def kscomp(mu_sigma,trc_func,t_series,p_series,num_bins,*faux_args):
'''Compare a trace's histogramed fds to a normal distribution
mu_sigma = a tuple of the parameters mu and sigma
trc_func = Use etrace or atrace to act on p_series and t_series
t_series = The time series corrisponding to p_series
p_series = The data set which will be traced
num_bins = The number of bins used to histogram the fds
faux_args = Auxilary arguments for trc_func
Returns
-------
ks_2samp: KS static and a p-value in a tuple
mkr_trc: An array of bin positions for cnt_trc and cnt_norm
cnt_trc: An array of bin values for trace's histogramed fds
cnt_norm: An array of bin values for a scaled normal distribution
'''
mu,sigma = mu_sigma
trc = trc_func('price',t_series,p_series,mu,sigma,*faux_args)
fds_trc = np.array(ufuncs.get_fds(trc))
cnt_trc,mkr_trc = np.histogram(fds_trc,num_bins)
mkr_trc=mkr_trc[1:]
std = np.sqrt(np.mean((t_series-t_series.shift(1))[1:].values))
cnt_norm = normpdf(mkr_trc,0,std)
cnt_norm = cnt_norm*max(cnt_trc)/max(cnt_norm)
return ks_2samp(cnt_norm, cnt_trc),mkr_trc,cnt_trc,cnt_norm
def makeplots(a,b):
dat = func('price',t_series,p_series,a,b,*func_auxargs)
fig, ax = plt.subplots(figsize=(10,2))
ax.set_title('Data After Trace')
ax.set_ylabel('wiener',fontsize=13)
ax.set_xlabel('time',fontsize=13)
ax.set_xlim(min(t_series),max(t_series))
ax.plot(t_series,dat)
dat_fds = np.array(ufuncs.get_fds(dat))
dat_cnt,dat_mkr = np.histogram(dat_fds,bar_num)
norm_hist = np.histogram(dat_fds,bar_num)[0]
cnt_norm = normpdf(dat_mkr,0,np.std(dat_fds))
cnt_norm = cnt_norm*max(dat_cnt)/max(cnt_norm)
ksstat,pval = ks_2samp(cnt_norm, dat_cnt)
print 'KS test using scaled fds of traced dat and a fit gaussian:'
print 'Statistic value =',ksstat
print 'Two sided p-value =',pval
fig,ax1 = plt.subplots(figsize=(10,4))
ax1.set_title('Finite Difference Series Comparison')
ax1.set_ylabel('count',fontsize=13)
ax1.set_xlabel('difference',fontsize=13)
wdth = float(max(dat_mkr)-min(dat_mkr))/bar_num
plt.bar(dat_mkr[:-1],dat_cnt,label='traced fds (scaled by dt)',width = wdth,align='center')
plt.plot(dat_mkr,cnt_norm,label='fit gaussian',color='m')
ax1.set_xlim(min(dat_mkr)-wdth,max(dat_mkr)+wdth)
plt.legend(loc='best')
plt.show()
