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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
import numpy as np
from numba import jit, njit
import time
from warnings import warn
class SinusAmplitudeModulationStimulus(AbstractStimulus):
@ -25,10 +23,10 @@ class SinusAmplitudeModulationStimulus(AbstractStimulus):
return self.amplitude * am * carrier
def get_stimulus_start_ms(self):
def get_stimulus_start_s(self):
return self.start_time
def get_stimulus_duration_ms(self):
def get_stimulus_duration_s(self):
return self.duration
def get_amplitude(self):
@ -42,24 +40,25 @@ class SinusAmplitudeModulationStimulus(AbstractStimulus):
start_time = self.start_time
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
@jit(nopython=True)
def convert_to_array(carrier_freq, amplitude, modulation_freq, contrast, start_time, duration, time_start, total_time, step_size):
#@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_s):
# 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:
carrier = np.sin(2 * np.pi * carrier_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/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_s))
values = amplitude * carrier * modulation
return values
# 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:
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))
# 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:
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(
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:
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(
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))
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!")
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))
return values