Finish basic session3.

This removes the csv2rec.  We should add pandas material later.

2 files changed, 65 insertions, 126 deletions

diff --git a/scipy/basic/README.txt b/scipy/basic/README.txt
index 4cf4303..35d9c22 100644
--- a/scipy/basic/README.txt
+++ b/scipy/basic/README.txt
@@ -43,8 +43,7 @@ Basic Tutorial:
    - NumPy array basics: 1D arrays.
    - Reading data files.
    - More on NumPy: multi-dimensional arrays, slicing, elementary image
-     processing.
-   - Some powertools: pandas dataframe basics.
+     processing, random numbers.
    - Using SciPy for Linear Algebra, FFT's, root finding and integrating
      ODEs.
 
@@ -54,11 +53,7 @@ Basic Tutorial:
   A laptop is definitely recommended since this will be a hands-on
   session.  You will need to have Python (2.7/3.x would work fine),
   IPython (0.10.x), Numpy (1.x), SciPy (>0.5.x), matplotlib (any recent
-  version), Mayavi-3.x and above.
+  version).
 
-  On Windows and Mac OS X it is easiest to install these by installing
-  the Enthought Python Distribution.  On GNU/Linux these are typically
-  available along with the distribution.  The packages are all
-  available on Ubuntu.  FOSSEE provides a live Ubuntu-based DVD with
-  all the necessary packages.  It can be downloaded here:
-  http://fossee.in/download#DVDs
+  On Linux, Windows and Mac OS X it is easiest to install these by installing
+  the Enthought Canopy.
diff --git a/scipy/basic/session3.tex b/scipy/basic/session3.tex
index 6d603a5..2c923fd 100644
--- a/scipy/basic/session3.tex
+++ b/scipy/basic/session3.tex
@@ -1,14 +1,14 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %Tutorial slides on Python.
 %
-% Author: FOSSEE 
+% Author: FOSSEE
 % Copyright (c) 2009, FOSSEE, IIT Bombay
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 \documentclass[14pt,compress]{beamer}
 %\documentclass[draft]{beamer}
 %\documentclass[compress,handout]{beamer}
-%\usepackage{pgfpages} 
+%\usepackage{pgfpages}
 %\pgfpagesuselayout{2 on 1}[a4paper,border shrink=5mm]
 
 % Modified from: generic-ornate-15min-45min.de.tex
@@ -44,7 +44,7 @@
 % Macros
 \setbeamercolor{emphbar}{bg=blue!20, fg=black}
 \newcommand{\emphbar}[1]
-{\begin{beamercolorbox}[rounded=true]{emphbar} 
+{\begin{beamercolorbox}[rounded=true]{emphbar}
       {#1}
  \end{beamercolorbox}
 }
@@ -78,11 +78,11 @@
 Python}
 \subtitle{More on numpy arrays}
 
-\author[Prabhu] {FOSSEE}
+\author[FOSSEE] {FOSSEE}
 
 \institute[FOSSEE -- IITB] {Department of Aerospace Engineering\\IIT Bombay}
-\date[] {SciPy India, 2014\\
-December, 2014
+\date[] {SciPy India 2016\\
+Mumbai
 }
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
@@ -109,7 +109,7 @@ December, 2014
 }
 
 % If you wish to uncover everything in a step-wise fashion, uncomment
-% the following command: 
+% the following command:
 %\beamerdefaultoverlayspecification{<+->}
 
 %\includeonlyframes{current,current1,current2,current3,current4,current5,current6}
@@ -143,7 +143,7 @@ In []: c = array([[11,12,13],
                   [31,32,33]])
 
 In []: c
-Out[]: 
+Out[]:
 array([[11, 12, 13],
        [21, 22, 23],
        [31, 32, 33]])
@@ -155,16 +155,16 @@ array([[11, 12, 13],
 \begin{small}
   \begin{lstlisting}
 In []: ones((3,5))
-Out[]: 
+Out[]:
 array([[ 1.,  1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.,  1.]])
 
