Version 0.1.0 now in beta

README.md

@@ -1,22 +1,25 @@
 [![Build Status](https://travis-ci.org/williamjameshandley/spherical_kde.svg?branch=master)](https://travis-ci.org/williamjameshandley/spherical_kde)
 [![codecov](https://codecov.io/gh/williamjameshandley/spherical_kde/branch/master/graph/badge.svg)](https://codecov.io/gh/williamjameshandley/spherical_kde)
 [![PyPI version](https://badge.fury.io/py/spherical_kde.svg)](https://badge.fury.io/py/spherical_kde)
-[![Documentation Status](https://readthedocs.org/projects/pip/badge/?version=stable)](http://pip.pypa.io/en/stable/?badge=stable)
+[![Documentation Status](https://readthedocs.org/projects/spherical-kde/badge/?version=latest)](http://spherical-kde.readthedocs.io/en/latest/?badge=latest)
+
 
 
 
 Spherical Kernel Density Estimation
 ===================================
 
 These packages allow you to do rudimentary kernel density estimation on a
-sphere. Extreme alpha development status.
+sphere. Suggestions for improvements/extensions welcome.
 
 The fundamental principle is to replace the traditional Gaussian function used
 in 
 [kernel density estimation](https://en.wikipedia.org/wiki/Kernel_density_estimation)
 with the
 [Von Mises-Fisher distribution](https://en.wikipedia.org/wiki/Von_Mises-Fisher_distribution).
 
+Bandwidth estimation is still rough-and-ready.
+
 ![](https://raw.github.com/williamjameshandley/spherical_kde/master/plot.png)
 
 Example Usage
@@ -26,7 +29,7 @@ Example Usage
 import numpy
 from spherical_kde import SphericalKDE
 import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
+import cartopy.crs
 from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec
 
 # Choose a seed for deterministic plot
@@ -37,10 +40,10 @@ fig = plt.figure(figsize=(10, 10))
 gs_vert = GridSpec(3, 1)
 gs_lower = GridSpecFromSubplotSpec(1, 2, subplot_spec=gs_vert[1])
 
-fig.add_subplot(gs_vert[0], projection=ccrs.Mollweide())
-fig.add_subplot(gs_lower[0], projection=ccrs.Orthographic())
-fig.add_subplot(gs_lower[1], projection=ccrs.Orthographic(-10, 45))
-fig.add_subplot(gs_vert[2], projection=ccrs.PlateCarree())
+fig.add_subplot(gs_vert[0], projection=cartopy.crs.Mollweide())
+fig.add_subplot(gs_lower[0], projection=cartopy.crs.Orthographic())
+fig.add_subplot(gs_lower[1], projection=cartopy.crs.Orthographic(-10, 45))
+fig.add_subplot(gs_vert[2], projection=cartopy.crs.PlateCarree())
 
 # Choose parameters for samples
 nsamples = 100
@@ -69,7 +72,7 @@ for ax in fig.axes:
     ax.gridlines()
     ax.coastlines(linewidth=0.1)
     kde_green.plot(ax, 'g')
-    kde_green.plot_decra_samples(ax)
+    kde_green.plot_samples(ax)
     kde_red.plot(ax, 'r')
     kde_blue.plot(ax, 'b')
 

docs/source/conf.py

@@ -26,8 +26,11 @@
 # The short X.Y version
 version = ''
 # The full version, including alpha/beta/rc tags
-release = '0.0.6'
+release = '0.1.0'
 
+import sys
+import os
+sys.path.append(os.path.abspath('../../'))
 
 # -- General configuration ---------------------------------------------------
 

setup.py

@@ -8,7 +8,7 @@
     long_description = ''
 
 setup(name='spherical_kde',
-      version='0.0.6',
+      version='0.1.0',
       author='Will Handley',
       author_email='[email protected]',
       url='https://github.com/williamjameshandley/spherical_kde',
@@ -17,7 +17,7 @@
       install_requires=['cartopy', 'pytest', 'numpy', 'scipy', 'matplotlib', 'pypandoc', 'numpydoc'],
       license='MIT',
       classifiers=[
-      'Development Status :: 3 - Alpha',
+      'Development Status :: 4 - Beta',
       'Intended Audience :: Developers',
       'Natural Language :: English',
       'License :: OSI Approved :: MIT License',

spherical_kde/__init__.py

@@ -1,3 +1,5 @@
+""" The spherical kernel density estimator class. """
+
 import matplotlib
 import numpy
 import cartopy.crs
@@ -93,6 +95,7 @@ def plot(self, ax, colour='g', **kwargs):
         ax : matplotlib.axes.Axes
             matplotlib axis to plot on. This must be constructed with a
             `cartopy.crs.projection`:
+
             >>> import cartopy
             >>> import matplotlib.pyplot as plt
             >>> fig = plt.subplots()
@@ -139,13 +142,14 @@ def plot(self, ax, colour='g', **kwargs):
                     transform=cartopy.crs.PlateCarree(), *kwargs)
 
     def plot_samples(self, ax, nsamples=None, **kwargs):
-        """ Plot equally weighted samples on an axis. 
-        
+        """ Plot equally weighted samples on an axis.
+
         Parameters
         ----------
         ax : matplotlib.axes.Axes
             matplotlib axis to plot on. This must be constructed with a
             `cartopy.crs.projection`:
+
             >>> import cartopy
             >>> import matplotlib.pyplot as plt
             >>> fig = plt.subplots()
@@ -158,7 +162,7 @@ def plot_samples(self, ax, nsamples=None, **kwargs):
         Keywords
         --------
         Any other keywords are passed to `matplotlib.axes.Axes.plot`
-        
+
         """
         ra, dec = self._samples(nsamples)
         ax.plot(ra, dec, 'k.', transform=cartopy.crs.PlateCarree(), *kwargs)

spherical_kde/distributions.py

@@ -1,3 +1,9 @@
+""" Module containing the kernel function for the spherical KDE.
+
+For more detail, see:
+https://en.wikipedia.org/wiki/Von_Mises-Fisher_distribution
+"""
+
 import numpy
 import scipy.optimize
 from spherical_kde.utils import (cartesian_from_polar,
@@ -106,12 +112,12 @@ def VonMises_std(phi, theta):
 
     Returns
     -------
-        solution for 
-        
+        solution for
+
         ..math:: 1/tanh(x) - 1/x = R,
 
-        where 
-        
+        where
+
         ..math:: R = || \sum_i^N x_i || / N
 
     Notes

spherical_kde/utils.py

@@ -1,3 +1,11 @@
+""" Utilities
+
+* General stable functions
+* Transforming coordinates
+* Computing rotations
+* Performing spherical integrals
+"""
+
 import numpy
 from scipy.integrate import dblquad