def report_trajectories(res, name_obs=False):
r = Report()
f = r.figure()
import networkx as nx
G0 = create_graph_trajectories0(res['trajectories'], label_state=False,
name_obs=name_obs)
f.data('tree0', nx.to_pydot(G0).create_png(), mime=MIME_PNG)
G0 = create_graph_trajectories0(res['trajectories'], label_state=True,
name_obs=name_obs)
f.data('tree1', nx.to_pydot(G0).create_png(), mime=MIME_PNG)
#
# G1 = create_graph_trajectories(res['decisions'])
# d1 = nx.to_pydot(G1) # d is a pydot graph object, dot options can be easily set
# # r.data('tree1', d.create_pdf(), mime=MIME_PDF)
# f.data('tree1', d1.create_png(), mime=MIME_PNG)
#
#
#
# if 'decisions_dis' in res:
# G2 = create_graph_trajectories(res['decisions_dis'])
# d2 = nx.to_pydot(G2) # d is a pydot graph object, dot options can be easily set
# f.data('tree2', d2.create_png(), mime=MIME_PNG)
return r
def file_to_dot(infile):
interactions = defaultdict(lambda: 0)
es = list(edges(infile))
for a, b, users in es:
interactions[a, b] = users
keys, ratio = compute_ratios(interactions,
lambda k: interactions[k, k] > 5)
G = nx.Graph()
for a, b, users in es:
if a > b and a in keys and b in keys and users > 0 and ratio[a, b] > 0:
rat = (ratio[a, b] + ratio[b, a]) / 2
G.add_edge(a, b, {
'ratio': rat,
'users': users,
'connection': rat**-1
})
btwn = nx.edge_betweenness_centrality(G, weight='connection')
GG = nx.Graph()
GG.add_edges_from([(a, b, {
'weight': val
}) for (a, b), val in btwn.items()])
# GG.add_edges_from([(a, b, merge({'betweenness':btwn[a,b]}, G[a][b]))
# for a,b in G.edges_iter()])
for node in GG.nodes_iter():
GG.node[node]['height'] = GG.node[node]['width'] = size(G, node)
GG.node[node]['color'] = color(G, node)
Gtree = nx.minimum_spanning_tree(GG)
Gtree_dot = nx.to_pydot(Gtree)
return Gtree_dot.to_string()
def parse_tree(self, string, explicit=False, display=False):
'''
Returns graph representation of the equation tree of string,
as a networkx graph.
If explicit is True, then '-x' -> '0-x'
'''
stack, indices = self.parse(string)
tree = EquationTreeParser._create_tree(stack, indices)
if explicit:
idx = -1
for node, data in tree.nodes(data=True):
if data['label'] == '-' and len(tree[node]) == 1:
nbr = tree[node].keys()[0]
tree.remove_edge(node, nbr)
tree.add_node(idx, label='0')
tree.add_edge(node, idx, label='left')
tree.add_edge(node, nbr, label='right')
idx -= 1
if display:
_, image_path = tempfile.mkstemp()
pydot_graph = nx.to_pydot(tree)
pydot_graph.write_png(image_path)
cv2.imshow('equation tree', cv2.imread(image_path))
cv2.waitKey()
cv2.destroyAllWindows()
return tree
def build_graph(self, G):
G.add_edge("A", "B")
G.add_edge("A", "C")
G.add_edge("B", "C")
G.add_edge("A", "D")
G.add_node("E")
return G, nx.to_pydot(G)
def visualize_mcts_tree(mcts, depth, filename):
"""
Creates a small subgraph for visualization with a
number of levels equal to 2 + depth labelled with the
MCTS values from mcts and saves it as filename.png
"""
# Find root of the MCTS tree
mcts_root = nx.topological_sort(mcts.digraph)[0]
# root = GameState()
subgraph = nx.DiGraph()
# Don't include the empty board (the root) in the graphs
# for first_move in mcts.digraph.successors(root):
print(mcts_root)
print(type(mcts_root))
for first_move in mcts.digraph.successors(mcts_root):
add_edges(mcts.digraph, subgraph, first_move, depth)
dot_graph = nx.to_pydot(subgraph)
for node in dot_graph.get_nodes():
attr = node.get_attributes()
try:
node.set_label('{}{}/{}\n{:.2f}'.format(attr['state'],
int(attr['w']),
int(attr['n']),
float(attr['uct'])))
except KeyError:
pass
dot_graph.set_graph_defaults(fontname='Courier')
dot_graph.set_rankdir('LR')
dot_graph.write_png('{}.png'.format(filename))
def dump(self, output=False):
"""Prints out network and returns networkx graph
Prints the devices, links, and flows associated with the network, and
returns a pydot object with the network graph.
Parameters
----------
output : boolean, optional
Specifies whether to print the network information (the default is
False).
