{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "__Simulators__\n",
        "\n",
        "These scripts simulates many 1D Gaussian datasets with a low signal to noise ratio, which are used to demonstrate\n",
        "model-fitting."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "%matplotlib inline\n",
        "from pyprojroot import here\n",
        "workspace_path = str(here())\n",
        "%cd $workspace_path\n",
        "print(f\"Working Directory has been set to `{workspace_path}`\")\n",
        "\n",
        "import numpy as np\n",
        "from os import path\n",
        "\n",
        "import autofit as af\n",
        "import util"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "__Gaussian x1 low snr (centre fixed to 50.0)__\n",
        "\n",
        "This is used for demonstrating expectation propagation, whereby a shared `centre` parameter is inferred from a sample \n",
        "of `total_datasets` 1D Gaussian datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "total_datasets = 50\n",
        "\n",
        "for i in range(total_datasets):\n",
        "    dataset_path = path.join(\n",
        "        \"dataset\", \"example_1d\", f\"gaussian_x1__low_snr\", f\"dataset_{i}\"\n",
        "    )\n",
        "    gaussian = af.ex.Gaussian(centre=50.0, normalization=0.5, sigma=5.0)\n",
        "    util.simulate_dataset_1d_via_gaussian_from(\n",
        "        gaussian=gaussian, dataset_path=dataset_path\n",
        "    )"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "__Gaussian x1 low snr (centre drawn from parent Gaussian distribution to 50.0)__\n",
        "\n",
        "This is used for demonstrating expectation propagation and hierachical modeling, whereby a the `centre` parameters \n",
        "of a sample of `total_datasets` 1D Gaussian datasets are drawn from a Gaussian distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "\n",
        "total_datasets = 10\n",
        "\n",
        "gaussian_parent_model = af.Model(\n",
        "    af.ex.Gaussian,\n",
        "    centre=af.GaussianPrior(mean=50.0, sigma=10.0, lower_limit=0.0, upper_limit=100.0),\n",
        "    normalization=0.5,\n",
        "    sigma=5.0,\n",
        ")\n",
        "\n",
        "for i in range(total_datasets):\n",
        "    dataset_path = path.join(\n",
        "        \"dataset\", \"example_1d\", f\"gaussian_x1__hierarchical\", f\"dataset_{i}\"\n",
        "    )\n",
        "\n",
        "    gaussian = gaussian_parent_model.random_instance()\n",
        "\n",
        "    util.simulate_dataset_1d_via_gaussian_from(\n",
        "        gaussian=gaussian, dataset_path=dataset_path\n",
        "    )\n"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "__Gaussian x2 offset centre__\n",
        "\n",
        "This is used for demonstrating the benefits of graphical models over fitting one-by-one, because it creates a \n",
        "degeneracy in the offset of the centres of the two Gaussians."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "total_datasets = 10\n",
        "\n",
        "for i in range(total_datasets):\n",
        "    dataset_path = path.join(\n",
        "        \"dataset\", \"example_1d\", f\"gaussian_x2__offset_centres\", f\"dataset_{i}\"\n",
        "    )\n",
        "\n",
        "    sigma_0_prior = af.GaussianPrior(\n",
        "        lower_limit=0.0, upper_limit=20.0, mean=10.0, sigma=10.0\n",
        "    )\n",
        "    while True:\n",
        "        try:\n",
        "            sigma_0_value = sigma_0_prior.value_for(unit=np.random.random(1))\n",
        "            break\n",
        "        except af.exc.PriorLimitException:\n",
        "            continue\n",
        "\n",
        "    sigma_1_prior = af.GaussianPrior(\n",
        "        lower_limit=0.0, upper_limit=20.0, mean=10.0, sigma=10.0\n",
        "    )\n",
        "    while True:\n",
        "        try:\n",
        "            sigma_1_value = sigma_1_prior.value_for(unit=np.random.random(1))\n",
        "            break\n",
        "        except af.exc.PriorLimitException:\n",
        "            continue\n",
        "\n",
        "    gaussian_0 = af.ex.Gaussian(centre=40.0, normalization=1.0, sigma=sigma_0_value)\n",
        "    gaussian_1 = af.ex.Gaussian(centre=60.0, normalization=1.0, sigma=sigma_1_value)\n",
        "\n",
        "    util.simulate_dataset_1d_via_profile_1d_list_from(\n",
        "        profile_1d_list=[gaussian_0, gaussian_1], dataset_path=dataset_path\n",
        "    )\n"
      ],
      "outputs": [],
      "execution_count": null
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "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": "3.6.1"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}