Source code for epsproc.util.selectors

#*************** Selection functions

import numpy as np

# Selector function for matrix elements in Xarray
[docs]def matEleSelector(da, thres = None, inds = None, dims = None, sq = False, drop=True): """ Select & threshold raw matrix elements in an Xarray Parameters ---------- da : Xarray Set of matrix elements to sub-select thres : float, optional, default None Threshold value for abs(matElement), keep only elements > thres. This is *element-wise*. inds : dict, optional, default None Dicitonary of additional selection criteria, in name:value format. These correspond to parameter dimensions in the Xarray structure. E.g. inds = {'Type':'L','Cont':'A2'} dims : str or list of strs, dimensions to look for max & threshold, default None Set for *dimension-wise* thresholding. If set, this is used *instead* of element-wise thresholding. List of dimensions, which will be checked vs. threshold for max value, according to abs(dim.max) > threshold This allows for consistent selection of continuous parameters over a dimension, by a threshold. sq : bool, optional, default False Squeeze output singleton dimensions. drop : bool, optional, default True Passed to da.where() for thresholding, drop coord labels for values below threshold. Returns ------- daOut Xarray structure of selected matrix elements. Note that Nans are dropped if possible. Example ------- >>> daOut = matEleSelector(da, inds = {'Type':'L','Cont':'A2'}) """ # Iterate over other selection criteria # This may return view or copy - TBC - but seems to work as expected. # if inds is not None: da = da.sel(inds) # Fors inds as dict, e.g. {'Type':'L','it':1,'Cont':'A2'} # May want to send as list, or automate vs. dim names? # NOTE - in current dev code this is used to reindex, so .squeeze() casuses issues! # Reduce dims by thesholding on abs values # Do this after selection to ensure Nans removed. if (thres is not None) and (dims is None): daOut = da.where(np.abs(da) > thres, drop = drop) else: daOut = da # If dims is set, check over dims for consistency. # WILL this just produce same results as thres then squeeze...? if (dims is not None) and (thres is not None): daOut = daOut.where(np.abs(da).max(dim = dims) > thres, drop = drop) if sq: daOut = daOut.squeeze() # Squeeze dims. return daOut
# Select over vals from data structure (list) # Currently only used in IO.matEleGroupDim
[docs]def dataGroupSel(data, dInd): a = data[0] dataSub = [] uVals = np.unique(a[dInd,:]) for val in uVals: # Get matching terms and subset data # iSel = np.nonzero(a[dInd,:]==val) iSel = (a[dInd,:]==val) dataSub.append([data[0][:,iSel], data[1][iSel]]) return dataSub
# Xarray groupby + compare values # STARTED... but not finished. For basic diff along a dimension, just use da.diff(dim), see # def groupCmp(data, dim): # """ # Basic routine to compare sets of values by dimension, using Xarray groupby functionality. # # Parameters # ---------- # data : Xarray # Data for comparison # # dim : str # Dimension label for grouping # # Returns # ------- # # """ # # dGroup = data.groupby(dim) # # # Check differences between groups # for gTest in dGroup: