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| -rw-r--r-- | cbse/rise.css | 21 | ||||
| -rw-r--r-- | cbse/stats_plots.ipyml | 443 | ||||
| -rw-r--r-- | cbse/stdlib.ipyml | 347 |
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diff --git a/cbse/README.md b/cbse/README.md new file mode 100644 index 0000000..a09490f --- /dev/null +++ b/cbse/README.md @@ -0,0 +1,12 @@ +## 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 @@ +6.930830303506971291e+01 1.950570203348928500e+00 +5.076032587226054460e+01 1.968615715715650527e+00 +6.285323760945500027e+01 1.343008070563283418e+00 +7.068897251770047774e+01 1.602208201785916941e+00 +7.855575649137608707e+01 1.515231745959232823e+00 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1.750620647982633615e+00 +7.107706593555913344e+01 1.597893888509045901e+00 +7.504897940666698730e+01 1.664466695270478080e+00 +5.698579572299380658e+01 1.659222122103459984e+00 +6.543288085386846831e+01 1.713484701936906163e+00 +4.808865783696988672e+01 2.109078716378748553e+00 +7.014690314095311408e+01 1.560346619725913264e+00 +8.575885336562197381e+01 1.908869356619086632e+00 +7.719610459731612195e+01 1.711365885648766128e+00 +6.997728697499780992e+01 1.381206111441030338e+00 +6.306612372698030811e+01 1.586602768408121644e+00 +6.885643100290478458e+01 1.636465278582771798e+00 +6.616529774636667582e+01 1.456442393840382987e+00 +6.446565885803174467e+01 1.458092352780755219e+00 +5.080314466991057998e+01 2.121796211750642591e+00 +6.349031616958593105e+01 1.723911969656314325e+00 +6.320189575072709687e+01 1.702838265973882326e+00 +4.875875986665490558e+01 1.671161025329898520e+00 +5.481407554505388191e+01 1.713161230306921023e+00 +6.716838657586048100e+01 1.341905545247733089e+00 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/dev/null +++ b/cbse/images/four_plot.png diff --git a/cbse/images/penguins.png b/cbse/images/penguins.png Binary files differnew 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 |
