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Merge pull request #2 from juliakrizanova/new_ito
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add ito experiments
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@rozsatib
rozsatib authored Jul 1, 2024
2 parents 4b22727 + 0bed68f commit d252689
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Showing 2 changed files with 118 additions and 30 deletions.
127 changes: 103 additions & 24 deletions mozaik/experiments/vision.py
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Expand Up @@ -1222,9 +1222,16 @@ class MeasureNaturalImagesWithEyeMovement(VisualExperiment):
"""
Stimulate the model with a natural image with simulated eye movement.
This experiment presents a movie that is generated by translating a
static image along a pre-specified path (presumably containing path
that corresponds to eye-movements).
This experiment supports presenting natural images with flexible paths
for simulated eye movements, allowing for a more dynamic and varied stimulus
presentation. It can handle multiple images and corresponding eye paths for
each trial, enabling the experiment to simulate more natural viewing conditions.
It is also able to read eye movement paths from Junji hdf5 format files, facilitating
integration with data from real eye-tracking experiments.
The stimuli are presented by translating static images
along pre-specified paths that correspond to eye movements, with the paths and times passed directly to the stimulus.
Parameters
----------
Expand All @@ -1236,45 +1243,117 @@ class MeasureNaturalImagesWithEyeMovement(VisualExperiment):
----------------
stimulus_duration : float
The duration of single presentation of the stimulus.
The duration of single presentation of the stimulus, representing the maximum length of each trial.
num_trials : int
Number of trials each each stimulus is shown.
Number of trials, each showcasing a different image and eye movement path.
size : float
The size of the image in degrees of visual field
The size of the image in degrees of visual field.
Notes
-----
Currently this implementation bound to have the image and the eye path saved in in files *./image_naturelle_HIGH.bmp* and *./eye_path.pickle*.
In future we need to make this more general.
The implementation requires specifying directories for data and stimuli. Eye movement data and images are loaded
from files in specified formats, including hdf5 for eye positions. It supports reading and processing of eye
position data to filter positions outside the periods of image presentation and calculates the period of eye
movements based on trial start and end times from the data file. This process allows for a dynamic construction of
eye movement paths and their associated stimuli, presenting them according to the derived parameters for each trial.
Paths for eye movement data files and image files are constructed dynamically, supporting a variety of stimulus
presentations and making the experiment more adaptable to different kinds of datasets.
"""

required_parameters = ParameterSet({
'stimulus_duration' : float,
'num_trials' : int,
'size' : float,
'data_dir': str,
'data_filename': str,
'stim_dir': str,
})


def generate_stimuli(self):
def __init__(self,model,parameters):
VisualExperiment.__init__(self, model,parameters)

# for k in range(0, self.parameters.num_trials):
# self.stimuli.append(
# topo.NaturalImageWithEyeMovement(
# frame_duration = self.frame_duration,
# size_x=model.visual_field.size_x,
# size_y=model.visual_field.size_y,
# location_x=0.0,
# location_y=0.0,
# background_luminance=self.background_luminance,
# duration=self.parameters.stimulus_duration,
# density=self.density,
# trial=k,
# size=self.parameters.size, # x size of image
# eye_movement_period=6.66, # eye movement period
# eye_path_location='./eye_path.pickle',
# image_location='./image_naturelle_HIGHC.bmp'))

# Modify MeasureNaturalImagesWithEyeMovement (in mozaik/experiments/vision.py) to:
# Be able to present flexible path images and eye_paths
# Be able to present multiple images and eye_paths
# Be able to read eye paths in from Junji hdf5 format
# Pass positions and times to stimulus
import h5py
import os
import numpy as np
# basedir = "/projects/nextcloud_data/csng_test/files/Latency_shortening"
# basedir = "/home/rozsa/dev/Julia_analysis/Latency_shortening"
# data_dir = os.path.join(basedir, "data")
# data_filename = "HIME_20140904_rec5_blk2_eye_position.hdf5"
# stim_dir = os.path.join(basedir, "stimuli")


