3125 lines
133 KiB
Python
3125 lines
133 KiB
Python
from __future__ import annotations
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import asyncio
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import gzip
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import hashlib
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import json
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import math
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import mimetypes
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import os
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import re
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import struct
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Optional
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from geoalchemy2.shape import from_shape
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from shapely.geometry import Polygon
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from sqlalchemy import String, cast, delete, func, or_, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from ..config import settings
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from ..models import ResultAssetORM, ResultCatalogStateORM, ResultIssueORM, ResultProductORM
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from .admin_region_lookup_service import lookup_admin_region_for_point
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from .landsar_sbas_service import landsar_sbas_service
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from .sbas_insar_production_service import sbas_insar_production_service
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SBAS_INSAR_CATALOG_NAME = "sbas_insar"
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JOB_TYPE_REBUILD_SBAS_INSAR_CATALOG = "REBUILD_SBAS_INSAR_CATALOG"
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TASK_TYPE_REBUILD_SBAS_INSAR_CATALOG = "REBUILD_SBAS_INSAR_CATALOG"
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_READY_STATUSES = {
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"PRODUCTS_READY",
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"MONITOR_POINTS_READY",
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"WORKFLOW_COMPLETED",
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"LANDSAR_SBAS_COMPLETED",
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"LANDSAR_SBAS_PARTIAL",
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}
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_REQUIRED_ASSET_ROLES = {"primary_geotiff", "quality_geotiff"}
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_LANDSAR_REQUIRED_ASSET_ROLES = {"primary_geotiff"}
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_WGS84_GEOGCS_WKT = (
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'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],'
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'PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]]'
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)
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_CORE_ASSETS = (
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("run_manifest", "Run manifest", "run_manifest.json", True, False),
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("stack_manifest", "Stack manifest", "stack_manifest.json", True, False),
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("workflow_summary", "Workflow summary", "workflow_summary.json", False, False),
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("product_summary", "Product summary", "product_summary.json", False, False),
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("quality_summary", "Quality summary", "quality_summary.json", False, False),
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("monitor_points_summary", "Monitor points summary", "monitor_points_summary.json", False, False),
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(
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"point_vector_summary",
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"LOS point-vector summary",
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"publish/vectors/los_rate_points_summary.json",
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False,
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False,
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),
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(
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"point_vector_geojson_gz",
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"LOS point-vector GeoJSON.gz",
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"publish/vectors/los_rate_points.geojson.gz",
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False,
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False,
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),
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(
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"primary_geocoded_preview",
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"LOS velocity preview, toward radar positive",
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"publish/geotiff/los_rate_toward_m_per_year.hls.geo_preview.png",
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False,
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True,
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),
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(
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"quality_geocoded_preview",
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"LOS velocity sigma preview",
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"publish/geotiff/los_sigma_m_per_year.cc.geo_preview.png",
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False,
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False,
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),
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(
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"primary_geotiff",
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"LOS velocity GeoTIFF, toward radar positive",
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"publish/geotiff/los_rate_toward_m_per_year.tif",
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True,
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True,
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),
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(
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"alternate_geotiff",
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"LOS velocity GeoTIFF, away from radar positive",
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"publish/geotiff/los_rate_away_m_per_year.tif",
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False,
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False,
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),
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(
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"quality_geotiff",
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"LOS velocity sigma GeoTIFF",
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"publish/geotiff/los_sigma_m_per_year.tif",
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True,
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False,
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),
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(
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"primary_rgb_geotiff",
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"LOS velocity RGB GeoTIFF",
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"publish/geotiff/los_rate_toward_m_per_year.hls.geo_rgb.tif",
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False,
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False,
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),
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(
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"quality_rgb_geotiff",
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"LOS velocity sigma RGB GeoTIFF",
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"publish/geotiff/los_sigma_m_per_year.cc.geo_rgb.tif",
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False,
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False,
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),
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("gamma_phase_rate", "Gamma phase-rate GeoTIFF", "publish/geotiff/ts_rate_rad_per_year.tif", False, False),
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("gamma_sigma_rate", "Gamma sigma-rate GeoTIFF", "publish/geotiff/sigma_rate_rad_per_year.tif", False, False),
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("height_correction", "Height correction GeoTIFF", "publish/geotiff/hgt_correction_m.tif", False, False),
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)
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_LANDSAR_CORE_ASSETS = (
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("run_manifest", "Run manifest", "run_manifest.json", True, False),
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("stack_manifest", "Task/Input_Data manifest", "stack_manifest.json", True, False),
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("workflow_summary", "LandSAR SBAS workflow summary", "workflow_summary.json", True, False),
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("product_summary", "LandSAR SBAS product summary", "product_summary.json", False, False),
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("quality_summary", "LandSAR SBAS quality summary", "quality_summary.json", False, False),
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("command_manifest", "LandSAR SBAS command manifest", "landsar_command_manifest.json", False, False),
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("native_console_log", "LandSAR SBAS console log", "native_logs", False, False),
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("primary_preview", "LandSAR LOS preview", "publish/landsar/preview.png", False, True),
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("primary_geotiff", "LandSAR LOS time-series GeoTIFF", "publish/landsar/los_timeseries.tif", True, True),
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("secondary_geotiff", "LandSAR post-raster GeoTIFF", "publish/landsar/post_raster.tif", False, False),
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)
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_EXPERT_GAMMA_CORE_ASSETS = (
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("run_manifest", "Run manifest", "run_manifest.json", True, False),
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("stack_manifest", "Stack manifest", "stack_manifest.json", True, False),
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("workflow_summary", "Workflow summary", "workflow_summary.json", False, False),
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("monitor_points_summary", "Monitor points summary", "monitor_points_summary.json", False, False),
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(
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"unwrapped_phase_summary",
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"Expert Gamma geocoded unwrapped phase summary",
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"publish/geotiff/unwrapped/unwrapped_phase_summary.json",
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False,
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False,
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),
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(
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"unwrapped_phase_radar_colorbar",
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"Expert Gamma rmg.cm unwrapped phase radar colorbar",
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"publish/geotiff/unwrapped/unwrapped_phase_rmg_colorbar.png",
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False,
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False,
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),
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(
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"point_vector_summary",
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"Expert Gamma LOS point-vector summary",
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"publish/vectors/los_rate_points_summary.json",
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False,
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False,
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),
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(
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"point_vector_geojson_gz",
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"Expert Gamma LOS point-vector GeoJSON.gz",
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"publish/vectors/los_rate_points.geojson.gz",
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False,
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False,
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),
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(
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"primary_geocoded_preview",
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"Expert Gamma geo_los_def_rate RGB PNG preview",
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"publish/geotiff/geo_los_def_rate_rgb_preview.png",
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False,
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True,
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),
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(
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"primary_rate_color_preview",
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"Expert Gamma pure geo_los_def_rate hls.cm PNG preview",
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"publish/geotiff/geo_los_def_rate_pure_hls_preview.png",
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False,
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False,
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),
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(
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"primary_geotiff",
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"Expert Gamma geo_los_def_rate GeoTIFF",
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"publish/geotiff/geo_los_def_rate.tif",
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True,
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True,
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),
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(
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"primary_rgb_geotiff",
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"Expert Gamma geo_los_def_rate RGB GeoTIFF",
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"publish/geotiff/geo_los_def_rate_rgb.tif",
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False,
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False,
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),
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(
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"primary_colorbar",
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"Expert Gamma hls.cm deformation-rate colorbar",
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"publish/geotiff/geo_los_def_rate_hls_colorbar.png",
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False,
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False,
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),
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(
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"monitor_points",
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"Expert Gamma disp_prt_2d point time series",
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"publish/points/disp_point.txt",
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False,
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False,
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),
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(
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"monitor_point_items",
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"Expert Gamma disp_prt_2d column definitions",
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"publish/points/items.txt",
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False,
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False,
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),
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(
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"monitor_point_selection",
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"Expert Gamma disp_prt_2d selected radar points",
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"publish/points/disp_point_sel.txt",
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False,
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False,
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),
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(
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"monitor_point_selection_metadata",
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"Expert Gamma monitor point selection metadata",
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"publish/points/disp_point_selection.json",
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False,
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False,
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),
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)
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def _utcnow() -> datetime:
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return datetime.utcnow()
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def _normalize_path(path: str | os.PathLike[str]) -> str:
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return os.path.normpath(os.path.abspath(os.fspath(path)))
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def _read_json(path: Path) -> dict[str, Any]:
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with path.open("r", encoding="utf-8") as fp:
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payload = json.load(fp)
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return payload if isinstance(payload, dict) else {}
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def _safe_read_json(path: Path) -> dict[str, Any]:
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if not path.is_file():
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return {}
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try:
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return _read_json(path)
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except Exception:
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return {}
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def _safe_float(value: Any) -> Optional[float]:
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try:
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parsed = float(value)
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except (TypeError, ValueError):
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return None
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if parsed != parsed:
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return None
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return parsed
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def _safe_int(value: Any) -> Optional[int]:
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try:
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return int(float(value))
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except (TypeError, ValueError):
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return None
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def _read_gamma_key_values(path: Path) -> dict[str, str]:
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values: dict[str, str] = {}
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if not path.is_file():
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return values
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for line in path.read_text(encoding="utf-8", errors="ignore").splitlines():
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if ":" not in line:
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continue
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key, raw_value = line.split(":", 1)
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key = key.strip()
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value = raw_value.strip().split()[0] if raw_value.strip() else ""
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if key:
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values[key] = value
