polars rolling bin without truncating end

mre.py

import polars as pl
import numpy as np

def windowed_bin(df, window_size, column, binedges, precision=5, log_precision=False):
    step_size = window_size
    
    def bin_values(values):
        bins = np.histogram(values, bins=binedges)[0]
        return bins.tolist()
    
    if not log_precision:
        bin_labels = [f"bin_{binedges[i]:.{precision}f}" for i in range(1, len(binedges))]
    else:
        bin_labels = [f"bin_{binedges[i]:.{precision}e}" for i in range(1, len(binedges))]
    
    rolling_bin_df = (
        df
        .set_sorted("pos")
        .group_by_dynamic(index_column="pos", every=f"{step_size}i",
                          period=f"{window_size}i", offset="0i", closed="left",
                          include_boundaries=True)
        .agg([
            pl.map_groups(pl.col(column), bin_values).alias("bins")
        ])
        .rename({"_lower_boundary": "start", "_upper_boundary": "end"})
    )
    
    rolling_bin_df = (rolling_bin_df.with_columns([
        pl.col('bins').list.get(i).alias(label)
        for i, label in enumerate(bin_labels)
        ])
        .select(pl.exclude('bins')))
    
    return rolling_bin_df

# Create a sample DataFrame
df = pl.DataFrame({
    'pos': [1, 2, 3, 4, 5, 6, 7, 8, 9],
    'value': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
})

# Define the window size, column to bin, and binedges
window_size = 3
column = 'value'
binedges = [0.0, 0.5, 1.0]

# Expected windows: [0, 3), [3, 6), [6, 9), [9, 10)
result = windowed_bin(df, window_size, column, binedges)

print(result)