{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Title**: Slope Element\n",
    "\n",
    "**Dependencies**: Bokeh\n",
    "\n",
    "**Backends**: [Matplotlib](../matplotlib/VLine.ipynb), [Bokeh](./VLine.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import holoviews as hv\n",
    "\n",
    "hv.extension('bokeh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``Slope`` element is a type of annotation that plots a line with arbitrary slope and y-intercept.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient = 2\n",
    "y_intercept = 15\n",
    "\n",
    "# create random data\n",
    "xpts = np.arange(0, 20)\n",
    "ypts = gradient * xpts + y_intercept + np.random.normal(0, 4, 20)\n",
    "\n",
    "scatter = hv.Scatter((xpts, ypts))\n",
    "slope = hv.Slope(gradient, y_intercept)\n",
    "\n",
    "scatter.opts(size=10) * slope.opts(color='red', line_width=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Slope` maybe also be directly be calculated from a set of Scatter points using the ``Slope.from_scatter`` method, which will infer the gradient and y-intercept automatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal = hv.Scatter(np.random.randn(20, 2))\n",
    "\n",
    "normal.opts(size=10) * hv.Slope.from_scatter(normal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For full documentation and the available style and plot options, use ``hv.help(hv.Slope).``"
   ]
  }
 ],
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