{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UgqzEwle2xCd"
   },
   "source": [
    "## **IMPORT PACKAGES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3X7UsV832B6j"
   },
   "outputs": [],
   "source": [
    "from math import pi\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from pypulseq.Sequence.sequence import Sequence\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\n",
    "import pickle"
   ]
  },
  {
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "incomplete input (3637307858.py, line 20)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[2], line 20\u001b[1;36m\u001b[0m\n\u001b[1;33m    block_duration_raster = grad_raster_time,   ## !!! ВВЕДЕНИЕ ЗНАЧЕНИЙ РАСТРА\u001b[0m\n\u001b[1;37m                                                                               ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m incomplete input\n"
     ]
    }
   ],
   "source": [
    "## !!! ФИКСИРАВАННОЕ ВРЕМЯ НАРАСТАНИЯ ГРАДИЕНТА - КРАТНО РАСТРУ (СДЕЛАЛА ВРУЧНУЮ)\n",
    "dG = 252e-6\n",
    "\n",
    "\n",
    "## !!! ВВЕДЕНИЕ ЗНАЧЕНИЙ РАСТРА\n",
    "rf_raster_time = 0.000004\n",
    "grad_raster_time = 0.000004\n",
    "\n",
    "# Set system limits\n",
    "system = pp.Opts(\n",
    "    max_grad=32,\n",
    "    grad_unit=\"mT/m\",\n",
    "    max_slew=130,\n",
    "    slew_unit=\"T/m/s\",\n",
    "    rf_ringdown_time=100e-6,       ## !!! КРАТНО РАСТРУ - ВРУЧНУЮ\n",
    "    rf_dead_time=100e-6,           ## !!! КРАТНО РАСТРУ - ВРУЧНУЮ\n",
    "    adc_dead_time=12e-6,           ## !!! КРАТНО РАСТРУ - ВРУЧНУЮ\n",
    "    rf_raster_time = rf_raster_time,            ## !!! ВВЕДЕНИЕ ЗНАЧЕНИЙ РАСТРА\n",
    "    grad_raster_time = grad_raster_time,        ## !!! ВВЕДЕНИЕ ЗНАЧЕНИЙ РАСТРА\n",
    "    block_duration_raster = grad_raster_time,   ## !!! ВВЕДЕНИЕ ЗНАЧЕНИЙ РАСТРА"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "namespace(t_BW_product_ex=3.8,\n",
       "          t_BW_product_ref=4.2,\n",
       "          t_ex=0.002048,\n",
       "          t_ref=0.00256,\n",
       "          dG=0.00025,\n",
       "          sl_nb=3.0,\n",
       "          sl_thkn=0.004,\n",
       "          sl_gap=100.0,\n",
       "          FoV_f=0.2,\n",
       "          FoV_ph=0.2,\n",
       "          Nf=128.0,\n",
       "          Np=128.0,\n",
       "          BW_pixel=500.0,\n",
       "          TE=0.015000000000000001,\n",
       "          TR=0.5,\n",
       "          FA=90.0)"
      ]
     },
     "execution_count": 8,
     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ssnNwiQH4q_0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User inputs setup\n"
     ]
    }
   ],
   "source": [
    "nsa = 1  # Number of averages\n",
    "n_slices = 3  # Number of slices\n",
    "Nx = 128\n",
    "Ny = 128\n",
    "fov = 22e-3  # mm\n",
    "slice_thickness = 5e-3  # s\n",
    "slice_gap = 15e-3  # s\n",
    "rf_flip = 90  # degrees\n",
    "rf_offset = 0\n",
    "print('User inputs setup')"
   ]
  },
  {
   "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": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XHs1LT965kqg"
   },
   "outputs": [],
   "source": [
    "system = Opts(max_grad=32, grad_unit='mT/m', max_slew=130, slew_unit='T/m/s', \n",
    "              grad_raster_time=10e-6, rf_ringdown_time=10e-6, \n",
    "              rf_dead_time=100e-6)\n",
    "seq = Sequence(system)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_one\n",
    "system = Opts(max_grad=37.5, grad_unit='mT/m', max_slew=125, slew_unit='T/m/s', \n",
    "              grad_raster_time=10e-6)\n",
    "seq = Sequence(system)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ee-xBrpa7Zyn"
   },
   "source": [
    "## **TIME CONSTANTS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u2dW2nRf7obq"
   },
   "outputs": [],
   "source": [
    "TE = 20e-3  # s\n",
    "TR = 0.5  # s\n",
    "tau = TE / 2  # s\n",
    "readout_time = 6.4e-3\n",
    "pre_time = 8e-4  # s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OTw7M03g79bH"
   },
   "source": [
    "## **RF**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XDZyQrbL8I3Q"
   },
   "outputs": [],
   "source": [
    "flip90 = round(rf_flip * pi / 180, 3)\n",
    "flip180 = 180 * pi / 180\n",
    "rf90, gz90, _ = make_sinc_pulse(flip_angle=flip90, system=system, duration=4e-3, \n",
    "                                slice_thickness=slice_thickness, apodization=0.5, \n",
    "                                time_bw_product=4, return_gz = True)\n",
    "rf180, gz180, _ = make_sinc_pulse(flip_angle=flip180, system=system, \n",
