{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qick import *\n",
    "from qick.rfboard import *\n",
    "\n",
    "import numpy as np\n",
    "from numpy.fft import fft, fftshift\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import scipy.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "try {\n",
       "require(['notebook/js/codecell'], function(codecell) {\n",
       "  codecell.CodeCell.options_default.highlight_modes[\n",
       "      'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n",
       "  Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n",
       "      Jupyter.notebook.get_cells().map(function(cell){\n",
       "          if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n",
       "  });\n",
       "});\n",
       "} catch (e) {};\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "try {\n",
       "require(['notebook/js/codecell'], function(codecell) {\n",
       "  codecell.CodeCell.options_default.highlight_modes[\n",
       "      'magic_text/x-csrc'] = {'reg':[/^%%pybind11/]};\n",
       "  Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n",
       "      Jupyter.notebook.get_cells().map(function(cell){\n",
       "          if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n",
       "  });\n",
       "});\n",
       "} catch (e) {};\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "QICK configuration:\n",
      "\n",
      "\tBoard: ZCU216\n",
      "\n",
      "\tSoftware version: 0.2.228\n",
      "\tFirmware timestamp: Wed Jan 31 08:18:41 2024\n",
      "\n",
      "\tGlobal clocks (MHz): tProcessor 349.997, RF reference 245.760\n",
      "\n",
      "\t16 signal generator channels:\n",
      "\t0:\taxis_sg_int4_v1 - envelope memory 4096 samples (13.333 us)\n",
      "\t\tfs=4915.200 MHz, fabric=307.200 MHz, 16-bit DDS, range=1228.800 MHz\n",
      "\t\tDAC tile 0, blk 0 is 0_228, on JHC1\n",
      "\t1:\taxis_sg_int4_v1 - envelope memory 4096 samples (13.333 us)\n",
      "\t\tfs=4915.200 MHz, fabric=307.200 MHz, 16-bit DDS, range=1228.800 MHz\n",
      "\t\tDAC tile 0, blk 1 is 1_228, on JHC2\n",
      "\t2:\taxis_sg_int4_v1 - envelope memory 4096 samples (13.333 us)\n",
      "\t\tfs=4915.200 MHz, fabric=307.200 MHz, 16-bit DDS, range=1228.800 MHz\n",
      "\t\tDAC tile 0, blk 2 is 2_228, on JHC1\n",
      "\t3:\taxis_sg_int4_v1 - envelope memory 4096 samples (13.333 us)\n",
      "\t\tfs=4915.200 MHz, fabric=307.200 MHz, 16-bit DDS, range=1228.800 MHz\n",
      "\t\tDAC tile 0, blk 3 is 3_228, on JHC2\n",
      "\t4:\taxis_sg_int4_v1 - envelope memory 4096 samples (9.524 us)\n",
      "\t\tfs=6881.280 MHz, fabric=430.080 MHz, 16-bit DDS, range=1720.320 MHz\n",
      "\t\tDAC tile 1, blk 0 is 0_229, on JHC1\n",
      "\t5:\taxis_sg_int4_v1 - envelope memory 4096 samples (9.524 us)\n",
      "\t\tfs=6881.280 MHz, fabric=430.080 MHz, 16-bit DDS, range=1720.320 MHz\n",
      "\t\tDAC tile 1, blk 1 is 1_229, on JHC2\n",
      "\t6:\taxis_sg_int4_v1 - envelope memory 4096 samples (9.524 us)\n",
      "\t\tfs=6881.280 MHz, fabric=430.080 MHz, 16-bit DDS, range=1720.320 MHz\n",
      "\t\tDAC tile 1, blk 2 is 2_229, on JHC1\n",
      "\t7:\taxis_sg_int4_v1 - envelope memory 4096 samples (9.524 us)\n",
      "\t\tfs=6881.280 MHz, fabric=430.080 MHz, 16-bit DDS, range=1720.320 MHz\n",
