DOC: fix formatting of a return value of polynomial fit methods

Fixes numpy#19897

The 2nd return value of the following methods/functions were badly
formatted and the list was all appearing in a single line. Changed them
to separate points which are rendered nicely.
 - numpy.polyfit
 - numpy.ma.polyfit
 - numpy.polynomial.polynomial.polyfit
 - numpy.polynomial.polynomial.Polynomial.fit
 - numpy.polynomial.chebyshev.chebfit
 - numpy.polynomial.chebyshev.Chebyshev.fit
 - numpy.polynomial.hermite.hermfit
 - numpy.polynomial.hermite.Hermite.fit
 - numpy.polynomial.hermite_e.hermefit
 - numpy.polynomial.hermite_e.HermiteE.fit
 - numpy.polynomial.laguerre.lagfit
 - numpy.polynomial.laguerre.Laguerre.fit
 - numpy.polynomial.legendre.legfit
 - numpy.polynomial.legendre.Legendre.fit

Also fixed erroneous links to `numpy.full` which were actually referring
to the `full` argument. Changed those to code strings (double backticks)
from single backticks.

Also fixed formatting issues in the 3rd return value of numpy.polyfit
(and hence also numpy.ma.polyfit).

Author: Samyak2

numpy/lib/polynomial.py

@@ -510,13 +510,19 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
         coefficients for `k`-th data set are in ``p[:,k]``.
 
     residuals, rank, singular_values, rcond
-        Present only if `full` = True.  Residuals is sum of squared residuals
-        of the least-squares fit, the effective rank of the scaled Vandermonde
-        coefficient matrix, its singular values, and the specified value of
-        `rcond`. For more details, see `linalg.lstsq`.
+        These values are only returned if ``full == True``
+
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the effective rank of the scaled Vandermonde
+           coefficient matrix
+        - singular_values -- singular values of the scaled Vandermonde
+           coefficient matrix
+        - rcond -- value of `rcond`.
+
+        For more details, see `numpy.linalg.lstsq`.
 
     V : ndarray, shape (M,M) or (M,M,K)
-        Present only if `full` = False and `cov`=True.  The covariance
+        Present only if ``full == False`` and ``cov == True``.  The covariance
         matrix of the polynomial coefficient estimates.  The diagonal of
         this matrix are the variance estimates for each coefficient.  If y
         is a 2-D array, then the covariance matrix for the `k`-th data set
@@ -527,7 +533,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
     -----
     RankWarning
         The rank of the coefficient matrix in the least-squares fit is
-        deficient. The warning is only raised if `full` = False.
+        deficient. The warning is only raised if ``full == False``.
 
         The warnings can be turned off by
 

numpy/polynomial/_polybase.py

@@ -958,12 +958,12 @@ class domain in NumPy 1.4 and ``None`` in later versions.
             of interest, do ``new_series.convert().coef``.
 
         [resid, rank, sv, rcond] : list
-            These values are only returned if `full` = True
+            These values are only returned if ``full == True``
 
-            resid -- sum of squared residuals of the least squares fit
-            rank -- the numerical rank of the scaled Vandermonde matrix
-            sv -- singular values of the scaled Vandermonde matrix
-            rcond -- value of `rcond`.
+            - resid -- sum of squared residuals of the least squares fit
+            - rank -- the numerical rank of the scaled Vandermonde matrix
+            - sv -- singular values of the scaled Vandermonde matrix
+            - rcond -- value of `rcond`.
 
             For more details, see `linalg.lstsq`.
 

numpy/polynomial/chebyshev.py

@@ -1598,20 +1598,20 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None):
         `k`.
 
     [residuals, rank, singular_values, rcond] : list
-        These values are only returned if `full` = True
+        These values are only returned if ``full == True``
 
-        resid -- sum of squared residuals of the least squares fit
-        rank -- the numerical rank of the scaled Vandermonde matrix
-        sv -- singular values of the scaled Vandermonde matrix
-        rcond -- value of `rcond`.
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
 
         For more details, see `numpy.linalg.lstsq`.
 
     Warns
     -----
     RankWarning
         The rank of the coefficient matrix in the least-squares fit is
-        deficient. The warning is only raised if `full` = False.  The
+        deficient. The warning is only raised if ``full == False``.  The
         warnings can be turned off by
 
         >>> import warnings

numpy/polynomial/hermite.py

@@ -1324,20 +1324,20 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None):
         `k`.
 
