"""
Module for optical constants of dust grains.
"""
import os
import zipfile
import urllib.request
import h5py
import numpy as np
from astropy.io import fits
from typeguard import typechecked
[docs]
@typechecked
def add_optical_constants(input_path: str, database: h5py._hl.files.File) -> None:
"""
Function for adding the optical constants of crystalline and amorphous
MgSiO3 and Fe to the database.
Parameters
----------
input_path : str
Folder where the data is located.
database : h5py._hl.files.File
Database.
Returns
-------
None
NoneType
"""
if not os.path.exists(input_path):
os.makedirs(input_path)
url = "https://home.strw.leidenuniv.nl/~stolker/species/optical_constants.zip"
data_file = os.path.join(input_path, "optical_constants.zip")
if not os.path.isfile(data_file):
print("\nDownloading optical constants (87 kB)...", end="", flush=True)
urllib.request.urlretrieve(url, data_file)
print(" [DONE]")
print("\nUnpacking optical constants...", end="", flush=True)
with zipfile.ZipFile(data_file, "r") as zip_ref:
zip_ref.extractall(input_path)
print(" [DONE]")
print("\nAdding optical constants of MgSiO3...", end="")
nk_file = os.path.join(
input_path,
"optical_constants/mgsio3/crystalline/" "mgsio3_jaeger_98_scott_96_axis1.dat",
)
data = np.loadtxt(nk_file)
database.create_dataset("dust/mgsio3/crystalline/axis_1", data=data)
nk_file = os.path.join(
input_path,
"optical_constants/mgsio3/crystalline/" "mgsio3_jaeger_98_scott_96_axis2.dat",
)
data = np.loadtxt(nk_file)
database.create_dataset("dust/mgsio3/crystalline/axis_2", data=data)
nk_file = os.path.join(
input_path,
"optical_constants/mgsio3/crystalline/" "mgsio3_jaeger_98_scott_96_axis3.dat",
)
data = np.loadtxt(nk_file)
database.create_dataset("dust/mgsio3/crystalline/axis_3", data=data)
nk_file = os.path.join(
input_path,
"optical_constants/mgsio3/amorphous/" "mgsio3_jaeger_2003_reformat.dat",
)
data = np.loadtxt(nk_file)
database.create_dataset("dust/mgsio3/amorphous", data=data)
print(" [DONE]")
print("Adding optical constants of Fe...", end="")
nk_file = os.path.join(
input_path, "optical_constants/fe/crystalline/fe_henning_1996.dat"
)
data = np.loadtxt(nk_file)
database.create_dataset("dust/fe/crystalline", data=data)
nk_file = os.path.join(
input_path, "optical_constants/fe/amorphous/fe_pollack_1994.dat"
)
data = np.loadtxt(nk_file)
database.create_dataset("dust/fe/amorphous", data=data)
print(" [DONE]")
[docs]
@typechecked
def add_cross_sections(input_path: str, database: h5py._hl.files.File) -> None:
"""
Function for adding the extinction cross section of
crystalline MgSiO3 for a log-normal and power-law size
distribution to the database.
Parameters
----------
input_path : str
Folder where the data is located.
database : h5py._hl.files.File
Database.
Returns
-------
None
NoneType
"""
if not os.path.exists(input_path):
os.makedirs(input_path)
url = "https://home.strw.leidenuniv.nl/~stolker/species/lognorm_mgsio3_c_ext.fits"
data_file = os.path.join(input_path, "lognorm_mgsio3_c_ext.fits")
print(
"\nDownloading log-normal dust cross sections (231 kB)...", end="", flush=True
)
urllib.request.urlretrieve(url, data_file)
print(" [DONE]")
print("\nAdding log-normal dust cross sections:")
with fits.open(os.path.join(input_path, "lognorm_mgsio3_c_ext.fits")) as hdu_list:
database.create_dataset(
"dust/lognorm/mgsio3/crystalline/cross_section", data=hdu_list[0].data
)
print(
f" - Data shape (n_wavelength, n_radius, n_sigma): {hdu_list[0].data.shape}"
)
database.create_dataset(
"dust/lognorm/mgsio3/crystalline/wavelength", data=hdu_list[1].data
)
data_range = f"{np.amin(hdu_list[1].data)} - {np.amax(hdu_list[1].data)}"
print(f" - Wavelength range: {data_range} um")
database.create_dataset(
"dust/lognorm/mgsio3/crystalline/radius_g", data=hdu_list[2].data
)
data_range = f"{np.amin(hdu_list[2].data)} - {np.amax(hdu_list[2].data)}"
print(f" - Mean geometric radius range: {data_range} um")
database.create_dataset(
"dust/lognorm/mgsio3/crystalline/sigma_g", data=hdu_list[3].data
)
data_range = f"{np.amin(hdu_list[3].data)} - {np.amax(hdu_list[3].data)}"
print(f" - Geometric standard deviation range: {data_range}")
url = "https://home.strw.leidenuniv.nl/~stolker/species/powerlaw_mgsio3_c_ext.fits"
data_file = os.path.join(input_path, "powerlaw_mgsio3_c_ext.fits")
print("\nDownloading power-law dust cross sections (231 kB)...", end="", flush=True)
urllib.request.urlretrieve(url, data_file)
print(" [DONE]")
print("\nAdding power-law dust cross sections:")
with fits.open(os.path.join(input_path, "powerlaw_mgsio3_c_ext.fits")) as hdu_list:
database.create_dataset(
"dust/powerlaw/mgsio3/crystalline/cross_section", data=hdu_list[0].data
)
print(
f" - Data shape (n_wavelength, n_radius, n_exponent): {hdu_list[0].data.shape}"
)
database.create_dataset(
"dust/powerlaw/mgsio3/crystalline/wavelength", data=hdu_list[1].data
)
data_range = f"{np.amin(hdu_list[1].data)} - {np.amax(hdu_list[1].data)}"
print(f" - Wavelength range: {data_range} um")
database.create_dataset(
"dust/powerlaw/mgsio3/crystalline/radius_max", data=hdu_list[2].data
)
data_range = f"{np.amin(hdu_list[2].data)} - {np.amax(hdu_list[2].data)}"
print(f" - Maximum grain radius range: {data_range} um")
database.create_dataset(
"dust/powerlaw/mgsio3/crystalline/exponent", data=hdu_list[3].data
)
data_range = f"{np.amin(hdu_list[3].data)} - {np.amax(hdu_list[3].data)}"
print(f" - Power-law exponent range: {data_range}")
