SpectralCausalityResult._summary_table#

SpectralCausalityResult._summary_table()[source]#

Build the structured summary: the ten largest integrated pairs.

Every ordered pair’s integrated measure, sorted descending and cut at ten, under a header with the variable, grid, and pair counts; the notes say what the entries are, warn about common driving, and restate that the measures describe the fitted model.

Return type:

SummaryTable

Returns:

The SummaryTable that summary(), str(), and the notebook renderer display.

Example

>>> import numpy as np
>>> from cultivars.multivariate.reduced_form.vector_autoregression import VAR
>>> y = np.random.default_rng(0).standard_normal((200, 3))
>>> causal = SpectralCausality(VAR(y, order=1).fit(), n_frequencies=16).compute()
>>> table = causal._summary_table()
>>> table.columns, len(table.rows), table.metadata[2]
(('pair (top 10 integrated)', 'integrated measure'), 6, ('Pairs', '6'))