Simulation Subpackage

Simulation Subpackage

This subpackage provides comprehensive simulation capabilities for CMB observations including signal, foreground, and noise components.

Modules

cmb

CMB power spectrum calculation and map generation with cosmic birefringence effects (isotropic and time-dependent models).

fg

Galactic foreground simulation including thermal dust and synchrotron emission with realistic spatial and spectral properties.

mask

Mask generation for observations including Galactic plane cuts, point source masking, CO emission masking, and apodization.

noise

Realistic noise simulation for LAT and SAT including white noise and 1/f components with proper spatial correlations.

sky

Complete sky simulation classes that combine CMB, foregrounds, and noise for LAT and SAT observations. Includes bandpass integration and component separation via harmonic ILC.

Classes

CMB

CMB simulation with cosmic birefringence

synfast_pol

Fast polarization map generation

Foreground

Foreground component simulation

BandpassInt

Bandpass integration utilities

HILC

Harmonic Internal Linear Combination

Mask

Observation mask generation

Noise

Noise realization generation

NoiseSpectra

Noise power spectra

LATsky

LAT observation simulation

SATsky

SAT observation simulation

LATskyC

LAT calibration simulation

SATskyC

SAT calibration simulation

Overview

The simulation subpackage provides comprehensive tools for generating realistic CMB observations including signal, foregrounds, and instrumental effects.

Main Classes

cobi.simulation.CMB

CMB map generator with cosmic birefringence support.

cobi.simulation.Foreground

Galactic foreground generator for CMB analysis.

cobi.simulation.Mask

Sky mask generator and manager for CMB analysis.

cobi.simulation.Noise

Instrumental noise simulator for Simons Observatory telescopes.

cobi.simulation.LATsky

cobi.simulation.SATsky

cobi.simulation.LATskyC

cobi.simulation.SATskyC

CMB Module

CMB Simulation Module

This module provides comprehensive functionality for simulating Cosmic Microwave Background (CMB) temperature and polarization maps with support for:

  • Lensed and unlensed CMB realizations

  • Cosmic birefringence effects (isotropic and anisotropic)

  • Custom power spectra from CAMB

  • Auxiliary scalar fields (for patchy τ models)

  • Beam convolution and coordinate transformations

Classes

CMB

Main class for generating CMB maps with optional birefringence rotation.

Functions

synfast_pol

Generate polarized CMB maps with optional auxiliary fields.

hp_alm2map_spin

Fast spin-weighted spherical harmonic transforms.

get_camb_cls

Compute CMB power spectra using CAMB.

Example

Generate a CMB realization with isotropic birefringence:

from cobi.simulation import CMB

cmb = CMB(
    nside=512,
    lmax=1500,
    beta=0.35,  # isotropic rotation angle in degrees
    lensing=True,
    libdir='./cmb_sims'
)

# Get rotated CMB map
cmb_map = cmb.get_map(idx=0, apply_beta=True)

Notes

The module uses lenspyx for efficient lensing and supports MPI parallelization for generating large simulation sets.

synfast_pol(nside, spectra)[source]

Generate polarized CMB maps (Q, U) with optional auxiliary field A.

Parameters:

  • nside (int) – HEALPix resolution parameter.

  • spectra (dict) – Power spectra dictionary with required ‘EE’ (and optional ‘BB’, ‘AA’, ‘AE’).

Return type:

(Q_map, U_map) or (Q_map, U_map, A_map) if 'AA' in spectra

class CMB(libdir, nside, model='iso', beta=None, mass=None, Acb=None, lensing=False, Acb_sim_config=None, verbose=True)[source]

Bases: object

CMB map generator with cosmic birefringence support.

This class handles the generation of CMB temperature and polarization maps with optional cosmic birefringence rotation. Supports three birefringence models:

  • iso: Isotropic (constant) rotation angle β

  • iso_td: Time-dependent isotropic model with axion mass parameter

  • aniso: Anisotropic (spatially-varying) rotation from patchy reionization

Parameters:

  • libdir (str) – Directory for caching simulation products and power spectra.

  • nside (int) – HEALPix resolution parameter (nside = 2^n).

  • model ({'iso', 'iso_td', 'aniso'}, default 'iso') – Birefringence model type.

  • beta (float, optional) – Isotropic rotation angle in degrees (required for model=’iso’).

  • mass (float, optional) – Axion mass parameter in units of 10^-22 eV. Must be 1, 1.5, or 6.4 (required for model=’iso_td’).

  • Acb (float, optional) – Amplitude of anisotropic birefringence power spectrum (required for model=’aniso’).

  • lensing (bool, default False) – Whether to include CMB lensing effects.

  • Acb_sim_config (dict, optional) – Configuration for anisotropic simulation batches with keys: - ‘alpha_vary_index’: [start, end] indices for varying α - ‘alpha_cons_index’: [start, end] indices for constant α - ‘null_alpha_index’: [start, end] indices for null test (α=0)

  • verbose (bool, default True) – Enable logging output.

nside

HEALPix resolution.

Type:

int

lmax

Maximum multipole (3*nside - 1).

Type:

int

model

Birefringence model type.

Type:

str

beta

Isotropic rotation angle in degrees.

Type:

float or None

lensing

Whether lensing is enabled.

Type:

bool

cls

CMB power spectra (TT, EE, BB, TE).

Type:

dict

Examples

Generate isotropic birefringence simulation:

cmb = CMB(
    libdir='./cmb_sims',
    nside=512,
    model='iso',
    beta=0.35,
    lensing=True
)

# Get rotated CMB map
cmb_map = cmb.get_map(idx=0, apply_beta=True)

Generate anisotropic birefringence:

cmb = CMB(
    libdir='./cmb_sims',
    nside=512,
    model='aniso',
    Acb=1e-6,
    lensing=True
)

# Get map with spatially-varying rotation
cmb_map = cmb.get_map(idx=0, apply_beta=True)

Notes

  • Uses CAMB for power spectrum generation

  • Supports efficient caching of realizations

  • MPI-aware for parallel simulation generation

  • Lensing implemented via lenspyx

__init__(libdir, nside, model='iso', beta=None, mass=None, Acb=None, lensing=False, Acb_sim_config=None, verbose=True)[source]
Parameters:
scale_invariant(Acb)[source]
__validate_Acb_sim_config__()[source]

Validate the Acb_sim_config dictionary structure.

Return type:

None

__get_alpha_mode__(idx)[source]

Determine which alpha mode to use for a given index.

Parameters: idx (int): The realization index.

Returns: str: One of ‘vary’, ‘constant’, or ‘null’

Parameters:

idx (int)

Return type:

str

__set_seeds__()[source]

Sets the seeds for the simulation.

Return type:

None

__set_workspace__()[source]

Set the workspace for the CMB simulations.

Return type:

None

__dl2cl__(arr, unit_only=False)[source]

Convert Dl to Cl.

Parameters:

arr (numpy.ndarray)

Return type:

numpy.ndarray

__td_eb__(dl=True)[source]

Read the E and B mode power spectra from the CMB power spectra data.

Return type:

numpy.ndarray

compute_powers()[source]

compute the CMB power spectra using CAMB.

Return type:

Dict[str, Any]

get_power(dl=True)[source]

Get the CMB power spectra.

Parameters: dl (bool): If True, return the power spectra with dl factor else without dl factor.

Returns: Dict[str, np.ndarray]: A dictionary containing the CMB power spectra.

Parameters:

dl (bool)

Return type:

Dict[str, numpy.ndarray]

get_unlensed_spectra(dl=True, dtype='d')[source]

Retrieve the unlensed scalar spectra from the power spectrum data.

Parameters: dl (bool, optional): If True, returns Dl (C_l * l * (l + 1) / 2π). Defaults to True. dtype (str, optional): Specifies the format of the returned spectra.

  • ‘d’ returns a dictionary with keys ‘tt’, ‘ee’, ‘bb’, ‘te’.

  • ‘a’ returns the full array of power spectra.

Defaults to ‘d’.

Returns: Union[Dict[str, Any], np.ndarray]:

  • A dictionary containing individual power spectra for ‘tt’, ‘ee’, ‘bb’, ‘te’ if dtype is ‘d’.

  • The full array of unlensed scalar power spectra if dtype is ‘a’.

Raises: ValueError: If dtype is not ‘d’ or ‘a’.

Parameters:
Return type:

Dict[str, Any] | numpy.ndarray

get_lensed_spectra(dl=True, dtype='d')[source]

Retrieve the lensed scalar spectra from the power spectrum data.

Parameters: dl (bool, optional): If True, returns Dl (C_l * l * (l + 1) / 2π). Defaults to True. dtype (str, optional): Specifies the format of the returned spectra.

  • ‘d’ returns a dictionary with keys ‘tt’, ‘ee’, ‘bb’, ‘te’.

  • ‘a’ returns the full array of power spectra.

Defaults to ‘d’.

Returns: Union[Dict[str, Any], np.ndarray]:

  • A dictionary containing individual power spectra for ‘tt’, ‘ee’, ‘bb’, ‘te’ if dtype is ‘d’.

  • The full array of lensed scalar power spectra if dtype is ‘a’.

Raises: ValueError: If dtype is not ‘d’ or ‘a’.

Parameters:
Return type:

Dict[str, Any] | numpy.ndarray

get_cb_unlensed_spectra(beta=0.0, dl=True, dtype='d', new=False)[source]

Calculate the cosmic birefringence (CB) unlensed spectra with a given rotation angle beta.

Parameters: beta (float, optional): The rotation angle in degrees for the cosmic birefringence effect. Defaults to 0.3 degrees. dl (bool, optional): If True, returns Dl (C_l * l * (l + 1) / 2π). Defaults to True. dtype (str, optional): Specifies the format of the returned spectra.

