Adding some basic content for the CBSE teachers.

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diff --git a/cbse/README.md b/cbse/README.md
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--- /dev/null
+++ b/cbse/README.md
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+## CBSE class material
+
+Python is in the school syllabus in India, YAY!
+
+This directory has some random material based on content required for the CBSE
+CS and IP courses for class eleven and twelve.
+
+These items are essentially things that are mentioned in the syllabus but not
+quite covered explicitly in the basic and advanced material as of now.
+
+The idea is to build the content out so that it can be used to train the CBSE
+teachers.
diff --git a/cbse/data/data.txt b/cbse/data/data.txt
new file mode 100644
index 0000000..51bddee
--- /dev/null
+++ b/cbse/data/data.txt
@@ -0,0 +1,100 @@
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diff --git a/cbse/images/four_plot.png b/cbse/images/four_plot.png
new file mode 100644
index 0000000..00a3a7a
--- /dev/null
+++ b/cbse/images/four_plot.png
diff --git a/cbse/images/penguins.png b/cbse/images/penguins.png
new file mode 100644
index 0000000..d288f93
--- /dev/null
+++ b/cbse/images/penguins.png
diff --git a/cbse/rise.css b/cbse/rise.css
new file mode 100644
index 0000000..11d733d
--- /dev/null
+++ b/cbse/rise.css
@@ -0,0 +1,21 @@
+/* Customizations for RISE slides
+ */
+
+/* Increase font size for the code cells.
+*/
+
+div.cell.code_cell {
+    font-size: 150%;
+}
+
+/*
+div.CodeMirror-lines {
+    font-size: 125%;
+}*/
+
+/* Tables were rendered out as tiny values
+ since the font-size was set to 12px somehow.
+*/
+.rendered_html table {
+    font-size: 100%
+}
diff --git a/cbse/stats_plots.ipyml b/cbse/stats_plots.ipyml
new file mode 100644
index 0000000..90c67e9
--- /dev/null
+++ b/cbse/stats_plots.ipyml
@@ -0,0 +1,443 @@
+cells:
+
+- markdown: |
+    # More plotting and elementary stats
+
+    ## A quick exercise
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    # Start with this.
+    %pylab inline
+
+  id: 0
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- markdown: |
+    ## Simple plotting
+
+    - Plot $x, -x, \sin(x), x \sin(x)$ in range $-5\pi$ to $5\pi$
+    - Add a legend
+    - Annotate the origin
+    - Set axes limits to the range of x
+
+    <img src="images/four_plot.png" alt="Sample plot to reproduce" width="50%"></img>
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    # Solution
+
+  id: 1
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- markdown: |
+    ## Mean, standard deviation, percentiles, ...
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    x = np.array([3, 2, 1, 4, 4, 5, 15, 24, 22, 25, 18, 32, 33])
+
+- code: |
+    np.mean(x)
+
+- code: |
+    np.median(x)
+
+- markdown: |
+    ## Variance, standard deviation and degrees of freedom
+
+    $$S^2 = \frac{\sum_{i=1}^n (X_i - \bar{X})^2}{n-1}$$
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    np.var(x, ddof=1)
+
+- markdown: |
+    `ddof=1` corresponds to $n-1$ in the denominator.
+
+- code: |
+    np.std(x, ddof=1)
+
+- markdown: |
+    ## Percentiles
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    np.percentile(x, 34)
+
+  id: 3
+
+- code: |
+    np.percentile(x, [25, 50, 75, 34])
+
+  id: 4
+
+- code: |
+    sorted(x)
+
+  id: 5
+  metadata:
+    slideshow:
+      slide_type: slide
+
+
+- markdown: |
+    ## More statistics from `scipy.stats`
+
+    Use the `scipy.stats` module for more stats related functions
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    import scipy.stats
+
+- code: |
+    scipy.stats?