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return values
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def _read_gamma_int_param(path: Path, key: str) -> Optional[int]:
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return _safe_int(_read_gamma_key_values(path).get(key))
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def _haversine_m(lon1: float, lat1: float, lon2: float, lat2: float) -> float:
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radius_m = 6371008.8
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phi1 = math.radians(lat1)
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phi2 = math.radians(lat2)
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dphi = math.radians(lat2 - lat1)
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dlambda = math.radians(lon2 - lon1)
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a = math.sin(dphi / 2) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(dlambda / 2) ** 2
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return 2 * radius_m * math.asin(min(1.0, math.sqrt(a)))
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def _parse_datetime(value: Any) -> Optional[datetime]:
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text = str(value or "").strip()
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if not text:
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return None
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if text.endswith("Z"):
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text = text[:-1] + "+00:00"
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try:
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return datetime.fromisoformat(text).replace(tzinfo=None)
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except ValueError:
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return None
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def _stable_digest(*parts: Any, length: int = 20) -> str:
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payload = "||".join(str(part or "") for part in parts)
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return hashlib.sha1(payload.encode("utf-8", errors="ignore")).hexdigest()[:length]
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def _asset_format(path: str) -> Optional[str]:
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lowered = path.lower()
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if lowered.endswith(".geojson.gz"):
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return "geojson.gz"
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ext = Path(path).suffix.lower()
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return {
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".bmp": "bmp",
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".csv": "csv",
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".geo": "gamma_binary",
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".gz": "gzip",
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".json": "json",
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".log": "log",
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".png": "png",
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".sh": "shell",
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".tif": "geotiff",
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".tiff": "geotiff",
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".txt": "text",
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}.get(ext)
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def _media_type(path: str) -> Optional[str]:
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lowered = path.lower()
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if lowered.endswith(".geojson.gz"):
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return "application/gzip"
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ext = Path(path).suffix.lower()
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explicit = {
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".bmp": "image/bmp",
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".csv": "text/csv",
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".gz": "application/gzip",
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".json": "application/json",
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".log": "text/plain",
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".png": "image/png",
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".sh": "text/x-shellscript",
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".tif": "image/tiff",
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".tiff": "image/tiff",
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".txt": "text/plain",
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}
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return explicit.get(ext) or mimetypes.guess_type(path)[0]
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|
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def _build_rgb_geotiff_preview(source: Path, target: Path) -> Optional[str]:
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if target.is_file():
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return str(target)
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if not source.is_file():
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return None
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try:
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import numpy as np
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import rasterio
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from PIL import Image
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from rasterio.enums import Resampling
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except Exception:
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return None
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try:
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with rasterio.open(source) as src:
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scale = max(src.width / 1600, src.height / 1600, 1)
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out_w = max(1, int(src.width / scale))
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out_h = max(1, int(src.height / scale))
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mask = src.dataset_mask(out_shape=(out_h, out_w)) > 0
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if src.count >= 3:
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data = src.read(
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[1, 2, 3],
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out_shape=(3, out_h, out_w),
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resampling=Resampling.bilinear,
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)
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rgb = np.moveaxis(data, 0, -1)
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if rgb.dtype != np.uint8:
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rgb = np.clip(rgb, 0, 255).astype("uint8")
|
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else:
|
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data = src.read(1, out_shape=(out_h, out_w), resampling=Resampling.bilinear).astype("float32")
|
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finite = np.isfinite(data)
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valid = finite & mask
|
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if np.any(valid):
|
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p2, p98 = np.nanpercentile(data[valid], [2, 98])
|
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if not np.isfinite(p2) or not np.isfinite(p98) or p98 <= p2:
|
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p2 = float(np.nanmin(data[valid]))
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p98 = float(np.nanmax(data[valid]))
|
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norm = np.clip((data - p2) / max(p98 - p2, 1e-6), 0, 1)
|
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else:
|
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norm = np.zeros_like(data, dtype="float32")
|
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gray = np.where(np.isfinite(norm), norm * 255, 0).astype("uint8")
|
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rgb = np.dstack([gray, gray, gray])
|
|
alpha = np.where(mask, 255, 0).astype("uint8")
|
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image = Image.fromarray(np.dstack([rgb, alpha]), mode="RGBA")
|
|
resampling = getattr(getattr(Image, "Resampling", Image), "LANCZOS")
|
|
image.thumbnail((1600, 1600), resampling)
|
|
target.parent.mkdir(parents=True, exist_ok=True)
|
|
image.save(target, "PNG", optimize=True)
|
|
return str(target)
|
|
except Exception:
|
|
return None
|
|
|
|
|
|
def _find_gamma_hls_colormap() -> Optional[Path]:
|
|
candidates = [
|
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Path(r"\\wsl.localhost\Ubuntu-24.04\usr\local\GAMMA_SOFTWARE-20240627\DISP\cmaps\hls.cm"),
|
|
Path(r"\\wsl$\Ubuntu-24.04\usr\local\GAMMA_SOFTWARE-20240627\DISP\cmaps\hls.cm"),
|
|
]
|
|
for candidate in candidates:
|
|
if candidate.is_file():
|
|
return candidate
|
|
return None
|
|
|
|
|
|
def _find_gamma_colormap(name: str) -> Optional[Path]:
|
|
safe_name = Path(str(name or "")).name
|
|
if not safe_name:
|
|
return None
|
|
candidates = [
|
|
Path(r"\\wsl.localhost\Ubuntu-24.04\usr\local\GAMMA_SOFTWARE-20240627\DISP\cmaps") / safe_name,
|
|
Path(r"\\wsl$\Ubuntu-24.04\usr\local\GAMMA_SOFTWARE-20240627\DISP\cmaps") / safe_name,
|
|
]
|
|
for candidate in candidates:
|
|
if candidate.is_file():
|
|
return candidate
|
|
return None
|
|
|
|
|
|
def _read_gamma_colormap(path: Path) -> list[tuple[int, int, int]]:
|
|
colors: list[tuple[int, int, int]] = []
|
|
if not path.is_file():
|
|
return colors
|
|
for line in path.read_text(encoding="utf-8", errors="ignore").splitlines():
|
|
parts = line.strip().split()
|
|
if len(parts) < 3:
|
|
continue
|
|
try:
|
|
red, green, blue = (max(0, min(255, int(float(value)))) for value in parts[:3])
|
|
except ValueError:
|
|
continue
|
|
colors.append((red, green, blue))
|
|
return colors
|
|
|
|
|
|
def _build_gamma_colormap_colorbar(
|
|
target: Path,
|
|
*,
|
|
colormap_name: str,
|
|
min_value: float,
|
|
max_value: float,
|
|
unit: str,
|
|
title: str,
|
|
) -> Optional[str]:
|
|
if target.is_file():
|
|
return str(target)
|
|
source = _find_gamma_colormap(colormap_name)
|
|
colors = _read_gamma_colormap(source) if source else []
|
|
if not colors:
|
|
return None
|
|
try:
|
|
import numpy as np
|
|
from PIL import Image, ImageDraw, ImageFont
|
|
|
|
width = 920
|
|
bar_height = 34
|
|
label_height = 78
|
|
margin_x = 48
|
|
margin_top = 14
|
|
target.parent.mkdir(parents=True, exist_ok=True)
|
|
image = Image.new("RGBA", (width, bar_height + label_height), (255, 255, 255, 0))
|
|
draw = ImageDraw.Draw(image)
|
|
gradient_width = width - margin_x * 2
|
|
color_array = np.asarray(colors, dtype=np.uint8)
|
|
for x in range(gradient_width):
|
|
idx = int(round((x / max(1, gradient_width - 1)) * (len(color_array) - 1)))
|
|
draw.line(
|
|
[(margin_x + x, margin_top), (margin_x + x, margin_top + bar_height)],
|
|
fill=tuple(int(value) for value in color_array[idx]) + (255,),
|
|
)
|
|
draw.rectangle(
|
|
[margin_x, margin_top, margin_x + gradient_width, margin_top + bar_height],
|
|
outline=(15, 23, 42, 180),
|
|
width=1,
|
|
)
|
|
try:
|
|
font = ImageFont.truetype("arial.ttf", 14)
|
|
small_font = ImageFont.truetype("arial.ttf", 12)
|
|
except Exception:
|
|
font = ImageFont.load_default()
|
|
small_font = font
|
|
ticks = [
|
|
(min_value, f"{min_value:g}"),
|
|
(0.0, "0"),
|
|
(max_value, f"{max_value:g}"),
|
|
]
|
|
for value, label in ticks:
|
|
ratio = (value - min_value) / max(max_value - min_value, 1e-6)
|
|
x = margin_x + int(round(ratio * gradient_width))
|
|
draw.line([(x, margin_top + bar_height), (x, margin_top + bar_height + 7)], fill=(15, 23, 42, 220), width=1)
|
|
text = f"{label} {unit}".strip()
|
|
bbox = draw.textbbox((0, 0), text, font=font)
|
|
draw.text((x - (bbox[2] - bbox[0]) / 2, margin_top + bar_height + 10), text, fill=(15, 23, 42, 255), font=font)
|
|
source_label = f"{title} / {colormap_name}"
|
|
source_bbox = draw.textbbox((0, 0), source_label, font=small_font)
|
|
draw.text(
|
|
(width - margin_x - (source_bbox[2] - source_bbox[0]), margin_top + bar_height + 34),
|
|
source_label,
|
|
fill=(71, 85, 105, 255),
|
|
font=small_font,
|
|
)
|
|
image.save(target, "PNG", optimize=True)
|
|
return str(target)
|
|
except Exception:
|
|
return None
|
|
|
|
|
|
def _build_gamma_hls_colorbar(target: Path, *, min_mm_year: float = -80.0, max_mm_year: float = 80.0) -> Optional[str]:
|
|
if target.is_file():
|
|
return str(target)
|
|
source = _find_gamma_hls_colormap()
|
|
colors = _read_gamma_colormap(source) if source else []
|
|
if not colors:
|
|
return None
|
|
try:
|
|
import numpy as np
|
|
from PIL import Image, ImageDraw, ImageFont
|
|
|
|
width = 900
|
|
bar_height = 34
|
|
label_height = 58
|
|
margin_x = 46
|
|
margin_top = 14
|
|
target.parent.mkdir(parents=True, exist_ok=True)
|
|
image = Image.new("RGBA", (width, bar_height + label_height), (255, 255, 255, 0))
|
|
draw = ImageDraw.Draw(image)
|
|
gradient_width = width - margin_x * 2
|
|
color_array = np.asarray(colors, dtype=np.uint8)
|
|
for x in range(gradient_width):
|
|
idx = int(round((x / max(1, gradient_width - 1)) * (len(color_array) - 1)))
|
|
draw.line(
|
|
[(margin_x + x, margin_top), (margin_x + x, margin_top + bar_height)],
|
|
fill=tuple(int(value) for value in color_array[idx]) + (255,),
|
|
)
|
|
draw.rectangle(
|
|
[margin_x, margin_top, margin_x + gradient_width, margin_top + bar_height],
|
|
outline=(15, 23, 42, 180),
|
|
width=1,
|
|
)
|
|
ticks = [
|
|
(min_mm_year, f"{min_mm_year:g}"),
|
|
(0.0, "0"),
|
|
(max_mm_year, f"{max_mm_year:g}"),
|
|
]
|
|
try:
|
|
font = ImageFont.truetype("arial.ttf", 14)
|
|
small_font = ImageFont.truetype("arial.ttf", 12)
|
|
except Exception:
|
|
font = ImageFont.load_default()
|
|
small_font = font
|
|
for value, label in ticks:
|
|
ratio = (value - min_mm_year) / max(max_mm_year - min_mm_year, 1e-6)
|
|
x = margin_x + int(round(ratio * gradient_width))
|
|
draw.line([(x, margin_top + bar_height), (x, margin_top + bar_height + 7)], fill=(15, 23, 42, 220), width=1)
|
|
text = f"{label} mm/yr"
|
|
bbox = draw.textbbox((0, 0), text, font=font)
|
|
draw.text((x - (bbox[2] - bbox[0]) / 2, margin_top + bar_height + 10), text, fill=(15, 23, 42, 255), font=font)
|
|
source_label = "Gamma hls.cm"
|
|
source_bbox = draw.textbbox((0, 0), source_label, font=small_font)
|
|
draw.text(
|
|
(width - margin_x - (source_bbox[2] - source_bbox[0]), margin_top + bar_height + 33),
|
|
source_label,
|
|
fill=(71, 85, 105, 255),
|
|
font=small_font,
|
|
)
|
|
image.save(target, "PNG", optimize=True)
|
|
return str(target)
|
|
except Exception:
|
|
return None
|
|
|
|
|
|
def _build_gamma_hls_rate_preview(
|
|
source: Path,
|
|
target: Path,
|
|
*,
|
|
coverage_source: Optional[Path] = None,
|
|
min_native: float = -0.08,
|
|
max_native: float = 0.08,
|
|
) -> Optional[str]:
|
|
if target.is_file():
|
|
return str(target)
|
|
if not source.is_file():
|
|
return None
|
|
colormap_path = _find_gamma_hls_colormap()
|
|
colors = _read_gamma_colormap(colormap_path) if colormap_path else []
|
|
if not colors:
|
|
return None
|
|
try:
|
|
import numpy as np
|
|
import rasterio
|
|
from PIL import Image
|
|
from rasterio.enums import Resampling
|
|
except Exception:
|
|
return None
|
|
|
|
try:
|
|
with rasterio.open(source) as src:
|
|
scale = max(src.width / 1600, src.height / 1600, 1)
|
|
out_w = max(1, int(src.width / scale))
|
|
out_h = max(1, int(src.height / scale))
|
|
data = src.read(1, out_shape=(out_h, out_w), resampling=Resampling.nearest).astype("float32")
|
|
coverage = np.ones((out_h, out_w), dtype=bool)
|
|
if coverage_source and coverage_source.is_file():
|
|
with rasterio.open(coverage_source) as coverage_src:
|
|
if coverage_src.width == src.width and coverage_src.height == src.height and coverage_src.count >= 3:
|
|
coverage_rgb = coverage_src.read(
|
|
[1, 2, 3],
|
|
out_shape=(3, out_h, out_w),
|
|
resampling=Resampling.nearest,
|
|
)
|
|
coverage = np.any(coverage_rgb != 0, axis=0)
|
|
valid = np.isfinite(data) & coverage & (data != 0.0)
|
|
ratio = np.clip((data - min_native) / max(max_native - min_native, 1e-12), 0.0, 1.0)
|
|
color_array = np.asarray(colors, dtype=np.uint8)
|
|
indices = np.rint(ratio * (len(color_array) - 1)).astype(np.int32)
|
|
rgb = color_array[np.clip(indices, 0, len(color_array) - 1)]
|
|
alpha = np.where(valid, 255, 0).astype(np.uint8)
|
|
image = Image.fromarray(np.dstack([rgb, alpha]), mode="RGBA")
|
|
resampling = getattr(getattr(Image, "Resampling", Image), "LANCZOS")
|
|
image.thumbnail((1600, 1600), resampling)
|
|
target.parent.mkdir(parents=True, exist_ok=True)
|
|
image.save(target, "PNG", optimize=True)
|
|
return str(target)
|
|
except Exception:
|
|
return None
|
|
|
|
|
|
def _parse_expert_disp_point_table(disp_point_path: Path) -> list[dict[str, Any]]:
|
|
if not disp_point_path.is_file():
|
|
return []
|
|
import csv
|
|
|
|
rows = list(csv.reader(disp_point_path.read_text(encoding="utf-8", errors="ignore").splitlines()))
|
|
if len(rows) < 2:
|
|
return []
|
|
header = [str(value or "").strip() for value in rows[0]]
|
|
dates = [value for value in header[5:] if value]
|
|
points: list[dict[str, Any]] = []
|
|
for index, row in enumerate(rows[1:], start=1):
|
|
if len(row) < 5:
|
|
continue
|
|
values = [str(value or "").strip() for value in row]
|
|
try:
|
|
img_x = int(float(values[0]))
|
|
img_y = int(float(values[1]))
|
|
except ValueError:
|
|
continue
|
|
displacements: list[dict[str, Any]] = []
|
|
for date_text, value_text in zip(dates, values[5:]):
|
|
try:
|
|
displacement = float(value_text)
|
|
except ValueError:
|
|
continue
|
|
date_clean = date_text.strip()
|
|
if len(date_clean) == 8 and date_clean.isdigit():
|
|
date_iso = f"{date_clean[0:4]}-{date_clean[4:6]}-{date_clean[6:8]}"
|
|
else:
|
|
date_iso = date_clean
|
|
displacements.append({"date": date_iso, "displacement_mm": displacement})
|
|
points.append(
|
|
{
|
|
"point_id": f"expert_point_{index:03d}",
|
|
"img_x": img_x,
|
|
"img_y": img_y,
|
|
"height_m": _safe_float(values[2]) if len(values) > 2 else None,
|
|
"deformation_rate_mm_per_year": _safe_float(values[3]) if len(values) > 3 else None,
|
|
"stdev_residual_phase_rad": _safe_float(values[4]) if len(values) > 4 else None,
|
|
"displacements": displacements,
|
|
}
|
|
)
|
|
return points
|
|
|
|
|
|
def _read_expert_sbas_dates(run_dir: Path) -> list[str]:
|
|
rmli_tab = run_dir / "sbas" / "RMLI_tab"
|
|
dates: list[str] = []
|
|
if rmli_tab.is_file():
|
|
for line in rmli_tab.read_text(encoding="utf-8", errors="ignore").splitlines():
|
|
parts = line.split()
|
|
if not parts:
|
|
continue
|
|
match = re.search(r"(\d{8})", Path(parts[0]).name)
|
|
if match:
|
|
raw = match.group(1)
|
|
dates.append(f"{raw[0:4]}-{raw[4:6]}-{raw[6:8]}")
|
|
if dates:
|
|
return dates
|
|
points = _parse_expert_disp_point_table(run_dir / "publish" / "points" / "disp_point.txt")
|
|
for point in points:
|
|
displacements = point.get("displacements") or []
|
|
if displacements:
|
|
return [str(item.get("date") or "") for item in displacements if item.get("date")]
|
|
return []
|
|
|
|
|
|
def _read_radar_float32(path: Path, *, width: int, img_x: int, img_y: int) -> Optional[float]:
|
|
if width <= 0 or img_x < 0 or img_y < 0 or not path.is_file():
|
|
return None
|
|
offset = (img_y * width + img_x) * 4
|
|
try:
|
|
if offset < 0 or offset + 4 > path.stat().st_size:
|
|
return None
|
|
with path.open("rb") as fp:
|
|
fp.seek(offset)
|
|
payload = fp.read(4)
|
|
if len(payload) != 4:
|
|
return None
|
|
value = struct.unpack(">f", payload)[0]
|
|
return value if math.isfinite(value) else None
|
|
except Exception:
|
|
return None
|
|
|
|
|
|
def _read_geo_rate_window(
|
|
*,
|
|
source_path: Path,
|
|
coverage_path: Path,
|
|
center_row: int,
|
|
center_col: int,
|
|
radius: int,
|
|
width: int,
|
|
height: int,
|
|
) -> tuple[Any, Any, int, int]:
|
|
import numpy as np
|
|
import rasterio
|
|
|
|
row0 = max(0, center_row - radius)
|
|
row1 = min(height - 1, center_row + radius)
|
|
col0 = max(0, center_col - radius)
|
|
col1 = min(width - 1, center_col + radius)
|
|
if row0 > row1 or col0 > col1:
|
|
return None, None, row0, col0
|
|
window = rasterio.windows.Window(col0, row0, col1 - col0 + 1, row1 - row0 + 1)
|
|
with rasterio.open(source_path) as src:
|
|
rate = src.read(1, window=window)
|
|
coverage = None
|
|
if coverage_path.is_file():
|
|
with rasterio.open(coverage_path) as cov:
|
|
if cov.width == width and cov.height == height and cov.count >= 3:
|
|
rgb = cov.read([1, 2, 3], window=window)
|
|
coverage = np.any(rgb != 0, axis=0)
|
