diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..95179b0 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,11 @@ +# To get started with Dependabot version updates, you'll need to specify which +# package ecosystems to update and where the package manifests are located. +# Please see the documentation for all configuration options: +# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates + +version: 2 +updates: + - package-ecosystem: "github-actions" # See documentation for possible values + directory: "/" # Location of package manifests + schedule: + interval: "monthly" diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index cf0a029..d79378d 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -2,30 +2,30 @@ name: publish to pypi on: release: types: [published] - + jobs: publish: runs-on: ubuntu-latest steps: #---------------------------------------------- - # check-out repo and set-up python - #---------------------------------------------- + # check-out repo and set-up python + #---------------------------------------------- - uses: actions/checkout@v3 - uses: actions/setup-python@v4 with: - python-version: "3.7" + python-version: "3.8" + #---------------------------------------------- + # ----- install & configure poetry ----- #---------------------------------------------- - # ----- install & configure poetry ----- - #---------------------------------------------- - name: Install and configure Poetry uses: snok/install-poetry@v1 with: - version: 1.3.2 virtualenvs-create: true virtualenvs-in-project: true + installer-parallel: true + #---------------------------------------------- + # load cached venv if cache exists #---------------------------------------------- - # load cached venv if cache exists - #---------------------------------------------- - name: Load cached venv id: cached-poetry-dependencies uses: actions/cache@v3 @@ -34,14 +34,14 @@ jobs: key: venv-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }} - name: Install dependencies #---------------------------------------------- - # install dependencies if cache does not exist - #---------------------------------------------- - run: poetry install + # install dependencies if cache does not exist + #---------------------------------------------- + run: poetry install --without docs if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' #---------------------------------------------- - # config poetry and publish to pypi - #---------------------------------------------- + # config poetry and publish to pypi + #---------------------------------------------- - name: Config run: poetry config pypi-token.pypi ${{ secrets.PYPI_TOKEN }} - - name: Publish + - name: Publish run: poetry publish --build diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 287c650..e58d164 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -7,46 +7,46 @@ jobs: strategy: fail-fast: false matrix: - python-version: [3.7, 3.8] + python-version: ['3.8', '3.9', '3.10', '3.11'] os: [ubuntu-latest, macos-latest] runs-on: ${{ matrix.os }} if: "! contains(toJSON(github.event.commits.*.message), '[skip-tests]')" steps: #---------------------------------------------- - # check-out repo and set-up python + # check-out repo and set-up python #---------------------------------------------- - - uses: actions/checkout@v2 - - uses: actions/setup-python@v2 + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} #---------------------------------------------- - # ----- install & configure poetry ----- - #---------------------------------------------- + # ----- install & configure poetry ----- + #---------------------------------------------- - name: Install and configure Poetry uses: snok/install-poetry@v1 with: - version: 1.1.6 virtualenvs-create: true virtualenvs-in-project: true + installer-parallel: true + #---------------------------------------------- + # load cached venv if cache exists #---------------------------------------------- - # load cached venv if cache exists - #---------------------------------------------- - name: Load cached venv id: cached-poetry-dependencies - uses: actions/cache@v2 + uses: actions/cache@v3 with: path: .venv key: venv-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }} #---------------------------------------------- - # install dependencies if cache does not exist - #---------------------------------------------- + # install dependencies if cache does not exist + #---------------------------------------------- - name: Install dependencies - run: poetry