def test_reflected_light():
pri = starry.Primary(starry.Map(amp=0), r=1)
sec = starry.Secondary(starry.Map(reflected=True), porb=1.0, r=1)
sys = starry.System(pri, sec)
t = np.concatenate((np.linspace(0.1, 0.4, 50), np.linspace(0.6, 0.9, 50)))
flux = sys.flux(t)
import theano
import theano.tensor as tt
import numpy as np
import starry
import matplotlib.pyplot as plt
import pytest
map = starry.Map(ydeg=1, reflected=True)
_b = tt.dvector("b")
_theta = tt.dvector("theta")
_bo = tt.dvector("bo")
_ro = tt.dscalar("ro")
_sigr = tt.dscalar("sigr")
_s = theano.function([_b, _theta, _bo, _ro, _sigr],
map.ops.sT(_b, _theta, _bo, _ro, _sigr))
def s(b, theta, bo, ro, sigr, n=0):
if hasattr(ro, "__len__"):
assert not (hasattr(b, "__len__") or hasattr(theta, "__len__")
or hasattr(bo, "__len__") or hasattr(sigr, "__len__"))
return [
_s([b], [theta], [bo], ro[i], sigr)[0, n] for i in range(len(ro))
]
elif hasattr(sigr, "__len__"):
assert not (hasattr(b, "__len__") or hasattr(theta, "__len__")
or hasattr(bo, "__len__") or hasattr(ro, "__len__"))
return [
_s([b], [theta], [bo], ro, sigr[i])[0, n] for i in range(len(sigr))
]
else:
def test_default_system_units():
pri = starry.Primary(starry.Map())
sec = starry.Secondary(starry.Map(), porb=1.0)
sys = starry.System(pri, sec)
assert sys.time_unit == u.day
def earth_eclipse(lmax=20):
"""Compute the error on the secondary eclipse of the Earth."""
npts = 1000
# Create our map
map = starry.Map(lmax)
map.load_image('earth')
# Compute. Ingress duration is
# dt = (2 REARTH) / (2 PI * 1 AU / 1 year) ~ 7 minutes
yo = 0
ro = 6.957e8 / 6.3781e6
time = np.linspace(0, 7 * 1.5, npts)
xo = np.linspace(-(ro + 1.5), -(ro - 1.5), npts, -1)
flux = np.array(map.flux(xo=xo, yo=yo, ro=ro))
# Compute at high precision
map_128 = starry.Map(lmax, multi=True)
map_128[:, :] = map[:, :]
flux128 = np.array(map_128.flux(xo=xo, yo=yo, ro=ro))
# Show
fig = pl.figure(figsize=(7, 6))
nim = 10
ax = [pl.subplot2grid((7, nim), (1, 0), colspan=nim, rowspan=3),
pl.subplot2grid((7, nim), (4, 0), colspan=nim, rowspan=3)]
fig.subplots_adjust(hspace=0.6)
ax[0].plot(time, flux / flux[0])
ax[1].plot(time, np.abs(flux - flux128))
ax[1].set_yscale('log')
ax[1].axhline(1e-3, color='k', ls='--', alpha=0.75, lw=0.5)
ax[1].axhline(1e-6, color='k', ls='--', alpha=0.75, lw=0.5)
ax[1].axhline(1e-9, color='k', ls='--', alpha=0.75, lw=0.5)
ax[1].annotate("ppt", xy=(1e-3, 1e-3), xycoords="data", xytext=(3, -3),
textcoords="offset points", ha="left", va="top", alpha=0.75)
ax[1].annotate("ppm", xy=(1e-3, 1e-6), xycoords="data", xytext=(3, -3),
textcoords="offset points", ha="left", va="top", alpha=0.75)
ax[1].annotate("ppb", xy=(1e-3, 1e-9), xycoords="data", xytext=(3, -3),
textcoords="offset points", ha="left", va="top", alpha=0.75)
ax[1].set_ylim(5e-17, 20.)
