def overlaps_vs_weight(w, o, eo, factor=1.2, name= path + '/weight_overlap_comp.pdf'):
fig, ax = plots.log_bin_plot(w, o, factor, xlabel=None, ylabel=None, title=None, label=r'$O^c_{ij}$')
fig, ax = plots.log_bin_plot(w, eo, factor, xlabel=None, ylabel=None, title=None, label=r'$O^f_{ij}$', fig=fig, ax=ax)
ax.set_ylabel(r'$O_{ij}$')
ax.set_xlabel(r'$w_{ij}$')
ax.set_title('')
ax.legend(loc=0)
fig.tight_layout()
fig.savefig(name)
return fig, ax
def plot_iet_aget(df, factor=1.4):
idx = df.c_iet_age_na.notnull()
fig, ax = plots.log_bin_plot(df.c_iet_age_na[idx], df.ovrl[idx], factor)
ax.set_xlabel(r'Residual Waiting Time, $\tau_r$')
ax.set_ylabel(r'$\langle O | \tau_r \rangle$')
ax.set_title('Overlap as a function of residual waiting time')
fig.savefig(run_path + 'age_iets.png')
return fig, ax
def plot_iet_rfresh(df, factor=1.4):
idx = df.c_iet_rfsh_na.notnull()
fig, ax = plots.log_bin_plot(df.c_iet_rfsh_na[idx], df.ovrl[idx], factor)
ax.set_xlabel(r'Relative freshness, $f$')
ax.set_ylabel(r'$\langle O | f \rangle$')
ax.set_title('Overlap as a function of relative freshness')
fig.savefig(run_path + 'rfsh_iets.png')
return fig, ax
def plot_overlap_logbin(df, factor=1.3, limit=8000):
df = df[df.w < limit]
fig, ax = plots.log_bin_plot(df.w,
df.ovrl,
factor,
label='Within Company Overlap')
fig, ax = plots.log_bin_plot(df.w,
df.e_ovrl,
factor,
label='Full network',
fig=fig,
ax=ax)
ax.set_xlabel(r'$w$')
ax.set_ylabel(r'$\langle O | w \rangle$')
ax.set_title('Overlap as a function of number of calls')
ax.legend()
fig.savefig(run_path + 'overlap_logbin_calls.png')
return fig, ax
def plot_btrns(df, factor=1.4):
idx = df.c_brtrn.notnull()
fig, ax = plots.log_bin_plot(df.c_brtrn[idx],
df.ovrl[idx],
factor,
label='Bursty Trains')
fig, ax = plots.log_bin_plot(df.c_wkn_t[idx],
df.ovrl[idx],
factor,
label='Number of Calls',
fig=fig,
ax=ax)
ax.set_xlabel(r'Residual Waiting Time, $\tau_r$')
ax.set_ylabel(r'$\langle O | \tau_r \rangle$')
ax.set_title('Overlap as a function of residual waiting time')
ax.legend()
fig.savefig(run_path + 'brtrn.png')
return fig, ax
def plot_overlap_rank_logbin(df, factor=1.3, limit=8000, overlap_rank=True):
df = df[df.w < limit]
fig, ax = plots.log_bin_plot(df.w,
rankdata(df.ovrl) / df.shape[0],
factor,
label='Within Company Overlap')
fig, ax = plots.log_bin_plot(df.w,
rankdata(df.e_ovrl) / df.shape[0],
factor,
label='Full network',
fig=fig,
ax=ax)
ax.set_xlabel(r'$w$')
ax.set_ylabel(r'$\langle Rank(O) | w \rangle$')
ax.set_title('Rank of overlap as a function of number of calls')
ax.legend()
fig.savefig(run_path + 'overlap_rank_logbin_calls.png')
return fig, ax
def plot_iet_mean(df, factor=1.3):
idx = df.c_iet_mu_na.notnull()
fig, ax = plots.log_bin_plot(df.c_iet_mu_na[idx],
df.ovrl[idx],
factor,
label='Naive')
idx = df.c_iet_mu_km.notnull()
fig, ax = plots.log_bin_plot(df.c_iet_mu_km[idx],
df.ovrl[idx],
factor,
label='KM',
fig=fig,
ax=ax)
ax.legend()
ax.set_xlabel(r'Mean inter-event time, $\tau$')
ax.set_ylabel(r'$\langle O | \tau \rangle$')
ax.set_title('Overlap as a function of mean inter-event time')
fig.savefig(run_path + 'mean_iets.png')
return fig, ax
def plot_iet_sig(df, factor=1.2):
idx = df.c_iet_sig_na.notnull()
fig, ax = plots.log_bin_plot(df.c_iet_sig_na[idx],
df.ovrl[idx],
factor,
label='Naive')
idx = df.c_iet_sig_km.notnull()
fig, ax = plots.log_bin_plot(df.c_iet_sig_km[idx],
df.ovrl[idx],
factor,
label='KM',
fig=fig,
ax=ax)
ax.legend()
ax.set_xlabel(r'Inter-event time standard deviation, $\sigma_{\tau}$')
ax.set_ylabel(r'$\langle O | \sigma_{\tau}\rangle$')
ax.set_title(
'Overlap as a function of inter-event time standard deviation')
fig.savefig(run_path + 'std_iets.png')
return fig, ax