def RC_sigma_rnd2(vel,
evel,
nrings,
guess0,
vary,
sigma=[],
mode="rotation",
delta=1,
ring="pixel",
rstart=4,
iter=5,
ring_position=[],
pixel_scale=1):
vrot0, vr20, pa0, inc0, X0, Y0, vsys0 = guess0
guess = [vrot0, vr20, pa0, inc0, X0, Y0, vsys0]
guess_copy = np.copy(guess)
n_sigma = len(sigma)
if n_sigma != 0:
e_vrot, e_vr2, e_pa, e_inc, e_x0, e_y0, e_vsys = sigma
[ny, nx] = vel.shape
x = np.arange(0, nx, 1)
y = np.arange(0, ny, 1)
mesh = np.meshgrid(x, y, sparse=True)
from pixel_params import pixels
import fit_params
from fit_params import fit
from fit_params import fit_polynomial
from fit_params import fit_linear
vary = [True, True, True, True, False, False, True]
#ring_position = np.arange(rstart,nrings,2)
#ring_rposition = np.arange(rstart,nrings,4)
vr1, vr21, vsys1, pa1, inc1 = np.asarray([]), np.asarray([]), np.asarray(
[]), np.asarray([]), np.asarray([])
vr0, vr20, vsys0, pa0, inc0, r0 = [], [], [], [], [], []
r = np.asarray([])
for n_iter in range(iter):
#for i in ring_position:
#vr0,vr20,vsys0,pa0,inc0 = np.asarray([]),np.asarray([]),np.asarray([]),np.asarray([]),np.asarray([])
#for n_iter in range(iter):
for i in ring_position:
sigma = []
sol = np.asarray(guess)
sol[0] = sol[0] + 10 * random.uniform(-1, 1)
sol[1] = sol[1] + 10 * random.uniform(-1, 1)
sol[2] = sol[2] + e_pa * random.uniform(-1, 1)
sol[3] = sol[3] + e_inc * random.uniform(-1, 1)
sol[4] = sol[4] #+(2/pixel_scale)*random.uniform(-1,1)
sol[5] = sol[5] #+(2/pixel_scale)*random.uniform(-1,1)
sol[6] = sol[6] + e_vsys * random.uniform(-1, 1)
try:
#XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess,ringpos = i, delta=4,ring = "pixel",pixel_scale=pixel_scale)
XY_mesh, vel_val, e_vel, f_pixel = pixels(
vel,
evel,
guess,
ringpos=i,
delta=2,
ring="ARCSEC",
pixel_scale=pixel_scale)
if f_pixel > 0.10:
Vrot, Vr2, Vsys, pa, inc, xi = fit("vsys", vel_val, e_vel,
XY_mesh, sol, vary,
mode, sigma)
if Vrot > 10 and Vrot < vmaxrot:
vr0.append(Vrot)
vr20.append(Vr2)
vsys0.append(Vsys)
pa0.append(pa)
inc0.append(inc)
r0.append(i)
else:
vr0.append(np.nan)
vr20.append(np.nan)
vsys0.append(np.nan)
pa0.append(np.nan)
inc0.append(np.nan)
r0.append(i)
#except(TypeError,ZeroDivisionError,ValueError):
except (1):
pass
r_sort, pa_sort = zip(*sorted(zip(r0, pa0)))
r_sort, inc_sort = zip(*sorted(zip(r0, inc0)))
r_sort, vr_sort = zip(*sorted(zip(r0, vr0)))
r_sort, vsys_sort = zip(*sorted(zip(r0, vsys0)))
r_sort, vr20_sort = zip(*sorted(zip(r0, vr20)))
max_r = np.nanmax(r_sort)
max_r = int(max_r)
inc_sort = np.asarray(inc_sort)
pa_sort = np.asarray(pa_sort)
r_final = np.asarray(r_sort)
vr_sort = np.asarray(vr_sort)
vsys_sort = np.asarray(vsys_sort)
vr20_sort = np.asarray(vr20_sort)
r_sort = np.asarray(r_sort)
R = np.unique(r_sort)
pa_mean_ring = np.asarray([])
inc_mean_ring = np.asarray([])
vr_mean_ring = np.asarray([])
vsys_mean_ring = np.asarray([])
vr20_mean_ring = np.asarray([])
r_mean_ring = np.asarray([])
pa_e = np.asarray([])
inc_e = np.asarray([])
vr_e = np.asarray([])
vsys_e = np.asarray([])
vr20_e = np.asarray([])
#print(len(r_sort),len(ring_position))
#for kk in range(max_r+1):
for kk in R:
mask = r_sort == kk
pa_i = pa_sort[mask]
inc_i = inc_sort[mask]
vr_i = vr_sort[mask]
vsys_i = vsys_sort[mask]
vr20_i = vr20_sort[mask]
if len(pa_i) > 0:
vr_mean_ring = np.append(vr_mean_ring, np.nanmean(vr_i))
vsys_mean_ring = np.append(vsys_mean_ring, np.nanmean(vsys_i))
vr20_mean_ring = np.append(vr20_mean_ring, np.nanmean(vr20_i))
pa_mean_ring = np.append(pa_mean_ring, np.nanmean(pa_i))
inc_mean_ring = np.append(inc_mean_ring, np.nanmean(inc_i))
r_mean_ring = np.append(r_mean_ring, kk)
vr_e = np.append(vr_e, np.nanstd(vr_i))
vr20_e = np.append(vr20_e, np.nanstd(vr20_i))
pa_e = np.append(pa_e, np.nanstd(pa_i))
inc_e = np.append(inc_e, np.nanstd(inc_i))
#else:
# vr_mean_ring = np.append(vr_mean_ring,20)
# pa_mean_ring = np.append(pa_mean_ring,median_pa)
# inc_mean_ring = np.append(inc_mean_ring,median_inc)
# r_mean_ring = np.append(r_mean_ring,kk)
return vr_e, vr20_e, pa_e, inc_e, vsys_e
def circ_mod(vel, evel, guess0, vary, n_it, rstart, rfinal, ring_space,
frac_pixel, delta, pixel_scale, bar_min_max, errors, config,
e_ISM):
vrot0, vr20, pa0, inc0, x0, y0, vsys0, vtan, theta_b = guess0
vmode = "circular"
[ny, nx] = vel.shape
shape = [ny, nx]
"""
CIRCULAR MODEL
"""
chisq_global = 1e10
PA, INC, XC, YC, VSYS = 0, 0, 0, 0, 0
Vrot, Vrad, Vsys, Vtan = [], [], [], []
R = 0
best_xy_pix = []
rings = np.arange(rstart, rfinal, ring_space)
for it in np.arange(n_it):
guess = [vrot0, vr20, pa0, inc0, x0, y0, vsys0, 0, theta_b]
vrot_tab, vrad_tab, vtan_tab = np.asarray([]), np.asarray(
[]), np.asarray([])
index = 0
nrings = len(rings)
n_annulus = nrings - 1
R_pos = np.asarray([])
for ring in rings:
from pixel_params import pixels
fpix = pixels(shape,
vel,
pa0,
inc0,
x0,
y0,
ring,
delta=delta,
pixel_scale=pixel_scale)
if fpix > frac_pixel:
v_rot_k, v_rad_k, v_tan_k = M_tab(pa0,
inc0,
x0,
y0,
theta_b,
ring,
delta,
index,
shape,
vel - vsys0,
evel,
pixel_scale=pixel_scale,
vmode=vmode)
vrot_tab = np.append(vrot_tab, v_rot_k)