-In []: ones_like([1, 2, 3, 4]) 
-Out[]: array([1, 1, 1, 1])   
+In []: ones_like([1, 2, 3, 4])
+Out[]: array([1, 1, 1, 1])
 
 In []: identity(2)
-Out[]: 
+Out[]:
 array([[ 1.,  0.],
        [ 0.,  1.]])
   \end{lstlisting}
@@ -178,7 +178,7 @@ Also available \alert{\typ{zeros, zeros_like, empty, empty_like}}
   \begin{small}
   \begin{lstlisting}
 In []: c
-Out[]: 
+Out[]:
 array([[11, 12, 13],
        [21, 22, 23],
        [31, 32, 33]])
@@ -200,14 +200,14 @@ Out[]: array([21, 22, 23])
   \begin{lstlisting}
 In []: c[1,1] = -22
 In []: c
-Out[]: 
+Out[]:
 array([[ 11,  12,  13],
        [ 21, -22,  23],
        [ 31,  32,  33]])
 
 In []: c[1] = 0
 In []: c
-Out[]: 
+Out[]:
 array([[11, 12, 13],
        [ 0,  0,  0],
        [31, 32, 33]])
@@ -227,12 +227,12 @@ In []: c[1,:]
 Out[]: array([0, 0, 0])
 
 In []: c[0:2,:]
-Out[]: 
+Out[]:
 array([[11, 12, 13],
        [ 0,  0,  0]])
 
 In []: c[1:3,:]
-Out[]: 
+Out[]:
 array([[ 0,  0,  0],
        [31, 32, 33]])
   \end{lstlisting}
@@ -244,17 +244,17 @@ array([[ 0,  0,  0],
 \begin{small}
   \begin{lstlisting}
 In []: c[:2,:]
-Out[]: 
+Out[]:
 array([[11, 12, 13],
        [ 0,  0,  0]])
 
 In []: c[1:,:]
-Out[]: 
+Out[]:
 array([[ 0,  0,  0],
        [31, 32, 33]])
 
 In []: c[1:,:2]
-Out[]: 
+Out[]:
 array([[ 0,  0],
        [31, 32]])
   \end{lstlisting}
@@ -267,18 +267,18 @@ array([[ 0,  0],
   \begin{small}
   \begin{lstlisting}
 In []: c[::2,:]
-Out[]: 
+Out[]:
 array([[11, 12, 13],
        [31, 32, 33]])
 
 In []: c[:,::2]
-Out[]: 
+Out[]:
 array([[11, 13],
        [ 0,  0],
        [31, 33]])
 
 In []: c[::2,::2]
-Out[]: 
+Out[]:
 array([[11, 13],
        [31, 33]])
   \end{lstlisting}
@@ -289,7 +289,7 @@ array([[11, 13],
   \frametitle{Shape of a matrix}
   \begin{lstlisting}
 In []: c
-Out[]: 
+Out[]:
 array([[11, 12, 13],
        [ 0,  0,  0],
        [31, 32, 33]])
@@ -365,7 +365,7 @@ In []: b = array([[3,2,-1,5],
                   [-1,0.5,-1,-7],
                   [9,-5,7,3]])
 In []: a + b
-Out[]: 
+Out[]:
 array([[  4. ,   3. ,   1. ,   4. ],
        [  4. ,   3. ,   3. ,   0. ],
        [  1. ,   1.5,  -2. ,  -4. ],
@@ -377,7 +377,7 @@ array([[  4. ,   3. ,   1. ,   4. ],
 \frametitle{Elementwise Multiplication}
 \begin{lstlisting}
 In []: a*b
-Out[]: 
+Out[]:
 array([[  3. ,   2. ,  -2. ,  -5. ],
        [  4. , -10. ,  -4. , -81. ],
        [ -2. ,   0.5,   1. , -21. ],
@@ -390,7 +390,7 @@ array([[  3. ,   2. ,  -2. ,  -5. ],
 \frametitle{Matrix Multiplication}
 \begin{lstlisting}
 In []: dot(a, b)
-Out[]: 
+Out[]:
 array([[ -6. ,   6. ,  -6. ,  -3. ],
        [-64. ,  38.5, -44. ,  35. ],
        [ 36. , -13.5,  24. ,  35. ],
@@ -406,7 +406,7 @@ array([[ -6. ,   6. ,  -6. ,  -3. ],
 \begin{small}
 \begin{lstlisting}
 In []: inv(a)
-Out[]: 
+Out[]:
 array([[-0.5 ,  0.55, -0.15,  0.7 ],
        [ 0.75, -0.5 ,  0.5 , -0.75],
        [ 0.5 , -0.15, -0.05, -0.1 ],
@@ -437,7 +437,7 @@ Out[]: 12
 In []: e = array([[3,2,4],[2,0,2],[4,2,3]])
 