     [residuals, rank, singular_values, rcond] : list
-        These values are only returned if `full` = True
+        These values are only returned if ``full == True``
 
-        resid -- sum of squared residuals of the least squares fit
-        rank -- the numerical rank of the scaled Vandermonde matrix
-        sv -- singular values of the scaled Vandermonde matrix
-        rcond -- value of `rcond`.
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
 
         For more details, see `numpy.linalg.lstsq`.
 
     Warns
     -----
     RankWarning
         The rank of the coefficient matrix in the least-squares fit is
-        deficient. The warning is only raised if `full` = False.  The
+        deficient. The warning is only raised if ``full == False``.  The
         warnings can be turned off by
 
         >>> import warnings

numpy/polynomial/hermite_e.py

@@ -1315,20 +1315,20 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None):
         `k`.
 
     [residuals, rank, singular_values, rcond] : list
-        These values are only returned if `full` = True
+        These values are only returned if ``full == True``
 
-        resid -- sum of squared residuals of the least squares fit
-        rank -- the numerical rank of the scaled Vandermonde matrix
-        sv -- singular values of the scaled Vandermonde matrix
-        rcond -- value of `rcond`.
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
 
         For more details, see `numpy.linalg.lstsq`.
 
     Warns
     -----
     RankWarning
         The rank of the coefficient matrix in the least-squares fit is
-        deficient. The warning is only raised if `full` = False.  The
+        deficient. The warning is only raised if ``full = False``.  The
         warnings can be turned off by
 
         >>> import warnings

numpy/polynomial/laguerre.py

@@ -1321,20 +1321,20 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None):
         `k`.
 
     [residuals, rank, singular_values, rcond] : list
-        These values are only returned if `full` = True
+        These values are only returned if ``full == True``
 
-        resid -- sum of squared residuals of the least squares fit
-        rank -- the numerical rank of the scaled Vandermonde matrix
-        sv -- singular values of the scaled Vandermonde matrix
-        rcond -- value of `rcond`.
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
 
         For more details, see `numpy.linalg.lstsq`.
 
     Warns
     -----
     RankWarning
         The rank of the coefficient matrix in the least-squares fit is
-        deficient. The warning is only raised if `full` = False.  The
+        deficient. The warning is only raised if ``full == False``.  The
         warnings can be turned off by
 
         >>> import warnings

numpy/polynomial/legendre.py

@@ -1339,20 +1339,20 @@ def legfit(x, y, deg, rcond=None, full=False, w=None):
         returned `coef`.
 
     [residuals, rank, singular_values, rcond] : list
-        These values are only returned if `full` = True
+        These values are only returned if ``full == True``
 
-        resid -- sum of squared residuals of the least squares fit
-        rank -- the numerical rank of the scaled Vandermonde matrix
-        sv -- singular values of the scaled Vandermonde matrix
-        rcond -- value of `rcond`.
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
 
         For more details, see `numpy.linalg.lstsq`.
 
     Warns
     -----
     RankWarning
         The rank of the coefficient matrix in the least-squares fit is
-        deficient. The warning is only raised if `full` = False.  The
+        deficient. The warning is only raised if ``full == False``.  The
         warnings can be turned off by
 
         >>> import warnings

numpy/polynomial/polynomial.py

@@ -1268,20 +1268,20 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None):
         fit to the data in `y`'s `k`-th column.
 
     [residuals, rank, singular_values, rcond] : list
-        These values are only returned if `full` = True
+        These values are only returned if ``full == True``
 
-        resid -- sum of squared residuals of the least squares fit
-        rank -- the numerical rank of the scaled Vandermonde matrix
-        sv -- singular values of the scaled Vandermonde matrix
-        rcond -- value of `rcond`.
+        - residuals -- sum of squared residuals of the least squares fit
+        - rank -- the numerical rank of the scaled Vandermonde matrix
+        - singular_values -- singular values of the scaled Vandermonde matrix
+        - rcond -- value of `rcond`.
 
         For more details, see `numpy.linalg.lstsq`.
 
     Raises
     ------
     RankWarning
         Raised if the matrix in the least-squares fit is rank deficient.
-        The warning is only raised if `full` == False.  The warnings can
+        The warning is only raised if ``full == False``.  The warnings can
         be turned off by:
 
         >>> import warnings