R_pos = np.append(R_pos, ring)
if np.nanmean(vrot_tab) < 0:
vrot_tab = abs(vrot_tab)
pa0 = pa0 - 180
if pa0 < 0: pa0 = pa0 + 360
guess = [
vrot_tab + 1, vrad_tab + 1, pa0, inc0, x0, y0, vsys0, vtan_tab,
theta_b
]
v_2D_mdl, kin_2D_modls, vrot, vsys0, pa0, inc0, x0, y0, xi_sq, n_data, Errors = fit(
shape,
vel,
evel,
guess,
vary,
vmode,
config,
R_pos,
fit_method="powell",
e_ISM=e_ISM,
pixel_scale=pixel_scale,
ring_space=ring_space)
if xi_sq < chisq_global:
PA, INC, XC, YC, VSYS, THETA = pa0, inc0, x0, y0, vsys0, theta_b
Vrot = vrot
chisq_global = xi_sq
best_vlos_2D_model = v_2D_mdl
best_kin_2D_models = kin_2D_modls
Rings = R_pos
std_errors = Errors
GUESS = [Vrot, 0, PA, INC, XC, YC, VSYS, 0, 0]
Vrot = np.array(Vrot)
if errors == 1:
from mcmc import fit_mcmc
res_mcmc = fit_mcmc(shape,
vel,
evel,
GUESS,
vary,
vmode,
"",
Rings,
fit_method="emcee",
e_ISM=e_ISM,
pixel_scale=pixel_scale,
ring_space=ring_space)
else:
std_Vrot, std_Vrad, std_pa, std_inc, std_x0, std_y0, std_Vsys, std_theta, std_Vtan = std_errors
res_mcmc = Vrot * 0, [std_Vrot, std_Vrot], Vrot * 0, [
std_Vrad, std_Vrad
], 0, [std_pa,
std_pa], 0, [std_inc, std_inc], 0, [std_x0, std_x0], 0, [
std_y0, std_y0
], 0, [std_Vsys,
std_Vsys], 0, [std_theta, std_theta
], Vrot * 0, [std_Vtan, std_Vtan]
return PA, INC, XC, YC, VSYS, 0, Rings, Vrot, 0 * Vrot, 0 * Vrot, best_vlos_2D_model, best_kin_2D_models, chisq_global, res_mcmc
def RC_emcee(vel,
evel,
nrings,
guess0,
vary,
sigma=[],
mode="rotation",
delta=1,
ring="pixel",
rstart=4,
iter=5,
pos=2,
pixel_scale=1):
rstart = int(rstart / pixel_scale)
vrot0, vr20, pa0, inc0, X0, Y0, vsys0 = guess0
guess = [vrot0, vr20, pa0, inc0, X0, Y0, vsys0]
guess_copy = np.copy(guess)
n_sigma = len(sigma)
if n_sigma != 0:
e_vrot, e_vr2, e_pa, e_inc, e_x0, e_y0, e_vsys = sigma
[ny, nx] = vel.shape
x = np.arange(0, nx, 1)
y = np.arange(0, ny, 1)
mesh = np.meshgrid(x, y, sparse=True)
from pixel_params import pixels
from emcee_fit import EMCEE
import fit_params
from fit_params import fit
from fit_params import fit_polynomial
from fit_params import fit_linear
vary = [True, True, True, True, False, False, True]
ring_position = np.arange(rstart, nrings, 2)
vr1, vr21, vsys1, pa1, inc1 = [], [], [], [], []
r = []
for i in ring_position:
vr0, vr20, vsys0, pa0, inc0 = [], [], [], [], []
for n_iter in range(iter):
#sigma = []
sol = np.asarray(guess)
sol[0] = sol[0] + 10 * random.uniform(-1, 1)
sol[1] = sol[1] + 10 * random.uniform(-1, 1)
sol[2] = sol[2] + e_pa * random.uniform(-1, 1)
sol[3] = sol[3] + e_inc * random.uniform(-1, 1)
sol[4] = sol[4] + (2 / pixel_scale) * random.uniform(-1, 1)
sol[5] = sol[5] + (2 / pixel_scale) * random.uniform(-1, 1)
sol[6] = sol[6] + e_vsys * random.uniform(-1, 1)
try:
XY_mesh, vel_val, e_vel, f_pixel = pixels(
vel,
evel,
sol,
ringpos=i,
delta=4,
ring="pixel",
pixel_scale=pixel_scale)
if f_pixel > 0.50:
res = EMCEE(XY_mesh, vel_val, e_vel, guess, sigma)
except (TypeError, ZeroDivisionError, ValueError):
pass
#except(1):pass
#return vr1,vr21,vsys1,pa1,inc1,r
return 0
def RC_sigma(vel,
evel,
nrings,
guess0,
vary,
sigma=[],
mode="rotation",
delta=1,
ring="pixel",
rstart=4,
iter=3,
pos=2,
plot=False,
model=False,
pixel_scale=1):
#rstart = int(rstart/pixel_scale)
vrot0, vr20, pa0, inc0, X0, Y0, vsys0 = guess0
guess = [vrot0, vr20, pa0, inc0, X0, Y0, vsys0]
n_sigma = len(sigma)
if n_sigma != 0:
e_vrot, e_vr2, e_pa, e_inc, e_x0, e_y0, e_vsys = sigma
[ny, nx] = vel.shape
x = np.arange(0, nx, 1)
y = np.arange(0, ny, 1)
mesh = np.meshgrid(x, y, sparse=True)
VLOS = np.zeros((ny, nx))
VROT = np.zeros((ny, nx))
VLOS = np.zeros((ny, nx))
VROT = np.zeros((ny, nx))
VT2 = np.zeros((ny, nx))
VR2 = np.zeros((ny, nx))
MODEL = np.zeros((ny, nx))
nrings = nrings * pixel_scale
#ring_position = np.arange(rstart,nrings,pos)
ring_position_arc = np.arange(rstart, nrings, 1)
ring_position_pix = np.arange(rstart, nrings, 1) / pixel_scale
ring_position = ring_position_arc
vsys = []
r = np.array([])
vsys_it = np.array([])
pa_free = np.array([])
guess0 = [vrot0, vr20, pa0, inc0, X0, Y0, vsys0]
pa_it_0 = []
inc_int_0 = []
frac_pix = 0.3333
for n_iter in range(5):
vary = [True, True, True, True, False, False, True]
#print(ring_position)
guess_test = guess0
for i in ring_position:
#XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess,ringpos = i, delta=2+n_iter/2.,ring = "pixel",pixel_scale=pixel_scale)
XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,
evel,
guess,
ringpos=i,
delta=2 + n_iter / 2.,
ring="ARCSEC",
pixel_scale=pixel_scale)
if f_pixel > frac_pix:
Vrot, Vr2, Vsys, pa, inc, xi = fit("vsys", vel_val, e_vel,
XY_mesh, guess, vary, mode,
sigma)
guess00 = [Vrot, 0, pa, inc, X0, Y0, Vsys]
guess_test = guess00
if Vrot > 10 and Vrot < vmaxrot:
vsys_it = np.append(vsys_it, Vsys)
r = np.append(r, i)
pa_free = np.append(pa_free, pa)
sigma_c_vsys = sigma_clip(vsys_it, sigma=2, maxiters=2)
vsys_it0 = np.nanmean(sigma_c_vsys)
sigma_c_pa = sigma_clip(pa_free, sigma=2, maxiters=2)
pa_free0 = np.nanmean(sigma_c_pa)
"""
fig=plt.figure(figsize=(3,2.))
