def get_last_only(folder, f):
os.chdir(folder + f + '/lastIter')
output = results.AgentOutput(path='agent_Sum(last).xml')
species = results.SpeciesOutput(output, 'OldieA')
requirements = {}
cells = species.find_cells(requirements)
t_a = (float(output.time))
cell = cells[0]
population_a = (float(cell.vars['population']))
biomass_a = (float(cell.vars['mass']))
growthrate_a = (float(cell.vars['growthRate']))
species = results.SpeciesOutput(output, 'OldieB')
requirements = {}
cells = species.find_cells(requirements)
cell = cells[0]
population_b = (float(cell.vars['population']))
biomass_b = (float(cell.vars['mass']))
growthrate_b = (float(cell.vars['growthRate']))
if growthrate_a == 0:
growthrate_b = abs(1 / growthrate_b)
gr = numpy.log(growthrate_b)
elif growthrate_b == 0:
growthrate_a = abs(growthrate_a / 1)
gr = numpy.log(growthrate_a)
elif growthrate_a == 0 and growthrate_b == 0:
gr = 0
else:
gr = abs(growthrate_a / growthrate_b)
gr = numpy.log(gr)
br = numpy.log(biomass_a / biomass_b)
pr = numpy.log(population_a / population_b)
ratio = [br, pr, gr]
time = t_a
return time, ratio
def get_biomass(sim_dir_path, a):
times1, biomass1, population1, growthrate1 = [], [], [], []
times2, biomass2, population2, growthrate2 = [], [], [], []
file_path = '/Volumes/Robyn_W_2/july_2018/paper/' + sim_dir_path
file_dir = file_path + '/agent_sum'
basic.unzip_files(file_dir + '.zip')
file_list = basic.file_list(file_dir)
for filename in file_list:
output = results.AgentOutput(path=filename)
species1 = results.SpeciesOutput(output, 'OldieA')
requirements = {}
cells = species1.find_cells(requirements)
times1.append(float(output.time))
if len(cells) == 0:
continue
cell = cells[0]
population1.append(float(cell.vars['population']))
biomass1.append(float(cell.vars['mass']))
growthrate1.append(float(cell.vars['growthRate']))
species2 = results.SpeciesOutput(output, 'OldieB')
cells = species2.find_cells(requirements)
times2.append(float(output.time))
if len(cells) == 0:
continue
cell = cells[0]
population2.append(float(cell.vars['population']))
biomass2.append(float(cell.vars['mass']))
growthrate2.append(float(cell.vars['growthRate']))
basic.rm_dir(file_dir)
lists_a, lists_b = [population1, biomass1,
growthrate1], [population2, biomass2, growthrate2]
t_a1, t_b1 = times1, times2
for i in range(3):
list1, list2, list3, list4 = t_a1, lists_a[i], t_b1, lists_b[i]
t_a, lists_a[i] = (list(x) for x in zip(
*sorted(zip(list1, list2), key=itemgetter(0))))
t_b, lists_b[i] = (list(x) for x in zip(
*sorted(zip(list3, list4), key=itemgetter(0))))
biomass_ratio, population_ratio, growthrate_ratio = [], [], []
for j in range(len(lists_a[1])):
if lists_a[1][j] == 0 and lists_b[1][j] == 0 or lists_a[1][
j] == 0 and lists_b[1][j] != 0 or lists_a[1][
j] != 0 and lists_b[1][j] == 0:
biomass_ratio.append(0)
else:
biomass_ratio.append(numpy.log(lists_a[1][j] / lists_b[1][j]))
if lists_a[0][j] == 0 and lists_b[0][j] == 0 or lists_a[0][
j] == 0 and lists_b[0][j] != 0 or lists_a[0][
j] != 0 and lists_b[0][j] == 0:
population_ratio.append(0)
else:
population_ratio.append(lists_a[0][j] / lists_b[0][j])
if lists_a[2][j] == 0 and lists_b[2][j] == 0 or lists_a[2][
j] == 0 and lists_b[2][j] != 0 or lists_a[2][
j] != 0 and lists_b[2][j] == 0:
growthrate_ratio.append(0)
else:
growthrate_ratio.append(lists_a[2][j] / lists_b[2][j])
return t_a, biomass_ratio, population_ratio, growthrate_ratio
def build_population_structure(sim_dir_path,
attribute='specific growth rate',
bins=numpy.linspace(0, 0.6, 90),
starting_time=2400,
biomass_names=[
'activeBiomassGrowth',
'activeBiomassRepair',
'inactiveBiomassGrowth',
'inactiveBiomassRepair'
]):
sim_dir_path = os.path.join(
'/Volumes/Robyn_W_2/july_2018/paper/comparison_clegg/' + sim_dir_path +
'/')
attr_values = []
total_pop = 0.0
file_dir = os.path.join(sim_dir_path, 'agent_State')