      "\t\tDAC tile 1, blk 3 is 3_229, on JHC2\n",
      "\t8:\taxis_signal_gen_v6 - envelope memory 16384 samples (1.667 us)\n",
      "\t\tfs=9830.400 MHz, fabric=614.400 MHz, 32-bit DDS, range=9830.400 MHz\n",
      "\t\tDAC tile 2, blk 0 is 0_230, on JHC3\n",
      "\t9:\taxis_signal_gen_v6 - envelope memory 16384 samples (1.667 us)\n",
      "\t\tfs=9830.400 MHz, fabric=614.400 MHz, 32-bit DDS, range=9830.400 MHz\n",
      "\t\tDAC tile 2, blk 1 is 1_230, on JHC4\n",
      "\t10:\taxis_signal_gen_v6 - envelope memory 16384 samples (1.667 us)\n",
      "\t\tfs=9830.400 MHz, fabric=614.400 MHz, 32-bit DDS, range=9830.400 MHz\n",
      "\t\tDAC tile 2, blk 2 is 2_230, on JHC3\n",
      "\t11:\taxis_signal_gen_v6 - envelope memory 16384 samples (1.667 us)\n",
      "\t\tfs=9830.400 MHz, fabric=614.400 MHz, 32-bit DDS, range=9830.400 MHz\n",
      "\t\tDAC tile 2, blk 3 is 3_230, on JHC4\n",
      "\t12:\taxis_signal_gen_v6 - envelope memory 16384 samples (1.667 us)\n",
      "\t\tfs=9830.400 MHz, fabric=614.400 MHz, 32-bit DDS, range=9830.400 MHz\n",
      "\t\tDAC tile 3, blk 0 is 0_231, on JHC3\n",
      "\t13:\taxis_signal_gen_v6 - envelope memory 16384 samples (1.667 us)\n",
      "\t\tfs=9830.400 MHz, fabric=614.400 MHz, 32-bit DDS, range=9830.400 MHz\n",
      "\t\tDAC tile 3, blk 1 is 1_231, on JHC4\n",
      "\t14:\taxis_signal_gen_v6 - envelope memory 16384 samples (1.667 us)\n",
      "\t\tfs=9830.400 MHz, fabric=614.400 MHz, 32-bit DDS, range=9830.400 MHz\n",
      "\t\tDAC tile 3, blk 2 is 2_231, on JHC3\n",
      "\t15:\taxis_sg_mux4_v3 - envelope memory 0 samples (0.000 us)\n",
      "\t\tfs=9830.400 MHz, fabric=614.400 MHz, 32-bit DDS, range=9830.400 MHz\n",
      "\t\tDAC tile 3, blk 3 is 3_231, on JHC4\n",
      "\n",
      "\t8 readout channels:\n",
      "\t0:\taxis_pfb_readout_v2 - controlled by PYNQ\n",
      "\t\tfs=2457.600 MHz, fabric=307.200 MHz, 32-bit DDS, range=307.200 MHz\n",
      "\t\tmaxlen 16384 accumulated, 16384 decimated (53.333 us)\n",
      "\t\ttriggered by output 7, pin 4, feedback to tProc input 0\n",
      "\t\tADC tile 2, blk 0 is 0_226, on JHC7\n",
      "\t1:\taxis_pfb_readout_v2 - controlled by PYNQ\n",
      "\t\tfs=2457.600 MHz, fabric=307.200 MHz, 32-bit DDS, range=307.200 MHz\n",
      "\t\tmaxlen 16384 accumulated, 16384 decimated (53.333 us)\n",
      "\t\ttriggered by output 7, pin 5, feedback to tProc input 1\n",
      "\t\tADC tile 2, blk 0 is 0_226, on JHC7\n",
      "\t2:\taxis_pfb_readout_v2 - controlled by PYNQ\n",
      "\t\tfs=2457.600 MHz, fabric=307.200 MHz, 32-bit DDS, range=307.200 MHz\n",
      "\t\tmaxlen 16384 accumulated, 16384 decimated (53.333 us)\n",
      "\t\ttriggered by output 7, pin 6, feedback to tProc input 2\n",
      "\t\tADC tile 2, blk 0 is 0_226, on JHC7\n",
      "\t3:\taxis_pfb_readout_v2 - controlled by PYNQ\n",
      "\t\tfs=2457.600 MHz, fabric=307.200 MHz, 32-bit DDS, range=307.200 MHz\n",
      "\t\tmaxlen 16384 accumulated, 16384 decimated (53.333 us)\n",
      "\t\ttriggered by output 7, pin 7, feedback to tProc input 3\n",
      "\t\tADC tile 2, blk 0 is 0_226, on JHC7\n",
      "\t4:\taxis_readout_v2 - controlled by PYNQ\n",
      "\t\tfs=2457.600 MHz, fabric=307.200 MHz, 32-bit DDS, range=2457.600 MHz\n",
