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    "# Weighted Belief Propagation Decoding\n",
    "\n",
    "This notebooks implements the *Weighted Belief Propagation* (BP) algorithm as proposed by Nachmani *et al.* in [1].\n",
    "The main idea is to leverage BP decoding by additional trainable weights that scale each outgoing variable node (VN) and check node (CN) message. These weights provide additional degrees of freedom and can be trained by stochastic gradient descent (SGD) to improve the BP performance for the given code. If all weights are initialized with *1*, the algorithm equals the *classical* BP algorithm and, thus, the concept can be seen as a generalized BP decoder.\n",
    "\n",
    "Our main focus is to show how Sionna can lower the barrier-to-entry for state-of-the-art research.\n",
    "For this, you will investigate:\n",
    "\n",
    "* How to implement the multi-loss BP decoding with Sionna\n",
    "* How a single scaling factor can lead to similar results\n",
    "* What happens for training of the 5G LDPC code\n",
    "\n",
    "The setup includes the following components:\n",
    "\n",
    "- LDPC BP Decoder\n",
    "- Gaussian LLR source\n",
    "\n",
    "Please note that we implement a simplified version of the original algorithm consisting of two major simplifications:\n",
    "\n",
    "1. ) Only outgoing variable node (VN) messages are weighted. This is possible as the VN operation is linear and it would only increase the memory complexity without increasing the *expressive* power of the neural network.\n",
    "\n",
    "2. ) We use the same shared weights for all iterations. This can potentially influence the final performance, however, simplifies the implementation and allows to run the decoder with different number of iterations.\n",
    "\n",
    "\n",
    "**Note**: If you are not familiar with all-zero codeword-based simulations please have a look into the [Bit-Interleaved Coded Modulation](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Bit_Interleaved_Coded_Modulation.html) example notebook first.\n",
    "\n",
    "## Table of Contents\n",
    "* [Configuration and Imports](#Configuration-and-Imports)\n",
    "* [Weighted BP for BCH Codes](#Weighted-BP-for-BCH-Codes)\n",
    "    * [Weights *before* Training and Simulation of BER](#Weights-before-Training-and-Simulation-of-BER)\n",
    "    * [Training](#Training)\n",
    "    * [Results](#Results)\n",
    "* [Further Experiments](#Further-Experiments)\n",
    "    * [Damped BP](#Damped-BP)\n",
    "    * [Learning the 5G LDPC Code](#Learning-the-5G-LDPC-Code)\n",
    "* [References](#References)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "![System Model](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:11:42.667130Z",
     "iopub.status.busy": "2026-02-13T15:11:42.666959Z",
     "iopub.status.idle": "2026-02-13T15:11:45.499039Z",
     "shell.execute_reply": "2026-02-13T15:11:45.497990Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda:0\n"
     ]
    }
   ],
   "source": [
    "# Import Sionna\n",
    "try:\n",
    "    import sionna.phy\n",
    "except ImportError as e:\n",
    "    import os\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 torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "sionna.phy.config.seed = 42 # Set seed for reproducible random number generation\n",
    "device = sionna.phy.config.device\n",
    "\n",
    "# Import required Sionna components\n",
    "from sionna.phy.fec.ldpc import LDPCBPDecoder, LDPC5GEncoder, LDPC5GDecoder, WeightedBPCallback\n",
    "from sionna.phy.fec.utils import GaussianPriorSource, load_parity_check_examples, llr2mi\n",
    "from sionna.phy.utils import ebnodb2no, hard_decisions\n",
    "from sionna.phy.utils.metrics import compute_ber\n",
    "from sionna.phy.utils.plotting import PlotBER\n",
    "from sionna.phy import Block\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weighted BP for BCH Codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we define the trainable model consisting of:\n",
    "\n",
    "- LDPC BP decoder\n",
    "- Gaussian LLR source\n",
    "\n",
    "The idea of the multi-loss function in [1]\u00a0is to average the loss overall iterations, i.e., not just the final estimate is evaluated. This requires to call the BP decoder *iteration-wise* by setting `num_iter=1` and `return_state=True` such that the decoder will perform a single iteration and returns its current estimate while also providing the internal messages for the next iteration.\n",
    "\n",
    "A few comments:\n",
    "\n",
    "- We assume the transmission of the all-zero codeword. This allows to train and analyze the decoder without the need of an encoder. Remark: The final decoder can be used for arbitrary codewords.\n",
    "- We directly generate the channel LLRs with `GaussianPriorSource`. The equivalent LLR distribution could be achieved by transmitting the all-zero codeword over an AWGN channel with BPSK modulation.\n",
    "- For the proposed *multi-loss* [1] (i.e., the loss is averaged over all iterations), we need to access the decoders intermediate output after each iteration. This is done by calling the decoding function multiple times while setting `return_state` to True, i.e., the decoder continuous the decoding process at the last message state.\n",
    "\n",
    "\n",
    "The BP decoder itself does not have any trainable weights. However, the LDPCBPDecoder API allows to register custom callback functions after each VN/CN node update step. In this tutorial, we use the `WeightedBPCallback` to apply trainable weights to each exchanged internal decoder message. Similarly, offset-corrected BP can be made trainable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:11:45.548231Z",
     "iopub.status.busy": "2026-02-13T15:11:45.547945Z",
     "iopub.status.idle": "2026-02-13T15:11:45.552872Z",
     "shell.execute_reply": "2026-02-13T15:11:45.552075Z"
    }
   },
   "outputs": [],
   "source": [
    "class WeightedBP(Block):\n",
    "    \"\"\"System model for BER simulations of weighted BP decoding.\n",
    "\n",
    "    This model uses `GaussianPriorSource` to mimic the LLRs after demapping of\n",
    "    QPSK symbols transmitted over an AWGN channel.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    pcm: ndarray\n",
    "        The parity-check matrix of the code under investigation.\n",
    "\n",
    "    num_iter: int\n",
    "        Number of BP decoding iterations.\n",
    "\n",
    "    Input\n",
    "    -----\n",
    "    batch_size: int\n",
    "        The batch_size used for the simulation.\n",
    "\n",
    "    ebno_db: float\n",
    "        A float defining the simulation SNR.\n",
    "\n",
    "    Output\n",
    "    ------\n",
    "    (u, u_hat, loss):\n",
    "        Tuple:\n",
    "\n",
    "    u: torch.Tensor\n",
    "        A tensor of shape `[batch_size, k] of 0s and 1s containing the transmitted information bits.\n",
    "\n",
    "    u_hat: torch.Tensor\n",
    "        A tensor of shape `[batch_size, k] of 0s and 1s containing the estimated information bits.\n",
    "\n",
    "    loss: torch.Tensor\n",
    "        Binary cross-entropy loss between `u` and `u_hat`.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, pcm, num_iter=5):\n",
    "        super().__init__()\n",
    "\n",
    "        # add trainable weights via decoder callbacks\n",
    "        # Pass PCM to enable proper weight application in padded format\n",
    "        self.edge_weights = WeightedBPCallback(num_edges=np.sum(pcm), pcm=pcm)\n",
    "\n",
    "        # init components\n",
    "        self.decoder = LDPCBPDecoder(pcm,\n",
    "                                     num_iter=1, # iterations are done via outer loop (to access intermediate results for multi-loss)\n",
    "                                     return_state=True, # decoder stores internal messages after call\n",
    "                                     hard_out=False, # we need to access soft-information\n",
    "                                     cn_update=\"boxplus\",\n",
    "                                     v2c_callbacks=[self.edge_weights]) # register callback to make the decoder trainable\n",
    "\n",
    "        # used to generate llrs during training (see example notebook on all-zero codeword trick)\n",
    "        self.llr_source = GaussianPriorSource()\n",
    "        self._num_iter = num_iter\n",
    "\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        noise_var = ebnodb2no(ebno_db,\n",
    "                              num_bits_per_symbol=2, # QPSK\n",
    "                              coderate=self.decoder.coderate)\n",
    "\n",
    "        # all-zero CW to calculate loss / BER\n",
    "        c = torch.zeros(batch_size, self.decoder.n, device=self.device)\n",
    "\n",
    "        # Gaussian LLR source\n",
    "        llr = self.llr_source([batch_size, self.decoder.n], no=noise_var)\n",
    "\n",
    "        # --- implement multi-loss as proposed by Nachmani et al. [1]---\n",
    "        loss = 0.0\n",
    "        msg_v2c = None # internal state of decoder\n",
    "        for i in range(self._num_iter):\n",
    "            c_hat, msg_v2c = self.decoder(llr, msg_v2c=msg_v2c) # perform one decoding iteration; decoder returns soft-values\n",
    "            loss = loss + F.binary_cross_entropy_with_logits(c_hat, c)  # add loss after each iteration\n",
    "\n",
    "        loss = loss / self._num_iter # scale loss by number of iterations\n",
    "\n",
    "        return c, c_hat, loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a parity-check matrix used for the experiment. We use the same BCH(63,45) code as in [1].\n",
    "The code can be replaced by any parity-check matrix of your choice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:11:45.554725Z",
     "iopub.status.busy": "2026-02-13T15:11:45.554600Z",
     "iopub.status.idle": "2026-02-13T15:11:45.752691Z",
     "shell.execute_reply": "2026-02-13T15:11:45.751802Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "n: 63, k: 45, coderate: 0.714\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "WeightedBP(\n",
       "  (edge_weights): WeightedBPCallback()\n",
       "  (decoder): LDPCBPDecoder()\n",
       "  (llr_source): GaussianPriorSource()\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcm_id = 1 # (63,45) BCH code parity check matrix\n",
    "pcm, k , n, coderate = load_parity_check_examples(pcm_id=pcm_id, verbose=True)\n",
    "\n",
    "num_iter = 10 # set number of decoding iterations\n",
    "\n",
    "# and initialize the model\n",
    "model = WeightedBP(pcm=pcm, num_iter=num_iter)\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: weighted BP tends to work better for small number of iterations.\n",
    "The effective gains (compared to the baseline with same number of iterations)\n",
    "vanish with more iterations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weights *before* Training and Simulation of BER\n",
    "\n",
    "Let us plot the weights after initialization of the decoder to verify that everything is properly initialized.\n",
    "This is equivalent the *classical* BP decoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:11:45.754589Z",
     "iopub.status.busy": "2026-02-13T15:11:45.754447Z",
     "iopub.status.idle": "2026-02-13T15:11:45.866346Z",
     "shell.execute_reply": "2026-02-13T15:11:45.865484Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of weights:  432\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count number of weights/edges\n",
    "print(\"Total number of weights: \", model.edge_weights.weights.numel())\n",
    "\n",
    "# and show the weight distribution\n",
    "model.edge_weights.show_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first simulate (and store) the BER performance *before* training.\n",
    "For this, we use the `PlotBER` class, which provides a convenient way to store the results for later comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:11:45.868232Z",
     "iopub.status.busy": "2026-02-13T15:11:45.868090Z",
     "iopub.status.idle": "2026-02-13T15:11:47.430775Z",
     "shell.execute_reply": "2026-02-13T15:11:47.429866Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      1.0 | 8.8190e-02 | 9.5900e-01 |        5556 |       63000 |          959 |        1000 |         0.2 |iter: 0/100\r",
      "      1.0 | 8.8190e-02 | 9.5900e-01 |        5556 |       63000 |          959 |        1000 |         0.2 |reached target bit errors\n",
      "      1.5 | 7.3762e-02 | 9.0600e-01 |        4647 |       63000 |          906 |        1000 |         0.0 |iter: 0/100\r",
      "      1.5 | 7.3762e-02 | 9.0600e-01 |        4647 |       63000 |          906 |        1000 |         0.0 |reached target bit errors\n",
      "      2.0 | 6.1825e-02 | 8.3300e-01 |        3895 |       63000 |          833 |        1000 |         0.0 |iter: 0/100\r",
      "      2.0 | 6.1825e-02 | 8.3300e-01 |        3895 |       63000 |          833 |        1000 |         0.0 |reached target bit errors\n",
      "      2.5 | 4.5746e-02 | 6.8400e-01 |        2882 |       63000 |          684 |        1000 |         0.0 |iter: 0/100\r",
      "      2.5 | 4.5746e-02 | 6.8400e-01 |        2882 |       63000 |          684 |        1000 |         0.0 |reached target bit errors\n",
      "      3.0 | 3.2635e-02 | 5.0600e-01 |        2056 |       63000 |          506 |        1000 |         0.0 |iter: 0/100\r",
      "      3.0 | 3.2635e-02 | 5.0600e-01 |        2056 |       63000 |          506 |        1000 |         0.0 |reached target bit errors\n",
      "      3.5 | 2.1746e-02 | 3.6700e-01 |        1370 |       63000 |          367 |        1000 |         0.0 |iter: 0/100\r",
      "      3.5 | 2.2770e-02 | 3.7400e-01 |        2869 |      126000 |          748 |        2000 |         0.0 |iter: 1/100\r",
      "      3.5 | 2.2770e-02 | 3.7400e-01 |        2869 |      126000 |          748 |        2000 |         0.0 |reached target bit errors\n",
      "      4.0 | 1.3921e-02 | 2.3100e-01 |         877 |       63000 |          231 |        1000 |         0.0 |iter: 0/100\r",
      "      4.0 | 1.4508e-02 | 2.3350e-01 |        1828 |      126000 |          467 |        2000 |         0.0 |iter: 1/100\r",
      "      4.0 | 1.4074e-02 | 2.3267e-01 |        2660 |      189000 |          698 |        3000 |         0.0 |iter: 2/100\r",
      "      4.0 | 1.4074e-02 | 2.3267e-01 |        2660 |      189000 |          698 |        3000 |         0.0 |reached target bit errors\n",
      "      4.5 | 6.7778e-03 | 1.3200e-01 |         427 |       63000 |          132 |        1000 |         0.0 |iter: 0/100\r",
      "      4.5 | 7.1667e-03 | 1.3550e-01 |         903 |      126000 |          271 |        2000 |         0.0 |iter: 1/100\r",
      "      4.5 | 6.9048e-03 | 1.2967e-01 |        1305 |      189000 |          389 |        3000 |         0.0 |iter: 2/100\r",
      "      4.5 | 6.9048e-03 | 1.3100e-01 |        1740 |      252000 |          524 |        4000 |         0.0 |iter: 3/100\r",
      "      4.5 | 6.9270e-03 | 1.2940e-01 |        2182 |      315000 |          647 |        5000 |         0.0 |iter: 4/100\r",
      "      4.5 | 6.9270e-03 | 1.2940e-01 |        2182 |      315000 |          647 |        5000 |         0.0 |reached target bit errors\n",
      "      5.0 | 4.2222e-03 | 8.5000e-02 |         266 |       63000 |           85 |        1000 |         0.0 |iter: 0/100\r",
      "      5.0 | 4.3968e-03 | 8.4000e-02 |         554 |      126000 |          168 |        2000 |         0.0 |iter: 1/100\r",
      "      5.0 | 4.0265e-03 | 7.9000e-02 |         761 |      189000 |          237 |        3000 |         0.0 |iter: 2/100\r",
      "      5.0 | 4.2103e-03 | 8.0750e-02 |        1061 |      252000 |          323 |        4000 |         0.0 |iter: 3/100\r",
      "      5.0 | 4.2032e-03 | 8.0400e-02 |        1324 |      315000 |          402 |        5000 |         0.0 |iter: 4/100\r",
      "      5.0 | 4.3280e-03 | 8.1167e-02 |        1636 |      378000 |          487 |        6000 |         0.0 |iter: 5/100\r",
      "      5.0 | 4.3469e-03 | 8.1286e-02 |        1917 |      441000 |          569 |        7000 |         0.1 |iter: 6/100\r",
      "      5.0 | 4.2837e-03 | 8.1250e-02 |        2159 |      504000 |          650 |        8000 |         0.1 |iter: 7/100\r",
      "      5.0 | 4.2837e-03 | 8.1250e-02 |        2159 |      504000 |          650 |        8000 |         0.1 |reached target bit errors\n",
      "      5.5 | 2.3651e-03 | 4.1000e-02 |         149 |       63000 |           41 |        1000 |         0.0 |iter: 0/100\r",
      "      5.5 | 1.8651e-03 | 3.5000e-02 |         235 |      126000 |           70 |        2000 |         0.0 |iter: 1/100\r",
      "      5.5 | 1.9788e-03 | 3.8667e-02 |         374 |      189000 |          116 |        3000 |         0.0 |iter: 2/100\r",
      "      5.5 | 1.9365e-03 | 3.8500e-02 |         488 |      252000 |          154 |        4000 |         0.0 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      5.5 | 1.8571e-03 | 3.7600e-02 |         585 |      315000 |          188 |        5000 |         0.0 |iter: 4/100\r",
      "      5.5 | 2.0397e-03 | 4.0333e-02 |         771 |      378000 |          242 |        6000 |         0.0 |iter: 5/100\r",
      "      5.5 | 2.0431e-03 | 4.0143e-02 |         901 |      441000 |          281 |        7000 |         0.1 |iter: 6/100\r",
      "      5.5 | 2.1151e-03 | 4.1750e-02 |        1066 |      504000 |          334 |        8000 |         0.1 |iter: 7/100\r",
      "      5.5 | 2.0899e-03 | 4.1778e-02 |        1185 |      567000 |          376 |        9000 |         0.1 |iter: 8/100\r",
      "      5.5 | 2.1365e-03 | 4.1700e-02 |        1346 |      630000 |          417 |       10000 |         0.1 |iter: 9/100\r",
      "      5.5 | 2.1876e-03 | 4.2364e-02 |        1516 |      693000 |          466 |       11000 |         0.1 |iter: 10/100\r",
      "      5.5 | 2.2222e-03 | 4.2417e-02 |        1680 |      756000 |          509 |       12000 |         0.1 |iter: 11/100\r",
      "      5.5 | 2.1661e-03 | 4.2231e-02 |        1774 |      819000 |          549 |       13000 |         0.1 |iter: 12/100\r",
      "      5.5 | 2.1474e-03 | 4.2143e-02 |        1894 |      882000 |          590 |       14000 |         0.1 |iter: 13/100\r",
      "      5.5 | 2.1450e-03 | 4.2333e-02 |        2027 |      945000 |          635 |       15000 |         0.1 |iter: 14/100\r",
      "      5.5 | 2.1450e-03 | 4.2333e-02 |        2027 |      945000 |          635 |       15000 |         0.1 |reached target bit errors\n",
      "      6.0 | 5.5556e-04 | 1.5000e-02 |          35 |       63000 |           15 |        1000 |         0.0 |iter: 0/100\r",
      "      6.0 | 7.7778e-04 | 1.7000e-02 |          98 |      126000 |           34 |        2000 |         0.0 |iter: 1/100\r",
      "      6.0 | 1.1111e-03 | 2.1333e-02 |         210 |      189000 |           64 |        3000 |         0.0 |iter: 2/100\r",
      "      6.0 | 1.0317e-03 | 1.9500e-02 |         260 |      252000 |           78 |        4000 |         0.0 |iter: 3/100\r",
      "      6.0 | 1.0286e-03 | 1.8600e-02 |         324 |      315000 |           93 |        5000 |         0.0 |iter: 4/100\r",
      "      6.0 | 9.7354e-04 | 1.8167e-02 |         368 |      378000 |          109 |        6000 |         0.0 |iter: 5/100\r",
      "      6.0 | 9.8186e-04 | 1.8143e-02 |         433 |      441000 |          127 |        7000 |         0.1 |iter: 6/100\r",
      "      6.0 | 9.8214e-04 | 1.8625e-02 |         495 |      504000 |          149 |        8000 |         0.1 |iter: 7/100\r",
      "      6.0 | 9.6649e-04 | 1.8889e-02 |         548 |      567000 |          170 |        9000 |         0.1 |iter: 8/100\r",
      "      6.0 | 9.6825e-04 | 1.8700e-02 |         610 |      630000 |          187 |       10000 |         0.1 |iter: 9/100\r",
      "      6.0 | 9.5238e-04 | 1.8727e-02 |         660 |      693000 |          206 |       11000 |         0.1 |iter: 10/100\r",
      "      6.0 | 9.7619e-04 | 1.9083e-02 |         738 |      756000 |          229 |       12000 |         0.1 |iter: 11/100\r",
      "      6.0 | 9.9634e-04 | 1.9077e-02 |         816 |      819000 |          248 |       13000 |         0.1 |iter: 12/100\r",
      "      6.0 | 1.0159e-03 | 1.8857e-02 |         896 |      882000 |          264 |       14000 |         0.1 |iter: 13/100\r",
      "      6.0 | 9.8201e-04 | 1.8600e-02 |         928 |      945000 |          279 |       15000 |         0.1 |iter: 14/100\r",
      "      6.0 | 9.7421e-04 | 1.8875e-02 |         982 |     1008000 |          302 |       16000 |         0.1 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.0 | 9.8506e-04 | 1.9059e-02 |        1055 |     1071000 |          324 |       17000 |         0.1 |iter: 16/100\r",
      "      6.0 | 9.6561e-04 | 1.8444e-02 |        1095 |     1134000 |          332 |       18000 |         0.1 |iter: 17/100\r",
      "      6.0 | 9.5823e-04 | 1.8421e-02 |        1147 |     1197000 |          350 |       19000 |         0.1 |iter: 18/100\r",
      "      6.0 | 9.7778e-04 | 1.8350e-02 |        1232 |     1260000 |          367 |       20000 |         0.2 |iter: 19/100\r",
      "      6.0 | 9.7279e-04 | 1.8286e-02 |        1287 |     1323000 |          384 |       21000 |         0.2 |iter: 20/100\r",
      "      6.0 | 9.6681e-04 | 1.8182e-02 |        1340 |     1386000 |          400 |       22000 |         0.2 |iter: 21/100\r",
      "      6.0 | 9.6273e-04 | 1.8174e-02 |        1395 |     1449000 |          418 |       23000 |         0.2 |iter: 22/100\r",
      "      6.0 | 9.6561e-04 | 1.8333e-02 |        1460 |     1512000 |          440 |       24000 |         0.2 |iter: 23/100\r",
      "      6.0 | 9.6381e-04 | 1.8360e-02 |        1518 |     1575000 |          459 |       25000 |         0.2 |iter: 24/100\r",
      "      6.0 | 9.7375e-04 | 1.8308e-02 |        1595 |     1638000 |          476 |       26000 |         0.2 |iter: 25/100\r",
      "      6.0 | 9.9647e-04 | 1.8481e-02 |        1695 |     1701000 |          499 |       27000 |         0.2 |iter: 26/100\r",
      "      6.0 | 9.8469e-04 | 1.8393e-02 |        1737 |     1764000 |          515 |       28000 |         0.2 |iter: 27/100\r",
      "      6.0 | 9.7756e-04 | 1.8276e-02 |        1786 |     1827000 |          530 |       29000 |         0.2 |iter: 28/100\r",
      "      6.0 | 9.7249e-04 | 1.8233e-02 |        1838 |     1890000 |          547 |       30000 |         0.2 |iter: 29/100\r",
      "      6.0 | 9.5648e-04 | 1.8065e-02 |        1868 |     1953000 |          560 |       31000 |         0.2 |iter: 30/100\r",
      "      6.0 | 9.5337e-04 | 1.8156e-02 |        1922 |     2016000 |          581 |       32000 |         0.2 |iter: 31/100\r",
      "      6.0 | 9.3699e-04 | 1.7879e-02 |        1948 |     2079000 |          590 |       33000 |         0.2 |iter: 32/100\r",
      "      6.0 | 9.4444e-04 | 1.8000e-02 |        2023 |     2142000 |          612 |       34000 |         0.3 |iter: 33/100\r",
      "      6.0 | 9.4444e-04 | 1.8000e-02 |        2023 |     2142000 |          612 |       34000 |         0.3 |reached target bit errors\n",
      "      6.5 | 2.6984e-04 | 9.0000e-03 |          17 |       63000 |            9 |        1000 |         0.0 |iter: 0/100\r",
      "      6.5 | 2.3810e-04 | 7.5000e-03 |          30 |      126000 |           15 |        2000 |         0.0 |iter: 1/100\r",
      "      6.5 | 2.5926e-04 | 7.6667e-03 |          49 |      189000 |           23 |        3000 |         0.0 |iter: 2/100\r",
      "      6.5 | 3.4127e-04 | 8.2500e-03 |          86 |      252000 |           33 |        4000 |         0.0 |iter: 3/100\r",
      "      6.5 | 4.0635e-04 | 8.6000e-03 |         128 |      315000 |           43 |        5000 |         0.0 |iter: 4/100\r",
      "      6.5 | 4.0212e-04 | 8.1667e-03 |         152 |      378000 |           49 |        6000 |         0.0 |iter: 5/100\r",
      "      6.5 | 4.1950e-04 | 8.8571e-03 |         185 |      441000 |           62 |        7000 |         0.1 |iter: 6/100\r",
      "      6.5 | 4.2857e-04 | 8.5000e-03 |         216 |      504000 |           68 |        8000 |         0.1 |iter: 7/100\r",
      "      6.5 | 4.1799e-04 | 8.3333e-03 |         237 |      567000 |           75 |        9000 |         0.1 |iter: 8/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.5 | 4.3175e-04 | 8.7000e-03 |         272 |      630000 |           87 |       10000 |         0.1 |iter: 9/100\r",
      "      6.5 | 4.7186e-04 | 8.7273e-03 |         327 |      693000 |           96 |       11000 |         0.1 |iter: 10/100\r",
      "      6.5 | 4.6561e-04 | 8.6667e-03 |         352 |      756000 |          104 |       12000 |         0.1 |iter: 11/100\r",
      "      6.5 | 4.5421e-04 | 8.8462e-03 |         372 |      819000 |          115 |       13000 |         0.1 |iter: 12/100\r",
      "      6.5 | 4.3651e-04 | 8.7143e-03 |         385 |      882000 |          122 |       14000 |         0.1 |iter: 13/100\r",
      "      6.5 | 4.3492e-04 | 8.6667e-03 |         411 |      945000 |          130 |       15000 |         0.1 |iter: 14/100\r",
      "      6.5 | 4.2262e-04 | 8.5000e-03 |         426 |     1008000 |          136 |       16000 |         0.1 |iter: 15/100\r",
      "      6.5 | 4.1643e-04 | 8.6471e-03 |         446 |     1071000 |          147 |       17000 |         0.1 |iter: 16/100\r",
      "      6.5 | 4.1358e-04 | 8.5556e-03 |         469 |     1134000 |          154 |       18000 |         0.1 |iter: 17/100\r",
      "      6.5 | 4.1771e-04 | 8.4737e-03 |         500 |     1197000 |          161 |       19000 |         0.1 |iter: 18/100\r",
      "      6.5 | 4.1587e-04 | 8.4000e-03 |         524 |     1260000 |          168 |       20000 |         0.2 |iter: 19/100\r",
      "      6.5 | 4.2328e-04 | 8.4286e-03 |         560 |     1323000 |          177 |       21000 |         0.2 |iter: 20/100\r",
      "      6.5 | 4.2496e-04 | 8.4091e-03 |         589 |     1386000 |          185 |       22000 |         0.2 |iter: 21/100\r",
      "      6.5 | 4.2029e-04 | 8.3913e-03 |         609 |     1449000 |          193 |       23000 |         0.2 |iter: 22/100\r",
      "      6.5 | 4.1733e-04 | 8.3750e-03 |         631 |     1512000 |          201 |       24000 |         0.2 |iter: 23/100\r",
      "      6.5 | 4.1841e-04 | 8.3600e-03 |         659 |     1575000 |          209 |       25000 |         0.2 |iter: 24/100\r",
      "      6.5 | 4.1819e-04 | 8.3846e-03 |         685 |     1638000 |          218 |       26000 |         0.2 |iter: 25/100\r",
      "      6.5 | 4.2328e-04 | 8.2593e-03 |         720 |     1701000 |          223 |       27000 |         0.2 |iter: 26/100\r",
      "      6.5 | 4.4388e-04 | 8.2143e-03 |         783 |     1764000 |          230 |       28000 |         0.2 |iter: 27/100\r",
      "      6.5 | 4.5211e-04 | 8.4828e-03 |         826 |     1827000 |          246 |       29000 |         0.2 |iter: 28/100\r",
      "      6.5 | 4.4762e-04 | 8.5000e-03 |         846 |     1890000 |          255 |       30000 |         0.2 |iter: 29/100\r",
      "      6.5 | 4.4752e-04 | 8.6129e-03 |         874 |     1953000 |          267 |       31000 |         0.2 |iter: 30/100\r",
      "      6.5 | 4.4544e-04 | 8.6875e-03 |         898 |     2016000 |          278 |       32000 |         0.2 |iter: 31/100\r",
      "      6.5 | 4.3771e-04 | 8.5758e-03 |         910 |     2079000 |          283 |       33000 |         0.2 |iter: 32/100\r",
      "      6.5 | 4.3044e-04 | 8.5000e-03 |         922 |     2142000 |          289 |       34000 |         0.3 |iter: 33/100\r",
      "      6.5 | 4.2449e-04 | 8.4857e-03 |         936 |     2205000 |          297 |       35000 |         0.3 |iter: 34/100\r",
      "      6.5 | 4.1843e-04 | 8.4167e-03 |         949 |     2268000 |          303 |       36000 |         0.3 |iter: 35/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.5 | 4.1870e-04 | 8.5135e-03 |         976 |     2331000 |          315 |       37000 |         0.3 |iter: 36/100\r",
      "      6.5 | 4.1896e-04 | 8.6053e-03 |        1003 |     2394000 |          327 |       38000 |         0.3 |iter: 37/100\r",
      "      6.5 | 4.1188e-04 | 8.5385e-03 |        1012 |     2457000 |          333 |       39000 |         0.3 |iter: 38/100\r",
      "      6.5 | 4.0635e-04 | 8.4250e-03 |        1024 |     2520000 |          337 |       40000 |         0.3 |iter: 39/100\r",
      "      6.5 | 4.0457e-04 | 8.4146e-03 |        1045 |     2583000 |          345 |       41000 |         0.3 |iter: 40/100\r",
      "      6.5 | 4.1156e-04 | 8.5000e-03 |        1089 |     2646000 |          357 |       42000 |         0.3 |iter: 41/100\r",
      "      6.5 | 4.0938e-04 | 8.4651e-03 |        1109 |     2709000 |          364 |       43000 |         0.3 |iter: 42/100\r",
      "      6.5 | 4.1017e-04 | 8.4545e-03 |        1137 |     2772000 |          372 |       44000 |         0.3 |iter: 43/100\r",
      "      6.5 | 4.0917e-04 | 8.4444e-03 |        1160 |     2835000 |          380 |       45000 |         0.3 |iter: 44/100\r",
      "      6.5 | 4.1718e-04 | 8.5435e-03 |        1209 |     2898000 |          393 |       46000 |         0.3 |iter: 45/100\r",
      "      6.5 | 4.1574e-04 | 8.5319e-03 |        1231 |     2961000 |          401 |       47000 |         0.4 |iter: 46/100\r",
      "      6.5 | 4.1865e-04 | 8.5000e-03 |        1266 |     3024000 |          408 |       48000 |         0.4 |iter: 47/100\r",
      "      6.5 | 4.2306e-04 | 8.6327e-03 |        1306 |     3087000 |          423 |       49000 |         0.4 |iter: 48/100\r",
      "      6.5 | 4.2794e-04 | 8.7000e-03 |        1348 |     3150000 |          435 |       50000 |         0.4 |iter: 49/100\r",
      "      6.5 | 4.2795e-04 | 8.7647e-03 |        1375 |     3213000 |          447 |       51000 |         0.4 |iter: 50/100\r",
      "      6.5 | 4.2979e-04 | 8.7885e-03 |        1408 |     3276000 |          457 |       52000 |         0.4 |iter: 51/100\r",
      "      6.5 | 4.2767e-04 | 8.7358e-03 |        1428 |     3339000 |          463 |       53000 |         0.4 |iter: 52/100\r",
      "      6.5 | 4.2651e-04 | 8.7222e-03 |        1451 |     3402000 |          471 |       54000 |         0.4 |iter: 53/100\r",
      "      6.5 | 4.3146e-04 | 8.8000e-03 |        1495 |     3465000 |          484 |       55000 |         0.4 |iter: 54/100\r",
      "      6.5 | 4.2829e-04 | 8.7679e-03 |        1511 |     3528000 |          491 |       56000 |         0.4 |iter: 55/100\r",
      "      6.5 | 4.2941e-04 | 8.8070e-03 |        1542 |     3591000 |          502 |       57000 |         0.4 |iter: 56/100\r",
      "      6.5 | 4.2666e-04 | 8.7586e-03 |        1559 |     3654000 |          508 |       58000 |         0.4 |iter: 57/100\r",
      "      6.5 | 4.3153e-04 | 8.7797e-03 |        1604 |     3717000 |          518 |       59000 |         0.4 |iter: 58/100\r",
      "      6.5 | 4.2672e-04 | 8.7333e-03 |        1613 |     3780000 |          524 |       60000 |         0.5 |iter: 59/100\r",
      "      6.5 | 4.2363e-04 | 8.7049e-03 |        1628 |     3843000 |          531 |       61000 |         0.5 |iter: 60/100\r",
      "      6.5 | 4.2729e-04 | 8.7742e-03 |        1669 |     3906000 |          544 |       62000 |         0.5 |iter: 61/100\r",
      "      6.5 | 4.2504e-04 | 8.7302e-03 |        1687 |     3969000 |          550 |       63000 |         0.5 |iter: 62/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.5 | 4.2262e-04 | 8.7031e-03 |        1704 |     4032000 |          557 |       64000 |         0.5 |iter: 63/100\r",
      "      6.5 | 4.2271e-04 | 8.7385e-03 |        1731 |     4095000 |          568 |       65000 |         0.5 |iter: 64/100\r",
      "      6.5 | 4.1991e-04 | 8.6667e-03 |        1746 |     4158000 |          572 |       66000 |         0.5 |iter: 65/100\r",
      "      6.5 | 4.2170e-04 | 8.7015e-03 |        1780 |     4221000 |          583 |       67000 |         0.5 |iter: 66/100\r",
      "      6.5 | 4.2110e-04 | 8.7059e-03 |        1804 |     4284000 |          592 |       68000 |         0.5 |iter: 67/100\r",
      "      6.5 | 4.2604e-04 | 8.7681e-03 |        1852 |     4347000 |          605 |       69000 |         0.5 |iter: 68/100\r",
      "      6.5 | 4.2472e-04 | 8.7714e-03 |        1873 |     4410000 |          614 |       70000 |         0.5 |iter: 69/100\r",
      "      6.5 | 4.1963e-04 | 8.6901e-03 |        1877 |     4473000 |          617 |       71000 |         0.5 |iter: 70/100\r",
      "      6.5 | 4.1843e-04 | 8.6806e-03 |        1898 |     4536000 |          625 |       72000 |         0.5 |iter: 71/100\r",
      "      6.5 | 4.1661e-04 | 8.6712e-03 |        1916 |     4599000 |          633 |       73000 |         0.6 |iter: 72/100\r",
      "      6.5 | 4.1527e-04 | 8.6351e-03 |        1936 |     4662000 |          639 |       74000 |         0.6 |iter: 73/100\r",
      "      6.5 | 4.1312e-04 | 8.6133e-03 |        1952 |     4725000 |          646 |       75000 |         0.6 |iter: 74/100\r",
      "      6.5 | 4.1145e-04 | 8.6053e-03 |        1970 |     4788000 |          654 |       76000 |         0.6 |iter: 75/100\r",
      "      6.5 | 4.0940e-04 | 8.5584e-03 |        1986 |     4851000 |          659 |       77000 |         0.6 |iter: 76/100\r",
      "      6.5 | 4.1249e-04 | 8.6026e-03 |        2027 |     4914000 |          671 |       78000 |         0.6 |iter: 77/100\r",
      "      6.5 | 4.1249e-04 | 8.6026e-03 |        2027 |     4914000 |          671 |       78000 |         0.6 |reached target bit errors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SNR to simulate the results\n",
    "ebno_dbs = np.array(np.arange(1, 7, 0.5))\n",
    "mc_iters = 100 # number of Monte Carlo iterations\n",
    "\n",
    "# we generate a new PlotBER() object to simulate, store and plot the BER results\n",
