import os
import numpy as np
from polsartools.utils.proc_utils import process_chunks_parallel
from polsartools.utils.utils import conv2d,time_it
from polsartools.utils.convert_matrices import T3_C3_mat, C3_T3_mat
from .fp_infiles import fp_c3t3files
[docs]
@time_it
def simulate_CP(in_dir, chi=45,psi=0, win=1, fmt="tif", cog=False,
ovr = [2, 4, 8, 16], comp=False,
max_workers=None,block_size=(512, 512),
progress_callback=None, # for QGIS plugin
):
"""
This function simulates Compact polarimetric C2 matrix (RHV, LHV, pi/4 etc) from full polarimetric C3/T3 matrix.
Examples
--------
>>> # Basic usage with default LC
>>> simulate_CP("/path/to/C3")
>>> # With chi, psi and COG GeoTIFF output
>>> simulate_CP("/path/to/C3", chi=-45, psi=0, fmt="tif", cog=True)
Parameters
----------
in_dir : str
Path to the input folder containing a supported polarimetric matrix.
chi : float, default=45
Ellipticity angle chi of the transmitted wave in degrees.
For circular polarization, chi = 45° (right circular) or -45° (left circular).
psi : float, default=0
Orientation angle psi of the transmitted wave in degrees.
For circular polarization, typically 0°.
fmt : {'tif', 'bin'}, default='tif'
Output format:
- 'tif': Cloud-optimized GeoTIFF (if cog_flag is True)
- 'bin': Raw binary format
cog : bool, default=False
Enable Cloud Optimized GeoTIFF output with internal overviews and tiling.
ovr : list[int], default=[2, 4, 8, 16]
Overview levels for pyramid generation (used with COGs).
comp : bool, default=False
If True, uses LZW compression for GeoTIFF outputs.
max_workers : int | None, default=None
Maximum number of parallel worker threads (defaults to all available CPUs).
block_size : tuple[int, int], default=(512, 512)
Size of processing blocks for chunked and parallel execution.
Returns
-------
None
The simulated CP C2 matrix elements
"""
write_flag=True
input_filepaths = fp_c3t3files(in_dir)
output_filepaths = []
os.makedirs(os.path.join(in_dir, "C2CP"), exist_ok=True)
if fmt == "bin":
output_filepaths.append(os.path.join(in_dir, "C2CP","C11.bin"))
output_filepaths.append(os.path.join(in_dir, "C2CP","C12_real.bin"))
output_filepaths.append(os.path.join(in_dir, "C2CP","C12_imag.bin"))
output_filepaths.append(os.path.join(in_dir, "C2CP","C22.bin"))
else:
output_filepaths.append(os.path.join(in_dir, "C2CP","C11.tif"))
output_filepaths.append(os.path.join(in_dir, "C2CP","C12_real.tif"))
output_filepaths.append(os.path.join(in_dir, "C2CP","C12_imag.tif"))
output_filepaths.append(os.path.join(in_dir, "C2CP","C22.tif"))
process_chunks_parallel(input_filepaths, list(output_filepaths),
win, write_flag,
process_chunk_sim_cp,
*[chi,psi],
block_size=block_size,
max_workers=max_workers, num_outputs=len(output_filepaths),
cog=cog, ovr=ovr, comp=comp,
progress_callback=progress_callback
)
# def process_chunk_yam4cfp(chunks, window_size, input_filepaths, model,*args):
def process_chunk_sim_cp(chunks, window_size, input_filepaths, *args, **kwargs):
chi=args[-2]
psi=args[-1]
if 'T11' in input_filepaths[0] and 'T22' in input_filepaths[5] and 'T33' in input_filepaths[8]:
t11_T1 = np.array(chunks[0])
t12_T1 = np.array(chunks[1])+1j*np.array(chunks[2])
t13_T1 = np.array(chunks[3])+1j*np.array(chunks[4])
t21_T1 = np.conj(t12_T1)
t22_T1 = np.array(chunks[5])
t23_T1 = np.array(chunks[6])+1j*np.array(chunks[7])
t31_T1 = np.conj(t13_T1)
t32_T1 = np.conj(t23_T1)
t33_T1 = np.array(chunks[8])
T3 = np.array([[t11_T1, t12_T1, t13_T1],
[t21_T1, t22_T1, t23_T1],
[t31_T1, t32_T1, t33_T1]])
T_T1 = T3_C3_mat(T3)
if 'C11' in input_filepaths[0] and 'C22' in input_filepaths[5] and 'C33' in input_filepaths[8]:
C11 = np.array(chunks[0])
C12 = np.array(chunks[1])+1j*np.array(chunks[2])
C13 = np.array(chunks[3])+1j*np.array(chunks[4])
C21 = np.conj(C12)
C22 = np.array(chunks[5])
C23 = np.array(chunks[6])+1j*np.array(chunks[7])
C31 = np.conj(C13)
C32 = np.conj(C23)
C33 = np.array(chunks[8])
T_T1 = np.array([[C11, C12, C13],
[C21, C22, C23],
[C31, C32, C33]])
if window_size>1:
kernel = np.ones((window_size,window_size),np.float32)/(window_size*window_size)
t11f = conv2d(T_T1[0,0,:,:],kernel)
t12f = conv2d(np.real(T_T1[0,1,:,:]),kernel)+1j*conv2d(np.imag(T_T1[0,1,:,:]),kernel)
t13f = conv2d(np.real(T_T1[0,2,:,:]),kernel)+1j*conv2d(np.imag(T_T1[0,2,:,:]),kernel)
t21f = np.conj(t12f)
t22f = conv2d(T_T1[1,1,:,:],kernel)
t23f = conv2d(np.real(T_T1[1,2,:,:]),kernel)+1j*conv2d(np.imag(T_T1[1,2,:,:]),kernel)
t31f = np.conj(t13f)
t32f = np.conj(t23f)
t33f = conv2d(T_T1[2,2,:,:],kernel)
T_T1 = np.array([[t11f, t12f, t13f], [t21f, t22f, t23f], [t31f, t32f, t33f]])
_,_,rows,cols = np.shape(T_T1)
psi = psi*np.pi/180
chi = chi*np.pi/180
CP11 = 0.5*((1+np.cos(2*psi)*np.cos(2*chi))*T_T1[0,0,:,:]+
0.5*(1-np.cos(2*psi)*np.cos(2*chi))*T_T1[1,1,:,:]+
(1/np.sqrt(2))*(np.sin(2*psi)*np.cos(2*chi))*(T_T1[0,1,:,:]+np.conj(T_T1[0,1,:,:]))+
(1j/np.sqrt(2))*np.sin(2*chi)*(T_T1[0,1,:,:]-np.conj(T_T1[0,1,:,:]))
)
CP12 = 0.5*((1/np.sqrt(2))*(1+np.cos(2*psi)*np.cos(2*chi))*T_T1[0,1,:,:]+
(1/np.sqrt(2))*(1-np.cos(2*psi)*np.cos(2*chi))*T_T1[1,2,:,:]+
(np.sin(2*psi)*np.cos(2*chi))*(T_T1[0,2,:,:]+0.5*T_T1[1,1,:,:])+
1j*np.sin(2*chi)*(T_T1[0,2,:,:]-0.5*T_T1[1,1,:,:])
)
CP22 = 0.5*(0.5*(1+np.cos(2*psi)*np.cos(2*chi))*T_T1[1,1,:,:]+
(1-np.cos(2*psi)*np.cos(2*chi))*T_T1[2,2,:,:]+
(1/np.sqrt(2))*(np.sin(2*psi)*np.cos(2*chi))*(T_T1[1,2,:,:]+np.conj(T_T1[1,2,:,:]))+
(1j/np.sqrt(2))*np.sin(2*chi)*(T_T1[1,2,:,:]-np.conj(T_T1[1,2,:,:]))
)
return np.real(CP11).astype(np.float32), np.real(CP12).astype(np.float32), \
np.imag(CP12).astype(np.float32), np.real(CP22).astype(np.float32)
