seim_sparse.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import SparseCoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def ricker_function(resolution, center, width):\n",
    "    \"\"\"Discrete sub-sampled Ricker (Mexican hat) wavelet\"\"\"\n",
    "    x = np.linspace(0, resolution - 1, resolution)\n",
    "    x = (\n",
    "        (2 / (np.sqrt(3 * width) * np.pi**0.25))\n",
    "        * (1 - (x - center) ** 2 / width**2)\n",
    "        * np.exp(-((x - center) ** 2) / (2 * width**2))\n",
    "    ) \n",
    "    return x\n",
    "\n",
    "\n",
    "def ricker_matrix(width, resolution, n_components):\n",
    "    \"\"\"Dictionary of Ricker (Mexican hat) wavelets\"\"\"\n",
    "    centers = np.linspace(0, resolution - 1, n_components)\n",
    "    D = np.empty((n_components, resolution))\n",
    "    for i, center in enumerate(centers):\n",
    "        D[i] = ricker_function(resolution, center, width)\n",
    "    D /= np.sqrt(np.sum(D**2, axis=1))[:, np.newaxis]\n",
    "    return D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "\n",
    "resolution = 1024  # 1000 samples\n",
    "subsampling = 2  # subsampling factor\n",
    "width = 100\n",
    "n_components = resolution // subsampling\n",
    "\n",
    "# Compute a wavelet dictionary\n",
    "D_multi = np.r_[\n",
    "    tuple(\n",
    "        ricker_matrix(width=w, resolution=resolution, n_components= n_components // 2)\n",
    "        for w in np.power(np.linspace(2,20,20),2)\n",
    "    )\n",
    "]\n",
    "\n",
    "time_consume = np.linspace(0, resolution - 1, resolution)/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = D_multi.shape[0]\n",
    "idx = np.arange(feature)\n",
    "k   = 120\n",
    "true_coef = 8 * np.exp(-idx / 50)\n",
    "# true_coef = 3.5 * rng.rand(feature)\n",
    "ri = rng.choice(feature, k, replace=False)\n",
    "mask = np.ones(feature, dtype=bool)\n",
    "mask[ri] = False\n",
    "true_coef[mask] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "seismic_data = D_multi.T.dot(true_coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f37d42e22f0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ZMmLECLnjjjvk66+/tmWNPCsAsXXRRRdJjx49lGPXXHNNgzKADh06JBMnTpSqqqqgsS5dushNN91keS7uCUDiSpTrN1b7obp1hlpDU0MACmjGzPovhbPBHIrL5dI23mtON1wgUTz44IPa2u1TpkwJay7dfcbOe4zZfGw2o7nz+/1yzTXXiN/vr3O8W7ductttt8V8PY19TxDh2QPNh2EYsmHDBuVY/YzDDz74QPr27SujR4+WZ599VjZt2qR8FiguLpYlS5bI/fffLwMGDJCTTz5Z5s2bF9E6G/u+wLMCmhuv1ysvv/yy8iXRI0eOyPDhw+Wll17Sfh+ob9GiRXLyyScrMwpSU1Nl1qxZkpqaanl93BOAxJUo169unQ6HQ/Lz8yNaW218JzmKABTQjB04cEA7ZucNV0SkdevWYa8BQOytXr1aXnrpJeVYUlKSTJs2zfJcpaWlUllZqRyL1T1GhPsMmrfHHntM+SbzM888Y2sZDKt01yP3BMB+Bw4ckNLSUuVYy5YtRUSkqqpKpk2bJmPHjtX2hjWzcuVKOf/882XcuHGyd+/esH+eZwWgcZx66qkya9YsZfZxaWmp/OIXv5CuXbvKjBkz5MMPP5Tdu3dLRUWFVFdXy/79++XLL7+Uxx57TAYOHChjxoxRbuampaXJf/7zn7D6zXFPABJXIl2/us/k5uZaLhdqBfeZo+z7Lwog4Rw6dEg7lpmZaeu5dPMVFhbaeh4ADefz+eTKK68MypQ45rrrrjN9gKovHu4xItxn0Hxt3bpV7r777qDjl1xyiZx++ukxX49hGHL48GHlGPcEwH5mAaGcnBwpKyuT8ePHy5IlSyI+1/vvvy8nn3yyvPPOO3LSSSdZ/jmeFYDGc9FFF0mLFi3kiiuukIMHDwaNb9myRfkcYcUpp5wis2fPlu7du4f1c9wTgMSVSNevbq12rzM9PV2cTqcyo7Q53WfIgAKaseLiYu1Yenq6refSzRetZsYAwvf73/9eW/O9ZcuWYX8BjYd7jAj3GTRf119/vZSXl9c5lpWVJX/+858bZT1HjhzRlvPhngDYzywA5fF45Gc/+5ktwadj9uzZIyNHjtSW/VPhWQFoXBMmTJBvvvlGrr32WvF6vRHP179/f/nnP/8pX331VdjBJxHuCUAiS6TrV7dWu9cpItKiRQvl8eZ0nyEABTRjqgahxyQnJ9t6Lt18ZmsAEDtvvfWWPProo9rxp556SrKzs8OaMx7uMaHWATRVs2fPlnfffTfo+P333297CQyruCcAsVVWVqYde/LJJ5X3iIyMDLnmmmvk7bffls2bN0tZWZkUFxfLpk2b5NVXX5VLLrnEdJP68OHDMnHiRCkpKbG0Ru4LQONr06aNPPfcc7J161Z57LHHpKCgIOw5vF6v3HXXXbJw4UK56KKLxOls2HYj9wQgcSXS9av7jN3rNJuzOd1nCEABzVh1dbV2zM6apyJH37IMdw0AYmPt2rVy2WWXiWEYyvEpU6bIRRddFPa88XCPCbUOoCkqLCyUX//610HHBw4cKNdee20jrOgo7glAbOn6MIiIMkvp0ksvlU2bNskLL7wgEydOlE6dOklqaqpkZGRI165d5cILL5TZs2fLmjVrZMyYMdq5t2zZIr/97W8trZH7AhAftm7dKg8//LA89thjsmvXrrB/vrq6WmbMmCGtW7eWCy+8UL755psGrYN7ApC4Eun61X3G7nWKsB8qQgAKaNZ0fV5ERNmMNBK6+Xw+n63nARCe/fv3y6RJk+TIkSPK8a5du8oLL7zQoLnj4R4jwn0Gzc9vf/vboKa2LpdLnn/++Qa/kWwH7glAbNXU1Fj+7N133y2vvPKKpQzJbt26ycKFC2Xq1Knaz7z00kuWNqC5LwCN6/Dhw3L11VdLt27d5PHHH5ft27dHNJ/f75d//etf0rdvX7npppvCfsOfewKQuBLp+tWt1e51ms3ZnO4z9of1ACQMs8i+z+ezNfKvu7GavbUAILpKS0tl/