2.26.5.8. pycsamt.agents.phase_analysis#

pycsamt.agents.phase_analysis#

PhaseAnalysisAgent — Phase tensor, strike, and dimensionality analysis.

Wraps:

  • build_phase_tensor_table()

  • plot_phase_tensor_psection()

  • plot_phase_tensor_rose()

  • estimate_strike_consensus()

  • plot_strike_analysis()

  • plot_strike_rose()

  • classify_dimensionality()

  • plot_dim_confidence_grid()

  • plot_impedance_mohr_circles()

  • plot_survey_fingerprint()

All figures are governed by PYCSAMT_SECTION and PYCSAMT_STYLE.

Classes

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

Run a full phase tensor, strike, and dimensionality survey analysis.
class pycsamt.agents.phase_analysis.PhaseAnalysisAgent(*, api_key=None, model=None, llm_provider='claude', skew_th=5.0, ellipt_th=0.1, band=None)[source]

Bases: BaseAgent

Run a full phase tensor, strike, and dimensionality survey analysis.

Parameters:

  • api_key (str)

  • model (str)

  • llm_provider (str)

  • skew_th (float) – Skewness |β| threshold for 3-D classification (°).

  • ellipt_th (float) – Ellipticity λ threshold for 2-D classification.

  • band ((T_min, T_max) or None) – Period band for strike estimation.

  • keys (Output data)

  • ----------

  • path (sites /)

  • period_range ([T_min, T_max], optional)

  • output_dir (str, optional)

  • run_mohr (bool, optional — also produce Mohr circles (default False))

  • run_fingerprint (bool, optional — produce fingerprint grid (default True))

  • keys

  • ----------------

  • (station (pt_table pandas DataFrame — full PT metrics per)

  • period)

  • (°) (strike_consensus float — consensus strike angle)

  • stations (strike_iqr float — IQR of strike across all)

  • per-(station (dim_table pandas DataFrame —)

  • classification (period))

  • n_1d

  • n_2d

  • class (n_3d int — count of observations per)

  • objects (figures dict — matplotlib Figure)

  • paths (figure_paths dict — saved file)

Examples

>>> agent = PhaseAnalysisAgent()
>>> result = agent.execute(
...     {"path": "/data/L22PLT", "output_dir": "/out/pt"}
... )
>>> result["strike_consensus"]
42.5
SYSTEM_PROMPT: str = 'You are an expert in MT phase tensor analysis and geological interpretation.\nGiven a survey phase tensor summary, write 4–5 sentences that:\n1. State the dominant dimensionality (1-D, 2-D, or 3-D) with evidence.\n2. Report the consensus geoelectric strike direction and its reliability.\n3. Identify periods / depth ranges where 3-D structure becomes significant.\n4. Note any anomalous stations (high skew, low ellipticity).\n5. Recommend whether to rotate data to strike before inversion.\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