#195: fix pylint warnings in scaler.py

romtools/vector_space/utils/scaler.py

@@ -46,19 +46,21 @@
 """
 ---
 ##**Notes**
-The scaler class is used to performed scaled POD. Scaling is applied to tensors of shape $\mathbb{R}^{ N_{\\mathrm{vars}} \\times N_{\\mathrm{x}} \\times N_s}$. These tensors are then reshaped into matrices when performing SVD.
+The scaler class is used to performed scaled POD.
+Scaling is applied to tensors of shape $\mathbb{R}^{ N_{\\mathrm{vars}} \\times N_{\\mathrm{x}} \\times N_s}$.
+These tensors are then reshaped into matrices when performing SVD.
 
 ___
 ##**Theory**
 
 *What is scaled POD, and why would I do it?*
 
 Standard POD computes a basis that minimizes the projection error in a standard Euclidean $\\ell^2$ inner product,
-i.e., for a snapshot matrix $\\mathbf{S} \\in \\mathbb{R}^{  N_{\\mathrm{vars}} N_{\\mathrm{x}} \\times N_s}$, POD computes the basis by solving the minimization problem
-(assuming no affine offset)
-$$ \\boldsymbol \\Phi = \\underset{ \\boldsymbol \\Phi_{\\*} \\in \\mathbb{R}^{ N_{\\mathrm{vars}} N_{\\mathrm{x}} \\times K} | \\boldsymbol
-\\Phi_{\\*}^T \\boldsymbol \\Phi_{\\*} = \\mathbf{I}}{ \\mathrm{arg \\; min} } \\| \\Phi_{\\*} \\Phi_{\\*}^T
-\\mathbf{S} - \\mathbf{S} \\|_2.$$
+i.e., for a snapshot matrix $\\mathbf{S} \\in \\mathbb{R}^{  N_{\\mathrm{vars}} N_{\\mathrm{x}} \\times N_s}$,
+POD computes the basis by solving the minimization problem (assuming no affine offset)
+$$ \\boldsymbol \\Phi = \\underset{ \\boldsymbol \\Phi_{\\*} \\in \\mathbb{R}^{ N_{\\mathrm{vars}} N_{\\mathrm{x}}
+\\times K} | \\boldsymbol \\Phi_{\\*}^T \\boldsymbol \\Phi_{\\*} = \\mathbf{I}}{ \\mathrm{arg \\; min} } \\| \\Phi_{\\*}
+\\Phi_{\\*}^T \\mathbf{S} - \\mathbf{S} \\|_2.$$
 In this minimization problem, errors are measured in a standard $\\ell^2$ norm.
 For most practical applications, where our snapshot matrix involves variables of different scales,
 this norm does not make sense (both intuitively, and on dimensional grounds).
@@ -69,9 +71,9 @@
 Defining $\\mathbf{S}_{\\*} = \\mathbf{W}^{-1} \\mathbf{S}$, where $\\mathbf{W}$ is a weighting matrix
 (e.g., a diagonal matrix containing the max absolute value of each state variable),
 we compute the basis as the solution to the minimization problem
-$$ \\boldsymbol \\Phi = \\mathbf{W} \\underset{ \\boldsymbol \\Phi_{\\*} \\in \\mathbb{R}^{N_{\\mathrm{vars}} N_{\\mathrm{x}} \\times K} |\\boldsymbol
-\\Phi_{\\*}^T \\boldsymbol \\Phi_{\\*} = \\mathbf{I}}{ \\mathrm{arg \\; min} } \\| \\Phi_{\\*} \\Phi_{\\*}^T
-\\mathbf{S}_{\\*} - \\mathbf{S}_{\\*} \\|_2.$$
+$$ \\boldsymbol \\Phi = \\mathbf{W} \\underset{ \\boldsymbol \\Phi_{\\*} \\in \\mathbb{R}^{N_{\\mathrm{vars}} N_{\\mathrm{x}}
+\\times K} |\\boldsymbol \\Phi_{\\*}^T \\boldsymbol \\Phi_{\\*} = \\mathbf{I}}{ \\mathrm{arg \\; min} } \\| \\Phi_{\\*}
+\\Phi_{\\*}^T \\mathbf{S}_{\\*} - \\mathbf{S}_{\\*} \\|_2.$$
 
 The Scaler encapsulates this information.
 
@@ -113,10 +115,10 @@ def __init__(self) -> None:
         pass
 
     def pre_scale(self, data_tensor: np.ndarray):
-        data_tensor = data_tensor
+        pass
 
     def post_scale(self, data_tensor):
-        data_tensor = data_tensor
+        pass
 
 
 class VectorScaler: