def main(args):
data_file_list = tl.get_data_file_list(args.library)
if args.list:
tl.print_list(data_file_list)
sys.exit(0)
data = tl.load_data_file(data_file_list, args.select)
if not args.static:
logging.debug('Remove zero velocity samples')
data = ntp.remove_non_positive_velocity_samples(data)
# Get basic data
ntp.process_data(data, ntp.process_lte_bw)
column_list = ['DL bandwidth', 'DL bandwidth full']
if args.select is None:
df = tl.concat_pandas_data([df[column_list] for df in data])
else:
df = data
print(df['DL bandwidth'].describe())
print('Percentage of each bandwidth:')
print(df['DL bandwidth'].dropna().value_counts(normalize=True))
bandwidth_transitions_count = df['DL bandwidth'].dropna().diff(
).value_counts(normalize=True)
print(
'Percentage of bandwidth transitions: ', bandwidth_transitions_count[
bandwidth_transitions_count.index != 0].sum())
plt.ion()
plt.figure()
plt.subplot2grid((1, 5), (0, 0), colspan=4)
dpl.plot_ts(df['DL bandwidth'].dropna(), 'DL bandwidth', 'MHz', 10,
[9.5, 15.5])
plt.subplot2grid((1, 5), (0, 4), colspan=1)
bandwidth_counts = df['DL bandwidth'].dropna().value_counts(normalize=True)
per10 = bandwidth_counts[bandwidth_counts.index == 10] * 100
per15 = bandwidth_counts[bandwidth_counts.index == 15] * 100
# print(per10,per15)
plt.bar(1,
per15,
width=0.4,
bottom=per10,
color='Orange',
align='center',
label='15 MHz')
plt.bar(1, per10, width=0.4, color='m', align='center', label='10 MHz')
plt.grid(True)
plt.legend()
plt.xlim([0, 2])
plt.xticks([])
plt.tight_layout()
if args.print:
plt.savefig(args.print, dpi=300, bbox_inches='tight')
input('Press any key')
def main(args):
data_file_list = tl.get_data_file_list(args.library)
if args.list:
tl.print_list(data_file_list)
sys.exit(0)
data = tl.load_data_file(data_file_list,args.select)
if args.ue == 'e398':
# Rename MAC downlink throughput in Application downlink throughput if need be
ntp.process_data(data,ntp.process_lte_rename_mac_to_app)
# Get basic data
ntp.process_data(data,ntp.process_lte_app_bw_prb_util)
ntp.process_data(data,ntp.process_lte_app_bw_prb_util_bw20)
ntp.process_data(data,ntp.process_lte_app_bw_prb_util_bw10)
ntp.process_data(data,ntp.process_lte_app_bw_prb_util_bw15)
# Spectral efficiency
ntp.process_data(data,ntp.process_se_bw_norm)
ntp.process_data(data,ntp.process_lte_se_rb)
column_list = ['DL bandwidth','DL bandwidth full',
'Application throughput downlink',
'PRB utilization DL','PRB utilization DL full',
'SE norm','SE RB norm']
if args.select is None:
df = dl.concat_pandas_data([df[column_list] for df in data ])
else:
df = data
print(df['SE RB norm'].describe())
print(df['SE norm'].describe())
print(df['Application throughput downlink'].describe())
# Normalize
f_norm = lambda x: x/1e6
plt.ion()
plt.figure()
plt.subplot2grid((2,1), (0,0))
dpl.plot_ts(df['Application throughput downlink'].dropna(),
'MAC DL throughput' if args.ue == 'e398' else 'App. throughput',
'Mbit/s',marker_size=10,ylim=None)
plt.subplot2grid((2,1), (1,0))
x = np.arange(0,155,1)
dpl.plot_ecdf(df['Application throughput downlink'].dropna().apply(f_norm),x,
'App. throughput','Mbit/s')
plt.figure()
dpl.plot_ecdf_pair(df['SE norm'].dropna(),
df['SE RB norm'].dropna(),np.linspace(0,8.5,86),
'SE norm','SE RB norm',
'bit/s/Hz')
input('Press any key')
def main(args):
data_file_list = tl.get_data_file_list(args.library)
if args.list:
tl.print_list(data_file_list)
sys.exit(0)
data = tl.load_data_file(data_file_list,args.select)
if not args.static:
logging.debug('Remove zero velocity samples')
data = ntp.remove_non_positive_velocity_samples(data)
# Get basic data
ntp.process_data(data,ntp.process_lte_bw)