Returns
-------
pydot
pydot object containing the network graph
"""
# Print network information if output is True
if output:
print "Devices:\n"
for device_id in self.devices:
print self.devices[device_id]
print "Links:\n"
for link_id in self.links:
print self.links[link_id]
print "Flows:\n"
for flow_id in self.flows:
print self.flows[flow_id]
# Convert the graph to a pydot object and return
return nx.to_pydot(self.g)
def dot_graph(filename='conversions'):
# Edges from Convert
dg = nx.DiGraph()
for a, b in convert.graph.edges():
cost = convert.graph.edge[a][b]['cost']
dg.add_edge(cls_name(a),
cls_name(b),
cost=cost,
penwidth=max(log(1. / (cost + 0.06)), 1))
# Edges from Append
for a, b in append.funcs:
if b is not object and a != b:
dg.add_edge(cls_name(b), cls_name(a), color='blue')
# Color edges
for n in convert.graph.nodes() + list(pluck(0, append.funcs)):
if issubclass(n, tuple(ooc_types)):
dg.node[cls_name(n)]['color'] = 'red'
# Convert to pydot
p = nx.to_pydot(dg)
p.set_overlap(False)
p.set_splines(True)
with open(filename + '.dot', 'w') as f:
f.write(p.to_string())
os.system('neato -Tpdf %s.dot -o %s.pdf' % (filename, filename))
print("Writing graph to %s.pdf" % filename)
os.system('neato -Tpng %s.dot -o %s.png' % (filename, filename))
print("Writing graph to %s.png" % filename)
def build_graph(self, G):
G.add_edge('A','B')
G.add_edge('A','C')
G.add_edge('B','C')
G.add_edge('A','D')
G.add_node('E')
return G, nx.to_pydot(G)
def parse_tree(self, string, explicit=False, display=False):
'''
Returns graph representation of the equation tree of string,
as a networkx graph.
If explicit is True, then '-x' -> '0-x'
'''
stack, indices = self.parse(string)
tree = ExpressionParser._create_tree(stack, indices)
if explicit:
idx = -1
for node, data in tree.nodes(data=True):
if data['label'] == '-' and len(tree[node]) == 1:
nbr = tree[node].keys()[0]
tree.remove_edge(node, nbr)
tree.add_node(idx, label='0')
tree.add_edge(node, idx, label='left')
tree.add_edge(node, nbr, label='right')
idx -= 1
if display:
"""
For some reason, displaying the tree doesn't work now.
"""
_, image_path = tempfile.mkstemp()
pydot_graph = nx.to_pydot(tree)
pydot_graph.write_png(image_path)
cv2.imshow('equation tree', cv2.imread(image_path))
cv2.waitKey()
cv2.destroyAllWindows()
return tree
def write_graph2dot(graph, other_graphs, c_fname, img_fname,
for_latex, multi_page, layout):
if pydot is None:
print('Pydot not found. Exporting using pycflow2dot.write_dot_file().')
dot_str = dump_dot_wo_pydot(graph, other_graphs, c_fname,
for_latex=for_latex, multi_page=multi_page)
dot_path = write_dot_file(dot_str, img_fname)
else:
# dump using networkx and pydot
if hasattr(nx,"nx_pydot"):
pydot_graph = nx.nx_pydot.to_pydot(graph)
else:
pydot_graph = nx.to_pydot(graph)
pydot_graph.set_splines('true')
if layout == 'twopi':
pydot_graph.set_ranksep(5)
pydot_graph.set_root('main')
else:
pydot_graph.set_overlap(False)
pydot_graph.set_rankdir('LR')
dot_path = img_fname +'.dot'
pydot_graph.write(dot_path, format='dot')
return dot_path
def pydot_checks(self, G):
G.add_edge('A', 'B')
G.add_edge('A', 'C')
G.add_edge('B', 'C')
G.add_edge('A', 'D')
G.add_node('E')
P = nx.to_pydot(G)
G2 = G.__class__(nx.from_pydot(P))
assert_graphs_equal(G, G2)
fname = tempfile.mktemp()
assert_true(P.write_raw(fname))
Pin = pydotplus.graph_from_dot_file(fname)
n1 = sorted([p.get_name() for p in P.get_node_list()])
n2 = sorted([p.get_name() for p in Pin.get_node_list()])
assert_true(n1 == n2)
e1 = [(e.get_source(), e.get_destination()) for e in P.get_edge_list()]
e2 = [(e.get_source(), e.get_destination())
for e in Pin.get_edge_list()]
assert_true(sorted(e1) == sorted(e2))
Hin = nx.drawing.nx_pydot.read_dot(fname)
Hin = G.__class__(Hin)
assert_graphs_equal(G, Hin)
def visualize_mcts_tree(mcts, depth, filename):
"""
Creates a small subgraph for visualization with a
number of levels equal to 2 + depth labelled with the
MCTS values from mcts and saves it as filename.png
"""
# Find root of the MCTS tree
mcts_root = nx.topological_sort(mcts.digraph)[0]
# root = GameState()
subgraph = nx.DiGraph()
# Don't include the empty board (the root) in the graphs
# for first_move in mcts.digraph.successors(root):
print(mcts_root)
print(type(mcts_root))
for first_move in mcts.digraph.successors(mcts_root):
add_edges(mcts.digraph, subgraph, first_move, depth)
dot_graph = nx.to_pydot(subgraph)
for node in dot_graph.get_nodes():
attr = node.get_attributes()
try:
node.set_label('{}{}/{}\n{:.2f}'.format(attr['state'],
int(attr['w']),
int(attr['n']),
float(attr['uct'])))
except KeyError:
pass
dot_graph.set_graph_defaults(fontname='Courier')
dot_graph.set_rankdir('LR')
dot_graph.write_png('{}.png'.format(filename))
def write_graph2dot(graph, other_graphs, c_fname, img_fname, for_latex,
multi_page, layout):
if pydot is None:
print('Pydot not found. Exporting using pycflow2dot.write_dot_file().')
