single_qubit_rabi.ipynb

{
 "cells": [
  {
   "attachments": {
    "qiskit_header.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![qiskit_header.png](attachment:qiskit_header.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Calibrating a qubit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "import pprint as pp\n",
    "\n",
    "from qiskit import IBMQ\n",
    "import qiskit.pulse as pulse\n",
    "import qiskit.pulse.pulse_lib as pulse_lib\n",
    "from qiskit.compiler import assemble\n",
    "from qiskit.qobj.utils import MeasLevel, MeasReturnType"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Loading a pulse backend\n",
    "To run an OpenPulse experiment, at first we need to load a backend object of your quantum computing system with `open_pulse = True`. Then you will be able to program a `pulse` experiment using the pulse module. For more information on how to use the `pulse` module see this [tutorial](../terra/5_pulse_schedules.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we use `ibmq_armonk`, a one qubit system publicly available on the IBM Q Experience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Credentials are already in use. The existing account in the session will be replaced.\n"
     ]
    }
   ],
   "source": [
    "IBMQ.load_account()\n",
    "ibmq_provider = IBMQ.get_provider(\n",
    "    hub='ibm-q',\n",
    "    group='open',\n",
    "    project='main'\n",
    ")\n",
    "backend = ibmq_provider.get_backend('ibmq_armonk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = backend.configuration()\n",
    "defaults = backend.defaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating our pulses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "User defined pulses can be created from the standard pulse library found in `pulse_lib`, and able to find calibrated pulses provided by the backend in `defaults.circuit_instruction_map`. In this notebook, we use a Gaussian pulse template from `pulse_lib.gaussian` and default measurement pulses from the `circuit_instruction_map`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "circ_inst_map = defaults.circuit_instruction_map\n",
    "\n",
    "measure = circ_inst_map.get('measure', qubits=config.meas_map[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# auto-phase the output measurement signal\n",
    "\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": [
    "In this notebook, we apply the backend default setting for drive and measurement pulse frequency. These frequencies are calibrated to be centered at resonanse frequency of corresponding qubit and readout resonator. To change this setting, you can refer to the [qiskit community tutorials](https://github.com/Qiskit/qiskit-community-tutorials)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Running a Rabi experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In this experiment, we apply a Rabi drive pulse to qubit0 and measure a signal from the qubit by changing the amplitude of the pulse (`drive_amps`) from 0 to 0.05. The returned signal is integrated and averaged over 512 shot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# qubit to use for exeperiment\n",
    "qubit = 0\n",
    "\n",
    "# exp configuration\n",
    "exps = 64\n",
    "shots = 512\n",
    "\n",
    "# Rabi pulse\n",
    "drive_amps = np.linspace(0, 1.0, exps)\n",
    "drive_samples = 2048\n",
    "drive_sigma = 256\n",
    "\n",
    "# scaling factor for data returned by backend\n",
    "# note: You may have to adjust this for the backend you use\n",
    "scale_factor=1e-10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Pulse commands are registered to `Schedule`s with information of `channel` and the timestep they occur at. You can create your `Schedule` by using schedule composition methods or their syntactic sugar as shown below:  \n",
    "\n",
    "- `|` (`union`): combine two `Schedule`s in parallel.\n",
    "- `+` (`append`): add a `Schedule` right after a `Schedule` to be appended.\n",
    "- `<<` (`shift`): shift `start_time` of `Schedule`.\n",
    "\n",
    "Once create, `Schedule`s can be combined to form a composite `Schedule`. More details are available in this [tutorial](../terra/5_pulse_schedules.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Create schedule\n",
    "schedules = []\n",
    "for ii, drive_amp in enumerate(drive_amps):\n",
    "    # drive pulse\n",
    "    rabi_pulse = pulse_lib.gaussian(duration=drive_samples, amp=drive_amp, sigma=drive_sigma, name='rabi_pulse_%d' % ii)\n",
    "    \n",
    "    # add commands to schedule\n",
    "    schedule = pulse.Schedule(name='Rabi Experiment at drive amp = %s' % drive_amp)\n",
    "    \n",
    "    schedule |= rabi_pulse(pulse.DriveChannel(qubit))\n",
