one.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from Analysis.analyze_reim import do_analysis\n",
    "from Analysis.util import get_intensity\n",
    "from test import reverse_get_formula, find_percentile_indices, sfg, shg1, shg2\n",
    "from Utilz.losses import calculate_MSE_metrics as mse_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_vectors_shg1 = np.load(\"Data/shg_freq_domain_ds.npy\")\n",
    "freq_vectors_shg2 = freq_vectors_shg1  # these are equivalent here\n",
    "freq_vectors_sfg = np.load(\"Data/sfg_freq_domain_ds.npy\")\n",
    "\n",
    "domain_spacing_1 = (\n",
    "    freq_vectors_shg1[1] - freq_vectors_shg1[0]\n",
    ")  # * 1e12  # scaled to be back in Hz\n",
    "domain_spacing_2 = freq_vectors_shg2[1] - freq_vectors_shg2[0]  # * 1e12\n",
    "domain_spacing_3 = freq_vectors_sfg[1] - freq_vectors_sfg[0]  # * 1e12\n",
    "\n",
    "sfg_original_freq = np.load(\"Data/sfg_original_freq_vector.npy\")\n",
    "sfg_original_time = np.load(\"Data/sfg_original_time_vector.npy\")\n",
    "sfg_original_time_ds = sfg_original_time[1] - sfg_original_time[0]\n",
    "\n",
    "# required lists for plotting the frequency domain\n",
    "freq_vectors_sfg_list = [freq_vectors_sfg, freq_vectors_sfg]\n",
    "freq_vectors_shg1_list = [freq_vectors_shg1, freq_vectors_shg1]\n",
    "freq_vectors_shg2_list = [freq_vectors_shg2, freq_vectors_shg2]\n",
    "\n",
    "# required lists for plotting the time domain\n",
    "sfg_time_vector_list = [sfg_original_time, sfg_original_time]\n",
    "shg1_time_vector_list = [sfg_original_time, sfg_original_time]\n",
    "shg2_time_vector_list = [sfg_original_time, sfg_original_time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((348,), (1892,), (32768,), (32768,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_vectors_sfg.shape, freq_vectors_shg1.shape, sfg_original_freq.shape, sfg_original_time.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_MSE_metrics(\n",
    "    data_dir,\n",
    "    model_save_name,\n",
    "    output_dir,\n",
    "    examples_list,\n",
    "    domain_type=\"time\",\n",
    "    which=\"sfg\",\n",
    "    normalize=True,\n",
    "):\n",
    "    if domain_type == \"time\":\n",
    "        return_vals = 1\n",
    "    else:\n",
    "        return_vals = 2\n",
    "    # init for shape\n",
    "    (\n",
    "        init_sfg_time_true,\n",
    "        init_sfg_time_pred,\n",
    "        shg1_time_true,\n",
    "        shg1_time_pred,\n",
    "        shg2_time_true,\n",
    "        shg2_time_pred,\n",
    "    ) = do_analysis(\n",
    "        output_dir,\n",
    "        data_dir,\n",
    "        model_save_name,\n",
    "        91,\n",
    "        0,\n",
    "        return_vals=return_vals,\n",
    "    )\n",
    "\n",
    "    # get the shape of the data\n",
    "    init_sfg_time_true = torch.tensor(init_sfg_time_true)\n",
    "    sfg_err_values = [\n",
    "        torch.zeros(init_sfg_time_true.shape) for _ in range(len(examples_list))\n",
    "    ]\n",
    "\n",
    "    i = 0\n",
    "    for file_idx, example_idx in examples_list:\n",
    "        print(f\"File: {file_idx}, Example: {example_idx}\")\n",
    "        (\n",
    "            sfg_time_true,\n",
    "            sfg_time_pred,\n",
    "            shg1_time_true,\n",
    "            shg1_time_pred,\n",
    "            shg2_time_true,\n",
    "            shg2_time_pred,\n",
    "        ) = do_analysis(\n",
    "            output_dir,\n",
    "            data_dir,\n",
    "            model_save_name,\n",
    "            file_idx,\n",
    "            example_idx,\n",
    "            return_vals=return_vals,\n",
    "        )\n",
    "\n",
    "        if which == \"sfg\":\n",
    "            pass\n",
    "        elif which == \"shg1\":\n",
    "            sfg_time_true = shg1_time_true\n",
    "            sfg_time_pred = shg1_time_pred\n",
    "        else:\n",
    "            sfg_time_true = shg2_time_true\n",
    "            sfg_time_pred = shg2_time_pred\n",
    "\n",
    "        sfg_time_true = torch.tensor(sfg_time_true)\n",
    "        sfg_time_pred = torch.tensor(sfg_time_pred)\n",
    "\n",
    "        sfg_time_true_intensity = get_intensity(sfg_time_true)\n",
    "        sfg_time_pred_intensity = get_intensity(sfg_time_pred)\n",
    "\n",
    "        # sfg_time_diff = torch.abs(sfg_time_true_intensity - sfg_time_pred_intensity)\n",
    "        sfg_time_diff = sfg_time_true_intensity - sfg_time_pred_intensity\n",
    "        if normalize:\n",
    "            sfg_time_diff /= sfg_time_true_intensity\n",
    "        sfg_err_values[i] = sfg_time_diff\n",
    "        i += 1\n",
    "\n",
    "    return np.array(sfg_err_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"/mnt/threeterra/SFG_reIm_h5/\"\n",
    "output_dir = \"/mnt/twoterra/outputs/14-02-2024/\"\n",
    "model_save_name = \"LSTM_200_epoch_125\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "which = \"sfg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all examples is the cartesian product of the two lists\n",
    "# first one from (91, 100), second one from (0, 100)\n",
    "all_example_indexes = [(i, j) for i in range(91, 100) for j in range(100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NpzFile 'metrics.npz' with keys: sfg_metric, shg1_metric, shg2_metric"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the metrics.npz file\n",
    "metrics = np.load(\"metrics.npz\")\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "sfg_new_metric_time = mse_metrics(\n",
    "    data_dir, model_save_name, output_dir, return_vals=1, normalize=True)\n",
    "sfg_new_metric_freq = mse_metrics(data_dir, model_save_name, output_dir, return_vals=2, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_from_arr(f_idx, e_idx, arr1, arr2, arr3):\n",
    "    idx = (f_idx - 91) * 100 + e_idx\n",
    "    return np.array([arr1[idx], arr2[idx], arr3[idx]])\n",
    "\n",
    "def find_percentile_of_value(arr, value):\n",
    "    # Count how many values are less than the vlue\n",
    "    less_than_count = np.sum(arr < value)\n",
    "\n",
    "    # Count how many values are equal to the value\n",
    "    equal_count = np.sum(arr == value)\n",
    "\n",
    "    # Calculate the rank of the value\n",
    "    rank = less_than_count + 0.5 * equal_count\n",
    "\n",
    "    # Calculate the percentile rank\n",
    "    percentile_rank = (rank / arr.size) * 100\n",
    "\n",
    "    return percentile_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.10794559067065636, 0.027044379936199854, 0.02787373897824377)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfg_new_metric_time[0].mean(), sfg_new_metric_time[1].mean(), sfg_new_metric_time[2].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.03537238, 0.05558425, 0.05412802])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_from_arr(98, 25, sfg_new_metric_time[0], sfg_new_metric_time[1], sfg_new_metric_time[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04049244, 0.08877644, 0.08862293], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_from_arr(98, 25, sfg_new_metric_freq[0], sfg_new_metric_freq[1], sfg_new_metric_freq[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.94444444444444"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_percentile_of_value(sfg_new_metric_time[0], get_from_arr(98, 25, sfg_new_metric_time[0], sfg_new_metric_time[1], sfg_new_metric_time[2])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88.5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_percentile_of_value(sfg_new_metric_time[1], get_from_arr(98, 25, sfg_new_metric_time[0], sfg_new_metric_time[1], sfg_new_metric_time[2])[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.16666666666667"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_percentile_of_value(sfg_new_metric_time[2], get_from_arr(98, 25, sfg_new_metric_time[0], sfg_new_metric_time[1], sfg_new_metric_time[2])[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the new metric is the metric_time and metric_freq added together and weigthed by 0.5\n",
    "new_metric_sfg = (0.5 * sfg_new_metric_time[0] + 0.5 * sfg_new_metric_freq[0])\n",
    "new_metric_shg1 = (0.5 * sfg_new_metric_time[1] + 0.5 * sfg_new_metric_freq[1])\n",
    "new_metric_shg2 = (0.5 * sfg_new_metric_time[2] + 0.5 * sfg_new_metric_freq[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.10341475082489708, 0.02629365314088183, 0.026694959123404527)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_metric_sfg.mean(), new_metric_shg1.mean(), new_metric_shg2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.03793241, 0.07218034, 0.07137548])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_from_arr(98, 25, new_metric_sfg, new_metric_shg1, new_metric_shg2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50.16666666666667, 93.38888888888889, 93.16666666666666)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    find_percentile_of_value(\n",
    "        new_metric_sfg,\n",
    "        get_from_arr(98, 25, new_metric_sfg, new_metric_shg1, new_metric_shg2)[0],\n",
    "    ),\n",
    "    find_percentile_of_value(\n",
    "        new_metric_shg1,\n",
    "        get_from_arr(98, 25, new_metric_sfg, new_metric_shg1, new_metric_shg2)[1],\n",
    "    ),\n",
    "    find_percentile_of_value(\n",
    "        new_metric_shg2,\n",
    "        get_from_arr(98, 25, new_metric_sfg, new_metric_shg1, new_metric_shg2)[2],\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10341475082489708"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((sfg_new_metric_time[0] + sfg_new_metric_freq[0]) / 2).mean() # sfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02629365314088183"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((sfg_new_metric_time[1] + sfg_new_metric_freq[1]) / 2).mean() # shg1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026694959123404527"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((sfg_new_metric_time[2] + sfg_new_metric_freq[2]) / 2).mean() # shg2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfg_metric = (sfg_new_metric_time[0] + sfg_new_metric_freq[0]) / 2\n",
