Feynman_Kac_Solver.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fI-kfxwkxWJv"
   },
   "source": [
    "# Solution of Linear PDEs in High Dimensions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5pbqiIgtxWJ0"
   },
   "source": [
    "In this Jupyter Notebook we illustrate a PDE solver based on the Feynman-Kac formula as proposed in\n",
    "\n",
    "- Beck, Christian, et al. *Solving stochastic differential equations and Kolmogorov equations by means of deep learning*. [arXiv 1806.00421](https://arxiv.org/abs/1806.00421).\n",
    "\n",
    "This notebook is partially based on the TensorFlow 1 code appended to the aforementioned preprint.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/janblechschmidt/PDEsByNNs/blob/main/Feynman_Kac_Solver.ipynb\" target=\"_parent\">\n",
    "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZxwN1C_kxWJ1"
   },
   "source": [
    "## Introduction\n",
    "\n",
    "We consider the solution by neural network methods of a class of partial differential equations which arise as the *backward Kolmogorov equation* of stochastic processes known as *Itô diffusions*.\n",
    "We begin with linear parabolic second-order partial differential equation in non-divergence form\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    \\partial_t u(t,x) + \\frac{1}{2} \\sigma \\sigma^T(t,x) : \\nabla^2 u(t,x) + \\mu(t,x) \\cdot \\nabla u(t,x) \n",
    "    &= 0, \t\t\t\\quad && (t,x) \\in [0,T) \\times \\mathbb R^d,\\\\\n",
    "    u(T,x) &= g(x), \t\\quad && x \\in \\mathbb R^d,\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "and subsequently move to more general PDEs.\n",
    "We consider the pure Cauchy problem, allowing the state variable $x$ to vary throughout $\\mathbb R^d$, were $d \\in \\mathbb{N}$ is the spatial dimension, \n",
    "$\\nabla u(t,x)$ and $\\nabla^2 u(t,x)$ denote the gradient and Hessian of the function $u$, respectively, the colon $:$ denotes the Frobenius inner product of $d \\times d$ matrices, i.e., $A:B = \\sum_{i,j=1}^d a_{ij} \\, b_{ij}$, and the dot $\\cdot$ the Euclidean inner product on $\\mathbb R^d$.\n",
    "We assume the coefficient functions $\\mu\\colon[0,T] \\times \\mathbb R^d \\to \\mathbb R^d$ (drift) and $\\sigma\\colon[0,T] \\times\\mathbb R^d \\to \\mathbb R^{d \\times d}$ (diffusion) to be globally Lipschitz continuous.\n",
    "Due to the stochastic process connection, the backward Kolmogorov equation is posed as a *final time problem* with data prescribed at time $t=T$ given by a function $g\\colon \\mathbb R^d \\to \\mathbb R$.\n",
    "The simple change of variables $t \\mapsto T - t$ leads to the more familiar initial value form\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "   \\partial_t u(t,x) - \\frac{1}{2} \\sigma \\sigma^T(t,x) : \\nabla^2 u(t,x) - \\mu(t,x) \\cdot \\nabla u(t,x) \n",
    "   &= 0, \t\t\t\\quad && (t,x) \\in (0,T] \\times \\mathbb R^d,\\\\\n",
    "   u(0,x) &= g(x), \t\\quad && x \\in\\mathbb R^d.\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "Equations in non-divergence form like the backward Kolmogorov equation with  leading term $\\sigma \\sigma^T(t,x) \\colon \\nabla^2u(t,x)$ typically arise in the context of stochastic differential equations (SDEs) due to the Itô formula.\n",
    "\n",
    "The goal of the computations that follow is to approximate the solution of the PDE at a fixed time $t=0$, i.e., the function $u(0,x)$ where $x$ varies over some $d$-dimensional hypercube $\\mathcal{D} := [a,b] \\subset \\mathbb{R}^d$ with endpoint vectors $a \\le b$ (component-wise). We denote this approximation by $u_\\theta(x)$, where $\\theta$ denotes a vector of parameters describing the degrees of freedom of the approximation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qfq3CVDQxWJ1"
   },
   "source": [
    "## Theoretical Background\n",
    "The method is based on the Feynman-Kac formula for Kolmogorov PDEs which connects such PDEs with the expectation of a stochastic process.\n",
    "\n",
    "Given a filtered probability space $(\\Omega, \\mathcal{F}, \\mathbb{P}; \\mathbb{F})$ equipped with the filtration $\\mathbb{F}=\\{\\mathcal{F}_t\\}_{t\\in[0,T]}$ induced by a Brownian motion $\\{W_t\\}_{t \\in [0,T]}$,\n",
    "the stochastic process $\\{X_t\\}_{t \\in [0,T]}$ governed by the SDE \n",
    "\n",
    "$$\n",
    "X_t = x + \\int_0^t \\mu(s, X_s) \\, ds + \\int_0^t \\sigma(s, X_s) \\, dW_s,\n",
    "\\qquad t \\in [0,T]\n",
    "$$\n",
    "\n",
    "describes the evolution of the state variable in $\\mathbb R^d$. The Feynman-Kac formula connects the solution of the (linear) PDE given above with this stochastic process.\n",
    "Specifically, its solution at a point $(t,x) \\in [0,T] \\times \\mathbb R^d$ is given by the conditional expectation\n",
    "\n",
    "$$\n",
    "u(t,x) = \\mathbb{E} [g(X_T) | X_t = x].\n",
    "$$\n",
    "\n",
    "This connection can be exploited to approximate the PDE solution at a fixed point in space and time $(t,x)$ by simulating many paths/realizations of the stochastic process\n",
    "$\\{X^j_\\tau\\}_{\\tau \\in [t,T]}$, which all start at $X_t=x$\n",
    "for $j=1,\\ldots,n_{samples}$ and approximating the expectation by the sample average\n",
    "\n",
    "$$\n",
    "u(t,x) \\approx \\frac{1}{n_{samples}} \\sum_{j=1}^{n_{samples}} g(X_T^j).\n",
    "$$\n",
    "\n",
    "The method reviewed here differs in that it constructs, for a fixed time $t_0$ (we set $t_0=0$ below), an approximation of the solution not only at a single point $(t_0,x) \\in [0,T] \\times \\mathbb{R}^d$ but throughout a bounded domain of interest $\\mathcal{D} \\subset \\mathbb{R}^d$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Kh4I9xXvRMc"
   },
   "source": [
    "## Solution Method\n",
    "The approach relies on\n",
    "\n",
    "1. the generation of training data by generating sample paths of the stochastic process $\\{X_t\\}_{t \\in [0,T]}$ and\n",
    "2. a neural network approximation of the unknown function $u(0,x) \\approx u_\\theta(x)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sdx4mFItGjCG"
   },
   "source": [
    "### Step 1: Generation of training data\n",
    "\n",
    "The neural network model is trained with data pairs $(x, y)$ such that the values of $x$ are realizations of a random variable $X \\sim \\mathsf U (\\mathcal D)$ and $y = g(X_T)$, where $X_T$ is the final value at $t=T$ of the stochastic process $\\{X_t\\}_{t \\in[0,T]}$ evolving according to the SDE\n",
    "\n",
    "$$\n",
    "X_T = x + \\int_0^T \\mu(s, X_s) \\, ds + \\int_0^T \\sigma(s, X_s) \\, dW_s.\n",
    "$$\n",
    "\n",
    "Each path of the process starts at a uniformly drawn initial point $X_0 = x$.\n",
    "We distinguish two cases:\n",
    "\n",
    "#### Simple case: Distribution of $X_T$ explicitly known\n",
    "\n",
    "When the probability distribution of $X_T$ is explicitly known, we can draw pairs $(x,y)$ directly.\n",
    "For example, when $\\{X_t\\}$ is a scaled Brownian motion with dynamics characterized by $\\mu(t,x) \\equiv 0$ and $\\sigma(t,x) \\equiv \\sigma$, the solution process is given by\n",
    "\n",
    "$$\n",
    "X_t = x + \\sigma \\, W_t, \\qquad 0 \\le t \\le T.\n",
    "$$\n",
    "\n",
    "Thus, we may draw $x$ from $\\mathsf U (\\mathcal D)$ and set $y := g(x + \\sqrt{T} \\, \\sigma \\, \\xi)$ where $\\xi \\sim \\mathsf N (0, \\mathrm{Id}_{d \\times d})$ is a random variable with a $d$-variate standard normal distribution.\n",
    "\n",
    "#### General case: Distribution of $X_T$ unknown\n",
    "\n",
    "When the solution process is not available in closed form, we can still generate approximate sample paths of $\\{X_t\\}_{t \\in [0,T]}$ numerically in order to sample from $X_T$.\n",
    "This can be done, for example, using the Euler-Maruyama scheme:\n",
    "\n",
    "$$\n",
    "\\tilde X_{n+1} := \\tilde X_{n}\n",
    "    + \\mu(t_n, \\tilde X_{n}) \\, (t_{n+1} - t_n)\n",
    "    + \\sigma(t_n, \\tilde X_{n}) \\, (W_{t_{n+1}} - W_{t_n}),\n",
    "\\qquad\n",
    "\\tilde X_0 := x,\n",
    "$$\n",
    "\n",
    "where $\\tilde X_n \\approx  X_{t_n}$ and $0=t_0 < t_1 < \\ldots < t_N = T$ is a discretization of the time domain.\n",
    "Note that the increment of a Brownian motion $(W_{t_{n+1}} - W_{t_n}) \\sim \\mathsf{N}(0,(t_{n+1} - t_n) I_{d\\times d})$ is normally distributed.\n",
    "Finally, we set $y := g(\\tilde X_N)$.\n",
    "\n",
    "*Note*: Strong convergence results for the Euler-Maruyama scheme (cf. [Kloeden, Platen (1992)](https://www.springer.com/de/book/9783540540625)) ensure that $\\tilde X_n \\to X_{t_n}$ pathwise as $N \\to \\infty$ and $\\sup |t_n - t_{n-1}| \\to 0$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3sdvYFcXGs8h"
   },
   "source": [
    "### Step 2: Neural network approximation\n",
    "The PDE solution $u \\colon [0,T] \\times \\mathcal D \\to \\mathbb R$ is now approximated by neural networks with structure as given in [Beck et al. (2018)](https://arxiv.org/abs/1806.00421) as\n",
    "\n",
    "    Input -> BN -> (Dense -> BN -> TanH) -> (Dense -> BN -> TanH) -> Dense -> BN -> Output\n",
    "    \n",
    "Here, `BN` denotes Batch Normalization, `Dense` a fully connected layer **without** bias term and activation and `TanH` the application of the componentwise hyperbolic tangent activation function\n",
    "\n",
    "$$\n",
    "\\tanh(x) = \\frac{e^x - e^{-x}}{e^x + e^{-x}}.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aYfwElhSxWJ3"
   },
   "source": [
    "## Example: Heat equation\n",
    "\n",
    "As an example, consider the Cauchy problem for the $d$-dimensional heat equation\n",
    "\n",
    "$$\n",
    "\\partial_t u(t,x) = \\Delta u (t,x), \\qquad\n",
    "u(0,x) = \\|x\\|^2,\n",
    "$$\n",
    "where $\\Delta u = \\sum_{i=1}^d \\partial^2 u/\\partial x_i^2$ denotes the Laplacian of $u$.\n",
    "\n",
    "The problem is posed as an initial value problem with drift coefficient $\\mu(t,x) \\equiv 0$ and diffusion coefficient $\\sigma(t,x) \\equiv \\sqrt 2 $. One easily verifies that the solution is given by\n",
    "\n",
    "$$\n",
    "u(t,x) = \\|x\\|^2 + 2 \\, t \\, d.\n",
    "$$\n",
    "\n",
    "Our domain of interest is chosen as the $d$-dimensional unit cube $\\mathcal D = [0,1]^d$.\n",
    "\n",
    "In the following, we pesent code fragments for solving the heat equation in 100 spatial dimensions. \n",
    "The running time is reduced significantly by GPU acceleration, such as is available when running this Jupyter notebook on Google Colab (roughly 1 hour). Remember to explicitly enable GPU mode in the settings.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/janblechschmidt/PDEsByNNs/blob/main/Feynman_Kac_Solver.ipynb\" target=\"_parent\">\n",
    "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bB5d-AayxWJ4"
   },
   "source": [
    "### 1. Initialize problem\n",
    "This code runs with current TensorFlow versions (tested with 2.3.0 and 2.4.1).\n",
    "First, we import some necessary packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i0JdyLWkxWJ4",
    "outputId": "2b91a57a-f477-4655-9e07-9bf288348816"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.4.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from time import time\n",
    "\n",
    "# Set data type\n",
    "DTYPE='float32'\n",
    "#DTYPE='float64'\n",
    "tf.keras.backend.set_floatx(DTYPE)\n",
    "print('TensorFlow version used: {}'.format(tf.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ki-CS2bqNCh-"
   },
   "source": [
    "Next, we set some problem specific data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "v7JbleagxWJ6"
   },
   "outputs": [],
   "source": [
    "# Final time\n",
    "T = tf.constant(1., dtype=DTYPE)\n",
    "\n",
    "# Spatial dimension\n",
    "dim = 100\n",
    "\n",
    "# Spatial domain of interest at t=0 (hyperrectangle)\n",
    "a = np.zeros((dim), dtype=DTYPE)\n",
    "b = np.ones((dim), dtype=DTYPE)\n",
    "\n",
    "# Diffusion coefficient is assumed to be constant\n",
    "sigma = np.sqrt(2, dtype=DTYPE)\n",
    "\n",
    "# Define initial time condition\n",
    "def fun_g(x):\n",
    "    return tf.reduce_sum(tf.pow(x,2), axis=1, keepdims=True)\n",
    "\n",
    "# Define exact reference solution \n",
    "def fun_u(t, x):\n",
    "    return tf.reduce_sum(tf.pow(x,2), axis=1, keepdims=True) + 2 * t * dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ved8NAK4PBtY"
   },
   "source": [
    "### 2. Generation of training data\n",
    "Next, we define a method to generate `num_samples` of training data $(x, y)$.\n",
    "We are in the setting where we know the distribution of $X_T$. \n",
    "Thus, it suffices to\n",
    "- draw $x$ uniformly from $\\mathcal D$, i.e.,\n",
    "$$X \\sim \\mathsf U(\\mathcal D),$$\n",
    "- set\n",
    "$$y := g(X + \\sigma \\, \\sqrt{T} \\,\\xi),$$\\\n",
    "where $\\xi \\sim \\mathsf N(0, \\mathrm{Id}_{d \\times d})$ is drawn from a $d$-variate standard normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "D2foRCyXQoUY"
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def draw_X(num_samples, a, b, sigma=1.0):\n",
    "    \"\"\" Function to draw num_samples pairs of starting values X_0 \n",
    "    and end points X_T of a stochastic process X with zero drift and\n",
    "    constant scalar diffusion. Starting points are drawn uniformly from the\n",
    "    hypercube [a,b] \\\\subset \\mathbb{R}^d. \"\"\"\n",
    "    dim = a.shape[0]\n",
    "    \n",
    "    X0 = a + tf.random.uniform((num_samples, dim), dtype=DTYPE) * (b-a)\n",
    "\n",
    "    # Initialize the array X\n",
    "    xi = tf.random.normal(shape=(num_samples, dim), dtype=DTYPE)\n",
    "    \n",
    "    XT = X0 + sigma * np.sqrt(T) * xi\n",
    "\n",
