def write_clipped_array(
self,
numpy_array: NumpyArray,
name: str,
a_min: float,
a_max: float = None,
attrs: Dict[str, str] = None,
): # pylint: disable=R0913
"""Write array with min and max values capped.
Args:
numpy_array: Numpy array
name: name to use for the OMX key
a_min: minimum value to clip array data
a_max: optional maximum value to clip array data
attrs: additional attribute key value pairs to write to OMX file
"""
if a_max is not None:
numpy_array = numpy_array.clip(a_min, a_max)
else:
numpy_array = numpy_array.clip(a_min)
self.write_array(numpy_array, name, attrs)
def __init__(self, data: numpy.array, samplerate: float) -> None:
if len(data.shape) > 2:
raise ValueError(
'data dimensions must be 1 or 2 but got {}'.format(
len(data.shape)))
if len(data.shape) == 1:
data = data.reshape([-1, 1])
_assertSamplerate(samplerate)
_assertDuration(data.shape[0])
self._data = data.clip(-1.0, 1.0)
self._samplerate = samplerate
def preprocess_minmax(img: np.array) -> np.array:
"""
Normalize image data to 98-2 percentiles
"""
im_min = np.percentile(img, 2)
im_max = np.percentile(img, 98)
im_range = im_max - im_min
# print(f'percentile 2 {im_min}, percentile 98 {im_max}, im_range {im_range}')
# Normalise to the percentile
img = img.astype(np.float32)
img = (img - im_min) / im_range
img = img.clip(0, 1)
return img
def float_2_label(x: np.array, bits: float) -> np.array:
assert abs(x).max() <= 1.0
x = (x + 1.) * (2**bits - 1) / 2
return x.clip(0, 2**bits - 1)