basic.unzip_files(file_dir + '.zip')
file_list = basic.file_list(file_dir)
all_growth = []
for filename in file_list:
output = results.AgentOutput(path=filename)
if output.time >= starting_time:
species = results.SpeciesOutput(output)
species.set_biomass_names(biomass_names)
attr_values.extend(species.get_attribute_values(attribute))
all_growth.append(species.get_attribute_values(attribute))
total_pop += species.population()
hist, bin_edges = numpy.histogram(attr_values, bins)
bin_using, frequencies = [], []
for i in range(len(hist)):
bin_using.append((bins[i + 1] + bins[i]) / 2)
frequencies.append(float(hist[i] / total_pop))
basic.rm_dir(file_dir)
return bin_using, frequencies, all_growth
def normalise_by_generation(sim_dir_path, max_generation):
results_path = basic.check_path(
os.path.join(sim_dir_path, 'lineage_results.xml'))
results_output = results.AgentOutput(path=results_path)
results_species = results.SpeciesOutput(results_output)
for generation in range(max_generation + 1):
cohort = [
c for c in results_species.members
if int(c.vars['generation']) == generation
]
growth_rates = [float(c.vars['growth_rate']) for c in cohort]
mean = numpy.mean(growth_rates)
for cell in cohort:
cell.vars['growth_rate'] = str(
float(cell.vars['growth_rate']) / mean)
results_species.update_agent_output()
save_path = os.path.join(sim_dir_path, 'norm_gen_lineage_results.xml')
results_output.write(output_path=save_path)
def get_population(sim_dir_path):
sim_dir_path = os.path.join(
'/Volumes/Robyn_W_2/july_2018/paper/comparison_clegg/' + sim_dir_path +
'/lastIter/')
output = results.AgentOutput(path=sim_dir_path + 'agent_State(last).xml')
species = results.SpeciesOutput(output)
cells = species.findcells()
age, total_biomass = [], []
for cell in cells:
if len(cells) == 0:
continue
ar = cell.vars['activeBiomassRepair']
ir = cell.vars['inactiveBiomassRepair']
ag = cell.vars['activeBiomassGrowth']
ig = cell.vars['inactiveBiomassGrowth']
total_biomass.append(ar + ir + ag + ig)
age.append(cell.vars['age'])
return age, biomass
def normalise_by_progenitor(sim_dir_path):
results_path = basic.check_path(
os.path.join(sim_dir_path, 'lineage_results.xml'))
results_output = results.AgentOutput(path=results_path)
results_species = results.SpeciesOutput(results_output)
prog_growth_rates = {}
for cell in results_species.members:
generation = cell.vars['generation']
if generation == '0':
family = cell.vars['family']
growth_rate = float(cell.vars['growth_rate'])
prog_growth_rates[family] = growth_rate
for cell in results_species.members:
family = cell.vars['family']
top = float(cell.vars['growth_rate'])
bottom = prog_growth_rates[family]
cell.vars['growth_rate'] = str(top / bottom)
results_species.update_agent_output()
save_path = os.path.join(sim_dir_path, 'norm_prog_lineage_results.xml')
results_output.write(output_path=save_path)
def calc_averages(results_path, max_generation):
results_path = basic.check_path(results_path)
results_output = results.AgentOutput(path=results_path)
results_species = results.SpeciesOutput(results_output)
results_species.change_header(
'family,genealogy,generation,growth_rate,std_growth_rate')
save_cells = []
min_num_cells = 10000000000
max_num_cells = 0
for generation in range(max_generation + 1):
cohort = [
c for c in results_species.members
if int(c.vars['generation']) == generation
]
genealogies = list(set([int(c.vars['genealogy']) for c in cohort]))
for genealogy in genealogies:
growth_rates = [
float(c.vars['growth_rate']) for c in cohort
if int(c.vars['genealogy']) == genealogy
]
num_cells = len(growth_rates)
min_num_cells = min(min_num_cells, num_cells)
max_num_cells = max(max_num_cells, num_cells)
if num_cells < 5:
print('Only ' + str(num_cells) + ' in point ' +
str(genealogy) + ', ' + str(generation))
cell = LineageCell(results_species)
cell.vars['genealogy'] = genealogy