    "shg1_metric = (sfg_new_metric_time[1] + sfg_new_metric_freq[1]) / 2\n",
    "shg2_metric = (sfg_new_metric_time[2] + sfg_new_metric_freq[2]) / 2\n",
    "\n",
    "# save these in a .npz file\n",
    "np.savez(\n",
    "    \"metrics.npz\",\n",
    "    sfg_metric=sfg_metric,\n",
    "    shg1_metric=shg1_metric,\n",
    "    shg2_metric=shg2_metric,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get(f_idx, e_idx):\n",
    "    idx = (f_idx - 91) * 100 + e_idx\n",
    "    return np.array([sfg_metric[idx], shg1_metric[idx], shg2_metric[idx]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.03793241, 0.07218034, 0.07137548]),\n",
       " array([0.00062407, 0.00049234, 0.00043857]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get(98, 25), get(93, 56)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.10341475082489708, 0.02629365314088183, 0.026694959123404527)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfg_metric.mean(), shg1_metric.mean(), shg2_metric.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50.16666666666667, 93.38888888888889, 93.16666666666666)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_percentile_of_value(sfg_metric, get(98, 25)[0]), find_percentile_of_value(shg1_metric, get(98, 25)[1]), find_percentile_of_value(shg2_metric, get(98, 25)[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6111111111111112, 0.3888888888888889, 0.3888888888888889)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_percentile_of_value(sfg_metric, get(93, 56)[0]), find_percentile_of_value(shg1_metric, get(93, 56)[1]), find_percentile_of_value(shg2_metric, get(93, 56)[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.15904048e-06, 9.16098725e-06, 2.96912640e-05, 4.33963469e-05,\n",
       "       1.53864657e-05, 5.05198568e-06, 4.90054917e-05, 8.62591241e-06,\n",
       "       3.60367761e-04, 2.54736119e-06, 3.34375254e-05, 7.27778941e-04,\n",
       "       2.50433186e-06, 1.28808344e-04, 1.31663755e-05, 2.16395798e-04,\n",
       "       6.75047515e-04, 1.67168633e-04, 3.24473693e-03, 7.00446981e-06,\n",
       "       1.87666435e-03, 2.37143777e-05, 2.33566470e-06, 3.75322270e-06,\n",
       "       8.07428387e-06, 2.21550654e-05, 1.29981330e-04, 1.81699943e-05,\n",
       "       1.21292681e-03, 1.29419950e-05, 5.99957311e-05, 4.87832904e-06,\n",
       "       4.52275213e-04, 5.83535817e-04, 9.42112310e-05, 5.97334641e-04,\n",
       "       8.55819162e-05, 1.63584831e-04, 3.37829930e-04, 9.36471508e-04,\n",
       "       1.57001286e-05, 8.59068648e-04, 1.10484753e-03, 1.03317806e-03,\n",
       "       4.82078804e-06, 1.66784987e-04, 4.26847109e-05, 6.65882226e-06,\n",
       "       7.00444216e-05, 4.54928067e-06, 1.13440819e-05, 8.26407086e-06,\n",
       "       1.43373138e-04, 1.53136789e-05, 2.20655488e-06, 5.67374673e-06,\n",
       "       2.29897050e-05, 3.32476688e-04, 8.78265768e-04, 7.94527805e-05,\n",
       "       4.82322685e-05, 2.35790649e-05, 4.98439593e-04, 5.56937039e-06,\n",
       "       2.74415070e-04, 1.16633964e-04, 2.77934538e-04, 4.66566155e-04,\n",
       "       2.00436389e-05, 4.69197994e-06, 2.47871285e-05, 5.92256198e-04,\n",
       "       1.09504722e-03, 7.11492830e-05, 1.19867989e-04, 5.11659891e-04,\n",
       "       5.33234270e-05, 3.81786813e-04, 3.25691479e-04, 2.16885048e-04,\n",
       "       9.69707617e-05, 3.63551109e-04, 3.57371973e-05, 2.14839001e-05,\n",
       "       5.63899579e-04, 2.49594668e-05, 1.79457838e-05, 1.41977347e-04,\n",
       "       3.46737623e-04, 4.49518702e-05, 5.68857125e-04, 6.27144254e-05,\n",
       "       1.90891384e-04, 1.58376897e-05, 2.81615328e-04, 1.41180935e-05,\n",
       "       9.11473126e-06, 9.15618293e-05, 8.57683244e-06, 3.38768518e-06,\n",
       "       1.25458086e-04, 2.38086141e-05, 8.22587317e-05, 6.50457514e-04,\n",
       "       1.15122471e-03, 4.84270131e-05, 1.93105952e-05, 1.30252936e-03,\n",
       "       2.87012255e-04, 2.94702349e-05, 3.51842667e-04, 1.63987759e-04,\n",
       "       3.48464855e-05, 1.46364437e-05, 3.56682017e-06, 3.74952884e-04,\n",
       "       1.19861841e-04, 6.47541310e-05, 2.60743104e-06, 4.54337896e-05,\n",
       "       2.81132408e-04, 5.96775906e-04, 7.21642398e-04, 8.01616552e-05,\n",
       "       4.26214137e-06, 4.50418429e-06, 2.89741251e-06, 7.63581411e-05,\n",
       "       5.21151640e-04, 1.42117729e-03, 2.13376610e-04, 7.87016761e-05,\n",
       "       4.10205685e-05, 7.93777872e-05, 5.91257194e-05, 4.73740984e-05,\n",
       "       4.38671850e-05, 1.51063784e-06, 1.27927677e-04, 4.85591545e-06,\n",
       "       3.52492352e-04, 5.57281950e-04, 5.80454325e-05, 1.55453636e-05,\n",
       "       2.07432080e-04, 9.07266585e-05, 5.21735940e-03, 3.50302798e-05,\n",
       "       4.27449617e-04, 3.38121754e-04, 4.88788763e-04, 9.75877902e-06,\n",
       "       1.56667945e-03, 3.72867794e-06, 4.13519228e-05, 5.60809021e-05,\n",
       "       4.69796360e-05, 1.49535326e-05, 7.30916236e-06, 9.83174687e-06,\n",
       "       3.27621907e-04, 3.57626391e-06, 1.47406070e-03, 1.63881225e-04,\n",
       "       4.00671779e-05, 1.77938800e-05, 1.16373616e-04, 2.28838006e-04,\n",
       "       1.22961914e-03, 2.39279962e-06, 1.28585219e-04, 1.77895947e-06,\n",
       "       3.81409845e-05, 3.46844627e-06, 7.90002698e-04, 4.44661651e-04,\n",
       "       3.94680355e-05, 1.04003222e-04, 9.39446090e-06, 8.69557407e-05,\n",
       "       2.61781793e-06, 1.71415850e-05, 8.30239733e-04, 5.74600068e-04,\n",
       "       3.43824126e-04, 4.08768801e-06, 1.69193736e-05, 4.26512968e-04,\n",
       "       1.23234396e-03, 8.78198080e-06, 2.83847883e-04, 1.25850085e-04,\n",
       "       1.32146952e-05, 2.28880890e-06, 1.99340226e-04, 1.24936114e-05,\n",
       "       2.79500068e-06, 5.11263730e-04, 2.58292246e-04, 6.30270961e-06,\n",
       "       8.15916865e-05, 3.10197902e-05, 7.59782968e-04, 1.94799650e-05,\n",
       "       8.60757882e-06, 1.17494556e-05, 3.03610752e-04, 2.56414292e-04,\n",
       "       2.80153090e-05, 2.60792277e-03, 1.43742682e-05, 2.71298835e-04,\n",
       "       3.34398937e-05, 2.00074181e-04, 2.26097727e-05, 8.76256527e-05,\n",
       "       5.30468460e-05, 3.68143286e-04, 1.22064527e-03, 1.30675116e-05,\n",
       "       5.61092893e-05, 5.07164164e-04, 9.26809735e-04, 1.35650143e-05,\n",
       "       1.21927925e-03, 1.84196673e-04, 4.57320333e-04, 2.88944193e-05,\n",
       "       2.38430846e-04, 5.64347720e-04, 6.36812329e-05, 8.21220630e-04,\n",
       "       8.55731014e-06, 8.45722825e-05, 1.07005826e-05, 5.84579422e-04,\n",
       "       7.22703000e-04, 6.92931877e-04, 4.19475655e-05, 1.68294748e-04,\n",
       "       3.55203985e-04, 5.40783338e-04, 2.96144659e-04, 9.56392978e-06,\n",
       "       6.92069443e-05, 1.67563360e-03, 2.43400245e-05, 5.78130828e-04,\n",
       "       4.51827327e-05, 1.02865511e-06, 4.46937454e-04, 5.64167625e-04,\n",
       "       4.69550112e-04, 1.66205864e-05, 1.95651810e-04, 1.93595151e-05,\n",
       "       1.09311429e-06, 4.24579594e-05, 5.33611514e-04, 5.71374083e-04,\n",
       "       6.22236548e-05, 8.21508365e-06, 1.23905804e-04, 4.61977215e-05,\n",
       "       1.12402166e-04, 2.22082628e-04, 7.16115665e-06, 6.99144402e-06,\n",
       "       4.47391241e-04, 1.22026489e-04, 5.36595413e-04, 2.26748743e-04,\n",
       "       8.97009391e-04, 6.53610099e-04, 1.16299452e-04, 3.41963205e-05,\n",
       "       7.89481390e-04, 6.69907138e-04, 3.65316431e-04, 6.13849959e-04,\n",
       "       1.27828869e-04, 7.42717471e-04, 1.06714597e-05, 2.34202118e-04,\n",
       "       7.02272082e-05, 7.44474586e-04, 3.10712523e-04, 1.60472553e-06,\n",
       "       2.01548115e-04, 3.22242209e-04, 2.42591777e-04, 9.21122191e-05,\n",
       "       1.13584712e-04, 5.05083473e-04, 5.61846187e-04, 3.31053336e-04,\n",
       "       1.60823311e-05, 1.51552231e-04, 1.82170970e-05, 8.06818571e-05,\n",
       "       4.44749632e-04, 3.48140049e-04, 1.13408358e-04, 3.44661166e-05,\n",
       "       1.04864994e-04, 2.68529722e-04, 2.20572605e-04, 3.46839479e-05,\n",
       "       6.03840090e-05, 1.24881335e-03, 4.57000033e-06, 9.62078397e-04,\n",
       "       2.28780298e-03, 5.01533468e-05, 8.58651474e-05, 3.33363714e-05,\n",
       "       1.45304206e-04, 5.30680663e-05, 1.17004449e-04, 1.08361390e-04,\n",
       "       1.37311436e-04, 1.86346268e-04, 1.47356157e-04, 1.38213609e-05,\n",
       "       1.32689356e-05, 1.49348823e-04, 4.89848253e-06, 8.08772238e-05,\n",
       "       6.40255257e-06, 1.15327898e-03, 4.40834374e-05, 6.58852659e-05,\n",
       "       1.03899409e-04, 2.40710979e-05, 5.91115444e-04, 8.00559210e-05,\n",
       "       3.37835968e-06, 4.67145255e-05, 8.55857434e-05, 9.86216910e-05,\n",
       "       3.24647190e-05, 1.49311396e-04, 7.77818961e-04, 5.17694480e-06,\n",
       "       7.55173505e-06, 1.11637513e-04, 1.78165537e-05, 1.84460423e-05,\n",
       "       8.20122776e-04, 1.84881326e-04, 9.77095738e-07, 9.89132750e-05,\n",
       "       1.14465263e-06, 4.53749417e-05, 2.93794292e-04, 9.68491004e-05,\n",
       "       2.92476179e-04, 8.48521013e-05, 1.22537639e-03, 8.92761545e-06,\n",
       "       1.80206407e-04, 6.09730669e-05, 7.57516091e-06, 8.73772387e-06,\n",
       "       7.30548709e-05, 7.29379317e-05, 1.50195498e-04, 1.26415375e-03,\n",
       "       3.00440661e-05, 4.65122808e-04, 8.20014859e-04, 1.93276617e-04,\n",
       "       4.58150271e-06, 1.67021444e-05, 1.32989526e-05, 2.44515354e-06,\n",
       "       1.23498385e-05, 1.01238060e-04, 7.85469485e-04, 2.45996926e-04,\n",
       "       5.97215512e-05, 1.10981731e-04, 2.40945519e-05, 2.44384340e-04,\n",
       "       1.05130865e-04, 2.52819700e-05, 5.05500884e-06, 9.73834976e-05,\n",
       "       7.25590507e-05, 3.29022878e-04, 5.92169672e-05, 4.10459688e-05,\n",
       "       6.02486944e-05, 4.35403563e-05, 1.45584039e-04, 9.69145003e-06,\n",
       "       4.66719765e-04, 2.16364249e-04, 3.59340629e-04, 2.88488682e-05,\n",
