├── .gitignore ├── .settings └── org.eclipse.core.resources.prefs ├── .travis.yml ├── AUTHORS.rst ├── CONTRIBUTING.rst ├── HISTORY.rst ├── LICENSE ├── MANIFEST.in ├── Makefile ├── README.rst ├── Tests ├── .gitignore ├── __init__.py ├── test_ChainContext.py ├── test_CosmoHammerSampler.py ├── test_InMemoryStorageUtil.py ├── test_LikelihoodComputationChain.py ├── test_MpiUtil.py ├── test_Params.py ├── test_ParticleSwarmOptimizer.py ├── test_PositionGenerators.py └── test_SampleFileUtil.py ├── cosmoHammer ├── ChainContext.py ├── ConcurrentMpiCosmoHammerSampler.py ├── Constants.py ├── CosmoHammerSampler.py ├── LikelihoodComputationChain.py ├── MpiCosmoHammerSampler.py ├── __init__.py ├── exceptions.py ├── modules │ ├── MultivarianteGaussianModule.py │ ├── PseudoCmbModule.py │ ├── RosenbrockModule.py │ └── __init__.py ├── pso │ ├── BestFitPositionGenerator.py │ ├── CurvatureFitter.py │ ├── MpiParticleSwarmOptimizer.py │ ├── ParticleSwarmOptimizer.py │ └── __init__.py └── util │ ├── FlatPositionGenerator.py │ ├── IPythonUtil.py │ ├── InMemoryStorageUtil.py │ ├── IterationStopCriteriaStrategy.py │ ├── MpiUtil.py │ ├── Params.py │ ├── SampleBallPositionGenerator.py │ ├── SampleFileUtil.py │ └── __init__.py ├── doc ├── .gitignore ├── Makefile ├── check_sphinx.py └── source │ ├── api.rst │ ├── authors.rst │ ├── conf.py │ ├── contributing.rst │ ├── history.rst │ ├── index.rst │ └── user │ ├── HowToCosmoHammer.rst │ ├── benchmark.png │ ├── benchmark.rst │ ├── chscheme.jpg │ ├── install.rst │ ├── parallelisation.jpg │ ├── parallelization.rst │ ├── pso.gif │ ├── pso.rst │ └── usage.rst ├── examples ├── .gitignore ├── DerivedParamterFileUtil.py ├── DummyCoreModule.py ├── DummyLikelihoodModule.py ├── README.rst ├── __init__.py ├── pseudo_cmb.png ├── runCosmoHammer.py ├── runCosmoHammerGaussian.py ├── runCosmoHammerPseudoCmb.py ├── runCosmoHammerRosenbrock.py └── wmap_tester.py ├── requirements.txt ├── setup.py └── tox.ini /.gitignore: -------------------------------------------------------------------------------- 1 | *.py[cod] 2 | 3 | # C extensions 4 | *.so 5 | 6 | # Packages 7 | *.egg 8 | *.egg-info 9 | dist 10 | build 11 | eggs 12 | parts 13 | bin 14 | var 15 | sdist 16 | develop-eggs 17 | .installed.cfg 18 | lib 19 | lib64 20 | .DS_Store 21 | *.log 22 | 23 | # Installer logs 24 | pip-log.txt 25 | 26 | # Unit test / coverage reports 27 | .coverage 28 | .tox 29 | nosetests.xml 30 | *junit-* 31 | htmlcov 32 | coverage.xml 33 | 34 | # Translations 35 | *.mo 36 | 37 | # Mr Developer 38 | .mr.developer.cfg 39 | .project 40 | .pydevproject 41 | 42 | # Complexity 43 | output/*.html 44 | output/*/index.html 45 | 46 | # Sphinx 47 | doc/build 48 | -------------------------------------------------------------------------------- /.settings/org.eclipse.core.resources.prefs: -------------------------------------------------------------------------------- 1 | eclipse.preferences.version=1 2 | encoding//doc/source/conf.py=utf-8 3 | encoding/setup.py=utf-8 4 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | language: python 2 | python: 3 | - "2.7" 4 | - "3.6" 5 | 6 | before_install: 7 | - sudo apt-get update 8 | - sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test 9 | - sudo apt-get -qq update 10 | - sudo apt-get -qq install g++-4.8 gcc-4.8 11 | - sudo ln -sf /usr/bin/gcc-4.8 /usr/bin/gcc 12 | - sudo ln -sf /usr/bin/g++-4.8 /usr/bin/g++ 13 | # We do this conditionally because it saves us some downloading if the 14 | # version is the same. 15 | - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then 16 | wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh; 17 | else 18 | wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; 19 | fi 20 | - bash miniconda.sh -b -p $HOME/miniconda 21 | - export PATH="$HOME/miniconda/bin:$PATH" 22 | - conda update --yes conda 23 | - travis_retry conda install --yes python=$TRAVIS_PYTHON_VERSION pip numpy scipy 24 | 25 | install: 26 | - travis_retry pip install coveralls 27 | - travis_retry pip install -r requirements.txt 28 | 29 | # command to run tests, e.g. python setup.py test 30 | script: coverage run --source cosmoHammer setup.py test 31 | after_success: 32 | coveralls 33 | -------------------------------------------------------------------------------- /AUTHORS.rst: -------------------------------------------------------------------------------- 1 | ======= 2 | Credits 3 | ======= 4 | 5 | Development Lead 6 | ---------------- 7 | 8 | - Joel Akeret (ETHZ) 9 | - Sebastian Seehars (ETHZ) 10 | 11 | Contributors 12 | ------------ 13 | 14 | - sibirrer (Simon Birrer) 15 | 16 | Citations 17 | --------- 18 | 19 | As you use **CosmoHammer** for your exciting discoveries, please cite the paper that describes the package: 20 | 21 | Akeret, J., Seehars, S., Amara, A, Refregier, A., and Csillaghy, A., Astronomy and Computing (2013) 22 | -------------------------------------------------------------------------------- /CONTRIBUTING.rst: -------------------------------------------------------------------------------- 1 | ============ 2 | Contributing 3 | ============ 4 | 5 | Contributions are welcome, and they are greatly appreciated! Every 6 | little bit helps, and credit will always be given. 7 | 8 | You can contribute in many ways: 9 | 10 | Types of Contributions 11 | ---------------------- 12 | 13 | Report Bugs 14 | ~~~~~~~~~~~ 15 | 16 | If you are reporting a bug, please include: 17 | 18 | * Your operating system name and version. 19 | * Any details about your local setup that might be helpful in troubleshooting. 20 | * Detailed steps to reproduce the bug. 21 | 22 | Fix Bugs 23 | ~~~~~~~~ 24 | 25 | Implement Features 26 | ~~~~~~~~~~~~~~~~~~ 27 | 28 | Write Documentation 29 | ~~~~~~~~~~~~~~~~~~~ 30 | 31 | CosmoHammer could always use more documentation, whether as part of the 32 | official CosmoHammer docs, in docstrings, or even on the web in blog posts, 33 | articles, and such. 34 | 35 | Submit Feedback 36 | ~~~~~~~~~~~~~~~ 37 | 38 | If you are proposing a feature: 39 | 40 | * Explain in detail how it would work. 41 | * Keep the scope as narrow as possible, to make it easier to implement. 42 | * Remember that this is a volunteer-driven project, and that contributions 43 | are welcome :) 44 | 45 | Pull Request Guidelines 46 | ----------------------- 47 | 48 | Before you submit a pull request, check that it meets these guidelines: 49 | 50 | 1. The pull request should include tests. 51 | 2. If the pull request adds functionality, the docs should be updated. Put 52 | your new functionality into a function with a docstring, and add the 53 | feature to the list in README.rst. 54 | 3. The pull request should work for Python 2.6, 2.7, and 3.3, and for PyPy. 55 | make sure that the tests pass for all supported Python versions. 56 | 57 | 58 | Tips 59 | ---- 60 | 61 | To run a subset of tests:: 62 | 63 | $ py.test test/test_CosmoHammerSampler.py -------------------------------------------------------------------------------- /HISTORY.rst: -------------------------------------------------------------------------------- 1 | .. :changelog: 2 | 3 | History 4 | ------- 5 | 6 | 0.6.1 (2018-03-05) 7 | ++++++++++++++++++ 8 | - Fixed numpy compatibility issue 9 | 10 | 0.6.0 (2016-08-15) 11 | ++++++++++++++++++ 12 | - Order preserving parameter objects 13 | - Fixing logging issue 14 | - Prettified examples 15 | 16 | 0.5.0 (2015-05-12) 17 | ++++++++++++++++++ 18 | - Flat package structure 19 | - Separation of sampling and modules 20 | - improved documentation 21 | 22 | 0.4.0 (2015-03-16) 23 | ++++++++++++++++++ 24 | - Support for Python 3 25 | - Change in package structure 26 | - Fixed bug in mpi pso 27 | 28 | 0.3.0 (2014-03-22) 29 | ++++++++++++++++++ 30 | - Better exception handling 31 | - Particle swarm optimization module 32 | - Higher test coverage 33 | - Some bug fixes 34 | - Extended documentation 35 | - Support for PyCamb 36 | 37 | 0.2.1 (2013-09-05) 38 | ++++++++++++++++++ 39 | - Some bug fixes 40 | 41 | 0.2.0 (2013-08-28) 42 | ++++++++++++++++++ 43 | - Revised version - not backwards compatible! 44 | - Bug fixes and major code refactoring 45 | - New documentation 46 | 47 | 0.1.0 (2013-01-03) 48 | ++++++++++++++++++ 49 | - First release 50 | 51 | 0.0.3 (2012-11-23) 52 | ++++++++++++++++++ 53 | - First release candidate 54 | 55 | 0.0.1 (2012-05-09) 56 | ++++++++++++++++++ 57 | - Initial creation 58 | 59 | 60 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include AUTHORS.rst 2 | include HISTORY.rst 3 | include README.rst 4 | include LICENSE 5 | include doc/* 6 | include doc/source/* 7 | include doc/source/user/* 8 | include Tests/* 9 | include examples/*.py 10 | include Makefile 11 | 12 | exclude .settings/* 13 | exclude .pydevproject -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | .PHONY: help clean clean-pyc clean-build list test test-all coverage docs release sdist 2 | 3 | help: 4 | @echo "clean-build - remove build artifacts" 5 | @echo "clean-pyc - remove Python file artifacts" 6 | @echo "lint - check style with flake8" 7 | @echo "test - run tests quickly with the default Python" 8 | @echo "test-all - run tests on every Python version with tox" 9 | @echo "coverage - check code coverage quickly with the default Python" 10 | @echo "docs - generate Sphinx HTML documentation, including API docs" 11 | @echo "sdist - package" 12 | 13 | clean: clean-build clean-pyc 14 | 15 | clean-build: 16 | rm -fr build/ 17 | rm -fr dist/ 18 | rm -fr *.egg-info 19 | 20 | clean-pyc: 21 | find . -name '*.pyc' -exec rm -f {} + 22 | find . -name '*.pyo' -exec rm -f {} + 23 | find . -name '*~' -exec rm -f {} + 24 | 25 | lint: 26 | flake8 cosmoHammer test 27 | 28 | test: 29 | python setup.py test 30 | 31 | test-all: 32 | tox 33 | 34 | coverage: 35 | coverage run --source cosmoHammer setup.py test 36 | coverage report -m 37 | coverage html 38 | open htmlcov/index.html 39 | 40 | docs: 41 | rm -f docs/cosmoHammer.rst 42 | rm -f docs/modules.rst 43 | sphinx-apidoc -o doc/api/ cosmoHammer 44 | $(MAKE) -C doc clean 45 | $(MAKE) -C doc html 46 | open doc/build/html/index.html 47 | 48 | sdist: clean 49 | # pip freeze > requirements.txt 50 | python setup.py sdist 51 | ls -l dist 52 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | ======================================================= 2 | Cosmological parameter estimation with the MCMC Hammer 3 | ======================================================= 4 | 5 | .. image:: https://badge.fury.io/py/cosmoHammer.png 6 | :target: http://badge.fury.io/py/cosmoHammer 7 | 8 | .. image:: https://travis-ci.org/cosmo-ethz/CosmoHammer.png?branch=master 9 | :target: https://travis-ci.org/cosmo-ethz/CosmoHammer 10 | 11 | .. image:: https://coveralls.io/repos/cosmo-ethz/CosmoHammer/badge.svg 12 | :target: https://coveralls.io/r/cosmo-ethz/CosmoHammer 13 | 14 | .. image:: https://readthedocs.org/projects/cosmohammer/badge/?version=latest 15 | :target: http://cosmohammer.readthedocs.io/en/latest/?badge=latest 16 | :alt: Documentation Status 17 | 18 | .. image:: http://img.shields.io/badge/arXiv-1212.1721-orange.svg?style=flat 19 | :target: http://arxiv.org/abs/1212.1721 20 | 21 | 22 | 23 | CosmoHammer is a framework which embeds `emcee `_ , an implementation by Foreman-Mackey et al. (2012) of the `Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler `_ by Goodman and Weare (2010). 24 | 25 | It gives the user the possibility to plug in modules for the computation of any desired likelihood. The major goal of the software is to reduce the complexity when one wants to extend or replace the existing computation by modules which fit the user's needs as well as to provide the possibility to easily use large scale computing environments. 26 | 27 | We published a `paper `_ in the `Astronomy and Computing Journal `_ which discusses the advantages and performance of our framework. 28 | 29 | This project has been realized in collaboration with the `Institute of 4D Technologies `_ of the `University of Applied Sciences and Arts Northwest Switzerland `_ (Fachhochschule Nordwestschweiz - FHNW). 30 | 31 | The development is coordinated on `GitHub `_ and contributions are welcome. The documentation of **CosmoHammer** is available at `readthedocs.org `_ and the package is distributed over `PyPI `_. 32 | 33 | For all public modules such as PyCamb, WMAP, Planck and more, see the cosmoHammerPlugins project at http://github.com/cosmo-ethz/CosmoHammerPlugins. 34 | 35 | -------------------------------------------------------------------------------- /Tests/.gitignore: -------------------------------------------------------------------------------- 1 | /__pycache__ 2 | -------------------------------------------------------------------------------- /Tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cosmo-ethz/CosmoHammer/b6c65ccff7c3f83623264b7d57e05310755f761b/Tests/__init__.py -------------------------------------------------------------------------------- /Tests/test_ChainContext.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test the CosmoHammerSampler module. 3 | 4 | Execute with py.test -v 5 | 6 | """ 7 | from __future__ import print_function, division, absolute_import, unicode_literals 8 | 9 | from cosmoHammer.ChainContext import ChainContext 10 | 11 | import numpy as np 12 | 13 | class TestCosmoHammerSampler(object): 14 | ctx = None 15 | params = np.array([[1,2,3],[4,5,6]]) 16 | 17 | def setup(self): 18 | self.ctx=ChainContext(self, self.params) 19 | 20 | def test_no_data(self): 21 | assert self.ctx.getParent() == self 22 | assert self.ctx.getParams() is self.params 23 | assert self.ctx.contains("key") == False 24 | assert self.ctx.getData() is not None 25 | assert len(list(self.ctx.getData().items())) == 0 26 | 27 | def test_add_data(self): 28 | assert self.ctx.contains("key") == False 29 | assert self.ctx.getData() is not None 30 | 31 | self.ctx.add("key", "value") 32 | 33 | assert self.ctx.contains("key") == True 34 | assert self.ctx.getData() is not None 35 | 36 | assert self.ctx.get("key") == "value" 37 | 38 | def test_remove_data(self): 39 | assert self.ctx.contains("key2") == False 40 | assert self.ctx.getData() is not None 41 | 42 | self.ctx.add("key2", "value") 43 | 44 | assert self.ctx.contains("key2") == True 45 | assert self.ctx.getData() is not None 46 | 47 | assert self.ctx.get("key2") == "value" 48 | 49 | self.ctx.remove("key2") 50 | assert self.ctx.contains("key2") == False 51 | 52 | def test_get_default(self): 53 | assert self.ctx.contains("aaa") == False 54 | assert self.ctx.get("aaa", "default") == "default" 55 | 56 | 57 | def test_additional_data(self): 58 | assert self.ctx.getData() is not None 59 | assert len(list(self.ctx.getData().items())) == 0 60 | 61 | self.ctx.getData()["moreData"] = "data" 62 | assert len(list(self.ctx.getData().items())) == 1 -------------------------------------------------------------------------------- /Tests/test_CosmoHammerSampler.py: -------------------------------------------------------------------------------- 1 | ##!/usr/bin/env python 2 | """ 3 | Test the CosmoHammerSampler module. 