def inclusion_plots_suite(data=None, out_dir=EXPERIMENT_OUT_DIR):
n_snapshots = 5
tagged_molecules = [('inclusion_body', ), (
'front',
'aggregate',
), (
'back',
'aggregate',
)]
# multigen plot
plot_settings = {}
plot_agents_multigen(data, plot_settings, out_dir)
# extract data for snapshots
bounds = LATTICE_CONFIG['multibody']['bounds']
agents, fields = format_snapshot_data(data)
# snapshots plot
plot_snapshots(bounds=bounds,
agents=agents,
fields=fields,
n_snapshots=n_snapshots,
out_dir=out_dir,
filename='inclusion_snapshots')
# tags plot
plot_tags(
data=data,
bounds=bounds,
tagged_molecules=tagged_molecules,
n_snapshots=n_snapshots,
convert_to_concs=False,
out_dir=out_dir,
)
def make_snapshot_function(data, bounds, agent_colors=None, **kwargs):
agent_colors = agent_colors or {}
multibody_agents, multibody_fields = format_snapshot_data(data)
# make the snapshot plot function
time_vec = list(multibody_agents.keys())
# get fields and agent colors
multibody_field_range = get_field_range(multibody_fields, time_vec)
multibody_agent_colors = get_agent_colors(multibody_agents)
multibody_agent_colors.update(agent_colors)
def plot_single_snapshot(t_index):
time_indices = np.array([t_index])
snapshot_time = [time_vec[t_index]]
fig = make_snapshots_figure(time_indices=time_indices,
snapshot_times=snapshot_time,
agents=multibody_agents,
agent_colors=multibody_agent_colors,
fields=multibody_fields,
field_range=multibody_field_range,
n_snapshots=1,
bounds=bounds,
default_font_size=12,
plot_width=PLOT_WIDTH,
scale_bar_length=0,
**kwargs)
return fig
return plot_single_snapshot, time_vec
def plot_fields_snapshots(output,
bounds=None,
include_fields=None,
n_snapshots=4,
colorbar_decimals=4,
scale_bar_length=5,
phylogeny_colors=False,
out_dir=None,
filename=None,
**kwargs):
agents, fields = format_snapshot_data(output)
fig1 = plot_snapshots(bounds=bounds,
agents=agents,
fields=fields,
phylogeny_names=phylogeny_colors,
n_snapshots=n_snapshots,
scale_bar_length=scale_bar_length,
colorbar_decimals=colorbar_decimals,
include_fields=include_fields,
out_dir=out_dir,
filename=filename,
**kwargs)
return fig1
def main():
out_dir = os.path.join(COMPOSITE_OUT_DIR, NAME)
os.makedirs(out_dir, exist_ok=True)
parser = argparse.ArgumentParser(description='lattice composite')
parser.add_argument('-exchange',
'-e',
action='store_true',
default=False,
help='simulate agents with exchange')
args = parser.parse_args()
bounds = [25, 25]
if args.exchange:
# GrowDivide agents with Exchange
data = test_lattice(exchange=True,
n_agents=3,
total_time=4000,
bounds=bounds)
else:
# GrowDivide agents
n_bins = [20, 20]
initial_field = np.zeros((n_bins[0], n_bins[1]))
initial_field[:, -1] = 100
data = test_lattice(n_agents=3,
total_time=4000,
bounds=bounds,
n_bins=n_bins,
initial_field=initial_field)
# format the data for plot_snapshots
agents, fields = format_snapshot_data(data)
initial_ids = list(data[0]['agents'].keys())
agent_ids = get_agent_ids(agents)
# make colors based on initial agents
agent_colors = {}
hues = [n / 360 for n in [120, 270, 300, 240, 360, 30, 60]]
for idx, initial_id in enumerate(initial_ids):
hue = hues[idx]
color = [hue] + DEFAULT_SV
for agent_id in agent_ids:
if agent_id.startswith(initial_id, 0, len(initial_id)):
agent_colors[agent_id] = color
plot_snapshots(
bounds,
agents=agents,
fields=fields,
n_snapshots=4,
agent_colors=agent_colors,
out_dir=out_dir,
filename=f"lattice_snapshots{'_exchange' if args.exchange else ''}")
def run_mother_machine(time=10, out_dir='out'):
config = get_mother_machine_config()
# configure the experiment
agent_ids = config.get('agent_ids', ['0'])
# get the environment composite
environment = Lattice(config.get('environment', {}))
composite = environment.generate({})
# add the agents
growth_division = GrowDivide(config.get('growth_division', {}))
for agent_id in agent_ids:
agent = growth_division.generate({'agent_id': agent_id})
composite.merge(composite=agent, path=('agents', agent_id))
experiment = Engine(
**{
'processes': composite['processes'],
'topology': composite['topology'],
'initial_state': config.get('initial_state', {}),
'progress_bar': True,
})
# simulate
settings = {'total_time': time, 'return_raw_data': True}
data = simulate_experiment(experiment, settings)
# agents plot
plot_settings = {'agents_key': 'agents'}
plot_agents_multigen(data, plot_settings, out_dir)
# snapshot plot
agents, fields = format_snapshot_data(data)
bounds = config['environment']['multibody']['bounds']
plot_snapshots(bounds,
agents=agents,
fields=fields,
n_snapshots=4,
out_dir=out_dir,
filename=f"mother_machine")
# make snapshot video
make_video(
data,
bounds,