    "ber_plot = PlotBER(\"Weighted BP\")\n",
    "\n",
    "# simulate and plot the BER curve of the untrained decoder\n",
    "ber_plot.simulate(model,\n",
    "                  ebno_dbs=ebno_dbs,\n",
    "                  batch_size=1000,\n",
    "                  num_target_bit_errors=2000, # stop sim after 2000 bit errors\n",
    "                  legend=\"Untrained\",\n",
    "                  soft_estimates=True,\n",
    "                  max_mc_iter=mc_iters,\n",
    "                  forward_keyboard_interrupt=False,\n",
    "                  compile_mode=None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training\n",
    "We now train the model for a fixed number of SGD training iterations.\n",
    "\n",
    "**Note**: this is a very basic implementation of the training loop.\n",
    "You can also try more sophisticated training loops with early stopping, different hyper-parameters or optimizers etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:11:47.432795Z",
     "iopub.status.busy": "2026-02-13T15:11:47.432658Z",
     "iopub.status.idle": "2026-02-13T15:11:55.938892Z",
     "shell.execute_reply": "2026-02-13T15:11:55.937575Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0/200 - Loss: 0.053 BER: 0.0129 BMI: 0.944\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 10/200 - Loss: 0.046 BER: 0.0118 BMI: 0.947\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 20/200 - Loss: 0.047 BER: 0.0129 BMI: 0.943\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 30/200 - Loss: 0.052 BER: 0.0139 BMI: 0.933\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 40/200 - Loss: 0.042 BER: 0.0129 BMI: 0.943\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 50/200 - Loss: 0.040 BER: 0.0120 BMI: 0.952\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 60/200 - Loss: 0.043 BER: 0.0138 BMI: 0.946\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 70/200 - Loss: 0.041 BER: 0.0134 BMI: 0.946\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 80/200 - Loss: 0.043 BER: 0.0136 BMI: 0.941\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 90/200 - Loss: 0.042 BER: 0.0136 BMI: 0.949\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 100/200 - Loss: 0.041 BER: 0.0127 BMI: 0.947\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 110/200 - Loss: 0.042 BER: 0.0138 BMI: 0.949\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 120/200 - Loss: 0.043 BER: 0.0137 BMI: 0.945\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 130/200 - Loss: 0.042 BER: 0.0133 BMI: 0.947\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 140/200 - Loss: 0.039 BER: 0.0127 BMI: 0.951\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 150/200 - Loss: 0.042 BER: 0.0127 BMI: 0.947\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 160/200 - Loss: 0.039 BER: 0.0128 BMI: 0.953\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 170/200 - Loss: 0.038 BER: 0.0126 BMI: 0.953\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 180/200 - Loss: 0.044 BER: 0.0133 BMI: 0.941\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 190/200 - Loss: 0.038 BER: 0.0125 BMI: 0.953\n"
     ]
    }
   ],
   "source": [
    "# training parameters\n",
    "batch_size = 1000\n",
    "train_iter = 200\n",
    "ebno_db = 4.0\n",
    "clip_value_grad = 10 # gradient clipping for stable training convergence\n",
    "\n",
    "# try also different optimizers or different hyperparameters\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)\n",
    "\n",
    "for it in range(0, train_iter):\n",
    "    optimizer.zero_grad()\n",
    "    b, llr, loss = model(batch_size, ebno_db)\n",
    "    loss.backward()\n",
    "    torch.nn.utils.clip_grad_value_(model.parameters(), clip_value_grad)\n",
    "    optimizer.step()\n",
    "\n",
    "    # calculate and print intermediate metrics\n",
    "    # only for information\n",
    "    # this has no impact on the training\n",
    "    if it%10==0: # evaluate every 10 iterations\n",
    "        with torch.no_grad():\n",
    "            # calculate ber from received LLRs\n",
    "            b_hat = hard_decisions(llr) # hard decided LLRs first\n",
    "            ber = compute_ber(b, b_hat)\n",
    "            # and print results\n",
    "            mi = llr2mi(llr, -2*b+1).item() # calculate bit-wise mutual information\n",
    "            l = loss.item() # copy loss for printing\n",
    "            print(f\"Iteration {it}/{train_iter} - Loss: {l:.3f} BER: {ber:.4f} BMI: {mi:.3f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "After training, the weights of the decoder have changed.\n",
    "In average, the weights are smaller after training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:11:55.941473Z",
     "iopub.status.busy": "2026-02-13T15:11:55.941239Z",
     "iopub.status.idle": "2026-02-13T15:11:56.072731Z",
     "shell.execute_reply": "2026-02-13T15:11:56.071765Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.edge_weights.show_weights() # show weights AFTER training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let us compare the new BER performance.\n",
    "For this, we can simply call the ber_plot.simulate() function again as it internally stores all previous results (if `add_results` is True)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:11:56.074807Z",
     "iopub.status.busy": "2026-02-13T15:11:56.074674Z",
     "iopub.status.idle": "2026-02-13T15:11:57.922422Z",
     "shell.execute_reply": "2026-02-13T15:11:57.921329Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      1.0 | 9.0159e-02 | 9.9800e-01 |        5680 |       63000 |          998 |        1000 |         0.0 |iter: 0/100\r",
      "      1.0 | 9.0159e-02 | 9.9800e-01 |        5680 |       63000 |          998 |        1000 |         0.0 |reached target bit errors\n",
      "      1.5 | 7.6524e-02 | 9.7200e-01 |        4821 |       63000 |          972 |        1000 |         0.0 |iter: 0/100\r",
      "      1.5 | 7.6524e-02 | 9.7200e-01 |        4821 |       63000 |          972 |        1000 |         0.0 |reached target bit errors\n",
      "      2.0 | 6.5016e-02 | 9.3300e-01 |        4096 |       63000 |          933 |        1000 |         0.0 |iter: 0/100\r",
      "      2.0 | 6.5016e-02 | 9.3300e-01 |        4096 |       63000 |          933 |        1000 |         0.0 |reached target bit errors\n",
      "      2.5 | 4.8714e-02 | 8.2400e-01 |        3069 |       63000 |          824 |        1000 |         0.0 |iter: 0/100\r",
      "      2.5 | 4.8714e-02 | 8.2400e-01 |        3069 |       63000 |          824 |        1000 |         0.0 |reached target bit errors\n",
      "      3.0 | 3.6317e-02 | 6.5800e-01 |        2288 |       63000 |          658 |        1000 |         0.0 |iter: 0/100\r",
      "      3.0 | 3.6317e-02 | 6.5800e-01 |        2288 |       63000 |          658 |        1000 |         0.0 |reached target bit errors\n",
      "      3.5 | 2.5873e-02 | 4.6900e-01 |        1630 |       63000 |          469 |        1000 |         0.0 |iter: 0/100\r",
      "      3.5 | 2.4238e-02 | 4.4900e-01 |        3054 |      126000 |          898 |        2000 |         0.0 |iter: 1/100\r",
      "      3.5 | 2.4238e-02 | 4.4900e-01 |        3054 |      126000 |          898 |        2000 |         0.0 |reached target bit errors\n",
      "      4.0 | 1.3413e-02 | 2.6400e-01 |         845 |       63000 |          264 |        1000 |         0.0 |iter: 0/100\r",
      "      4.0 | 1.3492e-02 | 2.6700e-01 |        1700 |      126000 |          534 |        2000 |         0.0 |iter: 1/100\r",
      "      4.0 | 1.3831e-02 | 2.7067e-01 |        2614 |      189000 |          812 |        3000 |         0.0 |iter: 2/100\r",
      "      4.0 | 1.3831e-02 | 2.7067e-01 |        2614 |      189000 |          812 |        3000 |         0.0 |reached target bit errors\n",
      "      4.5 | 5.9841e-03 | 1.2300e-01 |         377 |       63000 |          123 |        1000 |         0.0 |iter: 0/100\r",
      "      4.5 | 5.8016e-03 | 1.2100e-01 |         731 |      126000 |          242 |        2000 |         0.0 |iter: 1/100\r",
      "      4.5 | 6.1111e-03 | 1.2733e-01 |        1155 |      189000 |          382 |        3000 |         0.0 |iter: 2/100\r",
      "      4.5 | 6.2897e-03 | 1.3000e-01 |        1585 |      252000 |          520 |        4000 |         0.0 |iter: 3/100\r",
      "      4.5 | 6.3143e-03 | 1.2980e-01 |        1989 |      315000 |          649 |        5000 |         0.0 |iter: 4/100\r",
      "      4.5 | 6.3413e-03 | 1.3150e-01 |        2397 |      378000 |          789 |        6000 |         0.0 |iter: 5/100\r",
      "      4.5 | 6.3413e-03 | 1.3150e-01 |        2397 |      378000 |          789 |        6000 |         0.0 |reached target bit errors\n",
      "      5.0 | 3.2540e-03 | 6.9000e-02 |         205 |       63000 |           69 |        1000 |         0.0 |iter: 0/100\r",
      "      5.0 | 2.8968e-03 | 6.1000e-02 |         365 |      126000 |          122 |        2000 |         0.0 |iter: 1/100\r",
      "      5.0 | 2.8995e-03 | 6.3333e-02 |         548 |      189000 |          190 |        3000 |         0.0 |iter: 2/100\r",
      "      5.0 | 3.0437e-03 | 6.7250e-02 |         767 |      252000 |          269 |        4000 |         0.0 |iter: 3/100\r",
      "      5.0 | 3.0000e-03 | 6.6800e-02 |         945 |      315000 |          334 |        5000 |         0.0 |iter: 4/100\r",
      "      5.0 | 2.9868e-03 | 6.5500e-02 |        1129 |      378000 |          393 |        6000 |         0.0 |iter: 5/100\r",
      "      5.0 | 2.9909e-03 | 6.5286e-02 |        1319 |      441000 |          457 |        7000 |         0.1 |iter: 6/100\r",
      "      5.0 | 3.0774e-03 | 6.6875e-02 |        1551 |      504000 |          535 |        8000 |         0.1 |iter: 7/100\r",
      "      5.0 | 3.0600e-03 | 6.6000e-02 |        1735 |      567000 |          594 |        9000 |         0.1 |iter: 8/100\r",
      "      5.0 | 3.0587e-03 | 6.5500e-02 |        1927 |      630000 |          655 |       10000 |         0.1 |iter: 9/100\r",
      "      5.0 | 3.0260e-03 | 6.5000e-02 |        2097 |      693000 |          715 |       11000 |         0.1 |iter: 10/100\r",
      "      5.0 | 3.0260e-03 | 6.5000e-02 |        2097 |      693000 |          715 |       11000 |         0.1 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      5.5 | 6.6667e-04 | 1.9000e-02 |          42 |       63000 |           19 |        1000 |         0.0 |iter: 0/100\r",
      "      5.5 | 1.0794e-03 | 2.4000e-02 |         136 |      126000 |           48 |        2000 |         0.0 |iter: 1/100\r",
      "      5.5 | 1.1905e-03 | 2.6333e-02 |         225 |      189000 |           79 |        3000 |         0.0 |iter: 2/100\r",
      "      5.5 | 1.1190e-03 | 2.6500e-02 |         282 |      252000 |          106 |        4000 |         0.0 |iter: 3/100\r",
      "      5.5 | 1.0921e-03 | 2.6800e-02 |         344 |      315000 |          134 |        5000 |         0.0 |iter: 4/100\r",
      "      5.5 | 1.0979e-03 | 2.6667e-02 |         415 |      378000 |          160 |        6000 |         0.0 |iter: 5/100\r",
      "      5.5 | 1.1474e-03 | 2.8143e-02 |         506 |      441000 |          197 |        7000 |         0.1 |iter: 6/100\r",
      "      5.5 | 1.1905e-03 | 2.8875e-02 |         600 |      504000 |          231 |        8000 |         0.1 |iter: 7/100\r",
      "      5.5 | 1.1958e-03 | 2.9000e-02 |         678 |      567000 |          261 |        9000 |         0.1 |iter: 8/100\r",
      "      5.5 | 1.2159e-03 | 2.9300e-02 |         766 |      630000 |          293 |       10000 |         0.1 |iter: 9/100\r",
      "      5.5 | 1.2395e-03 | 3.0000e-02 |         859 |      693000 |          330 |       11000 |         0.1 |iter: 10/100\r",
      "      5.5 | 1.2368e-03 | 3.0000e-02 |         935 |      756000 |          360 |       12000 |         0.1 |iter: 11/100\r",
      "      5.5 | 1.2210e-03 | 2.9846e-02 |        1000 |      819000 |          388 |       13000 |         0.1 |iter: 12/100\r",
      "      5.5 | 1.2381e-03 | 2.9929e-02 |        1092 |      882000 |          419 |       14000 |         0.1 |iter: 13/100\r",
      "      5.5 | 1.2794e-03 | 3.0800e-02 |        1209 |      945000 |          462 |       15000 |         0.1 |iter: 14/100\r",
      "      5.5 | 1.2917e-03 | 3.0625e-02 |        1302 |     1008000 |          490 |       16000 |         0.1 |iter: 15/100\r",
      "      5.5 | 1.2923e-03 | 3.0471e-02 |        1384 |     1071000 |          518 |       17000 |         0.1 |iter: 16/100\r",
      "      5.5 | 1.2892e-03 | 3.0222e-02 |        1462 |     1134000 |          544 |       18000 |         0.1 |iter: 17/100\r",
      "      5.5 | 1.3099e-03 | 3.0737e-02 |        1568 |     1197000 |          584 |       19000 |         0.1 |iter: 18/100\r",
      "      5.5 | 1.3111e-03 | 3.0900e-02 |        1652 |     1260000 |          618 |       20000 |         0.2 |iter: 19/100\r",
      "      5.5 | 1.2971e-03 | 3.0667e-02 |        1716 |     1323000 |          644 |       21000 |         0.2 |iter: 20/100\r",
      "      5.5 | 1.3038e-03 | 3.0727e-02 |        1807 |     1386000 |          676 |       22000 |         0.2 |iter: 21/100\r",
      "      5.5 | 1.3002e-03 | 3.0739e-02 |        1884 |     1449000 |          707 |       23000 |         0.2 |iter: 22/100\r",
      "      5.5 | 1.2844e-03 | 3.0375e-02 |        1942 |     1512000 |          729 |       24000 |         0.2 |iter: 23/100\r",
      "      5.5 | 1.2787e-03 | 3.0240e-02 |        2014 |     1575000 |          756 |       25000 |         0.2 |iter: 24/100\r",
      "      5.5 | 1.2787e-03 | 3.0240e-02 |        2014 |     1575000 |          756 |       25000 |         0.2 |reached target bit errors\n",
      "      6.0 | 6.3492e-04 | 1.4000e-02 |          40 |       63000 |           14 |        1000 |         0.0 |iter: 0/100\r",
      "      6.0 | 4.6825e-04 | 1.2000e-02 |          59 |      126000 |           24 |        2000 |         0.0 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.0 | 5.1323e-04 | 1.2667e-02 |          97 |      189000 |           38 |        3000 |         0.0 |iter: 2/100\r",
      "      6.0 | 5.0794e-04 | 1.3000e-02 |         128 |      252000 |           52 |        4000 |         0.0 |iter: 3/100\r",
      "      6.0 | 5.3016e-04 | 1.3400e-02 |         167 |      315000 |           67 |        5000 |         0.0 |iter: 4/100\r",
      "      6.0 | 4.7884e-04 | 1.2333e-02 |         181 |      378000 |           74 |        6000 |         0.0 |iter: 5/100\r",
      "      6.0 | 4.6032e-04 | 1.1857e-02 |         203 |      441000 |           83 |        7000 |         0.1 |iter: 6/100\r",
      "      6.0 | 5.1389e-04 | 1.2875e-02 |         259 |      504000 |          103 |        8000 |         0.1 |iter: 7/100\r",
      "      6.0 | 4.8677e-04 | 1.2444e-02 |         276 |      567000 |          112 |        9000 |         0.1 |iter: 8/100\r",
      "      6.0 | 4.7778e-04 | 1.2500e-02 |         301 |      630000 |          125 |       10000 |         0.1 |iter: 9/100\r",
      "      6.0 | 4.9206e-04 | 1.2727e-02 |         341 |      693000 |          140 |       11000 |         0.1 |iter: 10/100\r",
      "      6.0 | 4.7354e-04 | 1.2250e-02 |         358 |      756000 |          147 |       12000 |         0.1 |iter: 11/100\r",
      "      6.0 | 4.7985e-04 | 1.2538e-02 |         393 |      819000 |          163 |       13000 |         0.1 |iter: 12/100\r",
      "      6.0 | 4.5918e-04 | 1.2071e-02 |         405 |      882000 |          169 |       14000 |         0.1 |iter: 13/100\r",
      "      6.0 | 4.6878e-04 | 1.2400e-02 |         443 |      945000 |          186 |       15000 |         0.1 |iter: 14/100\r",
      "      6.0 | 4.8611e-04 | 1.2688e-02 |         490 |     1008000 |          203 |       16000 |         0.1 |iter: 15/100\r",
      "      6.0 | 4.8833e-04 | 1.2706e-02 |         523 |     1071000 |          216 |       17000 |         0.1 |iter: 16/100\r",
      "      6.0 | 4.7972e-04 | 1.2778e-02 |         544 |     1134000 |          230 |       18000 |         0.1 |iter: 17/100\r",
      "      6.0 | 4.8120e-04 | 1.2737e-02 |         576 |     1197000 |          242 |       19000 |         0.1 |iter: 18/100\r",
      "      6.0 | 4.9286e-04 | 1.3000e-02 |         621 |     1260000 |          260 |       20000 |         0.2 |iter: 19/100\r",
      "      6.0 | 4.9887e-04 | 1.3286e-02 |         660 |     1323000 |          279 |       21000 |         0.2 |iter: 20/100\r",
      "      6.0 | 4.9134e-04 | 1.3091e-02 |         681 |     1386000 |          288 |       22000 |         0.2 |iter: 21/100\r",
      "      6.0 | 4.9689e-04 | 1.3217e-02 |         720 |     1449000 |          304 |       23000 |         0.2 |iter: 22/100\r",
      "      6.0 | 4.9868e-04 | 1.3333e-02 |         754 |     1512000 |          320 |       24000 |         0.2 |iter: 23/100\r",
      "      6.0 | 4.9397e-04 | 1.3280e-02 |         778 |     1575000 |          332 |       25000 |         0.2 |iter: 24/100\r",
      "      6.0 | 5.0366e-04 | 1.3615e-02 |         825 |     1638000 |          354 |       26000 |         0.2 |iter: 25/100\r",
      "      6.0 | 5.0617e-04 | 1.3704e-02 |         861 |     1701000 |          370 |       27000 |         0.2 |iter: 26/100\r",
      "      6.0 | 5.1417e-04 | 1.3821e-02 |         907 |     1764000 |          387 |       28000 |         0.2 |iter: 27/100\r",
      "      6.0 | 5.0575e-04 | 1.3655e-02 |         924 |     1827000 |          396 |       29000 |         0.2 |iter: 28/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.0 | 5.0106e-04 | 1.3533e-02 |         947 |     1890000 |          406 |       30000 |         0.2 |iter: 29/100\r",
      "      6.0 | 5.0077e-04 | 1.3484e-02 |         978 |     1953000 |          418 |       31000 |         0.2 |iter: 30/100\r",
      "      6.0 | 4.9702e-04 | 1.3469e-02 |        1002 |     2016000 |          431 |       32000 |         0.2 |iter: 31/100\r",
      "      6.0 | 4.9447e-04 | 1.3455e-02 |        1028 |     2079000 |          444 |       33000 |         0.2 |iter: 32/100\r",
      "      6.0 | 4.8506e-04 | 1.3147e-02 |        1039 |     2142000 |          447 |       34000 |         0.3 |iter: 33/100\r",
      "      6.0 | 4.8254e-04 | 1.3057e-02 |        1064 |     2205000 |          457 |       35000 |         0.3 |iter: 34/100\r",
      "      6.0 | 4.8589e-04 | 1.3139e-02 |        1102 |     2268000 |          473 |       36000 |         0.3 |iter: 35/100\r",
      "      6.0 | 4.8091e-04 | 1.3000e-02 |        1121 |     2331000 |          481 |       37000 |         0.3 |iter: 36/100\r",
      "      6.0 | 4.8162e-04 | 1.3105e-02 |        1153 |     2394000 |          498 |       38000 |         0.3 |iter: 37/100\r",
      "      6.0 | 4.8677e-04 | 1.3179e-02 |        1196 |     2457000 |          514 |       39000 |         0.3 |iter: 38/100\r",
      "      6.0 | 4.8889e-04 | 1.3250e-02 |        1232 |     2520000 |          530 |       40000 |         0.3 |iter: 39/100\r",
      "      6.0 | 4.8897e-04 | 1.3171e-02 |        1263 |     2583000 |          540 |       41000 |         0.3 |iter: 40/100\r",
      "      6.0 | 4.9282e-04 | 1.3262e-02 |        1304 |     2646000 |          557 |       42000 |         0.3 |iter: 41/100\r",
      "      6.0 | 4.8837e-04 | 1.3186e-02 |        1323 |     2709000 |          567 |       43000 |         0.3 |iter: 42/100\r",
      "      6.0 | 4.9026e-04 | 1.3205e-02 |        1359 |     2772000 |          581 |       44000 |         0.3 |iter: 43/100\r",
      "      6.0 | 4.9806e-04 | 1.3400e-02 |        1412 |     2835000 |          603 |       45000 |         0.3 |iter: 44/100\r",
      "      6.0 | 4.9517e-04 | 1.3348e-02 |        1435 |     2898000 |          614 |       46000 |         0.3 |iter: 45/100\r",
      "      6.0 | 4.9071e-04 | 1.3255e-02 |        1453 |     2961000 |          623 |       47000 |         0.4 |iter: 46/100\r",
      "      6.0 | 4.8810e-04 | 1.3208e-02 |        1476 |     3024000 |          634 |       48000 |         0.4 |iter: 47/100\r",
      "      6.0 | 4.9271e-04 | 1.3327e-02 |        1521 |     3087000 |          653 |       49000 |         0.4 |iter: 48/100\r",
      "      6.0 | 4.9397e-04 | 1.3340e-02 |        1556 |     3150000 |          667 |       50000 |         0.4 |iter: 49/100\r",
      "      6.0 | 4.8895e-04 | 1.3216e-02 |        1571 |     3213000 |          674 |       51000 |         0.4 |iter: 50/100\r",
      "      6.0 | 4.9573e-04 | 1.3365e-02 |        1624 |     3276000 |          695 |       52000 |         0.4 |iter: 51/100\r",
      "      6.0 | 4.9686e-04 | 1.3377e-02 |        1659 |     3339000 |          709 |       53000 |         0.4 |iter: 52/100\r",
      "      6.0 | 4.9677e-04 | 1.3352e-02 |        1690 |     3402000 |          721 |       54000 |         0.4 |iter: 53/100\r",
      "      6.0 | 5.0216e-04 | 1.3455e-02 |        1740 |     3465000 |          740 |       55000 |         0.4 |iter: 54/100\r",
      "      6.0 | 5.0142e-04 | 1.3446e-02 |        1769 |     3528000 |          753 |       56000 |         0.4 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.0 | 4.9819e-04 | 1.3368e-02 |        1789 |     3591000 |          762 |       57000 |         0.4 |iter: 56/100\r",
      "      6.0 | 4.9945e-04 | 1.3397e-02 |        1825 |     3654000 |          777 |       58000 |         0.4 |iter: 57/100\r",
      "      6.0 | 5.0336e-04 | 1.3475e-02 |        1871 |     3717000 |          795 |       59000 |         0.4 |iter: 58/100\r",
      "      6.0 | 5.0635e-04 | 1.3600e-02 |        1914 |     3780000 |          816 |       60000 |         0.5 |iter: 59/100\r",
      "      6.0 | 5.0690e-04 | 1.3590e-02 |        1948 |     3843000 |          829 |       61000 |         0.5 |iter: 60/100\r",
      "      6.0 | 5.0563e-04 | 1.3581e-02 |        1975 |     3906000 |          842 |       62000 |         0.5 |iter: 61/100\r",
      "      6.0 | 5.0642e-04 | 1.3571e-02 |        2010 |     3969000 |          855 |       63000 |         0.5 |iter: 62/100\r",
      "      6.0 | 5.0642e-04 | 1.3571e-02 |        2010 |     3969000 |          855 |       63000 |         0.5 |reached target bit errors\n",
      "      6.5 | 1.5873e-04 | 6.0000e-03 |          10 |       63000 |            6 |        1000 |         0.0 |iter: 0/100\r",
      "      6.5 | 1.7460e-04 | 6.5000e-03 |          22 |      126000 |           13 |        2000 |         0.0 |iter: 1/100\r",
      "      6.5 | 1.4815e-04 | 5.3333e-03 |          28 |      189000 |           16 |        3000 |         0.0 |iter: 2/100\r",
      "      6.5 | 1.6667e-04 | 5.2500e-03 |          42 |      252000 |           21 |        4000 |         0.0 |iter: 3/100\r",
      "      6.5 | 1.7460e-04 | 4.6000e-03 |          55 |      315000 |           23 |        5000 |         0.0 |iter: 4/100\r",
      "      6.5 | 1.7989e-04 | 4.6667e-03 |          68 |      378000 |           28 |        6000 |         0.0 |iter: 5/100\r",
      "      6.5 | 2.2902e-04 | 5.2857e-03 |         101 |      441000 |           37 |        7000 |         0.1 |iter: 6/100\r",
      "      6.5 | 2.5992e-04 | 6.0000e-03 |         131 |      504000 |           48 |        8000 |         0.1 |iter: 7/100\r",
      "      6.5 | 2.5573e-04 | 6.0000e-03 |         145 |      567000 |           54 |        9000 |         0.1 |iter: 8/100\r",
      "      6.5 | 2.4286e-04 | 5.9000e-03 |         153 |      630000 |           59 |       10000 |         0.1 |iter: 9/100\r",
      "      6.5 | 2.4242e-04 | 6.0909e-03 |         168 |      693000 |           67 |       11000 |         0.1 |iter: 10/100\r",
      "      6.5 | 2.3280e-04 | 6.0833e-03 |         176 |      756000 |           73 |       12000 |         0.1 |iter: 11/100\r",
      "      6.5 | 2.2222e-04 | 5.8462e-03 |         182 |      819000 |           76 |       13000 |         0.1 |iter: 12/100\r",
      "      6.5 | 2.3129e-04 | 5.8571e-03 |         204 |      882000 |           82 |       14000 |         0.1 |iter: 13/100\r",
      "      6.5 | 2.5503e-04 | 6.0667e-03 |         241 |      945000 |           91 |       15000 |         0.1 |iter: 14/100\r",
      "      6.5 | 2.4901e-04 | 6.0625e-03 |         251 |     1008000 |           97 |       16000 |         0.1 |iter: 15/100\r",
      "      6.5 | 2.5117e-04 | 6.2353e-03 |         269 |     1071000 |          106 |       17000 |         0.1 |iter: 16/100\r",
      "      6.5 | 2.4339e-04 | 6.1111e-03 |         276 |     1134000 |          110 |       18000 |         0.1 |iter: 17/100\r",
      "      6.5 | 2.4227e-04 | 6.1579e-03 |         290 |     1197000 |          117 |       19000 |         0.1 |iter: 18/100\r",
      "      6.5 | 2.4365e-04 | 6.1000e-03 |         307 |     1260000 |          122 |       20000 |         0.1 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.5 | 2.3961e-04 | 6.0476e-03 |         317 |     1323000 |          127 |       21000 |         0.2 |iter: 20/100\r",
      "      6.5 | 2.3665e-04 | 6.0000e-03 |         328 |     1386000 |          132 |       22000 |         0.2 |iter: 21/100\r",
      "      6.5 | 2.3395e-04 | 5.9565e-03 |         339 |     1449000 |          137 |       23000 |         0.2 |iter: 22/100\r",
      "      6.5 | 2.2950e-04 | 5.9167e-03 |         347 |     1512000 |          142 |       24000 |         0.2 |iter: 23/100\r",
      "      6.5 | 2.2222e-04 | 5.7600e-03 |         350 |     1575000 |          144 |       25000 |         0.2 |iter: 24/100\r",
      "      6.5 | 2.2466e-04 | 5.7692e-03 |         368 |     1638000 |          150 |       26000 |         0.2 |iter: 25/100\r",
      "      6.5 | 2.2516e-04 | 5.7778e-03 |         383 |     1701000 |          156 |       27000 |         0.2 |iter: 26/100\r",
      "      6.5 | 2.2959e-04 | 5.8929e-03 |         405 |     1764000 |          165 |       28000 |         0.2 |iter: 27/100\r",
      "      6.5 | 2.2715e-04 | 5.8966e-03 |         415 |     1827000 |          171 |       29000 |         0.2 |iter: 28/100\r",
      "      6.5 | 2.2487e-04 | 5.8333e-03 |         425 |     1890000 |          175 |       30000 |         0.2 |iter: 29/100\r",
      "      6.5 | 2.2376e-04 | 5.8710e-03 |         437 |     1953000 |          182 |       31000 |         0.2 |iter: 30/100\r",
      "      6.5 | 2.1825e-04 | 5.7812e-03 |         440 |     2016000 |          185 |       32000 |         0.2 |iter: 31/100\r",
      "      6.5 | 2.1597e-04 | 5.8182e-03 |         449 |     2079000 |          192 |       33000 |         0.2 |iter: 32/100\r",
      "      6.5 | 2.1709e-04 | 5.8529e-03 |         465 |     2142000 |          199 |       34000 |         0.3 |iter: 33/100\r",
      "      6.5 | 2.1587e-04 | 5.8571e-03 |         476 |     2205000 |          205 |       35000 |         0.3 |iter: 34/100\r",
      "      6.5 | 2.2090e-04 | 5.9167e-03 |         501 |     2268000 |          213 |       36000 |         0.3 |iter: 35/100\r",
      "      6.5 | 2.2222e-04 | 6.0541e-03 |         518 |     2331000 |          224 |       37000 |         0.3 |iter: 36/100\r",
      "      6.5 | 2.2891e-04 | 6.1579e-03 |         548 |     2394000 |          234 |       38000 |         0.3 |iter: 37/100\r",
      "      6.5 | 2.3036e-04 | 6.1538e-03 |         566 |     2457000 |          240 |       39000 |         0.3 |iter: 38/100\r",
      "      6.5 | 2.3056e-04 | 6.1500e-03 |         581 |     2520000 |          246 |       40000 |         0.3 |iter: 39/100\r",
      "      6.5 | 2.2687e-04 | 6.0488e-03 |         586 |     2583000 |          248 |       41000 |         0.3 |iter: 40/100\r",
      "      6.5 | 2.2336e-04 | 5.9524e-03 |         591 |     2646000 |          250 |       42000 |         0.3 |iter: 41/100\r",
      "      6.5 | 2.2665e-04 | 6.0000e-03 |         614 |     2709000 |          258 |       43000 |         0.3 |iter: 42/100\r",
      "      6.5 | 2.2872e-04 | 6.0227e-03 |         634 |     2772000 |          265 |       44000 |         0.3 |iter: 43/100\r",
      "      6.5 | 2.2963e-04 | 6.0667e-03 |         651 |     2835000 |          273 |       45000 |         0.3 |iter: 44/100\r",
      "      6.5 | 2.2809e-04 | 6.0435e-03 |         661 |     2898000 |          278 |       46000 |         0.3 |iter: 45/100\r",
      "      6.5 | 2.2965e-04 | 6.1489e-03 |         680 |     2961000 |          289 |       47000 |         0.4 |iter: 46/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.5 | 2.2718e-04 | 6.1042e-03 |         687 |     3024000 |          293 |       48000 |         0.4 |iter: 47/100\r",
      "      6.5 | 2.2514e-04 | 6.0612e-03 |         695 |     3087000 |          297 |       49000 |         0.4 |iter: 48/100\r",
      "      6.5 | 2.2286e-04 | 6.0200e-03 |         702 |     3150000 |          301 |       50000 |         0.4 |iter: 49/100\r",
      "      6.5 | 2.2222e-04 | 6.0588e-03 |         714 |     3213000 |          309 |       51000 |         0.4 |iter: 50/100\r",
      "      6.5 | 2.2131e-04 | 6.0385e-03 |         725 |     3276000 |          314 |       52000 |         0.4 |iter: 51/100\r",
      "      6.5 | 2.2372e-04 | 6.0377e-03 |         747 |     3339000 |          320 |       53000 |         0.4 |iter: 52/100\r",
      "      6.5 | 2.2222e-04 | 6.0000e-03 |         756 |     3402000 |          324 |       54000 |         0.4 |iter: 53/100\r",
      "      6.5 | 2.2136e-04 | 6.0000e-03 |         767 |     3465000 |          330 |       55000 |         0.4 |iter: 54/100\r",
      "      6.5 | 2.2222e-04 | 6.0000e-03 |         784 |     3528000 |          336 |       56000 |         0.4 |iter: 55/100\r",
      "      6.5 | 2.2139e-04 | 5.9825e-03 |         795 |     3591000 |          341 |       57000 |         0.4 |iter: 56/100\r",
      "      6.5 | 2.1976e-04 | 5.9655e-03 |         803 |     3654000 |          346 |       58000 |         0.4 |iter: 57/100\r",
      "      6.5 | 2.2411e-04 | 6.0339e-03 |         833 |     3717000 |          356 |       59000 |         0.4 |iter: 58/100\r",
      "      6.5 | 2.2513e-04 | 6.0667e-03 |         851 |     3780000 |          364 |       60000 |         0.5 |iter: 59/100\r",
      "      6.5 | 2.2717e-04 | 6.1148e-03 |         873 |     3843000 |          373 |       61000 |         0.5 |iter: 60/100\r",
      "      6.5 | 2.2939e-04 | 6.1774e-03 |         896 |     3906000 |          383 |       62000 |         0.5 |iter: 61/100\r",
      "      6.5 | 2.3079e-04 | 6.2381e-03 |         916 |     3969000 |          393 |       63000 |         0.5 |iter: 62/100\r",
      "      6.5 | 2.2991e-04 | 6.2031e-03 |         927 |     4032000 |          397 |       64000 |         0.5 |iter: 63/100\r",
      "      6.5 | 2.2930e-04 | 6.1846e-03 |         939 |     4095000 |          402 |       65000 |         0.5 |iter: 64/100\r",
      "      6.5 | 2.2727e-04 | 6.1515e-03 |         945 |     4158000 |          406 |       66000 |         0.5 |iter: 65/100\r",
      "      6.5 | 2.2459e-04 | 6.0746e-03 |         948 |     4221000 |          407 |       67000 |         0.5 |iter: 66/100\r",
      "      6.5 | 2.2456e-04 | 6.0882e-03 |         962 |     4284000 |          414 |       68000 |         0.5 |iter: 67/100\r",
      "      6.5 | 2.2567e-04 | 6.0870e-03 |         981 |     4347000 |          420 |       69000 |         0.5 |iter: 68/100\r",
      "      6.5 | 2.2880e-04 | 6.1429e-03 |        1009 |     4410000 |          430 |       70000 |         0.5 |iter: 69/100\r",
      "      6.5 | 2.3161e-04 | 6.1831e-03 |        1036 |     4473000 |          439 |       71000 |         0.5 |iter: 70/100\r",
      "      6.5 | 2.2928e-04 | 6.1389e-03 |        1040 |     4536000 |          442 |       72000 |         0.5 |iter: 71/100\r",
      "      6.5 | 2.2918e-04 | 6.1644e-03 |        1054 |     4599000 |          450 |       73000 |         0.5 |iter: 72/100\r",
      "      6.5 | 2.2694e-04 | 6.1081e-03 |        1058 |     4662000 |          452 |       74000 |         0.6 |iter: 73/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.5 | 2.2709e-04 | 6.1067e-03 |        1073 |     4725000 |          458 |       75000 |         0.6 |iter: 74/100\r",
      "      6.5 | 2.2786e-04 | 6.1316e-03 |        1091 |     4788000 |          466 |       76000 |         0.6 |iter: 75/100\r",
      "      6.5 | 2.2614e-04 | 6.0909e-03 |        1097 |     4851000 |          469 |       77000 |         0.6 |iter: 76/100\r",
      "      6.5 | 2.2690e-04 | 6.1026e-03 |        1115 |     4914000 |          476 |       78000 |         0.6 |iter: 77/100\r",
      "      6.5 | 2.2704e-04 | 6.0886e-03 |        1130 |     4977000 |          481 |       79000 |         0.6 |iter: 78/100\r",
      "      6.5 | 2.2698e-04 | 6.0875e-03 |        1144 |     5040000 |          487 |       80000 |         0.6 |iter: 79/100\r",
      "      6.5 | 2.2888e-04 | 6.0988e-03 |        1168 |     5103000 |          494 |       81000 |         0.6 |iter: 80/100\r",
      "      6.5 | 2.2842e-04 | 6.1098e-03 |        1180 |     5166000 |          501 |       82000 |         0.6 |iter: 81/100\r",
      "      6.5 | 2.2758e-04 | 6.0602e-03 |        1190 |     5229000 |          503 |       83000 |         0.6 |iter: 82/100\r",
      "      6.5 | 2.2543e-04 | 6.0000e-03 |        1193 |     5292000 |          504 |       84000 |         0.6 |iter: 83/100\r",
      "      6.5 | 2.2372e-04 | 5.9765e-03 |        1198 |     5355000 |          508 |       85000 |         0.6 |iter: 84/100\r",
      "      6.5 | 2.2351e-04 | 6.0000e-03 |        1211 |     5418000 |          516 |       86000 |         0.6 |iter: 85/100\r",
      "      6.5 | 2.2386e-04 | 6.0115e-03 |        1227 |     5481000 |          523 |       87000 |         0.7 |iter: 86/100\r",
      "      6.5 | 2.2258e-04 | 6.0114e-03 |        1234 |     5544000 |          529 |       88000 |         0.7 |iter: 87/100\r",
      "      6.5 | 2.2401e-04 | 6.0000e-03 |        1256 |     5607000 |          534 |       89000 |         0.7 |iter: 88/100\r",
