def plot_extinction(tag: str,
burnin: Optional[int] = None,
random: Optional[int] = None,
wavel_range: Optional[Tuple[float, float]] = None,
xlim: Optional[Tuple[float, float]] = None,
ylim: Optional[Tuple[float, float]] = None,
offset: Optional[Tuple[float, float]] = None,
output: str = 'extinction.pdf') -> None:
"""
Function to plot random samples of the extinction, either from fitting a size distribution
of enstatite grains (``dust_radius``, ``dust_sigma``, and ``dust_ext``), or from fitting
ISM extinction (``ism_ext`` and optionally ``ism_red``).
Parameters
----------
tag : str
Database tag with the samples.
burnin : int, None
Number of burnin steps to exclude. All samples are used if set to ``None``. Only required
after running MCMC with :func:`~species.analysis.fit_model.FitModel.run_mcmc`.
random : int, None
Number of randomly selected samples. All samples are used if set to ``None``.
wavel_range : tuple(float, float), None
Wavelength range (um) for the extinction. The default wavelength range (0.4, 10.) is used
if set to ``None``.
xlim : tuple(float, float), None
Limits of the wavelength axis. The range is set automatically if set to ``None``.
ylim : tuple(float, float)
Limits of the extinction axis. The range is set automatically if set to ``None``.
offset : tuple(float, float), None
Offset of the x- and y-axis label. Default values are used if set to ``None``.
output : str
Output filename.
Returns
-------
NoneType
None
"""
if burnin is None:
burnin = 0
if wavel_range is None:
wavel_range = (0.4, 10.)
mpl.rcParams['font.serif'] = ['Bitstream Vera Serif']
mpl.rcParams['font.family'] = 'serif'
plt.rc('axes', edgecolor='black', linewidth=2.2)
species_db = database.Database()
box = species_db.get_samples(tag)
samples = box.samples
if samples.ndim == 2 and random is not None:
ran_index = np.random.randint(samples.shape[0], size=random)
samples = samples[ran_index, ]
elif samples.ndim == 3:
if burnin > samples.shape[1]:
raise ValueError(
f'The \'burnin\' value is larger than the number of steps '
f'({samples.shape[1]}) that are made by the walkers.')
samples = samples[:, burnin:, :]
ran_walker = np.random.randint(samples.shape[0], size=random)
ran_step = np.random.randint(samples.shape[1], size=random)
samples = samples[ran_walker, ran_step, :]
plt.figure(1, figsize=(6, 3))
gridsp = mpl.gridspec.GridSpec(1, 1)
gridsp.update(wspace=0, hspace=0, left=0, right=1, bottom=0, top=1)
ax = plt.subplot(gridsp[0, 0])
ax.tick_params(axis='both',
which='major',
colors='black',
labelcolor='black',
direction='in',
width=1,
length=5,
labelsize=12,
top=True,
bottom=True,
left=True,
right=True,
labelbottom=True)
ax.tick_params(axis='both',
which='minor',
colors='black',
labelcolor='black',
direction='in',
width=1,
length=3,
labelsize=12,
top=True,
bottom=True,
left=True,
right=True,
labelbottom=True)
ax.set_xlabel('Wavelength (µm)', fontsize=12)
ax.set_ylabel('Extinction (mag)', fontsize=12)
if xlim is not None:
ax.set_xlim(xlim[0], xlim[1])
if ylim is not None:
ax.set_ylim(ylim[0], ylim[1])
if offset is not None:
ax.get_xaxis().set_label_coords(0.5, offset[0])
ax.get_yaxis().set_label_coords(offset[1], 0.5)
else:
ax.get_xaxis().set_label_coords(0.5, -0.22)
ax.get_yaxis().set_label_coords(-0.09, 0.5)
sample_wavel = np.linspace(wavel_range[0], wavel_range[1], 100)
if 'lognorm_radius' in box.parameters and 'lognorm_sigma' in box.parameters and \
'lognorm_ext' in box.parameters:
cross_optical, dust_radius, dust_sigma = dust_util.interp_lognorm([],
[],
None)
log_r_index = box.parameters.index('lognorm_radius')
sigma_index = box.parameters.index('lognorm_sigma')
ext_index = box.parameters.index('lognorm_ext')
log_r_g = samples[:, log_r_index]
sigma_g = samples[:, sigma_index]
dust_ext = samples[:, ext_index]
database_path = dust_util.check_dust_database()
with h5py.File(database_path, 'r') as h5_file:
cross_section = np.asarray(
h5_file['dust/lognorm/mgsio3/crystalline/cross_section'])
wavelength = np.asarray(
h5_file['dust/lognorm/mgsio3/crystalline/wavelength'])
cross_interp = RegularGridInterpolator(
(wavelength, dust_radius, dust_sigma), cross_section)
for i in range(samples.shape[0]):
cross_tmp = cross_optical['Generic/Bessell.V'](sigma_g[i],
10.**log_r_g[i])
n_grains = dust_ext[i] / cross_tmp / 2.5 / np.log10(np.exp(1.))