4 | 5 | Execute with py.test -v 6 | 7 | """ 8 | import numpy as np 9 | import os 10 | import tempfile 11 | 12 | from cosmoHammer import LikelihoodComputationChain 13 | from cosmoHammer.modules.PseudoCmbModule import PseudoCmbModule 14 | 15 | from cosmoHammer.util.IterationStopCriteriaStrategy import IterationStopCriteriaStrategy 16 | 17 | from cosmoHammer.CosmoHammerSampler import CosmoHammerSampler 18 | 19 | from cosmoHammer.util import SampleBallPositionGenerator 20 | from cosmoHammer.util import FlatPositionGenerator 21 | from cosmoHammer.util import InMemoryStorageUtil 22 | from cosmoHammer.util import SampleFileUtil 23 | 24 | class TestCosmoHammerSampler(object): 25 | params = None 26 | sampler = None 27 | 28 | def setup(self): 29 | """ 30 | Initialise some data vectors used for the comparisons by the other functions. 31 | """ 32 | self.params = np.array([[70, 40, 100, 3], 33 | [0.0226, 0.005, 0.1, 0.001], 34 | [0.122, 0.01, 0.99, 0.01], 35 | [2.1e-9, 1.48e-9, 5.45e-9, 1e-10], 36 | [0.96, 0.5, 1.5, 0.02], 37 | [0.09, 0.01, 0.8, 0.03], 38 | [1,0,2,0.4] ]) 39 | 40 | 41 | #the real means.. 42 | means = [70.704, 0.02256, 0.1115, 2.18474E-09, 0.9688, 0.08920] 43 | 44 | # ...and non-trivial covariance matrix. 45 | cov = np.array([[6.11E+00, 0, 0, 0, 0, 0], 46 | [7.19E-04, 3.26E-07, 0, 0, 0, 0], 47 | [-1.19E-02, -3.37E-07, 3.14E-05, 0, 0, 0], 48 | [-3.56E-11, 1.43E-14, 1.76E-13, 5.96E-21, 0, 0], 49 | [2.01E-02, 6.37E-06, -2.13E-05, 3.66E-13, 1.90E-04, 0], 50 | [1.10E-02, 2.36E-06, -1.92E-05, 8.70E-13, 7.32E-05, 2.23E-04]]) 51 | cov += cov.T - np.diag(cov.diagonal()) 52 | 53 | # Invert the covariance matrix 54 | icov = np.linalg.inv(cov) 55 | 56 | chain = LikelihoodComputationChain() 57 | pseudoLikelihood = PseudoCmbModule(icov, means) 58 | 59 | chain.addLikelihoodModule(pseudoLikelihood) 60 | chain.setup() 61 | 62 | posGen = FlatPositionGenerator() 63 | 64 | self.sampler = CosmoHammerSampler( 65 | params= self.params, 66 | likelihoodComputationChain=chain, 67 | filePrefix=self._getTempFilePrefix(), 68 | walkersRatio=10, 69 | burninIterations=1, 70 | sampleIterations=11, 71 | initPositionGenerator=posGen, 72 | storageUtil=InMemoryStorageUtil()) 73 | 74 | def test_init(self): 75 | self.sampler = CosmoHammerSampler( 76 | params= self.params, 77 | likelihoodComputationChain=LikelihoodComputationChain(), 78 | filePrefix=self._getTempFilePrefix(), 79 | walkersRatio=10, 80 | burninIterations=1, 81 | sampleIterations=1) 82 | 83 | assert isinstance(self.sampler.storageUtil, SampleFileUtil) 84 | assert isinstance(self.sampler.stopCriteriaStrategy, IterationStopCriteriaStrategy) 85 | assert isinstance(self.sampler.initPositionGenerator, SampleBallPositionGenerator) 86 | assert self.sampler.likelihoodComputationChain.params is not None 87 | 88 | 89 | def test_no_burn_in(self): 90 | self.sampler.resetSampler() 91 | self.sampler.burninIterations = 0; 92 | self.sampler.startSampling() 93 | 94 | assert self.sampler.storageUtil.samplesBurnin == None 95 | assert self.sampler.storageUtil.probBurnin == None 96 | 97 | def test_one_iter_burn_in(self): 98 | self.sampler.resetSampler() 99 | self.sampler.burninIterations = 1; 100 | self.sampler.walkersRatio = 10; 101 | self.sampler.startSampling() 102 | 103 | assert self.sampler.storageUtil.samplesBurnin is not None 104 | assert self.sampler.storageUtil.probBurnin is not None 105 | 106 | params = 7 107 | samples = params * self.sampler.burninIterations * self.sampler.walkersRatio 108 | assert self.sampler.storageUtil.samplesBurnin.shape == (samples, params) 109 | assert self.sampler.storageUtil.probBurnin.shape == (samples, ) 110 | 111 | def test_one_iter_sampling(self): 112 | self.sampler.resetSampler() 113 | self.sampler.burninIterations = 0; 114 | self.sampler.sampleIterations = 1; 115 | self.sampler.walkersRatio = 10; 116 | self.sampler.startSampling() 117 | 118 | params = 7 119 | samples = params * self.sampler.sampleIterations * self.sampler.walkersRatio 120 | 121 | assert self.sampler.storageUtil.samplesBurnin is None 122 | assert self.sampler.storageUtil.probBurnin is None 123 | assert self.sampler.storageUtil.samples.shape == (samples, params) 124 | assert self.sampler.storageUtil.prob.shape == (samples, ) 125 | 126 | def _getTempFilePrefix(self): 127 | return os.path.join(tempfile.mkdtemp(), "pseudoCmb") -------------------------------------------------------------------------------- /Tests/test_InMemoryStorageUtil.py: -------------------------------------------------------------------------------- 1 | ##!/usr/bin/env python 2 | """ 3 | Test the CosmoHammerSampler module. 4 | 5 | Execute with py.test -v 6 | 7 | """ 8 | from cosmoHammer.util.InMemoryStorageUtil import InMemoryStorageUtil 9 | 10 | import numpy as np 11 | 12 | class TestCosmoHammerSampler(object): 13 | storageUtil = None 14 | samples = np.array([[1,2,3],[4,5,6]]) 15 | prob = np.array([1,1]) 16 | 17 | def setup(self): 18 | self.storageUtil=InMemoryStorageUtil() 19 | 20 | def test_no_data(self): 21 | 22 | assert self.storageUtil.samplesBurnin == None 23 | assert self.storageUtil.probBurnin == None 24 | 25 | def test_persistBurninValues(self): 26 | self.storageUtil.persistBurninValues(self.samples, self.prob, []) 27 | 28 | assert self.storageUtil.samplesBurnin is not None 29 | assert self.storageUtil.probBurnin is not None 30 | assert self.storageUtil.samplesBurnin.shape == (2, 3) 31 | assert self.storageUtil.probBurnin.shape == (2, ) 32 | 33 | self.storageUtil.persistBurninValues(self.samples, self.prob, []) 34 | 35 | assert self.storageUtil.samplesBurnin is not None 36 | assert self.storageUtil.probBurnin is not None 37 | assert self.storageUtil.samplesBurnin.shape == (4, 3) 38 | assert self.storageUtil.probBurnin.shape == (4, ) 39 | 40 | def test_persistSamplingValues(self): 41 | self.storageUtil.persistSamplingValues(self.samples, self.prob, []) 42 | 43 | assert self.storageUtil.samples is not None 44 | assert self.storageUtil.prob is not None 45 | assert self.storageUtil.samples.shape == (2, 3) 46 | assert self.storageUtil.prob.shape == (2, ) 47 | 48 | self.storageUtil.persistSamplingValues(self.samples, self.prob, []) 49 | 50 | assert self.storageUtil.samples is not None 51 | assert self.storageUtil.prob is not None 52 | assert self.storageUtil.samples.shape == (4, 3) 53 | assert self.storageUtil.prob.shape == (4, ) 54 | 55 | -------------------------------------------------------------------------------- /Tests/test_LikelihoodComputationChain.py: -------------------------------------------------------------------------------- 1 | ##!/usr/bin/env python 2 | """ 3 | Test the LikelihoodComputationChain module. 4 | 5 | Execute with py.test -v 6 | 7 | """ 8 | 9 | import numpy as np 10 | from cosmoHammer import LikelihoodComputationChain 11 | from cosmoHammer.util import Params 12 | 13 | class TestLikelihoodComputationChain(object): 14 | 15 | def test_modules(self): 16 | chain = LikelihoodComputationChain() 17 | 18 | assert len(chain.getCoreModules())==0 19 | assert len(chain.getLikelihoodModules())==0 20 | 21 | coreModule = DummyModule() 22 | likeModule = DummyModule() 23 | chain.addCoreModule(coreModule) 24 | chain.addLikelihoodModule(likeModule) 25 | assert len(chain.getCoreModules())==1 26 | assert len(chain.getLikelihoodModules())==1 27 | 28 | chain.setup() 29 | assert coreModule.init 30 | assert likeModule.init 31 | 32 | like, data = chain([0]) 33 | 34 | assert coreModule.called 35 | assert likeModule.compLike 36 | 37 | assert like == DummyModule.like 38 | assert len(data) == 1 39 | assert data["data"] == DummyModule.data 40 | 41 | def test_isValid(self): 42 | chain = LikelihoodComputationChain() 43 | assert chain.isValid([0]) 44 | 45 | chain = LikelihoodComputationChain(min=[0]) 46 | assert chain.isValid([1]) 47 | assert chain.isValid([0]) 48 | assert not chain.isValid([-1]) 49 | 50 | chain = LikelihoodComputationChain(min=[0, 1]) 51 | assert chain.isValid([1, 2]) 52 | assert chain.isValid([0, 1]) 53 | assert not chain.isValid([-1, 1]) 54 | assert not chain.isValid([0, 0]) 55 | assert not chain.isValid([-1, 0]) 56 | 57 | chain = LikelihoodComputationChain(max=[1]) 58 | assert chain.isValid([1]) 59 | assert chain.isValid([0]) 60 | assert not chain.isValid([2]) 61 | 62 | chain = LikelihoodComputationChain(max=[1, 2]) 63 | assert chain.isValid([0, 1]) 64 | assert chain.isValid([1, 2]) 65 | assert not chain.isValid([2, 2]) 66 | assert not chain.isValid([1, 3]) 67 | assert not chain.isValid([2, 3]) 68 | 69 | chain = LikelihoodComputationChain(min=[0, 1], max=[1, 2]) 70 | assert chain.isValid([1, 2]) 71 | assert chain.isValid([0, 1]) 72 | assert chain.isValid([0, 1]) 73 | assert chain.isValid([1, 2]) 74 | assert not chain.isValid([-1, 1]) 75 | assert not chain.isValid([0, 0]) 76 | assert not chain.isValid([-1, 0]) 77 | assert not chain.isValid([2, 2]) 78 | assert not chain.isValid([1, 3]) 79 | assert not chain.isValid([2, 3]) 80 | 81 | like, data = chain([-1, 0]) 82 | assert like == -np.inf 83 | assert len(data) == 0 84 | 85 | like, data = chain([2, 3]) 86 | assert like == -np.inf 87 | assert len(data) == 0 88 | 89 | def test_createChainContext(self): 90 | chain = LikelihoodComputationChain() 91 | 92 | p = np.array([1,2]) 93 | ctx = chain.createChainContext(p) 94 | assert ctx is not None 95 | assert np.all(ctx.getParams() == p) 96 | 97 | def test_createChainContext_params_invalid(self): 98 | chain = LikelihoodComputationChain() 99 | chain.values = [] 100 | 101 | p = np.array([1,2]) 102 | ctx = chain.createChainContext(p) 103 | 104 | assert ctx is not None 105 | assert np.all(ctx.getParams() == p) 106 | 107 | def test_createChainContext_params(self): 108 | keys = ["a", "b"] 109 | params = Params((keys[0], 0), 110 | (keys[1], 1)) 111 | chain = LikelihoodComputationChain() 112 | chain.params = params 113 | 114 | p = np.array([1,2]) 115 | ctx = chain.createChainContext(p) 116 | 117 | assert ctx is not None 118 | assert np.all(ctx.getParams().keys == keys) 119 | assert np.all(ctx.getParams()[0] == p[0]) 120 | assert np.all(ctx.getParams()[1] == p[1]) 121 | 122 | 123 | 124 | class DummyModule(object): 125 | 126 | like = 0 127 | data = 1 128 | 129 | def __init__(self): 130 | self.init = False 131 | self.called = False 132 | self.compLike = False 133 | 134 | def setup(self): 135 | self.init = True 136 | 137 | def __call__(self, ctx): 138 | self.called = True 139 | ctx.getData()["data"] = self.data 140 | 141 | def computeLikelihood(self, ctx): 142 | self.compLike = True 143 | return self.like -------------------------------------------------------------------------------- /Tests/test_MpiUtil.py: -------------------------------------------------------------------------------- 1 | # Copyright (C) 2014 ETH Zurich, Institute for Astronomy 2 | 3 | ''' 4 | Created on Jul 29, 2014 5 | 6 | author: jakeret 7 | ''' 8 | from __future__ import print_function, division, absolute_import, unicode_literals 9 | 10 | import numpy as np 11 | from mock import patch 12 | 13 | from cosmoHammer.util import MpiUtil 14 | 15 | class TestMpiUtil(object): 16 | 17 | @patch("cosmoHammer.util.MpiUtil.MPI") 18 | def test_splitlist_1(self, mpi_mock): 19 | sequence = self._get_sequence(7) 20 | n = 1 21 | sList = MpiUtil.splitList(sequence, n) 22 | assert len(sList) == n 23 | for i in range(len(sequence)): 24 | assert np.all(sList[0][i] == sequence[i]) 25 | 26 | @patch("cosmoHammer.util.MpiUtil.MPI") 27 | def test_splitlist_2(self, mpi_mock): 28 | sequence = self._get_sequence(7) 29 | n = 2 30 | sList = MpiUtil.splitList(sequence, n) 31 | assert len(sList) == n 32 | 33 | assert len(sList[0]) == 4 34 | for i in range(4): 35 | assert np.all(sList[0][i] == sequence[0+i]) 36 | 37 | assert len(sList[1]) == 3 38 | for i in range(3): 39 | assert np.all(sList[1][i] == sequence[4+i]) 40 | 41 | @patch("cosmoHammer.util.MpiUtil.MPI") 42 | def test_splitlist_3(self, mpi_mock): 43 | sequence = self._get_sequence(7) 44 | n = 3 45 | sList = MpiUtil.splitList(sequence, n) 46 | assert len(sList) == n 47 | 48 | l0 = 2 49 | assert len(sList[0]) == l0 50 | for i in range(l0): 51 | assert np.all(sList[0][i] == sequence[0+i]) 52 | 53 | l1 = 3 54 | assert len(sList[1]) == l1 55 | for i in range(l1): 56 | assert np.all(sList[1][i] == sequence[2+i]) 57 | 58 | l2 = 2 59 | assert len(sList[2]) == l2 60 | for i in range(l2): 61 | assert np.all(sList[2][i] == sequence[5+i]) 62 | 63 | @patch("cosmoHammer.util.MpiUtil.MPI") 64 | def test_splitlist_equal(self, mpi_mock): 65 | sequence = self._get_sequence(10) 66 | 67 | n = 10 68 | sList = MpiUtil.splitList(sequence, n) 69 | assert len(sList) == n 70 | 71 | l0 = 1 72 | for k in range(n): 73 | assert len(sList[k]) == l0 74 | for i in range(l0): 75 | assert np.all(sList[k][i] == sequence[(k*l0)+i]) 76 | 77 | @patch("cosmoHammer.util.MpiUtil.MPI") 78 | def test_splitlist_80(self, mpi_mock): 79 | sequence = self._get_sequence(160) 80 | 81 | n = 80 82 | sList = MpiUtil.splitList(sequence, n) 83 | assert len(sList) == n 84 | 85 | l0 = 2 86 | for k in range(n): 87 | assert len(sList[k]) == l0 88 | for i in range(l0): 89 | assert np.all(sList[k][i] == sequence[(k*l0)+i]) 90 | 91 | 92 | def _get_sequence(self, lenght): 93 | sequence = (np.ones((lenght,4)).T * np.arange(lenght)).T 94 | sequence = [sequence[i] for i in range(len(sequence))] 95 | return sequence -------------------------------------------------------------------------------- /Tests/test_Params.py: -------------------------------------------------------------------------------- 1 | # Copyright (C) 2015 ETH Zurich, Institute for Astronomy 2 | 3 | ''' 4 | Created on Aug 5, 2015 5 | 6 | author: jakeret 7 | ''' 8 | from __future__ import print_function, division, absolute_import, unicode_literals 9 | from cosmoHammer.util import Params 10 | import pytest 11 | import numpy as np 12 | class TestParams(object): 13 | 14 | def test_attr_looup(self): 15 | params = Params(("attr1", 1), 16 | ("attr2", 2)) 17 | 18 | assert params.attr1 == 1 19 | assert params.attr2 == 2 20 | 21 | with pytest.raises(AttributeError): 22 | params.inexistent 23 | 24 | def test_get(self): 25 | params = Params(("attr1", 1), 26 | ("attr2", 2)) 27 | 28 | assert params.get("attr1") == 1 29 | assert params.get("attr2") == 2 30 | 31 | with pytest.raises(KeyError): 32 | params.get("inexistent") 33 | 34 | def test_params_access_1d(self): 35 | values = [1,2] 36 | params = Params(("attr1", values[0]), 37 | ("attr2", values[1])) 38 | 39 | assert np.all(params.values == values) 40 | 41 | def test_params_access_2d(self): 42 | values = np.array([[1,2,3,4], 43 | [5,6,7,8]]) 44 | params = Params(("attr1", values[0]), 45 | ("attr2", values[1])) 46 | 47 | assert np.all(params.values == values) 48 | 49 | 50 | def test_slicing(self): 51 | values = np.array([[1,2,3,4], 52 | [5,6,7,8]]) 53 | params = Params(("attr1", values[0]), 54 | ("attr2", values[1])) 55 | 56 | assert np.all(values[0] == values[0]) 57 | assert np.all(values[:, 1] == values[:, 1]) 58 | 59 | def test_names_access(self): 60 | keys = ["attr1","attr2"] 61 | params = Params((keys[0], 1), 62 | (keys[1], 2)) 63 | 64 | assert np.all(params.keys == keys) 65 | 66 | def test_names_immutability(self): 67 | keys = ["attr1","attr2"] 68 | params = Params((keys[0], 1), 69 | (keys[1], 2)) 70 | 71 | params.keys.append("irrelevant") 72 | 73 | assert np.all(params.keys == keys) 74 | 75 | 76 | def test_copy(self): 77 | params = Params(("attr1", 1), 78 | ("attr2", 2)) 79 | 80 | params2 = params.copy() 81 | 82 | assert np.all(params.keys == params2.keys) 83 | assert np.all(params.values == params2.values) 84 | 85 | def test_str(self): 86 | params = Params(("attr1", 1)) 87 | s = str(params) 88 | assert "=" in s 89 | 90 | def test_value_assignment(self): 91 | values = np.array([[1,2,3,4], 92 | [5,6,7,8]]) 93 | params = Params(("attr1", values[0]), 94 | ("attr2", values[1])) 95 | 96 | params[:,0] = 0 97 | assert np.all(params[:,0] == 0) 98 | 99 | def test_duplicated_key(self): 100 | with pytest.raises(KeyError): 101 | _ = Params(("attr1", 1), 102 | ("attr1", 2)) 103 | 104 | -------------------------------------------------------------------------------- /Tests/test_ParticleSwarmOptimizer.