def metabolic_pathways_HTML_alg1(pathway0, pathway1): redirectOut() # graph creation graph0 = to_graph_from_dict(pathway0) graph1 = to_graph_from_dict(pathway1) bft0 = graph0.breadth_first_traversal() dft0 = graph0.depth_first_traversal() add_to_dictionary(bft0) add_to_dictionary(dft0) bft1 = graph1.breadth_first_traversal() dft1 = graph1.depth_first_traversal() add_to_dictionary(bft1) add_to_dictionary(dft1) # Full Detail Output print "<h4>Full Detail Output</h4>" print "<p>Breadth First Traversal (BFT)</p>" print "<p class='pathway'>Pathway 1: ", ' '.join(bft0), "</p>" print "<p class='pathway'>Pathway 2: ", ' '.join(bft1), "</p>" print '<br>' print "<p>Depth First Traversal (DFT)</p>" print "<p class='pathway'>Pathway 1: ", ' '.join(dft0), "</p>" print "<p class='pathway'>Pathway 2: ", ' '.join(dft1), "</p>" print "<br>" show_dictionary() # Low Detail Output print "<h4>Low Detail Output</h4>" print "<p>Breadth First Search (BFT)</p>" print "<p class='pathway'>Pathway 1: ", ' '.join(renamed_path(bft0)), "</p>" print "<p class='pathway'>Pathway 2: ", ' '.join(renamed_path(bft1)), "</p>" print "<br>" print "<p>Depth First Search (DFT)</p>" print "<p class='pathway'>Pathway 1: ", ' '.join(renamed_path(dft0)), "</p>" print "<p class='pathway'>Pathway 2: ", ' '.join(renamed_path(dft1)), "</p>" print "<h4>Alignment Algorithms</h4>" print "<p>Global Alignment (BFT):</p>", '\n'.join(["<p class='alignment'>" + str(i) + "</p>" for i in needleman_wunsch(renamed_path(bft0), renamed_path(bft1))]) print "<p>Global Alignment (DFT):</p>", '\n '.join(["<p class='alignment'>" + str(i) + "</p>" for i in needleman_wunsch(renamed_path(dft0), renamed_path(dft1))]) print "<br>" print "<p>Local Alignment (BFT):</p>", '\n'.join(["<p class='alignment'>" + str(i) + "</p>" for i in local_alignment(renamed_path(bft0), renamed_path(bft1))]) print "<p>Local Alignment (DFT):</p>", '\n'.join(["<p class='alignment'>" + str(i) + "</p>" for i in local_alignment(renamed_path(dft0), renamed_path(dft1))]) print "<br>" print "<p>Semiglobal Alignment (BFT):</p>", '\n'.join(["<p class='alignment'>" + str(i) + "</p>" for i in semiglobal_alignment(renamed_path(bft0), renamed_path(bft1))]) print "<p>Semiglobal Alignment (DFT):</p>", '\n'.join(["<p class='alignment'>" + str(i) + "</p>" for i in semiglobal_alignment(renamed_path(dft0), renamed_path(dft1))]) print "<br>" print "<h4>Equality Identified</h4>" identify_equality(graph0, graph1, LOW) print "<br>" print "<h4>Differences Identified (from Pathway 1 to Pathway 2)</h4>" identify_differences(graph0, graph1, FULL) print "<br>" print "<h4>Differences Identified (from Pathway 2 to Pathway 1)</h4>" identify_differences(graph1, graph0, FULL)
def metabolic_pathways_HTML_alg3(pathway0, pathway1):
# graph creation
graph0 = to_graph_from_dict(pathway0)
graph1 = to_graph_from_dict(pathway1)
print "Graph 1"
for n,i in zip(range(len(graph0.get_nodes())),graph0.get_nodes()):
print n," - ",i.get_value()
start0 = input("Start Node Graph 1: ")
end0 = input("End Node Graph 1: ")
print ""
print "Graph 2"
for n,i in zip(range(len(graph1.get_nodes())),graph1.get_nodes()):
print n," - ",i.get_value()
start1 = (input("Start Node Graph 2: "))
end1 = (input("End Node Graph 2: "))
bft0 = graph0.breadth_first_traversal(start0,end0)