column_list = ['DL bandwidth','DL bandwidth full']
if args.select is None:
df = tl.concat_pandas_data([df[column_list] for df in data ])
else:
df = data
print(df['DL bandwidth'].describe())
print('Percentage of each bandwidth:')
print(df['DL bandwidth'].dropna().value_counts(normalize=True))
bandwidth_transitions_count = df['DL bandwidth'].dropna().diff().value_counts(normalize=True)
print('Percentage of bandwidth transitions: ',bandwidth_transitions_count[bandwidth_transitions_count.index != 0].sum())
plt.ion()
plt.figure()
plt.subplot2grid((1,5),(0,0),colspan=4)
dpl.plot_ts(df['DL bandwidth'].dropna(),
'DL bandwidth','MHz',
10,[9.5,15.5])
plt.subplot2grid((1,5),(0,4),colspan=1)
bandwidth_counts = df['DL bandwidth'].dropna().value_counts(normalize=True)
per10 = bandwidth_counts[bandwidth_counts.index == 10]*100
per15 = bandwidth_counts[bandwidth_counts.index == 15]*100
# print(per10,per15)
plt.bar(1,per15,width=0.4,bottom=per10,color='Orange',align='center',
label='15 MHz')
plt.bar(1,per10,width=0.4,color='m',align='center',
label='10 MHz')
plt.grid(True)
plt.legend()
plt.xlim([0,2])
plt.xticks([])
plt.tight_layout()
if args.print:
plt.savefig(args.print,dpi=300,bbox_inches='tight')
input('Press any key')
def main(args):
data_file_list = tl.get_data_file_list(args.library)
if args.list:
tl.print_list(data_file_list)
sys.exit(0)
data = tl.load_data_file(data_file_list, args.select)
if not args.static:
logging.debug('Remove non-positive velocity samples')
data = ntp.remove_non_positive_velocity_samples(data)
if args.select is None:
df = tl.concat_pandas_data([df['Velocity'] for df in data])
else:
df = data['Velocity']
print(df.describe())
plt.ion()
plt.figure()
dpl.plot_ts(df, 'Velocity', 'km/h', marker_size=2, ylim=None)
if args.print:
plt.savefig(args.print[0], dpi=300, bbox_inches='tight')
plt.figure()
x = np.arange(0, 210, 1)
plt.subplot2grid((2, 1), (0, 0))
dpl.plot_hist(df.dropna().astype(float),
'Velocity',
'km/h',
bins=20,
normed=False)
plt.xlim([np.min(x), np.max(x)])
plt.subplot2grid((2, 1), (1, 0))
dpl.plot_ecdf(df.dropna().astype(float), x, 'Velocity', 'km/h')
plt.xlim([np.min(x), np.max(x)])
if args.print:
plt.savefig(args.print[1], dpi=300, bbox_inches='tight')
plt.figure()
x = np.arange(-5, 220, 10)
dpl.plot_hist(df.dropna().astype(float),
'Velocity',
'km/h',
bins=x,
normed=False)
plt.xlim([np.min(x), np.max(x)])
if args.print:
plt.savefig(args.print[2], dpi=600, bbox_inches='tight')
input('Press any key')
def main(args):
data_file_list = tl.get_data_file_list(args.library)
if args.list:
tl.print_list(data_file_list)
sys.exit(0)
data = tl.load_data_file(data_file_list,args.select)
if not args.static:
logging.debug('Remove non-positive velocity samples')
data = ntp.remove_non_positive_velocity_samples(data)
if args.select is None:
df = tl.concat_pandas_data([df['Velocity'] for df in data ])
else:
df = data['Velocity']
print(df.describe())
plt.ion()
plt.figure()
dpl.plot_ts(df,'Velocity','km/h',marker_size=2,ylim=None)
if args.print:
plt.savefig(args.print[0],dpi=300,bbox_inches='tight')
plt.figure()
x = np.arange(0,210,1)
plt.subplot2grid((2,1),(0,0))
dpl.plot_hist(df.dropna().astype(float),'Velocity','km/h',bins=20,normed=False)
plt.xlim([np.min(x),np.max(x)])
plt.subplot2grid((2,1),(1,0))
dpl.plot_ecdf(df.dropna().astype(float),x,'Velocity','km/h')
plt.xlim([np.min(x),np.max(x)])
if args.print:
plt.savefig(args.print[1],dpi=300,bbox_inches='tight')
plt.figure()
x = np.arange(-5,220,10)
dpl.plot_hist(df.dropna().astype(float),'Velocity','km/h',bins=x,normed=False)
plt.xlim([np.min(x),np.max(x)])
if args.print:
plt.savefig(args.print[2],dpi=600,bbox_inches='tight')
input('Press any key')
