def gettmat():
# Partial derivatives with respect to av and ebv
av = 0.1
ebv = 0.1 / 2.5
A1 = f99_band.A_X(r_v=av / ebv, ebv=ebv)
A2 = f99_band.A_X(r_v=(av + 0.01) / ebv, ebv=ebv)
dAdAv = (A2 - A1) / 0.01
A3 = f99_band.A_X(r_v=av / (ebv + 0.001), ebv=ebv + 0.001)
dAdebv = (A3 - A1) / 0.001
# print 'f99 derivatives'
# print '{0[0]:6.2f}, {0[1]:6.2f}, {0[2]:6.2f}, {0[3]:6.2f}, {0[4]:6.2f}'.format(dAdAv)
# print '{0[0]:6.2f}, {0[1]:6.2f}, {0[2]:6.2f}, {0[3]:6.2f}, {0[4]:6.2f}'.format(dAdebv)
# The equation of interest is
# gammma0 = ans00 F0 + ans01 F1 + res
# gammma1 = ans10 F0 + ans11 F1 + res
# where F are the Fitzpatrick vectors (partial derivatives above) and
# the residues are perpendicular to a and b
# Note that the gammas are really gamma_X/(gamma_B-gamma_V)
norm_dAdebv = numpy.dot(dAdebv, dAdebv)
norm_dAdAv = numpy.dot(dAdAv, dAdAv)
cross = numpy.dot(dAdebv, dAdAv)
a = numpy.array([[norm_dAdAv, cross], [cross, norm_dAdebv]])
tmat = []
res = []
# c_n = []
cs = []
for s in ['gamma', 'rho1']:
c, cmin, cmax = numpy.percentile(
fit[s] / ((fit[s][:, 1] - fit[s][:, 2])[:, None]),
(50, 50 - 34, 50 + 34),
axis=0)
# print s,
# print "{:6.2f}, {:6.2f}, {:6.2f}, {:6.2f}, {:6.2f}".format(c[0],c[1],c[2],c[3],c[4])
cs.append(c)
# c_norm = numpy.linalg.norm(c)
# c_n.append(c_norm)
y = numpy.array([numpy.dot(c, dAdAv), numpy.dot(c, dAdebv)])
ans = numpy.linalg.solve(a, y)
tmat.append(ans)
ans = c - ans[1] * dAdebv - ans[0] * dAdAv
res.append(ans)
tmat = numpy.array(tmat)
gvec = numpy.array([[4.8, 3.89, 2.89, 2.22, 1.59],
[-3.83, -4.42, -5.42, -5.15, -4.71]])
avec = numpy.array([[0.96, 1, 1, 0.97, 0.77],
[1.77, 0.98, 0.12, -0.50, -0.53]])
return tmat
FancyArrowPatch.draw(self, renderer)
mpl.rcParams['font.size'] = 14
# Get the data
f = open('temp11.pkl', 'rb')
(fit, _) = pickle.load(f)
f.close()
# Determine the plane approximaion for Fitzpatrick
# Partial derivatives with respect to av and ebv
av = 0.1
ebv = 0.1 / 2.5
A1 = f99_band.A_X(r_v=av / ebv, ebv=ebv)
# pkl_file = open('fitz.pkl', 'r')
# a=pickle.load(pkl_file)
# pkl_file.close()
# AX = a[0]* av + a[1] * av**2 \
# + a[2]* ebv+ a[3] * ebv**2 \
# + a[4] * av* ebv \
# + a[5]* av**3 \
# + a[6] * ebv**3 \
# + a[7] * (av**2) * ebv \
# + a[8] * av * (ebv**2)
# plt.plot(A1-AX)
import sncosmo
import scipy
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
import f99_band
import emcee
import matplotlib as mpl
mpl.rcParams['font.size'] = 14
# Determine the plane approximaion for Fitzpatrick
# Partial derivatives with respect to av and ebv
av = 0.1
ebv = 0.1 / 2.5