sample_cross = np.zeros(sample_wavel.shape)
for j, item in enumerate(sample_wavel):
sample_cross[j] = cross_interp(
(item, 10.**log_r_g[i], sigma_g[i]))
sample_ext = 2.5 * np.log10(np.exp(1.)) * sample_cross * n_grains
ax.plot(sample_wavel,
sample_ext,
ls='-',
lw=0.5,
color='black',
alpha=0.5)
elif 'powerlaw_max' in box.parameters and 'powerlaw_exp' in box.parameters and \
'powerlaw_ext' in box.parameters:
cross_optical, dust_max, dust_exp = dust_util.interp_powerlaw([], [],
None)
r_max_index = box.parameters.index('powerlaw_max')
exp_index = box.parameters.index('powerlaw_exp')
ext_index = box.parameters.index('powerlaw_ext')
r_max = samples[:, r_max_index]
exponent = samples[:, exp_index]
dust_ext = samples[:, ext_index]
database_path = dust_util.check_dust_database()
with h5py.File(database_path, 'r') as h5_file:
cross_section = np.asarray(
h5_file['dust/powerlaw/mgsio3/crystalline/cross_section'])
wavelength = np.asarray(
h5_file['dust/powerlaw/mgsio3/crystalline/wavelength'])
cross_interp = RegularGridInterpolator(
(wavelength, dust_max, dust_exp), cross_section)
for i in range(samples.shape[0]):
cross_tmp = cross_optical['Generic/Bessell.V'](exponent[i],
10.**r_max[i])
n_grains = dust_ext[i] / cross_tmp / 2.5 / np.log10(np.exp(1.))
sample_cross = np.zeros(sample_wavel.shape)
for j, item in enumerate(sample_wavel):
sample_cross[j] = cross_interp(
(item, 10.**r_max[i], exponent[i]))
sample_ext = 2.5 * np.log10(np.exp(1.)) * sample_cross * n_grains
ax.plot(sample_wavel,
sample_ext,
ls='-',
lw=0.5,
color='black',
alpha=0.5)
elif 'ism_ext' in box.parameters:
ext_index = box.parameters.index('ism_ext')
ism_ext = samples[:, ext_index]
if 'ism_red' in box.parameters:
red_index = box.parameters.index('ism_red')
ism_red = samples[:, red_index]
else:
ism_red = np.full(samples.shape[0], 3.1)
for i in range(samples.shape[0]):
sample_ext = dust_util.ism_extinction(ism_ext[i], ism_red[i],
sample_wavel)
ax.plot(sample_wavel,
sample_ext,
ls='-',
lw=0.5,
color='black',
alpha=0.5)
else:
raise ValueError(
'The SamplesBox does not contain extinction parameters.')
print(f'Plotting extinction: {output}...', end='', flush=True)
plt.savefig(os.getcwd() + '/' + output, bbox_inches='tight')
plt.clf()
plt.close()
print(' [DONE]')
def apply_powerlaw_ext(wavelength: np.ndarray,
flux: np.ndarray,
r_max_interp: float,
exp_interp: float,
v_band_ext: float) -> np.ndarray:
"""
Internal function for applying extinction by dust to a spectrum.
wavelength : np.ndarray
Wavelengths (um) of the spectrum.
flux : np.ndarray
Fluxes (W m-2 um-1) of the spectrum.
r_max_interp : float
Maximum radius (um) of the power-law size distribution.
exp_interp : float
Exponent of the power-law size distribution.
v_band_ext : float
The extinction (mag) in the V band.
Returns
-------
np.ndarray
Fluxes (W m-2 um-1) with the extinction applied.
