jpivarski

jpivarski

jpivarski

jpivarski

# see https://gist.github.com/jpivarski/001867b9da51a47b93913a0b9809db3a

import concurrent.futures
import time
import glob
import tarfile
import json
import ast
import re
import gzip

jpivarski

# see https://gist.github.com/jpivarski/001867b9da51a47b93913a0b9809db3a

import concurrent.futures
import time
import glob
import tarfile
import json
import ast
import re
import gzip

jpivarski

jpivarski

import time
import numpy as np
import numba as nb

# This could also be done without a class, using @nb.jit directly.
# It just seems natural to use a class. (That *might* slow it down a little; haven't tested it.)

spec = [
  ("width", nb.int32),
  ("height", nb.int32),

jpivarski

Uproot version 5.0.0

Uproot version 5 has a few major new features, one removal (uproot.lazy), and is based on Awkward Array version 2 instead of version 1.

uproot.lazy → uproot.dask

@kkothari2001 upgraded Uproot from Awkward version 1 to version 2, the major part of which was replacing uproot.lazy, which is based on Awkward 1's virtual and partitioned lazy arrays, with the new Dask collection, dask-awkward. The entry point for this function is uproot.dask.

@kkothari2001 also simplified Uproot's Pandas backend, which used to "explode" ragged arrays from ROOT into Pandas DataFrames with a non-trivial MultiIndex. Now, it takes advantage of awkward-pandas to put ragged (and more complex) Awkward Arrays directly into Pandas columns.

jpivarski

jpivarski