def _min_max_scaler(ranges, feature_range=(0, 100)):
res = MinMaxScaler()
res.data_max_ = ranges[:, 1]
res.data_min_ = ranges[:, 0]
res.data_range_ = res.data_max_ - res.data_min_
res.scale_ = (feature_range[1] - feature_range[0]) / (ranges[:, 1] -
ranges[:, 0])
res.min_ = -res.scale_ * res.data_min_
res.n_samples_seen_ = 1
res.feature_range = feature_range
return res
temp_min = data.min()
if temp_min < min_val:
#print(f"New min: {min_val}")
min_val = temp_min
temp_max = data.max()
if temp_max > max_val:
#print(f"New max: {max_val}")
max_val = temp_max
except:
print(f"Did not work here: {file}")
scaler = MinMaxScaler(feature_range=(np.iinfo(np.int8).min,
np.iinfo(np.int8).max))
scaler.data_min_ = min_val
scaler.data_max_ = max_val
#######################################################
for file in tqdm(files[:]):
temp = os.path.join('temp', os.path.basename(file))
with open(temp, 'wb') as f:
f.write(zip.read(file))
data = np.fromfile(temp)
os.remove(temp)
try:
data = compressData(data, scaler, dtype=np.int8)
data = np.reshape(data, shape)
# return flat vector with values for V, position is stored in the array inds_heart
