def usdn_energy_v_hops(df_dict):
"""Plot usdn energy vs hops."""
try:
if 'app' in df_dict and 'pow' in df_dict:
pow_df = df_dict['pow']
app_df = df_dict['app']
else:
raise Exception('ERROR: Correct df(s) not in dict!')
except Exception:
traceback.print_exc()
sys.exit(0)
# Add hops to node_df. N.B. cols with NaN are always converted to float
hops = app_df[['src', 'hops']].groupby('src').agg(lambda x: mode(x)[0])
pow_df = pow_df.join(hops['hops'].astype(int))
df = pow_df.groupby('hops')['all_rdc'] \
.apply(lambda x: x.mean()) \
.reset_index() \
.set_index('hops')
x = df.index.tolist()
y = df['all_rdc'].tolist()
cpplot.plot_bar(df,
'usdn_energy_v_hops',
sim_dir,
x,
y,
xlabel='Hops',
ylabel='Radio duty cycle (%)')
def usdn_pdr_v_hops(df_dict):
"""Plot usdn energy vs hops."""
try:
if 'app' in df_dict:
app_df = df_dict['app']
else:
raise Exception('ERROR: Correct df(s) not in dict!')
except Exception:
traceback.print_exc()
sys.exit(0)
# Get hops for each node. N.B. cols with NaN are always converted to float
df = app_df[['src', 'hops']].groupby('src').agg(lambda x: mode(x)[0])
# Calculate PRR
df['pdr'] = app_df.groupby('src')['drpd'] \
.apply(lambda x: ratio(len(x), x.sum()))
df = df.groupby('hops')['pdr'] \
.apply(lambda x: x.mean()) \
.reset_index() \
.set_index('hops')
x = df.index.tolist()
y = df['pdr'].tolist()
cpplot.plot_bar(df,
'usdn_pdr_v_hops',
sim_dir,
x,
y,
xlabel='Hops',
ylabel='PDR (%)')
def atomic_op_times(df_dict):
"""Plot atomic op times."""
df = df_dict['atomic-op'].copy()
g = df.groupby('op_type')
data = pd.DataFrame()
for k, v in g:
data[k] = pd.Series(v['op_duration'].mean())
# # rearrage cols
# data = data[['NONE', 'CLCT', 'CONF', 'RACT', 'ASSC']]
# # rename cols
# data = data.rename(columns={'NONE': 'IND',
# 'CLCT': 'COLLECT',
# 'CONF': 'CONFIGURE',
# 'RACT': 'REACT',
# 'ASSC': 'ASSOCIATE'})
x = list(data.columns.values)
y = data.values.tolist()[0]
cpplot.plot_bar(df,
'atomic_op',
sim_dir,
x,
y,
xlabel='Op Type',
ylabel='Time(ms)')
def energy_v_hops(df_dict, **kwargs):
"""Plot energy vs hops."""
df_name = kwargs['df'] if 'df' in kwargs else None
filename = kwargs['file'] if 'file' in kwargs else 'energy_v_hops'
print('> Do energy_v_hops for ' + df_name)
df = df_dict[df_name].copy()
df.set_index('node', inplace=True)
if 'all' in df_dict:
df_all = df_dict['all'][df_dict['all']['node'] != 1]
df['hops'] = df_all.groupby('node').apply(lambda x: x.hops.value_counts().index[0])
elif 'node' in df_dict:
df['hops'] = df_dict['node']['hops']
# HACK: Why isnt the filter filtering for hops between 1 and 5?
df = df[(df.hops <= 5) & (df.hops >= 1)]
g = df.groupby('hops')
data = {}
for k, v in g:
data[k] = v.groupby('node').last()['all_rdc'].mean()
x = data.keys()
y = data.values()
if y is not list:
y = list(y)
cpplot.plot_bar(df, filename, sim_dir, x, y,
xlabel='Hops', ylabel='Radio Duty Cycle (%)')
print(' ... RDC mean: ' + str(np.mean(y)))
def atomic_op_times(df_dict, **kwargs):
"""Plot atomic op times."""
