doc/source/examples/00ComposedPulses.ipynb

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    "# Combining Pulse Templates\n",
    "\n",
    "So far we have seen how to define simple pulses using the `TablePulseTemplate` ([Modelling a Simple TablePulseTemplate](00SimpleTablePulse.ipynb)), `FunctionPulseTemplate` ([Modelling Pulses Using Functions And Expressions](00FunctionPulse.ipynb)) and `PointPulseTemplate` ([The PointPulseTemplate](00PointPulse.ipynb)) classes. These are the elementary building blocks to create pulses and we call them *atomic* pulse templates.\n",
    "\n",
    "We will now have a look at how to compose more complex pulse structures.\n",
    "\n",
    "## SequencePulseTemplate: Putting Pulses in a Sequence\n",
    "\n",
    "As the name suggests `SequencePulseTemplate` allows us to define a pulse as a sequence of already existing pulse templates which are run one after another. In the following example we have two templates created using `PointPulseTemplate` and want to define a higher-level pulse template that puts them in sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sequence parameters: {'v_0', 'v_1', 't_2', 't'}\n",
      "sequence measurements: {'M'}\n"
     ]
    }
   ],
   "source": [
    "from qupulse.pulses import PointPT, SequencePT\n",
    "# create our atomic \"low-level\" PointPTs\n",
    "first_point_pt = PointPT([(0,   'v_0'),\n",
    "                          (1,   'v_1', 'linear'),\n",
    "                          ('t', 'v_0+v_1', 'jump')],\n",
    "                         channel_names=('A',),\n",
    "                         measurements=[('M', 1, 't-1')])\n",
    "second_point_pt = PointPT([(0, 'v_0+v_1'),\n",
    "                           ('t_2', 'v_0', 'linear')],\n",
    "                          channel_names=('A',),\n",
    "                          measurements=[('M', 0, 1)])\n",
    "\n",
    "# define the SequencePT\n",
    "sequence_pt = SequencePT(first_point_pt, second_point_pt)\n",
    "\n",
    "print(\"sequence parameters: {}\".format(sequence_pt.parameter_names))\n",
    "print(\"sequence measurements: {}\".format(sequence_pt.measurement_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to note that all of the pulse templates used to create a `SequencePT` (we call those *subtemplates*) are defined on the same channels, in this case the channel `A` (otherwise we would encounter an exception). The `SequencePT` will also be defined on the same channel.\n",
    "\n",
    "The `SequencePT` will further have the union of all parameters defined in its subtemplates as its own parameter set. If two subtemplates defined parameters with the same name, they will be treated as the same parameters in the `SequencePT`.\n",
    "\n",
    "Finally, `SequencePT` will also expose all measurements defined in subtemplates. It is also possible to define additional measurements in the constructor of `SequencePT`. See [Definition of Measurements](01Measurements.ipynb) for me info about measurements.\n",
    "\n",
    "There are several cases where the above constraints represent a problem: Subtemplates might not all be defined on the same channel, subtemplates might define parameters with the same name which should still be treated as different parameters in the sequence or names of measurements defined by different subtemplates might collide. To deal with these, we can wrap a subtemplate with the `MappingPulseTemplate` class which allows us to rename parameters, channels and measurements or even derive parameter values from other parameters using mathematical expressions. You can learn how to do all this in [Mapping with the MappingPulseTemplate](00MappingTemplate.ipynb).\n",
    "\n",
    "In our example above, however, we were taking care not to encounter these problems yet. Let's plot all of them with some parameters to see the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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wsFDvvvuufdvPfvYz3XjjjVq6dOk13yM/P182m015eXkKCQlxWe1AbVF06Yo6z94kSTrwzFAF1q/n4YoA4Pq56+93rZojlJeXJ0kKDQ2ttE1GRoYGDx5cbtvQoUOVkZFRYfvi4mLl5+eXewAAAEi1KAiVlpZq+vTp6tu3r7p27Vppu9zcXDVt2rTctqZNmyo3N7fC9ikpKbLZbPZHVFSUS+sGAADeq9YEoaSkJO3fv1+rV6926XGTk5OVl5dnf5w4ccKlxwcAAN6rVkwemDp1qt59912lp6erefPmVbYNDw/XqVOnym07deqUwsPDK2xvtVpltVpdVisAAKg7PHpFyDAMTZ06VRs2bNCWLVvUunXra+4TFxenzZs3l9uWmpqquLg4d5UJAADqKI9eEUpKStIbb7yht99+W8HBwfZ5PjabTQEBAZKkhIQENWvWTCkpKZKkhx56SP3799fChQs1YsQIrV69Wnv27NHLL7/ssX4AAADv5NErQi+99JLy8vI0YMAARURE2B9r1qyxt8nOzlZOTo79eZ8+ffTGG2/o5ZdfVkxMjNatW6eNGzdWOcEaAACgIh69IuTILYy2bdt21bZRo0Zp1KhRbqgIAACYSa1ZNQYAAFDTCEIAAMC0CEIAAMC0CEIAAMC0CEIAAMC0CEIAAMC0CEIAAMC0CEIAAMC0CEIAAMC0CEIAAMC0CEIAAMC0CEIAAMC0CEIAAMC0CEIAAMC0CEIAAMC06jm7Q3FxsXbt2qXjx4+rqKhITZo0UWxsrFq3bu2O+gAAANzG4SC0Y8cOLV68WP/61790+fJl2Ww2BQQE6Ny5cyouLlabNm3029/+VlOmTFFwcLA7awYAAHAJh4bG7rzzTo0ZM0atWrXSRx99pAsXLujs2bP69ttvVVRUpG+++UZPPfWUNm/erPbt2ys1NdXddQMAAFw3h64IjRgxQuvXr5efn1+Fr7dp00Zt2rRRYmKiDhw4oJycHJcWCQAA4A4OBaH777/f4QN27txZnTt3rnZBAAAANYVVYwAAwLRcFoQSExM1aNAgVx0