      "\t\tmaxlen 16384 accumulated, 16384 decimated (53.333 us)\n",
      "\t\ttriggered by output 7, pin 8, feedback to tProc input -1\n",
      "\t\tADC tile 1, blk 0 is 0_225, on JHC5\n",
      "\t5:\taxis_readout_v2 - controlled by PYNQ\n",
      "\t\tfs=2457.600 MHz, fabric=307.200 MHz, 32-bit DDS, range=2457.600 MHz\n",
      "\t\tmaxlen 16384 accumulated, 16384 decimated (53.333 us)\n",
      "\t\ttriggered by output 7, pin 9, feedback to tProc input -1\n",
      "\t\tADC tile 1, blk 1 is 1_225, on JHC6\n",
      "\t6:\taxis_readout_v2 - controlled by PYNQ\n",
      "\t\tfs=2457.600 MHz, fabric=307.200 MHz, 32-bit DDS, range=2457.600 MHz\n",
      "\t\tmaxlen 16384 accumulated, 16384 decimated (53.333 us)\n",
      "\t\ttriggered by output 7, pin 10, feedback to tProc input -1\n",
      "\t\tADC tile 1, blk 2 is 2_225, on JHC5\n",
      "\t7:\taxis_readout_v2 - controlled by PYNQ\n",
      "\t\tfs=2457.600 MHz, fabric=307.200 MHz, 32-bit DDS, range=2457.600 MHz\n",
      "\t\tmaxlen 16384 accumulated, 16384 decimated (53.333 us)\n",
      "\t\ttriggered by output 7, pin 11, feedback to tProc input -1\n",
      "\t\tADC tile 1, blk 3 is 3_225, on JHC6\n",
      "\n",
      "\t4 digital output pins:\n",
      "\t0:\tSPARE0_1V8\n",
      "\t1:\tSPARE1_1V8\n",
      "\t2:\tSPARE2_1V8\n",
      "\t3:\tSPARE3_1V8\n",
      "\n",
      "\ttProc axis_tproc64x32_x8: program memory 1024 words, data memory 1024 words\n",
      "\t\texternal start pin: None\n"
     ]
    }
   ],
   "source": [
    "# Load bitstream with custom overlay\n",
    "soc = RFQickSoc216V1('./qick_rf216_v2.bit', clk_output=None)\n",
    "soccfg = soc\n",
    "\n",
    "print(soccfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################\n",
    "### Square-envelope finite-duration: one pulse ###\n",
    "##################################################\n",
    "class PulseTest(AveragerProgram):\n",
    "    def initialize(self):\n",
    "        cfg=self.cfg   \n",
    "        gen_ch = cfg[\"gen_ch\"]\n",
    "\n",
    "        # set the nyquist zone\n",
    "        self.declare_gen(ch=cfg[\"gen_ch\"], nqz=1)\n",
    "        \n",
    "        # convert frequency to DAC frequency (ensuring it is an available ADC frequency)\n",
    "        freq = self.freq2reg(cfg[\"pulse_freq\"], gen_ch=cfg['gen_ch'])\n",
    "        gain = cfg[\"pulse_gain\"]\n",
    "        \n",
    "        # Set pulse registers.\n",
    "        self.set_pulse_registers(ch=gen_ch,\n",
    "                                 style='const',\n",
    "                                 freq=freq,\n",
    "                                 phase=0,\n",
    "                                 phrst=1,\n",
    "                                 gain=gain,\n",
    "                                 length=self.us2cycles(cfg['pulse_length'], gen_ch=gen_ch))\n",
    "        \n",
    "        self.synci(200)  # give processor some time to configure pulses\n",
    "        \n",
    "        # Trigger.\n",
    "        self.trigger(pins=[0])\n",
    "        self.synci(200)\n",
    "    \n",
    "    def body(self):\n",
    "        # Pulse.\n",
    "        self.pulse(ch=self.cfg[\"gen_ch\"])\n",
    "        \n",
    "        # Period.\n",
    "        self.synci(self.us2cycles(self.cfg['period']))        \n",
