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eat-xi-sensitivity.py
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eat-xi-sensitivity.py
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import numpy as np
import matplotlib.pyplot as plt
# def generate_species_randomly(name, number, center_x, center_y, mean, cov):
# """
# Generate a group of animals
# Using 2d Guassian distribution.
# """
# return np.random.multivariate_normal(mean, cov, 5000).T
# kg
MASS = [
[753, 87.5, 3.94625],
[0, 0, 0],
[0, 0, 0]
]
# 只/km^2
DENSITY = [
[3.4130, 9.4514, 0],
[0, 0, 0],
[0, 0, 0]
]
# calorie/kg
ENERGY_PER_MASS = [
[1250000, 1180000, 1020000],
[0, 0, 0],
[0, 0, 0]
]
# 卡路里 / 千克 / 度
SPECIFIC_HEAT = [
[351.33843212, 358.50860421, 389.5793499],
[0, 0, 0],
[0, 0, 0]
]
# 边长, km
SIDE_LENGTH = 10.0
AREA = SIDE_LENGTH ** 2
if __name__ == "__main__":
###### Sub figures #####
fig, axes = plt.subplots(
nrows=1, ncols=1,
figsize=(12, 8)
)
axes.set_xlabel('x/km')
axes.set_ylabel('y/km')
# print(generate_species_randomly(
# ))
# common_cov = [[1000000, 0], [0, 1000000]]
number_of_animals_PER_ANIMAL = np.array(AREA * np.array(DENSITY), dtype=int)
# print(number_of_animals_PER_ANIMAL)
number_of_animals = np.sum(number_of_animals_PER_ANIMAL)
# 3 行 3 列，每个元素都是一个二维向量，两行表示 x, y 坐标，每行表示坐标位置
species = [[[np.empty((2, number_of_animals_PER_ANIMAL[x][y]))] for y in range(3)] for x in range(3)]
spe_name = [
['Cow', 'Sheep', 'Hare'],
['D', 'E', 'F'],
['G', 'H', 'J'],
]
COW = spe_name[0][0]
SHEEP = spe_name[0][1]
# HARE = spe_name[0][2]
total = np.array(
[
# This is the dragon's position
[0.0],
[0.0]
]
)
# # Number of all species
# TOTAL = number_of_animals
# klass_x = np.empty(TOTAL)
# klass_y = np.empty(TOTAL)
# print(species)
a = b = c = 0
for mu_x in range(3):
for mu_y in range(3):
# species[mu_x][mu_y] = np.random.multivariate_normal([(mu_x-1)*1000, (mu_y-1)*1000], common_cov, number_of_animals).T
species[mu_x][mu_y] = np.random.rand(2, number_of_animals_PER_ANIMAL[mu_x][mu_y]) * SIDE_LENGTH
# print(species[mu_x][mu_y].shape)
total = np.concatenate((total, species[mu_x][mu_y]), axis=1)
# if spe_name[mu_x][mu_y] != COW and spe_name[mu_x][mu_y] != SHEEP and spe_name[mu_x][mu_y] != HARE:
# continue
if spe_name[mu_x][mu_y] != COW and spe_name[mu_x][mu_y] != SHEEP:
continue
if spe_name[mu_x][mu_y] == COW:
a = axes.scatter(species[mu_x][mu_y][0], species[mu_x][mu_y][1], linewidth='1')
elif spe_name[mu_x][mu_y] == SHEEP:
b = axes.scatter(species[mu_x][mu_y][0], species[mu_x][mu_y][1], linewidth='1')
# elif spe_name[mu_x][mu_y] == HARE:
# c = axes.scatter(species[mu_x][mu_y][0], species[mu_x][mu_y][1], linewidth='1')
# plt.plot(total[0], total[1])
import save_fig as sf
axes.set_title('Intial distribution of three species')
axes.legend((a, b, c),
# (f'Cow \n {number_of_animals_PER_ANIMAL[0][0]}', f'Sheep \n {number_of_animals_PER_ANIMAL[0][1]}', f'Hare \n {number_of_animals_PER_ANIMAL[0][2]}'),
(f'Cow \n {number_of_animals_PER_ANIMAL[0][0]}', f'Sheep \n {number_of_animals_PER_ANIMAL[0][1]}'),
