{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "66d74b51",
   "metadata": {},
   "source": [
    "# Bisect Plot Examples\n",
    "\n",
    "## Introduction\n",
    "This notebook explains the use case of the `prepare_datasets`, and `plot_bisect` functions from the Plaid library. The function is used to generate bisect plots for different scenarios using file paths and PLAID objects.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cc83db7",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Importing Required Libraries\n",
    "from pathlib import Path\n",
    "import os\n",
    "\n",
    "from plaid import Dataset\n",
    "from plaid.post.bisect import plot_bisect, prepare_datasets\n",
    "from plaid import ProblemDefinition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5463897",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up Directories\n",
    "try:\n",
    "    dataset_directory = Path(__file__).parent.parent.parent / \"tests\" / \"post\"\n",
    "except NameError:\n",
    "    dataset_directory = Path(\"..\") / \"..\" / \"..\" / \"..\" / \"tests\" / \"post\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d0b6ba",
   "metadata": {},
   "source": [
    "## Prepare Datasets for comparision\n",
    "\n",
    "Assuming you have reference and predicted datasets, and a problem definition, The `prepare_datasets` function is used to obtain output scalars for subsequent analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62cd2d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load PLAID datasets and problem metadata objects\n",
    "ref_ds = Dataset(dataset_directory / \"dataset_ref\")\n",
    "pred_ds = Dataset(dataset_directory / \"dataset_near_pred\")\n",
    "problem = ProblemDefinition(dataset_directory / \"problem_definition\")\n",
    "\n",
    "# Get output scalars from reference and prediction dataset\n",
    "ref_out_scalars, pred_out_scalars, out_scalars_names = prepare_datasets(\n",
    "    ref_ds, pred_ds, problem, verbose=True\n",
    ")\n",
    "\n",
    "print(f\"{out_scalars_names = }\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a97cadde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get output scalar\n",
    "key = out_scalars_names[0]\n",
    "\n",
    "print(f\"KEY '{key}':\\n\")\n",
    "print(f\"ID{' ' * 5}--REF_out_scalars--{' ' * 7}--PRED_out_scalars--\")\n",
    "\n",
    "# Print output scalar values for both datasets\n",
    "index = 0\n",
    "for item1, item2 in zip(ref_out_scalars[key], pred_out_scalars[key]):\n",
    "    print(\n",
    "        f\"{str(index).ljust(2)}  |  {str(item1).ljust(20)}  |   {str(item2).ljust(20)}\"\n",
    "    )\n",
    "    index += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f8f097",
   "metadata": {},
   "source": [
    "## Plotting with File Paths\n",
    "\n",
    "Here, we load the datasets and problem metadata from file paths and use the `plot_bisect` function to generate a bisect plot for a specific scalar, in this case, \"scalar_2.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7efe426",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"=== Plot with file paths ===\")\n",
    "\n",
    "# Load PLAID datasets and problem metadata from files\n",
    "ref_path = dataset_directory / \"dataset_ref\"\n",
    "pred_path = dataset_directory / \"dataset_pred\"\n",
    "problem_path = dataset_directory / \"problem_definition\"\n",
    "\n",
    "# Using file paths to generate bisect plot on feature_2\n",
    "plot_bisect(ref_path, pred_path, problem_path, \"feature_2\", \"differ_bisect_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ded9db",
   "metadata": {},
   "source": [
    "## Plotting with PLAID\n",
    "\n",
    "In this section, we demonstrate how to use PLAID objects directly to generate a bisect plot. This can be advantageous when working with PLAID datasets in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a02cb933",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"=== Plot with PLAID objects ===\")\n",
    "\n",
    "# Load PLAID datasets and problem metadata objects\n",
    "ref_path = Dataset(dataset_directory / \"dataset_ref\")\n",
    "pred_path = Dataset(dataset_directory / \"dataset_pred\")\n",
    "problem_path = ProblemDefinition(dataset_directory / \"problem_definition\")\n",
    "\n",
    "# Using PLAID objects to generate bisect plot on feature_2\n",
    "plot_bisect(ref_path, pred_path, problem_path, \"feature_2\", \"equal_bisect_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "233a28a4",
   "metadata": {},
   "source": [
    "## Mixing with Scalar Index and Verbose\n",
    "\n",
    "In this final section, we showcase a mix of file paths and PLAID objects, incorporating a scalar index and enabling the verbose option when generating a bisect plot. This can provide more detailed information during the plotting process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59d5e0a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"=== Mix with scalar index and verbose ===\")\n",
    "\n",
    "# Mix\n",
    "ref_path = dataset_directory / \"dataset_ref\"\n",
    "pred_path = dataset_directory / \"dataset_near_pred\"\n",
    "problem_path = ProblemDefinition(dataset_directory / \"problem_definition\")\n",
    "\n",
    "# Using scalar index and verbose option to generate bisect plot\n",
    "scalar_index = 0\n",
    "plot_bisect(\n",
    "    ref_path,\n",
    "    pred_path,\n",
    "    problem_path,\n",
    "    scalar_index,\n",
    "    \"converge_bisect_plot\",\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "os.remove(\"converge_bisect_plot.png\")\n",
    "os.remove(\"differ_bisect_plot.png\")\n",
    "os.remove(\"equal_bisect_plot.png\")"
   ]
  }
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