mcmc.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mcmc.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "-DTsv2PZojGg"
      ],
      "toc_visible": true,
      "mount_file_id": "1T0pg_m0UubOOmMI2lQS-IDliA0TQNteK",
      "authorship_tag": "ABX9TyOThQeBs4xs/y2TdNCrsq6N",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/weldlabucsb/mcmc/blob/master/mcmc.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbDSkPEvnzQi",
        "colab_type": "text"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P1uhEQp4-4nZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Goal of this notebook is to create some simple code that can take a 1d array which we will suppose we have, i.e. it will be one of the inputs\n",
        "#and we will take it and compare it to simulated data.\n",
        "\n",
        "## Imports:\n",
        "\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_probability as tfp\n",
        "from scipy.integrate import odeint\n",
        "from scipy.interpolate import interp1d\n",
        "from scipy.integrate import solve_ivp\n",
        "from multiprocessing import Process, Queue\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.core.pylabtools import figsize\n",
        "\n",
        "class _TFColor(object):\n",
        "    \"\"\"Enum of colors used in TF docs.\"\"\"\n",
        "    red = '#F15854'\n",
        "    blue = '#5DA5DA'\n",
        "    orange = '#FAA43A'\n",
        "    green = '#60BD68'\n",
        "    pink = '#F17CB0'\n",
        "    brown = '#B2912F'\n",
        "    purple = '#B276B2'\n",
        "    yellow = '#DECF3F'\n",
        "    gray = '#4D4D4D'\n",
        "    def __getitem__(self, i):\n",
        "        return [\n",
        "            self.red,\n",
        "            self.orange,\n",
        "            self.green,\n",
        "            self.blue,\n",
        "            self.pink,\n",
        "            self.brown,\n",
        "            self.purple,\n",
        "            self.yellow,\n",
        "            self.gray,\n",
        "        ][i % 9]\n",
        "TFColor = _TFColor()\n",
        "\n",
        "tfd = tfp.distributions"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvJ0bdl2TT1L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 946
        },
        "cellView": "form",
        "outputId": "a2a1f162-fd04-4bff-89a5-9c269e8ea8e9"
      },
      "source": [
        "#@title Tensors Tests\n",
        "tf.tile([1./3,1./3,1./3], [2,])\n",
        "tf.ones((2,3))*tf.constant([1./3,1./3,1./3])\n",
        "amplitude = tf.constant([1., 2.])\n",
        "tf.ones((3, tf.shape(amplitude)[0]))*amplitude \n",
        "pixels = tf.cast(tf.range(0, 100), dtype=tf.float32)\n",
        "test = tfd.Normal(loc = [2., 5.], scale = [2., 0.5])\n",
        "tf.transpose(amplitude*tf.ones((100, 2)))*tf.reshape(test.prob(tf.reshape(tf.ones(( 2, tf.shape(pixels)[0]))*pixels, (tf.shape(pixels)[0], 2))), (2,100))\n",
        "# test.prob(tf.ones(( 2, tf.shape(pixels)[0]))*pixels)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 100), dtype=float32, numpy=\n",
              "array([[1.2098535e-01, 1.0104537e-14, 1.9947115e-01, 2.6766057e-04,\n",
              "        1.2098535e-01, 7.9788458e-01, 2.6995480e-02, 2.6766057e-04,\n",
              "        2.2159240e-03, 1.0104537e-14, 6.6915141e-05, 4.2927658e-32,\n",
              "        7.4336003e-07, 0.0000000e+00, 3.0379428e-09, 0.0000000e+00,\n",
              "        4.5673621e-12, 0.0000000e+00, 2.5261318e-15, 0.0000000e+00,\n",
              "        5.1398796e-19, 0.0000000e+00, 3.8472939e-23, 0.0000000e+00,\n",
              "        1.0594082e-27, 0.0000000e+00, 1.0731945e-32, 0.0000000e+00,\n",
              "        3.9994241e-38, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "       [2.4197070e-01, 2.0209074e-14, 3.9894229e-01, 5.3532113e-04,\n",
              "        2.4197070e-01, 1.5957692e+00, 5.3990960e-02, 5.3532113e-04,\n",
              "        4.4318479e-03, 2.0209074e-14, 1.3383028e-04, 8.5855316e-32,\n",
              "        1.4867201e-06, 0.0000000e+00, 6.0758856e-09, 0.0000000e+00,\n",
              "        9.1347242e-12, 0.0000000e+00, 5.0522635e-15, 0.0000000e+00,\n",
              "        1.0279759e-18, 0.0000000e+00, 7.6945878e-23, 0.0000000e+00,\n",
              "        2.1188165e-27, 0.0000000e+00, 2.1463891e-32, 0.0000000e+00,\n",
              "        7.9988482e-38, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
              "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00]],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrS6QGPbpVLN",
        "colab_type": "text"
      },
      "source": [
        "# Global Constants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXvqbNSppYit",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# number of \"pixels\" on the direction\n",
        "N = 100"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YO7ZdSbmoEe9",
        "colab_type": "text"
      },
      "source": [
        "# Definitions of the distributions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JlzKkwlJYiP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "8c2df4f0-efa9-458c-b4e6-d1875823d21c"
      },
      "source": [
        "def main_gaussian(sigma_right, sigma_left, sigma_center, lambda_right, lambda_left, lambda_center):\n",
        "  center_dist = tfd.Normal(loc = lambda_center, scale = sigma_center)\n",
        "  # print(sigma_right)\n",
        "  try:\n",
        "    pass\n",
        "    # print(sigma_right.shape)\n",
        "  except:\n",
        "    pass\n",
        "  left_dist = tfd.Normal(loc = lambda_left, scale = sigma_left)\n",
        "  right_dist = tfd.Normal(loc = lambda_right, scale = sigma_right)\n",
        "  try:\n",
        "    return tfd.Mixture(cat=tfd.Categorical(probs=tf.ones((tf.shape(sigma_right)[0],3))*tf.constant([1./3,1./3,1./3])),\n",
        "  components=[\n",
        "    center_dist,\n",
        "    left_dist,\n",
        "    right_dist\n",
        "  ])\n",
        "  except:\n",
        "    return tfd.Mixture(cat=tfd.Categorical(probs=[1./3,1./3,1./3]),\n",
        "  components=[\n",
        "    center_dist,\n",
        "    left_dist,\n",
        "    right_dist\n",
        "  ])\n",
        "\n",
        "#test:\n",
        "N = 100.\n",
        "x = tf.linspace(0., N*1, int(1e4))\n",
        "plt.plot(x, main_gaussian(15,10,40, 20, 75,50).prob(x));\n"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ItmASRwEoRld",
        "colab_type": "text"
      },
      "source": [
        "### Joint Distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6pRuUh0A0bZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "909b5cf7-edcd-479a-8fa2-30c2091772cd"
      },
      "source": [
        "#first try of function:\n",
        "#we assume as explained on my onenote doc, a 3 piece gaussian.\n",
        "\n",
        "\n",
        "\n",
        "# def od_sim_left_1d(pixel, ):\n",
        "def joint_dist_gen(amplitude, sigmas, sigma_right, sigma_left, sigma_center,\n",
        "                   lambda_right, lambda_left,\n",
        "                   lambda_center, N):\n",
        "  #we assume that each pixel is going to follow a normal distribution with the average given (lambda_i) given by what OD we expect from the \"parrent\" gaussians.\n",
        "  mix = main_gaussian(sigma_right, sigma_left, sigma_center, lambda_right, lambda_left, lambda_center)\n",
        "  pixels = tf.range(0, limit=N, delta=1.)\n",
        "  try:\n",