Pjxsm3bNuV4RkaGzJs3r8F1kEPdY+xkNh/3GTQnixcvlpkzZwYdv+GGG6Rv374xX09t3BOA2LK6iXLVVVfJPffcE9bcbrdb/vGPf8gPP/wgS5cuDRo3DEMefPBBmTNnTsh5dLgvANH1+eefy0UXXSQ7duxQjicnJ8uYMWNk5MiR0r17d8nNzZWkpCQ5dOiQ7Nq1S5YuXSrvvfee8ucNw5Ann3xSPvnkE3n//fclNzfX0pq4JwCJK5GuX91aoxEUYj+UABTQrJnd7GIVgLKj0SmA8FVXV8t5550ny5cvV44nJSXJG2+8Ib17927wOULdY+xkNh/3GTQXlZWVyhJ7BQUFMmPGjEZYUV3cE4DYsvJvvaCgQB577LEGze90OuXll1+W3r17K8v9zZs3TwoLCyUnJ0c7B/cFoHF8+umncsYZZyh7xblcLrn55pvllltuMc2KvOKKK8Tn88ncuXPljjvuUAaiVq5cKWPHjpXFixdLZmZmyHVxTwASVyJdv7q1xjIA1ZzuM5TgA5oxs+Z6FRUVtp6rvLw87DUAiA6/3y+XXHKJfPDBB8pxl8slc+bMkdGjR0d0nni4x4RaB9CUzJgxQ7777rug40888USDMxntxD0BiC0r/9ZvuOEGycjIaPA5unTpIhdeeKFyrLq6Wt555x3Tn+e+AMTe9u3bZcKECcrgU05Ojrz33nvyyCOPWCrJ6Xa75bLLLpPly5fLqFGjlJ9ZuXKlTJ8+3dLauCcAiSuRrl/dZ+xepwj7oSIEoIBmLTs7Wzum6wfTULr5zNYAwH6GYcg111wj8+bNU447HA558cUXZfLkyRGfKx7uMaHWATQVa9askUceeSTo+Pjx4+VnP/tZI6woWHp6uja7mnsCYL+srCzTcbfbLVdddVXE57nuuuu0Yx999JHpz/KsAMTe1VdfLcXFxUHHPR6PvPnmmw16CS0vL0/efvtt6dOnj3J81qxZsnDhwpDzcE8AElciXb+6z9i9TsMwlMF+szU0RQSggGbMrBxGUVGRrefSzWe2BgD2+/Wvfy0vv/yydvyxxx6TK6+80pZzxcM9JtQ6gKYgEAjItGnTpKamps7xlJQUeeqppxppVWq6L1rcEwD7tWzZ0nS8d+/ekpeXF/F5Bg4cKGlpacoxXanfY3hWAGLr/fff11ZBuOOOO+S0005r8NxpaWny2muvafvPPfDAAyHn4J4AJK5Eun51n7F7ncXFxWIYRlhraIoIQAHNWKtWrbRje/futfVcuvnM1gDAXnfeeac88cQT2vEZM2bITTfdZNv5UlNTtRtSsbrHiHCfQdP37LPPyhdffBF0/M4775SOHTs2wor0dNcj9wTAfqH+rZ966qm2nMflckn//v2VY5s3bzb9WZ4VgNh68sknlcezsrLk17/+dcTzd+vWTS699FLl2CeffCJr1641/XnuCUDiSqTrV/eZw4cPS3V1dYPXVR/3maPUNTAANAsdOnTQju3evdu289TU1MiBAwfCXgMA+zz00ENy3333acd/97vfyZ133mn7eTt06CDr1q0LOm7nPcZsvoyMjGb1ZhGap/fffz/oWOvWreXMM88MudFjRlcuoqysTDtvWlqaadCrse8Jx9YANAe5ubnSokULKS0tVY7beS106NBBPv7446DjxcXFUlZWpt2QOvazPCsA0VdZWal8ZhARmTRpUkT94Gq79NJL5e9//7tybNGiRdKrVy/Tn+eeACSuRLl+Q+2H2vWMxHeSowhAAc2Y2QZRqLcVw7Ft2zYJBAJhrwGAPZ5++mm59dZbtePXXXedPPzww1E5d8eOHZUPoHbeY8zma04PdUBte/fulZNPPjkqc3/99dfSu3dv5diIESNMe77ofu/v3LlTqqurxev12rFE7T2BTSU0Nx07dpQ1a9Yox+zsPWDWb6q0tNQ0AMWzAhAby5Ytk6qqKuVYQ/o+6QwdOlS8Xq8yi+Czzz4LWXGBewKQuBLl+g21H2rXfcDs792c9kMpwQc0Y7m5udq67xs3brTtPJs2bdKO9ejRw7bzAAj297//XW644Qbt+KWXXirPPPNM1M6vu8Z37dqlfSO7IXT3Ge4