def load_graph(fn):
"""Load graph_tool.Graph from weighted edge list."""
return gt.load_graph_from_csv(
fn,
directed=True,
eprop_types=('string', 'string', 'double'),
eprop_names=('fromId', 'toId', 'weight'),
string_vals=True,
hashed=True,
skip_first=True,
ecols=(2, 3),
)
import graph_tool.all as gt
# from graph_tool.all import *
import math
import matplotlib
g = gt.load_graph_from_csv(
"csv/graphdata_test.csv",
hashed=True,
eprop_types=['string', 'string'],
eprop_names=["source_handle", "target_handle", "weight"])
# import pdb; pdb.set_trace()
source_handle = g.ep["source_handle"]
weight = g.ep["weight"]
gt.graph_draw(g,
vertex_font_size=12,
vertex_text="g.ep.source_handle",
vertex_shape="double_circle",
vertex_fill_color="#729fcf",
vertex_pen_width=3,
edge_pen_width=1,
output="graph-draw.png",
output_size=(4000, 4000))
edgelist_df = pd.read_csv(processed_edge_list)
edgelist_df.head()
# In this "generic gene map", 1 denotes a generic gene and 0 is all other genes. A gene is considered generic if it had a high percentile from SOPHIE and the manually curated set based on the correlation plot seen [here](../pseudomonas_analysis/2_identify_generic_genes_pathways.ipynb).
# In[4]:
annot_df = pd.read_csv(generic_gene_map, sep='\t', index_col=0)
annot_df.head()
# In[5]:
G = gt.load_graph_from_csv(processed_edge_list,
skip_first=True,
directed=False,
hashed=True,
eprop_names=['weight'],
eprop_types=['float'])
# In[6]:
# add vertex property for generic genes
vprop_generic = G.new_vertex_property('bool')
for ix, v in enumerate(G.vertices()):
v_name = G.vp['name'][v]
v_label = annot_df.loc[v_name, 'label']
vprop_generic[v] = v_label
G.vertex_properties['is_generic'] = vprop_generic
# In[7]:
n_clusters=G.graph['number_communities'])
except:
D = [100]
zsvd.append(np.mean(D))
Y = fct.mds_shortest_paths(G, dimension)
D = fct.comp_clusters_communities(
Y,
G.graph['labels_communities'],
algo=False,
n_clusters=G.graph['number_communities'])
zmds.append(np.mean(D))
g = gt.load_graph_from_csv(G.graph['edgelist'],
directed=isDirected,
csv_options={
"delimiter": " ",
"quotechar": '"'
})
block = gt.minimize_nested_blockmodel_dl(
g,
B_min=G.graph['number_communities'],
B_max=G.graph['number_communities'])
num_block = block.levels[0].get_B()
block = block.levels[0].get_blocks()
partition = [0 for i in range(G.number_of_nodes())]
for i in range(G.number_of_nodes()): #for every node
partition[i] = block[i]
zsbm.append(ami(partition, G.graph['labels_communities']))
igraph = ig.Read_Edgelist(G.graph['edgelist'])
part = igraph.community_infomap()
pending = len(iterables)
nexts = cycle(iter(it).next for it in iterables)
while pending:
try:
for next in nexts:
yield next()
except StopIteration:
pending -= 1
nexts = cycle(islice(nexts, pending))
conceptnet_path = os.path.expanduser(
'~/project/KB_dump/conceptnet/conceptnet-en.csv')
g = load_graph_from_csv(conceptnet_path,
directed=False,
eprop_types=['string', 'string'],
string_vals=True)
prefix = '/c/en/'
entities = [
['capoeira', 'hand', 'cartwheel', 'shirt', 'handstand'],
['sunscreen', 'skateboarding', 'soccer', 'tan', 'rubbing'],
['cream', 'mascara', 'writing', 'lifting', 'dictaphone'],
]
blackListVertex = set([
find_vertex(g, prop=g.properties[('v', 'name')], match=prefix + b)[0]
for b in ['object', 'thing']
])
blackListEdge = set(['/r/DerivedFrom', '/r/RelatedTo'])
def plot_log_log_dist(g, fname):
(data_xs, data_ys) = deg_frequency(g.get_total_degrees(g.get_vertices()))
ys = np.divide(data_ys, np.sum(data_ys))
plt.clf()
plt.scatter(data_xs, ys, alpha=0.5, color='b', label='Dataset')
plt.legend(loc='lower left')
plt.xscale('log')
plt.yscale('log')
plt.xlim(0.5, 1500)
plt.ylim(0.0001, 0.5)
plt.xlabel("degree")
plt.ylabel("fraction of nodes")
plt.savefig(fname)
G = gt.load_graph_from_csv(FILENAME, csv_options={"delimiter": "\t"})
plot_log_log_dist(G, "dist.png")
# state1 = gt.minimize_blockmodel_dl(G, verbose=True)
N = len(G.get_vertices())
print(len(G.get_edges()))
knock_count = int(KNOCKOUT * N)
# to_remove = np.random.randint(0, N, knock_count)
# G.remove_vertex(to_remove)
# top_degree_nodes = [[idx[0], elem] for idx, elem in np.ndenumerate(G.get_total_degrees(G.get_vertices()))]
# top_degree_nodes.sort(key=lambda x: x[1], reverse=True)
# top_degree_nodes = top_degree_nodes[0:knock_count]
# top_degree_nodes = [i[1] for i in top_degree_nodes]
# G.remove_vertex(top_degree_nodes)
squeeze=True)
for i, node in enumerate(essentials.values):
node_ = ''.join(
node.split('-')) # eliminamos '-' de los nombres de las proteinas
essentials[i] = node_.replace(' ', '').upper(
) # eliminamos los ' ' inutiles de los nombres de las proteinas
############################################################################################################
#### Creacion del grafo
############################################################################################################
if args.format == 'csv': # si el formato es una lista de links hay que tratarlo distinto (csv)
graph = gp.load_graph_from_csv(args.data,
string_vals=True,
directed=args.is_directed,
csv_options={
"delimiter": "\t",
"quotechar": "#"
})
else: # si no es csv, que lo lea tranqui..
graph = gp.load_graph(args.data, fmt=args.format)
############################################################################################################
#### Distribucion de grado (todos los nodos)
############################################################################################################
v_degrees = np.array([v.out_degree() for v in graph.vertices()
]) # lista de grado por id de nodo
degrees, hist = np.unique(
v_degrees, # degrees: lista de grados existentes en la red
return_counts=True
) # hist: cada elemento k es el numero de nodos de grado k
