ColinP_Lightlab_Functions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drivers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\python.exe\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.executable)\n",
    "import lightlab.util.io as io\n",
    "from lightlab.util.sweep import NdSweeper\n",
    "import time\n",
    "import datetime\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Laser setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "<class 'bytes'> b'HEWLETT-PACKARD,HP8164A,DE38700815,V5.25(72630)\\n'\n",
      "The mainframe is: HP8164A\n",
      "Connected to the laser\n",
      "HEWLETT-PACKARD,HP8164A,DE38700815,V5.25(72630)\n"
     ]
    }
   ],
   "source": [
    "from lightlab.equipment.lab_instruments import hp816x_instr as hp816x_instr\n",
    "channel1 = hp816x_instr.hp816x()\n",
    "channel1.connect('GPIB0::30::INSTR', reset=0, forceTrans=1)\n",
    "print(channel1.query('*IDN?'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sourcemeter setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "connected\n"
     ]
    }
   ],
   "source": [
    "from lightlab.equipment.lab_instruments import Ke26XXA as Ke\n",
    "Keithley = Ke.Ke26XXA()\n",
    "Keithley.connect('GPIB0::29::INSTR')\n",
    "print(\"connected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dark current-voltage scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing progress in tempfile C:\\Users\\Admin\\progress-monitor\\swp_prog2tf3vo70\n",
      "Generic sweep in Voltage\n",
      "Dim-0...\n",
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      "Sweep completed!\n",
      "[1.07884e-06 4.18270e-05 8.24332e-05 1.22901e-04 1.63401e-04 2.03815e-04\n",
      " 2.44368e-04 2.84784e-04 3.25217e-04 3.65416e-04 4.05692e-04 4.45960e-04\n",
      " 4.86097e-04 5.25863e-04 5.65945e-04 6.06416e-04 6.45649e-04 6.85660e-04\n",
      " 7.25245e-04 7.63475e-04 8.02015e-04 8.40977e-04 8.79182e-04 9.18074e-04\n",
      " 9.56414e-04 9.94116e-04 1.03096e-03 1.06379e-03 1.10001e-03 1.13821e-03\n",
      " 1.17491e-03 1.20920e-03 1.24473e-03 1.28034e-03 1.31530e-03 1.35001e-03\n",
      " 1.38256e-03 1.41631e-03 1.45542e-03 1.48420e-03 1.51890e-03 1.55007e-03\n",
      " 1.58577e-03 1.62126e-03 1.64994e-03 1.68017e-03 1.71482e-03 1.74136e-03\n",
      " 1.77517e-03 1.80620e-03 1.83875e-03 1.87114e-03 1.89982e-03 1.92852e-03\n",
      " 1.95745e-03 1.98510e-03 2.01286e-03 2.03975e-03 2.07093e-03 2.09713e-03\n",
      " 2.12446e-03 2.15150e-03 2.17820e-03 2.20155e-03 2.22813e-03 2.25029e-03\n",
      " 2.27417e-03 2.29792e-03 2.31869e-03 2.34518e-03 2.37276e-03 2.39301e-03\n",
      " 2.41569e-03 2.43419e-03 2.45394e-03 2.47482e-03 2.49750e-03 2.51674e-03\n",
      " 2.53066e-03 2.54966e-03 2.57607e-03]\n",
      "Saving to file: C:\\Users\\Admin\\data\\test_modular_dark.pkl.gz\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def I_dark(vi,vf,steps,filename):\n",
    "    '''\n",
    "    vi=initial voltage in V\n",
    "    vf=final voltage in V\n",
    "    steps=steps inbetween vi and vf\n",
    "    filename=name of file to save data\n",
    "    '''\n",
    "    domain=numpy.arange(vi,vf+steps,steps)#sets up range of voltage values\n",
    "    channel1.setTLSState('off')#turns laser off\n",
    "    Keithley.setCurrentMeasurementRange(10e-3,chan='a')\n",
    "    Keithley.setCurrentLimit(10e-3,chan='a') \n",
    "    Keithley.outputenable(True)#enables keithley to send voltage\n",
