{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FFT + Average Firmware\n",
    "This firmware has one input ADC and one output DAC. ADC is connected to Tile 224 Channel 0. DAC is connected to Tile 229 Channel 3.\n",
    "\n",
    "### Input data path\n",
    "Sampling frequency of the ADC is set to 1.024 GSPS. This covers an input spectrum from -512 to +512 MHz. Inside the ADC block, signal is down-converted and decimated. The user can control the frequency of the NCO that is used for the down-conversion process inside the ADC.\n",
    "\n",
    "Data leaves the ADC block at a speed 8 times slower, which means the signal going into the logic is spanning from -64 MHz to +64 MHz. This data goes into the DDS + CIC block. The output of the DDS + CIC is connected to a harwdare implemented 64k points FFT. The output of the spectrum is fed into the Accumulator block, which will stream the data to the PS side of the FPGA.\n",
    "\n",
    "#### DDS + CIC block\n",
    "This block receives complex data sampled at 128 MSPS, coming from the output of the ADC. This block has two main components: DDS and CIC. DDS is used to apply an extra digital-down conversion process before decimation or spectrum analysis. This DDS has a frequency resolution of 30 mHz.\n",
    "\n",
    "The block has options to select which data is routed into its output. Input data, DDS data or Product data can be selected. Input means the Digital product is by-passed and the ADC data is sent down the chain. DDS option is useful for debugging, as it sends the high-quality, internally generated complex exponential signal to the output. Product is the options that applies the frequency modulation.\n",
    "\n",
    "Data from the previous DDS block is fed into the CIC filter, which applies a varying decimation. The user can chose any decimation from 1 to 1000. For high decimation factors, DDS down-conversion may be needed to fine tune the signal location.\n",
    "\n",
    "#### Windowed FFT block\n",
    "This block computes the hardware FFT with 65536-points. Data is applied a window before entering the FFT computation, which is useful for sine wave inputs. Available windows are \"rect\" and \"hanning\". These could be replace by any function as the coefficients are generated by numpy and uploaded into the window memory.\n",
    "\n",
    "#### Accumulator block\n",
    "This block receives the data from the FFT, computes the absolute value of the complex bins and accumulates the value. The number of accumulated values can be set by the provided functions. The block is always running, which means after the current accumulation is finished, the data can be retrieved and that will automatically launch the next averaging process.\n",
    "\n",
    "### Sine Wave Loop Back Example\n",
    "This example sends a output signal using the DAC, which is read back at the ADC side. The example shows how to properly configure both the DAC and ADC mixer. The user can play with different number of averages, decimation values and DDS frequencies. The example scales down the amplitude of the output tone to allow visualizing quantization noise at the FFT output.\n",
    "\n",
    "### Noise Example\n",
    "This example uses a external wide-band noise source with ~25 dB analog amplification to provide a good noise power to the ADC input. The user can play with different number of averages to check the noise variance is decreased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-16T03:22:04.751971Z",
     "start_time": "2024-12-16T03:22:04.729337Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../../qick_lib/')\n",
    "\n",
    "from fft_low_avg import *\n",
    "\n",
    "import numpy as np\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.fft import fft, fftshift\n",
    "#from slab import *\n",
    "#from slab.datamanagement import SlabFile\n",
    "import json\n",
    "import os\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-16T03:22:20.195801Z",
     "start_time": "2024-12-16T03:22:06.566537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resetting clocks: 204.8\n"
