Added matplotlib sections about images and lines
Aurélien Geron
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a1e4e9bb5e
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91c616af20
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@ -6,7 +6,7 @@
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"source": [
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"# Tools - matplotlib\n",
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"\n",
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"This notebook demonstrates how to use the matplotlib library to plot beautiful graphs."
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"*This notebook demonstrates how to use the matplotlib library to plot beautiful graphs.*"
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]
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},
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{
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@ -902,7 +902,9 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Lines"
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"## Lines\n",
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"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",
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"For example:"
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]
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},
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{
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@ -914,11 +916,20 @@
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"outputs": [],
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"source": [
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"from numpy.random import randn\n",
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"\n",
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"def plot_line(axis, slope, intercept, **kargs):\n",
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" xmin, xmax = axis.get_xlim()\n",
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" plt.plot([xmin, xmax], [xmin*slope+intercept, xmax*slope+intercept], **kargs)\n",
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"\n",
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"x = randn(1000)\n",
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"y = 3*x + 5 + randn(1000)*2\n",
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"y = 0.5*x + 5 + randn(1000)*2\n",
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"plt.axis([-2.5, 2.5, -5, 15])\n",
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"plt.scatter(x, y)\n",
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"plt.vlines(0, -numpy.inf, numpy.inf)\n",
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"plt.scatter(x, y, alpha=0.2)\n",
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"plt.plot(1, 0, \"ro\")\n",
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"plt.vlines(1, -5, 0, color=\"red\")\n",
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"plt.hlines(0, -2.5, 1, color=\"red\")\n",
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"plot_line(axis=plt.gca(), slope=0.5, intercept=5, color=\"magenta\")\n",
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"plt.grid(True)\n",
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"plt.show()"
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]
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},
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@ -954,7 +965,7 @@
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"execution_count": 36,
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"metadata": {
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"collapsed": false,
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"scrolled": false
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"scrolled": true
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},
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"outputs": [],
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"source": [
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@ -976,6 +987,155 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Images\n",
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"Reading, generating and plotting images in matplotlib is quite straightforward.\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import matplotlib.image as mpimg\n",
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"\n",
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"img = mpimg.imread('my_square_function.png')\n",
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"print img.shape, img.dtype"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"plt.imshow(img)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Tadaaa! You may want to hide the axes when you are displaying an image:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"plt.imshow(img)\n",
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"plt.axis('off')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"It's just as easy to generate your own image:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"img = numpy.arange(100*100).reshape(100, 100)\n",
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"print img\n",
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"plt.imshow(img)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"metadata": {
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"collapsed": false,
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"plt.imshow(img, cmap=\"hot\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can also generate an RGB image directly:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"img = numpy.empty((20,30,3))\n",
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"img[:, :10] = [0, 0, 0.6]\n",
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"img[:, 10:20] = [1, 1, 1]\n",
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"img[:, 20:] = [0.6, 0, 0]\n",
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"plt.imshow(img)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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",
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"You can select another interpolation algorithm, such as copying the color of the nearest pixel:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {
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"collapsed": false,
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"plt.imshow(img, interpolation=\"nearest\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -986,7 +1146,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 44,
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"metadata": {
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"collapsed": true
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},
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@ -1009,7 +1169,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 45,
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"metadata": {
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"collapsed": false
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},
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@ -1045,7 +1205,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 46,
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"metadata": {
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"collapsed": false
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},
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@ -1053,7 +1213,7 @@
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"source": [
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"Writer = animation.writers['ffmpeg']\n",
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"writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)\n",
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"line_ani.save('my_wiggly.mp4', writer=writer)"
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"line_ani.save('my_wiggly_animation.mp4', writer=writer)"
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]
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},
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{
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