"""

Bioinformatics Algorithms 1 (2015) Coursera Class. Interactive Text Code Challenge

in Chapter 2, Section 2.4 ("Some Hidden Messages Are More Elusive Than Others").

This is the approximate pattern matching problem. Given a text, a pattern, and a

Hamming distance d, find all patterns within Hamming distance d of pattern in the text.

Input: Strings pattern and text, as well as integer d (the Hamming distance).

Output: All starting positions where pattern appears as a substring of text with at

most d mismatches.

"""

# Calculate all patterns within a Hamming Distance d from pattern.

pats_to_search = gen_patterns_within_distance_d(pattern, d)

# # Search for each of the above patterns in text

# for pat in pats_to_search:

# patternindex.pat_index(pat, text)

# Iterate through the text, and compare the current pattern to see if it is an element

# of the pats_to_search.

indxlist = []

for i in range(len(text)):

if text[i:i+len(pattern)] in pats_to_search:

indxlist.append(i)

"""

This helper function is used to generate all patterns within a Hamming distance d

of the pattern given by pattern.

Input: A string, pattern, and an integer, d (the Hamming distance).

Output: A list of all patterns within Hamming distance d of the given pattern.

"""

alphabet = ['A', 'C', 'G', 'T']

# patlist = [ pattern ]

# for indx in range(len(pattern)):

# for nt in alphabet:

# newpat = pattern[:i] + nt + pattern[i+1:]

# patlist.append(newpat)

allpatlist = gen_list_of_patterns(alphabet, len(pattern))

patlist = []

for val in allpatlist:

hamdist = hamming.hamming(pattern, val)

if hamdist <= d:

patlist.append(val)

#print(patlist)

"""

Helper function used to generate all possible patterns of a size (given by k)

using the alphabet (given by alphabet).

"""

alphalen = len(alphabet)

mylist = []

temp = ''

for i in range(alphalen**k):

for j in range(k):

temp += alphabet[int(i/(4**j)) % alphalen]

mylist.append(temp)

temp = ''