autodoc warning fixes

docs/source/api.rst

@@ -21,7 +21,7 @@ Correlate
 
 
 Modulation/Demodulation
-============
+=======================
 
 Demodulation
 ------------
@@ -34,7 +34,7 @@ Estimation
 ============
 
 Kalman Filter
-------------
+-------------
 
 .. automodule:: cusignal.estimation.filters
     :members:

python/cusignal/convolution/convolve.py

@@ -330,6 +330,7 @@ def convolve2d(
     Convolve two 2-dimensional arrays.
     Convolve `in1` and `in2` with output size determined by `mode`, and
     boundary conditions determined by `boundary` and `fillvalue`.
+    
     Parameters
     ----------
     in1 : array_like
@@ -338,6 +339,7 @@ def convolve2d(
         Second input. Should have the same number of dimensions as `in1`.
     mode : str {'full', 'valid', 'same'}, optional
         A string indicating the size of the output:
+        
         ``full``
            The output is the full discrete linear convolution
            of the inputs. (Default)
@@ -350,6 +352,7 @@ def convolve2d(
            with respect to the 'full' output.
     boundary : str {'fill', 'wrap', 'symm'}, optional
         A flag indicating how to handle boundaries:
+        
         ``fill``
            pad input arrays with fillvalue. (default)
         ``wrap``
@@ -472,6 +475,7 @@ def choose_conv_method(in1, in2, mode="full", measure=False):
     >>> b = cp.random.randn(1000000)
     >>> method = cusignal.choose_conv_method(a, b, mode='same')
     >>> method
+    
     'fft'
 
     This can then be applied to other arrays of the same dtype and shape:

python/cusignal/convolution/correlate.py

@@ -85,9 +85,8 @@ def correlate(
     then
 
     .. math::
-
-          z[k] = (x * y)(k - N + 1)
-               = \sum_{l=0}^{||x||-1}x_l y_{l-k+N-1}^{*}
+        z[k] = (x * y)(k - N + 1)
+            = \sum_{l=0}^{||x||-1}x_l y_{l-k+N-1}^{*}
 
     for :math:`k = 0, 1, ..., ||x|| + ||y|| - 2`
 
@@ -175,6 +174,7 @@ def correlate2d(
         Second input. Should have the same number of dimensions as `in1`.
     mode : str {'full', 'valid', 'same'}, optional
         A string indicating the size of the output:
+        
         ``full``
            The output is the full discrete linear cross-correlation
            of the inputs. (Default)
@@ -187,6 +187,7 @@ def correlate2d(
            with respect to the 'full' output.
     boundary : str {'fill', 'wrap', 'symm'}, optional
         A flag indicating how to handle boundaries:
+        
         ``fill``
            pad input arrays with fillvalue. (default)
         ``wrap``
@@ -206,6 +207,7 @@ def correlate2d(
     --------
     Use 2D cross-correlation to find the location of a template in a noisy
     image:
+    
     >>> import cusignal
     >>> import cupy as cp
     >>> from scipy import misc
@@ -261,6 +263,7 @@ def correlate2d(
 def correlation_lags(in1_len, in2_len, mode="full"):
     r"""
     Calculates the lag / displacement indices array for 1D cross-correlation.
+    
     Parameters
     ----------
     in1_size : int
@@ -270,34 +273,41 @@ def correlation_lags(in1_len, in2_len, mode="full"):
     mode : str {'full', 'valid', 'same'}, optional
         A string indicating the size of the output.
         See the documentation `correlate` for more information.
+    
     See Also
     --------
     correlate : Compute the N-dimensional cross-correlation.
+    
     Returns
     -------
     lags : array
         Returns an array containing cross-correlation lag/displacement indices.
         Indices can be indexed with the np.argmax of the correlation to return
         the lag/displacement.
+    
     Notes
     -----
     Cross-correlation for continuous functions :math:`f` and :math:`g` is
     defined as:
+    
     .. math::
         \left ( f\star g \right )\left ( \tau \right )
         \triangleq \int_{t_0}^{t_0 +T}
         \overline{f\left ( t \right )}g\left ( t+\tau \right )dt
     Where :math:`\tau` is defined as the displacement, also known as the lag.
     Cross correlation for discrete functions :math:`f` and :math:`g` is
     defined as:
+    
     .. math::
         \left ( f\star g \right )\left [ n \right ]
         \triangleq \sum_{-\infty}^{\infty}
         \overline{f\left [ m \right ]}g\left [ m+n \right ]
     Where :math:`n` is the lag.
+    
     Examples
     --------
     Cross-correlation of a signal with its time-delayed self.
+    
     >>> import cusignal
     >>> import cupy as cp
     >>> from cupy.random import default_rng
@@ -307,6 +317,7 @@ def correlation_lags(in1_len, in2_len, mode="full"):
     >>> correlation = cusignal.correlate(x, y, mode="full")
     >>> lags = cusignal.correlation_lags(x.size, y.size, mode="full")
     >>> lag = lags[cp.argmax(correlation)]
+    
     """
 
     # calculate lag ranges in different modes of operation

python/cusignal/estimation/filters.py

@@ -161,6 +161,7 @@ class KalmanFilter(object):
     Define the process noise  for all Kalman Filter points:
 
         .. code::
+
             motion_noise = np.eye(dim_x, dtype=dt) * np.array(
                 [10.0, 10.0, 10.0, 10.0], dtype=dt
             )
@@ -181,6 +182,7 @@ class KalmanFilter(object):
     Results are in:
 
         .. code::
+
             kf.x[:, :, :]
 
     References