ascl_id stringclasses 752
values | repo_url stringclasses 737
values | file_path stringlengths 4 194 | content large_stringlengths 1 1M | language stringclasses 29
values | license_type stringclasses 24
values | size int64 1 1M |
|---|---|---|---|---|---|---|
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/posterior.py | from .likelihood import Likelihood
import numpy as np
import dill as pickle
import radvel
class Posterior(Likelihood):
"""Posterior object
Posterior object to be sent to the fitting routines.
It is essentially the same as the Likelihood object,
but priors are applied here.
Args:
likelihood... | Python | MIT | 8,517 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/nested_sampling.py | import os
import shutil
from typing import Optional
import h5py
import numpy as np
from pandas import DataFrame, read_hdf
from radvel.posterior import Posterior
def _run_dynesty(
post: Posterior,
output_dir: Optional[str] = None,
sampler_type: str = "static",
proceed: bool = False,
sampler_kwar... | Python | MIT | 19,002 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/report.py | import numpy as np
import pandas as pd
import subprocess
import os
import tempfile
import shutil
from operator import itemgetter
from jinja2 import Environment, PackageLoader, select_autoescape
import radvel
env = Environment(loader=PackageLoader('radvel', 'templates'))
print_basis = 'per tc tp e w k'
units = {
... | Python | MIT | 18,042 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/fitting.py | import warnings
import scipy.optimize
import numpy as np
import copy
import collections
import itertools
import radvel.likelihood
ALPHABET = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm',
'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
def maxlike_fitting(post, verbos... | Python | MIT | 14,826 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/model.py | import numpy as np
from collections import OrderedDict
from radvel import kepler
from radvel.basis import Basis
texdict = {
'per': 'P',
'logper': '\\ln{P}',
'logk': '\\ln{K}',
'tc': 'T\\rm{conj}',
'secosw': '\\sqrt{e}\\cos{\\omega}',
'sesinw': '\\sqrt{e}\\sin{\\omega}',
'ecosw': 'e\\cos{\... | Python | MIT | 12,768 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/utils.py | import os
import sys
from decimal import Decimal
from contextlib import contextmanager
import warnings
import numpy as np
from datetime import datetime, timedelta
from astropy import constants as c
from astropy import units as u
import radvel
from scipy.optimize import root
# Normalization.
# RV m/s of a 1.0 Jupiter... | Python | MIT | 20,162 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/cli.py | """
Command Line Interface
"""
import os
from argparse import ArgumentParser
import radvel.driver
from radvel.nested_sampling import BACKENDS
def main():
psr = ArgumentParser(
description="RadVel: The Radial Velocity Toolkit", prog='RadVel'
)
psr.add_argument('--version',
act... | Python | MIT | 12,716 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/basis.py | import warnings
import numpy as np
import pandas as pd
from collections import OrderedDict
from radvel.orbit import timeperi_to_timetrans, timetrans_to_timeperi
import radvel.model
BASIS_NAMES = ['per tp e w k', # The synth basis
'per tc secosw sesinw logk',
'per tc secosw sesinw k',
... | Python | MIT | 34,047 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/mcmc.py | import time
import curses
import sys
import os
import multiprocess as mp
import pandas as pd
import numpy as np
import emcee
import h5py
from radvel import utils
import radvel
class StateVars(object):
def __init__(self):
self.oac = 0
self.autosamples = []
self.automean = []
sel... | Python | MIT | 25,836 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/gp.py | import sys
import radvel
import scipy
from scipy import spatial
import abc
import numpy as np
import warnings
# implemented kernels & examples of their associated hyperparameters
KERNELS = {
"SqExp": ['gp_length', 'gp_amp'],
"Per": ['gp_per', 'gp_length', 'gp_amp'],
"QuasiPer": ['gp_per', 'gp_perlength', '... | Python | MIT | 17,043 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/tests/test_api.py | import sys
import copy
import time
import types
# pytest is now the test runner, but we don't need to import it in test files
import radvel
import radvel.driver
from radvel.nested_sampling import BACKENDS
import numpy as np
import scipy
import radvel.prior
class _args(types.SimpleNamespace):
outputdir = '/tmp/'
... | Python | MIT | 20,434 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/tests/test_cli.py | import subprocess
import sys
import radvel
import radvel.cli
def _run_cmd(cmd):
p = subprocess.Popen(cmd.split())
p.wait()
status = p.poll()
out,stderr = p.communicate()
return (status, out)
def test_help():
"""
Test command-line install
"""
cmd = 'radvel --help'
stat,o... | Python | MIT | 672 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/tests/test_coverage.py | """
Additional tests to improve code coverage for gp, likelihood, and mcmc modules.