  • ‘d’ returns a dictionary with keys ‘tt’, ‘ee’, ‘bb’, ‘te’, ‘eb’, ‘tb’.

  • ‘a’ returns an array of power spectra.

Defaults to ‘d’.

new (bool, optional): Determines the ordering of the spectra in the array if dtype is ‘a’.

If True, returns [TT, EE, BB, TE, EB, TB]. If False, returns [TT, TE, TB, EE, EB, BB]. Defaults to False.

Returns: Union[Dict[str, np.ndarray], np.ndarray]:

  • A dictionary containing individual CB unlensed power spectra for ‘tt’, ‘ee’, ‘bb’, ‘te’, ‘eb’, ‘tb’ if dtype is ‘d’.

  • An array of CB unlensed power spectra with ordering determined by the new parameter if dtype is ‘a’.

Raises: ValueError: If dtype is not ‘d’ or ‘a’.

Notes: The method applies a rotation by alpha degrees to the E and B mode spectra to account for cosmic birefringence.

Parameters:
Return type:

Dict[str, numpy.ndarray] | numpy.ndarray

get_cb_unlensed_mass_spectra(dl=True, dtype='d', new=False)[source]
Parameters:
Return type:

Dict[str, numpy.ndarray] | numpy.ndarray

get_cb_lensed_spectra(beta=0.0, dl=True, dtype='d', new=False)[source]

Calculate the cosmic birefringence (CB) lensed spectra with a given rotation angle beta.

Parameters: beta (float, optional): The rotation angle in degrees for the cosmic birefringence effect. Defaults to 0.3 degrees. dl (bool, optional): If True, returns Dl (C_l * l * (l + 1) / 2π). Defaults to True. dtype (str, optional): Specifies the format of the returned spectra.

  • ‘d’ returns a dictionary with keys ‘tt’, ‘ee’, ‘bb’, ‘te’, ‘eb’, ‘tb’.

  • ‘a’ returns an array of power spectra.

Defaults to ‘d’.

new (bool, optional): Determines the ordering of the spectra in the array if dtype is ‘a’.

If True, returns [TT, EE, BB, TE, EB, TB]. If False, returns [TT, TE, TB, EE, EB, BB]. Defaults to False.

Returns: Union[Dict[str, np.ndarray], np.ndarray]:

  • A dictionary containing individual CB lensed power spectra for ‘tt’, ‘ee’, ‘bb’, ‘te’, ‘eb’, ‘tb’ if dtype is ‘d’.

  • An array of CB lensed power spectra with ordering determined by the new parameter if dtype is ‘a’.

Raises: ValueError: If dtype is not ‘d’ or ‘a’.

Notes: The method applies a rotation by alpha degrees to the E and B mode spectra to account for cosmic birefringence.

Parameters:
Return type:

Dict[str, numpy.ndarray] | numpy.ndarray

get_cb_lensed_mass_spectra(dl=True, dtype='d', new=False)[source]
Parameters:
Return type:

Dict[str, numpy.ndarray] | numpy.ndarray

cl_aa()[source]

Compute the Cl_AA power spectrum for the anisotropic model.

cl_pp()[source]
get_iso_const_cb_real_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_td_cb_real_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_model_cb_real_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_const_cb_gaussian_lensed_QU(idx)[source]

Generate or retrieve the Q and U Stokes parameters after applying cosmic birefringence.

Parameters: idx (int): Index for the realization of the CMB map.

Returns: List[np.ndarray]: A list containing the Q and U Stokes parameter maps as NumPy arrays.

Notes: The method applies a rotation to the E and B mode spherical harmonics to simulate the effect of cosmic birefringence. If the map for the given idx exists in the specified directory, it reads the map from the file. Otherwise, it generates the Q and U maps, applies the birefringence, and saves the resulting map to a FITS file.

Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_td_cb_gaussian_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_model_cb_gaussian_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_model_cb_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

alpha_alm(idx)[source]

Generate the alpha alm for the anisotropic model.

Parameters: idx (int): Index for the realization of the CMB map.

Returns: np.ndarray: The alpha alm as a NumPy array.

Notes: The method generates the alpha alm for the anisotropic model. The alpha alm is generated as a random realization of the Cl_AA power spectrum. The behavior depends on Acb_sim_config: - ‘vary’: Each idx gets a unique seed (default) - ‘constant’: All indices in this range use the same fixed seed - ‘null’: Returns zeros (no rotation)

Parameters:

idx (int)

Return type:

numpy.ndarray

phi_alm(idx)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

grad_phi_alm(idx)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

alpha_map(idx)[source]

Generate a map of the rotation angle alpha for the anisotropic model.

Parameters: idx (int): Index for the realization of the CMB map.

Returns: np.ndarray: A map of the rotation angle alpha as a NumPy array.

Notes: The method generates a map of the rotation angle alpha for the anisotropic model. The map is generated as a random realization of the Cl_AA power spectrum.

Parameters:

idx (int)

Return type:

numpy.ndarray

get_aniso_real_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_aniso_gauss_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_aniso_model_cb_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_cb_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

Classes

class CMB(libdir, nside, model='iso', beta=None, mass=None, Acb=None, lensing=False, Acb_sim_config=None, verbose=True)[source]

Bases: object

CMB map generator with cosmic birefringence support.

This class handles the generation of CMB temperature and polarization maps with optional cosmic birefringence rotation. Supports three birefringence models:

  • iso: Isotropic (constant) rotation angle β

  • iso_td: Time-dependent isotropic model with axion mass parameter

  • aniso: Anisotropic (spatially-varying) rotation from patchy reionization

Parameters:

  • libdir (str) – Directory for caching simulation products and power spectra.

  • nside (int) – HEALPix resolution parameter (nside = 2^n).

  • model ({'iso', 'iso_td', 'aniso'}, default 'iso') – Birefringence model type.

  • beta (float, optional) – Isotropic rotation angle in degrees (required for model=’iso’).

  • mass (float, optional) – Axion mass parameter in units of 10^-22 eV. Must be 1, 1.5, or 6.4 (required for model=’iso_td’).

  • Acb (float, optional) – Amplitude of anisotropic birefringence power spectrum (required for model=’aniso’).

  • lensing (bool, default False) – Whether to include CMB lensing effects.

  • Acb_sim_config (dict, optional) – Configuration for anisotropic simulation batches with keys: - ‘alpha_vary_index’: [start, end] indices for varying α - ‘alpha_cons_index’: [start, end] indices for constant α - ‘null_alpha_index’: [start, end] indices for null test (α=0)

  • verbose (bool, default True) – Enable logging output.

nside

HEALPix resolution.

Type:

int

lmax

Maximum multipole (3*nside - 1).

Type:

int

model

Birefringence model type.

Type:

str

beta

Isotropic rotation angle in degrees.

Type:

float or None

lensing

Whether lensing is enabled.

Type:

bool

cls

CMB power spectra (TT, EE, BB, TE).

Type:

dict

Examples

Generate isotropic birefringence simulation:

cmb = CMB(
    libdir='./cmb_sims',
    nside=512,
    model='iso',
    beta=0.35,
    lensing=True
)

# Get rotated CMB map
cmb_map = cmb.get_map(idx=0, apply_beta=True)

Generate anisotropic birefringence:

cmb = CMB(
    libdir='./cmb_sims',
    nside=512,
    model='aniso',
    Acb=1e-6,
    lensing=True
)

# Get map with spatially-varying rotation
cmb_map = cmb.get_map(idx=0, apply_beta=True)

Notes

  • Uses CAMB for power spectrum generation

  • Supports efficient caching of realizations

  • MPI-aware for parallel simulation generation

  • Lensing implemented via lenspyx

__init__(libdir, nside, model='iso', beta=None, mass=None, Acb=None, lensing=False, Acb_sim_config=None, verbose=True)[source]
Parameters:
scale_invariant(Acb)[source]
__validate_Acb_sim_config__()[source]

Validate the Acb_sim_config dictionary structure.

Return type:

None

__get_alpha_mode__(idx)[source]

Determine which alpha mode to use for a given index.

Parameters: idx (int): The realization index.

Returns: str: One of ‘vary’, ‘constant’, or ‘null’

Parameters:

idx (int)

Return type:

str

__set_seeds__()[source]

Sets the seeds for the simulation.

Return type:

None

__set_workspace__()[source]

Set the workspace for the CMB simulations.

Return type:

None

__dl2cl__(arr, unit_only=False)[source]

Convert Dl to Cl.

Parameters:

arr (numpy.ndarray)

Return type:

numpy.ndarray

__td_eb__(dl=True)[source]

Read the E and B mode power spectra from the CMB power spectra data.

Return type:

numpy.ndarray

compute_powers()[source]

compute the CMB power spectra using CAMB.

Return type:

Dict[str, Any]

get_power(dl=True)[source]

Get the CMB power spectra.

Parameters: dl (bool): If True, return the power spectra with dl factor else without dl factor.

Returns: Dict[str, np.ndarray]: A dictionary containing the CMB power spectra.

Parameters:

dl (bool)

Return type:

Dict[str, numpy.ndarray]

get_unlensed_spectra(dl=True, dtype='d')[source]

Retrieve the unlensed scalar spectra from the power spectrum data.

Parameters: dl (bool, optional): If True, returns Dl (C_l * l * (l + 1) / 2π). Defaults to True. dtype (str, optional): Specifies the format of the returned spectra.

  • ‘d’ returns a dictionary with keys ‘tt’, ‘ee’, ‘bb’, ‘te’.

  • ‘a’ returns the full array of power spectra.