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test the CosmoHammerSampler module. 3 | 4 | Execute with py.test -v 5 | 6 | """ 7 | import numpy as np 8 | 9 | from cosmoHammer.ChainContext import ChainContext 10 | from cosmoHammer.pso.ParticleSwarmOptimizer import ParticleSwarmOptimizer 11 | from cosmoHammer.pso.ParticleSwarmOptimizer import Particle 12 | 13 | class TestCosmoHammerSampler(object): 14 | ctx = None 15 | params = np.array([[1,2,3],[4,5,6]]) 16 | 17 | def setup(self): 18 | self.ctx=ChainContext(self, self.params) 19 | 20 | def test_Particle(self): 21 | particle = Particle.create(2) 22 | assert particle.fitness == -np.inf 23 | 24 | assert particle == particle.pbest 25 | 26 | particle2 = particle.copy() 27 | assert particle.fitness == particle2.fitness 28 | assert particle.paramCount == particle2.paramCount 29 | assert (particle.position == particle2.position).all() 30 | assert (particle.velocity == particle2.velocity).all() 31 | 32 | 33 | particle.fitness = 1 34 | particle.updatePBest() 35 | 36 | assert particle.pbest.fitness == 1 37 | 38 | 39 | def test_setup(self): 40 | low = np.zeros(2) 41 | high = np.ones(2) 42 | pso = ParticleSwarmOptimizer(None, low, high, 10) 43 | 44 | assert pso.swarm is not None 45 | assert len(pso.swarm) == 10 46 | 47 | position = [part.position for part in pso.swarm] 48 | 49 | assert (position>=low).all() 50 | assert (position<=high).all() 51 | 52 | velocity = [part.velocity for part in pso.swarm] 53 | assert (velocity == np.zeros(2)).all() 54 | 55 | fitness = [part.fitness == 0 for part in pso.swarm] 56 | assert all(fitness) 57 | 58 | assert pso.gbest.fitness == -np.inf 59 | 60 | 61 | def test_optimize(self): 62 | low = np.zeros(2) 63 | high = np.ones(2) 64 | func = lambda p: (-np.random.rand(), None) 65 | pso = ParticleSwarmOptimizer(func, low, high, 10) 66 | 67 | maxIter=10 68 | swarms, gbests = pso.optimize(maxIter) 69 | assert swarms is not None 70 | assert gbests is not None 71 | assert len(swarms) == maxIter 72 | assert len(gbests) == maxIter 73 | 74 | fitness = [part.fitness != 0 for part in pso.swarm] 75 | assert all(fitness) 76 | 77 | assert pso.gbest.fitness != -np.inf 78 | 79 | -------------------------------------------------------------------------------- /Tests/test_PositionGenerators.py: -------------------------------------------------------------------------------- 1 | ##!/usr/bin/env python 2 | """ 3 | Test the TestSampleFileUtil module. 4 | 5 | Execute with py.test -v 6 | 7 | """ 8 | 9 | from cosmoHammer.util import SampleBallPositionGenerator 10 | from cosmoHammer.util import FlatPositionGenerator 11 | 12 | class TestPositionGenerators(object): 13 | 14 | nwalkers = 10 15 | 16 | def setup(self): 17 | self.sampler = DummySampler([1,2], 2, [1,1], self.nwalkers) 18 | 19 | def test_SampleBallPositionGenerator(self): 20 | gen = SampleBallPositionGenerator() 21 | gen.setup(self.sampler) 22 | 23 | pos = gen.generate() 24 | assert pos is not None 25 | assert len(pos) == self.nwalkers 26 | 27 | def test_FlatPositionGenerator(self): 28 | gen = FlatPositionGenerator() 29 | gen.setup(self.sampler) 30 | 31 | pos = gen.generate() 32 | assert pos is not None 33 | assert len(pos) == self.nwalkers 34 | 35 | class DummySampler(object): 36 | 37 | def __init__(self, paramValues, paramCount, paramWidths, nwalkers): 38 | self.paramValues = paramValues 39 | self.paramCount = paramCount 40 | self.paramWidths = paramWidths 41 | self.nwalkers = nwalkers -------------------------------------------------------------------------------- /Tests/test_SampleFileUtil.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test the TestSampleFileUtil module. 3 | 4 | Execute with py.test -v 5 | 6 | """ 7 | from __future__ import print_function, division, absolute_import, unicode_literals 8 | 9 | import tempfile 10 | import os 11 | import numpy 12 | 13 | import cosmoHammer.Constants as c 14 | from cosmoHammer.util.SampleFileUtil import SampleFileUtil 15 | 16 | class TestSampleFileUtil(object): 17 | 18 | prefix = "test" 19 | 20 | def createFileUtil(self): 21 | tempPath = tempfile.mkdtemp() 22 | tempPath = os.path.join(tempPath, self.prefix) 23 | fileUtil = SampleFileUtil(tempPath, True) 24 | return fileUtil, tempPath 25 | 26 | def test_not_master(self): 27 | tempPath = tempfile.mkdtemp() 28 | SampleFileUtil(tempPath, False) 29 | fileList = os.listdir(tempPath) 30 | assert len(fileList) == 0 31 | 32 | 33 | def test_persistBurninValues(self): 34 | fileUtil, tempPath = self.createFileUtil() 35 | 36 | pos = numpy.ones((10,5)) 37 | prob = numpy.zeros(10) 38 | 39 | fileUtil.persistBurninValues(pos, prob, None) 40 | 41 | cPos = numpy.loadtxt(tempPath + c.BURNIN_SUFFIX) 42 | cProb = numpy.loadtxt(tempPath + c.BURNIN_PROB_SUFFIX) 43 | 44 | assert (pos == cPos).all() 45 | assert (prob == cProb).all() 46 | 47 | 48 | def test_persistSamplingValues(self): 49 | fileUtil, tempPath = self.createFileUtil() 50 | 51 | pos = numpy.ones((10,5)) 52 | prob = numpy.zeros(10) 53 | 54 | fileUtil.persistSamplingValues(pos, prob, None) 55 | 56 | cPos = numpy.loadtxt(tempPath + c.FILE_SUFFIX) 57 | cProb = numpy.loadtxt(tempPath + c.PROB_SUFFIX) 58 | 59 | assert (pos == cPos).all() 60 | assert (prob == cProb).all() 61 | 62 | def test_importFromFile(self): 63 | fileUtil, tempPath = self.createFileUtil() 64 | 65 | pos = numpy.ones((10,5)) 66 | prob = numpy.zeros(10) 67 | 68 | fileUtil.persistSamplingValues(pos, prob, None) 69 | 70 | cPos = fileUtil.importFromFile(tempPath + c.FILE_SUFFIX) 71 | cProb = fileUtil.importFromFile(tempPath + c.PROB_SUFFIX) 72 | 73 | assert (pos == cPos).all() 74 | assert (prob == cProb).all() 75 | 76 | def test_storeRandomState(self): 77 | fileUtil, tempPath = self.createFileUtil() 78 | 79 | rstate = numpy.random.mtrand.RandomState() 80 | fileUtil.storeRandomState(tempPath+c.BURNIN_STATE_SUFFIX, rstate) 81 | 82 | cRstate = fileUtil.importRandomState(tempPath+c.BURNIN_STATE_SUFFIX) 83 | 84 | print(rstate.get_state()) 85 | oState = rstate.get_state() 86 | nState = cRstate.get_state() 87 | 88 | assert oState[0] == nState[0] 89 | assert all(oState[1] == nState[1]) 90 | assert oState[2] == nState[2] 91 | assert oState[3] == nState[3] 92 | assert oState[4] == nState[4] 93 | -------------------------------------------------------------------------------- /cosmoHammer/ChainContext.py: -------------------------------------------------------------------------------- 1 | 2 | PARENT_KEY = "key_parent" 3 | PARAMS_KEY = "key_params" 4 | DATA_KEY = "key_data" 5 | 6 | class ChainContext(object): 7 | """ 8 | Context holding a dict to store data and information durring the computation of the likelihood 9 | """ 10 | 11 | def __init__(self, parent, params): 12 | """ 13 | Constructor of the context 14 | """ 15 | 16 | self._data = dict() 17 | self.add(PARENT_KEY, parent) 18 | self.add(PARAMS_KEY, params) 19 | self.add(DATA_KEY, dict()) 20 | 21 | def add(self, key, value): 22 | """ 23 | Adds the value to the context using the key 24 | 25 | :param key: string 26 | key to use 27 | :param value: object 28 | the value to store 29 | 30 | """ 31 | self._data[key] = value 32 | 33 | def remove(self, key): 34 | """ 35 | Removes the value from the context 36 | 37 | :param key: string 38 | key to remove from the context 39 | """ 40 | assert key != None 41 | del(self._data[key]) 42 | 43 | def contains(self, key): 44 | """ 45 | Checks if the key is in the context 46 | 47 | :param key: string 48 | key to check 49 | 50 | :return: True if the key is in the context 51 | """ 52 | return key in self._data 53 | 54 | def get(self, key, default=None): 55 | """ 56 | Returns the value stored in the context at the key or the default value in the 57 | context doesn't contain the key 58 | 59 | :param key: string 60 | key to use 61 | :param default: string 62 | the default value to use if the key is not available 63 | """ 64 | if(self.contains(key)): 65 | return self._data[key] 66 | 67 | return default 68 | 69 | def getParams(self): 70 | """ 71 | Returns the currently processed parameters 72 | 73 | :return: The param of this context 74 | """ 75 | return self.get(PARAMS_KEY) 76 | 77 | def getParent(self): 78 | """ 79 | Returns the parent 80 | 81 | :return: The parent chain of this context 82 | """ 83 | return self.get(PARENT_KEY) 84 | 85 | def getData(self): 86 | """ 87 | Returns the data 88 | 89 | :return: The data of this context 90 | """ 91 | return self.get(DATA_KEY) 92 | -------------------------------------------------------------------------------- /cosmoHammer/ConcurrentMpiCosmoHammerSampler.py: -------------------------------------------------------------------------------- 1 | 2 | import multiprocessing 3 | from cosmoHammer.MpiCosmoHammerSampler import MpiCosmoHammerSampler 4 | 5 | 6 | class ConcurrentMpiCosmoHammerSampler(MpiCosmoHammerSampler): 7 | """ 8 | A sampler implementation extending the mpi sampler in order to allow to 9 | distribute the computation with MPI and using multiprocessing on a single node. 10 | 11 | :param threads: (optional) 12 | The number of threads to use for parallelization. If ``threads == 1``, 13 | then the ``multiprocessing`` module is not used but if 14 | ``threads > 1``, then a ``Pool`` object is created 15 | 16 | :param kwargs: key word arguments passed to the CosmoHammerSampler 17 | 18 | """ 19 | def __init__(self, threads=1, **kwargs): 20 | """ 21 | CosmoHammer sampler implementation 22 | 23 | """ 24 | 25 | self.threads = threads 26 | 27 | super(ConcurrentMpiCosmoHammerSampler, self).__init__(**kwargs) 28 | 29 | 30 | def _getMapFunction(self): 31 | if self.threads > 1: 32 | pool = multiprocessing.Pool(self.threads) 33 | return pool.map 34 | else: 35 | return map 36 | -------------------------------------------------------------------------------- /cosmoHammer/Constants.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | """ 3 | Some constants used by the samplers 4 | 5 | """ 6 | 7 | FILE_SUFFIX = ".out" 8 | 9 | LOG_FILE_SUFFIX = ".log" 10 | 11 | PROB_PREFIX = "prob" 12 | 13 | BURNIN_PREFIX = "burnin" 14 | 15 | STATE_PREFIX = "state" 16 | 17 | PROB_SUFFIX = PROB_PREFIX+FILE_SUFFIX 18 | 19 | BURNIN_SUFFIX = BURNIN_PREFIX+FILE_SUFFIX 20 | 21 | BURNIN_PROB_SUFFIX = BURNIN_PREFIX+PROB_PREFIX+FILE_SUFFIX 22 | 23 | BURNIN_STATE_SUFFIX = BURNIN_PREFIX+STATE_PREFIX+FILE_SUFFIX 24 | -------------------------------------------------------------------------------- /cosmoHammer/CosmoHammerSampler.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division, absolute_import, unicode_literals 2 | 3 | import emcee 4 | import numpy as np 5 | import logging 6 | import time 7 | 8 | import cosmoHammer 9 | import cosmoHammer.Constants as c 10 | 11 | from cosmoHammer import getLogger 12 | from cosmoHammer.util import SampleFileUtil 13 | from cosmoHammer.util import SampleBallPositionGenerator 14 | from cosmoHammer.util.IterationStopCriteriaStrategy import IterationStopCriteriaStrategy 15 | 16 | 17 | 18 | class CosmoHammerSampler(object): 19 | """ 20 | A complete sampler implementation taking care of correct setup, chain burn in and sampling. 21 | 22 | :param params: the parameter of the priors 23 | :param likelihoodComputationChain: the callable computation chain 24 | :param filePrefix: the prefix for the log and output files 25 | :param walkersRatio: the ratio of walkers and the count of sampled parameters 26 | :param burninIterations: number of iteration for burn in 27 | :param sampleIterations: number of iteration to sample 28 | :param stopCriteriaStrategy: the strategy to stop the sampling. 29 | Default is None an then IterationStopCriteriaStrategy is used 30 | :param initPositionGenerator: the generator for the init walker position. 31 | Default is None an then SampleBallPositionGenerator is used 32 | :param storageUtil: util used to store the results 33 | :param threadCount: The count of threads to be used for the computation. Default is 1 34 | :param reuseBurnin: Flag if the burn in should be reused. 35 | If true the values will be read from the file System. Default is False 36 | 37 | """ 38 | 39 | def __init__(self, params, likelihoodComputationChain, filePrefix, walkersRatio, burninIterations, 40 | sampleIterations, stopCriteriaStrategy=None, initPositionGenerator=None, 41 | storageUtil=None, threadCount=1, reuseBurnin=False, logLevel=logging.INFO, pool=None): 42 | """ 43 | CosmoHammer sampler implementation 44 | 45 | """ 46 | self.params = params 47 | self.likelihoodComputationChain = likelihoodComputationChain 48 | self.walkersRatio = walkersRatio 49 | self.reuseBurnin = reuseBurnin 50 | self.filePrefix = filePrefix 51 | self.threadCount = threadCount 52 | self.paramCount = len(self.paramValues) 53 | self.nwalkers = self.paramCount*walkersRatio 54 | self.burninIterations = burninIterations 55 | self.sampleIterations = sampleIterations 56 | 57 | assert likelihoodComputationChain is not None, "The sampler needs a chain" 58 | assert sampleIterations > 0, "CosmoHammer needs to sample for at least one iterations" 59 | 60 | if not hasattr(self.likelihoodComputationChain, "params"): 61 | self.likelihoodComputationChain.params = params 62 | 63 | # setting up the logging 64 | self._configureLogging(filePrefix+c.LOG_FILE_SUFFIX, logLevel) 65 | 66 | if self.isMaster(): self.log("Using CosmoHammer "+str(cosmoHammer.