Merge pull request #3 from mcara/pep8-tify

Fix math formatting in docstrings after PEP8 changes

wiimatch/lsq_optimizer.py

@@ -92,16 +92,16 @@ def build_lsq_eqs(images, masks, sigmas, degree, center=None,
     :py:func:`build_lsq_eqs` builds a system of linear equations
 
     .. math::
-        a \\cdot c = b
+        a \cdot c = b
 
     whose solution :math:`c` is a set of coefficients of (multivariate)
     polynomials that represent the "background" in each input image (these are
     polynomials that are "corrections" to intensities of input images) such
     that the following sum is minimized:
 
     .. math::
-        L = \sum^N_{n,m=1,n \\neq m} \sum_k\
-        \\frac{\\left[I_n(k) - I_m(k) - P_n(k) + P_m(k)\\right]^2}\
+        L = \sum^N_{n,m=1,n \neq m} \sum_k
+        \frac{\left[I_n(k) - I_m(k) - P_n(k) + P_m(k)\right]^2}
         {\sigma^2_n(k) + \sigma^2_m(k)}.
 
     In the above equation, index :math:`k=(k_1,k_2,...)` labels a position
@@ -112,15 +112,16 @@ def build_lsq_eqs(images, masks, sigmas, degree, center=None,
     corresponding coefficients as:
 
     .. math::
-        P_n(k_1,k_2,...) = \sum_{d_1=0,d_2=0,...}^{D_1,D_2,...} \
-        c_{d_1,d_2,...}^n \\cdot k_1^{d_1} \\cdot k_2^{d_2}  \\cdot \\ldots .
+        P_n(k_1,k_2,...) = \sum_{d_1=0,d_2=0,...}^{D_1,D_2,...}
+        c_{d_1,d_2,...}^n \cdot k_1^{d_1} \cdot k_2^{d_2}  \cdot \ldots .
 
     Coefficients :math:`c_{d_1,d_2,...}^n` are arranged in the vector :math:`c`
     in the following order:
 
     .. math::
-        (c_{0,0,\\ldots}^1,c_{1,0,\\ldots}^1,\\ldots,c_{0,0,\\ldots}^2,\
-        c_{1,0,\\ldots}^2,\\ldots).
+        (c_{0,0,\ldots}^1,c_{1,0,\ldots}^1,\ldots,c_{0,0,\ldots}^2,
+        c_{1,0,\ldots}^2,\ldots).
+
 
     Examples
     --------
@@ -276,7 +277,7 @@ def pinv_solve(matrix, free_term, nimages, tol=None):
     Solves a system of linear equations
 
     .. math::
-        a \\cdot c = b.
+        a \ cdot c = b.
 
     using Moore-Penrose pseudoinverse.
 
@@ -333,11 +334,11 @@ def pinv_solve(matrix, free_term, nimages, tol=None):
 
 
 def rlu_solve(matrix, free_term, nimages):
-    """
+    r"""
     Computes solution of a "reduced" system of linear equations
 
     .. math::
-        a' \\cdot c' = b'.
+        a' \cdot c' = b'.
 
     using LU-decomposition. If the original system contained a set of
     linearly-dependent equations, then the "reduced" system is formed by

wiimatch/match.py

@@ -119,17 +119,17 @@ def match_lsq(images, masks=None, sigmas=None, degree=0,
     :py:func:`match_lsq` builds a system of linear equations
 
     .. math::
-        a \\cdot c = b
+        a \cdot c = b
 
     whose solution :math:`c` is a set of coefficients of (multivariate)
     polynomials that represent the "background" in each input image (these are
     polynomials that are "corrections" to intensities of input images) such
     that the following sum is minimized:
 
     .. math::
-        L = \sum^N_{n,m=1,n \\neq m} \sum_k\
-    \\frac{\\left[I_n(k) - I_m(k) - P_n(k) + P_m(k)\\right]^2}\
-    {\sigma^2_n(k) + \sigma^2_m(k)}.
+        L = \sum^N_{n,m=1,n \neq m} \sum_k
+        \frac{\left[I_n(k) - I_m(k) - P_n(k) + P_m(k)\right]^2}
+        {\sigma^2_n(k) + \sigma^2_m(k)}.
 
     In the above equation, index :math:`k=(k_1,k_2,...)` labels a position
     in input image's pixel grid [NOTE: all input images share a common
@@ -139,15 +139,15 @@ def match_lsq(images, masks=None, sigmas=None, degree=0,
     corresponding coefficients as:
 
     .. math::
-        P_n(k_1,k_2,...) = \sum_{d_1=0,d_2=0,...}^{D_1,D_2,...} \
-        c_{d_1,d_2,...}^n \\cdot k_1^{d_1} \\cdot k_2^{d_2}  \\cdot \\ldots .
+        P_n(k_1,k_2,...) = \sum_{d_1=0,d_2=0,...}^{D_1,D_2,...}
+        c_{d_1,d_2,...}^n \cdot k_1^{d_1} \cdot k_2^{d_2}  \cdot \ldots .
 
     Coefficients :math:`c_{d_1,d_2,...}^n` are arranged in the vector :math:`c`
     in the following order:
 
     .. math::
-        (c_{0,0,\\ldots}^1,c_{1,0,\\ldots}^1,\\ldots,c_{0,0,\\ldots}^2,\
-        c_{1,0,\\ldots}^2,\\ldots).
+        (c_{0,0,\ldots}^1,c_{1,0,\ldots}^1,\ldots,c_{0,0,\ldots}^2,
+        c_{1,0,\ldots}^2,\ldots).
 
     :py:func:`match_lsq` returns coefficients of the polynomials that
     minimize *L*.