dot_str = dump_dot_wo_pydot(graph,
other_graphs,
c_fname,
for_latex=for_latex,
multi_page=multi_page)
dot_path = write_dot_file(dot_str, img_fname)
else:
# dump using networkx and pydot
pydot_graph = nx.to_pydot(graph)
pydot_graph.set_splines('true')
if layout == 'twopi':
pydot_graph.set_ranksep(5)
pydot_graph.set_root('main')
else:
pydot_graph.set_overlap(False)
pydot_graph.set_rankdir('LR')
dot_path = img_fname + '.dot'
pydot_graph.write(dot_path, format='dot')
return dot_path
def dot_graph(filename='conversions'):
# Edges from Convert
dg = nx.DiGraph()
for a, b in convert.graph.edges():
cost = convert.graph.edge[a][b]['cost']
dg.add_edge(cls_name(a), cls_name(b),
cost=cost,
penwidth=max(log(1./(cost + 0.06)), 1))
# Edges from Append
for a, b in append.funcs:
if b is not object and a != b:
dg.add_edge(cls_name(b), cls_name(a), color='blue')
# Color edges
for n in convert.graph.nodes() + list(pluck(0, append.funcs)):
if issubclass(n, tuple(ooc_types)):
dg.node[cls_name(n)]['color'] = 'red'
# Convert to pydot
p = nx.to_pydot(dg)
p.set_overlap(False)
p.set_splines(True)
with open(filename + '.dot', 'w') as f:
f.write(p.to_string())
os.system('neato -Tpdf %s.dot -o %s.pdf' % (filename, filename))
print("Writing graph to %s.pdf" % filename)
os.system('neato -Tpng %s.dot -o %s.png' % (filename, filename))
print("Writing graph to %s.png" % filename)
def build_graph(self, G):
G.add_edge('A', 'B')
G.add_edge('A', 'C')
G.add_edge('B', 'C')
G.add_edge('A', 'D')
G.add_node('E')
return G, nx.to_pydot(G)
def pydot_checks(self, G):
G.add_edge('A','B')
G.add_edge('A','C')
G.add_edge('B','C')
G.add_edge('A','D')
G.add_node('E')
P = nx.to_pydot(G)
G2 = G.__class__(nx.from_pydot(P))
assert_graphs_equal(G, G2)
fname = tempfile.mktemp()
assert_true( P.write_raw(fname) )
Pin = pydotplus.graph_from_dot_file(fname)
n1 = sorted([p.get_name() for p in P.get_node_list()])
n2 = sorted([p.get_name() for p in Pin.get_node_list()])
assert_true( n1 == n2 )
e1=[(e.get_source(),e.get_destination()) for e in P.get_edge_list()]
e2=[(e.get_source(),e.get_destination()) for e in Pin.get_edge_list()]
assert_true( sorted(e1)==sorted(e2) )
Hin = nx.drawing.nx_pydot.read_dot(fname)
Hin = G.__class__(Hin)
assert_graphs_equal(G, Hin)
def _graph2pydot(graph, wrap=10, tikz=False, rankdir='TB'):
"""Convert (possibly labeled) state graph to dot str.
@type graph: L{LabeledDiGraph}
@rtype: str
"""
pydot = import_pydot()
if pydot is None:
return None
dummy_nx_graph = nx.MultiDiGraph()
_states2dot_str(graph,
dummy_nx_graph,
wrap=wrap,
tikz=tikz,
rankdir=rankdir)
_transitions2dot_str(graph.transitions, dummy_nx_graph, tikz=tikz)
pydot_graph = nx.to_pydot(dummy_nx_graph)
_place_initial_states(graph, pydot_graph, tikz)
pydot_graph.set_overlap('false')
#pydot_graph.set_size('"0.25,1"')
#pydot_graph.set_ratio('"compress"')
pydot_graph.set_nodesep(0.5)
pydot_graph.set_ranksep(0.1)
return pydot_graph
def drawGraph(self, key=None, filename="graph", format="pdf"):
graph = self.graphs.get(key)
if( not graph ):
return
if( graph.number_of_nodes() > 1 ):
ag = nx.to_pydot(graph)
ag.write("%s.%s"%(filename,format),format=format)
def _graph2pydot(graph, wrap=10, tikz=False,
rankdir='TB'):
"""Convert (possibly labeled) state graph to dot str.
@type graph: L{LabeledDiGraph}
@rtype: str
"""
pydot = import_pydot()
if pydot is None:
return None
dummy_nx_graph = nx.MultiDiGraph()
_states2dot_str(graph, dummy_nx_graph, wrap=wrap, tikz=tikz,
rankdir=rankdir)
_transitions2dot_str(graph.transitions, dummy_nx_graph, tikz=tikz)
pydot_graph = nx.to_pydot(dummy_nx_graph)
_place_initial_states(graph, pydot_graph, tikz)
pydot_graph.set_overlap('false')
#pydot_graph.set_size('"0.25,1"')
#pydot_graph.set_ratio('"compress"')
pydot_graph.set_nodesep(0.5)
pydot_graph.set_ranksep(0.1)
return pydot_graph
def neato_from_networkx( g, min_node_size = 0.5, max_node_size = 2.0, min_edge_width = 1.0, max_edge_width = 5.0, legend_attribute=None, label_nodes_directly = False ) :
d = nx.to_pydot( g )
d.set_overlap(False)
# normalize node size
nodewidths = array( [float(n.get_width()) for n in d.get_nodes()] )
# done this way in case input has all the same size to avoid divide by zero
node_range = (nodewidths.max() - nodewidths.min())/(max_node_size - min_node_size)
for n in d.get_nodes() :
