{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UgqzEwle2xCd"
   },
   "source": [
    "## **IMPORT PACKAGES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3X7UsV832B6j"
   },
   "outputs": [],
   "source": [
    "from math import pi\n",
    "\n",
    "import numpy as np\n",
    "import pickle\n",
    "import math\n",
    "\n",
    "from pypulseq.Sequence.sequence import Sequence\n",
    "from pypulseq.calc_rf_center import calc_rf_center\n",
    "from pypulseq.calc_duration import calc_duration\n",
    "from pypulseq.make_adc import make_adc\n",
    "from pypulseq.make_delay import make_delay\n",
    "from pypulseq.make_sinc_pulse import make_sinc_pulse\n",
    "from pypulseq.make_trapezoid import make_trapezoid\n",
    "from pypulseq.opts import Opts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UQ4AWw9l4et_"
   },
   "source": [
    "## **USER INPUTS**\n",
    "\n",
    "These parameters are typically on the user interface of the scanner computer console "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "namespace(t_BW_product_ex=3.8,\n",
       "          t_BW_product_ref=4.2,\n",
       "          t_ex=0.00205,\n",
       "          t_ref=0.00256,\n",
       "          dG=0.00025,\n",
       "          sl_nb=1.0,\n",
       "          sl_thkn=0.004,\n",
       "          sl_gap=100.0,\n",
       "          FoV_f=0.256,\n",
       "          FoV_ph=0.256,\n",
       "          Nf=32.0,\n",
       "          Np=32.0,\n",
       "          BW_pixel=500.0,\n",
       "          TE=0.015000000000000001,\n",
       "          TR=0.4,\n",
       "          FA=90.0,\n",
       "          gamma=42576000.0,\n",
       "          G_amp_max=1609372.8)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('C:\\MRI_seq_files_mess\\SE_pulsequence_parameters.pickle', 'rb') as f:\n",
    "    params = pickle.load(f, fix_imports=True)\n",
    "params.gamma = 42.576e6 #TODO add to GUI\n",
    "params.G_amp_max = 37.8*1e-3*params.gamma #TODO add to GUI\n",
    "\n",
    "dG = 256e-6 #TODO: get from GUI correct value!\n",
    "rf_raster_time = 1e-6  #TODO add to GUI\n",
    "grad_raster_time = 10e-6  #TODO add to GUI\n",
    "\n",
    "params.t_ex = np.ceil(params.t_ex / grad_raster_time) * grad_raster_time\n",
    "params.TE = np.ceil(params.TE / grad_raster_time) * grad_raster_time #TODO in GUI correct TE\n",
    "params.t_ref = np.ceil(params.t_ref / grad_raster_time) * grad_raster_time\n",
    "\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ssnNwiQH4q_0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User inputs setup\n"
     ]
    }
   ],
   "source": [
    "params.nsa = 1  # Number of averages\n",
    "n_slices = 1 #params.sl_nb  # Number of slices #TODO integer from GUI\n",
    "Nx = 32 #TODO integer from GUI\n",
    "Ny = 32 #TODO integer from GUI\n",
    "fov = 256e-3  # mm\n",
    "slice_thickness = params.sl_thkn  # s\n",
    "slice_gap = params.sl_gap  # s\n",
    "rf_flip = params.FA  # degrees\n",
    "rf_offset = 0\n",
    "print('User inputs setup')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PeYeI0V45ZfD"
   },
   "source": [
    "## **SYSTEM LIMITS**\n",
    "Set the hardware limits and initialize sequence object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XHs1LT965kqg"
   },
   "outputs": [],
   "source": [
    "# Set system limits\n",
    "system = Opts(\n",
    "    max_grad=37.8,  #TODO add to GUI\n",
    "    grad_unit=\"mT/m\",\n",
    "    max_slew=121,  #TODO add to GUI\n",
    "    slew_unit=\"T/m/s\",\n",
    "    rf_ringdown_time=20e-6,    #TODO add to GUI     \n",
    "    rf_dead_time=100e-6,     #TODO add to GUI       \n",
    "    adc_dead_time=10e-6,    #TODO add to GUI        \n",
    "    rf_raster_time = rf_raster_time,            \n",
    "    grad_raster_time = grad_raster_time,        \n",
    "    block_duration_raster = grad_raster_time,   \n",
    ")\n",
    "seq = Sequence(system)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ee-xBrpa7Zyn"
   },
   "source": [
    "## **TIME CONSTANTS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u2dW2nRf7obq"
   },
   "outputs": [],
   "source": [
    "TE = 15e-3  # s\n",
    "TR = params.TR  # s\n",
    "tau = TE / 2  # s  #TODO - introduce in GUI\n",
