{
 "cells": [
  {
   "cell_type": "raw",
   "id": "33ce3e3e",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Simple Indexing\n",
    "===============\n",
    "\n",
    ":mod:`named_arrays` generalizes the :mod:`numpy` indexing model using :class:`dict`\\ s insted of :class:`tuple`\\ s.\n",
    "This means that indexing the array depends on the name of the axis instead of it's position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-23T15:28:18.682370Z",
     "start_time": "2025-08-23T15:28:16.737734Z"
    }
   },
   "outputs": [],
   "source": [
    "import named_arrays as na"
   ]
  },
  {
   "cell_type": "raw",
   "id": "eb91d331",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Say we have a given 2D array, with axis names `row` and `col`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b2439ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = na.arange(1, 7, \"a\").reshape(dict(row=2, col=3))\n",
    "a"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b3f15708",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "If we want to select the top-left element of `a`, we need to use a :class:`dict` with the row and column set to zero.\n",
    "It would be nice if we could use keyword arguments to ``__getitem__`` directly, but this is not supported by Python.\n",
    "See `PEP 637 <https://peps.python.org/pep-0637/>`_ for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "025ad3a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "a[dict(row=0, col=0)]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "2b30f23b",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "If we just want to select the first row, use a :class:`dict` with only the row set to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe039ae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "a[dict(row=0)]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6e6758b7",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "To select only the first two columns, we need to use a :class:`slice` object.\n",
    "Slicing notation is not supported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98dacfc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "a[dict(col=slice(2))]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9234b801",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "If the axis given by the item key does not exist, it is ignored.\n",
    "This allows vector and function indexing to work more intuitively and efficiently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecab6dda",
   "metadata": {},
   "outputs": [],
   "source": [
    "a[dict(foo=1)]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "5ad2df67",
   "metadata": {},
   "source": [
    "This document only describes simple indexing, but advanced indexing is also fully supported and will be discussed in later tutorials."
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}