API Reference¶

class twind.TigressWindModel(z0='H', verbose=False)¶

TIGRESS Wind Launching Model class

Parameters:	z0 (['H','2H','500','1000']) –

Examples

>>> from twind import *
>>> tw = TigressWindModel(z0='H')
>>> tw.set_axes()
>>> pdf = tw.build_Mpdf()

CoolMassFluxPDF(u, w, sfr=1.0, params=None)¶

Model of mass loading/flux PDF for cool gas

This utilizes generalized gamma (vout) and log-normal (cs) distributions.

Parameters:	u (array_like (xarray.DataArray)) – log vout w (array_like (xarray.DataArray)) – log cs sfr (float, array_like) – SFR surface density params (array_like or None) – (p_v, d_v, cs0, sigma) if None, cool_params attribute will be used
Returns:	pdf
Return type:	array_like (xarray.DataArray)

Notes

see Joint PDF Model

HotMassFluxPDF(u, w, sfr=1.0, params=None)¶

Model of mass loading/flux PDF for hot gas

This utilizes generalized gamma distributions in vBz and Mach, where vBz = sqrt(vout^2 + 5*cs^2) and Mach = vout/cs

Parameters:	u (array_like (xarray.DataArray)) – log vout w (array_like (xarray.DataArray)) – log cs sfr (float, array_like) – SFR surface density params (array_like or None) – (p_v, d_v, cs0, sigma) if None, cool_params attribute will be used
Returns:	pdf
Return type:	array_like (xarray.DataArray)

Notes

see Joint PDF Model

build_Mpdf(verbose=False)¶

Build mass loading/flux PDF

This will use axes attributes (logvout, logcs, sfr) set by set_axes method

Parameters:	verbose (bool) – print integrations of both cool and hot PDFs
Returns:	pdfs
Return type:	xarray.Dataset

build_model(ZISM=None, renormalize=True, energy_bias=True, verbose=False)¶

Build full PDFs for mass, momentum, energy, and metal PDFs

This will use axes attributes (logvout, logcs, sfr) set by set_axes method

Parameters:	ZISM (float) – set ZISM for metal PDF (if None, ZISM=0.02) renormalize (bool) – if True, momentum, energy, and metal PDFs are renormalized energy_bias (bool) – if True, apply energy bias factor in building the energy PDF verbose (bool) – print integrations of both cool and hot PDFs
Returns:	pdfs
Return type:	xarray.Dataset

reset_parameters(z0)¶

Reset parameters for different z0

This sets z0 attribute and calls _set_parameters() method.

Parameters:	z0 (['H','2H','500','1000']) –

set_axes(pdf=None, sfr=(-6, 2, 100), vout=(0, 4, 500), cs=(0, 4, 500), verbose=False)¶

Set axes (vout, cs, sfr) using xarray for convenient manipulations

If a simulated pdf is an input, model axes are set to be identical to those of the input pdf otherwise, axes are set for a given (log min, log max, N bins)

Key attributes, u = logvout, w = logcs, logsfr, vBz, and Mach, will be set

Parameters:

pdf (xarray.Dataset or xarray.DataArray) – a joint pdf from simulation
sfr (float, tuple, list) – a single value of SFR surface density, or log values of min, max, and No. of bins for SFR axis
vout (tuple, list) – log values of min, max, and No. of bins for vout axis
cs (tuple, list) – log values of min, max, and No. of bins for cs axis
verbose (bool) – print input ranges

show_parameters()¶: Print all parameters in readable forms

class twind.TigressWindSampler(z0='H', verbose=False)¶

Particle sampler for the TIGRESS Wind Model

Parameters:	z0 (['H','2H','500','1000']) –

Examples

>>> from twind import *
>>> sampler = TigressWindSampler()
>>> cool,hot=sampler.draw_mass(sfr0,mcool,mhot,area=area,dt=dt)

draw_energy(sfr, ecool, ehot, area=1.0, dt=1000.0)¶

Draw particles with fixed particle energy quanta

Parameters:	sfr (float, array_like) – SFR surface density in Msun/yr/kpc^2 ecool (float) – energy of cool gas in 10^51 erg ehot (float) – energy of hot gas in 10^51 erg area (float) – area in kpc^2 dt (float, array_like) – time interval over which particle is sampled
Returns:	cool, hot – dicts containg particle mass, 3 component velocity, sound speed, metallicity, and index of each particle in the corresponding input SFR surface density array, which will be used for reconstuction of time series
Return type:	dicts

draw_mass(sfr, mcool, mhot, area=1.0, dt=1000.0)¶

Draw particles with fixed particle mass quanta

Parameters:	sfr (float, array_like) – SFR surface density in Msun/yr/kpc^2 mcool (float) – Mass of cool gas in Msun mhot (float) – Mass of hot gas in Msun area (float) – area in kpc^2 dt (float, array_like) – time interval in yr over which particle is sampled
Returns:	cool, hot – dicts containg particle mass, 3 component velocity, sound speed, metallicity, and index of each particle in the corresponding input SFR surface density array, which will be used for reconstuction of time series
Return type:	dicts

get_refs(sfr)¶

Obtain reference rates and loading factors for a given SFR surface density using scaling relations

Parameters:	sfr (array_like) – SFR surface density
Returns:	refs (array_like) – reference mass, momemtum, energy, metal outflow rates eta (array_like) – mass, momemtum, energy, metal loading factors for total gas eta_cool (array_like) – mass, momemtum, energy, metal loading factors for cool gas eta_hot (array_like) – mass, momemtum, energy, metal loading factors for hot gas

twind.to_time_series(p, time)¶

Function to convert the particle data into time series

Parameters:	p (dict) – paticle data as returned by TigressWindSampler.draw method time (array_like) – time array corresponding to SFR time series used to sample particles
Returns:	out – time series of mass, momemtum, energy, and metals carried by sampled particles
Return type:	(m, p, E, mZ)

class twind.TigressSimContainer(z0='H', modelnames=['R2', 'R4', 'R8', 'R16', 'LGR2', 'LGR4', 'LGR8'], basedir='./')¶

Simulation PDF container for the TIGRESS simulation suite

Load all models at a given height.

Parameters:	z0 (['H','2H','500','1000']) – modelnames (['R2','R4','R8','R16','LGR2','LGR4','LGR8']) – list of model names to load

Examples

>>> from twind import *
>>> sim = TigressSimContainer(z0='H')

class twind.TigressSimLoader(name='R4', z0='H', basedir='./')¶

Simulated PDF loader for the TIGRESS simulation suite

Parameters:	name (['R2','R4','R8','R16','LGR2','LGR4','LGR8']) – z0 (['H','2H','500','1000']) –

Examples

>>> from twind import *
>>> sim = TigressSimContainer(model='R4',z0='H')

download(source='tigressdata', time_series=False)¶

Download simulation pdf data

Parameters:	source (['tigressdata','dataverse','cca']) –

Note

‘cca’ server is not yet available as a source

load(download=False, time_series=False)¶

Load simulation PDF

Parameters:	download (bool) – automatically call download() method if file doesn’t exist

pdf_reconstruction()¶: PDF reconstruction from mass loading PDF