added robustness to poor footprints

import networkx as nx

import numpy as np

import pandas as pd

import warnings

from autocnet.fileio.io_gdal import GeoDataset

from autocnet.fileio import io_hdf

        # This is brute force for now, could swap to an RTree at some point.

        adjacency_dict = {}

        valid_datasets = []

        for i, j in itertools.permutations(datasets,2):

            if not i.file_name in adjacency_dict.keys():

        # filter bad/malformed footprints

        for i in datasets:

            adjacency_dict[i.file_name] = []

            if not j.file_name in adjacency_dict.keys():

                adjacency_dict[j.file_name] = []

            fp = i.footprint

            if fp and fp.IsValid():

                valid_datasets.append(i)

            else:

                warnings.warn('Missing or invalid geospatial data for {}'.format(i.base_name))

        # Grab the footprints and test for intersection

        for i, j in itertools.permutations(valid_datasets, 2):

            i_fp = i.footprint

            j_fp = j.footprint

            try:

                if j_fp and i_fp and i_fp.Intersects(j_fp):

                if i_fp.Intersects(j_fp):

                    adjacency_dict[i.file_name].append(j.file_name)

                    adjacency_dict[j.file_name].append(i.file_name)

            except:

                warnings.warn('No or incorrect geospatial information for {} and/or {}'.format(i, j))

                warnings.warn('Failed to calculated intersection between {} and {}'.format(i,j))

        return cls(adjacency_dict)

            descriptors = node.descriptors

            # Load the neighbors of the current node into the FLANN matcher

            neighbors = self.neighbors(i)

            # if node has no neighbors, skip

            if not neighbors:

                continue

            for n in neighbors:

                neighbor_descriptors = self.node[n].descriptors

                self._fl.add(neighbor_descriptors, n)