CoolDwarf.star.atmo package

Submodules

CoolDwarf.star.atmo.atmo module

class CoolDwarf.star.atmo.atmo.AdiabaticIdealAtmosphere(structure: VoxelSphere, opac, radialResolution: int = 10, azimuthalResolution: int = 10, altitudinalResolution: int = 10, tol: Dict[str, float] = {'maxEChange': 0.0001, 'relax': 1e-06, 'volCheck': 0.01}, dof: int = 5, Omega: float = 0)

Bases: object

Attributes:

density

f

geostrophic_wind_velocity_field

gradP

k

lapT

Find the laplacian of the temperature field.

pressure

temperature

Methods

gradT([order, zThresh, corr])	Computes the temperature gradients in the radial, azimuthal, and altitudinal directions.
timestep_temperature(dt)	Calculate the change in temperature over some timestep, dt. The change in temperature is given by the following equation: .. math:: Delta T = lambda_{D} - lambda_{A} + lambda_{R}.

adiabatic_loss
advective_loss
density_profile
diffusive_loss
inject_energy
pressure_profile
radiative_loss
temperature_profile
timestep

adiabatic_loss() → float

advective_loss()

property density: ndarray[Any, dtype[float64]]

density_profile(r: float | ndarray[Any, dtype[float64]]) → float | ndarray[Any, dtype[float64]]

diffusive_loss()

property f: ndarray[Any, dtype[float64]]

property geostrophic_wind_velocity_field: Tuple[ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]]]

property gradP: Tuple[ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]]]

gradT(order: int = 1, zThresh: float = 1e-08, corr: bool = True) → Tuple[ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]]]

Computes the temperature gradients in the radial, azimuthal, and altitudinal directions.

Parameters:

orderint, default=1

The order of the derivative.

zThreshfloat, default=1e-8

The threshold value for the temperature gradient. If the absolute value of the gradient is less than zThresh, it is set to 0.

coorbool, default=True

If True, the temperature gradient is computed in spherical coordinates. If False, the gradient is computed in cartesian coordinates. More formally, if true then the gradient is computed as:

\[\nabla T = (\partial_r T, \frac{1}{r} \partial_{\theta} T, \frac{1}{r \sin(\phi)} \partial_{\phi} T)\]

If false, the gradient is computed as:

\[\nabla T = (\partial_x T, \partial_y T, \partial_z T)\]

A similar pattern is carried over to the laplacian.

Returns:

tuple: A tuple of 3D arrays representing the temperature gradients in the radial, azimuthal, and altitudinal directions.

The arrays are in the form of (tGradR, tGradTheta, tGradPhi).

Raises:

TypeError

If order is not an integer

ValueError

If order is less than 1.

inject_energy(theta, phi, r, energy)

property k

property lapT: Tuple[ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]], ndarray[Any, dtype[float64]]]

Find the laplacian of the temperature field. This is simply a wrapper around 2nd order gradient

Parameters:

corrbool, default=True

If True, the laplacian is computed in spherical coordinates. If False, the laplacian is computed in cartesian coordinates.

Returns:

lapTNDArray[np.float64]

The laplacian of the temperature field.

property pressure: ndarray[Any, dtype[float64]]

pressure_profile(r: float | ndarray[Any, dtype[float64]]) → float | ndarray[Any, dtype[float64]]

radiative_loss()

property temperature: ndarray[Any, dtype[float64]]

temperature_profile(r: float | ndarray[Any, dtype[float64]]) → float | ndarray[Any, dtype[float64]]

timestep(dt: float)

timestep_temperature(dt: float)

Calculate the change in temperature over some timestep, dt. The change in temperature is given by the following equation:

\[\Delta T = \lambda_{D} - \lambda_{A} + \lambda_{R}\]

Where \(\lambda_{D}\) is the diffusive loss, \(\lambda_{A}\) is the advective loss, and \(\lambda_{R}\) is the radiative loss.

Parameters:

dtfloat

The timestep to calculate the change in temperature over.

Returns:

deltaTfloat

The change in temperature over the timestep, dt.

Module contents