def latdur(self):
print 'latency vs duration'
filtered = util.lock_float_field(self.df, 'bandwidths', self.bws)
if filtered is None:
return self.latmeanbw()
g = sns.lmplot("latencies", "durations", data=filtered[['latencies', 'durations', 'bandwidths']].astype(float), col='bandwidths')
def latmean(self):
print 'loading latency vs mean...'
self.load_block_times()
filtered = util.lock_float_field(self.df, 'bandwidths', self.bws)
if filtered is None:
return self.latmeanbw()
g = sns.lmplot("latencies", "means", data=filtered[['latencies', 'means', 'bandwidths']], scatter=True, col='bandwidths')
g.set(ylim=(0, 200))
def latdur(self):
print('latency vs duration')
filtered = util.lock_float_field(self.df, 'bandwidths', self.bws)
if filtered is None:
return self.latmeanbw()
g = sns.lmplot("latencies",
"durations",
data=filtered[['latencies', 'durations',
'bandwidths']].astype(float),
col='bandwidths')
def latmean(self):
print('loading latency vs mean...')
self.load_block_times()
filtered = util.lock_float_field(self.df, 'bandwidths', self.bws)
if filtered is None:
return self.latmeanbw()
g = sns.lmplot("latencies",
"means",
data=filtered[['latencies', 'means', 'bandwidths']],
scatter=True,
col='bandwidths')
g.set(ylim=(0, 200))
def bwdur(self):
print 'bandwidth vs durations'
filtered = util.lock_float_field(self.df, 'latencies', self.lats)
if filtered is None:
return latmeanbw()
filter = filtered["bandwidths"] > 0
filtered = filtered[filter]
# use plain pyplot cause seaborn has semilog issues
plt.figure()
plt.scatter(filtered["bandwidths"].tolist(), filtered["means"].tolist())
plt.semilogx()
plt.title(self.wl)
plt.xlabel('bandwidth')
plt.ylabel('duration')
def bwdur(self):
print('bandwidth vs durations')
filtered = util.lock_float_field(self.df, 'latencies', self.lats)
if filtered is None:
return latmeanbw()
filter = filtered["bandwidths"] > 0
filtered = filtered[filter]
# use plain pyplot cause seaborn has semilog issues
plt.figure()
plt.scatter(filtered["bandwidths"].tolist(),
filtered["means"].tolist())
plt.semilogx()
plt.title(self.wl)
plt.xlabel('bandwidth')
plt.ylabel('duration')
def latmean_nodes(self):
print('loading latency vs mean all nodes displayed...')
self.load_block_times()
filtered = util.lock_float_field(self.df, 'bandwidths', self.bws)
if filtered is None:
return self.latmeanbw()
all_times_dict = {
'runids': [],
'latencies': [],
'bandwidths': [],
'times': []
}
for runid in filtered['runids']:
# get latency for runid
self.cur.execute(
'SELECT latency, bandwidth FROM runs where runid=?', (runid, ))
lat, bw = self.cur.fetchone()
# get block times from runid and populate bandwidths and latencies
for row in self.cur.execute(
'SELECT time FROM block_times where runid=?', (runid, )):
all_times_dict['runids'].append(runid)
all_times_dict['latencies'].append(lat)
all_times_dict['bandwidths'].append(bw)
all_times_dict['times'].append(row[0])
timesdf = pd.DataFrame.from_dict(all_times_dict)
g = sns.lmplot(
"latencies",
"times",
data=timesdf[['latencies', 'times']], # 'bandwidths']],
scatter=True,
scatter_kws={
'c': timesdf['runids'].tolist(),
'cmap': cm.Accent,
"alpha": .5
},
legend_out=True)
def latmean_nodes(self):
print 'loading latency vs mean all nodes displayed...'
self.load_block_times()
filtered = util.lock_float_field(self.df, 'bandwidths', self.bws)
if filtered is None:
return self.latmeanbw()
all_times_dict = {'runids': [], 'latencies': [], 'bandwidths': [], 'times': []}
for runid in filtered['runids']:
# get latency for runid
self.cur.execute('SELECT latency, bandwidth FROM runs where runid=?', (runid,))
lat, bw = self.cur.fetchone()
# get block times from runid and populate bandwidths and latencies
for row in self.cur.execute('SELECT time FROM block_times where runid=?', (runid,)):
all_times_dict['runids'].append(runid)
all_times_dict['latencies'].append(lat)
all_times_dict['bandwidths'].append(bw)
all_times_dict['times'].append(row[0])
timesdf = pd.DataFrame.from_dict(all_times_dict)
g = sns.lmplot("latencies", "times", data=timesdf[['latencies', 'times']],# 'bandwidths']],
scatter=True, scatter_kws={'c': timesdf['runids'].tolist(), 'cmap': cm.Accent, "alpha": .5}, legend_out=True)
