Merge pull request #728 from agriyakhetarpal/test-interactive-docs

Initial steps towards interactive documentation via JupyterLite

Author: rgommers

.gitignore

@@ -10,6 +10,7 @@ __pycache__
 *.egg-info
 dist
 build
+_build
 .installed.cfg
 
 # Installer logs
@@ -19,6 +20,11 @@ pip-log.txt
 .coverage
 .tox
 
+# Sphinx documentation
+doc/_build/
+doc/build/
+.jupyterlite.doit.db
+
 # Editors
 .idea
 .project

doc/source/_static/pywavelets.css

@@ -0,0 +1,27 @@
+/* Custom CSS rules for the interactive documentation button */
+
+.try_examples_button {
+    color: white;
+    background-color: #0054a6;
+    border: none;
+    padding: 5px 10px;
+    border-radius: 10px;
+    margin-bottom: 5px;
+    box-shadow: 0 2px 5px rgba(108,108,108,0.2);
+    font-weight: bold;
+    font-size: small;
+}
+
+.try_examples_button:hover {
+background-color: #0066cc;
+transform: scale(1.02);
+box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
+cursor: pointer;
+}
+
+.try_examples_button_container {
+    display: flex;
+    justify-content: flex-start;
+    gap: 10px;
+    margin-bottom: 20px;
+}

doc/source/conf.py

@@ -34,6 +34,7 @@
 # Add any Sphinx extension module names here, as strings. They can be extensions
 # coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
 extensions = [
+    'jupyterlite_sphinx',
     'matplotlib.sphinxext.plot_directive',
     'numpydoc',
     'sphinx.ext.autodoc',
@@ -193,6 +194,12 @@
 # so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = ['_static']
 
+# CSS files to include in the build. The file path should be relative to the
+# _static directory.
+html_css_files = [
+    "pywavelets.css",
+]
+
 # If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
 # using the given strftime format.
 html_last_updated_fmt = '%b %d, %Y'
@@ -282,8 +289,22 @@
 
 # -- Options for intersphinx extension ---------------------------------------
 
-# Intersphinx to get Numpy and other targets
+# Intersphinx to get NumPy, SciPy, and other targets
 intersphinx_mapping = {
     'numpy': ('https://numpy.org/devdocs', None),
-    'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),
+    'scipy': ('https://docs.scipy.org/doc/scipy/', None),
     }
+
+# -- Options for JupyterLite -------------------------------------------------
+
+global_enable_try_examples = True
+try_examples_global_button_text = "Try it in your browser!"
+try_examples_global_warning_text = (
+"""These interactive examples with JupyterLite are experimental and
+may not always work as expected. The execution of cells containing import
+statements can result in high bandwidth usage and may take a long time to
+load. They may not be in sync with the latest PyWavelets release.
+
+Shall you encounter any issues, please feel free to report them on the
+[PyWavelets issue tracker](https://github.com/PyWavelets/pywt/issues)."""
+)

doc/source/ref/cwt.rst

@@ -89,9 +89,11 @@ Continuous Wavelet Families
 A variety of continuous wavelets have been implemented. A list of the available
 wavelet names compatible with ``cwt`` can be obtained by:
 
-.. sourcecode:: python
+.. try_examples::
+  :button_text: Try it in your browser!
 
-    wavlist = 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``
@@ -205,7 +207,8 @@ sampled at 100 Hz, a center frequency of 1.0 corresponds to ~100 Hz at
 ``scale = 1``. This is above the Nyquist rate of 50 Hz, so for this
 particular wavelet, one would analyze a signal using ``scales >= 2``.
 
-.. sourcecode:: python
+.. try_examples::
+  :button_text: Try it in your browser!
 
     >>> import numpy as np
     >>> import pywt
@@ -235,7 +238,8 @@ the input frequency in this function is normalized by 1/dt, or the sampling
 frequency fs. This function is useful for specifying the transform as a function
 of frequency directly.
 
-.. sourcecode:: python
+.. try_examples::
+  :button_text: Try it in your browser!
 
     >>> import numpy as np
     >>> import pywt

doc/source/ref/other-functions.rst

@@ -64,12 +64,3 @@ Each can be loaded via a function of the same name.
 
 .. currentmodule:: pywt.data
 .. autofunction:: demo_signal
-
-**Example:**
-
-.. sourcecode:: python
-
-    >>> import pywt
-    >>> camera = pywt.data.camera()
-    >>> doppler = pywt.data.demo_signal('doppler', 1024)
-    >>> available_signals = pywt.data.demo_signal('list')

doc/source/ref/signal-extension-modes.rst

@@ -87,9 +87,10 @@ computations can be performed with the `periodization`_ mode:
   smallest possible number of decomposition coefficients. :ref:`IDWT <ref-idwt>` must be
   performed with the same mode.
 
-  **Example:**
+**Example:**
 
-  .. sourcecode:: python
+.. try_examples::
+  :button_text: Try it in your browser!
 
