#========================
"""
LOAD DATA
"""
list_of_data = []
for path in data_paths:
list_of_data += glob.glob(path)
list_of_data.sort(key=os.path.getmtime)
if last_nfiles > 0:
list_of_data = list_of_data[-last_nfiles:]
if len(list_of_data) == 0:
exit()
loader = DataLoader(list_of_data)
if td:
if eligibility_trace:
_child_stats = loader.child_stats
n = _child_stats[:, 0]
q = _child_stats[:, 3]
_episode = loader.episode
_score = loader.score
_v = np.sum(n * q, axis=1) / np.sum(n, axis=1)
values = np.zeros(_v.shape)
for idx, ep in enumerate(_episode):
idx_r = idx
weight = 1.0
_sum = 0
list_of_data = []
for path in data_paths:
list_of_data += glob.glob(path)
list_of_data = sorted(list_of_data, key=keyFile)
x = []
y_s = []
y_s_err = []
y_l = []
y_l_err = []
for f in list_of_data:
print('Processing: ', f)
try:
_tmp = DataLoader([f,])
except:
pass
_idx = np.ediff1d(_tmp.episode, to_end=-1) == -1
_cycle = _tmp.cycle[0]
_scores = _tmp.score[_idx]
s_mean = _scores.mean()
s_stddev = _scores.std() / np.sqrt(np.sum(np.absolute(_idx)))
_lines = _tmp.lines[_idx]
l_mean = _lines.mean()
l_stddev = _lines.std() / np.sqrt(np.sum(np.absolute(_idx)))
x.append(_cycle)
y_s.append(s_mean)
y_s_err.append(s_stddev)
y_l.append(l_mean)
y_f = offset_y + b_h
color = 'gray' + str(int(100*v))
canvas.create_rectangle(x_i, y_i, x_f, y_f, fill=color)
index = 0
if __name__ == '__main__':
master = Tk()
master.geometry('%dx%d'%(width, height))
master.resizable(False, False)
master.title('Replay')
list_of_data = []
for path in data_paths:
list_of_data += glob.glob(path)
data = DataLoader(data_paths)
canvas_frame = Frame(master)
canvas_frame.grid(row=0,column=0,rowspan=10,columnspan=5)
canvas_frame_2 = Frame(master)
canvas_frame_2.grid(row=5,column=5,rowspan=5,columnspan=5)
info_frame = Frame(master)
info_frame.grid(row=0,column=5,rowspan=3,columnspan=5)
control_frame = Frame(master)
control_frame.grid(row=3,column=5,rowspan=1,columnspan=5)
control_frame_2 = Frame(master)
control_frame_2.grid(row=4,column=5,rowspan=1,columnspan=5)