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get_data_for_si.py
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
import matplotlib as mpl
import matplotlib.gridspec as gridspec
import matplotlib
from basicFunctions import *
from plot_script_functions import get_dataMadisVersion
from plot_script_functions import pseudo_potential
from plot_script_functions import find_minima
def sim_OG(X):
[nr,nc] = X.shape
totalC,count=0,0
for i in range(nr):
corr = corrF(X[i],X[(i+1)%nr])
corr = (1.-corr)/2 # convert from (-1,1) to (0,1)
totalC+=corr
count+=1.
totalC=totalC/count
totalR,count=0,0
for i in range(nr):
corr = corrF(X[:,i],X[:,(i+1)%nr])
corr = (1.-corr)/2 # convert from (-1,1) to (0,1)
totalR+=corr
count+=1.
totalR=totalR/count
return (totalR+totalC)/2.
def corrF(x,y):
xbar = np.mean(x)
ybar = np.mean(y)
sx=np.std(x)
sy=np.std(y)
if sx==0 or sy==0 or len(x)==0:
return 1.
return np.sum((x-xbar)*(y-ybar))/(sx*sy)/len(x)
##############################
##############################
##############################
##############################
def getSpecialPatt(ext):
[dN,dD,dI] = get_data("traj_16x16x1_"+str(ext)+".dat","~/Research/signaling/paper1/data_ND_mcT_trajs_patt/",tstart=0)
kk = dN.keys()[0]
try:
[nr,dr,ns,ds,ntr,dtr,nts,dts] = get_thresholds(dN,dD)
except:
[nr,dr,ns,ds,ntr,dtr,nts,dts] = [5138.8,21.7,567.,1561.,2853.,791.,2853.,781.]
#iD = dtr+ (dts-dtr)/2.
#iN = nts+ (ntr-nts)/2.
#pattI = (dD[kk][0]>iD)*(dN[kk][0]<iN)*1.+(dD[kk][0]<iD)*(dN[kk][0]>iN)*2.
#pattF = (dD[kk][-1]>iD)*(dN[kk][-1]<iN)*1.+(dD[kk][-1]<iD)*(dN[kk][-1]>iN)*2.
pattI = dD[kk][0]
pattF = dD[kk][-1]
datD = dD[dD.keys()[0]]
datN = dN[dN.keys()[0]]
distD=[]
threshN={'S':nts,'R':ntr}
threshD={'S':dts,'R':dtr}
states,states2,states3 = getSimStates(datD,datN,threshD,threshN)
cc=[]
for k in range(len(datD)):
distD+=[sim_OG(datD[k])]
tmp = getCont(states2[k])
cc+=[tmp[1]/5.12]
time = np.arange(0,len(datD))*0.1
return pattI,pattF,distD,cc,time
################################
################################
################################
def getMistakesPatt(ext,nt,na):
if na==0:
[dN,dD,dI] = get_data("traj_16x16x1_"+str(ext)+"_s0.dat","~/Research/signaling/paper1/data_ND_mcT_trajs_mistakes/",tstart=0)
else:
[dN,dD,dI] = get_data("traj_16x16x1_"+str(nt)+"_n"+str(na)+"_"+str(ext)+"_s0.dat","~/Research/signaling/paper1/data_ND_mcT_trajs_mar2021/mistakes/",tstart=0)
kk = dN.keys()[0]
try:
[nr,dr,ns,ds,ntr,dtr,nts,dts] = get_thresholds(dN,dD)
except:
[nr,dr,ns,ds,ntr,dtr,nts,dts] = [5138.8,21.7,567.,1561.,2853.,791.,2853.,781.]
iD = dtr+ (dts-dtr)/2.
iN = nts+ (ntr-nts)/2.
pattI = dD[kk][0]#(dD[kk][0]>iD)*(dN[kk][0]<iN)*1.+(dD[kk][0]<iD)*(dN[kk][0]>iN)*2.
pattF = dD[kk][-1]#(dD[kk][-1]>iD)*(dN[kk][-1]<iN)*1.+(dD[kk][-1]<iD)*(dN[kk][-1]>iN)*2.