ax[0].set_xlim(0, time[-1])
ax[1].set_xlim(0, time[-1])
ax[1].set_xlabel("Time [minutes]", fontsize=16)
ax[0].set_ylabel("Normalized flux", fontsize=16, labelpad=15)
ax[1].set_ylabel("Relative error", fontsize=16)
# Plot the earth images
res = 100
ax_im = [pl.subplot2grid((7, nim), (0, n)) for n in range(nim)]
x, y = np.meshgrid(np.linspace(-1, 1, res), np.linspace(-1, 1, res))
map.axis = [0, 1, 0]
for n in range(nim):
i = int(np.linspace(0, npts - 1, nim)[n])
I = [map(theta=0, x=x[j], y=y[j]) for j in range(res)]
ax_im[n].imshow(I, origin="lower", interpolation="none", cmap='plasma',
extent=(-1, 1, -1, 1))
xm = np.linspace(xo[i] - ro + 1e-5, xo[i] + ro - 1e-5, 10000)
ax_im[n].fill_between(xm, yo - np.sqrt(ro ** 2 - (xm - xo[i]) ** 2),
yo + np.sqrt(ro ** 2 - (xm - xo[i]) ** 2),
color='w')
ax_im[n].axis('off')
ax_im[n].set_xlim(-1.05, 1.05)
ax_im[n].set_ylim(-1.05, 1.05)
fig.savefig("stability_earth.pdf", bbox_inches='tight')
def test_lightcurve(b, theta, ro, ydeg=1, ns=1000, nb=50, res=999, plot=False):
# Array over full occultation, including all singularities
xo = 0.0
yo = np.linspace(0, 1 + ro, ns, endpoint=True)
for pt in [ro, 1, 1 - ro, b + ro]:
if pt >= 0:
yo[np.argmin(np.abs(yo - pt))] = pt
if theta == 0:
xs = 0
ys = 1
else:
xs = 0.5
ys = -xs / np.tan(theta)
rxy2 = xs ** 2 + ys ** 2
if b == 0:
zs = 0
elif b == 1:
zs = -1
xs = 0
ys = 0
elif b == -1:
zs = 1
xs = 0
ys = 0
else:
zs = -np.sign(b) * np.sqrt(rxy2 / (b ** -2 - 1))
# Compute analytic
map = starry.Map(ydeg=ydeg, reflected=True)
map[1:, :] = 1
flux = map.flux(xs=xs, ys=ys, zs=zs, xo=xo, yo=yo, ro=ro)
# Compute numerical
flux_num = np.zeros_like(yo) * np.nan
computed = np.zeros(ns, dtype=bool)
(lat, lon), (x, y, z) = map.ops.compute_ortho_grid(res)
img = map.render(xs=xs, ys=ys, zs=zs, res=res).flatten()
for i, yoi in tqdm(enumerate(yo), total=len(yo)):
if (i == 0) or (i == ns - 1) or (i % (ns // nb) == 0):
idx = (x - xo) ** 2 + (y - yoi) ** 2 > ro ** 2
flux_num[i] = np.nansum(img[idx]) * 4 / res ** 2
computed[i] = True
# Interpolate over numerical result
f = interp1d(yo[computed], flux_num[computed], kind="cubic")
flux_num_interp = f(yo)
# Plot
if plot:
fig = plt.figure()
plt.plot(yo, flux, "C0-", label="starry", lw=2)
plt.plot(yo, flux_num, "C1o", label="brute")
plt.plot(yo, flux_num_interp, "C1-", lw=1)
plt.legend(loc="best")
plt.xlabel("impact parameter")
plt.ylabel("flux")
fig.savefig(
"test_lightcurve[{}-{}-{}].pdf".format(b, theta, ro),
bbox_inches="tight",
)
plt.close()
# Compare with very lax tolerance; we're mostly looking
# for gross outliers
diff = np.abs(flux - flux_num_interp)
assert np.max(diff) < 0.001
def test_show_moll():
map = starry.Map(ydeg=1, udeg=1)
map.show(file="tmp.pdf", projection="moll")
os.remove("tmp.pdf")
def test_show_ld():
map = starry.Map(udeg=2)
map.show(file="tmp.pdf")
os.remove("tmp.pdf")
ax[0, 0].set_visible(False)
ax[0, 1].set_visible(False)
ax[1, 0].set_ylabel("flux [normalized]", fontsize=10)
ax[1, 0].set_title("thermal phase curve", fontsize=12, fontweight="bold")
ax[1, 1].set_title("reflected phase curve", fontsize=12, fontweight="bold")
ax[2, 0].set_ylabel("flux [normalized]", fontsize=10)
ax[3, 0].set_title("thermal occultation", fontsize=12, fontweight="bold")
ax[3, 1].set_title("reflected occultation", fontsize=12, fontweight="bold")
for axis in np.append(ax[1, :], ax[3, :]):
axis.tick_params(labelsize=8)
axis.set_ylim(-0.05, 1.65)
# Show the true map
ax_top = fig.add_subplot(5, 1, 1)
ax_top.set_title("input", fontsize=12, fontweight="bold")
map = starry.Map(20)
map.load("earth", sigma=0.1)
map.show(ax=ax_top, projection="moll")
# Solve the least-squares problem for thermal & reflected phase curves
for j, rmoon in enumerate([0, 0.25]):
for i, reflected in enumerate([False, True]):
# Instantiate a map of the Earth
map = starry.Map(20, reflected=reflected)
map.load("earth", sigma=0.1)
obl = 23.5
porb = 365.25
prot = 1.0
pmoon = 1.37
amoon = 2.0
def test_I_stability(noon, plot=False):
