def main():
if not os.path.exists(OUT_DIR):
os.makedirs(OUT_DIR)
data, experiment_config = run_experiment(start_locations=[[0.3, 0.3],
[0.5, 0.5]], )
# extract data
multibody_config = experiment_config['environment']['multibody']
agents = {time: time_data['agents'] for time, time_data in data.items()}
fields = {time: time_data['fields'] for time, time_data in data.items()}
# agents plot
agents_settings = {'agents_key': 'agents'}
plot_agents_multigen(data, agents_settings, OUT_DIR, 'agents')
# snapshot plot
snapshot_data = {
'agents': agents,
'fields': fields,
'config': multibody_config,
}
snapshot_config = {
'out_dir': OUT_DIR,
'filename': 'agents_snapshots',
}
plot_snapshots(snapshot_data, snapshot_config)
# Colony Metrics Plot
embedded_ts = timeseries_from_data(data)
colony_metrics_ts = embedded_ts['colony_global']
colony_metrics_ts['time'] = embedded_ts['time']
path_ts = path_timeseries_from_embedded_timeseries(colony_metrics_ts)
fig = plot_colony_metrics(path_ts)
fig.savefig(os.path.join(OUT_DIR, 'colonies'))
def plot_colony_metrics(self, data, out_dir):
embedded_ts = timeseries_from_data(data)
colony_metrics_ts = embedded_ts['colony_global']
colony_metrics_ts['time'] = embedded_ts['time']
path_ts = path_timeseries_from_embedded_timeseries(colony_metrics_ts)
fig = plot_colony_metrics(path_ts)
fig.savefig(os.path.join(out_dir, 'colonies'))
def test_flagella_metabolism(seed=1):
random.seed(seed)
np.random.seed(seed)
# make the compartment and state
compartment = FlagellaExpressionMetabolism({'divide': False})
initial_state = get_flagella_metabolism_initial_state()
name = compartment.name
# run simulation
settings = {
'total_time': 60,
'emit_step': COMPARTMENT_TIMESTEP,
'initial_state': initial_state,
}
timeseries = simulate_compartment_in_experiment(compartment, settings)
# remove non-numerical data for timeseries comparison, convert to path_timeseries
del timeseries['chromosome']
del timeseries['ribosomes']
del timeseries['dimensions']
del timeseries['boundary']['divide']
del timeseries['fields']
del timeseries['null']
path_timeseries = path_timeseries_from_embedded_timeseries(timeseries)
# # save reference timeseries
# out_dir = os.path.join(COMPARTMENT_OUT_DIR, name)
# if not os.path.exists(out_dir):
# os.makedirs(out_dir)
# save_flat_timeseries(path_timeseries, out_dir)
reference = load_timeseries(
os.path.join(REFERENCE_DATA_DIR, name + '.csv'))
assert_timeseries_close(path_timeseries, reference)
def format_data_for_colony_metrics(data):
embedded_ts = timeseries_from_data(data)
colony_metrics_ts = embedded_ts['colony_global']
colony_metrics_ts['time'] = embedded_ts['time']
path_ts = path_timeseries_from_embedded_timeseries(colony_metrics_ts)
return path_ts
def plot_simulation_output(
timeseries_raw,
settings: Optional[Dict[str, Any]] = None,
out_dir=None,
filename='simulation',
):
'''
Plot simulation output, with rows organized into separate columns.
Arguments::
timeseries (dict): This can be obtained from simulation output with convert_to_timeseries()
settings (dict): Accepts the following keys:
* **column_width** (:py:class:`int`): the width (inches) of
each column in the figure
* **max_rows** (:py:class:`int`): ports with more states
than this number of states get wrapped into a new column
* **remove_zeros** (:py:class:`bool`): if True, timeseries
with all zeros get removed
* **remove_flat** (:py:class:`bool`): if True, timeseries
with all the same value get removed
* **remove_first_timestep** (:py:class:`bool`): if True,
skips the first timestep
* **skip_ports** (:py:class:`list`): entire ports that won't
be plotted
* **show_state** (:py:class:`list`): with
``[('port_id', 'state_id')]`` for all states that will be
highlighted, even if they are otherwise to be removed
TODO: Obsolete?
