Minor changes to the script of Least_square_fit.

1 files changed, 71 insertions, 2 deletions

diff --git a/least_square_fit/script.rst b/least_square_fit/script.rst
index 4aa5630..850223a 100644
--- a/least_square_fit/script.rst
+++ b/least_square_fit/script.rst
@@ -20,32 +20,55 @@
 Script
 ------
 
+.. L1
+
 {{{ Show the  first slide containing title, name of the production
 team along with the logo of MHRD }}}
 
+.. R1
+
 Hello friends and welcome to the tutorial on 'Least Square Fit'.
 
+.. L2
+
 {{{ Show the slide containing objectives }}}
 
+.. R2
+
 At the end of this tutorial, you will be able to,
 
  1. Generate the least square fit line for a
     given set of points.
 
+.. L3
+
 {{{ Switch to the pre-requisite slide }}}
 
+.. R3
+
 Before beginning this tutorial,we would suggest you to complete the 
 tutorial on "Using plot interactively", "Loading data from files"
 and "Getting started with arrays".
 
 Let us start this tutorial with the help of an example.
 
+.. L4
+
 {{{ Show the slide containing exercise 1 }}}
 
-We have an input file generated from a simple pendulum experiment.
+.. R4
+
+Generate a least square fit line for l v/s t^2 using the data in the file
+'pendulum.txt'.
+
+.. L5
 
 {{{ Open the file 'pendulum.txt' and show }}}
 
+.. R5
+
+We have an input file generated from a simple pendulum experiment.
+
 It contains two columns of data. The first column is the length of the
 pendulum and the second is the corresponding time period of the pendulum.
 
@@ -54,6 +77,8 @@ its length, we shall plot l vs t^2 and verify this.
 
 To read the input file and parse the data, we are going to use the
 loadtxt function.Switch to the terminal.
+
+.. L6
 ::
  
     ipython -pylab
@@ -61,10 +86,14 @@ loadtxt function.Switch to the terminal.
     l
     t
 
+.. R6
+
 We can see that l and t are two sequences containing length and time values
 correspondingly.
 
 Let us first plot l vs t^2.
+
+.. L7
 ::
 
     tsq = t * t
@@ -72,6 +101,8 @@ Let us first plot l vs t^2.
 
 {{{ switch to the plot window }}}
 
+.. R7
+
 We can see that there is a visible linear trend, but we do not get a
 straight line connecting them. We shall, therefore, generate a least
 square fit line.
@@ -80,56 +111,87 @@ We will first generate the two matrices tsq and A.
 Then we will use the ``lstsq`` function to find the
 values of m and c.
 
+.. L8
+
 {{{ Switch to the terminal }}}
 
+.. R8
+
 let us now generate the A matrix with l values.
 We shall first generate a 2 x 90 matrix with the first row as l values and the
 second row as ones. Then take the transpose of it. Type
 
+.. L9
 ::
 
     inter_mat = array((l, ones_like(l)))
     inter_mat
 
+.. R9
+
 We see that we have intermediate matrix. Now we need the transpose. Type
+
+.. L10
 ::
 
     A = inter_mat.T
     A
 
+.. R10
+
 Now we have both the matrices A and tsq. We only need to use the ``lstsq``
 Type
+
+.. L11
 ::
 
     result = lstsq(A, tsq)
 
+.. R11
+
 The result is a sequence of values. The first item in this sequence,
-is the matrix p i.e., the values of m and c. Hence, 
+is the matrix p i.e., the values of m and c. 
+
+.. L12
 ::
 
     m, c = result[0]
     m
     c
 
+.. R12
+
 Now that we have m and c, we need to generate the fitted values of t^2. Type
+
+.. L13
 ::
 
     tsq_fit = m * l + c
     plot(l, tsq, 'bo')
     plot(l, tsq_fit, 'r')
 
+.. R13
+
 We get the least square fit of l vs t^2.
 
+.. L14
+
 {{{ Show summary slide }}}
 
+.. R14
+
 This brings us to the end of the tutorial.In this tutorial,
 we have learnt to,
 
  1. Generate a least square fit using matrices.
  #. Use the function ``lstsq()`` to generate a least square fit line.
 
+.. L15
+
 {{{Show self assessment questions slide}}}
 
+.. R15
+
 Here are some self assessment questions for you to solve
 
 1. What does ones_like([1, 2, 3]) produce
@@ -142,9 +204,12 @@ Here are some self assessment questions for you to solve
 2. The plot of ``u`` vs ``v`` is a bunch of scattered points that show a
     linear trend. How do you find the least square fit line of ``u`` vs ``v``.
 
+.. L16
 
 {{{solution of self assessment questions on slide}}}
 
+.. R16
+
 And the answers,
 
 1. The function ``ones_like([1, 2, 3])`` will generate 'array([1, 1, 1])'.
@@ -157,8 +222,12 @@ And the answers,
     m, c = result[0]
     lst_line = m * u + c
 
+.. L17
+
 {{{ Show the thank you slide }}}
 
+.. R17
+
 Hope you have enjoyed this tutorial and found it useful.
 Thank you!