def find_single_result(self, attribute_name, attribute_value):
for result in self.members:
if str(result.vars[attribute_name]) == str(attribute_value):
return result
toolbox_basic.error_message('Could not find result with '+str(attribute_name),
'of '+str(attribute_value))
return None
def find_axis_from_label(self, label):
for ax, lab in self.subplots.iteritems():
if lab == label:
return ax
toolbox_basic.error_message('Could not find subplot with',
'label ' + str(label))
return None
def __init__(self, env_output, name=''):
search_pattern = './simulation/'
if not env_output.find('./simulation/bulk') == None:
search_pattern += 'bulk/'
if not (name == ''):
search_pattern += 'solute[@name="'+name+'"]'
else:
toolbox_basic.error_message('Please define which solute to use in',
env_output.path)
self.env_output = env_output
solute = env_output.find(search_pattern)
if solute == None:
toolbox_basic.error_message('Trouble finding solute from name:',
search_pattern)
self.name = solute.attrib['name']
self.unit = solute.attrib['unit']
self.grid_res = float(solute.attrib['resolution'])
self.grid_nI = int(solute.attrib['nI'])
self.grid_nJ = int(solute.attrib['nJ'])
self.grid_nK = int(solute.attrib['nK'])
self.three_dim = (self.grid_nK > 1)
temp = solute.text.translate(None,' ').split(';\n')
self.values = []
for value in temp:
if value == '' or value == '\n': continue
self.values.append(float(value))
def find_axis_from_label(self, label):
for ax, lab in self.subplots.iteritems():
if lab == label:
return ax
toolbox_basic.error_message('Could not find subplot with',
'label '+str(label))
return None
def calc_total_attibute(self, attribute):
if attribute == 'specific growth rate':
return self.calc_total_specific_growth_rate()
if self.attributes.count(attribute) < 1:
toolbox_basic.error_message('Species '+self.name,
'does not have attribute '+attribute)
exit()
return sum(self.get_attribute_values(attribute))
def get_mean_NN_dist(self, detail_level, mean='amean', dist='oNN_dist'):
rf = self.calc_nearest_neighbor_distances(detail_level)
dists = [e.vars[dist] for e in rf.events]
if not mean in ['amean', 'gmean', 'hmean']:
toolbox_basic.error_message('toolbix_elmer.Simulation.get_mean_NN_dist()',
'mean not recognised: %s'%(mean))
exec 'def mean(x): return %s(x)'%(mean)
return mean(dists)
def check_single_species(self):
if len(self.species) > 1:
toolbox_basic.error_message('More than one species in:',self.path)
return False
elif len(self.species) < 1:
toolbox_basic.error_message('No species present in:',self.path)
return False
else:
return True
def find_attribute_position(self, atttribute):
position = -1
for i, x in enumerate(self.header.split(',')):
if str(x) == str(attribute):
position = i
break
if position < 0:
msg = 'Could not find attribute "'+attribute
msg += '" for species "'+self.name+'" in '
toolbox_basic.error_message(msg, path)
return position
def calc_mean_attribute(self, attribute):
if attribute == 'specific growth rate':
return self.calc_mean_specific_growth_rate()
if self.attributes.count(attribute) < 1:
toolbox_basic.error_message('Species '+self.name,
'does not have attribute '+attribute)
exit()
values = self.get_attribute_values(attribute)
mean = numpy.mean(values)
std = numpy.std(values)
return mean, std
def get_mean_surf_concn(self, cell_type='all', mean='amean'):
if cell_type == 'all':
events = self.events
elif cell_type == 'consumers':
events = self.consumers()
elif cell_type == 'producers':
events = self.producers()
if not mean in ['amean', 'gmean', 'hmean']:
toolbox_basic.error_message('toolbix_elmer.Simulation.get_mean_NN_dist()',
'mean not recognised: %s'%(mean))
exec 'def mean(x): return %s(x)'%(mean)
return mean([e.vars['amean_surf_concn'] for e in events])
def setup_replicate_results(replicates_dir):
results_file = get_replicate_results(replicates_dir)
replicate_simulations = get_replicate_simulations(replicates_dir)
wrapping = replicate_simulations[0].wrapping
is_3d=replicate_simulations[0].is_3d
side_length=replicate_simulations[0].side_length
results_file.set_space_parameters(wrapping=wrapping, is_3d=is_3d, side_length=side_length)
for sim in replicate_simulations:
if not ((sim.wrapping == wrapping) and
(sim.is_3d == is_3d) and
(sim.side_length == side_length)):
toolbox_basic.error_message('toolbox_elmer.get_replicates_results():'+
'Replicates have different space parameters', replicates_dir)
exit()
results_file.write()
return results_file
def settle_competition(set_dir_path):
set_dir_path = basic.check_path(set_dir_path)
attributes = {'name' : 'competition',
'header' : 'speciesAwashout,speciesBwashout,numSims,pValue'}
results_file_path = os.path.join(set_dir_path, 'results.xml')
results_output = results.ResultsOutput(path=results_file_path)
result_set = results.ResultSet(results_output, attributes)
if len(result_set.members) > 0:
return result_set
dir_list = basic.subdir_list(set_dir_path)
dir_result = results.SingleResult()
washoutA = 0
washoutB = 0
for dir_name in dir_list:
last_path = os.path.join(dir_name, 'lastIter', 'agent_Sum(last).xml')
last_output = results.AgentOutput(path=last_path)
species_names = last_output.get_species_names()
if not len(species_names) == 2:
basic.error_message('There should be 2 species for a competition',
last_path)
last_speciesA = results.SpeciesOutput(last_output,
name=species_names[0])
populationA = float(last_speciesA.members[0].vars['population'])
last_speciesB = results.SpeciesOutput(last_output,
name=species_names[1])
populationB = float(last_speciesB.members[0].vars['population'])
if populationA < 1 and populationB > 0:
washoutA += 1
if populationB < 1 and populationA > 0:
washoutB += 1
if populationA < 1 and populationB < 1:
basic.error_message('Both species washed out in', last_path)
dir_result.vars['speciesAwashout'] = washoutA
dir_result.vars['speciesBwashout'] = washoutB
dir_result.vars['numSims'] = len(dir_list)
dir_result.vars['pValue'] = \
stats.binom.cdf(min(washoutA,washoutB),(washoutA+washoutB),0.5)
result_set.members.append(dir_result)
result_set.update_results_output()
results_output.write(results_file_path)
return result_set
def get_optima(set_dir_path, attribute, dir_group_process,
higher_is_fitter=True, starting_time=2400):
results_file_path = os.path.join(set_dir_path, 'results.xml')
results_output = results.ResultsOutput(path=results_file_path)
attributes = {'name':'optimum '+attribute,
'starting_time':str(starting_time),
'header':'directory,mean,std'}
optimum_set = results.ResultSet(results_output, attributes)
if len(optimum_set.members) > 0:
return optimum_set
attributes['name'] = attribute
result_set = results.ResultSet(results_output, attributes)
if result_set.members == []:
basic.error_message('Collate mean attributes before assembling optima',
set_dir_path)
exit()
args = [set_dir_path]
groups = dir_group_process(*args)
for group in groups:
single_result = results.SingleResult()
means = {}
for dir_name in group:
dir_name = os.path.basename(dir_name)
result = result_set.find_single_result('directory', dir_name)
means[dir_name] = float(result.vars['mean'])
if means == {}:
continue
if higher_is_fitter:
optimum = max(means.values())
else:
optimum = min(means.values())
fittest_dir = basic.get_key_from_dict_value(means, optimum)
fittest = result_set.find_single_result('directory', fittest_dir)
single_result.vars['directory'] = fittest.vars['directory']
single_result.vars['mean'] = fittest.vars['mean']
single_result.vars['std'] = fittest.vars['std']
optimum_set.members.append(single_result)
optimum_set.update_results_output()
results_output.write(results_file_path)
return optimum_set
def __init__(self, agent_output, name=''):
if not (name == ''):
search_pattern = './simulation/species[@name="'+name+'"]'
elif agent_output.check_single_species():
search_pattern = './simulation/species'
else:
toolbox_basic.error_message('Please define which species to use in',
agent_output.path)
self.agent_output = agent_output
species = self.agent_output.find(search_pattern)
self.name = species.attrib['name']
self.attributes = species.attrib['header'].split(',')
self.biomass_names = []
self.members = []
for line in species.text.translate(None,'\n').split(';'):
if line == '': break
variables = line.split(',')
cell = CellOutput(self)
for i, attribute in enumerate(self.attributes):
cell.vars[attribute] = variables[i]
self.members.append(cell)
def get_species_by_name(self, name):
for species in self.get_species_outputs():
if name == species.name:
return species
toolbox_basic.error_message('Species %s cannot be found in'%(name), self.path)
def get_solute(self, solute_name):
for solute in self.solute_outputs:
if solute.name == solute_name:
return solute
toolbox_basic.error_message('Could not find solute '+solute_name,
'in '+self.path)