install + run: poetry install --without docs if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' #---------------------------------------------- # run tests - #---------------------------------------------- + #---------------------------------------------- - name: Run tests run: | source .venv/bin/activate - poetry run pytest --cov tests/ \ No newline at end of file + poetry run pytest --cov tests/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..6d226c3 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,24 @@ +exclude: '^.md' +default_language_version: + python: python3.8 + + +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: "v4.5.0" + hooks: + - id: check-yaml + - id: end-of-file-fixer + - id: trailing-whitespace + + # Using this mirror lets us use mypyc-compiled black, which is about 2x faster +- repo: https://github.com/psf/black-pre-commit-mirror + rev: "23.11.0" + hooks: + - id: black + +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: "v0.1.6" + hooks: + - id: ruff + args: [--fix, --exit-non-zero-on-fix] diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..c566f4a --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,35 @@ +# Read the Docs configuration file for Sphinx projects +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the OS, Python version and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.8" + # You can also specify other tool versions: + # nodejs: "20" + # rust: "1.70" + # golang: "1.20" + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/conf.py + # You can configure Sphinx to use a different builder, for instance use the dirhtml builder for simpler URLs + # builder: "dirhtml" + # Fail on all warnings to avoid broken references + # fail_on_warning: true + +# Optionally build your docs in additional formats such as PDF and ePub +formats: + - pdf + # - epub + +# Optional but recommended, declare the Python requirements required +# to build your documentation +# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +python: + install: + - requirements: docs/requirements.txt diff --git a/.readthedocs.yml b/.readthedocs.yml deleted file mode 100644 index 52a680c..0000000 --- a/.readthedocs.yml +++ /dev/null @@ -1,25 +0,0 @@ -# .readthedocs.yml -# Read the Docs configuration file -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -# Required -version: 2 - -# Build documentation in the docs/ directory with Sphinx -sphinx: - configuration: docs/conf.py - -# Build documentation with MkDocs -#mkdocs: -# configuration: mkdocs.yml - -# Optionally build your docs in additional formats such as PDF -formats: - - pdf - -# Optionally set the version of Python and requirements required to build your docs -python: - version: 3.8 - install: - - requirements: docs/requirements.txt - system_packages: true \ No newline at end of file diff --git a/Changelog.rst b/Changelog.rst index 0196a00..3f2e75c 100644 --- a/Changelog.rst +++ b/Changelog.rst @@ -9,12 +9,12 @@ - Fitting functions (i.e., `drw_fit`) are now fully modular (allow user provided optimization function, prior function and etc.) - A new `addNoise` function to simulated random noise given measurement errors. - **Bug fixes:** #44 -- **API changes:** `n_iter` -> `n_opt` in fitting functions. +- **API changes:** `n_iter` -> `n_opt` in fitting functions. 0.3.0 (2021-01-07) +++++++++++++++++++++ -- update parameter initialization in fit functions; removed `de` option #26, #27 +- update parameter initialization in fit functions; removed `de` option #26, #27 - add few utils functions #30, #25 - add mcmc module #29 - ts simulation now support linear error @@ -48,4 +48,4 @@ First release! - Fully working CARMA kernels: `DRW_term`, `DHO_term` and `CARMA_term` - Functions to simulate CARMA time series given a kernel -- Functions to fit arbitrary time series to CARMA models (still having instability issues with CARMA models higher than DHO/CARMA(2,1)) \ No newline at end of file +- Functions to fit arbitrary time series to CARMA models (still having instability issues with CARMA models higher than DHO/CARMA(2,1)) diff --git a/README.md b/README.md index e51e72d..0fcf6fd 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ ![tests](https://github.com/ywx649999311/EzTao/workflows/tests/badge.svg) -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ywx649999311/EzTao/v0.4.1?filepath=docs/notebooks) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ywx649999311/EzTao/v0.4.2?filepath=docs/notebooks) [![Documentation