def run_sim(num_net, nets_inhm_attr, I9, nets_a, nets_g, vec_size_net,
vec_nmc_state, theta0, theta1, theta_setup, sampleinfo, sample_k,
mcJump, filename, jsim, jscid):
np.random.seed(jsim * num_net + jscid + 2026642028)
jattr = nets_inhm_attr[jscid]
jI9 = I9[jscid]
jA = nets_a[jscid]
jG = nets_g[jscid]
nn = int(vec_size_net[jscid])
jkk = sample_k[jscid]
nmc_state = vec_nmc_state[jscid]
[vv, ww, h, phi1, phi2, psi, qhat] = theta2param(theta1, jattr,
theta_setup, sampleinfo)
[jG1, jA1, p_th1_s1,
p_th1_s0] = gen_kCD_fixG(vv, ww, h, phi1, phi2, psi, qhat, jI9, nn, jkk,
mcJump, nmc_state, jG, jA)
## Could be more efficient.
[vv, ww, h, phi1, phi2, psi, qhat] = theta2param(theta0, jattr,
theta_setup, sampleinfo)
p_th0_s1 = potentialCwrap(vv, ww, h, phi1, phi2, psi, qhat, jI9, jG1, jA1,
nn)
p_th0_s0 = potentialCwrap(vv, ww, h, phi1, phi2, psi, qhat, jI9, jG, jA,
nn)
# if jsim>0 and jsim%5000==0:
# state2pickle(filename+'-kCD-state--scid-'+str(jscid+1).zfill(2)+'--sim-'+str(jsim+1).zfill(6),jG1,jA1)
# #print(f'Saving {posteriorfile}')
p_dTh_s1 = p_th1_s1 - p_th0_s1
p_dTh_s0 = p_th1_s0 - p_th0_s0
return (p_dTh_s0 - p_dTh_s1) #,mcstats
def gen_sample(num_nets, attr, I9, data_a, data_g, size_nets, sampleinfo,
numsim, vec_numsim_kCD, theta_draws, theta_setup, sample_k,
mcJump, jscid):
flag_largest_scid = size_nets.index(max(size_nets)) == jscid
#for jscid in range(num_nets):
#print(f'Simulation {js+1} {(datetime.now()).strftime("%Y-%m-%d-%H:%M:%S")}')
jattr = attr[jscid]
jI9 = I9[jscid]
jA = data_a[jscid]
jG = data_g[jscid]
nn = int(size_nets[jscid])
jkk = sample_k[jscid]
numsim_kCD = vec_numsim_kCD[jscid]
sim_a = np.zeros([numsim + 1, nn])
sim_g = np.zeros([numsim + 1, nn, nn])
sim_a[0, :] = jA
sim_g[0, :, :] = jG
np.random.seed(jscid + 2026642028)
for js in range(numsim):
theta_js = (theta_draws.iloc[js]).to_numpy()
[vv, ww, h, phi1, phi2, psi,
qhat] = theta2param(theta_js, jattr, theta_setup, sampleinfo)
[jG1, jA1, p_th1_s1,
p_th1_s0] = gen_kCD(vv, ww, h, phi1, phi2, psi, qhat, jI9, nn, jkk,
mcJump, numsim_kCD, jG, jA)
#print(f'fine={js:4d} prev={np.mean(jA1):5.4f}/{np.mean(jA):5.4f} density={np.mean(jG1.ravel()):5.4f}/{np.mean(jG.ravel()):5.4f}')
sim_a[js + 1, :] = np.copy(jA1) #np.copy
sim_g[js + 1, :, :] = np.copy(jG1)
#Multiple setups
#jG=np.copy(jG1)
#jA=np.copy(jA1)
if flag_largest_scid and js % 10 == 0:
systime_t = datetime.now()
print(
f'Largest scid {jscid:3d} ({size_nets[jscid]:3d}) sim {js:4d}/{numsim:3d} time {systime_t.strftime("%Y-%m-%d-%H:%M:%S")}.'
