2.26.5.13. pycsamt.agents.forward#

pycsamt.agents.forward#

ForwardModelAgent — Run 1-D, 2-D, or 3-D MT forward solvers.

Wraps pycsamt.forward:

1-D (dim=1)

MT1DForward on a LayeredModel.

2-D (dim=2)

MT2DForward (finite-difference TE + TM) on a Grid2D. Supports halfspace, 1-D-layer, or embedded conductive-anomaly models.

3-D (dim=3)

MT3DForward (quasi-3D profile stacking) on a Grid3D. Supports halfspace and block-anomaly models.

The agent also computes data–model RMS when observed sites are provided (1-D only), letting it act as a model-validation check before inversion.

Classes

ForwardModelAgent(*[, api_key, model, ...])

Run a 1-D, 2-D, or 3-D MT forward model.
class pycsamt.agents.forward.ForwardModelAgent(*, api_key=None, model=None, llm_provider='claude', dim=1, freqs=None)[source]

Bases: BaseAgent

Run a 1-D, 2-D, or 3-D MT forward model.

Parameters:

  • api_key (str)

  • model (dict or LayeredModel or None)

  • llm_provider (str)

  • dim (int, optional — overrides constructor dim for this call) – Forward solver dimensionality.

  • freqs (array-like, optional — overrides constructor default) – Frequencies (Hz). Defaults to 40 log-spaced points 10⁻⁴–10³ Hz.

  • keys (Output data)

  • ----------

  • model –

    1-D / 2-D from 1-D layers: {"resistivities": [...], "thicknesses": [...]}.

    2-D grid type override: add "type": "halfspace" | "anomaly" and grid parameters such as "bg_rho", "anomaly_rho", "anomaly_bounds".

    3-D grid type: "type": "halfspace" | "block_anomaly" with grid parameters.

  • dim

  • nx (int / float, optional (2-D grid))

  • nz (int / float, optional (2-D grid))

  • x_max (int / float, optional (2-D grid))

  • z_max (int / float, optional (2-D grid))

  • ny (int / float (3-D))

  • y_max (int / float (3-D))

  • nx_stations (int / float (3-D))

  • ny_stations (int / float (3-D))

  • n_stations (int, optional — number of surface receivers (2-D))

  • method (str, optional — "quasi3d" (default) for 3-D solver)

  • path (sites /)

  • freqs

  • output_dir (str, optional)

  • component ("xy" (default) or "yx" (1-D component selection))

  • keys

  • ----------------

  • int (dim)

  • 1-D) (layered_model LayeredModel (1-D / 2-D from)

  • 3-D) (grid Grid2D or Grid3D (2-D /)

  • ForwardResponse3D (response ForwardResponse / ForwardResponse2D /)

  • ρa (rho_a ndarray — 1-D)

  • (°) (phase ndarray — 1-D phase)

  • (n_freqs (rho_a_xy ndarray)

  • TE (n_stations) — 2-D)

  • phase (phase_yx ndarray — 3-D YX)

  • TM (rho_a_tm ndarray — 2-D)

  • phase

  • (n_freqs

  • XY (n_stations) — 3-D)

  • phase

  • YX (rho_a_yx ndarray — 3-D)

  • phase

  • ndarray (freqs)

  • None (rms float or)

  • dict (figure_paths)

  • dict

SYSTEM_PROMPT: str = 'You are an expert in MT forward modelling and resistivity earth models.\nGiven a forward model result, write 3-4 sentences that:\n1. Describe the model geometry (dimensionality, layers / grid, resistivity range).\n2. Comment on the synthetic ρa and phase response (frequency range, lateral variation for 2D/3D).\n3. If observed data are provided, interpret the data-model misfit (1-D only).\n4. Suggest which model parameters to adjust to better fit the data or geology.\nReply in plain English. No bullet points or markdown.\n'

Override in subclasses to give the LLM its domain expertise.

execute(input_data)[source]

Run this agent on input_data and return an AgentResult.

Subclasses must implement this method. The contract:

  • Reset self._last_cost = 0.0 at the top.

  • Record wall-clock time with t0 = time.time().

  • Return AgentResult(elapsed_seconds=time.time()-t0, cost_estimate_usd=self._last_cost, ...).

Parameters:

input_data (dict[str, Any])

Return type:

AgentResult

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