      "      6.5 | 2.2205e-04 | 5.9667e-03 |        1259 |     5670000 |          537 |       90000 |         0.7 |iter: 89/100\r",
      "      6.5 | 2.2275e-04 | 6.0110e-03 |        1277 |     5733000 |          547 |       91000 |         0.7 |iter: 90/100\r",
      "      6.5 | 2.2239e-04 | 6.0217e-03 |        1289 |     5796000 |          554 |       92000 |         0.7 |iter: 91/100\r",
      "      6.5 | 2.2256e-04 | 6.0215e-03 |        1304 |     5859000 |          560 |       93000 |         0.7 |iter: 92/100\r",
      "      6.5 | 2.2188e-04 | 6.0106e-03 |        1314 |     5922000 |          565 |       94000 |         0.7 |iter: 93/100\r",
      "      6.5 | 2.2139e-04 | 6.0000e-03 |        1325 |     5985000 |          570 |       95000 |         0.7 |iter: 94/100\r",
      "      6.5 | 2.2024e-04 | 5.9896e-03 |        1332 |     6048000 |          575 |       96000 |         0.7 |iter: 95/100\r",
      "      6.5 | 2.2091e-04 | 6.0103e-03 |        1350 |     6111000 |          583 |       97000 |         0.7 |iter: 96/100\r",
      "      6.5 | 2.1866e-04 | 5.9490e-03 |        1350 |     6174000 |          583 |       98000 |         0.7 |iter: 97/100\r",
      "      6.5 | 2.1902e-04 | 5.9899e-03 |        1366 |     6237000 |          593 |       99000 |         0.7 |iter: 98/100\r",
      "      6.5 | 2.1889e-04 | 5.9900e-03 |        1379 |     6300000 |          599 |      100000 |         0.8 |iter: 99/100\r",
      "      6.5 | 2.1889e-04 | 5.9900e-03 |        1379 |     6300000 |          599 |      100000 |         0.8 |reached max iterations\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ebno_dbs = np.array(np.arange(1, 7, 0.5))\n",
    "batch_size = 10000\n",
    "mc_ites = 100\n",
    "\n",
    "ber_plot.simulate(model,\n",
    "                  ebno_dbs=ebno_dbs,\n",
    "                  batch_size=1000,\n",
    "                  num_target_bit_errors=2000, # stop sim after 2000 bit errors\n",
    "                  legend=\"Trained\",\n",
    "                  max_mc_iter=mc_iters,\n",
    "                  soft_estimates=True,\n",
    "                  compile_mode=None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Experiments\n",
    "\n",
    "You will now see that the memory footprint can be drastically reduced by using the same weight for all messages.\n",
    "In the second part we will apply the concept to the 5G LDPC codes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Damped BP \n",
    "\n",
    "It is well-known that scaling of LLRs / messages can help to improve the performance of BP decoding in some scenarios [3,4].\n",
    "In particular, this works well for very short codes such as the code we are currently analyzing.\n",
    "\n",
    "We now follow the basic idea of [2] and scale all weights with the same scalar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:11:57.924906Z",
     "iopub.status.busy": "2026-02-13T15:11:57.924751Z",
     "iopub.status.idle": "2026-02-13T15:11:59.861263Z",
     "shell.execute_reply": "2026-02-13T15:11:59.860247Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      1.0 | 8.8556e-02 | 9.9200e-01 |        5579 |       63000 |          992 |        1000 |         0.0 |iter: 0/100\r",
      "      1.0 | 8.8556e-02 | 9.9200e-01 |        5579 |       63000 |          992 |        1000 |         0.0 |reached target bit errors\n",
      "      1.5 | 7.5921e-02 | 9.7300e-01 |        4783 |       63000 |          973 |        1000 |         0.0 |iter: 0/100\r",
      "      1.5 | 7.5921e-02 | 9.7300e-01 |        4783 |       63000 |          973 |        1000 |         0.0 |reached target bit errors\n",
      "      2.0 | 6.4794e-02 | 9.3000e-01 |        4082 |       63000 |          930 |        1000 |         0.0 |iter: 0/100\r",
      "      2.0 | 6.4794e-02 | 9.3000e-01 |        4082 |       63000 |          930 |        1000 |         0.0 |reached target bit errors\n",
      "      2.5 | 4.8952e-02 | 7.9600e-01 |        3084 |       63000 |          796 |        1000 |         0.0 |iter: 0/100\r",
      "      2.5 | 4.8952e-02 | 7.9600e-01 |        3084 |       63000 |          796 |        1000 |         0.0 |reached target bit errors\n",
      "      3.0 | 3.6683e-02 | 6.3000e-01 |        2311 |       63000 |          630 |        1000 |         0.0 |iter: 0/100\r",
      "      3.0 | 3.6683e-02 | 6.3000e-01 |        2311 |       63000 |          630 |        1000 |         0.0 |reached target bit errors\n",
      "      3.5 | 2.3619e-02 | 4.2800e-01 |        1488 |       63000 |          428 |        1000 |         0.0 |iter: 0/100\r",
      "      3.5 | 2.3048e-02 | 4.2250e-01 |        2904 |      126000 |          845 |        2000 |         0.0 |iter: 1/100\r",
      "      3.5 | 2.3048e-02 | 4.2250e-01 |        2904 |      126000 |          845 |        2000 |         0.0 |reached target bit errors\n",
      "      4.0 | 1.2683e-02 | 2.4900e-01 |         799 |       63000 |          249 |        1000 |         0.0 |iter: 0/100\r",
      "      4.0 | 1.2619e-02 | 2.4600e-01 |        1590 |      126000 |          492 |        2000 |         0.0 |iter: 1/100\r",
      "      4.0 | 1.2291e-02 | 2.3567e-01 |        2323 |      189000 |          707 |        3000 |         0.0 |iter: 2/100\r",
      "      4.0 | 1.2291e-02 | 2.3567e-01 |        2323 |      189000 |          707 |        3000 |         0.0 |reached target bit errors\n",
      "      4.5 | 6.2063e-03 | 1.3200e-01 |         391 |       63000 |          132 |        1000 |         0.0 |iter: 0/100\r",
      "      4.5 | 6.3016e-03 | 1.3250e-01 |         794 |      126000 |          265 |        2000 |         0.0 |iter: 1/100\r",
      "      4.5 | 6.0794e-03 | 1.2567e-01 |        1149 |      189000 |          377 |        3000 |         0.0 |iter: 2/100\r",
      "      4.5 | 6.0476e-03 | 1.2275e-01 |        1524 |      252000 |          491 |        4000 |         0.0 |iter: 3/100\r",
      "      4.5 | 6.0889e-03 | 1.2420e-01 |        1918 |      315000 |          621 |        5000 |         0.0 |iter: 4/100\r",
      "      4.5 | 6.0926e-03 | 1.2400e-01 |        2303 |      378000 |          744 |        6000 |         0.0 |iter: 5/100\r",
      "      4.5 | 6.0926e-03 | 1.2400e-01 |        2303 |      378000 |          744 |        6000 |         0.0 |reached target bit errors\n",
      "      5.0 | 2.6032e-03 | 6.2000e-02 |         164 |       63000 |           62 |        1000 |         0.0 |iter: 0/100\r",
      "      5.0 | 2.8651e-03 | 6.7500e-02 |         361 |      126000 |          135 |        2000 |         0.0 |iter: 1/100\r",
      "      5.0 | 2.8095e-03 | 6.4333e-02 |         531 |      189000 |          193 |        3000 |         0.0 |iter: 2/100\r",
      "      5.0 | 2.7460e-03 | 6.2750e-02 |         692 |      252000 |          251 |        4000 |         0.0 |iter: 3/100\r",
      "      5.0 | 2.7651e-03 | 6.3200e-02 |         871 |      315000 |          316 |        5000 |         0.0 |iter: 4/100\r",
      "      5.0 | 2.7831e-03 | 6.3167e-02 |        1052 |      378000 |          379 |        6000 |         0.0 |iter: 5/100\r",
      "      5.0 | 2.9048e-03 | 6.4143e-02 |        1281 |      441000 |          449 |        7000 |         0.1 |iter: 6/100\r",
      "      5.0 | 2.9464e-03 | 6.4625e-02 |        1485 |      504000 |          517 |        8000 |         0.1 |iter: 7/100\r",
      "      5.0 | 2.9541e-03 | 6.4444e-02 |        1675 |      567000 |          580 |        9000 |         0.1 |iter: 8/100\r",
      "      5.0 | 2.9460e-03 | 6.4400e-02 |        1856 |      630000 |          644 |       10000 |         0.1 |iter: 9/100\r",
      "      5.0 | 3.0303e-03 | 6.5091e-02 |        2100 |      693000 |          716 |       11000 |         0.1 |iter: 10/100\r",
      "      5.0 | 3.0303e-03 | 6.5091e-02 |        2100 |      693000 |          716 |       11000 |         0.1 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      5.5 | 1.3810e-03 | 2.9000e-02 |          87 |       63000 |           29 |        1000 |         0.0 |iter: 0/100\r",
      "      5.5 | 1.2619e-03 | 2.8000e-02 |         159 |      126000 |           56 |        2000 |         0.0 |iter: 1/100\r",
      "      5.5 | 1.4233e-03 | 3.2000e-02 |         269 |      189000 |           96 |        3000 |         0.0 |iter: 2/100\r",
      "      5.5 | 1.3770e-03 | 3.1250e-02 |         347 |      252000 |          125 |        4000 |         0.0 |iter: 3/100\r",
      "      5.5 | 1.3333e-03 | 3.0400e-02 |         420 |      315000 |          152 |        5000 |         0.0 |iter: 4/100\r",
      "      5.5 | 1.4074e-03 | 3.2833e-02 |         532 |      378000 |          197 |        6000 |         0.0 |iter: 5/100\r",
      "      5.5 | 1.4535e-03 | 3.3714e-02 |         641 |      441000 |          236 |        7000 |         0.1 |iter: 6/100\r",
      "      5.5 | 1.4345e-03 | 3.3500e-02 |         723 |      504000 |          268 |        8000 |         0.1 |iter: 7/100\r",
      "      5.5 | 1.3862e-03 | 3.2667e-02 |         786 |      567000 |          294 |        9000 |         0.1 |iter: 8/100\r",
      "      5.5 | 1.4032e-03 | 3.3600e-02 |         884 |      630000 |          336 |       10000 |         0.1 |iter: 9/100\r",
      "      5.5 | 1.3824e-03 | 3.2909e-02 |         958 |      693000 |          362 |       11000 |         0.1 |iter: 10/100\r",
      "      5.5 | 1.3373e-03 | 3.1917e-02 |        1011 |      756000 |          383 |       12000 |         0.1 |iter: 11/100\r",
      "      5.5 | 1.3126e-03 | 3.1385e-02 |        1075 |      819000 |          408 |       13000 |         0.1 |iter: 12/100\r",
      "      5.5 | 1.3197e-03 | 3.1357e-02 |        1164 |      882000 |          439 |       14000 |         0.1 |iter: 13/100\r",
      "      5.5 | 1.3206e-03 | 3.1333e-02 |        1248 |      945000 |          470 |       15000 |         0.1 |iter: 14/100\r",
      "      5.5 | 1.3571e-03 | 3.1938e-02 |        1368 |     1008000 |          511 |       16000 |         0.1 |iter: 15/100\r",
      "      5.5 | 1.3623e-03 | 3.1824e-02 |        1459 |     1071000 |          541 |       17000 |         0.1 |iter: 16/100\r",
      "      5.5 | 1.3386e-03 | 3.1278e-02 |        1518 |     1134000 |          563 |       18000 |         0.1 |iter: 17/100\r",
      "      5.5 | 1.3317e-03 | 3.1053e-02 |        1594 |     1197000 |          590 |       19000 |         0.1 |iter: 18/100\r",
      "      5.5 | 1.3365e-03 | 3.1100e-02 |        1684 |     1260000 |          622 |       20000 |         0.2 |iter: 19/100\r",
      "      5.5 | 1.3432e-03 | 3.1143e-02 |        1777 |     1323000 |          654 |       21000 |         0.2 |iter: 20/100\r",
      "      5.5 | 1.3312e-03 | 3.0864e-02 |        1845 |     1386000 |          679 |       22000 |         0.2 |iter: 21/100\r",
      "      5.5 | 1.3216e-03 | 3.0609e-02 |        1915 |     1449000 |          704 |       23000 |         0.2 |iter: 22/100\r",
      "      5.5 | 1.3069e-03 | 3.0458e-02 |        1976 |     1512000 |          731 |       24000 |         0.2 |iter: 23/100\r",
      "      5.5 | 1.3225e-03 | 3.0520e-02 |        2083 |     1575000 |          763 |       25000 |         0.2 |iter: 24/100\r",
      "      5.5 | 1.3225e-03 | 3.0520e-02 |        2083 |     1575000 |          763 |       25000 |         0.2 |reached target bit errors\n",
      "      6.0 | 8.8889e-04 | 2.1000e-02 |          56 |       63000 |           21 |        1000 |         0.0 |iter: 0/100\r",
      "      6.0 | 5.4762e-04 | 1.3500e-02 |          69 |      126000 |           27 |        2000 |         0.0 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.0 | 4.9735e-04 | 1.3333e-02 |          94 |      189000 |           40 |        3000 |         0.0 |iter: 2/100\r",
      "      6.0 | 5.1587e-04 | 1.3250e-02 |         130 |      252000 |           53 |        4000 |         0.0 |iter: 3/100\r",
      "      6.0 | 5.5873e-04 | 1.3200e-02 |         176 |      315000 |           66 |        5000 |         0.0 |iter: 4/100\r",
      "      6.0 | 5.0529e-04 | 1.2167e-02 |         191 |      378000 |           73 |        6000 |         0.0 |iter: 5/100\r",
      "      6.0 | 4.8753e-04 | 1.1857e-02 |         215 |      441000 |           83 |        7000 |         0.1 |iter: 6/100\r",
      "      6.0 | 4.8214e-04 | 1.1625e-02 |         243 |      504000 |           93 |        8000 |         0.1 |iter: 7/100\r",
      "      6.0 | 4.6384e-04 | 1.1444e-02 |         263 |      567000 |          103 |        9000 |         0.1 |iter: 8/100\r",
      "      6.0 | 5.0635e-04 | 1.2500e-02 |         319 |      630000 |          125 |       10000 |         0.1 |iter: 9/100\r",
      "      6.0 | 5.1082e-04 | 1.2636e-02 |         354 |      693000 |          139 |       11000 |         0.1 |iter: 10/100\r",
      "      6.0 | 5.2646e-04 | 1.3167e-02 |         398 |      756000 |          158 |       12000 |         0.1 |iter: 11/100\r",
      "      6.0 | 5.6166e-04 | 1.3692e-02 |         460 |      819000 |          178 |       13000 |         0.1 |iter: 12/100\r",
      "      6.0 | 5.5782e-04 | 1.3857e-02 |         492 |      882000 |          194 |       14000 |         0.1 |iter: 13/100\r",
      "      6.0 | 5.6720e-04 | 1.4133e-02 |         536 |      945000 |          212 |       15000 |         0.1 |iter: 14/100\r",
      "      6.0 | 5.7738e-04 | 1.3813e-02 |         582 |     1008000 |          221 |       16000 |         0.1 |iter: 15/100\r",
      "      6.0 | 5.8170e-04 | 1.3941e-02 |         623 |     1071000 |          237 |       17000 |         0.1 |iter: 16/100\r",
      "      6.0 | 5.7937e-04 | 1.3889e-02 |         657 |     1134000 |          250 |       18000 |         0.1 |iter: 17/100\r",
      "      6.0 | 5.8062e-04 | 1.3842e-02 |         695 |     1197000 |          263 |       19000 |         0.1 |iter: 18/100\r",
      "      6.0 | 5.8968e-04 | 1.4050e-02 |         743 |     1260000 |          281 |       20000 |         0.2 |iter: 19/100\r",
      "      6.0 | 5.9184e-04 | 1.4143e-02 |         783 |     1323000 |          297 |       21000 |         0.2 |iter: 20/100\r",
      "      6.0 | 5.9668e-04 | 1.4318e-02 |         827 |     1386000 |          315 |       22000 |         0.2 |iter: 21/100\r",
      "      6.0 | 5.8868e-04 | 1.4304e-02 |         853 |     1449000 |          329 |       23000 |         0.2 |iter: 22/100\r",
      "      6.0 | 5.8796e-04 | 1.4250e-02 |         889 |     1512000 |          342 |       24000 |         0.2 |iter: 23/100\r",
      "      6.0 | 5.9429e-04 | 1.4360e-02 |         936 |     1575000 |          359 |       25000 |         0.2 |iter: 24/100\r",
      "      6.0 | 5.8913e-04 | 1.4269e-02 |         965 |     1638000 |          371 |       26000 |         0.2 |iter: 25/100\r",
      "      6.0 | 5.9200e-04 | 1.4370e-02 |        1007 |     1701000 |          388 |       27000 |         0.2 |iter: 26/100\r",
      "      6.0 | 5.9127e-04 | 1.4357e-02 |        1043 |     1764000 |          402 |       28000 |         0.2 |iter: 27/100\r",
      "      6.0 | 5.9004e-04 | 1.4310e-02 |        1078 |     1827000 |          415 |       29000 |         0.2 |iter: 28/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.0 | 5.9048e-04 | 1.4367e-02 |        1116 |     1890000 |          431 |       30000 |         0.2 |iter: 29/100\r",
      "      6.0 | 5.8116e-04 | 1.4226e-02 |        1135 |     1953000 |          441 |       31000 |         0.2 |iter: 30/100\r",
      "      6.0 | 5.9177e-04 | 1.4313e-02 |        1193 |     2016000 |          458 |       32000 |         0.2 |iter: 31/100\r",
      "      6.0 | 5.8490e-04 | 1.4212e-02 |        1216 |     2079000 |          469 |       33000 |         0.2 |iter: 32/100\r",
      "      6.0 | 5.7656e-04 | 1.4088e-02 |        1235 |     2142000 |          479 |       34000 |         0.3 |iter: 33/100\r",
      "      6.0 | 5.7687e-04 | 1.3971e-02 |        1272 |     2205000 |          489 |       35000 |         0.3 |iter: 34/100\r",
      "      6.0 | 5.7187e-04 | 1.3917e-02 |        1297 |     2268000 |          501 |       36000 |         0.3 |iter: 35/100\r",
      "      6.0 | 5.7400e-04 | 1.3811e-02 |        1338 |     2331000 |          511 |       37000 |         0.3 |iter: 36/100\r",
      "      6.0 | 5.8521e-04 | 1.3947e-02 |        1401 |     2394000 |          530 |       38000 |         0.3 |iter: 37/100\r",
      "      6.0 | 5.7835e-04 | 1.3846e-02 |        1421 |     2457000 |          540 |       39000 |         0.3 |iter: 38/100\r",
      "      6.0 | 5.7976e-04 | 1.3950e-02 |        1461 |     2520000 |          558 |       40000 |         0.3 |iter: 39/100\r",
      "      6.0 | 5.7182e-04 | 1.3829e-02 |        1477 |     2583000 |          567 |       41000 |         0.3 |iter: 40/100\r",
      "      6.0 | 5.7181e-04 | 1.3833e-02 |        1513 |     2646000 |          581 |       42000 |         0.3 |iter: 41/100\r",
      "      6.0 | 5.7069e-04 | 1.3884e-02 |        1546 |     2709000 |          597 |       43000 |         0.3 |iter: 42/100\r",
      "      6.0 | 5.6241e-04 | 1.3727e-02 |        1559 |     2772000 |          604 |       44000 |         0.3 |iter: 43/100\r",
      "      6.0 | 5.6966e-04 | 1.3978e-02 |        1615 |     2835000 |          629 |       45000 |         0.3 |iter: 44/100\r",
      "      6.0 | 5.6556e-04 | 1.3913e-02 |        1639 |     2898000 |          640 |       46000 |         0.3 |iter: 45/100\r",
      "      6.0 | 5.6366e-04 | 1.3915e-02 |        1669 |     2961000 |          654 |       47000 |         0.4 |iter: 46/100\r",
      "      6.0 | 5.6382e-04 | 1.3938e-02 |        1705 |     3024000 |          669 |       48000 |         0.4 |iter: 47/100\r",
      "      6.0 | 5.6333e-04 | 1.3939e-02 |        1739 |     3087000 |          683 |       49000 |         0.4 |iter: 48/100\r",
      "      6.0 | 5.6286e-04 | 1.3940e-02 |        1773 |     3150000 |          697 |       50000 |         0.4 |iter: 49/100\r",
      "      6.0 | 5.6116e-04 | 1.3922e-02 |        1803 |     3213000 |          710 |       51000 |         0.4 |iter: 50/100\r",
      "      6.0 | 5.5922e-04 | 1.3865e-02 |        1832 |     3276000 |          721 |       52000 |         0.4 |iter: 51/100\r",
      "      6.0 | 5.6065e-04 | 1.3962e-02 |        1872 |     3339000 |          740 |       53000 |         0.4 |iter: 52/100\r",
      "      6.0 | 5.5556e-04 | 1.3889e-02 |        1890 |     3402000 |          750 |       54000 |         0.4 |iter: 53/100\r",
      "      6.0 | 5.5007e-04 | 1.3727e-02 |        1906 |     3465000 |          755 |       55000 |         0.4 |iter: 54/100\r",
      "      6.0 | 5.4847e-04 | 1.3679e-02 |        1935 |     3528000 |          766 |       56000 |         0.4 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.0 | 5.4581e-04 | 1.3614e-02 |        1960 |     3591000 |          776 |       57000 |         0.4 |iter: 56/100\r",
      "      6.0 | 5.3886e-04 | 1.3466e-02 |        1969 |     3654000 |          781 |       58000 |         0.4 |iter: 57/100\r",
      "      6.0 | 5.4049e-04 | 1.3475e-02 |        2009 |     3717000 |          795 |       59000 |         0.4 |iter: 58/100\r",
      "      6.0 | 5.4049e-04 | 1.3475e-02 |        2009 |     3717000 |          795 |       59000 |         0.4 |reached target bit errors\n",
      "      6.5 | 3.1746e-04 | 7.0000e-03 |          20 |       63000 |            7 |        1000 |         0.0 |iter: 0/100\r",
      "      6.5 | 3.4921e-04 | 7.5000e-03 |          44 |      126000 |           15 |        2000 |         0.0 |iter: 1/100\r",
      "      6.5 | 3.3333e-04 | 7.3333e-03 |          63 |      189000 |           22 |        3000 |         0.0 |iter: 2/100\r",
      "      6.5 | 2.9762e-04 | 6.7500e-03 |          75 |      252000 |           27 |        4000 |         0.0 |iter: 3/100\r",
      "      6.5 | 2.7302e-04 | 6.8000e-03 |          86 |      315000 |           34 |        5000 |         0.0 |iter: 4/100\r",
      "      6.5 | 2.8307e-04 | 7.1667e-03 |         107 |      378000 |           43 |        6000 |         0.0 |iter: 5/100\r",
      "      6.5 | 2.7438e-04 | 7.0000e-03 |         121 |      441000 |           49 |        7000 |         0.1 |iter: 6/100\r",
      "      6.5 | 2.5992e-04 | 6.7500e-03 |         131 |      504000 |           54 |        8000 |         0.1 |iter: 7/100\r",
      "      6.5 | 2.3986e-04 | 6.4444e-03 |         136 |      567000 |           58 |        9000 |         0.1 |iter: 8/100\r",
      "      6.5 | 2.4603e-04 | 6.6000e-03 |         155 |      630000 |           66 |       10000 |         0.1 |iter: 9/100\r",
      "      6.5 | 2.5253e-04 | 6.9091e-03 |         175 |      693000 |           76 |       11000 |         0.1 |iter: 10/100\r",
      "      6.5 | 2.6984e-04 | 7.2500e-03 |         204 |      756000 |           87 |       12000 |         0.1 |iter: 11/100\r",
      "      6.5 | 2.5763e-04 | 7.0000e-03 |         211 |      819000 |           91 |       13000 |         0.1 |iter: 12/100\r",
      "      6.5 | 2.5170e-04 | 6.8571e-03 |         222 |      882000 |           96 |       14000 |         0.1 |iter: 13/100\r",
      "      6.5 | 2.5503e-04 | 6.9333e-03 |         241 |      945000 |          104 |       15000 |         0.1 |iter: 14/100\r",
      "      6.5 | 2.5099e-04 | 7.0625e-03 |         253 |     1008000 |          113 |       16000 |         0.1 |iter: 15/100\r",
      "      6.5 | 2.4743e-04 | 7.0000e-03 |         265 |     1071000 |          119 |       17000 |         0.1 |iter: 16/100\r",
      "      6.5 | 2.4515e-04 | 7.0000e-03 |         278 |     1134000 |          126 |       18000 |         0.1 |iter: 17/100\r",
      "      6.5 | 2.3642e-04 | 6.7895e-03 |         283 |     1197000 |          129 |       19000 |         0.1 |iter: 18/100\r",
      "      6.5 | 2.3175e-04 | 6.7000e-03 |         292 |     1260000 |          134 |       20000 |         0.2 |iter: 19/100\r",
      "      6.5 | 2.3280e-04 | 6.6667e-03 |         308 |     1323000 |          140 |       21000 |         0.2 |iter: 20/100\r",
      "      6.5 | 2.4387e-04 | 6.6818e-03 |         338 |     1386000 |          147 |       22000 |         0.2 |iter: 21/100\r",
      "      6.5 | 2.4155e-04 | 6.6087e-03 |         350 |     1449000 |          152 |       23000 |         0.2 |iter: 22/100\r",
      "      6.5 | 2.4140e-04 | 6.5000e-03 |         365 |     1512000 |          156 |       24000 |         0.2 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.5 | 2.4698e-04 | 6.5600e-03 |         389 |     1575000 |          164 |       25000 |         0.2 |iter: 24/100\r",
      "      6.5 | 2.3993e-04 | 6.3846e-03 |         393 |     1638000 |          166 |       26000 |         0.2 |iter: 25/100\r",
      "      6.5 | 2.3986e-04 | 6.4074e-03 |         408 |     1701000 |          173 |       27000 |         0.2 |iter: 26/100\r",
      "      6.5 | 2.3866e-04 | 6.3571e-03 |         421 |     1764000 |          178 |       28000 |         0.2 |iter: 27/100\r",
      "      6.5 | 2.4138e-04 | 6.3793e-03 |         441 |     1827000 |          185 |       29000 |         0.2 |iter: 28/100\r",
      "      6.5 | 2.4233e-04 | 6.3667e-03 |         458 |     1890000 |          191 |       30000 |         0.2 |iter: 29/100\r",
      "      6.5 | 2.4219e-04 | 6.3548e-03 |         473 |     1953000 |          197 |       31000 |         0.2 |iter: 30/100\r",
      "      6.5 | 2.3810e-04 | 6.2500e-03 |         480 |     2016000 |          200 |       32000 |         0.2 |iter: 31/100\r",
      "      6.5 | 2.3906e-04 | 6.2424e-03 |         497 |     2079000 |          206 |       33000 |         0.2 |iter: 32/100\r",
      "      6.5 | 2.3903e-04 | 6.2353e-03 |         512 |     2142000 |          212 |       34000 |         0.3 |iter: 33/100\r",
      "      6.5 | 2.4172e-04 | 6.2571e-03 |         533 |     2205000 |          219 |       35000 |         0.3 |iter: 34/100\r",
      "      6.5 | 2.4427e-04 | 6.3056e-03 |         554 |     2268000 |          227 |       36000 |         0.3 |iter: 35/100\r",
      "      6.5 | 2.4453e-04 | 6.3514e-03 |         570 |     2331000 |          235 |       37000 |         0.3 |iter: 36/100\r",
      "      6.5 | 2.4311e-04 | 6.3421e-03 |         582 |     2394000 |          241 |       38000 |         0.3 |iter: 37/100\r",
      "      6.5 | 2.4542e-04 | 6.4872e-03 |         603 |     2457000 |          253 |       39000 |         0.3 |iter: 38/100\r",
      "      6.5 | 2.4325e-04 | 6.5000e-03 |         613 |     2520000 |          260 |       40000 |         0.3 |iter: 39/100\r",
      "      6.5 | 2.3926e-04 | 6.4146e-03 |         618 |     2583000 |          263 |       41000 |         0.3 |iter: 40/100\r",
      "      6.5 | 2.3507e-04 | 6.3095e-03 |         622 |     2646000 |          265 |       42000 |         0.3 |iter: 41/100\r",
      "      6.5 | 2.3699e-04 | 6.3023e-03 |         642 |     2709000 |          271 |       43000 |         0.3 |iter: 42/100\r",
      "      6.5 | 2.3377e-04 | 6.2273e-03 |         648 |     2772000 |          274 |       44000 |         0.3 |iter: 43/100\r",
      "      6.5 | 2.3386e-04 | 6.2222e-03 |         663 |     2835000 |          280 |       45000 |         0.3 |iter: 44/100\r",
      "      6.5 | 2.3223e-04 | 6.1739e-03 |         673 |     2898000 |          284 |       46000 |         0.3 |iter: 45/100\r",
      "      6.5 | 2.3776e-04 | 6.1915e-03 |         704 |     2961000 |          291 |       47000 |         0.4 |iter: 46/100\r",
      "      6.5 | 2.3876e-04 | 6.2292e-03 |         722 |     3024000 |          299 |       48000 |         0.4 |iter: 47/100\r",
      "      6.5 | 2.3648e-04 | 6.1837e-03 |         730 |     3087000 |          303 |       49000 |         0.4 |iter: 48/100\r",
      "      6.5 | 2.3333e-04 | 6.1000e-03 |         735 |     3150000 |          305 |       50000 |         0.4 |iter: 49/100\r",
      "      6.5 | 2.3094e-04 | 6.0784e-03 |         742 |     3213000 |          310 |       51000 |         0.4 |iter: 50/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.5 | 2.2863e-04 | 6.0192e-03 |         749 |     3276000 |          313 |       52000 |         0.4 |iter: 51/100\r",
      "      6.5 | 2.2881e-04 | 6.0377e-03 |         764 |     3339000 |          320 |       53000 |         0.4 |iter: 52/100\r",
      "      6.5 | 2.2957e-04 | 6.0370e-03 |         781 |     3402000 |          326 |       54000 |         0.4 |iter: 53/100\r",
      "      6.5 | 2.2684e-04 | 6.0000e-03 |         786 |     3465000 |          330 |       55000 |         0.4 |iter: 54/100\r",
      "      6.5 | 2.2619e-04 | 6.0000e-03 |         798 |     3528000 |          336 |       56000 |         0.4 |iter: 55/100\r",
      "      6.5 | 2.2445e-04 | 5.9649e-03 |         806 |     3591000 |          340 |       57000 |         0.4 |iter: 56/100\r",
      "      6.5 | 2.2578e-04 | 5.9655e-03 |         825 |     3654000 |          346 |       58000 |         0.4 |iter: 57/100\r",
      "      6.5 | 2.2464e-04 | 5.9661e-03 |         835 |     3717000 |          352 |       59000 |         0.4 |iter: 58/100\r",
      "      6.5 | 2.2354e-04 | 5.9500e-03 |         845 |     3780000 |          357 |       60000 |         0.5 |iter: 59/100\r",
      "      6.5 | 2.2248e-04 | 5.9508e-03 |         855 |     3843000 |          363 |       61000 |         0.5 |iter: 60/100\r",
      "      6.5 | 2.2350e-04 | 5.9839e-03 |         873 |     3906000 |          371 |       62000 |         0.5 |iter: 61/100\r",
      "      6.5 | 2.2600e-04 | 5.9683e-03 |         897 |     3969000 |          376 |       63000 |         0.5 |iter: 62/100\r",
      "      6.5 | 2.2421e-04 | 5.9219e-03 |         904 |     4032000 |          379 |       64000 |         0.5 |iter: 63/100\r",
      "      6.5 | 2.2247e-04 | 5.8923e-03 |         911 |     4095000 |          383 |       65000 |         0.5 |iter: 64/100\r",
      "      6.5 | 2.2246e-04 | 5.9091e-03 |         925 |     4158000 |          390 |       66000 |         0.5 |iter: 65/100\r",
      "      6.5 | 2.2317e-04 | 5.9552e-03 |         942 |     4221000 |          399 |       67000 |         0.5 |iter: 66/100\r",
      "      6.5 | 2.2199e-04 | 5.9265e-03 |         951 |     4284000 |          403 |       68000 |         0.5 |iter: 67/100\r",
      "      6.5 | 2.2337e-04 | 5.9420e-03 |         971 |     4347000 |          410 |       69000 |         0.5 |iter: 68/100\r",
      "      6.5 | 2.2200e-04 | 5.9429e-03 |         979 |     4410000 |          416 |       70000 |         0.5 |iter: 69/100\r",
      "      6.5 | 2.2110e-04 | 5.9296e-03 |         989 |     4473000 |          421 |       71000 |         0.5 |iter: 70/100\r",
      "      6.5 | 2.2266e-04 | 5.9722e-03 |        1010 |     4536000 |          430 |       72000 |         0.5 |iter: 71/100\r",
      "      6.5 | 2.2396e-04 | 5.9452e-03 |        1030 |     4599000 |          434 |       73000 |         0.6 |iter: 72/100\r",
      "      6.5 | 2.2866e-04 | 6.0135e-03 |        1066 |     4662000 |          445 |       74000 |         0.6 |iter: 73/100\r",
      "      6.5 | 2.2836e-04 | 6.0133e-03 |        1079 |     4725000 |          451 |       75000 |         0.6 |iter: 74/100\r",
      "      6.5 | 2.2807e-04 | 6.0000e-03 |        1092 |     4788000 |          456 |       76000 |         0.6 |iter: 75/100\r",
      "      6.5 | 2.2820e-04 | 6.0260e-03 |        1107 |     4851000 |          464 |       77000 |         0.6 |iter: 76/100\r",
      "      6.5 | 2.2833e-04 | 6.0513e-03 |        1122 |     4914000 |          472 |       78000 |         0.6 |iter: 77/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      6.5 | 2.2805e-04 | 6.0506e-03 |        1135 |     4977000 |          478 |       79000 |         0.6 |iter: 78/100\r",
      "      6.5 | 2.3135e-04 | 6.1250e-03 |        1166 |     5040000 |          490 |       80000 |         0.6 |iter: 79/100\r",
      "      6.5 | 2.3045e-04 | 6.0741e-03 |        1176 |     5103000 |          492 |       81000 |         0.6 |iter: 80/100\r",
      "      6.5 | 2.3268e-04 | 6.0610e-03 |        1202 |     5166000 |          497 |       82000 |         0.6 |iter: 81/100\r",
      "      6.5 | 2.3293e-04 | 6.0843e-03 |        1218 |     5229000 |          505 |       83000 |         0.6 |iter: 82/100\r",
      "      6.5 | 2.3186e-04 | 6.0714e-03 |        1227 |     5292000 |          510 |       84000 |         0.6 |iter: 83/100\r",
      "      6.5 | 2.3231e-04 | 6.0941e-03 |        1244 |     5355000 |          518 |       85000 |         0.6 |iter: 84/100\r",
      "      6.5 | 2.3330e-04 | 6.1047e-03 |        1264 |     5418000 |          525 |       86000 |         0.6 |iter: 85/100\r",
      "      6.5 | 2.3262e-04 | 6.1034e-03 |        1275 |     5481000 |          531 |       87000 |         0.7 |iter: 86/100\r",
      "      6.5 | 2.3250e-04 | 6.1023e-03 |        1289 |     5544000 |          537 |       88000 |         0.7 |iter: 87/100\r",
      "      6.5 | 2.3274e-04 | 6.0899e-03 |        1305 |     5607000 |          542 |       89000 |         0.7 |iter: 88/100\r",
      "      6.5 | 2.3351e-04 | 6.0889e-03 |        1324 |     5670000 |          548 |       90000 |         0.7 |iter: 89/100\r",
      "      6.5 | 2.3373e-04 | 6.1099e-03 |        1340 |     5733000 |          556 |       91000 |         0.7 |iter: 90/100\r",
      "      6.5 | 2.3361e-04 | 6.1087e-03 |        1354 |     5796000 |          562 |       92000 |         0.7 |iter: 91/100\r",
      "      6.5 | 2.3383e-04 | 6.0860e-03 |        1370 |     5859000 |          566 |       93000 |         0.7 |iter: 92/100\r",
      "      6.5 | 2.3387e-04 | 6.1064e-03 |        1385 |     5922000 |          574 |       94000 |         0.7 |iter: 93/100\r",
      "      6.5 | 2.3442e-04 | 6.1263e-03 |        1403 |     5985000 |          582 |       95000 |         0.7 |iter: 94/100\r",
      "      6.5 | 2.3214e-04 | 6.0729e-03 |        1404 |     6048000 |          583 |       96000 |         0.7 |iter: 95/100\r",
      "      6.5 | 2.3171e-04 | 6.0619e-03 |        1416 |     6111000 |          588 |       97000 |         0.7 |iter: 96/100\r",
      "      6.5 | 2.3113e-04 | 6.0612e-03 |        1427 |     6174000 |          594 |       98000 |         0.7 |iter: 97/100\r",
      "      6.5 | 2.3168e-04 | 6.0505e-03 |        1445 |     6237000 |          599 |       99000 |         0.7 |iter: 98/100\r",
      "      6.5 | 2.3206e-04 | 6.0500e-03 |        1462 |     6300000 |          605 |      100000 |         0.8 |iter: 99/100\r",
      "      6.5 | 2.3206e-04 | 6.0500e-03 |        1462 |     6300000 |          605 |      100000 |         0.8 |reached max iterations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get weights of trained model\n",
    "weights_bp = model.edge_weights.weights\n",
    "\n",
    "# calc mean value of weights\n",
    "damping_factor = weights_bp.mean()\n",
    "\n",
    "# set all weights to the SAME constant scaling\n",
    "weights_damped = torch.ones_like(weights_bp) * damping_factor\n",
    "\n",
    "# and apply the new weights\n",
    "with torch.no_grad():\n",
    "    model.edge_weights.weights.copy_(weights_damped)\n",
    "\n",
    "# let us have look at the new weights again\n",
    "model.edge_weights.show_weights()\n",
    "\n",
    "# and simulate the BER again\n",
    "leg_str = f\"Damped BP (scaling factor {damping_factor.item():.3f})\"\n",
    "ber_plot.simulate(model,\n",
    "                  ebno_dbs=ebno_dbs,\n",
    "                  batch_size=1000,\n",
    "                  num_target_bit_errors=2000, # stop sim after 2000 bit errors\n",
    "                  legend=leg_str,\n",
    "                  max_mc_iter=mc_iters,\n",
    "                  soft_estimates=True,\n",
    "                  compile_mode=None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at the results, we observe almost the same performance although we only scale by a single scalar.\n",
    "This implies that the number of weights of our model is by far too large and the memory footprint could be reduced significantly.\n",