    "    darksweep = NdSweeper()#lightlab sweeping module\n",
    "    darksweep.addActuation('Voltage', lambda v: Keithley.setVoltage(v,chan='a'),domain)#tells lightlab sweeper package to sweep voltage across domain \n",
    "    darksweep.addMeasurement('Current', lambda: Keithley.getCurrent(chan='a'))#tells lightlab sweeper package to measure current\n",
    "    darksweep.gather(3)#tells sweeper package to sweep after waiting 3 seconds\n",
    "    darksweep.saveObj(filename)#save file\n",
    "    Keithley.setVoltage(0)#sets voltage back to 0\n",
    "    darksweep.plot()#plots\n",
    "I_dark(0,4,50e-3,'test_modular_dark')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Light current-voltage scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing progress in tempfile C:\\Users\\Admin\\progress-monitor\\swp_progxe3cnw0k\n",
      "Generic sweep in Voltage\n",
      "Dim-0...\n",
      "1/81...\n",
      "2/81...\n",
      "3/81...\n",
      "4/81...\n",
      "5/81...\n",
      "6/81...\n",
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      "78/81...\n",
      "79/81...\n",
      "80/81...\n",
      "81/81...\n",
      "Sweep completed!\n",
      "[1.07169e-06 4.26495e-05 8.40521e-05 1.25296e-04 1.66792e-04 2.08058e-04\n",
      " 2.49244e-04 2.90425e-04 3.31811e-04 3.72975e-04 4.13908e-04 4.55041e-04\n",
      " 4.95803e-04 5.36414e-04 5.76985e-04 6.17902e-04 6.58076e-04 6.98214e-04\n",
      " 7.38025e-04 7.77880e-04 8.17399e-04 8.56498e-04 8.95253e-04 9.33841e-04\n",
      " 9.70745e-04 1.00865e-03 1.04620e-03 1.08416e-03 1.12130e-03 1.15822e-03\n",
      " 1.19485e-03 1.22841e-03 1.26488e-03 1.30065e-03 1.33685e-03 1.37125e-03\n",
      " 1.40455e-03 1.43945e-03 1.47234e-03 1.50661e-03 1.53844e-03 1.56786e-03\n",
      " 1.60196e-03 1.63507e-03 1.67068e-03 1.70407e-03 1.73428e-03 1.76523e-03\n",
      " 1.79412e-03 1.82745e-03 1.85694e-03 1.88403e-03 1.91328e-03 1.94242e-03\n",
      " 1.97567e-03 2.00167e-03 2.02946e-03 2.05414e-03 2.08027e-03 2.10776e-03\n",
      " 2.13754e-03 2.16103e-03 2.18703e-03 2.21143e-03 2.23766e-03 2.26159e-03\n",
      " 2.28185e-03 2.30228e-03 2.32656e-03 2.34962e-03 2.36772e-03 2.39042e-03\n",
      " 2.41721e-03 2.43589e-03 2.45602e-03 2.47777e-03 2.50371e-03 2.53124e-03\n",
      " 2.55382e-03 2.57842e-03 2.59888e-03]\n",
      "Saving to file: C:\\Users\\Admin\\data\\test_light_modular.pkl.gz\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def I_light(vi,vf,steps,filename,wave=.00000155,power=0):\n",
    "    '''\n",
    "    vi=initial voltage in V\n",
    "    vf=final voltage in V\n",
    "    steps=steps inbetween vi and vf\n",
    "    filename=name of file to save data\n",
    "    wave=wavelength in m\n",
    "    power=optical power in dbm\n",
    "    '''\n",
    "    domain=numpy.arange(vi,vf+steps,steps)\n",
    "    Keithley.setCurrentMeasurementRange(10e-3,chan='a')\n",
    "    Keithley.setCurrentLimit(10e-3,chan='a') \n",
    "    Keithley.outputenable(True)\n",
    "    channel1.setWavelength(wave)\n",
    "    channel1.setOutputPower(power)\n",
    "    channel1.setTLSState('on')\n",
    "    lightsweep = NdSweeper()\n",
    "    lightsweep.addActuation('Voltage', lambda v: Keithley.setVoltage(v,chan='a'),domain)\n",
    "    lightsweep.addMeasurement('Current', lambda: Keithley.getCurrent(chan='a'))\n",
    "    lightsweep.gather(3)\n",
