Merge branch 'dev' of https://github.com/USGS-Astrogeology/autocnet into sugar

            raise AttributeError('This edge does not yet have any matches computed.')

        matches, mask = self._clean(clean_keys)

        domain = self.source.handle.raster_size

        domain = self.source.geodata.raster_size

        # Massage the dataframe into the correct structure

        coords = self.source.get_keypoint_coordinates()

import itertools

import math

import os

import warnings

import dill as pickle

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_utils

from autocnet.fileio import io_hdf

from autocnet.control.control import C

from autocnet.fileio import io_hdf

from autocnet.fileio import io_json

from autocnet.matcher.matcher import FlannMatcher

import autocnet.matcher.suppression_funcs as spf

from autocnet.fileio import io_utils

from autocnet.fileio.io_gdal import GeoDataset

from autocnet.graph import markov_cluster

from autocnet.graph.edge import Edge

from autocnet.graph.node import Node

from autocnet.graph import markov_cluster

from autocnet.matcher.matcher import FlannMatcher

from autocnet.vis.graph_view import plot_graph

        self.node_counter = 0

        node_labels = {}

        self.node_name_map = {}

        self.graph_masks = pd.DataFrame()

        for node_name in self.nodes():

            image_name = os.path.basename(node_name)

        """

        _, self.clusters = func(self, *args, **kwargs)

    def apply_func_to_edges(self, function, *args, graph_mask_keys=[], **kwargs):

    def apply_func_to_edges(self, function, *args, **kwargs):

        """

        Iterates over edges using an optional mask and and applies the given function.

        If func is not an attribute of Edge, raises AttributeError

        graph_mask_keys : list

                          of keys in graph_masks

        """

        if graph_mask_keys:

            merged_graph_mask = self.graph_masks[graph_mask_keys].all(axis=1)

            edges_to_iter = merged_graph_mask[merged_graph_mask].index

        else:

            edges_to_iter = self.edges()

        if not isinstance(function, str):

            function = function.__name__

        for s, d in edges_to_iter:

            curr_edge = self.get_edge_data(s, d)

        for s, d, edge in self.edges_iter(data=True):

            try:

                func = getattr(curr_edge, function)

                func = getattr(edge, function)

            except:

                raise AttributeError(function, ' is not an attribute of Edge')

            else:

           boolean mask for edges in the minimum spanning tree

        """

        graph_mask = pd.Series(False, index=self.edges())

        self.graph_masks['mst'] = graph_mask

        mst = nx.minimum_spanning_tree(self)

        self.graph_masks['mst'][mst.edges()] = True

        return self.create_edge_subgraph(mst.edges())

    def to_filelist(self):

        """

        if not hasattr(self, '_keypoints'):

            raise AttributeError('No keypoints extracted for this node.')

        domain = self.handle.raster_size

        domain = self.geodata.raster_size

        self._keypoints['strength'] = self._keypoints.apply(func, axis=1)

        if not hasattr(self, 'suppression'):

from unittest.mock import patch

from unittest.mock import PropertyMock

from unittest.mock import MagicMock

from osgeo import ogr

import gdal

import numpy as np

from autocnet.examples import get_path

from autocnet.fileio import io_gdal

from .. import network

    def test_apply_func_to_edges(self):

        graph = self.graph.copy()

        graph.minimum_spanning_tree()

        mst_graph = graph.minimum_spanning_tree()

        try:

            graph.apply_func_to_edges('incorrect_func')

        except AttributeError:

            pass

        graph.extract_features(extractor_parameters={'nfeatures': 500})

        graph.match_features()

        graph.apply_func_to_edges("symmetry_check", graph_mask_keys=['mst'])

        mst_graph.extract_features(extractor_parameters={'nfeatures': 500})

        mst_graph.match_features()

        mst_graph.apply_func_to_edges("symmetry_check")

        self.assertFalse(graph[0][2].masks['symmetry'].all())

        self.assertFalse(graph[0][1].masks['symmetry'].all())

        try:

            self.assertTrue(graph[1][2].masks['symmetry'].all())

        except:

            pass

    def test_connected_subgraphs(self):

        subgraph_list = self.disconnected_graph.connected_subgraphs()

        self.assertEqual(test_sub_graph.edges(), sub_graph_from_matches.edges())

    def tearDown(self):

        pass

class TestGraphMasks(unittest.TestCase):

    @classmethod

    def setUpClass(cls):

        cls.test_dict = {"0": ["4", "2", "1", "3"],

    def test_minimum_spanning_tree(self):

        test_dict = {"0": ["4", "2", "1", "3"],

                     "1": ["0", "3", "2", "6", "5"],

                     "2": ["1", "0", "3", "4", "7"],

                     "3": ["2", "0", "1", "5"],

                     "6": ["1"],

                     "7": ["2"]}

        cls.graph = network.CandidateGraph.from_adjacency(cls.test_dict)

        cls.graph.minimum_spanning_tree()

        removed_edges = cls.graph.graph_masks['mst'][cls.graph.graph_masks['mst'] == False].index

        graph = network.CandidateGraph.from_adjacency(test_dict)

        mst_graph = graph.minimum_spanning_tree()

        print(len(mst_graph.edges()))

        cls.mst_graph = cls.graph.copy()

        cls.mst_graph.remove_edges_from(removed_edges)

        self.assertEqual(sorted(mst_graph.nodes()), sorted(graph.nodes()))

        self.assertEqual(len(mst_graph.edges()), len(graph.edges())-5)

    def test_mst_output(self):

        self.assertEqual(sorted(self.mst_graph.nodes()), sorted(self.graph.nodes()))

        self.assertEqual(self.mst_graph.number_of_edges(), self.graph.number_of_edges()-5)

 No newline at end of file

    def tearDown(self):

        pass

subpixel_register:

    clean_keys:

        - ratio

        - symmetry

        - fundamental

    template_size: 5

    threshold: 0.8

    search_size: 15

suppress:

    clean_keys:

        - fundamental

        - ratio

# Keyword arguments

    min_radius: 2