|
return rate, coverage, row0, col0
|
|
|
|
|
|
def _query_expert_gamma_point_timeseries(run_dir: Path, *, lon: float, lat: float) -> dict[str, Any]:
|
|
source_path = run_dir / "publish" / "geotiff" / "geo_los_def_rate.tif"
|
|
coverage_path = run_dir / "publish" / "geotiff" / "geo_los_def_rate_rgb.tif"
|
|
lt_path = run_dir / "dem" / f"{_safe_read_json(run_dir / 'run_manifest.json').get('reference_date') or ''}.lt_fine"
|
|
if not lt_path.is_file():
|
|
candidates = sorted((run_dir / "dem").glob("*.lt_fine"))
|
|
lt_path = candidates[0] if candidates else lt_path
|
|
mli_par = run_dir / "sbas" / "mli.ave.par"
|
|
disp_tab = run_dir / "sbas" / "disp.TS_tab"
|
|
if not source_path.is_file():
|
|
raise FileNotFoundError("geo_los_def_rate.tif is missing")
|
|
if not lt_path.is_file():
|
|
raise FileNotFoundError("Gamma lookup table *.lt_fine is missing")
|
|
if not mli_par.is_file():
|
|
raise FileNotFoundError("mli.ave.par is missing")
|
|
if not disp_tab.is_file():
|
|
raise FileNotFoundError("disp.TS_tab is missing")
|
|
|
|
try:
|
|
import numpy as np
|
|
import rasterio
|
|
except Exception as exc:
|
|
raise RuntimeError(f"point_query_dependency_unavailable: {exc}") from exc
|
|
|
|
with rasterio.open(source_path) as src:
|
|
width = int(src.width)
|
|
height = int(src.height)
|
|
if lon < src.bounds.left or lon > src.bounds.right or lat < src.bounds.bottom or lat > src.bounds.top:
|
|
raise ValueError("requested WGS84 coordinate is outside product geocoded bounds")
|
|
center_row, center_col = src.index(lon, lat)
|
|
transform = src.transform
|
|
|
|
valid_choice: Optional[dict[str, Any]] = None
|
|
for radius in [0, 1, 2, 4, 8, 16, 32, 64, 128, 256]:
|
|
rate, coverage, row0, col0 = _read_geo_rate_window(
|
|
source_path=source_path,
|
|
coverage_path=coverage_path,
|
|
center_row=int(center_row),
|
|
center_col=int(center_col),
|
|
radius=radius,
|
|
width=width,
|
|
height=height,
|
|
)
|
|
if rate is None:
|
|
continue
|
|
valid = np.isfinite(rate) & (rate != 0.0)
|
|
if coverage is not None:
|
|
valid &= coverage
|
|
if not np.any(valid):
|
|
continue
|
|
rows, cols = np.where(valid)
|
|
abs_rows = rows + row0
|
|
abs_cols = cols + col0
|
|
distances = (abs_rows - int(center_row)) ** 2 + (abs_cols - int(center_col)) ** 2
|
|
best_index = int(np.argmin(distances))
|
|
matched_row = int(abs_rows[best_index])
|
|
matched_col = int(abs_cols[best_index])
|
|
matched_lon, matched_lat = rasterio.transform.xy(transform, matched_row, matched_col, offset="center")
|
|
rate_value = float(rate[rows[best_index], cols[best_index]])
|
|
valid_choice = {
|
|
"geo_row": matched_row,
|
|
"geo_col": matched_col,
|
|
"lon": float(matched_lon),
|
|
"lat": float(matched_lat),
|
|
"los_rate_mm_per_year": rate_value * 1000.0,
|
|
"source_native_m_per_year": rate_value,
|
|
"search_radius_pixels": radius,
|
|
"distance_m": _haversine_m(lon, lat, float(matched_lon), float(matched_lat)),
|
|
}
|
|
break
|
|
|
|
if valid_choice is None:
|
|
raise ValueError("no valid deformation pixel found near requested WGS84 coordinate")
|
|
|
|
lt_offset = (int(valid_choice["geo_row"]) * width + int(valid_choice["geo_col"])) * 8
|
|
if lt_offset < 0 or lt_offset + 8 > lt_path.stat().st_size:
|
|
raise ValueError("matched geocoded pixel is outside lookup table")
|
|
with lt_path.open("rb") as fp:
|
|
fp.seek(lt_offset)
|
|
range_value, azimuth_value = struct.unpack(">ff", fp.read(8))
|
|
if not math.isfinite(range_value) or not math.isfinite(azimuth_value):
|
|
raise ValueError("lookup table returned invalid radar coordinates")
|
|
|
|
radar_width = _read_gamma_int_param(mli_par, "range_samples") or _read_gamma_int_param(mli_par, "width") or 0
|
|
radar_lines = _read_gamma_int_param(mli_par, "azimuth_lines") or _read_gamma_int_param(mli_par, "nlines") or 0
|
|
img_x = int(round(range_value))
|
|
img_y = int(round(azimuth_value))
|
|
if radar_width <= 0 or radar_lines <= 0:
|
|
raise ValueError("invalid radar image dimensions")
|
|
img_x = min(max(0, img_x), radar_width - 1)
|
|
img_y = min(max(0, img_y), radar_lines - 1)
|
|
|
|
raw_disp_paths = [
|
|
Path(_wsl_path_to_windows(line.strip()))
|
|
for line in disp_tab.read_text(encoding="utf-8", errors="ignore").splitlines()
|
|
if line.strip()
|
|
]
|
|
dates = _read_expert_sbas_dates(run_dir)
|
|
displacements: list[dict[str, Any]] = []
|
|
for index, disp_path in enumerate(raw_disp_paths):
|
|
value_m = _read_radar_float32(disp_path, width=radar_width, img_x=img_x, img_y=img_y)
|
|
date = dates[index] if index < len(dates) else f"epoch_{index + 1:03d}"
|
|
if value_m is None:
|
|
displacements.append({"date": date, "displacement_mm": None})
|
|
else:
|
|
displacements.append({"date": date, "displacement_mm": value_m * 1000.0})
|
|
|
|
if not any(item.get("displacement_mm") is not None for item in displacements):
|
|
raise ValueError("matched radar pixel has no readable displacement values")
|
|
|
|
return {
|
|
"schema": "insar.gamma-sbas-point-query/v1",
|
|
"source_tool": "disp.TS_tab_radar_pixel_sample",
|
|
"query": {"lon": lon, "lat": lat},
|
|
"matched": {
|
|
**valid_choice,
|
|
"used_nearest": int(valid_choice["geo_row"]) != int(center_row)
|
|
or int(valid_choice["geo_col"]) != int(center_col),
|
|
"input_geo_row": int(center_row),
|
|
"input_geo_col": int(center_col),
|
|
"radar_range": float(range_value),
|
|
"radar_azimuth": float(azimuth_value),
|
|
"img_x": img_x,
|
|
"img_y": img_y,
|
|
"radar_width": radar_width,
|
|
"radar_lines": radar_lines,
|
|
},
|
|
"unit": "mm",
|
|
"rate_unit": "mm/yr",
|
|
"displacement_count": len(displacements),
|
|
"displacements": displacements,
|
|
}
|
|
|
|
|
|
def _locate_radar_points_in_geocoded_product(run_dir: Path, points: list[dict[str, Any]]) -> dict[str, dict[str, Any]]:
|
|
targets: list[tuple[str, float, float]] = []
|
|
for point in points:
|
|
point_id = str(point.get("point_id") or "").strip()
|
|
img_x = _safe_float(point.get("img_x"))
|
|
img_y = _safe_float(point.get("img_y"))
|
|
if point_id and img_x is not None and img_y is not None:
|
|
targets.append((point_id, img_x, img_y))
|
|
if not targets:
|
|
return {}
|
|
|
|
source_path = run_dir / "publish" / "geotiff" / "geo_los_def_rate.tif"
|
|
coverage_path = run_dir / "publish" / "geotiff" / "geo_los_def_rate_rgb.tif"
|
|
manifest = _safe_read_json(run_dir / "run_manifest.json")
|
|
lt_path = run_dir / "dem" / f"{manifest.get('reference_date') or ''}.lt_fine"
|
|
if not lt_path.is_file():
|
|
candidates = sorted((run_dir / "dem").glob("*.lt_fine"))
|
|
lt_path = candidates[0] if candidates else lt_path
|
|
if not source_path.is_file() or not lt_path.is_file():
|
|
return {}
|
|
|
|
try:
|
|
import numpy as np
|
|
import rasterio
|
|
from rasterio.windows import Window
|
|
except Exception:
|
|
return {}
|
|
|
|
try:
|
|
with rasterio.open(source_path) as src:
|
|
width = int(src.width)
|
|
height = int(src.height)
|
|
transform = src.transform
|
|
expected_size = width * height * 2 * 4
|
|
if lt_path.stat().st_size < expected_size:
|
|
return {}
|
|
|
|
lookup = np.memmap(lt_path, dtype=">f4", mode="r", shape=(height, width, 2))
|
|
coarse_step = 16
|
|
coarse_best: dict[str, dict[str, Any]] = {
|
|
point_id: {"distance2": math.inf, "row": None, "col": None}
|
|
for point_id, _, _ in targets
|
|
}
|
|
for row0 in range(0, height, coarse_step * 128):
|
|
row1 = min(height, row0 + coarse_step * 128)
|
|
sampled = lookup[row0:row1:coarse_step, 0:width:coarse_step, :]
|
|
if sampled.size == 0:
|
|
continue
|
|
range_chunk = np.asarray(sampled[:, :, 0], dtype=np.float32)
|
|
azimuth_chunk = np.asarray(sampled[:, :, 1], dtype=np.float32)
|
|
valid = np.isfinite(range_chunk) & np.isfinite(azimuth_chunk) & (range_chunk >= 0.0) & (azimuth_chunk >= 0.0)
|
|
if not np.any(valid):
|
|
continue
|
|
for point_id, img_x, img_y in targets:
|
|
distances = (range_chunk - np.float32(img_x)) ** 2 + (azimuth_chunk - np.float32(img_y)) ** 2
|
|
distances = np.where(valid, distances, np.float32(np.inf))
|
|
flat_index = int(np.argmin(distances))
|
|
value = float(distances.flat[flat_index])
|
|
if value >= coarse_best[point_id]["distance2"]:
|
|
continue
|
|
local_row, local_col = np.unravel_index(flat_index, distances.shape)
|
|
coarse_best[point_id] = {
|
|
"distance2": value,
|
|
"row": int(row0 + local_row * coarse_step),
|
|
"col": int(local_col * coarse_step),
|
|
}
|
|
|
|
results: dict[str, dict[str, Any]] = {}
|
|
coverage_src = None
|
|
if coverage_path.is_file():
|
|
try:
|
|
candidate = rasterio.open(coverage_path)
|
|
if candidate.width == width and candidate.height == height and candidate.count >= 3:
|
|
coverage_src = candidate
|
|
else:
|
|
candidate.close()
|
|
except Exception:
|
|
coverage_src = None
|
|
|
|
try:
|
|
refine_radius = coarse_step * 12
|
|
for point_id, img_x, img_y in targets:
|
|
seed = coarse_best.get(point_id) or {}
|
|
seed_row = seed.get("row")
|
|
seed_col = seed.get("col")
|
|
if seed_row is None or seed_col is None:
|
|
continue
|
|
row0 = max(0, int(seed_row) - refine_radius)
|
|
row1 = min(height, int(seed_row) + refine_radius + 1)
|
|
col0 = max(0, int(seed_col) - refine_radius)
|
|
col1 = min(width, int(seed_col) + refine_radius + 1)
|
|
local = lookup[row0:row1, col0:col1, :]
|
|
range_local = np.asarray(local[:, :, 0], dtype=np.float32)
|
|
azimuth_local = np.asarray(local[:, :, 1], dtype=np.float32)
|
|
valid = np.isfinite(range_local) & np.isfinite(azimuth_local) & (range_local >= 0.0) & (azimuth_local >= 0.0)
|
|
if not np.any(valid):
|
|
continue
|
|
distances = (range_local - np.float32(img_x)) ** 2 + (azimuth_local - np.float32(img_y)) ** 2
|
|
distances = np.where(valid, distances, np.float32(np.inf))
|
|
flat_index = int(np.argmin(distances))
|
|
value = float(distances.flat[flat_index])
|
|
if not math.isfinite(value):
|
|
continue
|
|
local_row, local_col = np.unravel_index(flat_index, distances.shape)
|
|
row = int(row0 + local_row)
|
|
col = int(col0 + local_col)
|
|
lon, lat = rasterio.transform.xy(transform, row, col, offset="center")
|
|
rate_mm_per_year = None
|
|
coverage_valid = None
|
|
try:
|
|
rate_window = src.read(1, window=Window(col, row, 1, 1))
|
|
rate_value = float(rate_window[0, 0])
|
|
if math.isfinite(rate_value):
|
|
rate_mm_per_year = rate_value * 1000.0
|
|
except Exception:
|
|
rate_mm_per_year = None
|
|
if coverage_src is not None:
|
|
try:
|
|
rgb = coverage_src.read([1, 2, 3], window=Window(col, row, 1, 1))
|
|
coverage_valid = bool(np.any(rgb != 0))
|
|
except Exception:
|
|
coverage_valid = None
|
|
results[point_id] = {
|
|
"lon": float(lon),
|
|
"lat": float(lat),
|
|
"geo_row": row,
|
|
"geo_col": col,
|
|
"geo_match_distance_px": math.sqrt(value),
|
|
"geo_los_rate_mm_per_year": rate_mm_per_year,
|
|
"geo_coverage_valid": coverage_valid,
|
|
"geo_source": "gamma_lt_fine_nearest_inverse",
|
|
}
|
|
finally:
|
|
if coverage_src is not None:
|
|
coverage_src.close()
|
|
del lookup
|
|
return results
|
|
except Exception:
|
|
return {}
|
|
|
|
|
|
def _file_summary(path: Path) -> dict[str, Any]:
|
|
return {
|
|
"path": str(path),
|
|
"exists": path.is_file(),
|
|
"size_bytes": path.stat().st_size if path.is_file() else None,
|
|
}
|
|
|
|
|
|
def _build_expert_gamma_primary_geotiff_stats(path: Path) -> dict[str, Any]:
|
|
stats = {
|
|
**_file_summary(path),
|
|
"schema": "insar.gamma-sbas-expert-primary-geotiff-stats/v1",
|
|
"source": "expert_gamma_geo_los_def_rate",
|
|
"unit": "mm/yr",
|
|
"native_unit": "m/yr",
|
|
"scale_to_unit": 1000.0,
|
|
"zero_is_valid": False,
|
|
"validity_rule": "expert_rgb_coverage_finite_nonzero_values",
|
|
}
|
|
if not path.is_file():
|
|
return stats
|
|
|
|
try:
|
|
import numpy as np
|
|
import rasterio
|
|
from rasterio.windows import Window
|
|
except Exception as exc:
|
|
stats["error"] = f"raster_stats_dependency_unavailable: {exc}"
|
|
return stats
|
|
|
|
try:
|
|
coverage_path = path.with_name("geo_los_def_rate_rgb.tif")
|
|
coverage_mask_source = str(coverage_path) if coverage_path.is_file() else None
|
|
valid_count = 0
|
|
zero_count = 0
|
|
nonzero_count = 0
|
|
sample_count = 0
|
|
value_sum = 0.0
|
|
value_sumsq = 0.0
|
|
value_min: Optional[float] = None
|
|
value_max: Optional[float] = None
|
|
percentile_chunks: list[Any] = []
|
|
max_percentile_samples = 5_000_000
|
|
tile_size = 1024
|
|
|
|
with rasterio.open(path) as src:
|
|
coverage_src = None
|
|
if coverage_path.is_file():
|
|
try:
|
|
candidate = rasterio.open(coverage_path)
|
|
if candidate.width == src.width and candidate.height == src.height and candidate.count >= 3:
|
|
coverage_src = candidate
|
|
else:
|
|
candidate.close()
|
|
except Exception:
|
|
coverage_src = None
|
|
total_count = int(src.width * src.height)
|
|
stats.update(
|
|
{
|
|
"width": int(src.width),
|
|
"height": int(src.height),
|
|
"band_count": int(src.count),
|
|
"dtype": str(src.dtypes[0]) if src.dtypes else None,
|
|
"crs": str(src.crs) if src.crs else None,
|
|
"nodata": _safe_float(src.nodata),
|
|
"metadata_nodata_applied": _safe_float(src.nodata) == 0.0,
|
|
"coverage_mask_source": coverage_mask_source if coverage_src is not None else None,
|
|
"bounds": {
|
|
"left": _safe_float(src.bounds.left),
|
|
"bottom": _safe_float(src.bounds.bottom),
|
|
"right": _safe_float(src.bounds.right),
|
|
"top": _safe_float(src.bounds.top),
|
|
},
|
|
"total_count": total_count,
|
|
}
|
|
)
|
|
for row_off in range(0, src.height, tile_size):
|
|
for col_off in range(0, src.width, tile_size):
|
|
window = Window(
|
|
col_off=col_off,
|
|
row_off=row_off,
|
|
width=min(tile_size, src.width - col_off),
|
|
height=min(tile_size, src.height - row_off),
|
|
)
|
|
data = src.read(1, window=window, masked=False)
|
|
values = np.asarray(data, dtype=np.float64).reshape(-1)
|
|
if values.size == 0:
|
|
continue
|
|
finite = np.isfinite(values)
|
|
if coverage_src is not None:
|
|
coverage_rgb = coverage_src.read([1, 2, 3], window=window, masked=False)
|
|
coverage = np.any(coverage_rgb != 0, axis=0).reshape(-1)
|
|
else:
|
|
coverage = np.ones(values.shape, dtype=bool)
|
|
covered_values = values[finite & coverage]
|
|
zero_count += int(np.count_nonzero(covered_values == 0.0))
|
|
values = values[finite & coverage & (values != 0.0)]
|
|
if values.size == 0:
|
|
continue
|
|
scaled = values * 1000.0
|
|
valid_count += int(scaled.size)
|
|
nonzero_count += int(np.count_nonzero(values != 0.0))
|
|
value_sum += float(scaled.sum(dtype=np.float64))
|
|
value_sumsq += float(np.square(scaled, dtype=np.float64).sum(dtype=np.float64))
|
|
chunk_min = float(scaled.min())
|
|
chunk_max = float(scaled.max())
|
|
value_min = chunk_min if value_min is None else min(value_min, chunk_min)
|
|
value_max = chunk_max if value_max is None else max(value_max, chunk_max)
|
|
if sample_count < max_percentile_samples:
|
|
remaining = max_percentile_samples - sample_count
|
|
if scaled.size <= remaining:
|
|
sample = scaled
|
|
else:
|
|
step = max(1, int(np.ceil(scaled.size / remaining)))
|
|
sample = scaled[::step][:remaining]
|
|
percentile_chunks.append(sample.astype(np.float64, copy=True))
|
|
sample_count += int(sample.size)
|
|
|
|
if coverage_src is not None:
|
|
coverage_src.close()
|
|
|
|
stats["valid_count"] = valid_count
|
|
stats["zero_count"] = zero_count
|
|
stats["nonzero_count"] = nonzero_count
|
|
stats["valid_ratio"] = (valid_count / stats["total_count"]) if stats.get("total_count") else None
|
|
if valid_count <= 0:
|
|
return stats
|
|
|
|
mean = value_sum / valid_count
|
|
variance = max((value_sumsq / valid_count) - (mean * mean), 0.0)
|
|
stats.update(
|
|
{
|
|
"min": value_min,
|
|
"max": value_max,
|
|
"mean": mean,
|
|
"stddev": float(variance ** 0.5),
|
|
"sample_count": sample_count,
|
|
"percentiles_sampled": sample_count < valid_count,
|
|
}
|
|
)
|
|
|
|
if percentile_chunks:
|
|
percentile_values = np.concatenate(percentile_chunks)
|
|
p01, p05, median, p95, p99 = np.percentile(percentile_values, [1, 5, 50, 95, 99])
|
|
stats.update(
|
|
{
|
|
"p01": float(p01),
|
|
"p05": float(p05),
|
|
"median": float(median),
|
|
"p95": float(p95),
|
|
"p99": float(p99),
|
|
}
|
|
)
|
|
return stats
|
|
except Exception as exc:
|
|
stats["error"] = f"raster_stats_failed: {exc}"
|
|
return stats
|
|
|
|
|
|
def _build_expert_gamma_quality_summary(run_dir: Path) -> dict[str, Any]:
|
|
primary_stats = _build_expert_gamma_primary_geotiff_stats(
|
|
run_dir / "publish" / "geotiff" / "geo_los_def_rate.tif"
|
|
)
|
|
return {
|
|
"schema": "insar.gamma-sbas-derived-quality-summary/v1",
|
|
"source": "derived_from_expert_gamma_outputs",
|
|
"note": "Catalog/UI inspection stats derived from expert geo_los_def_rate.tif; quality_summary.json is not required for the expert workflow.",
|
|
"primary_geotiff": primary_stats,
|
|
}
|
|
|
|
|
|
def _pixel_center(transform: tuple[float, float, float, float, float, float], row: int, col: int) -> tuple[float, float]:
|
|
a, b, c, d, e, f = transform
|
|
x = c + (col + 0.5) * a + (row + 0.5) * b
|
|
y = f + (col + 0.5) * d + (row + 0.5) * e
|
|
return float(x), float(y)
|
|
|
|
|
|
def _normalize_crs_label(value: Any) -> Optional[str]:
|
|
text = str(value or "").strip()
|
|
if not text:
|
|
return None
|
|
upper = text.upper()
|
|
if "EPSG" in upper and "4326" in upper:
|
|
return "EPSG:4326"
|
|
if "WGS 84" in upper or "WGS_1984" in upper:
|
|
return "EPSG:4326"
|
|
return text[:240]
|
|
|
|
|
|
def _build_expert_gamma_point_vector(run_dir: Path, *, summary_context: Optional[dict[str, Any]] = None) -> dict[str, Any]:
|
|
source_path = run_dir / "publish" / "geotiff" / "geo_los_def_rate.tif"
|
|
coverage_path = run_dir / "publish" / "geotiff" / "geo_los_def_rate_rgb.tif"
|
|
output_path = run_dir / "publish" / "vectors" / "los_rate_points.geojson.gz"
|
|
summary_path = run_dir / "publish" / "vectors" / "los_rate_points_summary.json"
|
|
previous_summary = _safe_read_json(summary_path)
|
|
context = summary_context or {}
|
|
stack_dates = context.get("stack_dates") or []
|
|
admin_region = context.get("admin_region") if isinstance(context.get("admin_region"), dict) else {}
|
|
admin_names = admin_region.get("names") if isinstance(admin_region.get("names"), dict) else {}
|
|
fields = [
|
|
"run_id",
|
|
"row",
|
|
"col",
|
|
"lon",
|
|
"lat",
|
|
"los_rate_mm_per_year",
|
|
"source_native_m_per_year",
|
|
"date_start",
|
|
"date_end",
|
|
"reference_date",
|
|
"admin_province",
|
|
"admin_city",
|
|
]
|
|
summary: dict[str, Any] = {
|
|
"schema": "insar.gamma-sbas-expert-point-vector-summary/v1",
|
|
"generated_at": previous_summary.get("generated_at") or _utcnow().isoformat(timespec="seconds") + "Z",
|
|
"ready": False,
|
|
"feature_count": 0,
|
|
"output_geojson_gz": str(output_path),
|
|
"output_size_bytes": output_path.stat().st_size if output_path.is_file() else 0,
|
|
"fields": fields,
|
|
"source_geotiffs": {
|
|
"geo_los_def_rate": str(source_path),
|
|
"coverage_mask": str(coverage_path) if coverage_path.is_file() else None,
|
|
},
|
|
"unit": "mm/yr",
|
|
"native_unit": "m/yr",
|
|
"scale_to_unit": 1000.0,
|
|
"zero_is_valid": False,
|
|
"validity_rule": "expert_rgb_coverage_finite_nonzero_values",
|
|
"date_start": context.get("date_start") or (stack_dates[0] if stack_dates else None),
|
|
"date_end": context.get("date_end") or (stack_dates[-1] if stack_dates else None),
|
|
"reference_date": context.get("reference_date"),
|
|
"admin_region": {
|
|
"province": admin_names.get("province") or admin_region.get("province"),
|
|
"city": admin_names.get("city") or admin_region.get("city"),
|
|
},
|
|
"los_convention": context.get("los_sign_convention") or "Gamma expert geo_los_def_rate output; sign follows the expert workflow.",
|
|
"frontend_policy": "download_only; do not render full point GeoJSON in browser",
|
|
}
|
|
if not source_path.is_file():
|
|
_write_json_if_changed(summary_path, {**summary, "error": "source_geotiff_missing"})
|
|
return summary
|
|
if output_path.is_file() and summary_path.is_file():
|
|
input_paths = [source_path]
|
|
if coverage_path.is_file():
|
|
input_paths.append(coverage_path)
|
|
output_mtime = output_path.stat().st_mtime
|
|
if (
|
|
previous_summary.get("validity_rule") == "expert_rgb_coverage_finite_nonzero_values"
|
|
and previous_summary.get("zero_is_valid") is False
|
|
and all(output_mtime >= item.stat().st_mtime for item in input_paths)
|
|
):
|
|
return previous_summary
|
|
|
|
try:
|
|
import numpy as np
|
|
import rasterio
|
|
from rasterio.windows import Window
|
|
except Exception as exc:
|
|
summary["error"] = f"point_vector_dependency_unavailable: {exc}"
|
|
_write_json_if_changed(summary_path, summary)
|
|
return summary
|
|
|
|
try:
|
|
output_path.parent.mkdir(parents=True, exist_ok=True)
|
|
feature_count = 0
|
|
zero_count = 0
|
|
tile_size = 512
|
|
with rasterio.open(source_path) as src:
|
|
coverage_src = None
|
|
if coverage_path.is_file():
|
|
candidate = rasterio.open(coverage_path)
|
|
if candidate.width == src.width and candidate.height == src.height and candidate.count >= 3:
|
|
coverage_src = candidate
|
|
else:
|
|
candidate.close()
|
|
transform = tuple(float(value) for value in src.transform.to_gdal())
