def iter_procs(iD, sD, rD, ps, ct, minlim):
pr, D = 0, 0
pD = float('NaN')
h, r, u, nr, im, Si = ps
procs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
shuffle(procs)
for p in procs:
if p is 0: rD = bd.ResIn(rD, ps)
elif p is 1: rD = bd.res_flow(rD, ps)
elif p is 2: sD, iD = bd.immigration(sD, iD, ps)
elif p is 3: iD = bd.ind_flow(iD, ps)
elif p is 4: iD = bd.ind_disp(iD, ps)
elif p is 5: iD, rD, D = bd.consume(iD, rD, ps)
elif p is 6: iD = bd.grow(iD, ps)
elif p is 7: iD = bd.transition(iD, ps)
elif p is 8: iD = bd.maintenance(iD, ps)
elif p is 9: sD, iD, pr = bd.reproduce(sD, iD, ps)
avgQ = []
Sz = []
Ris = []
SpIDs = []
states = []
if len(list(iD)) > 0:
for k, v in iD.items():
Ris.append(v['q'] / (v['mt'] * v['sz']))
avgQ.append(v['q'])
Sz.append(v['sz'])
SpIDs.append(v['sp'])
states.append(v['st'])
avgQ = mean(avgQ)
Sz = mean(Sz)
N = len(states)
pD = states.count('d') / N
S = len(list(set(SpIDs)))
Ri = mean(Ris)
return [
iD, sD, rD, N, S,
len(list(rD)), ct + 1, pr, pD, avgQ, Sz, D, Ri
]
else:
return [
iD, sD, rD, 0, 0,
len(list(rD)), ct + 1, 0,
float('NaN'),
float('NaN'),
float('NaN'),
float('NaN'),
float('NaN')
]
def iter_procs(iD, sD, rD, ps, ct, minlim):
pr, D = 0, 0
pD = float('NaN')
h, r, u, nr, im, Si = ps
procs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
shuffle(procs)
for p in procs:
if p is 0: rD = bd.ResIn(rD, ps)
elif p is 1: rD = bd.res_flow(rD, ps)
elif p is 2: sD, iD = bd.immigration(sD, iD, ps)
elif p is 3: iD = bd.ind_flow(iD, ps)
elif p is 4: iD = bd.ind_disp(iD, ps)
elif p is 5: iD, rD, D = bd.consume(iD, rD, ps)
elif p is 6: iD = bd.grow(iD, ps)
elif p is 7: iD = bd.transition(iD, ps)
elif p is 8: iD = bd.maintenance(iD, ps)
elif p is 9: sD, iD, pr = bd.reproduce(sD, iD, ps)
avgQ = []
Sz = []
Ris = []
SpIDs = []
states = []
if len(list(iD)) > 0:
for k, v in iD.items():
Ris.append(v['q']/(v['mt']*v['sz']))
avgQ.append(v['q'])
Sz.append(v['sz'])
SpIDs.append(v['sp'])
states.append(v['st'])
avgQ = mean(avgQ)
Sz = mean(Sz)
N = len(states)
pD = states.count('d')/N
S = len(list(set(SpIDs)))
Ri = mean(Ris)
return [iD, sD, rD, N, S, len(list(rD)), ct+1, pr, pD, avgQ, Sz, D, Ri]
else: return [iD, sD, rD, 0, 0, len(list(rD)), ct+1, 0, float('NaN'),
float('NaN'), float('NaN'), float('NaN'), float('NaN')]
def nextFrame(arg):
""" Function called for each successive animation frame; arg is the frame number """
global ADs, ADList, AVG_DIST, SpecDisp, SpecMaint, SpecGrowth, fixed, p, BurnIn, t, num_sims, width, height, Rates, u0, rho, ux, uy, n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW, SpColorDict, GrowthDict, N_RD, P_RD, C_RD, DispDict, MaintDict, one9th, four9ths, one36th, barrier, gmax, dmax, maintmax, IndIDs, Qs, IndID, IndTimeIn, IndExitAge, IndX, IndY, Ind_scatImage, SpeciesIDs, EnvD, TY, tracer_scatImage, TTimeIn, TIDs, TExitAge, TX, RTypes, RX, RY, RID, RIDs, RVals, RTimeIn, RExitAge, resource_scatImage, bN, bS, bE, bW, bNE, bNW, bSE, bSW, ct1, Mu, Maint, motion, reproduction, speciation, seedCom, m, r, nNi, nP, nC, rmax, sim, RAD, splist, N, ct, splist2, WTs, Jcs, Sos, RDens, RDiv, RRich, S, ES, Ev, BP, SD, Nm, sk, T, R, LowerLimit, prod_i, prod_q, viscosity, alpha, Ts, Rs, PRODIs, Ns, TTAUs, INDTAUs, RDENs, RDIVs, RRICHs, Ss, ESs, EVs, BPs, SDs, NMAXs, SKs, MUs, MAINTs, PRODNs, PRODPs, PRODCs, lefts, bottoms, Gs, Ms, NRs, PRs, CRs, Ds, RTAUs, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, amp, freq, flux, pulse, phase, disturb, envgrads, barriers
ct += 1
#plot_system = 'yes'
plot_system = 'no'
# fluctuate flow according to amplitude, frequency, & phase
u1 = u0 + u0 * (amp * sin(2 * pi * ct * freq + phase))
if u1 > 1: u1 == 1.0
# Fluid dynamics
nN, nS, nE, nW, nNE, nNW, nSE, nSW, barrier = LBM.stream(
[nN, nS, nE, nW, nNE, nNW, nSE, nSW, barrier])
rho, ux, uy, n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW = LBM.collide(
viscosity, rho, ux, uy, n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW, u0)
# Inflow of tracers
if motion == 'white_noise' or motion == 'brown_noise':
numt = 10
TIDs, TTimeIn, TX, TY = bide.NewTracers(numt, motion, TIDs, TX, TY,
TTimeIn, width, height, 2)
elif ct == 1:
numt = 10
TIDs, TTimeIn, TX, TY = bide.NewTracers(numt, motion, TIDs, TX, TY,
TTimeIn, width, height, 2)
else:
numt = 1
TIDs, TTimeIn, TX, TY = bide.NewTracers(numt, motion, TIDs, TX, TY,
TTimeIn, width, height, u0)
# moving tracer particles
if len(TIDs) > 0:
if motion == 'fluid':
TIDs, TX, TY, TExitAge, TTimeIn = bide.fluid_movement(
'tracer', TIDs, TTimeIn, TExitAge, TX, TY, ux, uy, width,
height, u0)
else:
TIDs, TX, TY, TExitAge, TTimeIn = bide.nonfluid_movement(
'tracer', motion, TIDs, TTimeIn, TExitAge, TX, TY, ux, uy,
width, height, u0)
# Inflow of resources
if motion == 'white_noise' or motion == 'brown_noise':
u1 = 2
RTypes, RVals, RX, RY, RIDs, RID, RTimeIn = bide.ResIn(
motion, RTypes, RVals, RX, RY, RID, RIDs, RTimeIn, r, rmax, nNi, nP,
nC, width, height, u1)
# resource flow
Lists = [RTypes, RIDs, RID, RVals]
if len(RTypes) > 0:
if motion == 'fluid':
RTypes, RX, RY, RExitAge, RIDs, RID, RTimeIn, RVals = bide.fluid_movement(
'resource', Lists, RTimeIn, RExitAge, RX, RY, ux, uy, width,
height, u0)
else:
RTypes, RX, RY, RExitAge, RIDs, RID, RTimeIn, RVals = bide.nonfluid_movement(
'resource', motion, Lists, RTimeIn, RExitAge, RX, RY, ux, uy,
width, height, u0)
# Inflow of individuals (immigration)
if ct == 1:
SpeciesIDs, IndX, IndY, MaintDict, EnvD, GrowthDict, DispDict, SpColorDict, IndIDs, IndID, IndTimeIn, Qs, N_RD, P_RD, C_RD, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.immigration(
dmax, gmax, maintmax, motion, seedCom, 1, SpeciesIDs, IndX, IndY,
width, height, MaintDict, EnvD, envgrads, GrowthDict, DispDict,
SpColorDict, IndIDs, IndID, IndTimeIn, Qs, N_RD, P_RD, C_RD, nNi,
nP, nC, u1, alpha, GrowthList, MaintList, N_RList, P_RList,
C_RList, DispList, ADList)
else:
SpeciesIDs, IndX, IndY, MaintDict, EnvD, GrowthDict, DispDict, SpColorDict, IndIDs, IndID, IndTimeIn, Qs, N_RD, P_RD, C_RD, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.immigration(
dmax, gmax, maintmax, motion, 1, m, SpeciesIDs, IndX, IndY, width,
height, MaintDict, EnvD, envgrads, GrowthDict, DispDict,
SpColorDict, IndIDs, IndID, IndTimeIn, Qs, N_RD, P_RD, C_RD, nNi,
nP, nC, u1, alpha, GrowthList, MaintList, N_RList, P_RList,
C_RList, DispList, ADList)
# dispersal
Lists = [
SpeciesIDs, IndIDs, IndID, Qs, DispDict, GrowthList, MaintList,
N_RList, P_RList, C_RList, DispList, ADList, Qs
]
if len(SpeciesIDs) > 0:
if motion == 'fluid':
SpeciesIDs, IndX, IndY, IndExitAge, IndIDs, IndID, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList, Qs = bide.fluid_movement(
'individual', Lists, IndTimeIn, IndExitAge, IndX, IndY, ux, uy,
width, height, u0)
else:
SpeciesIDs, IndX, IndY, IndExitAge, IndIDs, IndID, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList, Qs = bide.nonfluid_movement(
'individual', motion, Lists, IndTimeIn, IndExitAge, IndX, IndY,
ux, uy, width, height, u0)
# Chemotaxis
#SpeciesIDs, Qs, IndIDs, ID, TimeIn, X, Y, GrowthDict, DispDict, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.chemotaxis(reproduction, speciation, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndX, IndY, width, height, GrowthDict, DispDict, SpColorDict, N_RD, P_RD, C_RD, MaintDict, EnvD, envgrads, nNi, nP, nC, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList)