n.set_width( min_node_size + (float(n.get_width()) - nodewidths.min()) / node_range )
n.set_fixedsize( "true" )
n.set_shape('circle')
# normalize edge width
edge_widths = array( [float(e.get_penwidth()) for e in d.get_edges()] )
edge_width_range = (edge_widths.max() - edge_widths.min())/(max_edge_width - min_edge_width)
for e in d.get_edges() :
e.set_penwidth( min_edge_width + (float(e.get_penwidth()) - edge_widths.min() )/edge_width_range )
# if the legend attribute is set, create a legend node
if label_nodes_directly :
if legend_attribute == None :
legend_attribute = 'labelval'
for n in d.get_nodes() :
n.set_label( n.get_attributes()[legend_attribute] )
else :
legend = pydot.Node( "legend" )
nodelist = [n.get_label()+": "+n.get_attributes()[legend_attribute] for n in d.get_nodes()]
nodelist.sort( lambda a,b : cmp( int( a.split(':')[0] ), int (b.split(':')[0] ) ))
legend.set_label( "\l".join([x for x in nodelist])+"\l" )
legend.set_shape("box")
d.add_node(legend)
return d
def get_dot(G_orig):
"""Change labels and colors to be presented with graphviz"""
G = G_orig.copy()
cluster_networks.relabel_graph(G)
tr = {}
for node in G.nodes_iter():
tr[node] = '"{}"'.format(node)
nx.relabel_nodes(G, tr, copy=False)
for node in G.nodes_iter():
label = str(node)
if len(label) > MAX_LABEL:
label = u'{}..."'.format(label[:MAX_LABEL])
G.node[node]['label'] = label
for node in cluster_networks.get_leaf_nodes(G):
G.node[node]['color'] = "blue"
for node in cluster_networks.hybrid_nodes(G):
G.node[node]['color'] = "#7BFF74" # light green
G.node[node]['style'] = 'filled'
for node in cluster_networks.get_root_nodes(G):
G.node[node]['color'] = "orange"
for node in cluster_networks.problematic_treechild_nodes(G):
G.node[node]['color'] = "#FF77EB" # light pink
G.node[node]['style'] = 'filled'
for u, v in cluster_networks.removable_edges(G):
G.edge[u][v]['color'] = "red"
for root in cluster_networks.get_root_nodes(G):
G.node[root]['label'] = '"R"'
dot = nx.to_pydot(G).to_string()
return dot.strip()
def dump(self, output=False):
"""Prints out network and returns networkx graph
Prints the devices, links, and flows associated with the network, and
returns a pydot object with the network graph.
Parameters
----------
output : boolean, optional
Specifies whether to print the network information (the default is
False).
Returns
-------
pydot
pydot object containing the network graph
"""
# Print network information if output is True
if output:
print "Devices:\n"
for device_id in self.devices:
print self.devices[device_id]
print "Links:\n"
for link_id in self.links:
print self.links[link_id]
print "Flows:\n"
for flow_id in self.flows:
print self.flows[flow_id]
# Convert the graph to a pydot object and return
return nx.to_pydot(self.g)
def graph(self, out_file=None):
if out_file is None:
doc_dir = self.config["dir"].get("doc")
out_file = os.path.join(doc_dir, "pipeline_viz.png")
if file_exists(out_file):
return out_file
pd = nx.to_pydot(self.dag)
pd.write_png(out_file, prog="dot")
def to_pydot(self, detailed=False):
"""Create GraphViz dot string from given AST.
@type ast: L{ASTNode}
@rtype: str
"""
g = ast_to_labeled_graph(self, detailed)
return nx.to_pydot(g)
def to_pydot(self, detailed=False):
"""Create GraphViz dot string from given AST.
@type ast: L{ASTNode}
@rtype: str
"""
g = ast_to_labeled_graph(self, detailed)
return nx.to_pydot(g)
def graph(self, out_file=None):
if out_file is None:
doc_dir = self.config["dir"].get("doc")
out_file = os.path.join(doc_dir, "pipeline_viz.png")
if file_exists(out_file):
return out_file
pd = nx.to_pydot(self.dag)
pd.write_png(out_file, prog="dot")
def plotGraph(self):
cntxt = self.context["disease"]
g = cntxt.getDocumentGraph()
ag = nx.to_pydot(g, strict=True)
gfile = os.path.join(
self.save_dir, "report_%s_unc%s_%s_critical.pdf" %
(self.proc_category, self.allow_uncertainty, self.currentCase))
ag.write(gfile, format="pdf")
def plotGraph(self):
cntxt = self.context["disease"]
g = cntxt.getDocumentGraph()
ag = nx.to_pydot(g, strict=True)
gfile = os.path.join(self.save_dir,
"report_%s_unc%s_%s_critical.pdf"%(self.proc_category,
self.allow_uncertainty,
self.currentCase))
ag.write(gfile,format="pdf")
def print_ascii_graph(model_):
"""
pip install img2txt.py
python -c
"""
from PIL import Image # NOQA
from six.moves import StringIO