|
|
# Convert GDAL geotransform (c, a, b, f, d, e) to affine tuple used by _pixel_center.
|
|
transform = (transform[1], transform[2], transform[0], transform[4], transform[5], transform[3])
|
|
nodata = _safe_float(src.nodata)
|
|
summary.update(
|
|
{
|
|
"width": int(src.width),
|
|
"height": int(src.height),
|
|
"crs": _normalize_crs_label(str(src.crs) if src.crs else None),
|
|
"nodata": nodata,
|
|
"metadata_nodata_applied": nodata == 0.0,
|
|
"coverage_mask_source": str(coverage_path) if coverage_src is not None else None,
|
|
"total_count": int(src.width * src.height),
|
|
}
|
|
)
|
|
with gzip.open(output_path, "wt", encoding="utf-8", compresslevel=6) as handle:
|
|
handle.write('{"type":"FeatureCollection","features":[\n')
|
|
first = True
|
|
for row_off in range(0, src.height, tile_size):
|
|
for col_off in range(0, src.width, tile_size):
|
|
window = Window(
|
|
col_off=col_off,
|
|
row_off=row_off,
|
|
width=min(tile_size, src.width - col_off),
|
|
height=min(tile_size, src.height - row_off),
|
|
)
|
|
data = src.read(1, window=window, masked=False).astype("float64", copy=False)
|
|
finite = np.isfinite(data)
|
|
coverage = np.ones(data.shape, dtype=bool)
|
|
if coverage_src is not None:
|
|
rgb = coverage_src.read([1, 2, 3], window=window, masked=False)
|
|
coverage = np.any(rgb != 0, axis=0)
|
|
covered = finite & coverage
|
|
zero_count += int(np.count_nonzero(data[covered] == 0.0))
|
|
valid = covered & (data != 0.0)
|
|
rows, cols = np.where(valid)
|
|
for local_row, local_col in zip(rows.tolist(), cols.tolist()):
|
|
row = int(row_off + local_row)
|
|
col = int(col_off + local_col)
|
|
lon, lat = _pixel_center(transform, row, col)
|
|
native_value = float(data[local_row, local_col])
|
|
properties = {
|
|
"run_id": context.get("run_id") or run_dir.name,
|
|
"row": row,
|
|
"col": col,
|
|
"lon": lon,
|
|
"lat": lat,
|
|
"los_rate_mm_per_year": native_value * 1000.0,
|
|
"source_native_m_per_year": native_value,
|
|
"date_start": summary.get("date_start"),
|
|
"date_end": summary.get("date_end"),
|
|
"reference_date": summary.get("reference_date"),
|
|
"admin_province": summary["admin_region"].get("province"),
|
|
"admin_city": summary["admin_region"].get("city"),
|
|
}
|
|
feature = {
|
|
"type": "Feature",
|
|
"geometry": {"type": "Point", "coordinates": [lon, lat]},
|
|
"properties": properties,
|
|
}
|
|
if not first:
|
|
handle.write(",\n")
|
|
handle.write(json.dumps(feature, ensure_ascii=False, separators=(",", ":")))
|
|
first = False
|
|
feature_count += 1
|
|
handle.write("\n]}\n")
|
|
if coverage_src is not None:
|
|
coverage_src.close()
|
|
summary.update(
|
|
{
|
|
"ready": output_path.is_file() and output_path.stat().st_size > 0,
|
|
"feature_count": feature_count,
|
|
"zero_count": zero_count,
|
|
"output_size_bytes": output_path.stat().st_size if output_path.is_file() else 0,
|
|
}
|
|
)
|
|
_write_json_if_changed(summary_path, summary)
|
|
return summary
|
|
except Exception as exc:
|
|
summary["error"] = f"point_vector_export_failed: {exc}"
|
|
_write_json_if_changed(summary_path, summary)
|
|
return summary
|
|
|
|
|
|
def _write_text_if_changed(path: Path, text: str) -> bool:
|
|
if path.is_file():
|
|
try:
|
|
if path.read_text(encoding="utf-8", errors="ignore") == text:
|
|
return False
|
|
except Exception:
|
|
pass
|
|
path.parent.mkdir(parents=True, exist_ok=True)
|
|
path.write_text(text, encoding="utf-8")
|
|
return True
|
|
|
|
|
|
def _write_json_if_changed(path: Path, payload: dict[str, Any]) -> bool:
|
|
text = json.dumps(payload, ensure_ascii=False, indent=2) + "\n"
|
|
return _write_text_if_changed(path, text)
|
|
|
|
|
|
_MONITOR_POINT_SELECTION_DEFINITIONS = [
|
|
(
|
|
"toward_high_rate_low_sigma",
|
|
"趋近雷达高形变低残差点",
|
|
"rate > 0，且绝对速率位于高分位，残差低，用于检查明显正向形变区域。",
|
|
),
|
|
(
|
|
"away_high_rate_low_sigma",
|
|
"远离雷达高形变低残差点",
|
|
"rate < 0，且绝对速率位于高分位，残差低，用于检查明显负向形变区域。",
|
|
),
|
|
(
|
|
"high_abs_rate_low_sigma",
|
|
"高绝对速率低残差点",
|
|
"不区分正负，优先选择绝对速率高且残差低的有效点。",
|
|
),
|
|
(
|
|
"stable_low_sigma",
|
|
"近零低残差代表点",
|
|
"绝对速率位于低分位且残差低，用于对照相对稳定区域。",
|
|
),
|
|
(
|
|
"center_valid",
|
|
"覆盖区中心有效点",
|
|
"从有效像元中选取最接近雷达网格中心的点，用于空间位置对照。",
|
|
),
|
|
]
|
|
|
|
|
|
def _read_expert_monitor_point_selection(points_dir: Path) -> dict[tuple[int, int], dict[str, Any]]:
|
|
selection_path = points_dir / "disp_point_sel.txt"
|
|
rows: list[dict[str, Any]] = []
|
|
if selection_path.is_file():
|
|
for index, line in enumerate(selection_path.read_text(encoding="utf-8", errors="ignore").splitlines()):
|
|
parts = line.strip().split()
|
|
if len(parts) < 2:
|
|
continue
|
|
try:
|
|
img_x = int(float(parts[0]))
|
|
img_y = int(float(parts[1]))
|
|
except ValueError:
|
|
continue
|
|
key, label, description = (
|
|
_MONITOR_POINT_SELECTION_DEFINITIONS[index]
|
|
if index < len(_MONITOR_POINT_SELECTION_DEFINITIONS)
|
|
else (
|
|
f"extra_representative_{index + 1:03d}",
|
|
f"补充代表点 {index + 1}",
|
|
"自动选点数量超过内置策略说明时的补充代表点。",
|
|
)
|
|
)
|
|
rows.append(
|
|
{
|
|
"selection_rank": index + 1,
|
|
"selection_key": key,
|
|
"selection_label": label,
|
|
"selection_description": description,
|
|
"img_x": img_x,
|
|
"img_y": img_y,
|
|
}
|
|
)
|
|
payload = {
|
|
"schema": "insar.gamma-sbas-expert-monitor-point-selection/v1",
|
|
"generated_at": _utcnow().isoformat(timespec="seconds") + "Z",
|
|
"source": "disp_point_sel.txt",
|
|
"selection_count": len(rows),
|
|
"strategy": "auto_representative_points",
|
|
"strategy_note": "自动选取趋近/远离雷达高形变、绝对高形变、近零稳定和中心有效点；时序仍由 Gamma disp_prt_2d 输出。",
|
|
"points": rows,
|
|
}
|
|
if rows:
|
|
_write_json_if_changed(points_dir / "disp_point_selection.json", payload)
|
|
return {(int(item["img_x"]), int(item["img_y"])): item for item in rows}
|
|
|
|
|
|
def _wsl_path_to_windows(path: str) -> str:
|
|
text = str(path or "").strip()
|
|
if text.startswith("/mnt/") and len(text) > 6 and text[6] == "/":
|
|
drive = text[5].upper()
|
|
return f"{drive}:{text[6:]}".replace("/", "\\")
|
|
return text
|
|
|
|
|
|
def _build_expert_unwrapped_phase_radar_browse(
|
|
run_dir: Path,
|
|
source_paths: list[Path],
|
|
*,
|
|
width: int,
|
|
lines: int,
|
|
) -> dict[str, dict[str, Any]]:
|
|
unwrapped_dir = run_dir / "publish" / "geotiff" / "unwrapped"
|
|
browse_by_pair: dict[str, dict[str, Any]] = {}
|
|
if not source_paths or width <= 0 or lines <= 0:
|
|
return browse_by_pair
|
|
try:
|
|
import numpy as np
|
|
from PIL import Image
|
|
except Exception:
|
|
return browse_by_pair
|
|
|
|
colormap_path = _find_gamma_colormap("rmg.cm")
|
|
colors = _read_gamma_colormap(colormap_path) if colormap_path else []
|
|
if not colors:
|
|
return browse_by_pair
|
|
color_array = np.asarray(colors, dtype=np.uint8)
|
|
unwrapped_dir.mkdir(parents=True, exist_ok=True)
|
|
_build_gamma_colormap_colorbar(
|
|
unwrapped_dir / "unwrapped_phase_rmg_colorbar.png",
|
|
colormap_name="rmg.cm",
|
|
min_value=-6.28,
|
|
max_value=6.28,
|
|
unit="rad",
|
|
title="Gamma unwrapped phase browse",
|
|
)
|
|
for source in source_paths:
|
|
pair_id = source.name.replace(".unw.atmsub_1", "")
|
|
output_bmp = unwrapped_dir / f"{source.name}.rdc_rmg.bmp"
|
|
output_png = unwrapped_dir / f"{source.name}.rdc_rmg_preview.png"
|
|
item = {
|
|
"pair_id": pair_id,
|
|
"radar_coordinates": True,
|
|
"browse_command": "rasdt_pwr <unw.atmsub_1> mli.ave <width> 1 - 1 1 -6.28 6.28 1 rmg.cm ... 1.0 0.35 24",
|
|
"colormap": "Gamma rmg.cm",
|
|
"display_range_rad": [-6.28, 6.28],
|
|
"bmp": _file_summary(output_bmp),
|
|
"preview": _file_summary(output_png),
|
|
"ready": False,
|
|
}
|
|
if not source.is_file():
|
|
item["error"] = "source_missing"
|
|
browse_by_pair[pair_id] = item
|
|
continue
|
|
if (
|
|
output_png.is_file()
|
|
and output_png.stat().st_mtime >= source.stat().st_mtime
|
|
and (not output_bmp.is_file() or output_bmp.stat().st_mtime >= source.stat().st_mtime)
|
|
):
|
|
item["bmp"] = _file_summary(output_bmp)
|
|
item["preview"] = _file_summary(output_png)
|
|
item["ready"] = output_png.stat().st_size > 0
|
|
browse_by_pair[pair_id] = item
|
|
continue
|
|
try:
|
|
data = np.fromfile(source, dtype=">f4", count=int(width) * int(lines)).reshape((int(lines), int(width)))
|
|
valid = np.isfinite(data) & (data != 0.0)
|
|
wrapped = ((data + 6.28) % 12.56) - 6.28
|
|
ratio = np.clip((wrapped + 6.28) / 12.56, 0.0, 1.0)
|
|
indices = np.rint(ratio * (len(color_array) - 1)).astype(np.int32)
|
|
rgb = color_array[np.clip(indices, 0, len(color_array) - 1)]
|
|
rgb = np.where(valid[..., None], rgb, 0).astype(np.uint8)
|
|
image = Image.fromarray(rgb, mode="RGB")
|
|
image.save(output_bmp, "BMP")
|
|
preview = image.copy()
|
|
resampling = getattr(getattr(Image, "Resampling", Image), "LANCZOS")
|
|
preview.thumbnail((1600, 1600), resampling)
|
|
preview.save(output_png, "PNG", optimize=True)
|
|
item.update(
|
|
{
|
|
"bmp": _file_summary(output_bmp),
|
|
"preview": _file_summary(output_png),
|
|
"valid_count": int(np.count_nonzero(valid)),
|
|
"ready": output_png.is_file() and output_png.stat().st_size > 0,
|
|
}
|
|
)
|
|
except Exception as exc:
|
|
item["error"] = str(exc)
|
|
browse_by_pair[pair_id] = item
|
|
return browse_by_pair
|
|
|
|
|
|
def _build_expert_unwrapped_phase_derivatives(run_dir: Path) -> dict[str, Any]:
|
|
unwrapped_dir = run_dir / "publish" / "geotiff" / "unwrapped"
|
|
summary_path = unwrapped_dir / "unwrapped_phase_summary.json"
|
|
final_tab = run_dir / "sbas" / "final_unw_tab"
|
|
manifest = _safe_read_json(run_dir / "run_manifest.json")
|
|
stack_manifest = _safe_read_json(run_dir / "stack_manifest.json")
|
|
stack = stack_manifest.get("stack") if isinstance(stack_manifest.get("stack"), dict) else {}
|
|
reference_date = str(
|
|
manifest.get("reference_date")
|
|
or stack.get("reference_date")
|
|
or (manifest.get("coregistration") or {}).get("reference_date")
|
|
or ""
|
|
).strip()
|
|
dem_par = run_dir / "dem" / f"{reference_date}_seg.dem_par" if reference_date else next(iter(sorted((run_dir / "dem").glob("*_seg.dem_par"))), run_dir / "dem" / "_missing_seg.dem_par")
|
|
lookup = run_dir / "dem" / f"{reference_date}.lt_fine" if reference_date else next(iter(sorted((run_dir / "dem").glob("*.lt_fine"))), run_dir / "dem" / "_missing.lt_fine")
|
|
mli_par = run_dir / "sbas" / "mli.ave.par"
|
|
previous = _safe_read_json(summary_path)
|
|
source_paths: list[Path] = []
|
|
if final_tab.is_file():
|
|
for line in final_tab.read_text(encoding="utf-8", errors="ignore").splitlines():
|
|
text = line.strip().split()
|
|
if text:
|
|
source_paths.append(Path(_wsl_path_to_windows(text[0])))
|
|
|
|
summary: dict[str, Any] = {
|
|
"schema": "insar.gamma-sbas-expert-unwrapped-phase-summary/v1",
|
|
"generated_at": previous.get("generated_at") or _utcnow().isoformat(timespec="seconds") + "Z",
|
|
"source_stage": "final_unw_tab",
|
|
"source_tab": _file_summary(final_tab),
|
|
"source_count": len(source_paths),
|
|
"ready": False,
|
|
"products": [],
|
|
"note": "Geocoded GeoTIFF derivatives from the final unwrapped phase files consumed by the expert Gamma SBAS inversion.",
|
|
}
|
|
if not source_paths:
|
|
_write_json_if_changed(summary_path, summary)
|
|
return summary
|
|
|
|
outputs = [
|
|
unwrapped_dir / f"{path.name}.geo.tif"
|
|
for path in source_paths
|
|
]
|
|
radar_outputs = [
|
|
unwrapped_dir / f"{path.name}.rdc_rmg_preview.png"
|
|
for path in source_paths
|
|
]
|
|
radar_colorbar = unwrapped_dir / "unwrapped_phase_rmg_colorbar.png"
|
|
previous_products = previous.get("products") if isinstance(previous.get("products"), list) else []
|
|
previous_has_radar_browse = (
|
|
len(previous_products) == len(source_paths)
|
|
and all((item.get("radar_browse") or {}).get("ready") for item in previous_products if isinstance(item, dict))
|
|
)
|
|
if (
|
|
summary_path.is_file()
|
|
and all(output.is_file() for output in outputs)
|
|
and all(output.is_file() for output in radar_outputs)
|
|
and radar_colorbar.is_file()
|
|
and previous_has_radar_browse
|
|
and all(
|
|
output.stat().st_mtime >= source.stat().st_mtime
|
|
for source, output in zip(source_paths, outputs)
|
|
if source.is_file()
|
|
)
|
|
and all(
|
|
output.stat().st_mtime >= source.stat().st_mtime
|
|
for source, output in zip(source_paths, radar_outputs)
|
|
if source.is_file()
|
|
)
|
|
):
|
|
return previous
|
|
|
|
try:
|
|
import numpy as np
|
|
import rasterio
|
|
from PIL import Image
|
|
from rasterio.transform import from_origin
|
|
except Exception as exc:
|
|
summary["error"] = f"unwrapped_phase_dependency_unavailable: {exc}"
|
|
_write_json_if_changed(summary_path, summary)
|
|
return summary
|
|
|
|
def read_gamma_param(path: Path, key: str) -> Optional[str]:
|
|
if not path.is_file():
|
|
return None
|
|
for line in path.read_text(encoding="utf-8", errors="ignore").splitlines():
|
|
parts = line.split()
|
|
if parts and parts[0].rstrip(":") == key.rstrip(":") and len(parts) > 1:
|
|
return parts[1]
|
|
return None
|
|
|
|
try:
|
|
rdc_width = _safe_int(read_gamma_param(mli_par, "range_samples"))
|
|
rdc_lines = _safe_int(read_gamma_param(mli_par, "azimuth_lines"))
|
|
dem_width = _safe_int(read_gamma_param(dem_par, "width"))
|
|
dem_lines = _safe_int(read_gamma_param(dem_par, "nlines"))
|
|
corner_lon = _safe_float(read_gamma_param(dem_par, "corner_lon"))
|
|
corner_lat = _safe_float(read_gamma_param(dem_par, "corner_lat"))
|
|
post_lon = _safe_float(read_gamma_param(dem_par, "post_lon"))
|
|
post_lat = _safe_float(read_gamma_param(dem_par, "post_lat"))
|
|
if None in (rdc_width, rdc_lines, dem_width, dem_lines, corner_lon, corner_lat, post_lon, post_lat):
|
|
raise RuntimeError("required Gamma geometry parameters are missing")
|
|
if not lookup.is_file():
|
|
raise FileNotFoundError(str(lookup))
|
|
|
|