xQHzRXZN+v1++//57287DPQE4qmfPntoxs6BQuFq0aKEdC1XKhmcFIDbMfs926dLFtvOkpKRIQUGBcmzXrl0hf557ApC4EuX6NftcLPZDnU6ndO/e3bbzxDsCUEAz169fP+XxlStX2naOFStWhH1+AJH717/+JVdffbW26eXkyZNl5syZ4nA4orYG3TVuGIasXr3alnPs2LFDDh48GNb5ATQOs2vSrmcPn8+nzfjgnoDm5pRTTtGOlZSU2HYes7mSk5NNf5ZnBSA2Dh06pB3Lzc219Vy6+czWcAz3BCBxJcr12717d0lJSVGOxWI/tFu3bpKammrbeeIdASigmTNrGKy7oYfryy+/VB5v166d5Ofn23IOAHUtWLBApk6dKn6/Xzl+xhlnyNy5c8XlckV1Hbp7jIj+3hAus3nMzg8g9vr06SMej0c5Ztc9YdWqVdoSQ9wT0Nyceuqp2rGioiLbznP48GHlcYfDEbKvDM8KQGxUVlbG7Fy6F9x0301q454AJK5EuX5dLpc2WGXXOv1+vyxfvlw51tzuMwSggGZOV+vZMAxZvHhxxPP7fD5ZsmSJcuz000+PeH4AwRYvXiznn3++1NTUKMeHDx8ub7zxhm29Vsx07dpV2rdvrxxbtGiRLefQzZOcnCxDhw615RwA7JGamqrdEI/2PUGEZw80P4MGDdKW2rOzxIxurvz8fElKSjL9WZ4VgNjQve0vYi0zKRwHDhxQHrdS+pN7ApC4Eun61e2Hrl27Vvbv39+gtdX29ddfS3FxsXKsuX0nIQAFNHNDhw7VPoi++eabEc+/aNEiOXLkiHJs7NixEc8PoK4vvvhCzj77bO0bjgMHDpT58+ebfgG125gxY5THP/zwQ+39wapAICBvv/22cmzYsGEhy/4ATcGbb74phmHY/mfEiBHK840YMUL7Mx999FHI9ep+/69fv16+++67SP5TiIj++aVLly40Fkez4/V6ZdSoUcqxL774wpZzVFVVacvqmPWgqo1nBSD6WrVqpR3bvHmzbeepqKiQ3bt3K8fatm1raQ7uCUDiSpTrV/edxDAMeeuttxq0vtrM9lSb234oASigmUtOTpZJkyYpx/7zn/9E/Mth1qxZYZ8XQMOsXr1axo8fr23u2adPH3nnnXckPT09puu64IILlMcrKyvl3//+d0RzL168WNvM+MILL4xobgDRobsniOifG6z6/vvvtZvq3BPQXOn+7W/atMmWTecPPvhAqqurlWODBg2yNAfPCkD0de7cWTv24Ycf2naeTz75RHtP6NKli6U5uCcAiStRrt9hw4ZJmzZtlGORficJBAIyd+5c5diQIUOkoKAgovkTDQEoADJ16lTl8dLSUnnxxRcbPO+ePXvktddeU45NmjQpZD14ANZt2rRJxo0bp+3B0L17d3nvvfckOzs7xis7+naP7o3Lxx9/XAzDaPDcjz/+uPK41+uV888/v8HzAoieHj16aGuuP/fccxH1qHjiiSe0Y7rnHaCpmzx5srRo0SLouGEY8txzz0U8/zPPPKMdGz9+vKU5eFYAoq9v377idruVY2+//baUlJTYcp5XXnlFOzZw4EBLc3BPABJXoly/TqdTpkyZohxbsmSJrFixItzl/eiNN96QH374QTnWHL+TEIACIBMmTNCWpHnggQe0NUtDueuuu7RvPk2fPr1BcwIItn37dhkzZoy2TnHHjh1l0aJFpmU3osntdsu1116rHPvmm29kzpw5DZp36dKlMn/+fOXY1KlTGyXYBsAa3XPA/v375dFHH23QnFu2bNG+ODNq1CjLpcCApiYtLU1+8YtfKMeef/552b59e4Pn/vTTT+Wdd95RjnXo0EEGDx5saR6eFYDoS01NlSFDhijHDh8+bPoSh1UbNmzQvvXvdDq1pbnq454AJK5Eun6vv/56cTrV4ZFbb7017PlERHw+n9x5553KsczMTLnssssaNG9CMwDAMIy//OUvhogo/0ydOjXs+d59913D4XAo5xs0aFAU/gZA87R3716ja9eu2uu3oKDA2LJlS2Mv09i3b5+RmpqqXGNeXp6xe/fusOYrLS01unXrppzP