"""

@return: the body of a form

"""

# define the tree string

tree_string = '(((Human:0.1, Chimpanzee:0.2):0.8, Gorilla:0.3):0.7, Orangutan:0.4, Gibbon:0.5);'

tree = Newick.parse(tree_string, Newick.NewickTree)

formatted_tree_string = Newick.get_narrow_newick_string(tree, 60)

# define the form objects

form_objects = [

Form.MultiLine('tree', 'newick tree', formatted_tree_string),

Form.MultiLine('model', 'DNA frequency mixture model',

get_sample_xml_string().strip()),

Form.Integer('ncols', 'sample this many nucleotide columns',

200, low=1, high=1000),

Form.Integer('seed', 'random number seed', 314159, low=0),

Form.RadioGroup('format', 'output format', [

Form.RadioItem('fastaformat', 'fasta alignment'),

Form.RadioItem('nexusformat', 'nexus alignment', True)])]

"""

@param fs: a FieldStorage object containing the cgi arguments

@return: a (response_headers, response_text) pair

"""

# parse the tree

try:

tree = Newick.parse(fs.tree, Newick.NewickTree)

tree.assert_valid()

except Newick.NewickSyntaxError as e:

raise HandlingError(str(e))

# get the normalized model

mixture_model = deserialize_mixture_model(fs.model)

# sample the alignment, possibly using a specified seed

try:

alignment = PhyLikelihood.simulate_alignment(

tree, mixture_model, fs.ncols, fs.seed)

except PhyLikelihood.SimulationError as e:

raise HandlingError(e)

# get the output string

output_string = ''

if fs.fastaformat:

# the output is the alignment

arr = []

for node in tree.gen_tips():

arr.append(alignment.get_fasta_sequence(node.name))

alignment_string = '\n'.join(arr)

output_string = alignment_string

elif fs.nexusformat:

# the output is the alignment and the tree

nexus = Nexus.Nexus()

nexus.tree = tree

nexus.alignment = alignment

output_string = str(nexus)

# print the results

response_headers = [('Content-Type', 'text/plain')]

"""

@return: a multi line xml string

"""

# define the model

kappa = 2

category_weights = [1, 4, 5]

nt_weights_list = [

[1, 4, 4, 1],

[2, 3, 3, 2],

[1, 1, 1, 1]

]

# create the model using the definition

root = ET.Element('model')

root.set('kappa', str(float(kappa)))

for category_weight, nt_weights in zip(category_weights, nt_weights_list):

category = ET.SubElement(root, 'category')

category.set('weight', str(float(category_weight)))

distribution = ET.SubElement(category, 'distribution')

for nt, weight in zip(list('ACGT'), nt_weights):

terminal = ET.SubElement(distribution, 'nt')

terminal.set('symbol', nt)

terminal.set('weight', str(float(weight)))

# modify the contents so that the tree is shown as indented

XmlUtil.indent(root)

# get the string representing the tree

tree = ET.ElementTree(root)

out = StringIO()

tree.write(out)

"""

Convert the xml string to a mixture model.

@param xml_string: an xml string defining the mixture model

@return: an unscaled mixture model object

"""

# define the variables that define the model

kappa = None

category_weights = []

nt_dicts = []

# get the variables that define the model

element_tree = ET.parse(StringIO(xml_string))

root = element_tree.getroot()

kappa = float(root.get('kappa'))

for category in root:

category_weights.append(float(category.get('weight')))

distribution = category.find('distribution')

nt_dict = {}

for terminal in distribution:

nt_dict[terminal.get('symbol')] = float(terminal.get('weight'))

total = sum(nt_dict.values())

for nt in nt_dict:

nt_dict[nt] /= total

nt_dicts.append(nt_dict)

# create a mixture model from the variables that define the model

rate_matrix_objects = []

for nt_dict in nt_dicts:

rate_matrix_object = RateMatrix.get_unscaled_hky85_rate_matrix(

nt_dict, kappa)

rate_matrix_objects.append(rate_matrix_object)

total = float(sum(category_weights))

category_distribution = [weight / total for weight in category_weights]

mixture_model = SubModel.MixtureModel(

category_distribution, rate_matrix_objects)

mixture_model.normalize()