cell.vars['generation'] = generation
cell.vars['growth_rate'] = numpy.mean(growth_rates)
cell.vars['std_growth_rate'] = numpy.std(growth_rates)
save_cells.append(cell)
print('All lineages had between ' + str(min_num_cells) + ' and ' +
str(max_num_cells) + ' cells')
results_species.members = save_cells
results_species.update_agent_output()
dir_name = os.path.dirname(results_path)
base_name = 'mean_' + os.path.basename(results_path)
save_path = os.path.join(dir_name, base_name)
results_output.write(output_path=save_path)
def build_life_history(sim_dir_path,
attribute1='specific growth rate',
attribute2='age',
cell_id=(1, 0),
biomass_names=[
'activeBiomassGrowth', 'activeBiomassRepair',
'inactiveBiomassGrowth', 'inactiveBiomassRepair'
]):
sim_dir_path = os.path.join(
'//Volumes/Robyn_W_2/july_2018/paper/comparison_clegg/' +
sim_dir_path + '/')
time, generation, spec_growth, age, inact_rep, act_rep, inact_gro, act_gro, total_biomass = [], [], [], [], [], [], [], [], []
requirements = {'family': str(cell_id[0]), 'genealogy': str(cell_id[1])}
file_dir = os.path.join(sim_dir_path, 'agent_State')
basic.unzip_files(file_dir + '.zip')
file_list = basic.file_list(file_dir)
for filename in file_list:
#print filename
output = results.AgentOutput(path=filename)
species = results.SpeciesOutput(output)
cells = species.find_cells(requirements)
time.append(output.time)
if len(cells) == 0:
continue
cell = cells[0]
generation.append(cell.vars['generation'])
ar = cell.vars['activeBiomassRepair']
act_rep.append(ar)
ir = cell.vars['inactiveBiomassRepair']
inact_rep.append(ir)
ag = cell.vars['activeBiomassGrowth']
act_gro.append(ag)
ig = cell.vars['inactiveBiomassGrowth']
inact_gro.append(ig)
total_biomass.append(ar + ir + ag + ig)
spec_growth.append(cell.get_specific_growth_rate(biomass_names))
age.append(cell.vars[attribute2])
basic.rm_dir(file_dir)
return time, generation, spec_growth, age, inact_rep, act_rep, inact_gro, act_gro
def plot_lineages(axis,
results_path,
max_generation,
line_width=1.0,
error_bars=True):
results_path = basic.check_path(results_path)
results_output = results.AgentOutput(path=results_path)
results_species = results.SpeciesOutput(results_output)
if error_bars and results_species.attributes.count('std_growth_rate') == 0:
print('Could not find data for error bars')
error_bars = False
for cell in results_species.members:
generation = int(cell.vars['generation'])
if generation <= max_generation - 1:
family = int(cell.vars['family'])
genealogy = int(cell.vars['genealogy'])
growth_rate = float(cell.vars['growth_rate'])
if error_bars:
eb_x = [generation + 1]
eb_y = [growth_rate]
eb_e = [float(cell.vars['std_growth_rate'])]
requirements = {
'family': family,
'genealogy': genealogy,
'generation': generation + 1
}
temp = results_species.find_cells(requirements)
if not temp == []:
oldie = temp[0]
oldie_grow = float(oldie.vars['growth_rate'])
axis.plot([generation + 1, generation + 2],
[growth_rate, oldie_grow],
'r',
linewidth=line_width,
solid_capstyle='round',
zorder=5 + numpy.random.randint(5))
if error_bars:
eb_x.append(generation + 2)
eb_y.append(oldie_grow)
eb_e.append(float(oldie.vars['std_growth_rate']))
requirements['genealogy'] = genealogy + 2**generation
temp = results_species.find_cells(requirements)
if not temp == []:
newbie = temp[0]
newbie_grow = float(newbie.vars['growth_rate'])
axis.plot([generation + 1, generation + 2],
[growth_rate, newbie_grow],
'b',
linewidth=line_width,
solid_capstyle='round',
zorder=5 + numpy.random.randint(5))
if error_bars:
eb_x.append(generation + 2)
eb_y.append(newbie_grow)
eb_e.append(float(newbie.vars['std_growth_rate']))
if error_bars:
axis.errorbar(eb_x,
eb_y,
yerr=eb_e,
ecolor='grey',
fmt=None,
elinewidth=line_width / 2,
capsize=1,
zorder=1)
axis.set_xlabel('Generations')
axis.set_ylabel('Normalised growth rate')
