def comparemeanphase(iters, ran=10):
ratios = []
avgs = []
for i in range(ran):
R = 10
L = R * (i + 1)
o = simulate(1, iters, 1, 100, R, 10, 0, L)[4]
phase = []
for j in range(iters):
avg = np.sum([x[3] for x in o[0][i]])
phase.append(avg)
fullavg = np.average(phase)
avgs.append(fullavg)
ratios.append(R / L)
for i in range(ran):
L = 60
R = L * (i + 1) // 10
o = simulate(1, iters, 1, 100, R, 10, 0, L)[4]
phase = []
for j in range(iters):
avg = np.sum([x[3] for x in o[0][i]])
phase.append(avg)
fullavg = np.average(phase)
avgs.append(fullavg)
ratios.append(R / L)
plt.scatter(ratios, avgs)
plt.xlabel("ratio of Resources to locusts")
plt.ylabel("Average gregarization")
plt.title(
f"mean gregarization as a function of resources to locusts. {iters} iterations, 1x100 grid",
wrap=True)
plt.show()
def proplocusts(sims, gens=2, iters=10):
fig, axs = plt.subplots(sims)
for i in range(sims):
locust.K = (i + 1) * 5
props = simulate(gens, iters)[2]
print(props)
axs[i].scatter(range(gens + 1), props)
axs[i].set_title(f'Threshold: {locust.K}', wrap=True)
fig.suptitle(
f'y-axis: gregarizing as portion of total. gens: {gens}, iters: {iters}, grid: 1x100, Resources: 50, cutoff: 10',
fontsize=10)
plt.show()
def meancontact(iters):
o = simulate(1, iters, 1, 100, 50, 10, 0, 30)
locustData = o[4]
contact = []
for i in range(iters):
avg = np.average([x[1] for x in locustData[0][i]])
contact.append(avg)
plt.scatter(range(iters), contact)
plt.show()
#meancontact(1000)
def resourcedensity(iters, intervals, resources=50, N=60):
locusts, grid, props, gridData, locustData = simulate(
1, iters, 1, 100, resources, 10, 0, int(N / 2))
fig, axs = plt.subplots(intervals)
gridhist = []
for i in range(iters):
if i % (iters / intervals) == 0:
gridhist.append(gridData[0][i])
for j in range(intervals):
axs[j].scatter(range(len(gridhist[j])), gridhist[j])
for ax in axs.flat:
ax.set(xlabel='location', ylabel='locusts')
fig.suptitle(
f'Evolution of locust density over {iters} iterations. ' +
f'gens: 1, grid: 1x100, Resources: {resources}, locusts: {N/2}, gregarization threshold: {locust.K}',
wrap=True)
plt.show()
def trackphase(iters, intervals, numrows=100, resources=50, G=30):
o = simulate(1, iters, 1, numrows, 50, 10, 0, G)
locustData = o[4]
fig, axs = plt.subplots(intervals)
locusthist = []
for i in range(iters):
if i % (iters / intervals) == 0 or i == 0:
locusthist.append(locustData[0][i])
for j in range(intervals):
axs[j].scatter(range(len(locusthist[j])), locusthist[j])
for ax in axs.flat:
ax.set(xlabel='locust', ylabel='phase')
fig.suptitle(
f'Evolution of locust phase over {iters} iterations (WITHOUT resource regeneration). '
+
f'gens: 1, iters: {iters}, grid: 1x{numrows}, Resources: {resources}, gregarizing locusts: {G}, gregarization threshold: {locust.K}',
wrap=True)
plt.show()
def meanvalue(iters, K):
locustData = simulate(K, 1, iters, 1, 100, 50, 10, 0, 30)
eff = []
greg = []
for i in range(iters):
toeff = np.average([x[0] for x in locustData[0][i]])
eff.append(toeff)
togreg = np.average([x[1] for x in locustData[0][i]])
greg.append(togreg)
#plt.scatter(range(iters), cons, c='r')
#plt.scatter(range(iters), walked, c='b')
plt.plot(range(iters), eff, c='g')
plt.scatter(range(iters), greg, c='b')
plt.xlabel("iterations")
plt.title(
f"Foraging efficiency/gregarization over time for gregarization threshold of {K}. {gridpoint.R} resources, {locust.N} locusts. Foraging efficiency is green and gregarization is blue",
wrap=True)
plt.show()
def singlelocust(iters):
"""tracking the location and contact level of a single locust over a generation
"""
o = simulate(1, iters, 1, 100, 50, 10, 0, 30)
locustData = o[4]
billx = []
billc = []
for i in range(iters):
locustcont = [x[0] for x in locustData[0][i]]
locustx = [x[5] for x in locustData[0][i]]
