Output files and array layouts

Directory structure

path_output/
├── grn_s/                     # Static library, models and metadata
├── grn_d/
│   ├── qseis/                 # Layered dynamic library, or
│   ├── qssp/                  # Spherical dynamic library
│   └── results_each/          # Dynamic per-point arrays and job/cache metadata
└── results/
    ├── static/                # Static tensors and projected CSV files
    └── dynamic/               # Assembled dynamic CSV files

Configuration parsing creates library/result roots. Libraries contain their own backend-specific files and green_lib_info.json; retain these together.

Filename patterns

Let n be an observation plane ID and z a depth formatted to two decimals.

Quantity/mode	Fault result	Fixed-depth result
Static tensor, all modes	stress_tensor_plane<n>.npy	stress_tensor_dep_<z>.npy
Static CFS, mode 0	cfs_static_plane<n>.csv	cfs_static_dep_<z>.csv
Static CFS, mode 1	cfs_os_static_plane<n>.csv	cfs_os_static_dep_<z>.csv
Static CFS, mode 2	cfs_oop_static_plane<n>.csv	cfs_oop_static_dep_<z>.csv
Dynamic CFS, mode 0	cfs_dynamic_plane<n>.csv	cfs_dynamic_dep_<z>.csv
Dynamic CFS, mode 1	cfs_os_dynamic_plane<n>.csv	cfs_os_dynamic_dep_<z>.csv
Dynamic CFS, mode 2	cfs_oop_dynamic_plane<n>.csv	cfs_oop_dynamic_dep_<z>.csv

Other keys are normal_vector, rupture_vector, normal_stress and shear_stress; optimal-rake mode adds rake. OOP outputs use numbered quantities such as normal_vector1 and normal_vector2. Dynamic tensor CSVs use the key stress_ned and include the mode marker, e.g. stress_ned_os_dynamic_plane1.csv.

Depth filenames have only two decimal places, so nearby depths can produce the same name. Different receiver modes retain separate projected filenames, but static tensor files are shared.

Shapes

For N receivers and T time samples:

Data	Shape
Static tensor NPY	(N, 6)
Static scalar CSV	(N, 1)
Static vector CSV	(N, 3)
Dynamic per-point tensor NPY	(T, 6)
Dynamic per-point vector NPY	(T, 3)
Dynamic per-point scalar NPY	(T,)
Dynamic scalar CSV	(N, T)
Dynamic vector CSV	(3*N, T)
Dynamic tensor CSV	(6*N, T)

All CSVs omit headers and index columns. Dynamic vector/tensor rows are receiver-major: all components of receiver 0, then receiver 1, etc. Tensor order is [NN, NE, ND, EE, ED, DD]. Vectors use NED. Stress is in Pa, orientation angles in degrees, unit vectors dimensionless.

from pathlib import Path
import numpy as np

result = Path("my-output/results/dynamic")
raw = np.loadtxt(result / "stress_ned_dynamic_plane1.csv", delimiter=",", ndmin=2)
n_receivers = raw.shape[0] // 6
stress = raw.reshape(n_receivers, 6, raw.shape[1]).transpose(0, 2, 1)
# stress.shape == (n_receivers, time_samples, 6)

Geographic grid order

Let n_lat = cal_grid_num(obs_lat_range, obs_delta_lat) and likewise for longitude. The flattened index is i_lat * n_lon + i_lon. Reshape a scalar snapshot to (n_lat, n_lon).

Preserve the INI, input CSVs and row order with your outputs; result CSVs do not carry receiver coordinates or a time column. Parallel depth preparation additionally saves obs_plane_<z>.npy under grn_d/results_each/.

Per-point cache names

Dynamic filenames begin with latitude, longitude and depth, each to four decimals, separated by underscores. The tensor cache uses *_stress_ned.npy and *_stress_ned.json. See reuse rules before treating existing files as complete.