        "    amp = tf.transpose(tf.ones((tf.shape(pixels)[0], tf.shape(amplitude)[0]))*amplitude)\n",
        "    # amp = amplitude\n",
        "    pixels = pixels*tf.ones((mix.batch_shape[0], tf.shape(pixels)[0]))\n",
        "    probs = tf.reshape(mix.prob(tf.reshape(pixels, (N, mix.batch_shape[0]))), (mix.batch_shape[0], N))\n",
        "    # print(\"pixels shape \", pixels.shape)\n",
        "    #print(\"amp \", amp)\n",
        "  except:\n",
        "    amp = amplitude\n",
        "    probs = mix.prob(pixels)\n",
        "    # print(\"test\")\n",
        "  # print(\"mix shape\", mix.batch_shape)\n",
        "    \n",
        "  # print(\"prob shape\", probs.shape)\n",
        "  # print(amp.shape)\n",
        "  density = probs*amp #N sized tensor with the OD density of each pixel.\n",
        "  #simulated distribution:\n",
        "  #assume a poissonian dist for the # of counts:\n",
        "  #tfd.Poisson(rate=density)\n",
        "  #assume a normal distribution:\n",
        "  return tfd.Normal(loc = density, scale=sigmas)\n",
        "\n",
        "def closed_joint_dist_gen(N):\n",
        "  #second = lambda r0, gamma, delta, I0, R0, care: self.joint_dist_gen_tr(r0, gamma, I0, R0, care, delta)\n",
        "  first = [# x_tail\n",
        "    tfd.Uniform(low=0., high=1.*N),\n",
        "    #lambda_center\n",
        "    lambda x: tfd.Uniform(low=N/2.-x, high=N/2.+x),\n",
        "    #lambda_left\n",
        "    lambda lambda_center, x: tfd.Uniform(low=0., high=N/2.-x),\n",
        "    #lambda_right:\n",
        "    lambda lambda_left, lambda_center, x: tfd.Uniform(low = N/2.+x, high=N),\n",
        "    #sigma_center\n",
        "    tfd.Uniform(low = 0., high = 1.),\n",
        "    #sigma_left\n",
        "    tfd.Uniform(low = 0., high = 1.),\n",
        "    #sigma_right\n",
        "    tfd.Uniform(low = 0., high = 1.),\n",
        "    #production of the N scales of the N pixels:\n",
        "    tfd.Independent(tfd.Uniform(low = 0.*tf.ones((N,)), high = 1.*tf.ones((N,))), reinterpreted_batch_ndims=1),\n",
        "    #amplitude\n",
        "    tfd.Normal(loc=1., scale=0.5)\n",
        "    ]\n",
        "    \n",
        "  \n",
        "  return tfd.JointDistributionSequential(first+[\n",
        "    # likelihood\n",
        "    #   Using Independent to ensure the log_prob is not incorrectly broadcasted\n",
        "    lambda amplitude , sigmas, sigma_right, sigma_left, sigma_center, lambda_right, lambda_left, lambda_center: #tfd.Independent(\n",
        "    tfd.Independent(\n",
        "        joint_dist_gen(amplitude, sigmas, sigma_right, sigma_left, sigma_center, lambda_right, lambda_left, lambda_center, N),#) #,\n",
        "        reinterpreted_batch_ndims=1)\n",
        "    #), reinterpreted_batch_ndims=1)\n",
        "    ,\n",
        "    #lambda _, care, R0, I0, delta, gamma, r0: tfd.Poisson(rate=np.float32(care*self.Z))\n",
        "    ])\n",
        "dist = closed_joint_dist_gen(100)\n",
        "dist.resolve_graph()"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(('x', ()),\n",
              " ('lambda_center', ('x',)),\n",
              " ('lambda_left', ('lambda_center', 'x')),\n",
              " ('lambda_right', ('lambda_left', 'lambda_center', 'x')),\n",
              " ('sigma_center', ()),\n",
              " ('sigma_left', ()),\n",
              " ('sigma_right', ()),\n",
              " ('sigmas', ()),\n",
              " ('amplitude', ()),\n",
              " ('x1',\n",
              "  ('amplitude',\n",
              "   'sigmas',\n",
              "   'sigma_right',\n",
              "   'sigma_left',\n",
              "   'sigma_center',\n",
              "   'lambda_right',\n",
              "   'lambda_left',\n",
              "   'lambda_center')))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekIG2VB8_UDy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9dee4bb8-d928-4565-e6ab-4d5ccdec4e35"
      },
      "source": [
        "sm = dist.sample()\n",
        "sm"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Tensor: shape=(), dtype=float32, numpy=20.689535>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=29.332184>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=13.706285>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=97.33265>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=0.037354827>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=0.11924994>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=0.56203985>,\n",
              " <tf.Tensor: shape=(100,), dtype=float32, numpy=\n",
              " array([0.5378003 , 0.43373168, 0.4505    , 0.31671083, 0.80298305,\n",
              "        0.41707516, 0.8261064 , 0.8553922 , 0.63771915, 0.78866637,\n",
              "        0.33386815, 0.61364424, 0.21225715, 0.18499279, 0.6765022 ,\n",
              "        0.18014657, 0.75396883, 0.53522444, 0.97033167, 0.82831335,\n",
              "        0.5864692 , 0.7145835 , 0.51841986, 0.8330029 , 0.6962609 ,\n",
              "        0.6806638 , 0.09186745, 0.5739355 , 0.48853588, 0.57064176,\n",
              "        0.8069316 , 0.6711159 , 0.8636261 , 0.10735381, 0.20186639,\n",
              "        0.92575634, 0.05894363, 0.43990326, 0.69244146, 0.37673283,\n",
              "        0.32168388, 0.95081675, 0.51851296, 0.442492  , 0.03596389,\n",
              "        0.97919333, 0.50568867, 0.9281957 , 0.43940032, 0.2881837 ,\n",
              "        0.8251296 , 0.5448798 , 0.27730763, 0.50034535, 0.55639553,\n",
              "        0.41401553, 0.6769203 , 0.118276  , 0.33664775, 0.02354753,\n",
              "        0.48025703, 0.34878993, 0.5037124 , 0.6743572 , 0.92765224,\n",
              "        0.9547199 , 0.7387105 , 0.45394135, 0.12776637, 0.79596937,\n",
              "        0.5078535 , 0.57672894, 0.44038785, 0.06195354, 0.18807113,\n",
              "        0.5340655 , 0.4270358 , 0.8133209 , 0.7553183 , 0.06729364,\n",
              "        0.7521397 , 0.10418618, 0.26500714, 0.91094327, 0.9868187 ,\n",
              "        0.91695774, 0.22328544, 0.20171773, 0.36060762, 0.8338424 ,\n",
              "        0.05490303, 0.79174066, 0.10963011, 0.00870132, 0.47643995,\n",
              "        0.84162796, 0.900741  , 0.44107616, 0.20814252, 0.8618996 ],\n",
              "       dtype=float32)>,\n",
              " <tf.Tensor: shape=(), dtype=float32, numpy=1.1738999>,\n",
              " <tf.Tensor: shape=(100,), dtype=float32, numpy=\n",
              " array([ 1.04352498e+00,  4.08374608e-01,  4.38321859e-01,  3.26646596e-01,\n",
              "         7.80282974e-01,  8.58863771e-01,  3.51661712e-01, -2.52528965e-01,\n",
              "        -7.64199138e-01,  8.00473467e-02, -8.07790533e-02, -1.64863989e-01,\n",
              "         3.47781450e-01, -6.29544184e-02, -4.81736660e-01,  2.58393615e-01,\n",
              "        -2.83128619e-01,  1.93161234e-01, -1.74748623e+00,  8.45196486e-01,\n",
              "         4.02745932e-01, -1.11851037e+00, -4.75829691e-01, -1.38503641e-01,\n",
              "         8.02210331e-01, -9.10989404e-01,  6.74862822e-05, -4.47507292e-01,\n",
              "         1.18670389e-01, -2.44735807e-01, -1.54940337e-01,  7.03049719e-01,\n",
              "         3.97266448e-01,  1.21126883e-01, -7.27742389e-02,  1.30756438e+00,\n",
              "         6.20303638e-02,  3.00204217e-01, -1.10840607e+00, -3.80016387e-01,\n",
              "        -4.42189872e-02,  6.38195515e-01, -6.08822145e-02,  1.51175201e-01,\n",
              "        -7.54043460e-03, -9.49586093e-01, -4.64555889e-01, -1.18308328e-01,\n",