    "    # Return simulated paths as well as increments of Brownian motion\n",
    "    return tf.stack([X0, XT], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BzkSIzzgT9zK"
   },
   "source": [
    "### 3. Set up neural network model\n",
    "\n",
    "The next function returns a neural network model in which one may change the used activation function (default: $\\tanh$) as well as the number of hidden neurons (default: $n_{hidden}=100$) in each of the hidden layers.\n",
    "\n",
    "    Input -> BN -> (Dense -> BN -> TanH) -> (Dense -> BN -> TanH) -> Dense -> BN -> Output\n",
    "    \n",
    "Here, `BN` denotes Batch Normalization, `Dense` indicates a fully connected layer **without** bias term and activation, `TanH` refers to the componentwise application of the hyperbolic tangent activation function.\n",
    "\n",
    "The chosen model contains a total of $11\\,944$ unknown parameters allotted to the network components as \n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\underbrace{4\\cdot d}_{\\texttt{BN0}}\n",
    "+ \\underbrace{d\\cdot n}_{\\texttt{Dense1}}\n",
    "+ \\underbrace{4\\cdot n}_{\\texttt{BN1}}\n",
    "+ \\underbrace{n\\cdot n}_{\\texttt{Dense2}}\n",
    "+ \\underbrace{4\\cdot n}_{\\texttt{BN2}} &\n",
    "+ \\underbrace{n\\cdot 1}_{\\texttt{Dense3}}\n",
    "+ \\underbrace{4\\cdot 1}_{\\texttt{BN3}}\n",
    "\\\\\n",
    "&\\quad= 4\\,(d + 1) + n\\,(d+9) + n^2\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "-UHnKtyvxWJ8"
   },
   "outputs": [],
   "source": [
    "def init_model(dim, activation='tanh',\n",
    "               num_hidden_neurons=100,\n",
    "               num_hidden_layers=2,\n",
    "               initializer=tf.keras.initializers.GlorotUniform()):\n",
    "    model = tf.keras.Sequential()\n",
    "    model.add(tf.keras.layers.Input(dim))\n",
    "    model.add(tf.keras.layers.BatchNormalization(epsilon=1e-6))\n",
    "    for _ in range(num_hidden_layers):\n",
    "        model.add(tf.keras.layers.Dense(num_hidden_neurons,\n",
    "                                activation=None,\n",
    "                                use_bias=False,\n",
    "                                kernel_initializer=initializer\n",
    "                                ))\n",
    "        model.add(tf.keras.layers.BatchNormalization(epsilon=1e-6))\n",
    "        model.add(tf.keras.layers.Activation(activation))\n",
    "    \n",
    "    model.add(tf.keras.layers.Dense(1,\n",
    "                                    activation=None,\n",
    "                                    use_bias=False,\n",
    "                                    kernel_initializer=initializer\n",
    "                                    ))\n",
    "    model.add(tf.keras.layers.BatchNormalization(epsilon=1e-6))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wzNT0vhQxWJ9"
   },
   "source": [
    "### 4. Define functions for the objective and its gradient\n",
    "In the next step, we define the loss function, i.e., the function to be minimized in the neural network's training.\n",
    "To achieve $u_\\theta(T,x) \\approx u(T,x) = \\mathbb{E}[g(X_T) | X_0=x]$, we minimize the mean-square error (MSE)\n",
    "\n",
    "$$\n",
    "\\mathbb{E} [ |g(X_T) - u_\\theta(T,x)|^2 ],\n",
    "$$\n",
    "\n",
    "where the expectation is taken with respect to the joint distribution of initial values and sample paths.\n",
    "The existence of a unique function $u$ which minimizes this objective and solves the PDE is based on Prop. 2.7 of [Beck et al. (2018)](https://arxiv.org/abs/1806.00421).\n",
    "\n",
    "For batches of size $m$, we approximate this quantity by the sample mean\n",
    "\n",
    "$$\n",
    "\\frac{1}{m} \\sum_{i=1}^m |y^i - u_\\theta(T,x^i)|^2,\n",
    "$$\n",
    "\n",
    "where $x^i$ is drawn uniformly from ${\\mathsf U}(\\mathcal D)$ and $y^i$ is a realization of $g(X_T^i)$ where $X_0^i = x^i$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "RrjDa9XFxWJ9"
   },
   "outputs": [],
   "source": [
    "def loss_fn(X, y, model, training=False):\n",
    "    \"\"\" This function computes the mean squared error between the current model\n",
    "    prediction model(X) and the values y.\n",
    "    Inputs:\n",
    "        X - approximation to the state process X\n",
    "        y - target value\n",
    "        model - model of neural network approximating x -> u(T,x)\n",
    "        training - boolean flag to indicate training\n",
    "    \"\"\"\n",
    "\n",
    "    X0 = X[:,:,0]\n",
    "    y_pred = model(X0, training)\n",
    "    \n",
    "    # Return mean squared error\n",
    "    return tf.reduce_mean(tf.math.squared_difference(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U7tGl-0MxWJ9"
   },
   "source": [
    "The next step uses the automatic differentiation functionality of TensorFlow to compute the gradient of the loss function with respect to the unknowns $\\theta$, called `trainable_variables` in TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "CRM5hJvjxWJ-"
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def compute_grad(X, y, model, training=False):\n",
    "    \"\"\" This function computes the gradient of the loss function w.r.t.\n",
    "    the trainable variables theta.\n",
    "    Inputs:\n",
    "        X - approximation to the state process X\n",
    "        y - target value\n",
    "        model - model of neural network approximating x -> u(T,x)\n",
    "        training - boolean flag to indicate training\n",
    "    \"\"\"\n",
    "    with tf.GradientTape() as tape:\n",
    "        loss = loss_fn(X, y, model, training)\n",
    "    grad = tape.gradient(loss, model.trainable_variables)\n",
    "    return loss, grad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g4b957hTxWJ_"
   },
   "source": [
    "### 5. Solve the PDE\n",
    "\n",
    "Next, we initialize the model and choose an optimizer as well as a learning rate schedule.\n",
    "\n",
    "The optimizer which performs the gradient descent step and in particular its step size (aka learning rate) have to be chosen carefully.\n",
    "The speed of convergence can depend strongly on the initialization of the unknown network parameters $\\theta$ as well as the optimizer settings, e.g., choosing the learning rate too high may yield a divergent sequence while values which are too low may result in slow convergence.\n",
    "\n",
    "In addition, we define a test data set $(X_{test}, Y_{test})$ consisting of uniformly drawn points $x^i_{test} \\subset \\mathcal D$ and target values $y^i_{test} = u(T,x^i_{test}).$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "O2DmiMmTE4Xl"
   },
   "outputs": [],
   "source": [
    "# Original experiment\n",
    "model = init_model(dim=dim, num_hidden_neurons=200)\n",
    "lr = tf.keras.optimizers.schedules.PiecewiseConstantDecay([250001,500001],[1e-3,1e-4,1e-5])\n",
    "suffix='orig'\n",
    "\n",
    "# Exponential decay\n",
    "#model = init_model(dim=dim, num_hidden_neurons=200)\n",
    "#lr = tf.keras.optimizers.schedules.ExponentialDecay(1e-1,100000,.1)\n",
    "#suffix='exp'\n",
    "\n",
    "# More layers and neurons experiment\n",
    "#model = init_model(dim=dim, num_hidden_neurons=300, num_hidden_layers=3)\n",
    "#lr = tf.keras.optimizers.schedules.PiecewiseConstantDecay([250001,500001],[1e-3,1e-4,1e-5])\n",
    "#suffix='more'\n",
    "\n",
    "# Even more\n",
    "#model = init_model(dim=dim, num_hidden_neurons=400, num_hidden_layers=4)\n",
    "#lr = tf.keras.optimizers.schedules.PiecewiseConstantDecay([250001,500001],[1e-3,1e-4,1e-5])\n",
    "#suffix='even_more'\n",
    "\n",
    "# Deep ReLU Networks\n",
    "#model = init_model(dim=dim, num_hidden_neurons=200, num_hidden_layers=4, initializer=tf.keras.initializers.HeNormal())\n",
    "#lr = tf.keras.optimizers.schedules.PiecewiseConstantDecay([250001,500001],[1e-3,1e-4,1e-5])\n",
    "#suffix='relu'\n",
    "\n",
    "# Set number of training epochs\n",
    "num_epochs = 750001\n",
    "\n",
    "# Set batch size\n",
    "batch_size = 8192\n",
    "\n",
    "# Choose an optimizer\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=lr, epsilon=1e-8)\n",
    "\n",
    "# Initialize list containing history of losses\n",
    "hist_loss = []\n",
    "error_hist = []\n",
    "\n",
    "# Randomly choose a test set from \\mathcal{D} to approximate\n",
    "# errors by means of Monte Carlo sampling\n",
    "n_test = 1000000\n",
    "X = draw_X(n_test, a, b, sigma=sigma)\n",
    "Xtest = X[:,:,0]\n",
    "# Compute exact solution\n",
    "Ytest = fun_u(T,Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PHGGkULeT6UZ"
   },
   "source": [
    "Next, we train the model.\n",
    "Here, we decided to make this explicit in order to show what happens in detail.\n",
    "Note, however, that one might implement a `Python generator` or `tf.keras.utils.Sequential` object to provide the training data and use the `fit()` method from the `tf.keras.Model` object to train the model.\n",
    "\n",
    "**Note**: The following code cell has a runtime of about 60 minutes on Google Colab with GPU acceleration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y9-doxyhDxJY",
    "outputId": "4190d338-419c-46c7-93a0-5471c1be2889",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Define a training step as a TensorFlow function to increase speed of training\n",
    "@tf.function\n",
    "def train_step():\n",
    "    # Draw batch of random paths\n",
    "    X = draw_X(batch_size, a, b, sigma=sigma)\n",
    "    \n",
    "    # Evaluate g at X_T\n",
    "    y = fun_g(X[:,:,-1])\n",
    "\n",
    "    # And compute the loss as well as the gradient\n",
    "    loss, grad = compute_grad(X, y, model, training=True)\n",
    "\n",
    "    # Perform gradient step\n",
    "    optimizer.apply_gradients(zip(grad, model.trainable_variables))\n",
    "    return loss\n",
    "\n",
    "# Start timer\n",
    "t0 = time()\n",
    "\n",
    "\n",
    "# Set interval to estimate errors\n",
    "log_interval = 1000\n",
    "\n",
    "\n",
    "# Initialize header of output\n",
    "print('  Iter        Loss   L1_rel   L2_rel   Linf_rel |   L1_abs   L2_abs   Linf_abs  |    Time  Stepsize')\n",
    "\n",
    "# Loop to train model\n",
    "for i in range(num_epochs):\n",
    "\n",
    "    # Perform training step\n",
    "    loss = train_step()\n",
    "    hist_loss.append(loss)\n",
    "\n",
    "    if i % log_interval == 0:\n",
    "        \n",
    "        # Compute current prediction on test set\n",
    "        Ypred = model(Xtest, training=False)\n",
    "        \n",
    "        # Compute absolute and relative errors\n",
    "        abs_error = np.abs(Ypred - Ytest)\n",
    "        rel_error = abs_error/Ytest\n",
    "        L2_rel = tf.sqrt(tf.reduce_mean(tf.pow(rel_error, 2))).numpy()\n",
    "        L1_rel = tf.reduce_mean(tf.abs(rel_error)).numpy()\n",
    "        Linf_rel = tf.reduce_max(tf.abs(rel_error)).numpy()\n",
    "        \n",
    "        L2_abs = tf.sqrt(tf.reduce_mean(tf.pow(abs_error, 2))).numpy()\n",
    "        L1_abs = tf.reduce_mean(tf.abs(abs_error)).numpy()\n",
    "        Linf_abs = tf.reduce_max(tf.abs(abs_error)).numpy()\n",
    "        total_time = time()-t0\n",
    "        stepsize = optimizer.lr(optimizer.iterations).numpy()\n",
    "        err = (i, loss.numpy(), L1_rel, L2_rel, Linf_rel, L1_abs, L2_abs, Linf_abs, total_time, stepsize)\n",
    "        error_hist.append(err)\n",
    "        print('{:5d} {:12.4f} {:8.4f} {:8.4f}   {:8.4f} | {:8.4f} {:8.4f}   {:8.4f}  |  {:6.1f}  {:6.2e}'.format(*err))\n",
    "print(time()-t0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FlT3PuBVxWKC"
   },
   "source": [
    "### Plot of the training history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "id": "MIKk28sRxWKC",
    "outputId": "d30ca45e-ab9e-4d23-d6c8-6eb1b75f7d66"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'training loss')"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 14})\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(10,6))\n",
    "ax.semilogy(range(len(hist_loss)), hist_loss,'k-')\n",
    "ax.set_xlabel('epoch')\n",
    "ax.set_ylabel('training loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wSUsWDowpgM1"
   },
   "source": [
    "### Plot of the absolute and relative errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 415
    },
    "id": "ToR6G-zRoceu",
    "outputId": "e025120e-1dc8-4c64-d617-a6eee87e6f01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.legend.Legend at 0x7f222a316588>,)"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(9,6))\n",
    "ax.set_prop_cycle(color=['royalblue', 'darkorange', 'darkcyan'])\n",
    "start_idx = 0\n",
    "xrange = [e[0] for e in error_hist[start_idx:]]\n",
    "ax.semilogy(xrange, [e[2:5] for e in error_hist[start_idx:]])\n",
    "ax.semilogy(xrange, [e[5:8] for e in error_hist[start_idx:]], linestyle='--')\n",
    "ax.set_xlabel('$n_{epoch}$')\n",
    "ax.set_ylabel('Errors')\n",
    "ax.legend(['$L^1$', '$L^2$', '$L^{\\infty}$']),\n",
    "#plt.savefig('Errors_HeatEquation_dim_{:03d}_{:s}.pdf'.format(dim,suffix), bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "Pi_X89POUGwN"
   },
   "outputs": [],
   "source": [
    "SAVE_CSV = False\n",
    "if SAVE_CSV:\n",
    "    import csv\n",
    "    with open('heat_dim_{:03d}_{:s}.csv'.format(dim, suffix),'w') as out:\n",
    "        csv_out=csv.writer(out)\n",
    "        csv_out.writerow(['Iter', 'Loss', 'L1_rel', 'L2_rel', 'Linf_rel', 'L1_abs', 'L2_abs', 'Linf_abs', 'Time', 'Stepsize'])\n",
    "        for row in error_hist:\n",
    "            csv_out.writerow(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7m9vYreOKN0p"
   },
   "source": [
