{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f543de9b-3858-45dd-bddf-cddddcc60da3",
   "metadata": {},
   "source": [
    "# Part 1: Getting Started with Sionna"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c55670f1-3ee6-481d-9661-d59e61074818",
   "metadata": {},
   "source": [
    "This tutorial will guide you through Sionna, from its basic principles to the implementation of a point-to-point link with a 5G NR compliant code and a 3GPP channel model.\n",
    "You will also learn how to write custom trainable layers by implementing a state of the art neural receiver, and how to train and evaluate end-to-end communication systems.\n",
    "\n",
    "The tutorial is structured in four notebooks:\n",
    "\n",
    "- **Part I: Getting started with Sionna**\n",
    "\n",
    "- Part II: Differentiable Communication Systems\n",
    "\n",
    "- Part III: Advanced Link-level Simulations\n",
    "\n",
    "- Part IV: Toward Learned Receivers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b226f39",
   "metadata": {},
   "source": [
    "The [official documentation](https://nvlabs.github.io/sionna/index.html) provides key material on how to use Sionna and how its components are implemented."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c4211f-beee-4142-9289-c385d0180877",
   "metadata": {},
   "source": [
    "* [Imports & Basics](#Imports-&-Basics)\n",
    "* [A note on random number generation](#A-note-on-random-number-generation)\n",
    "* [Sionna Data-flow and Design Paradigms](#Sionna-Data-flow-and-Design-Paradigms)\n",
    "* [Hello, Sionna!](#Hello,-Sionna!)\n",
    "* [Communication Systems as Models](#Communication-Systems-as-sionna-blocks)\n",
    "* [Forward Error Correction](#Forward-Error-Correction-(FEC))\n",
    "* [Eager vs. Compiled Mode](#Eager-vs-Compiled-Mode)\n",
    "* [Exercise](#Exercise)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a184ac-1f64-407f-9a24-c53d40799be2",
   "metadata": {},
   "source": [
    "## Imports & Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d84bd69e-59f2-4a42-8dd3-730c8c1d821e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:36.011592Z",
     "iopub.status.busy": "2026-08-06T15:53:36.011476Z",
     "iopub.status.idle": "2026-08-06T15:53:42.250127Z",
     "shell.execute_reply": "2026-08-06T15:53:42.249292Z"
    }
   },
   "outputs": [],
   "source": [
    "import os # Configure which GPU\n",
    "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n",
    "    gpu_num = 0 # Use \"\" to use the CPU\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n",
    "\n",
    "# Import Sionna\n",
    "try:\n",
    "    import sionna.phy\n",
    "except ImportError as e:\n",
    "    import sys\n",
    "    if 'google.colab' in sys.modules:\n",
    "       # Install Sionna in Google Colab\n",
    "       print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n",
    "       os.system(\"pip install sionna\")\n",
    "       os.kill(os.getpid(), 5)\n",
    "    else:\n",
    "       raise e\n",
    "\n",
    "# Import PyTorch\n",
    "import torch\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# For plotting\n",
    "%matplotlib inline\n",
    "# also try %matplotlib widget\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# for performance measurements\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d471a3",
   "metadata": {},
   "source": [
    "We can now access Sionna functions within the `sn` namespace.\n",
    "\n",
    "**Hint**: In Jupyter notebooks, you can run bash commands with `!`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec4eb0ab-a34e-45e7-9055-a1555d6f0775",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:42.252794Z",
     "iopub.status.busy": "2026-08-06T15:53:42.252515Z",
     "iopub.status.idle": "2026-08-06T15:53:42.501892Z",
     "shell.execute_reply": "2026-08-06T15:53:42.500800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu Aug  6 17:53:42 2026       \r\n",
      "+-----------------------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 580.173.02             Driver Version: 580.173.02     CUDA Version: 13.0     |\r\n",
      "+-----------------------------------------+------------------------+----------------------+\r\n",
      "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                                         |                        |               MIG M. |\r\n",
      "|=========================================+========================+======================|\r\n",
      "|   0  NVIDIA GeForce RTX 3090        Off |   00000000:01:00.0 Off |                  N/A |\r\n",
      "|  0%   47C    P8             23W /  350W |       4MiB /  24576MiB |      0%      Default |\r\n",
      "|                                         |                        |                  N/A |\r\n",
      "+-----------------------------------------+------------------------+----------------------+\r\n",
      "|   1  NVIDIA GeForce RTX 3090        Off |   00000000:4D:00.0 Off |                  N/A |\r\n",
      "|  0%   46C    P0             24W /  350W |      24MiB /  24576MiB |      0%      Default |\r\n",
      "|                                         |                        |                  N/A |\r\n",
      "+-----------------------------------------+------------------------+----------------------+\r\n",
      "\r\n",
      "+-----------------------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                              |\r\n",
      "|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\r\n",
      "|        ID   ID                                                               Usage      |\r\n",
      "|=========================================================================================|\r\n",
      "|    1   N/A  N/A         4054844      C   ...jects/sionna/.venv/bin/python         16MiB |\r\n",
      "+-----------------------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "865f1607-afc4-4f89-a79f-676c61591bb7",
   "metadata": {},
   "source": [
    "## A note on random number generation\n",
    "When Sionna is loaded, it instantiates random number generators (RNGs) for [Python](https://docs.python.org/3/library/random.html#alternative-generator),\n",
    "[NumPy](https://numpy.org/doc/stable/reference/random/generator.html), and [PyTorch](https://pytorch.org/docs/stable/generated/torch.Generator.html). You can optionally set a seed which will make all of your\n",
    "results deterministic, as long as only these RNGs are used. In the cell below,\n",
    "you can see how this seed is set and how the different RNGs can be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "953a834a-349d-471b-af47-0e7af410e8c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:42.504682Z",
     "iopub.status.busy": "2026-08-06T15:53:42.504432Z",
     "iopub.status.idle": "2026-08-06T15:53:42.654740Z",
     "shell.execute_reply": "2026-08-06T15:53:42.653892Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "5\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "sionna.phy.config.seed = 40\n",
    "\n",
    "# Python RNG - use instead of\n",
    "# import random\n",
    "# random.randint(0, 10)\n",
    "print(sionna.phy.config.py_rng.randint(0,10))\n",
    "\n",
    "# NumPy RNG - use instead of\n",
    "# import numpy as np\n",
    "# np.random.randint(0, 10)\n",
    "print(sionna.phy.config.np_rng.integers(0,10))\n",
    "\n",
    "# PyTorch RNG - use instead of\n",
    "# import torch\n",
    "# torch.randint(0, 10, (1,))\n",
    "torch_gen = sionna.phy.config.torch_rng()\n",
    "device = sionna.phy.config.device\n",
    "print(torch.randint(0, 10, (1,), device=device, generator=torch_gen).item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6294919-0e78-4d6b-835f-55631e6264f6",
   "metadata": {},
   "source": [
    "## Sionna Data-flow and Design Paradigms\n",
    "\n",
    "Sionna inherently parallelizes simulations via *batching*, i.e., each element in the batch dimension is simulated independently.\n",
    "\n",
    "This means the first tensor dimension is always used for *inter-frame* parallelization similar to an outer *for-loop* in Matlab/NumPy simulations, but operations can be operated in parallel.\n",
    "\n",
    "To keep the dataflow efficient, Sionna follows a few simple design principles:\n",
    "\n",
    "* Signal-processing components are implemented as an individual Sionna Blocks..\n",
    "* `torch.float32` is used as preferred datatype and `torch.complex64` for complex-valued datatypes, respectively.  \n",
    "This allows simpler re-use of components (e.g., the same scrambling layer can be used for binary inputs and LLR-values).\n",
    "* `torch.float64`/`torch.complex128` are available when high precision is needed.\n",
    "* Models can be developed in *eager mode* allowing simple (and fast) modification of system parameters.\n",
    "* Number crunching simulations can be executed with *torch.compile* for acceleration.\n",
    "* Whenever possible, components are automatically differentiable via [autograd](https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html) to simplify the deep learning design-flow.\n",
    "* Code is structured into sub-packages for different tasks such as channel coding, mapping,... (see [API documentation](https://nvlabs.github.io/sionna/phy/api/phy.html) for details).\n",
    "\n",
    "These paradigms simplify the re-useability and reliability of our components for a wide range of communications related applications."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce869c5-5169-4c66-833c-8144414cdda4",
   "metadata": {},
   "source": [
    "## Hello, Sionna!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b52449ca-532c-4c45-8e03-464832717200",
   "metadata": {},
   "source": [
    "Let's start with a very simple simulation: Transmitting QAM symbols over an AWGN channel. We will implement the system shown in the figure below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576ea7ca-3dda-4c09-868d-1e2440d35b00",
   "metadata": {},
   "source": [
    "![QAM AWGN](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "777704cf-0855-4984-86df-b615890811ae",
   "metadata": {},
   "source": [
    "We will use upper case for naming simulation parameters that are used throughout this notebook\n",
    "\n",
    "Every layer needs to be initialized once before it can be used.\n",
    "\n",
    "**Tip**: Use the [API documentation](https://nvlabs.github.io/sionna/phy/api/phy.html) to find an overview of all existing components.\n",
    "You can directly access the signature and the docstring within jupyter via `Shift+TAB`.\n",
    "\n",
    "*Remark*: Most layers are defined to be complex-valued.\n",
    "\n",
    "We first need to create a QAM constellation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21ccb7b5-b590-4abb-95f7-d939cb283f02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:42.656698Z",
     "iopub.status.busy": "2026-08-06T15:53:42.656563Z",
     "iopub.status.idle": "2026-08-06T15:53:42.828683Z",
     "shell.execute_reply": "2026-08-06T15:53:42.827741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NUM_BITS_PER_SYMBOL = 2 # QPSK\n",
    "constellation = sionna.phy.mapping.Constellation(\"qam\", NUM_BITS_PER_SYMBOL)\n",
    "\n",
    "constellation.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ded88c1-1576-42a5-a180-5bd8da533470",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Task:** Try to change the modulation order, e.g., to 16-QAM."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcb7b6f1-5dbc-4335-b633-177da8e8a5e3",
   "metadata": {
    "tags": []
   },
   "source": [
    "We then need to setup a mapper to map bits into constellation points. The mapper takes as parameter the constellation.\n",
    "\n",
    "We also need to setup a corresponding demapper to compute log-likelihood ratios (LLRs) from received noisy samples."
