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plot directed graph (python matplotlib)
#!/usr/bin/python
# programmer : zhuxp
# usage:
import sys
from getopt import getopt
import networkx as nx
import matplotlib.pyplot as plt
def show_help():
print >>sys.stderr,"\n\nplotCausalGraph.py: drawing causal graph from LiNGAM output equation matrix B"
print >>sys.stderr,"Library Dependence: networkx , matplotlib\n\n"
print >>sys.stderr,"Usage: plotCausalGraph.py -m matrix -t threshold[default:0.5]"
print >>sys.stderr,"Options:"
print >>sys.stderr," --matrix,-m file.mat B matrix file generated from LiNGAM"
print >>sys.stderr," --threshold,-t threshold draw the line if the absolute value of line weight is bigger than threshold"
print >>sys.stderr," --direct,-d draw the arrow"
print >>sys.stderr,"Matrix File Example:"
print >>sys.stderr,"H3K9me3\t0"
print >>sys.stderr,"H3K4me3\t-1\t0"
print >>sys.stderr,"H3K4me1\t-0.3\t1\t0"
exit(0)
def Main():
if len(sys.argv)==1: show_help()
opts,restlist = getopt(sys.argv[1:],"m:oht:d",\
["matrix=","threshold=","help","direct"])
threshold=0.5
direct=False
for o, a in opts:
if o in ("-m","--matrix"): M = a
if o in ("-h","--help"): show_help()
if o in ("-t","--threshold"): threshold=float(a)
if o in ("-d","--direct"): direct=True
if not 'M' in dir():
show_help()
try:
f=open(M)
except:
print >>sys.stderr,"Can't open file",M
show_help()
max=0
nodes=[]
edges=[]
pos={}
edges_col=[]
col={}
rank={}
lines=f.readlines()
i=0
maxcols=0
for line in lines:
line=line.strip()
if line[0]=="#":continue
a=line.split("\t")
nodes.append(a[0])
rank[a[0]]=0
for k,x in enumerate(a[1:]):
if k==i: continue
x=float(x)
if x>threshold or x<-threshold:
if k < len(nodes) and rank[a[0]] < rank[nodes[k]]+1:
rank[a[0]]=rank[nodes[k]]+1
if col.has_key(rank[a[0]]):
col[rank[a[0]]]+=1
else:
col[rank[a[0]]]=1
#pos[a[0]]=(rank[a[0]],col[rank[a[0]]]+rank[a[0]]%2*0.5+rank[a[0]]*0.111)
pos[a[0]]=(rank[a[0]],col[rank[a[0]]])
i+=1
for e in col.values():
if e > maxcols: maxcols=e
for e in pos.keys():
a=pos[e]
pos[e]=(a[0],float(a[1]+0.05*(rank[e]%3))/float(col[rank[e]]+1)*maxcols)
j=0
G=nx.DiGraph()
G.add_nodes_from(nodes)
edges=[]
for line in lines:
line=line.strip()
if line[0]=="#":continue
a=line.split("\t")
for i,x in enumerate(a[1:]):
if i==j:continue
x=float(x)
if max<abs(x):max=abs(x)
if x>threshold:
edges.append((nodes[i],nodes[j],{'color':'green','weight':x}))
# edges_col.append(x)
elif x<-threshold:
edges.append((nodes[i],nodes[j],{'color':'red','weight':x}))
# edges_col.append(x)
j+=1
G.add_edges_from(edges)
e=G.edges()
for i in e:
edges_col.append(G[i[0]][i[1]]['weight'])
nx.draw(G,edge_cmap=plt.get_cmap("RdYlGn"),edgelist=e,edge_color=edges_col,pos=pos,node_color="y",edge_vmin=-max,edge_vmax=max,linewidths=0,width=2,arrows=direct,node_size=100,font_size=10)
#nx.draw(G,edge_cmap=plt.get_cmap("RdYlGn"),edge_list=edges,edge_color=edges_col,pos=pos,node_color="y",edge_vmin=-max,edge_vmax=max,linewidths=0,width=2)
#nx.draw(G,pos=pos,node_color="y",linewidths=0,width=2)
plt.colorbar()
plt.show()
if __name__=="__main__":
Main()
#!/usr/bin/python
####################### import np_pylab.py #################
###### download from https://networkx.lanl.gov/trac/ticket/423
import networkx as nx
import matplotlib.patches as patches
import matplotlib.collections as collections
import numpy as np
import math
import matplotlib.cbook as cb
import matplotlib.colors as mcolors
import matplotlib as mpl
import matplotlib.cm as cm
def get_color_dict(color,item_list,vmin=None,vmax=None,cmap=None):
""" Determine color rgb color values given a data list.