# Forage
#SpeciesIDs, Qs, IndIDs, ID, TimeIn, X, Y, GrowthDict, DispDict, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.density_forage(RVals, RX, RY, reproduction, speciation, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndX, IndY, width, height, GrowthDict, DispDict, SpColorDict, N_RD, P_RD, C_RD, MaintDict, EnvD, envgrads, nNi, nP, nC, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList)
PRODI, PRODN, PRODC, PRODP = 0, 0, 0, 0
p1, TNQ1, TPQ1, TCQ1 = metrics.getprod(Qs)
# Consume
RTypes, RVals, RIDs, RID, RTimeIn, RExitAge, RX, RY, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndX, IndY, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.consume(
RTypes, RVals, RIDs, RID, RX, RY, RTimeIn, RExitAge, SpeciesIDs, Qs,
IndIDs, IndID, IndTimeIn, IndX, IndY, width, height, GrowthDict, N_RD,
P_RD, C_RD, DispDict, GrowthList, MaintList, N_RList, P_RList, C_RList,
DispList, ADList)
# Reproduction
SpeciesIDs, Qs, IndIDs, ID, TimeIn, X, Y, GrowthDict, DispDict, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.reproduce(
reproduction, speciation, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn,
IndX, IndY, width, height, GrowthDict, DispDict, SpColorDict, N_RD,
P_RD, C_RD, MaintDict, EnvD, envgrads, nNi, nP, nC, GrowthList,
MaintList, N_RList, P_RList, C_RList, DispList, ADList)
# maintenance
SpeciesIDs, X, Y, IndExitAge, IndIDs, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.maintenance(
SpeciesIDs, IndX, IndY, IndExitAge, SpColorDict, MaintDict, EnvD,
IndIDs, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList,
C_RList, DispList, ADList)
# transition to or from dormancy
Sp_IDs, IDs, Qs, GrowthList, MaintList, ADList = bide.transition(
SpeciesIDs, IndIDs, Qs, GrowthList, MaintList, ADList)
p2, TNQ2, TPQ2, TCQ2 = metrics.getprod(Qs)
PRODI = p2 - p1
PRODN = TNQ2 - TNQ1
PRODP = TPQ2 - TPQ1
PRODC = TCQ2 - TCQ1
# disturbance
if np.random.binomial(1, disturb * u0) == 1:
SpeciesIDs, X, Y, IndExitAge, IndIDs, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.decimate(
SpeciesIDs, IndX, IndY, IndExitAge, SpColorDict, MaintDict, EnvD,
IndIDs, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList,
C_RList, DispList, ADList)
ax = fig.add_subplot(111)
plt.tick_params(axis='both',
which='both',
bottom='off',
top='off',
left='off',
right='off',
labelbottom='off',
labelleft='off')
if len(SpeciesIDs) >= 1: RAD, splist = bide.GetRAD(SpeciesIDs)
else: RAD, splist, N, S = [], [], 0, 0
N, S, tt, rr = sum(RAD), len(RAD), len(TIDs), len(RIDs)
numD = ADList.count('d')
if N != len(ADList):
print N, len(SpeciesIDs), len(ADList)
print "N != len(ADList)"
sys.exit()
if N > 0:
Title = [
'Individuals consume resources, grow, reproduce, and die as they move through the environment. \nAverage speed on the x-axis is '
+ str(u0) + ' units per time step. ' + str(len(TExitAge)) +
' tracers have passed through.\nN: ' + str(N) + ', S: ' + str(S) +
', tracers: ' + str(tt) + ', resources: ' + str(rr) + ', ct: ' +
str(ct) + ', %dormant: ' + str(round((numD / N) * 100, 2))
]
else:
Title = [
'Individuals consume resources, grow, reproduce, and die as they move through the environment. \nAverage speed on the x-axis is '
+ str(u0) + ' units per time step. ' + str(len(TExitAge)) +
' tracers have passed through.\nN: ' + str(N) + ', S: ' + str(S) +
', tracers: ' + str(tt) + ', resources: ' + str(rr) + ', ct: ' +
str(ct) + ', %dormant: nan'
]
txt.set_text(' '.join(Title))
ax.set_ylim(0, height)
ax.set_xlim(0, width)
if plot_system == 'yes':
##### PLOTTING THE SYSTEM ##############################################
resource_scatImage.remove()
tracer_scatImage.remove()
Ind_scatImage.remove()
colorlist = []
sizelist = []
for i, val in enumerate(SpeciesIDs):
if ADList[i] == 'a':
colorlist.append('red')
elif ADList[i] == 'd':
colorlist.append('0.3')
sizelist.append(min(Qs[i]) * 1000)
resource_scatImage = ax.scatter(RX,
RY,
s=RVals * 100,
c='w',
edgecolor='SpringGreen',
lw=0.6,
alpha=0.3)
Ind_scatImage = ax.scatter(IndX,
IndY,
s=sizelist,
c=colorlist,
edgecolor='0.2',
lw=0.2,
alpha=0.9)
tracer_scatImage = ax.scatter(TX,
TY,
s=200,
c='r',
marker='*',
lw=0.0,
alpha=0.6)
Ns.append(N)
if N == 0 and BurnIn == 'not done':
Ns = [Ns[-1]] # only keep the most recent N value
BurnIn = 'done'
if ct > 200 and BurnIn == 'not done':
if len(Ns) > 100:
AugmentedDickeyFuller = sta.adfuller(Ns)
val, p = AugmentedDickeyFuller[0:2]
if p >= 0.05:
Ns.pop(0)
elif p < 0.05 or isnan(p) == True:
BurnIn = 'done'
Ns = [Ns[-1]] # only keep the most recent N value
if ct > 300 and BurnIn == 'not done':
Ns = [Ns[-1]] # only keep the most recent N value
BurnIn = 'done'
if BurnIn == 'done':
PRODIs.append(PRODI)
PRODNs.append(PRODN)
PRODPs.append(PRODP)
PRODCs.append(PRODC)
if len(RExitAge) > 0:
RTAUs.append(mean(RExitAge))
if len(IndExitAge) > 0:
INDTAUs.append(mean(IndExitAge))
if len(TExitAge) > 0:
TTAUs.append(mean(TExitAge))
# Examining the resource RAD
if len(RTypes) > 0:
RRAD, Rlist = bide.GetRAD(RTypes)
RDens = len(RTypes) / (height * width)
RDiv = float(metrics.Shannons_H(RRAD))
RRich = len(Rlist)
RDENs.append(RDens)
RDIVs.append(RDiv)
RRICHs.append(RRich)
# Number of tracers, resource particles, and individuals
T, R, N = len(TIDs), len(RIDs), len(SpeciesIDs)
Ts.append(T)
Rs.append(R)
Ss.append(S)
if N >= 1:
if R >= 1:
q = min([10, R])
#avg_dist = spatial.avg_dist(X, RX, Y, RY, q)
avg_dist = spatial.nearest_neighbor(X, RX, Y, RY, q)
AVG_DIST.append(avg_dist)
spD = DispDict.values()
spM = MaintDict.values()
spG = GrowthDict.values()
SpecDisp.append(mean(spD))
SpecMaint.append(mean(spM))
SpecGrowth.append(mean(spG))
RAD, splist = bide.GetRAD(SpeciesIDs)
RAD, splist = zip(*sorted(zip(RAD, splist), reverse=True))
RAD = list(RAD)
S = len(RAD)
Ss.append(S)
# Evenness, Dominance, and Rarity measures
Ev = metrics.e_var(RAD)
EVs.append(Ev)
ES = metrics.e_simpson(RAD)
ESs.append(ES)
if len(Ns) == 1:
splist2 = list(splist)
if len(Ns) > 1:
wt = metrics.WhittakersTurnover(splist, splist2)
jc = metrics.jaccard(splist, splist2)
so = metrics.sorensen(splist, splist2)
splist2 = list(splist)
WTs.append(wt)
Jcs.append(jc)
Sos.append(so)
Nm, BP = [max(RAD), Nm / N]
NMAXs.append(Nm)
BPs.append(BP)
SD = metrics.simpsons_dom(RAD)
SDs.append(SD)
sk = stats.skew(RAD)
SKs.append(sk)
Gs.append(mean(GrowthList))
Ms.append(mean(MaintList))
Ds.append(mean(DispList))
numD = ADList.count('d')
ADs.append(numD / len(ADList))
Nmeans = [sum(x) / len(x) for x in zip(*N_RList)]
NRs.append(mean(Nmeans))
Pmeans = [sum(x) / len(x) for x in zip(*P_RList)]
PRs.append(mean(Pmeans))
Cmeans = [sum(x) / len(x) for x in zip(*C_RList)]
CRs.append(mean(Cmeans))
#process = psutil.Process(os.getpid())
#mem = round(process.get_memory_info()[0] / float(2 ** 20), 1)
# return the memory usage in MB
if len(Ns) > 100:
t = time.clock() - t
#print sim, ' N:', int(round(mean(Ns))), 'S:', int(round(mean(Ss))), 'WT:', round(mean(WTs),2), ': flow:', u0, 'time:', round(t,1), 'seconds', ': Ttaus:',round(mean(TTimeIn)), round(mean(TExitAge)), ': Etau:', round(width/u0) #' MB:',int(round(mem)), 'p-val =', round(p,3)
#print sim, ' N:', int(round(mean(Ns))), 'S:', int(round(mean(Ss))), ': flow:', u0, 'time:', round(t,1), 'seconds', ' height:', str(height), ' Avg dist:', round(mean(AVG_DIST),3), ' f(dormant):',round(mean(ADs),3)
print sim, ' N:', int(round(mean(Ns))), 'S:', int(round(
mean(Ss))), ' flow:', u0, 'time:', round(
t,
1), 'Ttaus:', round(mean(TExitAge), 2), ': Etau:', round(
(width - 1) / u0, 2), 'dormant:', round(mean(ADs), 3)
t = time.clock()
SString = str(splist).strip('()')