# import networkx as nx
import copy
model = copy.deepcopy(model_)
assert model is not model_
# model.graph.setdefault('graph', {})['size'] = '".4,.4"'
model.graph.setdefault('graph', {})['size'] = '".3,.3"'
model.graph.setdefault('graph', {})['height'] = '".3,.3"'
pydot_graph = nx.to_pydot(model)
png_str = pydot_graph.create_png(prog='dot')
sio = StringIO()
sio.write(png_str)
sio.seek(0)
pil_img = Image.open(sio) # NOQA
logger.info('pil_img.size = %r' % (pil_img.size, ))
# def print_ascii_image(pil_img):
# img2txt = ut.import_module_from_fpath('/home/joncrall/venv/bin/img2txt.py')
# import sys
# pixel = pil_img.load()
# width, height = pil_img.size
# bgcolor = None
# #fill_string =
# img2txt.getANSIbgstring_for_ANSIcolor(img2txt.getANSIcolor_for_rgb(bgcolor))
# fill_string = "\x1b[49m"
# fill_string += "\x1b[K" # does not move the cursor
# sys.stdout.write(fill_string)
# img_ansii_str = img2txt.generate_ANSI_from_pixels(pixel, width, height, bgcolor)
# sys.stdout.write(img_ansii_str)
def print_ascii_image(pil_img):
# https://gist.github.com/cdiener/10491632
SC = 1.0
GCF = 1.0
WCF = 1.0
img = pil_img
S = (int(round(img.size[0] * SC * WCF * 3)),
int(round(img.size[1] * SC)))
img = np.sum(np.asarray(img.resize(S)), axis=2)
logger.info('img.shape = %r' % (img.shape, ))
img -= img.min()
chars = np.asarray(list(' .,:;irsXA253hMHGS#9B&@'))
img = (1.0 - img / img.max())**GCF * (chars.size - 1)
logger.info('\n'.join((''.join(r) for r in chars[img.astype(int)])))
print_ascii_image(pil_img)
pil_img.close()
pass
def write_networkx_to_dot(dg, filename='mydask'):
import os
p = nx.to_pydot(dg)
p.set_rankdir('BT')
with open(filename + '.dot', 'w') as f:
f.write(p.to_string())
os.system('dot -Tpdf %s.dot -o %s.pdf' % (filename, filename))
os.system('dot -Tpng %s.dot -o %s.png' % (filename, filename))
print("Writing graph to %s.pdf" % filename)
def write_dot(G, path):
# requires graphviz, pygraphviz, pydot
g = nx.to_pydot(G)
for n in g.get_nodes():
n.set_label(G.node_labels[int(n.get_name())])
for e in g.get_edges():
key = (int(e.get_destination()), int(e.get_source()))
s = '\\n'.join(list(str(x) for x in G.edge_labels[key]))
e.set_label(s)
g.write_dot(path)
def write_networkx_to_dot(dg, filename='mydask'):
import os
p = nx.to_pydot(dg)
p.set_rankdir('BT')
with open(filename + '.dot', 'w') as f:
f.write(p.to_string())
os.system('dot -Tpdf %s.dot -o %s.pdf' % (filename, filename))
os.system('dot -Tpng %s.dot -o %s.png' % (filename, filename))
print("Writing graph to %s.pdf" % filename)
def compare_graph_network(self, goldnet, filename, topn=None):
import networkx as nx
#import Image as im
import matplotlib as plt
import pylab as P
import pydot
G = nx.DiGraph()
net = self
if topn != None:
net = self.copy()
net.set_top_edges_percent(topn)
# Add all of the nodes
for gene in net.network.keys():
G.add_node(gene)
Gp = nx.to_pydot(G)
if goldnet == []:
for gene1 in net.network.keys():
for gene2 in net.network.keys():
if net.network[gene1][gene2] != 0:
Gp.add_edge(pydot.Edge(gene1, gene2, style="bold"))
else:
for gene1 in net.network.keys():
for gene2 in net.network.keys():
# Figure out of node is correct, apply color/style based on that
# If true positive, bold line
if net.network[gene1][gene2] != 0 and goldnet.network[gene1][gene2] != 0:
# thick line
Gp.add_edge(pydot.Edge(gene1, gene2, style="bold"))
# If false positive, dashed line
elif net.network[gene1][gene2] != 0 and goldnet.network[gene1][gene2] == 0:
Gp.add_edge(pydot.Edge(gene1, gene2, style="dashed"))
# If false negative, dotted line
elif net.network[gene1][gene2] == 0 and goldnet.network[gene1][gene2] != 0:
Gp.add_edge(pydot.Edge(gene1, gene2, style="dotted"))
# now output your graph to a file and display it
outstem = filename
dot = pydot.Dot(Gp)
Gp.set_size('18!')
Gp.set_rankdir('RL')
Gp.set_page('20')
Gp.set_ranksep(0.5)
Gp.set_nodesep(0.25)
#Gp.write_pdf(outstem + '_dot.pdf', prog='dot') # writes Gp to png file #use
#Gp.write_pdf(outstem + '_neato.pdf', prog='neato') # writes Gp to png file #use
#Gp.write_pdf(outstem + '_fdp.pdf', prog='fdp') # writes Gp to png file #use
#Gp.write_pdf(outstem + '_twopi.pdf', prog='twopi') # writes Gp to png file #use
Gp.write_pdf(outstem + '_circo.pdf', prog='circo') # writes Gp to png file #use
def show_pydot(g):
"""
Display a networkx graph using pydot.
