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Merge pull request #3 from IdeasLabUT/issue-1-fix
Fixes - including issue #1
2 parents d7fb1b9 + 59b3a61 commit e362710

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.gitignore

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DyNetworkX.egg-info/
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tutorials/facebook-wall.txt.anon
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tutorials/execs.email.linesnum
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# venv
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.venv

README.rst renamed to README.md

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==========
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DyNetworkX is a Python package for the study of dynamic network analysis (DNA).
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DyNetworkX used to be a fork of `NetworkX <https://networkx.github.io/documentation/stable/index.html>`_
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package. Thus, implementation, documentation and the development of DyNetworkX is heavily
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DyNetworkX used to be a fork of [NetworkX](https://networkx.github.io/documentation/stable/index.html) package. Thus, implementation, documentation and the development of DyNetworkX is heavily
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influenced by NetworkX.
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**Documentation:** https://dynetworkx.readthedocs.io/
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computer scientists, and social scientists. Overall, everyone interested
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in analyzing dynamic networks.
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Install
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-------
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You can install the latest version of DyNetworkX using pip:
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```python
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pip install dynetworkx
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```
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Python
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------
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NetworkX uses to provide more features such as numerical linear algebra and
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drawing. In order to make the most out of NetworkX you will want to know how
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to write basic programs in Python. Among the many guides to Python, we
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recommend the `Python documentation <https://docs.python.org/3/>`_.
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recommend the [Python documentation](https://docs.python.org/3/).
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License
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-------
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Released under the 3-Clause BSD license (see `dydoc/source/license.rst`)::
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Released under the 3-Clause BSD license (see [LICENSE.txt](LICENSE.txt)):
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Copyright (C) 2018 IDEASLab @ The University of Toledo.
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dynetworkx/classes/impulsedigraph.py

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from networkx.classes.digraph import DiGraph
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from networkx.classes.multidigraph import MultiDiGraph
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import dynetworkx as dnx
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from networkx.exception import NetworkXError
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from sortedcontainers import SortedList, SortedDict
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from sortedcontainers import SortedDict
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import random
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import math
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from timeit import default_timer as timer

dynetworkx/classes/impulsegraph.py

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from networkx.classes.graph import Graph
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from networkx.exception import NetworkXError
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from networkx.classes.multigraph import MultiGraph
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from networkx.classes.reportviews import NodeView, EdgeView, NodeDataView
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from sortedcontainers import SortedList, SortedDict
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from networkx.classes.reportviews import NodeDataView
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from sortedcontainers import SortedDict
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import random
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import math
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from timeit import default_timer as timer
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from sklearn.linear_model import LinearRegression
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from itertools import product
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from dynetworkx.classes.intervalgraph import IntervalGraph
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class ImpulseGraph(object):
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"""Base class for undirected impulse graphs.

dynetworkx/classes/intervaldigraph.py

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from networkx.classes.digraph import DiGraph
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from networkx.classes.multidigraph import MultiDiGraph
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import dynetworkx as dnx
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from networkx.exception import NetworkXError
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from dynetworkx.classes.intervaltree import IntervalTree
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from sortedcontainers import SortedList, SortedDict
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from sortedcontainers import SortedDict
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import random
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import math
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from timeit import default_timer as timer

dynetworkx/classes/intervalgraph.py

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from networkx.exception import NetworkXError
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from dynetworkx.classes.intervaltree import IntervalTree
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from networkx.classes.multigraph import MultiGraph
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from networkx.classes.reportviews import NodeView, EdgeView, NodeDataView
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from networkx.classes.reportviews import NodeDataView
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from sortedcontainers import SortedList, SortedDict
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import random
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import math
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True
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With specific overlapping interval:
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>>> G.has_edge(1, 2, begin=2)
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True
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>>> G.has_edge(2, 4, begin=12)

dynetworkx/classes/intervaltree.py

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import networkx as nx
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from sortedcontainers import SortedDict, SortedList
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import operator
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class Node:

dynetworkx/classes/snapshotgraph.py

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from networkx.classes.graph import Graph
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import numpy as np
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from networkx import adjacency_matrix, from_numpy_array
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from sortedcontainers import SortedDict, SortedList
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from sortedcontainers import SortedDict
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import copy
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[0 0 0 0 0 0 0]]
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"""
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if sbunch and (start or end):
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raise ValueError('Either sbunch or (start and end) can be specified.')
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elif sbunch:
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