def process_yield_results(results_path, inflow=3.24):
glucose_results = process_default_results(results_path, 'glucose')
biomass_results = process_default_results(results_path, 'totalBiomass')
attributes = {
'header': 'directory,mean,std',
'starting_time': '240',
'name': 'yield'
}
results_output = toolbox_results.ResultsOutput(path=results_path)
result_set = toolbox_results.ResultSet(results_output, attributes)
for g_result in glucose_results.members:
dirname = g_result.vars['directory']
mean_g = g_result.vars['mean']
std_g = g_result.vars['std']
b_result = biomass_results.find_single_result('directory', dirname)
mean_b = b_result.vars['mean']
std_b = b_result.vars['std']
y_result = toolbox_results.SingleResult()
y_result.vars['directory'] = dirname
y_result.vars['accumulation'] = g_result.vars['accumulation']
y_result.vars['segregation'] = g_result.vars['segregation']
y_result.vars['repair'] = g_result.vars['repair']
if mean_b == 0.0:
y_result.vars['mean'] = 'nan'
y_result.vars['std'] = 'nan'
else:
mean_y = mean_b / (inflow - mean_g)
cv_g = (std_g / mean_g)
cv_b = (std_b / mean_b)
y_result.vars['mean'] = mean_y
y_result.vars['std'] = mean_y * math.sqrt(cv_g**2 + cv_b**2)
result_set.members.append(y_result)
return result_set
def process_beta_results(results_path):
attributes = {
'header': 'directory,mean,std',
'starting_time': '240',
'name': 'specific growth rate'
}
results_output = toolbox_results.ResultsOutput(path=results_path)
result_set = toolbox_results.ResultSet(results_output, attributes)
segregation = []
for result in result_set.members:
dir = result.vars['directory']
# 'a', 'm', or 's'
result.vars['segregation'] = dir[0]
if not dir[0] in segregation: segregation.append(dir[0])
# 'repair' is 'n' to ensure a solid line in the plot
result.vars['repair'] = 'n'
# 'accumulation' is actually the value of beta
result.vars['accumulation'] = 0.01 * float(dir[2:4])
# gGrowth rate mean & standard deviation
result.vars['mean'] = float(result.vars['mean'])
result.vars['std'] = float(result.vars['std'])
result.vars['optimal'] = False
for seg in segregation:
seg_set = [
r for r in result_set.members if r.vars['segregation'] == seg
]
means = [r.vars['mean'] for r in seg_set]
seg_set[means.index(max(means))].vars['optimal'] = True
return result_set
def process_erjavec_results(results_path):
attributes = {
'header': 'directory,mean,std',
'starting_time': '240',
'name': 'specific growth rate'
}
results_output = toolbox_results.ResultsOutput(path=results_path)
results_set = toolbox_results.ResultSet(results_output, attributes)
for result in results_set.members:
dir = result.vars['directory']
result.vars['segregation'] = dir[0]
result.vars['repair'] = 'n'
result.vars['accumulation'] = 0.1 * float(dir[1:3])
result.vars['mean'] = float(result.vars['mean'])
result.vars['std'] = float(result.vars['std'])
return results_set
def process_optima_results(results_path, attribute):
attributes = {
'header': 'directory,mean,std',
'starting_time': '240',
'name': 'optimum ' + attribute
}
results_output = toolbox_results.ResultsOutput(path=results_path)
result_set = toolbox_results.ResultSet(results_output, attributes)
for result in result_set.members:
dir = result.vars['directory']
result.vars['segregation'] = dir[0]
result.vars['repair'] = 'n'
# This is actually the value of beta
result.vars['accumulation'] = 0.01 * float(dir[5:7])
result.vars['mean'] = 0.01 * float(dir[2:4])
result.vars['std'] = 0.005
return result_set
def process_default_results(results_path, attribute):
attributes = {
'header': 'directory,mean,std',
'starting_time': '240',
'name': attribute
}
results_output = toolbox_results.ResultsOutput(path=results_path)
results_set = toolbox_results.ResultSet(results_output, attributes)