# FB found this unstable limit. The instability comes
# from two places:
# 1. The upward recursion in the I integral is unstable.
# We need to either implement a tridiagonal solver as
# in the J integral and/or refine our computation of kappa.
# 2. The terminator parameter b = 1, which causes `get_angles` to
# oscillate between different integration codes. When b
# approaches unity, we should switch to the regular starry
# solver, since the terminator is so close to the limb that its
# presence doesn't matter.
xo, yo, zo, ro = (
31.03953239062832,
23.892679948795926,
1.0,
39.10406741663172,
)
# Exactly noon?
if noon:
xs = 0.0
else:
xs = 0.1
ys = 0.0
zs = 1.0
xo = np.linspace(xo - 2, xo + 2, 1000)
# Compute analytic
map = starry.Map(ydeg=10, reflected=True)
map[10, :] = 1
flux1 = map.flux(xo=xo, yo=yo, zo=zo, ro=ro, xs=xs, ys=ys, zs=zs)
if noon:
# The flux above should be *exactly* equal to 2/3 the flux of a
# linearly-limb darkened source with u_1 = 1.0, since linear
# limb darkening weights the surface brightness by the same
# cosine-like profile (2/3 is the geometrical albedo of a
# perfect Lambert sphere)
map_e = starry.Map(ydeg=10, udeg=1)
map_e[10, :] = 1
map_e[1] = 1
flux2 = (2.0 / 3.0) * map_e.flux(xo=xo, yo=yo, zo=zo, ro=ro)
atol = 1e-12
else:
# Compute numerical
res = 500
x, y, z = map.ops.compute_ortho_grid(res)
image = map.render(xs=xs, ys=ys, zs=zs, res=res).flatten()
flux2 = np.zeros_like(flux1)
for k in range(len(xo)):
idx = (x - xo[k]) ** 2 + (y - yo) ** 2 > ro ** 2
flux2[k] = np.nansum(image[idx])
flux2 *= 4 / res ** 2
atol = 1e-3
# Plot it
if plot:
plt.plot(xo, flux1)
plt.plot(xo, flux2)
plt.show()
# Compare
assert np.allclose(flux1, flux2, atol=atol)
def map():
return starry.Map(1, reflected=True)
# -*- coding: utf-8 -*-
"""
There's a bug in tt.mgrid that causes different
behavior whether it's compiled or not. We implemented
some hacks in `starry` to circumvent this.
See docstring of `compute_ortho_grid` in "core.py"
"""
import starry
import pytest
import theano.tensor as tt
import itertools
import numpy as np
map = starry.Map(1)
@pytest.mark.parametrize("compile", [True, False])
def test_ortho_grid(compile):
for res in np.arange(30, 101):
if compile:
(lat, lon), (x, y, z) = map.ops.compute_ortho_grid(res)
else:
latlon, xyz = map.ops.compute_ortho_grid(
tt.as_tensor_variable(res))
x, y, z = xyz.eval()
assert len(x) == res**2
"""55 cancrie e secondary eclipse example."""
