Siglent.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author:  Areg Danagoulian (aregjan@mit.edu), with initial code from Nolan Kowitt\n",
    "\n",
    "**License: see LICENSE**\n",
    "\n",
    "The code below connects to a Siglent scope, and reads data from it. It has been tested with SIGLENT SDS 1202X-E.\n",
    "\n",
    "In all examples below we use Channel1 of the scope -- C1 for short. You can always change it to C2.\n",
    "\n",
    "* Documentation: https://siglentna.com/wp-content/uploads/dlm_uploads/2017/10/ProgrammingGuide_forSDS-1-1.pdf\n",
    "\n",
    "Requirements:\n",
    "\n",
    "* pip install pyvisa\n",
    "* pip install pyvisa-py\n",
    "* pip install zeroconf #slow!\n",
    "* pip install typing-extensions --upgrade\n",
    "* go to https://www.ni.com/en/support/downloads/drivers/download/unpackaged.ni-visa.487529.html and install the drivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyvisa\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import struct\n",
    "import re\n",
    "#from tqdm.notebook import tqdm\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scope_settings(scope):\n",
    "    div=float(scope.query('C1:VDIV?')[8:16]) #volts per division -- this is not safe, regexp would be better\n",
    "    ofst=float(scope.query('C1:OFST?')[8:16]) #offset in volts\n",
    "\n",
    "    trdl_query=scope.query('TRDL?') #centering of the horizontal axis, time\n",
    "    trdl=float(re.search(r\"TRDL(.*)S\", trdl_query).group(1)) #first do a regex, then pick the first match. Convert to float\n",
    "\n",
    "    #print(scope.query('TDIV?')) #times per division\n",
    "    tdiv_query=scope.query('TDIV?') #times per division\n",
    "    tdiv=float(re.search(r\"TDIV(.*)S\", tdiv_query).group(1)) #first do a regex, then pick the first match. Convert to float\n",
    "\n",
    "\n",
    "    #print(scope.query('SARA?')) #sampling rate\n",
    "    sara_query=scope.query('SARA?') #sampling rate\n",
    "    sara=float(re.search(r\"SARA(.*)S\", sara_query).group(1)) #first do a regex, then pick the first match. Convert to float\n",
    "    return div,ofst,trdl,tdiv,sara"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def acquire_data(scope,time_measurement,baseline_length, positive_polarity): #need the scope handler, time (in sec), as well as the polarity of the expected pulse\n",
    "#Here we take the pulses and integrate them (for many detectors the integral of the pulse is proportional to the deposited energy)\n",
    "#.   scope:  the handler for the scope object\n",
    "#.   time_measurement:  the measurement time, in SECONDS\n",
    "#    baseline_length:  the length of baseline, in seconds.  It's computed from the beginning of the waveform\n",
    "#    positive_polarity:  logical. Is your sensor outputing positive pulse?  Then YES.  If it's negative then NO.\n",
    "\n",
    "    print(\"Acquiring data for %d seconds.\" % time_measurement)\n",
    "    t_start = time.time() #time start\n",
    "    data=np.empty(0) #this is the array that we will fill\n",
    "    #VM = float(re.search(r\"C1:PAVA? AMPL,(.*)V\", scope.query('C1:PAVA? AMPL')).group(1))\n",
    "    #print(VM)\n",
    "    scope.baud_rate = 115200\n",
    "    div,ofst,trdl,tdiv,sara = get_scope_settings(scope)\n",
    "    cymo_total=0\n",
    "    VM=0\n",
    "    while(time.time()-t_start<=time_measurement): #the main loop\n",
    "        scope.write('C1:WF? DAT2') #three options:  DAT2(just data); DESC(descriptor); ALL\n",
    "        raw_data =scope.read_raw()\n",
    "        samples=int(raw_data[13:22].decode(\"utf-8\")) #this provides the descriptor, with the number of samples at the end\n",
    "        integer_array = np.array([int(byte) for byte in raw_data[22:22+samples]])\n",
    "        #now clean up the problem of >127, as the range in y starts from 0, and goes to the top of the screen(127)\n",
    "        #then cycles from the bottom of the screen to the origin\n",
    "        integer_array[integer_array>127]=integer_array[integer_array>127]-255\n",
    "        v = (integer_array * div/25) - ofst #claculate the actual voltages\n",
    "        time_s=np.arange(samples)\n",