       "       4.17795178e-04, 7.27866427e-04, 9.61143269e-06, 4.48172243e-04,\n",
       "       1.92256484e-05, 6.63342944e-05, 1.98688504e-04, 3.60611011e-05,\n",
       "       5.43163973e-04, 6.63117855e-04, 4.30739565e-05, 2.81383866e-04,\n",
       "       3.69411900e-05, 1.27485209e-05, 8.22350965e-04, 1.44051382e-05,\n",
       "       4.25997750e-05, 6.47689922e-06, 8.03444709e-04, 4.09925357e-04,\n",
       "       1.55571994e-04, 1.20761237e-04, 6.97837619e-04, 7.59118993e-05,\n",
       "       1.05991494e-04, 1.11295712e-05, 1.46824996e-05, 7.52062215e-06,\n",
       "       1.37700852e-06, 2.47288226e-05, 3.62695049e-04, 4.72396991e-04,\n",
       "       7.06743322e-06, 7.26629398e-04, 5.93843106e-05, 9.87621479e-06,\n",
       "       6.46495073e-06, 2.79578118e-04, 1.06264044e-04, 3.45360022e-04,\n",
       "       1.64724799e-04, 1.18683283e-05, 4.02949139e-04, 8.61564113e-05,\n",
       "       2.57750035e-05, 4.42956616e-06, 9.27429846e-06, 2.42254464e-04,\n",
       "       2.97438266e-04, 5.60420449e-06, 1.99262882e-04, 4.17109477e-05,\n",
       "       6.18213307e-05, 1.66824553e-03, 4.03890415e-04, 1.96688488e-05,\n",
       "       1.96808264e-06, 4.16493604e-05, 8.55589169e-05, 3.18364648e-04,\n",
       "       6.35224336e-04, 2.23079449e-04, 2.83449863e-05, 3.28779075e-04,\n",
       "       2.92543405e-06, 7.99583631e-06, 1.10468327e-05, 2.10056052e-04,\n",
       "       1.48507288e-05, 2.57319305e-04, 2.71237259e-05, 4.77680471e-04,\n",
       "       8.53475212e-05, 6.29769493e-05, 1.12085854e-05, 3.62153602e-04,\n",
       "       3.91219335e-04, 6.24044405e-05, 5.61033958e-05, 3.71693692e-04,\n",
       "       9.89224995e-04, 4.88920850e-05, 1.89991406e-04, 9.80428831e-06,\n",
       "       9.78120952e-04, 5.47353629e-05, 8.70942131e-06, 3.31196170e-05,\n",
       "       2.83148947e-05, 3.12661105e-05, 1.30315733e-04, 2.81556597e-04,\n",
       "       7.79897382e-04, 9.62241902e-05, 2.00956893e-05, 6.84752420e-04,\n",
       "       3.06022645e-04, 7.33833353e-04, 5.02925250e-05, 1.16743636e-03,\n",
       "       1.63470034e-03, 8.99506675e-04, 9.65265644e-05, 2.12199113e-04,\n",
       "       1.05799030e-04, 9.42596525e-05, 1.79021790e-05, 5.99187706e-06,\n",
       "       7.70878160e-06, 5.22919698e-04, 5.09923324e-04, 1.34328739e-05,\n",
       "       4.40744217e-04, 1.34734310e-05, 2.88409443e-04, 1.23696498e-06,\n",
       "       9.48501747e-06, 2.38133493e-04, 1.82345451e-04, 8.95191533e-06,\n",
       "       1.65348826e-03, 7.07916615e-06, 2.78685529e-05, 5.04701282e-04,\n",
       "       9.89709399e-04, 3.07263108e-04, 1.38037241e-04, 7.20736862e-05,\n",
       "       1.01893766e-05, 3.23938571e-06, 1.07132764e-05, 1.74417882e-03,\n",
       "       1.70722575e-04, 4.93082152e-05, 3.48537927e-04, 3.56270139e-05,\n",
       "       5.04822965e-05, 4.56682719e-05, 2.55256073e-05, 1.00459991e-04,\n",
       "       5.55761682e-04, 7.64951750e-04, 3.63329251e-04, 7.99638592e-03,\n",
       "       4.13376343e-04, 1.91050640e-04, 3.75199420e-06, 9.81243677e-04,\n",
       "       1.23546139e-04, 5.67416682e-05, 1.48765448e-05, 4.26075596e-04,\n",
       "       2.78983091e-04, 2.18207599e-04, 7.28231362e-06, 1.01281586e-03,\n",
       "       8.24653020e-04, 6.16722173e-05, 6.36338955e-04, 3.84336054e-05,\n",
       "       2.82720775e-05, 2.01759845e-04, 7.95607775e-05, 1.51691147e-05,\n",
       "       1.82520642e-04, 1.24345097e-05, 1.62580836e-05, 1.30335893e-05,\n",
       "       1.53997771e-05, 1.19109194e-04, 2.76268314e-04, 1.63750622e-06,\n",
       "       4.04639635e-04, 9.45504726e-05, 2.43688337e-04, 1.10133886e-04,\n",
       "       4.68315084e-05, 5.88798721e-05, 2.23930416e-04, 1.90383944e-05,\n",
       "       7.90318882e-04, 6.67398053e-05, 1.34700610e-06, 1.25929830e-04,\n",
       "       5.99978666e-04, 1.16440460e-04, 6.68766588e-05, 1.13697151e-05,\n",
       "       7.28650542e-04, 5.54482453e-04, 6.58041681e-04, 5.51650184e-04,\n",
       "       4.72210741e-06, 8.41754299e-05, 8.57881605e-05, 1.13866176e-03,\n",
       "       5.72724857e-05, 9.69592111e-06, 1.77483707e-05, 3.29889852e-04,\n",
       "       1.97731919e-04, 5.64880156e-06, 1.46296697e-05, 7.45875377e-06,\n",
       "       1.53852350e-04, 7.10726963e-05, 1.05497136e-04, 3.96634015e-04,\n",
       "       5.38143249e-05, 2.65313807e-04, 4.00996141e-05, 1.97209974e-05,\n",
       "       9.74924551e-05, 1.40107615e-04, 6.80782599e-04, 5.68738615e-04,\n",
       "       3.25440888e-05, 1.60258096e-05, 1.81320065e-04, 1.90865685e-04,\n",
       "       8.47182309e-05, 9.70543129e-04, 1.88022503e-04, 1.31805996e-06,\n",
       "       1.64418423e-04, 1.64634708e-04, 2.13558596e-05, 1.21331705e-05,\n",
       "       1.33957328e-05, 1.15375100e-04, 6.53455209e-05, 8.86990165e-06,\n",
       "       1.01034436e-03, 4.67898026e-05, 1.79838062e-05, 1.26083369e-05,\n",
       "       3.14618155e-05, 1.14054645e-04, 1.11167508e-04, 6.34565542e-04,\n",
       "       6.81841280e-04, 1.37846742e-04, 5.52898091e-05, 1.62364493e-04,\n",
       "       1.29356135e-06, 7.94563039e-06, 1.55649555e-04, 8.88331560e-05,\n",
       "       3.81163176e-04, 7.14344671e-04, 8.25739116e-05, 4.36076625e-05,\n",
       "       2.78678373e-04, 6.78189872e-06, 7.90218746e-06, 3.03760171e-04,\n",
       "       6.93746551e-05, 7.24636720e-06, 1.50037711e-04, 4.52705659e-03,\n",
       "       4.49330037e-05, 1.64739813e-05, 4.08835505e-04, 2.98047325e-06,\n",
       "       4.49349813e-04, 6.80426019e-04, 3.99960816e-04, 3.35390032e-05,\n",
       "       3.17151891e-04, 8.50066281e-05, 1.33484418e-05, 2.17934852e-04,\n",
       "       1.61316766e-05, 2.95960872e-05, 7.47676939e-04, 4.05899278e-04,\n",
       "       3.17949103e-04, 2.91131524e-04, 3.92787944e-04, 3.65307824e-06,\n",
       "       6.69045112e-05, 4.80857489e-05, 3.82516795e-04, 1.95879707e-04,\n",
       "       7.52974229e-05, 4.77952883e-03, 3.05246096e-04, 3.64193193e-06,\n",
       "       8.77421175e-04, 6.40376646e-04, 2.34890035e-06, 2.42077658e-05,\n",
       "       2.53266135e-05, 5.89884236e-04, 2.73247697e-05, 7.39422976e-05,\n",
       "       8.05728632e-06, 3.08005183e-05, 4.02114732e-04, 2.38307234e-06,\n",
       "       1.07644264e-05, 2.94871803e-04, 9.17237339e-05, 1.21368642e-03,\n",
       "       8.21388979e-03, 3.41307327e-06, 4.03736864e-04, 1.59824165e-04,\n",
       "       4.78523143e-05, 3.89911933e-04, 1.62163378e-05, 8.42278041e-06,\n",
       "       4.72598913e-05, 1.82992357e-04, 8.61721637e-05, 8.95798075e-05,\n",
       "       2.76648934e-05, 5.53930986e-05, 8.32949809e-06, 3.65029518e-05,\n",
       "       4.76171612e-04, 4.47797647e-04, 1.26476734e-04, 8.62030720e-04,\n",
       "       6.31624542e-04, 1.16357594e-04, 4.89371196e-05, 5.55745646e-05,\n",
       "       3.35819641e-05, 7.13214395e-05, 3.26202397e-04, 1.10431679e-03,\n",
       "       3.51120136e-04, 2.25842348e-04, 2.74534628e-04, 2.95046339e-04,\n",
       "       1.81025558e-03, 3.27244270e-05, 6.86240601e-05, 4.80082554e-06,\n",
       "       1.11568377e-04, 7.07688596e-05, 2.87364601e-05, 2.07287318e-04,\n",
       "       7.63237858e-06, 5.49671631e-06, 7.68557766e-06, 1.95258854e-05,\n",
       "       1.84080054e-05, 7.28575969e-06, 3.10835057e-05, 1.49366620e-04,\n",
       "       4.93989246e-05, 1.39046504e-04, 3.40998231e-05, 1.82481614e-04,\n",
       "       6.36861441e-05, 2.38598353e-04, 3.76077787e-06, 3.35711316e-04,\n",
       "       5.05962635e-05, 1.61433080e-03, 1.60848358e-04, 6.21662592e-04,\n",
       "       2.88525422e-04, 6.04711158e-06, 8.30474733e-07, 5.04612444e-06,\n",
       "       2.25232543e-05, 1.05451072e-04, 1.36825649e-04, 1.16284045e-05,\n",
       "       7.20059052e-06, 1.19517463e-04, 1.42688944e-03, 5.99017767e-05,\n",
       "       2.13698004e-05, 1.30837818e-03, 3.93277645e-04, 5.14800486e-04,\n",
       "       1.84296223e-05, 2.27876935e-05, 9.17492653e-05, 1.36611479e-05,\n",
       "       1.57745217e-05, 5.51802805e-06, 1.39798831e-05, 5.16272150e-04,\n",
       "       2.14713436e-05, 1.90219525e-05, 3.20166262e-04, 9.54111383e-05,\n",
       "       5.31002806e-05, 6.22655079e-06, 2.81605375e-04, 8.07541655e-05,\n",
       "       1.46989642e-05, 1.08224431e-05, 8.91697855e-05, 8.39360146e-05,\n",
       "       2.60320434e-04, 6.88780055e-05, 8.64003523e-05, 5.28872124e-06,\n",
       "       3.99831079e-05, 1.30951294e-05, 3.48194517e-05, 1.54909576e-05,\n",
       "       3.89932648e-05, 1.07051101e-05, 7.28274836e-06, 1.15832054e-05,\n",
       "       3.76932876e-05, 4.57930350e-04, 6.90469099e-03, 2.75332932e-05,\n",
       "       8.95812536e-06, 7.01442244e-04, 1.77387497e-03, 1.09741513e-05,\n",
       "       6.38836491e-06, 1.30778099e-05, 3.22259848e-06, 2.27217151e-06,\n",
       "       1.65299585e-04, 1.79322706e-05, 1.51564709e-05, 2.43344301e-04,\n",
       "       1.96422079e-05, 4.17502124e-05, 7.59406430e-06, 2.53695744e-05,\n",
       "       1.15161776e-04, 8.80045918e-05, 8.32783408e-05, 4.89206604e-06,\n",
       "       1.24452399e-05, 5.47233185e-06, 7.73895532e-04, 2.45280011e-04,\n",
       "       9.96269318e-06, 5.23876370e-05, 4.50256834e-04, 3.86207103e-05,\n",
       "       5.42474800e-06, 7.54616667e-06, 2.86760624e-04, 2.36167281e-04,\n",
       "       1.03671141e-06, 4.62086900e-05, 9.49621681e-05, 4.78579386e-06,\n",
       "       4.14211769e-04, 3.51412775e-04, 4.85000637e-04, 1.48661347e-04,\n",
       "       6.94651448e-04, 1.32620067e-03, 1.06666876e-05, 1.66591053e-04,\n",
       "       1.34721895e-05, 9.05237994e-06, 3.38124053e-04, 7.89451005e-05,\n",
       "       3.64935142e-04, 2.47768214e-04, 4.23864840e-04, 5.53117097e-05,\n",
       "       1.23748876e-04, 5.01762005e-03, 7.11464672e-05, 4.33726200e-05,\n",
       "       2.88560909e-06, 1.10814035e-04, 7.33943380e-05, 2.18018686e-04,\n",
       "       1.55344833e-06, 9.95273331e-06, 9.38025405e-05, 1.21831699e-05,\n",
       "       7.28938394e-05, 5.67842275e-04, 9.38146695e-05, 6.56589255e-05,\n",
       "       3.80143407e-04, 8.09847043e-05, 9.28740250e-04, 5.31655620e-04,\n",
       "       5.13185980e-03, 1.57127710e-04, 4.67305072e-04, 2.16974604e-05,\n",
       "       2.62878719e-04, 7.66809353e-06, 6.39941618e-06, 2.70807122e-05,\n",
       "       5.15705397e-05, 1.78663558e-05, 1.49827087e-04, 3.70357666e-05],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfg_new_metric_freq[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.26512957e-32, 2.97647606e-31, 8.97797270e-32, 1.18571194e-30,\n",
       "       4.49084874e-31, 1.65296341e-32, 1.78527582e-31, 4.30356005e-32,\n",
       "       8.64219591e-31, 4.54042833e-32, 1.03225529e-30, 3.38573760e-29,\n",
       "       1.24011789e-32, 4.78451704e-30, 5.01586402e-31, 4.22899562e-31,\n",
       "       7.19602659e-30, 5.86621710e-30, 9.92414322e-30, 2.12588638e-32,\n",
       "       6.65140369e-30, 8.13298563e-32, 7.41963394e-32, 1.02342864e-31,\n",
       "       2.18760886e-31, 6.33147400e-31, 1.21687749e-30, 4.83705934e-32,\n",
       "       9.83152254e-30, 4.12723825e-31, 1.85762773e-31, 1.49847729e-32,\n",
       "       6.66593835e-31, 1.96788531e-30, 4.27815950e-31, 1.00986259e-30,\n",
       "       3.00491386e-31, 2.44053843e-30, 1.39144827e-29, 2.75625044e-30,\n",
       "       4.74593807e-31, 3.75581015e-30, 5.74109974e-30, 2.94167537e-30,\n",
       "       1.33730585e-31, 3.21713883e-30, 1.96425800e-31, 1.82105481e-31,\n",
       "       1.73655442e-30, 1.45348337e-31, 3.58590829e-31, 1.14221505e-31,\n",
       "       4.88231234e-31, 4.15472131e-31, 6.05468399e-32, 1.81180437e-31,\n",
       "       7.51454277e-31, 2.13185493e-31, 2.36585767e-30, 3.11862601e-30,\n",
       "       1.28734934e-31, 8.62419486e-31, 1.53279507e-30, 1.46045087e-31,\n",
       "       3.55675814e-31, 2.95580913e-30, 7.46642649e-31, 1.25029604e-30,\n",
       "       9.14793743e-32, 1.28401352e-31, 7.86869242e-31, 9.10671987e-30,\n",
       "       8.45499947e-30, 2.69100656e-31, 3.70825134e-30, 1.60715468e-30,\n",
       "       1.91041210e-30, 1.80282559e-30, 1.67540539e-30, 6.81681587e-31,\n",
       "       3.33631393e-31, 1.14607145e-30, 6.04490823e-31, 1.12334272e-31,\n",
       "       1.73072058e-30, 1.30815394e-31, 4.04855771e-31, 2.24114825e-30,\n",
       "       1.06389884e-30, 1.21314379e-31, 1.52447126e-30, 2.17820490e-31,\n",
       "       6.02451105e-31, 4.52487225e-31, 8.06127319e-31, 4.38199814e-32,\n",
       "       2.79674233e-32, 3.32677615e-30, 2.99607743e-31, 6.11819243e-32,\n",
       "       9.32054996e-32, 9.37915364e-32, 3.12319012e-30, 1.99611034e-30,\n",
       "       3.70175133e-30, 1.44842731e-30, 5.13265079e-31, 3.08928924e-30,\n",
       "       1.65435389e-30, 1.56427865e-31, 9.42815871e-31, 8.57663530e-31,\n",
       "       3.99891376e-31, 3.92186645e-32, 9.59755873e-32, 1.10455421e-30,\n",
       "       5.03263437e-30, 1.37778292e-30, 7.21444141e-32, 1.98956298e-31,\n",
       "       9.72355291e-31, 2.39299485e-30, 1.81754767e-30, 2.72810962e-31,\n",
       "       7.61846940e-32, 1.23193559e-31, 8.54818904e-32, 2.00179522e-30,\n",
       "       1.27787587e-30, 4.39077571e-30, 7.73921106e-30, 2.91788237e-31,\n",
       "       1.07227925e-31, 2.13764000e-31, 2.21632289e-31, 1.26940401e-31,\n",
       "       1.54300608e-31, 4.56776203e-33, 2.62027777e-31, 1.31850934e-31,\n",
       "       1.27289280e-30, 3.00675600e-30, 1.88406330e-31, 4.20962536e-31,\n",
       "       1.06757739e-30, 3.36968151e-30, 1.93537136e-29, 1.09719458e-30,\n",
       "       1.08672675e-30, 1.15376397e-30, 1.32857277e-30, 2.96857521e-32,\n",
       "       5.99245805e-30, 1.36025943e-32, 1.24423463e-30, 2.09842405e-30,\n",
       "       1.45598893e-30, 4.82711524e-32, 1.98647284e-31, 2.75553814e-31,\n",
       "       1.10587904e-30, 1.13206324e-31, 4.69220030e-30, 5.80268398e-30,\n",
       "       8.10454830e-31, 4.88094172e-31, 2.08619064e-30, 6.91915629e-31,\n",
       "       6.62523521e-31, 6.50078292e-32, 4.37269341e-30, 4.86693835e-32,\n",
       "       1.44135365e-30, 9.40683771e-32, 2.40956161e-30, 1.10963385e-30,\n",
       "       1.24467889e-31, 3.34136094e-30, 2.93919755e-32, 2.77752364e-31,\n",
       "       8.33905684e-32, 4.69144685e-31, 3.54677867e-30, 1.85126367e-30,\n",
       "       1.45776459e-30, 1.54663436e-32, 4.83157519e-31, 1.30594423e-30,\n",
       "       4.92975812e-30, 2.53037925e-31, 9.28249521e-31, 3.24599035e-31,\n",
       "       3.87995156e-32, 6.24517462e-32, 3.74253478e-30, 3.39600835e-31,\n",
       "       8.77356716e-32, 8.43571798e-31, 1.17896998e-29, 1.74888933e-31,\n",
       "       2.33049109e-30, 4.57812215e-31, 2.36500605e-30, 5.56234335e-31,\n",
       "       2.70573903e-31, 3.18587275e-31, 8.77108369e-30, 4.02035243e-30,\n",
       "       8.15279247e-31, 7.04238071e-29, 4.37999351e-31, 1.99657001e-30,\n",
       "       9.28001726e-31, 7.17609171e-30, 1.73680724e-31, 1.86899875e-31,\n",
       "       1.51772637e-30, 2.75656246e-30, 3.52134323e-30, 5.31914074e-32,\n",
       "       1.42687664e-30, 1.47795572e-30, 2.00822320e-30, 9.78155298e-32,\n",
       "       2.18622953e-30, 6.91462340e-30, 1.28881487e-29, 1.07484041e-31,\n",
       "       2.79615029e-30, 1.69987411e-30, 1.97787198e-31, 2.54782815e-30,\n",
       "       2.32673402e-31, 1.32417614e-30, 4.07958165e-32, 1.77471604e-30,\n",
       "       2.33191673e-30, 2.35439538e-30, 1.42850599e-31, 2.56331213e-31,\n",
       "       1.10739420e-30, 1.55452141e-30, 8.20382962e-31, 2.65913256e-31,\n",
       "       2.10709224e-30, 5.70911614e-30, 6.67982658e-31, 1.54918833e-30,\n",
       "       1.52499080e-31, 3.10069622e-33, 2.80521082e-30, 1.14277258e-29,\n",
       "       2.19217377e-30, 3.74917928e-31, 3.74194458e-30, 5.56386303e-31,\n",
       "       2.97089593e-32, 9.42057818e-31, 4.70019667e-30, 1.83488556e-30,\n",
       "       2.28247506e-30, 2.01086270e-31, 7.36799248e-31, 1.44828079e-30,\n",
       "       3.76929505e-31, 1.75067220e-30, 2.72404976e-32, 5.54107113e-32,\n",
       "       1.49908773e-30, 2.01773079e-30, 1.81459069e-30, 5.24317425e-30,\n",
       "       2.79652160e-29, 2.19415443e-30, 3.52404103e-30, 9.29693498e-31,\n",
       "       2.33728112e-30, 1.90821383e-30, 1.11942506e-30, 2.62008494e-30,\n",
       "       3.76353442e-31, 2.91110612e-30, 3.22579418e-31, 9.00858020e-30,\n",
       "       1.30351627e-31, 1.94255790e-31, 7.64114116e-32, 6.05694476e-33,\n",
       "       5.58915120e-31, 2.09324746e-31, 5.20845560e-31, 1.93074815e-30,\n",
       "       6.41581760e-32, 1.43002236e-30, 1.29176814e-30, 1.12721012e-30,\n",
       "       5.43354584e-31, 3.98537348e-30, 6.71549625e-31, 3.97393902e-30,\n",
       "       1.16820328e-30, 1.06861709e-30, 3.81056374e-30, 1.06827601e-30,\n",
       "       4.17974711e-31, 7.65777055e-30, 5.81545455e-31, 1.13942473e-30,\n",
       "       2.30700405e-31, 2.61694639e-29, 1.24200253e-31, 4.07862309e-30,\n",
       "       4.65055507e-29, 1.79380758e-30, 2.38810479e-30, 1.07387249e-30,\n",
       "       6.02636222e-31, 8.34877936e-32, 2.38924832e-30, 4.03609794e-31,\n",
       "       4.62706315e-31, 5.90055132e-30, 1.44089116e-30, 4.61087236e-31,\n",
       "       4.41686078e-31, 3.55938335e-30, 1.43821369e-31, 1.25693730e-30,\n",
       "       1.78465304e-31, 3.91045390e-29, 1.25062932e-30, 5.62430695e-31,\n",
       "       1.07858274e-30, 7.85014359e-31, 2.28379161e-30, 1.98269222e-30,\n",
       "       9.18044690e-32, 1.52864494e-31, 2.64940967e-30, 2.77172897e-30,\n",
       "       8.99885594e-32, 4.94040640e-30, 3.41255865e-30, 1.37636036e-31,\n",
       "       2.08618480e-32, 2.31871677e-30, 5.04522034e-31, 5.72223973e-31,\n",
       "       3.34257555e-30, 4.45746847e-31, 2.07807388e-32, 3.61744675e-30,\n",
       "       3.11095432e-32, 1.26681691e-30, 3.91329743e-30, 1.85565005e-30,\n",
       "       1.11606484e-30, 3.26257611e-30, 1.18304356e-30, 2.75409416e-31,\n",
       "       4.86626938e-30, 2.02631105e-31, 1.08767258e-31, 2.89577814e-31,\n",
       "       3.25542722e-31, 1.57444655e-31, 5.54688419e-31, 3.80070915e-30,\n",
       "       1.21333293e-31, 1.56169672e-30, 2.77724515e-30, 7.01788418e-31,\n",
       "       1.32880409e-31, 5.35215179e-31, 3.47777080e-31, 6.64649015e-32,\n",
       "       5.77701577e-32, 1.20559112e-31, 2.04772827e-30, 4.72491929e-31,\n",
       "       2.05326020e-31, 3.31063182e-30, 6.57762969e-32, 4.28902295e-31,\n",
       "       2.51274688e-30, 7.92800974e-31, 1.38415118e-31, 2.97764239e-31,\n",
       "       5.24274712e-31, 9.94325469e-31, 1.27152274e-30, 1.01982627e-30,\n",
       "       1.62797071e-30, 1.26472378e-31, 5.43664896e-30, 2.79289182e-31,\n",
       "       1.40249980e-30, 6.95402381e-31, 5.22363960e-30, 5.19565400e-31,\n",
       "       1.11859046e-30, 2.29951390e-30, 2.75352193e-31, 1.34323683e-30,\n",
       "       6.37993681e-31, 2.90852708e-31, 1.01259340e-30, 1.22926816e-31,\n",
       "       5.57623186e-30, 2.10231297e-30, 1.48995028e-30, 6.08244929e-31,\n",
       "       9.95235701e-32, 5.13286097e-31, 2.77547626e-30, 4.37918995e-31,\n",
       "       8.82375054e-31, 1.02610524e-31, 3.79851389e-30, 1.12763146e-29,\n",
       "       4.14816534e-31, 5.97877163e-31, 2.17982243e-30, 3.97306934e-31,\n",
       "       2.41525834e-30, 3.09990391e-31, 3.58621885e-31, 2.02693030e-31,\n",
       "       3.95510797e-32, 7.70891969e-31, 9.16639737e-30, 5.16900300e-30,\n",
       "       1.91799548e-31, 1.90085945e-29, 1.65226508e-30, 2.62716217e-31,\n",
       "       1.75112512e-31, 7.95799143e-31, 4.34814326e-31, 1.24996060e-30,\n",
       "       5.70280759e-30, 5.88320523e-32, 1.26279991e-30, 1.50208289e-30,\n",
       "       7.80216508e-31, 2.12516037e-32, 3.17309448e-32, 7.00579211e-31,\n",
       "       9.91665654e-31, 1.79332654e-31, 2.44021136e-30, 2.29219024e-31,\n",
       "       2.36015873e-31, 5.01935712e-30, 1.15481242e-30, 6.57305549e-31,\n",
       "       5.34908646e-32, 1.21735323e-30, 2.56803518e-30, 9.25189850e-31,\n",
       "       1.91327598e-30, 5.06724055e-30, 8.94125672e-31, 7.38257021e-30,\n",