     ]
    }
   ],
   "source": [
    "# Load bitstream with custom overlay\n",
    "soc = TopSoc('./fft_low_avg.bit', ignore_version=True, force_init_clks=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-18T23:18:02.987226Z",
     "start_time": "2022-11-18T23:17:47.798510Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 = 13.454, Y1 = -91.007\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f952e3f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f2e9f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##########################################\n",
    "### Simple Sine Loop-back (ADC output) ###\n",
    "##########################################\n",
    "# Frequency and gain.\n",
    "#fout = 234\n",
    "f_adc = 13.45\n",
    "#g = 0.99\n",
    "\n",
    "# DAC mixer.\n",
    "#soc.set_fmix(f=fout, mtype=\"out\")\n",
    "\n",
    "# ADC mixer.\n",
    "soc.set_fmix(f=f_adc, mtype=\"in\")\n",
    "\n",
    "# DAC Gain.\n",
    "#soc.iq.set_iq(i=g,q=g)\n",
    "\n",
    "# Get data.\n",
    "[xi,xq] = soc.buff_adc.getdata(t=1)\n",
    "x = xi + 1j*xq\n",
    "\n",
    "# ADU to V.\n",
    "# 500 --> 2^15\n",
    "x = x*0.5/2**15\n",
    "\n",
    "# Spectrum.\n",
    "w = np.hanning(len(x))\n",
    "Aw = len(w)/np.sum(w)\n",
    "xw = Aw*x*w\n",
    "Y = fftshift(fft(xw)/len(xw))\n",
    "F = np.linspace(-soc.fs/2,soc.fs/2,len(Y))\n",
    "\n",
    "# Normalize to dBm.\n",
    "R = 50;\n",
    "Y = 10*np.log10((np.abs(Y)**2)/(2*R*1e-3));\n",
    "\n",
    "# Max.\n",
    "[F1,Y1] = soc.findPeak(F,Y);\n",
    "print('F1 = {:.3f}, Y1 = {:.3f}'.format(F1,Y1));\n",
    "\n",
    "plt.figure(1,dpi=150)\n",
    "plt.plot(F,Y)\n",
    "plt.plot(F1,Y1,'r*');\n",
    "plt.xlabel(\"F [MHz]\");\n",
    "plt.ylabel(\"Power [dBm]\");\n",
    "\n",
    "# Time domain signal.\n",
    "n = np.arange(len(x))\n",
    "t = n/soc.fs\n",
    "\n",
    "plt.figure(2,dpi=150)\n",
    "plt.plot(t,xi,label='xi')\n",
    "plt.plot(t,xq,label='xq')\n",
    "plt.legend()\n",
    "plt.xlabel(\"t [us]\");\n",
    "plt.ylabel(\"ADC Units\");\n",
    "plt.xlim([0,20]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-18T23:18:24.046616Z",
     "start_time": "2022-11-18T23:18:15.574291Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 = 0.000, Y1 = -89.296\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f2b1f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9408fdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################\n",
    "### Simple Sine Loop-back (DDS + CIC output) ###\n",
    "################################################\n",
    "# Frequency and gain.\n",
    "#fout = 234\n",
    "f_adc = 13.45\n",
    "#g = 0.99\n",
    "\n",
    "# DAC mixer.\n",
    "#soc.set_fmix(f=fout, mtype=\"out\")\n",
    "\n",
    "# ADC mixer.\n",
    "soc.set_fmix(f=f_adc, mtype=\"in\")\n",
    "\n",
    "# DAC Gain.\n",
    "#soc.iq.set_iq(i=g,q=g)\n",
    "\n",
    "# Decimation.\n",
    "D = 300\n",
    "\n",
    "# Data source.\n",
    "src = \"input\"\n",
    "\n",
    "# Change decimation/source.\n",
    "if D == 1:\n",
    "    soc.ddscic.outsel(data=src, cic=\"no\")\n",
    "else:\n",
    "    soc.ddscic.outsel(data=src, cic=\"yes\")\n",
    "    soc.ddscic.decimation(D)\n",
    "    \n",
    "# Configure DDS + CIC.\n",
    "soc.ddscic.ddsfreq(f=-0.45e6)\n",
    "soc.ddscic.set_qprod(9)\n",
    "\n",
    "# Get data.\n",
    "[xi,xq] = soc.buff_dds.getdata(t=1)\n",
    "x = xi + 1j*xq\n",
    "\n",
    "# ADU to V.\n",
    "# 500 --> 2^15\n",
    "x = x*0.5/2**15\n",
    "\n",
    "# Spectrum.\n",
    "w = np.hanning(len(x))\n",
    "Aw = len(w)/np.sum(w)\n",
    "xw = Aw*x*w\n",
    "Y = fftshift(fft(xw)/len(xw))\n",
    "F = np.linspace(-soc.fs/D/2,soc.fs/D/2,len(Y))\n",
    "\n",
    "# Normalize to dBm.\n",
    "R = 50;\n",
    "Y = 10*np.log10((np.abs(Y)**2)/(2*R*1e-3));\n",
    "\n",
    "# Max.\n",
    "[F1,Y1] = soc.findPeak(F,Y);\n",
    "print('F1 = {:.3f}, Y1 = {:.3f}'.format(F1,Y1));\n",
    "\n",
