Attempt to recreate figure 3 from Maheswaranathan et al, 2018

cell07.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows (1) a fit using the default of just two optimization alternations and (2) another fit with 15 alternations. Both are done with regularization weights of 1.25 for sparsity and 1.7 for nuclear norm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "from matplotlib import pyplot as plt\n",
    "from nems import models\n",
    "import numpy as np\n",
    "\n",
    "with h5py.File('rgc_whitenoise.h5', 'r') as f:\n",
    "    stimulus = np.array(f['stimulus'])\n",
    "    rates = np.array(f['firing_rates'])\n",
    "stim_dim = stimulus.shape[0]\n",
    "hist_dim = 40\n",
    "cell = 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Fitting with only 2 optimization alternations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 965x497 with 17 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lnln = models.LNLN(stimulus, rates[cell], (stim_dim, hist_dim), num_subunits=4)\n",
    "lnln.add_regularizer('W', 'sparse', gamma=1.25)\n",
    "lnln.add_regularizer('W', 'nucnorm', gamma=1.7)\n",
    "lnln.fit(disp=0)\n",
    "\n",
    "# Removed code for plotting..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the figure above the nonlinearity of the first subunit dips below zero and the third subunit filter is flipped in sign and its nonlinearity is reversed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Fitting with 15 optimization alternations\n",
    "Below is the code I used to fit the LNLN model (including the stdout output of the fitting function) followed by a figure showing the results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During fitting of the filter the test/train scores drop quite severly at times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 48/48 [00:00<00:00, 152059.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting random seed to: 4701\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│      Initial parameters      │\n",
      "╘══════════════════════════════╛\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │    0.00425 │      -1.84 │       17.1 │    -0.0418 │\n",
      "│      Train │   0.000812 │      -1.96 │       17.2 │     -0.046 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│         4.71 │        2.633 │        388.5 │            5 │\n",
      "│        9.343 │        2.754 │        14.08 │            5 │\n",
      "│         13.9 │        2.633 │        10.86 │            5 │\n",
      "│        18.42 │        2.265 │        20.33 │            5 │\n",
      "│        22.96 │         2.73 │        61.41 │          2.5 │\n",
      "│        27.49 │        3.908 │        11.04 │          2.5 │\n",
      "│         33.1 │        3.314 │         4.75 │          2.5 │\n",
      "│        40.53 │        51.95 │        245.2 │          2.5 │\n",
      "│        45.91 │        35.87 │        126.1 │          2.5 │\n",
      "│        51.63 │        43.03 │        213.6 │          2.5 │\n",
      "│        57.53 │        35.61 │        186.6 │          2.5 │\n",
      "│        63.52 │        31.31 │        147.8 │          2.5 │\n",
      "│        69.57 │         31.9 │        148.7 │          2.5 │\n",
      "│        76.51 │        34.92 │        170.2 │          2.5 │\n",
      "│        83.16 │        31.22 │          145 │          2.5 │\n",
      "│        89.86 │        29.84 │        150.2 │          2.5 │\n",
      "│        96.52 │        37.12 │        182.3 │          2.5 │\n",
      "│        102.5 │        37.85 │        175.7 │          2.5 │\n",
      "│        109.9 │        32.77 │        148.6 │          2.5 │\n",
      "│        116.1 │        35.81 │        176.1 │          2.5 │\n",
      "│        122.4 │        36.91 │        180.5 │          2.5 │\n",
      "│        128.3 │         34.7 │        164.7 │          2.5 │\n",
      "│        133.8 │        35.87 │        172.4 │          2.5 │\n",
      "│        140.3 │        30.81 │          136 │          2.5 │\n",
      "│        146.2 │        41.83 │        208.6 │          2.5 │\n",
      "│        152.7 │        32.34 │        149.4 │          2.5 │\n",
      "│        159.2 │        33.38 │        157.8 │          2.5 │\n",
      "│        165.6 │        39.16 │        199.2 │          2.5 │\n",
      "│        172.9 │        31.84 │        143.4 │          2.5 │\n",
      "│        178.7 │        37.36 │          180 │          2.5 │\n",
      "│        184.7 │        33.04 │        155.9 │          2.5 │\n",
      "│        190.5 │        35.09 │        168.1 │          2.5 │\n",
      "│        196.3 │        38.13 │        184.4 │          2.5 │\n",
      "│        202.2 │         37.2 │        178.9 │          2.5 │\n",
      "│        209.2 │        29.69 │        135.1 │          2.5 │\n",
      "│        215.2 │         34.6 │        172.6 │          2.5 │\n",
      "│        221.4 │        38.03 │        186.9 │          2.5 │\n",
      "│        227.7 │        36.52 │        170.2 │          2.5 │\n",
      "│        233.6 │        37.06 │        175.3 │          2.5 │\n",
      "│        240.1 │        30.48 │        139.2 │          2.5 │\n",
      "│        240.1 │        30.48 │        139.2 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 89.9506 ms\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │     0.0353 │      -1.94 │         17 │    -0.0345 │\n",
      "│      Train │     0.0363 │      -2.18 │       17.2 │    -0.0377 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.507 │            0 │        143.3 │            5 │\n",
      "│        5.022 │            0 │        24.85 │          2.5 │\n",
      "│        7.599 │            0 │        7.491 │         1.25 │\n",
      "│        10.11 │            0 │        2.525 │        0.625 │\n",
      "│        12.74 │            0 │       0.8036 │       0.3125 │\n",
      "│        15.45 │            0 │       0.2298 │       0.1562 │\n",
      "│        18.16 │            0 │      0.03907 │      0.07812 │\n",
      "│        20.73 │            0 │      0.01742 │      0.03906 │\n",
      "│        23.33 │            0 │     0.004835 │      0.01953 │\n",
      "│        25.94 │            0 │     0.001285 │     0.009766 │\n",
      "│        28.52 │            0 │    0.0003423 │     0.004883 │\n",
      "│        28.52 │            0 │    0.0003423 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 28.5216 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │     0.0303 │    -0.0315 │       16.7 │   -0.00337 │\n",
      "│      Train │     0.0816 │     0.0368 │       16.8 │    0.00368 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        4.742 │        2.494 │          350 │            5 │\n",
      "│        9.399 │        2.388 │        41.47 │          2.5 │\n",
      "│        14.18 │        2.709 │        7.412 │          2.5 │\n",
      "│        19.18 │        2.509 │        2.814 │          2.5 │\n",
      "│        23.87 │        2.232 │        7.152 │          2.5 │\n",
      "│        29.04 │        2.269 │        6.129 │          2.5 │\n",
      "│        34.29 │        2.104 │         5.37 │          2.5 │\n",
      "│        39.48 │        2.022 │        5.826 │          2.5 │\n",
      "│        45.58 │        2.018 │        5.418 │          2.5 │\n",
      "│        51.31 │        2.081 │        5.321 │          2.5 │\n",
      "│        57.44 │         1.93 │        5.148 │          2.5 │\n",
      "│        64.06 │        2.036 │        5.824 │          2.5 │\n",
      "│        69.51 │        2.279 │        7.176 │          2.5 │\n",
      "│        75.82 │        2.476 │        7.036 │          2.5 │\n",
      "│        82.35 │        2.638 │        9.021 │          2.5 │\n",
      "│        88.58 │        2.948 │        11.47 │          2.5 │\n",
      "│        93.87 │        3.344 │        14.22 │          2.5 │\n",
      "│        98.98 │        3.216 │        12.78 │          2.5 │\n",
      "│        105.7 │        3.185 │        13.25 │          2.5 │\n",
      "│        111.9 │        3.087 │         10.8 │          2.5 │\n",
      "│        118.1 │        3.274 │        11.44 │          2.5 │\n",
      "│        124.4 │        3.306 │         10.4 │          2.5 │\n",
      "│        130.5 │        3.506 │        12.92 │          2.5 │\n",
      "│        136.2 │        3.737 │        14.31 │          2.5 │\n",
      "│        141.3 │        3.746 │        16.65 │          2.5 │\n",
      "│        146.9 │        3.744 │         16.5 │          2.5 │\n",
      "│        153.5 │        3.654 │        15.84 │          2.5 │\n",
      "│        159.6 │        3.838 │        16.34 │          2.5 │\n",
      "│        165.1 │        3.625 │        15.34 │          2.5 │\n",
      "│        170.7 │        3.434 │        13.92 │          2.5 │\n",
      "│        176.6 │        3.463 │        13.56 │          2.5 │\n",
      "│        182.5 │        3.574 │        14.01 │          2.5 │\n",
      "│        187.9 │        3.558 │        14.59 │          2.5 │\n",
      "│        193.4 │        3.432 │        12.11 │          2.5 │\n",
      "│        198.6 │        3.358 │        12.26 │          2.5 │\n",
      "│        204.6 │        3.216 │        10.45 │          2.5 │\n",
      "│        210.9 │        3.375 │        11.96 │          2.5 │\n",
      "│          217 │        3.371 │        11.37 │          2.5 │\n",
      "│        222.8 │        3.285 │        11.87 │          2.5 │\n",
      "│        229.1 │        3.288 │        10.25 │          2.5 │\n",
      "│        229.1 │        3.288 │        10.25 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 3 min., 49.09 s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.318 │      0.389 │       16.3 │      0.049 │\n",
      "│      Train │      0.378 │      0.464 │       16.3 │     0.0615 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.552 │            0 │        52.07 │            5 │\n",
      "│        5.062 │            0 │        18.76 │          2.5 │\n",
      "│        7.584 │            0 │         8.38 │         1.25 │\n",
      "│        10.15 │            0 │        3.206 │        0.625 │\n",
      "│        12.67 │            0 │        1.047 │       0.3125 │\n",
      "│        15.19 │            0 │       0.3314 │       0.1562 │\n",
      "│        17.71 │            0 │       0.1034 │      0.07812 │\n",
      "│        20.22 │            0 │      0.02545 │      0.03906 │\n",
      "│        22.94 │            0 │     0.002818 │      0.01953 │\n",
      "│        25.45 │            0 │     0.003261 │     0.009766 │\n",
      "│        27.98 │            0 │    0.0007498 │     0.004883 │\n",
      "│        27.98 │            0 │    0.0007498 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 27.9788 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.405 │      0.953 │       15.3 │      0.158 │\n",
      "│      Train │      0.469 │       1.17 │       14.9 │      0.213 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        4.778 │        2.266 │        291.8 │            5 │\n",
      "│        9.554 │         2.52 │        40.96 │          2.5 │\n",
      "│        14.72 │        2.601 │        1.763 │          2.5 │\n",
      "│        19.44 │        2.557 │        3.147 │          2.5 │\n",
      "│        24.24 │        2.311 │         4.32 │          2.5 │\n",
      "│        29.14 │        2.266 │        3.283 │          2.5 │\n",
      "│        34.22 │        2.186 │        3.339 │          2.5 │\n",
      "│        40.13 │        2.034 │        4.188 │          2.5 │\n",
      "│        46.94 │        1.885 │        4.795 │          2.5 │\n",
      "│        52.55 │        1.935 │        4.506 │          2.5 │\n",
      "│        59.11 │        1.929 │        4.522 │          2.5 │\n",
      "│        65.27 │        1.775 │        4.755 │          2.5 │\n",
      "│        70.59 │        1.728 │        3.968 │          2.5 │\n",
      "│        78.15 │        1.658 │        3.519 │          2.5 │\n",
      "│        85.29 │        1.599 │        3.417 │          2.5 │\n",
      "│        91.42 │        1.647 │        3.847 │          2.5 │\n",
      "│        96.92 │        1.583 │        3.522 │          2.5 │\n",
      "│        103.3 │        1.593 │        3.202 │          2.5 │\n",
      "│          110 │        1.591 │        4.038 │          2.5 │\n",
      "│        115.6 │        1.515 │        4.341 │          2.5 │\n",
      "│        122.4 │        1.473 │        4.282 │          2.5 │\n",
      "│        129.3 │        1.844 │        6.326 │          2.5 │\n",
      "│        136.2 │        1.838 │         7.63 │          2.5 │\n",
      "│        141.7 │        2.185 │        9.268 │          2.5 │\n",
      "│        146.9 │        1.966 │        7.641 │          2.5 │\n",
      "│        152.1 │        1.781 │        5.384 │          2.5 │\n",
      "│        158.4 │        1.818 │        3.725 │          2.5 │\n",
      "│        165.1 │        1.935 │        4.727 │          2.5 │\n",
      "│        170.5 │        1.914 │        5.629 │          2.5 │\n",
      "│        176.2 │        1.956 │         4.62 │          2.5 │\n",
      "│        181.5 │        1.904 │        5.009 │          2.5 │\n",
      "│        187.4 │        1.868 │        4.049 │          2.5 │\n",
      "│        193.1 │        1.929 │        3.966 │          2.5 │\n",
      "│        199.7 │        2.014 │        4.536 │          2.5 │\n",
      "│        205.9 │        2.151 │        5.449 │          2.5 │\n",
      "│        211.6 │        2.279 │        6.978 │          2.5 │\n",
      "│        217.3 │        2.249 │        5.808 │          2.5 │\n",
      "│        223.2 │        2.244 │        5.248 │          2.5 │\n",
      "│          229 │         2.25 │         5.51 │          2.5 │\n",
      "│        235.5 │         2.37 │        6.274 │          2.5 │\n",
      "│        235.5 │         2.37 │        6.274 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 3 min., 55.467 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.433 │       1.13 │       15.1 │      0.179 │\n",
      "│      Train │      0.479 │       1.32 │       14.9 │      0.216 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.644 │            0 │        27.34 │            5 │\n",
      "│        5.187 │            0 │        8.847 │          2.5 │\n",
      "│        7.848 │            0 │        3.604 │         1.25 │\n",
      "│        10.52 │            0 │        1.368 │        0.625 │\n",
      "│        13.18 │            0 │       0.4737 │       0.3125 │\n",
      "│        15.73 │            0 │       0.1897 │       0.1562 │\n",
      "│        18.29 │            0 │      0.03828 │      0.07812 │\n",
      "│        20.98 │            0 │      0.01835 │      0.03906 │\n",
      "│        23.58 │            0 │     0.008471 │      0.01953 │\n",
      "│        26.13 │            0 │     0.002264 │     0.009766 │\n",
      "│        28.69 │            0 │    0.0006122 │     0.004883 │\n",
      "│        28.69 │            0 │    0.0006122 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 28.6879 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.479 │       1.26 │       14.7 │      0.221 │\n",
      "│      Train │      0.521 │       1.49 │       14.4 │      0.262 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.027 │        2.161 │        266.6 │            5 │\n",
      "│        10.01 │        2.459 │        38.88 │          2.5 │\n",
      "│        15.04 │        2.638 │        3.258 │          2.5 │\n",
      "│        19.96 │        2.628 │         2.22 │          2.5 │\n",
      "│        24.85 │        2.361 │        3.458 │          2.5 │\n",
      "│        29.89 │        2.238 │        3.761 │          2.5 │\n",
      "│        35.14 │        2.166 │        3.631 │          2.5 │\n",
      "│        40.39 │        2.028 │        4.003 │          2.5 │\n",
      "│        47.12 │        2.014 │        3.542 │          2.5 │\n",
      "│        54.64 │          1.9 │        3.804 │          2.5 │\n",
      "│        62.24 │        2.155 │        7.538 │          2.5 │\n",
      "│        69.14 │        2.343 │         10.7 │          2.5 │\n",
      "│        74.75 │        2.692 │        11.69 │          2.5 │\n",
      "│        79.93 │        2.421 │        9.916 │          2.5 │\n",
      "│        84.89 │         2.26 │        6.977 │          2.5 │\n",
      "│        90.45 │        2.168 │        4.916 │          2.5 │\n",
      "│        95.68 │        2.072 │        4.893 │          2.5 │\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "│        101.8 │        1.981 │        3.401 │          2.5 │\n",
      "│        108.6 │        2.044 │        3.312 │          2.5 │\n",
      "│        114.8 │        1.979 │        3.523 │          2.5 │\n",
      "│        121.5 │        2.177 │        4.633 │          2.5 │\n",
      "│        127.1 │        2.133 │        4.595 │          2.5 │\n",
      "│        132.1 │        2.018 │        3.813 │          2.5 │\n",
      "│        138.5 │        2.043 │        3.457 │          2.5 │\n",
      "│        144.8 │        2.064 │        4.446 │          2.5 │\n",
      "│        150.8 │        2.033 │        5.026 │          2.5 │\n",
      "│        157.2 │        2.046 │        4.971 │          2.5 │\n",
      "│        163.6 │        1.989 │        5.231 │          2.5 │\n",
      "│        170.9 │        2.055 │        5.525 │          2.5 │\n",
      "│        177.9 │        2.375 │          6.9 │          2.5 │\n",
      "│        184.1 │        2.532 │        7.622 │          2.5 │\n",
