The goal of this assignment is to work with files, iterators, strings, and string formatting in Python.
You will be doing your work in a Jupyter notebook for this
assignment. You may choose to work on this assignment on a hosted
environment (e.g. tiger)
or on your own local installation of Jupyter and Python. You should use
Python 3.12 for your work. (Older versions may work, but your code will
be checked with Python 3.12.) To use tiger, use the credentials you
received. If you work remotely, make sure to download the .ipynb file to
turn in. If you choose to work locally, Anaconda or miniforge are
probably the easiest ways to install and manage Python. If you work
locally, you may launch Jupyter Lab either from the Navigator
application (anaconda) or via the command-line as
jupyter-lab or jupyter lab.
In this assignment, we will be working with texts from Project Gutenberg which is a library of eBooks which includes a lot of literature for which U.S. copyright has expired. In this assignment, you will process these files, count vowels and consonants, and convert some of the text. We will use not only English texts but those from other languages to demonstrate the benefits of Unicode encoding.
A template notebook is provided with a cell to help download the files given a URL. You will read this data, do some calculations, and write a new output file in a similar format. We will be working with three books, but write your code so that it can be adapted to work with other texts as well. The three texts are:
The assignment is due at 11:59pm on Wednesday, October 16.
You should submit the completed notebook file required for this
assignment on Blackboard. The
filename of the notebook should be a4.ipynb. You
should not turn in the output files (Part 4) as your
notebook should contain the code to create it.
Please make sure to follow instructions to receive full credit. Use a markdown cell to Label each part of the assignment with the number of the section you are completing. You may put the code for each part into one or more cells. Do not use external libraries for reading, parsing, or writing the data files.
The first cell of your notebook should be a markdown cell with a line for your name and a line for your Z-ID. If you wish to add other information (the assignment name, a description of the assignment), you may do so after these two lines.
Use the download_text function in the provided template notebook to download files. Then, write
a function that given the filename, opens the local file and reads the
downloaded file, returning the processed lines as a list. Project
Gutenberg includes metadata at the beginning of the text and license
information at the end of the text. The actual text is contained between
the lines
*** START OF THE PROJECT GUTENBERG EBOOK <TITLE> ***
and
*** END OF THE PROJECT GUTENBERG EBOOK <TITLE> ***
where <TITLE> is the name of the book. Read (and
discard) lines until you hit the START tag. Then, read in all
the lines up to but not including the END tag into a list of
strings. Remove any leading and trailing whitespace from the lines. You
will need to figure out how to find the START and END
tags and which lines should be included.
strip will be useful to remove whitespace.Next, write a function to loop through the list of lines to count the vowels and consonants in the texts. For our purposes, we will count any of the letters in the string “aeiouyàâæèéêëîïôùûüÿœ” as vowels. A consonant is any alphabetic character that is not a vowel. You will need to convert the lines to lowercase before checking whether the individual characters are vowels or consonants or something else, like punctuation. Return both the number of vowels and the number of consonants
is* functions in the string class to check
for letters.Next, write a function that creates a new version of the text where the vowels with diacritics (àâèéêëîïôùûüÿ) are converted to versions without them (e.g. á becomes a, î becomes i). In addition, the digraphs æ and œ should be expanded to ae and oe, respectively. Also, make sure to include the uppercase variants of all these conversions, but use a programmatic way to do this from the lowercase conversions. Your output should be a new list with the converted lines of text. You may not use a series of if or elif statements for this task.
Write the converted text (from Part 3) to a file. In addition, put
your name at the beginning of the file as the converter, then a line
marking the start of the text (*** START OF THE EBOOK ***),
the converted text, and then a line marking the end of the text
(*** END OF THE EBOOK ***). Finally, use your function from
Part 2 to compute the number of vowels and consonants before
and after conversion. Compute the change as the value
after minus the value before divided by the value before. Multiply by
100 to obtain a percentage and print with a + or - sign depending on the
change. (Use the format mini-language for this.) The statistics need to
be right-aligned so your output should look like:
From Project Gutenberg (www.gutenberg.org)
Converted by: Iamma Huskie
*** START OF THE EBOOK ***
...
*** END OF THE EBOOK ***
Number of vowels before: 360122
Number of vowels after: 360385
Conversion Change: +0.07%
Number of consonants before: 426580
Number of consonants after: 426580
Conversion Change: +0.00%
The name of the file should be the original filename with
-converted added. For example,
2650-0-converted.txt.
print function calls to write a few data items to
stdout first, then when you are satisfied the format is
correct, write all of the data items to a file.print
function.with statement to make sure all data is written
to the file (or make sure to call close).w flag to open to be
able to write to a file.!cat command:
!cat 2650-0-converted.txtNow, we wish to test the differences between two texts, specifically to investigate the hypothesis that one text has a greater percent of vowels than the other. For this part, write a single function that takes two urls and uses the functions written in Parts 1-3 to compute the contingency table showing the number of vowels and consonants for each text with their totals. Make sure to do the conversion in Part 3 as well. Right-align the results. Then, compute the chi-squared test for the vowels and consonants and test it on Pride and Prejudice vs. Du côté de chez Swann (two different languages) and Pride and Prejudice vs. Sense and Sensibility (same language, same author). Report the p-value. Use the function scipy.stats.chi2_contingency to do this calculation. Note that it does not need the totals, only the upper-left 2x2 square. Sample results (Updated 2024-10-16):
>>> compare_texts('2650-0.txt', '1342-0.txt')
# of Vowels # of Consonants Total
2650-0.txt 360385 426580 786965
1342-0.txt 227573 336368 563941
Total 587958 762948 1350906
p-value: 0.0
>>> compare_texts('161-0.txt', '1342-0.txt')
# of Vowels # of Consonants Total
161-0.txt 212215 313598 525813
1342-0.txt 227573 336368 563941
Total 439788 649966 1089754
p-value: 0.9560997372450746*** lines
in Part 4 (5 pts)