HahnEcho.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import numpy as np\n",
    "from scipy.stats import logistic\n",
    "from scipy.optimize import minimize\n",
    "from scipy import interpolate\n",
    "from IPython.display import clear_output, Javascript, Markdown\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "import gc\n",
    "import datetime\n",
    "import time\n",
    "import mat73\n",
    "import random\n",
    "import matplotlib\n",
    "import _spin_params as spin_pars\n",
    "from _create_pdf import create_spins, create_spins_inhomo_g\n",
    "from _helper_functions import tsallis, cauchy, find_nearest, odeintz, smooth_data, find_nearest_sorted_left, find_nearest_sorted\n",
    "from _steady_state import a_steady, a_steady_with_sz\n",
    "from _mbes import mbes, mbes_soc\n",
    "from _do_calculation import solve_mbes, solve_mbes_cpmg, get_pulses_from_cavity_field, solve_mbes_wait, solve_mbes_only_decay, solve_mbes_cpmg_time\n",
    "# matplotlib.pyplot.switch_backend('nbagg')\n",
    "# %qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.340878677112328\n"
     ]
    }
   ],
   "source": [
    "gcoll = spin_pars.gcoll\n",
    "q = spin_pars.q\n",
    "quali = spin_pars.quali\n",
    "fr = spin_pars.fr\n",
    "kappa = np.pi*fr/quali  # HWHM\n",
    "delta = spin_pars.delta  # detuning cavity to spin central frequency ws-wc\n",
    "deltac = spin_pars.deltac  # detuning drive to cavity wc-wp\n",
    "gperp = spin_pars.gperp  # HWHM\n",
    "gpar = spin_pars.gpar  # 0.001 #HWHM\n",
    "gammaq = spin_pars.gammaq/3\n",
    "nbins = spin_pars.nbins  # 701#5000#20\n",
    "g_number = spin_pars.g_number  # how many bins for inhomogeneous coupling\n",
    "#g0 = gcoll/sqrt(nspins)\n",
    "print(kappa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def g_inhomo_function(x, sigma):\n",
    "    return np.exp(-x**2/sigma**2)\n",
    "\n",
    "\n",
    "x_start = 5\n",
    "x_stop = 20  # µm\n",
    "x = np.linspace(x_start, x_stop, g_number)\n",
    "sigma = 5\n",
    "g_temp = g_inhomo_function(x, sigma)\n",
    "g_list = g_temp/np.sum(g_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'weight')"
      ]
     },
     "execution_count": 55,
     "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": [
    "spins, gs, pdf = create_spins_inhomo_g(\n",
    "    gcoll, g_list, q, delta, gammaq, nbins, holes=False, inhomo=True, hyperfine=True)\n",
    "plt.figure()\n",
    "plt.plot(spins[:len(pdf)], pdf/max(pdf))\n",
    "plt.xlabel('$\\Delta f$ (MHz)')\n",
    "plt.ylabel('weight')\n",
    "# %qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "initsteady = (0.001, 0.001)\n",
    "fstart = 60\n",
    "deltaarr = np.linspace(-fstart*2*np.pi, fstart*2*np.pi, 2000)\n",
    "etasteady = 0.00001\n",
    "losasteady = np.zeros(np.shape(deltaarr))\n",
    "losareal = np.zeros(np.shape(deltaarr))\n",
    "losaimag = np.zeros(np.shape(deltaarr))\n",
    "gs_copl = gs[:len(pdf)]/np.linalg.norm(gs[:len(pdf)])*gcoll\n",
    "newinit = initsteady\n",
    "for i, delt in enumerate(deltaarr):\n",
    "    ar, ai = a_steady(etasteady, delt, gs_copl,\n",
    "                      spins[:len(pdf)], gpar, gperp, kappa, newinit)\n",
    "    newinit = (ar, ai)\n",
    "    losasteady[i] = ai**2+ar**2\n",
    "    losareal[i] = ar\n",
    "    losaimag[i] = ai\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.xlabel('$\\Delta f$ (MHz)')\n",
    "plt.ylabel('|A|² (dB)')\n",
    "plt.plot(-deltaarr/2/np.pi, 10*np.log10(losasteady/0.0000000001))\n",
    "#deltaarr[find(losasteady == max(losasteady))]/2/pi\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "pumptime = 40e-3  # 5*np.pi/gcoll\n",
    "waittime = 200e-3\n",
    "decaytime = 8000e-3\n",
    "dt = 0.004\n",
    "numsteps = int(pumptime/dt)\n",
    "tlistpump = np.arange(0, pumptime, dt)\n",
