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#!/usr/bin/env python3 """ v1.0 20250731 Muhammet Nergizci, Uni of Leeds ======== Overview ======== This script interpolates the inSAR epoch regarding the given csv file by applying linear interpolation. ===== Usage ===== LiCSBAS_cum_interpolate.py -t TS_folder --csv csv.file -t Path to the folder. Default: TS_GEOCml10GACOSmask. --csv Path to the CSV file """ #%% Import import pandas as pd from LiCSBAS_meta import * import getopt import os import sys import time import numpy as np import datetime as dt import h5py as h5 import cmcrameri.cm as cmc import xarray as xr class Usage(Exception): """Usage context manager""" def __init__(self, msg): self.msg = msg #%% Main def main(argv=None): #%% Check argv if argv == None: argv = sys.argv start = time.time() print("\n{} ver{} {} {}".format(os.path.basename(argv[0]), ver, date, author), flush=True) print("{} {}".format(os.path.basename(argv[0]), ' '.join(argv[1:])), flush=True) #%% Set default tsadir = [] cumname = 'cum_filt.h5' csvfile = [] csv = None cmap_wrap = cmc.romaO compress = 'gzip' modelfile = '' nopngs = False #%% Read options try: try: opts, args = getopt.getopt(argv[1:], "ht:", ["help", "csv="]) except getopt.error as msg: raise Usage(msg) for o, a in opts: if o == '-h' or o == '--help': print(__doc__) return 0 elif o == '-t': tsadir = a elif o == '--csv': csv = a #%%file if not tsadir: raise Usage('No tsa directory given, -t is not optional!') elif not os.path.isdir(tsadir): raise Usage('No {} dir exists!'.format(tsadir)) elif not os.path.exists(os.path.join(tsadir, cumname)): raise Usage('No {} exists in {}!'.format(cumname, tsadir)) ###This is needed for if not csv: raise Usage('No CSV file given, --csv is not optional!, this is needed for interseismic analysis') elif not os.path.exists(csv): raise Usage('No {} exists!'.format(csv)) except Usage as err: print("\nERROR:", file=sys.stderr, end='') print(" "+str(err.msg), file=sys.stderr) print("\nFor help, use -h or --help.\n", file=sys.stderr) return 2 #%%Directory and file setting tsadir = os.path.abspath(tsadir) cumfile = os.path.join(tsadir, cumname) csvfile = os.path.abspath(csv) #%%cumfile cumfile_xr = xr.load_dataset(cumfile) cum_xr = cumfile_xr["cum"] imdates = cumfile_xr["imdates"].values # breakpoint() time_idx = pd.to_datetime(imdates.astype(str), errors='coerce') if pd.isna(time_idx).any(): bad = np.where(pd.isna(time_idx))[0][:5] raise ValueError(f"Bad date format in imdates: {imdates[bad]}") # attach time; rename phony dims if present da = cum_xr # <— use the loaded variable da = da.assign_coords({da.dims[0]: time_idx}).rename({da.dims[0]: "time"}) ren = {d: ("y" if i==1 else "x") for i, d in enumerate(da.dims[1:], 1) if d.startswith("phony_dim")} if ren: da = da.rename(ren) da = da.sortby("time") # ensure ascending #%%csv file csv_dt = ( pd.read_csv(csvfile)["date"] .astype(str) .pipe(pd.to_datetime, errors="coerce") .dropna() .sort_values() .unique() ) #%%linear interpolation interp= da.interp(time=csv_dt, method="linear") ##TODO: the method can be changed later if needed, e.g. quadratic but I get some issues with that now. ##Linear extrapolation t_days = da.time.values.astype("datetime64[D]").astype(int) g_days = interp.time.values.astype("datetime64[D]").astype(int) t0, tN = int(t_days[0]), int(t_days[-1]) # slopes from first and last segments dt0 = float(t_days[1] - t_days[0]) dtN = float(t_days[-1] - t_days[-2]) if dt0 == 0 or dtN == 0: raise ValueError("Duplicate timestamps in InSAR time axis.") slope_start = (da.isel(time=1) - da.isel(time=0)) / dt0 # (y,x) slope_end = (da.isel(time=-1) - da.isel(time=-2)) / dtN # (y,x) # masks and time offsets (as DataArrays for clean broadcasting) time_idx_interp = interp["time"] before = xr.DataArray(g_days < t0, dims=["time"], coords={"time": time_idx_interp}) after = xr.DataArray(g_days > tN, dims=["time"], coords={"time": time_idx_interp}) d_before = xr.DataArray((g_days - t0).astype(float), dims=["time"], coords={"time": time_idx_interp}) d_after = xr.DataArray((g_days - tN).astype(float), dims=["time"], coords={"time": time_idx_interp}) # predicted values outside span (broadcast: (time) with (y,x)) pred_before = da.isel(time=0) + slope_start * d_before pred_after = da.isel(time=-1) + slope_end * d_after # fill only outside interp = interp.where(~before, pred_before) interp = interp.where(~after, pred_after) #%% Saving results cumfh5 = h5.File(cumfile, 'r+') # source (for copying datasets) cumifile = os.path.join(tsadir, 'cum_filt_interpolate.h5') if os.path.exists(cumifile): os.remove(cumifile) cumih5 = h5.File(cumifile, 'w') # copy datasets if present indices = ['coh_avg', 'hgt', 'n_loop_err', 'n_unw', 'slc.mli', 'maxTlen', 'n_gap', 'n_ifg_noloop', 'resid_rms', 'E.geo', 'N.geo', 'U.geo', 'filtwidth_yr', 'filtwidth_km', 'deramp_flag', 'hgt_linear_flag', 'demerr_flag', 'masked', 'vel', 'vintercept', 'corner_lat', 'corner_lon', 'post_lat', 'post_lon', 'refarea'] for index in indices: if index in list(cumih5.keys()): del cumih5[index] if index in list(cumfh5.keys()): # read array content data_in = cumfh5[index][...] cumih5.create_dataset(index, data=data_in) else: print(' No {} field found in {}. Skip.'.format(index, cumname)) ##interp_time interp_time = interp.assign_coords( time = pd.to_datetime(interp.time.values).strftime("%Y%m%d").astype(np.int32) ) if 'imdates' in cumih5: del cumih5['imdates'] cumih5.create_dataset('imdates', data=interp_time.time.values, dtype=np.int32) if 'cum' in cumih5: del cumih5['cum'] cumih5.create_dataset('cum', data=interp.values.astype(np.float32), dtype=np.float32, compression=compress) cumih5.close() cumfh5.close() #%% 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)) #%% main if __name__ == "__main__": sys.exit(main())