6XQa69evj9LfBGg+RowYobzGRowYEfHcVVVVRtu2bZXzJycnG998801Y8/l8PmP48OHae+E777wT8ZqBRLZjxw4jKSlJeX2cfvrphs/nC3vOkpISo0uXLtrr7tFHHw1rPp4VgOh75plntNes1+s1li5d2uC5S0pKjF69emnnHzNmTFjzcU8A7KG7Ju++++6onTORrt+f/exn2v9GL774Ytjz3XHHHdr5brvttgavM5GRAQVARESuvfZa6datm3Js9uzZcu+991qea9WqVTJlyhRtWm1D32wGUFdRUZGMGzdOvvvuO+V4q1atZNGiRdKxY8cYr0y9lv/7v/9Tjh04cEAmTJigLR9YX1VVlZx//vmyadMm5fjVV18tPXr0aPBaAUSf1+uV+++/XzlWWVkpkyZNspyVEQgEZNq0abJkyRLl+NixY+WMM85o8FqBpqBdu3Zyyy23KMcWLVokU6ZMkZqaGsvzHT58WMaOHSvff/+9crx9+/byy1/+Mqw18qwARN/ll18uubm5yrHq6mo599xz5eOPPw573v3798vEiRNl7dq12s/87ne/C2tO7glA4kqk6/eBBx4Qr9erHJs+fbq89957lud6+eWXtd9x8vLy5LbbbmvQGhOdw9DtEANodj744AMZN26cNnB02WWXyWOPPaZ9YDUMQ2bOnCk33nijlJaWKj9z5ZVXyt/+9jfb1gw0V1VVVTJq1Cj5/PPPleMej0dmzpwpJ510ku3nTktLa1BQq6KiQvr06aMNmHXp0kX+/ve/a0uDiIisW7dOrrjiCvn666+V461atZK1a9dKXl5e2OsDUNfIkSOVm1AjRoyQjz76KOL5DcOQkSNHagNHrVq1kpdeekkmTpyonWP79u0ybdo0effdd5XjKSkpsmLFCjnhhBMiXi+Q6CoqKqRfv36yceNG5fiAAQPkL3/5i5x66qmm88yfP19uvvlm2bx5s+lnJkyY0KA18qwARNezzz5rGiB2u93y29/+Vn7zm9+ELOHt8/lk9uzZcscdd8iuXbu0nzvjjDO05TrNcE8A1LZu3SplZWWWPtu7d2/l8euvv97yyyIdO3aUtLQ0y+sTSazr984775T77rtPOebxeOSPf/yj3HzzzeLxeJSfKSsrk7vvvtv0hfu///3vcvnll0e0zkRFAApAHbfeeqs89NBD2vGMjAyZMmWKjB49Wjp27Cgej0f2798vX331lcydO1c2bNig/dlu3brJ8uXLlU2QAYRn27ZtjZbZFMnm88qVK2Xw4MFSVVWl/czo0aPlvPPOk5NOOkkyMzPlyJEjsmHDBpk/f74sWLBA/H6/8uccDocsXLiQTAfAJtEOQImI7NixQ/r16yeHDh3SfmbAgAFy0UUXSb9+/SQnJ0cqKirku+++k3fffVfmzZtnej954YUX5JprrrFlrUBTsHbtWjn11FOlvLxcOe5wOGTw4MFy9tlnS8+ePaV169bi8/lk3759smLFCnnzzTdlzZo1puf43e9+Jw8//HCD18izAhBdgUBAJkyYEDIglJKSImPGjJGRI0fKCSecIDk5OeL1eqWwsFB27twpn3zyibz77rumgSeRo2/9r1y5UgoKChq0Xu4JQDDdc3q0LF68WEaOHBn2zyXK9evz+WTEiBHy2WefaT9z/PHHy9SpU2XIkCHSpk0b8fv9smPHDlmyZInMmTNHDhw4oP3ZSy65RGbPnh3xOhMVASgAdfj9frnwwgvl9ddft3Xe1q1by5IlS6Rr1662zgs0V4kagBIR+fe//y1TpkzRPkg21JNPPik33HCDrXMCzVksAlAiRxsKn3HGGdoN8Yb67W9/K4888oitcwJNwcKFC+Xcc8+V6upq2+e+9NJL5R//+Ic4HI6I5uFZAYiuoqIiGTNmjCxfvjyq58nIyJB3331XBg0aFNE83BOAuhIlACWSONfvvn37ZPjw4dpSfw112mmnyTvvvCOpqam2zptI6AEFoA6XyyVz586Viy++2LY5O3ToIIsWLSL4BEBERM4//3yZM2eOpKSk2DKfy+XiyyOQwIYNGyb//e9/JScnx7Y5/+///o/gE6Bx1llnyVtvvSWZmZm2zvub3/zGluCTCM8KQLRlZWX92P8tWnr27CmffPJJxMEnEe4JQCJLlOs3Pz9fFi1aJH369LFtzjFjxsj8+fObdfBJhAAUAAWv1ytz5syRxx57LOwar/Wde+65smzZMunZs6dNqwPQFFx44YWydOlS6dWrV0TzdOjQQd59912+PAIJbsSIEbJs2TIZNmxYRPO0bNlS/vnPf8qf/vQnm1YGNE1nnnmmfP311xFfcyIi7du3l7feekseffRRW4JPx/CsAERXZmamzJ07V2bNmmXrSyAul0tuuOEGWb58ua39aLknAIkrUa7fdu3ayaeffipXX311RM80Ho9Hbr/9dnnnnXckIyPDxhUmJkrwATC1fft2+eMf/yivvPKKVFRUWP650047TW6//XY588wzo7g6oPlK5BJ8tfl8Pnn++eflz3/+s2zZssXyz7Vp00amT58uN998c7N/mwiIlpkzZ8q2bduCjnfo0EGuuOKKqJzTMAyZM2eOPPTQQ/LNN99Y/rmsrCyZNm2a3HrrrbZuogHNwWuvvSaPPPKILFu2LKyf69ixo0yfPl2uu+66qP4u5lkBiL6KigqZM2eOPP3007Jy5coGzZGXlye/+MUv5Prrr5f27dvbvMKfcE8AEqsEX22JdP1+/vnnct9998nChQvFavjE7XbLBRdcIH/4wx94Eb8WAlAALCkqKpKFCxfKokWLZM2aNbJt2zYpKSkRn88nLVq0kPz8fOnRo4cMHTpUJk6cKCeccEJjLxlAAgkEAvLpp5/KwoULZfny5bJx40YpLCyU8vJySU5OluzsbOnatav07dtXzjzzTBk1apR4PJ7GXjaAKFq+fLksWLBAvvrqK/n222/l4MGDUlpaKklJSZKZmSmdOnWSPn36yLhx42Ts2LFsJgERWrdunSxcuFA+//xz+fbbb2XPnj1SWloqhmFIixYtpE2bNtKlSxcZMGCAjBs3Tk499dSYro9nBSA2tmzZIl988YV88cUXsnr1ajl48KAUFRVJUVGR1NTUSGZmpmRnZ0t2drZ06dJFBg0aJIMGDZK+ffvG9JrjngAkrkS6fn/44Qd5++235ZNPPpH169fL7t275ciRI+JwOCQjI0Pat28vJ554oowaNUomTJgg+fn5jbLOeEYACgAAAAAAAAAAALaiBxQAAAAAAAAAAABsRQAKAAAAAAAAAAAAtiIABQAAAAAAAAAAAFsRgAIAAAAAAAAAAICtCEABAAAAAAAAAADAVgSgAAAAAAAAAAAAYCsCUAAAAAAAAAAAALAVASgAAAAAAAAAAADYigAUAAAAAAAAAAAAbEUACgAAAAAAAAAAALYiAAUAAAAAAAAAAABbEYACAAAAAAAAAACArQhAAQAAAAAAAAAAwFYEoAAAAAAAAAAAAGArAlAAAAAAAAAAAACwFQEoAAAAAAAAAAAA2IoAFAAAAAAAAAAAAGxFAAoAAAAAAAAAAAC2IgAFAAAAAAAAAAAAWxGAAgAAAAAAAAAAgK0IQAEAAAAAAAAAAMBWBKAAAAAAAAAAAABgKwJQAAAAAAAAAAAAsBUBKAAAAAAAAAAAANiKABQAAAAAAAAAAABsRQAKAAAAAAAAAAAAtiIABQAAAAAAAAAAAFsRgAIAAAAAAAAAAICtCEABAAAAAAAAAADAVgSgAAAAAAAAAAAAYCsCUAAAAAAAAAAAALDV/wecVeajloMfIQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time_consume, seismic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5120, 1024)\n"
     ]
    }
   ],
   "source": [
    "print(D_multi.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cy_env",
   "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": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}