{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# InSAR time series analysis with HyP3 and MintPy\n",
"\n",
"This notebook shows how to do time-series analysis using HyP3 product with MintPy. It requires `hyp3_sdk` and `MintPy`:\n",
"\n",
"+ run `conda install --yes -c conda-forge hyp3_sdk ipywidgets` to install `hyp3_sdk`\n",
"+ check the [installation page](https://github.com/insarlab/MintPy/blob/main/docs/installation.md) to install `MintPy`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 0. Initial setup of the notebook\n",
"\n",
"The 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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"# verify mintpy install\n",
"try:\n",
" #from mintpy.objects.insar_vs_gps import plot_insar_vs_gps_scatter\n",
" #from mintpy.unwrap_error_phase_closure import plot_num_triplet_with_nonzero_integer_ambiguity\n",
" #from mintpy import workflow, view, tsview, plot_network, plot_transection, plot_coherence_matrix\n",
" from mintpy import view, tsview\n",
"except ImportError:\n",
" raise ImportError(\"Can not import mintpy!\")\n",
"\n",
"# utils function\n",
"def configure_template_file(outName, CONFIG_TXT): \n",
" \"\"\"Write configuration files for MintPy to process HyP3 product\"\"\"\n",
" if os.path.isfile(outName):\n",
" with open(outName, \"w\") as fid:\n",
" fid.write(CONFIG_TXT)\n",
" print('write configuration to file: {}'.format(outName))\n",
"\n",
" else:\n",
" with open(outName, \"a\") as fid:\n",
" fid.write(\"\\n\" + CONFIG_TXT)\n",
" print('add the following to file: \\n{}'.format(outName))\n",
"\n",
"# define the work directory\n",
"#work_dir = os.path.abspath(os.path.join(os.getcwd(), 'mintpy')) #OpenSARLab at ASF\n",
"proj_name = 'Ridgecrest'\n",
"proj_dir = os.path.join('/media/jzhu4/data/hyp3-mintpy', proj_name) #Local\n",
"hyp3_dir = os.path.join(proj_dir, 'hyp3')\n",
"work_dir = os.path.join(proj_dir, 'mintpy') #Local\n",
"\n",
"if not os.path.isdir(proj_dir):\n",
" os.makedirs(proj_dir)\n",
" print('Create directory: {}'.format(proj_dir))\n",
" \n",
"if not os.path.isdir(hyp3_dir):\n",
" os.makedirs(hyp3_dir)\n",
" print('Create directory: {}'.format(hyp3_dir))\n",
" \n",
"if not os.path.isdir(work_dir):\n",
" os.makedirs(work_dir)\n",
" print('Create directory: {}'.format(work_dir))\n",
" \n",
"os.chdir(work_dir)\n",
"print('Go to work directory: {}'.format(work_dir))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Run MintPy routine workflow `smallbaselineApp.py`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1 Prepare the template file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"CONFIG_TXT = f'''# vim: set filetype=cfg:\n",
"mintpy.load.processor = hyp3\n",
"##---------interferogram datasets:\n",
"mintpy.load.unwFile = {hyp3_dir}/*/*unw_phase_clip.tif\n",
"mintpy.load.corFile = {hyp3_dir}/*/*corr_clip.tif\n",
"##---------geometry datasets:\n",
"mintpy.load.demFile = {hyp3_dir}/*/*dem_clip.tif\n",
"mintpy.load.incAngleFile = {hyp3_dir}/*/*lv_theta_clip.tif\n",
"mintpy.load.waterMaskFile = {hyp3_dir}/*/*water_mask_clip.tif\n",
"'''\n",
"print(CONFIG_TXT)\n",
"configName = os.path.join(work_dir, \"{}.txt\".format(proj_name))\n",
"configure_template_file(configName, CONFIG_TXT)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 Load the data produced from HyP3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The example dataset is from 2019 Ridgecrest, CA earthquake. The dataset 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 at the tutorial directory of (https://github.com/ASFHyP3/hyp3-docs/tree/develop/docs )."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# verify / prepare input dataset\n",
"\n",
"os.chdir(hyp3_dir)\n",
"\n",
"use_staged_data = True\n",
"\n",
"zip_file_name ='Ridgecrest.zip'\n",
"\n",
"if all(os.path.isfile(os.path.join(work_dir, 'inputs', i)) for i in ['ifgramStack.h5', 'geometryGeo.h5']):\n",
" print(\"Required inputs for mintpy already exists.\")\n",
"\n",
"else:\n",
" if use_staged_data:\n",
" # Check if a stage file from S3 already exist, if not try and download it\n",
" zip_file = os.path.join(hyp3_dir, zip_file_name)\n",
" if not os.path.isfile(zip_file):\n",
" !wget https://jzhu-hyp3-dev.s3.us-west-2.amazonaws.com/hyp3-mintpy/{zip_file_name}\n",
" #!aws s3 cp s3://jzhu-hyp3-dev/hyp3-mintpy-example/{zip_file_name} {zip_file_name}\n",
" # verify if download was succesfull\n",
" if os.path.isfile(zip_file_name):\n",
" import zipfile, glob\n",
" \n",
" with zipfile.ZipFile(zip_file, 'r') as fzip:\n",
" fzip.extractall(hyp3_dir)\n",
" # unzip zip files extracted from the zip_file\n",
" files = glob.glob(\"./????_*.zip\")\n",
" for file in files:\n",
" with zipfile.ZipFile(file) as f:\n",
" f.extractall(hyp3_dir)\n",
" \n",
" print('S3 pre-staged data retrieval was successfull')\n",
"\n",
" else:\n",
" msg = 'No staged data. Setting use_staged_data = False and re-run this cell.'\n",
" print(msg)\n",
"\n",
" else:\n",
" print(\"Using HyP3-sdk to download and prepare the input data for MintPy\")\n",
" print(\"please refer the notebook\")\n",
" os.chdir(os.path.dirname(work_dir))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3 Run Time-series Analysis application"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! smallbaselineApp.py --work-dir {work_dir} {configName}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4 Display the analysis results\n",
"\n",
"There are a few scripts used to display the analysis results. There are in the `${MINTPY_HOME}/src/mintpy/cli`. Here we show two majoy disaply scripts."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"os.chdir(proj_dir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"view.main(['mintpy/velocity.h5'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tsview.main(['mintpy/timeseries.h5'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 4
}