Merge branch 'master' of https://github.com/Nyxas/neuronModel

models/LIFACnoise.py

@@ -226,7 +226,7 @@ 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:
-            warn("Search was stopped no value was found!")
+            warn("Search was stopped. Upper and lower bounds converged without finding a value closer than threshold!")
             return middle
 
 

run_Fitter.py

@@ -49,16 +49,14 @@ def fit_cell_base(parameter):
           "\n error: {:.2f}".format(sum(error)))
 
 
-def fit_all_cells_parallel_sync(cells, start_parameters, results_base_folder):
+def fit_all_cells_parallel_sync(cells, start_parameters, thread_pool, results_base_folder):
     parameter = []
     for cell in cells:
         for i, s_pars in enumerate(start_parameters):
             parameter.append((cell, i, s_pars, results_base_folder))
 
-    core_count = mp.cpu_count()
-    pool = mp.Pool(core_count - 1)
     time1 = time.time()
-    pool.map(fit_cell_base, parameter)
+    thread_pool.map(fit_cell_base, parameter)
     time2 = time.time()
     print("Time taken for all cells and start parameters ({:}): {:.2f}s".format(len(parameter), time2 - time1))
 
@@ -273,17 +271,29 @@ def save_fitting_run_info(cell_data, parameters, start_parameters, plot=False, s
 
 
 def test_effect_of_refractory_period():
-    ref_periods = np.arange(0.0006, 0.001, 0.0015)
+    ref_periods = [0.0006, 0.001, 0.0015]
-
+    counter = 0
-    cells = [c for c in icelldata_of_dir("./data/")]
+    core_count = mp.cpu_count()
+    pool = mp.Pool(core_count - 1)
+    for cell in icelldata_of_dir("./data/"):
+        counter += 1
+        if counter < 10:
+            continue
+        elif counter >= 14:
+            return
         start_parameters_base = [p for p in iget_start_parameters()]
         for ref_period in ref_periods:
-        results_base_folder = "./test_routines/ref_period_{:.3f}/".format(ref_period)
+            print(cell.get_data_path())
+            print("ref period: {:.4f}".format(ref_period))
+            results_base_folder = "./test_routines/ref_period_{:.4f}/".format(ref_period)
             all_start_parameters = copy.deepcopy(start_parameters_base)
 
             for par_set in all_start_parameters:
                 par_set["refractory_period"] = ref_period
-        fit_all_cells_parallel_sync(cells, all_start_parameters, results_base_folder)
+            fit_all_cells_parallel_sync([cell], all_start_parameters, pool, results_base_folder)
+
+        del cell
+
 
 if __name__ == '__main__':
     main()

sam_experiments.py

@@ -4,6 +4,7 @@ from models.LIFACnoise import LifacNoiseModel
 import numpy as np
 import matplotlib.pyplot as plt
 import helperFunctions as hF
+from CellData import CellData
 
 
 def main():
@@ -12,8 +13,23 @@ def main():
                   'v_base': 0, 'step_size': 5e-05, 'dend_tau': 0.0008667253013050408, 'v_zero': 0, 'v_offset': -6.25,
                   'noise_strength': 0.03337309379328535, 'a_zero': 2, 'threshold': 1, 'delta_a': 0.0726267312975076}
     eod_freq = 658
-
+    cell_data = CellData("./data/2012-12-13-ao-invivo-1/")
     model = LifacNoiseModel(parameters)
+    mean_duration = np.mean(cell_data.get_sam_durations())
+    contrasts = cell_data.get_sam_contrasts()
+    spiketimes = cell_data.get_sam_spiketimes()
+    for i, m_freq in enumerate(cell_data.get_sam_delta_frequencies()):
+
+        stimulus = SAM(eod_freq, contrasts[i], m_freq)
+        prob_desnity_function_model = generate_pdf(model, stimulus, sim_length=mean_duration)
+        for spikes in spiketimes[i]:
+            prob_density_cell = spiketimes_calculate_pdf(spikes, cell_data.get_sampling_interval())
+
+            plt.plot(prob_density_cell)
+            plt.plot(prob_desnity_function_model)
+            plt.show()
+            plt.close()
+
 
     # __init__(carrier_frequency, contrast, modulation_frequency, start_time=0, duration=np.inf, amplitude=1)
     mod_freqs = np.arange(-60, eod_freq*4 + 61, 10)
@@ -58,6 +74,19 @@ def generate_pdf(model, stimulus, trials=4, sim_length=3, kernel_width=0.005):
     return mean_rate
 
 
+def spiketimes_calculate_pdf(spikes, step_size, kernel_width=0.005):
+    length = int(spikes[-1] / step_size)+1
+    binary = np.zeros(length)
+    spikes = [int(s / step_size) for s in spikes]
+    for s_idx in spikes:
+        binary[s_idx] = 1
+
+    kernel = gaussian_kernel(kernel_width, step_size)
+    rate = np.convolve(binary, kernel, mode='same')
+
+    return rate
+
+
 def gaussian_kernel(sigma, dt):
     x = np.arange(-4. * sigma, 4. * sigma, dt)
     y = np.exp(-0.5 * (x / sigma) ** 2) / np.sqrt(2. * np.pi) / sigma

variableEffect.py

@@ -46,8 +46,8 @@ def main():
 
 
 def test_effect_of_two_variables():
-    eod_freqs = np.arange(100, 1001, 20)
+    eod_freqs = np.arange(100, 1001, 50)
-    ref_periods = np.arange(0, 0.00201, 0.0001)
+    ref_periods = np.arange(0, 0.00201, 0.0002)
     variables = ("bf", "vs", "sc", "cv", "burst", "f_inf_s", "f_zero_s")
     colorbar_labels = ("Frequency in Hz", "Vector strength", "serial correlation lag=1", "Coefficient of Variation",
                        "Burstiness", "f_inf slope", "f_zero slope")