    "However, isn't it fascinating to see that this simple concept of weighted BP leads to the same results as the concept of *damped BP*?\n",
    "\n",
    "**Note**: for more iterations it could be beneficial to implement an individual damping per iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Learning the 5G LDPC Code\n",
    "\n",
    "In this Section, you will experience what happens if we apply the same concept to the 5G LDPC code (including rate matching).\n",
    "\n",
    "For this, we need to define a new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:11:59.863472Z",
     "iopub.status.busy": "2026-02-13T15:11:59.863330Z",
     "iopub.status.idle": "2026-02-13T15:11:59.868506Z",
     "shell.execute_reply": "2026-02-13T15:11:59.867651Z"
    }
   },
   "outputs": [],
   "source": [
    "class WeightedBP5G(Block):\n",
    "    \"\"\"System model for BER simulations of weighted BP decoding for 5G LDPC codes.\n",
    "\n",
    "    This model uses `GaussianPriorSource` to mimic the LLRs after demapping of\n",
    "    QPSK symbols transmitted over an AWGN channel.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    k: int\n",
    "        Number of information bits per codeword.\n",
    "\n",
    "    n: int\n",
    "        Codeword length.\n",
    "\n",
    "    num_iter: int\n",
    "        Number of BP decoding iterations.\n",
    "\n",
    "    Input\n",
    "    -----\n",
    "    batch_size: int\n",
    "        The batch_size used for the simulation.\n",
    "\n",
    "    ebno_db: float\n",
    "        A float defining the simulation SNR.\n",
    "\n",
    "    Output\n",
    "    ------\n",
    "    (u, u_hat, loss):\n",
    "        Tuple:\n",
    "\n",
    "    u: torch.Tensor\n",
    "        A tensor of shape `[batch_size, k] of 0s and 1s containing the transmitted information bits.\n",
    "\n",
    "    u_hat: torch.Tensor\n",
    "        A tensor of shape `[batch_size, k] of 0s and 1s containing the estimated information bits.\n",
    "\n",
    "    loss: torch.Tensor\n",
    "        Binary cross-entropy loss between `u` and `u_hat`.\n",
    "    \"\"\"\n",
    "    def __init__(self, k, n, num_iter=20):\n",
    "        super().__init__()\n",
    "\n",
    "        self._k = k\n",
    "        self._n = n\n",
    "\n",
    "        # we need to initialize an encoder for the 5G parameters\n",
    "        self.encoder = LDPC5GEncoder(k, n)\n",
    "\n",
    "        # add trainable weights via decoder callbacks\n",
    "        # Pass PCM to enable proper weight application in padded format\n",
    "        self.edge_weights = WeightedBPCallback(\n",
    "                            num_edges=int(np.sum(self.encoder.pcm)),\n",
    "                            pcm=self.encoder.pcm)\n",
    "\n",
    "        self.decoder = LDPC5GDecoder(self.encoder,\n",
    "                                     num_iter=1, # iterations are done via outer loop (to access intermediate results for multi-loss)\n",
    "                                     return_state=True,\n",
    "                                     hard_out=False,\n",
    "                                     prune_pcm=False,\n",
    "                                     cn_update=\"boxplus\",\n",
    "                                     v2c_callbacks=[self.edge_weights,]) # register callback\n",
    "\n",
    "        self.llr_source = GaussianPriorSource()\n",
    "        self._num_iter = num_iter\n",
    "        self._coderate = k/n\n",
    "\n",
    "    def call(self, batch_size, ebno_db):\n",
    "\n",
    "        noise_var = ebnodb2no(ebno_db,\n",
    "                              num_bits_per_symbol=2, # QPSK\n",
    "                              coderate=self._coderate)\n",
    "\n",
    "        # BPSK modulated all-zero CW\n",
    "        c = torch.zeros(batch_size, self._k, device=self.device) # decoder only returns info bits\n",
    "\n",
    "        # use fake llrs from GA\n",
    "        # works as BP is symmetric\n",
    "        llr = self.llr_source([batch_size, self._n], no=noise_var)\n",
    "\n",
    "        # --- implement multi-loss is proposed by Nachmani et al. ---\n",
    "        loss = 0.0\n",
    "        msg_v2c = None\n",
    "        for i in range(self._num_iter):\n",
    "            c_hat, msg_v2c = self.decoder(llr, msg_v2c=msg_v2c) # perform one decoding iteration; decoder returns soft-values\n",
    "            loss = loss + F.binary_cross_entropy_with_logits(c_hat, c)  # add loss after each iteration\n",
    "\n",
    "        loss = loss / self._num_iter\n",
    "\n",
    "        return c, c_hat, loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:11:59.870541Z",
     "iopub.status.busy": "2026-02-13T15:11:59.870416Z",
     "iopub.status.idle": "2026-02-13T15:13:02.896937Z",
     "shell.execute_reply": "2026-02-13T15:13:02.895813Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.6584e-01 | 1.0000e+00 |       66336 |      400000 |         1000 |        1000 |         0.1 |iter: 0/100\r",
      "      0.0 | 1.6584e-01 | 1.0000e+00 |       66336 |      400000 |         1000 |        1000 |         0.1 |reached target bit errors\n",
      "     0.25 | 1.4654e-01 | 1.0000e+00 |       58616 |      400000 |         1000 |        1000 |         0.1 |iter: 0/100\r",
      "     0.25 | 1.4654e-01 | 1.0000e+00 |       58616 |      400000 |         1000 |        1000 |         0.1 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0.5 | 1.2308e-01 | 9.9800e-01 |       49234 |      400000 |          998 |        1000 |         0.1 |iter: 0/100\r",
      "      0.5 | 1.2308e-01 | 9.9800e-01 |       49234 |      400000 |          998 |        1000 |         0.1 |reached target bit errors\n",
      "     0.75 | 9.6780e-02 | 9.8700e-01 |       38712 |      400000 |          987 |        1000 |         0.1 |iter: 0/100\r",
      "     0.75 | 9.6780e-02 | 9.8700e-01 |       38712 |      400000 |          987 |        1000 |         0.1 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      1.0 | 6.7312e-02 | 9.3700e-01 |       26925 |      400000 |          937 |        1000 |         0.1 |iter: 0/100\r",
      "      1.0 | 6.7312e-02 | 9.3700e-01 |       26925 |      400000 |          937 |        1000 |         0.1 |reached target bit errors\n",
      "     1.25 | 4.1515e-02 | 8.2000e-01 |       16606 |      400000 |          820 |        1000 |         0.1 |iter: 0/100\r",
      "     1.25 | 4.1515e-02 | 8.2000e-01 |       16606 |      400000 |          820 |        1000 |         0.1 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      1.5 | 2.2400e-02 | 6.2300e-01 |        8960 |      400000 |          623 |        1000 |         0.1 |iter: 0/100\r",
      "      1.5 | 2.2400e-02 | 6.2300e-01 |        8960 |      400000 |          623 |        1000 |         0.1 |reached target bit errors\n",
      "     1.75 | 8.9475e-03 | 3.6600e-01 |        3579 |      400000 |          366 |        1000 |         0.1 |iter: 0/100\r",
      "     1.75 | 8.9475e-03 | 3.6600e-01 |        3579 |      400000 |          366 |        1000 |         0.1 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.0 | 3.1650e-03 | 1.8000e-01 |        1266 |      400000 |          180 |        1000 |         0.1 |iter: 0/100\r",
      "      2.0 | 2.9875e-03 | 1.8100e-01 |        2390 |      800000 |          362 |        2000 |         0.2 |iter: 1/100\r",
      "      2.0 | 2.9875e-03 | 1.8100e-01 |        2390 |      800000 |          362 |        2000 |         0.2 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.25 | 1.0200e-03 | 7.5000e-02 |         408 |      400000 |           75 |        1000 |         0.1 |iter: 0/100\r",
      "     2.25 | 9.7500e-04 | 7.0500e-02 |         780 |      800000 |          141 |        2000 |         0.2 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.25 | 9.2833e-04 | 7.1000e-02 |        1114 |     1200000 |          213 |        3000 |         0.3 |iter: 2/100\r",
      "     2.25 | 9.9125e-04 | 7.2500e-02 |        1586 |     1600000 |          290 |        4000 |         0.4 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.25 | 9.0750e-04 | 6.9600e-02 |        1815 |     2000000 |          348 |        5000 |         0.5 |iter: 4/100\r",
      "     2.25 | 9.0167e-04 | 6.8833e-02 |        2164 |     2400000 |          413 |        6000 |         0.7 |iter: 5/100\r",
      "     2.25 | 9.0167e-04 | 6.8833e-02 |        2164 |     2400000 |          413 |        6000 |         0.7 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.5250e-04 | 2.3000e-02 |         101 |      400000 |           23 |        1000 |         0.1 |iter: 0/100\r",
      "      2.5 | 2.8375e-04 | 2.4500e-02 |         227 |      800000 |           49 |        2000 |         0.2 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.9917e-04 | 2.4000e-02 |         359 |     1200000 |           72 |        3000 |         0.3 |iter: 2/100\r",
      "      2.5 | 2.8063e-04 | 2.3750e-02 |         449 |     1600000 |           95 |        4000 |         0.4 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.8050e-04 | 2.4000e-02 |         561 |     2000000 |          120 |        5000 |         0.5 |iter: 4/100\r",
      "      2.5 | 2.4458e-04 | 2.1333e-02 |         587 |     2400000 |          128 |        6000 |         0.7 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.3143e-04 | 2.1000e-02 |         648 |     2800000 |          147 |        7000 |         0.8 |iter: 6/100\r",
      "      2.5 | 2.4281e-04 | 2.2250e-02 |         777 |     3200000 |          178 |        8000 |         0.9 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.4000e-04 | 2.1444e-02 |         864 |     3600000 |          193 |        9000 |         1.0 |iter: 8/100\r",
      "      2.5 | 2.4150e-04 | 2.1500e-02 |         966 |     4000000 |          215 |       10000 |         1.1 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.5114e-04 | 2.2000e-02 |        1105 |     4400000 |          242 |       11000 |         1.2 |iter: 10/100\r",
      "      2.5 | 2.4396e-04 | 2.2250e-02 |        1171 |     4800000 |          267 |       12000 |         1.3 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.4038e-04 | 2.2000e-02 |        1250 |     5200000 |          286 |       13000 |         1.4 |iter: 12/100\r",
      "      2.5 | 2.3571e-04 | 2.1857e-02 |        1320 |     5600000 |          306 |       14000 |         1.5 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.3400e-04 | 2.2667e-02 |        1404 |     6000000 |          340 |       15000 |         1.6 |iter: 14/100\r",
      "      2.5 | 2.4016e-04 | 2.2750e-02 |        1537 |     6400000 |          364 |       16000 |         1.8 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.5706e-04 | 2.3000e-02 |        1748 |     6800000 |          391 |       17000 |         1.9 |iter: 16/100\r",
      "      2.5 | 2.5028e-04 | 2.2722e-02 |        1802 |     7200000 |          409 |       18000 |         2.0 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.4289e-04 | 2.2368e-02 |        1846 |     7600000 |          425 |       19000 |         2.1 |iter: 18/100\r",
      "      2.5 | 2.3675e-04 | 2.2100e-02 |        1894 |     8000000 |          442 |       20000 |         2.2 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.3536e-04 | 2.2000e-02 |        1977 |     8400000 |          462 |       21000 |         2.3 |iter: 20/100\r",
      "      2.5 | 2.3023e-04 | 2.1818e-02 |        2026 |     8800000 |          480 |       22000 |         2.4 |iter: 21/100\r",
      "      2.5 | 2.3023e-04 | 2.1818e-02 |        2026 |     8800000 |          480 |       22000 |         2.4 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.0000e-05 | 3.0000e-03 |          12 |      400000 |            3 |        1000 |         0.1 |iter: 0/100\r",
      "     2.75 | 3.5000e-05 | 4.5000e-03 |          28 |      800000 |            9 |        2000 |         0.2 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.7500e-05 | 5.6667e-03 |          57 |     1200000 |           17 |        3000 |         0.3 |iter: 2/100\r",
      "     2.75 | 4.4375e-05 | 6.5000e-03 |          71 |     1600000 |           26 |        4000 |         0.4 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.3000e-05 | 6.2000e-03 |          86 |     2000000 |           31 |        5000 |         0.5 |iter: 4/100\r",
      "     2.75 | 4.4167e-05 | 6.1667e-03 |         106 |     2400000 |           37 |        6000 |         0.7 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.8571e-05 | 6.1429e-03 |         136 |     2800000 |           43 |        7000 |         0.8 |iter: 6/100\r",
      "     2.75 | 4.5937e-05 | 5.8750e-03 |         147 |     3200000 |           47 |        8000 |         0.9 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.1667e-05 | 5.5556e-03 |         150 |     3600000 |           50 |        9000 |         1.0 |iter: 8/100\r",
      "     2.75 | 4.0000e-05 | 5.5000e-03 |         160 |     4000000 |           55 |       10000 |         1.1 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.0682e-05 | 5.8182e-03 |         179 |     4400000 |           64 |       11000 |         1.2 |iter: 10/100\r",
      "     2.75 | 4.0417e-05 | 5.6667e-03 |         194 |     4800000 |           68 |       12000 |         1.3 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.1923e-05 | 6.0000e-03 |         218 |     5200000 |           78 |       13000 |         1.4 |iter: 12/100\r",
      "     2.75 | 4.1786e-05 | 6.0714e-03 |         234 |     5600000 |           85 |       14000 |         1.5 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9833e-05 | 6.0000e-03 |         239 |     6000000 |           90 |       15000 |         1.6 |iter: 14/100\r",
      "     2.75 | 4.0000e-05 | 6.0625e-03 |         256 |     6400000 |           97 |       16000 |         1.8 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.0441e-05 | 6.1176e-03 |         275 |     6800000 |          104 |       17000 |         1.9 |iter: 16/100\r",
      "     2.75 | 3.9861e-05 | 6.0000e-03 |         287 |     7200000 |          108 |       18000 |         2.0 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9079e-05 | 6.1053e-03 |         297 |     7600000 |          116 |       19000 |         2.1 |iter: 18/100\r",
      "     2.75 | 3.9250e-05 | 6.1500e-03 |         314 |     8000000 |          123 |       20000 |         2.2 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.0000e-05 | 6.1905e-03 |         336 |     8400000 |          130 |       21000 |         2.3 |iter: 20/100\r",
      "     2.75 | 3.9545e-05 | 6.0909e-03 |         348 |     8800000 |          134 |       22000 |         2.4 |iter: 21/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.0000e-05 | 6.0435e-03 |         368 |     9200000 |          139 |       23000 |         2.5 |iter: 22/100\r",
      "     2.75 | 3.9479e-05 | 6.0833e-03 |         379 |     9600000 |          146 |       24000 |         2.6 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.0800e-05 | 6.0800e-03 |         408 |    10000000 |          152 |       25000 |         2.7 |iter: 24/100\r",
      "     2.75 | 3.9327e-05 | 5.8846e-03 |         409 |    10400000 |          153 |       26000 |         2.9 |iter: 25/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.1296e-05 | 5.7407e-03 |         446 |    10800000 |          155 |       27000 |         3.0 |iter: 26/100\r",
      "     2.75 | 4.0893e-05 | 5.7143e-03 |         458 |    11200000 |          160 |       28000 |         3.1 |iter: 27/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9741e-05 | 5.5862e-03 |         461 |    11600000 |          162 |       29000 |         3.2 |iter: 28/100\r",
      "     2.75 | 3.8750e-05 | 5.4667e-03 |         465 |    12000000 |          164 |       30000 |         3.3 |iter: 29/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8710e-05 | 5.5806e-03 |         480 |    12400000 |          173 |       31000 |         3.4 |iter: 30/100\r",
      "     2.75 | 3.7891e-05 | 5.5000e-03 |         485 |    12800000 |          176 |       32000 |         3.5 |iter: 31/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7348e-05 | 5.4848e-03 |         493 |    13200000 |          181 |       33000 |         3.6 |iter: 32/100\r",
      "     2.75 | 3.7206e-05 | 5.4412e-03 |         506 |    13600000 |          185 |       34000 |         3.7 |iter: 33/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7571e-05 | 5.4571e-03 |         526 |    14000000 |          191 |       35000 |         3.8 |iter: 34/100\r",
      "     2.75 | 3.7361e-05 | 5.4444e-03 |         538 |    14400000 |          196 |       36000 |         4.0 |iter: 35/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8514e-05 | 5.4324e-03 |         570 |    14800000 |          201 |       37000 |         4.1 |iter: 36/100\r",
      "     2.75 | 3.8289e-05 | 5.3947e-03 |         582 |    15200000 |          205 |       38000 |         4.2 |iter: 37/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7821e-05 | 5.3846e-03 |         590 |    15600000 |          210 |       39000 |         4.3 |iter: 38/100\r",
      "     2.75 | 3.8500e-05 | 5.4250e-03 |         616 |    16000000 |          217 |       40000 |         4.4 |iter: 39/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9085e-05 | 5.3902e-03 |         641 |    16400000 |          221 |       41000 |         4.5 |iter: 40/100\r",
      "     2.75 | 3.8750e-05 | 5.3571e-03 |         651 |    16800000 |          225 |       42000 |         4.6 |iter: 41/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9012e-05 | 5.3721e-03 |         671 |    17200000 |          231 |       43000 |         4.7 |iter: 42/100\r",
      "     2.75 | 3.8864e-05 | 5.3864e-03 |         684 |    17600000 |          237 |       44000 |         4.8 |iter: 43/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.1111e-05 | 5.4000e-03 |         740 |    18000000 |          243 |       45000 |         4.9 |iter: 44/100\r",
      "     2.75 | 4.0815e-05 | 5.4130e-03 |         751 |    18400000 |          249 |       46000 |         5.1 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.0691e-05 | 5.4043e-03 |         765 |    18800000 |          254 |       47000 |         5.2 |iter: 46/100\r",
      "     2.75 | 4.1406e-05 | 5.4375e-03 |         795 |    19200000 |          261 |       48000 |         5.3 |iter: 47/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.1071e-05 | 5.4694e-03 |         805 |    19600000 |          268 |       49000 |         5.4 |iter: 48/100\r",
      "     2.75 | 4.0850e-05 | 5.4400e-03 |         817 |    20000000 |          272 |       50000 |         5.5 |iter: 49/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.0931e-05 | 5.4706e-03 |         835 |    20400000 |          279 |       51000 |         5.6 |iter: 50/100\r",
      "     2.75 | 4.0673e-05 | 5.4615e-03 |         846 |    20800000 |          284 |       52000 |         5.7 |iter: 51/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.0660e-05 | 5.4717e-03 |         862 |    21200000 |          290 |       53000 |         5.8 |iter: 52/100\r",
      "     2.75 | 4.0602e-05 | 5.5000e-03 |         877 |    21600000 |          297 |       54000 |         5.9 |iter: 53/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.1182e-05 | 5.5273e-03 |         906 |    22000000 |          304 |       55000 |         6.0 |iter: 54/100\r",
      "     2.75 | 4.0670e-05 | 5.5000e-03 |         911 |    22400000 |          308 |       56000 |         6.2 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.0263e-05 | 5.4386e-03 |         918 |    22800000 |          310 |       57000 |         6.3 |iter: 56/100\r",
      "     2.75 | 3.9871e-05 | 5.3966e-03 |         925 |    23200000 |          313 |       58000 |         6.4 |iter: 57/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9831e-05 | 5.3729e-03 |         940 |    23600000 |          317 |       59000 |         6.5 |iter: 58/100\r",
      "     2.75 | 3.9667e-05 | 5.3667e-03 |         952 |    24000000 |          322 |       60000 |         6.6 |iter: 59/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9139e-05 | 5.3279e-03 |         955 |    24400000 |          325 |       61000 |         6.7 |iter: 60/100\r",
      "     2.75 | 3.9073e-05 | 5.2903e-03 |         969 |    24800000 |          328 |       62000 |         6.8 |iter: 61/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9048e-05 | 5.2698e-03 |         984 |    25200000 |          332 |       63000 |         6.9 |iter: 62/100\r",
      "     2.75 | 3.9258e-05 | 5.3281e-03 |        1005 |    25600000 |          341 |       64000 |         7.0 |iter: 63/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9077e-05 | 5.3538e-03 |        1016 |    26000000 |          348 |       65000 |         7.1 |iter: 64/100\r",
      "     2.75 | 3.9432e-05 | 5.3939e-03 |        1041 |    26400000 |          356 |       66000 |         7.3 |iter: 65/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9328e-05 | 5.3731e-03 |        1054 |    26800000 |          360 |       67000 |         7.4 |iter: 66/100\r",
      "     2.75 | 3.9154e-05 | 5.3676e-03 |        1065 |    27200000 |          365 |       68000 |         7.5 |iter: 67/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8949e-05 | 5.4058e-03 |        1075 |    27600000 |          373 |       69000 |         7.6 |iter: 68/100\r",
      "     2.75 | 3.8929e-05 | 5.4143e-03 |        1090 |    28000000 |          379 |       70000 |         7.7 |iter: 69/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8908e-05 | 5.3944e-03 |        1105 |    28400000 |          383 |       71000 |         7.8 |iter: 70/100\r",
      "     2.75 | 3.8681e-05 | 5.4167e-03 |        1114 |    28800000 |          390 |       72000 |         7.9 |iter: 71/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8425e-05 | 5.4110e-03 |        1122 |    29200000 |          395 |       73000 |         8.0 |iter: 72/100\r",
      "     2.75 | 3.8007e-05 | 5.3784e-03 |        1125 |    29600000 |          398 |       74000 |         8.1 |iter: 73/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7700e-05 | 5.3467e-03 |        1131 |    30000000 |          401 |       75000 |         8.2 |iter: 74/100\r",
      "     2.75 | 3.8322e-05 | 5.4079e-03 |        1165 |    30400000 |          411 |       76000 |         8.4 |iter: 75/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8117e-05 | 5.4156e-03 |        1174 |    30800000 |          417 |       77000 |         8.5 |iter: 76/100\r",
      "     2.75 | 3.8205e-05 | 5.4359e-03 |        1192 |    31200000 |          424 |       78000 |         8.6 |iter: 77/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7785e-05 | 5.3924e-03 |        1194 |    31600000 |          426 |       79000 |         8.7 |iter: 78/100\r",
      "     2.75 | 3.8313e-05 | 5.3875e-03 |        1226 |    32000000 |          431 |       80000 |         8.8 |iter: 79/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7994e-05 | 5.3704e-03 |        1231 |    32400000 |          435 |       81000 |         8.9 |iter: 80/100\r",
      "     2.75 | 3.8537e-05 | 5.4146e-03 |        1264 |    32800000 |          444 |       82000 |         9.0 |iter: 81/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8464e-05 | 5.4096e-03 |        1277 |    33200000 |          449 |       83000 |         9.1 |iter: 82/100\r",
      "     2.75 | 3.8125e-05 | 5.3810e-03 |        1281 |    33600000 |          452 |       84000 |         9.2 |iter: 83/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8147e-05 | 5.3765e-03 |        1297 |    34000000 |          457 |       85000 |         9.3 |iter: 84/100\r",
      "     2.75 | 3.7878e-05 | 5.3488e-03 |        1303 |    34400000 |          460 |       86000 |         9.5 |iter: 85/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7672e-05 | 5.3333e-03 |        1311 |    34800000 |          464 |       87000 |         9.6 |iter: 86/100\r",
      "     2.75 | 3.8097e-05 | 5.3750e-03 |        1341 |    35200000 |          473 |       88000 |         9.7 |iter: 87/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7809e-05 | 5.3708e-03 |        1346 |    35600000 |          478 |       89000 |         9.8 |iter: 88/100\r",
      "     2.75 | 3.7528e-05 | 5.3222e-03 |        1351 |    36000000 |          479 |       90000 |         9.9 |iter: 89/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7280e-05 | 5.2967e-03 |        1357 |    36400000 |          482 |       91000 |        10.0 |iter: 90/100\r",
      "     2.75 | 3.7609e-05 | 5.2609e-03 |        1384 |    36800000 |          484 |       92000 |        10.1 |iter: 91/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7500e-05 | 5.2258e-03 |        1395 |    37200000 |          486 |       93000 |        10.2 |iter: 92/100\r",
      "     2.75 | 3.8005e-05 | 5.2766e-03 |        1429 |    37600000 |          496 |       94000 |        10.3 |iter: 93/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8526e-05 | 5.3158e-03 |        1464 |    38000000 |          505 |       95000 |        10.4 |iter: 94/100\r",
      "     2.75 | 3.9609e-05 | 5.3021e-03 |        1521 |    38400000 |          509 |       96000 |        10.6 |iter: 95/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9201e-05 | 5.2474e-03 |        1521 |    38800000 |          509 |       97000 |        10.7 |iter: 96/100\r",
      "     2.75 | 3.9337e-05 | 5.2347e-03 |        1542 |    39200000 |          513 |       98000 |        10.8 |iter: 97/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9268e-05 | 5.2424e-03 |        1555 |    39600000 |          519 |       99000 |        10.9 |iter: 98/100\r",
      "     2.75 | 3.8975e-05 | 5.2000e-03 |        1559 |    40000000 |          520 |      100000 |        11.0 |iter: 99/100\r",
      "     2.75 | 3.8975e-05 | 5.2000e-03 |        1559 |    40000000 |          520 |      100000 |        11.0 |reached max iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 0.0000e+00 | 0.0000e+00 |           0 |      400000 |            0 |        1000 |         0.1 |iter: 0/100\r",
      "      3.0 | 1.2500e-06 | 5.0000e-04 |           1 |      800000 |            1 |        2000 |         0.2 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 8.3333e-07 | 3.3333e-04 |           1 |     1200000 |            1 |        3000 |         0.3 |iter: 2/100\r",
      "      3.0 | 1.8750e-06 | 7.5000e-04 |           3 |     1600000 |            3 |        4000 |         0.4 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 1.5000e-06 | 6.0000e-04 |           3 |     2000000 |            3 |        5000 |         0.5 |iter: 4/100\r",
      "      3.0 | 2.5000e-06 | 6.6667e-04 |           6 |     2400000 |            4 |        6000 |         0.7 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.5000e-06 | 7.1429e-04 |           7 |     2800000 |            5 |        7000 |         0.8 |iter: 6/100\r",
      "      3.0 | 2.5000e-06 | 7.5000e-04 |           8 |     3200000 |            6 |        8000 |         0.9 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.0556e-06 | 7.7778e-04 |          11 |     3600000 |            7 |        9000 |         1.0 |iter: 8/100\r",
      "      3.0 | 3.0000e-06 | 8.0000e-04 |          12 |     4000000 |            8 |       10000 |         1.1 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.7273e-06 | 7.2727e-04 |          12 |     4400000 |            8 |       11000 |         1.2 |iter: 10/100\r",
      "      3.0 | 2.7083e-06 | 7.5000e-04 |          13 |     4800000 |            9 |       12000 |         1.3 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.5000e-06 | 6.9231e-04 |          13 |     5200000 |            9 |       13000 |         1.4 |iter: 12/100\r",
      "      3.0 | 5.1786e-06 | 9.2857e-04 |          29 |     5600000 |           13 |       14000 |         1.5 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.8333e-06 | 8.6667e-04 |          29 |     6000000 |           13 |       15000 |         1.6 |iter: 14/100\r",
      "      3.0 | 4.5313e-06 | 8.1250e-04 |          29 |     6400000 |           13 |       16000 |         1.8 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.2647e-06 | 7.6471e-04 |          29 |     6800000 |           13 |       17000 |         1.9 |iter: 16/100\r",
      "      3.0 | 4.0278e-06 | 7.2222e-04 |          29 |     7200000 |           13 |       18000 |         2.0 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.0789e-06 | 7.8947e-04 |          31 |     7600000 |           15 |       19000 |         2.1 |iter: 18/100\r",
      "      3.0 | 3.8750e-06 | 7.5000e-04 |          31 |     8000000 |           15 |       20000 |         2.2 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.9286e-06 | 7.6190e-04 |          33 |     8400000 |           16 |       21000 |         2.3 |iter: 20/100\r",
      "      3.0 | 3.9773e-06 | 8.1818e-04 |          35 |     8800000 |           18 |       22000 |         2.4 |iter: 21/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.1304e-06 | 8.6957e-04 |          38 |     9200000 |           20 |       23000 |         2.5 |iter: 22/100\r",
      "      3.0 | 5.0000e-06 | 9.1667e-04 |          48 |     9600000 |           22 |       24000 |         2.6 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.9000e-06 | 9.2000e-04 |          49 |    10000000 |           23 |       25000 |         2.7 |iter: 24/100\r",
      "      3.0 | 4.7115e-06 | 8.8462e-04 |          49 |    10400000 |           23 |       26000 |         2.9 |iter: 25/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.5370e-06 | 8.5185e-04 |          49 |    10800000 |           23 |       27000 |         3.0 |iter: 26/100\r",
      "      3.0 | 4.3750e-06 | 8.2143e-04 |          49 |    11200000 |           23 |       28000 |         3.1 |iter: 27/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.2241e-06 | 7.9310e-04 |          49 |    11600000 |           23 |       29000 |         3.2 |iter: 28/100\r",
      "      3.0 | 5.5000e-06 | 8.6667e-04 |          66 |    12000000 |           26 |       30000 |         3.3 |iter: 29/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.3226e-06 | 8.3871e-04 |          66 |    12400000 |           26 |       31000 |         3.4 |iter: 30/100\r",
      "      3.0 | 5.2344e-06 | 8.4375e-04 |          67 |    12800000 |           27 |       32000 |         3.5 |iter: 31/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.0758e-06 | 8.1818e-04 |          67 |    13200000 |           27 |       33000 |         3.6 |iter: 32/100\r",
      "      3.0 | 5.2941e-06 | 8.2353e-04 |          72 |    13600000 |           28 |       34000 |         3.7 |iter: 33/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.1429e-06 | 8.0000e-04 |          72 |    14000000 |           28 |       35000 |         3.8 |iter: 34/100\r",
      "      3.0 | 5.0000e-06 | 7.7778e-04 |          72 |    14400000 |           28 |       36000 |         4.0 |iter: 35/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.8649e-06 | 7.5676e-04 |          72 |    14800000 |           28 |       37000 |         4.1 |iter: 36/100\r",
      "      3.0 | 4.7368e-06 | 7.3684e-04 |          72 |    15200000 |           28 |       38000 |         4.2 |iter: 37/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.7436e-06 | 7.4359e-04 |          74 |    15600000 |           29 |       39000 |         4.3 |iter: 38/100\r",
      "      3.0 | 4.6250e-06 | 7.2500e-04 |          74 |    16000000 |           29 |       40000 |         4.4 |iter: 39/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.6341e-06 | 7.3171e-04 |          76 |    16400000 |           30 |       41000 |         4.5 |iter: 40/100\r",
      "      3.0 | 4.5238e-06 | 7.1429e-04 |          76 |    16800000 |           30 |       42000 |         4.6 |iter: 41/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.4186e-06 | 6.9767e-04 |          76 |    17200000 |           30 |       43000 |         4.7 |iter: 42/100\r",
      "      3.0 | 5.0568e-06 | 7.2727e-04 |          89 |    17600000 |           32 |       44000 |         4.8 |iter: 43/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.9444e-06 | 7.1111e-04 |          89 |    18000000 |           32 |       45000 |         4.9 |iter: 44/100\r",
      "      3.0 | 4.9457e-06 | 7.3913e-04 |          91 |    18400000 |           34 |       46000 |         5.1 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.8404e-06 | 7.2340e-04 |          91 |    18800000 |           34 |       47000 |         5.2 |iter: 46/100\r",
      "      3.0 | 4.8437e-06 | 7.2917e-04 |          93 |    19200000 |           35 |       48000 |         5.3 |iter: 47/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.8469e-06 | 7.5510e-04 |          95 |    19600000 |           37 |       49000 |         5.4 |iter: 48/100\r",
      "      3.0 | 5.0500e-06 | 8.0000e-04 |         101 |    20000000 |           40 |       50000 |         5.5 |iter: 49/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.0000e-06 | 8.0392e-04 |         102 |    20400000 |           41 |       51000 |         5.6 |iter: 50/100\r",
      "      3.0 | 4.9519e-06 | 8.0769e-04 |         103 |    20800000 |           42 |       52000 |         5.7 |iter: 51/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.9057e-06 | 8.1132e-04 |         104 |    21200000 |           43 |       53000 |         5.8 |iter: 52/100\r",
      "      3.0 | 5.0463e-06 | 8.1481e-04 |         109 |    21600000 |           44 |       54000 |         5.9 |iter: 53/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.3182e-06 | 8.3636e-04 |         117 |    22000000 |           46 |       55000 |         6.0 |iter: 54/100\r",
      "      3.0 | 5.3571e-06 | 8.5714e-04 |         120 |    22400000 |           48 |       56000 |         6.2 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.3509e-06 | 8.5965e-04 |         122 |    22800000 |           49 |       57000 |         6.3 |iter: 56/100\r",