This function is used to take a sequence of data and convert it
into a dictionary which of possible rgb values. It can take a
number of different types of data explained below. If no colormap
is given it will return the grayscale for scalar values.
Parameters:
-----------
color: A string, scalar, or iterable of either.
This can be a color in a variety of formats. A matplotlib
a single color, which can be a matplotlib color specification
e.g. 'rgbcmykw', a hex color string '#00FFFF', a standard
html name like 'aqua', or a numeric value to be scaled with
vmax or vmin. It can also be a list of any of these types
as well as a dictionary of any of these types.
item_list: a list
A list of keys which correspond to the values given in
color.
vmin: A scalar
The minimum value if scalar values are given for color
vmax: A scalar
The maximum value if scalar values are given for color
cmap: A matplotlib colormap
A colormap to be used if scalar values are given.
Returns:
-------
color_dict: dict
A dictionary of rgb colors keyed by values in item_list
"""
CC = mcolors.ColorConverter()
if cb.is_string_like(color):
return {}.fromkeys(item_list,CC.to_rgb(color))
elif cb.is_scalar(color):
CN = mcolors.Normalize(0.0,1.0)
if cmap is not None:
return {}.fromkeys(item_list,cmap(CN(color)))
else:
return {}.fromkeys(item_list,CC.to_rgb(str(CN(color))))
elif cb.iterable(color) and not cb.is_string_like(color):
try:
vals = [color[i] for i in item_list]
except:
vals = color
keys = item_list
if len(item_list)>len(vals):
raise nx.NetworkXError("Must provide a value for each item")
if cb.alltrue([cb.is_string_like(c) for c in vals]):
return dict(zip(keys,[CC.to_rgb(v) for v in vals]))
elif cb.alltrue([cb.is_scalar(c) for c in vals]):
if vmin is None:
vmin = float(min(vals))
if vmax is None:
vmax = float(max(vals))
CN = mcolors.Normalize(vmin,vmax)
if cmap is not None:
return dict(zip(keys,[cmap(CN(v)) for v in vals]))
else:
return dict(zip(keys,[CC.to_rgb(str(CN(v))) for v in vals]))
elif cb.alltrue([cb.iterable(c) and not cb.is_string(c) for c in vals]):
return dict(zip(keys,vals))
else:
raise nx.NetworkXError("Could not convert colors")
def is_weighted(G):
""" Determine if a graph G is weighted
Checks each edge to see if it has attribute 'weight' if it does
return True, otherwise false. This checks if the entire graph is
weighted not partial.
Parameters:
----------
G: A networkx Graph
Returns:
--------
weighted : A bool
Determines whether the graph is weighted.
"""
weighted = True
for (u,v) in G.edges():
weighted = weighted and ('weight' in G.edge[u][v])
if not weighted:
return weighted
return weighted
def is_weighted(self):
""" Determine if a graph G is weighted
Checks each edge to see if it has attribute 'weight' if it does
return True, otherwise false. This checks if the entire graph is
weighted not partial.
Parameters:
----------
G: A networkx Graph
Returns:
--------
weighted : A bool
Determines whether the graph is weighted.
"""
weighted = True
for (u,v) in self.edges():
weighted = weighted and ('weight' in self.edge[u][v])
if not weighted:
return weighted
return weighted
def edge_width_weight(G,edgelist=None):
"""Automatically calculate a normalized reasonable line width for
a weighted graph
Parameters:
-----------
G: A networkx Graph
edgelist: A list
Edges to calculate the weights for if None, usesall edges
Returns:
--------
weight_dict: A dictionary
Line weights that displays nicely in matplotlib.
