Args: signal: input 1 dimension signal to be dropped
(self, signal: NdarrayOrTensor)
| 135 | self.boundaries = boundaries |
| 136 | |
| 137 | def __call__(self, signal: NdarrayOrTensor) -> NdarrayOrTensor: |
| 138 | """ |
| 139 | Args: |
| 140 | signal: input 1 dimension signal to be dropped |
| 141 | """ |
| 142 | self.randomize(None) |
| 143 | self.magnitude = self.R.uniform(low=self.boundaries[0], high=self.boundaries[1]) |
| 144 | |
| 145 | length = signal.shape[-1] |
| 146 | mask = torch.zeros(round(self.magnitude * length)) |
| 147 | trange = torch.arange(length) |
| 148 | loc = trange[torch.randint(0, trange.size(0), (1,))] |
| 149 | signal = convert_to_tensor(paste(signal, mask, (loc,))) |
| 150 | |
| 151 | return signal |
| 152 | |
| 153 | |
| 154 | class SignalRandAddSine(RandomizableTransform): |
nothing calls this directly
no test coverage detected