       "       8.82060545e-33, 2.70369637e-32, 1.69746686e-31, 2.71141032e-30,\n",
       "       4.03602882e-31, 6.97883285e-31, 9.06254705e-31, 1.04897411e-30,\n",
       "       2.50481577e-30, 1.27326480e-31, 3.03872484e-31, 1.50842480e-31,\n",
       "       1.22016173e-30, 1.85317982e-30, 1.58168490e-31, 1.25464264e-30,\n",
       "       3.64545474e-30, 1.46922100e-31, 3.28584737e-31, 2.67078711e-31,\n",
       "       9.21488830e-31, 1.88875294e-31, 2.87070203e-31, 1.14917654e-30,\n",
       "       9.88424299e-31, 9.41818652e-32, 4.47327736e-30, 8.74706223e-31,\n",
       "       1.78865553e-30, 2.26473375e-30, 3.71986480e-31, 2.48916985e-30,\n",
       "       7.77444128e-30, 2.21603725e-30, 1.43611273e-30, 4.72890619e-30,\n",
       "       5.04812682e-30, 2.70941593e-30, 2.69817895e-31, 1.99551145e-31,\n",
       "       5.42031391e-30, 2.66928022e-30, 2.64453832e-31, 8.46706230e-32,\n",
       "       1.99596308e-31, 1.76492145e-30, 9.17876169e-30, 3.97217908e-32,\n",
       "       1.62355913e-30, 4.25577091e-32, 6.66889580e-30, 3.36456311e-32,\n",
       "       2.85995248e-32, 5.36279613e-31, 3.84410915e-30, 2.43291737e-31,\n",
       "       4.57431260e-31, 1.79253508e-31, 1.32644734e-31, 9.30107554e-31,\n",
       "       3.23208698e-30, 1.13908675e-30, 2.16661138e-30, 1.63848753e-30,\n",
       "       2.99572008e-31, 8.83571966e-32, 3.08591905e-31, 2.67757056e-30,\n",
       "       1.87385872e-31, 1.48545246e-31, 1.05003854e-30, 1.29119273e-31,\n",
       "       1.36808852e-31, 1.23081368e-30, 8.26035773e-31, 2.72585498e-30,\n",
       "       1.11503407e-30, 2.46561821e-30, 1.11370172e-30, 3.16411422e-29,\n",
       "       2.62116263e-30, 5.08124454e-30, 1.08050060e-32, 5.08188928e-30,\n",
       "       3.16373302e-30, 1.91511704e-31, 4.50520347e-32, 1.21520885e-30,\n",
       "       9.56379759e-31, 6.40577183e-31, 1.25077101e-31, 4.11372091e-30,\n",
       "       2.48392357e-30, 1.55636195e-30, 2.30157675e-30, 1.04060731e-30,\n",
       "       8.90874725e-31, 4.57803855e-30, 2.09720399e-30, 4.74940184e-32,\n",
       "       7.44614028e-31, 4.20117214e-32, 5.76961192e-32, 5.46477743e-32,\n",
       "       4.31403517e-32, 1.88954322e-30, 7.62582259e-31, 4.71204199e-32,\n",
       "       1.04565526e-30, 3.44135808e-30, 6.52934920e-31, 3.87203777e-30,\n",
       "       8.64587662e-31, 1.48872469e-31, 6.72480194e-30, 7.02628062e-32,\n",
       "       2.25928077e-30, 1.65961126e-30, 3.73220691e-32, 7.07035120e-31,\n",
       "       2.00968514e-30, 2.60874302e-30, 8.52327797e-32, 3.06659064e-31,\n",
       "       1.98017083e-30, 1.30566512e-30, 2.10536098e-30, 1.68174008e-30,\n",
       "       1.36159227e-31, 2.58497508e-31, 3.08758426e-31, 4.18527297e-30,\n",
       "       1.57416683e-30, 2.34216779e-31, 8.18053967e-32, 8.09058437e-31,\n",
       "       2.79966624e-30, 1.56123858e-31, 2.56302766e-31, 1.35409426e-31,\n",
       "       3.44906899e-31, 1.23308050e-30, 2.80157520e-31, 1.49684178e-30,\n",
       "       2.10374439e-31, 5.58088193e-30, 1.19176545e-30, 5.75685287e-31,\n",
       "       2.90501588e-31, 4.49012934e-31, 1.05602557e-30, 1.73171326e-30,\n",
       "       9.60595763e-32, 2.29937086e-31, 4.45241525e-31, 4.22644085e-30,\n",
       "       1.81118834e-30, 1.16159132e-29, 1.01466355e-29, 3.90910646e-32,\n",
       "       5.79779863e-31, 5.75228518e-30, 9.35536105e-32, 3.35047334e-32,\n",
       "       3.30760659e-32, 2.85619417e-30, 1.53537430e-31, 2.08590180e-31,\n",
       "       1.13065562e-29, 1.24275435e-30, 5.02772628e-32, 3.77902814e-31,\n",
       "       8.96116901e-32, 1.75582670e-30, 2.61535889e-30, 1.99410956e-30,\n",
       "       2.65096734e-30, 3.81313553e-30, 1.88744764e-30, 8.87885020e-31,\n",
       "       3.51578018e-32, 2.28983902e-31, 3.26345745e-30, 1.92918841e-30,\n",
       "       1.32359701e-29, 7.63923592e-31, 2.83232674e-30, 1.57940643e-31,\n",
       "       8.84333240e-31, 1.84329257e-31, 2.03352506e-31, 5.48091638e-31,\n",
       "       2.25329290e-30, 2.03168259e-31, 6.19791575e-31, 2.16123239e-29,\n",
       "       1.47510094e-31, 4.38559592e-31, 1.52987073e-30, 8.64411526e-32,\n",
       "       1.19913392e-30, 1.63660806e-30, 6.15218083e-30, 1.33639802e-31,\n",
       "       1.05421211e-30, 1.91199498e-30, 2.04482391e-31, 1.00380813e-30,\n",
       "       4.85904255e-32, 8.15720481e-31, 2.21966435e-30, 1.16985995e-29,\n",
       "       8.79554677e-30, 7.60154816e-31, 3.99693611e-31, 9.92864847e-32,\n",
       "       1.01893515e-30, 1.56672673e-31, 1.02498057e-30, 3.77930650e-30,\n",
       "       2.73182724e-30, 1.86942166e-29, 1.03624670e-30, 9.13938923e-32,\n",
       "       3.33012246e-30, 2.01387460e-30, 3.96900848e-32, 7.11547889e-31,\n",
       "       6.48146191e-31, 1.38214776e-30, 7.38234762e-31, 2.52931895e-31,\n",
       "       3.07593158e-31, 9.34925200e-32, 8.98884619e-30, 7.03014271e-32,\n",
       "       2.92511119e-31, 1.13795574e-30, 2.84939699e-30, 1.13589298e-30,\n",
       "       3.15623054e-29, 9.27629024e-32, 1.26151966e-30, 1.49860681e-30,\n",
       "       1.93795184e-31, 1.21769968e-30, 4.82965725e-32, 2.30876824e-31,\n",
       "       1.56328253e-31, 4.00819838e-30, 1.92702720e-30, 1.91004817e-30,\n",
       "       8.83698332e-32, 1.62504054e-31, 2.68043698e-31, 1.18530192e-30,\n",
       "       4.17654412e-31, 1.40780025e-30, 4.18609384e-31, 2.63520048e-30,\n",
       "       2.01229775e-30, 5.72913706e-31, 1.00148705e-30, 2.00527337e-30,\n",
       "       8.94312752e-32, 2.86982464e-31, 9.13894478e-31, 4.12869690e-30,\n",
       "       6.57005422e-30, 8.72512563e-32, 7.68651242e-31, 8.52519360e-30,\n",
       "       6.11096894e-30, 1.27074127e-30, 2.32867923e-31, 1.42563920e-31,\n",
       "       3.66881341e-30, 2.84679792e-30, 2.52100374e-31, 5.70751596e-31,\n",
       "       2.21296521e-31, 1.76040083e-31, 2.09201833e-32, 5.62698049e-31,\n",
       "       6.45211393e-32, 2.20467139e-32, 1.06992320e-31, 4.66146818e-30,\n",
       "       8.11962567e-31, 2.84866574e-30, 9.15971470e-32, 4.44462012e-30,\n",
       "       3.19993190e-31, 1.39046594e-30, 1.06786950e-31, 1.01115366e-30,\n",
       "       1.37364790e-30, 9.85918296e-30, 6.35176821e-31, 1.70756184e-30,\n",
       "       8.85578888e-31, 2.14004800e-32, 2.26283691e-32, 1.26970905e-31,\n",
       "       6.69637801e-31, 5.05769027e-30, 1.23405174e-30, 2.97417162e-31,\n",
       "       2.12845187e-31, 2.77384764e-31, 4.72204883e-30, 2.67957934e-31,\n",
       "       7.09233576e-32, 3.94560829e-30, 1.16113028e-30, 2.52258022e-30,\n",
       "       3.32885095e-31, 5.58418826e-31, 2.77406299e-31, 3.80747769e-31,\n",
       "       4.02006843e-31, 1.72386217e-32, 1.94099343e-31, 1.67132125e-29,\n",
       "       7.45687889e-32, 6.97959316e-31, 1.27708480e-30, 1.36518717e-30,\n",
       "       4.99511372e-31, 1.69851493e-31, 5.71038605e-31, 2.92136226e-30,\n",
       "       4.25489270e-31, 3.27538159e-32, 2.03841761e-30, 1.96015364e-31,\n",
       "       7.06111933e-31, 2.45979227e-30, 2.75536967e-30, 1.43734594e-31,\n",
       "       1.42582584e-30, 4.43739420e-32, 1.17680632e-31, 4.21959919e-31,\n",
       "       1.02117533e-31, 3.42003992e-31, 1.97925977e-31, 3.49023463e-32,\n",
       "       1.05504539e-30, 5.55767558e-32, 2.41127181e-29, 1.14985388e-31,\n",
       "       2.58089635e-31, 2.17968419e-30, 5.53326933e-30, 1.70979027e-31,\n",
       "       1.73618329e-31, 3.70889568e-32, 1.04335127e-31, 6.08800022e-33,\n",
       "       5.22278394e-31, 6.82699083e-32, 4.45540430e-31, 7.29232356e-31,\n",
       "       1.07915583e-32, 9.65246466e-31, 2.40403453e-31, 8.77998125e-32,\n",
       "       3.59637418e-31, 2.11551349e-30, 2.16556961e-30, 1.31532646e-32,\n",
       "       3.77587076e-31, 1.75270040e-31, 6.98601897e-30, 5.26569334e-30,\n",
       "       2.69889671e-31, 1.42178459e-30, 1.81590050e-31, 1.40943642e-31,\n",
       "       1.55836509e-31, 1.14410301e-31, 6.35501727e-31, 5.25401635e-31,\n",
       "       5.14700597e-33, 1.07870010e-30, 2.76463012e-30, 2.19383951e-32,\n",
       "       9.31415456e-31, 4.75547509e-31, 6.90616755e-31, 7.49341584e-31,\n",
       "       2.01072202e-30, 3.82583952e-30, 1.77652649e-31, 5.65944012e-31,\n",
       "       7.87947793e-32, 2.96697196e-31, 1.08020821e-30, 1.94721956e-30,\n",
       "       1.09919170e-30, 6.58808423e-30, 1.29851783e-30, 2.26139135e-31,\n",
       "       3.42784159e-30, 2.31046383e-29, 5.49932086e-31, 6.62807427e-31,\n",
       "       7.97913869e-32, 1.34753912e-31, 1.91133019e-31, 6.64262943e-30,\n",
       "       4.22299343e-32, 2.70220243e-31, 2.90676676e-30, 3.48323520e-31,\n",
       "       1.71224788e-31, 1.52276914e-30, 1.94725040e-30, 2.34424268e-30,\n",
       "       1.43099115e-30, 1.76225486e-30, 3.13816668e-30, 1.70858800e-30,\n",
       "       1.93563377e-29, 9.51298990e-31, 1.42027224e-30, 5.89691302e-31,\n",
       "       1.19023166e-30, 2.90008092e-32, 1.71317487e-32, 4.02635997e-32,\n",
       "       1.39499288e-31, 3.71964311e-31, 2.72925959e-30, 1.00648826e-30],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfg_new_metric_freq[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfg_new_metric = (sfg_new_metric[0] + sfg_new_metric[3], sfg_new_metric[1] + sfg_new_metric[4], sfg_new_metric[2] + sfg_new_metric[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.02754626e+13, 5.09537767e+13, 2.89692523e+13, 1.02410606e+15,\n",
       "       1.45175078e+14, 8.25004150e+12, 1.79021394e+13, 2.29793840e+13,\n",
       "       1.08913261e+14, 5.80410329e+12, 2.17729086e+14, 4.04390768e+15,\n",
       "       1.45993372e+12, 9.30311454e+14, 1.46625935e+14, 1.54805830e+14,\n",
       "       1.08356496e+15, 5.46465699e+14, 2.04369728e+15, 3.64929001e+12,\n",
       "       9.54251228e+14, 2.72778531e+13, 3.41269284e+13, 4.15474093e+13,\n",
       "       1.65439974e+14, 3.33544399e+14, 3.27525727e+14, 9.14104043e+12,\n",
       "       9.39663047e+14, 9.18458988e+13, 3.68132892e+13, 3.43350146e+12,\n",
       "       7.95972685e+13, 2.92328778e+14, 7.46136385e+13, 1.03253875e+14,\n",