unwrapped_dir.mkdir(parents=True, exist_ok=True)
|
|
radar_browse_by_pair = _build_expert_unwrapped_phase_radar_browse(
|
|
run_dir,
|
|
source_paths,
|
|
width=int(rdc_width),
|
|
lines=int(rdc_lines),
|
|
)
|
|
lut = np.fromfile(lookup, dtype=">c8").reshape((int(dem_lines), int(dem_width)))
|
|
rng = np.rint(lut.real).astype(np.int32)
|
|
az = np.rint(lut.imag).astype(np.int32)
|
|
valid_lut = (
|
|
np.isfinite(lut.real)
|
|
& np.isfinite(lut.imag)
|
|
& (rng >= 0)
|
|
& (rng < int(rdc_width))
|
|
& (az >= 0)
|
|
& (az < int(rdc_lines))
|
|
)
|
|
del lut
|
|
transform = from_origin(float(corner_lon), float(corner_lat), abs(float(post_lon)), abs(float(post_lat)))
|
|
products: list[dict[str, Any]] = []
|
|
preview_limit = 6
|
|
for source in source_paths:
|
|
if not source.is_file():
|
|
products.append({"source": str(source), "ready": False, "error": "source_missing"})
|
|
continue
|
|
pair_id = source.name.replace(".unw.atmsub_1", "")
|
|
output_tif = unwrapped_dir / f"{source.name}.geo.tif"
|
|
output_preview = unwrapped_dir / f"{source.name}.geo_preview.png"
|
|
data = np.fromfile(source, dtype=">f4", count=int(rdc_width) * int(rdc_lines)).reshape((int(rdc_lines), int(rdc_width)))
|
|
geo = np.full((int(dem_lines), int(dem_width)), np.nan, dtype=np.float32)
|
|
geo[valid_lut] = data[az[valid_lut], rng[valid_lut]]
|
|
finite_nonzero = np.isfinite(geo) & (geo != 0.0)
|
|
with rasterio.open(
|
|
output_tif,
|
|
"w",
|
|
driver="GTiff",
|
|
width=int(dem_width),
|
|
height=int(dem_lines),
|
|
count=1,
|
|
dtype="float32",
|
|
crs=_WGS84_GEOGCS_WKT,
|
|
transform=transform,
|
|
nodata=np.nan,
|
|
compress="deflate",
|
|
) as dst:
|
|
dst.write(geo, 1)
|
|
if np.any(finite_nonzero) and len(products) < preview_limit:
|
|
valid_values = geo[finite_nonzero]
|
|
p02, p98 = np.nanpercentile(valid_values, [2, 98])
|
|
if not np.isfinite(p02) or not np.isfinite(p98) or p98 <= p02:
|
|
p02 = float(np.nanmin(valid_values))
|
|
p98 = float(np.nanmax(valid_values))
|
|
norm = np.clip((geo - p02) / max(p98 - p02, 1e-6), 0, 1)
|
|
gray = np.where(finite_nonzero, norm * 255, 0).astype("uint8")
|
|
alpha = np.where(finite_nonzero, 255, 0).astype("uint8")
|
|
rgba = np.dstack([gray, gray, gray, alpha])
|
|
image = Image.fromarray(rgba, mode="RGBA")
|
|
resampling = getattr(getattr(Image, "Resampling", Image), "LANCZOS")
|
|
image.thumbnail((1600, 1600), resampling)
|
|
image.save(output_preview, "PNG", optimize=True)
|
|
products.append(
|
|
{
|
|
"pair_id": pair_id,
|
|
"source": str(source),
|
|
"geotiff": _file_summary(output_tif),
|
|
"preview": _file_summary(output_preview),
|
|
"radar_browse": radar_browse_by_pair.get(pair_id) or {},
|
|
"valid_count": int(np.count_nonzero(finite_nonzero)),
|
|
"unit": "rad",
|
|
"description": "Final model-corrected unwrapped phase used by Gamma SBAS inversion, geocoded for inspection.",
|
|
"ready": output_tif.is_file() and output_tif.stat().st_size > 0,
|
|
}
|
|
)
|
|
del data, geo, finite_nonzero
|
|
summary.update(
|
|
{
|
|
"generated_at": _utcnow().isoformat(timespec="seconds") + "Z",
|
|
"ready": bool(products) and all(item.get("ready") for item in products),
|
|
"products": products,
|
|
}
|
|
)
|
|
_write_json_if_changed(summary_path, summary)
|
|
return summary
|
|
except Exception as exc:
|
|
summary["error"] = f"unwrapped_phase_export_failed: {exc}"
|
|
_write_json_if_changed(summary_path, summary)
|
|
return summary
|
|
|
|
|
|
def _write_expert_monitor_point_derivatives(run_dir: Path) -> dict[str, Any]:
|
|
points_dir = run_dir / "publish" / "points"
|
|
disp_point_path = points_dir / "disp_point.txt"
|
|
items_path = points_dir / "items.txt"
|
|
selection_by_xy = _read_expert_monitor_point_selection(points_dir)
|
|
points = _parse_expert_disp_point_table(disp_point_path)
|
|
geo_locations = _locate_radar_points_in_geocoded_product(run_dir, points)
|
|
out_dir = run_dir / "publish" / "monitor_points"
|
|
summary_path = run_dir / "monitor_points_summary.json"
|
|
monitor_outputs: list[dict[str, Any]] = []
|
|
if points:
|
|
out_dir.mkdir(parents=True, exist_ok=True)
|
|
expected_names = set()
|
|
for index in range(1, len(points) + 1):
|
|
point_id = f"expert_point_{index:03d}"
|
|
expected_names.update(
|
|
{
|
|
f"{point_id}_timeseries.csv",
|
|
f"{point_id}_timeseries.png",
|
|
f"{point_id}_metadata.json",
|
|
}
|
|
)
|
|
for old_path in out_dir.iterdir():
|
|
if old_path.is_file() and old_path.name not in expected_names:
|
|
try:
|
|
old_path.unlink()
|
|
except Exception:
|
|
pass
|
|
|
|
for point in points:
|
|
point_id = str(point["point_id"])
|
|
csv_path = out_dir / f"{point_id}_timeseries.csv"
|
|
png_path = out_dir / f"{point_id}_timeseries.png"
|
|
metadata_path = out_dir / f"{point_id}_metadata.json"
|
|
displacements = point.get("displacements") or []
|
|
csv_lines = ["date,displacement_mm"]
|
|
csv_lines.extend(f"{item['date']},{item['displacement_mm']:.6f}" for item in displacements)
|
|
curve_needs_refresh = _write_text_if_changed(csv_path, "\n".join(csv_lines) + "\n")
|
|
selection = selection_by_xy.get((int(point.get("img_x") or 0), int(point.get("img_y") or 0)), {})
|
|
geo_location = geo_locations.get(point_id) or {}
|
|
|
|
metadata = {
|
|
"schema": "insar.gamma-sbas-expert-monitor-point/v1",
|
|
"point_id": point_id,
|
|
"source_tool": "disp_prt_2d",
|
|
"selection_rank": selection.get("selection_rank"),
|
|
"selection_key": selection.get("selection_key"),
|
|
"selection_label": selection.get("selection_label"),
|
|
"selection_description": selection.get("selection_description"),
|
|
"img_x": point.get("img_x"),
|
|
"img_y": point.get("img_y"),
|
|
"lon": geo_location.get("lon"),
|
|
"lat": geo_location.get("lat"),
|
|
"geo_row": geo_location.get("geo_row"),
|
|
"geo_col": geo_location.get("geo_col"),
|
|
"geo_match_distance_px": geo_location.get("geo_match_distance_px"),
|
|
"geo_los_rate_mm_per_year": geo_location.get("geo_los_rate_mm_per_year"),
|
|
"geo_coverage_valid": geo_location.get("geo_coverage_valid"),
|
|
"geo_source": geo_location.get("geo_source"),
|
|
"height_m": point.get("height_m"),
|
|
"deformation_rate_mm_per_year": point.get("deformation_rate_mm_per_year"),
|
|
"stdev_residual_phase_rad": point.get("stdev_residual_phase_rad"),
|
|
"displacement_count": len(displacements),
|
|
"displacements": displacements,
|
|
"source_files": {
|
|
"items": str(items_path),
|
|
"disp_point": str(disp_point_path),
|
|
},
|
|
}
|
|
_write_json_if_changed(metadata_path, metadata)
|
|
|
|
try:
|
|
import matplotlib
|
|
|
|
matplotlib.use("Agg")
|
|
import matplotlib.pyplot as plt
|
|
from matplotlib.dates import DateFormatter
|
|
|
|
if curve_needs_refresh or not png_path.is_file():
|
|
x_values = [datetime.fromisoformat(str(item["date"])) for item in displacements]
|
|
x_labels = [str(item["date"]) for item in displacements]
|
|
y_values = [float(item["displacement_mm"]) for item in displacements]
|
|
fig_width = max(8.0, len(x_values) * 1.2)
|
|
fig, ax = plt.subplots(figsize=(fig_width, 4.5), dpi=150)
|
|
ax.plot(x_values, y_values, marker="o", markersize=5, linewidth=1.8, color="#1d4ed8")
|
|
ax.axhline(0, color="#94a3b8", linewidth=0.8)
|
|
ax.set_title(
|
|
f"Gamma SBAS {point_id} rate={point.get('deformation_rate_mm_per_year') or 0:.3f} mm/yr",
|
|
fontsize=10,
|
|
)
|
|
ax.set_xlabel("Date")
|
|
ax.set_ylabel("Displacement (mm)")
|
|
ax.grid(True, alpha=0.25)
|
|
ax.set_xticks(x_values)
|
|
ax.set_xticklabels(x_labels, rotation=35, ha="right")
|
|
ax.xaxis.set_major_formatter(DateFormatter("%Y-%m-%d"))
|
|
fig.tight_layout()
|
|
fig.savefig(png_path)
|
|
plt.close(fig)
|
|
except Exception:
|
|
if not png_path.is_file():
|
|
png_path.write_bytes(b"")
|
|
|
|
monitor_outputs.append(
|
|
{
|
|
"point_id": point_id,
|
|
"metadata": metadata,
|
|
"files": {
|
|
"png": _file_summary(png_path),
|
|
"csv": _file_summary(csv_path),
|
|
"metadata": _file_summary(metadata_path),
|
|
},
|
|
}
|
|
)
|
|
|
|
summary = {
|
|
"schema": "insar.gamma-sbas-expert-monitor-points-summary/v1",
|
|
"generated_at": _utcnow().isoformat(timespec="seconds") + "Z",
|
|
"mode": "expert_disp_prt_2d",
|
|
"source_tool": "disp_prt_2d",
|
|
"source_files": {
|
|
"items": _file_summary(items_path),
|
|
"disp_point": _file_summary(disp_point_path),
|
|
"selection": _file_summary(points_dir / "disp_point_selection.json"),
|
|
},
|
|
"monitor_points": [
|
|
{
|
|
"point_id": point.get("point_id"),
|
|
"selection_rank": selection_by_xy.get((int(point.get("img_x") or 0), int(point.get("img_y") or 0)), {}).get("selection_rank"),
|
|
"selection_key": selection_by_xy.get((int(point.get("img_x") or 0), int(point.get("img_y") or 0)), {}).get("selection_key"),
|
|
"selection_label": selection_by_xy.get((int(point.get("img_x") or 0), int(point.get("img_y") or 0)), {}).get("selection_label"),
|
|
"selection_description": selection_by_xy.get((int(point.get("img_x") or 0), int(point.get("img_y") or 0)), {}).get("selection_description"),
|
|
"img_x": point.get("img_x"),
|
|
"img_y": point.get("img_y"),
|
|
"lon": (geo_locations.get(str(point.get("point_id"))) or {}).get("lon"),
|
|
"lat": (geo_locations.get(str(point.get("point_id"))) or {}).get("lat"),
|
|
"geo_row": (geo_locations.get(str(point.get("point_id"))) or {}).get("geo_row"),
|
|
"geo_col": (geo_locations.get(str(point.get("point_id"))) or {}).get("geo_col"),
|
|
"geo_match_distance_px": (geo_locations.get(str(point.get("point_id"))) or {}).get("geo_match_distance_px"),
|
|
"geo_los_rate_mm_per_year": (geo_locations.get(str(point.get("point_id"))) or {}).get("geo_los_rate_mm_per_year"),
|
|
"geo_coverage_valid": (geo_locations.get(str(point.get("point_id"))) or {}).get("geo_coverage_valid"),
|
|
"geo_source": (geo_locations.get(str(point.get("point_id"))) or {}).get("geo_source"),
|
|
"height_m": point.get("height_m"),
|
|
"deformation_rate_mm_per_year": point.get("deformation_rate_mm_per_year"),
|
|
"stdev_residual_phase_rad": point.get("stdev_residual_phase_rad"),
|
|
"displacement_count": len(point.get("displacements") or []),
|
|
"displacements": point.get("displacements") or [],
|
|
}
|
|
for point in points
|
|
],
|
|
"monitor_outputs": monitor_outputs,
|
|
"ready": bool(monitor_outputs)
|
|
and all(
|
|
(item.get("files") or {}).get("png", {}).get("exists")
|
|
and (item.get("files") or {}).get("csv", {}).get("exists")
|
|
and (item.get("files") or {}).get("metadata", {}).get("exists")
|
|
for item in monitor_outputs
|
|
),
|
|
}
|
|
if points:
|
|
_write_json_if_changed(summary_path, summary)
|
|
return summary
|
|
|
|
|
|
def _bbox_polygon(
|
|
min_lon: Optional[float],
|
|
min_lat: Optional[float],
|
|
max_lon: Optional[float],
|
|
max_lat: Optional[float],
|
|
):
|
|
if None in (min_lon, min_lat, max_lon, max_lat):
|
|
return None
|
|
if min_lon == max_lon or min_lat == max_lat:
|
|
return None
|
|
return Polygon(
|
|
[
|
|
(min_lon, min_lat),
|
|
(max_lon, min_lat),
|
|
(max_lon, max_lat),
|
|
(min_lon, max_lat),
|
|
(min_lon, min_lat),
|
|
]
|
|
)
|
|
|
|
|
|
def _stack_dates_from_manifest(stack_manifest: dict[str, Any], manifest: dict[str, Any], stack: dict[str, Any]) -> list[str]:
|
|
values: list[str] = []
|
|
for source in (
|
|
stack_manifest.get("dates"),
|
|
stack.get("dates"),
|
|
manifest.get("dates"),
|
|
[scene.get("date") for scene in stack_manifest.get("scenes") or [] if isinstance(scene, dict)],
|
|
[scene.get("date") for scene in manifest.get("scenes") or [] if isinstance(scene, dict)],
|
|
):
|
|
if not isinstance(source, list):
|
|
continue
|
|
for item in source:
|
|
text = str(item or "").strip()
|
|
if text:
|
|
values.append(text)
|
|
return sorted(dict.fromkeys(values))
|
|
|
|
|
|
class SbasInsarCatalogService:
|
|
def get_run_root(self) -> str:
|
|
root = Path(settings.GAMMA_SBAS_WORK_ROOT or Path(settings.BACKEND_DIR) / "runtime" / "sbas_insar_production")
|
|
run_root = root / "runs"
|
|
run_root.mkdir(parents=True, exist_ok=True)
|
|
return _normalize_path(run_root)
|
|
|
|
def get_run_roots(self) -> list[str]:
|
|
roots = [self.get_run_root()]
|
|
try:
|
|
landsar_root = landsar_sbas_service.configured_run_root()
|
|
if landsar_root not in roots:
|
|
roots.append(landsar_root)
|
|
except Exception:
|
|
pass
|
|
return roots
|
|
|
|
def _iter_run_manifest_paths(self, run_root: Optional[str] = None) -> list[str]:
|
|
roots = [run_root] if run_root else self.get_run_roots()
|
|
manifest_paths: list[str] = []
|
|
for raw_root in roots:
|
|
root = Path(raw_root)
|
|
if not root.is_dir():
|
|
continue
|
|
manifest_paths.extend(
|
|
_normalize_path(path)
|
|
for path in sorted(root.glob("*/run_manifest.json"))
|
|
if self._is_publish_ready(path.parent, _safe_read_json(path))
|
|
)
|
|
return sorted(dict.fromkeys(manifest_paths))
|
|
|
|
@staticmethod
|
|
def _is_landsar_manifest(manifest: dict[str, Any]) -> bool:
|
|
return str(manifest.get("processor_code") or "").strip().lower() == "landsar_sbas"
|
|
|
|
@staticmethod
|
|
def _is_expert_gamma_manifest(manifest: dict[str, Any], run_dir: Optional[Path] = None) -> bool:
|
|
execution_mode = str(manifest.get("execution_mode") or "").strip().lower()
|
|
if execution_mode == "expert_manifest_script_workflow":
|
|
return True
|
|
if run_dir is not None and (run_dir / "publish" / "geotiff" / "geo_los_def_rate.tif").is_file():
|
|
return True
|
|
return False
|
|
|
|
def _asset_definitions_for_manifest(self, manifest: dict[str, Any], run_dir: Optional[Path] = None):
|
|
if self._is_landsar_manifest(manifest):
|
|
return _LANDSAR_CORE_ASSETS
|
|
if self._is_expert_gamma_manifest(manifest, run_dir):
|
|
return _EXPERT_GAMMA_CORE_ASSETS
|
|
return _CORE_ASSETS
|
|
|
|
def _required_roles_for_manifest(self, manifest: dict[str, Any], run_dir: Optional[Path] = None) -> set[str]:
|
|
if self._is_expert_gamma_manifest(manifest, run_dir):
|
|
return {"primary_geotiff"}
|
|
return _LANDSAR_REQUIRED_ASSET_ROLES if self._is_landsar_manifest(manifest) else _REQUIRED_ASSET_ROLES
|
|
|
|
def _is_publish_ready(self, run_dir: Path, manifest: dict[str, Any]) -> bool:
|
|
status = str(manifest.get("status") or "").strip().upper()
|
|
asset_defs = self._asset_definitions_for_manifest(manifest, run_dir)
|
|
required_roles = self._required_roles_for_manifest(manifest, run_dir)
|
|
required_outputs_ready = all(
|
|
(run_dir / relative_path).is_file()
|
|
for role, _name, relative_path, is_required, _is_primary in asset_defs
|
|
if role in required_roles and is_required
|
|
)
|
|
return status in _READY_STATUSES or required_outputs_ready
|
|
|
|
def _tree_fingerprint(self, manifest_paths: list[str]) -> str:
|
|
records: list[dict[str, Any]] = []
|
|
for raw_path in manifest_paths:
|
|
manifest_path = Path(raw_path)
|
|
run_dir = manifest_path.parent
|
|
manifest_payload = _safe_read_json(manifest_path)
|
|
tracked_paths = [
|
|
manifest_path,
|
|
run_dir / "monitor_points_summary.json",
|
|
]
|
|
if not self._is_expert_gamma_manifest(manifest_payload, run_dir):
|
|
tracked_paths.extend(
|
|
[
|
|
run_dir / "product_summary.json",
|
|
run_dir / "quality_summary.json",
|
|
]
|
|
)
|
|
else:
|
|
tracked_paths.extend(
|
|
[
|
|
run_dir / "diff_dir" / "bprep_file.png",
|
|
run_dir / "diff_dir" / "mean.cc_mask.bmp",
|
|
run_dir / "sbas" / "final_unw_tab",
|
|
]
|
|
)
|
|
diff_dir = run_dir / "diff_dir"
|
|
if diff_dir.is_dir():
|
|
tracked_paths.extend(sorted(diff_dir.glob("*.adf.unw.bmp")))
|
|
tracked_paths.extend(
|
|
run_dir / relative_path
|
|
for _role, _name, relative_path, _required, _primary in self._asset_definitions_for_manifest(
|
|
manifest_payload, run_dir
|
|
)
|
|
if not (run_dir / relative_path).is_dir()
|
|
)
|
|
for path in tracked_paths:
|
|
if not path.exists():
|
|
continue
|
|
stat = path.stat()
|
|
records.append(
|
|
{
|
|
"path": str(path.relative_to(run_dir)).replace("\\", "/"),
|
|
"run": run_dir.name,
|
|
"size": stat.st_size,
|
|
"mtime_ns": stat.st_mtime_ns,
|
|
}
|
|
)
|
|
encoded = json.dumps(records, sort_keys=True, ensure_ascii=True)
|
|
return hashlib.sha256(encoded.encode("utf-8")).hexdigest()
|
|
|
|
async def _get_or_create_catalog_state(self, db: AsyncSession, *, storage_root: str) -> ResultCatalogStateORM:
|
|
result = await db.execute(
|
|
select(ResultCatalogStateORM).where(ResultCatalogStateORM.catalog_name == SBAS_INSAR_CATALOG_NAME)
|
|
)
|
|
state = result.scalar_one_or_none()
|
|
if state is None:
|
|
state = ResultCatalogStateORM(
|
|
catalog_name=SBAS_INSAR_CATALOG_NAME,
|
|
product_family="timeseries",
|
|
storage_root=storage_root,
|
|
status="READY",
|
|
needs_rebuild=False,
|
|
)
|
|
db.add(state)
|
|
await db.flush()
|
|
elif state.storage_root != storage_root:
|
|
state.storage_root = storage_root
|
|
if state.product_family != "timeseries":
|
|
state.product_family = "timeseries"
|
|
return state
|
|
|
|
def _asset_row(
|
|
self,
|
|
run_dir: Path,
|
|
*,
|
|
role: str,
|
|
name: str,
|
|
relative_path: str,
|
|
is_required: bool,
|
|
is_primary: bool,
|
|
) -> ResultAssetORM:
|
|
absolute_path = run_dir / relative_path
|
|
exists = absolute_path.is_file()
|
|
return ResultAssetORM(
|
|
asset_role=role[:32],
|
|
asset_name=name,
|
|
relative_path=relative_path.replace("\\", "/"),
|
|
absolute_path=_normalize_path(absolute_path),
|
|
format=_asset_format(relative_path),
|
|
media_type=_media_type(relative_path),
|
|
is_required=is_required,
|
|
is_primary=is_primary,
|
|
exists_flag=exists,
|
|
file_size=absolute_path.stat().st_size if exists else None,
|
|
srid=4326 if (
|
|
(relative_path.lower().endswith((".tif", ".tiff")) and "/geotiff/" in relative_path)
|
|
or (relative_path.lower().endswith((".tif", ".tiff")) and "/landsar/" in relative_path)
|
|
or relative_path.lower().endswith(".geojson.gz")
|
|
) else None,
|
|
)
|
|
|
|
def _monitor_asset_rows(self, run_dir: Path) -> list[ResultAssetORM]:
|
|
monitor_dir = run_dir / "publish" / "monitor_points"
|
|
if not monitor_dir.is_dir():
|
|
return []
|
|
rows: list[ResultAssetORM] = []
|
|
for path in sorted(monitor_dir.iterdir()):
|
|
if not path.is_file():
|
|
continue
|
|
suffix = path.suffix.lower()
|
|
if suffix not in {".png", ".csv", ".json"}:
|
|
continue
|
|
role = {
|
|
".png": "monitor_point_curve",
|
|
".csv": "monitor_point_csv",
|
|
".json": "monitor_point_metadata",
|
|
}[suffix]
|
|
relative_path = str(path.relative_to(run_dir)).replace("\\", "/")
|
|
rows.append(
|
|
self._asset_row(
|
|
run_dir,
|
|
role=role,
|
|
name=path.name,
|
|
relative_path=relative_path,
|
|
is_required=False,
|
|
is_primary=False,
|
|
)
|
|
)
|
|
return rows
|
|
|
|
def _unwrapped_phase_asset_rows(self, run_dir: Path) -> list[ResultAssetORM]:
|
|
unwrapped_dir = run_dir / "publish" / "geotiff" / "unwrapped"
|
|
if not unwrapped_dir.is_dir():
|
|
return []
|
|
rows: list[ResultAssetORM] = []
|
|
for path in sorted(unwrapped_dir.iterdir()):
|
|
if not path.is_file():
|
|
continue
|
|
lowered = path.name.lower()
|
|
if lowered in {"unwrapped_phase_summary.json", "unwrapped_phase_rmg_colorbar.png"}:
|
|
continue
|
|
if lowered.endswith(".rdc_rmg_preview.png"):
|
|
role = "unwrapped_phase_radar_preview"
|
|
name = f"Radar-coordinate rmg unwrapped phase preview {path.name}"
|
|
elif lowered.endswith(".rdc_rmg.bmp"):
|
|
role = "unwrapped_phase_radar_bmp"
|
|
name = f"Gamma radar-coordinate rmg unwrapped phase BMP {path.name}"
|
|
elif lowered.endswith((".tif", ".tiff")):
|
|
role = "unwrapped_phase_geotiff"
|
|
name = f"Geocoded unwrapped phase {path.name}"
|
|
elif lowered.endswith(".png"):
|
|
role = "unwrapped_phase_preview"
|
|
name = f"Unwrapped phase preview {path.name}"
|
|
else:
|
|
continue
|
|
relative_path = str(path.relative_to(run_dir)).replace("\\", "/")
|
|
rows.append(
|
|
self._asset_row(
|
|
run_dir,
|
|
role=role,
|
|
name=name,
|
|
relative_path=relative_path,
|
|
is_required=False,
|
|
is_primary=False,
|
|
)
|
|
)
|
|
return rows
|
|
|
|
def _gamma_intermediate_qc_asset_rows(self, run_dir: Path) -> list[ResultAssetORM]:
|
|
rows: list[ResultAssetORM] = []
|
|
|
|
static_assets = (
|
|
(
|
|
"gamma_qc_baseline_plot",
|
|
"Gamma baseline network plot",
|
|
run_dir / "diff_dir" / "bprep_file.png",
|
|
),
|
|
(
|
|
"gamma_qc_mean_coherence",
|
|
"Gamma mean coherence mask",
|
|
run_dir / "diff_dir" / "mean.cc_mask.bmp",
|
|
),
|
|
)
|
|
for role, name, path in static_assets:
|
|
if not path.is_file():
|
|
continue
|
|
rows.append(
|
|
self._asset_row(
|
|
run_dir,
|
|
role=role,
|
|
name=f"{name} {path.name}",
|
|
relative_path=str(path.relative_to(run_dir)).replace("\\", "/"),
|
|
is_required=False,
|
|
is_primary=False,
|
|
)
|
|
)
|
|
|
|
diff_dir = run_dir / "diff_dir"
|
|
final_tab = run_dir / "sbas" / "final_unw_tab"
|
|
pair_ids: list[str] = []
|
|
if final_tab.is_file():
|
|
try:
|
|
for line in final_tab.read_text(encoding="utf-8", errors="ignore").splitlines():
|
|
raw_path = line.strip().split()[0] if line.strip() else ""
|
|
if not raw_path:
|
|
continue
|
|
name = Path(_wsl_path_to_windows(raw_path)).name
|
|
pair_id = name.replace(".unw.atmsub_1", "").replace(".unw", "")
|
|
if pair_id and pair_id not in pair_ids:
|
|
pair_ids.append(pair_id)
|
|
except OSError:
|
|
pair_ids = []
|
|
|
|
unwrapped_paths: list[Path] = []
|
|
for pair_id in pair_ids:
|
|
path = diff_dir / f"{pair_id}.adf.unw.bmp"
|
|
if path.is_file():
|
|
unwrapped_paths.append(path)
|
|
if not unwrapped_paths and diff_dir.is_dir():
|
|
unwrapped_paths = sorted(diff_dir.glob("*.adf.unw.bmp"))
|
|
|
|
if len(unwrapped_paths) > 3:
|
|
last_index = len(unwrapped_paths) - 1
|
|
indexes = sorted({round(index * last_index / 2) for index in range(3)})
|
|
unwrapped_paths = [unwrapped_paths[index] for index in indexes]
|
|
|
|
for path in unwrapped_paths:
|
|
rows.append(
|
|
self._asset_row(
|
|
run_dir,
|
|
role="gamma_qc_unwrapped_phase",
|
|
name=f"Gamma representative filtered unwrapped phase {path.name}",
|
|
relative_path=str(path.relative_to(run_dir)).replace("\\", "/"),
|
|
is_required=False,
|
|
is_primary=False,
|
|
)
|
|
)
|
|
return rows
|
|
|
|
def _build_product(self, manifest_path: str) -> ResultProductORM:
|
|
manifest_file = Path(manifest_path)
|
|
run_dir = manifest_file.parent
|
|
manifest = _read_json(manifest_file)
|
|
if not self._is_publish_ready(run_dir, manifest):
|
|
raise ValueError(f"run is not publish-ready: {manifest.get('status') or 'UNKNOWN'}")
|
|
if self._is_landsar_manifest(manifest):
|
|
return self._build_landsar_product(run_dir, manifest_file, manifest)
|
|
|
|
detail = sbas_insar_production_service.get_run_detail(run_dir.name)
|
|
coverage = detail.get("geographic_coverage") or {}
|
|
stack_manifest = _safe_read_json(run_dir / "stack_manifest.json")
|
|
monitor_summary = _safe_read_json(run_dir / "monitor_points_summary.json")
|
|
workflow_summary = _safe_read_json(run_dir / "workflow_summary.json")
|
|
is_expert_gamma = self._is_expert_gamma_manifest(manifest, run_dir)
|
|
product_summary = {} if is_expert_gamma else _safe_read_json(run_dir / "product_summary.json")
|
|
quality_summary = (
|
|
_build_expert_gamma_quality_summary(run_dir)
|
|
if is_expert_gamma
|
|
else _safe_read_json(run_dir / "quality_summary.json")
|
|
)
|
|
point_vector_summary = _safe_read_json(run_dir / "publish" / "vectors" / "los_rate_points_summary.json")
|
|
asset_definitions = self._asset_definitions_for_manifest(manifest, run_dir)
|
|
if is_expert_gamma:
|
|
_build_rgb_geotiff_preview(
|
|
run_dir / "publish" / "geotiff" / "geo_los_def_rate_rgb.tif",
|
|
run_dir / "publish" / "geotiff" / "geo_los_def_rate_rgb_preview.png",
|
|
)
|
|
_build_gamma_hls_rate_preview(
|
|
run_dir / "publish" / "geotiff" / "geo_los_def_rate.tif",
|
|
run_dir / "publish" / "geotiff" / "geo_los_def_rate_pure_hls_preview.png",
|
|
coverage_source=run_dir / "publish" / "geotiff" / "geo_los_def_rate_rgb.tif",
|
|
min_native=-0.08,
|
|
max_native=0.08,
|
|
)
|
|
_build_gamma_hls_colorbar(
|
|
run_dir / "publish" / "geotiff" / "geo_los_def_rate_hls_colorbar.png",
|
|
min_mm_year=-80.0,
|
|
max_mm_year=80.0,
|
|
)
|
|
point_vector_context = {
|
|
"run_id": str(manifest.get("run_id") or run_dir.name).strip() or run_dir.name,
|
|
"reference_date": str(
|
|
manifest.get("reference_date")
|
|
or (stack_manifest.get("stack") or {}).get("reference_date")
|
|
or ""
|
|
).strip() or None,
|
|
"stack_dates": _stack_dates_from_manifest(stack_manifest, manifest, stack_manifest.get("stack") or {}),
|
|
"los_sign_convention": "Gamma expert geo_los_def_rate output; sign and unit semantics follow the expert workflow.",
|
|
"admin_region": coverage.get("admin_region"),
|
|
}
|
|
point_vector_context["date_start"] = (
|
|
point_vector_context["stack_dates"][0] if point_vector_context["stack_dates"] else None
|
|
)
|
|
point_vector_context["date_end"] = (
|
|
point_vector_context["stack_dates"][-1] if point_vector_context["stack_dates"] else None
|
|
)
|
|
point_vector_summary = _build_expert_gamma_point_vector(
|
|
run_dir,
|
|
summary_context=point_vector_context,
|
|
)
|
|
unwrapped_phase_summary = _build_expert_unwrapped_phase_derivatives(run_dir)
|
|
monitor_summary = _write_expert_monitor_point_derivatives(run_dir) or monitor_summary
|
|
else:
|
|
unwrapped_phase_summary = {}
|
|
|
|
bbox = coverage.get("bbox") or {}
|
|
min_lon = _safe_float(bbox.get("min_lon"))
|
|
min_lat = _safe_float(bbox.get("min_lat"))
|
|
max_lon = _safe_float(bbox.get("max_lon"))
|
|
max_lat = _safe_float(bbox.get("max_lat"))
|
|
poly = _bbox_polygon(min_lon, min_lat, max_lon, max_lat)
|
|
|
|
run_id = str(manifest.get("run_id") or run_dir.name).strip() or run_dir.name
|
|
stack = stack_manifest.get("stack") or manifest.get("stack") or {}
|
|
stack_id = str(manifest.get("stack_id") or stack_manifest.get("stack_id") or stack.get("stack_id") or "").strip()
|
|
stack_dates = _stack_dates_from_manifest(stack_manifest, manifest, stack)
|
|
reference_date = str(
|
|
manifest.get("reference_date")
|
|
or stack.get("reference_date")
|
|
or (manifest.get("coregistration") or {}).get("reference_date")
|
|
or ""
|
|
).strip() or None
|
|
display_name = stack_id or f"Gamma SBAS {run_id}"
|
|
product_id = str(manifest.get("product_id") or "").strip() or f"gamma_sbas_{run_id}"
|
|
if len(product_id) > 64:
|
|
product_id = f"gamma_sbas_{_stable_digest(product_id, run_dir, length=32)}"
|
|
|
|
assets: list[ResultAssetORM] = [
|
|
self._asset_row(
|
|
run_dir,
|
|
role=role,
|
|
name=name,
|
|
relative_path=relative_path,
|
|
is_required=is_required,
|
|
is_primary=is_primary,
|
|
)
|
|
for role, name, relative_path, is_required, is_primary in asset_definitions
|
|
]
|
|
assets.extend(self._monitor_asset_rows(run_dir))
|
|
if is_expert_gamma:
|
|
assets.extend(self._unwrapped_phase_asset_rows(run_dir))
|
|
assets.extend(self._gamma_intermediate_qc_asset_rows(run_dir))
|
|
preview_asset = next((asset for asset in assets if asset.asset_role == "primary_geocoded_preview" and asset.exists_flag), None)
|
|
primary_asset = next((asset for asset in assets if asset.asset_role == "primary_geotiff" and asset.exists_flag), None)
|
|
missing_required = [asset for asset in assets if asset.is_required and not asset.exists_flag]
|
|
default_los_product = product_summary.get("default_los_product")
|
|
los_sign_convention = product_summary.get("los_sign_convention")
|
|
if is_expert_gamma:
|
|
default_los_product = default_los_product or "geo_los_def_rate"
|
|
los_sign_convention = (
|
|
los_sign_convention
|
|
or "Gamma expert geo_los_def_rate output; sign and unit semantics follow the expert workflow."
