Checkpoint while collecting metadata #31
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sfinkens
reviewed
Sep 11, 2020
| # get set of processed files in case of a restart | ||
| wanted = set(filenames) | ||
| done = set( | ||
| filename for filename, in session.query(FileMetadata.filename) |
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Unfortunately yes, because the query returns tuples (of one item) and therefore I need to unpack them.
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Have not yet seen a better way to get directly a list for a 1 column query.
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Ok I see. What about for filename, _ in ... ?
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Another idea:
import itertools as it
done = set(it.chain(*session.query(FileMetadata.filename)))Which is another way to flatten the list of one element tuples.
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Or another way without any import:
done = set(next(zip(*session.query(FileMetadata.filename))))
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| with sqlite3.connect(dbfile) as con: | ||
| mda = pd.read_sql('select * from metadata', con) | ||
| mda = mda.set_index(['platform', 'level_1']) | ||
| mda.fillna(value=np.nan, inplace=True) | ||
| for col in mda.columns: | ||
| if 'time' in col: | ||
| mda[col] = mda[col].astype('datetime64[ns]') |
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Does the new solution convert time columns in the dataframe to datetime64 and replace NULL items with NaN? I don't remember why, but I think this was needed for the overlap computation to work correctly.
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That's a nice use case for the system tests actually
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I was a bit surprised by these lines and did a test and it worked, meaning the types were conserved when writing and re-reading into the DataFrame.
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Magic! Curious how the system tests will behave.
Thanks, I learned these things while writing application databases for dashboards using |
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@carloshorn I ran the system tests on this PR and got the following error: |
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Ohh, strange that I did not run into it before... Luckily, pandas timestamps can be converted using |
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You can run them yourself as follows: |
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This PR closes #25 by writing a checkpoint database while looping through all filenames.
This allows to restart in case of an interruption.