traces_file = "gary_decker_traces.txt"
seed = 2
npr.seed(seed)
model_number = 8 # Decker dog
original_gs, g_parameters = ms.get_original_params(model_number)
original_gs = np.array(original_gs)
print original_gs, "\n"
lower_bounds = 0.5 * original_gs
upper_bounds = 2.0 * original_gs
times = np.arange(solve_start, solve_end + solve_timestep, solve_timestep)
ap = ap_simulator.APSimulator()
ap.DefineStimulus(stimulus_magnitude, stimulus_duration, stimulus_period,
stimulus_start_time)
ap.DefineSolveTimes(solve_start, solve_end, solve_timestep)
ap.DefineModel(model_number)
ap.SetNumberOfSolves(num_solves)
true_params = np.loadtxt(params_file)
expt_traces = np.loadtxt(traces_file)
best_params_file = "gary_decker_optimize_best_fits.txt"
best_params = np.loadtxt(best_params_file)
#print "best_params:", best_params
num_expts = best_params.shape[0]
def test_ap_simulator(self):
stim_amp = -25.5
stim_duration = 2
stim_period = 1000
stim_start = 10.
solve_start = 0.
sampling_timestep = 0.2
ap = ap_simulator.APSimulator()
ap.DefineProtocol(protocol_number)
ap.DefineModel(model_number)
how_many_repeats = 1000
start = time.time()
for _ in xrange(how_many_repeats):
test_trace = ap.SolveForVoltageTraceWithParams(params)
#example_loglikelihood_1 = example_loglikelihood(test_trace)
example_loglikelihood_1 = ap.ExampleLogLikelihoodFunction(
test_trace)
time_taken = time.time() - start
print "\nTime taken by ap_simulator SolveForVoltageTraceWithParams: {} s\n".format(
round(time_taken, 2))
start = time.time()
for _ in xrange(how_many_repeats):
test_trace = ap.SolveForVoltageTraceWithParams(params)
numpy_loglikelihood = example_loglikelihood(test_trace)
time_taken = time.time() - start
print "\nTime taken by numpy example_loglikelihood: {} s\n".format(
round(time_taken, 2))
print "numpy_loglikelihood =", numpy_loglikelihood
EXE_NAME = "/home/rossj/chaste-build/projects/RossJ/apps/APApp" # this needs to be more general for chaste build path
process = subprocess.Popen(EXE_NAME,
False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stdin=subprocess.PIPE)
PROVENANCE_HEADER = "This version of Chaste was compiled on:"
reading_values, looking_for_blank = False, False
while (not reading_values):
line = process.stdout.readline()
if line.startswith(PROVENANCE_HEADER):
looking_for_blank = True
elif looking_for_blank and line.strip() == "":
reading_values = True
process.stdin.write(str(protocol_number) + '\n')
process.stdin.write(str(model_number) + '\n')
start = time.time()
for _ in xrange(how_many_repeats):
output_string_to_c = " ".join(map(str, params)) + '\n'
#print "output_string_to_c =", output_string_to_c
process.stdin.write(output_string_to_c)
from_chaste = process.stdout.readline()
#print "from_chaste =", from_chaste
example_loglikelihood_2 = float(from_chaste)
time_taken = time.time() - start
print "\nTime taken by APApp: {} s\n".format(round(time_taken, 2))
if (example_loglikelihood_1 == example_loglikelihood_2):
print "The two outputted 'log-likelihoods' are the same, don't worry."
