Change Log#

v0.3#

v0.3.10 (2024-04-04)#

Fixed

  • Documentation build errors

v0.3.9 (2024-04-04)#

Added

  • Instructions for installation via PyPI and on Google Colab

Changed

  • Updated minimal example notebook

Fixed

  • Windows’ PyPI wheels

v0.3.8 (2024-03-28)#

Added

  • PyPI badge to README

  • Precompiled wheels to PyPI

v0.3.7 (2024-03-28)#

Fixed

  • Changed wheel creation trigger behavior

v0.3.6 (2024-03-28)#

Fixed

  • Explicitly set author-email in pyproject.toml to avoid display issues on PyPI

v0.3.5 (2024-03-28)#

Fixed

  • Due to PyPI maintainance, v0.3.4 was not published to the package index. It should happen now.

v0.3.4 (2024-03-28)#

Added

  • Source distribution released to PyPI

  • Add instructions to build sdist to documentation

v0.3.3 (2024-03-28)#

Added

  • Optional dependencies can be used during installation

  • Instructions for Conda environment

  • Prepare project for release to PyPI

Changed

  • Code style for C++/CUDA source

  • Build instructions have been updated

Fixed

  • Installation in conda environments and on systems with multiple compilers and Python versions

  • Small correction to example in docs landing page

v0.3.2 (2024-03-07)#

Added

  • Build and test badge to documentation

  • Azimuthal average test

Fixed

  • Resolved bug where list input in azimuthal_average was throwing an error

v0.3.1 (2024-03-05)#

Added

  • Documentation is now hosted on ReadtheDocs

  • Build and tests (currently, only Python and C++; CUDA not possible on GitHub actions)

Changed

  • README file

  • Updates to developers documentation

v0.3.0 (2024-03-01)#

Added

  • Image windowing function can now be used as an input for ddm function.

  • Documentation (developer guidelines for contributors, installation, doc-strings, etc.)

  • Noise estimators

  • Intermediate scattering function

  • Logo

  • support for fixed expression in fit_multik

  • general function azimuthal_average_array to perform an azimuthal average on a 3D ndarray

Changed

  • fit_multik also returns the standard error of the parameters

  • refactored functions for C++ & CUDA

  • improved azimuthal average (changed behaviour of input bins variable)

Fixed

  • mergesort & resample bugs with different dtypes

  • initial fit at reference k value in fit_multik was not done properly

  • CUDA compilation works well on various systems

v0.2#

v0.2.0 (2023-04-20)#

Added

  • ImageStructureFunction provides methods to retrieve the full plane representation from half-plane.

  • SINGLE_PRECISION option at install time.

  • Errors evaluated in AzimuthalAverage can be used in fit_multik

  • In fit_multik, the user can now fix q-dependent parameter values via fixed_params.

  • In fit_multik, the user can now fix q-dependent parameters range via fixed_params_min and fixed_params_max.

  • Results from fit_multik also include the k parameter for convenience.

Changed

  • Now ImageStructureFunction data and err are stored using half-plane representation.

  • Fit models are not saved to file due to incompatibilities with dill package.

  • Updated pytest.

Fixed

  • Passed parameters in fit_multik are not changed by the function.

Removed

  • Removed unused functions and modules.

v0.1#

v0.1.3 (2023-04-04)#

Added

  • Reader function for raw image metadata (supports .tif and .nd2)

  • basic pytest routines

  • chunkify function for time analysis (for non-stationary processes)

  • variance in python backend

  • uncertainty in azimuthal average (selectable via flag in azimuthal_average function)

  • fit function to fit a model for multiple k/q values at once

  • copyright information

  • script to generate fit models for the intermediate scattering function and image structure function

  • formatted (custom) binary file outputs (and readers/parsers) for the ImageStructureFunction and the AzimuthalAverage classes

Changed

  • Now save method in ImageStructureFunction and AzimuthalAverage allow the usage of the filename without the argument keyword.

  • read_images and related functions allow for the selection of color channels when reading image files.

  • Improved speed in cuda fft2 step.

  • moved from static to dynamic library compilation

  • sped up the azimuthal average computation

Fixed

  • fixed a bug in azimuthal average resample method

  • fixed scaling issue in python backend fft mode

  • fixed bug in variance calculation in cuda backend

  • computing lag=0 in ddm python backend was causing an error due to faulty array slicing, this was fixed.

v0.1.2 (2023-01-24)#

Added

  • New power_spec and var properties in ImageStructureFunction and AzimuthalAverage classes, containing the average power spectrum of the input images and the variance of the temporal fluctuations of the 2D Fourier transformed images.

  • len() method in ImageStructureFunction class, which returns the length of the underlying data.

Fixed

  • Fixed memory leak.

  • Fixed evaluation of RAM memory in cuda core functions.

v0.1.1 (2023-01-20)#

Added

  • New shape property in AzimuthalAverage class.

  • Unified read_images function with support for Nikon .nd2 files.

Changed

  • azimuthal_average method now works only with ImageStructureFunction objects. The generic method was moved to _azimuthal_average.

  • pixel_size and delta_t properties of ImageStructureFunction class can now be set by simple assignment and work as previous set_pixel_size and set_delta_t methods.

  • Improved performance of azimuthal_average when weights are not set.

  • Improved speed of optimization step in cuda functions.

Fixed

  • Fixed bug in image sequence reader.

  • Fixed fatal error on import in macOS systems when using conda environment.

Removed

  • set_pixel_size and set_delta_t methods of ImageStructureFunction class have been substituted with simple assignment.

  • Removed unused dependencies.

v0.1.0 (2023-01-16)#

Added

  • Image reading functions.

  • Image structure function calculation on CPU (python and C++) and GPU.

  • Azimuthal average calculation on CPU (python only).

  • Basic windowing functions for image preprocessing.

  • Basic central cross mask for azimuthal average computation.

  • Basic weights (sector average) functions for azimuthal average.

  • Objects loading and saving.

  • Lag selection functions.

  • Fit interface with lmfit.