epsproc.plot.hvPlotters module

ePSproc plotting functions with Holoviews + Bokeh.

Aim: simple plotters for different datatypes, interactive in Jupyter Notebook + HTML formats.

15/07/20 Debugged, still pretty basic but running. 05/07/20 v1 in development.

See

Todo

  • Plotting style mapping & options. Currently having HV issues here.
  • Currently set only for XC datatypes from single dataSet, will want to enable stacking etc. here.
  • Errorbar or spread plots, currently having issues getting these working for multidim data.
epsproc.plot.hvPlotters.XCplot(dataXS, lineDashList={'L': 'dashed', 'M': 'solid', 'V': 'dashed'})[source]

Plot XC data using Holoviews.

Currently optional stuff hard-coded here, will produce plots [sigma, beta] showing all data. Rather crude, needs some more style mapping.

Parameters:
  • dataXS (Xarray) – Xarray dataarray containing XC data in standard format.
  • lineDashList (dict, optional, default = {'L': 'dashed', 'M': 'solid', 'V': 'dashed'}) – Set line types for calculation gauge.
Returns:

layout
Return type:

hv object

Examples

>>> plotObj, _,_ = XCplot(dataXS[0])
>>> plotObj
epsproc.plot.hvPlotters.hvdsConv(dataXS)[source]

Basic conversion for XS data from Xarray to Holoviews.

This will drop stacked Sym dims, and sum of Total to reduce - may not be appropriate in all cases?

epsproc.plot.hvPlotters.setPlotters(hvBackend='bokeh', width=500)[source]

Set some plot options - Seaborn style + HV defaults.

May have some issues with scope here - TBC. Should just run on function import?

Update: now moved to module import.

Parameters:
  • hvBackend (str or list of strs, optional, default = 'bokeh') – Backend(s) for holoviews to load. Can call bokeh, matplotlib or plotly
  • width (int, optional, default = 500) – Setting for plot width, in pixels.