import starry
import matplotlib.pyplot as plt
import numpy as np
import astropy.units as u
# Config
starry.config.lazy = False
# Star
star_map = starry.Map(udeg=2, amp=1)
star_map[1:] = [0.5, 0.25]
star = starry.Primary(star_map,
m=0.905,
mass_unit=u.M_sun,
r=0.943,
length_unit=u.R_sun)
# Planet
kwargs = dict(
m=7.99,
mass_unit=u.earthMass,
porb=0.73654737,
r=1.875,
length_unit=u.earthRad,
inc=83.59,
t0=0.400473685,
)
# Time arrays (secondary eclipse ingress / full phase curve)
t_ingress = np.linspace(0, 0.002, 1000)
# -*- coding: utf-8 -*-
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import starry
ydeg = 10
theta = np.linspace(-180, 180, 1000)
s = np.zeros(((ydeg + 1)**2, len(theta)))
map = starry.Map(ydeg, lazy=False)
n = 0
for l in range(ydeg + 1):
for m in range(-l, l + 1):
map.reset()
if l > 0:
map[l, m] = 1.0
s[n] = map.flux(theta=theta)
n += 1
# Set up the plot
fig, ax = plt.subplots(ydeg + 1,
2 * ydeg + 1,
figsize=(16, 10),
sharex=True,
sharey=True)
fig.subplots_adjust(hspace=0)
for axis in ax.flatten():
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.spines['bottom'].set_visible(False)
axis.spines['left'].set_visible(False)
def map(request):
(nw, ) = request.param
map = starry.Map(ydeg=5, udeg=2, nw=nw)
return map
# -*- coding: utf-8 -*-
"""
Show the effect a rotating spot has on an absorption line.
"""
import matplotlib.pyplot as plt
import numpy as np
import starry
import paparazzi as pp
from matplotlib.animation import FuncAnimation
# Generate two maps
ydeg = 20
N = (ydeg + 1)**2
map1 = starry.Map(ydeg)
map1.add_spot(amp=-0.03, sigma=0.05, lat=30, lon=0)
map1.inc = 90
map2 = starry.Map(ydeg)
map2.load("spot")
map2.inc = 40
for map, name in zip([map1, map2], ["spot1", "spot2"]):
# Get the map coeffs
y1 = np.array(map.y.eval())[1:]
# Generate the dataset
vsini = 40.0 # km/s
nt = 100
theta = np.linspace(-180, 180, nt)
def map(request):
ydeg, udeg, nw, rv, reflected = request.param
map = starry.Map(ydeg=ydeg, udeg=udeg, nw=nw, reflected=reflected, rv=rv)
map.reflected = reflected
return map
def test_show_with_figure():
map = starry.Map(ydeg=1, udeg=1)
fig, ax = plt.subplots(1)
map.show(ax=ax, file="tmp.pdf", projection="ortho")
os.remove("tmp.pdf")
def test_amplitude():
"""Test the amplitude attribute of a multi-wavelength map."""
map = starry.Map(ydeg=1, nw=5)
assert np.allclose(map.amp, np.ones(5))
map.amp = 10.0
assert np.allclose(map.amp, 10.0 * np.ones(5))
def test_show_colorbar():
map = starry.Map(ydeg=1, udeg=1)
map.show(file="tmp.pdf", projection="ortho", colorbar=True)
os.remove("tmp.pdf")
def test_edges(xs,
ys,
zs,
ro,
y=[1, 1, 1],
ns=100,
nb=50,
res=999,
atol=1e-2,
plot=False):
# Instantiate
ydeg = np.sqrt(len(y) + 1) - 1
map = starry.Map(ydeg=ydeg, reflected=True)
map[1:, :] = y
# bo - ro singularities
singularities = [ro - 1, 0, ro, 1, 1 - ro, 1 + ro]
labels = [
"$b_o = r_o - 1$",
"$b_o = 0$",
"$b_o = r_o$",
"$b_o = 1$",
"$b_o = 1 - r_o$",
"$b_o = 1 + r_o$",
"grazing",
"grazing",
]