gs2 = GridSpec(1, 1)
gs2.update(left=0.15, right=0.99,top=0.99,hspace=0.0,bottom=0.19,wspace=0)
ax0=plt.subplot(gs2[0,0])
ax0.scatter(r,pa_free,marker = "o", color = "crimson",s = 9,zorder = 0)
ax0.scatter(r,sigma_c_pa,marker = "o", color = "dodgerblue",s = 10,zorder = 1)
#ax0.scatter(r,vsys_it,marker = "o", color = "crimson",s = 9,zorder = 0)
#ax0.scatter(r,sigma_c_vsys,marker = "o", color = "dodgerblue",s = 10,zorder = 1)
ax0.set_xlabel("Ring position (arcsec)",fontsize = 10)
ax0.set_ylabel("P.A. (degree)",fontsize = 10)
#ax0.set_ylabel("$v_\mathrm{sys}~~(km/s)$",fontsize = 10)
AXIS(ax0,tickscolor = "k")
ax0.set_facecolor('#e8ebf2')
#ax0.set_ylim(int(np.min(vsys_it))-10,int(np.max(vsys_it))+10)
#ax0.set_ylim(int(np.min(pa_free))-10,int(np.max(pa_free))+10)
#plt.savefig("/home/carlos/Documents/PhD_Thesis/figures/pa.png",dpi = 300)
plt.show()
"""
vrot_it = [vrot0]
vrot_it_2 = [vrot0]
vrot_it_3 = [vrot0]
guess00 = [vrot_it[0], vr20, pa_free0, inc0, X0, Y0, vsys_it0]
pa_it_1,inc_it_1,vr_it_1,vr2_it_1,vsys_it_1, r_gal_1 = [],[],[],[],[], []
pa_it_2, inc_it_2, vr_it_2, vr2_it_2, vsys_it_2, r_gal_2 = np.asarray(
[]), np.asarray([]), np.asarray([]), np.asarray([]), np.asarray(
[]), np.asarray([])
pa_it_3, inc_it_3, vr_it_3, vr2_it_3, vsys_it_3, r_gal_3 = np.asarray(
[]), np.asarray([]), np.asarray([]), np.asarray([]), np.asarray(
[]), np.asarray([])
pa_it_4, inc_it_4, vr_it_4, vr2_it_4, vsys_it_4, r_gal_4 = np.asarray(
[]), np.asarray([]), np.asarray([]), np.asarray([]), np.asarray(
[]), np.asarray([])
pa_it, inc_it, vr_it, vr2_it = [], [], [], []
r = np.asarray([])
for n_iter in range(10):
vary = [True, True, True, True, False, False, False]
ring_position = np.arange(rstart, nrings, 1 + n_iter)
for i in ring_position:
#XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess0,ringpos = i, delta=2+n_iter/2.,ring = "arcsec",pixel_scale=pixel_scale)
XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,
evel,
guess0,
ringpos=i,
delta=2 + n_iter / 2.,
ring="ARCSEC",
pixel_scale=pixel_scale)
if f_pixel > frac_pix:
Vrot, Vr2, Vsys, pa, inc, xi = fit("vsys", vel_val, e_vel,
XY_mesh, guess00, vary,
mode, sigma)
if Vrot > 10 and Vrot < vmaxrot:
#if 1>0:
pa_it.append(pa)
inc_it.append(inc)
r = np.append(r, i)
#a=fit_polynomial(r,pa_it)
#plt.plot(r,fit_params.polynomial(r,a[0],a[1],a[2],a[3],a[4],a[5]),"ro")
#b=fit_polynomial(r,inc_it)
#plt.plot(r,fit_params.polynomial(r,b[0],b[1],b[2],b[3],a[4],a[5]),"bo")
#
# THE MASKED IN SIGMA CLIP IS TRUE for clipped values:
# You have to remove or replace the TRUE values
#
#
pa_it_array = np.asarray(pa_it)
sigma_c_pa = sigma_clip(pa_it_array, sigma=1, maxiters=1)
mask_pa = sigma_c_pa.mask
pa_it_array[mask_pa] = np.nanmean(sigma_c_pa)
legfit = np.polynomial.legendre.legfit(r, pa_it_array, 5)
legval_pa = np.polynomial.legendre.legval(r, legfit)
inc_it_array = np.asarray(inc_it)
sigma_c_inc = sigma_clip(inc_it_array, sigma=1, maxiters=1)
mask_inc = sigma_c_inc.mask
inc_it_array[mask_inc] = np.nanmean(sigma_c_inc)
legfit = np.polynomial.legendre.legfit(r, inc_it_array, 5)
legval_inc = np.polynomial.legendre.legval(r, legfit)
"""
fig=plt.figure(figsize=(3,2.))
gs2 = GridSpec(1, 1)
gs2.update(left=0.15, right=0.99,top=0.99,hspace=0.0,bottom=0.19,wspace=0)
ax0=plt.subplot(gs2[0,0])
ax0.plot(r,legval_pa,"k-",markersize = 1,alpha = 0.7)
ax0.scatter(r,pa_it_array,marker = "o", color = "dodgerblue",s = 9,zorder = 2)
ax0.scatter(r,pa_it,marker = "o", color = "crimson",s = 9,zorder = 0)
#ax0.plot(r,legval_inc,"k-",markersize = 1,alpha = 0.7)
#ax0.scatter(r,inc_it_array,marker = "o", color = "dodgerblue",s = 9,zorder = 2)
#ax0.scatter(r,inc_it,marker = "o", color = "crimson",s = 9,zorder = 0)
#ax0.plot(r,legval_inc,"r-",markersize = 1,alpha = 0.7)
#ax0.plot(r,inc_it,"ko",markersize = 2)
ax0.set_xlabel("Ring position (arcsec)",fontsize = 10)
#ax0.set_ylabel("P.A. (degree)",fontsize = 10)
ax0.set_ylabel("inc. (degree)",fontsize = 10)
#ax0.set_ylim(int(np.min(pa_it))-10,int(np.max(pa_it))+10)
ax0.set_ylim(int(np.min(inc_it))-10,int(np.max(inc_it))+10)
AXIS(ax0,tickscolor = "k")
ax0.set_facecolor('#e8ebf2')
#plt.savefig("/home/carlos/Documents/PhD_Thesis/figures/inc_legendre.png",dpi = 300)
plt.show()
"""
f_pa = interp1d(r, legval_pa)
f_inc = interp1d(r, legval_inc)
#
# Estimate vsys
#
k = 0
vr_it_0 = 50
vr2_it_0 = 0
vsys_it_n = vsys_it0
for i in ring_position:
#XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess0,ringpos = i, delta=2+n_iter/2.,ring = "arcsec",pixel_scale=pixel_scale)
XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,
evel,
guess0,
ringpos=i,
delta=1 + n_iter / 2.,
ring="ARCSEC",
pixel_scale=pixel_scale)
if f_pixel > frac_pix:
vary = [True, True, False, False, False, False, True]
if k >= len(legval_pa):
k = -1
guess = [
vr_it_0, vr2_it_0, legval_pa[k], legval_pa[k], X0, Y0,
vsys_it_n
]
Vrot, Vr2, Vsys, pa, inc, xi = fit("vsys", vel_val, e_vel,
XY_mesh, guess, vary, mode,
sigma)
k = k + 1
if Vrot > 10 and Vrot < vmaxrot:
vr2_it_0 = Vr2
vr_it_0 = Vrot
vsys_it_n = Vsys
vrot_it.append(Vrot)
vr_it_1.append(Vrot)
vsys_it_1.append(Vsys)
r_gal_1.append(i)
sigma_c = sigma_clip(vsys_it_1, sigma=2, maxiters=3)
mask_vsys = sigma_c.mask
vsys_it0 = np.nanmean(sigma_c)
"""
fig=plt.figure(figsize=(3,2.))
gs2 = GridSpec(1, 1)
gs2.update(left=0.15, right=0.99,top=0.99,hspace=0.0,bottom=0.19,wspace=0)
ax0=plt.subplot(gs2[0,0])
ax0.plot(r_gal_1,vsys_it_1,"ko",markersize = 1)
#ax0.plot(r,pa_it,"ko",markersize = 1)
#ax0.plot(r,legval_inc,"r-",markersize = 1)
#ax0.plot(r,inc_it,"ko",markersize = 1)
ax0.set_xlabel("Ring position (arcsec)",fontsize = 10)
ax0.set_ylabel("vsys (km/s)",fontsize = 10)
AXIS(ax0,tickscolor = "k")
ax0.set_facecolor('#e8ebf2')
#plt.savefig("/home/carlos/Documents/PhD_Thesis/figures/inc.png",dpi = 300)
plt.show()
"""
vsys_it_1 = np.asarray(vsys_it_1)
"""
fig=plt.figure(figsize=(3,2.))