"""
if edgelist is None:
edgelist = G.edges()
lw = {}
for (u,v) in edgelist:
lw[(u,v)] = G.edge[u][v]['weight']
maxw = max(lw.values())
minw = float(min(lw.values())) #to ensure floats later
return dict(zip(lw.keys(), \
map((lambda x: 19.5*((x-minw)/(maxw-minw)) + 0.5), \
lw.values())))
def edge_color_weight(G,edgelist=None):
"""Automatically calculate a normalized reasonable color for
a weighted graph
Parameters:
-----------
G: A networkx Graph
edgelist: A list
Edges to calculate the weights for if None, uses all edges
Returns:
--------
weight_dict: A dictionary
Values between 0-1 that displays nicely in matplotlib.
"""
cl = {}
if edgelist is None:
edgelist=G.edges()
for (u,v) in edgelist:
cl[(u,v)] = G[u][v]['weight']
maxw = max(cl.values())
minw = float(min(cl.values()))
return dict(zip(cl.keys(), \
map((lambda x: (x-minw)/(maxw - minw)),cl.values())))
def draw(G, pos=None, ax=None, hold=None, **kwds):
"""Draw the graph G with Matplotlib (pylab).
Draw the graph as a simple representation with no node
labels or edge labels and using the full Matplotlib figure area
and no axis labels by default. See draw_networkx() for more
full-featured drawing that allows title, axis labels etc.
Parameters
----------
G : graph
A networkx graph
pos : dictionary, optional
A dictionary with nodes as keys and positions as values.
If not specified a spring layout positioning will be computed.
See networkx.layout for functions that compute node positions.
ax : Matplotlib Axes object, optional
Draw the graph in specified Matplotlib axes.
hold: bool, optional
Set the Matplotlib hold state. If True subsequent draw
commands will be added to the current axes.
**kwds: optional keywords
See networkx.draw_networkx() for a description of optional keywords.
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> nx.draw(G)
>>> nx.draw(G,pos=nx.spring_layout(G)) # use spring layout
See Also
--------
draw_networkx()
draw_networkx_nodes()
draw_networkx_edges()
draw_networkx_labels()
draw_networkx_edge_labels()
Notes
-----
This function has the same name as pylab.draw and pyplot.draw
so beware when using
>>> from networkx import *
since you might overwrite the pylab.draw function.
Good alternatives are:
With pylab:
>>> import pylab as P #
>>> import networkx as nx
>>> G=nx.dodecahedral_graph()
>>> nx.draw(G) # networkx draw()
>>> P.draw() # pylab draw()
With pyplot
>>> import matplotlib.pyplot as plt
>>> import networkx as nx
>>> G=nx.dodecahedral_graph()
>>> nx.draw(G) # networkx draw()
>>> plt.draw() # pyplot draw()
Also see the NetworkX drawing examples at
http://networkx.lanl.gov/gallery.html
"""
try:
import matplotlib.pylab as pylab
except ImportError:
raise ImportError, "Matplotlib required for draw()"
except RuntimeError:
print "Matplotlib unable to open display"
raise
cf=pylab.gcf()
cf.set_facecolor('w')
if ax is None:
if cf._axstack() is None:
ax=cf.add_axes((0,0,1,1))
else:
ax=cf.gca()
# allow callers to override the hold state by passing hold=True|False
b = pylab.ishold()
h = kwds.pop('hold', None)
if h is not None:
pylab.hold(h)
try:
ax.set_axis_off()
draw_networkx(G,pos=pos,ax=ax,**kwds)
pylab.draw_if_interactive()
except:
pylab.hold(b)
raise
pylab.hold(b)
return
def draw_networkx(G, pos=None, with_labels=True, **kwds):
"""Draw the graph G using Matplotlib.
Draw the graph with Matplotlib with options for node positions,
labeling, titles, and many other drawing features.
See draw() for simple drawing without labels or axes.
Parameters
----------
G : graph
A networkx graph
pos : dictionary, optional
A dictionary with nodes as keys and positions as values.
If not specified a spring layout positioning will be computed.
See networkx.layout for functions that compute node positions.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
with_labels: bool, optional
Set to True (default) to draw labels on the nodes.
nodelist: list, optional
Draw only specified nodes (default G.nodes())
edgelist: list
Draw only specified edges(default=G.edges())
node_size: scalar or array
Size of nodes (default=300). If an array is specified it must be the
same length as nodelist.
node_color: color string, or array of floats
Node color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as nodelist.
