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using for loop to install conda package

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"""
Text-based visual representations of graphs
"""
import sys
import warnings
from collections import defaultdict
import networkx as nx
from networkx.utils import open_file
__all__ = ["forest_str", "generate_network_text", "write_network_text"]
class _AsciiBaseGlyphs:
empty = "+"
newtree_last = "+-- "
newtree_mid = "+-- "
endof_forest = " "
within_forest = ": "
within_tree = "| "
class AsciiDirectedGlyphs(_AsciiBaseGlyphs):
last = "L-> "
mid = "|-> "
backedge = "<-"
class AsciiUndirectedGlyphs(_AsciiBaseGlyphs):
last = "L-- "
mid = "|-- "
backedge = "-"
class _UtfBaseGlyphs:
# Notes on available box and arrow characters
# https://en.wikipedia.org/wiki/Box-drawing_character
# https://stackoverflow.com/questions/2701192/triangle-arrow
empty = "╙"
newtree_last = "╙── "
newtree_mid = "╟── "
endof_forest = " "
within_forest = "╎ "
within_tree = "│ "
class UtfDirectedGlyphs(_UtfBaseGlyphs):
last = "└─╼ "
mid = "├─╼ "
backedge = "╾"
class UtfUndirectedGlyphs(_UtfBaseGlyphs):
last = "└── "
mid = "├── "
backedge = "─"
def generate_network_text(
graph, with_labels=True, sources=None, max_depth=None, ascii_only=False
):
"""Generate lines in the "network text" format
This works via a depth-first traversal of the graph and writing a line for
each unique node encountered. Non-tree edges are written to the right of
each node, and connection to a non-tree edge is indicated with an ellipsis.
This representation works best when the input graph is a forest, but any
graph can be represented.
This notation is original to networkx, although it is simple enough that it
may be known in existing literature. See #5602 for details. The procedure
is summarized as follows:
1. Given a set of source nodes (which can be specified, or automatically
discovered via finding the (strongly) connected components and choosing one
node with minimum degree from each), we traverse the graph in depth first
order.
2. Each reachable node will be printed exactly once on it's own line.
3. Edges are indicated in one of three ways:
a. a parent "L-style" connection on the upper left. This corresponds to
a traversal in the directed DFS tree.
b. a backref "<-style" connection shown directly on the right. For
directed graphs, these are drawn for any incoming edges to a node that
is not a parent edge. For undirected graphs, these are drawn for only
the non-parent edges that have already been represented (The edges that
have not been represented will be handled in the recursive case).
c. a child "L-style" connection on the lower right. Drawing of the
children are handled recursively.
4. The children of each node (wrt the directed DFS tree) are drawn
underneath and to the right of it. In the case that a child node has already
been drawn the connection is replaced with an ellipsis ("...") to indicate
that there is one or more connections represented elsewhere.
5. If a maximum depth is specified, an edge to nodes past this maximum
depth will be represented by an ellipsis.
Parameters
----------
graph : nx.DiGraph | nx.Graph
Graph to represent
with_labels : bool | str
If True will use the "label" attribute of a node to display if it
exists otherwise it will use the node value itself. If given as a
string, then that attribute name will be used instead of "label".
Defaults to True.
sources : List
Specifies which nodes to start traversal from. Note: nodes that are not
reachable from one of these sources may not be shown. If unspecified,
the minimal set of nodes needed to reach all others will be used.
max_depth : int | None
The maximum depth to traverse before stopping. Defaults to None.
ascii_only : Boolean
If True only ASCII characters are used to construct the visualization
Yields
------
str : a line of generated text
"""
is_directed = graph.is_directed()
if is_directed:
glyphs = AsciiDirectedGlyphs if ascii_only else UtfDirectedGlyphs
succ = graph.succ
pred = graph.pred
else:
glyphs = AsciiUndirectedGlyphs if ascii_only else UtfUndirectedGlyphs
succ = graph.adj
pred = graph.adj
if isinstance(with_labels, str):
label_attr = with_labels
elif with_labels:
label_attr = "label"
else:
label_attr = None
if max_depth == 0:
yield glyphs.empty + " ..."
elif len(graph.nodes) == 0:
yield glyphs.empty
else:
# If the nodes to traverse are unspecified, find the minimal set of
# nodes that will reach the entire graph
if sources is None:
sources = _find_sources(graph)
