def compute_solutions(res):
""" Compute solution from original and reconstructed parameters
"""
# configure comparison
omega = 0.5
orig_conf = get_base_config(res.A.orig, res.B.orig, omega)
rec_conf = get_base_config(res.A.rec, res.B.rec, omega)
init = np.random.uniform(0, 2 * np.pi, size=orig_conf.o_vec.shape)
# solve systems
orig_sols, ots = solve_system(orig_conf, init=init)
rec_sols, rts = solve_system(rec_conf, init=init)
assert (ots == rts).all()
# aggregate data
df = pd.DataFrame()
for orig_slice, rec_slice, t in zip(orig_sols.T, rec_sols.T, rts):
for i, (orig_val, rec_val) in enumerate(zip(orig_slice, rec_slice)):
df = df.append([{
'time': t,
'theta': orig_val,
'oscillator': i,
'source': 'orig'
}, {
'time': t,
'theta': rec_val,
'oscillator': i,
'source': 'rec'
}],
ignore_index=True)
return df
def compute_solutions(res):
""" Compute solution from original and reconstructed parameters
"""
# configure comparison
omega = 0.5
orig_conf = get_base_config(res.A.orig, res.B.orig, omega)
rec_conf = get_base_config(res.A.rec, res.B.rec, omega)
init = np.random.uniform(0, 2*np.pi, size=orig_conf.o_vec.shape)
# solve systems
orig_sols, ots = solve_system(orig_conf, init=init)
rec_sols, rts = solve_system(rec_conf, init=init)
assert (ots == rts).all()
# aggregate data
df = pd.DataFrame()
for orig_slice, rec_slice, t in zip(orig_sols.T, rec_sols.T, rts):
for i, (orig_val, rec_val) in enumerate(zip(orig_slice, rec_slice)):
df = df.append([
{'time': t, 'theta': orig_val, 'oscillator': i, 'source': 'orig'},
{'time': t, 'theta': rec_val, 'oscillator': i, 'source': 'rec'}
], ignore_index=True)
return df
def simulate_system(bundle, reps=10, check_laplacian=True):
""" Generate data from system setup
"""
# lonely investigation :'(
if check_laplacian:
investigate_laplacian(bundle.graph)
# solve system on network
corr_mats = []
var_sers = []
all_sols = []
for _ in trange(reps):
sols, ts = solve_system(bundle.system_config)
cmat = compute_correlation_matrix(sols)
vser = compute_cluster_num(sols, len(bundle.graph.nodes()))
corr_mats.append(cmat)
var_sers.append(vser)
all_sols.append(sols)
bundle['all_sols'] = all_sols
bundle['corr_mats'] = np.array(corr_mats)
bundle['var_sers'] = np.array(var_sers)
bundle['ts'] = ts
return bundle
def process(bundle_pack, reps=10, skipper=1):
""" Solve system bundle and return data
"""
# assemble final bundle from pack
repr_bundle = bundle_pack[0]
data = []
for bundle in tqdm(bundle_pack):
all_sols = []
for _ in range(reps):
sols, ts = solve_system(bundle.system_config, force_mod=False)
sols, ts = reduce_information_content(sols, ts, step=skipper)
all_sols.append(sols)
data.append((bundle.system_config, all_sols))
# reconstruct parameters
rec_a, rec_b = reconstruct_coupling_params(
DW({
'A': repr_bundle.system_config.A,
'B': repr_bundle.system_config.B,
'Phi': repr_bundle.system_config.Phi,
'dt': repr_bundle.system_config.dt,
'ts': ts,
'skip': skipper
}), data, verbose=False)
return DW({
'A': DW({'orig': repr_bundle.system_config.A, 'rec': rec_a}),
'B': DW({'orig': repr_bundle.system_config.B, 'rec': rec_b}),
})
def simulate_system(bundle, reps=10, check_laplacian=True):
""" Generate data from system setup
"""
# lonely investigation :'(
if check_laplacian:
investigate_laplacian(bundle.graph)
# solve system on network
corr_mats = []
var_sers = []
all_sols = []
for _ in trange(reps):
sols, ts = solve_system(bundle.system_config)
cmat = compute_correlation_matrix(sols)
vser = compute_cluster_num(sols, len(bundle.graph.nodes()))
corr_mats.append(cmat)
var_sers.append(vser)
all_sols.append(sols)
bundle['all_sols'] = all_sols
bundle['corr_mats'] = np.array(corr_mats)
bundle['var_sers'] = np.array(var_sers)
bundle['ts'] = ts
return bundle
def process(bundle_pack, reps=10, skipper=1):
""" Solve system bundle and return data
"""
# assemble final bundle from pack
repr_bundle = bundle_pack[0]
data = []
for bundle in tqdm(bundle_pack):
all_sols = []
for _ in range(reps):
sols, ts = solve_system(bundle.system_config, force_mod=False)
sols, ts = reduce_information_content(sols, ts, step=skipper)
all_sols.append(sols)
data.append((bundle.system_config, all_sols))
# reconstruct parameters
rec_a, rec_b = reconstruct_coupling_params(DW({
'A':
repr_bundle.system_config.A,
'B':
repr_bundle.system_config.B,
'Phi':
repr_bundle.system_config.Phi,
'dt':
repr_bundle.system_config.dt,
'ts':
ts,
'skip':
skipper
}),
data,
verbose=False)
return DW({
'A': DW({
'orig': repr_bundle.system_config.A,
'rec': rec_a
}),
'B': DW({
'orig': repr_bundle.system_config.B,
'rec': rec_b
}),
})
def test_valid_input(self):
variables = [
[6, 4863, 8761, 654],
[4863, 4521899, 8519938, 620707],
[8761, 8519938, 21022091, 905925],
[654, 620707, 905925, 137902]
]
results = [714, 667832, 1265493, 100583]
result = solve_system(variables, results)
expected_result = [
6.701336536389188,
0.07836603673386534,
0.015041331199344957,
0.24605633258014775
]
self.assertEqual(result, expected_result)
def recover_master(p, t, shadows, moduli):
k0 = utils.solve_system(shadows, moduli)
return k0 - t*p
def recover_master(p, t, shadows, moduli):
k0 = utils.solve_system(shadows, moduli)
return k0 - t * p