A1 = f99_band.A_X(r_v=av / ebv, ebv=ebv)
# pkl_file = open('fitz.pkl', 'r')
# a=pickle.load(pkl_file)
# pkl_file.close()
# AX = a[0]* av + a[1] * av**2 \
# + a[2]* ebv+ a[3] * ebv**2 \
# + a[4] * av* ebv \
# + a[5]* av**3 \
# + a[6] * ebv**3 \
# + a[7] * (av**2) * ebv \
# + a[8] * av * (ebv**2)
# plt.plot(A1-AX)
correction = [fit['Delta']+ fit['c'][:,i][:,None] + fit['alpha'][:,i][:,None]*fit['EW'][:,:, 0] \
+ fit['beta'][:,i][:,None]*fit['EW'][:,:, 1] + fit['eta'][:,i][:,None]*fit['sivel']
for i in xrange(5)]
correction = numpy.array(correction)
# correction = correction - correction[2,:,:]
# correction_median = numpy.median(correction,axis=1)
cind = [0, 1, 2, 3, 4]
cname = ['U', 'B', 'V', 'R', 'I']
mpl.rcParams['font.size'] = 14
import f99_band
A_X = f99_band.A_X(r_v=2.44, ebv=0.2 / 2.44)
A_X26 = A_X / (A_X[1] - A_X[2])
A_X = f99_band.A_X(r_v=2.1, ebv=0.2 / 2.1)
A_X21 = A_X / (A_X[1] - A_X[2])
fig, axes = plt.subplots(nrows=5)
for i in xrange(5):
(y, ymin, ymax) = numpy.percentile(correction[cind[i], :, :],
(50, 50 - 34, 50 + 34),
axis=0)
err = numpy.sqrt(color_cov[:, 1, 1] + ((ymax - ymin) / 2)**2)
#axes[i,0].errorbar(y,y-color_obs[:,i],xerr=[((ymax-ymin)/2),((ymax-ymin)/2)], yerr=[err,err],fmt='.',alpha=0.4)
#axes[i,0].errorbar(color_obs[:,i],y-color_obs[:,i],xerr=[numpy.sqrt(color_cov[:,i,i]),numpy.sqrt(color_cov[:,i,i])], yerr=[err,err],fmt='.',alpha=0.4)
# axes[i,0].errorbar(color_obs[:,i],y,xerr=[numpy.sqrt(color_cov[:,i,i]),numpy.sqrt(color_cov[:,i,i])], yerr=[((ymax-ymin)/2),((ymax-ymin)/2)],fmt='.',alpha=0.4)
axes[i].errorbar(
mag_obs[:, 1] - mag_obs[:, 2],
# gvec = numpy.array([[4.8,3.89,2.89,2.22,1.59],[-3.83,-4.42,-5.42,-5.15,-4.71]])
# avec = numpy.array([[0.96,1,1,0.97,0.77],[1.77,0.98,0.12,-0.50,-0.53]])
# # print avec[0]
# # print avec[1]
# # print tmat[0,0],tmat[0,1]
# print tmat[0,0]*avec[0]+tmat[0,1]*avec[1]
# print tmat[1,0]*avec[0]+tmat[1,1]*avec[1]
# test= gvec[0]*Egamma[0]+ gvec[1]*Egamma[1]
# print test
# test= avec[0]*myavebv[0]+ avec[1]*myavebv[1]
# print test
# wwefwe
draw = numpy.random.multivariate_normal(myavebv, newmat, 100)
fidA1 = f99_band.A_X(r_v=myavebv[0] / myavebv[1], ebv=myavebv[1])
# fidA1 = numpy.delete(fidA1-fidA1[2],2)
temp = []
for a in draw:
A1 = f99_band.A_X(r_v=a[0] / a[1], ebv=a[1])
A1 = numpy.delete(A1 - A1[2], 2)
# A1 = fidA1 + (A1-fidA1)*2
temp.append(A1)
temp = numpy.array(temp)
tempperc = numpy.percentile(temp, (50, 50 - 34, 50 + 34), axis=0)
# wefwe
import sncosmo
# def lnprob(p, x, y ,yerr):
# # p is ebv and r_v
# f99 = sncosmo.F99Dust(r_v =p[1])