    "tlistdecay = np.arange(0, decaytime, dt)\n",
    "tlistwait = np.arange(0, waittime, dt)\n",
    "\n",
    "\n",
    "# test time it takes to calculate\n",
    "\n",
    "\n",
    "init = np.ones(len(spins)*2+1)*(-1)\n",
    "init[0] = 0\n",
    "init[1::2] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Pulse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "asolpump, asoldecay, infodict = solve_mbes(\n",
    "    mbes, init, pdf, tlistpump, tlistdecay, 0.001, kappa, deltac, gs, gperp, spins, gpar)\n",
    "alos_linear = np.hstack((abs(asolpump[:, 0])**2, abs(asoldecay[:, 0])**2))\n",
    "szlos_linear = np.hstack((np.real(np.sum(asolpump[:, 2::2], axis=1)), np.real(\n",
    "    np.sum(asoldecay[:, 2::2], axis=1))))\n",
    "\n",
    "\n",
    "tplot_linear = np.linspace(\n",
    "    0, 1000*(pumptime+decaytime), len(tlistpump)+len(tlistdecay))\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "ax1.plot(tplot_linear, alos_linear)\n",
    "ax1.set_xlabel('Time (ns)')\n",
    "ax1.set_ylabel('Cavity population')\n",
    "\n",
    "ax2.plot(tplot_linear, szlos_linear)\n",
    "ax2.set_xlabel('Time (ns)')\n",
    "ax2.set_ylabel('Inversion');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibrate Power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "pulsei = np.ones((len(tlistpump)))\n",
    "pin = np.arange(-30, -16, 1)\n",
    "aref = 3080\n",
    "\n",
    "alos = np.zeros((len(pin), len(tlistpump)+len(tlistdecay)))\n",
    "alosr = np.zeros((len(pin), len(tlistpump)+len(tlistdecay)))\n",
    "alosi = np.zeros((len(pin), len(tlistpump)+len(tlistdecay)))\n",
    "szlos = np.zeros(np.shape(alos))\n",
    "szall = np.zeros((len(pin), len(tlistpump)+len(tlistdecay), nbins))\n",
    "small = np.zeros(np.shape(szall))\n",
    "smlos = np.zeros(np.shape(alos))\n",
    "\n",
    "for ctr, p in enumerate(pin):\n",
    "    dr = aref*10**(p/20.)\n",
    "    asolpump, asoldecay, infodict = solve_mbes(\n",
    "        mbes, init, pdf, tlistpump, tlistdecay, dr, kappa, deltac, gs, gperp, spins, gpar)\n",
    "    alos[ctr, :] = np.hstack((abs(asolpump[:, 0])**2, abs(asoldecay[:, 0])**2))\n",
    "    alosr[ctr, :] = np.hstack((asolpump[:, 0].real, asoldecay[:, 0].real))\n",
    "    alosi[ctr, :] = np.hstack((asolpump[:, 0].imag, asoldecay[:, 0].imag))\n",
    "    szlos[ctr, :] = np.hstack((np.real(np.sum(asolpump[:, 2::2], axis=1)), np.real(\n",
    "        np.sum(asoldecay[:, 2::2], axis=1))))\n",
    "    szall[ctr, :, :] = np.vstack(\n",
    "        (np.real(asolpump[:, 2::2]), np.real(asoldecay[:, 2::2])))\n",
    "    small[ctr, :, :] = np.vstack(\n",
    "        (np.real(asolpump[:, 1::2]), np.real(asoldecay[:, 1::2])))\n",
    "    smlos[ctr, :] = np.hstack((np.real(np.sum(asolpump[:, 1::2], axis=1)), np.real(\n",
    "        np.sum(asoldecay[:, 1::2], axis=1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tplot = np.linspace(0, 1000*(pumptime+decaytime),\n",
    "                    len(tlistpump)+len(tlistdecay))\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "ax1.pcolor(tplot, pin, alos, shading=\"auto\")\n",
    "ax1.set_xlabel('Time (ns)')\n",
    "ax1.set_ylabel('Input power (dB)')\n",
    "\n",
    "ax2.pcolor(tplot, pin, szlos, shading=\"auto\")\n",
    "ax2.set_xlabel('Time (ns)')\n",
    "ax2.set_ylabel('Input power (dB)')\n",
    "maxAmpl = abs(asolpump).max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CPMG sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "ptest = -5\n",
    "drive = aref*10**(ptest/20.)\n",
    "solpump, solwait, solpump2, soldecay, infodict = solve_mbes_cpmg_time(\n",
    "    mbes, init, pdf, tlistpump, tlistwait, tlistdecay, drive, kappa, deltac, gs, gperp, spins, gpar)\n",
    "asol2 = np.hstack((abs(solpump[:, 0])**2, abs(solwait[:, 0])\n",
    "                  ** 2, abs(solpump2[:, 0])**2, abs(soldecay[:, 0])**2))\n",
