chat1

import polars as pl
from datetime import timedelta

def propagate_indicator(df):
    # EnsureSample the dataframe is sorted by timestampDataFrame
    df = df.sort('timestamp'pl.DataFrame({
    "stuff": [None, None, 1, 1, 1, None, None, 1, 1, None, 1, None, 1, 1, 1, 1]
})

    # Create a helperforward-looking column to trackcompare with the current 'stuff' column
df = df.with_column(
    pl.col("stuff").shift(-1).alias("next_stuff")
)

# Identify rows where indicatora ischange from 1 to None or None to 1 will occur
    df = df.with_column(
    (pl.col('indicator'"stuff") != pl.col("next_stuff")).alias("will_change")
)

# Count the number of changes from 1 to None, indicating the end of a group of 1s
count_groups = df.filter((pl.col("stuff") == 1) & (pl.col("next_stuff").alias('start_indicator'is_null()
    )).count()

print("Number of #contiguous Calculategroups theof timestamp up to which the value 1 should be propagated
    df = df.with_column(
        pl.when(pl.col('start_indicator'))
        .then(pl.col('timestamp') + timedelta(seconds=30))
        .otherwise(None)
        .alias('propagate_until')
    )

    # Forward fill the 'propagate_until' to get the maximum propagate time
    df = df.with_column(
        pl.col('propagate_until').fill_null('forward').alias('propagation_time')
    )

    # Use a window function to propagate the indicator
    df = df.with_columns(
        pl.when(pl.col('timestamp') <= pl.col('propagation_time'))
        .then(1)
        .otherwise(pl.col('indicator'))
        .alias('indicator_propagated')
    )

    # Drop helper columns
    df = df.drop(['start_indicator'1s:", 'propagate_until', 'propagation_time'])

    return df

# Example DataFrame
data = {
    'timestamp': [pl.datetime("2023-01-01 00:00:00") + timedelta(seconds=i) for i in range(120)],
    'indicator': [1 if i in [0, 50, 70, 100] else None for i in range(120)]
}
df = pl.DataFrame(data)

# Apply the function
result_df = propagate_indicator(df)
print(result_df)count_groups)