      "      3.0 | 5.4741e-06 | 8.6207e-04 |         127 |    23200000 |           50 |       58000 |         6.4 |iter: 57/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.3814e-06 | 8.4746e-04 |         127 |    23600000 |           50 |       59000 |         6.5 |iter: 58/100\r",
      "      3.0 | 5.5417e-06 | 8.6667e-04 |         133 |    24000000 |           52 |       60000 |         6.6 |iter: 59/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.5738e-06 | 9.0164e-04 |         136 |    24400000 |           55 |       61000 |         6.7 |iter: 60/100\r",
      "      3.0 | 5.6048e-06 | 9.0323e-04 |         139 |    24800000 |           56 |       62000 |         6.8 |iter: 61/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.5159e-06 | 8.8889e-04 |         139 |    25200000 |           56 |       63000 |         6.9 |iter: 62/100\r",
      "      3.0 | 5.9375e-06 | 9.0625e-04 |         152 |    25600000 |           58 |       64000 |         7.0 |iter: 63/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 6.0769e-06 | 9.2308e-04 |         158 |    26000000 |           60 |       65000 |         7.1 |iter: 64/100\r",
      "      3.0 | 6.0227e-06 | 9.2424e-04 |         159 |    26400000 |           61 |       66000 |         7.3 |iter: 65/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.9328e-06 | 9.1045e-04 |         159 |    26800000 |           61 |       67000 |         7.4 |iter: 66/100\r",
      "      3.0 | 6.1029e-06 | 9.5588e-04 |         166 |    27200000 |           65 |       68000 |         7.5 |iter: 67/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 6.0145e-06 | 9.4203e-04 |         166 |    27600000 |           65 |       69000 |         7.6 |iter: 68/100\r",
      "      3.0 | 5.9286e-06 | 9.2857e-04 |         166 |    28000000 |           65 |       70000 |         7.7 |iter: 69/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.8803e-06 | 9.2958e-04 |         167 |    28400000 |           66 |       71000 |         7.8 |iter: 70/100\r",
      "      3.0 | 6.0417e-06 | 9.4444e-04 |         174 |    28800000 |           68 |       72000 |         7.9 |iter: 71/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.9589e-06 | 9.3151e-04 |         174 |    29200000 |           68 |       73000 |         8.0 |iter: 72/100\r",
      "      3.0 | 6.0811e-06 | 9.5946e-04 |         180 |    29600000 |           71 |       74000 |         8.1 |iter: 73/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 6.0000e-06 | 9.4667e-04 |         180 |    30000000 |           71 |       75000 |         8.2 |iter: 74/100\r",
      "      3.0 | 6.1842e-06 | 9.7368e-04 |         188 |    30400000 |           74 |       76000 |         8.4 |iter: 75/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 6.1364e-06 | 9.7403e-04 |         189 |    30800000 |           75 |       77000 |         8.5 |iter: 76/100\r",
      "      3.0 | 6.0577e-06 | 9.6154e-04 |         189 |    31200000 |           75 |       78000 |         8.6 |iter: 77/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 6.0443e-06 | 9.6203e-04 |         191 |    31600000 |           76 |       79000 |         8.7 |iter: 78/100\r",
      "      3.0 | 6.0000e-06 | 9.6250e-04 |         192 |    32000000 |           77 |       80000 |         8.8 |iter: 79/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.9259e-06 | 9.5062e-04 |         192 |    32400000 |           77 |       81000 |         8.9 |iter: 80/100\r",
      "      3.0 | 5.8537e-06 | 9.3902e-04 |         192 |    32800000 |           77 |       82000 |         9.0 |iter: 81/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.7831e-06 | 9.2771e-04 |         192 |    33200000 |           77 |       83000 |         9.1 |iter: 82/100\r",
      "      3.0 | 5.7143e-06 | 9.1667e-04 |         192 |    33600000 |           77 |       84000 |         9.2 |iter: 83/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.6471e-06 | 9.0588e-04 |         192 |    34000000 |           77 |       85000 |         9.3 |iter: 84/100\r",
      "      3.0 | 5.5814e-06 | 8.9535e-04 |         192 |    34400000 |           77 |       86000 |         9.5 |iter: 85/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.5460e-06 | 8.9655e-04 |         193 |    34800000 |           78 |       87000 |         9.6 |iter: 86/100\r",
      "      3.0 | 5.6250e-06 | 9.0909e-04 |         198 |    35200000 |           80 |       88000 |         9.7 |iter: 87/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.5618e-06 | 8.9888e-04 |         198 |    35600000 |           80 |       89000 |         9.8 |iter: 88/100\r",
      "      3.0 | 5.6111e-06 | 9.1111e-04 |         202 |    36000000 |           82 |       90000 |         9.9 |iter: 89/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.8516e-06 | 9.3407e-04 |         213 |    36400000 |           85 |       91000 |        10.0 |iter: 90/100\r",
      "      3.0 | 5.7880e-06 | 9.2391e-04 |         213 |    36800000 |           85 |       92000 |        10.1 |iter: 91/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.7258e-06 | 9.1398e-04 |         213 |    37200000 |           85 |       93000 |        10.2 |iter: 92/100\r",
      "      3.0 | 5.6649e-06 | 9.0426e-04 |         213 |    37600000 |           85 |       94000 |        10.3 |iter: 93/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.6579e-06 | 9.1579e-04 |         215 |    38000000 |           87 |       95000 |        10.4 |iter: 94/100\r",
      "      3.0 | 5.5990e-06 | 9.0625e-04 |         215 |    38400000 |           87 |       96000 |        10.6 |iter: 95/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.5412e-06 | 8.9691e-04 |         215 |    38800000 |           87 |       97000 |        10.7 |iter: 96/100\r",
      "      3.0 | 5.5102e-06 | 8.9796e-04 |         216 |    39200000 |           88 |       98000 |        10.8 |iter: 97/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.6061e-06 | 9.0909e-04 |         222 |    39600000 |           90 |       99000 |        10.9 |iter: 98/100\r",
      "      3.0 | 5.6750e-06 | 9.4000e-04 |         227 |    40000000 |           94 |      100000 |        11.0 |iter: 99/100\r",
      "      3.0 | 5.6750e-06 | 9.4000e-04 |         227 |    40000000 |           94 |      100000 |        11.0 |reached max iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |      400000 |            0 |        1000 |         0.1 |iter: 0/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |      800000 |            0 |        2000 |         0.2 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     1200000 |            0 |        3000 |         0.3 |iter: 2/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     1600000 |            0 |        4000 |         0.4 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     2000000 |            0 |        5000 |         0.6 |iter: 4/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     2400000 |            0 |        6000 |         0.7 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     2800000 |            0 |        7000 |         0.8 |iter: 6/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     3200000 |            0 |        8000 |         0.9 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     3600000 |            0 |        9000 |         1.0 |iter: 8/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     4000000 |            0 |       10000 |         1.1 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     4400000 |            0 |       11000 |         1.2 |iter: 10/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     4800000 |            0 |       12000 |         1.3 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     5200000 |            0 |       13000 |         1.4 |iter: 12/100\r",
      "     3.25 | 7.1429e-07 | 7.1429e-05 |           4 |     5600000 |            1 |       14000 |         1.5 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 6.6667e-07 | 6.6667e-05 |           4 |     6000000 |            1 |       15000 |         1.7 |iter: 14/100\r",
      "     3.25 | 6.2500e-07 | 6.2500e-05 |           4 |     6400000 |            1 |       16000 |         1.8 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 5.8824e-07 | 5.8824e-05 |           4 |     6800000 |            1 |       17000 |         1.9 |iter: 16/100\r",
      "     3.25 | 5.5556e-07 | 5.5556e-05 |           4 |     7200000 |            1 |       18000 |         2.0 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 5.2632e-07 | 5.2632e-05 |           4 |     7600000 |            1 |       19000 |         2.1 |iter: 18/100\r",
      "     3.25 | 6.2500e-07 | 1.0000e-04 |           5 |     8000000 |            2 |       20000 |         2.2 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 5.9524e-07 | 9.5238e-05 |           5 |     8400000 |            2 |       21000 |         2.3 |iter: 20/100\r",
      "     3.25 | 5.6818e-07 | 9.0909e-05 |           5 |     8800000 |            2 |       22000 |         2.4 |iter: 21/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 5.4348e-07 | 8.6957e-05 |           5 |     9200000 |            2 |       23000 |         2.5 |iter: 22/100\r",
      "     3.25 | 5.2083e-07 | 8.3333e-05 |           5 |     9600000 |            2 |       24000 |         2.7 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 5.0000e-07 | 8.0000e-05 |           5 |    10000000 |            2 |       25000 |         2.8 |iter: 24/100\r",
      "     3.25 | 4.8077e-07 | 7.6923e-05 |           5 |    10400000 |            2 |       26000 |         2.9 |iter: 25/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.6296e-07 | 7.4074e-05 |           5 |    10800000 |            2 |       27000 |         3.0 |iter: 26/100\r",
      "     3.25 | 4.4643e-07 | 7.1429e-05 |           5 |    11200000 |            2 |       28000 |         3.1 |iter: 27/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.3103e-07 | 6.8966e-05 |           5 |    11600000 |            2 |       29000 |         3.2 |iter: 28/100\r",
      "     3.25 | 4.1667e-07 | 6.6667e-05 |           5 |    12000000 |            2 |       30000 |         3.3 |iter: 29/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.0323e-07 | 6.4516e-05 |           5 |    12400000 |            2 |       31000 |         3.5 |iter: 30/100\r",
      "     3.25 | 3.9063e-07 | 6.2500e-05 |           5 |    12800000 |            2 |       32000 |         3.6 |iter: 31/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.7879e-07 | 6.0606e-05 |           5 |    13200000 |            2 |       33000 |         3.8 |iter: 32/100\r",
      "     3.25 | 3.6765e-07 | 5.8824e-05 |           5 |    13600000 |            2 |       34000 |         3.9 |iter: 33/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.5714e-07 | 5.7143e-05 |           5 |    14000000 |            2 |       35000 |         4.1 |iter: 34/100\r",
      "     3.25 | 4.1667e-07 | 8.3333e-05 |           6 |    14400000 |            3 |       36000 |         4.2 |iter: 35/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.0541e-07 | 8.1081e-05 |           6 |    14800000 |            3 |       37000 |         4.4 |iter: 36/100\r",
      "     3.25 | 3.9474e-07 | 7.8947e-05 |           6 |    15200000 |            3 |       38000 |         4.5 |iter: 37/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.8462e-07 | 7.6923e-05 |           6 |    15600000 |            3 |       39000 |         4.7 |iter: 38/100\r",
      "     3.25 | 3.7500e-07 | 7.5000e-05 |           6 |    16000000 |            3 |       40000 |         4.8 |iter: 39/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.6585e-07 | 7.3171e-05 |           6 |    16400000 |            3 |       41000 |         5.0 |iter: 40/100\r",
      "     3.25 | 4.1667e-07 | 9.5238e-05 |           7 |    16800000 |            4 |       42000 |         5.1 |iter: 41/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.0698e-07 | 9.3023e-05 |           7 |    17200000 |            4 |       43000 |         5.3 |iter: 42/100\r",
      "     3.25 | 3.9773e-07 | 9.0909e-05 |           7 |    17600000 |            4 |       44000 |         5.4 |iter: 43/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.8889e-07 | 8.8889e-05 |           7 |    18000000 |            4 |       45000 |         5.5 |iter: 44/100\r",
      "     3.25 | 3.8043e-07 | 8.6957e-05 |           7 |    18400000 |            4 |       46000 |         5.7 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.7234e-07 | 8.5106e-05 |           7 |    18800000 |            4 |       47000 |         5.8 |iter: 46/100\r",
      "     3.25 | 3.6458e-07 | 8.3333e-05 |           7 |    19200000 |            4 |       48000 |         6.0 |iter: 47/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.5714e-07 | 8.1633e-05 |           7 |    19600000 |            4 |       49000 |         6.1 |iter: 48/100\r",
      "     3.25 | 3.5000e-07 | 8.0000e-05 |           7 |    20000000 |            4 |       50000 |         6.3 |iter: 49/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.4314e-07 | 7.8431e-05 |           7 |    20400000 |            4 |       51000 |         6.4 |iter: 50/100\r",
      "     3.25 | 3.3654e-07 | 7.6923e-05 |           7 |    20800000 |            4 |       52000 |         6.6 |iter: 51/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.3019e-07 | 7.5472e-05 |           7 |    21200000 |            4 |       53000 |         6.7 |iter: 52/100\r",
      "     3.25 | 3.2407e-07 | 7.4074e-05 |           7 |    21600000 |            4 |       54000 |         6.9 |iter: 53/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.5455e-07 | 9.0909e-05 |          10 |    22000000 |            5 |       55000 |         7.0 |iter: 54/100\r",
      "     3.25 | 4.4643e-07 | 8.9286e-05 |          10 |    22400000 |            5 |       56000 |         7.2 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.3860e-07 | 8.7719e-05 |          10 |    22800000 |            5 |       57000 |         7.3 |iter: 56/100\r",
      "     3.25 | 4.3103e-07 | 8.6207e-05 |          10 |    23200000 |            5 |       58000 |         7.4 |iter: 57/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.2373e-07 | 8.4746e-05 |          10 |    23600000 |            5 |       59000 |         7.6 |iter: 58/100\r",
      "     3.25 | 4.5833e-07 | 1.0000e-04 |          11 |    24000000 |            6 |       60000 |         7.7 |iter: 59/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.5082e-07 | 9.8361e-05 |          11 |    24400000 |            6 |       61000 |         7.9 |iter: 60/100\r",
      "     3.25 | 4.4355e-07 | 9.6774e-05 |          11 |    24800000 |            6 |       62000 |         8.0 |iter: 61/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.3651e-07 | 9.5238e-05 |          11 |    25200000 |            6 |       63000 |         8.2 |iter: 62/100\r",
      "     3.25 | 4.2969e-07 | 9.3750e-05 |          11 |    25600000 |            6 |       64000 |         8.3 |iter: 63/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.2308e-07 | 9.2308e-05 |          11 |    26000000 |            6 |       65000 |         8.5 |iter: 64/100\r",
      "     3.25 | 4.5455e-07 | 1.0606e-04 |          12 |    26400000 |            7 |       66000 |         8.6 |iter: 65/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.4776e-07 | 1.0448e-04 |          12 |    26800000 |            7 |       67000 |         8.8 |iter: 66/100\r",
      "     3.25 | 4.4118e-07 | 1.0294e-04 |          12 |    27200000 |            7 |       68000 |         8.9 |iter: 67/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.3478e-07 | 1.0145e-04 |          12 |    27600000 |            7 |       69000 |         9.0 |iter: 68/100\r",
      "     3.25 | 4.2857e-07 | 1.0000e-04 |          12 |    28000000 |            7 |       70000 |         9.2 |iter: 69/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.2254e-07 | 9.8592e-05 |          12 |    28400000 |            7 |       71000 |         9.3 |iter: 70/100\r",
      "     3.25 | 4.1667e-07 | 9.7222e-05 |          12 |    28800000 |            7 |       72000 |         9.5 |iter: 71/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.1096e-07 | 9.5890e-05 |          12 |    29200000 |            7 |       73000 |         9.6 |iter: 72/100\r",
      "     3.25 | 4.0541e-07 | 9.4595e-05 |          12 |    29600000 |            7 |       74000 |         9.8 |iter: 73/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.0000e-07 | 9.3333e-05 |          12 |    30000000 |            7 |       75000 |         9.9 |iter: 74/100\r",
      "     3.25 | 3.9474e-07 | 9.2105e-05 |          12 |    30400000 |            7 |       76000 |        10.1 |iter: 75/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.8961e-07 | 9.0909e-05 |          12 |    30800000 |            7 |       77000 |        10.2 |iter: 76/100\r",
      "     3.25 | 3.8462e-07 | 8.9744e-05 |          12 |    31200000 |            7 |       78000 |        10.3 |iter: 77/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.7975e-07 | 8.8608e-05 |          12 |    31600000 |            7 |       79000 |        10.5 |iter: 78/100\r",
      "     3.25 | 4.0625e-07 | 1.0000e-04 |          13 |    32000000 |            8 |       80000 |        10.6 |iter: 79/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.0123e-07 | 9.8765e-05 |          13 |    32400000 |            8 |       81000 |        10.8 |iter: 80/100\r",
      "     3.25 | 3.9634e-07 | 9.7561e-05 |          13 |    32800000 |            8 |       82000 |        10.9 |iter: 81/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 3.9157e-07 | 9.6386e-05 |          13 |    33200000 |            8 |       83000 |        11.1 |iter: 82/100\r",
      "     3.25 | 4.1667e-07 | 1.0714e-04 |          14 |    33600000 |            9 |       84000 |        11.2 |iter: 83/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.1176e-07 | 1.0588e-04 |          14 |    34000000 |            9 |       85000 |        11.4 |iter: 84/100\r",
      "     3.25 | 4.0698e-07 | 1.0465e-04 |          14 |    34400000 |            9 |       86000 |        11.5 |iter: 85/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 4.0230e-07 | 1.0345e-04 |          14 |    34800000 |            9 |       87000 |        11.6 |iter: 86/100\r",
      "     3.25 | 3.9773e-07 | 1.0227e-04 |          14 |    35200000 |            9 |       88000 |        11.8 |iter: 87/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 5.3371e-07 | 1.1236e-04 |          19 |    35600000 |           10 |       89000 |        11.9 |iter: 88/100\r",
      "     3.25 | 5.5556e-07 | 1.2222e-04 |          20 |    36000000 |           11 |       90000 |        12.0 |iter: 89/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 5.4945e-07 | 1.2088e-04 |          20 |    36400000 |           11 |       91000 |        12.1 |iter: 90/100\r",
      "     3.25 | 5.7065e-07 | 1.3043e-04 |          21 |    36800000 |           12 |       92000 |        12.3 |iter: 91/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 5.6452e-07 | 1.2903e-04 |          21 |    37200000 |           12 |       93000 |        12.4 |iter: 92/100\r",
      "     3.25 | 6.9149e-07 | 1.3830e-04 |          26 |    37600000 |           13 |       94000 |        12.5 |iter: 93/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 6.8421e-07 | 1.3684e-04 |          26 |    38000000 |           13 |       95000 |        12.7 |iter: 94/100\r",
      "     3.25 | 6.7708e-07 | 1.3542e-04 |          26 |    38400000 |           13 |       96000 |        12.8 |iter: 95/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 6.7010e-07 | 1.3402e-04 |          26 |    38800000 |           13 |       97000 |        12.9 |iter: 96/100\r",
      "     3.25 | 6.6327e-07 | 1.3265e-04 |          26 |    39200000 |           13 |       98000 |        13.0 |iter: 97/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 6.8182e-07 | 1.4141e-04 |          27 |    39600000 |           14 |       99000 |        13.2 |iter: 98/100\r",
      "     3.25 | 6.7500e-07 | 1.4000e-04 |          27 |    40000000 |           14 |      100000 |        13.3 |iter: 99/100\r",
      "     3.25 | 6.7500e-07 | 1.4000e-04 |          27 |    40000000 |           14 |      100000 |        13.3 |reached max iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 0.0000e+00 | 0.0000e+00 |           0 |      400000 |            0 |        1000 |         0.1 |iter: 0/100\r",
      "      3.5 | 0.0000e+00 | 0.0000e+00 |           0 |      800000 |            0 |        2000 |         0.3 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 0.0000e+00 | 0.0000e+00 |           0 |     1200000 |            0 |        3000 |         0.4 |iter: 2/100\r",
      "      3.5 | 3.7500e-06 | 2.5000e-04 |           6 |     1600000 |            1 |        4000 |         0.5 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.0000e-06 | 2.0000e-04 |           6 |     2000000 |            1 |        5000 |         0.6 |iter: 4/100\r",
      "      3.5 | 2.5000e-06 | 1.6667e-04 |           6 |     2400000 |            1 |        6000 |         0.8 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 2.1429e-06 | 1.4286e-04 |           6 |     2800000 |            1 |        7000 |         0.9 |iter: 6/100\r",
      "      3.5 | 1.8750e-06 | 1.2500e-04 |           6 |     3200000 |            1 |        8000 |         1.0 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.3333e-06 | 2.2222e-04 |          12 |     3600000 |            2 |        9000 |         1.1 |iter: 8/100\r",
      "      3.5 | 3.0000e-06 | 2.0000e-04 |          12 |     4000000 |            2 |       10000 |         1.3 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 2.7273e-06 | 1.8182e-04 |          12 |     4400000 |            2 |       11000 |         1.4 |iter: 10/100\r",
      "      3.5 | 2.5000e-06 | 1.6667e-04 |          12 |     4800000 |            2 |       12000 |         1.5 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 2.3077e-06 | 1.5385e-04 |          12 |     5200000 |            2 |       13000 |         1.6 |iter: 12/100\r",
      "      3.5 | 2.1429e-06 | 1.4286e-04 |          12 |     5600000 |            2 |       14000 |         1.8 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 2.0000e-06 | 1.3333e-04 |          12 |     6000000 |            2 |       15000 |         1.9 |iter: 14/100\r",
      "      3.5 | 1.8750e-06 | 1.2500e-04 |          12 |     6400000 |            2 |       16000 |         2.0 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.7647e-06 | 1.1765e-04 |          12 |     6800000 |            2 |       17000 |         2.2 |iter: 16/100\r",
      "      3.5 | 1.6667e-06 | 1.1111e-04 |          12 |     7200000 |            2 |       18000 |         2.3 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.5789e-06 | 1.0526e-04 |          12 |     7600000 |            2 |       19000 |         2.4 |iter: 18/100\r",
      "      3.5 | 1.5000e-06 | 1.0000e-04 |          12 |     8000000 |            2 |       20000 |         2.5 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.4286e-06 | 9.5238e-05 |          12 |     8400000 |            2 |       21000 |         2.7 |iter: 20/100\r",
      "      3.5 | 1.3636e-06 | 9.0909e-05 |          12 |     8800000 |            2 |       22000 |         2.8 |iter: 21/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.3043e-06 | 8.6957e-05 |          12 |     9200000 |            2 |       23000 |         2.9 |iter: 22/100\r",
      "      3.5 | 1.2500e-06 | 8.3333e-05 |          12 |     9600000 |            2 |       24000 |         3.0 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.2000e-06 | 8.0000e-05 |          12 |    10000000 |            2 |       25000 |         3.2 |iter: 24/100\r",
      "      3.5 | 1.3462e-06 | 1.1538e-04 |          14 |    10400000 |            3 |       26000 |         3.3 |iter: 25/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.2963e-06 | 1.1111e-04 |          14 |    10800000 |            3 |       27000 |         3.4 |iter: 26/100\r",
      "      3.5 | 1.2500e-06 | 1.0714e-04 |          14 |    11200000 |            3 |       28000 |         3.5 |iter: 27/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.2069e-06 | 1.0345e-04 |          14 |    11600000 |            3 |       29000 |         3.7 |iter: 28/100\r",
      "      3.5 | 1.1667e-06 | 1.0000e-04 |          14 |    12000000 |            3 |       30000 |         3.8 |iter: 29/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.1290e-06 | 9.6774e-05 |          14 |    12400000 |            3 |       31000 |         3.9 |iter: 30/100\r",
      "      3.5 | 1.0937e-06 | 9.3750e-05 |          14 |    12800000 |            3 |       32000 |         4.0 |iter: 31/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.0606e-06 | 9.0909e-05 |          14 |    13200000 |            3 |       33000 |         4.1 |iter: 32/100\r",
      "      3.5 | 1.0294e-06 | 8.8235e-05 |          14 |    13600000 |            3 |       34000 |         4.2 |iter: 33/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.0000e-06 | 8.5714e-05 |          14 |    14000000 |            3 |       35000 |         4.4 |iter: 34/100\r",
      "      3.5 | 9.7222e-07 | 8.3333e-05 |          14 |    14400000 |            3 |       36000 |         4.5 |iter: 35/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 9.4595e-07 | 8.1081e-05 |          14 |    14800000 |            3 |       37000 |         4.6 |iter: 36/100\r",
      "      3.5 | 9.2105e-07 | 7.8947e-05 |          14 |    15200000 |            3 |       38000 |         4.7 |iter: 37/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 8.9744e-07 | 7.6923e-05 |          14 |    15600000 |            3 |       39000 |         4.8 |iter: 38/100\r",
      "      3.5 | 8.7500e-07 | 7.5000e-05 |          14 |    16000000 |            3 |       40000 |         4.9 |iter: 39/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 8.5366e-07 | 7.3171e-05 |          14 |    16400000 |            3 |       41000 |         5.1 |iter: 40/100\r",
      "      3.5 | 8.3333e-07 | 7.1429e-05 |          14 |    16800000 |            3 |       42000 |         5.2 |iter: 41/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 8.1395e-07 | 6.9767e-05 |          14 |    17200000 |            3 |       43000 |         5.3 |iter: 42/100\r",
      "      3.5 | 7.9545e-07 | 6.8182e-05 |          14 |    17600000 |            3 |       44000 |         5.4 |iter: 43/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 7.7778e-07 | 6.6667e-05 |          14 |    18000000 |            3 |       45000 |         5.5 |iter: 44/100\r",
      "      3.5 | 7.6087e-07 | 6.5217e-05 |          14 |    18400000 |            3 |       46000 |         5.6 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 7.4468e-07 | 6.3830e-05 |          14 |    18800000 |            3 |       47000 |         5.8 |iter: 46/100\r",
      "      3.5 | 7.2917e-07 | 6.2500e-05 |          14 |    19200000 |            3 |       48000 |         5.9 |iter: 47/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 7.1429e-07 | 6.1224e-05 |          14 |    19600000 |            3 |       49000 |         6.0 |iter: 48/100\r",
      "      3.5 | 7.0000e-07 | 6.0000e-05 |          14 |    20000000 |            3 |       50000 |         6.1 |iter: 49/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 6.8627e-07 | 5.8824e-05 |          14 |    20400000 |            3 |       51000 |         6.2 |iter: 50/100\r",
      "      3.5 | 6.7308e-07 | 5.7692e-05 |          14 |    20800000 |            3 |       52000 |         6.3 |iter: 51/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 6.6038e-07 | 5.6604e-05 |          14 |    21200000 |            3 |       53000 |         6.5 |iter: 52/100\r",
      "      3.5 | 6.4815e-07 | 5.5556e-05 |          14 |    21600000 |            3 |       54000 |         6.6 |iter: 53/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 6.3636e-07 | 5.4545e-05 |          14 |    22000000 |            3 |       55000 |         6.7 |iter: 54/100\r",
      "      3.5 | 6.2500e-07 | 5.3571e-05 |          14 |    22400000 |            3 |       56000 |         6.8 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 6.1404e-07 | 5.2632e-05 |          14 |    22800000 |            3 |       57000 |         6.9 |iter: 56/100\r",
      "      3.5 | 6.0345e-07 | 5.1724e-05 |          14 |    23200000 |            3 |       58000 |         7.0 |iter: 57/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.9322e-07 | 5.0847e-05 |          14 |    23600000 |            3 |       59000 |         7.2 |iter: 58/100\r",
      "      3.5 | 5.8333e-07 | 5.0000e-05 |          14 |    24000000 |            3 |       60000 |         7.3 |iter: 59/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.7377e-07 | 4.9180e-05 |          14 |    24400000 |            3 |       61000 |         7.4 |iter: 60/100\r",
      "      3.5 | 5.6452e-07 | 4.8387e-05 |          14 |    24800000 |            3 |       62000 |         7.5 |iter: 61/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.5556e-07 | 4.7619e-05 |          14 |    25200000 |            3 |       63000 |         7.6 |iter: 62/100\r",
      "      3.5 | 5.4687e-07 | 4.6875e-05 |          14 |    25600000 |            3 |       64000 |         7.7 |iter: 63/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.3846e-07 | 4.6154e-05 |          14 |    26000000 |            3 |       65000 |         7.9 |iter: 64/100\r",
      "      3.5 | 5.3030e-07 | 4.5455e-05 |          14 |    26400000 |            3 |       66000 |         8.0 |iter: 65/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.2239e-07 | 4.4776e-05 |          14 |    26800000 |            3 |       67000 |         8.1 |iter: 66/100\r",
      "      3.5 | 5.1471e-07 | 4.4118e-05 |          14 |    27200000 |            3 |       68000 |         8.2 |iter: 67/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.0725e-07 | 4.3478e-05 |          14 |    27600000 |            3 |       69000 |         8.3 |iter: 68/100\r",
      "      3.5 | 5.0000e-07 | 4.2857e-05 |          14 |    28000000 |            3 |       70000 |         8.4 |iter: 69/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.9296e-07 | 4.2254e-05 |          14 |    28400000 |            3 |       71000 |         8.6 |iter: 70/100\r",
      "      3.5 | 4.8611e-07 | 4.1667e-05 |          14 |    28800000 |            3 |       72000 |         8.7 |iter: 71/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.7945e-07 | 4.1096e-05 |          14 |    29200000 |            3 |       73000 |         8.8 |iter: 72/100\r",
      "      3.5 | 4.7297e-07 | 4.0541e-05 |          14 |    29600000 |            3 |       74000 |         8.9 |iter: 73/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.6667e-07 | 4.0000e-05 |          14 |    30000000 |            3 |       75000 |         9.0 |iter: 74/100\r",
      "      3.5 | 4.6053e-07 | 3.9474e-05 |          14 |    30400000 |            3 |       76000 |         9.1 |iter: 75/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.5455e-07 | 3.8961e-05 |          14 |    30800000 |            3 |       77000 |         9.3 |iter: 76/100\r",
      "      3.5 | 4.4872e-07 | 3.8462e-05 |          14 |    31200000 |            3 |       78000 |         9.4 |iter: 77/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.4304e-07 | 3.7975e-05 |          14 |    31600000 |            3 |       79000 |         9.5 |iter: 78/100\r",
      "      3.5 | 4.3750e-07 | 3.7500e-05 |          14 |    32000000 |            3 |       80000 |         9.6 |iter: 79/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.3210e-07 | 3.7037e-05 |          14 |    32400000 |            3 |       81000 |         9.7 |iter: 80/100\r",
      "      3.5 | 4.2683e-07 | 3.6585e-05 |          14 |    32800000 |            3 |       82000 |         9.8 |iter: 81/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.2169e-07 | 3.6145e-05 |          14 |    33200000 |            3 |       83000 |        10.0 |iter: 82/100\r",
      "      3.5 | 4.1667e-07 | 3.5714e-05 |          14 |    33600000 |            3 |       84000 |        10.1 |iter: 83/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.1176e-07 | 3.5294e-05 |          14 |    34000000 |            3 |       85000 |        10.2 |iter: 84/100\r",
      "      3.5 | 4.0698e-07 | 3.4884e-05 |          14 |    34400000 |            3 |       86000 |        10.3 |iter: 85/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.0230e-07 | 3.4483e-05 |          14 |    34800000 |            3 |       87000 |        10.4 |iter: 86/100\r",
      "      3.5 | 3.9773e-07 | 3.4091e-05 |          14 |    35200000 |            3 |       88000 |        10.5 |iter: 87/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.9326e-07 | 3.3708e-05 |          14 |    35600000 |            3 |       89000 |        10.6 |iter: 88/100\r",
      "      3.5 | 3.8889e-07 | 3.3333e-05 |          14 |    36000000 |            3 |       90000 |        10.8 |iter: 89/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.8462e-07 | 3.2967e-05 |          14 |    36400000 |            3 |       91000 |        10.9 |iter: 90/100\r",
      "      3.5 | 3.8043e-07 | 3.2609e-05 |          14 |    36800000 |            3 |       92000 |        11.0 |iter: 91/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.7634e-07 | 3.2258e-05 |          14 |    37200000 |            3 |       93000 |        11.1 |iter: 92/100\r",
      "      3.5 | 3.7234e-07 | 3.1915e-05 |          14 |    37600000 |            3 |       94000 |        11.2 |iter: 93/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.9474e-07 | 4.2105e-05 |          15 |    38000000 |            4 |       95000 |        11.3 |iter: 94/100\r",