# Populate the stack with each:
# 1. parent node in the DFS tree (or None for root nodes),
# 2. the current node in the DFS tree
# 2. a list of indentations indicating depth
# 3. a flag indicating if the node is the final one to be written.
# Reverse the stack so sources are popped in the correct order.
last_idx = len(sources) - 1
stack = [
(None, node, [], (idx == last_idx)) for idx, node in enumerate(sources)
][::-1]
num_skipped_children = defaultdict(lambda: 0)
seen_nodes = set()
while stack:
parent, node, indents, this_islast = stack.pop()
if node is not Ellipsis:
skip = node in seen_nodes
if skip:
# Mark that we skipped a parent's child
num_skipped_children[parent] += 1
if this_islast:
# If we reached the last child of a parent, and we skipped
# any of that parents children, then we should emit an
# ellipsis at the end after this.
if num_skipped_children[parent] and parent is not None:
# Append the ellipsis to be emitted last
next_islast = True
try_frame = (node, Ellipsis, indents, next_islast)
stack.append(try_frame)
# Redo this frame, but not as a last object
next_islast = False
try_frame = (parent, node, indents, next_islast)
stack.append(try_frame)
continue
if skip:
continue
seen_nodes.add(node)
if not indents:
# Top level items (i.e. trees in the forest) get different
# glyphs to indicate they are not actually connected
if this_islast:
this_prefix = indents + [glyphs.newtree_last]
next_prefix = indents + [glyphs.endof_forest]
else:
this_prefix = indents + [glyphs.newtree_mid]
next_prefix = indents + [glyphs.within_forest]
else:
# For individual tree edges distinguish between directed and
# undirected cases
if this_islast:
this_prefix = indents + [glyphs.last]
next_prefix = indents + [glyphs.endof_forest]
else:
this_prefix = indents + [glyphs.mid]
next_prefix = indents + [glyphs.within_tree]
if node is Ellipsis:
label = " ..."
suffix = ""
children = []
else:
if label_attr is not None:
label = str(graph.nodes[node].get(label_attr, node))
else:
label = str(node)
# Determine:
# (1) children to traverse into after showing this node.
# (2) parents to immediately show to the right of this node.
if is_directed:
# In the directed case we must show every successor node
# note: it may be skipped later, but we don't have that
# information here.
children = list(succ[node])
# In the directed case we must show every predecessor
# except for parent we directly traversed from.
handled_parents = {parent}
else:
# Showing only the unseen children results in a more
# concise representation for the undirected case.
children = [
child for child in succ[node] if child not in seen_nodes
]
# In the undirected case, parents are also children, so we
# only need to immediately show the ones we can no longer
# traverse
handled_parents = {*children, parent}
if max_depth is not None and len(indents) == max_depth - 1:
# Use ellipsis to indicate we have reached maximum depth
if children:
children = [Ellipsis]
handled_parents = {parent}
# The other parents are other predecessors of this node that
# are not handled elsewhere.
other_parents = [p for p in pred[node] if p not in handled_parents]
if other_parents:
if label_attr is not None:
other_parents_labels = ", ".join(
[
str(graph.nodes[p].get(label_attr, p))
for p in other_parents
]
)
else:
other_parents_labels = ", ".join(
[str(p) for p in other_parents]
)
suffix = " ".join(["", glyphs.backedge, other_parents_labels])
else:
suffix = ""
# Emit the line for this node, this will be called for each node
# exactly once.
yield "".join(this_prefix + [label, suffix])
# Push children on the stack in reverse order so they are popped in
# the original order.
for idx, child in enumerate(children[::-1]):
next_islast = idx == 0
try_frame = (node, child, next_prefix, next_islast)
stack.append(try_frame)
@open_file(1, "w")
def write_network_text(
graph,
path=None,
with_labels=True,
sources=None,
max_depth=None,
ascii_only=False,
end="\n",
):
"""Creates a nice text representation of a graph
This works via a depth-first traversal of the graph and writing a line for
each unique node encountered. Non-tree edges are written to the right of
each node, and connection to a non-tree edge is indicated with an ellipsis.
This representation works best when the input graph is a forest, but any
graph can be represented.
Parameters
----------
graph : nx.DiGraph | nx.Graph
Graph to represent
path : string or file or callable or None
Filename or file handle for data output.
if a function, then it will be called for each generated line.
if None, this will default to "sys.stdout.write"
with_labels : bool | str
If True will use the "label" attribute of a node to display if it
exists otherwise it will use the node value itself. If given as a
string, then that attribute name will be used instead of "label".
Defaults to True.
sources : List
Specifies which nodes to start traversal from. Note: nodes that are not
reachable from one of these sources may not be shown. If unspecified,
the minimal set of nodes needed to reach all others will be used.
max_depth : int | None
The maximum depth to traverse before stopping. Defaults to None.
ascii_only : Boolean
If True only ASCII characters are used to construct the visualization
end : string
The line ending character
Examples
--------
>>> graph = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
>>> nx.write_network_text(graph)
╙── 0
├─╼ 1
│ ├─╼ 3
│ └─╼ 4
└─╼ 2
├─╼ 5
└─╼ 6
>>> # A near tree with one non-tree edge
>>> graph.add_edge(5, 1)
>>> nx.write_network_text(graph)
╙── 0
├─╼ 1 ╾ 5
│ ├─╼ 3
│ └─╼ 4
└─╼ 2
├─╼ 5
│ └─╼ ...
└─╼ 6
>>> graph = nx.cycle_graph(5)
>>> nx.write_network_text(graph)
╙── 0
├── 1
│ └── 2
│ └── 3
│ └── 4 ─ 0
└── ...
>>> graph = nx.generators.barbell_graph(4, 2)
>>> nx.write_network_text(graph)
╙── 4
├── 5
│ └── 6
│ ├── 7
│ │ ├── 8 ─ 6
│ │ │ └── 9 ─ 6, 7
│ │ └── ...
│ └── ...
└── 3
├── 0
│ ├── 1 ─ 3
│ │ └── 2 ─ 0, 3
│ └── ...
└── ...
>>> graph = nx.complete_graph(5, create_using=nx.Graph)
>>> nx.write_network_text(graph)
╙── 0
├── 1
│ ├── 2 ─ 0
│ │ ├── 3 ─ 0, 1
│ │ │ └── 4 ─ 0, 1, 2
│ │ └── ...
│ └── ...
└── ...
>>> graph = nx.complete_graph(3, create_using=nx.DiGraph)
>>> nx.write_network_text(graph)
╙── 0 ╾ 1, 2
├─╼ 1 ╾ 2
│ ├─╼ 2 ╾ 0
│ │ └─╼ ...
│ └─╼ ...
└─╼ ...
"""
if path is None:
# The path is unspecified, write to stdout
_write = sys.stdout.write
elif hasattr(path, "write"):
# The path is already an open file
_write = path.write
elif callable(path):
# The path is a custom callable
_write = path
else:
raise TypeError(type(path))
for line in generate_network_text(
graph,
with_labels=with_labels,
sources=sources,
max_depth=max_depth,
ascii_only=ascii_only,
):
_write(line + end)
def _find_sources(graph):
"""
Determine a minimal set of nodes such that the entire graph is reachable
"""
# For each connected part of the graph, choose at least
# one node as a starting point, preferably without a parent
if graph.is_directed():
# Choose one node from each SCC with minimum in_degree
sccs = list(nx.strongly_connected_components(graph))
# condensing the SCCs forms a dag, the nodes in this graph with
# 0 in-degree correspond to the SCCs from which the minimum set
# of nodes from which all other nodes can be reached.
scc_graph = nx.condensation(graph, sccs)
supernode_to_nodes = {sn: [] for sn in scc_graph.nodes()}
# Note: the order of mapping differs between pypy and cpython
# so we have to loop over graph nodes for consistency
mapping = scc_graph.graph["mapping"]
for n in graph.nodes:
sn = mapping[n]
supernode_to_nodes[sn].append(n)
sources = []
for sn in scc_graph.nodes():
if scc_graph.in_degree[sn] == 0:
scc = supernode_to_nodes[sn]
node = min(scc, key=lambda n: graph.in_degree[n])
sources.append(node)
else:
# For undirected graph, the entire graph will be reachable as
# long as we consider one node from every connected component
sources = [
min(cc, key=lambda n: graph.degree[n])
for cc in nx.connected_components(graph)
]
sources = sorted(sources, key=lambda n: graph.degree[n])
return sources
def forest_str(graph, with_labels=True, sources=None, write=None, ascii_only=False):
"""Creates a nice utf8 representation of a forest
This function has been superseded by
:func:`nx.readwrite.text.generate_network_text`, which should be used
instead.
Parameters
----------
graph : nx.DiGraph | nx.Graph
Graph to represent (must be a tree, forest, or the empty graph)
with_labels : bool
If True will use the "label" attribute of a node to display if it
exists otherwise it will use the node value itself. Defaults to True.
sources : List
Mainly relevant for undirected forests, specifies which nodes to list
first. If unspecified the root nodes of each tree will be used for
directed forests; for undirected forests this defaults to the nodes
with the smallest degree.
write : callable
Function to use to write to, if None new lines are appended to
a list and returned. If set to the `print` function, lines will
be written to stdout as they are generated. If specified,
this function will return None. Defaults to None.
ascii_only : Boolean
If True only ASCII characters are used to construct the visualization
Returns
-------
str | None :
utf8 representation of the tree / forest
Examples
--------
>>> graph = nx.balanced_tree(r=2, h=3, create_using=nx.DiGraph)
>>> print(nx.forest_str(graph))
╙── 0
├─╼ 1
│ ├─╼ 3
│ │ ├─╼ 7
│ │ └─╼ 8
│ └─╼ 4
│ ├─╼ 9
│ └─╼ 10
└─╼ 2
├─╼ 5
│ ├─╼ 11
│ └─╼ 12
└─╼ 6
├─╼ 13
└─╼ 14
>>> graph = nx.balanced_tree(r=1, h=2, create_using=nx.Graph)
>>> print(nx.forest_str(graph))
╙── 0
└── 1
└── 2
>>> print(nx.forest_str(graph, ascii_only=True))
+-- 0
L-- 1
L-- 2
"""
msg = (
"\nforest_str is deprecated as of version 3.1 and will be removed "
"in version 3.3. Use generate_network_text or write_network_text "
"instead.\n"
)
warnings.warn(msg, DeprecationWarning)
if len(graph.nodes) > 0:
if not nx.is_forest(graph):
raise nx.NetworkXNotImplemented("input must be a forest or the empty graph")
printbuf = []
if write is None:
_write = printbuf.append
else:
_write = write
write_network_text(
graph,
_write,
with_labels=with_labels,
sources=sources,
ascii_only=ascii_only,
end="",
)
if write is None:
# Only return a string if the custom write function was not specified
return "\n".join(printbuf)