    "readout_time = round(1/params.BW_pixel, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OTw7M03g79bH"
   },
   "source": [
    "## **RF**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XDZyQrbL8I3Q"
   },
   "outputs": [],
   "source": [
    "rf90_phase = np.pi / 2\n",
    "rf180_phase = 0\n",
    "\n",
    "flip90 = round(params.FA * pi / 180, 3)\n",
    "flip180 = round(180 * pi / 180)\n",
    "rf90, gz90, _ = make_sinc_pulse(flip_angle = flip90, system = system, duration = params.t_ex, \n",
    "                                slice_thickness = params.sl_thkn, apodization=0.3, \n",
    "                                time_bw_product = round(params.t_BW_product_ex,8), return_gz = True)\n",
    "\n",
    "rf180, gz180, _ = make_sinc_pulse(flip_angle = flip180, system = system, duration = params.t_ref, \n",
    "                                  slice_thickness = params.sl_thkn, apodization=0.3, \n",
    "                                  time_bw_product = round(params.t_BW_product_ref,8), phase_offset=90 * pi/180, return_gz = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Slice selection gradients**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "namespace(type='trap',\n",
       "          channel='z',\n",
       "          amplitude=1608174.0276862227,\n",
       "          rise_time=0.00032,\n",
       "          flat_time=0.0015300000000000001,\n",
       "          fall_time=0.00032,\n",
       "          area=2975.121951219512,\n",
       "          flat_area=2460.506262359921,\n",
       "          delay=0,\n",
       "          first=0,\n",
       "          last=0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gz_reph rephase gradient after gz90\n",
    "t_gz_reph = np.ceil(params.t_ex / 2 / grad_raster_time) * grad_raster_time\n",
    "gz_reph = make_trapezoid(channel='z', system=system, area=-gz90.area / 2)\n",
    "t_gz_spoil = np.ceil(params.t_ref / 2 / grad_raster_time) * grad_raster_time\n",
    "# gz_spoil spoil gradients around \n",
    "gz_spoil = make_trapezoid(channel='z', system=system, area=gz90.area*3)\n",
    "gz_spoil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RFSHuUOG9LHK"
   },
   "source": [
    "## **READOUT gradients & events**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q8p-CttI9dk9"
   },
   "outputs": [],
   "source": [
    "delta_k = 1 / fov\n",
    "k_width = Nx * delta_k\n",
    "# gx readout gradient\n",
    "t_gx = np.ceil(readout_time / grad_raster_time) * grad_raster_time\n",
    "gx = make_trapezoid(channel='x', system=system, flat_area=k_width, \n",
    "                    flat_time=t_gx)\n",
    "# gx_pre readout prephase gradient\n",
    "\n",
    "\n",
    "t_gx_pre = np.ceil(readout_time / 2 / grad_raster_time) * grad_raster_time\n",
    "gx_pre = make_trapezoid(channel='x', system=system, flat_area=k_width / 2, \n",
    "                        flat_time=t_gx_pre)\n",
    "\n",
    "t_gx_spoil = np.ceil(readout_time / 2 / grad_raster_time) * grad_raster_time\n",
    "gx_spoil = make_trapezoid(channel='x', system=system, area=gz90.area * 0.75,\n",
    "                          duration=t_gx_spoil)\n",
    "\n",
    "\n",
    "gx.rise_time = np.ceil(gx.rise_time / grad_raster_time) * grad_raster_time\n",
    "gx.flat_time = np.ceil(gx.flat_time / grad_raster_time) * grad_raster_time\n",
    "adc = make_adc(num_samples=Nx, duration=gx.flat_time, delay=gx.rise_time)\n",
    "#adc = make_adc(num_samples=Nx, duration=gx.flat_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o829kzm8kVFB"
   },
   "source": [
    "## **PREPHASE AND REPHASE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m5zA1bMakTVs"
   },
   "outputs": [],
   "source": [
    "phase_areas = (np.arange(Ny) - (Ny / 2)) * delta_k\n",
    "#gz_reph = make_trapezoid(channel='z', system=system, area=-gz90.area / 2,\n",
    "  #                       duration=2.5e-3)\n",
    "#gx_pre = make_trapezoid(channel='x', system=system, flat_area=k_width / 2, \n",
    "#                        flat_time=readout_time / 2)\n",
    "t_gy_pre = np.ceil(params.t_ex / 2 / grad_raster_time) * grad_raster_time\n",
    "gy_pre = make_trapezoid(channel='y', system=system, area=phase_areas[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3F5JUpE9-4lo"
   },
   "source": [
    "## **DELAYS**\n",