    "                                  duration=2.5e-3, \n",
    "                                  slice_thickness=slice_thickness, \n",
    "                                  apodization=0.5, \n",
    "                                time_bw_product=4, phase_offset=90 * pi/180, return_gz = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RFSHuUOG9LHK"
   },
   "source": [
    "## **READOUT**\n",
    "Readout gradients and related events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q8p-CttI9dk9"
   },
   "outputs": [],
   "source": [
    "delta_k = 1 / fov\n",
    "k_width = Nx * delta_k\n",
    "gx = make_trapezoid(channel='x', system=system, flat_area=k_width, \n",
    "                    flat_time=readout_time)\n",
    "adc = make_adc(num_samples=Nx, duration=gx.flat_time, delay=gx.rise_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o829kzm8kVFB"
   },
   "source": [
    "## **PREPHASE AND REPHASE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
    "gy_pre = make_trapezoid(channel='y', system=system, area=phase_areas[-1], \n",
    "                        duration=4e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5Css5esAkYHo"
   },
   "source": [
    "## **SPOILER**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R1DOmoKKkawr"
   },
   "outputs": [],
   "source": [
    "gz_spoil = make_trapezoid(channel='z', system=system, area=gz90.area * 4,\n",
    "                          duration=pre_time * 4)"
   ]
  },
  {
   "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": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aOKRJclb_mDQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "delay_1: namespace(type='delay', delay=0.0025050000000000003)\n",
      "delay_2: namespace(type='delay', delay=0.0025050000000000003)\n",
      "delay_TR: namespace(type='delay', delay=0.47059999999999996)\n"
     ]
    }
   ],
   "source": [
    "delay1 = tau - calc_duration(rf90) / 2 - calc_duration(gx_pre)\n",
    "delay1 -= calc_duration(gz_spoil) - calc_duration(rf180) / 2\n",
    "delay1 = make_delay(delay1)\n",
    "delay2 = tau - calc_duration(rf180) / 2 - calc_duration(gz_spoil)\n",
    "delay2 -= calc_duration(gx) / 2\n",
    "delay2 = make_delay(delay2)\n",
    "delay_TR = TR - calc_duration(rf90) / 2 - calc_duration(gx) / 2 - TE\n",
    "delay_TR -= calc_duration(gy_pre)\n",
    "delay_TR = make_delay(delay_TR)\n",
    "print(f'delay_1: {delay1}')\n",
    "print(f'delay_2: {delay1}')\n",
    "print(f'delay_TR: {delay_TR}')"
   ]
  },
  {
   "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": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B8ZmVkkrAXnK"
   },
   "outputs": [],
   "source": [
    "# Prepare RF offsets. This is required for multi-slice acquisition\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(nsa):  # Averages\n",
    "  for j in range(n_slices):  # Slices\n",
    "    # Apply RF offsets\n",
    "    freq_offset = gz90.amplitude * z[j]\n",
    "    rf90.freq_offset = freq_offset\n",
    "\n",
    "    freq_offset = gz180.amplitude * z[j]\n",
    "    rf180.freq_offset = freq_offset\n",
    "\n",
    "    for i in range(Ny):  # Phase encodes\n",
    "      seq.add_block(rf90, gz90)\n",
    "      gy_pre = make_trapezoid(channel='y', system=system, \n",
    "                              area=phase_areas[-i -1], duration=4e-3)\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, adc)\n",
    "      gy_pre = make_trapezoid(channel='y', system=system, \n",
    "                              area=-phase_areas[-j -1], duration=4e-3)\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": 30,
   "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, 0.1))"
   ]
  },
  {
   "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": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6iN0aeuuqKRe"
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[31], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mseq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt2_se_pypulseq_colab.seq\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\MRI_seq\\pypulseq\\Sequence\\sequence.py:1703\u001b[0m, in \u001b[0;36mSequence.write\u001b[1;34m(self, name, create_signature)\u001b[0m\n\u001b[0;32m   1690\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrite\u001b[39m(\u001b[38;5;28mself\u001b[39m, name: \u001b[38;5;28mstr\u001b[39m, create_signature: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1691\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1692\u001b[0m \u001b[38;5;124;03m    Write the sequence data to the given filename using the open file format for MR sequences.