     >>> import pywt
     >>> print(pywt.Modes.modes)
@@ -104,7 +105,8 @@ signal to an even length prior to using periodic boundary conditions.
 Notice that you can use any of the following ways of passing wavelet and mode
 parameters:
 
-.. sourcecode:: python
+.. try_examples::
+  :button_text: Try it in your browser!
 
   >>> import pywt
   >>> (a, d) = pywt.dwt([1,2,3,4,5,6], 'db2', 'smooth')
@@ -142,8 +144,8 @@ Padding using PyWavelets Signal Extension Modes - ``pad``
 
 .. autofunction:: pad
 
-Pywavelets provides a function, :func:`pad`, that operate like
-:func:`numpy.pad`, but supporting the PyWavelets signal extension modes
+Pywavelets provides a function, :func:`pad`, that operates like
+:func:`numpy.pad`, but supports the PyWavelets signal extension modes
 discussed above. For efficiency, the DWT routines in PyWavelets do not
 expclitly create padded signals using this function. It can be used to manually
 prepad signals to reduce boundary effects in functions such as :func:`cwt` and

doc/source/ref/wavelets.rst

@@ -48,7 +48,8 @@ Custom discrete wavelets are also supported through the
 
   **Example:**
 
-  .. sourcecode:: python
+  .. try_examples::
+    :button_text: Try it in your browser!
 
     >>> import pywt
     >>> wavelet = pywt.Wavelet('db1')
@@ -128,7 +129,8 @@ Custom discrete wavelets are also supported through the
 
   **Example:**
 
-  .. sourcecode:: python
+  .. try_examples::
+    :button_text: Try it in your browser!
 
     >>> def format_array(arr):
     ...     return "[%s]" % ", ".join(["%.14f" % x for x in arr])
@@ -171,7 +173,8 @@ Approximating wavelet and scaling functions - ``Wavelet.wavefun()``
 
   **Example:**
 
-  .. sourcecode:: python
+  .. try_examples::
+    :button_text: Try it in your browser!
 
     >>> import pywt
     >>> wavelet = pywt.Wavelet('db2')
@@ -186,7 +189,8 @@ Approximating wavelet and scaling functions - ``Wavelet.wavefun()``
 
   **Example:**
 
-  .. sourcecode:: python
+  .. try_examples::
+    :button_text: Try it in your browser!
 
     >>> import pywt
     >>> wavelet = pywt.Wavelet('bior3.5')
@@ -239,7 +243,8 @@ from plain Python lists of filter coefficients and a *filter bank-like* object.
 
   **Example:**
 
-  .. sourcecode:: python
+  .. try_examples::
+    :button_text: Try it in your browser!
 
     >>> import pywt, math
     >>> c = math.sqrt(2)/2
@@ -273,7 +278,8 @@ from plain Python lists of filter coefficients and a *filter bank-like* object.
 
   **Example:**
 
-  .. sourcecode:: python
+  .. try_examples::
+    :button_text: Try it in your browser!
 
     >>> import pywt
     >>> wavelet = pywt.ContinuousWavelet('gaus1')
@@ -328,7 +334,8 @@ from plain Python lists of filter coefficients and a *filter bank-like* object.
 
   **Example:**
 
-  .. sourcecode:: python
+  .. try_examples::
+    :button_text: Try it in your browser!
 
     >>> import pywt
     >>> wavelet = pywt.ContinuousWavelet('gaus1')
@@ -358,7 +365,8 @@ Approximating wavelet functions - ``ContinuousWavelet.wavefun()``
 
   **Example:**
 
-  .. sourcecode:: python
+  .. try_examples::
+    :button_text: Try it in your browser!
 
     >>> import pywt
     >>> wavelet = pywt.ContinuousWavelet('gaus1')
@@ -375,7 +383,8 @@ Approximating wavelet functions - ``ContinuousWavelet.wavefun()``
 
   **Example:**
 
-  .. sourcecode:: python
+  .. try_examples::
+    :button_text: Try it in your browser!
 
     >>> import pywt
     >>> wavelet = pywt.DiscreteContinuousWavelet('db1')

doc/source/try_examples.json

@@ -0,0 +1,3 @@
+{
+    "global_min_height": "600px"
+}

pywt/_cwt.py

@@ -96,7 +96,7 @@ def cwt(data, scales, wavelet, sampling_period=1., method='conv', axis=-1):
     >>> coef, freqs=pywt.cwt(y,np.arange(1,129),'gaus1')
     >>> plt.matshow(coef) # doctest: +SKIP
     >>> plt.show() # doctest: +SKIP
-    ----------
+
     >>> import pywt
     >>> import numpy as np
     >>> import matplotlib.pyplot as plt

pywt/data/_wavelab_signals.py

@@ -63,6 +63,16 @@ def demo_signal(name='Bumps', n=None):
     test signals are provided with permission of Dr. Donoho to encourage
     reproducible research.
 
+    Examples
+    --------
+    >>> import pywt
+    >>> camera = pywt.data.camera()
+    >>> doppler = pywt.data.demo_signal('doppler', 1024)
+    >>> available_signals = pywt.data.demo_signal('list')
+    >>> print(available_signals)
+
+
+
     """
     if name.lower() == 'list':
         return _implemented_signals