def transform_streamels(self, streamels):
check_streamels_1D(streamels)
check_streamels_continuous(streamels)
check_streamels_range(streamels, 0, 1)
n = streamels.size
streamels2 = np.zeros(n + 2, streamel_dtype)
streamels2['kind'][:] = ValueFormats.Continuous
streamels2['lower'][:n] = 0
streamels2['upper'][:n] = +1
# this is the mean
streamels2['lower'][n] = np.mean(streamels['lower'])
streamels2['upper'][n] = np.mean(streamels['upper'])
# this is the spread of the data
streamels2['lower'][n + 1] = 0
# XXX: we should find a different bounded
streamels2['upper'][n + 1] = (np.max(streamels['upper']) -
np.min(streamels['lower']))
# Save this so we can enforce it later
self.lower = streamels2['lower'].copy()
self.upper = streamels2['upper'].copy()
streamels2['default'] = self.transform_value(streamels['default'])
return streamels2
def transform_streamels(self, streamels):
check_streamels_2D(streamels)
check_streamels_range(streamels, 0, 1)
_, W = streamels.shape
streamels2 = make_streamels_1D_float(nstreamels=W, lower=0, upper=1)
return streamels2
def transform_streamels(self, streamels):
check_streamels_2D(streamels)
check_streamels_continuous(streamels)
check_streamels_range(streamels, 0, 1)
N, _ = streamels.shape
shape2 = N,
streamels2 = np.empty(shape=shape2, dtype=streamel_dtype)
streamels2['kind'].flat[:] = ValueFormats.Continuous
streamels2['lower'].flat[:] = 0
streamels2['upper'].flat[:] = 1
streamels2['default'] = self.transform_value(streamels['default'])
return streamels2
def transform_streamels(self, streamels):
check_streamels_1D(streamels)
check_streamels_continuous(streamels)
check_streamels_range(streamels, 0.0, 1.0)
M = streamels.size
n = M - 2
streamels2 = np.zeros(n, streamel_dtype)
streamels2['kind'][:] = ValueFormats.Continuous
streamels2['lower'][:] = 0
streamels2['upper'][:] = +1
streamels2['default'] = self.transform_value(streamels['default'])
return streamels2
