Documentation fixes (#1122)

* small doc fixes

* corrected typo in tracers.py

* fixed typo in correlations.py

* HOD log vs log10

* Fixed C_ell docs

---------

Co-authored-by: Nikolina Sarcevic <[email protected]>

pyccl/cells.py

@@ -46,8 +46,9 @@ def angular_cl(
             method backed up by `quad` when it fails) and 'spline' (the
             integrand is splined and then integrated numerically).
         non_limber_integration_method (string) : integration method to be used
-            for the non-Limber integrals. Possibilities: 'FKEM','MATTER'.
-            See N5K (arXiv:2212.04291) paper for more information.
+            for the non-Limber integrals. Currently the only method implemented
+            is ``'FKEM'`` (see the `N5K paper <https://arxiv.org/abs/2212.04291>`_
+            for details).
         p_of_k_a_lin (:class:`~pyccl.pk2d.Pk2D`, :obj:`str` or :obj:`None`): 
             3D linear Power spectrum to project, for special use in
             PT calculations using the FKEM non-limber integration technique.

pyccl/correlations.py

@@ -110,7 +110,7 @@ def correlation(cosmo, *, ell, C_ell, theta, type='NN', method='fftlog'):
     method = method.lower()
 
     if type not in correlation_types:
-        raise ValueError(f"Invalud correlation type {type}.")
+        raise ValueError(f"Invalid correlation type {type}.")
 
     if method not in correlation_methods.keys():
         raise ValueError(f"Invalid correlation method {method}.")

pyccl/halos/profiles/hod.py

@@ -68,6 +68,12 @@ class HaloProfileHOD(HaloProfile):
     here are roughly compatible with those found in the latter
     paper.
 
+    .. warning:: Note that :math:`\\sigma_{{\\rm ln}M}` is defined
+        so that all logarithms of mass entering the definition of
+        :math:`\\bar{N}_c(M,a)` are natural logarithms, and not
+        decimal. This is different from the convention used in some
+        of the papers above, which used :math:`\\log_{10}`.
+
     See :class:`~pyccl.halos.profiles_2pt.Profile2ptHOD` for a
     description of the Fourier-space two-point correlator of the
     HOD profile.

pyccl/pk2d.py

@@ -54,9 +54,9 @@ class Pk2D(CCLObject):
             splines. Note that extrapolation is either in :math:`\\log(P(k))` or
             in :math:`P(k)`, depending on the value of ``is_logp``.
         is_logp (:obj:`bool`):
-            If True, ``pkfunc``/``pkarr`` return/hold the natural logarithm of the
+            If True, ``pkarr`` holds the natural logarithm of the
             power spectrum. Otherwise, the true value of the power spectrum is
-            expected. If ``is_logp`` is ``True``, arrays are interpolate in
+            expected. If ``is_logp`` is ``True``, arrays are interpolated in
             log-space.
 
     .. automethod:: __call__

pyccl/tracers.py

@@ -873,11 +873,11 @@ def WeakLensingTracer(cosmo, *, dndz, has_shear=True, ia_bias=None,
             lensing shear contribution from this tracer.
         ia_bias (:obj:`tuple`): A tuple of arrays
             ``(z, A_IA(z))`` giving the intrinsic alignment amplitude
-            ``A_IA(z)``. If ``None``, the tracer is assumped to not have
+            ``A_IA(z)``. If ``None``, the tracer is assumed to not have
             intrinsic alignments.
         use_A_ia (:obj:`bool`): set to ``True`` to use the conventional IA
             normalization. Set to ``False`` to use the raw input amplitude,
-            which will usually be 1 for use with perturbaion theory IA
+            which will usually be 1 for use with perturbation theory IA
             modeling.
         n_samples (:obj:`int`): number of samples over which the lensing
             kernel is desired. These will be equi-spaced in radial distance.

readthedocs/api/pyccl.nl_pt.bacco_lbias.rst

@@ -0,0 +1,7 @@
+pyccl.nl\_pt.bacco\_lbias module
+================================
+
+.. automodule:: pyccl.nl_pt.bacco_lbias
+   :members:
+   :undoc-members:
+   :show-inheritance:

readthedocs/api/pyccl.nl_pt.rst

@@ -7,6 +7,7 @@ Submodules
 .. toctree::
    :maxdepth: 4
 
+   pyccl.nl_pt.bacco_lbias
    pyccl.nl_pt.ept
    pyccl.nl_pt.lpt
    pyccl.nl_pt.tracers