RADString = str(RAD).strip('()')
RADString = str(RAD).strip('[]')
IndRTD = str(IndExitAge).strip('[]')
TRTD = str(TExitAge).strip('[]')
RRTD = str(RExitAge).strip('[]')
OUT1 = open(GenPath + 'examples/SimData.csv', 'a')
OUT2 = open(GenPath + 'examples/RADs.csv', 'a')
OUT3 = open(GenPath + 'examples/Species.csv', 'a')
OUT4 = open(GenPath + 'examples/IndRTD.csv', 'a')
OUT5 = open(GenPath + 'examples/TracerRTD.csv', 'a')
OUT6 = open(GenPath + 'examples/ResRTD.csv', 'a')
#TTAUs = np.mean(TExitAge), np.mean(TTimeIn)
outlist = [
sim, motion,
mean(PRODIs),
mean(PRODNs),
mean(PRODPs),
mean(PRODCs), r, nNi, nP, nC, rmax, gmax, maintmax, dmax,
barriers, alpha, seedCom, u0, width - 0.2, height, viscosity,
N, m,
mean(RTAUs),
mean(TExitAge),
mean(INDTAUs),
mean(RDENs),
mean(RDIVs),
mean(RRICHs),
mean(Ss),
mean(ESs),
mean(EVs),
mean(BPs),
mean(SDs),
mean(NMAXs),
mean(SKs), T, R, speciation,
mean(WTs),
mean(Jcs),
mean(Sos),
mean(Gs),
mean(Ms),
mean(NRs),
mean(PRs),
mean(CRs),
mean(Ds), amp, flux, freq, phase, disturb,
mean(SpecGrowth),
mean(SpecDisp),
mean(SpecMaint),
mean(AVG_DIST),
mean(ADs)
]
outlist = str(outlist).strip('[]')
print >> OUT1, outlist
print >> OUT2, RADString
print >> OUT3, SString
print >> OUT4, ct1, ',', sim, ',', IndRTD
print >> OUT5, ct1, ',', sim, ',', TRTD
print >> OUT6, ct1, ',', sim, ',', RRTD
OUT1.close()
OUT2.close()
OUT3.close()
OUT4.close()
OUT5.close()
OUT6.close()
ct1 += 1
ct = 0
Rates = np.roll(Rates, -1, axis=0)
u0 = Rates[0]
n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW, barrier, rho, ux, uy, bN, bS, bE, bW, bNE, bNW, bSE, bSW = LBM.SetLattice(
u0, viscosity, width, height, lefts, bottoms, barriers)
u1 = u0 + u0 * (amp * sin(2 * pi * ct * freq + phase))
RDens, RDiv, RRich, S, ES, Ev, BP, SD, Nm, sk, Mu, Maint, ct, IndID, RID, N, ct1, T, R, PRODI, PRODQ = [
0
] * 21
ADList, ADs, AVG_DIST, SpecDisp, SpecMaint, SpecGrowth, SpColorList, GrowthList, MaintList, N_RList, P_RList, C_RList, RColorList, DispList = [
list([]) for _ in xrange(14)
]
RAD, splist, IndTimeIn, SpeciesIDs, IndX, IndY, IndIDs, Qs, IndExitAge, TX, TY, TExitAge, TIDs, TTimeIn, RX, RY, RIDs, RTypes, RExitAge, RTimeIn, RVals, Gs, Ms, NRs, PRs, CRs, Ds, Ts, Rs, PRODIs, PRODNs, PRODPs, PRODCs, Ns, RTAUs, TTAUs, INDTAUs, RDENs, RDIVs, RRICHs, Ss, ESs, EVs, BPs, SDs, NMAXs, SKs, MUs, MAINTs, WTs, Jcs, Sos, splist2 = [
list([]) for _ in xrange(53)
]
p = 0
BurnIn = 'not done'
#if u0 in Rates:
if u0 == max(Rates):
print '\n'
sim += 1
if sim > num_sims:
print "simplex finished"
sys.exit()
width, height, alpha, motion, reproduction, speciation, seedCom, m, r, nNi, nP, nC, rmax, gmax, maintmax, dmax, amp, freq, flux, pulse, phase, disturb, envgrads, barriers, Rates = rp.get_rand_params(
fixed)
for i in range(barriers):
lefts.append(np.random.uniform(0.2, .8))
bottoms.append(np.random.uniform(0.1, 0.7))
n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW, barrier, rho, ux, uy, bN, bS, bE, bW, bNE, bNW, bSE, bSW = LBM.SetLattice(
u0, viscosity, width, height, lefts, bottoms, barriers)
u1 = u0 + u0 * (amp * sin(2 * pi * ct * freq + phase))
SpColorDict, GrowthDict, MaintDict, EnvD, N_RD, P_RD, C_RD, RColorDict, DispDict, EnvD = {}, {}, {}, {}, {}, {}, {}, {}, {}, {}
####################### REPLACE ENVIRONMENT ########################
ax = fig.add_subplot(111)
def nextFrame(arg):
""" Function called for each successive animation frame; arg is the frame number """
global mmax, pmax, ADs, ADList, AVG_DIST, SpecDisp, SpecMaint, SpecGrowth
global fixed, p, BurnIn, t, num_sims, width, height, Rates, u0, rho, ux, uy
global n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW, SpColorDict, GrowthDict, N_RD
global P_RD, C_RD, DispDict, MaintDict, one9th, four9ths, one36th, barrier
global gmax, dmax, maintmax, IndIDs, Qs, IndID, IndTimeIn, IndExitAge, IndX
global IndY, Ind_scatImage, SpeciesIDs, EnvD, TY, tracer_scatImage, TTimeIn
global TIDs, TExitAge, TX, RTypes, RX, RY, RID, RIDs, RVals, RTimeIn, RExitAge
global resource_scatImage, bN, bS, bE, bW, bNE, bNW, bSE, bSW, ct1, Mu, Maint
global motion, reproduction, speciation, seedCom, m, r, nNi, nP, nC, rmax, sim
global RAD, splist, N, ct, splist2, WTs, Jcs, Sos, RDens, RDiv, RRich, S, ES
global Ev, BP, SD, Nm, sk, T, R, LowerLimit, prod_i, prod_q, viscosity, alpha
global Ts, Rs, PRODIs, Ns, TTAUs, INDTAUs, RDENs, RDIVs, RRICHs, Ss, ESs, EVs
global BPs, SDs, NMAXs, SKs, MUs, MAINTs, PRODNs, PRODPs, PRODCs, lefts, bottoms
global Gs, Ms, NRs, PRs, CRs, Ds, RTAUs, GrowthList, MaintList, N_RList, P_RList
global C_RList, DispList, amp, freq, flux, pulse, phase, disturb, envgrads
global barriers, MainFactorDict, RPFDict
ct += 1
#plot_system = 'yes'
plot_system = 'no'
# fluctuate flow according to amplitude, frequency, & phase
u1 = u0 + u0*(amp * sin(2*pi * ct * freq + phase))
if u1 > 1: u1 == 1.0
# Fluid dynamics
nN, nS, nE, nW, nNE, nNW, nSE, nSW, barrier = LBM.stream([nN, nS, nE, nW, nNE, nNW, nSE, nSW, barrier])
rho, ux, uy, n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW = LBM.collide(viscosity, rho, ux, uy, n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW, u0)
# Inflow of resources
if motion == 'white_noise' or motion == 'brown_noise':
u1 = 2
RTypes, RVals, RX, RY, RIDs, RID, RTimeIn = bide.ResIn(motion, RTypes, RVals, RX, RY, RID, RIDs, RTimeIn, r, rmax, nNi, nP, nC, width, height, u1)
# resource flow
Lists = [RTypes, RIDs, RID, RVals]
if len(RTypes) > 0:
if motion == 'fluid': RTypes, RX, RY, RExitAge, RIDs, RID, RTimeIn, RVals = bide.fluid_movement('resource', Lists, RTimeIn, RExitAge, RX, RY, ux, uy, width, height, u0)
else: RTypes, RX, RY, RExitAge, RIDs, RID, RTimeIn, RVals = bide.nonfluid_movement('resource', motion, Lists, RTimeIn, RExitAge, RX, RY, ux, uy, width, height, u0)
# Inflow of individuals (immigration)
if ct == 1:
SpeciesIDs, IndX, IndY, MaintDict, MainFactorDict, RPFDict, EnvD, GrowthDict, DispDict, SpColorDict, IndIDs, IndID, IndTimeIn, Qs, N_RD, P_RD, C_RD, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.immigration(mmax, pmax, dmax, gmax, maintmax, motion, seedCom, 1, SpeciesIDs, IndX, IndY, width, height, MaintDict, MainFactorDict, RPFDict, EnvD, envgrads, GrowthDict, DispDict, SpColorDict, IndIDs, IndID, IndTimeIn, Qs, N_RD, P_RD, C_RD, nNi, nP, nC, u1, alpha, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList)
else:
SpeciesIDs, IndX, IndY, MaintDict, MainFactorDict, RPFDict, EnvD, GrowthDict, DispDict, SpColorDict, IndIDs, IndID, IndTimeIn, Qs, N_RD, P_RD, C_RD, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.immigration(mmax, pmax, dmax, gmax, maintmax, motion, 1, m, SpeciesIDs, IndX, IndY, width, height, MaintDict, MainFactorDict, RPFDict, EnvD, envgrads, GrowthDict, DispDict, SpColorDict, IndIDs, IndID, IndTimeIn, Qs, N_RD, P_RD, C_RD, nNi, nP, nC, u1, alpha, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList)
# dispersal
Lists = [SpeciesIDs, IndIDs, IndID, Qs, DispDict, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList]
if len(SpeciesIDs) > 0:
if motion == 'fluid': SpeciesIDs, IndX, IndY, IndExitAge, IndIDs, IndID, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList, Qs = bide.fluid_movement('individual', Lists, IndTimeIn, IndExitAge, IndX, IndY, ux, uy, width, height, u0)
else: SpeciesIDs, IndX, IndY, IndExitAge, IndIDs, IndID, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.nonfluid_movement('individual', motion, Lists, IndTimeIn, IndExitAge, IndX, IndY, ux, uy, width, height, u0)
# Chemotaxis
#SpeciesIDs, Qs, IndIDs, ID, TimeIn, X, Y, GrowthDict, DispDict, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.chemotaxis(reproduction, speciation, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndX, IndY, width, height, GrowthDict, DispDict, SpColorDict, N_RD, P_RD, C_RD, MaintDict, MainFactorDict, RPFDict, EnvD, envgrads, nNi, nP, nC, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList)
# Forage
#SpeciesIDs, Qs, IndIDs, ID, TimeIn, X, Y, GrowthDict, DispDict, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.density_forage(RVals, RX, RY, reproduction, speciation, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndX, IndY, width, height, GrowthDict, DispDict, SpColorDict, N_RD, P_RD, C_RD, MaintDict, MainFactorDict, RPFDict, EnvD, envgrads, nNi, nP, nC, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList)
PRODI, PRODN, PRODC, PRODP = 0, 0, 0, 0
p1, TNQ1, TPQ1, TCQ1 = metrics.getprod(Qs)
# Consume
#RTypes, RVals, RIDs, RID, RTimeIn, RExitAge, RX, RY, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndX, IndY, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.consume(RTypes, RVals, RIDs, RID, RX, RY, RTimeIn, RExitAge, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndX, IndY, width, height, GrowthDict, N_RD, P_RD, C_RD, DispDict, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList)
# Reproduction
SpeciesIDs, Qs, IndIDs, ID, TimeIn, X, Y, GrowthDict, DispDict, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.reproduce(reproduction, speciation, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndX, IndY, width, height, GrowthDict, DispDict, SpColorDict, N_RD, P_RD, C_RD, MaintDict, MainFactorDict, RPFDict, EnvD, envgrads, nNi, nP, nC, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList)
# maintenance
#SpeciesIDs, X, Y, IndExitAge, IndIDs, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.maintenance(SpeciesIDs, IndX, IndY, IndExitAge, SpColorDict, MaintDict, MainFactorDict, RPFDict, EnvD, IndIDs, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList)
# transition to or from dormancy
Sp_IDs, IDs, Qs, GrowthList, MaintList, ADList = bide.transition(SpeciesIDs, IndIDs, Qs, GrowthList, MaintList, MainFactorDict, RPFDict, ADList)
p2, TNQ2, TPQ2, TCQ2 = metrics.getprod(Qs)
PRODI = p2 - p1
PRODN = TNQ2 - TNQ1
PRODP = TPQ2 - TPQ1
PRODC = TCQ2 - TCQ1
# disturbance
#if np.random.binomial(1, disturb*u0) == 1: SpeciesIDs, X, Y, IndExitAge, IndIDs, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList = bide.decimate(SpeciesIDs, IndX, IndY, IndExitAge, SpColorDict, MaintDict, MainFactorDict, RPFDict, EnvD, IndIDs, IndTimeIn, Qs, GrowthList, MaintList, N_RList, P_RList, C_RList, DispList, ADList)
ax = fig.add_subplot(111)
plt.tick_params(axis='both', which='both', bottom='off', top='off', left='off', right='off', labelbottom='off', labelleft='off')
if len(SpeciesIDs) >= 1: RAD, splist = bide.GetRAD(SpeciesIDs)
else: RAD, splist, N, S = [], [], 0, 0
N, S, tt, rr = sum(RAD), len(RAD), len(TIDs), len(RIDs)
numD = ADList.count('d')
if N != len(ADList):
print N, len(SpeciesIDs), len(ADList)
print "N != len(ADList)"
sys.exit()
if N > 0:
Title = ['Individuals consume resources, grow, reproduce, and die as they move through the environment. \nAverage speed on the x-axis is '+str(u0)+' units per time step. '+str(len(TExitAge))+' tracers have passed through.\nN: '+str(N)+', S: '+str(S)+', tracers: '+str(tt)+', resources: '+str(rr)+', ct: '+str(ct)+', %dormant: '+str(round((numD/N)*100, 2))]
else:
Title = ['Individuals consume resources, grow, reproduce, and die as they move through the environment. \nAverage speed on the x-axis is '+str(u0)+' units per time step. '+str(len(TExitAge))+' tracers have passed through.\nN: '+str(N)+', S: '+str(S)+', tracers: '+str(tt)+', resources: '+str(rr)+', ct: '+str(ct)+', %dormant: nan']
txt.set_text(' '.join(Title))
ax.set_ylim(0, height)
ax.set_xlim(0, width)
if plot_system == 'yes':
##### PLOTTING THE SYSTEM ##############################################
resource_scatImage.remove()
tracer_scatImage.remove()
Ind_scatImage.remove()
colorlist = []
sizelist = []
for i, val in enumerate(SpeciesIDs):
if ADList[i] == 'a':
colorlist.append('red')
elif ADList[i] == 'd':
colorlist.append('0.3')
sizelist.append(min(Qs[i]) * 1000)
resource_scatImage = ax.scatter(RX, RY, s = RVals*100, c = 'w', edgecolor = 'SpringGreen', lw = 0.6, alpha=0.3)
Ind_scatImage = ax.scatter(IndX, IndY, s = sizelist, c = colorlist, edgecolor = '0.2', lw = 0.2, alpha=0.9)
tracer_scatImage = ax.scatter(TX, TY, s = 200, c = 'r', marker='*', lw=0.0, alpha=0.6)
Ns.append(N)
if N == 0 and BurnIn == 'not done':
Ns = [Ns[-1]] # only keep the most recent N value
BurnIn = 'done'
if ct > 200 and BurnIn == 'not done':
if len(Ns) > 100:
AugmentedDickeyFuller = sta.adfuller(Ns)
val, p = AugmentedDickeyFuller[0:2]
if p >= 0.05:
Ns.pop(0)
elif p < 0.05 or isnan(p) == True:
BurnIn = 'done'
Ns = [Ns[-1]] # only keep the most recent N value
if ct > 300 and BurnIn == 'not done':
Ns = [Ns[-1]] # only keep the most recent N value
BurnIn = 'done'
if BurnIn == 'done':
PRODIs.append(PRODI)
PRODNs.append(PRODN)
PRODPs.append(PRODP)
PRODCs.append(PRODC)
if len(RExitAge) > 0:
RTAUs.append(mean(RExitAge))
if len(IndExitAge) > 0:
INDTAUs.append(mean(IndExitAge))
if len(TExitAge) > 0:
TTAUs.append(mean(TExitAge))
# Examining the resource RAD
if len(RTypes) > 0:
RRAD, Rlist = bide.GetRAD(RTypes)
RDens = len(RTypes)/(height*width)
RDiv = float(metrics.Shannons_H(RRAD))
RRich = len(Rlist)
RDENs.append(RDens)
RDIVs.append(RDiv)
RRICHs.append(RRich)
# Number of tracers, resource particles, and individuals
T, R, N = len(TIDs), len(RIDs), len(SpeciesIDs)
Ts.append(T)
Rs.append(R)
Ss.append(S)
if N >= 1:
if R >= 1:
q = min([10, R])
avg_dist = spatial.avg_dist(IndX, RX, IndY, RY, q)
avg_dist = spatial.nearest_neighbor(IndX, RX, IndY, RY, q)
AVG_DIST.append(avg_dist)
spD = DispDict.values()
spM = MaintDict.values()
spMF = MainFactorDict.values()
spRPF = RPFDict.values()
spG = GrowthDict.values()
SpecDisp.append(mean(spD))
SpecMaint.append(mean(spM))
SpecGrowth.append(mean(spG))
RAD, splist = bide.GetRAD(SpeciesIDs)
RAD, splist = zip(*sorted(zip(RAD, splist), reverse=True))
RAD = list(RAD)
S = len(RAD)
Ss.append(S)
# Evenness, Dominance, and Rarity measures
Ev = metrics.e_var(RAD)
EVs.append(Ev)
ES = metrics.e_simpson(RAD)
ESs.append(ES)
if len(Ns) == 1:
splist2 = list(splist)
if len(Ns) > 1:
wt = metrics.WhittakersTurnover(splist, splist2)
jc = metrics.jaccard(splist, splist2)
so = metrics.sorensen(splist, splist2)
splist2 = list(splist)
WTs.append(wt)
Jcs.append(jc)
Sos.append(so)
Nm, BP = [max(RAD), Nm/N]
NMAXs.append(Nm)
BPs.append(BP)
SD = metrics.simpsons_dom(RAD)
SDs.append(SD)
sk = stats.skew(RAD)
SKs.append(sk)
Gs.append(mean(GrowthList))
Ms.append(mean(MaintList))
Ds.append(mean(DispList))
numD = ADList.count('d')
ADs.append(numD/len(ADList))
Nmeans = [sum(x)/len(x) for x in zip(*N_RList)]
NRs.append(mean(Nmeans))
Pmeans = [sum(x)/len(x) for x in zip(*P_RList)]
PRs.append(mean(Pmeans))
Cmeans = [sum(x)/len(x) for x in zip(*C_RList)]
CRs.append(mean(Cmeans))
#process = psutil.Process(os.getpid())
#mem = round(process.get_memory_info()[0] / float(2 ** 20), 1)
# return the memory usage in MB
if len(Ns) > 100:
t = time.clock() - t
#print sim, ' N:', int(round(mean(Ns))), 'S:', int(round(mean(Ss))), 'WT:', round(mean(WTs),2), ': flow:', u0, 'time:', round(t,1), 'seconds', ': Ttaus:',round(mean(TTimeIn)), round(mean(TExitAge)), ': Etau:', round(width/u0) #' MB:',int(round(mem)), 'p-val =', round(p,3)