__version__)) 67 | 68 | # The sampler object 69 | self._sampler = self.createEmceeSampler(likelihoodComputationChain, pool=pool) 70 | 71 | if(storageUtil is None): 72 | storageUtil = self.createSampleFileUtil() 73 | 74 | self.storageUtil = storageUtil 75 | 76 | if(stopCriteriaStrategy is None): 77 | stopCriteriaStrategy = self.createStopCriteriaStrategy() 78 | 79 | stopCriteriaStrategy.setup(self) 80 | self.stopCriteriaStrategy = stopCriteriaStrategy 81 | 82 | if(initPositionGenerator is None): 83 | initPositionGenerator = self.createInitPositionGenerator() 84 | 85 | initPositionGenerator.setup(self) 86 | self.initPositionGenerator = initPositionGenerator 87 | 88 | 89 | def _configureLogging(self, filename, logLevel): 90 | logger = getLogger() 91 | logger.setLevel(logLevel) 92 | fh = logging.FileHandler(filename, "w") 93 | fh.setLevel(logLevel) 94 | # create console handler with a higher log level 95 | ch = logging.StreamHandler() 96 | ch.setLevel(logging.ERROR) 97 | # create formatter and add it to the handlers 98 | formatter = logging.Formatter('%(asctime)s %(levelname)s:%(message)s') 99 | fh.setFormatter(formatter) 100 | ch.setFormatter(formatter) 101 | # add the handlers to the logger 102 | for handler in logger.handlers[:]: 103 | logger.removeHandler(handler) 104 | logger.addHandler(fh) 105 | logger.addHandler(ch) 106 | # logging.basicConfig(format='%(asctime)s %(levelname)s:%(message)s', 107 | # filename=filename, filemode='w', level=logLevel) 108 | 109 | 110 | def createStopCriteriaStrategy(self): 111 | """ 112 | Returns a new instance of a stop criteria stategy 113 | """ 114 | return IterationStopCriteriaStrategy() 115 | 116 | def createSampleFileUtil(self): 117 | """ 118 | Returns a new instance of a File Util 119 | """ 120 | return SampleFileUtil(self.filePrefix, reuseBurnin=self.reuseBurnin) 121 | 122 | def createInitPositionGenerator(self): 123 | """ 124 | Returns a new instance of a Init Position Generator 125 | """ 126 | return SampleBallPositionGenerator() 127 | 128 | @property 129 | def paramValues(self): 130 | return self.params[:,0] 131 | 132 | @property 133 | def paramWidths(self): 134 | return self.params[:,3] 135 | 136 | def startSampling(self): 137 | """ 138 | Launches the sampling 139 | """ 140 | try: 141 | if self.isMaster(): self.log(self.__str__()) 142 | if(self.burninIterations>0): 143 | 144 | if(self.reuseBurnin): 145 | pos, prob, rstate = self.loadBurnin() 146 | datas = [None]*len(pos) 147 | 148 | else: 149 | pos, prob, rstate, datas = self.startSampleBurnin() 150 | else: 151 | pos = self.createInitPos() 152 | prob = None 153 | rstate = None 154 | datas = None 155 | # Starting from the final position in the burn-in chain, sample for 1000 156 | # steps. 157 | self.log("start sampling after burn in") 158 | start = time.time() 159 | self.sample(pos, prob, rstate, datas) 160 | end = time.time() 161 | self.log("sampling done! Took: " + str(round(end-start,4))+"s") 162 | 163 | # Print out the mean acceptance fraction. In general, acceptance_fraction 164 | # has an entry for each walker 165 | self.log("Mean acceptance fraction:"+ str(round(np.mean(self._sampler.acceptance_fraction), 4))) 166 | finally: 167 | if self._sampler.pool is not None: 168 | try: 169 | self._sampler.pool.close() 170 | except AttributeError: 171 | pass 172 | try: 173 | self.storageUtil.close() 174 | except AttributeError: 175 | pass 176 | 177 | 178 | 179 | def loadBurnin(self): 180 | """ 181 | loads the burn in form the file system 182 | """ 183 | self.log("reusing previous burn in") 184 | 185 | pos = self.storageUtil.importFromFile(self.filePrefix+c.BURNIN_SUFFIX)[-self.nwalkers:] 186 | 187 | prob = self.storageUtil.importFromFile(self.filePrefix+c.BURNIN_PROB_SUFFIX)[-self.nwalkers:] 188 | 189 | rstate= self.storageUtil.importRandomState(self.filePrefix+c.BURNIN_STATE_SUFFIX) 190 | 191 | self.log("loading done") 192 | return pos, prob, rstate 193 | 194 | 195 | def startSampleBurnin(self): 196 | """ 197 | Runs the sampler for the burn in 198 | """ 199 | self.log("start burn in") 200 | start = time.time() 201 | p0 = self.createInitPos() 202 | pos, prob, rstate, data = self.sampleBurnin(p0) 203 | end = time.time() 204 | self.log("burn in sampling done! Took: " + str(round(end-start,4))+"s") 205 | self.log("Mean acceptance fraction for burn in:" + str(round(np.mean(self._sampler.acceptance_fraction), 4))) 206 | 207 | self.resetSampler() 208 | 209 | return pos, prob, rstate, data 210 | 211 | 212 | def resetSampler(self): 213 | """ 214 | Resets the emcee sampler in the master node 215 | """ 216 | if self.isMaster(): 217 | self.log("Reseting emcee sampler") 218 | # Reset the chain to remove the burn-in samples. 219 | self._sampler.reset() 220 | 221 | 222 | 223 | def sampleBurnin(self, p0): 224 | """ 225 | Run the emcee sampler for the burnin to create walker which are independent form their starting position 226 | """ 227 | 228 | counter = 1 229 | for pos, prob, rstate, datas in self._sampler.sample(p0, iterations=self.burninIterations): 230 | if self.isMaster(): 231 | self.storageUtil.persistBurninValues(pos, prob, datas) 232 | if(counter%10==0): 233 | self.log("Iteration finished:" + str(counter)) 234 | 235 | counter = counter + 1 236 | 237 | if self.isMaster(): 238 | self.log("storing random state") 239 | self.storageUtil.storeRandomState(self.filePrefix+c.BURNIN_STATE_SUFFIX, rstate) 240 | 241 | return pos, prob, rstate, datas 242 | 243 | 244 | def sample(self, burninPos, burninProb=None, burninRstate=None, datas=None): 245 | """ 246 | Starts the sampling process 247 | """ 248 | counter = 1 249 | for pos, prob, _, datas in self._sampler.sample(burninPos, lnprob0=burninProb, rstate0=burninRstate, 250 | blobs0=datas, iterations=self.sampleIterations): 251 | if self.isMaster(): 252 | self.log("Iteration done. Persisting", logging.DEBUG) 253 | self.storageUtil.persistSamplingValues(pos, prob, datas) 254 | 255 | if(self.stopCriteriaStrategy.hasFinished()): 256 | break 257 | 258 | if(counter%10==0): 259 | self.log("Iteration finished:" + str(counter)) 260 | 261 | counter = counter + 1 262 | 263 | 264 | def isMaster(self): 265 | """ 266 | Returns True. Can be overridden for multitasking i.e. with MPI 267 | """ 268 | return True 269 | 270 | def log(self, message, level=logging.INFO): 271 | """ 272 | Logs a message to the logfile 273 | """ 274 | getLogger().log(level, message) 275 | 276 | 277 | def createEmceeSampler(self, callable, **kwargs): 278 | """ 279 | Factory method to create the emcee sampler 280 | """ 281 | if self.isMaster(): self.log("Using emcee "+str(emcee.__version__)) 282 | return emcee.EnsembleSampler(self.nwalkers, 283 | self.paramCount, 284 | callable, 285 | threads=self.threadCount, 286 | **kwargs) 287 | 288 | def createInitPos(self): 289 | """ 290 | Factory method to create initial positions 291 | """ 292 | return self.initPositionGenerator.generate() 293 | 294 | 295 | def getChain(self): 296 | """ 297 | Returns the sample chain 298 | """ 299 | return self._sampler.chain 300 | 301 | def __str__(self, *args, **kwargs): 302 | """ 303 | Returns the string representation of the sampler config 304 | """ 305 | desc = "Sampler: " + str(type(self))+"\n" \ 306 | "configuration: \n" \ 307 | " Params: " +str(self.paramValues)+"\n" \ 308 | " Burnin iterations: " +str(self.burninIterations)+"\n" \ 309 | " Samples iterations: " +str(self.sampleIterations)+"\n" \ 310 | " Walkers ratio: " +str(self.walkersRatio)+"\n" \ 311 | " Reusing burn in: " +str(self.reuseBurnin)+"\n" \ 312 | " init pos generator: " +str(self.initPositionGenerator)+"\n" \ 313 | " stop criteria: " +str(self.stopCriteriaStrategy)+"\n" \ 314 | " storage util: " +str(self.storageUtil)+"\n" \ 315 | "likelihoodComputationChain: \n" + str(self.likelihoodComputationChain) \ 316 | +"\n" 317 | 318 | return desc 319 | -------------------------------------------------------------------------------- /cosmoHammer/LikelihoodComputationChain.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division, absolute_import, unicode_literals 2 | import numpy as np 3 | from collections import deque 4 | import os 5 | 6 | from cosmoHammer.ChainContext import ChainContext 7 | from cosmoHammer.exceptions import LikelihoodComputationException 8 | from cosmoHammer import getLogger 9 | from cosmoHammer.util import Params 10 | 11 | class LikelihoodComputationChain(object): 12 | """ 13 | Implementation of a likelihood computation chain. 14 | """ 15 | 16 | def __init__(self, min=None, max=None): 17 | """ 18 | Constructor for the likelihood chain 19 | 20 | :param min: array 21 | lower bound for the parameters 22 | :param max: array 23 | upper bound for the parameters 24 | """ 25 | self.min = min 26 | self.max = max 27 | self._likelihoodModules = deque(); 28 | self._coreModules = deque(); 29 | 30 | 31 | def getCoreModules(self): 32 | """pointer to the likelihood module list """ 33 | return self._coreModules 34 | 35 | def getLikelihoodModules(self): 36 | """pointer to the core module list """ 37 | return self._likelihoodModules 38 | 39 | def addLikelihoodModule(self, module): 40 | """ 41 | adds a module to the likelihood module list 42 | 43 | :param module: callable 44 | the callable module to add for the likelihood computation 45 | """ 46 | self.getLikelihoodModules().append(module) 47 | 48 | def addCoreModule(self, module): 49 | """ 50 | adds a module to the likelihood module list 51 | 52 | :param module: callable 53 | the callable module to add for the computation of the data 54 | """ 55 | self.getCoreModules().append(module) 56 | 57 | 58 | def isValid(self, p): 59 | """ 60 | checks if the given parameters are valid 61 | """ 62 | if(self.min is not None): 63 | for i in range(len(p)): 64 | if (p[i]self.max[i]): 71 | getLogger().debug("Params out of bounds i="+str(i)+" params "+str(p)) 72 | return False 73 | 74 | return True 75 | 76 | 77 | def setup(self): 78 | """sets up the chain and its modules """ 79 | for cModule in self.getCoreModules(): 80 | cModule.setup() 81 | 82 | for cModule in self.getLikelihoodModules(): 83 | cModule.setup() 84 | 85 | 86 | def __call__(self, p): 87 | """ 88 | Computes the log likelihood by calling all the core and likelihood modules. 89 | 90 | :param p: the parameter array for which the likelihood should be evaluated 91 | 92 | :return: the current likelihood and a dict with additional data 93 | """ 94 | try: 95 | getLogger().debug("pid: %s, processing: %s"%(os.getpid(), p)) 96 | if not self.isValid(p): 97 | raise LikelihoodComputationException() 98 | 99 | ctx = self.createChainContext(p) 100 | 101 | self.invokeCoreModules(ctx) 102 | 103 | likelihood = self.computeLikelihoods(ctx) 104 | getLogger().debug("pid: %s, processed. Returning: %s"%(os.getpid(), likelihood)) 105 | return likelihood, ctx.getData() 106 | except LikelihoodComputationException: 107 | getLogger().debug("pid: %s, processed. Returning: %s"%(os.getpid(), -np.inf)) 108 | return -np.inf, [] 109 | 110 | def createChainContext(self, p): 111 | """ 112 | Returns a new instance of a chain context 113 | """ 114 | try: 115 | p = Params(*zip(self.params.keys, p)) 116 | except Exception: 117 | # no params or params has no keys 118 | pass 119 | return ChainContext(self, p) 120 | 121 | def invokeCoreModules(self, ctx): 122 | """ 123 | Iterates thru the core modules and invokes them 124 | """ 125 | for cModule in self.getCoreModules(): 126 | self.invokeCoreModule(cModule, ctx) 127 | 128 | 129 | def invokeCoreModule(self, coreModule, ctx): 130 | """ 131 | Invokes the given module with the given ChainContext 132 | """ 133 | coreModule(ctx) 134 | 135 | 136 | def computeLikelihoods(self, ctx): 137 | """ 138 | Computes the likelihoods by iterating thru all the modules. 139 | Sums up the log likelihoods. 140 | """ 141 | likelihood = 0 142 | 143 | for lModule in self.getLikelihoodModules(): 144 | likelihood += self.invokeLikelihoodModule(lModule, ctx) 145 | 146 | return likelihood 147 | 148 | def invokeLikelihoodModule(self, likelihoodModule, ctx): 149 | """ 150 | Invokes the given module with the given ChainContext 151 | """ 152 | return likelihoodModule.computeLikelihood(ctx) 153 | 154 | def __str__(self, *args, **kwargs): 155 | s = "Core Modules: \n " 156 | s = s + "\n ".join([type(o).__name__ for o in self.getCoreModules()]) 157 | 158 | s = s + "\nLikelihood Modules: \n " 159 | s = s + "\n ".join([type(o).__name__ for o in self.getLikelihoodModules()]) 160 | return s 161 | -------------------------------------------------------------------------------- /cosmoHammer/MpiCosmoHammerSampler.py: -------------------------------------------------------------------------------- 1 | 2 | from cosmoHammer import CosmoHammerSampler 3 | 4 | from cosmoHammer.util.SampleFileUtil import SampleFileUtil 5 | from cosmoHammer.util.MpiUtil import MpiPool, mpiBCast 6 | 7 | class MpiCosmoHammerSampler(CosmoHammerSampler): 8 | """ 9 | A sampler implementation extending the regular sampler in order to allow for distributing 10 | the computation with MPI. 11 | 12 | :param kwargs: 13 | key word arguments passed to the CosmoHammerSampler 14 | 15 | """ 16 | def __init__(self, **kwargs): 17 | """ 18 | CosmoHammer sampler implementation 19 | 20 | """ 21 | self.pool = MpiPool(self._getMapFunction()) 22 | self.rank = self.pool.rank 23 | 24 | super(MpiCosmoHammerSampler, self).__init__(pool=self.pool, **kwargs) 25 | 26 | 27 | 28 | def _getMapFunction(self): 29 | """ 30 | Returns the build in map function 31 | """ 32 | return map 33 | 34 | def createSampleFileUtil(self): 35 | """ 36 | Returns a new instance of a File Util 37 | """ 38 | return SampleFileUtil(self.filePrefix, self.isMaster(), reuseBurnin=self.reuseBurnin) 39 | 40 | 41 | def sampleBurnin(self, p0): 42 | """ 43 | Starts the sampling process. The master node (mpi rank = 0) persists the result to the disk 44 | """ 45 | p0 = mpiBCast(p0) 46 | 47 | self.log("MPI Process rank "+ str(self.rank)+" starts sampling") 48 | return super(MpiCosmoHammerSampler, self).sampleBurnin(p0); 49 | 50 | def sample(self, burninPos, burninProb, burninRstate, datas): 51 | """ 52 | Starts the sampling process. The master node (mpi rank = 0) persists the result to the disk 53 | """ 54 | burninPos = mpiBCast(burninPos) 55 | burninProb = mpiBCast(burninProb) 56 | burninRstate = mpiBCast(burninRstate) 57 | 58 | self.log("MPI Process rank "+ str(self.rank)+" starts sampling") 59 | super(MpiCosmoHammerSampler, self).sample(burninPos, burninProb, burninRstate, datas); 60 | 61 | 62 | def loadBurnin(self): 63 | """ 64 | loads the burn in form the file system 65 | """ 66 | if(self.isMaster()): 67 | pos, prob, rstate = super(MpiCosmoHammerSampler, self).loadBurnin() 68 | else: 69 | pos, prob, rstate = [] 70 | 71 | pos = mpiBCast(pos) 72 | prob = mpiBCast(prob) 73 | rstate = mpiBCast(rstate) 74 | 75 | self.log("loading done") 76 | return pos, prob, rstate 77 | 78 | def createInitPos(self): 79 | """ 80 | Factory method to create initial positions 81 | """ 82 | #bcast the positions to ensure that all mpi nodes start at the same position 83 | return mpiBCast(super(MpiCosmoHammerSampler, self).createInitPos()) 84 | 85 | 86 | def isMaster(self): 87 | """ 88 | Returns true if the rank is 0 89 | """ 90 | return self.pool.isMaster() -------------------------------------------------------------------------------- /cosmoHammer/__init__.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | # Author: Joel Akeret 4 | # Contact: jakeret@phys.ethz.ch 5 | """ 6 | This is the CosmoHammer package. 7 | """ 8 | 9 | __version__ = '0.6.1' 10 | __author__ = 'Joel Akeret' 11 | __credits__ = 'Institute for Astronomy ETHZ, Institute of 4D Technologies FHNW' 12 | 13 | def getLogger(): 14 | return logging.getLogger(__name__) 15 | 16 | 17 | from cosmoHammer.CosmoHammerSampler import CosmoHammerSampler 18 | from cosmoHammer.MpiCosmoHammerSampler import MpiCosmoHammerSampler 19 | from cosmoHammer.ConcurrentMpiCosmoHammerSampler import ConcurrentMpiCosmoHammerSampler 20 | 21 | from cosmoHammer.LikelihoodComputationChain import LikelihoodComputationChain 22 | from cosmoHammer.ChainContext import ChainContext 23 | 24 | from cosmoHammer.pso.ParticleSwarmOptimizer import ParticleSwarmOptimizer 25 | from cosmoHammer.pso.MpiParticleSwarmOptimizer import MpiParticleSwarmOptimizer 26 | -------------------------------------------------------------------------------- /cosmoHammer/exceptions.py: -------------------------------------------------------------------------------- 1 | #Created on Nov 11, 2013 2 | #author: jakeret 3 | 4 | 5 | class LikelihoodComputationException(Exception): 6 | ''' 7 | Exception for likelihood computation 8 | ''' 9 | def __init__(self): 10 | ''' 11 | Constructor 12 | ''' 13 | pass 14 | 15 | class InvalidLikelihoodException(LikelihoodComputationException): 16 | """ 17 | Exception for invalid likelihoods e.g. -loglike >= 0.0 18 | """ 19 | 20 | def __init__(self, params=None): 21 | self.params = params 22 | 23 | -------------------------------------------------------------------------------- /cosmoHammer/modules/MultivarianteGaussianModule.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division, absolute_import, unicode_literals 2 | 3 | import numpy as np 4 | from cosmoHammer import getLogger 5 | 6 | class MultivarianteGaussianModule(object): 7 | """ 8 | Chain for computing the likelihood of a multivariante gaussian distribution 9 | """ 10 | def __init__(self, icov, mu): 11 | self.icov = icov 12 | self.mu = mu 13 | 14 | 15 | def computeLikelihood(self, ctx): 16 | x = ctx.getParams() 17 | diff = x-self.mu 18 | return -np.dot(diff,np.dot(self.icov,diff))/2.0 19 | 20 | def setup(self): 21 | getLogger().info("Multivariante Gaussian setup") 22 | 23 | 24 | -------------------------------------------------------------------------------- /cosmoHammer/modules/PseudoCmbModule.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division, absolute_import, unicode_literals 2 | 3 | import numpy as np 4 | from cosmoHammer import getLogger 5 | 6 | 7 | #the real means.. 8 | WMAP7_MEANS = [70.704, 0.02256, 0.1115, 2.18474E-09, 0.9688, 0.08920] 9 | 10 | # ...and non-trivial covariance matrix. 11 | _cov = np.array([[6.11E+00, 0, 0, 0, 0, 0], 12 | [7.19E-04, 3.26E-07, 0, 0, 0, 0], 13 | [-1.19E-02, -3.37E-07, 3.14E-05, 0, 0, 0], 14 | [-3.56E-11, 1.43E-14, 1.76E-13, 5.96E-21, 0, 0], 15 | [2.01E-02, 6.37E-06, -2.13E-05, 3.66E-13, 1.90E-04, 0], 16 | [1.10E-02, 2.36E-06, -1.92E-05, 8.70E-13, 7.32E-05, 2.23E-04]]) 17 | _cov += _cov.T - np.diag(_cov.diagonal()) 18 | 19 | # Invert the covariance matrix 20 | WMAP7_ICOV = np.linalg.inv(_cov) 21 | 22 | 23 | class PseudoCmbModule(object): 24 | """ 25 | Chain for computing the likelihood of a multivariante gaussian distribution 26 | """ 27 | def __init__(self, icov=WMAP7_ICOV, mu=WMAP7_MEANS, min_sz=0, max_sz=2): 28 | self.icov = icov 29 | self.mu = mu 30 | self.a = min_sz 31 | self.b = max_sz 32 | 33 | 34 | def computeLikelihood(self, ctx): 35 | x = ctx.getParams() 36 | 37 | diff = x[:6]-self.mu 38 | 39 | lnprob = -np.dot(diff,np.dot(self.icov,diff))/2.0 40 | 41 | lnprob -= np.log(self.b-self.a) 42 | return lnprob 43 | 44 | def setup(self): 45 | getLogger().info("Pseudo cmb setup") 46 | 47 | 48 | -------------------------------------------------------------------------------- /cosmoHammer/modules/RosenbrockModule.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division, absolute_import, unicode_literals 2 | from cosmoHammer import getLogger 3 | 4 | class RosenbrockModule(object): 5 | """ 6 | A module for the computation of the rosenbrock likelihood 7 | """ 8 | 9 | def __init__(self): 10 | self.a1 = 100.0 11 | self.a2 = 20.0 12 | 13 | def computeLikelihood(self, ctx): 14 | p = ctx.getParams() 15 | return -(self.a1 * (p.y - p.x**2)**2 + (1 - p.x)**2) / self.a2 16 | 17 | def setup(self): 18 | getLogger().info("Rosenbrock setup") -------------------------------------------------------------------------------- /cosmoHammer/modules/__init__.py: -------------------------------------------------------------------------------- 1 | from cosmoHammer.modules.RosenbrockModule import RosenbrockModule 2 | from cosmoHammer.modules.MultivarianteGaussianModule import MultivarianteGaussianModule 3 | from cosmoHammer.modules.PseudoCmbModule import PseudoCmbModule 4 | -------------------------------------------------------------------------------- /cosmoHammer/pso/BestFitPositionGenerator.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Created on Oct 22, 2013 3 | 4 | @author: J.Akeret 5 | ''' 6 | from __future__ import print_function, division, absolute_import, unicode_literals 7 | 8 | import numpy 9 | from cosmoHammer.pso.ParticleSwarmOptimizer import ParticleSwarmOptimizer 10 | from cosmoHammer.pso.CurvatureFitter import CurvatureFitter 11 | 12 | class BestFitPositionGenerator(object): 13 | ''' 14 | A position generator which uses a particle swarm optimization algorithm 15 | to find the best fit value and the collapsed swarm to estimate the curvature matrix 16 | at that point. The optimization process can be parallelized over 17 | MPI and python multiprocessing. 18 | 19 | :param mpi: True if a MPI implementation of the PSO should be used. Default is False 20 | :param threads: Number of multiprocessing thread that should be started. Default is 1 21 | :param particleCount: Number of particle to use for the optimization. If none 22 | the number is derrived according to the size of the parameter space. Default is None 23 | :param maxIter: the maximal number of iterations. Default will be set to MAX_PSO_ITER 24 | 25 | ''' 26 | 27 | MAX_PSO_ITER = 1000 28 | 29 | MIN_PARTICLE_COUNT = 20 30 | 31 | BEST_FILE_NAME = "_best_fit_global.out" 32 | 33 | BEST_INFO_FILE_NAME = "_best_fit_info.out" 34 | 35 | def __init__(self, mpi=False, threads=1, particleCount=None, maxIter=None): 36 | """ 37 | default constructor 38 | """ 39 | self.mpi = mpi 40 | self.threads = threads 41 | self.particleCount = particleCount 42 | 43 | self.maxIter = maxIter 44 | if(self.maxIter is None): 45 | self.maxIter = self.MAX_PSO_ITER 46 | 47 | 48 | def setup(self, sampler): 49 | """ 50 | setup the generator 51 | """ 52 | self.sampler = sampler 53 | 54 | def generate(self): 55 | """ 56 | generates the positions by running the PSO and using the chain's min and max and then calling 57 | the paraboloid fitter in order to estimate the covariance matrix. The position will then 58 | be generated by drawing position from a multivariant gaussian distribution defined by 59 | the best fit and the estimated covariance matrix. 60 | The progress of the PSO is successively stored to a the disk. 61 | """ 62 | 63 | chain = self.sampler.likelihoodComputationChain 64 | 65 | if(self.particleCount is None): 66 | self.particleCount = self.get_particle_count() 67 | 68 | if(self.mpi): 69 | #only import when needed in order to avoid an error in case mpi4py is not installed 70 | from cosmoHammer.sampler.util.pso.MpiParticleSwarmOptimizer import MpiParticleSwarmOptimizer 71 | 72 | pso = MpiParticleSwarmOptimizer(chain, chain.min, chain.max, self.particleCount, threads=self.threads) 73 | else: 74 | pso = ParticleSwarmOptimizer(chain, chain.min, chain.max, self.particleCount, threads=self.threads) 75 | 76 | swarm = [] 77 | with open(self.sampler.filePrefix+self.BEST_FILE_NAME, "w") as f: 78 | for i, cswarm in enumerate(pso.sample(self.maxIter)): 79 | self._save(f, i, pso) 80 | if(i>=0): 81 | swarm.append(cswarm) 82 | 83 | self._save(f, i+1, pso) 84 | self.sampler.log("Best fit found after %s iteration: %f %s"%(i+1, pso.gbest.fitness, pso.gbest.position)) 85 | 86 | 87 | fswarm = [] 88 | for i in range(1,5): 89 | fswarm += swarm[-i] 90 | 91 | self._storeSwarm(fswarm) 92 | 93 | fitter = CurvatureFitter(fswarm, pso.gbest) 94 | mean, _cov = fitter.fit() 95 | 96 | self._storeFit(pso.gbest, _cov) 97 | 98 | # dim = len(mean)-1 99 | # sigma = 0.4 100 | # factor = _cov[dim,dim] / numpy.sqrt(sigma) 101 | # _cov[:-1,dim] = _cov[:-1,dim]/factor 102 | # _cov[dim,:-1] = _cov[dim,:-1]/factor 103 | # _cov[dim,dim] = sigma 104 | # print "" 105 | # fitter = ParaboloidFitter(fswarm, pso.gbest, True) 106 | # mean, _cov = fitter.fit() 107 | sigma = numpy.sqrt(numpy.diag(_cov)) 108 | print("=> found sigma:", sigma) 109 | 110 | # fitter = ParaboloidFitter(pso.swarm, pso.gbest) 111 | # mean, _cov = fitter.fit() 112 | # sigma = numpy.sqrt(numpy.diag(_cov)) 113 | # print "=> found sigma:", sigma 114 | 115 | samples = numpy.random.multivariate_normal(mean, _cov, self.sampler.nwalkers) 116 | # print numpy.std(samples, axis=0) 117 | return samples 118 | 119 | # self.sampler.paramValues = pso.gbest.position 120 | # self.sampler.paramWidths = self.sampler.paramValues * self.SPREAD_FACTOR 121 | # generator = SampleBallPositionGenerator() 122 | # generator.setup(self.sampler) 123 | # return generator.generate() 124 | 125 | 126 | 127 | 128 | def get_particle_count(self): 129 | """ 130 | Generates the number of particles to use by using a logarithmic function of the parameter count 131 | """ 132 | return int(self.MIN_PARTICLE_COUNT + self.MIN_PARTICLE_COUNT*numpy.log(self.sampler.paramCount)) 133 | 134 | def __str__(self, *args, **kwargs): 135 | return "BestFitPositionGenerator: particleCount=%s, mpi=%s, threads=%s"%(self.particleCount, self.mpi, self.threads) 136 | 137 | def _save(self, f, i, pso): 138 | if(pso.isMaster()): 139 | particle = pso.gbest 140 | f.write("%s\t%f\t"%(i, particle.fitness)) 141 | f.write("\t".join([str(p) for p in particle.position])) 142 | f.write("\n") 143 | f.flush() 144 | 145 | def _storeFit(self, gbest, _cov): 146 | with open(self.sampler.filePrefix+self.BEST_INFO_FILE_NAME, "w") as f: 147 | f.write("#Best fit: %s\n"%(gbest.fitness)) 148 | f.write(", ".join([str(i) for i in gbest.position])) 149 | f.write("\n#Estimated covariance matrix:\n") 150 | for row in _cov: 151 | f.write ("[" + ", ".join([str(i) for i in row]) + "]\n") 152 | 153 | def _storeSwarm(self, swarm): 154 | with open(self.sampler.filePrefix+"swarm", "w") as f: 155 | for particle in swarm: 156 | f.write(str(particle.fitness)) 157 | f.write("\t") 158 | f.write("\t".join([str(p) for p in particle.position])) 159 | f.write("\n") -------------------------------------------------------------------------------- /cosmoHammer/pso/CurvatureFitter.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Created on Oct 30, 2013 3 | 4 | @author: J.Akeret 5 | ''' 6 | from __future__ import print_function, division, absolute_import, \ 7 | unicode_literals 8 | 9 | from numpy.linalg.linalg import norm 10 | from scipy.optimize.minpack import leastsq 11 | from scipy.optimize import minimize 12 | import numpy 13 | import sys 14 | 15 | def parabola(p, theta, thetabar=1): 16 | """ 17 | Computation of the paraboloid for the given curvature matrix and samples. 18 | :param p: list of samples 19 | :param theta: vector containing the lower triangle of the matrix and the offset from the true mean 20 | 21 | :return: vector y from f(x,p) 22 | """ 23 | 24 | leng, dim = theta.shape 25 | corrm, v, mu = transform(dim, p) 26 | 27 | # _cov = corr2cov(corrm, v) 28 | # R = numpy.linalg.inv(_cov) 29 | if(any(v==0)): 30 | vi = v 31 | else: 32 | vi = numpy.diag(1/v) 33 | 34 | R = numpy.dot(vi, numpy.dot(numpy.linalg.inv(corrm), vi)) 35 | 36 | v = numpy.zeros(leng) 37 | for i,thetaj in enumerate(theta): 38 | thetaj = thetaj / thetabar 39 | v[i] = numpy.dot(thetaj.T,numpy.dot(R, thetaj)) #+ numpy.dot(thetaj, mu) 40 | 41 | return numpy.array(v) 42 | 43 | 44 | def errfunc(p,theta,delta, thetabar): 45 | """ 46 | Error function defined by f(theta) - delta 47 | :param p: list of samples 48 | :param theta: the curvature matrix. see parabola def 49 | :param delta: the measured values 50 | """ 51 | return parabola(p, theta, thetabar) - delta 52 | 53 | def errfunc2(p,theta,delta, thetabar): 54 | """ 55 | Error function defined by f(theta) - delta 56 | :param p: the curvature matrix. see parabola def 57 | :param theta: list of samples 58 | :param delta: the measured values 59 | """ 60 | return sum((parabola(p, theta, thetabar) - delta)**2) 61 | 62 | def transform(dim, p): 63 | """ 64 | Transforms a vector containg the lower triangle of a matrix into a symmetric matrix 65 | 66 | :param p: the vector 67 | 68 | :return: the matrix and left over values 69 | """ 70 | corrm = numpy.identity(dim) 71 | k=0 72 | for i in range(1,dim): 73 | for j in range(0,i): 74 | corrm[i,j]= p[k] 75 | k +=1 76 | 77 | corrm += corrm.T - numpy.diag(corrm.diagonal()) 78 | 79 | vars = p[k:k+dim] 80 | mu = p[k+dim:] 81 | 82 | return corrm, vars, mu 83 | 84 | def reverse(dim, R, vars): 85 | """ 86 | Transforms a symmetric matrix into a vector containig the lower triangle 87 | 88 | :param R: the symmetric matrix 89 | 90 | :return: the vector 91 | """ 92 | p = numpy.zeros(dim*(dim-1)/2) 93 | k=0 94 | for i in range(1,dim): 95 | for j in range(0,i): 96 | p[k] = R[i,j] 97 | k +=1 98 | 99 | p = numpy.append(p, vars) 100 | return numpy.append(p, numpy.zeros_like(vars)) 101 | 102 | def bound(x): 103 | dim = int(1./2 * (numpy.sqrt(8*len(x)+1)-1)) 104 | _, stds, _ = transform(dim, x) 105 | return stds 106 | 107 | 108 | class CurvatureFitter(object): 109 | ''' 110 | Fits a paraboloid centered around the global best fit of the PSO by estimating a curvarture 111 | matrix with the particle given in the swarm 112 | 113 | :param swarm: list of particles 114 | :param gbest: the global best particle at the last iteration 115 | ''' 116 | 117 | 118 | def __init__(self, swarm, gbest): 119 | ''' 120 | Constructor 121 | ''' 122 | self.swarm = swarm 123 | self.gbest = gbest 124 | 125 | def fit(self): 126 | """ 127 | Fits the paraboloid to the swarm particles 128 | 129 | :return: the mean = global best position and the estimated covariance matrix 130 | """ 131 | 132 | scale = 10**0 133 | dim = len(self.gbest.position) 134 | 135 | x = numpy.array([particle.position * scale for particle in self.swarm]) 136 | theta = (x - self.gbest.position * scale) #/ (self.gbest.position * scale) 137 | norms = numpy.array(list(map(norm, theta))) 138 | 139 | b = (norms < 0.1) 140 | theta = theta[b] 141 | fitness = numpy.array([particle.fitness * scale for particle in self.swarm]) 142 | 143 | fitness = fitness[b] 144 | delta = -2*(fitness - self.gbest.fitness * scale) 145 | 146 | _cov = self.minimize1(dim, theta, delta) 147 | _cov = self.minimize2(dim, theta, delta) 148 | 149 | return self.gbest.position, _cov 150 | 151 | def minimize1(self, dim, theta, delta): 152 | p0Cor = numpy.random.uniform(-1,1,dim**2).reshape(dim, dim) 153 | p0Cor = p0Cor - numpy.diag(p0Cor) + numpy.identity(dim) 154 | 155 | p0 = reverse(dim, numpy.identity(dim), numpy.ones(dim)/20) 156 | popt, _,infodict,mesg,_ = leastsq(errfunc, p0, args=(theta, delta, self.gbest.position),full_output=True) 157 | print(mesg) 158 | 159 | 160 | ss_err=(infodict['fvec']**2).sum() 161 | ss_tot=((delta-delta.mean())**2).sum() 162 | rsquared=1-(ss_err/ss_tot) 163 | print("rsquared", rsquared) 164 | 165 | corrm, var, mu = transform(dim, popt) 166 | var = var * self.gbest.position 167 | _cov = corr2cov(corrm, var) 168 | 169 | print("used mu:", mu) 170 | print("found _cov:\n", _cov) 171 | 172 | sigma = numpy.sqrt(numpy.diag(_cov)) 173 | print( "=> found sigma:", sigma) 174 | 175 | return _cov 176 | 177 | def minimize2(self, dim, theta, delta): 178 | cons = ( 179 | {'type': 'ineq', 180 | 'fun' : lambda x: bound(x)}) 181 | 182 | p0 = reverse(dim, numpy.identity(dim), numpy.ones(dim)*self.gbest.position/10) 183 | res = minimize(errfunc2, p0, args=(theta, delta, self.gbest.position), constraints=cons, method='SLSQP', options={'disp': True, "ftol":10**-17}) 184 | popt=res.x 185 | 186 | corrm, var, mu = transform(dim, popt) 187 | var = var * self.gbest.position 188 | _cov = corr2cov(corrm, var) 189 | 190 | print("used mu:", mu) 191 | print("found _cov:\n", _cov) 192 | 193 | sigma = numpy.sqrt(numpy.diag(_cov)) 194 | print( "=> found sigma:", sigma) 195 | 196 | return _cov 197 | 198 | def corr2cov(corrm, var): 199 | dim = len(var) 200 | covm = numpy.empty((dim,dim)) 201 | for i in range(len(corrm)): 202 | for j in range(len(corrm)): 203 | covm[i,j] = corrm[i,j]*var[i]*var[j] 204 | 205 | return covm 206 | 207 | def rescale(_cov, v, dim): 208 | #rescaling 209 | cov2 = numpy.empty((dim, dim)) 210 | for i in range(dim): 211 | for j in range(dim): 212 | cov2[i,j] = _cov[i,j] * v[i] * v[j] 213 | return cov2 -------------------------------------------------------------------------------- /cosmoHammer/pso/MpiParticleSwarmOptimizer.