              "        -2.77600288e-02, -8.82463977e-02, -7.34864593e-01, -4.90653545e-01,\n",
              "        -2.99393803e-01,  5.71012616e-01, -4.81581330e-01, -1.59578517e-01,\n",
              "        -3.13918263e-01, -2.55099803e-01, -2.65374273e-01,  1.37820039e-02,\n",
              "         5.43062210e-01, -1.93624407e-01,  1.28046942e+00, -6.23741925e-01,\n",
              "         6.13672376e-01,  1.18668616e+00,  9.09595728e-01, -8.76896322e-01,\n",
              "        -1.99258387e-01, -1.67738307e+00, -4.70141768e-01, -3.95375162e-01,\n",
              "        -2.56851465e-01, -2.00902359e-04, -2.37325966e-01,  1.00320446e+00,\n",
              "        -2.72485852e-01,  4.12888795e-01,  1.19236566e-01,  2.61564106e-02,\n",
              "        -9.09738362e-01, -1.90235481e-01,  2.95342982e-01,  9.40304771e-02,\n",
              "         9.29602981e-01, -3.42229903e-01,  1.51360109e-01, -1.61769137e-01,\n",
              "         3.76595445e-02,  1.74767137e-01, -1.30535975e-01,  8.53401184e-01,\n",
              "        -4.86680567e-02, -2.78315367e-03,  2.20633581e-01,  7.28614628e-01,\n",
              "         4.84030426e-01, -8.92895758e-02,  1.58931896e-01,  5.48753619e-01],\n",
              "       dtype=float32)>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBpQaT87Qiyf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f0d559f0-559d-4234-83fe-d08a758a9866"
      },
      "source": [
        "#sample the joint distribution:\n",
        "dist.log_prob(sm)"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=-68.820755>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OTaSxYXBoa9D",
        "colab_type": "text"
      },
      "source": [
        "# Data Import"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ty8OTXZYDhlt",
        "colab_type": "text"
      },
      "source": [
        "We will take some data and turn it a 1d array with the data along a given direction.\n",
        "\n",
        "The idea is that the data is a 2d matrix, we will interpolate it to get a function OD(x, y) that will provide the optical density at pixels x,y. We want to be able to slice it, so that we give a direction like \\[0,1] or \\[1,1] and with N bins. Each bin would represent a pixel. \n",
        "\n",
        "We have used the DataManager made by Peter to get a CSV file that I will use for this run."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vTRmA0rls47",
        "colab_type": "text"
      },
      "source": [
        "Load the optical density (could add some kind of interworking with matlab maybe).\n",
        "\n",
        "All of the od < 0 should be equal to 0."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpFe4lyrRv9h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bc38bc25-8a0c-4e9d-9ba0-6477ef076a31"
      },
      "source": [
        "#load csv file:\n",
        "od = np.loadtxt(r\"/content/drive/My Drive/Colab Notebooks/Weld Lab/data/optical density run 18.csv\", delimiter=\",\", encoding='utf-8-sig').T\n",
        "od[od < 0] = 0 #isn't this so easy with numpy?? How do you even do the same on matlab? Is it as easy, maybe?\n",
        "od.shape"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41, 390)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgLoa-I4Fcqe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import scipy:\n",
        "from scipy.interpolate import RectBivariateSpline"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mfkloDfZloRT",
        "colab_type": "text"
      },
      "source": [
        "Creation of the function with interpolation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlPt_Gl1Igvr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = range(od.shape[0])\n",
        "Y = range(od.shape[1])\n",
        "optical_density = RectBivariateSpline(X, Y, od)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwDdwXf_JaG0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1ad05bb8-12f4-44a6-cb17-724bd1d301bd"
      },
      "source": [
        "optical_density(201,0)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.92475813e-24]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlH-wkayJi07",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "aa47973b-2aa5-4659-c5c0-b34d58a10f93"
      },
      "source": [
        "(od >= 0)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-DTsv2PZojGg",
        "colab_type": "text"
      },
      "source": [
        "## Helper function to cut through the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUyDsZQrJitw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#this function will provide an array of points, so X, and Y where X[i], Y[i]\n",
        "#are places where to evaluate this the function along a given direction.\n",
        "def points(start, end, Nx, Ny, N):\n",
        "  x1 = start[0]\n",
        "  y1 = start[1]\n",
        "  x2, y2 = end\n",
        "  line = lambda x: (y2-y1)/(x2-x1)*(x-x1)+y1\n",
        "  \n",
        "  \n",
        "  xinit = (x2*y1-x1*y2)/(y1-y2)\n",
        "  xfinal = (Ny*(x1-x2)-x1*y2+x2*y1)/(y1-y2)\n",
        "  print(f\"xinit is {xinit:.2f}, xfinal is {xfinal:.2f}.\")\n",
        "  if xinit<0:\n",
        "    xinit = 0\n",
        "  elif xinit > Nx:\n",
        "    xinit = Nx\n",
        "  if xfinal > Nx:\n",
        "    xfinal = Nx\n",
        "  elif xfinal < 0:\n",
        "    xfinal = 0\n",
        "  \n",
        "  X = np.linspace(xinit, xfinal, N)\n",
        "  Y = line(X)\n",
        "  return (X, Y)\n"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J31YUjgBpC8w",
        "colab_type": "text"
      },
      "source": [
        "## Extraction of the slice"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUlcloN_S6MP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "2192bd4b-0a42-4fd9-b3f9-77fb475f784d"
      },
      "source": [
        "X, Y = points((15,196),(25,189), od.shape[0], od.shape[1], N)\n",
        "plt.plot(X, Y)\n",
        "plt.xlim(0, od.shape[0])\n",
        "plt.ylim(0, od.shape[1])"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "xinit is 295.00, xfinal is -262.14.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 390.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3rMu8k6bnFh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "a62305bb-ab4f-45aa-9057-4c68ddb2aa19"
      },
      "source": [
        "Slice = optical_density(X, Y, grid=False)\n",