"""
database_path = dust_util.check_dust_database()
with h5py.File(database_path, 'r') as h5_file:
dust_cross = np.asarray(h5_file['dust/powerlaw/mgsio3/crystalline/cross_section'])
dust_wavel = np.asarray(h5_file['dust/powerlaw/mgsio3/crystalline/wavelength'])
dust_r_max = np.asarray(h5_file['dust/powerlaw/mgsio3/crystalline/radius_max'])
dust_exp = np.asarray(h5_file['dust/powerlaw/mgsio3/crystalline/exponent'])
dust_interp = RegularGridInterpolator((dust_wavel, dust_r_max, dust_exp),
dust_cross,
method='linear',
bounds_error=True)
read_filt = read_filter.ReadFilter('Generic/Bessell.V')
filt_trans = read_filt.get_filter()
cross_phot = np.zeros((dust_r_max.shape[0], dust_exp.shape[0]))
for i in range(dust_r_max.shape[0]):
for j in range(dust_exp.shape[0]):
cross_interp = interp1d(dust_wavel,
dust_cross[:, i, j],
kind='linear',
bounds_error=True)
cross_tmp = cross_interp(filt_trans[:, 0])
integral1 = np.trapz(filt_trans[:, 1]*cross_tmp, filt_trans[:, 0])
integral2 = np.trapz(filt_trans[:, 1], filt_trans[:, 0])
# Filter-weighted average of the extinction cross section
cross_phot[i, j] = integral1/integral2
cross_interp = interp2d(dust_exp,
dust_r_max,
cross_phot,
kind='linear',
bounds_error=True)
cross_v_band = cross_interp(exp_interp, 10.**r_max_interp)[0]
r_max_full = np.full(wavelength.shape[0], 10.**r_max_interp)
exp_full = np.full(wavelength.shape[0], exp_interp)
cross_new = dust_interp(np.column_stack((wavelength, r_max_full, exp_full)))
n_grains = v_band_ext / cross_v_band / 2.5 / np.log10(np.exp(1.))
return flux * np.exp(-cross_new*n_grains)
def plot_extinction(
tag: str,
burnin: Optional[int] = None,
random: Optional[int] = None,
wavel_range: Optional[Tuple[float, float]] = None,
xlim: Optional[Tuple[float, float]] = None,
ylim: Optional[Tuple[float, float]] = None,
offset: Optional[Tuple[float, float]] = None,
output: Optional[str] = "extinction.pdf",
) -> None:
"""
Function to plot random samples of the extinction, either from fitting a size distribution
of enstatite grains (``dust_radius``, ``dust_sigma``, and ``dust_ext``), or from fitting
ISM extinction (``ism_ext`` and optionally ``ism_red``).
Parameters
----------
tag : str
Database tag with the samples.
burnin : int, None
Number of burnin steps to exclude. All samples are used if set to ``None``. Only required
after running MCMC with :func:`~species.analysis.fit_model.FitModel.run_mcmc`.
random : int, None
Number of randomly selected samples. All samples are used if set to ``None``.
wavel_range : tuple(float, float), None
Wavelength range (um) for the extinction. The default wavelength range (0.4, 10.) is used
if set to ``None``.
xlim : tuple(float, float), None
Limits of the wavelength axis. The range is set automatically if set to ``None``.
ylim : tuple(float, float)
Limits of the extinction axis. The range is set automatically if set to ``None``.
offset : tuple(float, float), None
Offset of the x- and y-axis label. Default values are used if set to ``None``.
output : str
Output filename for the plot. The plot is shown in an
interface window if the argument is set to ``None``.
Returns
-------
NoneType
None
"""
if burnin is None:
burnin = 0
if wavel_range is None:
wavel_range = (0.4, 10.0)
mpl.rcParams["font.serif"] = ["Bitstream Vera Serif"]
mpl.rcParams["font.family"] = "serif"
plt.rc("axes", edgecolor="black", linewidth=2.2)
species_db = database.Database()
box = species_db.get_samples(tag)
samples = box.samples
if samples.ndim == 2 and random is not None:
ran_index = np.random.randint(samples.shape[0], size=random)
samples = samples[ran_index, ]
elif samples.ndim == 3:
if burnin > samples.shape[1]:
raise ValueError(
f"The 'burnin' value is larger than the number of steps "
f"({samples.shape[1]}) that are made by the walkers.")
