVd+rxlVVdR3wLOCJwN9V1Z1VdXdVXTyJbTqtqpZX1W+q6tdt23ur6lftNvS7zb+oql8BXwZ2b9v/HPhEVX29qu6vqhur6ookWwMvBt7S1vzfwAeBA9v3/ZomcD/xEWyPpGlikJM0U15RVVtU1aKq+suqWptk0yQntbss7wC+BWwxsjuvT4uAw9pdjLe3YXFbmiA14vox3ndzz/O7gLFOvtgKeBQPzorRPt+mfb4tcPUY79sWuK6qfjNGXz/Gqre3rZ9tHm/7xqt5EbARcFPPOk8CHt/2Hw4E+G6S5Un+v8ltkqTp4EGzkobpMJrdm8+uqpuT7E6zyzVtf/WxjuuB46rquHUs0896xnILD85E/bht2w64seezdxinpu2SzBsjzN0JbNrz+gl91tvb1s82j2ddNd8DbDVWAK2qm2lmAknyXOCCJN+qqqseQQ2SBsQZOUnDtDnNcXG3J9kSOGZU/y9pjk1bV9s/AW9O8uz2LM/NkuyfZPOpFldV9wGfA45LsnmSRcBbgZGTFU4B3pbkGe1n79gu813gJuD4tp5Nkjynfc8Pgecl2S7JY4C/fwSlTWWbTwVen2TfJBsk2SbJzlV1E80xcCckWdD27ZBkb2iOB0x7IgpwG02wvO8R1C5pgAxykobp/wKPppn5+k/gq6P6/xF4ZZozWj/Uti0FTm93//1xVS2jmSk6kSZgXEV7YP+A/DXNLNo1NMf3nUlzogFVdTbNcX1nAquBc4Et2wD4MpqTBn4O3EBzrB1V9XXgLOBy4FKakyImZSrbXFXfpTlZ44PAKprjFRe13a+j2ZX843a9nwd+q+17JnBJkjXAl4C/qaprJ1u7pMFK1SPd4yBJkqRhckZOkiSpowxykiRJHWWQkyRJ6iiDnCRJUkcZ5CRJkjrKICdJktRRBjlJkqSOMshJkiR1lEFOkiSpowxykiRJHWWQkyRJ6iiDnCRJUkcZ5CRJkjrKICdJktRRBjlJkqSOMshJkiR1lEFOkiSpowxykiRJHTVv2AUMy1ZbbVWLFy8edhmSJEkTuvTSS2+pqoWj29fbILd48WKWLVs27DIkSZImlOS6sdrdtSpJktRRBjlJkqSOMshJkiR11Hp7jJxml8VHnD/sEmbMiuP3H3YJkqQ5whk5SZKkjjLISZIkdZRBTpIkqaMMcpIkSR1lkJMkSeoog5wkSVJHGeQkSZI6yiAnSZLUUQY5SZKkjjLISZIkdZRBTpIkqaMMcpIkSR1lkJMkSeoog5wkSVJHGeQkSZI6yiAnSZLUUQY5SZKkjjLISZIkdZRBTpIkqaMMcpIkSR1lkJMkSeoog5wkSVJHGeQkSZI6akaCXJKNk5ya5Lokq5P8IMmLe/r3TXJFkruSfCPJop6+JHlfklvbx/uTpKd/cfueu9p1PH8mtkmSJGnYZmpGbh5wPbA38BjgKOBzbQjbCjinbdsSWAac1fPeQ4BXALsBTwNeCrypp/+zwA+AxwHvAD6fZOF0bowkSdJsMCNBrqrurKqlVbWiqu6vqvOAa4FnAAcAy6vq7Kq6G1gK7JZk5/btBwEnVNUNVXUjcAJwMECSpwBPB46pqrVV9QXgR8CSmdguSZKkYRrKMXJJtgaeAiwHdgEuG+mrqjuBq9t2Rve3z3v7rqmq1eP0j/7cQ5IsS7Js5cqVg9gUSZKkoZnxIJdkI+AzwOlVdQUwH1g1arFVwObt89H9q4D57XFyE733Iarq5Kras6r2XLjQva+SJKnbZjTIJdkA+DRwL3Bo27wGWDBq0QXA6nH6FwBrqqr6eK8kSdKcNWNBrp1BOxXYGlhSVb9uu5bTnMgwstxmwA5t+8P62+e9fdsn2XycfkmSpDlr3gx+1seApwLPr6q1Pe1fBP53kiXA+cDRwOXtbleATwFvTfKvQAGHAR8GqKork/wQOCbJkcCLac5snRUnOyw+4vxhlzCjVhy//7BLkCRpvTIjQa69LtybgHuAm3suA/emqvpMG+JOBM4ALgEO7Hn7ScD2NGejApzSto04EDgNuA34OfDKqvJMBkmSNOfNSJCrquuArKP/AmDncfoKOLx9jNW/AthnykVKkiR1TN/HyCV51TjtrxxcOZIkSerXZE52OHWc9pMHUYgkSZImZ8Jdq0m2b59ukOS3eegu0u2Bu6ejMEmSJK1bP8fIXUVztmho7rjQ62aaW2pJkiRphk0Y5KpqA4Ak36yqvae/JEmSJPWj72PkDHGSJEmzS9+XH2mPjzsO2J3mHqcPqKrtBluWJEmSJjKZ68idSXOM3GHAXdNTjiRJkvo1mSC3C/Ccqrp/uoqRJElS/yZzHblvAXtMVyGSJEmanMnMyK0AvpbkHJrLjjygqo4eZFGSJEma2GSC3GbAl4GNgG2npxxJkiT1q+8gV1Wvn85CJEmSNDmTufzI9uP1VdU1gylHkiRJ/ZrMrtXeW3WNqPbPDQdWkSRJkvoymV2rDznDNckTgGOAiwZdlCRJkiY2mcuPPERV3Qy8BXjvwKqRJElS3x5xkGvtBGw6iEIkSZI0OZM52eEiHjwmDpoAtwvwzkEXJUmSpIlN5mSHU0a9vhO4rKp+NsB6JEmS1KfJnOxw+nQWIkmSpMnp+xi5JBslOTbJNUnubv88NsmjprNASZIkjW0yu1bfDzwLeDNwHbAIOApYAPzt4EuTJEnSukwmyL0K2K2qbm1f/zTJ94HLMMhJkiTNuMlcfiSTbJckSdI0mkyQOxv4cpL9kjw1yYuAc9t2SZIkzbDJ7Fo9HDgS+AjwROBG4LPAu6ehLkmSJE1gwhm5JM9J8r6qureqjq6qHatq06p6MrAx8PTpL1OSJEmj9bNr9R+Ab43T9w3gHf18UJJDkyxLck+S00b17ZvkiiR3JflGkkU9fUnyviS3to/3J0lP/+L2PXe163h+P/VIkiR1XT9Bbnfgq+P0XQA8o8/P+gXNbthP9DYm2Qo4h+ZSJlsCy4CzehY5BHgFsBvwNOClwJt6+j8L/AB4HE2o/HyShX3WJEmS1Fn9BLkFwHgX/d0I2LyfD6qqc6rqXODWUV0HAMur6uyquhtYCuyWZOe2/yDghKq6oapuBE4ADgZI8hSaXbvHVNXaqvoC8CNgST81SZIkdVk/Qe4K4IXj9L2w7Z+KXWiuRQdAVd0JXN22P6y/fd7bd01VrR6n/yGSHNLu3l22cuXKKZYtSZI0XP0EuQ8CJyU5IMkGAEk2SHIA8HHgA1OsYT6walTbKh6c6RvdvwqY3x4nN9F7H6KqTq6qPatqz4UL3fsqSZK6bcLLj1TVmUmeAJwObJzkFmAr4G6aXZqfnWINa2h23/ZaAKwep38BsKaqKslE75UkSZqz+rqOXFV9IMkpwO/TnFRwK/CdqrpjADUspzkODoAkmwE7tO0j/bsB321f7zaqb/skm/fsXt0NOHMAdUmSJM1qfd/ZoaruqKqvVdWZ7Z+TCnFJ5iXZBNgQ2DDJJknmAV8Edk2ypO0/Gri8qkaOvfsU8NYk2yR5InAYcFpb05XAD4Fj2vX9Ec2ZrV+YTG2SJEldNJlbdE3VkcBa4AjgNe3zI6tqJc1ZpscBtwHPBg7sed9JwJdpzkb9L+D8tm3EgcCe7XuPB17ZrlOSJGlOm8wtuqakqpbSXFpkrL4LgJ3H6Sua24MdPk7/CmCfAZQoSZLUKTM5IydJkqQBMshJkiR1lEFOkiSpowxykiRJHWWQkyRJ6iiDnCRJUkfN2OVHJE3d4iPOH3YJM2rF8fsPuwRJmtWckZMkSeoog5wkSVJHGeQkSZI6yiAnSZLUUQY5SZKkjjLISZIkdZRBTpIkqaMMcpIkSR1lkJMkSeoog5wkSVJHGeQkSZI6yiAnSZLUUQY5SZKkjjLISZIkdZRBTpIkqaMMcpIkSR1lkJMkSeoog5wkSVJHGeQkSZI6yiAnSZLUUQY5SZKkjpo37AIkaTosPuL8YZcwo1Ycv/+wS5A0BHNiRi7Jlkm+mOTOJNcl+bNh1yRJkjTd5sqM3EeAe4Gtgd2B85NcVlXLh1qVJEnSNOp8kEuyGbAE2LWq1gAXJ/kS8FrgiKEWJ0mznLugpW5LVQ27hilJsgfwH1X16J62twF7V9XLRi17CHBI+3In4KczVujM2gq4ZdhFdIRj1R/HqX+OVX8cp/45Vv2Z6+O0qKoWjm7s/IwcMB9YNaptFbD56AWr6mTg5JkoapiSLKuqPYddRxc4Vv1xnPrnWPXHceqfY9Wf9XWc5sLJDmuABaPaFgCrh1CLJEnSjJkLQe5KYF6SJ/e07QZ4ooMkSZrTOh/kqupO4BzgnUk2S/Ic4OXAp4db2VDN+d3HA+RY9cdx6p9j1R/HqX+OVX/Wy3Hq/MkO0FxHDvgE8ALgVuCIqjpzuFVJkiRNrzkR5CRJktZHnd+1KkmStL4yyEmSJHWUQW6OSLJxklPbe82uTvKDJC8edl2zVZIzktyU5I4kVyZ5w7Brms2SPDnJ3UnOGHYts1WSC9sxWtM+5uoFx6csyYFJftLeH/vqJHsNu6bZpud7NPK4L8mHh13XbJVkcZJ/TXJbkpuTnJhkLlwrd0IGubljHnA9sDfwGOAo4HNJFg+zqFnsvcDiqloA/CHw7iTPGHJNs9lHgO8Nu4gOOLSq5rePnYZdzGyU5AXA+4DX01y4/XnANUMtahbq+R7Np7mP+Frg7CGXNZt9FPhv4Ldo7rm+N/CXwyxophjk5oiqurOqllbViqq6v6rOA64FDCdjqKrlVXXPyMv2scMQS5q1khwI3A78vyGXornhWOCdVfWf7f9VN1bVjcMuapZ7JU1IuWjYhcxivw18rqrurqqbga8Cuwy5phlhkJujkmwNPAUvjDyuJB9NchdwBXAT8K9DLmnWSbIAeCdw2LBr6Yj3JrklybeT7DPsYmabJBsCewILk1yV5IZ2F9ijJ3rveu4g4FPlZSbW5R+BA5NsmmQb4MU0YW7OM8jNQUk2Aj4DnF5VVwy7ntmqqv6SZtfOXjQXlb5n3e9YL70LOLWqrh92IR3wdmB7YBuaC5N+OYmzvA+1NbARzQzTXjS7wPYAjhxiTbNaku1odhOePuxaZrlv0szA3QHcACwDzh1mQTPFIDfHJNmA5q4W9wKHDrmcWa+q7quqi4EnAX8x7HpmkyS7A88HPjjkUjqhqi6pqtVVdU9VnQ58G3jJsOuaZda2f364qm6qqluAD+A4rcvrgIur6tphFzJbtT/3vkbzC/lmwFbAY2mOxZzzDHJzSJIAp9L81rukqn495JK6ZB4eIzfaPsBi4OdJbgbeBixJ8v1hFtUhBWTYRcwmVXUbzWyJuwj79zqcjZvIlsC2wIntL1K3Ap9kPfkFwSA3t3wMeCrwsqpaO9HC66skj28vfzA/yYZJ9gP+FPj3Ydc2y5xME253bx8fB84H9hteSbNTki2S7JdkkyTzkrya5mzMrw27tlnok8Bft/8OHwu8BThvuCXNTkn+gGZXvWerrkM7s3st8Bftv78taI4rvGyohc0Qg9wckWQR8CaaH7g391x76NXDrWxWKprdqDcAtwH/B3hLVf3LUKuaZarqrqq6eeQBrAHurqqVw65tFtoIeDewErgF+GvgFVXlteQe7l00l7K5EvgJ8APguKFWNHsdBJxTVauHXUgHHAC8iObf4FXAb4C/HWpFM8R7rUqSJHWUM3KSJEkdZZCTJEnqKIOcJElSRxnkJEmSOsogJ0mS1FEGOUmSpI4yyEnSLJRkryReh07SOhnkJHVekhVJ1rYXwf5lkk8mmd/H+y5M8oZRbZVkx+mrtj9VdVFV7TTsOiTNbgY5SXPFy6pqPvB04JnAkTNdQJJ5M/2ZktZvBjlJc0pV3Qh8Bdg1yWOTnJdkZZLb2udPAkhyHLAXcGI7k3dikm+1q7msbfuTdtmXJvlhktuT/EeSp418Xjsb+PYklwN3JtmxndU7KMnPk9yS5B3j1ZvkJUl+nGR1khuTvK1t3yfJDe3zP+m57d6aJPckubDt2zjJ/2k/65dJPp7k0QMfWEmzkkFO0pySZFvgJTT38NyA5ibti4DtgLXAiQBV9Q7gIuDQqppfVYdW1fPa1ezWtp2V5OnAJ2juZfw44CTgS0k27vnYPwX2B7aguccjwHOBnYB9gaOTPHWckk8F3lRVmwO7Av8+eoGqOqutZz7wROAa4LNt9/uAp9DcZ3lHmpusH93HUEmaAwxykuaKc5PcDlwMfBN4T1XdWlVfqKq72huPHwfsPcn1vhE4qaouqar7qup04B7g93qW+VBVXV9Va3vajq2qtVV1GXAZsNs46/818DtJFlTVbVX1/fEKSbIBcCZwYVWdlCRtfX9bVb9qt/E9wIGT3EZJHWWQkzRXvKKqtqiqRVX1l1W1NsmmSU5Kcl2SO4BvAVsk2XAS610EHNbuVr29DYvb0syMjbh+jPfd3PP8LmC8ky+W0MwgXpfkm0l+fx21HAdsDvyv9vVCYFPg0p7avtq2S1oPeGCupLnsMJrdm8+uqpuT7E6zyzVtf/WxjuuB46rquHUs0896xn5j1feAlyfZCDgU+BxNUHyIJAfS7MJ9ZlX9um2+hWZ38S7tsYGS1jPOyEmayzanCTq3J9kSOGZU/y+B7Sdo+yfgzUmencZmSfZPsvlUi0vyqCSvTvKYNpzdAdw3xnJ7AB+mmXVcOdJeVfe39X0wyePbZbdJst9Ua5PUDQY5SXPZ/wUeTTNz9Z80ux17/SPwyvaM1g+1bUuB09tdlX9cVctojkM7EbgNuAo4eIA1vhZY0e76fTPwmjGWeTnwWODinjNXv9L2vb2t6T/bdVxAMwspaT2Qqke8R0CSJElD5IycJElSRxnkJEmSOsogJ0mS1FEGOUmSpI4yyEmSJHWUQU6SJKmjDHKSJEkdZZCTJEnqqP8fGBMnWrvb3PEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viziphant.patterns.plot_patterns_statistics_all(patterns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "079c0771",
   "metadata": {},
   "source": [
    "# References\n",
    "1. Grün et al (2002a) DOI: 10.1162/089976602753284455\n",
    "2. Grün et al (2002b) DOI: 10.1162/089976602753284464\n",
    "3. Grün et al (2010) DOI: 10.1007/978-1-4419-5675-0_10\n",
    "4. Torre et al (2013) DOI: 10.3389/fncom.2013.00132\n",
    "5. Stella et al (2019) DOI: 10.1016/j.biosystems.2019.104022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "633369d0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}