Simple Inventory

This recipe demonstrates how the DASDAE inventory can be used with a simple deployment. In this scenario, the interrogator is connected to a telemetry cable (with unknown geometry/coupling) that then connects to a trenched cable through two E2000 connectors housed in an E2000 coupler. The sensing fiber runs 300 m east, turns north for 120 m, then leaves the trench and continues 300 m east on the surface (Figure 1).

Figure 1: Map of a simple fiber deployment with trenched and loose coupling sections

This page is an illustrative inventory example for the proposal. The exact Python API names may shift as the DASDAE inventory implementation evolves.

Field Notes

The tap test defines four points along the sensing fiber. Coordinates are WGS84 latitude, longitude, and elevation with values centered in the gardens of Versailles (just for fun).

Table 1: Tap-test points used to define deployment geometry.

Point	Description	Optical Distance (m)	Latitude	Longitude	Elevation (m)
A	Start of sensing fiber after E2000 coupler	50	48.807461	2.103905	115
B	90-degree bend in trenched section	350	48.807461	2.108000	115
C	Cable exits trench and becomes loose	470	48.808539	2.108000	115
D	End of loose surface-laid section	770	48.808539	2.112095	115

The deployment uses a telemetry (lead-in) cable to connect to a separate sensing cable.

Table 2: Fiber and cable types used in the simple deployment.

Cable	Role	Fiber Type	Construction	Notes
Telemetry lead-in	Interrogator to E2000 coupler	Single-mode	Armored patch cable	Not used for sensing
Solifos BRUsens DAS AC3	DAS sensing fiber	DAS sensing cable	Armored sensing cable	Trenched from A-C, loose from C-D

Build The Inventory

Start by transcribing the field notes into a table. This gives the inventory a single source for geometry and distance intervals. Normally one would probably just read this from a CSV.

import pandas as pd

tap_points = pd.DataFrame(
    {
        "point": ["A", "B", "C", "D"],
        "description": [
            "Start of sensing fiber after E2000 coupler",
            "90-degree bend in trenched section",
            "Cable exits trench and becomes loose",
            "End of loose surface-laid section",
        ],
        "distance": [50.0, 350.0, 470.0, 770.0],
        "latitude": [48.807461, 48.807461, 48.808539, 48.808539],
        "longitude": [2.103905, 2.108000, 2.108000, 2.112095],
        "elevation": [115.0, 115.0, 115.0, 115.0],
    }
).set_index("point")

tap_points["coordinates"] = list(
    zip(tap_points["longitude"], tap_points["latitude"], tap_points["elevation"])
)
total_optical_length = tap_points.loc["D", "distance"] - tap_points.loc["A", "distance"]

Acquisition

The acquisition describes the interrogator and the configuration used to produce a data stream.

import dascore.core.inventory as inv

interrogator = inv.Interrogator(
    manufacturer="Example Instruments",
    model="DAS-1000",
    serial_number="RUDAS88",
)

acquisition = inv.Acquisition(
    code="RAW",
    location_code="",
    data_category="DAS",
    data_type="strain",
    data_units="strain",
    interrogator=interrogator,
    start_time="2026-06-01",
    sample_rate=250.0,
    gauge_length=2.0,
    spatial_interval=1.0,
    extra_fields={"software_version": "v0.2.1"},
)

Cables And Fibers

The lead-in cable, sensing cable, and E2000 coupler are independent resources. The optical path can refer to them without duplicating metadata.

The inventory implementation generates unique internal IDs automatically.

telemetry_cable = inv.Cable(
    name="Telemetry lead-in cable",
    owner="Example Operator",
    fiber_count=1,
    notes="Armored patch lead-in cable from interrogator.",
)

sensing_cable = inv.Cable(
    name="Solifos BRUsens DAS AC3 cable",
    owner="Example Operator",
    manufacturer="Solifos",
    model="BRUsens DAS AC3",
    fiber_count=4,
)

e2000_coupler = inv.Enclosure(
    name="E2000 coupler",
    enclosure_type="coupler",
)

Optical Path

The optical path describes the components encountered by the light, along with geometry, coupling, and labels that can later be projected onto patch coordinates.

Components

telemetry_lead = inv.FiberSegment(
    name="Telemetry lead-in fiber",
    optical_length=tap_points.loc["A", "distance"],
    fiber_standard="OS2",
    color="yellow",
    notes="Not used for sensing.",
    container=telemetry_cable,
)
lead_in_connector = inv.Connector(
    name="Telemetry lead-in E2000 connector",
    connector_type="E2000",
    container=e2000_coupler,
)
sensing_connector = inv.Connector(
    name="Sensing cable E2000 connector",
    connector_type="E2000",
    container=e2000_coupler,
)
sensing_fiber = inv.FiberSegment(
    name="Solifos BRUsens DAS AC3 sensing fiber",
    optical_length=total_optical_length,
    fiber_type="das_sensing_cable",
    fiber_standard="Solifos BRUsens DAS AC3",
    buffer_type="armored_sensing_cable",
    color="blue",
    container=sensing_cable,
)

Geometry

unknown_lead_in = inv.Geometry(optical_length=50)
east_trench = inv.Geometry(
    optical_length=300.0,
    geometry_type="linear",
    coordinates=(tap_points.loc["A", "coordinates"], tap_points.loc["B", "coordinates"]),
)
north_trench = inv.Geometry(
    optical_length=120.0,
    geometry_type="linear",
    coordinates=(tap_points.loc["B", "coordinates"], tap_points.loc["C", "coordinates"]),
)
surface_run = inv.Geometry(
    optical_length=300.0,
    geometry_type="linear",
    coordinates=(tap_points.loc["C", "coordinates"], tap_points.loc["D", "coordinates"]),
)

Coupling Conditions

lead_in_coupling = inv.CouplingCondition(optical_length=50)
trenched = inv.CouplingCondition(
    optical_length=420.0,
    coupling_type="trench",
    medium="soil",
    attachment="direct_burial",
)
loose = inv.CouplingCondition(
    optical_length=300.0,
    coupling_type="surface",
    attachment="loose",
)

Annotations

trenched_label = inv.OpticalPathAnnotation(distance=50.0, optical_length=420.0, label="trenched")
loose_label = inv.OpticalPathAnnotation(distance=470.0, optical_length=300.0, label="loose")

Assemble The Path

optical_path = inv.OpticalPath(
    name="Simple DAS experiment",
    start_time="2026-06-01",
    optical_components=(
        telemetry_lead,
        lead_in_connector,
        sensing_connector,
        sensing_fiber,
    ),
    geometries=(unknown_lead_in, east_trench, north_trench, surface_run),
    coupling_conditions=(lead_in_coupling, trenched, loose),
    annotations=(trenched_label, loose_label),
).validate()

Inventory

The fiber array links the acquisition and optical path into one patch-resolvable observing identity.

fiber_array = inv.FiberArray(
    code="SIMP",
    name="Simple trench array",
    start_time="2026-06-01",
    acquisitions=(acquisition,),
    optical_paths=(optical_path,),
    dims="distance,time",
    tag="simple",
)

network = inv.Network(
    code="XX",
    name="Simple example network",
    fiber_arrays=(fiber_array,),
)

inventory = inv.Inventory(
    networks=(network,),
)

The shareable resources (cable, enclosures) can be manually added to the Inventory’s resources, but if not, they will be populated automatically when the inventory is created.