       "       4.20884519e+13, 4.88667083e+14, 1.66491719e+15, 4.25757311e+14,\n",
       "       1.17468978e+14, 5.13317833e+14, 8.64067820e+14, 4.28050425e+14,\n",
       "       5.16775919e+13, 1.23541830e+15, 3.59501813e+13, 3.60005218e+13,\n",
       "       8.15811549e+14, 2.34176910e+13, 2.13330181e+14, 3.17192058e+13,\n",
       "       1.25282071e+14, 4.85849146e+14, 1.85695712e+13, 3.68590878e+13,\n",
       "       1.59445805e+14, 2.70757174e+13, 5.88879200e+14, 8.03956195e+14,\n",
       "       5.03669008e+13, 2.99457858e+14, 1.97766482e+14, 1.28369323e+14,\n",
       "       1.31620728e+14, 2.16533095e+15, 1.12802967e+14, 2.03067655e+14,\n",
       "       2.33909781e+13, 8.31734416e+13, 5.39882551e+14, 2.07771885e+15,\n",
       "       2.99302382e+15, 3.91574362e+13, 1.51331218e+15, 2.19427561e+14,\n",
       "       5.04909024e+14, 3.49951488e+14, 5.64087579e+14, 8.09839595e+13,\n",
       "       3.72411924e+13, 1.90522519e+14, 8.67884482e+13, 1.56522442e+13,\n",
       "       2.52359520e+14, 2.42531903e+13, 1.92891991e+14, 1.11448416e+15,\n",
       "       1.64808682e+14, 2.22656586e+13, 2.51461407e+14, 3.40173746e+13,\n",
       "       1.11164978e+14, 1.31575199e+14, 1.66790394e+14, 9.75336772e+12,\n",
       "       7.56164054e+12, 1.07764840e+15, 3.77497670e+14, 7.57890068e+12,\n",
       "       7.99362897e+13, 2.29390478e+13, 4.08692008e+14, 4.59090865e+14,\n",
       "       4.96564317e+14, 5.27860947e+14, 2.07857201e+14, 6.18301495e+14,\n",
       "       6.59826922e+14, 3.48544808e+13, 1.65336135e+14, 1.27584422e+14,\n",
       "       1.58756400e+14, 4.21259966e+12, 8.48147473e+13, 8.62063650e+13,\n",
       "       2.91333015e+15, 6.55814255e+14, 2.46472520e+13, 2.38872794e+13,\n",
       "       2.16579374e+14, 4.49957924e+14, 2.95964497e+14, 1.07350595e+14,\n",
       "       1.28053266e+13, 1.11957427e+14, 6.74003488e+13, 3.17678313e+14,\n",
       "       2.91032974e+14, 1.61250514e+15, 7.90015447e+14, 3.56520580e+13,\n",
       "       1.59707753e+13, 2.58364572e+13, 9.30887088e+13, 2.24767690e+13,\n",
       "       1.77197759e+13, 1.34618767e+12, 4.20801846e+13, 3.11583274e+13,\n",
       "       1.57138011e+14, 3.00080313e+14, 3.42020631e+13, 3.61971940e+14,\n",
       "       1.27589627e+14, 5.53553279e+14, 3.24980611e+15, 6.31640147e+14,\n",
       "       1.46843755e+14, 9.72166599e+13, 2.10849860e+14, 6.84752022e+12,\n",
       "       1.34874480e+15, 4.53337838e+12, 5.00533380e+14, 3.08139224e+14,\n",
       "       2.16625444e+14, 2.06562752e+13, 4.26749181e+13, 1.79575841e+14,\n",
       "       1.50069791e+14, 6.21712345e+13, 5.85664376e+14, 1.12695668e+15,\n",
       "       4.26559810e+14, 9.63255943e+13, 3.68447052e+14, 1.08649685e+14,\n",
       "       7.37872111e+13, 1.23472133e+13, 8.06326332e+14, 9.21102741e+12,\n",
       "       7.76313661e+14, 8.67159737e+13, 3.47625347e+14, 1.56674254e+14,\n",
       "       2.11655916e+13, 8.48530615e+14, 6.81786590e+12, 4.83500477e+13,\n",
       "       9.63919739e+12, 8.49237037e+13, 5.12269147e+14, 2.55499276e+14,\n",
       "       2.64548849e+14, 5.00182440e+12, 1.24234014e+14, 2.19862095e+14,\n",
       "       6.68508295e+14, 1.25890941e+14, 1.03082827e+14, 6.10670981e+13,\n",
       "       3.20227961e+13, 6.47247006e+13, 1.65501925e+15, 1.38613553e+14,\n",
       "       2.88127304e+13, 7.13593726e+13, 1.42489129e+15, 2.54281415e+13,\n",
       "       4.13488592e+14, 1.97803990e+14, 3.33418155e+14, 9.86160068e+13,\n",
       "       2.75833524e+13, 4.03201956e+14, 1.43323162e+15, 1.61178473e+15,\n",
       "       2.47678147e+14, 7.58056015e+15, 5.42412229e+14, 2.84122173e+14,\n",
       "       3.77817745e+14, 1.25635798e+15, 5.74608112e+13, 2.27060286e+13,\n",
       "       3.45254120e+14, 3.66336887e+14, 2.22473529e+15, 7.43894950e+12,\n",
       "       1.12806898e+15, 1.89443687e+14, 2.32969584e+14, 4.92288488e+13,\n",
       "       4.77883945e+14, 2.08851922e+15, 2.00695111e+15, 1.22930993e+13,\n",
       "       1.67827347e+15, 2.52370610e+14, 4.97874210e+13, 3.25498928e+14,\n",
       "       2.51904640e+13, 1.54117092e+14, 7.96595194e+12, 2.49748129e+14,\n",
       "       3.15350007e+14, 2.84844404e+14, 1.04310789e+14, 8.84932064e+13,\n",
       "       2.49779488e+14, 1.88787129e+14, 7.48393321e+13, 1.18272762e+14,\n",
       "       1.80424301e+15, 8.36204519e+14, 1.61138724e+14, 2.43915999e+14,\n",
       "       1.12026945e+14, 9.97430498e+11, 4.23992163e+14, 1.85595193e+15,\n",
       "       3.81098593e+14, 5.13859434e+14, 1.65852358e+15, 2.22133428e+14,\n",
       "       1.11945071e+13, 1.08582025e+15, 1.07358321e+15, 3.08935278e+14,\n",
       "       9.41378560e+14, 1.85799491e+14, 2.40636530e+14, 4.84839963e+14,\n",
       "       5.28446953e+13, 3.36850992e+14, 5.15992373e+12, 1.40030998e+13,\n",
       "       2.94816382e+14, 9.52974914e+14, 2.72686479e+14, 1.41500995e+15,\n",
       "       2.92605643e+15, 2.16827388e+14, 9.31387833e+14, 3.17217157e+14,\n",
       "       3.84426443e+14, 2.58312606e+14, 1.23724740e+14, 4.00668591e+14,\n",
       "       6.15338266e+13, 4.66442175e+14, 1.92345556e+14, 3.02794275e+15,\n",
       "       4.14900972e+13, 2.92696508e+13, 2.83765566e+13, 4.18223853e+12,\n",
       "       8.20737171e+13, 5.81646959e+13, 5.90294012e+13, 4.29963801e+14,\n",
       "       3.09491608e+13, 2.44256789e+14, 1.61474114e+14, 1.48676798e+14,\n",
       "       2.01809704e+14, 1.82561665e+15, 3.66795796e+14, 8.49015563e+14,\n",
       "       2.02580858e+14, 2.13611003e+14, 7.70067530e+14, 1.85716279e+14,\n",
       "       4.29109476e+13, 1.23547470e+15, 1.19113470e+14, 1.01732263e+15,\n",
       "       2.74118085e+13, 4.32741102e+15, 2.78613383e+13, 4.60477656e+14,\n",
       "       1.19782422e+16, 4.40741958e+14, 6.50366553e+14, 2.01568733e+14,\n",
       "       2.20458712e+14, 3.16032548e+13, 1.63827078e+15, 1.05165214e+14,\n",
       "       6.11332414e+13, 9.70038444e+14, 2.14065491e+14, 1.35606125e+14,\n",
       "       1.42576414e+14, 8.50292504e+14, 2.21265729e+14, 5.01660419e+14,\n",
       "       4.58429624e+13, 5.29503099e+15, 3.62997129e+14, 1.73801911e+14,\n",
       "       2.05647768e+14, 6.62655294e+13, 3.47254436e+14, 4.10961120e+14,\n",
       "       1.49552182e+13, 4.42097173e+13, 1.39475790e+15, 1.21709844e+15,\n",
       "       2.44361547e+13, 5.52226187e+14, 6.03092223e+14, 2.37969087e+14,\n",
       "       1.24179703e+13, 4.69355407e+14, 1.68820896e+14, 2.99659638e+14,\n",
       "       7.28788055e+14, 9.24836415e+13, 5.42603239e+12, 5.03651796e+14,\n",
       "       1.54622279e+13, 1.24365832e+15, 8.64916510e+14, 9.60316438e+14,\n",
       "       1.59666352e+14, 1.06136343e+15, 1.17843438e+14, 2.78812124e+13,\n",
       "       7.94104543e+14, 2.42575372e+13, 9.29432486e+13, 2.64764632e+14,\n",
       "       4.65230654e+13, 1.91343075e+13, 9.94554910e+13, 5.27793157e+14,\n",
       "       3.27102206e+13, 1.42133426e+14, 3.88152080e+14, 5.84661057e+13,\n",
       "       8.14411108e+13, 2.31131661e+14, 1.24896225e+14, 3.76249481e+13,\n",
       "       6.69975968e+12, 4.25586482e+13, 2.30212341e+14, 1.06275211e+14,\n",
       "       2.14447393e+13, 8.40398094e+14, 1.35906762e+13, 1.75258737e+14,\n",
       "       9.71415092e+14, 9.72012936e+13, 8.43018191e+13, 2.99766397e+13,\n",
       "       9.18638717e+13, 1.35486313e+14, 6.27222885e+14, 5.21243746e+14,\n",
       "       4.93834265e+14, 3.36313228e+13, 7.08473442e+14, 1.41032820e+14,\n",
       "       1.87144599e+14, 1.01911681e+14, 2.43010458e+15, 1.52809683e+14,\n",
       "       1.78193606e+14, 2.82873878e+14, 3.74073818e+13, 1.97938735e+14,\n",
       "       2.86704734e+14, 7.08915039e+13, 2.03373887e+14, 1.00688197e+14,\n",
       "       6.26491665e+14, 2.96964690e+14, 2.68938406e+14, 4.73793125e+13,\n",
       "       1.57787726e+13, 1.63449061e+14, 3.73499114e+14, 8.16839745e+13,\n",
       "       4.78322969e+14, 3.55967746e+13, 7.95962961e+14, 1.78416872e+15,\n",
       "       1.29094739e+14, 1.13697169e+14, 2.83413599e+14, 6.16706715e+13,\n",
       "       8.45669798e+14, 7.75837429e+13, 6.82052899e+13, 1.06051643e+14,\n",
       "       2.98911752e+13, 3.86514437e+14, 2.30627925e+15, 4.28664419e+15,\n",
       "       4.39877406e+14, 1.95542334e+15, 3.89008134e+14, 2.14992858e+14,\n",
       "       1.92558668e+14, 1.38373686e+14, 6.60802701e+13, 1.07063016e+14,\n",
       "       9.36973727e+14, 2.14925011e+13, 1.67644140e+14, 3.63364792e+14,\n",
       "       2.89663414e+14, 1.17815351e+13, 3.43770904e+12, 1.01108619e+14,\n",
       "       1.22773009e+14, 3.50142553e+13, 9.94237278e+14, 4.73340531e+13,\n",
       "       4.98201928e+13, 5.40731591e+14, 1.91710151e+14, 6.52162378e+13,\n",
       "       1.25561427e+13, 3.69362876e+14, 7.51618270e+14, 1.13336585e+14,\n",
       "       2.53225555e+14, 1.37762725e+15, 2.15624104e+14, 8.16020678e+14,\n",
       "       8.00238866e+11, 5.78660223e+12, 6.82056107e+13, 7.08870419e+14,\n",
       "       7.00651964e+13, 1.10869425e+14, 9.15587035e+14, 2.11336440e+14,\n",
       "       1.02438566e+15, 6.86207956e+13, 2.93168545e+14, 4.05700725e+13,\n",
       "       1.60534476e+14, 4.63652604e+14, 4.36598389e+13, 1.83414111e+14,\n",
       "       9.69091347e+14, 2.75840615e+13, 7.53533700e+13, 9.58085168e+13,\n",
       "       9.11833119e+13, 4.63374338e+13, 8.05618543e+13, 2.55071202e+14,\n",
       "       7.25411229e+14, 1.23461489e+13, 7.22694536e+14, 1.18162688e+14,\n",
       "       1.69011714e+14, 1.30453876e+15, 1.80730479e+14, 3.54401744e+14,\n",
       "       2.11917979e+15, 3.02728875e+14, 2.83454026e+14, 5.88593353e+14,\n",
       "       6.94045924e+14, 4.62108295e+14, 7.25119012e+13, 3.91495886e+13,\n",
       "       1.29829618e+15, 4.95133431e+14, 5.04807611e+13, 4.93649199e+13,\n",
       "       3.32136319e+13, 2.62154512e+14, 2.19127698e+15, 4.48099845e+12,\n",
       "       2.74579421e+14, 1.20510588e+13, 9.22038556e+14, 2.67837562e+13,\n",
       "       1.57220345e+13, 6.13459907e+13, 1.09987020e+15, 4.19488912e+13,\n",
       "       5.61831853e+13, 4.53539455e+13, 2.32032933e+13, 1.74215901e+14,\n",
       "       3.77120590e+14, 3.46843327e+14, 3.02323742e+14, 3.43228970e+14,\n",
       "       4.80158018e+13, 1.43485604e+13, 4.02278271e+14, 3.59719228e+14,\n",
       "       5.24049016e+13, 2.49820702e+13, 1.50995996e+14, 1.36347729e+13,\n",
       "       2.13107979e+13, 3.11209796e+14, 1.81482446e+14, 4.01365555e+14,\n",
       "       2.54415177e+14, 3.88195794e+14, 1.66607585e+14, 5.47208804e+15,\n",
       "       4.04406975e+14, 7.96108110e+14, 3.87155261e+12, 1.22071522e+15,\n",
       "       2.89441180e+15, 1.10024215e+14, 9.66064128e+12, 1.69833940e+14,\n",
       "       1.93600413e+14, 7.62331160e+13, 5.91404602e+13, 5.92579098e+14,\n",
       "       3.66227426e+14, 4.76479502e+14, 3.47659114e+14, 2.53711071e+14,\n",
       "       2.24163271e+14, 9.18763742e+14, 3.65335898e+14, 5.72981253e+12,\n",
       "       1.41896683e+14, 4.40412566e+12, 4.17881971e+13, 1.30498589e+13,\n",
       "       1.45818701e+13, 1.53880074e+15, 1.02124290e+14, 3.32374949e+13,\n",
       "       1.90049007e+14, 1.06743318e+15, 9.00474098e+13, 6.49025090e+14,\n",
       "       1.74766749e+14, 3.06664114e+13, 1.16756484e+15, 1.00634854e+13,\n",
       "       2.84694632e+14, 6.53641951e+14, 4.53627454e+13, 1.17561980e+14,\n",
       "       1.96837262e+14, 1.08749845e+15, 1.69383690e+13, 5.56698169e+13,\n",
       "       2.52946872e+14, 2.34472264e+14, 2.72494850e+14, 2.11267559e+14,\n",
       "       2.64947790e+13, 5.97690039e+13, 2.91310164e+13, 6.85436297e+14,\n",
       "       3.27814577e+14, 4.02314733e+13, 1.16529131e+13, 1.18241412e+14,\n",
       "       3.47142545e+14, 4.19172731e+13, 1.54762066e+14, 8.04889853e+13,\n",
       "       1.46086257e+14, 5.06890265e+14, 3.66556323e+13, 3.52648594e+14,\n",
       "       3.83371438e+13, 1.60193957e+15, 1.03817727e+15, 1.94012870e+14,\n",
       "       4.47612330e+13, 6.56802063e+13, 1.64401179e+14, 2.65592486e+14,\n",
       "       1.40838543e+13, 1.23180622e+14, 1.80642237e+14, 8.47661086e+14,\n",
       "       5.43831701e+14, 1.61486813e+15, 1.41421374e+15, 3.86483988e+13,\n",
       "       1.11993672e+14, 1.53208245e+15, 3.50533161e+13, 5.13546742e+12,\n",
       "       7.92279626e+12, 5.25228556e+14, 2.91127429e+13, 9.29832741e+13,\n",
       "       2.32519445e+15, 1.89008348e+14, 9.75261887e+12, 5.56844052e+13,\n",
       "       2.10623569e+13, 3.30158511e+14, 1.62858445e+15, 2.69819604e+14,\n",
       "       3.45159451e+14, 1.29793122e+15, 5.73397121e+14, 1.78946729e+14,\n",
       "       8.79009081e+12, 2.59129855e+14, 5.11733170e+14, 4.94750127e+14,\n",
       "       2.50696885e+15, 3.64783330e+14, 2.33077658e+15, 7.25883598e+13,\n",
       "       1.82907538e+14, 3.84954139e+13, 4.39890400e+13, 2.06818021e+14,\n",
       "       7.61414736e+14, 8.12009900e+13, 1.80742155e+14, 2.59012490e+15,\n",
       "       3.00257468e+13, 1.42486820e+14, 2.74907676e+14, 4.26643777e+13,\n",
       "       1.93148031e+14, 1.52407057e+14, 2.26125115e+15, 2.18009347e+13,\n",
       "       1.25061480e+14, 3.46760077e+14, 1.28425791e+14, 1.61882662e+14,\n",
       "       9.27220148e+12, 1.46313962e+14, 3.27918150e+14, 1.62256803e+15,\n",
       "       2.26291031e+15, 1.51902507e+14, 1.09993951e+14, 1.51247205e+13,\n",
       "       1.59779993e+14, 5.05209685e+13, 1.91657316e+14, 1.49790883e+15,\n",
       "       5.47878801e+14, 3.19265458e+15, 1.37911456e+14, 5.42702067e+13,\n",
       "       5.80734602e+14, 2.66958858e+14, 2.41738086e+13, 2.65920065e+14,\n",
       "       2.72570373e+14, 5.24948543e+14, 4.24033680e+14, 9.00231841e+13,\n",
       "       2.12166798e+14, 1.87325556e+13, 2.53525999e+15, 1.55893509e+14,\n",
       "       1.55183069e+14, 2.40442254e+14, 5.75156140e+14, 1.34152665e+14,\n",
       "       5.48159329e+15, 1.66135008e+13, 1.73526741e+14, 2.07707544e+14,\n",
       "       3.58354358e+13, 1.55138231e+14, 2.06280850e+13, 4.29751577e+13,\n",
       "       5.05515578e+13, 9.26740567e+14, 2.96589838e+14, 9.78108141e+14,\n",
       "       4.74043450e+13, 2.16136524e+13, 4.85051386e+13, 2.13442772e+14,\n",
       "       2.10903600e+14, 1.78008291e+14, 7.67543642e+13, 3.97741889e+14,\n",
       "       2.66549383e+14, 7.67532299e+13, 2.85233227e+14, 1.00666163e+15,\n",
       "       4.17869176e+13, 4.32078378e+13, 1.22508298e+14, 9.55745544e+14,\n",
       "       1.74254523e+15, 1.83070496e+13, 1.21241011e+14, 1.39515708e+15,\n",
       "       8.99020522e+14, 4.89975901e+14, 9.72071184e+13, 2.71678317e+13,\n",
       "       6.43549049e+14, 8.25150519e+14, 1.34382651e+14, 7.65918904e+13,\n",
       "       2.39089061e+14, 7.44649931e+13, 2.55284426e+12, 1.65974873e+14,\n",
       "       1.85256982e+13, 2.95782243e+12, 2.55054777e+13, 1.69596526e+15,\n",
       "       2.32806023e+14, 8.45772571e+14, 1.25757084e+13, 5.70261579e+14,\n",
       "       6.06330907e+13, 1.90318525e+14, 3.50613382e+13, 1.71369048e+14,\n",
       "       3.29047843e+14, 1.45170850e+15, 1.21764989e+14, 2.69898238e+14,\n",
       "       1.20293353e+14, 2.33803284e+12, 1.22372747e+13, 2.87834161e+13,\n",
       "       5.73106368e+14, 6.68121589e+14, 4.13028143e+14, 3.60590973e+13,\n",
       "       6.13041713e+13, 4.84486167e+13, 3.12283268e+15, 1.57342457e+14,\n",
       "       1.76223207e+13, 5.51218009e+14, 1.53927313e+14, 4.32932025e+14,\n",
       "       7.23998504e+13, 2.19218043e+14, 9.01738868e+13, 6.88502796e+13,\n",
       "       9.68097677e+13, 3.30446685e+12, 8.40236922e+13, 2.99340505e+15,\n",
       "       2.79288357e+13, 2.06783465e+14, 2.16874594e+14, 2.12373001e+14,\n",
       "       1.96355896e+14, 2.40631234e+13, 9.00948217e+13, 3.77457843e+14,\n",
       "       7.03145229e+14, 6.46620622e+12, 7.53479003e+14, 5.05409770e+13,\n",
       "       1.19780558e+14, 3.11211217e+14, 5.20121703e+14, 3.30576101e+13,\n",
       "       1.98943638e+14, 4.78144587e+12, 1.70045809e+13, 1.88260928e+14,\n",
       "       2.06833874e+13, 1.33111046e+14, 2.12441525e+13, 5.67937968e+12,\n",
       "       3.01672596e+14, 1.52606725e+13, 4.16528865e+15, 4.15763950e+13,\n",
       "       1.22394225e+14, 2.80922426e+14, 7.49649291e+14, 6.54167446e+13,\n",
       "       1.94953744e+13, 1.55792969e+13, 3.58934622e+13, 1.66191146e+12,\n",
       "       6.26101597e+13, 1.16606076e+13, 5.48111331e+13, 1.00846939e+14,\n",
       "       2.84278361e+12, 6.16589879e+14, 3.57626465e+13, 1.10093072e+13,\n",
       "       8.11569904e+13, 9.76851632e+14, 4.87164102e+14, 3.76535464e+12,\n",
       "       1.22860351e+14, 7.40054335e+13, 1.25961730e+15, 1.66023316e+15,\n",
       "       4.71952685e+14, 2.04669057e+14, 5.89117546e+13, 1.55786787e+13,\n",
       "       8.89697215e+13, 4.92156258e+13, 5.10697771e+13, 1.56296909e+14,\n",
       "       1.46277278e+12, 3.83055981e+14, 5.60381005e+14, 4.11794454e+12,\n",
       "       1.38199090e+14, 1.56972165e+14, 3.54091047e+14, 5.71396040e+13,\n",
       "       2.77849398e+14, 2.34138604e+15, 9.33406331e+13, 7.86842015e+13,\n",
       "       1.63485296e+13, 5.59965081e+13, 1.62856786e+14, 4.74645818e+14,\n",
       "       1.53334992e+14, 1.91756819e+15, 1.76583254e+14, 7.86086705e+13,\n",
       "       5.53504520e+14, 3.99524715e+15, 5.49508601e+13, 1.41424622e+14,\n",
       "       1.74466782e+13, 3.83470575e+13, 2.90646661e+13, 7.31253204e+14,\n",
       "       9.03763342e+12, 5.19987335e+13, 8.67077770e+14, 4.34721791e+13,\n",
       "       2.51293754e+13, 2.50759482e+14, 3.78572697e+14, 7.37495233e+14,\n",
       "       2.10918708e+14, 3.53424314e+14, 3.08026469e+14, 2.15146571e+14,\n",
       "       3.28197149e+15, 1.47936383e+14, 1.87740044e+14, 1.35205032e+14,\n",
       "       2.05850722e+14, 3.25992035e+12, 4.27954951e+12, 8.56486238e+12,\n",
       "       2.13359495e+13, 7.71726887e+13, 7.78103864e+14, 3.73661510e+14])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfg_new_metric[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfg_new_metric[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if which == \"sfg\":\n",
    "    signal = sfg\n",
    "elif which == \"shg1\":\n",
    "    signal = shg1\n",
    "else:\n",
    "    signal = shg2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_type = \"time\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "if domain_type == \"time\":\n",
    "    factor = 1e12\n",
    "    x_label = \"time (ps)\"\n",
    "elif domain_type == \"wavelength\":\n",
    "    factor = 1e9\n",
    "    x_label = \"wavelength (nm)\"\n",
    "elif domain_type == \"frequency\" or domain_type == \"freq\":\n",
    "    factor = 1e-12\n",
    "    x_label = \"frequency (THz)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "examples = [reverse_get_formula(idx) for idx in find_percentile_indices(signal)['0-10']]\n",
    "sfg_errs = calculate_MSE_metrics(data_dir, model_save_name, output_dir, examples, domain_type, which)\n",
    "\n",
    "sfg_errs_copy = sfg_errs.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(91, 0),\n",
       " (91, 5),\n",
       " (91, 9),\n",
       " (91, 12),\n",
       " (91, 19),\n",
       " (91, 27),\n",
       " (91, 31),\n",
       " (91, 83),\n",
       " (91, 95),\n",
       " (91, 96),\n",
       " (91, 99),\n",
       " (92, 13),\n",
       " (92, 24),\n",
       " (92, 32),\n",
       " (92, 37),\n",
       " (92, 51),\n",
       " (92, 53),\n",
       " (92, 69),\n",
       " (92, 71),\n",
       " (92, 78),\n",
       " (92, 80),\n",
       " (92, 85),\n",
       " (93, 19),\n",
       " (93, 27),\n",
       " (93, 34),\n",
       " (93, 49),\n",
       " (93, 56),\n",
       " (93, 66),\n",
       " (93, 67),\n",
       " (93, 87),\n",
       " (94, 36),\n",
       " (94, 44),\n",
       " (94, 50),\n",
       " (94, 52),\n",
       " (94, 76),\n",
       " (94, 82),\n",
       " (95, 12),\n",
       " (95, 45),\n",
       " (95, 46),\n",
       " (95, 56),\n",
       " (95, 64),\n",
       " (95, 65),\n",
       " (95, 89),\n",
       " (96, 11),\n",
       " (96, 13),\n",
       " (96, 16),\n",
       " (96, 29),\n",
       " (96, 35),\n",
       " (96, 46),\n",
       " (96, 50),\n",
       " (96, 63),\n",
       " (96, 65),\n",
       " (96, 67),\n",
       " (96, 68),\n",
       " (96, 79),\n",
       " (96, 86),\n",
       " (96, 98),\n",
       " (97, 16),\n",
       " (97, 27),\n",
       " (97, 28),\n",
       " (97, 34),\n",
       " (97, 44),\n",
       " (97, 72),\n",
       " (97, 79),\n",
       " (98, 5),\n",
       " (98, 46),\n",
       " (98, 49),\n",
       " (98, 54),\n",
       " (98, 65),\n",
       " (98, 66),\n",
       " (98, 85),\n",
       " (98, 97),\n",
       " (99, 5),\n",
       " (99, 6),\n",
       " (99, 11),\n",
       " (99, 13),\n",
       " (99, 21),\n",
       " (99, 23),\n",
       " (99, 25),\n",
       " (99, 28),\n",
       " (99, 31),\n",
       " (99, 35),\n",
       " (99, 43),\n",
       " (99, 48),\n",
       " (99, 51),\n",
       " (99, 60),\n",
       " (99, 76),\n",
       " (99, 93),\n",
       " (99, 94),\n",
       " (99, 95)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X values - range with the length of the temporal dimension\n",
    "if \"shg\" in which:\n",
    "    if \"freq\" in domain_type:\n",
    "        x_values = freq_vectors_shg1 * factor\n",
    "    else:\n",
    "        x_values = factor * sfg_original_time\n",
    "else:\n",
    "    if \"freq\" in domain_type:\n",
    "        x_values = freq_vectors_sfg * factor\n",
    "    else:\n",
    "        x_values = factor * sfg_original_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32768, (32768,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfg_errs.shape[1], x_values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (0,) and (3000,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 36\u001b[0m\n\u001b[1;32m     33\u001b[0m mean_minus_std \u001b[38;5;241m=\u001b[39m (SFG_mean_errors \u001b[38;5;241m-\u001b[39m SFG_std_errors)\n\u001b[1;32m     35\u001b[0m \u001b[38;5;66;03m# Plot the mean\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mSFG_mean_errors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mMean\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mblue\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;66;03m# Create the shaded standard deviation area\u001b[39;00m\n\u001b[1;32m     39\u001b[0m plt\u001b[38;5;241m.\u001b[39mfill_between(x_values, mean_minus_std, mean_plus_std, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlightgray\u001b[39m\u001b[38;5;124m'\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMean ± STD\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/matplotlib/pyplot.py:3578\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3570\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m   3571\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m   3572\u001b[0m     \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3576\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   3577\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3578\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3579\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3580\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3581\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3582\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3583\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3584\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1721\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1478\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1479\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1480\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1718\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1719\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1720\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1721\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1722\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1723\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/matplotlib/axes/_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    301\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    302\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 303\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/matplotlib/axes/_base.py:499\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    496\u001b[0m     axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m    498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 499\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    500\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    501\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m    502\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    503\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (0,) and (3000,)"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "# Assuming sfg_errs is initialized and has shape (90, 32768)\n",
    "\n",
    "# Compute the mean and standard deviation across the example dimension\n",
    "SFG_mean_errors = np.mean(sfg_errs, axis=0)\n",
    "SFG_std_errors = np.std(sfg_errs, axis=0)\n",
    "    \n",
    "# Create the plot\n",
    "plt.figure(figsize=(20, 6))  # Wide figure for better visibility\n",
    "\n",
    "# NOTE: Uncomment this part to zoom in.\n",
    "## For time - sfg\n",
    "SFG_mean_errors = SFG_mean_errors[15_000:18_000]\n",
    "SFG_std_errors = SFG_std_errors[15_000:18_000]\n",
    "x_values = x_values[15_000:18_000]\n",
    "## For time - shg\n",
    "# SFG_mean_errors = SFG_mean_errors[12_000:18_000]\n",
    "# SFG_std_errors = SFG_std_errors[12_000:18_000]\n",
    "# x_values = x_values[12_000:18_000]\n",
    "## For freq - sfg\n",
    "# SFG_mean_errors = SFG_mean_errors[100:]\n",
    "# SFG_std_errors = SFG_std_errors[100:]\n",
    "# x_values = x_values[100:]\n",
    "## For freq - shg\n",
    "# SFG_mean_errors = SFG_mean_errors[300:1500]\n",
    "# SFG_std_errors = SFG_std_errors[300:1500]\n",
    "# x_values = x_values[300:1500]\n",
    "\n",
    "mean_plus_std = (SFG_mean_errors + SFG_std_errors)\n",
    "mean_minus_std = (SFG_mean_errors - SFG_std_errors)\n",
    "\n",
    "# Plot the mean\n",
    "plt.plot(x_values, SFG_mean_errors, label='Mean', color='blue')\n",
    "\n",
    "# Create the shaded standard deviation area\n",
    "plt.fill_between(x_values, mean_minus_std, mean_plus_std, color='lightgray', alpha=0.5, label='Mean ± STD')\n",
    "\n",
    "# Adding labels and title for clarity\n",
    "plt.xlabel(x_label)\n",
    "plt.ylabel('Error')\n",
    "plt.title(f'Mean Error for {which} with Shaded Standard Deviation Band in {domain_type}')\n",
    "\n",
    "highlighted_examples = [(93, 56), (94, 59), (99, 31)]\n",
    "# highlighted_examples = random.sample(examples, 5)\n",
    "more_errors = calculate_MSE_metrics(data_dir, model_save_name, output_dir, highlighted_examples, domain_type=domain_type, which=which)\n",
    "\n",
    "for example, error in zip(highlighted_examples, more_errors):\n",
    "    # draw each error on the previously existing plot with a different color\n",
    "    plt.plot(x_values, error[15_000:18_000], label=f\"Example {example}\", alpha=0.5)\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "/tmp/ipykernel_139432/970226061.py:7: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"o\" (-> marker='o'). The keyword argument will take precedence.\n",
      "  plt.errorbar(x_values, SFG_mean_errors, yerr=SFG_std_errors, fmt='o', ecolor='orangered', capsize=5, linestyle='-', marker='.', color='blue', label='Mean SFG Errors with STD')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = np.arange(len(SFG_mean_errors)) *10\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Mean Errors')\n",
    "plt.title('Error Bar Plot: Mean Errors with Standard Deviation')\n",
    "plt.legend()\n",
    "plt.errorbar(x_values, SFG_mean_errors, yerr=SFG_std_errors, fmt='o', ecolor='orangered', capsize=5, linestyle='-', marker='.', color='blue', label='Mean SFG Errors with STD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_143617/1258885821.py:19: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.errorbar(x_values[15_000:18_000], SFG_mean_errors[15_000:18_000], yerr=SFG_std_errors[15_000:18_000], fmt='-', ecolor='lightgray', elinewidth=0.5, capsize=0, linestyle='-', color='blue', label='Mean Errors with STD', errorevery=1000)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efc82088b50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming sfg_errs is initialized and has shape (90, 32768)\n",
    "\n",
    "sfg_errs = sfg_errs_copy\n",
    "\n",
    "# Compute the mean and standard deviation across the example dimension\n",
    "SFG_mean_errors = np.mean(sfg_errs, axis=0)\n",
    "SFG_std_errors = np.std(sfg_errs, axis=0)\n",
    "\n",
    "# X values - range with the length of the temporal dimension\n",
    "x_values = np.arange(SFG_mean_errors.shape[0])\n",
    "\n",
    "# Due to the large number of points, consider plotting in a wider format\n",
    "plt.figure(figsize=(20, 6))  # Adjust the figure size as needed\n",
    "\n",
    "# Plotting with error bars\n",
    "# plt.errorbar(x_values, SFG_mean_errors, yerr=SFG_std_errors, fmt='-', ecolor='lightgray', elinewidth=0.5, capsize=0, linestyle='-', color='blue', label='Mean Errors with STD', errorevery=1000)\n",
    "plt.errorbar(x_values[15_000:18_000], SFG_mean_errors[15_000:18_000], yerr=SFG_std_errors[15_000:18_000], fmt='-', ecolor='lightgray', elinewidth=0.5, capsize=0, linestyle='-', color='blue', label='Mean Errors with STD', errorevery=1000)\n",
    "\n",
    "# Adding labels and title for clarity\n",
    "plt.xlabel('Temporal Index')\n",
    "plt.ylabel('Error')\n",
    "plt.title('Error Across Example Dimension')\n",
    "plt.legend()\n",
    "\n",
    "# Given the large data size, you might want to limit the x-axis or use a more dense error bar representation\n",
    "# plt.xlim(0, 32768)  # Adjust or remove this line as needed\n",
    "\n",
    "# Show the plot\n",
    "# plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}