Issue Running in Jupyter Notebook for convert_parquet Function

[sarakh1999]

Take a look at the following function:

import pandas as pd import polars as pl import pyarrow as pa import pyarrow.parquet as pq from cytotable import convert from parsl.config import Config from parsl.executors import ThreadPoolExecutor import random import sqlite3

Constants for columns

COLUMNS = ( "TableNumber", "ImageNumber", "ObjectNumber", "Metadata_Well", "Metadata_Plate", "Cytoplasm_Parent_Cells", "Cytoplasm_Parent_Nuclei", )

Modified convert_parquet function to read data chunk by chunk

def convert_parquet( input_file, output_file, cols=COLUMNS, chunk_size=150000, thread=2, initial_offset=0, offset_step=100 ):

conn = sqlite3.connect(input_file)

# Get total number of rows in the image_table for processing
total_rows = pd.read_sql_query("SELECT COUNT(*) as count FROM image_table", conn)['count'][0]

# Define the schema
schema = pa.schema([
        ('Metadata_TableNumber', pa.int64()),
        ('Metadata_ImageNumber', pa.int64()),
        ('Metadata_Well', pa.string()),
        ('Metadata_Plate', pa.string()),
        ('cytoplasm_Metadata_TableNumber', pa.int64()),
        ('cytoplasm_Metadata_ImageNumber', pa.int64()),
        ('cytoplasm_Metadata_ObjectNumber', pa.int64()),
        ('cells_Metadata_ObjectNumber', pa.int64()),
        ('nuclei_Metadata_ObjectNumber', pa.int64())
    ])

# Create a Parquet writer
pq_writer = pq.ParquetWriter(output_file, schema, compression='gzip')

offset = initial_offset

while offset < total_rows:
    query_limit = f"LIMIT {offset_step} OFFSET {offset}"
    image_df = pd.read_sql_query(f"SELECT * FROM image_table {query_limit}", conn)
    cytoplasm_df = pd.read_sql_query(f"SELECT * FROM cytoplasm_table {query_limit}", conn)
    cells_df = pd.read_sql_query(f"SELECT * FROM cells_table {query_limit}", conn)
    nuclei_df = pd.read_sql_query(f"SELECT * FROM nuclei_table {query_limit}", conn)

    image_pl = pl.from_pandas(image_df)
    cytoplasm_pl = pl.from_pandas(cytoplasm_df)
    cells_pl = pl.from_pandas(cells_df)
    nuclei_pl = pl.from_pandas(nuclei_df)

    image_filtered = image_pl.select(['Metadata_TableNumber', 'Metadata_ImageNumber', 'Metadata_Well', 'Metadata_Plate'])

    # Perform join operations
    result = (
        image_filtered
        .join(cytoplasm_pl, on=['Metadata_TableNumber', 'Metadata_ImageNumber'], how='left')
        .join(cells_pl, left_on=['Metadata_TableNumber', 'Metadata_ImageNumber', 'Metadata_ObjectNumber'], right_on=['Metadata_TableNumber', 'Metadata_ImageNumber', 'Metadata_Cytoplasm_Parent_Cells'], how='left')
        .join(nuclei_pl, left_on=['Metadata_TableNumber', 'Metadata_ImageNumber', 'Metadata_ObjectNumber'], right_on=['Metadata_TableNumber', 'Metadata_ImageNumber', 'Metadata_Cytoplasm_Parent_Nuclei'], how='left')
    )

    # Convert the result to an Arrow table
    result_arrow = result.to_arrow()

    # Write the table to the Parquet file
    pq_writer.write_table(result_arrow)

    offset += offset_step

# Close the Parquet writer
pq_writer.close()

conn.close()

"""Convert sqlite profiles to parquet"""

hash_str = str(random.getrandbits(128))
parsl_config = Config(
                    executors=[
                        ThreadPoolExecutor(
                            max_threads=thread
                        )
                    ],
                    run_dir=f'./runinfo/{hash_str}'
                )

convert(
    source_path=input_file,
    dest_path=output_file,
    identifying_columns=cols,
    dest_datatype='parquet',
    chunk_size=chunk_size,
    preset="cell-health-cellprofiler-to-cytominer-database",
    joins=None,  # No joins needed here since it's already handled
    reload_parsl_config=True,
    parsl_config=parsl_config,
    sort_output=False, 
)

When I run it in jupyter notebook, the function does not work and raise some error regarding threading, but when I run the exact same code on python script it has no issue. I guess the issue is kernel and multithreading in jupyter notebook that this library can't handle.

[d33bs]

Thanks so much @sarakh1999 for raising this issue! I've noticed that parallel or multithreaded processing does sometimes have issues within Jupyter environments (whether through Parsl, built-ins, or otherwise). That said, I've regularly used the latest revisions of CytoTable inside of local Jupyter environments and also through Google Colab.

Could I ask for more detail surrounding your Python version, Jupyter environment, OS, and other system information that might be helpful for debugging? If you use environment files (like a conda .yml or pyproject.toml file) or lockfiles (such as poetry.lock or conda-lock.yml) these also might be helpful in helping to navigate through any dependency issues that may be happening.