dft0 = graph0.depth_first_traversal(start0,end0)
add_to_dictionary(bft0)
add_to_dictionary(dft0)
bft1 = graph1.breadth_first_traversal(start1,end1)
dft1 = graph1.depth_first_traversal(start1,end1)
add_to_dictionary(bft1)
add_to_dictionary(dft1)
# Full Detail Output
redirectOut()
print "<h4>Full Detail Output</h4>"
print "<p>Breadth First Traversal (BFT)</p>"
print "<p class='pathway'>Pathway 1: ", ' '.join(bft0), "</p>"
print "<p class='pathway'>Pathway 2: ", ' '.join(bft1), "</p>"
print '<br>'
print "<p>Depth First Traversal (DFT)</p>"
print "<p class='pathway'>Pathway 1: ", ' '.join(dft0), "</p>"
print "<p class='pathway'>Pathway 2: ", ' '.join(dft1), "</p>"
print "<br>"
show_dictionary()
# Low Detail Output
print "<h4>Low Detail Output</h4>"
print "<p>Breadth First Search (BFT)</p>"
print "<p class='pathway'>Pathway 1: ", ' '.join(renamed_path(bft0)), "</p>"
print "<p class='pathway'>Pathway 2: ", ' '.join(renamed_path(bft1)), "</p>"
print "<br>"
print "<p>Depth First Search (DFT)</p>"
print "<p class='pathway'>Pathway 1: ", ' '.join(renamed_path(dft0)), "</p>"
print "<p class='pathway'>Pathway 2: ", ' '.join(renamed_path(dft1)), "</p>"
print "<h4>Alignment Algorithms</h4>"
print "<p>Global Alignment (BFT):</p>", '\n'.join(["<p class='alignment'>" + str(i) + "</p>" for i in needleman_wunsch(renamed_path(bft0), renamed_path(bft1))])
print "<p>Global Alignment (DFT):</p>", '\n '.join(["<p class='alignment'>" + str(i) + "</p>" for i in needleman_wunsch(renamed_path(dft0), renamed_path(dft1))])
print "<br>"
print "<p>Local Alignment (BFT):</p>", '\n'.join(["<p class='alignment'>" + str(i) + "</p>" for i in local_alignment(renamed_path(bft0), renamed_path(bft1))])
print "<p>Local Alignment (DFT):</p>", '\n'.join(["<p class='alignment'>" + str(i) + "</p>" for i in local_alignment(renamed_path(dft0), renamed_path(dft1))])
print "<br>"
print "<p>Semiglobal Alignment (BFT):</p>", '\n'.join(["<p class='alignment'>" + str(i) + "</p>" for i in semiglobal_alignment(renamed_path(bft0), renamed_path(bft1))])
print "<p>Semiglobal Alignment (DFT):</p>", '\n'.join(["<p class='alignment'>" + str(i) + "</p>" for i in semiglobal_alignment(renamed_path(dft0), renamed_path(dft1))])
print "<br>"
print "<h4>Equality Identified</h4>"
identify_equality(graph0, graph1, LOW)
print "<br>"
print "<h4>Differences Identified (from Pathway 1 to Pathway 2)</h4>"
identify_differences(graph0, graph1, FULL)
print "<br>"
print "<h4>Differences Identified (from Pathway 2 to Pathway 1)</h4>"
identify_differences(graph1, graph0, FULL)
s_list = []
s_count = get_input_int('How many sequences will you align?')
for idx in range(s_count):
next_seq = input(f'Input sequence #{idx+1}')
s_list.append(next_seq)
select_center = get_input_boolean('Do you want to input the center star sequence? [Y/N]')
if select_center:
center_star_seq = input('Input the center star sequence')
center_star_st1s = []
center_star_st2s = []
for seq in s_list:
if seq == center_star_seq:
continue
V, paths = needleman_wunsch(center_star_seq, seq, ins_cost, del_cost, match_cost, mismatch_cost)
st1, st2, score = reconstruct(center_star_seq, seq, V, paths)
center_star_st1s.append(st1)
center_star_st2s.append(st2)
else:
print("Finding center star...")
