def plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr):
"""Plot dt scan"""
dt_vec = np.linspace(-90e-3, 50e-3, 100)
sg_vec = []
for model_dt in dt_vec:
protocol = stdputil.Protocol(
["pre", "post", "post", "post"], [model_dt, 20e-3, 20e-3], 0.1, 60.0, prot_id="%.2fms" % model_dt
)
model_sg = stdputil.protocol_outcome(protocol, best_ind_dict)
sg_vec.append(model_sg)
try:
sg_good_sol_vec = pickle.load(open("sg_good_sol_vec.pkl", "rb"))
except IOError:
sg_good_sol_vec = []
for _, good_sol in enumerate(good_solutions):
sg_ind = []
for model_dt in dt_vec:
protocol = stdputil.Protocol(
["pre", "post", "post", "post"], [model_dt, 20e-3, 20e-3], 0.1, 60.0, prot_id="%.2fms" % model_dt
)
model_sg = stdputil.protocol_outcome(protocol, good_sol)
sg_ind.append(model_sg)
sg_good_sol_vec.append(sg_ind)
pickle.dump(sg_good_sol_vec, open("sg_good_sol_vec.pkl", "wb"))
fig3, ax3 = plt.subplots(figsize=(10, 10), facecolor="white")
ax3.set_rasterization_zorder(1)
for sg_ind in sg_good_sol_vec:
ax3.plot(dt_vec * 1000.0, sg_ind, lw=1, color="lightblue", zorder=0)
ax3.plot(dt_vec * 1000.0, sg_vec, marker="o", lw=1, color="darkblue", label="Best model")
ax3.errorbar(
dt,
sg,
yerr=stderr,
fmt="o",
color="red",
ms=10,
ecolor="red",
elinewidth=3,
capsize=5,
capthick=3,
zorder=10000,
label="In vitro",
)
ax3.axhline(y=1, color="k", linestyle="--")
ax3.axvline(color="k", linestyle="--")
ax3.set_xlabel(r"$\Delta t$ (ms)")
ax3.set_ylabel("EPSP amplitude change")
ax3.legend()
fig3.tight_layout()
fig3.savefig("figures/graupner_dtscan.eps", rasterized=True, dpi=72)
def plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr):
"""Plot dt scan"""
dt_vec = np.linspace(-90e-3, 50e-3, 100)
sg_vec = []
for model_dt in dt_vec:
protocol = stdputil.Protocol(
['pre', 'post', 'post', 'post'], [model_dt, 20e-3, 20e-3], 0.1,
60.0, prot_id='%.2fms' % model_dt)
model_sg = stdputil.protocol_outcome(protocol, best_ind_dict)
sg_vec.append(model_sg)
try:
sg_good_sol_vec = pickle.load(open( "sg_good_sol_vec.pkl", "rb" ))
except IOError:
sg_good_sol_vec = []
for i, good_sol in enumerate(good_solutions):
#print(len(good_solutions), i)
sg_ind = []
for model_dt in dt_vec:
protocol = stdputil.Protocol(
['pre', 'post', 'post', 'post'], [model_dt, 20e-3, 20e-3],
0.1, 60.0, prot_id='%.2fms' % model_dt)
model_sg = stdputil.protocol_outcome(protocol, good_sol)
sg_ind.append(model_sg)
sg_good_sol_vec.append(sg_ind)
pickle.dump(sg_good_sol_vec, open( "sg_good_sol_vec.pkl", "wb" ))
fig3, ax3 = plt.subplots(figsize=(10, 10), facecolor='white')
ax3.set_rasterization_zorder(1)
for sg_ind in sg_good_sol_vec:
ax3.plot(dt_vec * 1000.0, sg_ind, lw=1, color='lightblue', zorder=0)
ax3.plot(dt_vec * 1000.0, sg_vec, marker='o', lw=1, color='darkblue',
label='Best model')
ax3.errorbar(dt, sg, yerr=stderr, fmt='o', color='red', ms=10,
ecolor='red', elinewidth=3, capsize=5, capthick=3,
zorder=10000, label='In vitro')
ax3.axhline(y=1, color='k', linestyle='--')
ax3.axvline(color='k', linestyle='--')
ax3.set_xlabel(r'$\Delta t$ (ms)')
ax3.set_ylabel('EPSP amplitude change')
ax3.legend()
fig3.tight_layout()
fig3.savefig('figures/graupner_dtscan.eps', rasterized=True, dpi=72)
def test_protocol_outcome():
"""
Test against Fig. 3 of Graupner and Brunel (2012).
TODO, Ask permission to Michael to add testing files.
Notes
-----
Data used for the test were kindly provprot_ided by Dr. M. Graupner.
Fig. 3F cannot be reproduced because generated using an approximate
solution.
"""
# Case 1: Fig. 3B
mgspike = np.loadtxt(
'/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/post_spike.dat')
dt = mgspike[:, 0]
outcome = np.zeros(len(dt))
for i in xrange(len(dt)):
p = stdputil.Protocol(['pre', 'post'], [dt[i] * 1e-3], 5.0, 200.0)
outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal)
npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05)
# Case 2: Fig. 3D
mgspike = np.loadtxt(
'/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/'
'post_burst_100.dat')
dt = mgspike[:, 0]
outcome = np.zeros(len(dt))
for i in xrange(len(dt)):
p = stdputil.Protocol(['post', 'post', 'pre'],
[11.5e-3, -dt[i] * 1e-3], 5.0, 100.0)
outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal)
npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05)
# Case 3: Fig. 3E
mgspike = np.loadtxt(
'/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/'
'post_burst_30.dat')
dt = mgspike[:, 0]
outcome = np.zeros(len(dt))
for i in xrange(len(dt)):
p = stdputil.Protocol(['post', 'post', 'pre'],
[11.5e-3, -dt[i] * 1e-3], 5.0, 30.0)
outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal)
npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05)
def compute_synaptic_gain_with_lists(self, param_values):
"""Compute synaptic gain for all protocols.
:param param_values: iterable
Parameters list
"""
param_dict = self.get_param_dict(param_values)
syn_gain = [stdputil.protocol_outcome(protocol, param_dict)
for protocol in self.protocols]
return syn_gain
def compute_synaptic_gain_with_lists(self, param_values):
"""Compute synaptic gain for all protocols.
:param param_values: iterable
Parameters list
"""
param_dict = self.get_param_dict(param_values)
syn_gain = [
stdputil.protocol_outcome(protocol, param_dict)
for protocol in self.protocols
]
return syn_gain
def test_protocol_outcome():
"""
Test against Fig. 3 of Graupner and Brunel (2012).
TODO, Ask permission to Michael to add testing files.
Notes
-----
Data used for the test were kindly provprot_ided by Dr. M. Graupner.
Fig. 3F cannot be reproduced because generated using an approximate
solution.
"""
# Case 1: Fig. 3B
mgspike = np.loadtxt('/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/post_spike.dat')
dt = mgspike[:, 0]
outcome = np.zeros(len(dt))
for i in xrange(len(dt)):
p = stdputil.Protocol(['pre', 'post'], [dt[i] * 1e-3], 5.0, 200.0)
outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal)
npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05)
# Case 2: Fig. 3D
mgspike = np.loadtxt('/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/post_burst_100.dat')
dt = mgspike[:, 0]
outcome = np.zeros(len(dt))
for i in xrange(len(dt)):
p = stdputil.Protocol(['post', 'post', 'pre'], [11.5e-3, -dt[i] * 1e-3], 5.0, 100.0)
outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal)
npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05)
# Case 3: Fig. 3E
mgspike = np.loadtxt('/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/post_burst_30.dat')
dt = mgspike[:, 0]
outcome = np.zeros(len(dt))
for i in xrange(len(dt)):
p = stdputil.Protocol(['post', 'post', 'pre'], [11.5e-3, -dt[i] * 1e-3], 5.0, 30.0)
outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal)
npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05)
def evaluate_with_lists(self, param_values):
"""Evaluate individual
:param param_values: iterable
Parameters list
"""
param_dict = self.get_param_dict(param_values)
err = []
for protocol, sg, stderr in zip(self.protocols, self.sg, self.stderr):
res = stdputil.protocol_outcome(protocol, param_dict)
err.append(numpy.abs(sg - res) / stderr)
return err
def evaluate_with_lists(self, param_values):
"""Evaluate individual
:param param_values: iterable
Parameters list
"""
param_dict = self.get_param_dict(param_values)
err = []
for protocol, sg, stderr in \
zip(self.protocols, self.sg, self.stderr):
res = stdputil.protocol_outcome(protocol, param_dict)
err.append(numpy.abs(sg - res) / stderr)
return err
def plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr):
"""Plot dt scan"""
dt_vec = np.linspace(-90e-3, 50e-3, 100)
sg_vec = []
for model_dt in dt_vec:
protocol = stdputil.Protocol(
["pre", "post", "post", "post"],
[model_dt, 20e-3, 20e-3],
0.1,
60.0,
prot_id="%.2fms" % model_dt,
)
model_sg = stdputil.protocol_outcome(protocol, best_ind_dict)
sg_vec.append(model_sg)
try:
sg_good_sol_vec = pickle.load(open("sg_good_sol_vec.pkl", "rb"))
except IOError:
sg_good_sol_vec = []
for _, good_sol in enumerate(good_solutions):
sg_ind = []
for model_dt in dt_vec:
protocol = stdputil.Protocol(
["pre", "post", "post", "post"],
[model_dt, 20e-3, 20e-3],
0.1,
60.0,
prot_id="%.2fms" % model_dt,
)
model_sg = stdputil.protocol_outcome(protocol, good_sol)
sg_ind.append(model_sg)
sg_good_sol_vec.append(sg_ind)
pickle.dump(sg_good_sol_vec, open("sg_good_sol_vec.pkl", "wb"))
fig3, ax3 = plt.subplots(figsize=(10, 10), facecolor="white")
ax3.set_rasterization_zorder(1)
for sg_ind in sg_good_sol_vec:
ax3.plot(dt_vec * 1000.0, sg_ind, lw=1, color="lightblue", zorder=0)
ax3.plot(
dt_vec * 1000.0,
sg_vec,
marker="o",
lw=1,
color="darkblue",
label="Best model",
)
ax3.errorbar(
dt,
sg,
yerr=stderr,
fmt="o",
color="red",
ms=10,
ecolor="red",
elinewidth=3,
capsize=5,
capthick=3,
zorder=10000,
label="In vitro",
)
ax3.axhline(y=1, color="k", linestyle="--")
ax3.axvline(color="k", linestyle="--")
ax3.set_xlabel(r"$\Delta t$ (ms)")
ax3.set_ylabel("EPSP amplitude change")
ax3.legend()
fig3.tight_layout()
fig3.savefig("figures/graupner_dtscan.eps", rasterized=True, dpi=72)