def test_no_ion_selective_setter(self):
protocol = protocols.SingleActionPotentialProtocol()
paci_baseline = paci_2018.PaciModel()
ion_selective_scales = {'I_CaL': .4, 'I_NaCa': .4}
paci_no_ion = paci_2018.PaciModel()
baseline_trace = paci_baseline.generate_response(protocol)
paci_baseline.no_ion_selective = ion_selective_scales
baseline_no_ion_trace = paci_baseline.generate_response(
protocol, is_no_ion_selective=True)
tst.assert_raises(AssertionError, tst.assert_array_equal,
baseline_trace.y, baseline_no_ion_trace.y,
'generate_no_ion_trace() does not update trace')
def test_no_ion_selective_IK1_no_effect(self):
protocol = protocols.SingleActionPotentialProtocol()
paci_k1_increase = paci_2018.PaciModel(
updated_parameters={'G_K1': 1.4})
ion_selective_scales = {'I_K1': .4}
paci_k1_no_ion = paci_2018.PaciModel(
no_ion_selective_dict=ion_selective_scales)
trace_ion_selective = paci_k1_increase.generate_response(protocol)
trace_no_ion_selective = paci_k1_no_ion.generate_response(
protocol, is_no_ion_selective=True)
tst.assert_raises(AssertionError, tst.assert_array_equal,
trace_ion_selective.y, trace_no_ion_selective.y,
'generate_no_ion_trace() does not update trace')
def plot_baseline_irregular_pacing_trace():
model = paci_2018.PaciModel()
trace = model.generate_response(
protocol=protocols.IrregularPacingProtocol(
duration=10,
stimulation_offsets=[0.6, 0.4, 1., 0.1, 0.2, 0.0, 0.8, 0.9]))
trace.plot()
trace.pacing_info.plot_peaks_and_apd_ends(trace=trace)
plt.savefig(
'figures/Irregular Pacing Figure/baseline_irregular_pacing_trace.svg')
def plot_all_in_system_of_equations(duration=10):
model = paci_2018.PaciModel()
model.generate_response(
protocol=protocols.SingleActionPotentialProtocol(duration=duration))
for i in range(len(model.y_names)):
plt.figure()
plt.plot([timestamp[i] for timestamp in model.full_y])
plt.savefig('figures/before_hold_i_na_480/{}.png'.format(
model.y_names[i]))
def test_generate_trace_SAP(self):
protocol = protocols.SingleActionPotentialProtocol()
baseline_model = paci_2018.PaciModel()
baseline_model.generate_response(protocol)
self.assertTrue(
len(baseline_model.t) > 100, 'Paci errored in less than .4s')
self.assertTrue(
min(baseline_model.y_voltage) < -.01,
'baseline Paci min is greater than .01')
self.assertTrue(
max(baseline_model.y_voltage) < .06,
'baseline Paci max is greater than .06')
def plot_baseline_voltage_clamp_trace():
model = paci_2018.PaciModel()
steps = [
protocols.VoltageClampStep(duration=0.9, voltage=-0.08),
protocols.VoltageClampStep(duration=0.7, voltage=-0.12),
protocols.VoltageClampStep(duration=0.5, voltage=-0.06),
protocols.VoltageClampStep(duration=0.9, voltage=-0.04),
protocols.VoltageClampStep(duration=0.95, voltage=0.02),
protocols.VoltageClampStep(duration=0.9, voltage=0.04),
]
trace = model.generate_response(
protocol=protocols.VoltageClampProtocol(steps=steps))
trace.plot_with_currents()
plt.savefig(
'figures/Voltage Protocol Figure/baseline_voltage_clamp_trace.svg')
def main():
"""Run parameter tuning or voltage clamp protocol experiments here
"""
paci_model = paci_2018.PaciModel()
paci_model.generate_response(protocol=VC_PROTOCOL_ZERO)
fig = plt.figure(figsize=(10, 5))
ax_1 = fig.add_subplot(511)
ax_1.plot([1000 * i for i in paci_model.t],
[i * 1000 for i in paci_model.y_voltage],
'b',
label='Voltage')
plt.xlabel('Time (ms)')
plt.ylabel(r'$V_m$ (mV)')
I_NaCa = []
I_CaL = []
Cai = []
CaSR = []
i = 0
for currents in paci_model.current_response_info.currents:
I_CaL.append(currents[4].value)
I_NaCa.append(currents[8].value)
Cai.append(paci_model.full_y[i][2])
CaSR.append(paci_model.full_y[i][1])
i = i + 1
ax_2 = fig.add_subplot(512)
ax_2.plot([1000 * i for i in paci_model.t], I_CaL, 'r--', label='I_CaL')
plt.ylabel(r'$I_{CaL}$ (nA/nF)')
ax_3 = fig.add_subplot(513)
ax_3.plot([1000 * i for i in paci_model.t], I_NaCa, 'r--', label='I_NaCa')
plt.ylabel(r'$I_{NaCa}$ (nA/nF)')
ax_4 = fig.add_subplot(514)
ax_4.plot([1000 * i for i in paci_model.t], Cai, 'r--', label='Ca_i')
plt.ylabel(r'$Cai$ (nA/nF)')
ax_5 = fig.add_subplot(515)
ax_5.plot([1000 * i for i in paci_model.t], CaSR, 'r--', label='CaSR')
plt.ylabel(r'$Ca_{SR}$ (nA/nF)')
plt.show()
def generate_restitution_curve():
max_pacing_rate = 1.1
model = paci_2018.PaciModel()
cycle_lengths = []
pacing_rates = np.arange(0.1, max_pacing_rate, 0.1)
for i in pacing_rates:
print('Generating trace at pacing rate: {}'.format(i))
stimulation_count = 8
curr_trace = model.generate_response(
protocol=protocols.IrregularPacingProtocol(
duration=20,
stimulation_offsets=[i for _ in range(stimulation_count)]))
plt.figure()
curr_trace.plot()
curr_trace.pacing_info.plot_peaks_and_apd_ends(trace=curr_trace)
plt.savefig('figures/Restitution Curve/{}_PacingRate.svg'.format(i))
relevant_peaks = curr_trace.pacing_info.peaks[1:1+stimulation_count]
curr_cycle_lengths = []
for j in range(1, len(relevant_peaks)):
curr_cycle_lengths.append(relevant_peaks[j] - relevant_peaks[j-1])
print('Curr cycle lengths: {}'.format(curr_cycle_lengths))
# Remove outliers
if i == 0.5:
median = 0.871659106127219
else:
median = np.median(np.array(curr_cycle_lengths))
print('Median: {}'.format(median))
cycle_lengths.append(median)
print('Pacing rates')
print(pacing_rates)
print('Cycle lengths')
print(cycle_lengths)
plt.figure()
plt.plot(pacing_rates, cycle_lengths)
plt.xlabel('Pacing Rate (s)')
plt.ylabel('Cycle Length (s)')
plt.savefig('figures/Restitution Curve/Restitution Curve.svg')