)
return [sim_a, sim_g, jscid]
def sim_ctrfPrice_jscid_jctrf(num_nets, attr, I9, data_a, data_g, size_nets,
vec_numsim_kCD, thetastars, theta_setups,
sampleinfo, sample_k, gridDeltaPrice, mcJump,
numsim, filename, cpuinfo, jcpu):
[ncpu, nscid, nctrf] = cpuinfo
[jscid, jctrf] = Jcpu2JscidJctrf(jcpu, nscid, nctrf)
[
theta_setupModel, theta_setupRestrictNet, theta_setupFixedNet,
theta_setupNoNetData
] = theta_setups
[
thetastarModel_draws, thetastarRestrictNet_draws,
thetastarFixedNet_draws, thetastarNoNetData_draws
] = thetastars
#for jprice,dPrice in enumerate(gridDeltaPrice):
jprice = jctrf
#dPrice = gridDeltaPrice[jprice]
print(jcpu, jscid, jctrf)
#if size_nets[jscid]==max(size_nets):
# print(f'Largest school (scid={jscid:2d}, size={size_nets[jscid]:3d}) delta price = {dPrice:3.0f}, {(datetime.now()).strftime("%Y-%m-%d-%H:%M:%S")}')
jattr = (attr[jscid]).copy() #fresh copy for each scenario
jattr.price = jattr.price + gridDeltaPrice[jprice]
jI9 = I9[jscid]
nn = int(size_nets[jscid])
jkk = sample_k[jscid]
nmc_state = vec_numsim_kCD[jscid]
sim_a = np.zeros([numsim + 1, nn])
sim_g = np.zeros([numsim + 1, nn, nn])
sim_a[0, :] = np.copy(data_a[jscid])
sim_g[0, :, :] = np.copy(data_g[jscid])
# (1) Model
#[vv,ww,h,phi,qhat,I9grade] = theta2param(thetastar,jattr,theta_setup,sampleinfo)
jA = np.copy(data_a[jscid])
jG = np.copy(data_g[jscid])
for js in range(numsim):
thetastar = (thetastarModel_draws.iloc[js]).to_numpy()
[vv, ww, h, phi1, phi2, psi,
qhat] = theta2param(thetastar, jattr, theta_setupModel, sampleinfo)
np.random.seed(js * num_nets + jscid + 2026642028)
[jG1, jA1, p_th1_s1,
p_th1_s0] = gen_kCD(vv, ww, h, phi1, phi2, psi, qhat, jI9, nn, jkk,
mcJump, nmc_state, jG, jA)
sim_a[js + 1, :] = jA1
sim_g[js + 1, :, :] = jG1
simdata = [np.copy(sim_a), np.copy(sim_g), jprice, jscid]
# (2a) Restrict network adjustmens: True estimates but Fixed net
#[vv,ww,h,phi,qhat,I9grade] = theta2param(thetastarFixedNet,jattr,theta_setupFixedNet,sampleinfo)
jA = np.copy(data_a[jscid])
jG = np.copy(data_g[jscid])
for js in range(numsim):
thetastar = (thetastarRestrictNet_draws.iloc[js]).to_numpy()
[vv, ww, h, phi1, phi2, psi,
qhat] = theta2param(thetastar, jattr, theta_setupRestrictNet,
sampleinfo)
np.random.seed(js * num_nets + jscid + 2026642028)
[jG1, jA1, p_th1_s1,
p_th1_s0] = gen_kCD_fixG(vv, ww, h, phi1, phi2, psi, qhat, jI9, nn,
jkk, mcJump, nmc_state, jG, jA)
sim_a[js + 1, :] = jA1
sim_g[js + 1, :, :] = jG1
#Multiple setups: v0 with reset & v1 without
#jG=np.copy(jG1)
#jA=np.copy(jA1)
simdataRestrictNet = [np.copy(sim_a), np.copy(sim_g), jprice, jscid]
# (2) Exogenous (fixed) network
jA = np.copy(data_a[jscid])
