drippy.data

EDA data container.

Classes

EDAData

Validated data container for EDA analysis.

Module Contents

class drippy.data.EDAData(y: collections.abc.Iterable[float], x: collections.abc.Iterable | None = None, t: collections.abc.Iterable[float] | None = None, factors: dict[str, collections.abc.Iterable] | None = None)[source]

Validated data container for EDA analysis.

Parameters:

  • y – Response variable. Must be 1D and non-empty.

  • x – Continuous predictor or single categorical factor. Must match len(y) if provided.

  • t – Continuous index variable (e.g. time, 1/B, position). Not restricted to real time. Must match len(y) if provided.

  • factors – Named factor arrays for multi-factor/DOE/comparative plots. Each value must match len(y).

y[source]
x = None[source]
t = None[source]
factors = None[source]
run_sequence_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.univariate.run_sequence_plot.

lag_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.univariate.lag_plot.

histogram(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.univariate.histogram.

normal_probability_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes, float | None][source]

Delegates to drippy.univariate.normal_probability_plot.

four_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, numpy.ndarray][source]

Delegates to drippy.univariate.four_plot.

ppcc_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, numpy.ndarray][source]

Delegates to drippy.univariate.ppcc_plot.

weibull_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.univariate.weibull_plot.

probability_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.univariate.probability_plot.

box_cox_linearity_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.univariate.box_cox_linearity_plot.

box_cox_normality_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, numpy.ndarray][source]

Delegates to drippy.univariate.box_cox_normality_plot.

bootstrap_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.univariate.bootstrap_plot.

spectral_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.timeseries.spectral_plot.

autocorrelation_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.timeseries.autocorrelation_plot.

complex_demodulation_amplitude_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegate to timeseries.complex_demodulation_amplitude_plot.

complex_demodulation_phase_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegate to drippy.timeseries.complex_demodulation_phase_plot.

scatter_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.onefactor.scatter_plot.

box_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.onefactor.box_plot.

bihistogram(**kwargs: Any) → tuple[matplotlib.figure.Figure, numpy.ndarray][source]

Delegates to drippy.onefactor.bihistogram.

qq_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.onefactor.qq_plot.

mean_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.onefactor.mean_plot.

sd_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.onefactor.sd_plot.

doe_scatter_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.multifactor.doe_scatter_plot (Phase 2).

doe_mean_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.multifactor.doe_mean_plot (Phase 2).

doe_sd_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.multifactor.doe_sd_plot (Phase 2).

contour_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.multifactor.contour_plot (Phase 2).

six_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, tuple[matplotlib.axes.Axes, matplotlib.axes.Axes, matplotlib.axes.Axes, matplotlib.axes.Axes, matplotlib.axes.Axes, matplotlib.axes.Axes]][source]

Delegates to drippy.regression.six_plot (Phase 3).

linear_correlation_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegate to drippy.regression.linear_correlation_plot (Phase 3).

linear_intercept_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegate to drippy.regression.linear_intercept_plot (Phase 3).

linear_slope_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.regression.linear_slope_plot (Phase 3).

linear_residual_sd_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegate to drippy.regression.linear_residual_sd_plot (Phase 3).

block_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.comparative.block_plot (Phase 4).

youden_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.comparative.youden_plot (Phase 4).

star_plot(**kwargs: Any) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]

Delegates to drippy.comparative.star_plot (Phase 4).

_validate_y() → None[source]

Validate y array.

_validate_and_convert_x(x: collections.abc.Iterable | None) → numpy.ndarray | None[source]

Validate and convert x array.

_validate_and_convert_t(t: collections.abc.Iterable[float] | None) → numpy.ndarray | None[source]

Validate and convert t array.

_validate_and_convert_factors(factors: dict[str, collections.abc.Iterable] | None) → dict[str, numpy.ndarray] | None[source]

Validate and convert factors dict.