Changelog¶
v0.5.3 [22 May 2018]¶
Bugfixes - Fixed a bug introduced in v0.5.1 where using bin_ave=False in angular_average_nd would fail.
v0.5.2 [17 May 2018]¶
Enhancements - Added ability to calculate the variance of an angularly averaged quantity. - Removed a redundant calculation of the bin weights in angular_average
Internals - Updated version numbers of dev requirements.
v0.5.1 [4 May 2018]¶
Enhancements - Added ability to not have dimensionless power spectra from get_power. - Also return linearly-spaced radial bin edges from angular_average_nd - Python 3 compatibility
Bugfixes - Fixed bug where field was modified in-place unexpectedly in angular_average - Now correctly flattens weights before getting the field average in angular_average_nd
v0.5.0 [7 Nov 2017]¶
Features - Input boxes to get_power no longer need to have same length on every dimension. - New angular_average_nd function to average over first n dimensions of an array.
Enhancements - Huge (5x or so) speed-up for angular_average function (with resulting speedup for get_power). - Huge memory reduction in fft/ifft routines, with potential loss of some speed (TODO: optimise) - Better memory consumption in PowerBox classes, at the expense of an API change (cached properties no
longer cached, or properties).
- Modified fftshift in dft to handle astropy Quantity objects (bit of a hack really)
Bugfixes - Fixed issue where if the boxlength was passed as an integer (to fft/ifft), then incorrect results occurred. - Fixed issue where incorrect first_edge assignment in get_power resulted in bad power spectrum. No longer require this arg.
v0.4.3 [29 March 2017]¶
Bugfixes - Fixed volume normalisation in get_power.
v0.4.2 [28 March 2017]¶
Features - Added ability to cross-correlate boxes in get_power.
v0.4.1¶
Bugfixes - Fixed cubegrid return value for dft functions when input boxes have different sizes on each dimension.
v0.4.0¶
Features - Added fft/ifft wrappers which consistently return fourier transforms with arbitrary Fourier conventions. - Boxes now may be composed with arbitrary Fourier conventions. - Documentation!
Enhancements - New test to compare LogNormalPowerBox with standard PowerBox. - New project structure to make for easier location of functions. - Code quality improvements - New tests, better coverage.
Bugfixes - Fixed incorrect boxsize for an odd number of cells - Ensure mean density is correct in LogNormalPowerBox
v0.3.2¶
Bugfixes - Fixed bug in pyFFTW cache setting
v0.3.1¶
Enhancements - New interface with pyFFTW to make fourier transforms ~twice as fast. No difference to the API.
v0.3.0¶
Features - New functionality in get_power function to measure power-spectra of discrete samples.
Enhancements - Added option to not store discrete positions in class (just return them) - get_power now more streamlined and intuitive in its API
v0.2.3 [11 Jan 2017]¶
Enhancements
- Improved estimation of power (in get_power
) for lowest k bin.
v0.2.2 [11 Jan 2017]¶
Bugfixes - Fixed a bug in which the output power spectrum was a factor of sqrt(2) off in normalisation
v0.2.1 [10 Jan 2017]¶
Bugfixes
- Fixed output of create_discrete_sample
when not randomising positions.
Enhancements - New option to set bounds of discrete particles to (0, boxlength) rather than centring at 0.
v0.2.0 [10 Jan 2017]¶
Features
- New LogNormalPowerBox
class for creating log-normal fields
Enhancements
- Restructuring of code for more flexibility after creation. Now requires cached_property
package.
v0.1.0 [27 Oct 2016]¶
First working version. Only Gaussian fields working.