      "│        190.7 │        2.516 │        7.802 │          2.5 │\n",
      "│        196.6 │         2.57 │        7.516 │          2.5 │\n",
      "│        202.8 │        2.866 │        8.209 │          2.5 │\n",
      "│        209.1 │        3.008 │        9.686 │          2.5 │\n",
      "│        215.1 │        3.181 │        10.09 │          2.5 │\n",
      "│        221.7 │        3.163 │        10.15 │          2.5 │\n",
      "│        227.6 │        2.992 │        9.913 │          2.5 │\n",
      "│        233.5 │        2.924 │        8.865 │          2.5 │\n",
      "│        239.6 │        2.781 │        7.676 │          2.5 │\n",
      "│        239.6 │        2.781 │        7.676 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 3 min., 59.5606 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.393 │      0.607 │       15.7 │      0.116 │\n",
      "│      Train │      0.456 │      0.942 │       15.4 │      0.162 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│         2.78 │            0 │        53.69 │            5 │\n",
      "│        5.547 │            0 │         15.1 │          2.5 │\n",
      "│        8.326 │            0 │         6.14 │         1.25 │\n",
      "│        11.11 │            0 │        2.199 │        0.625 │\n",
      "│        13.91 │            0 │       0.7457 │       0.3125 │\n",
      "│        16.77 │            0 │        0.225 │       0.1562 │\n",
      "│        19.52 │            0 │      0.06287 │      0.07812 │\n",
      "│        22.28 │            0 │      0.01511 │      0.03906 │\n",
      "│        25.05 │            0 │     0.006989 │      0.01953 │\n",
      "│        27.76 │            0 │     0.001845 │     0.009766 │\n",
      "│        30.57 │            0 │    0.0004064 │     0.004883 │\n",
      "│        30.57 │            0 │    0.0004064 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 30.5673 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.443 │       1.11 │         15 │      0.188 │\n",
      "│      Train │      0.494 │       1.38 │       14.7 │      0.235 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.013 │        2.071 │        245.6 │            5 │\n",
      "│        10.11 │        2.421 │        40.23 │          2.5 │\n",
      "│        15.23 │        2.614 │        3.945 │          2.5 │\n",
      "│        20.26 │         2.55 │        1.677 │          2.5 │\n",
      "│        25.31 │        2.261 │        2.826 │          2.5 │\n",
      "│        30.36 │        2.029 │        3.392 │          2.5 │\n",
      "│        35.93 │        1.983 │         4.34 │          2.5 │\n",
      "│        42.89 │        2.097 │        5.836 │          2.5 │\n",
      "│        50.22 │        2.398 │        7.106 │          2.5 │\n",
      "│        57.65 │        2.465 │        7.848 │          2.5 │\n",
      "│        64.37 │        2.584 │        9.278 │          2.5 │\n",
      "│        72.48 │         2.44 │        7.625 │          2.5 │\n",
      "│        80.92 │        2.648 │        8.064 │          2.5 │\n",
      "│        89.13 │        3.061 │        14.39 │          2.5 │\n",
      "│        97.04 │        6.935 │        36.86 │          2.5 │\n",
      "│        102.4 │        6.996 │        24.83 │          2.5 │\n",
      "│        108.5 │        7.426 │         27.6 │          2.5 │\n",
      "│        115.8 │        7.402 │         25.1 │          2.5 │\n",
      "│        124.2 │        8.739 │         36.1 │          2.5 │\n",
      "│        132.5 │        14.48 │         71.5 │          2.5 │\n",
      "│        137.8 │        11.65 │        46.06 │          2.5 │\n",
      "│        144.3 │        13.58 │        54.89 │          2.5 │\n",
      "│        150.5 │        14.25 │        63.21 │          2.5 │\n",
      "│        156.3 │        16.91 │         79.7 │          2.5 │\n",
      "│        161.9 │        17.04 │        69.27 │          2.5 │\n",
      "│        169.3 │        14.75 │        56.74 │          2.5 │\n",
      "│        175.1 │        18.47 │        92.99 │          2.5 │\n",
      "│          181 │        16.21 │        74.14 │          2.5 │\n",
      "│        187.2 │        16.55 │        73.91 │          2.5 │\n",
      "│        193.9 │        16.74 │        77.73 │          2.5 │\n",
      "│        199.8 │           18 │        85.28 │          2.5 │\n",
      "│        206.1 │        19.38 │        96.68 │          2.5 │\n",
      "│        213.3 │         15.8 │         67.6 │          2.5 │\n",
      "│        221.1 │        14.38 │        64.58 │          2.5 │\n",
      "│        228.3 │        14.14 │        66.74 │          2.5 │\n",
      "│        233.9 │        19.63 │        97.38 │          2.5 │\n",
      "│        240.3 │        15.61 │        57.57 │          2.5 │\n",
      "│        246.7 │        15.85 │        73.62 │          2.5 │\n",
      "│          253 │        17.67 │        87.42 │          2.5 │\n",
      "│        259.8 │        17.43 │        80.59 │          2.5 │\n",
      "│        259.8 │        17.43 │        80.59 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 19.7573 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │     0.0168 │      -1.87 │       19.6 │     -0.378 │\n",
      "│      Train │     0.0299 │      -1.67 │       19.6 │     -0.365 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.785 │            0 │        77.79 │            5 │\n",
      "│        5.566 │            0 │        19.53 │          2.5 │\n",
      "│        8.688 │            0 │        8.802 │         1.25 │\n",
      "│        11.55 │            0 │        3.935 │        0.625 │\n",
      "│        14.32 │            0 │        1.718 │       0.3125 │\n",
      "│        17.13 │            0 │       0.6521 │       0.1562 │\n",
      "│         19.9 │            0 │       0.2243 │      0.07812 │\n",
      "│        22.67 │            0 │      0.05379 │      0.03906 │\n",
      "│        25.42 │            0 │     0.006381 │      0.01953 │\n",
      "│        28.37 │            0 │    0.0007445 │     0.009766 │\n",
      "│        28.37 │            0 │    0.0007445 │     0.009766 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 10 iterations!\n",
      "Finished optimizing f. Elapsed time: 28.3739 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │     0.0843 │    0.00284 │       16.8 │     -0.012 │\n",
      "│      Train │      0.142 │      0.136 │       16.7 │     0.0114 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.295 │        2.379 │        394.7 │            5 │\n",
      "│        10.41 │        2.359 │        20.73 │            5 │\n",
      "│        15.52 │        2.165 │        11.36 │            5 │\n",
      "│        20.71 │        1.847 │        19.17 │          2.5 │\n",
      "│        25.84 │        1.894 │        5.063 │          2.5 │\n",
      "│        31.05 │        1.793 │        2.894 │          2.5 │\n",
      "│        36.31 │        1.743 │        2.552 │          2.5 │\n",
      "│        41.73 │        1.666 │        2.789 │          2.5 │\n",
      "│        47.09 │        1.569 │        3.289 │          2.5 │\n",
      "│        54.41 │         1.63 │        2.709 │          2.5 │\n",
      "│        61.81 │        1.811 │        4.449 │          2.5 │\n",
      "│        67.68 │        1.695 │        5.939 │          2.5 │\n",
      "│        74.48 │         1.62 │        5.277 │          2.5 │\n",
      "│        81.16 │         1.73 │        5.858 │          2.5 │\n",
      "│        86.81 │        1.805 │        6.368 │          2.5 │\n",
      "│        92.86 │        1.683 │        4.796 │          2.5 │\n",
      "│        99.71 │        1.801 │        5.788 │          2.5 │\n",
      "│        105.5 │        1.668 │        5.629 │          2.5 │\n",
      "│        111.6 │        1.538 │        4.633 │          2.5 │\n",
      "│        117.2 │        1.567 │        3.258 │          2.5 │\n",
      "│        123.4 │        1.521 │        2.826 │          2.5 │\n",
      "│        129.4 │        1.541 │        2.666 │          2.5 │\n",
      "│        136.5 │        1.575 │        3.495 │          2.5 │\n",
      "│        142.4 │        1.557 │        3.447 │          2.5 │\n",
      "│        148.4 │        1.493 │        3.358 │          2.5 │\n",
      "│        154.2 │        1.443 │        2.874 │          2.5 │\n",
      "│        161.4 │        1.606 │        3.206 │          2.5 │\n",
      "│        168.8 │         1.63 │        3.797 │          2.5 │\n",
      "│        174.6 │        1.531 │        5.822 │          2.5 │\n",
      "│          181 │        1.569 │        4.325 │          2.5 │\n",
      "│        186.5 │        1.571 │        4.507 │          2.5 │\n",
      "│        193.4 │        1.671 │        4.937 │          2.5 │\n",
      "│        199.9 │        1.687 │        4.815 │          2.5 │\n",