    "    schedule |= measure << schedule.duration\n",
    " \n",
    "    schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may plot any of the experiment schedules with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAKrCAYAAADCophVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzde5ScV3nn+99+q+vS1a1uSS3JwkK+RdgCISOM7zcMNiSESQKJmTWTC/eYADNrziQhyawMOSchMCdkHUKYJHAISUhCYE4ISbgOTIABjC+AnTQWbUmmrVu75VbfL9V1r9rnj+pWZFndb0td1XvXru9nLS/J9Va3HlvS07/ee7/Pa6y1AgAAQHNFrgsAAAAIESELAACgBQhZAAAALUDIAgAAaAFCFgAAQAt0uS5Akowx90m6T5K6u7tfsnfvXscVAQhJPp+XJGWzWceVAAhJPp/XkSNHJq2128933fg2wmHv3r32e4886roMAAEZHByUJB04cMBxJQBCMjg4qDvvuP1Ra+3157vuxUrW2dLptOsSAARmz549rksAEKC43uJdyEokEq5LABCY3t5e1yUACFBcb/Hu4Hu1WnVdAoDATE9Pa3p62nUZAAIT11e8W8kql8uuSwAQmJMnT0qStm7d6rgSACFZ7i0r8W4lCwAAIASELAAAgBYgZAEAALQAIQsAAKAFvDv4ns5kXJcAIDBXX3216xIABCiut3gXshIRi2sAmovH6QBohbje4l2iYU4WgGabmprS1NSU6zIABCaur3i3ksWcLADNNjIyIkkaGBhwXAmAkCz3lpV4t5IFAAAQAkIWAABACxCyAAAAWoCQBQAA0ALeHXzPMCcLQJPt3bvXdQkAAhTXW7wLWRFzsgA0Gd+8AWiFuN7iXaKpVCquSwAQmPHxcY2Pj7suA0Bg4vqKdytZhCwAzXbq1ClJ0o4dOxxXAiAky71lJd6tZAEAAISAkAUAANAChCwAAIAWIGQBAAC0gHcH3zPd3a5LABCYffv2uS4BQIDieot3ISsyxnUJAAKTTCZdlwAgQHG9xbvtQkY4AGi2sbExjY2NuS4DQGDi+gohC0DwCFkAWqHtQhYAAEAICFkAAAAtQMgCAABoAUIWAABAC3g3wqGbOVkAmmz//v2uSwAQoLje4l3IMszJAtBkiUTCdQkAAhTXW7zbLiyXy65LABCY0dFRjY6Oui4DQGDi+op3IatarbouAUBgJiYmNDEx4boMAIGJ6yvehSwAAIAQELIAAABagJAFAADQAoQsAACAFvBuhEM2m3VdAoDAHDhwwHUJAAIU11tYyQIAAGgB70IWc7IANNvIyIhGRkZclwEgMHF9xbuQxZwsAM02NTWlqakp12UACExcX/EuZAEAAISAkAUAANAChCwAAIAW8G6Eg2RcFwAgMImI7ycBNF9cb/EuZGWz3a5LABCY/dde67oEAAGK6y18ewcAANAC3oWsUqnkugQAgTlx/LhOHD/uugwAgYnrK95tF9ZqNdclAAjMzOysJOlyx3UACMtyb1mJdytZAAAAISBkAQAAtAAhCwAAoAW8O5NlDHOyADRXMpl0XQKAAMX1Fu9CVnc3c7IANNe+fftclwAgQHG9he1CAACAFvAuZDEnC0CzHT16VEePHnVdBoDAxPUV77YLmZMFoNnm5+ddlwAgQHG9xbuVLAAAgBAQsgAAAFqAkAUAANAC3p3JYk4WgGZLp1OuSwAQoLje4l3IYk4WgGZ7/vNf4LoEAAGK6y1sFwIAALSAdyGrWGROFoDmGh4e1vDwsOsyAAQmrq94t11YrzMnC0Bz5XI51yUACFBcb/FuJQsAACAEhCwAAIAWIGQBAAC0gHdnsqKI3AeguRgNA6AV4nqLdyErk8m4LgFAYK655hrXJQAIUFxvYdkIAACgBbwLWcVi0XUJAAJz5MgRHTlyxHUZAAIT11e82y6s1+uuSwAQmEKh4LoEAAGK6y3erWQBAACEgJAFAADQAoQsAACAFvDuTFYUJVyXACAwvb29rksAEKC43uJFyDLG3CfpPknavXu342oAhGbPnj2uSwAQoLje4sV2obX2o9ba662112/bts11OQAAAOvmRcg6G7daA2i2Q4ce16FDj7suA0Bg4vqKF9uFZ7PWui4BQGBKpbLrEgAEKK63eLeSBQAAEAJCFgAAQAsQsgAAAFrAuzNZiQRzsgA0V19fn+sSAAQorrd4F7LS6bTrEgAE5qqrrnJdAoAAxfUWtgsBAABawLuQxZwsAM02NDSkoaEh12UACExcX/Fuu5A5WQCarVKpuC4BQIDieot3K1kAAAAhIGQBAAC0ACELAACgBbw7k8WcLADNtmXzZtclAAhQXG/xLmQxJwtAs11+xRWuSwAQoLjewnYhAABAC3gXsvJ55mQBaK6Djz2mg4895roMAIGJ6yvebRdKzMkC0Fy1et11CQACFNdbvFvJAgAACAEhCwAAoAUIWQAAAC3g3Zmsri7vSgLQ5gYGBlyXACBAcb3Fu0STSqVclwAgMLt373ZdAoAAxfUWtgsBAABawLuQlc/nXZcAIDCDg4MaHBx0XQaAwMT1Fe9CFgAAQAgIWQAAAC1AyAIAAGgBQhYAAEALeDfCgTlZAJpt+/btrksAEKC43uJdomFOFoBm27Vrl+sSAAQorrd4t11orXVdAoDA1Go11Wo112UACExcX/EuZBUKBdclAAjMwYMHdfDgQddlAAhMXF/xLmQBAACEgJAFAADQAoQsAACAFiBkAQAAtIB3IxySyaTrEgAEZufOna5LABCguN5CyAIQPEIWgFaI6y3ebRfWmZMFoMkqlYoqlYrrMgAEJq6veBeyiszJAtBkQ0NDGhoacl0GgMDE9RXvQhYAAEAICFkAAAAtQMgCAABoAUIWAABACzDCAUDwLr30UtclAAhQXG8hZAEI3o4dO1yXACBAcb3Fu+3Cer3uugQAgSkWiyoWi67LABCYuL7iXciiEQJotsOHD+vw4cOuywAQmLi+4l3IAgAACAEhCwAAoAUIWQAAAC1AyAIAAGgB70Y4pFIp1yUACMzu3btdlwAgQHG9xbuQ1dXlXUkA2tzAwIDrEgAEKK63eLddWGNOFoAmy+fzyufzrssAEJi4vuJdyCoxJwtAkz3xxBN64oknXJcBIDBxfcW7kAUAABACQhYAAEALELIAAABagJAFAADQAt7NS2BOFoBmu+yyy1yXACBAcb3Fu5DFnCwAzbZ161bXJQAIUFxv8W67sFaruS4BQGByuZxyuZzrMgAEJq6veBeySqWS6xIABGZ4eFjDw8OuywAQmLi+4l3IAgAACAEhCwAAoAUIWQAAAC1AyAIAAGgB7+YlpNJp1yUACMyVV17pugQAAYrrLd6FrK5EwnUJAALT39/vugQAAYrrLd5tF1aZkwWgyebm5jQ3N+e6DACBiesr3oWsMnOyADTZsWPHdOzYMddlAAhMXF/xLmQBAACEgJAFAADQAoQsAACAFiBkAQAAtIB3IxzSzMkC0GR79uxxXQKAAMX1Fu9CVoI5WQCarLe313UJAAIU11u8C1nVatV1CfBYvS4tFIwKZalSNZKkZJdVT9qqt1syxnGB8NL09LQkaevWrY4rARCS5d6yEi9CljHmPkn3SdLOnTsdVwMf1a00vWA0NhNpNmdUqkpGy4nKKp2UtvTWtXOL1ZZeS9jCM5w8eVISIQtAcy33lpV4EbKstR+V9FFJ2rt3r3VcDjxTLEsnJiKNz0Sq1qW+bqvNPZIxjT8q1kqFsjQ2E2lmQdq5pa7nbq8rnXRcOACgo3kRsoCVzOeNnnw60vSCUX+PVXfq2e8xRsqmpWzaarEoHZ+ItFg2umpnTb2Zja8ZAACJkAWPzeSMhk8ltFCQtvdbJdYwcKQnI6WTVhNzkapV6XmX1rQp2/paAQA4F3Oy4KXFovTk05FyxbUHrGVdCWlHX10zi0ZPnk6oWG5dnQAArMS7lax0hv2dTleqSE+OJTS3GGlHf13RRRxijyJp2yaryTmjo4lIV++qq4vpIB3r6quvdl0CgADF9RbvQlYiYnGtk9Xq0onxSBNzRgN9da3nj0NXQtraazU2EymTkq68pM5dhx0qm2XPGEDzxfUW7xINc7I629PTkU5NR+rPWiWbsPKUSkq9GavRyUjjcySsTjU1NaWpqSnXZQAITFxf8S5klcscoOlUs4tGI5ORkgmd9y7Ci9WTkaykk+MJLRab93nRPkZGRjQyMuK6DACBiesr3oUsdKZqTTo5EalQkvqzzR+VtqXHar5gdHIiUq3e9E8PAMCzELLghVPTkSbnjLa2aFp7FElbeuo6Pds47wUAQKsRsuDcQkE6NRUpk5SSLbwVI52UEkZ6ajJirAMAoOUIWXBubCbSYsmorwXbhOfa3GM1t2g0PssffQBAa3k3wiHDnKyOMp83mpiL1JvemPEKUSRlUtLTs5G29deVTbf+14R7e/fudV0CgADF9Rbvvp2PmJPVMepWOjVlVCgb9Wxgtu7rtsoVpKen+LPWKTKZDN/AAWi6uL7i3VeZSqXiugRskJmc0cR8pP7sxg4JjSKpN211ei7SfJ5D8J1gfHxc4+PjrssAEJi4vkLIghO1emPwaLXW3JlYa9WTkQolo6dnjGzrj4LBsVOnTunUqVOuywAQmLi+4l3IQmeYXjCamjctmYm1FsZIfdm6JmcjzS6ymgUAaD5CFjZcrS49PRPJqjFWwZVsWipWpdOsZgEAWoCQhQ03vWA0s2DU3+0+2fR1W03Ns5oFAGg+QhY2VL3emItVt42HN7vGahYAoFX8m5PV3e26BLTQTM5oJufuLNb59HVbTS1Ems9b9ff4UxeaZ9++fa5LABCguN7i3UpWtJH38mND1a00NmtUq7s9i3Wu7pRULBuN80zDYCWTSSWTHv2hAxCEuL7iXchihEO4cgWj2VykTRm/VouMkXoyVtMLPNMwVGNjYxobG3NdBoDAxPUVQhY2zEzOqFhuPNbGN9m01WJRms6xmhUiQhaAVmi7kIUwlSrS+GykTEobOt19rRKR1JVo1Firu64GABACQhY2xPSC0UJR6vVsq/Bsm7qt5haNphc8TIEAgLZDyELL1erS6blICdNYLfJVqkuqWWl8zqjubxYEALQJQhZabjZnNL9o1OfB8NE4fRmr2VykBR4cDQBYJ+/mZHUzJyso1koTc0bVmh/DR+NkUtJcwWhywTAzKyD79+93XQKAAMX1Fu9WsoyPp6Jx0XJFaToXeX0W62zGSNmU1eQ84xxCkkgklEh4vFcNoC3F9RXvQla5zFe2kEwvRCqUGo+vaRe9GavFgjgAH5DR0VGNjo66LgNAYOL6inchq1qtui4BTVKuShOzxtuxDSuJlsc5zDHOIRQTExOamJhwXQaAwMT1Fe9CFsIxkzNaKJq22So8W2/Gaj5vNLfYRukQAOAVQhZaom6liblIRn6PbVhJOilVatLkPCELAHBxCFloiVyhsQrUjqtYy3rSjecZ5kuuKwEAtCNCFlpiesHf5xSuVU9aypekWbYMAQAXwbs5Wdls1nUJWKdavRGy0sn2OvB+riiSItM4W/acLbat/1s63YEDB1yXACBAcb2FlSw03ULBaLFklE2371bhsmzaaj4faZEtQwDABfIuZDEnq/1NLxiVKo2VrHaXXd4yzHn3VwUXYGRkRCMjI67LABCYuL7i3VcO5mS1t1JFmpyP1BPAKpbU2O5MdUnjs4aZWW1sampKU1NTrssAEJi4vuJdyEJ7m80ZLRYVxFbhst6MVa7IzCwAwIUhZKFprJUmF0zbzsZaSaqrMTNrisfsAAAuACELTZMrSLOLkXraeDbWSnrSjecw8tBoAMBaeRiyWC1oVzO5SMWS1N3Gs7FWkk1bLRaZmdWuElGkRORhuwPQ1uL6iodzsrpdl4CLUF16BE27z8ZaSWJpZtbEXKQdm2uKAvxvDNn+a691XQKAAMX1Fr61Q1PM541yRRPkVuGy5YdG5wokLABAPO9CVqnE1Md2NL1gVK01DomHKpOSiuXGBHi0lxPHj+vE8eOuywAQmLi+4l3IqtVqrkvABSqWpalcFORZrLMZI6WTjW3RKn9M28rM7KxmZmddlwEgMHF9xbuQhfYzt2iULyrorcJlPRmrXMFoLs9qFgBgdYQsrMvZs7ESHfCnKdUlVZcegA0AwGo64MsiWmmxKM0tRkFNeI+TTUnTuUiliutKAAA+8y5kmRDv/w/YQsGoWJa6064r2TjdaatiScoV+bPaLpLJpJLJAJ5YDsArcX3Fu3vBuruZk9VOZheNjFFHzY1KJqRqzWh20WhgU+es4LWzffv2uS4BQIDieot3K1loH40J6JF6OmircFl3ymp6PlK56roSAICvvAtZzMlqH3OLRvlAH6MTpydjtVhq/D+A/44ePaqjR4+6LgNAYOL6infbhczJag91K00uROqKpE58JFxXonFn5dS80fb+zlvJazfz8/OuSwAQoLje0oFfHtEMuYLRQj7sx+jEyaasZhcjFVh8BQCcByELF2Um17irMNPBN2xl01K+1Dj8DwDAuQhZuGDVWmObLJ1sPGqmU0VRYwDr5Hykeucu6AEAVuBdyGJOlv/m80a5ounIuwrP1ZO2ms8bLRZcV4LVpNMppdMdeIcGgJaK6yveHXxnTpb/ZnJGlZpRKknIyqSkubw0m4+0KVt3XQ5W8Pznv8B1CQACFNdbvFvJgt9KFWlqIVI2RcCSGtulqaQ0MWtUI2MBAM7iXcgqFrlVy2dzeaPFojrqWYVxetJWiyWj+Txb3b4aHh7W8PCw6zIABCaur3gXsup15mT5bHreyKoxJwoN6aRUqhjN5AhZvsrlcsrlcq7LABCYuL7iXciCvxrjCiL1sFX4LNmU1RSP2QEAnIWQhTWbXXqMTjbtuhL/8JgdAMC5CFlYE2ul6YVIiQ59jE6croRUrxOyAAD/yrsRDhFfwb1ULEsLBaNutgpXlElKszmjao0za75hNAyAVojrLd6FrEwm47oEnMd8ofEYnYFNrivxV3faaqHQuMtw6ybCqE+uueYa1yUACFBcb2HZCLGsbTxGR2o8Rgbnl+pqPHJoZoEtQwCAhyGrWCy6LgHnyJekuXzEY3TWoDslTeYilSquK8HZjhw5oiNHjrguA0Bg4vqKdyGrXmdstm9mFyPlS1I3dxXGyqat8kUOwPumUCioUOABkwCaK66veBey4Jd6XZqcN0ompIjcEGv5wPsUW4YA0PEIWVjVQsFoIW/YKrwAPWmr2cVIi+x8A0BHI2RhVbOLRqVK49ExWJvu1NI5NrYMAaCjeReyoogBQ76o1qTJOaNMSjLkhTWLIqkrkibnI9VZAPRCb2+vent7XZcBIDBxfcXDOVmcrvbF3KJRrmjUnyUpXKiejNVCvrHV2t/D/z/X9uzZ47oEAAGK6y3erWTBH1MLRjUrJb2L4v7LJKVipbHdCgDoTN6FLG6z9kOxLM3kImVTritpT8Y0gtbkXOMxO3Dr0KHHdejQ467LABCYuL7iXciylq0VH8wuGi2WGnOfcHF6Mla5ouEAvAdKpbJKpbLrMgAEJq6veBGyjDH3GWMeMcY8Mjc357qcjmdt49B2JB6jsx7JLqlmmZkFAJ3Kiy+h1tqPWmuvt9Ze39/f77qcjpcrSHN5o54Mq1jrlU01tl2LLKIAQMfxImTBL/OFRijo5jzWumXTVvmSNF9gNQsAOo13940lEszJcslaaXrBqCtiNlYzJKLG/9O5RaMd/awMutLX1+e6BAABiust3oWsdJo5WS7lio1H6XDgvXmyaWl6IVKxXFeG1UEnrrrqKtclAAhQXG9huxDPMLsYqcBWYVNl01aLJWZmAUCn8S5kMSfLnVpdmpg1SnWxVdhMiajxF21ijsfsuDI0NKShoSHXZQAITFxf8S5kMSfLndmlx+j0cldh0/VmrObzRjkOwDtRqVRUqVRclwEgMHF9xbuQBXem5o0qNSnl3Um99pdJLU/RJ2QBQKcgZEGSVFh6jE4P9x20hDGNoDUxZ1Spuq4GALARCFmQ1FhhWSxKPdxV2DLLj9nhADwAdAbvNoaYk7Xx6nVpci5qHNAmdrdMMrH8yCKjbX2Wmws20JbNm12XACBAcb3Fu5DFnKyNN18wms9z4H0j9KStZhcjLZbq6s24rqZzXH7FFa5LABCguN7CugU0tWBUrBgGZW6AbFrKlxrn3wAAYfOu0+fzzMnaSKWKNDkfcRZrgxjTuHtzYtaoWnNdTec4+NhjOvjYY67LABCYuL7iXciS+GK/kc4ceGercMP0ZqwWChyA30i1el21et11GQACE9dXPAxZ2Ch1K43PRUqYxlRybIxUV2O6/tQCIQsAQsaX1g62WJAW8oZVLAeyaWk2F6lYdl0JAKBVCFkdbC4fqViRMknXlXSe7pRVviTNsWUIAMHyboRDV5d3JQWpWpPGeRi0M10JyaixXbt9c00RvwctNTAw4LoEAAGK6y3eJZpUijkCG2EmZ7RQMOrLslXoSm+31dxiY0bZ5h5+H1pp9+7drksAEKC43sJ2YQdanjpetzwM2qVMUipWjKbmWMYCgBB5F7Ly+bzrEoK3UDCazkVMeHfMmMYE+MmFSAUOwLfU4OCgBgcHXZcBIDBxfcW7kIXWm1owKpakbnZmnevNWOWK0vQCfxUBIDR09g5TqkgTc5G6Uxx490EUSamEdHqGCfAAEBpCVoeZmjfKFRqHruGHTd1W8wWjmRypFwBCQsjqILV6Y2RAV4IJ7z5JdjVuRhifa9yMAAAIg3f3ljEnq3WmF4zmFhnb4KNN3VYzC5Hm85ZxDi2wfft21yUACFBcb/Eu0TAnqzXqZ62UMLbBP92pxvT38VlmZrXCrl27XJcAIEBxvcW7TSNr+QLTCvN5o5mFSJs4i+Wt3kxdUwuRckXXlYSnVqupVuPOAgDNFddXvAtZhULBdQlBmpozKlUMYxs81pOR8qXGg6PRXAcPHtTBgwddlwEgMHF9hW7eAUoVaTIXKZtmFctnxjTGOUzMM84BAEJAyOoAk/ORFgtSDxPevbf8PMPpBcY5AEC7I2QFrlJtDLpMMrahLaS6JFlpbDZSre66GgDAevBlN3BTC0ZzecY2tJO+rNVsjuGkANDuvLuZP5lMui4hGNWaNDYTKTJSV8J1NVirdFKazUunZ422brKKyFrrtnPnTtclAAhQXG8hZAVsat5odtFoM6tYbae/22p6IdJszmrrJn7/1ouQBaAV4nqLd9uFdeZkNUW11jjXY9R4bAvaSybVOE/39AyP2mmGSqWiSqXiugwAgYnrK96FrCJzsppiaqFxpoezWO2rL7u8msV+4XoNDQ1paGjIdRkAAhPXV7wLWVi/ak