Kalepy

- `kalepy/`
- `__init__.py`
- Import desired API methods into module namespace. Use `__all__` in both `kernels.py` and `utils.py`.
- `kde_base.py`
- `class KDE`
- Introduce `helper` argument upon initialization which determines if extra checks and verbose feedback are given.
- Introcuce `bw_rescale` initialization argument to rescale the bw-matrix by some factor (matrix, or array).
- `pdf_grid()` [new-function]
- Convenience / wrapper function to calculate the PDF given the edges of a grid.
- `kernels.py`
- Introduce `helper` parameter, see `class KDE`
- Allow the `keep` parameter to be `True` in which case all parameters are kept, or `False` and none are kept (same as `None`).
- `_check_reflect()`
- Add additional checks for where the reflection boundaries are relative to the data-values and bandwidth.
- `_resample_reflect()`
- BUG: reflection was actually a periodic boundary (ish), instead of reflection. Not sure why it was still behaving well in testing...
- BUG: reflection was unnecessarily duplicating (already reflected) data, making fewer new points valid.
- `utils.py`
- `ave_std()` [new-function]
- Calculation of (optionally) *weighted* average and standard-deviation.
- `bound_indices()`
- Allow boundaries to be `None` (for no boundaries)
- `percentiles()` [new-function]
- Copied from `zcode.math.statistic`, allows for weighted percentiles.
- `stats_str()`
- Copied function from `zcode.math.math_core` with more extended functionality.
- `trapz_dens_to_mass()`
- New argument `axis` to integrate only along target axes.
- `trapz_nd()`
- New argument `axis` to integrate only along target axes.
- `notebooks/`
- `init.ipy` [new-file]
- Convenience script for setting up the imports in each notebook file
- `utils.ipynb` [new-file]
- New notebook for testing/exploring the `utils.py` submodule.

- Module renamed from `kdes` to `kalepy`.
- Notebooks are now included in travis unit testing.
- Added skeleton for sphinx documentation; not written yet.

- `README.md`
- Added installation information and basic examples.
- `kalepy/`
- `bandwidths.py`
- `kde_base.py` [new-file]
- `class KDE` [new-class]
- Primary API for using the `kalepy` package. Uses passed data and options to construct KDEs by interfacing with `Kernel` instances.
- The `KDE` class calculates the bandwidth and constructs a `kernel` instance, and handles passing the data and covariance matrix to the kernel as needed.
- `pdf()`
- Interface to the kernel instance method: `kernel.pdf()`
- `resample()`
- Interface to the kernel instance method: `kernel.resample()`
- `kernels.py` [new-file]
- Stores classes and methods for handling the kernels and their underlying distribution functions.
- NOTE: some of the scaling and normalization does not work properly in multi-dimensions for all kernels.
- `class Kernel`
- Stores a covariance-matrix and uses it as needed with a `Distribution` class instance.
- `class Distribution`
- Subclassed to implement particular distribution functions to use in a kernel.
- Agnostic of the data and covariance. The `Kernel` class handles the covariance matrix and appropriately transforming the data.
- `class Gaussian(Distribution)`
- Gaussian/Normal distribution function with infinite support.
- `class Box_Asym(Distribution)`
- Boxcar/rectangle/uniform function with finite support.
- `class Parabola(Distribution)`
- Epanechnikov kernel-function with finite support.
- `class Triweight`
- Cubic kernel, similar to Parabola but with additional smooth derivatives.
- WARNING: does not currently work in multiple-dimensions (normalization is off).
- `get_all_distribution_classes()`
- Method to retrieve a list of all `Distribution` sub-classes. Mostly used for testing.
- `get_distribution_class()`
- Convert from the argument to a `Distribution` subclass as needed. This argument can convert from a string specification of a distribution function to return the actual class.
- `utils.py`
- `class Test_Base`
- Base-class to use in unittests.
- `add_cov()`
- Given a covariance matrix, use a Cholesky decomposition to transform the given data to have that covariance.
- `allclose()` [new-function]
- Convenience function for unittests.
- `alltrue()` [new-function]
- Convenience function for unittests.
- `array_str()` [new-function]
- Format an array (or elements of) for printing.
- `bins()` [new-function]
- Generate bin- edges, centers and widths all together.
- `bound_indices()`
- Find the indices of parameter space arrays within given bounds.
- `cov_from_var_cor()`
- Construct a covariance matrix given a set of variances of parameters, and the correlations between them.
- `matrix_invert()`
- Invert a matrix, following back to SVD if it initially fails.
- `rem_cov()`
- Given a covariance matrix, use a Cholesky decomposition to remove that covariance from the given data.
- `stats_str()` [new-function]
- Method for calculating percentiles of given data and returning them as a str.
- `trapz_dens_to_mass()`
- Use the ndimensional trapezoid rule to convert from densities on a grid to masses (e.g. PDF to PMF).
- `tests/`
- `test_distributions.py`
- Test the underlying distribution functions.
- `test_kde.py`
- Test the top-level KDE class and the accuracy of KDE calculation of PDFs and resampling.
- `test_kernels.py` [new-file]
- Tests of the kernels directly.
- `test_utils.py`
- Test the utility functions.

- `notebooks/`
- `kernels.ipynb` [new-file]
- Examining / testing the behavior of different kernels specifically.
- `demo.ipynb` [new-file]
- Currently includes the material used in the `README.rst`, should be expanded as a quick demonstration / tutorial of the package.

- `kdes/`
- `__init__.py`
- `class KDE`
- Base class for KDE calculations, modeled roughly on the `scipy.stats.gaussian_kde` class.
- Allows for multidimensional PDF calculation and resampling of data, in multi-dimensional parameter spaces.
- Reflecting boundary conditions are available in multiple dimensions, both for PDF calculation and resampling.
- `utils.py`
- General utility functions for the package. Methods extracted from the `zcode` package.
- `midpoints()`
- Calculate the midpoints between values in an array, either in log or linear space.
- `minmax()`
- Calculate the extrema of a given dataset. Allows for comparison with previous extrema, setting limits, or 'stretching' the return values by a given amount.
- `spacing()`
- Construct a linear or log spacing between the given extrema.
- `tests/`
- `test_kde.py`
- Basic tests for the `KDE` base class and its operations.
- `test_util.py`
- Basic tests for the utility methods.

- `notebooks/`
- `kde.ipynb`
- Includes basic examples and tests with plots. Mostly the same tests as in the `kdes/tests/` directory, but with plots.