plot_type='fields',
step=100,
out_dir=out_dir,
filename=f"mother_machine",
)
def make_tags_function(data,
bounds,
agent_colors=None,
tagged_molecules=None,
tag_colors=None,
convert_to_concs=False,
**kwargs):
agent_colors = agent_colors or {}
tag_colors = tag_colors or {}
multibody_agents, multibody_fields = format_snapshot_data(data)
# make the snapshot plot function
time_vec = list(multibody_agents.keys())
time_indices = np.array(range(0, len(time_vec)))
# get agent colors, and ranges
tag_ranges, tag_colors = get_tag_ranges(agents=multibody_agents,
tagged_molecules=tagged_molecules,
time_indices=time_indices,
convert_to_concs=convert_to_concs,
tag_colors=tag_colors)
# make the function for a single snapshot
def plot_single_tags(t_index):
time_index = np.array([t_index])
snapshot_time = [time_vec[t_index]]
fig = make_tags_figure(time_indices=time_index,
snapshot_times=snapshot_time,
agents=multibody_agents,
agent_colors=agent_colors,
tagged_molecules=tagged_molecules,
convert_to_concs=convert_to_concs,
tag_ranges=tag_ranges,
tag_colors=tag_colors,
n_snapshots=1,
bounds=bounds,
default_font_size=12,
plot_width=PLOT_WIDTH,
scale_bar_length=0,
**kwargs)
return fig
return plot_single_tags, time_vec
def main():
parser = argparse.ArgumentParser(description='access data from db')
parser.add_argument('experiment_id', type=str, default=False)
parser.add_argument('--multigen', '-1', action='store_true', default=False)
parser.add_argument('--fields', '-2', action='store_true', default=False)
parser.add_argument('--tags', '-3', action='store_true', default=False)
parser.add_argument('--single_tags',
'-4',
action='store_true',
default=False)
parser.add_argument('--all', '-a', action='store_true', default=False)
args = parser.parse_args()
experiment_id = args.experiment_id
# make a directory for the figures
out_dir = f'out/analyze/{experiment_id}'
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# retrieve the data
output, bounds = access(experiment_id)
del output[0.0]
for t in list(output.keys()):
if t > 43200: # cutoff at 12 hours
del output[t]
agents, fields = format_snapshot_data(output)
# run the plot functions
if args.multigen or args.all:
agent_colors = get_agent_colors(agents)
plot_phylogeny_fig(output, bounds, agent_colors, out_dir)
plot_multigen_fig(output, agent_colors, out_dir)
if args.fields or args.all:
plot_fields_fig(output, bounds, out_dir)
if args.tags or args.all:
plot_tags_fig(output, bounds, out_dir)
if args.single_tags or args.all:
plot_single_tags(agents, bounds, out_dir)
def run_growth_division(
out_dir='out',
animate=True,
):
n_agents = 2
agent_ids = [
str(agent_id)
for agent_id in range(n_agents)]
# configure the multibody process
bounds = DEFAULT_BOUNDS
multibody_config = {
'animate': animate,
# 'jitter_force': 1e0,
'bounds': bounds}
body_config = {
'bounds': bounds,
'agent_ids': agent_ids}
multibody_config.update(agent_body_config(body_config))
# experiment settings
experiment_settings = {
'progress_bar': False,
'display_info': False}
# run the test
gd_data = test_growth_division(
config=multibody_config,
growth_rate=0.05,
growth_rate_noise=0.001,
division_volume=volume_from_length(4, 1),
total_time=100,
experiment_settings=experiment_settings)
agents, fields = format_snapshot_data(gd_data)
return plot_snapshots(
bounds, agents=agents, fields=fields, out_dir=out_dir)
def main():
total_time = 7000
bounds = [15, 15]
data = run_sim(
total_time=total_time,
bounds=bounds,
)
out_dir = 'out/stochastic_expression'
os.makedirs(out_dir, exist_ok=True)
# format the data for plot_snapshots
agents, fields = format_snapshot_data(data)
# save snapshots figure
plot_snapshots(
bounds,
agents=agents,
fields=fields,
n_snapshots=4,
out_dir=out_dir,
filename='snapshots'
)
# tags plot
n_snapshots = 5
time_vec = list(agents.keys())
time_indices = np.round(np.linspace(0, len(time_vec) - 1, n_snapshots)).astype(int)
snapshot_times = [time_vec[i] for i in time_indices]
tagged_molecules = [
('RNA', 'C'),
('Protein', 'X'),
]
make_tags_figure(
agents=agents,
bounds=bounds,
n_snapshots=n_snapshots,
time_indices=time_indices,
snapshot_times=snapshot_times,
convert_to_concs=False,
tagged_molecules=tagged_molecules,
out_dir=out_dir,
filename='tags'
)
# make snapshot video
tagged_molecules = [
('RNA', 'C'), # use RNA_counts?
('Protein', 'X'),
]
make_video(
data,
bounds,
plot_type='tags',
step=100, # render every nth snapshot
tagged_molecules=tagged_molecules,
out_dir=out_dir,
filename=f'snapshots_video',
)
div_number = 0
for t in data.keys():
if len(data[t]['agents'].keys()) > div_number:
print(f"time {t}: agent_ids {list(data[t]['agents'].keys())}")
div_number += 1