   ]
  },
  {
   "cell_type": "code",
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    "execution": {
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   "outputs": [],
   "source": [
    "mapper = sionna.phy.mapping.Mapper(constellation=constellation)\n",
    "\n",
    "# The demapper uses the same constellation object as the mapper\n",
    "demapper = sionna.phy.mapping.Demapper(\"app\", constellation=constellation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "980fe9ce",
   "metadata": {},
   "source": [
    "**Tip**: You can access the signature+docstring via `?` command and print the complete class definition via `??` operator.\n",
    "\n",
    "Obviously, you can also access the source code via [https://github.com/nvlabs/sionna/](https://github.com/nvlabs/sionna/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10f6b1c2",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class Mapper(Block):\n",
      "    r\"\"\"\n",
      "    Maps binary tensors to points of a constellation.\n",
      "\n",
      "    This class defines a block that maps a tensor of binary values\n",
      "    to a tensor of points from a provided constellation.\n",
      "\n",
      "    :param constellation_type: Type of constellation. One of \"qam\", \"pam\", or \"custom\".\n",
      "        For \"custom\", an instance of :class:`~sionna.phy.mapping.Constellation`\n",
      "        must be provided.\n",
      "    :param num_bits_per_symbol: The number of bits per constellation symbol,\n",
      "        e.g., 4 for QAM16. Only required for ``constellation_type`` in [\"qam\", \"pam\"].\n",
      "    :param constellation: If no constellation is provided, ``constellation_type``\n",
      "        and ``num_bits_per_symbol`` must be provided. Defaults to `None`.\n",
      "    :param return_indices: If enabled, symbol indices are additionally returned.\n",
      "        Defaults to `False`.\n",
      "    :param precision: Precision used for internal calculations and outputs.\n",
      "        If set to `None`, :attr:`~sionna.phy.config.Config.precision` is used.\n",
      "    :param device: Device for tensor operations. If `None`,\n",
      "        :attr:`~sionna.phy.config.Config.device` is used.\n",
      "\n",
      "    :input bits: [..., n], `torch.float` or `torch.int`.\n",
      "        Tensor with binary entries.\n",
      "\n",
      "    :output x: [..., n/Constellation.num_bits_per_symbol], `torch.complex`.\n",
      "        Mapped constellation symbols.\n",
      "    :output ind: [..., n/Constellation.num_bits_per_symbol], `torch.int32`.\n",
      "        Symbol indices corresponding to the constellation symbols.\n",
      "        Only returned if ``return_indices`` is set to `True`.\n",
      "\n",
      "    .. rubric:: Notes\n",
      "\n",
      "    The last input dimension must be an integer multiple of the\n",
      "    number of bits per constellation symbol.\n",
      "\n",
      "    To avoid synchronizing accelerator devices in this performance-critical\n",
      "    path, input values are not checked at runtime. Every entry must be exactly\n",
      "    zero or one; passing non-binary values has undefined behavior.\n",
      "\n",
      "    .. rubric:: Examples\n",
      "\n",
      "    .. code-block:: python\n",
      "\n",
      "        import torch\n",
      "        from sionna.phy.mapping import Mapper\n",
      "\n",
      "        mapper = Mapper(\"qam\", 4)  # 16-QAM\n",
      "        bits = torch.randint(0, 2, (10, 100))\n",
      "        symbols = mapper(bits)\n",
      "        print(symbols.shape)\n",
      "        # torch.Size([10, 25])\n",
      "    \"\"\"\n",
      "\n",
      "    def __init__(\n",
      "        self,\n",
      "        constellation_type: Optional[str] = None,\n",
      "        num_bits_per_symbol: Optional[int] = None,\n",
      "        constellation: Optional[Constellation] = None,\n",
      "        return_indices: bool = False,\n",
      "        precision: Optional[Precision] = None,\n",
      "        device: Optional[str] = None,\n",
      "        **kwargs: Any,\n",
      "    ) -> None:\n",
      "        super().__init__(precision=precision, device=device, **kwargs)\n",
      "        self._constellation = Constellation.check_or_create(\n",
      "            constellation_type=constellation_type,\n",
      "            num_bits_per_symbol=num_bits_per_symbol,\n",
      "            constellation=constellation,\n",
      "            precision=precision,\n",
      "            device=device,\n",
      "        )\n",
      "        self._return_indices = return_indices\n",
      "        n = self.constellation.num_bits_per_symbol\n",
      "        self._bit_positions = torch.arange(\n",
      "            n - 1, -1, -1, dtype=torch.int32, device=self.device\n",
      "        )\n",
      "\n",
      "    @property\n",
      "    def constellation(self) -> Constellation:\n",
      "        \"\"\"Constellation used by the Mapper\"\"\"\n",
      "        return self._constellation\n",
      "\n",
      "    def call(\n",
      "        self, bits: torch.Tensor\n",
      "    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\n",
      "        num_bits_per_symbol = self.constellation.num_bits_per_symbol\n",
      "        if bits.dim() == 0 or bits.shape[-1] % num_bits_per_symbol != 0:\n",
      "            raise ValueError(\n",
      "                \"The last dimension of `bits` must be a multiple of \"\n",
      "                \"`num_bits_per_symbol`.\"\n",
      "            )\n",
      "\n",
      "        # Convert to int32\n",
      "        bits = bits.to(dtype=torch.int32)\n",
      "\n",
      "        # Reshape last dimensions to the desired format\n",
      "        n1 = bits.shape[-1] // num_bits_per_symbol\n",
      "        new_shape = [n1, num_bits_per_symbol]\n",
      "        bits = split_dim(bits, new_shape, axis=bits.dim() - 1)\n",
      "\n",
      "        # Use bitwise left shift to compute powers of two\n",
      "        shifted_bits = torch.bitwise_left_shift(bits, self._bit_positions)\n",
      "\n",
      "        # Compute the integer representation using bitwise operations\n",
      "        int_rep = shifted_bits.sum(dim=-1).to(torch.int32)\n",
      "\n",
      "        # Map integers to constellation symbols\n",
      "        x = self._constellation()[int_rep]\n",
      "\n",
      "        if self._return_indices:\n",
      "            return x, int_rep\n",
      "        else:\n",
      "            return x\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the class definition of the Mapper class\n",
    "import inspect\n",
    "print(inspect.getsource(sionna.phy.mapping.Mapper))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41c23abb-5163-4e08-a2c8-a4b912046b30",
   "metadata": {},
   "source": [
    "As can be seen, the `Mapper` class inherits from `Block`, i.e., implements a *Sionna Block*. These blocks can be connected by simply feeding the output of one block to the next block. This allows to simply build complex systems. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5d88fc6-f18d-49e7-b66c-98c6361f4a31",
   "metadata": {},
   "source": [
    "Sionna provides as utility a binary source to sample uniform i.i.d. bits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c64222f-1a4b-42da-a37a-3379034de9cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:42.850605Z",
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     "shell.execute_reply": "2026-08-06T15:53:42.852303Z"
    }
   },
   "outputs": [],
   "source": [
    "binary_source = sionna.phy.mapping.BinarySource()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bdaf6cb-5de8-4306-8701-b0e75bbc3fcf",
   "metadata": {},
   "source": [
    "Finally, we need the AWGN channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2665b1a1-945a-4b8c-bc35-49af1b0c26ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:42.854672Z",
     "iopub.status.busy": "2026-08-06T15:53:42.854557Z",
     "iopub.status.idle": "2026-08-06T15:53:42.856924Z",
     "shell.execute_reply": "2026-08-06T15:53:42.856296Z"
    }
   },
   "outputs": [],
   "source": [
    "awgn_channel = sionna.phy.channel.AWGN()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261ad816-d4cf-424a-9dff-61444637e4dd",
   "metadata": {},
   "source": [
    "Sionna provides a utility function to compute the noise power spectral density ratio $N_0$ from the energy per bit to noise power spectral density ratio $E_b/N_0$ in dB and a variety of parameters such as the coderate and the number of bits per symbol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4feb569f-dcc3-465f-94c5-e5d25fad97b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:42.858754Z",
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     "shell.execute_reply": "2026-08-06T15:53:42.925839Z"
    }
   },
   "outputs": [],
   "source": [
    "no = sionna.phy.utils.ebnodb2no(ebno_db=10.0,\n",
    "                        num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                        coderate=1.0) # Coderate set to 1 as we do uncoded transmission here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efcbe1b0-39d9-4d0f-98bc-84343a46099c",