If numeric values are specified they will be mapped to
colors using the cmap and vmin,vmax parameters. Can also be a
dictionary keyed by node, and can be in any matplotlib acceptable
color value.
node_shape: string
The shape of the node. Specification is as matplotlib.scatter
marker, one of 'so^>v<dph8' (default='o').
alpha: float
The node transparency (default=1.0)
cmap: Matplotlib colormap
Colormap for mapping intensities of nodes (default=None)
vmin,vmax: floats
Minimum and maximum for node colormap scaling (default=None)
width: float
Line width of edges (default =1.0)
edge_color: color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
edge_ cmap: Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax: floats
Minimum and maximum for edge colormap scaling (default=None)
style: string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
labels: dictionary
Node labels in a dictionary keyed by node of text labels (default=None)
font_size: int
Font size for text labels (default=12)
font_color: string
Font color string (default='k' black)
font_weight: string
Font weight (default='normal')
font_family: string
Font family (default='sans-serif')
Notes
-----
Any keywords not listed above are passed through to draw_networkx_nodes(),
draw_networkx_edges(), and draw_networkx_labels(). For finer control
of drawing you can call those functions directly.
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> nx.draw(G)
>>> nx.draw(G,pos=nx.spring_layout(G)) # use spring layout
>>> import pylab
>>> limits=pylab.axis('off') # turn of axis
Also see the NetworkX drawing examples at
http://networkx.lanl.gov/gallery.html
See Also
--------
draw()
draw_networkx_nodes()
draw_networkx_edges()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib.pylab as pylab
except ImportError:
raise ImportError, "Matplotlib required for draw()"
except RuntimeError:
print "Matplotlib unable to open display"
raise
if pos is None:
pos=nx.drawing.spring_layout(G) # default to spring layout
node_patches=draw_networkx_nodes(G, pos, **kwds)
edge_patches=draw_networkx_edges(G, pos, node_patches, **kwds)
if with_labels:
draw_networkx_labels(G, pos, **kwds)
pylab.draw_if_interactive()
def draw_networkx_nodes(G, pos,
nodelist=None,
node_size=300,
node_color='r',
node_shape='o',
alpha=1.0,
cmap=None,
vmin=None,
vmax=None,
ax=None,
linewidth=None,
**kwds):
"""Draw the nodes of the graph G.
This draws only the nodes of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
If not specified a spring layout positioning will be computed.
See networkx.layout for functions that compute node positions.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
nodelist: list, optional
Draw only specified nodes (default G.nodes())
edgelist: list
Draw only specified edges(default=G.edges())
node_size: scalar or array
Size of nodes (default=300). If an array is specified it must be the
same length as nodelist.
node_color: color string, or array of floats
Node color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as nodelist.
If numeric values are specified they will be mapped to
colors using the cmap and vmin,vmax parameters. See
matplotlib.scatter for more details.
node_shape: string
The shape of the node. Specification is as matplotlib.scatter
marker, one of 'so^>v<dph8' (default='o').
alpha: float
The node transparency (default=1.0)
cmap: Matplotlib colormap
Colormap for mapping intensities of nodes (default=None)
vmin,vmax: floats
Minimum and maximum for node colormap scaling (default=None)
width`: float
Line width of edges (default =1.0)
Notes
-----
Any keywords not listed above are passed through to Matplotlib's
scatter function.
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> nodes=nx.draw_networkx_nodes(G,pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
http://networkx.lanl.gov/gallery.html
See Also
--------
draw()
draw_networkx()
draw_networkx_edges()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib.pylab as pylab
import numpy
except ImportError:
raise ImportError, "Matplotlib required for draw()"
except RuntimeError:
print "Matplotlib unable to open display"
raise
if ax is None:
ax=pylab.gca()
if nodelist is None:
nodelist=G.nodes()
if not nodelist or len(nodelist)==0: # empty nodelist, no drawing
return None
try:
xy=numpy.asarray([pos[v] for v in nodelist])
except KeyError,e:
raise nx.NetworkXError('Node %s has no position.'%e)
except ValueError:
raise nx.NetworkXError('Bad value in node positions.')