gs2 = GridSpec(1, 1)
gs2.update(left=0.15, right=0.99,top=0.99,hspace=0.0,bottom=0.19,wspace=0)
ax0=plt.subplot(gs2[0,0])
binwidth = 2
min_v,max_v=int(np.min(vsys_it_1)),int(np.max(vsys_it_1))+1
bins=np.arange(min_v,max_v+0.01,binwidth)
n, bins, patches = ax0.hist(vsys_it_1,edgecolor='k',bins=bins,linewidth=0.1 )
ax0.set_xlabel("$v_\mathrm{sys}$ (km/s)")
ax0.set_ylabel("Frecuency")
AXIS(ax0,tickscolor = "k")
ax0.set_facecolor('#e8ebf2')
#plt.savefig("/home/carlos/Documents/PhD_Thesis/figures/vsys_hist.png",dpi = 300)
plt.show()
"""
sigma_c_vsys = sigma_clip(vsys_it_1, sigma=1, maxiters=2)
vsys_it_ = vsys_it_1[~sigma_c_vsys.mask]
vsys_final = np.nanmedian(vsys_it_)
# The final values of vsys
median_vsys = vsys_final
sigma_vsys = np.nanstd(vsys_it_1)
#
# Prepare initial conditions for the next iteration
#
#PA:
pa_it = np.asarray(pa_it)
sigma_c_pa = sigma_clip(pa_it, sigma=2, maxiters=3)
mask_pa = sigma_c_pa.mask
pa_it_next = np.nanmean(pa_it[~mask_pa])
pa_it[mask_pa] = pa_it_next
legfit = np.polynomial.legendre.legfit(r, pa_it, 5)
legval_pa = np.polynomial.legendre.legval(r, legfit)
#plt.plot(r,pa_it,"ko")
#plt.plot(r,sigma_c_pa,"ro")
#plt.plot(r,legval_pa,"b-")
#plt.show()
#inc:
inc_it = np.asarray(inc_it)
sigma_c = sigma_clip(inc_it, sigma=2, maxiters=3)
mask_inc = sigma_c.mask
inc_it_next = np.nanmean(sigma_c)
inc_it[mask_inc] = inc_it_next
legfit = np.polynomial.legendre.legfit(r, inc_it, 5)
legval_inc = np.polynomial.legendre.legval(r, legfit)
r_sort, vr_sort = zip(*sorted(zip(r_gal_1, vr_it_1)))
max_r = int(np.nanmax(r_sort))
r_sort = np.asarray(r_sort)
vr_sort = np.asarray(vr_sort)
vr_mean_ring = np.asarray([])
r_mean_ring = np.asarray([])
for kk in range(max_r + 1):
mask = r_sort == kk
s = vr_sort[mask]
if len(s) > 0:
vr_mean_ring = np.append(vr_mean_ring, np.nanmean(s))
r_mean_ring = np.append(r_mean_ring, kk)
else:
vr_mean_ring = np.append(vr_mean_ring, 20)
r_mean_ring = np.append(r_mean_ring, kk)
legfit = np.polynomial.legendre.legfit(r_mean_ring, vr_mean_ring, 5)
legval_vrot = np.polynomial.legendre.legval(r_mean_ring, legfit)
f = interp1d(r_mean_ring, legval_vrot)
def vr_interp(w):
try:
Vr_interp = f(w)
except (ValueError):
Vr_interp = 60
if np.isfinite(Vr_interp) == True:
return Vr_interp
else:
return 50
#plt.plot(r_mean_ring,legval_vrot,"b-")
#plt.plot(r_mean_ring,vr_mean_ring,"ko")
#plt.plot(r,vr_interp(r),"ro")
#plt.show()
r_it_fix = np.array([])
vr_it = 50
for n_iter in range(10):
ring_position = np.arange(rstart, nrings, 1 + n_iter)
for i in ring_position:
#XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess0,ringpos = i, delta=2+n_iter,ring = "pixel",pixel_scale=pixel_scale)
XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,
evel,
guess0,
ringpos=i,
delta=1 + n_iter / 2.,
ring="ARCSEC",
pixel_scale=pixel_scale)
if f_pixel > frac_pix:
guess = [
vr_it, vr20, pa_it_next, inc_it_next, X0, Y0, vsys_final
]
vary = [True, True, True, True, False, False, False]
Vrot, Vr2, Vsys, pa, inc, xi = fit("vsys", vel_val, e_vel,
XY_mesh, guess, vary, mode,
sigma)
if Vrot > 10 and Vrot < vmaxrot:
vr_it = Vrot
pa_it_2 = np.append(pa_it_2, pa)
inc_it_2 = np.append(inc_it_2, inc)
r_it_fix = np.append(r_it_fix, i)
#plt.plot(r_it_fix,pa_it_2,"ko")
#plt.plot(r_it_fix,inc_it_2,"ko")
#plt.show()
#inc:
sigma_c_inc = sigma_clip(inc_it_2, sigma=2, maxiters=1)
mask_inc = sigma_c_inc.mask
inc_it_mean = np.nanmedian(sigma_c_inc[~mask_inc])
#PA:
sigma_c_pa = sigma_clip(pa_it_2, sigma=2, maxiters=1)
mask_pa = sigma_c_pa.mask
pa_it_mean = np.nanmedian(sigma_c_pa[~mask_pa])
pa_it_2[mask_pa] = pa_it_mean
# Error in pa & inc
e_pa = np.nanstd(sigma_c_pa)
e_inc = np.nanstd(sigma_c_pa)
#sigma = [0,0,2*e_pa,2*e_inc,1,1,1]
#plt.plot(r_it_fix,inc_it_2,"ro")
#plt.show()
#fix inclination:
PA_final_vals = np.asarray([])
r_PA = np.asarray([])
vr_it = 50
vr2_it = 10
guess_it = [vr_it, vr2_it, pa_it_mean, inc_it_mean, X0, Y0, vsys_final]
for n_iter in range(10):
ring_position = np.arange(rstart, nrings, 1 + n_iter)
for i in ring_position:
#XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess0,ringpos = i, delta=2+n_iter,ring = "pixel",pixel_scale=pixel_scale)
XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,
evel,
guess_it,
ringpos=i,
delta=2 + n_iter / 2.,
ring="ARCSEC",
pixel_scale=pixel_scale)
if f_pixel > frac_pix and i > 0:
guess = [
vr_it, vr2_it, pa_it_mean, inc_it_mean, X0, Y0, vsys_final
]
vary = [True, True, True, False, False, False, False]
Vrot, Vr2, Vsys, pa, inc, xi = fit("vsys", vel_val, e_vel,
XY_mesh, guess, vary, mode,
sigma)
if Vrot > 10 and Vrot < vmaxrot:
vr_it = Vrot
vr2_it = Vr2
PA_final_vals = np.append(PA_final_vals, pa)
r_PA = np.append(r_PA, i)
median_pa = np.nanmedian(PA_final_vals)
sigma_pa = np.nanstd(PA_final_vals)
#fix PA:
inc_final_vals = np.asarray([])
r_inc = np.asarray([])
vr_it = 50
for n_iter in range(10):
ring_position = np.arange(rstart, nrings, 1 + n_iter)
for i in ring_position:
#XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess0,ringpos = i, delta=2+n_iter,ring = "pixel",pixel_scale=pixel_scale)
XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,
evel,
guess0,
ringpos=i,
delta=2 + n_iter / 2.,
ring="ARCSEC",
pixel_scale=pixel_scale)
if f_pixel > frac_pix:
guess = [
vr_it, vr20, median_pa, inc_it_mean, X0, Y0, vsys_final
]
vary = [True, True, False, True, False, False, False]
Vrot, Vr2, Vsys, pa, inc, xi = fit("vsys", vel_val, e_vel,
XY_mesh, guess, vary, mode,
sigma)
if Vrot > 10 and Vrot < vmaxrot:
vr_it = Vrot
inc_final_vals = np.append(inc_final_vals, inc)
r_inc = np.append(r_inc, i)
median_inc = np.nanmedian(inc_final_vals)
sigma_inc = np.nanstd(inc_final_vals)
sigma_f = [500, 500, 1 * sigma_pa, 1 * sigma_inc, 1, 1, 1]
inc_final = [] #np.asarray([])
pa_final = [] #np.asarray([])
r_final = [] #np.asarray([])
vr_inc_pa = [] #np.asarray([])
vr_it_0 = 50 #vr_interp(i)