   "metadata": {},
   "source": [
    "We now have all the components we need to transmit QAM symbols over an AWGN channel.\n",
    "\n",
    "Sionna natively supports multi-dimensional tensors.\n",
    "\n",
    "Most layers operate at the last dimension and can have arbitrary input shapes (preserved at output)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5747f93e-c89e-41b5-bc77-b6eb13aba4d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:42.929071Z",
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     "shell.execute_reply": "2026-08-06T15:53:43.117915Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of bits:  torch.Size([64, 1024])\n",
      "Shape of x:  torch.Size([64, 512])\n",
      "Shape of y:  torch.Size([64, 512])\n",
      "Shape of llr:  torch.Size([64, 1024])\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 64 # How many examples are processed by Sionna in parallel\n",
    "\n",
    "bits = binary_source([BATCH_SIZE,\n",
    "                      1024]) # Blocklength\n",
    "print(\"Shape of bits: \", bits.shape)\n",
    "\n",
    "x = mapper(bits)\n",
    "print(\"Shape of x: \", x.shape)\n",
    "\n",
    "y = awgn_channel(x, no)\n",
    "print(\"Shape of y: \", y.shape)\n",
    "\n",
    "llr = demapper(y, no)\n",
    "print(\"Shape of llr: \", llr.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca7a98b",
   "metadata": {},
   "source": [
    "In *Eager* mode, we can directly access the values of each tensor. This simplifies debugging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "abffb7f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:43.120710Z",
     "iopub.status.busy": "2026-08-06T15:53:43.120577Z",
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     "shell.execute_reply": "2026-08-06T15:53:43.160309Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 8 transmitted bits: tensor([0., 1., 1., 1., 1., 0., 1., 0.], device='cuda:0')\n",
      "First 4 transmitted symbols: tensor([ 0.7100-0.7100j, -0.7100-0.7100j, -0.7100+0.7100j, -0.7100+0.7100j])\n",
      "First 4 received symbols: tensor([ 0.5900-0.5200j, -0.8000-0.6700j, -0.6300+0.7200j, -0.8800+0.4300j])\n",
      "First 8 demapped llrs: tensor([-33.3600,  29.5700,  45.0800,  38.0200,  35.8000, -40.6700,  49.7500,\n",
      "        -24.1700])\n"
     ]
    }
   ],
   "source": [
    "num_samples = 8 # how many samples shall be printed\n",
    "num_symbols = int(num_samples/NUM_BITS_PER_SYMBOL)\n",
    "\n",
    "print(f\"First {num_samples} transmitted bits: {bits[0,:num_samples]}\")\n",
    "print(f\"First {num_symbols} transmitted symbols: {np.round(x.cpu()[0,:num_symbols], 2)}\")\n",
    "print(f\"First {num_symbols} received symbols: {np.round(y.cpu()[0,:num_symbols], 2)}\")\n",
    "print(f\"First {num_samples} demapped llrs: {np.round(llr.cpu()[0,:num_samples], 2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d92339a-44ae-45d9-a59c-bde99e3bd656",
   "metadata": {},
   "source": [
    "Let's visualize the received noisy samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "682509f0-e734-4aec-bc05-773bd576de16",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-06T15:53:43.409751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.axes().set_aspect(1)\n",
    "plt.grid(True)\n",
    "plt.title('Channel output')\n",
    "plt.xlabel('Real Part')\n",
    "plt.ylabel('Imaginary Part')\n",
    "plt.scatter(y.cpu().real.flatten().numpy(), y.cpu().imag.flatten().numpy())\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d415e3e0-65ee-488f-a29b-5909dce48ede",
   "metadata": {},
   "source": [
    "**Task:** One can play with the SNR to visualize the impact on the received samples.\n",
    "\n",
    "**Advanced Task:** Compare the LLR distribution for \"app\" demapping with \"maxlog\" demapping.\n",
    "The [Bit-Interleaved Coded Modulation](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Bit_Interleaved_Coded_Modulation.html) example notebook can be helpful for this task.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfab1249-a6c1-430a-a204-a9c631a82700",
   "metadata": {},
   "source": [
    "## Communication Systems as Sionna Blocks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc184ba-c090-4443-9cd6-c217b3f64052",
   "metadata": {},
   "source": [
    "It is typically more convenient to wrap a Sionna-based communication system into a Sionna Block acting as end-to-end model.\n",
    "\n",
    "These models can be simply built by stacking different Sionna components (i.e., Sionna Blocks).\n",
    "\n",
    "The following cell implements the previous system as a end-to-end model.\n",
    "\n",
    "The key functions that need to be defined are `__init__()`, which instantiates the required components, and `__call()__`, which performs forward pass through the end-to-end system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee71bdbf-14c7-464f-b90d-450cb7b070b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:43.412640Z",
     "iopub.status.busy": "2026-08-06T15:53:43.412508Z",
     "iopub.status.idle": "2026-08-06T15:53:43.416595Z",
     "shell.execute_reply": "2026-08-06T15:53:43.416042Z"
    }
   },
   "outputs": [],
   "source": [
    "class UncodedSystemAWGN(sionna.phy.Block):\n",
    "    def __init__(self, num_bits_per_symbol, block_length):\n",
    "        \"\"\"\n",
    "        A Sionna Block for uncoded transmission over the AWGN channel\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        num_bits_per_symbol: int\n",
    "            The number of bits per constellation symbol, e.g., 4 for QAM16.\n",
    "\n",
    "        block_length: int\n",
    "            The number of bits per transmitted message block (will be the codeword length later).\n",
    "\n",
    "        Input\n",
    "        -----\n",
    "        batch_size: int\n",
    "            The batch_size of the Monte-Carlo simulation.\n",
    "\n",
    "        ebno_db: float\n",
    "            The `Eb/No` value (=rate-adjusted SNR) in dB.\n",
    "\n",
    "        Output\n",
    "        ------\n",
    "        (bits, llr):\n",
    "            Tuple:\n",
    "\n",
    "        bits: torch.Tensor\n",
    "            A tensor of shape `[batch_size, block_length] of 0s and 1s\n",
    "            containing the transmitted information bits.\n",
    "\n",
    "        llr: torch.Tensor\n",
    "            A tensor of shape `[batch_size, block_length] containing the\n",
    "            received log-likelihood-ratio (LLR) values.\n",
    "        \"\"\"\n",
    "\n",
    "        super().__init__() # Must call the block initializer\n",
    "\n",
    "        self.num_bits_per_symbol = num_bits_per_symbol\n",
    "        self.block_length = block_length\n",
    "        self.constellation = sionna.phy.mapping.Constellation(\"qam\", self.num_bits_per_symbol)\n",
    "        self.mapper = sionna.phy.mapping.Mapper(constellation=self.constellation)\n",
    "        self.demapper = sionna.phy.mapping.Demapper(\"app\", constellation=self.constellation)\n",
    "        self.binary_source = sionna.phy.mapping.BinarySource()\n",
    "        self.awgn_channel = sionna.phy.channel.AWGN()\n",
    "\n",
    "    # @torch.compile # Enable compilation to speed things up\n",
    "    def call(self, batch_size, ebno_db):\n",
    "\n",
    "        # no channel coding used; we set coderate=1.0\n",
    "        no = sionna.phy.utils.ebnodb2no(ebno_db,\n",
    "                                num_bits_per_symbol=self.num_bits_per_symbol,\n",
    "                                coderate=1.0)\n",
    "\n",
    "        bits = self.binary_source([batch_size, self.block_length]) # Blocklength set to 1024 bits\n",
    "        x = self.mapper(bits)\n",
    "        y = self.awgn_channel(x, no)\n",
    "        llr = self.demapper(y,no)\n",
    "        return bits, llr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2397775-1a9d-467c-8048-0949a25bda02",
   "metadata": {},
   "source": [
    "We need first to instantiate the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3212d737-3ae0-4158-983b-6d0667cc1dd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:43.418011Z",
     "iopub.status.busy": "2026-08-06T15:53:43.417883Z",
     "iopub.status.idle": "2026-08-06T15:53:43.420817Z",
     "shell.execute_reply": "2026-08-06T15:53:43.420283Z"
    }
   },
   "outputs": [],
   "source": [
    "model_uncoded_awgn = UncodedSystemAWGN(num_bits_per_symbol=NUM_BITS_PER_SYMBOL, block_length=1024)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e449509-4ba0-4e14-8a6a-91e55a7efec4",
   "metadata": {},
   "source": [
    "Sionna provides a utility to easily compute and plot the bit error rate (BER)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "34534be8-947d-4d29-8e58-1c7d0dce96c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:43.422261Z",
     "iopub.status.busy": "2026-08-06T15:53:43.422149Z",
     "iopub.status.idle": "2026-08-06T15:53:43.723867Z",