for result in results_set.members:
dir = result.vars['directory']
result.vars['segregation'] = dir[0]
result.vars['repair'] = dir[1]
result.vars['accumulation'] = 0.01 * float(dir[2:4])
result.vars['mean'] = float(result.vars['mean'])
result.vars['std'] = float(result.vars['std'])
# Convert from g L-1 to mg L-1
if attribute == 'glucose' or attribute == 'totalBiomass':
result.vars['mean'] *= 1000
result.vars['std'] *= 1000
return results_set
def calc_roughness(sim_dir_path, iterate_number):
sim = toolbox_idynomics.SimulationDirectory(sim_dir_path)
agent_output = sim.get_single_iterate(iterate_number).agent_output
nI, nJ, nK, gridres = sim.find_domain_dimensions()
# Need agents coordinates and domain size
# Matrix of agentgrid of size xdom/gridres x ydom/gridres - xdom and ydom are height and width of computational domain
# Convert agent coordinates to agentgrid elements - 1 signifies presence
# Define bulk and biofilm as separate bodies
# Identify biofilm front points as agent occupied elements with at least one bulk neighbour, set equivalent elements to 1 in equivalent matrix, frontgrid
# create a grid of zeros
agentgrid = numpy.zeros((nI, nJ), dtype=int)
#set elements of the grid that have a cell in to have a value of 1 - all other grids should remain as 0
max_x = 0
newmax_x = 0
for r in agent_output.get_all_cells():
x, y = float(r.vars['locationX']), float(r.vars['locationY'])
newx = x
x, y = x / gridres, y / gridres
x, y = int(x), int(y)
if agentgrid[x, y] == 0:
agentgrid[x, y] = 1
if x > max_x:
max_x = x
if newx > newmax_x:
newmax_x = newx
#set front cells to have a value of 2, making sure that they aren't on the edges where the biofilm wraps around
for i in range(max_x + 2):
for j in range(nJ):
if agentgrid[i, j] < 1:
continue
# Look at the level below, if we're not on the bottom
if i > 0:
if agentgrid[i - 1, j] == 0:
agentgrid[i, j] = 2
continue
# Always apply cyclic search for left/right in the level below
if agentgrid[i - 1, (j - 1) % nJ] == 0:
agentgrid[i, j] = 2
continue
if agentgrid[i - 1, (j + 1) % nJ] == 0:
agentgrid[i, j] = 2
continue
# Look at the level above, if we're not at the top
if i < nI - 1:
if agentgrid[i + 1, j] == 0:
agentgrid[i, j] = 2
continue
# Always apply cyclic search for left/right in the level above
if agentgrid[i + 1, (j - 1) % nJ] == 0:
agentgrid[i, j] = 2
continue
if agentgrid[i + 1, (j + 1) % nJ] == 0:
agentgrid[i, j] = 2
continue
# Always apply cyclic search for left/right in the same level
if agentgrid[i, (j - 1) % nJ] == 0:
agentgrid[i, j] = 2
continue
if agentgrid[i, (j + 1) % nJ] == 0:
agentgrid[i, j] = 2
continue
frontgrid = numpy.zeros((nI), dtype=int)
for i in range(max_x + 2):
for j in range(nJ):
if agentgrid[i, j] == 2:
frontgrid[i] += 1
Cfx = numpy.zeros((nI, ), dtype=float)
Pf = 0
num = 0
for i in range(max_x + 1):
temp = frontgrid[i] / nJ
Cfx[i] = 1.0 * temp
Pf += temp
num += (i + 1) * temp
Xf = num / Pf
Sigmaf = 0
num2 = 0
for j in range(nI):
num2 += abs((j + 1) - Xf) * Cfx[j]
Sigmaf = num2 / Pf
Sigma = Sigmaf / Xf
attributes = {
'name': 'Sigmaf',
'name2': 'Sigma',
'name3': 'Xf',
'name4': 'Pf',
'name5': 'height',
'header': 'value,value2,value3,value4,value5'
}
results_file_path = os.path.join(sim_dir_path, 'roughness.xml')
results_output = toolbox_results.ResultsOutput(path=results_file_path)
result_set = toolbox_results.ResultSet(results_output, attributes)
single_result = toolbox_results.SingleResult()
single_result.vars['value'] = Sigmaf
single_result.vars['value2'] = Sigma
single_result.vars['value3'] = Xf
single_result.vars['value4'] = Pf
single_result.vars['value5'] = max_x
result_set.members.append(single_result)
result_set.update_results_output()
results_output.write(results_file_path)
return Sigmaf, max_x