if i < len(x) / 2:
M2 = x[i + len(x) / 2]
result.append(p[0] + p[1] * M2 + p[2] * (a_r0[i] ** 2))
else:
result.append(p[i - len(x) / 2 + 3])
return np.asarray(result)
errfunc = lambda p, x, cov: np.dot(cov, (x - fitfunc(p, x)).T)
errfunc2 = lambda p, x, cov: np.dot(cov, (x - fitfunc2(p, x)).T)
nofit = False
if not nofit:
# udres, udchi2, udpvalue = a.xyerrfits(udx2bstraps,r02M2bstraps,errfunc,[1,1],enablecheckcov = 0)
udres, udchi2, dof, udpvalue, cov = a.xyerrfits_c(r02M2bstraps, udx2bstraps, errfunc, [1, 1], enablecheckcov=0)
# udsysres, udsyschi2, dof, udsyspvalue, cov = a.xyerrfits_c(udsys, udxnsys,errfunc,[1,1],cov,0)
matplotlib.rcParams.update({"font.size": 10})
pdfplot = PdfPages(os.path.join(destdir, "udb_var2.pdf"))
for i in range(0, N2):
plt.errorbar(
r02M2bstraps[0][i],
udx2bstraps[0][i],
xerr=np.std(r02M2bstraps, axis=0)[i],
yerr=np.std(udx2bstraps, axis=0)[i],
color=plotcolors[i],
label=plotlabels[i],
marker=plotmarkers[i],
def checkcov(cov1): cov = np.zeros(cov1.shape) for i in range(0,cov1.shape[0]): for j in range(0,cov1.shape[1]): if i == j or i == j+cov1.shape[0]/2 or i == j-cov1.shape[0]/2: cov[i,j] = cov1[i,j] return cov cut = 0.158655254 ############################################################################################# # Fits ############################################################################################# if fittingvar == 0: udres, udchi2, uddof, udpvalue, udcov = a.xyerrfits_c(r02M2bstraps,udNxbstraps,errfunc,[1,1]) print "ud: " print "A = "+str(udres[0][0])+"+-"+str(np.std(udres[:,0])) print "B = "+str(udres[0][1])+"+-"+str(np.std(udres[:,1])) print "pvalue: "+str(udpvalue[0]) print "red. chi2: "+str(udchi2[0]/uddof) ufres, ufchi2, ufdof, ufpvalue, ufcov = a.xyerrfits_c(r02M2bstraps,ufNxbstraps,errfunc,[1,1]) print "uf: " print "A = "+str(ufres[0][0])+"+-"+str(np.std(ufres[:,0])) print "B = "+str(ufres[0][1])+"+-"+str(np.std(ufres[:,1]))