def fill_nice(r0, M95lo, M68lo, Mmedi, M68hi, M95hi):
gpl.fill_between(r0, M95lo, M95hi, color='black', alpha=0.2, lw=0.1)
gpl.plot(r0, M95lo, color='black', lw=0.4)
gpl.plot(r0, M95hi, color='black', lw=0.3)
gpl.fill_between(r0, M68lo, M68hi, color='black', alpha=0.4, lw=0.1)
gpl.plot(r0, M68lo, color='black', lw=0.4)
gpl.plot(r0, M68hi, color='black', lw=0.3)
gpl.plot(r0, Mmedi, 'r', lw=1)
gpl.axvline(gp.rstarhalf, color='gray', lw=0.5) # [pc]
gpl.xlim([r0[0], r0[-1]])
return
def plot_data(r0, prof, pop):
if prof=='nu':
# get 2D data here
# Rbin [Rscale], Binmin [Rscale], Binmax [Rscale], Nu(R)/Nu(0) [1], error [1]
DATA_2D = np.transpose(np.loadtxt(gp.files.nufiles[pop], unpack=False, skiprows=1))
Nudat = DATA_2D[4-1]; Nuerr = DATA_2D[5-1]
Nudat *= gp.Nu0pc[pop]; Nuerr *= gp.Nu0pc[pop]
dat = Nudat; err = Nuerr
elif prof=='sig':
DATA = np.transpose(np.loadtxt(gp.files.sigfiles[pop], unpack=False, skiprows=1))
sigdat = DATA[4-1]; sigerr = DATA[5-1]
sigdat *= gp.maxvlos[pop]; sigerr *= gp.maxvlos[pop]
dat = sigdat; err = sigerr
gpl.fill_between(r0, dat-err, dat+err, color='blue', alpha=0.2, lw=1)
return
from scipy.interpolate import splrep, splev
import gl_plot as gpl
xnew = np.array([ 0. , 40.156, 58.552, 74.426, 91.87 , 110.166,
130.44 , 155.967, 192.224, 248.297, 255.215, 262.119,
269.01 , 275.887])
ynew = np.array([ 2426.404, 2294.581, 1828.642, 1144.447, 918.291, 681.606,
321.61 , 200.627, 116.21 , 19.241, 17.438, 14.488,
12.037, 10. ])
errorinpercent = npr.uniform(0.05, 0.15, len(xnew))
gpl.plot(xnew,ynew,'.',lw=2)
gpl.fill_between(xnew,ynew*(1.-errorinpercent),ynew*(1.+errorinpercent),alpha=0.5,color='blue')
# gives warning
pdb.set_trace()
tcknu = splrep(xnew, ynew, k=2, s=0.1) # interpolation in real space
gpl.plot(xnew, splev(xnew,splrep(xnew,ynew, k=2, s=0.1)))
# try with third order
tcknu = splrep(xnew, ynew, k=3, s=0.1) # interpolation in real space
# better, uses weights
w = 1/(errorinpercent*ynew)
tcknu = splrep(xnew, ynew, w=w, k=2, s=0.1) # interpolation in real space