16erqxisUjdNpX99mrWdxpCABth5AVoMn5xipWP6tYba8/azU1H2ma1SwAaDuErMBUqtKpqUiJiLNYIcikpFq9sTLJahYAtBdCVmCmc425WKxihaOv22p2sfH7CgBoH96tdTDC4eItz8UyYi5WSDIpaa4gPT1t1N/D3KyLcemll7ouAUCA4noLISsgE3ONZ95t7mEVKzT93VaT85Gm5q229/P7e6F27NjhugQAAYrrLd5tF9Y5eHJRSpXGWayuiDsKQ5RJSXUrnZqOVK25rqb9FItFFYtF12UACExcX/EuZNEIL87YTKTZxcZ2EsK0OWs1vWA0Pst+4YU6fPiwDh8+7LoMAIGJ6yvehSxcuIVCY4Ujm5YS/I4GK9klJRPS6FRChbLragAAcfiS3ObqVhqdjJQvGm3qZhUrdP1Zq/m8dGoyEk+gAgC/EbLa3OS80fhcpP6eugy7SMGLIqm322pstrE9DADwFyGrjZUq0lMTjRWN7pTrarBRejON3/uRSQ7BA4DPvLsPLZUiLazV6FSkmUWjbZvYN+o0W3utJueNTs9G2jXAHblxdu/e7boEAAGK6y3ehayuLu9K8tJCQTo9G6knzeDRTpTsklKJxoDSgU2NEQ9Y2cDAgOsSAAQorrd4t11YY05WrFpdGploHHbvzbCK1an6slZzi0Yjk5Hq/DFYVT6fVz6fd10GgMDE9RXvQlaJOVmxxmYinZ6NtKWXw+6dLBE1gtbYdKTJOf4grOaJJ57QE0884boMAIGJ6yvehSysbj5vNDIRKdUlpXkCUcfLphtjPE5ORFrk+xMA8Aohq42Uq9Lx8UiLpca8JEBqHIKfy0c6ORGpxm47AHiDkNUm6rZxDmtizmhgk2WbEGdEkbSlp66xmUijk/yVBgBf0JHbxPis0ehUpE0ZqyR3E+Ic6aSUSUojU5Gm5kngAOAD7+YlMCfr2ebzRifGEzKSejKuq4Gv+rJWE3NGx09HyqZr6k67rsgfl112mesSAAQorrd4t5LFnKxnKlelE+ORckVpSy/nsLC6gU1Ws4tGJ8Y5n3W2rVu3auvWra7LABCYuL7iXciq1XhOyLJaXTp+OtL4rNFAL+ewEC+KGmH81HSkE+PMz1qWy+WUy+VclwEgMHF9xbuQVSqVXJfghbptrGCNTkXq77FKssCHNUonpd6M1chE4yC8JWhpeHhYw8PDrssAEJi4vuJdyIJkrfTUZKSRiUjZtOXhz7hgPRkpmWgE9bEZlkABwAVClmes1ZmtnlSX1MtBd1yk/p7GEtax0wmdJmgBwIYjZHlkOWAdHYuUMI27xYD12NJrVatJT44ldHqWoAUAG4mQ5QlrpdOzjdvvE+ZfVyGA9drSa1WtScfGEppkhhYAbBjvjlOn0p033KdupVNTkU6cjmQlbWZUA5rImMajd6YWjH54KqFqraZLNnfW3apXXnml6xIABCiut3gXsroSnTXOvFqTTkxEemoiUjIhbWYFCy1gTGOG1syi0fCphEqVup67ra5Eh6xl9/f3uy4BQIDieot3IavaQXOy8iXp2OlIp2ci9WYs09zRUssrWgsFo6NjkYoV6fLtdWU64O7Vubk5SYQtAM213FtW4l3IKnfInKzpBaNjY5Fm85G29tSVSrquCJ1iU3fj+ZdPTUQqlIyu2lkP/iaLY8eOSZIOHDjguBIAIVnuLSvxLmSFrlpr3EE4OhWpVJZ29NUVdciWDfyRSUnbEo1zWqVKQru31XTJFtsx28btqLkAACAASURBVIcAsBEIWRvEWml20WhkMtLknFEmKW3r66zDx/BLV0La0d941uGR0YRmFuvavc0Gv6oFABulJSHLGHNc0nFr7V2t+PztZrHYWL0an41UqjRuqU8Rb+EBYxp/HksV6fRMpPm8dMnmup6ztc6TBgBgnTZ0c8AYc5Mx5qvGmAVjzLwx5svGmGAPSRTL0snxSD840aWT45GSCatLNhOw4J90srGqZSQdG4v0gxOJxpZ2xXVlANC+NuzLvTHmZknfkDQq6beWXv4Pku43xtxqrT0oSek2n5NlrZQrSlPzkU7PRcoVpO6ktGOzVcTWIDxmTONQfE9amssbHXnK6Oms0Y7NVgOb6m199+uePXtclwAgQHG9ZSPXVD4kqSzpTmvtqCQZY/5W0iFJ/4+kV0pSok3nZFWqjTNXk/NGM7lIhZLUnZJ29FkOtqOtRNG/TonPFYx+eMpoNB1p66a6tvVZ9Wetutrsr2lvb6/rEgAEKK63rCtkGWN2qxGQflSSkfRNSf/Hed63R9INkv58OWBJkrV21BjzaUlvMsbstNaOVavV9ZS0oUoVaaFgNJc3mpk3ypWM6lbqTVtdslkcakdb60o0nj5Qt1K+KI1ORjo9I/WkrQb6rDZlrTZ1t8f29/T0tCRp69atjisBEJLl3rKSi26PxpjNkr4labekj0h6XNJLJf1vSd3nvP2GpR8fOs+neljSmyW9RNIXy+XyxZbUUtZK5apUKBstFo1mF6VcIVKhLNXrUiYpbc5aJdvgCw5wISIj9XZLvd1W5aqULxoNP904Y5hJS/3ddfX1WGXTUjbtZ+g6efKkJEIWgOZa7i0rWU87/DVJV0h6s7X2L5Ze+xNjzAcl/adz3nvp0o+jerbl13ato5amsVaq1BqBqlwxKlYawSpXMCqWjUqVxqyryEiZlNWWHrXd1glwsVJdUqrXarOsKjWpWDZ6eibS6PTStaTUnWyscHWnrdJJKdXVCF58AwKg06yn7b1G0mlJf3XO67+nZ4es7NKP5xvnXjznPU1Vt43gVKs3VpxqdalWN6rVG2Gp8aNRuSqVyo1AVakbVWtStSpV64190GSi8cViU3fj52wFotMlE1Kyu/F3wtrGucTSWWcTpcY3IF0JKZGQUgmrTMqqOy11RVJXonG2KxFJXVHj7GIi0pkfjRE3iwBoa+sJWVdJ+p619hkPG7TWPm2MmT3nvfmlH89362Dm7PfM5ar6yGdOXVAhdml2ol36R0vBytql16w56+eNf+r1RgBb/sfWn9nNG42eQ+vAejS+uTGq15/5erT0d8uo8XctMo1QdeYfScbYxoyZpde0/Lr+9edrNTYyKUl6+MkL6y0AsJqxkUklkpv6Vrq+UQv4y53tfFuCy6+9yxhznzFGv/bGqzeoLACdYHk0TKlDno0KYGOk02mZRO+KA27WE7KOSnqeMSZx9mqWMeY5kjaf897vLf14i6SPnXPtZjUWme6w1o696MAB++1vP7COsgDgmfL5xmJ6NtuSUwkAOlQ+n9fOS3aMr3R9PZthn5V0iaTXn/P6r5/7RmvtsKRHJL3OGLN8CF5LP3+dpK9ba8ckKcH+HIAmy2azBCwATRfXV9azkvV+ST8r6U+NMS+RNCTpLjVWqybP8/7/pMZ4h/uNMf996bX/qEbQ+5XlN7XTnCwA7WFqakqSNDAw4LgSACFZ7i0ruehlI2vtjKQ7JP2jGqtZv6fGHYIvk7R4nvc/qEYIOy7pdyW9R9KwGhPgv7/8Pl/nZAFoXyMjIxoZGXFdBoDAxPWVdR18t9aelHTveS5dscL7H5J093p+TQAAgHbAASgAAIAWIGQBAAC0ACELAACgBbx7mlgms+JMLwC4KHv37nVdAoAAxfUW70JWxJwsAE3GN28AWiGut3iXaCqViusSAARmfHxc4+MrDmUGgIsS11e8W8kiZAFotlOnGo9P3bFjh+NKAIRkubesxLuVLAAAgBAQsgAAAFqAkAUAANAChCwAAIAW8O7ge6a723UJAAKzb98+1yUACFBcb/EuZEXGuC4BQGCSyaTrEgAEKK63eLddyAiHsLxw3ws0NTn5rNf/7M8+pk9+8pNN/bWes/OSpn0ua61+57f/L734wAFd/5Lr9OEP/4kk6Ytf+IJuufkm3XbrLXrpnXfooQcfbNqvidYZGxvT2NiY6zIABCaur3i3kkXIaj/WWllrL2ha/1ve8tYWVrR+f/OJT+ip0VE9+s//rCiKNDHRGDj30rvu0o+/+tUyxugHP/iB3vD6X9Cj//wvjqtFnOVGuHPnTseVAAhJXMjybiUL7eHEiRO67sUv1n33/aJuuvEGvfMd79BL77xDN95wvd773t99xns/+MEP6uabbtRdd71UTz75pCTpfe97rz70h3+44uf/8Vf9mH7t196l2269RTfdeIMeeeSR837cTTfeoBMnTjzjY8fGxvRjP/rKMx/74AMPSJK+9rWv6e6Xv1x33H6bXv8LP69cLrfir/+xP/uYfv3Xf+NMcNy+vTHEsre3V2ZpS3txcfHMzwEAOBchCxftySeH9da3/qK++71H9N73vU/f/Nb9eujh7+iBb39bP/jBD868r6+/Tw9/57u677636Td+/dfX/PkL+YIeePAhfeADf6B3vuPta/64T//t3+rue+7RAw8+pAcfelj7r71WU5OT+v33/54+9/nP6/5vP6AXv/g6/dEf/fcVP8exo8f093//Gb30zjv00z/9Wg0PD5+59vnPfU4vue7Fet3r7tUf/8mH11wXAKCzELJw0S677DLdeOONkqR/+Ie/1x2336bbb7tVhw4d0uHDh8687957XydJet3rXqfvfve7a/78977uXknSbbffroWFBc3Ozq7p46677jp94hOf0Pve914NDQ1p06ZN+u73vqfDhw/rla+4R7fdeos++cm/0cjJkRU/R7lcUiad0Te/db/e+IY3PiPk/cRP/qQe/ed/0ac++Sm993ffs+b/HgBAZ/HuTBbaRzablSQdP35cH/rQh/SNb3xTW7Zs0S+97W0qFUtn3nf2ltqF7K6duxVnjFFXV5fq9fqZ14ql0rkfpttuv11f/vJX9JUvf1lv/6W36Z3/4T9qy+bNetnLX66/+IuPr+nXvvTSS/UTP/mTkhqh6h3nWUm77fbbdfztxzU1OamBbdvW/h8GAOgI3q1kdTMnq+0sLCyoJ5tVf3+/xsdP65/+6X894/rff+YzkqTPfOYzuvHGm9b8eZc/7qEHH1RfX5/6+/t12WWXa/D7g5KkwcFBnTh+/Fkfd/LkSe3YsUNvfNOb9Po3vEHf//6gbrjhBn3n4YfPnAlbXFzUD3/4wxV/7X/zb35C93/rW5Kkb3/7fv3Inj2SpCeffFLW2jO/fqlU0taBgTX/N8GN/fv3a//+/a7LABCYuL7i3UoWB4nbz/79+3XttS/SS667Ts997i7dfPPNz7g+OzurW26+Sal0Wn/+53+x5s+bzmR0+223qlKpnDn79FM/9VP61Kc+qRtvuF7XX3+D9iyFn7Pdf//9+tAfflDJZFI9PT36fz/6p9q2fbs+/JGP6M1vfpPKS6tf7373b+l5z3veeX/t//zLv6y3vuUt+uM//iP19PTqj/7ojyVJn/vsZ/WpT31SyWRSmUy3Pv7xv+TPbBtIJBKuSwAQoLjeYpa/K/fFC1/4QvvQw99xXQYc+/FX/Zh+973v03XXXee6FARgdHRUkrRr1y7HlQAIyejoqJ6/95pHrbXXn++6d9uF1WrVdQkAAjMxMaGJiQnXZQAITFxf8W67EJ3ll3/5P+s7Dz/8jNfe/vZ36Ev/88sb8uv/7L//d8+as/Xbv/Me3XPPPRvy6wMAwkXIglMf+MAfOP31P/mp/+H01wcAhMu77UIAAIAQELIAAABawLvtwuUBlwDQLAcOHHBdAoAAxfUWVrIAAABawLuQVS6XXZcAIDAjIyMaGVn5WZUAcDHi+op3IYs5WQCabWpqSlNTU67LABCYuL7iXcgCAAAIASELAACgBQhZAAAALeDdCAfJuC4AQGASEd9PAmi+uN7iXcjKZrtdlwAgMPuvvdZ1CQACFNdb+PYOAACgBbwLWaVSyXUJAAJz4vhxnTh+3HUZAAIT11e82y6s1WquSwAQmJnZWUnS5Y7rABCW5d6yEu9WsgAAAEJAyAIAAGgBQhYAAEALeHcmyxjmZAFormQy6boEAAGK6y3ehazubuZkAWiuffv2uS4BQIDiegvbhQAAAC3gXchiThaAZjt69KiOHj3qugwAgYnrK95tFzInC0Czzc/Puy4BQIDieot3K1kAAAAhIGQBAAC0ACELAACgBbw7k8WcLADNlk6nXJcAIEBxvcW7kMWcLADN9vznv8B1CQACFNdb2C4EAABoAe9CVrHInCwAzTU8PKzh4WHXZQAITFxf8W67sF5nThaA5srlcq5LABCguN7i3UoWAABACAhZAAAALeDddiEA+MqWq7IljjQAaLD58qrXvQtZUcTiGoDmasZomNrT86oMPiVbqDShIgAhSJweU9aksitd9y5kZTIZ1yUACMw111yzro+3xYoqj42qNrGgaGtPk6oC0O6uMltlpBWnqHsXsgDAN7XROdVOLyhx6WaZVMJ1OQA8Ue9efeK7d3tzxWLRdQkAAnPkyBEdOXLkoj7WWqvayIwUGQIWgGf44cTIqte9W8mq1+uuSwAQmEKhcNEfa+eKqk0tKurnKAOAZypWVh+g7t1KFgD4pD6Tl82VZXrTrksB0GYIWQCwitrUoiTJJGiXAC4MXQMAVmBrddmJnEw26boUAG3IuzNZUcTBUgDN1dvbe1EfZ/MV2UJFJkPIAvBsPanVZ/B5F7IyGc49AGiuPXv2XNTH2cWS6qWqEpvoSwCe7aptu1a9znYhAKzA5stSuSoxugHARfAuZK3nVmsAOJ9Dhx7XoUOPX/DH1RdKkoyMWXGgM4AOduT0iVWve7ddaK11XQKAwJRKqz/EdSX16UUGkAJYUbm2+rNMvVvJAgAf2GJFdqEk0+3d96IA2gQhCwDOw+bLsqWqlObOQgAXh5AFAOdRXyzLFqsyGVayAFwc77pHIsH5BwDN1dfXd8EfY/NlqV5n0juAFW1KZ1e97kXIMsbcJ+k+Sdq9e7fjagCE5qqrrrrgj6kvlCTuKgSwiisGLl31uhffollrP2qtvd5ae/22bdtclwMAsrMFmbQX34cCaFNehKyzMScLQLMNDQ1paGhoze+3papsviyT5vgCgJUdGju+6nXvvk1jThaAZqtUVp9lc67GnYU1mR7uLASwsmq9uup171ayAMA1W6jIlitsFwJYF0IWAJzDFipStS6TZLsQwMUjZAHAOWy+LHFyAcA6ebcWzpwsAM22ZfPmC3p/fb4ok+R7UACr6+/uXfW6dyErnU67LgFAYC6/4oo1v9fWrex8UUp51x4BeOayLTtXvc63agBwtlJFtlSVSbGqDmB9vAtZ+TxzsgA018HHHtPBxx5b03ttoSJbqbGSBSDW0NNHV73uXcjitCmAZqvV66rV62t6ry0srWQxiBRAjLpdva94GLIAwB1brEq1ukwXIQvA+hCyAOAstlBhQR1AUxCyAOAs9VxR6qI1Alg/7052dnV5VxKANjcwMLCm91nbGN/AnYUA1mJLtm/V694lmlQq5boEAIHZvXv32t5YrskWGd8AYG2eu3nHqtdZEweAJbbI+AYAzeNdyMrn865LABCYwcFBDQ4Oxr7PFquy5RoPhgawJgdPDa963buQBQCu2EJFKlclQhaAJiBkAcASW6xIkkxkHFcCIASELABYYgsVSQQsAM1ByAKAJXahIJOiLQJoDu9uoWFOFoBm2759e+x7bN2qnitzHgvAmm3r2bzqde8SDXOyADTbrl27Yt9jixWpXJNhfAOANXpO/7ZVr3u3Lm4tDw0D0Fy1Wk21Wm31Ny3PyGIlC8Aa1er1Va97F7IKhYLrEgAE5uDBgzp48OCq72nMyGLaO4C1e3zs6KrXvQtZAOCCLVakap2HQwNoGroJAEiypaokI2MY4QCgOQhZAKDGdiEANBMhCwAk2XxZhq1CAE3k3b3KyWTSdQkAArNz587Y99jFkpQkZAFYux2btq56nZAFIHhxIctWarLlmkwXdxYCWLtLYkKWd9+21ZmTBaDJKpWKKpXKitdtqSpVaqxkAbggldrqZzm96yhF5mQBaLKhoSENDQ2teN0WK42VLGZkAbgAh08fX/W6dyELADZcsdqY9s4jdQA0ESELQMezxYpUszIJWiKA5qGjAOh4tliVOA8KoMkIWQA6Xj1X5HE6AJrOuwMIjHAA0GyXXnrpitestbJzRQ69A7hgO/u2rXqdkAUgeDt27Fj5YrkmW6oSsgBcsO29m1e97t36eL1ed10CgMAUi0UVi8XzXrPFCncWArgopUp51evehayVGiEAXKzDhw/r8OHD571mC5XGSlaSlSwAF+aJiZOrXvcuZAHARrLFqlSpS4QsAE1GyALQ0WyhIlkrExnXpQAIDCELQEezhTKdEEBL0FoAdLT6XFGGQ+8AWsC7zpJKpVyXACAwu3fvPu/rtlKTXSxLjG8AcBF29W9f9bp3Iaury7uSALS5gYGB875ui1WpXGUlC8BF2drTv+p177YLa8zJAtBk+Xxe+Xz+Wa/bYkW2XGMQKYCLki+vPnbKu5BVYk4WgCZ74okn9MQTTzzrdVuoyJarDCIFcFGenHxq1evehSwA2Cg2X2Z8A4CWIWQB6Fg2X5ZEwALQGoQsAB2rMb6B81gAWoOQBaAjNcY3lKQ0IQtAa3h32pM5WQCa7bLLLnvWa2ceDJ3xrg0CaBPP3XzJqte96y7MyQLQbFu3bn3WazZfli3XFPVlHFQEIARbsptWve7ddmGtVnNdAoDA5HI55XK5Z7xmCxWpXJWSbBcCuDiLpcKq170LWaVSyXUJAAIzPDys4eHhZ7zWGN8gxjcAuGhHp0ZXve5dyAKAjVBfKEpdtEAArUOHAdBxbN3KzhZkuLMQQAsRsgB0HFusyBarMumk61IABIyQBaDj2MVyY3xDmruZAbSOdx0mlU67LgFAYK688spn/LvNl2VLFSm9+u3XALCay7fsXPW6dyGrK8EZCQDN1d/f/4x/t4tlqc6dhQDWp6+7d9Xr3m0XVpmTBaDJ5ubmNDc3d+bf6/MF7iwEsG7zhdyq173rMmXmZAFosmPHjunYsWOSJFury84UeJwOgHU7MTO26nXvQhYAtJItVGSLFQ69A2g5QhaAjtI49F4jZAFoOUIWgI5iCxXZckVKEbIAtBYhC0BHsfkKdxYC2BDefSuXZk4WgCbbs2fPmZ/X5wpSgoAFYP2uGti16nXvQlaCOVkAmqy3tzHLxlbrqk8vynTzOB0A69eT7l71unchq1qtui4BQGCmp6clSZujbtlChZAFoClm8gurXt/QM1nGmGuMMf9ojJkxxiwaY+43xrz87PeUy+WNLAlABzh58qROnjyp+kJRNl+R6U65LglAAJ6aPb3q9Q0LWcaYH5H0oKRbJL1f0rsk9Ur6ijHmno2qA0DnsvNFqVqXYdo7gA2wkduF/03SZkkvsdYOSpIx5q8kDUn6Y2PMXmut3cB6AHSY+mROJs25TwAbY03fzhlj3miMscaYu40xv2WMOWGMKRhjvmOMuXnpPS81xnx7aRvwaWPMu8/6+B5JPynpG8sBS5KstTlJH5N0taQbmvpfBgBnscWK6rMFmR62CgFsjAtdM/+/Jb1G0h9K+m1JV0n6X8aY10j6e0n3S/pVSYcl/Y4x5ueXPu5aSWlJD53ncz689CMhC0DL2MWy6otlmSwhC8DGuNDtwoSkm621ZUkyxjwu6bOSPi3pFmvtI0uv/5mkE5LeKekTki5d+vjR83zO5dd2SVI6k7nAkgBgdVdffbUqR05LI+M8TgdA0/zItueuev1CV7I+vBywlty/9ON3lgOWJC2957uSnrf0Unbpx9J5Pmfx7PckIg6kAmiu7u5uZeaqMinOYwFonmxq9YWhC/2W7ujZ/2KtnTHGSNKx87x3RtLA0s/zSz+eb5x75uz35MYmNfT7n7jAsgBgZXOFRdUXSuqrdskMMSMLQHPMTs0oklZsKhcasmoX+PqyU0s/nm/+/PJrP2eMeaUk3fI7v3SBZQEAAGysdDqtlK2vmKU26nDCQTW2Cm85z7Wbl378t9ba7+zdu9d+75FHN6gsAJ1gcLBxU/OBAwccVwIgJIODg7rzjtsnV7q+IQeglkY1fF7SXcaYFy2/bozplfRWST9U4wwXAABAEDbyNpv/IuluNUY+/IGkeUm/qMZ24asZRAoAAEKyYSHLWjtsjLlNjVlbvyEpJemfJf2YtfarG1UHAADARjC+LSAdOHDA3v/tB1yXASAgxWJjUkyGOXwAmqhYLGrH9m2PWmuvP99176byRczJAtBkhCsArRDXW7xLNJVKxXUJAAIzPj6u8fFx12UACExcX/FuJYuQBaDZTp1qjOrbsWOH40oAhGS5t6zEu5UsAACAEBCyAAAAWoCQBQAA0AKELAAAgBbw7uB7prvbdQkAArNv3z7XJQAIUFxv8S5kRca4LgFAYJLJpOsSAAQorrd4t13ICAcAzTY2NqaxsTHXZQAITFxfIWQBCB4hC0ArtF3IAgAACAEhCwAAoAUIWQAAr/Vt6tVb3/qWM/9erVZ15RWX63X33rvuz338+HG97GV36UUvulZvfMPrVS6XV3zv33ziE7ryist126236LZbb9Fffvzjq37uYrGou+56qW695WbdeMP1eu97f3fd9aK9ELIAAF7r6enRoccfV6FQkCR9/etf16WXXtqUz/1//ta79c53vlPf//5j2rx5s/7qr/5y1ff/9M/8jB548CE98OBDesMb37jqe9PptL7whS/qwYce1gMPPqSvfvWr+u53v9uUutEevAtZ3czJAtBk+/fv1/79+12XgXV45St/VF/5ypclSX/3d5/Wvfe+btX3v+9979Xb7rtPP/rKV2jfC56vz332s3r3f/2vuvmmG/Xa175GlUpF1lp985vf1Gte81pJ0r//2Z/TF77whYuq7w8/+EG99KV36pabbzqzYmWMUW9vr6TGTV3VSkVmaUzRo48+qnvuvlu33nKz7rrrpVpYWLioXxduxfUV70KWYU4WgCZLJBJKJBKuy8A6/My99+ozf/d3KhaLGvrBD3T9DdfHfsyxY8f0hS9+Sf/j//tb/eIvvlV33HmnHv7Od9WdyegrX/6ypqem1L95s7q6GiMjd+3apadPnVr1c37us5/VLTffpF/4+Z/TU089JUn62te+piefHNY3vvFNPfDgQxr8l0E98O1vS5JqtZpuu/UW/chVV+plL3u5brjhBpXLZb3pjW/Q773//XrwoYf1uc99ngWGNhXXV7wLWavthwPAxRgdHdXo6KjrMrAOL3zhC3Xy5En93ac/rVe+8kfX9DGveOUrlEwmtW/fPtVqNb3iFa+QJL1g3z6dOHnygmv4sVe9Sj8YelwPPfwdvezlL9cvve0+SdLXv/Y1ff3rX9ftt92qO26/TU/88Ak9+eSTkhpfhB948CEdOnxEjz76iB5/fEg//OETuuSSnXrJS14iSerr6zsT9NBe4vqKd7+r1WrVdQkAAjMxMSGpsVKB9vWqH/9x/eZv/qa+9D+/pOnp6dj3p1NpSVIURUomk2d2SqIoUq1a1daBAc3Nzqparaqrq0ujo6N6zipnvQYGBs78/A1veKN+693vliRZa/XLv/IrevOb37LSh2rz5s2648479dV/+qruvufuNf33wn/LvWUl3q1kAQBwPr/wC6/Xb/yX39C+fS9syuczxujOO+/UP/7jP0iSPvXJv9GrX/3qFd9/9uDJL33xi7r66mskSXffc4/++q//WrlcTpJ06tQpTUyMa3JiQrOzs5KkQqGg//31r+t5V1+t5z3vap0+PaZHH31UkrSwsMACQ6C8W8kCAOB8du3apbe//R1N/Zy//Tvv0Zve9Ea95z3v0YuuvVavf/0bVnzvRz78YX3pS19UV1eXtmzZoo985COSpLvvvltHjhzWPXe/XJLU09OrP/3Yx5TP5/VLb7tPtVpN9Xpdr/3pn9arXvUqSdJffPwv9a5f/VUViwVlMt363Oc/f+aQPMJhrLWua3iGvXv32u898qjrMgAEZHBwUJJ04MABx5UACMng4KDuvOP2R621570Tg+1CAACAFvBuuzCbzbouAUBgWMEK1yf++q/14Q//yTNeu+nmm/WBD/zBRX/O3//99+sf/+EfnvHaa177Wr3rXb920Z8TYYrrLd5tF1533XX2m9+633UZAAAAsfo29bbPdiFzsgA028jIiEZGRlyXASAwcX3Fu5DFbawAmm1qakpTU1OuywAQmLi+4l3IAgAACAEhCwAAoAUIWQAAAC3g3QgHybguAEBgEhHfTwJovrje4l3Iyma7XZcAIDD7r73WdQkAAhTXW/j2DgAAoAW8C1mlUsl1CQACc+L4cZ04ftx1GQACE9dXvNsurNVqrksAEJiZ2VlJ0uWO6wAQluXeshLvVrIAAABCQMgCAABoAUIWAABAC3h3JssY5mQBaK5kMum6BAABiust3oWs7m7mZAForn379rkuAUCA4noL24UAAAAt4F3IYk4WgGY7evSojh496roMAIGJ6yvebRcyJwtAs83Pz7suAUCA4nqLdytZAAAAISBkAQAAtAAhCwAAoAW8O5PFnCwAzZZOp1yXACBAcb3Fu5DFnCwAzfb857/AdQkAAhTXW9guBAAAaAHvQlaxyJwsAM01PDys4eFh12UACExcX/Fuu7BeZ04WgObK5XKuSwAQoLje4t1KFgAAQAgIWQAAAC1AyAIAAGgB785kRRG5D0BzMRoGQCvE9RbvQlYmk3FdAoDAXHPNNa5LABCguN7CshEAAEALeBeyisWi6xIABObIJH0gZAAAC+hJREFUkSM6cuSI6zIABCaur3i3XViv112XACAwhULBdQkAAhTXW7xbyQIAAAgBIQsAAKAFCFkAAAAt4N2ZrChKuC4BQGB6e3tdlwAgQHG9xbuQlcmkXZcAIDB79uxxXQKAAMX1FrYLAQAAWsC7kMWt1gCa7dChx3Xo0OOuywAQmLi+4t12obXWdQkAAlMqlV2XACBAcb3Fu5UsAACAEBCyAAAAWoCQBQAA0ALenclKJJiTBaC5+vr6XJcAIEBxvcW7kJVOMycLQHNdddVVrksAEKC43sJ2IQAAQAt4F7KYkwWg2YaGhjQ0NOS6DACBiesr3m0XMicLQLNVKhXXJQAIUFxv8WIlyxhznzHmEWPMI3Nzc67LAQAAWDcvQpa19qPW2uuttdf39/e7LgcAAGDdvAhZAAAAofHuTBZzsgA025bNm12XACBAcb3Fu5DFnCwAzXb5FVe4LgFAgOJ6C9uFAAAALeBdyMrnmZMFoLkOPvaYDj72mOsyAAQmrq94t10oMScLQHPV6nXXJQAIUFxv8W4lCwAAIASELAAAgBYgZAEAALSAd2eyurq8KwlAmxsYGHBdAoAAxfUW7xJNKpVyXQKAwOzevdt1CQACFNdb2C4EAABoAe9CVj6fd10CgMAMDg5qcHDQdRkAAhPXV7wLWQAAACEgZAEAALQAIQsAAKAFCFkAAAAt4N0IB+ZkAWi27du3uy4BQIDieot3iYY5WQCabdeuXa5LABCguN7i3XahtdZ1CQACU6vVVKvVXJcBIDBxfcW7kFUoFFyXACAwBw8e1MGDB12XASAwcX3Fu5AFAAAQAkIWAABACxCyAAD/f3v3HmpZVQdw/PtrHneysXHuNZ8z5kwqIRE2WBiJhJGvIgv8QwiSCoQeUESUIoT90R8FPSGSHqY9taxIhChLob/SNEedm+P16hUnMYfm5q1hmPfqj72unnOdew9jZ9+1Z53vBw5n77UPM7/9u2uv+d299lkjqQUWWZIkSS3o3BIOq1atKh2CpMqccsoppUOQVKFBY4tFlqTqWWRJasOgsaVz04WHXSdL0pAdOHCAAwcOlA5DUmUGjSudK7L2uk6WpCGbnJxkcnKydBiSKjNoXOlckSVJklQDiyxJkqQWWGRJkiS1wCJLkiSpBS7hIKl6p512WukQJFVo0NhikSWpeieddFLpECRVaNDY0rnpwsOHD5cOQVJl9u7dy969e0uHIakyg8aVzhVZDoSShm379u1s3769dBiSKjNoXOlckSVJklQDiyxJkqQWWGRJkiS1wCJLkiSpBZ1bwmH16tWlQ5BUmY0bN5YOQVKFBo0tnSuyVq7sXEiSjnETExOlQ5BUoUFjS+emCw+5TpakIduzZw979uwpHYakygwaVzpXZO1znSxJQzY1NcXU1FTpMCRVZtC40rkiS5IkqQYWWZIkSS2wyJIkSWqBRZYkSVILOrdegutkSRq2M844o3QIkio0aGzpXJHlOlmShm18fLx0CJIqNGhs6dx04aFDh0qHIKkyu3fvZvfu3aXDkFSZQeNK54qsffv2lQ5BUmWmp6eZnp4uHYakygwaVzpXZEmSJNXAIkuSJKkFFlmSJEktsMiSJElqQefWS1g9NlY6BEmV2bRpU+kQJFVo0NjSuSJr5YoVpUOQVJl169aVDkFShQaNLZ2bLjzoOlmShmxubo65ubnSYUiqzKBxpXNF1n7XyZI0ZDMzM8zMzJQOQ1JlBo0rnSuyJEmSamCRJUmS1AKLLEmSpBZYZEmSJLWgc0s4jLlOlqQhO+uss0qHIKlCg8aWzhVZK1wnS9KQrV27tnQIkio0aGzp3HThwYMHS4cgqTKzs7PMzs6WDkNSZQaNK527k7V///7SIUiqzDPPPAPA+Ph44Ugk1WR+bFlM5+5kSZIk1cAiS5IkqQUWWZIkSS2wyJIkSWpB5x58H1uzpnQIkipzzjnnlA5BUoUGjS2dK7JWvMqba5KG67jjjisdgqQKDRpbOlfRuE6WpGHbtWsXu3btKh2GpMoMGlc6dyfLdbIkDduOHTsAmJiYKByJpJrMjy2L6dydLEmSpBpYZEmSJLXAIkuSJKkFnXgmKyKuBa7Nu/tee/zabSXj6ZgTgX+VDqJDzEc/89HPfLycOelnPvqZj36vJB+vX+xApJT+v3CGLCIeSCmdXzqOrjAf/cxHP/PRz3y8nDnpZz76mY9+w86H04WSJEktsMiSJElqQReLrO+WDqBjzEc/89HPfPQzHy9nTvqZj37mo99Q89G5Z7IkSZJq0MU7WZIkScc8iyxJkqQWdKbIiojLIuLxiJiOiOtKx7NcIuLpiHg0IrZGxAO5bTwi7o6IJ/L7+tweEfGtnKNHImJL2eiHIyJujoidEbGtp+2ocxAR1+TPPxER15Q4l2FYJB83RsSzuZ9sjYgreo5dn/PxeERc2tNexTUVERsj4t6I+HtETEbEp3L7SPaRJfIxkn0kItZExP0R8XDOxxdz+6aIuC+f2+0RsTq3j+X96Xz8zJ4/64h5OpYskY9bImKmp3+cl9urvl7mRcSKiHgoIu7K+8vTP1JKxV/ACuBJYDOwGngYOLd0XMt07k8DJy5o+wpwXd6+Dvhy3r4C+B0QwAXAfaXjH1IOLgK2ANteaQ6AceCp/L4+b68vfW5DzMeNwGeP8Nlz8/UyBmzK19GKmq4p4FRgS94+HpjK5z2SfWSJfIxkH8k/57V5exVwX/65/wK4OrffBHwsb38cuClvXw3cvlSeSp/fEPNxC3DVET5f9fXSc56fAX4G3JX3l6V/dOVO1tuA6ZTSUyml/cBtwJWFYyrpSuDWvH0r8P6e9h+lxl+AEyLi1BIBDlNK6c/A7ILmo83BpcDdKaXZlNK/gbuBy9qPfvgWycdirgRuSyntSynNANM011M111RK6bmU0t/y9n+Bx4DTGdE+skQ+FlN1H8k/5915d1V+JeBi4I7cvrB/zPebO4B3RUSweJ6OKUvkYzFVXy8AEbEBeA/w/bwfLFP/6EqRdTqwo2f/Hyw9aNQkAX+IiAej+e+FAE5OKT2Xt/8JnJy3RylPR5uDUcjNJ/Pt/Jvnp8YYsXzkW/dvofntfOT7yIJ8wIj2kTwVtBXYSVMMPAm8kFI6mD/Se24vnnc+PgdMUHE+Ukrz/eNLuX98PSLGclv1/QP4BvA54HDen2CZ+kdXiqxRdmFKaQtwOfCJiLio92Bq7lOO9Dob5gCA7wBvAM4DngO+Wjac5RcRa4FfAZ9OKf2n99go9pEj5GNk+0hK6VBK6TxgA83dhTcWDqmohfmIiDcB19Pk5a00U4CfLxjisomI9wI7U0oPlvj7u1JkPQts7NnfkNuql1J6Nr/vBH5DM0A8Pz8NmN935o+PUp6ONgdV5yal9HweOA8D3+Ol29QjkY+IWEVTUPw0pfTr3DyyfeRI+Rj1PgKQUnoBuBd4O82018p8qPfcXjzvfHwdsIu683FZnmZOKaV9wA8Znf7xDuB9EfE0zZT4xcA3Wab+0ZUi66/A2flp/9U0D5vdWTim1kXEayLi+Plt4BJgG825z3+T4xrgt3n7TuBD+dsgFwBzPdMltTnaHPweuCQi1udpkktyWxUWPHv3AZp+Ak0+rs7fiNkEnA3cT0XXVH4e4gfAYymlr/UcGsk+slg+RrWPRMTrIuKEvP1q4N00z6ndC1yVP7awf8z3m6uAe/Kd0MXydExZJB/be34hCZrnj3r7R7XXS0rp+pTShpTSmTR9/J6U0gdZrv4x6Mn45XrRfMNhimYu/YbS8SzTOW+m+bbCw8Dk/HnTzP/+CXgC+CMwntsD+HbO0aPA+aXPYUh5+DnN9MYBmnnuj76SHAAfoXkYcRr4cOnzGnI+fpzP95F8sZ/a8/kbcj4eBy7vaa/imgIupJkKfATYml9XjGofWSIfI9lHgDcDD+Xz3gZ8IbdvpvlHcBr4JTCW29fk/el8fPOgPB1LryXycU/uH9uAn/DSNxCrvl4W5OadvPTtwmXpH/63OpIkSS3oynShJElSVSyyJEmSWmCRJUmS1AKLLEmSpBZYZEmSJLXAIkuSJKkFFlmSJEkt+B/oNzfMT5pwewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedules[-1].draw(channels=[pulse.DriveChannel(qubit), pulse.MeasureChannel(qubit)],\n",
    "                   label=True, plot_range=(0, 4096))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "After `Schedule`s are now build for the Rabi experiment. We now assemble the Qobj to be sent to the quantum computing system. We use `meas_level=MeasLevel.KERNELED` (otherwise known as measurement level `meas_level=1`)  to get the default kernel integrated results from the system, and we automatically average them with `meas_return=MeasReturnType.AVERAGE` (otherwise known as `meas_return='avg'`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/daniel/anaconda3/envs/QiskitStandard/lib/python3.6/site-packages/qiskit/providers/models/pulsedefaults.py:155: UserWarning: `qubit_freq_est` and `meas_freq_est` now have units of Hertz(Hz) rather than gigahertz(GHz).\n",
      "  warnings.warn('`qubit_freq_est` and `meas_freq_est` now have units of '\n"
     ]
    }
   ],
   "source": [
    "rabi_qobj = assemble(schedules, backend, \n",
    "                     meas_level=MeasLevel.KERNELED, \n",
    "                     meas_return=MeasReturnType.AVERAGE, \n",
    "                     shots=shots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After building our rabi experiment Qobj we now submit it to the backend to be run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "job = backend.run(rabi_qobj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We receive a job object for our experiment which we may be used to monitor and get the results of the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5e1c4a5a97290b0018034d57'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.job_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<JobStatus.DONE: 'job has successfully run'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.status()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now ask for the result from the quantum computing system, which will block until completion (or timeout)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "rabi_result = job.result(timeout=3600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Analyze the result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The result for our experiment is stored in `pulse.MemorySlot(qubit)`. Looking at the signal amplitude, we will observe an oscillation of the signal as a function of the drive amplitude. This is known as a Rabi oscillation and corresponds to the qubit being periodically driven to its excited state and back again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we extract the data from the returned system `memory`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "qubit_rabi_data = np.ones(exps, dtype=np.complex_)\n",
    "for i in range(exps):\n",
    "    qubit_rabi_data[i] = rabi_result.get_memory(i)[qubit]*scale_factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now plot and fit the extracted signal to a sinusoid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pi Amplitude -0.010302\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": [
    "rabi_amp_data = get_amplitude(qubit_rabi_data)\n",
    "\n",
    "fit_func = lambda x,A,B,Omega,phi: (A*np.cos(2*np.pi*x/Omega+phi)+B)\n",
    "\n",
    "#Fit the data\n",
    "fitparams, conv = curve_fit(fit_func, drive_amps, rabi_amp_data, [7.0,0.0,1.0,0])\n",
    "\n",
    "#get the pi amplitude\n",
    "pi_amp = (np.pi-fitparams[3])*fitparams[2]/4/np.pi\n",
    "\n",
    "plt.scatter(drive_amps, 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, 1)\n",
    "plt.ylim(-8, 8)\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)\n",
    "\n",
    "print('Pi Amplitude %f'%(pi_amp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the fit we obtain the Rabi oscillation frequency. We may use this to extract the amplitude of a $\\pi$ pulse. This is the required amplitude to drive the qubit from the ground state ($|0\\rangle$) to the excited state ($|1\\rangle$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The QObj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The experiment is specified in the `rabi_qobj`, an instance of `PulseQobj`.\n",
    "\n",
    "> backend.run(rabi_qobj)\n",
    "\n",
    "The backend must thus be able to parse the `PulseQobj` object. It contains many elements, such as readout and qubit frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.993427855]\n",
      "[4.974289865312971]\n"
     ]
    }
   ],
   "source": [
    "print(rabi_qobj.config.meas_lo_freq)\n",
    "print(rabi_qobj.config.qubit_lo_freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The pulse shapes are defined in the QObj as sample pulses and/or parametric pulses (comming in future releases). The example below shows a sample pulse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PulseLibraryItem(name='rabi_pulse_9', samples=array([3.75900549e-07+0.j, 1.13600564e-06+0.j, 1.90730826e-06+0.j, ...,\n",
      "       1.90730826e-06+0.j, 1.13600564e-06+0.j, 3.75900549e-07+0.j]))\n"
     ]
    }
   ],
   "source": [
    "print(rabi_qobj.config.pulse_library[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The QObj also contains the experiments to run, specified as a list. Each element contains a header and a list of instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'header': {'memory_slots': 1,\n",
      "            'name': 'Rabi Experiment at drive amp = 0.06349206349206349'},\n",
      " 'instructions': [{'ch': 'd0', 'name': 'rabi_pulse_4', 't0': 0},\n",
      "                  {'duration': 16000,\n",
      "                   'memory_slot': [0],\n",
      "                   'name': 'acquire',\n",
      "                   'qubits': [0],\n",
      "                   't0': 2048},\n",
      "                  {'ch': 'm0', 'name': 'M_m0_5e3c', 't0': 2048}]}\n"
     ]
    }
   ],
   "source": [
    "pp.pprint(rabi_qobj.experiments[4].to_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The backend must therefore be able to parse all the elements in the Qobj. Note that many elements in the Qobj come from the default values of the device, such as qubit LO frequencies. These values are obtained from the backend through\n",
    "\n",
    "```\n",
    "defaults = backend.defaults()\n",
    "```\n",
    "\n",
    "Additional information on the backend is also found in the configutation, obtained through\n",
    "```\n",
    "config = backend.configuration()\n",
    "```\n",
    "The configuration holds information such as the sample length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T01:29:36.602627Z",
     "start_time": "2019-08-22T01:29:36.594016Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Dec 11 12:02:58 2019 EST</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  }
 ],
 "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": "3.6.9"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}