Deep Learning Operator

download_dl_model

 download_dl_model (models:str|list=None, path:str=None)

	Type	Default	Details
models	str \| list	None	deep learning models to be downloaded, all available models by default
path	str	None	directory to save these models, inside installed Moraine package by default

Deep learning models have to be installed before use, download them by:

download_dl_model()

Downloading https://raw.githubusercontent.com/kanglcn/n2f/refs/heads/main/n2f.onnx to /work/projects/jinwook/radarlab/kangl/moraine/moraine/dl_model/n2f.onnx
File '/work/projects/jinwook/radarlab/kangl/moraine/moraine/dl_model/n2f.onnx' downloaded successfully.
Downloading https://raw.githubusercontent.com/kanglcn/n2f/refs/heads/main/n2fs3d.onnx to /work/projects/jinwook/radarlab/kangl/moraine/moraine/dl_model/n2fs3d.onnx
File '/work/projects/jinwook/radarlab/kangl/moraine/moraine/dl_model/n2fs3d.onnx' downloaded successfully.
Downloading https://raw.githubusercontent.com/kanglcn/n2ft/refs/heads/main/n2ft.onnx to /work/projects/jinwook/radarlab/kangl/moraine/moraine/dl_model/n2ft.onnx
File '/work/projects/jinwook/radarlab/kangl/moraine/moraine/dl_model/n2ft.onnx' downloaded successfully.
Downloading https://raw.githubusercontent.com/kanglcn/n2ft/refs/heads/main/n2ft.onnx.data to /work/projects/jinwook/radarlab/kangl/moraine/moraine/dl_model/n2ft.onnx.data
File '/work/projects/jinwook/radarlab/kangl/moraine/moraine/dl_model/n2ft.onnx.data' downloaded successfully.

source

n2f

 n2f (intf:numpy.ndarray, chunks:tuple=None, depths:tuple=(0, 0),
      model:str=None)

	Type	Default	Details
intf	ndarray		interferogram, 2d np.complex64 or cp.complex64
chunks	tuple	None	chunksize, intf.shape by default
depths	tuple	(0, 0)	width of the boundary
model	str	None	path to the model in onnx format, use the model comes with this package by default

Usage:

import zarr
import numpy as np
import holoviews as hv
hv.extension('bokeh')
hv.output(widget_location='bottom')
from bokeh.models import WheelZoomTool
from holoviews import opts

ⓘ

rslc_zarr = zarr.open('../CLI/raw/rslc.zarr/',mode='r')
intf = rslc_zarr[:,:,7]*rslc_zarr[:,:,13].conj()

filtered_intf = n2f(intf,depths=(10,10))

CPU times: user 2min 44s, sys: 9.54 s, total: 2min 54s
Wall time: 7.04 s

raw_intf_plot = mr.ras_plot(np.angle(intf))
filtered_intf_plot = mr.ras_plot(np.angle(filtered_intf))
(raw_intf_plot+filtered_intf_plot).opts(
    opts.Image(
        cmap='colorwheel',width=600, height=600, colorbar=True,invert_yaxis=True,
        default_tools=['pan',WheelZoomTool(zoom_on_axis=False),'save','reset','hover'],
        active_tools=['wheel_zoom'])
)

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n2fs3d

 n2fs3d (adi:numpy.ndarray, intf:numpy.ndarray, chunks:tuple=None,
         depths:tuple=(0, 0), model:str=None)

	Type	Default	Details
adi	ndarray		amplitude dispersion index, 2d np.float32 or cp.float32
intf	ndarray		interferogram, 2d np.complex64 or cp.complex64
chunks	tuple	None	chunksize, intf.shape by default
depths	tuple	(0, 0)	width of the boundary
model	str	None	path to the model in onnx format, use the model comes with this package by default

Usage:

import zarr
import numpy as np
import holoviews as hv
hv.extension('bokeh')
hv.output(widget_location='bottom')
from bokeh.models import WheelZoomTool
from holoviews import opts

ⓘ

rslc_zarr = zarr.open('../CLI/raw/rslc.zarr/',mode='r')
intf = rslc_zarr[:,:,7]*rslc_zarr[:,:,13].conj()
adi = zarr.open('../CLI/ps/adi.zarr/',mode='r')[:]

filtered_intf = n2fs3d(adi,intf,depths=(200,200))

