def plot_commSizeDist(nc, ncName):
"""Plot community size distribution."""
bins = binner.Bins(int, 1, nc.getGiantSize(), 'linlog', 1.5)
binned_data = bins.bin_count_divide(nc.getCommunitySizes())
# Set plotting properties
l, b, r, t = 0.1, 0.1, 0.98, (0.93 if print_titles else 0.98)
axes_rect = [l, b, r - l, t - b] #[left, bottom, width, height]
pylab.rcParams.update(fig_utils.get_rcParams(float(fig_width_cm)),
fig_ratio=0.8,
font_sizes=font_sizes)
fig = pylab.figure()
ax = fig.add_axes(axes_rect)
ax.loglog(bins.centers, binned_data, 'b.-')
ax.set_xlabel(r"Community size")
ax.set_ylabel(r"Number of communities")
if print_titles:
ax.set_title(r"Community size distribution (binned)")
# Adjust axis
V = ax.axis()
ax.axis((1, bins.bin_limits[-1], V[2], V[3]))
# Save figure.
fig_utils.savefig(fig, ncName + "_sizeDist", save_formats)
def plot_commSizeDist(nc, ncName):
"""Plot community size distribution."""
bins = binner.Bins(int, 1, nc.getGiantSize(), 'linlog', 1.5)
binned_data = bins.bin_count_divide(nc.getCommunitySizes())
# Set plotting properties
l, b, r, t = 0.1, 0.1, 0.98, (0.93 if print_titles else 0.98)
axes_rect = [l,b,r-l,t-b] #[left, bottom, width, height]
pylab.rcParams.update(fig_utils.get_rcParams(float(fig_width_cm)),
fig_ratio=0.8,
font_sizes=font_sizes)
fig = pylab.figure()
ax = fig.add_axes(axes_rect)
ax.loglog(bins.centers, binned_data, 'b.-')
ax.set_xlabel(r"Community size")
ax.set_ylabel(r"Number of communities")
if print_titles:
ax.set_title(r"Community size distribution (binned)")
# Adjust axis
V = ax.axis()
ax.axis((1, bins.bin_limits[-1], V[2], V[3]))
# Save figure.
fig_utils.savefig(fig, ncName + "_sizeDist", save_formats)
def create_motif_figure(N_h, N_v):
"""Create a figure for plotting motifs.
`event_types` is needed to construct colors used in the labels of
events.
"""
# Physical size of the output.
#t_cm_scatter, l_cm_scatter = 1.0, 1.2 # scatter left margin in cm
#h_cm_scatter, w_cm_scatter = 8.0, 8.0 # scatter plot width in cm
w_cm, h_cm = 2.05, 1.85 # width and height of motif in cm
t_cm, b_cm = 0.3, 0.6 # top and bottom margin in cm
l_cm, r_cm = 0.30, 0.30 # left and right margin in cm
ws_cm, hs_cm = 0.16, 0.36 # wspace and hspace in cm
# Derived physical sizes of the figure.
w_cm = l_cm+r_cm+N_h*w_cm+(N_h-1)*ws_cm
h_cm = t_cm+b_cm+N_v*h_cm+(N_v-1)*hs_cm
# Set plotting properties
font_sizes = {'text': 14, 'title':14}
params = fig_utils.get_rcParams(w_cm, h_cm/w_cm, font_sizes)
pylab.rcParams.update(params)
subplot_specs = {'left':l_cm/w_cm, 'right':1-r_cm/w_cm,
'top':1-t_cm/h_cm, 'bottom':b_cm/h_cm,
'wspace':ws_cm/w_cm, 'hspace':hs_cm/h_cm}
#motif_colors = {2: "#990033", 3: "#0000ff", 4:"#C850BE", 5:"#64C55F"}
fig = pylab.figure()
fig.subplots_adjust(**subplot_specs)
i_ax = fig_utils.SubplotHelper(N_v,N_h)
return fig, i_ax
def plot_avgCommWeight(nc, ncName, net):
"""Plot average weight of communities."""
def w_gen(comm, net):
w_avg_total = np.mean(list(net.weights))
for c in comm:
subnet = transforms.getSubnet(net,c)
if len(subnet):
w = np.sum(list(subnet.weights))/(len(subnet)*w_avg_total)
yield len(subnet), w
bins = binner.Bins(int, 1, nc.getGiantSize(), 'linlog', 1.5)
perc = (0.1, 0.25, 0.5, 0.75, 0.9)
binned_data = bins.bin_percentile(w_gen(nc, net), perc)
# Set plotting properties
l, b, r, t = 0.1, 0.1, 0.98, (0.93 if print_titles else 0.98)
axes_rect = [l,b,r-l,t-b] #[left, bottom, width, height]
pylab.rcParams.update(fig_utils.get_rcParams(float(fig_width_cm)),
fig_ratio=0.8,
font_sizes=font_sizes)
fig = pylab.figure()
ax = fig.add_axes(axes_rect)
plot_styles = ('b:', 'b--', 'r-', 'b--', 'b:')
labels = (r"%d$^{\mathrm{th}}$ percentile" % int(100*perc[0]),
r"%d$^{\mathrm{th}}$ percentile" % int(100*perc[1]),
r"Median",
r"%d$^{\mathrm{th}}$ percentile" % int(100*perc[3]),
r"%d$^{\mathrm{th}}$ percentile" % int(100*perc[4]))
for p, data, sty, lbl in zip(perc, binned_data, plot_styles, labels):
ax.loglog(bins.centers, data, sty, label=lbl)
ax.set_xlabel(r"Community size")
ax.set_ylabel(r"$\langle w_{\mathrm{comm}} \rangle /"
r"\langle w_{\mathrm{total}} \rangle$")
if print_titles:
ax.set_title(r"Ratio of avg community weigth to avg total weight (binned)")
ax.legend(loc='best')
# Adjust axis
V = ax.axis()
ax.axis((1, bins.bin_limits[-1], V[2], V[3]))
# Save figure.
fig_utils.savefig(fig, ncName + "_weightDist", save_formats)
def plot_avgCommWeight(nc, ncName, net):
"""Plot average weight of communities."""
def w_gen(comm, net):
w_avg_total = np.mean(list(net.weights))
for c in comm:
subnet = transforms.getSubnet(net, c)
if len(subnet):
w = np.sum(list(subnet.weights)) / (len(subnet) * w_avg_total)
yield len(subnet), w
bins = binner.Bins(int, 1, nc.getGiantSize(), 'linlog', 1.5)
perc = (0.1, 0.25, 0.5, 0.75, 0.9)
binned_data = bins.bin_percentile(w_gen(nc, net), perc)
# Set plotting properties
l, b, r, t = 0.1, 0.1, 0.98, (0.93 if print_titles else 0.98)
axes_rect = [l, b, r - l, t - b] #[left, bottom, width, height]
pylab.rcParams.update(fig_utils.get_rcParams(float(fig_width_cm)),
fig_ratio=0.8,
font_sizes=font_sizes)
fig = pylab.figure()
ax = fig.add_axes(axes_rect)
plot_styles = ('b:', 'b--', 'r-', 'b--', 'b:')
labels = (r"%d$^{\mathrm{th}}$ percentile" % int(100 * perc[0]),
r"%d$^{\mathrm{th}}$ percentile" % int(100 * perc[1]), r"Median",
r"%d$^{\mathrm{th}}$ percentile" % int(100 * perc[3]),
r"%d$^{\mathrm{th}}$ percentile" % int(100 * perc[4]))
for p, data, sty, lbl in zip(perc, binned_data, plot_styles, labels):
ax.loglog(bins.centers, data, sty, label=lbl)
ax.set_xlabel(r"Community size")
ax.set_ylabel(r"$\langle w_{\mathrm{comm}} \rangle /"
r"\langle w_{\mathrm{total}} \rangle$")
if print_titles:
ax.set_title(
r"Ratio of avg community weigth to avg total weight (binned)")
ax.legend(loc='best')
# Adjust axis
V = ax.axis()
ax.axis((1, bins.bin_limits[-1], V[2], V[3]))
# Save figure.
fig_utils.savefig(fig, ncName + "_weightDist", save_formats)
def plot_topology_chart(motifs, chartFileName, plot_single_motifs=False):
"""Create topology chart of motifs.
Parameters
----------
motifs : iterable
The typed motifs for which the chart is produced. The order is
not important (the names of the motifs are independent of the
order).
chartFileName : str
File name where the chart is saved. The suffix should be some
image file format. If empty, no plot is created.
plot_single_motifs : bool
If True, every untyped motif is plotted in a separate figure.
Returns
-------
labels : {int, str}
A dictionary where the key is the hash of the untyped motif and
the value gives a textual name for the motif that is shown in
the motif chart.
"""
def get_label(h, v_label, labels, lvl):
v_label[lvl] += 1
v_label[lvl+1:] = [0]*(3-lvl)
label = "-".join(map(str, v_label[:lvl+1]))
labels[h] = label
return label
graph_data = {}
hash_types = ('graph','digraph','multigraph','motif')
# First find out the topology tree. We go through hashes in
# increasing detail: first undirected graph, then digraph, then
# multigraph, and finally motif that includes also the timings.
for m in motifs:
data = graph_data
for hash_type in hash_types:
h = m.get_hash(hash_type, type_map=m.basic_type_map)#, canonical_labels=True)
clabels = m.get_node_roles(hash_type, latex=True)
if h not in data:
m_cp = m.copy()
m_cp.remap_types(m.basic_type_map)
node_labels = dict((i,r"$%s$" % str(r)) for i,r in clabels.iteritems())
data[h] = (m_cp,node_labels,{})
data = data[h][-1]
# Get max size of a row.
N_v, N_h = 0, 0
for m, tmp, digraph_data in graph_data.itervalues():
#N_v += 1
for m, tmp, multigraph_data in digraph_data.itervalues():
#N_v += 1
for m, tmp, motif_data in multigraph_data.itervalues():
N_v += 1
N_h = max(N_h, 4 + len(motif_data))
# Physical size of the output.
w_cm_motif, h_cm_motif = 2.05, 1.85 # width and height of motif in cm
t_cm, b_cm = 1.0, 0.5 # top and bottom margin in cm
l_cm, r_cm = 0.30, 0.30 # left and right margin in cm
ws_cm, hs_cm = 0.13, 0.36 # wspace and hspace in cm
# Derived physical sizes of the figure.
w_cm = l_cm+r_cm+N_h*w_cm_motif+(N_h-1)*ws_cm
h_cm = t_cm+b_cm+N_v*h_cm_motif+(N_v-1)*hs_cm
# Set plotting properties
font_sizes = {'text': 14, 'title':13}
params = fig_utils.get_rcParams(w_cm, h_cm/w_cm, font_sizes)
pylab.rcParams.update(params)
subplot_specs = {'left':l_cm/w_cm, 'right':1-r_cm/w_cm,
'top':1-t_cm/h_cm, 'bottom':b_cm/h_cm,
'wspace':ws_cm/w_cm_motif, 'hspace':hs_cm/h_cm_motif}
labelFontSize=9
orderFontSize=7
node_size=10
node_label_size=0.8*node_size
# Create figure and subplot helper.
fig = pylab.figure()
fig.subplots_adjust(**subplot_specs)
prev_coords = mf.init_prev_coords()
i_ax = fig_utils.SubplotHelper(N_v,N_h)
# Go through the data depth first, plotting the motifs in each branch.
v_label = [0,0,0,0]
labels = {} # To map untyped hashes to their labels.