"""
fd = tempfile.NamedTemporaryFile()
fd.close()
p = nx.to_pydot(g)
p.write_jpg(fd.name)
imdisp(fd.name)
os.remove(fd.name)
def main():
global graph
with open('tftpsrv.text.fixed.txt') as f:
lines = f.readlines()
graph_function(lines, 'strcpy')
#graph_function(lines, 'FillScreen')
#graph_function(lines, 'sprintf')
pydot_graph = nx.to_pydot(graph)
pydot_graph.write_png('unsafe.png')
def layer_dag_to_svg():
model = get_model()
graph = nx.DiGraph()
for layer in model.layers:
graph.add_node(layer["name"], layer_attributes=layer)
for layer in model.layers:
for inputLayer in layer.get("inputs", []):
graph.add_edge(model.layers[inputLayer]["name"], layer["name"], label=model.layers[inputLayer]["outputs"])
pydot_graph = nx.to_pydot(graph)
pydot_graph.set_rankdir("LR")
svg = pydot_graph.create_svg(prog="dot")
return Response(svg, mimetype="image/svg+xml")
def nx2dot(self, G):
H = G.copy()
for node1_id, node2_id in H.edges():
removed_list = []
for key, value in H.get_edge_data(node1_id, node2_id).items():
if isinstance(value, dict):
removed_list.append(key)
for key in removed_list:
del H.edge[node1_id][node2_id][key]
D = nx.to_pydot(H)
return D
def print_ascii_graph(model_):
"""
pip install img2txt.py
python -c
"""
from PIL import Image
from six.moves import StringIO
#import networkx as netx
import copy
model = copy.deepcopy(model_)
assert model is not model_
# model.graph.setdefault('graph', {})['size'] = '".4,.4"'
model.graph.setdefault('graph', {})['size'] = '".3,.3"'
model.graph.setdefault('graph', {})['height'] = '".3,.3"'
pydot_graph = netx.to_pydot(model)
png_str = pydot_graph.create_png(prog='dot')
sio = StringIO()
sio.write(png_str)
sio.seek(0)
pil_img = Image.open(sio)
print('pil_img.size = %r' % (pil_img.size,))
#def print_ascii_image(pil_img):
# img2txt = ut.import_module_from_fpath('/home/joncrall/venv/bin/img2txt.py')
# import sys
# pixel = pil_img.load()
# width, height = pil_img.size
# bgcolor = None
# #fill_string =
# img2txt.getANSIbgstring_for_ANSIcolor(img2txt.getANSIcolor_for_rgb(bgcolor))
# fill_string = "\x1b[49m"
# fill_string += "\x1b[K" # does not move the cursor
# sys.stdout.write(fill_string)
# img_ansii_str = img2txt.generate_ANSI_from_pixels(pixel, width, height, bgcolor)
# sys.stdout.write(img_ansii_str)
def print_ascii_image(pil_img):
#https://gist.github.com/cdiener/10491632
SC = 1.0
GCF = 1.0
WCF = 1.0
img = pil_img
S = (int(round(img.size[0] * SC * WCF * 3)), int(round(img.size[1] * SC)))
img = np.sum( np.asarray( img.resize(S) ), axis=2)
print('img.shape = %r' % (img.shape,))
img -= img.min()
chars = np.asarray(list(' .,:;irsXA253hMHGS#9B&@'))
img = (1.0 - img / img.max()) ** GCF * (chars.size - 1)
print( "\n".join( ("".join(r) for r in chars[img.astype(int)]) ) )
print_ascii_image(pil_img)
pil_img.close()
pass
def write_networkx_to_dot(dg, filename='mydask'):
import os
try:
p = nx.to_pydot(dg)
except AttributeError:
raise ImportError("Can not find pydot module. Please install.\n"
" pip install pydot")
p.set_rankdir('BT')
with open(filename + '.dot', 'w') as f:
f.write(p.to_string())
os.system('dot -Tpdf %s.dot -o %s.pdf' % (filename, filename))
os.system('dot -Tpng %s.dot -o %s.png' % (filename, filename))
print("Writing graph to %s.pdf" % filename)
def write_networkx_to_dot(dg, filename='mydask'):
import os
try:
p = nx.to_pydot(dg)
except AttributeError:
raise ImportError("Can not find pydot module. Please install.\n"
" pip install pydot")
p.set_rankdir('BT')
with open(filename + '.dot', 'w') as f:
f.write(p.to_string())
os.system('dot -Tpdf %s.dot -o %s.pdf' % (filename, filename))
os.system('dot -Tpng %s.dot -o %s.png' % (filename, filename))
print("Writing graph to %s.pdf" % filename)
def write_graph2dot(graph, c_fname, img_fname, graph_opts):
if pydot is None:
# dump using simple logic
dot_str = dump_dot_wo_pydot(graph, c_fname, graph_opts)
dot_path = write_dot_file_wo_pydot(dot_str, img_fname)
else:
# dump using networkx and pydot
if hasattr(nx, "nx_pydot"):
pydot_graph = nx.drawing.nx_pydot.to_pydot(graph)
else:
pydot_graph = nx.to_pydot(graph)
dot_path = write_dot_file_with_pydot(pydot_graph)
return dot_path
def layer_dag_to_svg():
model = get_model()
graph = nx.DiGraph()
for layer in model.layers:
graph.add_node(layer['name'], layer_attributes=layer)
for layer in model.layers:
for inputLayer in layer.get("inputs", []):
graph.add_edge(model.layers[inputLayer]['name'],
layer['name'],
label=model.layers[inputLayer]['outputs'])
pydot_graph = nx.to_pydot(graph)
pydot_graph.set_rankdir("LR")
svg = pydot_graph.create_svg(prog="dot")
return Response(svg, mimetype="image/svg+xml")
def show_pydot(g):
"""
Display a networkx graph using pydot.
Parameters
----------
g : networkx.Graph
Graph to visualize.
"""
fd = tempfile.NamedTemporaryFile()
fd.close()
p = nx.to_pydot(g)
p.write_jpg(fd.name)
imdisp(fd.name)
os.remove(fd.name)
def main(*args):
if len(args) != 3:
print 'Usage: {} <input file> <output file>'.format(args[0])
return 2
try:
NFA = nx.read_dot(args[1])
except IOError:
print '{}: file does not exist or is not readable. Terminating.'.format(
args[1])
return 3
remove_quotes(NFA)
Sigma, q0, F = NFA_parameters(NFA)
# for node in NFA.nodes(data=True):
# print('{}'.format(node))
# for edge in NFA.edges(data=True):
# print edge
t = input_transition(NFA, Sigma)
# print '*** input_transition() result ***'
# for node in sorted(t):
# print('{} {}'.format(node, t[node]))
q0_closure = lambda_closure(NFA, q0)
DFA = powerset_construction(t, q0_closure, Sigma)
DFA = rebuild_pretty(DFA, q0_closure, F)
# print '=== powerset_construction() results ==='
# for node in DFA.nodes(data=True):
# print('{}'.format(node))
# for edge in DFA.edges(data=True):
# print edge
DFAdot = nx.to_pydot(DFA)
DFAdot.set_rankdir(RANKDIR)
DFAdot.set_charset(CHARSET)
try:
DFAdot.write(args[2])
except IOError:
print '{}: IO error writing file. Terminating.'.format(args[2])
return 3
return 0
def _graph2pydot(graph, wrap=10, latex=False):
"""Convert (possibly labeled) state graph to dot str.
@type graph: L{LabeledDiGraph}
@rtype: str
"""
if _pydot_missing():
return None
dummy_nx_graph = nx.MultiDiGraph()
_states2dot_str(graph, dummy_nx_graph, wrap=wrap, latex=latex)
_transitions2dot_str(graph.transitions, dummy_nx_graph, latex)
pydot_graph = nx.to_pydot(dummy_nx_graph)
pydot_graph.set_overlap("false")
return pydot_graph
def layoutgraph(g):
"""Given a Graph object, """
#g = G.make_networkx()
#G.colors_to_networkx(g)
#networkx.write_gml(g.copy(), dirname+'dolphins_gamma%s.gml'%gamma)
pd = networkx.to_pydot(g)
#pd.to_string()
prog = 'neato'
#prog = 'fdp'
p = subprocess.Popen('%s -Goverlap=scalexy -Gsplines=true -Gstart=5'%prog,
stdin=subprocess.PIPE,stdout=subprocess.PIPE,
shell=True)
out = p.communicate(pd.to_string())
#from fitz import interactnow
gnew = networkx.from_pydot(pydot.graph_from_dot_data(out[0]))
return gnew
def main():
bibfile = sys.argv[1]
with open(bibfile) as bibtex_file:
bibtex = bibtexparser.load(bibtex_file)
stats = quick_stats(bibtex)
for k, v in stats.iteritems():
print "{0:>5} --> {1:}".format(v, k)
citations = {}
tags = {}
for key, entry in bibtex.entries_dict.iteritems():
cites = entry.get('cites')
if cites:
citations[key] = unpack(cites)
t = entry.get('tags')
if t:
tags[key] = t
G = nx.DiGraph()
for key, entry in citations.iteritems():
for cit in entry:
G.add_edge(key, cit)
pydot_G = nx.to_pydot(G)
for node in pydot_G.get_nodes():
t = node.get_name()
if t and tags.get(t):
node.set('nodetype', tags.get(t))
pydot_G.set('rankdir', 'LR')
pydot_G.set('style', 'dashed')
pydot_G.write('bib.dot')
call(["gvpr -c -f filter.gvpr bib.dot > bib_nice.dot"], shell=True)
call([
"ccomps -x bib_nice.dot | dot | gvpack -array1 | neato -Tpng -n2 -o bib.png"
],
shell=True)
def analyzeReport(self, report ):
"""
given an individual radiology report, creates a pyConTextGraph
object that contains the context markup
report: a text string containing the radiology reports
"""
context = self.context
targets = self.targets
modifiers = self.modifiers
context.reset()
splitter = helpers.sentenceSplitter()
# alternatively you can skip the default exceptions and add your own
# splitter = helpers.sentenceSpliter(useDefaults = False)
#splitter.addExceptionTerms("Dr.","Mr.","Mrs.",M.D.","R.N.","L.P.N.",addCaseVariants=True)
splitter.addExceptionTerms("Ms.","D.O.",addCaseVariants=True)
splitter.deleteExceptionTerms("A.B.","B.S.",deleteCaseVariants=True)
sentences = splitter.splitSentences(report)
count = 0
for s in sentences:
#print s
context.setRawText(s)
context.cleanText()
context.markItems(modifiers, mode="modifier")
context.markItems(targets, mode="target")
g= context.getCurrentGraph()
ic=0
context.pruneMarks()
context.dropMarks('Exclusion')
context.applyModifiers()
#context.pruneModifierRelationships()
#context.dropInactiveModifiers()
print context
self.outString += context.getXML()+u"\n"
context.commit()
count += 1
print context.getSectionText()
raw_input('continue')
context.computeDocumentGraph()
ag = nx.to_pydot(context.getDocumentGraph(), strict=True)
ag.write("case%03d.pdf"%self.currentCase,format="pdf")
def layoutgraph(g):
"""Given a Graph object, """
#g = G.make_networkx()
#G.colors_to_networkx(g)
#networkx.write_gml(g.copy(), dirname+'dolphins_gamma%s.gml'%gamma)
pd = networkx.to_pydot(g)
#pd.to_string()
prog = 'neato'
#prog = 'fdp'
p = subprocess.Popen('%s -Goverlap=scalexy -Gsplines=true -Gstart=5' %
prog,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
shell=True)
out = p.communicate(pd.to_string())
#from fitz import interactnow
gnew = networkx.from_pydot(pydot.graph_from_dot_data(out[0]))
return gnew
def produce_graph(nxgraph, sizedict, filename):
"""
Writes a networkx graph to the specified file as a .jpg, with node sizes being equal to the values in
the accompanying dictionary.
NB: Graphviz must be installed locally for this to work.
