def check_and_draw(base_graph, abstr):
'''
Parameters
----------
base_graph: a base graph
abstr: an abstract graph
Returns
-------
check if EVERY node in base_graph is in any abstr.graph.node['contracted']
'''
nodeset = set([a for n, d in abstr.nodes(data=True) for a in d['contracted']])
broken = []
for n in base_graph.nodes():
if n not in nodeset:
broken.append(n)
base_graph.node[n]['colo'] = .5
if len(broken) > 0:
print "FOUND SOMETHING BROKEN:"
draw.set_ids(base_graph)
base_graph.graph['info'] = 'failed to see these:%s' % str(broken)
edraw.draw_graph(base_graph, vertex_label='id', vertex_color='colo', edge_label=None, size=20)
for e, d in abstr.nodes(data=True):
d['label'] = str(d.get('contracted', ''))
edraw.draw_graph(abstr, vertex_label='label', vertex_color=None, edge_label=None, size=20)
return False
return True
def _draw(self,
seq,
file_name=None,
mutations=None,
scores=None,
fold=None):
seqs = [('header_placeholder', seq)]
graphs = fold(seqs)
graph = graphs.next()
graph = self._update_graph(graph, scores, mutations)
opts = {
'size': 14,
'font_size': 9,
'layout': 'KK',
'vertex_border': False,
'vertex_size': 400,
'vertex_alpha': 0.5,
'invert_colormap': True,
'secondary_vertex_label': 'alternative',
'vertex_color': 'stability',
'colormap': 'OrRd',
'edge_label': None,
'edge_alpha': 0.2,
'dark_edge_alpha': 0.8,
'ignore_for_layout': 'nesting'
}
draw_graph(graph, file_name=file_name, **opts)
def display_edge_predictions(g, tr_graphs, tr_targets,
te_graphs, te_targets, preds,
vertex_color='typeof', size=15):
"""display_edge_predictions."""
tr_roots = [gg.graph['roots'] for gg in tr_graphs]
graph = g.copy()
for (u, v), t in zip(tr_roots, tr_targets):
if t == 1:
graph.edge[u][v]['color'] = 'gray'
te_roots = [gg.graph['roots'] for gg in te_graphs]
for (u, v), p, t in zip(te_roots, preds, te_targets):
# for all predicted edges
if p == 1:
# true positive
if t == 1:
graph.edge[u][v]['color'] = 'limegreen'
# false positive
if t == 0:
graph.add_edge(u, v)
graph.edge[u][v]['nesting'] = True
graph.edge[u][v]['color'] = 'gray'
if p == 0:
# false negative
if t == 1:
graph.edge[u][v]['color'] = 'crimson'
draw_graph(graph, size=size, vertex_color=vertex_color,
vertex_size=100, vertex_label=None, edge_label=None,
edge_color='color', edge_alpha=1,
ignore_for_layout='nesting', dark_edge_alpha=.9,
dark_edge_color='color')
return graph
def getGraph9(peak, alpha):
seq, stru = peak
bg = fgb.BulgeGraph.from_dotbracket(stru)
dict = {}
i = 0
G = nx.Graph()
for key2 in bg.defines.keys():
G.add_node(i, label=key2[0])
dict[key2] = i
i += 1
for key1 in bg.edges:
for key2 in bg.edges[key1]:
G.add_edge(dict[key1], dict[key2], label='v')
if DEBUG == 1:
print(seq)
print(stru)
display.draw_graph(G,
size=15,
node_size=1500,
font_size=24,
node_border=True,
size_x_to_y_ratio=3)
return G
def show_graph(g, vertex_color='typeof', size=15, vertex_label=None):
"""show_graph."""