samples = samples[:, burnin:, :]
ran_walker = np.random.randint(samples.shape[0], size=random)
ran_step = np.random.randint(samples.shape[1], size=random)
samples = samples[ran_walker, ran_step, :]
plt.figure(1, figsize=(6, 3))
gridsp = mpl.gridspec.GridSpec(1, 1)
gridsp.update(wspace=0, hspace=0, left=0, right=1, bottom=0, top=1)
ax = plt.subplot(gridsp[0, 0])
ax.tick_params(
axis="both",
which="major",
colors="black",
labelcolor="black",
direction="in",
width=1,
length=5,
labelsize=12,
top=True,
bottom=True,
left=True,
right=True,
labelbottom=True,
)
ax.tick_params(
axis="both",
which="minor",
colors="black",
labelcolor="black",
direction="in",
width=1,
length=3,
labelsize=12,
top=True,
bottom=True,
left=True,
right=True,
labelbottom=True,
)
ax.set_xlabel("Wavelength (µm)", fontsize=12)
ax.set_ylabel("Extinction (mag)", fontsize=12)
if xlim is not None:
ax.set_xlim(xlim[0], xlim[1])
if ylim is not None:
ax.set_ylim(ylim[0], ylim[1])
if offset is not None:
ax.get_xaxis().set_label_coords(0.5, offset[0])
ax.get_yaxis().set_label_coords(offset[1], 0.5)
else:
ax.get_xaxis().set_label_coords(0.5, -0.22)
ax.get_yaxis().set_label_coords(-0.09, 0.5)
sample_wavel = np.linspace(wavel_range[0], wavel_range[1], 100)
if ("lognorm_radius" in box.parameters
and "lognorm_sigma" in box.parameters
and "lognorm_ext" in box.parameters):
cross_optical, dust_radius, dust_sigma = dust_util.interp_lognorm([],
[],
None)
log_r_index = box.parameters.index("lognorm_radius")
sigma_index = box.parameters.index("lognorm_sigma")
ext_index = box.parameters.index("lognorm_ext")
log_r_g = samples[:, log_r_index]
sigma_g = samples[:, sigma_index]
dust_ext = samples[:, ext_index]
database_path = dust_util.check_dust_database()
with h5py.File(database_path, "r") as h5_file:
cross_section = np.asarray(
h5_file["dust/lognorm/mgsio3/crystalline/cross_section"])
wavelength = np.asarray(
h5_file["dust/lognorm/mgsio3/crystalline/wavelength"])
cross_interp = RegularGridInterpolator(
(wavelength, dust_radius, dust_sigma), cross_section)
for i in range(samples.shape[0]):
cross_tmp = cross_optical["Generic/Bessell.V"](sigma_g[i],
10.0**log_r_g[i])
n_grains = dust_ext[i] / cross_tmp / 2.5 / np.log10(np.exp(1.0))
sample_cross = np.zeros(sample_wavel.shape)
for j, item in enumerate(sample_wavel):
sample_cross[j] = cross_interp(
(item, 10.0**log_r_g[i], sigma_g[i]))
sample_ext = 2.5 * np.log10(np.exp(1.0)) * sample_cross * n_grains
ax.plot(sample_wavel,
sample_ext,
ls="-",
lw=0.5,
color="black",
alpha=0.5)
elif ("powerlaw_max" in box.parameters and "powerlaw_exp" in box.parameters
and "powerlaw_ext" in box.parameters):
cross_optical, dust_max, dust_exp = dust_util.interp_powerlaw([], [],
None)
r_max_index = box.parameters.index("powerlaw_max")
exp_index = box.parameters.index("powerlaw_exp")
ext_index = box.parameters.index("powerlaw_ext")
r_max = samples[:, r_max_index]
exponent = samples[:, exp_index]
dust_ext = samples[:, ext_index]
database_path = dust_util.check_dust_database()
with h5py.File(database_path, "r") as h5_file:
cross_section = np.asarray(
h5_file["dust/powerlaw/mgsio3/crystalline/cross_section"])
wavelength = np.asarray(
h5_file["dust/powerlaw/mgsio3/crystalline/wavelength"])
cross_interp = RegularGridInterpolator(
(wavelength, dust_max, dust_exp), cross_section)
for i in range(samples.shape[0]):
cross_tmp = cross_optical["Generic/Bessell.V"](exponent[i],
10.0**r_max[i])
n_grains = dust_ext[i] / cross_tmp / 2.5 / np.log10(np.exp(1.0))
sample_cross = np.zeros(sample_wavel.shape)
for j, item in enumerate(sample_wavel):
sample_cross[j] = cross_interp(
(item, 10.0**r_max[i], exponent[i]))
sample_ext = 2.5 * np.log10(np.exp(1.0)) * sample_cross * n_grains
ax.plot(sample_wavel,
sample_ext,
ls="-",
lw=0.5,
color="black",
alpha=0.5)
elif "ism_ext" in box.parameters:
ext_index = box.parameters.index("ism_ext")
ism_ext = samples[:, ext_index]
if "ism_red" in box.parameters:
red_index = box.parameters.index("ism_red")
ism_red = samples[:, red_index]
else:
# Use default ISM redenning (R_V = 3.1) if ism_red was not fitted
ism_red = np.full(samples.shape[0], 3.1)
for i in range(samples.shape[0]):
sample_ext = dust_util.ism_extinction(ism_ext[i], ism_red[i],
sample_wavel)
ax.plot(sample_wavel,
sample_ext,
ls="-",
lw=0.5,
color="black",
alpha=0.5)
else:
raise ValueError(
"The SamplesBox does not contain extinction parameters.")