    "        time_s=trdl-(7.0*tdiv)+time_s/sara \n",
    "\n",
    "        v_baseline = np.sum(v[time_s<trdl-(7.*tdiv)+baseline_length])/len(v[time_s<trdl-(7.*tdiv)+baseline_length])\n",
    "\n",
    "        if(positive_polarity): #positive pulse\n",
    "            v_sum=np.sum(v-v_baseline)\n",
    "        else:\n",
    "            v_sum=np.sum(v_baseline-v)\n",
    "\n",
    "        if(v_sum!=VM):\n",
    "            data=np.append(data,v_sum)\n",
    "            VM=v_sum\n",
    "\n",
    "#Now let's try to determine the dead time\n",
    "        if((time.time()-t_start)%2<=0.05): #every two seconds read the cymometer.  This is probably wrong!!\n",
    "            cymo_query=scope.query('CYMOMETER?') #sampling rate\n",
    "#            print(cymo_query)\n",
    "            cymo=float(re.search(r\"CYMT (.*)Hz\", cymo_query).group(1)) #first do a regex, then pick the first match. Convert to float\n",
    "            cymo_total+=cymo*2\n",
    "    print(\"Acquired %d waveforms\\n\" % data.size)\n",
    "    print(\"Total triggers: %d\" % cymo_total)\n",
    "    live_time = float(time_measurement)*float(data.size)/float(cymo_total)\n",
    "    live_time\n",
    "    data.size\n",
    "    cymo_total\n",
    "    time_measurement\n",
    "    print(\"Effective live time: %f seconds\" % live_time)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's start by checking for resources.  List 'em. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('ASRL1::INSTR', 'ASRL2::INSTR', 'USB0::0xF4ED::0xEE3A::SDS1EEFD7R4087::INSTR')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rm = pyvisa.ResourceManager()\n",
    "rm.list_resources() #this will show a number of devices, which will hopefully include something like the next line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Siglent Technologies,SDS1202X-E,SDS1EEFD7R4087,1.3.27\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#scope = rm.open_resource('USB0::0x1AB1::0x0588::DS1ET171304397::INSTR') # you typically get an identifyer like this\n",
    "if(len(rm.list_resources())==3):\n",
    "    scope = rm.open_resource(rm.list_resources()[2]) #update the identifier\n",
    "    if(scope.open):\n",
    "        print(scope.query(\"*IDN?\")) #should return the brand and model\n",
    "    else:\n",
    "        print(\"No connection to a scope.\")\n",
    "else:\n",
    "    print(\"Couldn't identify a scope. Check for connection, and then go to the above cell.\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's first get some interesting parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C1:PAVA PKPK,6.320000E+00V\n",
      "\n",
      "C1:PAVA MAX,4.640000E+00V\n",
      "\n",
      "C1:PAVA MIN,-1.680000E+00V\n",
      "\n",
      "C1:PAVA AMPL,4.480000E+00V\n",
      "\n",
      "CYMT 1.01E+01Hz\n",
      "\n",
      "TRDL -0.00E+00S\n",
      "\n",
      "TDIV 1.00E-07S\n",
      "\n",
      "SARA 1.00E+09Sa/s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(scope.query('C1:PAVA? PKPK')) #peak to peak\n",
    "print(scope.query('C1:PAVA? MAX')) #max\n",
    "print(scope.query('C1:PAVA? MIN')) #min\n",
    "print(scope.query('C1:PAVA? AMPL'))#negative overshoot\n",
    "#print(scope.query('c1:PAVA? ALL')) #EVERYTHING\n",
    "print(scope.query('CYMOMETER?')) # this is VERY useful -- will allow calculation of dead times\n",
    "print(scope.query('TRDL?')) #centering of the horizontal axis, time\n",
    "print(scope.query('TDIV?')) #times per division\n",
    "print(scope.query('SARA?')) #sampling rate\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For documentation on waveform transfer, see p. 141 in \n",
    "https://siglentna.com/wp-content/uploads/dlm_uploads/2017/10/ProgrammingGuide_forSDS-1-1.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let's transfer and plot actual waveforms!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.436249732971192e-05\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "verbose=False\n",
    "\n",
    "div,ofst,trdl,tdiv,sara = get_scope_settings(scope)\n",
    "if verbose:\n",
    "    print(ofst)\n",
    "    print(trdl)\n",
    "    print(tdiv)\n",
    "    print(sara)\n",
