def plot_residuals(data):
n = len(data)
xs = np.arange(n)/float(n)
test_ks = np.exp(np.linspace(np.log(0.1),np.log(1000),1000))
G = fit_gaussian.fit_gaussian(data)
plt.figure()
resids = [fit_gaussian.rms_residual(k,data) for k in test_ks]
plt.loglog(test_ks,resids,color="green")
best_k = test_ks[np.argmin(resids)]
plt.axvline(best_k,color="green")
plt.axvline(G.k,color="cyan")
plt.figure()
mue, ae, be = fit_gaussian.fit_all_but_k(best_k, data)
plt.plot(xs, data, color="black", label="data")
plt.plot(xs, (ae*fit_gaussian.vonmises_histogram(best_k,mue,n)+be),
color="green", label="exhaustive best fit")
plt.plot(xs, G.histogram(n), color="cyan", label="best fit")
plt.plot([G.mu,G.mu],[G.b,G.b+G.amplitude(n)], color="red", label="height")
print "Amplitude over RMS:", G.amplitude(n)/np.std(data-G.histogram(n))
plt.legend(loc="best")
return data
plt.figure()
resids = [fit_gaussian.rms_residual(k,data) for k in test_ks]
plt.loglog(test_ks,resids,color="green")
best_k = test_ks[np.argmin(resids)]
plt.axvline(best_k,color="green")
plt.axvline(G.k,color="cyan")
plt.figure()
mue, ae, be = fit_gaussian.fit_all_but_k(best_k, data)
plt.plot(xs, data, color="black", label="data")
plt.plot(xs, (ae*fit_gaussian.vonmises_histogram(best_k,mue,n)+be),
color="green", label="exhaustive best fit")
plt.plot(xs, G.histogram(n), color="cyan", label="best fit")
plt.plot([G.mu,G.mu],[G.b,G.b+G.amplitude(n)], color="red", label="height")
print "Amplitude over RMS:", G.amplitude(n)/np.std(data-G.histogram(n))
plt.legend(loc="best")
return data
if __name__=='__main__':
import scipy.io
plot_residuals(scipy.io.read_array("noisy-1"))
plt.figure()
for factor in [1,2,4,8]:
plt.plot(fit_gaussian.vonmises_histogram(1000,0.5,64,factor=factor), label="factor %d" % factor)
plt.legend(loc="best")
plt.show()