start_time_total = timer()
best_d = np.inf
center_star_seq = ''
center_star_st1s = []
center_star_st2s = []
for idx1, seq1 in enumerate(s_list):
d_sum = 0
st1_list = []
st2_list = []
for idx2, seq2 in enumerate(s_list):
def metabolic_pathways_HTML_alg15(pathway0, pathway1):
global end0, end1, n
# graph creation
g1 = generate_graph(pathway0)
g2 = generate_graph(pathway1)
graph0 = to_graph_from_dict(pathway0)
graph1 = to_graph_from_dict(pathway1)
if (not params):
print("Graph 1")
for n, i in zip(range(len(graph0.get_nodes())), graph0.get_nodes()):
print(n, " - ", i.get_value(), "->",
[node.get_value() for node in i.get_edges()])
end0 = input("End Node Graph 1: ")
print("")
print("Graph 2")
for n, i in zip(range(len(graph1.get_nodes())), graph1.get_nodes()):
print(n, " - ", i.get_value(), "->",
[node.get_value() for node in i.get_edges()])
end1 = (input("End Node Graph 2: "))
n = (input("Maximum cycles: "))
paths0 = []
for node in graph0:
if (node.get_value() != graph0[end0].get_value()):
paths0 += graph0.get_cyclic_paths(node, graph0[end0], [], n)
paths1 = []
for node1 in graph1:
if (node1.get_value() != graph1[end1].get_value()):
paths1 += graph1.get_cyclic_paths(node1, graph1[end1], [], n)
if (len(paths0) == 0):
print("Not avaliable paths for graph 0")
return
if (len(paths1) == 0):
print("Not avaliable paths for graph 1")
return
"""
print("Paths Graph 0:")
for n,i in zip(range(len(paths0)),paths0):
out = ""
for node in i:
out+=str(node.get_value())+"->"
out = out[0:-2]
print(n," - ",out)
selected_Path0 = input("Select Path of graph 0: ")
print("")
print("Paths Graph 1:")
for n,i in zip(range(len(paths1)),paths1):
out = ""
for node in i:
out+=str(node.get_value())+"->"
out = out[0:-2]
print(n," - ",out)
selected_Path1 = input("Select Path of graph 1: ")
paths0 = [paths0[selected_Path0]]
paths1 = [paths1[selected_Path1]]"""
else:
paths0 = graph0.get_cyclic_paths(graph0[start0], graph0[end0], [], n)
paths1 = graph1.get_cyclic_paths(graph1[start1], graph1[end1], [], n)
redirectOut(5)
print("<h3>Algorithm 1.5</h3>")
print("Paths Graph 1:<br>")
for n, i in zip(range(len(paths0)), paths0):
out = ""
for node in i:
out += str(node.get_value()) + "->"
out = out[0:-2]
print("".join([str(1), ".", str(n), " - ", out]))
print("<br>")
print("<br>")
print("Paths Graph 2:<br>")
for n, i in zip(range(len(paths1)), paths1):
out = ""
for node in i:
out += str(node.get_value()) + "->"
out = out[0:-2]
print("".join([str(2), ".", str(n), " - ", out]))
print("<br>")
print("<h3>G1 ------------------------------------------G2</h3>")
print('<img src="data:image/png;base64,', g1, '" alt="Red dot" />')
print('<img src="data:image/png;base64,', g2, '" alt="Red dot" />')
maxglobNeed = [[-100]]
maxlocalAlg = [[-100]]
maxSemiglobal = [[-100]]
for p1 in range(len(paths0)):
path0 = to_graph_from_dict(path2Dic(paths0[p1]))
for p2 in range(len(paths1)):
path1 = to_graph_from_dict(path2Dic(paths1[p2]))
reset_dictionary()
bft0 = path0.breadth_first_traversal()
bft1 = path1.breadth_first_traversal()
add_to_dictionary(bft0)
add_to_dictionary(bft1)
# Full Detail Output
pathStr1 = "<p class='pathway'>Pathway 1." + str(
p1) + ": " + ' '.join(bft0) + "</p>"
pathStr2 = "<p class='pathway'>Pathway 2" + "." + str(
p2) + ": " + ' '.join(bft1) + "</p>"
print("<h4>Full Detail Output - Algorithm 1.4</h4>")
print(pathStr1)
print(pathStr2)
print("<br>")
show_dictionary()
# Low Detail Output
print("<h4>Alignment Algorithms</h4>")
