pyccl.pk2d module

class pyccl.pk2d.Pk2D(pkfunc=None, a_arr=None, lk_arr=None, pk_arr=None, is_logp=True, extrap_order_lok=1, extrap_order_hik=2, cosmo=None)[source]

Bases: object

A power spectrum class holding the information needed to reconstruct an arbitrary function of wavenumber and scale factor.

Parameters:
  • ( (pkfunc) – obj:function): a function returning a floating point number or numpy array with the signature f(k,a), where k is a wavenumber (in units of Mpc^-1) and a is the scale factor. The function must able to take numpy arrays as k. The function must return the value(s) of the power spectrum (or its natural logarithm, depending on the value of is_logp. The power spectrum units should be compatible with those used by CCL (e.g. if you’re passing a matter power spectrum, its units should be Mpc^3). If this argument is not None, this function will be sampled at the values of k and a used internally by CCL to store the linear and non-linear power spectra.
  • a_arr (array) – an array holding values of the scale factor
  • lk_arr (array) – an array holding values of the natural logarithm of the wavenumber (in units of Mpc^-1).
  • pk_arr (array) – a 2D array containing the values of the power spectrum at the values of the scale factor and the wavenumber held by a_arr and lk_arr. The shape of this array must be [na,nk], where na is the size of a_arr and nk is the size of lk_arr. This array can be provided in a flattened form as long as the total size matches nk*na. The array can hold the values of the natural logarithm of the power spectrum, depending on the value of is_logp. If pkfunc is not None, then a_arr, lk_arr and pk_arr are ignored. However, either pkfunc or all of the last three array must be non-None. Note that, if you pass your own Pk array, you are responsible of making sure that it is sufficiently well sampled (i.e. the resolution of a_arr and lk_arr is high enough to sample the main features in the power spectrum). For reference, CCL will use bicubic interpolation to evaluate the power spectrum at any intermediate point in k and a.
  • extrap_order_lok (int) – extrapolation order to be used on k-values below the minimum of the splines (use 0, 1 or 2). Note that the extrapolation will be done in either log(P(k)) or P(k), depending on the value of is_logp.
  • extrap_order_hik (int) – extrapolation order to be used on k-values above the maximum of the splines (use 0, 1 or 2). Note that the extrapolation will be done in either log(P(k)) or P(k), depending on the value of is_logp.
  • is_logp (boolean) – if True, pkfunc/pkarr return/hold the natural logarithm of the power spectrum. Otherwise, the true value of the power spectrum is expected. Note that arrays will be interpolated in log space if is_logp is set to True.
  • cosmo (Cosmology) – Cosmology object. The cosmology object is needed in order if pkfunc is not None. The object is used to determine the sampling rate in scale factor and wavenumber.
eval(k, a, cosmo)[source]

Evaluate power spectrum.

Parameters:
  • k (float or array_like) – wavenumber value(s) in units of Mpc^-1.
  • a (float) – value of the scale factor
  • cosmo (Cosmology) – Cosmology object. The cosmology object is needed in order to evaluate the power spectrum outside the interpolation range in a. E.g. if you want to evaluate the power spectrum at a very small a, not covered by the arrays you passed when initializing this object, the power spectrum will be extrapolated from the earliest available value using the linear growth factor (for which a cosmology is needed).
  • a_arr (array) – an array holding values of the scale factor.
Returns:

value(s) of the power spectrum.
Return type:

float or array_like