df_name = kwargs['df'] if 'df' in kwargs else None
packets = kwargs['packets'] if 'packets' in kwargs else None
filename = kwargs['file'] if 'file' in kwargs else 'atomic_op_times'
df = df_dict[df_name].copy()
# print(df[df['type'] == 'CLCT'])
# Filter df for packet types in packets
if 'type' in df and packets is not None:
df = df[df['type'].isin(packets)]
g = df.groupby('type')
data = pd.DataFrame()
for k, v in g:
data[k] = pd.Series(v['op_duration'].mean())
# # rename cols
data = data.rename(columns={'CONF': 'CONFIGURE',
'CLCT': 'COLLECT',
'RACT': 'REACT'})
data = data[['CONFIGURE', 'COLLECT', 'REACT']]
x = list(data.columns.values)
y = data.values.tolist()[0]
print(' ... Op time mean: ' + str(np.mean(y)))
print(' ... Op time median: ' + str(np.median(y)))
print(' ... Op time max: ' + str(np.max(y)))
cpplot.plot_bar(df, filename, sim_dir, x, y,
xlabel='Op Type', ylabel='Time(ms)')
def graph_traffic_ratio(df_dict, **kwargs):
"""Graph ratio of traffic for packets."""
try:
if 'sdn' in df_dict and 'icmp' in df_dict:
sdn_df = df_dict['sdn']
icmp_df = df_dict['icmp']
else:
raise Exception('ERROR: Correct df(s) not in dict!')
except Exception:
traceback.print_exc()
sys.exit(0)
cpplot.plot_bar(sdn_df, icmp_df, 'traffic_ratio', sim_dir)
def usdn_traffic_ratio(df_dict):
"""Plot traffic ratios."""
try:
if 'app' in df_dict and 'icmp' in df_dict:
app_df = df_dict['app']
icmp_df = df_dict['icmp']
if 'sdn' in df_dict:
sdn_df = df_dict['sdn']
else:
sdn_df = None
else:
raise Exception('ERROR: Correct df(s) not in dict!')
except Exception:
traceback.print_exc()
sys.exit(0)
cpplot.plot_bar(app_df, sdn_df, icmp_df, 'traffic_ratio', sim_dir)
def pdr_v_hops(df_dict, **kwargs):
"""Plot pdr vs hops."""
df_name = kwargs['df'] if 'df' in kwargs else None
packets = kwargs['packets'] if 'packets' in kwargs else None
filename = kwargs['file'] if 'file' in kwargs else 'pdr_v_hops'
print('> Do pdr_v_hops for ' + str(packets) + ' in ' + df_name)
if packets is None:
raise Exception('ERROR: No packets to search for!')
df = df_dict[df_name].copy()
print(' ... Total for df (' + df_name + ') = ' + str(df.shape[0]))
if 'type' in df:
df = df[df['type'].isin(packets)]
missed = df.received.str.count('missed').sum()
received = df.received.str.count('correct').sum()
if 'drpd' not in df:
df['drpd'] = np.where(df['received'] == 'missed', True, False)
total = df.shape[0]
print(' ... Total: ' + str(total))
print(' ... Received: ' + str(received))
print(' ... Missed: ' + str(missed))
print(' ... Total PDR: ' + str(ratio(total, missed)))
df_pdr = df.groupby('hops')['drpd'].apply(lambda x: ratio(len(x), x.sum()))
df_pdr = df_pdr.groupby('hops') \
.apply(lambda x: x.mean()) \
.reset_index() \
.set_index('hops')
x = df_pdr.index.tolist()
y = df_pdr['drpd'].tolist()
cpplot.plot_bar(df_pdr, filename, sim_dir, x, y,
xlabel='Hops', ylabel='End-to-end PDR (%)')
def graph_pdr(df_dict, **kwargs):
global hlp
"""Graph end-to-end PDR."""
