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| -rw-r--r-- | scipy/basic/session2.tex | 297 |
1 files changed, 201 insertions, 96 deletions
diff --git a/scipy/basic/session2.tex b/scipy/basic/session2.tex index 359d1c8..f17d137 100644 --- a/scipy/basic/session2.tex +++ b/scipy/basic/session2.tex @@ -79,11 +79,11 @@ Python} \subtitle{More plotting, lists and numpy arrays} -\author[Prabhu] {FOSSEE} +\author[FOSSEE] {FOSSEE} \institute[FOSSEE -- IITB] {Department of Aerospace Engineering\\IIT Bombay} -\date[] {India\\ -2016 +\date[] {SciPy India 2016\\ +Mumbai } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @@ -197,13 +197,13 @@ Out[]: <matplotlib.text.Text object at 0x98746ec> \begin{frame}[fragile] \frametitle{Lists: Introduction} \begin{lstlisting} - In []: time = [0, 1, 2, 3] - - In []: distance = [7, 11, 15, 19] +In []: time = [0., 1., 2, 3] +In []: distance = [7., 11, 15, 19] \end{lstlisting} -What are \typ{x} and \typ{y}?\\ +What are \typ{time} and \typ{distance}?\\ \begin{center} + \large \alert{\typ{lists!!}} \end{center} \end{frame} @@ -243,8 +243,14 @@ Out[]: [3, 5, 7] \end{lstlisting} \end{frame} -\begin{frame}[fragile] +\begin{frame}[plain,fragile] \frametitle{List: Slicing \ldots} + \vspace*{-0.1in} + \begin{small} + \begin{block}{Remember\ldots} + \kwrd{In []: p = [ 2, 3, 5, 7]} +\end{block} +\end{small} \begin{lstlisting} In []: p[0:4:2] Out[]: [2, 5] @@ -262,6 +268,9 @@ Out[]: [7, 5, 3, 2] \begin{frame}[fragile] \frametitle{List: Slicing} + \begin{block}{Remember\ldots} + \kwrd{In []: p = [ 2, 3, 5, 7]} + \end{block} What is the output of the following? \begin{lstlisting} In []: p[1::2] @@ -298,7 +307,6 @@ Let us look at the Simple Pendulum experiment. \begin{tabular}{| c | c | c |} \hline $L$ & $T$ & $T^2$ \\ \hline -0.1 & 0.69 & \\ \hline 0.2 & 0.90 & \\ \hline 0.3 & 1.19 & \\ \hline 0.4 & 1.30 & \\ \hline @@ -306,7 +314,6 @@ $L$ & $T$ & $T^2$ \\ \hline 0.6 & 1.58 & \\ \hline 0.7 & 1.77 & \\ \hline 0.8 & 1.83 & \\ \hline -0.9 & 1.94 & \\ \hline \end{tabular} \end{small}\\ \alert{$L \alpha T^2$} @@ -316,12 +323,12 @@ $L$ & $T$ & $T^2$ \\ \hline \begin{frame}[fragile] \frametitle{Lets use lists} \begin{lstlisting} -In []: L = [0.1, 0.2, 0.3, 0.4, 0.5, - 0.6, 0.7, 0.8, 0.9] +In []: L = [0.2, 0.3, 0.4, 0.5, + 0.6, 0.7, 0.8] -In []: t = [0.69, 0.90, 1.19, - 1.30, 1.47, 1.58, - 1.77, 1.83, 1.94] +In []: t = [0.90, 1.19, 1.30, + 1.47, 1.58, 1.77, + 1.83] \end{lstlisting} \alert{Gotcha}: Make sure \typ{L} and \typ{t} have the same number of elements @@ -342,6 +349,17 @@ In []: print len(L), len(t) \end{frame} \begin{frame}[fragile] +\frametitle{Looping with \texttt{for}} +\begin{lstlisting} +In []: for time in t: + ....: print(time*time) + ....: + ....: +\end{lstlisting} +This will print the square of each item in the list, \typ{t} +\end{frame} + +\begin{frame}[fragile] \frametitle{Plotting $L$ vs $T^2$} \begin{lstlisting} In []: tsq = [] @@ -354,8 +372,8 @@ In []: for time in t: \end{lstlisting} This gives \typ{tsq} which is the list of squares of \typ{t} values. \begin{lstlisting} -In []: print len(L), len(t), len(tsq) -Out[]: 9 9 9 +In []: print(len(L), len(t), len(tsq)) +Out[]: (7, 7, 7) In []: plot(L, tsq) \end{lstlisting} @@ -365,25 +383,84 @@ In []: plot(L, tsq) \begin{figure} \includegraphics[width=3.5in]{data/L-TSq-limited.png} \end{figure} +\inctime{10} \end{frame} \begin{frame}[fragile] -\frametitle{This seems tedious} -\begin{itemize} +\frametitle{Don't repeat yourself: functions} +\noindent Let us define a function to square the list +\begin{lstlisting} +In []: def sqr(arr): + ...: result = [] + ...: for x in arr: + ...: result.append(x*x) + ...: return result + ...: + +In []: tsq = sqr(t) + +\end{lstlisting} %$ +\end{frame} + +\begin{frame}[fragile] + \frametitle{More on defining functions} + \begin{itemize} + \item Consider the function \texttt{f(x) = x\textasciicircum{}2} + \item Let's write a Python function, equivalent to this + \end{itemize} + \begin{lstlisting} + In[]: def f(x): + ....: return x*x + ....: + + In[]: f(1) + In[]: f(2) + \end{lstlisting} + \begin{itemize} + \item \texttt{def} is a keyword + \item \texttt{f} is the name of the function + \item \texttt{x} the parameter of the function (local variable) + \item \texttt{return} is a keyword + \end{itemize} +\end{frame} + +\begin{frame}[fragile] + \frametitle{Aside: Exercise} + \begin{itemize} + \item Write a function called \typ{mysum(a, b)} that returns sum of two + arguments. + \end{itemize} + \pause +\begin{lstlisting} +In []: def mysum(a, b): + ...: return a + b + ...: +In []: mysum(1, 2) + +In []: mysum([1, 2], [3, 4]) +\end{lstlisting} +\end{frame} + +\begin{frame}[fragile] + \frametitle{This seems tedious} + + \begin{itemize} + \item Do we have to write a function just to get the square of a list? \item Lists \begin{itemize} \item Nice - \item Not too convenient + \item Not too convenient for math \item Slow \end{itemize} \item Enter NumPy arrays \begin{itemize} \item Fixed size, data type - \item Fast \item Very convenient + \item Fast \end{itemize} -\end{itemize} + \end{itemize} + \inctime{10} \end{frame} \subsection{\num\ arrays} @@ -395,13 +472,12 @@ In []: t = array(t) In []: tsq = t*t -In []: print tsq +In []: print(tsq) In []: plot(L, tsq) # works! \end{lstlisting} %$ \end{frame} - \begin{frame}[fragile] \frametitle{Speed?} @@ -409,12 +485,9 @@ In []: plot(L, tsq) # works! \begin{lstlisting} In []: t = range(1000000) -In []: tsq = [] -In []: for time in t: - ....: tsq.append(time*time) - ....: - ....: +In []: tsq = sqr(t) + \end{lstlisting} %$ \noindent Now try it with @@ -427,43 +500,6 @@ In []: tsq = t*t \ldots \end{frame} -\begin{frame}[fragile] -\frametitle{How fast is this?} -\noindent Lets define a function for the list -\begin{lstlisting} -In []: def sqr(arr): - ...: result = [] - ...: for x in arr: - ...: result.append(x*x) - ...: return result - ...: - -In []: tsq = sqr(t) - -\end{lstlisting} %$ -\end{frame} - -\begin{frame}[fragile] - \frametitle{Aside: Defining functions} - \begin{itemize} - \item Consider the function \texttt{f(x) = x\textasciicircum{}2} - \item Let's write a Python function, equivalent to this - \end{itemize} - \begin{lstlisting} - In[]: def f(x): - ....: return x*x - ....: - - In[]: f(1) - In[]: f(2) - \end{lstlisting} - \begin{itemize} - \item \texttt{def} is a keyword - \item \texttt{f} is the name of the function - \item \texttt{x} the parameter of the function - \item \texttt{return} is a keyword - \end{itemize} -\end{frame} \begin{frame}[fragile] \frametitle{IPython tip: Timing} @@ -482,7 +518,7 @@ In []: %timeit? \item \typ{\%time}: less accurate, one measurement \end{itemize} -\inctime{15} +\inctime{10} \end{frame} @@ -496,19 +532,41 @@ In []: %timeit? \end{frame} \begin{frame}[fragile] + \frametitle{Solution} +\begin{lstlisting} +In []: t = linspace(0, 10, 100000) +In []: %timeit sqr(t) +In []: %timeit t*t +\end{lstlisting} + \inctime{5} +\end{frame} + +\begin{frame}[fragile] \frametitle{The \num\ module} - \begin{itemize} - \item Efficient, powerful array type - \item Abstracts out standard operations on arrays - \item Convenience functions - \item \typ{ipython -pylab} imports part of numpy - \item Without the Pylab mode do: - \end{itemize} - \begin{lstlisting} -In []: import numpy + \begin{itemize} + \item Efficient, powerful array type + \item Abstracts out standard operations on arrays + \item Convenience functions + \item \typ{ipython --pylab} imports part of numpy + \end{itemize} +\end{frame} +\begin{frame}[fragile] + \frametitle{Without Pylab} +\begin{lstlisting} In []: from numpy import * - \end{lstlisting} +In []: x = linspace(0, 1) +\end{lstlisting} + Note that we had done this ``import'' earlier! +\begin{lstlisting} +# Can also do this: +In []: import numpy +In []: x = numpy.linspace(0, 1) +# or +In []: import numpy as np +In []: x = np.linspace(0, 1) +\end{lstlisting} + Note the use of \typ{numpy.linspace} \end{frame} \begin{frame} @@ -532,8 +590,8 @@ In []: from numpy import * In []: a = array([1,2,3,4]) In []: b = array([2,3,4,5]) -In []: print a[0], a[-1] -1, 4 +In []: print(a[0], a[-1]) +(1, 4) In []: a[0] = -1 In []: a[0] = 1 @@ -551,7 +609,11 @@ Out[]: array([2, 6, 12, 20]) In []: a/b Out[]: array([0, 0, 0, 0]) \end{lstlisting} -Operations are elementwise, types matter. + \begin{itemize} + \item Operations are \alert{element-wise} + \item Types matter + \end{itemize} + \inctime{10} \end{frame} \begin{frame}[fragile] @@ -573,8 +635,8 @@ In []: x *= 2*pi/10 In []: y = sin(x) In []: y = cos(x) In []: x[0] = -1 -In []: print x[0], x[-1] --1.0 10.0 +In []: print(x[0], x[-1]) +(-1.0, 10.0) \end{lstlisting} \end{frame} @@ -613,29 +675,58 @@ In []: a[1] # The second row array([10,11,12,-1]) In []: a[1] = 0 # Entire row to zero. \end{lstlisting} - +\inctime{10} \end{frame} -\begin{frame}[fragile] +\begin{frame}[plain,fragile] \frametitle{Slicing arrays} \vspace*{-0.2in} \begin{lstlisting} In []: a = array([[1,2,3], [4,5,6], ...: [7,8,9]]) In []: a[0,1:3] +\end{lstlisting} + \pause + \vspace*{-0.1in} +\begin{lstlisting} Out[]: array([2, 3]) + In []: a[1:,1:] +\end{lstlisting} + \pause + \vspace*{-0.1in} +\begin{lstlisting} Out[]: array([[5, 6], - [8, 9]]) + [8, 9]]) + In []: a[:,2] +\end{lstlisting} + \pause + \vspace*{-0.1in} +\begin{lstlisting} Out[]: array([3, 6, 9]) +\end{lstlisting} +\end{frame} + +\begin{frame}[plain,fragile] + \frametitle{Slicing arrays ...} + \vspace*{-0.2in} +\begin{lstlisting} +In []: a = array([[1,2,3], [4,5,6], + ...: [7,8,9]]) + In []: a[0::2,0::2] # Striding... +\end{lstlisting} + \pause + \vspace*{-0.1in} +\begin{lstlisting} Out[]: array([[1, 3], [7, 9]]) # Slices refer to the same memory! \end{lstlisting} \end{frame} + \begin{frame}[fragile] \frametitle{Array creation functions} \begin{itemize} @@ -650,6 +741,7 @@ Out[]: array([[1, 3], May pass an optional \typ{dtype=} keyword argument For more dtypes see: \typ{numpy.typeDict} + \end{frame} \begin{frame}[fragile] @@ -657,16 +749,19 @@ Out[]: array([[1, 3], \vspace*{-0.25in} \begin{lstlisting} In []: a = array([1,2,3], dtype=float) +In []: ones_like(a) +Out[]: array([ 1., 1., 1.]) + In []: ones( (2, 3) ) Out[]: array([[ 1., 1., 1.], [ 1., 1., 1.]]) + In []: identity(3) Out[]: array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) -In []: ones_like(a) -Out[]: array([ 1., 1., 1., 1.]) \end{lstlisting} + \inctime{15} \end{frame} \begin{frame}[fragile] @@ -684,6 +779,12 @@ Out[]: array([ 1., 1., 1., 1.]) \item Inplace operators: \typ{a += b}, or \typ{add(a, b, a)} \alert{What happens if \typ{a} is \typ{int} and \typ{b} is \typ{float?}} + \end{itemize} +\end{frame} + +\begin{frame}[fragile] + \frametitle{Array math} + \begin{itemize} \item Logical operations: \typ{==, !=, <, >}, etc. \item \typ{sin(x), arcsin(x), sinh(x)}, \typ{exp(x), sqrt(x)} etc. @@ -710,7 +811,7 @@ In []: x.shape Out[]: (90,) \end{lstlisting} - \inctime{20} + \inctime{10} \end{frame} @@ -729,8 +830,9 @@ Out[]: (90,) \begin{frame}[fragile] \frametitle{Learn more} + \small \begin{itemize} - \item \url{http://wiki.scipy.org/Tentative_NumPy_Tutorial} + \item \url{https://docs.scipy.org/doc/numpy-dev/user/quickstart.html} \item \url{http://numpy.org} \end{itemize} \end{frame} @@ -744,7 +846,8 @@ Out[]: (90,) \item Array creation, dtypes \item Math \item \typ{loadtxt} - \end{itemize} + \end{itemize} + \inctime{5} \end{frame} \begin{frame}[fragile] @@ -787,16 +890,18 @@ In []: !ls do: \begin{lstlisting} In []: ? +In []: %cd? \end{lstlisting} \end{frame} \begin{frame}[fragile] - \frametitle{Exercise} - \begin{itemize} - \item Plot L versus T square with dots - \item No line connecting points - \end{itemize} + \frametitle{Exercise} + \begin{itemize} + \item Plot L versus T square with dots + \item No line connecting points + \end{itemize} + \inctime{10} \end{frame} \begin{frame}[fragile] @@ -842,7 +947,7 @@ Out[]: 0.25979158313283879 \item Introduction to \num\ arrays \end{itemize} -\inctime{10} +\inctime{5} \end{frame} \end{document} |