syms = { # a dict from symbol to (numsides, angle)
's' : (4,math.pi/4.0,0), # square
'o' : (0,0,3), # circle
'^' : (3,0,0), # triangle up
'>' : (3,math.pi/2.0,0), # triangle right
'v' : (3,math.pi,0), # triangle down
'<' : (3,3*math.pi/2.0,0), # triangle left
'd' : (4,0,0), # diamond
'p' : (5,0,0), # pentagram
'h' : (6,0,0), # hexagon
'8' : (8,0,0), # octagon
'+' : (4,0,0), # plus
'x' : (4,math.pi/4.0,0) # cross
}
temp_x = map(lambda p: p[0],pos.values())
temp_y = map(lambda p: p[1],pos.values())
minx = np.amin(temp_x)
maxx = np.amax(temp_x)
miny = np.amin(temp_y)
maxy = np.amax(temp_y)
w = max(maxx-minx,1.0)
h = max(maxy-miny,1.0)
#for scaling
axcorn = ax.get_position().get_points()
xperc = (axcorn[1][0]-axcorn[0][0])*.5
yperc = (axcorn[1][1]-axcorn[0][1])*.5
area2radius = lambda a: math.sqrt((a*w*h)/(ax.figure.get_figheight()*ax.figure.get_figwidth()*ax.figure.dpi*ax.figure.dpi*math.pi*xperc*yperc))
if cb.iterable(node_size):
try:
vals = node_size.values()
except:
vals = node_size
node_size = dict(zip(nodelist,map(area2radius,vals)))
else:
node_size = {}.fromkeys(nodelist,area2radius(node_size))
for n in node_size:
if node_size[n] == 0.0:
node_size[n] = .00001
if cmap is None:
cmap = cm.get_cmap(mpl.rcParams['image.cmap'])
n_colors = get_color_dict(node_color,nodelist,vmin,vmax,cmap)
sym = syms[node_shape]
numsides,rotation,symstyle=syms[node_shape]
node_patches = {}
for n in nodelist:
if symstyle==0:
node_patches[n] = patches.RegularPolygon(pos[n],
numsides,
orientation=rotation,
radius=node_size[n],
facecolor=n_colors[n],
edgecolor='k',
alpha=alpha,
linewidth=linewidth,
zorder=2)
elif symstyle==3:
node_patches[n] = patches.Circle(pos[n],
radius=node_size[n],
facecolor=n_colors[n],
edgecolor='k',
alpha=alpha,
linewidth=linewidth,
zorder=2)
ax.add_patch(node_patches[n])
#node_collection = collections.PatchCollection(node_patches.values(),
# match_original=True)
#ax.add_collection(node_collection)
# the pad is a little hack to deal with the fact that we don't
# want to transform all the symbols whose scales are in points
# to data coords to get the exact bounding box for efficiency
# reasons. It can be done right if this is deemed important
temp_x = xy[:,0]
temp_y = xy[:,1]
minx = np.amin(temp_x)
maxx = np.amax(temp_x)
miny = np.amin(temp_y)
maxy = np.amax(temp_y)
w = maxx-minx
h = maxy-miny
padx, pady = 0.05*w, 0.05*h
corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady)
ax.update_datalim(corners)
# ax.autoscale()
ax.autoscale_view()
ax.set_aspect('equal')
# pylab.axes(ax)
#pylab.sci(node_collection)
#node_collection.set_zorder(2)
return node_patches
def draw_networkx_edges(G, pos, node_patches=None,
edgelist=None,
width=None,
edge_color=None,
style='solid',
alpha=None,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
arrow_style=None,
connection_style='arc3',
color_weights=False,
width_weights=False,
**kwds):
"""Draw the edges of the graph G
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
If not specified a spring layout positioning will be computed.
See networkx.layout for functions that compute node positions.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
alpha: float
The edge transparency (default=1.0)
width`: float
Line width of edges (default =1.0)
edge_color: color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
edge_ cmap: Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax: floats
Minimum and maximum for edge colormap scaling (default=None)
style: string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
arrow: Bool
Whether to draw arrows or not for directed graphs
arrow_style: string
Arrow style used by matplotlib see FancyArrowPatch
connection_style: string
Connection style used by matplotlib, see FancyArrowPatch
color_weights: Bool
Whether to color the edges of a graph by their weight if the
graph has any.
width_weights: Bool
Whether to vary the thicknes of an edge by their weight, if the
graph has any.