'''
int_or_float = (int, np.int32, np.int64, float, np.float32, np.float64)
settings = settings or {}
plot_fontsize = 8
plt.rc('font', size=plot_fontsize)
plt.rc('axes', titlesize=plot_fontsize)
# get settings
column_width = settings.get('column_width', 3)
max_rows = settings.get('max_rows', 25)
remove_zeros = settings.get('remove_zeros', True)
remove_flat = settings.get('remove_flat', False)
skip_ports = settings.get('skip_ports', [])
remove_first_timestep = settings.get('remove_first_timestep', False)
# make a flat 'path' timeseries, with keys being path
top_level = list(timeseries_raw.keys())
timeseries = path_timeseries_from_embedded_timeseries(timeseries_raw)
time_vec = timeseries.pop('time')
if remove_first_timestep:
time_vec = time_vec[1:]
# remove select states from timeseries
removed_states = set()
for path, series in timeseries.items():
if path[0] in skip_ports:
removed_states.add(path)
elif remove_flat:
if series.count(series[0]) == len(series):
removed_states.add(path)
elif remove_zeros:
if all(v == 0 for v in series):
removed_states.add(path)
for path in removed_states:
del timeseries[path]
# get figure columns
# get length of each top-level port
port_lengths = {}
for path in timeseries.keys():
if path[0] in top_level:
if path[0] not in port_lengths:
port_lengths[path[0]] = 0
port_lengths[path[0]] += 1
n_data = [length for port, length in port_lengths.items() if length > 0]
columns = []
for n_states in n_data:
new_cols = n_states / max_rows
if new_cols > 1:
for col in range(int(new_cols)):
columns.append(max_rows)
mod_states = n_states % max_rows
if mod_states > 0:
columns.append(mod_states)
else:
columns.append(n_states)
# make figure and plot
n_cols = len(columns)
n_rows = max(columns)
fig = plt.figure(figsize=(n_cols * column_width,
n_rows * column_width / 3))
grid = plt.GridSpec(n_rows, n_cols)
row_idx = 0
col_idx = 0
for port in port_lengths.keys():
# get this port's timeseries
port_timeseries = {}
for path, ts in timeseries.items():
if path[0] is port:
next_path = path[1:]
if any(isinstance(item, tuple) for item in next_path):
next_path = tuple([
item[0] if isinstance(item, tuple) else item
for item in next_path
])
port_timeseries[next_path] = ts
for state_id, series in sorted(port_timeseries.items()):
if remove_first_timestep:
series = series[1:]
# not enough data points -- this state likely did not exist throughout the entire simulation
if len(series) != len(time_vec):
continue
ax = fig.add_subplot(grid[row_idx,
col_idx]) # grid is (row, column)
if not all(isinstance(state, int_or_float) for state in series):
# check if series is a list of ints or floats
ax.title.set_text(
str(port) + ': ' + str(state_id) + ' (non numeric)')
else:
# plot line at zero if series crosses the zero line
if any(x == 0.0
for x in series) or (any(x < 0.0 for x in series)
and any(x > 0.0 for x in series)):
zero_line = [0 for t in time_vec]
ax.plot(time_vec, zero_line, 'k--')
# plot the series
ax.plot(time_vec, series)
if isinstance(state_id, tuple):
# new line for each store
state_id = '\n'.join(state_id)
ax.title.set_text(str(port) + '\n' + str(state_id))
if row_idx == columns[col_idx] - 1:
# if last row of column
set_axes(ax, True)
ax.set_xlabel('time (s)')
row_idx = 0
col_idx += 1
else:
set_axes(ax)
row_idx += 1
ax.set_xlim([time_vec[0], time_vec[-1]])
if out_dir:
plt.subplots_adjust(wspace=column_width / 3, hspace=column_width / 3)
_save_fig_to_dir(fig, filename, out_dir)
return fig
def plot_simulation_output(timeseries_raw, settings={}, out_dir='out', filename='simulation'):
'''
plot simulation output, with rows organized into separate columns.
