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#!/usr/bin/env python3 """ This script will take the statistics options from LiCSBAS130_sb_inv.py, and identifies and removes pixels that are not associated with a loop closure. Aggressive nullification will produce more no_loop_pixels, so run this before that to remove/mask pixels that natively have no loops. =============== Input & output files =============== Inputs in GEOCml*/ (): - yyyymmdd_yyyymmdd/ - yyyymmdd_yyyymmdd.cc - EQA.dem_par - slc.mli.par Inputs in GEOCml*/ (r): - yyyymmdd_yyyymmdd/ - yyyymmdd_yyyymmdd.unw Outputs in GEOCml*/: - yyyymmdd_yyyymmdd/ : - *.unw : unw with noloop pixels nulled - *_orig.unw : original, unnulled unw - no_loop_ifg/ : - yyyymmdd_yyyymmdd/ : Isolated interferograms fully not in any loop Outputs in TS_GEOCml*/ : - 130results*/ - n_gap*[.png] : Number of gaps in SB network - n_ifg_noloop*[.png] : Number of ifgs with no loop - maxTlen*[.png] : Max length of continous SB network in year ===== Usage ===== LiCSBAS130_sb_inv.py -d ifgdir [--n_para int] [--n_unw_r_thre float] [--null_mask] [--backup] """ #%% Change log ''' v1.14.2 20230706 Jack McGrath, Uni of Leeds - Re-edit for only nulling no-loop pixels v1.0 20220929 Qi Ou, Uni of Leeds - Original implementation ''' # - network/network13*.png : Figures of the network #%% Import from LiCSBAS_meta import * import getopt import os import sys import re import time import psutil import h5py as h5 import numpy as np import datetime as dt import multiprocessing as multi import cmcrameri.cm as cmc import LiCSBAS_io_lib as io_lib import LiCSBAS_inv_lib as inv_lib import LiCSBAS_tools_lib as tools_lib import LiCSBAS_loop_lib as loop_lib import LiCSBAS_plot_lib as plot_lib import argparse import shutil class CustomFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter): ''' Use a multiple inheritance approach to use features of both classes. The ArgumentDefaultsHelpFormatter class adds argument default values to the usage help message The RawDescriptionHelpFormatter class keeps the indentation and line breaks in the ___doc___ ''' pass class Usage(Exception): """Usage context manager""" def __init__(self, msg): self.msg = msg def init_args(): # read inputs parser = argparse.ArgumentParser(description=__doc__, formatter_class=CustomFormatter) parser.add_argument('-f', dest='frame_dir', default="./", help="directory of LiCSBAS output of a particular frame") parser.add_argument('-d', dest='ifg_dir', default="GEOCml10GACOS", help="folder containing unw files") parser.add_argument('-t', dest='ts_dir', default="TS_GEOCml10GACOS", help="folder containing time series") parser.add_argument('-m', dest='memory_size', default=2048, type=float, help="Max memory size for each patch in MB") parser.add_argument('-l', dest='ifg_list', default=None, type=str, help="text file containing a list of ifgs") parser.add_argument('--n_unw_r_thre', metavar="THRE", type=float, help="Threshold of n_unw (number of used unwrap data) \n (Note this value is ratio to the number of images; i.e., 1.5*n_im) \n Larger number (e.g. 2.5) makes processing faster but result sparser. \n (Default: 1 and 0.5 for C- and L-band, respectively)") parser.add_argument('--n_para', type=int, help="Number of parallel processing (Default: # of usable CPU)") parser.add_argument('--null_mask', dest='nullify', default=True, action='store_false', help="Create mask of no-loop pixels rather than null") parser.add_argument('--backup', dest='backup', default=False, action='store_true', help="Create a backup of the un-nulled data") args = parser.parse_args() return args def main(): start = time.time() ver='1.14.2'; date=20230708; author="Qi Ou and COMET dev