    "plt.figure(1,dpi=150)\n",
    "plt.plot(F,Y)\n",
    "plt.plot(F1,Y1,'r*');\n",
    "plt.xlabel(\"F [MHz]\");\n",
    "plt.ylabel(\"Power [dBm]\");\n",
    "\n",
    "# Time domain signal.\n",
    "n = np.arange(len(x))\n",
    "t = n/soc.fs\n",
    "\n",
    "plt.figure(2,dpi=150)\n",
    "plt.plot(t,xi,label='xi')\n",
    "plt.plot(t,xq,label='xq')\n",
    "#print(np.max(xq))\n",
    "#plt.legend()\n",
    "plt.xlabel(\"t [us]\");\n",
    "plt.ylabel(\"ADC Units\");\n",
    "plt.xlim([0,20]);\n",
    "plt.vlines(-46*1e-3, -200, -80, colors = 'r', linestyles = '--');\n",
    "plt.vlines((-46+26/2)*1e-3, -200, -80, colors = 'r', linestyles = '--');\n",
    "plt.vlines((-46-26/2)*1e-3, -200, -80, colors = 'r', linestyles = '--');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-18T23:19:33.209970Z",
     "start_time": "2022-11-18T23:18:33.878707Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of averages = 400\n",
      "F1 = 0.000, Y1 = 67.293\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8fbadac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##########################################\n",
    "### Simple Sine Loop-back (ACC output) ###\n",
    "##########################################\n",
    "# Frequency and gain.\n",
    "#fout = 234\n",
    "f_adc = 13.45\n",
    "#g = 0.99\n",
    "\n",
    "# DAC mixer.\n",
    "#soc.set_fmix(f=fout, mtype=\"out\")\n",
    "\n",
    "# ADC mixer.\n",
    "soc.set_fmix(f=f_adc, mtype=\"in\")\n",
    "\n",
    "# DAC Gain.\n",
    "#soc.iq.set_iq(i=g,q=g)\n",
    "\n",
    "# Decimation.\n",
    "D = 300\n",
    "\n",
    "# Number of averages.\n",
    "N = 400\n",
    "\n",
    "# Data source.\n",
    "src = \"input\"\n",
    "\n",
    "# Change decimation/source.\n",
    "if D == 1:\n",
    "    soc.ddscic.outsel(data=src, cic=\"no\")\n",
    "else:\n",
    "    soc.ddscic.outsel(data=src, cic=\"yes\")\n",
    "    soc.ddscic.decimation(D)\n",
    "    \n",
    "# Configure DDS + CIC.\n",
    "soc.ddscic.ddsfreq(f=-0.45e6)\n",
    "soc.ddscic.set_qprod(14)\n",
    "\n",
    "# Configure Number of Averages.\n",
    "soc.acc.setavg(N=N)\n",
    "\n",
    "# Start accumulator block.\n",
    "soc.acc.start()\n",
    "\n",
    "# Get data.\n",
    "[x,Navg] = soc.acc.transfer()\n",
    "x = x/Navg\n",
    "print(\"Number of averages = {}\".format(Navg))\n",
    "\n",
    "# Spectrum.\n",
    "r = np.random.normal(scale=1/2**20,size=len(x))\n",
    "x = x + r\n",
    "Y = x\n",
    "#Y_Max = np.max(Y)\n",
    "Y_Max = 100\n",
    "Y = 10*np.log10(Y/Y_Max)\n",
    "F = np.linspace(-soc.fs/D/2,soc.fs/D/2,len(Y))\n",
    "\n",
    "\n",
    "# Max.\n",
    "[F1,Y1] = soc.findPeak(F,Y);\n",
    "print('F1 = {:.3f}, Y1 = {:.3f}'.format(F1,Y1));\n",
    "\n",
    "# Plot.\n",
    "plt.figure(2,dpi=150)\n",
    "plt.plot(F*1000,Y)\n",
    "#plt.plot(Fmax1*1000, Ymax1,'*r')\n",
    "#plt.plot(Fmax2*1000, Ymax2,'*r')\n",
    "plt.xlabel('F [kHz]');\n",
    "plt.ylabel('Power [dB]');\n",
    "#plt.vlines(-46, 0, 80, colors = 'r', linestyles = '--');\n",
    "plt.vlines(-46+26/2, 40, 80, colors = 'r', linestyles = '--');\n",
    "plt.vlines(-46-26/2, 40, 80, colors = 'r', linestyles = '--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fridge Data Collection-Fridge Temp 15mK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-18T23:27:17.654640Z",
     "start_time": "2022-11-18T23:26:15.598601Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/xilinx/jupyter_notebooks/Morgan/20221108/Updated_Firmware_20221115/data/00014_fft_test.h5\n",
      "Trace taken at time:  11/18/2022, 17:26:15\n",
