{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysing your results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import vstt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import\n",
    "\n",
    "To import an experiment from a psydat file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = vstt.Experiment(\"example.psydat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditions\n",
    "\n",
    "The trial conditions are in `trial_list`, each element in this list is a dict of trial conditions that defines a trial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment.trial_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be more easily viewed if converted to a pandas DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(experiment.trial_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `weight` of a trial is how many times it should be repeated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "The results of the trials are in `stats` which provides both the raw data and calculated statistics as a pandas DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = experiment.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- each row in this DataFrame contains the data from a single movement to a target (and optionally back to the center)\n",
    "- they are in the order that they were shown in the experiment\n",
    "- `condition_index` is the index of the corresponding trial conditions in `trial_index`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Plot statistics\n",
    "Here we plot a few of the calculated statics for each target, identifying each set of conditions with a different color:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(ncols=3, figsize=(18, 6))\n",
    "for ax, statistic in zip(\n",
    "    axs, [\"to_target_reaction_time\", \"to_target_movement_time\", \"to_target_distance\"]\n",
    "):\n",
    "    ax.set_title(f\"{statistic}\")\n",
    "    for condition_index, df in stats.groupby(\"condition_index\"):\n",
    "        ax.plot(df.index, df[statistic], label=f\"Condition {condition_index}\")\n",
    "    ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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