removed __all__.

sigpy/alg.py

@@ -5,9 +5,6 @@
 if config.cupy_enabled:
     import cupy as cp
 
-__all__ = ['PowerMethod', 'ConjugateGradient', 'GradientMethod',
-           'PrimalDualHybridGradient', 'AltMin', 'Alg']
-
 
 class Alg(object):
     """Abstraction for iterative algorithm.
@@ -64,7 +61,7 @@ class PowerMethod(Alg):
         max_iter (int): Maximum number of iterations.
 
     Attributes:
-        max_eig (float): Maximum eigenvalue of A.
+        float: Maximum eigenvalue of `A`.
     """
 
     def __init__(self, A, x, max_iter=30):
@@ -250,6 +247,7 @@ class NewtonsMethod(Alg):
         hessf (function): Function to compute Hessian of f at x,
         proxHg (function): Function to compute proximal operator of g.
         x (array): Optimization variable.
+
     """
 
     def __init__(self, gradf, hessf, proxHg, x,
@@ -299,16 +297,17 @@ class PrimalDualHybridGradient(Alg):
     Args:
         proxfc (function): Function to compute proximal operator of f^*.
         proxg (function): Function to compute proximal operator of g.
-        A (function): Function to compute linear mapping A.
-        AH (function): Function to compute adjoint linear mapping of A.
+        A (function): Function to compute a linear mapping.
+        AH (function): Function to compute the adjoint linear mapping of `A`.
         x (array): Primal solution.
         u (array): Dual solution.
         tau (float): Primal step-size.
         sigma (float): Dual step-size.
         theta (float): Primal extrapolation parameter.
-        P (function): Function to compute precondition x.
-        D (function): Function to compute precondition u.
+        P (function): Function to compute precondition primal variable.
+        D (function): Function to compute precondition dual variable.
         max_iter (int): Maximum number of iterations.
+
     """
 
     def __init__(

sigpy/app.py

@@ -7,9 +7,6 @@
     import cupy as cp
 
 
-__all__ = ["MaxEig", "LinearLeastSquares", "SecondOrderConeConstraint"]
-
-
 class App(object):
     """Iterative algorithm application. Each App has its own Alg.
 
@@ -94,10 +91,10 @@ class LinearLeastSquares(App):
     .. math::
         \min_x \frac{1}{2} \| W^{0.5} (A x - y) \|_2^2 + g(G x) + \frac{\lambda}{2} \| R x \|_2^2
 
-    Three algorithms can be used: ConjugateGradient, GradientMethod,
-    and PrimalDualHybridGradient. If alg_name is None, ConjugateGradient is used
-    when proxg is not specified. If proximal operator is specified,
-    then GradientMethod is used when G is specified, and PrimalDualHybridGradient is
+    Three algorithms can be used: `ConjugateGradient`, `GradientMethod`,
+    and `PrimalDualHybridGradient`. If `alg_name` is None, `ConjugateGradient` is used
+    when `proxg` is not specified. If `proxg` is specified,
+    then `GradientMethod` is used when `G` is specified, and `PrimalDualHybridGradient` is
     used otherwise.
 
     Args:
@@ -106,18 +103,18 @@ class LinearLeastSquares(App):
         x (array): Solution.
         proxg (Prox): Proximal operator of g.
         lamda (float): l2 regularization parameter.
-        g (None or function): Regularization function. Only used for when save_objs is true.
+        g (None or function): Regularization function. Only used for when `save_objs` is true.
         G (None or Linop): Regularization linear operator.
         R (None or Linop): l2 regularization linear operator.
         weights (float or array): Weights for least squares.
-        alg_name (str): {'ConjugateGradient', 'GradientMethod', 'PrimalDualHybridGradient'}.
-        alpha (None or float): Step size for GradientMethod.
-        accelerate (bool): Toggle Nesterov acceleration for GradientMethod.
+        alg_name (str): {`'ConjugateGradient'`, `'GradientMethod'`, `'PrimalDualHybridGradient'`}.
+        alpha (None or float): Step size for `GradientMethod`.
+        accelerate (bool): Toggle Nesterov acceleration for `GradientMethod`.
         max_power_iter (int): Maximum number of iterations for power method. 
-            Used for GradientMethod and PrimalDualHybridGradient when alpha is not specified.
-        tau (float): Primal step-size for PrimalDualHybridGradient.
-        sigma (float): Dual step-size for PrimalDualHybridGradient.
-        theta (float): Primal extrapolation parameter for PrimalDualHybridGradient.
+            Used for `GradientMethod` and `PrimalDualHybridGradient` when `alpha` is not specified.
+        tau (float): Primal step-size for `PrimalDualHybridGradient`.
+        sigma (float): Dual step-size for `PrimalDualHybridGradient`.
+        theta (float): Primal extrapolation parameter for `PrimalDualHybridGradient`.
 
     """
     def __init__(self, A, y, x, proxg=None,

sigpy/conv.py

@@ -2,11 +2,6 @@
 from sigpy import fft, util, config
 
 
-__all__ = ['convolve', 'correlate',
-           'cudnn_convolve', 'cudnn_convolve_backward_filter',
-           'cudnn_convolve_backward_data']
-
-
 def convolve(input1, input2, axes=None, mode='full'):
     """Multi-dimensional convolution.
 

sigpy/fft.py

@@ -5,9 +5,6 @@
     import cupy as cp
 
 
-__all__ = ['fft', 'ifft']
-
-
 def fft(input, oshape=None, axes=None, center=True, norm='ortho'):
     """FFT function that supports centering.
 

sigpy/interp.py

@@ -8,9 +8,6 @@
     import cupy as cp
 
 
-__all__ = ['interp', 'gridding']
-
-
 def interp(input, width, table, coord):
     """Interpolation from array to points specified by coordinates.
 

sigpy/nufft.py

@@ -2,9 +2,6 @@
 from sigpy import fft, util, interp
 
 
-__all__ = ['nufft', 'nufft_adjoint', 'estimate_shape']
-
-
 def nufft(input, coord, oversamp=1.25, width=4.0, n=128):
     """Non-uniform Fast Fourier Transform.