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