Binning demonstration on locally generated fake data

In this example, we generate a table with random data simulating a single event dataset. We showcase the binning method, first on a simple single table using the bin_partition method and then in the distributed method bin_dataframe, using daks dataframes. The first method is never really called directly, as it is simply the function called by the bin_dataframe on each partition of the dask dataframe.

import dask
import numpy as np
import pandas as pd
import dask.dataframe

import matplotlib.pyplot as plt

from sed.binning import bin_partition, bin_dataframe

%matplotlib widget

Generate Fake Data

n_pts = 100000
cols = ["posx", "posy", "energy"]
df = pd.DataFrame(np.random.randn(n_pts, len(cols)), columns=cols)
df

	posx	posy	energy
0	1.503362	0.407499	-0.051490
1	-0.464478	0.046401	0.873805
2	0.264292	-1.302149	-1.327216
3	-1.762067	0.840551	1.654777
4	-1.056144	0.918105	0.400613
...	...	...	...
99995	0.671038	0.703163	0.361904
99996	-1.720362	-0.105392	0.969213
99997	0.723874	-0.308329	-1.854372
99998	-0.568478	-0.366917	1.368988
99999	0.313023	0.366284	0.331397

100000 rows × 3 columns

Define the binning range

binAxes = ["posx", "posy", "energy"]
nBins = [120, 120, 120]
binRanges = [(-2, 2), (-2, 2), (-2, 2)]
coords = {ax: np.linspace(r[0], r[1], n) for ax, r, n in zip(binAxes, binRanges, nBins)}

Compute the binning along the pandas dataframe

%%time
res = bin_partition(
    part=df,
    bins=nBins,
    axes=binAxes,
    ranges=binRanges,
    hist_mode="numba",
)

CPU times: user 1.21 s, sys: 19.9 ms, total: 1.23 s
Wall time: 1.23 s

fig, axs = plt.subplots(1, 3, figsize=(8, 2.5), constrained_layout=True)
for i in range(3):
    axs[i].imshow(res.sum(i))

Transform to dask dataframe

ddf = dask.dataframe.from_pandas(df, npartitions=50)
ddf

Dask DataFrame Structure:

	posx	posy	energy
npartitions=50
0	float64	float64	float64
2000	...	...	...
...	...	...	...
98000	...	...	...
99999	...	...	...

Dask Name: from_pandas, 1 graph layer

Compute distributed binning on the partitioned dask dataframe

In this example, the small dataset does not give significant improvement over the pandas implementation, at least using this number of partitions. A single partition would be faster (you can try…) but we use multiple for demonstration purposes.

%%time
res = bin_dataframe(
    df=ddf,
    bins=nBins,
    axes=binAxes,
    ranges=binRanges,
    hist_mode="numba",
)

CPU times: user 389 ms, sys: 557 ms, total: 946 ms
Wall time: 509 ms

fig, axs = plt.subplots(1, 3, figsize=(8, 2.5), constrained_layout=True)
for dim, ax in zip(binAxes, axs):
    res.sum(dim).plot(ax=ax)