STY, DOC: Fix issues from prettier and linters

Author: agriyakhetarpal

doc/source/regression/dwt-idwt.md

@@ -91,6 +91,7 @@ function:
 # for documentation tests -->
 <!-- though the doctests might not be needed anymore because MyST is testing -->
 <!-- these by executing the notebooks? -->
+
 ```{code-cell}
 int(pywt.dwt_coeff_len(data_len=len(x), filter_len=w.dec_len, mode='symmetric'))
 ```
@@ -143,10 +144,9 @@ print(pywt.idwt(cA, cD, 'sym3', 'periodization'))
 ### Passing `None` instead of coefficients data to {func}`idwt`
 
 Now, we showcase some tips & tricks. Passing `None` as one of the coefficient
-arrays parameters is similar to passing a *zero-filled* array. The results are
+arrays parameters is similar to passing a _zero-filled_ array. The results are
 simply the same:
 
-
 ```{code-cell}
 print(pywt.idwt([1,2,0,1], None, 'db2', 'symmetric'))
 ```

doc/source/regression/gotchas.md

@@ -41,17 +41,18 @@ The results are:
 ```{code-cell}
 numpy.all(numpy.isnan(cA))
 ```
-<!-- True -->
 
+<!-- True -->
 
 ```{code-cell}
 numpy.all(numpy.isnan(cD))
 ```
-<!-- True -->
 
+<!-- True -->
 
 ```{code-cell}
 rec = pywt.idwt(cA, cD, wavelet, mode)
 numpy.all(numpy.isnan(rec))
 ```
+
 <!-- True -->

doc/source/regression/multilevel.md

@@ -94,8 +94,6 @@ cA2
 cD2
 ```
 
-
-
 ```{code-cell}
 [(cA2, cD2)] = pywt.swt(cA1, db1, level=1, start_level=1)
 ```

doc/source/regression/wavelet.md

@@ -27,7 +27,7 @@ kernelspec:
 ## Wavelet families and builtin Wavelets names
 
 {class}`Wavelet` objects are really a handy carriers of a bunch of DWT-specific
-data like *quadrature mirror filters* and some general properties associated
+data like _quadrature mirror filters_ and some general properties associated
 with them.
 
 At first let's go through the methods of creating a {class}`Wavelet` object.
@@ -108,6 +108,7 @@ print(w.family_name)
 
 <!-- # int() is for normalizing longs and ints for doctest -->
 <!-- TODO: FIXME: note: might not be needed anymore -->
+
 ```{code-cell}
 int(w.dec_len)
 ```
@@ -122,21 +123,18 @@ int(w.rec_len)
 w.orthogonal
 ```
 
-
 ```{code-cell}
 w.biorthogonal
 ```
 
-
 3. Symmetry ({attr}`~Wavelet.symmetry`):
 
 ```{code-cell}
 print(w.symmetry)
 ```
 
-
 4. Number of vanishing moments for the scaling function `phi` ({attr}`~Wavelet.vanishing_moments_phi`)
-and the wavelet function `psi` ({attr}`~Wavelet.vanishing_moments_psi`), associated with the filters:
+   and the wavelet function `psi` ({attr}`~Wavelet.vanishing_moments_psi`), associated with the filters:
 
 ```{code-cell}
  w.vanishing_moments_phi
@@ -150,7 +148,7 @@ Now when we know a bit about the builtin Wavelets, let's see how to create
 {ref}`custom Wavelets <custom-wavelets>` objects. These can be done in two ways:
 
 1. Passing the filter bank object that implements the `filter_bank` attribute. The
-attribute must return four filters coefficients.
+   attribute must return four filters coefficients.
 
