def scatter_plot(M,Label,delta=100):
x = []
y = []
count = 0
for i in M:
if i[1][0] < 0.5:
print 'Boosting', Label[count],count
x.append(i[0])
y.append(i[1]*5.)
else:
x.append(i[0])
y.append(i[1])
count+=1
Mark = ['x','x','o','o','v','1','1','8','8','D','D']
Mark = ['x','x','o','o','v','v','1','1','8','8','D','D','o','o']
print len(x),len(Mark)
pyplot.plot_multi(x,y,Label,logx=True,limx=(0,100),limy=(0,1),loc=1,make_marker=True,M=Mark,
showleg=True,save='kowasaki')
fid = open('intstat.dat','w')
for i in Label:
fid.write((' %s'%(i)))
fid.write('\n')
for i in range(delta):
fid.write((' %.2f'%(i)))
for j in range(len(y)):
fid.write((' %.4f'%(y[j][i])))
fid.write('\n')
fid.close()
def scatter_plot_sort(M,Label,sort, delta=100):
x = []
y = []
print Label
count = 0
yf = np.zeros((delta))
new_label = []
for j,i in enumerate(M):
if Label[j] == sort:
new_label.append(Label[j])
if i[1][0] < 0.5:
x.append(i[0])
y.append(i[1]*5.)
else:
x.append(i[0])
y.append(i[1])
yf += y[count][0:delta]
count+=1
yf = yf/len(new_label)
new_label.append('average')
print len(x[0])
x.append(x[0][0:delta])
y.append(yf)
pyplot.plot(x[0][0:delta],yf,'average',logx=True,limx=(0,100),limy=(0,1),save='average')
Mark = ['x','x','o','o','v','v','1','1','8','8','D','D']
Mark = ['x','x','o','o','v','1','1','8','8','D','D']
pyplot.plot_multi(x,y,new_label,logx=True,limx=(0,100),limy=(0,1),loc=1,make_marker=True,M=Mark,
showleg=True,save='kowasaki_%s'%sort)
fid = open('intstat_avg_%s.dat'%sort,'w')
for j in range(yf.shape[0]):
fid.write(('%.2f %.4f\n'%(x[0][j],yf[j])))
fid.close()
def find_networks(M, VW, L, n_finish=1, n_start=0, delta=30, rcut=1.0, ndna=25):
#The total number of frames we are going to look at
x=np.arange(n_start,n_finish,delta)
print len(x)
#Find the number of connections at specific points x
import MD.canalysis.connections as con
import MD.analysis.connections as conn
import MD.analysis.graph as graph
try:
connections = util.pickle_load('con.pkl')
except:
try:
C = util.pickle_load('C.pkl')
G = util.pickle_load('G.pkl')
except:
G=M.cord_auto(['G'])
C=M.cord_auto(['C'])
util.pickle_dump(C,'C.pkl')
util.pickle_dump(G,'G.pkl')
connections = con.connections(C[x],G[x],L,rcut=rcut)
util.pickle_dump(connections,'con.pkl')
#plot total number of connections
num_connections=conn.num_connections(connections,VW.shape[1])
con_all = []
for i in range(len(connections)):
con_all.append(len(connections[i])/2.)
pyplot.plot(x,num_connections,xlabel='Time',
ylabel='hyrbid. density', save='connections')
pyplot.plot(x,con_all,xlabel='Time',
ylabel='hyrbid', save='connections_all')
plt.close()
#get the info
networks, num_networks, deg, neighbors, num_n, gr = graph.grapher(connections,VW.shape[1],ndna)
util.pickle_dump(networks,'net.pkl')
#plot the number of neighbors at each timesteps
pyplot_eps.plot(x,num_n,xlabel='Time',
ylabel='num neighbors', save='neighbors')
plt.close()
print 'making plot'
net = []
for i in networks:
net.append(len(i))
pyplot_eps.plot(x,net,xlabel='t',ylabel='networks',save='net')
label = ['networks','1','2','3','4','5','6','7','8','9']
pyplot_eps.plot(x,networks,xlabel='t',ylabel='Networks',save='net')
pyplot.plot_multi(x,deg,label,xlabel='time',ylabel='number',save='con_net')
return x, networks, connections