jG = np.copy(data_g[jscid])
for js in range(numsim):
thetastarFixedNet = (thetastarFixedNet_draws.iloc[js]).to_numpy()
[vv, ww, h, phi1, phi2, psi,
qhat] = theta2param(thetastarFixedNet, jattr, theta_setupFixedNet,
sampleinfo)
np.random.seed(js * num_nets + jscid + 2026642028)
[jG1, jA1, p_th1_s1,
p_th1_s0] = gen_kCD_fixG(vv, ww, h, phi1, phi2, psi, qhat, jI9, nn,
jkk, mcJump, nmc_state, jG, jA)
sim_a[js + 1, :] = jA1
sim_g[js + 1, :, :] = jG1
simdataFixedNet = [np.copy(sim_a), np.copy(sim_g), jprice, jscid]
# (3) Model with No Net Data (no local PE)
#[vv,ww,h,phi,qhat,I9grade] = theta2param(thetastarNoPE,jattr,theta_setupNoPE,sampleinfo)
jA = np.copy(data_a[jscid])
jG = np.copy(data_g[jscid])
for js in range(numsim):
thetastarNoNetData = (thetastarNoNetData_draws.iloc[js]).to_numpy()
[vv, ww, h, phi1, phi2, psi,
qhat] = theta2param(thetastarNoNetData, jattr, theta_setupNoNetData,
sampleinfo)
np.random.seed(js * num_nets + jscid + 2026642028)
[jG1, jA1, p_th1_s1,
p_th1_s0] = gen_kCD_fixG(vv, ww, h, phi1, phi2, psi, qhat, jI9, nn,
jkk, mcJump, nmc_state, jG, jA)
sim_a[js + 1, :] = jA1
sim_g[js + 1, :, :] = jG1
simdataNoNetData = [np.copy(sim_a), np.copy(sim_g), jprice, jscid]
# COMPUTE STATS
columnnames = ['scid', 'dprice', 'sim', 'netsize']
for jstats in [
'prev', 'density', 'avgDeg', 'minDeg', 'maxDeg', 'AGA', 'IAGIA',
'tri', 'HI', 'CHI', 'FSI'
]:
for jctrfLabel in ['Model', 'RestrictNet', 'FixedNet', 'noNetData']:
columnnames.append(jstats + '-' + jctrfLabel)
stats_ctrfPrice = pd.DataFrame(data=np.zeros([(numsim + 1), 48],
dtype=float),
columns=columnnames)
for jscenario, ctrfdata in enumerate(
[simdata, simdataRestrictNet, simdataFixedNet, simdataNoNetData]):
[sim_a, sim_g, jprice, jscid] = ctrfdata
for s in range(numsim + 1):
A = sim_a[s] #subdimensional array
G = sim_g[s] #subdimensional array
n = len(A)
stats_ctrfPrice.iloc[s, 0:4] = np.array([jscid, jprice, s, n])
jstats = np.array(
stateStats(G, A, n)[:-1] + homophily(G, A, n, True))
jcolumns = [j for j in range(jscenario + 4, jscenario + 44 + 1, 4)]
stats_ctrfPrice.iloc[s, jcolumns] = jstats
return stats_ctrfPrice #end sim_ctrfPrice_jscid_jctrf
def sim_state_spills(
num_nets,
grid_nnosmoke,
jattr0,
I9,
jA0,
jG0,
size_nets,
vec_numsim_kCD,
thetastar_draws,
thetastarFixedNet_draws,
thetastarNoNetData_draws,
theta_setup,
theta_setupFixedNet,
theta_setupNoNetData,
sampleinfo,
sample_k,
mcJump,
numsim,
jcpu):
nscid=num_nets
nctrf=3
[jscid,jctrf] = Jcpu2JscidJctrf(jcpu,nscid,nctrf)
jI9 = I9[jscid].copy()
nn = int(size_nets[jscid])
nnosmoke = np.round(nn*grid_nnosmoke[jscid]).astype(int)
jkk = sample_k[jscid]
nmc_state = vec_numsim_kCD[jscid]