    "       \n",
    "config={\"gen_ch\"      : 4,\n",
    "        \"reps\"        : 3,\n",
    "        \"period\"      : 20,\n",
    "        \"style\"       : 'const',\n",
    "        \"pulse_length\": 11.2,\n",
    "        \"pulse_gain\"  : 30000, # [DAC units]\n",
    "        \"pulse_freq\"  : 500, # [MHz]\n",
    "       }\n",
    "\n",
    "prog = PulseTest(soccfg, config)\n",
    "prog.config_all(soccfg)\n",
    "\n",
    "########################\n",
    "### RF Board Setting ###\n",
    "########################\n",
    "freq = config['pulse_freq']\n",
    "nqz = 1\n",
    "\n",
    "# Set Nyquist Zone.\n",
    "dacid = soc['gens'][config['gen_ch']]['dac']\n",
    "soc.rf.set_nyquist(dacid, nqz=nqz)\n",
    "\n",
    "# Set attenuator on DAC.\n",
    "soc.rfb_set_gen_rf(config['gen_ch'], 15, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "soc.tproc.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####################################################\n",
    "### Square-envelope finite-duration: three pulses ###\n",
    "#####################################################\n",
    "class PulseTest(AveragerProgram):\n",
    "    def initialize(self):\n",
    "        cfg=self.cfg   \n",
    "        gen_ch = cfg[\"gen_ch\"][0]\n",
    "\n",
    "        # set the nyquist zone\n",
    "        self.declare_gen(ch=cfg[\"gen_ch\"][0], nqz=1)\n",
    "        \n",
    "        # convert frequency to DAC frequency (ensuring it is an available ADC frequency)\n",
    "        freq = self.freq2reg(cfg[\"pulse_freq\"][0], gen_ch=cfg['gen_ch'][0])\n",
    "        gain = cfg[\"pulse_gain\"][0]\n",
    "        \n",
    "        # Set pulse registers.\n",
    "        self.set_pulse_registers(ch=cfg[\"gen_ch\"][0],\n",
    "                                 style='const',\n",
    "                                 freq=freq,\n",
    "                                 phase=0,\n",
    "                                 phrst=1,\n",
    "                                 gain=gain,\n",
    "                                 length=self.us2cycles(cfg['pulse_length'], gen_ch=cfg['gen_ch'][0]))\n",
    "        \n",
    "        # set the nyquist zone\n",
    "        self.declare_gen(ch=cfg[\"gen_ch\"][1], nqz=1)\n",
    "        \n",
    "        # convert frequency to DAC frequency (ensuring it is an available ADC frequency)\n",
    "        freq = self.freq2reg(cfg[\"pulse_freq\"][1], gen_ch=cfg['gen_ch'][1])\n",
    "        gain = cfg[\"pulse_gain\"][1]\n",
    "        \n",
    "        # Set pulse registers.\n",
    "        self.set_pulse_registers(ch=cfg[\"gen_ch\"][1],\n",
    "                                 style='const',\n",
    "                                 freq=freq,\n",
    "                                 phase=0,\n",
    "                                 phrst=1,\n",
    "                                 gain=gain,\n",
    "                                 length=self.us2cycles(cfg['pulse_length'], gen_ch=cfg['gen_ch'][1]))\n",
    "        \n",
    "        # set the nyquist zone\n",
    "        self.declare_gen(ch=cfg[\"gen_ch\"][2], nqz=1)\n",
    "        \n",
    "        # convert frequency to DAC frequency (ensuring it is an available ADC frequency)\n",
    "        freq = self.freq2reg(cfg[\"pulse_freq\"][2], gen_ch=cfg['gen_ch'][2])\n",