"""
import copy
import numpy as np
import pytest
import importlib
import radvel
import radvel.gp
import radvel.likelihood
# radvel.__init__ does `from .mcmc import *` which shadows the module
# with the mcmc function. Import the actua... | Python | MIT | 19,856 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/tests/test_nb.py | import os
import sys
from glob import glob
# pytest is now the test runner, but we don't need to import it in test files
import nbformat
from nbconvert.preprocessors import ExecutePreprocessor
def test_notebook():
"""
Run though a single notebook tutorial
"""
# Test just one notebook to avoid pytest ... | Python | MIT | 1,165 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/templates/tab_params.tex | \begin{deluxetable}{lrrr}
\tablecaption{ {{ title }} }
\tablehead{
\colhead{Parameter} &
\colhead{Credible Interval} &
\colhead{Maximum Likelihood} &
\colhead{Units}
}
\startdata
\sidehead{\bf{Modified MCMC Step Parameters}}
{% for row in fitting_basis_rows %}
{{ row }} \\
{% endfor %}
\hline
\sidehead{\bf... | LaTeX | MIT | 685 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/templates/fig_corner.tex | {# Template to define the radvel multipanel plot #}
\begin{figure*}[!h]
\centering
{# quotes and tildes needed to render the curly braces around infile with nospaces #}
{{'\\includegraphics[height=8.0in,width=6.0in,keepaspectratio]{'~infile~'}'}}
\caption{Posterior distributions for all free parameters.}
\end{figure*}... | LaTeX | MIT | 321 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/templates/tab_crit.tex | \begin{deluxetable}{lrrr}
\tablecaption{ {{ title }} }
\tablehead{
\colhead{Criterion} &
\colhead{Final Value}
}
\startdata
{% for row in rows %}
{{ row }} \\
{% endfor %}
\enddata
\end{deluxetable} | LaTeX | MIT | 204 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/templates/tab_rv.tex | \begin{deluxetable}{lrrc}
\tablecaption{ {{ title }} }
\tablehead{
\colhead{Time} &
\colhead{RV} &
\colhead{RV Unc.} &
\colhead{Inst.} \\
\colhead{(JD)} &
\colhead{(m s$^{-1}$)} &
\colhead{(m s$^{-1}$)} &
\colhead{}
}
\startdata
{% for row in rows %}
{{ row }} \\
{% endfor %}
\tablecomments{ {{ ... | LaTeX | MIT | 357 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/templates/tab_prior_summary.tex | \begin{deluxetable}{l}
\tablecaption{ {{ title }} }
\tablehead{}
\startdata
{% for row in rows %}
{{ row }} \\
{% endfor %}
\enddata
\end{deluxetable}
| LaTeX | MIT | 153 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/templates/fig_rv_multipanel.tex | {# Template to define the radvel multipanel plot #}
\begin{figure*}[!h]
\centering
{# quotes and tildes needed to render the curly braces around infile with nospaces #}
{{'\\includegraphics[height=8.0in,width=6.0in,keepaspectratio]{'~infile~'}'}}
\caption{ Best-fit {{ report.num_planets }}-planet Keplerian orbital mo... | LaTeX | MIT | 1,188 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/templates/tab_derived.tex | \begin{deluxetable}{lrrr}
\tablecaption{ {{ title }} }
\tablehead{
\colhead{Parameter} &
\colhead{Credible Interval} &
\colhead{Maximum Likelihood} &
\colhead{Units}
}
\startdata
{% for row in derived_rows %}
{{ row }} \\
{% endfor %}
\enddata
\end{deluxetable} | LaTeX | MIT | 271 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/templates/fig_corner_derived_pars.tex | {# Template to define the radvel multipanel plot #}
\begin{figure*}[!h]
\centering
{# quotes and tildes needed to render the curly braces around infile with nospaces #}
{{'\\includegraphics[height=8.0in,width=6.0in,keepaspectratio]{'~infile~'}'}}
\caption{Posterior distributions for all derived parameters.}
\end{figur... | LaTeX | MIT | 324 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/templates/tab_comparison.tex | {{ coldefs }}
\tablecaption{Model Comparison}
{{ head }}
\startdata
{% for row in rows %}
{{ row }} \\
{% endfor %}
\enddata
\label{tab:comp}
\end{deluxetable*}
| LaTeX | MIT | 163 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/templates/report.tex | \documentclass{emulateapj}
\usepackage{graphicx,textcomp,fancyhdr,hyperref}
\begin{document}
\pagestyle{fancy}
\pagenumbering{gobble}
\chead{Summary of \texttt{RadVel} results for {{ report.starname_tex }} }
% Model comparison table
{{ tab_comparison }}
% Table of best fit parameters
{{ tab_params }}
% Table of deri... | LaTeX | MIT | 808 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/plot/__init__.py | import numpy as np
import matplotlib