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Created on Oct 28, 2013 3 | 4 | @author: J.Akeret 5 | ''' 6 | from __future__ import print_function, division, absolute_import, \ 7 | unicode_literals 8 | 9 | import multiprocessing 10 | import numpy 11 | 12 | from cosmoHammer.util.MpiUtil import MpiPool, mpiBCast 13 | from cosmoHammer.pso.ParticleSwarmOptimizer import ParticleSwarmOptimizer 14 | 15 | 16 | 17 | class MpiParticleSwarmOptimizer(ParticleSwarmOptimizer): 18 | """ 19 | PSO with support for MPI to distribute the workload over multiple nodes 20 | """ 21 | 22 | def __init__(self, func, low, high, particleCount=25, threads=1): 23 | self.threads = threads 24 | pool = MpiPool(self._getMapFunction()) 25 | super(MpiParticleSwarmOptimizer, self).__init__(func, low, high, particleCount=particleCount, pool=pool) 26 | 27 | 28 | def _getMapFunction(self): 29 | if self.threads > 1: 30 | pool = multiprocessing.Pool(self.threads) 31 | return pool.map 32 | else: 33 | return map 34 | 35 | def _converged(self, it, p, m, n): 36 | 37 | if(self.isMaster()): 38 | converged = super(MpiParticleSwarmOptimizer, self)._converged(it, p, m, n) 39 | else: 40 | converged = False 41 | 42 | converged = mpiBCast(converged) 43 | return converged 44 | 45 | def _get_fitness(self,swarm): 46 | mapFunction = self.pool.map 47 | 48 | mpiSwarm = mpiBCast(swarm) 49 | 50 | pos = numpy.array([part.position for part in mpiSwarm]) 51 | results = mapFunction(self.func, pos) 52 | lnprob = numpy.array([l[0] for l in results]) 53 | for i, particle in enumerate(swarm): 54 | particle.fitness = lnprob[i] 55 | particle.position = pos[i] 56 | 57 | def isMaster(self): 58 | return self.pool.isMaster() -------------------------------------------------------------------------------- /cosmoHammer/pso/ParticleSwarmOptimizer.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Created on Sep 30, 2013 3 | 4 | @author: J. Akeret 5 | ''' 6 | from __future__ import print_function, division, absolute_import, unicode_literals 7 | from copy import copy 8 | from math import floor 9 | import math 10 | import multiprocessing 11 | import numpy 12 | 13 | class ParticleSwarmOptimizer(object): 14 | ''' 15 | Optimizer using a swarm of particles 16 | 17 | :param func: 18 | A function that takes a vector in the parameter space as input and 19 | returns the natural logarithm of the posterior probability for that 20 | position. 21 | 22 | :param low: array of the lower bound of the parameter space 23 | :param high: array of the upper bound of the parameter space 24 | :param particleCount: the number of particles to use. 25 | :param threads: (optional) 26 | The number of threads to use for parallelization. If ``threads == 1``, 27 | then the ``multiprocessing`` module is not used but if 28 | ``threads > 1``, then a ``Pool`` object is created and calls to 29 | ``lnpostfn`` are run in parallel. 30 | 31 | :param pool: (optional) 32 | An alternative method of using the parallelized algorithm. If 33 | provided, the value of ``threads`` is ignored and the 34 | object provided by ``pool`` is used for all parallelization. It 35 | can be any object with a ``map`` method that follows the same 36 | calling sequence as the built-in ``map`` function. 37 | 38 | ''' 39 | 40 | 41 | def __init__(self, func, low, high, particleCount=25, threads=1, pool=None): 42 | ''' 43 | Constructor 44 | ''' 45 | self.func = func 46 | self.low = low 47 | self.high = high 48 | self.particleCount = particleCount 49 | self.threads = threads 50 | self.pool = pool 51 | 52 | if self.threads > 1 and self.pool is None: 53 | self.pool = multiprocessing.Pool(self.threads) 54 | 55 | self.paramCount = len(self.low) 56 | 57 | self.swarm = self._initSwarm() 58 | self.gbest = Particle.create(self.paramCount) 59 | 60 | def _initSwarm(self): 61 | swarm = [] 62 | for _ in range(self.particleCount): 63 | swarm.append(Particle(numpy.random.uniform(self.low, self.high, size=self.paramCount), numpy.zeros(self.paramCount))) 64 | 65 | return swarm 66 | 67 | def sample(self, maxIter=1000, c1=1.193, c2=1.193, p=0.7, m=10**-3, n=10**-2): 68 | """ 69 | Launches the PSO. Yields the complete swarm per iteration 70 | 71 | :param maxIter: maximum iterations 72 | :param c1: cognitive weight 73 | :param c2: social weight 74 | :param p: stop criterion, percentage of particles to use 75 | :param m: stop criterion, difference between mean fitness and global best 76 | :param n: stop criterion, difference between norm of the particle vector and norm of the global best 77 | """ 78 | self._get_fitness(self.swarm) 79 | i = 0 80 | while True: 81 | 82 | 83 | for particle in self.swarm: 84 | if ((self.gbest.fitness) particle.pbest.fitness): 91 | particle.updatePBest() 92 | 93 | if(i>=maxIter): 94 | print("max iteration reached! stoping") 95 | return 96 | 97 | if(self._converged(i, p=p,m=m, n=n)): 98 | if(self.isMaster()): 99 | print("converged after %s iterations!"%i) 100 | print("best fit found: ", self.gbest.fitness, self.gbest.position) 101 | return 102 | 103 | 104 | for particle in self.swarm: 105 | 106 | w = 0.5 + numpy.random.uniform(0,1,size=self.paramCount)/2 107 | #w=0.72 108 | part_vel = w * particle.velocity 109 | cog_vel = c1 * numpy.random.uniform(0,1,size=self.paramCount) * (particle.pbest.position - particle.position) 110 | soc_vel = c2 * numpy.random.uniform(0,1,size=self.paramCount) * (self.gbest.position - particle.position) 111 | particle.velocity = part_vel + cog_vel + soc_vel 112 | particle.position = particle.position + particle.velocity 113 | 114 | self._get_fitness(self.swarm) 115 | 116 | swarm = [] 117 | for particle in self.swarm: 118 | swarm.append(particle.copy()) 119 | yield swarm 120 | 121 | i+=1 122 | 123 | def optimize(self, maxIter=1000, c1=1.193, c2=1.193, p=0.7, m=10**-3, n=10**-2): 124 | """ 125 | Runs the complete optimiziation. 126 | 127 | :param maxIter: maximum iterations 128 | :param c1: cognitive weight 129 | :param c2: social weight 130 | :param p: stop criterion, percentage of particles to use 131 | :param m: stop criterion, difference between mean fitness and global best 132 | :param n: stop criterion, difference between norm of the particle vector and norm of the global best 133 | 134 | :return swarms, gBests: the swarms and the global bests of all iterations 135 | """ 136 | 137 | swarms = [] 138 | gBests = [] 139 | for swarm in self.sample(maxIter,c1,c2,p,m,n): 140 | swarms.append(swarm) 141 | gBests.append(self.gbest.copy()) 142 | 143 | return swarms, gBests 144 | 145 | def _get_fitness(self,swarm): 146 | 147 | # If the `pool` property of the pso has been set (i.e. we want 148 | # to use `multiprocessing`), use the `pool`'s map method. Otherwise, 149 | # just use the built-in `map` function. 150 | if self.pool is not None: 151 | mapFunction = self.pool.map 152 | else: 153 | mapFunction = map 154 | 155 | pos = numpy.array([part.position for part in swarm]) 156 | results = mapFunction(self.func, pos) 157 | lnprob = numpy.array([l[0] for l in results]) 158 | for i, particle in enumerate(swarm): 159 | particle.fitness = lnprob[i] 160 | 161 | def _converged(self, it, p, m, n): 162 | # test = self._convergedSpace2(p=p) 163 | # print(test) 164 | fit = self._convergedFit(it=it, p=p, m=m) 165 | if(fit): 166 | space = self._convergedSpace(it=it, p=p, m=n) 167 | return space 168 | else: 169 | return False 170 | 171 | def _convergedFit(self, it, p, m): 172 | bestSort = numpy.sort([particle.pbest.fitness for particle in self.swarm])[::-1] 173 | meanFit = numpy.mean(bestSort[1:int(math.floor(self.particleCount*p))]) 174 | # print( "best %f, meanFit %f, ration %f"%( self.gbest[0], meanFit, abs((self.gbest[0]-meanFit)))) 175 | return (abs(self.gbest.fitness-meanFit) ['key1', 'key2'] 26 | 27 | $ print(params.key1) 28 | > [1, 2, 3] 29 | 30 | $ params[:,0] = 0 31 | 32 | $ print(params.values) 33 | > [[0 2 3] 34 | [0 2 3]] 35 | 36 | $ print(params[:,1]) 37 | > [2 2] 38 | 39 | """ 40 | 41 | def __init__(self, *args): 42 | 43 | values = [] 44 | self._keys = [] 45 | for k,v in args: 46 | if k in self._keys: 47 | raise KeyError("Duplicated key '%s'"%k) 48 | 49 | self.__dict__[k] = v 50 | self._keys.append(k) 51 | values.append(v) 52 | self._values = np.array(values) 53 | 54 | def __getitem__(self, slice): 55 | return self.values[slice] 56 | 57 | def __setitem__(self, slice, value): 58 | self.values[slice] = value 59 | 60 | def __str__(self): 61 | return ",".join(("%s=%s"%(k,v) for k,v in zip(self.keys, self.values))) 62 | 63 | @property 64 | def keys(self): 65 | return copy(self._keys) 66 | 67 | @property 68 | def values(self): 69 | return self._values 70 | 71 | def get(self, key): 72 | return self.__dict__[key] 73 | 74 | def copy(self): 75 | return Params(*zip(self.keys, self.values)) -------------------------------------------------------------------------------- /cosmoHammer/util/SampleBallPositionGenerator.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | 4 | 5 | class SampleBallPositionGenerator(object): 6 | """ 7 | Generates samples in a very thight n-dimensional ball 8 | """ 9 | 10 | def setup(self, sampler): 11 | """ 12 | setup the generator 13 | """ 14 | self.sampler = sampler 15 | 16 | def generate(self): 17 | """ 18 | generates the positions 19 | """ 20 | 21 | return [self.sampler.paramValues+np.random.normal(size=self.sampler.paramCount)*self.sampler.paramWidths for i in range(self.sampler.nwalkers)] 22 | 23 | def __str__(self, *args, **kwargs): 24 | return "SampleBallPositionGenerator" -------------------------------------------------------------------------------- /cosmoHammer/util/SampleFileUtil.py: -------------------------------------------------------------------------------- 1 | 2 | import pickle 3 | import numpy as np 4 | import cosmoHammer.Constants as c 5 | 6 | class SampleFileUtil(object): 7 | """ 8 | Util for handling sample files 9 | 10 | :param filePrefix: the prefix to use 11 | :param master: True if the sampler instance is the master 12 | :param reuseBurnin: True if the burn in data from a previous run should be used 13 | 14 | """ 15 | 16 | def __init__(self, filePrefix, master=True, reuseBurnin=False): 17 | self.filePrefix = filePrefix 18 | 19 | if(master): 20 | if(reuseBurnin): 21 | mode = "r" 22 | else: 23 | mode = "w" 24 | self.samplesFileBurnin = open(self.filePrefix+c.BURNIN_SUFFIX, mode) 25 | self.probFileBurnin = open(self.filePrefix+c.BURNIN_PROB_SUFFIX, mode) 26 | 27 | self.samplesFile = open(self.filePrefix+c.FILE_SUFFIX, "w") 28 | self.probFile = open(self.filePrefix+c.PROB_SUFFIX, "w") 29 | 30 | def importFromFile(self, filePath): 31 | values = np.loadtxt(filePath, dtype=float) 32 | return values 33 | 34 | def storeRandomState(self, filePath, randomState): 35 | with open(filePath,'wb') as f: 36 | pickle.dump(randomState, f) 37 | 38 | def importRandomState(self, filePath): 39 | with open(filePath,'rb') as f: 40 | state = pickle.load(f) 41 | return state 42 | 43 | def persistBurninValues(self, pos, prob, data): 44 | self.persistValues(self.samplesFileBurnin, self.probFileBurnin, pos, prob, data) 45 | 46 | def persistSamplingValues(self, pos, prob, data): 47 | self.persistValues(self.samplesFile, self.probFile, pos, prob, data) 48 | 49 | 50 | def persistValues(self, posFile, probFile, pos, prob, data): 51 | """ 52 | Writes the walker positions and the likelihood to the disk 53 | """ 54 | posFile.write("\n".join(["\t".join([str(q) for q in p]) for p in pos])) 55 | posFile.write("\n") 56 | posFile.flush() 57 | 58 | probFile.write("\n".join([str(p) for p in prob])) 59 | probFile.write("\n") 60 | probFile.flush(); 61 | 62 | def close(self): 63 | self.samplesFileBurnin.close() 64 | self.probFileBurnin.close() 65 | self.samplesFile.close() 66 | self.probFile.close() 67 | 68 | def __str__(self, *args, **kwargs): 69 | return "SampleFileUtil" -------------------------------------------------------------------------------- /cosmoHammer/util/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | from cosmoHammer.util.SampleFileUtil import SampleFileUtil 3 | from cosmoHammer.util.InMemoryStorageUtil import InMemoryStorageUtil 4 | from cosmoHammer.util.SampleBallPositionGenerator import SampleBallPositionGenerator 5 | from cosmoHammer.util.FlatPositionGenerator import FlatPositionGenerator 6 | from cosmoHammer.util.Params import Params 7 | -------------------------------------------------------------------------------- /doc/.gitignore: -------------------------------------------------------------------------------- 1 | /api 2 | /build 3 | -------------------------------------------------------------------------------- /doc/Makefile: -------------------------------------------------------------------------------- 1 | # Makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line. 5 | SPHINXOPTS = 6 | SPHINXBUILD = sphinx-build 7 | PAPER = 8 | BUILDDIR = build 9 | 10 | # Internal variables. 11 | PAPEROPT_a4 = -D latex_paper_size=a4 12 | PAPEROPT_letter = -D latex_paper_size=letter 13 | ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source 14 | # the i18n builder cannot share the environment and doctrees with the others 15 | I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source 16 | 17 | .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext 18 | 19 | help: 20 | @echo "Please use \`make ' where is one of" 21 | @echo " html to make standalone HTML files" 22 | @echo " dirhtml to make HTML files named index.html in directories" 23 | @echo " singlehtml to make a single large HTML file" 24 | @echo " pickle to make pickle files" 25 | @echo " json to make JSON files" 26 | @echo " htmlhelp to make HTML files and a HTML help project" 27 | @echo " qthelp to make HTML files and a qthelp project" 28 | @echo " devhelp to make HTML files and a Devhelp project" 29 | @echo " epub to make an epub" 30 | @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" 31 | @echo " latexpdf to make LaTeX files and run them through pdflatex" 32 | @echo " text to make text files" 33 | @echo " man to make manual pages" 34 | @echo " texinfo to make Texinfo files" 35 | @echo " info to make Texinfo files and run them through makeinfo" 36 | @echo " gettext to make PO message catalogs" 37 | @echo " changes to make an overview of all changed/added/deprecated items" 38 | @echo " linkcheck to check all external links for integrity" 39 | @echo " doctest to run all doctests embedded in the documentation (if enabled)" 40 | 41 | clean: 42 | -rm -rf $(BUILDDIR)/* 43 | 44 | html: 45 | $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html 46 | @echo 47 | @echo "Build finished. 