Add support for TrebleDAS / Terra15 Interrogators

docs/user-guide/data-formats.md

@@ -20,14 +20,15 @@ os.chdir("../_data")
 
 ## Implemented file formats
 
-The formats that are currently implemented are: ASN, FEBUS, OPTASENSE and SINTELA. To read them you have to specifiy which one you want in the `engine` argument in {py:func}`xdas.open_dataarray` for a single file or {py:func}`xdas.open_mfdataarray` for multiple files:
+The formats that are currently implemented are: ASN, FEBUS, OPTASENSE, SINTELA and TERRA15. To read them you have to specifiy which one you want in the `engine` argument in {py:func}`xdas.open_dataarray` for a single file or {py:func}`xdas.open_mfdataarray` for multiple files:
 
 | DAS constructor   | `engine` argument |
 |:-----------------:|:-----------------:|
 | ASN               | `"asn"`           |
 | FEBUS             | `"febus"`         |
 | OPTASENSE         | `"optasense"`     |
 | SINTELA           | `"sintela"`       |
+| TERRA15           | `"terra15"`       |
 
 ## Extending *xdas* with your file format
 

xdas/io/__init__.py

@@ -1,2 +1,2 @@
-from . import asn, febus, optasense, sintela
+from . import asn, febus, optasense, sintela, terra15
 from .core import get_free_port

xdas/io/terra15.py

@@ -0,0 +1,24 @@
+from datetime import datetime, timezone
+
+import h5py
+import numpy as np
+
+from ..core.dataarray import DataArray
+from ..virtual import VirtualSource
+
+
+def read(fname, tz=timezone.utc):
+    with h5py.File(fname, "r") as file:
+        ti = np.datetime64(
+            datetime.fromtimestamp(file["data_product"]["gps_time"][0], tz=tz)
+        ).astype("datetime64[ms]")
+        tf = np.datetime64(
+            datetime.fromtimestamp(file["data_product"]["gps_time"][-1], tz=tz)
+        ).astype("datetime64[ms]")
+        d0 = file.attrs["sensing_range_start"]
+        dx = file.attrs["dx"]
+        data = VirtualSource(file["data_product"]["data"])
+    nt, nd = data.shape
+    t = {"tie_indices": [0, nt - 1], "tie_values": [ti, tf]}
+    d = {"tie_indices": [0, nd - 1], "tie_values": [d0, d0 + (nd - 1) * dx]}
+    return DataArray(data, {"time": t, "distance": d})