{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n",
    "<dl class=\"dl-horizontal\">\n",
    "  <dt>Title</dt> <dd> Points Element</dd>\n",
    "  <dt>Dependencies</dt> <dd>Plotly</dd>\n",
    "    <dt>Backends</dt> <dd><a href='../bokeh/Points.ipynb'>Bokeh</a></dd> <dd><a href='../matplotlib/Points.ipynb'>Matplotlib</a></dd> <dd><a href='./Points.ipynb'>Plotly</a></dd>\n",
    "</dl>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import holoviews as hv\n",
    "from holoviews import dim, opts\n",
    "\n",
    "hv.extension('plotly')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``Points`` element visualizes as markers placed in a space of two independent variables, traditionally denoted ``x`` and ``y``. In HoloViews, the names ``'x'`` and ``'y'`` are used as the default key dimensions (``kdims``) of the element.  We can see this from the default axis labels when visualizing a simple ``Points`` element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(12)\n",
    "coords = np.random.rand(50,2)\n",
    "points = hv.Points(coords)\n",
    "\n",
    "points.opts(color='black', marker='x', size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the random ``x`` values and random ``y`` values are *both* considered to be the coordinates, with no dependency between them (compare this to the different way that [``Scatter``](./Scatter.ipynb) elements are defined). You can think of ``Points`` as simply marking positions in some two-dimensional space. Such positions can be sliced by specifying a 2D region of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(points + points[0.6:0.8,0.2:0.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the simplest ``Points`` element simply marks positions in a two-dimensional space without any associated value, value dimensions (``vdims``) are also supported. Here is an example with two additional quantities for each point, declared as the ``vdims``s ``z`` and ``size`` (visualized as the color and size of the dots, respectively):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(10)\n",
    "data = np.random.rand(100,4)\n",
    "\n",
    "points = hv.Points(data, vdims=['z', 'size'])\n",
    "(points + points[0.3:0.7, 0.3:0.7].hist()).opts(\n",
    "    opts.Points(color='z', size=dim('size')*20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The marker shape specified above can be any supported by [matplotlib](http://matplotlib.org/api/markers_api.html), e.g. ``s``, ``d``, or ``o``; the other options select the color and size of the marker.  For convenience with the [bokeh backend](Bokeh_Backend), the matplotlib marker options are supported using a compatibility function in HoloViews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Although the  ``Scatter`` element is superficially similar to the [``Points``](./Points.ipynb) element (they can generate plots that look identical), the two element types are semantically quite different. The fundamental difference is that [Scatter](./Scatter.ipynb) is used to visualize data where the *y* variable is *dependent*, unlike ``Points``. This semantic difference also explains why the histogram generated by the ``hist`` call above visualizes the distribution of a different dimension than it does for [``Scatter``](./Scatter.ipynb) (because here *z*, not *y*, is the first ``vdim``).\n",
    "\n",
    "This difference means that ``Points`` elements can most naturally overlay with other elements that express independent variables in two-dimensional space, such as [``Raster``](./Raster.ipynb) types like [``Image``](./Image.ipynb). Conversely, ``Scatter`` expresses a dependent relationship between *x* and *y* and thus most naturally overlays with ``Chart`` types such as [``Curve``](./Curve.ipynb).\n",
    "\n",
    "For full documentation and the available style and plot options, use ``hv.help(hv.Points).``"
   ]
  }
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