Description
Introduction
The purpose of this assignment is to make sure you understand elementary plotting
concepts covered in class, which can be reviewed in the class notes (available at the
classes system). You will use matplotlib/python to produce plots, whose documentation is available at http://matplotlib.sourceforge.net/. The data for
the exercises of this assignment are in four files: stocks.dat (problem 1), actions-fall2007.dat (problem 2), microprocessors.dat (problem 3) and genes.dat (problem 4).
They are packed into a single zip file called: data.zip (http://vgc.poly.edu/
projects/gx5003-fall2014/week8/data/data.zip).
Problem 1 – Principles of plotting and connected symbols plot
The file stocks.dat has the stock quote at the start of each month from January 2006
to September 2008 for Apple Inc. (AAPL) and Microsoft Corporation (MSFT). Below
we present the first three lines and the last two lines of this file.
month , a p pl e , m i c r o s o f t
2 0 0 8 −0 9 , 1 4 0. 9 1 , 2 5. 1 6
2 0 0 8 −0 8 , 1 6 9. 5 3 , 2 7. 2 9
2 0 0 6 −0 2 , 6 8. 4 9 , 2 5. 9 2
2 0 0 6 −0 1 , 7 5. 5 1 , 2 7. 0 6
(a) Apply the principles of plotting described in class (see slides available at the
classes system) and in the class notes to generate a simple connected symbol plot
(see class notes) for all Apple’s stock quotes in the file stocks.dat. You should
submit: the code to create the plot as a python file called problem1a.py (that receives the input stocks.dat filename as a command-line parameter); a screenshot
of the plot called problem1a.png; and a text file named problem1a.txt with an
explanation of the plotting principles you used to make this a clear plot.
(b) Using the quote of January 2006 as a baseline, directly compare the progress
of Apple’s and Microsoft’s stock price by generating a plot using superposition
(both curves in the same plot). You should submit: the code to create the plot as
a python file called problem1b.py (that receives the input stocks.dat filename as a
command-line parameter); a screenshot of the plot called problem1b.png; and a
text file named problem1b.txt with the conclusions you can draw from this plot.
(c) Repeat item b, but now using juxtaposition: split the two curves (i.e. Apple’s
stock price relative to January 2006 and Microsoft’s stock price relative to January 2006) into two different plots. You should submit: the code to create the
plots as a python file called problem1c.py (that receives the input stocks.dat filename as a command-line parameter); a screenshot of the juxtaposed plots called
problem1c.png; and a text file named problem1c.txt describing which technique
(superpostion vs. juxtaposition) makes more sense for this data and why.
Problem 2 – Histogram and number of bins
During a Scientific Visualization Course at University of Utah we collected all the
assignments of the students into a data file for analysis. The file actions-fall-2007.dat
has all the timestamps of all the actions of all the students in all the assignments: a total
of 132131 actions. The first three lines of this file are:
tim e st am p
2007−09−15 2 1: 2 4: 5 6
2007−09−15 2 1: 2 5: 1 6
. . .
Create a histogram for the distribution of these timestamps and highlight the following due dates in the histogram:
Assignment Due Date
0 2007-09-18 12:00:00
1 2007-09-18 12:00:00
2 2007-10-04 12:00:00
3 2007-10-25 12:00:00
4 2007-11-27 12:00:00
5 2007-12-15 12:00:00
6 2007-12-11 12:00:00
You should submit: the code to create the plot as a python file called problem2.py
(that receives the input actions-fall-2007.dat filename as a command-line parameter).
Also submit text files with answers for the following:
(a) How did you select the bins for the histogram and why? (submit answers in file
problem2a.txt).
(b) What hypothesis can you make about the amount of work (i.e. number of actions)
for the different assignments just by looking to this histogram? (submit answers
in file problem2b.txt).
(c) What pattern can you observe for the amount of work (i.e. number of actions)
close to the deadlines? (submit answers in file problem2c.txt).
Problem 3 – Dot plots for labeled data
Each line of the file microprocessors.dat (except for the header line) has two quantitative values associated with a label. The quantitative values are “year of introduction”
and “number of transistors”, and the label is the name of a “microprocessor” (e.g. 286,
386, 486, Pentium 4). See the first three lines of this file:
P r o c e s s o r , Yea r o f I n t r o d u c t i o n , T r a n s i s t o r s
Pe nti um 4 p r o c e s s o r , 2 0 0 0 , 4 2 0 0 0 0 0 0
2 8 6 , 1 9 8 2 , 1 2 0 0 0 0
. . .
Generate two dot plots horizontally juxtaposed for these microprocessors: one for
“year of introduction” and the other for “number of transistors”. For “number of transistors” dot plot use log base 10 scale. You should submit: the code to create the plots as a
python file called problem3.py (that receives the input microprocessors.dat filename as
a command-line parameter); a screenshot of the juxtaposed plots called problem3.png.
Problem 4 – Correlation, scatterplots and regression
Let A, B, C, D be four genes. A scientist measured the activity (i.e. the expression) of
these genes in 100 different conditions. The results are given in file genes.dat. Here
are the first three lines of this file:
A, B , C ,D
0 . 6 3 6 2 4 4 , 0 . 2 3 9 4 3 0 , 0 . 7 4 5 6 5 0 , 0 . 9 0 0 1 9 8
0 . 3 4 2 9 7 4 , 0 . 8 0 0 6 7 6 , 0 . 3 7 5 3 9 9 , 0 . 4 5 7 8 1 8
. . .
(a) Generate a 4 x 4 matrix of scatter plots to understand correlations between the
four genes. Visually analyze the plot and rank the genes B, C, D in descending order of correlation to A. You should submit: the code to create the plot as
a python file called problem4a.py (that receives the input genes.dat filename as
a command-line parameter); a screenshot of the scattermatrix plot called problem4a.png.
(Extra) Now draw a linear best fit line in the plots of A with its most correlated gene, a
cubic best fit curve in the plots of A with its second most correlated gene and a
degree-5 polynomial best fit curve in the plots of A with its most uncorrelated
gene. You should submit: the code to create the plot as a python file called
problem4b.py (that receives the input genes.dat filename as a command-line parameter); a screenshot of the scattermatrix plot with the requested lines called
problem4b.png.
How to submit your assignment?
Your assignment should be submitted using the NYU Classes system. You should submit all the requested files in each problem in a zip file named NetID_assignment_8.zip,
where you should change NetID by your NYU Net ID.
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Grading
We should be able to reproduce your plots by running your scripts with the specified
parameters. The grading is going to be done based on your plots and justifications
for the choices you made. It is part of the assignment to understand (and research
if necessary) the concepts asked.
That is especially true for the extra points in this
assignment. Try to test your code before submitting: your script solves the problem
when the plot is created as requested.
References
• Matplotlib reference (installation and documentation): http://matplotlib.
org/
• Elementary plotting concepts: Check slides and notes in the classes system
Consult the Matplotlib documentation to find out how to create each plot. Googling
for example code for the requested plots is encouraged.
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