#print sim, ' N:', int(round(mean(Ns))), 'S:', int(round(mean(Ss))), ': flow:', u0, 'time:', round(t,1), 'seconds', ' height:', str(height), ' Avg dist:', round(mean(AVG_DIST),3), ' f(dormant):',round(mean(ADs),3)
print sim, ' N:', int(round(mean(Ns))), 'S:', int(round(mean(Ss))), ' flow:', u0, 'time:', round(t,1), 'Ttaus:', round(mean(TExitAge),2), ': Etau:', round((width-1)/u0,2), 'dormant:', round(mean(ADs),3)
t = time.clock()
SString = str(splist).strip('()')
RADString = str(RAD).strip('()')
RADString = str(RAD).strip('[]')
IndRTD = str(IndExitAge).strip('[]')
TRTD = str(TExitAge).strip('[]')
RRTD = str(RExitAge).strip('[]')
OUT1 = open(GenPath + '/SimData.csv','a')
#TTAUs = np.mean(TExitAge), np.mean(TTimeIn)
outlist = [sim,motion,mean(PRODIs),mean(PRODNs),mean(PRODPs),mean(PRODCs),r,nNi,nP,nC,rmax,gmax,maintmax,dmax,barriers,alpha,seedCom,u0,width-0.2,height,viscosity,N,m,mean(RTAUs), mean(TExitAge) ,mean(INDTAUs),mean(RDENs),mean(RDIVs),mean(RRICHs),mean(Ss),mean(ESs),mean(EVs),mean(BPs),mean(SDs),mean(NMAXs),mean(SKs),T,R,speciation,mean(WTs),mean(Jcs),mean(Sos),mean(Gs),mean(Ms),mean(NRs),mean(PRs),mean(CRs),mean(Ds),amp,flux,freq,phase,disturb, mean(SpecGrowth), mean(SpecDisp), mean(SpecMaint), mean(AVG_DIST), mean(ADs)]
outlist = str(outlist).strip('[]')
print>>OUT1, outlist
OUT1.close()
ct1 += 1
ct = 0
Rates = np.roll(Rates, -1, axis=0)
u0 = Rates[0]
n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW, barrier, rho, ux, uy, bN, bS, bE, bW, bNE, bNW, bSE, bSW = LBM.SetLattice(u0, viscosity, width, height, lefts, bottoms, barriers)
u1 = u0 + u0*(amp * sin(2*pi * ct * freq + phase))
RDens, RDiv, RRich, S, ES, Ev, BP, SD, Nm, sk, Mu, Maint, ct, IndID, RID, N, ct1, T, R, PRODI, PRODQ = [0]*21
ADList, ADs, AVG_DIST, SpecDisp, SpecMaint, SpecGrowth, SpColorList, GrowthList, MaintList, N_RList, P_RList, C_RList, RColorList, DispList = [list([]) for _ in xrange(14)]
RAD, splist, IndTimeIn, SpeciesIDs, IndX, IndY, IndIDs, Qs, IndExitAge, TX, TY, TExitAge, TIDs, TTimeIn, RX, RY, RIDs, RTypes, RExitAge, RTimeIn, RVals, Gs, Ms, NRs, PRs, CRs, Ds, Ts, Rs, PRODIs, PRODNs, PRODPs, PRODCs, Ns, RTAUs, TTAUs, INDTAUs, RDENs, RDIVs, RRICHs, Ss, ESs, EVs,BPs, SDs, NMAXs, SKs, MUs, MAINTs, WTs, Jcs, Sos, splist2 = [list([]) for _ in xrange(53)]
p = 0
BurnIn = 'not done'
#if u0 in Rates:
if u0 == max(Rates):
print '\n'
sim += 1
if sim > num_sims:
print "model finished"
sys.exit()
width, height, alpha, motion, reproduction, speciation, seedCom, m, r, nNi, nP, nC, rmax, gmax, maintmax, dmax, amp, freq, flux, pulse, phase, disturb, envgrads, barriers, Rates, pmax, mmax = rp.get_rand_params(fixed)
for i in range(barriers):
lefts.append(np.random.uniform(0.2, .8))
bottoms.append(np.random.uniform(0.1, 0.7))
n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW, barrier, rho, ux, uy, bN, bS, bE, bW, bNE, bNW, bSE, bSW = LBM.SetLattice(u0, viscosity, width, height, lefts, bottoms, barriers)
u1 = u0 + u0*(amp * sin(2*pi * ct * freq + phase))
SpColorDict, GrowthDict, MaintDict, MainFactorDict, RPFDict, EnvD, N_RD, P_RD, C_RD, RColorDict, DispDict, EnvD = {}, {}, {}, {}, {}, {}, {}, {}, {}, {}
####################### REPLACE ENVIRONMENT ########################
ax = fig.add_subplot(111)
def nextFrame(arg):
""" Function called for each successive animation frame; arg is the frame number """
global mmax, pmax, ADs, ADList, AVG_DIST, SpecDisp, SpecMaint, SpecGrowth
global fixed, p, BurnIn, t, num_sims, width, height, Rates, GrowthDict, RD
global DispDict, MaintDict, gmax, dmax, maintmax, IndIDs, Qs, EVList
global IndID, IndX, IndY, Ind_scatImage, SpeciesIDs, TLList
global RX, RY, RID, RIDs, RVals, EnvD, resource_scatImage, Mu, Maint
global seedCom, m, r, sim
global N, ct, RDens, RDiv, RRich, T, R, LowerLimit, prod_i, prod_q
global Ts, Rs, PRODIs, Ns, RDENs, RDIVs, RRICHs, GrowthList, MaintList, RList
global MUs, MAINTs, PRODNs, PRODPs, PRODCs, Gs, Ms, Ds
global DispList, envgrads, MainFactorDict, RPFDict, enzyme_field, u0
global TrophicComplexityLevel, SpatialComplexityLevel, encList, std
global ResourceComplexityLevel, BiologicalComplexityLevel
global Ragg, Iagg, static, Deadlist
numDead = 0
ct += 1
#print ct
encounters = 0
# Inflow of resources
RList, RVals, RX, RY, RIDs, RID = bide.ResIn(std, ct, RList, RVals, RX, RY, RID,\
RIDs, r, width, height, u0, TrophicComplexityLevel, SpatialComplexityLevel, \
ResourceComplexityLevel, BiologicalComplexityLevel)
# Immigration
SpeciesIDs, IndX, IndY, MaintDict, MainFactorDict, RPFDict, EnvD, GrowthDict,\
DispDict, IndIDs, IndID, Qs, RD, GrowthList, MaintList, DispList, ADList, EVList,\
TLList = bide.immigration(std, mmax, pmax, dmax, gmax, maintmax, seedCom, m, \
SpeciesIDs, IndX, IndY, width, height, MaintDict, MainFactorDict, RPFDict, EnvD, envgrads,\
GrowthDict, DispDict, IndIDs, IndID, Qs, RD, u0, GrowthList, MaintList, \
DispList, ADList, EVList, TLList, ct, TrophicComplexityLevel, \
SpatialComplexityLevel, ResourceComplexityLevel, BiologicalComplexityLevel)
# Dispersal
if SpatialComplexityLevel < 3:
SpeciesIDs, Qs, IndIDs, ID, IndX, IndY, GrowthDict, DispDict, GrowthList, \
MaintList, DispList, ADList, EVList, TLList, RList, RVals, RX, RY, RIDs, RID, numDead = bide.dispersal(SpeciesIDs,\
Qs, IndIDs, IndID, IndX, IndY, width, height, GrowthDict, \
DispDict, RD, MaintDict, MainFactorDict, RPFDict, EnvD, envgrads, \
GrowthList, MaintList, DispList, ADList, EVList, TLList, RList, RVals, RX, RY, RIDs, RID, TrophicComplexityLevel, \
SpatialComplexityLevel, ResourceComplexityLevel, BiologicalComplexityLevel, numDead)
elif SpatialComplexityLevel == 3:
RList, RVals, RIDs, RID, RX, RY, SpeciesIDs, Qs, IndIDs, IndID, IndX, IndY, width, height, GD, RD, DispD, GrowthList,\
MaintList, DispList, ADList, TLList, EVList, numDead = bide.chemotaxis(RList, RVals, RIDs, RID, RX, RY, SpeciesIDs, Qs, IndIDs, IndID,\
IndX, IndY, width, height, GrowthDict, RD, DispDict, GrowthList, MaintList, DispList, ADList, TLList, EVList,\
TrophicComplexityLevel, SpatialComplexityLevel, ResourceComplexityLevel, BiologicalComplexityLevel, numDead)
# Resource Dispersal
if SpatialComplexityLevel == 1:
RList, RVals, RX, RY, RID, RIDs = bide.res_dispersal(ct, RList, RVals, RX, RY, RID, RIDs, r,\
width, height, u0, TrophicComplexityLevel, SpatialComplexityLevel, \
ResourceComplexityLevel, BiologicalComplexityLevel)
PRODI = 0
p1 = len(Qs)
# Consume
RList, RVals, RIDs, RID, RX, RY, SpeciesIDs, Qs, encounters = bide.consume(enzyme_field, \
RList, RVals, RIDs, RID, RX, RY, SpeciesIDs, Qs, IndIDs, IndID, IndX, IndY, \
width, height, GrowthDict, MaintDict, RD, DispDict, GrowthList, MaintList, DispList, ADList, \
TLList, EVList, TrophicComplexityLevel, SpatialComplexityLevel, \
ResourceComplexityLevel, BiologicalComplexityLevel)
# Reproduction
SpeciesIDs, Qs, IndIDs, ID, IndX, IndY, GrowthDict, DispDict, GrowthList, MaintList, \
DispList, ADList, EVList, TLList, RList, RVals, RX, RY, RID, RIDs, numDead = bide.reproduce(SpeciesIDs, Qs, IndIDs, IndID, \
IndX, IndY, width, height, GrowthDict, DispDict, RD, MaintDict, MainFactorDict, \
RPFDict, EnvD, envgrads, GrowthList, MaintList, DispList, ADList, EVList, TLList, RList, RVals, RX, RY, RID, RIDs, \
TrophicComplexityLevel, SpatialComplexityLevel, ResourceComplexityLevel, \
BiologicalComplexityLevel, numDead)
# maintenance
SpeciesIDs, IndX, IndY, IndIDs, Qs, GrowthList, MaintList, DispList, ADList,\