 ```{code-cell}
 class MyHaarFilterBank(object):

doc/source/regression/wp.md

@@ -23,6 +23,7 @@ kernelspec:
 ```
 
 <!-- not sure if this is needed anymore? -->
+
 ```{code-cell}
 ---
 tags: [hide-input]
@@ -51,7 +52,6 @@ def format_array(a):
     return numpy.array2string(a, precision=5, separator=' ', suppress_small=True)
 ```
 
-
 ## Create Wavelet Packet structure
 
 Okay, let's create a sample {class}`WaveletPacket` object:
@@ -115,7 +115,6 @@ print(wp['a'].data)
 print(wp['a'].path)
 ```
 
-
 - 2nd level:
 
 ```{code-cell}
@@ -126,7 +125,6 @@ print(wp['aa'].data)
 print(wp['aa'].path)
 ```
 
-
 - 3rd level:
 
 ```{code-cell}
@@ -225,7 +223,6 @@ print([node.path for node in wp.get_level(3, 'freq')])
 Note that {meth}`WaveletPacket.get_level` also performs automatic decomposition
 until it reaches the specified `level`.
 
-
 ## Reconstructing data from Wavelet Packets
 
 ```{code-cell}
@@ -284,7 +281,6 @@ print([n.path for n in new_wp.get_leaf_nodes(False)])
 print([n.path for n in new_wp.get_leaf_nodes(True)])
 ```
 
-
 ## Removing nodes from Wavelet Packet tree
 
 Let's create some sample data:
@@ -366,7 +362,7 @@ print(wp.a)
 **Remember that you should not rely on the attribute access.**
 
 2. At the first attempt to access the node, it is computed via the decomposition
-of its parent node (which is the `wp` object itself).
+   of its parent node (which is the `wp` object itself).
 
 ```{code-cell}
 print(wp['a'])

doc/source/regression/wp2d.md

@@ -27,6 +27,7 @@ kernelspec:
 ## Import pywt
 
 <!-- TODO: check if needed anymore -->
+
 ```{code-cell}
 from __future__ import print_function
 import pywt
@@ -159,6 +160,7 @@ print(wp['aaaa'].data)
 ```
 
 <!-- not sure what "Ups" means in the next sentence -->
+
 Ups, we have reached the maximum level of decomposition for the `'aaaa'` path,
 which, by the way, was:
 
@@ -186,42 +188,34 @@ just like with {class}`Node` and {class}`WaveletPacket` for the 1D case.).
 print(wp['av'].data)
 ```
 
-
 ```{code-cell}
 print(wp['av'].path)
 ```
 
-
 ```{code-cell}
 print(wp['av'].node_name)
 ```
 
-
 ```{code-cell}
 print(wp['av'].parent.path)
 ```
 
-
 ```{code-cell}
 print(wp['av'].parent.data)
 ```
 
-
 ```{code-cell}
 print(wp['av'].level)
 ```
 
-
 ```{code-cell}
 print(wp['av'].maxlevel)
 ```
 
-
 ```{code-cell}
 print(wp['av'].mode)
 ```
 
-
 ### Collecting nodes
 
 We can get all nodes on the particular level using the
@@ -247,7 +241,6 @@ len(wp.get_level(1))
 print([node.path for node in wp.get_level(1)])
 ```
 
-
 - 2nd level of decomposition:
 
 ```{code-cell}
@@ -314,7 +307,7 @@ Note: just remember to not assign to the `node.data parameter directly (TODO).
 
 And reconstruct the data from the `a`, `d`, `vh`, `vv`, `vd` and `h`
 packets (Note that `va` node was not set and the WP tree is "not complete"
-\- the `va` branch will be treated as *zero-array*):
+\- the `va` branch will be treated as _zero-array_):
 
 ```{code-cell}
 print(new_wp.reconstruct(update=False))
@@ -371,7 +364,7 @@ print([n.path for n in new_wp.get_leaf_nodes()])
 ```
 
 Passing the `decompose = True` parameter to the method will force the WP
-object to do a full decomposition up to the *maximum level* of decomposition:
+object to do a full decomposition up to the _maximum level_ of decomposition:
 
 ```{code-cell}
 paths = [n.path for n in new_wp.get_leaf_nodes(decompose=True)]