globNeed = needleman_wunsch(renamed_path(bft0), renamed_path(bft1))
localAlg = local_alignment(renamed_path(bft0), renamed_path(bft1))
SemilocalAlg = semiglobal_alignment(renamed_path(bft0),
renamed_path(bft1))
if (globNeed[0] > maxglobNeed[0][0]):
maxglobNeed = [globNeed, pathStr1, pathStr2]
if (localAlg[0] > maxlocalAlg[0][0]):
maxlocalAlg = [localAlg, pathStr1, pathStr2]
if (SemilocalAlg[0] > maxSemiglobal[0][0]):
maxSemiglobal = [SemilocalAlg, pathStr1, pathStr2]
print(
"<p>Global Alignment (BFT): ",
globNeed[0] if globNeed[0] <= 0 else "+" + str(globNeed[0]),
"<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" +
str(i) + "</p>" for i in globNeed[1:]
]))
print("<br>")
print(
"<p>Local Alignment (BFT): ",
localAlg[0] if localAlg[0] <= 0 else "+" + str(localAlg[0]),
"<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" +
str(i) + "</p>" for i in localAlg[1:]
]))
print("<br>")
print(
"<p>Semiglobal Alignment (BFT): ",
SemilocalAlg[0] if SemilocalAlg[0] <= 0 else "+" +
str(SemilocalAlg[0]), "<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" +
str(i) + "</p>" for i in SemilocalAlg[1:]
]))
print("<br>")
print("<h4>MAX Global Alignment</h4>")
print(maxglobNeed[1])
print(maxglobNeed[2])
print(
"<p>Global Alignment (BFT): ",
maxglobNeed[0][0] if maxglobNeed[0][0] <= 0 else "+" +
str(maxglobNeed[0][0]), "<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in maxglobNeed[0][1:]
]))
print("<br>")
print("<h4>MAX Local Alignment</h4>")
print(maxlocalAlg[1])
print(maxlocalAlg[2])
print(
"<p>Local Alignment (BFT): ",
maxlocalAlg[0][0] if maxlocalAlg[0][0] <= 0 else "+" +
str(maxlocalAlg[0][0]), "<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in maxlocalAlg[0][1:]
]))
print("<br>")
print("<h4>MAX Semiglobal Alignment</h4>")
print(maxSemiglobal[1])
print(maxSemiglobal[2])
print(
"<p>Semiglobal Alignment (BFT): ",
maxSemiglobal[0][0] if maxSemiglobal[0][0] <= 0 else "+" +
str(maxSemiglobal[0][0]), "<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in maxSemiglobal[0][1:]
]))
print("<br>")
def metabolic_pathways_HTML_alg13(pathway0, pathway1):
# graph creation
g1 = generate_graph(pathway0)
g2 = generate_graph(pathway1)
graph0 = to_graph_from_dict(pathway0)
graph1 = to_graph_from_dict(pathway1)
print("Graph 1")
for n, i in zip(range(len(graph0.get_nodes())), graph0.get_nodes()):
print(n, " - ", i.get_value(), " -> ",
[node.get_value() for node in i.get_edges()])
start0 = input("Start Node Graph 1: ")
end0 = input("End Node Graph 1: ")
print("")
print("Graph 2")
for n, i in zip(range(len(graph1.get_nodes())), graph1.get_nodes()):
print(n, " - ", i.get_value(), " -> ",
[node.get_value() for node in i.get_edges()])
start1 = (input("Start Node Graph 2: "))
end1 = (input("End Node Graph 2: "))
bft0 = graph0.breadth_first_traversal(start0, end0)
dft0 = graph0.depth_first_traversal(start0, end0)
add_to_dictionary(bft0)
add_to_dictionary(dft0)
bft1 = graph1.breadth_first_traversal(start1, end1)
dft1 = graph1.depth_first_traversal(start1, end1)
add_to_dictionary(bft1)
add_to_dictionary(dft1)
# Low Detail Output
globNeed = [
needleman_wunsch(renamed_path(bft0), renamed_path(bft1)),
needleman_wunsch(renamed_path(dft0), renamed_path(dft1))
]
localAlg = [
local_alignment(renamed_path(bft0), renamed_path(bft1)),
local_alignment(renamed_path(dft0), renamed_path(dft1))
]
SemilocalAlg = [
semiglobal_alignment(renamed_path(bft0), renamed_path(bft1)),
semiglobal_alignment(renamed_path(dft0), renamed_path(dft1))