axis.set_xlim([1, max_generation + 1])
def get_all(folder, f):
sim_dir_path = basic.check_path(folder + f + '/')
file_dir = os.path.join(sim_dir_path, 'agent_Sum')
basic.unzip_files(file_dir + '.zip')
file_list = basic.file_list(file_dir)
t_a, population_a, biomass_a, growthrate_a = [], [], [], []
t_b, population_b, biomass_b, growthrate_b = [], [], [], []
last_pop_a, last_biomass_a, last_growthrate_a = [], [], []
last_pop_b, last_biomass_b, last_growthrate_b = [], [], []
for filename in file_list:
output = results.AgentOutput(path=filename)
species = results.SpeciesOutput(output, 'OldieA')
requirements = {}
cells = species.find_cells(requirements)
#single_result = results.SingleResult()
t_a.append(float(output.time))
if len(cells) == 0:
continue
cell = cells[0]
population_a.append(float(cell.vars['population']))
biomass_a.append(float(cell.vars['mass']))
growthrate_a.append(float(cell.vars['growthRate']))
species = results.SpeciesOutput(output, 'OldieB')
requirements = {}
cells = species.find_cells(requirements)
#single_result = results.SingleResult()
t_b.append(float(output.time))
if len(cells) == 0:
continue
cell = cells[0]
population_b.append(float(cell.vars['population']))
biomass_b.append(float(cell.vars['mass']))
growthrate_b.append(float(cell.vars['growthRate']))
basic.rm_dir(file_dir)
lists_a, lists_b = [population_a, biomass_a,
growthrate_a], [population_b, biomass_b, growthrate_b]
t_a1, t_b1 = t_a, t_b
for i in range(3):
list1, list2, list3, list4 = t_a1, lists_a[i], t_b1, lists_b[i]
t_a, lists_a[i] = (list(x) for x in zip(
*sorted(zip(list1, list2), key=itemgetter(0))))
t_b, lists_b[i] = (list(x) for x in zip(
*sorted(zip(list3, list4), key=itemgetter(0))))
biomass_ratio, population_ratio, growthrate_ratio = [], [], []
for j in range(len(lists_a[1])):
biomass_ratio.append(numpy.log(lists_a[1][j] / lists_b[1][j]))
population_ratio.append(numpy.log(lists_a[0][j] / lists_b[0][j]))
if lists_a[2][j] == 0 and lists_b[2][j] == 0:
growthrate_ratio.append(0)
elif lists_a[2][j] == 0:
lists_b[2][j] = abs(1 / (lists_b[2][j]))
growthrate_ratio.append(numpy.log(lists_b[2][j]))
elif lists_b[2][j] == 0:
lists_a[2][j] = abs((lists_a[2][j]) / 1)
growthrate_ratio.append(numpy.log(lists_a[2][j]))
else:
growthrate_ratio.append(
abs(numpy.log(lists_a[2][j] / lists_b[2][j])))
if j == len(lists_a[1]) - 1:
last_pop_a.append(lists_a[0][j])
last_pop_b.append(lists_b[0][j])
last_biomass_a.append(lists_a[1][j])
last_biomass_b.append(lists_b[1][j])
last_growthrate_a.append(lists_a[2][j])
last_growthrate_b.append(lists_b[2][j])
return t_a, [biomass_ratio, population_ratio, growthrate_ratio]
axisA, axisB, axisC = fig.add_subplot(311), fig.add_subplot(
312), fig.add_subplot(313)
axisA.set_title('A', loc='left'), axisB.set_title(
'B', loc='left'), axisC.set_title('C', loc='left')
ax = [axisC, axisA, axisB, axisC, axisA, axisB]
color = [colors[4], colors[0], colors[2], colors[5], colors[1], colors[3]]
#axisA, axisA, axisB, axisB, axisC, axisC]
#color = [colors[0], colors[1], colors[2], colors[3], colors[4], colors[5]]
for b in range(6):
c = (b * 3) + a
input_path = os.path.join(
'/Volumes/Robyn_W_2/july_2018/paper/comparison_clegg/steady_state/'
+ names3[c] + '/lastIter/')
results_path = os.path.join(input_path, 'agent_State(last).xml')
agent_output = toolbox_results.AgentOutput(path=results_path)
species = toolbox_results.SpeciesOutput(agent_output)
aging_extras.scatter_population(ax[b],
species,
'totalBiomass',
'age',
color=color[b],
markersize=ms)
axisA.set_xlim([285, 630])
axisA.set_ylim([0.0, 1.0])
axisA.set_title('Age & Size Distributions', fontsize=10)
plt.setp(axisA.get_xticklabels(), visible=False)
axisB.set_xlim([285, 630])
axisB.set_ylim([0.0, 1.0])
axisB.set_ylabel(r'Cellular age $P_{dam}/(P_{act}+P_{dam})$', fontsize=10)
plt.setp(axisB.get_xticklabels(), visible=False)
axisC.set_xlim([285, 630])