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The epub file is in $(BUILDDIR)/epub." 97 | 98 | latex: 99 | $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex 100 | @echo 101 | @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." 102 | @echo "Run \`make' in that directory to run these through (pdf)latex" \ 103 | "(use \`make latexpdf' here to do that automatically)." 104 | 105 | latexpdf: 106 | $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex 107 | @echo "Running LaTeX files through pdflatex..." 108 | $(MAKE) -C $(BUILDDIR)/latex all-pdf 109 | @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." 110 | 111 | text: 112 | $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text 113 | @echo 114 | @echo "Build finished. The text files are in $(BUILDDIR)/text." 115 | 116 | man: 117 | $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man 118 | @echo 119 | @echo "Build finished. The manual pages are in $(BUILDDIR)/man." 120 | 121 | texinfo: 122 | $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo 123 | @echo 124 | @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." 125 | @echo "Run \`make' in that directory to run these through makeinfo" \ 126 | "(use \`make info' here to do that automatically)." 127 | 128 | info: 129 | $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo 130 | @echo "Running Texinfo files through makeinfo..." 131 | make -C $(BUILDDIR)/texinfo info 132 | @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." 133 | 134 | gettext: 135 | $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale 136 | @echo 137 | @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." 138 | 139 | changes: 140 | $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes 141 | @echo 142 | @echo "The overview file is in $(BUILDDIR)/changes." 143 | 144 | linkcheck: 145 | $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck 146 | @echo 147 | @echo "Link check complete; look for any errors in the above output " \ 148 | "or in $(BUILDDIR)/linkcheck/output.txt." 149 | 150 | doctest: 151 | $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest 152 | @echo "Testing of doctests in the sources finished, look at the " \ 153 | "results in $(BUILDDIR)/doctest/output.txt." 154 | -------------------------------------------------------------------------------- /doc/check_sphinx.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Created on Dec 2, 2013 3 | 4 | @author: jakeret 5 | ''' 6 | import py 7 | import subprocess 8 | def test_linkcheck(tmpdir): 9 | doctrees = tmpdir.join("doctrees") 10 | htmldir = tmpdir.join("html") 11 | subprocess.check_call( 12 | ["sphinx-build", "-blinkcheck", 13 | "-d", str(doctrees), "./source", str(htmldir)]) 14 | 15 | def test_build_docs(tmpdir): 16 | doctrees = tmpdir.join("doctrees") 17 | htmldir = tmpdir.join("html") 18 | subprocess.check_call([ 19 | "sphinx-build", "-bhtml", 20 | "-d", str(doctrees), "./source", str(htmldir)]) -------------------------------------------------------------------------------- /doc/source/api.rst: -------------------------------------------------------------------------------- 1 | .. _api: 2 | 3 | API 4 | *** 5 | 6 | .. automodule:: cosmoHammer 7 | 8 | This page details the methods and classes provided by the ``cosmoHammer`` module. 9 | 10 | 11 | Samplers 12 | -------- 13 | 14 | :mod:`CosmoHammerSampler` Module 15 | =================================== 16 | 17 | Standard usage of ``CosmoHammer`` involves instantiating an 18 | :class:`CosmoHammerSampler`. 19 | 20 | .. autoclass:: cosmoHammer.CosmoHammerSampler 21 | :members: 22 | 23 | 24 | :mod:`MpiCosmoHammerSampler` Module 25 | =================================== 26 | 27 | To distribute ``CosmoHammer`` in a cluster involves instantiating an 28 | :class:`MpiCosmoHammerSampler`. 29 | 30 | .. autoclass:: cosmoHammer.MpiCosmoHammerSampler 31 | :show-inheritance: 32 | 33 | :mod:`ConcurrentMpiCosmoHammerSampler` Module 34 | ============================================= 35 | 36 | To distribute ``CosmoHammer`` in a cluster and to spawn multiple processes involves instantiating an 37 | :class:`ConcurrentMpiCosmoHammerSampler`. 38 | 39 | .. autoclass:: cosmoHammer.ConcurrentMpiCosmoHammerSampler 40 | :members: 41 | :show-inheritance: 42 | 43 | CosmoHammer Chains 44 | ------------------------------------- 45 | 46 | ``CosmoHammer`` comes with a plain vanillia chain implementation 47 | :class:`LikelihoodComputationChain`. 48 | 49 | .. autoclass:: cosmoHammer.LikelihoodComputationChain 50 | :members: 51 | 52 | .. autoclass:: cosmoHammer.ChainContext 53 | :members: 54 | 55 | CosmoHammer Exceptions 56 | ------------------------------------- 57 | 58 | ``CosmoHammer`` may raise the following exceptions while execution 59 | 60 | .. automodule:: cosmoHammer.exceptions 61 | :members: 62 | :undoc-members: 63 | :show-inheritance: 64 | 65 | 66 | CosmoHammer Utils 67 | ------------------------------------- 68 | 69 | .. autoclass:: cosmoHammer.util.Params 70 | :members: 71 | 72 | .. autoclass:: cosmoHammer.util.SampleBallPositionGenerator 73 | :members: 74 | 75 | .. autoclass:: cosmoHammer.util.FlatPositionGenerator 76 | :members: 77 | 78 | .. autoclass:: cosmoHammer.util.SampleFileUtil 79 | :members: 80 | 81 | .. autoclass:: cosmoHammer.util.InMemoryStorageUtil 82 | :members: 83 | 84 | 85 | 86 | ParticleSwarmOptimizer Package 87 | ------------------------------ 88 | 89 | :mod:`ParticleSwarmOptimizer` Module 90 | ==================================== 91 | 92 | .. autoclass:: cosmoHammer.ParticleSwarmOptimizer 93 | :members: 94 | 95 | 96 | :mod:`MpiParticleSwarmOptimizer` Module 97 | ======================================= 98 | 99 | .. autoclass:: cosmoHammer.MpiParticleSwarmOptimizer 100 | :members: 101 | 102 | -------------------------------------------------------------------------------- /doc/source/authors.rst: -------------------------------------------------------------------------------- 1 | .. include:: ../../AUTHORS.rst -------------------------------------------------------------------------------- /doc/source/conf.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # 3 | # CosmoHammer documentation build configuration file, created by 4 | # sphinx-quickstart on Fri Oct 12 13:39:48 2012. 5 | # 6 | # This file is execfile()d with the current directory set to its containing dir. 7 | # 8 | # Note that not all possible configuration values are present in this 9 | # autogenerated file. 10 | # 11 | # All configuration values have a default; values that are commented out 12 | # serve to show the default. 13 | 14 | import sys, os 15 | 16 | # If extensions (or modules to document with autodoc) are in another directory, 17 | # add these directories to sys.path here. If the directory is relative to the 18 | # documentation root, use os.path.abspath to make it absolute, like shown here. 19 | #sys.path.insert(0, os.path.abspath('../../cosmoHammer')) 20 | sys.path.insert(0, os.path.abspath('../..')) 21 | import cosmoHammer 22 | from cosmoHammer import __version__ 23 | 24 | 25 | # -- General configuration ----------------------------------------------------- 26 | 27 | # If your documentation needs a minimal Sphinx version, state it here. 28 | #needs_sphinx = '1.0' 29 | 30 | # Add any Sphinx extension module names here, as strings. They can be extensions 31 | # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. 32 | extensions = ['sphinx.ext.autodoc', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode'] 33 | 34 | # Add any paths that contain templates here, relative to this directory. 35 | templates_path = ['_templates'] 36 | 37 | # The suffix of source filenames. 38 | source_suffix = '.rst' 39 | 40 | # The encoding of source files. 41 | #source_encoding = 'utf-8-sig' 42 | 43 | # The master toctree document. 44 | master_doc = 'index' 45 | 46 | # General information about the project. 47 | project = u'CosmoHammer' 48 | copyright = u'2014, Joel Akeret' 49 | 50 | # The version info for the project you're documenting, acts as replacement for 51 | # |version| and |release|, also used in various other places throughout the 52 | # built documents. 53 | # 54 | # The short X.Y version. 55 | version = __version__ 56 | # The full version, including alpha/beta/rc tags. 57 | release = __version__ 58 | 59 | # The language for content autogenerated by Sphinx. Refer to documentation 60 | # for a list of supported languages. 61 | #language = None 62 | 63 | # There are two options for replacing |today|: either, you set today to some 64 | # non-false value, then it is used: 65 | #today = '' 66 | # Else, today_fmt is used as the format for a strftime call. 67 | #today_fmt = '%B %d, %Y' 68 | 69 | # List of patterns, relative to source directory, that match files and 70 | # directories to ignore when looking for source files. 71 | exclude_patterns = [] 72 | 73 | # The reST default role (used for this markup: `text`) to use for all documents. 74 | #default_role = None 75 | 76 | # If true, '()' will be appended to :func: etc. cross-reference text. 77 | #add_function_parentheses = True 78 | 79 | # If true, the current module name will be prepended to all description 80 | # unit titles (such as .. function::). 81 | #add_module_names = True 82 | 83 | # If true, sectionauthor and moduleauthor directives will be shown in the 84 | # output. They are ignored by default. 85 | #show_authors = False 86 | 87 | # The name of the Pygments (syntax highlighting) style to use. 88 | pygments_style = 'sphinx' 89 | 90 | # A list of ignored prefixes for module index sorting. 91 | #modindex_common_prefix = [] 92 | 93 | 94 | # -- Options for HTML output --------------------------------------------------- 95 | 96 | # The theme to use for HTML and HTML Help pages. See the documentation for 97 | # a list of builtin themes. 98 | #html_theme = 'agogo' 99 | 100 | # Theme options are theme-specific and customize the look and feel of a theme 101 | # further. For a list of options available for each theme, see the 102 | # documentation. 103 | #html_theme_options = { 104 | # "headerbg": "#52ADE7", 105 | # "headercolor1": "#000000", 106 | # "headercolor2": "#52ADE7", 107 | # "linkcolor": "#000000", 108 | # "headerlinkcolor": "#000000", 109 | # "bodyfont": "sans-serif;" 110 | # } 111 | 112 | # Add any paths that contain custom themes here, relative to this directory. 113 | #html_theme_path = [] 114 | 115 | # The name for this set of Sphinx documents. If None, it defaults to 116 | # " v documentation". 117 | #html_title = None 118 | 119 | # A shorter title for the navigation bar. Default is the same as html_title. 120 | #html_short_title = None 121 | 122 | # The name of an image file (relative to this directory) to place at the top 123 | # of the sidebar. 124 | #html_logo = None 125 | 126 | # The name of an image file (within the static path) to use as favicon of the 127 | # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 128 | # pixels large. 129 | #html_favicon = None 130 | 131 | # Add any paths that contain custom static files (such as style sheets) here, 132 | # relative to this directory. They are copied after the builtin static files, 133 | # so a file named "default.css" will overwrite the builtin "default.css". 134 | # html_static_path = ['_static'] 135 | 136 | # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, 137 | # using the given strftime format. 138 | #html_last_updated_fmt = '%b %d, %Y' 139 | 140 | # If true, SmartyPants will be used to convert quotes and dashes to 141 | # typographically correct entities. 142 | #html_use_smartypants = True 143 | 144 | # Custom sidebar templates, maps document names to template names. 145 | #html_sidebars = {} 146 | 147 | # Additional templates that should be rendered to pages, maps page names to 148 | # template names. 149 | #html_additional_pages = {} 150 | 151 | # If false, no module index is generated. 152 | #html_domain_indices = True 153 | 154 | # If false, no index is generated. 155 | #html_use_index = True 156 | 157 | # If true, the index is split into individual pages for each letter. 158 | #html_split_index = False 159 | 160 | # If true, links to the reST sources are added to the pages. 161 | #html_show_sourcelink = True 162 | 163 | # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. 164 | #html_show_sphinx = True 165 | 166 | # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. 167 | #html_show_copyright = True 168 | 169 | # If true, an OpenSearch description file will be output, and all pages will 170 | # contain a tag referring to it. The value of this option must be the 171 | # base URL from which the finished HTML is served. 172 | #html_use_opensearch = '' 173 | 174 | # This is the file name suffix for HTML files (e.g. ".xhtml"). 175 | #html_file_suffix = None 176 | 177 | # Output file base name for HTML help builder. 178 | htmlhelp_basename = 'CosmoHammerdoc' 179 | 180 | 181 | # -- Options for LaTeX output -------------------------------------------------- 182 | 183 | latex_elements = { 184 | # The paper size ('letterpaper' or 'a4paper'). 185 | #'papersize': 'letterpaper', 186 | 187 | # The font size ('10pt', '11pt' or '12pt'). 188 | #'pointsize': '10pt', 189 | 190 | # Additional stuff for the LaTeX preamble. 191 | #'preamble': '', 192 | } 193 | 194 | # Grouping the document tree into LaTeX files. List of tuples 195 | # (source start file, target name, title, author, documentclass [howto/manual]). 196 | latex_documents = [ 197 | ('index', 'CosmoHammer.tex', u'CosmoHammer Documentation', 198 | u'Joel Akeret', 'manual'), 199 | ] 200 | 201 | # The name of an image file (relative to this directory) to place at the top of 202 | # the title page. 203 | #latex_logo = None 204 | 205 | # For "manual" documents, if this is true, then toplevel headings are parts, 206 | # not chapters. 207 | #latex_use_parts = False 208 | 209 | # If true, show page references after internal links. 210 | #latex_show_pagerefs = False 211 | 212 | # If true, show URL addresses after external links. 213 | #latex_show_urls = False 214 | 215 | # Documents to append as an appendix to all manuals. 216 | #latex_appendices = [] 217 | 218 | # If false, no module index is generated. 219 | #latex_domain_indices = True 220 | 221 | 222 | # -- Options for manual page output -------------------------------------------- 223 | 224 | # One entry per manual page. List of tuples 225 | # (source start file, name, description, authors, manual section). 226 | man_pages = [ 227 | ('index', 'cosmohammer', u'CosmoHammer Documentation', 228 | [u'Joel Akeret'], 1) 229 | ] 230 | 231 | # If true, show URL addresses after external links. 232 | #man_show_urls = False 233 | 234 | 235 | # -- Options for Texinfo output ------------------------------------------------ 236 | 237 | # Grouping the document tree into Texinfo files. List of tuples 238 | # (source start file, target name, title, author, 239 | # dir menu entry, description, category) 240 | texinfo_documents = [ 241 | ('index', 'CosmoHammer', u'CosmoHammer Documentation', 242 | u'Joel Akeret', 'CosmoHammer', 'One line description of project.', 243 | 'Miscellaneous'), 244 | ] 245 | 246 | # Documents to append as an appendix to all manuals. 