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> edges=nx.draw_networkx_edges(G,pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
http://networkx.lanl.gov/gallery.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pylab as pylab
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter,Colormap
from matplotlib.collections import LineCollection
import numpy
except ImportError:
raise ImportError, "Matplotlib required for draw()"
except RuntimeError:
print "Matplotlib unable to open display"
raise
if ax is None:
ax=pylab.gca()
if edgelist is None:
edgelist=G.edges()
if not edgelist or len(edgelist)==0: # no edges!
return None
# set edge positions
edge_pos=numpy.asarray([(pos[e[0]],pos[e[1]]) for e in edgelist])
if width is None and width_weights and is_weighted(G):
lw = edge_width_weight(G,edgelist)
if alpha is None:
alpha = 0.75
elif width is None:
lw = {}.fromkeys(edgelist,1.0)
elif cb.iterable(width):
try:
lwvals = width.values()
except:
lwvals = width
lw = dict(zip(edgelist,lwvals))
elif cb.is_scalar(width):
lw = {}.fromkeys(edgelist,width)
else:
raise nx.NetworkXError("Must provide a single scalar value or a list \
of values for line width or None")
if edge_cmap is None:
edge_cmap = cm.get_cmap(mpl.rcParams['image.cmap'])
if edge_color is None and color_weights and is_weighted(G):
edge_color = edge_color_weight(G,edgelist)
if alpha is None:
alpha = 0.75
elif edge_color is None:
edge_color = 'k'
e_colors = get_color_dict(edge_color,edgelist,edge_vmin,edge_vmax,edge_cmap)
edge_patches = {}
if arrow_style is None:
if G.is_directed():
arrow_style = '-|>'
else:
arrow_style = '-'
if node_patches is None:
node_patches = {}.fromkeys(G.nodes(),None)
for (u,v) in edgelist:
edge_patches[(u,v)] = patches.FancyArrowPatch(posA=pos[u],
posB=pos[v],
arrowstyle=arrow_style,
connectionstyle=connection_style,
patchA=node_patches[u],
patchB=node_patches[v],
shrinkA=0.0,
shrinkB=0.0,
mutation_scale=20.0,
alpha=alpha,
color=e_colors[(u,v)],
lw = lw[(u,v)],
linestyle=style,
zorder=1)
ax.add_patch(edge_patches[(u,v)])
# update view
minx = numpy.amin(numpy.ravel(edge_pos[:,:,0]))
maxx = numpy.amax(numpy.ravel(edge_pos[:,:,0]))
miny = numpy.amin(numpy.ravel(edge_pos[:,:,1]))
maxy = numpy.amax(numpy.ravel(edge_pos[:,:,1]))
w = maxx-minx
h = maxy-miny
padx, pady = 0.05*w, 0.05*h
corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady)
ax.update_datalim( corners)
ax.autoscale_view()
return edge_patches
def draw_networkx_labels(G, pos,
labels=None,
font_size=12,
font_color='k',
font_family='sans-serif',
font_weight='normal',
alpha=1.0,
ax=None,
**kwds):
"""Draw node labels on the graph G
Parameters
----------
G : graph
A networkx graph
pos : dictionary, optional
A dictionary with nodes as keys and positions as values.
If not specified a spring layout positioning will be computed.
See networkx.layout for functions that compute node positions.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
alpha: float
The text transparency (default=1.0)
labels: dictionary
Node labels in a dictionary keyed by node of text labels (default=None)
font_size: int
Font size for text labels (default=12)
font_color: string
Font color string (default='k' black)
font_weight: string
Font weight (default='normal')
font_family: string
Font family (default='sans-serif')
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> labels=nx.draw_networkx_labels(G,pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
http://networkx.lanl.gov/gallery.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_edges()
draw_networkx_edge_labels()
"""
try:
import matplotlib.pylab as pylab
import matplotlib.cbook as cb
except ImportError:
raise ImportError, "Matplotlib required for draw()"
except RuntimeError:
print "Matplotlib unable to open display"
raise
if ax is None:
ax=pylab.gca()
if labels is None:
labels=dict(zip(G.nodes(),G.nodes()))
text_items={} # there is no text collection so we'll fake one
for (n,label) in labels.items():
(x,y)=pos[n]
if not cb.is_string_like(label):
label=str(label) # this will cause "1" and 1 to be labeled the same
t=ax.text(x, y,
label,
size=font_size,
color=font_color,
family=font_family,
weight=font_weight,
horizontalalignment='center',
verticalalignment='center',
transform = ax.transData,
clip_on=True,
)
text_items[n]=t
return text_items
def draw_networkx_edge_labels(G, pos,
edge_labels=None,
font_size=10,
font_color='k',
font_family='sans-serif',
font_weight='normal',
alpha=1.0,
bbox=None,
ax=None,
**kwds):
"""Draw edge labels.