# matplotlib.use('agg')
from matplotlib import pyplot as pl
from matplotlib.cm import nipy_spectral
from matplotlib import rcParams
from matplotlib.offsetbox import AnchoredText
latex = {
'ms': r'm s$^{\mathregular{-1}}$',
'BJDTDB': r'BJD$_{\mathregular{TDB}}$'
}
telfmts_d... | Python | MIT | 5,328 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/plot/mcmc_plots.py | import numpy as np
import corner
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib import pyplot as pl
from matplotlib import rcParams
from radvel import plot
"""
Module for plotting results of MCMC analysis, including:
- trend plot
- autocorrelation plot
- corner plot of fitted paramet... | Python | MIT | 9,111 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | radvel/plot/orbit_plots.py | import warnings
import numpy as np
import pandas as pd
from matplotlib import rcParams, gridspec
from matplotlib import pyplot as pl
from matplotlib.ticker import MaxNLocator
from astropy.time import Time
import copy
import radvel
from radvel import plot
from radvel.plot import mcmc_plots
from radvel.utils import t_t... | Python | MIT | 33,881 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | example_planets/k2-131_celerite.py | import numpy as np
import pandas as pd
import os
import radvel
# This setup file is provided to illustrate how to set up a config
# file for use with Dan Foreman-Mackey's `celerite` package.
# Data from Dai+ 2017
instnames = ['harps-n', 'pfs'] # no spaces in instrument names
data = pd.read_csv(os.path.join(radvel.DA... | Python | MIT | 2,595 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | example_planets/k2-131.py | import numpy as np
import pandas as pd
import os
import radvel
"""
This config file uses Gaussian Process (GP) regression to model stellar activity.
Don't start here. Make sure you understand how RadVel works using the basic configuration
files (radvel/example_planets/epic203771098.py and radvel/example_planets/HD164... | Python | MIT | 5,423 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | example_planets/HD164922.py | # Example Keplerian fit configuration file
# Required packages for setup
import os
import pandas as pd
import numpy as np
import radvel
N_PLANETS = 2
# Define global planetary system and dataset parameters
starname = 'HD164922'
nplanets = N_PLANETS # number of planets in the system
if nplanets != 2:
starname ... | Python | MIT | 5,058 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | example_planets/epic203771098.py | # Example Keplerian fit configuration file
# Required packages for setup
import os
import pandas as pd
import numpy as np
import radvel
# Define global planetary system and dataset parameters
starname = 'epic203771098'
nplanets = 2 # number of planets in the system
instnames = ['j'] # list of instrument names. ... | Python | MIT | 4,096 |
[ascl:1801.012] | https://github.com/California-Planet-Search/radvel | .github/workflows/ci.yml | name: CI
on:
push:
branches:
- main
- master
- next-release
- github-actions
pull_request:
branches:
- main
- master
- next-release
release:
types: [published]
jobs:
test:
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
... | YAML | MIT | 3,938 |
[ascl:1902.008] | https://github.com/Goobley/Radynversion | Inn2.py | import torch
from torch import nn
import numpy as np
from FrEIA.framework import InputNode, OutputNode, Node, ReversibleGraphNet
from FrEIA.modules import rev_multiplicative_layer, permute_layer
from .loss import mse, mse_tv, mmd_multiscale_on
from scipy.interpolate import interp1d
from copy import deepcopy
from it... | Python | MIT | 28,047 |
[ascl:1902.008] | https://github.com/Goobley/Radynversion | README.md | ## Radynversion: Learning to Invert a Solar Flare Atmosphere with Invertible Neural Networks
This repository contains the code for Radynversion, an Invertible Neural Network (INN) based tool that infers solar atmospheric properties during a solar flare, based on a model trained on RADYN simulations (references not lim... | Markdown | MIT | 3,758 |
[ascl:1902.008] | https://github.com/Goobley/Radynversion | ExportSimpleLineBlobForTraining.py | import os
os.environ['CDF_LIB'] = '/usr/local/cdf/lib'
import radynpy.RadynCdfLoader as RadynCdfLoader
import numpy as np
from scipy.signal import savgol_filter
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
import torch
import pickle
from tqdm import tqdm
from concurrent.futures import ProcessP... | Python | MIT | 7,629 |