vr2_it_0 = 0
guess0 = [vrot0, vr20, pa0, inc0, X0, Y0, vsys0]
for n_iter in range(5):
ring_position = np.arange(rstart, nrings, 1 + n_iter)
for i in ring_position:
#XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess0,ringpos = i, delta=2+n_iter,ring = "pixel",pixel_scale=pixel_scale)
XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,
evel,
guess0,
ringpos=i,
delta=2 + n_iter / 2.,
ring="ARCSEC",
pixel_scale=pixel_scale)
if f_pixel > frac_pix:
guess = [
vr_it_0, vr20, median_pa, inc_it_mean, X0, Y0, vsys_final
]
vary = [True, True, True, True, False, False, False]
Vrot, Vr2, Vsys, pa, inc, xi = fit("vsys", vel_val, e_vel,
XY_mesh, guess, vary, mode,
sigma_f)
#guess0 = guess
if Vrot > 10 and Vrot < vmaxrot:
vr_it_0 = Vrot
vr2_it_0 = Vr2
r_final.append(i)
pa_final.append(pa)
inc_final.append(inc)
vr_inc_pa.append(Vrot)
r_sort, pa_sort = zip(*sorted(zip(r_final, pa_final)))
r_sort, inc_sort = zip(*sorted(zip(r_final, inc_final)))
r_sort, vr_sort = zip(*sorted(zip(r_final, vr_inc_pa)))
max_r = int(np.nanmax(r_sort))
inc_sort = np.asarray(inc_sort)
pa_sort = np.asarray(pa_sort)
r_final = np.asarray(r_sort)
vr_sort = np.asarray(vr_sort)
r_sort = np.asarray(r_sort)
pa_mean_ring = np.asarray([])
inc_mean_ring = np.asarray([])
vr_mean_ring = np.asarray([])
r_mean_ring = np.asarray([])
for kk in range(max_r + 1):
mask = r_sort == kk
pa_i = pa_sort[mask]
inc_i = inc_sort[mask]
vr_i = vr_sort[mask]
if len(pa_i) > 0:
vr_mean_ring = np.append(vr_mean_ring, np.nanmean(vr_i))
pa_mean_ring = np.append(pa_mean_ring, np.nanmean(pa_i))
inc_mean_ring = np.append(inc_mean_ring, np.nanmean(inc_i))
r_mean_ring = np.append(r_mean_ring, kk)
else:
vr_mean_ring = np.append(vr_mean_ring, 20)
pa_mean_ring = np.append(pa_mean_ring, median_pa)
inc_mean_ring = np.append(inc_mean_ring, median_inc)
r_mean_ring = np.append(r_mean_ring, kk)
legfit = np.polynomial.legendre.legfit(r_mean_ring, inc_mean_ring, 6)
legval_inc = np.polynomial.legendre.legval(r_mean_ring, legfit)
legfit = np.polynomial.legendre.legfit(r_mean_ring, pa_mean_ring, 6)
legval_pa = np.polynomial.legendre.legval(r_mean_ring, legfit)
legfit = np.polynomial.legendre.legfit(r_mean_ring, vr_mean_ring, 6)
legval_vr = np.polynomial.legendre.legval(r_mean_ring, legfit)
"""
plt.plot(r_mean_ring,pa_mean_ring,"bo")
plt.plot(r_mean_ring,legval_pa,"k-")
plt.plot(r_mean_ring,inc_mean_ring,"bo")
plt.plot(r_mean_ring,legval_inc,"k-")
plt.show()
plt.plot(r_mean_ring,vr_mean_ring,"bo")
plt.plot(r_mean_ring,legval_vr,"k-")
plt.show()
"""
f_pa = interp1d(r_mean_ring, legval_pa)
f_inc = interp1d(r_mean_ring, legval_inc)
f_vr = interp1d(r_mean_ring, legval_vr)
vc_final = np.asarray([])
vrad_final = np.asarray([])
ring_position = r_mean_ring
R = np.asarray([])
k = 0
guess_final = [50, 20, median_pa, median_inc, X0, Y0, vsys_final]
vr_temp = 50 #f_vr(1)
vrad_temp = 0
sigma = []
nrings = 50
ring_position = np.arange(0, nrings, 1)
frac_pix = 0.25
xi_0 = 0
for i in ring_position:
#XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess_final,ringpos = i, delta= 4,ring = "pixel",pixel_scale=pixel_scale)
XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,
evel,
guess_final,
ringpos=i,
delta=1,
ring="ARCSEC",
pixel_scale=pixel_scale)
if f_pixel > frac_pix and i > 0:
#guess = [f_vr(i),vrad_temp,median_pa,median_inc,X0,Y0,vsys_final]
guess = [
vr_temp, vrad_temp, median_pa, median_inc, X0, Y0, vsys_final
]
vary = [True, True, False, False, False, False, False]
Vrot, Vr2, Vsys, pa, inc, xi = fit("vsys", vel_val, e_vel, XY_mesh,
guess, vary, mode, sigma)
if Vrot > 10 and Vrot < vmaxrot:
if i <= 10:
#if 1>0:
vr_temp = Vrot
vrad_temp = Vr2
xi_0 = xi
vc_final = np.append(vc_final, Vrot)
vrad_final = np.append(vrad_final, Vr2)
R = np.append(R, i)
for mm, nn in zip(XY_mesh[0], XY_mesh[1]):
MODEL[nn][mm] = Vlos(mm, nn, Vrot, Vr2, pa, inc, X0,
Y0, Vsys) - Vsys
#"""
else:
if abs(xi_0 - xi) < 5 or xi < xi_0:
xi_0 = xi
vc_final = np.append(vc_final, Vrot)
vrad_final = np.append(vrad_final, Vr2)
R = np.append(R, i)
for mm, nn in zip(XY_mesh[0], XY_mesh[1]):
MODEL[nn][mm] = Vlos(mm, nn, Vrot, Vr2, pa, inc,
X0, Y0, Vsys) - Vsys
"""
else:
sigma_f = [500,500,2*sigma_pa,2*sigma_inc,1,1,1]
vary = [True,True,True,True,False,False,False]
XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess_final,ringpos = i, delta= 2 ,ring = "ARCSEC",pixel_scale=pixel_scale)
guess = [f_vr(i),vr_temp,vrad_temp,f_pa(i),f_inc(i),X0,Y0,vsys_final]
Vrot , Vr2, Vsys, pa, inc ,xi = fit("vsys",vel_val,e_vel,XY_mesh,guess,vary,mode,sigma_f)
print("xi2=",xi)
"""
#plt.plot(r_gal_4,vr_it_4,"ko")
#plt.plot(r_gal_4,vr_it_4, color = "k",linestyle='-', alpha = 0.6)
#plt.plot(r_gal_4,legval_Vr, color = "r",linestyle='-', alpha = 0.6)
#plt.plot(r_gal_4,vr2_it_4,"bo")
#plt.plot(r_gal_4,vr2_it_4, color = "b",linestyle='-', alpha = 0.6)
#plt.show()
MODEL[MODEL == 0] = np.nan
return median_pa, median_inc, median_vsys, sigma_pa, sigma_inc, sigma_vsys, R, vc_final, vrad_final, MODEL
def RC_sigma_rnd(vel,
evel,
nrings,
guess0,
vary,
sigma=[],
mode="rotation",
delta=1,
ring="pixel",
rstart=4,
iter=5,
ring_position=[],
pixel_scale=1):
rstart = int(rstart / pixel_scale)
vrot0, vr20, pa0, inc0, X0, Y0, vsys0 = guess0
guess = [vrot0, vr20, pa0, inc0, X0, Y0, vsys0]
guess_copy = np.copy(guess)
n_sigma = len(sigma)
if n_sigma != 0:
e_vrot, e_vr2, e_pa, e_inc, e_x0, e_y0, e_vsys = sigma
[ny, nx] = vel.shape
x = np.arange(0, nx, 1)
y = np.arange(0, ny, 1)
mesh = np.meshgrid(x, y, sparse=True)
from pixel_params import pixels
import fit_params
from fit_params import fit
from fit_params import fit_polynomial
from fit_params import fit_linear
vary = [True, True, True, True, False, False, True]
#ring_position = np.arange(rstart,nrings,2)
#ring_position = np.arange(rstart,nrings,4)
vr1, vr21, vsys1, pa1, inc1 = np.asarray([]), np.asarray([]), np.asarray(