]
# Full Detail Output
redirectOut(3)
print("<h4>Alignment Algorithms 1.3</h4>")
print("<h4>Full Detail Output</h4>")
print("<p>Breadth First Traversal (BFT)</p>")
print("<p class='pathway'>Pathway 1: ", ' '.join(bft0), "</p>")
print("<p class='pathway'>Pathway 2: ", ' '.join(bft1), "</p>")
print('<br>')
print("<p>Depth First Traversal (DFT)</p>")
print("<p class='pathway'>Pathway 1: ", ' '.join(dft0), "</p>")
print("<p class='pathway'>Pathway 2: ", ' '.join(dft1), "</p>")
print("<br>")
print("<h3>G1 ------------------------------------------G2</h3>")
print('<img src="data:image/png;base64,', g1, '" alt="Red dot" />')
print('<img src="data:image/png;base64,', g2, '" alt="Red dot" />')
show_dictionary()
# Low Detail Output
print("<h4>Low Detail Output</h4>")
print("<p>Breadth First Search (BFT)</p>")
print("<p class='pathway'>Pathway 1: ", ' '.join(renamed_path(bft0)),
"</p>")
print("<p class='pathway'>Pathway 2: ", ' '.join(renamed_path(bft1)),
"</p>")
print("<br>")
print("<p>Depth First Search (DFT)</p>")
print("<p class='pathway'>Pathway 1: ", ' '.join(renamed_path(dft0)),
"</p>")
print("<p class='pathway'>Pathway 2: ", ' '.join(renamed_path(dft1)),
"</p>")
print("<h4>Alignment Algorithms</h4>")
print(
"<p>Global Alignment (BFT): ",
globNeed[0][0] if globNeed[0][0] <= 0 else "+" + str(globNeed[0][0]),
"<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in globNeed[0][1:]
]))
print(
"<p>Global Alignment (DFT): ",
globNeed[1][0] if globNeed[1][0] <= 0 else "+" + str(globNeed[1][0]),
"<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in globNeed[0][1:]
]))
print("<br>")
print(
"<p>Local Alignment (BFT): ",
localAlg[0][0] if localAlg[0][0] <= 0 else "+" + str(localAlg[0][0]),
"<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in localAlg[0][1:]
]))
print(
"<p>Local Alignment (DFT): ",
localAlg[0][0] if localAlg[0][0] <= 0 else "+" + str(localAlg[0][0]),
"<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in localAlg[0][1:]
]))
print("<br>")
print(
"<p>Semiglobal Alignment (DFT): ",
SemilocalAlg[0][0] if SemilocalAlg[0][0] <= 0 else "+" +
str(SemilocalAlg[0][0]), "<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in SemilocalAlg[0][1:]
]))
print(
"<p>Semiglobal Alignment (DFT): ",
SemilocalAlg[0][0] if SemilocalAlg[0][0] <= 0 else "+" +
str(SemilocalAlg[0][0]), "<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in SemilocalAlg[0][1:]
]))
print("<br>")
print("<h4>Equality Identified</h4>")
identify_equality(graph0, graph1, LOW)
print("<br>")
print("<h4>Differences Identified (from Pathway 1 to Pathway 2)</h4>")
identify_differences(graph0, graph1, FULL)
print("<br>")
print("<h4>Differences Identified (from Pathway 2 to Pathway 1)</h4>")
identify_differences(graph1, graph0, FULL)
def metabolic_pathways_HTML_alg1(pathway0, pathway1):
redirectOut(1)
# graph creation
g1 = generate_graph(pathway0)
g2 = generate_graph(pathway1)
graph0 = to_graph_from_dict(pathway0)
graph1 = to_graph_from_dict(pathway1)
bft0 = graph0.breadth_first_traversal()
dft0 = graph0.depth_first_traversal()
add_to_dictionary(bft0)
add_to_dictionary(dft0)
bft1 = graph1.breadth_first_traversal()
dft1 = graph1.depth_first_traversal()
add_to_dictionary(bft1)
add_to_dictionary(dft1)
# Low Detail Output
globNeed = [
needleman_wunsch(renamed_path(bft0), renamed_path(bft1)),
needleman_wunsch(renamed_path(dft0), renamed_path(dft1))
]
localAlg = [
local_alignment(renamed_path(bft0), renamed_path(bft1)),