[ascl:1902.008] | https://github.com/Goobley/Radynversion | __init__.py | from .Inn2 import *
from .loss import *
from .utils import *
| Python | MIT | 61 |
[ascl:1902.008] | https://github.com/Goobley/Radynversion | Radynversion.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Radynversion Training Notebook\n",
"\n",
"This notebook is used to train the Radynversion neural network. It requires the following packages:\n",
"\n",
"`numpy`\n",
"\n",
"`scipy`\n",
"\n",
"`matplotl... | Jupyter Notebook | MIT | 293,591 |
[ascl:1902.008] | https://github.com/Goobley/Radynversion | loss.py | import torch
def mse(inp, target):
return torch.mean((inp - target)**2)
def tv_chan(inp, target):
l = inp.shape[-1]-1
tvIn = torch.sum(torch.abs(inp[:, :, 1:] - inp[:, :, :-1]), dim=2)
tvTarget = torch.sum(torch.abs(target[:, :, 1:] - target[:, :, :-1]), dim=2)
return torch.mean(torch.abs(tvTarget... | Python | MIT | 2,482 |
[ascl:1902.008] | https://github.com/Goobley/Radynversion | utils.py | from .Inn2 import RadynversionNet
import os
import numpy as np
from scipy.interpolate import interp1d
import torch
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.colors import PowerNorm,LinearSegmentedColormap
import matplotlib.ticker
__all__ = ["create_model","obs_files","interp_to_radyn_gri... | Python | MIT | 15,131 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | CHANGELOG.md | # Changelog
All notable changes to `light-curve-python` will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [Unreleased]
### Added
--
### Changed
--
### Deprec... | Markdown | Unknown | 30,854 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | README.md | # `light-curve` processing toolbox for Python
A Python wrapper for the [`light-curve-feature`](https://github.com/light-curve/light-curve-feature) and
[`light-curve-dmdt`](https://github.com/light-curve/light-curve-dmdt) Rust crates, providing high-performance
time-series feature extraction for astrophysics.
[![PyPI ... | Markdown | Unknown | 44,241 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | .pre-commit-config.yaml | # See https://pre-commit.com for more information
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v6.0.0
hooks:
- id: trailing-whitespace # trims trailing whitespace
- id: end-of-file-fixer # ensures that a file is either em... | YAML | Unknown | 2,109 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | CLAUDE.md | # CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Project Overview
`light-curve-python` is a hybrid Rust/Python package for time-series feature extraction in astrophysics. It wraps the
`light-curve-feature` and `light-curve-dmdt` Rust crates via PyO... | Markdown | Unknown | 3,843 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | .github/dependabot.yml | # 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://help.github.com/github/administering-a-repository/configuration-options-for-dependency-update... | YAML | Unknown | 642 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | .github/copilot-instructions.md | # GitHub Copilot Instructions for light-curve-python
## Project Overview
`light-curve-python` is a high-performance Python package for time-series feature extraction, particularly for
astrophysical light curves. It's a hybrid Rust/Python project that wraps the [
`light-curve-feature`](https://github.com/light-curve/l... | Markdown | Unknown | 8,014 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | .github/workflows/publish.yml | name: Publish Python packages
on:
push:
tags:
push:
pull_request:
repository_dispatch:
workflow_dispatch:
jobs:
check-version:
name: Check the tag corresponds to the crate version
runs-on: ubuntu-slim
steps:
- uses: actions/checkout@v6
- name: Set up Rust toolchain
u... | YAML | Unknown | 5,929 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | .github/workflows/test.yml | name: Test
on: [ push, pull_request, workflow_dispatch ]
jobs:
py_build_deps:
outputs:
output: ${{ steps.extract_from_toml.outputs.output }}
runs-on: ubuntu-slim
defaults:
run:
working-directory: light-curve
steps:
- uses: actions/checkout@v6
with:
subm... | YAML | Unknown | 6,781 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/README.md | # `light-curve` processing toolbox for Python
A Python wrapper for the [`light-curve-feature`](https://github.com/light-curve/light-curve-feature) and
[`light-curve-dmdt`](https://github.com/light-curve/light-curve-dmdt) Rust crates, providing high-performance
time-series feature extraction for astrophysics.
