def time_analysis(arrp, rtt):
# deadline-miss rate
miss_rate = sum(arrp['miss'])*1.0/len(arrp['miss'])
print len(arrp)
miss_rate = 0
# throughput
thru = 0
nitem = 0
thru_all = []
for item in arrp:
if item['mcs'] >= 0:
curr_thru = lte_utils.mcs_to_throughput(item['mcs'])*(1 if ((item['iter'] < 5) and (item['iter'] > -1) and (item['duration'] < (2000-rtt))) else 0)
thru += curr_thru
# miss_rate += (1 if ((item['duration'] > (2000-rtt) and item['iter'] < 5) or (item['iter'] == -1)) else 0)
miss_rate += (1 if ((item['duration'] > (2000-rtt) ) or (item['iter'] == -1)) else 0)
nitem += 1
# if miss_rate > 0:
# pdb.set_trace()
thru_all.append(curr_thru)
# thru = thru*1.0/len(arrp)
# miss_rate = miss_rate*1.0/len(arrp)
print nitem
thru = thru*1.0/nitem
miss_rate = miss_rate*1.0/nitem
# get cdf of throughput
obj = astat(thru_all)
thru_cdf = []
for x in np.arange(0, 35, 0.2):
thru_cdf.append(obj.get_cdf(x))
# pprint(result)
return [miss_rate, thru, thru_cdf]
# rtt_range = [700]
var_range = [1]
snr_range = [30]
# snr_range = [30]
run = 0
# proc time for mcs
t_t_range = []
for mcs in mcs_range:
t_t_range.append(lte_utils.mcs_to_time(mcs, 1))
# throughput for mcs
th_range = []
for mcs in mcs_range:
th_range.append(lte_utils.mcs_to_throughput(mcs))
for mcs in mcs_range:
for exp in exp_range:
for num_bss in num_bss_range:
for num_ants in num_ants_range:
for lmax in lmax_range:
for rtt in rtt_range:
for var in var_range:
for snr in snr_range:
nprocs = num_bss * int(math.ceil(1.0 * lmax / 1000))
num_cores_bs = int(math.ceil(1.0 * lmax / 1000))
# global is not dependent on input parameters
max_cores = int(math.ceil(1.0 * lmax / 1000)) * num_bss