      "      3.5 | 3.9063e-07 | 4.1667e-05 |          15 |    38400000 |            4 |       96000 |        11.5 |iter: 95/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.8660e-07 | 4.1237e-05 |          15 |    38800000 |            4 |       97000 |        11.6 |iter: 96/100\r",
      "      3.5 | 3.8265e-07 | 4.0816e-05 |          15 |    39200000 |            4 |       98000 |        11.7 |iter: 97/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.7879e-07 | 4.0404e-05 |          15 |    39600000 |            4 |       99000 |        11.8 |iter: 98/100\r",
      "      3.5 | 3.7500e-07 | 4.0000e-05 |          15 |    40000000 |            4 |      100000 |        11.9 |iter: 99/100\r",
      "      3.5 | 3.7500e-07 | 4.0000e-05 |          15 |    40000000 |            4 |      100000 |        11.9 |reached max iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |      400000 |            0 |        1000 |         0.1 |iter: 0/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |      800000 |            0 |        2000 |         0.2 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     1200000 |            0 |        3000 |         0.3 |iter: 2/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     1600000 |            0 |        4000 |         0.5 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     2000000 |            0 |        5000 |         0.6 |iter: 4/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     2400000 |            0 |        6000 |         0.7 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     2800000 |            0 |        7000 |         0.8 |iter: 6/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     3200000 |            0 |        8000 |         0.9 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     3600000 |            0 |        9000 |         1.0 |iter: 8/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     4000000 |            0 |       10000 |         1.2 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     4400000 |            0 |       11000 |         1.3 |iter: 10/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     4800000 |            0 |       12000 |         1.4 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     5200000 |            0 |       13000 |         1.5 |iter: 12/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     5600000 |            0 |       14000 |         1.6 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     6000000 |            0 |       15000 |         1.7 |iter: 14/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     6400000 |            0 |       16000 |         1.9 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     6800000 |            0 |       17000 |         2.0 |iter: 16/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     7200000 |            0 |       18000 |         2.1 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     7600000 |            0 |       19000 |         2.2 |iter: 18/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     8000000 |            0 |       20000 |         2.3 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     8400000 |            0 |       21000 |         2.4 |iter: 20/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     8800000 |            0 |       22000 |         2.6 |iter: 21/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     9200000 |            0 |       23000 |         2.7 |iter: 22/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     9600000 |            0 |       24000 |         2.8 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    10000000 |            0 |       25000 |         2.9 |iter: 24/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    10400000 |            0 |       26000 |         3.0 |iter: 25/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    10800000 |            0 |       27000 |         3.1 |iter: 26/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    11200000 |            0 |       28000 |         3.3 |iter: 27/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    11600000 |            0 |       29000 |         3.4 |iter: 28/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    12000000 |            0 |       30000 |         3.5 |iter: 29/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    12400000 |            0 |       31000 |         3.6 |iter: 30/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    12800000 |            0 |       32000 |         3.7 |iter: 31/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    13200000 |            0 |       33000 |         3.8 |iter: 32/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    13600000 |            0 |       34000 |         3.9 |iter: 33/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    14000000 |            0 |       35000 |         4.1 |iter: 34/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    14400000 |            0 |       36000 |         4.2 |iter: 35/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    14800000 |            0 |       37000 |         4.3 |iter: 36/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    15200000 |            0 |       38000 |         4.4 |iter: 37/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    15600000 |            0 |       39000 |         4.5 |iter: 38/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    16000000 |            0 |       40000 |         4.6 |iter: 39/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    16400000 |            0 |       41000 |         4.7 |iter: 40/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    16800000 |            0 |       42000 |         4.8 |iter: 41/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    17200000 |            0 |       43000 |         5.0 |iter: 42/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    17600000 |            0 |       44000 |         5.1 |iter: 43/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    18000000 |            0 |       45000 |         5.2 |iter: 44/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    18400000 |            0 |       46000 |         5.3 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    18800000 |            0 |       47000 |         5.4 |iter: 46/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    19200000 |            0 |       48000 |         5.5 |iter: 47/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    19600000 |            0 |       49000 |         5.6 |iter: 48/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    20000000 |            0 |       50000 |         5.7 |iter: 49/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    20400000 |            0 |       51000 |         5.8 |iter: 50/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    20800000 |            0 |       52000 |         6.0 |iter: 51/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    21200000 |            0 |       53000 |         6.1 |iter: 52/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    21600000 |            0 |       54000 |         6.2 |iter: 53/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    22000000 |            0 |       55000 |         6.3 |iter: 54/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    22400000 |            0 |       56000 |         6.4 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    22800000 |            0 |       57000 |         6.5 |iter: 56/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    23200000 |            0 |       58000 |         6.6 |iter: 57/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    23600000 |            0 |       59000 |         6.7 |iter: 58/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    24000000 |            0 |       60000 |         6.8 |iter: 59/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    24400000 |            0 |       61000 |         6.9 |iter: 60/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    24800000 |            0 |       62000 |         7.1 |iter: 61/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    25200000 |            0 |       63000 |         7.2 |iter: 62/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    25600000 |            0 |       64000 |         7.3 |iter: 63/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    26000000 |            0 |       65000 |         7.4 |iter: 64/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    26400000 |            0 |       66000 |         7.5 |iter: 65/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    26800000 |            0 |       67000 |         7.6 |iter: 66/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    27200000 |            0 |       68000 |         7.7 |iter: 67/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    27600000 |            0 |       69000 |         7.8 |iter: 68/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    28000000 |            0 |       70000 |         7.9 |iter: 69/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    28400000 |            0 |       71000 |         8.1 |iter: 70/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    28800000 |            0 |       72000 |         8.2 |iter: 71/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    29200000 |            0 |       73000 |         8.3 |iter: 72/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    29600000 |            0 |       74000 |         8.4 |iter: 73/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    30000000 |            0 |       75000 |         8.5 |iter: 74/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    30400000 |            0 |       76000 |         8.6 |iter: 75/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    30800000 |            0 |       77000 |         8.7 |iter: 76/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    31200000 |            0 |       78000 |         8.8 |iter: 77/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    31600000 |            0 |       79000 |         8.9 |iter: 78/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    32000000 |            0 |       80000 |         9.1 |iter: 79/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    32400000 |            0 |       81000 |         9.2 |iter: 80/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    32800000 |            0 |       82000 |         9.3 |iter: 81/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    33200000 |            0 |       83000 |         9.4 |iter: 82/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    33600000 |            0 |       84000 |         9.5 |iter: 83/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    34000000 |            0 |       85000 |         9.6 |iter: 84/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    34400000 |            0 |       86000 |         9.7 |iter: 85/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    34800000 |            0 |       87000 |         9.8 |iter: 86/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    35200000 |            0 |       88000 |         9.9 |iter: 87/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    35600000 |            0 |       89000 |        10.1 |iter: 88/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    36000000 |            0 |       90000 |        10.2 |iter: 89/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    36400000 |            0 |       91000 |        10.3 |iter: 90/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    36800000 |            0 |       92000 |        10.4 |iter: 91/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    37200000 |            0 |       93000 |        10.5 |iter: 92/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    37600000 |            0 |       94000 |        10.6 |iter: 93/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    38000000 |            0 |       95000 |        10.7 |iter: 94/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    38400000 |            0 |       96000 |        10.8 |iter: 95/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    38800000 |            0 |       97000 |        10.9 |iter: 96/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    39200000 |            0 |       98000 |        11.0 |iter: 97/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    39600000 |            0 |       99000 |        11.2 |iter: 98/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    40000000 |            0 |      100000 |        11.3 |iter: 99/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |    40000000 |            0 |      100000 |        11.3 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 3.8 dB.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ystKt5EGwdlcDAABYRYIVAAAAPjU2NurgwYPGdbASrE6nU59//rkk6Q9/+IOampq0ZcsWnT17VnV1dXr99ddlt9tVU1OjRYsWeZxj69atRs3Rm2++OaB1+DtUy1wmID8/XyUlJW71Tb2NP3DggHHokq+Er+teaWmpzzisuPbaa432/v37LY2ZOHGi0V6/fr1aWlp89q+rq3M78KszFBUVuV1b2Y1aXFxstGNjY3X55ZcHPS4AAABfSLACAADAp+LiYrW2thrX/hKTVn355Zeqra2VJA0aNEg7d+7UlClTZLPZFBYWplmzZumXv/ylJGnLli0e638GUh4g0HWkpqZq0KBBkqSTJ0/queeeM+5FRERo5MiRHseZX2t3jffEdc/qa/C+pKWlGe2DBw9aOhxq7ty5Rvv48eNatWqVz/6/+93vLB861RGHDx/WK6+8Ylxfeumllv7WzHVaJ02a1AmRAQAA+EaCFQAAAD6Z645KwdvBan5d/89//vN59Tcl6Qc/+IEkyeFw6Msvv3S753Q6jR2Vdrtd6enpPp9nXkffvn0VFxfns7/NZtMtt9xiXL/88stG++qrr/Z6KJc5CdmnTx+v87vuBePgqISEBCUkJEiSWltbtWPHDr9jkpKS3Hb9PvLII9q6davHvmvXrlVubu4Fx+lJc3Oz3nzzTaWnp6u+vt74fOnSpbLZbH7Hb9++3WhPnTq1U2IEAADwJbS7AwAAAEDPZk5MhoSEaM6cOQGNT05O1vLly73OO2HCBI0bN87j2Msuu8xoOxwOt3u7d+82dn9OmTJFF110kc84zOuwmiTOzs7W888/L6ktEegSrF28wXTbbbfpqaeekiRt3rzZUrJx5cqV+uCDD1RbW6umpiZlZmZq9uzZys7O1oABA3T8+HG99dZb2rBhgyRp1qxZeuONNwKK6/PPP/dYi7a1tVU1NTUqLS1VQ0OD273bb79dCxcu9Dt3fX29kUwODQ11K+sAAADQVUiwAgAAwCfzTlOHw6HNmzcHNP6KK67wOa9rl6onNTU1RnvgwIFu98zlAbKysvzGYV6H1QTp5MmTFR0dbZQycPF1QJbdbjfaDQ0Nio6O9tjPlVSMioqyFIs/c+bMMRKseXl5WrFihd8xw4YN08aNG5WZmamGhgY5HA6tWbNGa9asOa/v3LlzNX/+/IATrKdPn7b8NxMeHq6lS5fqscce87pD2Oy9994z6uLeeOONio2NDSg2AACAYKBEAAAAALxyOBzGQVQdZT6Aycy1o9Tbfen/T60fMGDAefVM8/PzJbW9yu/vgKv267CaYA0PD9e0adPO+9zXeHOcJ06c8NrPde/SSy+1FIs/ycnJuv766yVJ5eXl+uijjyyNmzhxovbs2eO1xMLFF1+s5cuX69VXXw1KnC42m01RUVGKi4tTdna2/vjHP6qiokLLli1TWFiYpTlee+01o33fffcFNT4AAACrbE7XEacAAABAF6mqqjJ2pJaUlCgxMdFjv3nz5mn16tW666673A5AOn78uAYPHiyp7TCqjz/+uNNjtqqurk7R0dFyOp1at26dbr/9do/9kpKSVFJSokcffVS///3vg/Ls119/XXfccYck6d5779VLL70U0PiysjLt2LFD//rXv/Sd73xHcXFxmjx5ssLDw4MSXzBVVVVp8ODBam5uVnx8vMrKyhQSwv4RAADQ9fgPBAAAAF3O/Lr+yZMnPfapqKjQm2++KUl64IEH3O6ZywNMnz49+AFeALvdrtTUVElSQUGBxz6VlZUqLS2VJN1www1Be/bMmTM1fPhwSW0HU1VXVwc0fvjw4Zo3b56WLFmiBQsWKDMzs0cmVyXphRdeMOri/uxnPyO5CgAAug3/hQAAAKDLmQ+ccr3qb9bS0qJ7771XZ8+e1W233abrrrvO7b55jJX6q13tzjvvlNSW5KyoqDjv/vLly+V0OjVo0CBlZGQE7bm9evXSr371K0ltB0CtWrUqaHP3JI2NjVq5cqWktjqyd999dzdHBAAA/peRYAUAAECXc+1gjYmJ0bPPPqu//OUvxm7E4uJiZWZmasuWLRo6dKjH19zT0tL0+OOP68knn7RcT7Ur/ehHP1J8fLzq6+uVlZVl1H89e/ascnNzjeTgE088YbneqFWzZ882DuF6+umndebMmaDO3xOsWrVKVVVVkqTf/va3Qf8OAQAAAkENVgAAAHS5kSNH6osvvtCrr76qZcuW6ciRIwoPD1fv3r1VW1srqW1nYkFBgUaMGNHN0XZMSUmJJk+ebCQCo6OjVV9fr9bWVknSgw8+qGeeeaZTnr1jxw5NmDBBkvT4449r2bJlnfKc7nDmzBnFx8fr1KlTGj9+vD788MPuDgkAAPyPI8EKAACALtXQ0KCoqCg5HA6VlZUpKipKS5cu1bvvvqv//Oc/SkhI0KxZs7R48WLZ7fbuDveCVFVVKTc3V/n5+aqoqFBkZKRSUlKUk5OjGTNmdHd4AAAACAISrAAAAAAAAADQQdRgBQAAAAAAAIAOIsEKAAAAAAAAAB1EghUAAAAAAAAAOogEKwAAAAAAAAB0EAlWAAAAAAAAAOggEqwAAAAAAAAA0EEkWAEAAAAAAACgg0iwAgAAAAAAAEAHkWAFAAAAAAAAgA4iwQoAAAAAAAAAHfRf9xsRRJpDPUsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate model\n",
    "num_iter = 10\n",
    "k = 400\n",
    "n = 800\n",
    "\n",
    "model5G = WeightedBP5G(k, n, num_iter=num_iter)\n",
    "model5G.to(device)\n",
    "\n",
    "# generate baseline BER\n",
    "ebno_dbs = np.array(np.arange(0, 4, 0.25))\n",
    "mc_iters = 100 # number of monte carlo iterations\n",
    "ber_plot_5G = PlotBER(\"Weighted BP for 5G LDPC\")\n",
    "\n",
    "# simulate the untrained performance\n",
    "ber_plot_5G.simulate(model5G,\n",
    "                     ebno_dbs=ebno_dbs,\n",
    "                     batch_size=1000,\n",
    "                     num_target_bit_errors=2000, # stop sim after 2000 bit errors\n",
    "                     legend=\"Untrained\",\n",
    "                     soft_estimates=True,\n",
    "                     max_mc_iter=mc_iters,\n",
    "                     compile_mode=None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's train this new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:13:02.899250Z",
     "iopub.status.busy": "2026-02-13T15:13:02.899109Z",
     "iopub.status.idle": "2026-02-13T15:16:23.884707Z",
     "shell.execute_reply": "2026-02-13T15:16:23.883553Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0/200 - Loss: 0.169 BER: 0.0194 BMI: 0.927\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 10/200 - Loss: 0.174 BER: 0.0227 BMI: 0.917\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 20/200 - Loss: 0.174 BER: 0.0219 BMI: 0.920\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 30/200 - Loss: 0.168 BER: 0.0195 BMI: 0.928\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 40/200 - Loss: 0.172 BER: 0.0212 BMI: 0.922\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 50/200 - Loss: 0.169 BER: 0.0195 BMI: 0.927\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 60/200 - Loss: 0.171 BER: 0.0218 BMI: 0.921\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 70/200 - Loss: 0.176 BER: 0.0235 BMI: 0.913\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 80/200 - Loss: 0.168 BER: 0.0192 BMI: 0.930\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 90/200 - Loss: 0.174 BER: 0.0215 BMI: 0.920\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 100/200 - Loss: 0.172 BER: 0.0212 BMI: 0.922\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 110/200 - Loss: 0.171 BER: 0.0208 BMI: 0.923\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 120/200 - Loss: 0.174 BER: 0.0229 BMI: 0.916\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 130/200 - Loss: 0.170 BER: 0.0211 BMI: 0.924\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 140/200 - Loss: 0.173 BER: 0.0219 BMI: 0.920\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 150/200 - Loss: 0.176 BER: 0.0235 BMI: 0.914\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 160/200 - Loss: 0.170 BER: 0.0206 BMI: 0.924\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 170/200 - Loss: 0.173 BER: 0.0224 BMI: 0.918\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 180/200 - Loss: 0.173 BER: 0.0221 BMI: 0.918\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 190/200 - Loss: 0.172 BER: 0.0211 BMI: 0.923\n"
     ]
    }
   ],
   "source": [
    "# training parameters\n",
    "batch_size = 1000\n",
    "train_iter = 200\n",
    "clip_value_grad = 10 # gradient clipping seems to be important\n",
    "\n",
    "# smaller training SNR as the new code is longer (=stronger) than before\n",
    "ebno_db = 1.5 # rule of thumb: train at ber = 1e-2\n",
    "\n",
    "# try also different optimizers or different hyperparameters\n",
    "optimizer = torch.optim.Adam(model5G.parameters(), lr=1e-2)\n",
    "\n",
    "# and let's go\n",
    "for it in range(0, train_iter):\n",
    "    optimizer.zero_grad()\n",
    "    b, llr, loss = model5G(batch_size, ebno_db)\n",
    "    loss.backward()\n",
    "    torch.nn.utils.clip_grad_value_(model5G.parameters(), clip_value_grad)\n",
    "    optimizer.step()\n",
    "\n",
    "    # calculate and print intermediate metrics\n",
    "    if it%10==0:\n",
    "        with torch.no_grad():\n",
    "            # calculate ber\n",
    "            b_hat = hard_decisions(llr)\n",
    "            ber = compute_ber(b, b_hat)\n",
    "            # and print results\n",
    "            mi = llr2mi(llr, -2*b+1).item() # calculate bit-wise mutual information\n",
    "            l = loss.item()\n",
    "            print(f\"Iteration {it}/{train_iter} - Loss: {l:.3f} BER: {ber:.4f} BMI: {mi:.3f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now simulate the new results and compare it to the untrained results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T15:16:23.887302Z",
     "iopub.status.busy": "2026-02-13T15:16:23.887051Z",
     "iopub.status.idle": "2026-02-13T15:17:31.027344Z",
     "shell.execute_reply": "2026-02-13T15:17:31.026476Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.6711e-01 | 1.0000e+00 |       66844 |      400000 |         1000 |        1000 |         0.1 |iter: 0/100\r",
      "      0.0 | 1.6711e-01 | 1.0000e+00 |       66844 |      400000 |         1000 |        1000 |         0.1 |reached target bit errors\n",
      "     0.25 | 1.4866e-01 | 1.0000e+00 |       59464 |      400000 |         1000 |        1000 |         0.1 |iter: 0/100\r",
      "     0.25 | 1.4866e-01 | 1.0000e+00 |       59464 |      400000 |         1000 |        1000 |         0.1 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0.5 | 1.2359e-01 | 9.9800e-01 |       49434 |      400000 |          998 |        1000 |         0.1 |iter: 0/100\r",
      "      0.5 | 1.2359e-01 | 9.9800e-01 |       49434 |      400000 |          998 |        1000 |         0.1 |reached target bit errors\n",
      "     0.75 | 9.6253e-02 | 9.8100e-01 |       38501 |      400000 |          981 |        1000 |         0.1 |iter: 0/100\r",
      "     0.75 | 9.6253e-02 | 9.8100e-01 |       38501 |      400000 |          981 |        1000 |         0.1 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      1.0 | 6.8867e-02 | 9.4700e-01 |       27547 |      400000 |          947 |        1000 |         0.1 |iter: 0/100\r",
      "      1.0 | 6.8867e-02 | 9.4700e-01 |       27547 |      400000 |          947 |        1000 |         0.1 |reached target bit errors\n",
      "     1.25 | 4.0942e-02 | 8.2000e-01 |       16377 |      400000 |          820 |        1000 |         0.1 |iter: 0/100\r",
      "     1.25 | 4.0942e-02 | 8.2000e-01 |       16377 |      400000 |          820 |        1000 |         0.1 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      1.5 | 2.1050e-02 | 5.9200e-01 |        8420 |      400000 |          592 |        1000 |         0.1 |iter: 0/100\r",
      "      1.5 | 2.1050e-02 | 5.9200e-01 |        8420 |      400000 |          592 |        1000 |         0.1 |reached target bit errors\n",
      "     1.75 | 8.6850e-03 | 3.6500e-01 |        3474 |      400000 |          365 |        1000 |         0.1 |iter: 0/100\r",
      "     1.75 | 8.6850e-03 | 3.6500e-01 |        3474 |      400000 |          365 |        1000 |         0.1 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.0 | 3.5325e-03 | 1.9000e-01 |        1413 |      400000 |          190 |        1000 |         0.1 |iter: 0/100\r",
      "      2.0 | 3.2287e-03 | 1.7400e-01 |        2583 |      800000 |          348 |        2000 |         0.2 |iter: 1/100\r",
      "      2.0 | 3.2287e-03 | 1.7400e-01 |        2583 |      800000 |          348 |        2000 |         0.2 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.25 | 8.5250e-04 | 6.8000e-02 |         341 |      400000 |           68 |        1000 |         0.1 |iter: 0/100\r",
      "     2.25 | 9.3250e-04 | 7.1000e-02 |         746 |      800000 |          142 |        2000 |         0.2 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.25 | 9.1333e-04 | 7.0667e-02 |        1096 |     1200000 |          212 |        3000 |         0.3 |iter: 2/100\r",
      "     2.25 | 9.1938e-04 | 6.9500e-02 |        1471 |     1600000 |          278 |        4000 |         0.4 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.25 | 8.7750e-04 | 6.7000e-02 |        1755 |     2000000 |          335 |        5000 |         0.5 |iter: 4/100\r",
      "     2.25 | 8.7583e-04 | 6.7500e-02 |        2102 |     2400000 |          405 |        6000 |         0.7 |iter: 5/100\r",
      "     2.25 | 8.7583e-04 | 6.7500e-02 |        2102 |     2400000 |          405 |        6000 |         0.7 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 1.3500e-04 | 1.8000e-02 |          54 |      400000 |           18 |        1000 |         0.1 |iter: 0/100\r",
      "      2.5 | 1.4250e-04 | 1.5500e-02 |         114 |      800000 |           31 |        2000 |         0.2 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 1.4917e-04 | 1.7667e-02 |         179 |     1200000 |           53 |        3000 |         0.3 |iter: 2/100\r",
      "      2.5 | 1.2937e-04 | 1.6500e-02 |         207 |     1600000 |           66 |        4000 |         0.4 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 1.6300e-04 | 1.8400e-02 |         326 |     2000000 |           92 |        5000 |         0.5 |iter: 4/100\r",
      "      2.5 | 1.6250e-04 | 1.8667e-02 |         390 |     2400000 |          112 |        6000 |         0.7 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 1.9643e-04 | 1.9714e-02 |         550 |     2800000 |          138 |        7000 |         0.8 |iter: 6/100\r",
      "      2.5 | 2.0625e-04 | 2.0375e-02 |         660 |     3200000 |          163 |        8000 |         0.9 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.0333e-04 | 2.0667e-02 |         732 |     3600000 |          186 |        9000 |         1.0 |iter: 8/100\r",
      "      2.5 | 2.0950e-04 | 2.1300e-02 |         838 |     4000000 |          213 |       10000 |         1.1 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 2.0068e-04 | 2.1000e-02 |         883 |     4400000 |          231 |       11000 |         1.2 |iter: 10/100\r",
      "      2.5 | 1.9437e-04 | 2.0833e-02 |         933 |     4800000 |          250 |       12000 |         1.3 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 1.9269e-04 | 2.0769e-02 |        1002 |     5200000 |          270 |       13000 |         1.4 |iter: 12/100\r",
      "      2.5 | 1.9036e-04 | 2.0429e-02 |        1066 |     5600000 |          286 |       14000 |         1.5 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 1.9633e-04 | 2.0467e-02 |        1178 |     6000000 |          307 |       15000 |         1.6 |iter: 14/100\r",
      "      2.5 | 1.9641e-04 | 2.0500e-02 |        1257 |     6400000 |          328 |       16000 |         1.8 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 1.9382e-04 | 2.0706e-02 |        1318 |     6800000 |          352 |       17000 |         1.9 |iter: 16/100\r",
      "      2.5 | 1.9014e-04 | 2.0556e-02 |        1369 |     7200000 |          370 |       18000 |         2.0 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 1.9263e-04 | 2.0737e-02 |        1464 |     7600000 |          394 |       19000 |         2.1 |iter: 18/100\r",
      "      2.5 | 1.9150e-04 | 2.0600e-02 |        1532 |     8000000 |          412 |       20000 |         2.2 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 1.8845e-04 | 2.0762e-02 |        1583 |     8400000 |          436 |       21000 |         2.3 |iter: 20/100\r",
      "      2.5 | 1.8580e-04 | 2.0455e-02 |        1635 |     8800000 |          450 |       22000 |         2.4 |iter: 21/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 1.9543e-04 | 2.0696e-02 |        1798 |     9200000 |          476 |       23000 |         2.5 |iter: 22/100\r",
      "      2.5 | 1.9823e-04 | 2.0667e-02 |        1903 |     9600000 |          496 |       24000 |         2.6 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      2.5 | 1.9610e-04 | 2.0440e-02 |        1961 |    10000000 |          511 |       25000 |         2.7 |iter: 24/100\r",
      "      2.5 | 1.9346e-04 | 2.0462e-02 |        2012 |    10400000 |          532 |       26000 |         2.9 |iter: 25/100\r",
      "      2.5 | 1.9346e-04 | 2.0462e-02 |        2012 |    10400000 |          532 |       26000 |         2.9 |reached target bit errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 2.2500e-05 | 4.0000e-03 |           9 |      400000 |            4 |        1000 |         0.1 |iter: 0/100\r",
      "     2.75 | 4.0000e-05 | 6.5000e-03 |          32 |      800000 |           13 |        2000 |         0.2 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 5.0000e-05 | 7.0000e-03 |          60 |     1200000 |           21 |        3000 |         0.3 |iter: 2/100\r",
      "     2.75 | 4.2500e-05 | 6.5000e-03 |          68 |     1600000 |           26 |        4000 |         0.4 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 6.1500e-05 | 6.2000e-03 |         123 |     2000000 |           31 |        5000 |         0.5 |iter: 4/100\r",
      "     2.75 | 5.4583e-05 | 6.3333e-03 |         131 |     2400000 |           38 |        6000 |         0.7 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 5.1071e-05 | 5.7143e-03 |         143 |     2800000 |           40 |        7000 |         0.8 |iter: 6/100\r",
      "     2.75 | 4.6875e-05 | 5.5000e-03 |         150 |     3200000 |           44 |        8000 |         0.9 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.9167e-05 | 5.4444e-03 |         177 |     3600000 |           49 |        9000 |         1.0 |iter: 8/100\r",
      "     2.75 | 4.8500e-05 | 5.7000e-03 |         194 |     4000000 |           57 |       10000 |         1.1 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.5455e-05 | 5.4545e-03 |         200 |     4400000 |           60 |       11000 |         1.2 |iter: 10/100\r",
      "     2.75 | 4.1875e-05 | 5.0833e-03 |         201 |     4800000 |           61 |       12000 |         1.3 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.9231e-05 | 4.8462e-03 |         204 |     5200000 |           63 |       13000 |         1.4 |iter: 12/100\r",
      "     2.75 | 3.7321e-05 | 4.7143e-03 |         209 |     5600000 |           66 |       14000 |         1.5 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.0833e-05 | 4.8000e-03 |         245 |     6000000 |           72 |       15000 |         1.6 |iter: 14/100\r",
      "     2.75 | 4.0312e-05 | 4.8125e-03 |         258 |     6400000 |           77 |       16000 |         1.8 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8824e-05 | 4.7059e-03 |         264 |     6800000 |           80 |       17000 |         1.9 |iter: 16/100\r",
      "     2.75 | 4.1389e-05 | 4.6667e-03 |         298 |     7200000 |           84 |       18000 |         2.0 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 4.1184e-05 | 4.8421e-03 |         313 |     7600000 |           92 |       19000 |         2.1 |iter: 18/100\r",
      "     2.75 | 3.9875e-05 | 4.8000e-03 |         319 |     8000000 |           96 |       20000 |         2.2 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8690e-05 | 4.7143e-03 |         325 |     8400000 |           99 |       21000 |         2.3 |iter: 20/100\r",
      "     2.75 | 3.9318e-05 | 4.7727e-03 |         346 |     8800000 |          105 |       22000 |         2.4 |iter: 21/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7717e-05 | 4.6087e-03 |         347 |     9200000 |          106 |       23000 |         2.5 |iter: 22/100\r",
      "     2.75 | 3.7292e-05 | 4.6250e-03 |         358 |     9600000 |          111 |       24000 |         2.6 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6200e-05 | 4.5600e-03 |         362 |    10000000 |          114 |       25000 |         2.7 |iter: 24/100\r",
      "     2.75 | 3.6827e-05 | 4.6538e-03 |         383 |    10400000 |          121 |       26000 |         2.9 |iter: 25/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6667e-05 | 4.5556e-03 |         396 |    10800000 |          123 |       27000 |         3.0 |iter: 26/100\r",
      "     2.75 | 3.6786e-05 | 4.6071e-03 |         412 |    11200000 |          129 |       28000 |         3.1 |iter: 27/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7759e-05 | 4.6207e-03 |         438 |    11600000 |          134 |       29000 |         3.2 |iter: 28/100\r",
      "     2.75 | 3.6750e-05 | 4.5333e-03 |         441 |    12000000 |          136 |       30000 |         3.3 |iter: 29/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7097e-05 | 4.5161e-03 |         460 |    12400000 |          140 |       31000 |         3.4 |iter: 30/100\r",
      "     2.75 | 3.6641e-05 | 4.5312e-03 |         469 |    12800000 |          145 |       32000 |         3.5 |iter: 31/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6288e-05 | 4.5455e-03 |         479 |    13200000 |          150 |       33000 |         3.6 |iter: 32/100\r",
      "     2.75 | 3.7574e-05 | 4.6176e-03 |         511 |    13600000 |          157 |       34000 |         3.7 |iter: 33/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7000e-05 | 4.5714e-03 |         518 |    14000000 |          160 |       35000 |         3.8 |iter: 34/100\r",
      "     2.75 | 3.7083e-05 | 4.5556e-03 |         534 |    14400000 |          164 |       36000 |         4.0 |iter: 35/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.8446e-05 | 4.6486e-03 |         569 |    14800000 |          172 |       37000 |         4.1 |iter: 36/100\r",
      "     2.75 | 3.7434e-05 | 4.5263e-03 |         569 |    15200000 |          172 |       38000 |         4.2 |iter: 37/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7372e-05 | 4.5385e-03 |         583 |    15600000 |          177 |       39000 |         4.3 |iter: 38/100\r",
      "     2.75 | 3.7000e-05 | 4.5250e-03 |         592 |    16000000 |          181 |       40000 |         4.4 |iter: 39/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6524e-05 | 4.5122e-03 |         599 |    16400000 |          185 |       41000 |         4.5 |iter: 40/100\r",
      "     2.75 | 3.6726e-05 | 4.5000e-03 |         617 |    16800000 |          189 |       42000 |         4.6 |iter: 41/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6105e-05 | 4.4651e-03 |         621 |    17200000 |          192 |       43000 |         4.7 |iter: 42/100\r",
      "     2.75 | 3.5852e-05 | 4.4545e-03 |         631 |    17600000 |          196 |       44000 |         4.8 |iter: 43/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6111e-05 | 4.4667e-03 |         650 |    18000000 |          201 |       45000 |         4.9 |iter: 44/100\r",