[![PyPI ... | Markdown | Unknown | 44,241 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/Cargo.toml | [package]
name = "light-curve-python"
version = "0.12.0"
authors = [
"Konstantin Malanchev <hombit@gmail.com>",
"Anastasia Lavrukhina <lavrukhina.ad@gmail.com>",
"Sergey Karpov",
"Etienne Russeil",
]
description = "Feature extractor from noisy time series"
readme = "README.md"
repository = "https://gith... | TOML | Unknown | 1,869 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/pyproject.toml | [build-system]
requires = ["maturin>=1.0,<2.0"]
build-backend = "maturin"
[project]
name = "light-curve"
dependencies = ["numpy"]
requires-python = ">=3.10"
classifiers = [
"Intended Audience :: Science/Research",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python",
"Programming ... | TOML | Unknown | 6,958 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/__init__.py | # Import all Python features
from .light_curve_py import *
# Hide Python features with Rust equivalents
from .light_curve_ext import *
# Hide Rust Extractor with universal Python Extractor
from .light_curve_py import Extractor
from .light_curve_ext import __version__
| Python | Unknown | 271 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_ext.py | from .light_curve import *
# A hack to bypass the lack of package support in PyO3
# https://github.com/PyO3/pyo3/issues/1517#issuecomment-808664021
def __register_submodules():
import sys
sys.modules["light_curve.light_curve_ext.ln_prior"] = ln_prior
__register_submodules()
def feature_from_json(s: str) ... | Python | Unknown | 445 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/minuit_lsq.py | """Re-implementation of iminuit.cost.LeastSquares with an arbitrary data format"""
from typing import Callable, Dict, Tuple
import numpy as np
try:
from iminuit import Minuit
except ImportError:
LeastSquares = None
else:
# Following https://iminuit.readthedocs.io/en/stable/notebooks/generic_least_squares... | Python | Unknown | 896 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/__init__.py | from .features.adnormal import *
from .features.amplitude import *
from .features.beyondnstd import *
from .features.bins import *
from .features.color_of_median import *
from .features.cusum import *
from .features.eta import *
from .features.etae import *
from .features.excvar import *
from .features.extractor import... | Python | Unknown | 1,144 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/dataclass_field.py | import sys
if sys.version_info >= (3, 10):
from dataclasses import field as dataclass_field
else:
from dataclasses import field as _field
def dataclass_field(*, kw_only, **kwargs):
return _field(**kwargs)
__all__ = ["dataclass_field"]
| Python | Unknown | 259 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/warnings.py | import warnings
class ExperimentalWarning(UserWarning):
pass
def warn_experimental(msg):
warnings.warn(msg, category=ExperimentalWarning, stacklevel=2)
def mark_experimental(f, msg=None):
def inner(f):
message = msg
if message is None:
full_name = f"{f.__module__}.{f.__clas... | Python | Unknown | 596 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/minuit_ml.py | """Maximum-likelihood based cost function"""
from typing import Callable, Dict, Tuple
import numpy as np
try:
from iminuit import Minuit
from scipy.special import erf
except ImportError:
MaximumLikelihood = None
else:
class MaximumLikelihood:
errordef = Minuit.LIKELIHOOD
def __init_... | Python | Unknown | 3,030 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/pdiffmperc.py | from dataclasses import dataclass
import numpy as np
from ..dataclass_field import dataclass_field
from ._base import BaseSingleBandFeature
@dataclass()
class PercentDifferenceMagnitudePercentile(BaseSingleBandFeature):
quantile: float = dataclass_field(default=0.25, kw_only=True)
def _eval_single_band(sel... | Python | Unknown | 829 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/skew.py | from ._base import BaseSingleBandFeature
class Skew(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
try:
from scipy.stats import skew
except ImportError:
raise ImportError("scipy is required for Skew feature, please install it")
return skew(m... | Python | Unknown | 420 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/beyondnstd.py | from dataclasses import dataclass
import numpy as np
from ._base import BaseSingleBandFeature
@dataclass()
class BeyondNStd(BaseSingleBandFeature):
nstd: float = 1.0
def _eval_single_band(self, t, m, sigma=None):
mean = np.mean(m)
std = np.std(m, ddof=1)
return np.count_nonzero(np.a... | Python | Unknown | 453 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/percampl.py | import numpy as np
from ._base import BaseSingleBandFeature
class PercentAmplitude(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
