Alexander/P-unit_model
2
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

improve search for v-offset

Browse Source
This commit is contained in:
AlexanderOtt 2020-03-27 10:22:56 +01:00
parent cef2f7a016
commit c841bed77e
1 changed files with 13 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

22
models/LIFACnoise.py
View File

@ -244,9 +244,9 @@ class LifacNoiseModel(AbstractModel):
test_model = self.get_model_copy()
simulation_length = 5
v_search_step_size = 1000
v_search_step_size = 100
current_v_offset = 0
current_v_offset = -400
current_freq = test_v_offset(test_model, current_v_offset, base_stimulus, simulation_length)
@ -256,9 +256,6 @@ class LifacNoiseModel(AbstractModel):
current_v_offset += v_search_step_size
current_freq = test_v_offset(test_model, current_v_offset, base_stimulus, simulation_length)
# if current_v_offset == 0:
# return -1000
lower_bound = current_v_offset - v_search_step_size
upper_bound = current_v_offset
@ -275,26 +272,33 @@ def binary_search_base_freq(model: LifacNoiseModel, base_stimulus, goal_frequenc
frequency = test_v_offset(model, middle, base_stimulus, simulation_length)
# print('{:.1f}, {:.1f}, {:.1f}, {:.1f} vs {:.1f} '.format(lower_bound, middle, upper_bound, frequency, goal_frequency))
if abs(frequency - goal_frequency) < threshold:
return middle
elif frequency < goal_frequency:
lower_bound = middle
elif frequency > goal_frequency:
upper_bound = middle
elif abs(upper_bound-lower_bound) < 0.001:
warn("Search was stopped no value was found!")
return middle
else:
print('lower bound: {:.1f}, middle: {:.1f}, upper_bound: {:.1f}, frequency: {:.1f} vs goal: {:.1f} '.format(lower_bound, middle, upper_bound, frequency, goal_frequency))
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!")
return middle
def test_v_offset(model: LifacNoiseModel, v_offset, base_stimulus, simulation_length):
model.set_variable("v_offset", v_offset)
_, spiketimes = model.simulate_fast(base_stimulus, simulation_length)
v, spiketimes = model.simulate_fast(base_stimulus, simulation_length)
freq = hF.mean_freq_of_spiketimes_after_time_x(spiketimes, simulation_length / 3)
# if freq > 10000:
# from IPython import embed
# import matplotlib.pyplot as plt
# embed()
return freq