Partial inputs - Pakistan flood tutorial #154
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…ind a signal for floods with pca and tsne
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weiji14
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Feb 15, 2024
src/datamodule.py
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| date = chip.tags()["date"] # YYYY-MM-DD | ||
| year, month, day = self.normalize_timestamp(date) | ||
| ( | ||
| year, | ||
| month, | ||
| day, | ||
| year_non_norm, | ||
| month_non_norm, | ||
| day_non_norm, | ||
| ) = self.normalize_timestamp(date) |
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We are already returning the non-normalized date as a YYYY-MM-DD string no? Instead of using ts.append(batch["timestep_non_norm"]) in your partial-inputs-flood-tutorial.ipynb notebook, could you use ts.append(batch["date"])?
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Fixed this. It worked, thanks!
src/datamodule.py
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| @@ -175,7 +184,7 @@ def train_dataloader(self): | |||
| self.trn_ds, | |||
| batch_size=self.batch_size, | |||
| num_workers=self.num_workers, | |||
| shuffle=True, | |||
| shuffle=False, | |||
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We should be shuffling the mini-batches in the train_dataloader, is there a reason to set it to False here?
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| shuffle=False, | |
| shuffle=True, |
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I rolled back changes to the datamodule in this PR, so it is the same as in main. I think this should be examined in a separate PR since no training is occurring for the purpose of this one.
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weiji14
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This is a tutorial following the style of #149 to show how to generate embeddings from partial inputs and find a signal for a major monsoon flood in Padidan, Pakistan (August 2022) with PCA and t-SNE.
Modifications (aside from AOI and time):