|
|
)
|
|
color_policy = {
|
|
"schema": "insar.gamma-sbas-color-policy/v1",
|
|
"source": "expert_gamma_command",
|
|
"browse_command": "rasdt_pwr los_def_rate ... -0.08 0.08 0 hls.cm ... 24",
|
|
"colormap": "Gamma hls.cm",
|
|
"data_range_native": [-0.08, 0.08],
|
|
"display_range_mm_per_year": [-80.0, 80.0],
|
|
"note": "The RGB browse GeoTIFF is generated by Gamma with hls.cm. Treat it as the expert browse standard unless the project defines a separate cartographic standard.",
|
|
}
|
|
else:
|
|
default_los_product = default_los_product or "los_rate_toward_m_per_year"
|
|
los_sign_convention = los_sign_convention or "toward radar positive; away from radar negative"
|
|
color_policy = product_summary.get("color_policy")
|
|
|
|
produced_at = (
|
|
_parse_datetime(monitor_summary.get("generated_at"))
|
|
or _parse_datetime(product_summary.get("generated_at"))
|
|
or _parse_datetime(workflow_summary.get("generated_at"))
|
|
or _parse_datetime(manifest.get("updated_at"))
|
|
or _parse_datetime(manifest.get("created_at"))
|
|
)
|
|
center = coverage.get("center") or {}
|
|
admin_region = coverage.get("admin_region") or lookup_admin_region_for_point(center.get("lon"), center.get("lat"))
|
|
scene_count = (
|
|
_safe_int(manifest.get("scene_count"))
|
|
or len(stack_manifest.get("scenes") or [])
|
|
or len(stack_dates)
|
|
)
|
|
|
|
summary_json = {
|
|
"schema": "insar.gamma-sbas-result-catalog-summary/v1",
|
|
"run_id": run_id,
|
|
"stack_id": stack_id or None,
|
|
"stack": stack,
|
|
"reference_date": reference_date,
|
|
"stack_dates": stack_dates,
|
|
"stack_size": len(stack_dates),
|
|
"date_start": stack_dates[0] if stack_dates else None,
|
|
"date_end": stack_dates[-1] if stack_dates else None,
|
|
"scene_count": scene_count,
|
|
"pair_count": _safe_int(manifest.get("pair_count")),
|
|
"status": manifest.get("status"),
|
|
"next_stage": manifest.get("next_stage"),
|
|
"los_sign_convention": los_sign_convention,
|
|
"default_los_product": default_los_product,
|
|
"color_policy": color_policy,
|
|
"center": center or None,
|
|
"admin_region": admin_region,
|
|
"geographic_coverage": coverage,
|
|
"quality": quality_summary,
|
|
"monitor_points": monitor_summary,
|
|
"point_vector": point_vector_summary,
|
|
"unwrapped_phase": unwrapped_phase_summary,
|
|
"workflow": {
|
|
"status": ((manifest.get("workflow") or {}).get("status")),
|
|
"summary": ((manifest.get("workflow") or {}).get("summary")) or workflow_summary,
|
|
},
|
|
"source_run_dir": str(run_dir),
|
|
}
|
|
|
|
product = ResultProductORM(
|
|
product_id=product_id,
|
|
catalog_name=SBAS_INSAR_CATALOG_NAME,
|
|
product_family="timeseries",
|
|
product_type="sbas_insar",
|
|
display_name=display_name,
|
|
task_name="Gamma SBAS-InSAR",
|
|
task_alias=run_id,
|
|
stack_key=stack_id or run_id,
|
|
run_key=run_id,
|
|
profile_code=str(stack.get("relative_orbit") or manifest.get("relative_orbit") or "").strip() or None,
|
|
engine_code="gamma",
|
|
engine_version=str((manifest.get("engine") or {}).get("version") or "").strip() or None,
|
|
package_schema=str(manifest.get("schema") or "").strip() or "insar.gamma-sbas-run/v1",
|
|
package_layout="gamma_sbas_expert_workflow_run",
|
|
processor_code="gamma_ipta_sbas",
|
|
runtime_id=settings.GAMMA_SBAS_RUNTIME_ID,
|
|
status="READY" if not missing_required else "INCOMPLETE",
|
|
health_status="OK" if not missing_required else "WARN",
|
|
publish_dir=_normalize_path(run_dir / "publish"),
|
|
manifest_path=_normalize_path(manifest_file),
|
|
source_primary_path=primary_asset.absolute_path if primary_asset else None,
|
|
native_output_dir=_normalize_path(run_dir),
|
|
preview_path=preview_asset.absolute_path if preview_asset else None,
|
|
primary_asset_path=primary_asset.absolute_path if primary_asset else None,
|
|
summary_json=summary_json,
|
|
tags_json={
|
|
"sensor": stack.get("satellite") or manifest.get("platform"),
|
|
"orbit_direction": stack.get("orbit_direction") or manifest.get("direction"),
|
|
"product": "Gamma SBAS",
|
|
"workflow_mode": "expert_document" if is_expert_gamma else "legacy_gamma",
|
|
"admin_region": (admin_region or {}).get("display_name") if isinstance(admin_region, dict) else None,
|
|
},
|
|
min_lon=min_lon,
|
|
min_lat=min_lat,
|
|
max_lon=max_lon,
|
|
max_lat=max_lat,
|
|
geom=from_shape(poly, srid=4326) if poly is not None else None,
|
|
coverage_polygon=(coverage.get("geojson") or coverage.get("scene_footprints_geojson")),
|
|
produced_at=produced_at,
|
|
published_at=produced_at,
|
|
)
|
|
for asset in assets:
|
|
product.assets.append(asset)
|
|
if asset.is_required and not asset.exists_flag:
|
|
product.issues.append(
|
|
ResultIssueORM(
|
|
asset=asset,
|
|
issue_code="MISSING_REQUIRED_ASSET",
|
|
severity="ERROR",
|
|
status="OPEN",
|
|
scope="file",
|
|
message=f"Required SBAS asset is missing: {asset.relative_path}",
|
|
)
|
|
)
|
|
if not preview_asset:
|
|
product.issues.append(
|
|
ResultIssueORM(
|
|
issue_code="MISSING_PREVIEW",
|
|
severity="WARN",
|
|
status="OPEN",
|
|
scope="product",
|
|
message="Primary geocoded preview PNG is missing.",
|
|
)
|
|
)
|
|
if poly is None:
|
|
product.issues.append(
|
|
ResultIssueORM(
|
|
issue_code="MISSING_COVERAGE",
|
|
severity="WARN",
|
|
status="OPEN",
|
|
scope="product",
|
|
message="No valid EPSG:4326 geographic coverage bbox was found.",
|
|
)
|
|
)
|
|
return product
|
|
|
|
def _build_landsar_product(self, run_dir: Path, manifest_file: Path, manifest: dict[str, Any]) -> ResultProductORM:
|
|
try:
|
|
detail = landsar_sbas_service.get_run_detail(run_dir.name)
|
|
except Exception:
|
|
detail = {}
|
|
coverage = detail.get("geographic_coverage") or manifest.get("geographic_coverage") or {}
|
|
stack_manifest = _safe_read_json(run_dir / "stack_manifest.json")
|
|
product_summary = _safe_read_json(run_dir / "product_summary.json")
|
|
quality_summary = _safe_read_json(run_dir / "quality_summary.json")
|
|
workflow_summary = _safe_read_json(run_dir / "workflow_summary.json")
|
|
|
|
bbox = coverage.get("bbox") or {}
|
|
min_lon = _safe_float(bbox.get("min_lon"))
|
|
min_lat = _safe_float(bbox.get("min_lat"))
|
|
max_lon = _safe_float(bbox.get("max_lon"))
|
|
max_lat = _safe_float(bbox.get("max_lat"))
|
|
poly = _bbox_polygon(min_lon, min_lat, max_lon, max_lat)
|
|
|
|
run_id = str(manifest.get("run_id") or run_dir.name).strip() or run_dir.name
|
|
stack_id = str(manifest.get("stack_id") or run_id).strip()
|
|
stack_dates = _stack_dates_from_manifest(stack_manifest, manifest, {})
|
|
if not stack_dates:
|
|
stack_dates = [str(item or "").strip() for item in manifest.get("dates") or [] if str(item or "").strip()]
|
|
display_name = str(manifest.get("run_label") or f"LandSAR SBAS {run_id}").strip()
|
|
product_id = str(manifest.get("product_id") or "").strip() or f"landsar_sbas_{run_id}"
|
|
if len(product_id) > 64:
|
|
product_id = f"landsar_sbas_{_stable_digest(product_id, run_dir, length=32)}"
|
|
|
|
assets: list[ResultAssetORM] = [
|
|
self._asset_row(
|
|
run_dir,
|
|
role=role,
|
|
name=name,
|
|
relative_path=relative_path,
|
|
is_required=is_required,
|
|
is_primary=is_primary,
|
|
)
|
|
for role, name, relative_path, is_required, is_primary in _LANDSAR_CORE_ASSETS
|
|
if not (run_dir / relative_path).is_dir()
|
|
]
|
|
native_logs_dir = run_dir / "native_logs"
|
|
if native_logs_dir.is_dir():
|
|
for path in sorted(native_logs_dir.rglob("*")):
|
|
if not path.is_file():
|
|
continue
|
|
relative_path = str(path.relative_to(run_dir)).replace("\\", "/")
|
|
assets.append(
|
|
self._asset_row(
|
|
run_dir,
|
|
role="native_log" if path.suffix.lower() == ".log" else "native_parameter",
|
|
name=path.name,
|
|
relative_path=relative_path,
|
|
is_required=False,
|
|
is_primary=False,
|
|
)
|
|
)
|
|
task_publish_root = run_dir / "publish" / "landsar"
|
|
if task_publish_root.is_dir():
|
|
for path in sorted(task_publish_root.rglob("*")):
|
|
if not path.is_file():
|
|
continue
|
|
relative_path = str(path.relative_to(run_dir)).replace("\\", "/")
|
|
if relative_path in {item.relative_path for item in assets}:
|
|
continue
|
|
role = "landsar_task_geotiff" if path.suffix.lower() in {".tif", ".tiff"} else "landsar_task_asset"
|
|
assets.append(
|
|
self._asset_row(
|
|
run_dir,
|
|
role=role,
|
|
name=path.name,
|
|
relative_path=relative_path,
|
|
is_required=False,
|
|
is_primary=False,
|
|
)
|
|
)
|
|
|
|
preview_asset = next((asset for asset in assets if asset.asset_role == "primary_preview" and asset.exists_flag), None)
|
|
primary_asset = next((asset for asset in assets if asset.asset_role == "primary_geotiff" and asset.exists_flag), None)
|
|
missing_required = [asset for asset in assets if asset.is_required and not asset.exists_flag]
|
|
|
|
produced_at = (
|
|
_parse_datetime(workflow_summary.get("ended_at"))
|
|
or _parse_datetime(product_summary.get("generated_at"))
|
|
or _parse_datetime(manifest.get("ended_at"))
|
|
or _parse_datetime(manifest.get("created_at"))
|
|
)
|
|
center = coverage.get("center") or {}
|
|
admin_region = coverage.get("admin_region") or lookup_admin_region_for_point(center.get("lon"), center.get("lat"))
|
|
scene_count = _safe_int(manifest.get("scene_count")) or len(stack_dates)
|
|
task_count = _safe_int(manifest.get("task_count"))
|
|
|
|
summary_json = {
|
|
"schema": "insar.landsar-sbas-result-catalog-summary/v1",
|
|
"run_id": run_id,
|
|
"stack_id": stack_id or None,
|
|
"reference_date": stack_dates[0] if stack_dates else None,
|
|
"stack_dates": stack_dates,
|
|
"stack_size": len(stack_dates),
|
|
"date_start": manifest.get("date_start") or (stack_dates[0] if stack_dates else None),
|
|
"date_end": manifest.get("date_end") or (stack_dates[-1] if stack_dates else None),
|
|
"scene_count": scene_count,
|
|
"task_count": task_count,
|
|
"pair_count": _safe_int(manifest.get("pair_count")),
|
|
"status": manifest.get("status"),
|
|
"next_stage": manifest.get("next_stage"),
|
|
"los_sign_convention": product_summary.get("los_sign_convention") or "LandSAR LOS output; semantics pending algorithm confirmation.",
|
|
"default_los_product": product_summary.get("default_los_product") or "los_timeseries",
|
|
"center": center or None,
|
|
"admin_region": admin_region,
|
|
"geographic_coverage": coverage,
|
|
"quality": quality_summary,
|
|
"workflow": workflow_summary,
|
|
"source_run_dir": str(run_dir),
|
|
"output_semantics_note": product_summary.get("output_semantics_note"),
|
|
}
|
|
|
|
product = ResultProductORM(
|
|
product_id=product_id,
|
|
catalog_name=SBAS_INSAR_CATALOG_NAME,
|
|
product_family="timeseries",
|
|
product_type="sbas_insar",
|
|
display_name=display_name,
|
|
task_name="LandSAR SBAS-InSAR",
|
|
task_alias=run_id,
|
|
stack_key=stack_id or run_id,
|
|
run_key=run_id,
|
|
profile_code="lt1_landsar_sbas",
|
|
engine_code="landsar",
|
|
engine_version=None,
|
|
package_schema=str(manifest.get("schema") or "").strip() or "insar.landsar-sbas-run/v1",
|
|
package_layout="landsar_sbas_console_run",
|
|
processor_code="landsar_sbas",
|
|
runtime_id="landsar_console",
|
|
status="READY" if not missing_required else "INCOMPLETE",
|
|
health_status="OK" if not missing_required else "WARN",
|
|
publish_dir=_normalize_path(run_dir / "publish"),
|
|
manifest_path=_normalize_path(manifest_file),
|
|
source_primary_path=primary_asset.absolute_path if primary_asset else None,
|
|
native_output_dir=_normalize_path(run_dir),
|
|
preview_path=preview_asset.absolute_path if preview_asset else None,
|
|
primary_asset_path=primary_asset.absolute_path if primary_asset else None,
|
|
summary_json=summary_json,
|
|
tags_json={
|
|
"sensor": "LT1",
|
|
"product": "LandSAR SBAS",
|
|
"processor_code": "landsar_sbas",
|
|
"admin_region": (admin_region or {}).get("display_name") if isinstance(admin_region, dict) else None,
|
|
},
|
|
min_lon=min_lon,
|
|
min_lat=min_lat,
|
|
max_lon=max_lon,
|
|
max_lat=max_lat,
|
|
geom=from_shape(poly, srid=4326) if poly is not None else None,
|
|
coverage_polygon=(coverage.get("geojson") or coverage.get("scene_footprints_geojson")),
|
|
produced_at=produced_at,
|
|
published_at=produced_at,
|
|
)
|
|
for asset in assets:
|
|
product.assets.append(asset)
|
|
if asset.is_required and not asset.exists_flag:
|
|
product.issues.append(
|
|
ResultIssueORM(
|
|
asset=asset,
|
|
issue_code="MISSING_REQUIRED_ASSET",
|
|
severity="ERROR",
|
|
status="OPEN",
|
|
scope="file",
|
|
message=f"Required LandSAR SBAS asset is missing: {asset.relative_path}",
|
|
)
|
|
)
|
|
if not preview_asset:
|
|
product.issues.append(
|
|
ResultIssueORM(
|
|
issue_code="MISSING_PREVIEW",
|
|
severity="WARN",
|
|
status="OPEN",
|
|
scope="product",
|
|
message="LandSAR SBAS preview PNG is missing.",
|
|
)
|
|
)
|
|
if poly is None:
|
|
product.issues.append(
|
|
ResultIssueORM(
|
|
issue_code="MISSING_COVERAGE",
|
|
severity="WARN",
|
|
status="OPEN",
|
|
scope="product",
|
|
message="No valid EPSG:4326 geographic coverage bbox was found.",
|
|
)
|
|
)
|
|
return product
|
|
|
|
async def rebuild_catalog(self, db: AsyncSession, *, full_rebuild: bool = True) -> dict[str, Any]:
|
|
run_root = self.get_run_root()
|
|
run_roots = self.get_run_roots()
|
|
manifest_paths = await asyncio.to_thread(self._iter_run_manifest_paths)
|
|
fingerprint = await asyncio.to_thread(self._tree_fingerprint, manifest_paths)
|
|
state = await self._get_or_create_catalog_state(db, storage_root=run_root)
|
|
state.status = "REBUILDING"
|
|
state.needs_rebuild = False
|
|
state.last_message = "SBAS catalog rebuild in progress"
|
|
await db.commit()
|
|
|
|
if full_rebuild:
|
|
await db.execute(delete(ResultProductORM).where(ResultProductORM.catalog_name == SBAS_INSAR_CATALOG_NAME))
|
|
await db.commit()
|
|
|
|
registered = 0
|
|
failed = 0
|
|
issue_count = 0
|
|
details: list[dict[str, Any]] = []
|
|
for manifest_path in manifest_paths:
|
|
try:
|
|
product = await asyncio.to_thread(self._build_product, manifest_path)
|
|
product_issue_count = len(product.issues)
|
|
product_id = product.product_id
|
|
product_status = product.status
|
|
db.add(product)
|
|
await db.flush()
|
|
await db.commit()
|
|
registered += 1
|
|
issue_count += product_issue_count
|
|
details.append(
|
|
{
|
|
"manifest_path": manifest_path,
|
|
"product_id": product_id,
|
|
"status": product_status,
|