Status](https://readthedocs.org/projects/eztao/badge/?version=latest)](https://eztao.readthedocs.io/en/latest/) ascl:2201.001 # EzTao (易道) @@ -15,12 +15,12 @@ pip install git+https://github.com/ywx649999311/EzTao.git ``` ### Dependencies >``` ->python = "^3.7" +>python = ">=3.8,<3.12" >celerite = ">= 0.3.0" ->matplotlib = "^3.3.0" >scipy = "> 1.5.0" >numba = ">= 0.51.0" ->emcee = ">=3.0.0" +>matplotlib = ">=3.3.3" +>emcee = ">=0.3.0" >``` ## Quick Examples @@ -72,7 +72,7 @@ ax.plot(freq, best_psd(freq), label='Best-fit PSD') ``` ![drw_psd](include/drw_psd.png) -__Note:__ How well the input and best-fit PSD match is up to how good the best-fit parameters are, which is highly influenced by the quality of the input time series. +__Note:__ How well the input and best-fit PSD match is up to how good the best-fit parameters are, which is highly influenced by the quality of the input time series. For more examples, please check out the [online documentation](https://eztao.readthedocs.io/en/latest/) or run the tutorial notebooks at -> [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ywx649999311/EzTao/v0.4.0?filepath=docs/notebooks). @@ -81,7 +81,7 @@ For more examples, please check out the [online documentation](https://eztao.rea `poetry` is used to solve dependencies and to build/publish this package. Below shows how setup the environment for development (assuming you already have `poetry` installed on your machine). _**Warning:**_ `poetry` is having issue installing `llvmlite = 0.34.0` (used for `eztao = ^0.4.0`) under Python 3.9. The issue disappears for Python 3.8. 1. Clone this repository, and enter the repository folder. -2. Create a python virtual environment and activate it (the virtual environment name must be '.venv'). +2. Create a python virtual environment and activate it (the virtual environment name must be '.venv'). ``` python -m venv .venv source .venv/bin/activate @@ -91,7 +91,7 @@ For more examples, please check out the [online documentation](https://eztao.rea poetry install ``` -Now you should be ready to start adding new features. Be sure to checkout the normal practice regarding how to use `poetry` on its website. When you are ready to push, also make sure the poetry.lock file is checked-in if any dependency has changed. +Now you should be ready to start adding new features. Be sure to checkout the normal practice regarding how to use `poetry` on its website. When you are ready to push, also make sure the poetry.lock file is checked-in if any dependency has changed. ## Citation We are working on a paper to describe the full implementation of **EzTao**. In the meantime, if you find **EzTao** useful for your research, please consider acknowledging **EzTao** using the following: @@ -112,4 +112,3 @@ archivePrefix = {ascl}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } ``` - diff --git a/docs/conf.py b/docs/conf.py index 9506efa..2fa19fb 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,7 +23,7 @@ # -- Project information ----------------------------------------------------- project = "EzTao" -copyright = "2020, Weixiang Yu" +copyright = "2020-2023, Weixiang Yu" author = "Weixiang Yu" # The full version, including alpha/beta/rc tags diff --git a/docs/index.rst b/docs/index.rst index 0fee9a7..9f7c3a4 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,8 +1,8 @@ EzTao ===== -**EzTao** is a Python toolkit for conducting time-series analysis using continuous-time autoregressive moving average (CARMA) processes. It uses `celerite`_ (a fast gaussian processes regression library) to compute the likelihood of a set of proposed CARMA parameters given the input time series. Comparing to existing tools for performing CARMA analysis in Python which use *Kalman filter* to evaluate the likelihood function (e.g., `Kali`_), **EzTao** offers a more scalable solution (see the `celerite paper`_ for a comparison). +**EzTao** is a Python toolkit for conducting time-series analysis using continuous-time autoregressive moving average (CARMA) processes. It uses `celerite`_ (a fast gaussian processes regression library) to compute the likelihood of a set of proposed CARMA parameters given the input time series. Comparing to existing tools for performing CARMA analysis in Python which use *Kalman filter* to evaluate the likelihood function (e.g., `Kali`_), **EzTao** offers a more scalable solution (see the `celerite paper`_ for a comparison). -**EzTao** consist of tools to both simulate CARMA processes and fit (maximum likelihood estimation or MLE) time series to CARMA