def plot_size_cdf(df,
cdf_ax,
cumsum_ax,
cdf_kws=None,
cumsum_kws=None,
**kwargs):
sizes = df[df.Sign > 0].Size
if cdf_kws is None:
cdf_kws = {}
cdf_kws.update(kwargs)
pu.cdf(sizes, ax=cdf_ax, **cdf_kws)
if cumsum_kws is None:
cumsum_kws = {}
cumsum_kws.update(kwargs)
sizes.sort_values().reset_index(drop=True).cumsum().plot(ax=cumsum_ax,
**cumsum_kws)
pu.cleanup_axis_bytes(cdf_ax.xaxis, format='%(value).0f %(symbol)s')
cdf_ax.set_ylabel('CDF')
cdf_ax.set_xlabel('Memory Allocation Size')
pu.cleanup_axis_bytes(cumsum_ax.yaxis,
maxN=5,
format='%(value).0f %(symbol)s')
cumsum_ax.set_ylabel('Cumsum of Sizes')
cumsum_ax.set_xlabel('Memory Allocations')
def plot_jcts(df, fifo, **kwargs):
ax = pu.cdf(df.JCT.dt.total_seconds(), label='SRTF')
ax = pu.cdf(fifo.JCT.dt.total_seconds(), label='FIFO', ax=ax)
ax.set_xlabel('JCT (s)')
ax.set_ylabel('CDF')
ax.legend()
return ax
def plot_jcts(df, **kwargs):
ax = kwargs.pop('ax', None)
if ax is None:
ax = plt.gca()
for col in df.columns:
pu.cdf(df[col].dt.total_seconds(), label=col, ax=ax, **kwargs)
ax.set_xlabel('JCT (s)')
ax.set_ylabel('CDF')
ax.legend()
return ax
def plot_ratio(df, **kwargs):
df = df[df.Network.str.contains("eval")].set_index('Network')
ratio = df.TransferTime / df['20iter-avg']
ax = pu.cdf(ratio, **kwargs)
#ax.axvline(1)
ax.set_xlabel('Transfer Time to Inference Latency Ratio')
return ax