     "shell.execute_reply": "2026-08-06T15:53:43.723203Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -3.0 | 1.5825e-01 | 1.0000e+00 |      324103 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -2.579 | 1.4700e-01 | 1.0000e+00 |      301059 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -2.158 | 1.3469e-01 | 1.0000e+00 |      275847 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -1.737 | 1.2324e-01 | 1.0000e+00 |      252386 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -1.316 | 1.1249e-01 | 1.0000e+00 |      230371 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -0.895 | 1.0066e-01 | 1.0000e+00 |      206157 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -0.474 | 9.0547e-02 | 1.0000e+00 |      185440 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -0.053 | 7.9997e-02 | 1.0000e+00 |      163834 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    0.368 | 7.0215e-02 | 1.0000e+00 |      143800 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    0.789 | 6.0873e-02 | 1.0000e+00 |      124667 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    1.211 | 5.1930e-02 | 1.0000e+00 |      106353 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    1.632 | 4.4062e-02 | 1.0000e+00 |       90238 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    2.053 | 3.6719e-02 | 1.0000e+00 |       75201 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    2.474 | 3.0021e-02 | 1.0000e+00 |       61483 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    2.895 | 2.4250e-02 | 1.0000e+00 |       49663 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    3.316 | 1.8932e-02 | 1.0000e+00 |       38772 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    3.737 | 1.4715e-02 | 1.0000e+00 |       30136 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    4.158 | 1.1146e-02 | 1.0000e+00 |       22828 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    4.579 | 8.3711e-03 | 1.0000e+00 |       17144 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "      5.0 | 5.8662e-03 | 9.9950e-01 |       12014 |     2048000 |         1999 |        2000 |         0.0 |reached target block errors\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "EBN0_DB_MIN = -3.0 # Minimum value of Eb/N0 [dB] for simulations\n",
    "EBN0_DB_MAX = 5.0 # Maximum value of Eb/N0 [dB] for simulations\n",
    "BATCH_SIZE = 2000 # How many examples are processed by Sionna in parallel\n",
    "\n",
    "ber_plots = sionna.phy.utils.PlotBER(\"AWGN\")\n",
    "ber_plots.simulate(model_uncoded_awgn,\n",
    "                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                  batch_size=BATCH_SIZE,\n",
    "                  num_target_block_errors=100, # simulate until 100 block errors occurred\n",
    "                  legend=\"Uncoded\",\n",
    "                  soft_estimates=True,\n",
    "                  max_mc_iter=100, # run 100 Monte-Carlo simulations (each with batch_size samples)\n",
    "                  show_fig=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e75524",
   "metadata": {},
   "source": [
    "The `sionna.phy.utils.PlotBER` object stores the results and allows to add additional simulations to the previous curves.\n",
    "\n",
    "*Remark*: In Sionna, a block error is defined to happen if for two tensors at least one position in the last dimension differs (i.e., at least one bit wrongly received per codeword).\n",
    "The bit error rate the total number of erroneous positions divided by the total number of transmitted bits."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c8f75c-65c8-4440-a8bd-fca7e3d8f35f",
   "metadata": {},
   "source": [
    "## Forward Error Correction (FEC)\n",
    "\n",
    "We now add channel coding to our transceiver to make it more robust against transmission errors. For this, we will use [5G compliant low-density parity-check (LDPC) codes and Polar codes](https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3214).\n",
    "You can find more detailed information in the notebooks [Bit-Interleaved Coded Modulation (BICM)](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Bit_Interleaved_Coded_Modulation.html) and [5G Channel Coding and Rate-Matching: Polar vs. LDPC Codes](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/5G_Channel_Coding_Polar_vs_LDPC_Codes.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b6800065",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:43.725961Z",
     "iopub.status.busy": "2026-08-06T15:53:43.725822Z",
     "iopub.status.idle": "2026-08-06T15:53:43.737872Z",
     "shell.execute_reply": "2026-08-06T15:53:43.737274Z"
    }
   },
   "outputs": [],
   "source": [
    "k = 12\n",
    "n = 20\n",
    "\n",
    "encoder = sionna.phy.fec.ldpc.LDPC5GEncoder(k, n)\n",
    "decoder = sionna.phy.fec.ldpc.LDPC5GDecoder(encoder, hard_out=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39673cbe",
   "metadata": {},
   "source": [
    "Let us encode some random input bits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6e940732",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:43.739881Z",
     "iopub.status.busy": "2026-08-06T15:53:43.739766Z",
     "iopub.status.idle": "2026-08-06T15:53:43.772081Z",
     "shell.execute_reply": "2026-08-06T15:53:43.771346Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input bits are: \n",
      " tensor([[0., 0., 1., 0., 0., 1., 1., 0., 0., 0., 0., 1.]], device='cuda:0')\n",
      "Encoded bits are: \n",
      " tensor([[0., 1., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 1., 0., 1., 1.,\n",
      "         0., 1.]], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 1 # one codeword in parallel\n",
    "u = binary_source([BATCH_SIZE, k])\n",
    "print(\"Input bits are: \\n\", u)\n",
    "\n",
    "c = encoder(u)\n",
    "print(\"Encoded bits are: \\n\", c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99a5303a",
   "metadata": {},
   "source": [
    "One of the fundamental paradigms of Sionna is batch-processing.\n",
    "Thus, the example above could be executed for arbitrary batch-sizes to simulate `batch_size` codewords in parallel.\n",
    "\n",
    "However, Sionna can do more - it supports *N*-dimensional input tensors and, thereby, allows the processing of multiple samples of multiple users and several antennas in a single command line.\n",
    "Let's say we want to encode `batch_size` codewords of length `n` for each of the `num_users` connected to each of the `num_basestations`. \n",
    "This means in total we transmit `batch_size` * `n` * `num_users` * `num_basestations` bits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e89a7fc4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:43.773824Z",
     "iopub.status.busy": "2026-08-06T15:53:43.773700Z",
     "iopub.status.idle": "2026-08-06T15:53:43.806075Z",
     "shell.execute_reply": "2026-08-06T15:53:43.805487Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of u:  torch.Size([10, 4, 5, 500])\n",
      "Shape of c:  torch.Size([10, 4, 5, 1000])\n",
      "Total number of processed bits:  200000\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 10 # samples per scenario\n",
    "num_basestations = 4\n",
    "num_users = 5 # users per basestation\n",
    "n = 1000 # codeword length per transmitted codeword\n",
    "coderate = 0.5 # coderate\n",
    "\n",
    "k = int(coderate * n) # number of info bits per codeword\n",
    "\n",
    "# instantiate a new encoder for codewords of length n\n",
    "encoder = sionna.phy.fec.ldpc.LDPC5GEncoder(k, n)\n",
    "\n",
    "# the decoder must be linked to the encoder (to know the exact code parameters used for encoding)\n",
    "decoder = sionna.phy.fec.ldpc.LDPC5GDecoder(encoder,\n",
    "                                    hard_out=True, # binary output or provide soft-estimates\n",
    "                                    return_infobits=True, # or also return (decoded) parity bits\n",
    "                                    num_iter=20, # number of decoding iterations\n",
    "                                    cn_update=\"boxplus-phi\") # also try \"minsum\" decoding\n",
    "\n",
    "# draw random bits to encode\n",
    "u = binary_source([BATCH_SIZE, num_basestations, num_users, k])\n",
    "print(\"Shape of u: \", u.shape)\n",
    "\n",
    "# We can immediately encode u for all users, basetation and samples\n",
    "# This all happens with a single line of code\n",
    "c = encoder(u)\n",
    "print(\"Shape of c: \", c.shape)\n",
    "\n",
    "print(\"Total number of processed bits: \", c.numel())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f19f80a",
   "metadata": {},
   "source": [
    "This works for arbitrary dimensions and allows a simple extension of the designed system to multi-user or multi-antenna scenarios.\n",
    "\n",
    "Let us now replace the LDPC code by a Polar code. The API remains similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b05b3fd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:43.808089Z",
     "iopub.status.busy": "2026-08-06T15:53:43.807973Z",
     "iopub.status.idle": "2026-08-06T15:53:43.815279Z",
     "shell.execute_reply": "2026-08-06T15:53:43.814650Z"
    }
   },
   "outputs": [],
   "source": [
    "k = 64\n",
    "n = 128\n",
    "\n",
    "encoder = sionna.phy.fec.polar.Polar5GEncoder(k, n)\n",
    "decoder = sionna.phy.fec.polar.Polar5GDecoder(encoder,\n",
    "                                      dec_type=\"SCL\") # you can also use \"SCL\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f00d4b2",
   "metadata": {},
   "source": [
    "*Advanced Remark:* The 5G Polar encoder/decoder class directly applies rate-matching and the additional CRC concatenation. \n",
    "This is all done internally and transparent to the user.\n",
    "\n",
    "In case you want to access low-level features of the Polar codes, please use `sionna.phy.fec.polar.PolarEncoder` and the desired decoder (`sionna.phy.fec.polar.PolarSCDecoder`, `sionna.phy.fec.polar.PolarSCLDecoder` or `sionna.phy.fec.polar.PolarBPDecoder`).\n",
    "\n",
    "Further details can be found in the tutorial notebook on [5G Channel Coding and Rate-Matching: Polar vs. LDPC Codes](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/5G_Channel_Coding_Polar_vs_LDPC_Codes.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708efce7-2145-4637-981a-5a38a315a9b6",
   "metadata": {},
   "source": [
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xVivowSUQHgJqEgLL3+9uhJQAk0kwALmLGzunVjWCcsV1i4sUkybgaWMBdJZrgnr2e23327X3zx27JgVUFjKcJWyigBLPCH2xowZYy1hLKTOJLXMmca6nkxSyzU4n6+UlpZmrV3En61fv14QYMSgMaEsrk2Wk+rQoYMVhs6SUSwvxYS5WP2++tWvypo1a+yLgQlY1MjzxRdfLPPnz29QHPrKi25TAkogsgmoSIvs8tPcK4GoJYCgwsrlnXAp4ubEmsWC6UyhwcACEtsI7EesIbIQcH/4wx+sleyJJ56QWbNm2Rgz4swQVsSKIco43+jRo+Wcc86RH/zgB8IC6g3NmcZSUM8//7w9Bpcqa4ByPqx9uGKd0Zyex+/du1e6detmrYEITPbDtco7+SfPbGeVA7ZpUgJKIDoIqEiLjnLWu1QCrY4AAmjRokXCyErEC++vvPKKnHfeeXa5JoL5t27dat2FF1xwwSn37wglZyOxXljAWJ8TtymxbggnhBxuTkRejx49hPNgETv//POlXbt2dvkorF24OIlF4zPWLlYluOKKK+w758DtiUDE7co+3q5S3LIITqyDuD3J34ABA05ZToptKtCcEtN3JRAdBFSkRUc5610qgVZH4KqrrrJB+cSLIYhYGJ2RmZdeeql1gxL0f9ttt9mpNBBBJO9RlFi5WJ+T0aEIMcQXwf7Ei7F4OnFpV155pT3/448/bs9HDBsi7qKLLrKCC6sZxyG++Ix7srCwUB588EG7DijWPNypuE+5PvliYAGuVifhfmXpqeuvv14eeughex0GCrC/Z549PzvH6rsSUAKtl4Cu3dl6y1bvLEIJVNaUyCOzMm3uk+Iy5GcTiiP0TvzPNuKjKVYirF2IGyaTZYHzPGPlwuJF/NiePXusJYq/+/btawUTLkdiz4gFW7hwoQwdOtRuZzJcxFS/fv3sOyMziSnD+sV8aZwLSxnB/GwbNmyYFU/EjsXFxdkF1hmwwGeWkeL6iLCcnBxh0ltcs7ycmDaEIPthPeP83DvHIBRZfor84mJdvny5nSyXGLaioiJrGWSpKm9LYGOk4dLaF1jfXDhLZqz7nhQcXSs9sybIdQOflY4pAxtDo98rAVcTUJHm6uLRzEUjARVpgZc6E9B6L3xOHBjJn+kqfM38770NIcXL+3xscyxcvj57H4erEzHnK3lf09c+TdmmIq0p1PQYJRB+Ar5rivDnS3OgBJSAEvCbgLdA40BvMXWmk/myTHlvQ4g5YszzXJ7bfH32Pq4hgcY5va/peR39rASUQPQR0Ji06CtzvWMloASUgBJQAkogAgioSIuAQtIsKgEloASUgBJQAtFHQEVa9JW53rESUAJKQAkoASUQAQRUpEVAIWkWlYASUAJKQAkogegjoCIt+spc71gJuI7A8OHD7QS0rstYK8gQU3uwZqivwRWt4Pb0FpRAqyagIq1VF6/enBKIDAIslD5nzpzIyGyE5ZJVGRDBLC2lSQkogcgioCItsspLc6sEWiUBZvhXkdYyRcuqCqmpqT6nD2mZK+pZlYASCBYBFWnBIqnnUQJKoMkEsKSVlJTI1KlTm3wOPfB0AtOmTbPrmrIUFUJNkxJQApFFQEVaZJWX5lYJtEoCLMl01113yaNmHc4VK1a0ynsM9U0Ri/bkk0/KxIkTpX///g2uchDqfOn1lIAS8J+AijT/WemeSkAJtCCBa665xi5ajlhTi1rzQGNBu+eee+yAgT59+qhAax5OPVoJhI2ALgsVNvR6YSWgBDwJYE175JFHZMeOHTJ79ux6oUbQOy9NZyaA5Yw1TBFo69evl0mTJgnClwXhNSkBJRCZBFSkRWa5aa6VQKskwELgvHJzc+tFGgMKtm/f3irvN5g31a1bN2sxGz16tBVnPXv2lKysrIDWMA1mfvRcSkAJNJ+AirTmM9QzKAElEGQCCDXcniQGFBQXF9vP+l/DBNLT060gQ6ypOGuYk36jBCKJgIq0SCotzasSiCICCDVNSkAJKIFoJqADB6K59PXelYASUAJKQAkoAdcSUJHm2qLRjCkBJaAElIASUALRTEBFWjSXvt67ElACSkAJKAEl4FoCKtJcWzSaMSWgBJSAElACSiCaCahIi+bS13tXAkpACSgBJaAEXEtARZpri0YzpgSUgBJQAkpACUQzARVp0Vz6eu9KQAkoASWgBJSAawmoSHNt0WjGlIASUAJKQAkogWgmoCItmktf710JKAEloASUgBJwLQEVaa4tGs2YElACSkAJKAElEM0EVKRFc+nrvSsBJaAElIASUAKuJaAizbVFoxlTAkpACSgBJaAEopmAirRoLn29dyWgBJSAElACSsC1BFSkubZoNGNKQAkoASWgBJRANBNQkRbNpa/3rgSUgBJQAkpACbiWgIo01xaNZkwJKAEloASUgBKIZgIq0qK59PXelYASUAJKQAkoAdcSUJHm2qLRjCkBJaAElIASUALRTEBFWjSXvt67ElACSkAJKAEl4FoCKtJcWzSaMSWgBJSAElACSiCaCahIi+bS13tXAkpACSgBJaAEXEtARZpri0YzpgSUgBJQAkpACUQzARVp0Vz6eu9KQAkoASWgBJSAawmoSHNt0WjGlIASUAJKQAkogWgmoCItmktf710JKAEloASUgBJwLQEVaa4tGs2YElACSkAJKAElEM0E4qL55vXelYBbCNQdr5HNhz+WY7VHpKq2vD5bdcer5YuCaebvGGkblyW920+o/04/KIFoJ1BVWyY7iz+TippCKShbLZU1pRbJ0eoDsvHQB2bbWslsmyc5KQMlPjY52nHp/UcgARVpEVhomuXWR6DueK1sL/lUVhdMl+PH6+pvsLquUj7c8jOJjUmQ4Z1vUZFWT0Y/KAEIxMj6Q+/L1qLZUl5dKEerD1osRRXb5dNdf5CENikyNu8H0jGln+JSAhFJQN2dEVlsmunWRiA2Jk7yMi+Q4oqdcuDo2vrbQ7BhISg7tl/ys8bWb9cPSkAJiCTEpkinlP5SXnVIiiq2SU3dMYsFC9vh8s1SayzU7ZPzJb6NWtH09xKZBFSkRWa5aa5bGYGYmFjJzTzPWMomnnZnsW3ipU+Hy6Rb+jmnfacblEC0E+iXPUk6pw2TuDaJp6CIkVgZmnOjdEjuK23M86VJCUQiARVpkVhqmudWSSDB9PZHd/+2JMamn3J/KXHZdrs2NKdg0T+UgCWQnthZBmZfLWkJOacQyTYWtj6m05Mc3/6U7fqHEogkAirSIqm0NK+tmoAva5pa0Vp1kevNBYmAtzVNrWhBAqunCTsBFWlhLwLNgBI4ScDbmqZWtJNs9JMSaIiAtzUt2wwUIHRArWgNEdPtkUIgZKM7d+/eLTU1NcL79u3b7WcgOdu7desmcXFx4rzzXU5OjuTl5UlSUhJ/alICrZ6ApzVt/aG3NRat1Ze43mCwCGBNW3twhpRVHTSxaJMlW2PRgoVWzxNGAi0q0hBg8+fPl8rKynox5txrTEyM81H4vGfPHvv3jh07zBQEx+3n/fv3S2ZmpowePdq+Dx8+3L7XH6gflEArJIA17bwe98iBI6s1Fq0Vlq/eUssQcKxptXXVakVrGcR61jAQiDGC6IQiCtLFHWG2efPmemsZIsyxkPGem5trrWZc0tnuWNScd75bsWKFbNiwwQqzoqKiesE2fvz4ViHWiouLZdu2bcayuFUKzefSkhIpKT4xGSP33xpTRmaGDBkyWFKSUyQ/v6e1lrr1PrH8YvXl5fwueQ9Fqq6rkDWHXpeh2beFdGQa1uwxY8ZImzZtJM9YsXv06BGK223SNZyy4Z3kvNs/Wul/lI3jcaB8+Oy2xDNCWfByvCe8hyKVHtsnhcc2mGk5hkrb2HahuKS9BmXBC6OC8x6yi/t5IYwllMm2bVtl3/69UlF5TA4WnJhXzs9TRNxueOFGjDxbkhKSbHuD3oi0FFSRNm3aNPnoo4+s5ax79+72x0qlkpiYWC/GAgWENY0fFi+sbPzQ0JVY1SJZrC1fvlxmzf5ICg7vN5MwlklNbZUwoSmv1pxizVD4hLgUMdMXSWpCuky+6SYZcfYI191yWVmZvPrqq/UW3nBkMDZepLY6HFc2162tFZ69O++804q28OSi4atOnTrV1gns4WmVb/iI1vMN9R9C6Nprr5WJEye6KhyENmD9+vWndNBDTT7W6Fbz8xUJqvmh8bugXOh4I9IoG97dklasWC6fLvxUdu7eLuU1R82qJrSjtVJbFxrxHC4OMWay46T4NIk5HiN1FW3kllunyAXnjwlXdpp03aCItEWLFsmcOXME61nnzp1tpT5u3LgWqTywrnGtEmN1ikSxxkNM/md+9J4cT6ySnv17SOdu2ZKanvLlK7VJBRkpB5WVlsmmtdvkSOlR2bZup6QkZkrf3AG2UiMGMdyJHj+NzGuvvSaHDh2yv2Usv47FNxJ7YoEwRZwRogAH3nnGEhIS5Pbbb3eFVY3OGgKNDhudNMrGaQyd90DuN9L2dcqGOvfYsWO2fG688Ubp379/WK1qiEY6NTw7I0eOlN69e9d7TJxnJ9JYB5JffpfOywnZcYMhwbO9Kasqlv7DekmnrtnSPjtTEhITpF12ViC3GXH7Vh2rkjXLN0jVsWpZu3yjaW8ypFuHPJkyZYqt0yPhhpol0ngwqTQQHVi4Jk+eLBdffHGLiDNvmN5i7fLLL7fWNbcPMnjzzTfl5X/9Tbr2yZYLLz1Xcrp18r61qPkbwTb3g8XyybuL5CuXXi0/+tGPwn7vCLMHH3xQUlNT5bbbbrPxkGHPVJgygGDDMv7rX//aioDnnnsuTDk5edm77rrLWs6++tWvWpF28pvo+4RQ+9WvfiVpaWny1FNPSYcOHcIG4YEHHrCdmlC2AWG72UYu7HQkVq5cKfxOsaqFK9He/PP1v0uX3u3lrNFDpc/A/HBlJezXRbAt/mSZTP/b+zL23PHym9/8Jux58icDsY+a5M+O3vtQQVBpr1u3Ts455xzb0x41apTtdXvv2xJ/Y3VxBhTMnDnTikUn9s2tQm2riQV4693p0rZDrHzlxomtvhfTWLnTk8vp1lFiYmNkyaKlkpXRTvr07tPYYS32PZaJ2bNny5IlS+Sxxx6TgQMHtti1IuHETlxaly5d5L333pPY2FgTTzgkbFmnM0ij8/TTT9sOWdgy4pILY6HCejhr1iwpLy+XQYMGhaz+9URAWzBjxgxBqBHeguU1mpMTl0a4AJZFvEvh8BJsM7HOb74zXZJNe3OFaW86m7o2mlNsXKxlkJaZKp/Mmi+pyWkyaOAg1yNpkkjjofzHP/4hR48elRtuuEGuv/56+yOkUg91cqbpwKqHWIuPj7cVl9uEGr2rf78xTcpqi+SCS84x5ubWbWb293eQ+KVQq66plsVzl8q5o861Vix/jw/Wfgg0rEb/+c9/5J577ol6geZwdYQaz9kLL7wgY8eOlYyMDOfrkL0j0F566SW57777VKB5UHeE2t/+9jcr0jp27GgHfXjs0qIfnbbg5ptvtp3maBdoDmxHqCHSCgoKrCU6lG2SbW+mT5OjdUVy/kRtb5xycYRacmpbeWf6f2TsmAslPf3UFV6cfd3yHrCqch5KetQPPfSQ4GYM5Y/PFzh8/1TemJUZLUmFji/eTYl8bdyyTjp0zjBqPnpdnL7KJC09VQYO7yN1sdWyfPkyX7u0+Dbc9fy2+S1jodV0kgAjCC+88ELp2rWrtVif/CZ0n3imiY8jDk3TqQT4vRIDhgWY33EoE88M1zz33HPD3g6E8r79uRZWTtomZiZANIUybd9u2pvN66W9bW+i24LmzR0PztBzBkjb9HhZaAZTuD0FJNJ4IKks+eERrxPuYFVPuPT0sYAg1MgnFhE3CbWi4kJJSI6TTmaQgKbTCaSYgRPtO6fLsjCJNALlDx48qALt9KKxW3B1IgaIQQ1HcgYKhOPakXBNyoY4XSzCoUx4MBgoEO6OeijvOZBrIdRI/H5DmQqNMExIjZUcM0hA0+kEEGr5/bvJgoULTv/SZVv8FmkIH1ycWNAY6UVgtRuTM6Jm4cKFrhJqO3bstMOe1Yrm+1eTZkRadpcOsmrVKt87tPBWrAE0cmpF8w0aa1o4RRqdQ7Wi+S4btjoiLRyWNOpcFWm+y8YRaaG2pO00orCq9lhUD0zzXSIntiYkxkuvAfmy5LMlZ9rNFd/5JdIQaIwiGjBggLTU1BrBpOGM9HzllVdk8eLFIXcB+LqXkuISOwcaU21oOp0APZvU9LZSsP/A6V+GYAuWNAJ9W/sUG01FSWwabHbu3NnUUzTrOBo5p8Fr1ola6cGUDb9ffsehTFjS8GK4cVLdUHJo6FrEppFC7dUpKSm1c6DR+dV0OgFi05iGZM/uvad/6bItfk1XzWgqhnkTHOpWC5o3V4QaFTtCjV6exkx4E9K/lYASUAJKQAkoATcTaNSShhUNE/q9994bUUsxOTFqCLQ33njDjrBxc0Fo3pSAElACSkAJKAEl4EngjCLNcXN+7Wtfk8GDB0ecSdsRapiaGQod6ngNT9D6WQkoASWgBJSAElACgRBoVKQhbCLZVeg5kID5ajQpASWgBJSAElACSiASCDQo0hw3J/OPOcGPZ7oh5jCqqqqS6urTV4Tmu3Am4tOc2Z/VmhbOktBrKwF3EqDeak49xdQXoQ7adydJzZUSUALBJNCgSJs2zcxWbFYUYGi3P8OrX3/9dXn88cfl5z//uTz//PN2sXXW/tuyZYv83//9n5SWlgYz3wGdC7cn98G0HGpNCwid7qwEooLABx98YEMi6urqpKSkRJ544gnbsUO4UY8xoz+dUBITUz/55JN2KSZGu1K//eIXv7D137///W+7H/PJMbksx5I4JyPkWb5JkxJQAkrAXwINijQsaYHMf4OoQwTRm/z444/l5ZdftiKPig1h5FRW/mYs2PupNS3YRPV8SqB1EMCK9uyzzwodTccT8OKLL8qCBQtsvbV582YrwPbu3WutbSwf9tprr8mePXvs4vMffvihPa6wsNCsmLHcHjN37lwrynbt2mWPoZOKSKPjq0kJKAEl4C8Bn1NwINA6dOggF1xwgV9WNOdiLB3z7W9/W5566ilZuXKlncGdSSjbtm1rF2eml8lnKr3k5GS56KKLpEePHnbx5o0bN9qFebF4nXXWWXafTZs2CRUfvVDSlVdeKT179rS90XfeecfGyuXm5vq1Vh3WNCyCuD1xefpjHXTuS9+VgBJovQSYxBjr2L59++Q73/mOZGdn24XCqbsmTZpk13Rlxng6ocxHxmomTKxLZ5SR41jS+BsrGfWLs34lx/Pdj3/8YwsPq1xzXKqttwT0zpSAEmiIgE9LGlaxK664IuA50Zg49rnnnrMTyDqDDdasWWMrNSqwv//97zJ9+nQ5cOCAILKo4OhZfvHFF3aSTITd7373OyvKli1bJs8884x8+umnVqi9/fbbtlJEsLE/eQzUdYllkMrYEX0NQdHtSkAJRA8B5lJkku6ysrJ6SxjiDJcl9dZ7771nV1qZOXOmrXOWLl0ql1xyifD30KFDZcKECXYeSYi1a9eu3lrWr18/K+zefffdegtd9FDVO1UCSiAYBHyKtEBdnU5GcBUw3QVWMmI1WO8P6xquTnqQVIL0RP/rv/7LVoqffPKJXWsOQTdixAi7Gj2uBCahZV9cpV/5ylfkv//7v+Xqq6+24g9hNmvWLOnevbt07NjRLyuakz9cnvSaeekAAoeKviuB6CVAfYK7csqUKdayTyeSemfs2LG2LsMaRlwt80QSY8a+WMoQZ4cOHRKEGPUciWNxac6bN8+GffTp08cuoYfrdPXq1WEP+WhqKW8+/JEcKN9gVkwJ7WoGTc2vHqcEWhOB00Sa4+rEjRjoUh+ILUaDPvDAA9ZShmWMc8TExNQzY2mXrKwsu8QUlRw9VdwIW7dulc6dO9t9qTipKPPz84WKDtfrZZddZt0RWNjYH6sYIi2Q5O3yDOTYaNu3prpGSgpLpbIitAs2Rxtnvd/wEsB1ST2EmzIlJUUYQED906lTJ1vHEKvWq1cvW/+kp6fLn//8ZzsIiTqMUe+4QZ14W45BoGHpd7Zde+211gr3pz/9KeSLnweL7NK9f5ePN/9CFu/+kxQcXdtssUaHndhlBmlocjkBU1Z1tXVSa8qrJedoOF53XCrKK6T48InQJpdTCWn2fIq0QAYMeOaWHmViYqLEx8dby1lFRUWDMRjsQ0KcEbB7zjnnyMUXX2wrSo6jkiNuzOml9u7d28ajsS/uStYRpVINNHFvnL8lLGkbDs2UjYf/I0WVOwLNlt/7b1y7VV578S158el/1r8+n7/CPEQnRpH5faJGdiw6XCxzP1gkWze23L00koWI/prfGFZh3Oskfm9YgLHKaDqVwLqDb0thxdZTN4bgLyz/DHA6++yzrdhChCEeCPrnHSs+1jNcn3xHCAiuTjqMdD55//zzz60wo7N5/vnn2/08O6V4Fb773e9agRap8WhHju2TLwr+JbO2/FI+3Pxos8QaDKZOnSqPPvqoPPbYYzZmb+3ateoObsLv/VD5RtlZskgqqouacLQfh5iy2rv7gPzlGdPWPPOK/PP5aTLvw0VypKTMj4MD24VncdOarTLzzdmBHejCvavrKmSj0QLFlTuDkjufAweacmYqJnqRuDtpkHBzDhkyxJr5PSst73Mj1tq3b28rO9YHzcjIEB5aFnT2TFSKEydOtEG4DDjo0qXLKRY6z33P9BmRRiwJQg/LWjDThsMzZX/pSslqmyc92423r6yk3GBewv6Q5/5noWR3ai8dOrWz5+YHHuxeDg/i8sWrJTm1rQwc1jeo9xDqk/HQbCv8RDqmDpTMpB4huTzuehoj3GYMpmF03wsvvGAbeiwzmk4S+HTnc5Ka0NE8L+Okl3lu2rXtefLLFvyEpZ+Y2T/84Q/Wak+dgxWNjuANN9xgY82oI4g5o5665ppr7FQcDG6iPsJKRhwuUw7Nnj3bljHl3rVrV9v5dLKOR+DBBx+0g6HOVBc6+zf1Pcc8pkv2/1G21WU19RQ+jyus2GE627VSWrXXiLXXZUfRAtlSOMeWVVL7Mok54e31eaz3RkTaSy+9ZAeQYXlE5DIi9nvf+57teHvX+97H698nCewqWSKrC96QTqZeo73pmnaWtI0PXtnTpuzfXSAz/jlTzh0/QmqqamT1sg1SsOegXH3rZfUDZE7mqOmfaoyRYdfWPfLZ3OUy5RvXNv1ELjjyWM0RWbDzWUlP7GbrtLzM860maGrWThNpu3fvti5GKqFA0o033igbNpi4BWPCZgkpRmgycpMe6Z133mkDa3knloMKj4rr7rvvtn8To4bFgcoNdykWNFwJw4YNs65OJx+4Q+nRIgARdk1JVLqOkGzK8Y0ds/fICtlSNEe2mkrMEWq8BzN1ze0sl15zkQwcfkI84U3esGqT7N6xT6qOVZsyqDXCqp/k9+shRYeKZe3yjbJ3135JTEqQIecMlPbZWbJu5SbZs3OftOuQZc7TRzp17WRMzcWyY/Nu2WSsdUWFJcbdecL0XFFeacXhtk07bOPUd3AvyenSUTat3yqH9hdKedlRSctMlwmTxkhMG5MZlyXvhyY/a0yLizXc9TRAWFi+8Y1v2MafQTD8xunEEI+JW4znjGcFMcfzgxUHazQDarDwEKuJ9c17+8iRI21HBqGBdQehzrPFdq7L1A90RHj+7rnnntM6PW4qopLKXcYCPdM8M7Nla7uLQibWiC2jvmEQABYvEmX12Wef2XIhNIPQDeoqxNXAgQOt2CKulr+Ji/3Rj35kLaZMz0FYBqzHjBlj6z46qY7HAKH3wx/+sEnWf3/Lqkt/kYX7npakI8F9BuvqqqTO/HOSp1gbdFWxbC9/X3Lq7pCENv57Nq666iobA8hyfcwxh9UZ0QZHrJe0I/CmU81UKPAmzpm2gXaAuGWeLX7vtDeEwPA8sR/PE88NswPwTFHfI/7YjzaFmQR8bUdArlq1ysYsYzDg+cODw7mLiorsgDee1/POO8/mzeERrvfKmhLZXjRPNhd+aJ6dT+xz44i1YOYJY8ANd15p6vY2Mvf9T+WDt+bI4LP6S+cenUw7slF2b99r2pR20ndIb9MuZMv+PQdkxaIvDLMj0iE7U4aMHGifg2ULVpo66Yj06NlV+g3pI5lZaVKw77AsW7BCDh8qkr07TqwIhFfoUEGhrFm5QQ6b9x75XaX3gHxJSk6UmdNnGQNFB9m/74Ccde4Q6TMwNB26QHgSu3mofLP1qm0zdVpu1tgTZZN1YZPE2mlKDJFGgxGoSJs8ebLP+yCwlhcJkeYkptLgRbrppptsI0OFxoNCA0cFygPnJCpOeriDBg2yDy5TeTQ1VdWWyWe7/iwHUzs09RQ+j9tVsliq6yrtd4WV26Vw79R6sba1aLupxLr4PC7QjYf2H5ZFsz8zgmqH9B/aR/L79pDP5q+QtSs2SJ8BPY2JukAKDxYbUZYoK5eskaXm4cjKzpCUtBQpKSqVXdv2yvyPFktqmhngYXovPCAXTBhpeknrjTl7odkvVSB/tKzcxqR9sWStfPzuPGnXMUuqKqpk++adpmc1UhYYdyju1yHnDJD4xASxBwV6MyHY/5SHxgjo/CysnOPMe8uINRqYw4cPC88SLnkaC4QUs9LTkCCoaCTYzm8c8UZjwIAWRjcjFGiEGF2IBYZ9vbcjDkhMd4NY4LkhnhSBR1zVm2++aa12WKb5rrmpzlhS9pSbuROvOSZztv26uac75fijVYfs38XHdsmyfX+TraaMnEanJS1riFpenom4Wl5O+vrXv26Z8jd1EusY8+4kGn5eWNAQEE69mWcEnmdiO+Xqeazn98H43MZYtGrrzMoHJ/VUME7b4DmO1ZVJfLK5WBvzMjFFcqrzo8Hj+AIO/G6xKlOvY9Hkd4tVzfme5wJXMSP5eU7wnvDM8PzAF0GHyGNQB8KLjj6dGzopTKOCuEL89e3b1x5Dx+j++++3zxuWT+/tnItjyRttEM8qovv999+3+aIdQ7DzXSAp3YROby+aH/TnZmfxQqmqLZcqUw7biufKvrKV5tkxYi1rnOwpXWWy6L9oPtP9IM7STNvRJTdHjpkY5aULV8oS82q3IV1WGwNAZrt06xbdvGG7TLrhYpn17gLT0d8snUxH3hSb7cjvMkJugWlzOvfIkf27DlhhN/HqcTLPtCHLzLnaG8/QwYKDJhtt5NCBQnnv9Q+NoaDUCLMk2bOrwLQz22TiNRfKS8+9LqPGniVduufYDuiZ8t3Yd7Em4iqzZ2nQy+WYEc/lxgVNnXmwYpMcqtxs67SeX7Y7PQMUaz5FGtYmp7Jp7EaD9b3T4+RhpKHxTlSC5AuXAw93U5NjSVuw7S3ZnNDyo5VOijWR/h1ubWq2Tzmu2gT1F5teCsLoaBlxf2KsWeXSzvRarph8sSw3vZi1qzbah2mLiSnr3rOLXHXL5aY3E2fjCRZ9skwystLlhq9eKWuWrpMVRsjFm0Zkx9bdUlVZK1+/7xpjgSuR16fOsKJuwxebZLN5SC7plyvFdWYKlM/XSVLbJCM6qiTDPKDX3zHJWtI8RfUpGfbzjwRjzIjvvKtlHxoTx3G4Yots8xJrfmbRr91oRIi1pKEgdpLpZrCGYbFxxBoxmDTqNC40TlgPOI5td911l22AEGg0EPzevbfPmDHD5mXOnDny/e9/335mqgeuhRikLHCzEuDuxHX6lfkGdkLo7i6bJyMmV8qHWx5tYK+mbUZYeCZiOTzF2qCO14StA+BdF3n/7eQ7NTXV+djge0sKNC66f4PItZO/Y6wa6Q3mwfMLf6X7yv2vSWnFznprWmJcunTPGGXEwHh585H/lbxbL5eEuMbv3/PazmeY4B1B+DA6lrnqeE7Yxm8coYULmgFkt9xyixV0bKMdwEL261//2naIsJBRBog9RB0dmyNHjtjwGYwAWEqZJYDOPh0oOi+e2wnVeeutt+xzRmw0ljM6OoTWEKpAZ4sRwFj3Ao2FzuhkhMrROea5me/cdlDeeSZxQzsJy9q2IiPWjiDWRHIzvuJ8FZR3rJFJxhuT2DZR9hmvzefzVph2IEF69c+T7Zt2yuql620IzvLFX8jl119kxNTZ1iJasO+grFy0Wgad3V8uuWa8LPx4icz5zwLp0auHLP10pZw3YYSxig2VWe/MkxWfrZEdW3bLh+bz2ecONlazdvKFaaOWL1wlI8cMM+1TlQwe3s8ItnE2H825sTjT12rXuzjo9ZmZDdHc90khTyeZmNuiym1WrPXKMl4wU6cN6Gisk35YNlwj0hqDPWrUKDu5Lg91c8QAIq38SI1xC7a8QGvsnpr6fSdjUh4/6QLpb8zLyaanQU+HlNkuQzp1zpaOpleyyQzi2L5pl1Qb92d+31zpmNPe7lNqLGlHS49KXp8e0i23szUnixFp640Qg2teX9O77ZcnW2W7pKSmmBE3lXKg4LCkGdM0D2lapnEDnD9E0jPSrEDMN8Kwo7lmMFKSqefjO29v8YfG9nA8xFrPdhMktXx40IQADQ49ftwuxFEyoSmdnocffljWrTOi2FjMaCxoZGg8cN1jBcBdxt/MtYWlmJ4+8ZmEDXhvd1yl/J4pF6x3BLrjMmImfCzOeV7WnOaVkVkeyTQIVGw1X1qLm3e+xo+m0SmvKbQ9UtMh19QIgb1GpI3OuU9yc3s0sqfztX8ybY+JfTpSuUcSY1PrxVmvzHHSNXOElO56QY7XBhCU5lz6y3c631icsSTzbOBmpB6iY8FADVybJMIEeE74mxfPCKP7cedzDo5FzNEpocPPiF2eQ8QeggyLHc8RzwadGO/tdKKwXjP4g2eJDhbPrmOJxlrK88T5A03mduR4TI15bkLT5vB81tQF/4nBDVls2o+y0jJJTG5rLF+HrdsTXp26djQd/zQpNcYD2peh5wyyFjZYFew9aOY6LZYJ/S+07VCPXl3l6JFy0z7tkGJjLRs20ogx0z7RRlGehNlUmpGeGe2NF8Ac39MYBzqaNq/WjDJNNIaJYecNMV6gIFkJY81I4xDVZ4g16rSK2hNWNmtd4cfRSDpNpBFvwXB0HopArGlkAFOzs7pAI9cN+Gt/eqr+nJR7I97tgp73SN9eposTxERczV4zcMCz0O0gAtPj3NduW9CuFBvbRtoaV2ZKSlt7TqbL8ExtzPcxxveRmpFiK6oDpifDg8VDVmfcEknmuGLzIDDcGZ8/2zp1zTYBoQeMafqwjU3jgaiuqjam/3hTUaUZd4bYGICEhFgTC3LMVoQFb8ySRPN90JL5vcYY14knv6Cd28eJjh+vkwrz0FTWGLEktX70aXycxMcmGgdW1SCeBosZMTe4SohRoqLH7cLqG4g2/qYXz7NDI4Q1jZHN/E45B7E1PFve2zk3sW00ToQn8JvG/UMIARNKB9rb93Ebp2xqExMnXVJGy4q3EuVeE8cVzLRk91+Me+CEy5PzJsVlSJ6J4+hlnhtcN13Sz7L1WTCviZUF6yQNOo0MjTd1HoI3FAmhgEUHwdGcTqdnXk0oqsTHmtH1sUmem5v9OSk2S3p3uMRYZs4XR5zFtzlR9zT15MSTIaqwIPO7ZaAGq8tgwcLdzHOB+EJYYeHylZyyQuSxD5YuLGqONZnniGeRd2I0KWO+P3jQ1Ifm3J7bmcwY0Yd44zOdIn4XuDf5jfBbaapFuvSAsdhUjZFx+Rf4uo0mb9tbuty6UavrTqwHG9cmUTqkGJds5oWyL2tjk8/rfSBTcBwx7UflxkpZbLwwySnJxprVRzaajn1GuzTrfqwzPvZa8wM8bFyVn89faUNiiDmrMHVUbXWt6dSnyoE9BcaTc1T2GfdlWyPyGPwWG9fGuj4zszJMWVVLGyPO25r2KTk1WXJMnPTwUQOl4miFxBsLXlysmc7LxDwnmbYvGKmmWqR4iynv/O8E43T156iqMYPu9r8ildWF9duS49ub8Bpi08ZLfsaF0jl9qHnu/atrGhRpKFp/RRq9etwuPGw0NN/61rfsj70+hy764Ii0Mb3ukWG9hgU1ZxWmsT9YtsGKDEec9TKxT7gF1mZNld0lm4JyPQYIvP3ah7JwzlJ7viEjTMSwSaeYTo3g6Z7fRdq1zzSxahvlH8//25RnrAweMUB6EsM2d5n89dlXrFuT/cZOHC3LjJt0yYLlZt/p5mGJsT0a3Jn4/z96+xP5aMYn9qFqZ4JER405K2iWJ5t5898xs6xh9b7u5qEJjlvYOa/3QwOnFDOa8ORDM1YqC1ONEHjSOaRZ7zQMjnsTSxeVPgKNjgYWA1wxbCdImWBorMNY13Br0pA8/vjj1rpAQ8JSaAgx7+3XXXednUoG986rr75qO0c8s7hIg9Xoe0JApHVPHiNLX0+SS373iOdXzf687sAMK9J8ibM2gQwd9CMnsMQtTD1Ah/LSSy+1ZcMktIhpLDShSMyvNtWM/v3pT39qBUAortnUa5zd9XZJje8kXTKGS3PFmZMHhBSdECxelMH48eOtYGVNVFz8iCSsVrfeeuspwtnXbxsRRblhdUbkId6witG54Vmj04IoRJRh3SZ2k3hPz+0sgcg1CUegA4VAY1+eSUcMOnkP9L3EiLQOdePkkp4nlgcL9PiG9l+8+/+MUWC5sTbV1osz2pseGefJiow/ya6SzQ0dGtB2gvjf+Md71r2IBewrN10iQ0cOMRayQ8YDs9kG8yNge5ng/oHD+tgY5VnvzZdVn62VlPRk6W08MwOMi3LB7M9lp4mHPrDvkJw9erD0NwPfBq3qZ9qWeXZwGxa3uPhYyevdw3w/xITVrDUD3vZKQryxno0cJDndA5sXtbGbrDUeyaItmaZcgluflZopa5iK65iJS6Odyc087xRxFmiddppIa+zGvL+nQfrjH/9oHwJ6PfSGaCzcmhyR1lBsSXPz3Tl9uGQl5Znh6SfEGWItmKnPoJ5y4ZHzjGXs5ELNBFeONMGU9DJijRBj9Od5488x7zm2F4ircr/pvbAfLtEePbuZ7XFmNOhe6dw1R4aeO9CMBM2VZDOQIMeYrXdt220HHeD3ZxRPB2OGTjCxB9s37pQaU9Fl57SzVrpzzTWDGWdzzEy/U70/r8UfGsRZPuVjejSd0gYJD82Ooh1BKyasWAykwYqG+4bgczovNAKILhpotiOwsN5885vftI0KGXAsO4g4YmNwWyIsvLcT8E6DxehDJnimEaLXT2VJHA2WuWCn2DZxZhg+1prmWVG889U2rp30zzbr8hqrGdYzLGeBVmTe5/T1N/FOP/vZz2zjjVjG3cwIWOa0+8c//iEPPfRQyEQa9RBigAEgWGncnAZmXxM04c9v9i4Tc4mI4hlgOhNGw/Ic8JtHOCOU6KDguqeeZqoTLI48P1iQeZYIDUA8MRiD0ZYIOkQa1jCeJ+olRj7zjjhj0AErzuC2xHrnvZ1OVP/+/a1blM4Tx3AdDBUcxzPdVG+OMdib33N80J8bYgM7pw2TbGM9w0KTa8RZeiKWWeNJ8dNKc6bfnennG2HUSa4zMcdwSM9KlVzTdgwybUKcaT8uvmqcaRvaGUvYPhOb1layTIeemQMuuXa8rDZxy4cOHJZM47Ls1K2jHXRgNLOUFpfKWecNtZ38Dmbfy667SFaZfbHUDRrRz/wmUs2Agw5y7W2T5Itl6+Sg8exkmvjpFOMVSjPWuJvvvk7S0oPj6uTe62pjgl4uCXHJkhbfWbp0Gm7bmbz0C6zlrKl12mkiDXcnlZm/Qov9GCnzgx/8wAZ20jjgx8ekzBIqWAAI+uRhpJdDz4aeEw8cPSYeKvz/c4wljgeUhxcrAY0OjQ/rfPKwMKEkPxRG32Cl4AHmnDx8gZihHZHWEhVjv/aXC6+OKf2bNNT2TA+M811fM+SY15kSIo2Xk3KMK7POPCFYkNp8OUVGZ/Pg4NKk0sR9SiJGrasZfYP707pMnROY9zEXm5jACaNMA1dXz7sd1rQgp+O1bVr8ofEUZ0HOvj0dLhOC9p3E75QGhRcNEi8SMTeILxKNEmvZ0ljhBiXxHW5OjvPebncw/xE3M8FM70Cj4pyLvyMpndfDLGqe3K/FxJnDgsB06hnWAaaxp0OJCIAxiUB16h1nhQE6oGzDQ0B9A9c844bDcokoQDxT1pQBddi//vUvW/dhIaLDSuNOXUgdiPUGcU5diNUmkpIv61VT8++INF/HU/9jdXb4OJ4c2gMnYd3i5SQ6QCTc/LQrTltAmcKdMANGaMLdOR/7N7SduE4GLrAvzx2J59SNqXvGSCPKukhOqokR/lKcBTWfpm3o0q2TfOM+354NxNQlV4+37QhtiPM7ye/dXfKMmKs1bUWc6TQ6abJpX3CLxiYYt+WXG5lag4EHWC6dsuMr4tDy+nS3bRGGB2f/W+6+1jmda98TY9Pkgrz/J9lt+9cbAZqT2dNEGg0Kw5axBPjTc6BhYD4ZhBo9Ix4UftyMjEGkUVlRafHAcD5cDQRnItKoNIkRYATc//7v/9pYBCo1YjVY9JieLpUjPV22IeoQkDzMuFgRcOQToeeG1K/D5W7Ixml5sELMPHCeiW24P72T3de4Or0T20+c4vRjvPd129/BfmgCvT/PxsHzWEdUsY3fNOLAU7ixnWfJ13a+cxL7OA2Ksy2S3ofn3FpfwbdkvqmTsLowtxaJuoVEh5LEFAzUUdR/iC6sbUxWSznROcQ1x7xqLA1Fo88+rCJBfBsjBZ944gm5/vrr7bmYMBtrDzGFv/3tb+390TFEiNNZRXRr8k2goefF994nt3oeh6UMqxll7FjEnD2xTtNmeG93vufYSEiIs04pg0Py7DTEo8F2xBgD4uTUtsLWUwknhK/n+U60Oafuy/cn6jXPPSPjc0JsigzpODlo5XIaMWfggL+WNCowejqIJ4Y+01NlyDJijN4NvRiEGgKLyglLG6qZxGd6q1yLCpDhzUxsi1UAVxDuImaiRohxDL1aBBsjPbkuFaRTwfpbfFjS6AW3hCXN3zzofqEl4Dw0BGs21eTc0jmmQqKzwqTQnqmh7Z77RPpnKulQJALVGTHbUMKCw7xcxDIhyuh00gFl+SfcXazzSV2F1RMxR92Gi4z4Jiw31C24oTkHiThDPAMvm6WnOBdWIuo8gtwd611DedHtzSeAxQ3mDKrxTA1t99wnUj6H6tmJFB5uyWcwy+U0kYb5noYBoeWIqcZunIbl97//vY0bwFXDJITEFWAVyMvLs2Z/3JwILCpAX4lKkLlviKthtBs9IGJrEI2INyxxxI+QL2agppJjhBSCzt9E5UrAKq4O4h80RQ+BYD40LUGN/NF58BYRDW1viTy09nPSMcPq1VBCpBEoTueQKVSw5LM/nUE6mMQ4OZ1X9qGO4hjCL5x6iL+psxACbEfs0RHFtYpnAXFHPdRQPdhQ3nR74ASwdFIW3paxhrYHfgU9Qgm0PIHT3J0IIn7UTLBJgCfiqbFEBYb5n6BcphfAvYmli8qISg1XJe5NXvQkEVhMH0D8hyMEuQ7HkLg++3C8sy/+ar7HisayUGyn0sUl4W+iZ8sxLTVowN986H5KQAmEngB1B/UaVn/cXVjhqUe86wPcZtRLuCz/8pe/yNNPP23FGJOoOmLMyT31iS/BxXbqL96ptxgEQgeUbXSEqTO5Nh1NTUpACSiBhgicZkljR3ofxE5QoTSWqKAw3+POxB1JBYcLgOBL/sZFQHwZZmcqSaxpxK+xP71Vp2fqeR0Cbjkvx1Kpcn6GWCMEmckdF4KzCLtnHILnOXx9xi3hWOd8fa/blIASaL0EWNKJhOhCgLHUFqM6sXT5Sli/iAekg0i8GgmLvtOx9HWM9zYsbogzLGq4Q7HGIdIY8ETdRafWVx3ofR79Wwkogegk4FOkMWKGisQflye9QZa2oeJD2GF9IziXuAx6ic8884x1GTDfDUG0xJcxFw7rtRG/QbwaAZx85/RoEXnkgVFVzGXD4tL0SBlBijXuT3/6kx10gGDj+v4kBCeuTtyc3jEK/hyv+ygBJRDZBKhjnnzySRvv+stf/tKGZBDcj+WeEbdO/YOowu3MIAA6h6wYwcAOYtNYGYJwCSxxuKL5noEI1E/e53DWemQONgY58U7HE0seYSCsZfzee+81KBIjm7bmXgkogWAQOM3dyUlxeVJ5EUNBj+9MLk8qOCbfRAQhoIhnIyHApprJGtnO8U5MEJNtMhiACtHZxv6MmHIS27GmYdjQUDkAAArYSURBVI2j10qP09mXChFrHNf1HLLrHNvQu+PqZGQPcSGalIASiD4CzLlF7CtWMjprTh3CQCcnOdM68PeLL75oLV3UN1i8cHdeffXVzq7WO4CHgOR5DubvctL48eOFF3WhZ93zk5/8xAo0zq1JCSgBJeCLgE9LGjsylQa9PKxZ/pj3qXwcgeZ5IbY7AsvZTq/Te5vznee7E4fmvS+9Wqdy9dy/oc+4GRhWf9VVV9kebEP76XYloARaPwHqE6z3/tQh1GmOiKKz6FjbmkLJU6A5xzvndv7WdyWgBJSAJ4EGRRojNnE7fvzxx9Zy5XlQpH3GokdcG2vC6ajOSCs9za8SUAJKQAkogegk0KBIAwdmfwL3/bWmuREhVjRm+8bFkWfiQDQpASWgBJSAElACSiASCJxRpBGbxoS0DAgg2NUft6ebbpoYEGLdiGO7++671YrmpsLRvCgBJaAElIASUAJnJHBGkcaRWNNYSYDRmATsR1Jiyg3ctbm5uTqiM5IKTvOqBJSAElACSkAJSKMiDWvaD3/4Q/nss89kyZIlfk95EW62TKjLcHcWzGUBZF9Bu6HMY2KSGc1q/lUd8z0nUyjz4sZr1dbUWjYpqY1PntwS+SdAnN8I085oOp0A8xbChrV1w5EYickKAJp8E6Bs+P36Grzl+4jgbGWqJjwWkeZlCc7dN34WJm0nMVgulMmZPUHbG9/Uj9cdl/Kj5ZKWnuZ7BxdtbVSkkVcGETBnEPOTsbC52ydfJA7t+eeft4sY33777a5wc/bI7SHxsYmyb/cBFxW/e7JypPSoHNhbKEOGDg5LpqhEWcqH+EVNpxNg6gnYMBdYOBKTYc8x6wJr8k2AsmH+tuaMPvV95jNvHT16tCxfvtyuIHPmPaPzWyZeJ+HNCWXqkdvdtDdJ2t40AP2YMZZsWbdTzhk1ooE93LPZL5FGdh2h9uqrr8q6detcK9QQaMwizoSUbhFo8Ms3gxbaxqXIfhVp4DgtVZZXSumhMunTu89p34ViA40bjZyKNN+0EWms8jFkyBDfO7TwVuYZY3Z+Tb4JUDYskRfqKT0QacxBqctb+S4X2iNSqGcVyM/LN+1NsrY3votFaqprZN+OAzJ44KAG9nDPZr9FGllGqGFSJz4N96e/s/2H6nZxcb700kt26RZmBw/1g3Gm++yZ30sy0zrIrq17pay08eW2znSu1vYdJvnCQyWSnpIlXbt0C8vtYUk7++yzraV49+7dYcmDWy+Kq3Pnzp12bkNWCAlHQqQhBhzLRDjy4NZr8nvFu8FyfoEskxeM+0GksfILL7d7WIJxv4GcA/c8L1z1oV7lJj+/p7T7sr3BS6HpJAFCaw7uN+uJJ6VJbo/8k1+49FPsoyYFkjfcDiyYjhiiYWNZFCaX9Z5wNpBzNndfYiJWrVpll28hb/fdd19AC6839/r+HI+4JW/z5y2Qmrpq6ZbXWWLjYv05tFXvwwOzZcN2mfvuYrlswiT5yqQrw3K/TGyalpZmrTWIAZY2C7XrKCw33shFEWgFBQXyi1/8wi7dxvJu4Uh0uHB3Yk1DsIU6xicc9+zPNYlFe+KJJ+zKMJQN9UwoU3Z2to1VpmwIFyBmMZxtQSjv/UzXIhaNZQjXrFljV8/BSh/KVN/ezP3UtDdV2t58CZ9YtP17CuTff31PLh57qdx805RQFkuTrhWwSOMqDCag8v7Zz34mR48elU6dOklWVlbIg1bJC+ZkVkZgjdABAwbI/fffb9cD5Tu3JRhtXLdZliz+XJLTkyWzXZokJEbvkjBY0Dav3y6z31kg1SUx8uOHfxLWIsMKQSA0azWylBm/a/6O1sQzvmXLFiHE4f3337fr6IaTBfMcsv4lnUJEW6itE+G8d1/XxoLG9EishUxfm6X8wpGw4L3wwgs2Li2cbUE47t3XNbGeMbMA5dKrVy9hKbJwpKzML9ubz5ZKSlpbaZvSVpLNK1oTBoEdW3fLLNPebFu9V5767dMRgaJJIo07c9b3ZL06Zw41elA0aqEYYYT1DBO7s1g7a+WxYDGWPbcmejcdOrSX3Tv2yZoV6yQuIU7iE+Ol2ixEj1UtGixrPCjlZeXW5btm+SaZ+e85UnG4Vr71rW8a62d44tGc3wvWNBoZ3mfMmGGnnOnatasd1chvOxosawizkpISe++rV6+2gpUl1R544IGwxaM55eMIM0QjDSGijXdStFjWKBusNE5ox/Tp0229h+U31PFoTrlgTaMD6rQFrPdcXl5uR3zyzESDZY0yoU3CaMCsAi+//LJdeowQoXCF3ZxobzrI7u17ZM3KDVJhRjOmZ6WZUY0V0iY2RuLj450ibLXvWM6OHjlq7r1CNq3bLu++9rHsXLtf7r33v42GcX88GgUTYyrl480tIeZR+93vfifEJ0yYMEH69OljR7O0RHwEDwI9SMzI9FaIi7vyyivtqLNQm/qbw+3zZUvk/Q/flf2H9kjb1ETJ7tJeUtNbfy+n6liNFJn4s4ojlbJ/22Hp32+Q3Dz5Zte5p/md0QHgt8bvGEGAxaC1J6ZSIP6M52rBggVCA3zHHXeEXaB5c59qlnpz4tMYJET5REOibCgjBrjQyN500012kfdwCTRv5rQF69evr4+Ro2xaoh3wvm64/6azwIvfJEYL2sLLL788bALNm8fSZZ/LJwtmy9adm6VNQox07JIlWR3CM52Od95a8u/amjo5VFAkNVW1sn3NHhuDdsdtd1ojU0teN5jnDopII0OMLiKW58knn7TzkjE3GWINq0RzA1odYUZMF40mFVSkijPPwoPX0mVLZe/+PfLFyi/kQMFBz69b5WfmQcvvmSddOneRm1wozjyh87uj0amqqrK/bX7jrT3xvDKCk0bfjeLMkz9CjcRzxCsaEmVDGWGhGTVqVNisZ2dizTPDs+O0CTw/rT1hLeNF7JmbxJknd6x87898X4pLimTlypWyfesOz69b5WfqsT79e0tGWobcftsdESXOnAIJmkhzTug8oLwPHjzYuiHoVTiCjf0cl6jz7hxLEKwzKSIPOT0TzPuOMGMyXYaZT5kyJeIsZ8496rsSUAJKQAkoASWgBPwhEHSR5nlRX4KN77GsYar3trAhxpxh3Kj+bdu2WfMx+zm+fURaJLk1PXnoZyWgBJSAElACSkAJ+EugRUWaZyYcwcY2zOCYwJ13Zz8GIzixFYgxTMe8VJQ5hPRdCSgBJaAElIASiBYCIRNp0QJU71MJKAEloASUgBJQAsEgENCKA8G4oJ5DCSgBJaAElIASUAJKoHECKtIaZ6R7KAEloASUgBJQAkog5ARUpIUcuV5QCSgBJaAElIASUAKNE1CR1jgj3UMJKAEloASUgBJQAiEnoCIt5Mj1gkpACSgBJaAElIASaJyAirTGGekeSkAJKAEloASUgBIIOQEVaSFHrhdUAkpACSgBJaAElEDjBFSkNc5I91ACSkAJKAEloASUQMgJqEgLOXK9oBJQAkpACSgBJaAEGiegIq1xRrqHElACSkAJKAEloARCTkBFWsiR6wWVgBJQAkpACSgBJdA4ARVpjTPSPZSAElACSkAJKAElEHICKtJCjlwvqASUgBJQAkpACSiBxgmoSGucke6hBJSAElACSkAJKIGQE1CRFnLkekEloASUgBJQAkpACTROQEVa44x0DyWgBJSAElACSkAJhJyAirSQI9cLKgEloASUgBJQAkqgcQL/H9DhU++tqriMAAAAAElFTkSuQmCC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "39b597a6-8eef-4ba5-91e7-f030c5fedae4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:43.816961Z",
     "iopub.status.busy": "2026-08-06T15:53:43.816837Z",
     "iopub.status.idle": "2026-08-06T15:53:43.820749Z",
     "shell.execute_reply": "2026-08-06T15:53:43.820108Z"
    }
   },
   "outputs": [],
   "source": [
    "class CodedSystemAWGN(sionna.phy.Block):\n",
    "    def __init__(self, num_bits_per_symbol, n, coderate):\n",
    "        super().__init__() # Must call the Sionna block initializer\n",
    "\n",
    "        self.num_bits_per_symbol = num_bits_per_symbol\n",
    "        self.n = n\n",
    "        self.k = int(n*coderate)\n",
    "        self.coderate = coderate\n",
    "        self.constellation = sionna.phy.mapping.Constellation(\"qam\", self.num_bits_per_symbol)\n",
    "\n",
    "        self.mapper = sionna.phy.mapping.Mapper(constellation=self.constellation)\n",
    "        self.demapper = sionna.phy.mapping.Demapper(\"app\", constellation=self.constellation)\n",
    "\n",
    "        self.binary_source = sionna.phy.mapping.BinarySource()\n",
    "        self.awgn_channel = sionna.phy.channel.AWGN()\n",
    "\n",
    "        self.encoder = sionna.phy.fec.ldpc.LDPC5GEncoder(self.k, self.n)\n",
    "        self.decoder = sionna.phy.fec.ldpc.LDPC5GDecoder(self.encoder, hard_out=True)\n",
    "\n",
    "    # @torch.compile # Activate compilation to speed things up\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        no = sionna.phy.utils.ebnodb2no(ebno_db, num_bits_per_symbol=self.num_bits_per_symbol, coderate=self.coderate)\n",
    "\n",
    "        bits = self.binary_source([batch_size, self.k])\n",
    "        codewords = self.encoder(bits)\n",
    "        x = self.mapper(codewords)\n",
    "        y = self.awgn_channel(x, no)\n",
    "        llr = self.demapper(y,no)\n",
    "        bits_hat = self.decoder(llr)\n",
    "        return bits, bits_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "056e25da-3d0a-416d-ac6c-98b15bc042e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:43.822257Z",
     "iopub.status.busy": "2026-08-06T15:53:43.822145Z",
     "iopub.status.idle": "2026-08-06T15:53:57.084730Z",
     "shell.execute_reply": "2026-08-06T15:53:57.083733Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -3.0 | 2.7954e-01 | 1.0000e+00 |      572498 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "   -2.429 | 2.6426e-01 | 1.0000e+00 |      541203 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "   -1.857 | 2.4687e-01 | 1.0000e+00 |      505587 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "   -1.286 | 2.2690e-01 | 1.0000e+00 |      464691 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "   -0.714 | 2.0228e-01 | 1.0000e+00 |      414272 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "   -0.143 | 1.7119e-01 | 1.0000e+00 |      350602 |     2048000 |         2000 |        2000 |         0.2 |reached target block errors\n",
      "    0.429 | 1.1315e-01 | 9.9000e-01 |      231740 |     2048000 |         1980 |        2000 |         0.2 |reached target block errors\n",
      "      1.0 | 2.0587e-02 | 4.8650e-01 |       42162 |     2048000 |          973 |        2000 |         0.2 |reached target block errors\n",
      "    1.571 | 1.9603e-04 | 1.1833e-02 |        6022 |    30720000 |          355 |       30000 |         3.6 |reached max iterations\n",
      "    2.143 | 1.6276e-07 | 3.3333e-05 |           5 |    30720000 |            1 |       30000 |         3.6 |reached max iterations\n",
      "    2.714 | 0.0000e+00 | 0.0000e+00 |           0 |    30720000 |            0 |       30000 |         3.6 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 2.7 dB.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CODERATE = 0.5\n",
    "BATCH_SIZE = 2000\n",
    "\n",
    "model_coded_awgn = CodedSystemAWGN(num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "                                   n=2048,\n",
    "                                   coderate=CODERATE)\n",
    "ber_plots.simulate(model_coded_awgn,\n",
    "                   ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 15),\n",
    "                   batch_size=BATCH_SIZE,\n",
    "                   num_target_block_errors=500,\n",
    "                   legend=\"Coded\",\n",
    "                   soft_estimates=False,\n",
    "                   max_mc_iter=15,\n",
