temporary-directory: null  # Directory for local disk like /tmp, /scratch, or /local

visualization:
  engine: null  # Default visualization engine to use when calling `.visualize()` on a collection

tokenize:
  ensure-deterministic: false  # If true, tokenize will error instead of falling back to uuids

dataframe:
  backend: "pandas"  # Backend dataframe library for input IO and data creation
  shuffle:
    method: null
    compression: null  # compression for on disk-shuffling. Partd supports ZLib, BZ2, SNAPPY
  parquet:
    metadata-task-size-local: 512  # Number of files per local metadata-processing task
    metadata-task-size-remote: 1  # Number of files per remote metadata-processing task
    minimum-partition-size: 75000000
  convert-string: null  # Whether to convert string-like data to pyarrow strings
  query-planning: null  # Whether to use dask-expr

array:
  backend: "numpy"  # Backend array library for input IO and data creation
  chunk-size: "128MiB"
  rechunk:
    method: "tasks"  # Rechunking method to use
    threshold: 4
  svg:
    size: 120  # pixels
  slicing:
    split-large-chunks: null  # How to handle large output chunks in slicing. Warns by default.

optimization:
  annotations:
    fuse: true  # Automatically fuse compatible annotations on layers
  fuse:
    active: null  # Treat as false for dask.dataframe, true for everything else
    ave-width: 1
    max-width: null  # 1.5 + ave_width * log(ave_width + 1)
    max-height: .inf
    max-depth-new-edges: null  # ave_width * 1.5
    subgraphs: null  # true for dask.dataframe, false for everything else
    rename-keys: true

admin:
  traceback:
    shorten:
      - concurrent[\\\/]futures[\\\/]
      - dask[\\\/](base|core|local|multiprocessing|optimization|threaded|utils)\.py
      - dask[\\\/]array[\\\/]core\.py
      - dask[\\\/]dataframe[\\\/](core|methods)\.py
      - distributed[\\\/](client|scheduler|utils|worker)\.py
      - tornado[\\\/]gen\.py
      - pandas[\\\/]core[\\\/]
