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import km
import numpy as np
from collections import defaultdict
network = {}
mapper = km.KeplerMapper(verbose=0)
data = np.zeros((3,3))
projected_data = mapper.fit_transform( data, projection="sum", scaler=None )
f = open('mapper.txt','r')
nodes = defaultdict(list)
links = defaultdict(list)
custom = defaultdict(list)
dat = f.readline()
lens = f.readline()
param = [float(i) for i in f.readline().split(" ")]
nums = [int(i) for i in f.readline().split(" ")]
num_nodes = nums[0]
num_edges = nums[1]
for i in range(0,num_nodes):
point = [float(j) for j in f.readline().split(" ")]
nodes[ str(int(point[0])) ] = [ int(point[0]), point[1], int(point[2]) ]
links[ str(int(point[0])) ] = []
custom[ int(point[0]) ] = point[1]
m = min([custom[i] for i in range(0,num_nodes)])
M = max([custom[i] for i in range(0,num_nodes)])
for i in range(0,num_edges):
edge = [int(j) for j in f.readline().split(" ")]
links[ str(edge[0]) ].append( str(edge[1]) )
links[ str(edge[1]) ].append( str(edge[0]) )
network["nodes"] = nodes
network["links"] = links
network["meta"] = lens
mapper.visualize(network, color_function = "Lens average", path_html="mapper_visualization_output.html", title=dat,
graph_link_distance=30, graph_gravity=0.1, graph_charge=-120, custom_tooltips=custom, width_html=0,
height_html=0, show_tooltips=True, show_title=True, show_meta=True, res=param[0],gain=param[1], minimum=m,maximum=M)
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