From f11984e25b6a794599f21fc0dca62b9c6df906f4 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 9 Apr 2024 19:36:17 +0530
Subject: [PATCH] DOC: Fix indentation for some interactive examples

---
 doc/source/ref/cwt.rst                    | 32 +++++++++----------
 doc/source/ref/signal-extension-modes.rst |  6 ++--
 doc/source/ref/wavelets.rst               | 38 +++++++++++------------
 3 files changed, 38 insertions(+), 38 deletions(-)

diff --git a/doc/source/ref/cwt.rst b/doc/source/ref/cwt.rst
index f0d5ab539..bbf47b320 100644
--- a/doc/source/ref/cwt.rst
+++ b/doc/source/ref/cwt.rst
@@ -92,8 +92,8 @@ wavelet names compatible with ``cwt`` can be obtained by:
 .. try_examples::
   :button_text: Try it in your browser!
 
-    >>> import pywt
-    >>> wavelist = pywt.wavelist(kind='continuous')
+  >>> import pywt
+  >>> wavelist = pywt.wavelist(kind='continuous')
 
 Here is an overview of all available wavelets for ``cwt``. Note, that they can be
 customized by passing parameters such as ``center_frequency`` and ``bandwidth_frequency``
@@ -210,12 +210,12 @@ particular wavelet, one would analyze a signal using ``scales >= 2``.
 .. try_examples::
   :button_text: Try it in your browser!
 
-    >>> import numpy as np
-    >>> import pywt
-    >>> dt = 0.01  # 100 Hz sampling
-    >>> frequencies = pywt.scale2frequency('cmor1.5-1.0', [1, 2, 3, 4]) / dt
-    >>> frequencies
-    array([ 100.        ,   50.        ,   33.33333333,   25.        ])
+  >>> import numpy as np
+  >>> import pywt
+  >>> dt = 0.01  # 100 Hz sampling
+  >>> frequencies = pywt.scale2frequency('cmor1.5-1.0', [1, 2, 3, 4]) / dt
+  >>> frequencies
+  array([ 100.        ,   50.        ,   33.33333333,   25.        ])
 
 The CWT in PyWavelets is applied to discrete data by convolution with samples
 of the integral of the wavelet. If ``scale`` is too low, this will result in
@@ -241,14 +241,14 @@ of frequency directly.
 .. try_examples::
   :button_text: Try it in your browser!
 
-    >>> import numpy as np
-    >>> import pywt
-    >>> dt = 0.01  # 100 Hz sampling
-    >>> fs = 1 / dt
-    >>> frequencies = np.array([100, 50, 33.33333333, 25]) / fs # normalize
-    >>> scale = pywt.frequency2scale('cmor1.5-1.0', frequencies)
-    >>> scale
-    array([ 1.,  2.,  3.,  4.])
+  >>> import numpy as np
+  >>> import pywt
+  >>> dt = 0.01  # 100 Hz sampling
+  >>> fs = 1 / dt
+  >>> frequencies = np.array([100, 50, 33.33333333, 25]) / fs # normalize
+  >>> scale = pywt.frequency2scale('cmor1.5-1.0', frequencies)
+  >>> scale
+  array([ 1.,  2.,  3.,  4.])
 
 
 .. plot:: pyplots/cwt_scaling_demo.py
diff --git a/doc/source/ref/signal-extension-modes.rst b/doc/source/ref/signal-extension-modes.rst
index 9ea76a66c..a93661ea1 100644
--- a/doc/source/ref/signal-extension-modes.rst
+++ b/doc/source/ref/signal-extension-modes.rst
@@ -92,9 +92,9 @@ computations can be performed with the `periodization`_ mode:
 .. try_examples::
   :button_text: Try it in your browser!
 
-    >>> import pywt
-    >>> print(pywt.Modes.modes)
-    ['zero', 'constant', 'symmetric', 'periodic', 'smooth', 'periodization', 'reflect', 'antisymmetric', 'antireflect']
+  >>> import pywt
+  >>> print(pywt.Modes.modes)
+  ['zero', 'constant', 'symmetric', 'periodic', 'smooth', 'periodization', 'reflect', 'antisymmetric', 'antireflect']
 
 The following figure illustrates how a short signal (red) gets extended (black)
 outside of its original extent. Note that periodization first extends the
diff --git a/doc/source/ref/wavelets.rst b/doc/source/ref/wavelets.rst
index 8fe6c319a..854699987 100644
--- a/doc/source/ref/wavelets.rst
+++ b/doc/source/ref/wavelets.rst
@@ -241,25 +241,25 @@ The Wavelet object created in this way is a standard :class:`Wavelet` instance.
 The following example illustrates the way of creating custom Wavelet objects
 from plain Python lists of filter coefficients and a *filter bank-like* object.
 
-  **Example:**
-
-  .. try_examples::
-    :button_text: Try it in your browser!
-
-    >>> import pywt, math
-    >>> c = math.sqrt(2)/2
-    >>> dec_lo, dec_hi, rec_lo, rec_hi = [c, c], [-c, c], [c, c], [c, -c]
-    >>> filter_bank = [dec_lo, dec_hi, rec_lo, rec_hi]
-    >>> myWavelet = pywt.Wavelet(name="myHaarWavelet", filter_bank=filter_bank)
-    >>>
-    >>> class HaarFilterBank(object):
-    ...     @property
-    ...     def filter_bank(self):
-    ...         c = math.sqrt(2)/2
-    ...         dec_lo, dec_hi, rec_lo, rec_hi = [c, c], [-c, c], [c, c], [c, -c]
-    ...         return [dec_lo, dec_hi, rec_lo, rec_hi]
-    >>> filter_bank = HaarFilterBank()
-    >>> myOtherWavelet = pywt.Wavelet(name="myHaarWavelet", filter_bank=filter_bank)
+**Example:**
+
+.. try_examples::
+  :button_text: Try it in your browser!
+
+  >>> import pywt, math
+  >>> c = math.sqrt(2)/2
+  >>> dec_lo, dec_hi, rec_lo, rec_hi = [c, c], [-c, c], [c, c], [c, -c]
+  >>> filter_bank = [dec_lo, dec_hi, rec_lo, rec_hi]
+  >>> myWavelet = pywt.Wavelet(name="myHaarWavelet", filter_bank=filter_bank)
+  >>>
+  >>> class HaarFilterBank(object):
+  ...     @property
+  ...     def filter_bank(self):
+  ...         c = math.sqrt(2)/2
+  ...         dec_lo, dec_hi, rec_lo, rec_hi = [c, c], [-c, c], [c, c], [c, -c]
+  ...         return [dec_lo, dec_hi, rec_lo, rec_hi]
+  >>> filter_bank = HaarFilterBank()
+  >>> myOtherWavelet = pywt.Wavelet(name="myHaarWavelet", filter_bank=filter_bank)
 
 
 .. _ContinuousWavelet: