import numpy as np
import scipy.io
import importlib
import common
importlib.reload(common)
from scipy import signal
output_directory = common.set_output_directory('cursor_gaze_movie\\')
is_load_fresh = False
LOC_CODE = np.array([
[3, 7], # - uppermost left, lowermost right
[4, 8], # - upper left, lower right
[5, 1], # - lower left, upper right
[6, 2]
]) # lowermost left, uppermost right
colour_location = np.array([[0.5, 0.25, 0.6], [1, 0, 1], [1, 0, 0],
[1, 0.36, 0], [1, 1, 0], [0, 1, 0], [0, 1, 1],
[0, 0, 1]])
mat1 = scipy.io.loadmat(file_name="..\\data_manager\\CheckFiles2\\fname.mat"
) # 'samples', 'GAZE', 'PUPIL', 'type'
CursorData = common.CursorData
dispatch_dictionary = {"CursorData": CursorData}
class Sizes:
target = 76.9231
import scipy.io
import numpy as np
import common
import time
import os
import matplotlib.pyplot as plt
import FieldTrip2
import subprocess
output_directory = common.set_output_directory('prepare_cursor_gaze\\')
start = time.time()
mat = scipy.io.loadmat('..\\visual_input\\movie_cursor_eye\\test.mat')
cursor_xy = mat['CURSOR_XY2'][0, 0]
#np.save(file=output_directory + 'test.npy', arr=cursor_xy)
# #np.savetxt(fname=output_directory + 'test.npy', X=cursor_xy)
# del cursor_xy
# array = np.load(file=output_directory + 'test.npy', allow_pickle=True)
dimensions = cursor_xy.shape
class Number:
axes = 2
frames = 360
trials = 480
cursors = 3
Number.axes = dimensions[0]
cmd = [executable_path, 'a', output_file, input_path + '/*']
system = subprocess.Popen(cmd,
stderr=subprocess.STDOUT,
stdout=subprocess.PIPE)
try:
log_print(system.communicate()[0].decode('ascii'))
except Exception as e:
# log_print(e)
log_print('communicate failed?')
log_print(time.time() - start)
log_output_path = common.set_output_directory("backup_project\\")
executable_path = "D:\\Program Files (x86)\\7za920\\7za"
parent_input_path = 'D:\\JOINT.ACTION\\JointActionRevision\\'
parent_output_path = 'E:\\JOINT_ACTION_BACKUPS\\joint-action-backup-2020.01.19\\'
##
filename = log_output_path + datetime.now().strftime(
"%Y-%m-%d %H.%M.%S") + '.txt'
f = open(filename, 'w')
directories = get_sorted_subdirectories(parent_input_path)
start = time.time()
import time
import numpy as np
import scipy.io
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use("Qt5Agg")
import importlib
import common
importlib.reload(common)
import pandas as pd
from scipy import stats
output_directory = common.set_output_directory('eye_single_trial\\')
is_generate_heatmaps_and_distance = True
x_edges = np.arange(start=-13, stop=+13, step=.1)
y_edges = np.arange(start=-13, stop=+13, step=.1)
epoch_length1 = 2.5
limit_seconds1 = [[-epoch_length1, 0], [0, +epoch_length1]]
common.Number.frames1 = int(epoch_length1 * common.FS.eye)
plt.hot()
def generate_subject_code():
subject_code = np.empty([common.Number.subjects, 3])
globals().clear()
import pandas as pd
import datetime
from tabulate import tabulate
import numpy as np
from pyarrow import feather
import common
output_directory = common.set_output_directory("casual_hours_calculator\\")
def my_tabulate(results):
if isinstance(results, type(pd.Series())):
results = pd.DataFrame(results)
print(tabulate(results, headers='keys', tablefmt='psql'))
class WorkDay:
date = None
timesheet = None
summary = None
def __init__(self, date, timesheet, summary):
self.date = date
self.timesheet = timesheet
self.summary = summary
class Summary:
start = None
import pandas as pd
from pdf2image import convert_from_path
import time
import os
import common
output_directory = common.set_output_directory('trajectories\\')
data_path = r'D:\JOINT.ACTION\JointActionRevision\analysis\JointActionStatisticsR2\Figure2_new'
number_of_control = 3
number_of_visibility = 2
number_of_figure = 2
str_control = ['HP Solo', 'LP Solo', 'Joint']
str_visibility = ['Visible', 'Invisible']
str_figure = ["endpoint", "trajectory"]
figure_list = []
i = 0
for VISIBILITY in str_visibility:
for CONTROL in str_control:
figure_list.append([VISIBILITY, CONTROL, i])
i += 1
figure_key = pd.DataFrame(figure_list, columns=['visibility', 'control', 'i'])
for FIGURE in [0]:
figure_list = []
import numpy as np
import scipy.io
import mat73
import importlib
import common
importlib.reload(common)
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from scipy import stats
from PIL import Image
output_directory = common.set_output_directory(
'collate_single_trial_results\\')
## ----- data structures
class SingleTrialValues:
control_codes = None
accuracy = None
RT = None
MT = None
curvature = None
endpoint_displacement = None
gaze_distance = None
behavioral_coupling = None
task_cue = None
neural_coupling = None
import moviepy.video.io.ImageSequenceClip
import os
import common
import time
import numpy as np
output_directory = common.set_output_directory("save_movies\\")
sessions_to_use = np.array(range(0, 20))
for SESSION in sessions_to_use:
common.print_stars()
print(SESSION)
if SESSION == 1:
print('missing eye data...')
continue
start = time.time()
image_folder = 'D:\\JOINT.ACTION\\JointActionRevision\\analysis\\Unity2\\RESULTS2\\' + common.Labels.session2[SESSION] + '\\'
image_files = [image_folder + '/' + img for img in os.listdir(image_folder) if img.endswith(".png")]
clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(image_files, fps=30)
clip.write_videofile(output_directory + common.Labels.session2[SESSION] + '.mp4')
stop = time.time()
print(stop-start)