{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a7e1ece6",
   "metadata": {},
   "source": [
    "# Analysing Samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d09269b",
   "metadata": {},
   "source": [
    "{nb-download}`Download this as a Jupyter notebook <analysing_samples.ipynb>`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b253a871",
   "metadata": {},
   "source": [
    "This guide covers how to use `pyXla` to conduct landscape analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79bc3162",
   "metadata": {},
   "source": [
    "The first step is to load a sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b51d9fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyxla.sampling import HilbertCurveSampler\n",
    "from pyxla import load_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "995acb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = HilbertCurveSampler(\n",
    "    sample_size=100, dim=2, return_neighbourhood=True\n",
    ")\n",
    "\n",
    "sample = {\n",
    "    \"X\": sampler,\n",
    "    \"F\": lambda x: (x**2).sum(),\n",
    "    \"V\": lambda x: ((x**2).sum() - 3),\n",
    "    \"D\": lambda x1, x2: ((x1 - x2)**2).sum(), #must take a pair of arguments and real number\n",
    "    # N file is provided by the sampler since return_neighbourhood=True\n",
    "}\n",
    "\n",
    "load_data(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dddb7a49",
   "metadata": {},
   "source": [
    "The next step, is to import the explainable landscape analysis (xLA) feature of choice. For instance we import the `distr_f` (distribution of objective values) below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d73cceaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyxla import distr_f"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94e637b4",
   "metadata": {},
   "source": [
    "Lastly, invoke the imported feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8804c8b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat, plot = distr_f(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5be4ba",
   "metadata": {},
   "source": [
    "Each xLA feature in `pyXla` produces as output both visual and numerical outputs. For most features these outputs are returned as a pair."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "434a8458",
   "metadata": {},
   "source": [
    "The numerical output is the first item in the pair:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1164940f",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68659247",
   "metadata": {},
   "source": [
    "The visual output is the second item in the pair:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce2099f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6e5dfb",
   "metadata": {},
   "source": [
    "For a complete reference of all the xLA features implemented in `pyXla` see the {doc}`API reference <../api-reference/pyxla>`."
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}