Running of automatic saturation measurement on AD2200 setup

saturation.ipynb

{
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pulsestreamer as ps\n",
    "import time\n",
    "from scipy.optimize import curve_fit\n",
    "import visa\n",
    "from ThorlabsPM100 import ThorlabsPM100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm = visa.ResourceManager()\n",
    "inst = rm.open_resource('USB0::0x1313::0x8078::P0034869::INSTR', timeout=1)\n",
    "power_meter = ThorlabsPM100(inst=inst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "power_meter.configure.scalar.power()\n",
    "print(\"Measurement type :\", power_meter.getconfigure)\n",
    "print(\"Current value    :\", power_meter.read)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pulsestreamer.pulse_streamer.constant(ps.OutputState([0], 0.04, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Measurement loop for saturation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "vol_range = np.linspace(0.0, 0.1, 20)\n",
    "pow_range = np.zeros_like(vol_range)\n",
    "counts = np.zeros_like(vol_range)\n",
    "for i, vol in enumerate(vol_range):\n",
    "    pulsestreamer.pulse_streamer.constant(ps.OutputState([0], vol, 0))\n",
    "    time.sleep(6)\n",
    "    pow_range[i] = power_meter.read\n",
    "    counts[i] = np.mean(counterlogic.countdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting and fitting [Momenzadeh Thesis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(P, Fs, Ps):\n",
    "    return Fs/(1+(Ps/P))\n",
    "\n",
    "popt,_ = curve_fit(objective, pow_range*0.738, counts, p0 = [400000, 30e-6])\n",
    "\n",
    "x_fit = np.linspace(pow_range.min()*0.738, pow_range.max()*0.738, 100)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_fit*1e6, objective(x_fit, *popt), 'r-')\n",
    "ax.plot(pow_range*0.738*1e6, counts, 'b.')\n",
    "\n",
    "ax.ticklabel_format(axis='both', style='sci', scilimits=(0,0))\n",
    "ax.set_xlabel('Power (uW)')\n",
    "ax.set_ylabel('Counts/s')\n",
    "plt.show()\n",
    "print('Fs, Ps',*popt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save routine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "filelabel = 'tip_R03C-F7-C9_LT'\n",
    "parameters = {'Fs':popt[0], 'Ps': popt[1]}\n",
    "data =  {'Pow (W), Counts (c/s)': np.array((pow_range*0.738, counts)).T}\n",
    "filepath = savelogic.get_path_for_module(module_name='Saturation')\n",
    "savelogic.save_data(data, parameters=parameters, filepath=filepath, filelabel=filelabel, plotfig=fig, delimiter='\\t')"
   ]
  }
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