    "### Plot slice of solution and rel. error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 714
    },
    "id": "Do1Sfm8zxWKC",
    "outputId": "f4c6e344-7bdb-453f-ac8b-826d965cac69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$x_2$')"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ngrid = 400\n",
    "\n",
    "# Choose indices to plot\n",
    "idx0 = 0\n",
    "idx1 = 1\n",
    "\n",
    "# Determine remaining indices\n",
    "idx = np.arange(dim)\n",
    "idx_remaining = np.setdiff1d(idx,[idx0,idx1])\n",
    "# Set remaining values to midpoints\n",
    "val_remaining = (a+b)/2\n",
    "\n",
    "# Meshgrid for plots\n",
    "xspace = np.linspace(a[idx0], b[idx0], ngrid + 1, dtype=DTYPE)\n",
    "yspace = np.linspace(a[idx1], b[idx1], ngrid + 1, dtype=DTYPE)\n",
    "X,Y = np.meshgrid(xspace, yspace)\n",
    "\n",
    "# Append remaining values\n",
    "Z = np.repeat(val_remaining[idx_remaining].reshape(1,-1), (ngrid+1)**2,axis=0)\n",
    "Xgrid = np.vstack([X.flatten(),Y.flatten()]).T\n",
    "Xgrid = np.hstack([Xgrid,Z])\n",
    "\n",
    "# Relative error\n",
    "urel =tf.abs(model(Xgrid,training=False) -  fun_u(T,Xgrid))/fun_u(T,Xgrid)\n",
    "Urel = urel.numpy().reshape(ngrid+1,ngrid+1)\n",
    "# Slice of solution\n",
    "U = model(Xgrid,training=False).numpy().reshape(ngrid+1,ngrid+1)\n",
    "\n",
    "# Plot solution of heat equation\n",
    "fig = plt.figure(figsize=(11,6))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot_surface(X,Y,U,cmap='viridis')\n",
    "ax.view_init(45,210)\n",
    "ax.set_xlabel('$x_1$')\n",
    "ax.set_ylabel('$x_2$')\n",
    "#plt.savefig('Sol_HeatEquation_dim_{:03d}_{:s}.pdf'.format(dim,suffix), bbox_inches='tight', dpi=300)\n",
    "\n",
    "# Plot relative error of heat equation\n",
    "fig = plt.figure(figsize=(9,6))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot_surface(X,Y,Urel,cmap='viridis')\n",
    "ax.view_init(45,210)\n",
    "ax.set_xlabel('$x_1$')\n",
    "ax.set_ylabel('$x_2$')\n",
    "#plt.savefig('RelError_HeatEquation_dim_{:03d}_{:s}.pdf'.format(dim,suffix), bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DXYmJ5A-QHKI"
   },
   "source": [
    "## Example: Option pricing\n",
    "In this example, we study the Black-Scholes formula, one of the basic models used in mathematical finance to determine the fair value of derivatives.\n",
    "\n",
    "We consider a financial market with 100 uncorrelated risky assets whose underlying dynamics is given by a geometric Brownian motion, i.e., a stochastic process $\\{X_t\\}_{t \\in [0,T]}$ governed by the SDE \n",
    "\n",
    "$$\n",
    "X_t = x + \\int_0^t \\mu(s,X_s) \\, ds + \\int_0^t \\sigma(s,X_s) \\, dW_s,\n",
    "\\qquad t \\in [0,T]\n",
    "$$\n",
    "\n",
    "with $\\mu(s,x) \\equiv -0.05 \\, x$ and $\\sigma(s,x) \\equiv \\mathrm{diag}(0.105 \\, x_1, 0.110 \\, x_2, \\ldots, 0.600 \\, x_{100})$.\n",
    "\n",
    "Here, we consider a derivative whose fair value at $(t,x)$ is determined by the PDE\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "   \\partial_t u(t,x) - \\frac{1}{2} \\sum_{i=1}^d | \\sigma_i x_i |^2 \\, \\partial^2_{x_i} u(t,x) - \\sum_{i=1}^d \\mu_i x_i \\partial_{x_i} u(t,x)\n",
    "       &= 0, \t\t\t\\quad && (t,x) \\in (0,T] \\times \\mathbb R_+^d,\\\\\n",
    "   u(0,x) &= g(x), \t\\quad && x \\in\\mathbb R_+^d.\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "with\n",
    "$g(x) := \\exp (-r \\, T) \\max \\{\n",
    "[ \\max_{i\\in\\{1, \\ldots, 100\\}} x_i ] - 100, 0\n",
    "\\}.\n",
    "$\n",
    "\n",
    "\n",
    "\n",
    "The model is taken from [Beck et al., Sec. 3.3](https://arxiv.org/abs/1806.00421)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "Py4fp7IZRZpI"
   },
   "outputs": [],
   "source": [
    "# Final time\n",
    "T = tf.constant(1., dtype=DTYPE)\n",
    "\n",
    "# Spatial dimensions\n",
    "dim = 100\n",
    "\n",
    "# Domain-of-interest at t=0\n",
    "a = 90 * tf.ones((dim), dtype=DTYPE)\n",
    "b = 110 * tf.ones((dim), dtype=DTYPE)\n",
    "\n",
    "# Interest rate\n",
    "r = tf.constant(1./20, dtype=DTYPE)\n",
    "\n",
    "# Drift\n",
    "mu = tf.constant(-1./20, dtype=DTYPE)\n",
    "\n",
    "# Strike price\n",
    "K = tf.constant(100., dtype=DTYPE)\n",
    "\n",
    "# Diffusion/volatility\n",
    "sigma = 1./10 + 1./200*tf.range(1, dim+1, dtype=DTYPE)\n",
    "\n",
    "# Define terminal condition, i.e., payoff at maturity\n",
    "def fun_g(x):\n",
    "    return tf.exp(-r*T) * tf.maximum(tf.reduce_max(x, axis=1, keepdims=True) - K, 0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "BOkzanumSla5"
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def draw_X(num_samples, a, b):\n",
    "    \"\"\" Function to draw num_samples many pairs of uniformly drawn starting\n",
    "    values X_0 and end points X_T of a stochastic process X with zero drift and\n",
    "    constant scalar diffusion. Starting points are drawn uniformly from the\n",
    "    hypercube [a,b] \\\\subset \\mathbb{R}^d. \"\"\"\n",
    "    dim = a.shape[0]\n",
    "    \n",
    "    X0 = a + tf.random.uniform((num_samples, dim), dtype=DTYPE) * (b-a)\n",
    "    \n",
    "    # Initialize the array X\n",
    "    xi = tf.random.normal(shape=(num_samples, dim), dtype=DTYPE)\n",
    "    XT = X0 * tf.exp( (mu- tf.square(sigma)/2) * T + sigma * tf.sqrt(T) * xi)\n",
    "\n",
    "    # Return simulated paths as well as increments of Brownian motion\n",
    "    return tf.stack([X0, XT], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "Uy7jx6k-dKzD"
   },
   "outputs": [],
   "source": [
    "# Initialize model\n",
    "model = init_model(dim=dim, num_hidden_neurons=dim+100)\n",
    "\n",
    "# Set up step size\n",
    "lr = tf.keras.optimizers.schedules.PiecewiseConstantDecay([250001,500001],[1e-3,1e-4,1e-5])\n",
    "\n",
    "# Set number of training epochs\n",
    "num_epochs = 750001\n",
    "\n",
    "# Set batch size\n",
    "batch_size = 8192\n",
    "\n",
    "# Choose an optimizer\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=lr, epsilon=1e-8)\n",
    "\n",
    "# Initialize list containing history of losses\n",
    "hist_loss = []\n",
    "error_hist = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o4D2oAz5vJpR"
   },
   "source": [
    "Draw a test set with 250 000 samples and estimate the target values by means of Monte-Carlo estimation with $32 \\times 32 000$ samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O92cmffUvCt1",
    "outputId": "0b2e78a1-ffa4-4030-f2f1-dd0fbae2dd35",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Randomly choose a test set from \\mathcal{D} to approximate\n",
    "# errors by means of Monte Carlo sampling\n",
    "n_test = 250000\n",
    "X = draw_X(n_test, a, b)\n",
    "Xtest = X[:,:,0]\n",
    "Xtest = tf.convert_to_tensor(Xtest, dtype=DTYPE)\n",
    "\n",
    "# Prepare Ytest\n",
    "Ytest = tf.zeros((Xtest.shape[0],1),dtype=DTYPE)\n",
    "\n",
    "# Determine values at Xtest through Monte-Carlo sampling\n",
    "# Set batch size and mc_samples, results in b_size*mc_samples many iterates\n",
    "b_size = 32\n",
    "mc_samples = 32000\n",
    "\n",
    "@tf.function\n",
    "def mc_step(y):\n",
    "    # Draw standard normal random variables\n",
    "    Xi = tf.random.normal(shape=(b_size, dim), dtype=DTYPE)\n",
    "    # Determine update\n",
    "    upd = tf.exp( (mu- tf.square(sigma)/2) * T + sigma * tf.sqrt(T) * Xi)\n",
    "    # Determine X_T\n",
    "    XT = tf.reshape(Xtest, shape=[n_test, dim, 1])*tf.transpose(upd)\n",
    "    return y + tf.reduce_sum(tf.reshape(fun_g(XT),[n_test, b_size]), axis=1,keepdims=True)/(b_size*mc_samples)\n",
    "\n",
    "for i in range(mc_samples):\n",
    "    if i%100==0:\n",
    "        print(i,'/',mc_samples)\n",
    "    Ytest=mc_step(Ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Janmi9aXUX44",
    "outputId": "fdff7fab-2f4c-4cdc-b6b9-6abc3e4a2c0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Iter        Loss   L1_rel   L2_rel   Linf_rel |   L1_abs   L2_abs   Linf_abs  |    Time\n",
      "    0   29924.8789   0.9938   0.9938     0.9990 | 156.6507 156.6663   165.7923  |     0.6  1.00e-03\n",
      " 1000   28752.9238   0.9939   0.9939     1.0184 | 156.6331 156.6350   161.8890  |     5.6  1.00e-03\n",
      " 2000   28955.1094   0.9876   0.9877     1.0142 | 155.6460 155.6476   160.7105  |    10.5  1.00e-03\n",
      " 3000   28425.1367   0.9813   0.9814     1.0102 | 154.6427 154.6440   159.3448  |    15.5  1.00e-03\n",
      " 4000   28706.2090   0.9751   0.9751     1.0047 | 153.6634 153.6646   158.0876  |    20.5  1.00e-03\n",
      " 5000   28245.5801   0.9690   0.9691     0.9985 | 152.7031 152.7041   157.0111  |    25.5  1.00e-03\n",
      " 6000   27585.2227   0.9624   0.9625     0.9928 | 151.6704 151.6713   155.8571  |    30.5  1.00e-03\n",
      " 7000   27575.7246   0.9553   0.9554     0.9876 | 150.5548 150.5557   154.7141  |    35.4  1.00e-03\n",
      " 8000   26774.1270   0.9496   0.9497     0.9802 | 149.6553 149.6560   153.8670  |    40.4  1.00e-03\n",
      " 9000   26162.2812   0.9437   0.9438     0.9744 | 148.7273 148.7281   152.9260  |    45.3  1.00e-03\n",
      "10000   26399.2109   0.9372   0.9373     0.9697 | 147.6905 147.6913   151.5009  |    50.3  1.00e-03\n",
      "11000   26861.9531   0.9312   0.9313     0.9630 | 146.7472 146.7479   150.4364  |    55.2  1.00e-03\n",
      "12000   26487.8984   0.9245   0.9246     0.9566 | 145.6960 145.6966   149.4508  |    60.2  1.00e-03\n",
      "13000   25973.8262   0.9182   0.9183     0.9517 | 144.7002 144.7008   148.2129  |    65.2  1.00e-03\n",
      "14000   25560.7422   0.9119   0.9119     0.9451 | 143.7034 143.7040   147.2574  |    70.1  1.00e-03\n",
      "15000   25375.0625   0.9057   0.9058     0.9386 | 142.7382 142.7387   145.9253  |    75.1  1.00e-03\n",
      "16000   24967.4941   0.9001   0.9002     0.9336 | 141.8463 141.8469   144.9352  |    80.0  1.00e-03\n",
      "17000   24423.7520   0.8929   0.8929     0.9267 | 140.7073 140.7078   143.7255  |    85.0  1.00e-03\n",
      "18000   24548.5391   0.8876   0.8877     0.9233 | 139.8713 139.8719   142.4231  |    90.0  1.00e-03\n",
      "19000   24531.8125   0.8815   0.8816     0.9163 | 138.9225 138.9230   141.5089  |    94.9  1.00e-03\n",
      "20000   24127.0273   0.8742   0.8743     0.9076 | 137.7766 137.7770   140.7688  |    99.9  1.00e-03\n",
      "21000   23562.7207   0.8676   0.8677     0.9008 | 136.7254 136.7259   139.6187  |   104.9  1.00e-03\n",
      "22000   23441.0977   0.8614   0.8615     0.8974 | 135.7568 135.7573   138.4073  |   109.8  1.00e-03\n",
      "23000   23165.2188   0.8553   0.8553     0.8913 | 134.7839 134.7843   137.1582  |   114.8  1.00e-03\n",
      "24000   22641.9258   0.8481   0.8482     0.8876 | 133.6546 133.6550   136.0884  |   119.8  1.00e-03\n",
      "25000   22777.0508   0.8435   0.8436     0.8798 | 132.9280 132.9284   134.9088  |   124.8  1.00e-03\n",
      "26000   21997.8965   0.8360   0.8361     0.8750 | 131.7451 131.7457   133.7084  |   129.7  1.00e-03\n",
      "27000   21467.0977   0.8298   0.8299     0.8662 | 130.7722 130.7727   132.4389  |   134.7  1.00e-03\n",
      "28000   21441.4023   0.8246   0.8247     0.8626 | 129.9506 129.9511   131.6505  |   139.7  1.00e-03\n",
      "29000   21279.1914   0.8172   0.8173     0.8514 | 128.7860 128.7864   130.7105  |   144.6  1.00e-03\n",
      "30000   20998.8086   0.8132   0.8133     0.8510 | 128.1463 128.1467   129.8902  |   149.6  1.00e-03\n",
      "31000   21171.8320   0.8042   0.8043     0.8383 | 126.7412 126.7417   128.9575  |   154.6  1.00e-03\n",
      "32000   20653.9531   0.7989   0.7990     0.8331 | 125.9029 125.9033   127.7071  |   159.6  1.00e-03\n",
      "33000   20397.2598   0.7937   0.7937     0.8288 | 125.0788 125.0792   127.3189  |   164.6  1.00e-03\n",
      "34000   20092.2852   0.7851   0.7852     0.8195 | 123.7327 123.7331   125.7038  |   169.5  1.00e-03\n",
      "35000   19847.4531   0.7806   0.7806     0.8134 | 123.0149 123.0153   125.1335  |   174.5  1.00e-03\n",
      "36000   20034.7969   0.7755   0.7756     0.8106 | 122.2172 122.2176   123.6554  |   179.5  1.00e-03\n",
      "37000   19896.4297   0.7679   0.7680     0.8018 | 121.0099 121.0103   122.5277  |   184.5  1.00e-03\n",
      "38000   19227.5703   0.7599   0.7599     0.7926 | 119.7473 119.7477   121.3613  |   189.4  1.00e-03\n",
      "39000   18824.6777   0.7545   0.7546     0.7872 | 118.9044 118.9048   120.7903  |   194.4  1.00e-03\n",
      "40000   18792.2188   0.7471   0.7471     0.7782 | 117.7412 117.7419   120.5541  |   199.4  1.00e-03\n",
      "41000   18512.3750   0.7422   0.7423     0.7737 | 116.9688 116.9692   118.5294  |   204.3  1.00e-03\n",
      "42000   18741.8047   0.7357   0.7358     0.7680 | 115.9478 115.9482   118.0180  |   209.3  1.00e-03\n",
      "43000   17766.4707   0.7298   0.7299     0.7632 | 115.0126 115.0130   116.5384  |   214.3  1.00e-03\n",
      "44000   18321.6836   0.7241   0.7242     0.7555 | 114.1163 114.1167   115.7080  |   219.2  1.00e-03\n",
      "45000   17806.2812   0.7172   0.7172     0.7457 | 113.0209 113.0213   115.2553  |   224.2  1.00e-03\n",
      "46000   16982.2422   0.7096   0.7097     0.7403 | 111.8225 111.8230   113.1372  |   229.2  1.00e-03\n",
      "47000   16922.4648   0.7047   0.7048     0.7343 | 111.0611 111.0614   112.5462  |   234.2  1.00e-03\n",
      "48000   16139.3447   0.6973   0.6974     0.7269 | 109.8972 109.8977   112.3515  |   239.2  1.00e-03\n",
      "49000   16238.5254   0.6917   0.6918     0.7238 | 109.0146 109.0150   110.6666  |   244.1  1.00e-03\n",
      "50000   16594.7617   0.6856   0.6857     0.7143 | 108.0567 108.0577   110.8684  |   249.1  1.00e-03\n",
