Qutrit_Tests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting stuff up\n",
    "Pick your favorite open pulse compatible backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import qiskit.pulse as pulse\n",
    "import qiskit.pulse.pulse_lib as pulse_lib\n",
    "from qiskit.compiler import assemble\n",
    "\n",
    "import qiskit\n",
    "qiskit.__qiskit_version__\n",
    "from qiskit.tools.jupyter import backend_overview, backend_monitor\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "import numpy as np\n",
    "import pickle\n",
    "import matplotlib.pyplot as plotter\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/earnestq/lib/python3.7/site-packages/qiskit/providers/ibmq/ibmqfactory.py:181: UserWarning: Credentials are already in use. The existing account in the session will be replaced.\n",
      "  warnings.warn('Credentials are already in use. The existing '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import IBMQ\n",
    "## Put in your infor\n",
    "# IBMQ.save_account()\n",
    "IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def qubit_drive_freq_sweep(drive_duration=128,drive_strength=0.002,drive_qubits=[1],drive_span_MHz=20,drive_freq_steps_MHz=1.0,freq_shift_GHz=0.0):\n",
    "    # drive pulse parameters\n",
    "    drive_sigma = 16\n",
    "    schedules = []\n",
    "    schedule_LOs = []\n",
    "\n",
    "    for ii,qubit in enumerate(drive_qubits):\n",
    "        center_frequency_GHz = backend_defaults.qubit_freq_est[qubit]\n",
    "        center_frequency_GHz = center_frequency_GHz - freq_shift_GHz\n",
    "        print (center_frequency_GHz)\n",
    "        frequency_min = center_frequency_GHz - drive_span_MHz/2.e3\n",
    "        frequency_max = center_frequency_GHz + drive_span_MHz/2.e3\n",
    "        frequencies_GHz = np.arange(frequency_min, frequency_max, drive_freq_steps_MHz/1e3)\n",
    "        # creating drive pulse\n",
    "        drive_pulse = pulse_lib.gaussian(duration=drive_duration, amp=drive_strength,\n",
    "                                         sigma=drive_sigma, name='mydrivepulse')\n",
    "        drive_pulse_qubit = drive_pulse(backend_devicespec.q[qubit].drive)\n",
    "\n",
    "\n",
    "\n",
    "        # measurement pulse parameters\n",
    "        meas_amp = 0.05\n",
    "        meas_samples = 1200\n",
    "        meas_sigma = 4\n",
    "        meas_risefall = 25\n",
    "\n",
    "        # creating measurement pulse\n",
    "        meas_pulse = pulse_lib.gaussian_square(duration=meas_samples, amp=meas_amp,\n",
    "                                               sigma=meas_sigma, risefall=meas_risefall, \n",
    "                                               name='mymeasurepulse')\n",
    "        meas_pulse_qubit = meas_pulse(backend_devicespec.q[qubit].measure)\n",
    "\n",
    "        # create acquire pulse\n",
    "        acq_cmd=pulse.Acquire(duration=meas_samples)\n",
    "        acq_cmd_qubit = acq_cmd(backend_devicespec.q, backend_devicespec.mem)\n",
    "\n",
    "        # combined measure and acquire pulse\n",
    "        measure_and_acquire_qubit = meas_pulse_qubit | acq_cmd_qubit\n",
    "\n",
    "        # scalefactor for received data\n",
    "        scale_factor = 1e-10\n",
    "\n",
    "\n",
    "\n",
    "        num_shots_per_frequency = 512\n",
    "        for jj, drive_frequency in enumerate(frequencies_GHz):\n",
    "            # start an empty schedule with a label\n",
    "            this_schedule = pulse.Schedule(name=\"Frequency = {}\".format(drive_frequency))\n",
    "            this_schedule += drive_pulse_qubit\n",
    "            this_schedule += measure_and_acquire_qubit << this_schedule.duration\n",
    "\n",
    "            schedules.append(this_schedule)\n",
    "            thisLO = pulse.LoConfig({backend_devicespec.q[qubit].drive: drive_frequency})\n",
    "            schedule_LOs.append(thisLO)\n",
    "        \n",
    "    VNASweep_experiment_qobj = assemble(schedules, backend = backend, \n",
    "                            meas_level=1, meas_return='avg', \n",
    "                            shots=num_shots_per_frequency,\n",
    "                            schedule_los = schedule_LOs\n",
    "                            )\n",
    "    return VNASweep_experiment_qobj, schedules, schedule_LOs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def qubit_rabi_pow_sweep(drive_duration=128,drive_start=0.005,drive_stop=0.05,drive_steps=0.005,drive_qubits=[1],drive_freq=5.0):\n",
    "    # drive pulse parameters\n",
    "    drive_sigma = 16\n",
    "    Rabi_schedules = []\n",
    "    Rabi_schedule_LOs = []\n",
    "    for ii,qubit in enumerate(drive_qubits):\n",
    "        drive_powers = np.arange(drive_start, drive_stop, drive_steps)\n",
    "        # creating drive pulse\n",
    "        drive_pulse = pulse_lib.gaussian(duration=drive_duration, amp=drive_start,\n",
    "                                         sigma=drive_sigma, name='mydrivepulse')\n",
    "        drive_pulse_qubit = drive_pulse(backend_devicespec.q[qubit].drive)\n",
    "\n",
    "\n",
    "\n",
    "        # measurement pulse parameters\n",
    "        meas_amp = 0.05\n",
    "        meas_samples = 1200\n",
    "        meas_sigma = 4\n",
    "        meas_risefall = 25\n",
    "\n",
    "        # creating measurement pulse\n",
    "        meas_pulse = pulse_lib.gaussian_square(duration=meas_samples, amp=meas_amp,\n",
    "                                               sigma=meas_sigma, risefall=meas_risefall, \n",
    "                                               name='mymeasurepulse')\n",
    "        meas_pulse_qubit = meas_pulse(backend_devicespec.q[qubit].measure)\n",
    "\n",
    "        # create acquire pulse\n",
    "        acq_cmd=pulse.Acquire(duration=meas_samples)\n",
    "        acq_cmd_qubit = acq_cmd(backend_devicespec.q, backend_devicespec.mem)\n",
    "\n",
    "        # combined measure and acquire pulse\n",
    "        measure_and_acquire_qubit = meas_pulse_qubit | acq_cmd_qubit\n",
    "\n",
    "        # scalefactor for received data\n",
    "        scale_factor = 1e-10\n",
    "\n",
    "\n",
    "\n",
    "        num_shots_per_frequency = 512\n",
    "        for ii, drive_power in enumerate(drive_powers):\n",
    "            rabi_pulse = pulse_lib.gaussian(duration=drive_duration, amp=drive_power, \n",
    "                                            sigma=drive_sigma, name='rabi_pulse_{}'.format(ii))\n",
    "            rabi_pulse_qubit = rabi_pulse(backend_devicespec.q[qubit].drive)\n",
    "            # start an empty schedule with a label\n",
    "            this_schedule = pulse.Schedule(name=\"Rabi drive = {}\".format(drive_power))\n",
    "            this_schedule += rabi_pulse_qubit\n",
    "            this_schedule += measure_and_acquire_qubit << this_schedule.duration\n",
    "\n",
    "            Rabi_schedules.append(this_schedule)\n",
    "            thisLO = pulse.LoConfig({backend_devicespec.q[qubit].drive: drive_freq})\n",
    "            Rabi_schedule_LOs.append(thisLO)\n",
    "        \n",
    "    Rabi_power_qobj = assemble(Rabi_schedules, backend = backend, \n",
    "                            meas_level=1, meas_return='avg', \n",
    "                            shots=num_shots_per_frequency,\n",
    "                            schedule_los = Rabi_schedule_LOs\n",
    "                            )\n",
    "    return Rabi_power_qobj, Rabi_schedules, Rabi_schedule_LOs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def T1_measurement(qubit=0, qubit_freq=1.0,pi_pulse_pow=0.2,pi_pulse_duration=128,time_max_us=500.0,time_step_us=2.0):\n",
    "    # T1 experiment parameters\n",
    "    drive_sigma = 6\n",
    "    times_us = np.arange(1, time_max_us, time_step_us)\n",
    "    num_shots_per_point = 512\n",
    "\n",
    "    # drive parameters\n",
    "\n",
    "    # create schedules for Ramsey experiment \n",
    "    T1_schedules = []\n",
    "    T1_schedule_LOs = []\n",
    "\n",
    "    T1_pulse = pulse_lib.gaussian(duration=pi_pulse_duration, amp=pi_pulse_pow, \n",
    "                                    sigma=drive_sigma, name='T1_pulse')\n",
    "    T1_pulse_qubit = T1_pulse(backend_devicespec.q[qubit].drive)\n",
    "    thisLO = pulse.LoConfig({backend_devicespec.q[qubit].drive: qubit_freq})\n",
    "    for ii, delay_time_us in enumerate(times_us):\n",
    "        # start an empty schedule with a label\n",
    "        this_schedule = pulse.Schedule(name=\"T1 delay = {} us\".format(qubit_freq))\n",
    "        this_schedule += T1_pulse_qubit\n",
    "        this_schedule |= (measure_and_acquire_qubit << int(delay_time_us*1000/dt))\n",
    "\n",
    "        T1_schedules.append(this_schedule)\n",
    "        T1_schedule_LOs.append(thisLO)\n",
    "\n",
    "    T1_experiment_qobj = assemble (T1_schedules, backend = backend, \n",
    "                                meas_level=1, meas_return='avg',\n",
    "                                shots=num_shots_per_point,\n",
    "                                schedule_los = T1_schedule_LOs\n",
    "                                    )\n",
    "    return T1_experiment_qobj, T1_schedules, T1_schedule_LOs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# test_qobj, test_schedules, test_LO_schedules = qubit_rabi_pow_sweep(drive_steps=0.002)\n",
    "# print (len(test_schedules))\n",
    "# # np.arange(0.01, 0.05,0.007)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_num = 1\n",
    "# test_schedules[20].draw(channels_to_plot=[backend_devicespec.q[test_num].measure, \n",
    "#                                            backend_devicespec.q[test_num].drive,\n",
    "#                                            #backend_devicespec.q[qubit].acquire,\n",
    "#                                          ],\n",
    "#                                          scaling=10.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IBMQ.provider()\n",
    "provider = IBMQ.get_provider(hub='ibm-q-dev', group='support', project='default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQBackend('ibmq_poughkeepsie') from IBMQ(hub='ibm-q-dev', group='support', project='default')>,\n",
       " <IBMQBackend('ibmq_boeblingen') from IBMQ(hub='ibm-q-dev', group='support', project='default')>,\n",
       " <IBMQBackend('ibmq_singapore') from IBMQ(hub='ibm-q-dev', group='support', project='default')>,\n",
       " <IBMQBackend('ibmq_almaden') from IBMQ(hub='ibm-q-dev', group='support', project='default')>,\n",
       " <IBMQBackend('ibmq_johannesburg') from IBMQ(hub='ibm-q-dev', group='support', project='default')>,\n",
       " <IBMQBackend('ibmq_rochester') from IBMQ(hub='ibm-q-dev', group='support', project='default')>,\n",
       " <IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q-dev', group='support', project='default')>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provider.backends()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = provider.get_backend('ibmq_boeblingen')\n",
    "backend_config = backend.configuration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%qiskit_backend_monitor` not found.\n"
     ]
    }
   ],
   "source": [
    "%qiskit_backend_monitor backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/earnestq/lib/python3.7/site-packages/qiskit/pulse/channels/device_specification.py:46: DeprecationWarning: DeviceSpecification is deprecated.Instead of DeviceSpecification, use PulseChannelSpec.\n",
      "  'Instead of DeviceSpecification, use PulseChannelSpec.', DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "backend_defaults = backend.defaults()\n",
    "backend_devicespec = pulse.DeviceSpecification.create_from(backend)\n",
    "dt = backend_config.dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.8472947497933605"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend_defaults.qubit_freq_est[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate amplitude from complex signal\n",
    "def get_amplitude(vec):\n",
    "    i_signal = np.imag(vec)\n",
    "    r_signal = np.real(vec)\n",
    "\n",
    "    mvec = [np.mean(r_signal), np.mean(i_signal)]\n",
    "\n",
    "    src_mat = np.vstack((r_signal - mvec[0], i_signal - mvec[1])).T\n",
    "    (_, _, v_mat) = np.linalg.svd(src_mat)\n",
    "\n",
    "    dvec = v_mat[0, 0:2]\n",
    "\n",
    "    if dvec.dot(mvec) < 0:\n",
    "        dvec = -dvec\n",
    "\n",
    "    return src_mat.dot(dvec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Find the qubit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the frequency range that will be swept in search of the qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "old_codes = ['5dc86e28bb6fd100115aacbc', '5dc86e2e0d505d0012dfe992', '5dc86e347820a700116595cb', '5dc86e3a0d505d0012dfe993', '5dc86e400d505d0012dfe994', '5dc86e457820a700116595cc', '5dc86e4b6c4dd90011508683', '5dc86e506c4dd90011508684', '5dc86e56bb6fd100115aacbe', '5dc86e5b6c4dd90011508685', '5dc86e610d505d0012dfe995', '5dc86e66867adf00111319b4', '5dc86e6cbb6fd100115aacc0', '5dc86e717820a700116595ce', '5dc86e776622b000110091f9', '5dc86e7c0d505d0012dfe996', '5dc86e8173c4410011ef04a9', '5dc86e877a1785001174c496', '5dc86e8c70b70d00122d0129', '5dc86e9270b70d00122d012b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    print (\"About to run\")\n",
    "    time.sleep(1)\n",
    "    qubit_job_codes = []\n",
    "    for i in range(20):\n",
    "        sweep_qobj, schedules, schedule_LOs = qubit_drive_freq_sweep(drive_qubits=[i])\n",
    "        time.sleep(0.1)\n",
    "        job = backend.run(sweep_qobj)\n",
    "        time.sleep(2)\n",
    "        print (job.job_id())\n",
    "        time.sleep(2)\n",
    "        qubit_job_codes.append(job.job_id())\n",
    "    print (qubit_job_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'qubit_job_codes' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-3fc54de158b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve_job\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqubit_job_codes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[0m\u001b[1;32m      2\u001b[0m \u001b[0mVNASweep_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'qubit_job_codes' is not defined"
     ]
    }
   ],
   "source": [
    "job = backend.retrieve_job(qubit_job_codes[0])\n",
    "VNASweep_results = job.result(timeout=3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.752420191956177\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "shapes (20,40) and (2,) not aligned: 40 (dim 1) != 2 (dim 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-fa81aa039020>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplotter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_X\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_amplitude\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_Y\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[0m\u001b[1;32m     22\u001b[0m \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxvline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcenter_frequency_GHz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-12-61d0f4fb553e>\u001b[0m in \u001b[0;36mget_amplitude\u001b[0;34m(vec)\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0mdvec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mdvec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0msrc_mat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: shapes (20,40) and (2,) not aligned: 40 (dim 1) != 2 (dim 0)"
     ]
    }
   ],
   "source": [
    "qubit = 18\n",
    "job = backend.retrieve_job(old_codes[qubit])\n",
    "VNASweep_results = job.result(timeout=3600)\n",
    "drive_span_MHz=20\n",
    "drive_freq_steps_MHz=1.0\n",
    "center_frequency_GHz = backend_defaults.qubit_freq_est[qubit]\n",
    "print (center_frequency_GHz)\n",
    "center_frequency_GHz = center_frequency_GHz \n",
    "scale_factor=1.0\n",
    "\n",
    "frequency_min = center_frequency_GHz - drive_span_MHz/2.e3\n",
    "frequency_max = center_frequency_GHz + drive_span_MHz/2.e3\n",
    "frequencies_GHz = np.arange(frequency_min, frequency_max, drive_freq_steps_MHz/1e3)\n",
    "plot_X = frequencies_GHz\n",
    "plot_Y = []\n",
    "for kk, drive_frequency in enumerate(frequencies_GHz):\n",
    "    thisfrequency_results = VNASweep_results.get_memory(kk)*scale_factor\n",
    "    plot_Y.append( thisfrequency_results[qubit]  )\n",
    "\n",
    "import matplotlib.pyplot as plotter\n",
    "plotter.plot(plot_X, get_amplitude(plot_Y))\n",
    "plotter.axvline(center_frequency_GHz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.697297169266484\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4.847297169266485"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweep_qobj, schedules, schedule_LOs = qubit_drive_freq_sweep(drive_strength=0.35,drive_qubits=[1],freq_shift_GHz=0.15)\n",
    "backend_defaults.qubit_freq_est[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two photon process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "4.896900365638736\n",
      "5dcb09ca6c129f0011a81599\n",
      "1\n",
      "4.697297169266484\n",
      "5dcb09d07069d20011460061\n",
      "2\n",
      "4.553656554150497\n",
      "5dcb09d5491a8100123b25f6\n",
      "3\n",
      "4.624331194461392\n",
      "5dcb09db491a8100123b25f7\n",
      "4\n",
      "4.216183279759661\n",
      "5dcb09e1b3fed10012292945\n",
      "5\n",
      "4.761607192206396\n",
      "5dcb09e61896660011bb283d\n",
      "6\n",
      "4.576867284366795\n",
      "5dcb09ec5eb52e00110c5baf\n",
      "7\n",
      "4.396024207803126\n",
      "5dcb09f22592de0011c9320d\n",
      "8\n",
      "4.508592808845852\n",
      "5dcb09f72592de0011c9320e\n",
      "9\n",
      "4.616633465544842\n",
      "5dcb09fd491a8100123b25f9\n",
      "10\n",
      "4.805298927928206\n",
      "5dcb0a03684d760011534d5d\n",
      "11\n",
      "4.371705870615484\n",
      "5dcb0a091896660011bb283f\n",
      "12\n",
      "4.582563137710962\n",
      "5dcb0a0e1896660011bb2840\n",
      "13\n",
      "4.485560601622505\n",
      "5dcb0a14b3fed10012292949\n",
      "14\n",
      "4.405080859933487\n",
      "5dcb0a1a2592de0011c93210\n",
      "15\n",
      "4.432274241287666\n",
      "5dcb0a20f2b4ea001160718e\n",
      "16\n",
      "4.605623987546261\n",
      "5dcb0a26491a8100123b25fc\n",
      "17\n",
      "4.439829544070661\n",
      "5dcb0a2bf2b4ea001160718f\n",
      "18\n",
      "4.602420191956177\n",
      "5dcb0a310a67070011e8da76\n",
      "19\n",
      "4.885361892935842\n",
      "5dcb0a365eb52e00110c5bb2\n",
      "['5dcb09ca6c129f0011a81599', '5dcb09d07069d20011460061', '5dcb09d5491a8100123b25f6', '5dcb09db491a8100123b25f7', '5dcb09e1b3fed10012292945', '5dcb09e61896660011bb283d', '5dcb09ec5eb52e00110c5baf', '5dcb09f22592de0011c9320d', '5dcb09f72592de0011c9320e', '5dcb09fd491a8100123b25f9', '5dcb0a03684d760011534d5d', '5dcb0a091896660011bb283f', '5dcb0a0e1896660011bb2840', '5dcb0a14b3fed10012292949', '5dcb0a1a2592de0011c93210', '5dcb0a20f2b4ea001160718e', '5dcb0a26491a8100123b25fc', '5dcb0a2bf2b4ea001160718f', '5dcb0a310a67070011e8da76', '5dcb0a365eb52e00110c5bb2']\n"
     ]
    }
   ],
   "source": [
    "twophoton_job_codes = []\n",
    "for i in range(20):\n",
    "    print (i)\n",
    "    sweep_qobj, schedules, schedule_LOs = qubit_drive_freq_sweep(drive_strength=0.35,drive_qubits=[i],freq_shift_GHz=0.15)\n",
    "    time.sleep(0.1)\n",
    "    job = backend.run(sweep_qobj)\n",
    "    time.sleep(2)\n",
    "    print (job.job_id())\n",
    "    time.sleep(2)\n",
    "    twophoton_job_codes.append(job.job_id())\n",
    "print (twophoton_job_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp_codes = ['5dc86f30bb6fd100115aacc1', '5dc86f356c4dd90011508689', '5dc86f3bbb6fd100115aacc2', '5dc86f416c4dd9001150868c', '5dc86f46de13de0011d5cc47', '5dc86f4bde13de0011d5cc49', '5dc86f5170b70d00122d012e', '5dc86f5770b70d00122d0130', '5dc86f5dbb6fd100115aacc4', '5dc86f62bb6fd100115aacc5', '5dc86f68de13de0011d5cc4b', '5dc86f6d70b70d00122d0131', '5dc86f737a1785001174c498', '5dc86f796c4dd9001150868f', '5dc86f7e867adf00111319bd', '5dc86f8473c4410011ef04b0', '5dc86f8a6622b000110091fd', '5dc86f8fbb6fd100115aacc6', '5dc86f9570b70d00122d0132', '5dc86f9a7a1785001174c499']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x134e18748>]"
      ]
     },
     "execution_count": 47,
     "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": [
    "complex_vec = plot_Y[np.argmin(get_amplitude(plot_Y))]\n",
    "freq = plot_X[np.argmin(get_amplitude(plot_Y))]\n",
    "plotter.plot(freq,abs(complex_vec),\"*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The qubit 01 frequency is 4.589830\n",
      "4.439829544070661\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x13372f4a8>"
      ]
     },
     "execution_count": 159,
     "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": [
    "drive_freq_steps_MHz= 1.0\n",
    "drive_span_MHz = 20.0\n",
    "freq_shift_GHz = 0.15\n",
    "qubit = -3\n",
    "print (\"The qubit 01 frequency is %f\" %backend_defaults.qubit_freq_est[qubit])\n",
    "job = backend.retrieve_job(twophoton_job_codes[qubit])\n",
    "VNASweep_results = job.result(timeout=3600)\n",
    "\n",
    "\n",
    "### . Getting the correct frequencies\n",
    "center_frequency_GHz = backend_defaults.qubit_freq_est[qubit]\n",
    "center_frequency_GHz = center_frequency_GHz - freq_shift_GHz\n",
    "print (center_frequency_GHz)\n",
    "frequency_min = center_frequency_GHz - drive_span_MHz/2.e3\n",
    "frequency_max = center_frequency_GHz + drive_span_MHz/2.e3\n",
    "frequencies_GHz = np.arange(frequency_min, frequency_max, drive_freq_steps_MHz/1e3)\n",
    "\n",
    "## Back to the usual\n",
    "plot_X = frequencies_GHz\n",
    "plot_Y = []\n",
    "for kk, drive_frequency in enumerate(frequencies_GHz):\n",
    "    thisfrequency_results = VNASweep_results.get_memory(kk)*scale_factor\n",
    "    plot_Y.append(  thisfrequency_results[qubit])  \n",
    "\n",
    "import matplotlib.pyplot as plotter\n",
    "plotter.plot(plot_X, get_amplitude(plot_Y),\"--*\")\n",
    "complex_vec = plot_Y[np.argmin(get_amplitude(plot_Y))]\n",
    "freq = plot_X[np.argmin(get_amplitude(plot_Y))]\n",
    "two_photon_peak.append(freq)\n",
    "plotter.plot(freq,get_amplitude(complex_vec),\".\")\n",
    "plotter.axvline(freq)\n",
    "# plotter.axvline(4.6950)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "4.896900365638736\n",
      "1\n",
      "4.697297169266484\n",
      "2\n",
      "4.553656554150497\n",
      "3\n",
      "4.624331194461392\n",
      "4\n",
      "4.216183279759661\n",
      "5\n",
      "4.761607192206396\n",
      "6\n",
      "4.576867284366795\n",
      "7\n",
      "4.396024207803126\n",
      "8\n",
      "4.508592808845852\n",
      "9\n",
      "4.616633465544842\n",
      "10\n",
      "4.805298927928206\n",
      "11\n",
      "4.371705870615484\n",
      "12\n",
      "4.582563137710962\n",
      "13\n",
      "4.485560601622505\n",
      "14\n",
      "4.405080859933487\n",
      "15\n",
      "4.432274241287666\n",
      "16\n",
      "4.605623987546261\n",
      "17\n",
      "4.439829544070661\n",
      "18\n",
      "4.602420191956177\n",
      "19\n",
      "4.885361892935842\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": [
    "two_photon_peak = []\n",
    "freq_shift_GHz = 0.15\n",
    "for jj in range(20):\n",
    "    qubit = jj\n",
    "    print (qubit)\n",
    "    job = backend.retrieve_job(twophoton_job_codes[qubit])\n",
    "    VNASweep_results = job.result(timeout=3600)\n",
    "    \n",
    "    \n",
    "    ### . Getting the correct frequencies\n",
    "    center_frequency_GHz = backend_defaults.qubit_freq_est[qubit]\n",
    "    center_frequency_GHz = center_frequency_GHz - freq_shift_GHz\n",
    "    print (center_frequency_GHz)\n",
    "    frequency_min = center_frequency_GHz - drive_span_MHz/2.e3\n",
    "    frequency_max = center_frequency_GHz + drive_span_MHz/2.e3\n",
    "    frequencies_GHz = np.arange(frequency_min, frequency_max, drive_freq_steps_MHz/1e3)\n",
    "    \n",
    "    ## Back to the usual\n",
    "    plot_X = frequencies_GHz\n",
    "    plot_Y = []\n",
    "    for kk, drive_frequency in enumerate(frequencies_GHz):\n",
    "        thisfrequency_results = VNASweep_results.get_memory(kk)*scale_factor\n",
    "        plot_Y.append(  thisfrequency_results[qubit]  )\n",
    "\n",
    "    import matplotlib.pyplot as plotter\n",
    "    plotter.plot(plot_X, get_amplitude(plot_Y),\"--*\")\n",
    "    complex_vec = plot_Y[np.argmin(get_amplitude(plot_Y))]\n",
    "    freq = plot_X[np.argmin(get_amplitude(plot_Y))]\n",
    "    two_photon_peak.append(freq)\n",
    "    plotter.plot(freq,get_amplitude(complex_vec),\".\")\n",
    "    plotter.axvline(freq)\n",
    "    # plotter.axvline(4.6950)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15199636219316925\n"
     ]
    }
   ],
   "source": [
    "testtest = [4.894900365638739, 4.695297169266487, 4.5526565541505, 4.621331194461394, 4.207183279759661, 4.7606071922063995, 4.573867284366798, 4.390024207803128, 4.504592808845854, 4.611633465544844, 4.8022989279282084, 4.365705870615486, 4.582563137710966, 4.480560601622507, 4.400080859933489, 4.425274241287667, 4.6046239875462645, 4.4338295440706625, 4.599420191956179, 4.876361892935843]\n",
    "point = 0\n",
    "print (backend_defaults.qubit_freq_est[point]-testtest[point])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.15199636219316925, 0.1519975805268734, 0.15101767012394784, 0.15298718022445446, 0.15900657322260336, 0.1509995784394027, 0.15300025279023366, 0.15599601648593264, 0.15398763425062345, 0.15500496982881717, 0.15300541142502677, 0.15599811253074147, 0.14999023494484298, 0.1550140852602464, 0.15501477548158338, 0.15699608519940522, 0.15100461474790805, 0.15599241160900412, 0.15298762922990328, 0.15901575519297229]\n"
     ]
    }
   ],
   "source": [
    "two_photon_freq_diff = []\n",
    "for i in range(len(testtest)):\n",
    "    two_photon_freq_diff.append(backend_defaults.qubit_freq_est[i]-testtest[i])\n",
    "print (two_photon_freq_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.894900365638739, 4.695297169266487, 4.5526565541505, 4.621331194461394, 4.207183279759661, 4.7606071922063995, 4.573867284366798, 4.390024207803128, 4.504592808845854, 4.611633465544844, 4.8022989279282084, 4.365705870615486, 4.582563137710966, 4.480560601622507, 4.400080859933489, 4.425274241287667, 4.6046239875462645, 4.4338295440706625, 4.599420191956179, 4.876361892935843]\n",
      "0.1589999999999998\n"
     ]
    }
   ],
   "source": [
    "print (two_photon_peak)\n",
    "point = 4\n",
    "print (backend_defaults.qubit_freq_est[point]-two_photon_peak[point])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Rabi experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 photon rabi\n",
    "### $E_{01}$ Rabi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "onephonton_chevron_codes = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n"
     ]
    }
   ],
   "source": [
    "print(len(np.arange(0.0,0.65,0.025)))\n",
    "old_2p_rabi_codes = ['5dca1a876622b00011009854', '5dca1a89de13de0011d5d27d', '5dca1a8abb6fd100115ab30c', '5dca1a8b70b70d00122d0734', '5dca1a8d867adf0011132026', '5dca1a8ede13de0011d5d27e', '5dca1a90bb6fd100115ab310', '5dca1a91de13de0011d5d27f', '5dca1a9370b70d00122d0735', '5dca1a9470b70d00122d0736', '5dca1a960d505d0012dff039', '5dca1a97bb6fd100115ab312', '5dca1a986c4dd90011508cd6', '5dca1a9a70b70d00122d0738', '5dca1a9b7820a70011659bd4', '5dca1a9d73c4410011ef0b2b', '5dca1a9e867adf0011132028', '5dca1aa0de13de0011d5d281', '5dca1aa10d505d0012dff03a', '5dca1aa273c4410011ef0b2c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Experiment on Qubit [0], at frequency 4.894900 GHz\n",
      "Running Experiment on Qubit [1], at frequency 4.695297 GHz\n",
      "Running Experiment on Qubit [2], at frequency 4.552657 GHz\n",
      "Running Experiment on Qubit [3], at frequency 4.621331 GHz\n",
      "Running Experiment on Qubit [4], at frequency 4.207183 GHz\n",
      "Running Experiment on Qubit [5], at frequency 4.760607 GHz\n",
      "Running Experiment on Qubit [6], at frequency 4.573867 GHz\n",
      "Running Experiment on Qubit [7], at frequency 4.390024 GHz\n",
      "Running Experiment on Qubit [8], at frequency 4.504593 GHz\n",
      "Running Experiment on Qubit [9], at frequency 4.611633 GHz\n",
      "Running Experiment on Qubit [10], at frequency 4.802299 GHz\n",
      "Running Experiment on Qubit [11], at frequency 4.365706 GHz\n",
      "Running Experiment on Qubit [12], at frequency 4.582563 GHz\n",
      "Running Experiment on Qubit [13], at frequency 4.480561 GHz\n",
      "Running Experiment on Qubit [14], at frequency 4.400081 GHz\n",
      "Running Experiment on Qubit [15], at frequency 4.425274 GHz\n",
      "Running Experiment on Qubit [16], at frequency 4.604624 GHz\n",
      "Running Experiment on Qubit [17], at frequency 4.433830 GHz\n",
      "Running Experiment on Qubit [18], at frequency 4.599420 GHz\n",
      "Running Experiment on Qubit [19], at frequency 4.876362 GHz\n",
      "['5dcb0dcd5eb52e00110c5bbc', '5dcb0dceb3fed1001229295e', '5dcb0dd01896660011bb284d', '5dcb0dd20a67070011e8da84', '5dcb0dd3684d760011534d6f', '5dcb0dd50a67070011e8da85', '5dcb0dd65eb52e00110c5bbe', '5dcb0dd87069d20011460074', '5dcb0dda7069d20011460076', '5dcb0ddcb3fed10012292962', '5dcb0dde2592de0011c9321d', '5dcb0ddff2b4ea001160719e', '5dcb0de1f2b4ea001160719f', '5dcb0de2684d760011534d71', '5dcb0de47069d20011460077', '5dcb0de6b3fed10012292964', '5dcb0de7b3fed10012292965', '5dcb0de91896660011bb284f', '5dcb0dea491a8100123b2609', '5dcb0decb3fed10012292966']\n"
     ]
    }
   ],
   "source": [
    "two_photon_rabi_codes = []\n",
    "\n",
    "for i in range(20):\n",
    "    qubit_freq = two_photon_peak[i]\n",
    "    drive_qubit = [i]\n",
    "    print (\"Running Experiment on Qubit %s, at frequency %f GHz\"%(drive_qubit,qubit_freq))\n",
    "    sweep_qobj, schedules, schedule_LOs = qubit_rabi_pow_sweep(drive_start=0.0,drive_stop=0.65,drive_steps=0.025,drive_qubits=drive_qubit,drive_freq=qubit_freq)\n",
    "    job = backend.run(sweep_qobj)\n",
    "    two_photon_rabi_codes.append(job.job_id())\n",
    "\n",
    "print (two_photon_rabi_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "5dcb0dda7069d20011460076\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x13ba926a0>"
      ]
     },
     "execution_count": 285,
     "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": [
    "plot_Y = []\n",
    "drive_powers = np.arange(0.0,0.65,0.025)\n",
    "qubit = 8\n",
    "print (qubit+1)\n",
    "print (two_photon_rabi_codes[qubit])\n",
    "job = backend.retrieve_job(two_photon_rabi_codes[qubit])\n",
    "Rabi_results = job.result(timeout=3600)\n",
    "    \n",
    "\n",
    "plot_Y = []\n",
    "for jj, drive_power in enumerate(drive_powers):\n",
    "    thispower_results = Rabi_results.get_memory(jj)*scale_factor\n",
    "    plot_Y.append( thispower_results[qubit] )\n",
    "\n",
    "plot_X = drive_powers\n",
    "\n",
    "plot_Y = plot_Y-np.mean(plot_Y)\n",
    "plotter.plot(plot_X, get_amplitude(plot_Y),\"-*\")\n",
    "plotter.axvline(0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "twop_pi = [0.2,0.18,0.55,0.1,0.1,0.52,0.15,0.2,0.29,0.0,0.3,0.2,0.22,0.2,0.2,0.2,0.2,0.2,0.2,0.4]\n",
    "print (len(twop_rabi_pow_pi))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T1 measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=0\n",
    "\n",
    "T1_experiment_qobj, T1_schedules, T1_schedule_LOs = T1_measurement(qubit_freq=two_photon_peak[i],pi_pulse_pow=0.2,pi_pulse_duration=128,time_max_us=500.0,time_step_us=2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting T1 Experiment on Qubit [0], at frequency 4.894900 GHz with power 0.200000 (arb)\n",
      "Submitting T1 Experiment on Qubit [1], at frequency 4.695297 GHz with power 0.180000 (arb)\n",
      "Submitting T1 Experiment on Qubit [2], at frequency 4.552657 GHz with power 0.550000 (arb)\n",
      "Submitting T1 Experiment on Qubit [3], at frequency 4.621331 GHz with power 0.100000 (arb)\n",
      "Submitting T1 Experiment on Qubit [4], at frequency 4.207183 GHz with power 0.100000 (arb)\n",
      "Submitting T1 Experiment on Qubit [5], at frequency 4.760607 GHz with power 0.520000 (arb)\n",
      "Submitting T1 Experiment on Qubit [6], at frequency 4.573867 GHz with power 0.150000 (arb)\n",
      "Submitting T1 Experiment on Qubit [7], at frequency 4.390024 GHz with power 0.200000 (arb)\n",
      "Submitting T1 Experiment on Qubit [8], at frequency 4.504593 GHz with power 0.290000 (arb)\n",
      "Submitting T1 Experiment on Qubit [9], at frequency 4.611633 GHz with power 0.000000 (arb)\n",
      "Submitting T1 Experiment on Qubit [10], at frequency 4.802299 GHz with power 0.300000 (arb)\n",
      "Submitting T1 Experiment on Qubit [11], at frequency 4.365706 GHz with power 0.200000 (arb)\n",
      "Submitting T1 Experiment on Qubit [12], at frequency 4.582563 GHz with power 0.220000 (arb)\n",
      "Submitting T1 Experiment on Qubit [13], at frequency 4.480561 GHz with power 0.200000 (arb)\n",
      "Submitting T1 Experiment on Qubit [14], at frequency 4.400081 GHz with power 0.200000 (arb)\n",
      "Submitting T1 Experiment on Qubit [15], at frequency 4.425274 GHz with power 0.200000 (arb)\n",
      "Submitting T1 Experiment on Qubit [16], at frequency 4.604624 GHz with power 0.200000 (arb)\n",
      "Submitting T1 Experiment on Qubit [17], at frequency 4.433830 GHz with power 0.200000 (arb)\n",
      "Submitting T1 Experiment on Qubit [18], at frequency 4.599420 GHz with power 0.200000 (arb)\n",
      "Submitting T1 Experiment on Qubit [19], at frequency 4.876362 GHz with power 0.400000 (arb)\n",
      "['5dcb60f72592de0011c93366', '5dcb60fab3fed10012292aac', '5dcb60fd5eb52e00110c5d05', '5dcb60fff2b4ea00116072ce', '5dcb61016c129f0011a816e3', '5dcb61030a67070011e8dbba', '5dcb6106491a8100123b2732', '5dcb61085eb52e00110c5d07', '5dcb610a0a67070011e8dbbc', '5dcb610c5eb52e00110c5d08', '5dcb610f2592de0011c93368', '5dcb6111b3fed10012292aad', '5dcb6113684d760011534ec3', '5dcb61165eb52e00110c5d0a', '5dcb6119491a8100123b2733', '5dcb611c6c129f0011a816e5', '5dcb611f7069d200114601bf', '5dcb61226c129f0011a816e6', '5dcb6124b3fed10012292ab0', '5dcb6128f2b4ea00116072d4']\n"
     ]
    }
   ],
   "source": [
    "T1_codes = []\n",
    "\n",
    "for i in range(20):\n",
    "    qubit_freq = two_photon_peak[i]\n",
    "    drive_qubit = [i]\n",
    "    qubit_pow = twop_pi[i]\n",
    "\n",
    "    print (\"Submitting T1 Experiment on Qubit %s, at frequency %f GHz with power %f (arb)\"%(drive_qubit,qubit_freq,qubit_pow))\n",
    "    T1_experiment_qobj, T1_schedules, T1_schedule_LOs = T1_measurement(qubit=i, qubit_freq=two_photon_peak[i],pi_pulse_pow=twop_pi[i],pi_pulse_duration=128,time_max_us=500.0,time_step_us=2.0)\n",
    "    job = backend.run(T1_experiment_qobj)\n",
    "    T1_codes.append(job.job_id())\n",
    "\n",
    "print (T1_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13b4edfd0>]"
      ]
     },
     "execution_count": 302,
     "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": [
    "qubit = 1\n",
    "job = backend.retrieve_job(T1_codes[qubit])\n",
    "T1_results = job.result(timeout=3600)\n",
    "time_max_us=500.0\n",
    "time_step_us=2.0\n",
    "times_us = np.arange(1, time_max_us, time_step_us)\n",
    "print (len(times_us))\n",
    "plot_X = times_us\n",
    "plot_Y = []\n",
    "for jj, delay_time_us in enumerate(times_us):\n",
    "    thisdelay_results = T1_results.get_memory(jj)*scale_factor\n",
    "    plot_Y.append( thisdelay_results[qubit] )\n",
    "\n",
    "plotter.plot(plot_X, plot_Y,\"--*\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WTF is that dip about?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13b6ff9b0>]"
      ]
     },
     "execution_count": 300,
     "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": [
    "qubit = 19\n",
    "job = backend.retrieve_job(T1_codes[qubit])\n",
    "T1_results = job.result(timeout=3600)\n",
    "time_max_us=500.0\n",
    "time_step_us=2.0\n",
    "times_us = np.arange(1, time_max_us, time_step_us)\n",
    "print (len(times_us))\n",
    "plot_X = times_us\n",
    "plot_Y = []\n",
    "for jj, delay_time_us in enumerate(times_us):\n",
    "    thisdelay_results = T1_results.get_memory(jj)*scale_factor\n",
    "    plot_Y.append( thisdelay_results[qubit] )\n",
    "\n",
    "plotter.plot(plot_X, plot_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T1 = 74.23507524950999 us\n",
      "T1 from backend = 50.59515676617817 us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Measured signal, a.u.')"
      ]
     },
     "execution_count": 313,
     "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": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "fit_func = lambda x,A,T1,B: (A*np.exp(-x/T1)+B)\n",
    "fit_func2 = lambda x,A,B: (A*np.exp(-x/59.8)+B)\n",
    "\n",
    "#Fit the data\n",
    "fitparams, conv = curve_fit(fit_func, plot_X,\n",
    "                                      get_amplitude(plot_Y),\n",
    "                                      [-1.0,50,-11])\n",
    "fitparams2, conv2 = curve_fit(fit_func2, plot_X,\n",
    "                                      get_amplitude(plot_Y),\n",
    "                                      [-1.0,-11])\n",
    "\n",
    "print(f\"T1 = {fitparams[1]} us\")\n",
    "print(f\"T1 from backend = {backend.properties().qubits[qubit][0].value} us\")\n",
    "\n",
    "plotter.scatter(plot_X, get_amplitude(plot_Y))\n",
    "plotter.plot(plot_X, fit_func(plot_X, *fitparams), color='red')\n",
    "plotter.plot(plot_X, fit_func2(plot_X, *fitparams2), color='black')\n",
    "plotter.xlim(0, np.max(plot_X))\n",
    "plotter.xlabel('Delay before measurement, ($\\mu$s)', fontsize=20)\n",
    "plotter.ylabel('Measured signal, a.u.', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from scipy.optimize import curve_fit\n",
    "\n",
    "# fit_func = lambda x,A,B,T,phi: (A*np.cos(2*np.pi*x/T+phi)+B)\n",
    "\n",
    "# #Fit the data\n",
    "# fitparams, conv = curve_fit(fit_func, plot_X, plot_Y, [3.0  ,0.0  ,0.04  ,0])\n",
    "\n",
    "# #get the pi amplitude\n",
    "# first_peak = abs(np.pi-fitparams[3])*fitparams[2]/(2*np.pi)\n",
    "# pi_amp = abs(fitparams[2]/2)\n",
    "\n",
    "# plotter.scatter(plot_X, plot_Y)\n",
    "# plotter.plot(plot_X, fit_func(plot_X, *fitparams), color='red')\n",
    "# plotter.axvline(first_peak, color='black', linestyle='dashed')\n",
    "# plotter.axvline(first_peak + pi_amp, color='black', linestyle='dashed')\n",
    "# plotter.xlabel('Pulse amplitude, a.u.', fontsize=20)\n",
    "# plotter.ylabel('Signal, a.u.', fontsize=20)\n",
    "# plotter.title('Rough Pi Amplitude Calibration', fontsize=20)\n",
    "\n",
    "# print('Pi Amplitude %f'%(pi_amp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "qubit_rabi_data = np.ones(num_Rabi_points, dtype=np.complex_)\n",
    "for i in range(num_Rabi_points):\n",
    "    qubit_rabi_data[i] = Rabi_results.get_memory(i)[qubit]*scale_factor\n",
    "rabi_amp_data = get_amplitude(qubit_rabi_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "saved_data = dict()\n",
    "saved_data['rabi_amp_data'] = rabi_amp_data\n",
    "saved_data['drive_amplitudes'] = drive_powers\n",
    "saved_data['drive_samples'] = drive_samples\n",
    "with open('rabi_exp_data_with_varying_drive_amplitudes.pkl', 'wb') as handle:\n",
    "    pickle.dump(saved_data, handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('rabi_exp_data_with_varying_drive_amplitudes.pkl', 'rb') as handle:\n",
    "    loaded_data = pickle.load(handle)\n",
    "rabi_amp_data = loaded_data['rabi_amp_data']\n",
    "drive_amps = loaded_data['drive_amplitudes']\n",
    "drive_samples = loaded_data['drive_samples']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0.07)"
      ]
     },
     "execution_count": 134,
     "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": [
    "#rabi_amp_data = get_amplitude(qubit_rabi_data)\n",
    "\n",
    "# fit_func = lambda x,A,B,T,phi: (A*np.cos(2*np.pi*x/T+phi)+B)\n",
    "\n",
    "#Fit the data\n",
    "# fitparams, conv = curve_fit(fit_func, drive_amps, rabi_amp_data, [1.0,0.0,0.02,0])\n",
    "\n",
    "#get the pi amplitude\n",
    "# pi_amp = (np.pi-fitparams[3])*fitparams[2]/2/np.pi\n",
    "\n",
    "plt.scatter(drive_amps, rabi_amp_data)\n",
    "plt.axvline(0.04)\n",
    "# plt.plot(drive_amps, fit_func(drive_amps, *fitparams), color='red')\n",
    "# plt.axvline(pi_amp, color='black', linestyle='dashed')\n",
    "plt.xlim(0, 0.07)\n",
    "# # plt.ylim(-2, 2)\n",
    "# plt.xlabel('Pulse amplitude, a.u.', fontsize=20)\n",
    "# plt.ylabel('Signal, a.u.', fontsize=20)\n",
    "# plt.title('Rough Pi Amplitude Calibration', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### ^^^^^^^ Need to fix the rabi oscillation fit.   Not sure what is going wrong here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rabi Oscillation with varying duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rabi experiment parameters\n",
    "num_Rabi_points = 70\n",
    "num_freq_points = 20\n",
    "num_shots_per_point = 512\n",
    "rough_frequency_qubit = backend_devicespec.q[qubit].drive\n",
    "backend_defaults = backend.defaults()\n",
    "rough_qubit_freq = backend_defaults.qubit_freq_est[qubit]\n",
    "sig_ma = 0.006\n",
    "\n",
    "# drive_frequencies = np.linspace(rough_frequency_qubit-sig_ma,rough_frequency_qubit+sig_ma,num_freq_points)\n",
    "\n",
    "# drive parameters\n",
    "duration_min = 4\n",
    "duration_max = 60\n",
    "drive_durations = np.array([int(i) for i in range(duration_min, duration_max, 1)])\n",
    "drive_samples = 128\n",
    "drive_sigma = 16\n",
    "# Note: for codes[2] the max was 100 and steps of 2.\n",
    "# For codes[3] the max was 60 and steps of 1. 5db86a45e440250018ba81a3\n",
    "\n",
    "# create schedules for Rabi experiment \n",
    "Rabi_schedules = []\n",
    "Rabi_schedule_LOs = []\n",
    "\n",
    "mm=19\n",
    "for ii, drive_duration in enumerate(drive_durations):\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_duration, amp=0.1, \n",
    "                                    sigma=drive_sigma, name='rabi_pulse_{}'.format(ii))\n",
    "    rabi_pulse_qubit = rabi_pulse(backend_devicespec.q[qubit].drive)\n",
    "    # start an empty schedule with a label\n",
    "    this_schedule = pulse.Schedule(name=\"Rabi drive = {}\".format(drive_power))\n",
    "    this_schedule += rabi_pulse_qubit\n",
    "    this_schedule += measure_and_acquire_qubit << this_schedule.duration\n",
    "    \n",
    "    Rabi_schedules.append(this_schedule)\n",
    "    thisLO = pulse.LoConfig({backend_devicespec.q[qubit].drive: rough_qubit_freq})\n",
    "    Rabi_schedule_LOs.append(thisLO)\n",
    "    \n",
    "Rabi_schedules[-1].draw(channels_to_plot=[backend_devicespec.q[qubit].measure, \n",
    "                                           backend_devicespec.q[qubit].drive,\n",
    "                                           #backend_devicespec.q[qubit].acquire,\n",
    "                                         ],\n",
    "                                         scaling=10.0)  \n",
    "\n",
    "rabi_experiment_qobj = assemble (Rabi_schedules, backend = backend, \n",
    "                            meas_level=1, meas_return='avg',\n",
    "                            shots=num_shots_per_point,\n",
    "                            schedule_los = Rabi_schedule_LOs\n",
    "                                )\n",
    "\n",
    "# job = backend.run(rabi_experiment_qobj)\n",
    "# onephonton_chevron_codes.append(job.job_id())\n",
    "\n",
    "# print (onephonton_chevron_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5db86a45e440250018ba81a3'"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "rabi_duration_drive_codes = [\"5db76a49676a4200188da865\",\"5db856749b911f001903c6a3\",\"5db864bb889b23001aee5ec6\",\"5db86a45e440250018ba81a3\"] # First job was power 0.05.  Second was 0.01. Third 0.1 with 4-100 durations, steps of 2.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13d069898>]"
      ]
     },
     "execution_count": 157,
     "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": [
    "Y_plots= []\n",
    "job = backend.retrieve_job(rabi_duration_drive_codes[3])\n",
    "Rabi_results = job.result(timeout=3600)\n",
    "    \n",
    "\n",
    "plot_Y = []\n",
    "for jj, drive_power in enumerate(drive_durations):\n",
    "    thispower_results = Rabi_results.get_memory(jj)*scale_factor\n",
    "    plot_Y.append( thispower_results[qubit] )\n",
    "Y_plots.append(plot_Y)\n",
    "plot_X = drive_durations\n",
    "import matplotlib.pyplot as plotter\n",
    "plot_Y = plot_Y-np.mean(plot_Y)\n",
    "plotter.plot(plot_X, plot_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "qubit_rabi_data = np.ones(len(drive_durations), dtype=np.complex_)\n",
    "for i in range(len(drive_durations)):\n",
    "    qubit_rabi_data[i] = Rabi_results.get_memory(i)[qubit]*scale_factor\n",
    "rabi_amp_data = get_amplitude(qubit_rabi_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "saved_data = dict()\n",
    "saved_data['rabi_amp_data'] = rabi_amp_data\n",
    "saved_data['drive_amplitudes'] = drive_powers\n",
    "saved_data['drive_samples'] = drive_samples\n",
    "with open('rabi_exp_data_with_varying_drive_amplitudes.pkl', 'wb') as handle:\n",
    "    pickle.dump(saved_data, handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('rabi_exp_data_with_varying_drive_amplitudes.pkl', 'rb') as handle:\n",
    "    loaded_data = pickle.load(handle)\n",
    "rabi_amp_data = loaded_data['rabi_amp_data']\n",
    "drive_amps = loaded_data['drive_amplitudes']\n",
    "drive_samples = loaded_data['drive_samples']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x13affa748>"
      ]
     },
     "execution_count": 161,
     "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": [
    "#rabi_amp_data = get_amplitude(qubit_rabi_data)\n",
    "\n",
    "fit_func = lambda x,A,B,T,phi: (A*np.cos(2*np.pi*x/T+phi)+B)\n",
    "\n",
    "#Fit the data\n",
    "fitparams, conv = curve_fit(fit_func, drive_durations, rabi_amp_data, [1.0,0.0,0.02,0])\n",
    "\n",
    "#get the pi amplitude\n",
    "pi_amp = (np.pi-fitparams[3])*fitparams[2]/2/np.pi\n",
    "\n",
    "plt.scatter(drive_durations, rabi_amp_data)\n",
    "# plt.plot(drive_amps, fit_func(drive_amps, *fitparams), color='red')\n",
    "# plt.axvline(pi_amp, color='black', linestyle='dashed')\n",
    "# # plt.xlim(0, 0.05)\n",
    "# # plt.ylim(-2, 2)\n",
    "# plt.xlabel('Pulse amplitude, a.u.', fontsize=20)\n",
    "# plt.ylabel('Signal, a.u.', fontsize=20)\n",
    "# plt.title('Rough Pi Amplitude Calibration', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 photon rabi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.pulse.channels.device_specification.DeviceSpecification at 0x132b64f28>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend_devicespec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rabi experiment parameters\n",
    "num_Rabi_points = 90\n",
    "num_freq_points = 20\n",
    "num_shots_per_point = 512\n",
    "rough_frequency_qubit = backend_devicespec.q[qubit].drive\n",
    "backend_defaults = backend.defaults()\n",
    "rough_qubit_freq = 4.847297169266485\n",
    "sig_ma = 0.006\n",
    "\n",
    "# drive_frequencies = np.linspace(rough_frequency_qubit-sig_ma,rough_frequency_qubit+sig_ma,num_freq_points)\n",
    "\n",
    "# drive parameters\n",
    "drive_power_min = 0.0\n",
    "drive_power_max = 0.3\n",
    "drive_powers = np.linspace(drive_power_min, drive_power_max, num_Rabi_points)\n",
    "drive_samples = 128\n",
    "drive_sigma = 16\n",
    "\n",
    "# create schedules for Rabi experiment \n",
    "Rabi_schedules = []\n",
    "Rabi_schedule_LOs = []\n",
    "\n",
    "mm=19\n",
    "for ii, drive_power in enumerate(drive_powers):\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_samples, amp=drive_power, \n",
    "                                    sigma=drive_sigma, name='rabi_pulse_{}'.format(ii))\n",
    "    rabi_pulse_qubit = rabi_pulse(backend_devicespec.q[qubit].drive)\n",
    "    \n",
    "    # measurement pulse parameters\n",
    "    meas_amp = 0.05\n",
    "    meas_samples = 1200\n",
    "    meas_sigma = 4\n",
    "    meas_risefall = 25\n",
    "\n",
    "    # creating measurement pulse\n",
    "    meas_pulse = pulse_lib.gaussian_square(duration=meas_samples, amp=meas_amp,\n",
    "                                           sigma=meas_sigma, risefall=meas_risefall, \n",
    "                                           name='mymeasurepulse')\n",
    "    meas_pulse_qubit = meas_pulse(backend_devicespec.q[qubit].measure)\n",
    "\n",
    "    # create acquire pulse\n",
    "    acq_cmd=pulse.Acquire(duration=meas_samples)\n",
    "    acq_cmd_qubit = acq_cmd(backend_devicespec.q, backend_devicespec.mem)\n",
    "\n",
    "    # combined measure and acquire pulse\n",
    "    measure_and_acquire_qubit = meas_pulse_qubit | acq_cmd_qubit\n",
    "\n",
    "    # scalefactor for received data\n",
    "    scale_factor = 1e-10\n",
    "    \n",
    "    \n",
    "    # start an empty schedule with a label\n",
    "    this_schedule = pulse.Schedule(name=\"Rabi drive = {}\".format(drive_power))\n",
    "    this_schedule += rabi_pulse_qubit\n",
    "    this_schedule += measure_and_acquire_qubit << this_schedule.duration\n",
    "    \n",
    "    Rabi_schedules.append(this_schedule)\n",
    "    thisLO = pulse.LoConfig({backend_devicespec.q[qubit].drive: rough_qubit_freq})\n",
    "    Rabi_schedule_LOs.append(thisLO)\n",
    "    \n",
    "Rabi_schedules[-1].draw(channels_to_plot=[backend_devicespec.q[qubit].measure, \n",
    "                                           backend_devicespec.q[qubit].drive,\n",
    "                                           #backend_devicespec.q[qubit].acquire,\n",
    "                                         ],\n",
    "                                         scaling=10.0)  \n",
    "\n",
    "rabi_experiment_qobj = assemble (Rabi_schedules, backend = backend, \n",
    "                            meas_level=1, meas_return='avg',\n",
    "                            shots=num_shots_per_point,\n",
    "                            schedule_los = Rabi_schedule_LOs\n",
    "                                )\n",
    "\n",
    "# job = backend.run(rabi_experiment_qobj)\n",
    "# onephonton_chevron_codes.append(job.job_id())\n",
    "\n",
    "# print (onephonton_chevron_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5dcb0494f2b4ea001160716b'"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "twoprabi_codes = [\"5dcafed81896660011bb280d\",\"5dcb00cd0a67070011e8da43\",\"5dcb0494f2b4ea001160716b\"] #spot 1 should not do anything....lets see what happens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.0057971  0.0115942  0.0173913  0.02318841 0.02898551\n",
      " 0.03478261 0.04057971 0.04637681 0.05217391 0.05797101 0.06376812\n",
      " 0.06956522 0.07536232 0.08115942 0.08695652 0.09275362 0.09855072\n",
      " 0.10434783 0.11014493 0.11594203 0.12173913 0.12753623 0.13333333\n",
      " 0.13913043 0.14492754 0.15072464 0.15652174 0.16231884 0.16811594\n",
      " 0.17391304 0.17971014 0.18550725 0.19130435 0.19710145 0.20289855\n",
      " 0.20869565 0.21449275 0.22028986 0.22608696 0.23188406 0.23768116\n",
      " 0.24347826 0.24927536 0.25507246 0.26086957 0.26666667 0.27246377\n",
      " 0.27826087 0.28405797 0.28985507 0.29565217 0.30144928 0.30724638\n",
      " 0.31304348 0.31884058 0.32463768 0.33043478 0.33623188 0.34202899\n",
      " 0.34782609 0.35362319 0.35942029 0.36521739 0.37101449 0.37681159\n",
      " 0.3826087  0.3884058  0.3942029  0.4       ]\n"
     ]
    }
   ],
   "source": [