# Sort graphs by the number of nodes and edges.
graph_hashes = [(m.nof_nodes, m.nof_edges, h) for h,(m,tmp1,tmp2) in graph_data.iteritems()]
for h in map(operator.itemgetter(-1), sorted(graph_hashes)):
m, node_labels, digraph_data = graph_data[h]
# Update label
label = get_label(h, v_label, labels, lvl=0)
# Plot motif m as graph.
ax = i_ax.add_subplot(fig)
mf.plot_motif(ax, m, prev_coords, node_color="white", node_edge_color="black",
label=label, labelFontSize=labelFontSize, orderFontSize=orderFontSize,
topology="graph", node_size=node_size,
node_labels=node_labels, node_label_size=node_label_size)
if ax.is_first_row():
ax.set_title("Graph")
# Sort digraphs by the number of directed edges (they all have
# the same number of nodes because the undirected underlying
# graph is the same).
digraph_hashes = [(m.nof_dir_edges, h) for h,(m,tmp1,tmp2) in digraph_data.iteritems()]
for h in map(operator.itemgetter(-1), sorted(digraph_hashes)):
m, node_labels, multigraph_data = digraph_data[h]
i_ax.set_index_at(col=2)
label = get_label(h, v_label, labels, lvl=1)
# Plot motif m as digraph.
ax = i_ax.add_subplot(fig)
mf.plot_motif(ax, m, prev_coords, node_color="white", node_edge_color="black",
label=label, labelFontSize=labelFontSize, orderFontSize=orderFontSize,
topology="digraph", node_size=node_size,
node_labels=node_labels, node_label_size=node_label_size)
if ax.is_first_row():
ax.set_title("Digraph")
# Sort digraphs by the total number of events.
multigraph_hashes = [(m.nof_events, h) for h,(m,tmp1,tmp2) in multigraph_data.iteritems()]
for h in map(operator.itemgetter(-1), sorted(multigraph_hashes)):
m, node_labels, motif_data = multigraph_data[h]
i_ax.set_index_at(col=3)
label = get_label(h, v_label, labels, lvl=2)
# Plot motif m as multigraph.
ax = i_ax.add_subplot(fig)
mf.plot_motif(ax, m, prev_coords, node_color="white", node_edge_color="black",
label=label, labelFontSize=labelFontSize, orderFontSize=orderFontSize,
topology="multigraph", node_size=node_size,
node_labels=node_labels, node_label_size=node_label_size)
if ax.is_first_row():
ax.set_title("Multigraph")
i_ax.right()
# Sort motifs by their hash.
for h in sorted(motif_data):
m, node_labels, tmp = motif_data[h]
i_ax.right()
label = get_label(h, v_label, labels, lvl=3)
# Plot motif m as motif.
ax = i_ax.add_subplot(fig)
mf.plot_motif(ax, m, prev_coords, node_color="white", node_edge_color="black",
label=label, labelFontSize=labelFontSize, orderFontSize=orderFontSize,
topology="motif", node_size=node_size,
node_labels=node_labels, node_label_size=node_label_size)
if ax.is_first_row() and i_ax.col==5:
ax.set_title("Motifs ...")
if plot_single_motifs and chartFileName:
# Plot the motif alone in a separate figure.
params = fig_utils.get_rcParams(w_cm_motif, h_cm_motif/w_cm_motif, font_sizes)
pylab.rcParams.update(params)
fig_motif = pylab.figure()
mf.plot_motif(fig_motif.add_axes([0.,0.,1.,1.]), m, prev_coords, node_color="white", node_edge_color="black",
label="",orderFontSize=orderFontSize,
topology="motif", node_size=node_size,
node_labels=node_labels, node_label_size=node_label_size)
motifFileName = "%s/motif_%s.pdf" % ("/".join(chartFileName.split("/")[:-1]), label)
fig_utils.savefig(fig_motif, motifFileName, verbose=True)
i_ax.next_row()
if chartFileName:
fig_utils.savefig(fig, chartFileName, verbose=True)
return labels
def plot_topology_chart(motifs, chartFileName, plot_single_motifs=False):
"""Create topology chart of motifs.
