for i in range(max_gen):
p=x[-1];q=1-p
x+=[(w11*p*p+w01*p*q)/(w11*p*p+2*w01*p*q+w00*q*q)]
return pd.Series(x)
df=[];dom=[]
H = [0, 0.5, 1, 2]
index = map(lambda h: '$h$={}'.format(h), H)
max_gen = 200
for h in H:
df+=[f(x0,s,h,max_gen)]
dom+=[(1+s,1+h*s, 1)]
df=pd.DataFrame(df,index=index).T
dom=pd.DataFrame(dom,index=index,columns=['AA','aA','aa']).T.iloc[::-1]
plt.figure(figsize=(4, 2.5), dpi=dpi)
# ax=plt.subplot(1,3,1);
df.plot(ax=plt.gca(), linewidth=1, legend=False, color=pplt.getColorMap(len(H)));
plt.ylim([0, 1.01])
plt.xlabel('Generations');
plt.ylabel('Carrier Frequency')
# pd.Series(utl.sig((np.arange(max_gen)+1)*s/2 + utl.logit(x0))).plot(style='--',linewidth=2,color='k')
plt.grid()
# ax=plt.subplot(1,3,2);
# df=2*df*(1-df)
# df.plot(ax=ax,linewidth=2,legend=False);plt.xlabel('Generations');plt.ylabel('Heterozygosity ($2pq$)')
# plt.grid();plt.ylim([0,0.55])
# ax=plt.subplot(1,3,3);
# dom.plot(ax=ax,grid=True,linewidth=2);plt.xlabel('Genotype');plt.ylabel('Relative Fitness')
plt.legend(loc='best');
plt.gca().locator_params(nbins=3);
plt.gcf().subplots_adjust(bottom=0.2)
df = pd.concat([a[col].rank(ascending=False).loc[v.FBID.values] for col in paddings], axis=1)
pvals = a[paddings].apply(lambda xy: getPvalAUC(xy, v))
# df = pd.DataFrame(a[col].rank(ascending=False).loc[v.FBID.values])
dff = pd.DataFrame(
[pd.Series(df[col].sort_values(ascending=False).values, index=range(1, df.shape[0] + 1), name=col) for col
in
df.columns]).T
dff.columns = map(lambda x: 'AUC={}, {}'.format(1 - np.round(x[3] / Genes.shape[0], 2), pvals[x[0]]),
zip(dff.columns, dff.median(), dff.min(), dff.mean()))
dff = N - dff
if df.shape[1] == 1:
dff.plot(ax=plt.gca(), color='r')
else:
dff.plot(ax=plt.gca(), colors=pplt.getColorMap(df.shape[1]), rotation=90)
# plt.title('padding={:.0f}K'.format(padding / 1000))
plt.plot([1, df.shape[0]], [1, N], ls="--", c=".3")
xticks = np.unique(np.append((np.round(plt.xticks()[0][:-1])).astype(int), df.shape[0]))
if v.shape[0] < 10:
plt.xticks(xticks, v.name, rotation=30)
else:
plt.xticks(xticks)
yticks = plt.yticks()[0]
# yticks=yticks.max()-yticks
plt.xlim([1, df.shape[0]])
# plt.yticks(yticks,N-yticks)
plt.ylim([0, 16995])
plt.yticks([0, 2000, 7000, 12000, 17000], [17000, 15000, 10000, 5000, 0])
