import uuid
import json
import logging
import numpy as np
import pyarrow.parquet as pq
import pyarrow as pa
import pandas as pd
_logger = logging.getLogger('ibllib')
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def load(filename):
"""
Loads parquet file into pandas dataframe
:param filename:
:return:
"""
table = pq.read_table(filename)
try:
metadata = json.loads(table.schema.metadata[b'one_metadata'])
except KeyError:
_logger.debug("No parquet metadata in %s" % filename)
metadata = {}
df = table.to_pandas()
return df, metadata
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def save(filename, table, metadata=None):
"""
Save pandas dataframe to parquet
:param filename:
:param table:
:param metadata:
:return:
"""
# cf https://towardsdatascience.com/saving-metadata-with-dataframes-71f51f558d8e
# from dataframe to parquet
table = pa.Table.from_pandas(table)
# Add user metadata
table = table.replace_schema_metadata({
'one_metadata': json.dumps(metadata or {}).encode(),
**table.schema.metadata
})
# Save to parquet.
pq.write_table(table, filename)
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def uuid2np(eids_uuid):
return np.asfortranarray(
np.array([np.frombuffer(eid.bytes, dtype=np.int64) for eid in eids_uuid]))
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def str2np(eids_str):
"""
Converts uuid string or list of uuid strings to int64 numpy array with 2 cols
Returns [0, 0] for None list entries
"""
if isinstance(eids_str, str):
eids_str = [eids_str]
return uuid2np([uuid.UUID(eid) if eid else uuid.UUID('0' * 32) for eid in eids_str])
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def np2uuid(eids_np):
if isinstance(eids_np, pd.DataFrame) | isinstance(eids_np, pd.Series):
eids_np = eids_np.to_numpy()
if eids_np.ndim >= 2:
return [uuid.UUID(bytes=npu.tobytes()) for npu in eids_np]
else:
return uuid.UUID(bytes=eids_np.tobytes())
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def np2str(eids_np):
eids = np2uuid(eids_np)
eids = str(eids) if isinstance(eids, uuid.UUID) else [str(u) for u in np2uuid(eids_np)]
return eids
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def is_np_id(id):
"""
The purpose of this is to correctly identify ids even as object arrays
:param id:
:return:
"""
# TODO Document and test
id = np.asarray(id)
is_int = id.dtype == int or np.all(isinstance(x, int) for x in id)
return id.shape[1] == 2 and is_int