billx.append(locustx[0])
billc.append(locustcont[0])
plt.scatter(range(iters), billx, c='r')
plt.scatter(range(iters), billc, c='b')
plt.xlabel(f'Time (seconds)')
plt.ylabel(f'Contact level (orange), Location (blue)')
plt.title(
f'Trajectory of a single locust over {iters} iterations. Threshold: {locust.K}, {locust.N} locusts, {gridpoint.R} resources'
)
plt.show()
def multlocusts(iters, plots=5):
"""tracking the location and contact level of multiple locusts over a generation
"""
o = simulate(1, iters, 1, 100, 50, 10, 0, 2)
fig, axs = plt.subplots(plots)
for p in range(plots):
locustData = o[4]
billx = []
billc = []
for i in range(iters):
locustcont = [x[1] for x in locustData[0][i]]
locustx = [x[0] for x in locustData[0][i]]
billx.append(locustx[p])
billc.append(locustcont[p])
#axs[p].scatter(range(iters), billx)
axs[p].scatter(range(iters), billc)
#axs[p].set(ylabel=f'Contact level (orange), Location (blue)')
plt.xlabel(f'Time (seconds)')
fig.suptitle(
f'Contact levels of {plots} locusts over {iters} iterations. y axis is location. Threshold: {locust.K}, {locust.N} locusts, {gridpoint.R} resources'
)
plt.show()
def distfromorigin(iters, L=30, plots=10):
fig, axs = plt.subplots(plots)
for p in range(plots):
locust.p = .7
locust.T = (p + 1) * 1000
locust.K = 3
o = simulate(1, iters, 1, 100, 50, 10, 0, L)[4]
loc = []
phase = []
for i in range(iters):
#avgl=np.average([np.absolute(x[0]-50) for x in o[0][i]])
#loc.append(avgl)
avgp = np.average([x[3] for x in o[0][i]])
phase.append(avgp)
axs[p].scatter(range(iters), phase)
axs[p].set_title(f'Timescale = {locust.T}', wrap=True)
#text= axs[p].text(4000, .05, f'Timescale: {locust.T} seconds \n Probability: {locust.p}', wrap=True)
#text.set_path_effects([path_effects.Normal()])
#text.set_path_effects([path_effects.Normal()])
plt.xlabel("Time (seconds)")
fig.suptitle(
f"Change in gregarization based on timescale. 1x100 grid, 50 resources, {L} locusts, 'p' = {locust.p}",
wrap=True)
plt.show()
def meanphase(iters, R=50, L=30):
fig, axs = plt.subplots(2)
locust.K = 20
o = simulate(1, iters, 1, 100, R, 10, 0, L)
locustData = o[4]
eff = []
greg = []
#point = [0, 0]
for i in range(iters):
avg = np.average([x[4] for x in locustData[0][i]])
eff.append(avg)
gregs = np.sum([x[3] for x in locustData[0][i]])
greg.append(gregs)
#if i >= 100 and point == [0,0] and phase[i] == phase[i-1]:
#point = [i, phase[i]]
axs[0].plot(range(iters), eff)
plt.xlabel("Time (iterations)")
axs[0].set_ylabel("Mean efficiency")
axs[1].plot(range(iters), greg)
axs[0].set_ylabel("Mean gregarization")
fig.suptitle(
f"Does mean foraging efficiency reach a steady state? Parameters: {iters} iterations, {R} resouces, {L} locusts on 1x100 grid, probability of {locust.p}, threshold of {locust.K}",
wrap=True)
plt.show()
def lines(iters, locusts=30):
"""tracking the location and contact level of multiple locusts over a generation
"""
locust.p = .7
locust.K = 10
o = simulate(1, iters, 1, 100, 50, 10, 0, locusts)
for l in range(locusts):
locustData = o[4]
billx = []
billc = []
for i in range(iters):
#locustcont = [x[1] for x in locustData[0][i]]
locustx = [x[0] for x in locustData[0][i]]
billx.append(locustx[l])
#billc.append(locustcont[p])
plt.scatter(range(iters), billx)
#axs[p].scatter(range(iters), billc)
#axs[p].set(ylabel=f'Contact level (orange), Location (blue)')
plt.xlabel(f'Time (seconds)')
plt.ylabel('position')
plt.title(
f'Trajectory of {locusts} locusts over {iters} iterations. Threshold: {locust.K}, {locust.N} locusts, {gridpoint.R} resources, probability of {locust.p}'
)
plt.show()
def showresources():
locusts, grid, props, gridData, locustData = simulate(0, 0)
dist = [x.locusts for x in grid[0]]
print(sum(dist))
plt.scatter(range(len(grid[0])), dist)
plt.show()