Issue 43 #44
Issue 43 #44
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Hi Stefan, thanks for doing this, and sorry it took me so long to review it. I left a couple of very minor comments on the code - I'm happy to accept the PR as is and merge it now, or if you feel like making any of those indicated changes that's also fine with me. Just reply one way or the other!
| self._yerr_raw = values | ||
| # now apply the same transformation that was applied to the ydata | ||
| if self.ytransform is None: | ||
| self._yerr_transformed = values | ||
| elif isinstance(self.ytransform, Transformer): | ||
| self._yerr_transformed = self.ytransform.transform(values) | ||
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| def _ensure_tensor(self, values): |
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In principle, I think this could be moved outside the context of the class, as maybe it would make sense as a more general utility? But I think this is great for now, and we can think about that later.
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I'd say that this method could stay as an internal method as I don't see any other utility for it outside of the scope of making sure the data is in the correct form. But if you think that we could generalize it more and that it could be used externally, we can do that too.
| warnings.warn(('The function expects a torch.Tensor as input.' | ||
| 'Your data will be converted to a tensor.'), | ||
| stacklevel=2) | ||
| values = torch.as_tensor(values, dtype=torch.float32) |
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Could we change the dtype here to be more general? I can see float32 being the default, but maybe we should have a self._default_dtype to cast to, which defaults to torch.float32? Again, this isn't a showstopper.
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Sure, I'll think about it.
| values = torch.as_tensor(values, dtype=torch.float32) | ||
| return values | ||
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| def _ensure_dim(self, values): |
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This is a great start, we can figure out how to add a threshold later
I added methods to deal with #43 as well as #35.