models. The current version of **EzTao** is built on top of `celerite`_, future versions will take advantage of `celerite2`_ (still under active development) for a better integration with other probabilistic programing libraries such as `PyMC3`_. +**EzTao** consist of tools to both simulate CARMA processes and fit (maximum likelihood estimation or MLE) time series to CARMA models. The current version of **EzTao** is built on top of `celerite`_, future versions will take advantage of `celerite2`_ (still under active development) for a better integration with other probabilistic programing libraries such as `PyMC3`_. Installation ------------ @@ -49,4 +49,4 @@ Changelog .. _celerite paper: https://iopscience.iop.org/article/10.3847/1538-3881/aa9332 .. _celerite2: https://github.com/exoplanet-dev/celerite2 .. _PyMC3: https://docs.pymc.io/ -.. _pip: https://pip.pypa.io/en/stable/ \ No newline at end of file +.. _pip: https://pip.pypa.io/en/stable/ diff --git a/docs/python/carma.rst b/docs/python/carma.rst index eb3baa5..ba06b5e 100644 --- a/docs/python/carma.rst +++ b/docs/python/carma.rst @@ -3,7 +3,7 @@ CARMA CARMA GP kernels ---------------- -.. note:: `term` is a `celerite` nomenclature, a `term` is just a GP kernel. For example, a ``DRW_term`` object defines a DRW kernel. +.. note:: `term` is a `celerite` nomenclature, a `term` is just a GP kernel. For example, a ``DRW_term`` object defines a DRW kernel. .. automodule:: eztao.carma.CARMATerm :members: @@ -12,4 +12,4 @@ CARMA GP kernels CARMA utility functions ----------------------- .. automodule:: eztao.carma.model_utils - :members: \ No newline at end of file + :members: diff --git a/docs/python/ts.rst b/docs/python/ts.rst index 96aa504..08f8aa1 100644 --- a/docs/python/ts.rst +++ b/docs/python/ts.rst @@ -23,4 +23,4 @@ MCMC Time series utility functions ----------------------------- .. automodule:: eztao.ts.utils - :members: \ No newline at end of file + :members: diff --git a/docs/python/viz.rst b/docs/python/viz.rst index dbe232a..afbf9df 100644 --- a/docs/python/viz.rst +++ b/docs/python/viz.rst @@ -2,4 +2,4 @@ Visualization Tools =================== .. automodule:: eztao.viz.mpl_viz - :members: \ No newline at end of file + :members: diff --git a/docs/requirements.txt b/docs/requirements.txt index 11caed7..ba7088d 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,13 +1,13 @@ # docs/requirements.txt +jinja2<3.1 sphinx==3.4.1 sphinx_rtd_theme==0.5.0 nbsphinx>=0.8.6 sphinx-copybutton==0.3.1 toml==0.10.2 ipykernel -numba==0.51.0 -celerite==0.4.0 -scipy==1.5.0 -emcee==3.0.0 -matplotlib==3.3.3 -jinja2<3.1 +numba>=0.51.0 +celerite>=0.4.0 +scipy>=1.5.0 +emcee>=3.0.0 +matplotlib>=3.3.3 diff --git a/environment.yml b/environment.yml index c95cea2..4c39690 100644 --- a/environment.yml +++ b/environment.yml @@ -3,11 +3,11 @@ channels: - defaults - conda-forge dependencies: - - python=3.7 + - python=3.8 - matplotlib - numpy - emcee - pip: - corner - - eztao==0.4.1 + - eztao==0.4.2 - importlib-metadata diff --git a/noxfile.py b/noxfile.py new file mode 100644 index 0000000..52767f1 --- /dev/null +++ b/noxfile.py @@ -0,0 +1,8 @@ +from nox_poetry import session + + +@session(python=["3.8", "3.9", "3.10", "3.11"]) +def tests(session): + session.install("pytest", ".") + session.install("joblib", ".") + session.run("pytest") diff --git a/poetry.lock b/poetry.lock index fe76b9e..199e98c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,51 +1,86 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + [[package]] name = "alabaster" -version = "0.7.12" +version = "0.7.13" description = "A configurable sidebar-enabled Sphinx theme" -category = "dev" optional = false -python-versions = "*" +python-versions = ">=3.6" +files = [ + {file = "alabaster-0.7.13-py3-none-any.whl", hash = "sha256:1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3"}, + {file = "alabaster-0.7.13.tar.gz", hash = "sha256:a27a4a084d5e690e16e01e03ad2b2e552c61a65469419b907243193de1a84ae2"}, +] + +[[package]] +name = "argcomplete" +version = "3.1.6" +description = "Bash tab completion for argparse" +optional = false +python-versions = ">=3.8" +files = [ + {file = "argcomplete-3.1.6-py3-none-any.whl", hash = "sha256:71f4683bc9e6b0be85f2b2c1224c47680f210903e23512cfebfe5a41edfd883a"}, + {file = 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(AGN) time-series analysis." -authors = ["Weixiang Yu "] +authors = ["Weixiang Yu "] readme = "README.md" license = "MIT" repository = "https://github.com/ywx649999311/EzTao" @@ -11,7 +11,7 @@ include = [ ] [tool.poetry.dependencies] -python = "^3.7" +python = ">=3.8,<4.0" celerite = ">= 0.3.0" matplotlib = "^3.3.0" scipy = ">= 1.5" @@ -19,17 +19,49 @@ numba = ">= 0.51.0" importlib-metadata = ">= 2.0.0" emcee = ">=3.0.0" -[tool.poetry.dev-dependencies] -toml = "^0.10.1" +[tool.poetry.group.dev.dependencies] +pandas = "^1.1.4" +pre-commit = "^3.5.0" +black = "^23.11.0" +ruff = "^0.1.6" + +[tool.poetry.group.test.dependencies] pytest = "^6.0.1" pytest-cov = "^2.10.1" +nox = "^2023.4.22" +nox-poetry = "^1.0.3" +poetry-plugin-export = "^1.6.0" joblib = ">=1.2.0" -pandas = "^1.1.4" + +[tool.poetry.group.docs.dependencies] +toml = "^0.10.1" Sphinx = "^3.4.1" sphinx-rtd-theme = "^0.5.0" nbsphinx = ">=0.8.6" sphinx-copybutton = "^0.3.1" -black = "^21.7b0" + +[tool.black] +target-version = ["py38"] +line-length = 88 + +[tool.ruff] +target-version = "py38" +line-length = 88 +ignore-init-module-imports = true +ignore = [ + "E741", # Allow ambiguous variable names + "PLR0911", # Allow many return statements + "PLR0913", # Allow many arguments to functions + "PLR0915", # Allow many statements + "PLR2004", # Allow magic numbers in comparisons + "F401", # unused imports + "F403", # import * + "F405", # undefined import due to F403 + "F841", + "E722", + "E731", +] +exclude=["docs/conf.py"] [build-system] requires = ["poetry_core>=1.0.0"] diff --git a/src/eztao/carma/CARMATerm.py b/src/eztao/carma/CARMATerm.py index c9b621a..96640de 100644 --- a/src/eztao/carma/CARMATerm.py +++ b/src/eztao/carma/CARMATerm.py @@ -1,11 +1,11 @@ """ -A collection of GP kernels that express the autovariance structure of CARMA models using +A collection of GP kernels that express the autovariance structure of CARMA models using celerite. """ import numpy as np from celerite import terms -from numba import njit, float64, complex128, int32, vectorize +from numba import njit, float64, complex128 import warnings __all__ = ["acf", "DRW_term", "DHO_term", "CARMA_term"] @@ -143,7 +143,7 @@ def acf(arroots, arparam, maparam): root_k = arroots[np.roll(root_idx, j)] denom *= (root_k - arroots) * (np.conj(root_k) + arroots) - return sigma ** 2 * num_left * num_right / denom + return sigma**2 * num_left * num_right / denom class DRW_term(terms.Term): @@ -255,7 +255,6 @@ class CARMA_term(terms.Term): """ def __init__(self, log_arpars, log_mapars, *args, **kwargs): - arpar_temp = "log_a{}" mapar_temp = "log_b{}" arpar_names = ("log_a1",) diff --git a/src/eztao/carma/model_utils.py b/src/eztao/carma/model_utils.py index 8e13e68..f6de114 100644 --- a/src/eztao/carma/model_utils.py +++ b/src/eztao/carma/model_utils.py @@ -20,10 +20,10 @@ def drw_psd(amp, tau): # convert amp, tau to CARMA parameters a0 = 1 / tau - sigma2 = 2 * amp ** 2 * a0 + sigma2 = 2 * amp**2 * a0 def psd(f): - return sigma2 / (a0 ** 2 + (2 * np.pi * f) ** 2) + return sigma2 / (a0**2 + (2 * np.pi * f) ** 2) return psd @@ -136,7 +136,7 @@ def drw_sf(amp, tau): """ def sf(lag): - return np.sqrt(amp ** 2 * (1 - drw_acf(tau)(lag))) + return np.sqrt(2 * amp**2 * (1 - drw_acf(tau)(lag))) return sf @@ -156,6 +156,6 @@ def carma_sf(arparams, maparams): amp2 = gpTerm.get_rms_amp() ** 2 def sf(lag): - return np.sqrt(amp2 * (1 - carma_acf(arparams, maparams)(lag))) + return np.sqrt(2 * amp2 * (1 - carma_acf(arparams, maparams)(lag))) return sf diff --git a/src/eztao/ts/__init__.py b/src/eztao/ts/__init__.py index c82d3ba..f4724ab 100644 --- a/src/eztao/ts/__init__.py +++ b/src/eztao/ts/__init__.py @@ -1,4 +1,4 @@ from .utils import * from .carma_fit import * from .carma_sim import * -from .carma_mcmc import mcmc \ No newline at end of file +from .carma_mcmc import mcmc diff --git a/src/eztao/ts/carma_fit.py b/src/eztao/ts/carma_fit.py index eb49c1e..0e5859a 100644 --- a/src/eztao/ts/carma_fit.py +++ b/src/eztao/ts/carma_fit.py @@ -3,7 +3,6 @@ """ import numpy as np -from math import ceil from scipy.optimize import minimize from scipy.stats import median_abs_deviation as mad import celerite @@ -54,10 +53,10 @@ def neg_fcoeff_ll(log_fcoeffs, y, gp): try: gp.kernel.set_log_fcoeffs(log_fcoeffs) neg_ll = -gp.log_likelihood(y) - except celerite.solver.LinAlgError as c: + except celerite.solver.LinAlgError: # print(c) pass - except Exception as e: + except Exception: pass return neg_ll @@ -84,10 +83,10 @@ def neg_param_ll(log_params, y, gp): try: gp.set_parameter_vector(log_params) neg_ll = -gp.log_likelihood(y) - except celerite.solver.LinAlgError as c: + except celerite.solver.LinAlgError: # print(c) pass - except Exception