Apply current workspace changes

2026-04-22 01:52:21 +08:00
parent cf34b9a8ed
commit cb518b431f
449 changed files with 92252 additions and 321 deletions
@@ -0,0 +1,81 @@
+#!/usr/bin/env python
+import argparse
+import sys
+import h5py
+import numpy as np
+from ssa import  SSA
+import time, math
+from mintpy.utils import readfile, writefile, ptime, utils as ut
+from mintpy.objects import timeseries
+
+def create_parser():
+    parser=argparse.ArgumentParser()
+    parser.add_argument('timeseries_file', 
+                    help='time-series file to do SSA \n'
+                    'i.e.:geo_timeseries_GACOS_ramp_demErr.h5 (MintPy)\n')
+    parser.add_argument("-w", "--window", 
+                    type=int,
+                    help='Widow length (default is 7) ')
+    parser.add_argument('-o', '--output', dest='outfile', help='output file name (default ssa.h5)')
+    # args=parser.parse_args()
+    # computing
+    # parser = arg_group.add_memory_argument(parser)
+    return parser
+def cmd_line_parse(iargs=None):
+    parser = create_parser()
+    inps = parser.parse_args(args=iargs)
+    inps.key = readfile.read_attribute(inps.timeseries_file)['FILE_TYPE']
+    if inps.key not in ['timeseries', 'giantTimeseries', 'HDFEOS']:
+        raise Exception('input file is {}, NOT timeseries!'.format(inps.key))
+    if not inps.window:
+        inps.window = int(7)
+    if not inps.outfile:
+        outname = 'ssa'
+        outname += '.h5'
+        inps.outfile = outname
+    tsobj = timeseries(inps.timeseries_file)
+    return inps
+def is_number(s):
+    if s==0:
+        return False
+    elif math.isnan(s):
+         return False
+    else:
+        return True
+def main(iargs=None):
+    # if cmd:
+         # iargs = cmd.split()[1:]
+    inps = cmd_line_parse(iargs)
+    date_list = timeseries(inps.timeseries_file).get_date_list()
+    data, atr = readfile.read(inps.timeseries_file)
+    length, width = int(atr['LENGTH']), int(atr['WIDTH'])
+    N_dates=len(date_list)
+    # with h5py.File(inps.timeseries_file,'r') as f:
+    #     data = f['timeseries'][:]
+    #     date_list = f['date'][:]
+    data_matrix = np.empty((len(date_list),length,width))
+    data_matrix[:] = np.nan
+    start_time = time.time()
+    for i in range(length):
+        for j in range(width):
+            if is_number(sum(data[:,i,j])):
+                a=SSA(data[:,i,j], inps.window)
+                data_matrix[:,i,j]=a.reconstruct([0,1])  # Grouping recostructed components, by defaults it takes F1 and F2 as trend
+    m, s = divmod(time.time()-start_time, 60)
+    block= [0,N_dates,0,length,0,width]
+    writefile.layout_hdf5(inps.outfile, metadata=atr, ref_file=inps.timeseries_file)
+    writefile.write_hdf5_block(inps.outfile,
+                          data=data_matrix,
+                          datasetName='timeseries',
+                          block=block,
+                          print_msg=False)
+
+
+    print(inps.timeseries_file)
+    print(inps.window)
+    # print(date_list)
+    print(length,width)
+    print('time used: {:02.0f} mins {:02.1f} secs.'.format(m, s))
+if __name__ == '__main__':
+    main(sys.argv[1:])    
+