pqm: Pixel quality metrics (CLI)

Commands of moraine.cli.pqm (source); the same functions are called in Python as mc.<name>(...) with import moraine.cli as mc. The help below is generated from the docstrings, like moraine COMMAND --help.

command	summary
temp-coh	temporal coherence.

temp-coh

temporal coherence.

moraine temp-coh --intf STR --rslc STR [--t_coh STR] [--image_pairs FILE] [--chunks INT INT] [--cuda | --no-cuda] [--processes VALUE] [--n_workers VALUE] [--threads_per_worker VALUE]

argument	type	default	description
--intf	str	required	input (filtered) interferograms or coherence, complex64, shape (n_points, n_image_pairs) or (nlines, width, n_image_pairs)
--rslc	str	required	input rslc stack or phase history, complex64, shape (n_points, nimages) or (nlines, width, nimages)
--t_coh	str	None	output temporal coherence, shape (n_points,) or (nlines, width)
--image_pairs	np.ndarray	None	image pairs (reference, secondary) of intf, shape (n_image_pairs, 2); all image pairs by default
--chunks	int | tuple[int, int]	None	point chunk size or (azimuth, range) chunk size, same as intf by default
--cuda	bool	False	if use cuda for processing, false by default
--processes		None	use processes (True) or threads (False) for the workers, only for cpu processing. Default: False
--n_workers		None	number of workers. Default: 1 for cpu, one per GPU for cuda
--threads_per_worker		None	tasks a worker runs at the same time. Default: 1

Global options: --json, --traceback, --log FILE, -q / --quiet (conventions).

In a pipeline (examples/02_ps.toml, step n2f_temp_coh):

[[step]]
name = "n2f_temp_coh"
run = "temp-coh"
intf = "ps/ras_n2f_intf.zarr"
rslc = "raw/rslc.zarr"
t_coh = "ps/ras_temp_coh.zarr"
image_pairs = "ps/pairs_seq.txt"
cuda = false                      # reads the stack once and reduces it: the CPU is faster than starting a GPU worker

Also in examples/04_refine.toml (step temp_coh).

In Python:

import moraine.cli as mc
mc.temp_coh(intf, rslc, t_coh=None, image_pairs=None, chunks=None, cuda=False, processes=None, n_workers=None, threads_per_worker=None)

The array function mr.temp_coh does the same on numpy / cupy arrays in memory.