as e: + except Exception: pass return neg_ll @@ -670,4 +669,4 @@ def carma_fit( except: pass - return best_fit_return \ No newline at end of file + return best_fit_return diff --git a/src/eztao/ts/carma_sim.py b/src/eztao/ts/carma_sim.py index ff22791..d2ecf6d 100644 --- a/src/eztao/ts/carma_sim.py +++ b/src/eztao/ts/carma_sim.py @@ -12,7 +12,27 @@ __all__ = ["gpSimFull", "gpSimRand", "gpSimByTime", "addNoise", "pred_lc"] -def gpSimFull(carmaTerm, SNR, duration, N, nLC=1, log_flux=True): +def gp_sample(gp, size=None, seed=None): + """Sample Celerite GP with a fixed seed""" + + if seed is not None: + rng = np.random.default_rng(seed=seed) + else: + rng = np.random.default_rng() + + gp._recompute() + if size is None: + n = rng.standard_normal(len(gp._t)) + else: + n = rng.standard_normal((len(gp._t), size)) + + n = gp.solver.dot_L(n) + if size is None: + return gp.mean.get_value(gp._t) + n[:, 0] + return gp.mean.get_value(gp._t)[None, :] + n.T + + +def gpSimFull(carmaTerm, SNR, duration, N, nLC=1, log_flux=True, lc_seed=None): """ Simulate CARMA time series using uniform sampling. @@ -25,6 +45,7 @@ def gpSimFull(carmaTerm, SNR, duration, N, nLC=1, log_flux=True): nLC (int, optional): Number of time series to simulate. Defaults to 1. log_flux (bool): Whether the flux/y values are in astronomical magnitude. This argument affects how errors are assigned. Defaults to True. + lc_seed (int): Random seed for time series simulation. Defaults to None. Raises: RuntimeError: If the input CARMA term/model is not stable, thus cannot be @@ -44,9 +65,14 @@ def gpSimFull(carmaTerm, SNR, duration, N, nLC=1, log_flux=True): "The covariance matrix of the provided CARMA term is not positive definite!" ) + if lc_seed is not None: + rng = np.random.default_rng(seed=lc_seed) + else: + rng = np.random.default_rng() + t = np.linspace(0, duration, N) noise = carmaTerm.get_rms_amp() / SNR - yerr = np.random.lognormal(0, 0.25, N) * noise + yerr = rng.lognormal(0, 0.25, N) * noise yerr = yerr[np.argsort(np.abs(yerr))] # small->large # init GP and solve matrix @@ -55,7 +81,7 @@ def gpSimFull(carmaTerm, SNR, duration, N, nLC=1, log_flux=True): # simulate, assign yerr based on y t = np.repeat(t[None, :], nLC, axis=0) - y = gp_sim.sample(size=nLC) + y = gp_sample(gp_sim, size=nLC, seed=lc_seed) # format yerr making it heteroscedastic yerr = np.repeat(yerr[None, :], nLC, axis=0) @@ -77,7 +103,16 @@ def gpSimFull(carmaTerm, SNR, duration, N, nLC=1, log_flux=True): def gpSimRand( - carmaTerm, SNR, duration, N, nLC=1, log_flux=True, season=True, full_N=10_000 + carmaTerm, + SNR, + duration, + N, + nLC=1, + log_flux=True, + season=True, + full_N=10_000, + lc_seed=None, + downsample_seed=None, ): """ Simulate CARMA time series randomly downsampled from a much denser full time series. @@ -95,12 +130,17 @@ def gpSimRand( to True. full_N (int, optional): The number of data points in the full time series (before downsampling). Defaults to 10_000. + lc_seed (int): Random seed for full time series simulation. Defaults to None. + downsample_seed (int): Random seed for downsampling the simulated full time + series. Defaults to None. Returns: (array(float), array(float), array(float)): Time stamps (default in day), y values and measurement errors of the simulated time series. """ - t, y, yerr = gpSimFull(carmaTerm, SNR, duration, full_N, nLC=nLC, log_flux=log_flux) + t, y, yerr = gpSimFull( + carmaTerm, SNR, duration, full_N, nLC=nLC, log_flux=log_flux, lc_seed=lc_seed + ) t = np.atleast_2d(t) y = np.atleast_2d(y) yerr = np.atleast_2d(yerr) @@ -116,7 +156,7 @@ def gpSimRand( mask1 = add_season(t[i]) else: mask1 = np.ones(t[i].shape[0], dtype=bool) - mask2 = downsample_byN(t[i, mask1], N) + mask2 = downsample_byN(t[i, mask1], N, seed=downsample_seed) tOut[i, :] = t[i, mask1][mask2] yOut[i, :] = y[i, mask1][mask2] yerrOut[i, :] = yerr[i, mask1][mask2] @@ -127,7 +167,7 @@ def gpSimRand( return tOut, yOut, yerrOut -def gpSimByTime(carmaTerm, SNR, t, factor=10, nLC=1, log_flux=True): +def gpSimByTime(carmaTerm, SNR, t, factor=10, nLC=1, log_flux=True, lc_seed=None): """ Simulate CARMA time series at desired time stamps. @@ -147,6 +187,7 @@ def gpSimByTime(carmaTerm, SNR, t, factor=10, nLC=1, log_flux=True): nLC (int, optional): Number of time series to simulate. Defaults to 1. log_flux (bool): Whether the flux/y values are in astronomical magnitude. This argument affects how errors are assigned. Defaults to True. + lc_seed (int): Random seed for time series simulation. Defaults to None. Returns: (array(float), array(float), array(float)): Time stamps (default in day), y @@ -158,7 +199,7 @@ def gpSimByTime(carmaTerm, SNR, t, factor=10, nLC=1, log_flux=True): # simulate full LC tFull, yFull, yerrFull = gpSimFull( - carmaTerm, SNR, duration, N=N, nLC=nLC, log_flux=log_flux + carmaTerm, SNR, duration, N=N, nLC=nLC, log_flux=log_flux, lc_seed=lc_seed ) tFull = np.atleast_2d(tFull) yFull = np.atleast_2d(yFull) @@ -177,7 +218,7 @@ def gpSimByTime(carmaTerm, SNR, t, factor=10, nLC=1, log_flux=True): return tOut, yOut, yerrOut -def addNoise(y, yerr): +def addNoise(y, yerr, seed=None): """ Add (gaussian) noise to the input simulated time series given the measurement uncertainties. @@ -185,12 +226,19 @@ def addNoise(y, yerr): y (array(float)): The 'clean' time series. yerr (array(float)): The measurement uncertainties for the input time series. + seed (int): Random seed for simulating noise. Defaults to None. Returns: array(float): A new time series with simulated (gaussian ) noise added on top. """ - vec_norm = np.vectorize(np.random.normal, signature="(n),(n)->(n)") + + if seed is not None: + rng = np.random.default_rng(seed=seed) + else: + rng = np.random.default_rng() + + vec_norm = np.vectorize(rng.normal, signature="(n),(n)->(n)") noise = vec_norm(np.zeros_like(y), yerr) return y + noise diff --git a/src/eztao/ts/utils.py b/src/eztao/ts/utils.py index efa4446..3e231fc 100644 --- a/src/eztao/ts/utils.py +++ b/src/eztao/ts/utils.py @@ -3,28 +3,34 @@ """ import numpy as np -from numba import njit from functools import partial from scipy.stats import median_abs_deviation as mad __all__ = ["downsample_byN", "add_season", "downsample_byTime", "median_clip"] -def downsample_byN(t, nObs): +def downsample_byN(t, nObs, seed): """ Randomly choose N data points from a given time series. Args: t (array(float)): Time stamps of the original time series. N (int): The number of entries in the final time series. + seed (int): Random seed for downsampling. Defaults to None. Returns: A 1d array booleans indicating which data points to keep. """ + + if seed is not None: + rng = np.random.default_rng(seed=seed) + else: + rng = np.random.default_rng() + # random choose index idx = np.arange(len(t)) mask = np.zeros_like(idx, dtype=np.bool_) - true_idx = np.random.choice(idx, nObs, replace=False) + true_idx = rng.choice(idx, nObs, replace=False) # assign chosen index to 1/True mask[true_idx] = 1 @@ -101,7 +107,7 @@ def median_clip(y, num_sigma=3): # compute residual & sigma res = np.abs(y - med) - sigma = mad(res, scale='normal') + sigma = mad(res, scale="normal") # set clipping thresh hold raise_bar = True diff --git a/src/eztao/viz/eztao.rc b/src/eztao/viz/eztao.rc index 8c4a75c..48eb149 100644 --- a/src/eztao/viz/eztao.rc +++ b/src/eztao/viz/eztao.rc @@ -744,4 +744,4 @@ pgf.texsystem : pdflatex #animation.embed_limit : 20.0 ## Limit, in MB, of size of base64 encoded ## animation in HTML (i.e. IPython notebook) -#mpl_toolkits.legacy_colorbar: True \ No newline at end of file +#mpl_toolkits.legacy_colorbar: True diff --git a/src/eztao/viz/mpl_viz.py b/src/eztao/viz/mpl_viz.py index e42cdf2..7412872 100644 --- a/src/eztao/viz/mpl_viz.py +++ b/src/eztao/viz/mpl_viz.py @@ -5,7 +5,6 @@ import matplotlib.pyplot as plt import matplotlib as mpl import numpy as np -import celerite from eztao.carma.CARMATerm import * from eztao.ts.carma_sim import pred_lc @@ -161,7 +160,7 @@ def plot_dho_ll( ranges (list, optional): Parameters ranges (in natural log) within which to plot the surface. Defaults to [(None, None), (None, None), (None, None), (None, None)]. - nLevels (int, optional): The number of levels in the final contour plot. + nLevels (int, optional): The number of levels in the final contour plot. Defaults to 10. Keyword Args: diff --git a/tests/test_CARMAFit.py b/tests/test_CARMAFit.py index ede8d2d..6c9ff16 100644 --- a/tests/test_CARMAFit.py +++ b/tests/test_CARMAFit.py @@ -4,7 +4,6 @@ import numpy as np from eztao.carma import DRW_term, DHO_term, CARMA_term from eztao.ts import * -from celerite import GP from joblib import Parallel, delayed import pytest @@ -111,7 +110,6 @@ def test_log_drw_init(): def test_sample_carma(): - p1, q1 = (4, 3) ar1, ma1 = sample_carma(p1, q1) assert len(ar1) == p1 @@ -126,7 +124,6 @@ def test_sample_carma(): def test_drwFit(): - for kernel in [drw1, drw2, drw3]: t, y, yerr = gpSimRand(kernel, 50, 365 * 10.0, 200, nLC=200, season=False) best_fit_drw = np.array( @@ -144,8 +141,9 @@ def test_drwFit(): def test_dhoFit(): - - t1, y1, yerr1 = gpSimRand(dho1, 100, 365 * 10.0, 200, nLC=100, season=False) + t1, y1, yerr1 = gpSimRand( + dho1, 100, 365 * 10.0, 200, nLC=100, season=False, lc_seed=10 + ) best_fit_dho1 = np.array( Parallel(n_jobs=-1)( delayed(dho_fit)(t1[i], y1[i], yerr1[i]) for i in range(len(t1)) @@ -160,7 +158,9 @@ def test_dhoFit(): assert np.percentile(diff1, 75) < 0.25 # the second test will down scale lc by 1e6 - t2, y2, yerr2 = gpSimRand(dho2, 100, 365 * 10.0, 200, nLC=100, season=False) + t2, y2, yerr2 = gpSimRand( + dho2, 100, 365 * 10.0, 200, nLC=100, season=False, lc_seed=10 + ) best_fit_dho2 = np.array( Parallel(n_jobs=-1)( delayed(dho_fit)(t2[i], y2[i] / 1e6, yerr2[i] / 1e6) for i in range(len(t2)) @@ -176,8 +176,9 @@ def test_dhoFit(): def test_carmaFit(): - - t1, y1, yerr1 = gpSimRand(carma30, 200, 365 * 10.0, 1000, nLC=150, season=False) + t1, y1, yerr1 = gpSimRand( + carma30, 200, 365 * 10.0, 1000, nLC=150, season=False, lc_seed=10 + ) best_fit_carma1 = np.array( Parallel(n_jobs=-1)( delayed(carma_fit)(t1[i], y1[i] / 1e-6, yerr1[i] / 1e-6, 3, 0, n_opt=30) @@ -194,7 +195,9 @@ def test_carmaFit(): assert np.percentile(diff1, 75) < 0.4 # the second test will down scale lc by 1e6 - t2, y2, yerr2 = gpSimRand(carma31, 300, 365 * 10.0, 1000, nLC=150, season=False) + t2, y2, yerr2 = gpSimRand( + carma31, 300, 365 * 10.0, 1000, nLC=150, season=False, lc_seed=10 + ) best_fit_carma2 = np.array( Parallel(n_jobs=-1)( delayed(carma_fit)(t2[i], y2[i], yerr2[i], 3, 1, n_opt=50) diff --git a/tests/test_CARMASim.py b/tests/test_CARMASim.py index 6fc9c6a..be6a814 100644 --- a/tests/test_CARMASim.py +++ b/tests/test_CARMASim.py @@ -26,9 +26,30 @@ def test_invalidSim(): t, y, yerr = gpSimFull(carma_invalid, 20, 365 * 10.0, 10000) +def test_simFullSeed(): + """Test the lc_seed option of gpSimFull.""" + + # SNR = 20 + nLC = 5 + for kernel in test_kernels: + t, y1, yerr1 = gpSimFull(kernel, 20, 365 * 10.0, 1000, nLC=nLC, lc_seed=10) + t, y2, yerr2 = gpSimFull(kernel, 20, 365 * 10.0, 1000, nLC=nLC, lc_seed=10) + + # agree between two separate calls + for i in range(nLC): + assert np.allclose(y1[i], y2[i]) + assert np.allclose(yerr1[i], yerr2[i]) + + # disagree between different simulations from one call + assert not np.allclose(y1[0], y1[nLC - 1]) + assert not np.allclose(y1[1], y1[nLC - 2]) + assert not np.allclose(yerr1[0], yerr1[nLC - 1]) + assert not np.allclose(yerr1[1], yerr1[nLC - 2]) + + def test_simRand(): """Test function gpSimRand.""" - # SNR = 10 + # SNR = 20 for kernel in test_kernels: t, y, yerr = gpSimRand(kernel, 20, 365 * 10.0, 150, nLC=100, season=False) log_amp = np.log(kernel.get_rms_amp()) @@ -53,6 +74,26 @@ def test_simRand(): assert (np.argsort(yF) == np.argsort(-yerrF)).all() +def test_simRandSeed(): + """Test the downsample_seed option of gpSimRand.""" + + # SNR = 20 + nLC = 1 + for kernel in test_kernels: + t1, y1, yerr1 = gpSimRand( + kernel, 20, 365 * 10.0, 150, nLC=nLC, lc_seed=10, downsample_seed=10 + ) + t2, y2, yerr2 = gpSimRand( + kernel, 20, 365 * 10.0, 150, nLC=nLC, lc_seed=10, downsample_seed=10 + ) + t3, y3, yerr3 = gpSimRand( + kernel, 20, 365 * 10.0, 150, nLC=nLC, lc_seed=10, downsample_seed=20 + ) + + assert np.allclose(t1, t2) + assert not np.allclose(t1, t3) + + def test_simByTime(): """Test function gpSimByTime.""" t = np.sort(np.random.uniform(0, 3650, 5000)) @@ -72,3 +113,22 @@ def test_simByTime(): # test single LC simulation tOut, yOut, yerrOut = gpSimByTime(dho2, SNR, t, nLC=1) assert tOut.shape[0] == yOut.shape[0] == yerrOut.shape[0] == t.shape[0] + + +def test_addNoiseSeed(): + """Test the seed option of addNoise.""" + + nLC = 1 + t1, y1, yerr1 = gpSimRand( + test_kernels[-1], 20, 365 * 10.0, 150, nLC=nLC, lc_seed=10, downsample_seed=10 + ) + t2, y2, yerr2 = gpSimRand( + test_kernels[-1], 20, 365 * 10.0, 150, nLC=nLC, lc_seed=10, downsample_seed=10 + ) + + noise_y1 = addNoise(y1, yerr1, seed=10) + noise_y2 = addNoise(y2, yerr2, seed=10) + noise_y3 = addNoise(y1, yerr1, seed=20) + + assert np.allclose(noise_y1, noise_y2) + assert not np.allclose(noise_y1, noise_y3) diff --git a/tests/test_CARMAUtils.py b/tests/test_CARMAUtils.py index 20f9b80..e000534 100644 --- a/tests/test_CARMAUtils.py +++ b/tests/test_CARMAUtils.py @@ -47,7 +47,7 @@ def test_acf(): ), atol=1e-10, ) - + ar = np.array([0.04, 0.0027941]) ma = np.array([0.004672, 0.0257]) dho_acf = carma_acf(ar, ma) @@ -65,4 +65,4 @@ def test_sf(): carma_sf(np.array([1 / tau]), np.array([kernel.get_perturb_amp()]))(t) ), atol=1e-10, - ) \ No newline at end of file + )