      "     2.75 | 3.6250e-05 | 4.4348e-03 |         667 |    18400000 |          204 |       46000 |         5.1 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.5851e-05 | 4.3617e-03 |         674 |    18800000 |          205 |       47000 |         5.2 |iter: 46/100\r",
      "     2.75 | 3.5260e-05 | 4.3125e-03 |         677 |    19200000 |          207 |       48000 |         5.3 |iter: 47/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.4898e-05 | 4.3061e-03 |         684 |    19600000 |          211 |       49000 |         5.4 |iter: 48/100\r",
      "     2.75 | 3.7400e-05 | 4.3600e-03 |         748 |    20000000 |          218 |       50000 |         5.5 |iter: 49/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6912e-05 | 4.3529e-03 |         753 |    20400000 |          222 |       51000 |         5.6 |iter: 50/100\r",
      "     2.75 | 3.6971e-05 | 4.3846e-03 |         769 |    20800000 |          228 |       52000 |         5.7 |iter: 51/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7217e-05 | 4.4151e-03 |         789 |    21200000 |          234 |       53000 |         5.8 |iter: 52/100\r",
      "     2.75 | 3.6898e-05 | 4.4074e-03 |         797 |    21600000 |          238 |       54000 |         5.9 |iter: 53/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7182e-05 | 4.4182e-03 |         818 |    22000000 |          243 |       55000 |         6.0 |iter: 54/100\r",
      "     2.75 | 3.7277e-05 | 4.4286e-03 |         835 |    22400000 |          248 |       56000 |         6.2 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6667e-05 | 4.3684e-03 |         836 |    22800000 |          249 |       57000 |         6.3 |iter: 56/100\r",
      "     2.75 | 3.7284e-05 | 4.4483e-03 |         865 |    23200000 |          258 |       58000 |         6.4 |iter: 57/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.7161e-05 | 4.4237e-03 |         877 |    23600000 |          261 |       59000 |         6.5 |iter: 58/100\r",
      "     2.75 | 3.6833e-05 | 4.4167e-03 |         884 |    24000000 |          265 |       60000 |         6.6 |iter: 59/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6803e-05 | 4.4590e-03 |         898 |    24400000 |          272 |       61000 |         6.7 |iter: 60/100\r",
      "     2.75 | 3.6492e-05 | 4.4677e-03 |         905 |    24800000 |          277 |       62000 |         6.8 |iter: 61/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6468e-05 | 4.4921e-03 |         919 |    25200000 |          283 |       63000 |         6.9 |iter: 62/100\r",
      "     2.75 | 3.6602e-05 | 4.5312e-03 |         937 |    25600000 |          290 |       64000 |         7.0 |iter: 63/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6154e-05 | 4.4923e-03 |         940 |    26000000 |          292 |       65000 |         7.1 |iter: 64/100\r",
      "     2.75 | 3.5682e-05 | 4.4545e-03 |         942 |    26400000 |          294 |       66000 |         7.3 |iter: 65/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.5634e-05 | 4.4776e-03 |         955 |    26800000 |          300 |       67000 |         7.4 |iter: 66/100\r",
      "     2.75 | 3.5809e-05 | 4.5147e-03 |         974 |    27200000 |          307 |       68000 |         7.5 |iter: 67/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6268e-05 | 4.5507e-03 |        1001 |    27600000 |          314 |       69000 |         7.6 |iter: 68/100\r",
      "     2.75 | 3.6000e-05 | 4.5286e-03 |        1008 |    28000000 |          317 |       70000 |         7.7 |iter: 69/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.5951e-05 | 4.5211e-03 |        1021 |    28400000 |          321 |       71000 |         7.8 |iter: 70/100\r",
      "     2.75 | 3.5972e-05 | 4.5278e-03 |        1036 |    28800000 |          326 |       72000 |         7.9 |iter: 71/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.6062e-05 | 4.5753e-03 |        1053 |    29200000 |          334 |       73000 |         8.0 |iter: 72/100\r",
      "     2.75 | 3.5980e-05 | 4.5946e-03 |        1065 |    29600000 |          340 |       74000 |         8.1 |iter: 73/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.5700e-05 | 4.6000e-03 |        1071 |    30000000 |          345 |       75000 |         8.2 |iter: 74/100\r",
      "     2.75 | 3.5592e-05 | 4.5921e-03 |        1082 |    30400000 |          349 |       76000 |         8.4 |iter: 75/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.5260e-05 | 4.5714e-03 |        1086 |    30800000 |          352 |       77000 |         8.5 |iter: 76/100\r",
      "     2.75 | 3.5513e-05 | 4.5897e-03 |        1108 |    31200000 |          358 |       78000 |         8.6 |iter: 77/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.5443e-05 | 4.5823e-03 |        1120 |    31600000 |          362 |       79000 |         8.7 |iter: 78/100\r",
      "     2.75 | 3.5094e-05 | 4.5625e-03 |        1123 |    32000000 |          365 |       80000 |         8.8 |iter: 79/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.4877e-05 | 4.5432e-03 |        1130 |    32400000 |          368 |       81000 |         8.9 |iter: 80/100\r",
      "     2.75 | 3.4512e-05 | 4.5000e-03 |        1132 |    32800000 |          369 |       82000 |         9.0 |iter: 81/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.4548e-05 | 4.5060e-03 |        1147 |    33200000 |          374 |       83000 |         9.1 |iter: 82/100\r",
      "     2.75 | 3.4554e-05 | 4.5119e-03 |        1161 |    33600000 |          379 |       84000 |         9.2 |iter: 83/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.4382e-05 | 4.5059e-03 |        1169 |    34000000 |          383 |       85000 |         9.4 |iter: 84/100\r",
      "     2.75 | 3.4128e-05 | 4.4884e-03 |        1174 |    34400000 |          386 |       86000 |         9.5 |iter: 85/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.3793e-05 | 4.4483e-03 |        1176 |    34800000 |          387 |       87000 |         9.6 |iter: 86/100\r",
      "     2.75 | 3.3409e-05 | 4.3977e-03 |        1176 |    35200000 |          387 |       88000 |         9.7 |iter: 87/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.3567e-05 | 4.4157e-03 |        1195 |    35600000 |          393 |       89000 |         9.8 |iter: 88/100\r",
      "     2.75 | 3.4028e-05 | 4.4667e-03 |        1225 |    36000000 |          402 |       90000 |         9.9 |iter: 89/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.4148e-05 | 4.4725e-03 |        1243 |    36400000 |          407 |       91000 |        10.0 |iter: 90/100\r",
      "     2.75 | 3.4049e-05 | 4.4674e-03 |        1253 |    36800000 |          411 |       92000 |        10.1 |iter: 91/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.3844e-05 | 4.4624e-03 |        1259 |    37200000 |          415 |       93000 |        10.2 |iter: 92/100\r",
      "     2.75 | 3.3910e-05 | 4.5106e-03 |        1275 |    37600000 |          424 |       94000 |        10.4 |iter: 93/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.3816e-05 | 4.5158e-03 |        1285 |    38000000 |          429 |       95000 |        10.5 |iter: 94/100\r",
      "     2.75 | 3.3568e-05 | 4.5000e-03 |        1289 |    38400000 |          432 |       96000 |        10.6 |iter: 95/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.3428e-05 | 4.5155e-03 |        1297 |    38800000 |          438 |       97000 |        10.8 |iter: 96/100\r",
      "     2.75 | 3.3342e-05 | 4.5306e-03 |        1307 |    39200000 |          444 |       98000 |        10.9 |iter: 97/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     2.75 | 3.3182e-05 | 4.5253e-03 |        1314 |    39600000 |          448 |       99000 |        11.1 |iter: 98/100\r",
      "     2.75 | 3.3075e-05 | 4.5400e-03 |        1323 |    40000000 |          454 |      100000 |        11.2 |iter: 99/100\r",
      "     2.75 | 3.3075e-05 | 4.5400e-03 |        1323 |    40000000 |          454 |      100000 |        11.2 |reached max iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 0.0000e+00 | 0.0000e+00 |           0 |      400000 |            0 |        1000 |         0.2 |iter: 0/100\r",
      "      3.0 | 2.5000e-06 | 1.0000e-03 |           2 |      800000 |            2 |        2000 |         0.3 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 5.8333e-06 | 1.6667e-03 |           7 |     1200000 |            5 |        3000 |         0.5 |iter: 2/100\r",
      "      3.0 | 4.3750e-06 | 1.2500e-03 |           7 |     1600000 |            5 |        4000 |         0.6 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 4.0000e-06 | 1.2000e-03 |           8 |     2000000 |            6 |        5000 |         0.8 |iter: 4/100\r",
      "      3.0 | 3.3333e-06 | 1.0000e-03 |           8 |     2400000 |            6 |        6000 |         0.9 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.8571e-06 | 8.5714e-04 |           8 |     2800000 |            6 |        7000 |         1.1 |iter: 6/100\r",
      "      3.0 | 2.5000e-06 | 7.5000e-04 |           8 |     3200000 |            6 |        8000 |         1.2 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.2222e-06 | 6.6667e-04 |           8 |     3600000 |            6 |        9000 |         1.4 |iter: 8/100\r",
      "      3.0 | 2.0000e-06 | 6.0000e-04 |           8 |     4000000 |            6 |       10000 |         1.5 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.7273e-06 | 6.3636e-04 |          12 |     4400000 |            7 |       11000 |         1.7 |iter: 10/100\r",
      "      3.0 | 2.5000e-06 | 5.8333e-04 |          12 |     4800000 |            7 |       12000 |         1.8 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.5000e-06 | 6.1538e-04 |          13 |     5200000 |            8 |       13000 |         2.0 |iter: 12/100\r",
      "      3.0 | 2.3214e-06 | 5.7143e-04 |          13 |     5600000 |            8 |       14000 |         2.1 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.3333e-06 | 6.0000e-04 |          14 |     6000000 |            9 |       15000 |         2.2 |iter: 14/100\r",
      "      3.0 | 2.1875e-06 | 5.6250e-04 |          14 |     6400000 |            9 |       16000 |         2.4 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.0588e-06 | 5.2941e-04 |          14 |     6800000 |            9 |       17000 |         2.5 |iter: 16/100\r",
      "      3.0 | 2.2222e-06 | 6.1111e-04 |          16 |     7200000 |           11 |       18000 |         2.7 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.1053e-06 | 5.7895e-04 |          16 |     7600000 |           11 |       19000 |         2.8 |iter: 18/100\r",
      "      3.0 | 2.0000e-06 | 5.5000e-04 |          16 |     8000000 |           11 |       20000 |         3.0 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 1.9048e-06 | 5.2381e-04 |          16 |     8400000 |           11 |       21000 |         3.1 |iter: 20/100\r",
      "      3.0 | 1.8182e-06 | 5.0000e-04 |          16 |     8800000 |           11 |       22000 |         3.3 |iter: 21/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 1.9565e-06 | 5.6522e-04 |          18 |     9200000 |           13 |       23000 |         3.4 |iter: 22/100\r",
      "      3.0 | 1.8750e-06 | 5.4167e-04 |          18 |     9600000 |           13 |       24000 |         3.6 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 1.8000e-06 | 5.2000e-04 |          18 |    10000000 |           13 |       25000 |         3.7 |iter: 24/100\r",
      "      3.0 | 2.3077e-06 | 5.7692e-04 |          24 |    10400000 |           15 |       26000 |         3.9 |iter: 25/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.3148e-06 | 5.9259e-04 |          25 |    10800000 |           16 |       27000 |         4.0 |iter: 26/100\r",
      "      3.0 | 2.5893e-06 | 6.0714e-04 |          29 |    11200000 |           17 |       28000 |         4.2 |iter: 27/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.5862e-06 | 6.2069e-04 |          30 |    11600000 |           18 |       29000 |         4.3 |iter: 28/100\r",
      "      3.0 | 2.8333e-06 | 6.6667e-04 |          34 |    12000000 |           20 |       30000 |         4.5 |iter: 29/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.9839e-06 | 6.7742e-04 |          37 |    12400000 |           21 |       31000 |         4.6 |iter: 30/100\r",
      "      3.0 | 2.8906e-06 | 6.5625e-04 |          37 |    12800000 |           21 |       32000 |         4.8 |iter: 31/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.8788e-06 | 6.6667e-04 |          38 |    13200000 |           22 |       33000 |         4.9 |iter: 32/100\r",
      "      3.0 | 3.0147e-06 | 6.7647e-04 |          41 |    13600000 |           23 |       34000 |         5.1 |iter: 33/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.9286e-06 | 6.5714e-04 |          41 |    14000000 |           23 |       35000 |         5.2 |iter: 34/100\r",
      "      3.0 | 2.8472e-06 | 6.3889e-04 |          41 |    14400000 |           23 |       36000 |         5.4 |iter: 35/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.7703e-06 | 6.2162e-04 |          41 |    14800000 |           23 |       37000 |         5.5 |iter: 36/100\r",
      "      3.0 | 2.7632e-06 | 6.3158e-04 |          42 |    15200000 |           24 |       38000 |         5.7 |iter: 37/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.6923e-06 | 6.1538e-04 |          42 |    15600000 |           24 |       39000 |         5.8 |iter: 38/100\r",
      "      3.0 | 2.6250e-06 | 6.0000e-04 |          42 |    16000000 |           24 |       40000 |         5.9 |iter: 39/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.6220e-06 | 6.0976e-04 |          43 |    16400000 |           25 |       41000 |         6.1 |iter: 40/100\r",
      "      3.0 | 2.7976e-06 | 6.4286e-04 |          47 |    16800000 |           27 |       42000 |         6.2 |iter: 41/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.7907e-06 | 6.5116e-04 |          48 |    17200000 |           28 |       43000 |         6.4 |iter: 42/100\r",
      "      3.0 | 2.7841e-06 | 6.5909e-04 |          49 |    17600000 |           29 |       44000 |         6.5 |iter: 43/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.7778e-06 | 6.6667e-04 |          50 |    18000000 |           30 |       45000 |         6.7 |iter: 44/100\r",
      "      3.0 | 2.7717e-06 | 6.7391e-04 |          51 |    18400000 |           31 |       46000 |         6.8 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.8191e-06 | 6.8085e-04 |          53 |    18800000 |           32 |       47000 |         7.0 |iter: 46/100\r",
      "      3.0 | 2.7604e-06 | 6.6667e-04 |          53 |    19200000 |           32 |       48000 |         7.1 |iter: 47/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.8061e-06 | 6.7347e-04 |          55 |    19600000 |           33 |       49000 |         7.3 |iter: 48/100\r",
      "      3.0 | 2.7500e-06 | 6.6000e-04 |          55 |    20000000 |           33 |       50000 |         7.4 |iter: 49/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.8922e-06 | 7.0588e-04 |          59 |    20400000 |           36 |       51000 |         7.5 |iter: 50/100\r",
      "      3.0 | 2.8846e-06 | 7.1154e-04 |          60 |    20800000 |           37 |       52000 |         7.7 |iter: 51/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.8774e-06 | 7.1698e-04 |          61 |    21200000 |           38 |       53000 |         7.8 |iter: 52/100\r",
      "      3.0 | 2.8241e-06 | 7.0370e-04 |          61 |    21600000 |           38 |       54000 |         8.0 |iter: 53/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 2.8636e-06 | 7.2727e-04 |          63 |    22000000 |           40 |       55000 |         8.1 |iter: 54/100\r",
      "      3.0 | 2.9911e-06 | 7.5000e-04 |          67 |    22400000 |           42 |       56000 |         8.3 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.3333e-06 | 7.7193e-04 |          76 |    22800000 |           44 |       57000 |         8.4 |iter: 56/100\r",
      "      3.0 | 3.2759e-06 | 7.5862e-04 |          76 |    23200000 |           44 |       58000 |         8.6 |iter: 57/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.2203e-06 | 7.4576e-04 |          76 |    23600000 |           44 |       59000 |         8.7 |iter: 58/100\r",
      "      3.0 | 3.2083e-06 | 7.5000e-04 |          77 |    24000000 |           45 |       60000 |         8.9 |iter: 59/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.8525e-06 | 7.5410e-04 |          94 |    24400000 |           46 |       61000 |         9.0 |iter: 60/100\r",
      "      3.0 | 3.8710e-06 | 7.7419e-04 |          96 |    24800000 |           48 |       62000 |         9.1 |iter: 61/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.8492e-06 | 7.7778e-04 |          97 |    25200000 |           49 |       63000 |         9.3 |iter: 62/100\r",
      "      3.0 | 3.9063e-06 | 7.9687e-04 |         100 |    25600000 |           51 |       64000 |         9.4 |iter: 63/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.9231e-06 | 8.0000e-04 |         102 |    26000000 |           52 |       65000 |         9.6 |iter: 64/100\r",
      "      3.0 | 3.8636e-06 | 7.8788e-04 |         102 |    26400000 |           52 |       66000 |         9.7 |iter: 65/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.9179e-06 | 8.0597e-04 |         105 |    26800000 |           54 |       67000 |         9.9 |iter: 66/100\r",
      "      3.0 | 3.8603e-06 | 7.9412e-04 |         105 |    27200000 |           54 |       68000 |        10.0 |iter: 67/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.8043e-06 | 7.8261e-04 |         105 |    27600000 |           54 |       69000 |        10.2 |iter: 68/100\r",
      "      3.0 | 3.7500e-06 | 7.7143e-04 |         105 |    28000000 |           54 |       70000 |        10.3 |iter: 69/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.8028e-06 | 7.8873e-04 |         108 |    28400000 |           56 |       71000 |        10.5 |iter: 70/100\r",
      "      3.0 | 3.9236e-06 | 8.1944e-04 |         113 |    28800000 |           59 |       72000 |        10.6 |iter: 71/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.8699e-06 | 8.0822e-04 |         113 |    29200000 |           59 |       73000 |        10.7 |iter: 72/100\r",
      "      3.0 | 3.8176e-06 | 7.9730e-04 |         113 |    29600000 |           59 |       74000 |        10.9 |iter: 73/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.7667e-06 | 7.8667e-04 |         113 |    30000000 |           59 |       75000 |        11.0 |iter: 74/100\r",
      "      3.0 | 3.7171e-06 | 7.7632e-04 |         113 |    30400000 |           59 |       76000 |        11.2 |iter: 75/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.6688e-06 | 7.6623e-04 |         113 |    30800000 |           59 |       77000 |        11.3 |iter: 76/100\r",
      "      3.0 | 3.7179e-06 | 7.8205e-04 |         116 |    31200000 |           61 |       78000 |        11.5 |iter: 77/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.6709e-06 | 7.7215e-04 |         116 |    31600000 |           61 |       79000 |        11.6 |iter: 78/100\r",
      "      3.0 | 3.6562e-06 | 7.7500e-04 |         117 |    32000000 |           62 |       80000 |        11.7 |iter: 79/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.6111e-06 | 7.6543e-04 |         117 |    32400000 |           62 |       81000 |        11.9 |iter: 80/100\r",
      "      3.0 | 3.5671e-06 | 7.5610e-04 |         117 |    32800000 |           62 |       82000 |        12.0 |iter: 81/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.5241e-06 | 7.4699e-04 |         117 |    33200000 |           62 |       83000 |        12.2 |iter: 82/100\r",
      "      3.0 | 3.4821e-06 | 7.3810e-04 |         117 |    33600000 |           62 |       84000 |        12.3 |iter: 83/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.5000e-06 | 7.5294e-04 |         119 |    34000000 |           64 |       85000 |        12.5 |iter: 84/100\r",
      "      3.0 | 3.4593e-06 | 7.4419e-04 |         119 |    34400000 |           64 |       86000 |        12.6 |iter: 85/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.5920e-06 | 7.5862e-04 |         125 |    34800000 |           66 |       87000 |        12.8 |iter: 86/100\r",
      "      3.0 | 3.5511e-06 | 7.5000e-04 |         125 |    35200000 |           66 |       88000 |        12.9 |iter: 87/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.5112e-06 | 7.4157e-04 |         125 |    35600000 |           66 |       89000 |        13.0 |iter: 88/100\r",
      "      3.0 | 3.4722e-06 | 7.3333e-04 |         125 |    36000000 |           66 |       90000 |        13.2 |iter: 89/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.4341e-06 | 7.2527e-04 |         125 |    36400000 |           66 |       91000 |        13.3 |iter: 90/100\r",
      "      3.0 | 3.4783e-06 | 7.3913e-04 |         128 |    36800000 |           68 |       92000 |        13.5 |iter: 91/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.4677e-06 | 7.4194e-04 |         129 |    37200000 |           69 |       93000 |        13.6 |iter: 92/100\r",
      "      3.0 | 3.4574e-06 | 7.4468e-04 |         130 |    37600000 |           70 |       94000 |        13.8 |iter: 93/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.4211e-06 | 7.3684e-04 |         130 |    38000000 |           70 |       95000 |        13.9 |iter: 94/100\r",
      "      3.0 | 3.5417e-06 | 7.3958e-04 |         136 |    38400000 |           71 |       96000 |        14.0 |iter: 95/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.5052e-06 | 7.3196e-04 |         136 |    38800000 |           71 |       97000 |        14.2 |iter: 96/100\r",
      "      3.0 | 3.4694e-06 | 7.2449e-04 |         136 |    39200000 |           71 |       98000 |        14.3 |iter: 97/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.0 | 3.5859e-06 | 7.3737e-04 |         142 |    39600000 |           73 |       99000 |        14.5 |iter: 98/100\r",
      "      3.0 | 3.5500e-06 | 7.3000e-04 |         142 |    40000000 |           73 |      100000 |        14.6 |iter: 99/100\r",
      "      3.0 | 3.5500e-06 | 7.3000e-04 |         142 |    40000000 |           73 |      100000 |        14.6 |reached max iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |      400000 |            0 |        1000 |         0.1 |iter: 0/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |      800000 |            0 |        2000 |         0.3 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     1200000 |            0 |        3000 |         0.4 |iter: 2/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     1600000 |            0 |        4000 |         0.6 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     2000000 |            0 |        5000 |         0.7 |iter: 4/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     2400000 |            0 |        6000 |         0.9 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     2800000 |            0 |        7000 |         1.0 |iter: 6/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     3200000 |            0 |        8000 |         1.1 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     3600000 |            0 |        9000 |         1.3 |iter: 8/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     4000000 |            0 |       10000 |         1.4 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     4400000 |            0 |       11000 |         1.6 |iter: 10/100\r",
      "     3.25 | 0.0000e+00 | 0.0000e+00 |           0 |     4800000 |            0 |       12000 |         1.7 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 5.7692e-07 | 7.6923e-05 |           3 |     5200000 |            1 |       13000 |         1.8 |iter: 12/100\r",
      "     3.25 | 8.9286e-07 | 1.4286e-04 |           5 |     5600000 |            2 |       14000 |         2.0 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 1.5000e-06 | 2.0000e-04 |           9 |     6000000 |            3 |       15000 |         2.1 |iter: 14/100\r",
      "     3.25 | 1.4063e-06 | 1.8750e-04 |           9 |     6400000 |            3 |       16000 |         2.3 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 1.3235e-06 | 1.7647e-04 |           9 |     6800000 |            3 |       17000 |         2.4 |iter: 16/100\r",
      "     3.25 | 1.2500e-06 | 1.6667e-04 |           9 |     7200000 |            3 |       18000 |         2.5 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 1.1842e-06 | 1.5789e-04 |           9 |     7600000 |            3 |       19000 |         2.7 |iter: 18/100\r",
      "     3.25 | 1.1250e-06 | 1.5000e-04 |           9 |     8000000 |            3 |       20000 |         2.8 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 1.0714e-06 | 1.4286e-04 |           9 |     8400000 |            3 |       21000 |         3.0 |iter: 20/100\r",
      "     3.25 | 1.0227e-06 | 1.3636e-04 |           9 |     8800000 |            3 |       22000 |         3.1 |iter: 21/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 9.7826e-07 | 1.3043e-04 |           9 |     9200000 |            3 |       23000 |         3.3 |iter: 22/100\r",
      "     3.25 | 9.3750e-07 | 1.2500e-04 |           9 |     9600000 |            3 |       24000 |         3.4 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 9.0000e-07 | 1.2000e-04 |           9 |    10000000 |            3 |       25000 |         3.5 |iter: 24/100\r",
      "     3.25 | 8.6538e-07 | 1.1538e-04 |           9 |    10400000 |            3 |       26000 |         3.7 |iter: 25/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 8.3333e-07 | 1.1111e-04 |           9 |    10800000 |            3 |       27000 |         3.8 |iter: 26/100\r",
      "     3.25 | 8.0357e-07 | 1.0714e-04 |           9 |    11200000 |            3 |       28000 |         4.0 |iter: 27/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 1.0345e-06 | 1.3793e-04 |          12 |    11600000 |            4 |       29000 |         4.1 |iter: 28/100\r",
      "     3.25 | 1.0000e-06 | 1.3333e-04 |          12 |    12000000 |            4 |       30000 |         4.2 |iter: 29/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 9.6774e-07 | 1.2903e-04 |          12 |    12400000 |            4 |       31000 |         4.4 |iter: 30/100\r",
      "     3.25 | 9.3750e-07 | 1.2500e-04 |          12 |    12800000 |            4 |       32000 |         4.5 |iter: 31/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 9.0909e-07 | 1.2121e-04 |          12 |    13200000 |            4 |       33000 |         4.7 |iter: 32/100\r",
      "     3.25 | 8.8235e-07 | 1.1765e-04 |          12 |    13600000 |            4 |       34000 |         4.8 |iter: 33/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 8.5714e-07 | 1.1429e-04 |          12 |    14000000 |            4 |       35000 |         4.9 |iter: 34/100\r",
      "     3.25 | 8.3333e-07 | 1.1111e-04 |          12 |    14400000 |            4 |       36000 |         5.1 |iter: 35/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 8.1081e-07 | 1.0811e-04 |          12 |    14800000 |            4 |       37000 |         5.2 |iter: 36/100\r",
      "     3.25 | 7.8947e-07 | 1.0526e-04 |          12 |    15200000 |            4 |       38000 |         5.3 |iter: 37/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.6923e-07 | 1.0256e-04 |          12 |    15600000 |            4 |       39000 |         5.4 |iter: 38/100\r",
      "     3.25 | 7.5000e-07 | 1.0000e-04 |          12 |    16000000 |            4 |       40000 |         5.6 |iter: 39/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.3171e-07 | 9.7561e-05 |          12 |    16400000 |            4 |       41000 |         5.7 |iter: 40/100\r",
      "     3.25 | 8.3333e-07 | 1.1905e-04 |          14 |    16800000 |            5 |       42000 |         5.8 |iter: 41/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 8.1395e-07 | 1.1628e-04 |          14 |    17200000 |            5 |       43000 |         5.9 |iter: 42/100\r",
      "     3.25 | 7.9545e-07 | 1.1364e-04 |          14 |    17600000 |            5 |       44000 |         6.1 |iter: 43/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 1.0556e-06 | 1.5556e-04 |          19 |    18000000 |            7 |       45000 |         6.2 |iter: 44/100\r",
      "     3.25 | 1.0326e-06 | 1.5217e-04 |          19 |    18400000 |            7 |       46000 |         6.3 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 1.0106e-06 | 1.4894e-04 |          19 |    18800000 |            7 |       47000 |         6.4 |iter: 46/100\r",
      "     3.25 | 9.8958e-07 | 1.4583e-04 |          19 |    19200000 |            7 |       48000 |         6.6 |iter: 47/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 9.6939e-07 | 1.4286e-04 |          19 |    19600000 |            7 |       49000 |         6.7 |iter: 48/100\r",
      "     3.25 | 9.5000e-07 | 1.4000e-04 |          19 |    20000000 |            7 |       50000 |         6.8 |iter: 49/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 9.3137e-07 | 1.3725e-04 |          19 |    20400000 |            7 |       51000 |         6.9 |iter: 50/100\r",
      "     3.25 | 9.1346e-07 | 1.3462e-04 |          19 |    20800000 |            7 |       52000 |         7.1 |iter: 51/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 8.9623e-07 | 1.3208e-04 |          19 |    21200000 |            7 |       53000 |         7.2 |iter: 52/100\r",
      "     3.25 | 8.7963e-07 | 1.2963e-04 |          19 |    21600000 |            7 |       54000 |         7.3 |iter: 53/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 8.6364e-07 | 1.2727e-04 |          19 |    22000000 |            7 |       55000 |         7.4 |iter: 54/100\r",
      "     3.25 | 8.4821e-07 | 1.2500e-04 |          19 |    22400000 |            7 |       56000 |         7.6 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 8.3333e-07 | 1.2281e-04 |          19 |    22800000 |            7 |       57000 |         7.7 |iter: 56/100\r",
      "     3.25 | 8.1897e-07 | 1.2069e-04 |          19 |    23200000 |            7 |       58000 |         7.8 |iter: 57/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 8.0508e-07 | 1.1864e-04 |          19 |    23600000 |            7 |       59000 |         7.9 |iter: 58/100\r",
      "     3.25 | 7.9167e-07 | 1.1667e-04 |          19 |    24000000 |            7 |       60000 |         8.1 |iter: 59/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.7869e-07 | 1.1475e-04 |          19 |    24400000 |            7 |       61000 |         8.2 |iter: 60/100\r",
      "     3.25 | 7.6613e-07 | 1.1290e-04 |          19 |    24800000 |            7 |       62000 |         8.3 |iter: 61/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.5397e-07 | 1.1111e-04 |          19 |    25200000 |            7 |       63000 |         8.4 |iter: 62/100\r",
      "     3.25 | 7.8125e-07 | 1.2500e-04 |          20 |    25600000 |            8 |       64000 |         8.6 |iter: 63/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.6923e-07 | 1.2308e-04 |          20 |    26000000 |            8 |       65000 |         8.7 |iter: 64/100\r",
      "     3.25 | 7.5758e-07 | 1.2121e-04 |          20 |    26400000 |            8 |       66000 |         8.8 |iter: 65/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.4627e-07 | 1.1940e-04 |          20 |    26800000 |            8 |       67000 |         8.9 |iter: 66/100\r",
      "     3.25 | 8.0882e-07 | 1.3235e-04 |          22 |    27200000 |            9 |       68000 |         9.1 |iter: 67/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.9710e-07 | 1.3043e-04 |          22 |    27600000 |            9 |       69000 |         9.2 |iter: 68/100\r",
      "     3.25 | 7.8571e-07 | 1.2857e-04 |          22 |    28000000 |            9 |       70000 |         9.3 |iter: 69/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.7465e-07 | 1.2676e-04 |          22 |    28400000 |            9 |       71000 |         9.4 |iter: 70/100\r",
      "     3.25 | 7.6389e-07 | 1.2500e-04 |          22 |    28800000 |            9 |       72000 |         9.5 |iter: 71/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.8767e-07 | 1.3699e-04 |          23 |    29200000 |           10 |       73000 |         9.7 |iter: 72/100\r",
      "     3.25 | 7.7703e-07 | 1.3514e-04 |          23 |    29600000 |           10 |       74000 |         9.8 |iter: 73/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.6667e-07 | 1.3333e-04 |          23 |    30000000 |           10 |       75000 |         9.9 |iter: 74/100\r",
      "     3.25 | 7.5658e-07 | 1.3158e-04 |          23 |    30400000 |           10 |       76000 |        10.0 |iter: 75/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.4675e-07 | 1.2987e-04 |          23 |    30800000 |           10 |       77000 |        10.2 |iter: 76/100\r",
      "     3.25 | 7.3718e-07 | 1.2821e-04 |          23 |    31200000 |           10 |       78000 |        10.3 |iter: 77/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.2785e-07 | 1.2658e-04 |          23 |    31600000 |           10 |       79000 |        10.4 |iter: 78/100\r",
      "     3.25 | 7.1875e-07 | 1.2500e-04 |          23 |    32000000 |           10 |       80000 |        10.5 |iter: 79/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.4074e-07 | 1.3580e-04 |          24 |    32400000 |           11 |       81000 |        10.7 |iter: 80/100\r",
      "     3.25 | 7.6220e-07 | 1.4634e-04 |          25 |    32800000 |           12 |       82000 |        10.8 |iter: 81/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.5301e-07 | 1.4458e-04 |          25 |    33200000 |           12 |       83000 |        10.9 |iter: 82/100\r",
