pyuncertainnumber.calibration.data_peeling.plots

Attributes

FONTSIZE

FIGSIZE

color_dict

cmap

COLORMAP

Functions

c_(k[, alpha])

breakout(n)

plot_peeling(x, a, b[, p, axes3d, figsize, grid, label])

Plotting function for data peeling results.

plot_one_fuzzy_grad(a_fuzzy[, p, data, ax, figsize, ...])

plot_box(box2d[, ax, figsize, facecolor, edgecolor, ...])

plot_peeling_nx2(X, a, b[, p, max_level, label, grid, ...])

plot_peeling_3d(x, a, b)

plot_peeling_nxd(x, a, b[, fx, p, figsize, aspect, ...])

plot_scattermatrix(x[, bins, GS, figsize, aspect, ...])

plot_fuzzy(fuzzy[, p, data, ax, figsize, grid, color, ...])

fuzzy: An (l,d_,2) array with projections

plot_peeling_nxd_back(ux, c[, p, figsize, aspect, ...])

_draw_peeling_cell(ax, x, b, fx, p, i, j, labels[, ...])

plot_peeling_nxd_all(x, a, b[, fx, p, figsize, ...])

plot_peeling_one(x, a, b, i, j[, fx, p, figsize, ...])

Module Contents

pyuncertainnumber.calibration.data_peeling.plots.FONTSIZE = 22
pyuncertainnumber.calibration.data_peeling.plots.FIGSIZE
pyuncertainnumber.calibration.data_peeling.plots.color_dict
pyuncertainnumber.calibration.data_peeling.plots.c_(k, alpha=1)
pyuncertainnumber.calibration.data_peeling.plots.cmap
pyuncertainnumber.calibration.data_peeling.plots.COLORMAP
pyuncertainnumber.calibration.data_peeling.plots.breakout(n)
pyuncertainnumber.calibration.data_peeling.plots.plot_peeling(x: numpy.typing.NDArray, a, b, p=None, axes3d=False, figsize='medium', grid=True, label='X')

Plotting function for data peeling results.

Parameters:

  • x (NDArray) – data set of iid observations

  • a – sequence of subindices for each level

  • b – sequence of boxes or enclosing sets

  • p – upper violation probability (membership value)

pyuncertainnumber.calibration.data_peeling.plots.plot_one_fuzzy_grad(a_fuzzy, p=None, data=None, ax=None, figsize=None, grid=None, color=None, baseline_alpha=0.4, linewidth=0.1, colormap=None, xlabel='$X$', ylabel='$1-\\delta$', flip=False)
pyuncertainnumber.calibration.data_peeling.plots.plot_box(box2d, ax=None, figsize=(10, 10), facecolor=None, edgecolor=None, alpha=None, label=None, zorder=None, grid=True)
pyuncertainnumber.calibration.data_peeling.plots.plot_peeling_nx2(X, a, b, p: list = None, max_level: int = None, label='X', grid=True, savefig: str = None, figsize=None, baseline_alpha=0.075)
pyuncertainnumber.calibration.data_peeling.plots.plot_peeling_3d(x, a, b)
pyuncertainnumber.calibration.data_peeling.plots.plot_peeling_nxd(x, a, b, fx=None, p: list = None, figsize=None, aspect='auto', label='X', marker='s', markercolor='grey', boxcolor='blue2', grid=True, baseline_alpha=0.075)
pyuncertainnumber.calibration.data_peeling.plots.plot_scattermatrix(x, bins=10, GS=None, figsize=None, aspect='auto', color=None, marker='s', alpha=None, edgecolors='face', grid=True, label='X')
pyuncertainnumber.calibration.data_peeling.plots.plot_fuzzy(fuzzy, p=None, data=None, ax=None, figsize=None, grid=False, color=None, baseline_alpha=0.4, linewidth=0.1, colormap=None, xlabel=None, ylabel='$1-\\delta$', flip=False)

fuzzy: An (l,d_,2) array with projections

pyuncertainnumber.calibration.data_peeling.plots.plot_peeling_nxd_back(ux, c, p: list = None, figsize=None, aspect='auto', xlabel='X', ylabel='$1-\\delta$', marker='s', markercolor='grey', boxcolor='blue2', colormap=None, grid=True, baseline_alpha=0.85)
pyuncertainnumber.calibration.data_peeling.plots._draw_peeling_cell(ax, x, b, fx, p, i, j, labels, aspect='auto', marker='s', markercolor='grey', boxcolor='blue2', grid=True, baseline_alpha=0.075)
pyuncertainnumber.calibration.data_peeling.plots.plot_peeling_nxd_all(x, a, b, fx=None, p: list = None, figsize=None, aspect='auto', label='X', marker='s', markercolor='grey', boxcolor='blue2', grid=True, baseline_alpha=0.075, return_axes=False)
pyuncertainnumber.calibration.data_peeling.plots.plot_peeling_one(x, a, b, i, j, fx=None, p: list = None, figsize=(4, 4), aspect='auto', label='X', marker='s', markercolor='grey', boxcolor='blue2', grid=True, baseline_alpha=0.075)