OAADA7ZxePl+ZZs2ayceHC0wAAMB7uCwIzZ0711WHAgAAqBFcwgEAAKbl9BWhiRMnVvn68uXLq10MAABATXI6CH3//fflnl++fFn79+/X+fPnmSwNAAC8itNBaMOGDVdtKy0t1QMPPKC2bdu6pCgAAICa4JI5Qj4+Ppo5c6YWLVrkisMBAADUCJdNlj5y5IiuXLniqsMBAAC4ndNDYzNnziz33DAM5eTk6L333lNiYqLLCgMAAHA3p4PQ3r17yz338fFRkyZNtHDhwmuuKAMAAKhNnA5CW7dudUcdAAAANc6jN1RMT09XfHy8IiMjZbFYtHHjxmvus2rVKsXExCgwMFARERGaOHGizp496/5iAQBAneOyIPTEE084PTRWWFiomJgYLVmyxKH2O3bsUEJCgiZNmqQvv/xSa9eu1aeffqr77ruvOiUDAACTc9l3jZ08eVInTpxwap9hw4Zp2LBhDrfPyMhQq1at9OCDD0qSWrdurfvvv1/z5s1z6n0BAAAkF14Rev3117VlyxZXHa5CcXFxOnHihN5//30ZhqFTp05p3bp1Gj58eKX7FBcXKz8/v9wDAABA8rIvXe3bt69WrVqlMWPGqH79+goPD5fNZqtyaC0lJUU2m83+iIqKqsGKAQBAbVatobHCwkKlpaUpOztbly5dKvda2bCVOxw4cEAPPfSQZs+eraFDhyonJ0ePPvqopkyZomXLllW4T3Jycrl7H+Xn5xOGAACApGreR2j48OEqKipSYWGhQkNDdebMGQUGBiosLMytQSglJUV9+/bVo48+Kknq3r27GjRooFtuuUV/+tOfFBERcdU+VqtVVqvVbTUBAADv5fTQ2IwZMxQfH6/vv/9eAQEB2rlzp44fP66bbrpJCxYscEeNdkVFRfLxKV+yr6+vpB/ucA0AAOAMp4NQZmamHn74Yfn4+MjX11fFxcWKiorS/Pnz9cQTTzh1rIKCAmVmZiozM1OSlJWVpczMTGVnZ0v6YVgrISHB3j4+Pl5vvfWWXnrpJR09elQ7duzQgw8+qN69eysyMtLZrgAAAJNzemjMz8/PflUmLCxM2dnZ6tSpk2w2m9PL5/fs2aOBAwfan5fN5UlMTNSKFSuUk5NjD0WSNGHCBF24cEEvvviiHn74YTVs2FCDBg1i+TwAAKgWp4NQbGysdu/erXbt2ql///6aPXu2zpw5o7///e/q2rWrU8caMGBAlUNaK1asuGrbtGnTNG3aNGfLBgAAuIrTQ2Nz5861T0p+9tln1ahRIz3wwAP67rvv9PLLL7u8QAAAAHdx+opQz5497T+HhYXpww8/dGlBAAAANcWrbqgIAADgSg4FoTvuuEM7d+68ZrsLFy5o3rx5Dn+JKgAAgCc5NDQ2atQo3XXXXbLZbIqPj1fPnj0VGRkpf39/ff/99zpw4IC2b9+u999/XyNGjNBf/vIXd9cNAABw3RwKQpMmTdK4ceO0du1arVmzRi+//LLy8vIkSRaLRZ07d9bQoUO1e/duderUya0FAwAAuIrDk6WtVqvGjRuncePGSZLy8vJ08eJF3XDDDfLz83NbgQAAAO5SrS9dlWT/NncAAABvxaoxAABgWgQhAABgWgQhAABgWgQhAABgWtUKQufPn9err76q5ORknTt3TpL0+eef6+TJky4tDgAAwJ2cXjX2xRdfaPDgwbLZbDp27Jjuu+8+hYaG6q233lJ2drZWrlzpjjoBAABczukrQjNnztSECRP0zTffyN/f3759+PDhSk9Pd2lxAAAA7uR0ENq9e7fuv//+q7Y3a9ZMubm5LikKAACgJjgdhKxWq/Lz86/afujQITVp0sQlRQEAANQEp4PQnXfeqWeeeUaXL1+W9MN3jWVnZ2vWrFm66667XF4gAACAuzgdhBYuXKiCggKFhYXp4sWL6t+/v6KjoxUcHKxnn33WHTUCAAC4hdOrxmw2m1JTU7V9+3Z98cUXKigoUI8ePTR48GB31AcAAOA21f7S1X79+qlfv36urAUAAKBGOR2Enn/++Qq3WywW+fv7Kzo6Wrfeeqt8fX2vuzgAAAB3cjoILVq0SN99952KiorUqFEjSdL333+vwMBABQUF6fTp02rTpo22bt2qqKgolxcMAADgKk5Plp47d6569eqlb775RmfPntXZs2d16NAh3XzzzVq8eLGys7MVHh6uGTNmuKNeAAAAl3H6itBTTz2l9evXq23btvZt0dHRWrBgge666y4dPXpU8+fPZyk9AACo9Zy+IpSTk6MrV65ctf3KlSv2O0tHRkbqwoUL118dAACAGzkdhAYOHKj7779fe/futW/bu3evHnjgAQ0aNEiStG/fPrVu3dp1VQIAALiB00Fo2bJlCg0N1U033SSr1Sqr1aqePXsqNDRUy5YtkyQFBQVp4cKFLi8WAADAlZyeIxQeHq7U1FR9/fXXOnTokCSpQ4cO6tChg73NwIEDXVchAACAm1T7hoodO3ZUx44dXVkLAABAjapWEPr222/1zjvvKDs7W5cuXSr32nPPPeeSwgAAANzN6SC0efNm3XnnnWrTpo2+/vprde3aVceOHZNhGOrRo4c7agQAAHALpydLJycn65FHHtG+ffvk7++v9evX68SJE+rfv79GjRrljhoBAADcwukg9NVXXykhIUGSVK9ePV28eFFBQUF65plnNG/ePKeOlZ6ervj4eEVGRspisWjjxo3X3Ke4uFhPPvmkWrZsKavVqlatWmn58uXOdgMAAMD5obEGDRrY5wVFREToyJEj6tKliyTpzJkzTh2rsLBQMTExmjhxon71q185tM/o0aN16tQpLVu2TNHR0crJyVFpaalznQAAAFA1gtDPfvYzbd++XZ06ddLw4cP18MMPa9++fXrrrbf0s5/9zKljDRs2TMOGDXO4/Ycffqi0tDQdPXpUoaGhkqRWrVo59Z4AAABlnB4ae+6553TzzTdLkubMmaPbbrtNa9asUatWrew3VHSXd955Rz179tT8+fPVrFkztW/fXo888oguXrxY6T7FxcXKz88v9wAAAJCqcUWoTZs29p8bNGigpUuXurSgqhw9elTbt2+Xv7+/NmzYoDNnzuh3v/udzp49q9dee63CfVJSUjRnzpwaqxEAAHgPp68ItWnTRmfPnr1q+/nz58uFJHcoLS2VxWLRqlWr1Lt3bw0fPlzPPfecXn/99UqvCiUnJysvL8/+OHHihFtrBAAA3sPpK0LHjh1TSUnJVduLi4t18uRJlxRVmYiICDVr1kw2m82+rVOnTjIMQ99++63atWt31T5l34cGAADwUw4HoXfeecf+86ZNm8qFkZKSEm3evNntE5f79u2rtWvXqqCgQEFBQZKkQ4cOycfHR82bN3frewMAgLrH4SA0cuRISZLFYlFiYmK51/z8/NSqVSunv3G+oKBAhw8ftj/PyspSZmamQkND1aJFCyUnJ+vkyZNauXKlJOnuu+/WH//4R917772aM2eOzpw5o0cffVQTJ05UQECAU+8NAADgcBAqu1dP69attXv3bjVu3Pi633zPnj3lvql+5syZkqTExEStWLFCOTk5ys7Otr8eFBSk1NRUTZs2TT179tQNN9yg0aNH609/+tN11wIAAMzHYhiG4ekialJ+fr5sNpvy8vIUEhLi6XIAlyu6dEWdZ2+SJB14ZqgC61fru5UBoFZx199vh/6FfP755x0+4IMPPljtYgAAAGqSQ0Fo0aJFDh3MYrEQhAAAgNdwKAhlZWW5uw4AAIAa5/QNFX/MMAyZbIoRAACoQ6oVhFauXKlu3bopICBAAQEB6t69u/7+97+7ujYAAAC3cno5yXPPPaff//73mjp1qvr27StJ2r59u6ZMmaIzZ85oxowZLi8SAADAHZwOQi+88IJeeuklJSQk2Lfdeeed6tKli/7whz8QhAAAgNdwemgsJydHffr0uWp7nz59lJOT45KiAAAAaoLTQSg6OlpvvvnmVdvXrFlT4ZeeAgAA1FZOD43NmTNHY8aMUXp6un2O0I4dO7R58+YKAxIAAEBt5fAVof3790uS7rrrLu3atUuNGzfWxo0btXHjRjVu3FiffvqpfvnLX7qtUAAAAFdz+IpQ9+7d1atXL02ePFm/+c1v9I9//MOddQEAALidw1eE0tLS1KVLFz388MOKiIjQhAkT9Mknn7izNgAAALdyOAjdcsstWr58uXJycvTCCy8oKytL/fv3V/v27TVv3jzl5ua6s04AAACXc3rVWIMGDXTvvfcqLS1Nhw4d0qhRo7RkyRK1aNFCd955pztqBAAAcIvr+q6x6OhoPfHEE3rqqacUHBys9957z1V1AQAAuJ3Ty+fLpKena/ny5Vq/fr18fHw0evRoTZo0yZW1AQAAuJVTQei///2vVqxYoRUrVujw4cPq06ePnn/+eY0ePVoNGjRwV40AAABu4XAQGjZsmD7++GM1btxYCQkJmjhxojp06ODO2gAAANzK4SDk5+endevW6ec//7l8fX3dWRMAAECNcDgIvfPOO+6sAwAAoMZd16oxAAAAb0YQAgAApkUQAgAApkUQAgAApkUQAgAApkUQAgAApkUQAgAApkUQAgAApkUQAgAApkUQAgAApkUQAgAApkUQAgAApuXRIJSenq74+HhFRkbKYrFo48aNDu+7Y8cO1atXTzfeeKPb6gMAAHWbR4NQYWGhYmJitGTJEqf2O3/+vBISEnTbbbe5qTIAAGAG9Tz55sOGDdOwYcOc3m/KlCm6++675evr69RVJAAAgB/zujlCr732mo4ePaqnn37aofbFxcXKz88v9wDqMsPwdAUA4D28Kgh98803evzxx/WPf/xD9eo5djErJSVFNpvN/oiKinJzlYDnlJYa+vkL2z1dBgB4Da8JQiUlJbr77rs1Z84ctW/f3uH9kpOTlZeXZ3+cOHHCjVUCNc8wDBVduqLC4iu67bk0ZZ0plCR1jghRgJ+vh6sDgNrNo3OEnHHhwgXt2bNHe/fu1dSpUyVJpaWlMgxD9erV00cffaRBgwZdtZ/VapXVaq3pcoEaUXYF6EBO+SHf1o0b6N1p/WSxWDxUGQB4B68JQiEhIdq3b1+5bX/961+1ZcsWrVu3Tq1bt/ZQZYBnlJYa5a4AlekcEaJ3p/WTjw8hCACuxaNBqKCgQIcPH7Y/z8rKUmZmpkJDQ9WiRQslJyfr5MmTWrlypXx8fNS1a9dy+4eFhcnf3/+q7UBd99MQ9P+vAEkBfr5cCQIAB3k0CO3Zs0cDBw60P585c6YkKTExUStWrFBOTo6ys7M9VR5Q6/wwH6hEP39he7kQtHlmf64AAUA1WAzDXItt8/PzZbPZlJeXp5CQEE+XAzisovlAhCAAZuGuv99eM0cIMLOK5gMxFwgArh9BCKjFKhsKe3daPwXWZy4QAFwvghBQC5UFoFFLMxgKAwA3IggBtUxl9wZiKAwAXI8gBNQilc0FWjsljqEwAHADghBQS1R2byACEAC4D0EI8DDuDQQAnkMQAjyIewMBgGcRhAAP4d5AAOB5BCGgBhmGoYuXS2QY4t5AAFALEISAGlLZsniGwgDAcwhCQA2oaBhMYigMADyNIAS4WWXL4i0WKcCPoTAA8CSCEOAmLIsHgNqPIAS4AcviAcA7EIQAF2NZPAB4D4IQ4AIsiwcA70QQAq4Ty+IBwHsRhIDrwLJ4APBuBCGgmlgWDwDejyAEOIll8QBQdxCEACewLB4A6haCEOAglsUDQN1DEAKuobKhMJbFA4D3IwgBlSgLQKOWZjAUBgB1FEEIqEBl9wZiKAwA6haCEPATlc0FWjsljqEwAKhjCELAj1R2byACEADUTQQhQNwbCADMiiAE0+PeQABgXgQhmBr3BgIAcyMIwXQMw9DFyyUyDHFvIAAwOYIQTKWyZfEMhQGAOfl48s3T09MVHx+vyMhIWSwWbdy4scr2b731lm6//XY1adJEISEhiouL06ZNm2qmWHi9smGwiu4NRAgCAHPyaBAqLCxUTEyMlixZ4lD79PR03X777Xr//ff12WefaeDAgYqPj9fevXvdXCm8XUXL4r+cM1QHnhmq9x5kPhAAmJXFMAzD00VIksVi0YYNGzRy5Ein9uvSpYvGjBmj2bNnO9Q+Pz9fNptNeXl5CgkJqUal8CYsiweAusFdf7+9eo5QaWmpLly4oNDQ0ErbFBcXq7i42P48Pz+/0raoW1gWDwC4Fo8OjV2vBQsWqKCgQKNHj660TUpKimw2m/0RFRVVgxXCUyqaD8RcIADAT3ntFaE33nhDc+bM0dtvv62wsLBK2yUnJ2vmzJn25/n5+YShOopl8QAAZ3llEFq9erUmT56stWvXavDgwVW2tVqtslqtNVQZPIVl8QCA6vC6IPTPf/5TEydO1OrVqzVixAhPl4NaoKK7Q0vcIRoAcG0eDUIFBQU6fPiw/XlWVpYyMzMVGhqqFi1aKDk5WSdPntTKlSsl/TAclpiYqMWLF+vmm29Wbm6uJCkgIEA2m80jfYBnVfZt8RaLFODHUBgAoGoenSy9Z88excbGKjY2VpI0c+ZMxcbG2pfC5+TkKDs7297+5Zdf1pUrV5SUlKSIiAj746GHHvJI/fAcwzBUWHzlqhC0eWZ/NbDWU2D9eoQgAMA11Zr7CNUU7iPk/VgWDwDmw32EAPFt8QAA1yIIwStUdodolsUDAK4HQQi1WlkAGrU0g6EwAIDLEYRQa1V2byCGwgAArkIQQq1U2VygtVPiGAoDALgMQQi1TmX3BiIAAQBcjSCEWqOyCdHMBQIAuAtBCLUC9wYCAHgCQQgex72BAACeQhCCRxiGoYuXS2QY4t5AAACPIQihxlW2LJ6hMABATSMIoUZVNAwmMRQGAPAMghBqTGXL4i0WKcCPoTAAQM0jCMHtWBYPAKitCEJwK5bFAwBqM4IQ3IZl8QCA2o4gBJdiWTwAwJsQhOAyLIsHAHgbghBcgmXxAABvRBDCdWNZPADAWxGEUG0siwcAeDuCEKqFZfEAgLqAIASnsSweAFBXEITgsMqGwlgWDwDwVgQhXFNZABq1NIOhMABAnUIQQpUquzcQQ2EAgLqAIIRKVTYXaO2UOIbCAAB1AkEIFars3kAEIABAXUIQQjncGwgAYCYEIdhxbyAAgNkQhCCJewMBAMyJIGRihmHo4uUSGYa4NxAAwJQIQiZV2bJ4hsIAAGbi48k3T09PV3x8vCIjI2WxWLRx48Zr7rNt2zb16NFDVqtV0dHRWrFihdvrrGvKhsEqujcQIQgAYCYevSJUWFiomJgYTZw4Ub/61a+u2T4rK0sjRozQlClTtGrVKm3evFmTJ09WRESEhg4dWgMVe7/KlsVbLFKAH0NhAABz8WgQGjZsmIYNG+Zw+6VLl6p169ZauHChJKlTp07avn27Fi1aRBCqQlVzgbgCBAAwM6+aI5SRkaHBgweX2zZ06FBNnz690n2Ki4tVXFxsf56fn19p27rq4uUSdZ69qdw2QhAAAB6eI+Ss3NxcNW3atNy2pk2bKj8/XxcvXqxwn5SUFNlsNvsjKiqqJkqt1ZgLBADAD7zqilB1JCcna+bMmfbn+fn5pgtDAX6+OvDM0HLPmQsEAICXBaHw8HCdOnWq3LZTp04pJCREAQEBFe5jtVpltVprorxay2KxKLC+V51qAABqhFcNjcXFxWnz5s3ltqWmpiouLs5DFQEAAG/m0SBUUFCgzMxMZWZmSvpheXxmZqays7Ml/TCslZCQYG8/ZcoUHT16VI899pi+/vpr/fWvf9Wbb76pGTNmeKJ8AADg5TwahPbs2aPY2FjFxsZKkmbOnKnY2FjNnj1bkpSTk2MPRZLUunVrvffee0pNTVVMTIwWLlyoV199laXzAACgWiyGYRieLqIm5efny2azKS8vTyEhIZ4uBwAAOMBdf7+9ao4QAACAKxGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAaRGEAACAadXzdAE1zTAMSVJ+fr6HKwEAAI4q+7td9nfcVUwXhM6ePStJioqK8nAlAADAWWfPnpXNZnPZ8UwXhEJDQyVJ2dnZLv1F1nb5+fmKiorSiRMnFBIS4ulyagz9pt9mQL/ptxnk5eWpRYsW9r/jrmK6IOTj88O0KJvNZqr/gMqEhITQbxOh3+ZCv83FrP0u+zvusuO59GgAAABehCAEAABMy3RByGq16umnn5bVavV0KTWKftNvM6Df9NsM6Ldr+20xXL0ODQAAwEuY7ooQAABAGYIQAAAwLYIQAAAwLYIQAAAwLVMEoXPnzumee+5RSEiIGjZsqEmTJqmgoKDKfQYMGCCLxVLuMWXKlBqquHqWLFmiVq1ayd/fXzfffLM+/fTTKtuvXbtWHTt2lL+/v7p166b333+/hip1LWf6vWLFiqvOq7+/fw1W6xrp6emKj49XZGSkLBaLNm7ceM19tm3bph49eshqtSo6OlorVqxwe52u5my/t23bdtX5tlgsys3NrZmCXSAlJUW9evVScHCwwsLCNHLkSB08ePCa+3n757s6/a4rn++XXnpJ3bt3t98wMS4uTh988EGV+3j7+Zac77erzrcpgtA999yjL7/8UqmpqXr33XeVnp6u3/72t9fc77777lNOTo79MX/+/BqotnrWrFmjmTNn6umnn9bnn3+umJgYDR06VKdPn66w/b///W+NHTtWkyZN0t69ezVy5EiNHDlS+/fvr+HKr4+z/ZZ+uBvrj8/r8ePHa7Bi1ygsLFRMTIyWLFniUPusrCyNGDFCAwcOVGZmpqZPn67Jkydr06ZNbq7UtZztd5mDBw+WO+dhYWFuqtD10tLSlJSUpJ07dyo1NVWXL1/WkCFDVFhYWOk+deHzXZ1+S3Xj8928eXP9+c9/1meffaY9e/Zo0KBB+sUvfqEvv/yywvZ14XxLzvdbctH5Nuq4AwcOGJKM3bt327d98MEHhsViMU6ePFnpfv379zceeuihGqjQNXr37m0kJSXZn5eUlBiRkZFGSkpKhe1Hjx5tjBgxoty2m2++2bj//vvdWqerOdvv1157zbDZbDVUXc2QZGzYsKHKNo899pjRpUuXctvGjBljDB061I2VuZcj/d66dashyfj+++9rpKaacPr0aUOSkZaWVmmbuvL5/jFH+l0XP99lGjVqZLz66qsVvlYXz3eZqvrtqvNd568IZWRkqGHDhurZs6d92+DBg+Xj46Ndu3ZVue+qVavUuHFjde3aVcnJySoqKnJ3udVy6dIlffbZZxo8eLB9m4+PjwYPHqyMjIwK98nIyCjXXpKGDh1aafvaqDr9lqSCggK1bNlSUVFR1/y/jbqiLpzv63HjjTcqIiJCt99+u3bs2OHpcq5LXl6eJFX5xZN18Xw70m+p7n2+S0pKtHr1ahUWFiouLq7CNnXxfDvSb8k157vOf+lqbm7uVZfB69Wrp9DQ0CrnCdx9991q2bKlIiMj9cUXX2jWrFk6ePCg3nrrLXeX7LQzZ86opKRETZs2Lbe9adOm+vrrryvcJzc3t8L23jR3ojr97tChg5YvX67u3bsrLy9PCxYsUJ8+ffTll1+qefPmNVG2R1R2vvPz83Xx4kUFBAR4qDL3ioiI0NKlS9WzZ08VFxfr1Vdf1YABA7Rr1y716NHD0+U5rbS0VNOnT1ffvn3VtWvXStvVhc/3jzna77r0+d63b5/i4uL0v//9T0FBQdqwYYM6d+5cYdu6dL6d6berzrfXBqHHH39c8+bNq7LNV199Ve3j/3gOUbdu3RQREaHbbrtNR44cUdu2bat9XHhWXFxcuf+76NOnjzp16qS//e1v+uMf/+jByuAOHTp0UIcOHezP+/TpoyNHjmjRokX6+9//7sHKqicpKUn79+/X9u3bPV1KjXK033Xp892hQwdlZmYqLy9P69atU2JiotLS0ioNBXWFM/121fn22iD08MMPa8KECVW2adOmjcLDw6+aOHvlyhWdO3dO4eHhDr/fzTffLEk6fPhwrQtCjRs3lq+vr06dOlVu+6lTpyrtY3h4uFPta6Pq9Pun/Pz8FBsbq8OHD7ujxFqjsvMdEhJSZ68GVaZ3795eGSSmTp1qX+xxrf/brQuf7zLO9PunvPnzXb9+fUVHR0uSbrrpJu3evVuLFy/W3/72t6va1qXz7Uy/f6q659tr5wg1adJEHTt2rPJRv359xcXF6fz58/rss8/s+27ZskWlpaX2cOOIzMxMST9caq9t6tevr5tuukmbN2+2bystLdXmzZsrHVuNi4sr116SUlNTqxyLrW2q0++fKikp0b59+2rleXWlunC+XSUzM9OrzrdhGJo6dao2bNigLVu2qHXr1tfcpy6c7+r0+6fq0ue7tLRUxcXFFb5WF853Zarq909V+3xf93RrL3DHHXcYsbGxxq5du4zt27cb7dq1M8aOHWt//dtvvzU6dOhg7Nq1yzAMwzh8+LDxzDPPGHv27DGysrKMt99+22jTpo1x6623eqoL17R69WrDarUaK1asMA4cOGD89re/NRo2bGjk5uYahmEY48ePNx5//HF7+x07dhj16tUzFixYYHz11VfG008/bfj5+Rn79u3zVBeqxdl+z5kzx9i0aZNx5MgR47PPPjN+85vfGP7+/saXX37pqS5Uy4ULF4y9e/cae/fuNSQZzz33nLF3717j+PHjhmEYxuOPP26MHz/e3v7o0aNGYGCg8eijjxpfffWVsWTJEsPX19f48MMPPdWFanG234sWLTI2btxofPPNN8a+ffuMhx56yPDx8TE+/vhjT3XBaQ888IBhs9mMbdu2GTk5OfZHUVGRvU1d/HxXp9915fP9+OOPG2lpaUZWVpbxxRdfGI8//rhhsViMjz76yDCMunm+DcP5frvqfJsiCJ09e9YYO3asERQUZISEhBj33nuvceHCBfvrWVlZhiRj69athmEYRnZ2tnHrrbcaoaGhhtVqNaKjo41HH33UyMvL81APHPPCCy8YLVq0MOrXr2/07t3b2Llzp/21/v37G4mJieXav/nmm0b79u2N+vXrG126dDHee++9Gq7YNZzp9/Tp0+1tmzZtagwfPtz4/PPPPVD19SlbFv7TR1lfExMTjf79+1+1z4033mjUr1/faNOmjfHaa6/VeN3Xy9l+z5s3z2jbtq3h7+9vhIaGGgMGDDC2bNnimeKrqaL+Sip3/uri57s6/a4rn++JEycaLVu2NOrXr280adLEuO222+xhwDDq5vk2DOf77arzbTEMw3DuGhIAAEDd4LVzhAAAAK4XQQgAAJgWQQgAAJgWQQgAAJgWQQgAAJgWQQgAAJgWQQgAAJgWQQgAAJgWQQhAjZswYYJGjhzpsfcfP3685s6d65JjXbp0Sa1atdKePXtccjwANYs7SwNwKYvFUuXrTz/9tGbMmCHDMNSwYcOaKepH/vOf/2jQoEE6fvy4goKCXHLMF198URs2bLjqiy8B1H4EIQAulZuba/95zZo1mj17tg4ePGjfFhQU5LIAUh2TJ09WvXr1tHTpUpcd8/vvv1d4eLg+//xzdenSxWXHBeB+DI0BcKnw8HD7w2azyWKxlNsWFBR01dDYgAEDNG3aNE2fPl2NGjVS06ZN9corr6iwsFD33nuvgoODFR0drQ8++KDce+3fv1/Dhg1TUFCQmjZtqvHjx+vMmTOV1lZSUqJ169YpPj6+3PZWrVpp7ty5mjhxooKDg9WiRQu9/PLL9tcvXbqkqVOnKiIiQv7+/mrZsqVSUlLsrzdq1Eh9+/bV6tWrr/O3B6CmEYQA1Aqvv/66GjdurE8//VTTpk3TAw88oFGjRqlPnz76/PPPNWTIEI0fP15FRUWSpPPnz2vQoEGKjY3Vnj179OGHH+rUqVMaPXp0pe/xxRdfKC8vTz179rzqtYULF6pnz57au3evfve73+mBBx6wX8l6/vnn9c477+jNN9/UwYMHtWrVKrVq1arc/r1799Ynn3ziul8IgBpBEAJQK8TExOipp55Su3btlJycLH9/fzVu3Fj33Xef2rVrp9mzZ+vs2bP64osvJP0wLyc2NlZz585Vx44dFRsbq+XLl2vr1q06dOhQhe9x/Phx+fr6Kiws7KrXhg8frt/97neKjo7WrFmz1LhxY23dulWSlJ2drXbt2qlfv35q2bKl+vXrp7Fjx5bbPzIyUsePH3fxbwWAuxGEANQK3bt3t//s6+urG264Qd26dbNva9q0qSTp9OnTkn6Y9Lx161b7nKOgoCB17NhRknTkyJEK3+PixYuyWq0VTuj+8fuXDeeVvdeECROUmZmpDh066MEHH9RHH3101f4BAQH2q1UAvEc9TxcAAJLk5+dX7rnFYim3rSy8lJaWSpIKCgoUHx+vefPmXXWsiIiICt+jcePGKioq0qVLl1S/fv1rvn/Ze/Xo0UNZWVn64IMP9PHHH2v06NEaPHiw1q1bZ29/7tw5NWnSxNHuAqglCEIAvFKPHj20fv16tWrVSvXqOfZP2Y033ihJOnDggP1nR4WEhGjMmDEaM2aMfv3rX+uOO+7QuXPnFBoaKumHiduxsbFOHROA5zE0BsArJSUl6dy5cxo7dqx2796tI0eOaNOmTbr33ntVUlJS4T5NmjRRjx49tH37dqfe67nnntM///lPff311zp06JDWrl2r8PDwcvdB+uSTTzRkyJDr6RIADyAIAfBKkZGR2rFjh0pKSjRkyBB169ZN06dPV8OGDeXjU/k/bZMnT9aqVauceq/g4GDNnz9fPXv2VK9evXTs2DG9//779vfJyMhQXl6efv3rX19XnwDUPG6oCMBULl68qA4dOmjNmjWKi4tzyTHHjBmjmJgYPfHEEy45HoCawxUhAKYSEBCglStXVnnjRWdcunRJ3bp104wZM1xyPAA1iytCAADAtLgiBAAATIsgBAAATIsgBAAATIsgBAAATIsgBAAATIsgBAAATIsgBAAATIsgBAAATIsgBAAATOv/AdkP52/heJpSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Yv541a5b+53/+RwsXLlT79u2tx5SWlrrjR4APYOV6APBfbg1Cf/nLX1RcXKx+/fopNjbW+li8eLF1n/z8fBUUFNQ6prKyUr/5zW9qHfPCCy+440eAD2DlegDwXx5z15ir0EcI53PuHWUJUaFaOTFFLYICmDcEAG7k83eNAZ6AlesBwL8QhIBz0HgRAPwHQ2NAA2i8CACeg6ExwMVovAgAvo8gBJxHzTvKEqJCJcnaeJEwBADejyAENILGiwDguwhCgI1ovAgAvocgBNiIxosA4HsIQoAdLMNkqyalKGdqqnU7V4UAwDsRhIAmOLfxIhOoAcA7EYSAJrIMlVkwgRoAvA9BCLgATKAGAO9GEAIuABOoAcC7EYSAC9TQBOohcxkmAwBPRxACHISV6wHA+xCEAAdqaOV6hskAwDMRhAAHM5tNDJMBgJcgCAFOwDAZAHgHghDgJA0Nk9F4EQA8h8nws3fkkpISRUREqLi4WOHh4Y0fAFwgwzBUVFappOfWWrd1jQ3XyokpMptNbqwMALyHsz6/uSIEOJllmOzcxotD5mZxZQgA3IwgBLhAzcaLCVGhklifDAA8AUEIcBFL48WVE1Os21ifDADciyAEuBjrkwGA5yAIAS7G+mQA4DkIQoAbsD4ZAHgGghDgRjReBAD3IggBbkbjRQBwHxoqAh6ivsaLSe1aaem4ZJlMNF4E4N9oqAj4uPoaLzKBGgCciyAEeBDLHWVMoAYA1yAIAR6GCdQA4DoEIcADMYEaAFzDrUFo5syZ6t27t8LCwhQdHa1hw4Zp165djR63dOlSdenSRSEhIerRo4c++eQTF1QLuJbZbKrTZ8iyJEd1NWEIABzBrUFo48aNSk9P15YtW7RmzRqdPn1a/fv3V1lZWYPHfPHFFxo1apTuv/9+bdu2TcOGDdOwYcO0Y8cOF1YOuAYr1wOAc3nU7fNHjhxRdHS0Nm7cqBtvvLHefUaOHKmysjKtXLnSuu26667TVVddpfnz5zf6Gtw+D29kGIbKK6s0ZG6W8o6e/Y9CztRUXRwaxK31APyCX9w+X1xcLEmKjIxscJ/s7GylpqbW2jZgwABlZ2fXu39FRYVKSkpqPQBvw8r1AOAcHhOEqqurNXnyZPXp00fdu3dvcL/CwkK1adOm1rY2bdqosLCw3v1nzpypiIgI6yM+Pt6hdQOuxMr1AOBYHhOE0tPTtWPHDr3//vsOfd6MjAwVFxdbHwcOHHDo8wOuxMr1AOBYHhGEJkyYoJUrV2rDhg269NJLz7tvTEyMDh06VGvboUOHFBMTU+/+wcHBCg8Pr/UAvBkr1wOA47g1CBmGoQkTJmjZsmVav369EhISGj0mOTlZ69atq7VtzZo1Sk5OdlaZgEei8SIAXDi3BqH09HS9/fbbevfddxUWFqbCwkIVFhbq1KlT1n3S0tKUkZFh/frhhx/Wp59+qjlz5uiHH37QM888o5ycHE2YMMEdPwLgVjReBIAL49bb5xu67ff111/XmDFjJEn9+vVT+/bttWjRIuv3ly5dqqlTp+qnn35Sp06dNHv2bA0aNMim1+T2efii+lau7xobrpUTU2Q2c3s9AO/nrM9vj+oj5AoEIfgqwzA0fH62cvYft27rGhuuVZNS6DUEwOv5RR8hAE1X846yhKhQSWdvr2eYDAAaRhACfAiNFwHAPgQhwAfReBEAbEMQAnwQjRcBwDYEIcBHNdR4katCAPBfBCHAx53beJEJ1ADwXwQhwA9YhsosLBOoq6sJQwD8G0EI8BP1TaAeMjeLK0MA/BpBCPAT9BkCgLoIQoAfoc8QANRGEAL8EH2GAOAsghDgh+gzBABnEYQAP9VQn6EhcxkmA+A/CEKAnzu3z1De0TKGyQD4DYIQAJlMJq2cmFJnmIw7ygD4OpNh57tcRUWFtm7dqv3796u8vFytW7dWYmKiEhISnFWjQ5WUlCgiIkLFxcUKDw9v/ADAjxiGoaKySiU9t9a6rWtsuFZOTJHZbHJjZQD8nbM+vwNt3XHz5s16+eWX9Y9//EOnT59WRESEmjdvrmPHjqmiokIdOnTQ7373O40bN05hYWEOKxCA61iGyZLatVLO/uOS/tt4cdWkFJlMhCEAvsWmobHbb79dI0eOVPv27fWvf/1LJ0+eVFFRkX7++WeVl5frxx9/1NSpU7Vu3TpdfvnlWrNmjbPrBuAkNF4E4E9sGhr761//qvvuu0/NmjVr9Al37typgoIC3XLLLQ4p0NEYGgNsV1ZxRt2mr7Z+3TU2XEvHJatFUABXhwC4lLM+v+2eI+TtCEKA7QzD0PD52dZhMoukdq20dFwyYQiAyzjr85u7xgA0iMaLAHydw4LQPffco5tvvtlRTwfAQzTUeJE+QwB8gcOC0CWXXKJ27do56ukAeJhzGy8ygRqAL2COEAC7MIEagDswRwiAR2DlegC+xOaGihb33Xffeb+/cOHCJhcDwPNZJlCXV1Zp+Pxs7SwokfTfCdShwXa/rQCA29j9jnX8eO3baE+fPq0dO3boxIkTTJYG/ETNCdQ1l+QYMjdLKyemMEwGwGvYHYSWLVtWZ1t1dbXGjx+vyy67zCFFAfAONSdQ7ywosa5cT58hAN7CIXOEzGazpkyZopdeeskRTwfAi7ByPQBv5rDJ0nv37tWZM2cc9XQAvIjZbKrTZyjpubUa/EqWqqsJQwA8l91DY1OmTKn1tWEYKigo0KpVq3TPPfc4rDAA3oWV6wF4I7uD0LZt22p9bTab1bp1a82ZM6fRO8oA+Laad5QNmZulvKNl1saLF4cGEYYAeBwaKgJwChovAnAkn2youGnTJg0dOlRxcXEymUxavnx5o8e888476tmzp1q0aKHY2Fjdd999Kioqcn6xAOxC40UA3sBhQej3v/+93UNjZWVl6tmzp+bNm2fT/ps3b1ZaWpruv/9+fffdd1q6dKm+/PJLPfDAA00pGYATsXI9AG/gsBawBw8e1IEDB+w6ZuDAgRo4cKDN+2dnZ6t9+/aaNGmSJCkhIUEPPvigZs2aZdfrAnANGi8C8HQOuyL0xhtvaP369Y56unolJyfrwIED+uSTT2QYhg4dOqQPPvhAgwYNavCYiooKlZSU1HoAcK1zV663NF5kmAyAu3nVoqt9+vTRO++8o5EjRyooKEgxMTGKiIg479DazJkzFRERYX3Ex8e7sGIAFjReBOCJmnTXWFlZmTZu3Kj8/HxVVlbW+p5l2MruQkwmLVu2TMOGDWtwn507dyo1NVWPPPKIBgwYoIKCAj3++OPq3bu3FixYUO8xFRUVqqiosH5dUlKi+Ph47hoD3MQwjFrDZNLZO8pWTkyR2cwwGYD6OeuuMbuD0LZt2zRo0CCVl5errKxMkZGROnr0qFq0aKHo6Gjt27evaYXYEIRGjx6t//znP1q6dKl1W1ZWlm644Qb98ssvio2NbfR1uH0ecD/DMDR8fra18aJ0NgzReBFAQzzm9vlHHnlEQ4cO1fHjx9W8eXNt2bJF+/fv19VXX60XXnjBYYXVp7y8XGZz7ZIDAgIkiUvrgBepeUdZQlSoJFkbL/JvGYAr2R2EcnNz9eijj8psNisgIEAVFRWKj4/X7Nmz9fvf/96u5yotLVVubq5yc3MlSXl5ecrNzVV+fr4kKSMjQ2lpadb9hw4dqo8++kh/+ctftG/fPm3evFmTJk3SNddco7i4OHt/FABuZLmjbOXEFOu2pOfWMoEagEvZHYSaNWtmvSoTHR1tDS0RERF23z6fk5OjxMREJSYmSjq7jlliYqKmTZsmSSooKLA+vySNGTNGL774ol599VV1795dw4cPV+fOnfXRRx/Z+2MA8BDnNl5kAjUAV7J7jlD//v01ZswY/fa3v9UDDzygb7/9VpMmTdJbb72l48ePa+vWrc6q1SGYIwR4HiZQA2iMx8wRmjFjhnVS8vPPP69WrVpp/PjxOnLkiP72t785rDAA/qPmyvUWlpXruTIEwJlYdBWAxzAMo9bK9ZKUMzWVlesBeM4VIQBwloYmUA9+JUvV1X71fzYALmJTELrtttu0ZcuWRvc7efKkZs2aZfMiqgBQn/pWrmeYDIAz2LTo6vDhw3XHHXcoIiJCQ4cOVVJSkuLi4hQSEqLjx49r586dysrK0ieffKLBgwfrT3/6k7PrBuDDLH2Gag6TWfoMMUwGwJFsniNUUVGhpUuXavHixcrKylJxcfHZJzCZ1LVrVw0YMED333+/rrjiCqcWfKGYIwR4l7KKM+o2fbX1666x4Vo6LpmV6wE/4zFLbFgUFxfr1KlTuvjii9WsWTOHFeRsBCHAu9S3HIckJbVrpaXjkglDgJ/wuMnSERERiomJ8aoQBMD71FyO49yV68srq9xYGQBfwF1jADye5W6yVZNSlDM11bp9yNwslVWcYRI1gCYjCAHwGpbGi5YrQ3lHy9Rt+mrWJwPQZAQhAF7FZDJp5cSUOsNkrE8GoCnoLA3AK7E+GeBfPGqy9IkTJ/T3v/9dGRkZOnbsmCTpm2++0cGDBx1WGACcD+uTAXAEmxoq1vTtt98qNTVVERER+umnn/TAAw8oMjJSH330kfLz8/Xmm286o04AqIPGiwAulN1XhKZMmaIxY8boxx9/VEhIiHX7oEGDtGnTJocWBwCNOd/6ZNxRBqAxdgehr776Sg8++GCd7ZdccokKCwsdUhQA2Ku+9cm4owxAY+wOQsHBwSopKamzfffu3WrdurVDigIAe9F4EUBT2B2Ebr/9dj377LM6ffq0pLNvPvn5+XryySd1xx13OLxAALBVQ40XuSoEoCF2B6E5c+aotLRU0dHROnXqlPr27auOHTsqLCxMzz//vDNqBAC7nNt40TKBmjAE4FxN7iOUlZWlb7/9VqWlperVq5dSU1MbP8gD0EcI8B+sXA/4Do9bfd5bEYQA/8HK9YDvcNbnt919hF555ZV6t5tMJoWEhKhjx4668cYbFRAQcMHFAcCFqNlnaPj8bO0sOHujh2UCdWiw3W+BAHyM3VeEEhISdOTIEZWXl6tVq7O3qh4/flwtWrRQy5YtdfjwYXXo0EEbNmxQfHy8U4q+EFwRAvzTuUtyJESFauXEFIbJAC/hMUtszJgxQ71799aPP/6ooqIiFRUVaffu3br22mv18ssvKz8/XzExMXrkkUccViQAXChWrgdQH7uvCF122WX68MMPddVVV9Xavm3bNt1xxx3at2+fvvjiC91xxx0qKChwZK0OwRUhwL9VVxsaMjfLOkwmSTlTU1mSA/BwHnNFqKCgQGfOnKmz/cyZM9bO0nFxcTp58uSFVwcADmY2m+r0GbIsyVFdzZUhwN/YHYRuuukmPfjgg9q2bZt127Zt2zR+/HjdfPPNkqTt27crISHBcVUCgAOxcj0AC7uD0IIFCxQZGamrr75awcHBCg4OVlJSkiIjI7VgwQJJUsuWLTVnzhyHFwsAjlJzSY6EqFBJNF4E/FGT+wj98MMP2r17tySpc+fO6ty5s0MLcxbmCAE4F40XAc9HQ0UHIQgBOBeNFwHP5zENFSXp559/1ooVK5Sfn6/Kyspa33vxxRcdUhgAuAqNFwH/Zfe/7nXr1un2229Xhw4d9MMPP6h79+766aefZBiGevXq5YwaAcDpaq5cX7Px4pC5WTReBHyY3ZOlMzIy9Nhjj2n79u0KCQnRhx9+qAMHDqhv374aPny4M2oEAJeh8SLgX+wOQt9//73S0tIkSYGBgTp16pRatmypZ599VrNmzbLruTZt2qShQ4cqLi5OJpNJy5cvb/SYiooKPf3002rXrp2Cg4PVvn17LVy40N4fAwAaZDKZtHJiijUMSWeHybijDPA9dgeh0NBQ67yg2NhY7d271/q9o0eP2vVcZWVl6tmzp+bNm2fzMSNGjNC6deu0YMEC7dq1S++9957X3LEGwHvQeBHwD3bPEbruuuuUlZWlK664QoMGDdKjjz6q7du366OPPtJ1111n13MNHDhQAwcOtHn/Tz/9VBs3btS+ffsUGRkpSWrfvr1drwkAtqrZeNFyR5ml8eKqSSnMGQJ8gN1XhF588UVde+21kqTMzEzdcsstWrx4sdq3b29tqOgsK1asUFJSkmbPnq1LLrlEl19+uR577DGdOnWqwWMqKipUUlJS6wEAtqLxIuDb7L4i1KFDB+ufQ0NDNX/+fIcWdD779u1TVlaWQkJCtGzZMh09elQPPfSQioqK9Prrr9d7zMyZM5WZmemyGgH4HssdZSsnplgbLyY9t5Y+Q4APsPuKUIcOHVRUVFRn+4kTJ2qFJGeorq6WyWTSO++8o2uuuUaDBg3Siy++qDfeeKPBq0IZGRkqLi62Pg4cOODUGgH4rhZBAbXWJ2MCNeD97A5CP/30k6qqqupsr6io0MGDBx1SVENiY2N1ySWXKCIiwrrtiiuukGEY+vnnn+s9Jjg4WOHh4bUeANAUlmEyJlADvsPmobEVK1ZY/7x69epaYaSqqkrr1q1z+sTlPn36aOnSpSotLVXLli0lSbt375bZbNall17q1NcGAIkJ1ICvsXmtMbP57MUjk8lU5zJws2bN1L59e82ZM0dDhgyx+cVLS0u1Z88eSVJiYqJefPFF3XTTTYqMjFTbtm2VkZGhgwcP6s0337Tuf8UVV+i6665TZmamjh49qrFjx6pv37567bXXbHpN1hoD4AiGYai8skpD5mYp72iZJClnaqouDg0iDAFO4KzPb5uHxqqrq1VdXa22bdvq8OHD1q+rq6tVUVGhXbt22RWCJCknJ0eJiYlKTEyUJE2ZMkWJiYmaNm2aJKmgoED5+fnW/Vu2bKk1a9boxIkTSkpK0l133aWhQ4fqlVdeset1AeBC1ZxAbcEwGeB9WH0eAC5AfSvXd40NZ5gMcDC3rj5vzxWXSZMmNbkYAPA2NVeutwyTWfoMMUwGeD6brgglJCTY9mQmk/bt23fBRTkTV4QAOEtZxRlrnyHp7JWhpeOSWbkecAC3XhHKy8tz2AsCgK+y9BmqeTdZt+mrabwIeDC7+wjVZBgGjcQA4P/UXI7j3JXryyvr9l8D4H5NCkJvvvmmevTooebNm6t58+a68sor9dZbbzm6NgDwOpa7yc5duX7I3CyVVZzhP4+Ah2nSoqvjx4/XoEGDtGTJEi1ZskS33Xabxo0bp5deeskZNQKA17E0XrRcGco7WqZu01dr+PxswhDgQey+fT4hIUGZmZlKS0urtf2NN97QM8884/HziZgsDcCVqqsNDZmbpZ0FJdZtNF4E7Of2hooWBQUFuv766+tsv/7661VQUOCQogDAV5jNpjrDZDReBDyH3UGoY8eOWrJkSZ3tixcvVqdOnRxSFAD4kprrk1lY1idjmAxwL5sXXbXIzMzUyJEjtWnTJvXp00eStHnzZq1bt67egAQAoPEi4KlsviK0Y8cOSdIdd9yhrVu3KioqSsuXL9fy5csVFRWlL7/8Ur/+9a+dVigAeLuG1idjAjXgPjZfEbryyivVu3dvjR07VnfeeafefvttZ9YFAD7r3MaLOfuPc2UIcBObrwht3LhR3bp106OPPqrY2FiNGTNGn3/+uTNrAwCfZBkmYwI14H42B6EbbrhBCxcuVEFBgebOnau8vDz17dtXl19+uWbNmqXCwkJn1gkAPoUJ1IBnsLuPUE179uzR66+/rrfeekuFhYW67bbbtGLFCkfW53D0EQLgSQzDqDWBWqLPEFAfZ31+X1AQkqSysjK98847ysjI0IkTJ1RV5dnr6RCEAHgiVq4Hzs9jGipabNq0SWPGjFFMTIwef/xx/b//9/+0efNmhxUGAP7EMoHawrJyPXeUAc5lVx+hX375RYsWLdKiRYu0Z88eXX/99XrllVc0YsQIhYaGOqtGAPB5NfsMDZ+fbV2Sw7JyfWiw3W3fANjA5qGxgQMHau3atYqKilJaWpruu+8+de7c2dn1ORxDYwA8nWEYKiqrVNJzayVJCVGhWjkxhWEy+DVnfX7b/F+MZs2a6YMPPtCQIUMUEBDgsAIAALXVXLl+Z0GJdeX6pHattHRcMmEIcCCb5witWLFCv/rVrwhBAOACJpNJKyemqGvsf//na2m8yJwhwHEu+K4xb8PQGABvcu4wmXT2jrKVE1NkNnNlCP7D4+4aAwA4H40XAeciCAGAh7PcUfZd5gAlRJ29Q9eycj1hCLgwBCEA8AINrVw/+JUslVWcIRABTUQQAgAvQuNFwLEIQgDgRWoOk517R1l5pWcvcQR4IoIQAHgZyzDZqkkpypmaat0+ZC7DZIC9CEIA4KVqNl6UZG28yDAZYDuCEAB4MRovAheGhooA4APqa7zIkhzwJTRUBAA0qL7Gi0ygBhpHEAIAH2G5o4wJ1IDt3BqENm3apKFDhyouLk4mk0nLly+3+djNmzcrMDBQV111ldPqAwBvwwRqwD5uDUJlZWXq2bOn5s2bZ9dxJ06cUFpamm655RYnVQYA3quhCdQMkwF1ecxkaZPJpGXLlmnYsGGN7nvnnXeqU6dOCggI0PLly5Wbm2vz6zBZGoC/OHcCdUJUqFZOTFGLoAAmUMPrMFn6/7z++uvat2+fpk+fbtP+FRUVKikpqfUAAH/AMBnQOK8KQj/++KOeeuopvf322woMDLTpmJkzZyoiIsL6iI+Pd3KVAOA56DMEnJ/XBKGqqir99re/VWZmpi6//HKbj8vIyFBxcbH1ceDAASdWCQCex2w21VmOw7JyfXU1YQj+zWuC0MmTJ5WTk6MJEyYoMDBQgYGBevbZZ/Xvf/9bgYGBWr9+fb3HBQcHKzw8vNYDAPxNfX2GdhaUaMjcLK4Mwa/ZNr7kAcLDw7V9+/Za2/785z9r/fr1+uCDD5SQkOCmygDAO1j6DJVXVmnI3CzlHS3TzoISFZVV6uLQICZQwy+5NQiVlpZqz5491q/z8vKUm5uryMhItW3bVhkZGTp48KDefPNNmc1mde/evdbx0dHRCgkJqbMdAFA/y8r1KyemqNv01ZLODpN1jQ3X0nHJ3FEGv+PWobGcnBwlJiYqMTFRkjRlyhQlJiZq2rRpkqSCggLl5+e7s0QA8EktggLqDJNxRxn8kcf0EXIV+ggBwFmGYai8skrD52drZ8F/W4t8lzlAocFeM3MCfoI+QgAAh7IMk517Rxnrk8GfEIQAwM/ReBH+jCAEAKDxIvwWc4QAAFbnrk8mSV1jw7VyYorMZu4mg/swRwgA4HQ0XoS/IQgBAGqxNF78LnOAEqJCJcnaeJEwBF9DEAIA1FGz8aJF0nNrmUANn0MQAgA06NzGi0yghq9hsjQA4LyYQA1PwGRpAIBbMIEavowgBABoFBOo4asIQgAAmzQ0gXrwKyzJAe9FEAIA2IWV6+FLCEIAALvUHCY7d0mO8soqN1YG2I8gBACwGyvXw1cQhAAATcbK9fB2BCEAwAVh5Xp4MxoqAgAcgsaLcCYaKgIAPBqNF+GNCEIAAIeh8SK8DUEIAOBQNF6ENyEIAQCcgsaL8AYEIQCAU9B4Ed6AIAQAcBoaL8LTEYQAAE5H40V4KoIQAMAlaLwIT0RDRQCAS9XXeDGpXSstHZcsk4nGi6gfDRUBAD6hvsaLTKCGuxCEAAAuZ7mjjAnUcDeCEADALZhADU9AEAIAuE1DE6gZJoOrEIQAAG5lNpvoMwS3IQgBANyOYTK4i1uD0KZNmzR06FDFxcXJZDJp+fLl593/o48+0q233qrWrVsrPDxcycnJWr16tWuKBQA4FX2G4A5uDUJlZWXq2bOn5s2bZ9P+mzZt0q233qpPPvlEX3/9tW666SYNHTpU27Ztc3KlAABXqG+YzLJyfXU1YQiO5zENFU0mk5YtW6Zhw4bZdVy3bt00cuRITZs2zab9aagIAJ7PMAwNn5+tnP3Hrdu6xoZr1aQUmi76KWd9fgc67JncoLq6WidPnlRkZGSD+1RUVKiiosL6dUlJiStKAwBcAEufofLKKg2Zm6W8o2XaWVCiorJKXRwaRBiCw3j1ZOkXXnhBpaWlGjFiRIP7zJw5UxEREdZHfHy8CysEADSVZeX6lRNTrNssw2TcUQZH8dog9O677yozM1NLlixRdHR0g/tlZGSouLjY+jhw4IALqwQAXKgWQQG1luPYWVDCHWVwGK8cGnv//fc1duxYLV26VKmpqefdNzg4WMHBwS6qDADgaDWHyYbPz9bOgrNTHCyNF0ODvfKjDB7C664Ivffee7r33nv13nvvafDgwe4uBwDgApZhMhovwtHcGoRKS0uVm5ur3NxcSVJeXp5yc3OVn58v6eywVlpamnX/d999V2lpaZozZ46uvfZaFRYWqrCwUMXFxe4oHwDgYjRehKO5NQjl5OQoMTFRiYmJkqQpU6YoMTHReit8QUGBNRRJ0t/+9jedOXNG6enpio2NtT4efvhht9QPAHA9Gi/CkTymj5Cr0EcIAHyDYRgqKqtU0nNrrduS2rXS0nHJ3F7vg5z1+e11c4QAAJD+O0xW844yVq6HvQhCAACvZbmjjAnUaCqCEADAqzGBGheCIAQA8HpMoEZTMVkaAOAz6ptA3TU2XCsnpshsZgK1N2OyNAAAjahvAvXOghINmZvFlSHUiyAEAPAplgnU32UOUEJUqCRZV64nDOFcBCEAgM9h5XrYiiAEAPBZrFyPxhCEAAA+q+Yw2bl3lNF4ERJBCADg41i5HudDEAIA+AUaL6I+BCEAgN+g8SLORUNFAIDfofGi96GhIgAADkLjRVgQhAAAfonGi5AIQgAAP0bjRRCEAAB+j8aL/osgBADwezRe9F8EIQAA1HDjRa4K+TaCEAAANZzbeJEJ1L6NIAQAwDksQ2UWTKD2XQQhAADqwQRq/0AQAgCgHkyg9g8EIQAAGsDK9b6PIAQAQCNYud53EYQAALBBQyvXM0zm3QhCAADYyGw2MUzmYwhCAADYgWEy30IQAgDATg0Nk9F40fuYDD/7GyspKVFERISKi4sVHh7e+AEAADTAMAwVlVUq6bm11m1dY8O1cmKKzGaTGyvzPc76/OaKEAAATWQZJju38eKQuVlcGfISBCEAAC5AzcaLCVGhklifzJu4NQht2rRJQ4cOVVxcnEwmk5YvX97oMZ999pl69eql4OBgdezYUYsWLXJ6nQAAnI+l8eLKiSnWbaxP5h3cGoTKysrUs2dPzZs3z6b98/LyNHjwYN10003Kzc3V5MmTNXbsWK1evdrJlQIA0DjWJ/M+HjNZ2mQyadmyZRo2bFiD+zz55JNatWqVduzYYd1255136sSJE/r0009teh0mSwMAnMkwDJVXVmn4/GztLCixbt/57AC1CAp0Y2XejcnSkrKzs5Wamlpr24ABA5Sdnd3gMRUVFSopKan1AADAWRpanwyeyauCUGFhodq0aVNrW5s2bVRSUqJTp07Ve8zMmTMVERFhfcTHx7uiVACAnzOZTGoRFODuMtAIrwpCTZGRkaHi4mLr48CBA+4uCQDgJ5o3C9DOZwdo57MD1LwZocgTedVgZUxMjA4dOlRr26FDhxQeHq7mzZvXe0xwcLCCg4NdUR4AALWcvSrkVR+1fserrgglJydr3bp1tbatWbNGycnJbqoIAAB4M7cGodLSUuXm5io3N1fS2dvjc3NzlZ+fL+nssFZaWpp1/3Hjxmnfvn164okn9MMPP+jPf/6zlixZokceecQd5QMAAC/n1iCUk5OjxMREJSYmSpKmTJmixMRETZs2TZJUUFBgDUWSlJCQoFWrVmnNmjXq2bOn5syZo7///e8aMGCAW+oHAADezWP6CLkKfYQAAPA+9BECAABwMIIQAADwWwQhAADgtwhCAADAbxGEAACA3yIIAQAAv0UQAgAAfosgBAAA/BZBCAAA+C2CEAAA8FsEIQAA4LcIQgAAwG8RhAAAgN8iCAEAAL9FEAIAAH6LIAQAAPwWQQgAAPgtghAAAPBbBCEAAOC3CEIAAMBvEYQAAIDfIggBAAC/RRACAAB+K9DdBbiaYRiSpJKSEjdXAgAAbGX53LZ8jjuK3wWhoqIiSVJ8fLybKwEAAPYqKipSRESEw57P74JQZGSkJCk/P9+hJ9IflZSUKD4+XgcOHFB4eLi7y/FqnEvH4Dw6DufScTiXjlFcXKy2bdtaP8cdxe+CkNl8dlpUREQEv5AOEh4ezrl0EM6lY3AeHYdz6TicS8ewfI477Pkc+mwAAABehCAEAAD8lt8FoeDgYE2fPl3BwcHuLsXrcS4dh3PpGJxHx+FcOg7n0jGcdR5NhqPvQwMAAPASfndFCAAAwIIgBAAA/BZBCAAA+C2CEAAA8Ft+EYSOHTumu+66S+Hh4brooot0//33q7S09LzH9OvXTyaTqdZj3LhxLqrYc8ybN0/t27dXSEiIrr32Wn355Zfn3X/p0qXq0qWLQkJC1KNHD33yyScuqtTz2XMuFy1aVOf3LyQkxIXVeqZNmzZp6NChiouLk8lk0vLlyxs95rPPPlOvXr0UHBysjh07atGiRU6v0xvYey4/++yzOr+TJpNJhYWFrinYQ82cOVO9e/dWWFiYoqOjNWzYMO3atavR43ivrK0p59FR75N+EYTuuusufffdd1qzZo1WrlypTZs26Xe/+12jxz3wwAMqKCiwPmbPnu2Caj3H4sWLNWXKFE2fPl3ffPONevbsqQEDBujw4cP17v/FF19o1KhRuv/++7Vt2zYNGzZMw4YN044dO1xcueex91xKZ7vQ1vz9279/vwsr9kxlZWXq2bOn5s2bZ9P+eXl5Gjx4sG666Sbl5uZq8uTJGjt2rFavXu3kSj2fvefSYteuXbV+L6Ojo51UoXfYuHGj0tPTtWXLFq1Zs0anT59W//79VVZW1uAxvFfW1ZTzKDnofdLwcTt37jQkGV999ZV12z//+U/DZDIZBw8ebPC4vn37Gg8//LALKvRc11xzjZGenm79uqqqyoiLizNmzpxZ7/4jRowwBg8eXGvbtddeazz44INOrdMb2HsuX3/9dSMiIsJF1XknScayZcvOu88TTzxhdOvWrda2kSNHGgMGDHBiZd7HlnO5YcMGQ5Jx/Phxl9TkrQ4fPmxIMjZu3NjgPrxXNs6W8+io90mfvyKUnZ2tiy66SElJSdZtqampMpvN2rp163mPfeeddxQVFaXu3bsrIyND5eXlzi7XY1RWVurrr79WamqqdZvZbFZqaqqys7PrPSY7O7vW/pI0YMCABvf3F005l5JUWlqqdu3aKT4+Xr/61a/03XffuaJcn8LvpONdddVVio2N1a233qrNmze7uxyPU1xcLEnnXRiU38vG2XIeJce8T/p8ECosLKxz6TYwMFCRkZHnHdv+7W9/q7ffflsbNmxQRkaG3nrrLd19993OLtdjHD16VFVVVWrTpk2t7W3atGnwvBUWFtq1v79oyrns3LmzFi5cqI8//lhvv/22qqurdf311+vnn392Rck+o6HfyZKSEp06dcpNVXmn2NhYzZ8/Xx9++KE+/PBDxcfHq1+/fvrmm2/cXZrHqK6u1uTJk9WnTx917969wf14rzw/W8+jo94nvXb1+aeeekqzZs067z7ff/99k5+/5hyiHj16KDY2Vrfccov27t2ryy67rMnPC9giOTlZycnJ1q+vv/56XXHFFfrrX/+qP/zhD26sDP6qc+fO6ty5s/Xr66+/Xnv37tVLL72kt956y42VeY709HTt2LFDWVlZ7i7Fq9l6Hh31Pum1QejRRx/VmDFjzrtPhw4dFBMTU2dC6pkzZ3Ts2DHFxMTY/HrXXnutJGnPnj1+EYSioqIUEBCgQ4cO1dp+6NChBs9bTEyMXfv7i6acy3M1a9ZMiYmJ2rNnjzNK9FkN/U6Gh4erefPmbqrKd1xzzTV86P+fCRMmWG/GufTSS8+7L++VDbPnPJ6rqe+TXjs01rp1a3Xp0uW8j6CgICUnJ+vEiRP6+uuvrceuX79e1dXV1nBji9zcXElnLw/7g6CgIF199dVat26ddVt1dbXWrVtXK4HXlJycXGt/SVqzZk2D+/uLppzLc1VVVWn79u1+8/vnKPxOOldubq7f/04ahqEJEyZo2bJlWr9+vRISEho9ht/LuppyHs/V5PfJC55u7QVuu+02IzEx0di6dauRlZVldOrUyRg1apT1+z///LPRuXNnY+vWrYZhGMaePXuMZ5991sjJyTHy8vKMjz/+2OjQoYNx4403uutHcIv333/fCA4ONhYtWmTs3LnT+N3vfmdcdNFFRmFhoWEYhjF69Gjjqaeesu6/efNmIzAw0HjhhReM77//3pg+fbrRrFkzY/v27e76ETyGvecyMzPTWL16tbF3717j66+/Nu68804jJCTE+O6779z1I3iEkydPGtu2bTO2bdtmSDJefPFFY9u2bcb+/fsNwzCMp556yhg9erR1/3379hktWrQwHn/8ceP777835s2bZwQEBBiffvqpu34Ej2HvuXzppZeM5cuXGz/++KOxfft24+GHHzbMZrOxdu1ad/0IHmH8+PFGRESE8dlnnxkFBQXWR3l5uXUf3isb15Tz6Kj3Sb8IQkVFRcaoUaOMli1bGuHh4ca9995rnDx50vr9vLw8Q5KxYcMGwzAMIz8/37jxxhuNyMhIIzg42OjYsaPx+OOPG8XFxW76Cdxn7ty5Rtu2bY2goCDjmmuuMbZs2WL9Xt++fY177rmn1v5LliwxLr/8ciMoKMjo1q2bsWrVKhdX7LnsOZeTJ0+27tumTRtj0KBBxjfffOOGqj2L5Rbucx+Wc3fPPfcYffv2rXPMVVddZQQFBRkdOnQwXn/9dZfX7YnsPZezZs0yLrvsMiMkJMSIjIw0+vXrZ6xfv949xXuQ+s6hpFq/Z7xXNq4p59FR75Om/ysAAADA73jtHCEAAIALRRACAAB+iyAEAAD8FkEIAAD4LYIQAADwWwQhAADgtwhCAADAbxGEAACA3yIIAXC5MWPGaNiwYW57/dGjR2vGjBkOea7Kykq1b99eOTk5Dnk+AK5FZ2kADmUymc77/enTp+uRRx6RYRi66KKLXFNUDf/+97918803a//+/WrZsqVDnvPVV1/VsmXL6iykCcDzEYQAOFRhYaH1z4sXL9a0adO0a9cu67aWLVs6LIA0xdixYxUYGKj58+c77DmPHz+umJgYffPNN+rWrZvDnheA8zE0BsChYmJirI+IiAiZTKZa21q2bFlnaKxfv36aOHGiJk+erFatWqlNmzZ67bXXVFZWpnvvvVdhYWHq2LGj/vnPf9Z6rR07dmjgwIFq2bKl2rRpo9GjR+vo0aMN1lZVVaUPPvhAQ4cOrbW9ffv2mjFjhu677z6FhYWpbdu2+tvf/mb9fmVlpSZMmKDY2FiFhISoXbt2mjlzpvX7rVq1Up8+ffT+++9f4NkD4GoEIQAe4Y033lBUVJS+/PJLTZw4UePHj9fw4cN1/fXX65tvvlH//v01evRolZeXS5JOnDihm2++WYmJicrJydGnn36qQ4cOacSIEQ2+xrfffqvi4mIlJSXV+d6cOXOUlJSkbdu26aGHHtL48eOtV7JeeeUVrVixQkuWLNGuXbv0zjvvqH379rWOv+aaa/T555877oQAcAmCEACP0LNnT02dOlWdOnVSRkaGQkJCFBUVpQceeECdOnXStGnTVFRUpG+//VbS2Xk5iYmJmjFjhrp06aLExEQtXLhQGzZs0O7du+t9jf379ysgIEDR0dF1vjdo0CA99NBD6tixo5588klFRUVpw4YNkqT8/Hx16tRJKSkpateunVJSUjRq1Khax8fFxWn//v0OPisAnI0gBMAjXHnlldY/BwQE6OKLL1aPHj2s29q0aSNJOnz4sKSzk543bNhgnXPUsmVLdenSRZK0d+/eel/j1KlTCg4OrndCd83XtwznWV5rzJgxys3NVefOnTVp0iT961//qnN88+bNrVerAHiPQHcXAACS1KxZs1pfm0ymWtss4aW6ulqSVFpaqqFDh2rWrFl1nis2Nrbe14iKilJ5ebkqKysVFBTU6OtbXqtXr17Ky8vTP//5T61du1YjRoxQamqqPvjgA+v+x44dU+vWrW39cQF4CIIQAK/Uq1cvffjhh2rfvr0CA217K7vqqqskSTt37rT+2Vbh4eEaOXKkRo4cqd/85je67bbbdOzYMUVGRko6O3E7MTHRrucE4H4MjQHwSunp6Tp27JhGjRqlr776Snv37tXq1at17733qqqqqt5jWrdurV69eikrK8uu13rxxRf13nvv6YcfftDu3bu1dOlSxcTE1OqD9Pnnn6t///4X8iMBcAOCEACvFBcXp82bN6uqqkr9+/dXjx49NHnyZF100UUymxt+axs7dqzeeecdu14rLCxMs2fPVlJSknr37q2ffvpJn3zyifV1srOzVVxcrN/85jcX9DMBcD0aKgLwK6dOnVLnzp21ePFiJScnO+Q5R44cqZ49e+r3v/+9Q54PgOtwRQiAX2nevLnefPPN8zZetEdlZaV69OihRx55xCHPB8C1uCIEAAD8FleEAACA3yIIAQAAv0UQAgAAfosgBAAA/BZBCAAA+C2CEAAA8FsEIQAA4LcIQgAAwG8RhAAAgN/6/87odjf0xUZaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qupulse.pulses.plotting import plot\n",
    "\n",
    "parameters = dict(t=3,\n",
    "                  t_2=2,\n",
    "                  v_0=1,\n",
    "                  v_1=1.4)\n",
    "\n",
    "_ = plot(first_point_pt, parameters, sample_rate=100)\n",
    "_ = plot(second_point_pt, parameters, sample_rate=100)\n",
    "_ = plot(sequence_pt, parameters, sample_rate=100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RepetitionPulseTemplate: Repeating a Pulse\n",
    "\n",
    "If we simply want to repeat some pulse template a fixed number of times, we can make use of the `RepetitionPulseTemplate`. In the following, we will reuse one of our `PointPT`s, `first_point_pt` and use it to create a new pulse template that repeats it `n_rep` times, where `n_rep` will be a parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "repetition parameters: {'t', 'v_0', 'v_1', 'n_rep'}\n",
      "repetition measurements: {'M'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qupulse.pulses import RepetitionPT\n",
    "\n",
    "repetition_pt = RepetitionPT(first_point_pt, 'n_rep')\n",
    "\n",
    "print(\"repetition parameters: {}\".format(repetition_pt.parameter_names))\n",
    "print(\"repetition measurements: {}\".format(repetition_pt.measurement_names))\n",
    "\n",
    "# let's plot to see the results\n",
    "parameters['n_rep'] = 5 # add a value for our n_rep parameter\n",
    "_ = plot(repetition_pt, parameters, sample_rate=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same remarks that were made about `SequencePT` also hold for `RepetitionPT`: it will expose all parameters and measurements defined by its subtemplate and will be defined on the same channels.\n",
    "\n",
    "## ForLoopPulseTemplate: Repeat a Pulse with a Varying Loop Parameter\n",
    "\n",
    "The `RepetitionPT` simple repeats the exact same subtemplate a given number of times. Sometimes, however, it is rather required to vary the parameters of a subtemplate in a loop, for example when trying to determine the best value for a parameter of a given pulse. This is what the `ForLoopPulseTemplate` is intended for. As the name suggests, its behavior mimics that for `for-loop` constructs in programming languages by repeating its content - the subtemplate - for a number of times while at the same time supplying a loop parameter that iterates over a range of values.\n",
    "\n",
    "In the following we make use of this to vary the value of parameter `t` in `first_point_pt` over several iterations. More specifically, we will have all a `first_point_pt` pulse for all even values of `t` between `t_start` and `t_end` which are new parameters. For the plot we will set them to `t_start = 4` and `t_end = 13`, i.e., `t = 4, 6, 8, 10, 12`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for loop parameters: {'v_0', 'v_1', 't_end', 't_start'}\n",
      "for loop measurements: {'M'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qupulse.pulses import ForLoopPT\n",
    "\n",
    "for_loop_pt = ForLoopPT(first_point_pt, 't', ('t_start', 't_end', 2))\n",
    "\n",
    "print(\"for loop parameters: {}\".format(for_loop_pt.parameter_names))\n",
    "print(\"for loop measurements: {}\".format(for_loop_pt.measurement_names))\n",
    "\n",
    "# plot it\n",
    "parameters['t_start'] = 4\n",
    "parameters['t_end'] = 13\n",
    "_ = plot(for_loop_pt, parameters, sample_rate=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second argument to `ForLoopPT`'s constructor is the name of the loop parameter. This has to be a parameter that is defined by the subtemplate. The third argument defined the range of the loop. The syntax of the range is similar to that of the `range()` command in Python, i.e., a tuple `(start_value, end_value, step)`. As seen above, inserting parameter values or even expressions is okay. As in `range()`, the `end_value` is exclusive.\n",
    "\n",
    "As for `SequencePT` and `RepetitionPT`, `ForLoopPT` exposes all parameters defined by the subtemplate except for the loop parameter, `t` in the above example. If expressions are used in the range definition and they make use of additional parameters, these are also exposed by `ForLoopPT`. \n",
    "\n",
    "`ForLoopPT` also exposes measurements defined by subtemplates.\n",
    "\n",
    "## AtomicMultiChannelPulseTemplate: Run Pulses in Parallel on Different Channels\n",
    "\n",
    "So far we have only looked at pulses that affect the time-domain aspect of combining pulses. Another way to combine pulses is to parallelise them by executing them on different channels at the same time. This is of course already supported by simply creating atomic pulse templates (`TablePT`, `PointPT`, `FunctionPT`) on multiple channels. However, sometimes it is necessary to put already existing pulses in parallel. Instead of having to define a new atomic pulse template for this, we can make use of the `AtomicMuliChannelPulseTemplate` class. To learn more about how this works, see [Multi-Channel Pulses](00MultiChannelTemplates.ipynb).\n",
    "\n",
    "## Combining Combined Pulses\n",
    "\n",
    "Our examples above have build combined higher-level pulses (`SequencePT`, `RepetitionPT`, `ForLoopPT`) on atomic subtemplates only. However, this is not a requirement. We can use `SequencePT`, `RepetitionPT` and `ForLoopPT` using any `PulseTemplate` objects as subtemplates allowing us to build arbitrarily complex pulses out of only a handful of primitives."
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}