      "51000   16194.3633   0.6794   0.6795     0.7106 | 107.0682 107.0685   108.4135  |   254.1  1.00e-03\n",
      "52000   16169.6924   0.6720   0.6721     0.7042 | 105.8999 105.9004   107.4613  |   259.1  1.00e-03\n",
      "53000   15876.0898   0.6674   0.6674     0.6989 | 105.1707 105.1712   106.6279  |   264.1  1.00e-03\n",
      "54000   15720.1143   0.6602   0.6603     0.6909 | 104.0428 104.0432   105.3248  |   269.1  1.00e-03\n",
      "55000   15554.2656   0.6548   0.6549     0.6837 | 103.1867 103.1871   104.5526  |   274.1  1.00e-03\n",
      "56000   15409.2129   0.6470   0.6471     0.6803 | 101.9580 101.9585   103.5205  |   279.1  1.00e-03\n",
      "57000   15002.5117   0.6390   0.6391     0.6669 | 100.7010 100.7014   102.0973  |   284.1  1.00e-03\n",
      "58000   14552.8799   0.6350   0.6351     0.6608 | 100.0753 100.0757   101.4614  |   289.1  1.00e-03\n",
      "59000   14318.6553   0.6297   0.6297     0.6557 |  99.2373  99.2378   101.1489  |   294.0  1.00e-03\n",
      "60000   14591.3340   0.6221   0.6222     0.6525 |  98.0385  98.0389    99.4635  |   299.1  1.00e-03\n",
      "61000   14345.3379   0.6164   0.6165     0.6440 |  97.1426  97.1431    99.2046  |   304.0  1.00e-03\n",
      "62000   13871.4375   0.6087   0.6088     0.6401 |  95.9242  95.9247    97.4697  |   309.0  1.00e-03\n",
      "63000   13916.6279   0.6043   0.6043     0.6288 |  95.2301  95.2305    96.5957  |   314.0  1.00e-03\n",
      "64000   14190.3818   0.5983   0.5983     0.6262 |  94.2848  94.2852    95.6768  |   319.0  1.00e-03\n",
      "65000   13792.1777   0.5915   0.5916     0.6236 |  93.2170  93.2175    94.7294  |   324.0  1.00e-03\n",
      "66000   13218.7363   0.5857   0.5858     0.6093 |  92.3087  92.3092    93.9242  |   329.0  1.00e-03\n",
      "67000   13054.3975   0.5791   0.5791     0.6037 |  91.2650  91.2655    93.1861  |   334.0  1.00e-03\n",
      "68000   13114.6787   0.5721   0.5722     0.6013 |  90.1547  90.1551    91.4256  |   339.0  1.00e-03\n",
      "69000   12824.4238   0.5646   0.5647     0.5934 |  88.9831  88.9835    90.2253  |   343.9  1.00e-03\n",
      "70000   12176.0332   0.5582   0.5583     0.5916 |  87.9694  87.9700    89.5574  |   348.9  1.00e-03\n",
      "71000   12414.4785   0.5527   0.5527     0.5807 |  87.0952  87.0956    88.3532  |   353.9  1.00e-03\n",
      "72000   12355.8975   0.5462   0.5463     0.5738 |  86.0847  86.0851    87.4097  |   358.9  1.00e-03\n",
      "73000   11945.4180   0.5406   0.5407     0.5655 |  85.1945  85.1948    87.0564  |   363.9  1.00e-03\n",
      "74000   11275.8652   0.5341   0.5341     0.5601 |  84.1633  84.1637    85.6646  |   368.8  1.00e-03\n",
      "75000   11534.0898   0.5279   0.5280     0.5560 |  83.1983  83.1988    84.4853  |   373.8  1.00e-03\n",
      "76000   12084.8936   0.5221   0.5222     0.5482 |  82.2830  82.2835    83.9882  |   378.8  1.00e-03\n",
      "77000   11889.5410   0.5160   0.5160     0.5431 |  81.3091  81.3097    82.7680  |   383.7  1.00e-03\n",
      "78000   10942.7715   0.5079   0.5080     0.5335 |  80.0404  80.0409    81.3342  |   388.7  1.00e-03\n",
      "79000   10906.5117   0.5023   0.5023     0.5304 |  79.1551  79.1556    80.5070  |   393.7  1.00e-03\n",
      "80000   10796.4902   0.4954   0.4954     0.5177 |  78.0649  78.0653    79.4512  |   398.7  1.00e-03\n",
      "81000   10847.7949   0.4922   0.4923     0.5125 |  77.5730  77.5735    79.6028  |   403.6  1.00e-03\n",
      "82000   10479.7373   0.4860   0.4860     0.5044 |  76.5905  76.5910    78.0584  |   408.6  1.00e-03\n",
      "83000   10446.8887   0.4782   0.4783     0.5024 |  75.3658  75.3663    76.6166  |   413.5  1.00e-03\n",
      "84000   10072.3711   0.4709   0.4710     0.4949 |  74.2135  74.2139    75.4678  |   418.5  1.00e-03\n",
      "85000   10044.6953   0.4654   0.4655     0.4915 |  73.3489  73.3495    74.9090  |   423.5  1.00e-03\n",
      "86000   10193.1846   0.4597   0.4597     0.4794 |  72.4409  72.4413    73.8208  |   428.5  1.00e-03\n",
      "87000   10174.3682   0.4528   0.4528     0.4715 |  71.3600  71.3605    72.5600  |   433.4  1.00e-03\n",
      "88000    9969.2578   0.4456   0.4456     0.4680 |  70.2222  70.2228    72.0024  |   438.4  1.00e-03\n",
      "89000    9816.7510   0.4406   0.4406     0.4634 |  69.4337  69.4342    70.6862  |   443.4  1.00e-03\n",
      "90000    9390.1455   0.4349   0.4350     0.4591 |  68.5404  68.5411    70.0167  |   448.3  1.00e-03\n",
      "91000    9283.7500   0.4271   0.4272     0.4457 |  67.3167  67.3175    69.2947  |   453.3  1.00e-03\n",
      "92000    9658.3721   0.4215   0.4215     0.4426 |  66.4218  66.4224    67.6935  |   458.3  1.00e-03\n",
      "93000    9432.2607   0.4152   0.4152     0.4340 |  65.4347  65.4353    66.9087  |   463.2  1.00e-03\n",
      "94000    9059.2109   0.4095   0.4095     0.4264 |  64.5382  64.5390    66.2742  |   468.1  1.00e-03\n",
      "95000    8597.9941   0.4044   0.4045     0.4246 |  63.7360  63.7365    65.0580  |   473.1  1.00e-03\n",
      "96000    8883.2188   0.3961   0.3961     0.4149 |  62.4183  62.4189    63.7067  |   478.0  1.00e-03\n",
      "97000    8545.1758   0.3900   0.3901     0.4109 |  61.4642  61.4649    62.8621  |   483.0  1.00e-03\n",
      "98000    8748.6768   0.3852   0.3852     0.4023 |  60.6983  60.6989    61.9283  |   488.0  1.00e-03\n",
      "99000    8274.7334   0.3787   0.3787     0.3938 |  59.6831  59.6838    61.0762  |   492.9  1.00e-03\n",
      "100000    8547.8506   0.3734   0.3735     0.3895 |  58.8512  58.8518    60.1024  |   497.9  1.00e-03\n",
      "101000    8333.1543   0.3662   0.3662     0.3836 |  57.7109  57.7115    58.8870  |   502.9  1.00e-03\n",
      "102000    7823.5527   0.3593   0.3593     0.3781 |  56.6204  56.6210    58.0278  |   507.8  1.00e-03\n",
      "103000    7805.9658   0.3526   0.3527     0.3708 |  55.5697  55.5703    56.8604  |   512.8  1.00e-03\n",
      "104000    7831.5552   0.3466   0.3467     0.3646 |  54.6277  54.6283    55.9735  |   517.7  1.00e-03\n",
      "105000    7484.9512   0.3419   0.3419     0.3571 |  53.8852  53.8861    55.2280  |   522.7  1.00e-03\n",
      "106000    7591.3735   0.3340   0.3340     0.3501 |  52.6302  52.6309    53.8757  |   527.6  1.00e-03\n",
      "107000    7790.8682   0.3294   0.3294     0.3436 |  51.9112  51.9122    53.3565  |   532.6  1.00e-03\n",
      "108000    7464.9248   0.3211   0.3212     0.3386 |  50.6055  50.6063    52.1315  |   537.5  1.00e-03\n",
      "109000    7296.0967   0.3154   0.3155     0.3327 |  49.7082  49.7091    51.0983  |   542.5  1.00e-03\n",
      "110000    7095.2344   0.3088   0.3088     0.3247 |  48.6614  48.6621    50.2022  |   547.5  1.00e-03\n",
      "111000    6806.1274   0.3037   0.3037     0.3166 |  47.8575  47.8582    49.0009  |   552.4  1.00e-03\n",
      "112000    6882.3789   0.2964   0.2964     0.3125 |  46.7031  46.7040    48.0685  |   557.4  1.00e-03\n",
      "113000    6928.4053   0.2910   0.2910     0.3056 |  45.8615  45.8622    47.1076  |   562.3  1.00e-03\n",
      "114000    6862.4443   0.2858   0.2858     0.2989 |  45.0382  45.0390    46.5472  |   567.3  1.00e-03\n",
      "115000    6749.9575   0.2766   0.2766     0.2944 |  43.5893  43.5902    44.8583  |   572.3  1.00e-03\n",
      "116000    6802.1797   0.2715   0.2715     0.2867 |  42.7852  42.7859    43.9926  |   577.2  1.00e-03\n",
      "117000    6432.9712   0.2651   0.2651     0.2792 |  41.7769  41.7777    43.0261  |   582.2  1.00e-03\n",
      "118000    7072.2637   0.2595   0.2595     0.2723 |  40.8948  40.8956    42.1480  |   587.1  1.00e-03\n",
      "119000    6141.9277   0.2544   0.2544     0.2646 |  40.0922  40.0934    41.4726  |   592.1  1.00e-03\n",
      "120000    6359.4717   0.2460   0.2460     0.2627 |  38.7614  38.7622    40.1512  |   597.1  1.00e-03\n",
      "121000    6400.3813   0.2416   0.2416     0.2563 |  38.0728  38.0736    39.2711  |   602.0  1.00e-03\n",
      "122000    6041.9448   0.2356   0.2356     0.2460 |  37.1296  37.1305    38.3355  |   607.0  1.00e-03\n",
      "123000    5829.7412   0.2288   0.2288     0.2442 |  36.0583  36.0592    37.3005  |   612.0  1.00e-03\n",
      "124000    5591.8740   0.2227   0.2227     0.2383 |  35.0917  35.0928    36.3840  |   616.9  1.00e-03\n",
      "125000    5896.4575   0.2162   0.2162     0.2281 |  34.0677  34.0686    35.1237  |   621.9  1.00e-03\n",
      "126000    6075.4346   0.2098   0.2099     0.2207 |  33.0698  33.0707    34.2977  |   626.9  1.00e-03\n",
      "127000    5635.2700   0.2048   0.2048     0.2171 |  32.2714  32.2725    33.5997  |   631.8  1.00e-03\n",
      "128000    5713.0386   0.1986   0.1987     0.2101 |  31.3036  31.3046    32.5253  |   636.8  1.00e-03\n",
      "129000    5757.3267   0.1905   0.1905     0.2002 |  30.0194  30.0208    31.6328  |   641.8  1.00e-03\n",
      "130000    5750.3320   0.1867   0.1867     0.1945 |  29.4250  29.4272    31.1765  |   646.7  1.00e-03\n",
      "131000    5275.0488   0.1804   0.1804     0.1905 |  28.4339  28.4352    29.7562  |   651.7  1.00e-03\n",
      "132000    5643.4404   0.1739   0.1739     0.1852 |  27.4059  27.4074    28.8013  |   656.7  1.00e-03\n",
      "133000    5584.6768   0.1667   0.1667     0.1778 |  26.2670  26.2683    27.5784  |   661.7  1.00e-03\n",
      "134000    5389.3379   0.1613   0.1613     0.1705 |  25.4161  25.4174    26.7230  |   666.7  1.00e-03\n",
      "135000    5177.4717   0.1562   0.1562     0.1680 |  24.6108  24.6125    25.8373  |   671.6  1.00e-03\n",
      "136000    5075.4668   0.1493   0.1493     0.1583 |  23.5251  23.5266    25.0559  |   676.6  1.00e-03\n",
      "137000    5147.1875   0.1419   0.1419     0.1530 |  22.3538  22.3553    23.5818  |   681.6  1.00e-03\n",
      "138000    5086.0430   0.1372   0.1372     0.1453 |  21.6234  21.6252    23.2495  |   686.6  1.00e-03\n",
      "139000    5101.0234   0.1309   0.1310     0.1418 |  20.6308  20.6325    21.9326  |   691.6  1.00e-03\n",
      "140000    5022.2715   0.1245   0.1245     0.1345 |  19.6227  19.6242    20.7552  |   696.6  1.00e-03\n",
      "141000    4978.9409   0.1190   0.1190     0.1268 |  18.7564  18.7582    20.3202  |   701.6  1.00e-03\n",
      "142000    5435.4033   0.1130   0.1130     0.1221 |  17.8012  17.8029    19.9289  |   706.6  1.00e-03\n",
      "143000    5090.4194   0.1064   0.1064     0.1159 |  16.7638  16.7657    18.0713  |   711.5  1.00e-03\n",
      "144000    5219.4033   0.1014   0.1014     0.1084 |  15.9870  15.9893    17.4904  |   716.5  1.00e-03\n",
      "145000    4962.1738   0.0954   0.0954     0.1028 |  15.0362  15.0382    16.1675  |   721.5  1.00e-03\n",
      "146000    5130.4404   0.0883   0.0883     0.0989 |  13.9182  13.9204    15.0507  |   726.5  1.00e-03\n",
      "147000    4872.4199   0.0840   0.0840     0.0915 |  13.2338  13.2378    14.7223  |   731.5  1.00e-03\n",
      "148000    4762.8950   0.0759   0.0760     0.0846 |  11.9663  11.9689    13.2250  |   736.5  1.00e-03\n",
      "149000    4772.5996   0.0710   0.0710     0.0790 |  11.1840  11.1867    12.3453  |   741.5  1.00e-03\n",
      "150000    5154.4697   0.0650   0.0650     0.0754 |  10.2337  10.2384    11.6006  |   746.5  1.00e-03\n",
      "151000    5086.7822   0.0603   0.0603     0.0688 |   9.5006   9.5035    10.7337  |   751.5  1.00e-03\n",
      "152000    4743.4902   0.0542   0.0542     0.0626 |   8.5372   8.5413     9.6888  |   756.5  1.00e-03\n",
      "153000    4874.1152   0.0499   0.0499     0.0572 |   7.8631   7.8671     9.0583  |   761.4  1.00e-03\n",
      "154000    5180.0586   0.0412   0.0413     0.0491 |   6.4971   6.5014     7.5639  |   766.4  1.00e-03\n",
      "155000    5141.0498   0.0359   0.0359     0.0435 |   5.6600   5.6650     6.9268  |   771.4  1.00e-03\n",
      "156000    5104.6143   0.0312   0.0312     0.0392 |   4.9179   4.9236     5.9973  |   776.4  1.00e-03\n",
      "157000    4910.5566   0.0274   0.0274     0.0342 |   4.3117   4.3183     5.4733  |   781.4  1.00e-03\n",
      "158000    4667.1680   0.0222   0.0222     0.0299 |   3.4977   3.5062     4.6991  |   786.4  1.00e-03\n",
      "159000    5103.8145   0.0170   0.0171     0.0246 |   2.6798   2.6900     3.7999  |   791.4  1.00e-03\n",
      "160000    4952.4341   0.0122   0.0124     0.0205 |   1.9289   1.9476     3.2164  |   796.3  1.00e-03\n",
      "161000    4651.5566   0.0079   0.0081     0.0149 |   1.2502   1.2720     2.4235  |   801.3  1.00e-03\n",
      "162000    4673.1836   0.0052   0.0055     0.0154 |   0.8178   0.8720     2.5610  |   806.3  1.00e-03\n",
      "163000    4785.2588   0.0017   0.0021     0.0097 |   0.2614   0.3274     1.4880  |   811.3  1.00e-03\n",
      "164000    4803.0229   0.0019   0.0024     0.0091 |   0.3083   0.3822     1.4559  |   816.3  1.00e-03\n",
      "165000    4728.2607   0.0014   0.0017     0.0091 |   0.2146   0.2700     1.4158  |   821.3  1.00e-03\n",
      "166000    5012.8804   0.0014   0.0017     0.0076 |   0.2157   0.2717     1.2095  |   826.2  1.00e-03\n",
      "167000    5066.7930   0.0018   0.0022     0.0094 |   0.2851   0.3531     1.5281  |   831.2  1.00e-03\n",
      "168000    4687.7217   0.0013   0.0016     0.0079 |   0.2046   0.2584     1.2205  |   836.2  1.00e-03\n",
      "169000    4987.9775   0.0014   0.0018     0.0093 |   0.2276   0.2856     1.4888  |   841.2  1.00e-03\n",
      "170000    4928.9385   0.0012   0.0016     0.0079 |   0.1958   0.2452     1.1765  |   846.2  1.00e-03\n",
      "171000    5294.6187   0.0018   0.0022     0.0083 |   0.2861   0.3462     1.3088  |   851.1  1.00e-03\n",
      "172000    4763.4302   0.0015   0.0019     0.0095 |   0.2349   0.2948     1.5707  |   856.1  1.00e-03\n",
      "173000    4826.4814   0.0019   0.0023     0.0094 |   0.3002   0.3611     1.4316  |   861.1  1.00e-03\n",