    "    lightsweep.saveObj(filename)\n",
    "    Keithley.setVoltage(0)\n",
    "    lightsweep.plot()\n",
    "I_light(0,4,50e-3,'test_light_modular')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Photocurrent scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing progress in tempfile C:\\Users\\Admin\\progress-monitor\\swp_progg8v3pedk\n",
      "Generic sweep in Voltage\n",
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      "Sweep completed!\n",
      "OrderedDict([('Voltage', array([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
      "       0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  , 1.05,\n",
      "       1.1 , 1.15, 1.2 , 1.25, 1.3 , 1.35, 1.4 , 1.45, 1.5 , 1.55, 1.6 ,\n",
      "       1.65, 1.7 , 1.75, 1.8 , 1.85, 1.9 , 1.95, 2.  , 2.05, 2.1 , 2.15,\n",
      "       2.2 , 2.25, 2.3 , 2.35, 2.4 , 2.45, 2.5 , 2.55, 2.6 , 2.65, 2.7 ,\n",
      "       2.75, 2.8 , 2.85, 2.9 , 2.95, 3.  , 3.05, 3.1 , 3.15, 3.2 , 3.25,\n",
      "       3.3 , 3.35, 3.4 , 3.45, 3.5 , 3.55, 3.6 , 3.65, 3.7 , 3.75, 3.8 ,\n",
      "       3.85, 3.9 , 3.95, 4.  ])), ('Current', array([1.20997e-06, 1.20640e-06, 1.19090e-06, 1.19209e-06, 1.20997e-06,\n",
      "       1.25885e-06, 1.23978e-06, 1.21474e-06, 1.26481e-06, 1.27316e-06,\n",
      "       1.22786e-06, 1.20521e-06, 1.22309e-06, 1.23501e-06, 1.25885e-06,\n",
      "       1.24454e-06, 1.26958e-06, 1.29104e-06, 1.29580e-06, 1.26600e-06,\n",
      "       1.26958e-06, 1.27435e-06, 1.27792e-06, 1.27316e-06, 1.24335e-06,\n",
      "       1.28627e-06, 1.26839e-06, 1.25170e-06, 1.29342e-06, 1.28031e-06,\n",
      "       1.28031e-06, 1.28388e-06, 1.30415e-06, 1.26719e-06, 1.29104e-06,\n",
      "       1.28984e-06, 1.30415e-06, 1.31249e-06, 1.32322e-06, 1.31130e-06,\n",
      "       1.27435e-06, 1.30296e-06, 1.31845e-06, 1.32084e-06, 1.27912e-06,\n",
      "       1.28508e-06, 1.31130e-06, 1.29938e-06, 1.33872e-06, 1.37925e-06,\n",
      "       1.36018e-06, 1.77813e-03, 1.81083e-03, 1.86888e-03, 1.91372e-03,\n",
      "       1.95929e-03, 1.98998e-03, 2.01253e-03, 2.04731e-03, 2.09215e-03,\n",
      "       2.12229e-03, 2.13989e-03, 2.17414e-03, 2.23759e-03, 2.27288e-03,\n",
      "       2.29622e-03, 2.32311e-03, 2.33591e-03, 2.35698e-03, 2.36012e-03,\n",
      "       2.39676e-03, 2.42071e-03, 2.45697e-03, 2.47951e-03, 2.50213e-03,\n",
      "       2.51644e-03, 2.53604e-03, 2.57106e-03, 2.60058e-03, 2.62517e-03,\n",
      "       2.64472e-03]))])\n",
      "Printing progress in tempfile C:\\Users\\Admin\\progress-monitor\\swp_proguzarngxf\n",
      "Generic sweep in Voltage\n",
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      "Sweep completed!\n",
      "OrderedDict([('Voltage', array([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
      "       0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  , 1.05,\n",
      "       1.1 , 1.15, 1.2 , 1.25, 1.3 , 1.35, 1.4 , 1.45, 1.5 , 1.55, 1.6 ,\n",
      "       1.65, 1.7 , 1.75, 1.8 , 1.85, 1.9 , 1.95, 2.  , 2.05, 2.1 , 2.15,\n",
      "       2.2 , 2.25, 2.3 , 2.35, 2.4 , 2.45, 2.5 , 2.55, 2.6 , 2.65, 2.7 ,\n",
      "       2.75, 2.8 , 2.85, 2.9 , 2.95, 3.  , 3.05, 3.1 , 3.15, 3.2 , 3.25,\n",
      "       3.3 , 3.35, 3.4 , 3.45, 3.5 , 3.55, 3.6 , 3.65, 3.7 , 3.75, 3.8 ,\n",
      "       3.85, 3.9 , 3.95, 4.  ])), ('Current', array([1.09434e-06, 4.56905e-05, 9.01008e-05, 1.34369e-04, 1.78647e-04,\n",