      "│        206.4 │        1.711 │        5.244 │          2.5 │\n",
      "│        212.2 │        1.652 │        4.446 │          2.5 │\n",
      "│        217.8 │        1.688 │        4.347 │          2.5 │\n",
      "│        224.8 │        1.742 │         3.84 │          2.5 │\n",
      "│        230.8 │        1.861 │        4.926 │          2.5 │\n",
      "│        237.6 │        1.897 │        5.282 │          2.5 │\n",
      "│        243.7 │        1.906 │        5.179 │          2.5 │\n",
      "│        243.7 │        1.906 │        5.179 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 3.70923 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.319 │      0.552 │       16.1 │     0.0718 │\n",
      "│      Train │      0.384 │      0.673 │       15.8 │      0.118 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.635 │            0 │        62.13 │            5 │\n",
      "│        5.319 │            0 │        17.07 │          2.5 │\n",
      "│        8.025 │            0 │        5.913 │         1.25 │\n",
      "│        10.75 │            0 │        2.261 │        0.625 │\n",
      "│        13.43 │            0 │       0.7695 │       0.3125 │\n",
      "│        16.16 │            0 │       0.2775 │       0.1562 │\n",
      "│        18.94 │            0 │       0.1015 │      0.07812 │\n",
      "│        21.76 │            0 │     0.006708 │      0.03906 │\n",
      "│        24.61 │            0 │      0.01317 │      0.01953 │\n",
      "│        27.37 │            0 │     0.003229 │     0.009766 │\n",
      "│        30.18 │            0 │     0.000819 │     0.004883 │\n",
      "│        30.18 │            0 │     0.000819 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 30.1791 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.422 │       1.07 │       15.2 │      0.173 │\n",
      "│      Train │       0.49 │       1.33 │       14.7 │      0.232 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.228 │        2.264 │        297.6 │            5 │\n",
      "│        10.71 │        2.408 │        39.18 │          2.5 │\n",
      "│        16.52 │        2.543 │        4.332 │          2.5 │\n",
      "│        21.88 │        2.593 │         2.17 │          2.5 │\n",
      "│        27.24 │        2.417 │        2.636 │          2.5 │\n",
      "│        32.47 │        2.324 │        3.311 │          2.5 │\n",
      "│        37.94 │        2.249 │        2.932 │          2.5 │\n",
      "│        43.26 │        2.115 │        4.039 │          2.5 │\n",
      "│        50.02 │        2.085 │        4.249 │          2.5 │\n",
      "│        55.85 │        2.037 │        4.205 │          2.5 │\n",
      "│        62.93 │        2.313 │        6.586 │          2.5 │\n",
      "│        69.07 │        2.496 │        7.254 │          2.5 │\n",
      "│        75.57 │         2.58 │        6.826 │          2.5 │\n",
      "│        81.54 │        2.621 │         7.18 │          2.5 │\n",
      "│        87.43 │        2.535 │        6.814 │          2.5 │\n",
      "│        94.13 │        2.513 │        6.626 │          2.5 │\n",
      "│          100 │        2.531 │        7.446 │          2.5 │\n",
      "│          107 │        2.528 │        6.376 │          2.5 │\n",
      "│        114.1 │        2.561 │        6.496 │          2.5 │\n",
      "│        120.1 │        2.576 │        6.912 │          2.5 │\n",
      "│        126.3 │        2.641 │        7.033 │          2.5 │\n",
      "│        132.6 │         2.65 │         5.81 │          2.5 │\n",
      "│        138.5 │        2.632 │        5.441 │          2.5 │\n",
      "│        143.9 │        2.662 │        5.374 │          2.5 │\n",
      "│        150.2 │        2.688 │        5.015 │          2.5 │\n",
      "│        156.2 │        2.693 │        5.269 │          2.5 │\n",
      "│        162.2 │        2.662 │        5.146 │          2.5 │\n",
      "│        168.3 │        2.688 │        5.685 │          2.5 │\n",
      "│        174.3 │        2.592 │        5.748 │          2.5 │\n",
      "│        180.2 │        2.558 │        5.594 │          2.5 │\n",
      "│        187.2 │        2.583 │         5.48 │          2.5 │\n",
      "│          193 │        2.542 │        5.496 │          2.5 │\n",
      "│        198.5 │        2.487 │        5.875 │          2.5 │\n",
      "│        205.5 │        2.483 │        5.668 │          2.5 │\n",
      "│        211.6 │        2.521 │        6.019 │          2.5 │\n",
      "│        217.6 │        2.489 │        5.674 │          2.5 │\n",
      "│          223 │        2.472 │        4.765 │          2.5 │\n",
      "│        229.6 │        2.541 │        4.699 │          2.5 │\n",
      "│        235.3 │        2.594 │        5.995 │          2.5 │\n",
      "│        241.6 │        2.615 │        5.745 │          2.5 │\n",
      "│        241.6 │        2.615 │        5.745 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 1.57988 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.466 │       1.24 │       14.8 │      0.209 │\n",
      "│      Train │      0.502 │        1.4 │       14.6 │      0.242 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.751 │            0 │        17.29 │            5 │\n",
      "│        5.514 │            0 │        6.848 │          2.5 │\n",
      "│        8.227 │            0 │         3.06 │         1.25 │\n",
      "│        10.93 │            0 │        1.145 │        0.625 │\n",
      "│        13.66 │            0 │       0.4571 │       0.3125 │\n",
      "│        16.71 │            0 │       0.1093 │       0.1562 │\n",
      "│         19.6 │            0 │      0.07548 │      0.07812 │\n",
      "│        22.34 │            0 │      0.02489 │      0.03906 │\n",
      "│        25.43 │            0 │     0.007497 │      0.01953 │\n",
      "│        28.28 │            0 │     0.002232 │     0.009766 │\n",
      "│        31.01 │            0 │    0.0006282 │     0.004883 │\n",
      "│        31.01 │            0 │    0.0006282 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 31.0118 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.477 │       1.25 │       14.7 │      0.219 │\n",
      "│      Train │      0.535 │       1.52 │       14.3 │      0.278 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.512 │        2.103 │        264.5 │            5 │\n",
      "│        11.47 │        2.433 │        34.93 │          2.5 │\n",
      "│        16.97 │        2.593 │        6.859 │          2.5 │\n",
      "│        22.57 │         2.59 │        1.519 │          2.5 │\n",
      "│        28.05 │        2.365 │        3.629 │          2.5 │\n",
      "│        34.18 │        2.279 │        3.489 │          2.5 │\n",
      "│         39.7 │        2.047 │        4.143 │          2.5 │\n",
      "│        46.59 │        1.959 │        4.396 │          2.5 │\n",
      "│        54.37 │          2.1 │         6.32 │          2.5 │\n",
      "│        59.92 │        1.988 │        6.049 │          2.5 │\n",
      "│        67.25 │        2.062 │        5.127 │          2.5 │\n",
      "│        75.09 │        2.302 │        6.934 │          2.5 │\n",
      "│        80.78 │        2.303 │        9.045 │          2.5 │\n",
      "│        86.66 │        2.166 │        7.017 │          2.5 │\n",
      "│        93.28 │        2.192 │        5.349 │          2.5 │\n",
      "│        100.1 │        2.294 │        6.295 │          2.5 │\n",
      "│        105.5 │        2.487 │        6.365 │          2.5 │\n",
      "│        111.4 │        2.415 │        5.836 │          2.5 │\n",
      "│        117.1 │        2.362 │        5.241 │          2.5 │\n",
      "│        123.9 │        2.508 │        5.375 │          2.5 │\n",
      "│        130.9 │        2.578 │        5.868 │          2.5 │\n",
      "│        137.2 │        2.693 │        6.909 │          2.5 │\n",
      "│        143.5 │        2.798 │        6.473 │          2.5 │\n",
      "│        149.4 │        2.907 │        6.002 │          2.5 │\n",
      "│        154.9 │        2.916 │        6.247 │          2.5 │\n",
      "│        161.6 │        2.898 │        6.198 │          2.5 │\n",
      "│        167.6 │         2.87 │        6.935 │          2.5 │\n",
      "│        173.8 │        2.839 │        7.079 │          2.5 │\n",
      "│        179.4 │        2.897 │        7.186 │          2.5 │\n",
      "│        185.2 │        2.935 │         7.16 │          2.5 │\n",
      "│          192 │        2.928 │        6.633 │          2.5 │\n",
      "│        199.1 │         2.95 │        6.848 │          2.5 │\n",
      "│        205.7 │        2.977 │        5.534 │          2.5 │\n",
      "│        211.5 │        3.065 │        5.532 │          2.5 │\n",
      "│        219.7 │        3.108 │        5.672 │          2.5 │\n",
      "│        226.8 │        3.372 │        9.073 │          2.5 │\n",
      "│        232.5 │        3.024 │        9.605 │          2.5 │\n",
      "│        238.6 │        2.867 │        7.832 │          2.5 │\n",
      "│          244 │        2.752 │        6.545 │          2.5 │\n",
      "│        249.8 │        2.618 │        5.216 │          2.5 │\n",
      "│        249.8 │        2.618 │        5.216 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 9.82348 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.474 │       1.21 │       14.8 │      0.215 │\n",
      "│      Train │      0.542 │       1.54 │       14.3 │      0.279 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.802 │            0 │        10.18 │            5 │\n",
      "│        5.574 │            0 │        7.053 │          2.5 │\n",
      "│         8.32 │            0 │        2.695 │         1.25 │\n",
      "│        11.01 │            0 │        1.013 │        0.625 │\n",
      "│        13.67 │            0 │       0.1792 │       0.3125 │\n",
      "│        16.35 │            0 │        0.124 │       0.1562 │\n",
      "│        19.05 │            0 │      0.03919 │      0.07812 │\n",
      "│        21.75 │            0 │       0.0125 │      0.03906 │\n",
      "│        24.45 │            0 │      0.00364 │      0.01953 │\n",
      "│        27.14 │            0 │     0.001089 │     0.009766 │\n",
      "│        29.84 │            0 │    0.0003591 │     0.004883 │\n",
      "│        29.84 │            0 │    0.0003591 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 29.8394 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.472 │       1.21 │       14.8 │      0.212 │\n",
      "│      Train │      0.547 │       1.59 │       14.2 │      0.291 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.233 │        2.205 │        277.2 │            5 │\n",
      "│        10.66 │        2.483 │        38.72 │          2.5 │\n",
      "│        16.01 │         2.61 │        3.047 │          2.5 │\n",
      "│        21.09 │          2.6 │        1.731 │          2.5 │\n",
      "│        26.15 │        2.401 │        2.767 │          2.5 │\n",
      "│        31.67 │        2.258 │        3.386 │          2.5 │\n",
      "│        37.09 │        2.091 │        3.954 │          2.5 │\n",
      "│        42.59 │        1.925 │        4.487 │          2.5 │\n",
      "│         50.6 │        1.931 │         6.23 │          2.5 │\n",
      "│        58.33 │        2.247 │        6.849 │          2.5 │\n",
      "│        65.37 │        2.321 │          8.6 │          2.5 │\n",
      "│         72.3 │        2.391 │        8.941 │          2.5 │\n",
      "│        79.29 │        2.445 │        8.338 │          2.5 │\n",
      "│        87.09 │        2.545 │        7.613 │          2.5 │\n",
      "│        94.51 │        2.665 │        7.366 │          2.5 │\n",
      "│        100.4 │        2.636 │        7.776 │          2.5 │\n",
      "│        105.8 │        2.372 │        6.407 │          2.5 │\n",
      "│        111.2 │        2.233 │        5.388 │          2.5 │\n",
      "│        117.2 │        2.146 │        4.584 │          2.5 │\n",
      "│        124.5 │        2.196 │        3.325 │          2.5 │\n",
      "│        130.1 │        2.126 │        4.008 │          2.5 │\n",
      "│        136.6 │        2.017 │        3.593 │          2.5 │\n",
      "│        142.9 │        2.103 │        3.697 │          2.5 │\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "│        148.4 │        2.058 │        3.843 │          2.5 │\n",
      "│        154.5 │         2.03 │        3.155 │          2.5 │\n",
      "│        160.3 │            2 │        3.204 │          2.5 │\n",
      "│          167 │        1.994 │        2.467 │          2.5 │\n",
      "│        174.4 │        2.001 │        2.591 │          2.5 │\n",
      "│        180.8 │        1.918 │        2.292 │          2.5 │\n",
      "│        187.3 │         1.95 │        2.396 │          2.5 │\n",
      "│        193.9 │        2.008 │        2.653 │          2.5 │\n",
      "│        201.3 │        2.101 │        3.207 │          2.5 │\n",
      "│        208.4 │        2.207 │        5.268 │          2.5 │\n",
      "│        214.3 │        2.306 │        5.377 │          2.5 │\n",
      "│        221.2 │         2.45 │        5.133 │          2.5 │\n",
      "│        226.8 │        2.434 │         5.74 │          2.5 │\n",
      "│        233.6 │        2.563 │        5.811 │          2.5 │\n",
      "│        241.2 │        2.676 │        5.771 │          2.5 │\n",
      "│        247.3 │        2.762 │        7.509 │          2.5 │\n",
      "│        253.3 │        2.865 │        6.817 │          2.5 │\n",
      "│        253.3 │        2.865 │        6.817 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 13.2596 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.483 │       1.12 │       14.7 │      0.222 │\n",
      "│      Train │      0.523 │       1.41 │       14.5 │       0.26 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.789 │            0 │        27.12 │            5 │\n",
      "│          5.5 │            0 │        7.953 │          2.5 │\n",
      "│        8.212 │            0 │        3.327 │         1.25 │\n",
      "│        11.02 │            0 │        1.326 │        0.625 │\n",
      "│        13.75 │            0 │       0.4472 │       0.3125 │\n",
      "│        16.47 │            0 │       0.1014 │       0.1562 │\n",
      "│         19.2 │            0 │      0.03548 │      0.07812 │\n",
      "│        21.99 │            0 │      0.01639 │      0.03906 │\n",
      "│        24.82 │            0 │     0.004187 │      0.01953 │\n",
      "│        27.59 │            0 │     0.001236 │     0.009766 │\n",
      "│        30.29 │            0 │    0.0003437 │     0.004883 │\n",
      "│        30.29 │            0 │    0.0003437 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 30.2882 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.495 │       1.24 │       14.6 │      0.236 │\n",
      "│      Train │      0.541 │       1.55 │       14.2 │      0.285 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.317 │        2.146 │        266.2 │            5 │\n",
      "│        12.44 │        2.487 │        33.15 │          2.5 │\n",
      "│         17.6 │        2.562 │          5.4 │          2.5 │\n",
      "│        22.97 │        2.577 │         1.85 │          2.5 │\n",
      "│        28.92 │        2.338 │        2.948 │          2.5 │\n",
      "│        35.07 │        2.014 │         4.34 │          2.5 │\n",
      "│        42.54 │        1.874 │         4.79 │          2.5 │\n",
      "│        50.73 │        2.085 │        7.229 │          2.5 │\n",
      "│        56.74 │        2.047 │         7.85 │          2.5 │\n",
      "│        63.37 │        2.123 │        6.076 │          2.5 │\n",
      "│        69.52 │        1.972 │        4.781 │          2.5 │\n",
      "│        75.99 │        1.956 │        4.319 │          2.5 │\n",
      "│        82.09 │        1.944 │        3.457 │          2.5 │\n",
      "│        87.95 │        1.967 │        2.878 │          2.5 │\n",
      "│        93.44 │        1.991 │        2.558 │          2.5 │\n",
      "│        100.8 │        2.076 │        2.424 │          2.5 │\n",
      "│        108.9 │         2.02 │        2.493 │          2.5 │\n",
      "│          116 │        2.007 │        3.764 │          2.5 │\n",
      "│        122.6 │        1.952 │        5.093 │          2.5 │\n",
      "│        130.6 │        2.061 │        5.076 │          2.5 │\n",
      "│          137 │        2.109 │        6.477 │          2.5 │\n",
      "│        142.9 │        1.939 │        5.045 │          2.5 │\n",
      "│        148.9 │        1.834 │        3.524 │          2.5 │\n",
      "│        155.2 │        1.903 │        2.879 │          2.5 │\n",
      "│        160.8 │        1.787 │        2.877 │          2.5 │\n",
      "│          167 │         1.75 │        2.203 │          2.5 │\n",
      "│        173.1 │        1.693 │        2.566 │          2.5 │\n",
      "│          180 │        1.748 │        3.108 │          2.5 │\n",
      "│        186.3 │        1.841 │        3.948 │          2.5 │\n",
      "│        192.2 │        1.789 │        3.546 │          2.5 │\n",
      "│        198.3 │        1.918 │        3.248 │          2.5 │\n",
      "│        204.3 │        1.906 │        3.322 │          2.5 │\n",
      "│          212 │         1.91 │        3.012 │          2.5 │\n",
      "│        219.1 │        2.189 │        4.293 │          2.5 │\n",
      "│        225.4 │        2.296 │        4.966 │          2.5 │\n",
      "│        231.2 │        2.212 │         4.43 │          2.5 │\n",
