fit no dend_tau increased v_offset baseline sim to 9 from 5

models/LIFACnoise.py

@@ -184,7 +184,7 @@ class LifacNoiseModel(AbstractModel):
 
     def find_v_offset(self, goal_baseline_frequency, base_stimulus, threshold=2, border=50000):
         test_model = self.get_model_copy()
-        simulation_length = 5
+        simulation_length = 9
 
         v_search_step_size = 100
 
@@ -229,8 +229,8 @@ def binary_search_base_freq(model: LifacNoiseModel, base_stimulus, goal_frequenc
             raise ValueError("binary_search_base_freq() - LifacNoiseModel: Goal frequency might be nan?")
 
         if abs(upper_bound - lower_bound) < 0.0001:
-            print("v_offset search stopped. bounds converged! freq: {:.2f}, lower {:.1f}, upper: {:.1f}"
+            print("v_offset search stopped. bounds converged! freq: {:.2f}, bounds: {:.0f}"
-                  .format(frequency, lower_bound, upper_bound))
+                  .format(frequency, lower_bound))
             warn("Search was stopped. Upper and lower bounds converged without finding a value closer than threshold!")
             return middle
 

run_Fitter.py

@@ -14,8 +14,8 @@ from helperFunctions import plot_errors
 
 import multiprocessing as mp
 
-SAVE_DIRECTORY = "./results/ref_and_tau/no_ref_period"
+SAVE_DIRECTORY = "./results/ref_and_tau/no_dend_tau/"
-SAVE_DIRECTORY_BEST = "./results/ref_and_tau/nrp_best"
+SAVE_DIRECTORY_BEST = "./results/ref_and_tau/ndt_best"
 # [vs, sc, cv, isi_hist, bursty, f_inf, f_inf_slope, f_zero, f_zero_slope, f0_curve]
 ERROR_WEIGHTS = (1, 1, 1, 1, 1, 1, 1, 1, 0, 1)
 
@@ -31,7 +31,7 @@ def main():
         quit()
 
     # test_single_cell("data/invivo/2010-11-08-al-invivo-1/")
-    test_single_cell("data/ref_and_dend_set/2012-12-21-am-invivo-1/")
+    test_single_cell("data/final/2012-12-21-am-invivo-1/")
 
     # start_parameters = [p for p in iget_start_parameters()]
     # cell_data = CellData("data/invivo_bursty/2014-03-19-ae-invivo-1/")
@@ -45,7 +45,7 @@ def test_single_cell(path):
     for i, p in enumerate(start_parameters):
         fitter = Fitter()
         fitter.set_data_reference_values(cell_data)
-        fmin, res_par = fitter.fit_routine_no_ref_period(p, ERROR_WEIGHTS)
+        fmin, res_par = fitter.fit_routine_no_dend_tau(p, ERROR_WEIGHTS)
 
         cell_path = os.path.split(cell_data.get_data_path())[-1]
 
@@ -60,7 +60,7 @@ def fit_cell_base(parameters):
     time1 = time.time()
     fitter = Fitter()
     fitter.set_data_reference_values(parameters[0])
-    fmin, res_par = fitter.fit_routine_no_ref_period(parameters[2], ERROR_WEIGHTS)
+    fmin, res_par = fitter.fit_routine_no_dend_tau(parameters[2], ERROR_WEIGHTS)
 
     cell_data = parameters[0]
     cell_name = os.path.split(cell_data.get_data_path())[-1]

test.py

@@ -19,7 +19,7 @@ import helperFunctions as hF
 import models.smallModels as sM
 from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus
 from matplotlib import gridspec
-from plottools.axes import labelaxes_params
+# from plottools.axes import labelaxes_params
 
 
 
@@ -34,30 +34,19 @@ from plottools.axes import labelaxes_params
 
 fit = ModelFit("results/final_2/2012-07-12-ag-invivo-1/start_parameter_4_err_6.11/")
 model = fit.get_model()
+print(model.parameters)
+eodf = fit.get_cell_data().get_eod_frequency()
+baseline = BaselineModel(model, eod_frequency=eodf)
+print("model BF: {:.2f}".format(baseline.get_baseline_frequency()))
 
-base_stim = SinusoidalStepStimulus(fit.get_cell_data().get_eod_frequency(), 0, 0)
+model_no_dend = model.get_model_copy()
-time_list = []
+model_no_dend.parameters["dend_tau"] = 0
-freq_list = []
+model_no_dend.parameters["input_scaling"] = 20
-con_freq_list = []
+
-duration = 10
+baseline = BaselineModel(model_no_dend, eod_frequency=eodf)
-step = model.get_sampling_interval()
+print("no dend BF: {:.2f}".format(baseline.get_baseline_frequency()))
-g = gaussian_kernel(0.005, step)
+baseline.plot_interspike_interval_histogram()
-for i in range(20):
+
-    print(i)
+v1, spikes = model_no_dend.simulate(SinusoidalStepStimulus(eodf, 0, 0), 2)
-    v1, spikes = model.simulate(base_stim, duration)
+plt.plot(v1)
-
-    binary = np.zeros(int(np.rint(duration / step)))
-    for s in spikes:
-        binary[int(np.rint(s/step))] = 1
-    rate = np.convolve(binary, g, mode='same')
-    con_freq_list.append(rate)
-
-    time, freq = hF.calculate_time_and_frequency_trace(spikes, model.get_sampling_interval())
-    time_list.append(time)
-    freq_list.append(freq)
-rates = np.array(con_freq_list)
-mean_rate = np.mean(rates, axis=0)
-mean_time, mean_freq = hF.calculate_mean_of_frequency_traces(time_list, freq_list, model.get_sampling_interval())
-plt.plot(np.arange(0, 10, step), mean_rate, alpha=0.5)
-plt.plot(mean_time, mean_freq, alpha=0.5)
 plt.show()