degrees = [len(g.neighbors(u)) for u in g.nodes()]
print(('num nodes=%d' % len(g)))
print(('num edges=%d' % len(g.edges())))
print(('num non edges=%d' % len(list(nx.non_edges(g)))))
print(('max degree=%d' % max(degrees)))
print(('median degree=%d' % np.percentile(degrees, 50)))
draw_graph(g, size=size,
vertex_color=vertex_color, vertex_label=vertex_label,
vertex_size=200, edge_label=None)
# display degree distribution
size = int((max(degrees) - min(degrees)) / 1.5)
plt.figure(figsize=(size, 3))
plt.title('Degree distribution')
_bins = np.arange(min(degrees), max(degrees) + 2) - .5
n, bins, patches = plt.hist(degrees, _bins,
alpha=0.3,
facecolor='navy', histtype='bar',
rwidth=0.8, edgecolor='k')
labels = np.array([str(int(i)) for i in n])
for xi, yi, label in zip(bins, n, labels):
plt.text(xi + 0.5, yi, label, ha='center', va='bottom')
plt.xticks(bins + 0.5)
plt.xlim((min(degrees) - 1, max(degrees) + 1))
plt.ylim((0, max(n) * 1.1))
plt.xlabel('Node degree')
plt.ylabel('Counts')
plt.grid(linestyle=":")
plt.show()
def draw_print(seq, shape, file_name= 'lol.svg',stru=None):
'''seq+shape => image'''
if stru != None:
brack = stru
else:
brack = rnafold.fold(seq)
graph = util.sequence_dotbracket_to_graph(seq,brack)
graph.graph['structure']= brack
graph = annotate(graph.copy(), shape)
ed.draw_graph(graph, size=5, layout="RNA",vertex_color='col',
vertex_label=None, edge_alpha=0.4, vertex_size=150,
vertex_border=False, file_name=file_name)
def draw_match(GA, GB, pairings, size=10):
G = nx.disjoint_union(GA, GB)
for i, jj in enumerate(pairings):
j = len(GA) + jj
if G.node[i]['label'] == G.node[j]['label']:
G.add_edge(i, j, label=i, nesting=True)
draw_graph(G,
size=size,
vertex_border=False,
vertex_size=400,
edge_label=None,
edge_alpha=.2,
dark_edge_color='label',
dark_edge_dotted=False,
dark_edge_alpha=1,
colormap='Set2',
ignore_for_layout='nesting')
def display_edge_predictions(g,
tr_graphs,
tr_targets,
te_graphs,
te_targets,
preds,
vertex_color='typeof',
size=15):
"""display_edge_predictions."""
tr_roots = [gg.graph['roots'] for gg in tr_graphs]
graph = g.copy()
for (u, v), t in zip(tr_roots, tr_targets):
if t == 1:
graph.edge[u][v]['color'] = 'gray'
te_roots = [gg.graph['roots'] for gg in te_graphs]
for (u, v), p, t in zip(te_roots, preds, te_targets):
# for all predicted edges
if p == 1:
# true positive
if t == 1:
graph.edge[u][v]['color'] = 'limegreen'
# false positive
if t == 0:
graph.add_edge(u, v)
graph.edge[u][v]['nesting'] = True
graph.edge[u][v]['color'] = 'gray'
if p == 0:
# false negative
if t == 1:
graph.edge[u][v]['color'] = 'crimson'
draw_graph(graph,
size=size,
vertex_color=vertex_color,
vertex_size=100,
vertex_label=None,
edge_label=None,
edge_color='color',
edge_alpha=1,
ignore_for_layout='nesting',
dark_edge_alpha=.9,
dark_edge_color='color')
return graph
def draw_match(GA, GB, pairings, size=10):
G = nx.disjoint_union(GA, GB)
for i, jj in enumerate(pairings):
j = len(GA) + jj
if G.node[i]['label'] == G.node[j]['label']:
G.add_edge(i, j, label=i, nesting=True)
draw_graph(
G,
size=size,
vertex_border=False,
vertex_size=400,
edge_label=None,
edge_alpha=.2,
dark_edge_color='label',
dark_edge_dotted=False,
dark_edge_alpha=1,
colormap='Set2',
ignore_for_layout='nesting')
def show_graph(g, vertex_color='typeof', size=15, vertex_label=None):
"""show_graph."""