median = np.median(m)
return np.max((np.max(m) - median, median - np.min(m)))
@property
def size_single_band(self):
return 1
... | Python | Unknown | 353 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/eta.py | import numpy as np
from ._base import BaseSingleBandFeature
class Eta(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
n = len(m)
m_std = np.var(m, ddof=1)
m_sum = np.sum((m[1:] - m[:-1]) ** 2)
return m_sum / ((n - 1) * m_std)
@property
def size_sing... | Python | Unknown | 373 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/extractor.py | from dataclasses import dataclass
from typing import Collection, Union
import numpy as np
from light_curve.light_curve_ext import Extractor as _RustExtractor
from light_curve.light_curve_ext import _FeatureEvaluator as _RustBaseFeature
from ..dataclass_field import dataclass_field
from ._base import BaseSingleBandFe... | Python | Unknown | 1,395 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/magnpratio.py | from dataclasses import dataclass
from ..dataclass_field import dataclass_field
from ._base import BaseSingleBandFeature
@dataclass()
class MagnitudePercentageRatio(BaseSingleBandFeature):
quantile_numerator: float = dataclass_field(default=0.4, kw_only=True)
quantile_denominator: float = dataclass_field(def... | Python | Unknown | 967 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/color_of_median.py | from dataclasses import dataclass
from ._base import BaseMultiBandFeature
from .median import Median
@dataclass()
class ColorOfMedian(BaseMultiBandFeature):
"""Difference of median magnitudes in two bands."""
blue_band: str
red_band: str
@property
def is_band_required(self) -> bool:
ret... | Python | Unknown | 826 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/_base.py | from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Optional, Sequence
import numpy as np
from light_curve.light_curve_py.dataclass_field import dataclass_field
from light_curve.light_curve_py.warnings import mark_experimental
@dataclass
class BaseMultiBandFeature(ABC):
@pro... | Python | Unknown | 5,564 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/stdev.py | import numpy as np
from ._base import BaseSingleBandFeature
class StandardDeviation(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
return np.std(m, ddof=1)
@property
def size_single_band(self):
return 1
__all__ = ("StandardDeviation",)
| Python | Unknown | 294 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/median.py | import numpy as np
from ._base import BaseSingleBandFeature
class Median(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
return np.median(m)
@property
def size_single_band(self):
return 1
__all__ = ("Median",)
| Python | Unknown | 267 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/excvar.py | import numpy as np
from ._base import BaseSingleBandFeature
class ExcessVariance(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
m_mean = np.mean(m)
d_mean = np.mean(np.power(sigma, 2))
m_std = np.std(m, ddof=1)
return (m_std**2 - d_mean) / m_mean**2
@p... | Python | Unknown | 409 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/stetsonk.py | import numpy as np
from ._base import BaseSingleBandFeature
class StetsonK(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
m_mean = np.average(m, weights=np.power(sigma, -2))
numerator = np.sum(np.abs((m - m_mean) / sigma))
chisq = np.sum(((m - m_mean) / sigma) ** 2... | Python | Unknown | 463 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/mean.py | import numpy as np
from ._base import BaseSingleBandFeature
class Mean(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
return np.mean(m)
@property
def size_single_band(self):
return 1
__all__ = ("Mean",)
| Python | Unknown | 261 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/otsusplit.py | import numpy as np
from ._base import BaseSingleBandFeature
class OtsuSplit(BaseSingleBandFeature):
"""Otsu threshholding algorithm
Difference of subset means, standard deviation of the lower subset, standard deviation
of the upper subset and lower-to-all observation count ratio for two subsets of magni... | Python | Unknown | 2,685 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/linfit.py | import numpy as np
from ._base import BaseSingleBandFeature
from ._lstsq import least_squares
class LinearFit(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
n = len(t)
slope, chi2 = least_squares(t, m, sigma)
red_chi2 = chi2 / (n - 2)
weighted_t2 = np.av... | Python | Unknown | 662 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/roms.py | import numpy as np
from ._base import BaseSingleBandFeature
class Roms(BaseSingleBandFeature):
r"""Robust median statistic
$$
\frac1{N-1} \sum_{i=0}^{N-1} \frac{|m_i - \mathrm{median}(m_i)|}{\sigma_i}
$$
For non-variable data, it should be less than one.
