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)