local_alignment(renamed_path(dft0), renamed_path(dft1))
]
SemilocalAlg = [
semiglobal_alignment(renamed_path(bft0), renamed_path(bft1)),
semiglobal_alignment(renamed_path(dft0), renamed_path(dft1))
]
redirectOut(1)
print("<h4>Alignment Algorithms 1</h4>")
print("<h4>Full Detail Output</h4>")
print("<p>Breadth First Traversal (BFT)</p>")
print("<p class='pathway'>Pathway 1: ", ' '.join(bft0), "</p>")
print("<p class='pathway'>Pathway 2: ", ' '.join(bft1), "</p>")
print('<br>')
print("<p>Depth First Traversal (DFT)</p>")
print("<p class='pathway'>Pathway 1: ", ' '.join(dft0), "</p>")
print("<p class='pathway'>Pathway 2: ", ' '.join(dft1), "</p>")
print("<br>")
print("<h3>G1 ------------------------------------------G2</h3>")
print('<img src="data:image/png;base64,', g1, '" alt="Red dot" />')
print('<img src="data:image/png;base64,', g2, '" alt="Red dot" />')
show_dictionary()
# Low Detail Output
print("<h4>Low Detail Output</h4>")
print("<p>Breadth First Search (BFT)</p>")
print("<p class='pathway'>Pathway 1: ", ' '.join(renamed_path(bft0)),
"</p>")
print("<p class='pathway'>Pathway 2: ", ' '.join(renamed_path(bft1)),
"</p>")
print("<br>")
print("<p>Depth First Search (DFT)</p>")
print("<p class='pathway'>Pathway 1: ", ' '.join(renamed_path(dft0)),
"</p>")
print("<p class='pathway'>Pathway 2: ", ' '.join(renamed_path(dft1)),
"</p>")
print("<h4>Alignment Algorithms</h4>")
print(
"<p>Global Alignment (BFT): ",
globNeed[0][0] if globNeed[0][0] <= 0 else "+" + str(globNeed[0][0]),
"<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in globNeed[0][1:]
]))
print(
"<p>Global Alignment (DFT): ",
globNeed[1][0] if globNeed[1][0] <= 0 else "+" + str(globNeed[1][0]),
"<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in globNeed[0][1:]
]))
print("<br>")
print(
"<p>Local Alignment (BFT): ",
localAlg[0][0] if localAlg[0][0] <= 0 else "+" + str(localAlg[0][0]),
"<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in localAlg[0][1:]
]))
print(
"<p>Local Alignment (DFT): ",
localAlg[0][0] if localAlg[0][0] <= 0 else "+" + str(localAlg[0][0]),
"<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in localAlg[0][1:]
]))
print("<br>")
print(
"<p>Semiglobal Alignment (DFT): ",
SemilocalAlg[0][0] if SemilocalAlg[0][0] <= 0 else "+" +
str(SemilocalAlg[0][0]), "<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in SemilocalAlg[0][1:]
]))
print(
"<p>Semiglobal Alignment (DFT): ",
SemilocalAlg[0][0] if SemilocalAlg[0][0] <= 0 else "+" +
str(SemilocalAlg[0][0]), "<br>", '\n'.join([
"<p class='alignment' style='font-family:Courier'>" + str(i) +
"</p>" for i in SemilocalAlg[0][1:]
]))
print("<br>")
print("<h4>Equality Identified</h4>")
identify_equality(graph0, graph1, LOW)
print("<br>")
print("<h4>Differences Identified (from Pathway 1 to Pathway 2)</h4>")
identify_differences(graph0, graph1, FULL)
print("<br>")
print("<h4>Differences Identified (from Pathway 2 to Pathway 1)</h4>")
identify_differences(graph1, graph0, FULL)
def metabolic_pathways_HTML_alg4(pathway0, pathway1):
# graph creation
graph0 = to_graph_from_dict(pathway0)
graph1 = to_graph_from_dict(pathway1)
print "Graph 1"
for n, i in zip(range(len(graph0.get_nodes())), graph0.get_nodes()):
print n, " - ", i.get_value()
start0 = input("Start Node Graph 1: ")
end0 = input("End Node Graph 1: ")
print ""
print "Graph 2"
for n, i in zip(range(len(graph1.get_nodes())), graph1.get_nodes()):
print n, " - ", i.get_value()