        "plt.plot(Slice)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f25305579e8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LyNMi0iwiO0d5XkTkURGpFpHtIrIs8s1UauJaej3kuZykOOK3vxKe/q9j0VUkTeQn/qfA5WM8fwVQGfq4HXj8xJul1NTF+xh0CN6GDrSHriJr3EA3xrwBtI+xy1XAMybobSBXRGZGqoFKTVZzzwDFWfE7Bh2G3+RCJxepyInE36SzgZphj2tD244jIreLyGYR2dzS0hKBt1bqeInQQx9aoEt76CqCprXIaIxZbYxZboxZXlRUNJ1vrZKEMSahAr2jT3voKnIiEeh1QNmwx6WhbUpNu26PjwFfIK4nFQGkOe0UZqZQ19kf66YoC4lEoL8A3Bwa7bIC6DLGNETgdZWatPCQxXie9h9WmufiSLs71s1QFjLu7VxE5NfABUChiNQC3wScAMaYJ4A1wEeBasAN3Bqtxio1nkSY9h9Wlu9ia01HrJuhLGTcQDfGXD/O8wa4K2ItUuoExPPNoY81Jz+dNTsa8PkDOOzxO2ZeJQ79KVKW0tztARIj0MvyXPgDhoYuT6yboixCA11ZSkvvACl229AoknhWlu8CoEbr6CpCNNCVpcTzzaGPVZYXCvQODXQVGRroylJaegYoTIByC8DM3DTsNqGmXYcuqsjQQFeWEu83hx7OabcxMydNe+gqYjTQlaUkwizR4cp0LLqKIA10ZRmD/gDtbm9CjEEPK8tP15KLihgNdGUZTd0ejIEZOfG90uJwc/JdtPYO0O/1x7opygI00JVlhEsXc0LDARNBeOhirdbRVQRooCvLqEnAQC8NDV3UOrqKBA10ZRk17f3YbcLMBCq5lOWnAzq5SEWGBrqyjCPtbmblpiXUuihFmamkOW3UdOiFUXXiEucnX6lx1HS4h2ZfJgoRoSzPpT10FREa6MoyatrdCVU/DyvL17HoKjI00JUluL0+Wnu9Q6NGEsmcfBe1Hf0EV6JWauo00JUlhCfnJGKgl+al0zvgG7ph9I7aLnbXd2vAq0kb9wYXSiWCcA26LC89xi2ZvKFldDvc/G1fM1/93Xb8AcNJJVlcvWw21581hxxX/C8HrGJPe+jKEhJxUlFY+ELuw69U8ZVnt3F2eT4PXLWYjFQ73/nzXv75Dzti3EKVKLSHrizhSLubjBQ7+RkpsW7KpIXHoq/d28yli0r4/vVLSXPauWllOXf96l121nXFuIUqUWgPXVlCbYebsnxXQtzY4lhZaU5OnZ3Np5eX8sMbl5HmtA89N78ok5p2N15fIIYtVIlCe+jKEo60u5lbkBHrZkzZn+4+b8R/jCoKMwiY4PEtKM6MQctUItEeukp4xhhq2vsTblLRcKP9ZTGvMPiP1IGW3ulsjkpQGugq4bX2eukf9DMnP/FGuIxnXlEw0A+29sW4JSoRaKCrhBe+hVsijkEfT3aak8LMVA10NSEa6CrhJeKyuZNRUZjBAQ10NQEa6CrhhQO9NIFr6GOZV5jBgRYNdDU+DXSV8I60uynKSiU9xT7+zgloXlEGrb0DdHsGY90UFec00FXCq2nvt2y5BT4Y6XJIyy5qHBroKuEdaXcn5BouEzVfR7qoCdJAVwlt0B+gocvaPfSyfBc2gf1aR1fj0EBXCe1gax8BA3MSeJboeFIddkrzXNpDV+PSQFcJ7fX3WwBYUZEf45ZE17zCDA626mxRNTYNdJXQ1u5tZmFJpmWHLIZVFGVwsKVPb3qhxqSBrhJWt2eQTYfauejkklg3JeoqCjPo8/pp7hmIdVNUHJtQoIvI5SLyvohUi8jXR3j+70WkRUS2hj4+F/mmKnW09VWt+AKGi04ujnVTom5eYXClRZ1gpMYybqCLiB14DLgCWARcLyKLRtj1WWPMGaGPJyPcTqWOs3ZvMznpTpbNyY11U6JOF+lSEzGRHvrZQLUx5oAxxgv8Brgqus1SamyBgOFv7zfzoYVFOOzWrxzOzE4jzWnTC6NqTBP5TZgN1Ax7XBvadqxPish2EXlORMpGeiERuV1ENovI5paWlik0V6mgHXVdtPZ6uejkolg3ZVrYbEJ5ga7posYWqa7Nn4ByY8zpwMvAz0bayRiz2hiz3BizvKgoOX4RVXSs3duMCHx4ofXr52HzizJ11UU1pokEeh0wvMddGto2xBjTZowJX35/EjgzMs1TamSvvd/M0rLchLwp9FTNL8rgSLubAZ8/1k1RcWoigb4JqBSReSKSAlwHvDB8BxGZOezhx4E9kWuiUkdr7vawvbaLi0+x/nDF4eYXZ+IPGI60uWPdFBWnxr1JtDHGJyJ3Ay8BduBpY8wuEfk2sNkY8wLwJRH5OOAD2oG/j2KbVZL75cYjAFxx6owYt2R6zS8KDl2sbu6lsiQrxq1R8WjcQAcwxqwB1hyz7RvDPr8fuD+yTVPqeG6vj2feOsSli0qoCAVcsqgIDV3crzeMVqOw/ngvZSm/21xLh3uQL3yoItZNmXauFAezc9N11UU1Kg10lTB8/gA/XneAM+fmsbzc2otxjaaiKEN76GpUGugqYazZ2UhtR39S9s7D5hdlsr+5VxfpUiPSQFcJwRjDj17fT0VRBpck2eiW4eYXZ9Ln9dPY7Yl1U1Qc0kBXCWHToQ521Xfz+fMrsNkk1s2JmQWhC8H7m7WOro6nga4Swp93NpDisPHxJbNi3ZSYml+sI13U6DTQVdwzxvDKnibOnV9ARuqERtpaVlFmKllpDqqbNdDV8TTQVdx7v6mHmvZ+Ll2UXBOJRiIiwQuj2kNXI9BAV3Hvld1NAFx8SvIsxDWWBcUa6GpkGugq7r28p5klZbmUZKfFuilxYX5RJk3dA/R4BmPdFBVnNNBVXGvq9rCtppNLtXc+ZP7QEgA60kUdTQNdxbVX9zQDaP18mPnF4aGLWnZRR9NAV3Ht5d2NlOWns7AkuRbiGsucfBdOu2gdXR1HA13Frb4BH2/ub+OSU0oQSd7JRMdy2m3MLdA1XdTxNNBV3Fpf3YrXF+DSJJ7qP5rK4kw2Heqgoas/1k1RcUQDXU2bLvcg7X3eCe//ZnUrrhR70q6sOJZ7Lqpk0Bfg5qfeodP9wfd0b2M3f9nZEMOWqVjSQFdRZYxhy+EO/uG3WznrwVe46rH1+PyBCX3thv1tnFWeT4pDf0yPtWhWNqtvXs7hNje3/WwzNe1u7n9+Bx99ZB13/OJdttd2xrqJKgb0N0VF1Vd/t51PPr6Bl3Y2smp+ATXt/azd2zzu1zV3e6hu7mXV/IJpaGViWjm/gEeuO4N3j3Rw/vde43eba7h5ZTk56U4efbU61s1TMZDcC2OoqFpX1cLv363l71eV89XLTiLNYeO8777Gz98+zEcWjz0M8a0DbQCsml84HU1NWFecNpP/+tQSXt/Xwj0XVbKgOJNcl5OHX6lid303i2Zlx7qJahppD11FhdcX4Fsv7GJugYv7P3oymakOHHYbN5wzh3VVrRwYZ4TGhuo2stMcGkgTcM2yUh65bikLQuPTb101j6xUBz94rSrGLVPTTQNdRcUzbx1if0sf37hyEakO+9D2684qw2ETfrnxyJhfv+FAKysqCrAn8drnU5XjcnLLqnLW7GhkX1NPrJujppEGuoq45h4PD79SxQUnFXHRyUdP2S/OTuOyU2fwu8019Hv9I359TbubmvZ+rZ+fgM+eNw9Xip0frNVaejLRQFcRZYzhX1/cw4DPzzeuXDTihKCbVsyl2+PjT9vqR3yNDftbAVi1QOvnU5WfkcJNK+by4vZ6mnv0dnXTyRjDC9vq+bvvr+ft0LWg6aKBriLqsdeqeWFbPXdfWElF0cjT9c+Zl8/CkkyeefvQiDc73rC/jcLMFCqLdbr/ibjy9FkETHA8v5oeexu7uW7123zp1++xs76LB17cPa039NZAVxHz3+/V8p9/3ccnzpjFly5eMOp+IsLNK8vZWdfNOwfbj3rOGMOG/W2snF+o0/1P0OJZ2eS5nKyvmt5eYrLadKidKx9dz/tNPTx49Wl875Ons6u+m5d2NU5bGzTQQxq7PLxZ3Up1c6+uMz0FG6pb+dpz21lZUcD3rl0ybhh/clkpeS4nP1534Kjt+1t6aekZ0Pp5BNhswqoFhayvbpnWXqIVub0+Nh1qH/X72Dfg477fbmNmbhpr77uAG86ZwzXLSqkoyuChl/fhD0zP918DHajtcHPZw29w45MbueSh1zntW3/l0z96a1LT1JPZ4bY+7vjFFuYVZvDETWdOaGZneoqdm1bM5ZU9zUctMvX7d+sANNAj5LwFhTR1D+g9SE9At2eQG5/cyKeeeIsH1+whMEI4f+fPe6npcPOf1y4hPyMFALtN+MolC9nX1MuL20e+XhRpSR/og/4A9/z6PfwBw+qbzuSR687g3ksq2VrTybVPbKCm3R3rJsY1t9fHF36+BZtNeOqWs8hJd074a29aWU6Kw8ZT6w8CwVrvE6/v55pls5lbkBGtJieV80IXltdVaR19Kro9g9z81DvsqO3iklNK+PG6g3z1uW0MDlu+Yn1VKz9/+zC3rprHORVHd0Q+dtpMTp6RxSOvVE14yYsTYfmZoofb+njgxT2ct6CAm1eWYztmXPN/vPQ+7x3p5LEblh01e/HcBYXc9tNNfPLxDXz32tOZkZ2GK8VOSXYaaU77sW+TlIwx3P/8Dt5v6uFnt55NWb5rUl9flJXKNUtn8/sttdyyspwv/2YrFYUZPHDVqVFqcfIpy3dRXuDizepWPnvevFg3Z9rUtLt5dU8Tr+9roSzfxZ0XLGBGzuRuYRgO8511XTx24zI+sqiEH6yt5r9e3kd9Zz9LynIRhD9uraOiKIOvXX7Sca9hswn3XrKQO36xheffq+PTy8sidYgjsnSgr9nRwD89t53+QT+v7Gnir7ub+N61p1Oa52LQH+ClXY2sfuMAN62Yy8dOn3nU155Vns9zX1zFLU+/w60/2TS0Pc/l5NHrl3J+ZdF0H07c+cmbh/jj1nr+8bKT+NDCqX0/Pnf+PH6zqYZPPr4BXyDALz93Dhmplv6xnHbnLijkD+/VMegP4LRb949yYwx/e7+Fh1/Zx7baLgDKC1ysq2rl2U013LKqnC9+eD55oZLIeL77573srOvihzd+0Nm75+JKCjJT+c6f9/DukU4wkJ3u5PFPnzFqR++yxSUsKc3h4Zf38fEls6LaIZRYXSxZvny52bx586S/zjPoD0486QhOPunqH+QTZ8xmTsEHvcOu/kH+66/v88xbhzmjLJfvX7+UN6tbeeDF3YgIM3LSONTahy9gWDQzm+fvXDXqN7nT7WVrTSf9Xj+9Az6eXHeQfc093HfpQu68YMFxPf5ksfFAGzc+uZELTy7mR//rzBP6Pnz2p5tYu7eZ737yND5z1pwItlIB/GVnA3f84l1++4WVnD3PmksRbzrUzvf+spdNhzqYk+/iphVzuWRRCfMKMzjS5ubhV/fxh/fqqCjK5L/vXEVW2tilwcYuDx/63mtcu7yUB68+7YTbt6G6lRue3Mi/fOwUPnd+xQm9lohsMcYsH/G5RAv0P7xXx73Pbj1qm9MuXH/2HG49dx4vbqtn9boD9Hh83HbePP7p8pOHLtLVtLv5zl/24vUFWFCcyYKiTC45pYQc18Trvm6vj/uf38Eft9Zz2eISHrthGQ4L93pG0tTt4WOPric7zcEf7j6X7HF+OcbT0NXPOwfb+fiSWTpUMQq63IMsfeCv3H1RJf9w6cJYNyeiGrr6eXDNXv60rZ7irFS+dHElnzmrbMS/RN6sbuXmp9/h4pOLeWKcTsgDL+7mpxsO8bevXjDpUuJobnpqIzvqunjjaxee0O+MpQK9vjP4y1+W76IsP51AAB5dW8Wzm2qGhgZduqiEr1yyMGoLOxljeHLdQf5tzR6+fHElX7HYL8lYvL4A161+i72NPfzxrnOpLMmKdZPUBFz12JvYBZ6/89xYNyUiAgHDj9cd4JFXq/AHDHd8eD53fHg+6SljlzOeWn+QB17czX2XLuSeiytH3Ke9z8u531nLFafO4KHPnBGxNu+s6+LK76/nrgvn84+XnTzl1xkr0BOuWDkrN51PLJ191LYHrz6N28+v4IVt9Xx4YRFLynKj2gYR4fMfqmBvYw/fX1vFiooCVibBMLtAwPCtP+3i3dBFZA3zxHH+gkIef30/zd0eirMnd3Ew3ngG/dz326qtpqsAAAnvSURBVG38z44GLl1UwjeuXDThXvRnzy1nZ10XD72yj0Wzsrl4hNsbPr3+IB6fnzsvnB/Rdp86O4ePL5nFU+sPcsvK8qichwnVCkTkchF5X0SqReTrIzyfKiLPhp7fKCLlkW7oeMoLM/jSxZVRD/Phvn3VYsoLMrj32fdo6x2YtveNhcNtfVz347f51cYjfOFDFcddRFbx7RNLZ+G0C5//+ZZRF0VLBO19Xm58ciNrdjbwLx87hdU3nTmpkoiI8O/XnMbiWdnc/av3jpup3O0Z5GdvHeLyxTNYUBz5Dst9H1mIz2947LXoLJo2bslFROzAPuBSoBbYBFxvjNk9bJ87gdONMXeIyHXA1caYz4z1ulMtucSbXfVdXP3DDZxdns+DV5921MVZK+jo8/LclloeenkfDpvwf/5uEZ86s1Rr3QnopV2N3PGLLVy2aAY/vHFZRC/ou70+3jvSyZF2N70eHz0DPrLTHJxfWcTCksyI/LwcaXNz89Mbaejy8PBnzuCK06beqWjpGeC61W/R2OXhmdvO5sy5+dR19vPtP+3ipV1NvHjPeZw6O+eE2zySF7fXc/a8fIqzptZDP6EauoisBL5ljLks9Ph+AGPMvw/b56XQPm+JiANoBIrMGC9ulUAH+PU7R/jn/96BMbCyooArl8zElWInEAAD2IShdb39AcOgP8Cg32C3CU67Daddhn7gw98yY8BgsImQYrfhtNuCrxH8b+h1BnzB1/L5Awz6AxjAYbPhsAtOu2CT4IcI+PxmaJ+MFAcZqXay0hxkpjrJSnOQ5rTT1O2hrqOfA629rN3bzKZDHfgDhgtOKuLfrzmNmTnpMfkeq8h4ct0B/vV/9nDjOXMoy3ex8UAbO+u7Kc1LZ/GsbE6ZmU1WmhO7CHYbDPgC9Hv99A/6SXHYyEl3kp3mpMfjo76zn9oON9vruthR24VvlOntM7LTuOCkIj562kxWzS84ahBBIGDo9gzS4R7EGDPqgm6767u55SfvMOgP8NQtyzlz7omP1mnu9vCZ1W/T0jPAJ5bO4rebawG458IFo9bX48GJBvq1wOXGmM+FHt8EnGOMuXvYPjtD+9SGHu8P7dN6zGvdDtwOMGfOnDMPHz489aOKM/Wd/Tz/bi2/3VzLEYvMLj2pJItLF5Vw6aISTi/N0V65BRhj+OYLu3jmreDv3vyiDJaU5lLb2c+e+m56BnyTer3MVAcnzcjinHn5nFNRQGVxJllpDjJSHDR0e1hf1cIb+1p5fV8LvQM+8jNSWFKaQ1ufl8YuD629Awz/d2DZnFw+d34FH1lUMhT8bx9o4/M/20xmmoOf33Z2REshDV39fOZHb1PT4ebqpbO57yMnMTs3vjstcRPow1mphz5cIGCo6XBjDNhCARgwJvQRHGLptNtw2AS/Mfj8Bq8/wPDTIKFeuIjgDxh8gQBeXwB/wGAI9t4dod59isMW7ME7BIfNdlRP3BcIvW/o68JfA+AOjavvHfDR4xmkx+Oj3+unODuV2bnplOa5KMpKnfbvn4o+f8Cw8UAblSVZR53jQMBQ39WPZ9CPPxDcL9VpI91pJ91pZ8AXoNszSFf/IBkpDmbnpU94qQfPoJ/X97Xw4vYGqpp6KMpKZWZOGsVZaeRlpJDnctLe5+WZtw5zpN1NYWYKqQ47/YN+Ot1eKooyeeazZzMrCmHb0eels3+QeYWJsdzEiY5yqQOGz1ctDW0baZ/aUMklB0jKNTttNtF1SFRcs4dWYTyWzSaU5o19DWiy0+fD0px2Lls8g8vGuTn4refO4+XdTby0qxG7TUh32snLSOHWVeUTnuE5WXkZKVF77ek2kUDfBFSKyDyCwX0dcMMx+7wA3AK8BVwLrB2rfq6UUiOx24TLT53B5aeOHfxqZOMGujHGJyJ3Ay8BduBpY8wuEfk2sNkY8wLwFPBzEakG2gmGvlJKqWk0oYlFxpg1wJpjtn1j2Oce4FORbZpSSqnJSK5FSJRSysI00JVSyiI00JVSyiI00JVSyiI00JVSyiI00JVSyiJidoMLEWkBprqYSyGQjLcxT8bjTsZjhuQ87mQ8Zpj8cc81xox4E9+YBfqJEJHNo61lYGXJeNzJeMyQnMedjMcMkT1uLbkopZRFaKArpZRFJGqgr451A2IkGY87GY8ZkvO4k/GYIYLHnZA1dKWUUsdL1B66UkqpY2igK6WURSRcoIvI5SLyvohUi8jXY92eaBCRMhF5TUR2i8guEflyaHu+iLwsIlWh/+fFuq3RICJ2EXlPRF4MPZ4nIhtD5/xZEbHG7WVCRCRXRJ4Tkb0iskdEVibDuRaRr4R+vneKyK9FJM2K51pEnhaR5tCtOsPbRjy/EvRo6Pi3i8iyybxXQgW6iNiBx4ArgEXA9SKyKLatigofcJ8xZhGwArgrdJxfB141xlQCr4YeW9GXgT3DHn8X+H/GmAVAB3BbTFoVPY8AfzHGnAwsIXjslj7XIjIb+BKw3BhzKsGb51yHNc/1T4HLj9k22vm9AqgMfdwOPD6ZN0qoQAfOBqqNMQeMMV7gN8BVMW5TxBljGowx74Y+7yH4Cz6b4LH+LLTbz4BPxKaF0SMipcDHgCdDjwW4CHgutIuljltEcoAPEbzrF8YYrzGmkyQ41wRvsJMeug+xC2jAgufaGPMGwTu5DTfa+b0KeMYEvQ3kisjMib5XogX6bKBm2OPa0DbLEpFyYCmwESgxxjSEnmoESmLUrGh6GPgaEAg9LgA6jTG+0GOrnfN5QAvwk1CZ6UkRycDi59oYUwf8J3CEYJB3AVuw9rkebrTze0IZl2iBnlREJBP4PXCvMaZ7+HOhm3BbasypiFwJNBtjtsS6LdPIASwDHjfGLAX6OKa8YtFznUewNzoPmAVkcHxZIilE8vwmWqDXAWXDHpeGtlmOiDgJhvkvjTHPhzY3hf/8Cv2/OVbti5JzgY+LyCGC5bSLCNaXc0N/loP1znktUGuM2Rh6/BzBgLf6ub4EOGiMaTHGDALPEzz/Vj7Xw412fk8o4xIt0DcBlaEr4SkEL6K8EOM2RVyobvwUsMcY89Cwp14Abgl9fgvwx+luWzQZY+43xpQaY8oJntu1xpgbgdeAa0O7Weq4jTGNQI2InBTadDGwG4ufa4KllhUi4gr9vIeP27Ln+hijnd8XgJtDo11WAF3DSjPjM8Yk1AfwUWAfsB/437FuT5SO8TyCf4JtB7aGPj5KsJ78KlAFvALkx7qtUfweXAC8GPq8AngHqAZ+B6TGun0RPtYzgM2h8/0HIC8ZzjXwf4G9wE7g50CqFc818GuC1wkGCf5Fdtto5xcQgiP59gM7CI4CmvB76dR/pZSyiEQruSillBqFBrpSSlmEBrpSSlmEBrpSSlmEBrpSSlmEBrpSSlmEBrpSSlnE/wc2kMHiRY8ytQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZDgXlFlwmUTH",