      "174000    4954.6641   0.0015   0.0019     0.0080 |   0.2433   0.2975     1.2600  |   866.0  1.00e-03\n",
      "175000    4803.3936   0.0016   0.0020     0.0099 |   0.2587   0.3219     1.5305  |   871.0  1.00e-03\n",
      "176000    4937.9072   0.0019   0.0022     0.0096 |   0.2980   0.3543     1.4275  |   875.9  1.00e-03\n",
      "177000    4876.2471   0.0014   0.0017     0.0070 |   0.2170   0.2705     1.1277  |   880.9  1.00e-03\n",
      "178000    4514.4941   0.0012   0.0015     0.0068 |   0.1859   0.2318     1.0446  |   885.9  1.00e-03\n",
      "179000    4709.6729   0.0020   0.0023     0.0078 |   0.3089   0.3674     1.2453  |   890.8  1.00e-03\n",
      "180000    5086.3193   0.0013   0.0016     0.0075 |   0.2047   0.2579     1.2097  |   895.8  1.00e-03\n",
      "181000    4803.0430   0.0015   0.0019     0.0103 |   0.2366   0.2979     1.5871  |   900.8  1.00e-03\n",
      "182000    4724.5186   0.0015   0.0018     0.0083 |   0.2310   0.2866     1.2702  |   905.8  1.00e-03\n",
      "183000    4932.7871   0.0015   0.0019     0.0090 |   0.2445   0.3034     1.3904  |   910.8  1.00e-03\n",
      "184000    4614.9023   0.0015   0.0018     0.0084 |   0.2304   0.2899     1.3365  |   915.7  1.00e-03\n",
      "185000    4620.2793   0.0014   0.0017     0.0097 |   0.2187   0.2740     1.4405  |   920.7  1.00e-03\n",
      "186000    4578.7402   0.0012   0.0015     0.0088 |   0.1896   0.2389     1.3068  |   925.7  1.00e-03\n",
      "187000    4787.0542   0.0014   0.0018     0.0083 |   0.2229   0.2795     1.2848  |   930.7  1.00e-03\n",
      "188000    4836.3076   0.0013   0.0016     0.0072 |   0.1973   0.2470     1.1246  |   935.7  1.00e-03\n",
      "189000    4702.3047   0.0012   0.0015     0.0075 |   0.1895   0.2388     1.1349  |   940.7  1.00e-03\n",
      "190000    4802.8359   0.0013   0.0016     0.0090 |   0.2016   0.2533     1.4167  |   945.7  1.00e-03\n",
      "191000    5061.6929   0.0024   0.0028     0.0107 |   0.3779   0.4404     1.6625  |   950.7  1.00e-03\n",
      "192000    5070.3672   0.0020   0.0024     0.0089 |   0.3211   0.3798     1.4461  |   955.7  1.00e-03\n",
      "193000    4961.1250   0.0012   0.0015     0.0076 |   0.1906   0.2400     1.1507  |   960.7  1.00e-03\n",
      "194000    4682.6631   0.0012   0.0016     0.0075 |   0.1937   0.2441     1.1691  |   965.7  1.00e-03\n",
      "195000    4728.0605   0.0013   0.0016     0.0074 |   0.2084   0.2586     1.0949  |   970.7  1.00e-03\n",
      "196000    5007.4482   0.0017   0.0021     0.0124 |   0.2621   0.3366     2.0640  |   975.7  1.00e-03\n",
      "197000    4923.1050   0.0014   0.0017     0.0095 |   0.2173   0.2691     1.4499  |   980.7  1.00e-03\n",
      "198000    4575.9102   0.0017   0.0021     0.0085 |   0.2652   0.3286     1.3639  |   985.7  1.00e-03\n",
      "199000    4651.2593   0.0012   0.0015     0.0079 |   0.1880   0.2369     1.2257  |   990.7  1.00e-03\n",
      "200000    4562.3418   0.0015   0.0018     0.0086 |   0.2316   0.2928     1.4083  |   995.7  1.00e-03\n",
      "201000    4903.7725   0.0013   0.0016     0.0075 |   0.1986   0.2504     1.1642  |  1000.7  1.00e-03\n",
      "202000    4683.9800   0.0011   0.0014     0.0079 |   0.1776   0.2231     1.2560  |  1005.7  1.00e-03\n",
      "203000    4493.4873   0.0015   0.0019     0.0090 |   0.2385   0.2981     1.4189  |  1010.7  1.00e-03\n",
      "204000    5142.0947   0.0015   0.0019     0.0102 |   0.2372   0.2981     1.6970  |  1015.8  1.00e-03\n",
      "205000    5109.9175   0.0011   0.0014     0.0066 |   0.1719   0.2154     1.0089  |  1020.7  1.00e-03\n",
      "206000    4827.9814   0.0019   0.0023     0.0085 |   0.2960   0.3558     1.3440  |  1025.7  1.00e-03\n",
      "207000    4450.7046   0.0019   0.0023     0.0098 |   0.2910   0.3573     1.5263  |  1030.8  1.00e-03\n",
      "208000    4522.2622   0.0018   0.0022     0.0097 |   0.2773   0.3416     1.5613  |  1035.8  1.00e-03\n",
      "209000    4719.1240   0.0012   0.0015     0.0067 |   0.1862   0.2326     1.1001  |  1040.8  1.00e-03\n",
      "210000    4616.6118   0.0014   0.0017     0.0079 |   0.2136   0.2670     1.2640  |  1045.8  1.00e-03\n",
      "211000    4542.6260   0.0022   0.0026     0.0103 |   0.3418   0.4092     1.7069  |  1050.8  1.00e-03\n",
      "212000    4932.3770   0.0026   0.0032     0.0150 |   0.4119   0.4999     2.4923  |  1055.8  1.00e-03\n",
      "213000    5118.1846   0.0012   0.0016     0.0102 |   0.1955   0.2481     1.7057  |  1060.8  1.00e-03\n",
      "214000    4844.2007   0.0020   0.0024     0.0111 |   0.3133   0.3821     1.6469  |  1065.8  1.00e-03\n",
      "215000    4838.4746   0.0016   0.0019     0.0088 |   0.2462   0.3047     1.4042  |  1070.8  1.00e-03\n",
      "216000    4613.7734   0.0015   0.0019     0.0097 |   0.2355   0.2955     1.4912  |  1075.8  1.00e-03\n",
      "217000    4671.3276   0.0013   0.0016     0.0094 |   0.1984   0.2507     1.5539  |  1080.8  1.00e-03\n",
      "218000    4643.3906   0.0020   0.0024     0.0098 |   0.3098   0.3748     1.5853  |  1085.8  1.00e-03\n",
      "219000    4682.7100   0.0014   0.0018     0.0097 |   0.2241   0.2801     1.4327  |  1090.8  1.00e-03\n",
      "220000    5199.7793   0.0012   0.0015     0.0090 |   0.1838   0.2321     1.4971  |  1095.8  1.00e-03\n",
      "221000    4667.2983   0.0036   0.0040     0.0130 |   0.5719   0.6282     1.9651  |  1100.8  1.00e-03\n",
      "222000    4786.1494   0.0013   0.0017     0.0081 |   0.2089   0.2614     1.3302  |  1105.8  1.00e-03\n",
      "223000    4701.5835   0.0014   0.0018     0.0083 |   0.2266   0.2840     1.2987  |  1110.8  1.00e-03\n",
      "224000    4708.4243   0.0014   0.0017     0.0084 |   0.2186   0.2747     1.3054  |  1115.9  1.00e-03\n",
      "225000    4728.3350   0.0012   0.0015     0.0070 |   0.1875   0.2354     1.0930  |  1120.9  1.00e-03\n",
      "226000    4769.9258   0.0014   0.0018     0.0102 |   0.2230   0.2855     1.6524  |  1125.9  1.00e-03\n",
      "227000    4726.3193   0.0014   0.0017     0.0068 |   0.2143   0.2649     1.1170  |  1130.9  1.00e-03\n",
      "228000    4823.9043   0.0014   0.0018     0.0085 |   0.2208   0.2763     1.3395  |  1135.9  1.00e-03\n",
      "229000    4713.1670   0.0014   0.0017     0.0087 |   0.2174   0.2742     1.4229  |  1140.9  1.00e-03\n",
      "230000    4674.5557   0.0012   0.0015     0.0084 |   0.1845   0.2322     1.3009  |  1145.9  1.00e-03\n",
      "231000    4709.5176   0.0014   0.0018     0.0084 |   0.2204   0.2785     1.3501  |  1150.9  1.00e-03\n",
      "232000    5064.0132   0.0013   0.0016     0.0080 |   0.2043   0.2557     1.2304  |  1155.9  1.00e-03\n",
      "233000    4635.6885   0.0015   0.0019     0.0087 |   0.2444   0.3046     1.4078  |  1160.9  1.00e-03\n",
      "234000    4659.2227   0.0020   0.0024     0.0103 |   0.3097   0.3735     1.5357  |  1165.9  1.00e-03\n",
      "235000    4943.2568   0.0019   0.0023     0.0098 |   0.3056   0.3708     1.6307  |  1170.9  1.00e-03\n",
      "236000    4927.3271   0.0016   0.0020     0.0130 |   0.2569   0.3232     1.9371  |  1175.9  1.00e-03\n",
      "237000    4650.6914   0.0014   0.0017     0.0081 |   0.2148   0.2666     1.2977  |  1180.8  1.00e-03\n",
      "238000    4903.9873   0.0019   0.0023     0.0088 |   0.2933   0.3578     1.4101  |  1185.8  1.00e-03\n",
      "239000    4819.0083   0.0015   0.0019     0.0076 |   0.2442   0.3011     1.1902  |  1190.8  1.00e-03\n",
      "240000    4772.9414   0.0012   0.0015     0.0082 |   0.1878   0.2373     1.3716  |  1195.8  1.00e-03\n",
      "241000    4583.6963   0.0012   0.0015     0.0075 |   0.1874   0.2344     1.2016  |  1200.8  1.00e-03\n",
      "242000    4945.4248   0.0029   0.0033     0.0109 |   0.4615   0.5246     1.7060  |  1205.8  1.00e-03\n",
      "243000    4895.3428   0.0017   0.0021     0.0090 |   0.2678   0.3295     1.4549  |  1210.8  1.00e-03\n",
      "244000    4906.5127   0.0014   0.0017     0.0083 |   0.2163   0.2723     1.2805  |  1215.8  1.00e-03\n",
      "245000    4993.9961   0.0019   0.0024     0.0123 |   0.3039   0.3814     2.0389  |  1220.7  1.00e-03\n",
      "246000    5032.5435   0.0017   0.0021     0.0087 |   0.2675   0.3240     1.3921  |  1225.7  1.00e-03\n",
      "247000    4862.7549   0.0019   0.0023     0.0088 |   0.2976   0.3570     1.4026  |  1230.7  1.00e-03\n",
      "248000    4736.1006   0.0014   0.0017     0.0083 |   0.2189   0.2737     1.3065  |  1235.8  1.00e-03\n",
      "249000    4640.6885   0.0022   0.0025     0.0080 |   0.3414   0.3988     1.2742  |  1240.7  1.00e-03\n",
      "250000    4887.3447   0.0013   0.0016     0.0079 |   0.1977   0.2480     1.1889  |  1245.7  1.00e-03\n",
      "251000    4746.6348   0.0013   0.0016     0.0084 |   0.2012   0.2535     1.3356  |  1250.8  1.00e-04\n",
      "252000    4847.0728   0.0012   0.0015     0.0083 |   0.1838   0.2320     1.2329  |  1255.9  1.00e-04\n",
      "253000    4766.3877   0.0012   0.0015     0.0078 |   0.1861   0.2346     1.2920  |  1261.0  1.00e-04\n",
      "254000    5130.4424   0.0011   0.0014     0.0073 |   0.1746   0.2203     1.1743  |  1266.0  1.00e-04\n",
      "255000    4746.2070   0.0012   0.0015     0.0075 |   0.1894   0.2378     1.1801  |  1271.1  1.00e-04\n",
      "256000    5069.6475   0.0011   0.0014     0.0072 |   0.1806   0.2271     1.1343  |  1276.3  1.00e-04\n",
      "257000    4624.2422   0.0011   0.0014     0.0073 |   0.1693   0.2134     1.0767  |  1281.4  1.00e-04\n",
      "258000    4546.2202   0.0011   0.0013     0.0065 |   0.1685   0.2123     1.0549  |  1286.4  1.00e-04\n",
      "259000    4995.4844   0.0011   0.0014     0.0070 |   0.1761   0.2213     1.1274  |  1291.5  1.00e-04\n",
      "260000    4883.1406   0.0011   0.0014     0.0071 |   0.1781   0.2237     1.1512  |  1296.5  1.00e-04\n",
      "261000    4795.3633   0.0014   0.0017     0.0076 |   0.2164   0.2656     1.2547  |  1301.6  1.00e-04\n",
      "262000    4844.4092   0.0011   0.0014     0.0067 |   0.1719   0.2161     1.0842  |  1306.7  1.00e-04\n",
      "263000    4918.6182   0.0011   0.0014     0.0068 |   0.1727   0.2171     1.0989  |  1311.7  1.00e-04\n",
      "264000    4809.3945   0.0012   0.0015     0.0069 |   0.1849   0.2310     1.0907  |  1316.8  1.00e-04\n",
      "265000    4667.1865   0.0012   0.0015     0.0071 |   0.1885   0.2352     1.1408  |  1321.9  1.00e-04\n",
      "266000    5077.1533   0.0011   0.0014     0.0065 |   0.1742   0.2186     1.0161  |  1326.9  1.00e-04\n",
      "267000    5232.7627   0.0012   0.0015     0.0067 |   0.1858   0.2318     1.0891  |  1332.0  1.00e-04\n",
      "268000    5085.5479   0.0012   0.0015     0.0069 |   0.1882   0.2343     1.1092  |  1337.1  1.00e-04\n",
      "269000    5059.4971   0.0014   0.0017     0.0070 |   0.2147   0.2625     1.1238  |  1342.1  1.00e-04\n",
      "270000    4788.0859   0.0012   0.0015     0.0068 |   0.1894   0.2354     1.0681  |  1347.2  1.00e-04\n",
      "271000    5049.4160   0.0011   0.0014     0.0067 |   0.1802   0.2250     1.0532  |  1352.3  1.00e-04\n",
      "272000    4818.3843   0.0011   0.0013     0.0065 |   0.1673   0.2100     1.0484  |  1357.4  1.00e-04\n",
      "273000    4676.5586   0.0012   0.0015     0.0068 |   0.1872   0.2326     1.0921  |  1362.4  1.00e-04\n",
      "274000    4596.8613   0.0012   0.0015     0.0069 |   0.1970   0.2434     1.1108  |  1367.5  1.00e-04\n",
      "275000    4482.3711   0.0011   0.0014     0.0068 |   0.1772   0.2216     1.1056  |  1372.5  1.00e-04\n",
      "276000    4780.0615   0.0011   0.0014     0.0066 |   0.1795   0.2241     1.0593  |  1377.6  1.00e-04\n",
      "277000    4909.0415   0.0013   0.0015     0.0069 |   0.1978   0.2442     1.0850  |  1382.6  1.00e-04\n",
      "278000    4972.6729   0.0013   0.0016     0.0071 |   0.2049   0.2518     1.1133  |  1387.7  1.00e-04\n",
      "279000    4884.5610   0.0011   0.0013     0.0065 |   0.1673   0.2102     1.0158  |  1392.8  1.00e-04\n",
      "280000    4558.4951   0.0012   0.0015     0.0070 |   0.1859   0.2313     1.0928  |  1397.8  1.00e-04\n",
      "281000    5004.6167   0.0012   0.0015     0.0066 |   0.1906   0.2367     1.0519  |  1402.9  1.00e-04\n",
      "282000    4760.8525   0.0011   0.0013     0.0063 |   0.1657   0.2084     0.9871  |  1408.0  1.00e-04\n",
      "283000    4665.3159   0.0011   0.0014     0.0063 |   0.1771   0.2215     1.0069  |  1413.0  1.00e-04\n",
      "284000    4678.4336   0.0011   0.0014     0.0064 |   0.1698   0.2132     0.9838  |  1418.1  1.00e-04\n",
      "285000    4754.7773   0.0011   0.0014     0.0063 |   0.1800   0.2247     0.9997  |  1423.1  1.00e-04\n",
      "286000    4640.1230   0.0012   0.0015     0.0066 |   0.1874   0.2329     1.0182  |  1428.2  1.00e-04\n",
      "287000    4921.6865   0.0012   0.0015     0.0066 |   0.1925   0.2384     1.0184  |  1433.3  1.00e-04\n",
      "288000    4791.2178   0.0011   0.0014     0.0065 |   0.1783   0.2228     1.0141  |  1438.3  1.00e-04\n",
      "289000    4866.8877   0.0011   0.0014     0.0064 |   0.1709   0.2143     0.9980  |  1443.4  1.00e-04\n",
      "290000    4636.6680   0.0011   0.0014     0.0064 |   0.1710   0.2144     1.0068  |  1448.5  1.00e-04\n",
      "291000    4733.1938   0.0013   0.0016     0.0066 |   0.2009   0.2475     1.0327  |  1453.5  1.00e-04\n",
      "292000    4928.2910   0.0011   0.0014     0.0063 |   0.1780   0.2224     1.0143  |  1458.5  1.00e-04\n",
      "293000    4783.1328   0.0011   0.0014     0.0062 |   0.1790   0.2235     0.9735  |  1463.6  1.00e-04\n",
      "294000    4968.9326   0.0011   0.0014     0.0066 |   0.1710   0.2146     1.0180  |  1468.6  1.00e-04\n",
      "295000    5026.4248   0.0011   0.0014     0.0063 |   0.1788   0.2234     1.0239  |  1473.7  1.00e-04\n",
      "296000    4945.9502   0.0011   0.0014     0.0063 |   0.1756   0.2195     0.9936  |  1478.7  1.00e-04\n",
