def plotScalingFactor():
r=2*1e-8
l = 5e4
dpi = 300
j = 0
for nu0 in [0.005, 0.1]:
for s in [0.025, 0.1]:
t = np.arange(0, 2 * (utl.logit(0.995) - utl.logit(nu0)) / s + 1., 1)
fig, ax = plt.subplots(2, 1, figsize=(5.5, 2.5), dpi=dpi, sharex=True);
nu(t, s=s, nu0=nu0).plot(color='k', legend=False, ax=ax[0])
pplt.annotate(r'$s$={}, $\nu_0=${} ({} Sweep)'.format(s, nu0, ('Soft', 'Hard')[nu0 == 0.005]), fontsize=7,
ax=ax[0])
pplt.setSize(ax=ax[0], fontsize=6)
ax[0].set_ylabel(r'$\nu_t$')
#
H0 = H(t[0], s=s, nu0=nu0)
Ht = H(t, s=s, nu0=nu0)
df = pd.DataFrame([np.log(Ht / H0), -2 * r * t * l], columns=t, index=['log(Growth)', r'log(Decay)']).T
df['log(Growth) + log(Decay)'] = df.sum(1)
df.plot(ax=ax[1], grid=True, linewidth=2);
ax[1].set_xlabel('Generations');
ax[1].set_ylabel('Log(Scaling Factor)')
ax[1].axvline(df.iloc[1:, 2].abs().idxmin(), color='k', linestyle='--', linewidth=0.5)
# if j != 3:
# ax[1].legend_.remove()
# else:
ax[1].legend(['log(Growth)', r'log(Decay)', 'log(Growth) + log(Decay)'], bbox_to_anchor=(1.45, .75),
prop={'size': 6})
pplt.setSize(ax[1], fontsize=6)
plt.tight_layout(pad=0.1, rect=[0, 0, 0.7, 1])
plt.gcf().subplots_adjust(bottom=0.15)
pplt.savefig('decayFactors{}'.format(j), dpi=dpi)
j += 1
def nu(t, s=0.025, nu0=0.005): return pd.Series(utl.sig(0.5 * s * t + utl.logit(nu0)), index=t) def plotLDDecayTheoretical(sim):
def H(t, s=0.025, nu0=0.005): return 2 * utl.sig(0.5 * s * t + utl.logit(nu0)) * ( 1 - utl.sig(0.5 * s * t + utl.logit(nu0))) def nu(t, s=0.025, nu0=0.005): return pd.Series(utl.sig(0.5 * s * t + utl.logit(nu0)), index=t)
def estimateS(y):
eps=1e-3
y[0]=y[0].apply(lambda x: min(1-eps,max(x,eps)))
y[59]=y[59].apply(lambda x: min(1-eps,max(x,eps)))
s=(2./59 * (utl.logit(y[59])-utl.logit(y[0]))).rename('s')
return s