# Find where the occultor grazes the terminator
rs = np.sqrt(xs**2 + ys**2 + zs**2)
b = -zs / rs
theta = -np.arctan2(xs, ys)
tol = 1e-15
nx = 10
c = np.cos(theta)
s = np.sin(theta)
t = np.tan(theta)
q2 = c**2 + b**2 * s**2
# Bottom / top half of occultor
for sgn0 in [1, -1]:
# Successively refine x array
xest = 0
xdel = ro
for j in range(10):
x = np.linspace(xest - xdel, xest + xdel, nx)
# Divide & conquer
yomax = 1 + ro
yomin = -1 - ro
niter = 0
xest = 0
while niter < 100 and np.abs(yomax - yomin) > tol:
yo_ = 0.5 * (yomax + yomin)
y = yo_ + sgn0 * np.sqrt(ro**2 - x**2)
try:
# Scan the x axis for an intersection
for i in range(nx):
# There are two solutions to the quadratic; pick
# the one that's actually on the ellipse
p = (x[i] * c - b * s * np.sqrt(q2 - x[i]**2)) / q2
yt1 = p * s + b * np.sqrt(1 - p**2) * c
xr = x[i] * c + yt1 * s
yr = -x[i] * s + yt1 * c
arg1 = np.abs(xr**2 + (yr / b)**2 - 1)
p = (x[i] * c + b * s * np.sqrt(q2 - x[i]**2)) / q2
yt2 = p * s + b * np.sqrt(1 - p**2) * c
xr = x[i] * c + yt2 * s
yr = -x[i] * s + yt2 * c
arg2 = np.abs(xr**2 + (yr / b)**2 - 1)
if arg1 < arg2:
if arg1 < 1e-6:
yt = yt1
else:
continue
elif arg2 < arg1:
if arg2 < 1e-6:
yt = yt2
else:
continue
else:
continue
if (sgn0 == -1) and (y[i] < yt):
# Part of the occultor has dipped below the terminator
yomin = yo_
xest = x[i]
raise StopIteration
if (sgn0 == 1) and (y[i] > yt):
# Part of the occultor has dipped above the terminator
yomax = yo_
xest = x[i]
raise StopIteration
except StopIteration:
niter += 1
continue
else:
niter += 1
if sgn0 == -1:
# The occultor is above the terminator everywhere
yomax = yo_
else:
# The occultor is below the terminator everywhere
yomin = yo_
# Increase res by 10x
xdel /= 10
singularities.append(yo_)
# Arrays over singularities
yo_s = np.zeros((8, ns))
logdelta = np.append(-np.inf, np.linspace(-16, -2, ns // 2 - 1))
delta = np.concatenate((-(10**logdelta[::-1]), 10**logdelta))
for i, pt in enumerate(singularities):
yo_s[i] = pt + delta
yo_s = yo_s[np.argsort(singularities)]
labels = list(np.array(labels)[np.argsort(singularities)])
# Array over full occultation
yo_full = np.linspace(yo_s[0, 0], yo_s[-1, -1], ns, endpoint=True)
# All
yo = np.concatenate((yo_full.reshape(1, -1), yo_s))
# Compute analytic
flux = np.zeros_like(yo)
msg = [["" for n in range(yo.shape[1])] for m in range(yo.shape[0])]
for i in range(len(yo)):
for k in tqdm(range(ns)):
try:
flux[i, k] = map.flux(xs=xs,
ys=ys,
zs=zs,
xo=0,
yo=yo[i, k],
ro=ro)
except Exception as e:
flux[i, k] = 0.0
msg[i][k] = str(e).split("\n")[0]
# Compute numerical
flux_num = np.zeros_like(yo) * np.nan
flux_num_interp = np.zeros_like(yo) * np.nan
x, y, z = map.ops.compute_ortho_grid(res)
img = map.render(xs=xs, ys=ys, zs=zs, res=res).flatten()
for i in range(len(yo)):
for k in tqdm(range(ns)):
idx = x**2 + (y - yo[i, k])**2 > ro**2
flux_num_interp[i, k] = np.nansum(img[idx]) * 4 / res**2
if (k == 0) or (k == ns - 1) or (k % (ns // nb) == 0):
flux_num[i, k] = flux_num_interp[i, k]
