def test_occultations():
"""Test occultation light curves."""
# Let's do the l = 3 Earth
map = Map(3)
map.load_image('earth')
# Rotate the map about a random axis
ux = np.random.random()
uy = np.random.random() * (1 - ux)
uz = np.sqrt(1 - ux**2 - uy**2)
axis = [ux, uy, uz]
npts = 30
theta = np.linspace(0, 360, npts, endpoint=False)
# Small occultor
ro = 0.3
xo = np.linspace(-1 - ro - 0.1, 1 + ro + 0.1, npts)
yo = 0
# Analytical and numerical fluxes
map.axis = axis
sF = np.array(map.flux(theta=theta, xo=xo, yo=yo, ro=ro))
nF = np.array(map.flux(theta=theta, xo=xo, yo=yo, ro=ro, numerical=True))
# Compute the (relative) error
error = np.max(np.abs(sF - nF))
# Our numerical integration scheme isn't the most accurate,
# so let's be lenient here!
assert error < 0.03
def test_phasecurves():
"""Test transit light curve generation."""
# Let's do the l = 3 Earth
map = Map(3)
map.load_image('earth')
# Compute the starry phase curve about a random axis
ux = np.random.random()
uy = np.random.random() * (1 - ux)
uz = np.sqrt(1 - ux ** 2 - uy ** 2)
map.axis = [ux, uy, uz]
theta = np.linspace(0, 360, 25, endpoint=False)
sF = map.flux(theta=theta)
# Compute the flux numerically
nF = [NumericalFlux(map, t) for t in theta]
# Compute the error
error = np.max(np.abs((sF - nF) / sF))
# We're computing the numerical integral at very low precision
# so that this test doesn't take forever, so let's be lenient here!
assert error < 1e-4
def test_small(benchmark=0.1):
"""Small occultor."""
# Let's do the l = 5 Earth
map = Map(5)
map.load_image('earth')
# Occultation properties
npts = 10550
ro = 0.1
xo = np.linspace(-1 - ro, 1 + ro, npts)
yo = np.linspace(-0.1, 0.1, npts)
theta = np.linspace(0, 90, npts)
map.axis = [1, 1, 1] / np.sqrt(3)
# Analytical and numerical fluxes
t = np.zeros(10)
for i in range(10):
tstart = time.time()
map.flux(theta=theta, xo=xo, yo=yo, ro=ro)
t[i] = time.time() - tstart
t = np.mean(t)
# Print
print("Time [Benchmark]: %.3f [%.3f]" % (t, benchmark))
"""Smiley spherical harmonic example."""
from starry import Map
import matplotlib.pyplot as pl
import numpy as np
# Generate a sample starry map
map = Map(5)
map[5, -3] = -2
map[5, 0] = 2
map[5, 4] = 1
map.axis = [0, 1, 0]
# Render it under consecutive rotations
nax = 9
fig, ax = pl.subplots(1, nax, figsize=(3 * nax, 3))
theta = np.linspace(-90, 90, nax, endpoint=True)
x = np.linspace(-1, 1, 300)
y = np.linspace(-1, 1, 300)
x, y = np.meshgrid(x, y)
for i in range(nax):
I = [map(theta=theta[i], x=x[j], y=y[j]) for j in range(300)]
ax[i].imshow(I, origin="lower", interpolation="none", cmap='plasma')
ax[i].axis('off')
# Save
pl.savefig('smiley.pdf', bbox_inches='tight')
"""Earth spherical harmonic example."""
from starry import Map
import matplotlib.pyplot as pl
import numpy as np
# Generate a sample starry map
m = Map(10)
m.load_image('earth')
# Start centered at longitude 180 W
m.axis = [0, 1, 0]
m.rotate(-180)
# Render it under consecutive rotations
nax = 8
res = 300
fig, ax = pl.subplots(1, nax, figsize=(3 * nax, 3))
theta = np.linspace(0, 360, nax, endpoint=False)
x, y = np.meshgrid(np.linspace(-1, 1, res), np.linspace(-1, 1, res))
for i in range(nax):
# starry functions accept vector arguments, but not matrix arguments,
# so we need to iterate below:
I = [m(theta=-theta[i], x=x[j], y=y[j]) for j in range(res)]
ax[i].imshow(I, origin="lower", interpolation="none", cmap='plasma')
ax[i].axis('off')
# Save
pl.savefig('earth.pdf', bbox_inches='tight')
ax[-1, j].set_xlabel(r"$m = %d$" % m, labelpad=30, fontsize=12)
# Rotate about this vector
ux = np.array([1., 0., 0.])
uy = np.array([0., 1., 0.])
map = Map(lmax)
theta = np.linspace(0, 360, nt, endpoint=False)
thetan = np.linspace(0, 360, nn, endpoint=False)
for i, l in enumerate(range(lmax + 1)):
for j, m in enumerate(range(l + 1)):
nnull = 0
for axis, zorder, color in zip([ux, uy], [1, 0], ['C0', 'C1']):
map.reset()
map[0, 0] = 0
map[l, m] = 1
map.axis = axis
flux = map.flux(theta=theta)
ax[i, j].plot(theta, flux, lw=1, zorder=zorder, color=color)
fluxn = map.flux(theta=thetan, numerical=True)
ax[i, j].plot(thetan, fluxn, '.', ms=2, zorder=zorder, color=color)
if np.max(np.abs(flux)) < 1e-10:
nnull += 1
# If there's no light curve, make sure our plot range
# isn't too tight, as it will zoom in on floating point error
if nnull == 2:
ax[i, j].set_ylim(-1, 1)
# Force the scale for the constant map
ax[0, 0].set_ylim(0.886 + 1, 0.886 - 1)
# Hack a legend
axleg = pl.axes([0.7, 0.7, 0.15, 0.15])
