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SC-SAM example¶
Load SC-SAM example data into a list of galaxy objects.
import matplotlib.pyplot as plt
import numpy as np
from synthesizer.emission_models import PacmanEmission
from synthesizer.emission_models.attenuation import PowerLaw
from synthesizer.grid import Grid
from synthesizer.load_data.load_scsam import load_SCSAM
if __name__ == "__main__":
# Define the grid
grid_name = "test_grid.hdf5"
grid_dir = "../../tests/test_grid/"
grid = Grid(grid_name, grid_dir=grid_dir)
# Define the emission model
model = PacmanEmission(
grid,
tau_v=0.33,
dust_curve=PowerLaw(slope=-1),
fesc=0.1,
fesc_ly_alpha=0.5,
)
# Load example SC-SAM SF history (just contains 10 galaxies)
test_data = "../../tests/data/sc-sam_sfhist.dat"
# Obtain galaxy objects using different methods:
# Particle method
particle_galaxies, _, _ = load_SCSAM(test_data, "particle")
# Paramteric method, interpolating the grid using scipy's
# nearest ND interpolator
parametric_NNI_galaxies, _, _ = load_SCSAM(
test_data, "parametric_NNI", grid
)
# Paramteric method, interpolating the grid using scipy's
# regular grid interpolator
parametric_RGI_galaxies, _, _ = load_SCSAM(
test_data, "parametric_RGI", grid
)
# Set up arrays to store galaxy SEDs
particle_SEDs = []
parametric_NNI_SEDs = []
parametric_RGI_SEDs = []
# Spectrum that we want
# (e.g. incident, nebular, intrinsic, emergent)
spectrum = "emergent"
# Loop over each galaxy object
for i in range(len(particle_galaxies)):
# Get SEDs for the particle galaxy object
particle_galaxy = particle_galaxies[i]
particle_galaxy.stars.get_spectra(model)
particle_sed = particle_galaxy.stars.spectra[spectrum]
particle_SEDs.append(particle_sed.lnu)
# Get SEDs for the parametric NNI galaxy object
parametric_NNI_galaxy = parametric_NNI_galaxies[i]
parametric_NNI_galaxy.stars.get_spectra(model)
parametric_sed = parametric_NNI_galaxy.stars.spectra[spectrum]
parametric_NNI_SEDs.append(parametric_sed.lnu)
# Get SEDs for the parametric RGI galaxy object
parametric_RGI_galaxy = parametric_RGI_galaxies[i]
parametric_RGI_galaxy.stars.get_spectra(model)
parametric_sed = parametric_RGI_galaxy.stars.spectra[spectrum]
parametric_RGI_SEDs.append(parametric_sed.lnu)
# Plot SEDs
for lnu in particle_SEDs:
plt.plot(np.log10(particle_sed.lam), np.log10(lnu))
plt.xlabel(r"$\log_{10}(\lambda/\rm{\AA})$")
plt.ylabel(
r"$\log_{10}(L_\nu/\rm{erg\,s^{-1}\,Hz^{-1}\,M_{\odot}^{-1}})$"
)
plt.xlim(0, 8)
plt.ylim(10, 35)
plt.title(f"simple particle method - {spectrum}")
plt.grid(color="whitesmoke")
plt.show()
for lnu in parametric_NNI_SEDs:
plt.plot(np.log10(parametric_sed.lam), np.log10(lnu))
plt.xlabel(r"$\log_{10}(\lambda/\rm{\AA})$")
plt.ylabel(
r"$\log_{10}(L_\nu/\rm{erg\,s^{-1}\,Hz^{-1}\,M_{\odot}^{-1}})$"
)
plt.xlim(0, 8)
plt.ylim(10, 35)
plt.title(f"NNI parametric method - {spectrum}")
plt.grid(color="whitesmoke")
plt.show()
for lnu in parametric_RGI_SEDs:
plt.plot(np.log10(parametric_sed.lam), np.log10(lnu))
plt.xlabel(r"$\log_{10}(\lambda/\rm{\AA})$")
plt.ylabel(
r"$\log_{10}(L_\nu/\rm{erg\,s^{-1}\,Hz^{-1}\,M_{\odot}^{-1}})$"
)
plt.xlim(0, 8)
plt.ylim(10, 35)
plt.title(f"RGI parametric method - {spectrum}")
plt.grid(color="whitesmoke")
plt.show()
Total running time of the script: (0 minutes 1.207 seconds)