[]), np.asarray([]), np.asarray([])
vr1, vr21, vsys1, pa1, inc1, r1 = [], [], [], [], [], []
r = np.asarray([])
for n_iter in range(iter):
#for i in ring_position:
vr0, vr20, vsys0, pa0, inc0 = np.asarray([]), np.asarray(
[]), np.asarray([]), np.asarray([]), np.asarray([])
#for n_iter in range(iter):
for i in ring_position:
sigma = []
sol = np.asarray(guess)
sol[0] = sol[0] + 10 * random.uniform(-1, 1)
sol[1] = sol[1] + 10 * random.uniform(-1, 1)
sol[2] = sol[2] + e_pa * random.uniform(-1, 1)
sol[3] = sol[3] + e_inc * random.uniform(-1, 1)
sol[4] = sol[4] #+(2/pixel_scale)*random.uniform(-1,1)
sol[5] = sol[5] #+(2/pixel_scale)*random.uniform(-1,1)
sol[6] = sol[6] + e_vsys * random.uniform(-1, 1)
try:
XY_mesh, vel_val, e_vel, f_pixel = pixels(
vel,
evel,
guess,
ringpos=i,
delta=4,
ring="pixel",
pixel_scale=pixel_scale)
if f_pixel > 0.10:
Vrot, Vr2, Vsys, pa, inc, xi = fit("vsys", vel_val, e_vel,
XY_mesh, sol, vary,
mode, sigma)
if Vrot > 10 and Vrot < vmaxrot:
vr0 = np.append(vr0, Vrot)
vr20 = np.append(vr20, Vr2)
vsys0 = np.append(vsys0, Vsys)
pa0 = np.append(pa0, pa)
inc0 = np.append(inc0, inc)
if n_iter == 0:
r1.append(i)
else:
vr0 = np.append(vr0, Vrot)
vr20 = np.append(vr20, Vr2)
vsys0 = np.append(vsys0, Vsys)
pa0 = np.append(pa0, pa)
inc0 = np.append(inc0, inc)
if n_iter == 0:
r1.append(i)
#else: print(i,"aqtuiiii")
except (TypeError, ZeroDivisionError, ValueError):
pass
#if len(vr0) > 0 and len(vr20)>0 and len(vsys0) >0 and len(pa0) >0 and len(inc0) >0:
#if 1>0:
#vr1=np.append(vr1,vr0)
#vr21 = np.append(vr21,vr20)
#vsys1 = np.append(vsys1,vsys0)
#inc1 = np.append(inc1,inc0)
#pa1 = np.append(pa1,pa0)
#if n_iter == 0:
# r1.append(i)
if len(vr0) != 0:
vr1.append(vr0)
vr21.append(vr20)
vsys1.append(vsys0)
inc1.append(inc0)
pa1.append(pa0)
return vr1, vr21, vsys1, pa1, inc1, r1
def bisym_mod(vel, evel, guess0, vary, rstart, rfinal, ring_space, frac_pixel, r_back, delta, pixel_scale, r_bar_max):
plt.imshow(vel)
plt.show()
vrot0,vr20,pa0,inc0,x0,y0,vsys0,vtan,theta_b = guess0
vmode = "bisymmetric"
[ny,nx] = vel.shape
"""
BYSIMETRIC MODEL
"""
chisq_global = 1e10
PA, INC, XC,YC,VSYS = 0,0,0,0,0
Vrot, Vrad, Vsys,Vtan = [],[],[],[]
R = 0
if r_back < ring_space:
r_back = ring_space
#for jj in range(0,r_back,ring_space):
for jj in range(1):
for it in range(5):
guess = [vrot0,vr20,pa0,inc0,x0,y0,vsys0,0,theta_b]
#theta_b = np.arctan(np.tan(theta_b*np.pi/180-pa0*np.pi/180)/np.cos(inc0*np.pi/180))*180/np.pi
xi_sq_array = np.asarray([])
N = np.asarray([])
vrot_model,vrad_model,vtan_model = np.asarray([]),np.asarray([]),np.asarray([])
los_vel = np.array([])
e_los_vel = np.array([])
x_pos = np.array([])
y_pos = np.array([])
los_vel = []
e_los_vel = []
x_pos = []
y_pos = []
xy_pos = []
r = []
for j in np.arange(rstart,rfinal-jj,ring_space):
XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess,ringpos = j, delta=delta,ring = "ARCSEC",pixel_scale=pixel_scale)
npixels = len(XY_mesh[0])
if j < 10:
f_pixel = 1
if f_pixel > frac_pixel:
if j < r_bar_max:
# Create bisymetric model
w_sys,w_rot,w_rad,w_tan,vrot,vrad,vsys,vtan = M_radial(XY_mesh,0,0,pa0,inc0,x0,y0,vsys0,0,theta_b,vel_val-vsys0,e_vel,vmode)
vrot_model,vrad_model,vtan_model = np.append(vrot_model,vrot),np.append(vrad_model,vrad),np.append(vtan_model,vtan)
else:
# Create ciruclar model
w_rot,w_rad,vrot,vrad = M_radial(XY_mesh,0,0,pa0,inc0,x0,y0,vsys0,0,theta_b,vel_val-vsys0,e_vel,vmode= "circular")
vrot_model,vrad_model,vtan_model = np.append(vrot_model,vrot),np.append(vrad_model,0),np.append(vtan_model,0)
r.append(j)
los_vel.append(vel_val)
e_los_vel.append(e_vel)
x_pos.append(XY_mesh[0])
y_pos.append(XY_mesh[1])
xy_pos.append(XY_mesh)
N = len(los_vel)
guess = [vrot_model,vrad_model,pa0,inc0,x0,y0,vsys0, vtan_model,theta_b]
vrot , vrad, vsys0, pa0, inc0 , x0, y0, vtan, theta_b, xi_sq, n_data = fit("vsys",los_vel,e_los_vel,xy_pos,guess,vary,vmode,fit_method = "Powell", sigma = [], r_bar_max = r_bar_max)
print(pa0, inc0 , x0, y0, theta_b,xi_sq)
if 1.1*xi_sq < chisq_global:
PA, INC, XC,YC,VSYS,THETA = pa0, inc0, x0, y0,vsys0,theta_b
Vrot = vrot
Vrad = vrad
Vtan = vtan
chisq_global = xi_sq
R = r
MODEL = np.zeros((ny,nx))
for k in range(N):
for mm,nn in zip(xy_pos[k][0],xy_pos[k][1]):
MODEL[nn][mm] = Vlos(mm,nn,Vrot[k],Vrad[k],PA,INC,XC,YC,VSYS,Vtan[k],THETA,vmode) - VSYS
MODEL[MODEL == 0] = np.nan
#plt.imshow(MODEL, vmin = -260, vmax = 260, origin = "l", cmap = califa)
#plt.show()
"""
#los_vel,e_los_vel,x_pos,y_pos,xy_pos = [],[],[],[],[]
vrad_,vrot_,vtan_ = [],[],[]
R2 = []
ring_space = 1
for j in np.arange(1,35,ring_space):
xy_pos = []
los_vel = []
e_los_vel = []
los_vel = np.array([])
XY_mesh, vel_val, e_vel, f_pixel = pixels(vel,evel,guess,ringpos = j, delta=0.5,ring = "ARCSEC",pixel_scale=pixel_scale)
npixels = len(XY_mesh[0])
xy_pos.append(XY_mesh)
#los_vel.append(vel_val)
los_vel = [vel_val]
#los_vel = np.append(los_vel,vel_val)
e_los_vel.append(e_vel)
if f_pixel > 0.5:
# Create model
w_sys,w_rot,w_rad,w_tan,vrot,vrad,vsys,vtan = M_radial(XY_mesh,0,0,PA,INC,XC,YC,VSYS,0,0,vel_val-VSYS,e_vel,vmode)
vrot_model,vrad_model,vtan_model = np.append(vrot_model,vrot),np.append(vrad_model,vrad),np.append(vtan_model,vtan)
R2.append(j)
vary = [True,True,False,False,False,False,False,True,False]
guess = [[vrot],[vrad],PA,INC,XC,YC,VSYS,[vtan],THETA]
vel_val, e_vel, XY_mesh = np.array([vel_val]), np.array([e_vel]),[XY_mesh]
vrot0 , vrad0, vsys, pa, inc , x0, y0, vtan0, theta, xi_sq, n_data = fit("vsys",vel_val,e_vel,xy_pos,guess,vary,vmode,fit_method = "Powell", sigma = [], r_bar_max = r_bar_max)
vrot_.append(vrot0)
vtan_.append(vtan0)
vrad_.append(vrad0)
print(PA, INC, XC,YC,VSYS,theta, chisq_global)
"""
#plt.plot(R,Vrot,"b-", label = "min Xi")
#plt.plot(R,Vrad,"g-")
#plt.plot(R,Vtan,"r-")
#plt.plot(R2,vrot_,"b--", label = "fix params")
#plt.plot(R2,vrad_,"g--")
