Alexander/P-unit_model
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improve internal stimulus handling

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This commit is contained in:
a.ott 2020-02-18 16:30:32 +01:00
parent 251ca4f2ef
commit af2172c81b
1 changed files with 15 additions and 16 deletions
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31
stimuli/SinusAmplitudeModulation.py
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@ -1,8 +1,6 @@
from stimuli.AbstractStimulus import AbstractStimulus from stimuli.AbstractStimulus import AbstractStimulus
import numpy as np import numpy as np
from numba import jit, njit from numba import jit, njit
import time
from warnings import warn
class SinusAmplitudeModulationStimulus(AbstractStimulus): class SinusAmplitudeModulationStimulus(AbstractStimulus):
@ -25,10 +23,10 @@ class SinusAmplitudeModulationStimulus(AbstractStimulus):
return self.amplitude * am * carrier return self.amplitude * am * carrier
def get_stimulus_start_ms(self): def get_stimulus_start_s(self):
return self.start_time return self.start_time
def get_stimulus_duration_ms(self): def get_stimulus_duration_s(self):
return self.duration return self.duration
def get_amplitude(self): def get_amplitude(self):
@ -42,24 +40,25 @@ class SinusAmplitudeModulationStimulus(AbstractStimulus):
start_time = self.start_time start_time = self.start_time
duration = self.duration duration = self.duration
values = convert_to_array(carrier, amp, mod_freq, contrast, start_time, duration, time_start, total_time, step_size) values = convert_to_array(carrier, amp, mod_freq, contrast, start_time, duration, time_start, total_time, step_size/1000)
return values return values
@jit(nopython=True) #@jit(nopython=True) # makes it slower?
def convert_to_array(carrier_freq, amplitude, modulation_freq, contrast, start_time, duration, time_start, total_time, step_size): def convert_to_array(carrier_freq, amplitude, modulation_freq, contrast, start_time, duration, time_start, total_time, step_size_s):
# if the whole stimulus time has the amplitude modulation just built it at once; # if the whole stimulus time has the amplitude modulation just built it at once;
if time_start >= start_time and start_time+duration < time_start+total_time: if time_start >= start_time and start_time+duration < time_start+total_time:
carrier = np.sin(2 * np.pi * carrier_freq * np.arange(start_time, total_time-start_time, step_size/1000)) carrier = np.sin(2 * np.pi * carrier_freq * np.arange(start_time, total_time - start_time, step_size_s))
modulation = 1 + contrast * np.sin(2 * np.pi * modulation_freq * np.arange(start_time, total_time-start_time, step_size/1000)) modulation = 1 + contrast * np.sin(2 * np.pi * modulation_freq * np.arange(start_time, total_time - start_time, step_size_s))
values = amplitude * carrier * modulation values = amplitude * carrier * modulation
return values return values
# if it is split into parts with and without amplitude modulation built it in parts: # if it is split into parts with and without amplitude modulation built it in parts:
values = np.array([]) values = np.empty(1)
if time_start < start_time: if time_start < start_time:
carrier_before_am = np.sin(2 * np.pi * carrier_freq * np.arange(time_start, start_time, step_size / 1000)) carrier_before_am = np.sin(2 * np.pi * carrier_freq * np.arange(time_start, start_time, step_size_s))
values = np.concatenate((values, amplitude * carrier_before_am)) values = np.concatenate((values, amplitude * carrier_before_am))
# there is at least a second part of the stimulus that contains the amplitude: # there is at least a second part of the stimulus that contains the amplitude:
@ -68,14 +67,14 @@ def convert_to_array(carrier_freq, amplitude, modulation_freq, contrast, start_t
if duration is np.inf: if duration is np.inf:
carrier_during_am = np.sin( carrier_during_am = np.sin(
2 * np.pi * carrier_freq * np.arange(start_time, time_start+total_time, step_size / 1000)) 2 * np.pi * carrier_freq * np.arange(start_time, time_start + total_time, step_size_s))
am = 1 + contrast * np.sin( am = 1 + contrast * np.sin(
2 * np.pi * modulation_freq * np.arange(start_time, time_start+total_time, step_size / 1000)) 2 * np.pi * modulation_freq * np.arange(start_time, time_start + total_time, step_size_s))
else: else:
carrier_during_am = np.sin( carrier_during_am = np.sin(
2 * np.pi * carrier_freq * np.arange(start_time, start_time + duration, step_size / 1000)) 2 * np.pi * carrier_freq * np.arange(start_time, start_time + duration, step_size_s))
am = 1 + contrast * np.sin( am = 1 + contrast * np.sin(
2 * np.pi * modulation_freq * np.arange(start_time, start_time + duration, step_size / 1000)) 2 * np.pi * modulation_freq * np.arange(start_time, start_time + duration, step_size_s))
values = np.concatenate((values, amplitude * am * carrier_during_am)) values = np.concatenate((values, amplitude * am * carrier_during_am))
else: else:
@ -83,7 +82,7 @@ def convert_to_array(carrier_freq, amplitude, modulation_freq, contrast, start_t
print("Given stimulus time parameters (start, total) result in no part of it containing the amplitude modulation!") print("Given stimulus time parameters (start, total) result in no part of it containing the amplitude modulation!")
if time_start+total_time > start_time+duration: if time_start+total_time > start_time+duration:
carrier_after_am = np.sin(2 * np.pi * carrier_freq * np.arange(start_time+duration, time_start+total_time, step_size/1000)) carrier_after_am = np.sin(2 * np.pi * carrier_freq * np.arange(start_time + duration, time_start + total_time, step_size_s))
values = np.concatenate((values, amplitude*carrier_after_am)) values = np.concatenate((values, amplitude*carrier_after_am))
return values return values