EVList, TLList, RList, RVals, RX, RY, RIDs, RID, numDead = bide.maintenance(SpeciesIDs, IndX, IndY, MaintDict, MainFactorDict, \
RPFDict, EnvD, IndIDs, Qs, GrowthList, MaintList, DispList, ADList, EVList, TLList, RList, RVals, RX, RY, RIDs, RID,\
TrophicComplexityLevel, SpatialComplexityLevel, ResourceComplexityLevel, \
BiologicalComplexityLevel, numDead)
# transition to or from dormancy
SpeciesIDs, IndX, IndY, GrowthList, DispList, ADList, EVList, IndIDs, Qs, GrowthList, \
MaintList, TLList, RList, RVals, RX, RY, RIDs, RID, numDead = bide.transition(SpeciesIDs, IndX, IndY, GrowthList, DispList, ADList, EVList,\
IndIDs, Qs, MaintList, TLList, RList, RVals, RX, RY, RIDs, RID, MainFactorDict, RPFDict, \
TrophicComplexityLevel, SpatialComplexityLevel, ResourceComplexityLevel, \
BiologicalComplexityLevel, numDead)
p2 = len(Qs)
PRODI = p2 - p1
ax1 = fig.add_subplot(2,2,1) # initiate 1st plot
ax2 = fig.add_subplot(2,2,2) # initiate 2nd plot
ax3 = fig.add_subplot(2,2,3) # initiate 3rd plot
ax4 = fig.add_subplot(2,2,4) # initiate 4th plot
plt.tick_params(axis='both', which='both', bottom='off', top='off', \
left='off', right='off', labelbottom='off', labelleft='off')
N = len(IndIDs)
if N == 0 or N > 20000:
TrophicComplexityLevel = choice([1,2,3]) #
SpatialComplexityLevel = choice([1,2,3]) # goes up to 3
ResourceComplexityLevel = choice([1,2,3]) # goes up to 3 but needs enzyme breakdown
BiologicalComplexityLevel = 2
RDens, RDiv, RRich, Mu, Maint, ct, IndID, RID, N, T, R, PRODI, PRODQ = [0]*13
ADList, ADs, AVG_DIST, SpecDisp, SpecMaint, SpecGrowth, GrowthList, MaintList, RVals, \
DispList, EVList, TLList = [list([]) for _ in xrange(12)]
SpeciesIDs, IndX, IndY, IndIDs, Qs, RX, RY, RIDs, RList, RVals, Gs, Ms, \
Ds, Rs, PRODIs, Ns, RDENs, RDIVs, RRICHs, MUs, MAINTs, encList, Ragg, Iagg = [list([]) for _ in xrange(24)]
if u0 == max(Rates):
#sim += 1
width, height, seedCom, m, r, gmax, maintmax, dmax, envgrads, Rates, pmax, mmax, std = rp.get_rand_params(fixed)
GrowthDict, MaintDict, MainFactorDict, RPFDict, EnvD, RD, DispDict,\
EnvD = {}, {}, {}, {}, {}, {}, {}, {}
enzyme_field = [0]*(width*height)
p = 0
BurnIn = 'not done'
Ns.append(N)
rr = len(RIDs)
numD = ADList.count('d')
pD = 0.0
if N > 0:
pD = round((numD/N*100), 2)
Title = ['Active individuals (red) consume resources, grow, reproduce, go dormant (gray) and die in complex resource-limited environment.\n \
Resource complexity: ' + str(ResourceComplexityLevel) + ', Trophic complexity: ' + str(TrophicComplexityLevel) + ', \
Spatial complexity: ' + str(SpatialComplexityLevel) + ' N: '+str(N)+', Resources: '+str(rr)+', ct: '+str(ct)+ ', \
%Dormant: '+str(pD) + '\n# of inflowing resources: ' + str(r) + ', Aggregation: ' + str(round(std,2))]
txt.set_text(' '.join(Title))
#ax1.set_ylim(0, height)
#ax1.set_xlim(0, width)
plot_system = 'yes'
if plot_system == 'yes':
##### PLOTTING THE SYSTEM ##############################################
resource_scatImage.remove()
Ind_scatImage.remove()
plot2.remove()
plot3.remove()
plot4.remove()
colorlist = []
ind_sizelist = []
res_sizelist = []
for val in RVals:
res_sizelist.append(val*200)
for i, val in enumerate(SpeciesIDs):
if ADList[i] == 'a':
colorlist.append('red')
elif ADList[i] == 'd':
colorlist.append('0.3')
ind_sizelist.append(Qs[i] * 1000)
resource_scatImage = ax1.scatter(RX, RY, s = res_sizelist, c = 'w',
edgecolor = 'SpringGreen', lw = 2.0, alpha=0.7)
Ind_scatImage = ax1.scatter(IndX, IndY, s = ind_sizelist, c = colorlist,
edgecolor = '0.2', lw = 0.2, alpha=0.8)
ax2 = fig.add_subplot(2,2,2) # initiate 2nd plot
ax3 = fig.add_subplot(2,2,3) # initiate 3rd plot
ax4 = fig.add_subplot(2,2,4) # initiate 4th plot
plot2 = ax2.scatter([0],[0], alpha=0)
plot3 = ax3.scatter([0],[0], alpha=0)
plot4 = ax4.scatter([0],[0], alpha=0)
if len(Ns) >= 50:
if BurnIn == 'not done':
AugmentedDickeyFuller = sta.adfuller(Ns)
val, p = AugmentedDickeyFuller[0:2]
if p >= 0.05:
Ns.pop(0)
elif p < 0.05 or isnan(p) == True:
BurnIn = 'done'
Ns = [Ns[-1]] # only keep the most recent N value
if ct == 100:
BurnIn = 'done'
Ns = [Ns[-1]] # only keep the most recent N value
if BurnIn == 'done':
PRODIs.append(PRODI)
if len(RList) > 0:
RDens = len(RList)/(height*width)
RDENs.append(RDens)
R = len(RX)
Rs.append(R)
encList.append(encounters)
if N >= 1:
if N >= 2:
Imor = spatial.morisitas(IndX, IndY, width, height)
Iagg.append(Imor)
if R >= 1:
q = min([20, R])
#avg_dist1 = spatial.avg_dist(IndX, RX, IndY, RY, q)
avg_dist2 = spatial.nearest_neighbor(IndX, RX, IndY, RY, q)
AVG_DIST.append(avg_dist2)
if R >= 2:
Rmor = spatial.morisitas(RX, RY, width, height)
Ragg.append(Rmor)
spD = DispDict.values()
spM = MaintDict.values()
spG = GrowthDict.values()
SpecDisp.append(mean(spD))
SpecMaint.append(mean(spM))
SpecGrowth.append(mean(spG))
Gs.append(mean(GrowthList))
Ms.append(mean(MaintList))
Ds.append(mean(DispList))
numD = ADList.count('d')
ADs.append(numD/len(ADList))
Deadlist.append(numDead)
if len(Ns) >= 20:
print '%4s' % sim, ' r:','%4s' % r, ' R:','%4s' % int(round(mean(Rs))), ' N:','%5s' % int(round(mean(Ns))), \
' Dormant:', '%5s' % round(mean(ADs),3), ' Encounters:','%5s' % round(mean(encList),2), ' Spatial:', SpatialComplexityLevel, \
'Resource:', ResourceComplexityLevel, 'Trophic:', TrophicComplexityLevel#, \
#'Agg(I):', round(mean(Iagg), 2), 'Agg(R):', round(mean(Ragg),2)
Rates = np.roll(Rates, -1, axis=0)
u0 = Rates[0]
if static == 'no':
TrophicComplexityLevel = choice([1,2,3,4]) #
SpatialComplexityLevel = choice([1,2,3]) # goes up to 3
ResourceComplexityLevel = choice([1,2,3]) # goes up to 3 but needs enzyme breakdown
BiologicalComplexityLevel = 2
RDens, RDiv, RRich, Mu, Maint, ct, IndID, RID, N, T, R, PRODI, PRODQ, numD = [0]*14
ADList, ADs, AVG_DIST, SpecDisp, SpecMaint, SpecGrowth, GrowthList, MaintList, RVals, \
DispList, EVList, TLList, Deadlist = [list([]) for _ in xrange(13)]
SpeciesIDs, IndX, IndY, IndIDs, Qs, RX, RY, RIDs, RList, RVals, Gs, Ms, \
Ds, Rs, PRODIs, Ns, RDENs, RDIVs, RRICHs, MUs, MAINTs, encList, Ragg, Iagg = [list([]) for _ in xrange(24)]
p = 0
BurnIn = 'not done'
if u0 == max(Rates):
sim += 1
width, height, seedCom, m, r, gmax, maintmax, dmax, envgrads, Rates, pmax, mmax, std = rp.get_rand_params(fixed)
GrowthDict, MaintDict, MainFactorDict, RPFDict, EnvD, RD, DispDict,\
EnvD = {}, {}, {}, {}, {}, {}, {}, {}
enzyme_field = [0]*(width*height)
####################### REPLACE ENVIRONMENT ########################
ax1 = fig.add_subplot(2,2,1)
ax2 = fig.add_subplot(2,2,2) # initiate first plot
ax3 = fig.add_subplot(2,2,3) # initiate first plot
ax4 = fig.add_subplot(2,2,4) # initiate first plot
if xi == 0: ResLists, ResDict, res_in = bide.ResIn(ResLists, ResDict, params, ct, ComplexityLevels)
# Immigration
elif xi == 1: IndDict, SpDicts = bide.immigration(IndDict, SpDicts, params, ct, ComplexityLevels)
# Individual Dispersal
elif xi == 2 and '-none-' not in SC: IndDict, ResList, ResDict, numDead = bide.dispersal(IndDict, SpDicts, ResLists, ResDict, params, ComplexityLevels, numDead)
# Resource Dispersal
elif xi == 3: ResLists = bide.res_dispersal(ResLists, params, ct, ComplexityLevels)
# Consumption
elif xi == 4: ResLists, ResDict, IndDict, encounters, numDead = bide.consume(IndDict, SpDicts, ResLists, ResDict, params, ComplexityLevels, numDead)
# Reproduction
elif xi == 5: PRODI, IndDicts, ResLists, ResDict, numDead = bide.reproduce(IndDict, SpDicts, ResLists, ResDict, params, ComplexityLevels, numDead)
# Maintenance
elif xi == 6: IndDict, ResLists, ResDict, numDead = bide.maintenance(IndDict, SpDicts, ResLists, ResDict, ComplexityLevels, numDead)
# Transition to or from dormancy
elif xi == 7: IndDict, ResLists, ResDict, numDead = bide.transition(IndDict, SpDicts, ResLists, ResDict, ComplexityLevels, numDead)
t2 = time.clock() - t1
if t2 >= 0.12: break
#if t2 < 0.12: time.sleep(0.12 - t2)
N = len(IndDict.keys())
Ns.append(N)
Rvs = ResLists[0]
Rvs.sort()
def nextFrame(arg): # arg is the frame number
print arg
plot_system = 'yes'
logdata = 'no'
global width, height, length, Rates, u0, shift, sign, rho, ux, uy, n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW
global SpColorDict, GrowthDict, ResUseDict, DispDict, MaintDict, one9th, four9ths, one36th, barrier
global IndIDs, Qs, IndID, IndTimeIn, IndExitAge, IndXcoords, IndYcoords, IndZcoords, Ind_scatImage, SpeciesIDs
global TracerYcoords, tracer_scatImage, TracerTimeIn, TracerZcoords, TracerIDs, TracerExitAge, TracerXcoords
global ResTypes, ResXcoords, ResYcoords, ResZcoords, ResID, ResIDs, ResVals, ResTimeIn, ResExitAge, resource_scatImage
global avg_Q, avg_maint, avg_disp, avg_res, avgTau, avg_growth, avg_prey, avg_symb, avg_parasite
global barrierN, barrierS, barrierE, barrierW, barrierNE, barrierNW, barrierSE, barrierSW
global BarrierWidth, BarrierHeight, BarrierXcoords1, BarrierYcoords1, BarrierXcoords2, BarrierYcoords2
global ct1, Mu, Maint, motion, D, reproduction, speciation, predators, parasites, symbionts
global env_gradient, seedcom, m, r, nr, rmax, sim, RAD, splist, N, TracerTau, IndTau, ct
global ResDens, ResDiv, ResRich, S, ES, Ev, BP, SD, Nm, sk, T, R, LowerLimit, prod_i, prod_q, viscosity, alpha
global Ts, Rs, PRODIs, PRODQs, Ns, TRACERTAUs, INDTAUs, RESDENs, RESDIVs, RESRICHs, Ss, ESs, EVs, BPs, SDs, NMAXs, SKs, MUs, MAINTs, RESTAUs
for step in range(1): # adjust number of steps for smooth animation
# inflow of tracers
TracerIDs, TracerTimeIn, TracerXcoords, TracerYcoords, TracerZcoords = bide.NewTracers(motion, TracerIDs, TracerXcoords, TracerYcoords, TracerZcoords, TracerTimeIn, width, height, length, u0, D)
# inflow of resources
ResTypes, ResVals, ResXcoords, ResYcoords, ResZcoords, ResIDs, ResID, ResTimeIn = bide.ResIn(motion, ResTypes, ResVals, ResXcoords, ResYcoords, ResZcoords, ResID, ResIDs, ResTimeIn, r, rmax, nr, width, height, length, u0, D)
# immigration
if ct == 0:
ct = 1
SpeciesIDs, IndXcoords, IndYcoords, IndZcoords, MaintDict, GrowthDict, DispDict, SpColorDict, IndIDs, IndID, IndTimeIn, Qs, ResUseDict = bide.immigration(motion, seedcom, SpeciesIDs, IndXcoords, IndYcoords, IndZcoords, width, height, length, MaintDict, GrowthDict, DispDict, SpColorDict, IndIDs, IndID, IndTimeIn, Qs, ResUseDict, nr, u0, alpha, D)
else:
SpeciesIDs, IndXcoords, IndYcoords, IndZcoords, MaintDict, GrowthDict, DispDict, SpColorDict, IndIDs, IndID, IndTimeIn, Qs, ResUseDict = bide.immigration(motion, m, SpeciesIDs, IndXcoords, IndYcoords, IndZcoords, width, height, length, MaintDict, GrowthDict, DispDict, SpColorDict, IndIDs, IndID, IndTimeIn, Qs, ResUseDict, nr, u0, alpha, D)
if motion == 'fluid' or motion == 'conveyor': # a 'conveyor' belt action wherein y-coordinates never change occurs when there is 0 turbulence
# stream & collide
nN, nS, nE, nW, nNE, nNW, nSE, nSW, barrier, shift, sign = LBM.stream([nN, nS, nE, nW, nNE, nNW, nSE, nSW, barrier, shift, sign])
rho, ux, uy, n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW = LBM.collide(viscosity, rho, ux, uy, n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW, u0)
# dispersal
Lists = [SpeciesIDs, IndIDs, IndID, Qs, DispDict]
if len(SpeciesIDs) > 0: SpeciesIDs, IndXcoords, IndYcoords, IndExitAge, IndIDs, IndID, IndTimeIn, Qs = bide.fluid_movement('individual', Lists, IndTimeIn, IndExitAge, IndXcoords, IndYcoords, ux, uy, width, height, u0)
# resource flow
Lists = [ResTypes, ResIDs, ResID, ResVals]
if len(ResTypes) > 0: ResTypes, ResXcoords, ResYcoords, ResExitAge, ResIDs, ResID, ResTimeIn, ResVals = bide.fluid_movement('resource', Lists, ResTimeIn, ResExitAge, ResXcoords, ResYcoords, ux, uy, width, height, u0)
# moving tracer particles
if len(TracerIDs) > 0: TracerIDs, TracerXcoords, TracerYcoords, TracerExitAge, TracerTimeIn = bide.fluid_movement('tracer', TracerIDs, TracerTimeIn, TracerExitAge, TracerXcoords, TracerYcoords, ux, uy, width, height, u0)
elif motion == 'random_walk' or motion == 'unidirectional':
# Moving tracer particles
if len(TracerIDs) > 0: TracerIDs, TracerExitAge, TracerTimeIn, TracerXcoords, TracerYcoords, TracerZcoords = bide.nonfluid_movement('tracer', motion, TracerIDs, TracerExitAge, TracerTimeIn, TracerXcoords, TracerYcoords, TracerZcoords, width, height, length, u0, D)
# Moving resource particles
if len(ResTypes) > 0:
Lists = [ResTypes, ResIDs, ResVals]
Lists, ResExitAge, ResTimeIn, Xcoords, Ycoords, Zcoords = bide.nonfluid_movement('resource', motion, Lists, ResExitAge, ResTimeIn, ResXcoords, ResYcoords, ResZcoords, width, height, length, u0, D)
ResTypes, ResIDs, ResVals = Lists
# Moving individuals
if len(SpeciesIDs) > 0:
Lists = [SpeciesIDs, IndIDs, Qs, DispDict]
Lists, IndExitAge, IndTimeIn, Xcoords, Ycoords, Zcoords = bide.nonfluid_movement('individual', motion, Lists, IndExitAge, IndTimeIn, IndXcoords, IndYcoords, IndZcoords, width, height, length, u0, D)
SpeciesIDs, IndIDs, Qs = Lists
# consume & reproduce
p1, q1 = [len(IndIDs), sum(Qs)]
ResTypes, ResVals, ResIDs, ResID, ResTimeIn, ResExitAge, ResXcoords, ResYcoords, ResZcoords, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndXcoords, IndYcoords, IndZcoords = bide.consume(ResTypes, ResVals, ResIDs, ResID, ResXcoords, ResYcoords, ResZcoords, ResTimeIn, ResExitAge, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndXcoords, IndYcoords, IndZcoords, width, height, length, GrowthDict, ResUseDict, DispDict, D)
SpeciesIDs, Qs, IDs, ID, TimeIn, Xcoords, Ycoords, Zcoords, GrowthDict, DispDict = bide.reproduce(reproduction, speciation, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndXcoords, IndYcoords, IndZcoords, width, height, length, GrowthDict, DispDict, SpColorDict, ResUseDict, MaintDict, D, nr)
PRODI, PRODQ = [len(IndIDs) - p1, sum(Qs) - q1]
# maintenance
SpeciesIDs, Xcoords, Ycoords, Zcoords, IndExitAge, IndIDs, IndTimeIn, Qs = bide.maintenance(SpeciesIDs, IndXcoords, IndYcoords, IndZcoords, IndExitAge, SpColorDict, MaintDict, IndIDs, IndTimeIn, Qs, D)
"""
# predation
p1, n1 = [len(PredIDs), len(IndIDs)]
PredIDs, PredID, PredTimeIn, PredXcoords, PredYcoords, PredZcoords, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndXcoords, IndYcoords, IndZcoords = bide.predation(predation(PredIDs, PredID, PredXcoords, PredYcoords, PredZcoords, PredTimeIn, IndExitAge, SpeciesIDs, Qs, IndIDs, IndID, IndTimeIn, IndXcoords, IndYcoords, IndZcoords, width, height, length, D)
pred_i, pred_n = [len(PredIDs) - p1, sum(IndIDs) - n1]
"""
########## PLOT THE SYSTEM #################################################
if D == 3: ax = fig.add_subplot(111, projection='3d')
else: ax = fig.add_subplot(111)
plt.tick_params(axis='both', which='both', bottom='off', top='off', left='off', right='off', labelbottom='off', labelleft='off')
if len(SpeciesIDs) >= 1: RAD, splist = bide.GetRAD(SpeciesIDs)
else: RAD, splist, N, S = [], [], 0, 0
N, S, tt, rr = sum(RAD), len(RAD), len(TracerIDs), len(ResIDs)
Title = ['Individuals consume resources, grow, reproduce, and die as they move through the environment.'
