Configuration#

gedih3 works out of the box with no configuration. By default, all files are stored under ~/gedi_data/:

Data Type	Default Location
Downloaded HDF5 files	`~/gedih3_db/soc/`
H3 database	`~/gedih3_db/h3/`
Temporary files	`~/gedih3_db/tmp/`

If the defaults work for you, skip this page entirely.

Customizing Storage Paths#

To use different storage locations, set environment variables or create a ~/.gedih3.env file.

Option A: Environment Variables#

export GH3_DEFAULT_DOWNLOAD_DIR=/data/gedi
export GH3_DEFAULT_H3_DIR=/data/gedi/h3_db
export GH3_DEFAULT_SOC_DIR=/data/gedi/soc
export GH3_DEFAULT_TMP_DIR=/data/gedi/tmp

Option B: create a `~/.gedih3.env` File#

GH3_DEFAULT_DOWNLOAD_DIR=/data/gedi
GH3_DEFAULT_H3_DIR=/data/gedi/h3_db
GH3_DEFAULT_SOC_DIR=/data/gedi/soc
GH3_DEFAULT_TMP_DIR=/data/gedi/tmp

Tip

You don’t need to set all the above environment variables, only the ones you want to modify - only setting GH3_DEFAULT_H3_DIR is enough for most applications.

Configuration Priority#

Settings are applied in this order (highest to lowest priority):

CLI arguments (e.g., -d /path/to/database)
Environment variables
~/.gedih3.env file
Package defaults (~/gedi_data/)

Dask Configuration#

gedih3 uses Dask for distributed processing. By default, it creates a local cluster sized to your machine. Most users don’t need to change this.

CLI Dask Flags#

Flag	Description
`-N`	Number of workers (default: half of all available cores)
`-T`	Threads per worker
`-M`	Memory per worker (e.g., `8GB`, default splits all memory equaly across workers)
`-P`	Dask dashboard port
`-s`	Connect to an existing Dask scheduler (e.g. tcp://localhost:8787)

Python: Dask Client#

from dask.distributed import Client

client = Client(n_workers=8, threads_per_worker=1, memory_limit='8GB')
# All gedih3 operations automatically use this client