API

pandisc.model

Functions to compute the pandisc model. For the formulation please refer to the documentation.

pandisc.model.disk(v_r, k, v_sigma, f, v_c, v_arr)

compute the co-rotating disk part of the model, the integrated model flux is normalized to f

Parameters:

• v_r (float) – float, rotation velocity of the disk

• k (float) – float, gradient of the angular distribution, defining the asymmetry of the line, should be in the range (-2/pi,2/pi)

• v_sigma (float) – float, sigma of the disk velocity dispersion

• f (float) – float, the integrated flux integrate(f_model dv) of the model

• v_c (float) – float, velocity of the line center

• v_arr (numpy.ndarray or float) – array, recording the axis to compute the model

Returns:

array of the computed model, in the same shape as input v_arr

Return type:

numpy.ndarray or float

pandisc.model.gaussian(v_g, f, v_c, v_arr)

compute the gaussian component of the model, the integrated flux is normalized to f

Parameters:

• v_g (float) – float, sigma of the gaussian peak

• f (float) – float, the integrated flux integrate(f_model dv) of the model

• v_c (float) – float, velocity of the line center

• v_arr (numpy.ndarray or float) – array, recording the axis to compute the model

Returns:

array of the computed model, in the same shape as input v_arr

Return type:

numpy.ndarray or float

pandisc.model.model(v_r, k, v_sigma, r, v_g, f, v_c, v_arr)

compute the full pandisc model

Parameters:

• v_r (float) – float, rotation velocity of the disk

• k (float) – float, gradient of the angular distribution, defining the asymmetry of the line, should be in the range (-2/pi,2/pi)

• v_sigma (float) – float, sigma of the disk velocity dispersion

• r (float) – float, fraction of the integrated flux in the disk component

• v_g (float) – float, sigma of the gaussian peak

• f (float) – float, the integrated flux integrate(f_model dv) of the model

• v_c (float) – float, velocity of the line center

• v_arr (numpy.ndarray or float) – array, recording the axis to compute the model

Returns:

array of the computed model, in the same shape as input v_arr

Return type:

numpy.ndarray or float

pandisc.model.model_parts(v_r, k, v_sigma, r, v_g, f, v_c, v_arr)

return the disk and gaussian components separately, useful for plotting

Parameters:

• v_r (float) – float, rotation velocity of the disk

• k (float) – float, gradient of the angular distribution, defining the asymmetry of the line, should be in the range (-2/pi,2/pi)

• v_sigma (float) – float, sigma of the disk velocity dispersion

• r (float) – float, fraction of the integrated flux in the disk component

• v_g (float) – float, sigma of the gaussian peak

• f (float) – float, the integrated flux integrate(f_model dv) of the model

• v_c (float) – float, velocity of the line center

• v_arr (numpy.ndarray or float) – array, recording the axis to compute the model

Returns:

(disk, gaussian) parts of the model, each part is in the same shape as input v_arr

Return type:

tuple[numpy.ndarray or float, numpy.ndarray or float]

pandisc.model.disk_fwhm(v_r, v_sigma)

estimate the line full width half maximum of the disk component

Parameters:

• v_r (float) – float, rotation velocity of the disk

• v_sigma (float) – float, sigma of the disk velocity dispersion

Returns:

float, the estimated line width

Return type:

float

pandisc.model.disk_peak_width(v_r, v_sigma)

estimate the line width between the two peaks of the disk component

Parameters:

• v_r (float) – float, rotation velocity of the disk

• v_sigma (float) – float, sigma of the disk velocity dispersion

Returns:

float, the estimated line width between peaks

Return type:

float

pandisc.model.disk_peak_flux(v_r, v_sigma, f)

estimate the flux density of the peaks of the disk component

Parameters:

• v_r (float) – float, rotation velocity of the disk

• v_sigma (float) – float, sigma of the disk velocity dispersion

• f (float) – float, the integrated flux of the disk component

Returns:

float, the estimated line width between peaks

Return type:

float

pandisc.model.w50m(v_r, v_sigma, r, v_g)

estimate the W50 of the line based on the line model

Parameters:

• v_r (float) – float, rotation velocity of the disk

• v_sigma (float) – float, sigma of the disk velocity dispersion

• r (float) – float, fraction of the integrated flux in the disk component

• v_g (float) – float, sigma of the gaussian peak

Returns:

float, W50 model

Return type:

float

pandisc.fit

Functions for MCMC fit formulation on H I spectra. If you are interested in applying the model on other spectral line, the accepted range of 25 km/s might be too small, so you may want to change pandisc.SIGMA_MAX to higher value

pandisc.fit.model_mcmc(para_mcmc, v_arr)

helper function for MCMC fit, to generate line model from the parameters used in fitting

Parameters:

• para_mcmc (list or tuple or numpy.ndarray) – list or tuple or array, containing the seven parameters used for MCMC fit, in the oder of (lg_v_r, k, v_sigma, r, lg_v_g, f, v_c)

• v_arr (numpy.ndarray or float) – array, recording the axis to compute the model

Returns:

array of the computed model, in the same shape as input v_arr

Return type:

numpy.ndarray or float

pandisc.fit.model_mcmc_parts(para_mcmc, v_arr)

return the disk and gaussian components separately using the set of the parameters used in MCMC fit

Parameters:

• para_mcmc (list or tuple or numpy.ndarray) – list or tuple or array, containing the seven parameters used for MCMC fit, in the oder of (lg_v_r, k, v_sigma, r, lg_v_g, f, v_c)

• v_arr (numpy.ndarray or float) – array, recording the axis to compute the model

Returns:

(disk, gaussian) parts of the model, each part is in the same shape as input v_arr

Return type:

tuple[numpy.ndarray or float, numpy.ndarray or float]

pandisc.fit.ln_priori_flat(para_mcmc, v_arr)

a flat priori function defined in the paper, only checking the range of the input parameters

Parameters:

• para_mcmc (list or tuple or numpy.ndarray) – list or tuple or array, containing the seven parameters used for MCMC fit, in the oder of (lg_v_r, k, v_sigma, r, lg_v_g, f, v_c)

• v_arr (numpy.ndarray) – array, recording the axis to compute the model

Returns:

float, log e of the priori

Return type:

float

pandisc.fit.ln_priori(para_mcmc, v_arr)

the priori function defined in the paper

Parameters:

• para_mcmc (list or tuple or numpy.ndarray) – list or tuple or array, containing the seven parameters used for MCMC fit, in the oder of (lg_v_r, k, v_sigma, r, lg_v_g, f, v_c)

• v_arr (numpy.ndarray) – array, recording the axis to compute the model

Returns:

float, log e of the priori

Return type:

float

pandisc.fit.ln_like(para_mcmc, v_arr, spec, sigma=2.23)

compute the log likelihood for the model parameter given the input spectrum, using a per-channel rms

Parameters:

• para_mcmc (list or tuple or numpy.ndarray) – list or tuple or array, containing the seven parameters used for MCMC fit, in the oder of (lg_v_r, k, v_sigma, r, lg_v_g, f, v_c)

• v_arr (numpy.ndarray) – array, recording the axis to compute the model

• spec (numpy.ndarray) – array, input spectrum to evaluate likelihood for the parameters, must have the same shape as v_arr

• sigma (float) – float, sigma per channel used for evaluating likelihood

Returns:

float, log likelihood of the input parameters

Return type:

float

pandisc.fit.ln_prob(para_mcmc, v_arr, spec, sigma=2.23)

compute the posterior likelihood of the input parameter, by combining priori and likelihood

Parameters:

• para_mcmc (list or tuple or numpy.ndarray) – list or tuple or array, containing the seven parameters used for MCMC fit, in the oder of (lg_v_r, k, v_sigma, r, lg_v_g, f, v_c)

• v_arr (numpy.ndarray) – array, recording the axis to compute the model

• spec (numpy.ndarray) – array, input spectrum to evaluate likelihood for the parameters, must have the same shape as v_arr

• sigma (float) – float, sigma per channel used for evaluating likelihood

Returns:

float, log posterior likelihood of the input parameters

Return type:

float