degrees = [len(g.neighbors(u)) for u in g.nodes()]
print(('num nodes=%d' % len(g)))
print(('num edges=%d' % len(g.edges())))
print(('num non edges=%d' % len(list(nx.non_edges(g)))))
print(('max degree=%d' % max(degrees)))
print(('median degree=%d' % np.percentile(degrees, 50)))
draw_graph(g,
size=size,
vertex_color=vertex_color,
vertex_label=vertex_label,
vertex_size=200,
edge_label=None)
# display degree distribution
size = int((max(degrees) - min(degrees)) / 1.5)
plt.figure(figsize=(size, 3))
plt.title('Degree distribution')
_bins = np.arange(min(degrees), max(degrees) + 2) - .5
n, bins, patches = plt.hist(degrees,
_bins,
alpha=0.3,
facecolor='navy',
histtype='bar',
rwidth=0.8,
edgecolor='k')
labels = np.array([str(int(i)) for i in n])
for xi, yi, label in zip(bins, n, labels):
plt.text(xi + 0.5, yi, label, ha='center', va='bottom')
plt.xticks(bins + 0.5)
plt.xlim((min(degrees) - 1, max(degrees) + 1))
plt.ylim((0, max(n) * 1.1))
plt.xlabel('Node degree')
plt.ylabel('Counts')
plt.grid(linestyle=":")
plt.show()
def getGraph10(peak, G1, alpha):
seq, stru = peak
bg = fgb.BulgeGraph.from_dotbracket(stru)
n = len(seq)
G = G1.copy()
i = n
dict = {}
for key2 in bg.defines.keys():
if key2[0] == 'h':
G.add_node(i, label=key2[0])
elif key2[0] == 's':
G.add_node(i, label=key2[0])
else:
G.add_node(i, label='i')
dict[key2] = i
i += 1
for key1 in bg.edges:
for key2 in bg.edges[key1]:
G.add_edge(dict[key1], dict[key2], label='v')
for i in range(n):
key = bg.get_node_from_residue_num(i + 1)
G.add_edge(i, dict[key], label='abb')
if DEBUG == 1:
print(seq)
print(stru)
display.draw_graph(G,
size=25,
node_size=2000,
font_size=14,
node_border=True,
size_x_to_y_ratio=3)
return G
def display_multiple_correspondences(Gs,
GB,
decomposition_function=None,
size=6,
n_graphs_per_line=6):
pos = get_pos_from_abstract_graph(GB)
if decomposition_function is None:
df1 = do_decompose(decompose_nodes_and_edges,
decompose_neighborhood(radius=1),
compose_function=decompose_context(radius=1))
df2 = do_decompose(decompose_nodes_and_edges,
decompose_neighborhood(radius=1),
compose_function=decompose_context(radius=2))
decomposition_function = do_decompose(
df1, df2, decompose_cycles, decompose_neighborhood(max_radius=3))
n = len(Gs)
n_rows = n // n_graphs_per_line
plt.figure(figsize=(n_graphs_per_line * size, size))
for i in range(n):
GA = Gs[i]
graph_A, pos_A, graph_B, pos_B, map_from_A_to_B = map_first_onto_second(
GA, GB, pos=pos, decomposition_function=decomposition_function)
colors = map_labels_to_colors([graph_A, graph_B])
kwargs = dict(size=None,
colormap='tab20c',
vertex_size=80,
edge_label=None,
vertex_color_dict=colors,
vmax=1,
vmin=0,
vertex_color='_label_',
vertex_label=None,
layout='spring',
ignore_for_layout='nesting')
plt.subplot(n_rows + 1, n_graphs_per_line, i + 1)
draw_graph(graph_A, pos=pos_A, **kwargs)
plt.show()
def display_correspondences(GA, GB, decomposition_function=None, size=4):
plt.figure(figsize=(4.2 * size, size))
graph_A, pos_A, graph_B, pos_B, map_from_A_to_B = map_first_onto_second(
GA, GB, decomposition_function=decomposition_function)
colors = map_labels_to_colors([graph_A, graph_B])
kwargs = dict(size=None,
colormap='tab20c',
vertex_size=80,
edge_label=None,
vertex_color_dict=colors,
vmax=1,
vmin=0,
vertex_color='_label_',
vertex_label=None,
layout='spring',
ignore_for_layout='nesting')
plt.subplot(1, 4, 1)
draw_graph(graph_A, pos=pos_A, **kwargs)
plt.subplot(1, 4, 2)
draw_graph(graph_B, pos=pos_B, **kwargs)
graph_A_i, pos_A_i, graph_B_i, pos_B_i, map_from_A_to_B_i = map_first_onto_second(
GB, GA, decomposition_function=decomposition_function)
plt.subplot(1, 4, 3)
draw_graph(graph_A_i, pos=pos_A_i, **kwargs)
plt.subplot(1, 4, 4)
draw_graph(graph_B_i, pos=pos_B_i, **kwargs)
plt.show()
if is_abstract_graph(GA) and is_abstract_graph(GB):
graph_A_base_nodes = [
u for u in graph_A.nodes() if 'isa' not in graph_A.nodes[u]
]
graph_A_base = nx.subgraph(graph_A, graph_A_base_nodes)
graph_B_base_nodes = [
u for u in graph_B.nodes() if 'isa' not in graph_B.nodes[u]
]
graph_B_base = nx.subgraph(graph_B, graph_B_base_nodes)
graph_A_i_base_nodes = [
u for u in graph_A_i.nodes() if 'isa' not in graph_A_i.nodes[u]
]
graph_A_i_base = nx.subgraph(graph_A_i, graph_A_i_base_nodes)
graph_B_i_base_nodes = [
u for u in graph_B_i.nodes() if 'isa' not in graph_B_i.nodes[u]
]
graph_B_i_base = nx.subgraph(graph_B_i, graph_B_i_base_nodes)
plt.figure(figsize=(4.2 * size, size))
plt.subplot(1, 4, 1)
draw_graph(graph_A_base, pos=pos_A, **kwargs)
plt.subplot(1, 4, 2)
draw_graph(graph_B_base, pos=pos_B, **kwargs)
plt.subplot(1, 4, 3)
draw_graph(graph_A_i_base, pos=pos_A_i, **kwargs)
plt.subplot(1, 4, 4)
draw_graph(graph_B_i_base, pos=pos_B_i, **kwargs)
plt.show()
def getGraph14(peak, all, upsc, struYes, alpha):
seq, stru2 = peak
if struYes == 1:
stru = stru2
else:
stru = ""
for i in range(len(seq)):
stru += "."
bgw = 1
bbw = 1
bg = fgb.BulgeGraph.from_dotbracket(stru)
n = len(seq)
if len(bg.defines.keys()) == 1: #Empty structure
G = nx.Graph()
for i in range(n):
G.add_node(i, label=seq[i], weight=1)
for i in range(n - 1):
G.add_edge(i, i + 1, label='bb', weight=1)
if DEBUG == 1:
print(seq)
print(stru)
display.draw_graph(G,
size=15,
node_size=1500,
font_size=24,
node_border=True,
size_x_to_y_ratio=3)
return G
n = len(seq)
dict = {}
i = 0
G = nx.Graph()
for key2 in bg.defines.keys():
lb = key2[0]
if lb != 's':
lb = 'u'
w = 10
else:
w = 10
G.add_node(i, label=lb, weight=w)
dict[key2] = i
i += 1
hairpins = {}
for key1 in bg.edges:
for key2 in bg.edges[key1]:
G.add_edge(dict[key1], dict[key2], label='v', weight=bgw)
if all == 0:
for j in range(n):
key = bg.get_node_from_residue_num(j + 1)
if key[0] == 'h' or G.number_of_nodes(
) == 1: #or key[0]=='m' or key[0]=='i':
if key not in hairpins:
hairpins[key] = []
hairpins[key].append(j)
elif all == 1:
for j in range(n):
key = bg.get_node_from_residue_num(j + 1)
if key[0] == 'h' or key[0] == 'i' or G.number_of_nodes() == 1:
if key not in hairpins:
hairpins[key] = []
hairpins[key].append(j)
elif all == 2:
for j in range(n):
key = bg.get_node_from_residue_num(j + 1)
if key[0] == 'i' or G.number_of_nodes() == 1:
if key not in hairpins:
hairpins[key] = []
hairpins[key].append(j)
elif all == 3:
for j in range(n):
key = bg.get_node_from_residue_num(j + 1)
if (key[0] == 's') or G.number_of_nodes() == 1:
if key not in hairpins:
hairpins[key] = []
hairpins[key].append(j)
elif all == 4:
for j in range(n):
key = bg.get_node_from_residue_num(j + 1)
if key not in hairpins:
hairpins[key] = []
hairpins[key].append(j)
elif all == 5:
for j in range(n):
key = bg.get_node_from_residue_num(j + 1)
if key[0] == 'm' or key[0] == 'f' or key[
0] == 't' or G.number_of_nodes() == 1:
if key not in hairpins:
hairpins[key] = []
hairpins[key].append(j)
else:
for j in range(n):
key = bg.get_node_from_residue_num(j + 1)
if (key[0] != 's') or G.number_of_nodes() == 1:
if key not in hairpins:
hairpins[key] = []
hairpins[key].append(j)
for key in hairpins:
st = min(hairpins[key])
ed = max(hairpins[key])
G.add_node(i, label=alpha[seq[st:st + 1]])
if key[0] != 'f':
G.add_edge(dict[key], i, label='abb', weight=bgw)
i += 1
if key[0] != 'i' and key[0] != 's':
for j in range(st + 1, ed):
G.add_node(i, label=alpha[seq[j:j + 1]])
G.add_edge(i - 1, i, label='bb', weight=bbw)
#if upsc==1:
G.add_edge(dict[key], i, label='abb', weight=bgw)
i += 1
if len(hairpins[key]) > 1:
G.add_node(i, label=alpha[seq[ed:ed + 1]])
G.add_edge(i - 1, i, label='bb', weight=bbw)
i += 1
#if key[0]!='t':
G.add_edge(i - 1, dict[key], label='abb', weight=bgw)
else:
j = st + 1
while (j in hairpins[key]):
G.add_node(i, label=alpha[seq[j:j + 1]])
G.add_edge(i - 1, i, label='bb', weight=bbw)
i += 1
j += 1
if j - st > 1:
G.add_edge(i - 1, dict[key], label='abb', weight=bgw)
elif upsc == 1:
G.add_edge(dict[key], i - 1, label='abb', weight=bgw)
if st != ed and j != ed + 1:
G.add_node(i, label=alpha[seq[ed:ed + 1]])
G.add_edge(dict[key], i, label='abb', weight=bgw)
if upsc == 1:
G.add_edge(dict[key], i, label='abb', weight=bgw)
i += 1
j = ed - 1
while (j in hairpins[key]):
G.add_node(i, label=alpha[seq[j:j + 1]])
G.add_edge(i - 1, i, label='bb', weight=bbw)
#if upsc==1:
G.add_edge(dict[key], i, label='abb', weight=bgw)
i += 1
j -= 1
if ed - j > 1 and upsc == 0:
G.add_edge(i - 1, dict[key], label='abb', weight=bgw)
if DEBUG == 1:
print(seq)
print(stru)
display.draw_graph(G,
size=15,
node_size=1500,
font_size=24,
node_border=True,
size_x_to_y_ratio=3,
vertex_color='red')
return G
def getGraph11(peak, G1, alpha):
seq, stru = peak
bps = getbps(stru)
bg = fgb.BulgeGraph.from_dotbracket(stru)
n = len(seq)
G = G1.copy()
i = n
dict = {}
for key2 in bg.defines.keys():
if key2[0] == 'h':
G.add_node(i, label=key2[0])
elif key2[0] == 's':
G.add_node(i, label=key2[0])
else:
G.add_node(i, label='i')
dict[key2] = i
i += 1
for key1 in bg.edges:
for key2 in bg.edges[key1]:
G.add_edge(dict[key1], dict[key2], label='v')
for i in range(n):
key = bg.get_node_from_residue_num(i + 1)
G.add_edge(i, dict[key], label='abb')
for i in range(1, n - 4):
j = bps[i]
if i < j:
if bps[i + 1] == i + 1 and bps[j - 1] == j - 1:
key = bg.get_node_from_residue_num(i + 2)
if key[0] == 'i' or key[0] == 'h':
G.add_edge(i, dict[key], label='abb')
G.add_edge(j, dict[key], label='abb')
elif bps[i + 1] == i + 1:
key = bg.get_node_from_residue_num(i + 2)
if key[0] == 'i' or key[0] == 'h':
G.add_edge(i, dict[key], label='abb')
elif bps[j - 1] == j - 1:
key = bg.get_node_from_residue_num(j)
if key[0] == 'i' or key[0] == 'h':
G.add_edge(j, dict[key], label='abb')
if j + 1 < n and bps[i - 1] == i - 1 and bps[j + 1] == j + 1:
key = bg.get_node_from_residue_num(i)
if key[0] == 'i' or key[0] == 'h':
G.add_edge(i, dict[key], label='abb')
G.add_edge(j, dict[key], label='abb')
elif bps[i - 1] == i - 1:
key = bg.get_node_from_residue_num(i)
if key[0] == 'i' or key[0] == 'h':
G.add_edge(i, dict[key], label='abb')
elif j + 1 < n and bps[j + 1] == j + 1:
key = bg.get_node_from_residue_num(j + 2)
if key[0] == 'i' or key[0] == 'h':
G.add_edge(j, dict[key], label='abb')
if DEBUG == 1:
print(seq)
print(stru)
display.draw_graph(G,
size=15,
node_size=1500,
font_size=24,
node_border=True,
size_x_to_y_ratio=3)
return G
def draw_bundled_graph(lattice,
pos=None,
n_iter=1,
n_voronoi_iter=2,
display_size=10,
display_nodes=True,
node_size=50,
display_node_density=True,
node_colormap='bwr',
edge_colormap='cividis_r',
edge_intensity=True):
if pos is None:
pos = forceatlas2_networkx_layout(
lattice,
niter=2000,
outboundAttractionDistribution=False, # Dissuade hubs
linLogMode=False, # NOT IMPLEMENTED
adjustSizes=False, # Prevent overlap (NOT IMPLEMENTED)
edgeWeightInfluence=2.0,
jitterTolerance=1.0, # Tolerance
barnesHutOptimize=False,
barnesHutTheta=1.2,
scalingRatio=2.0,
strongGravityMode=False,
gravity=0.01)
pos = get_canonical_orientation(pos)
points = np.array([pos[u] for u in pos])
N = len(points)
for iter in range(n_voronoi_iter):
vor = Voronoi(points, qhull_options='Qbb Qc Qx')
points = np.vstack([points, vor.vertices])
vor = Voronoi(points, qhull_options='Qbb Qc Qx')
M = len(vor.vertices)
id2hash = {i: u for i, u in enumerate(pos)}
hash2id = {u: i for i, u in enumerate(pos)}
points = np.array([pos[u] for u in pos])
all_points = np.vstack([points, vor.vertices])
all_pos = {i: x for i, x in enumerate(all_points)}
tri = Delaunay(all_points)
G = nx.Graph()
G.add_nodes_from(range(N + M))
color = np.zeros(N)
for i in range(N):
G.nodes[i]['label'] = 0
G.nodes[i]['type'] = 'lattice'
G.nodes[i]['pos'] = all_pos[i]
u = id2hash[i]
color[i] = lattice.nodes[u].get('num_type', 0)
for j in range(N, N + M):
G.nodes[j]['label'] = 1
G.nodes[j]['type'] = 'waypoint'
G.nodes[j]['pos'] = all_pos[j]
for simplex in tri.simplices:
for i in range(len(simplex) - 1):
u = simplex[i]
v = simplex[i + 1]
dist = np.sqrt(
np.sum(np.power(G.nodes[u]['pos'] - G.nodes[v]['pos'], 2)))
G.add_edge(u, v, label=0, weight=dist, count=0)
G.add_edge(simplex[0], simplex[-1], label=0, weight=dist, count=0)
for iter in range(n_iter):
for u, v in lattice.edges():
i = hash2id[u]
j = hash2id[v]
try:
path = nx.shortest_path(G, source=i, target=j, weight='weight')
for k in range(len(path) - 1):
a = path[k]
b = path[k + 1]
G.edges[a, b]['label'] = 1
G.edges[a, b]['count'] += 1
except Exception:
pass
# normalize thicknes
max_count = max(G.edges[i, j]['count'] for i, j in G.edges())
for i, j in G.edges():
G.edges[i, j]['thickness'] = 4 * G.edges[i, j].get(
'count', 0) / float(max_count)
# update weights
for i, j in G.edges():
G.edges[i, j]['weight'] = 1.0 / (1 + G.edges[i, j].get('count', 0))
G.edges[i, j]['count'] = 0
args = dict(pos=all_pos,
size=None,
vertex_size=0,
vertex_label=None,
edge_label=None,
edge_width='thickness',
edge_alpha=1,
edge_color='thickness',
edge_colormap=edge_colormap,
colormap=node_colormap)
plt.figure(figsize=(1.62 * display_size, display_size))
if edge_intensity:
draw_graph(G, **args)
else:
draw_graph(G, edge_fixed_color='k', **args)
x, y = points.T
if display_nodes:
colors = [lattice.nodes[u]['label'] for u in lattice.nodes()]
if len(set(colors)) == 1:
colors = 'w'
plt.scatter(x,
y,
s=node_size,
c=colors,
edgecolors='k',
linewidths=0.5,
cmap=node_colormap)
if display_node_density:
plot_pdf2D(points)
dx = (max(x) - min(x)) / 20
dy = (max(y) - min(y)) / 20
plt.xlim(min(x) - dx, max(x) + dx)
plt.ylim(min(y) - dy, max(y) + dy)
plt.show()
return pos
def getGraph8(peak, G1, alpha):
seq, stru = peak
bps = getbps(stru)
G = G1.copy() #nx.Graph()
pile = []
n = len(seq)
next_pos = 0
last_pos = 0
node = n
pendingexternal = -1
pendingstem = -1
while (next_pos < n):
last_pos = next_pos
while (next_pos < n and stru[next_pos] == '.'):
next_pos += 1
if next_pos > last_pos: #starts with unpaired rexion
G.add_node(node, label='EL')
#Combine
for i in range(last_pos, next_pos):
G.add_edge(i, node, label='s')
if pendingexternal != -1:
G.add_edge(node, pendingexternal, label='abb')
node += 1
pendingstem = node - 1
else:
pendingstem = pendingexternal
if next_pos == n:
break
#Start stem
i = next_pos
j = bps[i]
pile.append(i)
pile.append(j)
pendingexternal = node
next_pos = j + 1
while (i < j):
while (bps[i + 1] == j - 1):
i += 1
j -= 1
pile.append(i)
pile.append(j)
G.add_node(node, label='S')
#Combine
for p in range(len(pile)):
G.add_edge(pile[p], node, label='s')
pile = []
if pendingstem != -1:
G.add_edge(pendingstem, node, label='abb')
pendingstem = -1
node += 1
if hl(i + 1, j, stru):
G.add_node(node, label='HL')
G.add_edge(node - 1, node, label='abb')
#Combine
for p in range(i + 1, j):
G.add_edge(p, node, label='s')
node += 1
i = j
elif stru[i + 1] == '.' and stru[j - 1] == '.': #IL
i += 1
init_i = i
init_j = j
while (stru[i] == '.'):
i += 1
j = bps[i]
G.add_node(node, label='IL')
G.add_edge(node - 1, node, label='abb')
#Combine
for p in range(init_i, i):
G.add_edge(p, node, label='s')
for p in range(j + 1, init_j):
G.add_edge(p, node, label='s')
pendingstem = node
node += 1
elif stru[i + 1] == '.': #LB
i += 1
init_i = i
while (stru[i] == '.'):
i += 1
j = bps[i]
G.add_node(node, label='LB')
G.add_edge(node - 1, node, label='abb')
#Combine
for p in range(init_i, i):
G.add_edge(p, node, label='s')
pendingstem = node
node += 1
else: #RB
init_j = j
j -= 1
while (stru[j] == '.'):
j -= 1
i = bps[j]
G.add_node(node, label='RB')
G.add_edge(node - 1, node, label='abb')
#Combine
for p in range(j + 1, init_j):
G.add_edge(p, node, label='s')
pendingstem = node
node += 1
if DEBUG == 1:
print(seq)
print(stru)
display.draw_graph(G,
size=15,
node_size=1500,
font_size=24,
node_border=True,
size_x_to_y_ratio=3)
return G
def getGraph5(peak, stems, alpha):
seq, stru = peak
bps = getbps(stru)
G = nx.Graph()
pile = []
open = 0
close = 0
single = 0
j = 0
opending = -1
cpending = -1
for i in range(len(seq)):
#print(i,pile)
if stru[i] == ".":
if single == 1:
G.add_node(j, label=alpha[seq[i]])
G.add_edge(j - 1, j, label='bb')
j += 1
else:
single = 1
if open > 0:
G.add_node(j, label=alpha[seq[i]])
G.add_edge(j - 1, j, label='bb')
j += 1
open = 0
elif close > 0:
G.add_node(j, label=alpha[seq[i]])
G.add_edge(j - 1, j, label='bb')
j += 1
else: #it is the first node
G.add_node(j, label=alpha[seq[i]])
j += 1
elif stru[i] == "(":
if stems == 1:
lb = seq[i]
else:
lb = 'p'
if single == 1:
G.add_node(j, label=lb)
G.add_edge(j - 1, j, label='bb')
open = 1
pile.append((j, open))
j += 1
single = 0
elif open > 0:
if bps[i - 1] == bps[i] + 1:
G.add_node(j, label=lb)
G.add_edge(j - 1, j, label='bb')
open += 1
pile.append((j, open))
j += 1
else: #close loop
G.add_node(j, label=lb)
G.add_edge(j - 1, j, label='bb')
j += 1
pile.append((j - 1, open))
open = 1
elif close > 0:
G.add_node(j, label=lb)
G.add_edge(j - 1, j, label='bb')
open = 1
pile.append((j, open))
j += 1
else: #first nt
open = 1
G.add_node(j, label=lb)
pile.append((j, open))
j += 1
else: #stru[i]==")"
if stems == 1:
lb = seq[i]
else:
lb = 'p'
if single == 1:
G.add_node(j, label=lb)
G.add_edge(j - 1, j, label='bb')
k, w = pile.pop()
G.add_edge(k, j, label='bp')
j += 1
close = 1
single = 0
elif close > 0:
if bps[i] == bps[i - 1] - 1:
close += 1
G.add_node(j, label=lb)
G.add_edge(j - 1, j, label='bb')
k, w = pile.pop()
G.add_edge(k, j, label='bp')
j += 1
else:
G.add_node(j, label=lb)
G.add_edge(j - 1, j, label='bb')
k, w = pile.pop()
G.add_edge(k, j, label='bp')
j += 1
close = 1
else: #first nt
close = 1
if DEBUG == 1:
print(seq)
print(stru)
display.draw_graph(G,
size=15,
node_size=1500,
font_size=24,
node_border=True,
size_x_to_y_ratio=3)
return G