scatterpoints=1,
loc='lower left',
ncol=3,
fontsize=8)
sf.save_to_file('init-distribution')
# Delete it self
total = np.delete(total, 0, axis=1)
print(total.shape)
total_backup = np.array(total)
# np.savetxt('./data/species.txt', species)
np.savetxt('./data/total.txt', total)
# exit()
########## Find and eat ############
import find
import diff
accr = np.cumsum(number_of_animals_PER_ANIMAL)
def get_pos(idx):
# Calculate klass
index1 = 0
for i in range(len(accr)):
if idx < accr[i]:
index1 = i
break
x = index1 // 3
y = index1 % 3
return (x, y)
def get_base_consumption(weight):
"""
Weight is in kg,
return energy in calories
"""
m_d = weight
V_E = 2.25
period = 2 * 24
V_O2 = m_d * V_E * period
density_o2 = 1.429
m_O2 = V_O2 * density_o2
M_O2 = 32
n_O2 = m_O2 / M_O2
n_glucuse = n_O2 / 6
energy = 277485.66 * n_glucuse
return energy
def get_growth_consumption(mu):
period = 2 * 24
dmd = mu / 365 / 24 * period
rho_m = 1.12 * 1e3
rho_b = 1.23 * 1e3
r_b = 4
r_m = 5
coefficient_1 = rho_m + rho_b * (r_b ** 2) / (r_m ** 2)
coefficient_2 = (r_b ** 2) / (r_b ** 2 + r_m ** 2)
dS = dmd / coefficient_1 / coefficient_2
E_p = 17130 * 1000 / 4.184
E_b = 0.1 * E_p
coefficient_3 = rho_m * (r_m ** 2) / (r_b ** 2 + r_m ** 2) * E_p
coefficient_4 = rho_b * coefficient_2 * E_b
E_g = (coefficient_3 + coefficient_4) * dS
return E_g
def get_fly_energy(weight, distance):
"""
Weight in kg
distance in km
"""
m_d = weight
# v_d is in m/s
v_d = 5.70 * (m_d ** 0.16)
# 转为 m
L_d = distance * 1000
# 得到小时时间
temp_time = L_d / v_d / 60 / 60
E_v = 300 / 4.184
E_f = m_d * E_v * temp_time
return E_f
def get_fire_energy(weight, x, y):
c_p = SPECIFIC_HEAT[x][y]
m_p = MASS[x][y]
constant = 5
delta_T = 80 - 25
return c_p * m_p * constant * delta_T
def eat_when_age(age, xi):
print(f'Age: {age}')
dragon_pos = np.array([
[0],
[0]
])
# Get weight and mu
(mu, weight) = diff.get_mu_and_weight_at(age)
print(mu, weight)
# Recovery the eaten animals
total = np.array(total_backup)
ENERGY_GOT = 0
base_cons = get_base_consumption(weight)
print('Base consumption:', base_cons)
growth_cons = get_growth_consumption(mu)
print('Growth consumption:', growth_cons)
hurt_cons = xi * base_cons
print('Hurt consumption:', hurt_cons)
fly_cons = 0
fire_cos = 0
ENERGY_CONSUMPTION = base_cons + growth_cons + hurt_cons
# No reachable area
NOT_REACHABLE = np.inf
iter_times = 0
eaten = 0
eaten_each = [
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]
]
while eaten < number_of_animals:
iter_times += 1
print('=======================')
print(f'Iteration: {iter_times}')
idx = find.find_nearest(total, dragon_pos)
(x, y) = get_pos(idx)
# if spe_name[x][y] != COW and spe_name[x][y] != SHEEP and spe_name[x][y] != HARE:
if spe_name[x][y] != COW and spe_name[x][y] != SHEEP:
# 不可能出现
assert False
# print(spe_name[x][y])
total[0][idx] = total[1][idx] = NOT_REACHABLE
continue
######### Begin eaten ##########
eaten += 1
eaten_each[x][y] += 1
ENERGY_GOT += ENERGY_PER_MASS[x][y] * MASS[x][y]
temp1 = get_fire_energy(weight, x, y)