        "colab_type": "text"
      },
      "source": [
        "# Markov Chain Monte Carlo Set Up\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eA1duz8go0oH",
        "colab_type": "text"
      },
      "source": [
        "## Initial Step Set Up"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bObi30kHd_68",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creation of the distribution used:\n",
        "dist = closed_joint_dist_gen(int(N)) #N is the number of pixels in the slice, set globally\n",
        "#how many chains should we use?\n",
        "chains = 1\n",
        "#sample it:\n",
        "start = dist.sample(chains)\n",
        "step_size = 0.1"
      ],
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNFIDVyIquCl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 946
        },
        "outputId": "4dca42d3-b1ff-4a21-96cf-628cf34d3f68"
      },
      "source": [
        "start"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Tensor: shape=(1,), dtype=float32, numpy=array([48.20665], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([83.11667], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.5822948], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([98.38561], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.5758523], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.63156784], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.19571769], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1, 100), dtype=float32, numpy=\n",
              " array([[0.15525627, 0.07429612, 0.29699957, 0.46259522, 0.29016054,\n",
              "         0.82053745, 0.24206746, 0.38797474, 0.14111567, 0.8599329 ,\n",
              "         0.1481955 , 0.6395867 , 0.74338233, 0.7387494 , 0.62343526,\n",
              "         0.30039096, 0.2933327 , 0.19507706, 0.3301344 , 0.34572768,\n",
              "         0.48730493, 0.88718915, 0.41558242, 0.6438122 , 0.2676506 ,\n",
              "         0.91188586, 0.515144  , 0.9690287 , 0.3970574 , 0.8410324 ,\n",
              "         0.64213705, 0.05315208, 0.2898997 , 0.30305827, 0.5739678 ,\n",
              "         0.7857883 , 0.62541187, 0.767128  , 0.8679435 , 0.01176667,\n",
              "         0.22867918, 0.30359232, 0.40726268, 0.37231183, 0.14703321,\n",
              "         0.4181553 , 0.7345073 , 0.37581146, 0.26350284, 0.96988714,\n",
              "         0.12690294, 0.53813386, 0.46939957, 0.9898714 , 0.17523074,\n",
              "         0.57101846, 0.59079444, 0.6246009 , 0.72037446, 0.4616686 ,\n",
              "         0.79379284, 0.8710661 , 0.4669361 , 0.23038805, 0.598616  ,\n",
              "         0.64090645, 0.18880808, 0.07779622, 0.727128  , 0.40577412,\n",
              "         0.7087151 , 0.68089914, 0.7485982 , 0.18195999, 0.8309418 ,\n",
              "         0.35594845, 0.9283215 , 0.51063573, 0.38711166, 0.45441675,\n",
              "         0.4733144 , 0.9962728 , 0.558066  , 0.9546242 , 0.4059888 ,\n",
              "         0.22221732, 0.7714523 , 0.601176  , 0.7873088 , 0.05918074,\n",
              "         0.7064556 , 0.12590373, 0.14331329, 0.4633646 , 0.6930318 ,\n",
              "         0.9629513 , 0.8302305 , 0.43982208, 0.6012614 , 0.35047603]],\n",
              "       dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.39596564], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1, 100), dtype=float32, numpy=\n",
              " array([[-0.02216611,  0.06996039, -0.8680867 ,  0.21576023,  0.29888976,\n",
              "          0.05194075, -0.26139748, -0.31042576, -0.2141601 , -0.05807747,\n",
              "         -0.21162516,  0.48898488,  0.05143001,  0.05936814, -0.45594764,\n",
              "         -0.1178441 , -0.08430178,  0.35105705, -0.01652706,  0.6019754 ,\n",
              "         -0.5055399 ,  2.0214748 , -0.18859875, -0.8841175 ,  0.2279114 ,\n",
              "          0.03132597, -0.52608377, -1.1599779 , -0.6285406 ,  0.56751096,\n",
              "         -1.1923635 , -0.03325833, -0.506165  ,  0.4020098 ,  0.351093  ,\n",
              "         -1.1687126 , -0.36443985,  0.00862452,  0.17644194, -0.01440011,\n",
              "         -0.24634911,  0.23869891, -0.33559048,  0.4141903 , -0.28496614,\n",
              "          0.20012413, -0.17660993, -0.74213827, -0.4146991 ,  1.3883123 ,\n",
              "          0.09020788, -0.8902846 ,  0.15366592,  1.276095  ,  0.12171542,\n",
              "          0.55725706, -0.15314323,  0.17880024, -0.48886436,  0.22169356,\n",
              "         -0.43488634, -1.2644815 ,  0.20329894, -0.08513997,  0.0579686 ,\n",
              "          0.15000871, -0.07714796, -0.04138502,  0.15386507,  0.20703414,\n",
              "         -0.73144543,  0.01092755,  0.16329001,  0.09820573,  0.9741006 ,\n",
              "         -0.46130842, -0.29694217,  0.2420877 ,  0.17656426, -0.19109637,\n",
              "          0.37441668,  0.8937248 ,  0.34931216, -0.7431762 ,  0.2239791 ,\n",
              "         -0.18175618,  1.4243525 ,  0.19667912,  0.40474838,  0.01059362,\n",
              "          0.2400305 , -0.11273969,  0.00289627, -0.956082  , -0.7193893 ,\n",
              "         -0.31143773,  0.88736576, -0.40301567, -0.02175806,  0.55391747]],\n",
              "       dtype=float32)>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vqJ__Rn414s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "outputId": "0a8b3226-b656-4d2f-ebb1-5540ee005cbb"
      },
      "source": [
        "original_density = tf.reshape(tf.constant(Slice, dtype=tf.float32), (chains, N))\n",
        "original_density"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 100), dtype=float32, numpy=\n",
              "array([[ 6.69079572e-02,  7.15010837e-02,  5.27560748e-02,\n",
              "         7.09116384e-02,  9.75892246e-02,  7.34454766e-02,\n",
              "         7.91419372e-02,  1.26557812e-01,  1.22855581e-01,\n",
              "         9.10689533e-02,  8.95035416e-02,  6.18536659e-02,\n",
              "         1.36956004e-02,  1.15490366e-04,  3.80976446e-04,\n",
              "         4.39239739e-06,  8.37561034e-04,  7.24822530e-05,\n",
              "         7.47435188e-05,  2.51912716e-04, -5.52908285e-04,\n",
              "        -7.03916012e-04,  2.34676569e-04,  9.67155735e-04,\n",
              "         2.26369448e-04, -1.04150451e-04,  5.64145681e-04,\n",
              "        -1.27682078e-03, -3.34450253e-03,  9.60482983e-04,\n",
