analysis doc fixes

abacusnbody/analysis/power_spectrum.py

@@ -16,11 +16,11 @@
 from .cic import cic_serial
 
 
-__all__ = ['pk_to_xi',
-           'calc_power',
+__all__ = ['calc_power',
+           'calc_pk_from_deltak',
+           'pk_to_xi',
            'project_3d_to_poles',
            'get_k_mu_edges',
-           'calc_pk_from_deltak',
            ]
 
 MAX_THREADS = numba.config.NUMBA_NUM_THREADS
@@ -1093,15 +1093,16 @@ def calc_power(pos,
     Returns
     -------
     power : astropy.Table
-        The power spectrum in an astropy Table of length ``nbins_k``.
-        The columns are:
+        The power spectrum in an astropy Table of length ``nbins_k``. The columns are:
+
         - ``k_mid``: arithmetic bin centers of the k wavenumbers, shape ``(nbins_k,)``
         - ``k_avg``: mean wavenumber per (k, mu) wedge, shape ``(nbins_k,nbins_mu)``
         - ``mu_mid``: arithmetic bin centers of the mu angles, shape ``(nbins_k,nbins_mu)``
         - ``power``: mean power spectrum per (k, mu) wedge, shape ``(nbins_k,nbins_mu)``
         - ``N_mode``: number of modes per (k, mu) wedge, shape ``(nbins_k,nbins_mu)``
 
         If multipoles are requested via ``poles``, the table includes:
+
         - ``poles``: mean Legendre multipole coefficients, shape ``(nbins_k,len(poles))``
         - ``N_mode_poles``: number of modes per pole, shape ``(nbins_k,len(poles))``
 

abacusnbody/analysis/tpcf_corrfunc.py

@@ -38,24 +38,32 @@ def tpcf_multipole(s_mu_tcpf_result, mu_bins, order=0):
     Examples
     --------
     For demonstration purposes we create a randomly distributed set of points within a
-    periodic cube of length 250 Mpc/h.
-    >>> Npts = 100
-    >>> Lbox = 250.
-    >>> x = np.random.uniform(0, Lbox, Npts)
-    >>> y = np.random.uniform(0, Lbox, Npts)
-    >>> z = np.random.uniform(0, Lbox, Npts)
+    periodic cube of length 250 Mpc/h.::
+
+        >>> Npts = 100
+        >>> Lbox = 250.
+        >>> x = np.random.uniform(0, Lbox, Npts)
+        >>> y = np.random.uniform(0, Lbox, Npts)
+        >>> z = np.random.uniform(0, Lbox, Npts)
+
     We transform our *x, y, z* points into the array shape used by the pair-counter by
     taking the transpose of the result of `numpy.vstack`. This boilerplate transformation
-    is used throughout the `~halotools.mock_observables` sub-package:
-    >>> sample1 = np.vstack((x,y,z)).T
+    is used throughout the `~halotools.mock_observables` sub-package: ::
+
+        >>> sample1 = np.vstack((x,y,z)).T
+
     First, we calculate the correlation function using
-    `~halotools.mock_observables.s_mu_tpcf`.
-    >>> from halotools.mock_observables import s_mu_tpcf
-    >>> s_bins  = np.linspace(0.01, 25, 10)
-    >>> mu_bins = np.linspace(0, 1, 15)
-    >>> xi_s_mu = s_mu_tpcf(sample1, s_bins, mu_bins, period=Lbox)
-    Then, we can calculate the quadrapole of the correlation function:
-    >>> xi_2 = tpcf_multipole(xi_s_mu, mu_bins, order=2)
+    `~halotools.mock_observables.s_mu_tpcf`.::
+
+        >>> from halotools.mock_observables import s_mu_tpcf
+        >>> s_bins  = np.linspace(0.01, 25, 10)
+        >>> mu_bins = np.linspace(0, 1, 15)
+        >>> xi_s_mu = s_mu_tpcf(sample1, s_bins, mu_bins, period=Lbox)
+
+    Then, we can calculate the quadrapole of the correlation function: ::
+
+        >>> xi_2 = tpcf_multipole(xi_s_mu, mu_bins, order=2)
+
     """
 
     # process inputs