Parameters
----------
motifs : iterable
The typed motifs for which the chart is produced. The order is
not important (the names of the motifs are independent of the
order).
chartFileName : str
File name where the chart is saved. The suffix should be some
image file format. If empty, no plot is created.
plot_single_motifs : bool
If True, every untyped motif is plotted in a separate figure.
Returns
-------
labels : {int, str}
A dictionary where the key is the hash of the untyped motif and
the value gives a textual name for the motif that is shown in
the motif chart.
"""
def get_label(h, v_label, labels, lvl):
v_label[lvl] += 1
v_label[lvl + 1:] = [0] * (3 - lvl)
label = "-".join(map(str, v_label[:lvl + 1]))
labels[h] = label
return label
graph_data = {}
hash_types = ('graph', 'digraph', 'multigraph', 'motif')
# First find out the topology tree. We go through hashes in
# increasing detail: first undirected graph, then digraph, then
# multigraph, and finally motif that includes also the timings.
for m in motifs:
data = graph_data
for hash_type in hash_types:
h = m.get_hash(
hash_type,
type_map=m.basic_type_map) #, canonical_labels=True)
clabels = m.get_node_roles(hash_type, latex=True)
if h not in data:
m_cp = m.copy()
m_cp.remap_types(m.basic_type_map)
node_labels = dict(
(i, r"$%s$" % str(r)) for i, r in clabels.iteritems())
data[h] = (m_cp, node_labels, {})
data = data[h][-1]
# Get max size of a row.
N_v, N_h = 0, 0
for m, tmp, digraph_data in graph_data.itervalues():
#N_v += 1
for m, tmp, multigraph_data in digraph_data.itervalues():
#N_v += 1
for m, tmp, motif_data in multigraph_data.itervalues():
N_v += 1
N_h = max(N_h, 4 + len(motif_data))
# Physical size of the output.
w_cm_motif, h_cm_motif = 2.05, 1.85 # width and height of motif in cm
t_cm, b_cm = 1.0, 0.5 # top and bottom margin in cm
l_cm, r_cm = 0.30, 0.30 # left and right margin in cm
ws_cm, hs_cm = 0.13, 0.36 # wspace and hspace in cm
# Derived physical sizes of the figure.
w_cm = l_cm + r_cm + N_h * w_cm_motif + (N_h - 1) * ws_cm
h_cm = t_cm + b_cm + N_v * h_cm_motif + (N_v - 1) * hs_cm
# Set plotting properties
font_sizes = {'text': 14, 'title': 13}
params = fig_utils.get_rcParams(w_cm, h_cm / w_cm, font_sizes)
pylab.rcParams.update(params)
subplot_specs = {
'left': l_cm / w_cm,
'right': 1 - r_cm / w_cm,
'top': 1 - t_cm / h_cm,
'bottom': b_cm / h_cm,
'wspace': ws_cm / w_cm_motif,
'hspace': hs_cm / h_cm_motif
}
labelFontSize = 9
orderFontSize = 7
node_size = 10
node_label_size = 0.8 * node_size
# Create figure and subplot helper.
fig = pylab.figure()
fig.subplots_adjust(**subplot_specs)
prev_coords = mf.init_prev_coords()
i_ax = fig_utils.SubplotHelper(N_v, N_h)
# Go through the data depth first, plotting the motifs in each branch.
v_label = [0, 0, 0, 0]
labels = {} # To map untyped hashes to their labels.
