def plot_analytic(r0, prof, pop=0):
if gp.investigate == 'gaia':
if prof=="rho":
analytic = ga.rhogaiatot_3D(r0)
elif prof=="nr":
analytic = ga.nrgaiatot_3D(r0)
elif prof=="beta":
analytic = ga.betagaia(r0)
elif prof=="betastar":
b = ga.betagaia(r0)
analytic = b/(2.-b)
elif prof=="nu":
analytic_3D = ga.nugaiatot_3D(r0)
analytic = rho_INT_Rho(r0, analytic_3D)
elif prof=="sig":
analytic = 0.*r0 # TODO
elif gp.investigate == 'walk':
if prof=='rho':
analytic = ga.rhowalk_3D(r0)[pop]
elif prof=='nr':
analytic = ga.rhowalk_3D(r0)[pop] # TODO
elif prof=='beta':
analytic = ga.walker_delta(pop, r0) # TODO
elif prof=='betastar': # TODO
b = ga.walker_delta(pop, r0)
analytic = b/(2.-b)
elif prof=='nu': # TODO: mass ratio M_tracer/M_DM
pdb.set_trace()
analytic_3D = ga.rhowalk_3D(r0)[pop]
analytic = rho_INT_Rho(r0, analytic_3D)
elif prof=='sig':
analytic = 0.*r0 # TODO
gpl.plot(r0, analytic, 'b')
return analytic
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
# (c) 2013 [email protected] import numpy as np import pdb import gl_params import gl_plot as gpl from gl_project import * from gl_analytic import * r0 = np.arange(0.1,3.0,0.1) rho0 = rho_anf(r0,1.,1.e6) Rho0 = Sigma_anf(r0,1.,1.e6) gpl.ion() gpl.start() # gpl.yscale('linear') gpl.plot(r0, Rho0, color='blue', lw=2) # gpl.plot(r0, rho_INTDIRECT_Rho(r0, rho0), color='blue') try: Rhonu = rho_INT_Rho(r0,rho0) except Exception as detail: print('NaN in rho_INT_Rho') gpl.plot(r0, Rhonu, '.', color='red') # pdb.set_trace() print('error fraction determined/true = ',Rhonu/Rho0) print('gp.ascale = ',gp.ascale) print('gp.Mscale = ',gp.Mscale) gpl.ioff(); gpl.show()
import pdb
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
import gl_params
import gl_plot as gpl
from gl_project import *
from gl_int import *
from gl_analytic import *
gpl.ion()
gpl.start()
r0 = np.arange(0.05, 3.0, 0.11) * 1000
ascale = 1000.0
Mscale = 1.0e6
nuanf = rho_anf(r0, ascale, Mscale)
rhoanf = rho_anf(r0, ascale, Mscale) * (1.0 + npr.uniform(-1.0, 1.0, len(r0)))
gpl.plot(r0, rhoanf, ".", color="cyan", label=r"$\rho$")
gpl.plot(r0, rho_anf(r0, ascale, Mscale), color="cyan", lw=1)
betaanf = np.zeros(len(r0))
intbetaanf = np.zeros(len(r0))
# gpl.yscale('linear')
# surface density
Siganf = Sigma_anf(r0, ascale, Mscale)
gpl.plot(r0, Siganf, color="blue", lw=1)
Rhoself = rho_INT_Rho(r0, rhoanf)
gpl.plot(r0, Rhoself, ".", color="blue", label=r"$\Sigma$")
# gpl.plot(r0, M_anf(r0,1.,1.e6), color='blue')
# sigma_r^2
# gpl.plot(r0, sig_r_2_anf(r0, ascale, Mscale), color='green', lw=1)