def random_ebv(tau: float = 0.67, r_v: float = 2.3):
# Set up range of x
x = np.arange(0, 2, 0.01)
# Get curve of PDF.
a_exp = st.exponential(x=x, a=0.99, c=r_v / tau, d=0)
# Get random E(B-V) value from PDF.
ebv = st.value_from_pdf(x, a_exp)
return ebv
def random_x1(mu: float = 0.604, sigma_minus: float = 1.029, sigma_plus: float = 0.363):
"""
Generate a Type Ia supernova SALT2 x1 (stretch) value, using an asymmetric Gaussian distribution,
per Scolnic et al, 2016, ApJ 822, L35. Defaults are from PS1 distribution, same paper.
:param mu: Mean of the distribution.
:param sigma_minus: Sigma of the negative side of the distribution.
:param sigma_plus: Sigma of the positive side of the distribution.
:return: x1: float
"""
# Set up range of x
x = np.arange(-5, 4, 0.01)
# Get curve of PDF.
a_gauss = st.asymmetric_gaussian(x=x, mu=mu, sigma_minus=sigma_minus, sigma_plus=sigma_plus)
# Get random x1 value from PDF.
x1 = st.value_from_pdf(x, a_gauss / max(a_gauss))
return x1
def random_position(image: fits.HDUList,
hg_ra: float,
hg_dec: float,
limit: int = 10,
show: bool = False):
image, path = ff.path_or_hdu(image)
header = image[0].header
data = image[0].data
# Find host galaxy pixel
wcs_info = wcs.WCS(header)
hg_x, hg_y = wcs_info.all_world2pix(hg_ra, hg_dec, 0)
hg_x = int(np.round(hg_x))
hg_y = int(np.round(hg_y))
image_copy = data.copy()[hg_y - limit:hg_y + limit,
hg_x - limit:hg_x + limit]
noise = np.median(image_copy)
image_copy = image_copy - noise
image_flatten = image_copy.flatten(1)
i = st.value_from_pdf(np.arange(image_flatten.shape[0]),
image_flatten / max(image_flatten))
i = int(i)
x, y = np.unravel_index(i, image_copy.shape)
if show:
plt.imshow(image_copy)
plt.scatter(x, y)
plt.show()
plt.close()
plt.plot(image_flatten / max(image_flatten))
plt.show()
x += hg_x - limit + np.random.uniform(-0.5, 0.5)
y += hg_y - limit + np.random.uniform(-0.5, 0.5)
ra, dec = wcs_info.all_pix2world(x, y, 0)
if path:
image.close()
return x, y, ra, dec