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  {
   "cell_type": "markdown",
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   "source": [
    "# Quick start\n",
    "\n",
    "This section provides a quick introduction to Scipp.\n",
    "For in depth explanations refer to the sections in the user guide.\n",
    "We recommend importing Scipp with the alias `sc`, i.e., `import scipp as sc`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipp as sc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by creating some variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sc.arange('x', 5, unit='m')\n",
    "y = sc.linspace('y', 0.0, 1.0, num=4, unit='m')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Type the name of a variable at the end of a cell to generate an HTML representation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can initialize the values of a variable from a NumPy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "rng = np.random.default_rng(12345)\n",
    "var = sc.array(dims=['y', 'x'], values=rng.random((4, 5)))\n",
    "sc.show(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We combine the variables into a data array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = sc.DataArray(data=var, coords={'x': x, 'y': y})\n",
    "sc.show(array)\n",
    "array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables can have uncertainties.\n",
    "Scipp stores these as variances (the square of the standard deviation):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = array.copy()\n",
    "array.variances = np.square(rng.random((4, 5)))\n",
    "sc.show(array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = sc.Dataset(data={'a': var}, coords={'x': x, 'y': y, 'aux': x})\n",
    "dataset['b'] = array\n",
    "sc.show(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can slice variables, data arrays, and datasets using a dimension label and an index or a slice object like `i:j`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sliced = dataset['b']['x', 2]\n",
    "sc.show(sliced)\n",
    "sliced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also generate table representations (only 0-D and 1-D) and plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sc.table(dataset['y', 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sc.plot(dataset['a'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arithmetic operations can be combined with slicing and handle propagation of uncertainties and units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset['a']['y', 0:2] -= dataset['y', 0:2]['a']['x', 0]\n",
    "dataset['b'] *= 1.23 * sc.Unit('m/s')\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, type the imported name of the Scipp module at the end of a cell for a list of all current Scipp objects (variables, data arrays, datasets).\n",
    "Click on entries to expand nested sections:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sc"
   ]
  }
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