      "297000    4945.4160   0.0011   0.0014     0.0064 |   0.1790   0.2233     0.9995  |  1483.8  1.00e-04\n",
      "298000    4791.4995   0.0011   0.0014     0.0062 |   0.1734   0.2169     0.9895  |  1488.8  1.00e-04\n",
      "299000    4633.6191   0.0009   0.0012     0.0056 |   0.1497   0.1884     0.9047  |  1493.9  1.00e-04\n",
      "300000    4813.8252   0.0010   0.0013     0.0061 |   0.1607   0.2020     0.9511  |  1498.9  1.00e-04\n",
      "301000    4704.3569   0.0010   0.0013     0.0061 |   0.1591   0.2003     0.9753  |  1504.0  1.00e-04\n",
      "302000    4857.8076   0.0012   0.0015     0.0066 |   0.1854   0.2305     1.0364  |  1509.0  1.00e-04\n",
      "303000    4885.2949   0.0010   0.0012     0.0059 |   0.1521   0.1916     0.9382  |  1514.1  1.00e-04\n",
      "304000    4760.3721   0.0012   0.0015     0.0064 |   0.1909   0.2366     1.0296  |  1519.1  1.00e-04\n",
      "305000    4868.9072   0.0011   0.0014     0.0064 |   0.1793   0.2237     1.0181  |  1524.2  1.00e-04\n",
      "306000    4861.5415   0.0011   0.0014     0.0063 |   0.1749   0.2185     0.9892  |  1529.2  1.00e-04\n",
      "307000    4724.7065   0.0012   0.0015     0.0065 |   0.1845   0.2291     1.0455  |  1534.3  1.00e-04\n",
      "308000    4703.9170   0.0012   0.0014     0.0065 |   0.1815   0.2260     1.0423  |  1539.4  1.00e-04\n",
      "309000    4864.6709   0.0010   0.0013     0.0061 |   0.1579   0.1986     0.9818  |  1544.4  1.00e-04\n",
      "310000    5164.2998   0.0010   0.0013     0.0063 |   0.1631   0.2052     0.9896  |  1549.5  1.00e-04\n",
      "311000    4685.6367   0.0010   0.0013     0.0063 |   0.1633   0.2052     1.0108  |  1554.6  1.00e-04\n",
      "312000    4550.6934   0.0012   0.0015     0.0065 |   0.1862   0.2312     1.0476  |  1559.6  1.00e-04\n",
      "313000    4786.3267   0.0011   0.0013     0.0062 |   0.1665   0.2091     1.0047  |  1564.7  1.00e-04\n",
      "314000    4926.1045   0.0012   0.0015     0.0064 |   0.1862   0.2314     1.0248  |  1569.7  1.00e-04\n",
      "315000    4428.8413   0.0010   0.0013     0.0062 |   0.1639   0.2061     0.9782  |  1574.8  1.00e-04\n",
      "316000    4824.8711   0.0012   0.0015     0.0066 |   0.1851   0.2302     1.0320  |  1579.8  1.00e-04\n",
      "317000    4551.4912   0.0013   0.0016     0.0068 |   0.2007   0.2468     1.0624  |  1584.9  1.00e-04\n",
      "318000    4671.6123   0.0011   0.0014     0.0064 |   0.1744   0.2182     1.0084  |  1590.0  1.00e-04\n",
      "319000    5001.1338   0.0012   0.0015     0.0069 |   0.1943   0.2399     1.0849  |  1595.0  1.00e-04\n",
      "320000    4784.4014   0.0010   0.0013     0.0065 |   0.1616   0.2033     1.0246  |  1600.1  1.00e-04\n",
      "321000    4602.6650   0.0012   0.0015     0.0068 |   0.1864   0.2315     1.0725  |  1605.2  1.00e-04\n",
      "322000    4788.1182   0.0011   0.0014     0.0064 |   0.1716   0.2151     1.0025  |  1610.2  1.00e-04\n",
      "323000    4544.1987   0.0012   0.0015     0.0065 |   0.1913   0.2368     1.0213  |  1615.3  1.00e-04\n",
      "324000    4706.4346   0.0010   0.0013     0.0061 |   0.1569   0.1979     0.9417  |  1620.3  1.00e-04\n",
      "325000    4675.6646   0.0011   0.0014     0.0062 |   0.1711   0.2143     0.9999  |  1625.4  1.00e-04\n",
      "326000    4489.3916   0.0011   0.0014     0.0064 |   0.1762   0.2201     1.0340  |  1630.5  1.00e-04\n",
      "327000    4918.1514   0.0011   0.0014     0.0065 |   0.1809   0.2254     1.0445  |  1635.5  1.00e-04\n",
      "328000    4705.1035   0.0011   0.0014     0.0060 |   0.1703   0.2135     0.9719  |  1640.6  1.00e-04\n",
      "329000    4616.7764   0.0010   0.0013     0.0060 |   0.1638   0.2061     0.9637  |  1645.7  1.00e-04\n",
      "330000    4819.9414   0.0010   0.0012     0.0060 |   0.1512   0.1905     0.9611  |  1650.7  1.00e-04\n",
      "331000    4857.0425   0.0011   0.0014     0.0064 |   0.1698   0.2130     1.0248  |  1655.8  1.00e-04\n",
      "332000    4850.9780   0.0012   0.0015     0.0066 |   0.1919   0.2372     1.0691  |  1660.9  1.00e-04\n",
      "333000    4812.8208   0.0015   0.0018     0.0068 |   0.2312   0.2779     1.0925  |  1665.9  1.00e-04\n",
      "334000    5039.0000   0.0010   0.0013     0.0060 |   0.1589   0.1998     0.9472  |  1671.0  1.00e-04\n",
      "335000    5123.6973   0.0010   0.0012     0.0061 |   0.1541   0.1941     0.9865  |  1676.1  1.00e-04\n",
      "336000    4684.7529   0.0012   0.0015     0.0065 |   0.1960   0.2416     1.0529  |  1681.1  1.00e-04\n",
      "337000    4827.5469   0.0012   0.0015     0.0066 |   0.1915   0.2369     1.0560  |  1686.2  1.00e-04\n",
      "338000    4811.6880   0.0011   0.0014     0.0064 |   0.1765   0.2205     1.0363  |  1691.3  1.00e-04\n",
      "339000    4699.7129   0.0013   0.0016     0.0068 |   0.1986   0.2448     1.0670  |  1696.4  1.00e-04\n",
      "340000    4960.4312   0.0011   0.0013     0.0064 |   0.1662   0.2089     1.0038  |  1701.5  1.00e-04\n",
      "341000    4721.8413   0.0012   0.0014     0.0068 |   0.1832   0.2282     1.0693  |  1706.5  1.00e-04\n",
      "342000    4712.6973   0.0012   0.0015     0.0068 |   0.1842   0.2294     1.0621  |  1711.6  1.00e-04\n",
      "343000    4933.9717   0.0010   0.0013     0.0064 |   0.1626   0.2047     1.0031  |  1716.7  1.00e-04\n",
      "344000    4708.4087   0.0012   0.0015     0.0068 |   0.1898   0.2355     1.0694  |  1721.7  1.00e-04\n",
      "345000    5122.8423   0.0009   0.0012     0.0061 |   0.1479   0.1863     0.9460  |  1726.8  1.00e-04\n",
      "346000    4754.9277   0.0010   0.0012     0.0061 |   0.1552   0.1956     0.9568  |  1731.9  1.00e-04\n",
      "347000    4994.0752   0.0010   0.0012     0.0059 |   0.1524   0.1920     0.9240  |  1737.0  1.00e-04\n",
      "348000    4424.3467   0.0011   0.0014     0.0060 |   0.1704   0.2137     0.9719  |  1742.1  1.00e-04\n",
      "349000    4780.3599   0.0012   0.0015     0.0064 |   0.1890   0.2339     1.0066  |  1747.2  1.00e-04\n",
      "350000    4616.8271   0.0011   0.0014     0.0062 |   0.1786   0.2226     0.9777  |  1752.3  1.00e-04\n",
      "351000    5066.8057   0.0011   0.0014     0.0063 |   0.1775   0.2216     0.9664  |  1757.4  1.00e-04\n",
      "352000    4705.8604   0.0010   0.0013     0.0060 |   0.1623   0.2045     0.9485  |  1762.5  1.00e-04\n",
      "353000    4697.8833   0.0010   0.0013     0.0060 |   0.1600   0.2014     0.9235  |  1767.6  1.00e-04\n",
      "354000    4967.2236   0.0012   0.0015     0.0062 |   0.1889   0.2339     0.9603  |  1772.7  1.00e-04\n",
      "355000    4843.8135   0.0011   0.0014     0.0059 |   0.1813   0.2256     0.9478  |  1777.8  1.00e-04\n",
      "356000    4747.6455   0.0014   0.0017     0.0065 |   0.2174   0.2635     1.0284  |  1782.8  1.00e-04\n",
      "357000    4865.0518   0.0011   0.0013     0.0059 |   0.1696   0.2127     0.9566  |  1787.9  1.00e-04\n",
      "358000    5085.5078   0.0013   0.0016     0.0065 |   0.1985   0.2450     1.0168  |  1793.1  1.00e-04\n",
      "359000    4768.2563   0.0013   0.0016     0.0066 |   0.2014   0.2479     1.0237  |  1798.1  1.00e-04\n",
      "360000    4795.2354   0.0012   0.0015     0.0062 |   0.1891   0.2349     0.9690  |  1803.3  1.00e-04\n",
      "361000    4824.4985   0.0013   0.0016     0.0064 |   0.2125   0.2594     0.9967  |  1808.4  1.00e-04\n",
      "362000    4641.8950   0.0010   0.0013     0.0059 |   0.1626   0.2047     0.9310  |  1813.5  1.00e-04\n",
      "363000    4771.3838   0.0013   0.0016     0.0068 |   0.2011   0.2477     1.0470  |  1818.6  1.00e-04\n",
      "364000    4741.1035   0.0011   0.0014     0.0063 |   0.1738   0.2176     0.9890  |  1823.7  1.00e-04\n",
      "365000    4844.1294   0.0014   0.0017     0.0066 |   0.2156   0.2622     1.0375  |  1828.8  1.00e-04\n",
      "366000    4687.7373   0.0016   0.0019     0.0071 |   0.2515   0.2977     1.1136  |  1833.9  1.00e-04\n",
      "367000    4574.3232   0.0010   0.0012     0.0061 |   0.1501   0.1890     0.9507  |  1839.0  1.00e-04\n",
      "368000    4545.0493   0.0010   0.0013     0.0061 |   0.1636   0.2056     0.9528  |  1844.1  1.00e-04\n",
      "369000    4967.6323   0.0010   0.0013     0.0064 |   0.1643   0.2066     0.9866  |  1849.2  1.00e-04\n",
      "370000    4690.2207   0.0011   0.0014     0.0063 |   0.1727   0.2162     0.9687  |  1854.4  1.00e-04\n",
      "371000    4925.1353   0.0013   0.0016     0.0065 |   0.2071   0.2531     0.9993  |  1859.4  1.00e-04\n",
      "372000    4968.1445   0.0009   0.0011     0.0054 |   0.1414   0.1776     0.8696  |  1864.5  1.00e-04\n",
      "373000    5037.5352   0.0012   0.0015     0.0064 |   0.1895   0.2346     0.9861  |  1869.6  1.00e-04\n",
      "374000    4955.1328   0.0013   0.0015     0.0065 |   0.1981   0.2437     1.0012  |  1874.8  1.00e-04\n",
      "375000    4853.4023   0.0016   0.0019     0.0066 |   0.2472   0.2934     1.0475  |  1879.9  1.00e-04\n",
      "376000    4804.3022   0.0012   0.0015     0.0061 |   0.1847   0.2295     0.9845  |  1885.0  1.00e-04\n",
      "377000    4917.5786   0.0011   0.0014     0.0062 |   0.1736   0.2173     1.0024  |  1890.1  1.00e-04\n",
      "378000    4635.8320   0.0013   0.0016     0.0068 |   0.2109   0.2570     1.0381  |  1895.2  1.00e-04\n",
      "379000    4767.5923   0.0011   0.0013     0.0059 |   0.1668   0.2092     0.9587  |  1900.3  1.00e-04\n",
      "380000    5392.6182   0.0010   0.0012     0.0058 |   0.1508   0.1900     0.9081  |  1905.4  1.00e-04\n",
      "381000    4637.3745   0.0011   0.0013     0.0060 |   0.1692   0.2119     0.9378  |  1910.5  1.00e-04\n",
      "382000    4631.0527   0.0010   0.0012     0.0056 |   0.1513   0.1904     0.8779  |  1915.6  1.00e-04\n",
      "383000    4636.9160   0.0012   0.0015     0.0061 |   0.1934   0.2384     0.9820  |  1920.7  1.00e-04\n",
      "384000    4696.0234   0.0009   0.0012     0.0058 |   0.1462   0.1840     0.9167  |  1925.8  1.00e-04\n",
      "385000    4746.5625   0.0010   0.0013     0.0058 |   0.1622   0.2037     0.9342  |  1930.9  1.00e-04\n",
      "386000    4929.3398   0.0010   0.0012     0.0058 |   0.1524   0.1919     0.9316  |  1936.2  1.00e-04\n",
      "387000    4716.0078   0.0011   0.0013     0.0060 |   0.1658   0.2081     0.9725  |  1941.3  1.00e-04\n",
      "388000    4826.4951   0.0013   0.0016     0.0066 |   0.1996   0.2454     1.0594  |  1946.4  1.00e-04\n",
      "389000    5075.9756   0.0011   0.0013     0.0064 |   0.1674   0.2101     1.0359  |  1951.5  1.00e-04\n",
      "390000    4936.7896   0.0009   0.0012     0.0058 |   0.1445   0.1816     0.9392  |  1956.6  1.00e-04\n",
      "391000    4851.6494   0.0011   0.0014     0.0062 |   0.1713   0.2149     0.9970  |  1961.7  1.00e-04\n",
      "392000    4952.4751   0.0012   0.0015     0.0065 |   0.1868   0.2319     1.0499  |  1966.8  1.00e-04\n",
      "393000    4988.4385   0.0010   0.0013     0.0062 |   0.1634   0.2056     0.9906  |  1971.9  1.00e-04\n",
      "394000    4776.3672   0.0010   0.0012     0.0061 |   0.1547   0.1951     0.9796  |  1977.0  1.00e-04\n",
      "395000    4831.2642   0.0011   0.0014     0.0062 |   0.1726   0.2160     0.9950  |  1982.1  1.00e-04\n",
      "396000    4677.3955   0.0012   0.0015     0.0063 |   0.1863   0.2313     1.0223  |  1987.2  1.00e-04\n",
      "397000    4936.9434   0.0011   0.0014     0.0064 |   0.1779   0.2223     1.0266  |  1992.3  1.00e-04\n",
      "398000    4508.9072   0.0013   0.0016     0.0065 |   0.2125   0.2581     1.0428  |  1997.4  1.00e-04\n",
      "399000    4808.1641   0.0011   0.0014     0.0062 |   0.1803   0.2241     0.9848  |  2002.5  1.00e-04\n",
      "400000    4906.3203   0.0010   0.0013     0.0061 |   0.1576   0.1983     0.9790  |  2007.6  1.00e-04\n",
      "401000    5251.0884   0.0011   0.0013     0.0062 |   0.1703   0.2131     1.0063  |  2012.7  1.00e-04\n",
      "402000    5121.2090   0.0013   0.0016     0.0067 |   0.2087   0.2548     1.0768  |  2017.8  1.00e-04\n",
      "403000    4975.2881   0.0011   0.0013     0.0066 |   0.1685   0.2113     1.0620  |  2022.9  1.00e-04\n",
      "404000    4975.3574   0.0014   0.0017     0.0072 |   0.2238   0.2700     1.1635  |  2028.0  1.00e-04\n",
      "405000    4701.9575   0.0010   0.0013     0.0066 |   0.1630   0.2049     1.0738  |  2033.0  1.00e-04\n",
      "406000    4923.4648   0.0012   0.0014     0.0065 |   0.1838   0.2284     1.0472  |  2038.2  1.00e-04\n",
      "407000    4633.2524   0.0012   0.0015     0.0066 |   0.1902   0.2352     1.0703  |  2043.2  1.00e-04\n",
      "408000    4802.2070   0.0010   0.0013     0.0063 |   0.1655   0.2078     1.0205  |  2048.3  1.00e-04\n",
      "409000    4819.5820   0.0015   0.0018     0.0068 |   0.2386   0.2841     1.1009  |  2053.4  1.00e-04\n",
      "410000    4635.7754   0.0010   0.0012     0.0061 |   0.1513   0.1906     0.9812  |  2058.6  1.00e-04\n",
      "411000    4982.4502   0.0010   0.0013     0.0062 |   0.1591   0.2000     1.0069  |  2063.6  1.00e-04\n",
      "412000    4732.0132   0.0009   0.0012     0.0057 |   0.1443   0.1819     0.9218  |  2068.7  1.00e-04\n",
      "413000    4472.3228   0.0012   0.0014     0.0062 |   0.1832   0.2271     1.0046  |  2073.8  1.00e-04\n",
      "414000    4860.2734   0.0010   0.0013     0.0059 |   0.1647   0.2066     0.9427  |  2078.9  1.00e-04\n",
      "415000    4884.4810   0.0009   0.0012     0.0057 |   0.1470   0.1852     0.9222  |  2084.0  1.00e-04\n",
      "416000    4867.1426   0.0015   0.0018     0.0067 |   0.2397   0.2851     1.0485  |  2089.1  1.00e-04\n",
      "417000    4727.4702   0.0013   0.0016     0.0066 |   0.2110   0.2565     1.0335  |  2094.2  1.00e-04\n",
      "418000    4730.1333   0.0012   0.0015     0.0066 |   0.1933   0.2379     1.0338  |  2099.3  1.00e-04\n",
      "419000    4519.3350   0.0010   0.0012     0.0058 |   0.1510   0.1901     0.9126  |  2104.4  1.00e-04\n",
