removed unnecessary data and cleaned up notebooks more

examples/loading_and_saving.ipynb

@@ -0,0 +1,237 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Distributed\n",
+    "addprocs(8);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@everywhere using PulseInputDDM, Flatten"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate some data\n",
+    "\n",
+    "Create an instance of the `θchoice` [composite type](https://docs.julialang.org/en/v1/manual/types/#Composite-Types) which contains the 9 parameters of a choice DDM model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "θ_syn = θchoice(θz=θz(σ2_i = 1., B = 13., λ = -0.5, σ2_a = 10., σ2_s = 1.0,\n",
+    "    ϕ = 0.4, τ_ϕ = 0.02), bias=0.1, lapse=0.1);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xgen = collect(Flatten.flatten(θ_syn));"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate 20K trials of the synthetic data using those parameters. change `rng` to get a different set with the same parameters. `dt` specifies the temporal binning of the data. `1e-2` has worked well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, data = synthetic_data(;θ=θ_syn, ntrials=8_000, rng=2, dt=1e-2);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "choiceDDM{θchoice{θz{Float64}, Float64}}\n",
+       "  θ: θchoice{θz{Float64}, Float64}\n",
+       "  n: Int64 53\n",
+       "  cross: Bool false\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = choiceDDM(θ=θ_syn)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "choiceoptions\n",
+       "  fit: Array{Bool}((9,)) Bool[1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
+       "  lb: Array{Float64}((9,)) [0.0, 2.0, -5.0, 0.0, 0.0, 0.0, 0.005, -5.0, 0.0]\n",
+       "  ub: Array{Float64}((9,)) [30.0, 100.0, 5.0, 200.0, 10.0, 1.2, 1.0, 5.0, 1.0]\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "options = choiceoptions(lb=vcat([0., 2.,  -5., 0.,   0.,  0., 0.005, -5.0, 0.0]),\n",
+    "    ub = [30., 100., 5., 200., 10., 1.2,  1., 5.0, 1.0], \n",
+    "    fit = trues(9))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving and reloading modeling results and saving data\n",
+    "\n",
+    "Use the `save_choice_model` function to save the optimized model, the options used to define the optimization, and the confidence intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_file = \"./data/example_results.mat\"\n",
+    "save_choice_model(save_file, model, options)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "if you want to restart the optimization from where you stopped `reload_choice_model` will reload those model parameters and the `options`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "θ, options = reload_choice_model(save_file);"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the choice data using `save_choice_data` and then reload it using `load_choice_data`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import PulseInputDDM: save_choice_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_choice_data(\"./data/example_matfile_2.mat\", data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = load_choice_data(\"./data/example_matfile_2.mat\");"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading data and fitting a choice model from real data\n",
+    "\n",
+    "Because many neuroscientists use matlab, we use the [MAT.jl](https://github.com/JuliaIO/MAT.jl) package for IO. Data can be loaded using two conventions. One of these conventions is easier when data is saved within matlab as a .MAT file, and is described below. \n",
+    "\n",
+    "The package expects your data to live in a single .mat file which should contain a struct called `rawdata`. Each element of `rawdata` should have data for one behavioral trial and `rawdata` should contain the following fields with the specified structure:\n",
+    "\n",
+    " - `rawdata.leftbups`: row-vector containing the relative timing, in seconds, of left clicks on an individual trial. 0 seconds is the start of the click stimulus.\n",
+    " - `rawdata.rightbups`: row-vector containing the relative timing in seconds (origin at 0 sec) of right clicks on an individual trial. 0 seconds is the start of the click stimulus.\n",
+    " - `rawdata.T`: the duration of the trial, in seconds. The beginning of a trial is defined as the start of the click stimulus. The end of a trial is defined based on the behavioral event “cpoke_end”. This was the Hanks convention.\n",
+    " - `rawdata.pokedR`: `Bool` representing the animal choice (1 = right).\n",
+    " \n",
+    "The example file located at `../choice model/example_matfile.mat` adheres to this convention and can be loaded using the `load_choice_data` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = load_choice_data(\"./data/example_matfile.mat\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.10.2",
+   "language": "julia",
+   "name": "julia-1.10"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.10.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}