Usage

The pymcfost package provides classes to work with MCFOST image and line data.

Running MCFOST

The package provides a function to run MCFOST directly:

from pymcfost import run

# Run MCFOST with a parameter file
run("model.para")

# Run with additional options
run("model.para", options="-img 1.3")

# Run silently and save output to a log file
run("model.para", silent=True, logfile="mcfost.log")

Image Class

The Image class handles continuum images from MCFOST radiative transfer calculations.

Basic Usage

from pymcfost import Image
import matplotlib.plt as plt

# Load an image from a directory containing MCFOST output
image = Image(dir="path/to/mcfost/data")

# Plot the image
image.plot()

# save image to file
plt.savefig('mcfost-image.pdf',bbox_inches='tight')

# also show on screen
plt.show()

Advanced usage

from pymcfost import Image
import matplotlib.plt as plt

# Load an image from a directory containing MCFOST output
image = Image(dir="path/to/mcfost/data")

# set up the plotting page
fig, axes = plt.subplots(1,1,figsize=(8,8))

# Plot the image
image.plot(
    ax=axes,
    i=0,              # inclination index
    iaz=0,           # azimuth angle index
    vmin=0.,       # minimum value for colorscale
    vmax=50.,       # maximum value for colorscale
    dynamic_range=1e6, # ratio between max and min values
    colorbar=True,   # show colorbar
    psf_FWHM=None,   # convolve with Gaussian beam of this FWHM (in arcsec)
    plot_stars=True  # show stellar positions
    Tb=True          # use brightness temperature
)

plt.show()

Plotting model visibilities

from pymcfost import Image

# Load an image from a directory containing MCFOST output
image = Image(dir="path/to/mcfost/data")

# Calculate and plot model visibilities
baselines, vis, fim = image.calc_vis(
    i=0,       # inclination index
    iaz=0,     # azimuth index
    klambda=True  # plot in units of kilolambda
)

Line Class

The Line class handles spectral line data from MCFOST calculations.

Basic Usage

from pymcfost import Line

# Load line data
line = Line(dir="path/to/mcfost/data")

# Plot channel map
line.plot_map(
    i=0,           # inclination index
    iaz=0,         # azimuth angle index
    iTrans=0,      # transition index
    v=None,        # velocity in km/s (alternative to iv)
    iv=None,       # velocity channel index
    moment=None,   # moment map to plot (0=integrated intensity, 1=velocity, 2=dispersion)
    psf_FWHM=None, # beam FWHM in arcsec
    colorbar=True  # show colorbar
)

SED Class

The SED class handles spectral energy distributions and temperature structures.

Basic Usage

from pymcfost import SED

# Load SED data
sed = SED(dir="path/to/mcfost/data")

# Plot SED
sed.plot(
    i=0,           # inclination index
    iaz=0,         # azimuth angle index
    MC=False,      # plot Monte Carlo results
    contrib=True   # show individual contributions
)

# Plot temperature structure
sed.plot_T(
    iaz=0,         # azimuth angle index
    log=True       # use logarithmic scale
)

# Plot vertical temperature profile
sed.plot_Tz(
    r=100.0,       # radius in AU
    dr=5.0         # radial range to average over
)

Common Parameters

Many classes share some common parameters:

i: Index for inclination angle
iaz: Index for azimuth angle
psf_FWHM: FWHM of Gaussian beam for convolution (in arcsec)
bmaj, bmin, bpa: Beam major/minor axis and position angle
axes_unit: Units for axes (‘arcsec’, ‘au’, or ‘pixels’)
plot_stars: Whether to show stellar positions
colorbar: Whether to show colorbar

The plotting methods return matplotlib objects that can be further customized.

Disc Structure

The Disc class handles the spatial structure and density distribution of the disc.

Basic Usage

from pymcfost import Disc

# Load disc structure
disc = Disc(dir="path/to/mcfost/data")

# Get spatial coordinates
r = disc.r()  # radial coordinates
z = disc.z()  # vertical coordinates

# Add a spiral feature
new_density = disc.add_spiral(
    a=30,          # spiral parameter
    sigma=10,      # width in AU
    f=1.5,         # density enhancement
    rmin=20,       # inner radius
    rmax=100       # outer radius
)

Dust Model

The Dust_model class handles dust opacity properties.

Basic Usage

from pymcfost import Dust_model

# Load dust model
dust = Dust_model(dir="path/to/mcfost/data")

# Plot opacities
dust.plot_kappa(abs=True, scatt=True)

# Plot albedo
dust.plot_albedo()

# Save opacity data
dust.print_kappa(file="opacities.txt")

CASA Simulations

The package provides functions to create synthetic ALMA observations using CASA.

Basic Usage

from pymcfost import Image, CASA_simdata

# Load an image
image = Image(dir="path/to/mcfost/data")

# Quick simulation with beam convolution
pseudo_CASA_simdata(
    image,
    bmaj=0.5,        # beam major axis in arcsec
    bmin=0.3,        # beam minor axis in arcsec
    bpa=30,          # beam position angle in degrees
    rms=1e-4         # noise level
)

# Full CASA/ALMA simulation
CASA_simdata(
    image,
    obstime=3600,    # 1 hour observation
    config="alma.cycle6.3",  # ALMA configuration
    pwv=0.5,         # precipitable water vapor
    decl="-22d59m59.8"  # source declination
)

The CASA simulation functions require CASA to be installed on your system.

Directory Structure

pymcfost expects MCFOST output files to be organized in specific subdirectories:

data_th/: Contains SED and temperature data
data_disk/: Contains disc structure data
data_[wavelength]/: Contains image data at specific wavelengths
UV/: Contains dust opacity data

When specifying directories to pymcfost classes, you can either point to these specific subdirectories or to the parent directory containing them.