    "tplot = np.linspace(0, 3*pumptime + decaytime + waittime, len(asol2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$|a|^2$')"
      ]
     },
     "execution_count": 69,
     "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": [
    "plt.plot(tplot, asol2)\n",
    "#plt.ylim([0, 2e-16])\n",
    "plt.xlim([0, 2])\n",
    "plt.xlabel(\"Time (µs)\")\n",
    "plt.ylabel(\"$|a|^2$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scanning waittime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/scipy/optimize/minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n"
     ]
    }
   ],
   "source": [
    "waittimes = np.arange(100e-3, 2200e-3, 100e-3)\n",
    "ampls = np.zeros(waittimes.shape)\n",
    "\n",
    "for i, wt in enumerate(waittimes):\n",
    "    tlistwt = np.arange(0, wt, dt)\n",
    "    solpump, solwait, solpump2, soldecay, infodict = solve_mbes_cpmg_time(\n",
    "        mbes, init, pdf, tlistpump, tlistwt, tlistdecay, drive, kappa, deltac, gs, gperp, spins, gpar)\n",
    "    asol2_wt = np.hstack((abs(solpump[:, 0])**2, abs(solwait[:, 0])\n",
    "                         ** 2, abs(solpump2[:, 0])**2, abs(soldecay[:, 0])**2))\n",
    "    tidx = np.linspace(0, 3*pumptime + decaytime + wt, len(asol2))\n",
    "    valscan = pumptime+wt+2*pumptime+wt*0.8\n",
    "    idx, value = find_nearest(tidx, valscan)\n",
    "    ampls[i] = max(asol2_wt[idx:])\n",
    "\n",
    "\n",
    "def exp_fit(x, a, b, c):\n",
    "    return a*np.exp(-(x/b))\n",
    "\n",
    "\n",
    "popt, pcov = scipy.optimize.curve_fit(\n",
    "    exp_fit, waittimes, ampls, p0=[0.8, 0.2, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.37310316 0.20167275 1.        ]\n",
      "1.0202239941788163\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Echo amplitudes')"
      ]
     },
     "execution_count": 190,
     "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": [
    "print(popt)\n",
    "print(1/gperp)\n",
    "plt.plot(waittimes, ampls)\n",
    "plt.plot(waittimes, exp_fit(waittimes, *popt))\n",
    "plt.xlabel('waittimes (µs)')\n",
    "plt.ylabel(\"Echo amplitudes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare with data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n",
      "ERROR:root:ERROR: MATLAB type not supported: string, (uint32)\n"
     ]
    }
   ],
   "source": [
    "file_scan = \"/mnt/samba2/experiments/Quantum InterConnect/Cryogenics/Dilution Fridge/Measurements/20210124_DoulbeChipRes_10/timeResolved/scan_CPMG//SCRIPT_CPMG_1/scan_CPMG_5.mat\"\n",
    "data_ps = mat73.loadmat(file_scan)\n",
    "scan_values_ps = np.asarray(np.squeeze(data_ps['scan_values']), dtype=float)\n",
    "iq_data_ps = np.asarray(data_ps['IQdata_scan'])\n",
    "iq_data_ps = np.asarray([val for val in iq_data_ps])\n",
    "pl_idx = 5500\n",
    "idx_s = 1790\n",
    "wait_time_scan = 3e-7\n",
    "\n",
    "\n",
    "hori = float(data_ps['pars']['acqiris']['acqirisStruct']\n",
    "             ['horizontal']['sampInterval'])*1e9\n",
    "\n",
    "idx, value = find_nearest(scan_values_ps, wait_time_scan)\n",
    "a2plot = (iq_data_ps[idx, idx_s:pl_idx, 0]**2 +\n",
    "          iq_data_ps[idx, idx_s:pl_idx, 1]**2)\n",
    "aplotr = iq_data_ps[idx, idx_s:pl_idx, 0]\n",
    "aploti = iq_data_ps[idx, idx_s:pl_idx, 1]\n",
    "t_data = np.linspace(0, len(a2plot)*hori, len(a2plot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2.0)"
      ]
     },
     "execution_count": 121,
     "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": [
    "plt.plot(t_data/1000, smooth_data(a2plot, 25)-2e-5)\n",
    "plt.plot(tplot, asol2*0.0002)\n",
    "plt.xlabel(\"Time (µs)\")\n",
    "plt.ylabel(\"Amplitude\")\n",
    "plt.ylim([0, 0.0004])\n",
    "plt.xlim([0, 2])"
   ]
  },
  {
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}