              "         1.19403619e-02,  3.55237313e-02,  8.05626959e-02,\n",
              "         1.25617877e-01,  1.65984571e-01,  2.26217926e-01,\n",
              "         2.40791604e-01,  1.55293062e-01,  7.64923915e-02,\n",
              "         1.23983361e-01,  2.61320472e-01,  3.77028465e-01,\n",
              "         4.54105586e-01,  5.74854672e-01,  8.45498979e-01,\n",
              "         1.21892858e+00,  1.42897558e+00,  1.50237751e+00,\n",
              "         1.79798532e+00,  2.21455002e+00,  2.26186681e+00,\n",
              "         1.94721735e+00,  1.66151476e+00,  1.50621307e+00,\n",
              "         1.45682228e+00,  1.62510550e+00,  1.75529110e+00,\n",
              "         1.48380494e+00,  9.77944076e-01,  7.05085993e-01,\n",
              "         7.38224030e-01,  7.25344419e-01,  4.88052040e-01,\n",
              "         2.62558460e-01,  2.23559529e-01,  2.43794292e-01,\n",
              "         1.71356335e-01,  3.93034965e-02, -1.82652648e-03,\n",
              "         6.97252620e-03,  1.74089888e-04, -3.07547452e-04,\n",
              "         1.21187558e-03, -1.09913829e-03,  3.17546702e-03,\n",
              "         1.18135009e-02,  1.37882251e-02,  4.00535017e-03,\n",
              "        -7.98123796e-03, -4.19891952e-03, -1.54574541e-03,\n",
              "        -1.73492208e-02, -1.59103256e-02,  1.64303724e-02,\n",
              "         4.72658016e-02,  6.83133379e-02,  8.69675800e-02,\n",
              "         9.76889580e-02,  7.91569427e-02,  3.48307602e-02,\n",
              "        -1.40430336e-03,  1.79107562e-02,  9.16818827e-02,\n",
              "         1.26949325e-01,  9.45203677e-02,  9.43960324e-02,\n",
              "         1.46763712e-01,  1.67848080e-01,  9.88135412e-02,\n",
              "         3.26531492e-02]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZycJBBsE5qbU",
        "colab_type": "text"
      },
      "source": [
        "Log Prob to minimize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUaKmN3Eronn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def target_log_prob_tr(*init_state):\n",
        "  try:\n",
        "    #print(\"init st = \", init_state, \"\\n len init\", len(init_state))\n",
        "    inp = list(init_state)+[original_density]\n",
        "    return dist.log_prob(inp)\n",
        "    #print(\"inp \", inp, \"\\n\", len(inp))\n",
        "  except Exception as e:\n",
        "    # print(e)\n",
        "    inp = list(init_state[0])+[original_density]\n",
        "    # print(inp)\n",
        "    try:\n",
        "      return dist.log_prob(inp)\n",
        "    except Exception as e:\n",
        "      print(e)\n",
        "      print(init_state)\n",
        "      exit()"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4obGlcMt5yAK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Bijectors\n",
        "# Since HMC operates over unconstrained space, we need to transform the\n",
        "# samples so they live in real-space.\n",
        "unconstraining_bijectors = [\n",
        "    tfp.bijectors.Exp(),       # Maps a positive real to R. for x_tail\n",
        "    tfp.bijectors.Exp(), # for lambda center\n",
        "    tfp.bijectors.Exp(), # for lambda left\n",
        "    tfp.bijectors.Exp(), # for lambda right\n",
        "    tfp.bijectors.Exp(), # for sigma center\n",
        "    tfp.bijectors.Exp(), # for sigma left\n",
        "    tfp.bijectors.Exp(), # for sigma right\n",
        "    tfp.bijectors.Exp(), # for sigmai\n",
        "    tfp.bijectors.Exp(), # for amplitude\n",
        "\n",
        "    # tfp.bijectors.Sigmoid(),       # Maps [0,1] to R. for gamma\n",
        "    # tfp.bijectors.Sigmoid(),   # Maps [0,1] to R. for delta\n",
        "    # tfp.bijectors.Exp(),        # Maps a positive real to R. for I0\n",
        "    # tfp.bijectors.Exp(), # Maps a positive real to R. for R0\n",
        "    # tfp.bijectors.Sigmoid() # Maps [0,1] to R. for care\n",
        "]"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_0F-YWm79Ff",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Initial Step\n",
        "initial_chain = start[:-1]"
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHbAyxzc8b7K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0188d0a1-4b3e-496a-efa5-bdd1421089f6"
      },
      "source": [
        "#test:\n",
        "target_log_prob_tr(initial_chain)"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1,), dtype=float32, numpy=array([-355.51163], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaEw2cyTAr2S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import output\n",
        "# output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WfbmOV5LA6_2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "cellView": "code",
        "outputId": "b794a1bf-ab71-4bf4-ebf9-b62564c2671d"
      },
      "source": [
        "#@title Starting MCMC Code\n",
        "local_runtime = False #@param {type:\"boolean\"}\n",
        "# wrap the mcmc sampling call in a @tf.function to speed it up\n",
        "#@tf.function(autograph=False)\n",
        "calls = 0\n",
        "def graph_sample_chain(*args, **kwargs):\n",
        "  print(\"current = \",kwargs[\"current_state\"])\n",
        "  start_time = time.time()\n",
        "  out = tfp.mcmc.sample_chain(*args, **kwargs)\n",
        "  print(f\"It took: {(time.time()-start_time)/60} min\")\n",
        "  return out\n",
        "def trace_fn(_, pkr):\n",
        "    return (\n",
        "        #pkr.inner_results.inner_results.target_log_prob,\n",
        "        pkr.inner_results.inner_results.leapfrogs_taken,\n",
        "        pkr.inner_results.inner_results.has_divergence,\n",
        "        pkr.inner_results.inner_results.energy,\n",
        "        pkr.inner_results.inner_results.log_accept_ratio\n",
        "           )\n",
        "# r0, gamma, delta, I0, rho, _ = mdl_ols_batch.sample(nchain)\n",
        "# init_state = [b0, b1]\n",
        "# # step_size = [tf.cast(i, dtype=dtype) for i in [.1, .1]]\n",
        "# target_log_prob_fn = lambda *init_state: mdl_ols_batch.log_prob(\n",
        "#     list(init_state) + [Y_np])\n",
        "num_burnin_steps = 2500\n",
        "num_results = 5000\n",
        "\n",
        "\n",
        "\n",
        "print(step_size)\n",
        "kernel=tfp.mcmc.TransformedTransitionKernel(\n",
        "        inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(\n",
        "            target_log_prob_fn=target_log_prob_tr,\n",
        "            num_leapfrog_steps=2,\n",
        "            step_size=step_size,\n",
        "            state_gradients_are_stopped=False),\n",
        "        bijector=unconstraining_bijectors)\n",
        "\n",
        "kernel = tfp.mcmc.SimpleStepSizeAdaptation(\n",
        "    inner_kernel=kernel, num_adaptation_steps=int(num_burnin_steps * 0.8))\n",
        "\n",
        "#kernel.bootstrap_results(init_state)\n",
        "\n",
        "# # Sample from the chain.\n",
        "# [\n",
        "#     r0_samples,\n",
        "#     gamma_samples,\n",
        "#     delta_samples,\n",
        "#     I0_samples,\n",
        "#     posterior_care,\n",
        "# ],\n",
        "kernel_results = graph_sample_chain(\n",
        "    num_results=num_results,\n",
        "    num_burnin_steps=num_burnin_steps,\n",
        "    current_state=init, #put init in here if you want to start from init\n",
        "    #previous_kernel_results=kernel.bootstrap_results(initial_chain_state),\n",
        "    kernel = kernel, parallel_iterations=chains)\n",
