changed example names, added source links

.github/workflows/documentation.yml

@@ -4,7 +4,7 @@ name: documentation
 on:
   push:
     tags:
-      - '*'
+      - 'v*'
   workflow_dispatch:
 
 permissions:

.github/workflows/publish_release.yml

@@ -9,7 +9,7 @@ name: Publish Python 🐍 distribution 📦 to PyPI and Github
 on:
   push:
     tags:
-      - '*'
+      - 'v*'
   workflow_dispatch:
 
 jobs:

README.md

@@ -22,10 +22,10 @@ pip install git+https://github.com/Klugerlab/DysonEqualizer.git
 To import the package and apply the Dyson Equalizer to a test matrix
 
 ```python
-from dyson_equalizer.examples import generate_Y_with_correlated_noise
+from dyson_equalizer.examples import generate_Y_with_heteroskedastic_noise
 from dyson_equalizer.dyson_equalizer import DysonEqualizer
 
-Y = generate_Y_with_correlated_noise()
+Y = generate_Y_with_heteroskedastic_noise()
 de = DysonEqualizer(Y).compute()
 
 ```

docs/source/conf.py

@@ -1,7 +1,7 @@
-import numpy
 import os
 import sys
-from importlib.metadata import version
+from importlib.metadata import version, PackageNotFoundError
+import warnings
 
 sys.path.insert(0, os.path.abspath('..'))
 sys.path.insert(0, os.path.abspath('../..'))
@@ -17,11 +17,18 @@
 project = 'Dyson Equalizer'
 copyright = '2024, Boris Landa, Francesco Strino, Yuval Kluger'
 author = 'Boris Landa, Francesco Strino, Yuval Kluger'
-
-# scm version (https://pypi.org/project/setuptools-scm/7.0.5/)
-release_scm = version('dyson-equalizer')
+github_url = "https://github.com/KlugerLab/DysonEqualizer"
+
+# Get the version using scm (https://pypi.org/project/setuptools-scm/7.0.5/)
+try:
+    release_scm = version('dyson-equalizer')
+except PackageNotFoundError:
+    warnings.warn('The package "dyson-equalizer" is not installed. '
+                  'Run e.g. "pip install -e ." to make the SCM version available')
+    release_scm = '0.0.0-dev'
 release = '.'.join(release_scm.split('.')[:3])
 version = release
+branch = 'main' if 'dev' in release_scm else f'v{version}'
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
@@ -34,6 +41,7 @@
     'sphinx.ext.intersphinx',
     'sphinx.ext.coverage',
     'sphinx.ext.doctest',
+    'sphinx.ext.linkcode',
     'sphinx.ext.autosummary',
     'matplotlib.sphinxext.plot_directive',
 ]
@@ -64,7 +72,7 @@
     "icon_links": [
         {
             "name": "GitHub",
-            "url": "https://github.com/KlugerLab/DysonEqualizer",
+            "url": github_url,
             "icon": "fa-brands fa-square-github",
             "type": "fontawesome",
         },
@@ -78,3 +86,14 @@
 }
 
 plot_include_source = True
+
+
+def linkcode_resolve(domain, info):
+    """
+    Called by sphinx.ext.linkcode to add a link
+
+    See https://www.sphinx-doc.org/en/master/usage/extensions/linkcode.html
+    """
+    if domain == 'py' and info['module']:
+        filename = info['module'].replace('.', '/')
+        return f"{github_url}/tree/{branch}/{filename}.py"

docs/source/examples.rst

@@ -23,9 +23,9 @@ added to create the data matrix ``Y = X + E``. The noise ``E`` was homoskedastic
     import matplotlib.pyplot as plt
     import numpy as np
     from dyson_equalizer.dyson_equalizer import DysonEqualizer
-    from dyson_equalizer.examples import generate_Y_almost_homoskedastic
+    from dyson_equalizer.examples import generate_Y_with_almost_homoskedastic_noise
 
-    Y = generate_Y_almost_homoskedastic()
+    Y = generate_Y_with_almost_homoskedastic_noise()
     de = DysonEqualizer(Y).compute()
     de.plot_mp_eigenvalues_and_densities(show_only_significant=1)
 
@@ -55,9 +55,9 @@ Next, we compute the Dyson Equalizer and plot the eigenvalues distributions by
     import matplotlib.pyplot as plt
     import numpy as np
     from dyson_equalizer.dyson_equalizer import DysonEqualizer
-    from dyson_equalizer.examples import generate_Y_with_correlated_noise
+    from dyson_equalizer.examples import generate_Y_with_heteroskedastic_noise
 
-    Y = generate_Y_with_correlated_noise()
+    Y = generate_Y_with_heteroskedastic_noise()
     de = DysonEqualizer(Y).compute()
     de.plot_mp_eigenvalues_and_densities(show_only_significant=1)
 

dyson_equalizer/__init__.py

@@ -15,4 +15,10 @@
     .. [1] Landa B., Kluger Y., "The Dyson Equalizer: Adaptive Noise Stabilization for Low-Rank Signal
        Detection and Recovery," arXiv, https://arxiv.org/abs/2306.11263
 
-"""
+"""
+
+# Get the version from _version.py (added when building using scm)
+try:
+    from .version import __version__ # noqa
+except ModuleNotFoundError as e:
+    __version__ = '0.0.0-dev'

dyson_equalizer/examples.py

@@ -39,7 +39,7 @@ def generate_X(
     return X_20
 
 
-def generate_Y_almost_homoskedastic(
+def generate_Y_with_almost_homoskedastic_noise(
         m: int = 1000,
         n: int = 2000,
 
@@ -82,7 +82,7 @@ def generate_Y_almost_homoskedastic(
     return X + E
 
 
-def generate_Y_with_correlated_noise(
+def generate_Y_with_heteroskedastic_noise(
         m: int = 1000,
         n: int = 2000,
         noise_dimensions: int = 10,

tests/test_dyson_equalizer.py

@@ -3,26 +3,26 @@
 import numpy as np
 
 from dyson_equalizer.dyson_equalizer import DysonEqualizer
-from dyson_equalizer.examples import generate_Y_with_correlated_noise, generate_Y_almost_homoskedastic
+from dyson_equalizer.examples import generate_Y_with_heteroskedastic_noise, generate_Y_with_almost_homoskedastic_noise
 
 
 class DysonEqualizerTestCase(unittest.TestCase):
     def test_fig_1(self):
-        Y = generate_Y_almost_homoskedastic()
+        Y = generate_Y_with_almost_homoskedastic_noise()
         de = DysonEqualizer(Y)
         de.compute()
 
         self.assertEqual(20, de.r_hat)
 
     def test_fig_2(self):
-        Y = generate_Y_with_correlated_noise()
+        Y = generate_Y_with_heteroskedastic_noise()
         de = DysonEqualizer(Y)
         de.compute()
 
         self.assertEqual(20, de.r_hat)
 
     def test_transpose(self):
-        Y = generate_Y_with_correlated_noise()
+        Y = generate_Y_with_heteroskedastic_noise()
 
         de = DysonEqualizer(Y)
         de.compute()