{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Simulate Data from the SceneWalk Model"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "import sys\n",
        "from collections import OrderedDict\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from scipy.stats import norm as normal, truncnorm as truncated_normal\n",
        "import seaborn as sns\n",
        "from scenewalk.scenewalk_model_object import scenewalk as scenewalk_model\n",
        "from scenewalk.utils import loadData\n",
        "from scenewalk.simulation import simulate_dataset"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-04-01T00:00:01.876Z",
          "iopub.execute_input": "2020-04-01T00:00:01.880Z",
          "iopub.status.idle": "2020-04-01T00:00:02.857Z",
          "shell.execute_reply": "2020-04-01T00:00:02.868Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get some basis data"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataDict = loadData.load_data(\"spst_all\")\n",
        "dataDict = loadData.shorten_set(dataDict, 2)\n",
        "x_dat, y_dat, dur_dat, im_dat, densities_dat, d_range = loadData.dataDict2vars(dataDict)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shortening from 35 to 2\n"
          ]
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-04-01T00:00:02.863Z",
          "iopub.execute_input": "2020-04-01T00:00:02.865Z",
          "iopub.status.idle": "2020-04-01T00:00:02.891Z",
          "shell.execute_reply": "2020-04-01T00:00:02.998Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scenewalk Model\n",
        "Here you define the setup of the model."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "sw = scenewalk_model(\"subtractive\", \"zero\", \"off\", \"off\", \"off\", {'x': d_range[0], 'y': d_range[1]}, {\"logged_z\":True, \"coupled_oms\":True})\n",
        "\n",
        "sw.whoami()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": [
              "'I am a subtractive scenewalk model, initialized with zero activation, in 2 exponents mode, with om_i as a fraction, with logged z'"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-04-01T00:00:02.899Z",
          "iopub.execute_input": "2020-04-01T00:00:02.912Z",
          "iopub.status.idle": "2020-04-01T00:00:02.921Z",
          "shell.execute_reply": "2020-04-01T00:00:03.001Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It will tell you which parameters it needs and the order if you're passing them in as a list:"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "sw.get_param_list_order()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": [
              "['omegaAttention',\n",
              " 'omfrac',\n",
              " 'sigmaAttention',\n",
              " 'sigmaInhib',\n",
              " 'gamma',\n",
              " 'lamb',\n",
              " 'inhibStrength',\n",
              " 'zeta']"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-04-01T00:00:02.927Z",
          "iopub.execute_input": "2020-04-01T00:00:02.930Z",
          "iopub.status.idle": "2020-04-01T00:00:02.936Z",
          "shell.execute_reply": "2020-04-01T00:00:03.005Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_dict = OrderedDict({\n",
        "    \"omegaAttention\": 18.5,\n",
        "    \"omfrac\": 10,\n",
        "    \"sigmaAttention\": 6,\n",
        "    \"sigmaInhib\": 12,\n",
        "    \"gamma\" : 8,\n",
        "    \"lamb\" : 0.6,\n",
        "    \"inhibStrength\" :0.7,\n",
        "    \"zeta\" : -2,\n",
        "})"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-04-01T00:00:02.942Z",
          "iopub.execute_input": "2020-04-01T00:00:02.946Z",
          "iopub.status.idle": "2020-04-01T00:00:02.950Z",
          "shell.execute_reply": "2020-04-01T00:00:03.008Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sw.update_params(param_dict)\n",
        "#print(sw.get_params())\n",
        "print(\"have we provided all relevant parameters?:\", sw.check_params_for_config())\n",
        "print(\"are the parameters within the defined bounds?:\", sw.check_params_in_bounds())\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "have we provided all relevant parameters?: True\n",
            "are the parameters within the defined bounds?: True\n"
          ]
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2020-04-01T00:00:02.956Z",
          "iopub.execute_input": "2020-04-01T00:00:02.959Z",
          "iopub.status.idle": "2020-04-01T00:00:02.965Z",
          "shell.execute_reply": "2020-04-01T00:00:03.010Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Now Simulate"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sim_id = simulate_dataset.simulate(dur_dat, im_dat, densities_dat, sw, params=None, start_loc=\"center\", x_path=None, y_path=None, resample_durs=False, verbose = True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sub 0\n",
            "sub 1\n"
          ]
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-04-01T00:00:02.969Z",
          "iopub.execute_input": "2020-04-01T00:00:02.972Z",
          "iopub.status.idle": "2020-04-01T00:00:09.654Z",
          "shell.execute_reply": "2020-04-01T00:00:09.706Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load simulated data"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sim_dat_dict = loadData.load_sim_data(os.path.abspath(sim_id))"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-04-01T00:00:09.660Z",
          "iopub.execute_input": "2020-04-01T00:00:09.664Z",
          "iopub.status.idle": "2020-04-01T00:00:09.669Z",
          "shell.execute_reply": "2020-04-01T00:00:09.711Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## More settings for simulations"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Advanced settings include\n",
        "instead of params=None you can give it a dictionary of all parameters the model needs by subject. i.e."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_subj_dict = {\n",
        "    0 : param_dict,\n",
        "    1 : param_dict,\n",
        "    2 : param_dict,\n",
        "    3 : param_dict,\n",
        "    4 : param_dict\n",
        "}"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-04-01T00:00:09.675Z",
          "iopub.execute_input": "2020-04-01T00:00:09.679Z",
          "iopub.status.idle": "2020-04-01T00:00:09.684Z",
          "shell.execute_reply": "2020-04-01T00:00:09.715Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sim_id = simulate_dataset.simulate(dur_dat, im_dat, densities_dat, sw, params=param_subj_dict, start_loc=\"center\", x_path=None, y_path=None, resample_durs=False, verbose=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sub 0\n",
            "sub 1\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-04-01T00:00:09.689Z",
          "iopub.execute_input": "2020-04-01T00:00:09.694Z",
          "iopub.status.idle": "2020-04-01T00:00:16.248Z",
          "shell.execute_reply": "2020-04-01T00:00:16.268Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sw.get_params()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": [
              "OrderedDict([('omegaAttention', 18.5),\n",
              "             ('omegaInhib', 1.85),\n",
              "             ('sigmaAttention', 6),\n",
              "             ('sigmaInhib', 12),\n",
              "             ('gamma', 8),\n",
              "             ('lamb', 0.6),\n",
              "             ('inhibStrength', 0.7),\n",
              "             ('zeta', 0.01)])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-04-01T00:00:16.254Z",
          "iopub.execute_input": "2020-04-01T00:00:16.257Z",
          "iopub.status.idle": "2020-04-01T00:00:16.265Z",
          "shell.execute_reply": "2020-04-01T00:00:16.270Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "swtemp"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.0",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "name": "swtemp",
      "language": "python",
      "display_name": "swtemp"
    },
    "nteract": {
      "version": "0.22.0"
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  "nbformat_minor": 4
}