History¶
1.0.2 (2020-08-08)¶
Added¶
- Warnings about antimony keyword usage in tutorial,
ModelIO
class
Fixed¶
setup.py
setup requirements are automatically installedantimony
version incompatibility issue
1.0.0 (2020-07-18)¶
Added¶
Antimony
support andModelIO
class, giving easier entry point to load models- Support for custom species names in plotting and
Results
- Support for automatic
cpu
core detection - New logo for
cayenne
Changed¶
- Package name changed from
pyssa
tocayenne
- Update docs for new API
0.9.1 (2020-05-23)¶
Fix interpolation bug in Results.get_state
Fixed¶
Results.get_state
function now adds an epsilon to time
0.9.0 (2019-12-14)¶
Replace numba
implementation with Cython
implementation
Fixed¶
- Propensity calculation in all algorithms
- Considerable speed-up in algorithm runtimes
- Remove
volume
fromSimulation.simulate
parameters
Added¶
direct
algorithm inCython
tau_leaping
algorithm inCython
tau_adaptive
algorithm inCython
(experimental)Cython
to Azure pipeline- Accuracy tests from sbml-test-suite
- HOR property and tests for it
- Code coverage for
Cython
- Algorithms page to the documentation
- Examples page to the documentation
Changed¶
- Remove
numba
algorithms - Remove interpolation for direct algorithm
sim.plot
now plotspost
step curve- Updated tutorial page of the documentation
0.8.2 (2019-04-20)¶
Fixed¶
- Initialize
algorithms
submodule with__init__.py
- Update
setup.py
to allow submodule detection
0.8.0 (2019-04-13)¶
Added¶
Results.get_states
method - returns state at timet
- Accuracy tests for all algorithms
- Additional consistency checks for
X0
andk_det
Changed¶
- Refactor algorithms into sub module
algorithms
- Refactor algorithm independent tests
Fixed¶
- Indexing issue in propensity calculation in
direct
algorithm - Indexing issue in propensity calculation in
tau_leaping
algorithm - Address edge case X->2X in
tau_adaptive
algorithm
0.7.1 (2019-03-23)¶
Changed¶
- Refactor
tau_adaptive
- Rename
direct_naive
todirect
Fixed¶
- SSA part of
tau_adaptive
- Bug in linux compatibility of
tau_adaptive
0.7.0 (2019-02-02)¶
Added¶
- Support for the
tau_adaptive
algorithm - Support for multiprocessing
Fixed¶
- Transpose stoichiometric matrix
- Update references in docstrings
Changed¶
- Use
TINY
andHIGH
for status estimation - Use
np.int64
andnp.float64
explicitly
Chore¶
- Update dependencies
- Add azure pipelines for testing on Windows
0.6.0 (2018-12-16)¶
Added¶
- Updated
direct_naive
docstring - Support for the
tau_leaping
algorithm - Species name support for plotting
Fixed¶
- Check for sum propensities uses threshold instead of equality
- Add check for type of
max_iter
Changed¶
- Update
roulette_selection
to use np.searchsorted - Minor changes to
numpy
style usage
Chore¶
- Add
codecov
- Travis pypi autodepolyment
- Parameterize tests with algorithm name
- Add details about
tau_leaping
to docs and README
0.5.3 (2018-12-02)¶
Added¶
- plot to pypi
Changed¶
- fix bumpversion/black issue
- remove history from package long_description
0.5.0 (2018-12-01)¶
First public release!!
Added¶
- testpypi deployment
- pyup security checking
- readthedocs deployment
- Tutorials and documentation
- Plotting functionality through
Simulation.plot
Changed¶
Simulation.results
is now a property- Updated tests to support the new api changes
Chore¶
- Updated the README
0.4.0 (2018-11-23)¶
Added¶
Simulation
class - main class for running simulationsResults
class - for storing and acessing simulation resultsSimulation.simulate
function that returns an instance of theResults
class
Changed¶
- Refactor
get_kstoc
androulette_selection
intoutils.py
- Refactor
direct_naive
intodirect_naive.py
- Delete
pyssa.py
and replace withSimulation
class
Chore¶
- Add license and code-style badges
- Use
black
for code-formatting
0.2.0 (2018-11-10)¶
Added¶
- Naive implementation of the Gillepsie algorithm in
numba
- Tests - sanity checks, bifurcation and long running simulation
- CI on
travis
0.1.0 (2018-08-08)¶
- First commit