+
+- code: |
+    scipy.stats.mode(x)
+
+- code: |
+    scipy.stats.gmean(x)
+
+- code: |
+    scipy.stats.hmean(x)
+
+
+- markdown: |
+
+    ## Load the given `data.txt` file
+
+    - Load up the data file into numpy arrays.
+    - Plot the two columns one vs the other with points.
+    - Find the mean and standard deviation of the columns
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    x, y = loadtxt('data/data.txt', unpack=True)
+
+- code: |
+    # Find mean and standard deviation
+
+- code: |
+    # Your plotting code here...
+
+
+- markdown: |
+    ## More plotting functions
+
+    We explore more plotting functions below
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+
+- code: |
+    scatter(x, y, s=x, c=y);
+
+  id: 7
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    # Histogram
+    hist(y);
+
+  id: 8
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    hist(y, cumulative=True);
+
+  id: 9
+
+- code: |
+    # Boxplot
+    boxplot(y, showmeans=True);
+    grid()
+    axis('tight');
+
+  id: 10
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- markdown: |
+
+    ## Simple image processing
+
+    - Load the image.
+    - Show the image.
+    - Drop every alternate pixel to reduce the size of the image
+    - Crop the picture to only show the baby penguin.
+
+    <img src="images/penguins.png" alt="Sample plot" width="50%"></img>
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    imshow(img[:,:,3], cmap='gray');
+    colorbar();
+
+  id: 11
+
+- code: |
+    img = imread('images/penguins.png')
+    figure(figsize=(10, 5))
+    subplot(2, 2, 1)
+    imshow(img)
+    subplot(2, 2, 2)
+    imshow(img[::2,::2])
+    subplot(2, 2, 3)
+    imshow(img[225:,100:250])
+    subplot(2, 2, 4)
+    imshow(img[:,:,1])
+
+  id: 12
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- markdown: |
+
+    Use a for loop to plot the 3 channels of the image in a sub-plot,
+    i.e. r, g, b color channels.
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    # Solution.
+
+
+- markdown: |
+    ## Histogram of image pixel data
+
+    - Changing the dimensions of a numpy array.
+    - Making a 2D array into a 1D array.
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+
+- code: |
+    a = np.arange(9)
+    a.shape = (3, 3)
+    a.ravel()
+
+  id: 13
+
+- code: |
+    np.ravel(img[:, :, 0]).shape
+
+  id: 14
+
+- code: |
+    # Doing more
+    #print(numpy.ravel(img[:,:,0]).shape)
+    hist(numpy.ravel(img[:,:,3]));
+
+  id: 15
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    # Putting it together
+    print(numpy.ravel(img[:,:,0]).shape)
+    for color in [0, 1, 2, 3]:
+        subplot(2, 2, color + 1)
+        hist(numpy.ravel(img[:,:,color]), bins=40, density=True, edgecolor='b');
+
+  id: 17
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- markdown: |
+
+    ## Pie charts
+
+    | **Cancer**  | Lung | Breast | Colon | Prostate | Melanoma | Bladder |
+    |-------------|------|--------|-------|----------|----------|---------|
+    | **Numbers** | 42   |  50    |  32   |   55     |  9       |  12     |
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    # Solution
+    cancer = ['Lung', 'Breast', 'Colon', 'Prostate', 'Melanoma', 'Bladder']
+    numbers = [42, 50, 32, 55, 9, 12]
+    pie(numbers, labels=cancer);
+
+  id: 18
+  metadata:
+    slideshow:
+      slide_type: slide
+
+
+- markdown: |
+    ## More statistics
+
+    - Covariance, correlation
+    - Linear regression
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+
+- markdown: |
+    ## Covariance/correlation
+
+    $$Cov(X, Y) = E[(X-\mu_x)(Y - \mu_y)] = E[XY] - E[X]E[Y]$$
+
+    $$Corr(X, Y) = Cov(X, Y)/\sqrt{Var(X) Var(Y)}$$
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    # Loading some data
+    x, y = loadtxt('data/data.txt', unpack=True)
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    np.cov(x, y)
+
+
+- code: |
+    np.corrcoef(x, y)
+
+
+- markdown: |
+    ## Trying with other data
+
+    - Find the correlation coefficient and covariance
+    - Plot the data.
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    x1 = np.array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+    y1 = np.array([30, 33, 53, 73, 78, 85, 91, 92, 100, 120])
+
+- code: |
+    # Your solution.
+
+
+- markdown: |
+    ## Simple linear regression
+
+    - Already seen how to do least-square fits with numpy
+    - Simpler way with `scipy.stats.linregress`
+    - Can also use `np.polyfit`
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+
+- code: |
+    scipy.stats.linregress(x1, y1)
+
+- code: |
+    # Result is a named tuple (collections.namedtuple)!
+
+- code: |
+    res = scipy.stats.linregress(x1, y1)
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    res
+
+- code: |
+    res[0]
+
+- code: |
+    res.slope
+
+- code: |
+    # Using np.polyfit
+
+    np.polyfit(x1, y1, deg=1)
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    np.polyfit?
+
+- markdown: |
+    * Use `deg=1` for linear regression.
+    * Use `deg=2` for quadratic functions.
+    * ...
+
+
+# The lines below here may be deleted if you do not need them.
+# ---------------------------------------------------------------------------
+metadata:
+  celltoolbar: Slideshow
+  kernelspec:
+    display_name: Python 3
+    language: python
+    name: python3
+  language_info:
+    codemirror_mode:
+      name: ipython
+      version: 3
+    file_extension: .py
+    mimetype: text/x-python
+    name: python
+    nbconvert_exporter: python
+    pygments_lexer: ipython3
+    version: 3.6.0
+  rise:
+    scroll: true
+    transition: none
+nbformat: 4
+nbformat_minor: 2
diff --git a/cbse/stdlib.ipyml b/cbse/stdlib.ipyml
new file mode 100644
index 0000000..0b41aa0
--- /dev/null
+++ b/cbse/stdlib.ipyml
@@ -0,0 +1,347 @@
+cells:
+
+- markdown: |
+    # Some useful standard library modules
+
+    Focus is on the syllabus:
+
+    - `math`
+    - `statistics`
+    - `urllib`
+    - More info from: https://docs.python.org/3/library/
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- markdown: |
+    ## Basic mathematical functions: `math`
+
+    - Useful when `numpy` is not available
+    - Much simpler
+    - Does not handle lists or arrays
+    - Only scalars
+    - https://docs.python.org/3/library/math.html
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    import math
+
+- code: |
+    math?
+
+- code: |
+    math.sin(math.pi/4)
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    math.acos(0.5)*180/math.pi
+
+- code: |
+    math.degrees(math.acos(0.5))
+
+- code: |
+    dir(math)
+
+
+- code: |
+    math.fsum([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1])
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    math.frexp(2)
+
+- code: |
+    math.frexp(1.01235e5)
+
+- markdown: |
+    ## Differences from numpy
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+
+- code: |
+    import numpy as np
+    np.sin([1, 2, 3])
+
+- code: |
+    math.sin([1, 2, 3])
+
+- markdown: |
+
+    See: https://docs.python.org/3/library/math.html
+
+
+- markdown: |
+    ## `statistics` module
+
+    - New since Python 3.4
+    - Again useful primarily when `numpy` is not available
+    - https://docs.python.org/3/library/statistics.html
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    import statistics
+
+- code: |
+    statistics?
