def bigmip2dict(mip, time):
"""Convert a BigMip to a dictionary suitable for conversion to a Matlab
structure with scipy."""
if mip is None:
return np.array([])
matlab_data = {
'PhiMip': mip.phi,
'main_complex': convert.nodes2indices(mip.subsystem.nodes),
'MIP1': mip.cut.intact,
'MIP2': mip.cut.severed,
'current_state': mip.subsystem.network.current_state,
#'past_state': mip.subsystem.network.past_state,
'num_concepts': len(mip.unpartitioned_constellation),
'calculation_time': time,
'sum_small_phi': sum(c.phi for c in mip.unpartitioned_constellation),
'partition_only_concepts': [
concept2dict(c) for c in mip.partitioned_constellation if
convert.nodes2indices(c.mechanism) not in
[
convert.nodes2indices(upc.mechanism) for upc in
mip.unpartitioned_constellation
]
],
'concepts': [concept2dict(c) for c in
mip.unpartitioned_constellation]
}
return matlab_data
def concept2dict(c):
"""Convert a PyPhi Concept to a dictionary suitable for conversion to a
Matlab structure with scipy."""
return {
'phi': c.phi,
'mechanism': convert.nodes2indices(c.mechanism),
'cause': mice2dict(c.cause) if c.cause is not None else 'None',
'effect': mice2dict(c.effect) if c.effect is not None else 'None'
}
def mice2dict(mice):
"""Convert a PyPhi Mice to a dictionary suitable for conversion to a
Matlab structure with scipy."""
return {
'phi': mice.phi,
'direction': mice.direction,
'purview': convert.nodes2indices(mice.purview),
'partition': (
((convert.nodes2indices(mice.mip.partition[0].mechanism),
convert.nodes2indices(mice.mip.partition[0].purview)),
(convert.nodes2indices(mice.mip.partition[1].mechanism),
convert.nodes2indices(mice.mip.partition[1].purview)))
if mice.mip.partition is not None else 'None'
),
'repertoire': (
mice.mip.unpartitioned_repertoire if
mice.mip.unpartitioned_repertoire is not None else 'None'
),
'partitioned_repertoire': (
mice.mip.partitioned_repertoire if mice.mip.partitioned_repertoire
is not None else 'None'
)
}
def check_unpartitioned_small_phis(small_phis, unpartitioned_constellation):
assert len(small_phis) == len(unpartitioned_constellation)
for c in unpartitioned_constellation:
assert c.phi == small_phis[convert.nodes2indices(c.mechanism)]
def evaluate_animat(animat, gen, log, animat_string, matlab_results_dir):
log.info("\n[Animat] " + (" Generation " + str(gen) + " ").center(60, "-"))
print("running Animat: " + str(animat) + " Generation: " + str(gen) + "\n")
data_path = ANIMAT_PATH + str(animat) + "_" + str(gen) + "_"
#------- Make animat network for PyPhi analysis using animalysis ---------------
# Animalysis takes care of all the issues with the raw data:
# - Motors shouldn't connect back
# - Sensors shoudn't receive feedback
# - every 36 trials there is a transition which should not be counted as a causal transition
try:
animat_network = animalysis.Animat(data_path)
if animat_network.used_nodes:
# In DEBUG take only first 2 states (this is hacky, cause below l. 173 is must be more than 1 to work)
if DEBUG:
# TODO: not transitons but current state only now
current_states = animat_network.ranked_states[0:2]
else:
current_states = animat_network.ranked_states
# Compute results for this animat.
tic = time()
results = {
'animat': animat_network,
'state': tuple(filter(lambda x: x is not None, [
compute_phi_data(animat_network, state, log)
for state, count in current_states
]))
}
#import pdb; pdb.set_trace()
toc = time()
elapsed = toc - tic
used_nodes = tuple(animat_network.used_nodes)
# We have binary nodes only
num_states = 2**len(used_nodes)
number_of_connections = np.count_nonzero(animat_network.cm)
# Get the results in a form suitable for saving in a matfile.
matlab_results = {
'tpm': animat_network.tpm.reshape([num_states]+[len(used_nodes)], order='F'),
'connectivity_matrix': animat_network.cm,
'number_of_connections': number_of_connections,
'used_nodes': used_nodes,
'LifeStates': animat_network.ranked_states,
'mip_states': [
matlab_dict.bigmip2dict(mip, t) for mip, whole, t in results['state']
],
'whole_states': [
matlab_dict.constellation2dict(whole) for mip, whole, t in results['state']
],
'PhiMip': [mip.phi for mip, whole, t in results['state']],
#Todo: check what the result is if main complex is empty
'main_complex': [convert.nodes2indices(mip.subsystem.nodes) for mip, whole, t in results['state']],
'ssphi_whole_system': [sum(c.phi for c in whole) for mip, whole, t in results['state']],
'whole_num_concepts': [len(whole) for mip, whole, t in results['state']],
'mip_num_concepts': [len(mip.unpartitioned_constellation) for mip, whole, t in results['state']]
}
log.info('\n[Animat] Total time elapsed: ' + str(elapsed))
#import pdb; pdb.set_trace()
# Save the matlab results in a matfile for analysis with Matlab.
matfile_filename = os.path.join(matlab_results_dir, animat_string + "_" + str(gen))
scipy.io.savemat(matfile_filename, matlab_results, do_compression=True)
except ValueError:
print("[Animat] Animat network cannot be build.")
pass