        "#save the kernel_results to drive:\n",
        "[xtail_samples, lambdac_samples, lambdal_samples, lambdar_samples, sigmac_samples, sigmal_samples, sigmar_samples, sigmai_samples, amplitude_samples], extra = kernel_results\n",
        "#save data as plain npz files:\n",
        "base = r\"Desktop/covid19/\" if local_runtime else r\"/content/drive/My Drive/Colab Notebooks/Weld Lab/data/\"\n",
        "output_path = base + f\"out/output_run18_{time.strftime('%Y-%m-%d-%H:%M %Z', time.localtime())}.npz\"\n",
        "np.savez(output_path, xtail_samples, lambdac_samples, lambdal_samples, lambdar_samples, sigmac_samples, sigmal_samples, sigmar_samples, sigmai_samples, amplitude_samples)\n",
        "\n",
        "# notify.send(\"Finished!\")\n",
        "#np.savez(\"/content/drive/My Drive/Colab Notebooks/out/output_acceptance.npz\", tf.cast(extra.inner_results.inner_results.is_accepted,dtype=tf.float32)).numpy(), extra.inner_results.inner_results.accepted_results.step_size[-100:])\n",
        "# tau_samples = tf.floor(posterior_care * tf.cast(tf.size(cases),dtype=tf.float32))\n",
        "output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(0.0054721804, shape=(), dtype=float32)\n",
            "current =  [<tf.Tensor: shape=(1,), dtype=float32, numpy=array([16.54199], dtype=float32)>, <tf.Tensor: shape=(1,), dtype=float32, numpy=array([39.15755], dtype=float32)>, <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.8938131], dtype=float32)>, <tf.Tensor: shape=(1,), dtype=float32, numpy=array([91.62796], dtype=float32)>, <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.8651632], dtype=float32)>, <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.9555118], dtype=float32)>, <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.22146982], dtype=float32)>, <tf.Tensor: shape=(1, 100), dtype=float32, numpy=\n",
            "array([[0.06649459, 0.17247112, 0.9905001 , 0.15617588, 0.21489641,\n",
            "        0.15333958, 0.7566374 , 0.8423356 , 0.11635913, 0.28922942,\n",
            "        0.17449167, 0.8594504 , 0.9338984 , 0.34022266, 0.79693466,\n",
            "        0.47059858, 0.33562922, 0.18710056, 0.40023452, 0.6165842 ,\n",
            "        0.6727772 , 0.5651339 , 0.32354206, 0.22564912, 0.27666044,\n",
            "        0.9584269 , 0.65874267, 0.82348096, 0.30366033, 0.22221681,\n",
            "        0.35268563, 0.05015208, 0.46909785, 0.3537611 , 0.44923064,\n",
            "        0.22492181, 0.40249872, 0.6796479 , 0.20279248, 0.07510316,\n",
            "        0.52961713, 0.47962382, 0.29310033, 0.33454487, 0.6260215 ,\n",
            "        0.92184776, 0.945318  , 0.95695764, 0.6280155 , 0.8957753 ,\n",
            "        0.8827963 , 0.917143  , 0.7662849 , 0.8933538 , 0.4629858 ,\n",
            "        0.72940624, 0.5772071 , 0.9624629 , 0.85830563, 0.6932806 ,\n",
            "        0.66554123, 0.2938059 , 0.43574426, 0.3739012 , 0.72373   ,\n",
            "        0.3777765 , 0.1486726 , 0.25126123, 0.32048702, 0.19233052,\n",
            "        0.3460079 , 0.38408965, 0.60180277, 0.12428018, 0.30476305,\n",
            "        0.08725592, 0.11453557, 0.7118057 , 0.13500606, 0.3063146 ,\n",
            "        0.38879618, 0.3656976 , 0.33326992, 0.54184794, 0.35020933,\n",
            "        0.26909328, 0.57788235, 0.38985974, 0.4655614 , 0.19486727,\n",
            "        0.27299446, 0.01457192, 0.30015436, 0.09364519, 0.3461261 ,\n",
            "        0.62014425, 0.47537675, 0.11777846, 0.16935997, 0.2755525 ]],\n",
            "      dtype=float32)>, <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.71433556], dtype=float32)>]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow_probability/python/mcmc/sample.py:333: UserWarning: Tracing all kernel results by default is deprecated. Set the `trace_fn` argument to None (the future default value) or an explicit callback that traces the values you are interested in.\n",
            "  warnings.warn(\"Tracing all kernel results by default is deprecated. Set \"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LtfPiqkYDc7U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "e1d1111d-c910-4093-af83-a09140a690a4"
      },
      "source": [
        "#@title Calculate Acceptance Rate\n",
        "print(\"acceptance rate: {}\".format(\n",
        "    tf.reduce_mean(tf.cast(extra.inner_results.inner_results.is_accepted,dtype=tf.float32))))\n",
        "print(\"final step size: {}\".format(\n",
        "    tf.reduce_mean(extra.inner_results.inner_results.accepted_results.step_size[-100:])))\n",
        "# extra\n",
        "# step_size = [o[-1] for o in extra.inner_results.inner_results.accepted_results.step_size]\n",
        "step_size =  tf.reduce_mean(extra.inner_results.inner_results.accepted_results.step_size[-100:])\n",
        "tf.reduce_mean(extra.inner_results.inner_results.accepted_results.target_log_prob, axis=0)"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "acceptance rate: 0.4625999927520752\n",
            "final step size: 0.005472180433571339\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1,), dtype=float32, numpy=array([-148.33812], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jY9yBSOPD89k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2c9d450f-caaa-411a-87f9-cd799cedf41f"
      },
      "source": [
        "#@title Trace parameters graph\n",
        "plt.figure(figsize(14, 20))\n",
        "plt.subplot(611)\n",
        "lw = 1\n",
        "nchain = chains\n",
        "\n",
        "# for pretty colors later in the book.\n",
        "# xtail_samples\n",
        "# , \n",
        "# lambdac_samples, lambdal_samples, lambdar_samples,\n",
        "#  sigmac_samples,\n",
        "#   sigmal_samples, sigmar_samples, sigmai_samples, amplitude_samples]\n",
        "for i in range(nchain):\n",
        "  plt.plot(xtail_samples[:, i], label=f\"trace of xtail {i}\", c=TFColor[i], lw=lw)\n",
        "plt.title(\"Traces of unknown parameters\")\n",
        "leg = plt.legend(loc=\"upper right\")\n",
        "leg.get_frame().set_alpha(0.7)\n",
        "\n",
        "plt.subplot(612)\n",
        "\n",
        "for i in range(nchain):\n",
        "  plt.plot(lambdac_samples[:, i], label=f\"trace of lambda center {i}\", c=TFColor[i], lw=lw)\n",
        "\n",
        "plt.legend(loc=\"upper left\")\n",
        "\n",
        "plt.subplot(613)\n",
        "for i in range(nchain):\n",
        "  plt.plot(lambdal_samples[:,i], label=f\"trace of lambda left {i}\",\n",
        "     color=TFColor[i], lw=lw)\n",
        "plt.legend(loc=\"upper left\")\n",
        "\n",
        "plt.subplot(614)\n",
        "for i in range(nchain):\n",
        "  plt.plot(lambdar_samples[:,i], label=f\"trace of lambda right {i}\",\n",
        "     color=TFColor[i], lw=lw)\n",
        "plt.legend(loc=\"upper left\")\n",
        "\n",
        "plt.subplot(615)\n",
        "for i in range(nchain):\n",
        "  plt.plot(sigmac_samples[:,i], label=f\"trace of sigma center {i}\",\n",
        "     color=TFColor[i], lw=lw)\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.subplot(616)\n",
        "for i in range(nchain):\n",
        "  plt.plot(sigmal_samples[:,i], label=f\"trace of sigma left {i}\",\n",
        "     color=TFColor[i], lw=lw)\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.xlabel(\"Steps\")\n",
        "#plt.ylim(0, 1)\n",
        "plt.legend()"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f25141ff5f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 183