# Adjust the baseline
offset = np.nanmedian(flux[i]) - np.nanmedian(flux_num_interp[i])
flux_num_interp[i] += offset
flux_num[i] += offset
# Plot
if plot:
fig = plt.figure(figsize=(10, 8))
fig.subplots_adjust(hspace=0.35)
ax = [
plt.subplot2grid((40, 40), (0, 0), rowspan=15, colspan=40),
plt.subplot2grid((40, 40), (20, 0), rowspan=10, colspan=10),
plt.subplot2grid((40, 40), (20, 10), rowspan=10, colspan=10),
plt.subplot2grid((40, 40), (20, 20), rowspan=10, colspan=10),
plt.subplot2grid((40, 40), (20, 30), rowspan=10, colspan=10),
plt.subplot2grid((40, 40), (30, 0), rowspan=10, colspan=10),
plt.subplot2grid((40, 40), (30, 10), rowspan=10, colspan=10),
plt.subplot2grid((40, 40), (30, 20), rowspan=10, colspan=10),
plt.subplot2grid((40, 40), (30, 30), rowspan=10, colspan=10),
]
# Prepare image for plotting
img[(img < 0) | (img > 0.0)] = 1
img = img.reshape(res, res)
cmap = plt.get_cmap("plasma")
cmap.set_under("grey")
# Full light curve
ax[0].plot(yo[0], flux[0], "k-", lw=1)
ax[0].plot(yo[0], flux_num[0], "k.", lw=1)
ax[0].tick_params(labelsize=10)
ax[0].set_xlabel("$b_o$")
ax[0].set_ylabel("flux")
# Each singularity
for i in range(1, len(yo)):
ax[0].plot(yo[i], flux[i], lw=3, color="C{}".format(i - 1))
ax[i].plot(
2 + logdelta,
flux[i][:ns // 2],
lw=2,
color="C{}".format(i - 1),
)
ax[i].plot(
-(2 + logdelta)[::-1],
flux[i][ns // 2:],
lw=2,
color="C{}".format(i - 1),
)
ax[i].plot(2 + logdelta, flux_num[i][:ns // 2], "k.", ms=2)
ax[i].plot(-(2 + logdelta)[::-1],
flux_num[i][ns // 2:],
"k.",
ms=2)
ax[i].set_xticks([])
ax[i].set_yticks([])
# Show the map
axins = inset_axes(ax[i],
width="30%",
height="30%",
loc=4,
borderpad=1)
axins.imshow(
img,
origin="lower",
cmap=cmap,
extent=(-1, 1, -1, 1),
vmin=1e-8,
)
circ = plt.Circle(
(0, yo[i, ns // 2]),
ro,
fc="k",
ec="k",
clip_on=(ro > 0.75),
zorder=99,
)
axins.add_artist(circ)
axins.annotate(
labels[i - 1],
xy=(0.5, -0.1),
xycoords="axes fraction",
clip_on=False,
ha="center",
va="top",
fontsize=8,
)
axins.set_xlim(-1.01, 1.01)
axins.set_ylim(-1.01, 1.01)
axins.axis("off")
plt.show()
# Compare
if not np.allclose(flux, flux_num_interp, atol=atol):
index = np.unravel_index(np.argmax(np.abs(flux - flux_num_interp)),
flux.shape)
if index[0] > 0:
raise ValueError("Error in singular region {}/8: {}".format(
index[0], labels[index[0] - 1]))
foo = subprocess.check_output(['julia', "compare_to_batman_grad.jl"])
agol_grad_time[i] = float(foo.decode('utf-8'))
# pytransit
m = pytransit.Gimenez(nldc=len(u_g), interpolate=False)
tstart = time.time()
for k in range(10):
pytransit_flux = m(b, 0.1, u_g)
pytransit_time[i] = (time.time() - tstart) / 10
# starry (sph)
# Using the dense spherical harmonic
# integration algorithm (slow, since we don't
# actually need the majority of the terms!)
if N < 30:
map = starry.Map(ydeg=1, udeg=N)
map[1:] = u
tstart = time.time()
for k in range(10):
starry_ylm_flux = map.flux(xo=b, ro=0.1)
starry_ylm_time[i] = (time.time() - tstart) / 10
tstart = time.time()
else:
# Let's not even bother computing these!
starry_ylm_flux = np.zeros_like(b) * np.nan
starry_ylm_time[i] = np.nan
# starry (ld)
map = starry.Map(ydeg=0, udeg=N)
map[1:] = u
tstart = time.time()
nlam = 199
nt = 3
P = 1
inc = 90.0
ydeg = 5
vsini = 40.0
sigma = 7.5e-6
dlam = np.log(1.0 + 1.0 / R)
lam = np.arange(-(nlam // 2), nlam // 2 + 1) * dlam
t = np.linspace(-0.5 * P, 0.5 * P, nt + 1)[:-1]
doppler = pp.Doppler(lam, ydeg=ydeg, vsini=vsini, inc=inc, P=P)
D = doppler.D(t=t)
lam_padded = doppler.lam_padded
vT = np.ones_like(lam_padded)
vT = 1 - 0.5 * np.exp(-0.5 * lam_padded ** 2 / sigma ** 2)
map = starry.Map(ydeg)
map.load("vogtstar.jpg")
u = np.array(map.y.eval())
A = u.reshape(-1, 1).dot(vT.reshape(1, -1))
for i in tqdm(range(100)):
F = D.dot(A.reshape(-1)).reshape(nt, -1)
# Conv
A_t = tt.dmatrix()
F_t = theano.function(
[A_t], flux(lam, t, A_t, ydeg=ydeg, vsini=vsini, inc=inc, P=P)
)
for i in tqdm(range(100)):
foo = F_t(A)
def test_rotate_spectral():
map = starry.Map(1, nw=2)
map[1, 1, 0] = 1
map[1, -1, 1] = 1
map.rotate(np.array([0, 0, 1]), np.array(90.0))
assert np.allclose(map.y, [[1, 1], [1, 0], [0, 0], [0, -1]])
def __init__(self, lmax=5):
self.map = starry.Map(lmax, oblate=True, gdeg=0)
def test_X(xs, ys, zs, theta=0, ro=0.1, res=300, ydeg=2, tol=1e-3, plot=False):
# Params
npts = 250
xo = np.linspace(-1.5, 1.5, npts)
yo = np.linspace(-0.3, 0.5, npts)
theta = 0
ro = 0.1
res = 300
ydeg = 2
tol = 1e-3
# Instantiate
map = starry.Map(ydeg=ydeg, reflected=True)
# Analytic
X = map.amp * map.design_matrix(
xs=xs, ys=ys, zs=zs, theta=theta, xo=xo, yo=yo, ro=ro
)
# Numerical
(lat, lon), (x, y, z) = map.ops.compute_ortho_grid(res)
image = np.zeros((map.Ny, res * res))
image[0] = map.render(theta=theta, xs=xs, ys=ys, zs=zs, res=res).flatten()
n = 1
for l in range(1, map.ydeg + 1):
for m in range(-l, l + 1):
map.reset()
map[l, m] = 1
image[n] = (
map.render(theta=theta, xs=xs, ys=ys, zs=zs, res=res).flatten()
) - image[0]
n += 1
X_num = np.zeros_like(X)
for k in range(len(xo)):
idx = (x - xo[k]) ** 2 + (y - yo[k]) ** 2 > ro ** 2
for n in range(map.Ny):
X_num[k, n] = np.nansum(image[n][idx])
X_num *= 4 / res ** 2
# Plot
if plot:
fig, ax = plt.subplots(
ydeg + 1, 2 * ydeg + 1, figsize=(9, 6), sharex=True, sharey=True
)
for axis in ax.flatten():
axis.set_xticks([])
axis.set_yticks([])
axis.spines["top"].set_visible(False)
axis.spines["right"].set_visible(False)
axis.spines["bottom"].set_visible(False)
axis.spines["left"].set_visible(False)
n = 0
for i, l in enumerate(range(ydeg + 1)):
for j, m in enumerate(range(-l, l + 1)):
j += ydeg - l
med = np.median(X_num[:, n])
ax[i, j].plot(X[:, n] - med, lw=2)
ax[i, j].plot(X_num[:, n] - med, lw=1)
n += 1
fig.savefig(
"test_X_{}.pdf".format(datetime.now().strftime("%d%m%Y%H%M%S")),
bbox_inches="tight",
)
plt.close()
# Compare
diff = (X - X_num).flatten()
assert np.max(np.abs(diff)) < tol
import starry
import numpy as np
map = starry.Map(15, inc=40)
map.load("spot")
map.show(theta=np.linspace(0, 360, 100), res=300, file="spot.mp4")
def design_matrix(time,
ydeg=10,
nt=2,
period=1.0,
phase0=0.0,
fit_linear_term=False):
"""
Compute and return the design matrix.
Args:
time: The time array in TJD.
ydeg: The maximum spherical harmonic degree.
nt: The number of map temporal components.
phase0: The phase of the map at `t = 0` in degrees.