      "     3.25 | 7.4405e-07 | 1.4286e-04 |          25 |    33600000 |           12 |       84000 |        11.0 |iter: 83/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.3529e-07 | 1.4118e-04 |          25 |    34000000 |           12 |       85000 |        11.2 |iter: 84/100\r",
      "     3.25 | 7.2674e-07 | 1.3953e-04 |          25 |    34400000 |           12 |       86000 |        11.3 |iter: 85/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.1839e-07 | 1.3793e-04 |          25 |    34800000 |           12 |       87000 |        11.4 |iter: 86/100\r",
      "     3.25 | 7.1023e-07 | 1.3636e-04 |          25 |    35200000 |           12 |       88000 |        11.5 |iter: 87/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.0225e-07 | 1.3483e-04 |          25 |    35600000 |           12 |       89000 |        11.7 |iter: 88/100\r",
      "     3.25 | 7.2222e-07 | 1.4444e-04 |          26 |    36000000 |           13 |       90000 |        11.8 |iter: 89/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 7.1429e-07 | 1.4286e-04 |          26 |    36400000 |           13 |       91000 |        11.9 |iter: 90/100\r",
      "     3.25 | 7.0652e-07 | 1.4130e-04 |          26 |    36800000 |           13 |       92000 |        12.0 |iter: 91/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 6.9892e-07 | 1.3978e-04 |          26 |    37200000 |           13 |       93000 |        12.1 |iter: 92/100\r",
      "     3.25 | 6.9149e-07 | 1.3830e-04 |          26 |    37600000 |           13 |       94000 |        12.3 |iter: 93/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 6.8421e-07 | 1.3684e-04 |          26 |    38000000 |           13 |       95000 |        12.4 |iter: 94/100\r",
      "     3.25 | 6.7708e-07 | 1.3542e-04 |          26 |    38400000 |           13 |       96000 |        12.5 |iter: 95/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 6.7010e-07 | 1.3402e-04 |          26 |    38800000 |           13 |       97000 |        12.6 |iter: 96/100\r",
      "     3.25 | 6.6327e-07 | 1.3265e-04 |          26 |    39200000 |           13 |       98000 |        12.8 |iter: 97/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.25 | 6.5657e-07 | 1.3131e-04 |          26 |    39600000 |           13 |       99000 |        12.9 |iter: 98/100\r",
      "     3.25 | 6.5000e-07 | 1.3000e-04 |          26 |    40000000 |           13 |      100000 |        13.0 |iter: 99/100\r",
      "     3.25 | 6.5000e-07 | 1.3000e-04 |          26 |    40000000 |           13 |      100000 |        13.0 |reached max iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 0.0000e+00 | 0.0000e+00 |           0 |      400000 |            0 |        1000 |         0.1 |iter: 0/100\r",
      "      3.5 | 8.7500e-06 | 5.0000e-04 |           7 |      800000 |            1 |        2000 |         0.2 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.8333e-06 | 3.3333e-04 |           7 |     1200000 |            1 |        3000 |         0.4 |iter: 2/100\r",
      "      3.5 | 4.3750e-06 | 2.5000e-04 |           7 |     1600000 |            1 |        4000 |         0.5 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.5000e-06 | 2.0000e-04 |           7 |     2000000 |            1 |        5000 |         0.6 |iter: 4/100\r",
      "      3.5 | 2.9167e-06 | 1.6667e-04 |           7 |     2400000 |            1 |        6000 |         0.7 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 2.5000e-06 | 1.4286e-04 |           7 |     2800000 |            1 |        7000 |         0.9 |iter: 6/100\r",
      "      3.5 | 2.1875e-06 | 1.2500e-04 |           7 |     3200000 |            1 |        8000 |         1.0 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 2.2222e-06 | 2.2222e-04 |           8 |     3600000 |            2 |        9000 |         1.1 |iter: 8/100\r",
      "      3.5 | 2.0000e-06 | 2.0000e-04 |           8 |     4000000 |            2 |       10000 |         1.2 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.8182e-06 | 1.8182e-04 |           8 |     4400000 |            2 |       11000 |         1.3 |iter: 10/100\r",
      "      3.5 | 1.6667e-06 | 1.6667e-04 |           8 |     4800000 |            2 |       12000 |         1.5 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.5385e-06 | 1.5385e-04 |           8 |     5200000 |            2 |       13000 |         1.6 |iter: 12/100\r",
      "      3.5 | 1.4286e-06 | 1.4286e-04 |           8 |     5600000 |            2 |       14000 |         1.7 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.3333e-06 | 1.3333e-04 |           8 |     6000000 |            2 |       15000 |         1.8 |iter: 14/100\r",
      "      3.5 | 1.2500e-06 | 1.2500e-04 |           8 |     6400000 |            2 |       16000 |         2.0 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.1765e-06 | 1.1765e-04 |           8 |     6800000 |            2 |       17000 |         2.1 |iter: 16/100\r",
      "      3.5 | 1.1111e-06 | 1.1111e-04 |           8 |     7200000 |            2 |       18000 |         2.2 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 1.0526e-06 | 1.0526e-04 |           8 |     7600000 |            2 |       19000 |         2.3 |iter: 18/100\r",
      "      3.5 | 1.0000e-06 | 1.0000e-04 |           8 |     8000000 |            2 |       20000 |         2.4 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 9.5238e-07 | 9.5238e-05 |           8 |     8400000 |            2 |       21000 |         2.6 |iter: 20/100\r",
      "      3.5 | 9.0909e-07 | 9.0909e-05 |           8 |     8800000 |            2 |       22000 |         2.7 |iter: 21/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 8.6957e-07 | 8.6957e-05 |           8 |     9200000 |            2 |       23000 |         2.8 |iter: 22/100\r",
      "      3.5 | 8.3333e-07 | 8.3333e-05 |           8 |     9600000 |            2 |       24000 |         2.9 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 8.0000e-07 | 8.0000e-05 |           8 |    10000000 |            2 |       25000 |         3.1 |iter: 24/100\r",
      "      3.5 | 7.6923e-07 | 7.6923e-05 |           8 |    10400000 |            2 |       26000 |         3.2 |iter: 25/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 9.2593e-07 | 1.1111e-04 |          10 |    10800000 |            3 |       27000 |         3.3 |iter: 26/100\r",
      "      3.5 | 8.9286e-07 | 1.0714e-04 |          10 |    11200000 |            3 |       28000 |         3.4 |iter: 27/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 8.6207e-07 | 1.0345e-04 |          10 |    11600000 |            3 |       29000 |         3.5 |iter: 28/100\r",
      "      3.5 | 8.3333e-07 | 1.0000e-04 |          10 |    12000000 |            3 |       30000 |         3.7 |iter: 29/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 8.0645e-07 | 9.6774e-05 |          10 |    12400000 |            3 |       31000 |         3.8 |iter: 30/100\r",
      "      3.5 | 7.8125e-07 | 9.3750e-05 |          10 |    12800000 |            3 |       32000 |         3.9 |iter: 31/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 7.5758e-07 | 9.0909e-05 |          10 |    13200000 |            3 |       33000 |         4.0 |iter: 32/100\r",
      "      3.5 | 7.3529e-07 | 8.8235e-05 |          10 |    13600000 |            3 |       34000 |         4.2 |iter: 33/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 7.1429e-07 | 8.5714e-05 |          10 |    14000000 |            3 |       35000 |         4.3 |iter: 34/100\r",
      "      3.5 | 6.9444e-07 | 8.3333e-05 |          10 |    14400000 |            3 |       36000 |         4.4 |iter: 35/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 6.7568e-07 | 8.1081e-05 |          10 |    14800000 |            3 |       37000 |         4.5 |iter: 36/100\r",
      "      3.5 | 6.5789e-07 | 7.8947e-05 |          10 |    15200000 |            3 |       38000 |         4.6 |iter: 37/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 6.4103e-07 | 7.6923e-05 |          10 |    15600000 |            3 |       39000 |         4.8 |iter: 38/100\r",
      "      3.5 | 6.2500e-07 | 7.5000e-05 |          10 |    16000000 |            3 |       40000 |         4.9 |iter: 39/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 6.0976e-07 | 7.3171e-05 |          10 |    16400000 |            3 |       41000 |         5.0 |iter: 40/100\r",
      "      3.5 | 5.9524e-07 | 7.1429e-05 |          10 |    16800000 |            3 |       42000 |         5.1 |iter: 41/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.8140e-07 | 6.9767e-05 |          10 |    17200000 |            3 |       43000 |         5.3 |iter: 42/100\r",
      "      3.5 | 5.6818e-07 | 6.8182e-05 |          10 |    17600000 |            3 |       44000 |         5.4 |iter: 43/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.5556e-07 | 6.6667e-05 |          10 |    18000000 |            3 |       45000 |         5.5 |iter: 44/100\r",
      "      3.5 | 5.4348e-07 | 6.5217e-05 |          10 |    18400000 |            3 |       46000 |         5.6 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 6.3830e-07 | 8.5106e-05 |          12 |    18800000 |            4 |       47000 |         5.7 |iter: 46/100\r",
      "      3.5 | 6.2500e-07 | 8.3333e-05 |          12 |    19200000 |            4 |       48000 |         5.9 |iter: 47/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 6.1224e-07 | 8.1633e-05 |          12 |    19600000 |            4 |       49000 |         6.0 |iter: 48/100\r",
      "      3.5 | 6.0000e-07 | 8.0000e-05 |          12 |    20000000 |            4 |       50000 |         6.1 |iter: 49/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.8824e-07 | 7.8431e-05 |          12 |    20400000 |            4 |       51000 |         6.2 |iter: 50/100\r",
      "      3.5 | 5.7692e-07 | 7.6923e-05 |          12 |    20800000 |            4 |       52000 |         6.3 |iter: 51/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.6604e-07 | 7.5472e-05 |          12 |    21200000 |            4 |       53000 |         6.5 |iter: 52/100\r",
      "      3.5 | 5.5556e-07 | 7.4074e-05 |          12 |    21600000 |            4 |       54000 |         6.6 |iter: 53/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.9091e-07 | 9.0909e-05 |          13 |    22000000 |            5 |       55000 |         6.7 |iter: 54/100\r",
      "      3.5 | 5.8036e-07 | 8.9286e-05 |          13 |    22400000 |            5 |       56000 |         6.8 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.7018e-07 | 8.7719e-05 |          13 |    22800000 |            5 |       57000 |         7.0 |iter: 56/100\r",
      "      3.5 | 5.6034e-07 | 8.6207e-05 |          13 |    23200000 |            5 |       58000 |         7.1 |iter: 57/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.5085e-07 | 8.4746e-05 |          13 |    23600000 |            5 |       59000 |         7.2 |iter: 58/100\r",
      "      3.5 | 5.4167e-07 | 8.3333e-05 |          13 |    24000000 |            5 |       60000 |         7.3 |iter: 59/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.3279e-07 | 8.1967e-05 |          13 |    24400000 |            5 |       61000 |         7.4 |iter: 60/100\r",
      "      3.5 | 5.2419e-07 | 8.0645e-05 |          13 |    24800000 |            5 |       62000 |         7.6 |iter: 61/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.1587e-07 | 7.9365e-05 |          13 |    25200000 |            5 |       63000 |         7.7 |iter: 62/100\r",
      "      3.5 | 5.0781e-07 | 7.8125e-05 |          13 |    25600000 |            5 |       64000 |         7.8 |iter: 63/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 5.0000e-07 | 7.6923e-05 |          13 |    26000000 |            5 |       65000 |         7.9 |iter: 64/100\r",
      "      3.5 | 4.9242e-07 | 7.5758e-05 |          13 |    26400000 |            5 |       66000 |         8.0 |iter: 65/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.8507e-07 | 7.4627e-05 |          13 |    26800000 |            5 |       67000 |         8.1 |iter: 66/100\r",
      "      3.5 | 4.7794e-07 | 7.3529e-05 |          13 |    27200000 |            5 |       68000 |         8.2 |iter: 67/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.7101e-07 | 7.2464e-05 |          13 |    27600000 |            5 |       69000 |         8.4 |iter: 68/100\r",
      "      3.5 | 4.6429e-07 | 7.1429e-05 |          13 |    28000000 |            5 |       70000 |         8.5 |iter: 69/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.5775e-07 | 7.0423e-05 |          13 |    28400000 |            5 |       71000 |         8.6 |iter: 70/100\r",
      "      3.5 | 4.5139e-07 | 6.9444e-05 |          13 |    28800000 |            5 |       72000 |         8.7 |iter: 71/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.4521e-07 | 6.8493e-05 |          13 |    29200000 |            5 |       73000 |         8.8 |iter: 72/100\r",
      "      3.5 | 4.3919e-07 | 6.7568e-05 |          13 |    29600000 |            5 |       74000 |         8.9 |iter: 73/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.3333e-07 | 6.6667e-05 |          13 |    30000000 |            5 |       75000 |         9.0 |iter: 74/100\r",
      "      3.5 | 4.2763e-07 | 6.5789e-05 |          13 |    30400000 |            5 |       76000 |         9.2 |iter: 75/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.2208e-07 | 6.4935e-05 |          13 |    30800000 |            5 |       77000 |         9.3 |iter: 76/100\r",
      "      3.5 | 4.1667e-07 | 6.4103e-05 |          13 |    31200000 |            5 |       78000 |         9.4 |iter: 77/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.1139e-07 | 6.3291e-05 |          13 |    31600000 |            5 |       79000 |         9.5 |iter: 78/100\r",
      "      3.5 | 4.0625e-07 | 6.2500e-05 |          13 |    32000000 |            5 |       80000 |         9.6 |iter: 79/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 4.0123e-07 | 6.1728e-05 |          13 |    32400000 |            5 |       81000 |         9.7 |iter: 80/100\r",
      "      3.5 | 3.9634e-07 | 6.0976e-05 |          13 |    32800000 |            5 |       82000 |         9.8 |iter: 81/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.9157e-07 | 6.0241e-05 |          13 |    33200000 |            5 |       83000 |        10.0 |iter: 82/100\r",
      "      3.5 | 3.8690e-07 | 5.9524e-05 |          13 |    33600000 |            5 |       84000 |        10.1 |iter: 83/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.8235e-07 | 5.8824e-05 |          13 |    34000000 |            5 |       85000 |        10.2 |iter: 84/100\r",
      "      3.5 | 3.7791e-07 | 5.8140e-05 |          13 |    34400000 |            5 |       86000 |        10.3 |iter: 85/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.7356e-07 | 5.7471e-05 |          13 |    34800000 |            5 |       87000 |        10.4 |iter: 86/100\r",
      "      3.5 | 3.6932e-07 | 5.6818e-05 |          13 |    35200000 |            5 |       88000 |        10.5 |iter: 87/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.6517e-07 | 5.6180e-05 |          13 |    35600000 |            5 |       89000 |        10.6 |iter: 88/100\r",
      "      3.5 | 3.6111e-07 | 5.5556e-05 |          13 |    36000000 |            5 |       90000 |        10.7 |iter: 89/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.5714e-07 | 5.4945e-05 |          13 |    36400000 |            5 |       91000 |        10.9 |iter: 90/100\r",
      "      3.5 | 3.5326e-07 | 5.4348e-05 |          13 |    36800000 |            5 |       92000 |        11.0 |iter: 91/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.4946e-07 | 5.3763e-05 |          13 |    37200000 |            5 |       93000 |        11.1 |iter: 92/100\r",
      "      3.5 | 3.4574e-07 | 5.3191e-05 |          13 |    37600000 |            5 |       94000 |        11.2 |iter: 93/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.4211e-07 | 5.2632e-05 |          13 |    38000000 |            5 |       95000 |        11.3 |iter: 94/100\r",
      "      3.5 | 3.3854e-07 | 5.2083e-05 |          13 |    38400000 |            5 |       96000 |        11.4 |iter: 95/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.3505e-07 | 5.1546e-05 |          13 |    38800000 |            5 |       97000 |        11.5 |iter: 96/100\r",
      "      3.5 | 3.3163e-07 | 5.1020e-05 |          13 |    39200000 |            5 |       98000 |        11.7 |iter: 97/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      3.5 | 3.2828e-07 | 5.0505e-05 |          13 |    39600000 |            5 |       99000 |        11.8 |iter: 98/100\r",
      "      3.5 | 3.2500e-07 | 5.0000e-05 |          13 |    40000000 |            5 |      100000 |        11.9 |iter: 99/100\r",
      "      3.5 | 3.2500e-07 | 5.0000e-05 |          13 |    40000000 |            5 |      100000 |        11.9 |reached max iterations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |      400000 |            0 |        1000 |         0.1 |iter: 0/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |      800000 |            0 |        2000 |         0.2 |iter: 1/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     1200000 |            0 |        3000 |         0.3 |iter: 2/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     1600000 |            0 |        4000 |         0.5 |iter: 3/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     2000000 |            0 |        5000 |         0.6 |iter: 4/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     2400000 |            0 |        6000 |         0.7 |iter: 5/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     2800000 |            0 |        7000 |         0.8 |iter: 6/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     3200000 |            0 |        8000 |         0.9 |iter: 7/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     3600000 |            0 |        9000 |         1.0 |iter: 8/100\r",
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     4000000 |            0 |       10000 |         1.1 |iter: 9/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 0.0000e+00 | 0.0000e+00 |           0 |     4400000 |            0 |       11000 |         1.2 |iter: 10/100\r",
      "     3.75 | 2.0833e-07 | 8.3333e-05 |           1 |     4800000 |            1 |       12000 |         1.4 |iter: 11/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 1.9231e-07 | 7.6923e-05 |           1 |     5200000 |            1 |       13000 |         1.5 |iter: 12/100\r",
      "     3.75 | 1.7857e-07 | 7.1429e-05 |           1 |     5600000 |            1 |       14000 |         1.6 |iter: 13/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 1.6667e-07 | 6.6667e-05 |           1 |     6000000 |            1 |       15000 |         1.7 |iter: 14/100\r",
      "     3.75 | 1.5625e-07 | 6.2500e-05 |           1 |     6400000 |            1 |       16000 |         1.8 |iter: 15/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 1.4706e-07 | 5.8824e-05 |           1 |     6800000 |            1 |       17000 |         1.9 |iter: 16/100\r",
      "     3.75 | 1.3889e-07 | 5.5556e-05 |           1 |     7200000 |            1 |       18000 |         2.0 |iter: 17/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 1.3158e-07 | 5.2632e-05 |           1 |     7600000 |            1 |       19000 |         2.2 |iter: 18/100\r",
      "     3.75 | 1.2500e-07 | 5.0000e-05 |           1 |     8000000 |            1 |       20000 |         2.3 |iter: 19/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 1.1905e-07 | 4.7619e-05 |           1 |     8400000 |            1 |       21000 |         2.4 |iter: 20/100\r",
      "     3.75 | 1.1364e-07 | 4.5455e-05 |           1 |     8800000 |            1 |       22000 |         2.5 |iter: 21/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 1.0870e-07 | 4.3478e-05 |           1 |     9200000 |            1 |       23000 |         2.6 |iter: 22/100\r",
      "     3.75 | 1.0417e-07 | 4.1667e-05 |           1 |     9600000 |            1 |       24000 |         2.7 |iter: 23/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 1.0000e-07 | 4.0000e-05 |           1 |    10000000 |            1 |       25000 |         2.8 |iter: 24/100\r",
      "     3.75 | 9.6154e-08 | 3.8462e-05 |           1 |    10400000 |            1 |       26000 |         2.9 |iter: 25/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 9.2593e-08 | 3.7037e-05 |           1 |    10800000 |            1 |       27000 |         3.1 |iter: 26/100\r",
      "     3.75 | 8.9286e-08 | 3.5714e-05 |           1 |    11200000 |            1 |       28000 |         3.2 |iter: 27/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 8.6207e-08 | 3.4483e-05 |           1 |    11600000 |            1 |       29000 |         3.3 |iter: 28/100\r",
      "     3.75 | 8.3333e-08 | 3.3333e-05 |           1 |    12000000 |            1 |       30000 |         3.4 |iter: 29/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 8.0645e-08 | 3.2258e-05 |           1 |    12400000 |            1 |       31000 |         3.5 |iter: 30/100\r",
      "     3.75 | 7.8125e-08 | 3.1250e-05 |           1 |    12800000 |            1 |       32000 |         3.6 |iter: 31/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 7.5758e-08 | 3.0303e-05 |           1 |    13200000 |            1 |       33000 |         3.7 |iter: 32/100\r",
      "     3.75 | 7.3529e-08 | 2.9412e-05 |           1 |    13600000 |            1 |       34000 |         3.9 |iter: 33/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 7.1429e-08 | 2.8571e-05 |           1 |    14000000 |            1 |       35000 |         4.0 |iter: 34/100\r",
      "     3.75 | 6.9444e-08 | 2.7778e-05 |           1 |    14400000 |            1 |       36000 |         4.1 |iter: 35/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 6.7568e-08 | 2.7027e-05 |           1 |    14800000 |            1 |       37000 |         4.2 |iter: 36/100\r",
      "     3.75 | 6.5789e-08 | 2.6316e-05 |           1 |    15200000 |            1 |       38000 |         4.3 |iter: 37/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 6.4103e-08 | 2.5641e-05 |           1 |    15600000 |            1 |       39000 |         4.4 |iter: 38/100\r",
      "     3.75 | 6.2500e-08 | 2.5000e-05 |           1 |    16000000 |            1 |       40000 |         4.5 |iter: 39/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 6.0976e-08 | 2.4390e-05 |           1 |    16400000 |            1 |       41000 |         4.7 |iter: 40/100\r",
      "     3.75 | 5.9524e-08 | 2.3810e-05 |           1 |    16800000 |            1 |       42000 |         4.8 |iter: 41/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 5.8140e-08 | 2.3256e-05 |           1 |    17200000 |            1 |       43000 |         4.9 |iter: 42/100\r",
      "     3.75 | 5.6818e-08 | 2.2727e-05 |           1 |    17600000 |            1 |       44000 |         5.0 |iter: 43/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 5.5556e-08 | 2.2222e-05 |           1 |    18000000 |            1 |       45000 |         5.1 |iter: 44/100\r",
      "     3.75 | 5.4348e-08 | 2.1739e-05 |           1 |    18400000 |            1 |       46000 |         5.2 |iter: 45/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 5.3191e-08 | 2.1277e-05 |           1 |    18800000 |            1 |       47000 |         5.3 |iter: 46/100\r",
      "     3.75 | 5.2083e-08 | 2.0833e-05 |           1 |    19200000 |            1 |       48000 |         5.4 |iter: 47/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 5.1020e-08 | 2.0408e-05 |           1 |    19600000 |            1 |       49000 |         5.6 |iter: 48/100\r",
      "     3.75 | 5.0000e-08 | 2.0000e-05 |           1 |    20000000 |            1 |       50000 |         5.7 |iter: 49/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 4.9020e-08 | 1.9608e-05 |           1 |    20400000 |            1 |       51000 |         5.8 |iter: 50/100\r",
      "     3.75 | 4.8077e-08 | 1.9231e-05 |           1 |    20800000 |            1 |       52000 |         5.9 |iter: 51/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 4.7170e-08 | 1.8868e-05 |           1 |    21200000 |            1 |       53000 |         6.0 |iter: 52/100\r",
      "     3.75 | 4.6296e-08 | 1.8519e-05 |           1 |    21600000 |            1 |       54000 |         6.1 |iter: 53/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 4.5455e-08 | 1.8182e-05 |           1 |    22000000 |            1 |       55000 |         6.2 |iter: 54/100\r",
      "     3.75 | 4.4643e-08 | 1.7857e-05 |           1 |    22400000 |            1 |       56000 |         6.4 |iter: 55/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 4.3860e-08 | 1.7544e-05 |           1 |    22800000 |            1 |       57000 |         6.5 |iter: 56/100\r",
      "     3.75 | 4.3103e-08 | 1.7241e-05 |           1 |    23200000 |            1 |       58000 |         6.6 |iter: 57/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 4.2373e-08 | 1.6949e-05 |           1 |    23600000 |            1 |       59000 |         6.7 |iter: 58/100\r",
      "     3.75 | 4.1667e-08 | 1.6667e-05 |           1 |    24000000 |            1 |       60000 |         6.8 |iter: 59/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 4.0984e-08 | 1.6393e-05 |           1 |    24400000 |            1 |       61000 |         6.9 |iter: 60/100\r",
      "     3.75 | 4.0323e-08 | 1.6129e-05 |           1 |    24800000 |            1 |       62000 |         7.0 |iter: 61/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 3.9683e-08 | 1.5873e-05 |           1 |    25200000 |            1 |       63000 |         7.1 |iter: 62/100\r",
      "     3.75 | 3.9063e-08 | 1.5625e-05 |           1 |    25600000 |            1 |       64000 |         7.3 |iter: 63/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 3.8462e-08 | 1.5385e-05 |           1 |    26000000 |            1 |       65000 |         7.4 |iter: 64/100\r",
      "     3.75 | 3.7879e-08 | 1.5152e-05 |           1 |    26400000 |            1 |       66000 |         7.5 |iter: 65/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 3.7313e-08 | 1.4925e-05 |           1 |    26800000 |            1 |       67000 |         7.6 |iter: 66/100\r",
      "     3.75 | 3.6765e-08 | 1.4706e-05 |           1 |    27200000 |            1 |       68000 |         7.7 |iter: 67/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 3.6232e-08 | 1.4493e-05 |           1 |    27600000 |            1 |       69000 |         7.8 |iter: 68/100\r",
      "     3.75 | 3.5714e-08 | 1.4286e-05 |           1 |    28000000 |            1 |       70000 |         7.9 |iter: 69/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 3.5211e-08 | 1.4085e-05 |           1 |    28400000 |            1 |       71000 |         8.0 |iter: 70/100\r",
      "     3.75 | 3.4722e-08 | 1.3889e-05 |           1 |    28800000 |            1 |       72000 |         8.2 |iter: 71/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 3.4247e-08 | 1.3699e-05 |           1 |    29200000 |            1 |       73000 |         8.3 |iter: 72/100\r",
      "     3.75 | 3.3784e-08 | 1.3514e-05 |           1 |    29600000 |            1 |       74000 |         8.4 |iter: 73/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 3.3333e-08 | 1.3333e-05 |           1 |    30000000 |            1 |       75000 |         8.5 |iter: 74/100\r",
      "     3.75 | 3.2895e-08 | 1.3158e-05 |           1 |    30400000 |            1 |       76000 |         8.6 |iter: 75/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 3.2468e-08 | 1.2987e-05 |           1 |    30800000 |            1 |       77000 |         8.7 |iter: 76/100\r",
      "     3.75 | 3.2051e-08 | 1.2821e-05 |           1 |    31200000 |            1 |       78000 |         8.8 |iter: 77/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 3.1646e-08 | 1.2658e-05 |           1 |    31600000 |            1 |       79000 |         8.9 |iter: 78/100\r",
      "     3.75 | 3.1250e-08 | 1.2500e-05 |           1 |    32000000 |            1 |       80000 |         9.0 |iter: 79/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 3.0864e-08 | 1.2346e-05 |           1 |    32400000 |            1 |       81000 |         9.2 |iter: 80/100\r",
      "     3.75 | 3.0488e-08 | 1.2195e-05 |           1 |    32800000 |            1 |       82000 |         9.3 |iter: 81/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 3.0120e-08 | 1.2048e-05 |           1 |    33200000 |            1 |       83000 |         9.4 |iter: 82/100\r",
      "     3.75 | 2.9762e-08 | 1.1905e-05 |           1 |    33600000 |            1 |       84000 |         9.5 |iter: 83/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 2.9412e-08 | 1.1765e-05 |           1 |    34000000 |            1 |       85000 |         9.6 |iter: 84/100\r",
      "     3.75 | 2.9070e-08 | 1.1628e-05 |           1 |    34400000 |            1 |       86000 |         9.7 |iter: 85/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 2.8736e-08 | 1.1494e-05 |           1 |    34800000 |            1 |       87000 |         9.8 |iter: 86/100\r",
      "     3.75 | 2.8409e-08 | 1.1364e-05 |           1 |    35200000 |            1 |       88000 |         9.9 |iter: 87/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 2.8090e-08 | 1.1236e-05 |           1 |    35600000 |            1 |       89000 |        10.0 |iter: 88/100\r",
      "     3.75 | 2.7778e-08 | 1.1111e-05 |           1 |    36000000 |            1 |       90000 |        10.2 |iter: 89/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 2.7473e-08 | 1.0989e-05 |           1 |    36400000 |            1 |       91000 |        10.3 |iter: 90/100\r",
      "     3.75 | 2.7174e-08 | 1.0870e-05 |           1 |    36800000 |            1 |       92000 |        10.4 |iter: 91/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 2.6882e-08 | 1.0753e-05 |           1 |    37200000 |            1 |       93000 |        10.5 |iter: 92/100\r",
      "     3.75 | 2.6596e-08 | 1.0638e-05 |           1 |    37600000 |            1 |       94000 |        10.6 |iter: 93/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 2.6316e-08 | 1.0526e-05 |           1 |    38000000 |            1 |       95000 |        10.7 |iter: 94/100\r",
      "     3.75 | 2.6042e-08 | 1.0417e-05 |           1 |    38400000 |            1 |       96000 |        10.8 |iter: 95/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 2.5773e-08 | 1.0309e-05 |           1 |    38800000 |            1 |       97000 |        10.9 |iter: 96/100\r",
      "     3.75 | 2.5510e-08 | 1.0204e-05 |           1 |    39200000 |            1 |       98000 |        11.0 |iter: 97/100\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     3.75 | 2.5253e-08 | 1.0101e-05 |           1 |    39600000 |            1 |       99000 |        11.2 |iter: 98/100\r",
      "     3.75 | 2.5000e-08 | 1.0000e-05 |           1 |    40000000 |            1 |      100000 |        11.3 |iter: 99/100\r",
      "     3.75 | 2.5000e-08 | 1.0000e-05 |           1 |    40000000 |            1 |      100000 |        11.3 |reached max iterations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ebno_dbs = np.array(np.arange(0, 4, 0.25))\n",
    "batch_size = 1000\n",
    "mc_iters = 100\n",
    "\n",
    "ber_plot_5G.simulate(model5G,\n",
    "                     ebno_dbs=ebno_dbs,\n",
    "                     batch_size=batch_size,\n",
    "                     num_target_bit_errors=2000, # stop sim after 2000 bit errors\n",
    "                     legend=\"Trained\",\n",
    "                     max_mc_iter=mc_iters,\n",
    "                     soft_estimates=True,\n",
    "                     compile_mode=None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, we observe only very minor gains for the 5G LDPC code. We empirically observed that gain vanishes for more iterations and longer codewords, i.e., for most practical use-cases of the 5G LDPC code the gains are only minor.\n",
    "\n",
    "However, there may be other `codes on graphs` that benefit from the principle idea of weighted BP - or other channel setups? Feel free to adjust this notebook and train for your favorite code / channel.\n",
    "\n",
    "Other ideas for own experiments:\n",
    "\n",
    "- Implement weighted BP with unique weights per iteration.\n",
    "- Apply the concept to (scaled) min-sum decoding as in [5].\n",
    "- Can you replace the complete CN update by a neural network?\n",
    "- Verify the results from all-zero simulations for a *real* system simulation with explicit encoder and random data\n",
    "- What happens in combination with higher order modulation?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[1]\u00a0E. Nachmani, Y. Be\u2019ery and D. Burshtein, \"Learning to Decode Linear Codes Using Deep Learning,\"\n",
    "IEEE Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 341-346., 2016. https://arxiv.org/pdf/1607.04793.pdf\n",
    "\n",
    "[2] M. Lian, C. H\u00e4ger, and H. Pfister, \"What can machine learning teach us about communications?\" IEEE Information Theory Workshop (ITW), pp. 1-5. 2018.\n",
    "\n",
    "[3] ] M. Pretti, \u201cA message passing algorithm with damping,\u201d J. Statist. Mech.: Theory Practice, p. 11008, Nov. 2005.\n",
    "\n",
    "[4] J.S. Yedidia, W.T. Freeman and Y. Weiss, \"Constructing free energy approximations and Generalized Belief Propagation algorithms,\" IEEE Transactions on Information Theory, 2005.\n",
    "\n",
    "[5] E. Nachmani, E. Marciano, L. Lugosch, W. Gross, D. Burshtein and Y. Be\u2019ery, \"Deep learning methods for improved decoding of linear codes,\" IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp.119-131, 2018."
   ]
  }
 ],
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