    "        gain = cfg[\"pulse_gain\"][2]\n",
    "        \n",
    "        # Set pulse registers.\n",
    "        self.set_pulse_registers(ch=cfg[\"gen_ch\"][2],\n",
    "                                 style='const',\n",
    "                                 freq=freq,\n",
    "                                 phase=0,\n",
    "                                 phrst=1,\n",
    "                                 gain=gain,\n",
    "                                 length=self.us2cycles(cfg['pulse_length'], gen_ch=cfg['gen_ch'][2]))         \n",
    "        \n",
    "        self.synci(200)  # give processor some time to configure pulses\n",
    "        \n",
    "        # Trigger.\n",
    "        self.trigger(pins=[0])\n",
    "        self.synci(200)\n",
    "    \n",
    "    def body(self):\n",
    "        # Pulse.\n",
    "        self.pulse(ch=self.cfg[\"gen_ch\"][0])\n",
    "        self.pulse(ch=self.cfg[\"gen_ch\"][1])\n",
    "        self.pulse(ch=self.cfg[\"gen_ch\"][2])\n",
    "        \n",
    "        # Period.\n",
    "        self.synci(self.us2cycles(self.cfg['period']))        \n",
    "       \n",
    "config={\"gen_ch\"      : [4,5,12],\n",
    "        \"reps\"        : 3,\n",
    "        \"period\"      : 20,\n",
    "        \"style\"       : 'const',\n",
    "        \"pulse_length\": 11.2,\n",
    "        \"pulse_gain\"  : [30000,20000,10000], # [DAC units]\n",
    "        \"pulse_freq\"  : [500,500,3000], # [MHz]\n",
    "       }\n",
    "\n",
    "prog = PulseTest(soccfg, config)\n",
    "prog.config_all(soccfg)\n",
    "\n",
    "########################\n",
    "### RF Board Setting ###\n",
    "########################\n",
    "# Set attenuator on DAC.\n",
    "soc.rfb_set_gen_rf(config['gen_ch'][0], 15, 30)\n",
    "soc.rfb_set_gen_rf(config['gen_ch'][1], 0, 30)\n",
    "soc.rfb_set_gen_rf(config['gen_ch'][2], 10, 30)\n",
    "\n",
    "# Set filter.\n",
    "soc.rfb_set_gen_filter(config['gen_ch'][2], fc=2.1, ftype='bypass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "soc.tproc.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################\n",
    "### Long duration signal for spectrum analysis ###\n",
    "##################################################\n",
    "class MuxSGTest(AveragerProgram):\n",
    "    def initialize(self):\n",
    "        # Set the nyquist zone\n",
    "        self.declare_gen(ch=self.cfg[\"gen_ch\"], nqz=1,\n",
    "                         mux_freqs = self.cfg['pulse_freqs'],\n",
    "                         mux_gains = self.cfg['pulse_gains'])        \n",
    "        \n",
    "        # Pulse registers.\n",
    "        length = self.us2cycles(self.cfg['pulse_length'], gen_ch=self.cfg['gen_ch'])\n",
    "        self.set_pulse_registers(ch = self.cfg['gen_ch'], style = 'const', length=length, mask=[0,1,2,3])\n",
    "        \n",
    "        self.synci(200)\n",
    "    \n",
    "    def body(self):\n",
    "        # Trigger.\n",
    "        self.trigger(pins=[0],t=40)\n",
    "\n",
    "        # Pulses.\n",
    "        self.pulse(ch=self.cfg['gen_ch'])\n",
    "        self.pulse(ch=self.cfg['gen_ch'])\n",
    "        self.pulse(ch=self.cfg['gen_ch'])\n",
    "        self.pulse(ch=self.cfg['gen_ch'])\n",
    "        self.pulse(ch=self.cfg['gen_ch'])\n",
    "\n",
    "       \n",
    "config={\"gen_ch\"      : 15,\n",
    "        \"reps\"        : 1,\n",
    "        \"pulse_length\": 5000000, # 5 s\n",
    "        \"pulse_freqs\" : [7100,7200,7500,7700],\n",
    "        \"pulse_gains\" : [0.6, -0.6, 0.6, -0.6],\n",
    "        \"period\"      : 2,\n",
    "        \"phrst\"       : 1,\n",
    "       }\n",
    "\n",
    "prog = MuxSGTest(soccfg, config)\n",
    "prog.config_all(soccfg)\n",
    "\n",
    "########################\n",
    "### RF Board Setting ###\n",
    "########################\n",
    "# Set Nyquist Zone.\n",
    "nqz = 2\n",
    "dacid = soc['gens'][config['gen_ch']]['dac']\n",
    "soc.rf.set_nyquist(dacid, nqz=nqz, blocktype='dac')\n",
    "\n",
    "# Set attenuator on DAC.\n",
    "soc.rfb_set_gen_rf(config['gen_ch'], 15, 30)\n",
    "\n",
    "# Set filter.\n",
    "soc.rfb_set_gen_filter(config['gen_ch'], fc=3, ftype='bypass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "soc.tproc.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "soc.rfb_set_gen_rf(config['gen_ch'], 10, 10)\n",
    "soc.rfb_set_gen_filter(config['gen_ch'], fc=6, ftype='bandpass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebebb3c929d84075b57bbfaddbd00c22",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##########################################\n",
    "### One generator/readout and raw-data ###\n",
    "##########################################\n",
    "class ReadoutTest(AveragerProgram):\n",
    "    def initialize(self):\n",
    "        # Set the nyquist zone for the DACs.\n",
    "        self.declare_gen(ch=self.cfg[\"gen_ch\"], nqz=1)\n",
    "        \n",
    "        # DAC channel.\n",
    "        freq = self.freq2reg(self.cfg['pulse_freq'], gen_ch=self.cfg['gen_ch'], ro_ch=self.cfg['ro_ch'][0])\n",
    "        self.default_pulse_registers(ch     = self.cfg['gen_ch'], \n",
    "                                     style  = 'const', \n",
    "                                     freq   = freq,\n",
    "                                     gain   = self.cfg['pulse_gain'], \n",
    "                                     length = self.us2cycles(self.cfg['pulse_length'],gen_ch=self.cfg['gen_ch']))\n",
    "        \n",
    "        # Readout.\n",
    "        self.declare_readout(ch      = self.cfg['ro_ch'][0], \n",
    "                             length  = self.us2cycles(self.cfg['ro_length'], ro_ch = self.cfg['ro_ch'][0]),\n",
    "                             freq    = self.cfg['ro_freq'],\n",
    "                             gen_ch  = self.cfg['gen_ch'])\n",
    "        \n",
    "        # Registers.\n",
    "        self.set_pulse_registers(ch=self.cfg['gen_ch'], phase=0, phrst=0, mode=\"oneshot\")\n",
    "        \n",
    "        self.synci(200)  # give processor some time to configure pulses\n",
    "    \n",
    "    def body(self):       \n",
    "        self.measure(pulse_ch        = self.cfg['gen_ch'], \n",
    "                     adcs            = self.cfg['ro_ch'],\n",
    "                     pins            = [0],\n",
    "                     t               = self.us2cycles(self.cfg['pulse_start']),\n",
    "                     adc_trig_offset = self.us2cycles(self.cfg['ro_offset'], ro_ch = self.cfg['ro_ch'][0]),\n",