247 | #texinfo_appendices = [] 248 | 249 | # If false, no module index is generated. 250 | #texinfo_domain_indices = True 251 | 252 | # How to display URL addresses: 'footnote', 'no', or 'inline'. 253 | #texinfo_show_urls = 'footnote' 254 | try: 255 | import sphinx_eth_theme 256 | html_theme = "sphinx_eth_theme" 257 | html_theme_path = [sphinx_eth_theme.get_html_theme_path()] 258 | except ImportError: 259 | html_theme = 'default' -------------------------------------------------------------------------------- /doc/source/contributing.rst: -------------------------------------------------------------------------------- 1 | .. include:: ../../CONTRIBUTING.rst -------------------------------------------------------------------------------- /doc/source/history.rst: -------------------------------------------------------------------------------- 1 | .. include:: ../../HISTORY.rst -------------------------------------------------------------------------------- /doc/source/index.rst: -------------------------------------------------------------------------------- 1 | .. complexity documentation master file, created by 2 | sphinx-quickstart on Tue Jul 9 22:26:36 2013. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | .. include:: ../../README.rst 7 | 8 | User Guide 9 | ---------- 10 | 11 | .. toctree:: 12 | :maxdepth: 2 13 | 14 | user/install 15 | user/usage 16 | user/benchmark 17 | user/pso 18 | user/parallelization 19 | user/HowToCosmoHammer 20 | 21 | API Documentation 22 | ----------------- 23 | 24 | .. toctree:: 25 | :maxdepth: 2 26 | 27 | api 28 | 29 | 30 | Contents: 31 | --------- 32 | 33 | .. toctree:: 34 | :maxdepth: 2 35 | 36 | contributing 37 | authors 38 | history 39 | 40 | 41 | 42 | License 43 | ------- 44 | CosmoHammer is free software: you can redistribute it and/or modify 45 | it under the terms of the GNU General Public License as published by 46 | the Free Software Foundation, either version 3 of the License, or 47 | (at your option) any later version. 48 | 49 | CosmoHammer is distributed in the hope that it will be useful, 50 | but WITHOUT ANY WARRANTY; without even the implied warranty of 51 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 52 | GNU General Public License for more details. 53 | 54 | You should have received a copy of the GNU General Public License 55 | along with CosmoHammer. If not, see . 56 | -------------------------------------------------------------------------------- /doc/source/user/HowToCosmoHammer.rst: -------------------------------------------------------------------------------- 1 | .. _HowToCosmoHammer: 2 | 3 | How to... 4 | ========= 5 | 6 | When using CosmoHammer for sampling your own likelihood, the central component is the LikelihoodComputationChain. As shown in the Figure, the LikelihoodComputationChain is invoked by the sampler at every position in the Monte Carlo Markov chain in order to compute the likelihood of the proposed position in parameter space. The chain itself has three components: 7 | 8 | .. image:: chscheme.jpg 9 | :alt: Visulalization of the CosmoHammer LikelihoodComputationChain scheme. 10 | :align: left 11 | 12 | 13 | - **Context**: The context is a dictionary for storing information created during the evaluation of the likelihood. It at least contains the parameter values of the current position proposed by the sampler. 14 | 15 | - **CoreModules**: The CoreModules can be used to calculate information which is needed for the evaluation of the likelihood. The information can then stored in the context. 16 | 17 | - **LikelihoodModules**: The LikelihoodModules use the information in the context to calculate the likelihood of the proposed position and return the log-likelihood to the chain. 18 | 19 | The LikelihoodComputationChain (a) first stores the proposed parameters in the context , then (b) moves on and invokes all available CoreModules before (c) calling the LikelihoodModules. The resulting log-likelihood values are gathered, summed, and returned to the sampler. 20 | 21 | In the following, CoreModule and LikelihoodModule are explained in more detail. 22 | 23 | write own CoreModules 24 | --------------------- 25 | 26 | The minimal CoreModule is a callable module that takes only the context as an argument and has a setup routine for doing expansive calculations that can be precomputed. For every result that is to be stored in the context, you need to come up with a unique key which allows the other modules to get the information from the context. An example for such a minimal CoreModule can be found in the ``DummyCoreModule.py`` file in the examples:: 27 | 28 | class DummyCoreModule(object): 29 | """ 30 | Dummy Core Module for calculating the squares of parameters. 31 | """ 32 | 33 | def __init__(self): 34 | """ 35 | Constructor of the DummyCoreModule 36 | """ 37 | pass 38 | 39 | def __call__(self, ctx): 40 | """ 41 | Computes something and stores it in the context 42 | """ 43 | # Get the parameters from the context 44 | p = ctx.getParams() 45 | 46 | # Calculate something 47 | squares = p**2 48 | # Add the result to the context using a unique key 49 | ctx.add('squares_key', squares) 50 | 51 | # Store derived parameters for post processing 52 | derived_parms = sum(squares) % 2 53 | ctx.getData()["derived_params_key"] = derived_parms 54 | 55 | def setup(self): 56 | """ 57 | Sets up the core module. 58 | Tasks that need to be executed once per run 59 | """ 60 | #e.g. load data from files 61 | 62 | print("DummyCoreModule setup done") 63 | 64 | write own LikelihoodModules 65 | --------------------------- 66 | 67 | The minimal LikelihoodModule is a module with a computeLikelihood function that takes only the context as an argument and returns the likelihood and a setup routine for doing expansive calculations that can be precomputed. An example for such a minimal LikelihoodModule can be found in the ``DummyLikelihoodModule.py`` file in the examples.:: 68 | 69 | class DummyLikelihoodModule(object): 70 | """ 71 | Dummy object for calculating a likelihood 72 | """ 73 | 74 | def __init__(self): 75 | """ 76 | Constructor of the DummyLikelihoodModule 77 | """ 78 | pass 79 | 80 | def computeLikelihood(self, ctx): 81 | """ 82 | Computes the likelihood using information from the context 83 | """ 84 | # Get information from the context. This can be results from a core 85 | # module or the parameters coming from the sampler 86 | squares = ctx.get('squares_key') 87 | 88 | # Calculate a likelihood up to normalization 89 | lnprob = -sum(squares)/2.0 90 | 91 | # Return the likelihood 92 | return lnprob 93 | 94 | def setup(self): 95 | """ 96 | Sets up the likelihood module. 97 | Tasks that need to be executed once per run 98 | """ 99 | #e.g. load data from files 100 | 101 | print("DummyLikelihoodModule setup done") 102 | -------------------------------------------------------------------------------- /doc/source/user/benchmark.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cosmo-ethz/CosmoHammer/b6c65ccff7c3f83623264b7d57e05310755f761b/doc/source/user/benchmark.png -------------------------------------------------------------------------------- /doc/source/user/benchmark.rst: -------------------------------------------------------------------------------- 1 | .. _benchmark: 2 | 3 | Benchmark 4 | ============ 5 | 6 | Sampling the WMAP 7 year likelihood with CAMB required a large amount of computational power. We therefore decided to explore the possible benefits of cloud computing by means of CosmoHammer. One of the major advantages of this computing strategy is that the configuration of the cloud can be easily tailored to the problem at hand. In the cloud more computational power can be added within minutes by renting extra compute instances on demand, resulting in an optimised execution time. 7 | 8 | As cloud service provider we decided to use `Amazon EC2 `_. The high performance computing cluster consisted of one master node and several worker nodes. At the moment of the benchmarks one cc2.8xlarge Instance ships with 2 × Intel Xeon E5-2670, eight-core architecture with Hyper-Threading, resulting in 32 cores per node. We used a m1.large instance as master node mainly to benefit from the high I / O performance in order to reduce the loading time of the WMAP data. 9 | 10 | .. image:: benchmark.png 11 | :alt: Run time behaviour of CosmoHammer with changing number of cores using different parallelisation schemes. 12 | :align: right 13 | 14 | The results depicted in the figure have been realised with one to 64 worker nodes (32 - 2048 cores) and different combinations of processes and threads per node. The processes define the number of computations executed in parallel and the threads represent the number of cores used for one computation. 15 | 16 | As it can be seen CosmoHammer scales almost linearly with increasing number of computational cores. The best result was achieved using 64 nodes with 32 cores, four processes and eight threads. Using this configuration, the computation took about 16 Minutes. 17 | -------------------------------------------------------------------------------- /doc/source/user/chscheme.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cosmo-ethz/CosmoHammer/b6c65ccff7c3f83623264b7d57e05310755f761b/doc/source/user/chscheme.jpg -------------------------------------------------------------------------------- /doc/source/user/install.rst: -------------------------------------------------------------------------------- 1 | .. _install: 2 | 3 | Installation 4 | ============ 5 | 6 | Since ``CosmoHammer`` is a pure Python module, it should be pretty easy to install. 7 | 8 | At the command line via pip:: 9 | 10 | $ pip install cosmohammer 11 | 12 | This will install the package and all of the required dependencies. 13 | 14 | .. note:: If you wish to use `CosmoHammer` on a cluster with MPI you need to manually install `mpi4py `_. 15 | 16 | From source 17 | ----------- 18 | 19 | Once you've downloaded and unpacked the source, you can navigate into the 20 | root source directory and run: 21 | 22 | :: 23 | 24 | $ python setup.py build 25 | $ python setup.py install --user 26 | 27 | 28 | 29 | You might need to run this using ``sudo`` depending on your Python 30 | installation. 31 | 32 | Cosmological parameters from CMB data 33 | ------------------------------------------------------------------------ 34 | 35 | To estimate cosmological parameters you will need likelihood and core modules for CosmoHammer. 36 | See the cosmoHammerPlugins project `GitHub `_ for the modules publicly available at the moment. 37 | -------------------------------------------------------------------------------- /doc/source/user/parallelisation.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cosmo-ethz/CosmoHammer/b6c65ccff7c3f83623264b7d57e05310755f761b/doc/source/user/parallelisation.jpg -------------------------------------------------------------------------------- /doc/source/user/parallelization.rst: -------------------------------------------------------------------------------- 1 | .. _parallelization: 2 | 3 | Parallelization 4 | =============== 5 | 6 | .. image:: parallelisation.jpg 7 | :alt: Run time behaviour of CosmoHammer with changing number of cores using different parallelisation schemes. 8 | :align: left 9 | 10 | ``CosmoHammer`` allows for parallelizing the evaluation of the likelihood from a single computer to a large scale computing environment like a compute cluster or a cloud infrastructure. 11 | 12 | In the simple case where ``CosmoHammer`` is executed on a single physical computer with one or multiple threads the parallelization is either solved thru `OpenMP `_ or the built in Python `multiprocessing `_. In the case of OpenMP the parallelization is done by executing multiple threads within a shared-memory machine. This is typically a use case when your likelihood code is written in C/C++ or FORTRAN like the theory prediction module `CAMB `_. 13 | 14 | With multiprocessing it is important to note that the module spawns a certain number of Python processes within a machine in order to execute the code in parallel. This causes some overhead during the parallelization process so that a performance gain is only achieved if the computations are resource demanding. 15 | 16 | In the non-trivial case where ``CosmoHammer`` should take advantage of a computation cluster with multiple physical nodes like a cloud or grid computer the parallelization is solved by using the Message Passing Interface (MPI/`mpi4py `_). This requires that mpi4py is `installed `_ on your system. Note, however, that this way of parallelisation is only beneficial when the executed computations are time and resource consuming. Distributing the workload in a compute cluster always implies the transfer of information over the network which is typically slower than transferring information between local processes by an order of magnitude. Therefore, the advantage of additional computing resources and the disadvantage of the network overhead have to be weighted. 17 | 18 | When using a compute cluster the nodes often come with a large number of computational cores. Writing code that fully benefits from such a large number of cores is usually difficult. Therefore, it makes sense to split the workload also on the node since using a smaller number of cores per computation while performing multiple computations in parallel is typically more efficient. In this case ``CosmoHammer`` helps you to combine the parallelization schemes mention above. 19 | 20 | 21 | Examples 22 | -------- 23 | 24 | *Parallelization of* ``CosmoHammer`` *on a* **single machine** *with n cores.* 25 | 26 | 1) Using only Python multiprocessing: 27 | :: 28 | from cosmoHammer.CosmoHammerSampler import CosmoHammerSampler 29 | sampler = CosmoHammerSampler(params, likelihoodComputationChain, filePrefix, 30 | walkersRatio, burninIterations, sampleIterations, threadCount=n) 31 | 32 | 2) Using only OpenMP: 33 | 34 | ``$ export OMP_NUM_THREADS=n`` 35 | :: 36 | from cosmoHammer.CosmoHammerSampler import CosmoHammerSampler 37 | sampler = CosmoHammerSampler(params, likelihoodComputationChain, filePrefix, 38 | walkersRatio, burninIterations, sampleIterations) 39 | 40 | 3) Using OpenMP and Python multiprocessing (choose the the number m of OpenMP threads and the number k of multiprocessing threads such that k*m = n): 41 | 42 | ``$ export OMP_NUM_THREADS=m`` 43 | :: 44 | from cosmoHammer.CosmoHammerSampler import CosmoHammerSampler 45 | sampler = CosmoHammerSampler(params, likelihoodComputationChain, filePrefix, 46 | walkersRatio, burninIterations, sampleIterations, threadCount=k) 47 | 48 | 49 | 50 | *Parallelization of* ``CosmoHammer`` *on a* **cluster or cloud** *with N nodes and n cores per node. For distributing the workload between different nodes in the cluster, MPI has to be used. Run your python script with:* 51 | 52 | ``mpiexec -n $NUM ./