Parameters
----------
G : graph
A networkx graph
pos : dictionary, optional
A dictionary with nodes as keys and positions as values.
If not specified a spring layout positioning will be computed.
See networkx.layout for functions that compute node positions.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
alpha: float
The text transparency (default=1.0)
labels: dictionary
Node labels in a dictionary keyed by edge two-tuple of text
labels (default=None), Only labels for the keys in the dictionary
are drawn.
font_size: int
Font size for text labels (default=12)
font_color: string
Font color string (default='k' black)
font_weight: string
Font weight (default='normal')
font_family: string
Font family (default='sans-serif')
bbox: Matplotlib bbox
Specify text box shape and colors.
clip_on: bool
Turn on clipping at axis boundaries (default=True)
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> edge_labels=nx.draw_networkx_edge_labels(G,pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
http://networkx.lanl.gov/gallery.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_edges()
draw_networkx_labels()
"""
try:
import matplotlib.pylab as pylab
import matplotlib.cbook as cb
import numpy
except ImportError:
raise ImportError, "Matplotlib required for draw()"
except RuntimeError:
print "Matplotlib unable to open display"
raise
if ax is None:
ax=pylab.gca()
if edge_labels is None:
labels=dict(zip(G.edges(),[d for u,v,d in G.edges(data=True)]))
else:
labels = edge_labels
text_items={}
for ((n1,n2),label) in labels.items():
(x1,y1)=pos[n1]
(x2,y2)=pos[n2]
(x,y) = ((x1+x2)/2, (y1+y2)/2)
angle=numpy.arctan2(y2-y1,x2-x1)/(2.0*numpy.pi)*360 # degrees
# make label orientation "right-side-up"
if angle > 90:
angle-=180
if angle < - 90:
angle+=180
# transform data coordinate angle to screen coordinate angle
xy=numpy.array((x,y))
trans_angle=ax.transData.transform_angles(numpy.array((angle,)),
xy.reshape((1,2)))[0]
# use default box of white with white border
if bbox is None:
bbox = dict(boxstyle='round',
ec=(1.0, 1.0, 1.0),
fc=(1.0, 1.0, 1.0),
)
if not cb.is_string_like(label):
label=str(label) # this will cause "1" and 1 to be labeled the same
t=ax.text(x, y,
label,
size=font_size,
color=font_color,
family=font_family,
weight=font_weight,
horizontalalignment='center',
verticalalignment='center',
rotation=trans_angle,
transform = ax.transData,
bbox = bbox,
zorder = 1,
clip_on=True,
)
text_items[(n1,n2)]=t
return text_items
def draw_circular(G, **kwargs):
"""Draw the graph G with a circular layout"""
draw(G,circular_layout(G),**kwargs)
def draw_random(G, **kwargs):
"""Draw the graph G with a random layout."""
draw(G,random_layout(G),**kwargs)
def draw_spectral(G, **kwargs):
"""Draw the graph G with a spectral layout."""