# Sort graphs by the number of nodes and edges.
graph_hashes = [(m.nof_nodes, m.nof_edges, h)
for h, (m, tmp1, tmp2) in graph_data.iteritems()]
for h in map(operator.itemgetter(-1), sorted(graph_hashes)):
m, node_labels, digraph_data = graph_data[h]
# Update label
label = get_label(h, v_label, labels, lvl=0)
# Plot motif m as graph.
ax = i_ax.add_subplot(fig)
mf.plot_motif(ax,
m,
prev_coords,
node_color="white",
node_edge_color="black",
label=label,
labelFontSize=labelFontSize,
orderFontSize=orderFontSize,
topology="graph",
node_size=node_size,
node_labels=node_labels,
node_label_size=node_label_size)
if ax.is_first_row():
ax.set_title("Graph")
# Sort digraphs by the number of directed edges (they all have
# the same number of nodes because the undirected underlying
# graph is the same).
digraph_hashes = [(m.nof_dir_edges, h)
for h, (m, tmp1, tmp2) in digraph_data.iteritems()]
for h in map(operator.itemgetter(-1), sorted(digraph_hashes)):
m, node_labels, multigraph_data = digraph_data[h]
i_ax.set_index_at(col=2)
label = get_label(h, v_label, labels, lvl=1)
# Plot motif m as digraph.
ax = i_ax.add_subplot(fig)
mf.plot_motif(ax,
m,
prev_coords,
node_color="white",
node_edge_color="black",
label=label,
labelFontSize=labelFontSize,
orderFontSize=orderFontSize,
topology="digraph",
node_size=node_size,
node_labels=node_labels,
node_label_size=node_label_size)
if ax.is_first_row():
ax.set_title("Digraph")
# Sort digraphs by the total number of events.
multigraph_hashes = [(m.nof_events, h)
for h, (m, tmp1,
tmp2) in multigraph_data.iteritems()]
for h in map(operator.itemgetter(-1), sorted(multigraph_hashes)):
m, node_labels, motif_data = multigraph_data[h]
i_ax.set_index_at(col=3)
label = get_label(h, v_label, labels, lvl=2)
# Plot motif m as multigraph.
ax = i_ax.add_subplot(fig)
mf.plot_motif(ax,
m,
prev_coords,
node_color="white",
node_edge_color="black",
label=label,
labelFontSize=labelFontSize,
orderFontSize=orderFontSize,
topology="multigraph",
node_size=node_size,
node_labels=node_labels,
node_label_size=node_label_size)
if ax.is_first_row():
ax.set_title("Multigraph")
i_ax.right()
# Sort motifs by their hash.
for h in sorted(motif_data):
m, node_labels, tmp = motif_data[h]
i_ax.right()
label = get_label(h, v_label, labels, lvl=3)
# Plot motif m as motif.
ax = i_ax.add_subplot(fig)
mf.plot_motif(ax,
m,
prev_coords,
node_color="white",
node_edge_color="black",
label=label,
labelFontSize=labelFontSize,
orderFontSize=orderFontSize,
topology="motif",
node_size=node_size,
node_labels=node_labels,
node_label_size=node_label_size)
if ax.is_first_row() and i_ax.col == 5:
ax.set_title("Motifs ...")
if plot_single_motifs and chartFileName:
# Plot the motif alone in a separate figure.
params = fig_utils.get_rcParams(
w_cm_motif, h_cm_motif / w_cm_motif, font_sizes)
pylab.rcParams.update(params)
fig_motif = pylab.figure()
mf.plot_motif(fig_motif.add_axes([0., 0., 1., 1.]),
m,
prev_coords,
node_color="white",
node_edge_color="black",
label="",
orderFontSize=orderFontSize,
topology="motif",
node_size=node_size,
node_labels=node_labels,
node_label_size=node_label_size)
motifFileName = "%s/motif_%s.pdf" % ("/".join(
chartFileName.split("/")[:-1]), label)
fig_utils.savefig(fig_motif,
motifFileName,
verbose=True)
i_ax.next_row()
if chartFileName:
fig_utils.savefig(fig, chartFileName, verbose=True)
return labels