jA = np.copy(jA0)
jG = np.copy(jG0)
# Endog net
sim_a=np.zeros([numsim+1, nn])
sim_g=np.zeros([numsim+1, nn, nn])
sim_a[0,:] = np.copy(jA)
sim_g[0,:,:] = np.copy(jG)
print(jcpu, jscid, jctrf)
# MODEL
if jctrf==0:
for js in range(numsim):
np.random.seed(js+2026642028)
ids_nosmoke= np.sort(np.random.permutation(nn)[:nnosmoke])
jattr = jattr0.copy() #fresh copy for each scenario
jA = np.copy(jA0)
jG = np.copy(jG0)
jA[ids_nosmoke]=0
if thetastar_draws.loc[js,'vPrice']>0:
jattr.loc[ids_nosmoke,'price']=-1e12
#jattr.price[jattr.id.isin(ids_nosmoke)] without index=id-1
else:
jattr.loc[ids_nosmoke,'price']=1e12
# if thetastar_draws.loc[js,'vIncome']>0:
# jattr.loc[ids_nosmoke[jscid],'income']=-1e12
# else:
# jattr.loc[ids_nosmoke[jscid],'income']=1e12
thetastar=(thetastar_draws.iloc[js]).to_numpy()
[vv,ww,h,phi1,phi2,psi,qhat] = theta2param(thetastar,jattr,theta_setup,sampleinfo)
np.random.seed(js+2026642028)
[jG1,jA1,p_th1_s1,p_th1_s0] = gen_kCD(vv,ww,h,phi1,phi2,psi,qhat,jI9,
nn,jkk,mcJump,nmc_state,
jG,jA)
sim_a[js+1,:] = jA1.copy()
sim_g[js+1,:,:] = jG1.copy()
# EXOG (FIXED) NET
elif jctrf==1:
for js in range(numsim):
np.random.seed(js+2026642028)
ids_nosmoke= np.sort(np.random.permutation(nn)[:nnosmoke])
jattr = jattr0.copy() #fresh copy for each scenario
jA = np.copy(jA0)
jG = np.copy(jG0)
jA[ids_nosmoke]=0
if thetastar_draws.loc[js,'vPrice']>0:
jattr.loc[ids_nosmoke,'price']=-1e12
#jattr.price[jattr.id.isin(ids_nosmoke)] without index=id-1
else:
jattr.loc[ids_nosmoke,'price']=1e12
# if thetastar_draws.loc[js,'vIncome']>0:
# jattr.loc[ids_nosmoke[jscid],'income']=-1e12
# else:
# jattr.loc[ids_nosmoke[jscid],'income']=1e12
thetastarFixedNet = (thetastarFixedNet_draws.iloc[js]).to_numpy()
[vv,ww,h,phi1,phi2,psi,qhat] = theta2param(thetastarFixedNet,jattr,theta_setupFixedNet,sampleinfo)
np.random.seed(js+2026642028)
[jG1,jA1,p_th1_s1,p_th1_s0] = gen_kCD_fixG(vv,ww,h,phi1,phi2,psi,qhat,jI9,
nn,jkk,mcJump,nmc_state,
jG,jA)
sim_a[js+1,:] = jA1.copy()
sim_g[js+1,:,:] = jG1.copy()
# C NO NET DATA
elif jctrf==2:
for js in range(numsim):
np.random.seed(js+2026642028)
ids_nosmoke= np.sort(np.random.permutation(nn)[:nnosmoke])
jattr = jattr0.copy() #fresh copy for each scenario
jA = np.copy(jA0)
jG = np.copy(jG0)
jA[ids_nosmoke]=0
if thetastar_draws.loc[js,'vPrice']>0:
jattr.loc[ids_nosmoke,'price']=-1e12
#jattr.price[jattr.id.isin(ids_nosmoke)] without index=id-1
else:
jattr.loc[ids_nosmoke,'price']=1e12
# if thetastar_draws.loc[js,'vIncome']>0:
# jattr.loc[ids_nosmoke[jscid],'income']=-1e12
# else:
# jattr.loc[ids_nosmoke[jscid],'income']=1e12
thetastarNoNetData = (thetastarNoNetData_draws.iloc[js]).to_numpy()