    "                     )        \n",
    "\n",
    "config={\"ro_ch\"       : [5],\n",
    "        \"ro_length\"   : 40,\n",
    "        \"ro_freq\"     : 4000,\n",
    "        \"ro_offset\"   : 0,\n",
    "        \n",
    "        \"gen_ch\"      : 14,        \n",
    "        \"pulse_length\": 20,\n",
    "        \"pulse_start\" : 10,\n",
    "        \"pulse_gain\"  : 30000,\n",
    "        \"pulse_freq\"  : 4000,\n",
    "        \n",
    "        \"reps\"        : 1,\n",
    "        \"period\"      : 20\n",
    "       }\n",
    "\n",
    "########################\n",
    "### RF Board Setting ###\n",
    "########################\n",
    "# Nyquist Zone.\n",
    "nqz = 2\n",
    "\n",
    "#######\n",
    "# DAC #\n",
    "#######\n",
    "freq = config['pulse_freq']\n",
    "\n",
    "# Set Nyquist Zone.\n",
    "dacid = soc['gens'][config['gen_ch']]['dac']\n",
    "soc.rf.set_nyquist(dacid, nqz=nqz, blocktype='dac')\n",
    "\n",
    "# Set Filter.\n",
    "fc = freq/1000\n",
    "soc.rfb_set_gen_filter(config['gen_ch'], fc=fc, ftype='bandpass')\n",
    "#soc.rfb_set_gen_filter(config['gen_ch'], fc=2.5, ftype='lowpass')\n",
    "\n",
    "# Set attenuator on DAC.\n",
    "soc.rfb_set_gen_rf(config['gen_ch'], 15, 20)\n",
    "\n",
    "#######\n",
    "# ADC #\n",
    "#######\n",
    "# Set Filter.\n",
    "fc = freq/1000\n",
    "soc.rfb_set_ro_filter(config['ro_ch'][0], fc=fc, ftype='bandpass')\n",
    "\n",
    "# Set attenuator on ADC.\n",
    "soc.rfb_set_ro_rf(config['ro_ch'][0], 20)\n",
    "\n",
    "# Set Nyquist Zone.\n",
    "adcid = soc['readouts'][config['ro_ch'][0]]['adc']\n",
    "soc.rf.set_nyquist(adcid, nqz=nqz, blocktype='adc')\n",
    "\n",
    "prog = ReadoutTest(soccfg, config)\n",
    "iq_list = prog.acquire_decimated(soc, load_pulses=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Captured Data.\n",
    "plt.figure(dpi=200)\n",
    "\n",
    "# fs (MHz) and ts (us).\n",
    "fs = soccfg['readouts'][config['ro_ch'][0]]['fs']/8\n",
    "ts = 1000*1/fs\n",
    "\n",
    "yi = iq_list[0][0]\n",
    "yq = iq_list[0][1]\n",
    "t = ts*np.arange(len(iq_list[0][0]))\n",
    "\n",
    "plt.plot(t,yi)\n",
    "plt.plot(t,yq)\n",
    "#plt.xlim([2005,2040])\n",
    "    \n",
    "plt.xlabel('t [ns]');\n",
    "plt.legend(['I','Q']);\n",
    "#plt.savefig('time_sig_noise.png')\n",
    "\n",
    "# Save data.\n",
    "#fn = 'pulse_{}_{}_filter_p20.mat'.format(config['ro_freq'],config['pulse_freq'])\n",
    "#scipy.io.savemat(fn, {'xi':yi, 'xq':yq, 't':t,'fs':fs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Spectrum.\n",
    "plt.figure(dpi=200)\n",
    "\n",
    "# fs (MHz) and ts (us).\n",
    "fs = soccfg['readouts'][config['ro_ch'][0]]['fs']/8\n",
    "ts = 1000*1/fs\n",
    "\n",
    "yi = iq_list[0][0]\n",
    "yq = iq_list[0][1]\n",
    "x = yi + 1j*yq\n",
    "\n",
    "w = np.hanning(len(x))\n",
    "xw = x*w\n",
    "Y = fft(xw)/len(x)\n",
    "YdB = 20*np.log10(np.abs(Y))\n",
    "F = np.linspace(0,fs,len(Y))\n",
    "\n",
    "plt.plot(F, YdB);\n",
    "plt.xlim([0,fs/2]);\n",
    "#plt.savefig('spectrum_sig_noise.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "soc.rfb_set_bias(0,-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}