Defaults to ‘d’.

Returns: Union[Dict[str, Any], np.ndarray]:

  • A dictionary containing individual power spectra for ‘tt’, ‘ee’, ‘bb’, ‘te’ if dtype is ‘d’.

  • The full array of unlensed scalar power spectra if dtype is ‘a’.

Raises: ValueError: If dtype is not ‘d’ or ‘a’.

Parameters:
Return type:

Dict[str, Any] | numpy.ndarray

get_lensed_spectra(dl=True, dtype='d')[source]

Retrieve the lensed scalar spectra from the power spectrum data.

Parameters: dl (bool, optional): If True, returns Dl (C_l * l * (l + 1) / 2π). Defaults to True. dtype (str, optional): Specifies the format of the returned spectra.

  • ‘d’ returns a dictionary with keys ‘tt’, ‘ee’, ‘bb’, ‘te’.

  • ‘a’ returns the full array of power spectra.

Defaults to ‘d’.

Returns: Union[Dict[str, Any], np.ndarray]:

  • A dictionary containing individual power spectra for ‘tt’, ‘ee’, ‘bb’, ‘te’ if dtype is ‘d’.

  • The full array of lensed scalar power spectra if dtype is ‘a’.

Raises: ValueError: If dtype is not ‘d’ or ‘a’.

Parameters:
Return type:

Dict[str, Any] | numpy.ndarray

get_cb_unlensed_spectra(beta=0.0, dl=True, dtype='d', new=False)[source]

Calculate the cosmic birefringence (CB) unlensed spectra with a given rotation angle beta.

Parameters: beta (float, optional): The rotation angle in degrees for the cosmic birefringence effect. Defaults to 0.3 degrees. dl (bool, optional): If True, returns Dl (C_l * l * (l + 1) / 2π). Defaults to True. dtype (str, optional): Specifies the format of the returned spectra.

  • ‘d’ returns a dictionary with keys ‘tt’, ‘ee’, ‘bb’, ‘te’, ‘eb’, ‘tb’.

  • ‘a’ returns an array of power spectra.

Defaults to ‘d’.

new (bool, optional): Determines the ordering of the spectra in the array if dtype is ‘a’.

If True, returns [TT, EE, BB, TE, EB, TB]. If False, returns [TT, TE, TB, EE, EB, BB]. Defaults to False.

Returns: Union[Dict[str, np.ndarray], np.ndarray]:

  • A dictionary containing individual CB unlensed power spectra for ‘tt’, ‘ee’, ‘bb’, ‘te’, ‘eb’, ‘tb’ if dtype is ‘d’.

  • An array of CB unlensed power spectra with ordering determined by the new parameter if dtype is ‘a’.

Raises: ValueError: If dtype is not ‘d’ or ‘a’.

Notes: The method applies a rotation by alpha degrees to the E and B mode spectra to account for cosmic birefringence.

Parameters:
Return type:

Dict[str, numpy.ndarray] | numpy.ndarray

get_cb_unlensed_mass_spectra(dl=True, dtype='d', new=False)[source]
Parameters:
Return type:

Dict[str, numpy.ndarray] | numpy.ndarray

get_cb_lensed_spectra(beta=0.0, dl=True, dtype='d', new=False)[source]

Calculate the cosmic birefringence (CB) lensed spectra with a given rotation angle beta.

Parameters: beta (float, optional): The rotation angle in degrees for the cosmic birefringence effect. Defaults to 0.3 degrees. dl (bool, optional): If True, returns Dl (C_l * l * (l + 1) / 2π). Defaults to True. dtype (str, optional): Specifies the format of the returned spectra.

  • ‘d’ returns a dictionary with keys ‘tt’, ‘ee’, ‘bb’, ‘te’, ‘eb’, ‘tb’.

  • ‘a’ returns an array of power spectra.

Defaults to ‘d’.

new (bool, optional): Determines the ordering of the spectra in the array if dtype is ‘a’.

If True, returns [TT, EE, BB, TE, EB, TB]. If False, returns [TT, TE, TB, EE, EB, BB]. Defaults to False.

Returns: Union[Dict[str, np.ndarray], np.ndarray]:

  • A dictionary containing individual CB lensed power spectra for ‘tt’, ‘ee’, ‘bb’, ‘te’, ‘eb’, ‘tb’ if dtype is ‘d’.

  • An array of CB lensed power spectra with ordering determined by the new parameter if dtype is ‘a’.

Raises: ValueError: If dtype is not ‘d’ or ‘a’.

Notes: The method applies a rotation by alpha degrees to the E and B mode spectra to account for cosmic birefringence.

Parameters:
Return type:

Dict[str, numpy.ndarray] | numpy.ndarray

get_cb_lensed_mass_spectra(dl=True, dtype='d', new=False)[source]
Parameters:
Return type:

Dict[str, numpy.ndarray] | numpy.ndarray

cl_aa()[source]

Compute the Cl_AA power spectrum for the anisotropic model.

cl_pp()[source]
get_iso_const_cb_real_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_td_cb_real_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_model_cb_real_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_const_cb_gaussian_lensed_QU(idx)[source]

Generate or retrieve the Q and U Stokes parameters after applying cosmic birefringence.

Parameters: idx (int): Index for the realization of the CMB map.

Returns: List[np.ndarray]: A list containing the Q and U Stokes parameter maps as NumPy arrays.

Notes: The method applies a rotation to the E and B mode spherical harmonics to simulate the effect of cosmic birefringence. If the map for the given idx exists in the specified directory, it reads the map from the file. Otherwise, it generates the Q and U maps, applies the birefringence, and saves the resulting map to a FITS file.

Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_td_cb_gaussian_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_model_cb_gaussian_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_iso_model_cb_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

alpha_alm(idx)[source]

Generate the alpha alm for the anisotropic model.

Parameters: idx (int): Index for the realization of the CMB map.

Returns: np.ndarray: The alpha alm as a NumPy array.

Notes: The method generates the alpha alm for the anisotropic model. The alpha alm is generated as a random realization of the Cl_AA power spectrum. The behavior depends on Acb_sim_config: - ‘vary’: Each idx gets a unique seed (default) - ‘constant’: All indices in this range use the same fixed seed - ‘null’: Returns zeros (no rotation)

Parameters:

idx (int)

Return type:

numpy.ndarray

phi_alm(idx)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

grad_phi_alm(idx)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

alpha_map(idx)[source]

Generate a map of the rotation angle alpha for the anisotropic model.

Parameters: idx (int): Index for the realization of the CMB map.

Returns: np.ndarray: A map of the rotation angle alpha as a NumPy array.

Notes: The method generates a map of the rotation angle alpha for the anisotropic model. The map is generated as a random realization of the Cl_AA power spectrum.

Parameters:

idx (int)

Return type:

numpy.ndarray

get_aniso_real_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_aniso_gauss_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_aniso_model_cb_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

get_cb_lensed_QU(idx)[source]
Parameters:

idx (int)

Return type:

List[numpy.ndarray]

Functions

synfast_pol(nside, spectra)[source]

Generate polarized CMB maps (Q, U) with optional auxiliary field A.

Parameters:

  • nside (int) – HEALPix resolution parameter.

  • spectra (dict) – Power spectra dictionary with required ‘EE’ (and optional ‘BB’, ‘AA’, ‘AE’).

Return type:

(Q_map, U_map) or (Q_map, U_map, A_map) if 'AA' in spectra

Foreground Module

Foreground Simulation Module

This module handles Galactic foreground emission modeling for CMB analysis, including dust and synchrotron radiation with frequency-dependent properties.

The module provides:

  • Multi-frequency foreground map generation

  • Bandpass integration for realistic instrument response

  • PySM3 sky model integration (d1, s1 models)

  • Spatial templates for dust and synchrotron

  • Coordinate transformation support

Classes

BandpassInt

Handles bandpass profile integration for multi-frequency observations.

Foreground

Main class for generating dust and synchrotron foreground maps.

Example

Generate foreground maps for multiple frequencies:

from cobi.simulation import Foreground
import numpy as np

fg = Foreground(
    libdir='./fg_sims',
    nside=512,
    freqs=np.array([27, 39, 93, 145, 225, 280]),  # GHz
    models=['d1', 's1'],  # dust and synchrotron
    coord='C'  # Celestial coordinates
)

# Get foreground realization
fg_maps = fg.get_map(idx=0)  # Shape: (nfreqs, 3, npix)

Notes

Uses PySM3 for generating foreground templates with realistic spectral energy distributions. Supports bandpass integration for accurate modeling of detector response.

class BandpassInt(libdir)[source]

Bases: object

Parameters:

libdir (str)

__init__(libdir)[source]

Initializes the BandpassInt class, loading bandpass profiles from a specified file.

Parameters:

libdir (str)

get_profile(band)[source]

Retrieves the frequency and bandpass profile for a specified band.

Parameters: band (str): The frequency band for which to retrieve the profile.

Returns: Tuple[np.ndarray, np.ndarray]: A tuple containing two NumPy arrays:

  • nu: Array of frequencies (GHz) where the bandpass is defined.

  • bp: Array of bandpass values corresponding to the frequencies.

Parameters:

band (str)

Return type:

Tuple[numpy.ndarray, numpy.ndarray]

plot_profiles()[source]

Plots the bandpass profiles for all available bands.

The method iterates over all bands, retrieves the bandpass profile, and plots the profile as a function of frequency (GHz).

Return type:

None

class Foreground(libdir, nside, dust_model, sync_model, bandpass=False, verbose=True)[source]

Bases: object

Galactic foreground generator for CMB analysis.

This class generates multi-frequency Galactic foreground emission maps using PySM3 models for dust and synchrotron radiation. Supports bandpass integration for realistic detector response.