      "│        237.6 │        2.341 │         4.48 │          2.5 │\n",
      "│        243.8 │        2.211 │        4.044 │          2.5 │\n",
      "│        250.7 │        2.219 │        3.724 │          2.5 │\n",
      "│        256.2 │        2.187 │        3.601 │          2.5 │\n",
      "│        256.2 │        2.187 │        3.601 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 16.1604 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.509 │       1.34 │       14.4 │      0.251 │\n",
      "│      Train │       0.56 │       1.63 │         14 │      0.303 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.829 │            0 │        10.55 │            5 │\n",
      "│        5.698 │            0 │        5.267 │          2.5 │\n",
      "│        8.438 │            0 │         2.09 │         1.25 │\n",
      "│        11.17 │            0 │       0.9631 │        0.625 │\n",
      "│        14.06 │            0 │       0.1971 │       0.3125 │\n",
      "│        16.88 │            0 │       0.1182 │       0.1562 │\n",
      "│         19.6 │            0 │      0.04039 │      0.07812 │\n",
      "│        22.35 │            0 │      0.01199 │      0.03906 │\n",
      "│        25.13 │            0 │     0.003504 │      0.01953 │\n",
      "│        27.94 │            0 │     0.001135 │     0.009766 │\n",
      "│        30.71 │            0 │    0.0003646 │     0.004883 │\n",
      "│        30.71 │            0 │    0.0003646 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 30.7146 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.509 │       1.31 │       14.4 │       0.25 │\n",
      "│      Train │       0.57 │       1.68 │       13.9 │      0.316 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.181 │         2.16 │        250.7 │            5 │\n",
      "│        11.77 │        2.534 │        34.79 │          2.5 │\n",
      "│        17.15 │        2.579 │        6.495 │          2.5 │\n",
      "│        22.51 │        2.592 │        1.646 │          2.5 │\n",
      "│        27.94 │        2.361 │        2.959 │          2.5 │\n",
      "│         34.1 │        2.089 │        3.569 │          2.5 │\n",
      "│        41.56 │        2.141 │        7.159 │          2.5 │\n",
      "│        47.57 │        2.056 │        9.334 │          2.5 │\n",
      "│        53.35 │        1.962 │        7.491 │          2.5 │\n",
      "│        59.44 │        1.833 │        5.222 │          2.5 │\n",
      "│        65.91 │        1.871 │        4.478 │          2.5 │\n",
      "│         73.1 │        1.842 │        4.142 │          2.5 │\n",
      "│        79.54 │        1.765 │        3.436 │          2.5 │\n",
      "│        85.28 │         1.78 │        2.801 │          2.5 │\n",
      "│        90.76 │        1.777 │        2.178 │          2.5 │\n",
      "│        98.24 │        1.798 │        2.121 │          2.5 │\n",
      "│        104.9 │        1.845 │         2.56 │          2.5 │\n",
      "│        111.1 │        1.897 │        2.622 │          2.5 │\n",
      "│        117.7 │        1.864 │        2.589 │          2.5 │\n",
      "│        124.5 │        1.898 │        2.624 │          2.5 │\n",
      "│        131.3 │        1.844 │        2.791 │          2.5 │\n",
      "│        137.6 │        1.831 │        3.414 │          2.5 │\n",
      "│        143.5 │        1.809 │        2.849 │          2.5 │\n",
      "│        149.6 │        1.773 │        2.308 │          2.5 │\n",
      "│        156.6 │        1.817 │        2.049 │          2.5 │\n",
      "│        162.5 │         1.72 │        2.019 │          2.5 │\n",
      "│        168.7 │        1.649 │        2.334 │          2.5 │\n",
      "│        174.8 │        1.626 │         2.39 │          2.5 │\n",
      "│        181.5 │        1.625 │        2.218 │          2.5 │\n",
      "│        188.2 │        1.609 │        2.715 │          2.5 │\n",
      "│          196 │        1.625 │        3.891 │          2.5 │\n",
      "│        201.4 │        1.638 │        3.804 │          2.5 │\n",
      "│        207.3 │        1.594 │        3.145 │          2.5 │\n",
      "│          214 │        1.638 │        3.179 │          2.5 │\n",
      "│        220.9 │        1.604 │        2.825 │          2.5 │\n",
      "│        227.5 │        1.662 │         3.48 │          2.5 │\n",
      "│        233.2 │        1.705 │        3.671 │          2.5 │\n",
      "│          240 │        1.731 │        3.073 │          2.5 │\n",
      "│        246.3 │        1.787 │        3.372 │          2.5 │\n",
      "│        252.8 │        1.872 │        3.834 │          2.5 │\n",
      "│        252.8 │        1.872 │        3.834 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 12.7865 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.453 │       1.05 │       15.3 │      0.148 │\n",
      "│      Train │      0.523 │        1.4 │       14.7 │      0.233 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.821 │            0 │         32.9 │            5 │\n",
      "│        5.804 │            0 │        10.76 │          2.5 │\n",
      "│        8.622 │            0 │        4.634 │         1.25 │\n",
      "│        11.54 │            0 │        1.936 │        0.625 │\n",
      "│        14.38 │            0 │        0.715 │       0.3125 │\n",
      "│        17.09 │            0 │       0.2388 │       0.1562 │\n",
      "│        19.81 │            0 │      0.09086 │      0.07812 │\n",
      "│        22.51 │            0 │     0.009049 │      0.03906 │\n",
      "│        25.28 │            0 │     0.009253 │      0.01953 │\n",
      "│           28 │            0 │     0.001905 │     0.009766 │\n",
      "│         30.7 │            0 │    0.0003594 │     0.004883 │\n",
      "│         30.7 │            0 │    0.0003594 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 30.7026 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.512 │       1.32 │       14.4 │      0.251 │\n",
      "│      Train │      0.567 │       1.69 │       13.9 │      0.313 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.209 │        2.083 │        258.5 │            5 │\n",
      "│         10.4 │        2.455 │         36.6 │          2.5 │\n",
      "│        16.23 │        2.642 │        3.675 │          2.5 │\n",
      "│        21.69 │        2.592 │        1.674 │          2.5 │\n",
      "│        27.16 │        2.356 │        2.928 │          2.5 │\n",
      "│        32.47 │        2.065 │        3.936 │          2.5 │\n",
      "│        39.26 │        1.925 │        3.769 │          2.5 │\n",
      "│        46.93 │        2.248 │          8.1 │          2.5 │\n",
      "│        53.29 │        2.409 │        9.444 │          2.5 │\n",
      "│        61.91 │        2.756 │        9.948 │          2.5 │\n",
      "│        69.67 │        2.713 │        13.14 │          2.5 │\n",
      "│        76.67 │        2.707 │        12.09 │          2.5 │\n",
      "│        82.52 │        2.468 │        8.517 │          2.5 │\n",
      "│        88.24 │        2.329 │         5.75 │          2.5 │\n",
      "│        93.88 │        2.289 │        4.444 │          2.5 │\n",
      "│        99.34 │         2.22 │         3.42 │          2.5 │\n",
      "│        105.1 │        2.171 │        2.707 │          2.5 │\n",
      "│        111.3 │        2.129 │        2.445 │          2.5 │\n",
      "│        117.8 │        2.132 │        2.394 │          2.5 │\n",
      "│        125.4 │        2.108 │        2.605 │          2.5 │\n",
      "│        131.6 │        2.111 │        4.103 │          2.5 │\n",
      "│        137.9 │        2.052 │        3.178 │          2.5 │\n",
      "│        145.3 │        2.048 │        3.159 │          2.5 │\n",
      "│        151.5 │        2.071 │         4.07 │          2.5 │\n",
      "│        158.2 │        2.092 │         4.14 │          2.5 │\n",
      "│        164.8 │        2.066 │        4.024 │          2.5 │\n",
      "│        170.9 │         2.03 │        3.765 │          2.5 │\n",
      "│        176.8 │        2.003 │        3.615 │          2.5 │\n",
      "│        182.7 │        1.968 │        3.622 │          2.5 │\n",
      "│        188.3 │        2.033 │        3.774 │          2.5 │\n",
      "│        195.3 │        2.151 │        3.315 │          2.5 │\n",
      "│        201.9 │        2.235 │         4.04 │          2.5 │\n",
      "│        208.2 │        2.365 │        5.085 │          2.5 │\n",
      "│        215.3 │        2.583 │         5.66 │          2.5 │\n",
      "│        223.4 │        2.699 │        5.661 │          2.5 │\n",
      "│        230.4 │        2.814 │        7.626 │          2.5 │\n",
      "│        236.2 │        2.688 │        6.927 │          2.5 │\n",
      "│        243.3 │        2.659 │        6.065 │          2.5 │\n",
      "│        249.9 │        2.717 │        6.332 │          2.5 │\n",
      "│        255.4 │         2.56 │        5.601 │          2.5 │\n",
      "│        255.4 │         2.56 │        5.601 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 15.3719 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.487 │       1.12 │       14.7 │      0.223 │\n",
      "│      Train │      0.538 │       1.49 │       14.3 │      0.277 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.787 │            0 │        17.94 │            5 │\n",
      "│        5.539 │            0 │         6.93 │          2.5 │\n",
      "│        8.375 │            0 │        2.971 │         1.25 │\n",
      "│        11.12 │            0 │        1.171 │        0.625 │\n",
      "│        14.06 │            0 │        0.429 │       0.3125 │\n",
      "│        16.81 │            0 │       0.1693 │       0.1562 │\n",
      "│         19.7 │            0 │      0.02424 │      0.07812 │\n",
      "│        22.47 │            0 │      0.01725 │      0.03906 │\n",
      "│        25.23 │            0 │     0.005195 │      0.01953 │\n",
      "│        27.99 │            0 │     0.001635 │     0.009766 │\n",
      "│        30.94 │            0 │    0.0003757 │     0.004883 │\n",
      "│        30.94 │            0 │    0.0003757 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 30.9398 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.495 │       1.27 │       14.6 │      0.235 │\n",
      "│      Train │      0.559 │       1.64 │         14 │      0.305 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.191 │        2.124 │        250.8 │            5 │\n",
      "│         10.5 │        2.459 │        35.93 │          2.5 │\n",
      "│        15.65 │        2.628 │        4.564 │          2.5 │\n",
      "│        20.89 │        2.581 │        1.186 │          2.5 │\n",
      "│        26.16 │         2.27 │        3.134 │          2.5 │\n",
      "│        31.34 │        1.997 │        4.044 │          2.5 │\n",
      "│        38.86 │         1.89 │        4.342 │          2.5 │\n",
      "│        46.19 │        1.813 │        4.669 │          2.5 │\n",
      "│        54.15 │        1.874 │        5.238 │          2.5 │\n",
      "│        61.93 │        1.848 │        6.902 │          2.5 │\n",
      "│        68.43 │        1.945 │        7.818 │          2.5 │\n",
      "│        74.18 │        1.821 │          6.6 │          2.5 │\n",
      "│        80.25 │        1.772 │        5.084 │          2.5 │\n",
      "│        86.78 │        1.788 │        4.306 │          2.5 │\n",
      "│        93.38 │        1.917 │        4.009 │          2.5 │\n",
      "│        100.2 │         1.96 │        4.081 │          2.5 │\n",
      "│        106.2 │        1.957 │        3.878 │          2.5 │\n",
      "│        112.2 │        1.965 │        3.029 │          2.5 │\n",
      "│        118.4 │        1.966 │        1.863 │          2.5 │\n",
      "│        125.7 │        1.891 │        2.771 │          2.5 │\n",
      "│        131.6 │        1.936 │        2.916 │          2.5 │\n",
      "│        137.8 │        1.882 │        3.358 │          2.5 │\n",
      "│        144.6 │        1.878 │        3.092 │          2.5 │\n",
      "│        151.5 │        1.837 │        2.764 │          2.5 │\n",
      "│        157.7 │        1.806 │        2.929 │          2.5 │\n",
      "│          164 │        1.763 │        2.451 │          2.5 │\n",
      "│        170.5 │        1.731 │         2.66 │          2.5 │\n",
      "│        176.6 │        1.713 │        2.554 │          2.5 │\n",
      "│        182.5 │        1.681 │        2.907 │          2.5 │\n",
      "│        188.9 │        1.618 │        2.727 │          2.5 │\n",
      "│        195.4 │        1.614 │        2.369 │          2.5 │\n",
      "│        201.9 │        1.561 │        2.067 │          2.5 │\n",
      "│        207.9 │        1.501 │        2.229 │          2.5 │\n",
      "│        214.4 │        1.499 │        2.099 │          2.5 │\n",
      "│        220.4 │        1.496 │        1.781 │          2.5 │\n",
      "│        226.5 │        1.576 │        2.215 │          2.5 │\n",
      "│        232.7 │        1.646 │        2.273 │          2.5 │\n",
      "│        240.3 │        1.646 │        3.062 │          2.5 │\n",
      "│        246.2 │        1.906 │        3.238 │          2.5 │\n",
      "│          253 │        2.007 │        3.467 │          2.5 │\n",
      "│          253 │        2.007 │        3.467 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 12.9934 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.481 │       1.02 │       15.4 │      0.142 │\n",
      "│      Train │      0.543 │       1.35 │       14.8 │      0.218 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.735 │            0 │        29.38 │            5 │\n",
      "│        5.439 │            0 │        9.242 │          2.5 │\n",
      "│        8.155 │            0 │        3.997 │         1.25 │\n",
      "│        10.86 │            0 │        1.898 │        0.625 │\n",
      "│        13.62 │            0 │       0.7684 │       0.3125 │\n",
      "│        16.39 │            0 │       0.2783 │       0.1562 │\n",
      "│         19.2 │            0 │      0.08501 │      0.07812 │\n",
      "│        22.06 │            0 │      0.01455 │      0.03906 │\n",
      "│        24.81 │            0 │     0.006942 │      0.01953 │\n",
      "│        27.54 │            0 │     0.001148 │     0.009766 │\n",
      "│        30.24 │            0 │    0.0002852 │     0.004883 │\n",
      "│        30.24 │            0 │    0.0002852 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 30.2447 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.504 │       1.28 │       14.5 │      0.243 │\n",
      "│      Train │      0.566 │       1.65 │       13.9 │      0.313 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.043 │        2.092 │        247.1 │            5 │\n",
      "│        10.22 │         2.42 │         35.3 │          2.5 │\n",
      "│        15.35 │         2.64 │        5.268 │          2.5 │\n",
      "│        20.44 │        2.586 │        1.248 │          2.5 │\n",
      "│        25.68 │        2.315 │        3.437 │          2.5 │\n",
      "│         30.8 │         2.04 │        4.306 │          2.5 │\n",
      "│        37.65 │        2.029 │        4.216 │          2.5 │\n",
      "│        44.76 │         2.18 │        6.673 │          2.5 │\n",
      "│        52.25 │        2.295 │         7.77 │          2.5 │\n",
      "│         58.5 │        2.523 │        9.365 │          2.5 │\n",
      "│        64.47 │        2.531 │        8.363 │          2.5 │\n",
      "│        70.07 │        2.319 │        7.253 │          2.5 │\n",
      "│        75.97 │         2.22 │        5.599 │          2.5 │\n",
      "│         82.1 │        2.096 │        4.096 │          2.5 │\n",
      "│        90.05 │        2.057 │        3.765 │          2.5 │\n",
      "│        96.57 │        2.084 │        4.154 │          2.5 │\n",
      "│        102.4 │        2.019 │        3.151 │          2.5 │\n",
      "│        108.4 │        1.962 │        3.004 │          2.5 │\n",
      "│        114.7 │        1.926 │        2.718 │          2.5 │\n",
      "│        121.2 │        1.999 │         3.39 │          2.5 │\n",
      "│        126.7 │         1.86 │        2.923 │          2.5 │\n",
      "│          133 │        1.929 │         3.37 │          2.5 │\n",
      "│        138.8 │        1.876 │        3.037 │          2.5 │\n",
      "│        145.6 │        1.934 │        2.835 │          2.5 │\n",
      "│        152.5 │        1.952 │        3.266 │          2.5 │\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "│        158.5 │        1.911 │        3.852 │          2.5 │\n",
      "│        165.2 │        1.957 │        3.757 │          2.5 │\n",
      "│        173.8 │        2.158 │        5.088 │          2.5 │\n",
      "│        180.7 │        2.477 │        8.053 │          2.5 │\n",
      "│        187.2 │        2.475 │        8.388 │          2.5 │\n",
      "│        193.6 │        2.396 │        7.191 │          2.5 │\n",
      "│          202 │         2.57 │        6.441 │          2.5 │\n",
      "│        208.9 │        2.774 │        7.537 │          2.5 │\n",
      "│        215.1 │        2.802 │        7.174 │          2.5 │\n",
      "│          222 │         2.78 │        6.903 │          2.5 │\n",
      "│        229.1 │        2.635 │        5.626 │          2.5 │\n",
      "│        235.9 │        2.524 │        4.786 │          2.5 │\n",
      "│        243.6 │         2.52 │        4.635 │          2.5 │\n",
      "│        249.5 │        2.487 │        5.194 │          2.5 │\n",
      "│        257.1 │        2.511 │        5.101 │          2.5 │\n",
      "│        257.1 │        2.511 │        5.101 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 17.0643 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.457 │       1.11 │         15 │       0.19 │\n",
      "│      Train │       0.52 │       1.44 │       14.6 │      0.249 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        3.136 │            0 │        17.51 │            5 │\n",
      "│         6.08 │            0 │        9.317 │          2.5 │\n",
      "│        9.232 │            0 │        3.656 │         1.25 │\n",
      "│        12.12 │            0 │        1.375 │        0.625 │\n",
      "│        15.06 │            0 │       0.5161 │       0.3125 │\n",
      "│        18.02 │            0 │       0.1691 │       0.1562 │\n",
      "│        21.19 │            0 │       0.0678 │      0.07812 │\n",
      "│        24.41 │            0 │      0.02118 │      0.03906 │\n",
      "│        27.75 │            0 │     0.005834 │      0.01953 │\n",
      "│        30.89 │            0 │     0.001523 │     0.009766 │\n",
      "│        33.86 │            0 │    0.0003962 │     0.004883 │\n",
      "│        33.86 │            0 │    0.0003962 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 33.8589 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.476 │       1.22 │       14.8 │      0.217 │\n",
      "│      Train │      0.549 │       1.58 │       14.1 │      0.294 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│       Fitting filters        │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        5.504 │        2.038 │        242.7 │            5 │\n",
      "│        11.24 │          2.4 │        37.06 │          2.5 │\n",
      "│         16.8 │        2.652 │        5.041 │          2.5 │\n",
      "│        22.31 │        2.599 │        1.147 │          2.5 │\n",
      "│        27.87 │        2.262 │        3.341 │          2.5 │\n",
      "│        34.41 │        2.005 │         4.01 │          2.5 │\n",
      "│        42.23 │        1.861 │         4.27 │          2.5 │\n",
      "│        50.63 │        1.998 │        6.253 │          2.5 │\n",
      "│        58.66 │        2.091 │        8.798 │          2.5 │\n",
      "│        66.22 │         2.04 │        7.562 │          2.5 │\n",
      "│        73.27 │        1.982 │        6.324 │          2.5 │\n",
      "│        80.53 │        1.917 │        5.257 │          2.5 │\n",
      "│        88.63 │        2.006 │        4.279 │          2.5 │\n",
      "│        95.97 │        2.053 │        4.215 │          2.5 │\n",
      "│        102.3 │            2 │        3.535 │          2.5 │\n",
      "│          111 │        1.997 │        3.092 │          2.5 │\n",
      "│        119.4 │        1.988 │        3.283 │          2.5 │\n",
      "│        126.1 │        1.975 │        3.426 │          2.5 │\n",
      "│        134.9 │        1.941 │        3.575 │          2.5 │\n",
      "│        140.8 │        1.981 │        4.723 │          2.5 │\n",
      "│        146.2 │        1.905 │         4.12 │          2.5 │\n",
      "│          152 │        1.888 │        2.987 │          2.5 │\n",
      "│        158.9 │        1.881 │         3.36 │          2.5 │\n",
      "│        164.2 │        1.864 │        3.754 │          2.5 │\n",
      "│        169.9 │        1.838 │        4.838 │          2.5 │\n",
      "│        177.3 │        1.847 │        4.151 │          2.5 │\n",
      "│        183.7 │        2.225 │        8.246 │          2.5 │\n",
      "│        189.9 │        1.974 │        7.279 │          2.5 │\n",
      "│        195.9 │        1.942 │        6.259 │          2.5 │\n",
      "│        202.2 │        2.076 │        6.998 │          2.5 │\n",
      "│          208 │        2.003 │        6.847 │          2.5 │\n",
      "│        213.8 │        2.034 │        6.712 │          2.5 │\n",
      "│        219.1 │        1.885 │         4.85 │          2.5 │\n",
      "│        224.3 │        1.969 │        3.788 │          2.5 │\n",
      "│        230.2 │        2.103 │        3.777 │          2.5 │\n",
      "│        235.9 │        2.079 │        3.168 │          2.5 │\n",
      "│        242.4 │        2.075 │        3.041 │          2.5 │\n",
      "│        248.6 │        2.169 │        3.352 │          2.5 │\n",
      "│        255.2 │        2.285 │        4.787 │          2.5 │\n",
      "│        262.7 │        2.322 │        4.974 │          2.5 │\n",
      "│        262.7 │        2.322 │        4.974 │          2.5 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Finished optimizing W. Elapsed time: 4 min., 22.717 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.445 │      0.959 │       15.9 │     0.0875 │\n",
      "│      Train │      0.487 │       1.23 │       15.4 │      0.155 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "\n",
      "\n",
      "╒══════════════════════════════╕\n",
      "│     Fitting nonlinearity     │\n",
      "╘══════════════════════════════╛\n",
      "╭──────────────┬──────────────┬──────────────┬──────────────╮\n",
      "│   Time (s)   │ Primal resid │  Dual resid  │     rho      │\n",
      "├──────────────┼──────────────┼──────────────┼──────────────┤\n",
      "│        2.556 │            0 │        21.41 │            5 │\n",
      "│         5.06 │            0 │        12.29 │          2.5 │\n",
      "│        7.577 │            0 │        4.966 │         1.25 │\n",
      "│         10.1 │            0 │        2.471 │        0.625 │\n",
      "│        12.67 │            0 │       0.9692 │       0.3125 │\n",
      "│        15.21 │            0 │       0.3394 │       0.1562 │\n",
      "│         17.8 │            0 │       0.1137 │      0.07812 │\n",
      "│        20.42 │            0 │      0.01613 │      0.03906 │\n",
      "│         23.2 │            0 │     0.009382 │      0.01953 │\n",
      "│        25.88 │            0 │     0.001896 │     0.009766 │\n",
      "│        28.43 │            0 │    0.0004264 │     0.004883 │\n",
      "│        28.43 │            0 │    0.0004264 │     0.004883 │\n",
      "╰──────────────┴──────────────┴──────────────┴──────────────╯\n",
      "\n",
      "Converged after 11 iterations!\n",
      "Finished optimizing f. Elapsed time: 28.4315 s\n",
      "╭────────────┬────────────┬────────────┬────────────┬────────────╮\n",
      "│    Set     │     CC     │    LLI     │    RMSE    │    FEV     │\n",
      "├────────────┼────────────┼────────────┼────────────┼────────────┤\n",
      "│       Test │      0.494 │       1.26 │       14.6 │      0.233 │\n",
      "│      Train │      0.552 │       1.56 │       14.1 │      0.298 │\n",
      "╰────────────┴────────────┴────────────┴────────────┴────────────╯\n",
      "Done.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lnln = models.LNLN(stimulus, rates[cell], (stim_dim, hist_dim), num_subunits=4)\n",
    "lnln.add_regularizer('W', 'sparse', gamma=1.25)\n",
    "lnln.add_regularizer('W', 'nucnorm', gamma=1.7)\n",
    "lnln.fit(num_alt=15, max_iter=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show = lambda: 1  # Prevent tentbasis.plot from plotting prematurely\n",
    "lnln.plot()\n",
    "\n",
    "# Format to resemble figure 3\n",
    "bar_size = 55.5\n",
    "center_y = 27\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(12)\n",
    "fig.subplots_adjust(wspace=0.03, hspace=0.3)\n",
    "for ax in fig.get_axes():\n",
    "    if ax.is_first_row():\n",
    "        ax.set_ylim(center_y+np.array([-700/bar_size, 700/bar_size]))\n",
    "        if ax.is_first_col():\n",
    "            ax.set_xticks([0, 20, 40])\n",
    "            ax.set_xticklabels([400, 200, 0])\n",
    "            ax.set_yticks(center_y+np.array([-670/bar_size, 0, 670/bar_size]))\n",
    "            ax.set_yticklabels(['670', '0', '670'])\n",
    "            ax.set_xlabel('Time preceding spike (ms)', size=14)\n",
    "            ax.set_ylabel('Distance from\\nRF Center (um)', size=14)\n",
    "    else:\n",
    "        ax.set_xlim([-4, 4])\n",
    "        ax.set_ylim([-4, 24])\n",
    "        ax.spines['top'].set_visible(False)\n",
    "        ax.spines['right'].set_visible(False)\n",
    "        if ax.is_first_col():\n",
    "            ax.set_ylabel('Subunit output', size=14)\n",
    "            ax.set_xlabel('Input to nonlinearity (s.d.)', size=14)\n",
    "        else:\n",
    "            ax.set_xlabel('')\n",
    "            ax.set_ylabel('')\n",
    "            ax.set_xticklabels([])\n",
    "            ax.set_yticklabels([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above resembles figure 3 from the paper with the exception of\n",
    "- distorted filters\n",
    "- one failed subunit fit (third column)\n",
    "- reversed nonlinearities and flipped signs of the filters"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}