[930][evaluation] implement CSVReader #932
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| self.metric = eval_cfg.get("metric") | ||
| self.region = eval_cfg.get("region") | ||
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| def rename_channels(self) -> str: |
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you return a pd.DataFrame
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Also, personally, I would write this as a little helper function:
class CSVReader:
...
pd_data = pd.read_csv(self.csv_path, index_col=0)
self.data = _rename_channels(pd_data)
def _rename_channels(data) -> pd.DataFrame:
# No need for self.data here| def rename_channels(self) -> str: | ||
| """ | ||
| Rename channel names to include underscore between letters and digits. | ||
| E.g., 'z500' -> 'z_500', 't850' -> 't_850', '2t' -> '2t', '10ff' -> '10ff' |
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also, why do we need to do that renaming? I trust you it was necessary, just put a line in the docstring
| _logger.info(f"RUN {run_id}: Getting data...") | ||
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| reader = WeatherGenReader(run, run_id, private_paths) | ||
| type = run.get("type", "zarr") |
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thank you for putting a sensible default!
| and region == reader.region | ||
| and stream == reader.stream | ||
| ): | ||
| data = reader.data.values[np.newaxis, :, :, np.newaxis].T |
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style note: you colud have
else:
data = np.full(
(
len(available_data.samples),
len(available_data.fsteps),
len(available_data.channels),
1,
),
np.nan,
)| / f"{reader.run_id}_{stream}_{region}_{metric}_epoch{reader.epoch:05d}.json" | ||
| ) | ||
| _logger.debug(f"Looking for: {score_path}") | ||
| if hasattr(reader, "data") and reader.data is not None: |
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hasattr is not a good habit because it is really hard for humans and type checkers to figure out if an python object has an attribute. Here is what you can do, which then vscode can rename/check for you:
class Reader:
data: pd.DataFrame | None # Data attributes (if specified)
def __init__(self, eval_cfg: dict, run_id: str, private_paths: dict | None = None):
...
self.data = None
...
# No change to WG reader or CSVReader
# Now you can directly use:
if reader.data is not None:| self.csv_path = eval_cfg.get("csv_path") | ||
| assert self.csv_path is not None, "CSV path must be provided in the config." | ||
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| self.data = pd.read_csv(self.csv_path, index_col=0) |
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one thing I would do is cast all the values to np.float32 (or float). pandas tries to be very clever and would for example use int32 if the data allows. I am not sure if xarray can deal with that later.
2 participants
Description
Add reader to retrieve CSV scores from files generated with Quaver. @Jubeku
quaver scores can be plotted in the FastEvaluation package by adding this in the config (you need to have them locally first):
Issue Number
Closes #930
Is this PR a draft? Mark it as draft.
Checklist before asking for review
./scripts/actions.sh lint./scripts/actions.sh unit-test./scripts/actions.sh integration-testlaunch-slurm.py --time 60