-
Notifications
You must be signed in to change notification settings - Fork 0
/
eat-r-sensitivity.py
324 lines (287 loc) · 10.4 KB
/
eat-r-sensitivity.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
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, r_b = 0.8):
period = 2 * 24
dmd = mu / 365 / 24 * period
rho_m = 1.12 * 1e3
rho_b = 1.23 * 1e3
r_m = 1
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, r_b):
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, r_b)
print('Growth consumption:', growth_cons)
hurt_cons = 0.1 * 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('[\n')
print('\t#Base:\n')
print(f'\t{base_cons},\n')
print('\t#Grorth:\n')
print(f'\t{growth_cons},\n')
print('\t#Fly:\n')
print(f'\t{fly_cons},\n')
print('\t#Fire:\n')
print(f'\t{fire_cos},\n')
print('\t#Hurt:\n')
print(f'\t{hurt_cons}\n')
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
center = 0.8
r_bs = np.linspace(center * (1-percentage_max), center * (1+percentage_max), 9)
# print(xis)
# print(xis[1:])
# exit()
AGE = 280
eaten = np.array([eat_when_age(AGE, r_bs[0])[0]], dtype=int)
for xi in r_bs[1:]:
eaten = np.vstack([eaten, eat_when_age(AGE, xi)[0]])
print(eaten)
# eat_when_age(10)
# eat_when_age(20)
# eat_when_age(30)
# eat_when_age(40)
# eat_when_age(50)
# eat_when_age(60)