for k in range(0, self.parameters.num_trials):
self.stimuli.append(
topo.NaturalImageWithEyeMovement(
frame_duration = self.frame_duration,
size_x=self.model.visual_field.size_x,
size_y=self.model.visual_field.size_y,
location_x=0.0,
location_y=0.0,
background_luminance=self.background_luminance,
duration=self.parameters.stimulus_duration,
density=self.density,
trial=k,
size=self.parameters.size, # x size of image
eye_movement_period=6.66, # eye movement period
eye_path_location='./eye_path.pickle',
image_location='./image_naturelle_HIGHC.bmp'))
trial_label = f"/trial{k+1}"
with h5py.File(f"{self.parameters.data_dir}/{self.parameters.data_filename}", 'r') as data:
stimsetname = data['/stimulus_set_name'][...].astype('str')
# TODO: vybrat start_time, end_time, img_on, img_off time -> z coho vypocitam periodu (tym,ze to este vydelim nr of samples),
# odfiltrujem pozicie mimo ukazovania obrazku
eyepos = data[trial_label+"/eye_position"][...]
num_samples = eyepos.shape[0]
t_start = data[trial_label+"/trial_start_time"][...]
t_end = data[trial_label+"/trial_end_time"][...]
#pouzit t_start, t_end
period = (t_end - t_start) / num_samples
success = data[trial_label+"/success"][...]
image_id = f"{self.parameters.stim_dir}/{stimsetname}/{data[trial_label+'/stimulus_image_id'][...]}.png"

if not(success):
continue
else:
# TODO: odfiltrujem pozicie mimo ukazovania obrazku
times = np.linspace(t_start, t_end, num_samples)
mask_fv = (t_start < times) & (times < t_end)
filtered_eyepos = eyepos[mask_fv]
x_list = [filtered_eyepos[i][0] for i in range(len(filtered_eyepos))]
y_list = [filtered_eyepos[i][1] for i in range(len(filtered_eyepos))]
temp_dict = MozaikExtendedParameterSet({"x": x_list, "y": y_list})



trial_label = f"/trial{k}"
self.stimuli.append(
topo.NaturalImageWithEyeMovement(
frame_duration = self.frame_duration,
size_x=model.visual_field.size_x,
size_y=model.visual_field.size_y,
location_x=0.0,
location_y=0.0,
background_luminance=self.background_luminance,
duration=self.parameters.stimulus_duration, # maximum lenghth of trial
# duration = ((t_end - t_start) // 7 )* 7,
density=self.density,
trial=k,
size=self.parameters.size, # x size of image
img_id = image_id,
eye_movement_period = period,
eye_path = temp_dict,
image_location=image_id))

def do_analysis(self, data_store):
pass
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21 changes: 15 additions & 6 deletions mozaik/stimuli/vision/topographica_based.py
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Original file line number Diff line number Diff line change
Expand Up @@ -369,12 +369,12 @@ class NaturalImageWithEyeMovement(TopographicaBasedVisualStimulus):
size = SNumber(degrees, doc="The length of the longer axis of the image in visual degrees")
eye_movement_period = SNumber(ms, doc="The time between two consequitve eye movements recorded in the eye_path file")
image_location = SString(doc="Location of the image")
eye_path_location = SString(doc="Location of file containing the eye path (two columns of numbers)")
eye_path = SParameterSet(doc="Location of file containing the eye path (two columns of numbers)")
# POZN: eye_path = list of numpy arrays -> pouzit SParameterSet (funguje ako dictionary)


def frames(self):
self.time = 0
f = open(self.eye_path_location, 'rb')
self.eye_path = pickle.load(f, encoding="latin1")
self.pattern_sampler = imagen.image.PatternSampler(
size_normalization='fit_longest',
whole_pattern_output_fns=[MaximumDynamicRange()])
Expand All @@ -393,9 +393,18 @@ def frames(self):
pattern_sampler=self.pattern_sampler)

while True:
location = self.eye_path[int(numpy.floor(self.frame_duration * self.time / self.eye_movement_period))]
image.x = location[0]
image.y = location[1]
x = self.eye_path["x"]
y = self.eye_path["y"]


print(self.frame_duration, self.time, self.eye_movement_period)
print("EYE PATH, ", x)
index = int(numpy.floor(self.frame_duration * self.time / self.eye_movement_period))

if index > len(x)-1:
index = len(x)-1
image.x = x[index]
image.y = y[index]
yield (image(), [self.time])
self.time += 1

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