Parameters:

  • libdir (str) – Directory for caching foreground maps.

  • nside (int) – HEALPix resolution parameter.

  • dust_model (int) – PySM3 dust model number (e.g., 1 for d1, 6 for d6).

  • sync_model (int) – PySM3 synchrotron model number (e.g., 1 for s1, 3 for s3).

  • bandpass (bool, default False) – Whether to apply bandpass integration using detector profiles.

  • verbose (bool, default True) – Enable logging output.

nside

HEALPix resolution.

Type:

int

dust_model

Dust emission model identifier.

Type:

int

sync_model

Synchrotron emission model identifier.

Type:

int

bandpass

Whether bandpass integration is enabled.

Type:

bool

bp_profile

Bandpass profile handler (if enabled).

Type:

BandpassInt or None

dustQU(band)[source]

Generate dust Q/U polarization maps at given frequency.

Parameters:

band (str)

Return type:

numpy.ndarray

syncQU(band)[source]

Generate synchrotron Q/U polarization maps at given frequency.

Parameters:

band (str)

Return type:

numpy.ndarray

get_map(freqs, idx)

Get combined foreground map for multiple frequencies.

Examples

Generate foreground maps without bandpass:

fg = Foreground(
    libdir='./fg_sims',
    nside=512,
    dust_model=1,
    sync_model=1,
    bandpass=False
)

# Get dust emission at 145 GHz
dust_map = fg.dustQU('145')

With bandpass integration:

fg = Foreground(
    libdir='./fg_sims',
    nside=512,
    dust_model=1,
    sync_model=1,
    bandpass=True
)

# Generate multi-frequency maps
freqs = ['027', '039', '093', '145', '225', '280']
fg_maps = fg.get_map(freqs, idx=0)

Notes

  • Uses PySM3 for physically-motivated foreground models

  • Caches maps to disk for efficient reuse

  • Supports MPI parallelization

  • Output in μK_CMB units

__init__(libdir, nside, dust_model, sync_model, bandpass=False, verbose=True)[source]

Initializes the Foreground class for generating and handling dust and synchrotron foreground maps.

Parameters: libdir (str): Directory where the foreground maps will be stored. nside (int): HEALPix resolution parameter. dust_model (int): Model number for the dust emission. sync_model (int): Model number for the synchrotron emission. bandpass (bool, optional): If True, bandpass integration is applied. Defaults to False.

Parameters:
dustQU(band)[source]

Generates or retrieves the Q and U Stokes parameters for dust emission at a given frequency band.

Parameters: band (str): The frequency band.

Returns: np.ndarray: A NumPy array containing the Q and U maps.

Parameters:

band (str)

Return type:

numpy.ndarray

syncQU(band)[source]

Generates or retrieves the Q and U Stokes parameters for synchrotron emission at a given frequency band.

Parameters: band (str): The frequency band.

Returns: np.ndarray: A NumPy array containing the Q and U maps.

Parameters:

band (str)

Return type:

numpy.ndarray

class HILC[source]

Bases: object

This class is used to perform Harmonic ILC on the foregrounds.

__init__()[source]
harmonic_ilc_alm(alms, lbins=None)[source]

This method is used to perform Harmonic ILC on the foregrounds.

Parameters:

  • alms (np.ndarray) – The foregrounds in alm format.

  • lbins (Optional[List[int]], optional) – The list of ell bins.

Returns:

The Harmonic ILC results and the ILC filter.

Return type:

tuple

empirical_harmonic_covariance(alms)[source]

The method empirical_harmonic_covariance is used to compute the empirical harmonic covariance.

Parameters:

alms (np.ndarray) – The foregrounds in alm format.

Returns:

The empirical harmonic covariance.

Return type:

np.ndarray

regularized_inverse(cov)[source]

The method regularized_inverse is used to compute the regularized inverse of the covariance.

Parameters:

cov (np.ndarray) – The covariance.

Returns:

The regularized inverse of the covariance.

Return type:

np.ndarray

apply_harmonic_W(W, alms)[source]

The method apply_harmonic_W is used to apply the harmonic weights.

Parameters:

  • W (np.ndarray) – The harmonic weights.

  • alms (np.ndarray) – The foregrounds in alm format.

Returns:

The alms after applying the harmonic weights.

Return type:

np.ndarray

Classes

class BandpassInt(libdir)[source]

Bases: object

Parameters:

libdir (str)

__init__(libdir)[source]

Initializes the BandpassInt class, loading bandpass profiles from a specified file.

Parameters:

libdir (str)

get_profile(band)[source]

Retrieves the frequency and bandpass profile for a specified band.

Parameters: band (str): The frequency band for which to retrieve the profile.

Returns: Tuple[np.ndarray, np.ndarray]: A tuple containing two NumPy arrays:

  • nu: Array of frequencies (GHz) where the bandpass is defined.

  • bp: Array of bandpass values corresponding to the frequencies.

Parameters:

band (str)

Return type:

Tuple[numpy.ndarray, numpy.ndarray]

plot_profiles()[source]

Plots the bandpass profiles for all available bands.

The method iterates over all bands, retrieves the bandpass profile, and plots the profile as a function of frequency (GHz).

Return type:

None

class Foreground(libdir, nside, dust_model, sync_model, bandpass=False, verbose=True)[source]

Bases: object

Galactic foreground generator for CMB analysis.

This class generates multi-frequency Galactic foreground emission maps using PySM3 models for dust and synchrotron radiation. Supports bandpass integration for realistic detector response.

Parameters:

  • libdir (str) – Directory for caching foreground maps.

  • nside (int) – HEALPix resolution parameter.

  • dust_model (int) – PySM3 dust model number (e.g., 1 for d1, 6 for d6).

  • sync_model (int) – PySM3 synchrotron model number (e.g., 1 for s1, 3 for s3).

  • bandpass (bool, default False) – Whether to apply bandpass integration using detector profiles.

  • verbose (bool, default True) – Enable logging output.

nside

HEALPix resolution.

Type:

int

dust_model

Dust emission model identifier.

Type:

int

sync_model

Synchrotron emission model identifier.

Type:

int

bandpass

Whether bandpass integration is enabled.

Type:

bool

bp_profile

Bandpass profile handler (if enabled).

Type:

BandpassInt or None

dustQU(band)[source]

Generate dust Q/U polarization maps at given frequency.

Parameters:

band (str)

Return type:

numpy.ndarray

syncQU(band)[source]

Generate synchrotron Q/U polarization maps at given frequency.

Parameters:

band (str)

Return type:

numpy.ndarray

get_map(freqs, idx)

Get combined foreground map for multiple frequencies.

Examples

Generate foreground maps without bandpass:

fg = Foreground(
    libdir='./fg_sims',
    nside=512,
    dust_model=1,
    sync_model=1,
    bandpass=False
)

# Get dust emission at 145 GHz
dust_map = fg.dustQU('145')

With bandpass integration:

fg = Foreground(
    libdir='./fg_sims',
    nside=512,
    dust_model=1,
    sync_model=1,
    bandpass=True
)

# Generate multi-frequency maps
freqs = ['027', '039', '093', '145', '225', '280']
fg_maps = fg.get_map(freqs, idx=0)

Notes

  • Uses PySM3 for physically-motivated foreground models

  • Caches maps to disk for efficient reuse

  • Supports MPI parallelization

  • Output in μK_CMB units

__init__(libdir, nside, dust_model, sync_model, bandpass=False, verbose=True)[source]

Initializes the Foreground class for generating and handling dust and synchrotron foreground maps.

Parameters: libdir (str): Directory where the foreground maps will be stored. nside (int): HEALPix resolution parameter. dust_model (int): Model number for the dust emission. sync_model (int): Model number for the synchrotron emission. bandpass (bool, optional): If True, bandpass integration is applied. Defaults to False.

Parameters:
dustQU(band)[source]

Generates or retrieves the Q and U Stokes parameters for dust emission at a given frequency band.

Parameters: band (str): The frequency band.

Returns: np.ndarray: A NumPy array containing the Q and U maps.

Parameters:

band (str)

Return type:

numpy.ndarray

syncQU(band)[source]

Generates or retrieves the Q and U Stokes parameters for synchrotron emission at a given frequency band.

Parameters: band (str): The frequency band.

Returns: np.ndarray: A NumPy array containing the Q and U maps.

Parameters:

band (str)

Return type:

numpy.ndarray

class HILC[source]

Bases: object

This class is used to perform Harmonic ILC on the foregrounds.

__init__()[source]
harmonic_ilc_alm(alms, lbins=None)[source]

This method is used to perform Harmonic ILC on the foregrounds.

Parameters:

  • alms (np.ndarray) – The foregrounds in alm format.

  • lbins (Optional[List[int]], optional) – The list of ell bins.

Returns:

The Harmonic ILC results and the ILC filter.

Return type:

tuple

empirical_harmonic_covariance(alms)[source]

The method empirical_harmonic_covariance is used to compute the empirical harmonic covariance.

Parameters:

alms (np.ndarray) – The foregrounds in alm format.

Returns:

The empirical harmonic covariance.

Return type:

np.ndarray

regularized_inverse(cov)[source]

The method regularized_inverse is used to compute the regularized inverse of the covariance.

Parameters:

cov (np.ndarray) – The covariance.

Returns:

The regularized inverse of the covariance.

Return type:

np.ndarray

apply_harmonic_W(W, alms)[source]

The method apply_harmonic_W is used to apply the harmonic weights.

Parameters:

  • W (np.ndarray) – The harmonic weights.

  • alms (np.ndarray) – The foregrounds in alm format.