    "                   show_fig=True,\n",
    "                   forward_keyboard_interrupt=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df537cad",
   "metadata": {},
   "source": [
    "As can be seen, the `BerPlot` class uses multiple stopping conditions and stops the simulation after no error occurred at a specific SNR point."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e7902e8",
   "metadata": {},
   "source": [
    "**Task**: Replace the coding scheme by a Polar encoder/decoder or a convolutional code with Viterbi decoding."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c02ebb11",
   "metadata": {},
   "source": [
    "## Eager vs Compiled Mode\n",
    "\n",
    "So far, we have executed the example in *eager* mode. \n",
    "This allows to run PyTorch ops as if it was written NumPy and simplifies development and debugging.\n",
    "\n",
    "However, to unleash Sionna's full performance, we can use *torch.compile* which compiles the model for optimized execution.\n",
    "\n",
    "We refer to [PyTorch Compilation](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for further details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b3bc7a86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:57.086614Z",
     "iopub.status.busy": "2026-08-06T15:53:57.086469Z",
     "iopub.status.idle": "2026-08-06T15:53:58.245767Z",
     "shell.execute_reply": "2026-08-06T15:53:58.244775Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.compile(mode=\"default\") # enables compiled mode of the following function\n",
    "def run_compiled(batch_size, ebno_db):\n",
    "    return model_coded_awgn(batch_size, ebno_db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "64a7c186",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:53:58.247651Z",
     "iopub.status.busy": "2026-08-06T15:53:58.247344Z",
     "iopub.status.idle": "2026-08-06T15:54:14.179996Z",
     "shell.execute_reply": "2026-08-06T15:54:14.179055Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0., 0., 0.,  ..., 1., 0., 0.],\n",
       "         [1., 1., 1.,  ..., 1., 1., 0.],\n",
       "         [1., 1., 0.,  ..., 0., 1., 1.],\n",
       "         ...,\n",
       "         [1., 1., 0.,  ..., 1., 0., 1.],\n",
       "         [1., 0., 0.,  ..., 1., 0., 0.],\n",
       "         [1., 0., 1.,  ..., 1., 1., 1.]], device='cuda:0'),\n",
       " tensor([[0., 0., 0.,  ..., 1., 0., 0.],\n",
       "         [1., 1., 1.,  ..., 1., 1., 0.],\n",
       "         [1., 1., 0.,  ..., 0., 1., 1.],\n",
       "         ...,\n",
       "         [1., 1., 0.,  ..., 1., 0., 1.],\n",
       "         [1., 0., 0.,  ..., 1., 0., 0.],\n",
       "         [1., 0., 1.,  ..., 1., 1., 1.]], device='cuda:0'))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 10 # try also different batch sizes\n",
    "ebno_db = 1.5\n",
    "\n",
    "# run twice - compilation happens on first call\n",
    "run_compiled(batch_size, ebno_db)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76ffd1d",
   "metadata": {},
   "source": [
    "In compiled mode, the model is traced and optimized by PyTorch's compiler.\n",
    "\n",
    "The compilation happens when the function is first called with specific input shapes/types."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3664c47",
   "metadata": {},
   "source": [
    "We now compare the throughput of the different modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d3f22327",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:54:14.181940Z",
     "iopub.status.busy": "2026-08-06T15:54:14.181788Z",
     "iopub.status.idle": "2026-08-06T15:54:36.529202Z",
     "shell.execute_reply": "2026-08-06T15:54:36.528217Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Throughput in Eager mode: 8.599 Mbit/s\n",
      "Throughput in Compiled mode: 60.188 Mbit/s\n"
     ]
    }
   ],
   "source": [
    "repetitions = 4 # average over multiple runs\n",
    "batch_size = BATCH_SIZE # try also different batch sizes\n",
    "ebno_db = 1.5\n",
    "\n",
    "# Use CUDA events for accurate GPU timing\n",
    "# (CPU timers like time.perf_counter don't wait for async GPU operations)\n",
    "start_event = torch.cuda.Event(enable_timing=True)\n",
    "end_event = torch.cuda.Event(enable_timing=True)\n",
    "\n",
    "# --- eager mode ---\n",
    "# warmup to ensure GPU is ready\n",
    "_ = model_coded_awgn(batch_size, ebno_db)\n",
    "torch.cuda.synchronize()\n",
    "\n",
    "start_event.record()\n",
    "for _ in range(repetitions):\n",
    "    bits, bits_hat = model_coded_awgn(batch_size, ebno_db)\n",
    "end_event.record()\n",
    "torch.cuda.synchronize()\n",
    "\n",
    "elapsed_ms = start_event.elapsed_time(end_event)\n",
    "throughput_eager = bits.numel() * repetitions / (elapsed_ms / 1000) / 1e6\n",
    "\n",
    "print(f\"Throughput in Eager mode: {throughput_eager :.3f} Mbit/s\")\n",
    "\n",
    "# --- compiled mode ---\n",
    "# run once to compile (ignored for throughput)\n",
    "run_compiled(batch_size, ebno_db)\n",
    "torch.cuda.synchronize()\n",
    "\n",
    "start_event.record()\n",
    "for _ in range(repetitions):\n",
    "    bits, bits_hat = run_compiled(batch_size, ebno_db)\n",
    "end_event.record()\n",
    "torch.cuda.synchronize()\n",
    "\n",
    "elapsed_ms = start_event.elapsed_time(end_event)\n",
    "throughput_compiled = bits.numel() * repetitions / (elapsed_ms / 1000) / 1e6\n",
    "\n",
    "print(f\"Throughput in Compiled mode: {throughput_compiled :.3f} Mbit/s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "749d2355",
   "metadata": {},
   "source": [
    "Let's run the same simulation as above in compiled mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1de9bbf6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:54:36.531337Z",
     "iopub.status.busy": "2026-08-06T15:54:36.531186Z",
     "iopub.status.idle": "2026-08-06T15:55:03.004759Z",
     "shell.execute_reply": "2026-08-06T15:55:03.004060Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -3.0 | 2.7939e-01 | 1.0000e+00 |      572199 |     2048000 |         2000 |        2000 |        18.9 |reached target block errors\n",
      "   -2.273 | 2.5932e-01 | 1.0000e+00 |      531096 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -1.545 | 2.3566e-01 | 1.0000e+00 |      482638 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -0.818 | 2.0741e-01 | 1.0000e+00 |      424769 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "   -0.091 | 1.6784e-01 | 1.0000e+00 |      343735 |     2048000 |         2000 |        2000 |         0.0 |reached target block errors\n",
      "    0.636 | 7.6628e-02 | 9.1900e-01 |      156934 |     2048000 |         1838 |        2000 |         0.0 |reached target block errors\n",
      "    1.364 | 1.5206e-03 | 7.0875e-02 |       12457 |     8192000 |          567 |        8000 |         0.1 |reached target block errors\n",
      "    2.091 | 1.6602e-07 | 4.0000e-05 |          34 |   204800000 |            8 |      200000 |         3.5 |reached max iterations\n",
      "    2.818 | 0.0000e+00 | 0.0000e+00 |           0 |   204800000 |            0 |      200000 |         3.5 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 2.8 dB.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ber_plots.simulate(run_compiled,\n",
    "                   ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 12),\n",
    "                   batch_size=BATCH_SIZE,\n",
    "                   num_target_block_errors=500,\n",
    "                   legend=\"Coded (Compiled mode)\",\n",
    "                   soft_estimates=True,\n",
    "                   max_mc_iter=100,\n",
    "                   show_fig=True,\n",
    "                   forward_keyboard_interrupt=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57cdb153",
   "metadata": {},
   "source": [
    "**Task:** PyTorch offers various compiler backends nd compilation modes. Try\n",
    "experimenting with them.\n",
    "\n",
    "*Remark*: The effectiveness of compilation depends on the hardware and model complexity. On GPU with CUDA, torch.compile can provide substantial speedups by fusing operations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4765a0",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Simulate the coded bit error rate (BER) for a Polar coded and 64-QAM modulation.\n",
    "Assume a codeword length of n = 200 and coderate = 0.5.\n",
    "\n",
    "**Hint**: For Polar codes, successive cancellation list decoding (SCL) gives the best BER performance.\n",
    "However, successive cancellation (SC) decoding (without a list) is less complex.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cf855e19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:55:03.007258Z",
     "iopub.status.busy": "2026-08-06T15:55:03.007129Z",
     "iopub.status.idle": "2026-08-06T15:55:03.009779Z",
     "shell.execute_reply": "2026-08-06T15:55:03.009023Z"
    }
   },
   "outputs": [],
   "source": [
    "n = 200\n",
    "coderate = 0.5\n",
    "\n",
    "# *You can implement your code here*\n"
   ]
  }
 ],
 "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": "3.12.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}