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1440 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c1uSP6kEGF9p",
        "colab_type": "text"
      },
      "source": [
        "Recreate the initial data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4mcE-vwNFeqF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f4dc709b-7cd3-4a49-861d-9c9c5e045559"
      },
      "source": [
        "#let's find the mean parameters:\n",
        "amplitude = tf.reduce_mean(amplitude_samples)\n",
        "sigmas = tf.reduce_mean(sigmai_samples)\n",
        "sigmar = tf.reduce_mean(sigmar_samples)\n",
        "sigmal = tf.reduce_mean(sigmal_samples)\n",
        "sigmac = tf.reduce_mean(sigmac_samples)\n",
        "lambdar = tf.reduce_mean(lambdar_samples)\n",
        "lambdal = tf.reduce_mean(lambdal_samples)\n",
        "lambdac = tf.reduce_mean(lambdac_samples)\n",
        "\n",
        "simulated_dist = joint_dist_gen(amplitude, sigmas, sigmar, sigmal, sigmac, lambdar, lambdal, lambdac, N)\n",
        "amplitude"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=0.5200003>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kw6tS4naPMAg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "outputId": "e9d370e0-262f-4494-9b29-186a76da04d5"
      },
      "source": [
        "init = [\n",
        "        xtail_samples[-1],\n",
        "        lambdac_samples[-1],\n",
        "        lambdal_samples[-1],\n",
        "        lambdar_samples[-1],\n",
        "        sigmac_samples[-1],\n",
        "        sigmal_samples[-1],\n",
        "        sigmar_samples[-1],\n",
        "        sigmai_samples[-1],\n",
        "        amplitude_samples[-1]\n",
        "\n",
        "]\n",
        "init"
      ],
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Tensor: shape=(1,), dtype=float32, numpy=array([16.54199], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([39.15755], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.8938131], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([91.62796], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.8651632], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.9555118], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.22146982], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1, 100), dtype=float32, numpy=\n",
              " array([[0.06649459, 0.17247112, 0.9905001 , 0.15617588, 0.21489641,\n",
              "         0.15333958, 0.7566374 , 0.8423356 , 0.11635913, 0.28922942,\n",
              "         0.17449167, 0.8594504 , 0.9338984 , 0.34022266, 0.79693466,\n",
              "         0.47059858, 0.33562922, 0.18710056, 0.40023452, 0.6165842 ,\n",
              "         0.6727772 , 0.5651339 , 0.32354206, 0.22564912, 0.27666044,\n",
              "         0.9584269 , 0.65874267, 0.82348096, 0.30366033, 0.22221681,\n",
              "         0.35268563, 0.05015208, 0.46909785, 0.3537611 , 0.44923064,\n",
              "         0.22492181, 0.40249872, 0.6796479 , 0.20279248, 0.07510316,\n",
              "         0.52961713, 0.47962382, 0.29310033, 0.33454487, 0.6260215 ,\n",
              "         0.92184776, 0.945318  , 0.95695764, 0.6280155 , 0.8957753 ,\n",
              "         0.8827963 , 0.917143  , 0.7662849 , 0.8933538 , 0.4629858 ,\n",
              "         0.72940624, 0.5772071 , 0.9624629 , 0.85830563, 0.6932806 ,\n",
              "         0.66554123, 0.2938059 , 0.43574426, 0.3739012 , 0.72373   ,\n",
              "         0.3777765 , 0.1486726 , 0.25126123, 0.32048702, 0.19233052,\n",
              "         0.3460079 , 0.38408965, 0.60180277, 0.12428018, 0.30476305,\n",
              "         0.08725592, 0.11453557, 0.7118057 , 0.13500606, 0.3063146 ,\n",
              "         0.38879618, 0.3656976 , 0.33326992, 0.54184794, 0.35020933,\n",
              "         0.26909328, 0.57788235, 0.38985974, 0.4655614 , 0.19486727,\n",
              "         0.27299446, 0.01457192, 0.30015436, 0.09364519, 0.3461261 ,\n",
              "         0.62014425, 0.47537675, 0.11777846, 0.16935997, 0.2755525 ]],\n",
              "       dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.71433556], dtype=float32)>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NlfO2u_lPI_t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "outputId": "b264594b-a912-4132-f1a0-2db9d4c88646"
      },
      "source": [
        "initial_chain"
      ],
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Tensor: shape=(1,), dtype=float32, numpy=array([48.20665], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([83.11667], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.5822948], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([98.38561], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.5758523], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.63156784], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.19571769], dtype=float32)>,\n",
              " <tf.Tensor: shape=(1, 100), dtype=float32, numpy=\n",
              " array([[0.15525627, 0.07429612, 0.29699957, 0.46259522, 0.29016054,\n",
              "         0.82053745, 0.24206746, 0.38797474, 0.14111567, 0.8599329 ,\n",
              "         0.1481955 , 0.6395867 , 0.74338233, 0.7387494 , 0.62343526,\n",
              "         0.30039096, 0.2933327 , 0.19507706, 0.3301344 , 0.34572768,\n",
              "         0.48730493, 0.88718915, 0.41558242, 0.6438122 , 0.2676506 ,\n",
              "         0.91188586, 0.515144  , 0.9690287 , 0.3970574 , 0.8410324 ,\n",
              "         0.64213705, 0.05315208, 0.2898997 , 0.30305827, 0.5739678 ,\n",
              "         0.7857883 , 0.62541187, 0.767128  , 0.8679435 , 0.01176667,\n",
              "         0.22867918, 0.30359232, 0.40726268, 0.37231183, 0.14703321,\n",
              "         0.4181553 , 0.7345073 , 0.37581146, 0.26350284, 0.96988714,\n",
              "         0.12690294, 0.53813386, 0.46939957, 0.9898714 , 0.17523074,\n",
              "         0.57101846, 0.59079444, 0.6246009 , 0.72037446, 0.4616686 ,\n",
              "         0.79379284, 0.8710661 , 0.4669361 , 0.23038805, 0.598616  ,\n",
              "         0.64090645, 0.18880808, 0.07779622, 0.727128  , 0.40577412,\n",
              "         0.7087151 , 0.68089914, 0.7485982 , 0.18195999, 0.8309418 ,\n",
              "         0.35594845, 0.9283215 , 0.51063573, 0.38711166, 0.45441675,\n",
              "         0.4733144 , 0.9962728 , 0.558066  , 0.9546242 , 0.4059888 ,\n",
              "         0.22221732, 0.7714523 , 0.601176  , 0.7873088 , 0.05918074,\n",
              "         0.7064556 , 0.12590373, 0.14331329, 0.4633646 , 0.6930318 ,\n",
              "         0.9629513 , 0.8302305 , 0.43982208, 0.6012614 , 0.35047603]],\n",
              "       dtype=float32)>,\n",
              " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.39596564], dtype=float32)>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDe2UNRdHyUN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "87029e6e-2fc8-4c29-c638-54afc1de4ffe"
      },
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "# plt.plot(simulated_dist.sample())\n",
        "plt.plot(Slice)\n",
        "mg = main_gaussian(sigmar, sigmal, sigmac, lambdar, lambdal, lambdac)\n",
        "x = np.linspace(0, N*1., 1000)\n",
        "plt.plot(x, mg.prob(x)*amplitude)"
      ],
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2514edef60>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 198
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLgawGNgnuNf",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
}