Updates control - all unit tests nx2 compliant (4d94c457) · Commits · aflab / astrogeology / Autocnet

autocnet/control/control.py

+2 −1

Original line number	Diff line number	Diff line
		@@ -82,7 +82,7 @@ def identify_potential_overlaps(cg, cn, overlap=True):
		# Determine whether a 'real' lat/lon are to be used and reproject
		if overlap:
		row = p.iloc[0]
		lat, lon = cg.node[row.image_index].geodata.pixel_to_latlon(row.x, row.y)
		lat, lon = cg.node[row.image_index]['data'].geodata.pixel_to_latlon(row.x, row.y)
		else:
		lat, lon = 0,0

		@@ -178,6 +178,7 @@ class ControlNetwork(object):
		# The node_id is a composite key (image_id, correspondence_id), so just grab the image
		image_id = key[0]
		match_id = key[1]
		print(self.data.columns)
		self.data.loc[self._measure_id] = [point_id, image_id, match_id, edge, match_idx, *fields, 0, 0, np.inf, True]
		self._measure_id += 1

conftest.py

+44 −35

Original line number	Diff line number	Diff line
		@@ -7,19 +7,27 @@ import pytest

		from autocnet.control import control
		from autocnet.graph.network import CandidateGraph
		from autocnet.graph import edge, node
		from autocnet.graph.node import Node
		from plio.io.io_gdal import GeoDataset

		@pytest.fixture(scope='session')
		def candidategraph(node_a, node_b, node_c):
		# TODO: Getting this fixture from the global conf is causing deepycopy
		# to fail. Why?
		cg = CandidateGraph()

		# Create a candidategraph object - we instantiate a real CandidateGraph to
		# have access of networkx functionality we do not want to test and then
		# mock all autocnet functionality to control test behavior.
		edges = [(0,1), (0,2), (1,2)]
		edges = [(0,1,{'data':edge.Edge(0,1)}),
		(0,2,{'data':edge.Edge(0,2)}),
		(1,2,{'data':edge.Edge(1,2)})]

		cg.add_edges_from(edges)



		match_indices = [([0,1,2,3,4,5,6,7], [0,1,2,3,4,5,6,7]),
		([0,1,2,3,4,5,8,9], [0,1,2,3,4,5,8,9]),
		([0,1,2,3,4,5,8,9], [0,1,2,3,4,5,6,7])]
		@@ -40,9 +48,9 @@ def candidategraph(node_a, node_b, node_c):
		cg.get_matches = MagicMock(return_value=matches)

		# Mock in the node objects onto the candidate graph
		cg.node[0] = node_a
		cg.node[1] = node_b
		cg.node[2] = node_c
		cg.node[0]['data'] = node_a
		cg.node[1]['data'] = node_b
		cg.node[2]['data'] = node_c

		return cg

		@@ -86,38 +94,39 @@ def geodata_c():

		@pytest.fixture(scope='session')
		def controlnetwork_data():
		df = pd.DataFrame([[0, 0.0, 0.0, (0.0, 1.0), 0, 0.0, 0.0],
		[0, 1.0, 0.0, (0.0, 1.0), 0, 0.0, 0.0],
		[1, 0.0, 1.0, (0.0, 1.0), 1, 0.0, 0.0],
		[1, 1.0, 1.0, (0.0, 1.0), 1, 0.0, 0.0],
		[2, 0.0, 2.0, (0.0, 1.0), 2, 0.0, 0.0],
		[2, 1.0, 2.0, (0.0, 1.0), 2, 0.0, 0.0],
		[3, 0.0, 3.0, (0.0, 1.0), 3, 0.0, 0.0],
		[3, 1.0, 3.0, (0.0, 1.0), 3, 0.0, 0.0],
		[4, 0.0, 4.0, (0.0, 1.0), 4, 0.0, 0.0],
		[4, 1.0, 4.0, (0.0, 1.0), 4, 0.0, 0.0],
		[5, 0.0, 5.0, (0.0, 1.0), 5, 0.0, 0.0],
		[5, 1.0, 5.0, (0.0, 1.0), 5, 0.0, 0.0],
		[6, 0.0, 6.0, (0.0, 1.0), 6, 0.0, 0.0],
		[6, 1.0, 6.0, (0.0, 1.0), 6, 0.0, 0.0],
		[7, 0.0, 7.0, (0.0, 1.0), 7, 0.0, 0.0],
		[7, 1.0, 7.0, (0.0, 1.0), 7, 0.0, 0.0],
		[0, 2.0, 0.0, (0.0, 2.0), 0, 0.0, 0.0],
		[1, 2.0, 1.0, (0.0, 2.0), 1, 0.0, 0.0],
		[2, 2.0, 2.0, (0.0, 2.0), 2, 0.0, 0.0],
		[3, 2.0, 3.0, (0.0, 2.0), 3, 0.0, 0.0],
		[4, 2.0, 4.0, (0.0, 2.0), 4, 0.0, 0.0],
		[5, 2.0, 5.0, (0.0, 2.0), 5, 0.0, 0.0],
		[8, 0.0, 8.0, (0.0, 2.0), 6, 0.0, 0.0],
		[8, 2.0, 8.0, (0.0, 2.0), 6, 0.0, 0.0],
		[9, 0.0, 9.0, (0.0, 2.0), 7, 0.0, 0.0],
		[9, 2.0, 9.0, (0.0, 2.0), 7, 0.0, 0.0],
		[10, 1.0, 8.0, (1.0, 2.0), 6, 0.0, 0.0],
		[10, 2.0, 6.0, (1.0, 2.0), 6, 0.0, 0.0],
		[11, 1.0, 9.0, (1.0, 2.0), 7, 0.0, 0.0],
		[11, 2.0, 7.0, (1.0, 2.0), 7, 0.0, 0.0]],
		df = pd.DataFrame([[0, 0.0, 0.0, (0.0, 1.0), 0, 0.0, 0.0, 0, 0, np.inf, True],
		[0, 1.0, 0.0, (0.0, 1.0), 0, 0.0, 0.0, 0, 0, np.inf, True],
		[1, 0.0, 1.0, (0.0, 1.0), 1, 0.0, 0.0, 0, 0, np.inf, True],
		[1, 1.0, 1.0, (0.0, 1.0), 1, 0.0, 0.0, 0, 0, np.inf, True],
		[2, 0.0, 2.0, (0.0, 1.0), 2, 0.0, 0.0, 0, 0, np.inf, True],
		[2, 1.0, 2.0, (0.0, 1.0), 2, 0.0, 0.0, 0, 0, np.inf, True],
		[3, 0.0, 3.0, (0.0, 1.0), 3, 0.0, 0.0, 0, 0, np.inf, True],
		[3, 1.0, 3.0, (0.0, 1.0), 3, 0.0, 0.0, 0, 0, np.inf, True],
		[4, 0.0, 4.0, (0.0, 1.0), 4, 0.0, 0.0, 0, 0, np.inf, True],
		[4, 1.0, 4.0, (0.0, 1.0), 4, 0.0, 0.0, 0, 0, np.inf, True],
		[5, 0.0, 5.0, (0.0, 1.0), 5, 0.0, 0.0, 0, 0, np.inf, True],
		[5, 1.0, 5.0, (0.0, 1.0), 5, 0.0, 0.0, 0, 0, np.inf, True],
		[6, 0.0, 6.0, (0.0, 1.0), 6, 0.0, 0.0, 0, 0, np.inf, True],
		[6, 1.0, 6.0, (0.0, 1.0), 6, 0.0, 0.0, 0, 0, np.inf, True],
		[7, 0.0, 7.0, (0.0, 1.0), 7, 0.0, 0.0, 0, 0, np.inf, True],
		[7, 1.0, 7.0, (0.0, 1.0), 7, 0.0, 0.0, 0, 0, np.inf, True],
		[0, 2.0, 0.0, (0.0, 2.0), 0, 0.0, 0.0, 0, 0, np.inf, True],
		[1, 2.0, 1.0, (0.0, 2.0), 1, 0.0, 0.0, 0, 0, np.inf, True],
		[2, 2.0, 2.0, (0.0, 2.0), 2, 0.0, 0.0, 0, 0, np.inf, True],
		[3, 2.0, 3.0, (0.0, 2.0), 3, 0.0, 0.0, 0, 0, np.inf, True],
		[4, 2.0, 4.0, (0.0, 2.0), 4, 0.0, 0.0, 0, 0, np.inf, True],
		[5, 2.0, 5.0, (0.0, 2.0), 5, 0.0, 0.0, 0, 0, np.inf, True],
		[8, 0.0, 8.0, (0.0, 2.0), 6, 0.0, 0.0, 0, 0, np.inf, True],
		[8, 2.0, 8.0, (0.0, 2.0), 6, 0.0, 0.0, 0, 0, np.inf, True],
		[9, 0.0, 9.0, (0.0, 2.0), 7, 0.0, 0.0, 0, 0, np.inf, True],
		[9, 2.0, 9.0, (0.0, 2.0), 7, 0.0, 0.0, 0, 0, np.inf, True],
		[10, 1.0, 8.0, (1.0, 2.0), 6, 0.0, 0.0, 0, 0, np.inf, True],
		[10, 2.0, 6.0, (1.0, 2.0), 6, 0.0, 0.0, 0, 0, np.inf, True],
		[11, 1.0, 9.0, (1.0, 2.0), 7, 0.0, 0.0, 0, 0, np.inf, True],
		[11, 2.0, 7.0, (1.0, 2.0), 7, 0.0, 0.0, 0, 0, np.inf, True]],
		columns=['point_id', 'image_index', 'keypoint_index',
		'edge', 'match_idx', 'x', 'y'])
		'edge', 'match_idx', 'x', 'y','x_off', 'y_off',
		'corr', 'valid'])

		df.index.name = 'measure_id'

pytest.ini

+0 −2

Original line number	Diff line number	Diff line
		[pytest]
		addopts = --doctest-modules --cov-report term-missing --cov=autocnet
		filterwarnings =
		ignore::UserWarning

tests/conftest.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -44,7 +44,7 @@ def candidategraph():
		keypoints = pd.DataFrame(kps, columns=['x', 'y', 'response', 'size', 'angle',
		'octave', 'layer'])

		for i, n in cg.nodes_iter(data=True):
		for i, n in cg.nodes.data('data'):
		n.keypoints = keypoints
		n.descriptors = np.random.random(size=(3, 128))
		n.masks = masks