add DL example

c8338805 · Jens Henrik Göbbert · 8e1e513e · c8338805 · c8338805
Commit c8338805 authored 4 years ago by Jens Henrik Göbbert
--- a/001-Jupyter/001-Tutorials/001-Basic-Tutorials/001-IPython-Kernel/Plotting in the Notebook with Matplotlib.ipynb
+++ b/001-Jupyter/001-Tutorials/001-Basic-Tutorials/001-IPython-Kernel/Plotting in the Notebook with Matplotlib.ipynb
@@ -36,6 +36,7 @@
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
+    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
@@ -70,6 +71,7 @@
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
+    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
@@ -84,6 +86,7 @@
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
+    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
@@ -127,6 +130,7 @@
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
+    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
@@ -200,20 +204,17 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
-    "%matplotlib notebook"
+    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
+    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }

 %% Cell type:markdown id: tags:

 # Plotting with Matplotlib

 %% Cell type:markdown id: tags:

 IPython works with the [Matplotlib](http://matplotlib.org/) plotting library, which integrates Matplotlib with IPython's display system and event loop handling.

 %% Cell type:markdown id: tags:

 ## matplotlib mode

 %% Cell type:markdown id: tags:

 To make plots using Matplotlib, you must first enable IPython's matplotlib mode.

 To do this, run the `%matplotlib` magic command to enable plotting in the current Notebook.

 This magic takes an optional argument that specifies which Matplotlib backend should be used.  Most of the time, in the Notebook, you will want to use the `inline` backend, which will embed plots inside the Notebook:

 %% Cell type:code id: tags:

 ``` python
 %matplotlib inline
 ```

 %% Cell type:markdown id: tags:

 You can also use Matplotlib GUI backends in the Notebook, such as the Qt backend (`%matplotlib qt`). This will use Matplotlib's interactive Qt UI in a floating window to the side of your browser. Of course, this only works if your browser is running on the same system as the Notebook Server.  You can always call the `display` function to paste figures into the Notebook document.

 %% Cell type:markdown id: tags:

 ## Making a simple plot

 %% Cell type:markdown id: tags:

 With matplotlib enabled, plotting should just work.

 %% Cell type:code id: tags:

 ``` python
 import matplotlib.pyplot as plt
 import numpy as np
 ```

 %% Cell type:code id: tags:

 ``` python
 x = np.linspace(0, 3*np.pi, 500)
 plt.plot(x, np.sin(x**2))
 plt.title('A simple chirp');
 ```

 %% Cell type:markdown id: tags:

 These images can be resized by dragging the handle in the lower right corner.  Double clicking will return them to their original size.

 %% Cell type:markdown id: tags:

 One thing to be aware of is that by default, the `Figure` object is cleared at the end of each cell, so you will need to issue all plotting commands for a single figure in a single cell.

 %% Cell type:markdown id: tags:

 ## Loading Matplotlib demos with %load

 %% Cell type:markdown id: tags:

 IPython's `%load` magic can be used to load any Matplotlib demo by its URL:

 %% Cell type:code id: tags:

 ``` python
 # %load http://matplotlib.org/mpl_examples/showcase/integral_demo.py
 """
 Plot demonstrating the integral as the area under a curve.

 Although this is a simple example, it demonstrates some important tweaks:

    * A simple line plot with custom color and line width.
    * A shaded region created using a Polygon patch.
    * A text label with mathtext rendering.
    * figtext calls to label the x- and y-axes.
    * Use of axis spines to hide the top and right spines.
    * Custom tick placement and labels.
 """
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.patches import Polygon


 def func(x):
    return (x - 3) * (x - 5) * (x - 7) + 85


 a, b = 2, 9 # integral limits
 x = np.linspace(0, 10)
 y = func(x)

 fig, ax = plt.subplots()
 plt.plot(x, y, 'r', linewidth=2)
 plt.ylim(ymin=0)

 # Make the shaded region
 ix = np.linspace(a, b)
 iy = func(ix)
 verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]
 poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')
 ax.add_patch(poly)

 plt.text(0.5 * (a + b), 30, r"$\int_a^b f(x)\mathrm{d}x$",
         horizontalalignment='center', fontsize=20)

 plt.figtext(0.9, 0.05, '$x$')
 plt.figtext(0.1, 0.9, '$y$')

 ax.spines['right'].set_visible(False)
 ax.spines['top'].set_visible(False)
 ax.xaxis.set_ticks_position('bottom')

 ax.set_xticks((a, b))
 ax.set_xticklabels(('$a$', '$b$'))
 ax.set_yticks([])

 plt.show()
 ```

 %% Cell type:markdown id: tags:

 Matplotlib 1.4 introduces an interactive backend for use in the notebook,
 called 'nbagg'. You can enable this with `%matplotlib notebook`.

 With this backend, you will get interactive panning and zooming of matplotlib figures in your browser.

 %% Cell type:code id: tags:

 ``` python
-%matplotlib notebook
+%matplotlib widget
 ```

 %% Cell type:code id: tags:

 ``` python
 plt.figure()
 x = np.linspace(0, 5 * np.pi, 1000)
 for n in range(1, 4):
    plt.plot(np.sin(n * x))
 plt.show()
 ```

--- a/002-Methods/001-Computing/PyDeepLearningVersion.ipynb
+++ b/002-Methods/001-Computing/PyDeepLearningVersion.ipynb