[vv,ww,h,phi1,phi2,psi,qhat] = theta2param(thetastarNoNetData,jattr,theta_setupNoNetData,sampleinfo)
np.random.seed(js+2026642028)
[jG1,jA1,p_th1_s1,p_th1_s0] = gen_kCD_fixG(vv,ww,h,phi1,phi2,psi,qhat,jI9,
nn,jkk,mcJump,nmc_state,
jG,jA)
sim_a[js+1,:] = jA1.copy()
sim_g[js+1,:,:] = jG1.copy()
more_info = [jscid,jctrf,nn,numsim]
jctrfSpills = get_stats(sim_a,sim_g,jattr0,more_info)
return jctrfSpills
def sim_state_spills_old(
num_nets,
grid_nnosmoke,
jattr0,
I9,
jA0,
jG0,
size_nets,
vec_numsim_kCD,
thetastar_draws,
thetastarFixedNet_draws,
thetastarNoNetData_draws,
theta_setup,
theta_setupFixedNet,
theta_setupNoNetData,
sampleinfo,
sample_k,
mcJump,
numsim,
jscid):
jI9 = I9[jscid].copy()
nn = int(size_nets[jscid])
nnosmoke = np.round(nn*grid_nnosmoke[jscid]).astype(int)
jkk = sample_k[jscid]
nmc_state = vec_numsim_kCD[jscid]
jA = np.copy(jA0)
jG = np.copy(jG0)
# Endog net
sim_a=np.zeros([numsim+1, nn])
sim_g=np.zeros([numsim+1, nn, nn])
sim_a[0,:] = np.copy(jA)
sim_g[0,:,:] = np.copy(jG)
# Fixed net
sim_a_fixG=np.zeros([numsim+1, nn])
sim_g_fixG=np.zeros([numsim+1, nn, nn])
sim_a_fixG[0,:] = np.copy(jA)
sim_g_fixG[0,:,:] = np.copy(jG)
# No net data
sim_a_noNet=np.zeros([numsim+1, nn])
sim_g_noNet=np.zeros([numsim+1, nn, nn])
sim_a_noNet[0,:] = np.copy(jA)
sim_g_noNet[0,:,:] = np.copy(jG)
for js in range(numsim):
np.random.seed(js+2026642028)
ids_nosmoke= np.sort(np.random.permutation(nn)[:nnosmoke])
jattr = jattr0.copy() #fresh copy for each scenario
jA = np.copy(jA0)
jG = np.copy(jG0)
jA[ids_nosmoke]=0
if thetastar_draws.loc[js,'vPrice']>0:
jattr.loc[ids_nosmoke,'price']=-1e12
#jattr.price[jattr.id.isin(ids_nosmoke)] without index=id-1
else:
jattr.loc[ids_nosmoke,'price']=1e12
# if thetastar_draws.loc[js,'vIncome']>0:
# jattr.loc[ids_nosmoke[jscid],'income']=-1e12
# else:
# jattr.loc[ids_nosmoke[jscid],'income']=1e12
# A MODEl
thetastar=(thetastar_draws.iloc[js]).to_numpy()
[vv,ww,h,phi1,phi2,psi,qhat] = theta2param(thetastar,jattr,theta_setup,sampleinfo)
np.random.seed(js+2026642028)
[jG1,jA1,p_th1_s1,p_th1_s0] = gen_kCD(vv,ww,h,phi1,phi2,psi,qhat,jI9,
nn,jkk,mcJump,nmc_state,
jG,jA)
sim_a[js+1,:] = jA1.copy()
sim_g[js+1,:,:] = jG1.copy()
# B FIXED (EXOG) NET
thetastarFixedNet = (thetastarFixedNet_draws.iloc[js]).to_numpy()
[vv,ww,h,phi1,phi2,psi,qhat] = theta2param(thetastarFixedNet,jattr,theta_setupFixedNet,sampleinfo)
np.random.seed(js+2026642028)
[jG1,jA1,p_th1_s1,p_th1_s0] = gen_kCD_fixG(vv,ww,h,phi1,phi2,psi,qhat,jI9,
nn,jkk,mcJump,nmc_state,
jG,jA)
sim_a_fixG[js+1,:] = jA1.copy()
sim_g_fixG[js+1,:,:] = jG1.copy()
# C NO NET DATA
thetastarNoNetData = (thetastarNoNetData_draws.iloc[js]).to_numpy()