draw(G,spectral_layout(G),**kwargs)
def draw_spring(G, **kwargs):
"""Draw the graph G with a spring layout"""
draw(G,spring_layout(G),**kwargs)
def draw_shell(G, **kwargs):
"""Draw networkx graph with shell layout"""
nlist = kwargs.get('nlist', None)
if nlist != None:
del(kwargs['nlist'])
draw(G,shell_layout(G,nlist=nlist),**kwargs)
def draw_graphviz(G, prog="neato", **kwargs):
"""Draw networkx graph with graphviz layout"""
pos=nx.drawing.graphviz_layout(G,prog)
draw(G,pos,**kwargs)
def draw_nx(G,pos,**kwds):
"""For backward compatibility; use draw or draw_networkx"""
draw(G,pos,**kwds)
# fixture for nose tests
def setup_module(module):
from nose import SkipTest
try:
import pylab
except:
raise SkipTest("matplotlib not available")
def test():
import matplotlib.pylab as pyb
G=nx.path_graph(10,create_using=nx.DiGraph())
draw(G)
pyb.draw()
pyb.show()
################# end of nx_pylab.py #################
# programmer : zhuxp
# usage:
# import networkx as nx
# from matplotlib import mpl
import sys
from getopt import getopt
import matplotlib.pyplot as plt
import pylab
# import nx_pylab as nx2
def show_help():
print >>sys.stderr,"\n\nplotCausalGraph.py: drawing causal graph from LiNGAM output equation matrix B"
print >>sys.stderr,"Library Dependence: networkx , matplotlib\n\n"
print >>sys.stderr,"Usage: plotCausalGraph.py -m matrix -t threshold[default:0.5]"
print >>sys.stderr,"Options:"
print >>sys.stderr," --matrix,-m file.mat B matrix file generated from LiNGAM"
print >>sys.stderr," --threshold,-t threshold draw the line if the absolute value of line weight is bigger than threshold"
print >>sys.stderr,"Matrix File Example:"
print >>sys.stderr,"H3K9me3\t0"
print >>sys.stderr,"H3K4me3\t-1\t0"
print >>sys.stderr,"H3K4me1\t-0.3\t1\t0"
exit(0)
def Main():
if len(sys.argv)==1: show_help()
opts,restlist = getopt(sys.argv[1:],"m:oht:",\
["matrix=","threshold=","help"])
threshold=0.5
for o, a in opts:
if o in ("-m","matrix"): M = a
if o in ("-h","--help"): show_help()
if o in ("-t","--threshold"): threshold=float(a)
if not 'M' in dir():
show_help()
try:
f=open(M)
except:
print >>sys.stderr,"Can't open file",M
show_help()
max=0
nodes=[]
edges=[]
pos={}
edges_col=[]
col={}
rank={}
lines=f.readlines()
i=0
for line in lines:
line=line.strip()
if line[0]=="#":continue
a=line.split("\t")
nodes.append(a[0])
rank[a[0]]=0
for k,x in enumerate(a[1:]):
if k==i: continue
x=float(x)
if x>threshold or x<-threshold:
if k < len(nodes) and rank[a[0]] < rank[nodes[k]]+1:
rank[a[0]]=rank[nodes[k]]+1
if col.has_key(rank[a[0]]):
col[rank[a[0]]]+=1
else:
col[rank[a[0]]]=1
pos[a[0]]=(rank[a[0]],col[rank[a[0]]]+rank[a[0]]%2*0.5+rank[a[0]]*0.111)
i+=1
j=0
G=nx.DiGraph()
G.add_nodes_from(nodes)
for line in lines:
line=line.strip()
if line[0]=="#":continue
a=line.split("\t")
for i,x in enumerate(a[1:]):
if i==j:continue
x=float(x)
if max<abs(x):max=abs(x)
if x>threshold:
G.add_edge(nodes[i],nodes[j],{'weight':x})
elif x<-threshold:
G.add_edge(nodes[i],nodes[j],{'weight':x})
j+=1
fig=plt.figure(figsize=(8,10))
e=G.edges()
for i in e:
edges_col.append(G[i[0]][i[1]]['weight'])
# nx2.draw(G,edge_cmap=plt.get_cmap("RdYlGn"),edge_color=edges_col,pos=pos,node_color="y",edge_vmin=-max,edge_vmax=max,linewidth=0,width=2)
draw(G,edge_cmap=plt.get_cmap("RdYlGn"),edge_color=edges_col,pos=pos,node_color="y",edge_vmin=-max,edge_vmax=max,linewidth=0,width=2)
ax1=fig.add_axes([0.87,0.25,0.02,0.5])
norm=mpl.colors.Normalize(vmin=-max,vmax=max)
cmap=plt.get_cmap("RdYlGn")
cb1=mpl.colorbar.ColorbarBase(ax1,cmap=cmap,norm=norm,orientation='vertical')
plt.show()
if __name__=="__main__":
Main()
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