pycsamt.agents.static_shift

pycsamt.agents.static_shift

StaticShiftAgent — Detect and correct galvanic static shift.

Wraps pycsamt.emtools.ss:

• correct_ss_ama() — AMA correction (default)

• correct_ss_loess() — LOESS smooth correction

• estimate_ss_refmedian() — reference-median estimate

• ss_comparison_psection() — before/after section

• plot_ss_summary() — summary dashboard

• plot_ss_1d_curves() — per-station 1-D curves

Supported methods: "ama" (default), "loess", "refmedian", "bilateral".

Classes

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

Detect and correct galvanic static shift in MT/AMT data.

class pycsamt.agents.static_shift.StaticShiftAgent(*, api_key=None, model=None, llm_provider='claude', method='ama', half_window=3, pband=None, inplace=False)

Bases: BaseAgent

Detect and correct galvanic static shift in MT/AMT data.

Parameters:

• api_key (str)

• model (str)

• llm_provider (str)

• method (str, optional — overrides constructor default) – Correction algorithm. Default "ama" (adaptive moving average).

• half_window (int) – Spatial half-window for AMA / LOESS smoothing.

• pband ((T_min, T_max) or None) – Period band used to estimate shift factors.

• inplace (bool) – Modify the input Sites in-place. Default False (returns a copy).

• keys (Output data)

• ----------

• path (sites /)

• method

• output_dir (str, optional)

• keys

• ----------------

• removed (corrected_sites Sites with static shift)

• {station (shift_factors dict)

• before (rho_before ndarray (n_freq × n_sta) — log₁₀ ρa)

• after (rho_after ndarray — log₁₀ ρa)

• magnitude (delta_stats dict — min/max/mean shift)

• objects (figures dict — matplotlib Figure)

• paths (figure_paths dict — saved file)

Examples

>>> agent  = StaticShiftAgent(method="ama")
>>> result = agent.execute({"path": "/data/L22PLT",
...                         "output_dir": "/out/ss"})
>>> result["delta_stats"]
{'mean': 0.18, 'max': 0.42, 'n_shifted': 7}

SYSTEM_PROMPT: str = 'You are an expert in galvanic distortion and static-shift correction for magnetotelluric data.\nGiven a static-shift correction summary, write 3–4 sentences that:\n1. State whether significant static shift was detected.\n2. Identify stations with the largest corrections and their magnitude.\n3. Assess whether the correction method was appropriate for this dataset.\n4. Recommend any follow-up action (e.g., additional spatial filtering).\nReply in plain English. No bullet points or markdown.\n'

Override in subclasses to give the LLM its domain expertise.

execute(input_data)

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