|
"issues": product_issue_count,
|
|
}
|
|
)
|
|
except Exception as exc:
|
|
await db.rollback()
|
|
failed += 1
|
|
issue_count += 1
|
|
details.append({"manifest_path": manifest_path, "status": "error", "message": str(exc)})
|
|
|
|
await db.commit()
|
|
db_count_result = await db.execute(
|
|
select(func.count(ResultProductORM.id)).where(ResultProductORM.catalog_name == SBAS_INSAR_CATALOG_NAME)
|
|
)
|
|
db_count = int(db_count_result.scalar_one() or 0)
|
|
fingerprint = await asyncio.to_thread(self._tree_fingerprint, manifest_paths)
|
|
state = await self._get_or_create_catalog_state(db, storage_root=run_root)
|
|
state.manifest_count = len(manifest_paths)
|
|
state.manifest_fingerprint = fingerprint
|
|
state.db_count = db_count
|
|
state.issue_count = issue_count
|
|
state.needs_rebuild = False
|
|
state.status = "READY" if failed == 0 else "WARN"
|
|
now = _utcnow()
|
|
state.last_full_rebuild_at = now
|
|
state.last_incremental_scan_at = now
|
|
state.last_message = (
|
|
f"SBAS catalog rebuild finished: runs={len(manifest_paths)}, "
|
|
f"registered={registered}, failed={failed}, issues={issue_count}"
|
|
)
|
|
await db.commit()
|
|
return {
|
|
"catalog_name": SBAS_INSAR_CATALOG_NAME,
|
|
"storage_root": run_root,
|
|
"storage_roots": run_roots,
|
|
"run_count": len(manifest_paths),
|
|
"manifest_count": len(manifest_paths),
|
|
"manifest_fingerprint": fingerprint,
|
|
"registered": registered,
|
|
"failed": failed,
|
|
"issue_count": issue_count,
|
|
"details": details,
|
|
}
|
|
|
|
async def list_products(
|
|
self,
|
|
db: AsyncSession,
|
|
*,
|
|
limit: int = 100,
|
|
offset: int = 0,
|
|
status: Optional[str] = None,
|
|
query: Optional[str] = None,
|
|
admin_region: Optional[str] = None,
|
|
) -> dict[str, Any]:
|
|
safe_limit = max(1, min(int(limit or 100), 500))
|
|
safe_offset = max(0, int(offset or 0))
|
|
stmt = select(ResultProductORM).where(ResultProductORM.catalog_name == SBAS_INSAR_CATALOG_NAME)
|
|
count_stmt = select(func.count(ResultProductORM.id)).where(ResultProductORM.catalog_name == SBAS_INSAR_CATALOG_NAME)
|
|
if status:
|
|
stmt = stmt.where(ResultProductORM.status == status)
|
|
count_stmt = count_stmt.where(ResultProductORM.status == status)
|
|
if query:
|
|
like_value = f"%{query.strip()}%"
|
|
predicate = or_(
|
|
ResultProductORM.display_name.ilike(like_value),
|
|
ResultProductORM.product_id.ilike(like_value),
|
|
ResultProductORM.run_key.ilike(like_value),
|
|
ResultProductORM.stack_key.ilike(like_value),
|
|
)
|
|
stmt = stmt.where(predicate)
|
|
count_stmt = count_stmt.where(predicate)
|
|
if admin_region:
|
|
like_value = f"%{admin_region.strip()}%"
|
|
predicate = or_(
|
|
cast(ResultProductORM.summary_json, String).ilike(like_value),
|
|
cast(ResultProductORM.tags_json, String).ilike(like_value),
|
|
)
|
|
stmt = stmt.where(predicate)
|
|
count_stmt = count_stmt.where(predicate)
|
|
total_result = await db.execute(count_stmt)
|
|
total = int(total_result.scalar_one() or 0)
|
|
result = await db.execute(
|
|
stmt.order_by(ResultProductORM.published_at.desc().nullslast(), ResultProductORM.id.desc())
|
|
.offset(safe_offset)
|
|
.limit(safe_limit)
|
|
)
|
|
items: list[dict[str, Any]] = []
|
|
for product in result.scalars().all():
|
|
summary = product.summary_json or {}
|
|
items.append(
|
|
{
|
|
"id": product.id,
|
|
"product_id": product.product_id,
|
|
"display_name": product.display_name,
|
|
"run_key": product.run_key,
|
|
"stack_key": product.stack_key,
|
|
"engine_code": product.engine_code,
|
|
"processor_code": product.processor_code,
|
|
"runtime_id": product.runtime_id,
|
|
"status": product.status,
|
|
"health_status": product.health_status,
|
|
"preview_path": product.preview_path,
|
|
"primary_asset_path": product.primary_asset_path,
|
|
"reference_date": summary.get("reference_date"),
|
|
"date_start": summary.get("date_start"),
|
|
"date_end": summary.get("date_end"),
|
|
"stack_dates": summary.get("stack_dates") or [],
|
|
"stack_size": summary.get("stack_size") or len(summary.get("stack_dates") or []),
|
|
"scene_count": summary.get("scene_count"),
|
|
"pair_count": summary.get("pair_count"),
|
|
"los_sign_convention": summary.get("los_sign_convention"),
|
|
"color_policy": summary.get("color_policy"),
|
|
"center": summary.get("center") or ((summary.get("geographic_coverage") or {}).get("center")),
|
|
"admin_region": summary.get("admin_region") or ((summary.get("geographic_coverage") or {}).get("admin_region")),
|
|
"min_lon": product.min_lon,
|
|
"min_lat": product.min_lat,
|
|
"max_lon": product.max_lon,
|
|
"max_lat": product.max_lat,
|
|
"published_at": product.published_at,
|
|
}
|
|
)
|
|
return {
|
|
"items": items,
|
|
"total": total,
|
|
"limit": safe_limit,
|
|
"offset": safe_offset,
|
|
"has_more": safe_offset + len(items) < total,
|
|
}
|
|
|
|
async def get_product_detail(self, db: AsyncSession, *, product_db_id: int) -> Optional[dict[str, Any]]:
|
|
result = await db.execute(select(ResultProductORM).where(ResultProductORM.id == product_db_id))
|
|
product = result.scalar_one_or_none()
|
|
if product is None or product.catalog_name != SBAS_INSAR_CATALOG_NAME:
|
|
return None
|
|
assets_result = await db.execute(
|
|
select(ResultAssetORM)
|
|
.where(ResultAssetORM.product_ref_id == product.id)
|
|
.order_by(ResultAssetORM.is_primary.desc(), ResultAssetORM.asset_role.asc(), ResultAssetORM.id.asc())
|
|
)
|
|
issues_result = await db.execute(
|
|
select(ResultIssueORM)
|
|
.where(ResultIssueORM.product_ref_id == product.id)
|
|
.order_by(ResultIssueORM.severity.asc(), ResultIssueORM.id.asc())
|
|
)
|
|
summary = product.summary_json or {}
|
|
return {
|
|
"id": product.id,
|
|
"product_id": product.product_id,
|
|
"catalog_name": product.catalog_name,
|
|
"product_type": product.product_type,
|
|
"display_name": product.display_name,
|
|
"run_key": product.run_key,
|
|
"run_id": summary.get("run_id") or product.run_key,
|
|
"stack_key": product.stack_key,
|
|
"profile_code": product.profile_code,
|
|
"engine_code": product.engine_code,
|
|
"engine_version": product.engine_version,
|
|
"package_schema": product.package_schema,
|
|
"package_layout": product.package_layout,
|
|
"processor_code": product.processor_code,
|
|
"runtime_id": product.runtime_id,
|
|
"status": product.status,
|
|
"health_status": product.health_status,
|
|
"publish_dir": product.publish_dir,
|
|
"manifest_path": product.manifest_path,
|
|
"source_primary_path": product.source_primary_path,
|
|
"native_output_dir": product.native_output_dir,
|
|
"preview_path": product.preview_path,
|
|
"primary_asset_path": product.primary_asset_path,
|
|
"reference_date": summary.get("reference_date"),
|
|
"date_start": summary.get("date_start"),
|
|
"date_end": summary.get("date_end"),
|
|
"stack_dates": summary.get("stack_dates") or [],
|
|
"stack_size": summary.get("stack_size") or len(summary.get("stack_dates") or []),
|
|
"scene_count": summary.get("scene_count"),
|
|
"pair_count": summary.get("pair_count"),
|
|
"los_sign_convention": summary.get("los_sign_convention"),
|
|
"default_los_product": summary.get("default_los_product"),
|
|
"color_policy": summary.get("color_policy"),
|
|
"quality": summary.get("quality"),
|
|
"monitor_points": summary.get("monitor_points"),
|
|
"point_vector": summary.get("point_vector"),
|
|
"unwrapped_phase": summary.get("unwrapped_phase"),
|
|
"workflow": summary.get("workflow"),
|
|
"geographic_coverage": summary.get("geographic_coverage"),
|
|
"center": summary.get("center") or ((summary.get("geographic_coverage") or {}).get("center")),
|
|
"admin_region": summary.get("admin_region") or ((summary.get("geographic_coverage") or {}).get("admin_region")),
|
|
"coverage_polygon": product.coverage_polygon,
|
|
"min_lon": product.min_lon,
|
|
"min_lat": product.min_lat,
|
|
"max_lon": product.max_lon,
|
|
"max_lat": product.max_lat,
|
|
"produced_at": product.produced_at,
|
|
"published_at": product.published_at,
|
|
"registered_at": product.registered_at,
|
|
"updated_at": product.updated_at,
|
|
"assets": [
|
|
{
|
|
"id": asset.id,
|
|
"asset_role": asset.asset_role,
|
|
"asset_name": asset.asset_name,
|
|
"relative_path": asset.relative_path,
|
|
"absolute_path": asset.absolute_path,
|
|
"format": asset.format,
|
|
"media_type": asset.media_type,
|
|
"is_required": asset.is_required,
|
|
"is_primary": asset.is_primary,
|
|
"exists_flag": asset.exists_flag,
|
|
"file_size": asset.file_size,
|
|
"srid": asset.srid,
|
|
}
|
|
for asset in assets_result.scalars().all()
|
|
],
|
|
"issues": [
|
|
{
|
|
"id": issue.id,
|
|
"issue_code": issue.issue_code,
|
|
"severity": issue.severity,
|
|
"status": issue.status,
|
|
"scope": issue.scope,
|
|
"message": issue.message,
|
|
"detected_at": issue.detected_at,
|
|
}
|
|
for issue in issues_result.scalars().all()
|
|
],
|
|
}
|
|
|
|
async def query_point_timeseries(
|
|
self,
|
|
db: AsyncSession,
|
|
*,
|
|
product_db_id: int,
|
|
lon: float,
|
|
lat: float,
|
|
) -> Optional[dict[str, Any]]:
|
|
result = await db.execute(select(ResultProductORM).where(ResultProductORM.id == product_db_id))
|
|
product = result.scalar_one_or_none()
|
|
if product is None or product.catalog_name != SBAS_INSAR_CATALOG_NAME:
|
|
return None
|
|
manifest_path = str(product.manifest_path or "").strip()
|
|
run_dir = Path(manifest_path).parent if manifest_path else Path(str(product.native_output_dir or ""))
|
|
if not run_dir.is_dir():
|
|
raise FileNotFoundError("SBAS run directory not found")
|
|
manifest = _safe_read_json(run_dir / "run_manifest.json")
|
|
if not self._is_expert_gamma_manifest(manifest, run_dir):
|
|
raise ValueError("point time-series query is only available for expert Gamma SBAS products")
|
|
return _query_expert_gamma_point_timeseries(run_dir, lon=lon, lat=lat)
|
|
|
|
async def get_asset(self, db: AsyncSession, *, product_db_id: int, asset_id: int) -> Optional[ResultAssetORM]:
|
|
result = await db.execute(
|
|
select(ResultAssetORM)
|
|
.join(ResultProductORM, ResultProductORM.id == ResultAssetORM.product_ref_id)
|
|
.where(
|
|
ResultProductORM.id == product_db_id,
|
|
ResultProductORM.catalog_name == SBAS_INSAR_CATALOG_NAME,
|
|
ResultAssetORM.id == asset_id,
|
|
)
|
|
)
|
|
return result.scalar_one_or_none()
|
|
|
|
async def get_catalog_status(self, db: AsyncSession) -> dict[str, Any]:
|
|
run_root = self.get_run_root()
|
|
run_roots = self.get_run_roots()
|
|
manifest_paths = await asyncio.to_thread(self._iter_run_manifest_paths)
|
|
fingerprint = await asyncio.to_thread(self._tree_fingerprint, manifest_paths)
|
|
state = await self._get_or_create_catalog_state(db, storage_root=run_root)
|
|
db_count_result = await db.execute(
|
|
select(func.count(ResultProductORM.id)).where(ResultProductORM.catalog_name == SBAS_INSAR_CATALOG_NAME)
|
|
)
|
|
db_count = int(db_count_result.scalar_one() or 0)
|
|
needs_rebuild = (
|
|
state.manifest_count != len(manifest_paths)
|
|
or state.db_count != db_count
|
|
or state.manifest_fingerprint != fingerprint
|
|
)
|
|
state.manifest_count = len(manifest_paths)
|
|
state.db_count = db_count
|
|
state.needs_rebuild = needs_rebuild
|
|
state.last_incremental_scan_at = _utcnow()
|
|
state.status = "WARN" if needs_rebuild else "READY"
|
|
state.last_message = (
|
|
f"SBAS catalog rebuild required: runs={len(manifest_paths)}, db={db_count}"
|
|
if needs_rebuild
|
|
else "SBAS catalog is in sync"
|
|
)
|
|
await db.commit()
|
|
return {
|
|
"catalog_name": state.catalog_name,
|
|
"product_family": state.product_family,
|
|
"storage_root": state.storage_root,
|
|
"storage_roots": run_roots,
|
|
"status": state.status,
|
|
"needs_rebuild": state.needs_rebuild,
|
|
"run_count": len(manifest_paths),
|
|
"manifest_count": state.manifest_count,
|
|
"manifest_fingerprint": state.manifest_fingerprint,
|
|
"current_manifest_fingerprint": fingerprint,
|
|
"db_count": db_count,
|
|
"issue_count": state.issue_count,
|
|
"last_message": state.last_message,
|
|
"last_boot_check_at": state.last_boot_check_at,
|
|
"last_full_rebuild_at": state.last_full_rebuild_at,
|
|
"last_incremental_scan_at": state.last_incremental_scan_at,
|
|
}
|
|
|
|
async def bootstrap_catalog_on_startup_clean(self) -> dict[str, Any]:
|
|
from ..database import AsyncSessionLocal
|
|
|
|
if AsyncSessionLocal is None:
|
|
raise RuntimeError("Database session factory is not initialized.")
|
|
async with AsyncSessionLocal() as db:
|
|
run_root = self.get_run_root()
|
|
run_roots = self.get_run_roots()
|
|
manifest_paths = await asyncio.to_thread(self._iter_run_manifest_paths)
|
|
fingerprint = await asyncio.to_thread(self._tree_fingerprint, manifest_paths)
|
|
state = await self._get_or_create_catalog_state(db, storage_root=run_root)
|
|
db_count_result = await db.execute(
|
|
select(func.count(ResultProductORM.id)).where(ResultProductORM.catalog_name == SBAS_INSAR_CATALOG_NAME)
|
|
)
|
|
db_count = int(db_count_result.scalar_one() or 0)
|
|
needs_rebuild = (
|
|
state.manifest_count != len(manifest_paths)
|
|
or state.db_count != db_count
|
|
or state.manifest_fingerprint != fingerprint
|
|
)
|
|
state.last_boot_check_at = _utcnow()
|
|
await db.commit()
|
|
|
|
rebuilt = False
|
|
result: dict[str, Any] = {}
|
|
if needs_rebuild and settings.RESULT_CATALOG_AUTO_REBUILD_ON_STARTUP:
|
|
result = await self.rebuild_catalog(db, full_rebuild=True)
|
|
rebuilt = True
|
|
db_count = int(result.get("registered") or db_count)
|
|
else:
|
|
state.manifest_count = len(manifest_paths)
|
|
state.db_count = db_count
|
|
state.manifest_fingerprint = fingerprint if not needs_rebuild else state.manifest_fingerprint
|
|
state.needs_rebuild = needs_rebuild
|
|
state.status = "WARN" if needs_rebuild else "READY"
|
|
state.last_message = "SBAS boot check complete"
|
|
await db.commit()
|
|
|
|
return {
|
|
"storage_root": run_root,
|
|
"storage_roots": run_roots,
|
|
"manifest_count": len(manifest_paths),
|
|
"current_manifest_fingerprint": fingerprint,
|
|
"indexed_manifest_fingerprint": state.manifest_fingerprint,
|
|
"db_count": db_count,
|
|
"needs_rebuild": needs_rebuild and not rebuilt,
|
|
"rebuilt": rebuilt,
|
|
"queued": False,
|
|
"registered": result.get("registered"),
|
|
"failed": result.get("failed"),
|
|
}
|
|
|
|
|
|
sbas_insar_catalog_service = SbasInsarCatalogService()
|