shnitsel.vis.plot.filtration#

Attributes#

`shnitsel_blue`
`shnitsel_yellow`
`shnitsel_magenta`
`text_color`
`text_backgroundcolor`

Functions#

`check_thresholds`(…)	Display graphs illustrating
`validity_populations`(ds_or_da[, intersections])	Display two plots showing

Module Contents#

shnitsel_blue#

shnitsel_yellow = '#C4A000'#

shnitsel_magenta = '#7E5273'#

text_color = '#fff'#

text_backgroundcolor = (0, 0, 0, 0.2)#

check_thresholds(ds_or_da: shnitsel.data.tree.node.TreeNode[Any, xarray.Dataset | xarray.DataArray | shnitsel.data.dataset_containers.shared.ShnitselDataset], quantiles: Sequence[float] | None = None) → shnitsel.vis.support.multi_plot.MultiPlot#

check_thresholds(ds_or_da: xarray.Dataset | xarray.DataArray | shnitsel.data.dataset_containers.shared.ShnitselDataset, quantiles: Sequence[float] | None = None) → matplotlib.axes.Axes

Display graphs illustrating

how many trajectories meet each criterion throughout, and
quantiles of cumulative maxima over time for each criterion, indicating at what times a given proportion has failed the criterion

Parameters:

ds_or_da (xr.Dataset | xr.DataArray | ShnitselDataset | TreeNode[Any, xr.Dataset | xr.DataArray | ShnitselDataset]) – Data to plot. Can be flat or hierarchical format.
quantiles (Sequence[float] | None, optional) – Quantiles to display and mark on the right-hand graph, by default None.

Return type:

The matplotlib Axes object of the plots

validity_populations(ds_or_da, intersections=True)#

Display two plots showing 1. how many trajectories meet criteria (or combinations thereof) up to a given time 2. how many frames would remain if the ensemble were transected at a given time (see shnitsel.clean.transect())