Properly removing the dill dependency (024273ad) · Commits · aflab / astrogeology / Autocnet

autocnet/graph/network.py

+14 −59

Original line number	Diff line number	Diff line
		@@ -3,7 +3,6 @@ import os
		from time import gmtime, strftime
		import warnings

		import dill as pickle
		import networkx as nx
		import pandas as pd

		@@ -81,24 +80,6 @@ class CandidateGraph(nx.Graph):
		eq = False
		return eq

		@classmethod
		def from_graph(cls, graph):
		"""
		Return a graph object from a pickled file
		Parameters
		----------
		graph : str
		PATH to the graph object

		Returns
		-------
		graph : object
		CandidateGraph object
		"""
		with open(graph, 'rb') as f:
		graph = pickle.load(f)
		return graph

		@classmethod
		def from_filelist(cls, filelist, basepath=None):
		"""
		@@ -287,7 +268,6 @@ class CandidateGraph(nx.Graph):
		"""
		self.apply_func_to_edges('match', args, *kwargs)


		def decompose_and_match(self, args, *kwargs):
		"""
		For all edges in the graph, apply coupled decomposition followed by
		@@ -344,6 +324,20 @@ class CandidateGraph(nx.Graph):
		cycles.append((s,d,n))
		return cycles

		def minimum_spanning_tree(self):
		"""
		Calculates the minimum spanning tree of the graph

		Returns
		-------

		: DataFrame
		boolean mask for edges in the minimum spanning tree
		"""

		mst = nx.minimum_spanning_tree(self)
		return self.create_edge_subgraph(mst.edges())

		def apply_func_to_edges(self, function, args, *kwargs):
		"""
		Iterates over edges using an optional mask and and applies the given function.
		@@ -405,16 +399,6 @@ class CandidateGraph(nx.Graph):
		'''
		self.apply_func_to_edges('compute_fundamental_matrix', args, *kwargs)

		def refine_fundamental_matrix_matches(self, args, *kwargs):
		"""
		Refine the fundamental matrix matches using reprojective error

		See Also
		--------
		autocnet.transformation.transformations.FundamentalMatrix.refine_matches
		"""
		self.apply_func_to_edges('refine_fundamental_matrix_matches', args, *kwargs)

		def subpixel_register(self, args, *kwargs):
		'''
		Compute subpixel offsets for all edges using identical parameters
		@@ -445,20 +429,6 @@ class CandidateGraph(nx.Graph):
		'''
		self.apply_func_to_edges('overlap')

		def minimum_spanning_tree(self):
		"""
		Calculates the minimum spanning tree of the graph

		Returns
		-------

		: DataFrame
		boolean mask for edges in the minimum spanning tree
		"""

		mst = nx.minimum_spanning_tree(self)
		return self.create_edge_subgraph(mst.edges())

		def to_filelist(self):
		"""
		Generate a file list for the entire graph.
		@@ -493,21 +463,6 @@ class CandidateGraph(nx.Graph):
		self.cn = [n.point_to_correspondence_df for i, n in self.nodes_iter(data=True) if
		isinstance(n.point_to_correspondence_df, pd.DataFrame)]

		def to_json_file(self, outputfile):
		"""
		Write the edge structure to a JSON adjacency list

		Parameters
		----------

		outputfile : str
		PATH where the JSON will be written
		"""
		adjacency_dict = {}
		for n in self.nodes():
		adjacency_dict[n] = self.neighbors(n)
		io_json.write_json(adjacency_dict, outputfile)

		def island_nodes(self):
		"""
		Finds single nodes that are completely disconnected from the rest of the graph