team" print("\n{} ver{} {} {}".format(os.path.basename(sys.argv[0]), ver, date, author), flush=True) print("{} {}".format(os.path.basename(sys.argv[0]), ' '.join(sys.argv[1:])), flush=True) ## For parallel processing global n_para_gap, G, Aloop, unwpatch, imdates, incdir, ifgdir, length, width,\ coef_r2m, ifgdates, ref_unw, cycle, keep_incfile, resdir, restxtfile, \ cmap_vel, cmap_wrap, wavelength, args try: n_para = len(os.sched_getaffinity(0)) except: n_para = multi.cpu_count() os.environ["OMP_NUM_THREADS"] = "1" # Because np.linalg.lstsq use full CPU but not much faster than 1CPU. # Instead parallelize by multiprocessing cmap_vel = cmc.roma.reversed() cmap_noise = 'viridis' cmap_noise_r = 'viridis_r' cmap_wrap = cmc.romaO import win_compat; q = win_compat.get_compatible_context() args = init_args() # Check if assigned number of cores is greater than those availiable if args.n_para: if args.n_para < n_para: n_para = args.n_para # define input directories ifgdir = os.path.abspath(os.path.join(args.frame_dir, args.ifg_dir)) # to read .cc tsadir = os.path.abspath(os.path.join(args.frame_dir, args.ts_dir)) # to read 120.ref, to write cum.h5 # define output directories and files resultsdir = os.path.join(tsadir, 'results') # to save vel, vintercept, rms etc if not os.path.exists(resultsdir): os.mkdir(resultsdir) if n_para > 40: n_para = 40 # Don't fully jam the server #%% Read data information ### Get size mlipar = os.path.join(ifgdir, 'slc.mli.par') width = int(io_lib.get_param_par(mlipar, 'range_samples')) length = int(io_lib.get_param_par(mlipar, 'azimuth_lines')) speed_of_light = 299792458 #m/s radar_frequency = float(io_lib.get_param_par(mlipar, 'radar_frequency')) #Hz wavelength = speed_of_light/radar_frequency #meter coef_r2m = -wavelength/4/np.pi*1000 #rad -> mm, positive is -LOS ### Set n_unw_r_thre and cycle depending on L- or C-band if wavelength > 0.2: ## L-band if args.n_unw_r_thre is None: n_unw_r_thre = 0.5 else: n_unw_r_thre = args.n_unw_r_thre cycle = 1.5 # 2pi/cycle for comparison png elif wavelength <= 0.2: ## C-band if args.n_unw_r_thre is None: n_unw_r_thre = 1.0 else: n_unw_r_thre = args.n_unw_r_thre cycle = 3 # 3*2pi/cycle for comparison png else: # any other if args.n_unw_r_thre is None: n_unw_r_thre = 1.0 else: n_unw_r_thre = args.n_unw_r_thre cycle = 3 # 3*2pi/cycle for comparison png #%% Read date and network information ### Get all ifgdates in ifgdir if args.ifg_list: ifgdates = io_lib.read_ifg_list(args.ifg_list) else: ifgdates = tools_lib.get_ifgdates(ifgdir) ### Construct G and Aloop matrix for increment and n_gap G = inv_lib.make_sb_matrix(ifgdates) Aloop = loop_lib.make_loop_matrix(ifgdates) ### Extract no loop ifgs ns_loop4ifg = np.abs(Aloop).sum(axis=0) ixs_ifg_no_loop = np.where(ns_loop4ifg==0)[0] no_loop_ifg = [ifgdates[ix] for ix in ixs_ifg_no_loop] no_loop_dir = os.path.join(ifgdir, 'no_loop_ifg') if not os.path.exists(no_loop_dir): os.mkdir(no_loop_dir) print('Moving {} no loop ifgs to no_loop_ifg'.format(len(no_loop_ifg))) for ifg in no_loop_ifg: shutil.move(os.path.join(ifgdir, ifg), os.path.join(no_loop_dir, ifg)) # Reset IFG dates list ifgdates = list(set(ifgdates) - set(no_loop_ifg)).sort() imdates = tools_lib.ifgdates2imdates(ifgdates) n_ifg = len(ifgdates) n_im = len(imdates) n_unw_thre = int(n_unw_r_thre*n_im) ### Calc dt in year imdates_dt = ([dt.datetime.strptime(imd, '%Y%m%d').toordinal() for imd in imdates]) dt_cum = np.float32((np.array(imdates_dt)-imdates_dt[0])/365.25) #%% Get patch row number ### Check RAM mem_avail = (psutil.virtual_memory().available)/2**20 #MB if args.memory_size > mem_avail/2: print('\nNot enough memory available compared to mem_size ({} MB).'