def get_response_content(fs):
# get a properly formatted newick tree with branch lengths
tree = Newick.parse(fs.tree, SpatialTree.SpatialTree)
tree.assert_valid()
if tree.has_negative_branch_lengths():
msg = 'drawing a tree with negative branch lengths is not implemented'
raise HandlingError(msg)
tree.add_branch_lengths()
# get the dictionary mapping the branch name to the nucleotide
name_to_nt = {}
lines = Util.get_stripped_lines(fs.column.splitlines())
if lines:
name_to_nt = SnippetUtil.get_generic_dictionary(lines, 'name',
'nucleotide', list('acgtACGT'))
# augment the tips with the nucleotide letters
for name, nt in name_to_nt.items():
try:
node = tree.get_unique_node(name)
except Newick.NewickSearchError as e:
raise HandlingError(e)
if node.children:
msg = 'constraints on internal nodes are not implemented'
raise HandlingError(msg)
node.state = nt.upper()
# read the rate matrix
R = fs.matrix
# convert the rate matrix to a rate matrix object
states = list('ACGT')
rate_matrix_object = RateMatrix.RateMatrix(R.tolist(), states)
# simulate the ancestral nucleotides
rate_matrix_object.simulate_ancestral_states(tree)
# simulate a path on each branch
# this breaks up the branch into a linear sequence of nodes and adds color
for node in tree.gen_non_root_nodes():
simulate_branch_path(tree, node, rate_matrix_object)
# do the layout
EqualArcLayout.do_layout(tree)
# draw the image
try:
ext = Form.g_imageformat_to_ext[fs.imageformat]
return DrawTreeImage.get_tree_image(tree, (640, 480), ext)
except CairoUtil.CairoUtilError as e:
raise HandlingError(e)
def get_response_content(fs):
# get the tree
tree = Newick.parse(fs.tree, Newick.NewickTree)
tree.assert_valid()
# read the ordered states
ordered_states = Util.get_stripped_lines(StringIO(fs.states))
# read the matrix from the form data
R = fs.rate_matrix
if len(R) < 2:
raise HandlingError('the rate matrix should have at least two rows')
if len(ordered_states) != len(R):
msg_a = 'the number of ordered states should be the same '
msg_b = 'as the number of rows in the matrix'
raise HandlingError(msg_a + msg_b)
# get the dictionary mapping taxa to states
taxon_to_state = SnippetUtil.get_generic_dictionary(
StringIO(fs.assignments), 'taxon name', 'state name',
ordered_states)
# set the states for each of the tree tips
for node in tree.gen_tips():
node.state = taxon_to_state[node.name]
# create the rate matrix object
rate_matrix_object = RateMatrix.RateMatrix(R.tolist(), ordered_states)
# repeatedly reroot and calculate root state distributions
internal_nodes = list(tree.gen_internal_nodes())
for node in internal_nodes:
tree.reroot(node)
rate_matrix_object.add_probabilities(tree)
weights = [node.state_to_subtree_prob[state]
for state in ordered_states]
node.state_distribution = Util.weights_to_distribution(weights)
# define the response
out = StringIO()
# show the ancestral state distributions
for node in tree.gen_internal_nodes():
if node.name:
name = '\t'.join(str(p) for p in node.state_distribution)
print >> out, node.name, ':', name
# write the response
return out.getvalue()
def get_response_content(fs):
# get the tree
tree = Newick.parse(fs.tree, Newick.NewickTree)
tree.assert_valid()
# read the ordered states
ordered_states = Util.get_stripped_lines(StringIO(fs.states))
# read the matrix from the form data
R = fs.rate_matrix
if len(R) < 2:
raise HandlingError('the rate matrix should have at least two rows')
if len(ordered_states) != len(R):
msg_a = 'the number of ordered states should be the same '
msg_b = 'as the number of rows in the matrix'
raise HandlingError(msg_a + msg_b)
# get the dictionary mapping taxa to states
taxon_to_state = SnippetUtil.get_generic_dictionary(
StringIO(fs.assignments), 'taxon name', 'state name', ordered_states)
# set the states for each of the tree tips
for node in tree.gen_tips():
node.state = taxon_to_state[node.name]
# create the rate matrix object
rate_matrix_object = RateMatrix.RateMatrix(R.tolist(), ordered_states)
# repeatedly reroot and calculate root state distributions
internal_nodes = list(tree.gen_internal_nodes())
for node in internal_nodes:
tree.reroot(node)
rate_matrix_object.add_probabilities(tree)
weights = [
node.state_to_subtree_prob[state] for state in ordered_states
]
node.state_distribution = Util.weights_to_distribution(weights)
# define the response
out = StringIO()
# show the ancestral state distributions
for node in tree.gen_internal_nodes():
if node.name:
name = '\t'.join(str(p) for p in node.state_distribution)
print >> out, node.name, ':', name
# write the response
return out.getvalue()
