Minor changes to documentation.

docs/implementation.md

@@ -29,11 +29,9 @@ the work-around altogether.
 
 The Matlab implementation also bins the data somewhat differently in
 1d vs. the 2d case. This minor inconsistency was removed. The change
-is arguably insignificant as far the final results are concerned,
-but is a deviation nonetheless.
-
-In practical use, based on a handful of tests, both implementations
-yield indiscernible results.
+is arguably insignificant as far as the final results are concerned,
+but is a deviation nonetheless. In practical use, based on a handful
+of tests, both implementations yield indiscernible results.
 
 The 2d density is returned in matrix index order, also known as
 Cartesian indexing, in which the first index (the matrix row) refers

docs/installation.md

@@ -1,7 +1,8 @@
 Installation
 ------------
 
-The library is [available on PyPI][1] and can be readily installed via `pip`.
+The library is [available on PyPI][1] and can be readily installed
+via `pip`.
 
 System-wide installation — recommended on Windows:
 ```none
@@ -13,9 +14,10 @@ Per-user installation — recommended on Linux and MacOS:
 pip install KDE-diffusion --user
 ```
 
-Requires [NumPy][2] and [SciPy][3]. To remove the library, replace
-`install` with `uninstall` in the command from whichever method you
-chose.
+Requires [NumPy][2] and [SciPy][3], which `pip` will install
+automatically if they aren't already. Remove the library from your
+system with `pip uninstall KDE-diffusion`.
+
 
 [1]: https://pypi.org/project/KDE-diffusion/
 [2]: https://numpy.org/

kde_diffusion/__init__.py

@@ -8,8 +8,8 @@
 
 # Meta information
 __title__     = 'KDE-diffusion'
-__version__   = '1.0.1'
-__date__      = '2020–05–22'
+__version__   = '1.0.2'
+__date__      = '2021–02–03'
 __author__    = 'John Hennig'
 __copyright__ = 'John Hennig'
 __license__   = 'MIT'

kde_diffusion/kde1d.py

@@ -1,6 +1,4 @@
-"""
-Kernel density estimation via diffusion for 1-dimensional input data.
-"""
+"""Kernel density estimation via diffusion for 1-dimensional data."""
 __license__ = 'MIT'
 
 
@@ -28,7 +26,7 @@ def kde1d(x, n=1024, limits=None):
     observations of a random variable. They are binned on a grid of
     `n` points within the data `limits`, if specified, or within
     the limits given by the values' range. `n` will be coerced to the
-    next power of two if it isn't one to begin with.
+    next highest power of two if it isn't one to begin with.
 
     The limits may be given as a tuple (`xmin`, `xmax`) or a single
     number denoting the upper bound of a range centered at zero.
@@ -48,7 +46,7 @@ def kde1d(x, n=1024, limits=None):
     # Convert to array in case a list is passed in.
     x = array(x)
 
-    # Round up number of bins to the next power of two.
+    # Round up number of bins to next power of two.
     n = int(2**ceil(log2(n)))
 
     # Determine missing data limits.
@@ -76,7 +74,7 @@ def kde1d(x, n=1024, limits=None):
     (binned, edges) = histogram(x, bins=n, range=(xmin, xmax))
     grid = edges[:-1]
 
-    # Compute 2d discrete cosine transform. Adjust first component.
+    # Compute 2d discrete cosine transform, then adjust first component.
     transformed = dct(binned/N)
     transformed[0] /= 2
 
@@ -105,7 +103,7 @@ def ξγ(t, l=7):
     # Apply Gaussian filter with optimized kernel.
     smoothed = transformed * exp(-π**2 * ts/2 * k**2)
 
-    # Reverse transformation.
+    # Reverse transformation after adjusting first component.
     smoothed[0] *= 2
     inverse = idct(smoothed)
 

kde_diffusion/kde2d.py

@@ -1,6 +1,4 @@
-"""
-Kernel density estimation via diffusion for 2-dimensional input data.
-"""
+"""Kernel density estimation via diffusion for 2-dimensional data."""
 __license__ = 'MIT'
 
 
@@ -27,8 +25,9 @@ def kde2d(x, y, n=256, limits=None):
 
     The input is two lists/arrays `x` and `y` of numbers that represent
     discrete observations of a random variable with two coordinate
-    components. The observations are binned on a grid of n×n points,
-    where `n` must be a power of 2 or will be coerced to the next one.
+    components. The observations are binned on a grid of n×n points.
+    `n` will be coerced to the next highest power of two if it isn't
+    one to begin with.
 
     Data `limits` may be specified as a tuple of tuples denoting
     `((xmin, xmax), (ymin, ymax))`. If any of the values are `None`,
@@ -104,7 +103,7 @@ def kde2d(x, y, n=256, limits=None):
                                            range=((xmin, xmax), (ymin, ymax)))
     grid = (xedges[:-1], yedges[:-1])
 
-    # Compute discrete cosine transform. Adjust first component.
+    # Compute discrete cosine transform, then adjust first component.
     transformed = dctn(binned/N)
     transformed[0, :] /= 2
     transformed[:, 0] /= 2
@@ -164,7 +163,7 @@ def ψ(i, j, t):
     smoothed = transformed * outer(exp(-π**2 * k2 * tx2/2),
                                    exp(-π**2 * k2 * tx1/2))
 
-    # Reverse transformation.
+    # Reverse transformation after adjusting first component.
     smoothed[0, :] *= 2
     smoothed[:, 0] *= 2
     inverse = idctn(smoothed)

pyproject.toml

@@ -8,6 +8,8 @@ module = 'kde_diffusion'
 author = 'John Hennig'
 requires = ['NumPy', 'SciPy']
 requires-python = '>=3.6'
+license = 'MIT'
+home-page = 'https://pypi.org/project/KDE-diffusion'
 description-file = 'ReadMe.md'
 keywords = 'kernel density estimation, statistics'
 classifiers = [

tests/parent.py

@@ -1,6 +1,4 @@
-"""
-Adds the parent folder to the module search path.
-"""
+"""Adds the parent folder to the module search path."""
 __license__ = 'MIT'
 
 

tests/test_1d.py

@@ -1,6 +1,4 @@
-"""
-Tests the 1d kernel density estimation.
-"""
+"""Tests the 1d kernel density estimation."""
 __license__ = 'MIT'
 
 

tests/test_2d.py

@@ -1,6 +1,4 @@
-"""
-Tests the 2d kernel density estimation.
-"""
+"""Tests the 2d kernel density estimation."""
 __license__ = 'MIT'
 
 

verification/kde1d_refactored.py

@@ -1,6 +1,4 @@
-"""
-Demonstrates that refactoring barely affected 1d result.
-"""
+"""Demonstrates that refactoring barely affected 1d result."""
 __license__ = 'MIT'
 
 

verification/kde1d_reimplemented.py

@@ -1,6 +1,4 @@
-"""
-Reproduces exact same results as 1d reference implementation.
-"""
+"""Reproduces exact same results as 1d reference implementation."""
 __license__ = 'MIT'
 
 

verification/kde2d_refactored.py

@@ -1,6 +1,4 @@
-"""
-Demonstrates that refactoring did not affect 2d result.
-"""
+"""Demonstrates that refactoring did not affect 2d result."""
 __license__ = 'MIT'
 
 

verification/kde2d_reimplemented.py

@@ -1,6 +1,4 @@
-"""
-Reproduces exact same results as 2d reference implementation.
-"""
+"""Reproduces exact same results as 2d reference implementation."""
 __license__ = 'MIT'