      "420000    4853.8047   0.0011   0.0013     0.0064 |   0.1664   0.2086     1.0056  |  2109.5  1.00e-04\n",
      "421000    4747.4570   0.0012   0.0015     0.0067 |   0.1859   0.2305     1.0248  |  2114.6  1.00e-04\n",
      "422000    4749.5405   0.0010   0.0012     0.0057 |   0.1518   0.1912     0.9163  |  2119.7  1.00e-04\n",
      "423000    4687.6763   0.0013   0.0016     0.0066 |   0.2093   0.2547     1.0235  |  2124.8  1.00e-04\n",
      "424000    5028.1362   0.0011   0.0014     0.0063 |   0.1753   0.2187     1.0183  |  2129.9  1.00e-04\n",
      "425000    4830.9482   0.0010   0.0013     0.0064 |   0.1647   0.2067     0.9924  |  2135.0  1.00e-04\n",
      "426000    4692.6812   0.0011   0.0013     0.0064 |   0.1673   0.2098     0.9997  |  2140.1  1.00e-04\n",
      "427000    5204.1426   0.0011   0.0014     0.0063 |   0.1708   0.2135     0.9950  |  2145.2  1.00e-04\n",
      "428000    4965.7109   0.0010   0.0012     0.0060 |   0.1518   0.1913     0.9594  |  2150.3  1.00e-04\n",
      "429000    4812.5820   0.0012   0.0015     0.0066 |   0.1924   0.2373     1.0446  |  2155.4  1.00e-04\n",
      "430000    4933.8115   0.0010   0.0013     0.0062 |   0.1610   0.2021     1.0054  |  2160.5  1.00e-04\n",
      "431000    5195.1489   0.0013   0.0015     0.0066 |   0.1977   0.2424     1.0586  |  2165.6  1.00e-04\n",
      "432000    4984.1074   0.0011   0.0014     0.0063 |   0.1749   0.2180     1.0163  |  2170.7  1.00e-04\n",
      "433000    5073.3770   0.0010   0.0012     0.0061 |   0.1502   0.1893     0.9787  |  2175.8  1.00e-04\n",
      "434000    4598.3857   0.0009   0.0012     0.0060 |   0.1497   0.1888     0.9703  |  2180.9  1.00e-04\n",
      "435000    4812.4980   0.0015   0.0018     0.0071 |   0.2434   0.2882     1.1088  |  2186.0  1.00e-04\n",
      "436000    4801.8447   0.0010   0.0013     0.0064 |   0.1652   0.2072     1.0349  |  2191.1  1.00e-04\n",
      "437000    4855.8477   0.0010   0.0012     0.0060 |   0.1500   0.1891     0.9728  |  2196.2  1.00e-04\n",
      "438000    4745.3462   0.0011   0.0014     0.0062 |   0.1724   0.2152     0.9897  |  2201.3  1.00e-04\n",
      "439000    4635.0845   0.0010   0.0012     0.0062 |   0.1514   0.1907     0.9624  |  2206.4  1.00e-04\n",
      "440000    4847.7705   0.0010   0.0013     0.0060 |   0.1582   0.1992     0.9397  |  2211.5  1.00e-04\n",
      "441000    4929.7871   0.0011   0.0014     0.0062 |   0.1812   0.2253     0.9823  |  2216.6  1.00e-04\n",
      "442000    4775.2544   0.0009   0.0012     0.0056 |   0.1441   0.1817     0.9037  |  2221.7  1.00e-04\n",
      "443000    4697.6875   0.0010   0.0012     0.0060 |   0.1560   0.1968     0.9312  |  2226.8  1.00e-04\n",
      "444000    5033.4756   0.0009   0.0011     0.0058 |   0.1429   0.1800     0.9028  |  2231.9  1.00e-04\n",
      "445000    4831.9805   0.0013   0.0016     0.0066 |   0.2069   0.2524     1.0356  |  2236.9  1.00e-04\n",
      "446000    4923.6377   0.0010   0.0013     0.0060 |   0.1596   0.2012     0.9592  |  2242.0  1.00e-04\n",
      "447000    4661.3008   0.0010   0.0013     0.0060 |   0.1619   0.2041     0.9709  |  2247.1  1.00e-04\n",
      "448000    4963.8325   0.0011   0.0014     0.0063 |   0.1725   0.2158     0.9865  |  2252.2  1.00e-04\n",
      "449000    5051.1074   0.0013   0.0016     0.0069 |   0.2054   0.2505     1.0779  |  2257.3  1.00e-04\n",
      "450000    4747.8301   0.0010   0.0012     0.0064 |   0.1554   0.1957     1.0052  |  2262.4  1.00e-04\n",
      "451000    4664.2290   0.0014   0.0017     0.0071 |   0.2167   0.2622     1.1092  |  2267.5  1.00e-04\n",
      "452000    4511.0439   0.0010   0.0013     0.0064 |   0.1634   0.2054     0.9990  |  2272.5  1.00e-04\n",
      "453000    5046.6479   0.0013   0.0016     0.0067 |   0.2010   0.2462     1.0437  |  2277.6  1.00e-04\n",
      "454000    4763.8145   0.0009   0.0012     0.0057 |   0.1447   0.1825     0.9021  |  2282.7  1.00e-04\n",
      "455000    4817.8086   0.0010   0.0013     0.0060 |   0.1601   0.2015     0.9382  |  2287.8  1.00e-04\n",
      "456000    4643.2969   0.0010   0.0013     0.0060 |   0.1577   0.1986     0.9334  |  2292.8  1.00e-04\n",
      "457000    5046.2607   0.0010   0.0013     0.0058 |   0.1569   0.1980     0.9461  |  2297.9  1.00e-04\n",
      "458000    4716.4043   0.0012   0.0015     0.0063 |   0.1903   0.2352     1.0128  |  2303.0  1.00e-04\n",
      "459000    4594.9141   0.0015   0.0018     0.0067 |   0.2350   0.2800     1.0645  |  2308.1  1.00e-04\n",
      "460000    4614.7461   0.0010   0.0013     0.0061 |   0.1598   0.2011     0.9533  |  2313.1  1.00e-04\n",
      "461000    4963.0664   0.0011   0.0014     0.0062 |   0.1802   0.2247     1.0081  |  2318.2  1.00e-04\n",
      "462000    4674.4888   0.0014   0.0017     0.0068 |   0.2160   0.2617     1.0373  |  2323.2  1.00e-04\n",
      "463000    4833.2280   0.0017   0.0020     0.0069 |   0.2719   0.3155     1.1210  |  2328.3  1.00e-04\n",
      "464000    4567.1196   0.0011   0.0014     0.0063 |   0.1737   0.2170     0.9886  |  2333.4  1.00e-04\n",
      "465000    4858.0762   0.0010   0.0013     0.0063 |   0.1654   0.2075     0.9862  |  2338.4  1.00e-04\n",
      "466000    4838.9707   0.0013   0.0015     0.0066 |   0.1998   0.2445     1.0396  |  2343.5  1.00e-04\n",
      "467000    5213.4087   0.0010   0.0012     0.0059 |   0.1504   0.1895     0.9448  |  2348.5  1.00e-04\n",
      "468000    5007.7681   0.0011   0.0014     0.0065 |   0.1727   0.2155     1.0197  |  2353.6  1.00e-04\n",
      "469000    4760.0918   0.0010   0.0013     0.0063 |   0.1598   0.2010     0.9887  |  2358.7  1.00e-04\n",
      "470000    4600.7144   0.0009   0.0012     0.0061 |   0.1455   0.1836     0.9559  |  2363.8  1.00e-04\n",
      "471000    4933.9243   0.0015   0.0018     0.0074 |   0.2356   0.2811     1.1624  |  2368.8  1.00e-04\n",
      "472000    4749.4277   0.0009   0.0011     0.0058 |   0.1383   0.1736     0.9040  |  2373.9  1.00e-04\n",
      "473000    4857.4014   0.0009   0.0012     0.0058 |   0.1450   0.1831     0.9129  |  2379.0  1.00e-04\n",
      "474000    5127.5132   0.0012   0.0015     0.0065 |   0.1894   0.2343     1.0155  |  2384.1  1.00e-04\n",
      "475000    4925.5952   0.0011   0.0014     0.0064 |   0.1730   0.2159     1.0020  |  2389.1  1.00e-04\n",
      "476000    4573.5625   0.0011   0.0014     0.0064 |   0.1737   0.2167     1.0013  |  2394.2  1.00e-04\n",
      "477000    5110.2930   0.0014   0.0016     0.0067 |   0.2128   0.2579     1.0497  |  2399.3  1.00e-04\n",
      "478000    5110.1880   0.0013   0.0016     0.0068 |   0.2125   0.2577     1.0802  |  2404.4  1.00e-04\n",
      "479000    4800.7148   0.0010   0.0013     0.0066 |   0.1618   0.2032     1.0653  |  2409.4  1.00e-04\n",
      "480000    4531.9180   0.0011   0.0014     0.0067 |   0.1704   0.2132     1.0824  |  2414.5  1.00e-04\n",
      "481000    4813.4629   0.0012   0.0014     0.0067 |   0.1823   0.2263     1.0820  |  2419.5  1.00e-04\n",
      "482000    4532.1367   0.0009   0.0011     0.0055 |   0.1385   0.1732     0.8953  |  2424.6  1.00e-04\n",
      "483000    4832.5537   0.0011   0.0013     0.0063 |   0.1702   0.2126     1.0231  |  2429.7  1.00e-04\n",
      "484000    4557.6357   0.0010   0.0013     0.0065 |   0.1599   0.2010     1.0490  |  2434.7  1.00e-04\n",
      "485000    4779.0112   0.0010   0.0012     0.0061 |   0.1507   0.1899     0.9824  |  2439.8  1.00e-04\n",
      "486000    4673.1123   0.0012   0.0015     0.0066 |   0.1889   0.2332     1.0342  |  2444.9  1.00e-04\n",
      "487000    4731.2144   0.0013   0.0016     0.0066 |   0.2065   0.2513     1.0458  |  2449.9  1.00e-04\n",
      "488000    4621.8213   0.0010   0.0012     0.0062 |   0.1513   0.1907     1.0006  |  2455.0  1.00e-04\n",
      "489000    4831.4497   0.0010   0.0013     0.0062 |   0.1617   0.2031     0.9965  |  2460.1  1.00e-04\n",
      "490000    4894.8867   0.0014   0.0017     0.0066 |   0.2157   0.2605     1.0566  |  2465.2  1.00e-04\n",
      "491000    4625.4463   0.0015   0.0017     0.0069 |   0.2289   0.2735     1.0973  |  2470.3  1.00e-04\n",
      "492000    5224.2139   0.0009   0.0011     0.0057 |   0.1406   0.1767     0.9087  |  2475.4  1.00e-04\n",
      "493000    4673.5532   0.0010   0.0013     0.0063 |   0.1643   0.2060     1.0218  |  2480.4  1.00e-04\n",
      "494000    4768.0479   0.0011   0.0014     0.0065 |   0.1721   0.2148     1.0494  |  2485.5  1.00e-04\n",
      "495000    4702.0010   0.0010   0.0012     0.0062 |   0.1549   0.1949     1.0092  |  2490.6  1.00e-04\n",
      "496000    4698.7354   0.0010   0.0012     0.0064 |   0.1523   0.1920     1.0258  |  2495.6  1.00e-04\n",
      "497000    4836.3418   0.0010   0.0012     0.0064 |   0.1523   0.1922     1.0280  |  2500.7  1.00e-04\n",
      "498000    4697.5303   0.0012   0.0014     0.0066 |   0.1824   0.2268     1.0678  |  2505.8  1.00e-04\n",
      "499000    5131.5083   0.0017   0.0020     0.0072 |   0.2666   0.3099     1.1518  |  2510.9  1.00e-04\n",
      "500000    4822.4106   0.0009   0.0012     0.0063 |   0.1482   0.1872     1.0154  |  2516.0  1.00e-04\n",
      "501000    4839.1729   0.0011   0.0013     0.0065 |   0.1682   0.2108     1.0423  |  2521.0  1.00e-05\n",
      "502000    4676.6846   0.0011   0.0013     0.0064 |   0.1674   0.2098     1.0320  |  2526.1  1.00e-05\n",
      "503000    4722.6479   0.0011   0.0013     0.0064 |   0.1671   0.2094     1.0350  |  2531.1  1.00e-05\n",
      "504000    4708.7969   0.0011   0.0013     0.0064 |   0.1682   0.2107     1.0317  |  2536.1  1.00e-05\n",
      "505000    4874.6426   0.0010   0.0013     0.0063 |   0.1649   0.2068     1.0190  |  2541.2  1.00e-05\n",
      "506000    4924.5938   0.0011   0.0013     0.0064 |   0.1696   0.2121     1.0301  |  2546.2  1.00e-05\n",
      "507000    4955.2520   0.0010   0.0013     0.0063 |   0.1611   0.2025     1.0127  |  2551.2  1.00e-05\n",
      "508000    5048.3145   0.0011   0.0014     0.0064 |   0.1736   0.2165     1.0350  |  2556.3  1.00e-05\n",
      "509000    4668.7837   0.0010   0.0013     0.0064 |   0.1654   0.2075     1.0266  |  2561.3  1.00e-05\n",
      "510000    4716.3252   0.0011   0.0013     0.0064 |   0.1675   0.2097     1.0275  |  2566.4  1.00e-05\n",
      "511000    4668.0845   0.0010   0.0013     0.0063 |   0.1627   0.2043     1.0175  |  2571.4  1.00e-05\n",
      "512000    5166.6309   0.0010   0.0013     0.0063 |   0.1639   0.2057     1.0220  |  2576.5  1.00e-05\n",
      "513000    5278.3477   0.0011   0.0013     0.0064 |   0.1659   0.2079     1.0280  |  2581.5  1.00e-05\n",
      "514000    4937.5117   0.0010   0.0013     0.0063 |   0.1639   0.2056     1.0187  |  2586.6  1.00e-05\n",
      "515000    4771.3613   0.0010   0.0013     0.0063 |   0.1624   0.2039     1.0128  |  2591.7  1.00e-05\n",
      "516000    5132.0537   0.0010   0.0013     0.0063 |   0.1643   0.2061     1.0175  |  2596.8  1.00e-05\n",
      "517000    4945.0957   0.0011   0.0013     0.0063 |   0.1686   0.2111     1.0258  |  2601.8  1.00e-05\n",
      "518000    4698.3682   0.0011   0.0013     0.0063 |   0.1670   0.2092     1.0221  |  2606.9  1.00e-05\n",
      "519000    4491.7705   0.0011   0.0013     0.0063 |   0.1676   0.2099     1.0214  |  2611.9  1.00e-05\n",
      "520000    4811.4551   0.0011   0.0013     0.0063 |   0.1678   0.2102     1.0215  |  2617.0  1.00e-05\n",
      "521000    4901.1235   0.0011   0.0013     0.0063 |   0.1683   0.2106     1.0199  |  2622.0  1.00e-05\n",
      "522000    4866.8770   0.0011   0.0013     0.0063 |   0.1696   0.2121     1.0256  |  2627.1  1.00e-05\n",
      "523000    4646.6602   0.0010   0.0013     0.0063 |   0.1644   0.2063     1.0150  |  2632.1  1.00e-05\n",
      "524000    4895.1050   0.0011   0.0013     0.0063 |   0.1696   0.2120     1.0253  |  2637.2  1.00e-05\n",
      "525000    4967.7637   0.0010   0.0013     0.0063 |   0.1624   0.2038     1.0070  |  2642.3  1.00e-05\n",
      "526000    4701.6230   0.0011   0.0013     0.0063 |   0.1679   0.2100     1.0150  |  2647.3  1.00e-05\n",
      "527000    4712.0410   0.0010   0.0013     0.0063 |   0.1655   0.2072     1.0090  |  2652.4  1.00e-05\n",
      "528000    5172.4487   0.0011   0.0013     0.0063 |   0.1687   0.2108     1.0189  |  2657.4  1.00e-05\n",
      "529000    4685.0229   0.0011   0.0013     0.0063 |   0.1681   0.2101     1.0185  |  2662.5  1.00e-05\n",
      "530000    5006.1885   0.0010   0.0013     0.0062 |   0.1609   0.2019     0.9996  |  2667.6  1.00e-05\n",
      "531000    4836.6953   0.0010   0.0013     0.0062 |   0.1606   0.2015     1.0012  |  2672.6  1.00e-05\n",
      "532000    4473.3892   0.0010   0.0013     0.0063 |   0.1650   0.2066     1.0099  |  2677.7  1.00e-05\n",
      "533000    4703.3896   0.0011   0.0013     0.0063 |   0.1671   0.2090     1.0134  |  2682.8  1.00e-05\n",
      "534000    4741.8955   0.0011   0.0013     0.0063 |   0.1668   0.2086     1.0137  |  2687.8  1.00e-05\n",
      "535000    4849.3330   0.0010   0.0013     0.0062 |   0.1636   0.2050     1.0065  |  2692.9  1.00e-05\n",
      "536000    4597.2993   0.0010   0.0013     0.0062 |   0.1625   0.2037     1.0020  |  2698.0  1.00e-05\n",
      "537000    4615.0435   0.0011   0.0013     0.0063 |   0.1682   0.2102     1.0103  |  2703.1  1.00e-05\n",
      "538000    4807.2559   0.0011   0.0013     0.0063 |   0.1668   0.2087     1.0123  |  2708.1  1.00e-05\n",
      "539000    4673.0303   0.0010   0.0013     0.0063 |   0.1656   0.2073     1.0114  |  2713.2  1.00e-05\n",
      "540000    4732.8472   0.0010   0.0013     0.0062 |   0.1623   0.2036     1.0017  |  2718.2  1.00e-05\n",
      "541000    4582.8301   0.0010   0.0013     0.0062 |   0.1649   0.2064     1.0033  |  2723.3  1.00e-05\n",
      "542000    4884.0347   0.0010   0.0013     0.0062 |   0.1592   0.1999     0.9942  |  2728.4  1.00e-05\n",
