myprojects-hg-reborn

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#!/usr/bin/env python
import sys, time
import os
from numpy import *
import sociopatterns
from sociopatterns.loader import Loader
from sociopatterns.analysis import ContactGraphAnalyzer
from xml.dom import minidom

from sociopatterns.loader import TagMapping

import string
import scipy as s


M = TagMapping()

f = open('tag_mapping_high_presence.dat')
for line in f:
    (mapped_id, orig_id, t1, t2, type) = string.split(line[:-1])
    mapped_id = int(mapped_id)
    orig_id = int(orig_id)
    t1 = int(t1)
    t2 = int(t2)
    M.add_mapping(orig_id, mapped_id, t1, t2)
f.close()



# ================= CONFIGURE THESE PARAMETERS ==========================
XXTEA_CRYPTO_KEY = ( 0xf6e103d4, 0x77a739f6, 0x65eecead, 0xa40543a9 )
DECODE = 0
EXPERIMENT = "OBG"
TSTART = None # 1280613600  #     None
TSTOP = None #1280700000 #   None
TAG_ID_MIN = 1000
TAG_ID_MAX = 2000
DELTAT = 20         # aggregation window
K_MAX = 4           # filter out all nodes with degree > K_MAX
         # (useful for filtering away clusters of tags left together)
# =======================================================================

TAG_ID_SPAN = TAG_ID_MAX - TAG_ID_MIN

cum_adj = zeros((TAG_ID_SPAN, TAG_ID_SPAN), uint32)
onoff = zeros((TAG_ID_SPAN, TAG_ID_SPAN), uint32)
adj_prev = zeros((TAG_ID_SPAN, TAG_ID_SPAN), uint32)
time_first_seen = zeros((TAG_ID_SPAN, TAG_ID_SPAN), uint32)
time_last_seen = zeros((TAG_ID_SPAN, TAG_ID_SPAN), uint32)
frame_count = 0

loader = Loader(sys.argv[1:-1], experiment=EXPERIMENT, decode=DECODE, xxtea_crypto_key=XXTEA_CRYPTO_KEY, load_contacts=1, unique_contacts=1, load_sightings=0,
                mapping=M,start_time=TSTART, stop_time=TSTOP)

analyzer = ContactGraphAnalyzer(loader, DELTAT, get_graph=False, get_adj=True, tag_id_min=TAG_ID_MIN, tag_id_max=TAG_ID_MAX, isolated_nodes=True, weighted=False)

for frame in analyzer:
    frame_count += 1
    adj = frame['adj']
    frame_time = frame['time']

    tags_high_k = set(where(adj.sum(0) > K_MAX)[0])
    tags_ignore = list(tags_high_k)
    adj[tags_ignore,:] = 0
    adj[:,tags_ignore] = 0

    cum_adj += adj
    onoff += where(adj_prev != adj, 1, 0)

    time_first_seen = where(onoff == 1, frame_time, time_first_seen)
    time_last_seen = where(adj > 0, frame_time, time_last_seen)

    adj_prev = adj

    print '#%d' % frame_count, time.ctime(frame['time'])

direct_indirect_seen = cum_adj.sum(0)



node_attr_list=[]


node_label_list=[]

edge_attr_list=[]

edge_label_list=[]


for i in range(TAG_ID_SPAN):
    if not direct_indirect_seen[i]:
        continue

    node_attr_list.append("[")

    node_attr_list.append('%d' % i)
    node_attr_list.append('"%d"' % (TAG_ID_MIN+i))



    node_label_list.append("node")
    node_label_list.append("id")
    node_label_list.append("label")




    if cum_adj[i, i]:

        node_attr_list.append('%d' % cum_adj[i,i])
        node_attr_list.append('%d' % onoff[i,i])
        node_attr_list.append('%e' %  (onoff[i,i] / float(cum_adj[i,i]) / 2.0) )
        node_attr_list.append('%d' % time_first_seen[i,i])
        node_attr_list.append('%d' % time_last_seen[i,i])
        node_attr_list.append("1")
        node_attr_list.append("]")


        node_label_list.append("weight")
        node_label_list.append("count")
        node_label_list.append("intermittency")
        node_label_list.append("first_seen")
        node_label_list.append("last_seen")
        node_label_list.append("direct")
        node_label_list.append("")


                
        
    else:
        

        node_attr_list.append('0')
        node_attr_list.append("]")


        node_label_list.append("direct")
        node_label_list.append("")

for i in range(TAG_ID_SPAN):
    for j in range(i+1,TAG_ID_SPAN):
        if not cum_adj[i, j]:
            continue

        edge_attr_list.append("[")
        edge_attr_list.append('%d' % i)
        edge_attr_list.append('%d' % j)
        edge_attr_list.append('"%d (pp) %d"' % (TAG_ID_MIN+i, TAG_ID_MIN+j) )



        edge_attr_list.append('%d' % cum_adj[i,j])
        edge_attr_list.append('%d' % onoff[i,j])
        edge_attr_list.append('%d' % (onoff[i,j] / float(cum_adj[i,j]) / 2.0) )
        edge_attr_list.append('%d' % time_first_seen[i,j])
        edge_attr_list.append('%d' % time_last_seen[i,j])

        edge_attr_list.append('%d' % (TAG_ID_MIN+i) )
        edge_attr_list.append('%d' % (TAG_ID_MIN+j) )

        edge_attr_list.append("")



        edge_label_list.append("edge")
        edge_label_list.append("source")
        edge_label_list.append("target")
        edge_label_list.append("label")



        edge_label_list.append("weight")
        edge_label_list.append("count")
        edge_label_list.append("intermittency")
        edge_label_list.append("first_seen")
        edge_label_list.append("last_seen")

        edge_label_list.append("node_A")
        edge_label_list.append("node_B")

        edge_label_list.append("]")


# -----------------------------------



nodes_all_together=zip(node_label_list,node_attr_list)

n2=["%s %s" %(x,y) for x,y in nodes_all_together]

n3="\n".join(n2)




edges_all_together=zip(edge_label_list,edge_attr_list)


e2=["%s %s" %(x,y) for x,y in edges_all_together]

e3="\n".join(e2)

print e3

tot=n3+e3

#if needed, change the value of "directed"

ini="\n".join(["graph [", "directed 0",""])
end="\n".join(["","]" ])

tot=ini+tot+end



g=open("graph.gml", "w")
g.write(tot)
g.close()




print "So far so good"