      "       2.22893e-04, 2.66978e-04, 3.10997e-04, 3.55122e-04, 3.98557e-04,\n",
      "       4.42528e-04, 4.85713e-04, 5.29256e-04, 5.72375e-04, 6.15047e-04,\n",
      "       6.57579e-04, 6.99946e-04, 7.41322e-04, 7.82422e-04, 8.22480e-04,\n",
      "       8.64183e-04, 9.03918e-04, 9.43592e-04, 9.85346e-04, 1.02271e-03,\n",
      "       1.06075e-03, 1.09864e-03, 1.13258e-03, 1.16587e-03, 1.20190e-03,\n",
      "       1.24170e-03, 1.28136e-03, 1.31868e-03, 1.35486e-03, 1.39078e-03,\n",
      "       1.42932e-03, 1.47258e-03, 1.51124e-03, 1.54509e-03, 1.57814e-03,\n",
      "       1.60444e-03, 1.63462e-03, 1.66910e-03, 1.70509e-03, 1.73540e-03,\n",
      "       1.77387e-03, 1.80050e-03, 1.83999e-03, 1.87035e-03, 1.89783e-03,\n",
      "       1.92927e-03, 1.96077e-03, 1.99265e-03, 2.02395e-03, 2.05261e-03,\n",
      "       2.08489e-03, 2.11336e-03, 2.14238e-03, 2.16604e-03, 2.18928e-03,\n",
      "       2.21598e-03, 2.23723e-03, 2.25989e-03, 2.28618e-03, 2.30895e-03,\n",
      "       2.33255e-03, 2.35951e-03, 2.38402e-03, 2.40654e-03, 2.43340e-03,\n",
      "       2.45304e-03, 2.47497e-03, 2.49619e-03, 2.51636e-03, 2.53165e-03,\n",
      "       2.54432e-03, 2.56495e-03, 2.58839e-03, 2.60246e-03, 2.62392e-03,\n",
      "       2.66186e-03]))])\n",
      "Saving to file: C:\\Users\\Admin\\data\\test_modular_photocurrent.pkl.gz\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def I_ph(vi,vf,steps,filename,wave=.000001555,power=6):\n",
    "    '''\n",
    "    ****NOTWORKING***\n",
    "    vi=initial voltage in V\n",
    "    vf=final voltage in V\n",
    "    steps=steps inbetween vi and vf\n",
    "    filename=name of file to save data\n",
    "    wave=wavelength in m\n",
    "    power=optical power in dbm\n",
    "    '''\n",
    "    domain=numpy.arange(vi,vf+steps,steps)\n",
    "    Keithley.setVoltage(0)\n",
    "    Keithley.setCurrentMeasurementRange(10e-3,chan='a')\n",
    "    Keithley.setCurrentLimit(10e-3,chan='a') \n",
    "    Keithley.outputenable(True)\n",
    "    channel1.setTLSState('off')\n",
    "    channel1.setWavelength(wave)\n",
    "    channel1.setOutputPower(power)\n",
    "    darksweep = NdSweeper()\n",
    "    darksweep.addActuation('Voltage', lambda v: Keithley.setVoltage(v,chan='a'),domain)\n",
    "    darksweep.addMeasurement('Current', lambda: Keithley.getCurrent(chan='a'))\n",
    "    darksweep.gather(3)\n",
    "    storage=darksweep.data\n",
    "    Keithley.setVoltage(0)\n",
    "    channel1.setTLSState('on')\n",
    "    lightsweep=NdSweeper()\n",
    "    lightsweep.addActuation('Voltage', lambda v: Keithley.setVoltage(v,chan='a'),domain)\n",
    "    lightsweep.addMeasurement('Current', lambda: Keithley.getCurrent(chan='a'))\n",
    "    lightsweep.gather(3)\n",
    "    stor2=lightsweep.data\n",
    "    for i in range(len(storage[\"Current\"])):\n",
    "        darksweep.data[\"Current\"][i]=lightsweep.data[\"Current\"][i]-darksweep.data[\"Current\"][i]\n",
    "    Keithley.setVoltage(0)\n",
    "    darksweep.saveObj(filename)\n",
    "    darksweep.plot()\n",
    "I_ph(0,4,50e-3,'test_modular_photocurrent')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optical Power and Voltage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing progress in tempfile C:\\Users\\Admin\\progress-monitor\\swp_progm9tqwk5d\n",
      "Generic sweep in Voltage\n",
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      "Sweep completed!\n",