[vv,ww,h,phi1,phi2,psi,qhat] = theta2param(thetastarNoNetData,jattr,theta_setupNoNetData,sampleinfo)
np.random.seed(js+2026642028)
[jG1,jA1,p_th1_s1,p_th1_s0] = gen_kCD_fixG(vv,ww,h,phi1,phi2,psi,qhat,jI9,
nn,jkk,mcJump,nmc_state,
jG,jA)
sim_a_noNet[js+1,:] = jA1.copy()
sim_g_noNet[js+1,:,:] = jG1.copy()
if (jscid==0 and js%10==9):
print(f'Simulation {js+1} {(datetime.now()).strftime("%Y-%m-%d-%H:%M:%S")}')
more_info=[jscid,nn,numsim]
jctrfSpills = get_stats(sim_a,sim_g,jattr0,more_info)
if (jscid==0):
print(f'ctrfSpills stats computed {(datetime.now()).strftime("%Y-%m-%d-%H:%M:%S")}')
jctrfSpills_fixG = get_stats(sim_a_fixG,sim_g_fixG,jattr0,more_info)
if (jscid==0):
print(f'ctrfSpills_fixG stats computed {(datetime.now()).strftime("%Y-%m-%d-%H:%M:%S")}')
jctrfSpills_noNet = get_stats(sim_a_noNet,sim_g_noNet,jattr0,more_info)
if (jscid==0):
print(f'ctrfSpills_noNet stats computed {(datetime.now()).strftime("%Y-%m-%d-%H:%M:%S")}')
#print(f'completed scid={jscid}')
return jctrfSpills,jctrfSpills_fixG,jctrfSpills_noNet
def sim_state(num_nets,
attr,
I9,
data_a,
data_g,
size_nets,
vec_numsim_kCD,
thetastar_draws,
theta_setup,
sampleinfo,
sample_k,
mcJump,
numsim,
filename,
jscid):
simdata=[]
jattr = (attr[jscid]).copy() #fresh copy for each scenario
jI9 = I9[jscid]
nn = int(size_nets[jscid])
jkk = sample_k[jscid]
nmc_state = vec_numsim_kCD[jscid]
sim_a=np.zeros([numsim+1, nn])
sim_g=np.zeros([numsim+1, nn, nn])
sim_a[0,:] = np.copy(data_a[jscid])
sim_g[0,:,:] = np.copy(data_g[jscid])
# (1) Model
jA = np.copy(data_a[jscid])
jG = np.copy(data_g[jscid])
for js in range(numsim):
thetastar=(thetastar_draws.iloc[js]).to_numpy()
[vv,ww,h,phi1,phi2,psi,qhat] = theta2param(thetastar,jattr,theta_setup,sampleinfo)
np.random.seed(js*num_nets+jscid+2026642028)
[jG1,jA1,p_th1_s1,p_th1_s0] = gen_kCD(vv,ww,h,phi1,phi2,psi,qhat,jI9,
nn,jkk,mcJump,nmc_state,
jG,jA)
sim_a[js+1,:] = jA1
sim_g[js+1,:,:] = jG1
#Multiple setups: v0 with reset & v1 without
#jG=np.copy(jG1)
#jA=np.copy(jA1)
simdata.append([np.copy(sim_a), np.copy(sim_g), jscid])
columnnames = ['scid','sim','netsize','race',
'prev', 'density', 'avgDeg', 'minDeg', 'maxDeg', 'AGA', 'IAGIA', 'tri',
'HI','CHI','FSI']
stats_ctrfSchool = pd.DataFrame(data=np.zeros([(numsim+1),15],dtype=float),columns=columnnames)
for s in range(numsim+1):
A=sim_a[s] #subdimensional array
G=sim_g[s] #subdimensional array
n=len(A)
race=np.mean(jattr.race.to_numpy(float),dtype=float)
stats_ctrfSchool.iloc[s,0:4] = np.array([jscid,s,n,race])
stats_ctrfSchool.iloc[s,4:15]= np.array(stateStats(G,A,n)[:-1] + homophily(G,A,n,True))
return stats_ctrfSchool