fire_cos += temp1
ENERGY_CONSUMPTION += temp1
temp2 = get_fly_energy(weight, np.linalg.norm(dragon_pos-np.array([total[:,idx]]).T))
fly_cons += temp2
ENERGY_CONSUMPTION += temp2
print(f'Energy Delta this round: {ENERGY_PER_MASS[x][y] * MASS[x][y] - temp1 - temp2}')
print(f'Nearest point {spe_name[x][y]} eaten', total[0][idx], total[1][idx])
dragon_pos = np.array([total[:,idx]]).T
print(dragon_pos)
# axes.plot(total[0][idx],total[1][idx],'wo', mec='w')
total[0][idx] = total[1][idx] = NOT_REACHABLE
######### End eaten ##########
# if eaten % 75 == 0:
# # num_of_fig = int(4-eaten//75)
# # axes[num_of_fig].set_title(f'Eaten: {eaten}')
# # for i in range(len(total[0])):
# # (x, y) = get_pos(i)
# # if x == 0 and y == 0:
# # axes[num_of_fig].sactter(total[])
# axes.set_title(f'Eaten total: {eaten} \n Eaten cow: {eaten_each[0][0]} \n Eaten sheep: {eaten_each[0][1]} \n Eaten hare: {eaten_each[0][2]} \n')
# sf.save_to_file(f'age={age}-eaten-{eaten}')
if ENERGY_GOT * 0.57 * 0.7 >= ENERGY_CONSUMPTION:
print('Success got all energy')
print('Summary:')
print('=== ENERGY_CONSUMPTION ===')
print('E_n = ', ENERGY_CONSUMPTION)
print('[')
print('\t#Base:')
print(f'\t{base_cons},')
print('\t#Grorth:')
print(f'\t{growth_cons},')
print('\t#Fly:')
print(f'\t{fly_cons},')
print('\t#Fire:')
print(f'\t{fire_cos},')
print('\t#Hurt:')
print(f'\t{hurt_cons}')
print(']')
break
else:
print('ENERGY_GOT * 0.57 * 0.7', ENERGY_GOT * 0.57 * 0.7)
print('ENERGY_CONSUMPTION:', ENERGY_CONSUMPTION)
print('Still need these:', ENERGY_CONSUMPTION - ENERGY_GOT * 0.57 * 0.7)
print('Energy got:', ENERGY_GOT)
print('Fire energy need:', temp1)
print('Fly energy need:', temp2)
print(eaten_each)
print('################################################################')
return eaten_each
########## Find and eat ############
percentage_max = 0.08
xis = np.linspace(0.1 * (1-percentage_max), 0.1 * (1+percentage_max), 9)
# print(xis)
# print(xis[1:])
# exit()
AGE = 280
eaten = np.array([eat_when_age(AGE, xis[0])[0]], dtype=int)
for xi in xis[1:]:
eaten = np.vstack([eaten, eat_when_age(AGE, xi)[0]])
print(eaten)
# plt.close()
# fig, axes = plt.subplots(
# nrows=1, ncols=1,
# figsize=(12, 8)
# )
# axes.set_xlabel('Age/year')
# axes.set_ylabel('Energy consumption in two days/million calories')
# np.savetxt(f'./data/eaten-{years}.txt', eaten)
# axes.set_title('Energy consumption in two days changes with age')
# a = axes.plot(np.arange(0, years), eaten[:,0], label=COW)
# b = axes.plot(np.arange(0, years), eaten[:,1], label=SHEEP)
# c = axes.plot(np.arange(0, years), eaten[:,2], label=HARE)
# axes.legend()
# print(eaten[:,0].shape)
# print(eaten[:,1].shape)
# ENERGY_PER_MASS = np.array(ENERGY_PER_MASS)/1e6
# axes.plot(np.arange(0, years), eaten[:,0] * ENERGY_PER_MASS[0][0] * MASS[0][0] + eaten[:,1] * ENERGY_PER_MASS[0][1] * MASS[0][1])
# sf.save_to_file(f'Prey intake-age={years}')
# eat_when_age(10)
# eat_when_age(20)
# eat_when_age(30)
# eat_when_age(40)
# eat_when_age(50)
# eat_when_age(60)