coscon.cmb module

class coscon.cmb.Maps(names: Union[List[str], Tuple[str, …]], maps: np.ndarray, name: str = '')

Bases: object

A class for multiple healpix maps that wraps some healpy functions

In ring ordering, uK ($mathrm{mu K}$) unit.

property f_sky

classmethod from_fits(path: pathlib.Path, memmap: bool = True) → coscon.cmb.Maps

classmethod from_planck_2018(nside: int) → coscon.cmb.Maps

Use the Planck 2018 best-fit ΛCDM model to simulate a map

Use official release if nside is small enough.

classmethod from_pysm(freq: float, nside: int, preset_strings: List[str] = ['c1']) → coscon.cmb.Maps

maps: np.ndarray

property maps_dict

property maps_dict_fullname

mollview(n_std: Optional[int] = None, fwhm: Optional[float] = None, graticule: bool = True, **kwargs)

object wrap of healpy.mollview

Parameters

• n_std – if specified, set the range to be mean ± n_std * std

• fwhm – if specified, smooth map to this number of arcmin

property n_maps

name: str = ''

names: Union[List[str], Tuple[str, …]]

property spectra

Use anafast to calculate the spectra results are always 2D-array

property to_spectra

write(path: pathlib.Path, nest: bool = True, dtype: Optional[Union[numpy.dtype, List[numpy.dtype]]] = None, fits_IDL: bool = True, coord: str = 'G', partial: bool = False, column_names: Optional[Union[str, List[str]]] = None, column_units: Union[str, List[str]] = 'uK', extra_header: list = [], overwrite: bool = False)

class coscon.cmb.PowerSpectra(names: List[str], spectra: numpy.ndarray, l_min: int = 0, l: Optional[numpy.ndarray] = None, scale: str = 'Dl', name: str = '')

Bases: object

A class for power-spectra.

In [microK]^2 unit.

compare(*others: coscon.cmb.PowerSpectra, relative: bool = False) → pandas.core.frame.DataFrame

Return a tidy DataFrame comparing self and others.

compare_plot(*others: PowerSpectra, show: bool = True, relative: bool = False) → List[plotly.graph_objs._figure.Figure]

property dataframe

classmethod from_class(path: Optional[pathlib.Path] = None, url: Optional[str] = None, camb: bool = True, name: str = '') → coscon.cmb.PowerSpectra

read from CLASS’ .dat output

Parameters

camb (bool) – if True, assumed format = camb is used when

generating the .dat from CLASS

To self: migrated from abx’s convert_theory.py

classmethod from_dataframe(df: pandas.core.frame.DataFrame, scale: str = 'Dl') → coscon.cmb.PowerSpectra

classmethod from_planck_2018() → coscon.cmb.PowerSpectra

Use the Planck 2018 best-fit ΛCDM model

Officially released by Planck up to l equals 2508. see http://pla.esac.esa.int/pla/#cosmology and its description

classmethod from_planck_2018_extended() → coscon.cmb.PowerSpectra

Use the Planck 2018 best fit ΛCDM model with extended l-range up to 4902

This is reproduced using CLASS but with an extended l-range

intersect(*others: coscon.cmb.PowerSpectra) → List[pandas.core.frame.DataFrame]

l: Optional[numpy.ndarray] = None

property l_array

property l_max

exclusive l_max

l_min: int = 0

property n_spectra

name: str = ''

names: List[str]

scale: str = 'Dl'

spectra: numpy.ndarray

to_maps(nside: int, pixwin=False) → coscon.cmb.Maps

Use synfast to generate random maps

coscon.cmb.simmap_planck2018_cli()