def run_full_flow(self, flow, energy_harvest=None, routing_algs=None):
energy_harvest = energy_harvest or self.energy_harvest
routing_algs = routing_algs or self.the_routing_algs
can_sustain = []
el = copy.copy(self.energy_init)
seg_pos = 0
for seg_idx in range(NUM_SEGMENTS):
f = flow[seg_idx]
ra = routing_algs[seg_pos]
eh = energy_harvest[seg_idx]
consume_energy = sc.consume_energy_calculation(f, ra)
el = np.array(el) + np.array(eh) - np.array(consume_energy)
can_sustain.append(False if any(v < 0 for v in el) else True)
el = [ v if v > 0 else 0 for v in el ]
el = self.battery_limit(el)
seg_pos = 0 if seg_pos == NUM_SEGS_PERDAY - 1 else seg_pos + 1
return can_sustain
def step_3(self):
"""In this step the self.the_flow is set
"""
# Step 3: particle filter
print "Step 3: particle filter"
# Get the mean of flows in the previous step
# mean_flows = [0] * NUM_SEGS_PERDAY
# for i in range(len(self.flows_the_routing_algs)):
# mean_flows[i % NUM_SEGS_PERDAY] += self.flows_the_routing_algs[i]
# mean_flows = [ f / float(NUM_SEGMENTS / NUM_SEGS_PERDAY) for f in mean_flows ]
self.the_flow = [ 0, 0, 0 ]
mean_flows = [800, 200, 200]
sigmas = [ f * 0.2 for f in mean_flows ]
# Initialize
flows = [ mean_flows ] * NUM_PARTICLES
weights = [1.0 / NUM_PARTICLES] * NUM_PARTICLES
# Resample
flows = self.resample(flows, weights, sigmas)
seg_idx = 0
seg_pos = 0
els = [ self.energy_init for i in range(NUM_PARTICLES) ]
is_dead = [ False for i in range(NUM_PARTICLES) ]
tmp_cnt = 0
while (1):
print "*",
eh = self.energy_harvest[seg_idx]
for j in range(NUM_PARTICLES):
flow = flows[j][seg_pos]
ra = self.the_routing_algs[seg_pos]
consume_energy = sc.consume_energy_calculation(flow, ra)
els[j] = np.array(els[j]) + np.array(eh) - np.array(consume_energy)
day_begin = seg_idx / NUM_SEGS_PERDAY
day_end = seg_idx / NUM_SEGS_PERDAY + NUM_SEGS_PERDAY
if self.in_two_sigma(self.energy_harvest[day_begin:day_end]):
is_dead[j] = True if any(v < 0 for v in els[j]) else is_dead[j]
else:
tmp_cnt += 1
els[j] = [ v if v > 0 else 0 for v in els[j] ]
els[j] = self.battery_limit(els[j])
seg_pos = 0 if seg_pos == NUM_SEGS_PERDAY - 1 else seg_pos + 1
seg_idx = 0 if seg_idx == NUM_SEGMENTS - 1 else seg_idx + 1
if seg_idx == 0:
print ''
print
print 'Count: ' + str(tmp_cnt)
tmp_cnt = 0
print np.mean(flows, axis = 0),
print ' ',
print np.std(flows, axis = 0)
#print el_mean
best_sum = 0
best_flow = []
best_flow_idx = -1
d_cnt = 0
for f, d, k in zip(flows, is_dead, range(len(flows))):
if sum(f) > best_sum and not d:
best_sum = sum(f)
best_flow = f
best_flow_idx = k
elif d:
d_cnt += 1
print "dead: " + str(d_cnt)
print best_flow_idx,
print best_sum,
print best_flow
if sum(best_flow) > 0 and np.abs(sum(self.the_flow) - sum(best_flow)) < 1:
break
else:
self.the_flow = best_flow
els = [ self.energy_init for i in range(NUM_PARTICLES) ]
weights = self.assign_weight(is_dead, flows)
is_dead = [ False for i in range(NUM_PARTICLES) ]
flows = self.resample(flows, weights, sigmas)
