xn = np.arange(-10,10,0.01)
twocol = Paired_12.mpl_colors
plt.figure(figsize=(7,5))
plt.hist(ps,lw=0,facecolor=twocol[0],normed=True,bins=np.arange(-2,10,0.3),label="observed distribution")
plt.xlim([-2,10])
plt.ylim([0,0.5])
plt.plot(xn,stats.norm.pdf(xn),color=twocol[1],lw=3,label="null distribution")
plt.show()
peaks = cluster.cluster(spm)
peaks['pval'] = peakdistribution.peakp(peaks.peak.tolist())
bum = BUM.bumOptim(peaks["pval"].tolist(),starts=10)
modelfit = neuropower.TFpeakfit(peaks['peak'].tolist(),bum['pi1'])
xn = np.arange(-10,10,0.01)
twocol = Paired_12.mpl_colors
plt.figure(figsize=(7,5))
plt.hist(peaks['peak'].tolist(),lw=0,facecolor=twocol[0],normed=True,bins=np.arange(-2,10,0.3),label="observed distribution")
plt.xlim([-2,10])
plt.ylim([0,0.5])
plt.plot(xn,[(1-bum["pi1"])*peakdistribution.peakdens3D(p,1) for p in xn],color=twocol[3],lw=3,label="null distribution")
plt.plot(xn,[bum["pi1"]*peakdistribution.peakdens3D(p-modelfit['delta'],1) for p in xn],color=twocol[5],lw=3,label="alternative distribution")
plt.plot(xn,neuropower.mixprobdens(modelfit["delta"],bum["pi1"],xn),color=twocol[1],lw=3,label="fitted distribution")
plt.title("histogram")
plt.xlabel("peak height")
plt.ylabel("density")
plt.legend(loc="upper right",frameon=False)
plt.show()
newsubs = range(10,71)
for s in newsubs:
projected_effect = effect_cohen*np.sqrt(s)
powerpred = {k:1-neuropower.altCDF(v,projected_effect,modelfit['sigma'],exc=exc,method="RFT") for k,v in thresholds.items() if v!='nan'}
power_predicted.append(powerpred)
power_predicted_df = pd.DataFrame(power_predicted)
# figure modelfit
twocol = Paired_12.mpl_colors
xn = np.arange(-10,10,0.01)
nul = [1-bum['pi1']]*neuropower.nulPDF(xn,exc=exc,method="RFT")
alt = bum['pi1']*neuropower.altPDF(xn,mu=modelfit['mu'],sigma=modelfit['sigma'],exc=exc,method="RFT")
mix = neuropower.mixprobdens(xn,pi1=bum['pi1'],mu=modelfit['mu'],sigma=modelfit['sigma'],exc=2,method="RFT")
xn_p = np.arange(0,1,0.01)
alt_p = [1-bum['pi1']]*scipy.stats.beta.pdf(xn_p, bum['a'], 1)+1-bum['pi1']
null_p = [1-bum['pi1']]*len(xn_p)
mpl.rcParams['font.size']='11.0'
fig,axs=plt.subplots(1,2,figsize=(14,5))
fig.subplots_adjust(hspace=.5,wspace=0.3)
axs=axs.ravel()
axs[0].hist(peaks.pval,lw=0,normed=True,facecolor=twocol[0],bins=np.arange(0,1.1,0.1),label="observed distribution")
axs[0].set_ylim([0,3])
axs[0].plot(xn_p,null_p,color=twocol[3],lw=2,label="null distribution")
axs[0].plot(xn_p,alt_p,color=twocol[5],lw=2,label="alternative distribution")
axs[0].legend(loc="upper right",frameon=False)