      "543000    5018.4639   0.0011   0.0014     0.0063 |   0.1735   0.2159     1.0179  |  2733.4  1.00e-05\n",
      "544000    4696.4507   0.0011   0.0013     0.0063 |   0.1666   0.2084     1.0123  |  2738.5  1.00e-05\n",
      "545000    4695.3057   0.0010   0.0013     0.0062 |   0.1645   0.2059     1.0044  |  2743.6  1.00e-05\n",
      "546000    5044.3477   0.0010   0.0013     0.0062 |   0.1619   0.2030     0.9989  |  2748.7  1.00e-05\n",
      "547000    4684.7959   0.0011   0.0013     0.0062 |   0.1658   0.2075     1.0064  |  2753.7  1.00e-05\n",
      "548000    4481.6787   0.0011   0.0013     0.0063 |   0.1671   0.2089     1.0097  |  2758.8  1.00e-05\n",
      "549000    4895.3223   0.0010   0.0013     0.0061 |   0.1625   0.2037     0.9933  |  2763.9  1.00e-05\n",
      "550000    4587.9937   0.0011   0.0013     0.0063 |   0.1701   0.2122     1.0098  |  2769.0  1.00e-05\n",
      "551000    4759.8950   0.0010   0.0013     0.0062 |   0.1656   0.2071     1.0019  |  2774.0  1.00e-05\n",
      "552000    5100.6870   0.0011   0.0013     0.0063 |   0.1694   0.2113     1.0097  |  2779.1  1.00e-05\n",
      "553000    5200.5625   0.0010   0.0013     0.0061 |   0.1619   0.2030     0.9931  |  2784.2  1.00e-05\n",
      "554000    4930.3047   0.0011   0.0013     0.0062 |   0.1686   0.2105     0.9994  |  2789.3  1.00e-05\n",
      "555000    4730.3926   0.0010   0.0013     0.0061 |   0.1608   0.2017     0.9796  |  2794.3  1.00e-05\n",
      "556000    4705.4985   0.0011   0.0013     0.0061 |   0.1666   0.2083     0.9922  |  2799.4  1.00e-05\n",
      "557000    4949.9082   0.0010   0.0013     0.0061 |   0.1619   0.2031     0.9872  |  2804.5  1.00e-05\n",
      "558000    4827.9395   0.0011   0.0013     0.0062 |   0.1671   0.2089     0.9964  |  2809.5  1.00e-05\n",
      "559000    4787.5742   0.0011   0.0013     0.0062 |   0.1673   0.2091     0.9968  |  2814.6  1.00e-05\n",
      "560000    4901.2578   0.0010   0.0013     0.0061 |   0.1636   0.2049     0.9886  |  2819.7  1.00e-05\n",
      "561000    4982.5342   0.0010   0.0013     0.0061 |   0.1644   0.2058     0.9901  |  2825.0  1.00e-05\n",
      "562000    4794.6914   0.0010   0.0013     0.0061 |   0.1637   0.2050     0.9857  |  2830.1  1.00e-05\n",
      "563000    5158.9609   0.0010   0.0013     0.0061 |   0.1645   0.2060     0.9873  |  2835.2  1.00e-05\n",
      "564000    4890.5815   0.0010   0.0013     0.0060 |   0.1587   0.1994     0.9754  |  2840.2  1.00e-05\n",
      "565000    4472.5723   0.0011   0.0013     0.0061 |   0.1660   0.2077     0.9910  |  2845.3  1.00e-05\n",
      "566000    4740.5586   0.0011   0.0013     0.0062 |   0.1666   0.2083     0.9913  |  2850.4  1.00e-05\n",
      "567000    5043.8916   0.0010   0.0013     0.0062 |   0.1649   0.2065     0.9942  |  2855.5  1.00e-05\n",
      "568000    4911.5845   0.0011   0.0013     0.0062 |   0.1685   0.2104     0.9941  |  2860.5  1.00e-05\n",
      "569000    4797.7139   0.0011   0.0013     0.0062 |   0.1692   0.2112     0.9899  |  2865.6  1.00e-05\n",
      "570000    4960.5674   0.0010   0.0013     0.0062 |   0.1648   0.2063     0.9905  |  2870.7  1.00e-05\n",
      "571000    4908.3354   0.0010   0.0013     0.0061 |   0.1645   0.2059     0.9879  |  2875.7  1.00e-05\n",
      "572000    4584.6353   0.0010   0.0013     0.0061 |   0.1632   0.2045     0.9889  |  2880.8  1.00e-05\n",
      "573000    4880.3262   0.0010   0.0013     0.0061 |   0.1608   0.2018     0.9839  |  2885.9  1.00e-05\n",
      "574000    4841.1738   0.0010   0.0013     0.0062 |   0.1653   0.2068     0.9995  |  2891.0  1.00e-05\n",
      "575000    4879.6660   0.0011   0.0013     0.0063 |   0.1692   0.2112     1.0057  |  2896.1  1.00e-05\n",
      "576000    4646.1860   0.0010   0.0013     0.0062 |   0.1650   0.2065     1.0046  |  2901.1  1.00e-05\n",
      "577000    4843.6777   0.0011   0.0013     0.0063 |   0.1691   0.2111     1.0099  |  2906.2  1.00e-05\n",
      "578000    4664.5957   0.0010   0.0013     0.0062 |   0.1654   0.2069     0.9993  |  2911.3  1.00e-05\n",
      "579000    4805.1133   0.0010   0.0013     0.0062 |   0.1653   0.2068     1.0038  |  2916.4  1.00e-05\n",
      "580000    4806.7881   0.0011   0.0013     0.0062 |   0.1661   0.2076     1.0054  |  2921.4  1.00e-05\n",
      "581000    4681.8828   0.0010   0.0013     0.0062 |   0.1632   0.2044     1.0003  |  2926.5  1.00e-05\n",
      "582000    5203.5107   0.0011   0.0013     0.0062 |   0.1656   0.2070     1.0033  |  2931.6  1.00e-05\n",
      "583000    4655.8379   0.0010   0.0013     0.0062 |   0.1650   0.2063     1.0018  |  2936.7  1.00e-05\n",
      "584000    4974.4341   0.0011   0.0013     0.0063 |   0.1658   0.2073     1.0102  |  2941.8  1.00e-05\n",
      "585000    4672.7432   0.0011   0.0013     0.0063 |   0.1663   0.2078     1.0079  |  2946.9  1.00e-05\n",
      "586000    4722.6484   0.0010   0.0013     0.0062 |   0.1612   0.2021     1.0012  |  2951.9  1.00e-05\n",
      "587000    5099.0547   0.0010   0.0013     0.0062 |   0.1625   0.2035     1.0047  |  2957.0  1.00e-05\n",
      "588000    4952.2852   0.0011   0.0013     0.0063 |   0.1687   0.2104     1.0087  |  2962.1  1.00e-05\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-45-c7a21811e8d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;31m# Perform training step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m     \u001b[0mhist_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    826\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    853\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    854\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 855\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    856\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2941\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2942\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2943\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2945\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1917\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1918\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1919\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1920\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1921\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    558\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    561\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Define a training step as a TensorFlow function to increase speed of training\n",
    "@tf.function\n",
    "def train_step():\n",
    "    # Draw batch of random paths\n",
    "    X = draw_X(batch_size, a, b)\n",
    "    \n",
    "    # Evaluate g at X_T\n",
    "    y = fun_g(X[:,:,-1])\n",
    "\n",
    "    # And compute the loss as well as the gradient\n",
    "    loss, grad = compute_grad(X, y, model, training=True)\n",
    "    #hist_loss.append(loss)\n",
    "\n",
    "    # Perform gradient step\n",
    "    optimizer.apply_gradients(zip(grad, model.trainable_variables))\n",
    "    return loss\n",
    "\n",
    "# Start timer\n",
    "t0 = time()\n",
    "\n",
    "\n",
    "# Set interval to estimate errors\n",
    "log_interval = 1000\n",
    "\n",
    "\n",
    "# Initialize header of output\n",
    "print('  Iter        Loss   L1_rel   L2_rel   Linf_rel |   L1_abs   L2_abs   Linf_abs  |    Time')\n",
    "\n",
    "# Loop to train model\n",
    "for i in range(num_epochs):\n",
    "\n",
    "    # Perform training step\n",
    "    loss = train_step()\n",
    "    hist_loss.append(loss)\n",
    "\n",
    "    if i % log_interval == 0:\n",
    "        \n",
    "        # Compute current prediction on test set\n",
    "        Ypred = model(Xtest, training=False)\n",
    "        \n",
    "        # Compute absolute and relative errors\n",
    "        abs_error = np.abs(Ypred - Ytest)\n",
    "        rel_error = abs_error/Ytest\n",
    "        L2_rel = tf.sqrt(tf.reduce_mean(tf.pow(rel_error, 2))).numpy()\n",
    "        L1_rel = tf.reduce_mean(tf.abs(rel_error)).numpy()\n",
    "        Linf_rel = tf.reduce_max(tf.abs(rel_error)).numpy()\n",
    "        \n",
    "        L2_abs = tf.sqrt(tf.reduce_mean(tf.pow(abs_error, 2))).numpy()\n",
    "        L1_abs = tf.reduce_mean(tf.abs(abs_error)).numpy()\n",
    "        Linf_abs = tf.reduce_max(tf.abs(abs_error)).numpy()\n",
    "        \n",
    "        total_time = time()-t0\n",
    "        stepsize = optimizer.lr(optimizer.iterations)\n",
    "        err = (i, loss.numpy(), L1_rel, L2_rel, Linf_rel, L1_abs, L2_abs, Linf_abs, total_time, stepsize)\n",
    "        error_hist.append(err)\n",
    "        print('{:5d} {:12.4f} {:8.4f} {:8.4f}   {:8.4f} | {:8.4f} {:8.4f}   {:8.4f}  |  {:6.1f}  {:6.2e}'.format(*err))\n",
    "#Y = model.call((X,dw))\n",
    "print(time()-t0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P4ZUlkFL0QMX"
   },
   "source": [
    "### Plot of the training history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "id": "WlYZhvu50QMl",
    "outputId": "9ff4c99e-4730-47f4-e795-1ae120e367a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'training loss')"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(10,6))\n",
    "ax.semilogy(range(len(hist_loss)), hist_loss,'k-')\n",
    "ax.set_xlabel('epoch')\n",
    "ax.set_ylabel('training loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sIUPehHg0QMm"
   },
   "source": [
    "### Plot of the absolute and relative errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 398
    },
    "id": "ap_Vufdl0QMn",
    "outputId": "4ad0f7f2-8013-4265-f134-3e25124f4686"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(9,6))\n",
    "ax.set_prop_cycle(color=['royalblue', 'darkorange', 'darkcyan'])\n",
    "xrange = np.arange(len(error_hist))*log_interval\n",
    "ax.semilogy(xrange, [e[2:5] for e in error_hist])\n",
    "ax.semilogy(xrange, [e[5:8] for e in error_hist], linestyle='--')\n",
    "ax.set_xlabel('$n_{epoch}$')\n",
    "ax.set_ylabel('Errors')\n",
    "ax.legend(['$L^1$', '$L^2$', '$L^{\\infty}$']),\n",
    "plt.savefig('Errors_OptionPricing.pdf', bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EH4vzONY0QMn"
   },
   "source": [
    "### Plot slice of solution and rel. error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dbjc_5TDkWUf",
    "outputId": "33511132-138f-48df-f0cf-a670123f1fcd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 2000\n",
      "100 / 2000\n",
      "200 / 2000\n",
      "300 / 2000\n",
      "400 / 2000\n",
      "500 / 2000\n",
      "600 / 2000\n",
      "700 / 2000\n",
      "800 / 2000\n",
      "900 / 2000\n",
      "1000 / 2000\n",
      "1100 / 2000\n",
      "1200 / 2000\n",
      "1300 / 2000\n",
      "1400 / 2000\n",
      "1500 / 2000\n",
      "1600 / 2000\n",
      "1700 / 2000\n",
      "1800 / 2000\n",
      "1900 / 2000\n"
     ]
    }
   ],
   "source": [
    "ngrid = 100\n",
    "\n",
    "# Choose indices to plot\n",
    "idx0 = 0\n",
    "idx1 = 1\n",
    "\n",
    "# Determine remaining indices\n",
    "idx = np.arange(dim)\n",
    "idx_remaining = np.setdiff1d(idx,[idx0,idx1])\n",
    "# Set remaining values to midpoints\n",
    "val_remaining = np.array((a+b)/2)\n",
    "\n",
    "# Meshgrid for plots\n",
    "xspace = np.linspace(a[idx0], b[idx0], ngrid + 1, dtype=DTYPE)\n",
    "yspace = np.linspace(a[idx1], b[idx1], ngrid + 1, dtype=DTYPE)\n",
    "X,Y = np.meshgrid(xspace, yspace)\n",
    "\n",
    "# Append remaining values\n",
    "Z = np.repeat(val_remaining[idx_remaining].reshape(1,-1), (ngrid+1)**2,axis=0)\n",
    "Xgrid = np.vstack([X.flatten(),Y.flatten()]).T\n",
    "Xgrid = np.hstack([Xgrid,Z])\n",
    "\n",
    "n_test, dim = Xgrid.shape\n",
    "uest = tf.zeros((n_test,1),dtype=DTYPE)\n",
    "\n",
    "b_size = 512\n",
    "mc_samples=2000\n",
    "\n",
    "Xgrid = tf.convert_to_tensor(Xgrid, dtype=DTYPE)\n",
    "@tf.function\n",
    "def mc_step(y):\n",
    "    Xi = tf.random.normal(shape=(b_size, dim), dtype=DTYPE)\n",
    "    upd = tf.exp( (mu- tf.square(sigma)/2) * T + sigma * tf.sqrt(T) * Xi)\n",
    "    XT = tf.reshape(Xgrid, shape=[(ngrid+1)**2,dim,1])*tf.transpose(upd)\n",
    "    tmp = tf.reduce_sum(tf.reshape(fun_g(XT),[(ngrid+1)**2,b_size]), axis=1,keepdims=True)/(b_size*mc_samples)\n",
    "    return y + tmp\n",
    "\n",
    "#mc_step(uest)\n",
    "for i in range(mc_samples):\n",
    "    if i%100==0:\n",
    "        print(i,'/',mc_samples)\n",
    "\n",
    "    uest=mc_step(uest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 714
    },
    "id": "t4Bv1egQ0QMn",
    "outputId": "6f07cbd1-297e-461a-8793-2e0547f2a7d1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$x_2$')"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relative error\n",
    "urel =tf.abs(model(Xgrid,training=False) -  uest)/uest\n",
    "\n",
    "Urel = urel.numpy().reshape(ngrid+1,ngrid+1)\n",
    "# Slice of solution\n",
    "U = model(Xgrid,training=False).numpy().reshape(ngrid+1,ngrid+1)\n",
    "\n",
    "# Plot solution of heat equation\n",
    "fig = plt.figure(figsize=(11,6))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot_surface(X,Y,U,cmap='viridis')\n",
    "ax.view_init(45,210)\n",
    "ax.set_xlabel('$x_1$')\n",
    "ax.set_ylabel('$x_2$')\n",
    "#plt.savefig('Sol_OptionPricing.pdf', bbox_inches='tight', dpi=300)\n",
    "\n",
    "# Plot relative error of heat equation\n",
    "fig = plt.figure(figsize=(9,6))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot_surface(X,Y,Urel,cmap='viridis')\n",
    "ax.view_init(45,210)\n",
    "ax.set_xlabel('$x_1$')\n",
    "ax.set_ylabel('$x_2$')\n",
    "#plt.savefig('RelError_OptionPricing.pdf', bbox_inches='tight', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Feynman_Kac_Solver.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}