Lab 8, CSC 203




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Lab 8, CSC 203

This lab explores streams through the implementation of a program to read in a set of numbers
(representing points – specifically an image composed primarily of noise). Through implementing various
computational operations on these points they are transformed into a more coherent image (composed of
Streams are a useful tool to process collections. In particular, with a collection of data with a sequence of
commands/operations, streams allow for efficient processing (and can even leverage multicore
parallelism, which is beyond the scope of this lab, but good to know about). You can read more about
them here:
Also check the examples we did in class.
• To implement an entire program on your own which reads and writes data from and to a file
• To implement streams in order to compute various operations, including filter, map and collect.
Step 1
Start from one of your past labs or projects (consider lab 2 as a good option) and write code to read in the
data from the specified input file (see below).
This data represents a large number of points in space with an x, y and z value. Consider modifying your
existing Point class to use with this program, it may require adding more than just a data member for Z
(see tasks below). Your code should represent all the points in a collection that can be processed as a
The input data is stored in a file named “positions.txt” and is in the following format:
564.0, 414.0, 1
564.2765, 414.44946, 1
564.5011, 414.95673, 1
564.6649, 415.51572, 1
564.7596, 416.1191, 1
564.7776, 416.75833, 1

You can get a copy of these input points from Canvas. Write your code to take in the name of this input
file as a command line argument. In IntelliJ you need to add file name in Edit Configuration from Run
as argument.
To start with, just test reading in the points and writing them back out to a different file named
“drawMe.txt”. Consider using diff on the two files to make sure that you can read in the data and write
it out unchanged. You can also use the helper program provided, which displays points, using
Processing. The helper program can be found on Canvas and needs to be compiled with setting
Processing appropriately to your lab (similar to lab7).
For your reference, to start with the point file should look like:
Step 2
After you have confirmed that you can read and write the points unchanged, work through the following
operations using streams
• Remove all points that have a z value > 2.0.
• Scale down all the points by 0.5
• Translate all the points by {-150, -37}
Be sure that you use collect, filer, map of stream to complete these tasks. Now be sure to re-run
the helper program to see the hidden image!
Submit your files in the Canvas.
1) Pass file name as argument 10 Points
2) Remove point > 2 10
3) Scale 10
4) Translate 10
5) Using Stream 20
6) Discover hidden image 20
7) Submission 30