      "Saving to file: C:\\Users\\Admin\\data\\test_modular_pv.pkl.gz\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def P_v(vi,vf,steps,filename,wave=.00000155,power=0,laserchannel=1):\n",
    "    '''\n",
    "    vi=initial voltage in V\n",
    "    vf=final voltage in V\n",
    "    steps=steps inbetween vi and vf\n",
    "    filename=name of file to save data\n",
    "    wave=wavelength in m\n",
    "    power=optical power in dbm\n",
    "    '''\n",
    "    domain=numpy.linspace(vi,vf,steps)\n",
    "    Keithley.setCurrentMeasurementRange(10e-3,chan='a')\n",
    "    Keithley.setCurrentLimit(10e-3,chan='a') \n",
    "    Keithley.outputenable(True)\n",
    "    testsweep = NdSweeper()\n",
    "    channel1.setWavelength(wave)\n",
    "    channel1.setOutputPower(power)\n",
    "    channel1.setTLSState('on')\n",
    "    testsweep.addActuation('Voltage', lambda v: Keithley.setVoltage(v),domain)\n",
    "    testsweep.addMeasurement('Optical Power', lambda: channel1.readPWM(0,laserchannel))\n",
    "    testsweep.gather(3)\n",
    "    testsweep.saveObj(filename)\n",
    "    Keithley.setVoltage(0)\n",
    "    testsweep.plot()\n",
    "P_v(0,6,600,'test_modular_pv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optical Power and Wavelength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing progress in tempfile C:\\Users\\Admin\\progress-monitor\\swp_progjsx77rr0\n",
      "Generic sweep in Frequency\n",
      "Dim-0...\n",
      "1/100...\n",
      "2/100...\n",
      "3/100...\n",
      "4/100...\n",
      "5/100...\n",
      "6/100...\n",
      "7/100...\n",
      "8/100...\n",
      "9/100...\n",
      "10/100...\n",
      "11/100...\n",
      "12/100...\n",
      "13/100...\n",
      "14/100...\n",
      "15/100...\n",
      "16/100...\n",
      "17/100...\n",
      "18/100...\n",
      "19/100...\n",
      "20/100...\n",
      "21/100...\n",
      "22/100...\n",
      "23/100...\n",
      "24/100...\n",
      "25/100...\n",
      "26/100...\n",
      "27/100...\n",
      "28/100...\n",
      "29/100...\n",
      "30/100...\n",
      "31/100...\n",
      "32/100...\n",
      "33/100...\n",
      "34/100...\n",
      "35/100...\n",
      "36/100...\n",
      "37/100...\n",
      "38/100...\n",
      "39/100...\n",
      "40/100...\n"
     ]
    }
   ],
   "source": [
    "def P_w(wi,wf,steps,filename,wave=.00000155,power=0):\n",
    "    '''\n",
    "    wi=initial wavelength in m\n",
    "    wf=final wavelength in m\n",
    "    steps=number of steps inbetween wi and wf\n",
    "    filename=name of file to save data\n",
    "    wave=wavelength in m\n",
    "    power=optical power in dbm\n",
    "    '''\n",
    "    domain=numpy.linspace(wi,wf,steps)\n",
    "    Keithley.setCurrentMeasurementRange(10e-3,chan='a')\n",
    "    Keithley.setCurrentLimit(10e-3,chan='a') \n",
    "    channel1.setWavelength(wave)\n",
    "    channel1.setOutputPower(power)\n",
    "    channel1.setTLSState('on')\n",
    "    testsweep = NdSweeper()\n",
    "    testsweep.addActuation('Frequency', lambda f: channel1.setWavelength(f),domain)\n",
    "    testsweep.addMeasurement('Optical Power', lambda: channel1.readPWM(0,1))\n",
    "    testsweep.gather(3)\n",
    "    testsweep.saveObj(filename)\n",
    "    testsweep.plot()\n",
    "P_w(.00000148,.00000158,100,'test_modular_pw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}