.format(args.memory_size)) print('Reduce mem_size automatically to {} MB.'.format(int(mem_avail/2))) memory_size = int(mem_avail/2) else: memory_size = args.memory_size ### Determine if read cum on memory (fast) or hdf5 (slow) cum_size = int(n_im*length*width*4/2**20) #MB if memory_size > cum_size*2: print('Read cum data on memory (fast but need memory).') save_mem = False # read on memory memory_size_patch = memory_size - cum_size else: print('Read cum data in HDF5 (save memory but slow).') save_mem = True # read on hdf5 memory_size_patch = memory_size n_store_data = n_ifg*2+n_im*2+n_im*0.3 #not sure n_patch, patchrow = tools_lib.get_patchrow(width, length, n_store_data, memory_size_patch) #%% Display and output settings & parameters print('') print('Size of image (w,l) : {}, {}'.format(width, length)) print('# of images to be used : {}'.format(n_im)) print('# of ifgs to be used : {}'.format(n_ifg)) print('Threshold of used unw : {}'.format(n_unw_thre)) print('') print('Allowed memory size : {} MB'.format(memory_size)) print('Number of patches : {}'.format(n_patch)) # names for layers to be stored/pngs generated names = ['n_gap', 'n_ifg_noloop', 'maxTlen'] #%% For each patch for i_patch, rows in enumerate(patchrow): print('\nProcess {0}/{1}th line ({2}/{3}th patch)...'.format(rows[1], patchrow[-1][-1], i_patch+1, n_patch), flush=True) start2 = time.time() #%% Read data ### Allocate memory lengththis = rows[1] - rows[0] n_pt_all = lengththis*width unwpatch = np.zeros((n_ifg, lengththis, width), dtype=np.float32) ### For each ifg print(" Reading {0} ifg's unw data...".format(n_ifg), flush=True) countf = width*rows[0] countl = width*lengththis for i, ifgd in enumerate(ifgdates): suffix = '.unw' if i_patch == 0: unwfile = os.path.join(ifgdir, ifgd, ifgd + '.unw') if args.nullify and args.backup: ## Check for backed up data. THIS ASSUMES ALL DATA HAS BEEN NULLED THE SAME WAY trueorigfile = os.path.join(ifgdir, ifgd, ifgd + '_orig.unw') # If no trueorigfile exists, data is truely untouched if not os.path.exists(trueorigfile): shutil.move(unwfile, trueorigfile) suffix = '_orig13.unw' origfile = os.path.join(ifgdir, ifgd, ifgd + suffix) # Softlink (used for checking if null has already occurred for LiCSBAS12, softlink to save space) os.symlink(trueorigfile, origfile) else: # True orig exists - check if it is because loop err nullification has occurred suffix = '_orig12.unw' origfile = os.path.join(ifgdir, ifgd, ifgd + suffix) if os.path.exists(origfile): # orig12 exists - backup unw as orig1213 suffix = '_orig1213.unw' origfile = os.path.join(ifgdir, ifgd, ifgd + suffix) else: # orig12 doesn't exist. There should therefore be orig13 so no need to backup suffix = '_orig13.unw' origfile = os.path.join(ifgdir, ifgd, ifgd + suffix) shutil.move(unwfile, origfile) datafile = os.path.join(ifgdir, ifgd, ifgd + suffix) f = open(datafile, 'rb') f.seek(countf*4, os.SEEK_SET) #Seek for >=2nd patch, 4 means byte ### Read unw data (mm) at patch area unw = np.fromfile(f, dtype=np.float32, count=countl).reshape((lengththis, width))*coef_r2m unw[unw == 0] = np.nan # Fill 0 with nan unwpatch[i] = unw f.close() unwpatch = unwpatch.reshape((n_ifg, n_pt_all)).transpose() #(n_pt_all, n_ifg) #%% Remove points with less valid data than n_unw_thre ix_unnan_pt = np.where(np.sum(~np.isnan(unwpatch), axis=1) > n_unw_thre)[0] n_pt_unnan = len(ix_unnan_pt) unwpatch = unwpatch[ix_unnan_pt,:] ## keep only unnan data print(' {}/{} points removed due to not enough ifg data (e.g sea, incoherent areas)...'.format(n_pt_all-n_pt_unnan, n_pt_all), flush=True) #%% Compute number of gaps, ifg_noloop, maxTlen point-by-point if n_pt_unnan != 0: ns_gap_patch = np.zeros((n_pt_all), dtype=np.float32)*np.nan gap_patch = np.zeros((n_im-1, n_pt_all), dtype=np.int8) ns_ifg_noloop_patch = np.zeros((n_pt_all), dtype=np.float32)*np.nan maxTlen_patch = np.zeros((n_pt_all), dtype=np.float32)*np.nan print('\n Identifing gaps, and counting n_gap and n_ifg_noloop') ### Determine n_para n_pt_patch_min = 1000 if n_pt_patch_min*n_para > n_pt_unnan: ## Too much n_para n_para_gap = int(np.floor(n_pt_unnan/n_pt_patch_min)) if n_para_gap == 0: n_para_gap = 1 if n_para_gap > n_para: n_para_gap = n_para else: n_para_gap = n_para print(' with {} parallel processing...'.format(n_para_gap), flush=True) ### Devide unwpatch by n_para for parallel processing p = q.Pool(n_para_gap) _result = np.array(p.map(null_noloop_wrapper, range(n_para_gap)), dtype=object) p.close() ns_gap_patch[ix_unnan_pt] = np.hstack(_result[:, 0]) #n_pt gap_patch[:, ix_unnan_pt] = np.hstack(_result[:, 1]) #n_im-1, n_pt ns_ifg_noloop_patch[ix_unnan_pt] = np.hstack(_result[:, 2]) no_loop_log = np.hstack(_result[:, 3]).T if args.nullify: print('\n Removing {} IFG pixels that have no loop'.format(np.sum((no_loop_log == 1).flatten() > 0))) unwpatch[np.where(no_loop_log == 1)] = np.nan null_patch = np.zeros((n_ifg, n_pt_all), dtype=np.float32)*np.nan null_patch[:, ix_unnan_pt] = unwpatch.T ### maxTlen _maxTlen = np.zeros((n_pt_unnan), dtype=np.float32) #temporaly _Tlen = np.zeros((n_pt_unnan), dtype=np.float32) #temporaly for imx in range(n_im-1): _Tlen = _Tlen + (dt_cum[imx+1]-dt_cum[imx]) ## Adding dt _Tlen[gap_patch[imx, ix_unnan_pt]==1] = 0 ## reset to 0 if gap _maxTlen[_maxTlen<_Tlen] = _Tlen[_maxTlen<_Tlen] ## Set Tlen to maxTlen maxTlen_patch[ix_unnan_pt] = _maxTlen #%% Fill by np.nan if n_pt_unnan == 0 else: gap_patch = np.zeros((n_im-1, n_pt_all), dtype=np.int8) ns_gap_patch = np.zeros((n_pt_all), dtype=np.float32)*np.nan ns_ifg_noloop_patch = np.zeros((n_pt_all), dtype=np.float32)*np.nan maxTlen_patch = np.zeros((n_pt_all), dtype=np.float32)*np.nan null_patch = np.zeros((n_ifg, n_pt_all), dtype=np.float32) * np.nan #%% Output data and image #unwnull[:, rows[0]:rows[1], :] = unwpatch.reshape((n_ifg, lengththis, width)) ### Others openmode = 'w' if rows[0] == 0 else 'a' #w only 1st patch if args.nullify: ## For each ifg for i, ifgd in enumerate(ifgdates): ifgfile = os.path.join(ifgdir, ifgd, '{0}.unw'.format(ifgd)) # Write patch to file with open(ifgfile, openmode) as f: null_patch[i, :].astype(np.float32).tofile(f) ## velocity and noise indicies in results dir # names = ['n_gap', 'n_ifg_noloop', 'maxTlen'] # had to be defined earlier data = [ns_gap_patch, ns_ifg_noloop_patch, maxTlen_patch] for i in range(len(names)): file = os.path.join(resultsdir, '{}_preNullNoLoop'.format(names[i])) with open(file, openmode) as f: data[i].astype(np.float32).tofile(f) #%% Finish patch elapsed_time2 = int(time.time()-start2) hour2 = int(elapsed_time2/3600) minite2 = int(np.mod((elapsed_time2/60),60)) sec2 = int(np.mod(elapsed_time2,60)) print(" Elapsed time for {0}th patch: {1:02}h {2:02}m {3:02}s".format(i_patch+1, hour2, minite2, sec2), flush=True) if args.nullify: ## Write zeros file for n_no_loop for later masking zero_no_loop = np.zeros((length, width)) file = os.path.join(resultsdir, 'n_ifg_noloop_preNullNoLoop') data = io_lib.read_img(file, length, width) zero_no_loop[np.where(np.isnan(data))] = np.nan file = os.path.join(resultsdir, 'n_ifg_no_loop') with open(file, 'w') as f: zero_no_loop.tofile(f) #%% Output png images ### Velocity and noise indices cmins = [None, None, None] cmaxs = [None, None, None] cmaps = [cmap_noise_r, cmap_noise_r, cmap_noise] titles = ['Number of gaps in SB network', 'Number of ifgs with no loops', 'Max length of connected SB network (yr)'] if