 In []: eig(e)
-Out[]: 
+Out[]:
 (array([-1.,  8., -1.]),
  array([[-0.74535599,  0.66666667, -0.1931126 ],
         [ 0.2981424 ,  0.33333333, -0.78664085],
@@ -462,7 +462,7 @@ Out[]: 8.1240384046359608
   \begin{small}
   \begin{lstlisting}
 In []: svd(e)
-Out[]: 
+Out[]:
 (array(
 [[ -6.66666667e-01,  -1.23702565e-16,   7.45355992e-01],
  [ -3.33333333e-01,  -8.94427191e-01,  -2.98142397e-01],
@@ -488,7 +488,7 @@ Linear trend visible.
 
 \begin{frame}[fragile]
 \frametitle{$L$ vs. $T^2$ - Line}
-This line does not make any mathematical sense.
+This plot is not a straight line, can we do better?
 \vspace{-0.1in}
 \begin{figure}
 \includegraphics[width=4in]{data/L-Tsq-Line}
@@ -508,34 +508,43 @@ This is what our intention is.
 \frametitle{Matrix Formulation}
 \begin{itemize}
 \item We need to fit a line through points for the equation $T^2 = m \cdot L+c$
-\item In matrix form, the equation can be represented as $T_{sq} = A \cdot p$, where $T_{sq}$ is
-  $\begin{bmatrix}
+\item In matrix form, the equation can be represented as $T_{sq} = A \cdot p$,
+\item We need to find $p$ to plot the line
+\end{itemize}
+\end{frame}
+
+\begin{frame}
+  \begin{equation}
+  \begin{bmatrix}
   T^2_1 \\
   T^2_2 \\
   \vdots\\
   T^2_N \\
-  \end{bmatrix}$
-, A is   
-  $\begin{bmatrix}
+\end{bmatrix}
+= \begin{bmatrix}
   L_1 & 1 \\
   L_2 & 1 \\
   \vdots & \vdots\\
   L_N & 1 \\
-  \end{bmatrix}$
-  and p is 
-  $\begin{bmatrix}
+\end{bmatrix} \cdot
+\begin{bmatrix}
   m\\
   c\\
-  \end{bmatrix}$
-\item We need to find $p$ to plot the line
-\end{itemize}
+  \end{bmatrix}
+\end{equation}
+
+Or
+
+\[T_sq = A \cdot p \]
+
 \end{frame}
 
+
 \begin{frame}[fragile]
 \frametitle{Getting $L$ and $T^2$}
 %If you \alert{closed} IPython after session 2
 \begin{lstlisting}
-In []: L, t = loadtxt('pendulum.txt', 
+In []: L, t = loadtxt('pendulum.txt',
   ...:                unpack=True)
 In []: tsq = t*t
 \end{lstlisting}
@@ -577,13 +586,14 @@ In []: Tline = coef[0]*L + coef[1]
 In []: Tline.shape
 \end{lstlisting}
 \begin{itemize}
-\item Now plot \typ{Tline} vs. \typ{L}, to get the least squares fit line. 
+\item Now plot \typ{Tline} vs. \typ{L}, to get the least squares fit line.
 \end{itemize}
 \begin{lstlisting}
 In []: plot(L, Tline, 'r')
 
 In []: plot(L, tsq, 'o')
 \end{lstlisting}
+\inctime{10}
 \end{frame}
 
 \begin{frame}[fragile]
@@ -592,7 +602,6 @@ In []: plot(L, tsq, 'o')
 \begin{figure}
 \includegraphics[width=4in]{data/least-sq-fit}
 \end{figure}
-\inctime{10}
 \end{frame}
 
 \section{Random numbers}
@@ -611,7 +620,7 @@ In []: import numpy
 In []: numpy.random
     \end{lstlisting}
 \begin{itemize}
-    \item \typ{random.random}: produces uniform deviates in $[0, 1)$
+    \item \typ{random.random}: produces uniform deviates in $(0, 1)$
     \item \typ{random.normal}: draws random samples from a Gaussian
         distribution
     \item Useful to create a random matrix of any shape
@@ -621,11 +630,11 @@ In []: numpy.random
 \begin{frame}[fragile]
     \frametitle{Using the \typ{random} module}
 \begin{lstlisting}
-In []: x = random.random(size=1000)
-In []: y = random.random(size=1000)
+In []: x = random.random(size=100)
+In []: y = random.random(size=100)
 In []: scatter(x, y) # Scatter plot it.
 
-In []: x,y = random.normal(size=(2,1000))
+In []: x,y = random.normal(size=(2,100))
 In []: clf()
 In []: scatter(x, y)
 \end{lstlisting}
@@ -637,76 +646,14 @@ Note that \typ{size} can be a tuple.
     \frametitle{Using the \typ{random} module}
 \begin{lstlisting}
 In []: img = random.random(
-  .                    size=(200, 200))
+  ...:     size=(200, 200)
+  ...: )
 In []: clf()
 In []: imshow(img)
 \end{lstlisting}
-\inctime{5}
 \end{frame}
 
 
-\section{Record arrays}
-
-\begin{frame}[fragile]
-    \frametitle{Record arrays}
-    \begin{itemize}
-        \item Numpy arrays
-        \item Create custom data types
-        \item Consider: \\
-            \typ{[   (id0, x0), (id1, x1), ...]}
-        \item \typ{id}: int
-        \item \typ{x}: float
-
-    \end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]
-    \frametitle{Record arrays}
-    \begin{lstlisting}
-In []: typ = [('id', int), ('x', float)]
-In []: rec = numpy.zeros(10, dtype=typ)
-In []: rec['id'] = range(10)
-In []: rec['x'] = random.random(size=10)
-    \end{lstlisting}
-    \begin{itemize}
-        \item Data type can be any valid numpy dtype
-        \item Very convenient
-    \end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]
-    \frametitle{\typ{csv2rec}}
-    \begin{itemize}
-        \item CSV file $\rightarrow$ Record array
-        \item Very convenient and powerful
-        \item Automatic naming
-        \item Handles missing data
-        \item Handles different delimiters
-    \end{itemize}
-    \begin{lstlisting}
-In []: csv2rec?
-    \end{lstlisting}
-\end{frame}
-
-\begin{frame}[fragile]
-    \frametitle{\typ{csv2rec} example data}
-    \begin{itemize}
-        \item \typ{data.csv}
-        \item First column ``x''
-        \item Second column ``y''
-        \item Third column ``temp''
-    \end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]
-    \frametitle{\typ{csv2rec} example}
-    \begin{lstlisting}
-In []: data = csv2rec('data.csv')
-In []: scatter(data['x'], data['y'], 
-  ...:         c=data['temp'])
-In []: colorbar()
-    \end{lstlisting}
-\end{frame}
 
 \section{Summary}
 \begin{frame}
@@ -715,10 +662,8 @@ In []: colorbar()
   \item Matrices
   \item Least Squares
   \item Random numbers
-  \item Record arrays
-  \item \typ{csv2rec}
   \end{itemize}
-  \inctime{5}
+\inctime{10}
 \end{frame}
 
 \end{document}
@@ -827,4 +772,3 @@ eigenvalues?
 %% $N\times3$ array called \lstinline+data+, how would you obtain the third
 %% column of this data?
 %% \end{frame}
-