'\nAverage speed on the x-axis is '+str(u0)+' units per time step. '+str(len(TracerExitAge))+' tracers have passed through.',
'\nMotion is '+motion+'; N: '+str(N)+', S: '+str(S)+', tracers: '+str(tt)+', resources: '+str(rr)+', replicate: '+str(len(Ns))]
txt.set_text(' '.join(Title))
ax.set_ylim(0, height)
ax.set_xlim(0, width)
if D == 3: ax.set_zlim(0,length)
if plot_system == 'yes':
##### PLOTTING THE INDIVIDUALS ############################################
resource_scatImage.remove()
tracer_scatImage.remove()
Ind_scatImage.remove()
colorlist = []
sizelist = []
for i, val in enumerate(SpeciesIDs):
colorlist.append(SpColorDict[val])
sizelist.append(Qs[i] * 1000)
if D == 2: resource_scatImage = ax.scatter(ResXcoords, ResYcoords, s = ResVals, c = 'w', edgecolor = 'SpringGreen', lw = 0.6, alpha=0.7)
elif D == 3: resource_scatImage = ax.scatter(ResXcoords, ResYcoords, ResZcoords, s = ResVals, c = 'w', edgecolor = 'SpringGreen', lw = 0.6, alpha=0.2)
if D == 2: Ind_scatImage = ax.scatter(IndXcoords, IndYcoords, s = sizelist, c = colorlist, edgecolor = '0.2', lw = 0.2, alpha=0.9)
elif D == 3: Ind_scatImage = ax.scatter(IndXcoords, IndYcoords, IndZcoords, s = Qs, c = colorlist, edgecolor = '0.2', lw = 0.2, alpha=0.99)
if D == 2: tracer_scatImage = ax.scatter(TracerXcoords, TracerYcoords, s = 200, c = 'r', marker='*', lw=0.0, alpha=0.6)
elif D == 3: tracer_scatImage = ax.scatter(TracerXcoords, TracerYcoords, TracerZcoords, s = 200, c = 'r', marker='*', lw=0.0, alpha=0.8)
plt.draw()
if u0 == 1.0: LowerLimit = 50
elif u0 >= 0.5: LowerLimit = 40
elif u0 >= 0.1: LowerLimit = 20
elif u0 > 0.01: LowerLimit = 10
elif u0 == 0.01: LowerLimit = 5
# Record model values and reset, or not
if len(TracerExitAge) >= LowerLimit or N == 0:
PRODIs.append(PRODI)
PRODQs.append(PRODQ)
RESTAUs.append(np.mean(ResExitAge))
INDTAUs.append(np.mean(IndExitAge))
TRACERTAUs.append(np.mean(TracerExitAge))
# Examining the resource RAD
if len(ResTypes) > 0:
ResRAD, Rlist = bide.GetRAD(ResTypes)
ResDens = sum(ResTypes)/(height*width)
ResDiv = float(metrics.Shannons_H(ResRAD))
ResRich = len(Rlist)
RESDENs.append(ResDens)
RESDIVs.append(ResDiv)
RESRICHs.append(ResRich)
# Residence times for tracers and Inds
TracerTau, IndTau, ResTau = [np.mean(TracerExitAge), np.mean(IndExitAge), np.mean(ResExitAge)]
# Number of tracers, resource particles, and individuals
T, R, N = len(TracerIDs), len(ResIDs), len(SpeciesIDs)
if logdata == 'yes':
IndRTD = str(IndExitAge).strip('[]')
TracerRTD = str(TracerExitAge).strip('[]')
ResRTD = str(ResExitAge).strip('[]')
OUT4 = open(mydir + '/GitHub/hydrobide/results/simulated_data/IndRTD.csv','a')
OUT5 = open(mydir + '/GitHub/hydrobide/results/simulated_data/TracerRTD.csv','a')
OUT6 = open(mydir + '/GitHub/hydrobide/results/simulated_data/ResRTD.csv','a')
print>>OUT4, ct1,',', sim,',', IndRTD
print>>OUT5, ct1,',', sim,',', TracerRTD
print>>OUT6, ct1,',', sim,',', ResRTD
OUT4.close()
OUT5.close()
OUT6.close()
TracerExitAge, IndExitAge, ResExitAge= [],[],[]
Ts.append(T)
Rs.append(R)
if N == 0:
Ns.append(0)
Ss.append(0)
if N >= 1:
RAD, splist = bide.GetRAD(SpeciesIDs)
RAD, splist = zip(*sorted(zip(RAD, splist), reverse=True))
S = len(RAD)
Ss.append(S)
Ns.append(N)
# Specific Growth rate and Maintenance
mu, maint = [0.0, 0.0]
for i, sp in enumerate(splist):
mu += (RAD[i] * GrowthDict[sp])
maint += (RAD[i] * MaintDict[sp])
Mu, Maint = [mu/N, maint/N]
MUs.append(Mu)
MAINTs.append(Maint)
# Evenness, Dominance, and Rarity measures
Ev = metrics.e_var(RAD)
EVs.append(Ev)
ES = metrics.e_simpson(RAD)
ESs.append(ES)
Nm, BP = [max(RAD), Nm/N]
NMAXs.append(Nm)
BPs.append(BP)
SD = metrics.simpsons_dom(RAD)
SDs.append(SD)
sk = stats.skew(RAD)
SKs.append(sk)
process = psutil.Process(os.getpid())
mem = round(process.get_memory_info()[0] / float(2 ** 20), 1) # return the memory usage in MB
if len(Ns) >= 1 or N == 0:
ct = 0
T, R, PRODI, PRODQ, N, RESTAU, TRACERTAU, INDTAU, RESDENS, RESDIV, RESRICH, S, ES, EV, BP, SD, NMAX, SK, MU, MAINT = [np.mean(Ts), np.mean(Rs), np.mean(PRODIs), np.mean(PRODQs), np.mean(Ns), np.mean(RESTAUs), np.mean(TRACERTAUs), np.mean(INDTAUs), np.mean(RESDENs), np.mean(RESDIVs), np.mean(RESRICHs), np.mean(Ss), np.mean(ESs), np.mean(EVs), np.mean(BPs), np.mean(SDs), np.mean(NMAXs), np.mean(SKs), np.mean(MUs), np.mean(MAINTs)]
print ct, sim, ' N:', int(round(N)), 'S:', int(round(S)), ' pI:', int(prod_i), 'pQ:', int(prod_q), ': flow:', u0, ' MB:',int(round(mem))
if logdata == 'yes':
SString = str(splist).strip('()')
RADString = str(RAD).strip('()')
OUT1 = open(mydir + '/GitHub/hydrobide/results/simulated_data/SimData.csv','a')
OUT2 = open(mydir + '/GitHub/hydrobide/results/simulated_data/RADs.csv','a')
OUT3 = open(mydir + '/GitHub/hydrobide/results/simulated_data/Species.csv','a')
print>>OUT1, ct1,',', sim,',', motion,',', D,',', PRODI,',', PRODQ,',', r,',', nr,',', rmax,',', BarrierWidth,',', BarrierHeight,',', alpha,',', seedcom,',', LowerLimit,',', u0,',', width,',', height,',', viscosity,',', N,',', m,',',RESTAU,',',TRACERTAU,',', INDTAU,',', RESDENS,',', RESDIV,',', RESRICH,',', S,',', ES,',', EV,',', BP,',', SD,',', NMAX,',', SK,',', MU,',', MAINT,',',T,',',R
print>>OUT2, RADString
print>>OUT3, SString
OUT1.close()
OUT2.close()
OUT3.close()
ct1 += 1
if u0 == min(Rates):
SpColorDict, GrowthDict, MaintDict, ResUseDict, ResColorDict, DispDict = {}, {}, {}, {}, {}, {}
width, height, length, alpha, motion, D, reproduction, speciation, predators, parasites, env_gradient, seedcom, m, r, nr, rmax = get_rand_params()
sim += 1
alpha = np.random.uniform(0.9, 0.999)
print '\n'
Rates = np.roll(Rates, -1, axis=0)
u0 = Rates[0] # initial in-flow speed
Ts, Rs, PRODIs, PRODQs, Ns, RESTAUs, TRACERTAUs, INDTAUs, RESDENs, RESDIVs, RESRICHs, Ss, ESs, EVs, BPs, SDs, NMAXs, SKs, MUs, MAINTs = [], [],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]
TracerTau, IndTau, ResDens, ResDiv, ResRich, S, ES, Ev, BP, SD, Nm, sk, Mu, Maint = [0,0,0,0,0,0,0,0,0,0,0,0,0,0]
IndTimeIn, SpeciesIDs, IndXcoords, IndYcoords, IndZcoords, IndIDs, Qs, IndExitAge = [],[],[],[],[],[],[],[]
TracerXcoords, TracerYcoords, TracerZcoords, TracerExitAge, TracerIDs, TracerTimeIn = [],[],[],[],[],[]
ResXcoords, ResYcoords, ResZcoords, ResIDs, ResTypes, ResExitAge, ResTimeIn, ResVals = [],[],[],[],[],[],[],[]
if motion == 'fluid' or motion == 'conveyor':
n0, nN, nS, nE, nW, nNE, nNW, nSE, nSW, barrier, rho, ux, uy, barrierN, barrierS, barrierE, barrierW, barrierNE, barrierNW, barrierSE, barrierSW, BarrierXcoords1, BarrierYcoords1, BarrierXcoords2, BarrierYcoords2 = LBM.SetLattice(u0, viscosity, width, height, left1, bottom1, left2, bottom2, BarrierWidth, BarrierHeight, BarrierXcoords1, BarrierYcoords1, BarrierXcoords2, BarrierYcoords2)
if seedcom > 0:
# inflow of resources
ResTypes, ResVals, ResXcoords, ResYcoords, ResZcoords, ResIDs, ResID, ResTimeIn = bide.ResIn(motion, ResTypes, ResVals, ResXcoords, ResYcoords, ResZcoords, ResID, ResIDs, ResTimeIn, r, rmax, nr, width, height, length, u0, D)
# immigration
SpeciesIDs, IndXcoords, IndYcoords, IndZcoords, MaintDict, GrowthDict, DispDict, SpColorDict, IDs, ID, TimeIn, Qs, ResUseDict = bide.immigration(motion, m, SpeciesIDs, IndXcoords, IndYcoords, IndZcoords, width, height, length, MaintDict, GrowthDict, DispDict, SpColorDict, IndIDs, IndID, IndTimeIn, Qs, ResUseDict, nr, u0, alpha, D)
####################### REPLACE ENVIRONMENT ############################
if D == 3: ax = fig.add_subplot(111, projection='3d')
elif D == 2: ax = fig.add_subplot(111)