def suffix_tree_edges(word):
'''Returns the edge subsrings associated with the suffix tree for the given word.'''
# Most of the work is done by the generalized suffix tree script (see scripts folder).
gst = GeneralizedSuffixTree(word)
# Get a list of all edge substrings from the generalized suffix tree.
edges = [gst.edge_substring(e) for e in gst.edges.values()]
# Return the edges in suffix tree format (i.e. want endings $0 to be $).
# Note: This is necessary because we're using a generalized suffix tree, which uses $0, $1, ..., $N
# as the out of alphabet suffixes in order to distinguish between word 0, word 1, ..., word N.
return [e[:-1] if '$' in e else e for e in edges]
def suffix_tree_edges(word):
'''Returns the edge subsrings associated with the suffix tree for the given word.'''
# Most of the work is done by the generalized suffix tree script (see scripts folder).
gst = GeneralizedSuffixTree(word)
# Get a list of all edge substrings from the generalized suffix tree.
edges = [gst.edge_substring(e) for e in gst.edges.values()]
# Return the edges in suffix tree format (i.e. want endings $0 to be $).
# Note: This is necessary because we're using a generalized suffix tree, which uses $0, $1, ..., $N
# as the out of alphabet suffixes in order to distinguish between word 0, word 1, ..., word N.
return [e[:-1] if '$' in e else e for e in edges]
def longest_common_substring(string_list):
'''Returns the longest common substring among all strings in string_list.'''
# Construct the generalized suffix tree for the input text.
gst = GeneralizedSuffixTree(string_list)
# Find all nodes that are traversed by all words in text, meaning that the substring up to that node is in all words in text.
candidate_nodes = filter(lambda i: len(gst.nodes[i].words) == len(string_list), xrange(len(gst.nodes)))
# Get the deepest node of from the candidate nodes, where depth corresponds to substring length.
deepest_node = max(candidate_nodes, key=lambda i: gst.node_depth(i))
# Return the substring corresponding to a traversal up to the deepest node.
return gst.node_substring(deepest_node)
def longest_repeat_substring(word, n):
'''Returns the longest substring that appears at least n times in the given word.'''
# Construct the suffix tree.
gst = GeneralizedSuffixTree(word)
# Find all nodes with at least n children.
# The number of children a node has tells us how many times is associated substring appears within the string.
candidate_nodes = filter(lambda i: len(gst.nodes[i].children) >= n, xrange(len(gst.nodes)))
# Get the longest substring that appears at least n times.
# Recall: node depth = proper length of substring, i.e. the length discounting the out of alphabet characters.
best_node = max(candidate_nodes, key=lambda i: gst.node_depth(i))
return gst.node_substring(best_node)
def longest_common_substring(string_list):
'''Returns the longest common substring among all strings in string_list.'''
# Construct the generalized suffix tree for the input text.
gst = GeneralizedSuffixTree(string_list)
# Find all nodes that are traversed by all words in text, meaning that the substring up to that node is in all words in text.
candidate_nodes = filter(
lambda i: len(gst.nodes[i].words) == len(string_list),
xrange(len(gst.nodes)))
# Get the deepest node of from the candidate nodes, where depth corresponds to substring length.
deepest_node = max(candidate_nodes, key=lambda i: gst.node_depth(i))
# Return the substring corresponding to a traversal up to the deepest node.
return gst.node_substring(deepest_node)
def longest_repeat_substring(word, n):
'''Returns the longest substring that appears at least n times in the given word.'''
# Construct the suffix tree.
gst = GeneralizedSuffixTree(word)
# Find all nodes with at least n children.
# The number of children a node has tells us how many times is associated substring appears within the string.
candidate_nodes = filter(lambda i: len(gst.nodes[i].children) >= n,
xrange(len(gst.nodes)))
# Get the longest substring that appears at least n times.
# Recall: node depth = proper length of substring, i.e. the length discounting the out of alphabet characters.
best_node = max(candidate_nodes, key=lambda i: gst.node_depth(i))
return gst.node_substring(best_node)
def shortest_nonshared_substring(string_list):
'''Returns the shortest nonshared substring unique to the first word in string_list.'''
# Construct the generalized suffix tree for the input text.
gst = GeneralizedSuffixTree(string_list)
# Find all nodes that are traversed only by the first word in text, meaning that the substring up to that node is only in the first word.
candidate_nodes = filter(lambda i: gst.nodes[i].words == {0}, xrange(len(gst.nodes)))
# Filter out all nodes corresponding to the out of alphabet character unique to first word, as these are trivally only traveresed by the first word.
# If the out of alphabet character is the only character on the edge, then its parent must be traversed by another word.
candidate_nodes = filter(lambda i: gst.edge_substring(gst.edges[gst.nodes[i].parent,i]) != '$0', candidate_nodes)
# To get the shortest substring, only take the first character of the last edge, hence the substring has length parent_length + 1.
shortest = min(candidate_nodes, key=lambda i: gst.node_depth(gst.nodes[i].parent)+1)
# Shortest nonshared substring is the substring up to the first character of the edge leading to the optimal node.
return gst.node_substring(gst.nodes[shortest].parent) + gst.edge_substring(gst.edges[gst.nodes[shortest].parent,shortest])[0]
def shortest_nonshared_substring(string_list):
'''Returns the shortest nonshared substring unique to the first word in string_list.'''
# Construct the generalized suffix tree for the input text.
gst = GeneralizedSuffixTree(string_list)
# Find all nodes that are traversed only by the first word in text, meaning that the substring up to that node is only in the first word.
candidate_nodes = filter(lambda i: gst.nodes[i].words == {0},
xrange(len(gst.nodes)))
# Filter out all nodes corresponding to the out of alphabet character unique to first word, as these are trivally only traveresed by the first word.
# If the out of alphabet character is the only character on the edge, then its parent must be traversed by another word.
candidate_nodes = filter(
lambda i: gst.edge_substring(gst.edges[gst.nodes[i].parent, i]) !=
'$0', candidate_nodes)
# To get the shortest substring, only take the first character of the last edge, hence the substring has length parent_length + 1.
shortest = min(candidate_nodes,
key=lambda i: gst.node_depth(gst.nodes[i].parent) + 1)
# Shortest nonshared substring is the substring up to the first character of the edge leading to the optimal node.
return gst.node_substring(gst.nodes[shortest].parent) + gst.edge_substring(
gst.edges[gst.nodes[shortest].parent, shortest])[0]