+
+- code: |
+    statistics.mean([-1.0, 2.5, 3.25, 5.75])
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    statistics.median([1, 3, 5, 7])
+
+- code: |
+    statistics.median_low([1, 3, 5, 7])
+
+- code: |
+    statistics.median_high([1, 3, 5, 7])
+
+- code: |
+    statistics.median_grouped([1, 3, 5, 7])
+
+- code: |
+    statistics.mode([2, 3, 3, 5])
+
+- code: |
+    statistics.mode([2, 3, 3, 5, 5])
+
+- code: |
+    statistics.harmonic_mean([2.5, 3, 10])
+
+- code: |
+    statistics.stdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    statistics.pstdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
+
+- code: |
+    import numpy as np
+    np.std([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
+
+- code: |
+    np.std([1.5, 2.5, 2.5, 2.75, 3.25, 4.75], ddof=1)
+
+
+- markdown: |
+    ## `urllib` module
+
+    - Changed significantly in Python 3
+    - Very convenient
+    - https://docs.python.org/3/library/urllib.html
+
+    - Three parts:
+      - `urllib.request`
+      - `urllib.parse`
+      - `urllib.error`
+      - `urllib.robotparser`
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- markdown: |
+    ## Reading a web page
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    import urllib.request
+
+- code: |
+    f = urllib.request.urlopen('http://www.w3.org/')
+    f
+
+- code: |
+    # f can be used like a file object
+    print(f.read(300))
+
+- code: |
+    print(f.read(100))
+
+- code: |
+    f.close()
+    # close the request object
+
+- markdown: |
+    - `f.read()` will read everything.
+    - Note that the returned object is a "bytes" object
+
+
+- code: |
+    # Bytes and unicode
+    data = f.read(300)
+    type(data)
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- markdown: |
+    - Must use a suitable encoding to decode these bytes
+    - `'utf-8'` is good usually.
+
+- code: |
+    data.decode('utf-8')
+
+
+- code: |
+    # Using a with statement
+
+    with urllib.request.urlopen('http://www.w3.org/') as f:
+        print(f.read(300))
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- markdown: |
+    Avoids the need to explicitly close the request in the end.
+
+
+- markdown: |
+    ## More advanced tasks
+
+    - Lot more information in documentation
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    # Sending data to a CGI script via POST.
+    req = urllib.request.Request(url='https://localhost/cgi-bin/test.cgi',
+                          data=b'This data is passed to stdin of the CGI')
+
+- code: |
+    with urllib.request.urlopen(req) as f:
+        print(f.read().decode('utf-8'))
+
+
+- code: |
+    # A PUT request
+    DATA = b'some data'
+    req = urllib.request.Request(url='http://localhost:8080', data=DATA,method='PUT')
+    with urllib.request.urlopen(req) as f:
+        pass
+
+    print(f.status)
+    print(f.reason)
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+
+- markdown: |
+    ## Performing a GET with parameters
+
+    - Lets say you want to do a wikipedia search!
+    - Construct the parameters with `urllib.parse.urlencode`
+    - The wikipedia API requires this URL in a GET request
+
+    - https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch=python
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    import urllib.parse
+
+- code: |
+    params = urllib.parse.urlencode(
+        {'action': 'query',
+        'list': 'search', 'srsearch': 'python'}
+    )
+    params
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    url = 'https://en.wikipedia.org/w/api.php?%s' % params
+    url
+
+- code: |
+    with urllib.request.urlopen(url) as f:
+        print(f.read().decode('utf-8'))
+
+
+- markdown: |
+    ## More on parsing
+
+    - Use `urllib.parse` to parse/construct URLs.
+
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    import urllib.parse
+
+- code: |
+    urllib.parse.urlparse('http://google.com')
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- code: |
+    urllib.parse.urlparse(ftp://ubuntu.com')
+
+- code: |
+    urllib.parse.urlparse('https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch=python')
+
+- code: |
+    urllib.parse.urlsplit('https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch=python')
+
+
+- markdown: |
+    ## Robots.txt
+
+    - Learn more: https://www.robotstxt.org
+    - Helps control robots which parse a website.
+    - `urllib.robotparser` helps parse and detect setup
+
+  metadata:
+    slideshow:
+      slide_type: slide
+
+- markdown: |
+    ## More information
+
+    - See documentation for more information.
+    - https://docs.python.org/3/library/urllib.html
+
+
+  metadata:
+    slideshow:
+      slide_type: slide