    "Echo time (TE) and repetition time (TR). Here, TE is broken down into `delay1` and `delay2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aOKRJclb_mDQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "delay_1: namespace(type='delay', delay=0.00455)\n",
      "delay_2: namespace(type='delay', delay=0.00455)\n",
      "delay_TR: namespace(type='delay', delay=0.38268)\n"
     ]
    }
   ],
   "source": [
    "delay1 = tau - calc_duration(rf90) / 2 - calc_duration(gx_pre)\n",
    "delay1 -= calc_duration(gz_spoil) - calc_duration(rf180) / 2\n",
    "delay1 = np.ceil(delay1/seq.grad_raster_time)*seq.grad_raster_time\n",
    "delay1 = make_delay(delay1)\n",
    "\n",
    "delay2 = tau - calc_duration(rf180) / 2 - calc_duration(gz_spoil)\n",
    "delay2 -= calc_duration(gx) / 2\n",
    "delay2 = np.ceil(delay2/seq.grad_raster_time)*seq.grad_raster_time\n",
    "delay2 = make_delay(delay2)\n",
    "\n",
    "delay_TR = TR - calc_duration(rf90) / 2 - calc_duration(gx) / 2 - TE\n",
    "delay_TR -= calc_duration(gy_pre)\n",
    "delay_TR = np.ceil(delay_TR/seq.grad_raster_time)*seq.grad_raster_time\n",
    "delay_TR = make_delay(delay_TR)\n",
    "\n",
    "print(f'delay_1: {delay1}')\n",
    "print(f'delay_2: {delay1}')\n",
    "print(f'delay_TR: {delay_TR}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6Dq4wT-UAEOR"
   },
   "source": [
    "## **CONSTRUCT SEQUENCE**\n",
    "Construct sequence for one phase encode and multiple slices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B8ZmVkkrAXnK"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Must supply area or duration.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[60], line 19\u001b[0m\n\u001b[0;32m     17\u001b[0m seq\u001b[38;5;241m.\u001b[39madd_block(rf90, gz90)\n\u001b[0;32m     18\u001b[0m t_gy_pre \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mceil(params\u001b[38;5;241m.\u001b[39mt_ex \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m/\u001b[39m grad_raster_time) \u001b[38;5;241m*\u001b[39m grad_raster_time\n\u001b[1;32m---> 19\u001b[0m gy_pre \u001b[38;5;241m=\u001b[39m \u001b[43mmake_trapezoid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchannel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43my\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msystem\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msystem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m     20\u001b[0m \u001b[43m                        \u001b[49m\u001b[43marea\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mphase_areas\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     21\u001b[0m seq\u001b[38;5;241m.\u001b[39madd_block(gx_pre, gy_pre, gz_reph)\n\u001b[0;32m     22\u001b[0m seq\u001b[38;5;241m.\u001b[39madd_block(delay1)\n",
      "File \u001b[1;32mC:\\MRI_seq\\pypulseq\\make_trapezoid.py:157\u001b[0m, in \u001b[0;36mmake_trapezoid\u001b[1;34m(channel, amplitude, area, delay, duration, fall_time, flat_area, flat_time, max_grad, max_slew, rise_time, system)\u001b[0m\n\u001b[0;32m    155\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    156\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m area \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 157\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMust supply area or duration.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    158\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    159\u001b[0m         \u001b[38;5;66;03m# Find the shortest possible duration. First check if the area can be realized as a triangle.\u001b[39;00m\n\u001b[0;32m    160\u001b[0m         \u001b[38;5;66;03m# If not, then it must be a trapezoid.\u001b[39;00m\n\u001b[0;32m    161\u001b[0m         rise_time \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    162\u001b[0m             np\u001b[38;5;241m.\u001b[39mceil(np\u001b[38;5;241m.\u001b[39msqrt(np\u001b[38;5;241m.\u001b[39mabs(area) \u001b[38;5;241m/\u001b[39m max_slew) \u001b[38;5;241m/\u001b[39m system\u001b[38;5;241m.\u001b[39mgrad_raster_time)\n\u001b[0;32m    163\u001b[0m             \u001b[38;5;241m*\u001b[39m system\u001b[38;5;241m.\u001b[39mgrad_raster_time\n\u001b[0;32m    164\u001b[0m         )\n",
      "\u001b[1;31mValueError\u001b[0m: Must supply area or duration."
     ]
    }
   ],
   "source": [
    "# Prepare RF offsets. This is required for multi-slice acquisition\n",
    "\n",
    "phase_areas = (np.arange(Ny) - (Ny / 2)) * delta_k\n",
    "\n",
    "delta_z = n_slices * slice_gap\n",
    "#z = np.linspace((-delta_z / 2), (delta_z / 2), n_slices) + rf_offset\n",
    "\n",
    "for k in range(params.nsa):  # Averages\n",
    "  for j in range(n_slices):  # Slices\n",
    "     # Apply RF offsets\n",
    "     rf90.freq_offset = (gz90.amplitude * slice_thickness * (j - (n_slices - 1) / 2))\n",
    "     rf180.freq_offset = (gz180.amplitude * slice_thickness * (j - (n_slices - 1) / 2))\n",
    "     rf90.phase_offset = (rf90_phase - 2 * np.pi * rf90.freq_offset * calc_rf_center(rf90)[0])\n",
    "     rf180.phase_offset = (rf180_phase - 2 * np.pi * rf180.freq_offset * calc_rf_center(rf180)[0])\n",
    "\n",
    "     for i in range(Ny):  # Phase encodes\n",
    "      seq.add_block(rf90, gz90)\n",
    "      t_gy_pre = np.ceil(params.t_ex / 2 / grad_raster_time) * grad_raster_time\n",
    "      gy_pre = make_trapezoid(channel='y', system=system, \n",
    "                              area=phase_areas[-i -1])\n",
    "      seq.add_block(gx_pre, gy_pre, gz_reph)\n",
    "      seq.add_block(delay1)\n",
    "      seq.add_block(gz_spoil)\n",
    "      seq.add_block(rf180, gz180)\n",
    "      seq.add_block(gz_spoil)\n",
    "      seq.add_block(delay2)\n",
    "      #seq.add_block(gx_spoil) \n",
    "      seq.add_block(gx, adc)\n",
    "      #seq.add_block(gx_spoil)\n",
    "      gy_pre = make_trapezoid(channel='y', system=system, \n",
    "                              area=-phase_areas[-j -1])\n",
    "      seq.add_block(gy_pre, gz_spoil)\n",
    "      seq.add_block(delay_TR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l-YP9djBJCpC"
   },
   "source": [
    "## **PLOTTING TIMNG DIAGRAM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "d_iCUR4nfoH9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seq.plot(time_range=(0.000, 0.025))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timing check passed successfully\n"
     ]
    }
   ],
   "source": [
    "ok, error_report = seq.check_timing()\n",
    "if ok:\n",
    "    print(\"Timing check passed successfully\")\n",
    "else:\n",
    "    print(\"Timing check failed. Error listing follows:\")\n",
    "    [print(e) for e in error_report]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fYNgdWc_KiK7"
   },
   "source": [
    "## **GENERATING `.SEQ` FILE**\n",
    "Uncomment the code in the cell below to generate a `.seq` file and download locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6iN0aeuuqKRe"
   },
   "outputs": [],
   "source": [
    "seq.write('C:\\\\MRI_seq\\\\new_MRI_pulse_seq\\\\t1_se_maxGrad\\\\t1_SE_matrx32x32_maxGrad.seq')  # Save to disk\n",
    "# from google.colab import files\n",
    "# files.download('t2_se_pypulseq_colab.seq')  # Download locally\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Q0b5w-lKtfP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<xml.etree.ElementTree.ElementTree at 0x25ec7b47a30>,\n",
       " 'C:\\\\MRI_seq\\\\new_MRI_pulse_seq\\\\t1_se_maxGrad/t1_SE_matrx32x32_maxGrad.xml')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from py2jemris.seq2xml import seq2xml\n",
    "seq2xml(seq, seq_name='t1_SE_matrx32x32_maxGrad', out_folder='C:\\\\MRI_seq\\\\new_MRI_pulse_seq\\\\t1_se_maxGrad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "write_t2_se.ipynb",
   "private_outputs": true,
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}