def add_coupling_plots(
report: reporting.Report,
trials: typing.List[dict]
):
"""
Generates coupling report data from the analyzed coupling data and the
individual simulation trial results
:param report:
Grouped coupling data from the grouped simulation results
:param trials:
Simulation results for each trial run by the group
"""
distribution_traces = []
population_traces = []
index = 0
for trial in trials:
trial_data = reader.trial(trial['id'], paths.results())
index += 1
coupling_data = trial_data['couplings']
distribution_traces.append(dict(
x=coupling_data['distribution_profile']['x'],
y=coupling_data['distribution_profile']['y'],
type='scatter',
mode='lines',
name='{}'.format(index)
))
population_traces.append(dict(
x=coupling_data['population'],
type='box',
name='{}'.format(index),
boxpoints=False
))
report.add_plotly(
data=distribution_traces,
layout=plotting.create_layout(
title='Coupling Length Trial Distributions',
x_label='Expectation Value (au)',
y_label='Coupling Length (m)'
)
)
report.add_plotly(
data=population_traces,
layout=plotting.create_layout(
title='Coupling Length Trials',
x_label='Coupling Length (m)',
y_label='Trial Index (#)'
)
)
def load(
group_ids: list = None,
trial_ids: list = None,
row_filter: typing.Callable = None
):
"""
:param group_ids:
:param trial_ids:
:param row_filter:
:return:
"""
if not group_ids:
group_ids = []
groups = [reader.group(gid) for gid in group_ids]
if not trial_ids:
trial_ids = []
trials = [reader.trial(tid) for tid in trial_ids]
for g in groups:
trials += g['trials']
df = []
for trial in trials:
df.append(to_data_entry(trial))
trial['short_id'] = df[-1]['short_id']
df = pd.DataFrame(df).sort_values(
by=['coupling_length', 'gait_index', 'duty_cycle']
)
if row_filter is not None:
df['keep'] = df.apply(row_filter, axis=1)
df = df[df.keep]
df.drop('keep', 1)
keep_trial_ids = df['id'].tolist()
keep_trials = []
for trial in trials:
if trial['id'] in keep_trial_ids:
keep_trials.append(trial)
trials = keep_trials
df['order'] = np.arange(0, df.shape[0], 1)
df['relative_uncertainty'] = df.uncertainty / df.coupling_length
return dict(
trials=trials,
groups=groups,
df=df
)
from tracksim import reader as tk_reader import cauldron as cd # TRIAL_ID = 'G2-walk_BEB-515-2009-1-S-21_v0_st9_et21' TRIAL_ID = 'G2-walk_BEB-500-2014-1-S-1_v0_st9point5_et21' # TRIAL_ID = 'G2-walk_BEB-500-2014-1-S-4_v0_full' # TRIAL_ID = 'G2-walk2_BEB-500-2014-1-S-1_v0_st9point5_et21' cd.shared.trial_id = TRIAL_ID cd.shared.trial = tk_reader.trial(TRIAL_ID)