Returns:

The alms after applying the harmonic weights.

Return type:

np.ndarray

Mask Module

Mask Generation Module

This module provides tools for creating and managing sky masks used in CMB analysis, including galactic masks, point source masks, and survey footprints.

Features:

  • Multiple mask types (LAT, SAT, CO, point sources, galactic)

  • Apodization with multiple methods (C1, C2, Gaussian)

  • Galactic cut options

  • Combination of multiple mask types

  • Automatic mask caching for efficiency

Classes

Mask

Main class for creating and managing sky masks with apodization.

Example

Create a LAT survey mask with galactic cut:

from cobi.simulation import Mask

mask = Mask(
    libdir='./masks',
    nside=512,
    select='lat',
    apo_scale=1.0,  # degrees
    apo_method='C2',
    gal_cut=20  # degrees from galactic plane
)

# Get the mask
mask_map = mask.mask

Combine multiple masks:

mask = Mask(
    libdir='./masks',
    nside=512,
    select='lat+gal+ps',  # LAT + galactic + point sources
    apo_scale=1.0
)

Notes

Masks are cached to disk for reuse. The module supports NaMaster apodization methods for optimal mode-coupling correction in power spectrum estimation.

class Mask(libdir, nside, select, apo_scale=0.0, apo_method='C2', gal_cut=0, verbose=True)[source]

Bases: object

Sky mask generator and manager for CMB analysis.

This class creates and handles various types of sky masks including survey footprints (LAT/SAT), galactic plane cuts, point source masks, and CO line emission masks. Supports apodization for optimal power spectrum estimation.

Parameters:

  • libdir (str) – Directory for caching mask files.

  • nside (int) – HEALPix resolution parameter.

  • select (str) – Mask type selector. Can combine multiple masks with ‘+’: - ‘lat’: LAT survey footprint - ‘sat’: SAT survey footprint - ‘co’: CO line emission mask - ‘ps’: Point source mask - ‘gal’: Galactic plane mask Example: ‘lat+gal+ps’ combines LAT, galactic, and point source masks.

  • apo_scale (float, default 0.0) – Apodization scale in degrees. If 0, no apodization applied.

  • apo_method ({'C1', 'C2', 'Gaussian'}, default 'C2') – Apodization method compatible with NaMaster.

  • gal_cut (float, int, or str, default 0) – Galactic cut specification: - float < 1: f_sky fraction (e.g., 0.4 for 40% sky) - int > 1: percentage (e.g., 40 for 40% sky) - str: direct percentage ‘40’, ‘60’, ‘70’, ‘80’, ‘90’ Only used when ‘gal’ or ‘GAL’ in select.

  • verbose (bool, default True) – Enable logging output.

nside

HEALPix resolution.

Type:

int

mask

The combined mask array (values 0-1).

Type:

ndarray

select

Mask type identifier.

Type:

str

apo_scale

Apodization scale in degrees.

Type:

float

fsky

Sky fraction (computed from mask).

Type:

float

get_mask()

Returns the mask array.

Examples

Create LAT mask with galactic cut:

mask = Mask(
    libdir='./masks',
    nside=512,
    select='lat+gal',
    gal_cut=20,  # 20% sky
    apo_scale=1.0,
    apo_method='C2'
)

mask_array = mask.mask
print(f"Sky fraction: {mask.fsky:.3f}")

Simple point source mask:

ps_mask = Mask(
    libdir='./masks',
    nside=512,
    select='ps',
    apo_scale=0.5
)

Combined mask for full analysis:

full_mask = Mask(
    libdir='./masks',
    nside=512,
    select='lat+gal+ps+co',
    gal_cut=40,
    apo_scale=1.0
)

Notes

  • Masks are cached to disk for efficient reuse

  • Apodization reduces mode-coupling in power spectra

  • Multiple masks are combined via multiplication

  • Compatible with NaMaster (pymaster) workflows

__init__(libdir, nside, select, apo_scale=0.0, apo_method='C2', gal_cut=0, verbose=True)[source]

Initializes the Mask class for handling and generating sky masks.

Parameters: nside (int): HEALPix resolution parameter. libdir (Optional[str], optional): Directory where the mask may be saved or loaded from. Defaults to None.

Parameters:
Return type:

None

__load_mask_healper__()[source]

Loads a mask from a file.

Returns: np.ndarray: The mask array.

Return type:

numpy.ndarray

__load_mask__()[source]

Loads a mask from a file.

Returns: np.ndarray: The mask array.

Return type:

numpy.ndarray

__calc_fsky__(mask=None)[source]

Calculates the fraction of sky covered by the mask.

Returns: float: The fraction of sky covered by the mask.

Return type:

float

Classes

class Mask(libdir, nside, select, apo_scale=0.0, apo_method='C2', gal_cut=0, verbose=True)[source]

Bases: object

Sky mask generator and manager for CMB analysis.

This class creates and handles various types of sky masks including survey footprints (LAT/SAT), galactic plane cuts, point source masks, and CO line emission masks. Supports apodization for optimal power spectrum estimation.

Parameters:

  • libdir (str) – Directory for caching mask files.

  • nside (int) – HEALPix resolution parameter.

  • select (str) – Mask type selector. Can combine multiple masks with ‘+’: - ‘lat’: LAT survey footprint - ‘sat’: SAT survey footprint - ‘co’: CO line emission mask - ‘ps’: Point source mask - ‘gal’: Galactic plane mask Example: ‘lat+gal+ps’ combines LAT, galactic, and point source masks.

  • apo_scale (float, default 0.0) – Apodization scale in degrees. If 0, no apodization applied.

  • apo_method ({'C1', 'C2', 'Gaussian'}, default 'C2') – Apodization method compatible with NaMaster.

  • gal_cut (float, int, or str, default 0) – Galactic cut specification: - float < 1: f_sky fraction (e.g., 0.4 for 40% sky) - int > 1: percentage (e.g., 40 for 40% sky) - str: direct percentage ‘40’, ‘60’, ‘70’, ‘80’, ‘90’ Only used when ‘gal’ or ‘GAL’ in select.

  • verbose (bool, default True) – Enable logging output.

nside

HEALPix resolution.

Type:

int

mask

The combined mask array (values 0-1).

Type:

ndarray

select

Mask type identifier.

Type:

str

apo_scale

Apodization scale in degrees.

Type:

float

fsky

Sky fraction (computed from mask).

Type:

float

get_mask()

Returns the mask array.

Examples

Create LAT mask with galactic cut:

mask = Mask(
    libdir='./masks',
    nside=512,
    select='lat+gal',
    gal_cut=20,  # 20% sky
    apo_scale=1.0,
    apo_method='C2'
)

mask_array = mask.mask
print(f"Sky fraction: {mask.fsky:.3f}")

Simple point source mask:

ps_mask = Mask(
    libdir='./masks',
    nside=512,
    select='ps',
    apo_scale=0.5
)

Combined mask for full analysis:

full_mask = Mask(
    libdir='./masks',
    nside=512,
    select='lat+gal+ps+co',
    gal_cut=40,
    apo_scale=1.0
)

Notes

  • Masks are cached to disk for efficient reuse

  • Apodization reduces mode-coupling in power spectra

  • Multiple masks are combined via multiplication

  • Compatible with NaMaster (pymaster) workflows

__init__(libdir, nside, select, apo_scale=0.0, apo_method='C2', gal_cut=0, verbose=True)[source]

Initializes the Mask class for handling and generating sky masks.

Parameters: nside (int): HEALPix resolution parameter. libdir (Optional[str], optional): Directory where the mask may be saved or loaded from. Defaults to None.

Parameters:
Return type:

None

__load_mask_healper__()[source]

Loads a mask from a file.

Returns: np.ndarray: The mask array.

Return type:

numpy.ndarray

__load_mask__()[source]

Loads a mask from a file.

Returns: np.ndarray: The mask array.

Return type:

numpy.ndarray

__calc_fsky__(mask=None)[source]

Calculates the fraction of sky covered by the mask.

Returns: float: The fraction of sky covered by the mask.

Return type:

float

Noise Module

Noise Simulation Module

This module simulates realistic instrumental noise for Simons Observatory observations, including white noise, atmospheric noise, and correlated noise between detector pairs.

Features:

  • SO LAT and SAT noise models (v3.1.1)

  • Sensitivity modes (baseline, goal)

  • Atmospheric and 1/f noise components

  • Cross-frequency correlations

  • Map-based and harmonic-space realizations

  • Anisotropic noise from hit maps

Classes

Noise

Main class for generating noise realizations and spectra.

Functions

NoiseSpectra

Compute noise power spectra for SO telescopes.

Example

Generate noise maps for LAT baseline sensitivity:

from cobi.simulation import Noise
import numpy as np

noise = Noise(
    libdir='./noise_sims',
    nside=512,
    lmax=3000,
    freqs=np.array([27, 39, 93, 145, 225, 280]),
    fwhm=np.array([7.4, 5.1, 2.2, 1.4, 1.0, 0.9]),
    telescope='LAT',
    sensitivity_mode='baseline',
    fsky=0.4,
    atm_noise=True
)

# Get noise realization
noise_maps = noise.get_map(idx=0)  # Shape: (nfreqs, 3, npix)

Anisotropic noise from hit maps:

noise_aniso = Noise(
    libdir='./noise_sims',
    nside=512,
    nside_aniso=4096,  # high-res for hit maps
    isotropic=False,
    hitmap_file='hitmaps.h5'
)

Notes

Uses SO noise models v3.1.1 for realistic noise properties. Supports both isotropic and anisotropic noise based on actual survey hit maps.

