{
 "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> Image Element</dd>\n",
    "  <dt>Dependencies</dt> <dd>Matplotlib</dd>\n",
    "  <dt>Backends</dt> <dd><a href='./Image.ipynb'>Matplotlib</a></dd> <dd><a href='../bokeh/Image.ipynb'>Bokeh</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",
    "hv.extension('matplotlib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like ``Raster``, a HoloViews ``Image`` allows you to view 2D arrays using an arbitrary color map. Unlike ``Raster``, an ``Image`` is associated with a [2D coordinate system in continuous space](Continuous_Coordinates.ipynb), which is appropriate for values sampled from some underlying continuous distribution (as in a photograph or other measurements from locations in real space)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls = np.linspace(0, 10, 200)\n",
    "xx, yy = np.meshgrid(ls, ls)\n",
    "\n",
    "bounds=(-1,-1,1,1)   # Coordinate system: (left, bottom, top, right)\n",
    "img = hv.Image(np.sin(xx)*np.cos(yy), bounds=bounds)\n",
    "img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing, sampling, etc. on an ``Image`` all operate in this continuous space, whereas the corresponding operations on a ``Raster`` work on the raw array coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img + img[-0.5:0.5, -0.5:0.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how, because our declared coordinate system is continuous, we can slice with any floating-point value we choose. The appropriate range of the samples in the input numpy array will always be displayed, whether or not there are samples at those specific floating-point values. This also allows us to index by a floating value, since the ``Image`` is defined as a continuous space it will snap to the closest coordinate, to inspect the closest coordinate we can use the ``closest`` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%opts Points (color='black' marker='x' size=20)\n",
    "closest = img.closest((0.1,0.1))\n",
    "print('The value at position %s is %s' % (closest, img[0.1, 0.1]))\n",
    "img * hv.Points([img.closest((0.1,0.1))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also easily take cross-sections of the Image by using the sample method or collapse a dimension using the ``reduce`` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img.sample(x=0) + img.reduce(x=np.mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One additional way to create Image objects is via the separate [ImaGen](http://ioam.github.io/imagen) library, which creates parameterized streams of images for experiments, simulations, or machine-learning applications.\n",
    "\n",
    "For full documentation and the available style and plot options, use ``hv.help(hv.Image).``"
   ]
  }
 ],
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