def __getitem__(self, idx): # DATA doesn't have to be loaded until here!
y = self.labels[idx]
if not y == 0 or not y == 1:
return None, y
else:
start = self.times[idx]
end = start + 600
if self.medium:
# start -= 60*60 # REMOVE
start += 60 * 9 # take last minute of the 10 min sample
hrs_back = 24 # this means 25 samples as we go as far as exactly 24 hrs ago
x_np = np.zeros((hrs_back + 1, 16, 23960))
for i in range(hrs_back + 1):
hrs = hrs_back - i
min_start = start - hrs * 60 * 60
min_end = min_start + 60
minute = cd.get_data(self.pt, min_start, min_end)
x_np[i] = minute[:, :23960]
elif self.long:
# start -= 60*60*24
start += 60 * 9
days_back = 30 # days to look back
x_np = np.zeros((days_back + 1, 16, 23960))
for i in range(days_back + 1):
days = days_back - i
min_start = start - days * 24 * 60 * 60
min_end = min_start + 60
minute = cd.get_data(self.pt, min_start, min_end)
x_np[i] = minute[:, :23960]
else:
# start -= 60
start -= 600 * (
self.lookBack - 1
) # Go back 10s of minutes for more training info
x_np = cd.get_data(self.pt, start, end)
if self.nan_as_noise:
x_np = cd.fill_with_noise(self.pt, x_np)
else:
x_np[np.isnan(x_np)] = 0
# plt.plot(x_np[0])
# plt.show()
x = torch.tensor(x_np, dtype=torch.float32)
# print('X size', x.size())
return x, torch.tensor(y, dtype=torch.float32)
def __getitem__(self, i):
# print('Getting Item')
y = self.select_labels[i]
if y == 2 or y == -1:
y = 0
start = self.select_times[i]
end = start + 60
x_np = cd.get_data(self.pt, start, end)
if self.nan_as_noise:
x_np = cd.fill_with_noise(self.pt, x_np)
else:
x_np[np.isnan(x_np)] = 0
# plt.plot(x_np[0])
# plt.show()
x_np = x_np[:, :23960]
x = torch.tensor(x_np, dtype=torch.float32)
if self.transform:
# print(x.size)
x = self.transform(x)
return x, torch.tensor(y, dtype=torch.float32)
def __getitem__(self, idx): # DATA doesn't have to be loaded until here!
y = self.labels[idx]
if y != 1:
y = 0
start = self.times[idx]
x = torch.tensor(np.zeros((10, 16, 120, 120)), dtype=torch.float)
for i in range(10):
end = start + 60
tmp = cd.get_data(self.iPt, start, end)
tmp_trim = tmp[:, :23960]
if self.nan_as_noise:
tmp_trim = cd.fill_with_noise(self.pt, tmp_trim)
else:
tmp_trim[np.isnan(tmp_trim)] = 0
tmp_tensor = torch.tensor(tmp_trim, dtype=torch.float)
tmp_trans = self.transform(tmp_tensor)
x[i] = tmp_trans
start = end
return x, torch.tensor(y, dtype=torch.float)
def __getitem__(self, idx):
y_np = self.select_labels[idx]
if y_np == 2 or y_np == -1:
y_np = 0
start = self.select_times[idx]
x_np = {}
short_start = start - 600 * (
self.lookBack - 1) # Go back 10s of minutes for more training info
x_np_short = cd.get_data(self.pt, short_start, start + 600)
x_np_short[np.isnan(x_np_short)] = 0
x_np['short'] = x_np_short
med_start = start + 60 * 9 # take last minute of the 10 min sample
x_np_med = np.zeros((self.hrsBack + 1, 16, 23960))
for i in range(self.hrsBack + 1):
hrs = self.hrsBack - i
min_start = med_start - hrs * 60 * 60
min_end = min_start + 60
minute = cd.get_data(self.pt, min_start, min_end)
x_np_med[i] = minute[:, :23960]
x_np_med[np.isnan(x_np_med)] = 0
x_np['medium'] = x_np_med
long_start = start + 60 * 9
x_np_long = np.zeros((self.daysBack + 1, 16, 23960))
for i in range(self.daysBack + 1):
days = self.daysBack - i
min_start = long_start - days * 24 * 60 * 60
min_end = min_start + 60
minute = cd.get_data(self.pt, min_start, min_end)
x_np_long[i] = minute[:, :23960]
x_np_long[np.isnan(x_np_long)] = 0
x_np['long'] = x_np_long
return x_np, y_np