- Depends on: **magnitude**, **error... | Python | Unknown | 747 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/kurtosis.py | import numpy as np
from ._base import BaseSingleBandFeature
class Kurtosis(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
n = len(m)
m_mean = np.mean(m)
m_st = np.std(m, ddof=1) ** 4
m_sum = sum(np.power(m - m_mean, 4))
return (n * (n + 1) * m_sum) ... | Python | Unknown | 520 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/adnormal.py | from ._base import BaseSingleBandFeature
class AndersonDarlingNormal(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
try:
from scipy.stats import anderson
except ImportError:
raise ImportError("scipy is required for AndersonDarlingNormal feature, plea... | Python | Unknown | 522 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/ptp_var.py | import numpy as np
from ._base import BaseSingleBandFeature
class PeakToPeakVar(BaseSingleBandFeature):
r"""Peak-to-peak variation
$$
\frac{(m_i - \sigma_i)_\text{max} - (m_i + \sigma_i)_\text{min}}
{(m_i - \sigma_i)_\text{max} + (m_i + \sigma_i)_\text{min}}
$$
Input m must be non-negative (... | Python | Unknown | 1,056 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/etae.py | import numpy as np
from ._base import BaseSingleBandFeature
class EtaE(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
n = len(m)
m_std = np.var(m, ddof=1)
m_sum = np.sum(((m[1:] - m[:-1]) / (t[1:] - t[:-1])) ** 2)
return m_sum * (t[n - 1] - t[0]) ** 2 / ((n... | Python | Unknown | 426 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/intpercrange.py | from dataclasses import dataclass
from ..dataclass_field import dataclass_field
from ._base import BaseSingleBandFeature
@dataclass()
class InterPercentileRange(BaseSingleBandFeature):
quantile: float = dataclass_field(default=0.25, kw_only=True)
def _eval_single_band(self, t, m, sigma=None):
try:
... | Python | Unknown | 720 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/cusum.py | import numpy as np
from ._base import BaseSingleBandFeature
class Cusum(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
m_mean = np.mean(m)
m_std = np.std(m, ddof=1)
m_new = np.cumsum(m - m_mean)
result = m_new / (len(m) * m_std)
return np.ptp(result... | Python | Unknown | 409 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/medbufrperc.py | from dataclasses import dataclass
import numpy as np
from ..dataclass_field import dataclass_field
from ._base import BaseSingleBandFeature
@dataclass()
class MedianBufferRangePercentage(BaseSingleBandFeature):
quantile: float = dataclass_field(default=0.1, kw_only=True)
def _eval_single_band(self, t, m, s... | Python | Unknown | 578 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/lintrend.py | import numpy as np
from ._base import BaseSingleBandFeature
from ._lstsq import least_squares
class LinearTrend(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
n = len(t)
slope, chi2 = least_squares(t, m, None)
red_chi2 = chi2 / (n - 2)
sxx = np.var(t, ddo... | Python | Unknown | 499 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/weightmean.py | import numpy as np
from ._base import BaseSingleBandFeature
class WeightedMean(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
return np.average(m, weights=np.power(sigma, -2))
@property
def size_single_band(self):
return 1
__all__ = ("WeightedMean",)
| Python | Unknown | 309 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/bins.py | from dataclasses import dataclass
import numpy as np
from ..dataclass_field import dataclass_field
from ._base_meta import BaseMetaSingleBandFeature
@dataclass()
class Bins(BaseMetaSingleBandFeature):
window: float = dataclass_field(default=1.0, kw_only=True)
offset: float = dataclass_field(default=0.0, kw_... | Python | Unknown | 1,337 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/medabsdev.py | import numpy as np
from ._base import BaseSingleBandFeature
class MedianAbsoluteDeviation(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
return np.median((np.abs(m - np.median(m))))
@property
def size_single_band(self):
return 1
__all__ = ("MedianAbsoluteDeviati... | Python | Unknown | 326 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/redchi2.py | import numpy as np
from ._base import BaseSingleBandFeature
class ReducedChi2(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
n = len(m)
weights = sigma**-2
m_wmean = np.average(m, weights=weights)
s = (m - m_wmean) ** 2 * weights
return np.sum(s) / ... | Python | Unknown | 421 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/maxslope.py | import numpy as np
from ._base import BaseSingleBandFeature
class MaximumSlope(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
m_span = np.subtract(m[1:], m[:-1])
t_span = np.subtract(t[1:], t[:-1])
div = np.abs(np.divide(m_span, t_span))
return np.amax(div)... | Python | Unknown | 415 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/flux_n_not_det_before_fd.py | from dataclasses import dataclass
