EN 600.639: Final Project with Samantha Siow, Michael Norris, Aaron Lerner, and Isac Lee

• numpy pip install numpy
• opencv (on mac, brew tap homebrew/science then brew install opencv)

Our code is in the fft directory

$ python cli.py

#algorithms that match a single genome to a single substring

fftmatch.naive_string_match_index(text, pattern)
fftmatch.fft_match_index_n_log_n(text, pattern)

Naive 1-D FFT-based match-index algorithm

fftmatch.fft_match_index_n_log_m(text, pattern) Most efficient 1-D FFT-based FFT-based match-index algorithm

boyermoore.boyer_moore_match_index(text, pattern)

Used to benchmark all of our algorithms with

#algorithms that match multiple genomes to a single substring

fftmatch.fft_match_index_n_sq_log_n(texts, pattern)

Similar to the n log n 1-D algorithm

fftmatch.fft_match_index_n_sq_log_n\_naive(texts, pattern)

This uses the 1-D algorithm on each text individually from a list of texts.

fftmatch.fft_match_index_n_sq_log_m(texts, pattern)

This breaks up the text into smaller chunks of size 2*len(pattern) and does a 2-D FFT on the text.

cvmatch.cv_match_index(texts, pattern)

This uses openCV's template-matching algorithm to solve the match index problem

cvmatch.cv_match_index_chunk(texts, pattern)

This uses openCV's template-matching algorithm on size 2*len(pattern) chunks

Run with:

sh run_analysis.sh

Writes results to the results folder. Performs analysis on the time performance of the algorithms according to text length, as well as the time performance of the nlogm algorithm by the chunk size.