sbpiper (MIT License)
Copyright 2010-2018 Piero Dalle Pezze
v4.21.0 (Beyond the Kuiper Belt)
- added support for Copasi Optimisation task. This also uses the -e option.
- bugfix: added is_package_installed.r to MANIFEST.ini.
- SBpipe v4.18.0, sbpiper v1.8.0, sbpipe_snake v1.0.0 and above are released under MIT License.
Previous versions of these packages were released under GNU GPL v3.
- Improved project consistency and added warnings.
- Snakemake files moved to a separate repository (https://github.com/pdp10/sbpipe_snake.git).
- Added script for moving data sets and update indexes.
- Project and documentation clean-up.
- SBpipe is now available on pypi.org.
- Improved setup.py file for python packaging
- SBpipe tests no longer require CopasiSE.
- Documentation update.
- Added generate_tarball option to all the remaining pipelines in native SBpipe.
- Improved output messages.
- Added progress information for native SBpipe.
- Added PCA analysis for the best parameter estimates. Replaced conda channel "r" with "conda-forge".
- Improved data analysis scalability for parameter estimation (using Snakemake).
- Added checks whether a COPASI model can be loaded and executed correctly. This is based on Python bindings for COPASI.
- Optimisation of snakemake pipelines. Improved efficiency of rules for analyses.
- Bugfix - SGE and LSF job names now include a random string, avoiding potential interactions among multiple
SBpipe executions. Whilst this does not affect the results, it was still a performance-related bug.
- SBpipe R code is now an independent R package called sbpiper. This is imported by SBpipe as
an external dependency. Users can invoke SBpipe functions for data analysis directly from their R code.
- added option `exp_dataset_alpha` to `sim` pipeline. This option allows to plot experimental
data with an alpha level.
- data analysis for `sim` pipeline is scalable.
- improved yaml files for installing SBpipe using conda. SBpipe is now tested on Python 2.7 and 3.6.
- added transparencies and improved simulation plots combined with data set.
- added release.sh script for releasing SBpipe versions automatically.
- if `data_point_num` is [0, est_param_number], the analysis task for parameter estimation will
continue BUT the thresholds will be discarded.
- bug fix - conda build package after conda was upgraded to v3.x.x
- bug fix - constraints in parameter estimation using Copasi
- added scripts
- code optimisation for parameter estimation pipeline.
- Improved conda packaging.
- Improved import of parameter names for parameter estimation pipeline.
- Improved SBpipe packaging (snakemake is not a requirement)
- SBpipe pipelines are also available as snake files. Therefore, SBpipe can be run using Snakemake.
- SBpipe is now also available as conda package (installation+dependencies: conda install -c pdp10 sbpipe)
- The environment variable SBPIPE is no longer necessary.
- Anaconda can be used for installing SBpipe dependencies. This improves portability on Linux
and Windows OS.
- subprocess.Popen() and logging fileConfig() use `with .. as ..:` construct with Python3+.
- changed `chi^2` label to `obj val` in parameter estimation plots
- Added additional arguments to sbpipe
- Output is now coloured.
- Improved logging messages. Added log.debug() calls.
- Added sbpipe() function in main.py to facilitate programmatic use of sbpipe.
- Replaced Python getopt with argparse.
- Improved unit tests and nosetests with Travis-CI.
- Replaced INI configuration files with YAML configuration files.
- Skip heatmap and multiple time course plot if only one simulation is run. These plots are
- removed support for running R, Octave, and Java models directly as these can be run via
a Python model wrapper.
- bug fixes
- added prints to r plotting functions
- pipeline analyses are executed on cluster (local, sge, lsf) using sbpipe parcomp module.
- renamed option `pp_cpus` to `local_cpus`, after removal of parallel python.
- renamed value `pp` to `local` for option `cluster` after removal of parallel python.
- added support for Python 3. The code is now expected to work for Python 2.7+, 3.2, and 3.6.
- replaced parallel python with python multiprocessing package. This should facilitate
the transition to Python 3.
- removed deprecated source code for manually randomising parameters before parameter
estimation in Copasi files.
- Copasi and PL-based simulators now share a large amount of code.
- adapted programming language-based simulators to use ps1 and ps2 post-processing code.
All simulators support all the pipelines.
- moved post-processing code for ps1 and ps2 from Copasi to Simul.
- improved code cohesion by moving utility code into Simul class().
- improved output name consistency for report and plot files
- improved sorting of plots in latex/pdf report for ps1 pipeline
- added test case for stochastic double parameter scan.
- double parameter scans can be executed in parallel as repeats.
- added support for stochastic double parameter scans.
- added test case for stochastic single parameter scan.
- single parameter scans can be executed in parallel as repeats.
- added support for stochastic single parameter scans.
- modularised parallel computation within Copasi simulator
- added heatmap plot representing stochastic repeats for the time course simulation.
- moved R code from pipelines to R/
- redesign of simulation plots. Improvements plus based on melt function.
- removed remaining old gplots dependent code. Sbpipe only uses ggplot2 now.
- added two new plots useful for stochastic simulation
- improved reuse for all R plots.
- added plots reproducing all the single simulations per species.
- changed main script name from run_sbpipe.py to sbpipe.