from typing import Tuple
import numpy as np
from ..dataclass_field import dataclass_field
from ._base import BaseSingleBandFeature
@dataclass()
class FluxNNotDetBeforeFd(BaseSingleBandFeature):
"""Number of non-detections before the first detection for measurements of the flux.... | Python | Unknown | 2,172 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/magnitude_n_not_det_before_fd.py | from dataclasses import dataclass
from typing import Tuple
import numpy as np
from ..dataclass_field import dataclass_field
from ._base import BaseSingleBandFeature
@dataclass()
class MagnitudeNNotDetBeforeFd(BaseSingleBandFeature):
"""Number of non detections before the first detection for measurements of the ... | Python | Unknown | 2,267 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/_base_meta.py | from abc import abstractmethod
from dataclasses import dataclass, field
from typing import Collection, Union
from light_curve.light_curve_ext import Extractor as _RustExtractor
from light_curve.light_curve_ext import _FeatureEvaluator as _RustBaseFeature
from ..dataclass_field import dataclass_field
from ._base impor... | Python | Unknown | 1,485 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/_lstsq.py | import numpy as np
def least_squares(t, m, sigma):
if t.size < 3:
raise ValueError("Time series must have at least 3 points")
A = np.vstack([t, np.ones(len(t))]).T
if sigma is not None:
w = np.diag(1 / sigma)
A = np.dot(w, A)
m = np.dot(w, m.T)
(slope, _), residuals,... | Python | Unknown | 391 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/amplitude.py | import numpy as np
from ._base import BaseSingleBandFeature
class Amplitude(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
return 0.5 * np.ptp(m)
@property
def size_single_band(self):
return 1
__all__ = ("Amplitude",)
| Python | Unknown | 276 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/meanvar.py | import numpy as np
from ._base import BaseSingleBandFeature
class MeanVariance(BaseSingleBandFeature):
def _eval_single_band(self, t, m, sigma=None):
return np.std(m, ddof=1) / np.mean(m)
@property
def size_single_band(self):
return 1
__all__ = ("MeanVariance",)
| Python | Unknown | 297 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/rainbow/__init__.py | from .generic import *
| Python | Unknown | 23 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/rainbow/_base.py | from abc import abstractmethod
from dataclasses import dataclass
from typing import Dict, List, Tuple
import numpy as np
from light_curve.light_curve_py.dataclass_field import dataclass_field
from light_curve.light_curve_py.features._base import BaseMultiBandFeature
from light_curve.light_curve_py.features.rainbow._b... | Python | Unknown | 15,212 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/rainbow/_bands.py | from typing import Any, Dict
import numpy as np
from numpy.typing import ArrayLike, NDArray
class Bands:
"""Monochromatic passbands for Rainbow fit."""
@property
def names(self) -> NDArray:
"""Names of the bands"""
return self._names.copy()
@property
def index(self) -> NDArray[i... | Python | Unknown | 1,948 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/rainbow/generic.py | from dataclasses import dataclass
from typing import Dict, List, Tuple, Union
from light_curve.light_curve_py.dataclass_field import dataclass_field
from light_curve.light_curve_py.features.rainbow._base import BaseRainbowFit
from .bolometric import BaseBolometricTerm, bolometric_terms
from .temperature import BaseTe... | Python | Unknown | 5,326 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/rainbow/_parameters.py | from enum import IntEnum
from typing import Iterable, List
import numpy as np
from light_curve.light_curve_py.features.rainbow._bands import Bands
__all__ = ["create_parameters_class"]
def baseline_parameter_name(band: str) -> str:
return f"baseline_{band}"
def baseline_band_name(name: str) -> str:
if na... | Python | Unknown | 2,675 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/rainbow/temperature.py | from abc import abstractmethod
from dataclasses import dataclass
from typing import Dict, List, Union
import numpy as np
__all__ = ["temperature_terms", "BaseTemperatureTerm", "ConstantTemperatureTerm", "SigmoidTemperatureTerm"]
@dataclass()
class BaseTemperatureTerm:
"""Bolometric term for the Rainbow"""
... | Python | Unknown | 6,167 |
[ascl:2312.019] | https://github.com/light-curve/light-curve-python | light-curve/light_curve/light_curve_py/features/rainbow/bolometric.py | import math
from abc import abstractmethod
from dataclasses import dataclass
from typing import Dict, List, Union
import numpy as np
__all__ = [
"bolometric_terms",
"BaseBolometricTerm",
"SigmoidBolometricTerm",
"BazinBolometricTerm",
"LinexpBolometricTerm",
"DoublexpBolometricTerm",
]
@data... | Python | Unknown | 10,742 |
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