    "print (drive_powers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x133019668>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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eXXtc9jUQrF0wCxcOz4dWdSaFLM+iw8n2+DCKOt1+PLR4d8zRcwXDlTQ6EjQaT0C+NxbxVSBs6PSclgAR2p+qK6DGi/Z3sMmBQQuXYuWB5jP+7ZFLhuCiODNoopH0GXlZlhE3TS5Djvn0LJNwyes7PRicL+90ZfcMYvGOeizeUR/JIL4/2AK9WnmaF6BcuO3NLTBoVHjntgmn/X54URo0KgW2nGjH3OH5PRxND1anD+cPzcPkAdk40uKMbN/zLDo4fUE4fcG4lDRyu96PaMNHB6fSLANOtsUnkDfZvXh/cy2mDszBmG7VPtKAbnH4TuttyIUTVheeWLIf984ZhBHFp9NzrU4fCtK0sOg1GFeWGReWEwMGPxtTFBHvIg+UYNckTjwfKKEwG7Mhfc24+HssRCPpA/mYkgyMieLBEpCJtSabF4PzLbKuoXspofuWtCzLGDfxrDaXPyZrR6tS4tmrR8VNb3rR/Crc8/4O/PfH4/hhwalMigwFNdu9MOWY4rIWojr4we0T8dmOhkhwKsk0RIwM5AZpQscKEiQJaXH4MCQOSV9dhwcrDrTg9ulnNlgXza/CvgYbGDARMS25UZJlwLPXjD7td1anDyOL0rC73oZ540viQvtr7OKQx6ItN9m8sDp9CZtLSfpA7vAGoFUpz6hFjyhKw8G/zI0LK4FkEN5AOOaWtCzLgC1xmlJrd/qRZYot4XvJSPl3BNFosnuR343umGc5xSUfEKdAPrzQArcviLGlmRhbekoMqiTTiHaXHw5vQHYnmLauXk52jO8mlwTyONEySa23p+tkWGF8g5U3EIJWpTiNELBofhXeXH8Cu+tt+O15g2Tv6wBAfacXaiWDnBjv9dzKI1i+rwnbHjlP9nXEQtLXyBd8shsXPb/2jN+rlIq4UsusTh80SgYXDi84Qx61LNuIBpsnZu2aJnzBEBy+YKTL3h0efwjvbDyJS//1Y1woXc12L/LSzgzkCgYR5/Z44GirCwNyz3xoTKvIxh8vGox4SKu1O0/n90cj16zDJSMLUJAWnwE2Esh7Co6HmhyRfk888NsPd+KyF9ad8fuMrs8qXrK6jTYPd33GaDjnmDRod/sRCidGiC/pA3mHmxt7joWX1hzFyz8cjcs6Xph3DvwhFhV5JjxxxfDTmAEDc00YkGOSvZl1isET+wYNhMNY+Ple7Km3yc4RZlkWTTYv8i2nr6U8y4gjT14Ut8ZROMziaKszouwXjeFFabh9+gBY4uDL6AuGoVcrYxqe6DVKvDDvHEytyJZ9HQDQ6vRBo1ScRlGNxnf7m/C7j3edJu4lJ2ra3TF3KiWZBsyqzIkbdXdmZQ5unFQa89+yTFqwLBLWkE760kqHK4Cy7NhTceuqrbB7AjFrgfTXceYgDsElIwsxvixTduNhg1qFhy8achoflyDelC6bJwBfMBwppRDEaxyegAXw8vyqM5rhBLXtbigUzGkqgHLgjhkDcPv0/r0GpVCYPYP6Jgf0aiWGFlp6XIulS3rZ4Q1Ca5J3V0vka6tiXLOj+6Xj9Tiablw5prjHfyO7lzanPy5lnu5I+oy83X2mfgUBp0seHwVEMiyQ3cNa4mE8nGZQ41fT+6My/8yGZndjZrkpXSatCsvum36a+iLBcyuO4D+r47NTUioYTK3IjvmZAMDlL67Di6uq47KW3oL4LW9swXUvb4zLOu47d9BpOuTdQXYoDq/8rjhEvjYRBizRaOr04IoXf+xRmZP0ExLFJU/qQM6yLDpc/jM45ATFGQa0ufzw+OXfIpp1Ktw0uQwVeadv4ePp3N7u8uO41RWzjtfdmFluSpdKqUBlvjnm+Tcdb8N3+5tked/u2F3XieX7mhDuobbZL9OAmjhQEJ9aeqDXh5dRq0qI/nYsmLtKLvY46Pnv7NLqj2XA4guGMOmplfjvj/LPHTy9/BB21tpw9/vbY/57ZZ4Z/7puDAbFifXVHUkdyENhFvefPwjTB+XE/HeyXb56kfx6Df0yDXjssmEYmHv6F00yYZKLyZkJL95eh1nPrIbTFzuTsjp9GFZogUrByO5XubuuE29tOBGzwZtv0cVNyvb9zbVY8Olu9JQM55m12HKiTfbr47v9zdjbiwtQrlmLFkd8PpNfvrYZr6491uO/k9KKPQ7DdJ/tqAfAlUG7Q6tSot3ll5XNQxKtxdu5dWw50REz0cowanDpqMIeS3RyI6lr5CqlAr+ZObDHfy/O0EOrUmBvV3NPTr0Gtz8IpYKJyF8SRE/LMTJPy7W7/FArmR6bWIvmV6HN6YMnEEJRul7WJtKqg634x4rDuDbGIEVemg4tDm9cJGSPtjgxsBfrsIZOD3xBFv/87jD++rORsq2j3eXvkU0EcFxytz8k+6AUJ+dr7ZUjPqzQgqX3TkNplnzlju49m893NuDznQ1n9GwyDJpIE18OkBmQpXsaEQixp8lJdMemY22w6NUYUhAffn00kjojd/uDqG13x3SJr1y4FFe9tIGzOYP8Nk3/+r4aIx5dHtMn1Or0oShdh/45Rlkz4TYnV2bqLUBnmbQozjDIzgRosnuRZdTE1JrJM2sRCLFojwMDoLoHxgrJxPY2cE7y722ule36CITC6HQHeuzlAKe45PNe2Sjr7sDmCSAQYntt2Bk0KgwpsMCgke+BQnaqsaQTopFh1KBDRqpqZIq0q+bo7yXRuvv9HXhj3QnZ1tIbkjqQbzzWhmlPr8K+rpsxGvG2aWpzclrTsQLkovlVGFeWiUCQPYOaSHUNLn+fI95HW514cVU12mRu2jTbvWcwVggK0/UoTNPJXoNtd/nR7vLHDORnBBKVfNcHoaz19t0MK0zDkAKz7NTQUxzynh8qwVAYb284EVMXhhai9XgY9LxTzTCoZaf8WZ0+jClJBwBcM65fj4lWtkmbsGZnUpdW2l1cIIjFI4+etlTGweasrZeJSgAYUmCBNyCvhGm7y9fr9h0AjrW68PdlhzBjUI6suh5NNm/EEag7zh+Wf4bmsxw42sqZCMcaBiLXhz/ECXv5QvJdHx5/CCWZhjOmXAniSQ1tdXBBMdb0IoGCYfDol/tw56yBMeUvaMHq9CHXrEWuWYvRJRkxdV1mVubIzp5ZNL8KO2s7sfFYG+7ohSKaZdLAKmOZpzckdSAn3O10Y+yBDqvTh3SDGpV5ZlTkmWUVALL2kQ3fMUN+LvtdswdC0UfJhNRf5W5kNdu9GNUvXdb36Auj+6Vjxf0zUNDDA8Xq9GHe+BIML7Rg0/F22UpepVnG0/RmuqMny0I55GNVSgbnlKT3+JAFOK6/WaeWfce0aH4Vzn12DfplGvDEFcNjviYeMyAAd62M7uN6zTFpcTzOPq8ESR3I291+qBRMxGC4OxbNr8KNr22GzRPo8UKhhTanDwOyjbK+R1+YPbhvGypCLXPKnOWs+v3MSN2xO1iWxa/e2orZg/Mwb4J8qnJqpSJmWYVg0fwqePwhDPnTt1gwt7LXxrmcyLXoYIrT7nFcWSYW/6ZnDjmBRa+KC4+8zenDxP6Zvb4mHGbBML3z8KWitt0NpYLp1ec3y6SJiJ/FG0ldI+9w+ZHRQ12aIMekhTUOtK6bp5T3Ona+bF8Tpj+9KqKwRhvBUBgbj7X1WfsmgVzum9SiU/fY3GMYBjtrbdhT3ynrGt7ecALf7Gns9TV6jRJalQI2GRtqX+5qwA2vboKrB1oowNmIaZQM5g7Ll50aygdmrVr2XVsgFEaHO4CsHiQlAOCdjSdRsXApbDLvDv742R78+t3YHHKCeRNK8fat42MSGuRGUgfyy0cX4Q9zB/f6mhyzFq0On+wf/q1Ty3s1ZmVZFjXtbtnEolqdPlz78sY+ZVlJaaUnrjkNHG114m9LD6K+s+eHVp5F2+MUHS28tOZYxKuzN6TL3FA70uzA+qNW6HsRcVs0vwpFGQYwDGRtiP/1mwOY90rfE6QWvQp2j7wP+zDLNf9nD87t8TVGrRKhMCsrBRHghp9iDSVFozzbiKqyzLhpv0QjqUsrsSzNuqM824BB+Sa4/SEYZeLnegMhtDp8yLVoz+CRE5CxZ7kyi1Omy703OzMMGmxbeG5k6EMO7G+w46U1RyPWXbHXocbGY+1ocXhlKSG4/UHUd3pwTU6/Pl+bYdDIqsbY5uJkJPrizKcb1LKrQh5rdfIKis9dOwYapbx5oFalxA0TY4tUERCRMTkpiAB3X5Zk9V4abXf5seZwCyb1z+61xyAHkjoj31tv6zOru2ZcCb6+e5psQRwA9jfaMe3pVVhf3dbjayLTcjIFcnJz9sVaUSgYZJm0UMt4kzZ3TeL1xNIAgCY7N5gkF9WOWNrxmcRL06vRKePWvd3Zsx5QNDIMGtmpdq1OP6/PJM+ii8jIyoUOlx/7G+y9qiwSRlqHzBm5zRNAmr73GFHf4cFvP9yF3XXylgRjIakD+S9f24x/fS+vHCsf8MmGSUZul6k23ebqWe+6O/6z+ii+ktFDtMnmhU6tgCXGjUEGcapbOGqgXINai9ZwuiaxRr+746GLhuCRi4dSff9otLl8vL6Xq8YW9yijSgtWBz/z7fVHrXh+pbz31prDrbjo+bW9CttlRDJy+QI5y7Kwe4N9llZOCWfFv+GZtIE8HGY5LfI+bpAmmxeXv/AjlvOolYpFW8RxpecbJMOoxpzBub0OYkhbQ99DJwQfbqnBd/vlszgjzkCxaolyD2qRB8W6LgPsr3c39vmgGN0v/QzvSpoozjDwct25aESBrN6QLMuizeXjdQ1uOtaOZ787LKuRQmQ4qZdmZ7ZZgxsnlaJcRkZYmAX+ftVIXDi8d418EsjlHqaLhaStkdu9AYRZ9Kh8SGDQKrGrzoYaGd3S23iUNcw6Nf570zjZ1nDBsHwUpet71FmJhkmngkNGRoLNE+hxqjN6ok+rUlCn2vXlnxoLR1ud2FNnw+WjC2VpZP2jmx9lT3D7g2i0eVGWZZRFlzwYZjF7cC4v38lommqaQZ5+ShvRBuqlpGHQqPD45fJSh5UKBj87p2ctcgKtSgmzTpWQ6c6kDeSn3HB6D+RmrQpalQKtMlIQrU4fTFpVXK3luqNfpoG3prNJq5KVtfL2rRN6rXtanT5cPKIAe+ttGJRnpkq1E/Og+G5/M/629CDOH5Ynq75IX/h0ez0e+XwvNv9xDnJ76S+IhVqpwL+vH8vrtdEKiLIFcqcPWUZtnw/PQCgMXzAsm5iY3RvA4SYHKvPNfXq35pi0CZnuTNrSCqmZ9dWQYRgGOTJLhF48ogCPXNL3FN7lL/yIhxbvkWUNS/c24qLn1vISXDLr1LLzyHti7wAc1e635w3CiTY3LhpRQJ1qZ3X6MLYkA1dX9ePFyU7vClpyMCMONtox+JGl+GhrbZ+vzTDItw6hONXTkZHN04esBcHFz6/F7z7aJds69tbbcNVLG7CnvmeZYYJF88fi0Uvk66f0hKQN5GVZRvzrujEYykNSknDJ5UJVWSav2qYvGJZtW/bI53uxv9HOiwVi1qrg8ssTyG2eAO77YAe2nGjv9XWkDCXH57FofhX0GiX21Nt4cbIJxU0Ok99/fV8NbyDMq7mcIeM6AG4obfTjy3Gk2dHnay0Rcwn5Hvh3zBiA319Q2efr0g0aWZUyCZOsr2YnAFTkmWXZLfWFpC2tZJm0uHTUmTZisTC+PBM+GQWr9tTZkG3W9OmCbtHT168QI7j0t5+PhFopz1BDQ6cHn+9swHlDexfFsujUUCkY2QY9XL4gjFp+pa70rkyY5nRn9+9l7REryh5c0uv3kibjzgAAWh0+dLoDvGYIxpVnYu+fL4BRI1+5cHx576P5BJkGDY5ZnbKtwyYgkG872YEdNR24bVp/2dYTC1QycoZhXmMYpoVhmL00zkcDR1udWH/Uymti86ELh+Cxy4bJtpZb3tyC51f27fto0amp0w8JC4SADwtEo1LINp3WRDjkab2zZxQKBhlG+UwD3P4QjDzr3ekylDTI90IemFoeMrmkTChXRk52P3yokGqlAiatSrbrhGVZrDrU0uv0L0GGUR1ROpUDQgL52iOteGLJAQRjeCDICVqllTcAzKV0Lir4aGstbnp9S6KXgXDX+DAfShc39kz3giTNPQC8BZfWH7XiD5/sjmnDJhXNXQNaPbFWojFjUA5K+7Sfi5UAACAASURBVJimEwshTjtlWUZ8cecUTK3Ipvb+ZxgW8JDJzTJq8Pjlw1BVJo90rNXpQ4ZBzWsYzBsI4ckl+3nx8MXA7Q/h5te34GueJadOt182mQ2bJwClguF1vRB6r9ySAd1BpbTCsuwPDMOU0TgXLXCmy2peGcPSPY149Mt9+OKuKX2WP4Si0xNAKMz2OVEJAJP6Z/F66gsFmaS8cWIpAmG2T7neY60ufLi1Fg+cP4g604ZogDPo+3t55hejqL53NDRKBW8ZAp1aKYvkrtXpw/RB2bB5ghic3zc7R6dW4sZJZdTXQcA1F/lp0KsUDF5ZexwmrRpTBtJ7wEWvBeA39zB9UA6MWhWCYVaWkuCVY4oxsjidVyzJ7rrPb3ljC167eZxs/gbdEbcaOcMwtwO4HQBKSuQbaiBodwX65JATqJQKtDh8aHX4qAdyPsNABL+o6lv3QwxeumEsdtfbkG/R9SrDSRBxSfcGkUvZfnBj1yDOf1ZXy+qR2he+/91MQa9fvL0O+Wk6TB5AL2iJYeNUtzjAMAwG5PQsvysWE/tn8SIHANw9Y9QoZWOttEbum77v4Yn9szCxf9+6SmIxMNfUq9xxNLK75A32Ndhl9wGORtwCOcuyLwN4GQCqqqpk13nkM9VJQLQl5GCuWHmKVRHIoa2sUipwjgAnl8iwB0UuuZim6wvfH8GHW2uxdsFsausQi78vO4QpA7OpBnIxuPPdHSjNMuDlG+mrH/5ycpmg11v0atkGx0gC1JtTEUEgFEaz3YtMo0YWnv/m4+3QqhR97sqir3HiAyyXk1N3JC39kGiR84Gcgbwiz4QX5o3B4Py+M50vdtZjwMPf9KotIQZWpw8fba2NlFj6Ahl6oGkuIWb0nmWB2nZPr8NDYuD0BfE/b2/DmsOtvI9Jl0kBceHne3D1og0C1iGfAmJNuwtXL9rA29zZrJNPyjYyDc0jAdpdZ8PU/7cKm473TmkViye/OYBnlh/q83VrF8zCpSML+jSMlgNJG8j/9+pRuJOnowtpRMoxFJRt0uKSkYW8dgd6tRIsS1/KtrrFiQWf7I4IUfUFs04Fo0ZJNYBGe6SqeDZdM01E2Y7u52HzBPDtviY08mBEEKTr1bKwRRo7vYIemHIpIHoDIUx/ejU2H2/nrThp0anhpfyQJZgzOBdv3DyOl4BXpsxsHj5a5AB3jVv06lM+rzL7AEeDyj6EYZj3AcwEkM0wTB2AR1mW/S+Nc4uFEFNYrUqJK8cUySK8c7DJjg5XgJc2ulxStsR5hq9U7+B8C/Y9Tp+EZHX6oFUpMGtwLrJN2j6brsQZxur0UdV3dgv8PACO4naoqe9BGaFweIO9aonEWkfHycTPGgDAR3dM6lNDXSxyLTregzVk4lUuCqKNZyAHuGt1dHE6Lh5ZgBNtbll9gKNBJSNnWfY6lmULWJZVsyxbnOgg7vYH8dmOOtR18BfC+sc1o3kPEAnBWxtO4q73ereIIpBr7JnUuk08B2DkwqL5VWABlGYZeE1Ukm01bSrXqc+DfwBN02tkMf2wewN96necuQ66VLu1C2ZhxqCcyM98SwJyBXGAkxfmS2206NRQMPJk5CzL8s7IAe4ab7J7cbDJIauTU3ckZWllT50Nv/1wF1YdbBF0XFgGSc42p493o5NkZrTrji4ft/01afldjCzL4t4PduCLnfVU1+ENhOAPhiMPrL5QlK7HZaMKIwM5tOD2c5+HQcBU4v3nDcLXd0+jug6Ay8jNPBQpCS4fXYh/XXcOaFKmcy26CG1PreRv7vz17gbZtIFe+L4a/1xxmNdrFQqGG9OXgbvt9ocQDLOCaMFmnUp28/LuSMpA/t8fjwMAVh3i38x6aPEezP7f1dTX0ub092oeG41MowY3TS7DgFy6JR6nj8sk+Y6kMwyD5fuasaeub5EgISA7Db787cJ0PZ6/bgxGFtPncBem6QTZ2eWYtbLYd82szMG4Mn6j6AAwpMCCucPzqWfDhF317NWjeJs7H2i046OttbIM4rS5fLzvGwB4cO5gXDKS/o5ao1Lg4/+ZhEsE7NZNWhUcvvgKmyWV1kr3Wt/3B1v61K8gMGqUaLZzJsw0qX9tLj+GFfLj5ho0KlmkAq6u6oepA3N4j6QDXVkFZSlbfzCMyjwz8nhYiUUjHGapBq4pA7Ox/qE5go6pbnHimz2NuGFiKW9aKx88KZBnbHMHsKO2A6OK06larT31sxH4fGc9Jg/IxqWjevZSjYZZp0YozMrid9vm9GNcGf+/7+px8sxgqJUKQQ9agPtc5Lbk646kysjF6FcQ5Ji18ARCcPnpduFbHF5sOdHOm9LlDYSoB9B0gwZDCy2CgqFJp4KD8jqKMwxY9tvpOH9Y74JZ0Zj9zGr88TN5tu9CcKzViWe/O4x6ytRQoTjc4sBNr2/B3ga6u6UhBRY8dOEQ3pOdwKmeDm3J42AojHY3/ylTgHP62t9gp7oOAGi0ebB4e52gso1Zp5JdBro7kiqQi9GvIJCLSz51YDZa7D7elK7z/rEGj3xOV3ts9aEWfLKtTtAxZp067nW+WNCoFNQ9EL/YWY8bX9ssiF4ZEazy0FtLQ6cHlQuXYvF2/t+NXNroNk9AcDM30tOh3JzvcAfAskCOANvDZ787hFveoK+ttLvOhvs/2oUGAVTVZ34xCivun0F9Lb0hqUorAEf/uWpsMaZWZGPz8Xbe7jIkkLfYvVRoiGIpXRYdfSnbT7bVYX+DHVeN7duuiqA4XQ8WdGufqw624IVV1Xhh3hjeUgjZJi3aXXQfrkdbnFh7pBUaHuJQBHIEULs30OVUxL/pKpc2+rPLD+HznQ3Y9ej5/Nei1yDTqKEurpZuUOPb+6bx4pATZHRpktMujQpRPiRIhBNY0gXyaLrP5aP51foAoDzbiJsml/FmmPSFtQtm4dEv92HFgWYEQiwvb0iASNnS55ELrWG+eP05VNcAAHWdHmw72SHIbzLTqEGtABopHzh9nIStkBueBFAbxQBKtt9CWCsRSV3KnGm7QD47AEytyMb2R86jug6Aq0vzmYTufow/GEZNu5uqYmbEVEIAc2p9tRVf72nEY5cOg0YVn6JHUpVWCNZVW/HZDmGlhOIMAx67bBgG5pqprCHXokMozCIQYgVRujgpW/r0Q76MFTlBbgq+9EOAC+TtlEsrbn9QEPUQOJWR0RyPJzolQgK5WqmAWaui3kwTMvQiNw422fHWhhOCekVbuxyn/vEdP8oiX9g8ASgYwCSAKHCkxYn3NtXIamDeHUmXkQNcKWHryXZcOYZ/KQEAfEGO5yxkQKM3EKH+v/1sJHbUdvKa8krT08/Inb4gCtOFUec+3FKDZfua8dpN46itw+4JQKNSCNp6Th2YDY1KQZW5IkSLnECjUmD7I+dRDXYkIxdCgwSARTeORSFllU67JyDoAQtwn+MDH+3EVWP74byhedTWsq66DX/5ej8u40H5617C/HxnAz7f2UBNqMrm4RyThFx75MHs8AYFNWylICkDOXejCr/hpv5tFViw+ObeaVT0EW6ZWo7t7+3AiOI0/JxnfXru8HwMyqOzKyBw+YUHrvoOD1YdaqEaQO1e4cHi3KF5OJdikAC4fkhlvvDPmCbtEOB2gdeNL0G2AL40AFkUGG2eAG+pVgKVgsGyfc0YWZxONZC3OX1QKRhe18raBbPwxDcHsGxvE3zBMHQqBS4Y3ncJky/umj0Q8yYIk902y8Tm6Q3JGci9wYgrjhCEWM7Nh5aOMGF9CAmiswfnYfZgyW99Ghb/erLgY8w6NViWewjQ2qEUpOkxTqC7DcuycPlD0CgV1OqNj14qjqv/9saTAMtiPiVzh7GlGRhbKtztZ9vJDrS7/FSD523Tynnr9xPo1EpoVArqO0jO4ELDK4EgTLWIUJUAphof5Jp1gs91KiNPlVYkwekL8rJWIxDLMOGzDoDjZPOFNxBCq4MTieJjucUHYrZ3pqjtIa1Afs+cCsHHbDvZgate2oA3bxl/mh5IIrBsbxPc/iC1QO4LhqBWKATveN5cfwK76jqpBvJrxokze7Ho1NQzz4ZONxyeIFocXl5B1Or0Yd74EgwvsmDTMf5MNT5YvL0OGQYNZg3O5X2MWaeCRqWQTRkyFpKy2en0BWESEHzIIBFxiaKlI/yzc4rx9d1TBTVKlu5txLSnV1EbPAmGwnh2+SFsO9kh6Dg5zCXEgJQzaFIQf/XWVt46HtFIM6jRSZEa+pev92P8X1cIPi7DoEYHRV2RUJhFdYtDVAZp0dH3md3f6IA7EOI9e7FofhUevngIHlq8F5X5FqpCVS98Xy14BmNogQWHn7gQswfTLQn2hqTMyD+4fSIPR8hTINuzrjkiajrCmUaN4LoqqQvSUtpz+UJ4/vtqpBk0grbx2SYtBuebEaaoo3H9qxsxsjgdf5jLv3ZE9DbaKDJXdtZ2CtqxEXCa5BR55B7htFCAo0LavUGEwqwgKmdPaHf5ce6zP+Dxy4cJ9gTtn2MS3KztCVJ2xnq1EmolQ12hkjQ7hYAmj50vkjKQ83Fo7w6r04c5g3NRmmWAP9S3QTEfrD7Ugk53AFeM4c9nj2iSU6qvEfEeoRK2E/tn4dv7plNZA8HBRgfKBHJ8LXoVVAom4hhDAy5fUJDuDAFxa6fVAHaIaP4Cp7jkNk+ASgNWzNALwau/pJf9ksbl8n1N8AbCvGcvAC54punVVAM5y7KiaJksy+KBj3dh9uBcWYS8YiHpAnkwFMZ/Vh/F1IpsQeYScugGf7y1DoeaHcICOdEkp8QlJxK2tEWNhIJlWdi9wm8KhmGocsnDXSJPBlGZsBpKBQOnPygqAHeHUAlbAtKUFOJL2xsiqpSUeiFikWvRwahWwhsIC5q9ILDo6U5Fi5GwBbhrdumeJmQaNHEL5ElXI3f5Qvjf7w4LrgkDgMcfQk2bG4FQuO8X84BDBF85jXJG7hThhgNw2+0r/70OS/c0UlmHJxBCIMSK2ob/euYAahREd4BoswsfkLp5SjkOP3EhtYDHmUoID+QzBuXgm3umoTiDDpc8Mqgl4rt5fuVhjPrzct6icH2h0cad5/bp/XnL6RLQzsil7FTiLZyVdBm50y987Jngmz2NeODjXVjz+5lUxnydIm7UdIMaf7pkqCDX+97gEuGGA3AGAztqOqkZQZMdhpggePOUciprAIBQiMX48kz0yzAIPpZGPToaV1f1i2j8CEGGUUNVwlZKwFq6twk2T4AaZffBiwZj9eFWDC1Iw8UjCwQdu/DioYL0c/pCnkWHTX+cI3gKGOgK5HHUJE++QB7hbgu/KEnWSoup4fQFBdfrdWolbplKL3BNHZiNvX++IOLszRecFgmoStmeOyQXZVnCA6jDG0CHK4ASEcd2R5pBjY/umCTq2LoON/654ghumlyG4UVpktdy27T+oo5z+4P4dHs9qkozMKRAmCZJLIwqTseTVw4XZJwhF2WXNJPFuEKJ4eT3BqWCEdVvA7g5jHhm5ElXWhHqhhMNkrWSurLktXiFl1YA4LjVhZo2OkJRCgUDk1YlmJOuUDAwaVTUhhry03R49ZfjMHmg8KnE/11+GBc/v5bKOqTAGwjjk211ONrqlHwulmXR4vDCHxRexguEWDzy+V7enpZ9oSzbiOsnlAq6Vrtr/+sEaP/3BqLqKCaQH2524BtKpUAAONTkwHMrjqBNBC+9IE0XVxXEJAzkXBAWU1ohwd9FKQv9/M4p+MOFwsc0b359M55ZfojKGtYfteLJJftFSY2aEuA9GAvZJg0cvqAg/fCesL2mA7OfWY1dtZ2CjyVu7TQoiHZvEOOfXIm3NpwQfKxFp4JSwVATzjrZ5sI+gUYV3bX/aVF2yWcrdMoUAL7c2YC73ttOzXt3d10n/rHisKjE7j83jMUrN8bHeBlIwkA+ZUAWdjxyHkYUCfd5NFEureRadII0lQksFIWzdtR04pW1xyGG2jquLBPFImrJsfDVrgaMf3IFatuF7zQyu7jkNMx1O1x+HLO6RB1Lasgv/3BMcnNPjBIkAcMwSNerqWmjv7TmKG56Xbgpg9Xpw5whuRiUa8JlowqpTFReM64f9v35AuSLKGmk6dUId8lK0ICU3kG8kXQ1cpVSIboRlJemw6OXDuXtsdkbvIEQFq05htmDczGiWFg9laa5hNMXhEapEGReQPD8dWOorAHggnCLwwe9iMYR0Yhvc/p5G1L0BGLlJ6b0plIqoFYyqO/0SG7uidEij4ZJq8TSPY2479wKyVmwWAlbOSi7DMOIpsqSv8HmCVCRlbB7AmAYcd/Rp9vqsHRvI179JT310N6QdBn5hqNtePrbg6K24RadGjdPKUf/HGEqcLHQ6Q7gHysOY0+9cG9Fi14FO6WShtMb/MlqkRNkdT2YH/hol+RM2CWSjlm5cCnKHlyCQFcp4Z1NNSh7cAkqFy4VtQ7SexA7Fenyh9DhDvAeY+8NNk8AFpEPFNr4aEst/rVS3N9k0dOdim60eaAAYBUhD1HX4cGKAy3UqMx9IekC+ZYT7fj36qNQihyTPdzsQKNNOuWONF2FCGYRaJQKnGxzUeHminEHInjqmwO4/tWNktcAcJxpfZdanlD0zzFhXFkGDjc7JAcusYGcNPd0am79UvV4xGbk5IFCfEylPlAAjhoq9oHSYvfioufWUps3+O5AM5aIPBdxOKIVyLee6ESIhahrLqJVFKceU9IFcqcvCL1aCZVIPullL/yI19edkLyOyI0qIoh6AiEEQyyVbMsXCotizgDcDXGkWTpDAyDBQvg6KhcuxTl/+Q5bTnSAhfTAVZiux4xBOTAIZBSQ5h7nsamQ3Nzrn2PEgrmVKEoXViqi/UABpLkDqZUK7G+0RwZ5pKLT7RfFWAGAEUVp+Ow3kzGyWHh/LBrkYXm8jeuliLnmos0l4oGzYz9FEQ6v+AwU4BqeND58MRK2cnBzX5x3juguPs3ptJH90kTtTs4wDhCgvxELF40owEUjhA2aEFidPowuTke/TAMserUkPZ7+OSb8ZuZAwcdFHiiBMJQK4WPssfD45cNEB3IT5YDV4Q6gQqDBBYFZpxYky9ETpGi+RK8FoDeh3ReSLpC7fOL0KwiMWhUV+qEYU4nIBbS3CV4KQYtArMCTSavu2h2ERe9wCK6fUCrquOhMWMnQo7mJwaL5Vbjrve3YW2/D97+bKelcrQ4fPP4Q+mXqBavlWZ0+jOqXhp21Nlw3vp9ktsjMSv5a292hViqgVyupzRt0ugMRo2uhCIVZfLqtDoMLzJKycnLNeQNhqEQ+LLNNGsGOS1KQdKUVtwhbs2gYNXQC+fnD8rHr0fMFZRfRQQsAfAHpQeuppQfwweYaUceSbIvGgBQrQQ7X6vTBqFFiRmWuYP2N7vjjZ3tw1X/Wiz7epFVRoae+uvYYzv3HGlGSp4vmV+HqKs4I4p45FZLYI75gCKsOtqBJQmnEpKPzmbAsi1A4HOHrCwUD4MHFu7F8X7PktVidPujUCswZIu6aqyrLxIr7Z1CZAOaDpAvkr9xYhU9FWJsR0LpRlQpOVlNoJmt1+jCrknPCuWBYnuRs64sdDaIExABgQI4R0yuycfMbmyU3Xmc9sxoPfrpb1LGL5lehX6YBCobBE1cMlxS4Wuw+Sd8vrR0b518qPuEgwa7DJS0Ttjr9uPmNLVhzuEX0OaYNzEZZtnRtIoZhsONP5+P3F1SKOl6hYKjNYCyaXwWWBcqyjJKvuXgg6QI5wzCSvB3vnD0Qd80WXrvsjlUHW/C3pQcFZ6KL5lfhztmcJdp1E0olX0BSWCszK3NRkmnAjtpOyY3XTk9A0vdi0aupbN+l7thMWhVc/pDk6UG7RAs9Un7olDjdaXNLH3p59prR+J8ZAyStIxpSjBloKSB6AyH4gmHRbB6HN4BfvLQeX+5qkLwWPki6QP7U0gNYvF2YNVM0ZgzKwbQK6d6Q649a8eb6E6IuyrQudofUoSDOuFhcz4B07t/ZVAOWlcYWYVkWdo84EwUCi44Ot17Kgw0Aci1a9M82SvZjdHiD0jJyY1dGLnG682zRIgeAY61O3P3+Dhxssos+B61AbpfI89eoFNhyogM1beKmiIUi6QL5x1vrsL1GXCkBAGrb3dh8vF3yOpy+kCiWBkDP7s0TCCHMijOVWLtgVqTEA0ijubn83DrE0A8JfjWtPx4UoVsTay1SBqSun1CK7383EwYRDkPRcHilTR8OzDFhz2Pn46IR+ZLWYZOgRU7w2Jf7cPWiDZLWAXBDNF/tapDEvbbo6ARyly8EjVIheqeiVXEzEyn6oUhwioPiL8o315/Ae5trsP/xudLW4QuK4pADnN70KzdWYahEqQCPP4QMg1rUxZhr0UVubrGdewI7Bc2KCf2zRB8bjZmDcqjI4UrFr2cMEKxIGQ2VUgEzBe1tGt+Nxx/CSQqZZ4cE5UOCv/18hKTPlaA824jDT14oqUlPaxfJB0kVyH3BEPyhsCj3FwKjVgW3PyTZ2NbpDYjOyNVKBc6j4IiTZdJix5/OF308aQreOKlUko+pRqXAzVPKMDhf/IOpodODw80OTK/IkeSXufCSoaKPBYBdtZ148psDeOKK4RiUZxZ9nvOHScukAeDZ5YdQkWfGpaPE24nNqMzB27eOR65FuLgbAa15g1Na5OJNM2iJvBFIqddzmuTx4ZEnVWmF0OSkNrMA6QpqnkBI0jrWVVuxU4TUKk28emMV1EoGGpVSUuc+26TFo5cOw6h+4rm9S3Y34qbXt1BTthMLbyCEzcfb0eqQxibaXtMhmQn0ybY6rD7UKukcuWYdplXkiBJVIzDpuOQnKFFXJJKRS9gdbK/pwIurqiVl0gCw6VgbHvholygtcoIx/dKpP1h6ApVAzjDMXIZhDjEMU80wzIM0zikGnkAIZq1KUu2R1JOlUsw+uH0S3rplvOjjH/liL15de0zSGg402nHH21tR3eIQdTzDMEjvco6XAl8wJEoPPRqkvi5lq+oNhFDx8DeSPlcaLlLBUBg/+/d6vL+pVvQ5AFD5brad7MCK/dJ41+R+kzpvoGQYlGYZJA2fbTrWjr8vOwSPxOvtULMDn26vg5THwbPXjKbS1+EDyYGcYRglgBcBXAhgKIDrGIaRtn8ViaJ0Pfb8+QL8fGyx6HPQNJeQckFadGrJ9bX6Dg+W7WuWdINdMbpQsnbF17saMfiRbyXVUSMjzxIaWS5fEIEQK6mGaqLwoCcPASkTyADHXJFqLvHuxpN47Kt9ks4xKM+ES0YWICwxC757TgXW/F6aw1AaJQVEQss8G9g8fECjRj4eQDXLsscAgGGYDwBcDmA/hXPHHRPKs/DfX1aJ9uojWPj5HkwekC1a18Oil65JTsoQUuh2D18s/ZlMg+JGjpVSiyUPNCmfB40dGzGilhrI0w0aNHaKp+oB0gSzCKZV0KHs0gD5W+yeIAokDFXaPOLVOgn+ueIw1lVb8fH/iB9Q5AsapZUiANF7xLqu350GhmFuZxhmK8MwW1tbpdX1esKu2k7c9d52US40BPlpOswZkiepPMOyLN7fXCvYPisaFp1KciCPCHdJCFwAp2EhBSQ7khK4LBS49ZEHmwhzCwKzToWhBRZJ14dUjjJBhkF9msia2LWcLVnnw5/twfMitcgJaGXkdq/0B5zNE8CBRnFlTaGgEchjtXXPuPNZln2ZZdkqlmWrcnLkeXqfbHfj692NkrwdXb4gVuxvlqRJ7g2EEQqzkmiQNEaNXSIUGLvjsS/3YeJTKyWtw+7hpimllJrKs4144+ZxGFMivswjVos8Gjq1Et/cOw1XjDkjV+ENqe5ABI9fNhzrHpwt6Rw0MvL9DXaMeHQZVh6QVmv/sdqK6hZpssm0ArlKqUBRhjQ3KrNODacvKDkR4gMapZU6AP2ifi4GEJ+51G44pTgo/sJsdfhw21tb8ezVo/Czc8TV2h0+6Rnor6b1x3XjSkQfD3AGFUXpesHa29HQa5TocPnBsqxoKpZUXRGAuymkqPQBQKZRg/kTS9EvM7E88oo8E16YNwaVEuiLgHhVy2iI1YmPhk6tgMMXlJx4dLj8ogWzCCrzzdi28FzJD6e/SrDxIyDXvNMXlN33k0Yg3wKggmGYcgD1AK4FMI/CeQXjVMYljUcefS4xcFLIuMopiBDdNKUcN00pl3SODIMawTDLDTiJ3IKfOyQXg/OlBS2WZbFsXzNKMg2iB6X655jwlyuGS1oHANz25lZU5pvw+wvEMRKyTVpcMlI895tgV20nXlt3HA9dOAT5aeJ6Om/fOh46CQ964FQjWkr/IhRmYfcGkSaBQw5wMwtZIgzP5cApcwnpu56+ILm0wrJsEMBdAJYBOADgI5ZlpbXBRcJBArmE8WlThF4mvjzjDYRh1qkkBfKTbS58sLmGihKjFGR03VhSVPbmDi/AbdP6S1oHwzD47Yc78fnOetHn8AfDkrnOAFDT7sLRFvEMnJNtLqyrtkrecre5fPhiZ4OkMmBFnlnyDoWGGw4phUjNyAHg2e8OY9VB8WqOAPDbD3firQ0nJJ2jJNOIWZU5UEgYKuILKjxylmW/YVl2EMuyA1iWfZLGOcVAq1KgLMsgacupUyugYKRl5EMLLdjz2AWYPVj8dOauOhseXLwHTRJu0me/O4yHFouTjiWIBHIJNLcmm5fKA8kssQH87qaTGPjwUrS7pFH2jFqVpMGkL3Y24PpXN0mm651SQBT3mbh8Qby5/gSOtkqrS2tVCqiVjKRA7guGMKTAgkKB1nex8Pq641hzWBqhYsWBZhxrlSY7MGlAFl6/eTyVv6kvJNVk552zBmK1RB4qwzAwUtIklwJSX7N5xK9jd10n9jVIo6cNyDXhlinlkvQvrnppPf70+V5J6wCkN4DdfkI/lFZKkKpZ7+gyopaqCSL1Idts9+LRL/dhT514dhXA3TPzxpdghAQThYI0PZbeOw0XUJAusOikUXdDYZZTp5S5HEITSRXIaeG1m8bhFgm1K6F9fwAAIABJREFU5R+PWHHnu9sljfda9HQGYKSUmQCuVv+nS4eiNEt8zd7uCVC5KThKprRBHLWSkTSODkh3kWq2+RAMhyWP6EfMJURm5GTgTGqzEwD+fPlwXDxS3MwEbUiVsiU9LqkN+tp2Nyb8dQWW7G6UdB4+SKpA/tTSA/jrNwckn2dcWaYkhbzqFgeW7JH25VkomLc6fSFJVDsCjz8Et8hSQriribV8f5PkwCVVhMjtC0qWnwWA4UUWSRZeO+s6EAixks06LDo1sowa0boiNgrKhwQsy8IvgdO+bF8TrnhxneRrBOD+Hin3DTlW6ueiVSnQbPehXeL0LR8klfrhluPt0EsY9iD48YgVwXBYNN3NSYG/TWUAxheUpAQJcNvMoY9+i7tnV+D+8wYJPt7Z9QBo7PTi+RVH8IQEWtcjl0gzoXb5pQmZEdzV5eAkFJULl542wPPOphq8s6kGWpUCh564UPD5FAoG2x45T9RagCgtcgoDQTe+thneQEj0FGNtuxs7azsl75YALgCfkCAHEQiFMSDHiFyJ092n2DzyKyAmVSB3+ULIMUunHr205ijc/qDoQO7wBaFRKiRdlFlGLZbdNx0F6eIvptIsg2QvRaWCgUWnFiXOFB24WEgPXANzpVEYZ1XmYlBe/JzNu2Ptgll44psDWLa3Cb5gGDq1AhcMy8fDF0t7QIlFfQc3AR2gwOTRq5WSFCE73P6ua016SHruutHQSOg/9M8xYeUDMyWvQ6dWQKWQ1gTmi6QqrTh90kwlCIxapSShKadXnL1aNJQKBpX5ZknZ0tu3TsB95wrPorsjw6AWVYddu2AWLhyeD1UXi0iKyxAA7Guw4Z2NJ0UdCwAXjyzA7dOle0t+sLkGk59aCY9f2DWSa9HBrFXBHwpDq1JIMusgeGbZITy5RJys0ck2NxgA726qEf3+BFzZS3zA6nAHkK5XS9L/JtCqlFTOIxUMw3RptacyckHghlak/0lSWSsGjRKlFFxoPtpai2yTRhKNkQbEyqXmWnTINGoQYlkqgWvN4VY8/e0hXDW2WNQQS5vTB51aKblvEAiF0dBFqRRayrM6fZgyIAvzJpRi/dE20WYdBAca7Wi0CTtH9xLPu5tq8K6EnRIAyQGr0+2XxIyKxrpqK77YWY+/XDFc1K74271NeO3H43hp/lhkGqUNKF06qhBDCqQ5ffFBUmXkpVkGFEvURwCIU7r4QP7wxUOx+DdTJK9j0Zqj+HS7uAEYmyeAi55bS6VjzmXk4ho2jTYPLhlRgI/umITrJ5SiVQKTxyyxAXzNog2Y+v++l9xQkzL9+5/rx2Ld0TYcbLRLMusgSDMIZ2isXTALl40uhFpJZ6cEcIHc6QuKbryWZRkxrixT9PtH41irEx9trRPNXDnZ5sLmE+2SlA8J7po1EJ/tqKfSxO0NSZWRf3nXVCrnMWo5epkUfREakCJl6/AGsL/RTkVX/edji0Vvmy8eUYgHPt6FB86vxBMSx+NJ/dTuCUJMubzR5oXLH5LcdJViLuHwBsGykDyKTpBh0Ah+yJISTyDEggGolHgmlGchGGYRDLORB4QQLJhLz4AhmrorymPWG4BSwUhSySR4fuURbDnRLvma6wtJFchp4abJZbhSgrrdHz7ZjaIMPe6ZI47dQCC2yQicCjI06IdSdEHIdptGyYvcoEK38LTZIiYJgZwm5Q/gdktufwi+YEhQGcHq9CHPokWWUYNzSjMll3imVmRjakW2pHPQglQFRKIIKSWJo33N9YWkKa3Udbhx+Yvr8OMRq+Rz5Vl0GJRnFv1FbjzehmMSx54BMskoLhOua6fHSPD4QzhhdYnSBjkl2UpnIAgQbve2dsEsXBZlUCy1lJBn0eGCYXmiHk6dHum+lNEoytBjcL4ZboHN+UXzq5Bl1KIgTU+lxBMKs+hw+UVzyWc/sxqv/CDN2pAg2lxCDOyeoGT2DClfkc0JjfJVb0iaQN7hCmBXbadkrz4AOGF14fV1xyN2T0Lh9AYlccgJpJhLvLeZ8/r4RuJgEgB8ur0OM59ZDauI+rbDF4RWpaBSbxxWmIbvH5iBCeXCaqm5Fh10au79VQpGcilhYK4Ji+ZXYVih8KGgSEZOqbF35ZhifHvfdGSIaMrZPAFq6/ix2ooxf/kOe+qFG4Z7AyEcs7rgp5B0AFwgN2iUon0JCtP1GFOSIWkNpHwV6sp9aJSvekPSlFZoueEAwMEmO/781X5MKM8SdaE7vHRokL+/oBK/O79S0DHdt3TL9zej7MElkrZ00ZoeQi3wHN4AlWwc4Ewd+ueI44G3u/wYW5KOGyeXYcuJDsmlBLEYU5KBL++aIvrvoAmbJ4B0PZ1avVnkbgk4JfpFi7XSP8eE/Y/PFX08LcNkq9OH0f3SsLPWhmvH9ZPU6O8LqUAeAxFWggjmii8Ygj8UplITThfRECNDJ8v3NcEboDN0EtH0ECFle9XYYkweQKd2GgqzeH3dcYzqly6Y4fDqL8dF/v/y0eL7HwDHVpn29CrcPXsgbhaoyWPSqiSbWUejtt2Nez/YgfvOHYTpg4Q5b21+eA5omdeYteKlbIn6Ik251xa7F3e9vwMvzBsjWxbcFxbNr8J7m2qws3YP7plTgYI0+VQQk6a04uxy5aFR0pDCSvAFwxhWaEGBSKH/aOxvsOPpbw8KKvGQLZ0vSG/o5JRcqvDG69jSTFw6SrqJAgAoGOBvSw9i9SHhWtN2bwDHWp2S9EAI9Gol2l1+UfKx22s68MHmGtE0ve5QKRlsr+lEXYdwuWODRkUl8QFO9UCcIgL56+uOA4Bkq7hozHtlI7YcbxelZ3PFi+vw4qpqKusYWmjBTZPLoFLIG2qTJiM3a9U4pySdyoivSQJP2KJTY8k90ySvAQCOWZ349+qjuGJMkaASj9Xpw8iiNNi9QUwZmC25jECGIsSI/+yu64RJq6JSSiCTcmKaWKsOtuDeD3Zixf3TJY/6K7qoaWIe9Ev3NOLtjSdx7XhpNn4EYqVsW+xeLPrhGH5RVYzB+dIHVqLdcPiiexlwxYEWyWVAqWwRlmWxr8GGif2zRL1/d4zul47R/ejtwHpC0mTk5w7Nw+LfTKFi80TD7o0GIgqIAhuei+ZXYXC+BW5/kAojIdOowaOXDkVVqfCBjd9+uBN/X3ZI0vtHQ6wm+ak6LJ2aMJk1EAqadWmA6xvo1ArBu6W6Tg/+++NxNHbS6RUYNErcf94gVAkoeRFmB2lE02B2kHOSIo3Qc3oDYQRCLFVrNn8wLMkQng+SJpDTRJ5Z20VZE15L3VXbicte+BF766WJ9QNRgw0iAleH208tYGhUCtw8pRyVInw3Hd4gFXU9ArGmASRjpUX7E2su0emm79+YrtcILvNElA8prYVhGNwzpwJjS/mzPeQoA5JzsoCoYadTnwudYkWLw4tBC5fio611VM7XE5KmtPLPFYfx4xErPvm1OBnNaKiUCtE+hq0OH3bX2UCjBBo9ySgUne4ANRYAgAgvXmiJxEFBQCwanKaHuM/DrFNBJdGVh+DikQWilDZpUv4IxpdnokigNIWNMlME4Mo1LCCI2WR1+jCswIImuxdzhxdQYRNZnT70y9CDATC9MlfQOWkPbJFkqkOivWBfSJpAfrjZid11nWhxeKl0qV/54Rgq8kyCpWxpaJETSM3IB1CkuN313g4UpOnw35vG9f3iLgRCYXgCIWr0QwB4cd45ojjpNEWZAOABgbRQApsnINnsuDuev26MqHUA9AIWwGmSF2cY8Oov+ZfyFs2vwkOLd6Npv0+yhEP0OZ/4ej921HYKPqdKyWBWZQ6KM+h8RxqVAmatSpLnLR8kTSDfU9cJf5frCg1Ng0U/HMUFw/IFB/KGLrNkL4XBpCyjBgcenxupIQrBhP6ZKM+mF8gzjMKFs5yRqU56l5mYwRcAuHZ8Cc4dSldFMhxmBRt9v3XreITpzL1IAqHW0gzkFpEOTjYP3d0jACy8ZKio4wbkmPD6zeOpriXdqE5l5H1BLk0DsVK2y/dxFKo315/A334+UvT7A1zdUazj0RNX0BXoyTBo0NApzMhZr1Hi1RurUEHRzGHtkVZsONomWGSJFguB4Pcf78LmE+1YI9DsWw5O8wvfH8G3+5rw9d382VK/mTkQt0/rT63UBHC70Ga78NKIHH2DswmZBo1oX1W++Mk3O6V2qXuCUIPdyoVLUfbgEuys5UaUP9hSi7IHl6By4VJJ63huxRF8tLVW0jloQIzKnk6txLlD8yQZN3fH9pOd+PfqowgKHOfeXtOB+k7hXOueoFEpBHOm/cEwnltxBLvrhI+x9wZPIIQDjQ7B3HSaQRwQ37/gmDx0A/n6aiuufXmD4AfLe5tqMPGvKyWZN3fHdeNLqM1S9ISffCCP7lIrKehoEAhlJchBpQKAr3Y3YNVBYQMw9Z0eDH90Gb7a1SDpvaOR0aV7LUQ4q9Xhw8oDzVRvilN8ZWEB45f/3UxNlAngrg+HwB1bp8ePf6w4jF21dAN5hkGDUJjFVS9t4K17/fIPR6kNvRCINZeYMzgXMyuFTaX2BYcviI3H2gXbz7U6fGiye6lI2BJcO74EV40tpna+WPjJB3KA61LfMLEUX901VbJ5AYFQuzc5qFRAl3CWwJujw+WH0xekIlRFcPHIQvx73jmCsr7tNR249c2tqGlzU1vHKSlb/kE0EArD4QtGhmdowKRVwR8MC1KXtEcEs+itAzjFjd9+soP3JON3+5upKIVG47JRRXjoQuFSEPefX4n5k8qoriUjMo0s7N6xewMwapRUdysefwi17fTugVj4ydfIAZw28EKr8/38dWMEj9VanT7kW7QYW5qJdIOGCpXKolejzSmspEFKIDQDV2W+WTCPnARbWpxcIFrKlv8NSm7mDCO97Xv00BjfISOyDpr1YLEG153uAFVWE8DRIMcLVKZkWRahMEu9zEOap0LLgUSLnCZeWXsMz353GIefuJBqchWNpMjIW+xejHl8Ob7YKc4WLRbMOrXgRuOi+VUIhbn6Oo2JSqBrAEZgRh4JXBSZAHZvAD8cbkW7gO67M2IqQZEZISIjJ1OPtKY6AWBkcRp+Na1cEGuFlJho1oPXLpiFWZU5UAjUvZaDKWLzBLC7rlPQFGOHO4CBDy/F2xJMtWOB/G1CJ15tngC1ISkCch8SLXo5kBSBvN3tR4c7QFWYZvWhFjz1zQFBx4TCbMR5hRbS9GrBhg7k4qU5eHKs1YUbX9uMHTUdvI9xyEA/HFeWiSNPXohJA/izUAhjgGYArSrLxMMXDxU0tSpHRp5r0aEwXQ8WEFTS65Qh81x9qAWXvbBOkIAXuVZNWno1aYAbxKnMM8OgEXbtVZVm4DzKNFVCmRWjHsoXSVFaIR8QzQx0e00nFv1wDH+YO5h31tXm9CHMAjkCNbt7w+OXDwPDCCsXlWUbcdXYYqqaHhEpWwE1R4cvCJ1aATXFbbNSwUAJYdztgbkmvHTDORhWSM/NnGVZuPwhaJT8TTOuGFOE2YNzqWd8VqcP108oxbzxJXhvc02fJT1fMASLToUsE91avZhG9KldCt21aFQKLPvtdMHH3TFjANV1AOKFzYQgKQK5HFtnkiG4AyHeUp/Ndq7JmididLsniLGbm1aRg2kVdFkAYqRs508sxezBwgaq+oI3EMKTSw5gzpBc3sNamUYN5g4voLqO7TUd+Pl/NuDNW8ZjBk8dcKWCET3Q1BtemHcOJj31PYrS9bx6RFqVElsXnkd9HcRMRQgtk7bmi1TIYbgeCeQyDgUlRWmlQ4ZmlpiLUqtW4KIR+SjPpseb3nKiHXe/vwNtApg4/mCYmt41gUWngoIBXvvxOG+KW79MA/VBHLVSgbc3nsSOGv4UvuoWB9YcbkWYlosCxClkfrWrAf9aKVwfuy+olQq4fEFB14gcIBn5n7/ax/sakUMqgOD3H+/CHz/bI+iY0Y9/h78vO0h1HUUZeiy8eIgo0Tm+SIpAXpyhx+WjC6myNIxdGfmv3trK+6IclGfGv68fi4o8el9Yi92Hr3Y1wCqAuXLH21tx5b/XU1sDwO0M1EoFGmxe3hS3VYdasL6aLsVNqWBg0gqjZH6yrR63vbkFNBMto0a4+cjKA834eJs8KnhpejVvvv7+Bjtuf2srqlscVNdAAnl1i5P3NVKebcRtU8tFCZD1hUabFwca+U8jB0Nh2Dz/v70zj4+qOvv478yePSF7CCRA2MIOYREQZFERUdS6FIW6tFVra+3bWnytVK2i1rcW99fiUm37uheRKrKjQtnXsIawk32fSWYms5/3j5k7mUxmuZO5585cer+fTz4kZG7mnHvvPPecZ/k9dnx5qJb3Z54PaQlq/OTKgUzb+10WrpUZQ7IjbnMVDs6dcqzGwFu/hcW2jEvdi8RwtXmU/oSitzIIr24+jRSdClNLhGn1xpEaYQWhWzBLI+i14e6PSN0IrErRIzHk1W1mbDzRgF/OGSzY+/c2DXJ0Ybqgre98SU9UR1TNy/UbrdV3CqbZxHGh2QSVkggmxuXPZbEijzSrIxxDl63Dj/+2H0DXTcmn3H7Zl8cw+8/fCToWLiviidVHI9quChkv6G3VaofFLqgWOUdqQmSa5G1mm6CBcKB3rhU9g5Q/jkgMuZ6BO6O390i7xc6sgUtGooZ3TGfosnUY/+wmAJF95vly5zu78fIm4d1qHJeFIb//7/txy//uEOzv9fambGi3QCNwYQMXBDrdwH+76m4qIWyKW4pWBavdBbWSvwyC0FrkHMlaFfZeaOX9YGszC/tgA9xZEY/MGYyJERTAsMhR5pgzPAdXDua38+mqMI39PfKHf53A1Su+F2wcvqR7ZCX4xEa2L53V7fwJJbHBkZHE/6HSGy4L10qb2dZrlcBA5KTqkKRRwhLhTdnYYY1IVD8cvdmuOl0Uhk674CvQZqMVg3OTUWewYOHYvryqVlkZ8mF5KThwqY339ldvtgkagOb4r6uHRPR6s9UpuDgURyRpc3qzHUoFQYpAjZc5mo1WZCdrkZOqxdj+GbzuEXejDeEzeQBgcG4KppVkwepwhbUPOak672dGoxROYoMjI1HTq563fInqShJCbgPwNIDhACZRSvcLMahI0ZvtyE+PrENKOFpNNqgUBNeNzEdqgprXTdnQbsFQAQOd25fOwvJvTmLj8XpY7C7o1ApcOyIPT1wfXM/C4XLhoasGCZ4tsnJJGVZsqsTrW0/j6RtKw5ZUs2gq0Vtf/Yrbx0KlFDZ2AfjUDfAM1O16fLbgbkBfnC4KJY+ahwSNEkNyUwSP56xcUoZffnwI5dX8GzoYOm1IE1DCwZcbxxTgxghUB60OFxZP4Z+PHwkZSRpB1Tf9ifYMHgNwC4CVAoyl17Sabegj8FN95ZIyzF3xPWwOF6+b0l3VaRN0Rd4bIS6tSonfXhuZVjdfMpM0oNTtqghnvJSEYMOvZgi6M+AebOuO1sHupLwebAAwsm+aYGPwZfF7e1GYkYB3fsRPioEQwuSBAgBvfXcW/7OhApXLrwtbgPXzWSX4+awSJuPIT9Nh/XEL78C/odOOgQI2QImGV+4YB6WCQKNSCKbZxJGRGHljlkiIyqFLKT1JKRWuRXovYOVKANw3ZR1PPWO704W7ryiOWDQoHM1GK4bmpuCmcQW8lB0tdifaTDZBc6Y5uEpAPnorCgXB0LwU5DB4sNmd7rnxebBZ7E58tq8K55tNgo2DI1mr5J210my04tHPyyOSOIiERI0SlKJXjamFJC9NB5vDxVuTh2VTiZN17Zj2x628VR5f3XIaI5/aIHgNBgDcMr5Q8IeDL6IFOwkh9xNC9hNC9jc1NQn2dx0uF3565cCIgk58yUvVod7AbzukUyvx5A2lgqdBrlxShoxEDc40mngJce0624Jxz25CucDNCwB3hSQAtJjCF540tlvwwY7zqBV4O9lstGKqR2flxjEFYR9szUYrlq46gr3nWwQdB+DOXOFapoWjod2Cfx6o9lb/Cg1nDPlkrvz283L8eSOb9Vd+mvuhWmfgtwD62VWDcN2oPCZj0aoUqNF3osnIMyhucvd1FdrlBABj+6VjwWh2zSXCulYIIZsBBDrTT1BK1/B9I0rp2wDeBoCysjLBHnlalRK/mx+5BjIfCjMSoVYqePVm5Hp06tTCiv8Abh8sX8PcxkCugGN0YTq+fng6BmaHDxyeaTLi6a9OYEheCgoEjF+sXFKG7yubsPNsC5ZMKUJZcegHuN7bLV7485GkVfHWWjcwEMzyhfu7eh6GfN+FVma52xOL++Cjn05GMc/g8r3TBjAZBxC5Jnmr2eZdrAiNwWzH8VoDRhWmCRo34gi7IqeUzqWUjgzwxduIs8TqcMJodTDZDj0ydzD+/dhsXqJZaw7XYNjv1zMJaGQla9HMs9NJGwMJW45krQoj+6bxUpTzapEzuGmzPC6eZh4l6Sy02TmSNfy7SHnFoRjlkadGsCJnoXzIkZmsxdRBWbz0iWwOF843m9Bpi75ReSBSE9QghL/QW5uJnSEvr9bjznf34FS9sNW0HJLPI99e2YyRT23AkWpDTMfBbZmzBFaUA9wrcpPNCTOPbbzBbAMhbAwoAHyy9xJ2nwvvpmBpyLNTtMhL1YFPcx6WD7Yrh2QhSaPklc/OogjHl8KMBNw3bQAK0kLvflwuinaGhUkAsP5YHfZdaA37uvPNJsx66TtsqWhgMg6lgiBVp+adv91qsjERNQO6FhKR6PlHQlSGnBByMyGkGsAVANYSQjYIMyz+sFxxNbRbcN8H+3gFSxo7LMhIVEOrEt61UpSZiBEFqbxWf22e4FEkDQ8i4cX1Fbx6gXZ4m0oIn1qWk6LD7t/NwfWjwysaslDG5Nh9tgUXWs28CrWcLooUrYqZIc9N1eHJG0rDCjMZbQ64KLsHCgA8+/VJfLz3UtjXsZKw9eX60fm8xaoWTynCglHCqmRydDW6YBOMjupTRildDWC1QGPpFV4fqIDKhxxalQJbKxoxrSQL08NUzTW0C1sM5Mv8UfmYz/MGm1uai0E8fNi9JTNZy2tVwa3IkxkY8khYOLYvJhRlCLpl7k0+++IpRVg8pUiwMQSi0+aEi1KvfEAgrHYXxvVPR2GGsHUXvuSl6VCn57FL8T5k2T1Uno9AL+W+6ez89dz9xyoFUfKulTazu3BH6Co1wL1q0akVaOCRgtjYYWWi4BYpM4dk4x6GAaQ+SRq08DDkP54+ANt+O0vQphK+PLnmGK8OTmkJaowoSONVKMMXTsJB7ckJ16qELefuLROWb8LLmypDviY7RYvVD00TXJ/dl/w0Hep5fGZYSthGit3pQnWbOaI2dZGQqFFCo1Qwq+68DAy5nVnKECEE+WkJvFKpFk3sh9vK+gk+BsAd8V745g58eSh8T9LzzSZmfjjAXRTER/c6SatC/0w2Sm+AWyqVjx924/F6QXu5Al357A5PPruNRz77G1tP47m1JwQdhz+RCGexJD9NhzpDZ9gEBAMDzRd/fs9TyO5iiwnTX/wW64/VMxkHIQQrfzQBdzCyEZI35HOH5+Chq9hUqQH8c8l/OKl/ROXAkZCoVeJItZ5XUcvid/dg+dfsDEZmsobXg2LN4Rp8ui+8n7S3ZCVreWm0f7jnEt7793nB37/ZaMVN4/oCcHePD5fPvutcCw5G0AyjN6QlqMOmH26taMC8V7ahqpVf2mRvyEtLgMXuCvtQmVaShT/cOALJEfbVjASlgqCJR+5+q6ddZGYSu131rKE5zDTJJS+aNWe4sI1S/RlRkBo2pdDqcKJOb0F+uo5JsFOtVCAjUcMr3Y7T3mbFf80dgkfmhBeL+ueBanRYHLhjYn8m43Ab8tidj5VLyuBwuvCv8lqUFWeElUXQm+3IYxRD4UjlsSKvM1hQUd/BzOUFADeNLcCcYTlh86WH56dieL5wfVQDkZGoQYfVAbvTFXLOrZ4iNyG7jPlzuEoPQ6edd2vASJD8iryq1cx0O7lsQSneWjwh5GvONppw1UvfYcvJRmbjyEoOb8htDhdMNieTVDuOzGQtr1hAe6ewzS38yU7RwmxzhtWybjOzkW8AAJVSgbxUHWp4dI1nWYrOkcZDp511PjvgvkeKs5LCxiXONHbgbJOR2TiArnmGsxHcipxVHjkAvL3tLJ756jiTvy15Q/6Dt3bi+bXhg14s4fKIc1PZbcuyU7RoClMUpO/0ZAEwvBkvNJuwYuMp1IeJG3RYHEwb6g7ISsTYfukwhykmcTeVYHc+nr5xBK/gsluula0hv3lcXyy5InRmjMFsh1alYFKBzGGxO/Hu9nM4cDF0DOOZr0/i158eZjYOwDftL7QbjmUac9dYNMzSDyVtyCml0JvtTFIPOQ5easO8V7bhRG3w3n+NHh+cUNrFgZhQ1AdD80JvQ72pmAwNaK2hE69tPYNzzaFXUu0WB1IZrsjnjczHlz+fFnJ34HC60GFxMF19Xl2ai7H9Qpe7u1wUmUlqbDnZIGgvSH/mj8rHXZPDGHKGVZ0cKgXB89+cxHenQmsqsWy0wTE4JwV3Te6PhDB++OklWXhyQSnTB1yfRA30PBtdRIqkDbnZ5oTN6WL6FFUrFKio70B1W/DgEPfhZJl++Ourh+CFW0LnxGYna/HiD0aFNSzRwAWDWsIEGjssdiaaEpGgVBDsXzYX905ll47Z0G7BxuP1cIQoM1UoCK4cnI2qtk7eXZ56Q6fNiYstppCa5wOykgQXdvNHpVQgJ0UXNtvLwDieAwClBal47uZR6BtG72dMv3SmeeSAe3fgdFHc9pddgj/QJW3Iu7ZD7AxGnkfNLVRebEO71ZNzzu5pzoeMJA3umNgf/fqwS/vjK2V76Mmr8YiAzX390ZttmP/qdqw+FLwrfVOHFQ99eBBWJ5vcYAD4tqIR9//jQFCjNXTZOhT/91r8355LoFT4XpC+fH6gCjP/9F3IopMHZg7CS7eNEfy9/clL04V1v7l3B+zzLRxOF2yO0HoOZxpFVr+SAAAYWUlEQVRDL9aEgFtwHvR0txISaRtyE6ejwe6pnpmkgVpJQq4uZgzOQmaShum2efOJBkx+fnNItb06QyfKq/QhV4fRkpGoASEIWxSUqFGFrDCMliStCifq2nExxPl47psT2Hu+lWkMpa+nQjJYZhPXC5KL+wndC9IXrwJiCD9sY7sFt68UfkXoD5dLHgyXp48Ay/J8wL0zLHliHf6280LI1/3ms3I8sfoYs3EMXbYOv/m8HACb5s6SNuR5aTo8e9NIjGDUAQZwb4tzUkKvLr6vbML5FhPTbbNapUBDuzXkB3DN4VosfHNHt/JxoVEqCNIT1N50rUA0dVjxzFcnQsYVosWdkqkOmMnDrYLXHK4DAHx5uJbZKpjbsgfLXMlJ1cHppHBRNr0gfeGjgHjtK9uw93wr03sV6FqRBysKogD+967xWDCGXYUp4FbsVClI2NJ4lhK2QO8buvNF0nnk2SlaLGGsXwEAs4ZlIzu55wevtz0kewMf6dY2sw0apQKJAjaiDsS2pbNCypTWGyz4647zmDKwD0oL2OUJu+V9e35A/VvCaVUKzBsZviVcb+C01qtDpCByxUKfPjAFqw7WCNoL0hduRR4oBVHMexVwN6Z+bN6woBXXSgVhKhPAQQhBeqI6rJRtq5FtdlNv2jZGgqQNeb3BglaTDcPyUpip/QHA8psCBxm3L52F5WtP4l8eNUC+PSR7Q3ayO8jYFCLIWK+3gIKiyWhlmkETLojZ7lU+ZBvszErWBqyo9G8JZ3OyWwXr1EpkJWtRow/u4pk4oA9aTDaM65+Bcf0zBB8DR6guQduXzsIfvjqOtUfdJegs71UgvHyx3mxDebUBYwvTmadlpidqYOgM/rmx2J0w2Zze+A8rmo1W3DWZTXNnSbtWPtl3CfNf2w4Xg6YSfMhJ1YF7fKgUhOm2uU+S2zcdKpf84KU22J2U+bZ5zeEa/GlDRdDfcxK2qYwDWZMH9sHIICt+d6/TZCRplLx6nUbDyiXj8fDs4IHdOn2ntwUaS/JSdXjqhtKAzaZzUnVQeFbHrO9VwO2Lf+arEzhWE7hPwPHadtz9172oqGfnfuNIT1B742mB0JvZx9oAdzXw8ptGorQglVfbxkiQ9Ipcb3ZXD6oYlhsDwBcHq/HM1yew9TdX9fCjVXki3S/cMgrl1QZm22aVUoGbx/UNKFEr9rZ534VWrD1SF7QsvZ1hUwlffjU3uFTAyiVl+MVHB+FwUaZNbwF3jn8o+mYkoCiTnbQwR5JWFbJ1GhewX37TSByrbWd2rwKA3UXx1x3nMSQ3OeCDhTOerFfjAHDrhEKEWuql6FR4bdE4jClkF2tjjaQNeSvD1ky+6NRK6M121BssPd5vwegCHLykx+xhOczUDzlW3D424P9zPuH1x+phc7iYb5v7JGmh77TD4XQFfIhyBoKVJChfXl80LmzlpxCcbujArnMtWDy5KKCLL5hrjgVnGo3QqhQBU1CfumEE3tl+DjOGZOOHk9ho4HDkpGhBSPAmzGI0leCYPSwHv/j4EOYMzwm4A0nSqpgJ3omFpF0rbSIUFAC+ueQ9A1rnm01I1alEeaAACFgV1uUTZhNI8SczSQNKg/dCrNNbQAjwwY4LTN6fY/2xeox/dlNQJT9CCNMUSI7d51rw5JrjvES8WPOj9/bglSCutVGFaXht0ThBm2EHQ61UoE+iBh/tuRQw04qTkxBDi/zlzZXYdyF4pk6tvhO7zrbEfOERDZI25HqGgki+cP7NZ7460eOmvHdaMV6/czwTPXR/ln15NKi2clWbGTMGZ+HDn05m7hMOVhQkZvEL4G7o0GqyBZwrpRSPf3EE31awEzLj4HLJqwPkkp9p7MD0F7dix5nw7QKFIJQCop1hfUEwmozWgAbU0GmHRqXwpuOxgLsfP95bFfJ+3HSiAYve2Q2jhV8j7XhE0ob8sXnD8ODMQczfh8sYudDSsz/jwOxkJrKUgUhQK1HfHjg397qR+fi+shm5KTrBAyn+9EnSQKdWeIOaHFyurMrjXmBZ/AK4s1YAoDlAANhodeDjvVWobGDTtdyXvuluN0agXPLqtk5Ut3VCqxLnoxZKAfHBfxzAbX/ZyXwMnAHlisYCGdBrS3MxMCuJ6YKDbyenVpO7YXk8dCrqLZI25ENyk7FiUyXTKrWhy9ah5ImuG9D3prTYnfhsX1VYvXKhyErWwmJ3S9X6c7rBiAS1MqymhBBcMTATFc9eh7Li7kE+b+ccF4WCgLmLJyuFy63vmVrW6DHuOQwVKTlCVXfWenpXiuHOAEJ3CaozWJgHoAF+xS9fHKzBqYYOphlWfDs5tZltSEtQM0+aYIlkR+5wuvDYqiMhfV9CEOqmPNdkwtJVR3CYcecXDm4FGigF8XRjB0pykpnm03OEciNx/U3vmlzE3sXjEfAK5JvmzlGgQi6hSdaqkJagDrgirzN0QkHcwT8xSE8Mbsjr2y3eeA9LQhW/iO1+azZacXOYTk6tJhv6iBBrY4kks1bETLfzvSk1fjfl3vNuveUBWexTy4AudcVmo7XHe55uMGLqoExRxkEpxaOfH8H0wZm4eVxht989PHswNp9sxLSSTOaVexqVArdNKERJTs/2WU0irsgBBJXUrdF3IjdVJ9pqb9Gk/pgboGuWxe5Eq8kmSj474L5Hf1jWD1VtZlDaVd0qZhEd4E5Ddbkovj5ah7H90vH4/J7v02a2IUOkZAVWSNKQb186C0tXHfHqHbO+GZqNVswemoMtFY24dkSu96Y83+TuoVmcxU5t0JcBWUm4+4qiHgHedosd9e0WlOSy6QfoDyEEWysaoFMrehhyzic9ODdFlLH8KYiSX6fNiQS1UrSVcLCH+fC8VFFS7DiCVY5yO6W8NHFcPCuXlIFSionPbcGMIVne1NmcVB3gyeoWozAJcOslPbmgFMPyAt+Tj183nKk+kRhI0pDnpOoAT8BPrWR/M6xcUoZmoxVlyzdjfP8MPOAJsJ5vNiE/TYdEhs1jfenXJxF/WNizuCVZo8J3j16FRK14MrqZydqAUrZWhwsFaToUMZTS9SdQPvvtE/vhtrLCIEcIz74Lrfj+VBN+c82Qbq6nn84YKNoYAPei42RdOyYW9+kmq6xTK/HAzIEY2Zdtj0xfCCEY2y8N5VXdXY9nPQugN+8ah+2nW5gWJnEsDqHJFKhgSWpI1kfe4OnK88G9k5j7YgG3f7o4MxEHLrZ5/+9cswkDA1RassTmcMHo16dSoSAozkpiuqrxp0+SJqCU7eIpRdj5+BzRXAmPfl6Oq1/eFvB3hBBR0kIBoLxKjze+PdPNP00pZdINJhQ7zjRjyXt7e2hr56bq8Ph1wzEsTJcpoRlTmI5zzSav/g7gbrrcJ0mDa0rzmGdYcRjMduw939oj44tSii8P1TDvHcoayRry6YOzoFUpMHVQpmg3w/iiDBy42Oa9Gd67uwwv3Dya+fv6MvWPW/Gcn772uqN1+GjPJVHHkZmkQUscFMAka1UBg78vrq/Aq4w1Z3zhsoUWv7vHm0XVbLRh6O/X4bP9VaKNI5hwlsFsD9uomgWj+6WDUuBYdZfmyuEqPSYWZ4j2kAWA1YeqcfvKXT0ynEw2J3716WFsOdkg2lhYIFlDfvfUYrx/z0RRb4YJRRloMdm8zQwyk7XonymeCwEA0hNUWHe0rlvK5Sf7qvDhnouijiMzSYP6dku3cXRY7Ljm5e+x/li9aOPITtHCaHXg1rd2dhvLtxWNOFYbWLCJBVwK4vHadm8WVZ2hE3YnZdpD1R+u0tnfkK/YdApTnt8i2jg4xhSmYXRhGuw+O5OvH56OZwK4CFlS5IlhXGwxdfv/NhP7pstiIFlDXpiRiKklWaK+58wh2XjhllFIT1TjbJMRr24+jcYQLeBYYLI6oO+0d0u5PNNoxBCRgou+mG3ObuM43WhEZYMRImRAeuF02g9c7N4+q7HDKlqgc+iydbjxjR0Aund/ueUtd/GNWDnkQPAVeZ3Bgvx08VxvHOmJGvzrF9O7Fc3p1Erkpoo7Fi5m499RiovziCWxwQpJBjsB4NN9lzCmX7qoPr/CjEQs8ogNbTzegJc3V+KmceKI7QRLudSoFLA5XAFT8MQch1alwDMLRwCAaA8V37FwBpQbi9XhYtoM25dgKXWDspKxYnNlbAy5nw6OO4dcvHH443RRKBUE7/37PExWB37JsJ9rIAozEqEgwMXWwIZc6umHklyRm6wOPLbqKLaKoKPhT42+E+uP1eFcswlqJRGlkhLoWW6sVhIsHFuAlYsnAAAGi2TI/QukVAriLZCqbAiuvMdqLHOG5UDpJwmw6mdXAIBowd+cVB1SdCoQgm4FMB1WO7QqhSh6QBxpCWr8+bbR+PJwTTdXU53BggKRcsj9+fpILUY9vQEN7RZ8vr8K+y60ij4GjUqB/LQEXPJzrVxodv8crCWdVJCkIedKoQszxPVPA8CqA9X42YcHUV6lR1FmkmjZGb7l7yoFgcNFkaJVeTNYxMrb9i2QIoB3HDkpOlQ2uKtLlSL5VnJSdchL08FFaTcDmqBRYWBWEgpEdCVw3V9WPzTNm0U1vn8Gfjx9gKhxHKWC4NAlPcqrDV5Xk83hQrPRKkpVZyDy03Qw25zYVtmEivoOTCoOrd/OihduGeVNHeY4UdcOAuCfB6pjMiahkKRrhUutKswQf6tYVpQBSoFd51pwdWnPCjqWBGoVdcOYAswZngOdSrwccm4c6YkqvLH1rDcveGhuChJFkI31H8uiif0wZ3guvj3VhKYOCwZlJ2Pro1eJOg4ua+qpNcfgdFHvz9eNYt+XkiOU2+upBaUYy7DNXChGFKR53SqAu/VdLJjh46f3P1cf763Cx3urmDVjYY0kV+Rco9tYGPIx/dK93+elieOD5fBtFXXjmAJQ6n6o3fP+PjSH6GrPahw/mT4QaiXBCE+7tWULSvHrq4N37WE1FruT4rFVR/DswhGipKGGwupw4avyWlgdTjQbraLmkXNuL24D4KsLdM+0ARjrc++KiU6txNDcFFTUd4AAorkj/ak3WLDmcA3MNkeXUqdSHKVO1kjWkGtVCq+8rJgkaVVen6fdETu/msnmwMYTDZj+4rfYd56tcFgw0hM1uGpoDtZ4DFes/IylBaloNtq8iof/t/si7nxnt+jFOABw7cg8GK0OfH+qCROf24xXt4h3XTi3F3cZOFcTAcGZxg44Y3A+OLgFEAWw8vuzMRnD4ao2PPLJYZxrMiEnVQedSgGHk0JJxJEKYIkkXSsPzy7B7WWFovoegZ7bsU/2VeGTfbHZjj34jwPe7/0zNsQcyz1Ti3Gyrh0f77mEFZsqsfnXMz16GuLBlVgfrzUgN1WHk3XtOFnXLooSpD9TB2UiRavC33ddBKUQTaSKo9loxfWj8rD2aD0mF7vV/r48VIPnvjmJI09fI4qMrT9i95QNRv8+XC65GSP7puFUvVsX6PlbRuJoDdsepqyR5Io8RadGSY74edPcdoxrEhDL7dj2pbMwsbjL5xmrsUwrycJPrhyIE3Xt6LA6EItF+fD8VBACHKtxd2Rv6rDGbGWlVSkxZ3gO/u3pCCRm6iHgdjW9ced4FGYkIEGjxMolZagzWJCkUSJF5PgFBx99cjHgivcutpo8PychM0mDWyf0E606nBVRGXJCyJ8IIRWEkCOEkNWEEFGccK9uPu2VkBUTbutqE6k3ZrixDMlNAQFiPhaj1YHP9leDUuD1reK7eJK1KhRnJuG4p5KzscMqWg55IH4wodBbjMSylVkwCCG4dkQeyqsNsDlcqG/vRF6aTvQdLEcofXIxSdaqkJWsxaUWM6wOJ76taMTc4bmiZVmxJNpH9CYAj1NKHYSQFwE8DuCx6IcVHJPVgZc3V0KtGopJMYh+B8ociRXNRivumhLbscTLtvl384d7YxdNHVYMFEkjPhBXDs5GQXoCGjusWHWgBpMGiKMT78svZw/G0nlDoVEp3FWdMSwGAuLnc1OUmYgLLSZUtZqRoFHi2pHiZp6xgggVoCKE3AzgVkrpXeFeW1ZWRvfv39+r96ls6MA1L2/Da4vG4cYx4lRVygSnsd2C5WtPYu2RWjhpd234WLk37n1/r9flIzb+DzaO3jzY7li5CwDw6QNXRDWmK17YgmklWXgpiHb7fxJnGo1I1qrc9QcuCgpIakVOCDlAKe3hAxLSaXYfgE9DDOB+APcDQP/+/Xv9JrHMIZfpCVfV6EJsXTwWuxM7zzajJDsF7987SdT39mX70llY/s1JbDxeD4vdxbzpSSg2HK/Hm9+ewbLrS2OisxKPlOQke+WFYxEMZ0VYBx4hZDMh5FiAr4U+r3kCgAPAh8H+DqX0bUppGaW0LDu7913nY5lDLhOYQFWNYmOyOnDfB/ux4bh4youBiBd/MOAuSz9SbcCrWyrlz4uHGn0nfvTeHgz83TfY5ukwdjkQdkVOKZ0b6veEkLsBLAAwh4qQSFxnsMQsh1wmML7R/uU3iStPypGZrEV+mg7v7ziPj/ZewuuLxsWs80u8+IO5Hq6VDUa8tOEU/udW2bXS0G7B9jMtAICvj9ZixtDeLyrjiah85ISQeQBWAJhJKeX9eIvGR04pRYfVEZN8WJn45id/24fNJ91Calt+MxODssUREmNFND5yIX31lwuXwzkJ5iOPNjfqDQApADYRQg4TQv4S5d8LCyFENuIyASkt6FqBxzL9MB7gcrc1ytjXPMQL25fOwo1jCnqoZV4O5ySqYCeltESogfDlidVHMXNINq4ZkSf2W8vEOSMLurTpLTbnf/QDn/PV212x99XHC97AvJ9a5uVwTiRV2Wm2OfDhnks43SjtRqkybLhiUCbSPbnkr4mocRKvxEMQOt64XM+JpLRWauSMFZkgxEthUjwRD0HoeONyPSeSWpEfq3GXYCfFSDNCJn6JFz0PGZlYIClD/tHeSwCAb47WxXgkMvFGPOVvy8iIjSSWtv7b5i8O1uCLgzX/0dtmmZ7ES/62jIzYSMKQx1PZs0z8crn6P2VkwiEJ14q8bZaRkZEJjiRW5IC8bZaRkZEJhmQMubxtlpGRkQmMJFwrMjIyMjLBkQ25jIyMjMSRDbmMjIyMxJENuYyMjIzEkQ25jIyMjMSRDbmMjIyMxImqQ1Cv35SQJgAXe3l4FoBmAYcTS+S5xB+XyzwAeS7xSjRzKaKU9uhPFxNDHg2EkP2BWh1JEXku8cflMg9Anku8wmIusmtFRkZGRuLIhlxGRkZG4kjRkL8d6wEIiDyX+ONymQcgzyVeEXwukvORy8jIyMh0R4orchkZGRkZH2RDLiMjIyNx4sqQE0LmEUJOEULOEEL+O8DvCSHkNc/vjxBCxvM9VkyinMcFQshRQshhQsh+cUfeEx5zGUYI2UUIsRJCHo3kWLGJci5Suy53ee6tI4SQnYSQMXyPFZMo5yG1a7LQM4/DhJD9hJDpfI8NC6U0Lr4AKAGcBTAQgAZAOYBSv9fMB7AOAAEwBcAevsdKYR6e310AkBXr6xHBXHIATATwHIBHIzlWKnOR6HWZCiDD8/11Ev6sBJyHRK9JMrrikqMBVAh1TeJpRT4JwBlK6TlKqQ3AJwAW+r1mIYC/Uze7AaQTQvJ5HisW0cwj3gg7F0ppI6V0HwB7pMeKTDRziTf4zGUnpbTN8+NuAIV8jxWRaOYRb/CZi5F6LDeAJACU77HhiCdD3hdAlc/P1Z7/4/MaPseKRTTzANwXdyMh5AAh5H5mo+RHNOc1nq4JEP14pHxdfgz3DrA3x7IkmnkAErwmhJCbCSEVANYCuC+SY0MRT63eSID/88+NDPYaPseKRTTzAIBplNJaQkgOgE2EkApK6TZBR8ifaM5rPF0TIPrxSPK6EEJmwW0AOX9sPF2XaOYBSPCaUEpXA1hNCJkB4FkAc/keG4p4WpFXA+jn83MhgFqer+FzrFhEMw9QSrl/GwGshnvbFSuiOa/xdE2AKMcjxetCCBkN4F0ACymlLZEcKxLRzEOS14TD88AZRAjJivTYYH8wLr7g3h2cAzAAXQ7/EX6vuR7dg4R7+R4rkXkkAUjx+X4ngHnxfE18Xvs0ugc74+aaCDAXyV0XAP0BnAEwtbfnIc7nIcVrUoKuYOd4ADUeGxD1NYnJpEOcjPkAKuGO4D7h+b8HATzo+Z4AeNPz+6MAykIdK7V5wB21Lvd8HY/1PHjOJQ/uFUU7AL3n+9R4uybRzEWi1+VdAG0ADnu+9oc6VmrzkOg1ecwz1sMAdgGYLtQ1kUv0ZWRkZCROPPnIZWRkZGR6gWzIZWRkZCSObMhlZGRkJI5syGVkZGQkjmzIZWRkZCSObMhlZGRkJI5syGVkZGQkzv8DT4DD1w9kp3MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_plots= []\n",
    "job = backend.retrieve_job(twoprabi_codes[2])\n",
    "Rabi_results = job.result(timeout=3600)\n",
    "    \n",
    "\n",
    "plot_Y = []\n",
    "for jj, drive_power in enumerate(drive_powers):\n",
    "    thispower_results = Rabi_results.get_memory(jj)*scale_factor\n",
    "    plot_Y.append( thispower_results[qubit] )\n",
    "Y_plots.append(plot_Y)\n",
    "plot_X = drive_powers\n",
    "\n",
    "plot_Y = plot_Y-np.mean(plot_Y)\n",
    "plotter.plot(plot_X, plot_Y,\"--*\")\n",
    "plotter.axvline(0.185)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IQ Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so the varying duration rabi pulses are behaving a bit oddly.   In the meantime lets see how we can do with the IQ plots (also, worth trying to get the IQ plots of the 2 state.  \n",
    "\n",
    "\n",
    "Lets start off with defining a $\\pi$ pulse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_samples = 128\n",
    "drive_sigma = 16\n",
    "# pi_amp = 0.04\n",
    "pi_amp = 0.185\n",
    "pi_pulse = pulse_lib.gaussian(duration=drive_samples,\n",
    "                              amp=pi_amp, \n",
    "                              sigma=drive_sigma,\n",
    "                              name='pi_pulse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# measurement pulse parameters\n",
    "meas_amp = 0.2\n",
    "meas_samples = 200\n",
    "meas_sigma = 4\n",
    "meas_risefall = 25\n",
    "\n",
    "# creating measurement pulse\n",
    "meas_pulse = pulse_lib.gaussian_square(duration=meas_samples, amp=meas_amp,\n",
    "                                       sigma=meas_sigma, risefall=meas_risefall, \n",
    "                                       name='mymeasurepulse')\n",
    "meas_pulse_qubit = meas_pulse(backend_devicespec.q[qubit].measure)\n",
    "\n",
    "\n",
    "# create acquire pulse\n",
    "acq_cmd=pulse.Acquire(duration=meas_samples)\n",
    "acq_cmd_qubit = acq_cmd(backend_devicespec.q, backend_devicespec.mem)\n",
    "\n",
    "# combined measure and acquire pulse\n",
    "measure_and_acquire_qubit = meas_pulse_qubit | acq_cmd_qubit\n",
    "\n",
    "\n",
    "\n",
    "meas_map_idx = None\n",
    "for i, measure_group in enumerate(backend_config.meas_map):\n",
    "    if qubit in measure_group:\n",
    "        meas_map_idx = i\n",
    "        break\n",
    "assert meas_map_idx is not None, f\"Couldn't find qubit {qubit} in the meas_map!\"\n",
    "### Collect the necessary channels\n",
    "drive_chan = pulse.DriveChannel(qubit)\n",
    "meas_chan = pulse.MeasureChannel(qubit)\n",
    "acq_chan = pulse.AcquireChannel(qubit)\n",
    "# Create the base schedule\n",
    "# Start with drive pulse acting on the drive channel\n",
    "schedule = pulse.Schedule(name='Frequency sweep')\n",
    "schedule += drive_pulse(drive_chan)\n",
    "\n",
    "# In a new schedule, which we will use again later, add a measurement stimulus on the\n",
    "# measure channel pulse to trigger readout\n",
    "measure_schedule = meas_pulse(meas_chan)\n",
    "# Trigger data acquisition, and store classified values into respective memory slots\n",
    "measure_schedule += acq_cmd([pulse.AcquireChannel(i) for i in backend_config.meas_map[meas_map_idx]],\n",
    "                            [pulse.MemorySlot(i) for i in backend_config.meas_map[meas_map_idx]])\n",
    "# The left shift `<<` is special syntax meaning to shift the start time of the schedule by some duration\n",
    "schedule += measure_schedule << schedule.duration\n",
    "\n",
    "\n",
    "# Create the frequency settings for the sweep\n",
    "schedule_frequencies = [{drive_chan: freq} for freq in frequencies_GHz]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.691 , 4.6915, 4.692 , 4.6925, 4.693 , 4.6935, 4.694 , 4.6945,\n",
       "       4.695 , 4.6955, 4.696 , 4.6965, 4.697 , 4.6975, 4.698 , 4.6985])"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencies_GHz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create two schedules for the Rabi experiment \n",
    "# Ground state schedule\n",
    "gnd_schedule = pulse.Schedule(name=\"ground state\")\n",
    "gnd_schedule += measure_schedule\n",
    "\n",
    "# Excited state schedule\n",
    "exc_schedule = pulse.Schedule(name=\"excited state\")\n",
    "exc_schedule += pi_pulse(drive_chan)  # We found this in Part 2.A!\n",
    "exc_schedule += measure_schedule << exc_schedule.duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnd_schedule.draw(channels_to_plot=[drive_chan, meas_chan], scaling=4.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exc_schedule.draw(channels_to_plot=[drive_chan, meas_chan], scaling=4.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execution settings\n",
    "num_shots = 1024\n",
    "# rough_frequency_qubit = 4.847295222285821\n",
    "rough_frequency_qubit = 4.6950\n",
    "gnd_exc_program = assemble([gnd_schedule, exc_schedule],\n",
    "                           backend=backend,\n",
    "                           meas_level=1,\n",
    "                           meas_return='single',\n",
    "                           shots=num_shots,\n",
    "                           schedule_los=[{drive_chan: rough_frequency_qubit}] * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5dbc86486d90bb0018fb84f5\n"
     ]
    }
   ],
   "source": [
    "job = backend.run(gnd_exc_program)\n",
    "print(job.job_id())\n",
    "# job_monitor(job, quiet=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnd_exc_codes = [\"5db86fac99afd80019fc519a\",\"5db87285889b23001aee5f64\",\"5db872a199afd80019fc51ab\",\"5db872bfe440250018ba81d3\",\"5db875d7d3a8f10018a3e341\"] #1200 , #200, #2000, #20000 (error), #10000\n",
    "\n",
    "gnd_exc_codes_pow = [\"5db8795361a7870019954124\",\"5db87984f52ef7001ae785d4\",\"5db879c583968b00193a3cde\",\"5db879ddbb72de0018d8f1df\",\"5db879f6d3a8f10018a3e36d\"] #time fixed. 1200 samples   (above was power set to 0.05)sweeping power. #0.1, #0.2, #0.3, #0.01, #0.025\n",
    "\n",
    "gnd_exc_codes2 = [\"5db8b25ce440250018ba840b\",\"5db8b2aebb72de0018d8f3e2\",\"5db8b2c2889b23001aee6177\",\"5db8b2e6d3a8f10018a3e589\",\"5db8b314f52ef7001ae787f4\",\"5db8b32cd3a8f10018a3e58c\",\"5db8c296889b23001aee618a\",\"5db8c2ced3a8f10018a3e5a5\",\"5db8c2e6889b23001aee618c\"] # power set to 0.1 and varying the duration: #200, #600, #1200, #2000, #5000, #10000, #300, #400, #500\n",
    "\n",
    "gnd_f_codes = [\"5dbb23fdcc27e40018397ac5\"]\n",
    "gnd_e_codes = [\"5dbb28b5cc27e40018397adb\"]\n",
    "\n",
    "#Optimizing the qubit readout\n",
    "#2000samp with 0.1pow, #1200 samp with 0.15 pow, #500 samples with 0.25 pow, #500 sam 0.15 pow, #200 sam 0.2 pow\n",
    "g_e_codes = [\"5dbc509bcc27e40018397e7a\",\"5dbc544bcc27e40018397e86\",\"5dbc55ff1af28c0018cca78e\",\"5dbc5bb5cc27e40018397e99\", \"5dbc862123d1610018bf4ef0\"] \n",
    "g_f_codes = [\"5dbc50c823d1610018bf4e84\",\"5dbc5410cc27e40018397e84\",\"5dbc562626935800184e659b\",\"5dbc5b8f084c3b00195c8d1b\",\"5dbc86486d90bb0018fb84f5\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = backend.retrieve_job(g_e_codes[-2])\n",
    "gnd_e_results = job.result(timeout=3600)\n",
    "job = backend.retrieve_job(g_f_codes[-2])\n",
    "gnd_f_results = job.result(timeout=3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xIrqve58qZ5eWffyVla0zv/BwCZQPGyYB7GPHRBi0FkGqrITCwnpL6Gz7PpkAt8HQIsaSuBCwahkC76qVEvfMqlWSZVReLtXOO3dK2ukbb0i6aW2MwBYdSTffSSKKjuJHYQ8c326XO//WpKpKsqt27JD5Hzggcw4Lk9/9+kkMRGtYuBA2bRLh2Lu3PqAdzOcSWEhous0aDKdgROJ8p6WF0OkUYTh2TO66582TdNX16+Xu/cSJuvoDVVJCN0ADqvanDsvV1FbzD/x3VVV94PuGG+APf5D01+hoiWvMmyfZV8HUTDTOijIBboPhFIxInO+0tBD6fHJnPmeO3Kk/8IA8d++9UgRXUSGvr81MOkUcLNrKVdPcuBUVYmH8+tfyOCxMrIjBg6X6euFC2Lr19NaBCXAbDKfFxCTOd5prbmb58t1uCQZPmiTPWbvCpaTIwquUWBehRk2NWDBWfCIyUmIUu3eLqyw5uWGDwKbiDqfrhGswGIwl0VE0FSZoE5rq4aS1VEdnZsKECVIb4XDAc8/JAuv3izAkJNT3b2pLGtdVnCl2u8ROkpPhppukAWFurgifw9Fy3CFEevoYDKGKEYkOoN3jpY0XQo8HXn1V3E179oiA/OEP4qaprKxvohcV1T5312ciENYewRER9S3Kk5Lk9913S7PA2bMlKO9ymbiDwXCOGHdTB9Cme6CcSUqndY7XKw34hg6VSuqTJ+V3fr4EiEMJv1+Eq6JC0nXDw0XMUlKkq+yIERK4jolpWI3dCfcSMHR+9u7di1KKvXv3AvDmm29y++2306dPH1wuF6NHj+b111/v2EmeBmNJdABtFi8N1kRxueRu22rJkZgo5y9ZAn37ijhYe+h6PLIIW2012hurTUcgLpcI2fHj0KOHXGNOjhzPzhYr40c/kuOdcC8Bw/nLM888Q//+/fn9739PfHw8K1asYPr06Rw7dowFCxZ09PSaxIhEB9DcunXOcYqWXCuNB288genTpeVFZaXcndts8hqloHt3aaPRETQVDykvl3l16VIfmB4wQGolxo0TwfB666/dxB0MIcI//vEP4uPj6x5fd911HD58mGeeeaaBSOTl5TFgwICOmOIpGHdTB2GtW4ECcc6NS1vKZGo8eOMJgNyZHz0qgeBbbpH9q++8U9pdtAe2IP93tLKtSkqkEeGgQdJOZNAgsSyCLaYzGNqZQIGwGDlyJIcPH25wbO7cuQwbNoynn36agoKC9ppekxiRCBEaGwH5+We+LYJG4ZmRhn6+UUpn4PafmZkyIDQcXClZYHv2lM2ALFeNUrLVaCvS7KWEhwdnQtnt9efV1Ejw3eUSkXniCenbZFkYBkOIk52dzeDBgxscW7RoEddffz2//vWvSUpK4q677uLDDz/E39aZhk1gRCIECNwgbt8+uVF+7DHJUg3WuqgzFh5WpC9zoQPL3hwOGDVKWnDk5UnLb7+/fvDnnpPHDzwgd+D5+fD++7JHxDvvSEppZGTbfxAVFU1fpCUeAwdKKw7LFRYTU78nRXGx7F2xfPmpGxAZLni01ngqPSHXbO+TTz7hnXfe4dFHH21w/JJLLmHRokUcPnyYpUuX4vF4+M53vkO/fv34+c9/zp49e9ptjkYkOhhrcU9Lk8e/+53cKKekyJbOn3zSsC6s8WstQ6DZjCmtZdFcv17UZ8IECe4WFEhX1SNH4Fe/gmuvrd/wx2rw5/dLG4ywsPpW3hYu11m5dKy2Hmf8IV1yiWyGNHiwbIx02WXSyvzBB2W3uogI2VVvwwaxlhpbTYYLFq016TnpzP9gPuk56SEjFHv37mX69OlMnTqV2bNnN3lOZGQk99xzDx999BH79u1j3rx5vPbaawwcOJBf/OIX7TJPIxIdjMcDr7wi2Zuvvirr7vjx0qdOKemWsWqVWBmBIYbS0oZWhsPRTKZnrXroAQOpKTyOfustEYv4eBEFS1EsC8NyPVnY7TK4UvJvkAW5d2+pTzjDCHuTZ7tc4uY65eTa3fLCwuT3FVeItVNQIKr66qvw0EOy7eqPfiTXNGmSCGGg1RQii4KhY/BWeck6kEW/Lv3IOpCFt6o1c87PjqKiIm6++Wb69evHsiBjfiUlJZw4cQKPx0NERASudkrGMNlN7UAwWUuBzVnT0qR10uOPw+TJstbNnFlfmJyeLlbGnj3yfFaWnN94CwWtwaudOMa62fnSasI8PfCMmsRw/xHCC4+hIiPhyitlgOhocd1Mmyab/Bw5ItlDFRWiZBERMkG/XwYuKJBMqMAFOCysvlXG6bDb5T0TE6UduN0uLq7qahGEG2+UHfIKC+W5o0flwn/8Y6kQDwy6KyUiYX3IHo9YEQMHitU0a5bJbrqAcYY7cfd1k3UgC3dfN87wjk1q8Pl8fOc736GyspL3338fh8PR7LknT57kjTfeYMmSJWzYsIERI0bwn//5n8yaNYvu3bu3y3yNSLQxpytdcLnk5jhwF1GvV2LIVi3FxIn1a5zlVho4UEQiN1dit489JhaINX69mCiqq9L45vgMUhOXMXBrFjn9x1P9dg/Srr4a1q6l6pbbCI8MQykFn38uWU2xsfIGdrtYDAcP1qfHRkTIGzgcUnQXuFGQ3V4vElaAuaneTzU1cry4WC760kthzRp5TUSEpNz6fBKDKCoSq+eiiyS99fvflzE8nnpFDExztfbBNo37DIBSirTRacy8dCbOcKf8f95BVFdXc/fdd7Nr1y6ysrLo0aNHk+etXbuWP/7xj7z99tvY7Xbuvfdenn32WcaOHdvOMzYi0eo0thpO1xVCKeluPWuWrLkZGadurhYdLbHkHj0aFuI98IDUlGVkSLF04PiWG6ukBAoKFCNHxvD0zjSS+85k4mQHhz7xMv256fy1bAaZOU6uHlLA93c+hm1gP/FZDR8uE0pIgM2bJXDtcMiCHREhi3lpqVxwdLTELmJi5A0jIuSxUmLqHDok/rOoKIl3WB9UVJQE1K3d8K65Ro7v2yfiExUl7cqjokSg8vNh9Gh5/MQT4qO7+uqm+zGZAjpDAEopXBEdb03Onz+fFStW8Nxzz3H8+HGOHz9e99zIkSOJrE0Q+e///m/Ky8tZuHAh9913X7u5lprCiEQr0pTV4HRKPCHQUmjK/dTU5mozZsjaec89sk6PGSM7dlriobXEJIYMke0fHnmk4ZbVIGu5yyVrbY9ERWKyE9dri/nJ8Vfxjgdbz9kc65bG75YkMnD0eK7buxY1bRo6bR4Vr7xO5NpPUP37iyDs2iUCEBYmP3Z7fduOnj1FmVwucVFFRooVEB0t/46JEWugTx8RCsvV9e23Eku46CIRksceg7VrZcMjr7d+pzy7XT6w+++Xwr8PP5QPCprux2QK6AwhyMcffwzAj370o1Oe27NnDykpKQC88cYbJCYmtufUmsUErluR5jKMAksOAuvaFi+WG+/Fi2HKFNlmusynObLLw6iRmj//WYTlnXckRPDuu5KEtHixjGNZFYWFUtqwcaMk/qSny3MPPCCtjB55ROrM4uPhqw1eLivJJD7aR+UJD+6yf3FgeymXDvTyov4+VZ4KdHo6u6Y8xOuva3btDUPX1Ig1EBEhP1YGVFmZWAy9e4uZk5wsb3TrrRJnSE4WS2LOHIk9JCeLmKSlibLFxUksYc4csR527oQ77hCR+NvfYOxY+N73Gqa5LlwoipmYKK+55BLjTjJ0Gvbu3YvWuskfSyCAkBEIMJZEi5xpm4zGPZmsFFUrhmAFmK26tldegY8/lrWuogJOFGuu353ODa4sduxw8+tjaRQeU3WxYJtNzluyRNz3V10lx48fr4/RTpsmQe0ZM2BemmbWNA8OByxULv7rvyCqWvNhzQR62vLoE15A36h9/NWeRt7WcPKihlNT/C/Com30PLySMQOq2akGMNC7HvuoUfD117LIHzkiLh9rH+myMrEUamrqYw2vvy7nOp3iGioslPRVj0eC3nPm1HdqBQlUT5kigrNxo1zsNdfAZ5+JApaWyljbt4tJlZMDN98M//7vxp1kMLQhRiSa4WzaeSslrqCpU+Hvf5d/T5jQUDh69BD306pV4l46cEDiCt26gb3cy6TwLHIK+zHoZBal3plU+utdJlY2aFWV3FQ/95y8LqWfZmeOFy9OXnpJ0b07/PlVzXz7YlyvvooG7rvpAfb5FVeRxRrtZvl9b3N75qPEufsy+N2/8W38d5la/Ca6ohz8Gu1M4HPHeK4My8HmTJBCteRkUaPSUlHP6GiZQHy8XPz+/SIYe/bIov/YY6JqTz0lglJQALfdJqaQ1aXVon9/sRw2bBD1S0yUD7CsTGIWNlv9h7dggVgyPXoE38rDYDCcFUYkmiGYXnkOh6x9gSmnGRlyJ79+vZy/Z49YC1YMVev6ujStJVbg98t62/9iJ59tcnO5P4ut0W6qqpxE+OX8sNpvqqBAROXbb2W93LpFM8Obzg90Ft/EufmdJ40TxfDei/n8YNBq7B4fxUWaLxf+C3d1OHtsA7mpSxavF8+gatwkwo+vQ4+9ivisAxSfUBzvNp7LonYS++Mfccf3H8HpLUA9/pgEiN97T7rE5uRIEZvV56l/f3jrLXEhrV8vH0xxMaxcWZ+tVFoqgmJVTzc20Ww2qWkoKKhf/D0e2Yb0hhtkx7mRI+XxSy+ZneQMhnbCiEQzNNfO27Iw1q0Tz0pYWL2lYQlL375iKURHNxxTa3GpP/es5vKLvHTv5qSkRHHypMSEc3MVewakkR07k5IaJ2FlirJSeW11tWSlHjsm/1ZKkntsZV4mhmWxo6wfg49lEaFncH/5Mqbsy2K3UoQdjqay3M/71ddSUW1jgj2LbQ43n38Of7gilZFPzcTmjCb3+oVMqsyjorKEivn/RvSPH8Fls0FMouTWZmXJHX5hoaiTxyPuoYoKEY3Ro8UKSEgQgejeXZ63KgIjIsT9NGEC/OlPshd1YxPNZmtYVBf4JYwdK68xmwcZOhEpKSkhU+F9thiRaIbmsigtIejdG958U1xL1poVuKZNmSKiMHEiLFuq2bLGy/Arnbz+OtxVlM7ozCy63+bm4+vTePoZhVJiUeTtUQy6wUV+3qk96qIiNTUlXqpxorWisBDAySrcuMlirXYDMKYqi20n++H9ei/Oq+7A/uXnVJQpMkjldf8M7s9fyv+LfJh/vefm2j1pPHS/l4lhW/lETaG3ymV1xIN8X9nqqqP19Bl4p87AmeBAHSsUIfD55M4/J0fSWP1+MW9OnJCg9f33S26vUvWpWCAC8Yc/iNVxusU+8EtonB9sgtUGQ7sQMiKhlHoSuA2oBHKBOVrrEx07p1PXL0sI1q6Vm+X33pMbbKtzReCa5vOBv0bz18npzK/IYtO3booPz+AybxZ7VD96bsricNVMXC4XpbUWg0KzdY2XfI+zQZM+l1Pz3WPpjLOv5181E3mFOUiTC0UGaSxjJl5k4cyqFY3s6jFcvXkz+9VFjCOLt7mDcu1gIpnkevsyPiyL9b6ZrFjt5K473AxKz6Loskms2erie155T52ezrevZJGl3QwYoJkYno1yuyXonJ0td/ZLl8KOHfXFdHffLXs75OeLZRATIxfh8Yg1MGSIZDLdeOPpF/vAL8HUPlxQaK07tPCtM9Oa1ksoRf1WAiO01pcC3wI/7eD5NIklBE8+KTHc735X1kWfr/55q2OE1vDGn7z02pvFlmPiDvJ4YK3fTZJ/H3874iZnhxO7XcoHQDO3Jp3feubzA9KRdngaBx6qvOW8p2/l/pqX+Qv3tzRDMkjjYV5gEQv4rHI8CeV7sVPDEzzGYlK5Smczh5eJri5l27dR/PNjxfc3prFjwQv8PT4N93gla7fXS3VmFt94+zHKm0lY9mqquvcSXxuIWubliThUVUnBW1WV+NrWr5dUq8BOrFZjqu7dJfj8wx823dK7uf7oTe2BYTgvCQ8Pp6ysrKOn0WkpKysjvJW2Hg4ZkdBaf6y1tvo3rAeSOnI+p+Pdd6X2KzOz3vthNd6zah/S0uDl5U4qR7tJse3jw5NuCnwu0knjIf0Cz1elkZ8PVSc8HDqkceLFTRb76YebLJx4mMbbKMCPnUP0BWzUEEZ9qzxNJBU8ypOk1gmLheKPOo3HeQKFpojuuG3rKdPRhFHFFD5mKd+h+1LkAAAgAElEQVSjrMzPu+8pDpe4eG6hYvLk2s4aTidhE91c7NzHFscEEhIU4e+9JaLgdMoFpqdLubgVoLbqIVwuUc7MzIYFI2lp8Mc/SvHGiy+e2tK7VXZfMnR2evTowaFDh/D5fJ3ep9+eaK3x+XwcOnSo2ZYfZ0rIuJsaMRdY3tyTSqlUIBUgOTm5veZUhxWXCGy+B7L2LVki61pYGFx/Pezerfifo2n49ExyS53U+GVx9+ICNPecTMdNFlm4ySC1zlWUhZsaFB9yCxVEtTAbRQVR/Iaf8VP+FyceZrCswZjTeIexrCcWD3n+ftipph8VHKQPY8ihBwUc0z3JyJDEoRMnpKRh40ZFWFoag2fMpI9H43w8G9X7LqmTsGokYmKk6d769VLf4HZLJtRf/iLTs8rM66ar6vuGNJU+dro+JoYLgtjYWAAOHz5MVVVVB8+mcxEeHk5iYmLdZ3iutKtIKKVWAU30hOZnWut3a8/5GVANNNs/V2udAWQAjBkzpt1vMwID1FbzPY9HBGL3bslq6t0bvtmuSe7mZdN2J75K1yl9shtbDsuYSQapvMM0CkggkqrTCEQ9VUTwO35KLEVMZDV5DMRNFu8wjcnha/BUOfHhYDeD+AlP8xw/5gpy+DpmLJWqBzafWEBer3TM+OIL+M1v4L/+S6FiXLhcGsY3ke4FUssQHQ333ScCcv/98gPNu4eaSx9r7rjhgiM2NrbVFjrD2dOuIqG1ntzS80qp2cB3gOt1CNuYzWU+2WyyVlZUSIHbwE/SGVGSRaLNzQs1aTRWCS/OBpaDFwepZNQKxnQymXRG8/JjYz6LGct6BpDHK8yhgB6sqppIEnkAZDKJAnrzPZYzwFnA0PE9eGWejV/8Qqwiu11CCz16SGzZ6629kW+paV7jhT2YuEFz45nmfAZDSKFCZS1WSt0EPANM0loXBvu6MWPG6M2bN7fdxIJEa3E3vZihiaqRYOyCnQ+zT/ejd80+HuaFWhfTKa/EiVRLO/HyPPPZTz/SSa2NQZwZg/iWt/guOxjCgyzBS0zte8gObTIHRUSEiAHIOhwbKyLncknX7q5dxWtkdc447VodbA+TM+11YjAY2gSlVI7WeszpzguZwDXwByAGWKmU+lwptbijJxQsVjJORbnmpv3p/M+x+dxWspRN4ZLFtCXSTYW9ObeJqlu4Lcsiif0cOsu4fS6DKCWGf3F9gCgpvMTUCoaia9d6gbDmf/KklDeMGwevvSaCZ3WZtbbAbpFgMo9MUNpg6HQ0625SShWcxXgamKy1/vKMX6j1oLN4v1blTG5yrXOjo2Ujt7Vr4ZtNXn5WmsXGE/0Y2yubF699ns+TZvKvdU66FSoc0Zpj+7x4cNL0Rp6SvrqUGdjw48d+5teA4kb+ia9WeE55ByWC0JiYGDh8WFJ6QURv3TrZPmLhQhGVf//3c2yVZILSBkOno6WYRDzwJ+BgkGPZgZ8BrZOc286cSUO/wHN9PmlUGhYGpSVONtjdXEUW7xe7WbfNBV8oiorAbtPcUZDO1bYs1vjdZJAGUOdqql/QpUBONykiwaDwEdP8s0pcScXF8thuh4EDNAkOL3fOchIZqbjhBnkuKUn2qXA65XojIyVz9ay9RCYobTB0Ok4XuH5Ra70xmIGUUnbgv859Su3Dme4gF4h1bq9esHy5lAf4fNA9XvFplzT+fnAmJ6udUKKw2cTfX1nkZbzKYp/ux3iyWMaMRqmq9YFtH07iKKKYc93DVp8iQn6/WAeRkbV7XKC592Q6lx7Mwr7SzfIuaXi90iYkLAzmzpWW5kOGyDYO1g55Pt+pDQ5PiwlKGwydjpZE4lpge7ADaa1rlFLXAjvPeVZtTHM7yDV3k9tYUBr3ndu1S47PnQvTpyuefdbFr38tC3J4OKSkwP5yJ9uq3VziyWIzowHdIP1V0l4TqRcKB+K9O9uFVJNKepMiZF1TfDxc0t/L4Ows9kX2I3FlFvuGzcThcKGUbBM9fbq4onJy6vfSrqkRt5Pf37DBYdBCYVxMBkOnoVkPs9Z6tdbacyaD1b7Ge+7Talua2kHOusl94YWGC15TsVbr3OeflzqJAQPk5jg1Ve66v/xSxKFLFxGUl1+WLhRLa6aTw0hGs5kZLCMLN8nsw041T/BYbcW0n1TS+QMPE0nFWV/jqdXbDb+WysrarSD6OTkxzE1ixT62d3VzpMTJsmXS3hxk3pGRsm+Q3S71H1az1w0bxJoK3IXPYDCcX4RSdlO7YVkC+/Y1tBqaStDxeKSzRHLyqYuhxwOvviq97V5/HZ5+WoLYXi90i9MkRHvon6J5aJ4m8tV0/qjn8T2W1y7c2SxjBo/zBDXY2U8KbrLoQQFusjhCH/6D39CwzUbzhFHV4FwrUyqZfbU1GE37/3/7O0Xq5jReGP4Cz5WlEZ+g6N9fLAWrf192tnxGo0dLcNvqGH7VVVI7Z8ILBsP5yzkX0ymlXgRsWusHW2E+7UKwrnGtpcFpXp78zJ5d36MpPV02F8rPF998TQ18/rm0s9i0UfNvjnS+0z2LDdrNiyUzGFGSxdGYAfSuyGOAziWTSXhx4cVFFuPr3EIF9KgtsFtHTq1bStxETbueHHgIp5qf8CRP8NPaegsxADNIDegOe+prv/lGAtGLFin6X+Liyutk8S8rk+scN04EcsIESYvdskV2Dv3hD+WcM45JGAyGTkdrWBLX1v50KoJJ6/d65S568mTZfG3GjIZB7oED6x8PGiSupy5dIMHhZURpFpsLpPNrQQGst7kZn7yft2Mf4Cc8QwapBLb6fpgX6uIGy5hODqMZTQ4/VouIcfprWybrBj9xHOcxnuRdbuMQSbUV24tZyRRWMoVUMpoVCBA30kcfyaJv7ZV99dUNLSulxDW1bp1YFTk5IhAuV33xnREIg+H85ZxFQms9SGs9oDUmE2pYbqn9+yWIa8VbreN5edL09N57JcNp5kz4/e8htpeTbU7x86/TbmJ6ufg4JY24Zc8TGwtPqf8glQxAI918VV3hWyrppJPG93iD7hzjYb2QBWoh3eMkVvEys5nNEgCK6cZT/Du3soIM5uHEx0QyceDDiZdJZBJraxgssLKWoqLqRWDgQIk3PPGEWFggFpIlhFu2iKspN9e4lgyGC40LMiYRLM0Fs63j6enwwAOy1bPVsqhHD7jzu4rtV6ex97EXeK9nGsqmCAtXOF2Km7pks7c2DTZGeetSUaE+2JzHQGzUMIwd7GQIl1fnEF0qsYqDJHMNn9UGohtWUntxso6JRHRxUBXuZK2aSPdkJ0pJkNqae3U1xMVJl9rbboODB2Wbh8REOSc9XbKYqqvr4zYREeJmM0XSBsOFRVAxCaXUsNOdo7UOOl22M9FcxqZSkho6b55sp+B0isvm3ntlU7YrrlDM+3cXYV0D/PrvOgmPcjMoPIvVVRJMdl8u2VBeL/iq6xv+vcxcIilnrH0LX0SNp6SmB1lVgc0A62/nrXRUUCxmHluTZpKSAgU+F0VbpFajqEjOtdJW+/QR8XvnHVizpn7xt1xpKSmyX8YTT8i1PfywuNSys+V6TRarwXBhEGzg+itOn2Zz5j0kzgMsEdEannoK/vEPuev+9FMoKJA4xowZ8nxamqLvNWm8nz8T9xQnPT9S7N0rr6+qAh2ueKU6jWVVEmx2OuADl5eufZz09CkydqY1GYiOiJCfsjKw2RSVkTFcOh7eeks60vr9IiQREXD55XDokNQ/KCWLfuDiH1gDEmhdmEJpg+HCJFiRaCowHQfcWPvzSKvNqJPi9Up2U1SULNZxcfDXv4o/3+0WkdizB/LyFJeNd+HxSi1FdLSISe/ecod/5IjC43FBDVRWgaOHi/4DZD+fyEhFebU8F4jdLim6xcUyD78ftm2T37GxMr4Vf0hIkC2oo6LgP/5DsrL27hVBsLKUAvfptooITaG0wXBhEpRIaK1XN/PUO0qpXwH3AO+32qw6IU6nBLfz8sSXP2OGZAL17i2pslC/k93ixbL4TptWX3dRVCSL8tix4p7y++WnslICxtaun4cOnfreNTUiMGFhIgI7d8pY4eHSf6m0VOYUHy+iZLPVu5T27ROXUmJiw5iL1a8psCrduJgMhguP1ghcfwpMbYVxQharFXhLQVvrDnzVKmn496MfyeL91lvy3IQJkiU1YYI8TkyEOXOkPffAgTBihKSgbtwobqGYmPrA8fbtYqWsXCkWgIXdLo/HjJHOrgMGSBC6pkbcR4cOwdtvS3D6+98XEUlJqc9WsoLSgQJh0VRVusFguPBoDZG4FTjRCuOEJGeyBYJSEOPSxCgPZT6N3Q533SWL+axZ0sZDKQkCZ2RIG4+XXoK+fWVRLy+XGILNJov/RReJUPTuLVZJly5w++3iyurdWwQmJQW2bpXn8vJkfpWVIjT5+dIS5NprReTGjpWKabdbiugaZ20F0lxVusFguLAINrvpr00cjgCGAhcB/6c1JxVKnNEWCAGdA51uN+PdaWRlK8aPl9c0NZbNJm6huDiJKVRXw803S4wgJ0cW56IiqX5OTIQXXxT30fLlUgV98KA853LJeZddJpZCVZUIxQcfwCef1McYAiukW3IfmYatBoMBgg9cJzRxrBxYA/xEa72i9aYUWpzRFggBKqCyskj9w0ym3eGiR49Tu8cGjnXNNRLU7ttXLI/HHhPx8Hjgz3+WTKmrrxbrIzsbRo4Ut1T37hJ0Li6W+EZ0tDTdu/VWiWNUV4sVEygIZxJXMA1bDQZDyOxxfba0xx7XQe9YF2BJaLebdC2WRGAr7abGsmIe0LDNRUmJtB8fMlhzeJeXcruTgYMUK1fWxyuuuEI2EcrIkDELCiR4/eKL9fUZlvVjrAGDwWDRGfe4DlmC6fNUd2JtibZ3hghE48BvU2NZhXkxMfXH/X4p1Fv9mUanp/Mf++eTptLJ3a1RCm66SfpJde1a3zLEZoOePcV6sCrCQWIgZktpg8FwNpyzSCilZiqlZrXGZM4LalXA6VLnFPgtKBDX0eUXeRldkUWfcf2YFJ5Fxu+9zJ4NBw7Agw+KBdFU8Nmqi7DafZsMJYPBcDacc6twYAkiNn9phbHOG8428Gu5oxISZL+GDeudFF/sxnk8CzXeTUxPZ9Djmi2lDQbDuXLOMQmlVHLtOPtaZ0pnRnvEJNqLxtuq/uAHsr9DjwSNrSyYoEjTYwYVTzEYDBcU7RaT0Frv7yiBCHWCKcILpHGKbFmZxBhs9mCDIqcSdDzFYDAYmsAErtuIMynCszAFbAaDIdQIOiahlLoX+AEwGIhq/LzWukcrzqvTc0ZFeLWYAjaDwRBqBGVJKKWmA68Cu4Ek4D2koZ8NKAH+0FYT7KycrVVg3EMGgyGUCCpwrZTaCrwF/BaoAsZorbcopWKAlcBbWuun2nSmzRDKgWsTNDYYDKFKaweuLwLWaa1rkN0MYgG01qXA74Afnu1Ez2eMVWAwGDo7wYpECRBZ++9DwMUBzymge2tOymAwGAyhQbCB603ApcA/kXjEz5VS1UAl8HNgfdtMz2AwGAwdSbAi8RugX+2/f1777z8ilsgmIK31p2YwGAyGjibY7UvXU2staK1PAFOVUpFApNa6pA3nZzAYDIYO5KyL6bTWFa0pEEqp/6eU+kIp9blS6mOlVO/WGttgMBgMZ0ezIqGUekYp1TfYgZTwjFKqz1nO5Umt9aVa68uRGoyfn+U4BoPBYGglWrIkfgT0PMOxfgQkns1EGlklTsDsfmAwGAwdTEsxCQW8opRqt10IlFL/C9wPnASubeG8VCAVIDk5uX0mZzAYDBcgzVZcK6WWnOWYv9Ba729mzFU0bZ38TGv9bsB5PwWitNa/ON2bhXLFtcFgMIQqwVZcN2tJaK3ntO6UQGs9OchTlwErgNOKhMFgMBjajpBpFa6Uuijg4VRgR0fNxWAwGAxCa2xf2lr8Vik1BPAD+4B5HTwfg8FguOAJGZHQWn+3o+dgMBgMhoaEjLvJYDAYDKGHEQmDwWAwNIsRCYPBYGhltNZ4Kj0Es6lbqNNsTEIpVcgZVD2bPa4NBoNBBCI9J52sA1m4+7pJG52G6sQ7j7UUuH4e0xrDYDAYzghvlZesA1n069KPrANZzLx0Jq4IV0dP66xpqZjul+04D4PBYDgvcIY7cfd111kSznBnR0/pnGi2LUdnwbTlMBgMHYXWGm+VF2e4s4FLqbnjoUSwbTlM4NpgMBjOAiv2MP+D+aTnpDcIUiulcEW4QlYgzgQjEgaDwdAMLWUpNY49eKuab5jdmbOdjEgYDAZDEwRaCs9teI6S8pIGi7wVe9h3cl+LsYeWLI7OgBEJg8FgaALLUij0FvK/mf/L9X++nvTN9Yu8Uoq00Wm8cOsLLaa5NmVxdCbLwoiEwXCe0ZkWoI4m8LNq/Lk5w52M7jWaHcd2EGGPoKyqjMz9mQ3cSlbsAWj2M29scUTbo3luw3M89P5DncKyCJkGfwbD+UZHZLicb4VcrU3gdwLUfVbjksahUGQdzMKd5GbGpTNwRbhYcOUC/NrP61++jkYzoe+EU9xKp/vMLYtj5qUzcYQ5WLRxEYs2LGJI/BDW7V8X8nUUxpIwGNqAtvRDt1Yw9UKj8XfiqfTUfVaZ+zLJ3J9Jcmwyr2x7hdR/pJKekw7AgyMf5HuXfI9B3QedkubqqfQ0GKe5z9yyOHzVPnKO5DAkfgg7j+1kdO/RIV9HYUTCYGhFzmThONvxWxKfYIOpFxpaa/K9+azbv67uOwHqPquJ/SYyIXkC3xZ9i/ZrEp2JrN23lkUbFzHvg3ks/3o5Sa4kVu9bTWlFKSXlJTy3/jnmfzCfpV8sxZ0U3GdufT/xjngeueoRHrnyEZRSIe0iNMV0BkMr0cDtkORGo8k+mN2kC+JsXVGeSg/zP5hPvy792HdyHy/c+sIprorOUMjVFG0178DvpdpfjV3ZGZ88nrTRaYBYX44wB+k56Xy25zOyD2ZTVF7E4G6DGd5jOL1cvXhz+5ucKDuBM9LJ1UlXs/vEbvae2MsVva8gwZnA87c8j1Kqbu6Nr6Wxm6vxcx3hIjTFdAbDaWjtu7cGrp6D0rPHynwBGgRIm7MGTjenYCyFzljI1ZbuOet7SY5NRqN5YsoTdQtxoBso+2A2vWN7U1RexLCEYRSXF+Ot9PLm9jeprqmmRtdw1HOU93a9h7fCS4Q9gm8Kv2FEwgic4c66z9zv9zcITPv9/gbXBjT4fkLdRWhEwnBB0haLUuMF3BXhqrvLb84XHrgoBDOnYNMuO5KzEd+2Wii11vj9fkb1HMWqvFXsKd7DOzveOeU8Z7gTd5KbI54jXBx/Mcd8xxjTewyOcAd3XXwXXSK6UF5djtKKyppKjnqPUlFVQVxUHF/kf8HCjQvx+/1oresC08fLjrNu/zryvfms3rua5NjkJq8t1F2ERiQMFyStuSgFLoozLpnB87c832AB91R6yNyXWbdIAE0uCsHOKZQthbMVX2uRzi3OxZ3UcKE8W4tPa0365nRuWHoDS79YSnVNNRP6TiBzfyaeSs+p56NRKOZcNod1c9fx+p2vM7r3aIlDVJfiinRhs9kY2HUgsZGx3DP8HorKiyjwFrBowyIWbVyEp9LD5sObGdhtIDuO7WBUr1G8/c3b7C7azcrclYzsORJHmKPB+4a68BuRMFyQtNbdm7UoPvT+Q9z71r089P5DLPtyWYPnl36xlLziPFbtWYU7SSyMphaFUL+jDIZzaVWhEVecpqH7rSnRaU44Ao97q7xS11DppaKmgoKyAl7+/GXWH1jP0m1LG7zWEvKBcQPJPpQNwItbX2TDwQ1UVFcQGxlLL1cvLk64mMt7Xo67r5ucIzkAbD26lSHdh5BzJAetNTW6htyiXMb0GsOcy+bw5y/+TFlVGUe8R9hwcAMZWzJOcTGGcgzJiIThguRM7t5Ol3K6bv86CrwFfLT7Iwp9hazbv65ucfRWeck+mM3k/pPp37U/My6d0cAX3lQ+fWvcUXZUtkwwQqe1prSilPTNDV1w2QezGdRtENkHsxt8fk1VKzcnHIHHHWEOJiZPxBnhJCosioToBHo4exAVFsXqfavJ9+bXxYiWfbGMvOI8Ptr9EVU1VTzy4SMs2bqE477jHCw5SElFSZ24vzT1JdK/k86g7oOYPnw6fWP7EhsVi7uvG6UUdmXnrmF3ERkWSVlNGQDHy49T4CngeNlxsg5k1WXANY5XhGIikREJwwVLMG6bYFJOR/UaxbfHv6Vvl77sKtrVIPfdWjT3l+xnUsqk0xZNtYYrqSN7BZ1O6Ky5pf4jlVe2vdKkC25c0ri6xbsp0WnsvrMExVPpaeD791X7SBuTxspZK/nk/k+YO3Iu5dXlFPoKyS3O5bGPH6uPER3Mont0dwrLCsk+mM32gu3sLt5N9qFsesb0ZGyfsSy7cxlpo9OIiYwhJjKGCckT+HT/p4TbwxmbNJbUUany/0PPUeQWidss0ZnIPRffgx07/bv2J7c4l1G9RrH0i6XM/2A+CzcuZN3+dfRy9WpwcxFKGJEwGFogGPdJlD2KeEc8MRExDXLfoWP8zR2dLdOS0FmWV9/YvmityTuRVxfkTxudJqmkKB5e8XBdJlDg5wec4r5zhjvrrIE9J/awas8qxiWNq3PfxETGEBsVy/2X38/YvmOZeclMjpcdJ94Rz7r96wAY1WsU249tZ0CXAZRWlOLHj0JxZe8rKasuw4+f/7v6/5KxJQO/34+n0sPUIVNJ6ZrCpH6TWH9gPUc9R1m8eTELNy7kX3v+xep9q/H7/Ww6somy6jLKqsv44RU/ZO7lc8k+mE2/Lv3YfGgzFdUVvLX9LWp0zSnxilDAtOUwGFrgdLuMeau8ZB/K5saBN5JbnMuDIx/EZmt47xXY3+dsOFOfdSjvjOYIc1Cja/jbN3/jyj5XsvjWxcRExjQQ1ayDp279GdgfKftgNtenXM/O4zuZfsl0lFKUVpSSuT+Tyf0nk3cij5mXzqwb0/r8ou3RjOw5ki/zv6R7dHfe3fEuY/uOxRHmIIwwampq2Fawjf5d+1NdXY0z3ElJRQmpo1L5/OjnJMcm89mezyirKuP1L16nrKqMiLAIVuauRGvNLUtvwWazcaT0CChYlbeKrwu/ZuOhjVzU7SIOew5zz7B7UErhTnKTdTCLMX3GkHM4h7uG3cURzxF81b6Qa9FhRMJgaIHAvjtNLdKBC/LEfhMBWZRay2I4m0Kr0825I/FV+wizhdUtijabrcH8mhK4QJF0hjsZlzSOV7e9CsBrX77GD0b9gJe2vERuUS55xXnMvmx23UJrfX5r963lm8JvKPQVMrLnSAZ0HcC1Kddy1HuUfG8+f9r6JwrLCtF+qcz2Vnqp0TWUlpeSfSCbI54jrD+0nm5R3dh1fBdfFX5Fpb8Sm7IRRhiVupLSwlKcEU6qdTVKK5wRTrpFdaNrRFc2Hd6EDRtT35jKsB7DcPd18/wtz+MMd5KxJSMkBd3CVFwbDOeIFSRe9sUyaRDXilWzwVRYdyaCEb3mmvBZ53sqPaS9n8aArgPILc7l4u4X8/K2lxnafShdIrvw7M3P0tPVE6VU3efXNaIrL2x+gTB7GH6/n8HdBuOMdDL78tmUV5XzP6v/h5OVJ1FKEWmLxGF3cKLyBFFhUWg0o3uN5quCr+gZ05PDpYc5WXGyyeuzYyc5Jpmj3qMMjh/MkO5D+GfuPympLMGOnYiwCH4w6gcU+ApI/046MZExHZbdZCquDYZ2wspWCnSTtFYc4HxIiw0kmBhNYEyjqfiKK8LFxOSJrMxbSdaBLH6X9TuKy4rZdGgTfvw8vvJxFm6Q4jbL8sjcl4lGU1lTSbWuprKmEr/2M3XIVLYc3cKoXqMIs4XRPbI73aK7UUUVfvxUVFfgtDvJOZyDRmOr/c9hl9hBmC2MCCLq5l5DDYW+Qkb1GsWJ8hPERsTiq/Jhx44fPwO6DmBl7kq+Pvo1f/78z3VWZ6jWvYARCYPhjGkqvbStFvNQL7Q6GwIXxbNpQ6KUYsalM+gb2xdXuAu/9qOUIik2iTBbGMfLjrNww0IWblwIwMxLZzI0YSiDuw0mTIURZgujtLIUhbz/yMSR7CraRbgtHE+1h2O+Y5RWlhIXGYeyKTzVHsloiohhSPchXNf/OuKd8fRw9GBwt8FEhUfVzdeGDV+Njy1Ht+DXft7a/pYct9kYkTACGza2H9/Ol8e+5DdrfsOR0iMhmfYaiIlJGC4IWsukb85d0pZxgHMNfIcqwbielFKkjkpl2tBp9HD0aFB4eHXy1RwoOUBPV0+6R3dnzuVzqNJVLNqwiMHdB7Ph4AY8Iz24Ilxc0/8atNZEh0fTw9mDnUU76Rvbl6lvTMWv/XirvFwUdxHbCrdhx45Gc7LyJHGRcZysPIm30ktVWBVX9LmCR8c+yjMbnuHv3/ydyppKKmsqiVSRVOgK/PgB8Gs/JeUl+PFjw0a/rv3o26Uvn+z5RK4dTYGvgEc+fITJAyeH9A2AsSQM5z2tWTfQUnppqLsN2pNgivmCSdXVWpOxJYPHVz5eV6lsHcs5ksN9I+7j87TP+XT2p8y/cj6PXPkIC65cQFFZEXtO7OFPW/6E1rquCC51dCo9XD1IG5VGXnEeucW5HPUcRaPZWbQTEJcRwOBugymrKSPKHoW32kuCI4GvCr7i+Zzn+argK6YNnsbUwVMbZLMp5Lv3az+OcAfOMCd2u52YyBhuHnQzQ7sPrTsnIiyCAXEDzrgyvb0xImE472nNuoFgK4pb+qPu6D/6tiZYUQ7ms2zqu7OOpXRNYevRrXWLsFIKm83Gg6MeZEDcAOKi4li0cVGd2ykmMobU0an89vrfov2aAyUH0GgqaipwhjnpHdMbhaJrRFe6hnelf9f+jOo5ivLqcpxhTvI9+fTr2o9NBzdxzHeMJ7Of5MnsJ7ErO9ER0fR09CTCFoENG3abHYBwezgXx1/MjEtmMG/MPD594FN+MekXfOsSThgAACAASURBVHfod7lt8G0c8Rxp0T3ZkYWRFsbdZDjvac26gdO5laymcpn7M5mYPJG0MafuI3GueweEeq+fxgt7c9tzBuOia+67a+n7dEW4GJs0loUbFjIkfgg5h3PqPq+MnAz+tPVP5BXnkehMxFPp4cGRD/LBrg/wVfmIi4rDGe7E7/fz2b7P0GjCbLJMVtRUsHTbUro7ulNUVkS4LZzSytK69+zl6kVZTRn+Kj/aLzcCYWFh3Nf/PrYe3cqiTYvYcmQL45LG8fLUl+s+g8D4TOPvNdjPsi0JOZFQSj0KPAUkaK2PdfR8DJ2f1o4XtBQj8FR6eGXbK3grveQV5zHj0hnERMbUPd/4j37a0GkkOhODnlNHbVBzJpyu1qFxv6qWFr3mvruWvk+lFAuuXIBGk3M4h/HJ43GGO+sa/pVVlxGuwjlccpj+cf1JcCYw5/I5rDmwhoeSHuJQ6SGeXf8s2q+p0pLlFG2Lptxfjs1mo7i8mDmXzeHtHW9TWlWKK9xFdU01VyVdxbGyYwyPH86mw5twRDiICo/iQOkBxvYZy+bDm+nXpR+r966moqaCLUe21H2HzX2voVAYGVJ1EkqpvsBLwFBgdDAiYeokDKFEaUUpU/4yBV+VD0e4g5WzVjYQiZZ2SQtmsQ/1uglLDBxhDnzVvmZrHdpD2ALnYjUGXPbFMpZ8voRdRbvwVfkY1G0Qo3qN4vlbnq8757GPH2Nl7koKywsBsCs7fu0nPjoeX5WPmy+6mWtTriVzfyZV1VXsKd7DvpJ9dYs6fuga3ZVBXQcxJmkM86+YT0xkDOk56by67VVq/DXYlZ3JAyazv2Q/L9z6gsRNams/rGOBBYFtYTl21jqJ3wOPA6GjXAbDGeCKcPHAZQ8wPGE4D1z2wCkLuHVn/MSUJ7ArOyldU84oThLKdRPN+c87qpeUtWgv3ryY6169jil/mQIKlt25jC5RXUiKTWL/yf2MSBjBn7b8iWlvTOP2127HU+mhoqaiLsBco2uIiYghKiyKGwbeQMatGazZv4ZBcYMIt4eT0i2F3q7exEfHAzAofhAllSXkluTy4tYXeWjFQ4Ck4g6IG8ANA24ARV3fKkeYo8l+VIHX0ZEJESHjblJKTQUOaa23hZr5bDAEg3XHlzY6jVmXzWr2zk8pRaIzkfHJ48/YjXAmrrP2jl0EbhP66rZXWbN/DRP7TSR1VGqbuUxOd42eSg9LPl/C7qLdRIdHk7kvkxmXzMDd1836g+u5adBNhNnCeG7DcxSXF1NZU0nuiVxcES48VR40Gjt2wmxhDOo2CEe4g4wtGew6vovdRbuZc9mcOreWt8pLcmwyeUV5JHdNJudIDiMSRpB9IJvcolwSXYlMSJ5A9sFsHrjsgbr4gvW5TUyeyP6T++vayZ/N9bYF7SoSSqlVQM8mnvoZ8H+AG4IcJxVIBUhOTm61+RkMZ8uZtps4lzhJMHUTHRG7sKyc1XtXAzCg64C6YGtb1JAEe402ZcMR7qC8upwr+1xJTGQMS+9YSoGvAEeYg4dXPMygboP4dO+naDRVNVUARNgjqK6pJtweTpgtjI2HNzKg6wA+3P0h1f5qwmxhsqVpbZ3MyJ4j6R7dnYqaCj4/+jndorqxu3g3Wmum/GUK8Y545o6cy/O3PF/3/VmNB62mh1clXXXaTKf2dtu1q7tJaz1Zaz2i8Q+QB/QHtiml9gJJwBalVFOCgtY6Q2s9Rms9JiEhof0uwHDe0VrpqKdzqTTlimlLN0JHuHgs4cu4LYPZl81mf8n+BlXSrXWt1nfW3F7hgbgiXMy+fDa9YnrRv2t/Iu2RgFRA93T1xBXhYnTv0XSL6kYvZy8i7ZGE28PRWtM3ti+R9kjC7FLF3cvVi+KyYsJt4fiqpFHh2v1r2XB4A4O7DWbr0a18c+wbDp48yN3D7mZsn7GMSxpHoiORAl8Bu47vYsnnS+rmlr5Z9tVYtGkRNmzcPuR27MqOr9rX5HU3/k6tTZPampCISWitv9Ra99Bap2itU4CDwCit9dEOnprhPKClrS7PJAe9JUE5XaygvRftjopdWPs3pI05u1YiwdSYWN/Zsi+WMS5pXN01OsIcda+1xgGJBQyKG8TVyVc32PXOKsrbcmQL7n5ufnr1TxmeMJxh8cO4ceCNFPoKKaspw1vpJTkmmfKKckb3Hk0vZy+c4U6q/dVE2CNQWvHt8W/x+/1U11RTWFZIXnEe1/S/hpsH3UxUeBQK6Qpr1U9YWXBf5n/Ja1+8RkVNBe/tfK/FPSUCv9Nqf3XdpkltLRQhE5MwGNqClkz0M8lBP52pfyYtxdtj0e7oduFn00pEa83izYvJ3JfJxH4TmTdm3inzbvCdHczi+VueZ9Zls4i2R7Nww0I2HNrAxOSJoCD7YDbuvm6+f/n3yS3O5f1v32dw98FE2f5/e2ceHWV5L/7PM0smmSQkBAgEskBAQTRQEzQkyFILWK29YjerAdxqYqVqbave3z3ent/v9tyee6V1QdsKrQptsG5Vq5ZaFyxgEoIkrIoiSchCEgIJWSaTzPr8/pilk8lMMgkhMybP55wcwsz7vvOdZybP9/3u0X2ulRKXQmVTJU99/Smuuegaphin0NnbyaW/vZQoTRS9zl4aTA0kG5Op76jHKZzER8UTFxXH8bbjXJR0EWvmraH4cDES6eoJhcRit2DQGbh40sVkTsxEp9GxfOZyYvWxLivA6Zrn7ZTOPu3Tg82U8Hyma+at4cF3H/QmPVzo2omIVBJua0KhOG8GUgRD2bhDUSgDbYzh2LQjsedToBRZz1qYrCa2HdqG2Wampr2mT2DXc5z/Z+Z5f0/sfYL/3vPfGLQGTrSdYPbE2Vw06SJK60tZOWslLd0tTDZO5njbcR4rf4wH8x/EqDNic9h4+ZOXyZ2ey7rX1nGw+SCLZixCSFdjP+lOtNSipcXcwlnzWexOOxKJUW8kShtFRkIGZ81nWT17NTanjbr2OrosXWzat4nJxsmsyFhBfWc9j3/9cabGTmVL5RZK6kpwSAcmi4lLZ1xK7oxcKpsrB/0enk/Sw3CJSCWhUIwUgxV2hbpxj4QlEGjTjvTqaV/OV9ZQa0Q87pNg1pv/Z9Zl6aKkrgSD1kC31eVKyk3N5UDzAfJS83i36l0AGrsaSZ+QzuHmw3RYOjBZTFS1VRGti+ZvX/wNk92EVmh5+/jbGPVGr0UA0OvsBVxdXj1N/Ew2EzGOGA40H+CWrFv42eKfYbabuedv9/B+zfukTkilxdTCcwefY0L0BN747A3WLlhLaX0p0+One4sp957ai+aUhtzUXAqzCyNuqFREFdMNB1VMpxiMwYbYBPojC7QhDmeTHOicL0P1tAdfWXNScrj3ynv7jWn1HBfs/XoKAVPiUnj101f59iXfpr6zni3f3OIdvuNpabI0fSlr5q3hofceYmbizKCFgx4X1daDWzndfZopxinccfkdFOUUYbabkVKyYccGWkwtvFf9Hk6c6DV6b3aSzWnrowwAYnWx6DQ6Oqx9BwsJBBo03gaAHqI10VicFuL18cQb4mk1tyKFxOl0MilmEknGJASC+cnz+cM3/8ALR1+gtL4Um8OG1WGlobOBVZmr+hXRXWi+rMV0CsWIM9gQG1+klHRZuti8v39Ae6gZOoMFxsNVZDYcPLKeNZ9lU/kmflX6K5xOZ59jgr1fTxDZqDOSn5ZPk6mJK2dcya7aXdS017D98HZvtlfRoiI2X78ZgIfffxiHdHCy/eSADQDLGspYlbmKxTMW8+bNb1KYXehNb43Vx3JZ8mUcOH3AawHYnDZXqqvTleoq/Wp3E6MTvQqhz/tD9lMQAkGvsxeJpNPWSaOpEZu0YXFYkEjae9tpM7fR0dtBVWsVxYeLKcwu5OlrnyZ3Ri56rZ7ZE2f3yQSLNJSSUIwrBsr68WxyhW8VsvXQVtLi09h1cperlbTPBh8oAyfQY4MpgWCyRGKX2Fh9LDkpOXx+9nNi9DE8s/8ZNu3bNOj79VUcWyq3UJhdyG+/8Vs2X7+Z2UmzWTlrJaUN/1objwLeXbub9Anp6DQ6Nq7eGNTK8qxhXWcdy2cux6gzsvb1teT/IZ/vvfI9Hi99nH8c/wet5taQ36vNacPutHuVSiA0aEg2JmPQGLyV2eBSJHqNnmhtNDqNDod00GxupsHUwPHW4zx/4HlMVhPPHXyOp/Y9RaelE61Gy6OrHo1YS1IpCcW4YqBJb55NbvbE2Ugpebf6Xfae2suaF9fwzP5nvKmVnk1vU/kmHA4Hnb2dPLH3CX749g/ZXLEZp9PZ5845WBpqIFkioTV0IDxN84pyijBbzcybMs9bZewhkNLzVxyezJ14QzzLMpa57qBT871r6+mvVNNew/s175OXmkeyMdmrcKSUdPZ20tTVhNPp9K7hb677DQC3/fU23j3xLgLBm5+/ySMfPsLOup04pCPYW+tHW08bJrtp4PVAgAMsTovXEtGgIUoTxTWzr+EXy35BZmImTun0WiTd9m6aTE10WbqoaKzg4kkXc+zMMXJScvo0eYy0mwQVuFaMO4Jl/fgGp79/2fcprS+lx95Dj62HD6o/YO2CtQghvG6XJ/c+yUd1H3Gy/SQ17TVkT8tmz8k9WOwWKpoqyE/LpzC7cMAAoydjx+PHj4TW0P74xhl+lv8zonRRfbqreggUUA0W8PccW5BVQPHhYjbs2EB+Wj4FWQWUNrgykqrbqynIKmBL5RZK60vJS81DOiUbyzZisppYlbmK4m8Ve2Mj2w5tc22uSJq7mzFoDUEL0wbCLu2DHuPAQYulpe86IUk0JNJkakKr07Jg2gL0Wj3HW49jcVjQa/T02nt5+ZOXyUvNY9uhbUw2TsagM3jX2WQ1UXy42Ju+GwnWhbIkFAo3vnf29+fez9dmfY1YfSxd1i7qO+vZfng7Rp3R63aZnTSbfaf20W3rxu60e++UPS2hfe+cB+rF42s5DGZ9jDb+8gkhuD/3fn53/e/6bGC+xWu+79d3TQuzC70WgQeT1cTuOpdrqbS+FMDrPlqWscyrlDMSMthdu5v3at6jvbcdgLJTZbSY+27UGqFhTtIcvjrzq+i1+gFdRhcCicTqsLK3YS9zkuaQNTWLkttL+PmynzMtfhpXzLiCyuZK1sxbQ0ZiBtfMvoayhjJMVhObKzZT9HYR2w5t865HJMSplJJQKHzwWBkajYaiRUW88f03WJy6mFWZqyhtcG369155Lz+68kd09HZ4g+E6jc67qedMzwlYBRyIQO6YYO6wcBCoFQT0VQSDucg8FsWWyi3eY5xOJ5v3b2bNi2soqyvjnRPvkDcjj7iouD7vPy4qzqs0l2UsY9WsVSRGJwKQN8PlivK4oW669CbmT57Pty/5NgmGBG7/yu0jsgZJUUkhHadBw5meM5xsP4lAcLj5MD3WHn6999euGdwLb2dC1ATyU/P562d/pa6jjver3yc7JRspJaX1pWQmZgJQda4qIm4SQLmbFIoBiYuKY1nGMq/573Gj/CD7B3zc+DGrMldxou0E2SnZHD1zlCXpSyjMLsRsN2PUGb2FUznTc7jvyvu8rhHfojJ/d0wkFcH5uos8rSD8axtCcZEFGra0q3YXnZZOLHYLLd0trowgKfsV2fm6sADWLlxLt62bqbFTkVLyaMmjvHbsNQAyEjM4euYoUkgauhrQCu2Q4hGBaLO2BX3Okz7rmxrbaenknap3vJZV/vR8Pqz5kB57D7FRsVyRcgUVLRVcnXE1O07soKyuDIPWQH5qPqUNpdy28DYKFhQMOLFuNFFKQqEIQJ8ahtR8b+dOzx9pXFQcS9OXsrt2NytmrfDm5ftu8iariZK6Elp7Wnmq/CkEgvty7wP61moMFrcIJ6G0ggil0ND/mCkxUxAIWrpbMFlNJEUn8eejfwagsrmSxTMWc+MlNzI1dioajaaP0pkQPYEJ0RNwOp3c/Jeb2XFiB1qhZWrcVFoaW7hx3o2cNZ/lkaWP8PKnL1/Q9fG04fB1azlx9qmx2NO4x/t7u6Wdx8ofY1rsNN754h3MVjMWuwW9Vs/G1Rv7KAeIjFoa5W5SjBs8NRCdvZ10WboGzB7x7xHkaQfti8D1mOdf/z9uKSXZKdl8fvZz16zlJlc2ULCMn0hTEB58W0HUdtS6Asju9+d5fjAXmW8WUkFWAWa7GZ1Wx7oF64iLiiPJmIRTOik/VU5qXCr/W/K/LH52MQWvFdDZ2xnws2oxt1DRVMFU41S6bd2cMZ1BSskbn73BsTPHWL09pMkDAfGvkRgI/zoLLdoBjz3dfdqVpGDpRq/VU32umm5bNw+//zDbj2z/17HSdWxJXUlYa2mUklCMCzyVuSv/uJKFmxey6k+rvGmtgeiTzulO0fSktnrM/9IGV7qsb56/57U2V2xmw44NGLQGCnMKmRQzyXuXHShVNNLSHv3x3eQFgg07Ngyr0HD74e0UvV1E8aFi8lPzaettY/Xs1WQlZ3H7V25nWfoy3ql6h1Ndp2jtbuWt429xx1/vCBjrSDYmk5uaiwMHKXEprFuwDo3QcN1F19HS3UKHpSOIFCG8X4avsP0L7vyZlTiLmvYajFFGNEJDcnwy0bpo0ieks7t2t/d7sLliMw+999CgBYUXGtWWQzEuMFlN3PXmXRxpOUJDRwOpCalkJWfx+3/7/YCdX31TEn37DRVmF7KlYgu763azLH0ZRYv+dQfdZenyzit+v+Z9ZibMZHHa4oAxiVBahYTDJx3sNYc6Y9v3OiariVV/WkW3tZvYqFjeXfsuGo2mT7O/LksX619fz86TO+m196LX6Lln0T2cNp/m6Wufxmw3k2xM9q6jw+FgY+lGXv30VbQaLZkTM3E4HNilnf2N+6nvqh+R9fAoDX+rAVyWQyDF4N/uQyu06DV6kgxJCI2gx96DMcrIw3kPo9Vq2XZoGwC3LryVgqwC7v7b3WQmZlLVXsVjqx9jWty0Ef38VVsOhcIHo87IlTOuxKgzMtE40RuQHij7yONiKmsoIyUuhfKGcm9jtm5bNxJ3ARj/ml/gdDopPlxMVVsVO77YgdPpZE7SHCqbKvvk7IfaKiQcxXUDveZgcyp8LaJg1/FNj/VkknkUzQtHXqDZ1ExSdBLzJ83n+ouup8HUwOIZiyl6u4glzy5h7etrvS1BzHYzrx17jS5rFza7jStSrqCmo4ZTXad4YPED/OeS/yReH39elgGAXuiZGjO13+NatERpo8hPzSdKRPVdCz+F4pROkqKTiI6Kprm72WWZWkzcMPcGV0rshAyWpS/jo9qP+N3Hv6OqtYrtR7dTc66G14+9HjZLUwWuFWMez3CZA80HuGXBLdy+8HY0Go03LXOgoKBvwDU3NZfGrkaWpC8BXPMK5iTNoayhDIvDQmVTJdkp2VQ0VpAUk8RnZz9j0fRF1HbU9is8C/Ya/u05TnefHvXiuoGylXyL4PzxD/bfMO+GPtcpyCpg/YL17KzZydWzrg6cAdVQysrMlVS1V/Grlb/i5U9epvxUOV2WLspPlZNsTKakroRmUzPTJ0zH6XRSda6KLmsXsbpY/ln7T3rtrl5KZafKeHz14/y9+u+cbD9Ja08reo0eq9M65DXRaXRIEXiDvnjyxcxKnEWPrYcDpw8EPEYgmBIzhR5HD43djQB0WDsQCL754je5dMqlVLdX83HTxyQYEiipL2FO0hxOm0+zNG0pfzz8Rz6q/4il6Uu9n8doWZXKklCMeXw3vcqmSrRaLXFRcbSYWwYNCvoGZYtvLPYWkfnm7+ek5FDRWEFGQgYVjRVkJWdxvPU4l0y5hGhddJ/eQ/6xB487xtPTyL89x4PvPojdaR+x4rpQYh+hTLXbfmR7v7iEZ53TJ6Sz9dBWHnjngX6ya4SGKF0UGvEvt5tHnj59mNKX88qnr/D0x0/Tae3k8OnDJEQlsL9pP82mZn7yj5/gdDox282uaXj6eKzSSm1HLV2WLrosXVS3VfPS0Ze4af5NGPVGVs5ayaLpi4jT91VOoQSpzQ4zDnt/l5JWaLHb7Pw076fMmzSP7CnZAc9PMCSweMZiBIIojcvikEji9fF80fYFiTGJtPW0ce3sa6nvrMfisHCg+QBXTL+Chq4GAGYlzGLboW0UvV00qi1blCWhGPP436kbdUbvHa8nKDjQnb5v3YLv3a8nf99TD1FaX8qS9CXcdfldGHQGb2sOT18e/3TGwuxCNldsZnetqzX2uoXrvNf2bLgzE2dysv0kj656tE9/n+EQajrlYPMKglkannXeXbsbgDlJc6jtqPXK7h/sL1hQwPYj2/vI43ldT4vvuZPn8vnZzynKKaLH3kNVexUGjYHyU+W0mFuYGjuV1ZmrKWkoAQs4nA6SYpJwOp00mZr45Ue/ZMXMFUwyTqLD0sGtC2/lxotu5KEPH/K6g/TosWAZdP1arf2bBFqllWNtx7iu+Dp6nb10WjoxaAwgwOqwEqOJIUoXxfoF62ntaWVFxgr+fuLvaNCgEzq6bF1Mj5/OOfM5clNzaettIz4qHqPeSLQumt9f/3u0Wi3bD29nV+0uADITM0e1ZYtSEooxj/+m123rpqSuhOnx02nsamTj6o3D2oB9lYf/pnpf7n39Ar++m2tJXQkrZ61k68GtmG1m9jbsZU/9HpZnLKcop6iPYluSvuS8FYT/6w+2yQxU0BdqP6ayhrI+svufB3gtj921uynIKiDe4BoLKqUkPzWfXbW7KMwp5IHcB1jftB6HdGC2m/la5te8AezibxVTfa6agr8U0G3vRiM0SI2k29pNjD6GnTU7SY5NJi4qjhvm3sCGHRv6uJ0GUhC+wedAQWtw1UWc7jmNRDLNOI1Oayf/KPgHe2r38Opnr9LW00Z5Yzm3LryVWy67hdvfvJ0jzUeoaq9iduJsclNz2bh6o3cdiw8X8/zB59EIDX/+5M/cvehuihYVuZTq4e2UNozORDoPSkkoxgW+m55RZ8QhHbz66avkpuaSbEwesLdSKJlF/ptqoE3Ws0l6Rlc+svMRWswtROuiMVlNpMWn9dm8R3r62EjN2R7I0hBCEG+I5+5Fd7Nu4bo+z/ufB5Cfms/WQ1sB1+boO9faKZ2caDtB9blqDFoDOq2O/NR8jrcdJ3dGLuDKtorVx5I5MZPMpEzK6suYmzKXq2ddzdZDW3FKJzXnajjdfZpeh6u5XmNXI0adEa1dCwIc0pUNJaTol6U0NXoq3fZueh29rmOdjgH7QbWYW0gwJHDo9CE25G7gyNkjZCZmUt1e7f1cl6YvZffJ3cRHxXPOco6c6Tm88dkb3qr+gqwCdtbsZO6kuZQ1lLFu4Tpv59yiRUWsXTi6hZcqJqEYd5jtZu/geZ1GF7RT6EhnFnk2yY2rN6LT6JiTNIepsVO5bMplrMpcRXN3c78785EMUIZS9DaUawWSLVijv2Dn3TDvBmYmzGTlrJWUNZR540Ldtm721O3B4rBgtpkpP1VO1tQsqs9Vc+mUS6loquCpfU95W7abrK7Ro3Mnz+XA6QPYnDY+WP8Bb938FhdNuoiU+BSSopMoP1XOBMMEjFFGfpr3U36+7OfERcWh1+iZbJxMckxyH3ljo1xxFIHA5rT1UxB69AgEeqEnTue6TtaULPae2osQwtsOfVnGMu/7vvPyO5k1cRZpCWnMSpzF9Rdd77WodtXu4rkDz9HQ1cAHJz8gLzWvX6fd0S68VJaEYkwTyBII9Y46FPfMUGsYPNXLnte/deGt3jtr/55FF4IL2RdqKC0kfI/VaDT9MsBi9bEsy1hGTXsNAMszlnNX9l1EaV1tynOm57C/cT+tPa1sKncNP8qZnsPT+55m3uR5VDZXcqe4k3hDPLcuvJXnDz6PQPDZmc84evYoGqHhT0f+xMGig0Rpo3i8/HFMVhMWuwUdOuy42oW3WloxO8zYpK2P/LHaWNIS06hprSFOH4fZbqbb7lJwZQ1lTImbglFnpCCrgIKsvq024g3x3Hn5neyq2wUS/mv3f+FwOlzjVaWT6rZqVs5aSU1Hjbc9fThRxXSKMctAm1Yom/tgm9759NUJd9O2C8FQCu38jw0UmA9klfg2Rty0bxNP7n2SeZPmkRiTyDPfeIath7a6EgZS811psA1lZE/LZm/DXtIT0nnx6Iu0mFtwOp3EG+I58sMjvPTJSzy590nmTJzDF+e+oMvShclqYkLUBJw40Wl0tPa04sQ1QCglNoUfZP+AL9q+oKm7ifKGcoQUmB1mJJJkYzIrMlaQl55HZVNlwO+eyWrCZDV5Z3ifOHcCm93G3Elz+eDkB8xKnMXymcsvaK+mUIvplCWhGLMMlu/vu4EF2rSHm+XjT7BrR0qn15FiIAvNfw38jw0UmPfEN/wf8wS2DRoDk2Mmc+zsMabGTeWFoy9w75X3YrabvdlRGQkZroaBaYupaHRlm+09tZfO3k6unnk1sfpYKpsqmZ88n8/Pfs6PF/+Y62Zfx8o/raTT0olOoyMtIY0YfQxJhiS+d+n3MOgM7GvchxCC6XHTWT17NXvq9qB1aLE5baTFp5GXlsf+U/uZMWEGJXUl3u+Gfy1Jflo+ZQ1lLE9f7lVq/l1gw42yJBRjllDv9IdrEYRyXiR08RxNAinEYGtwPtaUxxKZFjuNVz59he/O/y7N3c1e6yVQurHZbiZaE81Nr9zEhyc/JN4Qz8NLHkZoXIONPO3cTVYTC55ZQHtvOwmGBErvKCXeEO+VeXXxam/A/PWbXidWH8uzB571ZqLdefmdxOpjWffGOsobyslNzaX4RtcEPX8L6jfX/carNIFRtS6VJaEY9wxmCXgY7sjQUK4fieNILySBLKRga3A+1pSvJZKXlkeTqalPTCPQZxMXFUezqZnKFldBZae1k50nd/L8muf7ZGIJIZgWO40YXQzR2mgAr5Lo7O3kdPdpzpnPMTFmIi8dfckVKHfY0Gl0ROuiiTfEewdRfWf+d2gyNXk7/fpbUP7WQiR+N1R2k2JME0o2N40NqwAACTpJREFUSCgVxsO9/vlce6xwIdYgWCW8fzqu/2eTbEwmb0YeAInRid72IP6zQtYvXE+3tZvj545z1fNXeTsGCyFINiaTmpDKpJhJlJ8qd/3bUE5GYoY3Q8vznptMTQFrSSJl8mAoKHeTQsGFDSSPxSD1UImUNfDMFDFZTd7ag0DyNHU1sfgPi+mx9WB1WFk9ZzXP3fAcsfpYntn/DLtrd3NV2lWU1JdQfqqcpJgk5k6ay1UZV42IO200UO4mhWIIXMhA8mgFqSN5U4qEQP1Q4kOeNOX3qt9jonEiX535Ve+6egoFpZSUN5bznfnfCVi5HwnveSRQSkKhGAOMtwD5cBhKfEij0bD9W9u9U+R8LQ7fDKuRbp0SiSgloVCMAcZbgHw4DNSSPZAFptFoSIlPGfCagYrlxhpKSSgUY4CR6ss0lgmU8RTIAoPBU1GDnTcYkewSDIbKblIoxgBfxqyZcOCf8eRvgZmsppD6dQ00TTAY4ZgyOBIoJaFQjBHC0fzty45/ei4Q0uY/nLTe4SiWSCBi3E1CiP8L3AWccT/0H1LKHeGTSKFQjHUCti8PwW0XaqGmL19Wl2DE1Em4lYRJSvmroZyn6iQUCsVIMpy4QajnRFJMQtVJKBQKxTAYan3DUNKPv4y1E5EWk/iREOKwEOI5IcTEYAcJIQqFEPuFEPvPnDkT7DCFQqG44HxZYw2hMqpKQgjxvhDiaICfG4DfAbOBrwBNwK+DXUdKuUVKuUhKuWjKlCmjJL1CoVD0Z6z354qYmIQvQoiZwNtSyssGO1bFJBQKRbiJpFhDqIQak4gYd5MQwre08UbgaLhkUSgUiqEwltOPIylw/agQ4iuABE4CoZUwKhQKheKCETFKQkq5LtwyKBQKhaIvEeNuUigUCkXkoZSEQqFQKIKilIRCoVAogqKUhEKhUCiCopSEQqFQKIISkcV0Q0EIcQaoDbccI8xk4Gy4hQgzag3UGnhQ63Bh1iBDSjloy4ovvZIYiwgh9odSCTmWUWug1sCDWofwroFyNykUCoUiKEpJKBQKhSIoSklEJlvCLUAEoNZArYEHtQ5hXAMVk1AoFApFUJQloVAoFIqgKCWhUCgUiqAoJRFBuMe2tgghxu0sDSFEmhDiQyHEp0KIT4QQ94dbptFGCBEthNgnhDjkXoP/F26ZwoUQQiuEOCCEeDvcsoQLIcRJIcQRIcRBIcSoT1hTMYkIQgixDDABfwxlKt9YxD18KkVKWSmEiAcqgDVSyk/DLNqoIVyTa2KllCYhhB74CLhfSrk3zKKNOkKInwCLgAlSyuvDLU84EEKcBBZJKcNSUKgsiQhCSrkbaAu3HOFEStkkpax0/94FHANmhFeq0UW6MLn/q3f/jLu7OSFEKvAN4A/hlmU8o5SEImJxzzq/HCgPrySjj9vNchBoAd6TUo67NQCeAB4CnOEWJMxI4F0hRIUQonC0X1wpCUVEIoSIA/4C/FhK2RlueUYbKaVDSvkVIBW4UggxrtyPQojrgRYpZUW4ZYkArpJSZgPXAhvcbulRQykJRcTh9sP/BdgupXwt3PKEEyllO/Ah8PVwyzLKLAH+ze2PfxG4WghRHF6RwoOU8pT73xbgdeDK0Xx9pSQUEYU7aPsscExK+Vi45QkHQogpQohE9+8xwCrgs/BKNbpIKf+PlDJVSjkT+D6wU0q5NsxijTpCiFh3AgdCiFhgNTCq2Y9KSUQQQog/A2XAXCFEgxDiznDLFAaWAOtw3TkedP9cF26hRpkU4EMhxGHgY1wxiXGbAjrOmQp8JIQ4BOwD/ialfGc0BVApsAqFQqEIirIkFAqFQhEUpSQUCoVCERSlJBQKhUIRFKUkFAqFQhEUpSQUCoUiAhlKw08hxDIhRKUQwi6E+I7fc+8IIdqH2yRRKQnFuEO4uE0IUS6EMAkhOt2dZ78RwrkzhRDS5ydxNGQOIss/feT4UbjkUFwwthJ6EWUdcBvwQoDnNuJKKx8WSkkoxiO/xdU0rhy4EbgJ1x/Z20KIB0K8xs+APKDrgkgYGve4ZVCMQQI1/BRCzHZbBhVCiD1CiHnuY09KKQ8ToM+VlPIDzuN7qhvuiQrFlxEhxBrgbuCHUspnfJ76uxCiGdgohPjA/Qc3EJ+Hu3W3p326q0hdMU7YAtwtpfxCCJGL64bn6gv5gsqSUIw37gdOAL8P8Nwvcc3zGJbrRgjxP+7hMCZ3xfx2IcS0Qc5Z4XYXXeb3+D+FEK8ORw7F2MTd9DIfeMXdIXgzrur8C4qyJBTjBiGEDpd75rdSSof/81LKDiHEh8DyYb5EMi5F0whMAX4K7BRCXCalHO/trhXnjwZod3cHHjWUklCMJyYDBqB2gGNqcTVRGzJSyjs8vwshtLj6cDUAVwG7h3NNhcKDlLJTCFEjhPiulPIVdzPMBVLKQxfydZW7SaHoz7AamgkhrhVClAohOgA7LgUBcPGISaYYNwRp+FkA3Olu+PcJcIP72CuEEA3Ad4HNQohPfK6zB3gF+Jr7OtcMRQ5lSSjGE2cBC5AxwDEZQNNQLyyEuAJ4E1e////BNVFOAnuB6CFLqhj3SClvDvJUv7RYKeXHuAZUBbrO0vORQ1kSinGDlNIOlALfEEL0++4LISYAK9zHDJUbgTPATVLKN92ZT80hnNfr/jfK7/GJw5BBoRhxlJJQjDc24XL//CDAc/8OJALPD+O6MYBN9u29XxDCeR6X1CWeB4QQacC8YcigUIw4yt2kGFdIKd8QQjwD/EYIMR94G9ffwU24KlafllL+cxiXfg/4sRDiCeAtXKmK/SapCSGeBZZLKee45WkQQuwHfiGEMOO6cfsP+hdRrQeeA2ZLKQcKvCsUI4pSEorxyD24qq1/CNwFGN2P/zvw6HAuKKXcIYR4GLjXfc0y4HrguN+hWvr/3d2MqwK8GJdl8RDgX/mtcZ+rKucUo4qaTKcY9wghZgD7gQPANwPVUPgcOxOowZVVssMd5wgL7jRbAdiAe6WUT4dLFsXYRcUkFOMeKeUpXIHnq3HFLELhr4AtnA3+gA9wKQiF4oKhLAmFYggIIaKABT4PHRjI8rjAsswF4t3/rZVSngmHHIqxjVISCoVCoQiKcjcpFAqFIihKSSgUCoUiKEpJKBQKhSIoSkkoFAqFIihKSSgUCoUiKP8fH1QgLCO1iHwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gnd_results = gnd_exc_results.get_memory(0)[:, qubit]\n",
    "exc_results = gnd_exc_results.get_memory(1)[:, qubit]\n",
    "f_results = gnd_f_results.get_memory(1)[:, qubit]\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot all the results\n",
    "# All results from the gnd_schedule are plotted in blue\n",
    "plt.scatter(np.real(gnd_results), np.imag(gnd_results), \n",
    "                s=5, cmap='viridis', c='blue', alpha=0.5, label='state_0')\n",
    "# All results from the exc_schedule are plotted in red\n",
    "plt.scatter(np.real(exc_results), np.imag(exc_results), \n",
    "                s=5, cmap='viridis', c='red', alpha=0.5, label='state_1')\n",
    "#e states\n",
    "plt.scatter(np.real(f_results), np.imag(e_results), \n",
    "                s=5, cmap='viridis', c='green', alpha=0.5, label='state_2')\n",
    "\n",
    "# Plot a large dot for the average result of the 0 and 1 states.\n",
    "mean_gnd = np.mean(gnd_results) # takes mean of both real and imaginary parts\n",
    "mean_exc = np.mean(exc_results)\n",
    "mean_f = np.mean(f_results)\n",
    "plt.scatter(np.real(mean_gnd), np.imag(mean_gnd), \n",
    "            s=200, cmap='viridis', c='blue',alpha=1.0, label='state_0_mean')\n",
    "plt.scatter(np.real(mean_exc), np.imag(mean_exc), \n",
    "            s=200, cmap='viridis', c='red',alpha=1.0, label='state_1_mean')\n",
    "plt.scatter(np.real(mean_f), np.imag(mean_f), \n",
    "            s=200, cmap='viridis', c='green',alpha=1.0, label='state_2_mean')\n",
    "\n",
    "plt.ylabel('I [a.u.]', fontsize=15)\n",
    "plt.xlabel('Q [a.u.]', fontsize=15)\n",
    "plt.legend([\"|0>\",\"|1>\",\"|2>\"],fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting prototypical chevrons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "chevron_codes  =['5db8c50715a70b001967dcda', '5db8c51061a787001995434f', '5db8c51983968b00193a3f26', '5db8c520d3a8f10018a3e5ac', '5db8c525889b23001aee618e', '5db8c52a99afd80019fc53ec', '5db8c536f52ef7001ae7880d', '5db8c53ff52ef7001ae7880f', '5db8c544d3a8f10018a3e5ae', '5db8c54a15a70b001967dcdd', '5db8c54ef52ef7001ae78811', '5db8c55115a70b001967dcdf', '5db8c556d3a8f10018a3e5b0', '5db8c55b15a70b001967dce1', '5db8c560f52ef7001ae78813', '5db8c56461a7870019954351', '5db8c568889b23001aee6190', '5db8c56c99afd80019fc53ee', '5db8c57015a70b001967dce3', '5db8c57483968b00193a3f28', '5db8c578a693ee001877ed25', '5db8c57d99afd80019fc53f0', '5db8c581a693ee001877ed27', '5db8c587f52ef7001ae78815', '5db8c58c15a70b001967dce5', '5db8c59383968b00193a3f2a', '5db8c59883968b00193a3f2c', '5db8c59f15a70b001967dce8', '5db8c5a5a693ee001877ed29', '5db8c5ac83968b00193a3f2e', '5db8c5b2e440250018ba8424', '5db8c5b715a70b001967dceb', '5db8c5bce440250018ba8426', '5db8c5c2a693ee001877ed2b', '5db8c5c961a7870019954353', '5db8c5ce61a7870019954355', '5db8c5d3f52ef7001ae78817', '5db8c5dabb72de0018d8f3fb', '5db8c5de99afd80019fc53f3', '5db8c5e4a693ee001877ed2f']\n",
    "qubit_rabi_frequencies = [4.8412952222858205, 4.841602914593513, 4.841910606901205, 4.842218299208898, 4.84252599151659, 4.842833683824282, 4.843141376131975, 4.843449068439667, 4.843756760747359, 4.844064453055052, 4.844372145362744, 4.844679837670436, 4.844987529978129, 4.845295222285821, 4.845602914593513, 4.845910606901206, 4.846218299208898, 4.84652599151659, 4.846833683824283, 4.847141376131975, 4.8474490684396665, 4.847756760747359, 4.848064453055051, 4.8483721453627435, 4.848679837670436, 4.848987529978128, 4.8492952222858206, 4.849602914593513, 4.849910606901205, 4.850218299208898, 4.85052599151659, 4.850833683824282, 4.851141376131975, 4.851449068439667, 4.851756760747359, 4.852064453055052, 4.852372145362744, 4.852679837670436, 4.852987529978129, 4.853295222285821]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['5db8c50715a70b001967dcda', '5db8c51061a787001995434f', '5db8c51983968b00193a3f26', '5db8c520d3a8f10018a3e5ac', '5db8c525889b23001aee618e', '5db8c52a99afd80019fc53ec', '5db8c536f52ef7001ae7880d', '5db8c53ff52ef7001ae7880f', '5db8c544d3a8f10018a3e5ae', '5db8c54a15a70b001967dcdd', '5db8c54ef52ef7001ae78811', '5db8c55115a70b001967dcdf', '5db8c556d3a8f10018a3e5b0', '5db8c55b15a70b001967dce1', '5db8c560f52ef7001ae78813', '5db8c56461a7870019954351', '5db8c568889b23001aee6190', '5db8c56c99afd80019fc53ee', '5db8c57015a70b001967dce3', '5db8c57483968b00193a3f28', '5db8c578a693ee001877ed25', '5db8c57d99afd80019fc53f0', '5db8c581a693ee001877ed27', '5db8c587f52ef7001ae78815', '5db8c58c15a70b001967dce5', '5db8c59383968b00193a3f2a', '5db8c59883968b00193a3f2c', '5db8c59f15a70b001967dce8', '5db8c5a5a693ee001877ed29', '5db8c5ac83968b00193a3f2e', '5db8c5b2e440250018ba8424', '5db8c5b715a70b001967dceb', '5db8c5bce440250018ba8426', '5db8c5c2a693ee001877ed2b', '5db8c5c961a7870019954353', '5db8c5ce61a7870019954355', '5db8c5d3f52ef7001ae78817', '5db8c5dabb72de0018d8f3fb', '5db8c5de99afd80019fc53f3', '5db8c5e4a693ee001877ed2f']\n",
      "[4.8412952222858205, 4.841602914593513, 4.841910606901205, 4.842218299208898, 4.84252599151659, 4.842833683824282, 4.843141376131975, 4.843449068439667, 4.843756760747359, 4.844064453055052, 4.844372145362744, 4.844679837670436, 4.844987529978129, 4.845295222285821, 4.845602914593513, 4.845910606901206, 4.846218299208898, 4.84652599151659, 4.846833683824283, 4.847141376131975, 4.8474490684396665, 4.847756760747359, 4.848064453055051, 4.8483721453627435, 4.848679837670436, 4.848987529978128, 4.8492952222858206, 4.849602914593513, 4.849910606901205, 4.850218299208898, 4.85052599151659, 4.850833683824282, 4.851141376131975, 4.851449068439667, 4.851756760747359, 4.852064453055052, 4.852372145362744, 4.852679837670436, 4.852987529978129, 4.853295222285821]\n"
     ]
    }
   ],
   "source": [
    "# Rabi experiment parameters\n",
    "num_Rabi_points = 70\n",
    "num_freq_points = 40\n",
    "num_shots_per_point = 512\n",
    "backend_defaults = backend.defaults()\n",
    "rough_qubit_freq = backend_defaults.qubit_freq_est[qubit]\n",
    "sig_ma = 0.006\n",
    "\n",
    "drive_frequencies = np.linspace(rough_qubit_freq-sig_ma,rough_qubit_freq+sig_ma,num_freq_points)\n",
    "\n",
    "# drive parameters\n",
    "duration_min = 4\n",
    "duration_max = 60\n",
    "drive_durations = np.array([int(i) for i in range(duration_min, duration_max, 1)])\n",
    "drive_samples = 128\n",
    "drive_sigma = 16\n",
    "# Note: for codes[2] the max was 100 and steps of 2.\n",
    "# For codes[3] the max was 60 and steps of 1. 5db86a45e440250018ba81a3\n",
    "\n",
    "# create schedules for Rabi experiment \n",
    "Rabi_schedules = []\n",
    "Rabi_schedule_LOs = []\n",
    "\n",
    "mm=39\n",
    "for ii, drive_duration in enumerate(drive_durations):\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_duration, amp=0.1, \n",
    "                                    sigma=drive_sigma, name='rabi_pulse_{}'.format(ii))\n",
    "    rabi_pulse_qubit = rabi_pulse(backend_devicespec.q[qubit].drive)\n",
    "    # start an empty schedule with a label\n",
    "    this_schedule = pulse.Schedule(name=\"Rabi drive = {}\".format(drive_power))\n",
    "    this_schedule += rabi_pulse_qubit\n",
    "    this_schedule += measure_and_acquire_qubit << this_schedule.duration\n",
    "    \n",
    "    Rabi_schedules.append(this_schedule)\n",
    "    thisLO = pulse.LoConfig({backend_devicespec.q[qubit].drive: drive_frequencies[mm]})\n",
    "    Rabi_schedule_LOs.append(thisLO)\n",
    "    \n",
    "\n",
    "\n",
    "rabi_experiment_qobj = assemble (Rabi_schedules, backend = backend, \n",
    "                            meas_level=1, meas_return='avg',\n",
    "                            shots=num_shots_per_point,\n",
    "                            schedule_los = Rabi_schedule_LOs\n",
    "                                )\n",
    "\n",
    "# job = backend.run(rabi_experiment_qobj)\n",
    "# job_code =  job.job_id()\n",
    "print (chevron_codes)\n",
    "print (qubit_rabi_frequencies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_plots= []\n",
    "for k in range(len(chevron_codes)):\n",
    "    job = backend.retrieve_job(chevron_codes[k])\n",
    "    Rabi_results = job.result(timeout=3600)\n",
    "    \n",
    "\n",
    "    plot_Y = []\n",
    "    for jj, drive_duration in enumerate(drive_durations):\n",
    "        thispower_results = Rabi_results.get_memory(jj)*scale_factor\n",
    "        plot_Y.append( thispower_results[qubit] )\n",
    "    Y_plots.append(plot_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13179db38>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SPpU8HhzxoLK0AKtlKnvb0WwD55Rnl0q3w48qcyGqLbTbdyUECeyMMSNSQf13nPOHF3sN5/wezvlOzvnOqqoqIW6reI7Z1AlHtSJtQa8yF4Kx/Np9enDYg53N5aIsRi/HlsYy6BgoHSORHqcPG2nhdMWEqIphAH4OoI9z/t3ch6QdTu88qsyFghyHtxijXoeq0sK8qWWf8M1jfHZelvx6htlkxDq7hQK7BMKxBE67guigNMyKCTFjvwzA+wBczRg7mn67UYDrqp7TNy96SZ69zISJPJmxZ+rXL5ExsAPAjmYrjo56kaCNSqLqnwwgkeS0cJoFIapiXuScM875Zs751vTbX4QYnNo5ZudRbxU3N5hPu087RzwoLTTI3tFvR7MNgUgcp10BWcehded2nNKMfaVo56lIOOdweOdRL/aMPY92nx4c9mB7sw16nTz59YwdTanfGCgdI64epx9lRUY02KTdiKYFFNhF4glFEYknJUnF+OZjmI8mRL2P3GZDUZyaCkp2sMaFNJYXobK0kAK7yHqcPmyst8i2UK5mFNhF4vCmKmJED+x50pf9cHq3p9T9YRbDGMOOZisOU2AXTSyRRP9EgOrXs0SBXSTOdGAXPRWTJ7tP+yZSWyM6FLK1fEezDSMzc5gO5u+Zs2I6PRVENJFUzN+32lBgF4nDmwq0UgV2re8+7ZsMoKm8GKWFyjj0a0dzKiVEs3ZxdDupVW8uKLCLxDE7jyKjHtZi4Q6wXky+7D7tm/BjvYIOLe6oK0OBXodD1BBMFD0OH0oK9FhVQQdrZIMCu0ic3nnUWU2iL/yUFBpgLjRoesY+H01gZDqE9Qo6uNhk1GNjvYVm7CLpdvrRXmeBTuYKKLWiwC4Sp28e9bZiSe5VU2bSdI79tCuAJAfaa5UzYwdS6Zhj4z5E40m5h6IpiSRHr9NP9es5oMAuEik2J2VkDtzQqv6J1Eag9XblzNiBVGCPxpPoSeeDiTCGp4OYjyWoB3sOKLCLIBxLYCYURV2ZNBsraizanrH3TvhRZNSjqVya34CWa3tTagGV6tmF1e1IVUBRK4HsUWAXwdlSR4l2zNktJriDEc32Lumf9GOd3ay4fGu1xYTG8qKzNfZEGD1OHwoNOtFOHssHFNhFINXmpAx7mQmJJNdkTTXnHP2TAdn7wyxle1PqRKV8O3dWTN0OP9bXWmDQU3jKFn3lRCDV5qQMLZc8Tvkj8M7FsEFhC6cZO5ptmPJHzv4wJ7nhnNPh1QKgwC4ChzcMxs5tHhKblnefZnacKm3hNIPy7NkJhGO4+Ycv4qO/PYQnuicQjqV6HY155hEIx6mVQI6UsY1PYxyz86gxm2CU6FfJGot2d5/2TaYC+zoFbU5aaL3djOICPQ6fmcUtW+kM9+V6vHsSx8d9GPXM4fHuSZgLDbiuw45KcwEAWjjNFQV2EWQ2J0mloqQARj3TZMlj/0QA9dYilBWJu4M3Wwa9DlsbreiiGfuKPHrUgeaKYvz1U1filaEZPHbUiSe6JxGIxGHQMaytUeYPcrWgwC4Cp28emxuskt1Pp2OoNmuz5LF/0q/Y/HrGjmYb/vvAAOaicRQX0D+pi5n0hfHy4Aw+fnUbDHodrmirwhVtVfiPt23EcyfdYAyiHSeZLyjHLrBkkmPCG5Z0xg6k8uxaC+zhWAKD7pBi8+sZ25tsSHLg2BhtVFqOPx5zgnPgbdten7oyGfW4fqMd+zrsMo1MOyiwC2w6GEE0kZSsIibDXmbSXI59wBVEIsmxXuEz9q2Nqd/OqJ59eR456sCWhjKsqqQGX2KhwC4wh8Sljhl2iwkTvrCm6qn7J1OtBJRaw55hKynA6soSHKHAflGnpwLocfrfMFsnwqLALjBnug+7VJuTMuwWE+ZjCfjDcUnvK6b+CT8KDTq0qKB167YmGw6PejX1g1UMjxx1QK9juGlzndxD0TQK7AJzeOcASB/YazR44EZfupWA3IdXL8f2Zis8oSjOzMzJPRTFSiY5Hj3qxGWtlagyF8o9HE2jwC4wpzcMc6FB8vK8Wo1tUuKco28ioKjDNS4ks1GJ8uxLOzQ6i/HZedy6jWbrYqPALjCHd17y2Tqw4FBrjQR2dzACTyiq+Px6xtoaM0oLDTgy6pV7KIr1yBEHiox6XNdOVS9io8AuMMestJuTMqotqV9ttbJJSak92Jei1zFsaSyjGfsSovEk/nxiAte216BEIefWahkFdoGlTk6SfsZeaNCjoqRAM4E90yNG6ZuTFtreZEP/ZABzUe0sYAvl+VNueOdiuJWqYSRBgV1AoUgc3rmYLKkYQFsHbvRPBlBbZoK1uEDuoSzbtiYrEklOG5UW8chRB8pLCnB5W6XcQ8kLFNgFJHW73vNpafdp34RfNQunGdsaaQF1MYFwDM/0TuGmzbWSNcbLd/RVFpBcm5MymiuKMTITUv1JStF4EoPuINarZOE0gzYqLe6J7klE4knalCQhCuwCkmtzUkZ7rQVz0QTOzIRkub9QBt1BxBJcdTN2gDYqLeap3inUW4uwrVG6xnj5jgK7gBzeOeh1DNUybb5oT58605teeFSr/nQP9naVzdiBcxuVRj20UQlIbUrqGvHgTWsqwJjyN5ppBQV2ATm9YdgtJtnOamyrNsOoZ+h1qjywTwRQoNepskkUbVR6vQF3ELNzMexaVS73UPIKBXYBObzzsuXXAaDAoENrtVn1M/a+yQDaakpVeZjx2hozSgr0OHyGNioBwMFhDwBgdwsFdimp71+Ogsm1OWmh9lqL6mfsg64g2qpL5R5GVlIblaw0Y0/rHPGgylyI5opiuYeSVyiwCySR5Jj0h2XZnLRQe50FrkAE7kBE1nFkK/N1bLCpNxDQRqVzOoc92N1STvl1iVFgF4grEEYiyWWriMnILDj2qTQdo5SvYy62N9NGJQAYn52D0xfGbsqvS06QwM4Yu54xdpIxNsAY+7wQ11Qbx2yqhl3ugJQJ7GrNs2c2ecmd0soFbVRK6RxJ5dd3UX5dcjkHdsaYHsB/A7gBQDuAOxhj7bleV20ym5MaZA7sZcVG1FuLVJtnH5+Vd5OXEGijUsrB4VmYTQasU+F+BLUTYsa+G8AA53yIcx4FcB+AWwS4rqpkNifVKiAgbai1qHjGrpyvYy62NdlwJM83Kh0cnsHOZpsqDkrRGiECez2AsQXvj6c/llcc3jmUFRlRqoCWpO11Fgy5g5iPJuQeyoo5vfOK+TrmYluTFTN5vFFpJhjBoDtE9esykWzxlDF2J2OsizHW5Xa7pbqtZJzesGLSB+21FiQ5cHIqIPdQVswp00ElQstsVDp0Jj/TMZ0jqeem+nV5CBHYHQAaF7zfkP7Y63DO7+Gc7+Sc76yqqhLgtsqihBr2jI5MawEV5tlTm7yU8XXMxTq7GbZiI/52SnuTmOXoHPGgwKDDpoYyuYeSl4QI7J0A2hhjqxhjBQBuB/CYANdVjWSS44wnhOYKZWyBb7AVwVxoQO+E+srttDJj1+sYrtlQg7/2uxCNJ+UejuQ6RzzY1mhFoUEv91DyUs6BnXMeB/AxAE8C6APwAOe8J9frqsmEP4xwLInVVcoI7IwxbKhT3w7UQDgGfziuicAOAPs67AiE43h1aEbuoUgqFImjx+mn+nUZCZJj55z/hXO+lnO+hnP+DSGuqSbD7lSbXCU1rWqvtaB/MoCkinqzT/jkbXsstCvaKlFcoMeTPZNyD0VSh0dnkUhyql+XEe08FcDQdBAAsKZKOf1N2uvSvdlVVJVx7qAS9efYAcBk1OPKtVV4undKVT9gc3Vw2AMdA7Y32+QeSt6iwC6AIXcIxQV62fqwL+bsDlQVpWPO7TrVxowdSKVjXIEIjozlT7fHg8MedNSVqb5kVc0osAtgaDqEVZUlimp01FZTCoOOqWoB1emdTx9Uoo0ZOwBctb4aBh3DU3mSjonEEzg65qU0jMwosAtgeDqI1QpKwwBAoUGP1upSlc3YUweVaGmnYlmREZeuqcATPZN5sQu12+FDJJ6khVOZUWDPUTiWwPjsvKIWTjPa69TVWkDug0rEsq/DjjMzc6rcMLZSB4dTG5N2tVB+XU4U2HM06pkD58AahZQ6LtRea8GUP4LpoDp6s6dq2LWThsm4rr0GjAFPdk/JPRTRdY54sKaqBBWlyllvykcU2HM0pMBSx4zM4dZq6M2eSHJM+uQ/qEQM1RYTtjVaNV/2mEhydI54KA2jABTYc5QpdVRkYFdRZYw7EEFc5QdsXMi+Djt6J/wYU1H56UodHfMiEI5TYFcACuw5GnaHUGUuhNlklHsob2AtLkj1ZlfBjN3hTQU8LQd2AJqetf/+4ChKCvS4tt0u91DyHgX2HA1Nh7BagbP1jA0qOdzake7DrsXFUwBoqSzBuhoznurRZp7dNxfDn447ccu2eqpfVwAK7Dkang4ppkfMYtrrLBh0BxGOKbs3e2ZzUm2Z9hZPM/ZttKPzjEc1i9kr8fCRcYRjSbxnd5PcQyGgwJ4T71wUnlAUqyuVVcO+0Nne7JPKLrVzeudhMRkUmdISyr6OGnAOPNOrrVk75xz3vjaKLY1WbKynNr1KQIE9B0PTyq2IycgsoPZPKjsdo5V2vRfSXmtBg61Ic3n2rjOzOO0K4u9ptq4YFNhzkCl1VHIqps6a2smZOSRaqRwKOoFKLIwxXNdux0sDM4jElZ0aW4l7XxuFudCAm7bUyj0UkkaBPQfD00EYdAyN5cVyD2VJBr0OdosJDoUH9nyYsQPAjmYbookkTk0G5R6KIGZDUfz5xARu3V6P4gJaNFUKCuw5GHKH0FReDKNe2V/GemsRxr3KDezBSBy++VheBPbMsYU9TvU0Z7uQhw6PIxpP4j2XUBpGSZQdkRRuON3VUenqbUWKnrFPnG3Xq92KmIym8mKYCw3oUUEJ6sVwznHvwVHsaLZhvd0i93DIAhTYs5RMcvUEdmsRJv1hxBPKPHvz3AEb2p+x63SpYwu7NTBjf3XIgyF3iEocFYgCe5acvnlE4knFtetdTL2tCIkkx1RAmfXTTq+2jsS7mI11Zeib8COh8lOV7j04irIiI966mRZNlYYCe5aU3PzrfJmZsFLTMecO2MiPjoAddRaEY0kMudW7gDodjOCJ7gm8fXs9TEa93MMh56HAnqXhdA27Etv1ni/TMTHTj0VpnN552C0mGBS+CC2UjvrMAqp68+x/ODSOWILj72nRVJHy41+SCIanQygp0KNKBbNMpc/YHRrtw76U1qpSFBp0qq6MeeyoEzuabWitNss9FLIICuxZGnSnjsNT0jmnSzEZ9agsLTi7SKk0Tl9+1LBnGPQ6rLeb0e1Q54x90hdG74QfezfUyD0UsgQK7FlSS0VMRr21SJG7TzMHbORTYAeAjvoy9Dh9qjwH9dl+FwDg6vXVMo+ELIUCexbCsQQc3nlFtxI4X72tSJEz9ulgBLGEdg/YWEpHnQX+cFyRP2wv5tl+F+qtRVhbo/yKsHxFgT0LZ2ZS55yqbcbu9M4rboaYCWz1eZRjB1Ilj4D6dqCGYwm8NDCNq9dXqyINma8osGchU6a2RgU17Bn11iKEY0nMhKJyD+V1nGc3Jym3344Y1tnN0OuY6ipjXhv2YD6WoDSMwlFgz0KmXW+LmmbstlTgVFpljDOP2gksZDLq0VpVim6HumbsB/pdMBl1uHRNhdxDIRdAgT0LQ+4QaiyFqjoC7GzJo8Ly7E7vPMwaP2BjKR31FlXN2DnneLbfhTetqaRNSQpHgT0Lw9NBVeXXgQWblBQ2Y8+HPuxL6agrgysQgSsQlnsoyzLoDmHUM4erKA2jeBTYszA0HcIqBR+Ht5iyIiPMhQZFztjzrSImY2OdunagHqAyR9WgwL5Cs6EovHMxVbQSOF+9TXm17KnNSfmVX89ozwR2leTZn+13Yb3dnLe/YakJBfYVGppOVcSoLRUDpPLsSpqxhyJxeOfy44CNxZhNRrRUFKtixu4Px9A54qE0jEpQYF8hNXV1PF/qwA3lNAKb8OVPH/aldNSVqSKwv3BqGvEkpzSMSqgqsHeNePBA15isYxh0h2DUMzQp+JzTpdRbi+APxxEIx+QeCoDUwimQP33YF9NRb8GoZw6+eWX8nSzl2X4XyoqM2NZolXsoZBkYQRv8AAAV/0lEQVRUFdj/dHwCX/9jr6y7JwfdQbRUlKiyxey59r3KSMeMpPcDNNjyOLCnd6D2KnjWnkxyPH/KhSvXVqny+z4f5fS3xBj7DmOsnzF2nDG2nzEm6o/z5opiBCNxWXdPDrqDqtpxupDS2vceH/ehsrQAdkt+Lp4C6jjc+rjDh+lglNIwKpLrj9+nAWzknG8GcArAF3If0tKaK1LpjzMzITFvs6RYIonRmTmsqVZffh1Q3oz9hMOLTfVled1zpLK0EHaLSdF59mf7XdAx4Mq1VXIPhSxTToGdc/4U5zyefvdVAA25D2lpzRWpgDoyLc8C4JmZOcSTXLUz9sqSQhQYdIqYsYcicQy4gtjcQDnbjjqLomfsB/pd2N5kg62kQO6hkGUSMmH2AQCPC3i9N2iwFUHH5JuxD6qw+ddCOh1L9WVXwIy9x+lHkgObG8rkHorsOurLMOAKYj6akHsob+Dyh3HC4aMyR5W5aGBnjD3DGOte5O2WBa/5EoA4gN9d4Dp3Msa6GGNdbrc7q8EWGvSosxbhjEeeGXsmsKupD/v56q1FipixHx/3AgA2UWBHR50FSQ70TyovHfOn4xMAgOva6bQkNbloFyvO+d4LfZ4x9o8AbgJwDb9AuQrn/B4A9wDAzp07sy5raakowciMTIHdlWr+peaGVfXWIjx70iX3MHB83IfaMhOqzfm7cJqxsT71w63b6ce2JpvMo3m9hw6PY1N9Gdpq6GxTNcm1KuZ6AJ8DcDPnXJJo21xRLGsqRq1pmIx6WxHcgQjCMXl/7T/h8GFTPc3WAaCuzIQqcyFeGZyWeyiv0z/pR4/Tj9u218s9FLJCuebYfwjADOBpxthRxtiPBRjTBTVXFMM7F4NvTtoNHZxzbQT2dMnjhE++joK++RiGp0PYQptdAACMMezrqMGBfrei8uwPHRqHUc9w81YK7GqTa1VMK+e8kXO+Nf32z0INbCmZypgzHmln7e5gBIFwXJXNvxZSQvvezOESNGM/58ZNtZiPJXBAAWkyAIgnkth/xImr1lWjnKphVEd128haMiWPEufZB12pHyRrqrUxY3d45esZcyy9cEoVMedcsqoCFSUF+MuJCbmHAgB4YWAa08EIbtshagUzEYnqAnumR8uZaWln7Govdcywl5mgY/LO2E+M+9BUXgxrMc0EM/Q6hn0b7Xi23yX7+geQSsPYio24ah2VOaqR6gJ7UYEedotJ+hm7O4ji9L3VzKjXwW4xyVrLfnzcR7P1Rbx1Uy3mogk8J3M6xjcfw1O9U7h5Sx0KDKoLEQQqDOyAPJUxA64gVleVQKdT//b3VPteeQL7dDACh3eeAvsiLllVjvKSAvzlxKSs4/jz8QlE40lKw6iYegO7xJuUhtwh1adhMuQ8cOPEeGrhlFoJvJFBr8O+jhr8tW9K1nTMQ4fH0VZdSovbKqbSwF4CdyCCUCR+8RcLYC4ah8M7r53AbivCpC+MRFL69sfHx31g7NymHPJ6N26qRSiawN9OZbc7O1fD0yEcOjOL23Y05HVzNrVTZWDPVMackSjPnjk1STOB3VqMeJJjyi99LfsJhxdrqkpRWnjRTc95ac/qCliLjbJVx+w/PA4dA27dRrXraqbKwC51+95MRUyryksdM+Rq38s5x7FxHzbTbH1JRr0O+9rteKZP+uqYZJLjocMOXNZaiRqVFwnkO1UHdqkqYwbdIejYufuqnVwHbkz5I3AHIrRwehE3bLIjGInjxdPSthh4bdgDh3ce76BFU9VTZWA3m4yoKCnAqES7TwfdQTSWF8Nk1EtyP7Gd26QkbWA/drajIy2cXshlrZUoK5I+HfPgoTGUFhpwXbtd0vsS4akysAOp2bNUB24MutTfI2ahogI9KkoKMC7xjP3EuA96HTt7HBxZnFGvw3XtNXi6bwqRuDTpmP5JPx454sA7djSgqEAbE5h8ptrA3lJRIkmOPZHkGJ4Oqb5HzPnqbdKXPB4b92JtjVkzv/mI6cZNtQiE43hpQPx0DOccX/9jLyxFRnxib5vo9yPiU21gb64ogdMXFn2ByemdRySe1NSMHcgcuCHdXgDOOU44fNhC+fVluay1EmaTAX8+Lv5mpSd7pvDy4Aw+de1aavOgEaoN7C2VqYXMMZE3Kg1kesRopCImo6WyBKOeOckqL8Y88/DOxejEpGUqMOhwbXsNnu6dRFDE/RrhWALf+Esv1tWY8Z7dTaLdh0hLtYG9WaJa9kGXNpp/nW9nsw2xBMfRMa8k9zvuSN1nCy2cLtv79jQjGInj3x/pFu0eP39xGGOeeXzl79ph0Ks2HJDzqPZvsrk8U/Iobp590B2CrdiouZ7UO5vLwRjQOeyR5H7Hx30o0Ouwlo5YW7ZtTTZ8/Jo27D/iwEOHxgW//qQvjP8+MIB9HTW4rLVS8OsT+ag2sFuLjbCYDOLP2DVwatJiyoqNWFdjxsERqQK7FxvqLNQtcIX+9eo27F5Vjn9/tBtD6bSgUL79RD/iSY4v3dgu6HWJ/FT7r4wxhpbKEtFn7EMaDewAsHtVOQ6fmUU8kRT1PvFEEidox2lW9DqGH9y+FQUGHf7190cEK388PDqLh4848OErVqFJIxvvyDmqDexAKs8+KuLiqXcuiulgFGuqtVXqmLGrpRyhaAK9E35R79M/GUAomsDOFpuo99Gq2rIifPu2zehx+vGtx0/mfL1kkuNrj/WgxlKIf3lLqwAjJEqj6sDeUlGM8dl5xESacQ5qrPnX+XavKgcAHBQ5z96ZTvdk7kdW7roOO/7xTS34xUvDeLZ/KqdrPdU7hWPjPvzb9etRQs3YNEnVgb25ogSJJBet54lWjsNbSo3FhOaKYkkCe721CLVlRaLeR+s+f8N6bKi14DMPHsd4DnsQ7uschd1iwi1bqYOjVqk8sItbGTPoDqJAr0ODTbsBaVdLOTpHPOBcnN7snHN0jsxiF6VhcmYy6vHD92zDfDSBK759ALf88EX855Mn8erQDKLx5f3WOuGbx99OufGOHQ3Qa+A0MLI4TQR2sSpjBl0htFQWa7q+d3dLOWbnYhhwCVtxkTHqmYM7EMEuSsMIYk1VKR772GW4+5o2GPQ6/Oj5Qdx+z6vY+vWncNe9hzEXvfBmpocPO5DkwDt3UgdHLVN1gq2qtBDFBXrxArs7iPV2bdddn82zj3jQJkKNeSbNs6uFArtQ2mrM+ESNGZ/Yuxb+cAyvDs7guVNu3PvaKDbVl+Gfr1yz6P+XTHI80DWGPavLz27wI9qk6qkoYwzNIjUDm4vGMTITEiXYKUlzRTGqzIWibVTqGplFWZERrRpdp5CbxWTEdR12/N9bN+HKtVX4yfODS7YgODjiwZmZObxrZ6PEoyRSU3VgB1KVMWLk2E9OBsA50F6r7RazjDHsbikXbQG184wHu1ps0FE+V3SfvHYtZudi+PXLI4t+/oGuMZgLDbhhY620AyOSU31gb6ooxphnXvCDmfsmAgCQF73Dd68qh9MXzqnSYjHTwQiG3CHspDSMJLY2WnH1+mrc87chBMKx133OH47hLycm8Hdb66jfeh5QfWBvqShBNJHEhE/YksfeCR/MhQZNV8RkZPLfnQK3F+gamU1fnypipPLJvWvhm4/hVy+NvO7jfzo2gXAsSWmYPKH6wJ6pjBkVeAG11+nHhjoLGNN+CmGd3QyzySB4OqZrxINCgw4bqZWAZDY1lGHvhhr89IUh+ObPzdof6BrDuhoz9cPPE6oP7C3p1f2haeHy7IkkR/9kQPP59Qy9jmGXCHn2zhEPtjRaUWigX/2l9Im9bfCH4/jFi8MAgFNTARwd8+KdOxvyYqJCNBDY7RYTbMVGQfuKn5kJYS6aQHse5NczdrWUY9AdwnQwIsj15qJxdDv9lIaRwcb6MlzfYccvXhyGby6GBzrHYNQz3LqNdprmC9UHdp2O4ZJVFXh1aEawa2aaYuXLjB0Adq9KBeAugfLsR0e9SCQ51a/L5O69bQhE4vjR84PYf8SBvRtqUFFaKPewiERUH9gBYM/qcozPzgt2TF6v0w+DjqGtJn9qrzfVW1Fo0OHg8Kwg1zs44gFjwPZmmrHLYUOtBW/dVIsfPz+ImVCUFk3zjDYC+5oKAMBrAuWIeyf8aK0uzavccIFBh21NVhwcEeY3n66RWay3W2AxGQW5Hlm5u/e2gbFUuvLNa6vkHg6RkCYC+9pqM2zFRrwyKExQ6nX68yq/nrF7VQV6nf431ECvVDyRxOHRWeym/Lqs1taY8cUbNuDLN22ghl95RtW9YjJ0OoY9q4XJs7sDEbgCkbzKr2fsbilHkgOHR724MocZXu+EH3PRBG1MUoAPv3m13EMgMhBkxs4Y+zRjjDPGZDsRd8/qCji8uefZ+zILp3k4Y9/ebEWRUY/fvDKSUxvfzrMbkyiwEyKHnAM7Y6wRwHUARnMfTvb2rE7l2XOdtfflYUVMRnGBAZ+8tg3P9LnwZM9k1tfpGvGgsbwI9jKTgKMjhCyXEDP27wH4HABxTmpYprbqUpSXFODVodwWUHsn/Ki3FsFaXCDQyNTlny5bhQ21Fnz1sZ6scu2pgzU82NVMs3VC5JJTYGeM3QLAwTk/JtB4spbKs5fj1aGZnNIIvU4/NtRqu1XvhRj1Onzz7ZvgCkTwn0+u/ODkkZk5TAejlF8nREYXDeyMsWcYY92LvN0C4IsAvrKcGzHG7mSMdTHGutxud67jXlQmzz6e5Rmo4VgCg+5gXqZhFtraaMX79zTjf189s+IdvS+cTv3d0o5TQuRz0cDOOd/LOd94/huAIQCrABxjjI0AaABwmDFmX+I693DOd3LOd1ZViVNTm8mzv5Jlnv3kZABJnp8Lp+f7zL51qDYX4gsPn0AssbzzNP3hGP7rrwPY0mhFa3X+bO4iRGmyTsVwzk9wzqs55y2c8xYA4wC2c86zX3XL0bk8e3aB/VwrAeqAZzYZ8bWbO9A34ccvXxpe1v/z/adPYyYUwX/c0kHNpgiRkSY2KGUwlsqzvzbkySrP3uv0500P9uXY12HH3g3V+N7Tpy9aRto/6cevXxnBHbubsLnBKs0ACSGLEiywp2fu00JdL1uXnq1nX3mevXfCjw21FjrGLY0xhq/dshGMAV/cfwLxJVIynHN85ZEemE0GfPa6dRKPkhByPk3N2IHs69mTSY6+ifxsJXAh9dYifOHGDXjh9DTu/M0hzEXfeFDyo0edODjiwef2rYetJD/LRAlREs0F9tbqUlRkkWc/45lL9WDP84qYxbxvTzP+z9s24rmTLtxxz6uv69keCMfwjb/0YUtDGd69izoIEqIEmgvsqTx7xYrr2Xud+dtKYDneu6cZP3nfTpycCuC2H72MkfSJVT945jSmgxF8/ZaN1GiKEIXQXGAHUm18nb7wivLsvRM+GHSMyvQu4Nr2Gtz74T3wz8dw249exh8OjeOXL4/g9l2N2NJIC6aEKIUmA/ulq1O7Hl8ZWv5abt9EAK3VpTAZ86cHeza2N9nw0EffhOJCPT7z4LHUgum+9XIPixCygCYD+5qqUlSWrqxvTK/TT/n1ZVpdVYqHP3oZrmuvwTdv3YRyWjAlRFE00Y/9fIwxXLK6Ai8PTiOeSMKgv/DPr5lgBJP+MOXXV6DKXIh73r9T7mEQQhahyRk7ANyypQ5T/gh+9fLIRV/bNxEAkJ+tegkh2qPZwH5tew2uWleF7z59Ck7vhRdRHz3qAGOpA4AJIUTtNBvYGWP4+i0bkeQcX/tjz5Kve+SIAw8eGsdHr1xDm2sIIZqg2cAOAI3lxfj4NW14smcKz/ROveHzA64gvrj/BHa12PCpa9fKMEJCCBGepgM7AHz4itVYW1OKrz7W87rt8PPRBO763WGYjHr8vzu2X3SBlRBC1ELz0cyo1+Ebt26CwzuPH/z19NmPf+2PPTg5FcD33r2VzuYkhGiK5gM7AOxqKce7dzbi5y8Mo3/Sj/1HxnFf5xjuumoNrlwrzqEfhBAiF03WsS/m8zesx9N9U/jEfUcx6pnD7pZyfHIv5dUJIdqTFzN2ALCVFOCLN25A/2QAJqMe/3XHNsqrE0I0KW9m7ABw2/Z6THjncVlbJeXVCSGalVeBnTGGf72mTe5hEEKIqCgXQQghGkOBnRBCNIYCOyGEaAwFdkII0RgK7IQQojEU2AkhRGMosBNCiMZQYCeEEI1hnHPpb8qYG8AZyW8snEoA03IPQkRafj56NvXS8vMt99maOecX7VwoS2BXO8ZYF+dcsyc5a/n56NnUS8vPJ/SzUSqGEEI0hgI7IYRoDAX27Nwj9wBEpuXno2dTLy0/n6DPRjl2QgjRGJqxE0KIxlBgvwjG2C8YYy7GWPeCj5Uzxp5mjJ1O/9cm5xizxRhrZIwdYIz1MsZ6GGN3pz+uleczMcYOMsaOpZ/va+mPr2KMvcYYG2CM3c8YK5B7rNlijOkZY0cYY39Kv6+JZ2OMjTDGTjDGjjLGutIf08T3JQAwxqyMsT8wxvoZY32MsUuFfD4K7Bf3KwDXn/exzwP4K+e8DcBf0++rURzApznn7QD2ALiLMdYO7TxfBMDVnPMtALYCuJ4xtgfAtwB8j3PeCmAWwAdlHGOu7gbQt+B9LT3bVZzzrQvKALXyfQkAPwDwBOd8PYAtSP0dCvd8nHN6u8gbgBYA3QvePwmgNv3nWgAn5R6jQM/5KIBrtfh8AIoBHAZwCVIbQQzpj18K4Em5x5flMzWkA8DVAP4EgGno2UYAVJ73MU18XwIoAzCM9BqnGM9HM/bs1HDOJ9J/ngRQI+dghMAYawGwDcBr0NDzpVMVRwG4ADwNYBCAl3MeT79kHEC9XOPL0fcBfA5AMv1+BbTzbBzAU4yxQ4yxO9Mf08r35SoAbgC/TKfRfsYYK4GAz0eBPUc89eNV1aVFjLFSAA8B+ATn3L/wc2p/Ps55gnO+FanZ7W4A62UekiAYYzcBcHHOD8k9FpFczjnfDuAGpFKEb174SZV/XxoAbAfwI875NgAhnJd2yfX5KLBnZ4oxVgsA6f+6ZB5P1hhjRqSC+u845w+nP6yZ58vgnHsBHEAqPWFljGUOcm8A4JBtYNm7DMDNjLERAPchlY75AbTxbOCcO9L/dQHYj9QPZa18X44DGOecv5Z+/w9IBXrBno8Ce3YeA/AP6T//A1K5adVhjDEAPwfQxzn/7oJPaeX5qhhj1vSfi5BaP+hDKsC/I/0yVT4f5/wLnPMGznkLgNsBPMs5/3to4NkYYyWMMXPmzwCuA9ANjXxfcs4nAYwxxtalP3QNgF4I+Hy0QekiGGO/B/AWpLqvTQH4KoBHADwAoAmpLpXv4px75BpjthhjlwN4AcAJnMvTfhGpPLsWnm8zgF8D0CM1iXmAc/51xthqpGa55QCOAHgv5zwi30hzwxh7C4DPcM5v0sKzpZ9hf/pdA4B7OeffYIxVQAPflwDAGNsK4GcACgAMAfgnpL9HIcDzUWAnhBCNoVQMIYRoDAV2QgjRGArshBCiMRTYCSFEYyiwE0KIxlBgJ4QQjaHATgghGkOBnRBCNOb/A3CHBUTdrkvrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_X = drive_durations\n",
    "import matplotlib.pyplot as plotter\n",
    "plot_Ynew = get_amplitude(plot_Y)\n",
    "plot_Y = plot_Y - np.mean(plot_Y)\n",
    "plotter.plot(plot_X, plot_Ynew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1310dca90>"
      ]
     },
     "execution_count": 41,
     "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": [
    "Y_amp_plots = []\n",
    "for i in range(len(Y_plots)):\n",
    "    Y_amp_plots.append(get_amplitude(Y_plots[i]))\n",
    "print (len(Y_amp_plots))\n",
    "plotter.pcolormesh(drive_durations,qubit_rabi_frequencies, Y_amp_plots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chevron pattern with power on x-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pow_chevron_codes = ['5db8c6b6889b23001aee6198', '5db8c6cbf52ef7001ae7881b', '5db8c6d2f52ef7001ae7881d', '5db8c6d6f52ef7001ae7881f', '5db8c6eb99afd80019fc53f7', '5db8c6f699afd80019fc53f9', '5db8c6fc83968b00193a3f30', '5db8c704889b23001aee619a', '5db8c70761a787001995435b', '5db8c70c99afd80019fc53fb', '5db8c710bb72de0018d8f401', '5db8c71361a787001995435d', '5db8c716e440250018ba842d', '5db8c71a99afd80019fc53fe', '5db8c71df52ef7001ae78821', '5db8c721f52ef7001ae78823', '5db8c725a693ee001877ed32', '5db8c72861a787001995435f', '5db8c72ba693ee001877ed34', '5db8c72fd3a8f10018a3e5b4', '5db8c732a693ee001877ed36', '5db8c73615a70b001967dcf1', '5db8c73b61a7870019954361', '5db8c73f61a7870019954363', '5db8c742889b23001aee619d', '5db8c74699afd80019fc5400', '5db8c749889b23001aee619f', '5db8c74ce440250018ba842f', '5db8c74f15a70b001967dcf3', '5db8c75383968b00193a3f32', '5db8c76715a70b001967dcf5', '5db8c76dbb72de0018d8f403', '5db8c770f52ef7001ae78825', '5db8c77461a7870019954365', '5db8c778a693ee001877ed38', '5db8c77ca693ee001877ed3a', '5db8c782bb72de0018d8f406', '5db8c78599afd80019fc5402', '5db8c789e440250018ba8431', '5db8c78de440250018ba8433']\n",
    "qubit_pow_rabi_frequencies = [4.8412952222858205, 4.841602914593513, 4.841910606901205, 4.842218299208898, 4.84252599151659, 4.842833683824282, 4.843141376131975, 4.843449068439667, 4.843756760747359, 4.844064453055052, 4.844372145362744, 4.844679837670436, 4.844987529978129, 4.845295222285821, 4.845602914593513, 4.845910606901206, 4.846218299208898, 4.84652599151659, 4.846833683824283, 4.847141376131975, 4.8474490684396665, 4.847756760747359, 4.848064453055051, 4.8483721453627435, 4.848679837670436, 4.848987529978128, 4.8492952222858206, 4.849602914593513, 4.849910606901205, 4.850218299208898, 4.85052599151659, 4.850833683824282, 4.851141376131975, 4.851449068439667, 4.851756760747359, 4.852064453055052, 4.852372145362744, 4.852679837670436, 4.852987529978129, 4.853295222285821]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['5db8c6b6889b23001aee6198', '5db8c6cbf52ef7001ae7881b', '5db8c6d2f52ef7001ae7881d', '5db8c6d6f52ef7001ae7881f', '5db8c6eb99afd80019fc53f7', '5db8c6f699afd80019fc53f9', '5db8c6fc83968b00193a3f30', '5db8c704889b23001aee619a', '5db8c70761a787001995435b', '5db8c70c99afd80019fc53fb', '5db8c710bb72de0018d8f401', '5db8c71361a787001995435d', '5db8c716e440250018ba842d', '5db8c71a99afd80019fc53fe', '5db8c71df52ef7001ae78821', '5db8c721f52ef7001ae78823', '5db8c725a693ee001877ed32', '5db8c72861a787001995435f', '5db8c72ba693ee001877ed34', '5db8c72fd3a8f10018a3e5b4', '5db8c732a693ee001877ed36', '5db8c73615a70b001967dcf1', '5db8c73b61a7870019954361', '5db8c73f61a7870019954363', '5db8c742889b23001aee619d', '5db8c74699afd80019fc5400', '5db8c749889b23001aee619f', '5db8c74ce440250018ba842f', '5db8c74f15a70b001967dcf3', '5db8c75383968b00193a3f32', '5db8c76715a70b001967dcf5', '5db8c76dbb72de0018d8f403', '5db8c770f52ef7001ae78825', '5db8c77461a7870019954365', '5db8c778a693ee001877ed38', '5db8c77ca693ee001877ed3a', '5db8c782bb72de0018d8f406', '5db8c78599afd80019fc5402', '5db8c789e440250018ba8431', '5db8c78de440250018ba8433']\n",
      "[4.8412952222858205, 4.841602914593513, 4.841910606901205, 4.842218299208898, 4.84252599151659, 4.842833683824282, 4.843141376131975, 4.843449068439667, 4.843756760747359, 4.844064453055052, 4.844372145362744, 4.844679837670436, 4.844987529978129, 4.845295222285821, 4.845602914593513, 4.845910606901206, 4.846218299208898, 4.84652599151659, 4.846833683824283, 4.847141376131975, 4.8474490684396665, 4.847756760747359, 4.848064453055051, 4.8483721453627435, 4.848679837670436, 4.848987529978128, 4.8492952222858206, 4.849602914593513, 4.849910606901205, 4.850218299208898, 4.85052599151659, 4.850833683824282, 4.851141376131975, 4.851449068439667, 4.851756760747359, 4.852064453055052, 4.852372145362744, 4.852679837670436, 4.852987529978129, 4.853295222285821]\n"
     ]
    }
   ],
   "source": [
    "# Rabi experiment parameters\n",
    "num_Rabi_points = 70\n",
    "num_freq_points = 40\n",
    "num_shots_per_point = 512\n",
    "backend_defaults = backend.defaults()\n",
    "rough_qubit_freq = backend_defaults.qubit_freq_est[qubit]\n",
    "sig_ma = 0.006\n",
    "\n",
    "drive_frequencies = np.linspace(rough_qubit_freq-sig_ma,rough_qubit_freq+sig_ma,num_freq_points)\n",
    "\n",
    "# drive parameters\n",
    "drive_power_min = 0.0\n",
    "drive_power_max = 0.15\n",
    "drive_powers = np.linspace(drive_power_min, drive_power_max, num_Rabi_points)\n",
    "drive_samples = 128\n",
    "drive_sigma = 16\n",
    "\n",
    "# create schedules for Rabi experiment \n",
    "Rabi_schedules = []\n",
    "Rabi_schedule_LOs = []\n",
    "\n",
    "mm=39\n",
    "for ii, drive_power in enumerate(drive_powers):\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_samples, amp=drive_power, \n",
    "                                    sigma=drive_sigma, name='rabi_pulse_{}'.format(ii))\n",
    "    rabi_pulse_qubit = rabi_pulse(backend_devicespec.q[qubit].drive)\n",
    "    # start an empty schedule with a label\n",
    "    this_schedule = pulse.Schedule(name=\"Rabi drive = {}\".format(drive_power))\n",
    "    this_schedule += rabi_pulse_qubit\n",
    "    this_schedule += measure_and_acquire_qubit << this_schedule.duration\n",
    "    \n",
    "    Rabi_schedules.append(this_schedule)\n",
    "    thisLO = pulse.LoConfig({backend_devicespec.q[qubit].drive: drive_frequencies[mm]})\n",
    "    Rabi_schedule_LOs.append(thisLO)\n",
    "    \n",
    "\n",
    "rabi_experiment_qobj = assemble (Rabi_schedules, backend = backend, \n",
    "                            meas_level=1, meas_return='avg',\n",
    "                            shots=num_shots_per_point,\n",
    "                            schedule_los = Rabi_schedule_LOs\n",
    "                                )\n",
    "\n",
    "# job = backend.run(rabi_experiment_qobj)\n",
    "# job_code =  job.job_id()\n",
    "print (pow_chevron_codes)\n",
    "print (qubit_pow_rabi_frequencies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_plots= []\n",
    "for k in range(len(pow_chevron_codes)):\n",
    "    job = backend.retrieve_job(pow_chevron_codes[k])\n",
    "    Rabi_results = job.result(timeout=3600)\n",
    "    \n",
    "\n",
    "    plot_Y = []\n",
    "    for jj, drive_power in enumerate(drive_powers):\n",
    "        thispower_results = Rabi_results.get_memory(jj)*scale_factor\n",
    "        plot_Y.append( thispower_results[qubit] )\n",
    "    Y_plots.append(plot_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x130fba0f0>]"
      ]
     },
     "execution_count": 43,
     "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": [
    "plot_X = drive_powers\n",
    "plot_Ynew = get_amplitude(plot_Y)\n",
    "plot_Y = plot_Y - np.mean(plot_Y)\n",
    "plotter.plot(plot_X, plot_Ynew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x131cc6d30>"
      ]
     },
     "execution_count": 46,
     "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": [
    "Y_amp_plots = []\n",
    "for i in range(len(Y_plots)):\n",
    "    Y_amp_plots.append(get_amplitude(Y_plots[i]))\n",
    "print (len(Y_amp_plots))\n",
    "plotter.pcolormesh(drive_powers,qubit_rabi_frequencies, Y_amp_plots)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}