\u001b[39;00m\n\u001b[0;32m   1693\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1701\u001b[0m \u001b[38;5;124;03m        Boolean flag to indicate if the file has to be signed.\u001b[39;00m\n\u001b[0;32m   1702\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1703\u001b[0m     \u001b[43mwrite_seq\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_signature\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\MRI_seq\\pypulseq\\Sequence\\write_seq.py:72\u001b[0m, in \u001b[0;36mwrite\u001b[1;34m(self, file_name, create_signature)\u001b[0m\n\u001b[0;32m     67\u001b[0m block_duration \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblock_durations[block_counter] \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblock_duration_raster\n\u001b[0;32m     69\u001b[0m )\n\u001b[0;32m     70\u001b[0m block_duration_rounded \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(np\u001b[38;5;241m.\u001b[39mround(block_duration))\n\u001b[1;32m---> 72\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mabs(block_duration_rounded \u001b[38;5;241m-\u001b[39m block_duration) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1e-6\u001b[39m\n\u001b[0;32m     74\u001b[0m s \u001b[38;5;241m=\u001b[39m id_format_str\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;241m*\u001b[39m(\n\u001b[0;32m     76\u001b[0m         block_counter \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     79\u001b[0m     )\n\u001b[0;32m     80\u001b[0m )\n\u001b[0;32m     81\u001b[0m output_file\u001b[38;5;241m.\u001b[39mwrite(s)\n",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    " seq.write('t2_se_pypulseq_colab.seq')  # Save to disk\n",
    "# from google.colab import files\n",
    "# files.download('t2_se_pypulseq_colab.seq')  # Download locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Q0b5w-lKtfP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'block_duration': 0.0041400000000000005,\n",
       " 'rf': namespace(type='rf',\n",
       "           signal=array([-9.52099400e-09+1.16598548e-24j, -2.57194516e-07+3.14972441e-23j,\n",
       "                         -1.19130264e-06+1.45892496e-22j, ...,\n",
       "                         -1.19130264e-06+1.45892496e-22j, -2.57194516e-07+3.14972441e-23j,\n",
       "                         -9.52099400e-09+1.16598548e-24j]),\n",
       "           t=array([5.0000e-07, 1.5000e-06, 2.5000e-06, ..., 3.9975e-03, 3.9985e-03,\n",
       "                    3.9995e-03]),\n",
       "           shape_dur=0.004,\n",
       "           delay=0.00010000000000000002,\n",
       "           freq_offset=-4500.0,\n",
       "           phase_offset=0.0,\n",
       "           dead_time=0.0001,\n",
       "           ringdown_time=1e-05,\n",
       "           use='undefined'),\n",
       " 'gx': None,\n",
       " 'gy': None,\n",
       " 'gz': namespace(type='trap',\n",
       "           channel='z',\n",
       "           amplitude=200000.0,\n",
       "           rise_time=4e-05,\n",
       "           flat_time=0.004,\n",
       "           fall_time=4e-05,\n",
       "           delay=6.000000000000001e-05,\n",
       "           area=808.0,\n",
       "           flat_area=800.0),\n",
       " 'adc': None}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq.block_events\n",
    "blk = seq.get_block(101).__dict__\n",
    "blk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<xml.etree.ElementTree.ElementTree at 0x1b2749f7850>,\n",
       " './t2_se_pypulseq_colab.xml')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from py2jemris.seq2xml import seq2xml\n",
    "seq2xml(seq, seq_name='t2_se_pypulseq_colab', out_folder='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'320000 3 9 0'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[18], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mseq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtse_pypulseq.seq\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\MRI_seq\\pypulseq\\Sequence\\sequence.py:1172\u001b[0m, in \u001b[0;36mSequence.read\u001b[1;34m(self, file_path, detect_rf_use)\u001b[0m\n\u001b[0;32m   1162\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread\u001b[39m(\u001b[38;5;28mself\u001b[39m, file_path: \u001b[38;5;28mstr\u001b[39m, detect_rf_use: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1163\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1164\u001b[0m \u001b[38;5;124;03m    Read `.seq` file from `file_path`.\u001b[39;00m\n\u001b[0;32m   1165\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1170\u001b[0m \u001b[38;5;124;03m        Path to `.seq` file to be read.