#plt.plot(R2,vtan_,"r--")
#plt.legend()
#plt.show()
R = np.asarray(R)
Vtan = np.asarray(Vtan)
Vrad = np.asarray(Vrad)
Vrot = np.asarray(Vrot)
return PA,INC,XC,YC,VSYS,THETA,R,Vrot,Vrad,Vtan,MODEL
def rad_mod(vel, evel, guess0, vary, rstart, rfinal, ring_space, frac_pixel,
r_back, delta, pixel_scale, r_bar_max):
vrot0, vr20, pa0, inc0, x0, y0, vsys0, vtan, theta_b = guess0
vmode = "radial"
[ny, nx] = vel.shape
"""
RADIAL MODEL
"""
chisq_global = 1e4
PA, INC, XC, YC, VSYS = 0, 0, 0, 0, 0
Vrot, Vrad, Vsys, Vtan = [], [], [], []
R = 0
if r_back < ring_space:
r_back = ring_space
for jj in range(0, r_back, ring_space):
for it in range(5):
guess = [vrot0, vr20, pa0, inc0, x0, y0, vsys0, 0, theta_b]
xi_sq_array = np.asarray([])
N = np.asarray([])
vrot_model, vrad_model, vtan_model = np.asarray([]), np.asarray(
[]), np.asarray([])
los_vel = np.array([])
e_los_vel = np.array([])
x_pos = np.array([])
y_pos = np.array([])
los_vel = []
e_los_vel = []
x_pos = []
y_pos = []
xy_pos = []
r = []
for j in np.arange(rstart, rfinal - jj, ring_space):
XY_mesh, vel_val, e_vel, f_pixel = pixels(
vel,
evel,
guess,
ringpos=j,
delta=delta,
ring="ARCSEC",
pixel_scale=pixel_scale)
npixels = len(XY_mesh[0])
if j < 10:
f_pixel = 1
if f_pixel > frac_pixel:
# Create model
try:
w_rot, w_rad, vrot, vrad = M_radial(
XY_mesh, 0, 0, pa0, inc0, x0, y0, vsys0, 0, 0,
vel_val - vsys0, e_vel)
vrot_model, vrad_model = np.append(
vrot_model, vrot), np.append(vrad_model, vrad)
except (np.linalg.LinAlgError):
vrot_model, vrad_model = np.append(vrot_model,
100), np.append(
vrad_model, 10)
pass
r.append(j)
los_vel.append(vel_val)
e_los_vel.append(e_vel)
x_pos.append(XY_mesh[0])
y_pos.append(XY_mesh[1])
xy_pos.append(XY_mesh)
#vary = [True,True,True,True,True,True,True]
N = len(los_vel)
guess = [
vrot_model, vrad_model, pa0, inc0, x0, y0, vsys0, vtan_model,
theta_b
]
vrot, vrad, vsys0, pa0, inc0, x0, y0, xi_sq, n_data = fit(
"vsys",
los_vel,
e_los_vel,
xy_pos,
guess,
vary,
vmode,
fit_method="Powell",
sigma=[])
if inc0 > 85:
pa0 = guess0[2]
x0, y0 = guess0[4], guess0[5]
inc0 = 55
frac_pixel = 0.6
xi_sq = 1e5
if 1.1 * xi_sq < chisq_global:
PA, INC, XC, YC, VSYS, THETA = pa0, inc0, x0, y0, vsys0, theta_b
#print(PA,INC)
Vrot = vrot
Vrad = vrad
chisq_global = xi_sq
R = r
MODEL = np.zeros((ny, nx))
for k in range(N):
for mm, nn in zip(xy_pos[k][0], xy_pos[k][1]):
MODEL[nn][mm] = Vlos(mm, nn, Vrot[k], Vrad[k], PA, INC,
XC, YC, VSYS, 0, 0, vmode) - VSYS
MODEL[MODEL == 0] = np.nan
#print("final = ", PA, INC, XC,YC,VSYS, chisq_global)
"""
plt.imshow(MODEL, origin = "l", cmap = califa)
plt.show()
plt.plot(R,Vrot,"k-")
plt.plot(R,Vrot,"ko")
plt.plot(R,Vrad,"r-")
plt.show()
"""
Vrot = np.array(Vrot)
R = np.array(R)
return PA, INC, XC, YC, VSYS, 0, R, Vrot, Vrad, 0 * Vrot, MODEL
def bisym_mod(vel, evel, guess0, vary, n_it, rstart, rfinal, ring_space,
frac_pixel, r_back, delta, pixel_scale, r_bar_max, errors,
config, e_ISM):
#def bisym_mod(vel, evel, guess0, vary, n_it=5,rstart = 2, rfinal = 55, ring_space = 2, frac_pixel = 0.7, r_back = 20, delta = 1, pixel_scale = 0.2, r_bar_max = 20):
vrot0, vr20, pa0, inc0, x0, y0, vsys0, vtan, theta_b = guess0
vmode = "bisymmetric"
[ny, nx] = vel.shape
shape = [ny, nx]
"""
BYSIMETRIC MODEL
"""
chisq_global = 1e4
PA, INC, XC, YC, VSYS = 0, 0, 0, 0, 0
Vrot, Vrad, Vsys, Vtan = [], [], [], []
R = 0
if r_back < ring_space:
r_back = ring_space
for jj in np.arange(0, r_back, ring_space):
for it in range(n_it):
guess = [vrot0, vr20, pa0, inc0, x0, y0, vsys0, 0, theta_b]
#theta_b = np.arctan(np.tan(theta_b*np.pi/180-pa0*np.pi/180)/np.cos(inc0*np.pi/180))*180/np.pi
xi_sq_array = np.asarray([])
N = np.asarray([])
vrot_model, vrad_model, vtan_model = np.asarray([]), np.asarray(
[]), np.asarray([])
los_vel = np.array([])
e_los_vel = np.array([])
x_pos = np.array([])
y_pos = np.array([])
los_vel = []
e_los_vel = []
x_pos = []
y_pos = []
xy_pos = []
r = []
for j in np.arange(rstart, rfinal - jj, ring_space):
XY_mesh, vel_val, e_vel, f_pixel = pixels(
vel,
evel,
guess,
ringpos=j,
delta=delta,
ring="ARCSEC",
pixel_scale=pixel_scale)
npixels = len(XY_mesh[0])
if j < 10:
f_pixel = 1
if f_pixel > frac_pixel:
if j < r_bar_max:
# Create bisymetric model
try:
w_sys, w_rot, w_rad, w_tan, vrot, vrad, vsys, vtan = M_radial(
XY_mesh, 0, 0, pa0, inc0, x0, y0, vsys0, 0,
theta_b, vel_val - vsys0, e_vel, vmode)
vrot_model, vrad_model, vtan_model = np.append(
vrot_model,
vrot), np.append(vrad_model, vrad), np.append(
vtan_model, vtan)
except (np.linalg.LinAlgError):
vrot_model, vrad_model, vtan_model = np.append(
vrot_model,
100), np.append(vrad_model,
10), np.append(vtan_model, 10)
else:
# Create ciruclar model
w_rot, w_rad, vrot, vrad = M_radial(XY_mesh,
0,
0,
pa0,
inc0,
x0,
y0,
vsys0,
0,
theta_b,
vel_val - vsys0,
e_vel,
vmode="circular")
vrot_model, vrad_model, vtan_model = np.append(
vrot_model,
vrot), np.append(vrad_model,
0), np.append(vtan_model, 0)
r.append(j)
los_vel.append(vel_val)
e_los_vel.append(e_vel)
x_pos.append(XY_mesh[0])
y_pos.append(XY_mesh[1])
xy_pos.append(XY_mesh)
guess = [
vrot_model + 1, vrad_model + 1, pa0, inc0, x0, y0, vsys0,
vtan_model + 1, theta_b
]
vrot, vrad, vsys0, pa0, inc0, x0, y0, vtan, theta_b, xi_sq, n_data = fit(
shape,
los_vel,
e_los_vel,
xy_pos,
guess,
vary,
vmode,
config,
fit_method="Powell",
r_bar_max=r_bar_max,
e_ISM=e_ISM)
if xi_sq < chisq_global:
PA, INC, XC, YC, VSYS, THETA = pa0, inc0, x0, y0, vsys0, theta_b
LOS_VEL, eLOS_VEL = los_vel, e_los_vel
Vrot = vrot
Vrad = vrad
Vtan = vtan
R = r
XY_PIX_POS = xy_pos
chisq_global = xi_sq
#print("chisq_global1 = ", chisq_global)
if it == n_it - 1:
vrot_poly = legendre(R, Vrot)
vrad_poly = legendre(R, Vrad)
vtan_poly = legendre(R, Vtan)
guess_end = [
vrot_poly, vrad_poly, PA, INC, XC, YC, VSYS, vtan_poly,
THETA
]
guess_end = [
vrot_poly, Vrad, PA, INC, XC, YC, VSYS, Vtan, THETA