Requires:
- timeseries (dict). This can be obtained from simulation output with convert_to_timeseries()
- settings (dict) with:
{
'max_rows': (int) ports with more states than this number of states get wrapped into a new column
'remove_zeros': (bool) if True, timeseries with all zeros get removed
'remove_flat': (bool) if True, timeseries with all the same value get removed
'remove_first_timestep': (bool) if True, skips the first timestep
'skip_ports': (list) entire ports that won't be plotted
'show_state': (list) with [('port_id', 'state_id')]
for all states that will be highlighted, even if they are otherwise to be removed
}
'''
plot_fontsize = 8
plt.rc('font', size=plot_fontsize)
plt.rc('axes', titlesize=plot_fontsize)
skip_keys = ['time']
# get settings
max_rows = settings.get('max_rows', 25)
remove_zeros = settings.get('remove_zeros', True)
remove_flat = settings.get('remove_flat', False)
skip_ports = settings.get('skip_ports', [])
remove_first_timestep = settings.get('remove_first_timestep', False)
# make a flat 'path' timeseries, with keys being path
top_level = list(timeseries_raw.keys())
timeseries = path_timeseries_from_embedded_timeseries(timeseries_raw)
time_vec = timeseries.pop('time')
if remove_first_timestep:
time_vec = time_vec[1:]
# remove select states from timeseries
removed_states = set()
for path, series in timeseries.items():
if path[0] in skip_ports:
removed_states.add(path)
elif remove_flat:
if series.count(series[0]) == len(series):
removed_states.add(path)
elif remove_zeros:
if all(v == 0 for v in series):
removed_states.add(path)
for path in removed_states:
del timeseries[path]
## get figure columns
# get length of each top-level port
port_lengths = {}
for path in timeseries.keys():
if path[0] in top_level:
if path[0] not in port_lengths:
port_lengths[path[0]] = 0
port_lengths[path[0]] += 1
n_data = [length for port, length in port_lengths.items() if length > 0]
columns = []
for n_states in n_data:
new_cols = n_states / max_rows
if new_cols > 1:
for col in range(int(new_cols)):
columns.append(max_rows)
mod_states = n_states % max_rows
if mod_states > 0:
columns.append(mod_states)
else:
columns.append(n_states)
# make figure and plot
n_cols = len(columns)
n_rows = max(columns)
fig = plt.figure(figsize=(n_cols * 3, n_rows * 1))
grid = plt.GridSpec(n_rows, n_cols)
row_idx = 0
col_idx = 0
for port in port_lengths.keys():
# get this port's states
port_timeseries = {path[1:]: ts for path, ts in timeseries.items() if path[0] is port}
for state_id, series in sorted(port_timeseries.items()):
if remove_first_timestep:
series = series[1:]
# not enough data points -- this state likely did not exist throughout the entire simulation
if len(series) != len(time_vec):
continue
ax = fig.add_subplot(grid[row_idx, col_idx]) # grid is (row, column)
if not all(isinstance(state, (int, float, np.int64, np.int32)) for state in series):
# check if series is a list of ints or floats
ax.title.set_text(str(port) + ': ' + str(state_id) + ' (non numeric)')
else:
# plot line at zero if series crosses the zero line
if any(x == 0.0 for x in series) or (any(x < 0.0 for x in series) and any(x > 0.0 for x in series)):
zero_line = [0 for t in time_vec]
ax.plot(time_vec, zero_line, 'k--')
# plot the series
ax.plot(time_vec, series)
ax.title.set_text(str(port) + ': ' + str(state_id))
if row_idx == columns[col_idx]-1:
# if last row of column
set_axes(ax, True)
ax.set_xlabel('time (s)')
row_idx = 0
col_idx += 1
else:
set_axes(ax)
row_idx += 1
ax.set_xlim([time_vec[0], time_vec[-1]])
# save figure
fig_path = os.path.join(out_dir, filename)
plt.subplots_adjust(wspace=0.8, hspace=1.0)
plt.savefig(fig_path, bbox_inches='tight')