\u001b[39;00m\n\u001b[0;32m   1171\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1172\u001b[0m     \u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdetect_rf_use\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdetect_rf_use\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\MRI_seq\\pypulseq\\Sequence\\read_seq.py:325\u001b[0m, in \u001b[0;36mread\u001b[1;34m(self, path, detect_rf_use)\u001b[0m\n\u001b[0;32m    314\u001b[0m         old_data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([amplitude, grad\u001b[38;5;241m.\u001b[39mshape_id, grad\u001b[38;5;241m.\u001b[39mdelay])\n\u001b[0;32m    315\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\n\u001b[0;32m    316\u001b[0m         [\n\u001b[0;32m    317\u001b[0m             amplitude,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    323\u001b[0m         ]\n\u001b[0;32m    324\u001b[0m     )\n\u001b[1;32m--> 325\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad_library\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamplitude_ID\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mold_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    326\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    327\u001b[0m     grad_prev_last[j] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[1;32mC:\\MRI_seq\\pypulseq\\event_lib.py:256\u001b[0m, in \u001b[0;36mEventLibrary.update_data\u001b[1;34m(self, key_id, old_data, new_data, data_type)\u001b[0m\n\u001b[0;32m    241\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdate_data\u001b[39m(\n\u001b[0;32m    242\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    243\u001b[0m     key_id: \u001b[38;5;28mint\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    246\u001b[0m     data_type: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(),\n\u001b[0;32m    247\u001b[0m ):\n\u001b[0;32m    248\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    249\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m    250\u001b[0m \u001b[38;5;124;03m    ----------\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    254\u001b[0m \u001b[38;5;124;03m    data_type : str\u001b[39;00m\n\u001b[0;32m    255\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 256\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mold_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_type\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\MRI_seq\\pypulseq\\event_lib.py:237\u001b[0m, in \u001b[0;36mEventLibrary.update\u001b[1;34m(self, key_id, old_data, new_data, data_type)\u001b[0m\n\u001b[0;32m    235\u001b[0m     data_string \u001b[38;5;241m=\u001b[39m data_string\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    236\u001b[0m     data_string \u001b[38;5;241m=\u001b[39m data_string\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 237\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeymap[data_string]\n\u001b[0;32m    239\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minsert(key_id, new_data, data_type)\n",
      "\u001b[1;31mKeyError\u001b[0m: '320000 3 9 0'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq.read('C:\\\\MRI_seq\\\\gre_pypulseq.seq')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'block_duration': 0.00317,\n",
       " 'rf': namespace(type='rf',\n",
       "           signal=array([-0.00198565+2.43172220e-19j, -0.00596096+7.30007627e-19j,\n",
       "                         -0.00994169+1.21750565e-18j, ..., -0.00994169+1.21750565e-18j,\n",
       "                         -0.00596096+7.30007627e-19j, -0.00198565+2.43172220e-19j]),\n",
       "           t=array([5.0000e-07, 1.5000e-06, 2.5000e-06, ..., 2.9975e-03, 2.9985e-03,\n",
       "                    2.9995e-03]),\n",
       "           shape_dur=0.003,\n",
       "           delay=9.999999999999999e-05,\n",
       "           freq_offset=0.0,\n",
       "           phase_offset=0.0,\n",
       "           dead_time=0.0001,\n",
       "           ringdown_time=1e-05),\n",
       " 'gx': None,\n",
       " 'gy': None,\n",
       " 'gz': namespace(type='trap',\n",
       "           channel='z',\n",
       "           amplitude=444444.0,\n",
       "           rise_time=7e-05,\n",
       "           flat_time=0.003,\n",
       "           fall_time=7e-05,\n",
       "           delay=2.9999999999999997e-05,\n",
       "           area=1364.44308,\n",
       "           flat_area=1333.332),\n",
       " 'adc': None}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blk = seq.get_block(1).__dict__\n",
    "blk"
   ]
  },
  {
   "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
}