prepare for testing of other variables

RelationAdaptionVariables.py

@@ -10,68 +10,60 @@ import functions as fu
 
 
 def main():
-    #boltzmann_vars = find_fitting_boltzmann()
-    #test_firerate_model(boltzmann_vars)
-    find_relation()
+    lifac_model = LIFACModel({"delta_a": 0})
+    stimulus_strengths = np.arange(20, 32, 1)
 
-def find_fitting_boltzmann():
-    lifac = LIFACModel({"delta_a": 0})
+    line_vars, boltzmann_vars = find_fitting_boltzmann(lifac_model, stimulus_strengths)
+    find_relation(line_vars, boltzmann_vars)
 
+
+def find_fitting_boltzmann(lifac_model, stimulus_strengths):
+    # Requieres a lifac model with adaption delta_a = 0, so just the base is fit
     frequencies = []
-    stim_strengths = np.arange(20, 32, 0.5)
-    duration = 0.5
-    for stim_strength in stim_strengths:
-        lifac.simulate(StepStimulus(0, duration, stim_strength), duration)
 
-        spiketimes = lifac.get_spiketimes()
+    duration = 0.2
+    for stim_strength in stimulus_strengths:
+        lifac_model.simulate(StepStimulus(0, duration, stim_strength), duration)
+
+        spiketimes = lifac_model.get_spiketimes()
         if len(spiketimes) == 0:
             frequencies.append(0)
             continue
-        time, freq = hf.calculate_isi_frequency(spiketimes, 0, lifac.get_sampling_interval()/1000)
+        time, freq = hf.calculate_isi_frequency(spiketimes, 0, lifac_model.get_sampling_interval() / 1000)
 
         frequencies.append(freq[-1])
 
-    popt, pcov = curve_fit(fu.line, stim_strengths, frequencies)
-    popt2, pcov = curve_fit(fu.full_boltzmann, stim_strengths, frequencies, p0=[700, 0, 5, 25], bounds=([0, 0, -np.inf, -np.inf], [3000, 0.001, np.inf, np.inf]))
+    popt, pcov = curve_fit(fu.line, stimulus_strengths, frequencies)
+    popt2, pcov = curve_fit(fu.full_boltzmann, stimulus_strengths, frequencies, p0=[700, 0, 5, 25], bounds=([0, 0, -np.inf, -np.inf], [3000, 0.001, np.inf, np.inf]))
     print("line:", popt)
     print("boltzmann:", popt2)
-    # plt.plot(stim_strengths, frequencies)
-    # plt.plot(stim_strengths, [fu.line(x, popt[0], popt[1]) for x in stim_strengths], '.')
-    # plt.plot(stim_strengths, [fu.full_boltzmann(x, popt2[0], popt2[1], popt2[2], popt2[3]) for x in stim_strengths], 'o')
+    # plt.plot(stimulus_strengths, frequencies)
+    # plt.plot(stimulus_strengths, [fu.line(x, popt[0], popt[1]) for x in stimulus_strengths], '.')
+    # plt.plot(stimulus_strengths, [fu.full_boltzmann(x, popt2[0], popt2[1], popt2[2], popt2[3]) for x in stimulus_strengths], 'o')
     # plt.show()
-    return popt2
+    return popt, popt2
 
 
-def test_firerate_model(boltzmann_vars):
-    fr_model = FirerateModel(params={"function_params": boltzmann_vars, "adaptation_factor": 0})
+def find_relation(line_vars, boltzmann_vars, stimulus_strengths):
+    # boltzmann_vars = [2.00728705e+02, 1.09905953e-12, 1.03639686e-01, 2.55002788e+01]
+    # line_vars = [5.10369405, -29.79774806]
+    # example values for base lifac (15.1.20) and stimulus 20-32
 
-    frequencies = []
-    stim_strengths = np.arange(0, 50, 0.5)
-    duration = 0.5
-    for stim_strength in stim_strengths:
-        fr_model.simulate(StepStimulus(0, duration, stim_strength), duration)
-
-        frequencies.append(fr_model.get_frequency()[-1])
-
-    plt.plot(stim_strengths, frequencies)
-    plt.plot(np.arange(20, 32, 1), [fu.line(x, 5.10369, -29.7977481) for x in np.arange(20, 32, 1)], 'o')
-    plt.show()
+    stimulus_step_size = 2
+    stimulus_range = np.arange(20, 32, stimulus_step_size)
 
 
-def find_relation():
-    stimulus_step_size = 1
-    stimulus_range = np.arange(20, 32, stimulus_step_size)
-    boltzmann_vars = [2.00728705e+02, 1.09905953e-12, 1.03639686e-01, 2.55002788e+01]
-    line_vars = [5.10369405, -29.79774806]
-    duration = 0.5
+    duration = 0.2
 
-    lifac_adaption_strength_range = np.arange(0, 3, 0.2)
+    lifac_adaption_strength_range = np.arange(0, 3, 0.5)
     firerate_adaption_variables = []
     for lifac_adaption_strength in lifac_adaption_strength_range:
-        lifac = LIFACModel({"delta_a": lifac_adaption_strength, "tau_a": 20}, "")
+        print(lifac_adaption_strength)
+        lifac = LIFACModel({"delta_a": lifac_adaption_strength, "tau_a": 20})
 
         adapted_frequencies = []
         for stim in stimulus_range:
+            print("stim:", stim)
             stimulus = StepStimulus(0, duration, stim)
             lifac.simulate(stimulus, duration)
             spiketimes = lifac.get_spiketimes()
@@ -111,12 +103,28 @@ def find_relation():
     plt.title("Relation of adaption strength variables:\n Small 'subplots' value for different stimulus strength")
     plt.xlabel("lifac adaption strength: delta_a")
     plt.ylabel("firerate adaption strength: alpha")
-    plt.savefig("figures/adaption_relation_stimulus_strength.png")
+    plt.savefig("figures/adaption_relation_stimulus_strength_stepsize_0-0001.png")
     plt.close()
     popt, pcov = curve_fit(fu.line, lifac_adaption_strength_range, mean_firerate_adaption_value)
 
     print(popt)
 
 
+def test_firerate_model( boltzmann_vars):
+    fr_model = FirerateModel(params={"function_params": boltzmann_vars, "adaptation_factor": 0})
+
+    frequencies = []
+    stim_strengths = np.arange(0, 50, 0.5)
+    duration = 0.5
+    for stim_strength in stim_strengths:
+        fr_model.simulate(StepStimulus(0, duration, stim_strength), duration)
+
+        frequencies.append(fr_model.get_frequency()[-1])
+
+    plt.plot(stim_strengths, frequencies)
+    plt.plot(np.arange(20, 32, 1), [fu.line(x, 5.10369, -29.7977481) for x in np.arange(20, 32, 1)], 'o')
+    plt.show()
+
+
 if __name__ == '__main__':
     main()