args.nullify: labels = ['_preNullNoLoop', ' before null_no_loop'] else: labels = ['',''] print('\nOutput noise png images...', flush=True) for i in range(len(names)): file = os.path.join(resultsdir, '{}{}}'.format(names[i], labels[0])) data = io_lib.read_img(file, length, width) pngfile = file + '.png' ## Get color range if None if cmins[i] is None: cmins[i] = np.nanpercentile(data, 1) if cmaxs[i] is None: cmaxs[i] = np.nanpercentile(data, 99) if cmins[i] == cmaxs[i]: cmins[i] = cmaxs[i]-1 # print(pngfile) plot_lib.make_im_png(data, pngfile, cmaps[i], '{}'.format(titles[i], labels[1]), cmins[i], cmaxs[i]) ### New unw pngs if n_para > 1 and n_ifg > 20: pool = q.Pool(processes=n_para) pool.map(null_png_wrapper, even_split(ifgdates, n_para)) else: null_png_wrapper(range(len(n_ifg))) #%% Finish elapsed_time = time.time()-start hour = int(elapsed_time/3600) minite = int(np.mod((elapsed_time/60),60)) sec = int(np.mod(elapsed_time,60)) print("\nElapsed time: {0:02}h {1:02}m {2:02}s".format(hour,minite,sec)) print('\n{} Successfully finished!!\n'.format(os.path.basename(sys.argv[0]))) print('Output directory: {}\n'.format(os.path.abspath(tsadir))) def null_noloop_wrapper(i): print(" Running {:2}/{:2}th patch...".format(i+1, n_para_gap), flush=True) n_pt_patch = int(np.ceil(unwpatch.shape[0]/n_para_gap)) n_im = G.shape[1]+1 n_loop, n_ifg = Aloop.shape if i*n_pt_patch >= unwpatch.shape[0]: # Nothing to do return ### n_gap and gap location ns_unw_unnan4inc = np.array([(G[:, j]* (~np.isnan(unwpatch[i*n_pt_patch:(i+1)*n_pt_patch]))) .sum(axis=1, dtype=np.int16) for j in range(n_im-1)]) #n_ifg*(n_pt,n_ifg) -> (n_im-1,n_pt) _ns_gap_patch = (ns_unw_unnan4inc==0).sum(axis=0) #n_pt _gap_patch = ns_unw_unnan4inc==0 del ns_unw_unnan4inc ### n_ifg_noloop # n_ifg*(n_pt,n_ifg)->(n_loop,n_pt) # Number of ifgs for each loop at eath point. # 3 means complete loop, 1 or 2 means broken loop. ns_ifg4loop = np.array([(np.abs(Aloop[j, :])* (~np.isnan(unwpatch[i*n_pt_patch:(i+1)*n_pt_patch]))) .sum(axis=1) for j in range(n_loop)]) bool_loop = (ns_ifg4loop==3) #(n_loop,n_pt) identify complete loop only #bad_bool_loop = (ns_ifg4loop != 3) # Identify incomplete loops (Loop x Pixel) del ns_ifg4loop # n_loop*(n_loop,n_pt)*n_pt->(n_ifg,n_pt) # Number of loops for each ifg at each point. ns_loop4ifg = np.array([( (np.abs(Aloop[:, j])*bool_loop.T).T* (~np.isnan(unwpatch[i*n_pt_patch:(i+1)*n_pt_patch, j])) ).sum(axis=0) for j in range(n_ifg)]) # <= This is the variable that contains the number of loops for each IFG for each pixel del bool_loop ns_ifg_noloop_ix = (ns_loop4ifg == 0).astype('int') # Int array of n_ifg x n_px of px with no loops ns_ifg_noloop_tmp = ns_ifg_noloop_ix.sum(axis=0) # Number of incomplete loops per pixel #del ns_loop4ifg ns_nan_ifg = np.isnan(unwpatch[i*n_pt_patch:(i+1)*n_pt_patch, :]).sum(axis=1) #n_pt, nan ifg count _ns_ifg_noloop_patch = ns_ifg_noloop_tmp - ns_nan_ifg # IFGs with no loop = IFGs with no loop - IFGs that are NaNs anyway (number no_loop per pixel) return _ns_gap_patch, _gap_patch, _ns_ifg_noloop_patch, ns_ifg_noloop_ix def null_png_wrapper(ifglist): for ifgd in ifglist: infile = os.path.join(ifgdir, ifgd, '{}.unw'.format(ifgd)) unw = io_lib.read_img(infile, length, width) pngfile = infile+'_noLoop_null.png' title = '{} (NoLoop Pixels Nulled)'.format(ifgd) plot_lib.make_im_png(np.angle(np.exp(1j*(unw/coef_r2m/cycle))*cycle), pngfile, cmap_wrap, title, vmin=-np.pi, vmax=np.pi, cbar=False) def even_split(a, n): """ Divide a list, a, in to n even parts""" n = min(n, len(a)) # to avoid empty lists k, m = divmod(len(a), n) return [a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n)] if __name__ == "__main__": sys.exit(main())