def __init__(self, simulation_directory, iterate_number):
self.number = iterate_number
self.min_max_concns = {}
agent_path = os.path.join(simulation_directory.agent_State,
'agent_State(%d).xml'%(iterate_number))
agent_path = toolbox_basic.check_path(agent_path)
self.agent_output = toolbox_results.AgentOutput(path=agent_path)
self.time = self.agent_output.time
agent_path = os.path.join(simulation_directory.agent_Sum,
'agent_Sum(%d).xml'%(iterate_number))
agent_path = toolbox_basic.check_path(agent_path)
self.agent_sum = toolbox_results.AgentOutput(path=agent_path)
env_path = os.path.join(simulation_directory.env_State,
'env_State(%d).xml'%(iterate_number))
env_path = toolbox_basic.check_path(env_path)
self.env_output = toolbox_results.EnvOutput(path=env_path)
env_path = os.path.join(simulation_directory.env_Sum,
'env_Sum(%d).xml'%(iterate_number))
env_path = toolbox_basic.check_path(env_path)
self.env_sum = toolbox_results.EnvOutput(path=env_path)
def single_time(fol, time):
os.chdir(fol + '/agent_State/')
axis = plt.subplot(111)
output = results.AgentOutput(path=time)
time = (output.time)
for species in output.species_outputs:
if species.members == []:
continue
for cell in species.members:
name = species.name
for (species_name,
colormap_name) in species_color_dict.iteritems():
if name == species_name:
norm = matplotlib.colors.Normalize(vmin=0, vmax=1)
colormap = matplotlib.cm.get_cmap(colormap_name, 256)
m = matplotlib.cm.ScalarMappable(norm=norm, cmap=colormap)
age = float(cell.vars['age'])
cell.color = m.to_rgba(age)
toolbox_idynomics.plot_cells_2d(axis, output)
output = results.EnvOutput(path='env_State(last).xml')
for solute in output.solute_outputs:
if solute.name == 'glucose':
solute_output = solute
toolbox_idynomics.solute_contour(axis,
solute_output,
interpolation='bicubic')
axis.text(250,
250,
'Time: ' + str(int(time)) + 'h',
va='top',
ha='right',
color='#bd0303',
fontsize=fs)
axis.set_xlabel(r'$\mu$m', fontsize=fs)
axis.set_ylabel(r'$\mu$m', fontsize=fs)
os.mkdir(fol + '/times/')
os.chdir(fol + '/times/')
plt.savefig('Biofilm time ' + time[12:-5] + '.png', bbox_inches='tight')
plt.close()
def single_time(fol, time_fn, count, arate=True):
biomass_names = [
'activeBiomassGrowth', 'activeBiomassRepair', 'inactiveBiomassGrowth',
'inactiveBiomassRepair'
]
ax = plt.subplot(111)
os.chdir(fol + '/agent_State/')
output = results.AgentOutput(path=time_fn)
time = (output.time)
ma1, ma2 = 0, 0
for species in output.species_outputs:
if species.members == []:
continue
if not arate:
all_growth = []
for cell in species.members:
all_growth.append(
float(cell.get_specific_growth_rate(biomass_names)))
ma = max(all_growth)
if ma1 == 0: ma1 = ma
else: ma2 = ma
for cell in species.members:
name = species.name
for (species_name, colormap_name) in species_color_dict.items():
if name == species_name:
colormap = matplotlib.cm.get_cmap(colormap_name, 256)
if not arate:
l0, l06 = numpy.log(0.001), numpy.log(0.6)
norm = matplotlib.colors.Normalize(vmin=l0, vmax=l06)
else:
norm = matplotlib.colors.Normalize(vmin=0, vmax=1)
m = matplotlib.cm.ScalarMappable(norm=norm, cmap=colormap)
if arate:
age = float(cell.vars['age'])
else:
gr = float(
cell.get_specific_growth_rate(biomass_names))
if gr == 0: gr = numpy.log(0.001)
else: gr = numpy.log(gr)
if arate:
cell.color = m.to_rgba(age)
else:
cell.color = m.to_rgba(gr)
toolbox_idynomics.plot_cells_2d(ax, output)
os.chdir(fol + '/env_State/')
output = results.EnvOutput(path=time_fn.replace('agent', 'env'))
for solute in output.solute_outputs:
if solute.name == 'glucose':
solute_output = solute
toolbox_idynomics.solute_contour(ax,
solute_output,
interpolation='bicubic')
ax.text(250,
250,
'Time: ' + str(count) + 'h',
va='top',
ha='right',
color='#bd0303',
fontsize=fs)
os.chdir(fol)
if arate:
plt.savefig(fol + '/times/' + str(count) + '.png',
bbox_inches='tight',
dpi=300)
else:
plt.savefig(fol + '/times_gr/' + str(count) + '.png',
bbox_inches='tight',
dpi=300)
plt.close()
return
growthrate = 0
if species.name == 'OldieA': OldieA_activelayer += growthrate
elif species.name == 'OldieB': OldieB_activelayer += growthrate
if species.name == 'OldieA':
if growthrate == 1:
cell.color = species_color_dict[0]
elif growthrate == 0:
cell.color = species_color_dict[1]
elif species.name == 'OldieB':
if growthrate == 1:
cell.color = species_color_dict[2]
elif growthrate == 0:
cell.color = species_color_dict[3]
toolbox_idynomics.plot_cells_2d(axis, output)
os.chdir(folder + f + '/env_State')
output = results.EnvOutput(path='env_State' + times[a][11:])
for solute in output.solute_outputs:
if solute.name == 'glucose':
solute_output = solute
toolbox_idynomics.solute_contour(axis,
solute_output,
interpolation='bicubic')
axis.text(250,
250,
'Time: ' + str(int(time)) + 'h',
va='top',
ha='right',
color='#bd0303',
fontsize=fs)
axis.set_xlabel(r'$\mu$m', fontsize=fs)
axis.set_ylabel(r'$\mu$m', fontsize=fs)
"""
lims = xlims[name]
for (species_name, colormap_name) in species_color_dict.items():
if name == species_name:
colormap = matplotlib.cm.get_cmap(colormap_name, 256)
norm = matplotlib.colors.Normalize(vmin=0, vmax=1)
m = matplotlib.cm.ScalarMappable(norm=norm, cmap=colormap)
age = float(cell.vars['age'])
cell.color = m.to_rgba(age)
spec_growth = float(cell.get_specific_growth_rate(biomass_names))
colors.append(cell.color)
growth_rates.append(spec_growth)
xloc.append(x)
yloc.append(y)
os.chdir(fi + '/env_State')
output = results.EnvOutput(path='env_State(1000).xml')
for solute in output.solute_outputs:
if solute.name != 'glucose': continue
array = solute.concentration_array()
solute = list(array)
concs = []
for a in range(len(colors)):
x, y = int(xloc[a] / 4), int(yloc[a] / 4)
conc = solute[x][y]
concs.append(conc)
ax2.scatter(conc,
growth_rates[a],
color=colors[a],
s=20,
edgecolors='gray',
linewidths=0.1)