df_name = kwargs['df'] if 'df' in kwargs else None
packets = kwargs['packets'] if 'packets' in kwargs else None
filename = kwargs['file'] if 'file' in kwargs else 'pdr'
xlabel = kwargs['xlabel'] if 'xlabel' in kwargs else packets
df = df_dict[df_name].copy()
if packets is not None:
print('> Do packet_pdr for ' + str(packets) + ' in ' + df_name)
df = df[df['type'].isin(packets)]
else:
print('> Do packet_pdr for ALL_PACKETS in ' + df_name)
total = df.shape[0]
missed = df.received.str.count('missed').sum()
received = df.received.str.count('correct').sum()
print(' ... Total: ' + str(total))
print(' ... Received: ' + str(received))
print(' ... Missed: ' + str(missed))
print(' ... Total PDR: ' + str(ratio(total, missed)))
if packets is not None:
if 'drpd' not in df:
df['drpd'] = np.where(df['received'] == 'missed', True, False)
df_pdr = df.groupby('type')['drpd'].apply(lambda x: ratio(len(x), x.sum()))
df_pdr = df_pdr.groupby('type') \
.apply(lambda x: x.mean()) \
.reset_index() \
.set_index('type')
x = df_pdr.index.tolist()
y = df_pdr['drpd'].tolist()
else:
x = [str(xlabel)]
y = [ratio(total, missed)]
cpplot.plot_bar(df, filename, sim_dir, x, y,
xlabel='Packet Type', ylabel='End-to-end PDR (%)')
def atomic_energy_v_hops(df_dict):
"""Plot atomic energy vs hops."""
try:
if 'atomic-energy' in df_dict:
df = df_dict['atomic-energy']
else:
raise Exception('ERROR: Correct df(s) not in dict!')
except Exception:
traceback.print_exc()
sys.exit(0)
g = df.groupby('hops')
data = {}
for k, v in g:
# ignore the timesync (0 hops)
if (k > 0):
data[k] = v.groupby('id').last()['all_rdc'].mean()
cpplot.plot_bar(df,
'atomic_energy_v_hops',
sim_dir,
data.keys(),
data.values(),
xlabel='Hops',
ylabel='Radio Duty Cycle (%)')
def atomic_vs_usdn(df_dict):
"""Plot atomic vs usdn collect pdr and energy."""
# PDR
if 'usdn' in sim_type:
# copy dfs
df_node = df_dict['node'].copy().reset_index()
df_sdn = df_dict['sdn'].copy()
# take only NSU and drop any dropped packets
df = df_sdn[(df_sdn['typ'] == 'NSU')]
df = df.rename(columns={'lat': 'collect_time'})
df = df[df['src'] != 1]
df = df.merge(df_node, left_on='src', right_on='id') # merge hops col
df = df[(df['hops'] > 0) & (df['hops'] <= 5)]
elif 'atomic' in sim_type:
df = df_dict['atomic-op'].copy()
df = df[df['op_type'] == 'CLCT']
df['collect_time'] = df['c_time'].astype(int)
df = df[df['hops'] != 0]
df['drpd'] = np.where(df['collect_time'] == 0, True, False)
else:
raise Exception('ERROR: Unknown types!')
df_pdr = df.groupby('hops')['drpd'] \
.apply(lambda x: ratio(len(x), x.sum()))
df_pdr = df_pdr.groupby('hops') \
.apply(lambda x: x.mean()) \
.reset_index() \
.set_index('hops')
x = df_pdr.index.tolist()
y = df_pdr['drpd'].tolist()
cpplot.plot_bar(df_pdr,
'atomic_vs_usdn_collect_pdr',
sim_dir,
x,
y,
xlabel='Hops',
ylabel='End-to-end PDR (%)')
# Energy
if 'usdn' in sim_type:
df_node = df_dict['node'].copy().reset_index()
df = df_dict['pow'].copy().reset_index()
df = df.merge(df_node, left_on='id', right_on='id') # merge hops col
df = df[(df['hops'] > 0) & (df['hops'] <= 5)]
elif 'atomic' in sim_type:
df = df_dict['atomic-energy'].copy()
df = df[df['op_type'] == 'CLCT']
df = df[df['hops'] != 0]
else:
raise Exception('ERROR: Unknown types!')
g = df.groupby('hops')
data = {}
for k, v in g:
data[k] = v.groupby('id').last()['all_rdc'].mean()
x = data.keys()
y = data.values()
cpplot.plot_bar(df,
'atomic_vs_usdn_collect_energy',
sim_dir,
x,
y,
xlabel='Hops',
ylabel='Radio Duty Cycle (%)')