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)