NoiseSpectra(sensitivity_mode, fsky, lmax, atm_noise, telescope, aso=False)[source]
class Noise(nside, fsky, telescope, sim='NC', atm_noise=False, nsplits=2, aso=False, verbose=True)[source]

Bases: object

Instrumental noise simulator for Simons Observatory telescopes.

This class generates realistic noise realizations for SO LAT and SAT observations based on the v3.1.1 noise models. Supports white noise, atmospheric/1f noise, and correlated noise between detector pairs within the same optical tube.

Parameters:

  • nside (int) – HEALPix resolution parameter.

  • fsky (float) – Sky fraction for noise power calculation (0 < fsky ≤ 1).

  • telescope ({'LAT', 'SAT'}) – Telescope identifier (Large Aperture or Small Aperture).

  • sim ({'NC', 'TOD'}, default 'NC') – Simulation type: - ‘NC’: Noise curves from SO models (analytic) - ‘TOD’: Time-ordered data based simulations (uses SO products)

  • atm_noise (bool, default False) – Include atmospheric and 1/f noise components.

  • nsplits (int, default 2) – Number of data splits (for split-based null tests).

  • verbose (bool, default True) – Enable logging output.

  • aso (bool)

nside

HEALPix resolution.

Type:

int

lmax

Maximum multipole (3*nside - 1).

Type:

int

sensitivity_mode

SO sensitivity mode (2 = baseline).

Type:

int

atm_noise

Whether atmospheric noise is included.

Type:

bool

Nell

Noise power spectra for each frequency and cross-frequency.

Type:

dict

telescope

Telescope identifier.

Type:

str

get_map(idx, freqs, fwhm, tube)

Generate noise map realization for given configuration.

get_spectra(idx, freqs, fwhm, tube)

Get noise power spectra for given configuration.

Examples

LAT baseline noise with atmosphere:

noise = Noise(
    nside=512,
    fsky=0.4,
    telescope='LAT',
    sim='NC',
    atm_noise=True,
    nsplits=2
)

# Generate noise for 6 LAT frequencies
freqs = np.array([27, 39, 93, 145, 225, 280])
fwhm = np.array([7.4, 5.1, 2.2, 1.4, 1.0, 0.9])
tube = np.array([0, 0, 1, 1, 2, 2])

noise_maps = noise.get_map(idx=0, freqs=freqs, fwhm=fwhm, tube=tube)

SAT white noise only:

noise_sat = Noise(
    nside=512,
    fsky=0.1,
    telescope='SAT',
    sim='NC',
    atm_noise=False
)

freqs = np.array([27, 39, 93, 145, 225, 280])
noise_maps = noise_sat.get_map(idx=0, freqs=freqs, fwhm=None, tube=None)

Notes

  • Uses SO v3.1.1 noise models (baseline sensitivity)

  • Correlated noise between frequency pairs in same tube

  • Supports independent realizations via idx parameter

  • MPI-aware for parallel generation

  • Output in μK_CMB units

__init__(nside, fsky, telescope, sim='NC', atm_noise=False, nsplits=2, aso=False, verbose=True)[source]

Initializes the Noise class for generating noise maps with or without atmospheric noise.

Parameters: nside (int): HEALPix resolution parameter. atm_noise (bool, optional): If True, includes atmospheric noise. Defaults to False. nhits (bool, optional): If True, includes hit count map. Defaults to False. nsplits (int, optional): Number of data splits to consider. Defaults to 2.

Parameters:
Return type:

None

property rand_alm: numpy.ndarray

Generates random spherical harmonic coefficients (alm) with a specified power spectrum.

Returns: np.ndarray: A complex array of spherical harmonic coefficients.

property cholesky_matrix_elements: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]

Computes the Cholesky matrix elements for the noise model.

Returns: Tuple of nine np.ndarray elements representing the Cholesky matrix components.

white_noise_maps()[source]
Return type:

numpy.ndarray

white_noise_maps_freq(freq)[source]

Generates white noise maps for a specific frequency band.

Returns: np.ndarray: An array of white noise maps for the specified frequency band.

Parameters:

freq (str)

Return type:

numpy.ndarray

atm_noise_maps_freq(idx, freq)[source]

Generates atmospheric noise maps using Cholesky decomposition.

Returns: np.ndarray: An array of atmospheric noise maps for different frequency bands.

Parameters:
Return type:

numpy.ndarray

atm_noise_maps(split, idx)[source]

Generates atmospheric noise maps using Cholesky decomposition.

Returns: np.ndarray: An array of atmospheric noise maps for different frequency bands.

Return type:

numpy.ndarray

noiseQU_NC(idx)[source]

Generates Q and U polarization noise maps based on the noise model.

Returns: np.ndarray: An array of Q and U noise maps.

Return type:

numpy.ndarray

noiseQU_NC_freq(idx, freq)[source]

Generates Q and U polarization noise maps for a specific frequency band.

Returns: np.ndarray: An array of Q and U noise maps for the specified frequency band.

Parameters:
Return type:

numpy.ndarray

noiseQU_TOD_sat(idx)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

noiseQU_TOD_lat(idx)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

noiseQU_TOD_sat_band(idx, freq)[source]
Parameters:
Return type:

numpy.ndarray

noiseQU_TOD_lat_band(idx, freq)[source]
Parameters:
Return type:

numpy.ndarray

noiseQU_TOD(idx)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

noiseQU_TOD_freq(idx, freq)[source]
Parameters:
Return type:

numpy.ndarray

noiseQU(idx=None)[source]

Generates Q and U polarization noise maps based on the noise model.

Returns: np.ndarray: An array of Q and U noise maps.

Parameters:

idx (int | None)

Return type:

numpy.ndarray

noiseQU_freq(idx, freq)[source]

Generates Q and U polarization noise maps for a specific frequency band.

Returns: np.ndarray: An array of Q and U noise maps for the specified frequency band.

Parameters:
Return type:

numpy.ndarray

Classes

class Noise(nside, fsky, telescope, sim='NC', atm_noise=False, nsplits=2, aso=False, verbose=True)[source]

Bases: object

Instrumental noise simulator for Simons Observatory telescopes.

This class generates realistic noise realizations for SO LAT and SAT observations based on the v3.1.1 noise models. Supports white noise, atmospheric/1f noise, and correlated noise between detector pairs within the same optical tube.

Parameters:

  • nside (int) – HEALPix resolution parameter.

  • fsky (float) – Sky fraction for noise power calculation (0 < fsky ≤ 1).

  • telescope ({'LAT', 'SAT'}) – Telescope identifier (Large Aperture or Small Aperture).

  • sim ({'NC', 'TOD'}, default 'NC') – Simulation type: - ‘NC’: Noise curves from SO models (analytic) - ‘TOD’: Time-ordered data based simulations (uses SO products)

  • atm_noise (bool, default False) – Include atmospheric and 1/f noise components.

  • nsplits (int, default 2) – Number of data splits (for split-based null tests).

  • verbose (bool, default True) – Enable logging output.

  • aso (bool)

nside

HEALPix resolution.

Type:

int

lmax

Maximum multipole (3*nside - 1).

Type:

int

sensitivity_mode

SO sensitivity mode (2 = baseline).

Type:

int

atm_noise

Whether atmospheric noise is included.

Type:

bool

Nell

Noise power spectra for each frequency and cross-frequency.

Type:

dict

telescope

Telescope identifier.

Type:

str

get_map(idx, freqs, fwhm, tube)

Generate noise map realization for given configuration.

get_spectra(idx, freqs, fwhm, tube)

Get noise power spectra for given configuration.

Examples

LAT baseline noise with atmosphere:

noise = Noise(
    nside=512,
    fsky=0.4,
    telescope='LAT',
    sim='NC',
    atm_noise=True,
    nsplits=2
)

# Generate noise for 6 LAT frequencies
freqs = np.array([27, 39, 93, 145, 225, 280])
fwhm = np.array([7.4, 5.1, 2.2, 1.4, 1.0, 0.9])
tube = np.array([0, 0, 1, 1, 2, 2])

noise_maps = noise.get_map(idx=0, freqs=freqs, fwhm=fwhm, tube=tube)

SAT white noise only:

noise_sat = Noise(
    nside=512,
    fsky=0.1,
    telescope='SAT',
    sim='NC',
    atm_noise=False
)

freqs = np.array([27, 39, 93, 145, 225, 280])
noise_maps = noise_sat.get_map(idx=0, freqs=freqs, fwhm=None, tube=None)

Notes

  • Uses SO v3.1.1 noise models (baseline sensitivity)

  • Correlated noise between frequency pairs in same tube

  • Supports independent realizations via idx parameter

  • MPI-aware for parallel generation

  • Output in μK_CMB units

__init__(nside, fsky, telescope, sim='NC', atm_noise=False, nsplits=2, aso=False, verbose=True)[source]

Initializes the Noise class for generating noise maps with or without atmospheric noise.

Parameters: nside (int): HEALPix resolution parameter. atm_noise (bool, optional): If True, includes atmospheric noise. Defaults to False. nhits (bool, optional): If True, includes hit count map. Defaults to False. nsplits (int, optional): Number of data splits to consider. Defaults to 2.

Parameters:
Return type:

None

property rand_alm: numpy.ndarray

Generates random spherical harmonic coefficients (alm) with a specified power spectrum.

Returns: np.ndarray: A complex array of spherical harmonic coefficients.

property cholesky_matrix_elements: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]

Computes the Cholesky matrix elements for the noise model.

Returns: Tuple of nine np.ndarray elements representing the Cholesky matrix components.

white_noise_maps()[source]
Return type:

numpy.ndarray

white_noise_maps_freq(freq)[source]

Generates white noise maps for a specific frequency band.

