31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227 | def run_wrapped_phase_sequential(
*,
slc_vrt_stack: VRTStack,
output_folder: Path,
ministack_size: int,
half_window: dict,
strides: Optional[dict[str, int]] = None,
mask_file: Optional[Filename] = None,
ps_mask_file: Optional[Filename] = None,
amp_mean_file: Optional[Filename] = None,
amp_dispersion_file: Optional[Filename] = None,
shp_method: ShpMethod = ShpMethod.NONE,
shp_alpha: float = 0.05,
shp_nslc: Optional[int] = None,
use_evd: bool = False,
beta: float = 0.00,
zero_correlation_threshold: float = 0.0,
similarity_nearest_n: int | None = None,
compressed_slc_plan: CompressedSlcPlan = CompressedSlcPlan.ALWAYS_FIRST,
max_num_compressed: int = 100,
output_reference_idx: int | None = None,
new_compressed_reference_idx: int | None = None,
cslc_date_fmt: str = "%Y%m%d",
write_closure_phase: bool = True,
write_crlb: bool = True,
block_shape: tuple[int, int] = (512, 512),
baseline_lag: Optional[int] = None,
max_workers: int = 1,
**tqdm_kwargs,
) -> tuple[
list[Path], list[Path], list[Path], list[Path], list[Path], list[Path], list[Path]
]:
"""Estimate wrapped phase using batches of ministacks."""
if strides is None:
strides = {"x": 1, "y": 1}
output_folder.mkdir(parents=True, exist_ok=True)
input_file_list = slc_vrt_stack.file_list
is_compressed = ["compressed" in str(f).lower() for f in slc_vrt_stack.file_list]
input_dates = _get_input_dates(input_file_list, is_compressed, cslc_date_fmt)
ministack_planner = MiniStackPlanner(
file_list=slc_vrt_stack.file_list,
dates=input_dates,
is_compressed=is_compressed,
output_folder=output_folder,
max_num_compressed=max_num_compressed,
output_reference_idx=output_reference_idx,
compressed_slc_plan=compressed_slc_plan,
)
ministacks = ministack_planner.plan(
ministack_size, compressed_idx=new_compressed_reference_idx
)
logger.info(f"File range start: {Path(slc_vrt_stack.file_list[0]).name}")
logger.info(f"File range end: {Path(slc_vrt_stack.file_list[-1]).name}")
logger.info(f"Output folder: {output_folder}")
logger.info(f"Number of ministacks of size {ministack_size}: {len(ministacks)}")
if shp_nslc is None:
shp_nslc = slc_vrt_stack.shape[0]
# list where each item is extended with output_slc_files from a ministack
output_slc_files: list[Path] = []
crlb_files: list[Path] = []
closure_phase_files: list[Path] = []
temp_coh_files: list[Path] = []
similarity_files: list[Path] = []
shp_count_files: list[Path] = []
# function to check if a ministack has already been processed
def already_processed(d: Path, search_ext: str = ".tif") -> bool:
return d.exists() and len(list(d.glob(f"*{search_ext}"))) > 0
# Solve each ministack using the current chunk (and the previous compressed SLCs)
for ministack in ministacks:
cur_output_folder = ministack.output_folder
if already_processed(cur_output_folder):
logger.info(f"Skipping {cur_output_folder} because it already exists.")
else:
cur_files = ministack.file_list
start_end = ministack.real_slc_date_range_str
logger.info(
f"Processing {len(cur_files)} SLCs. Output folder: {cur_output_folder}"
)
cur_vrt = VRTStack(
cur_files,
outfile=output_folder / f"{start_end}.vrt",
sort_files=False,
subdataset=slc_vrt_stack.subdataset,
)
run_wrapped_phase_single(
vrt_stack=cur_vrt,
ministack=ministack,
output_folder=cur_output_folder,
half_window=half_window,
strides=strides,
use_evd=use_evd,
beta=beta,
zero_correlation_threshold=zero_correlation_threshold,
mask_file=mask_file,
ps_mask_file=ps_mask_file,
amp_mean_file=amp_mean_file,
amp_dispersion_file=amp_dispersion_file,
shp_method=shp_method,
shp_alpha=shp_alpha,
shp_nslc=shp_nslc,
similarity_nearest_n=similarity_nearest_n,
write_closure_phase=write_closure_phase,
write_crlb=write_crlb,
block_shape=block_shape,
baseline_lag=baseline_lag,
max_workers=max_workers,
**tqdm_kwargs,
)
(
cur_output_files,
cur_crlb_files,
cur_closure_phase_files,
_cur_comp_slc_file,
temp_coh_file,
similarity_file,
shp_count_file,
) = _get_outputs_from_folder(cur_output_folder)
crlb_files.extend(cur_crlb_files)
closure_phase_files.extend(cur_closure_phase_files)
output_slc_files.extend(cur_output_files)
temp_coh_files.append(temp_coh_file)
similarity_files.append(similarity_file)
shp_count_files.append(shp_count_file)
##############################################
# Move the per-ministack files into the `output_folder`
def move_to_output(files: list[Path], output_folder: Path) -> list[Path]:
tmp: list[Path] = []
for p in files:
tmp.append(p.rename(output_folder / p.name))
return tmp
temp_coh_files = move_to_output(temp_coh_files, output_folder)
shp_count_files = move_to_output(shp_count_files, output_folder)
similarity_files = move_to_output(similarity_files, output_folder)
# Average the temporal coherence files in each ministack
full_span = ministack_planner.real_slc_date_range_str
avg_temp_coh_file = output_folder / f"temporal_coherence_average_{full_span}.tif"
avg_shp_count_file = output_folder / f"shp_counts_average_{full_span}.tif"
# we can pass the list of files to gdal_calc, which interprets it
# as a multi-band file
if len(temp_coh_files) > 1:
_average_or_rename(temp_coh_files, avg_temp_coh_file, "Float32")
_average_or_rename(shp_count_files, avg_shp_count_file, "Int16")
temp_coh_files.append(avg_temp_coh_file)
shp_count_files.append(avg_shp_count_file)
if len(similarity_files) > 1:
# Create one phase similarity raster on the whole wrapped time series
full_similarity_file = output_folder / f"similarity_full_{full_span}.tif"
create_similarities(
ifg_file_list=cur_output_files,
output_file=full_similarity_file,
# TODO: any of these configurable?
search_radius=11,
sim_type="median",
block_shape=block_shape,
nearest_n=similarity_nearest_n,
num_threads=2,
add_overviews=False,
)
similarity_files.append(full_similarity_file)
all_comp_slc_files = [ms.get_compressed_slc_info().path for ms in ministacks]
out_pl_slcs = []
for slc_fname in output_slc_files:
slc_fname.rename(output_folder / slc_fname.name)
out_pl_slcs.append(output_folder / slc_fname.name)
comp_slc_outputs = []
for p in all_comp_slc_files:
p.rename(output_folder / p.name)
comp_slc_outputs.append(output_folder / p.name)
return (
out_pl_slcs,
crlb_files,
closure_phase_files,
comp_slc_outputs,
temp_coh_files,
shp_count_files,
similarity_files,
)
|