- Added support for parameter estimation using non-Copasi models. Test using R model.
- Skip Java, Python, and R model tests if their dependencies are not satisfied.
- Optimised Java, Python, and R simulators. Report file names are passed as input argument.
Models do not need to be replicated.
- Java models can be used for model simulation in addition to Copasi.
- Python models can be used for model simulation in addition to Copasi.
- R models can be used for model simulation in addition to Copasi.
- improved threshold levels for Sampled PLE plots.
- added 20 tests including wrong configuration file settings.
- extensive refactory of unit tests.
- source code uses PEP8 standard
- source code cleaning and reformatting.
- improved source code by eliminating some warning highlighted by PyCharm.
- moved script core functions within sbpipe package.
- the copasi package is now a dynamically loaded simulator. Users can choose the simulator
to use in the configuration file.
- simulators are loaded dynamically. Uncoupling between simulators and pipelines.
- separation of code for generating data from pipeline package.
- improved source code modularisation for the whole program.
- extracted scripts (run_sbpipe and cleanup_sbpipe) from sbpipe/.
- sbpipe supports execution as a Python module (__main__.py).
- all Python imports are now absolute (in agreement with Python 3).
- improvements to program prints
- project renamed sbpipe
- added AIC, AICc, BIC to the parameter estimation summary table.
- randomisation of initial parameter values for parameter estimation is now only
performed by Copasi.
- added plots comparing model simulation vs experimental data in simulate pipeline.
- improving plot margins for simulate and single parameter scan pipelines.
- source code refractoring in parameter estimation analysis task.
- fixed a bug in parameter estimation pipeline related to the filtering of confidence
intervals from the complete data set.
- added ratios in parameter estimation summaries to investigate the distance between the
estimated parameter and its confidence intervals.
- plot polishing.
- separated options for plotting 2d correlations within 66%, 95%, or 99% confidence intervals.
- added 99% confidence intervals parameter estimation plots.
- added option to plot parameter estimation plots using the scientific notation.
- improved plots layout (fonts, legends).
- added option for y axis label to simulate and single parameter scan pipelines.
- Copasi models are fully consistent.
- table of estimated parameter and confidence values is in normal scale (not log10).
- completed source code documentation
- completed user and developer manuals.
- configured Python Sphinx for documenting SBpipe
- bug fixes.
- separation of pdf report code from pipelines.
- configuration sessions integrated in pipeline classes.
- pipelines converted to classes.
- added option for plotting parameter estimation results in log10 parameter space (default).
- improved heat palette for double parameter scan and coloured scatterplots.
- added test files for double parameter scan
- ported all Matlab code to Python / R
- added pipeline for double parameter scan (parsing, plots, report)
- further removal of deprecated files
- generated copasi files for parameter estimation now moved to Working_Folder/xx/
- improved insulin receptor model for testing.
- Copasi report files now in Models/ .
- Copasi experimental data files now in Models/ .
- added scripts for automatically installing Python and R package dependencies.
- use of sections in configuration
- separation of configuration file parsing from program logic.
- restructuring dataset parsing for simulate and single_param_scan.
- added parameter scan plot with homogeneous lines (useful for plotting param conf. interv.).
- replaced all prints with Python logging.
- improved LaTeX reports
- tested parameter estimation using Gillespie algorithm for model simulation.
- configured Travis-CI for continous integration tests.
- pipeline renaming.
- added computation for parameter confidence intervals.
- added plot for fit history.
- added 2D parameter correlations using 66% or 95% confidence levels from calculated PLE.
- added profile likelihood estimation based on intermediate estimations.
- cleaned pipeline output.
- added documentation for configuring Copasi.
- removed part of the deprecated code.
- internalised code for each pipeline; run_sbpipe.py is the main executor for sbpipe.
- bug fixes.
- models can now be simulated in parallel using PP, SGE, or LSF.
- separation of parallel code from param_estim__copasi pipeline. It is generic now.
- sbpipe should now be platform independent (untested yet).
- removed unused dependencies.
- better separation of test cases.
- pipeline steps can be executed separately.
- pipeline restructuring (separation of the steps: generate data, analyse data, and generate report).
- model parameters can now be estimated in parallel using PP, SGE, or LSF.
- removed old deprecated code.
- restructuring source code in the lib/ folder (now sbpipe/pipelines and sbpipe/utils).
- finalised skeleton for sb_param_estim pipeline.
- added parameter correlation plots for sb_param_estim pipeline.
- ported R gplots code to ggplot in sb_param_scan__single_perturb pipeline.
- ported R gplots code to ggplot in sb_simulate pipeline.
- sbpipe is now a Python package.
- added documentation (readme, developer_guide).
- added unit tests and setup.py.
- ported Bash / sed / grep and cut code to Python in sb_param_estim pipeline.
- ported Bash / sed / grep and cut code to Python in sb_param_scan__single_perturb pipeline.
- ported Bash / sed / grep and cut code to Python in sb_simulate pipeline.
- added param_estim__copasi.sh.
- improved configuration file.
- simulation time start, end, xaxis label and time step now replace the parameter `team`.
- adjusted sb_simulate.sh, sb_param_scan__single_perturb.sh, sb_sensitivity.sh.
- packaging of sb_modules in /bin.
- added test scripts.