Args:
nxgraph: A graph in the standard networkX format.
sizedict: A dictionary of values where keys are identical to the node names in nxgraph. Values will
determine the relative size of the appropriate nodes in the output graph.
filename: Name of the file you want to output to.
e.g.
sizedict = {'"Node1"': 45, '"Node2"': 98, '"Node3"': 67}
filename = 'outfile.jpg'
"""
#Takes a Networkx graph, and a series of numbers with the same indices as the node
#names; writes a jpg file with nodes of size determined by the series.
dotgraph = nx.to_pydot(nxgraph)
set_node_sizes(dotgraph, sizedict)
set_node_labels(dotgraph, sizedict)
dotgraph.set_nodesep(1)
dotgraph.write_jpg(filename)
def report_transitions(self, r):
Gd = self.create_transition_graph()
G = Gd['G']
name2state = Gd['name2state']
name2obs = Gd['name2obs']
name2obsset = Gd['name2obsset']
name2cmd = Gd['name2cmd']
policy = Gd['policy']
# f = r.figure()
import networkx as nx
# # pos = nx.spectral_layout(G)
# with f.plot('G', figsize=(5, 5)) as pylab: # @UnusedVariable
# # nx_draw_with_attrs(G, pos=pos, with_labels=True)
# nx.draw(G)
d = nx.to_pydot(G) # d is a pydot graph object, dot options can be easily set
# attributes get converted from networkx,
# use set methods to control dot attributes after creation
f = r.figure()
f.data('pydot1', d.create_pdf(), mime=MIME_PDF)
f.data('pydot2', d.create_png(), mime=MIME_PNG)
f = r.figure('policy')
gpolicy = get_policy_graph(policy)
f.data('pydot1', gpolicy.create_pdf(), mime=MIME_PDF)
f.data('pydot2', gpolicy.create_png(), mime=MIME_PNG)
r.text("name2state", str(name2state))
r.text("name2obs", str(name2obs))
r.text("name2obsset", str(name2obsset))
r.text("name2cmd", str(name2cmd))
def main():
bibfile = sys.argv[1]
with open(bibfile) as bibtex_file:
bibtex = bibtexparser.load(bibtex_file)
stats = quick_stats(bibtex)
for k,v in stats.iteritems():
print "{0:>5} --> {1:}".format(v, k)
citations = {}
tags = {}
for key, entry in bibtex.entries_dict.iteritems():
cites = entry.get('cites')
if cites:
citations[key] = unpack(cites)
t = entry.get('tags')
if t:
tags[key] = t
G = nx.DiGraph()
for key, entry in citations.iteritems():
for cit in entry:
G.add_edge(key, cit)
pydot_G = nx.to_pydot(G)
for node in pydot_G.get_nodes():
t = node.get_name()
if t and tags.get(t):
node.set('nodetype', tags.get(t))
pydot_G.set('rankdir', 'LR')
pydot_G.set('style', 'dashed')
pydot_G.write('bib.dot')
call(["gvpr -c -f filter.gvpr bib.dot > bib_nice.dot"], shell=True)
call(["ccomps -x bib_nice.dot | dot | gvpack -array1 | neato -Tpng -n2 -o bib.png"], shell=True)
def report_transitions(self, r):
Gd = self.create_transition_graph()
G = Gd['G']
name2state = Gd['name2state']
name2obs = Gd['name2obs']
name2obsset = Gd['name2obsset']
name2cmd = Gd['name2cmd']
policy = Gd['policy']
# f = r.figure()
import networkx as nx
# # pos = nx.spectral_layout(G)
# with f.plot('G', figsize=(5, 5)) as pylab: # @UnusedVariable
# # nx_draw_with_attrs(G, pos=pos, with_labels=True)
# nx.draw(G)
d = nx.to_pydot(
G) # d is a pydot graph object, dot options can be easily set
# attributes get converted from networkx,
# use set methods to control dot attributes after creation
f = r.figure()
f.data('pydot1', d.create_pdf(), mime=MIME_PDF)
f.data('pydot2', d.create_png(), mime=MIME_PNG)
f = r.figure('policy')
gpolicy = get_policy_graph(policy)
f.data('pydot1', gpolicy.create_pdf(), mime=MIME_PDF)
f.data('pydot2', gpolicy.create_png(), mime=MIME_PNG)
r.text("name2state", str(name2state))
r.text("name2obs", str(name2obs))
r.text("name2obsset", str(name2obsset))
r.text("name2cmd", str(name2cmd))
def dump_graphviz(self):
return # TODO: fix the add_node calls below
dgraph = nx.to_pydot(self._G)
for d in dgraph.get_node_list():
print d.get_name()
# Put the roots in the same subgraph so they can be put in the same rank in
# the graphviz graph. This puts them at the top of the graph
roots_graph = pydot.Subgraph(subgraph_name="roots")
roots = [n for n,d in self._G.in_degree().items() if d == 0]
for r in roots:
print repr(r)
[roots_graph.add_node(dgraph.get_node(str(repr(r)))) for r in roots]
roots_graph.set_rank('source')
dgraph.add_subgraph(roots_graph)
# Same for the bottom of the graph
leaves_graph = pydot.Subgraph(subgraph_name="leaves")
leaves = [n for n,d in self._G.out_degree().items() if d == 0]
[leaves_graph.add_node(dgraph.get_node(l)) for l in leaves]
leaves_graph.set_rank('sink')
dgraph.add_subgraph(leaves_graph)
dgraph.write_ps("a.ps", prog="dot")
def export_to_dot(self):
"""Exports the graph to a dot format (requires pydot library)."""
return nx.to_pydot(self).to_string()