85 lines
2.3 KiB
Python
85 lines
2.3 KiB
Python
from tqdm import tqdm
|
|
import time
|
|
import json
|
|
import os
|
|
import statistics
|
|
|
|
def stats(values):
|
|
ret={
|
|
"max":max(values),
|
|
"min":min(values),
|
|
"mean":statistics.mean(values),
|
|
"range":abs(max(values)-min(values)),
|
|
"sum":sum(values)
|
|
}
|
|
if len(values)>1:
|
|
ret["stdev"]=statistics.stdev(values)
|
|
else:
|
|
ret["stdev"]=0
|
|
return ret
|
|
|
|
last_seen=float('inf')
|
|
pbar=None
|
|
def callback(state):
|
|
try:
|
|
global pbar,last_seen
|
|
if state['n_seen']<last_seen:
|
|
if pbar:
|
|
pbar.close()
|
|
pbar=tqdm(total=state['d_total'],unit="Ly",unit_scale=True)
|
|
last_seen=state['n_seen']
|
|
pbar.n=round(state['d_total']-state['d_rem'],2)
|
|
pbar.set_description("[J:{depth} | Q:{queue_size} | D:{d_rem:.2f} Ly | S:{n_seen} ({prc_seen:.2f}%)] {system}".format(**state))
|
|
pbar.update(0)
|
|
except Exception as e:
|
|
print(e)
|
|
|
|
if not 'callback' in vars():
|
|
callback=None
|
|
import _ed_lrr
|
|
callback=None
|
|
r=_ed_lrr.PyRouter(callback)
|
|
r.load("stars.csv")
|
|
res=[]
|
|
done=set()
|
|
if os.path.isfile("res.json"):
|
|
with open("res.json","r") as fh:
|
|
try:
|
|
res=json.load(fh)
|
|
except:
|
|
pass
|
|
for v in res:
|
|
done.add((v[0],v[1]))
|
|
systems=r.resolve_systems("Sol","Colonia")
|
|
min_dt_total=60*10
|
|
print("Warming up...")
|
|
r.route([systems['Sol'],systems['Sol']],48.0,0.0,0.0,0) # warmup
|
|
print("Done")
|
|
|
|
for g in [0.0, 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]:
|
|
for w in [0.0, 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]:
|
|
if (g,w) in done:
|
|
continue
|
|
dt=[]
|
|
lr=[]
|
|
l=0
|
|
while sum(dt)<min_dt_total:
|
|
l+=1
|
|
print({'g':g,'w':w,'l':l})
|
|
t=time.time()
|
|
route=r.route([systems['Sol'],systems['Colonia']],48.0,g,w,0)
|
|
dt.append(time.time()-t)
|
|
lr.append(len(route))
|
|
print(stats(dt))
|
|
dt=sum(dt)/len(dt)
|
|
res.append((g,w,lr,dt))
|
|
with open("res.json","w") as of:
|
|
json.dump(res,of)
|
|
|
|
# for n in range(21):
|
|
# grid=r.get_grid(1<<n)
|
|
# max_v=max(grid.items(),key=lambda v:v[1])
|
|
# mean_v=sum(grid.values())/len(grid)
|
|
# print(1<<n,len(grid),mean_v,max_v)
|
|
|