Added matplotlib sections about images and lines

tools_matplotlib.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Tools - matplotlib\n",
     "\n",
-    "This notebook demonstrates how to use the matplotlib library to plot beautiful graphs."
+    "*This notebook demonstrates how to use the matplotlib library to plot beautiful graphs.*"
    ]
   },
   {
@@ -902,7 +902,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Lines"
+    "## Lines\n",
+    "You can draw lines simply using the `plot` function, as we have done so far. However, it is often convenient to create a utility function that plots a (seemingly) infinite line across the graph, given a slope and an intercept. You can also use the `hlines` and `vlines` functions that plot horizontal and vertical line segments.\n",
+    "For example:"
    ]
   },
   {
@@ -914,11 +916,20 @@
    "outputs": [],
    "source": [
     "from numpy.random import randn\n",
+    "\n",
+    "def plot_line(axis, slope, intercept, **kargs):\n",
+    "    xmin, xmax = axis.get_xlim()\n",
+    "    plt.plot([xmin, xmax], [xmin*slope+intercept, xmax*slope+intercept], **kargs)\n",
+    "\n",
     "x = randn(1000)\n",
-    "y = 3*x + 5 + randn(1000)*2\n",
+    "y = 0.5*x + 5 + randn(1000)*2\n",
     "plt.axis([-2.5, 2.5, -5, 15])\n",
-    "plt.scatter(x, y)\n",
+    "plt.scatter(x, y, alpha=0.2)\n",
-    "plt.vlines(0, -numpy.inf, numpy.inf)\n",
+    "plt.plot(1, 0, \"ro\")\n",
+    "plt.vlines(1, -5, 0, color=\"red\")\n",
+    "plt.hlines(0, -2.5, 1, color=\"red\")\n",
+    "plot_line(axis=plt.gca(), slope=0.5, intercept=5, color=\"magenta\")\n",
+    "plt.grid(True)\n",
     "plt.show()"
    ]
   },
@@ -954,7 +965,7 @@
    "execution_count": 36,
    "metadata": {
     "collapsed": false,
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [],
    "source": [
@@ -976,6 +987,155 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Images\n",
+    "Reading, generating and plotting images in matplotlib is quite straightforward.\n",
+    "\n",
+    "To read an image, just import the `matplotlib.image` module, and call its `imread` function, passing it the file name (or file object). This returns the image data, as a NumPy array. Let's try this with the `my_square_function.png` image we saved earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.image as mpimg\n",
+    "\n",
+    "img = mpimg.imread('my_square_function.png')\n",
+    "print img.shape, img.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have loaded a 288x432 image. Each pixel is represented by a 4-element array: red, green, blue, and alpha levels, stored as 32-bit floats between 0 and 1.  Now all we need to do is to call `imshow`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(img)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tadaaa! You may want to hide the axes when you are displaying an image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(img)\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's just as easy to generate your own image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "img = numpy.arange(100*100).reshape(100, 100)\n",
+    "print img\n",
+    "plt.imshow(img)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we did not provide RGB levels, the `imshow` function automatically maps values to a color gradient. By default, the color gradient goes from blue (for low values) to red (for high values), but you can select another color map.  For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(img, cmap=\"hot\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also generate an RGB image directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "img = numpy.empty((20,30,3))\n",
+    "img[:, :10] = [0, 0, 0.6]\n",
+    "img[:, 10:20] = [1, 1, 1]\n",
+    "img[:, 20:] = [0.6, 0, 0]\n",
+    "plt.imshow(img)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the `img` array is just quite small (20x30), when the `imshow` function displays it, it grows the image to the figure's size. By default it uses [bilinear interpolation](https://en.wikipedia.org/wiki/Bilinear_interpolation) to fill the added pixels. This is why the edges look blurry.\n",
+    "You can select another interpolation algorithm, such as copying the color of the nearest pixel:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(img, interpolation=\"nearest\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -986,7 +1146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 44,
    "metadata": {
     "collapsed": true
    },
@@ -1009,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
@@ -1045,7 +1205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
@@ -1053,7 +1213,7 @@
    "source": [
     "Writer = animation.writers['ffmpeg']\n",
     "writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)\n",
-    "line_ani.save('my_wiggly.mp4', writer=writer)"
+    "line_ani.save('my_wiggly_animation.mp4', writer=writer)"
    ]
   },
   {