datD = dD[dD.keys()[0]]
datN = dN[dN.keys()[0]]
distD=[]
threshN={'S':nts,'R':ntr}
threshD={'S':dts,'R':dtr}
states,states2,states3 = getSimStates(datD,datN,threshD,threshN)
cc=[]
for k in range(len(datD)):
distD+=[sim_OG(datD[k])]
tmp = getCont(states2[k])
cc+=[tmp[1]/5.12]
time = np.arange(0,len(datD))*0.1
return pattI,pattF,distD,cc,time
#######################
#######################
#######################
def getDevPatt(ext,nt,na):
if na==0:
[dN,dD,dI] = get_data("traj_16x16x1_"+str(ext)+"_s0.dat","~/Research/signaling/paper1/data_ND_mcT_trajs_dev/",tstart=0)
else:
[dN,dD,dI] = get_data("traj_16x16x1_"+str(nt)+"_n"+str(na)+"_"+str(ext)+"_s0.dat","~/Research/signaling/paper1/data_ND_mcT_trajs_mar2021/dev/",tstart=0)
kk = dN.keys()[0]
try:
[nr,dr,ns,ds,ntr,dtr,nts,dts] = get_thresholds(dN,dD)
except:
[nr,dr,ns,ds,ntr,dtr,nts,dts] = [5138.8,21.7,567.,1561.,2853.,791.,2853.,781.]
iD = dtr+ (dts-dtr)/2.
iN = nts+ (ntr-nts)/2.
pattI =dD[kk][0]# (dD[kk][0]>iD)*(dN[kk][0]<iN)*1.+(dD[kk][0]<iD)*(dN[kk][0]>iN)*2.
pattF = dD[kk][-1]#(dD[kk][-1]>iD)*(dN[kk][-1]<iN)*1.+(dD[kk][-1]<iD)*(dN[kk][-1]>iN)*2.
datD = dD[dD.keys()[0]]
datN = dN[dN.keys()[0]]
distD=[]
threshN={'S':nts,'R':ntr}
threshD={'S':dts,'R':dtr}
states,states2,states3 = getSimStates(datD,datN,threshD,threshN)
cc=[]
for k in range(len(datD)):
distD+=[sim_OG(datD[k])]
tmp = getCont(states2[k])
cc+=[tmp[1]/5.12]
time = np.arange(0,len(datD))*0.1
return pattI,pattF,distD,cc,time
##################3333
##################3333
def get_pseudo(nt,na):
# parameters for plotting
sim = 20
#sim_points = 10000
sim_points = 20000#150000
#n_eq = 1000
n_eq = 10000
bins = np.logspace(np.log10(0.1), np.log10(100000), num=61, base=10)
ncell = 16
N, D = get_dataMadisVersion('~/Research/signaling/paper1/data_ND_mcT_trajs_Oct2020/traj_16x16x1_'+str(nt)+'_n'+str(na)+'.dat',10000,25000,0)
U = pseudo_potential(N, D, bins)
x = np.zeros(bins.size - 1)
for i in range(x.size):
x[i] = (bins[i] + bins[i + 1]) / 2.
notch=bins[0:-1]
delta=bins[0:-1]
xint=[10, 30000]
yint=[0.1, 10000]
# find minima
i_s, j_s, i_r, j_r = find_minima(U, bins, x, x)
notch_thr_R, delta_thr_R, notch_thr_S, delta_thr_S = find_thresholds(x, U, i_s, j_s, i_r, j_r)
return [notch,delta,np.transpose(U),xint,yint,[bins[i_s], bins[i_r]], [bins[j_s], bins[j_r]],
notch_thr_R, delta_thr_R, notch_thr_S, delta_thr_S]
##################3333
##################3333
##################3333
##################3333
def getAvgs_rand():
tstart=10000
tfin=100000
xa,xb=[],[]
ya1,ya2,yb1,yb2=[],[],[],[]
yera1,yera2,yerb1,yerb2=[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp,tmp1,tmp2,tmp3=[],[],[],[]
for sim in range(20):
try:
fileN = "~/Research/signaling/paper1/figures_Oct2020/data/avgStates_16x16_"+nt+"_n"+str(na)+"_s"+str(sim)+".txt"
filX=pd.read_csv(fileN).dropna()
tmp+=list(filX['avgER'].values[tstart:tfin])
tmp1+=list(filX['avgES'].values[tstart:tfin])
except:
count=0
tmp = np.array(tmp)
tmp1 = np.array(tmp1)
if nt=='white':
xa+=[na*10.]
ya1+=[np.mean(tmp)]
ya2+=[np.mean(tmp1)]
yera1+=[np.std(tmp)]
yera2+=[np.std(tmp1)]
elif nt=='shot':
xb+=[na]
yb1+=[np.mean(tmp)]
yb2+=[np.mean(tmp1)]
yerb1+=[np.std(tmp)]
yerb2+=[np.std(tmp1)]
return xa,ya1,yera1,xb,yb1,yerb1,ya2,yb2,yera2,yerb2
############################33
############################33
############################33
def getAvgs_check():
tstart=10000
xa,xb=[],[]
ya1,ya2,ya3,ya4,yb1,yb2,yb3,yb4=[],[],[],[],[],[],[],[]
yera1,yera2,yera3,yera4,yerb1,yerb2,yerb3,yerb4=[],[],[],[],[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp,tmp1,tmp2,tmp3=[],[],[],[]
for sim in range(20):
try:
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/avgStates_16x16_"+nt+"_n"+str(na)+"_p8_s"+str(sim)+".txt"
filX=pd.read_csv(fileN).dropna()
tmp+=list(filX['avgER'].values[tstart:])
tmp1+=list(filX['avgES'].values[tstart:])
except:
count=0
if nt=='white':
xa+=[na*10.]
ya1+=[np.mean(tmp)]
ya2+=[np.mean(tmp1)]
yera1+=[np.std(tmp)]
yera2+=[np.std(tmp1)]
elif nt=='shot':
xb+=[na]
yb1+=[np.mean(tmp)]
yb2+=[np.mean(tmp1)]
yerb1+=[np.std(tmp)]
yerb2+=[np.std(tmp1)]
return xa,ya1,yera1,xb,yb1,yerb1,ya2,yb2,yera2,yerb2
#############
##################################3
##################################3
def getCont_rand_ind(sim):
tstart=10000
tfin=100000
xa,xb,ya,yb,yera,yerb=[],[],[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp=[]
try:
fileN = "~/Research/signaling/paper1/figures_Oct2020/data/Cont_16x16_"+nt+"_n"+str(na)+"_s"+str(sim)+".txt"
filX=pd.read_csv(fileN)#.dropna()
ind= np.argwhere(np.isnan(filX['contOpp']))[:,0]
filX['contOpp'][ind]=0
tmp+=list(filX['contOpp'].values[tstart:tfin]/5.12)
except:
count=0
tmp = np.array(tmp)
if nt=='white':
xa+=[na]
ya+=[np.mean(tmp)]
yera+=[np.std(tmp)]
elif nt=='shot':
xb+=[na]
yb+=[np.mean(tmp)]
yerb+=[np.std(tmp)]
return xa,ya,yera,xb,yb,yerb
def getSim_rand_ind(sim):
tstart=10000
tfin=100000
xa,xb,ya,yb,yera,yerb=[],[],[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp=[]
try:
fileN = "~/Research/signaling/paper1/figures_Oct2020/data/simM_16x16_"+nt+"_n"+str(na)+"_s"+str(sim)+".txt"
filX=pd.read_csv(fileN)#.dropna()
ind= np.argwhere(np.isnan(filX['Sim']))[:,0]
filX['Sim'][ind]=0
tmp+=list(filX['Sim'].values[tstart:tfin])
except:
count=0
tmp = np.array(tmp)
if nt=='white':
xa+=[na]
ya+=[np.mean(tmp)]
yera+=[np.std(tmp)]
elif nt=='shot':
xb+=[na]
yb+=[np.mean(tmp)]
yerb+=[np.std(tmp)]
return xa,ya,yera,xb,yb,yerb
def getSim_rand():
tstart=10000
tfin=100000
xa,xb,ya,yb,yera,yerb=[],[],[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp=[]
for sim in range(20):
try:
fileN = "~/Research/signaling/paper1/figures_Oct2020/data/simM_16x16_"+nt+"_n"+str(na)+"_s"+str(sim)+".txt"
filX=pd.read_csv(fileN)#.dropna()
ind= np.argwhere(np.isnan(filX['Sim']))[:,0]
filX['Sim'][ind]=0
tmp+=list(filX['Sim'].values[tstart:tfin])
except:
count=0
tmp = np.array(tmp)
if nt=='white':
xa+=[na]
ya+=[np.mean(tmp)]
yera+=[np.std(tmp)]
elif nt=='shot':
xb+=[na]
yb+=[np.mean(tmp)]
yerb+=[np.std(tmp)]
return xa,ya,yera,xb,yb,yerb
def getSimSP():
tstart=10000
xa,xb,ya,yb,yera,yerb=[],[],[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp=[]
for sim in range(20):
try:
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/simM_16x16_"+nt+"_n"+str(na)+"_p8_s"+str(sim)+".txt"
filX=pd.read_csv(fileN)#.dropna()
ind= np.argwhere(np.isnan(filX['Sim']))[:,0]
filX['Sim'][ind]=0
tmp+=list(filX['Sim'].values[tstart:])
except:
count=0
if nt=='white':
xa+=[na]
ya+=[np.mean(tmp)]
yera+=[np.std(tmp)]
elif nt=='shot':
xb+=[na]
yb+=[np.mean(tmp)]
yerb+=[np.std(tmp)]
return xa,ya,yera,xb,yb,yerb
##################3
def getContacts(Dir,ext):
tstart=10000
xa,xb,ya,yb,yera,yerb=[],[],[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp=[]
for sim in range(20):
try:
fileN = "~/Research/signaling/paper1/figures_mar2021/"+Dir+"/data/Cont_16x16_"+nt+"_n"+str(na)+"_"+ext+"_s"+str(sim)+".txt"
filX=pd.read_csv(fileN).dropna()
tmp+=list(filX['contOpp'].values[tstart:]/5.12)
except:
count=0
if nt=='white':
xa+=[na]
ya+=[np.mean(tmp)]
yera+=[np.std(tmp)]
elif nt=='shot':
xb+=[na]
yb+=[np.mean(tmp)]
yerb+=[np.std(tmp)]
return xa,ya,yera,xb,yb,yerb
def getSim(Dir,ext):
tstart=10000
xa,xb,ya,yb,yera,yerb=[],[],[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp=[]
for sim in range(20):
try:
fileN = "~/Research/signaling/paper1/figures_mar2021/"+Dir+"/data/simM_16x16_"+nt+"_n"+str(na)+"_"+ext+"_s"+str(sim)+".txt"
filX=pd.read_csv(fileN).dropna()
tmp+=list(filX['Sim'].values[tstart:])
except:
count=0
if nt=='white':
xa+=[na]
ya+=[np.mean(tmp)]
yera+=[np.std(tmp)]
elif nt=='shot':
xb+=[na]
yb+=[np.mean(tmp)]
yerb+=[np.std(tmp)]
return xa,ya,yera,xb,yb,yerb
##################3
##################3
##################3
##################3
def getSim_lattice(ext):
tstart=10000
xa,xb,ya,yb,yera,yerb=[],[],[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp=[]
for sim in range(20):
try:
fileN = "~/Research/signaling/paper1/figures_lattices/data/simM_"+ext+"x1_"+nt+"_nn"+str(na)+"_ss"+str(sim)+".txt"
print fileN
filX=pd.read_csv(fileN).dropna()
tmp+=list(filX['Sim'].values[tstart:])
except:
count=0
if nt=='white':
xa+=[na]
ya+=[np.mean(tmp)]
yera+=[np.std(tmp)]
elif nt=='shot':
xb+=[na]
yb+=[np.mean(tmp)]
yerb+=[np.std(tmp)]
return xa,ya,yera,xb,yb,yerb
def getContacts_lattice(ext,scale):
tstart=10000
xa,xb,ya,yb,yera,yerb=[],[],[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp=[]
for sim in range(20):
try:
fileN = "~/Research/signaling/paper1/figures_lattices/data/Cont_"+ext+"x1_"+nt+"_nn"+str(na)+"_ss"+str(sim)+".txt"
filX=pd.read_csv(fileN).dropna()
tmp+=list(filX['contOpp'].values[tstart:])
except:
count=0
tmp = np.array(tmp)
if nt=='white':
xa+=[na]
ya+=[np.mean(tmp/scale)]
yera+=[np.std(tmp/scale)]
elif nt=='shot':
xb+=[na]
yb+=[np.mean(tmp/scale)]
yerb+=[np.std(tmp/scale)]
return xa,ya,yera,xb,yb,yerb
############
############
############
##################3
##################3
##################3
##################3
def getSim_time(ext):
tstart=10000
xa,xb,ya,yb,yera,yerb=[],[],[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp=[]
for sim in range(20):
try:
if nt=='white':
fileN = "~/Research/signaling/paper1/figures_time/data/simM_"+str(ext)+"_16x16_"+nt+"_n"+str(int(na*np.sqrt(ext/0.1)))+"_ss"+str(sim)+".txt"
else:
fileN = "~/Research/signaling/paper1/figures_time/data/simM_"+str(ext)+"_16x16_"+nt+"_n"+str(na)+"_ss"+str(sim)+".txt"
filX=pd.read_csv(fileN).dropna()
tmp+=list(filX['Sim'].values[tstart:])
except:
count=0
if nt=='white':
xa+=[na]
ya+=[np.mean(tmp)]
yera+=[np.std(tmp)]
elif nt=='shot':
xb+=[na]
yb+=[np.mean(tmp)]
yerb+=[np.std(tmp)]
return xa,ya,yera,xb,yb,yerb
def getContacts_time(ext):
tstart=10000
scale = 100./(16.*16.*2.)
xa,xb,ya,yb,yera,yerb=[],[],[],[],[],[]
nAmp={'shot':np.arange(0,21,1),'white':np.arange(0,210,10)}
for nt in ['shot','white']:
for na in nAmp[nt]:
tmp=[]
for sim in range(20):
try:
if nt=='white':
fileN = "~/Research/signaling/paper1/figures_time/data/Cont_"+str(ext)+"_16x16_"+nt+"_n"+str(int(na*np.sqrt(ext/0.1)))+"_ss"+str(sim)+".txt"
else:
fileN = "~/Research/signaling/paper1/figures_time/data/Cont_"+str(ext)+"_16x16_"+nt+"_n"+str(na)+"_ss"+str(sim)+".txt"
filX=pd.read_csv(fileN).dropna()
tmp+=list(filX['contOpp'].values[tstart:])
except:
count=0
tmp = np.array(tmp)
if nt=='white':
xa+=[na]
ya+=[np.mean(tmp*scale)]
yera+=[np.std(tmp*scale)]
elif nt=='shot':
xb+=[na]
yb+=[np.mean(tmp*scale)]
yerb+=[np.std(tmp*scale)]
return xa,ya,yera,xb,yb,yerb
############
############
############
def getET_avg(df):
x,y = np.round(df['D'],0),np.round(df['N'],-1)
yval=(np.unique(y))
xval=(np.unique(x))
#plt.plot(yval)
#plt.plot(xval)
print len(xval),len(yval)
a,b,c=[],[],[]
std=[]
bk =False
count=0
for el in xval:
inds = np.argwhere(x==el)[:,0]
for el2 in yval:
ind2 = np.argwhere(y==el2)[:,0]
ind = np.intersect1d(inds,ind2)
if len(ind)>0:
a+=[el]
b+=[el2]
c+=[np.mean(df['time'].values[ind])]
std+=[np.std(df['time'].values[ind])]
count+=1
a = np.array(a)
b = np.array(b)
c = np.array(c)
std = np.array(std)
return [a,b,c,std]
###################
###################
###################
###################
def getETvSR_avg(df):
x,y = np.round(df['S'],3),np.round(df['R'],3)
yval=(np.unique(y))
xval=(np.unique(x))
#plt.plot(yval)
#plt.plot(xval)
print len(xval),len(yval)
a,b,c=[],[],[]
std=[]
bk =False
count=0
for el in xval:
inds = np.argwhere(x==el)[:,0]
for el2 in yval:
ind2 = np.argwhere(y==el2)[:,0]
ind = np.intersect1d(inds,ind2)
if len(ind)>0:
a+=[el]
b+=[el2]
c+=[np.mean(df['time'].values[ind])]
std+=[np.std(df['time'].values[ind])]
count+=1
a = np.array(a)
b = np.array(b)
c = np.array(c)
std = np.array(std)
return [a,b,c,std]
#####################
#####################
#####################
#####################
#####################
def getSim_checkFull_noise():
tfin=10000
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/simM_16x16_white_n0_p8_s0.txt"
filX=pd.read_csv(fileN)
n0w=filX['Sim'][:tfin]
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/simM_16x16_white_n50_p8_s0.txt"
filX=pd.read_csv(fileN)
n50=filX['Sim'][:tfin]
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/simM_16x16_white_n130_p8_s0.txt"
filX=pd.read_csv(fileN)
n130=filX['Sim'][:tfin]
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/simM_16x16_white_n200_p8_s0.txt"
filX=pd.read_csv(fileN)
n200=filX['Sim'][:tfin]
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/simM_16x16_shot_n0_p8_s0.txt"
filX=pd.read_csv(fileN)
n0s=filX['Sim'][:tfin]
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/simM_16x16_shot_n5_p8_s0.txt"
filX=pd.read_csv(fileN)
n5=filX['Sim'][:tfin]
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/simM_16x16_shot_n13_p8_s0.txt"
filX=pd.read_csv(fileN)
n13=filX['Sim'][:tfin]
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/simM_16x16_shot_n20_p8_s0.txt"
filX=pd.read_csv(fileN)
n20=filX['Sim'][:tfin]
x =np.arange(0,len(filX['Sim'][:tfin]))*0.1
return x,n0w,n0s,n50,n130,n200,n5,n13,n20
def getCont_checkFull_noise():
tfin=10000
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/Cont_16x16_white_n0_p8_s0.txt"
filX=pd.read_csv(fileN)
n0w=filX['contOpp'][:tfin]/5.12
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/Cont_16x16_white_n50_p8_s0.txt"
filX=pd.read_csv(fileN)
n50=filX['contOpp'][:tfin]/5.12
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/Cont_16x16_white_n130_p8_s0.txt"
filX=pd.read_csv(fileN)
n130=filX['contOpp'][:tfin]/5.12
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/Cont_16x16_white_n200_p8_s0.txt"
filX=pd.read_csv(fileN)
n200=filX['contOpp'][:tfin]/5.12
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/Cont_16x16_shot_n0_p8_s0.txt"
filX=pd.read_csv(fileN)
n0s=filX['contOpp'][:tfin]/5.12
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/Cont_16x16_shot_n5_p8_s0.txt"
filX=pd.read_csv(fileN)
n5=filX['contOpp'][:tfin]/5.12
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/Cont_16x16_shot_n13_p8_s0.txt"
filX=pd.read_csv(fileN)
n13=filX['contOpp'][:tfin]/5.12
fileN = "~/Research/signaling/paper1/figures_mar2021/patt/data/Cont_16x16_shot_n20_p8_s0.txt"
filX=pd.read_csv(fileN)
n20=filX['contOpp'][:tfin]/5.12
x =np.arange(0,len(filX['contOpp'][:tfin]))*0.1
return x,n0w,n0s,n50,n130,n200,n5,n13,n20
####################33
####################33
##################33
def qcorr_rand(nt,na):
tfin=10000
fileN= "~/Research/signaling/paper1/figures_Oct2020/data/ferr2_16x16_"+nt+"_n"+str(na)+"_s0.txt"
fileN2= "~/Research/signaling/paper1/figures_Oct2020/data/ferr_16x16_"+nt+"_n"+str(na)+"_s0.txt"
filX = pd.read_csv(fileN).dropna()
fil2 = pd.read_csv(fileN2).dropna()
yv1=fil2['qres'][0:tfin]
xv1 = np.arange(0,len(yv1))*0.1
yv2=filX['qres'][0:tfin]
xv2 = np.arange(0,len(yv2))*0.1
return xv1,yv1,xv2,yv2
def updatedF7():
tstart=10000
noise,old,new,oerr,nerr=[],[],[],[],[]
oldC,newC,oerrC,nerrC=[],[],[],[]
for na in [11,12,13]:
fileN = "~/Research/signaling/paper1/figures_Oct2020/data/simM_16x16_shot_n"+str(na)+"_s3.txt"
filX=pd.read_csv(fileN)#.dropna()
fileN = "~/Research/signaling/paper1/figures_Oct2020/data/simM_16x16_shot_n"+str(na)+"_s0.txt"
fil2=pd.read_csv(fileN)#.dropna()
noise+=[na]
old +=[np.mean(filX['Sim'])]
oerr +=[np.std(filX['Sim'])]
new +=[np.mean(fil2['Sim'])]
nerr +=[np.std(fil2['Sim'])]
fileN = "~/Research/signaling/paper1/figures_Oct2020/data/Cont_16x16_shot_n"+str(na)+"_s3.txt"
filX=pd.read_csv(fileN)#.dropna()
fileN = "~/Research/signaling/paper1/figures_Oct2020/data/Cont_16x16_shot_n"+str(na)+"_s0.txt"
fil2=pd.read_csv(fileN)#.dropna()
oldC+=[np.mean(filX['contOpp']/5.12)]
oerrC+=[np.std(filX['contOpp']/5.12)]
newC+=[np.mean(fil2['contOpp']/5.12)]
nerrC+=[np.std(fil2['contOpp']/5.12)]
return noise, old,new,oerr,nerr,oldC,newC,oerrC,nerrC
###########################################################
###########################################################
########### Get data sets #################################
###########################################################
###########################################################
'''
pattI,pattF,dist,cc,time = getSpecialPatt('p0')
fileo = open("data/fig2_a1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig2_a2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getSpecialPatt('p1')
fileo = open("data/fig2_b1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig2_b2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getSpecialPatt('p2')
fileo = open("data/fig2_c1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig2_c2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getSpecialPatt('p3')
fileo = open("data/fig2_d1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig2_d2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getSpecialPatt('p4')
fileo = open("data/fig2_e1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig2_e2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getSpecialPatt('p5')
fileo = open("data/fig2_f1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig2_f2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getSpecialPatt('p6')
fileo = open("data/fig2_g1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig2_g2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
## Figure 3
pattI,pattF,dist,cc,time = getMistakesPatt('m3','shot',0)
fileo = open("data/fig3_a1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig3_a2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getMistakesPatt('m26','shot',0)
fileo = open("data/fig3_b1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig3_b2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getMistakesPatt('m128','shot',0)
fileo = open("data/fig3_c1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig3_c2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getMistakesPatt('m192','shot',0)
fileo = open("data/fig3_d1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig3_d2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getMistakesPatt('m243','shot',0)
fileo = open("data/fig3_e1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig3_e2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getMistakesPatt('m253','shot',0)
fileo = open("data/fig3_f1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig3_f2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
### get Figure 4
pattI,pattF,dist,cc,time = getDevPatt('d1','shot',0)
fileo = open("data/fig4_a1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig4_a2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getDevPatt('d10','shot',0)
fileo = open("data/fig4_b1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig4_b2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getDevPatt('d25','shot',0)
fileo = open("data/fig4_c1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig4_c2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getDevPatt('d100','shot',0)
fileo = open("data/fig4_d1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig4_d2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getDevPatt('d1000','white',0)
fileo = open("data/fig4_e1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig4_e2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
pattI,pattF,dist,cc,time = getDevPatt('d2000','shot',0)
fileo = open("data/fig4_f1.txt",'w')
fileo.write("Initial,Final,row,column\n")
for i in range(len(pattI)):
for j in range(len(pattI[i])):
fileo.write("%s,%s,%s,%s\n" %(pattI[i][j],pattF[i][j],i,j))
fileo.close()
fileo = open("data/fig4_f2.txt",'w')
fileo.write("Sim,Cont,time\n")
for i in range(len(dist)):
fileo.write("%s,%s,%s\n" %(dist[i],cc[i],time[i]))
fileo.close()
#### Figure 5