Returns: np.ndarray: An array of white noise maps for the specified frequency band.

Parameters:

freq (str)

Return type:

numpy.ndarray

atm_noise_maps_freq(idx, freq)[source]

Generates atmospheric noise maps using Cholesky decomposition.

Returns: np.ndarray: An array of atmospheric noise maps for different frequency bands.

Parameters:
Return type:

numpy.ndarray

atm_noise_maps(split, idx)[source]

Generates atmospheric noise maps using Cholesky decomposition.

Returns: np.ndarray: An array of atmospheric noise maps for different frequency bands.

Return type:

numpy.ndarray

noiseQU_NC(idx)[source]

Generates Q and U polarization noise maps based on the noise model.

Returns: np.ndarray: An array of Q and U noise maps.

Return type:

numpy.ndarray

noiseQU_NC_freq(idx, freq)[source]

Generates Q and U polarization noise maps for a specific frequency band.

Returns: np.ndarray: An array of Q and U noise maps for the specified frequency band.

Parameters:
Return type:

numpy.ndarray

noiseQU_TOD_sat(idx)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

noiseQU_TOD_lat(idx)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

noiseQU_TOD_sat_band(idx, freq)[source]
Parameters:
Return type:

numpy.ndarray

noiseQU_TOD_lat_band(idx, freq)[source]
Parameters:
Return type:

numpy.ndarray

noiseQU_TOD(idx)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

noiseQU_TOD_freq(idx, freq)[source]
Parameters:
Return type:

numpy.ndarray

noiseQU(idx=None)[source]

Generates Q and U polarization noise maps based on the noise model.

Returns: np.ndarray: An array of Q and U noise maps.

Parameters:

idx (int | None)

Return type:

numpy.ndarray

noiseQU_freq(idx, freq)[source]

Generates Q and U polarization noise maps for a specific frequency band.

Returns: np.ndarray: An array of Q and U noise maps for the specified frequency band.

Parameters:
Return type:

numpy.ndarray

Functions

NoiseSpectra(sensitivity_mode, fsky, lmax, atm_noise, telescope, aso=False)[source]

Sky Module

Sky Simulation Module

This module provides a unified interface for generating complete multi-frequency sky simulations combining CMB, foregrounds, and noise with realistic instrumental effects and data processing.

The SkySimulation class orchestrates:

  • CMB signal with cosmic birefringence

  • Galactic foregrounds (dust, synchrotron)

  • Instrumental noise (LAT/SAT models)

  • Beam convolution

  • Component separation (ILC)

  • Survey masks

  • Coordinate transformations

Classes

SkySimulation

Main class for end-to-end sky simulation and processing.

HILC

Harmonic-space Internal Linear Combination for component separation.

Example

Generate a complete LAT simulation:

from cobi.simulation import SkySimulation
import numpy as np

sky = SkySimulation(
    libdir='./sky_sims',
    nside=512,
    freqs=np.array([27, 39, 93, 145, 225, 280]),
    fwhm=np.array([7.4, 5.1, 2.2, 1.4, 1.0, 0.9]),
    tube=np.array([0, 0, 1, 1, 2, 2]),  # tube assignments
    cb_model='iso',
    beta=0.35,
    lensing=True,
    sensitivity_mode='baseline',
    telescope='LAT',
    fsky=0.4
)

# Get full simulation (all components)
full_sky = sky.get_sim(idx=0, component='all')

# Get CMB-only
cmb_only = sky.get_sim(idx=0, component='cmb')

# Get ILC-cleaned map
cleaned = sky.get_ilc(idx=0)

Multi-component analysis:

# Individual components
cmb = sky.get_sim(idx=0, component='cmb')
fg = sky.get_sim(idx=0, component='fg')
noise = sky.get_sim(idx=0, component='noise')

# Verify: all = cmb + fg + noise
all_sky = sky.get_sim(idx=0, component='all')

Notes

This module provides the highest-level interface for simulation generation. It handles caching, parallelization, and ensures consistency across components. Supports both isotropic and anisotropic cosmic birefringence models.

class SkySimulation(libdir, nside, freqs, fwhm, tube, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, Acb_sim_config=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Bases: object

Unified multi-frequency sky simulation framework.

This is the highest-level class that orchestrates complete end-to-end simulations combining CMB, foregrounds, noise, and instrumental effects for SO LAT/SAT observations. Includes component separation via harmonic ILC.

Parameters:

  • libdir (str) – Base directory for all simulation products and caching.

  • nside (int) – HEALPix resolution parameter.

  • freqs (ndarray) – Array of observation frequencies in GHz (e.g., [27, 39, 93, 145, 225, 280]).

  • fwhm (ndarray) – Beam FWHM in arcminutes for each frequency.

  • tube (ndarray) – Optical tube assignment for each frequency (for correlated noise).

  • cb_model ({'iso', 'iso_td', 'aniso'}, default 'iso') – Cosmic birefringence model type.

  • beta (float, default 0.35) – Isotropic birefringence angle in degrees (for cb_model=’iso’).

  • mass (float, default 1.5) – Axion mass in 10^-22 eV units (for cb_model=’iso_td’). Must be 1, 1.5, or 6.4.

  • Acb (float, default 1e-6) – Anisotropic birefringence amplitude (for cb_model=’aniso’).

  • lensing (bool, default True) – Include CMB lensing effects.

  • dust_model (int, default 10) – PySM3 dust model number.

  • sync_model (int, default 5) – PySM3 synchrotron model number.

  • bandpass (bool, default True) – Apply bandpass integration for foregrounds.

  • alpha (float or array, default 0.0) – Miscalibration polarization angle(s) in degrees. - float: same angle for all frequencies - array: per-frequency angles (must match len(freqs))

  • alpha_err (float, default 0.0) – Gaussian uncertainty on miscalibration angle (degrees).

  • miscal_samples (int, optional) – Number of miscalibration angle realizations. Default: max index from Acb_sim_config or 300.

  • Acb_sim_config (dict, optional) – Anisotropic birefringence simulation batch configuration. Keys: ‘alpha_vary_index’, ‘alpha_cons_index’, ‘null_alpha_index’ Values: [start, end] index ranges.

  • noise_model ({'NC', 'TOD'}, default 'NC') – Noise simulation type (curves vs. time-ordered data).

  • atm_noise (bool, default True) – Include atmospheric/1f noise.

  • nsplits (int, default 2) – Number of data splits for null tests.

  • gal_cut (int, default 0) – Galactic mask cut (0, 40, 60, 70, 80, 90).

  • hilc_bins (int, default 10) – Number of multipole bins for harmonic ILC.

  • deconv_maps (bool, default False) – Deconvolve beam from output maps.

  • fldname_suffix (str, default '') – Additional suffix for output directory naming.

  • sht_backend ({'healpy', 'ducc0'}, default 'healpy') – Spherical harmonic transform backend.

  • verbose (bool, default True) – Enable logging output.

  • aso (bool)

nside

HEALPix resolution.

Type:

int

freqs

Observation frequencies.

Type:

ndarray

cmb

CMB simulation object.

Type:

CMB

foreground

Foreground simulation object.

Type:

Foreground

noise

Noise simulation object.

Type:

Noise

mask

Combined survey mask.

Type:

ndarray

fsky

Sky fraction.

Type:

float

config

Per-frequency configuration dictionary.

Type:

dict

get_sim(idx, component='all')

Get simulation realization (cmb/fg/noise/all).

get_ilc(idx, return_noise=False)

Get ILC-cleaned CMB map.

get_obs(idx, freqs_idx=None)

Get observed maps at specific frequencies.

Examples

Complete LAT simulation pipeline:

from cobi.simulation import SkySimulation
import numpy as np

sky = SkySimulation(
    libdir='./sims',
    nside=512,
    freqs=np.array([27, 39, 93, 145, 225, 280]),
    fwhm=np.array([7.4, 5.1, 2.2, 1.4, 1.0, 0.9]),
    tube=np.array([0, 0, 1, 1, 2, 2]),
    cb_model='iso',
    beta=0.35,
    lensing=True,
    dust_model=10,
    sync_model=5,
    alpha=0.0,  # no miscalibration
    atm_noise=True,
    gal_cut=40
)

# Get full simulation
full_map = sky.get_sim(idx=0, component='all')

# Get CMB-only
cmb_map = sky.get_sim(idx=0, component='cmb')

# Get ILC-cleaned map
cleaned_map = sky.get_ilc(idx=0)

Multi-frequency miscalibration:

sky = SkySimulation(
    libdir='./sims',
    nside=512,
    freqs=np.array([93, 145, 225]),
    fwhm=np.array([2.2, 1.4, 1.0]),
    tube=np.array([1, 1, 2]),
    alpha=[0.2, -0.1, 0.15],  # per-frequency
    alpha_err=0.05  # with uncertainty
)

Notes

  • Automatically manages caching and directory structure

  • MPI-aware for parallel processing

  • Supports batch processing with Acb_sim_config

  • ILC uses harmonic-space cleaning

  • All outputs in μK_CMB units

__init__(libdir, nside, freqs, fwhm, tube, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, Acb_sim_config=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Initialize the SkySimulation.

See class docstring for detailed parameter descriptions.