CPU times: user 2min 36s, sys: 12 s, total: 2min 48s
Wall time: 6.47 s

if is_cuda_available():
    intf_cp = cp.asarray(intf)
    adi_cp = cp.asarray(adi)
    filtered_intf_cp = n2fs3d(adi_cp,intf_cp)

CPU times: user 1 s, sys: 170 ms, total: 1.17 s
Wall time: 998 ms

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n2ft

 n2ft (x:numpy.ndarray, y:numpy.ndarray, intf:numpy.ndarray,
       chunks:int=None, k:int=128, model:str=None, cuda:bool=False)

	Type	Default	Details
x	ndarray		x coordinate, e.g., longitude, shape (n,) np.floating
y	ndarray		y coordinate, e.g., latitude, shape (n,) np.floating
intf	ndarray		interferogram, shape(n,) np.complex64
chunks	int	None	chunksize, intf.shape[0] by default
k	int	128	halo size for chunkwise processing
model	str	None	path to the model in onnx format, use the model comes with this package by default
cuda	bool	False	use gpu for inference

Usage:

import zarr

base = '../Tutorials/CLI/ps_processing/'
ps_can_rslc_zarr = zarr.open(base+'hix/ps_can_rslc.zarr/',mode='r')
ps_can_y = zarr.open(base+'hix/ps_can_n.zarr',mode='r')[:]
ps_can_x = zarr.open(base+'hix/ps_can_e.zarr',mode='r')[:]
ps_can_lon = zarr.open(base+'hix/ps_can_lon.zarr',mode='r')[:]
ps_can_lat = zarr.open(base+'hix/ps_can_lat.zarr',mode='r')[:]
ps_can_intf = ps_can_rslc_zarr[:,10]*ps_can_rslc_zarr[:,13].conj()

out = n2ft(ps_can_lon, ps_can_lat, ps_can_intf,)

CPU times: user 1min 55s, sys: 14.2 s, total: 2min 9s
Wall time: 12.7 s

out = n2ft(ps_can_lon, ps_can_lat, ps_can_intf, chunks=50000, cuda=True)

CPU times: user 7.61 s, sys: 2.03 s, total: 9.64 s
Wall time: 5.15 s

out = n2ft(ps_can_lon, ps_can_lat, ps_can_intf, chunks=20000)

CPU times: user 5min 10s, sys: 6.06 s, total: 5min 16s
Wall time: 19.4 s

import moraine as mr
import holoviews as hv
import numpy as np
import zarr
from holoviews import opts
from bokeh.models import WheelZoomTool
hv.extension('bokeh')

ⓘ

intf_plot = mr.pc_plot(np.angle(ps_can_intf),ps_can_y,ps_can_x)
intf_plot = intf_plot[0]*intf_plot[1]
intf_filt_plot = mr.pc_plot(np.angle(out),ps_can_y,ps_can_x)
intf_filt_plot = intf_filt_plot[0]*intf_filt_plot[1]

intf_plot = intf_plot.redim(
    x=hv.Dimension('lon', label='Longitude'), y=hv.Dimension('lat',label='Latitude'),
    z=hv.Dimension('intf',label='Phase',range=(-np.pi,np.pi)),
)
intf_filt_plot = intf_filt_plot.redim(
    x=hv.Dimension('lon', label='Longitude'), y=hv.Dimension('lat',label='Latitude'),
    z=hv.Dimension('intf',label='Phase',range=(-np.pi,np.pi)),
)

(intf_plot+intf_filt_plot).opts(
    opts.Image(
        cmap='colorwheel',width=600, height=400, colorbar=True,
        default_tools=['pan',WheelZoomTool(zoom_on_axis=False),'save','reset','hover'],
        active_tools=['wheel_zoom']
    ),
    opts.Points(
        color='intf', cmap='colorwheel',width=600, height=400, colorbar=True,
        default_tools=['pan',WheelZoomTool(zoom_on_axis=False),'save','reset','hover'],
        active_tools=['wheel_zoom']
    )
)