if output is None:
print("Plotting extinction...", end="", flush=True)
else:
print(f"Plotting extinction: {output}...", end="", flush=True)
print(" [DONE]")
if output is None:
plt.show()
else:
plt.savefig(output, bbox_inches="tight")
plt.clf()
plt.close()
def apply_lognorm_ext(wavelength: np.ndarray,
flux: np.ndarray,
radius_interp: float,
sigma_interp: float,
v_band_ext: float) -> np.ndarray:
"""
Internal function for applying extinction by dust to a spectrum.
wavelength : np.ndarray
Wavelengths (um) of the spectrum.
flux : np.ndarray
Fluxes (W m-2 um-1) of the spectrum.
radius_interp : float
Logarithm of the mean geometric radius (um) of the log-normal size distribution.
sigma_interp : float
Geometric standard deviation (dimensionless) of the log-normal size distribution.
v_band_ext : float
The extinction (mag) in the V band.
Returns
-------
np.ndarray
Fluxes (W m-2 um-1) with the extinction applied.
"""
database_path = dust_util.check_dust_database()
with h5py.File(database_path, 'r') as h5_file:
dust_cross = np.asarray(h5_file['dust/lognorm/mgsio3/crystalline/cross_section'])
dust_wavel = np.asarray(h5_file['dust/lognorm/mgsio3/crystalline/wavelength'])
dust_radius = np.asarray(h5_file['dust/lognorm/mgsio3/crystalline/radius_g'])
dust_sigma = np.asarray(h5_file['dust/lognorm/mgsio3/crystalline/sigma_g'])
dust_interp = RegularGridInterpolator((dust_wavel, dust_radius, dust_sigma),
dust_cross,
method='linear',
bounds_error=True)
read_filt = read_filter.ReadFilter('Generic/Bessell.V')
filt_trans = read_filt.get_filter()
cross_phot = np.zeros((dust_radius.shape[0], dust_sigma.shape[0]))
for i in range(dust_radius.shape[0]):
for j in range(dust_sigma.shape[0]):
cross_interp = interp1d(dust_wavel,
dust_cross[:, i, j],
kind='linear',
bounds_error=True)
cross_tmp = cross_interp(filt_trans[:, 0])
integral1 = np.trapz(filt_trans[:, 1]*cross_tmp, filt_trans[:, 0])
integral2 = np.trapz(filt_trans[:, 1], filt_trans[:, 0])
# Filter-weighted average of the extinction cross section
cross_phot[i, j] = integral1/integral2
cross_interp = interp2d(dust_sigma,
dust_radius,
cross_phot,
kind='linear',
bounds_error=True)
cross_v_band = cross_interp(sigma_interp, 10.**radius_interp)[0]
radius_full = np.full(wavelength.shape[0], 10.**radius_interp)
sigma_full = np.full(wavelength.shape[0], sigma_interp)
cross_new = dust_interp(np.column_stack((wavelength, radius_full, sigma_full)))
n_grains = v_band_ext / cross_v_band / 2.5 / np.log10(np.exp(1.))
return flux * np.exp(-cross_new*n_grains)