      "Trace taken at time:  11/18/2022, 17:27:17\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Only chunked datasets can be resized",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m/home/xilinx/jupyter_notebooks/slab/slab/datamanagement.py\u001b[0m in \u001b[0;36mappend_data\u001b[0;34m(self, f, key, data, forceInit)\u001b[0m\n\u001b[1;32m    400\u001b[0m                              \u001b[0mmaxshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 401\u001b[0;31m                              dtype=str(data.dtype))\n\u001b[0m\u001b[1;32m    402\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/h5py/_hl/group.py\u001b[0m in \u001b[0;36mcreate_dataset\u001b[0;34m(self, name, shape, dtype, data, **kwds)\u001b[0m\n\u001b[1;32m    108\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/h5py/_hl/group.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, name, obj)\u001b[0m\n\u001b[1;32m    276\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mHLObject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m                 \u001b[0mh5o\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlcpl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper (/build/h5py-G1Ccq1/h5py-2.7.1/h5py/_objects.c:2847)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper (/build/h5py-G1Ccq1/h5py-2.7.1/h5py/_objects.c:2805)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/h5o.pyx\u001b[0m in \u001b[0;36mh5py.h5o.link (/build/h5py-G1Ccq1/h5py-2.7.1/h5py/h5o.c:3899)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Unable to create link (name already exists)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-67-b71173c2e803>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;31m#        f.append_pt(('timestamp'), now)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m             \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mags'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/xilinx/jupyter_notebooks/slab/slab/datamanagement.py\u001b[0m in \u001b[0;36mappend\u001b[0;34m(self, dataset, pt)\u001b[0m\n\u001b[1;32m    423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 425\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;31m# def save_script(self, name=\"_script\"):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/xilinx/jupyter_notebooks/slab/slab/datamanagement.py\u001b[0m in \u001b[0;36mappend_data\u001b[0;34m(self, f, key, data, forceInit)\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0mShape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m             \u001b[0mShape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mShape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m             \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mShape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/h5py/_hl/dataset.py\u001b[0m in \u001b[0;36mresize\u001b[0;34m(self, size, axis)\u001b[0m\n\u001b[1;32m    345\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mphil\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Only chunked datasets can be resized\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    348\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Only chunked datasets can be resized"
     ]
    }
   ],
   "source": [
    "expt_path = os.path.join(os.getcwd(), 'data/')\n",
    "\n",
    "prefix = \"fft_test\"\n",
    "\n",
    "fname = get_next_filename(expt_path , prefix, suffix='.h5')\n",
    "\n",
    "\n",
    "fname = os.path.join(expt_path, fname)\n",
    "\n",
    "print(fname)\n",
    "\n",
    "f_adc = 13.45\n",
    "\n",
    "# ADC mixer.\n",
    "soc.set_fmix(f=f_adc, mtype=\"in\")\n",
    "\n",
    "# Decimation.\n",
    "D = 300\n",
    "\n",
    "# Number of averages.\n",
    "N = 400\n",
    "\n",
    "# Data source.\n",
    "src = \"input\"\n",
    "\n",
    "# Change decimation/source.\n",
    "if D == 1:\n",
    "    soc.ddscic.outsel(data=src, cic=\"no\")\n",
    "else:\n",
    "    soc.ddscic.outsel(data=src, cic=\"yes\")\n",
    "    soc.ddscic.decimation(D)\n",
    "    \n",
    "# Configure DDS + CIC.\n",
    "soc.ddscic.ddsfreq(f=-0.45e6)\n",
    "soc.ddscic.set_qprod(14)\n",
    "\n",
    "# Configure Number of Averages.\n",
    "soc.acc.setavg(N=N)\n",
    "\n",
    "# Start accumulator block.\n",
    "soc.acc.start()\n",
    "\n",
    "N_min = 2\n",
    "\n",
    "for ii in range(N_min):\n",
    "    \n",
    "    # Get data.\n",
    "    [x, Navg] = soc.acc.transfer()\n",
    "    t_str = time.strftime(\"%m/%d/%Y, %H:%M:%S\", time.localtime())\n",