    "\n",
    "scope.baud_rate = 115200\n",
    "time0=time.time()\n",
    "for i in range(1):\n",
    "    scope.write('C1:WF? DAT2') #three options:  DAT2(just data); DESC(descriptor); ALL\n",
    "    raw_data =scope.read_raw()\n",
    "print((time.time()-time0)/400.)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "samples=int(raw_data[13:22].decode(\"utf-8\")) #this provides the descriptor, with the number of samples at the end\n",
    "integer_array = np.array([int(byte) for byte in raw_data[22:22+samples]])\n",
    "#now clean up the problem of >127, as the range in y starts from 0, and goes to the top of the screen(127)\n",
    "#then cycles from the bottom of the screen to the origin\n",
    "integer_array[integer_array>127]=integer_array[integer_array>127]-255\n",
    "\n",
    "v = (integer_array * div/25) - ofst #claculate the actual voltages\n",
    "\n",
    "\n",
    "#now make an array of samples' time\n",
    "time_s=np.arange(samples)\n",
    "time_s=trdl-(7.0*tdiv)+time_s/sara \n",
    "\n",
    "if verbose:\n",
    "    print(samples)\n",
    "    print(integer_array)\n",
    "    print(v)\n",
    "    print(time_s)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))  \n",
    "ax.plot(time_s,v)\n",
    "ax.set_xlabel('time [s]')\n",
    "ax.set_ylabel('Voltage [V]')\n",
    "ax.set_ylim(-4*div-ofst,4*div-ofst)\n",
    "ax.set_xlim(-7*tdiv+trdl,7*tdiv+trdl)\n",
    "ax.grid(True,which='major', linestyle=':', linewidth='0.5', color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acquiring data for 5 seconds.\n",
      "Acquired 41 waveforms\n",
      "\n",
      "Total triggers: 85\n",
      "Effective live time: 2.406103 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Integral [V*samples]')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=acquire_data(scope,5,50e-9,False) #first argument is the scope handler. Next is time.  False denotes negative polarity.  The third argument denotes the time in s for the baseline estimation\n",
    "h = plt.hist(data,bins=50) #let's histogram data\n",
    "plt.xlabel(\"Integral [V*samples]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## You can ignore the rest.  It's code for histogramming and fitting spectral data from a NaI detector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gaussian_plusline(x,mu,sig,A,alpha,beta):\n",
    "    return alpha+x*beta+A*np.exp(-(x-mu)**2/2/sig**2)\n",
    "def gaussian(x,mu,sig,A):\n",
    "    return A*np.exp(-(x-mu)**2/2/sig**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0056714291298985 0.006776254649038748\n",
      "3.9133824537287 78.66138096948848\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ys,xs = np.histogram(data,bins=200,range=(0,150))\n",
    "\n",
    "xs = xs[1:]-xs[1]/2\n",
    "\n",
    "mask = np.logical_and(xs>60, xs<100)\n",
    "\n",
    "par,cov = curve_fit(gaussian_plusline,xs[mask],ys[mask],sigma=np.sqrt(ys[mask]+1),p0 = [80,3,50,0,0])\n",
    "h = plt.hist(data,bins=200,range=(0,150)) #let's histogram data\n",
    "\n",
    "xstoplt = np.linspace(60,100)\n",
    "ystoplt = gaussian_plusline(xstoplt,*par)\n",
    "\n",
    "plt.plot(xstoplt,ystoplt)\n",
    "plt.xlabel(\"Integral [V*samples]\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.savefig('my_plot.png') \n",
    "\n",
    "print(cov[1,1],cov[0,0])\n",
    "print(par[1],par[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03554894294822303 0.08280285242696021\n",
      "4.76898426939132 78.01363914634011\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#same as above, but just a gaussian fit\n",
    "ys,xs = np.histogram(data,bins=300,range=(0,150))\n",
    "\n",
    "xs = xs[1:]-xs[1]/2\n",
    "\n",
    "mask = np.logical_and(xs>75, xs<100)\n",
    "\n",
    "par,cov = curve_fit(gaussian,xs[mask],ys[mask],sigma=np.sqrt(ys[mask]+1),p0 = [80,5,400])\n",
    "h = plt.hist(data,bins=300,range=(0,150)) #let's histogram data\n",
    "\n",
    "xstoplt = np.linspace(75,100)\n",
    "ystoplt = gaussian(xstoplt,*par)\n",
    "\n",
    "plt.plot(xstoplt,ystoplt)\n",
    "\n",
    "\n",
    "print(cov[1,1],cov[0,0])\n",
    "print(par[1],par[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}