else:
print "ap_simulator:", example_loglikelihood_1
print "app: ", example_loglikelihood_2
print "numpy: ", numpy_loglikelihood
def do_for_trace(trace_number):
def sum_of_square_diffs(params,
expt_trace): #,expt_trace,upper_bounds,ap):
if np.any(params < 0) or np.any(params > upper_bounds):
#print test_gs
return np.inf
#ap.LoadStateVariables()
ap.SetToModelInitialConditions()
test_trace = ap.SolveForVoltageTraceWithParams(params)
return np.sum((test_trace - expt_trace)**2)
try:
trace_label = "Trace_2_2_{}_1".format(trace_number)
except:
print "No trace", trace_number
return None
expt_times = 1000. * all_APs[trace_label][:, 0]
expt_trace = 1000. * all_APs[trace_label][:, 1]
solve_start = expt_times[0]
solve_end = expt_times[-1]
solve_timestep = expt_times[1] - expt_times[0]
times = np.arange(solve_start, solve_end + solve_timestep, solve_timestep)
ap = ap_simulator.APSimulator()
ap.SetNumberOfSolves(num_solves)
ap.DefineSolveTimes(solve_start, solve_end, solve_timestep)
ap.DefineStimulus(stimulus_magnitude, stimulus_duration, stimulus_period,
stimulus_start_time)
ap.DefineModel(model_number)
ap.UseDataClamp(data_clamp_on, data_clamp_off)
ap.SetExperimentalTraceAndTimesForDataClamp(expt_times, expt_trace)
ap.SetExtracellularPotassiumConc(extra_K_conc)
for python_seed in xrange(1, 4):
x0 = original_gs * (1. + npr.randn(len(original_gs)))
x0[np.where(x0 < 0)] = 1e-5
print "x0 =", x0
original_obj_fun = sum_of_square_diffs(
x0, expt_trace) #,expt_trace,upper_bounds,ap)
print "original_obj_fun =", original_obj_fun
start = time.time()
res = so.minimize(sum_of_square_diffs,
x0,
args=(expt_trace, ),
method='Nelder-Mead')
time_taken = time.time() - start
best_gs = res.x
best_f = res.fun
print "\nTime taken: {} s\n".format(round(time_taken, 1))
#ap.LoadStateVariables()
ap.SetToModelInitialConditions()
best_fit_trace = ap.SolveForVoltageTraceWithParams(best_gs)
print "original_gs:", original_gs
print "best_gs:", best_gs
#ap.LoadStateVariables()
ap.SetToModelInitialConditions()
true_trace = ap.SolveForVoltageTraceWithParams(original_gs)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
ax.plot(expt_times, expt_trace, color='red', label='Expt')
ax.plot(times, true_trace, color='blue', label='Original')
ax.plot(times, best_fit_trace, color='green', label='Best fit')
ax.legend()
fig.tight_layout()
fig.savefig(output_dir +
"trace_{}_fit_to_model_{}_python_seed_{}.png".format(
trace_number, model_number, python_seed))
#plt.show()
plt.close()
return None
solve_timestep = expt_times[1] - expt_times[0]
data_clamp_on = 50
data_clamp_off = 52
num_solves = 100
stimulus_magnitude, stimulus_duration, stimulus_period, stimulus_start_time = 0, 1, 1000, 0
# need to make sure these are always same as in C++ protocol
# might have to change it to have protocols hard-coded into Python instead of C++
expt_times = np.arange(solve_start, solve_end + solve_timestep, solve_timestep)
num_pts = len(expt_times)
cell = ap.APSimulator()
cell.SetNumberOfSolves(num_solves)
cell.DefineStimulus(stimulus_magnitude, stimulus_duration, stimulus_period,
stimulus_start_time)
cell.DefineSolveTimes(solve_start, solve_end, solve_timestep)
cell.DefineModel(model)
cell.UseDataClamp(data_clamp_on, data_clamp_off)
cell.SetExperimentalTraceAndTimesForDataClamp(expt_times, expt_trace)
test_trace = cell.SolveForVoltageTraceWithParams(original_gs)
print "len(test_trace) =", len(test_trace)
print "len(expt_times) =", len(expt_times)
ax.plot(expt_times, test_trace, color='blue')
ax.axvline(data_clamp_on, color='green')
ax.axvline(data_clamp_off, color='green')
plt.show(block=True)