start1 = (input("Start Node Graph 2: "))
end1 = (input("End Node Graph 2: "))
n = int(input("Maximum cycles: "))
paths0 = graph0.get_cyclic_paths(graph0[start0], graph0[end0], [], n)
paths1 = graph1.get_cyclic_paths(graph1[start1], graph1[end1], [], n)
if (len(paths0) == 0):
print "Not avaliable paths for graph 0"
return
if (len(paths1) == 0):
print "Not avaliable paths for graph 1"
return
print "Paths Graph 0:"
for n, i in zip(range(len(paths0)), paths0):
out = ""
for node in i:
out += str(node.get_value()) + "->"
out = out[0:-2]
print n, " - ", out
selected_Path0 = input("Select Path of graph 0: ")
print ""
print "Paths Graph 1:"
for n, i in zip(range(len(paths1)), paths1):
out = ""
for node in i:
out += str(node.get_value()) + "->"
out = out[0:-2]
print n, " - ", out
selected_Path1 = input("Select Path of graph 1: ")
bft0 = graph0.breadth_first_traversal(selected_Path0)
dft0 = graph0.depth_first_traversal(selected_Path0)
add_to_dictionary(bft0)
add_to_dictionary(dft0)
bft1 = graph1.breadth_first_traversal(selected_Path1)
dft1 = graph1.depth_first_traversal(selected_Path1)
add_to_dictionary(bft1)
add_to_dictionary(dft1)
# Full Detail Output
redirectOut()
print "<h4>Full Detail Output</h4>"
print "<p>Breadth First Traversal (BFT)</p>"
print "<p class='pathway'>Pathway 1: ", ' '.join(bft0), "</p>"
print "<p class='pathway'>Pathway 2: ", ' '.join(bft1), "</p>"
print '<br>'
print "<p>Depth First Traversal (DFT)</p>"
print "<p class='pathway'>Pathway 1: ", ' '.join(dft0), "</p>"
print "<p class='pathway'>Pathway 2: ", ' '.join(dft1), "</p>"
print "<br>"
show_dictionary()
# Low Detail Output
print "<h4>Low Detail Output</h4>"
print "<p>Breadth First Search (BFT)</p>"
print "<p class='pathway'>Pathway 1: ", ' '.join(
renamed_path(bft0)), "</p>"
print "<p class='pathway'>Pathway 2: ", ' '.join(
renamed_path(bft1)), "</p>"
print "<br>"
print "<p>Depth First Search (DFT)</p>"
print "<p class='pathway'>Pathway 1: ", ' '.join(
renamed_path(dft0)), "</p>"
print "<p class='pathway'>Pathway 2: ", ' '.join(
renamed_path(dft1)), "</p>"
print "<h4>Alignment Algorithms</h4>"
print "<p>Global Alignment (BFT):</p>", '\n'.join([
"<p class='alignment'>" + str(i) + "</p>"
for i in needleman_wunsch(renamed_path(bft0), renamed_path(bft1))
])
print "<p>Global Alignment (DFT):</p>", '\n '.join([
"<p class='alignment'>" + str(i) + "</p>"
for i in needleman_wunsch(renamed_path(dft0), renamed_path(dft1))
])
print "<br>"
print "<p>Local Alignment (BFT):</p>", '\n'.join([
"<p class='alignment'>" + str(i) + "</p>"
for i in local_alignment(renamed_path(bft0), renamed_path(bft1))
])
print "<p>Local Alignment (DFT):</p>", '\n'.join([
"<p class='alignment'>" + str(i) + "</p>"
for i in local_alignment(renamed_path(dft0), renamed_path(dft1))
])
print "<br>"
print "<p>Semiglobal Alignment (BFT):</p>", '\n'.join([
"<p class='alignment'>" + str(i) + "</p>"
for i in semiglobal_alignment(renamed_path(bft0), renamed_path(bft1))
])
print "<p>Semiglobal Alignment (DFT):</p>", '\n'.join([
"<p class='alignment'>" + str(i) + "</p>"
for i in semiglobal_alignment(renamed_path(dft0), renamed_path(dft1))
])
print "<br>"
print "<h4>Equality Identified</h4>"
identify_equality(graph0, graph1, LOW)
print "<br>"
print "<h4>Differences Identified (from Pathway 1 to Pathway 2)</h4>"
identify_differences(graph0, graph1, FULL)
print "<br>"
print "<h4>Differences Identified (from Pathway 2 to Pathway 1)</h4>"
identify_differences(graph1, graph0, FULL)