]
vary_end = [
True, True, False, False, False, False, False, True, False
]
#vary_end = [True,True,True,True,True,True,True,True,True]
Vrot, Vrad, vsys0, pa0, inc0, x0, y0, Vtan, theta_b, xi_sq, n_data = fit(
shape,
LOS_VEL,
eLOS_VEL,
XY_PIX_POS,
guess_end,
vary_end,
vmode,
"",
fit_method="Powell",
r_bar_max=r_bar_max,
e_ISM=e_ISM)
#print("chisq_global2=", xi_sq)
MODEL_not_interp = np.zeros((ny, nx))
N = len(LOS_VEL)
for k in range(N):
for mm, nn in zip(XY_PIX_POS[k][0], XY_PIX_POS[k][1]):
MODEL_not_interp[nn][mm] = vlos(
mm, nn, Vrot[k], Vrad[k], PA, INC, XC, YC, VSYS,
Vtan[k], THETA, vmode) - VSYS
MODEL_interp = vlos_interp(XY_PIX_POS, R, Vrot, Vrad, PA, INC,
XC, YC, VSYS, Vtan, THETA, vmode,
shape, pixel_scale)
# For error estimation:
LoS, eLoS, XY_pixels = los_vel, e_los_vel, xy_pos
best = guess
MODEL_not_interp[MODEL_not_interp == 0] = np.nan
MODELS = [MODEL_interp, MODEL_not_interp]
R = np.asarray(R)
Vtan = np.asarray(Vtan)
Vrad = np.asarray(Vrad)
Vrot = np.asarray(Vrot)
PA_bar_major = pa_bar_sky(PA, INC, THETA)
PA_bar_minor = pa_bar_sky(PA, INC, THETA - 90)
if errors == 1:
from mcmc import fit_mcmc
res_mcmc = fit_mcmc(shape,
LOS_VEL,
eLOS_VEL,
XY_PIX_POS,
guess_end,
vary_end,
vmode,
"",
fit_method="emcee",
e_ISM=e_ISM)
else:
res_mcmc = Vrot * 0, [Vrot * 0, Vrot * 0], Vrot * 0, [
Vrot * 0, Vrot * 0
], 0, [0, 0], 0, [0, 0], 0, [0, 0], 0, [0, 0], 0, [0, 0], 0, [
0, 0
], Vrot * 0, [Vrot * 0, Vrot * 0]
return PA, INC, XC, YC, VSYS, THETA, R, Vrot, Vrad, Vtan, MODELS, PA_bar_major, PA_bar_minor, chisq_global, res_mcmc
def bisym_mod(vel, evel, guess0, vary, n_it, rstart, rfinal, ring_space,
frac_pixel, delta, pixel_scale, bar_min_max, errors, config,
e_ISM):
#def bisym_mod(vel, evel, guess0, vary, n_it=5,rstart = 2, rfinal = 55, ring_space = 2, frac_pixel = 0.7, r_back = 20, delta = 1, pixel_scale = 0.2, r_bar_max = 20):
vrot0, vr20, pa0, inc0, x0, y0, vsys0, vtan, theta_b = guess0
vmode = "bisymmetric"
[ny, nx] = vel.shape
shape = [ny, nx]
r_bar_min, r_bar_max = bar_min_max
"""
BYSIMETRIC MODEL
"""
chisq_global = 1e10
PA, INC, XC, YC, VSYS = 0, 0, 0, 0, 0
Vrot, Vrad, Vsys, Vtan = [], [], [], []
R = 0
best_xy_pix = []
rings = np.arange(rstart, rfinal, ring_space)
for it in np.arange(n_it):
guess = [vrot0, vr20, pa0, inc0, x0, y0, vsys0, 0, theta_b]
vrot_tab, vrad_tab, vtan_tab = np.asarray([]), np.asarray(
[]), np.asarray([])
index = 0
nrings = len(rings)
n_annulus = nrings - 1
R_pos = np.asarray([])
for ring in rings:
from pixel_params import pixels
fpix = pixels(shape,
vel,
pa0,
inc0,
x0,
y0,
ring,
delta=delta,
pixel_scale=pixel_scale)
if fpix > frac_pixel:
if ring >= r_bar_min and ring <= r_bar_max:
# Create bisymetric model
try:
v_rot_k, v_rad_k, v_tan_k = M_tab(
pa0,
inc0,
x0,
y0,
theta_b,
ring,
delta,
index,
shape,
vel - vsys0,
evel,
pixel_scale=pixel_scale,
vmode=vmode)
vrot_tab = np.append(vrot_tab, v_rot_k)
vrad_tab = np.append(vrad_tab, v_rad_k)
vtan_tab = np.append(vtan_tab, v_tan_k)
R_pos = np.append(R_pos, ring)
except (np.linalg.LinAlgError):
vrot_tab, vrad_tab, vtan_tab = np.append(
vrot_tab,
100), np.append(vrad_tab,
10), np.append(vtan_tab, 10)
R_pos = np.append(R_pos, ring)
else:
# Create ciruclar model
v_rot_k, v_rad_k, v_tan_k = M_tab(pa0,
inc0,
x0,
y0,
theta_b,
ring,
delta,
index,
shape,
vel - vsys0,
evel,
pixel_scale=pixel_scale,
vmode="circular")
vrot_tab = np.append(vrot_tab, v_rot_k)
vrad_tab = np.append(vrad_tab, 0)
vtan_tab = np.append(vtan_tab, 0)
R_pos = np.append(R_pos, ring)
if np.nanmean(vrot_tab) < 0:
vrot_tab = abs(vrot_tab)
pa0 = pa0 - 180
if pa0 < 0: pa0 = pa0 + 360
guess = [
vrot_tab, vrad_tab, pa0, inc0, x0, y0, vsys0, vtan_tab, theta_b
]
v_2D_mdl, kin_2D_modls, vrot, vrad, vsys0, pa0, inc0, x0, y0, vtan, theta_b, xi_sq, n_data, Errors = fit(
shape,
vel,
evel,
guess,
vary,
vmode,
config,
R_pos,
fit_method="Powell",
e_ISM=e_ISM,
pixel_scale=pixel_scale,
ring_space=ring_space)
if xi_sq < chisq_global:
PA, INC, XC, YC, VSYS, THETA = pa0, inc0, x0, y0, vsys0, theta_b
Vrot = vrot
Vrad = vrad
Vtan = vtan
chisq_global = xi_sq
best_vlos_2D_model = v_2D_mdl
best_kin_2D_models = kin_2D_modls
Rings = R_pos
std_errors = Errors
GUESS = [
vrot_tab, vrad_tab, PA, INC, XC, YC, VSYS, vtan_tab, THETA
]
GUESS = [Vrot, Vrad, PA, INC, XC, YC, VSYS, Vtan, THETA]
# Maybe the code has found the bar minor axis, in such case Vrad <0 and Vtan <0
if np.nanmean(Vrad) < 0 or np.nanmean(Vtan) < 0:
GUESS[-1] = GUESS[-1] - 90
NEW = GUESS[-1]
if NEW < 0: GUESS[-1] = 180 + NEW
VARY = [True, True, False, False, False, False, False, True, True]
v_2D_mdl_, kin_2D_modls_, vrot_, vrad_, _vsys0_, pa0_, inc0_, x0_, y0_, vtan_, theta_b_, xi_sq_, n_data_, Errors_ = fit(
shape,
vel,
evel,
GUESS,
VARY,
vmode,
config,
Rings,
fit_method="Powell",
e_ISM=e_ISM,
pixel_scale=pixel_scale,
ring_space=ring_space)
if xi_sq_ < chisq_global:
Vrot = vrot_
Vrad = vrad_
Vtan = vtan_
THETA = theta_b_
best_vlos_2D_model = v_2D_mdl_
best_kin_2D_models = kin_2D_modls_
std_errors = Errors_
Vtan = np.asarray(Vtan)
Vrad = np.asarray(Vrad)
Vrot = np.asarray(Vrot)
PA_bar_major = pa_bar_sky(PA, INC, THETA)
PA_bar_minor = pa_bar_sky(PA, INC, THETA - 90)
if errors == 1:
from mcmc import fit_mcmc
res_mcmc = fit_mcmc(shape,
vel,
evel,
GUESS,
vary,
vmode,
"",
Rings,
fit_method="emcee",
e_ISM=e_ISM,
pixel_scale=pixel_scale,
ring_space=ring_space)
else:
std_Vrot, std_Vrad, std_pa, std_inc, std_x0, std_y0, std_Vsys, std_theta, std_Vtan = std_errors
res_mcmc = Vrot * 0, [std_Vrot, std_Vrot], Vrot * 0, [
std_Vrad, std_Vrad
], 0, [std_pa,
std_pa], 0, [std_inc, std_inc], 0, [std_x0, std_x0], 0, [
std_y0, std_y0
], 0, [std_Vsys,
std_Vsys], 0, [std_theta, std_theta
], Vrot * 0, [std_Vtan, std_Vtan]
return PA, INC, XC, YC, VSYS, THETA, Rings, Vrot, Vrad, Vtan, best_vlos_2D_model, best_kin_2D_models, PA_bar_major, PA_bar_minor, chisq_global, res_mcmc