Parameters:
signalOnlyQU(idx, band)[source]
Parameters:
Return type:

numpy.ndarray

get_alpha(idx, band)[source]
Parameters:
obsQUwAlpha(idx, band, fwhm, alpha, apply_tranf=True, return_alms=False)[source]
Parameters:
Return type:

numpy.ndarray

obsQUfname(idx, band)[source]
Parameters:
Return type:

str

SaveObsQUs(idx, apply_mask=True, bands=None)[source]
Parameters:
Return type:

None

obsQU(idx, band)[source]
Parameters:
Return type:

numpy.ndarray

checkObsQU(idx, overwrite=False, what='filesize', bands=False)[source]
Parameters:

idx (int)

Return type:

bool

HILC_obsEB(idx, ret=None)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

class LATsky(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, Acb_sim_config=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Bases: SkySimulation

Parameters:
freqs = ['27', '39', '93', '145', '225', '280']
fwhm = [7.4, 5.1, 2.2, 1.4, 1.0, 0.9]
tube = ['LF', 'LF', 'MF', 'MF', 'HF', 'HF']
__init__(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, Acb_sim_config=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Initialize the SkySimulation.

See class docstring for detailed parameter descriptions.

Parameters:
class SATsky(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Bases: SkySimulation

Parameters:
freqs = ['27', '39', '93', '145', '225', '280']
fwhm = [91, 63, 30, 17, 11, 9]
tube = ['S1', 'S1', 'S2', 'S2', 'S3', 'S3']
__init__(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Initialize the SkySimulation.

See class docstring for detailed parameter descriptions.

Parameters:
class LATskyC(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', verbose=True)[source]

Bases: SkySimulation

Parameters:
freqs = ['93', '145']
fwhm = [2.2, 1.4]
tube = ['MF', 'MF']
__init__(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', verbose=True)[source]

Initialize the SkySimulation.

See class docstring for detailed parameter descriptions.

Parameters:
class SATskyC(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', verbose=True)[source]

Bases: SkySimulation

Parameters:
freqs = ['93', '145']
fwhm = [30, 17]
tube = ['S2', 'S2']
__init__(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', verbose=True)[source]

Initialize the SkySimulation.

See class docstring for detailed parameter descriptions.

Parameters:

Classes

class SkySimulation(libdir, nside, freqs, fwhm, tube, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, Acb_sim_config=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Bases: object

Unified multi-frequency sky simulation framework.

This is the highest-level class that orchestrates complete end-to-end simulations combining CMB, foregrounds, noise, and instrumental effects for SO LAT/SAT observations. Includes component separation via harmonic ILC.

Parameters:

  • libdir (str) – Base directory for all simulation products and caching.

  • nside (int) – HEALPix resolution parameter.

  • freqs (ndarray) – Array of observation frequencies in GHz (e.g., [27, 39, 93, 145, 225, 280]).

  • fwhm (ndarray) – Beam FWHM in arcminutes for each frequency.

  • tube (ndarray) – Optical tube assignment for each frequency (for correlated noise).

  • cb_model ({'iso', 'iso_td', 'aniso'}, default 'iso') – Cosmic birefringence model type.

  • beta (float, default 0.35) – Isotropic birefringence angle in degrees (for cb_model=’iso’).

  • mass (float, default 1.5) – Axion mass in 10^-22 eV units (for cb_model=’iso_td’). Must be 1, 1.5, or 6.4.

  • Acb (float, default 1e-6) – Anisotropic birefringence amplitude (for cb_model=’aniso’).

  • lensing (bool, default True) – Include CMB lensing effects.

  • dust_model (int, default 10) – PySM3 dust model number.

  • sync_model (int, default 5) – PySM3 synchrotron model number.

  • bandpass (bool, default True) – Apply bandpass integration for foregrounds.

  • alpha (float or array, default 0.0) – Miscalibration polarization angle(s) in degrees. - float: same angle for all frequencies - array: per-frequency angles (must match len(freqs))

  • alpha_err (float, default 0.0) – Gaussian uncertainty on miscalibration angle (degrees).

  • miscal_samples (int, optional) – Number of miscalibration angle realizations. Default: max index from Acb_sim_config or 300.

  • Acb_sim_config (dict, optional) – Anisotropic birefringence simulation batch configuration. Keys: ‘alpha_vary_index’, ‘alpha_cons_index’, ‘null_alpha_index’ Values: [start, end] index ranges.

  • noise_model ({'NC', 'TOD'}, default 'NC') – Noise simulation type (curves vs. time-ordered data).

  • atm_noise (bool, default True) – Include atmospheric/1f noise.

  • nsplits (int, default 2) – Number of data splits for null tests.

  • gal_cut (int, default 0) – Galactic mask cut (0, 40, 60, 70, 80, 90).

  • hilc_bins (int, default 10) – Number of multipole bins for harmonic ILC.

  • deconv_maps (bool, default False) – Deconvolve beam from output maps.

  • fldname_suffix (str, default '') – Additional suffix for output directory naming.

  • sht_backend ({'healpy', 'ducc0'}, default 'healpy') – Spherical harmonic transform backend.

  • verbose (bool, default True) – Enable logging output.

  • aso (bool)

nside

HEALPix resolution.

Type:

int

freqs

Observation frequencies.

Type:

ndarray

cmb

CMB simulation object.

Type:

CMB

foreground

Foreground simulation object.

Type:

Foreground

noise

Noise simulation object.

Type:

Noise

mask

Combined survey mask.

Type:

ndarray

fsky

Sky fraction.

Type:

float

config

Per-frequency configuration dictionary.

Type:

dict

get_sim(idx, component='all')

Get simulation realization (cmb/fg/noise/all).

get_ilc(idx, return_noise=False)

Get ILC-cleaned CMB map.

get_obs(idx, freqs_idx=None)

Get observed maps at specific frequencies.

Examples

Complete LAT simulation pipeline:

from cobi.simulation import SkySimulation
import numpy as np

sky = SkySimulation(
    libdir='./sims',
    nside=512,
    freqs=np.array([27, 39, 93, 145, 225, 280]),
    fwhm=np.array([7.4, 5.1, 2.2, 1.4, 1.0, 0.9]),
    tube=np.array([0, 0, 1, 1, 2, 2]),
    cb_model='iso',
    beta=0.35,
    lensing=True,
    dust_model=10,
    sync_model=5,
    alpha=0.0,  # no miscalibration
    atm_noise=True,
    gal_cut=40
)

# Get full simulation
full_map = sky.get_sim(idx=0, component='all')

# Get CMB-only
cmb_map = sky.get_sim(idx=0, component='cmb')

# Get ILC-cleaned map
cleaned_map = sky.get_ilc(idx=0)

Multi-frequency miscalibration:

sky = SkySimulation(
    libdir='./sims',
    nside=512,
    freqs=np.array([93, 145, 225]),
    fwhm=np.array([2.2, 1.4, 1.0]),
    tube=np.array([1, 1, 2]),
    alpha=[0.2, -0.1, 0.15],  # per-frequency
    alpha_err=0.05  # with uncertainty
)

Notes

  • Automatically manages caching and directory structure

  • MPI-aware for parallel processing

  • Supports batch processing with Acb_sim_config

  • ILC uses harmonic-space cleaning

  • All outputs in μK_CMB units

__init__(libdir, nside, freqs, fwhm, tube, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, Acb_sim_config=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Initialize the SkySimulation.

See class docstring for detailed parameter descriptions.

Parameters:
signalOnlyQU(idx, band)[source]
Parameters:
Return type:

numpy.ndarray

get_alpha(idx, band)[source]
Parameters:
obsQUwAlpha(idx, band, fwhm, alpha, apply_tranf=True, return_alms=False)[source]
Parameters:
Return type:

numpy.ndarray

obsQUfname(idx, band)[source]
Parameters:
Return type:

str

SaveObsQUs(idx, apply_mask=True, bands=None)[source]
Parameters:
Return type:

None

obsQU(idx, band)[source]
Parameters:
Return type:

numpy.ndarray

checkObsQU(idx, overwrite=False, what='filesize', bands=False)[source]
Parameters:

idx (int)

Return type:

bool

HILC_obsEB(idx, ret=None)[source]
Parameters:

idx (int)

Return type:

numpy.ndarray

class LATsky(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, Acb_sim_config=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Bases: SkySimulation

Parameters:
freqs = ['27', '39', '93', '145', '225', '280']
fwhm = [7.4, 5.1, 2.2, 1.4, 1.0, 0.9]
tube = ['LF', 'LF', 'MF', 'MF', 'HF', 'HF']
__init__(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, Acb_sim_config=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Initialize the SkySimulation.

See class docstring for detailed parameter descriptions.

Parameters:
class SATsky(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Bases: SkySimulation

Parameters:
freqs = ['27', '39', '93', '145', '225', '280']
fwhm = [91, 63, 30, 17, 11, 9]
tube = ['S1', 'S1', 'S2', 'S2', 'S3', 'S3']
__init__(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', sht_backend='healpy', verbose=True)[source]

Initialize the SkySimulation.

See class docstring for detailed parameter descriptions.

Parameters:
class LATskyC(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', verbose=True)[source]

Bases: SkySimulation

Parameters:
freqs = ['93', '145']
fwhm = [2.2, 1.4]
tube = ['MF', 'MF']
__init__(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', aso=False, atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', verbose=True)[source]

Initialize the SkySimulation.

See class docstring for detailed parameter descriptions.

Parameters:
class SATskyC(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', verbose=True)[source]

Bases: SkySimulation

Parameters:
freqs = ['93', '145']
fwhm = [30, 17]
tube = ['S2', 'S2']
__init__(libdir, nside, cb_model='iso', beta=0.35, mass=1.5, Acb=1e-06, lensing=True, dust_model=10, sync_model=5, bandpass=True, alpha=0.0, alpha_err=0.0, miscal_samples=None, noise_model='NC', atm_noise=True, nsplits=2, gal_cut=0, hilc_bins=10, deconv_maps=False, fldname_suffix='', verbose=True)[source]

Initialize the SkySimulation.

See class docstring for detailed parameter descriptions.

Parameters: