Dask Histogram

Common errors

• AttributeError: 'DaskHistogram' object has no attribute 'values'

  cause Attempting to access histogram data attributes (like 'values', 'counts', 'sum_weights') on a lazy DaskHistogram object before calling `.compute()`.
  fix Call `.compute()` on the DaskHistogram object first to get the concrete boost-histogram instance. Example: `computed_hist = dask_hist_obj.compute(); print(computed_hist.view())`

• TypeError: cannot pickle '_thread.RLock' object

  cause A non-serializable object (like a lock or certain complex Python objects) was inadvertently included in the Dask graph, making it impossible to send across processes in a distributed Dask setup.
  fix Ensure all objects passed into Dask operations or stored within the Dask graph are serializable. This often requires careful construction of custom functions or `boost-histogram` axis definitions. Sometimes restarting the Dask client or environment can resolve transient pickling issues.

• ValueError: Mismatched number of dimensions in fill data

  cause The number of data arrays provided to `Hist.fill()` does not match the number of axes defined in the underlying `boost-histogram` object.
  fix Review your `boost_histogram.Histogram` definition and the data arrays passed to `Hist.fill()`. For N-dimensional histograms, you need N 1D arrays, each corresponding to an axis.

• AttributeError: module 'dask_histogram' has no attribute 'factory'

  cause The `dask_histogram.factory` module or its specific functions were either removed, refactored, or are incompatible with the installed Dask version, especially after Dask 2024.12.
  fix Update `dask-histogram` to the latest version. If `factory` functions are still missing or problematic, consult the `dask-histogram` documentation for the current recommended way to create or manipulate histogram layers, as the API might have changed.

Warnings

• gotcha Dask histograms are lazy computations. They return a Dask object that needs to be explicitly computed using `.compute()` to obtain the final boost-histogram object with actual results. Failing to call `.compute()` will result in working with a Dask graph, not the histogram data itself.
  Fix: Always call `.compute()` on the Dask histogram object to get the final result. Example: `final_histogram = dask_histogram_obj.compute()`
• breaking Compatibility with Dask versions can be sensitive. For instance, `dask-histogram.factory` functionality was broken with `dask>=2024.12.0` and required an update in `dask-histogram==2024.12.0` to fix. Ensure your `dask-histogram` version is compatible with your `dask` version, especially after major Dask releases.
  Fix: Keep `dask-histogram` updated to its latest version to ensure compatibility with recent Dask releases. Check release notes for specific Dask version requirements.
• gotcha When using `dask_histogram.Hist.fill()`, the arguments (e.g., `x`, `y`) must be Dask arrays, not raw NumPy arrays or scalar values, unlike `boost-histogram`'s direct `fill()` method. This is a common mistake when migrating from `boost-histogram` to `dask-histogram`.
  Fix: Ensure all data intended for filling a `dask_histogram.Hist` object are Dask arrays. Convert NumPy arrays to Dask arrays first (e.g., `da.from_array(my_numpy_array)`).
• gotcha The internal Dask graph construction for `Hist.fill()` was optimized in version `2024.3.0` to delay the creation of the task graph until `.compute()` is called. This can affect users who were relying on inspecting the Dask graph immediately after calling `fill()` but before `compute()`.
  Fix: Adjust any custom graph introspection logic to account for delayed graph creation. The graph is fully formed only after `.compute()` is invoked or a Dask operation that triggers graph building occurs.

Install

• pip install dask-histogram Install stable version

Imports

• Hist

  from dask_histogram import Hist

• histogram

  from dask_histogram.routines import histogram

Quickstart

Demonstrates creating Dask histograms using both a NumPy-like routine and by wrapping a boost-histogram object with a Dask array. Remember to call `.compute()` to get the final histogram object.

import dask.array as da
from dask_histogram.routines import histogram
import boost_histogram as bh

# Create a large Dask array
x = da.random.normal(0, 1, size=(10_000_000,), chunks=1_000_000)

# Method 1: NumPy-like interface
bins = 50
range_min, range_max = -5, 5
dask_hist_numpy_like = histogram(x, bins=bins, range=(range_min, range_max))

print(f"NumPy-like Dask histogram (lazy): {dask_hist_numpy_like}")
computed_hist_numpy_like = dask_hist_numpy_like.compute()
print(f"Computed histogram (NumPy-like): {computed_hist_numpy_like.view()}")

# Method 2: boost-histogram like interface
from dask_histogram import Hist

bh_hist = (bh.Histogram(bh.axis.Regular(bins, range_min, range_max, metadata="x")))

dask_hist_bh_like = Hist.from_boost_histogram(bh_hist, x)

print(f"boost-histogram-like Dask histogram (lazy): {dask_hist_bh_like}")
computed_hist_bh_like = dask_hist_bh_like.compute()
print(f"Computed histogram (boost-histogram-like): {computed_hist_bh_like.view()}")