compute distances using cKDTree

[Duke-of-Lizard]

right now, we're doing quite an inefficient computation of minimum distance to nearest data point in distance.py. I'm familiar with scipy.sparse and scipy.spatial to pull it off. Here's what I tried, but the method .min on a csr.array was useless because I can't exlude the blank entries of the matrix that are assumed to be 0.

import scipy.spatial.distance
from scipy.sparse import csr_array
from scipy.spatial import cKDTree
import numpy as np

def distance_to_data(col, x_col="X", y_col="Y",max_search_dist = None, **tri):
    """Calculate spatial distance (cartesian distance in the current
    projection) to vertices with non-NaN values in the column col and
    store the distance in the column col_dist.
    """
    filt = ~tri["vertices"][col].isna()
    XA = tri["vertices"].loc[~filt][[x_col,y_col]].values
    XB = tri["vertices"].loc[filt][[x_col,y_col]].values
    if len(XB):
        if max_search_dist is None:
            tri["vertices"].loc[filt,'%s_dist' % col] = 0.0
            tri["vertices"].loc[~filt,'%s_dist' % col] = np.min(scipy.spatial.distance.cdist(XA, XB),axis=1)
        
        else:
            A = cKDTree(XA[~filt])
            B = cKDTree(XB)
            dist_mat = A.sparse_distance_matrix(B, max_search_dist)
            dist_nearest = csr_array(dist_mat).min(axis=1).toarray()
            tri["vertices"]['%s_dist' % col] = dist_nearest
    return tri