clim_dist_monthly: Extract climate data and compute distributions

In leonawicz/snapprep: Prepare And Curate Useful Data Sets From Raw SNAP Data

Description

Extract climate data and estimate monthly spatial probability distributions.

Usage

clim_dist_monthly(inputs, in_dir = snapdef()$ar5dir,
  out_dir = snapdef()$ar5dir_dist_monthly, na.rm = TRUE,
  density.args = list(n = 200, adjust = 0.1), sample.size = 10000,
  verbose = TRUE, overwrite = FALSE, move_akcan = TRUE,
  mc.cores = 32)

Arguments

inputs	data frame of inputs (one row). See details.
in_dir	input directory, e.g., snapdef()$ar5dir.
out_dir	output directory, e.g., snapdef()$ar5dir_dist_monthly
na.rm	logical, remove NAs.
density.args	arguments list passed to density.
sample.size	numeric, sample size.
verbose	logical, verbose progress.
overwrite	logical, overwrite existing files.
move_akcan	logical, relocate the AK-CAN domain from Political Boundaries subdirectory to top level as its own location group.
mc.cores	number of CPUs when processing years in parallel. Defaults to 32 assuming Atlas compute node context.

Details

inputs generally comes from clim_inputs_table. clim_dist_monthly processes data sets referred to by one row of this data frame at a time. Internally processing uses 32 CPUs on an Atlas compute node. It is expected that the different data sets in the full inputs be processed serially. See example call below.

Value

invisible, writes files.

Examples

## Not run: 
purrr::walk(1:nrow(inputs), ~slice(inputs, .x) %>% clim_dist_monthly())

## End(Not run)

leonawicz/snapprep documentation built on May 14, 2019, 8:54 a.m.