    "    print(\"Trace taken at time: \", t_str)\n",
    "    now = datetime.now().timestamp() #get a float number for \n",
    "    x = x/Navg\n",
    "    \n",
    "    #Spectrum\n",
    "    r = np.random.normal(scale=1/2**20,size=len(x))\n",
    "    x = x + r\n",
    "    Y = x\n",
    "    #Y_Max = np.max(Y)\n",
    "    Y_Max = 100\n",
    "    Y = 10*np.log10(Y/Y_Max)\n",
    "    F = np.linspace(-soc.fs/D/2,soc.fs/D/2,len(Y))\n",
    " \n",
    "    with SlabFile(fname, 'a') as f:\n",
    "        if ii == 0:\n",
    "            f.create_dataset('fpts', data=F)\n",
    "            f.create_dataset('mags', data=Y)\n",
    "        else:\n",
    "#        f.append_pt(('timestamp'), now)\n",
    "            f.append('mags', Y )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-18T23:01:53.040606Z",
     "start_time": "2022-11-18T23:01:53.013022Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/xilinx/jupyter_notebooks/Morgan/20221108/Updated_Firmware_20221115/data/00011_fft_test.h5\n",
      "(65536,)\n",
      "(65536,)\n",
      "<HDF5 dataset \"fpts\": shape (65536,), type \"<f8\">\n"
     ]
    }
   ],
   "source": [
    "print(fname)\n",
    "h5=h5py.File(fname)\n",
    "list(h5.keys())\n",
    "dset1 = h5['fpts']\n",
    "dset2 = h5['mags']\n",
    "print(dset1.shape)\n",
    "print(dset2.shape)\n",
    "print(dset1)\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-18T23:02:07.275426Z",
     "start_time": "2022-11-18T23:02:07.245000Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.21333333 -0.21332682 -0.21332031 ...  0.21332031  0.21332682\n",
      "  0.21333333]\n",
      "[50.25168721 50.03169901 50.23952775 ... 50.69360869 50.42843526\n",
      " 50.36267974]\n"
     ]
    }
   ],
   "source": [
    "with SlabFile(fname, 'r') as f:\n",
    "#    t_points = np.array(f['timestamp'])\n",
    "    f_points = np.array(f['fpts'])\n",
    "    power = np.array(f['mags'])\n",
    "    \n",
    "(print(f_points))\n",
    "print(power)\n",
    "#print(t_points.dtype)\n",
    "#print(power.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-18T20:01:31.825144Z",
     "start_time": "2022-11-18T20:01:30.382445Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bf0a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot.\n",
    "plt.figure(2,dpi=150)\n",
    "plt.plot(f_points[0],power[0])\n",
    "plt.xlabel('F [MHz]');\n",
    "plt.vlines(-(46+26/2)*1e-3, 40, 80, colors = 'r', linestyles = '--');\n",
    "plt.vlines(-(46-26/2)*1e-3, 40, 80, colors = 'r', linestyles = '--');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "###########################\n",
    "### Noise Experiment ###\n",
    "###########################\n",
    "# This experiment has a noise source connected into the ADC input.\n",
    "# Noise is amplified to provide a good power into the ADC.\n",
    "\n",
    "#######################\n",
    "### Set Input Mixer ###\n",
    "#######################\n",
    "# ADC tile and block.\n",
    "tile = soc.adcs['00']['tile']\n",
    "block = soc.adcs['00']['block']\n",
    "\n",
    "# Frequency.\n",
    "fin = 250\n",
    "soc.mixer.set_freq(tile=tile, block=block, f=fin, btype=\"adc\")\n",
    "\n",
    "\n",
    "################################\n",
    "### Get Accumulated Spectrum ###\n",
    "################################\n",
    "# FFT scaling.\n",
    "soc.fft.scale(soc.fft.SCALE_MIN)\n",
    "\n",
    "# Sampling frequency after CIC.\n",
    "fs = soc.fs/D\n",
    "ts = 1/fs\n",
    "\n",
    "# Get data.\n",
    "[x,Navg] = soc.acc.transfer()\n",
    "x = x/Navg\n",
    "print(\"Number of averages = {}\".format(Navg))\n",
    "\n",
    "# Spectrum.\n",
    "r = np.random.normal(scale=1/2**20,size=len(x))\n",
    "x = x + r\n",
    "Y = x\n",
    "Y = 20*np.log10(Y/np.max(Y))\n",
    "F = np.linspace(-fs/2,fs/2,len(Y))\n",
    "\n",
    "# Plot.\n",
    "plt.figure(2,dpi=150)\n",
    "plt.plot(F*1000,Y)\n",
    "plt.xlabel('F [kHz]');"
   ]
  }
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