Gb/bc #96
Gb/bc #96
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…simple integration into the forward pass
… active domain to alleviate the nearest neighbor seams
…d into fwp and qa
…n for new bc and windstats modules
| Distance in decimal degrees from coord to bias gid | ||
| """ | ||
| d, i = self.bias_tree.query(coord) | ||
| bias_gid = self.bias_gid_raster.flatten()[i] |
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Include a check that the index is in the raster?
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there's no distance threshold on this method so i should always be in the gid raster. I don't include a distance threshold because this is just a convenience method for debugging and investigation, not used in the actual bc module in a pipeline.
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There's no default threshold?
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nope its infinite haha
sup3r/bias/bias_transforms.py
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| def local_linear_bc(input, feature_name, bias_fp, lr_padded_slice, | ||
| out_range=None): | ||
| """Bias correct data using a simple global *scalar +adder method. |
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like site-by-site, right? yes, good catch
| except Exception as e: | ||
| msg = ('Sup3r ForwardPass chunk failed!') | ||
| logger.exception(msg) | ||
| raise RuntimeError(msg) from e | ||
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| @classmethod | ||
| def run(cls, strategy, node_index): | ||
| """This routine runs forward passes on all spatiotemporal chunks for |
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we lost part of the doc string here think. *for the given node index.
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oh man oops, either a merge conflict or me fat fingering with vim haha
| v_true = self.source_handler.data[..., v_idf] | ||
| u_true = self.bias_correct_source_data(u_true, u_feat) | ||
| v_true = self.bias_correct_source_data(v_true, v_feat) | ||
| data_true = np.hypot(u_true, v_true) |
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I always learn about new numpy methods reading your code lol
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i like this one even though it seems trivial, avoids silly errors like a single * instead of power **. I also thought you showed me this but maybe not haha
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