This notebook shows how to do time-series analysis using HyP3 product with MintPy. It requires hyp3_sdk and MintPy:
conda install --yes -c conda-forge hyp3_sdk ipywidgets to install hyp3_sdkMintPyThe cell below performs the intial setup of the notebook and must be run every time the notebook (re)starts. It imports necessary modules and defines the processing location.
%matplotlib inline
import os
import numpy as np
import matplotlib.pyplot as plt
# verify mintpy install
try:
#from mintpy.objects.insar_vs_gps import plot_insar_vs_gps_scatter
#from mintpy.unwrap_error_phase_closure import plot_num_triplet_with_nonzero_integer_ambiguity
#from mintpy import workflow, view, tsview, plot_network, plot_transection, plot_coherence_matrix
from mintpy import view, tsview
except ImportError:
raise ImportError("Can not import mintpy!")
# utils function
def configure_template_file(outName, CONFIG_TXT):
"""Write configuration files for MintPy to process HyP3 product"""
if os.path.isfile(outName):
with open(outName, "w") as fid:
fid.write(CONFIG_TXT)
print('write configuration to file: {}'.format(outName))
else:
with open(outName, "a") as fid:
fid.write("\n" + CONFIG_TXT)
print('add the following to file: \n{}'.format(outName))
# define the work directory
#work_dir = os.path.abspath(os.path.join(os.getcwd(), 'mintpy')) #OpenSARLab at ASF
proj_name = 'Ridgecrest'
proj_dir = os.path.join('/media/jzhu4/data/hyp3-mintpy', proj_name) #Local
hyp3_dir = os.path.join(proj_dir, 'hyp3')
work_dir = os.path.join(proj_dir, 'mintpy') #Local
if not os.path.isdir(proj_dir):
os.makedirs(proj_dir)
print('Create directory: {}'.format(proj_dir))
if not os.path.isdir(hyp3_dir):
os.makedirs(hyp3_dir)
print('Create directory: {}'.format(hyp3_dir))
if not os.path.isdir(work_dir):
os.makedirs(work_dir)
print('Create directory: {}'.format(work_dir))
os.chdir(work_dir)
print('Go to work directory: {}'.format(work_dir))
smallbaselineApp.py¶CONFIG_TXT = f'''# vim: set filetype=cfg:
mintpy.load.processor = hyp3
##---------interferogram datasets:
mintpy.load.unwFile = {hyp3_dir}/*/*unw_phase_clip.tif
mintpy.load.corFile = {hyp3_dir}/*/*corr_clip.tif
##---------geometry datasets:
mintpy.load.demFile = {hyp3_dir}/*/*dem_clip.tif
mintpy.load.incAngleFile = {hyp3_dir}/*/*inc_map_clip.tif
'''
print(CONFIG_TXT)
configName = os.path.join(work_dir, "{}.txt".format(proj_name))
configure_template_file(configName, CONFIG_TXT)
case data can be obtained through either downloading from the stagged server or producing with hyp3-sdk. As far as producing data from hyp3-sdk, we provide the prep_ts_hyp3 notebook( ).
# verify / prepare input dataset
os.chdir(hyp3_dir)
use_staged_data = True
zip_file_name ='Ridgecrest.zip'
if all(os.path.isfile(os.path.join(work_dir, 'inputs', i)) for i in ['ifgramStack.h5', 'geometryGeo.h5']):
print("Required inputs for mintpy already exists.")
else:
if use_staged_data:
# Check if a stage file from S3 already exist, if not try and download it
zip_file = os.path.join(hyp3_dir, zip_file_name)
if not os.path.isfile(zip_file):
!aws s3 cp s3://jzhu-hyp3-dev/hyp3-mintpy-example/{zip_file_name} {zip_file_name}
# verify if download was succesfull
if os.path.isfile(zip_file_name):
import zipfile, glob
with zipfile.ZipFile(zip_file, 'r') as fzip:
fzip.extractall(hyp3_dir)
# unzip zip files extracted from the zip_file
files = glob.glob("./????_*.zip")
for file in files:
with zipfile.ZipFile(file) as f:
f.extractall(hyp3_dir)
print('S3 pre-staged data retrieval was successfull')
else:
msg = 'No staged data. Setting use_staged_data = False and re-run this cell.'
print(msg)
else:
print("Using HyP3-sdk to download and prepare the input data for MintPy")
print("please refer the notebook")
os.chdir(os.path.dirname(work_dir))
! smallbaselineApp.py --work-dir {work_dir} {configName}
There are a few scripts used to display the analysis results. There are in the MINTPY_HOME/mintpy. Here we show two majoy disaply scripts.
os.chdir(proj_dir)
view.main(['mintpy/velocity.h5'])
tsview.main(['mintpy/timeseries.h5'])