"""
# Instantiate a `starry` map
map = starry.Map(ydeg=ydeg, udeg=0, reflected=True, nt=nt)
# Load the SPICE data
ephemFiles = glob.glob('../data/TESS_EPH_PRE_LONG_2018*.bsp')
tlsFile = '../data/tess2018338154046-41240_naif0012.tls'
solarSysFile = '../data/tess2018338154429-41241_de430.bsp'
#print(spice.tkvrsn('TOOLKIT'))
for ephFil in ephemFiles:
spice.furnsh(ephFil)
spice.furnsh(tlsFile)
spice.furnsh(solarSysFile)
# JD time range
allTJD = time + TJD0
nT = len(allTJD)
allET = np.zeros((nT, ), dtype=np.float)
for i, t in enumerate(allTJD):
allET[i] = spice.unitim(t, 'JDTDB', 'ET')
# Calculate positions of TESS, the Earth, and the Sun
tess = np.zeros((3, len(allET)))
sun = np.zeros((3, len(allET)))
for i, et in enumerate(allET):
outTuple = spice.spkezr('Mgs Simulation', et, 'J2000', 'NONE', 'Earth')
tess[0, i] = outTuple[0][0] * REARTH
tess[1, i] = outTuple[0][1] * REARTH
tess[2, i] = outTuple[0][2] * REARTH
outTuple = spice.spkezr('Sun', et, 'J2000', 'NONE', 'Earth')
sun[0, i] = outTuple[0][0] * REARTH
sun[1, i] = outTuple[0][1] * REARTH
sun[2, i] = outTuple[0][2] * REARTH
# Compute the linear starry model
t = (time - time[0]) / (time[-1] - time[0])
t = 2 * (t - 0.5)
X = np.empty((nT, map.Ny * map.nt))
for i in tqdm(range(len(time))):
# Find the rotation matrix `R` that rotates TESS onto the +z axis
r = np.sqrt(np.sum(tess[:, i]**2))
costheta = np.dot(tess[:, i], [0, 0, r])
axis = np.cross(tess[:, i], [0, 0, r])
sintheta = np.sqrt(np.sum(axis**2))
axis /= sintheta
R = starry.RAxisAngle(axis,
180. / np.pi * np.arctan2(sintheta, costheta))
# Rotate into this new frame. The Earth is still
# at the origin, TESS is along the +z axis, and
# the Sun is at `source`.
nx, ny, nz = np.dot(R, [0, 0, 1])
source = np.dot(R, sun[:, i])
source /= np.sqrt(np.sum(source**2, axis=0))
# We need to rotate the map of the Earth so the
# north pole is at (nx, ny, nz). We also need to
# rotate the Earth about the pole to get it to the
# correct phase. We'll do this with a compound
# rotation by an angle `theta` about the axis computed
# below.
phase = 2 * np.pi / period * (time[i]) + np.pi / 180. * phase0
cosphase = np.cos(phase)
sinphase = np.sin(phase)
map.axis = [
nz + nz * cosphase + nx * sinphase, (1 + ny) * sinphase,
-nx - nx * cosphase + nz * sinphase
]
costheta = 0.5 * (-1 + ny + cosphase + ny * cosphase)
sintheta = np.sqrt(1 - costheta**2)
theta = 180 / np.pi * np.arctan2(sintheta, costheta)
X[i] = map.linear_flux_model(t=t[i], theta=theta, source=source)
if fit_linear_term:
return X
else:
# Let's remove the Y_{0,0} from the design matrix and return
# the static constant term so that it can be subtracted from the data.
X00 = np.array(X[:, 0])
X = np.delete(X, [n * map.Ny for n in range(map.nt)], axis=1)
return X, X00
from multiprocessing import Pool
time, vels, verr = np.loadtxt('../data/vst222259.ascii', usecols=[1,2,3], unpack=True)
ha = np.loadtxt('../data/222259_mbcvel.ascii', usecols=[-2])
time = time[:-4]
vels = vels[:-4]
verr = verr[:-4]
ha = ha[:-7]
time -= 18706.5
map = starry.Map(ydeg=4, udeg=2, rv=True, lazy=False)
map.reset()
Prot = 2.85 # days
P = 8.1387 # days
e = 0.0
w = 0.0
inc = 90.0
tuse = time + 0.0
euse = verr + 0.0
vuse = vels + 0.0
bnds = ((12000, 24000), (0.04, 0.09), (-1.0, 0.0), (15,25), (0,1),(0,1), (-30,90), (-500,500), (0.0, 3.0), (0, 40.0), (0.0, 1.0), (0.16, 0.175), (-100000, 0), (-1000, 0.539), (0.0, 10.0))
def test_rotate_spectral():
map = starry.Map(1, nw=2)
map[1, 1, 0] = 1
map[1, -1, 1] = 1
map.rotate([0, 0, 1], 90.0)
assert np.allclose(map.y.eval(), [[1, 1], [1, 0], [0, 0], [0, -1]])
def test_bodies():
pri = starry.Primary(starry.Map())
sec = starry.Secondary(starry.Map(ydeg=1), porb=1.0)
sys = starry.System(pri, sec)
assert sys.primary == pri
assert sys.secondaries[0] == sec
